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'''simple docstring'''
from __future__ import annotations
import math
import random
from typing import Any
class UpperCAmelCase :
def __init__( self :Optional[int] )-> None:
A__ = []
A__ = 0
A__ = 0
def UpperCAmelCase_ ( self :List[str] )-> bool:
return self.head == self.tail
def UpperCAmelCase_ ( self :Union[str, Any] , lowercase_ :Any )-> None:
self.data.append(UpperCAmelCase_ )
A__ = self.tail + 1
def UpperCAmelCase_ ( self :Optional[int] )-> Any:
A__ = self.data[self.head]
A__ = self.head + 1
return ret
def UpperCAmelCase_ ( self :int )-> int:
return self.tail - self.head
def UpperCAmelCase_ ( self :str )-> None:
print(self.data )
print("**************" )
print(self.data[self.head : self.tail] )
class UpperCAmelCase :
def __init__( self :Tuple , lowercase_ :Any )-> None:
A__ = data
A__ = None
A__ = None
A__ = 1
def UpperCAmelCase_ ( self :List[str] )-> Any:
return self.data
def UpperCAmelCase_ ( self :Tuple )-> MyNode | None:
return self.left
def UpperCAmelCase_ ( self :List[Any] )-> MyNode | None:
return self.right
def UpperCAmelCase_ ( self :Union[str, Any] )-> int:
return self.height
def UpperCAmelCase_ ( self :List[Any] , lowercase_ :Any )-> None:
A__ = data
def UpperCAmelCase_ ( self :Tuple , lowercase_ :MyNode | None )-> None:
A__ = node
def UpperCAmelCase_ ( self :str , lowercase_ :MyNode | None )-> None:
A__ = node
def UpperCAmelCase_ ( self :Dict , lowercase_ :int )-> None:
A__ = height
def UpperCamelCase ( _lowerCamelCase : int ):
if node is None:
return 0
return node.get_height()
def UpperCamelCase ( _lowerCamelCase : Optional[Any] , _lowerCamelCase : Tuple ):
if a > b:
return a
return b
def UpperCamelCase ( _lowerCamelCase : Optional[Any] ):
print("left rotation node:" , node.get_data() )
A__ = node.get_left()
assert ret is not None
node.set_left(ret.get_right() )
ret.set_right(_SCREAMING_SNAKE_CASE )
A__ = my_max(get_height(node.get_right() ) , get_height(node.get_left() ) ) + 1
node.set_height(_SCREAMING_SNAKE_CASE )
A__ = my_max(get_height(ret.get_right() ) , get_height(ret.get_left() ) ) + 1
ret.set_height(_SCREAMING_SNAKE_CASE )
return ret
def UpperCamelCase ( _lowerCamelCase : Union[str, Any] ):
print("right rotation node:" , node.get_data() )
A__ = node.get_right()
assert ret is not None
node.set_right(ret.get_left() )
ret.set_left(_SCREAMING_SNAKE_CASE )
A__ = my_max(get_height(node.get_right() ) , get_height(node.get_left() ) ) + 1
node.set_height(_SCREAMING_SNAKE_CASE )
A__ = my_max(get_height(ret.get_right() ) , get_height(ret.get_left() ) ) + 1
ret.set_height(_SCREAMING_SNAKE_CASE )
return ret
def UpperCamelCase ( _lowerCamelCase : List[Any] ):
A__ = node.get_left()
assert left_child is not None
node.set_left(left_rotation(_SCREAMING_SNAKE_CASE ) )
return right_rotation(_SCREAMING_SNAKE_CASE )
def UpperCamelCase ( _lowerCamelCase : Any ):
A__ = node.get_right()
assert right_child is not None
node.set_right(right_rotation(_SCREAMING_SNAKE_CASE ) )
return left_rotation(_SCREAMING_SNAKE_CASE )
def UpperCamelCase ( _lowerCamelCase : Optional[int] , _lowerCamelCase : List[str] ):
if node is None:
return MyNode(_SCREAMING_SNAKE_CASE )
if data < node.get_data():
node.set_left(insert_node(node.get_left() , _SCREAMING_SNAKE_CASE ) )
if (
get_height(node.get_left() ) - get_height(node.get_right() ) == 2
): # an unbalance detected
A__ = node.get_left()
assert left_child is not None
if (
data < left_child.get_data()
): # new node is the left child of the left child
A__ = right_rotation(_SCREAMING_SNAKE_CASE )
else:
A__ = lr_rotation(_SCREAMING_SNAKE_CASE )
else:
node.set_right(insert_node(node.get_right() , _SCREAMING_SNAKE_CASE ) )
if get_height(node.get_right() ) - get_height(node.get_left() ) == 2:
A__ = node.get_right()
assert right_child is not None
if data < right_child.get_data():
A__ = rl_rotation(_SCREAMING_SNAKE_CASE )
else:
A__ = left_rotation(_SCREAMING_SNAKE_CASE )
A__ = my_max(get_height(node.get_right() ) , get_height(node.get_left() ) ) + 1
node.set_height(_SCREAMING_SNAKE_CASE )
return node
def UpperCamelCase ( _lowerCamelCase : Optional[Any] ):
while True:
A__ = root.get_right()
if right_child is None:
break
A__ = right_child
return root.get_data()
def UpperCamelCase ( _lowerCamelCase : Optional[Any] ):
while True:
A__ = root.get_left()
if left_child is None:
break
A__ = left_child
return root.get_data()
def UpperCamelCase ( _lowerCamelCase : Tuple , _lowerCamelCase : List[str] ):
A__ = root.get_left()
A__ = root.get_right()
if root.get_data() == data:
if left_child is not None and right_child is not None:
A__ = get_left_most(_SCREAMING_SNAKE_CASE )
root.set_data(_SCREAMING_SNAKE_CASE )
root.set_right(del_node(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) )
elif left_child is not None:
A__ = left_child
elif right_child is not None:
A__ = right_child
else:
return None
elif root.get_data() > data:
if left_child is None:
print("No such data" )
return root
else:
root.set_left(del_node(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) )
else: # root.get_data() < data
if right_child is None:
return root
else:
root.set_right(del_node(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) )
if get_height(_SCREAMING_SNAKE_CASE ) - get_height(_SCREAMING_SNAKE_CASE ) == 2:
assert right_child is not None
if get_height(right_child.get_right() ) > get_height(right_child.get_left() ):
A__ = left_rotation(_SCREAMING_SNAKE_CASE )
else:
A__ = rl_rotation(_SCREAMING_SNAKE_CASE )
elif get_height(_SCREAMING_SNAKE_CASE ) - get_height(_SCREAMING_SNAKE_CASE ) == -2:
assert left_child is not None
if get_height(left_child.get_left() ) > get_height(left_child.get_right() ):
A__ = right_rotation(_SCREAMING_SNAKE_CASE )
else:
A__ = lr_rotation(_SCREAMING_SNAKE_CASE )
A__ = my_max(get_height(root.get_right() ) , get_height(root.get_left() ) ) + 1
root.set_height(_SCREAMING_SNAKE_CASE )
return root
class UpperCAmelCase :
def __init__( self :Dict )-> None:
A__ = None
def UpperCAmelCase_ ( self :List[str] )-> int:
return get_height(self.root )
def UpperCAmelCase_ ( self :str , lowercase_ :Any )-> None:
print("insert:" + str(UpperCAmelCase_ ) )
A__ = insert_node(self.root , UpperCAmelCase_ )
def UpperCAmelCase_ ( self :Optional[int] , lowercase_ :Any )-> None:
print("delete:" + str(UpperCAmelCase_ ) )
if self.root is None:
print("Tree is empty!" )
return
A__ = del_node(self.root , UpperCAmelCase_ )
def __str__( self :Dict , )-> str: # a level traversale, gives a more intuitive look on the tree
A__ = ""
A__ = MyQueue()
q.push(self.root )
A__ = self.get_height()
if layer == 0:
return output
A__ = 0
while not q.is_empty():
A__ = q.pop()
A__ = " " * int(math.pow(2 , layer - 1 ) )
output += space
if node is None:
output += "*"
q.push(UpperCAmelCase_ )
q.push(UpperCAmelCase_ )
else:
output += str(node.get_data() )
q.push(node.get_left() )
q.push(node.get_right() )
output += space
A__ = cnt + 1
for i in range(1_00 ):
if cnt == math.pow(2 , UpperCAmelCase_ ) - 1:
A__ = layer - 1
if layer == 0:
output += "\n*************************************"
return output
output += "\n"
break
output += "\n*************************************"
return output
def UpperCamelCase ( ):
import doctest
doctest.testmod()
if __name__ == "__main__":
_test()
__lowerCAmelCase : Any =AVLtree()
__lowerCAmelCase : Any =list(range(10))
random.shuffle(lst)
for i in lst:
t.insert(i)
print(str(t))
random.shuffle(lst)
for i in lst:
t.del_node(i)
print(str(t))
| 237 |
"""simple docstring"""
def _a ( _SCREAMING_SNAKE_CASE ) -> bool:
if num < 0:
return False
snake_case_ = num
snake_case_ = 0
while num > 0:
snake_case_ = rev_num * 10 + (num % 10)
num //= 10
return num_copy == rev_num
if __name__ == "__main__":
import doctest
doctest.testmod()
| 347 | 0 |
'''simple docstring'''
import argparse
import os
import re
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_dummies.py
A_ = 'src/diffusers'
# Matches is_xxx_available()
A_ = re.compile(R"is\_([a-z_]*)_available\(\)")
# Matches from xxx import bla
A_ = re.compile(R"\s+from\s+\S*\s+import\s+([^\(\s].*)\n")
A_ = '\n{0} = None\n'
A_ = '\nclass {0}(metaclass=DummyObject):\n _backends = {1}\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, {1})\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, {1})\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, {1})\n'
A_ = '\ndef {0}(*args, **kwargs):\n requires_backends({0}, {1})\n'
def A_ ( snake_case ):
SCREAMING_SNAKE_CASE:Tuple = _re_backend.findall(_SCREAMING_SNAKE_CASE )
if len(_SCREAMING_SNAKE_CASE ) == 0:
return None
return "_and_".join(_SCREAMING_SNAKE_CASE )
def A_ ( ):
with open(os.path.join(_SCREAMING_SNAKE_CASE , "__init__.py" ) , "r" , encoding="utf-8" , newline="\n" ) as f:
SCREAMING_SNAKE_CASE:List[Any] = f.readlines()
# Get to the point we do the actual imports for type checking
SCREAMING_SNAKE_CASE:Union[str, Any] = 0
SCREAMING_SNAKE_CASE:str = {}
# Go through the end of the file
while line_index < len(_SCREAMING_SNAKE_CASE ):
# If the line contains is_backend_available, we grab all objects associated with the `else` block
SCREAMING_SNAKE_CASE:Union[str, Any] = find_backend(lines[line_index] )
if backend is not None:
while not lines[line_index].startswith("else:" ):
line_index += 1
line_index += 1
SCREAMING_SNAKE_CASE:Union[str, Any] = []
# Until we unindent, add backend objects to the list
while line_index < len(_SCREAMING_SNAKE_CASE ) and len(lines[line_index] ) > 1:
SCREAMING_SNAKE_CASE:Union[str, Any] = lines[line_index]
SCREAMING_SNAKE_CASE:Optional[Any] = _re_single_line_import.search(_SCREAMING_SNAKE_CASE )
if single_line_import_search is not None:
objects.extend(single_line_import_search.groups()[0].split(", " ) )
elif line.startswith(" " * 8 ):
objects.append(line[8:-2] )
line_index += 1
if len(_SCREAMING_SNAKE_CASE ) > 0:
SCREAMING_SNAKE_CASE:Optional[Any] = objects
else:
line_index += 1
return backend_specific_objects
def A_ ( snake_case , snake_case ):
if name.isupper():
return DUMMY_CONSTANT.format(_SCREAMING_SNAKE_CASE )
elif name.islower():
return DUMMY_FUNCTION.format(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
else:
return DUMMY_CLASS.format(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
def A_ ( snake_case=None ):
if backend_specific_objects is None:
SCREAMING_SNAKE_CASE:str = read_init()
# For special correspondence backend to module name as used in the function requires_modulename
SCREAMING_SNAKE_CASE:Tuple = {}
for backend, objects in backend_specific_objects.items():
SCREAMING_SNAKE_CASE:Optional[int] = "[" + ", ".join(F'''\"{b}\"''' for b in backend.split("_and_" ) ) + "]"
SCREAMING_SNAKE_CASE:Dict = "# This file is autogenerated by the command `make fix-copies`, do not edit.\n"
dummy_file += "from ..utils import DummyObject, requires_backends\n\n"
dummy_file += "\n".join([create_dummy_object(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) for o in objects] )
SCREAMING_SNAKE_CASE:Dict = dummy_file
return dummy_files
def A_ ( snake_case=False ):
SCREAMING_SNAKE_CASE:Any = create_dummy_files()
# For special correspondence backend to shortcut as used in utils/dummy_xxx_objects.py
SCREAMING_SNAKE_CASE:Dict = {"torch": "pt"}
# Locate actual dummy modules and read their content.
SCREAMING_SNAKE_CASE:Dict = os.path.join(_SCREAMING_SNAKE_CASE , "utils" )
SCREAMING_SNAKE_CASE:Union[str, Any] = {
backend: os.path.join(_SCREAMING_SNAKE_CASE , F'''dummy_{short_names.get(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )}_objects.py''' )
for backend in dummy_files.keys()
}
SCREAMING_SNAKE_CASE:str = {}
for backend, file_path in dummy_file_paths.items():
if os.path.isfile(_SCREAMING_SNAKE_CASE ):
with open(_SCREAMING_SNAKE_CASE , "r" , encoding="utf-8" , newline="\n" ) as f:
SCREAMING_SNAKE_CASE:List[Any] = f.read()
else:
SCREAMING_SNAKE_CASE:List[Any] = ""
for backend in dummy_files.keys():
if dummy_files[backend] != actual_dummies[backend]:
if overwrite:
print(
F'''Updating diffusers.utils.dummy_{short_names.get(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )}_objects.py as the main '''
"__init__ has new objects." )
with open(dummy_file_paths[backend] , "w" , encoding="utf-8" , newline="\n" ) as f:
f.write(dummy_files[backend] )
else:
raise ValueError(
"The main __init__ has objects that are not present in "
F'''diffusers.utils.dummy_{short_names.get(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )}_objects.py. Run `make fix-copies` '''
"to fix this." )
if __name__ == "__main__":
A_ = argparse.ArgumentParser()
parser.add_argument("--fix_and_overwrite", action="store_true", help="Whether to fix inconsistencies.")
A_ = parser.parse_args()
check_dummies(args.fix_and_overwrite)
| 139 |
"""simple docstring"""
import unittest
from transformers import SPIECE_UNDERLINE
from transformers.models.speechta import SpeechTaTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.tokenization_utils import AddedToken
from ...test_tokenization_common import TokenizerTesterMixin
__SCREAMING_SNAKE_CASE : Tuple = get_tests_dir('fixtures/test_sentencepiece_bpe_char.model')
@require_sentencepiece
@require_tokenizers
class __A (snake_case__ , unittest.TestCase):
'''simple docstring'''
__lowercase: Tuple = SpeechTaTokenizer
__lowercase: int = False
__lowercase: List[str] = True
def lowerCAmelCase ( self : Any ) ->str:
"""simple docstring"""
super().setUp()
# We have a SentencePiece fixture for testing
snake_case_ = SpeechTaTokenizer(UpperCAmelCase_ )
snake_case_ = AddedToken("""<mask>""" , lstrip=UpperCAmelCase_ , rstrip=UpperCAmelCase_ )
snake_case_ = mask_token
tokenizer.add_special_tokens({"""mask_token""": mask_token} )
tokenizer.add_tokens(["""<ctc_blank>"""] )
tokenizer.save_pretrained(self.tmpdirname )
def lowerCAmelCase ( self : Optional[Any] , UpperCAmelCase_ : Optional[Any] ) ->Dict:
"""simple docstring"""
snake_case_ = """this is a test"""
snake_case_ = """this is a test"""
return input_text, output_text
def lowerCAmelCase ( self : str , UpperCAmelCase_ : int , UpperCAmelCase_ : Any=False , UpperCAmelCase_ : Tuple=20 , UpperCAmelCase_ : Dict=5 ) ->List[Any]:
"""simple docstring"""
snake_case_ , snake_case_ = self.get_input_output_texts(UpperCAmelCase_ )
snake_case_ = tokenizer.encode(UpperCAmelCase_ , add_special_tokens=UpperCAmelCase_ )
snake_case_ = tokenizer.decode(UpperCAmelCase_ , clean_up_tokenization_spaces=UpperCAmelCase_ )
return text, ids
def lowerCAmelCase ( self : Union[str, Any] ) ->Optional[Any]:
"""simple docstring"""
snake_case_ = """<pad>"""
snake_case_ = 1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(UpperCAmelCase_ ) , UpperCAmelCase_ )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(UpperCAmelCase_ ) , UpperCAmelCase_ )
def lowerCAmelCase ( self : int ) ->str:
"""simple docstring"""
snake_case_ = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] , """<s>""" )
self.assertEqual(vocab_keys[1] , """<pad>""" )
self.assertEqual(vocab_keys[-4] , """œ""" )
self.assertEqual(vocab_keys[-2] , """<mask>""" )
self.assertEqual(vocab_keys[-1] , """<ctc_blank>""" )
self.assertEqual(len(UpperCAmelCase_ ) , 81 )
def lowerCAmelCase ( self : Optional[int] ) ->int:
"""simple docstring"""
self.assertEqual(self.get_tokenizer().vocab_size , 79 )
def lowerCAmelCase ( self : Optional[int] ) ->List[Any]:
"""simple docstring"""
snake_case_ = self.get_tokenizers(do_lower_case=UpperCAmelCase_ )
for tokenizer in tokenizers:
with self.subTest(F"""{tokenizer.__class__.__name__}""" ):
snake_case_ = tokenizer.vocab_size
snake_case_ = len(UpperCAmelCase_ )
self.assertNotEqual(UpperCAmelCase_ , 0 )
# We usually have added tokens from the start in tests because our vocab fixtures are
# smaller than the original vocabs - let's not assert this
# self.assertEqual(vocab_size, all_size)
snake_case_ = ["""aaaaa bbbbbb""", """cccccccccdddddddd"""]
snake_case_ = tokenizer.add_tokens(UpperCAmelCase_ )
snake_case_ = tokenizer.vocab_size
snake_case_ = len(UpperCAmelCase_ )
self.assertNotEqual(UpperCAmelCase_ , 0 )
self.assertEqual(UpperCAmelCase_ , UpperCAmelCase_ )
self.assertEqual(UpperCAmelCase_ , len(UpperCAmelCase_ ) )
self.assertEqual(UpperCAmelCase_ , all_size + len(UpperCAmelCase_ ) )
snake_case_ = tokenizer.encode("""aaaaa bbbbbb low cccccccccdddddddd l""" , add_special_tokens=UpperCAmelCase_ )
self.assertGreaterEqual(len(UpperCAmelCase_ ) , 4 )
self.assertGreater(tokens[0] , tokenizer.vocab_size - 1 )
self.assertGreater(tokens[-3] , tokenizer.vocab_size - 1 )
snake_case_ = {"""eos_token""": """>>>>|||<||<<|<<""", """pad_token""": """<<<<<|||>|>>>>|>"""}
snake_case_ = tokenizer.add_special_tokens(UpperCAmelCase_ )
snake_case_ = tokenizer.vocab_size
snake_case_ = len(UpperCAmelCase_ )
self.assertNotEqual(UpperCAmelCase_ , 0 )
self.assertEqual(UpperCAmelCase_ , UpperCAmelCase_ )
self.assertEqual(UpperCAmelCase_ , len(UpperCAmelCase_ ) )
self.assertEqual(UpperCAmelCase_ , all_size_a + len(UpperCAmelCase_ ) )
snake_case_ = tokenizer.encode(
""">>>>|||<||<<|<< aaaaabbbbbb low cccccccccdddddddd <<<<<|||>|>>>>|> l""" , add_special_tokens=UpperCAmelCase_ )
self.assertGreaterEqual(len(UpperCAmelCase_ ) , 6 )
self.assertGreater(tokens[0] , tokenizer.vocab_size - 1 )
self.assertGreater(tokens[0] , tokens[1] )
self.assertGreater(tokens[-3] , tokenizer.vocab_size - 1 )
self.assertGreater(tokens[-3] , tokens[-4] )
self.assertEqual(tokens[0] , tokenizer.eos_token_id )
self.assertEqual(tokens[-3] , tokenizer.pad_token_id )
def lowerCAmelCase ( self : Optional[Any] ) ->Tuple:
"""simple docstring"""
pass
def lowerCAmelCase ( self : List[str] ) ->Optional[Any]:
"""simple docstring"""
pass
def lowerCAmelCase ( self : List[str] ) ->List[str]:
"""simple docstring"""
snake_case_ = self.get_tokenizer()
snake_case_ = tokenizer.tokenize("""This is a test""" )
# fmt: off
self.assertListEqual(UpperCAmelCase_ , [SPIECE_UNDERLINE, """T""", """h""", """i""", """s""", SPIECE_UNDERLINE, """i""", """s""", SPIECE_UNDERLINE, """a""", SPIECE_UNDERLINE, """t""", """e""", """s""", """t"""] )
# fmt: on
self.assertListEqual(
tokenizer.convert_tokens_to_ids(UpperCAmelCase_ ) , [4, 32, 11, 10, 12, 4, 10, 12, 4, 7, 4, 6, 5, 12, 6] , )
snake_case_ = tokenizer.tokenize("""I was born in 92000, and this is falsé.""" )
self.assertListEqual(
UpperCAmelCase_ , [SPIECE_UNDERLINE, """I""", SPIECE_UNDERLINE, """w""", """a""", """s""", SPIECE_UNDERLINE, """b""", """o""", """r""", """n""", SPIECE_UNDERLINE, """i""", """n""", SPIECE_UNDERLINE, """92000""", """,""", SPIECE_UNDERLINE, """a""", """n""", """d""", SPIECE_UNDERLINE, """t""", """h""", """i""", """s""", SPIECE_UNDERLINE, """i""", """s""", SPIECE_UNDERLINE, """f""", """a""", """l""", """s""", """é""", """."""] )
snake_case_ = tokenizer.convert_tokens_to_ids(UpperCAmelCase_ )
# fmt: off
self.assertListEqual(UpperCAmelCase_ , [4, 30, 4, 20, 7, 12, 4, 25, 8, 13, 9, 4, 10, 9, 4, 3, 23, 4, 7, 9, 14, 4, 6, 11, 10, 12, 4, 10, 12, 4, 19, 7, 15, 12, 73, 26] )
# fmt: on
snake_case_ = tokenizer.convert_ids_to_tokens(UpperCAmelCase_ )
self.assertListEqual(
UpperCAmelCase_ , [SPIECE_UNDERLINE, """I""", SPIECE_UNDERLINE, """w""", """a""", """s""", SPIECE_UNDERLINE, """b""", """o""", """r""", """n""", SPIECE_UNDERLINE, """i""", """n""", SPIECE_UNDERLINE, """<unk>""", """,""", SPIECE_UNDERLINE, """a""", """n""", """d""", SPIECE_UNDERLINE, """t""", """h""", """i""", """s""", SPIECE_UNDERLINE, """i""", """s""", SPIECE_UNDERLINE, """f""", """a""", """l""", """s""", """é""", """."""] )
@slow
def lowerCAmelCase ( self : str ) ->Dict:
"""simple docstring"""
snake_case_ = [
"""Transformers (formerly known as pytorch-transformers and pytorch-pretrained-bert) provides """
"""general-purpose architectures (BERT, GPT, RoBERTa, XLM, DistilBert, XLNet...) for Natural """
"""Language Understanding (NLU) and Natural Language Generation (NLG) with over thirty-two pretrained """
"""models in one hundred plus languages and deep interoperability between Jax, PyTorch and TensorFlow.""",
"""BERT is designed to pre-train deep bidirectional representations from unlabeled text by jointly """
"""conditioning on both left and right context in all layers.""",
"""The quick brown fox jumps over the lazy dog.""",
]
# fmt: off
snake_case_ = {
"""input_ids""": [
[4, 32, 13, 7, 9, 12, 19, 8, 13, 18, 5, 13, 12, 4, 64, 19, 8, 13, 18, 5, 13, 15, 22, 4, 28, 9, 8, 20, 9, 4, 7, 12, 4, 24, 22, 6, 8, 13, 17, 11, 39, 6, 13, 7, 9, 12, 19, 8, 13, 18, 5, 13, 12, 4, 7, 9, 14, 4, 24, 22, 6, 8, 13, 17, 11, 39, 24, 13, 5, 6, 13, 7, 10, 9, 5, 14, 39, 25, 5, 13, 6, 63, 4, 24, 13, 8, 27, 10, 14, 5, 12, 4, 21, 5, 9, 5, 13, 7, 15, 39, 24, 16, 13, 24, 8, 12, 5, 4, 7, 13, 17, 11, 10, 6, 5, 17, 6, 16, 13, 5, 12, 4, 64, 40, 47, 54, 32, 23, 4, 53, 49, 32, 23, 4, 54, 8, 40, 47, 54, 32, 7, 23, 4, 69, 52, 43, 23, 4, 51, 10, 12, 6, 10, 15, 40, 5, 13, 6, 23, 4, 69, 52, 48, 5, 6, 26, 26, 26, 63, 4, 19, 8, 13, 4, 48, 7, 6, 16, 13, 7, 15, 4, 52, 7, 9, 21, 16, 7, 21, 5, 4, 61, 9, 14, 5, 13, 12, 6, 7, 9, 14, 10, 9, 21, 4, 64, 48, 52, 61, 63, 4, 7, 9, 14, 4, 48, 7, 6, 16, 13, 7, 15, 4, 52, 7, 9, 21, 16, 7, 21, 5, 4, 53, 5, 9, 5, 13, 7, 6, 10, 8, 9, 4, 64, 48, 52, 53, 63, 4, 20, 10, 6, 11, 4, 8, 27, 5, 13, 4, 6, 11, 10, 13, 6, 22, 39, 6, 20, 8, 4, 24, 13, 5, 6, 13, 7, 10, 9, 5, 14, 4, 18, 8, 14, 5, 15, 12, 4, 10, 9, 4, 8, 9, 5, 4, 11, 16, 9, 14, 13, 5, 14, 4, 24, 15, 16, 12, 4, 15, 7, 9, 21, 16, 7, 21, 5, 12, 4, 7, 9, 14, 4, 14, 5, 5, 24, 4, 10, 9, 6, 5, 13, 8, 24, 5, 13, 7, 25, 10, 15, 10, 6, 22, 4, 25, 5, 6, 20, 5, 5, 9, 4, 58, 7, 37, 23, 4, 49, 22, 32, 8, 13, 17, 11, 4, 7, 9, 14, 4, 32, 5, 9, 12, 8, 13, 55, 15, 8, 20, 26, 2],
[4, 40, 47, 54, 32, 4, 10, 12, 4, 14, 5, 12, 10, 21, 9, 5, 14, 4, 6, 8, 4, 24, 13, 5, 39, 6, 13, 7, 10, 9, 4, 14, 5, 5, 24, 4, 25, 10, 14, 10, 13, 5, 17, 6, 10, 8, 9, 7, 15, 4, 13, 5, 24, 13, 5, 12, 5, 9, 6, 7, 6, 10, 8, 9, 12, 4, 19, 13, 8, 18, 4, 16, 9, 15, 7, 25, 5, 15, 5, 14, 4, 6, 5, 37, 6, 4, 25, 22, 4, 46, 8, 10, 9, 6, 15, 22, 4, 17, 8, 9, 14, 10, 6, 10, 8, 9, 10, 9, 21, 4, 8, 9, 4, 25, 8, 6, 11, 4, 15, 5, 19, 6, 4, 7, 9, 14, 4, 13, 10, 21, 11, 6, 4, 17, 8, 9, 6, 5, 37, 6, 4, 10, 9, 4, 7, 15, 15, 4, 15, 7, 22, 5, 13, 12, 26, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
[4, 32, 11, 5, 4, 45, 16, 10, 17, 28, 4, 25, 13, 8, 20, 9, 4, 19, 8, 37, 4, 46, 16, 18, 24, 12, 4, 8, 27, 5, 13, 4, 6, 11, 5, 4, 15, 7, 57, 22, 4, 14, 8, 21, 26, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
],
"""attention_mask""": [
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
]
}
# fmt: on
self.tokenizer_integration_test_util(
expected_encoding=UpperCAmelCase_ , model_name="""microsoft/speecht5_asr""" , revision="""c5ef64c71905caeccde0e4462ef3f9077224c524""" , sequences=UpperCAmelCase_ , )
| 347 | 0 |
class __A:
def __init__( self ) -> Tuple:
'''simple docstring'''
__a = 0
__a = 0
__a = {}
def SCREAMING_SNAKE_CASE_ ( self , _snake_case ) -> List[Any]:
'''simple docstring'''
if vertex not in self.adjacency:
__a = {}
self.num_vertices += 1
def SCREAMING_SNAKE_CASE_ ( self , _snake_case , _snake_case , _snake_case ) -> Optional[Any]:
'''simple docstring'''
self.add_vertex(UpperCAmelCase_ )
self.add_vertex(UpperCAmelCase_ )
if head == tail:
return
__a = weight
__a = weight
def SCREAMING_SNAKE_CASE_ ( self ) -> Optional[Any]:
'''simple docstring'''
__a = self.get_edges()
for edge in edges:
__a , __a , __a = edge
edges.remove((tail, head, weight) )
for i in range(len(UpperCAmelCase_ ) ):
__a = list(edges[i] )
edges.sort(key=lambda _snake_case : e[2] )
for i in range(len(UpperCAmelCase_ ) - 1 ):
if edges[i][2] >= edges[i + 1][2]:
__a = edges[i][2] + 1
for edge in edges:
__a , __a , __a = edge
__a = weight
__a = weight
def __str__( self ) -> str:
'''simple docstring'''
__a = ''''''
for tail in self.adjacency:
for head in self.adjacency[tail]:
__a = self.adjacency[head][tail]
string += F"""{head} -> {tail} == {weight}\n"""
return string.rstrip('''\n''' )
def SCREAMING_SNAKE_CASE_ ( self ) -> Optional[int]:
'''simple docstring'''
__a = []
for tail in self.adjacency:
for head in self.adjacency[tail]:
output.append((tail, head, self.adjacency[head][tail]) )
return output
def SCREAMING_SNAKE_CASE_ ( self ) -> Optional[int]:
'''simple docstring'''
return self.adjacency.keys()
@staticmethod
def SCREAMING_SNAKE_CASE_ ( _snake_case=None , _snake_case=None ) -> List[str]:
'''simple docstring'''
__a = Graph()
if vertices is None:
__a = []
if edges is None:
__a = []
for vertex in vertices:
g.add_vertex(UpperCAmelCase_ )
for edge in edges:
g.add_edge(*UpperCAmelCase_ )
return g
class __A:
def __init__( self ) -> Dict:
'''simple docstring'''
__a = {}
__a = {}
def __len__( self ) -> Any:
'''simple docstring'''
return len(self.parent )
def SCREAMING_SNAKE_CASE_ ( self , _snake_case ) -> Tuple:
'''simple docstring'''
if item in self.parent:
return self.find(UpperCAmelCase_ )
__a = item
__a = 0
return item
def SCREAMING_SNAKE_CASE_ ( self , _snake_case ) -> Tuple:
'''simple docstring'''
if item not in self.parent:
return self.make_set(UpperCAmelCase_ )
if item != self.parent[item]:
__a = self.find(self.parent[item] )
return self.parent[item]
def SCREAMING_SNAKE_CASE_ ( self , _snake_case , _snake_case ) -> Any:
'''simple docstring'''
__a = self.find(UpperCAmelCase_ )
__a = self.find(UpperCAmelCase_ )
if roota == roota:
return roota
if self.rank[roota] > self.rank[roota]:
__a = roota
return roota
if self.rank[roota] < self.rank[roota]:
__a = roota
return roota
if self.rank[roota] == self.rank[roota]:
self.rank[roota] += 1
__a = roota
return roota
return None
@staticmethod
def SCREAMING_SNAKE_CASE_ ( _snake_case ) -> List[str]:
'''simple docstring'''
__a = graph.num_vertices
__a = Graph.UnionFind()
__a = []
while num_components > 1:
__a = {}
for vertex in graph.get_vertices():
__a = -1
__a = graph.get_edges()
for edge in edges:
__a , __a , __a = edge
edges.remove((tail, head, weight) )
for edge in edges:
__a , __a , __a = edge
__a = union_find.find(UpperCAmelCase_ )
__a = union_find.find(UpperCAmelCase_ )
if seta != seta:
if cheap_edge[seta] == -1 or cheap_edge[seta][2] > weight:
__a = [head, tail, weight]
if cheap_edge[seta] == -1 or cheap_edge[seta][2] > weight:
__a = [head, tail, weight]
for vertex in cheap_edge:
if cheap_edge[vertex] != -1:
__a , __a , __a = cheap_edge[vertex]
if union_find.find(UpperCAmelCase_ ) != union_find.find(UpperCAmelCase_ ):
union_find.union(UpperCAmelCase_ , UpperCAmelCase_ )
mst_edges.append(cheap_edge[vertex] )
__a = num_components - 1
__a = Graph.build(edges=UpperCAmelCase_ )
return mst | 6 |
"""simple docstring"""
import datasets
__SCREAMING_SNAKE_CASE : Tuple = '\\n@InProceedings{conneau2018xnli,\n author = "Conneau, Alexis\n and Rinott, Ruty\n and Lample, Guillaume\n and Williams, Adina\n and Bowman, Samuel R.\n and Schwenk, Holger\n and Stoyanov, Veselin",\n title = "XNLI: Evaluating Cross-lingual Sentence Representations",\n booktitle = "Proceedings of the 2018 Conference on Empirical Methods\n in Natural Language Processing",\n year = "2018",\n publisher = "Association for Computational Linguistics",\n location = "Brussels, Belgium",\n}\n'
__SCREAMING_SNAKE_CASE : Dict = '\\nXNLI is a subset of a few thousand examples from MNLI which has been translated\ninto a 14 different languages (some low-ish resource). As with MNLI, the goal is\nto predict textual entailment (does sentence A imply/contradict/neither sentence\nB) and is a classification task (given two sentences, predict one of three\nlabels).\n'
__SCREAMING_SNAKE_CASE : List[str] = '\nComputes XNLI score which is just simple accuracy.\nArgs:\n predictions: Predicted labels.\n references: Ground truth labels.\nReturns:\n \'accuracy\': accuracy\nExamples:\n\n >>> predictions = [0, 1]\n >>> references = [0, 1]\n >>> xnli_metric = datasets.load_metric("xnli")\n >>> results = xnli_metric.compute(predictions=predictions, references=references)\n >>> print(results)\n {\'accuracy\': 1.0}\n'
def _a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> List[Any]:
return (preds == labels).mean()
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION)
class __A (datasets.Metric):
'''simple docstring'''
def lowerCAmelCase ( self : str ) ->Any:
"""simple docstring"""
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
"""predictions""": datasets.Value("""int64""" if self.config_name != """sts-b""" else """float32""" ),
"""references""": datasets.Value("""int64""" if self.config_name != """sts-b""" else """float32""" ),
} ) , codebase_urls=[] , reference_urls=[] , format="""numpy""" , )
def lowerCAmelCase ( self : Dict , UpperCAmelCase_ : Optional[int] , UpperCAmelCase_ : Any ) ->int:
"""simple docstring"""
return {"accuracy": simple_accuracy(UpperCAmelCase_ , UpperCAmelCase_ )}
| 347 | 0 |
"""simple docstring"""
def lowercase ( _snake_case : Tuple ) ->Union[str, Any]:
"""simple docstring"""
__snake_case : Tuple = len(_SCREAMING_SNAKE_CASE )
__snake_case : int = sum(_SCREAMING_SNAKE_CASE )
__snake_case : Optional[Any] = [[False for x in range(s + 1 )] for y in range(n + 1 )]
for i in range(1 , n + 1 ):
__snake_case : Dict = True
for i in range(1 , s + 1 ):
__snake_case : Tuple = False
for i in range(1 , n + 1 ):
for j in range(1 , s + 1 ):
__snake_case : List[Any] = dp[i][j - 1]
if arr[i - 1] <= j:
__snake_case : Tuple = dp[i][j] or dp[i - 1][j - arr[i - 1]]
for j in range(int(s / 2 ) , -1 , -1 ):
if dp[n][j] is True:
__snake_case : Any = s - 2 * j
break
return diff
| 102 |
"""simple docstring"""
from ..utils import DummyObject, requires_backends
class __A (metaclass=snake_case__):
'''simple docstring'''
__lowercase: List[Any] = ["""sentencepiece"""]
def __init__( self : int , *UpperCAmelCase_ : Any , **UpperCAmelCase_ : List[str] ) ->List[Any]:
"""simple docstring"""
requires_backends(self , ["""sentencepiece"""] )
class __A (metaclass=snake_case__):
'''simple docstring'''
__lowercase: Optional[int] = ["""sentencepiece"""]
def __init__( self : Optional[int] , *UpperCAmelCase_ : int , **UpperCAmelCase_ : Tuple ) ->Dict:
"""simple docstring"""
requires_backends(self , ["""sentencepiece"""] )
class __A (metaclass=snake_case__):
'''simple docstring'''
__lowercase: Any = ["""sentencepiece"""]
def __init__( self : Any , *UpperCAmelCase_ : Union[str, Any] , **UpperCAmelCase_ : List[Any] ) ->List[Any]:
"""simple docstring"""
requires_backends(self , ["""sentencepiece"""] )
class __A (metaclass=snake_case__):
'''simple docstring'''
__lowercase: Dict = ["""sentencepiece"""]
def __init__( self : List[str] , *UpperCAmelCase_ : Dict , **UpperCAmelCase_ : int ) ->Optional[int]:
"""simple docstring"""
requires_backends(self , ["""sentencepiece"""] )
class __A (metaclass=snake_case__):
'''simple docstring'''
__lowercase: List[str] = ["""sentencepiece"""]
def __init__( self : Dict , *UpperCAmelCase_ : Optional[Any] , **UpperCAmelCase_ : int ) ->List[str]:
"""simple docstring"""
requires_backends(self , ["""sentencepiece"""] )
class __A (metaclass=snake_case__):
'''simple docstring'''
__lowercase: int = ["""sentencepiece"""]
def __init__( self : Tuple , *UpperCAmelCase_ : Dict , **UpperCAmelCase_ : Dict ) ->Optional[int]:
"""simple docstring"""
requires_backends(self , ["""sentencepiece"""] )
class __A (metaclass=snake_case__):
'''simple docstring'''
__lowercase: int = ["""sentencepiece"""]
def __init__( self : int , *UpperCAmelCase_ : Union[str, Any] , **UpperCAmelCase_ : Union[str, Any] ) ->Dict:
"""simple docstring"""
requires_backends(self , ["""sentencepiece"""] )
class __A (metaclass=snake_case__):
'''simple docstring'''
__lowercase: Optional[Any] = ["""sentencepiece"""]
def __init__( self : List[str] , *UpperCAmelCase_ : Any , **UpperCAmelCase_ : Optional[Any] ) ->Union[str, Any]:
"""simple docstring"""
requires_backends(self , ["""sentencepiece"""] )
class __A (metaclass=snake_case__):
'''simple docstring'''
__lowercase: Optional[int] = ["""sentencepiece"""]
def __init__( self : Optional[Any] , *UpperCAmelCase_ : List[str] , **UpperCAmelCase_ : List[Any] ) ->Tuple:
"""simple docstring"""
requires_backends(self , ["""sentencepiece"""] )
class __A (metaclass=snake_case__):
'''simple docstring'''
__lowercase: Any = ["""sentencepiece"""]
def __init__( self : List[Any] , *UpperCAmelCase_ : str , **UpperCAmelCase_ : int ) ->List[Any]:
"""simple docstring"""
requires_backends(self , ["""sentencepiece"""] )
class __A (metaclass=snake_case__):
'''simple docstring'''
__lowercase: Any = ["""sentencepiece"""]
def __init__( self : int , *UpperCAmelCase_ : Optional[int] , **UpperCAmelCase_ : List[Any] ) ->Tuple:
"""simple docstring"""
requires_backends(self , ["""sentencepiece"""] )
class __A (metaclass=snake_case__):
'''simple docstring'''
__lowercase: int = ["""sentencepiece"""]
def __init__( self : Optional[int] , *UpperCAmelCase_ : Optional[int] , **UpperCAmelCase_ : Union[str, Any] ) ->Union[str, Any]:
"""simple docstring"""
requires_backends(self , ["""sentencepiece"""] )
class __A (metaclass=snake_case__):
'''simple docstring'''
__lowercase: Tuple = ["""sentencepiece"""]
def __init__( self : Dict , *UpperCAmelCase_ : Union[str, Any] , **UpperCAmelCase_ : List[Any] ) ->Dict:
"""simple docstring"""
requires_backends(self , ["""sentencepiece"""] )
class __A (metaclass=snake_case__):
'''simple docstring'''
__lowercase: Union[str, Any] = ["""sentencepiece"""]
def __init__( self : List[Any] , *UpperCAmelCase_ : Dict , **UpperCAmelCase_ : List[Any] ) ->Optional[int]:
"""simple docstring"""
requires_backends(self , ["""sentencepiece"""] )
class __A (metaclass=snake_case__):
'''simple docstring'''
__lowercase: int = ["""sentencepiece"""]
def __init__( self : int , *UpperCAmelCase_ : List[Any] , **UpperCAmelCase_ : List[str] ) ->Union[str, Any]:
"""simple docstring"""
requires_backends(self , ["""sentencepiece"""] )
class __A (metaclass=snake_case__):
'''simple docstring'''
__lowercase: Tuple = ["""sentencepiece"""]
def __init__( self : int , *UpperCAmelCase_ : Tuple , **UpperCAmelCase_ : Optional[Any] ) ->str:
"""simple docstring"""
requires_backends(self , ["""sentencepiece"""] )
class __A (metaclass=snake_case__):
'''simple docstring'''
__lowercase: List[Any] = ["""sentencepiece"""]
def __init__( self : Dict , *UpperCAmelCase_ : str , **UpperCAmelCase_ : Optional[Any] ) ->int:
"""simple docstring"""
requires_backends(self , ["""sentencepiece"""] )
class __A (metaclass=snake_case__):
'''simple docstring'''
__lowercase: Union[str, Any] = ["""sentencepiece"""]
def __init__( self : Optional[Any] , *UpperCAmelCase_ : List[Any] , **UpperCAmelCase_ : Optional[int] ) ->Optional[int]:
"""simple docstring"""
requires_backends(self , ["""sentencepiece"""] )
class __A (metaclass=snake_case__):
'''simple docstring'''
__lowercase: Any = ["""sentencepiece"""]
def __init__( self : Optional[Any] , *UpperCAmelCase_ : List[Any] , **UpperCAmelCase_ : Dict ) ->Dict:
"""simple docstring"""
requires_backends(self , ["""sentencepiece"""] )
class __A (metaclass=snake_case__):
'''simple docstring'''
__lowercase: Any = ["""sentencepiece"""]
def __init__( self : List[Any] , *UpperCAmelCase_ : List[str] , **UpperCAmelCase_ : List[str] ) ->List[Any]:
"""simple docstring"""
requires_backends(self , ["""sentencepiece"""] )
class __A (metaclass=snake_case__):
'''simple docstring'''
__lowercase: int = ["""sentencepiece"""]
def __init__( self : Union[str, Any] , *UpperCAmelCase_ : Optional[Any] , **UpperCAmelCase_ : Optional[Any] ) ->List[str]:
"""simple docstring"""
requires_backends(self , ["""sentencepiece"""] )
class __A (metaclass=snake_case__):
'''simple docstring'''
__lowercase: int = ["""sentencepiece"""]
def __init__( self : Union[str, Any] , *UpperCAmelCase_ : Optional[Any] , **UpperCAmelCase_ : List[Any] ) ->Union[str, Any]:
"""simple docstring"""
requires_backends(self , ["""sentencepiece"""] )
class __A (metaclass=snake_case__):
'''simple docstring'''
__lowercase: List[Any] = ["""sentencepiece"""]
def __init__( self : Any , *UpperCAmelCase_ : Dict , **UpperCAmelCase_ : Optional[Any] ) ->List[str]:
"""simple docstring"""
requires_backends(self , ["""sentencepiece"""] )
class __A (metaclass=snake_case__):
'''simple docstring'''
__lowercase: Dict = ["""sentencepiece"""]
def __init__( self : int , *UpperCAmelCase_ : str , **UpperCAmelCase_ : Union[str, Any] ) ->List[Any]:
"""simple docstring"""
requires_backends(self , ["""sentencepiece"""] )
class __A (metaclass=snake_case__):
'''simple docstring'''
__lowercase: List[Any] = ["""sentencepiece"""]
def __init__( self : List[Any] , *UpperCAmelCase_ : int , **UpperCAmelCase_ : Optional[int] ) ->Optional[Any]:
"""simple docstring"""
requires_backends(self , ["""sentencepiece"""] )
class __A (metaclass=snake_case__):
'''simple docstring'''
__lowercase: Union[str, Any] = ["""sentencepiece"""]
def __init__( self : Union[str, Any] , *UpperCAmelCase_ : Any , **UpperCAmelCase_ : str ) ->Optional[int]:
"""simple docstring"""
requires_backends(self , ["""sentencepiece"""] )
class __A (metaclass=snake_case__):
'''simple docstring'''
__lowercase: Optional[int] = ["""sentencepiece"""]
def __init__( self : Tuple , *UpperCAmelCase_ : Dict , **UpperCAmelCase_ : Optional[int] ) ->Dict:
"""simple docstring"""
requires_backends(self , ["""sentencepiece"""] )
class __A (metaclass=snake_case__):
'''simple docstring'''
__lowercase: Dict = ["""sentencepiece"""]
def __init__( self : Optional[int] , *UpperCAmelCase_ : Dict , **UpperCAmelCase_ : List[str] ) ->Optional[Any]:
"""simple docstring"""
requires_backends(self , ["""sentencepiece"""] )
class __A (metaclass=snake_case__):
'''simple docstring'''
__lowercase: int = ["""sentencepiece"""]
def __init__( self : Dict , *UpperCAmelCase_ : Union[str, Any] , **UpperCAmelCase_ : Optional[int] ) ->Any:
"""simple docstring"""
requires_backends(self , ["""sentencepiece"""] )
class __A (metaclass=snake_case__):
'''simple docstring'''
__lowercase: List[str] = ["""sentencepiece"""]
def __init__( self : List[str] , *UpperCAmelCase_ : Dict , **UpperCAmelCase_ : Union[str, Any] ) ->Optional[Any]:
"""simple docstring"""
requires_backends(self , ["""sentencepiece"""] )
class __A (metaclass=snake_case__):
'''simple docstring'''
__lowercase: Any = ["""sentencepiece"""]
def __init__( self : List[str] , *UpperCAmelCase_ : List[Any] , **UpperCAmelCase_ : Optional[int] ) ->str:
"""simple docstring"""
requires_backends(self , ["""sentencepiece"""] )
| 347 | 0 |
'''simple docstring'''
from __future__ import annotations
a : List[str] = '#'
class UpperCamelCase_ :
def __init__( self ) -> None:
UpperCAmelCase : Tuple = {}
def _lowercase( self , A ) -> None:
UpperCAmelCase : Tuple = self._trie
for char in text:
if char not in trie:
UpperCAmelCase : Dict = {}
UpperCAmelCase : Optional[int] = trie[char]
UpperCAmelCase : Tuple = True
def _lowercase( self , A ) -> tuple | list:
UpperCAmelCase : List[Any] = self._trie
for char in prefix:
if char in trie:
UpperCAmelCase : Union[str, Any] = trie[char]
else:
return []
return self._elements(UpperCAmelCase_ )
def _lowercase( self , A ) -> tuple:
UpperCAmelCase : int = []
for c, v in d.items():
UpperCAmelCase : Any = [""" """] if c == END else [(c + s) for s in self._elements(UpperCAmelCase_ )]
result.extend(UpperCAmelCase_ )
return tuple(UpperCAmelCase_ )
a : Dict = Trie()
a : Dict = ('depart', 'detergent', 'daring', 'dog', 'deer', 'deal')
for word in words:
trie.insert_word(word)
def __lowerCamelCase ( _lowercase ) -> tuple:
UpperCAmelCase : int = trie.find_word(_SCREAMING_SNAKE_CASE )
return tuple(string + word for word in suffixes )
def __lowerCamelCase ( ) -> None:
print(autocomplete_using_trie("""de""" ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
main()
| 265 |
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_mobilevit import MobileViTImageProcessor
__SCREAMING_SNAKE_CASE : int = logging.get_logger(__name__)
class __A (snake_case__):
'''simple docstring'''
def __init__( self : str , *UpperCAmelCase_ : Dict , **UpperCAmelCase_ : int ) ->None:
"""simple docstring"""
warnings.warn(
"""The class MobileViTFeatureExtractor is deprecated and will be removed in version 5 of Transformers."""
""" Please use MobileViTImageProcessor instead.""" , UpperCAmelCase_ , )
super().__init__(*UpperCAmelCase_ , **UpperCAmelCase_ )
| 347 | 0 |
import argparse
import json
from collections import OrderedDict
from functools import partial
from pathlib import Path
import timm
import torch
from huggingface_hub import hf_hub_download
from transformers import LevitConfig, LevitForImageClassificationWithTeacher, LevitImageProcessor
from transformers.utils import logging
logging.set_verbosity_info()
__UpperCAmelCase = logging.get_logger()
def UpperCamelCase ( snake_case__ : Tuple , snake_case__ : Optional[int] , snake_case__ : List[Any] , snake_case__ : int , snake_case__ : str = True ) -> Dict:
print(F"""Converting {name}...""" )
with torch.no_grad():
if hidden_sizes == 128:
if name[-1] == "S":
UpperCamelCase : int = timm.create_model('levit_128s' , pretrained=_SCREAMING_SNAKE_CASE )
else:
UpperCamelCase : Optional[int] = timm.create_model('levit_128' , pretrained=_SCREAMING_SNAKE_CASE )
if hidden_sizes == 192:
UpperCamelCase : List[str] = timm.create_model('levit_192' , pretrained=_SCREAMING_SNAKE_CASE )
if hidden_sizes == 256:
UpperCamelCase : Optional[int] = timm.create_model('levit_256' , pretrained=_SCREAMING_SNAKE_CASE )
if hidden_sizes == 384:
UpperCamelCase : str = timm.create_model('levit_384' , pretrained=_SCREAMING_SNAKE_CASE )
from_model.eval()
UpperCamelCase : List[Any] = LevitForImageClassificationWithTeacher(_SCREAMING_SNAKE_CASE ).eval()
UpperCamelCase : Any = OrderedDict()
UpperCamelCase : str = from_model.state_dict()
UpperCamelCase : Optional[int] = list(from_model.state_dict().keys() )
UpperCamelCase : Optional[int] = list(our_model.state_dict().keys() )
print(len(_SCREAMING_SNAKE_CASE ) , len(_SCREAMING_SNAKE_CASE ) )
for i in range(len(_SCREAMING_SNAKE_CASE ) ):
UpperCamelCase : List[Any] = weights[og_keys[i]]
our_model.load_state_dict(_SCREAMING_SNAKE_CASE )
UpperCamelCase : List[str] = torch.randn((2, 3, 224, 224) )
UpperCamelCase : int = from_model(_SCREAMING_SNAKE_CASE )
UpperCamelCase : Dict = our_model(_SCREAMING_SNAKE_CASE ).logits
assert torch.allclose(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ), "The model logits don't match the original one."
UpperCamelCase : Optional[int] = name
print(_SCREAMING_SNAKE_CASE )
if push_to_hub:
our_model.save_pretrained(save_directory / checkpoint_name )
UpperCamelCase : Optional[int] = LevitImageProcessor()
image_processor.save_pretrained(save_directory / checkpoint_name )
print(F"""Pushed {checkpoint_name}""" )
def UpperCamelCase ( snake_case__ : Optional[int] , snake_case__ : Optional[int] = None , snake_case__ : str = True ) -> Optional[int]:
UpperCamelCase : Tuple = 'imagenet-1k-id2label.json'
UpperCamelCase : Any = 1000
UpperCamelCase : Optional[int] = (1, num_labels)
UpperCamelCase : Any = 'huggingface/label-files'
UpperCamelCase : str = num_labels
UpperCamelCase : Union[str, Any] = json.load(open(hf_hub_download(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , repo_type='dataset' ) , 'r' ) )
UpperCamelCase : Optional[int] = {int(_SCREAMING_SNAKE_CASE ): v for k, v in idalabel.items()}
UpperCamelCase : List[str] = idalabel
UpperCamelCase : List[str] = {v: k for k, v in idalabel.items()}
UpperCamelCase : int = partial(_SCREAMING_SNAKE_CASE , num_labels=_SCREAMING_SNAKE_CASE , idalabel=_SCREAMING_SNAKE_CASE , labelaid=_SCREAMING_SNAKE_CASE )
UpperCamelCase : str = {
'levit-128S': 128,
'levit-128': 128,
'levit-192': 192,
'levit-256': 256,
'levit-384': 384,
}
UpperCamelCase : Union[str, Any] = {
'levit-128S': ImageNetPreTrainedConfig(
hidden_sizes=[128, 256, 384] , num_attention_heads=[4, 6, 8] , depths=[2, 3, 4] , key_dim=[16, 16, 16] , drop_path_rate=0 , ),
'levit-128': ImageNetPreTrainedConfig(
hidden_sizes=[128, 256, 384] , num_attention_heads=[4, 8, 12] , depths=[4, 4, 4] , key_dim=[16, 16, 16] , drop_path_rate=0 , ),
'levit-192': ImageNetPreTrainedConfig(
hidden_sizes=[192, 288, 384] , num_attention_heads=[3, 5, 6] , depths=[4, 4, 4] , key_dim=[32, 32, 32] , drop_path_rate=0 , ),
'levit-256': ImageNetPreTrainedConfig(
hidden_sizes=[256, 384, 512] , num_attention_heads=[4, 6, 8] , depths=[4, 4, 4] , key_dim=[32, 32, 32] , drop_path_rate=0 , ),
'levit-384': ImageNetPreTrainedConfig(
hidden_sizes=[384, 512, 768] , num_attention_heads=[6, 9, 12] , depths=[4, 4, 4] , key_dim=[32, 32, 32] , drop_path_rate=0.1 , ),
}
if model_name:
convert_weight_and_push(
names_to_hidden_sizes[model_name] , _SCREAMING_SNAKE_CASE , names_to_config[model_name] , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
else:
for model_name, config in names_to_config.items():
convert_weight_and_push(names_to_hidden_sizes[model_name] , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
return config, expected_shape
if __name__ == "__main__":
__UpperCAmelCase = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--model_name''',
default=None,
type=str,
help='''The name of the model you wish to convert, it must be one of the supported Levit* architecture,''',
)
parser.add_argument(
'''--pytorch_dump_folder_path''',
default='''levit-dump-folder/''',
type=Path,
required=False,
help='''Path to the output PyTorch model directory.''',
)
parser.add_argument('''--push_to_hub''', action='''store_true''', help='''Push model and image processor to the hub''')
parser.add_argument(
'''--no-push_to_hub''',
dest='''push_to_hub''',
action='''store_false''',
help='''Do not push model and image processor to the hub''',
)
__UpperCAmelCase = parser.parse_args()
__UpperCAmelCase = args.pytorch_dump_folder_path
pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)
| 119 |
"""simple docstring"""
import argparse
import json
from pathlib import Path
import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import DeiTImageProcessor, ViTConfig, ViTForImageClassification, ViTImageProcessor, ViTModel
from transformers.utils import logging
logging.set_verbosity_info()
__SCREAMING_SNAKE_CASE : Tuple = logging.get_logger(__name__)
def _a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=False ) -> Any:
snake_case_ = []
for i in range(config.num_hidden_layers ):
# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
rename_keys.append((f"""blocks.{i}.norm1.weight""", f"""vit.encoder.layer.{i}.layernorm_before.weight""") )
rename_keys.append((f"""blocks.{i}.norm1.bias""", f"""vit.encoder.layer.{i}.layernorm_before.bias""") )
rename_keys.append((f"""blocks.{i}.attn.proj.weight""", f"""vit.encoder.layer.{i}.attention.output.dense.weight""") )
rename_keys.append((f"""blocks.{i}.attn.proj.bias""", f"""vit.encoder.layer.{i}.attention.output.dense.bias""") )
rename_keys.append((f"""blocks.{i}.norm2.weight""", f"""vit.encoder.layer.{i}.layernorm_after.weight""") )
rename_keys.append((f"""blocks.{i}.norm2.bias""", f"""vit.encoder.layer.{i}.layernorm_after.bias""") )
rename_keys.append((f"""blocks.{i}.mlp.fc1.weight""", f"""vit.encoder.layer.{i}.intermediate.dense.weight""") )
rename_keys.append((f"""blocks.{i}.mlp.fc1.bias""", f"""vit.encoder.layer.{i}.intermediate.dense.bias""") )
rename_keys.append((f"""blocks.{i}.mlp.fc2.weight""", f"""vit.encoder.layer.{i}.output.dense.weight""") )
rename_keys.append((f"""blocks.{i}.mlp.fc2.bias""", f"""vit.encoder.layer.{i}.output.dense.bias""") )
# projection layer + position embeddings
rename_keys.extend(
[
("""cls_token""", """vit.embeddings.cls_token"""),
("""patch_embed.proj.weight""", """vit.embeddings.patch_embeddings.projection.weight"""),
("""patch_embed.proj.bias""", """vit.embeddings.patch_embeddings.projection.bias"""),
("""pos_embed""", """vit.embeddings.position_embeddings"""),
] )
if base_model:
# layernorm + pooler
rename_keys.extend(
[
("""norm.weight""", """layernorm.weight"""),
("""norm.bias""", """layernorm.bias"""),
("""pre_logits.fc.weight""", """pooler.dense.weight"""),
("""pre_logits.fc.bias""", """pooler.dense.bias"""),
] )
# if just the base model, we should remove "vit" from all keys that start with "vit"
snake_case_ = [(pair[0], pair[1][4:]) if pair[1].startswith("""vit""" ) else pair for pair in rename_keys]
else:
# layernorm + classification head
rename_keys.extend(
[
("""norm.weight""", """vit.layernorm.weight"""),
("""norm.bias""", """vit.layernorm.bias"""),
("""head.weight""", """classifier.weight"""),
("""head.bias""", """classifier.bias"""),
] )
return rename_keys
def _a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=False ) -> Tuple:
for i in range(config.num_hidden_layers ):
if base_model:
snake_case_ = """"""
else:
snake_case_ = """vit."""
# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
snake_case_ = state_dict.pop(f"""blocks.{i}.attn.qkv.weight""" )
snake_case_ = state_dict.pop(f"""blocks.{i}.attn.qkv.bias""" )
# next, add query, keys and values (in that order) to the state dict
snake_case_ = in_proj_weight[
: config.hidden_size, :
]
snake_case_ = in_proj_bias[: config.hidden_size]
snake_case_ = in_proj_weight[
config.hidden_size : config.hidden_size * 2, :
]
snake_case_ = in_proj_bias[
config.hidden_size : config.hidden_size * 2
]
snake_case_ = in_proj_weight[
-config.hidden_size :, :
]
snake_case_ = in_proj_bias[-config.hidden_size :]
def _a ( _SCREAMING_SNAKE_CASE ) -> List[str]:
snake_case_ = ["""head.weight""", """head.bias"""]
for k in ignore_keys:
state_dict.pop(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
def _a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> str:
snake_case_ = dct.pop(_SCREAMING_SNAKE_CASE )
snake_case_ = val
def _a ( ) -> Any:
snake_case_ = """http://images.cocodataset.org/val2017/000000039769.jpg"""
snake_case_ = Image.open(requests.get(_SCREAMING_SNAKE_CASE , stream=_SCREAMING_SNAKE_CASE ).raw )
return im
@torch.no_grad()
def _a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> Any:
snake_case_ = ViTConfig()
snake_case_ = False
# dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
if vit_name[-5:] == "in21k":
snake_case_ = True
snake_case_ = int(vit_name[-12:-10] )
snake_case_ = int(vit_name[-9:-6] )
else:
snake_case_ = 1_000
snake_case_ = """huggingface/label-files"""
snake_case_ = """imagenet-1k-id2label.json"""
snake_case_ = json.load(open(hf_hub_download(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , repo_type="""dataset""" ) , """r""" ) )
snake_case_ = {int(_SCREAMING_SNAKE_CASE ): v for k, v in idalabel.items()}
snake_case_ = idalabel
snake_case_ = {v: k for k, v in idalabel.items()}
snake_case_ = int(vit_name[-6:-4] )
snake_case_ = int(vit_name[-3:] )
# size of the architecture
if "deit" in vit_name:
if vit_name[9:].startswith("""tiny""" ):
snake_case_ = 192
snake_case_ = 768
snake_case_ = 12
snake_case_ = 3
elif vit_name[9:].startswith("""small""" ):
snake_case_ = 384
snake_case_ = 1_536
snake_case_ = 12
snake_case_ = 6
else:
pass
else:
if vit_name[4:].startswith("""small""" ):
snake_case_ = 768
snake_case_ = 2_304
snake_case_ = 8
snake_case_ = 8
elif vit_name[4:].startswith("""base""" ):
pass
elif vit_name[4:].startswith("""large""" ):
snake_case_ = 1_024
snake_case_ = 4_096
snake_case_ = 24
snake_case_ = 16
elif vit_name[4:].startswith("""huge""" ):
snake_case_ = 1_280
snake_case_ = 5_120
snake_case_ = 32
snake_case_ = 16
# load original model from timm
snake_case_ = timm.create_model(_SCREAMING_SNAKE_CASE , pretrained=_SCREAMING_SNAKE_CASE )
timm_model.eval()
# load state_dict of original model, remove and rename some keys
snake_case_ = timm_model.state_dict()
if base_model:
remove_classification_head_(_SCREAMING_SNAKE_CASE )
snake_case_ = create_rename_keys(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
for src, dest in rename_keys:
rename_key(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
read_in_q_k_v(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
# load HuggingFace model
if vit_name[-5:] == "in21k":
snake_case_ = ViTModel(_SCREAMING_SNAKE_CASE ).eval()
else:
snake_case_ = ViTForImageClassification(_SCREAMING_SNAKE_CASE ).eval()
model.load_state_dict(_SCREAMING_SNAKE_CASE )
# Check outputs on an image, prepared by ViTImageProcessor/DeiTImageProcessor
if "deit" in vit_name:
snake_case_ = DeiTImageProcessor(size=config.image_size )
else:
snake_case_ = ViTImageProcessor(size=config.image_size )
snake_case_ = image_processor(images=prepare_img() , return_tensors="""pt""" )
snake_case_ = encoding["""pixel_values"""]
snake_case_ = model(_SCREAMING_SNAKE_CASE )
if base_model:
snake_case_ = timm_model.forward_features(_SCREAMING_SNAKE_CASE )
assert timm_pooled_output.shape == outputs.pooler_output.shape
assert torch.allclose(_SCREAMING_SNAKE_CASE , outputs.pooler_output , atol=1E-3 )
else:
snake_case_ = timm_model(_SCREAMING_SNAKE_CASE )
assert timm_logits.shape == outputs.logits.shape
assert torch.allclose(_SCREAMING_SNAKE_CASE , outputs.logits , atol=1E-3 )
Path(_SCREAMING_SNAKE_CASE ).mkdir(exist_ok=_SCREAMING_SNAKE_CASE )
print(f"""Saving model {vit_name} to {pytorch_dump_folder_path}""" )
model.save_pretrained(_SCREAMING_SNAKE_CASE )
print(f"""Saving image processor to {pytorch_dump_folder_path}""" )
image_processor.save_pretrained(_SCREAMING_SNAKE_CASE )
if __name__ == "__main__":
__SCREAMING_SNAKE_CASE : int = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--vit_name',
default='vit_base_patch16_224',
type=str,
help='Name of the ViT timm model you\'d like to convert.',
)
parser.add_argument(
'--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
)
__SCREAMING_SNAKE_CASE : int = parser.parse_args()
convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path)
| 347 | 0 |
'''simple docstring'''
import argparse
import json
import os
import sys
import tempfile
import unittest
from argparse import Namespace
from dataclasses import dataclass, field
from enum import Enum
from pathlib import Path
from typing import List, Literal, Optional
import yaml
from transformers import HfArgumentParser, TrainingArguments
from transformers.hf_argparser import make_choice_type_function, string_to_bool
# Since Python 3.10, we can use the builtin `|` operator for Union types
# See PEP 604: https://peps.python.org/pep-0604
a__ : int = sys.version_info >= (3, 1_0)
def _lowercase ( __A=None ,__A=None ):
'''simple docstring'''
return field(default_factory=lambda: default ,metadata=_SCREAMING_SNAKE_CASE )
@dataclass
class UpperCAmelCase__ :
__SCREAMING_SNAKE_CASE = 42
__SCREAMING_SNAKE_CASE = 42
__SCREAMING_SNAKE_CASE = 42
__SCREAMING_SNAKE_CASE = 42
@dataclass
class UpperCAmelCase__ :
__SCREAMING_SNAKE_CASE = 4_2
__SCREAMING_SNAKE_CASE = field(default='''toto''' , metadata={'''help''': '''help message'''})
@dataclass
class UpperCAmelCase__ :
__SCREAMING_SNAKE_CASE = False
__SCREAMING_SNAKE_CASE = True
__SCREAMING_SNAKE_CASE = None
class UpperCAmelCase__ ( snake_case__):
__SCREAMING_SNAKE_CASE = """titi"""
__SCREAMING_SNAKE_CASE = """toto"""
class UpperCAmelCase__ ( snake_case__):
__SCREAMING_SNAKE_CASE = """titi"""
__SCREAMING_SNAKE_CASE = """toto"""
__SCREAMING_SNAKE_CASE = 4_2
@dataclass
class UpperCAmelCase__ :
__SCREAMING_SNAKE_CASE = "toto"
def __lowerCamelCase ( self ) -> List[Any]:
__UpperCamelCase = BasicEnum(self.foo )
@dataclass
class UpperCAmelCase__ :
__SCREAMING_SNAKE_CASE = "toto"
def __lowerCamelCase ( self ) -> Optional[Any]:
__UpperCamelCase = MixedTypeEnum(self.foo )
@dataclass
class UpperCAmelCase__ :
__SCREAMING_SNAKE_CASE = None
__SCREAMING_SNAKE_CASE = field(default=snake_case__ , metadata={'''help''': '''help message'''})
__SCREAMING_SNAKE_CASE = None
__SCREAMING_SNAKE_CASE = list_field(default=[])
__SCREAMING_SNAKE_CASE = list_field(default=[])
@dataclass
class UpperCAmelCase__ :
__SCREAMING_SNAKE_CASE = list_field(default=[])
__SCREAMING_SNAKE_CASE = list_field(default=[1, 2, 3])
__SCREAMING_SNAKE_CASE = list_field(default=['''Hallo''', '''Bonjour''', '''Hello'''])
__SCREAMING_SNAKE_CASE = list_field(default=[0.1, 0.2, 0.3])
@dataclass
class UpperCAmelCase__ :
__SCREAMING_SNAKE_CASE = field()
__SCREAMING_SNAKE_CASE = field()
__SCREAMING_SNAKE_CASE = field()
def __lowerCamelCase ( self ) -> str:
__UpperCamelCase = BasicEnum(self.required_enum )
@dataclass
class UpperCAmelCase__ :
__SCREAMING_SNAKE_CASE = 42
__SCREAMING_SNAKE_CASE = field()
__SCREAMING_SNAKE_CASE = None
__SCREAMING_SNAKE_CASE = field(default='''toto''' , metadata={'''help''': '''help message'''})
__SCREAMING_SNAKE_CASE = list_field(default=['''Hallo''', '''Bonjour''', '''Hello'''])
if is_python_no_less_than_3_10:
@dataclass
class UpperCAmelCase__ :
__SCREAMING_SNAKE_CASE = False
__SCREAMING_SNAKE_CASE = True
__SCREAMING_SNAKE_CASE = None
@dataclass
class UpperCAmelCase__ :
__SCREAMING_SNAKE_CASE = None
__SCREAMING_SNAKE_CASE = field(default=snake_case__ , metadata={'''help''': '''help message'''})
__SCREAMING_SNAKE_CASE = None
__SCREAMING_SNAKE_CASE = list_field(default=[])
__SCREAMING_SNAKE_CASE = list_field(default=[])
class UpperCAmelCase__ ( unittest.TestCase):
def __lowerCamelCase ( self , lowercase , lowercase ) -> Optional[int]:
self.assertEqual(len(a._actions ) , len(b._actions ) )
for x, y in zip(a._actions , b._actions ):
__UpperCamelCase = {k: v for k, v in vars(UpperCAmelCase_ ).items() if k != """container"""}
__UpperCamelCase = {k: v for k, v in vars(UpperCAmelCase_ ).items() if k != """container"""}
# Choices with mixed type have custom function as "type"
# So we need to compare results directly for equality
if xx.get("""choices""" , UpperCAmelCase_ ) and yy.get("""choices""" , UpperCAmelCase_ ):
for expected_choice in yy["choices"] + xx["choices"]:
self.assertEqual(xx["""type"""](UpperCAmelCase_ ) , yy["""type"""](UpperCAmelCase_ ) )
del xx["type"], yy["type"]
self.assertEqual(UpperCAmelCase_ , UpperCAmelCase_ )
def __lowerCamelCase ( self ) -> Any:
__UpperCamelCase = HfArgumentParser(UpperCAmelCase_ )
__UpperCamelCase = argparse.ArgumentParser()
expected.add_argument("""--foo""" , type=UpperCAmelCase_ , required=UpperCAmelCase_ )
expected.add_argument("""--bar""" , type=UpperCAmelCase_ , required=UpperCAmelCase_ )
expected.add_argument("""--baz""" , type=UpperCAmelCase_ , required=UpperCAmelCase_ )
expected.add_argument("""--flag""" , type=UpperCAmelCase_ , default=UpperCAmelCase_ , const=UpperCAmelCase_ , nargs="""?""" )
self.argparsersEqual(UpperCAmelCase_ , UpperCAmelCase_ )
__UpperCamelCase = ["""--foo""", """1""", """--baz""", """quux""", """--bar""", """0.5"""]
((__UpperCamelCase ) , ) = parser.parse_args_into_dataclasses(UpperCAmelCase_ , look_for_args_file=UpperCAmelCase_ )
self.assertFalse(example.flag )
def __lowerCamelCase ( self ) -> List[Any]:
__UpperCamelCase = HfArgumentParser(UpperCAmelCase_ )
__UpperCamelCase = argparse.ArgumentParser()
expected.add_argument("""--foo""" , default=4_2 , type=UpperCAmelCase_ )
expected.add_argument("""--baz""" , default="""toto""" , type=UpperCAmelCase_ , help="""help message""" )
self.argparsersEqual(UpperCAmelCase_ , UpperCAmelCase_ )
def __lowerCamelCase ( self ) -> Union[str, Any]:
__UpperCamelCase = argparse.ArgumentParser()
expected.add_argument("""--foo""" , type=UpperCAmelCase_ , default=UpperCAmelCase_ , const=UpperCAmelCase_ , nargs="""?""" )
expected.add_argument("""--baz""" , type=UpperCAmelCase_ , default=UpperCAmelCase_ , const=UpperCAmelCase_ , nargs="""?""" )
# A boolean no_* argument always has to come after its "default: True" regular counter-part
# and its default must be set to False
expected.add_argument("""--no_baz""" , action="""store_false""" , default=UpperCAmelCase_ , dest="""baz""" )
expected.add_argument("""--opt""" , type=UpperCAmelCase_ , default=UpperCAmelCase_ )
__UpperCamelCase = [WithDefaultBoolExample]
if is_python_no_less_than_3_10:
dataclass_types.append(UpperCAmelCase_ )
for dataclass_type in dataclass_types:
__UpperCamelCase = HfArgumentParser(UpperCAmelCase_ )
self.argparsersEqual(UpperCAmelCase_ , UpperCAmelCase_ )
__UpperCamelCase = parser.parse_args([] )
self.assertEqual(UpperCAmelCase_ , Namespace(foo=UpperCAmelCase_ , baz=UpperCAmelCase_ , opt=UpperCAmelCase_ ) )
__UpperCamelCase = parser.parse_args(["""--foo""", """--no_baz"""] )
self.assertEqual(UpperCAmelCase_ , Namespace(foo=UpperCAmelCase_ , baz=UpperCAmelCase_ , opt=UpperCAmelCase_ ) )
__UpperCamelCase = parser.parse_args(["""--foo""", """--baz"""] )
self.assertEqual(UpperCAmelCase_ , Namespace(foo=UpperCAmelCase_ , baz=UpperCAmelCase_ , opt=UpperCAmelCase_ ) )
__UpperCamelCase = parser.parse_args(["""--foo""", """True""", """--baz""", """True""", """--opt""", """True"""] )
self.assertEqual(UpperCAmelCase_ , Namespace(foo=UpperCAmelCase_ , baz=UpperCAmelCase_ , opt=UpperCAmelCase_ ) )
__UpperCamelCase = parser.parse_args(["""--foo""", """False""", """--baz""", """False""", """--opt""", """False"""] )
self.assertEqual(UpperCAmelCase_ , Namespace(foo=UpperCAmelCase_ , baz=UpperCAmelCase_ , opt=UpperCAmelCase_ ) )
def __lowerCamelCase ( self ) -> List[str]:
__UpperCamelCase = HfArgumentParser(UpperCAmelCase_ )
__UpperCamelCase = argparse.ArgumentParser()
expected.add_argument(
"""--foo""" , default="""toto""" , choices=["""titi""", """toto""", 4_2] , type=make_choice_type_function(["""titi""", """toto""", 4_2] ) , )
self.argparsersEqual(UpperCAmelCase_ , UpperCAmelCase_ )
__UpperCamelCase = parser.parse_args([] )
self.assertEqual(args.foo , """toto""" )
__UpperCamelCase = parser.parse_args_into_dataclasses([] )[0]
self.assertEqual(enum_ex.foo , MixedTypeEnum.toto )
__UpperCamelCase = parser.parse_args(["""--foo""", """titi"""] )
self.assertEqual(args.foo , """titi""" )
__UpperCamelCase = parser.parse_args_into_dataclasses(["""--foo""", """titi"""] )[0]
self.assertEqual(enum_ex.foo , MixedTypeEnum.titi )
__UpperCamelCase = parser.parse_args(["""--foo""", """42"""] )
self.assertEqual(args.foo , 4_2 )
__UpperCamelCase = parser.parse_args_into_dataclasses(["""--foo""", """42"""] )[0]
self.assertEqual(enum_ex.foo , MixedTypeEnum.fourtytwo )
def __lowerCamelCase ( self ) -> str:
@dataclass
class UpperCAmelCase__ :
__SCREAMING_SNAKE_CASE = "toto"
__UpperCamelCase = HfArgumentParser(UpperCAmelCase_ )
__UpperCamelCase = argparse.ArgumentParser()
expected.add_argument(
"""--foo""" , default="""toto""" , choices=("""titi""", """toto""", 4_2) , type=make_choice_type_function(["""titi""", """toto""", 4_2] ) , )
self.argparsersEqual(UpperCAmelCase_ , UpperCAmelCase_ )
__UpperCamelCase = parser.parse_args([] )
self.assertEqual(args.foo , """toto""" )
__UpperCamelCase = parser.parse_args(["""--foo""", """titi"""] )
self.assertEqual(args.foo , """titi""" )
__UpperCamelCase = parser.parse_args(["""--foo""", """42"""] )
self.assertEqual(args.foo , 4_2 )
def __lowerCamelCase ( self ) -> Dict:
__UpperCamelCase = HfArgumentParser(UpperCAmelCase_ )
__UpperCamelCase = argparse.ArgumentParser()
expected.add_argument("""--foo_int""" , nargs="""+""" , default=[] , type=UpperCAmelCase_ )
expected.add_argument("""--bar_int""" , nargs="""+""" , default=[1, 2, 3] , type=UpperCAmelCase_ )
expected.add_argument("""--foo_str""" , nargs="""+""" , default=["""Hallo""", """Bonjour""", """Hello"""] , type=UpperCAmelCase_ )
expected.add_argument("""--foo_float""" , nargs="""+""" , default=[0.1, 0.2, 0.3] , type=UpperCAmelCase_ )
self.argparsersEqual(UpperCAmelCase_ , UpperCAmelCase_ )
__UpperCamelCase = parser.parse_args([] )
self.assertEqual(
UpperCAmelCase_ , Namespace(foo_int=[] , bar_int=[1, 2, 3] , foo_str=["""Hallo""", """Bonjour""", """Hello"""] , foo_float=[0.1, 0.2, 0.3] ) , )
__UpperCamelCase = parser.parse_args("""--foo_int 1 --bar_int 2 3 --foo_str a b c --foo_float 0.1 0.7""".split() )
self.assertEqual(UpperCAmelCase_ , Namespace(foo_int=[1] , bar_int=[2, 3] , foo_str=["""a""", """b""", """c"""] , foo_float=[0.1, 0.7] ) )
def __lowerCamelCase ( self ) -> List[Any]:
__UpperCamelCase = argparse.ArgumentParser()
expected.add_argument("""--foo""" , default=UpperCAmelCase_ , type=UpperCAmelCase_ )
expected.add_argument("""--bar""" , default=UpperCAmelCase_ , type=UpperCAmelCase_ , help="""help message""" )
expected.add_argument("""--baz""" , default=UpperCAmelCase_ , type=UpperCAmelCase_ )
expected.add_argument("""--ces""" , nargs="""+""" , default=[] , type=UpperCAmelCase_ )
expected.add_argument("""--des""" , nargs="""+""" , default=[] , type=UpperCAmelCase_ )
__UpperCamelCase = [OptionalExample]
if is_python_no_less_than_3_10:
dataclass_types.append(UpperCAmelCase_ )
for dataclass_type in dataclass_types:
__UpperCamelCase = HfArgumentParser(UpperCAmelCase_ )
self.argparsersEqual(UpperCAmelCase_ , UpperCAmelCase_ )
__UpperCamelCase = parser.parse_args([] )
self.assertEqual(UpperCAmelCase_ , Namespace(foo=UpperCAmelCase_ , bar=UpperCAmelCase_ , baz=UpperCAmelCase_ , ces=[] , des=[] ) )
__UpperCamelCase = parser.parse_args("""--foo 12 --bar 3.14 --baz 42 --ces a b c --des 1 2 3""".split() )
self.assertEqual(UpperCAmelCase_ , Namespace(foo=1_2 , bar=3.14 , baz="""42""" , ces=["""a""", """b""", """c"""] , des=[1, 2, 3] ) )
def __lowerCamelCase ( self ) -> Optional[Any]:
__UpperCamelCase = HfArgumentParser(UpperCAmelCase_ )
__UpperCamelCase = argparse.ArgumentParser()
expected.add_argument("""--required_list""" , nargs="""+""" , type=UpperCAmelCase_ , required=UpperCAmelCase_ )
expected.add_argument("""--required_str""" , type=UpperCAmelCase_ , required=UpperCAmelCase_ )
expected.add_argument(
"""--required_enum""" , type=make_choice_type_function(["""titi""", """toto"""] ) , choices=["""titi""", """toto"""] , required=UpperCAmelCase_ , )
self.argparsersEqual(UpperCAmelCase_ , UpperCAmelCase_ )
def __lowerCamelCase ( self ) -> List[Any]:
__UpperCamelCase = HfArgumentParser(UpperCAmelCase_ )
__UpperCamelCase = argparse.ArgumentParser()
expected.add_argument("""--foo""" , type=UpperCAmelCase_ , required=UpperCAmelCase_ )
expected.add_argument(
"""--required_enum""" , type=make_choice_type_function(["""titi""", """toto"""] ) , choices=["""titi""", """toto"""] , required=UpperCAmelCase_ , )
expected.add_argument("""--opt""" , type=UpperCAmelCase_ , default=UpperCAmelCase_ )
expected.add_argument("""--baz""" , default="""toto""" , type=UpperCAmelCase_ , help="""help message""" )
expected.add_argument("""--foo_str""" , nargs="""+""" , default=["""Hallo""", """Bonjour""", """Hello"""] , type=UpperCAmelCase_ )
self.argparsersEqual(UpperCAmelCase_ , UpperCAmelCase_ )
def __lowerCamelCase ( self ) -> Tuple:
__UpperCamelCase = HfArgumentParser(UpperCAmelCase_ )
__UpperCamelCase = {
"""foo""": 1_2,
"""bar""": 3.14,
"""baz""": """42""",
"""flag""": True,
}
__UpperCamelCase = parser.parse_dict(UpperCAmelCase_ )[0]
__UpperCamelCase = BasicExample(**UpperCAmelCase_ )
self.assertEqual(UpperCAmelCase_ , UpperCAmelCase_ )
def __lowerCamelCase ( self ) -> List[Any]:
__UpperCamelCase = HfArgumentParser(UpperCAmelCase_ )
__UpperCamelCase = {
"""foo""": 1_2,
"""bar""": 3.14,
"""baz""": """42""",
"""flag""": True,
"""extra""": 4_2,
}
self.assertRaises(UpperCAmelCase_ , parser.parse_dict , UpperCAmelCase_ , allow_extra_keys=UpperCAmelCase_ )
def __lowerCamelCase ( self ) -> Dict:
__UpperCamelCase = HfArgumentParser(UpperCAmelCase_ )
__UpperCamelCase = {
"""foo""": 1_2,
"""bar""": 3.14,
"""baz""": """42""",
"""flag""": True,
}
with tempfile.TemporaryDirectory() as tmp_dir:
__UpperCamelCase = os.path.join(UpperCAmelCase_ , """temp_json""" )
os.mkdir(UpperCAmelCase_ )
with open(temp_local_path + """.json""" , """w+""" ) as f:
json.dump(UpperCAmelCase_ , UpperCAmelCase_ )
__UpperCamelCase = parser.parse_yaml_file(Path(temp_local_path + """.json""" ) )[0]
__UpperCamelCase = BasicExample(**UpperCAmelCase_ )
self.assertEqual(UpperCAmelCase_ , UpperCAmelCase_ )
def __lowerCamelCase ( self ) -> List[str]:
__UpperCamelCase = HfArgumentParser(UpperCAmelCase_ )
__UpperCamelCase = {
"""foo""": 1_2,
"""bar""": 3.14,
"""baz""": """42""",
"""flag""": True,
}
with tempfile.TemporaryDirectory() as tmp_dir:
__UpperCamelCase = os.path.join(UpperCAmelCase_ , """temp_yaml""" )
os.mkdir(UpperCAmelCase_ )
with open(temp_local_path + """.yaml""" , """w+""" ) as f:
yaml.dump(UpperCAmelCase_ , UpperCAmelCase_ )
__UpperCamelCase = parser.parse_yaml_file(Path(temp_local_path + """.yaml""" ) )[0]
__UpperCamelCase = BasicExample(**UpperCAmelCase_ )
self.assertEqual(UpperCAmelCase_ , UpperCAmelCase_ )
def __lowerCamelCase ( self ) -> Any:
__UpperCamelCase = HfArgumentParser(UpperCAmelCase_ )
self.assertIsNotNone(UpperCAmelCase_ )
| 349 |
"""simple docstring"""
import unittest
import numpy as np
from transformers import RoFormerConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import jax.numpy as jnp
from transformers.models.roformer.modeling_flax_roformer import (
FlaxRoFormerForMaskedLM,
FlaxRoFormerForMultipleChoice,
FlaxRoFormerForQuestionAnswering,
FlaxRoFormerForSequenceClassification,
FlaxRoFormerForTokenClassification,
FlaxRoFormerModel,
)
class __A (unittest.TestCase):
'''simple docstring'''
def __init__( self : List[Any] , UpperCAmelCase_ : Union[str, Any] , UpperCAmelCase_ : Tuple=13 , UpperCAmelCase_ : List[Any]=7 , UpperCAmelCase_ : int=True , UpperCAmelCase_ : Dict=True , UpperCAmelCase_ : int=True , UpperCAmelCase_ : int=True , UpperCAmelCase_ : Dict=99 , UpperCAmelCase_ : str=32 , UpperCAmelCase_ : Tuple=5 , UpperCAmelCase_ : Union[str, Any]=4 , UpperCAmelCase_ : Any=37 , UpperCAmelCase_ : int="gelu" , UpperCAmelCase_ : Any=0.1 , UpperCAmelCase_ : List[str]=0.1 , UpperCAmelCase_ : Dict=512 , UpperCAmelCase_ : Optional[Any]=16 , UpperCAmelCase_ : Dict=2 , UpperCAmelCase_ : str=0.02 , UpperCAmelCase_ : str=4 , ) ->Tuple:
"""simple docstring"""
snake_case_ = parent
snake_case_ = batch_size
snake_case_ = seq_length
snake_case_ = is_training
snake_case_ = use_attention_mask
snake_case_ = use_token_type_ids
snake_case_ = use_labels
snake_case_ = vocab_size
snake_case_ = hidden_size
snake_case_ = num_hidden_layers
snake_case_ = num_attention_heads
snake_case_ = intermediate_size
snake_case_ = hidden_act
snake_case_ = hidden_dropout_prob
snake_case_ = attention_probs_dropout_prob
snake_case_ = max_position_embeddings
snake_case_ = type_vocab_size
snake_case_ = type_sequence_label_size
snake_case_ = initializer_range
snake_case_ = num_choices
def lowerCAmelCase ( self : Optional[int] ) ->str:
"""simple docstring"""
snake_case_ = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
snake_case_ = None
if self.use_attention_mask:
snake_case_ = random_attention_mask([self.batch_size, self.seq_length] )
snake_case_ = None
if self.use_token_type_ids:
snake_case_ = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
snake_case_ = RoFormerConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , is_decoder=UpperCAmelCase_ , initializer_range=self.initializer_range , )
return config, input_ids, token_type_ids, attention_mask
def lowerCAmelCase ( self : List[str] ) ->Dict:
"""simple docstring"""
snake_case_ = self.prepare_config_and_inputs()
snake_case_ , snake_case_ , snake_case_ , snake_case_ = config_and_inputs
snake_case_ = {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": attention_mask}
return config, inputs_dict
@require_flax
class __A (snake_case__ , unittest.TestCase):
'''simple docstring'''
__lowercase: Union[str, Any] = True
__lowercase: int = (
(
FlaxRoFormerModel,
FlaxRoFormerForMaskedLM,
FlaxRoFormerForSequenceClassification,
FlaxRoFormerForTokenClassification,
FlaxRoFormerForMultipleChoice,
FlaxRoFormerForQuestionAnswering,
)
if is_flax_available()
else ()
)
def lowerCAmelCase ( self : Optional[Any] ) ->Tuple:
"""simple docstring"""
snake_case_ = FlaxRoFormerModelTester(self )
@slow
def lowerCAmelCase ( self : Any ) ->List[str]:
"""simple docstring"""
for model_class_name in self.all_model_classes:
snake_case_ = model_class_name.from_pretrained("""junnyu/roformer_chinese_small""" , from_pt=UpperCAmelCase_ )
snake_case_ = model(np.ones((1, 1) ) )
self.assertIsNotNone(UpperCAmelCase_ )
@require_flax
class __A (unittest.TestCase):
'''simple docstring'''
@slow
def lowerCAmelCase ( self : str ) ->Dict:
"""simple docstring"""
snake_case_ = FlaxRoFormerForMaskedLM.from_pretrained("""junnyu/roformer_chinese_base""" )
snake_case_ = jnp.array([[0, 1, 2, 3, 4, 5]] )
snake_case_ = model(UpperCAmelCase_ )[0]
snake_case_ = 50_000
snake_case_ = (1, 6, vocab_size)
self.assertEqual(output.shape , UpperCAmelCase_ )
snake_case_ = jnp.array(
[[[-0.1_205, -1.0_265, 0.2_922], [-1.5_134, 0.1_974, 0.1_519], [-5.0_135, -3.9_003, -0.8_404]]] )
self.assertTrue(jnp.allclose(output[:, :3, :3] , UpperCAmelCase_ , atol=1E-4 ) )
| 347 | 0 |
'''simple docstring'''
import unittest
from transformers import EsmConfig, is_torch_available
from transformers.testing_utils import TestCasePlus, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers.models.esm.modeling_esmfold import EsmForProteinFolding
class A__ :
def __init__( self , UpperCamelCase__ , UpperCamelCase__=13 , UpperCamelCase__=7 , UpperCamelCase__=False , UpperCamelCase__=True , UpperCamelCase__=False , UpperCamelCase__=False , UpperCamelCase__=19 , UpperCamelCase__=32 , UpperCamelCase__=5 , UpperCamelCase__=4 , UpperCamelCase__=37 , UpperCamelCase__="gelu" , UpperCamelCase__=0.1 , UpperCamelCase__=0.1 , UpperCamelCase__=512 , UpperCamelCase__=16 , UpperCamelCase__=2 , UpperCamelCase__=0.02 , UpperCamelCase__=3 , UpperCamelCase__=4 , UpperCamelCase__=None , ) -> Optional[Any]:
'''simple docstring'''
A_ = parent
A_ = batch_size
A_ = seq_length
A_ = is_training
A_ = use_input_mask
A_ = use_token_type_ids
A_ = use_labels
A_ = vocab_size
A_ = hidden_size
A_ = num_hidden_layers
A_ = num_attention_heads
A_ = intermediate_size
A_ = hidden_act
A_ = hidden_dropout_prob
A_ = attention_probs_dropout_prob
A_ = max_position_embeddings
A_ = type_vocab_size
A_ = type_sequence_label_size
A_ = initializer_range
A_ = num_labels
A_ = num_choices
A_ = scope
def snake_case_ ( self ) -> Union[str, Any]:
'''simple docstring'''
A_ = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
A_ = None
if self.use_input_mask:
A_ = random_attention_mask([self.batch_size, self.seq_length] )
A_ = None
A_ = None
A_ = None
if self.use_labels:
A_ = ids_tensor([self.batch_size] , self.type_sequence_label_size )
A_ = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
A_ = ids_tensor([self.batch_size] , self.num_choices )
A_ = self.get_config()
return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
def snake_case_ ( self ) -> List[str]:
'''simple docstring'''
A_ = EsmConfig(
vocab_size=33 , hidden_size=self.hidden_size , pad_token_id=1 , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , initializer_range=self.initializer_range , is_folding_model=UpperCAmelCase_ , esmfold_config={"""trunk""": {"""num_blocks""": 2}, """fp16_esm""": False} , )
return config
def snake_case_ ( self , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ) -> List[str]:
'''simple docstring'''
A_ = EsmForProteinFolding(config=UpperCAmelCase_ ).float()
model.to(UpperCAmelCase_ )
model.eval()
A_ = model(UpperCAmelCase_ , attention_mask=UpperCAmelCase_ )
A_ = model(UpperCAmelCase_ )
A_ = model(UpperCAmelCase_ )
self.parent.assertEqual(result.positions.shape , (8, self.batch_size, self.seq_length, 14, 3) )
self.parent.assertEqual(result.angles.shape , (8, self.batch_size, self.seq_length, 7, 2) )
def snake_case_ ( self ) -> Optional[Any]:
'''simple docstring'''
A_ = self.prepare_config_and_inputs()
(
(
A_
) , (
A_
) , (
A_
) , (
A_
) , (
A_
) , (
A_
) ,
) = config_and_inputs
A_ = {"""input_ids""": input_ids, """attention_mask""": input_mask}
return config, inputs_dict
@require_torch
class A__ ( snake_case__ , snake_case__ , unittest.TestCase ):
lowercase = False
lowercase = (EsmForProteinFolding,) if is_torch_available() else ()
lowercase = ()
lowercase = {} if is_torch_available() else {}
lowercase = False
def snake_case_ ( self ) -> Tuple:
'''simple docstring'''
A_ = EsmFoldModelTester(self )
A_ = ConfigTester(self , config_class=UpperCAmelCase_ , hidden_size=37 )
def snake_case_ ( self ) -> Tuple:
'''simple docstring'''
self.config_tester.run_common_tests()
def snake_case_ ( self ) -> Any:
'''simple docstring'''
A_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*UpperCAmelCase_ )
@unittest.skip("""Does not support attention outputs""" )
def snake_case_ ( self ) -> Optional[Any]:
'''simple docstring'''
pass
@unittest.skip
def snake_case_ ( self ) -> Optional[int]:
'''simple docstring'''
pass
@unittest.skip("""Esm does not support embedding resizing""" )
def snake_case_ ( self ) -> Any:
'''simple docstring'''
pass
@unittest.skip("""Esm does not support embedding resizing""" )
def snake_case_ ( self ) -> Tuple:
'''simple docstring'''
pass
@unittest.skip("""ESMFold does not support passing input embeds!""" )
def snake_case_ ( self ) -> Any:
'''simple docstring'''
pass
@unittest.skip("""ESMFold does not support head pruning.""" )
def snake_case_ ( self ) -> Optional[int]:
'''simple docstring'''
pass
@unittest.skip("""ESMFold does not support head pruning.""" )
def snake_case_ ( self ) -> Tuple:
'''simple docstring'''
pass
@unittest.skip("""ESMFold does not support head pruning.""" )
def snake_case_ ( self ) -> str:
'''simple docstring'''
pass
@unittest.skip("""ESMFold does not support head pruning.""" )
def snake_case_ ( self ) -> List[str]:
'''simple docstring'''
pass
@unittest.skip("""ESMFold does not support head pruning.""" )
def snake_case_ ( self ) -> Any:
'''simple docstring'''
pass
@unittest.skip("""ESMFold does not output hidden states in the normal way.""" )
def snake_case_ ( self ) -> Dict:
'''simple docstring'''
pass
@unittest.skip("""ESMfold does not output hidden states in the normal way.""" )
def snake_case_ ( self ) -> List[Any]:
'''simple docstring'''
pass
@unittest.skip("""ESMFold only has one output format.""" )
def snake_case_ ( self ) -> List[Any]:
'''simple docstring'''
pass
@unittest.skip("""This test doesn't work for ESMFold and doesn't test core functionality""" )
def snake_case_ ( self ) -> Optional[Any]:
'''simple docstring'''
pass
@unittest.skip("""ESMFold does not support input chunking.""" )
def snake_case_ ( self ) -> str:
'''simple docstring'''
pass
@unittest.skip("""ESMFold doesn't respect you and it certainly doesn't respect your initialization arguments.""" )
def snake_case_ ( self ) -> List[str]:
'''simple docstring'''
pass
@unittest.skip("""ESMFold doesn't support torchscript compilation.""" )
def snake_case_ ( self ) -> Optional[Any]:
'''simple docstring'''
pass
@unittest.skip("""ESMFold doesn't support torchscript compilation.""" )
def snake_case_ ( self ) -> Any:
'''simple docstring'''
pass
@unittest.skip("""ESMFold doesn't support torchscript compilation.""" )
def snake_case_ ( self ) -> Any:
'''simple docstring'''
pass
@unittest.skip("""ESMFold doesn't support data parallel.""" )
def snake_case_ ( self ) -> str:
'''simple docstring'''
pass
@unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests.""" )
def snake_case_ ( self ) -> Union[str, Any]:
'''simple docstring'''
pass
@require_torch
class A__ ( snake_case__ ):
@slow
def snake_case_ ( self ) -> Tuple:
'''simple docstring'''
A_ = EsmForProteinFolding.from_pretrained("""facebook/esmfold_v1""" ).float()
model.eval()
A_ = torch.tensor([[0, 6, 4, 13, 5, 4, 16, 12, 11, 7, 2]] )
A_ = model(UpperCAmelCase_ )["""positions"""]
A_ = torch.tensor([2.5828, 0.7993, -10.9334] , dtype=torch.floataa )
self.assertTrue(torch.allclose(position_outputs[0, 0, 0, 0] , UpperCAmelCase_ , atol=1e-4 ) )
| 162 |
"""simple docstring"""
from __future__ import annotations
def _a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> bool:
snake_case_ = get_failure_array(_SCREAMING_SNAKE_CASE )
# 2) Step through text searching for pattern
snake_case_ , snake_case_ = 0, 0 # index into text, pattern
while i < len(_SCREAMING_SNAKE_CASE ):
if pattern[j] == text[i]:
if j == (len(_SCREAMING_SNAKE_CASE ) - 1):
return True
j += 1
# if this is a prefix in our pattern
# just go back far enough to continue
elif j > 0:
snake_case_ = failure[j - 1]
continue
i += 1
return False
def _a ( _SCREAMING_SNAKE_CASE ) -> list[int]:
snake_case_ = [0]
snake_case_ = 0
snake_case_ = 1
while j < len(_SCREAMING_SNAKE_CASE ):
if pattern[i] == pattern[j]:
i += 1
elif i > 0:
snake_case_ = failure[i - 1]
continue
j += 1
failure.append(_SCREAMING_SNAKE_CASE )
return failure
if __name__ == "__main__":
# Test 1)
__SCREAMING_SNAKE_CASE : Optional[int] = 'abc1abc12'
__SCREAMING_SNAKE_CASE : Optional[int] = 'alskfjaldsabc1abc1abc12k23adsfabcabc'
__SCREAMING_SNAKE_CASE : List[str] = 'alskfjaldsk23adsfabcabc'
assert kmp(pattern, texta) and not kmp(pattern, texta)
# Test 2)
__SCREAMING_SNAKE_CASE : int = 'ABABX'
__SCREAMING_SNAKE_CASE : Optional[Any] = 'ABABZABABYABABX'
assert kmp(pattern, text)
# Test 3)
__SCREAMING_SNAKE_CASE : Any = 'AAAB'
__SCREAMING_SNAKE_CASE : List[Any] = 'ABAAAAAB'
assert kmp(pattern, text)
# Test 4)
__SCREAMING_SNAKE_CASE : Optional[int] = 'abcdabcy'
__SCREAMING_SNAKE_CASE : str = 'abcxabcdabxabcdabcdabcy'
assert kmp(pattern, text)
# Test 5)
__SCREAMING_SNAKE_CASE : Any = 'aabaabaaa'
assert get_failure_array(pattern) == [0, 1, 0, 1, 2, 3, 4, 5, 2]
| 347 | 0 |
import os
import shutil
import tempfile
import unittest
import numpy as np
from transformers import AutoTokenizer, BarkProcessor
from transformers.testing_utils import require_torch, slow
@require_torch
class A ( unittest.TestCase ):
'''simple docstring'''
def lowerCamelCase__ (self : Optional[int] ) -> Dict:
"""simple docstring"""
lowercase__ = """ylacombe/bark-small"""
lowercase__ = tempfile.mkdtemp()
lowercase__ = """en_speaker_1"""
lowercase__ = """This is a test string"""
lowercase__ = """speaker_embeddings_path.json"""
lowercase__ = """speaker_embeddings"""
def lowerCamelCase__ (self : List[str] , **_UpperCAmelCase : str ) -> Optional[int]:
"""simple docstring"""
return AutoTokenizer.from_pretrained(self.checkpoint , **UpperCAmelCase_ )
def lowerCamelCase__ (self : Any ) -> Dict:
"""simple docstring"""
shutil.rmtree(self.tmpdirname )
def lowerCamelCase__ (self : List[Any] ) -> Dict:
"""simple docstring"""
lowercase__ = self.get_tokenizer()
lowercase__ = BarkProcessor(tokenizer=UpperCAmelCase_ )
processor.save_pretrained(self.tmpdirname )
lowercase__ = BarkProcessor.from_pretrained(self.tmpdirname )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer.get_vocab() )
@slow
def lowerCamelCase__ (self : Dict ) -> int:
"""simple docstring"""
lowercase__ = BarkProcessor.from_pretrained(
pretrained_processor_name_or_path=self.checkpoint , speaker_embeddings_dict_path=self.speaker_embeddings_dict_path , )
processor.save_pretrained(
self.tmpdirname , speaker_embeddings_dict_path=self.speaker_embeddings_dict_path , speaker_embeddings_directory=self.speaker_embeddings_directory , )
lowercase__ = self.get_tokenizer(bos_token="""(BOS)""" , eos_token="""(EOS)""" )
lowercase__ = BarkProcessor.from_pretrained(
self.tmpdirname , self.speaker_embeddings_dict_path , bos_token="""(BOS)""" , eos_token="""(EOS)""" , )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer_add_kwargs.get_vocab() )
def lowerCamelCase__ (self : Optional[Any] ) -> Any:
"""simple docstring"""
lowercase__ = BarkProcessor.from_pretrained(
pretrained_processor_name_or_path=self.checkpoint , speaker_embeddings_dict_path=self.speaker_embeddings_dict_path , )
lowercase__ = 35
lowercase__ = 2
lowercase__ = 8
lowercase__ = {
"""semantic_prompt""": np.ones(UpperCAmelCase_ ),
"""coarse_prompt""": np.ones((nb_codebooks_coarse, seq_len) ),
"""fine_prompt""": np.ones((nb_codebooks_total, seq_len) ),
}
# test providing already loaded voice_preset
lowercase__ = processor(text=self.input_string , voice_preset=UpperCAmelCase_ )
lowercase__ = inputs["""history_prompt"""]
for key in voice_preset:
self.assertListEqual(voice_preset[key].tolist() , processed_voice_preset.get(UpperCAmelCase_ , np.array([] ) ).tolist() )
# test loading voice preset from npz file
lowercase__ = os.path.join(self.tmpdirname , """file.npz""" )
np.savez(UpperCAmelCase_ , **UpperCAmelCase_ )
lowercase__ = processor(text=self.input_string , voice_preset=UpperCAmelCase_ )
lowercase__ = inputs["""history_prompt"""]
for key in voice_preset:
self.assertListEqual(voice_preset[key].tolist() , processed_voice_preset.get(UpperCAmelCase_ , np.array([] ) ).tolist() )
# test loading voice preset from the hub
lowercase__ = processor(text=self.input_string , voice_preset=self.voice_preset )
def lowerCamelCase__ (self : Tuple ) -> Dict:
"""simple docstring"""
lowercase__ = self.get_tokenizer()
lowercase__ = BarkProcessor(tokenizer=UpperCAmelCase_ )
lowercase__ = processor(text=self.input_string )
lowercase__ = tokenizer(
self.input_string , padding="""max_length""" , max_length=256 , add_special_tokens=UpperCAmelCase_ , return_attention_mask=UpperCAmelCase_ , return_token_type_ids=UpperCAmelCase_ , )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key] , encoded_processor[key].squeeze().tolist() )
| 305 |
"""simple docstring"""
from transformers import BertTokenizer, EncoderDecoderModel, SeqaSeqTrainer, SeqaSeqTrainingArguments
from transformers.testing_utils import TestCasePlus, require_torch, slow
from transformers.utils import is_datasets_available
if is_datasets_available():
import datasets
class __A (snake_case__):
'''simple docstring'''
@slow
@require_torch
def lowerCAmelCase ( self : Union[str, Any] ) ->Dict:
"""simple docstring"""
snake_case_ = EncoderDecoderModel.from_encoder_decoder_pretrained("""prajjwal1/bert-tiny""" , """prajjwal1/bert-tiny""" )
snake_case_ = BertTokenizer.from_pretrained("""bert-base-uncased""" )
snake_case_ = bertabert.config.encoder.vocab_size
snake_case_ = tokenizer.sep_token_id
snake_case_ = tokenizer.cls_token_id
snake_case_ = 128
snake_case_ = datasets.load_dataset("""cnn_dailymail""" , """3.0.0""" , split="""train[:1%]""" )
snake_case_ = datasets.load_dataset("""cnn_dailymail""" , """3.0.0""" , split="""validation[:1%]""" )
snake_case_ = train_dataset.select(range(32 ) )
snake_case_ = val_dataset.select(range(16 ) )
snake_case_ = 4
def _map_to_encoder_decoder_inputs(UpperCAmelCase_ : int ):
# Tokenizer will automatically set [BOS] <text> [EOS]
snake_case_ = tokenizer(batch["""article"""] , padding="""max_length""" , truncation=UpperCAmelCase_ , max_length=512 )
snake_case_ = tokenizer(batch["""highlights"""] , padding="""max_length""" , truncation=UpperCAmelCase_ , max_length=128 )
snake_case_ = inputs.input_ids
snake_case_ = inputs.attention_mask
snake_case_ = outputs.input_ids
snake_case_ = outputs.input_ids.copy()
snake_case_ = [
[-100 if token == tokenizer.pad_token_id else token for token in labels] for labels in batch["""labels"""]
]
snake_case_ = outputs.attention_mask
assert all(len(UpperCAmelCase_ ) == 512 for x in inputs.input_ids )
assert all(len(UpperCAmelCase_ ) == 128 for x in outputs.input_ids )
return batch
def _compute_metrics(UpperCAmelCase_ : Union[str, Any] ):
snake_case_ = pred.label_ids
snake_case_ = pred.predictions
# all unnecessary tokens are removed
snake_case_ = tokenizer.batch_decode(UpperCAmelCase_ , skip_special_tokens=UpperCAmelCase_ )
snake_case_ = tokenizer.batch_decode(UpperCAmelCase_ , skip_special_tokens=UpperCAmelCase_ )
snake_case_ = sum([int(pred_str[i] == label_str[i] ) for i in range(len(UpperCAmelCase_ ) )] ) / len(UpperCAmelCase_ )
return {"accuracy": accuracy}
# map train dataset
snake_case_ = train_dataset.map(
_map_to_encoder_decoder_inputs , batched=UpperCAmelCase_ , batch_size=UpperCAmelCase_ , remove_columns=["""article""", """highlights"""] , )
train_dataset.set_format(
type="""torch""" , columns=["""input_ids""", """attention_mask""", """decoder_input_ids""", """decoder_attention_mask""", """labels"""] , )
# same for validation dataset
snake_case_ = val_dataset.map(
_map_to_encoder_decoder_inputs , batched=UpperCAmelCase_ , batch_size=UpperCAmelCase_ , remove_columns=["""article""", """highlights"""] , )
val_dataset.set_format(
type="""torch""" , columns=["""input_ids""", """attention_mask""", """decoder_input_ids""", """decoder_attention_mask""", """labels"""] , )
snake_case_ = self.get_auto_remove_tmp_dir()
snake_case_ = SeqaSeqTrainingArguments(
output_dir=UpperCAmelCase_ , per_device_train_batch_size=UpperCAmelCase_ , per_device_eval_batch_size=UpperCAmelCase_ , predict_with_generate=UpperCAmelCase_ , evaluation_strategy="""steps""" , do_train=UpperCAmelCase_ , do_eval=UpperCAmelCase_ , warmup_steps=0 , eval_steps=2 , logging_steps=2 , )
# instantiate trainer
snake_case_ = SeqaSeqTrainer(
model=UpperCAmelCase_ , args=UpperCAmelCase_ , compute_metrics=_compute_metrics , train_dataset=UpperCAmelCase_ , eval_dataset=UpperCAmelCase_ , tokenizer=UpperCAmelCase_ , )
# start training
trainer.train()
| 347 | 0 |
import argparse
from collections import defaultdict
def _UpperCAmelCase ( SCREAMING_SNAKE_CASE__ : Tuple , SCREAMING_SNAKE_CASE__ : Tuple , SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : str , SCREAMING_SNAKE_CASE__ : Tuple ):
__UpperCamelCase =F'{file}_{class_name}_{test_name}'
done_test[_id] += 1
with open(_SCREAMING_SNAKE_CASE , 'r' ) as f:
__UpperCamelCase =f.readlines()
__UpperCamelCase =F'class {class_name}('
__UpperCamelCase =F'{4 * " "}def {test_name}('
__UpperCamelCase =F'{8 * " "}{correct_line.split()[0]}'
__UpperCamelCase =F'{16 * " "}{correct_line.split()[0]}'
__UpperCamelCase =False
__UpperCamelCase =False
__UpperCamelCase =False
__UpperCamelCase =False
__UpperCamelCase =0
__UpperCamelCase =0
__UpperCamelCase =[]
for line in lines:
if line.startswith(_SCREAMING_SNAKE_CASE ):
__UpperCamelCase =True
elif in_class and line.startswith(_SCREAMING_SNAKE_CASE ):
__UpperCamelCase =True
elif in_class and in_func and (line.startswith(_SCREAMING_SNAKE_CASE ) or line.startswith(_SCREAMING_SNAKE_CASE )):
__UpperCamelCase =len(line.split(correct_line.split()[0] )[0] )
count += 1
if count == done_test[_id]:
__UpperCamelCase =True
if in_class and in_func and in_line:
if ")" not in line:
continue
else:
__UpperCamelCase =True
if in_class and in_func and in_line and insert_line:
new_lines.append(F'{spaces * " "}{correct_line}' )
__UpperCamelCase =__UpperCamelCase =__UpperCamelCase =__UpperCamelCase =False
else:
new_lines.append(_SCREAMING_SNAKE_CASE )
with open(_SCREAMING_SNAKE_CASE , 'w' ) as f:
for line in new_lines:
f.write(_SCREAMING_SNAKE_CASE )
def _UpperCAmelCase ( SCREAMING_SNAKE_CASE__ : List[str] , SCREAMING_SNAKE_CASE__ : Optional[int]=None ):
if fail is not None:
with open(_SCREAMING_SNAKE_CASE , 'r' ) as f:
__UpperCamelCase ={l.strip() for l in f.readlines()}
else:
__UpperCamelCase =None
with open(_SCREAMING_SNAKE_CASE , 'r' ) as f:
__UpperCamelCase =f.readlines()
__UpperCamelCase =defaultdict(_SCREAMING_SNAKE_CASE )
for line in correct_lines:
__UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase =line.split(';' )
if test_failures is None or "::".join([file, class_name, test_name] ) in test_failures:
overwrite_file(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
if __name__ == "__main__":
_A = argparse.ArgumentParser()
parser.add_argument('--correct_filename', help='filename of tests with expected result')
parser.add_argument('--fail_filename', help='filename of test failures', type=str, default=None)
_A = parser.parse_args()
main(args.correct_filename, args.fail_filename)
| 62 |
"""simple docstring"""
# this script reports modified .py files under the desired list of top-level sub-dirs passed as a list of arguments, e.g.:
# python ./utils/get_modified_files.py utils src tests examples
#
# it uses git to find the forking point and which files were modified - i.e. files not under git won't be considered
# since the output of this script is fed into Makefile commands it doesn't print a newline after the results
import re
import subprocess
import sys
__SCREAMING_SNAKE_CASE : Tuple = subprocess.check_output('git merge-base main HEAD'.split()).decode('utf-8')
__SCREAMING_SNAKE_CASE : Tuple = subprocess.check_output(f"""git diff --name-only {fork_point_sha}""".split()).decode('utf-8').split()
__SCREAMING_SNAKE_CASE : Any = '|'.join(sys.argv[1:])
__SCREAMING_SNAKE_CASE : Optional[Any] = re.compile(Rf"""^({joined_dirs}).*?\.py$""")
__SCREAMING_SNAKE_CASE : List[str] = [x for x in modified_files if regex.match(x)]
print(' '.join(relevant_modified_files), end='')
| 347 | 0 |
"""simple docstring"""
from __future__ import annotations
def __UpperCAmelCase ( __lowerCamelCase ) -> int:
# preprocessing the first row
for i in range(1 , len(matrix[0] ) ):
matrix[0][i] += matrix[0][i - 1]
# preprocessing the first column
for i in range(1 , len(_SCREAMING_SNAKE_CASE ) ):
matrix[i][0] += matrix[i - 1][0]
# updating the path cost for current position
for i in range(1 , len(_SCREAMING_SNAKE_CASE ) ):
for j in range(1 , len(matrix[0] ) ):
matrix[i][j] += min(matrix[i - 1][j] , matrix[i][j - 1] )
return matrix[-1][-1]
if __name__ == "__main__":
import doctest
doctest.testmod()
| 16 |
"""simple docstring"""
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
WavaVecaConfig,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
WavaVecaForCTC,
WavaVecaForPreTraining,
WavaVecaProcessor,
logging,
)
from transformers.models.wavaveca.modeling_wavaveca import WavaVecaForSequenceClassification
logging.set_verbosity_info()
__SCREAMING_SNAKE_CASE : Tuple = logging.get_logger(__name__)
__SCREAMING_SNAKE_CASE : Tuple = {
'post_extract_proj': 'feature_projection.projection',
'encoder.pos_conv.0': 'encoder.pos_conv_embed.conv',
'self_attn.k_proj': 'encoder.layers.*.attention.k_proj',
'self_attn.v_proj': 'encoder.layers.*.attention.v_proj',
'self_attn.q_proj': 'encoder.layers.*.attention.q_proj',
'self_attn.out_proj': 'encoder.layers.*.attention.out_proj',
'self_attn_layer_norm': 'encoder.layers.*.layer_norm',
'fc1': 'encoder.layers.*.feed_forward.intermediate_dense',
'fc2': 'encoder.layers.*.feed_forward.output_dense',
'final_layer_norm': 'encoder.layers.*.final_layer_norm',
'encoder.layer_norm': 'encoder.layer_norm',
'adapter_layer': 'encoder.layers.*.adapter_layer',
'w2v_model.layer_norm': 'feature_projection.layer_norm',
'quantizer.weight_proj': 'quantizer.weight_proj',
'quantizer.vars': 'quantizer.codevectors',
'project_q': 'project_q',
'final_proj': 'project_hid',
'w2v_encoder.proj': 'lm_head',
'mask_emb': 'masked_spec_embed',
'pooling_layer.linear': 'projector',
'pooling_layer.projection': 'classifier',
}
__SCREAMING_SNAKE_CASE : List[Any] = [
'lm_head',
'quantizer.weight_proj',
'quantizer.codevectors',
'project_q',
'project_hid',
'projector',
'classifier',
]
def _a ( _SCREAMING_SNAKE_CASE ) -> List[str]:
snake_case_ = {}
with open(_SCREAMING_SNAKE_CASE , """r""" ) as file:
for line_number, line in enumerate(_SCREAMING_SNAKE_CASE ):
snake_case_ = line.strip()
if line:
snake_case_ = line.split()
snake_case_ = line_number
snake_case_ = words[0]
snake_case_ = value
return result
def _a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> Tuple:
for attribute in key.split(""".""" ):
snake_case_ = getattr(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
snake_case_ = None
for param_key in PARAM_MAPPING.keys():
if full_name.endswith(_SCREAMING_SNAKE_CASE ):
snake_case_ = PARAM_MAPPING[full_name.split(""".""" )[-1]]
snake_case_ = """param"""
if weight_type is not None and weight_type != "param":
snake_case_ = getattr(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ).shape
elif weight_type is not None and weight_type == "param":
snake_case_ = hf_pointer
for attribute in hf_param_name.split(""".""" ):
snake_case_ = getattr(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
snake_case_ = shape_pointer.shape
# let's reduce dimension
snake_case_ = value[0]
else:
snake_case_ = hf_pointer.shape
if hf_shape != value.shape:
raise ValueError(
f"""Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be"""
f""" {value.shape} for {full_name}""" )
if weight_type == "weight":
snake_case_ = value
elif weight_type == "weight_g":
snake_case_ = value
elif weight_type == "weight_v":
snake_case_ = value
elif weight_type == "bias":
snake_case_ = value
elif weight_type == "param":
for attribute in hf_param_name.split(""".""" ):
snake_case_ = getattr(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
snake_case_ = value
else:
snake_case_ = value
logger.info(f"""{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.""" )
def _a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> Tuple:
snake_case_ = None
for param_key in PARAM_MAPPING.keys():
if full_name.endswith(_SCREAMING_SNAKE_CASE ):
snake_case_ = PARAM_MAPPING[full_name.split(""".""" )[-1]]
snake_case_ = """param"""
if weight_type is not None and weight_type != "param":
snake_case_ = """.""".join([key, weight_type] )
elif weight_type is not None and weight_type == "param":
snake_case_ = """.""".join([key, hf_param_name] )
else:
snake_case_ = key
snake_case_ = value if """lm_head""" in full_key else value[0]
__SCREAMING_SNAKE_CASE : int = {
'W_a': 'linear_1.weight',
'W_b': 'linear_2.weight',
'b_a': 'linear_1.bias',
'b_b': 'linear_2.bias',
'ln_W': 'norm.weight',
'ln_b': 'norm.bias',
}
def _a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=None , _SCREAMING_SNAKE_CASE=None ) -> List[str]:
snake_case_ = False
for key, mapped_key in MAPPING.items():
snake_case_ = """wav2vec2.""" + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
if key in name or key.split("""w2v_model.""" )[-1] == name.split(""".""" )[0]:
snake_case_ = True
if "*" in mapped_key:
snake_case_ = name.split(_SCREAMING_SNAKE_CASE )[0].split(""".""" )[-2]
snake_case_ = mapped_key.replace("""*""" , _SCREAMING_SNAKE_CASE )
if "weight_g" in name:
snake_case_ = """weight_g"""
elif "weight_v" in name:
snake_case_ = """weight_v"""
elif "bias" in name:
snake_case_ = """bias"""
elif "weight" in name:
# TODO: don't match quantizer.weight_proj
snake_case_ = """weight"""
else:
snake_case_ = None
if hf_dict is not None:
rename_dict(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
else:
set_recursively(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
return is_used
return is_used
def _a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> Any:
snake_case_ = []
snake_case_ = fairseq_model.state_dict()
snake_case_ = hf_model.wavaveca.feature_extractor
for name, value in fairseq_dict.items():
snake_case_ = False
if "conv_layers" in name:
load_conv_layer(
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , hf_model.config.feat_extract_norm == """group""" , )
snake_case_ = True
else:
snake_case_ = load_wavaveca_layer(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
if not is_used:
unused_weights.append(_SCREAMING_SNAKE_CASE )
logger.warning(f"""Unused weights: {unused_weights}""" )
def _a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> Union[str, Any]:
snake_case_ = full_name.split("""conv_layers.""" )[-1]
snake_case_ = name.split(""".""" )
snake_case_ = int(items[0] )
snake_case_ = int(items[1] )
if type_id == 0:
if "bias" in name:
if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
raise ValueError(
f"""{full_name} has size {value.shape}, but"""
f""" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.""" )
snake_case_ = value
logger.info(f"""Feat extract conv layer {layer_id} was initialized from {full_name}.""" )
elif "weight" in name:
if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
raise ValueError(
f"""{full_name} has size {value.shape}, but"""
f""" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.""" )
snake_case_ = value
logger.info(f"""Feat extract conv layer {layer_id} was initialized from {full_name}.""" )
elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
if "bias" in name:
if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
raise ValueError(
f"""{full_name} has size {value.shape}, but"""
f""" {feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape} was found.""" )
snake_case_ = value
logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.""" )
elif "weight" in name:
if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
raise ValueError(
f"""{full_name} has size {value.shape}, but"""
f""" {feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape} was found.""" )
snake_case_ = value
logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.""" )
else:
unused_weights.append(_SCREAMING_SNAKE_CASE )
@torch.no_grad()
def _a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=None , _SCREAMING_SNAKE_CASE=None , _SCREAMING_SNAKE_CASE=True , _SCREAMING_SNAKE_CASE=False ) -> int:
if config_path is not None:
snake_case_ = WavaVecaConfig.from_pretrained(_SCREAMING_SNAKE_CASE )
else:
snake_case_ = WavaVecaConfig()
if is_seq_class:
snake_case_ = read_txt_into_dict(_SCREAMING_SNAKE_CASE )
snake_case_ = idalabel
snake_case_ = WavaVecaForSequenceClassification(_SCREAMING_SNAKE_CASE )
snake_case_ = WavaVecaFeatureExtractor(
feature_size=1 , sampling_rate=16_000 , padding_value=0 , do_normalize=_SCREAMING_SNAKE_CASE , return_attention_mask=_SCREAMING_SNAKE_CASE , )
feature_extractor.save_pretrained(_SCREAMING_SNAKE_CASE )
elif is_finetuned:
if dict_path:
snake_case_ = Dictionary.load(_SCREAMING_SNAKE_CASE )
# important change bos & pad token id since CTC symbol is <pad> and
# not <s> as in fairseq
snake_case_ = target_dict.pad_index
snake_case_ = target_dict.bos_index
snake_case_ = target_dict.eos_index
snake_case_ = len(target_dict.symbols )
snake_case_ = os.path.join(_SCREAMING_SNAKE_CASE , """vocab.json""" )
if not os.path.isdir(_SCREAMING_SNAKE_CASE ):
logger.error("""--pytorch_dump_folder_path ({}) should be a directory""".format(_SCREAMING_SNAKE_CASE ) )
return
os.makedirs(_SCREAMING_SNAKE_CASE , exist_ok=_SCREAMING_SNAKE_CASE )
snake_case_ = target_dict.indices
# fairseq has the <pad> and <s> switched
snake_case_ = 0
snake_case_ = 1
with open(_SCREAMING_SNAKE_CASE , """w""" , encoding="""utf-8""" ) as vocab_handle:
json.dump(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
snake_case_ = WavaVecaCTCTokenizer(
_SCREAMING_SNAKE_CASE , unk_token=target_dict.unk_word , pad_token=target_dict.pad_word , bos_token=target_dict.bos_word , eos_token=target_dict.eos_word , word_delimiter_token="""|""" , do_lower_case=_SCREAMING_SNAKE_CASE , )
snake_case_ = True if config.feat_extract_norm == """layer""" else False
snake_case_ = WavaVecaFeatureExtractor(
feature_size=1 , sampling_rate=16_000 , padding_value=0 , do_normalize=_SCREAMING_SNAKE_CASE , return_attention_mask=_SCREAMING_SNAKE_CASE , )
snake_case_ = WavaVecaProcessor(feature_extractor=_SCREAMING_SNAKE_CASE , tokenizer=_SCREAMING_SNAKE_CASE )
processor.save_pretrained(_SCREAMING_SNAKE_CASE )
snake_case_ = WavaVecaForCTC(_SCREAMING_SNAKE_CASE )
else:
snake_case_ = WavaVecaForPreTraining(_SCREAMING_SNAKE_CASE )
if is_finetuned or is_seq_class:
snake_case_ , snake_case_ , snake_case_ = fairseq.checkpoint_utils.load_model_ensemble_and_task(
[checkpoint_path] , arg_overrides={"""data""": """/""".join(dict_path.split("""/""" )[:-1] )} )
else:
snake_case_ = argparse.Namespace(task="""audio_pretraining""" )
snake_case_ = fairseq.tasks.setup_task(_SCREAMING_SNAKE_CASE )
snake_case_ , snake_case_ , snake_case_ = fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path] , task=_SCREAMING_SNAKE_CASE )
snake_case_ = model[0].eval()
recursively_load_weights(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , not is_finetuned )
hf_wavavec.save_pretrained(_SCREAMING_SNAKE_CASE )
if __name__ == "__main__":
__SCREAMING_SNAKE_CASE : str = argparse.ArgumentParser()
parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
parser.add_argument('--checkpoint_path', default=None, type=str, help='Path to fairseq checkpoint')
parser.add_argument('--dict_path', default=None, type=str, help='Path to dict of fine-tuned model')
parser.add_argument('--config_path', default=None, type=str, help='Path to hf config.json of model to convert')
parser.add_argument(
'--not_finetuned', action='store_true', help='Whether the model to convert is a fine-tuned model or not'
)
parser.add_argument(
'--is_seq_class',
action='store_true',
help='Whether the model to convert is a fine-tuned sequence classification model or not',
)
__SCREAMING_SNAKE_CASE : Any = parser.parse_args()
__SCREAMING_SNAKE_CASE : List[Any] = not args.not_finetuned and not args.is_seq_class
convert_wavaveca_checkpoint(
args.checkpoint_path,
args.pytorch_dump_folder_path,
args.config_path,
args.dict_path,
is_finetuned,
args.is_seq_class,
)
| 347 | 0 |
'''simple docstring'''
__lowerCAmelCase : List[Any] =range(2, 20 + 1)
__lowerCAmelCase : Optional[Any] =[10**k for k in range(ks[-1] + 1)]
__lowerCAmelCase : dict[int, dict[int, list[list[int]]]] ={}
def UpperCamelCase ( _lowerCamelCase : Tuple , _lowerCamelCase : Union[str, Any] , _lowerCamelCase : Dict , _lowerCamelCase : Union[str, Any] ):
A__ = sum(a_i[j] for j in range(_SCREAMING_SNAKE_CASE , len(_SCREAMING_SNAKE_CASE ) ) )
A__ = sum(a_i[j] * base[j] for j in range(min(len(_SCREAMING_SNAKE_CASE ) , _SCREAMING_SNAKE_CASE ) ) )
A__, A__ = 0, 0
A__ = n - i
A__ = memo.get(_SCREAMING_SNAKE_CASE )
if sub_memo is not None:
A__ = sub_memo.get(_SCREAMING_SNAKE_CASE )
if jumps is not None and len(_SCREAMING_SNAKE_CASE ) > 0:
# find and make the largest jump without going over
A__ = -1
for _k in range(len(_SCREAMING_SNAKE_CASE ) - 1 , -1 , -1 ):
if jumps[_k][2] <= k and jumps[_k][1] <= max_dn:
A__ = _k
break
if max_jump >= 0:
A__, A__, A__ = jumps[max_jump]
# since the difference between jumps is cached, add c
A__ = diff + c
for j in range(min(_SCREAMING_SNAKE_CASE , len(_SCREAMING_SNAKE_CASE ) ) ):
A__, A__ = divmod(_SCREAMING_SNAKE_CASE , 10 )
if new_c > 0:
add(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
else:
A__ = []
else:
A__ = {c: []}
A__ = sub_memo
if dn >= max_dn or c + diff >= base[k]:
return diff, dn
if k > ks[0]:
while True:
# keep doing smaller jumps
A__, A__ = next_term(_SCREAMING_SNAKE_CASE , k - 1 , i + dn , _SCREAMING_SNAKE_CASE )
diff += _diff
dn += terms_jumped
if dn >= max_dn or c + diff >= base[k]:
break
else:
# would be too small a jump, just compute sequential terms instead
A__, A__ = compute(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , i + dn , _SCREAMING_SNAKE_CASE )
diff += _diff
dn += terms_jumped
A__ = sub_memo[c]
# keep jumps sorted by # of terms skipped
A__ = 0
while j < len(_SCREAMING_SNAKE_CASE ):
if jumps[j][1] > dn:
break
j += 1
# cache the jump for this value digitsum(b) and c
sub_memo[c].insert(_SCREAMING_SNAKE_CASE , (diff, dn, k) )
return (diff, dn)
def UpperCamelCase ( _lowerCamelCase : Any , _lowerCamelCase : Optional[int] , _lowerCamelCase : str , _lowerCamelCase : Optional[int] ):
if i >= n:
return 0, i
if k > len(_SCREAMING_SNAKE_CASE ):
a_i.extend([0 for _ in range(k - len(_SCREAMING_SNAKE_CASE ) )] )
# note: a_i -> b * 10^k + c
# ds_b -> digitsum(b)
# ds_c -> digitsum(c)
A__ = i
A__, A__, A__ = 0, 0, 0
for j in range(len(_SCREAMING_SNAKE_CASE ) ):
if j >= k:
ds_b += a_i[j]
else:
ds_c += a_i[j]
while i < n:
i += 1
A__ = ds_c + ds_b
diff += addend
A__ = 0
for j in range(_SCREAMING_SNAKE_CASE ):
A__ = a_i[j] + addend
A__, A__ = divmod(_SCREAMING_SNAKE_CASE , 10 )
ds_c += a_i[j]
if addend > 0:
break
if addend > 0:
add(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
return diff, i - start_i
def UpperCamelCase ( _lowerCamelCase : List[str] , _lowerCamelCase : Tuple , _lowerCamelCase : Any ):
for j in range(_SCREAMING_SNAKE_CASE , len(_SCREAMING_SNAKE_CASE ) ):
A__ = digits[j] + addend
if s >= 10:
A__, A__ = divmod(_SCREAMING_SNAKE_CASE , 10 )
A__ = addend // 10 + quotient
else:
A__ = s
A__ = addend // 10
if addend == 0:
break
while addend > 0:
A__, A__ = divmod(_SCREAMING_SNAKE_CASE , 10 )
digits.append(_SCREAMING_SNAKE_CASE )
def UpperCamelCase ( _lowerCamelCase : int = 10**15 ):
A__ = [1]
A__ = 1
A__ = 0
while True:
A__, A__ = next_term(_SCREAMING_SNAKE_CASE , 20 , i + dn , _SCREAMING_SNAKE_CASE )
dn += terms_jumped
if dn == n - i:
break
A__ = 0
for j in range(len(_SCREAMING_SNAKE_CASE ) ):
a_n += digits[j] * 10**j
return a_n
if __name__ == "__main__":
print(f"""{solution() = }""")
| 237 |
"""simple docstring"""
import tempfile
import unittest
import numpy as np
import transformers
from transformers import GPTaTokenizer, GPTJConfig, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, tooslow
from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import jax
import jax.numpy as jnp
from transformers.modeling_flax_pytorch_utils import (
convert_pytorch_state_dict_to_flax,
load_flax_weights_in_pytorch_model,
)
from transformers.models.gptj.modeling_flax_gptj import FlaxGPTJForCausalLM, FlaxGPTJModel
if is_torch_available():
import torch
class __A :
'''simple docstring'''
def __init__( self : Dict , UpperCAmelCase_ : Tuple , UpperCAmelCase_ : Any=14 , UpperCAmelCase_ : Union[str, Any]=7 , UpperCAmelCase_ : Tuple=True , UpperCAmelCase_ : Optional[int]=True , UpperCAmelCase_ : Union[str, Any]=False , UpperCAmelCase_ : Union[str, Any]=True , UpperCAmelCase_ : str=99 , UpperCAmelCase_ : Union[str, Any]=32 , UpperCAmelCase_ : List[Any]=4 , UpperCAmelCase_ : Optional[int]=4 , UpperCAmelCase_ : int=4 , UpperCAmelCase_ : str=37 , UpperCAmelCase_ : Any="gelu" , UpperCAmelCase_ : str=0.1 , UpperCAmelCase_ : Union[str, Any]=0.1 , UpperCAmelCase_ : int=512 , UpperCAmelCase_ : Tuple=0.02 , ) ->List[str]:
"""simple docstring"""
snake_case_ = parent
snake_case_ = batch_size
snake_case_ = seq_length
snake_case_ = is_training
snake_case_ = use_input_mask
snake_case_ = use_token_type_ids
snake_case_ = use_labels
snake_case_ = vocab_size
snake_case_ = hidden_size
snake_case_ = rotary_dim
snake_case_ = num_hidden_layers
snake_case_ = num_attention_heads
snake_case_ = intermediate_size
snake_case_ = hidden_act
snake_case_ = hidden_dropout_prob
snake_case_ = attention_probs_dropout_prob
snake_case_ = max_position_embeddings
snake_case_ = initializer_range
snake_case_ = None
snake_case_ = vocab_size - 1
snake_case_ = vocab_size - 1
snake_case_ = vocab_size - 1
def lowerCAmelCase ( self : int ) ->Optional[int]:
"""simple docstring"""
snake_case_ = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
snake_case_ = None
if self.use_input_mask:
snake_case_ = random_attention_mask([self.batch_size, self.seq_length] )
snake_case_ = GPTJConfig(
vocab_size=self.vocab_size , n_embd=self.hidden_size , n_layer=self.num_hidden_layers , n_head=self.num_attention_heads , n_positions=self.max_position_embeddings , use_cache=UpperCAmelCase_ , bos_token_id=self.bos_token_id , eos_token_id=self.eos_token_id , pad_token_id=self.pad_token_id , rotary_dim=self.rotary_dim , )
return (config, input_ids, input_mask)
def lowerCAmelCase ( self : Dict ) ->Tuple:
"""simple docstring"""
snake_case_ = self.prepare_config_and_inputs()
snake_case_ , snake_case_ , snake_case_ = config_and_inputs
snake_case_ = {"""input_ids""": input_ids, """attention_mask""": attention_mask}
return config, inputs_dict
def lowerCAmelCase ( self : int , UpperCAmelCase_ : Union[str, Any] , UpperCAmelCase_ : List[Any] , UpperCAmelCase_ : List[str] , UpperCAmelCase_ : Dict ) ->Tuple:
"""simple docstring"""
snake_case_ = 20
snake_case_ = model_class_name(UpperCAmelCase_ )
snake_case_ = model.init_cache(input_ids.shape[0] , UpperCAmelCase_ )
snake_case_ = jnp.ones((input_ids.shape[0], max_decoder_length) , dtype="""i4""" )
snake_case_ = jnp.broadcast_to(
jnp.arange(input_ids.shape[-1] - 1 )[None, :] , (input_ids.shape[0], input_ids.shape[-1] - 1) )
snake_case_ = model(
input_ids[:, :-1] , attention_mask=UpperCAmelCase_ , past_key_values=UpperCAmelCase_ , position_ids=UpperCAmelCase_ , )
snake_case_ = jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]] , dtype="""i4""" )
snake_case_ = model(
input_ids[:, -1:] , attention_mask=UpperCAmelCase_ , past_key_values=outputs_cache.past_key_values , position_ids=UpperCAmelCase_ , )
snake_case_ = model(UpperCAmelCase_ )
snake_case_ = np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5]) ) )
self.parent.assertTrue(diff < 1E-3 , msg=F"""Max diff is {diff}""" )
def lowerCAmelCase ( self : Union[str, Any] , UpperCAmelCase_ : int , UpperCAmelCase_ : Union[str, Any] , UpperCAmelCase_ : Optional[Any] , UpperCAmelCase_ : Optional[Any] ) ->Dict:
"""simple docstring"""
snake_case_ = 20
snake_case_ = model_class_name(UpperCAmelCase_ )
snake_case_ = jnp.concatenate(
[attention_mask, jnp.zeros((attention_mask.shape[0], max_decoder_length - attention_mask.shape[1]) )] , axis=-1 , )
snake_case_ = model.init_cache(input_ids.shape[0] , UpperCAmelCase_ )
snake_case_ = jnp.broadcast_to(
jnp.arange(input_ids.shape[-1] - 1 )[None, :] , (input_ids.shape[0], input_ids.shape[-1] - 1) )
snake_case_ = model(
input_ids[:, :-1] , attention_mask=UpperCAmelCase_ , past_key_values=UpperCAmelCase_ , position_ids=UpperCAmelCase_ , )
snake_case_ = jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]] , dtype="""i4""" )
snake_case_ = model(
input_ids[:, -1:] , past_key_values=outputs_cache.past_key_values , attention_mask=UpperCAmelCase_ , position_ids=UpperCAmelCase_ , )
snake_case_ = model(UpperCAmelCase_ , attention_mask=UpperCAmelCase_ )
snake_case_ = np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5]) ) )
self.parent.assertTrue(diff < 1E-3 , msg=F"""Max diff is {diff}""" )
@require_flax
class __A (snake_case__ , snake_case__ , unittest.TestCase):
'''simple docstring'''
__lowercase: Any = (FlaxGPTJModel, FlaxGPTJForCausalLM) if is_flax_available() else ()
__lowercase: List[str] = (FlaxGPTJForCausalLM,) if is_flax_available() else ()
def lowerCAmelCase ( self : Tuple ) ->List[str]:
"""simple docstring"""
snake_case_ = FlaxGPTJModelTester(self )
def lowerCAmelCase ( self : int ) ->List[Any]:
"""simple docstring"""
for model_class_name in self.all_model_classes:
snake_case_ , snake_case_ , snake_case_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.check_use_cache_forward(UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ )
def lowerCAmelCase ( self : List[str] ) ->Any:
"""simple docstring"""
for model_class_name in self.all_model_classes:
snake_case_ , snake_case_ , snake_case_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.check_use_cache_forward_with_attn_mask(
UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ )
@tooslow
def lowerCAmelCase ( self : List[str] ) ->Optional[Any]:
"""simple docstring"""
snake_case_ = GPTaTokenizer.from_pretrained("""gpt2""" , pad_token="""<|endoftext|>""" , padding_side="""left""" )
snake_case_ = tokenizer(["""Hello this is a long string""", """Hey"""] , return_tensors="""np""" , padding=UpperCAmelCase_ , truncation=UpperCAmelCase_ )
snake_case_ = FlaxGPTJForCausalLM.from_pretrained("""EleutherAI/gpt-j-6B""" )
snake_case_ = False
snake_case_ = model.config.eos_token_id
snake_case_ = jax.jit(model.generate )
snake_case_ = jit_generate(
inputs["""input_ids"""] , attention_mask=inputs["""attention_mask"""] , pad_token_id=tokenizer.pad_token_id ).sequences
snake_case_ = tokenizer.batch_decode(UpperCAmelCase_ , skip_special_tokens=UpperCAmelCase_ )
snake_case_ = [
"""Hello this is a long string of text.\n\nI'm trying to get the text of the""",
"""Hey, I'm a little late to the party. I'm going to""",
]
self.assertListEqual(UpperCAmelCase_ , UpperCAmelCase_ )
@is_pt_flax_cross_test
def lowerCAmelCase ( self : int ) ->str:
"""simple docstring"""
snake_case_ , snake_case_ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__ ):
# prepare inputs
snake_case_ = self._prepare_for_class(UpperCAmelCase_ , UpperCAmelCase_ )
snake_case_ = {k: torch.tensor(v.tolist() ) for k, v in prepared_inputs_dict.items()}
# load corresponding PyTorch class
snake_case_ = model_class.__name__[4:] # Skip the "Flax" at the beginning
snake_case_ = getattr(UpperCAmelCase_ , UpperCAmelCase_ )
snake_case_ , snake_case_ = pt_inputs["""input_ids"""].shape
snake_case_ = np.random.randint(0 , seq_length - 1 , size=(batch_size,) )
for batch_idx, start_index in enumerate(UpperCAmelCase_ ):
snake_case_ = 0
snake_case_ = 1
snake_case_ = 0
snake_case_ = 1
snake_case_ = pt_model_class(UpperCAmelCase_ ).eval()
snake_case_ = model_class(UpperCAmelCase_ , dtype=jnp.floataa )
snake_case_ = convert_pytorch_state_dict_to_flax(pt_model.state_dict() , UpperCAmelCase_ )
snake_case_ = fx_state
with torch.no_grad():
snake_case_ = pt_model(**UpperCAmelCase_ ).to_tuple()
snake_case_ = fx_model(**UpperCAmelCase_ ).to_tuple()
self.assertEqual(len(UpperCAmelCase_ ) , len(UpperCAmelCase_ ) , """Output lengths differ between Flax and PyTorch""" )
for fx_output, pt_output in zip(UpperCAmelCase_ , UpperCAmelCase_ ):
self.assert_almost_equals(fx_output[:, -1] , pt_output[:, -1].numpy() , 4E-2 )
with tempfile.TemporaryDirectory() as tmpdirname:
pt_model.save_pretrained(UpperCAmelCase_ )
snake_case_ = model_class.from_pretrained(UpperCAmelCase_ , from_pt=UpperCAmelCase_ )
snake_case_ = fx_model_loaded(**UpperCAmelCase_ ).to_tuple()
self.assertEqual(
len(UpperCAmelCase_ ) , len(UpperCAmelCase_ ) , """Output lengths differ between Flax and PyTorch""" )
for fx_output_loaded, pt_output in zip(UpperCAmelCase_ , UpperCAmelCase_ ):
self.assert_almost_equals(fx_output_loaded[:, -1] , pt_output[:, -1].numpy() , 4E-2 )
@is_pt_flax_cross_test
def lowerCAmelCase ( self : List[Any] ) ->str:
"""simple docstring"""
snake_case_ , snake_case_ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__ ):
# prepare inputs
snake_case_ = self._prepare_for_class(UpperCAmelCase_ , UpperCAmelCase_ )
snake_case_ = {k: torch.tensor(v.tolist() ) for k, v in prepared_inputs_dict.items()}
# load corresponding PyTorch class
snake_case_ = model_class.__name__[4:] # Skip the "Flax" at the beginning
snake_case_ = getattr(UpperCAmelCase_ , UpperCAmelCase_ )
snake_case_ = pt_model_class(UpperCAmelCase_ ).eval()
snake_case_ = model_class(UpperCAmelCase_ , dtype=jnp.floataa )
snake_case_ = load_flax_weights_in_pytorch_model(UpperCAmelCase_ , fx_model.params )
snake_case_ , snake_case_ = pt_inputs["""input_ids"""].shape
snake_case_ = np.random.randint(0 , seq_length - 1 , size=(batch_size,) )
for batch_idx, start_index in enumerate(UpperCAmelCase_ ):
snake_case_ = 0
snake_case_ = 1
snake_case_ = 0
snake_case_ = 1
# make sure weights are tied in PyTorch
pt_model.tie_weights()
with torch.no_grad():
snake_case_ = pt_model(**UpperCAmelCase_ ).to_tuple()
snake_case_ = fx_model(**UpperCAmelCase_ ).to_tuple()
self.assertEqual(len(UpperCAmelCase_ ) , len(UpperCAmelCase_ ) , """Output lengths differ between Flax and PyTorch""" )
for fx_output, pt_output in zip(UpperCAmelCase_ , UpperCAmelCase_ ):
self.assert_almost_equals(fx_output[:, -1] , pt_output[:, -1].numpy() , 4E-2 )
with tempfile.TemporaryDirectory() as tmpdirname:
fx_model.save_pretrained(UpperCAmelCase_ )
snake_case_ = pt_model_class.from_pretrained(UpperCAmelCase_ , from_flax=UpperCAmelCase_ )
with torch.no_grad():
snake_case_ = pt_model_loaded(**UpperCAmelCase_ ).to_tuple()
self.assertEqual(
len(UpperCAmelCase_ ) , len(UpperCAmelCase_ ) , """Output lengths differ between Flax and PyTorch""" )
for fx_output, pt_output in zip(UpperCAmelCase_ , UpperCAmelCase_ ):
self.assert_almost_equals(fx_output[:, -1] , pt_output[:, -1].numpy() , 4E-2 )
@tooslow
def lowerCAmelCase ( self : List[Any] ) ->str:
"""simple docstring"""
for model_class_name in self.all_model_classes:
snake_case_ = model_class_name.from_pretrained("""EleutherAI/gpt-j-6B""" )
snake_case_ = model(np.ones((1, 1) ) )
self.assertIsNotNone(UpperCAmelCase_ )
| 347 | 0 |
'''simple docstring'''
import numpy as np
from numpy import ndarray
from scipy.optimize import Bounds, LinearConstraint, minimize
def A_ ( snake_case ):
return np.dot(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
class _snake_case :
def __init__( self : List[Any] ,*,
SCREAMING_SNAKE_CASE__ : float = np.inf ,SCREAMING_SNAKE_CASE__ : str = "linear" ,SCREAMING_SNAKE_CASE__ : float = 0.0 ,):
SCREAMING_SNAKE_CASE:Any = regularization
SCREAMING_SNAKE_CASE:Union[str, Any] = gamma
if kernel == "linear":
SCREAMING_SNAKE_CASE:Union[str, Any] = self.__linear
elif kernel == "rbf":
if self.gamma == 0:
raise ValueError("rbf kernel requires gamma" )
if not isinstance(self.gamma ,(float, int) ):
raise ValueError("gamma must be float or int" )
if not self.gamma > 0:
raise ValueError("gamma must be > 0" )
SCREAMING_SNAKE_CASE:Dict = self.__rbf
# in the future, there could be a default value like in sklearn
# sklear: def_gamma = 1/(n_features * X.var()) (wiki)
# previously it was 1/(n_features)
else:
SCREAMING_SNAKE_CASE:str = F'''Unknown kernel: {kernel}'''
raise ValueError(UpperCAmelCase_ )
def __UpperCamelCase ( self : Optional[Any] ,SCREAMING_SNAKE_CASE__ : ndarray ,SCREAMING_SNAKE_CASE__ : ndarray ):
return np.dot(UpperCAmelCase_ ,UpperCAmelCase_ )
def __UpperCamelCase ( self : Union[str, Any] ,SCREAMING_SNAKE_CASE__ : ndarray ,SCREAMING_SNAKE_CASE__ : ndarray ):
return np.exp(-(self.gamma * norm_squared(vectora - vectora )) )
def __UpperCamelCase ( self : Optional[Any] ,SCREAMING_SNAKE_CASE__ : list[ndarray] ,SCREAMING_SNAKE_CASE__ : ndarray ):
SCREAMING_SNAKE_CASE:List[Any] = observations
SCREAMING_SNAKE_CASE:Dict = classes
# using Wolfe's Dual to calculate w.
# Primal problem: minimize 1/2*norm_squared(w)
# constraint: yn(w . xn + b) >= 1
#
# With l a vector
# Dual problem: maximize sum_n(ln) -
# 1/2 * sum_n(sum_m(ln*lm*yn*ym*xn . xm))
# constraint: self.C >= ln >= 0
# and sum_n(ln*yn) = 0
# Then we get w using w = sum_n(ln*yn*xn)
# At the end we can get b ~= mean(yn - w . xn)
#
# Since we use kernels, we only need l_star to calculate b
# and to classify observations
((SCREAMING_SNAKE_CASE ) , ):List[Any] = np.shape(UpperCAmelCase_ )
def to_minimize(SCREAMING_SNAKE_CASE__ : ndarray ) -> float:
SCREAMING_SNAKE_CASE:List[Any] = 0
((SCREAMING_SNAKE_CASE ) , ):Union[str, Any] = np.shape(UpperCAmelCase_ )
for i in range(UpperCAmelCase_ ):
for j in range(UpperCAmelCase_ ):
s += (
candidate[i]
* candidate[j]
* classes[i]
* classes[j]
* self.kernel(observations[i] ,observations[j] )
)
return 1 / 2 * s - sum(UpperCAmelCase_ )
SCREAMING_SNAKE_CASE:List[Any] = LinearConstraint(UpperCAmelCase_ ,0 ,0 )
SCREAMING_SNAKE_CASE:Dict = Bounds(0 ,self.regularization )
SCREAMING_SNAKE_CASE:str = minimize(
UpperCAmelCase_ ,np.ones(UpperCAmelCase_ ) ,bounds=UpperCAmelCase_ ,constraints=[ly_contraint] ).x
SCREAMING_SNAKE_CASE:Optional[Any] = l_star
# calculating mean offset of separation plane to points
SCREAMING_SNAKE_CASE:Optional[int] = 0
for i in range(UpperCAmelCase_ ):
for j in range(UpperCAmelCase_ ):
s += classes[i] - classes[i] * self.optimum[i] * self.kernel(
observations[i] ,observations[j] )
SCREAMING_SNAKE_CASE:Tuple = s / n
def __UpperCamelCase ( self : str ,SCREAMING_SNAKE_CASE__ : ndarray ):
SCREAMING_SNAKE_CASE:int = sum(
self.optimum[n]
* self.classes[n]
* self.kernel(self.observations[n] ,UpperCAmelCase_ )
for n in range(len(self.classes ) ) )
return 1 if s + self.offset >= 0 else -1
if __name__ == "__main__":
import doctest
doctest.testmod()
| 139 |
"""simple docstring"""
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import CONFIG_MAPPING
__SCREAMING_SNAKE_CASE : Any = logging.get_logger(__name__)
class __A (snake_case__):
'''simple docstring'''
__lowercase: int = """upernet"""
def __init__( self : str , UpperCAmelCase_ : List[str]=None , UpperCAmelCase_ : str=512 , UpperCAmelCase_ : int=0.02 , UpperCAmelCase_ : Optional[Any]=[1, 2, 3, 6] , UpperCAmelCase_ : Optional[int]=True , UpperCAmelCase_ : Tuple=0.4 , UpperCAmelCase_ : Tuple=384 , UpperCAmelCase_ : Union[str, Any]=256 , UpperCAmelCase_ : str=1 , UpperCAmelCase_ : Tuple=False , UpperCAmelCase_ : Tuple=255 , **UpperCAmelCase_ : Dict , ) ->Union[str, Any]:
"""simple docstring"""
super().__init__(**UpperCAmelCase_ )
if backbone_config is None:
logger.info("""`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.""" )
snake_case_ = CONFIG_MAPPING["""resnet"""](out_features=["""stage1""", """stage2""", """stage3""", """stage4"""] )
elif isinstance(UpperCAmelCase_ , UpperCAmelCase_ ):
snake_case_ = backbone_config.get("""model_type""" )
snake_case_ = CONFIG_MAPPING[backbone_model_type]
snake_case_ = config_class.from_dict(UpperCAmelCase_ )
snake_case_ = backbone_config
snake_case_ = hidden_size
snake_case_ = initializer_range
snake_case_ = pool_scales
snake_case_ = use_auxiliary_head
snake_case_ = auxiliary_loss_weight
snake_case_ = auxiliary_in_channels
snake_case_ = auxiliary_channels
snake_case_ = auxiliary_num_convs
snake_case_ = auxiliary_concat_input
snake_case_ = loss_ignore_index
def lowerCAmelCase ( self : str ) ->Optional[Any]:
"""simple docstring"""
snake_case_ = copy.deepcopy(self.__dict__ )
snake_case_ = self.backbone_config.to_dict()
snake_case_ = self.__class__.model_type
return output
| 347 | 0 |
import tempfile
import unittest
import numpy as np
import transformers
from transformers import GPTaTokenizer, GPTJConfig, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, tooslow
from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import jax
import jax.numpy as jnp
from transformers.modeling_flax_pytorch_utils import (
convert_pytorch_state_dict_to_flax,
load_flax_weights_in_pytorch_model,
)
from transformers.models.gptj.modeling_flax_gptj import FlaxGPTJForCausalLM, FlaxGPTJModel
if is_torch_available():
import torch
class __A:
def __init__( self , _snake_case , _snake_case=14 , _snake_case=7 , _snake_case=True , _snake_case=True , _snake_case=False , _snake_case=True , _snake_case=99 , _snake_case=32 , _snake_case=4 , _snake_case=4 , _snake_case=4 , _snake_case=37 , _snake_case="gelu" , _snake_case=0.1 , _snake_case=0.1 , _snake_case=512 , _snake_case=0.02 , ) -> List[str]:
'''simple docstring'''
__a = parent
__a = batch_size
__a = seq_length
__a = is_training
__a = use_input_mask
__a = use_token_type_ids
__a = use_labels
__a = vocab_size
__a = hidden_size
__a = rotary_dim
__a = num_hidden_layers
__a = num_attention_heads
__a = intermediate_size
__a = hidden_act
__a = hidden_dropout_prob
__a = attention_probs_dropout_prob
__a = max_position_embeddings
__a = initializer_range
__a = None
__a = vocab_size - 1
__a = vocab_size - 1
__a = vocab_size - 1
def SCREAMING_SNAKE_CASE_ ( self ) -> Optional[int]:
'''simple docstring'''
__a = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
__a = None
if self.use_input_mask:
__a = random_attention_mask([self.batch_size, self.seq_length] )
__a = GPTJConfig(
vocab_size=self.vocab_size , n_embd=self.hidden_size , n_layer=self.num_hidden_layers , n_head=self.num_attention_heads , n_positions=self.max_position_embeddings , use_cache=UpperCAmelCase_ , bos_token_id=self.bos_token_id , eos_token_id=self.eos_token_id , pad_token_id=self.pad_token_id , rotary_dim=self.rotary_dim , )
return (config, input_ids, input_mask)
def SCREAMING_SNAKE_CASE_ ( self ) -> Tuple:
'''simple docstring'''
__a = self.prepare_config_and_inputs()
__a , __a , __a = config_and_inputs
__a = {'''input_ids''': input_ids, '''attention_mask''': attention_mask}
return config, inputs_dict
def SCREAMING_SNAKE_CASE_ ( self , _snake_case , _snake_case , _snake_case , _snake_case ) -> Tuple:
'''simple docstring'''
__a = 20
__a = model_class_name(UpperCAmelCase_ )
__a = model.init_cache(input_ids.shape[0] , UpperCAmelCase_ )
__a = jnp.ones((input_ids.shape[0], max_decoder_length) , dtype='''i4''' )
__a = jnp.broadcast_to(
jnp.arange(input_ids.shape[-1] - 1 )[None, :] , (input_ids.shape[0], input_ids.shape[-1] - 1) )
__a = model(
input_ids[:, :-1] , attention_mask=UpperCAmelCase_ , past_key_values=UpperCAmelCase_ , position_ids=UpperCAmelCase_ , )
__a = jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]] , dtype='''i4''' )
__a = model(
input_ids[:, -1:] , attention_mask=UpperCAmelCase_ , past_key_values=outputs_cache.past_key_values , position_ids=UpperCAmelCase_ , )
__a = model(UpperCAmelCase_ )
__a = np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5]) ) )
self.parent.assertTrue(diff < 1E-3 , msg=F"""Max diff is {diff}""" )
def SCREAMING_SNAKE_CASE_ ( self , _snake_case , _snake_case , _snake_case , _snake_case ) -> Dict:
'''simple docstring'''
__a = 20
__a = model_class_name(UpperCAmelCase_ )
__a = jnp.concatenate(
[attention_mask, jnp.zeros((attention_mask.shape[0], max_decoder_length - attention_mask.shape[1]) )] , axis=-1 , )
__a = model.init_cache(input_ids.shape[0] , UpperCAmelCase_ )
__a = jnp.broadcast_to(
jnp.arange(input_ids.shape[-1] - 1 )[None, :] , (input_ids.shape[0], input_ids.shape[-1] - 1) )
__a = model(
input_ids[:, :-1] , attention_mask=UpperCAmelCase_ , past_key_values=UpperCAmelCase_ , position_ids=UpperCAmelCase_ , )
__a = jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]] , dtype='''i4''' )
__a = model(
input_ids[:, -1:] , past_key_values=outputs_cache.past_key_values , attention_mask=UpperCAmelCase_ , position_ids=UpperCAmelCase_ , )
__a = model(UpperCAmelCase_ , attention_mask=UpperCAmelCase_ )
__a = np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5]) ) )
self.parent.assertTrue(diff < 1E-3 , msg=F"""Max diff is {diff}""" )
@require_flax
class __A( snake_case__ , snake_case__ , unittest.TestCase ):
snake_case_ = (FlaxGPTJModel, FlaxGPTJForCausalLM) if is_flax_available() else ()
snake_case_ = (FlaxGPTJForCausalLM,) if is_flax_available() else ()
def SCREAMING_SNAKE_CASE_ ( self ) -> List[str]:
'''simple docstring'''
__a = FlaxGPTJModelTester(self )
def SCREAMING_SNAKE_CASE_ ( self ) -> List[Any]:
'''simple docstring'''
for model_class_name in self.all_model_classes:
__a , __a , __a = self.model_tester.prepare_config_and_inputs()
self.model_tester.check_use_cache_forward(UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ )
def SCREAMING_SNAKE_CASE_ ( self ) -> Any:
'''simple docstring'''
for model_class_name in self.all_model_classes:
__a , __a , __a = self.model_tester.prepare_config_and_inputs()
self.model_tester.check_use_cache_forward_with_attn_mask(
UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ )
@tooslow
def SCREAMING_SNAKE_CASE_ ( self ) -> Optional[Any]:
'''simple docstring'''
__a = GPTaTokenizer.from_pretrained('''gpt2''' , pad_token='''<|endoftext|>''' , padding_side='''left''' )
__a = tokenizer(['''Hello this is a long string''', '''Hey'''] , return_tensors='''np''' , padding=UpperCAmelCase_ , truncation=UpperCAmelCase_ )
__a = FlaxGPTJForCausalLM.from_pretrained('''EleutherAI/gpt-j-6B''' )
__a = False
__a = model.config.eos_token_id
__a = jax.jit(model.generate )
__a = jit_generate(
inputs['''input_ids'''] , attention_mask=inputs['''attention_mask'''] , pad_token_id=tokenizer.pad_token_id ).sequences
__a = tokenizer.batch_decode(UpperCAmelCase_ , skip_special_tokens=UpperCAmelCase_ )
__a = [
'''Hello this is a long string of text.\n\nI\'m trying to get the text of the''',
'''Hey, I\'m a little late to the party. I\'m going to''',
]
self.assertListEqual(UpperCAmelCase_ , UpperCAmelCase_ )
@is_pt_flax_cross_test
def SCREAMING_SNAKE_CASE_ ( self ) -> str:
'''simple docstring'''
__a , __a = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__ ):
# prepare inputs
__a = self._prepare_for_class(UpperCAmelCase_ , UpperCAmelCase_ )
__a = {k: torch.tensor(v.tolist() ) for k, v in prepared_inputs_dict.items()}
# load corresponding PyTorch class
__a = model_class.__name__[4:] # Skip the "Flax" at the beginning
__a = getattr(UpperCAmelCase_ , UpperCAmelCase_ )
__a , __a = pt_inputs['''input_ids'''].shape
__a = np.random.randint(0 , seq_length - 1 , size=(batch_size,) )
for batch_idx, start_index in enumerate(UpperCAmelCase_ ):
__a = 0
__a = 1
__a = 0
__a = 1
__a = pt_model_class(UpperCAmelCase_ ).eval()
__a = model_class(UpperCAmelCase_ , dtype=jnp.floataa )
__a = convert_pytorch_state_dict_to_flax(pt_model.state_dict() , UpperCAmelCase_ )
__a = fx_state
with torch.no_grad():
__a = pt_model(**UpperCAmelCase_ ).to_tuple()
__a = fx_model(**UpperCAmelCase_ ).to_tuple()
self.assertEqual(len(UpperCAmelCase_ ) , len(UpperCAmelCase_ ) , '''Output lengths differ between Flax and PyTorch''' )
for fx_output, pt_output in zip(UpperCAmelCase_ , UpperCAmelCase_ ):
self.assert_almost_equals(fx_output[:, -1] , pt_output[:, -1].numpy() , 4E-2 )
with tempfile.TemporaryDirectory() as tmpdirname:
pt_model.save_pretrained(UpperCAmelCase_ )
__a = model_class.from_pretrained(UpperCAmelCase_ , from_pt=UpperCAmelCase_ )
__a = fx_model_loaded(**UpperCAmelCase_ ).to_tuple()
self.assertEqual(
len(UpperCAmelCase_ ) , len(UpperCAmelCase_ ) , '''Output lengths differ between Flax and PyTorch''' )
for fx_output_loaded, pt_output in zip(UpperCAmelCase_ , UpperCAmelCase_ ):
self.assert_almost_equals(fx_output_loaded[:, -1] , pt_output[:, -1].numpy() , 4E-2 )
@is_pt_flax_cross_test
def SCREAMING_SNAKE_CASE_ ( self ) -> str:
'''simple docstring'''
__a , __a = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__ ):
# prepare inputs
__a = self._prepare_for_class(UpperCAmelCase_ , UpperCAmelCase_ )
__a = {k: torch.tensor(v.tolist() ) for k, v in prepared_inputs_dict.items()}
# load corresponding PyTorch class
__a = model_class.__name__[4:] # Skip the "Flax" at the beginning
__a = getattr(UpperCAmelCase_ , UpperCAmelCase_ )
__a = pt_model_class(UpperCAmelCase_ ).eval()
__a = model_class(UpperCAmelCase_ , dtype=jnp.floataa )
__a = load_flax_weights_in_pytorch_model(UpperCAmelCase_ , fx_model.params )
__a , __a = pt_inputs['''input_ids'''].shape
__a = np.random.randint(0 , seq_length - 1 , size=(batch_size,) )
for batch_idx, start_index in enumerate(UpperCAmelCase_ ):
__a = 0
__a = 1
__a = 0
__a = 1
# make sure weights are tied in PyTorch
pt_model.tie_weights()
with torch.no_grad():
__a = pt_model(**UpperCAmelCase_ ).to_tuple()
__a = fx_model(**UpperCAmelCase_ ).to_tuple()
self.assertEqual(len(UpperCAmelCase_ ) , len(UpperCAmelCase_ ) , '''Output lengths differ between Flax and PyTorch''' )
for fx_output, pt_output in zip(UpperCAmelCase_ , UpperCAmelCase_ ):
self.assert_almost_equals(fx_output[:, -1] , pt_output[:, -1].numpy() , 4E-2 )
with tempfile.TemporaryDirectory() as tmpdirname:
fx_model.save_pretrained(UpperCAmelCase_ )
__a = pt_model_class.from_pretrained(UpperCAmelCase_ , from_flax=UpperCAmelCase_ )
with torch.no_grad():
__a = pt_model_loaded(**UpperCAmelCase_ ).to_tuple()
self.assertEqual(
len(UpperCAmelCase_ ) , len(UpperCAmelCase_ ) , '''Output lengths differ between Flax and PyTorch''' )
for fx_output, pt_output in zip(UpperCAmelCase_ , UpperCAmelCase_ ):
self.assert_almost_equals(fx_output[:, -1] , pt_output[:, -1].numpy() , 4E-2 )
@tooslow
def SCREAMING_SNAKE_CASE_ ( self ) -> str:
'''simple docstring'''
for model_class_name in self.all_model_classes:
__a = model_class_name.from_pretrained('''EleutherAI/gpt-j-6B''' )
__a = model(np.ones((1, 1) ) )
self.assertIsNotNone(UpperCAmelCase_ ) | 6 |
"""simple docstring"""
import os
import shutil
import tempfile
import unittest
import numpy as np
from transformers import AutoTokenizer, BarkProcessor
from transformers.testing_utils import require_torch, slow
@require_torch
class __A (unittest.TestCase):
'''simple docstring'''
def lowerCAmelCase ( self : Optional[int] ) ->Dict:
"""simple docstring"""
snake_case_ = """ylacombe/bark-small"""
snake_case_ = tempfile.mkdtemp()
snake_case_ = """en_speaker_1"""
snake_case_ = """This is a test string"""
snake_case_ = """speaker_embeddings_path.json"""
snake_case_ = """speaker_embeddings"""
def lowerCAmelCase ( self : List[str] , **UpperCAmelCase_ : str ) ->Optional[int]:
"""simple docstring"""
return AutoTokenizer.from_pretrained(self.checkpoint , **UpperCAmelCase_ )
def lowerCAmelCase ( self : Any ) ->Dict:
"""simple docstring"""
shutil.rmtree(self.tmpdirname )
def lowerCAmelCase ( self : List[Any] ) ->Dict:
"""simple docstring"""
snake_case_ = self.get_tokenizer()
snake_case_ = BarkProcessor(tokenizer=UpperCAmelCase_ )
processor.save_pretrained(self.tmpdirname )
snake_case_ = BarkProcessor.from_pretrained(self.tmpdirname )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer.get_vocab() )
@slow
def lowerCAmelCase ( self : Dict ) ->int:
"""simple docstring"""
snake_case_ = BarkProcessor.from_pretrained(
pretrained_processor_name_or_path=self.checkpoint , speaker_embeddings_dict_path=self.speaker_embeddings_dict_path , )
processor.save_pretrained(
self.tmpdirname , speaker_embeddings_dict_path=self.speaker_embeddings_dict_path , speaker_embeddings_directory=self.speaker_embeddings_directory , )
snake_case_ = self.get_tokenizer(bos_token="""(BOS)""" , eos_token="""(EOS)""" )
snake_case_ = BarkProcessor.from_pretrained(
self.tmpdirname , self.speaker_embeddings_dict_path , bos_token="""(BOS)""" , eos_token="""(EOS)""" , )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer_add_kwargs.get_vocab() )
def lowerCAmelCase ( self : Optional[Any] ) ->Any:
"""simple docstring"""
snake_case_ = BarkProcessor.from_pretrained(
pretrained_processor_name_or_path=self.checkpoint , speaker_embeddings_dict_path=self.speaker_embeddings_dict_path , )
snake_case_ = 35
snake_case_ = 2
snake_case_ = 8
snake_case_ = {
"""semantic_prompt""": np.ones(UpperCAmelCase_ ),
"""coarse_prompt""": np.ones((nb_codebooks_coarse, seq_len) ),
"""fine_prompt""": np.ones((nb_codebooks_total, seq_len) ),
}
# test providing already loaded voice_preset
snake_case_ = processor(text=self.input_string , voice_preset=UpperCAmelCase_ )
snake_case_ = inputs["""history_prompt"""]
for key in voice_preset:
self.assertListEqual(voice_preset[key].tolist() , processed_voice_preset.get(UpperCAmelCase_ , np.array([] ) ).tolist() )
# test loading voice preset from npz file
snake_case_ = os.path.join(self.tmpdirname , """file.npz""" )
np.savez(UpperCAmelCase_ , **UpperCAmelCase_ )
snake_case_ = processor(text=self.input_string , voice_preset=UpperCAmelCase_ )
snake_case_ = inputs["""history_prompt"""]
for key in voice_preset:
self.assertListEqual(voice_preset[key].tolist() , processed_voice_preset.get(UpperCAmelCase_ , np.array([] ) ).tolist() )
# test loading voice preset from the hub
snake_case_ = processor(text=self.input_string , voice_preset=self.voice_preset )
def lowerCAmelCase ( self : Tuple ) ->Dict:
"""simple docstring"""
snake_case_ = self.get_tokenizer()
snake_case_ = BarkProcessor(tokenizer=UpperCAmelCase_ )
snake_case_ = processor(text=self.input_string )
snake_case_ = tokenizer(
self.input_string , padding="""max_length""" , max_length=256 , add_special_tokens=UpperCAmelCase_ , return_attention_mask=UpperCAmelCase_ , return_token_type_ids=UpperCAmelCase_ , )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key] , encoded_processor[key].squeeze().tolist() )
| 347 | 0 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
SCREAMING_SNAKE_CASE : Union[str, Any] = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE : Union[str, Any] = {
'junnyu/roformer_chinese_small': 'https://huggingface.co/junnyu/roformer_chinese_small/resolve/main/config.json',
'junnyu/roformer_chinese_base': 'https://huggingface.co/junnyu/roformer_chinese_base/resolve/main/config.json',
'junnyu/roformer_chinese_char_small': (
'https://huggingface.co/junnyu/roformer_chinese_char_small/resolve/main/config.json'
),
'junnyu/roformer_chinese_char_base': (
'https://huggingface.co/junnyu/roformer_chinese_char_base/resolve/main/config.json'
),
'junnyu/roformer_small_discriminator': (
'https://huggingface.co/junnyu/roformer_small_discriminator/resolve/main/config.json'
),
'junnyu/roformer_small_generator': (
'https://huggingface.co/junnyu/roformer_small_generator/resolve/main/config.json'
),
# See all RoFormer models at https://huggingface.co/models?filter=roformer
}
class _UpperCAmelCase ( snake_case__ ):
'''simple docstring'''
lowerCamelCase__ ="""roformer"""
def __init__(self , a_=5_00_00 , a_=None , a_=7_68 , a_=12 , a_=12 , a_=30_72 , a_="gelu" , a_=0.1 , a_=0.1 , a_=15_36 , a_=2 , a_=0.02 , a_=1E-12 , a_=0 , a_=False , a_=True , **a_ , ):
'''simple docstring'''
super().__init__(pad_token_id=UpperCAmelCase_ , **UpperCAmelCase_ )
__snake_case : Optional[int] = vocab_size
__snake_case : List[Any] = hidden_size if embedding_size is None else embedding_size
__snake_case : int = hidden_size
__snake_case : Optional[int] = num_hidden_layers
__snake_case : str = num_attention_heads
__snake_case : Dict = hidden_act
__snake_case : Tuple = intermediate_size
__snake_case : List[Any] = hidden_dropout_prob
__snake_case : Tuple = attention_probs_dropout_prob
__snake_case : Tuple = max_position_embeddings
__snake_case : List[Any] = type_vocab_size
__snake_case : Dict = initializer_range
__snake_case : Any = layer_norm_eps
__snake_case : Optional[Any] = rotary_value
__snake_case : List[Any] = use_cache
class _UpperCAmelCase ( snake_case__ ):
'''simple docstring'''
@property
def SCREAMING_SNAKE_CASE (self ):
'''simple docstring'''
if self.task == "multiple-choice":
__snake_case : str = {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
else:
__snake_case : Optional[int] = {0: '''batch''', 1: '''sequence'''}
__snake_case : Union[str, Any] = {0: '''batch''', 1: '''sequence'''}
return OrderedDict(
[
('''input_ids''', dynamic_axis),
('''attention_mask''', dynamic_axis),
('''token_type_ids''', dynamic_axis),
] )
| 102 |
"""simple docstring"""
import argparse
import json
import os
import sys
import tempfile
import unittest
from argparse import Namespace
from dataclasses import dataclass, field
from enum import Enum
from pathlib import Path
from typing import List, Literal, Optional
import yaml
from transformers import HfArgumentParser, TrainingArguments
from transformers.hf_argparser import make_choice_type_function, string_to_bool
# Since Python 3.10, we can use the builtin `|` operator for Union types
# See PEP 604: https://peps.python.org/pep-0604
__SCREAMING_SNAKE_CASE : int = sys.version_info >= (3, 10)
def _a ( _SCREAMING_SNAKE_CASE=None , _SCREAMING_SNAKE_CASE=None ) -> Tuple:
return field(default_factory=lambda: default , metadata=_SCREAMING_SNAKE_CASE )
@dataclass
class __A :
'''simple docstring'''
__lowercase: int
__lowercase: float
__lowercase: str
__lowercase: bool
@dataclass
class __A :
'''simple docstring'''
__lowercase: int = 42
__lowercase: str = field(default="""toto""" , metadata={"""help""": """help message"""})
@dataclass
class __A :
'''simple docstring'''
__lowercase: bool = False
__lowercase: bool = True
__lowercase: Optional[bool] = None
class __A (snake_case__):
'''simple docstring'''
__lowercase: str = """titi"""
__lowercase: Any = """toto"""
class __A (snake_case__):
'''simple docstring'''
__lowercase: int = """titi"""
__lowercase: Optional[Any] = """toto"""
__lowercase: List[Any] = 42
@dataclass
class __A :
'''simple docstring'''
__lowercase: BasicEnum = "toto"
def lowerCAmelCase ( self : int ) ->List[Any]:
"""simple docstring"""
snake_case_ = BasicEnum(self.foo )
@dataclass
class __A :
'''simple docstring'''
__lowercase: MixedTypeEnum = "toto"
def lowerCAmelCase ( self : Optional[int] ) ->Optional[Any]:
"""simple docstring"""
snake_case_ = MixedTypeEnum(self.foo )
@dataclass
class __A :
'''simple docstring'''
__lowercase: Optional[int] = None
__lowercase: Optional[float] = field(default=snake_case__ , metadata={"""help""": """help message"""})
__lowercase: Optional[str] = None
__lowercase: Optional[List[str]] = list_field(default=[])
__lowercase: Optional[List[int]] = list_field(default=[])
@dataclass
class __A :
'''simple docstring'''
__lowercase: List[int] = list_field(default=[])
__lowercase: List[int] = list_field(default=[1, 2, 3])
__lowercase: List[str] = list_field(default=["""Hallo""", """Bonjour""", """Hello"""])
__lowercase: List[float] = list_field(default=[0.1, 0.2, 0.3])
@dataclass
class __A :
'''simple docstring'''
__lowercase: List[int] = field()
__lowercase: str = field()
__lowercase: BasicEnum = field()
def lowerCAmelCase ( self : Any ) ->str:
"""simple docstring"""
snake_case_ = BasicEnum(self.required_enum )
@dataclass
class __A :
'''simple docstring'''
__lowercase: int
__lowercase: "BasicEnum" = field()
__lowercase: "Optional[bool]" = None
__lowercase: "str" = field(default="""toto""" , metadata={"""help""": """help message"""})
__lowercase: "List[str]" = list_field(default=["""Hallo""", """Bonjour""", """Hello"""])
if is_python_no_less_than_3_10:
@dataclass
class __A :
'''simple docstring'''
__lowercase: bool = False
__lowercase: bool = True
__lowercase: bool | None = None
@dataclass
class __A :
'''simple docstring'''
__lowercase: int | None = None
__lowercase: float | None = field(default=snake_case__ , metadata={"""help""": """help message"""})
__lowercase: str | None = None
__lowercase: list[str] | None = list_field(default=[])
__lowercase: list[int] | None = list_field(default=[])
class __A (unittest.TestCase):
'''simple docstring'''
def lowerCAmelCase ( self : Optional[int] , UpperCAmelCase_ : argparse.ArgumentParser , UpperCAmelCase_ : argparse.ArgumentParser ) ->Optional[int]:
"""simple docstring"""
self.assertEqual(len(a._actions ) , len(b._actions ) )
for x, y in zip(a._actions , b._actions ):
snake_case_ = {k: v for k, v in vars(UpperCAmelCase_ ).items() if k != """container"""}
snake_case_ = {k: v for k, v in vars(UpperCAmelCase_ ).items() if k != """container"""}
# Choices with mixed type have custom function as "type"
# So we need to compare results directly for equality
if xx.get("""choices""" , UpperCAmelCase_ ) and yy.get("""choices""" , UpperCAmelCase_ ):
for expected_choice in yy["choices"] + xx["choices"]:
self.assertEqual(xx["""type"""](UpperCAmelCase_ ) , yy["""type"""](UpperCAmelCase_ ) )
del xx["type"], yy["type"]
self.assertEqual(UpperCAmelCase_ , UpperCAmelCase_ )
def lowerCAmelCase ( self : int ) ->Any:
"""simple docstring"""
snake_case_ = HfArgumentParser(UpperCAmelCase_ )
snake_case_ = argparse.ArgumentParser()
expected.add_argument("""--foo""" , type=UpperCAmelCase_ , required=UpperCAmelCase_ )
expected.add_argument("""--bar""" , type=UpperCAmelCase_ , required=UpperCAmelCase_ )
expected.add_argument("""--baz""" , type=UpperCAmelCase_ , required=UpperCAmelCase_ )
expected.add_argument("""--flag""" , type=UpperCAmelCase_ , default=UpperCAmelCase_ , const=UpperCAmelCase_ , nargs="""?""" )
self.argparsersEqual(UpperCAmelCase_ , UpperCAmelCase_ )
snake_case_ = ["""--foo""", """1""", """--baz""", """quux""", """--bar""", """0.5"""]
((snake_case_) , ) = parser.parse_args_into_dataclasses(UpperCAmelCase_ , look_for_args_file=UpperCAmelCase_ )
self.assertFalse(example.flag )
def lowerCAmelCase ( self : Optional[Any] ) ->List[Any]:
"""simple docstring"""
snake_case_ = HfArgumentParser(UpperCAmelCase_ )
snake_case_ = argparse.ArgumentParser()
expected.add_argument("""--foo""" , default=42 , type=UpperCAmelCase_ )
expected.add_argument("""--baz""" , default="""toto""" , type=UpperCAmelCase_ , help="""help message""" )
self.argparsersEqual(UpperCAmelCase_ , UpperCAmelCase_ )
def lowerCAmelCase ( self : List[Any] ) ->Union[str, Any]:
"""simple docstring"""
snake_case_ = argparse.ArgumentParser()
expected.add_argument("""--foo""" , type=UpperCAmelCase_ , default=UpperCAmelCase_ , const=UpperCAmelCase_ , nargs="""?""" )
expected.add_argument("""--baz""" , type=UpperCAmelCase_ , default=UpperCAmelCase_ , const=UpperCAmelCase_ , nargs="""?""" )
# A boolean no_* argument always has to come after its "default: True" regular counter-part
# and its default must be set to False
expected.add_argument("""--no_baz""" , action="""store_false""" , default=UpperCAmelCase_ , dest="""baz""" )
expected.add_argument("""--opt""" , type=UpperCAmelCase_ , default=UpperCAmelCase_ )
snake_case_ = [WithDefaultBoolExample]
if is_python_no_less_than_3_10:
dataclass_types.append(UpperCAmelCase_ )
for dataclass_type in dataclass_types:
snake_case_ = HfArgumentParser(UpperCAmelCase_ )
self.argparsersEqual(UpperCAmelCase_ , UpperCAmelCase_ )
snake_case_ = parser.parse_args([] )
self.assertEqual(UpperCAmelCase_ , Namespace(foo=UpperCAmelCase_ , baz=UpperCAmelCase_ , opt=UpperCAmelCase_ ) )
snake_case_ = parser.parse_args(["""--foo""", """--no_baz"""] )
self.assertEqual(UpperCAmelCase_ , Namespace(foo=UpperCAmelCase_ , baz=UpperCAmelCase_ , opt=UpperCAmelCase_ ) )
snake_case_ = parser.parse_args(["""--foo""", """--baz"""] )
self.assertEqual(UpperCAmelCase_ , Namespace(foo=UpperCAmelCase_ , baz=UpperCAmelCase_ , opt=UpperCAmelCase_ ) )
snake_case_ = parser.parse_args(["""--foo""", """True""", """--baz""", """True""", """--opt""", """True"""] )
self.assertEqual(UpperCAmelCase_ , Namespace(foo=UpperCAmelCase_ , baz=UpperCAmelCase_ , opt=UpperCAmelCase_ ) )
snake_case_ = parser.parse_args(["""--foo""", """False""", """--baz""", """False""", """--opt""", """False"""] )
self.assertEqual(UpperCAmelCase_ , Namespace(foo=UpperCAmelCase_ , baz=UpperCAmelCase_ , opt=UpperCAmelCase_ ) )
def lowerCAmelCase ( self : int ) ->List[str]:
"""simple docstring"""
snake_case_ = HfArgumentParser(UpperCAmelCase_ )
snake_case_ = argparse.ArgumentParser()
expected.add_argument(
"""--foo""" , default="""toto""" , choices=["""titi""", """toto""", 42] , type=make_choice_type_function(["""titi""", """toto""", 42] ) , )
self.argparsersEqual(UpperCAmelCase_ , UpperCAmelCase_ )
snake_case_ = parser.parse_args([] )
self.assertEqual(args.foo , """toto""" )
snake_case_ = parser.parse_args_into_dataclasses([] )[0]
self.assertEqual(enum_ex.foo , MixedTypeEnum.toto )
snake_case_ = parser.parse_args(["""--foo""", """titi"""] )
self.assertEqual(args.foo , """titi""" )
snake_case_ = parser.parse_args_into_dataclasses(["""--foo""", """titi"""] )[0]
self.assertEqual(enum_ex.foo , MixedTypeEnum.titi )
snake_case_ = parser.parse_args(["""--foo""", """42"""] )
self.assertEqual(args.foo , 42 )
snake_case_ = parser.parse_args_into_dataclasses(["""--foo""", """42"""] )[0]
self.assertEqual(enum_ex.foo , MixedTypeEnum.fourtytwo )
def lowerCAmelCase ( self : Dict ) ->str:
"""simple docstring"""
@dataclass
class __A :
'''simple docstring'''
__lowercase: Literal["titi", "toto", 42] = "toto"
snake_case_ = HfArgumentParser(UpperCAmelCase_ )
snake_case_ = argparse.ArgumentParser()
expected.add_argument(
"""--foo""" , default="""toto""" , choices=("""titi""", """toto""", 42) , type=make_choice_type_function(["""titi""", """toto""", 42] ) , )
self.argparsersEqual(UpperCAmelCase_ , UpperCAmelCase_ )
snake_case_ = parser.parse_args([] )
self.assertEqual(args.foo , """toto""" )
snake_case_ = parser.parse_args(["""--foo""", """titi"""] )
self.assertEqual(args.foo , """titi""" )
snake_case_ = parser.parse_args(["""--foo""", """42"""] )
self.assertEqual(args.foo , 42 )
def lowerCAmelCase ( self : Optional[int] ) ->Dict:
"""simple docstring"""
snake_case_ = HfArgumentParser(UpperCAmelCase_ )
snake_case_ = argparse.ArgumentParser()
expected.add_argument("""--foo_int""" , nargs="""+""" , default=[] , type=UpperCAmelCase_ )
expected.add_argument("""--bar_int""" , nargs="""+""" , default=[1, 2, 3] , type=UpperCAmelCase_ )
expected.add_argument("""--foo_str""" , nargs="""+""" , default=["""Hallo""", """Bonjour""", """Hello"""] , type=UpperCAmelCase_ )
expected.add_argument("""--foo_float""" , nargs="""+""" , default=[0.1, 0.2, 0.3] , type=UpperCAmelCase_ )
self.argparsersEqual(UpperCAmelCase_ , UpperCAmelCase_ )
snake_case_ = parser.parse_args([] )
self.assertEqual(
UpperCAmelCase_ , Namespace(foo_int=[] , bar_int=[1, 2, 3] , foo_str=["""Hallo""", """Bonjour""", """Hello"""] , foo_float=[0.1, 0.2, 0.3] ) , )
snake_case_ = parser.parse_args("""--foo_int 1 --bar_int 2 3 --foo_str a b c --foo_float 0.1 0.7""".split() )
self.assertEqual(UpperCAmelCase_ , Namespace(foo_int=[1] , bar_int=[2, 3] , foo_str=["""a""", """b""", """c"""] , foo_float=[0.1, 0.7] ) )
def lowerCAmelCase ( self : Optional[int] ) ->List[Any]:
"""simple docstring"""
snake_case_ = argparse.ArgumentParser()
expected.add_argument("""--foo""" , default=UpperCAmelCase_ , type=UpperCAmelCase_ )
expected.add_argument("""--bar""" , default=UpperCAmelCase_ , type=UpperCAmelCase_ , help="""help message""" )
expected.add_argument("""--baz""" , default=UpperCAmelCase_ , type=UpperCAmelCase_ )
expected.add_argument("""--ces""" , nargs="""+""" , default=[] , type=UpperCAmelCase_ )
expected.add_argument("""--des""" , nargs="""+""" , default=[] , type=UpperCAmelCase_ )
snake_case_ = [OptionalExample]
if is_python_no_less_than_3_10:
dataclass_types.append(UpperCAmelCase_ )
for dataclass_type in dataclass_types:
snake_case_ = HfArgumentParser(UpperCAmelCase_ )
self.argparsersEqual(UpperCAmelCase_ , UpperCAmelCase_ )
snake_case_ = parser.parse_args([] )
self.assertEqual(UpperCAmelCase_ , Namespace(foo=UpperCAmelCase_ , bar=UpperCAmelCase_ , baz=UpperCAmelCase_ , ces=[] , des=[] ) )
snake_case_ = parser.parse_args("""--foo 12 --bar 3.14 --baz 42 --ces a b c --des 1 2 3""".split() )
self.assertEqual(UpperCAmelCase_ , Namespace(foo=12 , bar=3.14 , baz="""42""" , ces=["""a""", """b""", """c"""] , des=[1, 2, 3] ) )
def lowerCAmelCase ( self : Optional[Any] ) ->Optional[Any]:
"""simple docstring"""
snake_case_ = HfArgumentParser(UpperCAmelCase_ )
snake_case_ = argparse.ArgumentParser()
expected.add_argument("""--required_list""" , nargs="""+""" , type=UpperCAmelCase_ , required=UpperCAmelCase_ )
expected.add_argument("""--required_str""" , type=UpperCAmelCase_ , required=UpperCAmelCase_ )
expected.add_argument(
"""--required_enum""" , type=make_choice_type_function(["""titi""", """toto"""] ) , choices=["""titi""", """toto"""] , required=UpperCAmelCase_ , )
self.argparsersEqual(UpperCAmelCase_ , UpperCAmelCase_ )
def lowerCAmelCase ( self : Optional[int] ) ->List[Any]:
"""simple docstring"""
snake_case_ = HfArgumentParser(UpperCAmelCase_ )
snake_case_ = argparse.ArgumentParser()
expected.add_argument("""--foo""" , type=UpperCAmelCase_ , required=UpperCAmelCase_ )
expected.add_argument(
"""--required_enum""" , type=make_choice_type_function(["""titi""", """toto"""] ) , choices=["""titi""", """toto"""] , required=UpperCAmelCase_ , )
expected.add_argument("""--opt""" , type=UpperCAmelCase_ , default=UpperCAmelCase_ )
expected.add_argument("""--baz""" , default="""toto""" , type=UpperCAmelCase_ , help="""help message""" )
expected.add_argument("""--foo_str""" , nargs="""+""" , default=["""Hallo""", """Bonjour""", """Hello"""] , type=UpperCAmelCase_ )
self.argparsersEqual(UpperCAmelCase_ , UpperCAmelCase_ )
def lowerCAmelCase ( self : Dict ) ->Tuple:
"""simple docstring"""
snake_case_ = HfArgumentParser(UpperCAmelCase_ )
snake_case_ = {
"""foo""": 12,
"""bar""": 3.14,
"""baz""": """42""",
"""flag""": True,
}
snake_case_ = parser.parse_dict(UpperCAmelCase_ )[0]
snake_case_ = BasicExample(**UpperCAmelCase_ )
self.assertEqual(UpperCAmelCase_ , UpperCAmelCase_ )
def lowerCAmelCase ( self : Optional[Any] ) ->List[Any]:
"""simple docstring"""
snake_case_ = HfArgumentParser(UpperCAmelCase_ )
snake_case_ = {
"""foo""": 12,
"""bar""": 3.14,
"""baz""": """42""",
"""flag""": True,
"""extra""": 42,
}
self.assertRaises(UpperCAmelCase_ , parser.parse_dict , UpperCAmelCase_ , allow_extra_keys=UpperCAmelCase_ )
def lowerCAmelCase ( self : Any ) ->Dict:
"""simple docstring"""
snake_case_ = HfArgumentParser(UpperCAmelCase_ )
snake_case_ = {
"""foo""": 12,
"""bar""": 3.14,
"""baz""": """42""",
"""flag""": True,
}
with tempfile.TemporaryDirectory() as tmp_dir:
snake_case_ = os.path.join(UpperCAmelCase_ , """temp_json""" )
os.mkdir(UpperCAmelCase_ )
with open(temp_local_path + """.json""" , """w+""" ) as f:
json.dump(UpperCAmelCase_ , UpperCAmelCase_ )
snake_case_ = parser.parse_yaml_file(Path(temp_local_path + """.json""" ) )[0]
snake_case_ = BasicExample(**UpperCAmelCase_ )
self.assertEqual(UpperCAmelCase_ , UpperCAmelCase_ )
def lowerCAmelCase ( self : Optional[int] ) ->List[str]:
"""simple docstring"""
snake_case_ = HfArgumentParser(UpperCAmelCase_ )
snake_case_ = {
"""foo""": 12,
"""bar""": 3.14,
"""baz""": """42""",
"""flag""": True,
}
with tempfile.TemporaryDirectory() as tmp_dir:
snake_case_ = os.path.join(UpperCAmelCase_ , """temp_yaml""" )
os.mkdir(UpperCAmelCase_ )
with open(temp_local_path + """.yaml""" , """w+""" ) as f:
yaml.dump(UpperCAmelCase_ , UpperCAmelCase_ )
snake_case_ = parser.parse_yaml_file(Path(temp_local_path + """.yaml""" ) )[0]
snake_case_ = BasicExample(**UpperCAmelCase_ )
self.assertEqual(UpperCAmelCase_ , UpperCAmelCase_ )
def lowerCAmelCase ( self : Dict ) ->Any:
"""simple docstring"""
snake_case_ = HfArgumentParser(UpperCAmelCase_ )
self.assertIsNotNone(UpperCAmelCase_ )
| 347 | 0 |
'''simple docstring'''
import os
from typing import Dict, List, Union
import tensorflow as tf
from keras_nlp.tokenizers import BytePairTokenizer
from tensorflow_text import pad_model_inputs
from .tokenization_gpta import GPTaTokenizer
class UpperCamelCase_ ( tf.keras.layers.Layer ):
def __init__( self , A , A , A = None , A = None ) -> Union[str, Any]:
super().__init__()
UpperCAmelCase : Union[str, Any] = pad_token_id
UpperCAmelCase : Optional[Any] = max_length
UpperCAmelCase : str = vocab
UpperCAmelCase : Optional[int] = merges
UpperCAmelCase : Optional[Any] = BytePairTokenizer(UpperCAmelCase_ , UpperCAmelCase_ , sequence_length=UpperCAmelCase_ )
@classmethod
def _lowercase( cls , A , *A , **A ) -> List[Any]:
UpperCAmelCase : List[Any] = [""" """.join(UpperCAmelCase_ ) for m in tokenizer.bpe_ranks.keys()]
UpperCAmelCase : str = tokenizer.get_vocab()
return cls(UpperCAmelCase_ , UpperCAmelCase_ , *UpperCAmelCase_ , **UpperCAmelCase_ )
@classmethod
def _lowercase( cls , A , *A , **A ) -> Any:
UpperCAmelCase : List[Any] = GPTaTokenizer.from_pretrained(UpperCAmelCase_ , *UpperCAmelCase_ , **UpperCAmelCase_ )
return cls.from_tokenizer(UpperCAmelCase_ , *UpperCAmelCase_ , **UpperCAmelCase_ )
@classmethod
def _lowercase( cls , A ) -> str:
return cls(**UpperCAmelCase_ )
def _lowercase( self ) -> Dict:
return {
"vocab": self.vocab,
"merges": self.merges,
"max_length": self.max_length,
"pad_token_id": self.pad_token_id,
}
def _lowercase( self , A , A = None ) -> Tuple:
UpperCAmelCase : Optional[int] = self.tf_tokenizer(UpperCAmelCase_ )
UpperCAmelCase : Optional[int] = tf.ones_like(UpperCAmelCase_ )
if self.pad_token_id is not None:
# pad the tokens up to max length
UpperCAmelCase : int = max_length if max_length is not None else self.max_length
if max_length is not None:
UpperCAmelCase , UpperCAmelCase : Any = pad_model_inputs(
UpperCAmelCase_ , max_seq_length=UpperCAmelCase_ , pad_value=self.pad_token_id )
return {"attention_mask": attention_mask, "input_ids": input_ids}
| 265 |
"""simple docstring"""
import logging
import os
from typing import Dict, List, Optional, Union
import torch
import torch.nn as nn
from accelerate.utils.imports import (
is_abit_bnb_available,
is_abit_bnb_available,
is_bnb_available,
)
from ..big_modeling import dispatch_model, init_empty_weights
from .dataclasses import BnbQuantizationConfig
from .modeling import (
find_tied_parameters,
get_balanced_memory,
infer_auto_device_map,
load_checkpoint_in_model,
offload_weight,
set_module_tensor_to_device,
)
if is_bnb_available():
import bitsandbytes as bnb
from copy import deepcopy
__SCREAMING_SNAKE_CASE : Any = logging.getLogger(__name__)
def _a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = None , _SCREAMING_SNAKE_CASE = None , _SCREAMING_SNAKE_CASE = None , _SCREAMING_SNAKE_CASE = None , _SCREAMING_SNAKE_CASE = None , _SCREAMING_SNAKE_CASE = False , ) -> Optional[Any]:
snake_case_ = bnb_quantization_config.load_in_abit
snake_case_ = bnb_quantization_config.load_in_abit
if load_in_abit and not is_abit_bnb_available():
raise ImportError(
"""You have a version of `bitsandbytes` that is not compatible with 8bit quantization,"""
""" make sure you have the latest version of `bitsandbytes` installed.""" )
if load_in_abit and not is_abit_bnb_available():
raise ValueError(
"""You have a version of `bitsandbytes` that is not compatible with 4bit quantization,"""
"""make sure you have the latest version of `bitsandbytes` installed.""" )
snake_case_ = []
# custom device map
if isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) and len(device_map.keys() ) > 1:
snake_case_ = [key for key, value in device_map.items() if value in ["""disk""", """cpu"""]]
# We keep some modules such as the lm_head in their original dtype for numerical stability reasons
if bnb_quantization_config.skip_modules is None:
snake_case_ = get_keys_to_not_convert(_SCREAMING_SNAKE_CASE )
# add cpu modules to skip modules only for 4-bit modules
if load_in_abit:
bnb_quantization_config.skip_modules.extend(_SCREAMING_SNAKE_CASE )
snake_case_ = bnb_quantization_config.skip_modules
# We add the modules we want to keep in full precision
if bnb_quantization_config.keep_in_fpaa_modules is None:
snake_case_ = []
snake_case_ = bnb_quantization_config.keep_in_fpaa_modules
modules_to_not_convert.extend(_SCREAMING_SNAKE_CASE )
# compatibility with peft
snake_case_ = load_in_abit
snake_case_ = load_in_abit
snake_case_ = get_parameter_device(_SCREAMING_SNAKE_CASE )
if model_device.type != "meta":
# quantization of an already loaded model
logger.warning(
"""It is not recommended to quantize a loaded model. """
"""The model should be instantiated under the `init_empty_weights` context manager.""" )
snake_case_ = replace_with_bnb_layers(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , modules_to_not_convert=_SCREAMING_SNAKE_CASE )
# convert param to the right dtype
snake_case_ = bnb_quantization_config.torch_dtype
for name, param in model.state_dict().items():
if any(module_to_keep_in_fpaa in name for module_to_keep_in_fpaa in keep_in_fpaa_modules ):
param.to(torch.floataa )
if param.dtype != torch.floataa:
snake_case_ = name.replace(""".weight""" , """""" ).replace(""".bias""" , """""" )
snake_case_ = getattr(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
if param is not None:
param.to(torch.floataa )
elif torch.is_floating_point(_SCREAMING_SNAKE_CASE ):
param.to(_SCREAMING_SNAKE_CASE )
if model_device.type == "cuda":
# move everything to cpu in the first place because we can't do quantization if the weights are already on cuda
model.cuda(torch.cuda.current_device() )
torch.cuda.empty_cache()
elif torch.cuda.is_available():
model.to(torch.cuda.current_device() )
else:
raise RuntimeError("""No GPU found. A GPU is needed for quantization.""" )
logger.info(
f"""The model device type is {model_device.type}. However, cuda is needed for quantization."""
"""We move the model to cuda.""" )
return model
elif weights_location is None:
raise RuntimeError(
f"""`weights_location` needs to be the folder path containing the weights of the model, but we found {weights_location} """ )
else:
with init_empty_weights():
snake_case_ = replace_with_bnb_layers(
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , modules_to_not_convert=_SCREAMING_SNAKE_CASE )
snake_case_ = get_quantized_model_device_map(
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , max_memory=_SCREAMING_SNAKE_CASE , no_split_module_classes=_SCREAMING_SNAKE_CASE , )
if offload_state_dict is None and device_map is not None and "disk" in device_map.values():
snake_case_ = True
snake_case_ = any(x in list(device_map.values() ) for x in ["""cpu""", """disk"""] )
load_checkpoint_in_model(
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , dtype=bnb_quantization_config.torch_dtype , offload_folder=_SCREAMING_SNAKE_CASE , offload_state_dict=_SCREAMING_SNAKE_CASE , keep_in_fpaa_modules=bnb_quantization_config.keep_in_fpaa_modules , offload_abit_bnb=load_in_abit and offload , )
return dispatch_model(_SCREAMING_SNAKE_CASE , device_map=_SCREAMING_SNAKE_CASE , offload_dir=_SCREAMING_SNAKE_CASE )
def _a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=None , _SCREAMING_SNAKE_CASE=None , _SCREAMING_SNAKE_CASE=None ) -> Tuple:
if device_map is None:
if torch.cuda.is_available():
snake_case_ = {"""""": torch.cuda.current_device()}
else:
raise RuntimeError("""No GPU found. A GPU is needed for quantization.""" )
logger.info("""The device_map was not initialized.""" """Setting device_map to `{'':torch.cuda.current_device()}`.""" )
if isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
if device_map not in ["auto", "balanced", "balanced_low_0", "sequential"]:
raise ValueError(
"""If passing a string for `device_map`, please choose 'auto', 'balanced', 'balanced_low_0' or """
"""'sequential'.""" )
snake_case_ = {}
special_dtypes.update(
{
name: bnb_quantization_config.torch_dtype
for name, _ in model.named_parameters()
if any(m in name for m in bnb_quantization_config.skip_modules )
} )
special_dtypes.update(
{
name: torch.floataa
for name, _ in model.named_parameters()
if any(m in name for m in bnb_quantization_config.keep_in_fpaa_modules )
} )
snake_case_ = {}
snake_case_ = special_dtypes
snake_case_ = no_split_module_classes
snake_case_ = bnb_quantization_config.target_dtype
# get max_memory for each device.
if device_map != "sequential":
snake_case_ = get_balanced_memory(
_SCREAMING_SNAKE_CASE , low_zero=(device_map == """balanced_low_0""") , max_memory=_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE , )
snake_case_ = max_memory
snake_case_ = infer_auto_device_map(_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE )
if isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
# check if don't have any quantized module on the cpu
snake_case_ = bnb_quantization_config.skip_modules + bnb_quantization_config.keep_in_fpaa_modules
snake_case_ = {
key: device_map[key] for key in device_map.keys() if key not in modules_not_to_convert
}
for device in ["cpu", "disk"]:
if device in device_map_without_some_modules.values():
if bnb_quantization_config.load_in_abit:
raise ValueError(
"""
Some modules are dispatched on the CPU or the disk. Make sure you have enough GPU RAM to fit
the quantized model. If you want to dispatch the model on the CPU or the disk while keeping
these modules in `torch_dtype`, you need to pass a custom `device_map` to
`load_and_quantize_model`. Check
https://huggingface.co/docs/accelerate/main/en/usage_guides/quantization#offload-modules-to-cpu-and-disk
for more details.
""" )
else:
logger.info(
"""Some modules are are offloaded to the CPU or the disk. Note that these modules will be converted to 8-bit""" )
del device_map_without_some_modules
return device_map
def _a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=None , _SCREAMING_SNAKE_CASE=None ) -> Tuple:
if modules_to_not_convert is None:
snake_case_ = []
snake_case_ , snake_case_ = _replace_with_bnb_layers(
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
if not has_been_replaced:
logger.warning(
"""You are loading your model in 8bit or 4bit but no linear modules were found in your model."""
""" this can happen for some architectures such as gpt2 that uses Conv1D instead of Linear layers."""
""" Please double check your model architecture, or submit an issue on github if you think this is"""
""" a bug.""" )
return model
def _a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=None , _SCREAMING_SNAKE_CASE=None , ) -> List[Any]:
snake_case_ = False
for name, module in model.named_children():
if current_key_name is None:
snake_case_ = []
current_key_name.append(_SCREAMING_SNAKE_CASE )
if isinstance(_SCREAMING_SNAKE_CASE , nn.Linear ) and name not in modules_to_not_convert:
# Check if the current key is not in the `modules_to_not_convert`
snake_case_ = """.""".join(_SCREAMING_SNAKE_CASE )
snake_case_ = True
for key in modules_to_not_convert:
if (
(key in current_key_name_str) and (key + "." in current_key_name_str)
) or key == current_key_name_str:
snake_case_ = False
break
if proceed:
# Load bnb module with empty weight and replace ``nn.Linear` module
if bnb_quantization_config.load_in_abit:
snake_case_ = bnb.nn.LinearabitLt(
module.in_features , module.out_features , module.bias is not None , has_fpaa_weights=_SCREAMING_SNAKE_CASE , threshold=bnb_quantization_config.llm_inta_threshold , )
elif bnb_quantization_config.load_in_abit:
snake_case_ = bnb.nn.Linearabit(
module.in_features , module.out_features , module.bias is not None , bnb_quantization_config.bnb_abit_compute_dtype , compress_statistics=bnb_quantization_config.bnb_abit_use_double_quant , quant_type=bnb_quantization_config.bnb_abit_quant_type , )
else:
raise ValueError("""load_in_8bit and load_in_4bit can't be both False""" )
snake_case_ = module.weight.data
if module.bias is not None:
snake_case_ = module.bias.data
bnb_module.requires_grad_(_SCREAMING_SNAKE_CASE )
setattr(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
snake_case_ = True
if len(list(module.children() ) ) > 0:
snake_case_ , snake_case_ = _replace_with_bnb_layers(
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
snake_case_ = has_been_replaced | _has_been_replaced
# Remove the last key for recursion
current_key_name.pop(-1 )
return model, has_been_replaced
def _a ( _SCREAMING_SNAKE_CASE ) -> Any:
# Create a copy of the model
with init_empty_weights():
snake_case_ = deepcopy(_SCREAMING_SNAKE_CASE ) # this has 0 cost since it is done inside `init_empty_weights` context manager`
snake_case_ = find_tied_parameters(_SCREAMING_SNAKE_CASE )
# For compatibility with Accelerate < 0.18
if isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
snake_case_ = sum(list(tied_params.values() ) , [] ) + list(tied_params.keys() )
else:
snake_case_ = sum(_SCREAMING_SNAKE_CASE , [] )
snake_case_ = len(_SCREAMING_SNAKE_CASE ) > 0
# Check if it is a base model
snake_case_ = False
if hasattr(_SCREAMING_SNAKE_CASE , """base_model_prefix""" ):
snake_case_ = not hasattr(_SCREAMING_SNAKE_CASE , model.base_model_prefix )
# Ignore this for base models (BertModel, GPT2Model, etc.)
if (not has_tied_params) and is_base_model:
return []
# otherwise they have an attached head
snake_case_ = list(model.named_children() )
snake_case_ = [list_modules[-1][0]]
# add last module together with tied weights
snake_case_ = set(_SCREAMING_SNAKE_CASE ) - set(_SCREAMING_SNAKE_CASE )
snake_case_ = list(set(_SCREAMING_SNAKE_CASE ) ) + list(_SCREAMING_SNAKE_CASE )
# remove ".weight" from the keys
snake_case_ = [""".weight""", """.bias"""]
snake_case_ = []
for name in list_untouched:
for name_to_remove in names_to_remove:
if name_to_remove in name:
snake_case_ = name.replace(_SCREAMING_SNAKE_CASE , """""" )
filtered_module_names.append(_SCREAMING_SNAKE_CASE )
return filtered_module_names
def _a ( _SCREAMING_SNAKE_CASE ) -> Union[str, Any]:
for m in model.modules():
if isinstance(_SCREAMING_SNAKE_CASE , bnb.nn.Linearabit ):
return True
return False
def _a ( _SCREAMING_SNAKE_CASE ) -> Optional[int]:
return next(parameter.parameters() ).device
def _a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> Optional[Any]:
# if it is not quantized, we quantize and offload the quantized weights and the SCB stats
if fpaa_statistics is None:
set_module_tensor_to_device(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , 0 , dtype=_SCREAMING_SNAKE_CASE , value=_SCREAMING_SNAKE_CASE )
snake_case_ = param_name
snake_case_ = model
if "." in tensor_name:
snake_case_ = tensor_name.split(""".""" )
for split in splits[:-1]:
snake_case_ = getattr(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
if new_module is None:
raise ValueError(f"""{module} has no attribute {split}.""" )
snake_case_ = new_module
snake_case_ = splits[-1]
# offload weights
snake_case_ = False
offload_weight(module._parameters[tensor_name] , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , index=_SCREAMING_SNAKE_CASE )
if hasattr(module._parameters[tensor_name] , """SCB""" ):
offload_weight(
module._parameters[tensor_name].SCB , param_name.replace("""weight""" , """SCB""" ) , _SCREAMING_SNAKE_CASE , index=_SCREAMING_SNAKE_CASE , )
else:
offload_weight(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , index=_SCREAMING_SNAKE_CASE )
offload_weight(_SCREAMING_SNAKE_CASE , param_name.replace("""weight""" , """SCB""" ) , _SCREAMING_SNAKE_CASE , index=_SCREAMING_SNAKE_CASE )
set_module_tensor_to_device(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , """meta""" , dtype=_SCREAMING_SNAKE_CASE , value=torch.empty(*param.size() ) )
| 347 | 0 |
import unittest
from transformers import SPIECE_UNDERLINE
from transformers.models.speechta import SpeechTaTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.tokenization_utils import AddedToken
from ...test_tokenization_common import TokenizerTesterMixin
__UpperCAmelCase = get_tests_dir('''fixtures/test_sentencepiece_bpe_char.model''')
@require_sentencepiece
@require_tokenizers
class lowerCAmelCase_ ( snake_case__ , unittest.TestCase ):
UpperCAmelCase__ : Tuple = SpeechTaTokenizer
UpperCAmelCase__ : int = False
UpperCAmelCase__ : List[str] = True
def snake_case_ ( self ) -> str:
super().setUp()
# We have a SentencePiece fixture for testing
UpperCamelCase : int = SpeechTaTokenizer(UpperCAmelCase_ )
UpperCamelCase : List[str] = AddedToken('<mask>', lstrip=UpperCAmelCase_, rstrip=UpperCAmelCase_ )
UpperCamelCase : Optional[Any] = mask_token
tokenizer.add_special_tokens({'mask_token': mask_token} )
tokenizer.add_tokens(['<ctc_blank>'] )
tokenizer.save_pretrained(self.tmpdirname )
def snake_case_ ( self, SCREAMING_SNAKE_CASE_ ) -> Dict:
UpperCamelCase : int = 'this is a test'
UpperCamelCase : Dict = 'this is a test'
return input_text, output_text
def snake_case_ ( self, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_=False, SCREAMING_SNAKE_CASE_=20, SCREAMING_SNAKE_CASE_=5 ) -> List[Any]:
UpperCamelCase , UpperCamelCase : List[Any] = self.get_input_output_texts(UpperCAmelCase_ )
UpperCamelCase : List[Any] = tokenizer.encode(UpperCAmelCase_, add_special_tokens=UpperCAmelCase_ )
UpperCamelCase : List[str] = tokenizer.decode(UpperCAmelCase_, clean_up_tokenization_spaces=UpperCAmelCase_ )
return text, ids
def snake_case_ ( self ) -> Optional[Any]:
UpperCamelCase : Tuple = '<pad>'
UpperCamelCase : List[Any] = 1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(UpperCAmelCase_ ), UpperCAmelCase_ )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(UpperCAmelCase_ ), UpperCAmelCase_ )
def snake_case_ ( self ) -> str:
UpperCamelCase : Optional[int] = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0], '<s>' )
self.assertEqual(vocab_keys[1], '<pad>' )
self.assertEqual(vocab_keys[-4], 'œ' )
self.assertEqual(vocab_keys[-2], '<mask>' )
self.assertEqual(vocab_keys[-1], '<ctc_blank>' )
self.assertEqual(len(UpperCAmelCase_ ), 81 )
def snake_case_ ( self ) -> int:
self.assertEqual(self.get_tokenizer().vocab_size, 79 )
def snake_case_ ( self ) -> List[Any]:
UpperCamelCase : int = self.get_tokenizers(do_lower_case=UpperCAmelCase_ )
for tokenizer in tokenizers:
with self.subTest(F"""{tokenizer.__class__.__name__}""" ):
UpperCamelCase : str = tokenizer.vocab_size
UpperCamelCase : str = len(UpperCAmelCase_ )
self.assertNotEqual(UpperCAmelCase_, 0 )
# We usually have added tokens from the start in tests because our vocab fixtures are
# smaller than the original vocabs - let's not assert this
# self.assertEqual(vocab_size, all_size)
UpperCamelCase : Optional[Any] = ['aaaaa bbbbbb', 'cccccccccdddddddd']
UpperCamelCase : Optional[int] = tokenizer.add_tokens(UpperCAmelCase_ )
UpperCamelCase : Union[str, Any] = tokenizer.vocab_size
UpperCamelCase : Optional[int] = len(UpperCAmelCase_ )
self.assertNotEqual(UpperCAmelCase_, 0 )
self.assertEqual(UpperCAmelCase_, UpperCAmelCase_ )
self.assertEqual(UpperCAmelCase_, len(UpperCAmelCase_ ) )
self.assertEqual(UpperCAmelCase_, all_size + len(UpperCAmelCase_ ) )
UpperCamelCase : Tuple = tokenizer.encode('aaaaa bbbbbb low cccccccccdddddddd l', add_special_tokens=UpperCAmelCase_ )
self.assertGreaterEqual(len(UpperCAmelCase_ ), 4 )
self.assertGreater(tokens[0], tokenizer.vocab_size - 1 )
self.assertGreater(tokens[-3], tokenizer.vocab_size - 1 )
UpperCamelCase : str = {'eos_token': '>>>>|||<||<<|<<', 'pad_token': '<<<<<|||>|>>>>|>'}
UpperCamelCase : str = tokenizer.add_special_tokens(UpperCAmelCase_ )
UpperCamelCase : str = tokenizer.vocab_size
UpperCamelCase : Union[str, Any] = len(UpperCAmelCase_ )
self.assertNotEqual(UpperCAmelCase_, 0 )
self.assertEqual(UpperCAmelCase_, UpperCAmelCase_ )
self.assertEqual(UpperCAmelCase_, len(UpperCAmelCase_ ) )
self.assertEqual(UpperCAmelCase_, all_size_a + len(UpperCAmelCase_ ) )
UpperCamelCase : Dict = tokenizer.encode(
'>>>>|||<||<<|<< aaaaabbbbbb low cccccccccdddddddd <<<<<|||>|>>>>|> l', add_special_tokens=UpperCAmelCase_ )
self.assertGreaterEqual(len(UpperCAmelCase_ ), 6 )
self.assertGreater(tokens[0], tokenizer.vocab_size - 1 )
self.assertGreater(tokens[0], tokens[1] )
self.assertGreater(tokens[-3], tokenizer.vocab_size - 1 )
self.assertGreater(tokens[-3], tokens[-4] )
self.assertEqual(tokens[0], tokenizer.eos_token_id )
self.assertEqual(tokens[-3], tokenizer.pad_token_id )
def snake_case_ ( self ) -> Tuple:
pass
def snake_case_ ( self ) -> Optional[Any]:
pass
def snake_case_ ( self ) -> List[str]:
UpperCamelCase : Optional[Any] = self.get_tokenizer()
UpperCamelCase : Tuple = tokenizer.tokenize('This is a test' )
# fmt: off
self.assertListEqual(UpperCAmelCase_, [SPIECE_UNDERLINE, 'T', 'h', 'i', 's', SPIECE_UNDERLINE, 'i', 's', SPIECE_UNDERLINE, 'a', SPIECE_UNDERLINE, 't', 'e', 's', 't'] )
# fmt: on
self.assertListEqual(
tokenizer.convert_tokens_to_ids(UpperCAmelCase_ ), [4, 32, 11, 10, 12, 4, 10, 12, 4, 7, 4, 6, 5, 12, 6], )
UpperCamelCase : Any = tokenizer.tokenize('I was born in 92000, and this is falsé.' )
self.assertListEqual(
UpperCAmelCase_, [SPIECE_UNDERLINE, 'I', SPIECE_UNDERLINE, 'w', 'a', 's', SPIECE_UNDERLINE, 'b', 'o', 'r', 'n', SPIECE_UNDERLINE, 'i', 'n', SPIECE_UNDERLINE, '92000', ',', SPIECE_UNDERLINE, 'a', 'n', 'd', SPIECE_UNDERLINE, 't', 'h', 'i', 's', SPIECE_UNDERLINE, 'i', 's', SPIECE_UNDERLINE, 'f', 'a', 'l', 's', 'é', '.'] )
UpperCamelCase : List[str] = tokenizer.convert_tokens_to_ids(UpperCAmelCase_ )
# fmt: off
self.assertListEqual(UpperCAmelCase_, [4, 30, 4, 20, 7, 12, 4, 25, 8, 13, 9, 4, 10, 9, 4, 3, 23, 4, 7, 9, 14, 4, 6, 11, 10, 12, 4, 10, 12, 4, 19, 7, 15, 12, 73, 26] )
# fmt: on
UpperCamelCase : Optional[Any] = tokenizer.convert_ids_to_tokens(UpperCAmelCase_ )
self.assertListEqual(
UpperCAmelCase_, [SPIECE_UNDERLINE, 'I', SPIECE_UNDERLINE, 'w', 'a', 's', SPIECE_UNDERLINE, 'b', 'o', 'r', 'n', SPIECE_UNDERLINE, 'i', 'n', SPIECE_UNDERLINE, '<unk>', ',', SPIECE_UNDERLINE, 'a', 'n', 'd', SPIECE_UNDERLINE, 't', 'h', 'i', 's', SPIECE_UNDERLINE, 'i', 's', SPIECE_UNDERLINE, 'f', 'a', 'l', 's', 'é', '.'] )
@slow
def snake_case_ ( self ) -> Dict:
UpperCamelCase : Union[str, Any] = [
'Transformers (formerly known as pytorch-transformers and pytorch-pretrained-bert) provides '
'general-purpose architectures (BERT, GPT, RoBERTa, XLM, DistilBert, XLNet...) for Natural '
'Language Understanding (NLU) and Natural Language Generation (NLG) with over thirty-two pretrained '
'models in one hundred plus languages and deep interoperability between Jax, PyTorch and TensorFlow.',
'BERT is designed to pre-train deep bidirectional representations from unlabeled text by jointly '
'conditioning on both left and right context in all layers.',
'The quick brown fox jumps over the lazy dog.',
]
# fmt: off
UpperCamelCase : Tuple = {
'input_ids': [
[4, 32, 13, 7, 9, 12, 19, 8, 13, 18, 5, 13, 12, 4, 64, 19, 8, 13, 18, 5, 13, 15, 22, 4, 28, 9, 8, 20, 9, 4, 7, 12, 4, 24, 22, 6, 8, 13, 17, 11, 39, 6, 13, 7, 9, 12, 19, 8, 13, 18, 5, 13, 12, 4, 7, 9, 14, 4, 24, 22, 6, 8, 13, 17, 11, 39, 24, 13, 5, 6, 13, 7, 10, 9, 5, 14, 39, 25, 5, 13, 6, 63, 4, 24, 13, 8, 27, 10, 14, 5, 12, 4, 21, 5, 9, 5, 13, 7, 15, 39, 24, 16, 13, 24, 8, 12, 5, 4, 7, 13, 17, 11, 10, 6, 5, 17, 6, 16, 13, 5, 12, 4, 64, 40, 47, 54, 32, 23, 4, 53, 49, 32, 23, 4, 54, 8, 40, 47, 54, 32, 7, 23, 4, 69, 52, 43, 23, 4, 51, 10, 12, 6, 10, 15, 40, 5, 13, 6, 23, 4, 69, 52, 48, 5, 6, 26, 26, 26, 63, 4, 19, 8, 13, 4, 48, 7, 6, 16, 13, 7, 15, 4, 52, 7, 9, 21, 16, 7, 21, 5, 4, 61, 9, 14, 5, 13, 12, 6, 7, 9, 14, 10, 9, 21, 4, 64, 48, 52, 61, 63, 4, 7, 9, 14, 4, 48, 7, 6, 16, 13, 7, 15, 4, 52, 7, 9, 21, 16, 7, 21, 5, 4, 53, 5, 9, 5, 13, 7, 6, 10, 8, 9, 4, 64, 48, 52, 53, 63, 4, 20, 10, 6, 11, 4, 8, 27, 5, 13, 4, 6, 11, 10, 13, 6, 22, 39, 6, 20, 8, 4, 24, 13, 5, 6, 13, 7, 10, 9, 5, 14, 4, 18, 8, 14, 5, 15, 12, 4, 10, 9, 4, 8, 9, 5, 4, 11, 16, 9, 14, 13, 5, 14, 4, 24, 15, 16, 12, 4, 15, 7, 9, 21, 16, 7, 21, 5, 12, 4, 7, 9, 14, 4, 14, 5, 5, 24, 4, 10, 9, 6, 5, 13, 8, 24, 5, 13, 7, 25, 10, 15, 10, 6, 22, 4, 25, 5, 6, 20, 5, 5, 9, 4, 58, 7, 37, 23, 4, 49, 22, 32, 8, 13, 17, 11, 4, 7, 9, 14, 4, 32, 5, 9, 12, 8, 13, 55, 15, 8, 20, 26, 2],
[4, 40, 47, 54, 32, 4, 10, 12, 4, 14, 5, 12, 10, 21, 9, 5, 14, 4, 6, 8, 4, 24, 13, 5, 39, 6, 13, 7, 10, 9, 4, 14, 5, 5, 24, 4, 25, 10, 14, 10, 13, 5, 17, 6, 10, 8, 9, 7, 15, 4, 13, 5, 24, 13, 5, 12, 5, 9, 6, 7, 6, 10, 8, 9, 12, 4, 19, 13, 8, 18, 4, 16, 9, 15, 7, 25, 5, 15, 5, 14, 4, 6, 5, 37, 6, 4, 25, 22, 4, 46, 8, 10, 9, 6, 15, 22, 4, 17, 8, 9, 14, 10, 6, 10, 8, 9, 10, 9, 21, 4, 8, 9, 4, 25, 8, 6, 11, 4, 15, 5, 19, 6, 4, 7, 9, 14, 4, 13, 10, 21, 11, 6, 4, 17, 8, 9, 6, 5, 37, 6, 4, 10, 9, 4, 7, 15, 15, 4, 15, 7, 22, 5, 13, 12, 26, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
[4, 32, 11, 5, 4, 45, 16, 10, 17, 28, 4, 25, 13, 8, 20, 9, 4, 19, 8, 37, 4, 46, 16, 18, 24, 12, 4, 8, 27, 5, 13, 4, 6, 11, 5, 4, 15, 7, 57, 22, 4, 14, 8, 21, 26, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
],
'attention_mask': [
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
]
}
# fmt: on
self.tokenizer_integration_test_util(
expected_encoding=UpperCAmelCase_, model_name='microsoft/speecht5_asr', revision='c5ef64c71905caeccde0e4462ef3f9077224c524', sequences=UpperCAmelCase_, )
| 119 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__SCREAMING_SNAKE_CASE : str = logging.get_logger(__name__)
__SCREAMING_SNAKE_CASE : Tuple = {
'microsoft/beit-base-patch16-224-pt22k': (
'https://huggingface.co/microsoft/beit-base-patch16-224-pt22k/resolve/main/config.json'
),
# See all BEiT models at https://huggingface.co/models?filter=beit
}
class __A (snake_case__):
'''simple docstring'''
__lowercase: Optional[int] = """beit"""
def __init__( self : List[str] , UpperCAmelCase_ : List[Any]=8_192 , UpperCAmelCase_ : Dict=768 , UpperCAmelCase_ : int=12 , UpperCAmelCase_ : Tuple=12 , UpperCAmelCase_ : List[Any]=3_072 , UpperCAmelCase_ : Tuple="gelu" , UpperCAmelCase_ : Dict=0.0 , UpperCAmelCase_ : List[str]=0.0 , UpperCAmelCase_ : Any=0.02 , UpperCAmelCase_ : Optional[Any]=1E-12 , UpperCAmelCase_ : int=224 , UpperCAmelCase_ : Tuple=16 , UpperCAmelCase_ : List[str]=3 , UpperCAmelCase_ : int=False , UpperCAmelCase_ : List[str]=False , UpperCAmelCase_ : Tuple=False , UpperCAmelCase_ : int=False , UpperCAmelCase_ : List[Any]=0.1 , UpperCAmelCase_ : List[str]=0.1 , UpperCAmelCase_ : Any=True , UpperCAmelCase_ : Dict=[3, 5, 7, 11] , UpperCAmelCase_ : Tuple=[1, 2, 3, 6] , UpperCAmelCase_ : Optional[int]=True , UpperCAmelCase_ : List[Any]=0.4 , UpperCAmelCase_ : Optional[Any]=256 , UpperCAmelCase_ : Optional[Any]=1 , UpperCAmelCase_ : int=False , UpperCAmelCase_ : Tuple=255 , **UpperCAmelCase_ : List[str] , ) ->Optional[Any]:
"""simple docstring"""
super().__init__(**UpperCAmelCase_ )
snake_case_ = vocab_size
snake_case_ = hidden_size
snake_case_ = num_hidden_layers
snake_case_ = num_attention_heads
snake_case_ = intermediate_size
snake_case_ = hidden_act
snake_case_ = hidden_dropout_prob
snake_case_ = attention_probs_dropout_prob
snake_case_ = initializer_range
snake_case_ = layer_norm_eps
snake_case_ = image_size
snake_case_ = patch_size
snake_case_ = num_channels
snake_case_ = use_mask_token
snake_case_ = use_absolute_position_embeddings
snake_case_ = use_relative_position_bias
snake_case_ = use_shared_relative_position_bias
snake_case_ = layer_scale_init_value
snake_case_ = drop_path_rate
snake_case_ = use_mean_pooling
# decode head attributes (semantic segmentation)
snake_case_ = out_indices
snake_case_ = pool_scales
# auxiliary head attributes (semantic segmentation)
snake_case_ = use_auxiliary_head
snake_case_ = auxiliary_loss_weight
snake_case_ = auxiliary_channels
snake_case_ = auxiliary_num_convs
snake_case_ = auxiliary_concat_input
snake_case_ = semantic_loss_ignore_index
class __A (snake_case__):
'''simple docstring'''
__lowercase: List[Any] = version.parse("""1.11""")
@property
def lowerCAmelCase ( self : Dict ) ->Mapping[str, Mapping[int, str]]:
"""simple docstring"""
return OrderedDict(
[
("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
] )
@property
def lowerCAmelCase ( self : Any ) ->float:
"""simple docstring"""
return 1E-4
| 347 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
a__ : str = {
'configuration_wav2vec2': ['WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP', 'Wav2Vec2Config'],
'feature_extraction_wav2vec2': ['Wav2Vec2FeatureExtractor'],
'processing_wav2vec2': ['Wav2Vec2Processor'],
'tokenization_wav2vec2': ['Wav2Vec2CTCTokenizer', 'Wav2Vec2Tokenizer'],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a__ : int = [
'WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST',
'Wav2Vec2ForAudioFrameClassification',
'Wav2Vec2ForCTC',
'Wav2Vec2ForMaskedLM',
'Wav2Vec2ForPreTraining',
'Wav2Vec2ForSequenceClassification',
'Wav2Vec2ForXVector',
'Wav2Vec2Model',
'Wav2Vec2PreTrainedModel',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a__ : Tuple = [
'TF_WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST',
'TFWav2Vec2ForCTC',
'TFWav2Vec2Model',
'TFWav2Vec2PreTrainedModel',
'TFWav2Vec2ForSequenceClassification',
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a__ : int = [
'FlaxWav2Vec2ForCTC',
'FlaxWav2Vec2ForPreTraining',
'FlaxWav2Vec2Model',
'FlaxWav2Vec2PreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_wavaveca import WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP, WavaVecaConfig
from .feature_extraction_wavaveca import WavaVecaFeatureExtractor
from .processing_wavaveca import WavaVecaProcessor
from .tokenization_wavaveca import WavaVecaCTCTokenizer, WavaVecaTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_wavaveca import (
WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST,
WavaVecaForAudioFrameClassification,
WavaVecaForCTC,
WavaVecaForMaskedLM,
WavaVecaForPreTraining,
WavaVecaForSequenceClassification,
WavaVecaForXVector,
WavaVecaModel,
WavaVecaPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_wavaveca import (
TF_WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST,
TFWavaVecaForCTC,
TFWavaVecaForSequenceClassification,
TFWavaVecaModel,
TFWavaVecaPreTrainedModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_wavaveca import (
FlaxWavaVecaForCTC,
FlaxWavaVecaForPreTraining,
FlaxWavaVecaModel,
FlaxWavaVecaPreTrainedModel,
)
else:
import sys
a__ : int = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 349 |
"""simple docstring"""
import os
import re
import warnings
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
if TYPE_CHECKING:
from ...tokenization_utils_base import TextInput
from ...utils import logging
__SCREAMING_SNAKE_CASE : Union[str, Any] = logging.get_logger(__name__)
__SCREAMING_SNAKE_CASE : List[Any] = {'vocab_file': 'spiece.model'}
__SCREAMING_SNAKE_CASE : int = {
'vocab_file': {
't5-small': 'https://huggingface.co/t5-small/resolve/main/spiece.model',
't5-base': 'https://huggingface.co/t5-base/resolve/main/spiece.model',
't5-large': 'https://huggingface.co/t5-large/resolve/main/spiece.model',
't5-3b': 'https://huggingface.co/t5-3b/resolve/main/spiece.model',
't5-11b': 'https://huggingface.co/t5-11b/resolve/main/spiece.model',
}
}
# TODO(PVP) - this should be removed in Transformers v5
__SCREAMING_SNAKE_CASE : Dict = {
't5-small': 512,
't5-base': 512,
't5-large': 512,
't5-3b': 512,
't5-11b': 512,
}
__SCREAMING_SNAKE_CASE : Optional[int] = '▁'
class __A (snake_case__):
'''simple docstring'''
__lowercase: Optional[int] = VOCAB_FILES_NAMES
__lowercase: Any = PRETRAINED_VOCAB_FILES_MAP
__lowercase: Any = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
__lowercase: List[str] = ["""input_ids""", """attention_mask"""]
def __init__( self : Optional[int] , UpperCAmelCase_ : Tuple , UpperCAmelCase_ : Optional[Any]="</s>" , UpperCAmelCase_ : Optional[Any]="<unk>" , UpperCAmelCase_ : Any="<pad>" , UpperCAmelCase_ : Tuple=100 , UpperCAmelCase_ : Optional[Any]=None , UpperCAmelCase_ : Optional[Dict[str, Any]] = None , UpperCAmelCase_ : Optional[int]=True , **UpperCAmelCase_ : Dict , ) ->None:
"""simple docstring"""
if extra_ids > 0 and additional_special_tokens is None:
snake_case_ = [F"""<extra_id_{i}>""" for i in range(UpperCAmelCase_ )]
elif extra_ids > 0 and additional_special_tokens is not None:
# Check that we have the right number of extra_id special tokens
snake_case_ = len(set(filter(lambda UpperCAmelCase_ : bool("""extra_id""" in str(UpperCAmelCase_ ) ) , UpperCAmelCase_ ) ) )
if extra_tokens != extra_ids:
raise ValueError(
F"""Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are"""
""" provided to T5Tokenizer. In this case the additional_special_tokens must include the extra_ids"""
""" tokens""" )
if legacy:
logger.warning_once(
F"""You are using the legacy behaviour of the {self.__class__}. This means that tokens that come after special tokens will not be properly handled. We recommend you to"""
""" read the related pull request available at https://github.com/huggingface/transformers/pull/24565""" )
snake_case_ = legacy
snake_case_ = {} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
eos_token=UpperCAmelCase_ , unk_token=UpperCAmelCase_ , pad_token=UpperCAmelCase_ , extra_ids=UpperCAmelCase_ , additional_special_tokens=UpperCAmelCase_ , sp_model_kwargs=self.sp_model_kwargs , legacy=UpperCAmelCase_ , **UpperCAmelCase_ , )
snake_case_ = vocab_file
snake_case_ = extra_ids
snake_case_ = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(UpperCAmelCase_ )
@staticmethod
def lowerCAmelCase ( UpperCAmelCase_ : str , UpperCAmelCase_ : Dict , UpperCAmelCase_ : Optional[Any] ) ->Union[str, Any]:
"""simple docstring"""
if pretrained_model_name_or_path in TaTokenizer.max_model_input_sizes:
snake_case_ = TaTokenizer.max_model_input_sizes[pretrained_model_name_or_path]
if init_max_model_length is not None and init_max_model_length != max_model_length:
return init_max_model_length
elif init_max_model_length is None:
warnings.warn(
"""This tokenizer was incorrectly instantiated with a model max length of"""
F""" {deprecated_max_model_length} which will be corrected in Transformers v5.\nFor now, this"""
""" behavior is kept to avoid breaking backwards compatibility when padding/encoding with"""
""" `truncation is True`.\n- Be aware that you SHOULD NOT rely on"""
F""" {pretrained_model_name_or_path} automatically truncating your input to"""
F""" {deprecated_max_model_length} when padding/encoding.\n- If you want to encode/pad to sequences"""
F""" longer than {deprecated_max_model_length} you can either instantiate this tokenizer with"""
""" `model_max_length` or pass `max_length` when encoding/padding.\n- To avoid this warning, please"""
""" instantiate this tokenizer with `model_max_length` set to your preferred value.""" , UpperCAmelCase_ , )
return max_model_length
@property
def lowerCAmelCase ( self : Optional[Any] ) ->Optional[Any]:
"""simple docstring"""
return self.sp_model.get_piece_size() + self._extra_ids
def lowerCAmelCase ( self : Any ) ->Optional[int]:
"""simple docstring"""
snake_case_ = {self.convert_ids_to_tokens(UpperCAmelCase_ ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def lowerCAmelCase ( self : List[str] , UpperCAmelCase_ : List[int] , UpperCAmelCase_ : Optional[List[int]] = None , UpperCAmelCase_ : bool = False ) ->List[int]:
"""simple docstring"""
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=UpperCAmelCase_ , token_ids_a=UpperCAmelCase_ , already_has_special_tokens=UpperCAmelCase_ )
# normal case: some special tokens
if token_ids_a is None:
return ([0] * len(UpperCAmelCase_ )) + [1]
return ([0] * len(UpperCAmelCase_ )) + [1] + ([0] * len(UpperCAmelCase_ )) + [1]
def lowerCAmelCase ( self : Any ) ->List[str]:
"""simple docstring"""
return list(
set(filter(lambda UpperCAmelCase_ : bool(re.search(R"""<extra_id_\d+>""" , UpperCAmelCase_ ) ) is not None , self.additional_special_tokens ) ) )
def lowerCAmelCase ( self : Dict ) ->str:
"""simple docstring"""
return [self._convert_token_to_id(UpperCAmelCase_ ) for token in self.get_sentinel_tokens()]
def lowerCAmelCase ( self : Dict , UpperCAmelCase_ : List[int] ) ->List[int]:
"""simple docstring"""
if len(UpperCAmelCase_ ) > 0 and token_ids[-1] == self.eos_token_id:
warnings.warn(
F"""This sequence already has {self.eos_token}. In future versions this behavior may lead to duplicated"""
""" eos tokens being added.""" )
return token_ids
else:
return token_ids + [self.eos_token_id]
def lowerCAmelCase ( self : str , UpperCAmelCase_ : List[int] , UpperCAmelCase_ : Optional[List[int]] = None ) ->List[int]:
"""simple docstring"""
snake_case_ = [self.eos_token_id]
if token_ids_a is None:
return len(token_ids_a + eos ) * [0]
return len(token_ids_a + eos + token_ids_a + eos ) * [0]
def lowerCAmelCase ( self : Optional[int] , UpperCAmelCase_ : List[int] , UpperCAmelCase_ : Optional[List[int]] = None ) ->List[int]:
"""simple docstring"""
snake_case_ = self._add_eos_if_not_present(UpperCAmelCase_ )
if token_ids_a is None:
return token_ids_a
else:
snake_case_ = self._add_eos_if_not_present(UpperCAmelCase_ )
return token_ids_a + token_ids_a
def __getstate__( self : Optional[Any] ) ->Tuple:
"""simple docstring"""
snake_case_ = self.__dict__.copy()
snake_case_ = None
return state
def __setstate__( self : Optional[Any] , UpperCAmelCase_ : List[Any] ) ->List[Any]:
"""simple docstring"""
snake_case_ = d
# for backward compatibility
if not hasattr(self , """sp_model_kwargs""" ):
snake_case_ = {}
snake_case_ = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(self.vocab_file )
def lowerCAmelCase ( self : int , UpperCAmelCase_ : "TextInput" , **UpperCAmelCase_ : Tuple ) ->List[str]:
"""simple docstring"""
if not self.legacy:
snake_case_ = SPIECE_UNDERLINE + text.replace(UpperCAmelCase_ , """ """ )
return super().tokenize(UpperCAmelCase_ , **UpperCAmelCase_ )
def lowerCAmelCase ( self : Dict , UpperCAmelCase_ : Tuple , **UpperCAmelCase_ : Any ) ->Tuple:
"""simple docstring"""
if not self.legacy:
snake_case_ = text.startswith(UpperCAmelCase_ )
if is_first:
snake_case_ = text[1:]
snake_case_ = self.sp_model.encode(UpperCAmelCase_ , out_type=UpperCAmelCase_ )
if not self.legacy and not is_first and not text.startswith(""" """ ) and tokens[0].startswith(UpperCAmelCase_ ):
snake_case_ = ([tokens[0][1:]] if len(tokens[0] ) > 1 else []) + tokens[1:]
return tokens
def lowerCAmelCase ( self : List[str] , UpperCAmelCase_ : List[Any] ) ->Tuple:
"""simple docstring"""
if token.startswith("""<extra_id_""" ):
snake_case_ = re.match(R"""<extra_id_(\d+)>""" , UpperCAmelCase_ )
snake_case_ = int(match.group(1 ) )
return self.vocab_size - num - 1
return self.sp_model.piece_to_id(UpperCAmelCase_ )
def lowerCAmelCase ( self : List[str] , UpperCAmelCase_ : Optional[Any] ) ->List[Any]:
"""simple docstring"""
if index < self.sp_model.get_piece_size():
snake_case_ = self.sp_model.IdToPiece(UpperCAmelCase_ )
else:
snake_case_ = F"""<extra_id_{self.vocab_size - 1 - index}>"""
return token
def lowerCAmelCase ( self : List[Any] , UpperCAmelCase_ : List[str] ) ->Optional[Any]:
"""simple docstring"""
snake_case_ = []
snake_case_ = """"""
snake_case_ = False
for token in tokens:
# make sure that special tokens are not decoded using sentencepiece model
if token in self.all_special_tokens:
if not prev_is_special:
out_string += " "
out_string += self.sp_model.decode(UpperCAmelCase_ ) + token
snake_case_ = True
snake_case_ = []
else:
current_sub_tokens.append(UpperCAmelCase_ )
snake_case_ = False
out_string += self.sp_model.decode(UpperCAmelCase_ )
return out_string.strip()
def lowerCAmelCase ( self : str , UpperCAmelCase_ : str , UpperCAmelCase_ : Optional[str] = None ) ->Tuple[str]:
"""simple docstring"""
if not os.path.isdir(UpperCAmelCase_ ):
logger.error(F"""Vocabulary path ({save_directory}) should be a directory""" )
return
snake_case_ = os.path.join(
UpperCAmelCase_ , (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(UpperCAmelCase_ ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file , UpperCAmelCase_ )
elif not os.path.isfile(self.vocab_file ):
with open(UpperCAmelCase_ , """wb""" ) as fi:
snake_case_ = self.sp_model.serialized_model_proto()
fi.write(UpperCAmelCase_ )
return (out_vocab_file,)
| 347 | 0 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
EulerAncestralDiscreteScheduler,
LMSDiscreteScheduler,
PNDMScheduler,
StableDiffusionInstructPixaPixPipeline,
UNetaDConditionModel,
)
from diffusers.image_processor import VaeImageProcessor
from diffusers.utils import floats_tensor, load_image, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
from ..pipeline_params import (
IMAGE_TO_IMAGE_IMAGE_PARAMS,
TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin
enable_full_determinism()
class A__ ( snake_case__ , snake_case__ , snake_case__ , unittest.TestCase ):
lowercase = StableDiffusionInstructPixaPixPipeline
lowercase = TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"""height""", """width""", """cross_attention_kwargs"""}
lowercase = TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
lowercase = IMAGE_TO_IMAGE_IMAGE_PARAMS
lowercase = IMAGE_TO_IMAGE_IMAGE_PARAMS
def snake_case_ ( self ) -> int:
'''simple docstring'''
torch.manual_seed(0 )
A_ = UNetaDConditionModel(
block_out_channels=(32, 64) , layers_per_block=2 , sample_size=32 , in_channels=8 , out_channels=4 , down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""") , up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""") , cross_attention_dim=32 , )
A_ = PNDMScheduler(skip_prk_steps=UpperCAmelCase_ )
torch.manual_seed(0 )
A_ = AutoencoderKL(
block_out_channels=[32, 64] , in_channels=3 , out_channels=3 , down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""] , up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""] , latent_channels=4 , )
torch.manual_seed(0 )
A_ = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=32 , intermediate_size=37 , layer_norm_eps=1e-0_5 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1000 , )
A_ = CLIPTextModel(UpperCAmelCase_ )
A_ = CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip""" )
A_ = {
"""unet""": unet,
"""scheduler""": scheduler,
"""vae""": vae,
"""text_encoder""": text_encoder,
"""tokenizer""": tokenizer,
"""safety_checker""": None,
"""feature_extractor""": None,
}
return components
def snake_case_ ( self , UpperCamelCase__ , UpperCamelCase__=0 ) -> List[str]:
'''simple docstring'''
A_ = floats_tensor((1, 3, 32, 32) , rng=random.Random(UpperCAmelCase_ ) ).to(UpperCAmelCase_ )
A_ = image.cpu().permute(0 , 2 , 3 , 1 )[0]
A_ = Image.fromarray(np.uinta(UpperCAmelCase_ ) ).convert("""RGB""" )
if str(UpperCAmelCase_ ).startswith("""mps""" ):
A_ = torch.manual_seed(UpperCAmelCase_ )
else:
A_ = torch.Generator(device=UpperCAmelCase_ ).manual_seed(UpperCAmelCase_ )
A_ = {
"""prompt""": """A painting of a squirrel eating a burger""",
"""image""": image,
"""generator""": generator,
"""num_inference_steps""": 2,
"""guidance_scale""": 6.0,
"""image_guidance_scale""": 1,
"""output_type""": """numpy""",
}
return inputs
def snake_case_ ( self ) -> List[str]:
'''simple docstring'''
A_ = """cpu""" # ensure determinism for the device-dependent torch.Generator
A_ = self.get_dummy_components()
A_ = StableDiffusionInstructPixaPixPipeline(**UpperCAmelCase_ )
A_ = sd_pipe.to(UpperCAmelCase_ )
sd_pipe.set_progress_bar_config(disable=UpperCAmelCase_ )
A_ = self.get_dummy_inputs(UpperCAmelCase_ )
A_ = sd_pipe(**UpperCAmelCase_ ).images
A_ = image[0, -3:, -3:, -1]
assert image.shape == (1, 32, 32, 3)
A_ = np.array([0.7526, 0.3750, 0.4547, 0.6117, 0.5866, 0.5016, 0.4327, 0.5642, 0.4815] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-3
def snake_case_ ( self ) -> List[Any]:
'''simple docstring'''
A_ = """cpu""" # ensure determinism for the device-dependent torch.Generator
A_ = self.get_dummy_components()
A_ = StableDiffusionInstructPixaPixPipeline(**UpperCAmelCase_ )
A_ = sd_pipe.to(UpperCAmelCase_ )
sd_pipe.set_progress_bar_config(disable=UpperCAmelCase_ )
A_ = self.get_dummy_inputs(UpperCAmelCase_ )
A_ = """french fries"""
A_ = sd_pipe(**UpperCAmelCase_ , negative_prompt=UpperCAmelCase_ )
A_ = output.images
A_ = image[0, -3:, -3:, -1]
assert image.shape == (1, 32, 32, 3)
A_ = np.array([0.7511, 0.3642, 0.4553, 0.6236, 0.5797, 0.5013, 0.4343, 0.5611, 0.4831] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-3
def snake_case_ ( self ) -> List[Any]:
'''simple docstring'''
A_ = """cpu""" # ensure determinism for the device-dependent torch.Generator
A_ = self.get_dummy_components()
A_ = StableDiffusionInstructPixaPixPipeline(**UpperCAmelCase_ )
A_ = sd_pipe.to(UpperCAmelCase_ )
sd_pipe.set_progress_bar_config(disable=UpperCAmelCase_ )
A_ = self.get_dummy_inputs(UpperCAmelCase_ )
A_ = [inputs["""prompt"""]] * 2
A_ = np.array(inputs["""image"""] ).astype(np.floataa ) / 255.0
A_ = torch.from_numpy(UpperCAmelCase_ ).unsqueeze(0 ).to(UpperCAmelCase_ )
A_ = image / 2 + 0.5
A_ = image.permute(0 , 3 , 1 , 2 )
A_ = image.repeat(2 , 1 , 1 , 1 )
A_ = sd_pipe(**UpperCAmelCase_ ).images
A_ = image[-1, -3:, -3:, -1]
assert image.shape == (2, 32, 32, 3)
A_ = np.array([0.5812, 0.5748, 0.5222, 0.5908, 0.5695, 0.7174, 0.6804, 0.5523, 0.5579] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-3
def snake_case_ ( self ) -> Union[str, Any]:
'''simple docstring'''
A_ = """cpu""" # ensure determinism for the device-dependent torch.Generator
A_ = self.get_dummy_components()
A_ = EulerAncestralDiscreteScheduler(
beta_start=0.00085 , beta_end=0.012 , beta_schedule="""scaled_linear""" )
A_ = StableDiffusionInstructPixaPixPipeline(**UpperCAmelCase_ )
A_ = sd_pipe.to(UpperCAmelCase_ )
sd_pipe.set_progress_bar_config(disable=UpperCAmelCase_ )
A_ = self.get_dummy_inputs(UpperCAmelCase_ )
A_ = sd_pipe(**UpperCAmelCase_ ).images
A_ = image[0, -3:, -3:, -1]
A_ = [round(UpperCAmelCase_ , 4 ) for x in image_slice.flatten().tolist()]
print(""",""".join([str(UpperCAmelCase_ ) for x in slice] ) )
assert image.shape == (1, 32, 32, 3)
A_ = np.array([0.7417, 0.3842, 0.4732, 0.5776, 0.5891, 0.5139, 0.4052, 0.5673, 0.4986] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-3
def snake_case_ ( self ) -> str:
'''simple docstring'''
super().test_inference_batch_single_identical(expected_max_diff=3e-3 )
def snake_case_ ( self ) -> Union[str, Any]:
'''simple docstring'''
A_ = self.get_dummy_components()
A_ = StableDiffusionInstructPixaPixPipeline(**UpperCAmelCase_ )
A_ = VaeImageProcessor(do_resize=UpperCAmelCase_ , do_normalize=UpperCAmelCase_ )
A_ = pipe.to(UpperCAmelCase_ )
pipe.set_progress_bar_config(disable=UpperCAmelCase_ )
A_ = pipe(**self.get_dummy_inputs_by_type(UpperCAmelCase_ , input_image_type="""pt""" ) )[0]
A_ = components["""vae"""]
A_ = self.get_dummy_inputs_by_type(UpperCAmelCase_ , input_image_type="""pt""" )
for image_param in self.image_latents_params:
if image_param in inputs.keys():
A_ = vae.encode(inputs[image_param] ).latent_dist.mode()
A_ = pipe(**UpperCAmelCase_ )[0]
A_ = np.abs(out - out_latents_inputs ).max()
self.assertLess(UpperCAmelCase_ , 1e-4 , """passing latents as image input generate different result from passing image""" )
@slow
@require_torch_gpu
class A__ ( unittest.TestCase ):
def snake_case_ ( self ) -> List[str]:
'''simple docstring'''
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def snake_case_ ( self , UpperCamelCase__=0 ) -> List[Any]:
'''simple docstring'''
A_ = torch.manual_seed(UpperCAmelCase_ )
A_ = load_image(
"""https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_pix2pix/example.jpg""" )
A_ = {
"""prompt""": """turn him into a cyborg""",
"""image""": image,
"""generator""": generator,
"""num_inference_steps""": 3,
"""guidance_scale""": 7.5,
"""image_guidance_scale""": 1.0,
"""output_type""": """numpy""",
}
return inputs
def snake_case_ ( self ) -> Dict:
'''simple docstring'''
A_ = StableDiffusionInstructPixaPixPipeline.from_pretrained(
"""timbrooks/instruct-pix2pix""" , safety_checker=UpperCAmelCase_ )
pipe.to(UpperCAmelCase_ )
pipe.set_progress_bar_config(disable=UpperCAmelCase_ )
pipe.enable_attention_slicing()
A_ = self.get_inputs()
A_ = pipe(**UpperCAmelCase_ ).images
A_ = image[0, -3:, -3:, -1].flatten()
assert image.shape == (1, 512, 512, 3)
A_ = np.array([0.5902, 0.6015, 0.6027, 0.5983, 0.6092, 0.6061, 0.5765, 0.5785, 0.5555] )
assert np.abs(expected_slice - image_slice ).max() < 1e-3
def snake_case_ ( self ) -> Union[str, Any]:
'''simple docstring'''
A_ = StableDiffusionInstructPixaPixPipeline.from_pretrained(
"""timbrooks/instruct-pix2pix""" , safety_checker=UpperCAmelCase_ )
A_ = LMSDiscreteScheduler.from_config(pipe.scheduler.config )
pipe.to(UpperCAmelCase_ )
pipe.set_progress_bar_config(disable=UpperCAmelCase_ )
pipe.enable_attention_slicing()
A_ = self.get_inputs()
A_ = pipe(**UpperCAmelCase_ ).images
A_ = image[0, -3:, -3:, -1].flatten()
assert image.shape == (1, 512, 512, 3)
A_ = np.array([0.6578, 0.6817, 0.6972, 0.6761, 0.6856, 0.6916, 0.6428, 0.6516, 0.6301] )
assert np.abs(expected_slice - image_slice ).max() < 1e-3
def snake_case_ ( self ) -> List[Any]:
'''simple docstring'''
A_ = StableDiffusionInstructPixaPixPipeline.from_pretrained(
"""timbrooks/instruct-pix2pix""" , safety_checker=UpperCAmelCase_ )
A_ = DDIMScheduler.from_config(pipe.scheduler.config )
pipe.to(UpperCAmelCase_ )
pipe.set_progress_bar_config(disable=UpperCAmelCase_ )
pipe.enable_attention_slicing()
A_ = self.get_inputs()
A_ = pipe(**UpperCAmelCase_ ).images
A_ = image[0, -3:, -3:, -1].flatten()
assert image.shape == (1, 512, 512, 3)
A_ = np.array([0.3828, 0.3834, 0.3818, 0.3792, 0.3865, 0.3752, 0.3792, 0.3847, 0.3753] )
assert np.abs(expected_slice - image_slice ).max() < 1e-3
def snake_case_ ( self ) -> str:
'''simple docstring'''
A_ = 0
def callback_fn(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ) -> None:
A_ = True
nonlocal number_of_steps
number_of_steps += 1
if step == 1:
A_ = latents.detach().cpu().numpy()
assert latents.shape == (1, 4, 64, 64)
A_ = latents[0, -3:, -3:, -1]
A_ = np.array([-0.2463, -0.4644, -0.9756, 1.5176, 1.4414, 0.7866, 0.9897, 0.8521, 0.7983] )
assert np.abs(latents_slice.flatten() - expected_slice ).max() < 5e-2
elif step == 2:
A_ = latents.detach().cpu().numpy()
assert latents.shape == (1, 4, 64, 64)
A_ = latents[0, -3:, -3:, -1]
A_ = np.array([-0.2644, -0.4626, -0.9653, 1.5176, 1.4551, 0.7686, 0.9805, 0.8452, 0.8115] )
assert np.abs(latents_slice.flatten() - expected_slice ).max() < 5e-2
A_ = False
A_ = StableDiffusionInstructPixaPixPipeline.from_pretrained(
"""timbrooks/instruct-pix2pix""" , safety_checker=UpperCAmelCase_ , torch_dtype=torch.floataa )
A_ = pipe.to(UpperCAmelCase_ )
pipe.set_progress_bar_config(disable=UpperCAmelCase_ )
pipe.enable_attention_slicing()
A_ = self.get_inputs()
pipe(**UpperCAmelCase_ , callback=UpperCAmelCase_ , callback_steps=1 )
assert callback_fn.has_been_called
assert number_of_steps == 3
def snake_case_ ( self ) -> Optional[Any]:
'''simple docstring'''
torch.cuda.empty_cache()
torch.cuda.reset_max_memory_allocated()
torch.cuda.reset_peak_memory_stats()
A_ = StableDiffusionInstructPixaPixPipeline.from_pretrained(
"""timbrooks/instruct-pix2pix""" , safety_checker=UpperCAmelCase_ , torch_dtype=torch.floataa )
A_ = pipe.to(UpperCAmelCase_ )
pipe.set_progress_bar_config(disable=UpperCAmelCase_ )
pipe.enable_attention_slicing(1 )
pipe.enable_sequential_cpu_offload()
A_ = self.get_inputs()
A_ = pipe(**UpperCAmelCase_ )
A_ = torch.cuda.max_memory_allocated()
# make sure that less than 2.2 GB is allocated
assert mem_bytes < 2.2 * 10**9
def snake_case_ ( self ) -> int:
'''simple docstring'''
A_ = self.get_inputs()
# resize to resolution that is divisible by 8 but not 16 or 32
A_ = inputs["""image"""].resize((504, 504) )
A_ = """timbrooks/instruct-pix2pix"""
A_ = StableDiffusionInstructPixaPixPipeline.from_pretrained(
UpperCAmelCase_ , safety_checker=UpperCAmelCase_ , )
pipe.to(UpperCAmelCase_ )
pipe.set_progress_bar_config(disable=UpperCAmelCase_ )
pipe.enable_attention_slicing()
A_ = pipe(**UpperCAmelCase_ )
A_ = output.images[0]
A_ = image[255:258, 383:386, -1]
assert image.shape == (504, 504, 3)
A_ = np.array([0.2726, 0.2529, 0.2664, 0.2655, 0.2641, 0.2642, 0.2591, 0.2649, 0.2590] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 5e-3
| 162 |
"""simple docstring"""
def _a ( _SCREAMING_SNAKE_CASE = 1_000_000 ) -> int:
snake_case_ = [i - 1 for i in range(limit + 1 )]
for i in range(2 , limit + 1 ):
if phi[i] == i - 1:
for j in range(2 * i , limit + 1 , _SCREAMING_SNAKE_CASE ):
phi[j] -= phi[j] // i
return sum(phi[2 : limit + 1] )
if __name__ == "__main__":
print(solution())
| 347 | 0 |
from ..utils import DummyObject, requires_backends
class __snake_case ( metaclass=lowerCamelCase__ ):
__lowerCamelCase : Dict = ["""sentencepiece"""]
def __init__( self , *snake_case__ , **snake_case__ ) -> List[str]:
'''simple docstring'''
requires_backends(self , ['''sentencepiece'''] )
class __snake_case ( metaclass=lowerCamelCase__ ):
__lowerCamelCase : List[Any] = ["""sentencepiece"""]
def __init__( self , *snake_case__ , **snake_case__ ) -> List[str]:
'''simple docstring'''
requires_backends(self , ['''sentencepiece'''] )
class __snake_case ( metaclass=lowerCamelCase__ ):
__lowerCamelCase : Optional[Any] = ["""sentencepiece"""]
def __init__( self , *snake_case__ , **snake_case__ ) -> Optional[int]:
'''simple docstring'''
requires_backends(self , ['''sentencepiece'''] )
class __snake_case ( metaclass=lowerCamelCase__ ):
__lowerCamelCase : Tuple = ["""sentencepiece"""]
def __init__( self , *snake_case__ , **snake_case__ ) -> List[Any]:
'''simple docstring'''
requires_backends(self , ['''sentencepiece'''] )
class __snake_case ( metaclass=lowerCamelCase__ ):
__lowerCamelCase : int = ["""sentencepiece"""]
def __init__( self , *snake_case__ , **snake_case__ ) -> List[str]:
'''simple docstring'''
requires_backends(self , ['''sentencepiece'''] )
class __snake_case ( metaclass=lowerCamelCase__ ):
__lowerCamelCase : Tuple = ["""sentencepiece"""]
def __init__( self , *snake_case__ , **snake_case__ ) -> Optional[int]:
'''simple docstring'''
requires_backends(self , ['''sentencepiece'''] )
class __snake_case ( metaclass=lowerCamelCase__ ):
__lowerCamelCase : List[Any] = ["""sentencepiece"""]
def __init__( self , *snake_case__ , **snake_case__ ) -> List[str]:
'''simple docstring'''
requires_backends(self , ['''sentencepiece'''] )
class __snake_case ( metaclass=lowerCamelCase__ ):
__lowerCamelCase : int = ["""sentencepiece"""]
def __init__( self , *snake_case__ , **snake_case__ ) -> List[str]:
'''simple docstring'''
requires_backends(self , ['''sentencepiece'''] )
class __snake_case ( metaclass=lowerCamelCase__ ):
__lowerCamelCase : Optional[int] = ["""sentencepiece"""]
def __init__( self , *snake_case__ , **snake_case__ ) -> Optional[Any]:
'''simple docstring'''
requires_backends(self , ['''sentencepiece'''] )
class __snake_case ( metaclass=lowerCamelCase__ ):
__lowerCamelCase : Union[str, Any] = ["""sentencepiece"""]
def __init__( self , *snake_case__ , **snake_case__ ) -> Union[str, Any]:
'''simple docstring'''
requires_backends(self , ['''sentencepiece'''] )
class __snake_case ( metaclass=lowerCamelCase__ ):
__lowerCamelCase : Optional[int] = ["""sentencepiece"""]
def __init__( self , *snake_case__ , **snake_case__ ) -> Tuple:
'''simple docstring'''
requires_backends(self , ['''sentencepiece'''] )
class __snake_case ( metaclass=lowerCamelCase__ ):
__lowerCamelCase : Tuple = ["""sentencepiece"""]
def __init__( self , *snake_case__ , **snake_case__ ) -> str:
'''simple docstring'''
requires_backends(self , ['''sentencepiece'''] )
class __snake_case ( metaclass=lowerCamelCase__ ):
__lowerCamelCase : List[Any] = ["""sentencepiece"""]
def __init__( self , *snake_case__ , **snake_case__ ) -> Union[str, Any]:
'''simple docstring'''
requires_backends(self , ['''sentencepiece'''] )
class __snake_case ( metaclass=lowerCamelCase__ ):
__lowerCamelCase : List[str] = ["""sentencepiece"""]
def __init__( self , *snake_case__ , **snake_case__ ) -> Any:
'''simple docstring'''
requires_backends(self , ['''sentencepiece'''] )
class __snake_case ( metaclass=lowerCamelCase__ ):
__lowerCamelCase : str = ["""sentencepiece"""]
def __init__( self , *snake_case__ , **snake_case__ ) -> Any:
'''simple docstring'''
requires_backends(self , ['''sentencepiece'''] )
class __snake_case ( metaclass=lowerCamelCase__ ):
__lowerCamelCase : Dict = ["""sentencepiece"""]
def __init__( self , *snake_case__ , **snake_case__ ) -> List[str]:
'''simple docstring'''
requires_backends(self , ['''sentencepiece'''] )
class __snake_case ( metaclass=lowerCamelCase__ ):
__lowerCamelCase : Any = ["""sentencepiece"""]
def __init__( self , *snake_case__ , **snake_case__ ) -> Optional[Any]:
'''simple docstring'''
requires_backends(self , ['''sentencepiece'''] )
class __snake_case ( metaclass=lowerCamelCase__ ):
__lowerCamelCase : Dict = ["""sentencepiece"""]
def __init__( self , *snake_case__ , **snake_case__ ) -> Dict:
'''simple docstring'''
requires_backends(self , ['''sentencepiece'''] )
class __snake_case ( metaclass=lowerCamelCase__ ):
__lowerCamelCase : Any = ["""sentencepiece"""]
def __init__( self , *snake_case__ , **snake_case__ ) -> Union[str, Any]:
'''simple docstring'''
requires_backends(self , ['''sentencepiece'''] )
class __snake_case ( metaclass=lowerCamelCase__ ):
__lowerCamelCase : Optional[Any] = ["""sentencepiece"""]
def __init__( self , *snake_case__ , **snake_case__ ) -> Dict:
'''simple docstring'''
requires_backends(self , ['''sentencepiece'''] )
class __snake_case ( metaclass=lowerCamelCase__ ):
__lowerCamelCase : List[str] = ["""sentencepiece"""]
def __init__( self , *snake_case__ , **snake_case__ ) -> Optional[Any]:
'''simple docstring'''
requires_backends(self , ['''sentencepiece'''] )
class __snake_case ( metaclass=lowerCamelCase__ ):
__lowerCamelCase : Any = ["""sentencepiece"""]
def __init__( self , *snake_case__ , **snake_case__ ) -> List[str]:
'''simple docstring'''
requires_backends(self , ['''sentencepiece'''] )
class __snake_case ( metaclass=lowerCamelCase__ ):
__lowerCamelCase : List[str] = ["""sentencepiece"""]
def __init__( self , *snake_case__ , **snake_case__ ) -> List[str]:
'''simple docstring'''
requires_backends(self , ['''sentencepiece'''] )
class __snake_case ( metaclass=lowerCamelCase__ ):
__lowerCamelCase : Union[str, Any] = ["""sentencepiece"""]
def __init__( self , *snake_case__ , **snake_case__ ) -> int:
'''simple docstring'''
requires_backends(self , ['''sentencepiece'''] )
class __snake_case ( metaclass=lowerCamelCase__ ):
__lowerCamelCase : Any = ["""sentencepiece"""]
def __init__( self , *snake_case__ , **snake_case__ ) -> Dict:
'''simple docstring'''
requires_backends(self , ['''sentencepiece'''] )
class __snake_case ( metaclass=lowerCamelCase__ ):
__lowerCamelCase : Optional[int] = ["""sentencepiece"""]
def __init__( self , *snake_case__ , **snake_case__ ) -> int:
'''simple docstring'''
requires_backends(self , ['''sentencepiece'''] )
class __snake_case ( metaclass=lowerCamelCase__ ):
__lowerCamelCase : List[Any] = ["""sentencepiece"""]
def __init__( self , *snake_case__ , **snake_case__ ) -> Optional[int]:
'''simple docstring'''
requires_backends(self , ['''sentencepiece'''] )
class __snake_case ( metaclass=lowerCamelCase__ ):
__lowerCamelCase : str = ["""sentencepiece"""]
def __init__( self , *snake_case__ , **snake_case__ ) -> List[Any]:
'''simple docstring'''
requires_backends(self , ['''sentencepiece'''] )
class __snake_case ( metaclass=lowerCamelCase__ ):
__lowerCamelCase : int = ["""sentencepiece"""]
def __init__( self , *snake_case__ , **snake_case__ ) -> Any:
'''simple docstring'''
requires_backends(self , ['''sentencepiece'''] )
class __snake_case ( metaclass=lowerCamelCase__ ):
__lowerCamelCase : Tuple = ["""sentencepiece"""]
def __init__( self , *snake_case__ , **snake_case__ ) -> Tuple:
'''simple docstring'''
requires_backends(self , ['''sentencepiece'''] )
class __snake_case ( metaclass=lowerCamelCase__ ):
__lowerCamelCase : Tuple = ["""sentencepiece"""]
def __init__( self , *snake_case__ , **snake_case__ ) -> Dict:
'''simple docstring'''
requires_backends(self , ['''sentencepiece'''] )
| 348 | from unittest import TestCase
from datasets import Dataset
from minhash_deduplication import deduplicate_dataset, make_duplicate_clusters
def lowerCAmelCase_ ( )-> int:
'''simple docstring'''
UpperCAmelCase : str ={
'''repo_name''': ['''test_repo1''', '''test_repo2''', '''test_repo3'''],
'''path''': ['''test_1.py''', '''test_2.py''', '''unit_test.py'''],
'''content''': ['''a ''' * 20, '''a ''' * 30, '''b ''' * 7],
}
UpperCAmelCase : Union[str, Any] =Dataset.from_dict(__lowerCAmelCase )
return dataset
class __snake_case ( lowerCamelCase__ ):
def UpperCAmelCase__ ( self ) -> Tuple:
'''simple docstring'''
UpperCAmelCase : List[str] =get_dataset()
UpperCAmelCase : Optional[int] =make_duplicate_clusters(snake_case__ , 0.85 )
self.assertEqual(len(duplicate_clusters[0] ) , 2 )
def UpperCAmelCase__ ( self ) -> Tuple:
'''simple docstring'''
UpperCAmelCase : str =get_dataset()
UpperCAmelCase , UpperCAmelCase : Tuple =deduplicate_dataset(snake_case__ )
self.assertEqual(len(snake_case__ ) , 2 )
print(snake_case__ )
self.assertEqual(duplicate_clusters[0][0]['''copies'''] , 2 )
self.assertEqual(duplicate_clusters[0][0]['''is_extreme'''] , snake_case__ )
| 348 | 1 |
import numpy as np
import pandas as pd
from sklearn.preprocessing import Normalizer
from sklearn.svm import SVR
from statsmodels.tsa.statespace.sarimax import SARIMAX
def lowerCAmelCase_ ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )-> float:
'''simple docstring'''
UpperCAmelCase : Union[str, Any] =np.array([[1, item, train_mtch[i]] for i, item in enumerate(__lowerCAmelCase )] )
UpperCAmelCase : Any =np.array(__lowerCAmelCase )
UpperCAmelCase : Dict =np.dot(np.dot(np.linalg.inv(np.dot(x.transpose() , __lowerCAmelCase ) ) , x.transpose() ) , __lowerCAmelCase )
return abs(beta[0] + test_dt[0] * beta[1] + test_mtch[0] + beta[2] )
def lowerCAmelCase_ ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )-> float:
'''simple docstring'''
UpperCAmelCase : Optional[Any] =(1, 2, 1)
UpperCAmelCase : Optional[int] =(1, 1, 0, 7)
UpperCAmelCase : int =SARIMAX(
__lowerCAmelCase , exog=__lowerCAmelCase , order=__lowerCAmelCase , seasonal_order=__lowerCAmelCase )
UpperCAmelCase : Union[str, Any] =model.fit(disp=__lowerCAmelCase , maxiter=6_00 , method='''nm''' )
UpperCAmelCase : Any =model_fit.predict(1 , len(__lowerCAmelCase ) , exog=[test_match] )
return result[0]
def lowerCAmelCase_ ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )-> float:
'''simple docstring'''
UpperCAmelCase : Union[str, Any] =SVR(kernel='''rbf''' , C=1 , gamma=0.1 , epsilon=0.1 )
regressor.fit(__lowerCAmelCase , __lowerCAmelCase )
UpperCAmelCase : Dict =regressor.predict(__lowerCAmelCase )
return y_pred[0]
def lowerCAmelCase_ ( __lowerCAmelCase )-> float:
'''simple docstring'''
train_user.sort()
UpperCAmelCase : Any =np.percentile(__lowerCAmelCase , 25 )
UpperCAmelCase : Union[str, Any] =np.percentile(__lowerCAmelCase , 75 )
UpperCAmelCase : Dict =qa - qa
UpperCAmelCase : Union[str, Any] =qa - (iqr * 0.1)
return low_lim
def lowerCAmelCase_ ( __lowerCAmelCase , __lowerCAmelCase )-> bool:
'''simple docstring'''
UpperCAmelCase : Union[str, Any] =0
UpperCAmelCase : Dict =0
for i in list_vote:
if i > actual_result:
UpperCAmelCase : Dict =not_safe + 1
else:
if abs(abs(__lowerCAmelCase ) - abs(__lowerCAmelCase ) ) <= 0.1:
safe += 1
else:
not_safe += 1
return safe > not_safe
if __name__ == "__main__":
# data_input_df = pd.read_csv("ex_data.csv", header=None)
__snake_case = [[1_82_31, 0.0, 1], [2_26_21, 1.0, 2], [1_56_75, 0.0, 3], [2_35_83, 1.0, 4]]
__snake_case = pd.DataFrame(
data_input, columns=['''total_user''', '''total_even''', '''days''']
)
__snake_case = Normalizer().fit_transform(data_input_df.values)
# split data
__snake_case = normalize_df[:, 2].tolist()
__snake_case = normalize_df[:, 0].tolist()
__snake_case = normalize_df[:, 1].tolist()
# for svr (input variable = total date and total match)
__snake_case = normalize_df[:, [1, 2]].tolist()
__snake_case = x[: len(x) - 1]
__snake_case = x[len(x) - 1 :]
# for linear regression & sarimax
__snake_case = total_date[: len(total_date) - 1]
__snake_case = total_user[: len(total_user) - 1]
__snake_case = total_match[: len(total_match) - 1]
__snake_case = total_date[len(total_date) - 1 :]
__snake_case = total_user[len(total_user) - 1 :]
__snake_case = total_match[len(total_match) - 1 :]
# voting system with forecasting
__snake_case = [
linear_regression_prediction(
trn_date, trn_user, trn_match, tst_date, tst_match
),
sarimax_predictor(trn_user, trn_match, tst_match),
support_vector_regressor(x_train, x_test, trn_user),
]
# check the safety of today's data
__snake_case = '''''' if data_safety_checker(res_vote, tst_user) else '''not '''
print('''Today\'s data is {not_str}safe.''')
| 348 | from typing import Callable, List, Optional, Tuple, Union
import torch
from transformers import CLIPTextModel, CLIPTokenizer
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin, TransformeraDModel, VQModel
from ...schedulers import VQDiffusionScheduler
from ...utils import logging
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
__snake_case = logging.get_logger(__name__) # pylint: disable=invalid-name
class __snake_case ( lowerCamelCase__ , lowerCamelCase__ ):
@register_to_config
def __init__( self , snake_case__ , snake_case__ = None , snake_case__ = None ) -> str:
'''simple docstring'''
super().__init__()
UpperCAmelCase : Optional[Any] =learnable
if self.learnable:
assert hidden_size is not None, "learnable=True requires `hidden_size` to be set"
assert length is not None, "learnable=True requires `length` to be set"
UpperCAmelCase : Any =torch.zeros(snake_case__ , snake_case__ )
else:
UpperCAmelCase : Union[str, Any] =None
UpperCAmelCase : Optional[int] =torch.nn.Parameter(snake_case__ )
class __snake_case ( lowerCamelCase__ ):
__lowerCamelCase : VQModel
__lowerCamelCase : CLIPTextModel
__lowerCamelCase : CLIPTokenizer
__lowerCamelCase : TransformeraDModel
__lowerCamelCase : LearnedClassifierFreeSamplingEmbeddings
__lowerCamelCase : VQDiffusionScheduler
def __init__( self , snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ , ) -> int:
'''simple docstring'''
super().__init__()
self.register_modules(
vqvae=snake_case__ , transformer=snake_case__ , text_encoder=snake_case__ , tokenizer=snake_case__ , scheduler=snake_case__ , learned_classifier_free_sampling_embeddings=snake_case__ , )
def UpperCAmelCase__ ( self , snake_case__ , snake_case__ , snake_case__ ) -> Optional[int]:
'''simple docstring'''
UpperCAmelCase : int =len(snake_case__ ) if isinstance(snake_case__ , snake_case__ ) else 1
# get prompt text embeddings
UpperCAmelCase : Optional[int] =self.tokenizer(
snake_case__ , padding='''max_length''' , max_length=self.tokenizer.model_max_length , return_tensors='''pt''' , )
UpperCAmelCase : int =text_inputs.input_ids
if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
UpperCAmelCase : List[str] =self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :] )
logger.warning(
'''The following part of your input was truncated because CLIP can only handle sequences up to'''
f''' {self.tokenizer.model_max_length} tokens: {removed_text}''' )
UpperCAmelCase : Optional[Any] =text_input_ids[:, : self.tokenizer.model_max_length]
UpperCAmelCase : List[Any] =self.text_encoder(text_input_ids.to(self.device ) )[0]
# NOTE: This additional step of normalizing the text embeddings is from VQ-Diffusion.
# While CLIP does normalize the pooled output of the text transformer when combining
# the image and text embeddings, CLIP does not directly normalize the last hidden state.
#
# CLIP normalizing the pooled output.
# https://github.com/huggingface/transformers/blob/d92e22d1f28324f513f3080e5c47c071a3916721/src/transformers/models/clip/modeling_clip.py#L1052-L1053
UpperCAmelCase : int =prompt_embeds / prompt_embeds.norm(dim=-1 , keepdim=snake_case__ )
# duplicate text embeddings for each generation per prompt
UpperCAmelCase : int =prompt_embeds.repeat_interleave(snake_case__ , dim=0 )
if do_classifier_free_guidance:
if self.learned_classifier_free_sampling_embeddings.learnable:
UpperCAmelCase : Optional[int] =self.learned_classifier_free_sampling_embeddings.embeddings
UpperCAmelCase : str =negative_prompt_embeds.unsqueeze(0 ).repeat(snake_case__ , 1 , 1 )
else:
UpperCAmelCase : str =[''''''] * batch_size
UpperCAmelCase : Tuple =text_input_ids.shape[-1]
UpperCAmelCase : Optional[Any] =self.tokenizer(
snake_case__ , padding='''max_length''' , max_length=snake_case__ , truncation=snake_case__ , return_tensors='''pt''' , )
UpperCAmelCase : Optional[Any] =self.text_encoder(uncond_input.input_ids.to(self.device ) )[0]
# See comment for normalizing text embeddings
UpperCAmelCase : Optional[int] =negative_prompt_embeds / negative_prompt_embeds.norm(dim=-1 , keepdim=snake_case__ )
# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
UpperCAmelCase : Optional[Any] =negative_prompt_embeds.shape[1]
UpperCAmelCase : Union[str, Any] =negative_prompt_embeds.repeat(1 , snake_case__ , 1 )
UpperCAmelCase : Optional[Any] =negative_prompt_embeds.view(batch_size * num_images_per_prompt , snake_case__ , -1 )
# For classifier free guidance, we need to do two forward passes.
# Here we concatenate the unconditional and text embeddings into a single batch
# to avoid doing two forward passes
UpperCAmelCase : int =torch.cat([negative_prompt_embeds, prompt_embeds] )
return prompt_embeds
@torch.no_grad()
def __call__( self , snake_case__ , snake_case__ = 100 , snake_case__ = 5.0 , snake_case__ = 1.0 , snake_case__ = 1 , snake_case__ = None , snake_case__ = None , snake_case__ = "pil" , snake_case__ = True , snake_case__ = None , snake_case__ = 1 , ) -> Union[ImagePipelineOutput, Tuple]:
'''simple docstring'''
if isinstance(snake_case__ , snake_case__ ):
UpperCAmelCase : Optional[int] =1
elif isinstance(snake_case__ , snake_case__ ):
UpperCAmelCase : Tuple =len(snake_case__ )
else:
raise ValueError(f'''`prompt` has to be of type `str` or `list` but is {type(snake_case__ )}''' )
UpperCAmelCase : Tuple =batch_size * num_images_per_prompt
UpperCAmelCase : List[str] =guidance_scale > 1.0
UpperCAmelCase : List[Any] =self._encode_prompt(snake_case__ , snake_case__ , snake_case__ )
if (callback_steps is None) or (
callback_steps is not None and (not isinstance(snake_case__ , snake_case__ ) or callback_steps <= 0)
):
raise ValueError(
f'''`callback_steps` has to be a positive integer but is {callback_steps} of type'''
f''' {type(snake_case__ )}.''' )
# get the initial completely masked latents unless the user supplied it
UpperCAmelCase : int =(batch_size, self.transformer.num_latent_pixels)
if latents is None:
UpperCAmelCase : Union[str, Any] =self.transformer.num_vector_embeds - 1
UpperCAmelCase : str =torch.full(snake_case__ , snake_case__ ).to(self.device )
else:
if latents.shape != latents_shape:
raise ValueError(f'''Unexpected latents shape, got {latents.shape}, expected {latents_shape}''' )
if (latents < 0).any() or (latents >= self.transformer.num_vector_embeds).any():
raise ValueError(
'''Unexpected latents value(s). All latents be valid embedding indices i.e. in the range 0,'''
f''' {self.transformer.num_vector_embeds - 1} (inclusive).''' )
UpperCAmelCase : Any =latents.to(self.device )
# set timesteps
self.scheduler.set_timesteps(snake_case__ , device=self.device )
UpperCAmelCase : Any =self.scheduler.timesteps.to(self.device )
UpperCAmelCase : Optional[int] =latents
for i, t in enumerate(self.progress_bar(snake_case__ ) ):
# expand the sample if we are doing classifier free guidance
UpperCAmelCase : Optional[Any] =torch.cat([sample] * 2 ) if do_classifier_free_guidance else sample
# predict the un-noised image
# model_output == `log_p_x_0`
UpperCAmelCase : Optional[int] =self.transformer(snake_case__ , encoder_hidden_states=snake_case__ , timestep=snake_case__ ).sample
if do_classifier_free_guidance:
UpperCAmelCase , UpperCAmelCase : str =model_output.chunk(2 )
UpperCAmelCase : Optional[int] =model_output_uncond + guidance_scale * (model_output_text - model_output_uncond)
model_output -= torch.logsumexp(snake_case__ , dim=1 , keepdim=snake_case__ )
UpperCAmelCase : Tuple =self.truncate(snake_case__ , snake_case__ )
# remove `log(0)`'s (`-inf`s)
UpperCAmelCase : Optional[Any] =model_output.clamp(-70 )
# compute the previous noisy sample x_t -> x_t-1
UpperCAmelCase : int =self.scheduler.step(snake_case__ , timestep=snake_case__ , sample=snake_case__ , generator=snake_case__ ).prev_sample
# call the callback, if provided
if callback is not None and i % callback_steps == 0:
callback(snake_case__ , snake_case__ , snake_case__ )
UpperCAmelCase : Optional[int] =self.vqvae.config.vq_embed_dim
UpperCAmelCase : Optional[Any] =(batch_size, self.transformer.height, self.transformer.width, embedding_channels)
UpperCAmelCase : Dict =self.vqvae.quantize.get_codebook_entry(snake_case__ , shape=snake_case__ )
UpperCAmelCase : Tuple =self.vqvae.decode(snake_case__ , force_not_quantize=snake_case__ ).sample
UpperCAmelCase : Union[str, Any] =(image / 2 + 0.5).clamp(0 , 1 )
UpperCAmelCase : Any =image.cpu().permute(0 , 2 , 3 , 1 ).numpy()
if output_type == "pil":
UpperCAmelCase : List[str] =self.numpy_to_pil(snake_case__ )
if not return_dict:
return (image,)
return ImagePipelineOutput(images=snake_case__ )
def UpperCAmelCase__ ( self , snake_case__ , snake_case__ ) -> torch.FloatTensor:
'''simple docstring'''
UpperCAmelCase , UpperCAmelCase : int =torch.sort(snake_case__ , 1 , descending=snake_case__ )
UpperCAmelCase : Union[str, Any] =torch.exp(snake_case__ )
UpperCAmelCase : Union[str, Any] =sorted_p_x_0.cumsum(dim=1 ) < truncation_rate
# Ensure that at least the largest probability is not zeroed out
UpperCAmelCase : Optional[Any] =torch.full_like(keep_mask[:, 0:1, :] , snake_case__ )
UpperCAmelCase : Tuple =torch.cat((all_true, keep_mask) , dim=1 )
UpperCAmelCase : int =keep_mask[:, :-1, :]
UpperCAmelCase : int =keep_mask.gather(1 , indices.argsort(1 ) )
UpperCAmelCase : Dict =log_p_x_0.clone()
UpperCAmelCase : List[Any] =-torch.inf # -inf = log(0)
return rv
| 348 | 1 |
import pickle
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, XGLMTokenizer, XGLMTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
__snake_case = get_tests_dir('''fixtures/test_sentencepiece.model''')
@require_sentencepiece
@require_tokenizers
class __snake_case ( lowerCamelCase__ , unittest.TestCase ):
__lowerCamelCase : Union[str, Any] = XGLMTokenizer
__lowerCamelCase : Dict = XGLMTokenizerFast
__lowerCamelCase : List[Any] = True
__lowerCamelCase : List[Any] = True
def UpperCAmelCase__ ( self ) -> Union[str, Any]:
'''simple docstring'''
super().setUp()
# We have a SentencePiece fixture for testing
UpperCAmelCase : str =XGLMTokenizer(snake_case__ , keep_accents=snake_case__ )
tokenizer.save_pretrained(self.tmpdirname )
def UpperCAmelCase__ ( self ) -> Union[str, Any]:
'''simple docstring'''
UpperCAmelCase : Dict ='''<pad>'''
UpperCAmelCase : int =1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(snake_case__ ) , snake_case__ )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(snake_case__ ) , snake_case__ )
def UpperCAmelCase__ ( self ) -> Optional[Any]:
'''simple docstring'''
UpperCAmelCase : List[str] =list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] , '''<s>''' )
self.assertEqual(vocab_keys[1] , '''<pad>''' )
self.assertEqual(len(snake_case__ ) , 1008 )
def UpperCAmelCase__ ( self ) -> Union[str, Any]:
'''simple docstring'''
self.assertEqual(self.get_tokenizer().vocab_size , 1008 )
def UpperCAmelCase__ ( self ) -> str:
'''simple docstring'''
UpperCAmelCase : int =XGLMTokenizer(snake_case__ , keep_accents=snake_case__ )
UpperCAmelCase : Optional[Any] =tokenizer.tokenize('''This is a test''' )
self.assertListEqual(snake_case__ , ['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est'''] )
self.assertListEqual(
tokenizer.convert_tokens_to_ids(snake_case__ ) , [value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]] , )
UpperCAmelCase : Dict =tokenizer.tokenize('''I was born in 92000, and this is falsé.''' )
self.assertListEqual(
snake_case__ , [
SPIECE_UNDERLINE + '''I''',
SPIECE_UNDERLINE + '''was''',
SPIECE_UNDERLINE + '''b''',
'''or''',
'''n''',
SPIECE_UNDERLINE + '''in''',
SPIECE_UNDERLINE + '''''',
'''9''',
'''2''',
'''0''',
'''0''',
'''0''',
''',''',
SPIECE_UNDERLINE + '''and''',
SPIECE_UNDERLINE + '''this''',
SPIECE_UNDERLINE + '''is''',
SPIECE_UNDERLINE + '''f''',
'''al''',
'''s''',
'''é''',
'''.''',
] , )
UpperCAmelCase : Dict =tokenizer.convert_tokens_to_ids(snake_case__ )
self.assertListEqual(
snake_case__ , [
value + tokenizer.fairseq_offset
for value in [8, 21, 84, 55, 24, 19, 7, 2, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 2, 4]
] , )
UpperCAmelCase : int =tokenizer.convert_ids_to_tokens(snake_case__ )
self.assertListEqual(
snake_case__ , [
SPIECE_UNDERLINE + '''I''',
SPIECE_UNDERLINE + '''was''',
SPIECE_UNDERLINE + '''b''',
'''or''',
'''n''',
SPIECE_UNDERLINE + '''in''',
SPIECE_UNDERLINE + '''''',
'''<unk>''',
'''2''',
'''0''',
'''0''',
'''0''',
''',''',
SPIECE_UNDERLINE + '''and''',
SPIECE_UNDERLINE + '''this''',
SPIECE_UNDERLINE + '''is''',
SPIECE_UNDERLINE + '''f''',
'''al''',
'''s''',
'''<unk>''',
'''.''',
] , )
@cached_property
def UpperCAmelCase__ ( self ) -> List[Any]:
'''simple docstring'''
return XGLMTokenizer.from_pretrained('''facebook/xglm-564M''' )
def UpperCAmelCase__ ( self ) -> List[str]:
'''simple docstring'''
with tempfile.NamedTemporaryFile() as f:
shutil.copyfile(snake_case__ , f.name )
UpperCAmelCase : str =XGLMTokenizer(f.name , keep_accents=snake_case__ )
UpperCAmelCase : Any =pickle.dumps(snake_case__ )
pickle.loads(snake_case__ )
def UpperCAmelCase__ ( self ) -> Any:
'''simple docstring'''
if not self.test_rust_tokenizer:
return
UpperCAmelCase : Dict =self.get_tokenizer()
UpperCAmelCase : Optional[Any] =self.get_rust_tokenizer()
UpperCAmelCase : Any ='''I was born in 92000, and this is falsé.'''
UpperCAmelCase : Optional[int] =tokenizer.tokenize(snake_case__ )
UpperCAmelCase : Optional[int] =rust_tokenizer.tokenize(snake_case__ )
self.assertListEqual(snake_case__ , snake_case__ )
UpperCAmelCase : Tuple =tokenizer.encode(snake_case__ , add_special_tokens=snake_case__ )
UpperCAmelCase : int =rust_tokenizer.encode(snake_case__ , add_special_tokens=snake_case__ )
self.assertListEqual(snake_case__ , snake_case__ )
UpperCAmelCase : Any =self.get_rust_tokenizer()
UpperCAmelCase : Union[str, Any] =tokenizer.encode(snake_case__ )
UpperCAmelCase : Optional[Any] =rust_tokenizer.encode(snake_case__ )
self.assertListEqual(snake_case__ , snake_case__ )
@slow
def UpperCAmelCase__ ( self ) -> Dict:
'''simple docstring'''
UpperCAmelCase : List[str] ='''Hello World!'''
UpperCAmelCase : Tuple =[2, 3_1227, 4447, 35]
self.assertListEqual(snake_case__ , self.big_tokenizer.encode(snake_case__ ) )
@slow
def UpperCAmelCase__ ( self ) -> Any:
'''simple docstring'''
UpperCAmelCase : Optional[Any] =(
'''This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) " [ ] ! : - . Also we will'''
''' add words that should not exsist and be tokenized to unk, such as saoneuhaoesuth'''
)
# fmt: off
UpperCAmelCase : Optional[Any] =[2, 1018, 67, 11, 1988, 2617, 5631, 278, 11, 3407, 48, 7_1630, 2_8085, 4, 3234, 157, 13, 6, 5, 6, 4, 3526, 768, 15, 659, 57, 298, 3983, 864, 129, 21, 6, 5, 1_3675, 377, 652, 7580, 1_0341, 155, 2817, 422, 1666, 7, 1674, 53, 113, 20_2277, 1_7892, 33, 60, 87, 4, 3234, 157, 61, 2667, 5_2376, 19, 88, 23, 735]
# fmt: on
self.assertListEqual(snake_case__ , self.big_tokenizer.encode(snake_case__ ) )
@slow
def UpperCAmelCase__ ( self ) -> Tuple:
'''simple docstring'''
UpperCAmelCase : Any ={
'''input_ids''': [[2, 10_8825, 1163, 15, 8_8010, 473, 1_5898, 157, 1_3672, 1857, 312, 8, 23_8021, 1163, 53, 1_3672, 1857, 312, 8, 5_3283, 18_2396, 8, 1_8566, 16, 3_6733, 4101, 8, 230, 24_4017, 12_2553, 7, 15, 13_2597, 4, 293, 1_2511, 7610, 4, 3414, 13_2597, 9, 4, 3_2361, 362, 4, 734, 2_8512, 3_2569, 18, 4, 3_2361, 2_6096, 1_4982, 73, 1_8715, 2_1433, 23_5261, 15, 492, 1_2427, 16, 53, 1_8715, 2_1433, 6_5454, 15, 2_3659, 563, 16, 278, 597, 2843, 595, 7931, 18_2396, 6_4186, 22, 886, 595, 13_2981, 53, 2_5540, 3449, 4_3982, 3_9901, 5951, 878, 330, 4, 2_7694, 8_0269, 312, 53, 6517, 1_1780, 611, 2_0408, 5], [2, 6, 13_2597, 67, 4_2897, 33, 592, 8, 16_3729, 2_5540, 361, 13_6997, 10_9514, 17_3230, 7, 501, 60, 10_2913, 196, 5631, 235, 6_3243, 473, 6, 23_1757, 74, 5277, 7905, 53, 3095, 3_7317, 22, 454, 18_3874, 5], [2, 268, 3_1298, 4_6530, 6, 13_2935, 4_3831, 7, 597, 32, 24, 3688, 9865, 5]],
'''attention_mask''': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]
} # noqa: E501
# fmt: on
self.tokenizer_integration_test_util(
expected_encoding=snake_case__ , model_name='''facebook/xglm-564M''' , padding=snake_case__ , )
| 348 | import unittest
import numpy as np
import torch
from diffusers import KarrasVePipeline, KarrasVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class __snake_case ( unittest.TestCase ):
@property
def UpperCAmelCase__ ( self ) -> int:
'''simple docstring'''
torch.manual_seed(0 )
UpperCAmelCase : Any =UNetaDModel(
block_out_channels=(32, 64) , layers_per_block=2 , sample_size=32 , in_channels=3 , out_channels=3 , down_block_types=('''DownBlock2D''', '''AttnDownBlock2D''') , up_block_types=('''AttnUpBlock2D''', '''UpBlock2D''') , )
return model
def UpperCAmelCase__ ( self ) -> Union[str, Any]:
'''simple docstring'''
UpperCAmelCase : Tuple =self.dummy_uncond_unet
UpperCAmelCase : Optional[int] =KarrasVeScheduler()
UpperCAmelCase : List[Any] =KarrasVePipeline(unet=snake_case__ , scheduler=snake_case__ )
pipe.to(snake_case__ )
pipe.set_progress_bar_config(disable=snake_case__ )
UpperCAmelCase : List[str] =torch.manual_seed(0 )
UpperCAmelCase : List[str] =pipe(num_inference_steps=2 , generator=snake_case__ , output_type='''numpy''' ).images
UpperCAmelCase : str =torch.manual_seed(0 )
UpperCAmelCase : str =pipe(num_inference_steps=2 , generator=snake_case__ , output_type='''numpy''' , return_dict=snake_case__ )[0]
UpperCAmelCase : Any =image[0, -3:, -3:, -1]
UpperCAmelCase : List[str] =image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 32, 32, 3)
UpperCAmelCase : int =np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1e-2
@slow
@require_torch
class __snake_case ( unittest.TestCase ):
def UpperCAmelCase__ ( self ) -> List[Any]:
'''simple docstring'''
UpperCAmelCase : Tuple ='''google/ncsnpp-celebahq-256'''
UpperCAmelCase : int =UNetaDModel.from_pretrained(snake_case__ )
UpperCAmelCase : Dict =KarrasVeScheduler()
UpperCAmelCase : Union[str, Any] =KarrasVePipeline(unet=snake_case__ , scheduler=snake_case__ )
pipe.to(snake_case__ )
pipe.set_progress_bar_config(disable=snake_case__ )
UpperCAmelCase : Any =torch.manual_seed(0 )
UpperCAmelCase : Tuple =pipe(num_inference_steps=20 , generator=snake_case__ , output_type='''numpy''' ).images
UpperCAmelCase : Optional[int] =image[0, -3:, -3:, -1]
assert image.shape == (1, 256, 256, 3)
UpperCAmelCase : Tuple =np.array([0.578, 0.5811, 0.5924, 0.5809, 0.587, 0.5886, 0.5861, 0.5802, 0.586] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
| 348 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__snake_case = logging.get_logger(__name__)
__snake_case = {
'''facebook/nllb-moe-54B''': '''https://huggingface.co/facebook/nllb-moe-54b/resolve/main/config.json''',
}
class __snake_case ( lowerCamelCase__ ):
__lowerCamelCase : Optional[Any] = """nllb-moe"""
__lowerCamelCase : Tuple = ["""past_key_values"""]
__lowerCamelCase : Union[str, Any] = {"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}
def __init__( self , snake_case__=12_8112 , snake_case__=1024 , snake_case__=12 , snake_case__=4096 , snake_case__=16 , snake_case__=12 , snake_case__=4096 , snake_case__=16 , snake_case__=0.05 , snake_case__=0.05 , snake_case__=True , snake_case__=True , snake_case__="relu" , snake_case__=1024 , snake_case__=0.1 , snake_case__=0.1 , snake_case__=0.0 , snake_case__=0.02 , snake_case__=2 , snake_case__=True , snake_case__=False , snake_case__="float32" , snake_case__=False , snake_case__=128 , snake_case__=64 , snake_case__=4 , snake_case__=4 , snake_case__=0.001 , snake_case__=0.001 , snake_case__="all" , snake_case__=False , snake_case__=False , snake_case__=1.0 , snake_case__=0.2 , snake_case__=1 , snake_case__=0 , snake_case__=2 , snake_case__=False , **snake_case__ , ) -> List[str]:
'''simple docstring'''
UpperCAmelCase : Union[str, Any] =vocab_size
UpperCAmelCase : str =max_position_embeddings
UpperCAmelCase : str =d_model
UpperCAmelCase : Tuple =encoder_ffn_dim
UpperCAmelCase : Tuple =encoder_layers
UpperCAmelCase : Dict =encoder_attention_heads
UpperCAmelCase : Dict =decoder_ffn_dim
UpperCAmelCase : Any =decoder_layers
UpperCAmelCase : Optional[Any] =decoder_attention_heads
UpperCAmelCase : List[str] =dropout
UpperCAmelCase : List[str] =attention_dropout
UpperCAmelCase : Union[str, Any] =activation_dropout
UpperCAmelCase : Any =activation_function
UpperCAmelCase : Tuple =init_std
UpperCAmelCase : Tuple =encoder_layerdrop
UpperCAmelCase : Union[str, Any] =decoder_layerdrop
UpperCAmelCase : Any =use_cache
UpperCAmelCase : Optional[int] =encoder_layers
UpperCAmelCase : Optional[Any] =scale_embedding # scale factor will be sqrt(d_model) if True
UpperCAmelCase : str =router_z_loss_coef
UpperCAmelCase : str =router_aux_loss_coef
UpperCAmelCase : Tuple =decoder_sparse_step
UpperCAmelCase : str =encoder_sparse_step
UpperCAmelCase : Optional[Any] =num_experts
UpperCAmelCase : Optional[int] =expert_capacity
UpperCAmelCase : Optional[int] =router_bias
if router_dtype not in ["float32", "float16", "bfloat16"]:
raise ValueError(f'''`router_dtype` must be one of \'float32\', \'float16\' or \'bfloat16\', got {router_dtype}''' )
UpperCAmelCase : Dict =router_dtype
UpperCAmelCase : Optional[Any] =router_ignore_padding_tokens
UpperCAmelCase : str =batch_prioritized_routing
UpperCAmelCase : Tuple =second_expert_policy
UpperCAmelCase : Any =normalize_router_prob_before_dropping
UpperCAmelCase : Union[str, Any] =moe_eval_capacity_token_fraction
UpperCAmelCase : Union[str, Any] =moe_token_dropout
UpperCAmelCase : int =output_router_logits
super().__init__(
pad_token_id=snake_case__ , bos_token_id=snake_case__ , eos_token_id=snake_case__ , is_encoder_decoder=snake_case__ , decoder_start_token_id=snake_case__ , **snake_case__ , )
| 348 | import qiskit
def lowerCAmelCase_ ( __lowerCAmelCase , __lowerCAmelCase )-> qiskit.result.counts.Counts:
'''simple docstring'''
UpperCAmelCase : Union[str, Any] =qiskit.Aer.get_backend('''aer_simulator''' )
UpperCAmelCase : List[str] =qiskit.QuantumCircuit(4 , 2 )
# encode inputs in qubits 0 and 1
if bita == 1:
qc_ha.x(0 )
if bita == 1:
qc_ha.x(1 )
qc_ha.barrier()
# use cnots to write XOR of the inputs on qubit2
qc_ha.cx(0 , 2 )
qc_ha.cx(1 , 2 )
# use ccx / toffoli gate to write AND of the inputs on qubit3
qc_ha.ccx(0 , 1 , 3 )
qc_ha.barrier()
# extract outputs
qc_ha.measure(2 , 0 ) # extract XOR value
qc_ha.measure(3 , 1 ) # extract AND value
# Execute the circuit on the qasm simulator
UpperCAmelCase : Dict =qiskit.execute(__lowerCAmelCase , __lowerCAmelCase , shots=10_00 )
# Return the histogram data of the results of the experiment
return job.result().get_counts(__lowerCAmelCase )
if __name__ == "__main__":
__snake_case = half_adder(1, 1)
print(f'Half Adder Output Qubit Counts: {counts}')
| 348 | 1 |
import math
def lowerCAmelCase_ ( __lowerCAmelCase , __lowerCAmelCase )-> str:
'''simple docstring'''
if 0 not in (x, y):
# We use the relation x^y = y*log10(x), where 10 is the base.
return y * math.logaa(__lowerCAmelCase )
else:
if x == 0: # 0 raised to any number is 0
return 0
elif y == 0:
return 1 # any number raised to 0 is 1
raise AssertionError('''This should never happen''' )
if __name__ == "__main__": # Main function
# Read two numbers from input and typecast them to int using map function.
# Here x is the base and y is the power.
__snake_case = '''Enter the base and the power separated by a comma: '''
__snake_case , __snake_case = map(int, input(prompt).split(''','''))
__snake_case , __snake_case = map(int, input(prompt).split(''','''))
# We find the log of each number, using the function res(), which takes two
# arguments.
__snake_case = res(xa, ya)
__snake_case = res(xa, ya)
# We check for the largest number
if resa > resa:
print('''Largest number is''', xa, '''^''', ya)
elif resa > resa:
print('''Largest number is''', xa, '''^''', ya)
else:
print('''Both are equal''')
| 348 | from __future__ import annotations
import unittest
from transformers import BlenderbotConfig, BlenderbotTokenizer, is_tf_available
from transformers.testing_utils import require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import TFAutoModelForSeqaSeqLM, TFBlenderbotForConditionalGeneration, TFBlenderbotModel
@require_tf
class __snake_case :
__lowerCamelCase : str = BlenderbotConfig
__lowerCamelCase : Optional[Any] = {}
__lowerCamelCase : Optional[int] = """gelu"""
def __init__( self , snake_case__ , snake_case__=13 , snake_case__=7 , snake_case__=True , snake_case__=False , snake_case__=99 , snake_case__=32 , snake_case__=2 , snake_case__=4 , snake_case__=37 , snake_case__=0.1 , snake_case__=0.1 , snake_case__=20 , snake_case__=2 , snake_case__=1 , snake_case__=0 , ) -> Optional[Any]:
'''simple docstring'''
UpperCAmelCase : Union[str, Any] =parent
UpperCAmelCase : Optional[int] =batch_size
UpperCAmelCase : Dict =seq_length
UpperCAmelCase : Optional[Any] =is_training
UpperCAmelCase : List[str] =use_labels
UpperCAmelCase : List[Any] =vocab_size
UpperCAmelCase : Optional[int] =hidden_size
UpperCAmelCase : Tuple =num_hidden_layers
UpperCAmelCase : Any =num_attention_heads
UpperCAmelCase : Optional[int] =intermediate_size
UpperCAmelCase : str =hidden_dropout_prob
UpperCAmelCase : Optional[int] =attention_probs_dropout_prob
UpperCAmelCase : str =max_position_embeddings
UpperCAmelCase : List[Any] =eos_token_id
UpperCAmelCase : Optional[int] =pad_token_id
UpperCAmelCase : Tuple =bos_token_id
def UpperCAmelCase__ ( self ) -> Optional[Any]:
'''simple docstring'''
UpperCAmelCase : List[Any] =ids_tensor([self.batch_size, self.seq_length - 1] , self.vocab_size )
UpperCAmelCase : List[Any] =tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size ) , 1 )
UpperCAmelCase : Tuple =tf.concat([input_ids, eos_tensor] , axis=1 )
UpperCAmelCase : str =ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
UpperCAmelCase : Optional[Any] =self.config_cls(
vocab_size=self.vocab_size , d_model=self.hidden_size , encoder_layers=self.num_hidden_layers , decoder_layers=self.num_hidden_layers , encoder_attention_heads=self.num_attention_heads , decoder_attention_heads=self.num_attention_heads , encoder_ffn_dim=self.intermediate_size , decoder_ffn_dim=self.intermediate_size , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , eos_token_ids=[2] , bos_token_id=self.bos_token_id , pad_token_id=self.pad_token_id , decoder_start_token_id=self.pad_token_id , **self.config_updates , )
UpperCAmelCase : List[str] =prepare_blenderbot_inputs_dict(snake_case__ , snake_case__ , snake_case__ )
return config, inputs_dict
def UpperCAmelCase__ ( self , snake_case__ , snake_case__ ) -> int:
'''simple docstring'''
UpperCAmelCase : Union[str, Any] =TFBlenderbotModel(config=snake_case__ ).get_decoder()
UpperCAmelCase : Any =inputs_dict['''input_ids''']
UpperCAmelCase : str =input_ids[:1, :]
UpperCAmelCase : Tuple =inputs_dict['''attention_mask'''][:1, :]
UpperCAmelCase : Tuple =inputs_dict['''head_mask''']
UpperCAmelCase : List[Any] =1
# first forward pass
UpperCAmelCase : List[str] =model(snake_case__ , attention_mask=snake_case__ , head_mask=snake_case__ , use_cache=snake_case__ )
UpperCAmelCase , UpperCAmelCase : str =outputs.to_tuple()
# create hypothetical next token and extent to next_input_ids
UpperCAmelCase : Union[str, Any] =ids_tensor((self.batch_size, 3) , config.vocab_size )
UpperCAmelCase : List[Any] =tf.cast(ids_tensor((self.batch_size, 3) , 2 ) , tf.inta )
# append to next input_ids and
UpperCAmelCase : Tuple =tf.concat([input_ids, next_tokens] , axis=-1 )
UpperCAmelCase : int =tf.concat([attention_mask, next_attn_mask] , axis=-1 )
UpperCAmelCase : Optional[int] =model(snake_case__ , attention_mask=snake_case__ )[0]
UpperCAmelCase : str =model(snake_case__ , attention_mask=snake_case__ , past_key_values=snake_case__ )[0]
self.parent.assertEqual(next_tokens.shape[1] , output_from_past.shape[1] )
# select random slice
UpperCAmelCase : List[Any] =int(ids_tensor((1,) , output_from_past.shape[-1] ) )
UpperCAmelCase : List[Any] =output_from_no_past[:, -3:, random_slice_idx]
UpperCAmelCase : Dict =output_from_past[:, :, random_slice_idx]
# test that outputs are equal for slice
tf.debugging.assert_near(snake_case__ , snake_case__ , rtol=1e-3 )
def lowerCAmelCase_ ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase=None , __lowerCAmelCase=None , __lowerCAmelCase=None , __lowerCAmelCase=None , __lowerCAmelCase=None , )-> str:
'''simple docstring'''
if attention_mask is None:
UpperCAmelCase : int =tf.cast(tf.math.not_equal(__lowerCAmelCase , config.pad_token_id ) , tf.inta )
if decoder_attention_mask is None:
UpperCAmelCase : Tuple =tf.concat(
[
tf.ones(decoder_input_ids[:, :1].shape , dtype=tf.inta ),
tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:] , config.pad_token_id ) , tf.inta ),
] , axis=-1 , )
if head_mask is None:
UpperCAmelCase : str =tf.ones((config.encoder_layers, config.encoder_attention_heads) )
if decoder_head_mask is None:
UpperCAmelCase : Union[str, Any] =tf.ones((config.decoder_layers, config.decoder_attention_heads) )
if cross_attn_head_mask is None:
UpperCAmelCase : int =tf.ones((config.decoder_layers, config.decoder_attention_heads) )
return {
"input_ids": input_ids,
"decoder_input_ids": decoder_input_ids,
"attention_mask": attention_mask,
"decoder_attention_mask": decoder_attention_mask,
"head_mask": head_mask,
"decoder_head_mask": decoder_head_mask,
"cross_attn_head_mask": cross_attn_head_mask,
}
@require_tf
class __snake_case ( lowerCamelCase__ , lowerCamelCase__ , unittest.TestCase ):
__lowerCamelCase : List[str] = (TFBlenderbotForConditionalGeneration, TFBlenderbotModel) if is_tf_available() else ()
__lowerCamelCase : Dict = (TFBlenderbotForConditionalGeneration,) if is_tf_available() else ()
__lowerCamelCase : Dict = (
{
"""conversational""": TFBlenderbotForConditionalGeneration,
"""feature-extraction""": TFBlenderbotModel,
"""summarization""": TFBlenderbotForConditionalGeneration,
"""text2text-generation""": TFBlenderbotForConditionalGeneration,
"""translation""": TFBlenderbotForConditionalGeneration,
}
if is_tf_available()
else {}
)
__lowerCamelCase : Union[str, Any] = True
__lowerCamelCase : Union[str, Any] = False
__lowerCamelCase : Union[str, Any] = False
def UpperCAmelCase__ ( self ) -> Dict:
'''simple docstring'''
UpperCAmelCase : List[str] =TFBlenderbotModelTester(self )
UpperCAmelCase : List[Any] =ConfigTester(self , config_class=snake_case__ )
def UpperCAmelCase__ ( self ) -> Optional[Any]:
'''simple docstring'''
self.config_tester.run_common_tests()
def UpperCAmelCase__ ( self ) -> List[Any]:
'''simple docstring'''
UpperCAmelCase : int =self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.check_decoder_model_past_large_inputs(*snake_case__ )
@require_tokenizers
@require_tf
class __snake_case ( unittest.TestCase ):
__lowerCamelCase : List[str] = ["""My friends are cool but they eat too many carbs."""]
__lowerCamelCase : Dict = """facebook/blenderbot-400M-distill"""
@cached_property
def UpperCAmelCase__ ( self ) -> int:
'''simple docstring'''
return BlenderbotTokenizer.from_pretrained(self.model_name )
@cached_property
def UpperCAmelCase__ ( self ) -> int:
'''simple docstring'''
UpperCAmelCase : int =TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name )
return model
@slow
def UpperCAmelCase__ ( self ) -> Any:
'''simple docstring'''
UpperCAmelCase : Optional[int] =self.tokenizer(self.src_text , return_tensors='''tf''' )
UpperCAmelCase : Optional[int] =self.model.generate(
model_inputs.input_ids , )
UpperCAmelCase : str =self.tokenizer.batch_decode(generated_ids.numpy() , skip_special_tokens=snake_case__ )[0]
assert (
generated_words
== " That's unfortunate. Are they trying to lose weight or are they just trying to be healthier?"
)
| 348 | 1 |
from math import cos, sin, sqrt, tau
from audio_filters.iir_filter import IIRFilter
def lowerCAmelCase_ ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase = 1 / sqrt(2 ) )-> IIRFilter:
'''simple docstring'''
UpperCAmelCase : Any =tau * frequency / samplerate
UpperCAmelCase : List[Any] =sin(__lowerCAmelCase )
UpperCAmelCase : Optional[Any] =cos(__lowerCAmelCase )
UpperCAmelCase : str =_sin / (2 * q_factor)
UpperCAmelCase : Tuple =(1 - _cos) / 2
UpperCAmelCase : Union[str, Any] =1 - _cos
UpperCAmelCase : Optional[Any] =1 + alpha
UpperCAmelCase : Union[str, Any] =-2 * _cos
UpperCAmelCase : Optional[Any] =1 - alpha
UpperCAmelCase : Union[str, Any] =IIRFilter(2 )
filt.set_coefficients([aa, aa, aa] , [ba, ba, ba] )
return filt
def lowerCAmelCase_ ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase = 1 / sqrt(2 ) )-> IIRFilter:
'''simple docstring'''
UpperCAmelCase : str =tau * frequency / samplerate
UpperCAmelCase : Any =sin(__lowerCAmelCase )
UpperCAmelCase : Union[str, Any] =cos(__lowerCAmelCase )
UpperCAmelCase : Tuple =_sin / (2 * q_factor)
UpperCAmelCase : List[str] =(1 + _cos) / 2
UpperCAmelCase : Union[str, Any] =-1 - _cos
UpperCAmelCase : List[Any] =1 + alpha
UpperCAmelCase : List[str] =-2 * _cos
UpperCAmelCase : List[Any] =1 - alpha
UpperCAmelCase : Any =IIRFilter(2 )
filt.set_coefficients([aa, aa, aa] , [ba, ba, ba] )
return filt
def lowerCAmelCase_ ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase = 1 / sqrt(2 ) )-> IIRFilter:
'''simple docstring'''
UpperCAmelCase : List[Any] =tau * frequency / samplerate
UpperCAmelCase : Optional[int] =sin(__lowerCAmelCase )
UpperCAmelCase : Union[str, Any] =cos(__lowerCAmelCase )
UpperCAmelCase : List[str] =_sin / (2 * q_factor)
UpperCAmelCase : int =_sin / 2
UpperCAmelCase : Any =0
UpperCAmelCase : str =-ba
UpperCAmelCase : int =1 + alpha
UpperCAmelCase : str =-2 * _cos
UpperCAmelCase : Tuple =1 - alpha
UpperCAmelCase : Union[str, Any] =IIRFilter(2 )
filt.set_coefficients([aa, aa, aa] , [ba, ba, ba] )
return filt
def lowerCAmelCase_ ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase = 1 / sqrt(2 ) )-> IIRFilter:
'''simple docstring'''
UpperCAmelCase : List[str] =tau * frequency / samplerate
UpperCAmelCase : List[str] =sin(__lowerCAmelCase )
UpperCAmelCase : Optional[Any] =cos(__lowerCAmelCase )
UpperCAmelCase : int =_sin / (2 * q_factor)
UpperCAmelCase : List[Any] =1 - alpha
UpperCAmelCase : List[Any] =-2 * _cos
UpperCAmelCase : int =1 + alpha
UpperCAmelCase : Optional[int] =IIRFilter(2 )
filt.set_coefficients([ba, ba, ba] , [ba, ba, ba] )
return filt
def lowerCAmelCase_ ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase = 1 / sqrt(2 ) , )-> IIRFilter:
'''simple docstring'''
UpperCAmelCase : Dict =tau * frequency / samplerate
UpperCAmelCase : Any =sin(__lowerCAmelCase )
UpperCAmelCase : Dict =cos(__lowerCAmelCase )
UpperCAmelCase : Optional[Any] =_sin / (2 * q_factor)
UpperCAmelCase : List[str] =10 ** (gain_db / 40)
UpperCAmelCase : List[Any] =1 + alpha * big_a
UpperCAmelCase : Union[str, Any] =-2 * _cos
UpperCAmelCase : Dict =1 - alpha * big_a
UpperCAmelCase : List[Any] =1 + alpha / big_a
UpperCAmelCase : Optional[Any] =-2 * _cos
UpperCAmelCase : Any =1 - alpha / big_a
UpperCAmelCase : Union[str, Any] =IIRFilter(2 )
filt.set_coefficients([aa, aa, aa] , [ba, ba, ba] )
return filt
def lowerCAmelCase_ ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase = 1 / sqrt(2 ) , )-> IIRFilter:
'''simple docstring'''
UpperCAmelCase : Optional[Any] =tau * frequency / samplerate
UpperCAmelCase : Optional[int] =sin(__lowerCAmelCase )
UpperCAmelCase : List[str] =cos(__lowerCAmelCase )
UpperCAmelCase : int =_sin / (2 * q_factor)
UpperCAmelCase : Optional[Any] =10 ** (gain_db / 40)
UpperCAmelCase : str =(big_a + 1) - (big_a - 1) * _cos
UpperCAmelCase : List[Any] =(big_a + 1) + (big_a - 1) * _cos
UpperCAmelCase : str =(big_a - 1) - (big_a + 1) * _cos
UpperCAmelCase : Dict =(big_a - 1) + (big_a + 1) * _cos
UpperCAmelCase : Optional[int] =2 * sqrt(__lowerCAmelCase ) * alpha
UpperCAmelCase : Optional[Any] =big_a * (pmc + aaa)
UpperCAmelCase : int =2 * big_a * mpc
UpperCAmelCase : Optional[int] =big_a * (pmc - aaa)
UpperCAmelCase : List[str] =ppmc + aaa
UpperCAmelCase : Optional[int] =-2 * pmpc
UpperCAmelCase : Optional[int] =ppmc - aaa
UpperCAmelCase : Any =IIRFilter(2 )
filt.set_coefficients([aa, aa, aa] , [ba, ba, ba] )
return filt
def lowerCAmelCase_ ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase = 1 / sqrt(2 ) , )-> IIRFilter:
'''simple docstring'''
UpperCAmelCase : Any =tau * frequency / samplerate
UpperCAmelCase : Any =sin(__lowerCAmelCase )
UpperCAmelCase : Any =cos(__lowerCAmelCase )
UpperCAmelCase : List[str] =_sin / (2 * q_factor)
UpperCAmelCase : Any =10 ** (gain_db / 40)
UpperCAmelCase : List[str] =(big_a + 1) - (big_a - 1) * _cos
UpperCAmelCase : Any =(big_a + 1) + (big_a - 1) * _cos
UpperCAmelCase : List[Any] =(big_a - 1) - (big_a + 1) * _cos
UpperCAmelCase : Optional[Any] =(big_a - 1) + (big_a + 1) * _cos
UpperCAmelCase : List[Any] =2 * sqrt(__lowerCAmelCase ) * alpha
UpperCAmelCase : List[Any] =big_a * (ppmc + aaa)
UpperCAmelCase : Tuple =-2 * big_a * pmpc
UpperCAmelCase : Optional[Any] =big_a * (ppmc - aaa)
UpperCAmelCase : Optional[int] =pmc + aaa
UpperCAmelCase : List[str] =2 * mpc
UpperCAmelCase : Dict =pmc - aaa
UpperCAmelCase : str =IIRFilter(2 )
filt.set_coefficients([aa, aa, aa] , [ba, ba, ba] )
return filt
| 348 | import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__snake_case = logging.get_logger(__name__)
__snake_case = {
'''asapp/sew-d-tiny-100k''': '''https://huggingface.co/asapp/sew-d-tiny-100k/resolve/main/config.json''',
# See all SEW-D models at https://huggingface.co/models?filter=sew-d
}
class __snake_case ( lowerCamelCase__ ):
__lowerCamelCase : Optional[Any] = """sew-d"""
def __init__( self , snake_case__=32 , snake_case__=768 , snake_case__=12 , snake_case__=12 , snake_case__=3072 , snake_case__=2 , snake_case__=512 , snake_case__=256 , snake_case__=True , snake_case__=True , snake_case__=("p2c", "c2p") , snake_case__="layer_norm" , snake_case__="gelu_python" , snake_case__=0.1 , snake_case__=0.1 , snake_case__=0.1 , snake_case__=0.0 , snake_case__=0.1 , snake_case__=0.02 , snake_case__=1e-7 , snake_case__=1e-5 , snake_case__="group" , snake_case__="gelu" , snake_case__=(64, 128, 128, 128, 128, 256, 256, 256, 256, 512, 512, 512, 512) , snake_case__=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1) , snake_case__=(10, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1) , snake_case__=False , snake_case__=128 , snake_case__=16 , snake_case__=True , snake_case__=0.05 , snake_case__=10 , snake_case__=2 , snake_case__=0.0 , snake_case__=10 , snake_case__=0 , snake_case__="mean" , snake_case__=False , snake_case__=False , snake_case__=256 , snake_case__=0 , snake_case__=1 , snake_case__=2 , **snake_case__ , ) -> int:
'''simple docstring'''
super().__init__(**snake_case__ , pad_token_id=snake_case__ , bos_token_id=snake_case__ , eos_token_id=snake_case__ )
UpperCAmelCase : Union[str, Any] =hidden_size
UpperCAmelCase : Union[str, Any] =feat_extract_norm
UpperCAmelCase : Optional[Any] =feat_extract_activation
UpperCAmelCase : List[str] =list(snake_case__ )
UpperCAmelCase : int =list(snake_case__ )
UpperCAmelCase : List[str] =list(snake_case__ )
UpperCAmelCase : str =conv_bias
UpperCAmelCase : Tuple =num_conv_pos_embeddings
UpperCAmelCase : Dict =num_conv_pos_embedding_groups
UpperCAmelCase : str =len(self.conv_dim )
UpperCAmelCase : Dict =num_hidden_layers
UpperCAmelCase : Optional[int] =intermediate_size
UpperCAmelCase : List[Any] =squeeze_factor
UpperCAmelCase : str =max_position_embeddings
UpperCAmelCase : int =position_buckets
UpperCAmelCase : Optional[int] =share_att_key
UpperCAmelCase : Optional[int] =relative_attention
UpperCAmelCase : Tuple =norm_rel_ebd
UpperCAmelCase : List[Any] =list(snake_case__ )
UpperCAmelCase : Dict =hidden_act
UpperCAmelCase : Optional[int] =num_attention_heads
UpperCAmelCase : Any =hidden_dropout
UpperCAmelCase : str =attention_dropout
UpperCAmelCase : Union[str, Any] =activation_dropout
UpperCAmelCase : str =feat_proj_dropout
UpperCAmelCase : Union[str, Any] =final_dropout
UpperCAmelCase : Optional[int] =layer_norm_eps
UpperCAmelCase : str =feature_layer_norm_eps
UpperCAmelCase : str =initializer_range
UpperCAmelCase : Any =vocab_size
if (
(len(self.conv_stride ) != self.num_feat_extract_layers)
or (len(self.conv_kernel ) != self.num_feat_extract_layers)
or (len(self.conv_dim ) != self.num_feat_extract_layers)
):
raise ValueError(
'''Configuration for convolutional layers is incorrect.'''
'''It is required that `len(config.conv_dim)` == `len(config.conv_stride)` == `len(config.conv_kernel)`,'''
f'''but is `len(config.conv_dim) = {len(self.conv_dim )}`, `len(config.conv_stride)'''
f'''= {len(self.conv_stride )}`, `len(config.conv_kernel) = {len(self.conv_kernel )}`.''' )
# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
UpperCAmelCase : Union[str, Any] =apply_spec_augment
UpperCAmelCase : Optional[Any] =mask_time_prob
UpperCAmelCase : Tuple =mask_time_length
UpperCAmelCase : str =mask_time_min_masks
UpperCAmelCase : Optional[int] =mask_feature_prob
UpperCAmelCase : Optional[Any] =mask_feature_length
UpperCAmelCase : List[Any] =mask_feature_min_masks
# ctc loss
UpperCAmelCase : str =ctc_loss_reduction
UpperCAmelCase : Optional[int] =ctc_zero_infinity
# sequence classification
UpperCAmelCase : Union[str, Any] =use_weighted_layer_sum
UpperCAmelCase : int =classifier_proj_size
@property
def UpperCAmelCase__ ( self ) -> List[Any]:
'''simple docstring'''
return functools.reduce(operator.mul , self.conv_stride , 1 )
| 348 | 1 |
import os
import jsonlines
import numpy as np
from tqdm import tqdm
__snake_case = 20_48
__snake_case = 40_96
__snake_case = 42
__snake_case = os.environ.pop('''PROCESS_TRAIN''', '''false''')
__snake_case = {'''null''': 0, '''short''': 1, '''long''': 2, '''yes''': 3, '''no''': 4}
def lowerCAmelCase_ ( __lowerCAmelCase )-> List[str]:
'''simple docstring'''
def choose_first(__lowerCAmelCase , __lowerCAmelCase=False ):
assert isinstance(__lowerCAmelCase , __lowerCAmelCase )
if len(__lowerCAmelCase ) == 1:
UpperCAmelCase : List[Any] =answer[0]
return {k: [answer[k]] for k in answer} if is_long_answer else answer
for a in answer:
if is_long_answer:
UpperCAmelCase : List[Any] ={k: [a[k]] for k in a}
if len(a['''start_token'''] ) > 0:
break
return a
UpperCAmelCase : Optional[Any] ={'''id''': example['''id''']}
UpperCAmelCase : Union[str, Any] =example['''annotations''']
UpperCAmelCase : List[Any] =annotation['''yes_no_answer''']
if 0 in yes_no_answer or 1 in yes_no_answer:
UpperCAmelCase : Optional[Any] =['''yes'''] if 1 in yes_no_answer else ['''no''']
UpperCAmelCase : Tuple =[]
UpperCAmelCase : List[str] =[]
UpperCAmelCase : Dict =['''<cls>''']
else:
UpperCAmelCase : List[str] =['''short''']
UpperCAmelCase : Tuple =choose_first(annotation['''short_answers'''] )
if len(out['''start_token'''] ) == 0:
# answer will be long if short is not available
UpperCAmelCase : Dict =['''long''']
UpperCAmelCase : List[str] =choose_first(annotation['''long_answer'''] , is_long_answer=__lowerCAmelCase )
UpperCAmelCase : Dict =[]
answer.update(__lowerCAmelCase )
# disregard some samples
if len(answer['''start_token'''] ) > 1 or answer["start_token"] == answer["end_token"]:
UpperCAmelCase : Any =True
else:
UpperCAmelCase : Tuple =False
UpperCAmelCase : int =['''start_token''', '''end_token''', '''start_byte''', '''end_byte''', '''text''']
if not all(isinstance(answer[k] , __lowerCAmelCase ) for k in cols ):
raise ValueError('''Issue in ID''' , example['''id'''] )
return answer
def lowerCAmelCase_ ( __lowerCAmelCase , __lowerCAmelCase=False )-> Optional[Any]:
'''simple docstring'''
UpperCAmelCase : Optional[int] =_get_single_answer(__lowerCAmelCase )
# bytes are of no use
del answer["start_byte"]
del answer["end_byte"]
# handle yes_no answers explicitly
if answer["category"][0] in ["yes", "no"]: # category is list with one element
UpperCAmelCase : str =example['''document''']['''tokens''']
UpperCAmelCase : int =[]
for i in range(len(doc['''token'''] ) ):
if not doc["is_html"][i]:
context.append(doc['''token'''][i] )
return {
"context": " ".join(__lowerCAmelCase ),
"answer": {
"start_token": -1_00, # ignore index in cross-entropy
"end_token": -1_00, # ignore index in cross-entropy
"category": answer["category"],
"span": answer["category"], # extra
},
}
# later, help in removing all no answers
if answer["start_token"] == [-1]:
return {
"context": "None",
"answer": {
"start_token": -1,
"end_token": -1,
"category": "null",
"span": "None", # extra
},
}
# handling normal samples
UpperCAmelCase : Optional[int] =['''start_token''', '''end_token''']
answer.update({k: answer[k][0] if len(answer[k] ) > 0 else answer[k] for k in cols} ) # e.g. [10] == 10
UpperCAmelCase : Optional[int] =example['''document''']['''tokens''']
UpperCAmelCase : Tuple =answer['''start_token''']
UpperCAmelCase : List[str] =answer['''end_token''']
UpperCAmelCase : Any =[]
for i in range(len(doc['''token'''] ) ):
if not doc["is_html"][i]:
context.append(doc['''token'''][i] )
else:
if answer["start_token"] > i:
start_token -= 1
if answer["end_token"] > i:
end_token -= 1
UpperCAmelCase : Dict =''' '''.join(context[start_token:end_token] )
# checking above code
if assertion:
UpperCAmelCase : str =doc['''is_html'''][answer['''start_token'''] : answer['''end_token''']]
UpperCAmelCase : str =doc['''token'''][answer['''start_token'''] : answer['''end_token''']]
UpperCAmelCase : Optional[Any] =''' '''.join([old[i] for i in range(len(__lowerCAmelCase ) ) if not is_html[i]] )
if new != old:
print('''ID:''' , example['''id'''] )
print('''New:''' , __lowerCAmelCase , end='''\n''' )
print('''Old:''' , __lowerCAmelCase , end='''\n\n''' )
return {
"context": " ".join(__lowerCAmelCase ),
"answer": {
"start_token": start_token,
"end_token": end_token - 1, # this makes it inclusive
"category": answer["category"], # either long or short
"span": new, # extra
},
}
def lowerCAmelCase_ ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase=20_48 , __lowerCAmelCase=40_96 , __lowerCAmelCase=True )-> str:
'''simple docstring'''
UpperCAmelCase : str =get_context_and_ans(__lowerCAmelCase , assertion=__lowerCAmelCase )
UpperCAmelCase : Dict =out['''answer''']
# later, removing these samples
if answer["start_token"] == -1:
return {
"example_id": example["id"],
"input_ids": [[-1]],
"labels": {
"start_token": [-1],
"end_token": [-1],
"category": ["null"],
},
}
UpperCAmelCase : Tuple =tokenizer(example['''question''']['''text'''] , out['''context'''] ).input_ids
UpperCAmelCase : List[str] =input_ids.index(tokenizer.sep_token_id ) + 1
# return yes/no
if answer["category"][0] in ["yes", "no"]: # category is list with one element
UpperCAmelCase : Tuple =[]
UpperCAmelCase : Optional[Any] =[]
UpperCAmelCase : str =input_ids[:q_len]
UpperCAmelCase : int =range(__lowerCAmelCase , len(__lowerCAmelCase ) , max_length - doc_stride )
for i in doc_start_indices:
UpperCAmelCase : str =i + max_length - q_len
UpperCAmelCase : Any =input_ids[i:end_index]
inputs.append(q_indices + slice )
category.append(answer['''category'''][0] )
if slice[-1] == tokenizer.sep_token_id:
break
return {
"example_id": example["id"],
"input_ids": inputs,
"labels": {
"start_token": [-1_00] * len(__lowerCAmelCase ),
"end_token": [-1_00] * len(__lowerCAmelCase ),
"category": category,
},
}
UpperCAmelCase : List[str] =out['''context'''].split()
UpperCAmelCase : Optional[Any] =splitted_context[answer['''end_token''']]
UpperCAmelCase : Optional[int] =len(
tokenizer(
''' '''.join(splitted_context[: answer['''start_token''']] ) , add_special_tokens=__lowerCAmelCase , ).input_ids )
UpperCAmelCase : Optional[int] =len(
tokenizer(''' '''.join(splitted_context[: answer['''end_token''']] ) , add_special_tokens=__lowerCAmelCase ).input_ids )
answer["start_token"] += q_len
answer["end_token"] += q_len
# fixing end token
UpperCAmelCase : Optional[int] =len(tokenizer(__lowerCAmelCase , add_special_tokens=__lowerCAmelCase ).input_ids )
if num_sub_tokens > 1:
answer["end_token"] += num_sub_tokens - 1
UpperCAmelCase : List[Any] =input_ids[answer['''start_token'''] : answer['''end_token'''] + 1] # right & left are inclusive
UpperCAmelCase : Any =answer['''start_token''']
UpperCAmelCase : int =answer['''end_token''']
if assertion:
UpperCAmelCase : List[str] =tokenizer.decode(__lowerCAmelCase )
if answer["span"] != new:
print('''ISSUE IN TOKENIZATION''' )
print('''OLD:''' , answer['''span'''] )
print('''NEW:''' , __lowerCAmelCase , end='''\n\n''' )
if len(__lowerCAmelCase ) <= max_length:
return {
"example_id": example["id"],
"input_ids": [input_ids],
"labels": {
"start_token": [answer["start_token"]],
"end_token": [answer["end_token"]],
"category": answer["category"],
},
}
UpperCAmelCase : List[Any] =input_ids[:q_len]
UpperCAmelCase : List[Any] =range(__lowerCAmelCase , len(__lowerCAmelCase ) , max_length - doc_stride )
UpperCAmelCase : Optional[Any] =[]
UpperCAmelCase : Optional[int] =[]
UpperCAmelCase : Optional[Any] =[]
UpperCAmelCase : List[Any] =[] # null, yes, no, long, short
for i in doc_start_indices:
UpperCAmelCase : Optional[Any] =i + max_length - q_len
UpperCAmelCase : Any =input_ids[i:end_index]
inputs.append(q_indices + slice )
assert len(inputs[-1] ) <= max_length, "Issue in truncating length"
if start_token >= i and end_token <= end_index - 1:
UpperCAmelCase : Union[str, Any] =start_token - i + q_len
UpperCAmelCase : List[str] =end_token - i + q_len
answers_category.append(answer['''category'''][0] ) # ["short"] -> "short"
else:
UpperCAmelCase : int =-1_00
UpperCAmelCase : List[Any] =-1_00
answers_category.append('''null''' )
UpperCAmelCase : Any =inputs[-1][start_token : end_token + 1]
answers_start_token.append(__lowerCAmelCase )
answers_end_token.append(__lowerCAmelCase )
if assertion:
if new != old and new != [tokenizer.cls_token_id]:
print('''ISSUE in strided for ID:''' , example['''id'''] )
print('''New:''' , tokenizer.decode(__lowerCAmelCase ) )
print('''Old:''' , tokenizer.decode(__lowerCAmelCase ) , end='''\n\n''' )
if slice[-1] == tokenizer.sep_token_id:
break
return {
"example_id": example["id"],
"input_ids": inputs,
"labels": {
"start_token": answers_start_token,
"end_token": answers_end_token,
"category": answers_category,
},
}
def lowerCAmelCase_ ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase=20_48 , __lowerCAmelCase=40_96 , __lowerCAmelCase=False )-> int:
'''simple docstring'''
UpperCAmelCase : int =get_strided_contexts_and_ans(
__lowerCAmelCase , __lowerCAmelCase , doc_stride=__lowerCAmelCase , max_length=__lowerCAmelCase , assertion=__lowerCAmelCase , )
return example
def lowerCAmelCase_ ( __lowerCAmelCase , __lowerCAmelCase )-> Dict:
'''simple docstring'''
with jsonlines.open(__lowerCAmelCase , '''a''' ) as writer:
for example in tqdm(__lowerCAmelCase , total=len(__lowerCAmelCase ) , desc='''Saving samples ... ''' ):
UpperCAmelCase : Dict =example['''labels''']
for ids, start, end, cat in zip(
example['''input_ids'''] , labels['''start_token'''] , labels['''end_token'''] , labels['''category'''] , ):
if start == -1 and end == -1:
continue # leave waste samples with no answer
if cat == "null" and np.random.rand() < 0.6:
continue # removing 50 % samples
writer.write(
{
'''input_ids''': ids,
'''start_token''': start,
'''end_token''': end,
'''category''': CATEGORY_MAPPING[cat],
} )
if __name__ == "__main__":
from datasets import load_dataset
from transformers import BigBirdTokenizer
__snake_case = load_dataset('''natural_questions''')
__snake_case = BigBirdTokenizer.from_pretrained('''google/bigbird-roberta-base''')
__snake_case = data['''train''' if PROCESS_TRAIN == '''true''' else '''validation''']
__snake_case = {
'''tokenizer''': tokenizer,
'''doc_stride''': DOC_STRIDE,
'''max_length''': MAX_LENGTH,
'''assertion''': False,
}
__snake_case = data.map(prepare_inputs, fn_kwargs=fn_kwargs)
__snake_case = data.remove_columns(['''annotations''', '''document''', '''id''', '''question'''])
print(data)
np.random.seed(SEED)
__snake_case = '''nq-training.jsonl''' if PROCESS_TRAIN == '''true''' else '''nq-validation.jsonl'''
save_to_disk(data, file_name=cache_file_name)
| 348 | import os
from argparse import ArgumentParser
from typing import List
import torch.utils.data
from datasets import Dataset, IterableDataset
from datasets.distributed import split_dataset_by_node
__snake_case = 4
__snake_case = 3
class __snake_case ( lowerCamelCase__ ):
pass
def lowerCAmelCase_ ( __lowerCAmelCase )-> List[str]:
'''simple docstring'''
for shard in shards:
for i in range(__lowerCAmelCase ):
yield {"i": i, "shard": shard}
def lowerCAmelCase_ ( )-> Optional[int]:
'''simple docstring'''
UpperCAmelCase : List[str] =int(os.environ['''RANK'''] )
UpperCAmelCase : Optional[Any] =int(os.environ['''WORLD_SIZE'''] )
UpperCAmelCase : List[Any] =ArgumentParser()
parser.add_argument('''--streaming''' , type=__lowerCAmelCase )
parser.add_argument('''--local_rank''' , type=__lowerCAmelCase )
parser.add_argument('''--num_workers''' , type=__lowerCAmelCase , default=0 )
UpperCAmelCase : Any =parser.parse_args()
UpperCAmelCase : List[str] =args.streaming
UpperCAmelCase : Tuple =args.num_workers
UpperCAmelCase : int ={'''shards''': [f'''shard_{shard_idx}''' for shard_idx in range(__lowerCAmelCase )]}
UpperCAmelCase : Optional[int] =IterableDataset.from_generator(__lowerCAmelCase , gen_kwargs=__lowerCAmelCase )
if not streaming:
UpperCAmelCase : List[Any] =Dataset.from_list(list(__lowerCAmelCase ) )
UpperCAmelCase : Dict =split_dataset_by_node(__lowerCAmelCase , rank=__lowerCAmelCase , world_size=__lowerCAmelCase )
UpperCAmelCase : List[Any] =torch.utils.data.DataLoader(__lowerCAmelCase , num_workers=__lowerCAmelCase )
UpperCAmelCase : Dict =NUM_SHARDS * NUM_ITEMS_PER_SHARD
UpperCAmelCase : str =full_size // world_size
expected_local_size += int(rank < (full_size % world_size) )
UpperCAmelCase : List[Any] =sum(1 for _ in dataloader )
if local_size != expected_local_size:
raise FailedTestError(f'''local_size {local_size} != expected_local_size {expected_local_size}''' )
if __name__ == "__main__":
main()
| 348 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__snake_case = logging.get_logger(__name__)
__snake_case = {
'''tanreinama/GPTSAN-2.8B-spout_is_uniform''': (
'''https://huggingface.co/tanreinama/GPTSAN-2.8B-spout_is_uniform/resolve/main/config.json'''
),
}
class __snake_case ( lowerCamelCase__ ):
__lowerCamelCase : List[str] = """gptsan-japanese"""
__lowerCamelCase : Union[str, Any] = [
"""past_key_values""",
]
__lowerCamelCase : Optional[int] = {
"""hidden_size""": """d_model""",
"""num_attention_heads""": """num_heads""",
"""num_hidden_layers""": """num_layers""",
}
def __init__( self , snake_case__=3_6000 , snake_case__=1280 , snake_case__=1024 , snake_case__=8192 , snake_case__=4096 , snake_case__=128 , snake_case__=10 , snake_case__=0 , snake_case__=16 , snake_case__=16 , snake_case__=128 , snake_case__=0.0 , snake_case__=1e-5 , snake_case__=False , snake_case__=0.0 , snake_case__="float32" , snake_case__=False , snake_case__=False , snake_case__=False , snake_case__=0.002 , snake_case__=False , snake_case__=True , snake_case__=3_5998 , snake_case__=3_5995 , snake_case__=3_5999 , **snake_case__ , ) -> List[Any]:
'''simple docstring'''
UpperCAmelCase : Optional[Any] =vocab_size
UpperCAmelCase : int =max_position_embeddings
UpperCAmelCase : Optional[int] =d_model
UpperCAmelCase : Optional[Any] =d_ff
UpperCAmelCase : Union[str, Any] =d_ext
UpperCAmelCase : List[Any] =d_spout
UpperCAmelCase : Optional[Any] =num_switch_layers
UpperCAmelCase : Optional[Any] =num_ext_layers
UpperCAmelCase : Dict =num_switch_layers + num_ext_layers
UpperCAmelCase : List[str] =num_heads
UpperCAmelCase : Optional[Any] =num_experts
UpperCAmelCase : Any =expert_capacity
UpperCAmelCase : List[str] =dropout_rate
UpperCAmelCase : Tuple =layer_norm_epsilon
UpperCAmelCase : Any =router_bias
UpperCAmelCase : Optional[int] =router_jitter_noise
UpperCAmelCase : Optional[int] =router_dtype
UpperCAmelCase : Union[str, Any] =router_ignore_padding_tokens
UpperCAmelCase : str =output_hidden_states
UpperCAmelCase : Optional[Any] =output_attentions
UpperCAmelCase : Any =initializer_factor
UpperCAmelCase : List[str] =output_router_logits
UpperCAmelCase : Union[str, Any] =use_cache
super().__init__(
separator_token_id=snake_case__ , pad_token_id=snake_case__ , eos_token_id=snake_case__ , **snake_case__ , )
| 348 | from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__snake_case = {'''configuration_opt''': ['''OPT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''OPTConfig''']}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__snake_case = [
'''OPT_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''OPTForCausalLM''',
'''OPTModel''',
'''OPTPreTrainedModel''',
'''OPTForSequenceClassification''',
'''OPTForQuestionAnswering''',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__snake_case = ['''TFOPTForCausalLM''', '''TFOPTModel''', '''TFOPTPreTrainedModel''']
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__snake_case = [
'''FlaxOPTForCausalLM''',
'''FlaxOPTModel''',
'''FlaxOPTPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_opt import OPT_PRETRAINED_CONFIG_ARCHIVE_MAP, OPTConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_opt import (
OPT_PRETRAINED_MODEL_ARCHIVE_LIST,
OPTForCausalLM,
OPTForQuestionAnswering,
OPTForSequenceClassification,
OPTModel,
OPTPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_opt import TFOPTForCausalLM, TFOPTModel, TFOPTPreTrainedModel
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_opt import FlaxOPTForCausalLM, FlaxOPTModel, FlaxOPTPreTrainedModel
else:
import sys
__snake_case = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 348 | 1 |
import inspect
import unittest
import numpy as np
from tests.test_modeling_common import floats_tensor
from transformers import MaskaFormerConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import MaskaFormerForUniversalSegmentation, MaskaFormerModel
if is_vision_available():
from transformers import MaskaFormerImageProcessor
if is_vision_available():
from PIL import Image
class __snake_case :
def __init__( self , snake_case__ , snake_case__=2 , snake_case__=True , snake_case__=False , snake_case__=10 , snake_case__=3 , snake_case__=32 * 8 , snake_case__=32 * 8 , snake_case__=4 , snake_case__=64 , ) -> Union[str, Any]:
'''simple docstring'''
UpperCAmelCase : int =parent
UpperCAmelCase : Any =batch_size
UpperCAmelCase : List[Any] =is_training
UpperCAmelCase : Optional[int] =use_auxiliary_loss
UpperCAmelCase : List[str] =num_queries
UpperCAmelCase : Dict =num_channels
UpperCAmelCase : int =min_size
UpperCAmelCase : int =max_size
UpperCAmelCase : str =num_labels
UpperCAmelCase : int =hidden_dim
UpperCAmelCase : List[str] =hidden_dim
def UpperCAmelCase__ ( self ) -> Optional[Any]:
'''simple docstring'''
UpperCAmelCase : List[str] =floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size] ).to(
snake_case__ )
UpperCAmelCase : Tuple =torch.ones([self.batch_size, self.min_size, self.max_size] , device=snake_case__ )
UpperCAmelCase : int =(
torch.rand([self.batch_size, self.num_labels, self.min_size, self.max_size] , device=snake_case__ ) > 0.5
).float()
UpperCAmelCase : Optional[int] =(torch.rand((self.batch_size, self.num_labels) , device=snake_case__ ) > 0.5).long()
UpperCAmelCase : Optional[int] =self.get_config()
return config, pixel_values, pixel_mask, mask_labels, class_labels
def UpperCAmelCase__ ( self ) -> List[str]:
'''simple docstring'''
UpperCAmelCase : Any =MaskaFormerConfig(
hidden_size=self.hidden_dim , )
UpperCAmelCase : List[Any] =self.num_queries
UpperCAmelCase : Dict =self.num_labels
UpperCAmelCase : Optional[int] =[1, 1, 1, 1]
UpperCAmelCase : List[str] =self.num_channels
UpperCAmelCase : Tuple =64
UpperCAmelCase : Any =128
UpperCAmelCase : str =self.hidden_dim
UpperCAmelCase : Optional[int] =self.hidden_dim
UpperCAmelCase : Union[str, Any] =self.hidden_dim
return config
def UpperCAmelCase__ ( self ) -> int:
'''simple docstring'''
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase : Tuple =self.prepare_config_and_inputs()
UpperCAmelCase : Tuple ={'''pixel_values''': pixel_values, '''pixel_mask''': pixel_mask}
return config, inputs_dict
def UpperCAmelCase__ ( self , snake_case__ , snake_case__ ) -> List[str]:
'''simple docstring'''
UpperCAmelCase : str =output.encoder_hidden_states
UpperCAmelCase : int =output.pixel_decoder_hidden_states
UpperCAmelCase : Optional[Any] =output.transformer_decoder_hidden_states
self.parent.assertTrue(len(snake_case__ ) , len(config.backbone_config.depths ) )
self.parent.assertTrue(len(snake_case__ ) , len(config.backbone_config.depths ) )
self.parent.assertTrue(len(snake_case__ ) , config.decoder_layers )
def UpperCAmelCase__ ( self , snake_case__ , snake_case__ , snake_case__ , snake_case__=False ) -> str:
'''simple docstring'''
with torch.no_grad():
UpperCAmelCase : Any =MaskaFormerModel(config=snake_case__ )
model.to(snake_case__ )
model.eval()
UpperCAmelCase : Union[str, Any] =model(pixel_values=snake_case__ , pixel_mask=snake_case__ )
UpperCAmelCase : Union[str, Any] =model(snake_case__ , output_hidden_states=snake_case__ )
self.parent.assertEqual(
output.transformer_decoder_last_hidden_state.shape , (self.batch_size, self.num_queries, self.hidden_dim) , )
# let's ensure the other two hidden state exists
self.parent.assertTrue(output.pixel_decoder_last_hidden_state is not None )
self.parent.assertTrue(output.encoder_last_hidden_state is not None )
if output_hidden_states:
self.check_output_hidden_state(snake_case__ , snake_case__ )
def UpperCAmelCase__ ( self , snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ ) -> int:
'''simple docstring'''
UpperCAmelCase : int =MaskaFormerForUniversalSegmentation(config=snake_case__ )
model.to(snake_case__ )
model.eval()
def comm_check_on_output(snake_case__ ):
# let's still check that all the required stuff is there
self.parent.assertTrue(result.transformer_decoder_last_hidden_state is not None )
self.parent.assertTrue(result.pixel_decoder_last_hidden_state is not None )
self.parent.assertTrue(result.encoder_last_hidden_state is not None )
# okay, now we need to check the logits shape
# due to the encoder compression, masks have a //4 spatial size
self.parent.assertEqual(
result.masks_queries_logits.shape , (self.batch_size, self.num_queries, self.min_size // 4, self.max_size // 4) , )
# + 1 for null class
self.parent.assertEqual(
result.class_queries_logits.shape , (self.batch_size, self.num_queries, self.num_labels + 1) )
with torch.no_grad():
UpperCAmelCase : Optional[int] =model(pixel_values=snake_case__ , pixel_mask=snake_case__ )
UpperCAmelCase : Optional[int] =model(snake_case__ )
comm_check_on_output(snake_case__ )
UpperCAmelCase : List[str] =model(
pixel_values=snake_case__ , pixel_mask=snake_case__ , mask_labels=snake_case__ , class_labels=snake_case__ )
comm_check_on_output(snake_case__ )
self.parent.assertTrue(result.loss is not None )
self.parent.assertEqual(result.loss.shape , torch.Size([1] ) )
@require_torch
class __snake_case ( lowerCamelCase__ , lowerCamelCase__ , unittest.TestCase ):
__lowerCamelCase : List[Any] = (MaskaFormerModel, MaskaFormerForUniversalSegmentation) if is_torch_available() else ()
__lowerCamelCase : int = {"""feature-extraction""": MaskaFormerModel} if is_torch_available() else {}
__lowerCamelCase : Dict = False
__lowerCamelCase : Any = False
__lowerCamelCase : Optional[Any] = False
__lowerCamelCase : str = False
def UpperCAmelCase__ ( self ) -> Dict:
'''simple docstring'''
UpperCAmelCase : int =MaskaFormerModelTester(self )
UpperCAmelCase : str =ConfigTester(self , config_class=snake_case__ , has_text_modality=snake_case__ )
def UpperCAmelCase__ ( self ) -> Union[str, Any]:
'''simple docstring'''
self.config_tester.run_common_tests()
def UpperCAmelCase__ ( self ) -> str:
'''simple docstring'''
UpperCAmelCase , UpperCAmelCase : Union[str, Any] =self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.create_and_check_maskaformer_model(snake_case__ , **snake_case__ , output_hidden_states=snake_case__ )
def UpperCAmelCase__ ( self ) -> Tuple:
'''simple docstring'''
UpperCAmelCase : Any =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_maskaformer_instance_segmentation_head_model(*snake_case__ )
@unittest.skip(reason='''Mask2Former does not use inputs_embeds''' )
def UpperCAmelCase__ ( self ) -> List[str]:
'''simple docstring'''
pass
@unittest.skip(reason='''Mask2Former does not have a get_input_embeddings method''' )
def UpperCAmelCase__ ( self ) -> Dict:
'''simple docstring'''
pass
@unittest.skip(reason='''Mask2Former is not a generative model''' )
def UpperCAmelCase__ ( self ) -> Union[str, Any]:
'''simple docstring'''
pass
@unittest.skip(reason='''Mask2Former does not use token embeddings''' )
def UpperCAmelCase__ ( self ) -> Optional[Any]:
'''simple docstring'''
pass
@require_torch_multi_gpu
@unittest.skip(
reason='''Mask2Former has some layers using `add_module` which doesn\'t work well with `nn.DataParallel`''' )
def UpperCAmelCase__ ( self ) -> Any:
'''simple docstring'''
pass
@unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.''' )
def UpperCAmelCase__ ( self ) -> Union[str, Any]:
'''simple docstring'''
pass
def UpperCAmelCase__ ( self ) -> int:
'''simple docstring'''
UpperCAmelCase , UpperCAmelCase : Union[str, Any] =self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
UpperCAmelCase : Tuple =model_class(snake_case__ )
UpperCAmelCase : str =inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
UpperCAmelCase : List[Any] =[*signature.parameters.keys()]
UpperCAmelCase : List[Any] =['''pixel_values''']
self.assertListEqual(arg_names[:1] , snake_case__ )
@slow
def UpperCAmelCase__ ( self ) -> int:
'''simple docstring'''
for model_name in ["facebook/mask2former-swin-small-coco-instance"]:
UpperCAmelCase : List[str] =MaskaFormerModel.from_pretrained(snake_case__ )
self.assertIsNotNone(snake_case__ )
def UpperCAmelCase__ ( self ) -> List[Any]:
'''simple docstring'''
UpperCAmelCase : str =(self.model_tester.min_size,) * 2
UpperCAmelCase : Optional[Any] ={
'''pixel_values''': torch.randn((2, 3, *size) , device=snake_case__ ),
'''mask_labels''': torch.randn((2, 10, *size) , device=snake_case__ ),
'''class_labels''': torch.zeros(2 , 10 , device=snake_case__ ).long(),
}
UpperCAmelCase : List[Any] =self.model_tester.get_config()
UpperCAmelCase : Dict =MaskaFormerForUniversalSegmentation(snake_case__ ).to(snake_case__ )
UpperCAmelCase : str =model(**snake_case__ )
self.assertTrue(outputs.loss is not None )
def UpperCAmelCase__ ( self ) -> Dict:
'''simple docstring'''
UpperCAmelCase , UpperCAmelCase : Optional[int] =self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.create_and_check_maskaformer_model(snake_case__ , **snake_case__ , output_hidden_states=snake_case__ )
def UpperCAmelCase__ ( self ) -> List[Any]:
'''simple docstring'''
UpperCAmelCase , UpperCAmelCase : Tuple =self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
UpperCAmelCase : Optional[int] =model_class(snake_case__ ).to(snake_case__ )
UpperCAmelCase : str =model(**snake_case__ , output_attentions=snake_case__ )
self.assertTrue(outputs.attentions is not None )
def UpperCAmelCase__ ( self ) -> List[str]:
'''simple docstring'''
if not self.model_tester.is_training:
return
UpperCAmelCase : Union[str, Any] =self.all_model_classes[1]
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase : Dict =self.model_tester.prepare_config_and_inputs()
UpperCAmelCase : Optional[Any] =model_class(snake_case__ )
model.to(snake_case__ )
model.train()
UpperCAmelCase : Tuple =model(snake_case__ , mask_labels=snake_case__ , class_labels=snake_case__ ).loss
loss.backward()
def UpperCAmelCase__ ( self ) -> List[str]:
'''simple docstring'''
UpperCAmelCase : List[str] =self.all_model_classes[1]
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase : List[Any] =self.model_tester.prepare_config_and_inputs()
UpperCAmelCase : str =True
UpperCAmelCase : Union[str, Any] =True
UpperCAmelCase : Union[str, Any] =model_class(snake_case__ ).to(snake_case__ )
model.train()
UpperCAmelCase : Optional[int] =model(snake_case__ , mask_labels=snake_case__ , class_labels=snake_case__ )
UpperCAmelCase : Union[str, Any] =outputs.encoder_hidden_states[0]
encoder_hidden_states.retain_grad()
UpperCAmelCase : List[str] =outputs.pixel_decoder_hidden_states[0]
pixel_decoder_hidden_states.retain_grad()
UpperCAmelCase : str =outputs.transformer_decoder_hidden_states[0]
transformer_decoder_hidden_states.retain_grad()
UpperCAmelCase : Any =outputs.attentions[0]
attentions.retain_grad()
outputs.loss.backward(retain_graph=snake_case__ )
self.assertIsNotNone(encoder_hidden_states.grad )
self.assertIsNotNone(pixel_decoder_hidden_states.grad )
self.assertIsNotNone(transformer_decoder_hidden_states.grad )
self.assertIsNotNone(attentions.grad )
__snake_case = 1e-4
def lowerCAmelCase_ ( )-> Dict:
'''simple docstring'''
UpperCAmelCase : str =Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
return image
@require_vision
@slow
class __snake_case ( unittest.TestCase ):
@cached_property
def UpperCAmelCase__ ( self ) -> List[Any]:
'''simple docstring'''
return "facebook/mask2former-swin-small-coco-instance"
@cached_property
def UpperCAmelCase__ ( self ) -> Optional[Any]:
'''simple docstring'''
return MaskaFormerImageProcessor.from_pretrained(self.model_checkpoints ) if is_vision_available() else None
def UpperCAmelCase__ ( self ) -> List[Any]:
'''simple docstring'''
UpperCAmelCase : Optional[Any] =MaskaFormerModel.from_pretrained(self.model_checkpoints ).to(snake_case__ )
UpperCAmelCase : str =self.default_image_processor
UpperCAmelCase : Dict =prepare_img()
UpperCAmelCase : List[str] =image_processor(snake_case__ , return_tensors='''pt''' ).to(snake_case__ )
UpperCAmelCase : int =inputs['''pixel_values'''].shape
# check size is divisible by 32
self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0 )
# check size
self.assertEqual(snake_case__ , (1, 3, 384, 384) )
with torch.no_grad():
UpperCAmelCase : List[str] =model(**snake_case__ )
UpperCAmelCase : List[str] =torch.tensor(
[[-0.2790, -1.0717, -1.1668], [-0.5128, -0.3128, -0.4987], [-0.5832, 0.1971, -0.0197]] ).to(snake_case__ )
self.assertTrue(
torch.allclose(
outputs.encoder_last_hidden_state[0, 0, :3, :3] , snake_case__ , atol=snake_case__ ) )
UpperCAmelCase : Union[str, Any] =torch.tensor(
[[0.8973, 1.1847, 1.1776], [1.1934, 1.5040, 1.5128], [1.1153, 1.4486, 1.4951]] ).to(snake_case__ )
self.assertTrue(
torch.allclose(
outputs.pixel_decoder_last_hidden_state[0, 0, :3, :3] , snake_case__ , atol=snake_case__ ) )
UpperCAmelCase : List[str] =torch.tensor(
[[2.1152, 1.7000, -0.8603], [1.5808, 1.8004, -0.9353], [1.6043, 1.7495, -0.5999]] ).to(snake_case__ )
self.assertTrue(
torch.allclose(
outputs.transformer_decoder_last_hidden_state[0, :3, :3] , snake_case__ , atol=snake_case__ ) )
def UpperCAmelCase__ ( self ) -> Optional[int]:
'''simple docstring'''
UpperCAmelCase : str =MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints ).to(snake_case__ ).eval()
UpperCAmelCase : Union[str, Any] =self.default_image_processor
UpperCAmelCase : List[str] =prepare_img()
UpperCAmelCase : Optional[int] =image_processor(snake_case__ , return_tensors='''pt''' ).to(snake_case__ )
UpperCAmelCase : Optional[Any] =inputs['''pixel_values'''].shape
# check size is divisible by 32
self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0 )
# check size
self.assertEqual(snake_case__ , (1, 3, 384, 384) )
with torch.no_grad():
UpperCAmelCase : List[str] =model(**snake_case__ )
# masks_queries_logits
UpperCAmelCase : str =outputs.masks_queries_logits
self.assertEqual(
masks_queries_logits.shape , (1, model.config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4) )
UpperCAmelCase : int =[
[-8.7839, -9.0056, -8.8121],
[-7.4104, -7.0313, -6.5401],
[-6.6105, -6.3427, -6.4675],
]
UpperCAmelCase : str =torch.tensor(snake_case__ ).to(snake_case__ )
self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3] , snake_case__ , atol=snake_case__ ) )
# class_queries_logits
UpperCAmelCase : Optional[int] =outputs.class_queries_logits
self.assertEqual(class_queries_logits.shape , (1, model.config.num_queries, model.config.num_labels + 1) )
UpperCAmelCase : List[str] =torch.tensor(
[
[1.8324, -8.0835, -4.1922],
[0.8450, -9.0050, -3.6053],
[0.3045, -7.7293, -3.0275],
] ).to(snake_case__ )
self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3] , snake_case__ , atol=snake_case__ ) )
def UpperCAmelCase__ ( self ) -> List[str]:
'''simple docstring'''
UpperCAmelCase : Tuple =MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints ).to(snake_case__ ).eval()
UpperCAmelCase : List[Any] =self.default_image_processor
UpperCAmelCase : List[Any] =image_processor(
[np.zeros((3, 800, 1333) ), np.zeros((3, 800, 1333) )] , segmentation_maps=[np.zeros((384, 384) ).astype(np.floataa ), np.zeros((384, 384) ).astype(np.floataa )] , return_tensors='''pt''' , )
UpperCAmelCase : Optional[int] =inputs['''pixel_values'''].to(snake_case__ )
UpperCAmelCase : str =[el.to(snake_case__ ) for el in inputs['''mask_labels''']]
UpperCAmelCase : Union[str, Any] =[el.to(snake_case__ ) for el in inputs['''class_labels''']]
with torch.no_grad():
UpperCAmelCase : Optional[int] =model(**snake_case__ )
self.assertTrue(outputs.loss is not None )
| 348 | import tempfile
import unittest
import numpy as np
import transformers
from transformers import GPTaTokenizer, GPTJConfig, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, tooslow
from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import jax
import jax.numpy as jnp
from transformers.modeling_flax_pytorch_utils import (
convert_pytorch_state_dict_to_flax,
load_flax_weights_in_pytorch_model,
)
from transformers.models.gptj.modeling_flax_gptj import FlaxGPTJForCausalLM, FlaxGPTJModel
if is_torch_available():
import torch
class __snake_case :
def __init__( self , snake_case__ , snake_case__=14 , snake_case__=7 , snake_case__=True , snake_case__=True , snake_case__=False , snake_case__=True , snake_case__=99 , snake_case__=32 , snake_case__=4 , snake_case__=4 , snake_case__=4 , snake_case__=37 , snake_case__="gelu" , snake_case__=0.1 , snake_case__=0.1 , snake_case__=512 , snake_case__=0.02 , ) -> str:
'''simple docstring'''
UpperCAmelCase : str =parent
UpperCAmelCase : Tuple =batch_size
UpperCAmelCase : Optional[int] =seq_length
UpperCAmelCase : Optional[int] =is_training
UpperCAmelCase : Tuple =use_input_mask
UpperCAmelCase : List[Any] =use_token_type_ids
UpperCAmelCase : Optional[Any] =use_labels
UpperCAmelCase : Union[str, Any] =vocab_size
UpperCAmelCase : List[Any] =hidden_size
UpperCAmelCase : Optional[int] =rotary_dim
UpperCAmelCase : Union[str, Any] =num_hidden_layers
UpperCAmelCase : List[Any] =num_attention_heads
UpperCAmelCase : Dict =intermediate_size
UpperCAmelCase : Union[str, Any] =hidden_act
UpperCAmelCase : Any =hidden_dropout_prob
UpperCAmelCase : Dict =attention_probs_dropout_prob
UpperCAmelCase : Union[str, Any] =max_position_embeddings
UpperCAmelCase : str =initializer_range
UpperCAmelCase : Optional[int] =None
UpperCAmelCase : List[Any] =vocab_size - 1
UpperCAmelCase : Optional[Any] =vocab_size - 1
UpperCAmelCase : List[Any] =vocab_size - 1
def UpperCAmelCase__ ( self ) -> Optional[int]:
'''simple docstring'''
UpperCAmelCase : List[str] =ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
UpperCAmelCase : List[Any] =None
if self.use_input_mask:
UpperCAmelCase : Optional[Any] =random_attention_mask([self.batch_size, self.seq_length] )
UpperCAmelCase : Dict =GPTJConfig(
vocab_size=self.vocab_size , n_embd=self.hidden_size , n_layer=self.num_hidden_layers , n_head=self.num_attention_heads , n_positions=self.max_position_embeddings , use_cache=snake_case__ , bos_token_id=self.bos_token_id , eos_token_id=self.eos_token_id , pad_token_id=self.pad_token_id , rotary_dim=self.rotary_dim , )
return (config, input_ids, input_mask)
def UpperCAmelCase__ ( self ) -> Dict:
'''simple docstring'''
UpperCAmelCase : Tuple =self.prepare_config_and_inputs()
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase : Union[str, Any] =config_and_inputs
UpperCAmelCase : Tuple ={'''input_ids''': input_ids, '''attention_mask''': attention_mask}
return config, inputs_dict
def UpperCAmelCase__ ( self , snake_case__ , snake_case__ , snake_case__ , snake_case__ ) -> List[Any]:
'''simple docstring'''
UpperCAmelCase : Any =20
UpperCAmelCase : Any =model_class_name(snake_case__ )
UpperCAmelCase : str =model.init_cache(input_ids.shape[0] , snake_case__ )
UpperCAmelCase : Any =jnp.ones((input_ids.shape[0], max_decoder_length) , dtype='''i4''' )
UpperCAmelCase : Optional[Any] =jnp.broadcast_to(
jnp.arange(input_ids.shape[-1] - 1 )[None, :] , (input_ids.shape[0], input_ids.shape[-1] - 1) )
UpperCAmelCase : Optional[Any] =model(
input_ids[:, :-1] , attention_mask=snake_case__ , past_key_values=snake_case__ , position_ids=snake_case__ , )
UpperCAmelCase : List[str] =jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]] , dtype='''i4''' )
UpperCAmelCase : Optional[Any] =model(
input_ids[:, -1:] , attention_mask=snake_case__ , past_key_values=outputs_cache.past_key_values , position_ids=snake_case__ , )
UpperCAmelCase : List[Any] =model(snake_case__ )
UpperCAmelCase : Any =np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5]) ) )
self.parent.assertTrue(diff < 1e-3 , msg=f'''Max diff is {diff}''' )
def UpperCAmelCase__ ( self , snake_case__ , snake_case__ , snake_case__ , snake_case__ ) -> List[Any]:
'''simple docstring'''
UpperCAmelCase : Dict =20
UpperCAmelCase : Dict =model_class_name(snake_case__ )
UpperCAmelCase : Tuple =jnp.concatenate(
[attention_mask, jnp.zeros((attention_mask.shape[0], max_decoder_length - attention_mask.shape[1]) )] , axis=-1 , )
UpperCAmelCase : Dict =model.init_cache(input_ids.shape[0] , snake_case__ )
UpperCAmelCase : int =jnp.broadcast_to(
jnp.arange(input_ids.shape[-1] - 1 )[None, :] , (input_ids.shape[0], input_ids.shape[-1] - 1) )
UpperCAmelCase : Optional[Any] =model(
input_ids[:, :-1] , attention_mask=snake_case__ , past_key_values=snake_case__ , position_ids=snake_case__ , )
UpperCAmelCase : Any =jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]] , dtype='''i4''' )
UpperCAmelCase : str =model(
input_ids[:, -1:] , past_key_values=outputs_cache.past_key_values , attention_mask=snake_case__ , position_ids=snake_case__ , )
UpperCAmelCase : Any =model(snake_case__ , attention_mask=snake_case__ )
UpperCAmelCase : Dict =np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5]) ) )
self.parent.assertTrue(diff < 1e-3 , msg=f'''Max diff is {diff}''' )
@require_flax
class __snake_case ( lowerCamelCase__ , lowerCamelCase__ , unittest.TestCase ):
__lowerCamelCase : Tuple = (FlaxGPTJModel, FlaxGPTJForCausalLM) if is_flax_available() else ()
__lowerCamelCase : Optional[Any] = (FlaxGPTJForCausalLM,) if is_flax_available() else ()
def UpperCAmelCase__ ( self ) -> int:
'''simple docstring'''
UpperCAmelCase : Union[str, Any] =FlaxGPTJModelTester(self )
def UpperCAmelCase__ ( self ) -> str:
'''simple docstring'''
for model_class_name in self.all_model_classes:
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase : Dict =self.model_tester.prepare_config_and_inputs()
self.model_tester.check_use_cache_forward(snake_case__ , snake_case__ , snake_case__ , snake_case__ )
def UpperCAmelCase__ ( self ) -> Dict:
'''simple docstring'''
for model_class_name in self.all_model_classes:
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase : int =self.model_tester.prepare_config_and_inputs()
self.model_tester.check_use_cache_forward_with_attn_mask(
snake_case__ , snake_case__ , snake_case__ , snake_case__ )
@tooslow
def UpperCAmelCase__ ( self ) -> Union[str, Any]:
'''simple docstring'''
UpperCAmelCase : Tuple =GPTaTokenizer.from_pretrained('''gpt2''' , pad_token='''<|endoftext|>''' , padding_side='''left''' )
UpperCAmelCase : Optional[Any] =tokenizer(['''Hello this is a long string''', '''Hey'''] , return_tensors='''np''' , padding=snake_case__ , truncation=snake_case__ )
UpperCAmelCase : Optional[int] =FlaxGPTJForCausalLM.from_pretrained('''EleutherAI/gpt-j-6B''' )
UpperCAmelCase : str =False
UpperCAmelCase : Union[str, Any] =model.config.eos_token_id
UpperCAmelCase : List[Any] =jax.jit(model.generate )
UpperCAmelCase : Dict =jit_generate(
inputs['''input_ids'''] , attention_mask=inputs['''attention_mask'''] , pad_token_id=tokenizer.pad_token_id ).sequences
UpperCAmelCase : Any =tokenizer.batch_decode(snake_case__ , skip_special_tokens=snake_case__ )
UpperCAmelCase : Tuple =[
'''Hello this is a long string of text.\n\nI\'m trying to get the text of the''',
'''Hey, I\'m a little late to the party. I\'m going to''',
]
self.assertListEqual(snake_case__ , snake_case__ )
@is_pt_flax_cross_test
def UpperCAmelCase__ ( self ) -> List[Any]:
'''simple docstring'''
UpperCAmelCase , UpperCAmelCase : List[str] =self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__ ):
# prepare inputs
UpperCAmelCase : Union[str, Any] =self._prepare_for_class(snake_case__ , snake_case__ )
UpperCAmelCase : List[str] ={k: torch.tensor(v.tolist() ) for k, v in prepared_inputs_dict.items()}
# load corresponding PyTorch class
UpperCAmelCase : Any =model_class.__name__[4:] # Skip the "Flax" at the beginning
UpperCAmelCase : Any =getattr(snake_case__ , snake_case__ )
UpperCAmelCase , UpperCAmelCase : Union[str, Any] =pt_inputs['''input_ids'''].shape
UpperCAmelCase : Tuple =np.random.randint(0 , seq_length - 1 , size=(batch_size,) )
for batch_idx, start_index in enumerate(snake_case__ ):
UpperCAmelCase : int =0
UpperCAmelCase : Optional[int] =1
UpperCAmelCase : Optional[int] =0
UpperCAmelCase : Union[str, Any] =1
UpperCAmelCase : List[str] =pt_model_class(snake_case__ ).eval()
UpperCAmelCase : Optional[int] =model_class(snake_case__ , dtype=jnp.floataa )
UpperCAmelCase : Any =convert_pytorch_state_dict_to_flax(pt_model.state_dict() , snake_case__ )
UpperCAmelCase : Union[str, Any] =fx_state
with torch.no_grad():
UpperCAmelCase : Any =pt_model(**snake_case__ ).to_tuple()
UpperCAmelCase : Dict =fx_model(**snake_case__ ).to_tuple()
self.assertEqual(len(snake_case__ ) , len(snake_case__ ) , '''Output lengths differ between Flax and PyTorch''' )
for fx_output, pt_output in zip(snake_case__ , snake_case__ ):
self.assert_almost_equals(fx_output[:, -1] , pt_output[:, -1].numpy() , 4e-2 )
with tempfile.TemporaryDirectory() as tmpdirname:
pt_model.save_pretrained(snake_case__ )
UpperCAmelCase : str =model_class.from_pretrained(snake_case__ , from_pt=snake_case__ )
UpperCAmelCase : int =fx_model_loaded(**snake_case__ ).to_tuple()
self.assertEqual(
len(snake_case__ ) , len(snake_case__ ) , '''Output lengths differ between Flax and PyTorch''' )
for fx_output_loaded, pt_output in zip(snake_case__ , snake_case__ ):
self.assert_almost_equals(fx_output_loaded[:, -1] , pt_output[:, -1].numpy() , 4e-2 )
@is_pt_flax_cross_test
def UpperCAmelCase__ ( self ) -> Optional[Any]:
'''simple docstring'''
UpperCAmelCase , UpperCAmelCase : Any =self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__ ):
# prepare inputs
UpperCAmelCase : Union[str, Any] =self._prepare_for_class(snake_case__ , snake_case__ )
UpperCAmelCase : Union[str, Any] ={k: torch.tensor(v.tolist() ) for k, v in prepared_inputs_dict.items()}
# load corresponding PyTorch class
UpperCAmelCase : int =model_class.__name__[4:] # Skip the "Flax" at the beginning
UpperCAmelCase : int =getattr(snake_case__ , snake_case__ )
UpperCAmelCase : Dict =pt_model_class(snake_case__ ).eval()
UpperCAmelCase : str =model_class(snake_case__ , dtype=jnp.floataa )
UpperCAmelCase : Optional[Any] =load_flax_weights_in_pytorch_model(snake_case__ , fx_model.params )
UpperCAmelCase , UpperCAmelCase : Optional[int] =pt_inputs['''input_ids'''].shape
UpperCAmelCase : Optional[int] =np.random.randint(0 , seq_length - 1 , size=(batch_size,) )
for batch_idx, start_index in enumerate(snake_case__ ):
UpperCAmelCase : str =0
UpperCAmelCase : Any =1
UpperCAmelCase : List[Any] =0
UpperCAmelCase : Tuple =1
# make sure weights are tied in PyTorch
pt_model.tie_weights()
with torch.no_grad():
UpperCAmelCase : Optional[Any] =pt_model(**snake_case__ ).to_tuple()
UpperCAmelCase : List[Any] =fx_model(**snake_case__ ).to_tuple()
self.assertEqual(len(snake_case__ ) , len(snake_case__ ) , '''Output lengths differ between Flax and PyTorch''' )
for fx_output, pt_output in zip(snake_case__ , snake_case__ ):
self.assert_almost_equals(fx_output[:, -1] , pt_output[:, -1].numpy() , 4e-2 )
with tempfile.TemporaryDirectory() as tmpdirname:
fx_model.save_pretrained(snake_case__ )
UpperCAmelCase : Tuple =pt_model_class.from_pretrained(snake_case__ , from_flax=snake_case__ )
with torch.no_grad():
UpperCAmelCase : Any =pt_model_loaded(**snake_case__ ).to_tuple()
self.assertEqual(
len(snake_case__ ) , len(snake_case__ ) , '''Output lengths differ between Flax and PyTorch''' )
for fx_output, pt_output in zip(snake_case__ , snake_case__ ):
self.assert_almost_equals(fx_output[:, -1] , pt_output[:, -1].numpy() , 4e-2 )
@tooslow
def UpperCAmelCase__ ( self ) -> List[str]:
'''simple docstring'''
for model_class_name in self.all_model_classes:
UpperCAmelCase : str =model_class_name.from_pretrained('''EleutherAI/gpt-j-6B''' )
UpperCAmelCase : Tuple =model(np.ones((1, 1) ) )
self.assertIsNotNone(snake_case__ )
| 348 | 1 |
import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin
@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->UnCLIP
class __snake_case ( lowerCamelCase__ ):
__lowerCamelCase : torch.FloatTensor
__lowerCamelCase : Optional[torch.FloatTensor] = None
def lowerCAmelCase_ ( __lowerCAmelCase , __lowerCAmelCase=0.999 , __lowerCAmelCase="cosine" , )-> str:
'''simple docstring'''
if alpha_transform_type == "cosine":
def alpha_bar_fn(__lowerCAmelCase ):
return math.cos((t + 0.008) / 1.008 * math.pi / 2 ) ** 2
elif alpha_transform_type == "exp":
def alpha_bar_fn(__lowerCAmelCase ):
return math.exp(t * -12.0 )
else:
raise ValueError(f'''Unsupported alpha_tranform_type: {alpha_transform_type}''' )
UpperCAmelCase : Optional[int] =[]
for i in range(__lowerCAmelCase ):
UpperCAmelCase : Tuple =i / num_diffusion_timesteps
UpperCAmelCase : List[Any] =(i + 1) / num_diffusion_timesteps
betas.append(min(1 - alpha_bar_fn(__lowerCAmelCase ) / alpha_bar_fn(__lowerCAmelCase ) , __lowerCAmelCase ) )
return torch.tensor(__lowerCAmelCase , dtype=torch.floataa )
class __snake_case ( lowerCamelCase__ , lowerCamelCase__ ):
@register_to_config
def __init__( self , snake_case__ = 1000 , snake_case__ = "fixed_small_log" , snake_case__ = True , snake_case__ = 1.0 , snake_case__ = "epsilon" , snake_case__ = "squaredcos_cap_v2" , ) -> Optional[int]:
'''simple docstring'''
if beta_schedule != "squaredcos_cap_v2":
raise ValueError('''UnCLIPScheduler only supports `beta_schedule`: \'squaredcos_cap_v2\'''' )
UpperCAmelCase : Any =betas_for_alpha_bar(snake_case__ )
UpperCAmelCase : Optional[int] =1.0 - self.betas
UpperCAmelCase : Optional[int] =torch.cumprod(self.alphas , dim=0 )
UpperCAmelCase : Optional[int] =torch.tensor(1.0 )
# standard deviation of the initial noise distribution
UpperCAmelCase : Optional[Any] =1.0
# setable values
UpperCAmelCase : Any =None
UpperCAmelCase : Union[str, Any] =torch.from_numpy(np.arange(0 , snake_case__ )[::-1].copy() )
UpperCAmelCase : List[str] =variance_type
def UpperCAmelCase__ ( self , snake_case__ , snake_case__ = None ) -> torch.FloatTensor:
'''simple docstring'''
return sample
def UpperCAmelCase__ ( self , snake_case__ , snake_case__ = None ) -> Optional[int]:
'''simple docstring'''
UpperCAmelCase : Union[str, Any] =num_inference_steps
UpperCAmelCase : Any =(self.config.num_train_timesteps - 1) / (self.num_inference_steps - 1)
UpperCAmelCase : List[Any] =(np.arange(0 , snake_case__ ) * step_ratio).round()[::-1].copy().astype(np.intaa )
UpperCAmelCase : Tuple =torch.from_numpy(snake_case__ ).to(snake_case__ )
def UpperCAmelCase__ ( self , snake_case__ , snake_case__=None , snake_case__=None , snake_case__=None ) -> Tuple:
'''simple docstring'''
if prev_timestep is None:
UpperCAmelCase : str =t - 1
UpperCAmelCase : Optional[int] =self.alphas_cumprod[t]
UpperCAmelCase : Dict =self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
UpperCAmelCase : Dict =1 - alpha_prod_t
UpperCAmelCase : int =1 - alpha_prod_t_prev
if prev_timestep == t - 1:
UpperCAmelCase : List[str] =self.betas[t]
else:
UpperCAmelCase : Dict =1 - alpha_prod_t / alpha_prod_t_prev
# For t > 0, compute predicted variance βt (see formula (6) and (7) from https://arxiv.org/pdf/2006.11239.pdf)
# and sample from it to get previous sample
# x_{t-1} ~ N(pred_prev_sample, variance) == add variance to pred_sample
UpperCAmelCase : Dict =beta_prod_t_prev / beta_prod_t * beta
if variance_type is None:
UpperCAmelCase : int =self.config.variance_type
# hacks - were probably added for training stability
if variance_type == "fixed_small_log":
UpperCAmelCase : Union[str, Any] =torch.log(torch.clamp(snake_case__ , min=1e-20 ) )
UpperCAmelCase : Optional[Any] =torch.exp(0.5 * variance )
elif variance_type == "learned_range":
# NOTE difference with DDPM scheduler
UpperCAmelCase : Any =variance.log()
UpperCAmelCase : Dict =beta.log()
UpperCAmelCase : Dict =(predicted_variance + 1) / 2
UpperCAmelCase : List[Any] =frac * max_log + (1 - frac) * min_log
return variance
def UpperCAmelCase__ ( self , snake_case__ , snake_case__ , snake_case__ , snake_case__ = None , snake_case__=None , snake_case__ = True , ) -> Union[UnCLIPSchedulerOutput, Tuple]:
'''simple docstring'''
UpperCAmelCase : str =timestep
if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type == "learned_range":
UpperCAmelCase , UpperCAmelCase : Union[str, Any] =torch.split(snake_case__ , sample.shape[1] , dim=1 )
else:
UpperCAmelCase : int =None
# 1. compute alphas, betas
if prev_timestep is None:
UpperCAmelCase : List[str] =t - 1
UpperCAmelCase : Optional[Any] =self.alphas_cumprod[t]
UpperCAmelCase : List[Any] =self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
UpperCAmelCase : Union[str, Any] =1 - alpha_prod_t
UpperCAmelCase : int =1 - alpha_prod_t_prev
if prev_timestep == t - 1:
UpperCAmelCase : List[Any] =self.betas[t]
UpperCAmelCase : Tuple =self.alphas[t]
else:
UpperCAmelCase : str =1 - alpha_prod_t / alpha_prod_t_prev
UpperCAmelCase : Optional[int] =1 - beta
# 2. compute predicted original sample from predicted noise also called
# "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
if self.config.prediction_type == "epsilon":
UpperCAmelCase : Tuple =(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
elif self.config.prediction_type == "sample":
UpperCAmelCase : Union[str, Any] =model_output
else:
raise ValueError(
f'''prediction_type given as {self.config.prediction_type} must be one of `epsilon` or `sample`'''
''' for the UnCLIPScheduler.''' )
# 3. Clip "predicted x_0"
if self.config.clip_sample:
UpperCAmelCase : Dict =torch.clamp(
snake_case__ , -self.config.clip_sample_range , self.config.clip_sample_range )
# 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
UpperCAmelCase : Optional[Any] =(alpha_prod_t_prev ** 0.5 * beta) / beta_prod_t
UpperCAmelCase : List[str] =alpha ** 0.5 * beta_prod_t_prev / beta_prod_t
# 5. Compute predicted previous sample µ_t
# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
UpperCAmelCase : List[Any] =pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample
# 6. Add noise
UpperCAmelCase : List[Any] =0
if t > 0:
UpperCAmelCase : str =randn_tensor(
model_output.shape , dtype=model_output.dtype , generator=snake_case__ , device=model_output.device )
UpperCAmelCase : Optional[int] =self._get_variance(
snake_case__ , predicted_variance=snake_case__ , prev_timestep=snake_case__ , )
if self.variance_type == "fixed_small_log":
UpperCAmelCase : Optional[Any] =variance
elif self.variance_type == "learned_range":
UpperCAmelCase : Tuple =(0.5 * variance).exp()
else:
raise ValueError(
f'''variance_type given as {self.variance_type} must be one of `fixed_small_log` or `learned_range`'''
''' for the UnCLIPScheduler.''' )
UpperCAmelCase : List[Any] =variance * variance_noise
UpperCAmelCase : int =pred_prev_sample + variance
if not return_dict:
return (pred_prev_sample,)
return UnCLIPSchedulerOutput(prev_sample=snake_case__ , pred_original_sample=snake_case__ )
def UpperCAmelCase__ ( self , snake_case__ , snake_case__ , snake_case__ , ) -> torch.FloatTensor:
'''simple docstring'''
UpperCAmelCase : str =self.alphas_cumprod.to(device=original_samples.device , dtype=original_samples.dtype )
UpperCAmelCase : int =timesteps.to(original_samples.device )
UpperCAmelCase : Union[str, Any] =alphas_cumprod[timesteps] ** 0.5
UpperCAmelCase : Any =sqrt_alpha_prod.flatten()
while len(sqrt_alpha_prod.shape ) < len(original_samples.shape ):
UpperCAmelCase : List[str] =sqrt_alpha_prod.unsqueeze(-1 )
UpperCAmelCase : Any =(1 - alphas_cumprod[timesteps]) ** 0.5
UpperCAmelCase : Optional[int] =sqrt_one_minus_alpha_prod.flatten()
while len(sqrt_one_minus_alpha_prod.shape ) < len(original_samples.shape ):
UpperCAmelCase : int =sqrt_one_minus_alpha_prod.unsqueeze(-1 )
UpperCAmelCase : Any =sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
return noisy_samples
| 348 | from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
__snake_case = {
'''configuration_bloom''': ['''BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''BloomConfig''', '''BloomOnnxConfig'''],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__snake_case = ['''BloomTokenizerFast''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__snake_case = [
'''BLOOM_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''BloomForCausalLM''',
'''BloomModel''',
'''BloomPreTrainedModel''',
'''BloomForSequenceClassification''',
'''BloomForTokenClassification''',
'''BloomForQuestionAnswering''',
]
if TYPE_CHECKING:
from .configuration_bloom import BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP, BloomConfig, BloomOnnxConfig
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_bloom_fast import BloomTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_bloom import (
BLOOM_PRETRAINED_MODEL_ARCHIVE_LIST,
BloomForCausalLM,
BloomForQuestionAnswering,
BloomForSequenceClassification,
BloomForTokenClassification,
BloomModel,
BloomPreTrainedModel,
)
else:
import sys
__snake_case = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 348 | 1 |
import inspect
import unittest
from datasets import load_dataset
from packaging import version
from transformers import BeitConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import (
MODEL_MAPPING,
BeitForImageClassification,
BeitForMaskedImageModeling,
BeitForSemanticSegmentation,
BeitModel,
)
from transformers.models.beit.modeling_beit import BEIT_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
import PIL
from PIL import Image
from transformers import BeitImageProcessor
class __snake_case :
def __init__( self , snake_case__ , snake_case__=100 , snake_case__=13 , snake_case__=30 , snake_case__=2 , snake_case__=3 , snake_case__=True , snake_case__=True , snake_case__=32 , snake_case__=4 , snake_case__=4 , snake_case__=37 , snake_case__="gelu" , snake_case__=0.1 , snake_case__=0.1 , snake_case__=10 , snake_case__=0.02 , snake_case__=3 , snake_case__=None , snake_case__=[0, 1, 2, 3] , ) -> Tuple:
'''simple docstring'''
UpperCAmelCase : List[str] =parent
UpperCAmelCase : Tuple =100
UpperCAmelCase : Tuple =batch_size
UpperCAmelCase : List[str] =image_size
UpperCAmelCase : List[Any] =patch_size
UpperCAmelCase : Any =num_channels
UpperCAmelCase : Dict =is_training
UpperCAmelCase : int =use_labels
UpperCAmelCase : str =hidden_size
UpperCAmelCase : int =num_hidden_layers
UpperCAmelCase : Tuple =num_attention_heads
UpperCAmelCase : Optional[Any] =intermediate_size
UpperCAmelCase : List[str] =hidden_act
UpperCAmelCase : Optional[int] =hidden_dropout_prob
UpperCAmelCase : List[Any] =attention_probs_dropout_prob
UpperCAmelCase : Optional[Any] =type_sequence_label_size
UpperCAmelCase : str =initializer_range
UpperCAmelCase : Union[str, Any] =scope
UpperCAmelCase : Optional[Any] =out_indices
UpperCAmelCase : Dict =num_labels
# in BeiT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
UpperCAmelCase : int =(image_size // patch_size) ** 2
UpperCAmelCase : Tuple =num_patches + 1
def UpperCAmelCase__ ( self ) -> List[str]:
'''simple docstring'''
UpperCAmelCase : Any =floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
UpperCAmelCase : str =None
UpperCAmelCase : int =None
if self.use_labels:
UpperCAmelCase : Union[str, Any] =ids_tensor([self.batch_size] , self.type_sequence_label_size )
UpperCAmelCase : Dict =ids_tensor([self.batch_size, self.image_size, self.image_size] , self.num_labels )
UpperCAmelCase : Any =self.get_config()
return config, pixel_values, labels, pixel_labels
def UpperCAmelCase__ ( self ) -> int:
'''simple docstring'''
return BeitConfig(
vocab_size=self.vocab_size , image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , is_decoder=snake_case__ , initializer_range=self.initializer_range , out_indices=self.out_indices , )
def UpperCAmelCase__ ( self , snake_case__ , snake_case__ , snake_case__ , snake_case__ ) -> List[Any]:
'''simple docstring'''
UpperCAmelCase : List[Any] =BeitModel(config=snake_case__ )
model.to(snake_case__ )
model.eval()
UpperCAmelCase : Tuple =model(snake_case__ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def UpperCAmelCase__ ( self , snake_case__ , snake_case__ , snake_case__ , snake_case__ ) -> Union[str, Any]:
'''simple docstring'''
UpperCAmelCase : Dict =BeitForMaskedImageModeling(config=snake_case__ )
model.to(snake_case__ )
model.eval()
UpperCAmelCase : List[str] =model(snake_case__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length - 1, self.vocab_size) )
def UpperCAmelCase__ ( self , snake_case__ , snake_case__ , snake_case__ , snake_case__ ) -> List[Any]:
'''simple docstring'''
UpperCAmelCase : List[Any] =self.type_sequence_label_size
UpperCAmelCase : Optional[int] =BeitForImageClassification(snake_case__ )
model.to(snake_case__ )
model.eval()
UpperCAmelCase : Optional[int] =model(snake_case__ , labels=snake_case__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
# test greyscale images
UpperCAmelCase : Any =1
UpperCAmelCase : Union[str, Any] =BeitForImageClassification(snake_case__ )
model.to(snake_case__ )
model.eval()
UpperCAmelCase : Union[str, Any] =floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
UpperCAmelCase : Optional[Any] =model(snake_case__ , labels=snake_case__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
def UpperCAmelCase__ ( self , snake_case__ , snake_case__ , snake_case__ , snake_case__ ) -> Dict:
'''simple docstring'''
UpperCAmelCase : int =self.num_labels
UpperCAmelCase : int =BeitForSemanticSegmentation(snake_case__ )
model.to(snake_case__ )
model.eval()
UpperCAmelCase : List[Any] =model(snake_case__ )
self.parent.assertEqual(
result.logits.shape , (self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2) )
UpperCAmelCase : str =model(snake_case__ , labels=snake_case__ )
self.parent.assertEqual(
result.logits.shape , (self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2) )
def UpperCAmelCase__ ( self ) -> List[Any]:
'''simple docstring'''
UpperCAmelCase : Optional[Any] =self.prepare_config_and_inputs()
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase : List[str] =config_and_inputs
UpperCAmelCase : Any ={'''pixel_values''': pixel_values}
return config, inputs_dict
@require_torch
class __snake_case ( lowerCamelCase__ , lowerCamelCase__ , unittest.TestCase ):
__lowerCamelCase : Tuple = (
(BeitModel, BeitForImageClassification, BeitForMaskedImageModeling, BeitForSemanticSegmentation)
if is_torch_available()
else ()
)
__lowerCamelCase : int = (
{
"""feature-extraction""": BeitModel,
"""image-classification""": BeitForImageClassification,
"""image-segmentation""": BeitForSemanticSegmentation,
}
if is_torch_available()
else {}
)
__lowerCamelCase : Tuple = False
__lowerCamelCase : Optional[Any] = False
__lowerCamelCase : Any = False
def UpperCAmelCase__ ( self ) -> List[str]:
'''simple docstring'''
UpperCAmelCase : Tuple =BeitModelTester(self )
UpperCAmelCase : Tuple =ConfigTester(self , config_class=snake_case__ , has_text_modality=snake_case__ , hidden_size=37 )
def UpperCAmelCase__ ( self ) -> List[Any]:
'''simple docstring'''
self.config_tester.run_common_tests()
@unittest.skip(reason='''BEiT does not use inputs_embeds''' )
def UpperCAmelCase__ ( self ) -> int:
'''simple docstring'''
pass
@require_torch_multi_gpu
@unittest.skip(reason='''BEiT has some layers using `add_module` which doesn\'t work well with `nn.DataParallel`''' )
def UpperCAmelCase__ ( self ) -> Dict:
'''simple docstring'''
pass
def UpperCAmelCase__ ( self ) -> Tuple:
'''simple docstring'''
UpperCAmelCase , UpperCAmelCase : Tuple =self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
UpperCAmelCase : Optional[Any] =model_class(snake_case__ )
self.assertIsInstance(model.get_input_embeddings() , (nn.Module) )
UpperCAmelCase : Any =model.get_output_embeddings()
self.assertTrue(x is None or isinstance(snake_case__ , nn.Linear ) )
def UpperCAmelCase__ ( self ) -> List[Any]:
'''simple docstring'''
UpperCAmelCase , UpperCAmelCase : Union[str, Any] =self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
UpperCAmelCase : Optional[Any] =model_class(snake_case__ )
UpperCAmelCase : Dict =inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
UpperCAmelCase : Union[str, Any] =[*signature.parameters.keys()]
UpperCAmelCase : Optional[Any] =['''pixel_values''']
self.assertListEqual(arg_names[:1] , snake_case__ )
def UpperCAmelCase__ ( self ) -> int:
'''simple docstring'''
UpperCAmelCase : Optional[int] =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*snake_case__ )
def UpperCAmelCase__ ( self ) -> Optional[int]:
'''simple docstring'''
UpperCAmelCase : List[str] =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*snake_case__ )
def UpperCAmelCase__ ( self ) -> List[str]:
'''simple docstring'''
UpperCAmelCase : Optional[Any] =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*snake_case__ )
def UpperCAmelCase__ ( self ) -> List[str]:
'''simple docstring'''
UpperCAmelCase : Union[str, Any] =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_semantic_segmentation(*snake_case__ )
def UpperCAmelCase__ ( self ) -> List[Any]:
'''simple docstring'''
if not self.model_tester.is_training:
return
UpperCAmelCase , UpperCAmelCase : Optional[int] =self.model_tester.prepare_config_and_inputs_for_common()
UpperCAmelCase : str =True
for model_class in self.all_model_classes:
# we don't test BeitForMaskedImageModeling
if model_class in [*get_values(snake_case__ ), BeitForMaskedImageModeling]:
continue
UpperCAmelCase : List[Any] =model_class(snake_case__ )
model.to(snake_case__ )
model.train()
UpperCAmelCase : Optional[int] =self._prepare_for_class(snake_case__ , snake_case__ , return_labels=snake_case__ )
UpperCAmelCase : str =model(**snake_case__ ).loss
loss.backward()
def UpperCAmelCase__ ( self ) -> Optional[int]:
'''simple docstring'''
UpperCAmelCase , UpperCAmelCase : Any =self.model_tester.prepare_config_and_inputs_for_common()
if not self.model_tester.is_training:
return
UpperCAmelCase : int =False
UpperCAmelCase : Any =True
for model_class in self.all_model_classes:
# we don't test BeitForMaskedImageModeling
if (
model_class in [*get_values(snake_case__ ), BeitForMaskedImageModeling]
or not model_class.supports_gradient_checkpointing
):
continue
UpperCAmelCase : List[str] =model_class(snake_case__ )
model.gradient_checkpointing_enable()
model.to(snake_case__ )
model.train()
UpperCAmelCase : Optional[int] =self._prepare_for_class(snake_case__ , snake_case__ , return_labels=snake_case__ )
UpperCAmelCase : List[Any] =model(**snake_case__ ).loss
loss.backward()
def UpperCAmelCase__ ( self ) -> Union[str, Any]:
'''simple docstring'''
UpperCAmelCase , UpperCAmelCase : Any =self.model_tester.prepare_config_and_inputs_for_common()
UpperCAmelCase : Tuple =_config_zero_init(snake_case__ )
for model_class in self.all_model_classes:
UpperCAmelCase : Dict =model_class(config=snake_case__ )
for name, param in model.named_parameters():
# we skip lambda parameters as these require special initial values
# determined by config.layer_scale_init_value
if "lambda" in name:
continue
if param.requires_grad:
self.assertIn(
((param.data.mean() * 1e9).round() / 1e9).item() , [0.0, 1.0] , msg=f'''Parameter {name} of model {model_class} seems not properly initialized''' , )
@slow
def UpperCAmelCase__ ( self ) -> List[Any]:
'''simple docstring'''
for model_name in BEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
UpperCAmelCase : Optional[Any] =BeitModel.from_pretrained(snake_case__ )
self.assertIsNotNone(snake_case__ )
def lowerCAmelCase_ ( )-> Optional[int]:
'''simple docstring'''
UpperCAmelCase : List[str] =Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
return image
@require_torch
@require_vision
class __snake_case ( unittest.TestCase ):
@cached_property
def UpperCAmelCase__ ( self ) -> Optional[Any]:
'''simple docstring'''
return BeitImageProcessor.from_pretrained('''microsoft/beit-base-patch16-224''' ) if is_vision_available() else None
@slow
def UpperCAmelCase__ ( self ) -> Tuple:
'''simple docstring'''
UpperCAmelCase : str =BeitForMaskedImageModeling.from_pretrained('''microsoft/beit-base-patch16-224-pt22k''' ).to(snake_case__ )
UpperCAmelCase : Optional[Any] =self.default_image_processor
UpperCAmelCase : str =prepare_img()
UpperCAmelCase : List[Any] =image_processor(images=snake_case__ , return_tensors='''pt''' ).pixel_values.to(snake_case__ )
# prepare bool_masked_pos
UpperCAmelCase : Tuple =torch.ones((1, 196) , dtype=torch.bool ).to(snake_case__ )
# forward pass
with torch.no_grad():
UpperCAmelCase : Any =model(pixel_values=snake_case__ , bool_masked_pos=snake_case__ )
UpperCAmelCase : Dict =outputs.logits
# verify the logits
UpperCAmelCase : str =torch.Size((1, 196, 8192) )
self.assertEqual(logits.shape , snake_case__ )
UpperCAmelCase : str =torch.tensor(
[[-3.2437, 0.5072, -13.9174], [-3.2456, 0.4948, -13.9401], [-3.2033, 0.5121, -13.8550]] ).to(snake_case__ )
self.assertTrue(torch.allclose(logits[bool_masked_pos][:3, :3] , snake_case__ , atol=1e-2 ) )
@slow
def UpperCAmelCase__ ( self ) -> Union[str, Any]:
'''simple docstring'''
UpperCAmelCase : Optional[Any] =BeitForImageClassification.from_pretrained('''microsoft/beit-base-patch16-224''' ).to(snake_case__ )
UpperCAmelCase : List[Any] =self.default_image_processor
UpperCAmelCase : Optional[int] =prepare_img()
UpperCAmelCase : Dict =image_processor(images=snake_case__ , return_tensors='''pt''' ).to(snake_case__ )
# forward pass
with torch.no_grad():
UpperCAmelCase : Optional[Any] =model(**snake_case__ )
UpperCAmelCase : Optional[Any] =outputs.logits
# verify the logits
UpperCAmelCase : Optional[int] =torch.Size((1, 1000) )
self.assertEqual(logits.shape , snake_case__ )
UpperCAmelCase : Union[str, Any] =torch.tensor([-1.2385, -1.0987, -1.0108] ).to(snake_case__ )
self.assertTrue(torch.allclose(logits[0, :3] , snake_case__ , atol=1e-4 ) )
UpperCAmelCase : List[Any] =281
self.assertEqual(logits.argmax(-1 ).item() , snake_case__ )
@slow
def UpperCAmelCase__ ( self ) -> Optional[Any]:
'''simple docstring'''
UpperCAmelCase : Union[str, Any] =BeitForImageClassification.from_pretrained('''microsoft/beit-large-patch16-224-pt22k-ft22k''' ).to(
snake_case__ )
UpperCAmelCase : Union[str, Any] =self.default_image_processor
UpperCAmelCase : Any =prepare_img()
UpperCAmelCase : Dict =image_processor(images=snake_case__ , return_tensors='''pt''' ).to(snake_case__ )
# forward pass
with torch.no_grad():
UpperCAmelCase : List[Any] =model(**snake_case__ )
UpperCAmelCase : Any =outputs.logits
# verify the logits
UpperCAmelCase : int =torch.Size((1, 2_1841) )
self.assertEqual(logits.shape , snake_case__ )
UpperCAmelCase : List[str] =torch.tensor([1.6881, -0.2787, 0.5901] ).to(snake_case__ )
self.assertTrue(torch.allclose(logits[0, :3] , snake_case__ , atol=1e-4 ) )
UpperCAmelCase : int =2396
self.assertEqual(logits.argmax(-1 ).item() , snake_case__ )
@slow
def UpperCAmelCase__ ( self ) -> Optional[Any]:
'''simple docstring'''
UpperCAmelCase : Tuple =BeitForSemanticSegmentation.from_pretrained('''microsoft/beit-base-finetuned-ade-640-640''' )
UpperCAmelCase : List[Any] =model.to(snake_case__ )
UpperCAmelCase : str =BeitImageProcessor(do_resize=snake_case__ , size=640 , do_center_crop=snake_case__ )
UpperCAmelCase : Union[str, Any] =load_dataset('''hf-internal-testing/fixtures_ade20k''' , split='''test''' )
UpperCAmelCase : List[Any] =Image.open(ds[0]['''file'''] )
UpperCAmelCase : List[str] =image_processor(images=snake_case__ , return_tensors='''pt''' ).to(snake_case__ )
# forward pass
with torch.no_grad():
UpperCAmelCase : Optional[Any] =model(**snake_case__ )
UpperCAmelCase : Dict =outputs.logits
# verify the logits
UpperCAmelCase : Any =torch.Size((1, 150, 160, 160) )
self.assertEqual(logits.shape , snake_case__ )
UpperCAmelCase : int =version.parse(PIL.__version__ ) < version.parse('''9.0.0''' )
if is_pillow_less_than_a:
UpperCAmelCase : Optional[Any] =torch.tensor(
[
[[-4.9225, -2.3954, -3.0522], [-2.8822, -1.0046, -1.7561], [-2.9549, -1.3228, -2.1347]],
[[-5.8168, -3.4129, -4.0778], [-3.8651, -2.2214, -3.0277], [-3.8356, -2.4643, -3.3535]],
[[-0.0078, 3.9952, 4.0754], [2.9856, 4.6944, 5.0035], [3.2413, 4.7813, 4.9969]],
] , device=snake_case__ , )
else:
UpperCAmelCase : List[str] =torch.tensor(
[
[[-4.8960, -2.3688, -3.0355], [-2.8478, -0.9836, -1.7418], [-2.9449, -1.3332, -2.1456]],
[[-5.8081, -3.4124, -4.1006], [-3.8561, -2.2081, -3.0323], [-3.8365, -2.4601, -3.3669]],
[[-0.0309, 3.9868, 4.0540], [2.9640, 4.6877, 4.9976], [3.2081, 4.7690, 4.9942]],
] , device=snake_case__ , )
self.assertTrue(torch.allclose(logits[0, :3, :3, :3] , snake_case__ , atol=1e-4 ) )
@slow
def UpperCAmelCase__ ( self ) -> Union[str, Any]:
'''simple docstring'''
UpperCAmelCase : List[Any] =BeitForSemanticSegmentation.from_pretrained('''microsoft/beit-base-finetuned-ade-640-640''' )
UpperCAmelCase : Union[str, Any] =model.to(snake_case__ )
UpperCAmelCase : Tuple =BeitImageProcessor(do_resize=snake_case__ , size=640 , do_center_crop=snake_case__ )
UpperCAmelCase : Dict =load_dataset('''hf-internal-testing/fixtures_ade20k''' , split='''test''' )
UpperCAmelCase : int =Image.open(ds[0]['''file'''] )
UpperCAmelCase : int =image_processor(images=snake_case__ , return_tensors='''pt''' ).to(snake_case__ )
# forward pass
with torch.no_grad():
UpperCAmelCase : List[str] =model(**snake_case__ )
UpperCAmelCase : Dict =outputs.logits.detach().cpu()
UpperCAmelCase : Dict =image_processor.post_process_semantic_segmentation(outputs=snake_case__ , target_sizes=[(500, 300)] )
UpperCAmelCase : Any =torch.Size((500, 300) )
self.assertEqual(segmentation[0].shape , snake_case__ )
UpperCAmelCase : List[str] =image_processor.post_process_semantic_segmentation(outputs=snake_case__ )
UpperCAmelCase : Tuple =torch.Size((160, 160) )
self.assertEqual(segmentation[0].shape , snake_case__ )
| 348 | import os
from typing import Dict, List, Tuple, TypeVar, Union
__snake_case = TypeVar('''T''')
__snake_case = Union[List[T], Tuple[T, ...]]
__snake_case = Union[T, List[T], Dict[str, T]]
__snake_case = Union[str, bytes, os.PathLike]
| 348 | 1 |
# We ignore warnings about stepping the scheduler since we step it ourselves during gradient accumulation
import warnings
from .state import AcceleratorState, GradientState
warnings.filterwarnings('''ignore''', category=UserWarning, module='''torch.optim.lr_scheduler''')
class __snake_case :
def __init__( self , snake_case__ , snake_case__ , snake_case__ = True , snake_case__ = False ) -> Optional[int]:
'''simple docstring'''
UpperCAmelCase : str =scheduler
UpperCAmelCase : Union[str, Any] =optimizers if isinstance(snake_case__ , (list, tuple) ) else [optimizers]
UpperCAmelCase : int =split_batches
UpperCAmelCase : int =step_with_optimizer
UpperCAmelCase : Union[str, Any] =GradientState()
def UpperCAmelCase__ ( self , *snake_case__ , **snake_case__ ) -> Tuple:
'''simple docstring'''
if not self.step_with_optimizer:
# No link between scheduler and optimizer -> just step
self.scheduler.step(*snake_case__ , **snake_case__ )
return
# Otherwise, first make sure the optimizer was stepped.
if not self.gradient_state.sync_gradients:
if self.gradient_state.adjust_scheduler:
self.scheduler._step_count += 1
return
for opt in self.optimizers:
if opt.step_was_skipped:
return
if self.split_batches:
# Split batches -> the training dataloader batch size is not changed so one step per training step
self.scheduler.step(*snake_case__ , **snake_case__ )
else:
# Otherwise the training dataloader batch size was multiplied by `num_processes`, so we need to do
# num_processes steps per training step
UpperCAmelCase : Tuple =AcceleratorState().num_processes
for _ in range(snake_case__ ):
# Special case when using OneCycle and `drop_last` was not used
if hasattr(self.scheduler , '''total_steps''' ):
if self.scheduler._step_count <= self.scheduler.total_steps:
self.scheduler.step(*snake_case__ , **snake_case__ )
else:
self.scheduler.step(*snake_case__ , **snake_case__ )
def UpperCAmelCase__ ( self ) -> Union[str, Any]:
'''simple docstring'''
return self.scheduler.get_last_lr()
def UpperCAmelCase__ ( self ) -> Union[str, Any]:
'''simple docstring'''
return self.scheduler.state_dict()
def UpperCAmelCase__ ( self , snake_case__ ) -> Any:
'''simple docstring'''
self.scheduler.load_state_dict(snake_case__ )
def UpperCAmelCase__ ( self ) -> List[Any]:
'''simple docstring'''
return self.scheduler.get_lr()
def UpperCAmelCase__ ( self , *snake_case__ , **snake_case__ ) -> List[str]:
'''simple docstring'''
return self.scheduler.print_lr(*snake_case__ , **snake_case__ )
| 348 | import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_big_bird import BigBirdTokenizer
else:
__snake_case = None
__snake_case = logging.get_logger(__name__)
__snake_case = {'''vocab_file''': '''spiece.model''', '''tokenizer_file''': '''tokenizer.json'''}
__snake_case = {
'''vocab_file''': {
'''google/bigbird-roberta-base''': '''https://huggingface.co/google/bigbird-roberta-base/resolve/main/spiece.model''',
'''google/bigbird-roberta-large''': (
'''https://huggingface.co/google/bigbird-roberta-large/resolve/main/spiece.model'''
),
'''google/bigbird-base-trivia-itc''': (
'''https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/spiece.model'''
),
},
'''tokenizer_file''': {
'''google/bigbird-roberta-base''': (
'''https://huggingface.co/google/bigbird-roberta-base/resolve/main/tokenizer.json'''
),
'''google/bigbird-roberta-large''': (
'''https://huggingface.co/google/bigbird-roberta-large/resolve/main/tokenizer.json'''
),
'''google/bigbird-base-trivia-itc''': (
'''https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/tokenizer.json'''
),
},
}
__snake_case = {
'''google/bigbird-roberta-base''': 40_96,
'''google/bigbird-roberta-large''': 40_96,
'''google/bigbird-base-trivia-itc''': 40_96,
}
__snake_case = '''▁'''
class __snake_case ( lowerCamelCase__ ):
__lowerCamelCase : Dict = VOCAB_FILES_NAMES
__lowerCamelCase : List[Any] = PRETRAINED_VOCAB_FILES_MAP
__lowerCamelCase : Dict = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
__lowerCamelCase : List[str] = BigBirdTokenizer
__lowerCamelCase : Any = ["""input_ids""", """attention_mask"""]
__lowerCamelCase : List[int] = []
def __init__( self , snake_case__=None , snake_case__=None , snake_case__="<unk>" , snake_case__="<s>" , snake_case__="</s>" , snake_case__="<pad>" , snake_case__="[SEP]" , snake_case__="[MASK]" , snake_case__="[CLS]" , **snake_case__ , ) -> List[Any]:
'''simple docstring'''
UpperCAmelCase : Any =AddedToken(snake_case__ , lstrip=snake_case__ , rstrip=snake_case__ ) if isinstance(snake_case__ , snake_case__ ) else bos_token
UpperCAmelCase : Optional[int] =AddedToken(snake_case__ , lstrip=snake_case__ , rstrip=snake_case__ ) if isinstance(snake_case__ , snake_case__ ) else eos_token
UpperCAmelCase : List[str] =AddedToken(snake_case__ , lstrip=snake_case__ , rstrip=snake_case__ ) if isinstance(snake_case__ , snake_case__ ) else unk_token
UpperCAmelCase : Union[str, Any] =AddedToken(snake_case__ , lstrip=snake_case__ , rstrip=snake_case__ ) if isinstance(snake_case__ , snake_case__ ) else pad_token
UpperCAmelCase : int =AddedToken(snake_case__ , lstrip=snake_case__ , rstrip=snake_case__ ) if isinstance(snake_case__ , snake_case__ ) else cls_token
UpperCAmelCase : str =AddedToken(snake_case__ , lstrip=snake_case__ , rstrip=snake_case__ ) if isinstance(snake_case__ , snake_case__ ) else sep_token
# Mask token behave like a normal word, i.e. include the space before it
UpperCAmelCase : List[Any] =AddedToken(snake_case__ , lstrip=snake_case__ , rstrip=snake_case__ ) if isinstance(snake_case__ , snake_case__ ) else mask_token
super().__init__(
snake_case__ , tokenizer_file=snake_case__ , bos_token=snake_case__ , eos_token=snake_case__ , unk_token=snake_case__ , sep_token=snake_case__ , pad_token=snake_case__ , cls_token=snake_case__ , mask_token=snake_case__ , **snake_case__ , )
UpperCAmelCase : Tuple =vocab_file
UpperCAmelCase : Optional[int] =False if not self.vocab_file else True
def UpperCAmelCase__ ( self , snake_case__ , snake_case__ = None ) -> List[int]:
'''simple docstring'''
UpperCAmelCase : int =[self.sep_token_id]
UpperCAmelCase : Optional[int] =[self.cls_token_id]
if token_ids_a is None:
return cls + token_ids_a + sep
return cls + token_ids_a + sep + token_ids_a + sep
def UpperCAmelCase__ ( self , snake_case__ , snake_case__ = None , snake_case__ = False ) -> List[int]:
'''simple docstring'''
if already_has_special_tokens:
if token_ids_a is not None:
raise ValueError(
'''You should not supply a second sequence if the provided sequence of '''
'''ids is already formatted with special tokens for the model.''' )
return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]
if token_ids_a is None:
return [1] + ([0] * len(snake_case__ )) + [1]
return [1] + ([0] * len(snake_case__ )) + [1] + ([0] * len(snake_case__ )) + [1]
def UpperCAmelCase__ ( self , snake_case__ , snake_case__ = None ) -> List[int]:
'''simple docstring'''
UpperCAmelCase : Optional[Any] =[self.sep_token_id]
UpperCAmelCase : Optional[int] =[self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]
def UpperCAmelCase__ ( self , snake_case__ , snake_case__ = None ) -> Tuple[str]:
'''simple docstring'''
if not self.can_save_slow_tokenizer:
raise ValueError(
'''Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '''
'''tokenizer.''' )
if not os.path.isdir(snake_case__ ):
logger.error(f'''Vocabulary path ({save_directory}) should be a directory''' )
return
UpperCAmelCase : Optional[int] =os.path.join(
snake_case__ , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(snake_case__ ):
copyfile(self.vocab_file , snake_case__ )
return (out_vocab_file,)
| 348 | 1 |
import secrets
from random import shuffle
from string import ascii_letters, ascii_lowercase, ascii_uppercase, digits, punctuation
def lowerCAmelCase_ ( __lowerCAmelCase = 8 )-> str:
'''simple docstring'''
UpperCAmelCase : Tuple =ascii_letters + digits + punctuation
return "".join(secrets.choice(__lowerCAmelCase ) for _ in range(__lowerCAmelCase ) )
def lowerCAmelCase_ ( __lowerCAmelCase , __lowerCAmelCase )-> str:
'''simple docstring'''
i -= len(__lowerCAmelCase )
UpperCAmelCase : Dict =i // 3
UpperCAmelCase : Optional[Any] =i % 3
# chars = chars_incl + random_letters(ascii_letters, i / 3 + remainder) +
# random_number(digits, i / 3) + random_characters(punctuation, i / 3)
UpperCAmelCase : List[str] =(
chars_incl
+ random(__lowerCAmelCase , quotient + remainder )
+ random(__lowerCAmelCase , __lowerCAmelCase )
+ random(__lowerCAmelCase , __lowerCAmelCase )
)
UpperCAmelCase : Tuple =list(__lowerCAmelCase )
shuffle(__lowerCAmelCase )
return "".join(__lowerCAmelCase )
# random is a generalised function for letters, characters and numbers
def lowerCAmelCase_ ( __lowerCAmelCase , __lowerCAmelCase )-> str:
'''simple docstring'''
return "".join(secrets.choice(__lowerCAmelCase ) for _ in range(__lowerCAmelCase ) )
def lowerCAmelCase_ ( __lowerCAmelCase , __lowerCAmelCase )-> List[Any]:
'''simple docstring'''
pass # Put your code here...
def lowerCAmelCase_ ( __lowerCAmelCase , __lowerCAmelCase )-> List[Any]:
'''simple docstring'''
pass # Put your code here...
def lowerCAmelCase_ ( __lowerCAmelCase , __lowerCAmelCase )-> Optional[int]:
'''simple docstring'''
pass # Put your code here...
def lowerCAmelCase_ ( __lowerCAmelCase , __lowerCAmelCase = 8 )-> bool:
'''simple docstring'''
if len(__lowerCAmelCase ) < min_length:
# Your Password must be at least 8 characters long
return False
UpperCAmelCase : List[Any] =any(char in ascii_uppercase for char in password )
UpperCAmelCase : int =any(char in ascii_lowercase for char in password )
UpperCAmelCase : Optional[int] =any(char in digits for char in password )
UpperCAmelCase : Optional[Any] =any(char in punctuation for char in password )
return upper and lower and num and spec_char
# Passwords should contain UPPERCASE, lowerase
# numbers, and special characters
def lowerCAmelCase_ ( )-> int:
'''simple docstring'''
UpperCAmelCase : Tuple =int(input('''Please indicate the max length of your password: ''' ).strip() )
UpperCAmelCase : Union[str, Any] =input(
'''Please indicate the characters that must be in your password: ''' ).strip()
print('''Password generated:''' , password_generator(__lowerCAmelCase ) )
print(
'''Alternative Password generated:''' , alternative_password_generator(__lowerCAmelCase , __lowerCAmelCase ) , )
print('''[If you are thinking of using this passsword, You better save it.]''' )
if __name__ == "__main__":
main()
| 348 | from collections.abc import Callable
from math import pi, sqrt
from random import uniform
from statistics import mean
def lowerCAmelCase_ ( __lowerCAmelCase )-> Optional[Any]:
'''simple docstring'''
def is_in_circle(__lowerCAmelCase , __lowerCAmelCase ) -> bool:
UpperCAmelCase : List[Any] =sqrt((x**2) + (y**2) )
# Our circle has a radius of 1, so a distance
# greater than 1 would land outside the circle.
return distance_from_centre <= 1
# The proportion of guesses that landed in the circle
UpperCAmelCase : List[Any] =mean(
int(is_in_circle(uniform(-1.0 , 1.0 ) , uniform(-1.0 , 1.0 ) ) )
for _ in range(__lowerCAmelCase ) )
# The ratio of the area for circle to square is pi/4.
UpperCAmelCase : Dict =proportion * 4
print(f'''The estimated value of pi is {pi_estimate}''' )
print(f'''The numpy value of pi is {pi}''' )
print(f'''The total error is {abs(pi - pi_estimate )}''' )
def lowerCAmelCase_ ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase = 0.0 , __lowerCAmelCase = 1.0 , )-> float:
'''simple docstring'''
return mean(
function_to_integrate(uniform(__lowerCAmelCase , __lowerCAmelCase ) ) for _ in range(__lowerCAmelCase ) ) * (max_value - min_value)
def lowerCAmelCase_ ( __lowerCAmelCase , __lowerCAmelCase = 0.0 , __lowerCAmelCase = 1.0 )-> None:
'''simple docstring'''
def identity_function(__lowerCAmelCase ) -> float:
return x
UpperCAmelCase : List[Any] =area_under_curve_estimator(
__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
UpperCAmelCase : Dict =(max_value * max_value - min_value * min_value) / 2
print('''******************''' )
print(f'''Estimating area under y=x where x varies from {min_value} to {max_value}''' )
print(f'''Estimated value is {estimated_value}''' )
print(f'''Expected value is {expected_value}''' )
print(f'''Total error is {abs(estimated_value - expected_value )}''' )
print('''******************''' )
def lowerCAmelCase_ ( __lowerCAmelCase )-> None:
'''simple docstring'''
def function_to_integrate(__lowerCAmelCase ) -> float:
return sqrt(4.0 - x * x )
UpperCAmelCase : Dict =area_under_curve_estimator(
__lowerCAmelCase , __lowerCAmelCase , 0.0 , 2.0 )
print('''******************''' )
print('''Estimating pi using area_under_curve_estimator''' )
print(f'''Estimated value is {estimated_value}''' )
print(f'''Expected value is {pi}''' )
print(f'''Total error is {abs(estimated_value - pi )}''' )
print('''******************''' )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 348 | 1 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import TransformeraDModel, VQDiffusionPipeline, VQDiffusionScheduler, VQModel
from diffusers.pipelines.vq_diffusion.pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings
from diffusers.utils import load_numpy, slow, torch_device
from diffusers.utils.testing_utils import require_torch_gpu
__snake_case = False
class __snake_case ( unittest.TestCase ):
def UpperCAmelCase__ ( self ) -> Union[str, Any]:
'''simple docstring'''
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
@property
def UpperCAmelCase__ ( self ) -> List[str]:
'''simple docstring'''
return 12
@property
def UpperCAmelCase__ ( self ) -> Optional[Any]:
'''simple docstring'''
return 12
@property
def UpperCAmelCase__ ( self ) -> Tuple:
'''simple docstring'''
return 32
@property
def UpperCAmelCase__ ( self ) -> Any:
'''simple docstring'''
torch.manual_seed(0 )
UpperCAmelCase : List[str] =VQModel(
block_out_channels=[32, 64] , in_channels=3 , out_channels=3 , down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D'''] , up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D'''] , latent_channels=3 , num_vq_embeddings=self.num_embed , vq_embed_dim=3 , )
return model
@property
def UpperCAmelCase__ ( self ) -> Optional[int]:
'''simple docstring'''
UpperCAmelCase : List[Any] =CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''' )
return tokenizer
@property
def UpperCAmelCase__ ( self ) -> Dict:
'''simple docstring'''
torch.manual_seed(0 )
UpperCAmelCase : Dict =CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=self.text_embedder_hidden_size , intermediate_size=37 , layer_norm_eps=1e-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1000 , )
return CLIPTextModel(snake_case__ )
@property
def UpperCAmelCase__ ( self ) -> List[Any]:
'''simple docstring'''
torch.manual_seed(0 )
UpperCAmelCase : Tuple =12
UpperCAmelCase : str =12
UpperCAmelCase : int ={
'''attention_bias''': True,
'''cross_attention_dim''': 32,
'''attention_head_dim''': height * width,
'''num_attention_heads''': 1,
'''num_vector_embeds''': self.num_embed,
'''num_embeds_ada_norm''': self.num_embeds_ada_norm,
'''norm_num_groups''': 32,
'''sample_size''': width,
'''activation_fn''': '''geglu-approximate''',
}
UpperCAmelCase : List[Any] =TransformeraDModel(**snake_case__ )
return model
def UpperCAmelCase__ ( self ) -> List[str]:
'''simple docstring'''
UpperCAmelCase : Any ='''cpu'''
UpperCAmelCase : Tuple =self.dummy_vqvae
UpperCAmelCase : str =self.dummy_text_encoder
UpperCAmelCase : Any =self.dummy_tokenizer
UpperCAmelCase : Optional[int] =self.dummy_transformer
UpperCAmelCase : Union[str, Any] =VQDiffusionScheduler(self.num_embed )
UpperCAmelCase : Optional[int] =LearnedClassifierFreeSamplingEmbeddings(learnable=snake_case__ )
UpperCAmelCase : Optional[Any] =VQDiffusionPipeline(
vqvae=snake_case__ , text_encoder=snake_case__ , tokenizer=snake_case__ , transformer=snake_case__ , scheduler=snake_case__ , learned_classifier_free_sampling_embeddings=snake_case__ , )
UpperCAmelCase : Tuple =pipe.to(snake_case__ )
pipe.set_progress_bar_config(disable=snake_case__ )
UpperCAmelCase : Tuple ='''teddy bear playing in the pool'''
UpperCAmelCase : Union[str, Any] =torch.Generator(device=snake_case__ ).manual_seed(0 )
UpperCAmelCase : Tuple =pipe([prompt] , generator=snake_case__ , num_inference_steps=2 , output_type='''np''' )
UpperCAmelCase : Tuple =output.images
UpperCAmelCase : Any =torch.Generator(device=snake_case__ ).manual_seed(0 )
UpperCAmelCase : int =pipe(
[prompt] , generator=snake_case__ , output_type='''np''' , return_dict=snake_case__ , num_inference_steps=2 )[0]
UpperCAmelCase : Any =image[0, -3:, -3:, -1]
UpperCAmelCase : List[Any] =image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 24, 24, 3)
UpperCAmelCase : Union[str, Any] =np.array([0.6551, 0.6168, 0.5008, 0.5676, 0.5659, 0.4295, 0.6073, 0.5599, 0.4992] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1e-2
def UpperCAmelCase__ ( self ) -> List[Any]:
'''simple docstring'''
UpperCAmelCase : List[str] ='''cpu'''
UpperCAmelCase : List[Any] =self.dummy_vqvae
UpperCAmelCase : int =self.dummy_text_encoder
UpperCAmelCase : Dict =self.dummy_tokenizer
UpperCAmelCase : Optional[Any] =self.dummy_transformer
UpperCAmelCase : List[str] =VQDiffusionScheduler(self.num_embed )
UpperCAmelCase : Any =LearnedClassifierFreeSamplingEmbeddings(
learnable=snake_case__ , hidden_size=self.text_embedder_hidden_size , length=tokenizer.model_max_length )
UpperCAmelCase : Any =VQDiffusionPipeline(
vqvae=snake_case__ , text_encoder=snake_case__ , tokenizer=snake_case__ , transformer=snake_case__ , scheduler=snake_case__ , learned_classifier_free_sampling_embeddings=snake_case__ , )
UpperCAmelCase : str =pipe.to(snake_case__ )
pipe.set_progress_bar_config(disable=snake_case__ )
UpperCAmelCase : List[str] ='''teddy bear playing in the pool'''
UpperCAmelCase : Any =torch.Generator(device=snake_case__ ).manual_seed(0 )
UpperCAmelCase : Optional[int] =pipe([prompt] , generator=snake_case__ , num_inference_steps=2 , output_type='''np''' )
UpperCAmelCase : Union[str, Any] =output.images
UpperCAmelCase : Union[str, Any] =torch.Generator(device=snake_case__ ).manual_seed(0 )
UpperCAmelCase : int =pipe(
[prompt] , generator=snake_case__ , output_type='''np''' , return_dict=snake_case__ , num_inference_steps=2 )[0]
UpperCAmelCase : Any =image[0, -3:, -3:, -1]
UpperCAmelCase : Dict =image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 24, 24, 3)
UpperCAmelCase : int =np.array([0.6693, 0.6075, 0.4959, 0.5701, 0.5583, 0.4333, 0.6171, 0.5684, 0.4988] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 2.0
assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1e-2
@slow
@require_torch_gpu
class __snake_case ( unittest.TestCase ):
def UpperCAmelCase__ ( self ) -> Union[str, Any]:
'''simple docstring'''
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def UpperCAmelCase__ ( self ) -> Optional[int]:
'''simple docstring'''
UpperCAmelCase : Optional[int] =load_numpy(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
'''/vq_diffusion/teddy_bear_pool_classifier_free_sampling.npy''' )
UpperCAmelCase : Dict =VQDiffusionPipeline.from_pretrained('''microsoft/vq-diffusion-ithq''' )
UpperCAmelCase : List[Any] =pipeline.to(snake_case__ )
pipeline.set_progress_bar_config(disable=snake_case__ )
# requires GPU generator for gumbel softmax
# don't use GPU generator in tests though
UpperCAmelCase : List[str] =torch.Generator(device=snake_case__ ).manual_seed(0 )
UpperCAmelCase : Optional[int] =pipeline(
'''teddy bear playing in the pool''' , num_images_per_prompt=1 , generator=snake_case__ , output_type='''np''' , )
UpperCAmelCase : Optional[int] =output.images[0]
assert image.shape == (256, 256, 3)
assert np.abs(expected_image - image ).max() < 2.0
| 348 | from __future__ import annotations
import unittest
import numpy as np
from transformers import BlipTextConfig
from transformers.testing_utils import require_tf, slow
from transformers.utils import is_tf_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
if is_tf_available():
import tensorflow as tf
from transformers import TFBlipTextModel
from transformers.models.blip.modeling_tf_blip import TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST
class __snake_case :
def __init__( self , snake_case__ , snake_case__=12 , snake_case__=7 , snake_case__=True , snake_case__=True , snake_case__=True , snake_case__=99 , snake_case__=32 , snake_case__=32 , snake_case__=2 , snake_case__=4 , snake_case__=37 , snake_case__=0.1 , snake_case__=0.1 , snake_case__=512 , snake_case__=0.02 , snake_case__=0 , snake_case__=None , ) -> Tuple:
'''simple docstring'''
UpperCAmelCase : List[Any] =parent
UpperCAmelCase : Optional[int] =batch_size
UpperCAmelCase : List[Any] =seq_length
UpperCAmelCase : Optional[int] =is_training
UpperCAmelCase : Union[str, Any] =use_input_mask
UpperCAmelCase : Tuple =use_labels
UpperCAmelCase : Union[str, Any] =vocab_size
UpperCAmelCase : Tuple =hidden_size
UpperCAmelCase : Dict =projection_dim
UpperCAmelCase : Optional[int] =num_hidden_layers
UpperCAmelCase : Dict =num_attention_heads
UpperCAmelCase : int =intermediate_size
UpperCAmelCase : Any =dropout
UpperCAmelCase : Union[str, Any] =attention_dropout
UpperCAmelCase : Union[str, Any] =max_position_embeddings
UpperCAmelCase : List[str] =initializer_range
UpperCAmelCase : str =scope
UpperCAmelCase : str =bos_token_id
def UpperCAmelCase__ ( self ) -> int:
'''simple docstring'''
UpperCAmelCase : int =ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
UpperCAmelCase : int =None
if self.use_input_mask:
UpperCAmelCase : Union[str, Any] =random_attention_mask([self.batch_size, self.seq_length] )
if input_mask is not None:
UpperCAmelCase : Optional[int] =input_mask.numpy()
UpperCAmelCase , UpperCAmelCase : List[Any] =input_mask.shape
UpperCAmelCase : Optional[Any] =np.random.randint(1 , seq_length - 1 , size=(batch_size,) )
for batch_idx, start_index in enumerate(snake_case__ ):
UpperCAmelCase : List[Any] =1
UpperCAmelCase : Tuple =0
UpperCAmelCase : List[Any] =self.get_config()
return config, input_ids, tf.convert_to_tensor(snake_case__ )
def UpperCAmelCase__ ( self ) -> Union[str, Any]:
'''simple docstring'''
return BlipTextConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , projection_dim=self.projection_dim , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , dropout=self.dropout , attention_dropout=self.attention_dropout , max_position_embeddings=self.max_position_embeddings , initializer_range=self.initializer_range , bos_token_id=self.bos_token_id , )
def UpperCAmelCase__ ( self , snake_case__ , snake_case__ , snake_case__ ) -> Dict:
'''simple docstring'''
UpperCAmelCase : Tuple =TFBlipTextModel(config=snake_case__ )
UpperCAmelCase : List[Any] =model(snake_case__ , attention_mask=snake_case__ , training=snake_case__ )
UpperCAmelCase : str =model(snake_case__ , training=snake_case__ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
self.parent.assertEqual(result.pooler_output.shape , (self.batch_size, self.hidden_size) )
def UpperCAmelCase__ ( self ) -> Optional[Any]:
'''simple docstring'''
UpperCAmelCase : List[str] =self.prepare_config_and_inputs()
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase : List[Any] =config_and_inputs
UpperCAmelCase : Optional[int] ={'''input_ids''': input_ids, '''attention_mask''': input_mask}
return config, inputs_dict
@require_tf
class __snake_case ( lowerCamelCase__ , unittest.TestCase ):
__lowerCamelCase : Optional[int] = (TFBlipTextModel,) if is_tf_available() else ()
__lowerCamelCase : Dict = False
__lowerCamelCase : Optional[Any] = False
__lowerCamelCase : Dict = False
def UpperCAmelCase__ ( self ) -> Optional[Any]:
'''simple docstring'''
UpperCAmelCase : str =BlipTextModelTester(self )
UpperCAmelCase : Optional[int] =ConfigTester(self , config_class=snake_case__ , hidden_size=37 )
def UpperCAmelCase__ ( self ) -> Optional[int]:
'''simple docstring'''
self.config_tester.run_common_tests()
def UpperCAmelCase__ ( self ) -> List[Any]:
'''simple docstring'''
UpperCAmelCase : Any =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*snake_case__ )
def UpperCAmelCase__ ( self ) -> Optional[int]:
'''simple docstring'''
pass
def UpperCAmelCase__ ( self ) -> Union[str, Any]:
'''simple docstring'''
pass
@unittest.skip(reason='''Blip does not use inputs_embeds''' )
def UpperCAmelCase__ ( self ) -> str:
'''simple docstring'''
pass
@unittest.skip(reason='''BlipTextModel has no base class and is not available in MODEL_MAPPING''' )
def UpperCAmelCase__ ( self ) -> Optional[int]:
'''simple docstring'''
pass
@unittest.skip(reason='''BlipTextModel has no base class and is not available in MODEL_MAPPING''' )
def UpperCAmelCase__ ( self ) -> str:
'''simple docstring'''
pass
@slow
def UpperCAmelCase__ ( self ) -> Tuple:
'''simple docstring'''
for model_name in TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
UpperCAmelCase : Optional[Any] =TFBlipTextModel.from_pretrained(snake_case__ )
self.assertIsNotNone(snake_case__ )
def UpperCAmelCase__ ( self , snake_case__=True ) -> Any:
'''simple docstring'''
super().test_pt_tf_model_equivalence(allow_missing_keys=snake_case__ )
| 348 | 1 |
import logging
import re
import pytorch_quantization
import pytorch_quantization.nn as quant_nn
import torch
from pytorch_quantization import calib
from pytorch_quantization.tensor_quant import QuantDescriptor
__snake_case = logging.getLogger(__name__)
__snake_case = 50 # max width of layer names
__snake_case = 70 # max width of quantizer names
def lowerCAmelCase_ ( __lowerCAmelCase )-> int:
'''simple docstring'''
UpperCAmelCase : Optional[int] =parser.add_argument_group('''quant_trainer arguments''' )
group.add_argument('''--wprec''' , type=__lowerCAmelCase , default=8 , help='''weight precision''' )
group.add_argument('''--aprec''' , type=__lowerCAmelCase , default=8 , help='''activation precision''' )
group.add_argument('''--quant-per-tensor''' , action='''store_true''' , help='''per tensor weight scaling''' )
group.add_argument('''--quant-disable''' , action='''store_true''' , help='''disable all quantizers''' )
group.add_argument('''--quant-disable-embeddings''' , action='''store_true''' , help='''disable all embeddings quantizers''' )
group.add_argument('''--quant-disable-keyword''' , type=__lowerCAmelCase , nargs='''+''' , help='''disable quantizers by keyword''' )
group.add_argument('''--quant-disable-layer-module''' , type=__lowerCAmelCase , help='''disable quantizers by keyword under layer.''' )
group.add_argument('''--quant-enable-layer-module''' , type=__lowerCAmelCase , help='''enable quantizers by keyword under layer''' )
group.add_argument('''--calibrator''' , default='''max''' , help='''which quantization range calibrator to use''' )
group.add_argument('''--percentile''' , default=__lowerCAmelCase , type=__lowerCAmelCase , help='''percentile for PercentileCalibrator''' )
group.add_argument('''--fuse-qkv''' , action='''store_true''' , help='''use the same scale factor for qkv''' )
group.add_argument('''--clip-gelu''' , metavar='''N''' , type=__lowerCAmelCase , help='''clip gelu output maximum value to N''' )
group.add_argument(
'''--recalibrate-weights''' , action='''store_true''' , help=(
'''recalibrate weight amaxes by taking the max of the weights.'''
''' amaxes will be computed with the current quantization granularity (axis).'''
) , )
def lowerCAmelCase_ ( __lowerCAmelCase )-> Dict:
'''simple docstring'''
if args.calibrator == "max":
UpperCAmelCase : Union[str, Any] ='''max'''
elif args.calibrator == "percentile":
if args.percentile is None:
raise ValueError('''Specify --percentile when using percentile calibrator''' )
UpperCAmelCase : Optional[Any] ='''histogram'''
elif args.calibrator == "mse":
UpperCAmelCase : str ='''histogram'''
else:
raise ValueError(f'''Invalid calibrator {args.calibrator}''' )
UpperCAmelCase : Tuple =QuantDescriptor(num_bits=args.aprec , calib_method=__lowerCAmelCase )
UpperCAmelCase : Optional[Any] =QuantDescriptor(num_bits=args.wprec , axis=(None if args.quant_per_tensor else (0,)) )
quant_nn.QuantLinear.set_default_quant_desc_input(__lowerCAmelCase )
quant_nn.QuantLinear.set_default_quant_desc_weight(__lowerCAmelCase )
def lowerCAmelCase_ ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase=False , __lowerCAmelCase=False )-> Optional[Any]:
'''simple docstring'''
logger.info('''Configuring Model for Quantization''' )
logger.info(f'''using quantization package {pytorch_quantization.__file__}''' )
if not calib:
if args.quant_disable_embeddings:
set_quantizer_by_name(__lowerCAmelCase , ['''embeddings'''] , which='''weight''' , _disabled=__lowerCAmelCase )
if args.quant_disable:
set_quantizer_by_name(__lowerCAmelCase , [''''''] , _disabled=__lowerCAmelCase )
if args.quant_disable_keyword:
set_quantizer_by_name(__lowerCAmelCase , args.quant_disable_keyword , _disabled=__lowerCAmelCase )
if args.quant_disable_layer_module:
set_quantizer_by_name(__lowerCAmelCase , [R'''layer.\d+.''' + args.quant_disable_layer_module] , _disabled=__lowerCAmelCase )
if args.quant_enable_layer_module:
set_quantizer_by_name(__lowerCAmelCase , [R'''layer.\d+.''' + args.quant_enable_layer_module] , _disabled=__lowerCAmelCase )
if args.recalibrate_weights:
recalibrate_weights(__lowerCAmelCase )
if args.fuse_qkv:
fuse_qkv(__lowerCAmelCase , __lowerCAmelCase )
if args.clip_gelu:
clip_gelu(__lowerCAmelCase , args.clip_gelu )
# if args.local_rank in [-1, 0] and not calib:
print_quant_summary(__lowerCAmelCase )
def lowerCAmelCase_ ( __lowerCAmelCase )-> Optional[Any]:
'''simple docstring'''
logger.info('''Enabling Calibration''' )
for name, module in model.named_modules():
if name.endswith('''_quantizer''' ):
if module._calibrator is not None:
module.disable_quant()
module.enable_calib()
else:
module.disable()
logger.info(f'''{name:80}: {module}''' )
def lowerCAmelCase_ ( __lowerCAmelCase , __lowerCAmelCase )-> List[str]:
'''simple docstring'''
logger.info('''Loading calibrated amax''' )
for name, module in model.named_modules():
if name.endswith('''_quantizer''' ):
if module._calibrator is not None:
if isinstance(module._calibrator , calib.MaxCalibrator ):
module.load_calib_amax()
else:
module.load_calib_amax('''percentile''' , percentile=args.percentile )
module.enable_quant()
module.disable_calib()
else:
module.enable()
model.cuda()
print_quant_summary(__lowerCAmelCase )
def lowerCAmelCase_ ( __lowerCAmelCase , __lowerCAmelCase )-> List[Any]:
'''simple docstring'''
def fusea(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ):
for mod in [qq, qk, qv]:
if not hasattr(__lowerCAmelCase , '''_amax''' ):
print(''' WARNING: NO AMAX BUFFER''' )
return
UpperCAmelCase : List[Any] =qq._amax.detach().item()
UpperCAmelCase : Dict =qk._amax.detach().item()
UpperCAmelCase : Dict =qv._amax.detach().item()
UpperCAmelCase : Any =max(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
qq._amax.fill_(__lowerCAmelCase )
qk._amax.fill_(__lowerCAmelCase )
qv._amax.fill_(__lowerCAmelCase )
logger.info(f''' q={q:5.2f} k={k:5.2f} v={v:5.2f} -> {amax:5.2f}''' )
for name, mod in model.named_modules():
if name.endswith('''.attention.self''' ):
logger.info(f'''FUSE_QKV: {name:{name_width}}''' )
fusea(mod.matmul_q_input_quantizer , mod.matmul_k_input_quantizer , mod.matmul_v_input_quantizer )
if args.quant_per_tensor:
fusea(mod.query._weight_quantizer , mod.key._weight_quantizer , mod.value._weight_quantizer )
def lowerCAmelCase_ ( __lowerCAmelCase , __lowerCAmelCase )-> str:
'''simple docstring'''
for name, mod in model.named_modules():
if name.endswith('''.output.dense''' ) and not name.endswith('''attention.output.dense''' ):
UpperCAmelCase : Union[str, Any] =mod._input_quantizer._amax.data.detach().item()
mod._input_quantizer._amax.data.detach().clamp_(max=__lowerCAmelCase )
UpperCAmelCase : Dict =mod._input_quantizer._amax.data.detach().item()
logger.info(f'''CLIP_GELU: {name:{name_width}} amax: {amax_init:5.2f} -> {amax:5.2f}''' )
def lowerCAmelCase_ ( __lowerCAmelCase )-> List[str]:
'''simple docstring'''
for name, mod in model.named_modules():
if hasattr(__lowerCAmelCase , '''_weight_quantizer''' ) and mod._weight_quantizer.axis is not None:
UpperCAmelCase : List[str] =mod.weight.shape[0]
UpperCAmelCase : Dict =mod._weight_quantizer._amax.detach()
UpperCAmelCase : List[Any] =torch.ones(__lowerCAmelCase , dtype=amax.dtype , device=amax.device ) * amax
print(f'''expanding {name} {amax} -> {mod._weight_quantizer._amax}''' )
def lowerCAmelCase_ ( __lowerCAmelCase )-> str:
'''simple docstring'''
for name, mod in model.named_modules():
if hasattr(__lowerCAmelCase , '''_weight_quantizer''' ):
if not hasattr(mod.weight_quantizer , '''_amax''' ):
print('''RECALIB: {name:{name_width}} WARNING: NO AMAX BUFFER''' )
continue
# determine which axes to reduce across
# e.g. a 4D tensor quantized per axis 0 should reduce over (1,2,3)
UpperCAmelCase : Optional[int] =set() if mod._weight_quantizer.axis is None else set(mod._weight_quantizer.axis )
UpperCAmelCase : Optional[Any] =set(range(len(mod.weight.size() ) ) ) - axis_set
UpperCAmelCase : Tuple =pytorch_quantization.utils.reduce_amax(mod.weight , axis=__lowerCAmelCase , keepdims=__lowerCAmelCase ).detach()
logger.info(f'''RECALIB: {name:{name_width}} {mod._weight_quantizer._amax.flatten()} -> {amax.flatten()}''' )
UpperCAmelCase : int =amax
def lowerCAmelCase_ ( __lowerCAmelCase , __lowerCAmelCase=25 , __lowerCAmelCase=1_80 , __lowerCAmelCase=None )-> Dict:
'''simple docstring'''
if ignore is None:
UpperCAmelCase : List[str] =[]
elif not isinstance(__lowerCAmelCase , __lowerCAmelCase ):
UpperCAmelCase : List[Any] =[ignore]
UpperCAmelCase : Dict =0
for name, mod in model.named_modules():
if not hasattr(__lowerCAmelCase , '''weight''' ):
continue
UpperCAmelCase : Union[str, Any] =max(__lowerCAmelCase , len(__lowerCAmelCase ) )
for name, mod in model.named_modules():
UpperCAmelCase : List[Any] =getattr(__lowerCAmelCase , '''_input_quantizer''' , __lowerCAmelCase )
UpperCAmelCase : str =getattr(__lowerCAmelCase , '''_weight_quantizer''' , __lowerCAmelCase )
if not hasattr(__lowerCAmelCase , '''weight''' ):
continue
if type(__lowerCAmelCase ) in ignore:
continue
if [True for s in ignore if type(__lowerCAmelCase ) is str and s in name]:
continue
UpperCAmelCase : Optional[int] =f'''Act:{input_q.extra_repr()}'''
UpperCAmelCase : Optional[Any] =f'''Wgt:{weight_q.extra_repr()}'''
UpperCAmelCase : Optional[Any] =f'''{name:{name_width}} {act_str} {wgt_str}'''
if len(__lowerCAmelCase ) <= line_width:
logger.info(__lowerCAmelCase )
else:
logger.info(f'''{name:{name_width}} {act_str}''' )
logger.info(f'''{' ':{name_width}} {wgt_str}''' )
def lowerCAmelCase_ ( __lowerCAmelCase )-> Union[str, Any]:
'''simple docstring'''
UpperCAmelCase : Union[str, Any] =0
for name, mod in model.named_modules():
if isinstance(__lowerCAmelCase , pytorch_quantization.nn.TensorQuantizer ):
print(f'''{name:80} {mod}''' )
count += 1
print(f'''{count} TensorQuantizers found in model''' )
def lowerCAmelCase_ ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )-> Optional[Any]:
'''simple docstring'''
UpperCAmelCase : Union[str, Any] =getattr(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
if quantizer_mod is not None:
assert hasattr(__lowerCAmelCase , __lowerCAmelCase )
setattr(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
else:
logger.warning(f'''{name} has no {quantizer}''' )
def lowerCAmelCase_ ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase="both" , **__lowerCAmelCase )-> List[Any]:
'''simple docstring'''
UpperCAmelCase : List[str] =f'''Warning: changing {which} quantizers of {name:{qname_width}}'''
for k, v in kwargs.items():
s += f''' {k}={v}'''
if which in ["input", "both"]:
set_quantizer(__lowerCAmelCase , __lowerCAmelCase , '''_input_quantizer''' , __lowerCAmelCase , __lowerCAmelCase )
if which in ["weight", "both"]:
set_quantizer(__lowerCAmelCase , __lowerCAmelCase , '''_weight_quantizer''' , __lowerCAmelCase , __lowerCAmelCase )
logger.info(__lowerCAmelCase )
def lowerCAmelCase_ ( __lowerCAmelCase , __lowerCAmelCase , **__lowerCAmelCase )-> List[Any]:
'''simple docstring'''
for name, mod in model.named_modules():
if hasattr(__lowerCAmelCase , '''_input_quantizer''' ) or hasattr(__lowerCAmelCase , '''_weight_quantizer''' ):
for n in names:
if re.search(__lowerCAmelCase , __lowerCAmelCase ):
set_quantizers(__lowerCAmelCase , __lowerCAmelCase , **__lowerCAmelCase )
elif name.endswith('''_quantizer''' ):
for n in names:
if re.search(__lowerCAmelCase , __lowerCAmelCase ):
UpperCAmelCase : Tuple =f'''Warning: changing {name:{name_width}}'''
for k, v in kwargs.items():
s += f''' {k}={v}'''
setattr(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
logger.info(__lowerCAmelCase )
| 348 | import torch
import torch.nn as nn
from transformers import CLIPConfig, CLIPVisionModel, PreTrainedModel
from ...utils import logging
__snake_case = logging.get_logger(__name__)
def lowerCAmelCase_ ( __lowerCAmelCase , __lowerCAmelCase )-> str:
'''simple docstring'''
UpperCAmelCase : Dict =nn.functional.normalize(__lowerCAmelCase )
UpperCAmelCase : Tuple =nn.functional.normalize(__lowerCAmelCase )
return torch.mm(__lowerCAmelCase , normalized_text_embeds.t() )
class __snake_case ( lowerCamelCase__ ):
__lowerCamelCase : List[str] = CLIPConfig
__lowerCamelCase : List[Any] = ["""CLIPEncoderLayer"""]
def __init__( self , snake_case__ ) -> Dict:
'''simple docstring'''
super().__init__(snake_case__ )
UpperCAmelCase : Dict =CLIPVisionModel(config.vision_config )
UpperCAmelCase : Optional[Any] =nn.Linear(config.vision_config.hidden_size , config.projection_dim , bias=snake_case__ )
UpperCAmelCase : int =nn.Parameter(torch.ones(17 , config.projection_dim ) , requires_grad=snake_case__ )
UpperCAmelCase : List[str] =nn.Parameter(torch.ones(3 , config.projection_dim ) , requires_grad=snake_case__ )
UpperCAmelCase : str =nn.Parameter(torch.ones(17 ) , requires_grad=snake_case__ )
UpperCAmelCase : Optional[int] =nn.Parameter(torch.ones(3 ) , requires_grad=snake_case__ )
@torch.no_grad()
def UpperCAmelCase__ ( self , snake_case__ , snake_case__ ) -> Tuple:
'''simple docstring'''
UpperCAmelCase : Union[str, Any] =self.vision_model(snake_case__ )[1] # pooled_output
UpperCAmelCase : Optional[Any] =self.visual_projection(snake_case__ )
# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
UpperCAmelCase : List[str] =cosine_distance(snake_case__ , self.special_care_embeds ).cpu().float().numpy()
UpperCAmelCase : Optional[Any] =cosine_distance(snake_case__ , self.concept_embeds ).cpu().float().numpy()
UpperCAmelCase : Tuple =[]
UpperCAmelCase : Dict =image_embeds.shape[0]
for i in range(snake_case__ ):
UpperCAmelCase : str ={'''special_scores''': {}, '''special_care''': [], '''concept_scores''': {}, '''bad_concepts''': []}
# increase this value to create a stronger `nfsw` filter
# at the cost of increasing the possibility of filtering benign images
UpperCAmelCase : str =0.0
for concept_idx in range(len(special_cos_dist[0] ) ):
UpperCAmelCase : Optional[Any] =special_cos_dist[i][concept_idx]
UpperCAmelCase : Union[str, Any] =self.special_care_embeds_weights[concept_idx].item()
UpperCAmelCase : str =round(concept_cos - concept_threshold + adjustment , 3 )
if result_img["special_scores"][concept_idx] > 0:
result_img["special_care"].append({concept_idx, result_img['''special_scores'''][concept_idx]} )
UpperCAmelCase : int =0.01
for concept_idx in range(len(cos_dist[0] ) ):
UpperCAmelCase : Any =cos_dist[i][concept_idx]
UpperCAmelCase : Optional[int] =self.concept_embeds_weights[concept_idx].item()
UpperCAmelCase : int =round(concept_cos - concept_threshold + adjustment , 3 )
if result_img["concept_scores"][concept_idx] > 0:
result_img["bad_concepts"].append(snake_case__ )
result.append(snake_case__ )
UpperCAmelCase : Optional[int] =[len(res['''bad_concepts'''] ) > 0 for res in result]
return images, has_nsfw_concepts
@torch.no_grad()
def UpperCAmelCase__ ( self , snake_case__ , snake_case__ ) -> Tuple:
'''simple docstring'''
UpperCAmelCase : Any =self.vision_model(snake_case__ )[1] # pooled_output
UpperCAmelCase : List[str] =self.visual_projection(snake_case__ )
UpperCAmelCase : Any =cosine_distance(snake_case__ , self.special_care_embeds )
UpperCAmelCase : Optional[Any] =cosine_distance(snake_case__ , self.concept_embeds )
# increase this value to create a stronger `nsfw` filter
# at the cost of increasing the possibility of filtering benign images
UpperCAmelCase : Optional[Any] =0.0
UpperCAmelCase : Any =special_cos_dist - self.special_care_embeds_weights + adjustment
# special_scores = special_scores.round(decimals=3)
UpperCAmelCase : str =torch.any(special_scores > 0 , dim=1 )
UpperCAmelCase : List[Any] =special_care * 0.01
UpperCAmelCase : Union[str, Any] =special_adjustment.unsqueeze(1 ).expand(-1 , cos_dist.shape[1] )
UpperCAmelCase : List[Any] =(cos_dist - self.concept_embeds_weights) + special_adjustment
# concept_scores = concept_scores.round(decimals=3)
UpperCAmelCase : str =torch.any(concept_scores > 0 , dim=1 )
return images, has_nsfw_concepts
| 348 | 1 |
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__snake_case = logging.get_logger(__name__)
class __snake_case ( lowerCamelCase__ ):
__lowerCamelCase : Union[str, Any] = """encoder-decoder"""
__lowerCamelCase : Union[str, Any] = True
def __init__( self , **snake_case__ ) -> str:
'''simple docstring'''
super().__init__(**snake_case__ )
assert (
"encoder" in kwargs and "decoder" in kwargs
), "Config has to be initialized with encoder and decoder config"
UpperCAmelCase : Tuple =kwargs.pop('''encoder''' )
UpperCAmelCase : Tuple =encoder_config.pop('''model_type''' )
UpperCAmelCase : Any =kwargs.pop('''decoder''' )
UpperCAmelCase : Optional[Any] =decoder_config.pop('''model_type''' )
from ..auto.configuration_auto import AutoConfig
UpperCAmelCase : Dict =AutoConfig.for_model(snake_case__ , **snake_case__ )
UpperCAmelCase : Optional[Any] =AutoConfig.for_model(snake_case__ , **snake_case__ )
UpperCAmelCase : Any =True
@classmethod
def UpperCAmelCase__ ( cls , snake_case__ , snake_case__ , **snake_case__ ) -> PretrainedConfig:
'''simple docstring'''
logger.info('''Set `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config''' )
UpperCAmelCase : Tuple =True
UpperCAmelCase : Dict =True
return cls(encoder=encoder_config.to_dict() , decoder=decoder_config.to_dict() , **snake_case__ )
def UpperCAmelCase__ ( self ) -> List[Any]:
'''simple docstring'''
UpperCAmelCase : Optional[Any] =copy.deepcopy(self.__dict__ )
UpperCAmelCase : List[Any] =self.encoder.to_dict()
UpperCAmelCase : List[str] =self.decoder.to_dict()
UpperCAmelCase : Dict =self.__class__.model_type
return output
| 348 | import argparse
import intel_extension_for_pytorch as ipex
import torch
from diffusers import DPMSolverMultistepScheduler, StableDiffusionPipeline
__snake_case = argparse.ArgumentParser('''Stable Diffusion script with intel optimization''', add_help=False)
parser.add_argument('''--dpm''', action='''store_true''', help='''Enable DPMSolver or not''')
parser.add_argument('''--steps''', default=None, type=int, help='''Num inference steps''')
__snake_case = parser.parse_args()
__snake_case = '''cpu'''
__snake_case = '''a lovely <dicoo> in red dress and hat, in the snowly and brightly night, with many brighly buildings'''
__snake_case = '''path-to-your-trained-model'''
__snake_case = StableDiffusionPipeline.from_pretrained(model_id)
if args.dpm:
__snake_case = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
__snake_case = pipe.to(device)
# to channels last
__snake_case = pipe.unet.to(memory_format=torch.channels_last)
__snake_case = pipe.vae.to(memory_format=torch.channels_last)
__snake_case = pipe.text_encoder.to(memory_format=torch.channels_last)
if pipe.requires_safety_checker:
__snake_case = pipe.safety_checker.to(memory_format=torch.channels_last)
# optimize with ipex
__snake_case = torch.randn(2, 4, 64, 64)
__snake_case = torch.rand(1) * 9_99
__snake_case = torch.randn(2, 77, 7_68)
__snake_case = (sample, timestep, encoder_hidden_status)
try:
__snake_case = ipex.optimize(pipe.unet.eval(), dtype=torch.bfloataa, inplace=True, sample_input=input_example)
except Exception:
__snake_case = ipex.optimize(pipe.unet.eval(), dtype=torch.bfloataa, inplace=True)
__snake_case = ipex.optimize(pipe.vae.eval(), dtype=torch.bfloataa, inplace=True)
__snake_case = ipex.optimize(pipe.text_encoder.eval(), dtype=torch.bfloataa, inplace=True)
if pipe.requires_safety_checker:
__snake_case = ipex.optimize(pipe.safety_checker.eval(), dtype=torch.bfloataa, inplace=True)
# compute
__snake_case = 6_66
__snake_case = torch.Generator(device).manual_seed(seed)
__snake_case = {'''generator''': generator}
if args.steps is not None:
__snake_case = args.steps
with torch.cpu.amp.autocast(enabled=True, dtype=torch.bfloataa):
__snake_case = pipe(prompt, **generate_kwargs).images[0]
# save image
image.save('''generated.png''')
| 348 | 1 |
from unittest import TestCase
from datasets import Dataset
from minhash_deduplication import deduplicate_dataset, make_duplicate_clusters
def lowerCAmelCase_ ( )-> int:
'''simple docstring'''
UpperCAmelCase : str ={
'''repo_name''': ['''test_repo1''', '''test_repo2''', '''test_repo3'''],
'''path''': ['''test_1.py''', '''test_2.py''', '''unit_test.py'''],
'''content''': ['''a ''' * 20, '''a ''' * 30, '''b ''' * 7],
}
UpperCAmelCase : Union[str, Any] =Dataset.from_dict(__lowerCAmelCase )
return dataset
class __snake_case ( lowerCamelCase__ ):
def UpperCAmelCase__ ( self ) -> Tuple:
'''simple docstring'''
UpperCAmelCase : List[str] =get_dataset()
UpperCAmelCase : Optional[int] =make_duplicate_clusters(snake_case__ , 0.85 )
self.assertEqual(len(duplicate_clusters[0] ) , 2 )
def UpperCAmelCase__ ( self ) -> Tuple:
'''simple docstring'''
UpperCAmelCase : str =get_dataset()
UpperCAmelCase , UpperCAmelCase : Tuple =deduplicate_dataset(snake_case__ )
self.assertEqual(len(snake_case__ ) , 2 )
print(snake_case__ )
self.assertEqual(duplicate_clusters[0][0]['''copies'''] , 2 )
self.assertEqual(duplicate_clusters[0][0]['''is_extreme'''] , snake_case__ )
| 348 | __snake_case = '''Input must be a string of 8 numbers plus letter'''
__snake_case = '''TRWAGMYFPDXBNJZSQVHLCKE'''
def lowerCAmelCase_ ( __lowerCAmelCase )-> bool:
'''simple docstring'''
if not isinstance(__lowerCAmelCase , __lowerCAmelCase ):
UpperCAmelCase : Optional[Any] =f'''Expected string as input, found {type(__lowerCAmelCase ).__name__}'''
raise TypeError(__lowerCAmelCase )
UpperCAmelCase : List[Any] =spanish_id.replace('''-''' , '''''' ).upper()
if len(__lowerCAmelCase ) != 9:
raise ValueError(__lowerCAmelCase )
try:
UpperCAmelCase : int =int(spanish_id_clean[0:8] )
UpperCAmelCase : Optional[int] =spanish_id_clean[8]
except ValueError as ex:
raise ValueError(__lowerCAmelCase ) from ex
if letter.isdigit():
raise ValueError(__lowerCAmelCase )
return letter == LOOKUP_LETTERS[number % 23]
if __name__ == "__main__":
import doctest
doctest.testmod()
| 348 | 1 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__snake_case = logging.get_logger(__name__)
__snake_case = {
'''facebook/xlm-roberta-xl''': '''https://huggingface.co/facebook/xlm-roberta-xl/resolve/main/config.json''',
'''facebook/xlm-roberta-xxl''': '''https://huggingface.co/facebook/xlm-roberta-xxl/resolve/main/config.json''',
# See all XLM-RoBERTa-XL models at https://huggingface.co/models?filter=xlm-roberta-xl
}
class __snake_case ( lowerCamelCase__ ):
__lowerCamelCase : Dict = """xlm-roberta-xl"""
def __init__( self , snake_case__=25_0880 , snake_case__=2560 , snake_case__=36 , snake_case__=32 , snake_case__=1_0240 , snake_case__="gelu" , snake_case__=0.1 , snake_case__=0.1 , snake_case__=514 , snake_case__=1 , snake_case__=0.02 , snake_case__=1e-05 , snake_case__=1 , snake_case__=0 , snake_case__=2 , snake_case__="absolute" , snake_case__=True , snake_case__=None , **snake_case__ , ) -> Optional[int]:
'''simple docstring'''
super().__init__(pad_token_id=snake_case__ , bos_token_id=snake_case__ , eos_token_id=snake_case__ , **snake_case__ )
UpperCAmelCase : Tuple =vocab_size
UpperCAmelCase : Optional[Any] =hidden_size
UpperCAmelCase : List[str] =num_hidden_layers
UpperCAmelCase : List[str] =num_attention_heads
UpperCAmelCase : str =hidden_act
UpperCAmelCase : Any =intermediate_size
UpperCAmelCase : Optional[Any] =hidden_dropout_prob
UpperCAmelCase : List[str] =attention_probs_dropout_prob
UpperCAmelCase : List[str] =max_position_embeddings
UpperCAmelCase : List[str] =type_vocab_size
UpperCAmelCase : Any =initializer_range
UpperCAmelCase : List[str] =layer_norm_eps
UpperCAmelCase : Optional[Any] =position_embedding_type
UpperCAmelCase : Tuple =use_cache
UpperCAmelCase : List[Any] =classifier_dropout
class __snake_case ( lowerCamelCase__ ):
@property
def UpperCAmelCase__ ( self ) -> Mapping[str, Mapping[int, str]]:
'''simple docstring'''
if self.task == "multiple-choice":
UpperCAmelCase : List[Any] ={0: '''batch''', 1: '''choice''', 2: '''sequence'''}
else:
UpperCAmelCase : Dict ={0: '''batch''', 1: '''sequence'''}
return OrderedDict(
[
('''input_ids''', dynamic_axis),
('''attention_mask''', dynamic_axis),
] )
| 348 | def lowerCAmelCase_ ( __lowerCAmelCase , __lowerCAmelCase )-> str:
'''simple docstring'''
if number < 0 or shift_amount < 0:
raise ValueError('''both inputs must be positive integers''' )
UpperCAmelCase : Dict =str(bin(__lowerCAmelCase ) )
binary_number += "0" * shift_amount
return binary_number
def lowerCAmelCase_ ( __lowerCAmelCase , __lowerCAmelCase )-> str:
'''simple docstring'''
if number < 0 or shift_amount < 0:
raise ValueError('''both inputs must be positive integers''' )
UpperCAmelCase : Any =str(bin(__lowerCAmelCase ) )[2:]
if shift_amount >= len(__lowerCAmelCase ):
return "0b0"
UpperCAmelCase : Optional[Any] =binary_number[: len(__lowerCAmelCase ) - shift_amount]
return "0b" + shifted_binary_number
def lowerCAmelCase_ ( __lowerCAmelCase , __lowerCAmelCase )-> str:
'''simple docstring'''
if number >= 0: # Get binary representation of positive number
UpperCAmelCase : Optional[Any] ='''0''' + str(bin(__lowerCAmelCase ) ).strip('''-''' )[2:]
else: # Get binary (2's complement) representation of negative number
UpperCAmelCase : int =len(bin(__lowerCAmelCase )[3:] ) # Find 2's complement of number
UpperCAmelCase : Any =bin(abs(__lowerCAmelCase ) - (1 << binary_number_length) )[3:]
UpperCAmelCase : Optional[Any] =(
'''1''' + '''0''' * (binary_number_length - len(__lowerCAmelCase )) + binary_number
)
if shift_amount >= len(__lowerCAmelCase ):
return "0b" + binary_number[0] * len(__lowerCAmelCase )
return (
"0b"
+ binary_number[0] * shift_amount
+ binary_number[: len(__lowerCAmelCase ) - shift_amount]
)
if __name__ == "__main__":
import doctest
doctest.testmod()
| 348 | 1 |
import json
import os
from functools import lru_cache
from typing import TYPE_CHECKING, List, Optional, Tuple
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
if TYPE_CHECKING:
from transformers.pipelines.conversational import Conversation
__snake_case = logging.get_logger(__name__)
__snake_case = {
'''vocab_file''': '''vocab.json''',
'''merges_file''': '''merges.txt''',
'''tokenizer_config_file''': '''tokenizer_config.json''',
}
__snake_case = {
'''vocab_file''': {'''facebook/blenderbot-3B''': '''https://huggingface.co/facebook/blenderbot-3B/resolve/main/vocab.json'''},
'''merges_file''': {'''facebook/blenderbot-3B''': '''https://huggingface.co/facebook/blenderbot-3B/resolve/main/merges.txt'''},
'''tokenizer_config_file''': {
'''facebook/blenderbot-3B''': '''https://huggingface.co/facebook/blenderbot-3B/resolve/main/tokenizer_config.json'''
},
}
__snake_case = {'''facebook/blenderbot-3B''': 1_28}
@lru_cache()
# Copied from transformers.models.roberta.tokenization_roberta.bytes_to_unicode
def lowerCAmelCase_ ( )-> str:
'''simple docstring'''
UpperCAmelCase : int =(
list(range(ord('''!''' ) , ord('''~''' ) + 1 ) ) + list(range(ord('''¡''' ) , ord('''¬''' ) + 1 ) ) + list(range(ord('''®''' ) , ord('''ÿ''' ) + 1 ) )
)
UpperCAmelCase : str =bs[:]
UpperCAmelCase : int =0
for b in range(2**8 ):
if b not in bs:
bs.append(__lowerCAmelCase )
cs.append(2**8 + n )
n += 1
UpperCAmelCase : Any =[chr(__lowerCAmelCase ) for n in cs]
return dict(zip(__lowerCAmelCase , __lowerCAmelCase ) )
def lowerCAmelCase_ ( __lowerCAmelCase )-> Tuple:
'''simple docstring'''
UpperCAmelCase : List[Any] =set()
UpperCAmelCase : int =word[0]
for char in word[1:]:
pairs.add((prev_char, char) )
UpperCAmelCase : Dict =char
return pairs
class __snake_case ( lowerCamelCase__ ):
__lowerCamelCase : Optional[int] = VOCAB_FILES_NAMES
__lowerCamelCase : str = PRETRAINED_VOCAB_FILES_MAP
__lowerCamelCase : int = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
__lowerCamelCase : int = ["""input_ids""", """attention_mask"""]
def __init__( self , snake_case__ , snake_case__ , snake_case__="replace" , snake_case__="<s>" , snake_case__="</s>" , snake_case__="</s>" , snake_case__="<s>" , snake_case__="<unk>" , snake_case__="<pad>" , snake_case__="<mask>" , snake_case__=False , **snake_case__ , ) -> str:
'''simple docstring'''
UpperCAmelCase : List[str] =AddedToken(snake_case__ , lstrip=snake_case__ , rstrip=snake_case__ ) if isinstance(snake_case__ , snake_case__ ) else bos_token
UpperCAmelCase : Tuple =AddedToken(snake_case__ , lstrip=snake_case__ , rstrip=snake_case__ ) if isinstance(snake_case__ , snake_case__ ) else eos_token
UpperCAmelCase : Optional[int] =AddedToken(snake_case__ , lstrip=snake_case__ , rstrip=snake_case__ ) if isinstance(snake_case__ , snake_case__ ) else sep_token
UpperCAmelCase : str =AddedToken(snake_case__ , lstrip=snake_case__ , rstrip=snake_case__ ) if isinstance(snake_case__ , snake_case__ ) else cls_token
UpperCAmelCase : int =AddedToken(snake_case__ , lstrip=snake_case__ , rstrip=snake_case__ ) if isinstance(snake_case__ , snake_case__ ) else unk_token
UpperCAmelCase : int =AddedToken(snake_case__ , lstrip=snake_case__ , rstrip=snake_case__ ) if isinstance(snake_case__ , snake_case__ ) else pad_token
# Mask token behave like a normal word, i.e. include the space before it
UpperCAmelCase : Optional[int] =AddedToken(snake_case__ , lstrip=snake_case__ , rstrip=snake_case__ ) if isinstance(snake_case__ , snake_case__ ) else mask_token
super().__init__(
errors=snake_case__ , bos_token=snake_case__ , eos_token=snake_case__ , unk_token=snake_case__ , sep_token=snake_case__ , cls_token=snake_case__ , pad_token=snake_case__ , mask_token=snake_case__ , add_prefix_space=snake_case__ , **snake_case__ , )
with open(snake_case__ , encoding='''utf-8''' ) as vocab_handle:
UpperCAmelCase : int =json.load(snake_case__ )
UpperCAmelCase : Any ={v: k for k, v in self.encoder.items()}
UpperCAmelCase : Optional[int] =errors # how to handle errors in decoding
UpperCAmelCase : List[str] =bytes_to_unicode()
UpperCAmelCase : Any ={v: k for k, v in self.byte_encoder.items()}
with open(snake_case__ , encoding='''utf-8''' ) as merges_handle:
UpperCAmelCase : Dict =merges_handle.read().split('''\n''' )[1:-1]
UpperCAmelCase : Dict =[tuple(merge.split() ) for merge in bpe_merges]
UpperCAmelCase : List[str] =dict(zip(snake_case__ , range(len(snake_case__ ) ) ) )
UpperCAmelCase : Optional[int] ={}
UpperCAmelCase : List[str] =add_prefix_space
# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
UpperCAmelCase : str =re.compile(r'''\'s|\'t|\'re|\'ve|\'m|\'ll|\'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+''' )
@property
# Copied from transformers.models.roberta.tokenization_roberta.RobertaTokenizer.vocab_size with Roberta->Blenderbot, RoBERTa->Blenderbot
def UpperCAmelCase__ ( self ) -> Any:
'''simple docstring'''
return len(self.encoder )
def UpperCAmelCase__ ( self ) -> int:
'''simple docstring'''
return dict(self.encoder , **self.added_tokens_encoder )
def UpperCAmelCase__ ( self , snake_case__ ) -> Optional[Any]:
'''simple docstring'''
if token in self.cache:
return self.cache[token]
UpperCAmelCase : Optional[Any] =tuple(snake_case__ )
UpperCAmelCase : str =get_pairs(snake_case__ )
if not pairs:
return token
while True:
UpperCAmelCase : str =min(snake_case__ , key=lambda snake_case__ : self.bpe_ranks.get(snake_case__ , float('''inf''' ) ) )
if bigram not in self.bpe_ranks:
break
UpperCAmelCase , UpperCAmelCase : Union[str, Any] =bigram
UpperCAmelCase : List[Any] =[]
UpperCAmelCase : Optional[int] =0
while i < len(snake_case__ ):
try:
UpperCAmelCase : int =word.index(snake_case__ , snake_case__ )
except ValueError:
new_word.extend(word[i:] )
break
else:
new_word.extend(word[i:j] )
UpperCAmelCase : Dict =j
if word[i] == first and i < len(snake_case__ ) - 1 and word[i + 1] == second:
new_word.append(first + second )
i += 2
else:
new_word.append(word[i] )
i += 1
UpperCAmelCase : Any =tuple(snake_case__ )
UpperCAmelCase : Union[str, Any] =new_word
if len(snake_case__ ) == 1:
break
else:
UpperCAmelCase : int =get_pairs(snake_case__ )
UpperCAmelCase : Tuple =''' '''.join(snake_case__ )
UpperCAmelCase : Tuple =word
return word
def UpperCAmelCase__ ( self , snake_case__ ) -> Optional[Any]:
'''simple docstring'''
UpperCAmelCase : Dict =[]
for token in re.findall(self.pat , snake_case__ ):
UpperCAmelCase : str =''''''.join(
self.byte_encoder[b] for b in token.encode('''utf-8''' ) ) # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
bpe_tokens.extend(bpe_token for bpe_token in self.bpe(snake_case__ ).split(''' ''' ) )
return bpe_tokens
def UpperCAmelCase__ ( self , snake_case__ ) -> List[str]:
'''simple docstring'''
return self.encoder.get(snake_case__ , self.encoder.get(self.unk_token ) )
def UpperCAmelCase__ ( self , snake_case__ ) -> Optional[int]:
'''simple docstring'''
return self.decoder.get(snake_case__ )
def UpperCAmelCase__ ( self , snake_case__ ) -> Dict:
'''simple docstring'''
UpperCAmelCase : List[Any] =''''''.join(snake_case__ )
UpperCAmelCase : int =bytearray([self.byte_decoder[c] for c in text] ).decode('''utf-8''' , errors=self.errors )
return text
def UpperCAmelCase__ ( self , snake_case__ , snake_case__ = None ) -> Tuple[str]:
'''simple docstring'''
if not os.path.isdir(snake_case__ ):
logger.error(f'''Vocabulary path ({save_directory}) should be a directory''' )
return
UpperCAmelCase : str =os.path.join(
snake_case__ , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] )
UpperCAmelCase : str =os.path.join(
snake_case__ , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''merges_file'''] )
with open(snake_case__ , '''w''' , encoding='''utf-8''' ) as f:
f.write(json.dumps(self.encoder , indent=2 , sort_keys=snake_case__ , ensure_ascii=snake_case__ ) + '''\n''' )
UpperCAmelCase : str =0
with open(snake_case__ , '''w''' , encoding='''utf-8''' ) as writer:
writer.write('''#version: 0.2\n''' )
for bpe_tokens, token_index in sorted(self.bpe_ranks.items() , key=lambda snake_case__ : kv[1] ):
if index != token_index:
logger.warning(
f'''Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.'''
''' Please check that the tokenizer is not corrupted!''' )
UpperCAmelCase : List[Any] =token_index
writer.write(''' '''.join(snake_case__ ) + '''\n''' )
index += 1
return vocab_file, merge_file
def UpperCAmelCase__ ( self , snake_case__ , snake_case__ = None , snake_case__ = False ) -> List[int]:
'''simple docstring'''
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=snake_case__ , token_ids_a=snake_case__ , already_has_special_tokens=snake_case__ )
if token_ids_a is None:
return [1] + ([0] * len(snake_case__ )) + [1]
return [1] + ([0] * len(snake_case__ )) + [1, 1] + ([0] * len(snake_case__ )) + [1]
def UpperCAmelCase__ ( self , snake_case__ , snake_case__ = None ) -> List[int]:
'''simple docstring'''
UpperCAmelCase : Any =[self.sep_token_id]
UpperCAmelCase : Tuple =[self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]
def UpperCAmelCase__ ( self , snake_case__ , snake_case__=False , **snake_case__ ) -> Any:
'''simple docstring'''
UpperCAmelCase : Dict =kwargs.pop('''add_prefix_space''' , self.add_prefix_space )
if (is_split_into_words or add_prefix_space) and (len(snake_case__ ) > 0 and not text[0].isspace()):
UpperCAmelCase : Dict =''' ''' + text
return (text, kwargs)
def UpperCAmelCase__ ( self , snake_case__ , snake_case__ = None ) -> str:
'''simple docstring'''
return token_ids_a + [self.eos_token_id]
def UpperCAmelCase__ ( self , snake_case__ ) -> List[int]:
'''simple docstring'''
UpperCAmelCase : List[Any] =[]
for is_user, text in conversation.iter_texts():
if is_user:
# We need to space prefix as it's being done within blenderbot
inputs.append(''' ''' + text )
else:
# Generated responses should contain them already.
inputs.append(snake_case__ )
UpperCAmelCase : List[str] =''' '''.join(snake_case__ )
UpperCAmelCase : str =self.encode(snake_case__ )
if len(snake_case__ ) > self.model_max_length:
UpperCAmelCase : List[Any] =input_ids[-self.model_max_length :]
logger.warning(f'''Trimmed input from conversation as it was longer than {self.model_max_length} tokens.''' )
return input_ids
| 348 | from dataclasses import asdict, dataclass
from typing import Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__snake_case = logging.get_logger(__name__)
# TODO Update this
__snake_case = {
'''facebook/esm-1b''': '''https://huggingface.co/facebook/esm-1b/resolve/main/config.json''',
# See all ESM models at https://huggingface.co/models?filter=esm
}
class __snake_case ( lowerCamelCase__ ):
__lowerCamelCase : Tuple = """esm"""
def __init__( self , snake_case__=None , snake_case__=None , snake_case__=None , snake_case__=768 , snake_case__=12 , snake_case__=12 , snake_case__=3072 , snake_case__=0.1 , snake_case__=0.1 , snake_case__=1026 , snake_case__=0.02 , snake_case__=1e-12 , snake_case__="absolute" , snake_case__=True , snake_case__=None , snake_case__=False , snake_case__=False , snake_case__=None , snake_case__=None , **snake_case__ , ) -> Union[str, Any]:
'''simple docstring'''
super().__init__(pad_token_id=snake_case__ , mask_token_id=snake_case__ , **snake_case__ )
UpperCAmelCase : List[str] =vocab_size
UpperCAmelCase : str =hidden_size
UpperCAmelCase : List[Any] =num_hidden_layers
UpperCAmelCase : Optional[Any] =num_attention_heads
UpperCAmelCase : str =intermediate_size
UpperCAmelCase : Any =hidden_dropout_prob
UpperCAmelCase : int =attention_probs_dropout_prob
UpperCAmelCase : Dict =max_position_embeddings
UpperCAmelCase : List[str] =initializer_range
UpperCAmelCase : Union[str, Any] =layer_norm_eps
UpperCAmelCase : Dict =position_embedding_type
UpperCAmelCase : Optional[Any] =use_cache
UpperCAmelCase : int =emb_layer_norm_before
UpperCAmelCase : List[str] =token_dropout
UpperCAmelCase : Optional[Any] =is_folding_model
if is_folding_model:
if esmfold_config is None:
logger.info('''No esmfold_config supplied for folding model, using default values.''' )
UpperCAmelCase : Optional[Any] =EsmFoldConfig()
elif isinstance(snake_case__ , snake_case__ ):
UpperCAmelCase : Optional[int] =EsmFoldConfig(**snake_case__ )
UpperCAmelCase : Tuple =esmfold_config
if vocab_list is None:
logger.warning('''No vocab_list supplied for folding model, assuming the ESM-2 vocabulary!''' )
UpperCAmelCase : Any =get_default_vocab_list()
else:
UpperCAmelCase : Tuple =vocab_list
else:
UpperCAmelCase : Optional[int] =None
UpperCAmelCase : Union[str, Any] =None
if self.esmfold_config is not None and getattr(self.esmfold_config , '''use_esm_attn_map''' , snake_case__ ):
raise ValueError('''The HuggingFace port of ESMFold does not support use_esm_attn_map at this time!''' )
def UpperCAmelCase__ ( self ) -> List[Any]:
'''simple docstring'''
UpperCAmelCase : Union[str, Any] =super().to_dict()
if isinstance(self.esmfold_config , snake_case__ ):
UpperCAmelCase : str =self.esmfold_config.to_dict()
return output
@dataclass
class __snake_case :
__lowerCamelCase : str = None
__lowerCamelCase : bool = True
__lowerCamelCase : bool = False
__lowerCamelCase : bool = False
__lowerCamelCase : bool = False
__lowerCamelCase : float = 0
__lowerCamelCase : bool = True
__lowerCamelCase : bool = False
__lowerCamelCase : int = 128
__lowerCamelCase : "TrunkConfig" = None
def UpperCAmelCase__ ( self ) -> Union[str, Any]:
'''simple docstring'''
if self.trunk is None:
UpperCAmelCase : str =TrunkConfig()
elif isinstance(self.trunk , snake_case__ ):
UpperCAmelCase : Optional[int] =TrunkConfig(**self.trunk )
def UpperCAmelCase__ ( self ) -> Any:
'''simple docstring'''
UpperCAmelCase : Optional[Any] =asdict(self )
UpperCAmelCase : Any =self.trunk.to_dict()
return output
@dataclass
class __snake_case :
__lowerCamelCase : int = 48
__lowerCamelCase : int = 1024
__lowerCamelCase : int = 128
__lowerCamelCase : int = 32
__lowerCamelCase : int = 32
__lowerCamelCase : int = 32
__lowerCamelCase : float = 0
__lowerCamelCase : float = 0
__lowerCamelCase : bool = False
__lowerCamelCase : int = 4
__lowerCamelCase : Optional[int] = 128
__lowerCamelCase : "StructureModuleConfig" = None
def UpperCAmelCase__ ( self ) -> List[str]:
'''simple docstring'''
if self.structure_module is None:
UpperCAmelCase : Any =StructureModuleConfig()
elif isinstance(self.structure_module , snake_case__ ):
UpperCAmelCase : str =StructureModuleConfig(**self.structure_module )
if self.max_recycles <= 0:
raise ValueError(f'''`max_recycles` should be positive, got {self.max_recycles}.''' )
if self.sequence_state_dim % self.sequence_state_dim != 0:
raise ValueError(
'''`sequence_state_dim` should be a round multiple of `sequence_state_dim`, got'''
f''' {self.sequence_state_dim} and {self.sequence_state_dim}.''' )
if self.pairwise_state_dim % self.pairwise_state_dim != 0:
raise ValueError(
'''`pairwise_state_dim` should be a round multiple of `pairwise_state_dim`, got'''
f''' {self.pairwise_state_dim} and {self.pairwise_state_dim}.''' )
UpperCAmelCase : Optional[int] =self.sequence_state_dim // self.sequence_head_width
UpperCAmelCase : Any =self.pairwise_state_dim // self.pairwise_head_width
if self.sequence_state_dim != sequence_num_heads * self.sequence_head_width:
raise ValueError(
'''`sequence_state_dim` should be equal to `sequence_num_heads * sequence_head_width, got'''
f''' {self.sequence_state_dim} != {sequence_num_heads} * {self.sequence_head_width}.''' )
if self.pairwise_state_dim != pairwise_num_heads * self.pairwise_head_width:
raise ValueError(
'''`pairwise_state_dim` should be equal to `pairwise_num_heads * pairwise_head_width, got'''
f''' {self.pairwise_state_dim} != {pairwise_num_heads} * {self.pairwise_head_width}.''' )
if self.pairwise_state_dim % 2 != 0:
raise ValueError(f'''`pairwise_state_dim` should be even, got {self.pairwise_state_dim}.''' )
if self.dropout >= 0.4:
raise ValueError(f'''`dropout` should not be greater than 0.4, got {self.dropout}.''' )
def UpperCAmelCase__ ( self ) -> List[Any]:
'''simple docstring'''
UpperCAmelCase : Union[str, Any] =asdict(self )
UpperCAmelCase : Tuple =self.structure_module.to_dict()
return output
@dataclass
class __snake_case :
__lowerCamelCase : int = 384
__lowerCamelCase : int = 128
__lowerCamelCase : int = 16
__lowerCamelCase : int = 128
__lowerCamelCase : int = 12
__lowerCamelCase : int = 4
__lowerCamelCase : int = 8
__lowerCamelCase : float = 0.1
__lowerCamelCase : int = 8
__lowerCamelCase : int = 1
__lowerCamelCase : int = 2
__lowerCamelCase : int = 7
__lowerCamelCase : int = 10
__lowerCamelCase : float = 1E-8
__lowerCamelCase : float = 1E5
def UpperCAmelCase__ ( self ) -> Union[str, Any]:
'''simple docstring'''
return asdict(self )
def lowerCAmelCase_ ( )-> Tuple:
'''simple docstring'''
return (
"<cls>",
"<pad>",
"<eos>",
"<unk>",
"L",
"A",
"G",
"V",
"S",
"E",
"R",
"T",
"I",
"D",
"P",
"K",
"Q",
"N",
"F",
"Y",
"M",
"H",
"W",
"C",
"X",
"B",
"U",
"Z",
"O",
".",
"-",
"<null_1>",
"<mask>",
)
| 348 | 1 |
import unittest
import numpy as np
import torch
from diffusers import DDIMPipeline, DDIMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device
from ..pipeline_params import UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS, UNCONDITIONAL_IMAGE_GENERATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin
enable_full_determinism()
class __snake_case ( lowerCamelCase__ , unittest.TestCase ):
__lowerCamelCase : List[Any] = DDIMPipeline
__lowerCamelCase : Optional[int] = UNCONDITIONAL_IMAGE_GENERATION_PARAMS
__lowerCamelCase : List[Any] = PipelineTesterMixin.required_optional_params - {
"""num_images_per_prompt""",
"""latents""",
"""callback""",
"""callback_steps""",
}
__lowerCamelCase : Any = UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS
__lowerCamelCase : Optional[int] = False
def UpperCAmelCase__ ( self ) -> Tuple:
'''simple docstring'''
torch.manual_seed(0 )
UpperCAmelCase : Any =UNetaDModel(
block_out_channels=(32, 64) , layers_per_block=2 , sample_size=32 , in_channels=3 , out_channels=3 , down_block_types=('''DownBlock2D''', '''AttnDownBlock2D''') , up_block_types=('''AttnUpBlock2D''', '''UpBlock2D''') , )
UpperCAmelCase : int =DDIMScheduler()
UpperCAmelCase : str ={'''unet''': unet, '''scheduler''': scheduler}
return components
def UpperCAmelCase__ ( self , snake_case__ , snake_case__=0 ) -> Union[str, Any]:
'''simple docstring'''
if str(snake_case__ ).startswith('''mps''' ):
UpperCAmelCase : List[str] =torch.manual_seed(snake_case__ )
else:
UpperCAmelCase : Union[str, Any] =torch.Generator(device=snake_case__ ).manual_seed(snake_case__ )
UpperCAmelCase : Any ={
'''batch_size''': 1,
'''generator''': generator,
'''num_inference_steps''': 2,
'''output_type''': '''numpy''',
}
return inputs
def UpperCAmelCase__ ( self ) -> Dict:
'''simple docstring'''
UpperCAmelCase : Tuple ='''cpu'''
UpperCAmelCase : Tuple =self.get_dummy_components()
UpperCAmelCase : str =self.pipeline_class(**snake_case__ )
pipe.to(snake_case__ )
pipe.set_progress_bar_config(disable=snake_case__ )
UpperCAmelCase : Union[str, Any] =self.get_dummy_inputs(snake_case__ )
UpperCAmelCase : int =pipe(**snake_case__ ).images
UpperCAmelCase : List[str] =image[0, -3:, -3:, -1]
self.assertEqual(image.shape , (1, 32, 32, 3) )
UpperCAmelCase : Any =np.array(
[1.0_00e00, 5.7_17e-01, 4.7_17e-01, 1.0_00e00, 0.0_00e00, 1.0_00e00, 3.0_00e-04, 0.0_00e00, 9.0_00e-04] )
UpperCAmelCase : List[Any] =np.abs(image_slice.flatten() - expected_slice ).max()
self.assertLessEqual(snake_case__ , 1e-3 )
def UpperCAmelCase__ ( self ) -> Tuple:
'''simple docstring'''
super().test_dict_tuple_outputs_equivalent(expected_max_difference=3e-3 )
def UpperCAmelCase__ ( self ) -> Tuple:
'''simple docstring'''
super().test_save_load_local(expected_max_difference=3e-3 )
def UpperCAmelCase__ ( self ) -> Any:
'''simple docstring'''
super().test_save_load_optional_components(expected_max_difference=3e-3 )
def UpperCAmelCase__ ( self ) -> Union[str, Any]:
'''simple docstring'''
super().test_inference_batch_single_identical(expected_max_diff=3e-3 )
@slow
@require_torch_gpu
class __snake_case ( unittest.TestCase ):
def UpperCAmelCase__ ( self ) -> Any:
'''simple docstring'''
UpperCAmelCase : Tuple ='''google/ddpm-cifar10-32'''
UpperCAmelCase : List[str] =UNetaDModel.from_pretrained(snake_case__ )
UpperCAmelCase : Tuple =DDIMScheduler()
UpperCAmelCase : List[str] =DDIMPipeline(unet=snake_case__ , scheduler=snake_case__ )
ddim.to(snake_case__ )
ddim.set_progress_bar_config(disable=snake_case__ )
UpperCAmelCase : int =torch.manual_seed(0 )
UpperCAmelCase : int =ddim(generator=snake_case__ , eta=0.0 , output_type='''numpy''' ).images
UpperCAmelCase : Union[str, Any] =image[0, -3:, -3:, -1]
assert image.shape == (1, 32, 32, 3)
UpperCAmelCase : Dict =np.array([0.1723, 0.1617, 0.1600, 0.1626, 0.1497, 0.1513, 0.1505, 0.1442, 0.1453] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
def UpperCAmelCase__ ( self ) -> Tuple:
'''simple docstring'''
UpperCAmelCase : Tuple ='''google/ddpm-ema-bedroom-256'''
UpperCAmelCase : List[Any] =UNetaDModel.from_pretrained(snake_case__ )
UpperCAmelCase : str =DDIMScheduler.from_pretrained(snake_case__ )
UpperCAmelCase : Union[str, Any] =DDIMPipeline(unet=snake_case__ , scheduler=snake_case__ )
ddpm.to(snake_case__ )
ddpm.set_progress_bar_config(disable=snake_case__ )
UpperCAmelCase : Optional[Any] =torch.manual_seed(0 )
UpperCAmelCase : List[str] =ddpm(generator=snake_case__ , output_type='''numpy''' ).images
UpperCAmelCase : Dict =image[0, -3:, -3:, -1]
assert image.shape == (1, 256, 256, 3)
UpperCAmelCase : Dict =np.array([0.0060, 0.0201, 0.0344, 0.0024, 0.0018, 0.0002, 0.0022, 0.0000, 0.0069] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
| 348 | import torch
from diffusers import KDPMaDiscreteScheduler
from diffusers.utils import torch_device
from .test_schedulers import SchedulerCommonTest
class __snake_case ( lowerCamelCase__ ):
__lowerCamelCase : Optional[int] = (KDPMaDiscreteScheduler,)
__lowerCamelCase : List[str] = 10
def UpperCAmelCase__ ( self , **snake_case__ ) -> str:
'''simple docstring'''
UpperCAmelCase : int ={
'''num_train_timesteps''': 1100,
'''beta_start''': 0.0001,
'''beta_end''': 0.02,
'''beta_schedule''': '''linear''',
}
config.update(**snake_case__ )
return config
def UpperCAmelCase__ ( self ) -> Tuple:
'''simple docstring'''
for timesteps in [10, 50, 100, 1000]:
self.check_over_configs(num_train_timesteps=snake_case__ )
def UpperCAmelCase__ ( self ) -> Optional[int]:
'''simple docstring'''
for beta_start, beta_end in zip([0.0_0001, 0.0001, 0.001] , [0.0002, 0.002, 0.02] ):
self.check_over_configs(beta_start=snake_case__ , beta_end=snake_case__ )
def UpperCAmelCase__ ( self ) -> str:
'''simple docstring'''
for schedule in ["linear", "scaled_linear"]:
self.check_over_configs(beta_schedule=snake_case__ )
def UpperCAmelCase__ ( self ) -> Dict:
'''simple docstring'''
for prediction_type in ["epsilon", "v_prediction"]:
self.check_over_configs(prediction_type=snake_case__ )
def UpperCAmelCase__ ( self ) -> str:
'''simple docstring'''
UpperCAmelCase : Optional[Any] =self.scheduler_classes[0]
UpperCAmelCase : Optional[int] =self.get_scheduler_config(prediction_type='''v_prediction''' )
UpperCAmelCase : Optional[Any] =scheduler_class(**snake_case__ )
scheduler.set_timesteps(self.num_inference_steps )
UpperCAmelCase : str =self.dummy_model()
UpperCAmelCase : Optional[Any] =self.dummy_sample_deter * scheduler.init_noise_sigma
UpperCAmelCase : Union[str, Any] =sample.to(snake_case__ )
for i, t in enumerate(scheduler.timesteps ):
UpperCAmelCase : str =scheduler.scale_model_input(snake_case__ , snake_case__ )
UpperCAmelCase : Any =model(snake_case__ , snake_case__ )
UpperCAmelCase : Union[str, Any] =scheduler.step(snake_case__ , snake_case__ , snake_case__ )
UpperCAmelCase : int =output.prev_sample
UpperCAmelCase : Dict =torch.sum(torch.abs(snake_case__ ) )
UpperCAmelCase : Optional[Any] =torch.mean(torch.abs(snake_case__ ) )
if torch_device in ["cpu", "mps"]:
assert abs(result_sum.item() - 4.69_34e-07 ) < 1e-2
assert abs(result_mean.item() - 6.11_12e-10 ) < 1e-3
else:
# CUDA
assert abs(result_sum.item() - 4.6_93_42_86_50_17_09_72e-07 ) < 1e-2
assert abs(result_mean.item() - 0.0002 ) < 1e-3
def UpperCAmelCase__ ( self ) -> int:
'''simple docstring'''
if torch_device == "mps":
return
UpperCAmelCase : Any =self.scheduler_classes[0]
UpperCAmelCase : Optional[int] =self.get_scheduler_config()
UpperCAmelCase : Optional[Any] =scheduler_class(**snake_case__ )
scheduler.set_timesteps(self.num_inference_steps )
UpperCAmelCase : Optional[int] =self.dummy_model()
UpperCAmelCase : Union[str, Any] =self.dummy_sample_deter * scheduler.init_noise_sigma
UpperCAmelCase : str =sample.to(snake_case__ )
for i, t in enumerate(scheduler.timesteps ):
UpperCAmelCase : Dict =scheduler.scale_model_input(snake_case__ , snake_case__ )
UpperCAmelCase : Union[str, Any] =model(snake_case__ , snake_case__ )
UpperCAmelCase : List[str] =scheduler.step(snake_case__ , snake_case__ , snake_case__ )
UpperCAmelCase : Optional[int] =output.prev_sample
UpperCAmelCase : Any =torch.sum(torch.abs(snake_case__ ) )
UpperCAmelCase : Union[str, Any] =torch.mean(torch.abs(snake_case__ ) )
if torch_device in ["cpu", "mps"]:
assert abs(result_sum.item() - 20.4125 ) < 1e-2
assert abs(result_mean.item() - 0.0266 ) < 1e-3
else:
# CUDA
assert abs(result_sum.item() - 20.4125 ) < 1e-2
assert abs(result_mean.item() - 0.0266 ) < 1e-3
def UpperCAmelCase__ ( self ) -> str:
'''simple docstring'''
if torch_device == "mps":
return
UpperCAmelCase : List[Any] =self.scheduler_classes[0]
UpperCAmelCase : Dict =self.get_scheduler_config()
UpperCAmelCase : List[str] =scheduler_class(**snake_case__ )
scheduler.set_timesteps(self.num_inference_steps , device=snake_case__ )
UpperCAmelCase : int =self.dummy_model()
UpperCAmelCase : Tuple =self.dummy_sample_deter.to(snake_case__ ) * scheduler.init_noise_sigma
for t in scheduler.timesteps:
UpperCAmelCase : Optional[Any] =scheduler.scale_model_input(snake_case__ , snake_case__ )
UpperCAmelCase : int =model(snake_case__ , snake_case__ )
UpperCAmelCase : str =scheduler.step(snake_case__ , snake_case__ , snake_case__ )
UpperCAmelCase : List[str] =output.prev_sample
UpperCAmelCase : List[str] =torch.sum(torch.abs(snake_case__ ) )
UpperCAmelCase : Dict =torch.mean(torch.abs(snake_case__ ) )
if str(snake_case__ ).startswith('''cpu''' ):
# The following sum varies between 148 and 156 on mps. Why?
assert abs(result_sum.item() - 20.4125 ) < 1e-2
assert abs(result_mean.item() - 0.0266 ) < 1e-3
else:
# CUDA
assert abs(result_sum.item() - 20.4125 ) < 1e-2
assert abs(result_mean.item() - 0.0266 ) < 1e-3
| 348 | 1 |
from __future__ import annotations
from fractions import Fraction
def lowerCAmelCase_ ( __lowerCAmelCase , __lowerCAmelCase )-> bool:
'''simple docstring'''
return (
num != den and num % 10 == den // 10 and (num // 10) / (den % 10) == num / den
)
def lowerCAmelCase_ ( __lowerCAmelCase )-> list[str]:
'''simple docstring'''
UpperCAmelCase : str =[]
UpperCAmelCase : Union[str, Any] =11
UpperCAmelCase : List[str] =int('''1''' + '''0''' * digit_len )
for num in range(__lowerCAmelCase , __lowerCAmelCase ):
while den <= 99:
if (num != den) and (num % 10 == den // 10) and (den % 10 != 0):
if is_digit_cancelling(__lowerCAmelCase , __lowerCAmelCase ):
solutions.append(f'''{num}/{den}''' )
den += 1
num += 1
UpperCAmelCase : Any =10
return solutions
def lowerCAmelCase_ ( __lowerCAmelCase = 2 )-> int:
'''simple docstring'''
UpperCAmelCase : str =1.0
for fraction in fraction_list(__lowerCAmelCase ):
UpperCAmelCase : str =Fraction(__lowerCAmelCase )
result *= frac.denominator / frac.numerator
return int(__lowerCAmelCase )
if __name__ == "__main__":
print(solution())
| 348 | import unittest
from transformers import is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, require_torch, slow
if is_flax_available():
import optax
from flax.training.common_utils import onehot
from transformers import AutoTokenizer, FlaxMTaForConditionalGeneration
from transformers.models.ta.modeling_flax_ta import shift_tokens_right
@require_torch
@require_sentencepiece
@require_tokenizers
@require_flax
class __snake_case ( unittest.TestCase ):
@slow
def UpperCAmelCase__ ( self ) -> Union[str, Any]:
'''simple docstring'''
UpperCAmelCase : Any =FlaxMTaForConditionalGeneration.from_pretrained('''google/mt5-small''' )
UpperCAmelCase : Tuple =AutoTokenizer.from_pretrained('''google/mt5-small''' )
UpperCAmelCase : List[str] =tokenizer('''Hello there''' , return_tensors='''np''' ).input_ids
UpperCAmelCase : List[Any] =tokenizer('''Hi I am''' , return_tensors='''np''' ).input_ids
UpperCAmelCase : Union[str, Any] =shift_tokens_right(snake_case__ , model.config.pad_token_id , model.config.decoder_start_token_id )
UpperCAmelCase : List[str] =model(snake_case__ , decoder_input_ids=snake_case__ ).logits
UpperCAmelCase : Any =optax.softmax_cross_entropy(snake_case__ , onehot(snake_case__ , logits.shape[-1] ) ).mean()
UpperCAmelCase : Union[str, Any] =-(labels.shape[-1] * loss.item())
UpperCAmelCase : List[str] =-84.9127
self.assertTrue(abs(mtf_score - EXPECTED_SCORE ) < 1e-4 )
| 348 | 1 |
import argparse
import json
from collections import OrderedDict
import torch
from huggingface_hub import cached_download, hf_hub_url
from transformers import AutoImageProcessor, CvtConfig, CvtForImageClassification
def lowerCAmelCase_ ( __lowerCAmelCase )-> int:
'''simple docstring'''
UpperCAmelCase : Optional[Any] =[]
embed.append(
(
f'''cvt.encoder.stages.{idx}.embedding.convolution_embeddings.projection.weight''',
f'''stage{idx}.patch_embed.proj.weight''',
) )
embed.append(
(
f'''cvt.encoder.stages.{idx}.embedding.convolution_embeddings.projection.bias''',
f'''stage{idx}.patch_embed.proj.bias''',
) )
embed.append(
(
f'''cvt.encoder.stages.{idx}.embedding.convolution_embeddings.normalization.weight''',
f'''stage{idx}.patch_embed.norm.weight''',
) )
embed.append(
(
f'''cvt.encoder.stages.{idx}.embedding.convolution_embeddings.normalization.bias''',
f'''stage{idx}.patch_embed.norm.bias''',
) )
return embed
def lowerCAmelCase_ ( __lowerCAmelCase , __lowerCAmelCase )-> str:
'''simple docstring'''
UpperCAmelCase : Optional[int] =[]
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.convolution.weight''',
f'''stage{idx}.blocks.{cnt}.attn.conv_proj_q.conv.weight''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.weight''',
f'''stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.weight''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.bias''',
f'''stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.bias''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.running_mean''',
f'''stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.running_mean''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.running_var''',
f'''stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.running_var''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.num_batches_tracked''',
f'''stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.num_batches_tracked''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.convolution.weight''',
f'''stage{idx}.blocks.{cnt}.attn.conv_proj_k.conv.weight''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.weight''',
f'''stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.weight''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.bias''',
f'''stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.bias''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.running_mean''',
f'''stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.running_mean''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.running_var''',
f'''stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.running_var''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.num_batches_tracked''',
f'''stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.num_batches_tracked''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.convolution.weight''',
f'''stage{idx}.blocks.{cnt}.attn.conv_proj_v.conv.weight''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.weight''',
f'''stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.weight''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.bias''',
f'''stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.bias''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.running_mean''',
f'''stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.running_mean''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.running_var''',
f'''stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.running_var''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.num_batches_tracked''',
f'''stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.num_batches_tracked''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_query.weight''',
f'''stage{idx}.blocks.{cnt}.attn.proj_q.weight''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_query.bias''',
f'''stage{idx}.blocks.{cnt}.attn.proj_q.bias''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_key.weight''',
f'''stage{idx}.blocks.{cnt}.attn.proj_k.weight''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_key.bias''',
f'''stage{idx}.blocks.{cnt}.attn.proj_k.bias''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_value.weight''',
f'''stage{idx}.blocks.{cnt}.attn.proj_v.weight''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_value.bias''',
f'''stage{idx}.blocks.{cnt}.attn.proj_v.bias''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.output.dense.weight''',
f'''stage{idx}.blocks.{cnt}.attn.proj.weight''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.output.dense.bias''',
f'''stage{idx}.blocks.{cnt}.attn.proj.bias''',
) )
attention_weights.append(
(f'''cvt.encoder.stages.{idx}.layers.{cnt}.intermediate.dense.weight''', f'''stage{idx}.blocks.{cnt}.mlp.fc1.weight''') )
attention_weights.append(
(f'''cvt.encoder.stages.{idx}.layers.{cnt}.intermediate.dense.bias''', f'''stage{idx}.blocks.{cnt}.mlp.fc1.bias''') )
attention_weights.append(
(f'''cvt.encoder.stages.{idx}.layers.{cnt}.output.dense.weight''', f'''stage{idx}.blocks.{cnt}.mlp.fc2.weight''') )
attention_weights.append(
(f'''cvt.encoder.stages.{idx}.layers.{cnt}.output.dense.bias''', f'''stage{idx}.blocks.{cnt}.mlp.fc2.bias''') )
attention_weights.append(
(f'''cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_before.weight''', f'''stage{idx}.blocks.{cnt}.norm1.weight''') )
attention_weights.append(
(f'''cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_before.bias''', f'''stage{idx}.blocks.{cnt}.norm1.bias''') )
attention_weights.append(
(f'''cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_after.weight''', f'''stage{idx}.blocks.{cnt}.norm2.weight''') )
attention_weights.append(
(f'''cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_after.bias''', f'''stage{idx}.blocks.{cnt}.norm2.bias''') )
return attention_weights
def lowerCAmelCase_ ( __lowerCAmelCase )-> Tuple:
'''simple docstring'''
UpperCAmelCase : str =[]
token.append((f'''cvt.encoder.stages.{idx}.cls_token''', '''stage2.cls_token''') )
return token
def lowerCAmelCase_ ( )-> Dict:
'''simple docstring'''
UpperCAmelCase : Optional[Any] =[]
head.append(('''layernorm.weight''', '''norm.weight''') )
head.append(('''layernorm.bias''', '''norm.bias''') )
head.append(('''classifier.weight''', '''head.weight''') )
head.append(('''classifier.bias''', '''head.bias''') )
return head
def lowerCAmelCase_ ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )-> Optional[int]:
'''simple docstring'''
UpperCAmelCase : List[Any] ='''imagenet-1k-id2label.json'''
UpperCAmelCase : str =10_00
UpperCAmelCase : List[Any] ='''huggingface/label-files'''
UpperCAmelCase : int =num_labels
UpperCAmelCase : int =json.load(open(cached_download(hf_hub_url(__lowerCAmelCase , __lowerCAmelCase , repo_type='''dataset''' ) ) , '''r''' ) )
UpperCAmelCase : List[str] ={int(__lowerCAmelCase ): v for k, v in idalabel.items()}
UpperCAmelCase : Any =idalabel
UpperCAmelCase : int ={v: k for k, v in idalabel.items()}
UpperCAmelCase : str =CvtConfig(num_labels=__lowerCAmelCase , idalabel=__lowerCAmelCase , labelaid=__lowerCAmelCase )
# For depth size 13 (13 = 1+2+10)
if cvt_model.rsplit('''/''' , 1 )[-1][4:6] == "13":
UpperCAmelCase : str =[1, 2, 10]
# For depth size 21 (21 = 1+4+16)
elif cvt_model.rsplit('''/''' , 1 )[-1][4:6] == "21":
UpperCAmelCase : Dict =[1, 4, 16]
# For wide cvt (similar to wide-resnet) depth size 24 (w24 = 2 + 2 20)
else:
UpperCAmelCase : Tuple =[2, 2, 20]
UpperCAmelCase : Union[str, Any] =[3, 12, 16]
UpperCAmelCase : int =[1_92, 7_68, 10_24]
UpperCAmelCase : Dict =CvtForImageClassification(__lowerCAmelCase )
UpperCAmelCase : Union[str, Any] =AutoImageProcessor.from_pretrained('''facebook/convnext-base-224-22k-1k''' )
UpperCAmelCase : List[str] =image_size
UpperCAmelCase : Dict =torch.load(__lowerCAmelCase , map_location=torch.device('''cpu''' ) )
UpperCAmelCase : Union[str, Any] =OrderedDict()
UpperCAmelCase : Optional[int] =[]
for idx in range(len(config.depth ) ):
if config.cls_token[idx]:
UpperCAmelCase : Tuple =list_of_state_dict + cls_token(__lowerCAmelCase )
UpperCAmelCase : Union[str, Any] =list_of_state_dict + embeddings(__lowerCAmelCase )
for cnt in range(config.depth[idx] ):
UpperCAmelCase : Union[str, Any] =list_of_state_dict + attention(__lowerCAmelCase , __lowerCAmelCase )
UpperCAmelCase : Tuple =list_of_state_dict + final()
for gg in list_of_state_dict:
print(__lowerCAmelCase )
for i in range(len(__lowerCAmelCase ) ):
UpperCAmelCase : Union[str, Any] =original_weights[list_of_state_dict[i][1]]
model.load_state_dict(__lowerCAmelCase )
model.save_pretrained(__lowerCAmelCase )
image_processor.save_pretrained(__lowerCAmelCase )
# Download the weights from zoo: https://1drv.ms/u/s!AhIXJn_J-blW9RzF3rMW7SsLHa8h?e=blQ0Al
if __name__ == "__main__":
__snake_case = argparse.ArgumentParser()
parser.add_argument(
'''--cvt_model''',
default='''cvt-w24''',
type=str,
help='''Name of the cvt model you\'d like to convert.''',
)
parser.add_argument(
'''--image_size''',
default=3_84,
type=int,
help='''Input Image Size''',
)
parser.add_argument(
'''--cvt_file_name''',
default=r'''cvtmodels\CvT-w24-384x384-IN-22k.pth''',
type=str,
help='''Input Image Size''',
)
parser.add_argument(
'''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.'''
)
__snake_case = parser.parse_args()
convert_cvt_checkpoint(args.cvt_model, args.image_size, args.cvt_file_name, args.pytorch_dump_folder_path)
| 348 | import unittest
import numpy as np
from diffusers import LMSDiscreteScheduler, OnnxStableDiffusionInpaintPipeline
from diffusers.utils.testing_utils import (
is_onnx_available,
load_image,
nightly,
require_onnxruntime,
require_torch_gpu,
)
from ..test_pipelines_onnx_common import OnnxPipelineTesterMixin
if is_onnx_available():
import onnxruntime as ort
class __snake_case ( lowerCamelCase__ , unittest.TestCase ):
# FIXME: add fast tests
pass
@nightly
@require_onnxruntime
@require_torch_gpu
class __snake_case ( unittest.TestCase ):
@property
def UpperCAmelCase__ ( self ) -> List[str]:
'''simple docstring'''
return (
"CUDAExecutionProvider",
{
"gpu_mem_limit": "15000000000", # 15GB
"arena_extend_strategy": "kSameAsRequested",
},
)
@property
def UpperCAmelCase__ ( self ) -> int:
'''simple docstring'''
UpperCAmelCase : List[Any] =ort.SessionOptions()
UpperCAmelCase : Optional[int] =False
return options
def UpperCAmelCase__ ( self ) -> Dict:
'''simple docstring'''
UpperCAmelCase : int =load_image(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
'''/in_paint/overture-creations-5sI6fQgYIuo.png''' )
UpperCAmelCase : Optional[Any] =load_image(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
'''/in_paint/overture-creations-5sI6fQgYIuo_mask.png''' )
UpperCAmelCase : List[str] =OnnxStableDiffusionInpaintPipeline.from_pretrained(
'''runwayml/stable-diffusion-inpainting''' , revision='''onnx''' , safety_checker=snake_case__ , feature_extractor=snake_case__ , provider=self.gpu_provider , sess_options=self.gpu_options , )
pipe.set_progress_bar_config(disable=snake_case__ )
UpperCAmelCase : Dict ='''A red cat sitting on a park bench'''
UpperCAmelCase : int =np.random.RandomState(0 )
UpperCAmelCase : Any =pipe(
prompt=snake_case__ , image=snake_case__ , mask_image=snake_case__ , guidance_scale=7.5 , num_inference_steps=10 , generator=snake_case__ , output_type='''np''' , )
UpperCAmelCase : Dict =output.images
UpperCAmelCase : Optional[int] =images[0, 255:258, 255:258, -1]
assert images.shape == (1, 512, 512, 3)
UpperCAmelCase : Tuple =np.array([0.2514, 0.3007, 0.3517, 0.1790, 0.2382, 0.3167, 0.1944, 0.2273, 0.2464] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-3
def UpperCAmelCase__ ( self ) -> Optional[Any]:
'''simple docstring'''
UpperCAmelCase : List[str] =load_image(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
'''/in_paint/overture-creations-5sI6fQgYIuo.png''' )
UpperCAmelCase : Tuple =load_image(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
'''/in_paint/overture-creations-5sI6fQgYIuo_mask.png''' )
UpperCAmelCase : List[str] =LMSDiscreteScheduler.from_pretrained(
'''runwayml/stable-diffusion-inpainting''' , subfolder='''scheduler''' , revision='''onnx''' )
UpperCAmelCase : int =OnnxStableDiffusionInpaintPipeline.from_pretrained(
'''runwayml/stable-diffusion-inpainting''' , revision='''onnx''' , scheduler=snake_case__ , safety_checker=snake_case__ , feature_extractor=snake_case__ , provider=self.gpu_provider , sess_options=self.gpu_options , )
pipe.set_progress_bar_config(disable=snake_case__ )
UpperCAmelCase : Union[str, Any] ='''A red cat sitting on a park bench'''
UpperCAmelCase : int =np.random.RandomState(0 )
UpperCAmelCase : str =pipe(
prompt=snake_case__ , image=snake_case__ , mask_image=snake_case__ , guidance_scale=7.5 , num_inference_steps=20 , generator=snake_case__ , output_type='''np''' , )
UpperCAmelCase : Dict =output.images
UpperCAmelCase : int =images[0, 255:258, 255:258, -1]
assert images.shape == (1, 512, 512, 3)
UpperCAmelCase : Union[str, Any] =np.array([0.0086, 0.0077, 0.0083, 0.0093, 0.0107, 0.0139, 0.0094, 0.0097, 0.0125] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-3
| 348 | 1 |
__snake_case = '''
# Transformers installation
! pip install transformers datasets
# To install from source instead of the last release, comment the command above and uncomment the following one.
# ! pip install git+https://github.com/huggingface/transformers.git
'''
__snake_case = [{'''type''': '''code''', '''content''': INSTALL_CONTENT}]
__snake_case = {
'''{processor_class}''': '''FakeProcessorClass''',
'''{model_class}''': '''FakeModelClass''',
'''{object_class}''': '''FakeObjectClass''',
}
| 348 | from unittest import TestCase
from datasets import Dataset
from minhash_deduplication import deduplicate_dataset, make_duplicate_clusters
def lowerCAmelCase_ ( )-> int:
'''simple docstring'''
UpperCAmelCase : str ={
'''repo_name''': ['''test_repo1''', '''test_repo2''', '''test_repo3'''],
'''path''': ['''test_1.py''', '''test_2.py''', '''unit_test.py'''],
'''content''': ['''a ''' * 20, '''a ''' * 30, '''b ''' * 7],
}
UpperCAmelCase : Union[str, Any] =Dataset.from_dict(__lowerCAmelCase )
return dataset
class __snake_case ( lowerCamelCase__ ):
def UpperCAmelCase__ ( self ) -> Tuple:
'''simple docstring'''
UpperCAmelCase : List[str] =get_dataset()
UpperCAmelCase : Optional[int] =make_duplicate_clusters(snake_case__ , 0.85 )
self.assertEqual(len(duplicate_clusters[0] ) , 2 )
def UpperCAmelCase__ ( self ) -> Tuple:
'''simple docstring'''
UpperCAmelCase : str =get_dataset()
UpperCAmelCase , UpperCAmelCase : Tuple =deduplicate_dataset(snake_case__ )
self.assertEqual(len(snake_case__ ) , 2 )
print(snake_case__ )
self.assertEqual(duplicate_clusters[0][0]['''copies'''] , 2 )
self.assertEqual(duplicate_clusters[0][0]['''is_extreme'''] , snake_case__ )
| 348 | 1 |
def lowerCAmelCase_ ( __lowerCAmelCase , __lowerCAmelCase )-> list:
'''simple docstring'''
UpperCAmelCase : Union[str, Any] =word.split()
def justify(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> str:
UpperCAmelCase : str =max_width - width
UpperCAmelCase : Optional[int] =len(__lowerCAmelCase )
if len(__lowerCAmelCase ) == 1:
# if there is only word in line
# just insert overall_spaces_count for the remainder of line
return line[0] + " " * overall_spaces_count
else:
UpperCAmelCase : List[str] =words_count - 1
# num_spaces_between_words_list[i] : tells you to insert
# num_spaces_between_words_list[i] spaces
# after word on line[i]
UpperCAmelCase : Dict =spaces_to_insert_between_words * [
overall_spaces_count // spaces_to_insert_between_words
]
UpperCAmelCase : Dict =(
overall_spaces_count % spaces_to_insert_between_words
)
# distribute spaces via round robin to the left words
for i in range(__lowerCAmelCase ):
num_spaces_between_words_list[i] += 1
UpperCAmelCase : Union[str, Any] =[]
for i in range(__lowerCAmelCase ):
# add the word
aligned_words_list.append(line[i] )
# add the spaces to insert
aligned_words_list.append(num_spaces_between_words_list[i] * ''' ''' )
# just add the last word to the sentence
aligned_words_list.append(line[-1] )
# join the aligned words list to form a justified line
return "".join(__lowerCAmelCase )
UpperCAmelCase : Optional[int] =[]
UpperCAmelCase : list[str] =[]
UpperCAmelCase : Optional[int] =0
for word in words:
if width + len(__lowerCAmelCase ) + len(__lowerCAmelCase ) <= max_width:
# keep adding words until we can fill out max_width
# width = sum of length of all words (without overall_spaces_count)
# len(word) = length of current word
# len(line) = number of overall_spaces_count to insert between words
line.append(__lowerCAmelCase )
width += len(__lowerCAmelCase )
else:
# justify the line and add it to result
answer.append(justify(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) )
# reset new line and new width
UpperCAmelCase , UpperCAmelCase : Optional[Any] =[word], len(__lowerCAmelCase )
UpperCAmelCase : List[str] =max_width - width - len(__lowerCAmelCase )
answer.append(''' '''.join(__lowerCAmelCase ) + (remaining_spaces + 1) * ''' ''' )
return answer
if __name__ == "__main__":
from doctest import testmod
testmod()
| 348 | from typing import Callable, List, Optional, Tuple, Union
import torch
from transformers import CLIPTextModel, CLIPTokenizer
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin, TransformeraDModel, VQModel
from ...schedulers import VQDiffusionScheduler
from ...utils import logging
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
__snake_case = logging.get_logger(__name__) # pylint: disable=invalid-name
class __snake_case ( lowerCamelCase__ , lowerCamelCase__ ):
@register_to_config
def __init__( self , snake_case__ , snake_case__ = None , snake_case__ = None ) -> str:
'''simple docstring'''
super().__init__()
UpperCAmelCase : Optional[Any] =learnable
if self.learnable:
assert hidden_size is not None, "learnable=True requires `hidden_size` to be set"
assert length is not None, "learnable=True requires `length` to be set"
UpperCAmelCase : Any =torch.zeros(snake_case__ , snake_case__ )
else:
UpperCAmelCase : Union[str, Any] =None
UpperCAmelCase : Optional[int] =torch.nn.Parameter(snake_case__ )
class __snake_case ( lowerCamelCase__ ):
__lowerCamelCase : VQModel
__lowerCamelCase : CLIPTextModel
__lowerCamelCase : CLIPTokenizer
__lowerCamelCase : TransformeraDModel
__lowerCamelCase : LearnedClassifierFreeSamplingEmbeddings
__lowerCamelCase : VQDiffusionScheduler
def __init__( self , snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ , ) -> int:
'''simple docstring'''
super().__init__()
self.register_modules(
vqvae=snake_case__ , transformer=snake_case__ , text_encoder=snake_case__ , tokenizer=snake_case__ , scheduler=snake_case__ , learned_classifier_free_sampling_embeddings=snake_case__ , )
def UpperCAmelCase__ ( self , snake_case__ , snake_case__ , snake_case__ ) -> Optional[int]:
'''simple docstring'''
UpperCAmelCase : int =len(snake_case__ ) if isinstance(snake_case__ , snake_case__ ) else 1
# get prompt text embeddings
UpperCAmelCase : Optional[int] =self.tokenizer(
snake_case__ , padding='''max_length''' , max_length=self.tokenizer.model_max_length , return_tensors='''pt''' , )
UpperCAmelCase : int =text_inputs.input_ids
if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
UpperCAmelCase : List[str] =self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :] )
logger.warning(
'''The following part of your input was truncated because CLIP can only handle sequences up to'''
f''' {self.tokenizer.model_max_length} tokens: {removed_text}''' )
UpperCAmelCase : Optional[Any] =text_input_ids[:, : self.tokenizer.model_max_length]
UpperCAmelCase : List[Any] =self.text_encoder(text_input_ids.to(self.device ) )[0]
# NOTE: This additional step of normalizing the text embeddings is from VQ-Diffusion.
# While CLIP does normalize the pooled output of the text transformer when combining
# the image and text embeddings, CLIP does not directly normalize the last hidden state.
#
# CLIP normalizing the pooled output.
# https://github.com/huggingface/transformers/blob/d92e22d1f28324f513f3080e5c47c071a3916721/src/transformers/models/clip/modeling_clip.py#L1052-L1053
UpperCAmelCase : int =prompt_embeds / prompt_embeds.norm(dim=-1 , keepdim=snake_case__ )
# duplicate text embeddings for each generation per prompt
UpperCAmelCase : int =prompt_embeds.repeat_interleave(snake_case__ , dim=0 )
if do_classifier_free_guidance:
if self.learned_classifier_free_sampling_embeddings.learnable:
UpperCAmelCase : Optional[int] =self.learned_classifier_free_sampling_embeddings.embeddings
UpperCAmelCase : str =negative_prompt_embeds.unsqueeze(0 ).repeat(snake_case__ , 1 , 1 )
else:
UpperCAmelCase : str =[''''''] * batch_size
UpperCAmelCase : Tuple =text_input_ids.shape[-1]
UpperCAmelCase : Optional[Any] =self.tokenizer(
snake_case__ , padding='''max_length''' , max_length=snake_case__ , truncation=snake_case__ , return_tensors='''pt''' , )
UpperCAmelCase : Optional[Any] =self.text_encoder(uncond_input.input_ids.to(self.device ) )[0]
# See comment for normalizing text embeddings
UpperCAmelCase : Optional[int] =negative_prompt_embeds / negative_prompt_embeds.norm(dim=-1 , keepdim=snake_case__ )
# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
UpperCAmelCase : Optional[Any] =negative_prompt_embeds.shape[1]
UpperCAmelCase : Union[str, Any] =negative_prompt_embeds.repeat(1 , snake_case__ , 1 )
UpperCAmelCase : Optional[Any] =negative_prompt_embeds.view(batch_size * num_images_per_prompt , snake_case__ , -1 )
# For classifier free guidance, we need to do two forward passes.
# Here we concatenate the unconditional and text embeddings into a single batch
# to avoid doing two forward passes
UpperCAmelCase : int =torch.cat([negative_prompt_embeds, prompt_embeds] )
return prompt_embeds
@torch.no_grad()
def __call__( self , snake_case__ , snake_case__ = 100 , snake_case__ = 5.0 , snake_case__ = 1.0 , snake_case__ = 1 , snake_case__ = None , snake_case__ = None , snake_case__ = "pil" , snake_case__ = True , snake_case__ = None , snake_case__ = 1 , ) -> Union[ImagePipelineOutput, Tuple]:
'''simple docstring'''
if isinstance(snake_case__ , snake_case__ ):
UpperCAmelCase : Optional[int] =1
elif isinstance(snake_case__ , snake_case__ ):
UpperCAmelCase : Tuple =len(snake_case__ )
else:
raise ValueError(f'''`prompt` has to be of type `str` or `list` but is {type(snake_case__ )}''' )
UpperCAmelCase : Tuple =batch_size * num_images_per_prompt
UpperCAmelCase : List[str] =guidance_scale > 1.0
UpperCAmelCase : List[Any] =self._encode_prompt(snake_case__ , snake_case__ , snake_case__ )
if (callback_steps is None) or (
callback_steps is not None and (not isinstance(snake_case__ , snake_case__ ) or callback_steps <= 0)
):
raise ValueError(
f'''`callback_steps` has to be a positive integer but is {callback_steps} of type'''
f''' {type(snake_case__ )}.''' )
# get the initial completely masked latents unless the user supplied it
UpperCAmelCase : int =(batch_size, self.transformer.num_latent_pixels)
if latents is None:
UpperCAmelCase : Union[str, Any] =self.transformer.num_vector_embeds - 1
UpperCAmelCase : str =torch.full(snake_case__ , snake_case__ ).to(self.device )
else:
if latents.shape != latents_shape:
raise ValueError(f'''Unexpected latents shape, got {latents.shape}, expected {latents_shape}''' )
if (latents < 0).any() or (latents >= self.transformer.num_vector_embeds).any():
raise ValueError(
'''Unexpected latents value(s). All latents be valid embedding indices i.e. in the range 0,'''
f''' {self.transformer.num_vector_embeds - 1} (inclusive).''' )
UpperCAmelCase : Any =latents.to(self.device )
# set timesteps
self.scheduler.set_timesteps(snake_case__ , device=self.device )
UpperCAmelCase : Any =self.scheduler.timesteps.to(self.device )
UpperCAmelCase : Optional[int] =latents
for i, t in enumerate(self.progress_bar(snake_case__ ) ):
# expand the sample if we are doing classifier free guidance
UpperCAmelCase : Optional[Any] =torch.cat([sample] * 2 ) if do_classifier_free_guidance else sample
# predict the un-noised image
# model_output == `log_p_x_0`
UpperCAmelCase : Optional[int] =self.transformer(snake_case__ , encoder_hidden_states=snake_case__ , timestep=snake_case__ ).sample
if do_classifier_free_guidance:
UpperCAmelCase , UpperCAmelCase : str =model_output.chunk(2 )
UpperCAmelCase : Optional[int] =model_output_uncond + guidance_scale * (model_output_text - model_output_uncond)
model_output -= torch.logsumexp(snake_case__ , dim=1 , keepdim=snake_case__ )
UpperCAmelCase : Tuple =self.truncate(snake_case__ , snake_case__ )
# remove `log(0)`'s (`-inf`s)
UpperCAmelCase : Optional[Any] =model_output.clamp(-70 )
# compute the previous noisy sample x_t -> x_t-1
UpperCAmelCase : int =self.scheduler.step(snake_case__ , timestep=snake_case__ , sample=snake_case__ , generator=snake_case__ ).prev_sample
# call the callback, if provided
if callback is not None and i % callback_steps == 0:
callback(snake_case__ , snake_case__ , snake_case__ )
UpperCAmelCase : Optional[int] =self.vqvae.config.vq_embed_dim
UpperCAmelCase : Optional[Any] =(batch_size, self.transformer.height, self.transformer.width, embedding_channels)
UpperCAmelCase : Dict =self.vqvae.quantize.get_codebook_entry(snake_case__ , shape=snake_case__ )
UpperCAmelCase : Tuple =self.vqvae.decode(snake_case__ , force_not_quantize=snake_case__ ).sample
UpperCAmelCase : Union[str, Any] =(image / 2 + 0.5).clamp(0 , 1 )
UpperCAmelCase : Any =image.cpu().permute(0 , 2 , 3 , 1 ).numpy()
if output_type == "pil":
UpperCAmelCase : List[str] =self.numpy_to_pil(snake_case__ )
if not return_dict:
return (image,)
return ImagePipelineOutput(images=snake_case__ )
def UpperCAmelCase__ ( self , snake_case__ , snake_case__ ) -> torch.FloatTensor:
'''simple docstring'''
UpperCAmelCase , UpperCAmelCase : int =torch.sort(snake_case__ , 1 , descending=snake_case__ )
UpperCAmelCase : Union[str, Any] =torch.exp(snake_case__ )
UpperCAmelCase : Union[str, Any] =sorted_p_x_0.cumsum(dim=1 ) < truncation_rate
# Ensure that at least the largest probability is not zeroed out
UpperCAmelCase : Optional[Any] =torch.full_like(keep_mask[:, 0:1, :] , snake_case__ )
UpperCAmelCase : Tuple =torch.cat((all_true, keep_mask) , dim=1 )
UpperCAmelCase : int =keep_mask[:, :-1, :]
UpperCAmelCase : int =keep_mask.gather(1 , indices.argsort(1 ) )
UpperCAmelCase : Dict =log_p_x_0.clone()
UpperCAmelCase : List[Any] =-torch.inf # -inf = log(0)
return rv
| 348 | 1 |
import json
from typing import TYPE_CHECKING, List, Optional, Tuple
from tokenizers import pre_tokenizers
from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_gpta import GPTaTokenizer
if TYPE_CHECKING:
from transformers.pipelines.conversational import Conversation
__snake_case = logging.get_logger(__name__)
__snake_case = {'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', '''tokenizer_file''': '''tokenizer.json'''}
__snake_case = {
'''vocab_file''': {
'''gpt2''': '''https://huggingface.co/gpt2/resolve/main/vocab.json''',
'''gpt2-medium''': '''https://huggingface.co/gpt2-medium/resolve/main/vocab.json''',
'''gpt2-large''': '''https://huggingface.co/gpt2-large/resolve/main/vocab.json''',
'''gpt2-xl''': '''https://huggingface.co/gpt2-xl/resolve/main/vocab.json''',
'''distilgpt2''': '''https://huggingface.co/distilgpt2/resolve/main/vocab.json''',
},
'''merges_file''': {
'''gpt2''': '''https://huggingface.co/gpt2/resolve/main/merges.txt''',
'''gpt2-medium''': '''https://huggingface.co/gpt2-medium/resolve/main/merges.txt''',
'''gpt2-large''': '''https://huggingface.co/gpt2-large/resolve/main/merges.txt''',
'''gpt2-xl''': '''https://huggingface.co/gpt2-xl/resolve/main/merges.txt''',
'''distilgpt2''': '''https://huggingface.co/distilgpt2/resolve/main/merges.txt''',
},
'''tokenizer_file''': {
'''gpt2''': '''https://huggingface.co/gpt2/resolve/main/tokenizer.json''',
'''gpt2-medium''': '''https://huggingface.co/gpt2-medium/resolve/main/tokenizer.json''',
'''gpt2-large''': '''https://huggingface.co/gpt2-large/resolve/main/tokenizer.json''',
'''gpt2-xl''': '''https://huggingface.co/gpt2-xl/resolve/main/tokenizer.json''',
'''distilgpt2''': '''https://huggingface.co/distilgpt2/resolve/main/tokenizer.json''',
},
}
__snake_case = {
'''gpt2''': 10_24,
'''gpt2-medium''': 10_24,
'''gpt2-large''': 10_24,
'''gpt2-xl''': 10_24,
'''distilgpt2''': 10_24,
}
class __snake_case ( lowerCamelCase__ ):
__lowerCamelCase : int = VOCAB_FILES_NAMES
__lowerCamelCase : str = PRETRAINED_VOCAB_FILES_MAP
__lowerCamelCase : List[Any] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
__lowerCamelCase : Union[str, Any] = ["""input_ids""", """attention_mask"""]
__lowerCamelCase : Union[str, Any] = GPTaTokenizer
def __init__( self , snake_case__=None , snake_case__=None , snake_case__=None , snake_case__="<|endoftext|>" , snake_case__="<|endoftext|>" , snake_case__="<|endoftext|>" , snake_case__=False , **snake_case__ , ) -> Any:
'''simple docstring'''
super().__init__(
snake_case__ , snake_case__ , tokenizer_file=snake_case__ , unk_token=snake_case__ , bos_token=snake_case__ , eos_token=snake_case__ , add_prefix_space=snake_case__ , **snake_case__ , )
UpperCAmelCase : Tuple =kwargs.pop('''add_bos_token''' , snake_case__ )
UpperCAmelCase : Optional[int] =json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__() )
if pre_tok_state.get('''add_prefix_space''' , snake_case__ ) != add_prefix_space:
UpperCAmelCase : Tuple =getattr(snake_case__ , pre_tok_state.pop('''type''' ) )
UpperCAmelCase : Any =add_prefix_space
UpperCAmelCase : List[Any] =pre_tok_class(**snake_case__ )
UpperCAmelCase : Optional[Any] =add_prefix_space
def UpperCAmelCase__ ( self , *snake_case__ , **snake_case__ ) -> BatchEncoding:
'''simple docstring'''
UpperCAmelCase : List[str] =kwargs.get('''is_split_into_words''' , snake_case__ )
assert self.add_prefix_space or not is_split_into_words, (
f'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
"to use it with pretokenized inputs."
)
return super()._batch_encode_plus(*snake_case__ , **snake_case__ )
def UpperCAmelCase__ ( self , *snake_case__ , **snake_case__ ) -> BatchEncoding:
'''simple docstring'''
UpperCAmelCase : Optional[Any] =kwargs.get('''is_split_into_words''' , snake_case__ )
assert self.add_prefix_space or not is_split_into_words, (
f'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
"to use it with pretokenized inputs."
)
return super()._encode_plus(*snake_case__ , **snake_case__ )
def UpperCAmelCase__ ( self , snake_case__ , snake_case__ = None ) -> Tuple[str]:
'''simple docstring'''
UpperCAmelCase : Any =self._tokenizer.model.save(snake_case__ , name=snake_case__ )
return tuple(snake_case__ )
def UpperCAmelCase__ ( self , snake_case__ ) -> List[int]:
'''simple docstring'''
UpperCAmelCase : Optional[Any] =[]
for is_user, text in conversation.iter_texts():
input_ids.extend(self.encode(snake_case__ , add_special_tokens=snake_case__ ) + [self.eos_token_id] )
if len(snake_case__ ) > self.model_max_length:
UpperCAmelCase : Dict =input_ids[-self.model_max_length :]
return input_ids
| 348 | import unittest
import numpy as np
import torch
from diffusers import KarrasVePipeline, KarrasVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class __snake_case ( unittest.TestCase ):
@property
def UpperCAmelCase__ ( self ) -> int:
'''simple docstring'''
torch.manual_seed(0 )
UpperCAmelCase : Any =UNetaDModel(
block_out_channels=(32, 64) , layers_per_block=2 , sample_size=32 , in_channels=3 , out_channels=3 , down_block_types=('''DownBlock2D''', '''AttnDownBlock2D''') , up_block_types=('''AttnUpBlock2D''', '''UpBlock2D''') , )
return model
def UpperCAmelCase__ ( self ) -> Union[str, Any]:
'''simple docstring'''
UpperCAmelCase : Tuple =self.dummy_uncond_unet
UpperCAmelCase : Optional[int] =KarrasVeScheduler()
UpperCAmelCase : List[Any] =KarrasVePipeline(unet=snake_case__ , scheduler=snake_case__ )
pipe.to(snake_case__ )
pipe.set_progress_bar_config(disable=snake_case__ )
UpperCAmelCase : List[str] =torch.manual_seed(0 )
UpperCAmelCase : List[str] =pipe(num_inference_steps=2 , generator=snake_case__ , output_type='''numpy''' ).images
UpperCAmelCase : str =torch.manual_seed(0 )
UpperCAmelCase : str =pipe(num_inference_steps=2 , generator=snake_case__ , output_type='''numpy''' , return_dict=snake_case__ )[0]
UpperCAmelCase : Any =image[0, -3:, -3:, -1]
UpperCAmelCase : List[str] =image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 32, 32, 3)
UpperCAmelCase : int =np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1e-2
@slow
@require_torch
class __snake_case ( unittest.TestCase ):
def UpperCAmelCase__ ( self ) -> List[Any]:
'''simple docstring'''
UpperCAmelCase : Tuple ='''google/ncsnpp-celebahq-256'''
UpperCAmelCase : int =UNetaDModel.from_pretrained(snake_case__ )
UpperCAmelCase : Dict =KarrasVeScheduler()
UpperCAmelCase : Union[str, Any] =KarrasVePipeline(unet=snake_case__ , scheduler=snake_case__ )
pipe.to(snake_case__ )
pipe.set_progress_bar_config(disable=snake_case__ )
UpperCAmelCase : Any =torch.manual_seed(0 )
UpperCAmelCase : Tuple =pipe(num_inference_steps=20 , generator=snake_case__ , output_type='''numpy''' ).images
UpperCAmelCase : Optional[int] =image[0, -3:, -3:, -1]
assert image.shape == (1, 256, 256, 3)
UpperCAmelCase : Tuple =np.array([0.578, 0.5811, 0.5924, 0.5809, 0.587, 0.5886, 0.5861, 0.5802, 0.586] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
| 348 | 1 |
def lowerCAmelCase_ ( __lowerCAmelCase )-> List[Any]:
'''simple docstring'''
UpperCAmelCase : List[Any] =[0] * len(__lowerCAmelCase )
UpperCAmelCase : Union[str, Any] =[]
UpperCAmelCase : Tuple =[1] * len(__lowerCAmelCase )
for values in graph.values():
for i in values:
indegree[i] += 1
for i in range(len(__lowerCAmelCase ) ):
if indegree[i] == 0:
queue.append(__lowerCAmelCase )
while queue:
UpperCAmelCase : Optional[Any] =queue.pop(0 )
for x in graph[vertex]:
indegree[x] -= 1
if long_dist[vertex] + 1 > long_dist[x]:
UpperCAmelCase : int =long_dist[vertex] + 1
if indegree[x] == 0:
queue.append(__lowerCAmelCase )
print(max(__lowerCAmelCase ) )
# Adjacency list of Graph
__snake_case = {0: [2, 3, 4], 1: [2, 7], 2: [5], 3: [5, 7], 4: [7], 5: [6], 6: [7], 7: []}
longest_distance(graph)
| 348 | import qiskit
def lowerCAmelCase_ ( __lowerCAmelCase , __lowerCAmelCase )-> qiskit.result.counts.Counts:
'''simple docstring'''
UpperCAmelCase : Union[str, Any] =qiskit.Aer.get_backend('''aer_simulator''' )
UpperCAmelCase : List[str] =qiskit.QuantumCircuit(4 , 2 )
# encode inputs in qubits 0 and 1
if bita == 1:
qc_ha.x(0 )
if bita == 1:
qc_ha.x(1 )
qc_ha.barrier()
# use cnots to write XOR of the inputs on qubit2
qc_ha.cx(0 , 2 )
qc_ha.cx(1 , 2 )
# use ccx / toffoli gate to write AND of the inputs on qubit3
qc_ha.ccx(0 , 1 , 3 )
qc_ha.barrier()
# extract outputs
qc_ha.measure(2 , 0 ) # extract XOR value
qc_ha.measure(3 , 1 ) # extract AND value
# Execute the circuit on the qasm simulator
UpperCAmelCase : Dict =qiskit.execute(__lowerCAmelCase , __lowerCAmelCase , shots=10_00 )
# Return the histogram data of the results of the experiment
return job.result().get_counts(__lowerCAmelCase )
if __name__ == "__main__":
__snake_case = half_adder(1, 1)
print(f'Half Adder Output Qubit Counts: {counts}')
| 348 | 1 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_big_bird import BigBirdTokenizer
else:
__snake_case = None
__snake_case = logging.get_logger(__name__)
__snake_case = {'''vocab_file''': '''spiece.model''', '''tokenizer_file''': '''tokenizer.json'''}
__snake_case = {
'''vocab_file''': {
'''google/bigbird-roberta-base''': '''https://huggingface.co/google/bigbird-roberta-base/resolve/main/spiece.model''',
'''google/bigbird-roberta-large''': (
'''https://huggingface.co/google/bigbird-roberta-large/resolve/main/spiece.model'''
),
'''google/bigbird-base-trivia-itc''': (
'''https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/spiece.model'''
),
},
'''tokenizer_file''': {
'''google/bigbird-roberta-base''': (
'''https://huggingface.co/google/bigbird-roberta-base/resolve/main/tokenizer.json'''
),
'''google/bigbird-roberta-large''': (
'''https://huggingface.co/google/bigbird-roberta-large/resolve/main/tokenizer.json'''
),
'''google/bigbird-base-trivia-itc''': (
'''https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/tokenizer.json'''
),
},
}
__snake_case = {
'''google/bigbird-roberta-base''': 40_96,
'''google/bigbird-roberta-large''': 40_96,
'''google/bigbird-base-trivia-itc''': 40_96,
}
__snake_case = '''▁'''
class __snake_case ( lowerCamelCase__ ):
__lowerCamelCase : Dict = VOCAB_FILES_NAMES
__lowerCamelCase : List[Any] = PRETRAINED_VOCAB_FILES_MAP
__lowerCamelCase : Dict = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
__lowerCamelCase : List[str] = BigBirdTokenizer
__lowerCamelCase : Any = ["""input_ids""", """attention_mask"""]
__lowerCamelCase : List[int] = []
def __init__( self , snake_case__=None , snake_case__=None , snake_case__="<unk>" , snake_case__="<s>" , snake_case__="</s>" , snake_case__="<pad>" , snake_case__="[SEP]" , snake_case__="[MASK]" , snake_case__="[CLS]" , **snake_case__ , ) -> List[Any]:
'''simple docstring'''
UpperCAmelCase : Any =AddedToken(snake_case__ , lstrip=snake_case__ , rstrip=snake_case__ ) if isinstance(snake_case__ , snake_case__ ) else bos_token
UpperCAmelCase : Optional[int] =AddedToken(snake_case__ , lstrip=snake_case__ , rstrip=snake_case__ ) if isinstance(snake_case__ , snake_case__ ) else eos_token
UpperCAmelCase : List[str] =AddedToken(snake_case__ , lstrip=snake_case__ , rstrip=snake_case__ ) if isinstance(snake_case__ , snake_case__ ) else unk_token
UpperCAmelCase : Union[str, Any] =AddedToken(snake_case__ , lstrip=snake_case__ , rstrip=snake_case__ ) if isinstance(snake_case__ , snake_case__ ) else pad_token
UpperCAmelCase : int =AddedToken(snake_case__ , lstrip=snake_case__ , rstrip=snake_case__ ) if isinstance(snake_case__ , snake_case__ ) else cls_token
UpperCAmelCase : str =AddedToken(snake_case__ , lstrip=snake_case__ , rstrip=snake_case__ ) if isinstance(snake_case__ , snake_case__ ) else sep_token
# Mask token behave like a normal word, i.e. include the space before it
UpperCAmelCase : List[Any] =AddedToken(snake_case__ , lstrip=snake_case__ , rstrip=snake_case__ ) if isinstance(snake_case__ , snake_case__ ) else mask_token
super().__init__(
snake_case__ , tokenizer_file=snake_case__ , bos_token=snake_case__ , eos_token=snake_case__ , unk_token=snake_case__ , sep_token=snake_case__ , pad_token=snake_case__ , cls_token=snake_case__ , mask_token=snake_case__ , **snake_case__ , )
UpperCAmelCase : Tuple =vocab_file
UpperCAmelCase : Optional[int] =False if not self.vocab_file else True
def UpperCAmelCase__ ( self , snake_case__ , snake_case__ = None ) -> List[int]:
'''simple docstring'''
UpperCAmelCase : int =[self.sep_token_id]
UpperCAmelCase : Optional[int] =[self.cls_token_id]
if token_ids_a is None:
return cls + token_ids_a + sep
return cls + token_ids_a + sep + token_ids_a + sep
def UpperCAmelCase__ ( self , snake_case__ , snake_case__ = None , snake_case__ = False ) -> List[int]:
'''simple docstring'''
if already_has_special_tokens:
if token_ids_a is not None:
raise ValueError(
'''You should not supply a second sequence if the provided sequence of '''
'''ids is already formatted with special tokens for the model.''' )
return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]
if token_ids_a is None:
return [1] + ([0] * len(snake_case__ )) + [1]
return [1] + ([0] * len(snake_case__ )) + [1] + ([0] * len(snake_case__ )) + [1]
def UpperCAmelCase__ ( self , snake_case__ , snake_case__ = None ) -> List[int]:
'''simple docstring'''
UpperCAmelCase : Optional[Any] =[self.sep_token_id]
UpperCAmelCase : Optional[int] =[self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]
def UpperCAmelCase__ ( self , snake_case__ , snake_case__ = None ) -> Tuple[str]:
'''simple docstring'''
if not self.can_save_slow_tokenizer:
raise ValueError(
'''Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '''
'''tokenizer.''' )
if not os.path.isdir(snake_case__ ):
logger.error(f'''Vocabulary path ({save_directory}) should be a directory''' )
return
UpperCAmelCase : Optional[int] =os.path.join(
snake_case__ , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(snake_case__ ):
copyfile(self.vocab_file , snake_case__ )
return (out_vocab_file,)
| 348 | from __future__ import annotations
import unittest
from transformers import BlenderbotConfig, BlenderbotTokenizer, is_tf_available
from transformers.testing_utils import require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import TFAutoModelForSeqaSeqLM, TFBlenderbotForConditionalGeneration, TFBlenderbotModel
@require_tf
class __snake_case :
__lowerCamelCase : str = BlenderbotConfig
__lowerCamelCase : Optional[Any] = {}
__lowerCamelCase : Optional[int] = """gelu"""
def __init__( self , snake_case__ , snake_case__=13 , snake_case__=7 , snake_case__=True , snake_case__=False , snake_case__=99 , snake_case__=32 , snake_case__=2 , snake_case__=4 , snake_case__=37 , snake_case__=0.1 , snake_case__=0.1 , snake_case__=20 , snake_case__=2 , snake_case__=1 , snake_case__=0 , ) -> Optional[Any]:
'''simple docstring'''
UpperCAmelCase : Union[str, Any] =parent
UpperCAmelCase : Optional[int] =batch_size
UpperCAmelCase : Dict =seq_length
UpperCAmelCase : Optional[Any] =is_training
UpperCAmelCase : List[str] =use_labels
UpperCAmelCase : List[Any] =vocab_size
UpperCAmelCase : Optional[int] =hidden_size
UpperCAmelCase : Tuple =num_hidden_layers
UpperCAmelCase : Any =num_attention_heads
UpperCAmelCase : Optional[int] =intermediate_size
UpperCAmelCase : str =hidden_dropout_prob
UpperCAmelCase : Optional[int] =attention_probs_dropout_prob
UpperCAmelCase : str =max_position_embeddings
UpperCAmelCase : List[Any] =eos_token_id
UpperCAmelCase : Optional[int] =pad_token_id
UpperCAmelCase : Tuple =bos_token_id
def UpperCAmelCase__ ( self ) -> Optional[Any]:
'''simple docstring'''
UpperCAmelCase : List[Any] =ids_tensor([self.batch_size, self.seq_length - 1] , self.vocab_size )
UpperCAmelCase : List[Any] =tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size ) , 1 )
UpperCAmelCase : Tuple =tf.concat([input_ids, eos_tensor] , axis=1 )
UpperCAmelCase : str =ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
UpperCAmelCase : Optional[Any] =self.config_cls(
vocab_size=self.vocab_size , d_model=self.hidden_size , encoder_layers=self.num_hidden_layers , decoder_layers=self.num_hidden_layers , encoder_attention_heads=self.num_attention_heads , decoder_attention_heads=self.num_attention_heads , encoder_ffn_dim=self.intermediate_size , decoder_ffn_dim=self.intermediate_size , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , eos_token_ids=[2] , bos_token_id=self.bos_token_id , pad_token_id=self.pad_token_id , decoder_start_token_id=self.pad_token_id , **self.config_updates , )
UpperCAmelCase : List[str] =prepare_blenderbot_inputs_dict(snake_case__ , snake_case__ , snake_case__ )
return config, inputs_dict
def UpperCAmelCase__ ( self , snake_case__ , snake_case__ ) -> int:
'''simple docstring'''
UpperCAmelCase : Union[str, Any] =TFBlenderbotModel(config=snake_case__ ).get_decoder()
UpperCAmelCase : Any =inputs_dict['''input_ids''']
UpperCAmelCase : str =input_ids[:1, :]
UpperCAmelCase : Tuple =inputs_dict['''attention_mask'''][:1, :]
UpperCAmelCase : Tuple =inputs_dict['''head_mask''']
UpperCAmelCase : List[Any] =1
# first forward pass
UpperCAmelCase : List[str] =model(snake_case__ , attention_mask=snake_case__ , head_mask=snake_case__ , use_cache=snake_case__ )
UpperCAmelCase , UpperCAmelCase : str =outputs.to_tuple()
# create hypothetical next token and extent to next_input_ids
UpperCAmelCase : Union[str, Any] =ids_tensor((self.batch_size, 3) , config.vocab_size )
UpperCAmelCase : List[Any] =tf.cast(ids_tensor((self.batch_size, 3) , 2 ) , tf.inta )
# append to next input_ids and
UpperCAmelCase : Tuple =tf.concat([input_ids, next_tokens] , axis=-1 )
UpperCAmelCase : int =tf.concat([attention_mask, next_attn_mask] , axis=-1 )
UpperCAmelCase : Optional[int] =model(snake_case__ , attention_mask=snake_case__ )[0]
UpperCAmelCase : str =model(snake_case__ , attention_mask=snake_case__ , past_key_values=snake_case__ )[0]
self.parent.assertEqual(next_tokens.shape[1] , output_from_past.shape[1] )
# select random slice
UpperCAmelCase : List[Any] =int(ids_tensor((1,) , output_from_past.shape[-1] ) )
UpperCAmelCase : List[Any] =output_from_no_past[:, -3:, random_slice_idx]
UpperCAmelCase : Dict =output_from_past[:, :, random_slice_idx]
# test that outputs are equal for slice
tf.debugging.assert_near(snake_case__ , snake_case__ , rtol=1e-3 )
def lowerCAmelCase_ ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase=None , __lowerCAmelCase=None , __lowerCAmelCase=None , __lowerCAmelCase=None , __lowerCAmelCase=None , )-> str:
'''simple docstring'''
if attention_mask is None:
UpperCAmelCase : int =tf.cast(tf.math.not_equal(__lowerCAmelCase , config.pad_token_id ) , tf.inta )
if decoder_attention_mask is None:
UpperCAmelCase : Tuple =tf.concat(
[
tf.ones(decoder_input_ids[:, :1].shape , dtype=tf.inta ),
tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:] , config.pad_token_id ) , tf.inta ),
] , axis=-1 , )
if head_mask is None:
UpperCAmelCase : str =tf.ones((config.encoder_layers, config.encoder_attention_heads) )
if decoder_head_mask is None:
UpperCAmelCase : Union[str, Any] =tf.ones((config.decoder_layers, config.decoder_attention_heads) )
if cross_attn_head_mask is None:
UpperCAmelCase : int =tf.ones((config.decoder_layers, config.decoder_attention_heads) )
return {
"input_ids": input_ids,
"decoder_input_ids": decoder_input_ids,
"attention_mask": attention_mask,
"decoder_attention_mask": decoder_attention_mask,
"head_mask": head_mask,
"decoder_head_mask": decoder_head_mask,
"cross_attn_head_mask": cross_attn_head_mask,
}
@require_tf
class __snake_case ( lowerCamelCase__ , lowerCamelCase__ , unittest.TestCase ):
__lowerCamelCase : List[str] = (TFBlenderbotForConditionalGeneration, TFBlenderbotModel) if is_tf_available() else ()
__lowerCamelCase : Dict = (TFBlenderbotForConditionalGeneration,) if is_tf_available() else ()
__lowerCamelCase : Dict = (
{
"""conversational""": TFBlenderbotForConditionalGeneration,
"""feature-extraction""": TFBlenderbotModel,
"""summarization""": TFBlenderbotForConditionalGeneration,
"""text2text-generation""": TFBlenderbotForConditionalGeneration,
"""translation""": TFBlenderbotForConditionalGeneration,
}
if is_tf_available()
else {}
)
__lowerCamelCase : Union[str, Any] = True
__lowerCamelCase : Union[str, Any] = False
__lowerCamelCase : Union[str, Any] = False
def UpperCAmelCase__ ( self ) -> Dict:
'''simple docstring'''
UpperCAmelCase : List[str] =TFBlenderbotModelTester(self )
UpperCAmelCase : List[Any] =ConfigTester(self , config_class=snake_case__ )
def UpperCAmelCase__ ( self ) -> Optional[Any]:
'''simple docstring'''
self.config_tester.run_common_tests()
def UpperCAmelCase__ ( self ) -> List[Any]:
'''simple docstring'''
UpperCAmelCase : int =self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.check_decoder_model_past_large_inputs(*snake_case__ )
@require_tokenizers
@require_tf
class __snake_case ( unittest.TestCase ):
__lowerCamelCase : List[str] = ["""My friends are cool but they eat too many carbs."""]
__lowerCamelCase : Dict = """facebook/blenderbot-400M-distill"""
@cached_property
def UpperCAmelCase__ ( self ) -> int:
'''simple docstring'''
return BlenderbotTokenizer.from_pretrained(self.model_name )
@cached_property
def UpperCAmelCase__ ( self ) -> int:
'''simple docstring'''
UpperCAmelCase : int =TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name )
return model
@slow
def UpperCAmelCase__ ( self ) -> Any:
'''simple docstring'''
UpperCAmelCase : Optional[int] =self.tokenizer(self.src_text , return_tensors='''tf''' )
UpperCAmelCase : Optional[int] =self.model.generate(
model_inputs.input_ids , )
UpperCAmelCase : str =self.tokenizer.batch_decode(generated_ids.numpy() , skip_special_tokens=snake_case__ )[0]
assert (
generated_words
== " That's unfortunate. Are they trying to lose weight or are they just trying to be healthier?"
)
| 348 | 1 |
import unittest
import numpy as np
import torch
from diffusers import KarrasVePipeline, KarrasVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class __snake_case ( unittest.TestCase ):
@property
def UpperCAmelCase__ ( self ) -> int:
'''simple docstring'''
torch.manual_seed(0 )
UpperCAmelCase : Any =UNetaDModel(
block_out_channels=(32, 64) , layers_per_block=2 , sample_size=32 , in_channels=3 , out_channels=3 , down_block_types=('''DownBlock2D''', '''AttnDownBlock2D''') , up_block_types=('''AttnUpBlock2D''', '''UpBlock2D''') , )
return model
def UpperCAmelCase__ ( self ) -> Union[str, Any]:
'''simple docstring'''
UpperCAmelCase : Tuple =self.dummy_uncond_unet
UpperCAmelCase : Optional[int] =KarrasVeScheduler()
UpperCAmelCase : List[Any] =KarrasVePipeline(unet=snake_case__ , scheduler=snake_case__ )
pipe.to(snake_case__ )
pipe.set_progress_bar_config(disable=snake_case__ )
UpperCAmelCase : List[str] =torch.manual_seed(0 )
UpperCAmelCase : List[str] =pipe(num_inference_steps=2 , generator=snake_case__ , output_type='''numpy''' ).images
UpperCAmelCase : str =torch.manual_seed(0 )
UpperCAmelCase : str =pipe(num_inference_steps=2 , generator=snake_case__ , output_type='''numpy''' , return_dict=snake_case__ )[0]
UpperCAmelCase : Any =image[0, -3:, -3:, -1]
UpperCAmelCase : List[str] =image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 32, 32, 3)
UpperCAmelCase : int =np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1e-2
@slow
@require_torch
class __snake_case ( unittest.TestCase ):
def UpperCAmelCase__ ( self ) -> List[Any]:
'''simple docstring'''
UpperCAmelCase : Tuple ='''google/ncsnpp-celebahq-256'''
UpperCAmelCase : int =UNetaDModel.from_pretrained(snake_case__ )
UpperCAmelCase : Dict =KarrasVeScheduler()
UpperCAmelCase : Union[str, Any] =KarrasVePipeline(unet=snake_case__ , scheduler=snake_case__ )
pipe.to(snake_case__ )
pipe.set_progress_bar_config(disable=snake_case__ )
UpperCAmelCase : Any =torch.manual_seed(0 )
UpperCAmelCase : Tuple =pipe(num_inference_steps=20 , generator=snake_case__ , output_type='''numpy''' ).images
UpperCAmelCase : Optional[int] =image[0, -3:, -3:, -1]
assert image.shape == (1, 256, 256, 3)
UpperCAmelCase : Tuple =np.array([0.578, 0.5811, 0.5924, 0.5809, 0.587, 0.5886, 0.5861, 0.5802, 0.586] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
| 348 | import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__snake_case = logging.get_logger(__name__)
__snake_case = {
'''asapp/sew-d-tiny-100k''': '''https://huggingface.co/asapp/sew-d-tiny-100k/resolve/main/config.json''',
# See all SEW-D models at https://huggingface.co/models?filter=sew-d
}
class __snake_case ( lowerCamelCase__ ):
__lowerCamelCase : Optional[Any] = """sew-d"""
def __init__( self , snake_case__=32 , snake_case__=768 , snake_case__=12 , snake_case__=12 , snake_case__=3072 , snake_case__=2 , snake_case__=512 , snake_case__=256 , snake_case__=True , snake_case__=True , snake_case__=("p2c", "c2p") , snake_case__="layer_norm" , snake_case__="gelu_python" , snake_case__=0.1 , snake_case__=0.1 , snake_case__=0.1 , snake_case__=0.0 , snake_case__=0.1 , snake_case__=0.02 , snake_case__=1e-7 , snake_case__=1e-5 , snake_case__="group" , snake_case__="gelu" , snake_case__=(64, 128, 128, 128, 128, 256, 256, 256, 256, 512, 512, 512, 512) , snake_case__=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1) , snake_case__=(10, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1) , snake_case__=False , snake_case__=128 , snake_case__=16 , snake_case__=True , snake_case__=0.05 , snake_case__=10 , snake_case__=2 , snake_case__=0.0 , snake_case__=10 , snake_case__=0 , snake_case__="mean" , snake_case__=False , snake_case__=False , snake_case__=256 , snake_case__=0 , snake_case__=1 , snake_case__=2 , **snake_case__ , ) -> int:
'''simple docstring'''
super().__init__(**snake_case__ , pad_token_id=snake_case__ , bos_token_id=snake_case__ , eos_token_id=snake_case__ )
UpperCAmelCase : Union[str, Any] =hidden_size
UpperCAmelCase : Union[str, Any] =feat_extract_norm
UpperCAmelCase : Optional[Any] =feat_extract_activation
UpperCAmelCase : List[str] =list(snake_case__ )
UpperCAmelCase : int =list(snake_case__ )
UpperCAmelCase : List[str] =list(snake_case__ )
UpperCAmelCase : str =conv_bias
UpperCAmelCase : Tuple =num_conv_pos_embeddings
UpperCAmelCase : Dict =num_conv_pos_embedding_groups
UpperCAmelCase : str =len(self.conv_dim )
UpperCAmelCase : Dict =num_hidden_layers
UpperCAmelCase : Optional[int] =intermediate_size
UpperCAmelCase : List[Any] =squeeze_factor
UpperCAmelCase : str =max_position_embeddings
UpperCAmelCase : int =position_buckets
UpperCAmelCase : Optional[int] =share_att_key
UpperCAmelCase : Optional[int] =relative_attention
UpperCAmelCase : Tuple =norm_rel_ebd
UpperCAmelCase : List[Any] =list(snake_case__ )
UpperCAmelCase : Dict =hidden_act
UpperCAmelCase : Optional[int] =num_attention_heads
UpperCAmelCase : Any =hidden_dropout
UpperCAmelCase : str =attention_dropout
UpperCAmelCase : Union[str, Any] =activation_dropout
UpperCAmelCase : str =feat_proj_dropout
UpperCAmelCase : Union[str, Any] =final_dropout
UpperCAmelCase : Optional[int] =layer_norm_eps
UpperCAmelCase : str =feature_layer_norm_eps
UpperCAmelCase : str =initializer_range
UpperCAmelCase : Any =vocab_size
if (
(len(self.conv_stride ) != self.num_feat_extract_layers)
or (len(self.conv_kernel ) != self.num_feat_extract_layers)
or (len(self.conv_dim ) != self.num_feat_extract_layers)
):
raise ValueError(
'''Configuration for convolutional layers is incorrect.'''
'''It is required that `len(config.conv_dim)` == `len(config.conv_stride)` == `len(config.conv_kernel)`,'''
f'''but is `len(config.conv_dim) = {len(self.conv_dim )}`, `len(config.conv_stride)'''
f'''= {len(self.conv_stride )}`, `len(config.conv_kernel) = {len(self.conv_kernel )}`.''' )
# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
UpperCAmelCase : Union[str, Any] =apply_spec_augment
UpperCAmelCase : Optional[Any] =mask_time_prob
UpperCAmelCase : Tuple =mask_time_length
UpperCAmelCase : str =mask_time_min_masks
UpperCAmelCase : Optional[int] =mask_feature_prob
UpperCAmelCase : Optional[Any] =mask_feature_length
UpperCAmelCase : List[Any] =mask_feature_min_masks
# ctc loss
UpperCAmelCase : str =ctc_loss_reduction
UpperCAmelCase : Optional[int] =ctc_zero_infinity
# sequence classification
UpperCAmelCase : Union[str, Any] =use_weighted_layer_sum
UpperCAmelCase : int =classifier_proj_size
@property
def UpperCAmelCase__ ( self ) -> List[Any]:
'''simple docstring'''
return functools.reduce(operator.mul , self.conv_stride , 1 )
| 348 | 1 |
from typing import Optional, Union
import torch
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...modeling_outputs import BaseModelOutputWithPoolingAndNoAttention, ImageClassifierOutputWithNoAttention
from ...modeling_utils import PreTrainedModel
from ...utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging
from .configuration_mobilenet_va import MobileNetVaConfig
__snake_case = logging.get_logger(__name__)
# General docstring
__snake_case = '''MobileNetV1Config'''
# Base docstring
__snake_case = '''google/mobilenet_v1_1.0_224'''
__snake_case = [1, 10_24, 7, 7]
# Image classification docstring
__snake_case = '''google/mobilenet_v1_1.0_224'''
__snake_case = '''tabby, tabby cat'''
__snake_case = [
'''google/mobilenet_v1_1.0_224''',
'''google/mobilenet_v1_0.75_192''',
# See all MobileNetV1 models at https://huggingface.co/models?filter=mobilenet_v1
]
def lowerCAmelCase_ ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase=None )-> int:
'''simple docstring'''
UpperCAmelCase : Dict ={}
if isinstance(__lowerCAmelCase , __lowerCAmelCase ):
UpperCAmelCase : Any =model.mobilenet_va
else:
UpperCAmelCase : str =model
UpperCAmelCase : Union[str, Any] ='''MobilenetV1/Conv2d_0/'''
UpperCAmelCase : Optional[Any] =backbone.conv_stem.convolution.weight
UpperCAmelCase : str =backbone.conv_stem.normalization.bias
UpperCAmelCase : Optional[Any] =backbone.conv_stem.normalization.weight
UpperCAmelCase : List[Any] =backbone.conv_stem.normalization.running_mean
UpperCAmelCase : List[Any] =backbone.conv_stem.normalization.running_var
for i in range(13 ):
UpperCAmelCase : Any =i + 1
UpperCAmelCase : Tuple =i * 2
UpperCAmelCase : Optional[Any] =backbone.layer[pt_index]
UpperCAmelCase : Optional[int] =f'''MobilenetV1/Conv2d_{tf_index}_depthwise/'''
UpperCAmelCase : Union[str, Any] =pointer.convolution.weight
UpperCAmelCase : str =pointer.normalization.bias
UpperCAmelCase : int =pointer.normalization.weight
UpperCAmelCase : str =pointer.normalization.running_mean
UpperCAmelCase : Dict =pointer.normalization.running_var
UpperCAmelCase : List[str] =backbone.layer[pt_index + 1]
UpperCAmelCase : List[Any] =f'''MobilenetV1/Conv2d_{tf_index}_pointwise/'''
UpperCAmelCase : Any =pointer.convolution.weight
UpperCAmelCase : int =pointer.normalization.bias
UpperCAmelCase : Dict =pointer.normalization.weight
UpperCAmelCase : List[Any] =pointer.normalization.running_mean
UpperCAmelCase : Optional[int] =pointer.normalization.running_var
if isinstance(__lowerCAmelCase , __lowerCAmelCase ):
UpperCAmelCase : Tuple ='''MobilenetV1/Logits/Conv2d_1c_1x1/'''
UpperCAmelCase : str =model.classifier.weight
UpperCAmelCase : Dict =model.classifier.bias
return tf_to_pt_map
def lowerCAmelCase_ ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )-> int:
'''simple docstring'''
try:
import numpy as np
import tensorflow as tf
except ImportError:
logger.error(
'''Loading a TensorFlow models in PyTorch, requires TensorFlow to be installed. Please see '''
'''https://www.tensorflow.org/install/ for installation instructions.''' )
raise
# Load weights from TF model
UpperCAmelCase : Tuple =tf.train.list_variables(__lowerCAmelCase )
UpperCAmelCase : Union[str, Any] ={}
for name, shape in init_vars:
logger.info(f'''Loading TF weight {name} with shape {shape}''' )
UpperCAmelCase : Dict =tf.train.load_variable(__lowerCAmelCase , __lowerCAmelCase )
UpperCAmelCase : int =array
# Build TF to PyTorch weights loading map
UpperCAmelCase : List[Any] =_build_tf_to_pytorch_map(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
for name, pointer in tf_to_pt_map.items():
logger.info(f'''Importing {name}''' )
if name not in tf_weights:
logger.info(f'''{name} not in tf pre-trained weights, skipping''' )
continue
UpperCAmelCase : Optional[Any] =tf_weights[name]
if "depthwise_weights" in name:
logger.info('''Transposing depthwise''' )
UpperCAmelCase : int =np.transpose(__lowerCAmelCase , (2, 3, 0, 1) )
elif "weights" in name:
logger.info('''Transposing''' )
if len(pointer.shape ) == 2: # copying into linear layer
UpperCAmelCase : int =array.squeeze().transpose()
else:
UpperCAmelCase : Tuple =np.transpose(__lowerCAmelCase , (3, 2, 0, 1) )
if pointer.shape != array.shape:
raise ValueError(f'''Pointer shape {pointer.shape} and array shape {array.shape} mismatched''' )
logger.info(f'''Initialize PyTorch weight {name} {array.shape}''' )
UpperCAmelCase : Optional[int] =torch.from_numpy(__lowerCAmelCase )
tf_weights.pop(__lowerCAmelCase , __lowerCAmelCase )
tf_weights.pop(name + '''/RMSProp''' , __lowerCAmelCase )
tf_weights.pop(name + '''/RMSProp_1''' , __lowerCAmelCase )
tf_weights.pop(name + '''/ExponentialMovingAverage''' , __lowerCAmelCase )
logger.info(f'''Weights not copied to PyTorch model: {', '.join(tf_weights.keys() )}''' )
return model
def lowerCAmelCase_ ( __lowerCAmelCase , __lowerCAmelCase )-> torch.Tensor:
'''simple docstring'''
UpperCAmelCase , UpperCAmelCase : Optional[int] =features.shape[-2:]
UpperCAmelCase , UpperCAmelCase : Tuple =conv_layer.stride
UpperCAmelCase , UpperCAmelCase : Union[str, Any] =conv_layer.kernel_size
if in_height % stride_height == 0:
UpperCAmelCase : Any =max(kernel_height - stride_height , 0 )
else:
UpperCAmelCase : Optional[Any] =max(kernel_height - (in_height % stride_height) , 0 )
if in_width % stride_width == 0:
UpperCAmelCase : Tuple =max(kernel_width - stride_width , 0 )
else:
UpperCAmelCase : List[Any] =max(kernel_width - (in_width % stride_width) , 0 )
UpperCAmelCase : List[str] =pad_along_width // 2
UpperCAmelCase : str =pad_along_width - pad_left
UpperCAmelCase : Dict =pad_along_height // 2
UpperCAmelCase : Dict =pad_along_height - pad_top
UpperCAmelCase : int =(pad_left, pad_right, pad_top, pad_bottom)
return nn.functional.pad(__lowerCAmelCase , __lowerCAmelCase , '''constant''' , 0.0 )
class __snake_case ( nn.Module ):
def __init__( self , snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ = 1 , snake_case__ = 1 , snake_case__ = False , snake_case__ = True , snake_case__ = True , ) -> None:
'''simple docstring'''
super().__init__()
UpperCAmelCase : str =config
if in_channels % groups != 0:
raise ValueError(f'''Input channels ({in_channels}) are not divisible by {groups} groups.''' )
if out_channels % groups != 0:
raise ValueError(f'''Output channels ({out_channels}) are not divisible by {groups} groups.''' )
UpperCAmelCase : Tuple =0 if config.tf_padding else int((kernel_size - 1) / 2 )
UpperCAmelCase : int =nn.Convad(
in_channels=snake_case__ , out_channels=snake_case__ , kernel_size=snake_case__ , stride=snake_case__ , padding=snake_case__ , groups=snake_case__ , bias=snake_case__ , padding_mode='''zeros''' , )
if use_normalization:
UpperCAmelCase : str =nn.BatchNormad(
num_features=snake_case__ , eps=config.layer_norm_eps , momentum=0.9997 , affine=snake_case__ , track_running_stats=snake_case__ , )
else:
UpperCAmelCase : Union[str, Any] =None
if use_activation:
if isinstance(snake_case__ , snake_case__ ):
UpperCAmelCase : Optional[int] =ACTaFN[use_activation]
elif isinstance(config.hidden_act , snake_case__ ):
UpperCAmelCase : Tuple =ACTaFN[config.hidden_act]
else:
UpperCAmelCase : Dict =config.hidden_act
else:
UpperCAmelCase : int =None
def UpperCAmelCase__ ( self , snake_case__ ) -> torch.Tensor:
'''simple docstring'''
if self.config.tf_padding:
UpperCAmelCase : Union[str, Any] =apply_tf_padding(snake_case__ , self.convolution )
UpperCAmelCase : List[str] =self.convolution(snake_case__ )
if self.normalization is not None:
UpperCAmelCase : str =self.normalization(snake_case__ )
if self.activation is not None:
UpperCAmelCase : str =self.activation(snake_case__ )
return features
class __snake_case ( lowerCamelCase__ ):
__lowerCamelCase : List[Any] = MobileNetVaConfig
__lowerCamelCase : str = load_tf_weights_in_mobilenet_va
__lowerCamelCase : Tuple = """mobilenet_v1"""
__lowerCamelCase : Any = """pixel_values"""
__lowerCamelCase : Optional[Any] = False
def UpperCAmelCase__ ( self , snake_case__ ) -> None:
'''simple docstring'''
if isinstance(snake_case__ , (nn.Linear, nn.Convad) ):
module.weight.data.normal_(mean=0.0 , std=self.config.initializer_range )
if module.bias is not None:
module.bias.data.zero_()
elif isinstance(snake_case__ , nn.BatchNormad ):
module.bias.data.zero_()
module.weight.data.fill_(1.0 )
__snake_case = r'''
This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it
as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
behavior.
Parameters:
config ([`MobileNetV1Config`]): Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
'''
__snake_case = r'''
Args:
pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See
[`MobileNetV1ImageProcessor.__call__`] for details.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
more detail.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
'''
@add_start_docstrings(
"""The bare MobileNetV1 model outputting raw hidden-states without any specific head on top.""" , lowerCamelCase__ , )
class __snake_case ( lowerCamelCase__ ):
def __init__( self , snake_case__ , snake_case__ = True ) -> Dict:
'''simple docstring'''
super().__init__(snake_case__ )
UpperCAmelCase : Any =config
UpperCAmelCase : str =32
UpperCAmelCase : Dict =max(int(depth * config.depth_multiplier ) , config.min_depth )
UpperCAmelCase : Union[str, Any] =MobileNetVaConvLayer(
snake_case__ , in_channels=config.num_channels , out_channels=snake_case__ , kernel_size=3 , stride=2 , )
UpperCAmelCase : str =[1, 2, 1, 2, 1, 2, 1, 1, 1, 1, 1, 2, 1]
UpperCAmelCase : List[str] =nn.ModuleList()
for i in range(13 ):
UpperCAmelCase : Optional[int] =out_channels
if strides[i] == 2 or i == 0:
depth *= 2
UpperCAmelCase : int =max(int(depth * config.depth_multiplier ) , config.min_depth )
self.layer.append(
MobileNetVaConvLayer(
snake_case__ , in_channels=snake_case__ , out_channels=snake_case__ , kernel_size=3 , stride=strides[i] , groups=snake_case__ , ) )
self.layer.append(
MobileNetVaConvLayer(
snake_case__ , in_channels=snake_case__ , out_channels=snake_case__ , kernel_size=1 , ) )
UpperCAmelCase : int =nn.AdaptiveAvgPoolad((1, 1) ) if add_pooling_layer else None
# Initialize weights and apply final processing
self.post_init()
def UpperCAmelCase__ ( self , snake_case__ ) -> Optional[int]:
'''simple docstring'''
raise NotImplementedError
@add_start_docstrings_to_model_forward(snake_case__ )
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC , output_type=snake_case__ , config_class=_CONFIG_FOR_DOC , modality='''vision''' , expected_output=_EXPECTED_OUTPUT_SHAPE , )
def UpperCAmelCase__ ( self , snake_case__ = None , snake_case__ = None , snake_case__ = None , ) -> Union[tuple, BaseModelOutputWithPoolingAndNoAttention]:
'''simple docstring'''
UpperCAmelCase : Optional[Any] =(
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
UpperCAmelCase : Optional[Any] =return_dict if return_dict is not None else self.config.use_return_dict
if pixel_values is None:
raise ValueError('''You have to specify pixel_values''' )
UpperCAmelCase : Union[str, Any] =self.conv_stem(snake_case__ )
UpperCAmelCase : Union[str, Any] =() if output_hidden_states else None
for i, layer_module in enumerate(self.layer ):
UpperCAmelCase : List[Any] =layer_module(snake_case__ )
if output_hidden_states:
UpperCAmelCase : Union[str, Any] =all_hidden_states + (hidden_states,)
UpperCAmelCase : int =hidden_states
if self.pooler is not None:
UpperCAmelCase : Optional[int] =torch.flatten(self.pooler(snake_case__ ) , start_dim=1 )
else:
UpperCAmelCase : Any =None
if not return_dict:
return tuple(v for v in [last_hidden_state, pooled_output, all_hidden_states] if v is not None )
return BaseModelOutputWithPoolingAndNoAttention(
last_hidden_state=snake_case__ , pooler_output=snake_case__ , hidden_states=snake_case__ , )
@add_start_docstrings(
"""
MobileNetV1 model with an image classification head on top (a linear layer on top of the pooled features), e.g. for
ImageNet.
""" , lowerCamelCase__ , )
class __snake_case ( lowerCamelCase__ ):
def __init__( self , snake_case__ ) -> None:
'''simple docstring'''
super().__init__(snake_case__ )
UpperCAmelCase : List[Any] =config.num_labels
UpperCAmelCase : int =MobileNetVaModel(snake_case__ )
UpperCAmelCase : Union[str, Any] =self.mobilenet_va.layer[-1].convolution.out_channels
# Classifier head
UpperCAmelCase : Union[str, Any] =nn.Dropout(config.classifier_dropout_prob , inplace=snake_case__ )
UpperCAmelCase : str =nn.Linear(snake_case__ , config.num_labels ) if config.num_labels > 0 else nn.Identity()
# Initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(snake_case__ )
@add_code_sample_docstrings(
checkpoint=_IMAGE_CLASS_CHECKPOINT , output_type=snake_case__ , config_class=_CONFIG_FOR_DOC , expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT , )
def UpperCAmelCase__ ( self , snake_case__ = None , snake_case__ = None , snake_case__ = None , snake_case__ = None , ) -> Union[tuple, ImageClassifierOutputWithNoAttention]:
'''simple docstring'''
UpperCAmelCase : List[Any] =return_dict if return_dict is not None else self.config.use_return_dict
UpperCAmelCase : Union[str, Any] =self.mobilenet_va(snake_case__ , output_hidden_states=snake_case__ , return_dict=snake_case__ )
UpperCAmelCase : List[str] =outputs.pooler_output if return_dict else outputs[1]
UpperCAmelCase : Tuple =self.classifier(self.dropout(snake_case__ ) )
UpperCAmelCase : List[str] =None
if labels is not None:
if self.config.problem_type is None:
if self.num_labels == 1:
UpperCAmelCase : Optional[Any] ='''regression'''
elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
UpperCAmelCase : Optional[Any] ='''single_label_classification'''
else:
UpperCAmelCase : Optional[int] ='''multi_label_classification'''
if self.config.problem_type == "regression":
UpperCAmelCase : Optional[int] =MSELoss()
if self.num_labels == 1:
UpperCAmelCase : Union[str, Any] =loss_fct(logits.squeeze() , labels.squeeze() )
else:
UpperCAmelCase : Any =loss_fct(snake_case__ , snake_case__ )
elif self.config.problem_type == "single_label_classification":
UpperCAmelCase : List[Any] =CrossEntropyLoss()
UpperCAmelCase : Any =loss_fct(logits.view(-1 , self.num_labels ) , labels.view(-1 ) )
elif self.config.problem_type == "multi_label_classification":
UpperCAmelCase : Optional[Any] =BCEWithLogitsLoss()
UpperCAmelCase : Dict =loss_fct(snake_case__ , snake_case__ )
if not return_dict:
UpperCAmelCase : Optional[int] =(logits,) + outputs[2:]
return ((loss,) + output) if loss is not None else output
return ImageClassifierOutputWithNoAttention(
loss=snake_case__ , logits=snake_case__ , hidden_states=outputs.hidden_states , )
| 348 | import os
from argparse import ArgumentParser
from typing import List
import torch.utils.data
from datasets import Dataset, IterableDataset
from datasets.distributed import split_dataset_by_node
__snake_case = 4
__snake_case = 3
class __snake_case ( lowerCamelCase__ ):
pass
def lowerCAmelCase_ ( __lowerCAmelCase )-> List[str]:
'''simple docstring'''
for shard in shards:
for i in range(__lowerCAmelCase ):
yield {"i": i, "shard": shard}
def lowerCAmelCase_ ( )-> Optional[int]:
'''simple docstring'''
UpperCAmelCase : List[str] =int(os.environ['''RANK'''] )
UpperCAmelCase : Optional[Any] =int(os.environ['''WORLD_SIZE'''] )
UpperCAmelCase : List[Any] =ArgumentParser()
parser.add_argument('''--streaming''' , type=__lowerCAmelCase )
parser.add_argument('''--local_rank''' , type=__lowerCAmelCase )
parser.add_argument('''--num_workers''' , type=__lowerCAmelCase , default=0 )
UpperCAmelCase : Any =parser.parse_args()
UpperCAmelCase : List[str] =args.streaming
UpperCAmelCase : Tuple =args.num_workers
UpperCAmelCase : int ={'''shards''': [f'''shard_{shard_idx}''' for shard_idx in range(__lowerCAmelCase )]}
UpperCAmelCase : Optional[int] =IterableDataset.from_generator(__lowerCAmelCase , gen_kwargs=__lowerCAmelCase )
if not streaming:
UpperCAmelCase : List[Any] =Dataset.from_list(list(__lowerCAmelCase ) )
UpperCAmelCase : Dict =split_dataset_by_node(__lowerCAmelCase , rank=__lowerCAmelCase , world_size=__lowerCAmelCase )
UpperCAmelCase : List[Any] =torch.utils.data.DataLoader(__lowerCAmelCase , num_workers=__lowerCAmelCase )
UpperCAmelCase : Dict =NUM_SHARDS * NUM_ITEMS_PER_SHARD
UpperCAmelCase : str =full_size // world_size
expected_local_size += int(rank < (full_size % world_size) )
UpperCAmelCase : List[Any] =sum(1 for _ in dataloader )
if local_size != expected_local_size:
raise FailedTestError(f'''local_size {local_size} != expected_local_size {expected_local_size}''' )
if __name__ == "__main__":
main()
| 348 | 1 |
import torch
from diffusers import KDPMaDiscreteScheduler
from diffusers.utils import torch_device
from .test_schedulers import SchedulerCommonTest
class __snake_case ( lowerCamelCase__ ):
__lowerCamelCase : Optional[int] = (KDPMaDiscreteScheduler,)
__lowerCamelCase : List[str] = 10
def UpperCAmelCase__ ( self , **snake_case__ ) -> str:
'''simple docstring'''
UpperCAmelCase : int ={
'''num_train_timesteps''': 1100,
'''beta_start''': 0.0001,
'''beta_end''': 0.02,
'''beta_schedule''': '''linear''',
}
config.update(**snake_case__ )
return config
def UpperCAmelCase__ ( self ) -> Tuple:
'''simple docstring'''
for timesteps in [10, 50, 100, 1000]:
self.check_over_configs(num_train_timesteps=snake_case__ )
def UpperCAmelCase__ ( self ) -> Optional[int]:
'''simple docstring'''
for beta_start, beta_end in zip([0.0_0001, 0.0001, 0.001] , [0.0002, 0.002, 0.02] ):
self.check_over_configs(beta_start=snake_case__ , beta_end=snake_case__ )
def UpperCAmelCase__ ( self ) -> str:
'''simple docstring'''
for schedule in ["linear", "scaled_linear"]:
self.check_over_configs(beta_schedule=snake_case__ )
def UpperCAmelCase__ ( self ) -> Dict:
'''simple docstring'''
for prediction_type in ["epsilon", "v_prediction"]:
self.check_over_configs(prediction_type=snake_case__ )
def UpperCAmelCase__ ( self ) -> str:
'''simple docstring'''
UpperCAmelCase : Optional[Any] =self.scheduler_classes[0]
UpperCAmelCase : Optional[int] =self.get_scheduler_config(prediction_type='''v_prediction''' )
UpperCAmelCase : Optional[Any] =scheduler_class(**snake_case__ )
scheduler.set_timesteps(self.num_inference_steps )
UpperCAmelCase : str =self.dummy_model()
UpperCAmelCase : Optional[Any] =self.dummy_sample_deter * scheduler.init_noise_sigma
UpperCAmelCase : Union[str, Any] =sample.to(snake_case__ )
for i, t in enumerate(scheduler.timesteps ):
UpperCAmelCase : str =scheduler.scale_model_input(snake_case__ , snake_case__ )
UpperCAmelCase : Any =model(snake_case__ , snake_case__ )
UpperCAmelCase : Union[str, Any] =scheduler.step(snake_case__ , snake_case__ , snake_case__ )
UpperCAmelCase : int =output.prev_sample
UpperCAmelCase : Dict =torch.sum(torch.abs(snake_case__ ) )
UpperCAmelCase : Optional[Any] =torch.mean(torch.abs(snake_case__ ) )
if torch_device in ["cpu", "mps"]:
assert abs(result_sum.item() - 4.69_34e-07 ) < 1e-2
assert abs(result_mean.item() - 6.11_12e-10 ) < 1e-3
else:
# CUDA
assert abs(result_sum.item() - 4.6_93_42_86_50_17_09_72e-07 ) < 1e-2
assert abs(result_mean.item() - 0.0002 ) < 1e-3
def UpperCAmelCase__ ( self ) -> int:
'''simple docstring'''
if torch_device == "mps":
return
UpperCAmelCase : Any =self.scheduler_classes[0]
UpperCAmelCase : Optional[int] =self.get_scheduler_config()
UpperCAmelCase : Optional[Any] =scheduler_class(**snake_case__ )
scheduler.set_timesteps(self.num_inference_steps )
UpperCAmelCase : Optional[int] =self.dummy_model()
UpperCAmelCase : Union[str, Any] =self.dummy_sample_deter * scheduler.init_noise_sigma
UpperCAmelCase : str =sample.to(snake_case__ )
for i, t in enumerate(scheduler.timesteps ):
UpperCAmelCase : Dict =scheduler.scale_model_input(snake_case__ , snake_case__ )
UpperCAmelCase : Union[str, Any] =model(snake_case__ , snake_case__ )
UpperCAmelCase : List[str] =scheduler.step(snake_case__ , snake_case__ , snake_case__ )
UpperCAmelCase : Optional[int] =output.prev_sample
UpperCAmelCase : Any =torch.sum(torch.abs(snake_case__ ) )
UpperCAmelCase : Union[str, Any] =torch.mean(torch.abs(snake_case__ ) )
if torch_device in ["cpu", "mps"]:
assert abs(result_sum.item() - 20.4125 ) < 1e-2
assert abs(result_mean.item() - 0.0266 ) < 1e-3
else:
# CUDA
assert abs(result_sum.item() - 20.4125 ) < 1e-2
assert abs(result_mean.item() - 0.0266 ) < 1e-3
def UpperCAmelCase__ ( self ) -> str:
'''simple docstring'''
if torch_device == "mps":
return
UpperCAmelCase : List[Any] =self.scheduler_classes[0]
UpperCAmelCase : Dict =self.get_scheduler_config()
UpperCAmelCase : List[str] =scheduler_class(**snake_case__ )
scheduler.set_timesteps(self.num_inference_steps , device=snake_case__ )
UpperCAmelCase : int =self.dummy_model()
UpperCAmelCase : Tuple =self.dummy_sample_deter.to(snake_case__ ) * scheduler.init_noise_sigma
for t in scheduler.timesteps:
UpperCAmelCase : Optional[Any] =scheduler.scale_model_input(snake_case__ , snake_case__ )
UpperCAmelCase : int =model(snake_case__ , snake_case__ )
UpperCAmelCase : str =scheduler.step(snake_case__ , snake_case__ , snake_case__ )
UpperCAmelCase : List[str] =output.prev_sample
UpperCAmelCase : List[str] =torch.sum(torch.abs(snake_case__ ) )
UpperCAmelCase : Dict =torch.mean(torch.abs(snake_case__ ) )
if str(snake_case__ ).startswith('''cpu''' ):
# The following sum varies between 148 and 156 on mps. Why?
assert abs(result_sum.item() - 20.4125 ) < 1e-2
assert abs(result_mean.item() - 0.0266 ) < 1e-3
else:
# CUDA
assert abs(result_sum.item() - 20.4125 ) < 1e-2
assert abs(result_mean.item() - 0.0266 ) < 1e-3
| 348 | from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__snake_case = {'''configuration_opt''': ['''OPT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''OPTConfig''']}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__snake_case = [
'''OPT_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''OPTForCausalLM''',
'''OPTModel''',
'''OPTPreTrainedModel''',
'''OPTForSequenceClassification''',
'''OPTForQuestionAnswering''',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__snake_case = ['''TFOPTForCausalLM''', '''TFOPTModel''', '''TFOPTPreTrainedModel''']
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__snake_case = [
'''FlaxOPTForCausalLM''',
'''FlaxOPTModel''',
'''FlaxOPTPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_opt import OPT_PRETRAINED_CONFIG_ARCHIVE_MAP, OPTConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_opt import (
OPT_PRETRAINED_MODEL_ARCHIVE_LIST,
OPTForCausalLM,
OPTForQuestionAnswering,
OPTForSequenceClassification,
OPTModel,
OPTPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_opt import TFOPTForCausalLM, TFOPTModel, TFOPTPreTrainedModel
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_opt import FlaxOPTForCausalLM, FlaxOPTModel, FlaxOPTPreTrainedModel
else:
import sys
__snake_case = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 348 | 1 |
import unittest
import numpy as np
import requests
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
from transformers.pytorch_utils import is_torch_greater_or_equal_than_1_11
else:
__snake_case = False
if is_vision_available():
from PIL import Image
from transformers import PixaStructImageProcessor
class __snake_case ( unittest.TestCase ):
def __init__( self , snake_case__ , snake_case__=7 , snake_case__=3 , snake_case__=18 , snake_case__=30 , snake_case__=400 , snake_case__=None , snake_case__=True , snake_case__=True , snake_case__=None , ) -> str:
'''simple docstring'''
UpperCAmelCase : Union[str, Any] =size if size is not None else {'''height''': 20, '''width''': 20}
UpperCAmelCase : int =parent
UpperCAmelCase : Tuple =batch_size
UpperCAmelCase : Union[str, Any] =num_channels
UpperCAmelCase : Optional[int] =image_size
UpperCAmelCase : Optional[int] =min_resolution
UpperCAmelCase : Tuple =max_resolution
UpperCAmelCase : Optional[int] =size
UpperCAmelCase : Union[str, Any] =do_normalize
UpperCAmelCase : Dict =do_convert_rgb
UpperCAmelCase : Any =[512, 1024, 2048, 4096]
UpperCAmelCase : Optional[int] =patch_size if patch_size is not None else {'''height''': 16, '''width''': 16}
def UpperCAmelCase__ ( self ) -> Dict:
'''simple docstring'''
return {"do_normalize": self.do_normalize, "do_convert_rgb": self.do_convert_rgb}
def UpperCAmelCase__ ( self ) -> str:
'''simple docstring'''
UpperCAmelCase : Dict ='''https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/australia.jpg'''
UpperCAmelCase : Union[str, Any] =Image.open(requests.get(snake_case__ , stream=snake_case__ ).raw ).convert('''RGB''' )
return raw_image
@unittest.skipIf(
not is_torch_greater_or_equal_than_1_11 , reason="""`Pix2StructImageProcessor` requires `torch>=1.11.0`.""" , )
@require_torch
@require_vision
class __snake_case ( lowerCamelCase__ , unittest.TestCase ):
__lowerCamelCase : Any = PixaStructImageProcessor if is_vision_available() else None
def UpperCAmelCase__ ( self ) -> Optional[Any]:
'''simple docstring'''
UpperCAmelCase : Tuple =PixaStructImageProcessingTester(self )
@property
def UpperCAmelCase__ ( self ) -> Any:
'''simple docstring'''
return self.image_processor_tester.prepare_image_processor_dict()
def UpperCAmelCase__ ( self ) -> List[str]:
'''simple docstring'''
UpperCAmelCase : Optional[int] =self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(snake_case__ , '''do_normalize''' ) )
self.assertTrue(hasattr(snake_case__ , '''do_convert_rgb''' ) )
def UpperCAmelCase__ ( self ) -> str:
'''simple docstring'''
UpperCAmelCase : Dict =self.image_processor_tester.prepare_dummy_image()
UpperCAmelCase : Dict =self.image_processing_class(**self.image_processor_dict )
UpperCAmelCase : List[Any] =2048
UpperCAmelCase : Optional[Any] =image_processor(snake_case__ , return_tensors='''pt''' , max_patches=snake_case__ )
self.assertTrue(torch.allclose(inputs.flattened_patches.mean() , torch.tensor(0.0606 ) , atol=1e-3 , rtol=1e-3 ) )
def UpperCAmelCase__ ( self ) -> Any:
'''simple docstring'''
UpperCAmelCase : List[str] =self.image_processing_class(**self.image_processor_dict )
# create random PIL images
UpperCAmelCase : List[Any] =prepare_image_inputs(self.image_processor_tester , equal_resolution=snake_case__ )
for image in image_inputs:
self.assertIsInstance(snake_case__ , Image.Image )
# Test not batched input
UpperCAmelCase : Optional[Any] =(
(self.image_processor_tester.patch_size['''height'''] * self.image_processor_tester.patch_size['''width'''])
* self.image_processor_tester.num_channels
) + 2
for max_patch in self.image_processor_tester.max_patches:
# Test not batched input
UpperCAmelCase : str =image_processor(
image_inputs[0] , return_tensors='''pt''' , max_patches=snake_case__ ).flattened_patches
self.assertEqual(
encoded_images.shape , (1, max_patch, expected_hidden_dim) , )
# Test batched
UpperCAmelCase : Dict =image_processor(
snake_case__ , return_tensors='''pt''' , max_patches=snake_case__ ).flattened_patches
self.assertEqual(
encoded_images.shape , (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim) , )
def UpperCAmelCase__ ( self ) -> Tuple:
'''simple docstring'''
UpperCAmelCase : List[Any] =self.image_processing_class(**self.image_processor_dict )
# create random PIL images
UpperCAmelCase : Dict =prepare_image_inputs(self.image_processor_tester , equal_resolution=snake_case__ )
for image in image_inputs:
self.assertIsInstance(snake_case__ , Image.Image )
# Test not batched input
UpperCAmelCase : Tuple =(
(self.image_processor_tester.patch_size['''height'''] * self.image_processor_tester.patch_size['''width'''])
* self.image_processor_tester.num_channels
) + 2
UpperCAmelCase : int =True
for max_patch in self.image_processor_tester.max_patches:
# Test not batched input
with self.assertRaises(snake_case__ ):
UpperCAmelCase : Union[str, Any] =image_processor(
image_inputs[0] , return_tensors='''pt''' , max_patches=snake_case__ ).flattened_patches
UpperCAmelCase : int ='''Hello'''
UpperCAmelCase : List[Any] =image_processor(
image_inputs[0] , return_tensors='''pt''' , max_patches=snake_case__ , header_text=snake_case__ ).flattened_patches
self.assertEqual(
encoded_images.shape , (1, max_patch, expected_hidden_dim) , )
# Test batched
UpperCAmelCase : Dict =image_processor(
snake_case__ , return_tensors='''pt''' , max_patches=snake_case__ , header_text=snake_case__ ).flattened_patches
self.assertEqual(
encoded_images.shape , (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim) , )
def UpperCAmelCase__ ( self ) -> Any:
'''simple docstring'''
UpperCAmelCase : List[str] =self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
UpperCAmelCase : str =prepare_image_inputs(self.image_processor_tester , equal_resolution=snake_case__ , numpify=snake_case__ )
for image in image_inputs:
self.assertIsInstance(snake_case__ , np.ndarray )
UpperCAmelCase : Optional[Any] =(
(self.image_processor_tester.patch_size['''height'''] * self.image_processor_tester.patch_size['''width'''])
* self.image_processor_tester.num_channels
) + 2
for max_patch in self.image_processor_tester.max_patches:
# Test not batched input
UpperCAmelCase : Union[str, Any] =image_processor(
image_inputs[0] , return_tensors='''pt''' , max_patches=snake_case__ ).flattened_patches
self.assertEqual(
encoded_images.shape , (1, max_patch, expected_hidden_dim) , )
# Test batched
UpperCAmelCase : List[str] =image_processor(
snake_case__ , return_tensors='''pt''' , max_patches=snake_case__ ).flattened_patches
self.assertEqual(
encoded_images.shape , (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim) , )
def UpperCAmelCase__ ( self ) -> str:
'''simple docstring'''
UpperCAmelCase : int =self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
UpperCAmelCase : Any =prepare_image_inputs(self.image_processor_tester , equal_resolution=snake_case__ , torchify=snake_case__ )
for image in image_inputs:
self.assertIsInstance(snake_case__ , torch.Tensor )
# Test not batched input
UpperCAmelCase : Any =(
(self.image_processor_tester.patch_size['''height'''] * self.image_processor_tester.patch_size['''width'''])
* self.image_processor_tester.num_channels
) + 2
for max_patch in self.image_processor_tester.max_patches:
# Test not batched input
UpperCAmelCase : str =image_processor(
image_inputs[0] , return_tensors='''pt''' , max_patches=snake_case__ ).flattened_patches
self.assertEqual(
encoded_images.shape , (1, max_patch, expected_hidden_dim) , )
# Test batched
UpperCAmelCase : Tuple =image_processor(
snake_case__ , return_tensors='''pt''' , max_patches=snake_case__ ).flattened_patches
self.assertEqual(
encoded_images.shape , (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim) , )
@unittest.skipIf(
not is_torch_greater_or_equal_than_1_11 , reason="""`Pix2StructImageProcessor` requires `torch>=1.11.0`.""" , )
@require_torch
@require_vision
class __snake_case ( lowerCamelCase__ , unittest.TestCase ):
__lowerCamelCase : Optional[int] = PixaStructImageProcessor if is_vision_available() else None
def UpperCAmelCase__ ( self ) -> int:
'''simple docstring'''
UpperCAmelCase : int =PixaStructImageProcessingTester(self , num_channels=4 )
UpperCAmelCase : int =3
@property
def UpperCAmelCase__ ( self ) -> Optional[int]:
'''simple docstring'''
return self.image_processor_tester.prepare_image_processor_dict()
def UpperCAmelCase__ ( self ) -> str:
'''simple docstring'''
UpperCAmelCase : Optional[Any] =self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(snake_case__ , '''do_normalize''' ) )
self.assertTrue(hasattr(snake_case__ , '''do_convert_rgb''' ) )
def UpperCAmelCase__ ( self ) -> Any:
'''simple docstring'''
UpperCAmelCase : Optional[Any] =self.image_processing_class(**self.image_processor_dict )
# create random PIL images
UpperCAmelCase : Any =prepare_image_inputs(self.image_processor_tester , equal_resolution=snake_case__ )
for image in image_inputs:
self.assertIsInstance(snake_case__ , Image.Image )
# Test not batched input
UpperCAmelCase : str =(
(self.image_processor_tester.patch_size['''height'''] * self.image_processor_tester.patch_size['''width'''])
* (self.image_processor_tester.num_channels - 1)
) + 2
for max_patch in self.image_processor_tester.max_patches:
# Test not batched input
UpperCAmelCase : Optional[int] =image_processor(
image_inputs[0] , return_tensors='''pt''' , max_patches=snake_case__ ).flattened_patches
self.assertEqual(
encoded_images.shape , (1, max_patch, expected_hidden_dim) , )
# Test batched
UpperCAmelCase : Dict =image_processor(
snake_case__ , return_tensors='''pt''' , max_patches=snake_case__ ).flattened_patches
self.assertEqual(
encoded_images.shape , (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim) , )
| 348 | import tempfile
import unittest
import numpy as np
import transformers
from transformers import GPTaTokenizer, GPTJConfig, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, tooslow
from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import jax
import jax.numpy as jnp
from transformers.modeling_flax_pytorch_utils import (
convert_pytorch_state_dict_to_flax,
load_flax_weights_in_pytorch_model,
)
from transformers.models.gptj.modeling_flax_gptj import FlaxGPTJForCausalLM, FlaxGPTJModel
if is_torch_available():
import torch
class __snake_case :
def __init__( self , snake_case__ , snake_case__=14 , snake_case__=7 , snake_case__=True , snake_case__=True , snake_case__=False , snake_case__=True , snake_case__=99 , snake_case__=32 , snake_case__=4 , snake_case__=4 , snake_case__=4 , snake_case__=37 , snake_case__="gelu" , snake_case__=0.1 , snake_case__=0.1 , snake_case__=512 , snake_case__=0.02 , ) -> str:
'''simple docstring'''
UpperCAmelCase : str =parent
UpperCAmelCase : Tuple =batch_size
UpperCAmelCase : Optional[int] =seq_length
UpperCAmelCase : Optional[int] =is_training
UpperCAmelCase : Tuple =use_input_mask
UpperCAmelCase : List[Any] =use_token_type_ids
UpperCAmelCase : Optional[Any] =use_labels
UpperCAmelCase : Union[str, Any] =vocab_size
UpperCAmelCase : List[Any] =hidden_size
UpperCAmelCase : Optional[int] =rotary_dim
UpperCAmelCase : Union[str, Any] =num_hidden_layers
UpperCAmelCase : List[Any] =num_attention_heads
UpperCAmelCase : Dict =intermediate_size
UpperCAmelCase : Union[str, Any] =hidden_act
UpperCAmelCase : Any =hidden_dropout_prob
UpperCAmelCase : Dict =attention_probs_dropout_prob
UpperCAmelCase : Union[str, Any] =max_position_embeddings
UpperCAmelCase : str =initializer_range
UpperCAmelCase : Optional[int] =None
UpperCAmelCase : List[Any] =vocab_size - 1
UpperCAmelCase : Optional[Any] =vocab_size - 1
UpperCAmelCase : List[Any] =vocab_size - 1
def UpperCAmelCase__ ( self ) -> Optional[int]:
'''simple docstring'''
UpperCAmelCase : List[str] =ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
UpperCAmelCase : List[Any] =None
if self.use_input_mask:
UpperCAmelCase : Optional[Any] =random_attention_mask([self.batch_size, self.seq_length] )
UpperCAmelCase : Dict =GPTJConfig(
vocab_size=self.vocab_size , n_embd=self.hidden_size , n_layer=self.num_hidden_layers , n_head=self.num_attention_heads , n_positions=self.max_position_embeddings , use_cache=snake_case__ , bos_token_id=self.bos_token_id , eos_token_id=self.eos_token_id , pad_token_id=self.pad_token_id , rotary_dim=self.rotary_dim , )
return (config, input_ids, input_mask)
def UpperCAmelCase__ ( self ) -> Dict:
'''simple docstring'''
UpperCAmelCase : Tuple =self.prepare_config_and_inputs()
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase : Union[str, Any] =config_and_inputs
UpperCAmelCase : Tuple ={'''input_ids''': input_ids, '''attention_mask''': attention_mask}
return config, inputs_dict
def UpperCAmelCase__ ( self , snake_case__ , snake_case__ , snake_case__ , snake_case__ ) -> List[Any]:
'''simple docstring'''
UpperCAmelCase : Any =20
UpperCAmelCase : Any =model_class_name(snake_case__ )
UpperCAmelCase : str =model.init_cache(input_ids.shape[0] , snake_case__ )
UpperCAmelCase : Any =jnp.ones((input_ids.shape[0], max_decoder_length) , dtype='''i4''' )
UpperCAmelCase : Optional[Any] =jnp.broadcast_to(
jnp.arange(input_ids.shape[-1] - 1 )[None, :] , (input_ids.shape[0], input_ids.shape[-1] - 1) )
UpperCAmelCase : Optional[Any] =model(
input_ids[:, :-1] , attention_mask=snake_case__ , past_key_values=snake_case__ , position_ids=snake_case__ , )
UpperCAmelCase : List[str] =jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]] , dtype='''i4''' )
UpperCAmelCase : Optional[Any] =model(
input_ids[:, -1:] , attention_mask=snake_case__ , past_key_values=outputs_cache.past_key_values , position_ids=snake_case__ , )
UpperCAmelCase : List[Any] =model(snake_case__ )
UpperCAmelCase : Any =np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5]) ) )
self.parent.assertTrue(diff < 1e-3 , msg=f'''Max diff is {diff}''' )
def UpperCAmelCase__ ( self , snake_case__ , snake_case__ , snake_case__ , snake_case__ ) -> List[Any]:
'''simple docstring'''
UpperCAmelCase : Dict =20
UpperCAmelCase : Dict =model_class_name(snake_case__ )
UpperCAmelCase : Tuple =jnp.concatenate(
[attention_mask, jnp.zeros((attention_mask.shape[0], max_decoder_length - attention_mask.shape[1]) )] , axis=-1 , )
UpperCAmelCase : Dict =model.init_cache(input_ids.shape[0] , snake_case__ )
UpperCAmelCase : int =jnp.broadcast_to(
jnp.arange(input_ids.shape[-1] - 1 )[None, :] , (input_ids.shape[0], input_ids.shape[-1] - 1) )
UpperCAmelCase : Optional[Any] =model(
input_ids[:, :-1] , attention_mask=snake_case__ , past_key_values=snake_case__ , position_ids=snake_case__ , )
UpperCAmelCase : Any =jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]] , dtype='''i4''' )
UpperCAmelCase : str =model(
input_ids[:, -1:] , past_key_values=outputs_cache.past_key_values , attention_mask=snake_case__ , position_ids=snake_case__ , )
UpperCAmelCase : Any =model(snake_case__ , attention_mask=snake_case__ )
UpperCAmelCase : Dict =np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5]) ) )
self.parent.assertTrue(diff < 1e-3 , msg=f'''Max diff is {diff}''' )
@require_flax
class __snake_case ( lowerCamelCase__ , lowerCamelCase__ , unittest.TestCase ):
__lowerCamelCase : Tuple = (FlaxGPTJModel, FlaxGPTJForCausalLM) if is_flax_available() else ()
__lowerCamelCase : Optional[Any] = (FlaxGPTJForCausalLM,) if is_flax_available() else ()
def UpperCAmelCase__ ( self ) -> int:
'''simple docstring'''
UpperCAmelCase : Union[str, Any] =FlaxGPTJModelTester(self )
def UpperCAmelCase__ ( self ) -> str:
'''simple docstring'''
for model_class_name in self.all_model_classes:
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase : Dict =self.model_tester.prepare_config_and_inputs()
self.model_tester.check_use_cache_forward(snake_case__ , snake_case__ , snake_case__ , snake_case__ )
def UpperCAmelCase__ ( self ) -> Dict:
'''simple docstring'''
for model_class_name in self.all_model_classes:
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase : int =self.model_tester.prepare_config_and_inputs()
self.model_tester.check_use_cache_forward_with_attn_mask(
snake_case__ , snake_case__ , snake_case__ , snake_case__ )
@tooslow
def UpperCAmelCase__ ( self ) -> Union[str, Any]:
'''simple docstring'''
UpperCAmelCase : Tuple =GPTaTokenizer.from_pretrained('''gpt2''' , pad_token='''<|endoftext|>''' , padding_side='''left''' )
UpperCAmelCase : Optional[Any] =tokenizer(['''Hello this is a long string''', '''Hey'''] , return_tensors='''np''' , padding=snake_case__ , truncation=snake_case__ )
UpperCAmelCase : Optional[int] =FlaxGPTJForCausalLM.from_pretrained('''EleutherAI/gpt-j-6B''' )
UpperCAmelCase : str =False
UpperCAmelCase : Union[str, Any] =model.config.eos_token_id
UpperCAmelCase : List[Any] =jax.jit(model.generate )
UpperCAmelCase : Dict =jit_generate(
inputs['''input_ids'''] , attention_mask=inputs['''attention_mask'''] , pad_token_id=tokenizer.pad_token_id ).sequences
UpperCAmelCase : Any =tokenizer.batch_decode(snake_case__ , skip_special_tokens=snake_case__ )
UpperCAmelCase : Tuple =[
'''Hello this is a long string of text.\n\nI\'m trying to get the text of the''',
'''Hey, I\'m a little late to the party. I\'m going to''',
]
self.assertListEqual(snake_case__ , snake_case__ )
@is_pt_flax_cross_test
def UpperCAmelCase__ ( self ) -> List[Any]:
'''simple docstring'''
UpperCAmelCase , UpperCAmelCase : List[str] =self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__ ):
# prepare inputs
UpperCAmelCase : Union[str, Any] =self._prepare_for_class(snake_case__ , snake_case__ )
UpperCAmelCase : List[str] ={k: torch.tensor(v.tolist() ) for k, v in prepared_inputs_dict.items()}
# load corresponding PyTorch class
UpperCAmelCase : Any =model_class.__name__[4:] # Skip the "Flax" at the beginning
UpperCAmelCase : Any =getattr(snake_case__ , snake_case__ )
UpperCAmelCase , UpperCAmelCase : Union[str, Any] =pt_inputs['''input_ids'''].shape
UpperCAmelCase : Tuple =np.random.randint(0 , seq_length - 1 , size=(batch_size,) )
for batch_idx, start_index in enumerate(snake_case__ ):
UpperCAmelCase : int =0
UpperCAmelCase : Optional[int] =1
UpperCAmelCase : Optional[int] =0
UpperCAmelCase : Union[str, Any] =1
UpperCAmelCase : List[str] =pt_model_class(snake_case__ ).eval()
UpperCAmelCase : Optional[int] =model_class(snake_case__ , dtype=jnp.floataa )
UpperCAmelCase : Any =convert_pytorch_state_dict_to_flax(pt_model.state_dict() , snake_case__ )
UpperCAmelCase : Union[str, Any] =fx_state
with torch.no_grad():
UpperCAmelCase : Any =pt_model(**snake_case__ ).to_tuple()
UpperCAmelCase : Dict =fx_model(**snake_case__ ).to_tuple()
self.assertEqual(len(snake_case__ ) , len(snake_case__ ) , '''Output lengths differ between Flax and PyTorch''' )
for fx_output, pt_output in zip(snake_case__ , snake_case__ ):
self.assert_almost_equals(fx_output[:, -1] , pt_output[:, -1].numpy() , 4e-2 )
with tempfile.TemporaryDirectory() as tmpdirname:
pt_model.save_pretrained(snake_case__ )
UpperCAmelCase : str =model_class.from_pretrained(snake_case__ , from_pt=snake_case__ )
UpperCAmelCase : int =fx_model_loaded(**snake_case__ ).to_tuple()
self.assertEqual(
len(snake_case__ ) , len(snake_case__ ) , '''Output lengths differ between Flax and PyTorch''' )
for fx_output_loaded, pt_output in zip(snake_case__ , snake_case__ ):
self.assert_almost_equals(fx_output_loaded[:, -1] , pt_output[:, -1].numpy() , 4e-2 )
@is_pt_flax_cross_test
def UpperCAmelCase__ ( self ) -> Optional[Any]:
'''simple docstring'''
UpperCAmelCase , UpperCAmelCase : Any =self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__ ):
# prepare inputs
UpperCAmelCase : Union[str, Any] =self._prepare_for_class(snake_case__ , snake_case__ )
UpperCAmelCase : Union[str, Any] ={k: torch.tensor(v.tolist() ) for k, v in prepared_inputs_dict.items()}
# load corresponding PyTorch class
UpperCAmelCase : int =model_class.__name__[4:] # Skip the "Flax" at the beginning
UpperCAmelCase : int =getattr(snake_case__ , snake_case__ )
UpperCAmelCase : Dict =pt_model_class(snake_case__ ).eval()
UpperCAmelCase : str =model_class(snake_case__ , dtype=jnp.floataa )
UpperCAmelCase : Optional[Any] =load_flax_weights_in_pytorch_model(snake_case__ , fx_model.params )
UpperCAmelCase , UpperCAmelCase : Optional[int] =pt_inputs['''input_ids'''].shape
UpperCAmelCase : Optional[int] =np.random.randint(0 , seq_length - 1 , size=(batch_size,) )
for batch_idx, start_index in enumerate(snake_case__ ):
UpperCAmelCase : str =0
UpperCAmelCase : Any =1
UpperCAmelCase : List[Any] =0
UpperCAmelCase : Tuple =1
# make sure weights are tied in PyTorch
pt_model.tie_weights()
with torch.no_grad():
UpperCAmelCase : Optional[Any] =pt_model(**snake_case__ ).to_tuple()
UpperCAmelCase : List[Any] =fx_model(**snake_case__ ).to_tuple()
self.assertEqual(len(snake_case__ ) , len(snake_case__ ) , '''Output lengths differ between Flax and PyTorch''' )
for fx_output, pt_output in zip(snake_case__ , snake_case__ ):
self.assert_almost_equals(fx_output[:, -1] , pt_output[:, -1].numpy() , 4e-2 )
with tempfile.TemporaryDirectory() as tmpdirname:
fx_model.save_pretrained(snake_case__ )
UpperCAmelCase : Tuple =pt_model_class.from_pretrained(snake_case__ , from_flax=snake_case__ )
with torch.no_grad():
UpperCAmelCase : Any =pt_model_loaded(**snake_case__ ).to_tuple()
self.assertEqual(
len(snake_case__ ) , len(snake_case__ ) , '''Output lengths differ between Flax and PyTorch''' )
for fx_output, pt_output in zip(snake_case__ , snake_case__ ):
self.assert_almost_equals(fx_output[:, -1] , pt_output[:, -1].numpy() , 4e-2 )
@tooslow
def UpperCAmelCase__ ( self ) -> List[str]:
'''simple docstring'''
for model_class_name in self.all_model_classes:
UpperCAmelCase : str =model_class_name.from_pretrained('''EleutherAI/gpt-j-6B''' )
UpperCAmelCase : Tuple =model(np.ones((1, 1) ) )
self.assertIsNotNone(snake_case__ )
| 348 | 1 |
import argparse
import copy
def lowerCAmelCase_ ( __lowerCAmelCase )-> Optional[Any]:
'''simple docstring'''
UpperCAmelCase : Optional[int] ={}
with open(__lowerCAmelCase ) as f:
for line in f:
if line.split()[0] not in dict_of_neighbours:
UpperCAmelCase : Any =[]
_list.append([line.split()[1], line.split()[2]] )
UpperCAmelCase : List[Any] =_list
else:
dict_of_neighbours[line.split()[0]].append(
[line.split()[1], line.split()[2]] )
if line.split()[1] not in dict_of_neighbours:
UpperCAmelCase : Dict =[]
_list.append([line.split()[0], line.split()[2]] )
UpperCAmelCase : Tuple =_list
else:
dict_of_neighbours[line.split()[1]].append(
[line.split()[0], line.split()[2]] )
return dict_of_neighbours
def lowerCAmelCase_ ( __lowerCAmelCase , __lowerCAmelCase )-> Union[str, Any]:
'''simple docstring'''
with open(__lowerCAmelCase ) as f:
UpperCAmelCase : Optional[Any] =f.read(1 )
UpperCAmelCase : Optional[Any] =start_node
UpperCAmelCase : List[str] =[]
UpperCAmelCase : Optional[Any] =start_node
UpperCAmelCase : str =0
while visiting not in first_solution:
UpperCAmelCase : Optional[Any] =1_00_00
for k in dict_of_neighbours[visiting]:
if int(k[1] ) < int(__lowerCAmelCase ) and k[0] not in first_solution:
UpperCAmelCase : List[Any] =k[1]
UpperCAmelCase : List[str] =k[0]
first_solution.append(__lowerCAmelCase )
UpperCAmelCase : Any =distance_of_first_solution + int(__lowerCAmelCase )
UpperCAmelCase : int =best_node
first_solution.append(__lowerCAmelCase )
UpperCAmelCase : Optional[Any] =0
for k in dict_of_neighbours[first_solution[-2]]:
if k[0] == start_node:
break
position += 1
UpperCAmelCase : Tuple =(
distance_of_first_solution
+ int(dict_of_neighbours[first_solution[-2]][position][1] )
- 1_00_00
)
return first_solution, distance_of_first_solution
def lowerCAmelCase_ ( __lowerCAmelCase , __lowerCAmelCase )-> str:
'''simple docstring'''
UpperCAmelCase : Tuple =[]
for n in solution[1:-1]:
UpperCAmelCase : List[str] =solution.index(__lowerCAmelCase )
for kn in solution[1:-1]:
UpperCAmelCase : Optional[int] =solution.index(__lowerCAmelCase )
if n == kn:
continue
UpperCAmelCase : Optional[Any] =copy.deepcopy(__lowerCAmelCase )
UpperCAmelCase : Tuple =kn
UpperCAmelCase : Any =n
UpperCAmelCase : List[Any] =0
for k in _tmp[:-1]:
UpperCAmelCase : Optional[int] =_tmp[_tmp.index(__lowerCAmelCase ) + 1]
for i in dict_of_neighbours[k]:
if i[0] == next_node:
UpperCAmelCase : Any =distance + int(i[1] )
_tmp.append(__lowerCAmelCase )
if _tmp not in neighborhood_of_solution:
neighborhood_of_solution.append(_tmp )
UpperCAmelCase : Optional[Any] =len(neighborhood_of_solution[0] ) - 1
neighborhood_of_solution.sort(key=lambda __lowerCAmelCase : x[index_of_last_item_in_the_list] )
return neighborhood_of_solution
def lowerCAmelCase_ ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )-> int:
'''simple docstring'''
UpperCAmelCase : Dict =1
UpperCAmelCase : Optional[Any] =first_solution
UpperCAmelCase : Dict =[]
UpperCAmelCase : Any =distance_of_first_solution
UpperCAmelCase : Any =solution
while count <= iters:
UpperCAmelCase : Tuple =find_neighborhood(__lowerCAmelCase , __lowerCAmelCase )
UpperCAmelCase : Dict =0
UpperCAmelCase : int =neighborhood[index_of_best_solution]
UpperCAmelCase : int =len(__lowerCAmelCase ) - 1
UpperCAmelCase : Union[str, Any] =False
while not found:
UpperCAmelCase : Dict =0
while i < len(__lowerCAmelCase ):
if best_solution[i] != solution[i]:
UpperCAmelCase : Union[str, Any] =best_solution[i]
UpperCAmelCase : Tuple =solution[i]
break
UpperCAmelCase : int =i + 1
if [first_exchange_node, second_exchange_node] not in tabu_list and [
second_exchange_node,
first_exchange_node,
] not in tabu_list:
tabu_list.append([first_exchange_node, second_exchange_node] )
UpperCAmelCase : Optional[Any] =True
UpperCAmelCase : Tuple =best_solution[:-1]
UpperCAmelCase : Dict =neighborhood[index_of_best_solution][best_cost_index]
if cost < best_cost:
UpperCAmelCase : Union[str, Any] =cost
UpperCAmelCase : int =solution
else:
UpperCAmelCase : Tuple =index_of_best_solution + 1
UpperCAmelCase : Union[str, Any] =neighborhood[index_of_best_solution]
if len(__lowerCAmelCase ) >= size:
tabu_list.pop(0 )
UpperCAmelCase : Tuple =count + 1
return best_solution_ever, best_cost
def lowerCAmelCase_ ( __lowerCAmelCase=None )-> Any:
'''simple docstring'''
UpperCAmelCase : Any =generate_neighbours(args.File )
UpperCAmelCase , UpperCAmelCase : int =generate_first_solution(
args.File , __lowerCAmelCase )
UpperCAmelCase , UpperCAmelCase : Any =tabu_search(
__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , args.Iterations , args.Size , )
print(f'''Best solution: {best_sol}, with total distance: {best_cost}.''' )
if __name__ == "__main__":
__snake_case = argparse.ArgumentParser(description='''Tabu Search''')
parser.add_argument(
'''-f''',
'''--File''',
type=str,
help='''Path to the file containing the data''',
required=True,
)
parser.add_argument(
'''-i''',
'''--Iterations''',
type=int,
help='''How many iterations the algorithm should perform''',
required=True,
)
parser.add_argument(
'''-s''', '''--Size''', type=int, help='''Size of the tabu list''', required=True
)
# Pass the arguments to main method
main(parser.parse_args())
| 348 | from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
__snake_case = {
'''configuration_bloom''': ['''BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''BloomConfig''', '''BloomOnnxConfig'''],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__snake_case = ['''BloomTokenizerFast''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__snake_case = [
'''BLOOM_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''BloomForCausalLM''',
'''BloomModel''',
'''BloomPreTrainedModel''',
'''BloomForSequenceClassification''',
'''BloomForTokenClassification''',
'''BloomForQuestionAnswering''',
]
if TYPE_CHECKING:
from .configuration_bloom import BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP, BloomConfig, BloomOnnxConfig
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_bloom_fast import BloomTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_bloom import (
BLOOM_PRETRAINED_MODEL_ARCHIVE_LIST,
BloomForCausalLM,
BloomForQuestionAnswering,
BloomForSequenceClassification,
BloomForTokenClassification,
BloomModel,
BloomPreTrainedModel,
)
else:
import sys
__snake_case = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 348 | 1 |
import json
import os
import unittest
from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class __snake_case ( lowerCamelCase__ , unittest.TestCase ):
__lowerCamelCase : List[Any] = MgpstrTokenizer
__lowerCamelCase : Optional[int] = False
__lowerCamelCase : Union[str, Any] = {}
__lowerCamelCase : List[Any] = False
def UpperCAmelCase__ ( self ) -> Any:
'''simple docstring'''
super().setUp()
# fmt: off
UpperCAmelCase : List[str] =['''[GO]''', '''[s]''', '''0''', '''1''', '''2''', '''3''', '''4''', '''5''', '''6''', '''7''', '''8''', '''9''', '''a''', '''b''', '''c''', '''d''', '''e''', '''f''', '''g''', '''h''', '''i''', '''j''', '''k''', '''l''', '''m''', '''n''', '''o''', '''p''', '''q''', '''r''', '''s''', '''t''', '''u''', '''v''', '''w''', '''x''', '''y''', '''z''']
# fmt: on
UpperCAmelCase : Tuple =dict(zip(snake_case__ , range(len(snake_case__ ) ) ) )
UpperCAmelCase : Union[str, Any] =os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['''vocab_file'''] )
with open(self.vocab_file , '''w''' , encoding='''utf-8''' ) as fp:
fp.write(json.dumps(snake_case__ ) + '''\n''' )
def UpperCAmelCase__ ( self , **snake_case__ ) -> Any:
'''simple docstring'''
return MgpstrTokenizer.from_pretrained(self.tmpdirname , **snake_case__ )
def UpperCAmelCase__ ( self , snake_case__ ) -> Any:
'''simple docstring'''
UpperCAmelCase : List[str] ='''tester'''
UpperCAmelCase : Tuple ='''tester'''
return input_text, output_text
@unittest.skip('''MGP-STR always lower cases letters.''' )
def UpperCAmelCase__ ( self ) -> Optional[int]:
'''simple docstring'''
pass
def UpperCAmelCase__ ( self ) -> Tuple:
'''simple docstring'''
UpperCAmelCase : Union[str, Any] =self.get_tokenizers(do_lower_case=snake_case__ )
for tokenizer in tokenizers:
with self.subTest(f'''{tokenizer.__class__.__name__}''' ):
UpperCAmelCase : Tuple ='''[SPECIAL_TOKEN]'''
tokenizer.add_special_tokens({'''cls_token''': special_token} )
UpperCAmelCase : List[str] =tokenizer.encode([special_token] , add_special_tokens=snake_case__ )
self.assertEqual(len(snake_case__ ) , 1 )
UpperCAmelCase : Tuple =tokenizer.decode(snake_case__ , skip_special_tokens=snake_case__ )
self.assertTrue(special_token not in decoded )
def UpperCAmelCase__ ( self ) -> Optional[Any]:
'''simple docstring'''
UpperCAmelCase : Any =self.get_tokenizers()
for tokenizer in tokenizers:
with self.subTest(f'''{tokenizer.__class__.__name__}''' ):
UpperCAmelCase , UpperCAmelCase : Dict =self.get_input_output_texts(snake_case__ )
UpperCAmelCase : List[str] =tokenizer.tokenize(snake_case__ )
UpperCAmelCase : Optional[int] =tokenizer.convert_tokens_to_ids(snake_case__ )
UpperCAmelCase : Optional[int] =tokenizer.encode(snake_case__ , add_special_tokens=snake_case__ )
self.assertListEqual(snake_case__ , snake_case__ )
UpperCAmelCase : Tuple =tokenizer.convert_ids_to_tokens(snake_case__ )
self.assertNotEqual(len(snake_case__ ) , 0 )
UpperCAmelCase : Any =tokenizer.decode(snake_case__ )
self.assertIsInstance(snake_case__ , snake_case__ )
self.assertEqual(text_a.replace(''' ''' , '''''' ) , snake_case__ )
@unittest.skip('''MGP-STR tokenizer only handles one sequence.''' )
def UpperCAmelCase__ ( self ) -> str:
'''simple docstring'''
pass
@unittest.skip('''inputs cannot be pretokenized in MgpstrTokenizer''' )
def UpperCAmelCase__ ( self ) -> Tuple:
'''simple docstring'''
pass
| 348 | import os
from typing import Dict, List, Tuple, TypeVar, Union
__snake_case = TypeVar('''T''')
__snake_case = Union[List[T], Tuple[T, ...]]
__snake_case = Union[T, List[T], Dict[str, T]]
__snake_case = Union[str, bytes, os.PathLike]
| 348 | 1 |
import asyncio
import os
import re
import sys
import tempfile
import unittest
from contextlib import contextmanager
from copy import deepcopy
from distutils.util import strtobool
from enum import Enum
from importlib.util import find_spec
from pathlib import Path
from unittest.mock import patch
import pyarrow as pa
import pytest
import requests
from packaging import version
from datasets import config
if config.PY_VERSION < version.parse('''3.8'''):
import importlib_metadata
else:
import importlib.metadata as importlib_metadata
def lowerCAmelCase_ ( __lowerCAmelCase , __lowerCAmelCase=False )-> Tuple:
'''simple docstring'''
try:
UpperCAmelCase : Union[str, Any] =os.environ[key]
except KeyError:
# KEY isn't set, default to `default`.
UpperCAmelCase : Optional[int] =default
else:
# KEY is set, convert it to True or False.
try:
UpperCAmelCase : Optional[int] =strtobool(__lowerCAmelCase )
except ValueError:
# More values are supported, but let's keep the message simple.
raise ValueError(f'''If set, {key} must be yes or no.''' )
return _value
__snake_case = parse_flag_from_env('''RUN_SLOW''', default=False)
__snake_case = parse_flag_from_env('''RUN_REMOTE''', default=False)
__snake_case = parse_flag_from_env('''RUN_LOCAL''', default=True)
__snake_case = parse_flag_from_env('''RUN_PACKAGED''', default=True)
# Compression
__snake_case = pytest.mark.skipif(not config.LZ4_AVAILABLE, reason='''test requires lz4''')
__snake_case = pytest.mark.skipif(not config.PY7ZR_AVAILABLE, reason='''test requires py7zr''')
__snake_case = pytest.mark.skipif(not config.ZSTANDARD_AVAILABLE, reason='''test requires zstandard''')
# Audio
__snake_case = pytest.mark.skipif(
# On Windows and OS X, soundfile installs sndfile
find_spec('''soundfile''') is None or version.parse(importlib_metadata.version('''soundfile''')) < version.parse('''0.12.0'''),
reason='''test requires sndfile>=0.12.1: \'pip install \"soundfile>=0.12.1\"\'; ''',
)
# Beam
__snake_case = pytest.mark.skipif(
not config.BEAM_AVAILABLE or config.DILL_VERSION >= version.parse('''0.3.2'''),
reason='''test requires apache-beam and a compatible dill version''',
)
# Dill-cloudpickle compatibility
__snake_case = pytest.mark.skipif(
config.DILL_VERSION <= version.parse('''0.3.2'''),
reason='''test requires dill>0.3.2 for cloudpickle compatibility''',
)
# Windows
__snake_case = pytest.mark.skipif(
sys.platform == '''win32''',
reason='''test should not be run on Windows''',
)
def lowerCAmelCase_ ( __lowerCAmelCase )-> List[Any]:
'''simple docstring'''
try:
import faiss # noqa
except ImportError:
UpperCAmelCase : List[str] =unittest.skip('''test requires faiss''' )(__lowerCAmelCase )
return test_case
def lowerCAmelCase_ ( __lowerCAmelCase )-> List[str]:
'''simple docstring'''
try:
import regex # noqa
except ImportError:
UpperCAmelCase : Any =unittest.skip('''test requires regex''' )(__lowerCAmelCase )
return test_case
def lowerCAmelCase_ ( __lowerCAmelCase )-> Any:
'''simple docstring'''
try:
import elasticsearch # noqa
except ImportError:
UpperCAmelCase : Optional[int] =unittest.skip('''test requires elasticsearch''' )(__lowerCAmelCase )
return test_case
def lowerCAmelCase_ ( __lowerCAmelCase )-> List[Any]:
'''simple docstring'''
try:
import sqlalchemy # noqa
except ImportError:
UpperCAmelCase : Union[str, Any] =unittest.skip('''test requires sqlalchemy''' )(__lowerCAmelCase )
return test_case
def lowerCAmelCase_ ( __lowerCAmelCase )-> Optional[Any]:
'''simple docstring'''
if not config.TORCH_AVAILABLE:
UpperCAmelCase : Any =unittest.skip('''test requires PyTorch''' )(__lowerCAmelCase )
return test_case
def lowerCAmelCase_ ( __lowerCAmelCase )-> Optional[Any]:
'''simple docstring'''
if not config.TF_AVAILABLE:
UpperCAmelCase : Any =unittest.skip('''test requires TensorFlow''' )(__lowerCAmelCase )
return test_case
def lowerCAmelCase_ ( __lowerCAmelCase )-> Optional[int]:
'''simple docstring'''
if not config.JAX_AVAILABLE:
UpperCAmelCase : Dict =unittest.skip('''test requires JAX''' )(__lowerCAmelCase )
return test_case
def lowerCAmelCase_ ( __lowerCAmelCase )-> Optional[Any]:
'''simple docstring'''
if not config.PIL_AVAILABLE:
UpperCAmelCase : List[str] =unittest.skip('''test requires Pillow''' )(__lowerCAmelCase )
return test_case
def lowerCAmelCase_ ( __lowerCAmelCase )-> Union[str, Any]:
'''simple docstring'''
try:
import transformers # noqa F401
except ImportError:
return unittest.skip('''test requires transformers''' )(__lowerCAmelCase )
else:
return test_case
def lowerCAmelCase_ ( __lowerCAmelCase )-> List[Any]:
'''simple docstring'''
try:
import tiktoken # noqa F401
except ImportError:
return unittest.skip('''test requires tiktoken''' )(__lowerCAmelCase )
else:
return test_case
def lowerCAmelCase_ ( __lowerCAmelCase )-> List[Any]:
'''simple docstring'''
try:
import spacy # noqa F401
except ImportError:
return unittest.skip('''test requires spacy''' )(__lowerCAmelCase )
else:
return test_case
def lowerCAmelCase_ ( __lowerCAmelCase )-> List[str]:
'''simple docstring'''
def _require_spacy_model(__lowerCAmelCase ):
try:
import spacy # noqa F401
spacy.load(__lowerCAmelCase )
except ImportError:
return unittest.skip('''test requires spacy''' )(__lowerCAmelCase )
except OSError:
return unittest.skip('''test requires spacy model \'{}\''''.format(__lowerCAmelCase ) )(__lowerCAmelCase )
else:
return test_case
return _require_spacy_model
def lowerCAmelCase_ ( __lowerCAmelCase )-> Union[str, Any]:
'''simple docstring'''
try:
import pyspark # noqa F401
except ImportError:
return unittest.skip('''test requires pyspark''' )(__lowerCAmelCase )
else:
return test_case
def lowerCAmelCase_ ( __lowerCAmelCase )-> Optional[int]:
'''simple docstring'''
try:
import joblibspark # noqa F401
except ImportError:
return unittest.skip('''test requires joblibspark''' )(__lowerCAmelCase )
else:
return test_case
def lowerCAmelCase_ ( __lowerCAmelCase )-> Optional[Any]:
'''simple docstring'''
if not _run_slow_tests or _run_slow_tests == 0:
UpperCAmelCase : Union[str, Any] =unittest.skip('''test is slow''' )(__lowerCAmelCase )
return test_case
def lowerCAmelCase_ ( __lowerCAmelCase )-> Optional[int]:
'''simple docstring'''
if not _run_local_tests or _run_local_tests == 0:
UpperCAmelCase : Any =unittest.skip('''test is local''' )(__lowerCAmelCase )
return test_case
def lowerCAmelCase_ ( __lowerCAmelCase )-> Union[str, Any]:
'''simple docstring'''
if not _run_packaged_tests or _run_packaged_tests == 0:
UpperCAmelCase : Optional[Any] =unittest.skip('''test is packaged''' )(__lowerCAmelCase )
return test_case
def lowerCAmelCase_ ( __lowerCAmelCase )-> Optional[Any]:
'''simple docstring'''
if not _run_remote_tests or _run_remote_tests == 0:
UpperCAmelCase : List[Any] =unittest.skip('''test requires remote''' )(__lowerCAmelCase )
return test_case
def lowerCAmelCase_ ( *__lowerCAmelCase )-> int:
'''simple docstring'''
def decorate(cls ):
for name, fn in cls.__dict__.items():
if callable(__lowerCAmelCase ) and name.startswith('''test''' ):
for decorator in decorators:
UpperCAmelCase : Optional[Any] =decorator(__lowerCAmelCase )
setattr(cls , __lowerCAmelCase , __lowerCAmelCase )
return cls
return decorate
class __snake_case ( lowerCamelCase__ ):
pass
class __snake_case ( lowerCamelCase__ ):
__lowerCamelCase : Optional[int] = 0
__lowerCamelCase : int = 1
__lowerCamelCase : Optional[int] = 2
@contextmanager
def lowerCAmelCase_ ( __lowerCAmelCase=OfflineSimulationMode.CONNECTION_FAILS , __lowerCAmelCase=1e-1_6 )-> Tuple:
'''simple docstring'''
UpperCAmelCase : Optional[Any] =requests.Session().request
def timeout_request(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , **__lowerCAmelCase ):
# Change the url to an invalid url so that the connection hangs
UpperCAmelCase : int ='''https://10.255.255.1'''
if kwargs.get('''timeout''' ) is None:
raise RequestWouldHangIndefinitelyError(
f'''Tried a call to {url} in offline mode with no timeout set. Please set a timeout.''' )
UpperCAmelCase : Union[str, Any] =timeout
try:
return online_request(__lowerCAmelCase , __lowerCAmelCase , **__lowerCAmelCase )
except Exception as e:
# The following changes in the error are just here to make the offline timeout error prettier
UpperCAmelCase : Dict =url
UpperCAmelCase : Union[str, Any] =e.args[0]
UpperCAmelCase : Optional[Any] =(max_retry_error.args[0].replace('''10.255.255.1''' , f'''OfflineMock[{url}]''' ),)
UpperCAmelCase : Any =(max_retry_error,)
raise
def raise_connection_error(__lowerCAmelCase , __lowerCAmelCase , **__lowerCAmelCase ):
raise requests.ConnectionError('''Offline mode is enabled.''' , request=__lowerCAmelCase )
if mode is OfflineSimulationMode.CONNECTION_FAILS:
with patch('''requests.Session.send''' , __lowerCAmelCase ):
yield
elif mode is OfflineSimulationMode.CONNECTION_TIMES_OUT:
# inspired from https://stackoverflow.com/a/904609
with patch('''requests.Session.request''' , __lowerCAmelCase ):
yield
elif mode is OfflineSimulationMode.HF_DATASETS_OFFLINE_SET_TO_1:
with patch('''datasets.config.HF_DATASETS_OFFLINE''' , __lowerCAmelCase ):
yield
else:
raise ValueError('''Please use a value from the OfflineSimulationMode enum.''' )
@contextmanager
def lowerCAmelCase_ ( *__lowerCAmelCase , **__lowerCAmelCase )-> Optional[Any]:
'''simple docstring'''
UpperCAmelCase : Tuple =str(Path().resolve() )
with tempfile.TemporaryDirectory(*__lowerCAmelCase , **__lowerCAmelCase ) as tmp_dir:
try:
os.chdir(__lowerCAmelCase )
yield
finally:
os.chdir(__lowerCAmelCase )
@contextmanager
def lowerCAmelCase_ ( )-> str:
'''simple docstring'''
import gc
gc.collect()
UpperCAmelCase : int =pa.total_allocated_bytes()
yield
assert pa.total_allocated_bytes() - previous_allocated_memory > 0, "Arrow memory didn't increase."
@contextmanager
def lowerCAmelCase_ ( )-> List[Any]:
'''simple docstring'''
import gc
gc.collect()
UpperCAmelCase : Optional[int] =pa.total_allocated_bytes()
yield
assert pa.total_allocated_bytes() - previous_allocated_memory <= 0, "Arrow memory wasn't expected to increase."
def lowerCAmelCase_ ( __lowerCAmelCase , __lowerCAmelCase )-> Dict:
'''simple docstring'''
return deepcopy(__lowerCAmelCase ).integers(0 , 1_00 , 10 ).tolist() == deepcopy(__lowerCAmelCase ).integers(0 , 1_00 , 10 ).tolist()
def lowerCAmelCase_ ( __lowerCAmelCase )-> Optional[Any]:
'''simple docstring'''
import decorator
from requests.exceptions import HTTPError
def _wrapper(__lowerCAmelCase , *__lowerCAmelCase , **__lowerCAmelCase ):
try:
return func(*__lowerCAmelCase , **__lowerCAmelCase )
except HTTPError as err:
if str(__lowerCAmelCase ).startswith('''500''' ) or str(__lowerCAmelCase ).startswith('''502''' ):
pytest.xfail(str(__lowerCAmelCase ) )
raise err
return decorator.decorator(_wrapper , __lowerCAmelCase )
class __snake_case :
def __init__( self , snake_case__ , snake_case__ , snake_case__ ) -> List[str]:
'''simple docstring'''
UpperCAmelCase : Union[str, Any] =returncode
UpperCAmelCase : int =stdout
UpperCAmelCase : Optional[int] =stderr
async def lowerCAmelCase_ ( __lowerCAmelCase , __lowerCAmelCase )-> List[Any]:
'''simple docstring'''
while True:
UpperCAmelCase : Union[str, Any] =await stream.readline()
if line:
callback(__lowerCAmelCase )
else:
break
async def lowerCAmelCase_ ( __lowerCAmelCase , __lowerCAmelCase=None , __lowerCAmelCase=None , __lowerCAmelCase=None , __lowerCAmelCase=False , __lowerCAmelCase=False )-> _RunOutput:
'''simple docstring'''
if echo:
print('''\nRunning: ''' , ''' '''.join(__lowerCAmelCase ) )
UpperCAmelCase : Any =await asyncio.create_subprocess_exec(
cmd[0] , *cmd[1:] , stdin=__lowerCAmelCase , stdout=asyncio.subprocess.PIPE , stderr=asyncio.subprocess.PIPE , env=__lowerCAmelCase , )
# note: there is a warning for a possible deadlock when using `wait` with huge amounts of data in the pipe
# https://docs.python.org/3/library/asyncio-subprocess.html#asyncio.asyncio.subprocess.Process.wait
#
# If it starts hanging, will need to switch to the following code. The problem is that no data
# will be seen until it's done and if it hangs for example there will be no debug info.
# out, err = await p.communicate()
# return _RunOutput(p.returncode, out, err)
UpperCAmelCase : int =[]
UpperCAmelCase : Dict =[]
def tee(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase="" ):
UpperCAmelCase : Dict =line.decode('''utf-8''' ).rstrip()
sink.append(__lowerCAmelCase )
if not quiet:
print(__lowerCAmelCase , __lowerCAmelCase , file=__lowerCAmelCase )
# XXX: the timeout doesn't seem to make any difference here
await asyncio.wait(
[
_read_stream(p.stdout , lambda __lowerCAmelCase : tee(__lowerCAmelCase , __lowerCAmelCase , sys.stdout , label='''stdout:''' ) ),
_read_stream(p.stderr , lambda __lowerCAmelCase : tee(__lowerCAmelCase , __lowerCAmelCase , sys.stderr , label='''stderr:''' ) ),
] , timeout=__lowerCAmelCase , )
return _RunOutput(await p.wait() , __lowerCAmelCase , __lowerCAmelCase )
def lowerCAmelCase_ ( __lowerCAmelCase , __lowerCAmelCase=None , __lowerCAmelCase=None , __lowerCAmelCase=1_80 , __lowerCAmelCase=False , __lowerCAmelCase=True )-> _RunOutput:
'''simple docstring'''
UpperCAmelCase : List[Any] =asyncio.get_event_loop()
UpperCAmelCase : Any =loop.run_until_complete(
_stream_subprocess(__lowerCAmelCase , env=__lowerCAmelCase , stdin=__lowerCAmelCase , timeout=__lowerCAmelCase , quiet=__lowerCAmelCase , echo=__lowerCAmelCase ) )
UpperCAmelCase : List[str] =''' '''.join(__lowerCAmelCase )
if result.returncode > 0:
UpperCAmelCase : Optional[Any] ='''\n'''.join(result.stderr )
raise RuntimeError(
f'''\'{cmd_str}\' failed with returncode {result.returncode}\n\n'''
f'''The combined stderr from workers follows:\n{stderr}''' )
# check that the subprocess actually did run and produced some output, should the test rely on
# the remote side to do the testing
if not result.stdout and not result.stderr:
raise RuntimeError(f'''\'{cmd_str}\' produced no output.''' )
return result
def lowerCAmelCase_ ( )-> Dict:
'''simple docstring'''
UpperCAmelCase : Optional[Any] =os.environ.get('''PYTEST_XDIST_WORKER''' , '''gw0''' )
UpperCAmelCase : Any =re.sub(R'''^gw''' , '''''' , __lowerCAmelCase , 0 , re.M )
return int(__lowerCAmelCase )
def lowerCAmelCase_ ( )-> List[Any]:
'''simple docstring'''
UpperCAmelCase : List[Any] =2_95_00
UpperCAmelCase : Dict =pytest_xdist_worker_id()
return port + uniq_delta
| 348 | import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_big_bird import BigBirdTokenizer
else:
__snake_case = None
__snake_case = logging.get_logger(__name__)
__snake_case = {'''vocab_file''': '''spiece.model''', '''tokenizer_file''': '''tokenizer.json'''}
__snake_case = {
'''vocab_file''': {
'''google/bigbird-roberta-base''': '''https://huggingface.co/google/bigbird-roberta-base/resolve/main/spiece.model''',
'''google/bigbird-roberta-large''': (
'''https://huggingface.co/google/bigbird-roberta-large/resolve/main/spiece.model'''
),
'''google/bigbird-base-trivia-itc''': (
'''https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/spiece.model'''
),
},
'''tokenizer_file''': {
'''google/bigbird-roberta-base''': (
'''https://huggingface.co/google/bigbird-roberta-base/resolve/main/tokenizer.json'''
),
'''google/bigbird-roberta-large''': (
'''https://huggingface.co/google/bigbird-roberta-large/resolve/main/tokenizer.json'''
),
'''google/bigbird-base-trivia-itc''': (
'''https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/tokenizer.json'''
),
},
}
__snake_case = {
'''google/bigbird-roberta-base''': 40_96,
'''google/bigbird-roberta-large''': 40_96,
'''google/bigbird-base-trivia-itc''': 40_96,
}
__snake_case = '''▁'''
class __snake_case ( lowerCamelCase__ ):
__lowerCamelCase : Dict = VOCAB_FILES_NAMES
__lowerCamelCase : List[Any] = PRETRAINED_VOCAB_FILES_MAP
__lowerCamelCase : Dict = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
__lowerCamelCase : List[str] = BigBirdTokenizer
__lowerCamelCase : Any = ["""input_ids""", """attention_mask"""]
__lowerCamelCase : List[int] = []
def __init__( self , snake_case__=None , snake_case__=None , snake_case__="<unk>" , snake_case__="<s>" , snake_case__="</s>" , snake_case__="<pad>" , snake_case__="[SEP]" , snake_case__="[MASK]" , snake_case__="[CLS]" , **snake_case__ , ) -> List[Any]:
'''simple docstring'''
UpperCAmelCase : Any =AddedToken(snake_case__ , lstrip=snake_case__ , rstrip=snake_case__ ) if isinstance(snake_case__ , snake_case__ ) else bos_token
UpperCAmelCase : Optional[int] =AddedToken(snake_case__ , lstrip=snake_case__ , rstrip=snake_case__ ) if isinstance(snake_case__ , snake_case__ ) else eos_token
UpperCAmelCase : List[str] =AddedToken(snake_case__ , lstrip=snake_case__ , rstrip=snake_case__ ) if isinstance(snake_case__ , snake_case__ ) else unk_token
UpperCAmelCase : Union[str, Any] =AddedToken(snake_case__ , lstrip=snake_case__ , rstrip=snake_case__ ) if isinstance(snake_case__ , snake_case__ ) else pad_token
UpperCAmelCase : int =AddedToken(snake_case__ , lstrip=snake_case__ , rstrip=snake_case__ ) if isinstance(snake_case__ , snake_case__ ) else cls_token
UpperCAmelCase : str =AddedToken(snake_case__ , lstrip=snake_case__ , rstrip=snake_case__ ) if isinstance(snake_case__ , snake_case__ ) else sep_token
# Mask token behave like a normal word, i.e. include the space before it
UpperCAmelCase : List[Any] =AddedToken(snake_case__ , lstrip=snake_case__ , rstrip=snake_case__ ) if isinstance(snake_case__ , snake_case__ ) else mask_token
super().__init__(
snake_case__ , tokenizer_file=snake_case__ , bos_token=snake_case__ , eos_token=snake_case__ , unk_token=snake_case__ , sep_token=snake_case__ , pad_token=snake_case__ , cls_token=snake_case__ , mask_token=snake_case__ , **snake_case__ , )
UpperCAmelCase : Tuple =vocab_file
UpperCAmelCase : Optional[int] =False if not self.vocab_file else True
def UpperCAmelCase__ ( self , snake_case__ , snake_case__ = None ) -> List[int]:
'''simple docstring'''
UpperCAmelCase : int =[self.sep_token_id]
UpperCAmelCase : Optional[int] =[self.cls_token_id]
if token_ids_a is None:
return cls + token_ids_a + sep
return cls + token_ids_a + sep + token_ids_a + sep
def UpperCAmelCase__ ( self , snake_case__ , snake_case__ = None , snake_case__ = False ) -> List[int]:
'''simple docstring'''
if already_has_special_tokens:
if token_ids_a is not None:
raise ValueError(
'''You should not supply a second sequence if the provided sequence of '''
'''ids is already formatted with special tokens for the model.''' )
return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]
if token_ids_a is None:
return [1] + ([0] * len(snake_case__ )) + [1]
return [1] + ([0] * len(snake_case__ )) + [1] + ([0] * len(snake_case__ )) + [1]
def UpperCAmelCase__ ( self , snake_case__ , snake_case__ = None ) -> List[int]:
'''simple docstring'''
UpperCAmelCase : Optional[Any] =[self.sep_token_id]
UpperCAmelCase : Optional[int] =[self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]
def UpperCAmelCase__ ( self , snake_case__ , snake_case__ = None ) -> Tuple[str]:
'''simple docstring'''
if not self.can_save_slow_tokenizer:
raise ValueError(
'''Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '''
'''tokenizer.''' )
if not os.path.isdir(snake_case__ ):
logger.error(f'''Vocabulary path ({save_directory}) should be a directory''' )
return
UpperCAmelCase : Optional[int] =os.path.join(
snake_case__ , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(snake_case__ ):
copyfile(self.vocab_file , snake_case__ )
return (out_vocab_file,)
| 348 | 1 |
import warnings
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__snake_case = logging.get_logger(__name__)
__snake_case = {
'''RUCAIBox/mvp''': '''https://huggingface.co/RUCAIBox/mvp/resolve/main/config.json''',
}
class __snake_case ( lowerCamelCase__ ):
__lowerCamelCase : Any = """mvp"""
__lowerCamelCase : Any = ["""past_key_values"""]
__lowerCamelCase : List[Any] = {"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}
def __init__( self , snake_case__=5_0267 , snake_case__=1024 , snake_case__=12 , snake_case__=4096 , snake_case__=16 , snake_case__=12 , snake_case__=4096 , snake_case__=16 , snake_case__=0.0 , snake_case__=0.0 , snake_case__="gelu" , snake_case__=1024 , snake_case__=0.1 , snake_case__=0.0 , snake_case__=0.0 , snake_case__=0.02 , snake_case__=0.0 , snake_case__=False , snake_case__=True , snake_case__=1 , snake_case__=0 , snake_case__=2 , snake_case__=True , snake_case__=2 , snake_case__=2 , snake_case__=False , snake_case__=100 , snake_case__=800 , **snake_case__ , ) -> Optional[Any]:
'''simple docstring'''
UpperCAmelCase : Optional[int] =vocab_size
UpperCAmelCase : List[Any] =max_position_embeddings
UpperCAmelCase : List[str] =d_model
UpperCAmelCase : List[str] =encoder_ffn_dim
UpperCAmelCase : int =encoder_layers
UpperCAmelCase : Any =encoder_attention_heads
UpperCAmelCase : List[Any] =decoder_ffn_dim
UpperCAmelCase : Optional[Any] =decoder_layers
UpperCAmelCase : int =decoder_attention_heads
UpperCAmelCase : Dict =dropout
UpperCAmelCase : List[str] =attention_dropout
UpperCAmelCase : List[str] =activation_dropout
UpperCAmelCase : int =activation_function
UpperCAmelCase : int =init_std
UpperCAmelCase : str =encoder_layerdrop
UpperCAmelCase : int =decoder_layerdrop
UpperCAmelCase : Union[str, Any] =classifier_dropout
UpperCAmelCase : Optional[Any] =use_cache
UpperCAmelCase : Tuple =encoder_layers
UpperCAmelCase : Union[str, Any] =scale_embedding # scale factor will be sqrt(d_model) if True
UpperCAmelCase : int =use_prompt
UpperCAmelCase : Union[str, Any] =prompt_length
UpperCAmelCase : Union[str, Any] =prompt_mid_dim
super().__init__(
pad_token_id=snake_case__ , bos_token_id=snake_case__ , eos_token_id=snake_case__ , is_encoder_decoder=snake_case__ , decoder_start_token_id=snake_case__ , forced_eos_token_id=snake_case__ , **snake_case__ , )
if self.forced_bos_token_id is None and kwargs.get('''force_bos_token_to_be_generated''' , snake_case__ ):
UpperCAmelCase : Dict =self.bos_token_id
warnings.warn(
f'''Please make sure the config includes `forced_bos_token_id={self.bos_token_id}` in future versions. '''
'''The config can simply be saved and uploaded again to be fixed.''' )
| 348 | from collections.abc import Callable
from math import pi, sqrt
from random import uniform
from statistics import mean
def lowerCAmelCase_ ( __lowerCAmelCase )-> Optional[Any]:
'''simple docstring'''
def is_in_circle(__lowerCAmelCase , __lowerCAmelCase ) -> bool:
UpperCAmelCase : List[Any] =sqrt((x**2) + (y**2) )
# Our circle has a radius of 1, so a distance
# greater than 1 would land outside the circle.
return distance_from_centre <= 1
# The proportion of guesses that landed in the circle
UpperCAmelCase : List[Any] =mean(
int(is_in_circle(uniform(-1.0 , 1.0 ) , uniform(-1.0 , 1.0 ) ) )
for _ in range(__lowerCAmelCase ) )
# The ratio of the area for circle to square is pi/4.
UpperCAmelCase : Dict =proportion * 4
print(f'''The estimated value of pi is {pi_estimate}''' )
print(f'''The numpy value of pi is {pi}''' )
print(f'''The total error is {abs(pi - pi_estimate )}''' )
def lowerCAmelCase_ ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase = 0.0 , __lowerCAmelCase = 1.0 , )-> float:
'''simple docstring'''
return mean(
function_to_integrate(uniform(__lowerCAmelCase , __lowerCAmelCase ) ) for _ in range(__lowerCAmelCase ) ) * (max_value - min_value)
def lowerCAmelCase_ ( __lowerCAmelCase , __lowerCAmelCase = 0.0 , __lowerCAmelCase = 1.0 )-> None:
'''simple docstring'''
def identity_function(__lowerCAmelCase ) -> float:
return x
UpperCAmelCase : List[Any] =area_under_curve_estimator(
__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
UpperCAmelCase : Dict =(max_value * max_value - min_value * min_value) / 2
print('''******************''' )
print(f'''Estimating area under y=x where x varies from {min_value} to {max_value}''' )
print(f'''Estimated value is {estimated_value}''' )
print(f'''Expected value is {expected_value}''' )
print(f'''Total error is {abs(estimated_value - expected_value )}''' )
print('''******************''' )
def lowerCAmelCase_ ( __lowerCAmelCase )-> None:
'''simple docstring'''
def function_to_integrate(__lowerCAmelCase ) -> float:
return sqrt(4.0 - x * x )
UpperCAmelCase : Dict =area_under_curve_estimator(
__lowerCAmelCase , __lowerCAmelCase , 0.0 , 2.0 )
print('''******************''' )
print('''Estimating pi using area_under_curve_estimator''' )
print(f'''Estimated value is {estimated_value}''' )
print(f'''Expected value is {pi}''' )
print(f'''Total error is {abs(estimated_value - pi )}''' )
print('''******************''' )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 348 | 1 |
from dataclasses import asdict, dataclass
from typing import Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__snake_case = logging.get_logger(__name__)
# TODO Update this
__snake_case = {
'''facebook/esm-1b''': '''https://huggingface.co/facebook/esm-1b/resolve/main/config.json''',
# See all ESM models at https://huggingface.co/models?filter=esm
}
class __snake_case ( lowerCamelCase__ ):
__lowerCamelCase : Tuple = """esm"""
def __init__( self , snake_case__=None , snake_case__=None , snake_case__=None , snake_case__=768 , snake_case__=12 , snake_case__=12 , snake_case__=3072 , snake_case__=0.1 , snake_case__=0.1 , snake_case__=1026 , snake_case__=0.02 , snake_case__=1e-12 , snake_case__="absolute" , snake_case__=True , snake_case__=None , snake_case__=False , snake_case__=False , snake_case__=None , snake_case__=None , **snake_case__ , ) -> Union[str, Any]:
'''simple docstring'''
super().__init__(pad_token_id=snake_case__ , mask_token_id=snake_case__ , **snake_case__ )
UpperCAmelCase : List[str] =vocab_size
UpperCAmelCase : str =hidden_size
UpperCAmelCase : List[Any] =num_hidden_layers
UpperCAmelCase : Optional[Any] =num_attention_heads
UpperCAmelCase : str =intermediate_size
UpperCAmelCase : Any =hidden_dropout_prob
UpperCAmelCase : int =attention_probs_dropout_prob
UpperCAmelCase : Dict =max_position_embeddings
UpperCAmelCase : List[str] =initializer_range
UpperCAmelCase : Union[str, Any] =layer_norm_eps
UpperCAmelCase : Dict =position_embedding_type
UpperCAmelCase : Optional[Any] =use_cache
UpperCAmelCase : int =emb_layer_norm_before
UpperCAmelCase : List[str] =token_dropout
UpperCAmelCase : Optional[Any] =is_folding_model
if is_folding_model:
if esmfold_config is None:
logger.info('''No esmfold_config supplied for folding model, using default values.''' )
UpperCAmelCase : Optional[Any] =EsmFoldConfig()
elif isinstance(snake_case__ , snake_case__ ):
UpperCAmelCase : Optional[int] =EsmFoldConfig(**snake_case__ )
UpperCAmelCase : Tuple =esmfold_config
if vocab_list is None:
logger.warning('''No vocab_list supplied for folding model, assuming the ESM-2 vocabulary!''' )
UpperCAmelCase : Any =get_default_vocab_list()
else:
UpperCAmelCase : Tuple =vocab_list
else:
UpperCAmelCase : Optional[int] =None
UpperCAmelCase : Union[str, Any] =None
if self.esmfold_config is not None and getattr(self.esmfold_config , '''use_esm_attn_map''' , snake_case__ ):
raise ValueError('''The HuggingFace port of ESMFold does not support use_esm_attn_map at this time!''' )
def UpperCAmelCase__ ( self ) -> List[Any]:
'''simple docstring'''
UpperCAmelCase : Union[str, Any] =super().to_dict()
if isinstance(self.esmfold_config , snake_case__ ):
UpperCAmelCase : str =self.esmfold_config.to_dict()
return output
@dataclass
class __snake_case :
__lowerCamelCase : str = None
__lowerCamelCase : bool = True
__lowerCamelCase : bool = False
__lowerCamelCase : bool = False
__lowerCamelCase : bool = False
__lowerCamelCase : float = 0
__lowerCamelCase : bool = True
__lowerCamelCase : bool = False
__lowerCamelCase : int = 128
__lowerCamelCase : "TrunkConfig" = None
def UpperCAmelCase__ ( self ) -> Union[str, Any]:
'''simple docstring'''
if self.trunk is None:
UpperCAmelCase : str =TrunkConfig()
elif isinstance(self.trunk , snake_case__ ):
UpperCAmelCase : Optional[int] =TrunkConfig(**self.trunk )
def UpperCAmelCase__ ( self ) -> Any:
'''simple docstring'''
UpperCAmelCase : Optional[Any] =asdict(self )
UpperCAmelCase : Any =self.trunk.to_dict()
return output
@dataclass
class __snake_case :
__lowerCamelCase : int = 48
__lowerCamelCase : int = 1024
__lowerCamelCase : int = 128
__lowerCamelCase : int = 32
__lowerCamelCase : int = 32
__lowerCamelCase : int = 32
__lowerCamelCase : float = 0
__lowerCamelCase : float = 0
__lowerCamelCase : bool = False
__lowerCamelCase : int = 4
__lowerCamelCase : Optional[int] = 128
__lowerCamelCase : "StructureModuleConfig" = None
def UpperCAmelCase__ ( self ) -> List[str]:
'''simple docstring'''
if self.structure_module is None:
UpperCAmelCase : Any =StructureModuleConfig()
elif isinstance(self.structure_module , snake_case__ ):
UpperCAmelCase : str =StructureModuleConfig(**self.structure_module )
if self.max_recycles <= 0:
raise ValueError(f'''`max_recycles` should be positive, got {self.max_recycles}.''' )
if self.sequence_state_dim % self.sequence_state_dim != 0:
raise ValueError(
'''`sequence_state_dim` should be a round multiple of `sequence_state_dim`, got'''
f''' {self.sequence_state_dim} and {self.sequence_state_dim}.''' )
if self.pairwise_state_dim % self.pairwise_state_dim != 0:
raise ValueError(
'''`pairwise_state_dim` should be a round multiple of `pairwise_state_dim`, got'''
f''' {self.pairwise_state_dim} and {self.pairwise_state_dim}.''' )
UpperCAmelCase : Optional[int] =self.sequence_state_dim // self.sequence_head_width
UpperCAmelCase : Any =self.pairwise_state_dim // self.pairwise_head_width
if self.sequence_state_dim != sequence_num_heads * self.sequence_head_width:
raise ValueError(
'''`sequence_state_dim` should be equal to `sequence_num_heads * sequence_head_width, got'''
f''' {self.sequence_state_dim} != {sequence_num_heads} * {self.sequence_head_width}.''' )
if self.pairwise_state_dim != pairwise_num_heads * self.pairwise_head_width:
raise ValueError(
'''`pairwise_state_dim` should be equal to `pairwise_num_heads * pairwise_head_width, got'''
f''' {self.pairwise_state_dim} != {pairwise_num_heads} * {self.pairwise_head_width}.''' )
if self.pairwise_state_dim % 2 != 0:
raise ValueError(f'''`pairwise_state_dim` should be even, got {self.pairwise_state_dim}.''' )
if self.dropout >= 0.4:
raise ValueError(f'''`dropout` should not be greater than 0.4, got {self.dropout}.''' )
def UpperCAmelCase__ ( self ) -> List[Any]:
'''simple docstring'''
UpperCAmelCase : Union[str, Any] =asdict(self )
UpperCAmelCase : Tuple =self.structure_module.to_dict()
return output
@dataclass
class __snake_case :
__lowerCamelCase : int = 384
__lowerCamelCase : int = 128
__lowerCamelCase : int = 16
__lowerCamelCase : int = 128
__lowerCamelCase : int = 12
__lowerCamelCase : int = 4
__lowerCamelCase : int = 8
__lowerCamelCase : float = 0.1
__lowerCamelCase : int = 8
__lowerCamelCase : int = 1
__lowerCamelCase : int = 2
__lowerCamelCase : int = 7
__lowerCamelCase : int = 10
__lowerCamelCase : float = 1E-8
__lowerCamelCase : float = 1E5
def UpperCAmelCase__ ( self ) -> Union[str, Any]:
'''simple docstring'''
return asdict(self )
def lowerCAmelCase_ ( )-> Tuple:
'''simple docstring'''
return (
"<cls>",
"<pad>",
"<eos>",
"<unk>",
"L",
"A",
"G",
"V",
"S",
"E",
"R",
"T",
"I",
"D",
"P",
"K",
"Q",
"N",
"F",
"Y",
"M",
"H",
"W",
"C",
"X",
"B",
"U",
"Z",
"O",
".",
"-",
"<null_1>",
"<mask>",
)
| 348 | from __future__ import annotations
import unittest
import numpy as np
from transformers import BlipTextConfig
from transformers.testing_utils import require_tf, slow
from transformers.utils import is_tf_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
if is_tf_available():
import tensorflow as tf
from transformers import TFBlipTextModel
from transformers.models.blip.modeling_tf_blip import TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST
class __snake_case :
def __init__( self , snake_case__ , snake_case__=12 , snake_case__=7 , snake_case__=True , snake_case__=True , snake_case__=True , snake_case__=99 , snake_case__=32 , snake_case__=32 , snake_case__=2 , snake_case__=4 , snake_case__=37 , snake_case__=0.1 , snake_case__=0.1 , snake_case__=512 , snake_case__=0.02 , snake_case__=0 , snake_case__=None , ) -> Tuple:
'''simple docstring'''
UpperCAmelCase : List[Any] =parent
UpperCAmelCase : Optional[int] =batch_size
UpperCAmelCase : List[Any] =seq_length
UpperCAmelCase : Optional[int] =is_training
UpperCAmelCase : Union[str, Any] =use_input_mask
UpperCAmelCase : Tuple =use_labels
UpperCAmelCase : Union[str, Any] =vocab_size
UpperCAmelCase : Tuple =hidden_size
UpperCAmelCase : Dict =projection_dim
UpperCAmelCase : Optional[int] =num_hidden_layers
UpperCAmelCase : Dict =num_attention_heads
UpperCAmelCase : int =intermediate_size
UpperCAmelCase : Any =dropout
UpperCAmelCase : Union[str, Any] =attention_dropout
UpperCAmelCase : Union[str, Any] =max_position_embeddings
UpperCAmelCase : List[str] =initializer_range
UpperCAmelCase : str =scope
UpperCAmelCase : str =bos_token_id
def UpperCAmelCase__ ( self ) -> int:
'''simple docstring'''
UpperCAmelCase : int =ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
UpperCAmelCase : int =None
if self.use_input_mask:
UpperCAmelCase : Union[str, Any] =random_attention_mask([self.batch_size, self.seq_length] )
if input_mask is not None:
UpperCAmelCase : Optional[int] =input_mask.numpy()
UpperCAmelCase , UpperCAmelCase : List[Any] =input_mask.shape
UpperCAmelCase : Optional[Any] =np.random.randint(1 , seq_length - 1 , size=(batch_size,) )
for batch_idx, start_index in enumerate(snake_case__ ):
UpperCAmelCase : List[Any] =1
UpperCAmelCase : Tuple =0
UpperCAmelCase : List[Any] =self.get_config()
return config, input_ids, tf.convert_to_tensor(snake_case__ )
def UpperCAmelCase__ ( self ) -> Union[str, Any]:
'''simple docstring'''
return BlipTextConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , projection_dim=self.projection_dim , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , dropout=self.dropout , attention_dropout=self.attention_dropout , max_position_embeddings=self.max_position_embeddings , initializer_range=self.initializer_range , bos_token_id=self.bos_token_id , )
def UpperCAmelCase__ ( self , snake_case__ , snake_case__ , snake_case__ ) -> Dict:
'''simple docstring'''
UpperCAmelCase : Tuple =TFBlipTextModel(config=snake_case__ )
UpperCAmelCase : List[Any] =model(snake_case__ , attention_mask=snake_case__ , training=snake_case__ )
UpperCAmelCase : str =model(snake_case__ , training=snake_case__ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
self.parent.assertEqual(result.pooler_output.shape , (self.batch_size, self.hidden_size) )
def UpperCAmelCase__ ( self ) -> Optional[Any]:
'''simple docstring'''
UpperCAmelCase : List[str] =self.prepare_config_and_inputs()
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase : List[Any] =config_and_inputs
UpperCAmelCase : Optional[int] ={'''input_ids''': input_ids, '''attention_mask''': input_mask}
return config, inputs_dict
@require_tf
class __snake_case ( lowerCamelCase__ , unittest.TestCase ):
__lowerCamelCase : Optional[int] = (TFBlipTextModel,) if is_tf_available() else ()
__lowerCamelCase : Dict = False
__lowerCamelCase : Optional[Any] = False
__lowerCamelCase : Dict = False
def UpperCAmelCase__ ( self ) -> Optional[Any]:
'''simple docstring'''
UpperCAmelCase : str =BlipTextModelTester(self )
UpperCAmelCase : Optional[int] =ConfigTester(self , config_class=snake_case__ , hidden_size=37 )
def UpperCAmelCase__ ( self ) -> Optional[int]:
'''simple docstring'''
self.config_tester.run_common_tests()
def UpperCAmelCase__ ( self ) -> List[Any]:
'''simple docstring'''
UpperCAmelCase : Any =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*snake_case__ )
def UpperCAmelCase__ ( self ) -> Optional[int]:
'''simple docstring'''
pass
def UpperCAmelCase__ ( self ) -> Union[str, Any]:
'''simple docstring'''
pass
@unittest.skip(reason='''Blip does not use inputs_embeds''' )
def UpperCAmelCase__ ( self ) -> str:
'''simple docstring'''
pass
@unittest.skip(reason='''BlipTextModel has no base class and is not available in MODEL_MAPPING''' )
def UpperCAmelCase__ ( self ) -> Optional[int]:
'''simple docstring'''
pass
@unittest.skip(reason='''BlipTextModel has no base class and is not available in MODEL_MAPPING''' )
def UpperCAmelCase__ ( self ) -> str:
'''simple docstring'''
pass
@slow
def UpperCAmelCase__ ( self ) -> Tuple:
'''simple docstring'''
for model_name in TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
UpperCAmelCase : Optional[Any] =TFBlipTextModel.from_pretrained(snake_case__ )
self.assertIsNotNone(snake_case__ )
def UpperCAmelCase__ ( self , snake_case__=True ) -> Any:
'''simple docstring'''
super().test_pt_tf_model_equivalence(allow_missing_keys=snake_case__ )
| 348 | 1 |
import math
import tensorflow as tf
from packaging import version
def lowerCAmelCase_ ( __lowerCAmelCase )-> Any:
'''simple docstring'''
UpperCAmelCase : Optional[int] =tf.convert_to_tensor(__lowerCAmelCase )
UpperCAmelCase : Optional[Any] =0.5 * (1.0 + tf.math.erf(x / tf.cast(tf.sqrt(2.0 ) , x.dtype ) ))
return x * cdf
def lowerCAmelCase_ ( __lowerCAmelCase )-> Union[str, Any]:
'''simple docstring'''
UpperCAmelCase : List[Any] =tf.convert_to_tensor(__lowerCAmelCase )
UpperCAmelCase : Union[str, Any] =tf.cast(math.pi , x.dtype )
UpperCAmelCase : str =tf.cast(0.044715 , x.dtype )
UpperCAmelCase : Optional[Any] =0.5 * (1.0 + tf.tanh(tf.sqrt(2.0 / pi ) * (x + coeff * tf.pow(__lowerCAmelCase , 3 )) ))
return x * cdf
def lowerCAmelCase_ ( __lowerCAmelCase )-> int:
'''simple docstring'''
UpperCAmelCase : str =tf.convert_to_tensor(__lowerCAmelCase )
return x * tf.tanh(tf.math.softplus(__lowerCAmelCase ) )
def lowerCAmelCase_ ( __lowerCAmelCase )-> Optional[Any]:
'''simple docstring'''
UpperCAmelCase : Optional[Any] =tf.convert_to_tensor(__lowerCAmelCase )
UpperCAmelCase : Union[str, Any] =tf.cast(0.044715 , x.dtype )
UpperCAmelCase : List[str] =tf.cast(0.7978845608 , x.dtype )
return 0.5 * x * (1.0 + tf.tanh(x * coeffa * (1.0 + coeffa * x * x) ))
def lowerCAmelCase_ ( __lowerCAmelCase )-> List[str]:
'''simple docstring'''
UpperCAmelCase : str =tf.convert_to_tensor(__lowerCAmelCase )
UpperCAmelCase : List[Any] =tf.cast(1.702 , x.dtype )
return x * tf.math.sigmoid(coeff * x )
def lowerCAmelCase_ ( __lowerCAmelCase )-> str:
'''simple docstring'''
return tf.clip_by_value(_gelu(__lowerCAmelCase ) , -10 , 10 )
def lowerCAmelCase_ ( __lowerCAmelCase , __lowerCAmelCase=-1 )-> Optional[Any]:
'''simple docstring'''
UpperCAmelCase , UpperCAmelCase : Optional[int] =tf.split(__lowerCAmelCase , 2 , axis=__lowerCAmelCase )
return a * tf.math.sigmoid(__lowerCAmelCase )
if version.parse(tf.version.VERSION) >= version.parse('''2.4'''):
def lowerCAmelCase_ ( __lowerCAmelCase )-> Union[str, Any]:
'''simple docstring'''
return tf.keras.activations.gelu(__lowerCAmelCase , approximate=__lowerCAmelCase )
__snake_case = tf.keras.activations.gelu
__snake_case = approximate_gelu_wrap
else:
__snake_case = _gelu
__snake_case = _gelu_new
__snake_case = {
'''gelu''': gelu,
'''gelu_10''': gelu_aa,
'''gelu_fast''': gelu_fast,
'''gelu_new''': gelu_new,
'''glu''': glu,
'''mish''': mish,
'''quick_gelu''': quick_gelu,
'''relu''': tf.keras.activations.relu,
'''sigmoid''': tf.keras.activations.sigmoid,
'''silu''': tf.keras.activations.swish,
'''swish''': tf.keras.activations.swish,
'''tanh''': tf.keras.activations.tanh,
}
def lowerCAmelCase_ ( __lowerCAmelCase )-> Dict:
'''simple docstring'''
if activation_string in ACTaFN:
return ACTaFN[activation_string]
else:
raise KeyError(f'''function {activation_string} not found in ACT2FN mapping {list(ACTaFN.keys() )}''' )
| 348 | import torch
import torch.nn as nn
from transformers import CLIPConfig, CLIPVisionModel, PreTrainedModel
from ...utils import logging
__snake_case = logging.get_logger(__name__)
def lowerCAmelCase_ ( __lowerCAmelCase , __lowerCAmelCase )-> str:
'''simple docstring'''
UpperCAmelCase : Dict =nn.functional.normalize(__lowerCAmelCase )
UpperCAmelCase : Tuple =nn.functional.normalize(__lowerCAmelCase )
return torch.mm(__lowerCAmelCase , normalized_text_embeds.t() )
class __snake_case ( lowerCamelCase__ ):
__lowerCamelCase : List[str] = CLIPConfig
__lowerCamelCase : List[Any] = ["""CLIPEncoderLayer"""]
def __init__( self , snake_case__ ) -> Dict:
'''simple docstring'''
super().__init__(snake_case__ )
UpperCAmelCase : Dict =CLIPVisionModel(config.vision_config )
UpperCAmelCase : Optional[Any] =nn.Linear(config.vision_config.hidden_size , config.projection_dim , bias=snake_case__ )
UpperCAmelCase : int =nn.Parameter(torch.ones(17 , config.projection_dim ) , requires_grad=snake_case__ )
UpperCAmelCase : List[str] =nn.Parameter(torch.ones(3 , config.projection_dim ) , requires_grad=snake_case__ )
UpperCAmelCase : str =nn.Parameter(torch.ones(17 ) , requires_grad=snake_case__ )
UpperCAmelCase : Optional[int] =nn.Parameter(torch.ones(3 ) , requires_grad=snake_case__ )
@torch.no_grad()
def UpperCAmelCase__ ( self , snake_case__ , snake_case__ ) -> Tuple:
'''simple docstring'''
UpperCAmelCase : Union[str, Any] =self.vision_model(snake_case__ )[1] # pooled_output
UpperCAmelCase : Optional[Any] =self.visual_projection(snake_case__ )
# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
UpperCAmelCase : List[str] =cosine_distance(snake_case__ , self.special_care_embeds ).cpu().float().numpy()
UpperCAmelCase : Optional[Any] =cosine_distance(snake_case__ , self.concept_embeds ).cpu().float().numpy()
UpperCAmelCase : Tuple =[]
UpperCAmelCase : Dict =image_embeds.shape[0]
for i in range(snake_case__ ):
UpperCAmelCase : str ={'''special_scores''': {}, '''special_care''': [], '''concept_scores''': {}, '''bad_concepts''': []}
# increase this value to create a stronger `nfsw` filter
# at the cost of increasing the possibility of filtering benign images
UpperCAmelCase : str =0.0
for concept_idx in range(len(special_cos_dist[0] ) ):
UpperCAmelCase : Optional[Any] =special_cos_dist[i][concept_idx]
UpperCAmelCase : Union[str, Any] =self.special_care_embeds_weights[concept_idx].item()
UpperCAmelCase : str =round(concept_cos - concept_threshold + adjustment , 3 )
if result_img["special_scores"][concept_idx] > 0:
result_img["special_care"].append({concept_idx, result_img['''special_scores'''][concept_idx]} )
UpperCAmelCase : int =0.01
for concept_idx in range(len(cos_dist[0] ) ):
UpperCAmelCase : Any =cos_dist[i][concept_idx]
UpperCAmelCase : Optional[int] =self.concept_embeds_weights[concept_idx].item()
UpperCAmelCase : int =round(concept_cos - concept_threshold + adjustment , 3 )
if result_img["concept_scores"][concept_idx] > 0:
result_img["bad_concepts"].append(snake_case__ )
result.append(snake_case__ )
UpperCAmelCase : Optional[int] =[len(res['''bad_concepts'''] ) > 0 for res in result]
return images, has_nsfw_concepts
@torch.no_grad()
def UpperCAmelCase__ ( self , snake_case__ , snake_case__ ) -> Tuple:
'''simple docstring'''
UpperCAmelCase : Any =self.vision_model(snake_case__ )[1] # pooled_output
UpperCAmelCase : List[str] =self.visual_projection(snake_case__ )
UpperCAmelCase : Any =cosine_distance(snake_case__ , self.special_care_embeds )
UpperCAmelCase : Optional[Any] =cosine_distance(snake_case__ , self.concept_embeds )
# increase this value to create a stronger `nsfw` filter
# at the cost of increasing the possibility of filtering benign images
UpperCAmelCase : Optional[Any] =0.0
UpperCAmelCase : Any =special_cos_dist - self.special_care_embeds_weights + adjustment
# special_scores = special_scores.round(decimals=3)
UpperCAmelCase : str =torch.any(special_scores > 0 , dim=1 )
UpperCAmelCase : List[Any] =special_care * 0.01
UpperCAmelCase : Union[str, Any] =special_adjustment.unsqueeze(1 ).expand(-1 , cos_dist.shape[1] )
UpperCAmelCase : List[Any] =(cos_dist - self.concept_embeds_weights) + special_adjustment
# concept_scores = concept_scores.round(decimals=3)
UpperCAmelCase : str =torch.any(concept_scores > 0 , dim=1 )
return images, has_nsfw_concepts
| 348 | 1 |
def lowerCAmelCase_ ( __lowerCAmelCase )-> str:
'''simple docstring'''
return " ".join(input_str.split()[::-1] )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 348 | import argparse
import intel_extension_for_pytorch as ipex
import torch
from diffusers import DPMSolverMultistepScheduler, StableDiffusionPipeline
__snake_case = argparse.ArgumentParser('''Stable Diffusion script with intel optimization''', add_help=False)
parser.add_argument('''--dpm''', action='''store_true''', help='''Enable DPMSolver or not''')
parser.add_argument('''--steps''', default=None, type=int, help='''Num inference steps''')
__snake_case = parser.parse_args()
__snake_case = '''cpu'''
__snake_case = '''a lovely <dicoo> in red dress and hat, in the snowly and brightly night, with many brighly buildings'''
__snake_case = '''path-to-your-trained-model'''
__snake_case = StableDiffusionPipeline.from_pretrained(model_id)
if args.dpm:
__snake_case = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
__snake_case = pipe.to(device)
# to channels last
__snake_case = pipe.unet.to(memory_format=torch.channels_last)
__snake_case = pipe.vae.to(memory_format=torch.channels_last)
__snake_case = pipe.text_encoder.to(memory_format=torch.channels_last)
if pipe.requires_safety_checker:
__snake_case = pipe.safety_checker.to(memory_format=torch.channels_last)
# optimize with ipex
__snake_case = torch.randn(2, 4, 64, 64)
__snake_case = torch.rand(1) * 9_99
__snake_case = torch.randn(2, 77, 7_68)
__snake_case = (sample, timestep, encoder_hidden_status)
try:
__snake_case = ipex.optimize(pipe.unet.eval(), dtype=torch.bfloataa, inplace=True, sample_input=input_example)
except Exception:
__snake_case = ipex.optimize(pipe.unet.eval(), dtype=torch.bfloataa, inplace=True)
__snake_case = ipex.optimize(pipe.vae.eval(), dtype=torch.bfloataa, inplace=True)
__snake_case = ipex.optimize(pipe.text_encoder.eval(), dtype=torch.bfloataa, inplace=True)
if pipe.requires_safety_checker:
__snake_case = ipex.optimize(pipe.safety_checker.eval(), dtype=torch.bfloataa, inplace=True)
# compute
__snake_case = 6_66
__snake_case = torch.Generator(device).manual_seed(seed)
__snake_case = {'''generator''': generator}
if args.steps is not None:
__snake_case = args.steps
with torch.cpu.amp.autocast(enabled=True, dtype=torch.bfloataa):
__snake_case = pipe(prompt, **generate_kwargs).images[0]
# save image
image.save('''generated.png''')
| 348 | 1 |
import json
import os
from typing import Optional, Tuple
import regex as re
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
__snake_case = logging.get_logger(__name__)
__snake_case = {
'''vocab_file''': '''vocab.json''',
'''merges_file''': '''merges.txt''',
}
__snake_case = {
'''vocab_file''': {'''ctrl''': '''https://raw.githubusercontent.com/salesforce/ctrl/master/ctrl-vocab.json'''},
'''merges_file''': {'''ctrl''': '''https://raw.githubusercontent.com/salesforce/ctrl/master/ctrl-merges.txt'''},
}
__snake_case = {
'''ctrl''': 2_56,
}
__snake_case = {
'''Pregnancy''': 16_86_29,
'''Christianity''': 76_75,
'''Explain''': 10_64_23,
'''Fitness''': 6_34_40,
'''Saving''': 6_31_63,
'''Ask''': 2_71_71,
'''Ass''': 9_59_85,
'''Joke''': 16_35_09,
'''Questions''': 4_56_22,
'''Thoughts''': 4_96_05,
'''Retail''': 5_23_42,
'''Feminism''': 16_43_38,
'''Writing''': 1_19_92,
'''Atheism''': 19_22_63,
'''Netflix''': 4_86_16,
'''Computing''': 3_96_39,
'''Opinion''': 4_32_13,
'''Alone''': 4_49_67,
'''Funny''': 5_89_17,
'''Gaming''': 4_03_58,
'''Human''': 40_88,
'''India''': 13_31,
'''Joker''': 7_71_38,
'''Diet''': 3_62_06,
'''Legal''': 1_18_59,
'''Norman''': 49_39,
'''Tip''': 7_26_89,
'''Weight''': 5_23_43,
'''Movies''': 4_62_73,
'''Running''': 2_34_25,
'''Science''': 20_90,
'''Horror''': 3_77_93,
'''Confession''': 6_05_72,
'''Finance''': 1_22_50,
'''Politics''': 1_63_60,
'''Scary''': 19_19_85,
'''Support''': 1_26_54,
'''Technologies''': 3_25_16,
'''Teenage''': 6_61_60,
'''Event''': 3_27_69,
'''Learned''': 6_74_60,
'''Notion''': 18_27_70,
'''Wikipedia''': 3_75_83,
'''Books''': 66_65,
'''Extract''': 7_60_50,
'''Confessions''': 10_27_01,
'''Conspiracy''': 7_59_32,
'''Links''': 6_36_74,
'''Narcissus''': 15_04_25,
'''Relationship''': 5_47_66,
'''Relationships''': 13_47_96,
'''Reviews''': 4_16_71,
'''News''': 42_56,
'''Translation''': 2_68_20,
'''multilingual''': 12_84_06,
}
def lowerCAmelCase_ ( __lowerCAmelCase )-> List[Any]:
'''simple docstring'''
UpperCAmelCase : List[Any] =set()
UpperCAmelCase : Dict =word[0]
for char in word[1:]:
pairs.add((prev_char, char) )
UpperCAmelCase : Dict =char
UpperCAmelCase : Tuple =set(__lowerCAmelCase )
return pairs
class __snake_case ( lowerCamelCase__ ):
__lowerCamelCase : Optional[Any] = VOCAB_FILES_NAMES
__lowerCamelCase : List[Any] = PRETRAINED_VOCAB_FILES_MAP
__lowerCamelCase : List[str] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
__lowerCamelCase : int = CONTROL_CODES
def __init__( self , snake_case__ , snake_case__ , snake_case__="<unk>" , **snake_case__ ) -> int:
'''simple docstring'''
super().__init__(unk_token=snake_case__ , **snake_case__ )
with open(snake_case__ , encoding='''utf-8''' ) as vocab_handle:
UpperCAmelCase : Optional[Any] =json.load(snake_case__ )
UpperCAmelCase : Dict ={v: k for k, v in self.encoder.items()}
with open(snake_case__ , encoding='''utf-8''' ) as merges_handle:
UpperCAmelCase : List[Any] =merges_handle.read().split('''\n''' )[1:-1]
UpperCAmelCase : Any =[tuple(merge.split() ) for merge in merges]
UpperCAmelCase : List[str] =dict(zip(snake_case__ , range(len(snake_case__ ) ) ) )
UpperCAmelCase : Tuple ={}
@property
def UpperCAmelCase__ ( self ) -> Tuple:
'''simple docstring'''
return len(self.encoder )
def UpperCAmelCase__ ( self ) -> int:
'''simple docstring'''
return dict(self.encoder , **self.added_tokens_encoder )
def UpperCAmelCase__ ( self , snake_case__ ) -> int:
'''simple docstring'''
if token in self.cache:
return self.cache[token]
UpperCAmelCase : Optional[int] =tuple(snake_case__ )
UpperCAmelCase : int =tuple(list(word[:-1] ) + [word[-1] + '''</w>'''] )
UpperCAmelCase : List[str] =get_pairs(snake_case__ )
if not pairs:
return token
while True:
UpperCAmelCase : Dict =min(snake_case__ , key=lambda snake_case__ : self.bpe_ranks.get(snake_case__ , float('''inf''' ) ) )
if bigram not in self.bpe_ranks:
break
UpperCAmelCase , UpperCAmelCase : Dict =bigram
UpperCAmelCase : Optional[int] =[]
UpperCAmelCase : int =0
while i < len(snake_case__ ):
try:
UpperCAmelCase : List[str] =word.index(snake_case__ , snake_case__ )
except ValueError:
new_word.extend(word[i:] )
break
else:
new_word.extend(word[i:j] )
UpperCAmelCase : Optional[int] =j
if word[i] == first and i < len(snake_case__ ) - 1 and word[i + 1] == second:
new_word.append(first + second )
i += 2
else:
new_word.append(word[i] )
i += 1
UpperCAmelCase : Union[str, Any] =tuple(snake_case__ )
UpperCAmelCase : Optional[Any] =new_word
if len(snake_case__ ) == 1:
break
else:
UpperCAmelCase : Tuple =get_pairs(snake_case__ )
UpperCAmelCase : Dict ='''@@ '''.join(snake_case__ )
UpperCAmelCase : int =word[:-4]
UpperCAmelCase : int =word
return word
def UpperCAmelCase__ ( self , snake_case__ ) -> Dict:
'''simple docstring'''
UpperCAmelCase : Union[str, Any] =[]
UpperCAmelCase : Dict =re.findall(r'''\S+\n?''' , snake_case__ )
for token in words:
split_tokens.extend(list(self.bpe(snake_case__ ).split(''' ''' ) ) )
return split_tokens
def UpperCAmelCase__ ( self , snake_case__ ) -> Union[str, Any]:
'''simple docstring'''
return self.encoder.get(snake_case__ , self.encoder.get(self.unk_token ) )
def UpperCAmelCase__ ( self , snake_case__ ) -> Optional[Any]:
'''simple docstring'''
return self.decoder.get(snake_case__ , self.unk_token )
def UpperCAmelCase__ ( self , snake_case__ ) -> Union[str, Any]:
'''simple docstring'''
UpperCAmelCase : int =''' '''.join(snake_case__ ).replace('''@@ ''' , '''''' ).strip()
return out_string
def UpperCAmelCase__ ( self , snake_case__ , snake_case__ = None ) -> Tuple[str]:
'''simple docstring'''
if not os.path.isdir(snake_case__ ):
logger.error(f'''Vocabulary path ({save_directory}) should be a directory''' )
return
UpperCAmelCase : Union[str, Any] =os.path.join(
snake_case__ , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] )
UpperCAmelCase : Any =os.path.join(
snake_case__ , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''merges_file'''] )
with open(snake_case__ , '''w''' , encoding='''utf-8''' ) as f:
f.write(json.dumps(self.encoder , indent=2 , sort_keys=snake_case__ , ensure_ascii=snake_case__ ) + '''\n''' )
UpperCAmelCase : Optional[Any] =0
with open(snake_case__ , '''w''' , encoding='''utf-8''' ) as writer:
writer.write('''#version: 0.2\n''' )
for bpe_tokens, token_index in sorted(self.bpe_ranks.items() , key=lambda snake_case__ : kv[1] ):
if index != token_index:
logger.warning(
f'''Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.'''
''' Please check that the tokenizer is not corrupted!''' )
UpperCAmelCase : str =token_index
writer.write(''' '''.join(snake_case__ ) + '''\n''' )
index += 1
return vocab_file, merge_file
# def decode(self, token_ids, skip_special_tokens=False, clean_up_tokenization_spaces=True):
# filtered_tokens = ' '.join(self.convert_ids_to_tokens(token_ids, skip_special_tokens=skip_special_tokens))
# tokens_generated_so_far = re.sub('(@@ )', '', string=filtered_tokens)
# tokens_generated_so_far = re.sub('(@@ ?$)', '', string=tokens_generated_so_far)
# return ''.join(tokens_generated_so_far)
| 348 | __snake_case = '''Input must be a string of 8 numbers plus letter'''
__snake_case = '''TRWAGMYFPDXBNJZSQVHLCKE'''
def lowerCAmelCase_ ( __lowerCAmelCase )-> bool:
'''simple docstring'''
if not isinstance(__lowerCAmelCase , __lowerCAmelCase ):
UpperCAmelCase : Optional[Any] =f'''Expected string as input, found {type(__lowerCAmelCase ).__name__}'''
raise TypeError(__lowerCAmelCase )
UpperCAmelCase : List[Any] =spanish_id.replace('''-''' , '''''' ).upper()
if len(__lowerCAmelCase ) != 9:
raise ValueError(__lowerCAmelCase )
try:
UpperCAmelCase : int =int(spanish_id_clean[0:8] )
UpperCAmelCase : Optional[int] =spanish_id_clean[8]
except ValueError as ex:
raise ValueError(__lowerCAmelCase ) from ex
if letter.isdigit():
raise ValueError(__lowerCAmelCase )
return letter == LOOKUP_LETTERS[number % 23]
if __name__ == "__main__":
import doctest
doctest.testmod()
| 348 | 1 |
def lowerCAmelCase_ ( __lowerCAmelCase , __lowerCAmelCase = False )-> bool:
'''simple docstring'''
if n == 2:
return True
if not n % 2 or n < 2:
return False
if n > 5 and n % 10 not in (1, 3, 7, 9): # can quickly check last digit
return False
if n > 3_31_70_44_06_46_79_88_73_85_96_19_81 and not allow_probable:
raise ValueError(
'''Warning: upper bound of deterministic test is exceeded. '''
'''Pass allow_probable=True to allow probabilistic test. '''
'''A return value of True indicates a probable prime.''' )
# array bounds provided by analysis
UpperCAmelCase : Union[str, Any] =[
20_47,
1_37_36_53,
25_32_60_01,
32_15_03_17_51,
2_15_23_02_89_87_47,
3_47_47_49_66_03_83,
3_41_55_00_71_72_83_21,
1,
3_82_51_23_05_65_46_41_30_51,
1,
1,
31_86_65_85_78_34_03_11_51_16_74_61,
3_31_70_44_06_46_79_88_73_85_96_19_81,
]
UpperCAmelCase : Any =[2, 3, 5, 7, 11, 13, 17, 19, 23, 29, 31, 37, 41]
for idx, _p in enumerate(__lowerCAmelCase , 1 ):
if n < _p:
# then we have our last prime to check
UpperCAmelCase : List[Any] =primes[:idx]
break
UpperCAmelCase , UpperCAmelCase : Union[str, Any] =n - 1, 0
# break up n -1 into a power of 2 (s) and
# remaining odd component
# essentially, solve for d * 2 ** s == n - 1
while d % 2 == 0:
d //= 2
s += 1
for prime in plist:
UpperCAmelCase : Union[str, Any] =False
for r in range(__lowerCAmelCase ):
UpperCAmelCase : Optional[Any] =pow(__lowerCAmelCase , d * 2**r , __lowerCAmelCase )
# see article for analysis explanation for m
if (r == 0 and m == 1) or ((m + 1) % n == 0):
UpperCAmelCase : str =True
# this loop will not determine compositeness
break
if pr:
continue
# if pr is False, then the above loop never evaluated to true,
# and the n MUST be composite
return False
return True
def lowerCAmelCase_ ( )-> None:
'''simple docstring'''
assert not miller_rabin(5_61 )
assert miller_rabin(5_63 )
# 2047
assert not miller_rabin(83_82_01 )
assert miller_rabin(83_82_07 )
# 1_373_653
assert not miller_rabin(17_31_60_01 )
assert miller_rabin(17_31_60_17 )
# 25_326_001
assert not miller_rabin(30_78_38_66_41 )
assert miller_rabin(30_78_38_66_53 )
# 3_215_031_751
assert not miller_rabin(1_71_30_45_57_48_01 )
assert miller_rabin(1_71_30_45_57_48_19 )
# 2_152_302_898_747
assert not miller_rabin(2_77_97_99_72_83_07 )
assert miller_rabin(2_77_97_99_72_83_27 )
# 3_474_749_660_383
assert not miller_rabin(1_13_85_00_23_90_94_41 )
assert miller_rabin(1_13_85_00_23_90_95_27 )
# 341_550_071_728_321
assert not miller_rabin(1_27_50_41_01_88_48_80_43_51 )
assert miller_rabin(1_27_50_41_01_88_48_80_43_91 )
# 3_825_123_056_546_413_051
assert not miller_rabin(7_96_66_46_44_58_50_77_87_79_18_67 )
assert miller_rabin(7_96_66_46_44_58_50_77_87_79_19_51 )
# 318_665_857_834_031_151_167_461
assert not miller_rabin(55_28_40_67_74_46_64_78_97_66_03_33 )
assert miller_rabin(55_28_40_67_74_46_64_78_97_66_03_59 )
# 3_317_044_064_679_887_385_961_981
# upper limit for probabilistic test
if __name__ == "__main__":
test_miller_rabin()
| 348 | def lowerCAmelCase_ ( __lowerCAmelCase , __lowerCAmelCase )-> str:
'''simple docstring'''
if number < 0 or shift_amount < 0:
raise ValueError('''both inputs must be positive integers''' )
UpperCAmelCase : Dict =str(bin(__lowerCAmelCase ) )
binary_number += "0" * shift_amount
return binary_number
def lowerCAmelCase_ ( __lowerCAmelCase , __lowerCAmelCase )-> str:
'''simple docstring'''
if number < 0 or shift_amount < 0:
raise ValueError('''both inputs must be positive integers''' )
UpperCAmelCase : Any =str(bin(__lowerCAmelCase ) )[2:]
if shift_amount >= len(__lowerCAmelCase ):
return "0b0"
UpperCAmelCase : Optional[Any] =binary_number[: len(__lowerCAmelCase ) - shift_amount]
return "0b" + shifted_binary_number
def lowerCAmelCase_ ( __lowerCAmelCase , __lowerCAmelCase )-> str:
'''simple docstring'''
if number >= 0: # Get binary representation of positive number
UpperCAmelCase : Optional[Any] ='''0''' + str(bin(__lowerCAmelCase ) ).strip('''-''' )[2:]
else: # Get binary (2's complement) representation of negative number
UpperCAmelCase : int =len(bin(__lowerCAmelCase )[3:] ) # Find 2's complement of number
UpperCAmelCase : Any =bin(abs(__lowerCAmelCase ) - (1 << binary_number_length) )[3:]
UpperCAmelCase : Optional[Any] =(
'''1''' + '''0''' * (binary_number_length - len(__lowerCAmelCase )) + binary_number
)
if shift_amount >= len(__lowerCAmelCase ):
return "0b" + binary_number[0] * len(__lowerCAmelCase )
return (
"0b"
+ binary_number[0] * shift_amount
+ binary_number[: len(__lowerCAmelCase ) - shift_amount]
)
if __name__ == "__main__":
import doctest
doctest.testmod()
| 348 | 1 |
from __future__ import annotations
def lowerCAmelCase_ ( __lowerCAmelCase , __lowerCAmelCase )-> tuple[int, int]:
'''simple docstring'''
if b == 0:
return (1, 0)
((UpperCAmelCase) , (UpperCAmelCase)) : int =extended_euclid(__lowerCAmelCase , a % b )
UpperCAmelCase : Dict =a // b
return (y, x - k * y)
def lowerCAmelCase_ ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )-> int:
'''simple docstring'''
((UpperCAmelCase) , (UpperCAmelCase)) : int =extended_euclid(__lowerCAmelCase , __lowerCAmelCase )
UpperCAmelCase : Dict =na * na
UpperCAmelCase : int =ra * x * na + ra * y * na
return (n % m + m) % m
def lowerCAmelCase_ ( __lowerCAmelCase , __lowerCAmelCase )-> int:
'''simple docstring'''
((UpperCAmelCase) , (UpperCAmelCase)) : Union[str, Any] =extended_euclid(__lowerCAmelCase , __lowerCAmelCase )
if b < 0:
UpperCAmelCase : List[str] =(b % n + n) % n
return b
def lowerCAmelCase_ ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )-> int:
'''simple docstring'''
UpperCAmelCase , UpperCAmelCase : List[str] =invert_modulo(__lowerCAmelCase , __lowerCAmelCase ), invert_modulo(__lowerCAmelCase , __lowerCAmelCase )
UpperCAmelCase : Optional[int] =na * na
UpperCAmelCase : Union[str, Any] =ra * x * na + ra * y * na
return (n % m + m) % m
if __name__ == "__main__":
from doctest import testmod
testmod(name='''chinese_remainder_theorem''', verbose=True)
testmod(name='''chinese_remainder_theorem2''', verbose=True)
testmod(name='''invert_modulo''', verbose=True)
testmod(name='''extended_euclid''', verbose=True)
| 348 | from dataclasses import asdict, dataclass
from typing import Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__snake_case = logging.get_logger(__name__)
# TODO Update this
__snake_case = {
'''facebook/esm-1b''': '''https://huggingface.co/facebook/esm-1b/resolve/main/config.json''',
# See all ESM models at https://huggingface.co/models?filter=esm
}
class __snake_case ( lowerCamelCase__ ):
__lowerCamelCase : Tuple = """esm"""
def __init__( self , snake_case__=None , snake_case__=None , snake_case__=None , snake_case__=768 , snake_case__=12 , snake_case__=12 , snake_case__=3072 , snake_case__=0.1 , snake_case__=0.1 , snake_case__=1026 , snake_case__=0.02 , snake_case__=1e-12 , snake_case__="absolute" , snake_case__=True , snake_case__=None , snake_case__=False , snake_case__=False , snake_case__=None , snake_case__=None , **snake_case__ , ) -> Union[str, Any]:
'''simple docstring'''
super().__init__(pad_token_id=snake_case__ , mask_token_id=snake_case__ , **snake_case__ )
UpperCAmelCase : List[str] =vocab_size
UpperCAmelCase : str =hidden_size
UpperCAmelCase : List[Any] =num_hidden_layers
UpperCAmelCase : Optional[Any] =num_attention_heads
UpperCAmelCase : str =intermediate_size
UpperCAmelCase : Any =hidden_dropout_prob
UpperCAmelCase : int =attention_probs_dropout_prob
UpperCAmelCase : Dict =max_position_embeddings
UpperCAmelCase : List[str] =initializer_range
UpperCAmelCase : Union[str, Any] =layer_norm_eps
UpperCAmelCase : Dict =position_embedding_type
UpperCAmelCase : Optional[Any] =use_cache
UpperCAmelCase : int =emb_layer_norm_before
UpperCAmelCase : List[str] =token_dropout
UpperCAmelCase : Optional[Any] =is_folding_model
if is_folding_model:
if esmfold_config is None:
logger.info('''No esmfold_config supplied for folding model, using default values.''' )
UpperCAmelCase : Optional[Any] =EsmFoldConfig()
elif isinstance(snake_case__ , snake_case__ ):
UpperCAmelCase : Optional[int] =EsmFoldConfig(**snake_case__ )
UpperCAmelCase : Tuple =esmfold_config
if vocab_list is None:
logger.warning('''No vocab_list supplied for folding model, assuming the ESM-2 vocabulary!''' )
UpperCAmelCase : Any =get_default_vocab_list()
else:
UpperCAmelCase : Tuple =vocab_list
else:
UpperCAmelCase : Optional[int] =None
UpperCAmelCase : Union[str, Any] =None
if self.esmfold_config is not None and getattr(self.esmfold_config , '''use_esm_attn_map''' , snake_case__ ):
raise ValueError('''The HuggingFace port of ESMFold does not support use_esm_attn_map at this time!''' )
def UpperCAmelCase__ ( self ) -> List[Any]:
'''simple docstring'''
UpperCAmelCase : Union[str, Any] =super().to_dict()
if isinstance(self.esmfold_config , snake_case__ ):
UpperCAmelCase : str =self.esmfold_config.to_dict()
return output
@dataclass
class __snake_case :
__lowerCamelCase : str = None
__lowerCamelCase : bool = True
__lowerCamelCase : bool = False
__lowerCamelCase : bool = False
__lowerCamelCase : bool = False
__lowerCamelCase : float = 0
__lowerCamelCase : bool = True
__lowerCamelCase : bool = False
__lowerCamelCase : int = 128
__lowerCamelCase : "TrunkConfig" = None
def UpperCAmelCase__ ( self ) -> Union[str, Any]:
'''simple docstring'''
if self.trunk is None:
UpperCAmelCase : str =TrunkConfig()
elif isinstance(self.trunk , snake_case__ ):
UpperCAmelCase : Optional[int] =TrunkConfig(**self.trunk )
def UpperCAmelCase__ ( self ) -> Any:
'''simple docstring'''
UpperCAmelCase : Optional[Any] =asdict(self )
UpperCAmelCase : Any =self.trunk.to_dict()
return output
@dataclass
class __snake_case :
__lowerCamelCase : int = 48
__lowerCamelCase : int = 1024
__lowerCamelCase : int = 128
__lowerCamelCase : int = 32
__lowerCamelCase : int = 32
__lowerCamelCase : int = 32
__lowerCamelCase : float = 0
__lowerCamelCase : float = 0
__lowerCamelCase : bool = False
__lowerCamelCase : int = 4
__lowerCamelCase : Optional[int] = 128
__lowerCamelCase : "StructureModuleConfig" = None
def UpperCAmelCase__ ( self ) -> List[str]:
'''simple docstring'''
if self.structure_module is None:
UpperCAmelCase : Any =StructureModuleConfig()
elif isinstance(self.structure_module , snake_case__ ):
UpperCAmelCase : str =StructureModuleConfig(**self.structure_module )
if self.max_recycles <= 0:
raise ValueError(f'''`max_recycles` should be positive, got {self.max_recycles}.''' )
if self.sequence_state_dim % self.sequence_state_dim != 0:
raise ValueError(
'''`sequence_state_dim` should be a round multiple of `sequence_state_dim`, got'''
f''' {self.sequence_state_dim} and {self.sequence_state_dim}.''' )
if self.pairwise_state_dim % self.pairwise_state_dim != 0:
raise ValueError(
'''`pairwise_state_dim` should be a round multiple of `pairwise_state_dim`, got'''
f''' {self.pairwise_state_dim} and {self.pairwise_state_dim}.''' )
UpperCAmelCase : Optional[int] =self.sequence_state_dim // self.sequence_head_width
UpperCAmelCase : Any =self.pairwise_state_dim // self.pairwise_head_width
if self.sequence_state_dim != sequence_num_heads * self.sequence_head_width:
raise ValueError(
'''`sequence_state_dim` should be equal to `sequence_num_heads * sequence_head_width, got'''
f''' {self.sequence_state_dim} != {sequence_num_heads} * {self.sequence_head_width}.''' )
if self.pairwise_state_dim != pairwise_num_heads * self.pairwise_head_width:
raise ValueError(
'''`pairwise_state_dim` should be equal to `pairwise_num_heads * pairwise_head_width, got'''
f''' {self.pairwise_state_dim} != {pairwise_num_heads} * {self.pairwise_head_width}.''' )
if self.pairwise_state_dim % 2 != 0:
raise ValueError(f'''`pairwise_state_dim` should be even, got {self.pairwise_state_dim}.''' )
if self.dropout >= 0.4:
raise ValueError(f'''`dropout` should not be greater than 0.4, got {self.dropout}.''' )
def UpperCAmelCase__ ( self ) -> List[Any]:
'''simple docstring'''
UpperCAmelCase : Union[str, Any] =asdict(self )
UpperCAmelCase : Tuple =self.structure_module.to_dict()
return output
@dataclass
class __snake_case :
__lowerCamelCase : int = 384
__lowerCamelCase : int = 128
__lowerCamelCase : int = 16
__lowerCamelCase : int = 128
__lowerCamelCase : int = 12
__lowerCamelCase : int = 4
__lowerCamelCase : int = 8
__lowerCamelCase : float = 0.1
__lowerCamelCase : int = 8
__lowerCamelCase : int = 1
__lowerCamelCase : int = 2
__lowerCamelCase : int = 7
__lowerCamelCase : int = 10
__lowerCamelCase : float = 1E-8
__lowerCamelCase : float = 1E5
def UpperCAmelCase__ ( self ) -> Union[str, Any]:
'''simple docstring'''
return asdict(self )
def lowerCAmelCase_ ( )-> Tuple:
'''simple docstring'''
return (
"<cls>",
"<pad>",
"<eos>",
"<unk>",
"L",
"A",
"G",
"V",
"S",
"E",
"R",
"T",
"I",
"D",
"P",
"K",
"Q",
"N",
"F",
"Y",
"M",
"H",
"W",
"C",
"X",
"B",
"U",
"Z",
"O",
".",
"-",
"<null_1>",
"<mask>",
)
| 348 | 1 |
import json
from typing import List, Optional, Tuple
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_bart import BartTokenizer
__snake_case = logging.get_logger(__name__)
__snake_case = {'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', '''tokenizer_file''': '''tokenizer.json'''}
# See all BART models at https://huggingface.co/models?filter=bart
__snake_case = {
'''vocab_file''': {
'''facebook/bart-base''': '''https://huggingface.co/facebook/bart-base/resolve/main/vocab.json''',
'''facebook/bart-large''': '''https://huggingface.co/facebook/bart-large/resolve/main/vocab.json''',
'''facebook/bart-large-mnli''': '''https://huggingface.co/facebook/bart-large-mnli/resolve/main/vocab.json''',
'''facebook/bart-large-cnn''': '''https://huggingface.co/facebook/bart-large-cnn/resolve/main/vocab.json''',
'''facebook/bart-large-xsum''': '''https://huggingface.co/facebook/bart-large-xsum/resolve/main/vocab.json''',
'''yjernite/bart_eli5''': '''https://huggingface.co/yjernite/bart_eli5/resolve/main/vocab.json''',
},
'''merges_file''': {
'''facebook/bart-base''': '''https://huggingface.co/facebook/bart-base/resolve/main/merges.txt''',
'''facebook/bart-large''': '''https://huggingface.co/facebook/bart-large/resolve/main/merges.txt''',
'''facebook/bart-large-mnli''': '''https://huggingface.co/facebook/bart-large-mnli/resolve/main/merges.txt''',
'''facebook/bart-large-cnn''': '''https://huggingface.co/facebook/bart-large-cnn/resolve/main/merges.txt''',
'''facebook/bart-large-xsum''': '''https://huggingface.co/facebook/bart-large-xsum/resolve/main/merges.txt''',
'''yjernite/bart_eli5''': '''https://huggingface.co/yjernite/bart_eli5/resolve/main/merges.txt''',
},
'''tokenizer_file''': {
'''facebook/bart-base''': '''https://huggingface.co/facebook/bart-base/resolve/main/tokenizer.json''',
'''facebook/bart-large''': '''https://huggingface.co/facebook/bart-large/resolve/main/tokenizer.json''',
'''facebook/bart-large-mnli''': '''https://huggingface.co/facebook/bart-large-mnli/resolve/main/tokenizer.json''',
'''facebook/bart-large-cnn''': '''https://huggingface.co/facebook/bart-large-cnn/resolve/main/tokenizer.json''',
'''facebook/bart-large-xsum''': '''https://huggingface.co/facebook/bart-large-xsum/resolve/main/tokenizer.json''',
'''yjernite/bart_eli5''': '''https://huggingface.co/yjernite/bart_eli5/resolve/main/tokenizer.json''',
},
}
__snake_case = {
'''facebook/bart-base''': 10_24,
'''facebook/bart-large''': 10_24,
'''facebook/bart-large-mnli''': 10_24,
'''facebook/bart-large-cnn''': 10_24,
'''facebook/bart-large-xsum''': 10_24,
'''yjernite/bart_eli5''': 10_24,
}
class __snake_case ( lowerCamelCase__ ):
__lowerCamelCase : Optional[int] = VOCAB_FILES_NAMES
__lowerCamelCase : Optional[Any] = PRETRAINED_VOCAB_FILES_MAP
__lowerCamelCase : str = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
__lowerCamelCase : List[Any] = ["""input_ids""", """attention_mask"""]
__lowerCamelCase : Optional[Any] = BartTokenizer
def __init__( self , snake_case__=None , snake_case__=None , snake_case__=None , snake_case__="replace" , snake_case__="<s>" , snake_case__="</s>" , snake_case__="</s>" , snake_case__="<s>" , snake_case__="<unk>" , snake_case__="<pad>" , snake_case__="<mask>" , snake_case__=False , snake_case__=True , **snake_case__ , ) -> Optional[Any]:
'''simple docstring'''
super().__init__(
snake_case__ , snake_case__ , tokenizer_file=snake_case__ , errors=snake_case__ , bos_token=snake_case__ , eos_token=snake_case__ , sep_token=snake_case__ , cls_token=snake_case__ , unk_token=snake_case__ , pad_token=snake_case__ , mask_token=snake_case__ , add_prefix_space=snake_case__ , trim_offsets=snake_case__ , **snake_case__ , )
UpperCAmelCase : Tuple =json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__() )
if pre_tok_state.get('''add_prefix_space''' , snake_case__ ) != add_prefix_space:
UpperCAmelCase : str =getattr(snake_case__ , pre_tok_state.pop('''type''' ) )
UpperCAmelCase : Union[str, Any] =add_prefix_space
UpperCAmelCase : Optional[int] =pre_tok_class(**snake_case__ )
UpperCAmelCase : List[str] =add_prefix_space
# the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
UpperCAmelCase : Optional[Any] ='''post_processor'''
UpperCAmelCase : Any =getattr(self.backend_tokenizer , snake_case__ , snake_case__ )
if tokenizer_component_instance:
UpperCAmelCase : str =json.loads(tokenizer_component_instance.__getstate__() )
# The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
if "sep" in state:
UpperCAmelCase : Union[str, Any] =tuple(state['''sep'''] )
if "cls" in state:
UpperCAmelCase : Any =tuple(state['''cls'''] )
UpperCAmelCase : Dict =False
if state.get('''add_prefix_space''' , snake_case__ ) != add_prefix_space:
UpperCAmelCase : List[Any] =add_prefix_space
UpperCAmelCase : Dict =True
if state.get('''trim_offsets''' , snake_case__ ) != trim_offsets:
UpperCAmelCase : Union[str, Any] =trim_offsets
UpperCAmelCase : int =True
if changes_to_apply:
UpperCAmelCase : Tuple =getattr(snake_case__ , state.pop('''type''' ) )
UpperCAmelCase : Optional[Any] =component_class(**snake_case__ )
setattr(self.backend_tokenizer , snake_case__ , snake_case__ )
@property
def UpperCAmelCase__ ( self ) -> str:
'''simple docstring'''
if self._mask_token is None:
if self.verbose:
logger.error('''Using mask_token, but it is not set yet.''' )
return None
return str(self._mask_token )
@mask_token.setter
def UpperCAmelCase__ ( self , snake_case__ ) -> str:
'''simple docstring'''
UpperCAmelCase : Optional[Any] =AddedToken(snake_case__ , lstrip=snake_case__ , rstrip=snake_case__ ) if isinstance(snake_case__ , snake_case__ ) else value
UpperCAmelCase : str =value
def UpperCAmelCase__ ( self , *snake_case__ , **snake_case__ ) -> BatchEncoding:
'''simple docstring'''
UpperCAmelCase : Any =kwargs.get('''is_split_into_words''' , snake_case__ )
if is_split_into_words and not self.add_prefix_space:
raise ValueError(
f'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
'''to use it with pretokenized inputs.''' )
return super()._batch_encode_plus(*snake_case__ , **snake_case__ )
def UpperCAmelCase__ ( self , *snake_case__ , **snake_case__ ) -> BatchEncoding:
'''simple docstring'''
UpperCAmelCase : Optional[int] =kwargs.get('''is_split_into_words''' , snake_case__ )
if is_split_into_words and not self.add_prefix_space:
raise ValueError(
f'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
'''to use it with pretokenized inputs.''' )
return super()._encode_plus(*snake_case__ , **snake_case__ )
def UpperCAmelCase__ ( self , snake_case__ , snake_case__ = None ) -> Tuple[str]:
'''simple docstring'''
UpperCAmelCase : str =self._tokenizer.model.save(snake_case__ , name=snake_case__ )
return tuple(snake_case__ )
def UpperCAmelCase__ ( self , snake_case__ , snake_case__=None ) -> Tuple:
'''simple docstring'''
UpperCAmelCase : List[str] =[self.bos_token_id] + token_ids_a + [self.eos_token_id]
if token_ids_a is None:
return output
return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]
def UpperCAmelCase__ ( self , snake_case__ , snake_case__ = None ) -> List[int]:
'''simple docstring'''
UpperCAmelCase : str =[self.sep_token_id]
UpperCAmelCase : Dict =[self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]
| 348 | import torch
from diffusers import KDPMaDiscreteScheduler
from diffusers.utils import torch_device
from .test_schedulers import SchedulerCommonTest
class __snake_case ( lowerCamelCase__ ):
__lowerCamelCase : Optional[int] = (KDPMaDiscreteScheduler,)
__lowerCamelCase : List[str] = 10
def UpperCAmelCase__ ( self , **snake_case__ ) -> str:
'''simple docstring'''
UpperCAmelCase : int ={
'''num_train_timesteps''': 1100,
'''beta_start''': 0.0001,
'''beta_end''': 0.02,
'''beta_schedule''': '''linear''',
}
config.update(**snake_case__ )
return config
def UpperCAmelCase__ ( self ) -> Tuple:
'''simple docstring'''
for timesteps in [10, 50, 100, 1000]:
self.check_over_configs(num_train_timesteps=snake_case__ )
def UpperCAmelCase__ ( self ) -> Optional[int]:
'''simple docstring'''
for beta_start, beta_end in zip([0.0_0001, 0.0001, 0.001] , [0.0002, 0.002, 0.02] ):
self.check_over_configs(beta_start=snake_case__ , beta_end=snake_case__ )
def UpperCAmelCase__ ( self ) -> str:
'''simple docstring'''
for schedule in ["linear", "scaled_linear"]:
self.check_over_configs(beta_schedule=snake_case__ )
def UpperCAmelCase__ ( self ) -> Dict:
'''simple docstring'''
for prediction_type in ["epsilon", "v_prediction"]:
self.check_over_configs(prediction_type=snake_case__ )
def UpperCAmelCase__ ( self ) -> str:
'''simple docstring'''
UpperCAmelCase : Optional[Any] =self.scheduler_classes[0]
UpperCAmelCase : Optional[int] =self.get_scheduler_config(prediction_type='''v_prediction''' )
UpperCAmelCase : Optional[Any] =scheduler_class(**snake_case__ )
scheduler.set_timesteps(self.num_inference_steps )
UpperCAmelCase : str =self.dummy_model()
UpperCAmelCase : Optional[Any] =self.dummy_sample_deter * scheduler.init_noise_sigma
UpperCAmelCase : Union[str, Any] =sample.to(snake_case__ )
for i, t in enumerate(scheduler.timesteps ):
UpperCAmelCase : str =scheduler.scale_model_input(snake_case__ , snake_case__ )
UpperCAmelCase : Any =model(snake_case__ , snake_case__ )
UpperCAmelCase : Union[str, Any] =scheduler.step(snake_case__ , snake_case__ , snake_case__ )
UpperCAmelCase : int =output.prev_sample
UpperCAmelCase : Dict =torch.sum(torch.abs(snake_case__ ) )
UpperCAmelCase : Optional[Any] =torch.mean(torch.abs(snake_case__ ) )
if torch_device in ["cpu", "mps"]:
assert abs(result_sum.item() - 4.69_34e-07 ) < 1e-2
assert abs(result_mean.item() - 6.11_12e-10 ) < 1e-3
else:
# CUDA
assert abs(result_sum.item() - 4.6_93_42_86_50_17_09_72e-07 ) < 1e-2
assert abs(result_mean.item() - 0.0002 ) < 1e-3
def UpperCAmelCase__ ( self ) -> int:
'''simple docstring'''
if torch_device == "mps":
return
UpperCAmelCase : Any =self.scheduler_classes[0]
UpperCAmelCase : Optional[int] =self.get_scheduler_config()
UpperCAmelCase : Optional[Any] =scheduler_class(**snake_case__ )
scheduler.set_timesteps(self.num_inference_steps )
UpperCAmelCase : Optional[int] =self.dummy_model()
UpperCAmelCase : Union[str, Any] =self.dummy_sample_deter * scheduler.init_noise_sigma
UpperCAmelCase : str =sample.to(snake_case__ )
for i, t in enumerate(scheduler.timesteps ):
UpperCAmelCase : Dict =scheduler.scale_model_input(snake_case__ , snake_case__ )
UpperCAmelCase : Union[str, Any] =model(snake_case__ , snake_case__ )
UpperCAmelCase : List[str] =scheduler.step(snake_case__ , snake_case__ , snake_case__ )
UpperCAmelCase : Optional[int] =output.prev_sample
UpperCAmelCase : Any =torch.sum(torch.abs(snake_case__ ) )
UpperCAmelCase : Union[str, Any] =torch.mean(torch.abs(snake_case__ ) )
if torch_device in ["cpu", "mps"]:
assert abs(result_sum.item() - 20.4125 ) < 1e-2
assert abs(result_mean.item() - 0.0266 ) < 1e-3
else:
# CUDA
assert abs(result_sum.item() - 20.4125 ) < 1e-2
assert abs(result_mean.item() - 0.0266 ) < 1e-3
def UpperCAmelCase__ ( self ) -> str:
'''simple docstring'''
if torch_device == "mps":
return
UpperCAmelCase : List[Any] =self.scheduler_classes[0]
UpperCAmelCase : Dict =self.get_scheduler_config()
UpperCAmelCase : List[str] =scheduler_class(**snake_case__ )
scheduler.set_timesteps(self.num_inference_steps , device=snake_case__ )
UpperCAmelCase : int =self.dummy_model()
UpperCAmelCase : Tuple =self.dummy_sample_deter.to(snake_case__ ) * scheduler.init_noise_sigma
for t in scheduler.timesteps:
UpperCAmelCase : Optional[Any] =scheduler.scale_model_input(snake_case__ , snake_case__ )
UpperCAmelCase : int =model(snake_case__ , snake_case__ )
UpperCAmelCase : str =scheduler.step(snake_case__ , snake_case__ , snake_case__ )
UpperCAmelCase : List[str] =output.prev_sample
UpperCAmelCase : List[str] =torch.sum(torch.abs(snake_case__ ) )
UpperCAmelCase : Dict =torch.mean(torch.abs(snake_case__ ) )
if str(snake_case__ ).startswith('''cpu''' ):
# The following sum varies between 148 and 156 on mps. Why?
assert abs(result_sum.item() - 20.4125 ) < 1e-2
assert abs(result_mean.item() - 0.0266 ) < 1e-3
else:
# CUDA
assert abs(result_sum.item() - 20.4125 ) < 1e-2
assert abs(result_mean.item() - 0.0266 ) < 1e-3
| 348 | 1 |
from __future__ import annotations
import unittest
from transformers import BlenderbotConfig, BlenderbotTokenizer, is_tf_available
from transformers.testing_utils import require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import TFAutoModelForSeqaSeqLM, TFBlenderbotForConditionalGeneration, TFBlenderbotModel
@require_tf
class __snake_case :
__lowerCamelCase : str = BlenderbotConfig
__lowerCamelCase : Optional[Any] = {}
__lowerCamelCase : Optional[int] = """gelu"""
def __init__( self , snake_case__ , snake_case__=13 , snake_case__=7 , snake_case__=True , snake_case__=False , snake_case__=99 , snake_case__=32 , snake_case__=2 , snake_case__=4 , snake_case__=37 , snake_case__=0.1 , snake_case__=0.1 , snake_case__=20 , snake_case__=2 , snake_case__=1 , snake_case__=0 , ) -> Optional[Any]:
'''simple docstring'''
UpperCAmelCase : Union[str, Any] =parent
UpperCAmelCase : Optional[int] =batch_size
UpperCAmelCase : Dict =seq_length
UpperCAmelCase : Optional[Any] =is_training
UpperCAmelCase : List[str] =use_labels
UpperCAmelCase : List[Any] =vocab_size
UpperCAmelCase : Optional[int] =hidden_size
UpperCAmelCase : Tuple =num_hidden_layers
UpperCAmelCase : Any =num_attention_heads
UpperCAmelCase : Optional[int] =intermediate_size
UpperCAmelCase : str =hidden_dropout_prob
UpperCAmelCase : Optional[int] =attention_probs_dropout_prob
UpperCAmelCase : str =max_position_embeddings
UpperCAmelCase : List[Any] =eos_token_id
UpperCAmelCase : Optional[int] =pad_token_id
UpperCAmelCase : Tuple =bos_token_id
def UpperCAmelCase__ ( self ) -> Optional[Any]:
'''simple docstring'''
UpperCAmelCase : List[Any] =ids_tensor([self.batch_size, self.seq_length - 1] , self.vocab_size )
UpperCAmelCase : List[Any] =tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size ) , 1 )
UpperCAmelCase : Tuple =tf.concat([input_ids, eos_tensor] , axis=1 )
UpperCAmelCase : str =ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
UpperCAmelCase : Optional[Any] =self.config_cls(
vocab_size=self.vocab_size , d_model=self.hidden_size , encoder_layers=self.num_hidden_layers , decoder_layers=self.num_hidden_layers , encoder_attention_heads=self.num_attention_heads , decoder_attention_heads=self.num_attention_heads , encoder_ffn_dim=self.intermediate_size , decoder_ffn_dim=self.intermediate_size , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , eos_token_ids=[2] , bos_token_id=self.bos_token_id , pad_token_id=self.pad_token_id , decoder_start_token_id=self.pad_token_id , **self.config_updates , )
UpperCAmelCase : List[str] =prepare_blenderbot_inputs_dict(snake_case__ , snake_case__ , snake_case__ )
return config, inputs_dict
def UpperCAmelCase__ ( self , snake_case__ , snake_case__ ) -> int:
'''simple docstring'''
UpperCAmelCase : Union[str, Any] =TFBlenderbotModel(config=snake_case__ ).get_decoder()
UpperCAmelCase : Any =inputs_dict['''input_ids''']
UpperCAmelCase : str =input_ids[:1, :]
UpperCAmelCase : Tuple =inputs_dict['''attention_mask'''][:1, :]
UpperCAmelCase : Tuple =inputs_dict['''head_mask''']
UpperCAmelCase : List[Any] =1
# first forward pass
UpperCAmelCase : List[str] =model(snake_case__ , attention_mask=snake_case__ , head_mask=snake_case__ , use_cache=snake_case__ )
UpperCAmelCase , UpperCAmelCase : str =outputs.to_tuple()
# create hypothetical next token and extent to next_input_ids
UpperCAmelCase : Union[str, Any] =ids_tensor((self.batch_size, 3) , config.vocab_size )
UpperCAmelCase : List[Any] =tf.cast(ids_tensor((self.batch_size, 3) , 2 ) , tf.inta )
# append to next input_ids and
UpperCAmelCase : Tuple =tf.concat([input_ids, next_tokens] , axis=-1 )
UpperCAmelCase : int =tf.concat([attention_mask, next_attn_mask] , axis=-1 )
UpperCAmelCase : Optional[int] =model(snake_case__ , attention_mask=snake_case__ )[0]
UpperCAmelCase : str =model(snake_case__ , attention_mask=snake_case__ , past_key_values=snake_case__ )[0]
self.parent.assertEqual(next_tokens.shape[1] , output_from_past.shape[1] )
# select random slice
UpperCAmelCase : List[Any] =int(ids_tensor((1,) , output_from_past.shape[-1] ) )
UpperCAmelCase : List[Any] =output_from_no_past[:, -3:, random_slice_idx]
UpperCAmelCase : Dict =output_from_past[:, :, random_slice_idx]
# test that outputs are equal for slice
tf.debugging.assert_near(snake_case__ , snake_case__ , rtol=1e-3 )
def lowerCAmelCase_ ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase=None , __lowerCAmelCase=None , __lowerCAmelCase=None , __lowerCAmelCase=None , __lowerCAmelCase=None , )-> str:
'''simple docstring'''
if attention_mask is None:
UpperCAmelCase : int =tf.cast(tf.math.not_equal(__lowerCAmelCase , config.pad_token_id ) , tf.inta )
if decoder_attention_mask is None:
UpperCAmelCase : Tuple =tf.concat(
[
tf.ones(decoder_input_ids[:, :1].shape , dtype=tf.inta ),
tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:] , config.pad_token_id ) , tf.inta ),
] , axis=-1 , )
if head_mask is None:
UpperCAmelCase : str =tf.ones((config.encoder_layers, config.encoder_attention_heads) )
if decoder_head_mask is None:
UpperCAmelCase : Union[str, Any] =tf.ones((config.decoder_layers, config.decoder_attention_heads) )
if cross_attn_head_mask is None:
UpperCAmelCase : int =tf.ones((config.decoder_layers, config.decoder_attention_heads) )
return {
"input_ids": input_ids,
"decoder_input_ids": decoder_input_ids,
"attention_mask": attention_mask,
"decoder_attention_mask": decoder_attention_mask,
"head_mask": head_mask,
"decoder_head_mask": decoder_head_mask,
"cross_attn_head_mask": cross_attn_head_mask,
}
@require_tf
class __snake_case ( lowerCamelCase__ , lowerCamelCase__ , unittest.TestCase ):
__lowerCamelCase : List[str] = (TFBlenderbotForConditionalGeneration, TFBlenderbotModel) if is_tf_available() else ()
__lowerCamelCase : Dict = (TFBlenderbotForConditionalGeneration,) if is_tf_available() else ()
__lowerCamelCase : Dict = (
{
"""conversational""": TFBlenderbotForConditionalGeneration,
"""feature-extraction""": TFBlenderbotModel,
"""summarization""": TFBlenderbotForConditionalGeneration,
"""text2text-generation""": TFBlenderbotForConditionalGeneration,
"""translation""": TFBlenderbotForConditionalGeneration,
}
if is_tf_available()
else {}
)
__lowerCamelCase : Union[str, Any] = True
__lowerCamelCase : Union[str, Any] = False
__lowerCamelCase : Union[str, Any] = False
def UpperCAmelCase__ ( self ) -> Dict:
'''simple docstring'''
UpperCAmelCase : List[str] =TFBlenderbotModelTester(self )
UpperCAmelCase : List[Any] =ConfigTester(self , config_class=snake_case__ )
def UpperCAmelCase__ ( self ) -> Optional[Any]:
'''simple docstring'''
self.config_tester.run_common_tests()
def UpperCAmelCase__ ( self ) -> List[Any]:
'''simple docstring'''
UpperCAmelCase : int =self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.check_decoder_model_past_large_inputs(*snake_case__ )
@require_tokenizers
@require_tf
class __snake_case ( unittest.TestCase ):
__lowerCamelCase : List[str] = ["""My friends are cool but they eat too many carbs."""]
__lowerCamelCase : Dict = """facebook/blenderbot-400M-distill"""
@cached_property
def UpperCAmelCase__ ( self ) -> int:
'''simple docstring'''
return BlenderbotTokenizer.from_pretrained(self.model_name )
@cached_property
def UpperCAmelCase__ ( self ) -> int:
'''simple docstring'''
UpperCAmelCase : int =TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name )
return model
@slow
def UpperCAmelCase__ ( self ) -> Any:
'''simple docstring'''
UpperCAmelCase : Optional[int] =self.tokenizer(self.src_text , return_tensors='''tf''' )
UpperCAmelCase : Optional[int] =self.model.generate(
model_inputs.input_ids , )
UpperCAmelCase : str =self.tokenizer.batch_decode(generated_ids.numpy() , skip_special_tokens=snake_case__ )[0]
assert (
generated_words
== " That's unfortunate. Are they trying to lose weight or are they just trying to be healthier?"
)
| 348 | import unittest
from transformers import is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, require_torch, slow
if is_flax_available():
import optax
from flax.training.common_utils import onehot
from transformers import AutoTokenizer, FlaxMTaForConditionalGeneration
from transformers.models.ta.modeling_flax_ta import shift_tokens_right
@require_torch
@require_sentencepiece
@require_tokenizers
@require_flax
class __snake_case ( unittest.TestCase ):
@slow
def UpperCAmelCase__ ( self ) -> Union[str, Any]:
'''simple docstring'''
UpperCAmelCase : Any =FlaxMTaForConditionalGeneration.from_pretrained('''google/mt5-small''' )
UpperCAmelCase : Tuple =AutoTokenizer.from_pretrained('''google/mt5-small''' )
UpperCAmelCase : List[str] =tokenizer('''Hello there''' , return_tensors='''np''' ).input_ids
UpperCAmelCase : List[Any] =tokenizer('''Hi I am''' , return_tensors='''np''' ).input_ids
UpperCAmelCase : Union[str, Any] =shift_tokens_right(snake_case__ , model.config.pad_token_id , model.config.decoder_start_token_id )
UpperCAmelCase : List[str] =model(snake_case__ , decoder_input_ids=snake_case__ ).logits
UpperCAmelCase : Any =optax.softmax_cross_entropy(snake_case__ , onehot(snake_case__ , logits.shape[-1] ) ).mean()
UpperCAmelCase : Union[str, Any] =-(labels.shape[-1] * loss.item())
UpperCAmelCase : List[str] =-84.9127
self.assertTrue(abs(mtf_score - EXPECTED_SCORE ) < 1e-4 )
| 348 | 1 |
import os
from typing import Dict, List, Tuple, TypeVar, Union
__snake_case = TypeVar('''T''')
__snake_case = Union[List[T], Tuple[T, ...]]
__snake_case = Union[T, List[T], Dict[str, T]]
__snake_case = Union[str, bytes, os.PathLike]
| 348 | import unittest
import numpy as np
from diffusers import LMSDiscreteScheduler, OnnxStableDiffusionInpaintPipeline
from diffusers.utils.testing_utils import (
is_onnx_available,
load_image,
nightly,
require_onnxruntime,
require_torch_gpu,
)
from ..test_pipelines_onnx_common import OnnxPipelineTesterMixin
if is_onnx_available():
import onnxruntime as ort
class __snake_case ( lowerCamelCase__ , unittest.TestCase ):
# FIXME: add fast tests
pass
@nightly
@require_onnxruntime
@require_torch_gpu
class __snake_case ( unittest.TestCase ):
@property
def UpperCAmelCase__ ( self ) -> List[str]:
'''simple docstring'''
return (
"CUDAExecutionProvider",
{
"gpu_mem_limit": "15000000000", # 15GB
"arena_extend_strategy": "kSameAsRequested",
},
)
@property
def UpperCAmelCase__ ( self ) -> int:
'''simple docstring'''
UpperCAmelCase : List[Any] =ort.SessionOptions()
UpperCAmelCase : Optional[int] =False
return options
def UpperCAmelCase__ ( self ) -> Dict:
'''simple docstring'''
UpperCAmelCase : int =load_image(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
'''/in_paint/overture-creations-5sI6fQgYIuo.png''' )
UpperCAmelCase : Optional[Any] =load_image(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
'''/in_paint/overture-creations-5sI6fQgYIuo_mask.png''' )
UpperCAmelCase : List[str] =OnnxStableDiffusionInpaintPipeline.from_pretrained(
'''runwayml/stable-diffusion-inpainting''' , revision='''onnx''' , safety_checker=snake_case__ , feature_extractor=snake_case__ , provider=self.gpu_provider , sess_options=self.gpu_options , )
pipe.set_progress_bar_config(disable=snake_case__ )
UpperCAmelCase : Dict ='''A red cat sitting on a park bench'''
UpperCAmelCase : int =np.random.RandomState(0 )
UpperCAmelCase : Any =pipe(
prompt=snake_case__ , image=snake_case__ , mask_image=snake_case__ , guidance_scale=7.5 , num_inference_steps=10 , generator=snake_case__ , output_type='''np''' , )
UpperCAmelCase : Dict =output.images
UpperCAmelCase : Optional[int] =images[0, 255:258, 255:258, -1]
assert images.shape == (1, 512, 512, 3)
UpperCAmelCase : Tuple =np.array([0.2514, 0.3007, 0.3517, 0.1790, 0.2382, 0.3167, 0.1944, 0.2273, 0.2464] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-3
def UpperCAmelCase__ ( self ) -> Optional[Any]:
'''simple docstring'''
UpperCAmelCase : List[str] =load_image(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
'''/in_paint/overture-creations-5sI6fQgYIuo.png''' )
UpperCAmelCase : Tuple =load_image(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
'''/in_paint/overture-creations-5sI6fQgYIuo_mask.png''' )
UpperCAmelCase : List[str] =LMSDiscreteScheduler.from_pretrained(
'''runwayml/stable-diffusion-inpainting''' , subfolder='''scheduler''' , revision='''onnx''' )
UpperCAmelCase : int =OnnxStableDiffusionInpaintPipeline.from_pretrained(
'''runwayml/stable-diffusion-inpainting''' , revision='''onnx''' , scheduler=snake_case__ , safety_checker=snake_case__ , feature_extractor=snake_case__ , provider=self.gpu_provider , sess_options=self.gpu_options , )
pipe.set_progress_bar_config(disable=snake_case__ )
UpperCAmelCase : Union[str, Any] ='''A red cat sitting on a park bench'''
UpperCAmelCase : int =np.random.RandomState(0 )
UpperCAmelCase : str =pipe(
prompt=snake_case__ , image=snake_case__ , mask_image=snake_case__ , guidance_scale=7.5 , num_inference_steps=20 , generator=snake_case__ , output_type='''np''' , )
UpperCAmelCase : Dict =output.images
UpperCAmelCase : int =images[0, 255:258, 255:258, -1]
assert images.shape == (1, 512, 512, 3)
UpperCAmelCase : Union[str, Any] =np.array([0.0086, 0.0077, 0.0083, 0.0093, 0.0107, 0.0139, 0.0094, 0.0097, 0.0125] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-3
| 348 | 1 |
import re
import string
from collections import Counter
import sacrebleu
import sacremoses
from packaging import version
import datasets
__snake_case = '''
@inproceedings{xu-etal-2016-optimizing,
title = {Optimizing Statistical Machine Translation for Text Simplification},
authors={Xu, Wei and Napoles, Courtney and Pavlick, Ellie and Chen, Quanze and Callison-Burch, Chris},
journal = {Transactions of the Association for Computational Linguistics},
volume = {4},
year={2016},
url = {https://www.aclweb.org/anthology/Q16-1029},
pages = {401--415
},
@inproceedings{post-2018-call,
title = "A Call for Clarity in Reporting {BLEU} Scores",
author = "Post, Matt",
booktitle = "Proceedings of the Third Conference on Machine Translation: Research Papers",
month = oct,
year = "2018",
address = "Belgium, Brussels",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/W18-6319",
pages = "186--191",
}
'''
__snake_case = '''\
WIKI_SPLIT is the combination of three metrics SARI, EXACT and SACREBLEU
It can be used to evaluate the quality of machine-generated texts.
'''
__snake_case = '''
Calculates sari score (between 0 and 100) given a list of source and predicted
sentences, and a list of lists of reference sentences. It also computes the BLEU score as well as the exact match score.
Args:
sources: list of source sentences where each sentence should be a string.
predictions: list of predicted sentences where each sentence should be a string.
references: list of lists of reference sentences where each sentence should be a string.
Returns:
sari: sari score
sacrebleu: sacrebleu score
exact: exact score
Examples:
>>> sources=["About 95 species are currently accepted ."]
>>> predictions=["About 95 you now get in ."]
>>> references=[["About 95 species are currently known ."]]
>>> wiki_split = datasets.load_metric("wiki_split")
>>> results = wiki_split.compute(sources=sources, predictions=predictions, references=references)
>>> print(results)
{\'sari\': 21.805555555555557, \'sacrebleu\': 14.535768424205482, \'exact\': 0.0}
'''
def lowerCAmelCase_ ( __lowerCAmelCase )-> List[Any]:
'''simple docstring'''
def remove_articles(__lowerCAmelCase ):
UpperCAmelCase : List[Any] =re.compile(R'''\b(a|an|the)\b''' , re.UNICODE )
return re.sub(__lowerCAmelCase , ''' ''' , __lowerCAmelCase )
def white_space_fix(__lowerCAmelCase ):
return " ".join(text.split() )
def remove_punc(__lowerCAmelCase ):
UpperCAmelCase : int =set(string.punctuation )
return "".join(ch for ch in text if ch not in exclude )
def lower(__lowerCAmelCase ):
return text.lower()
return white_space_fix(remove_articles(remove_punc(lower(__lowerCAmelCase ) ) ) )
def lowerCAmelCase_ ( __lowerCAmelCase , __lowerCAmelCase )-> Any:
'''simple docstring'''
return int(normalize_answer(__lowerCAmelCase ) == normalize_answer(__lowerCAmelCase ) )
def lowerCAmelCase_ ( __lowerCAmelCase , __lowerCAmelCase )-> Any:
'''simple docstring'''
UpperCAmelCase : Dict =[any(compute_exact(__lowerCAmelCase , __lowerCAmelCase ) for ref in refs ) for pred, refs in zip(__lowerCAmelCase , __lowerCAmelCase )]
return (sum(__lowerCAmelCase ) / len(__lowerCAmelCase )) * 1_00
def lowerCAmelCase_ ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )-> Dict:
'''simple docstring'''
UpperCAmelCase : Optional[int] =[rgram for rgrams in rgramslist for rgram in rgrams]
UpperCAmelCase : List[str] =Counter(__lowerCAmelCase )
UpperCAmelCase : Tuple =Counter(__lowerCAmelCase )
UpperCAmelCase : List[Any] =Counter()
for sgram, scount in sgramcounter.items():
UpperCAmelCase : Dict =scount * numref
UpperCAmelCase : int =Counter(__lowerCAmelCase )
UpperCAmelCase : int =Counter()
for cgram, ccount in cgramcounter.items():
UpperCAmelCase : List[str] =ccount * numref
# KEEP
UpperCAmelCase : Optional[int] =sgramcounter_rep & cgramcounter_rep
UpperCAmelCase : Tuple =keepgramcounter_rep & rgramcounter
UpperCAmelCase : Union[str, Any] =sgramcounter_rep & rgramcounter
UpperCAmelCase : Dict =0
UpperCAmelCase : Dict =0
for keepgram in keepgramcountergood_rep:
keeptmpscorea += keepgramcountergood_rep[keepgram] / keepgramcounter_rep[keepgram]
# Fix an alleged bug [2] in the keep score computation.
# keeptmpscore2 += keepgramcountergood_rep[keepgram] / keepgramcounterall_rep[keepgram]
keeptmpscorea += keepgramcountergood_rep[keepgram]
# Define 0/0=1 instead of 0 to give higher scores for predictions that match
# a target exactly.
UpperCAmelCase : int =1
UpperCAmelCase : List[str] =1
if len(__lowerCAmelCase ) > 0:
UpperCAmelCase : str =keeptmpscorea / len(__lowerCAmelCase )
if len(__lowerCAmelCase ) > 0:
# Fix an alleged bug [2] in the keep score computation.
# keepscore_recall = keeptmpscore2 / len(keepgramcounterall_rep)
UpperCAmelCase : str =keeptmpscorea / sum(keepgramcounterall_rep.values() )
UpperCAmelCase : Optional[int] =0
if keepscore_precision > 0 or keepscore_recall > 0:
UpperCAmelCase : Union[str, Any] =2 * keepscore_precision * keepscore_recall / (keepscore_precision + keepscore_recall)
# DELETION
UpperCAmelCase : str =sgramcounter_rep - cgramcounter_rep
UpperCAmelCase : Optional[int] =delgramcounter_rep - rgramcounter
UpperCAmelCase : List[Any] =sgramcounter_rep - rgramcounter
UpperCAmelCase : Any =0
UpperCAmelCase : List[str] =0
for delgram in delgramcountergood_rep:
deltmpscorea += delgramcountergood_rep[delgram] / delgramcounter_rep[delgram]
deltmpscorea += delgramcountergood_rep[delgram] / delgramcounterall_rep[delgram]
# Define 0/0=1 instead of 0 to give higher scores for predictions that match
# a target exactly.
UpperCAmelCase : Optional[int] =1
if len(__lowerCAmelCase ) > 0:
UpperCAmelCase : Optional[Any] =deltmpscorea / len(__lowerCAmelCase )
# ADDITION
UpperCAmelCase : Union[str, Any] =set(__lowerCAmelCase ) - set(__lowerCAmelCase )
UpperCAmelCase : Optional[int] =set(__lowerCAmelCase ) & set(__lowerCAmelCase )
UpperCAmelCase : Optional[int] =set(__lowerCAmelCase ) - set(__lowerCAmelCase )
UpperCAmelCase : List[str] =0
for addgram in addgramcountergood:
addtmpscore += 1
# Define 0/0=1 instead of 0 to give higher scores for predictions that match
# a target exactly.
UpperCAmelCase : List[Any] =1
UpperCAmelCase : List[str] =1
if len(__lowerCAmelCase ) > 0:
UpperCAmelCase : str =addtmpscore / len(__lowerCAmelCase )
if len(__lowerCAmelCase ) > 0:
UpperCAmelCase : str =addtmpscore / len(__lowerCAmelCase )
UpperCAmelCase : Union[str, Any] =0
if addscore_precision > 0 or addscore_recall > 0:
UpperCAmelCase : List[Any] =2 * addscore_precision * addscore_recall / (addscore_precision + addscore_recall)
return (keepscore, delscore_precision, addscore)
def lowerCAmelCase_ ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )-> str:
'''simple docstring'''
UpperCAmelCase : str =len(__lowerCAmelCase )
UpperCAmelCase : int =ssent.split(''' ''' )
UpperCAmelCase : int =csent.split(''' ''' )
UpperCAmelCase : Dict =[]
UpperCAmelCase : Tuple =[]
UpperCAmelCase : Tuple =[]
UpperCAmelCase : Any =[]
UpperCAmelCase : Dict =[]
UpperCAmelCase : List[Any] =[]
UpperCAmelCase : List[str] =[]
UpperCAmelCase : List[Any] =[]
UpperCAmelCase : Tuple =[]
UpperCAmelCase : Tuple =[]
for rsent in rsents:
UpperCAmelCase : Tuple =rsent.split(''' ''' )
UpperCAmelCase : List[str] =[]
UpperCAmelCase : Optional[Any] =[]
UpperCAmelCase : Union[str, Any] =[]
ragramslist.append(__lowerCAmelCase )
for i in range(0 , len(__lowerCAmelCase ) - 1 ):
if i < len(__lowerCAmelCase ) - 1:
UpperCAmelCase : Any =ragrams[i] + ''' ''' + ragrams[i + 1]
ragrams.append(__lowerCAmelCase )
if i < len(__lowerCAmelCase ) - 2:
UpperCAmelCase : Optional[int] =ragrams[i] + ''' ''' + ragrams[i + 1] + ''' ''' + ragrams[i + 2]
ragrams.append(__lowerCAmelCase )
if i < len(__lowerCAmelCase ) - 3:
UpperCAmelCase : Union[str, Any] =ragrams[i] + ''' ''' + ragrams[i + 1] + ''' ''' + ragrams[i + 2] + ''' ''' + ragrams[i + 3]
ragrams.append(__lowerCAmelCase )
ragramslist.append(__lowerCAmelCase )
ragramslist.append(__lowerCAmelCase )
ragramslist.append(__lowerCAmelCase )
for i in range(0 , len(__lowerCAmelCase ) - 1 ):
if i < len(__lowerCAmelCase ) - 1:
UpperCAmelCase : int =sagrams[i] + ''' ''' + sagrams[i + 1]
sagrams.append(__lowerCAmelCase )
if i < len(__lowerCAmelCase ) - 2:
UpperCAmelCase : Optional[int] =sagrams[i] + ''' ''' + sagrams[i + 1] + ''' ''' + sagrams[i + 2]
sagrams.append(__lowerCAmelCase )
if i < len(__lowerCAmelCase ) - 3:
UpperCAmelCase : Any =sagrams[i] + ''' ''' + sagrams[i + 1] + ''' ''' + sagrams[i + 2] + ''' ''' + sagrams[i + 3]
sagrams.append(__lowerCAmelCase )
for i in range(0 , len(__lowerCAmelCase ) - 1 ):
if i < len(__lowerCAmelCase ) - 1:
UpperCAmelCase : Any =cagrams[i] + ''' ''' + cagrams[i + 1]
cagrams.append(__lowerCAmelCase )
if i < len(__lowerCAmelCase ) - 2:
UpperCAmelCase : str =cagrams[i] + ''' ''' + cagrams[i + 1] + ''' ''' + cagrams[i + 2]
cagrams.append(__lowerCAmelCase )
if i < len(__lowerCAmelCase ) - 3:
UpperCAmelCase : List[Any] =cagrams[i] + ''' ''' + cagrams[i + 1] + ''' ''' + cagrams[i + 2] + ''' ''' + cagrams[i + 3]
cagrams.append(__lowerCAmelCase )
((UpperCAmelCase) , (UpperCAmelCase) , (UpperCAmelCase)) : Optional[Any] =SARIngram(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
((UpperCAmelCase) , (UpperCAmelCase) , (UpperCAmelCase)) : str =SARIngram(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
((UpperCAmelCase) , (UpperCAmelCase) , (UpperCAmelCase)) : Dict =SARIngram(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
((UpperCAmelCase) , (UpperCAmelCase) , (UpperCAmelCase)) : Union[str, Any] =SARIngram(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
UpperCAmelCase : Dict =sum([keepascore, keepascore, keepascore, keepascore] ) / 4
UpperCAmelCase : Union[str, Any] =sum([delascore, delascore, delascore, delascore] ) / 4
UpperCAmelCase : List[str] =sum([addascore, addascore, addascore, addascore] ) / 4
UpperCAmelCase : Any =(avgkeepscore + avgdelscore + avgaddscore) / 3
return finalscore
def lowerCAmelCase_ ( __lowerCAmelCase , __lowerCAmelCase = True , __lowerCAmelCase = "13a" , __lowerCAmelCase = True )-> Optional[int]:
'''simple docstring'''
if lowercase:
UpperCAmelCase : Optional[int] =sentence.lower()
if tokenizer in ["13a", "intl"]:
if version.parse(sacrebleu.__version__ ).major >= 2:
UpperCAmelCase : Union[str, Any] =sacrebleu.metrics.bleu._get_tokenizer(__lowerCAmelCase )()(__lowerCAmelCase )
else:
UpperCAmelCase : Union[str, Any] =sacrebleu.TOKENIZERS[tokenizer]()(__lowerCAmelCase )
elif tokenizer == "moses":
UpperCAmelCase : Optional[Any] =sacremoses.MosesTokenizer().tokenize(__lowerCAmelCase , return_str=__lowerCAmelCase , escape=__lowerCAmelCase )
elif tokenizer == "penn":
UpperCAmelCase : Union[str, Any] =sacremoses.MosesTokenizer().penn_tokenize(__lowerCAmelCase , return_str=__lowerCAmelCase )
else:
UpperCAmelCase : Union[str, Any] =sentence
if not return_str:
UpperCAmelCase : Optional[int] =normalized_sent.split()
return normalized_sent
def lowerCAmelCase_ ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )-> Union[str, Any]:
'''simple docstring'''
if not (len(__lowerCAmelCase ) == len(__lowerCAmelCase ) == len(__lowerCAmelCase )):
raise ValueError('''Sources length must match predictions and references lengths.''' )
UpperCAmelCase : int =0
for src, pred, refs in zip(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ):
sari_score += SARIsent(normalize(__lowerCAmelCase ) , normalize(__lowerCAmelCase ) , [normalize(__lowerCAmelCase ) for sent in refs] )
UpperCAmelCase : Optional[int] =sari_score / len(__lowerCAmelCase )
return 1_00 * sari_score
def lowerCAmelCase_ ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase="exp" , __lowerCAmelCase=None , __lowerCAmelCase=False , __lowerCAmelCase=False , __lowerCAmelCase=False , )-> int:
'''simple docstring'''
UpperCAmelCase : Any =len(references[0] )
if any(len(__lowerCAmelCase ) != references_per_prediction for refs in references ):
raise ValueError('''Sacrebleu requires the same number of references for each prediction''' )
UpperCAmelCase : Any =[[refs[i] for refs in references] for i in range(__lowerCAmelCase )]
UpperCAmelCase : int =sacrebleu.corpus_bleu(
__lowerCAmelCase , __lowerCAmelCase , smooth_method=__lowerCAmelCase , smooth_value=__lowerCAmelCase , force=__lowerCAmelCase , lowercase=__lowerCAmelCase , use_effective_order=__lowerCAmelCase , )
return output.score
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class __snake_case ( datasets.Metric ):
def UpperCAmelCase__ ( self ) -> str:
'''simple docstring'''
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
'''predictions''': datasets.Value('''string''' , id='''sequence''' ),
'''references''': datasets.Sequence(datasets.Value('''string''' , id='''sequence''' ) , id='''references''' ),
} ) , codebase_urls=[
'''https://github.com/huggingface/transformers/blob/master/src/transformers/data/metrics/squad_metrics.py''',
'''https://github.com/cocoxu/simplification/blob/master/SARI.py''',
'''https://github.com/tensorflow/tensor2tensor/blob/master/tensor2tensor/utils/sari_hook.py''',
'''https://github.com/mjpost/sacreBLEU''',
] , reference_urls=[
'''https://www.aclweb.org/anthology/Q16-1029.pdf''',
'''https://github.com/mjpost/sacreBLEU''',
'''https://en.wikipedia.org/wiki/BLEU''',
'''https://towardsdatascience.com/evaluating-text-output-in-nlp-bleu-at-your-own-risk-e8609665a213''',
] , )
def UpperCAmelCase__ ( self , snake_case__ , snake_case__ , snake_case__ ) -> str:
'''simple docstring'''
UpperCAmelCase : str ={}
result.update({'''sari''': compute_sari(sources=snake_case__ , predictions=snake_case__ , references=snake_case__ )} )
result.update({'''sacrebleu''': compute_sacrebleu(predictions=snake_case__ , references=snake_case__ )} )
result.update({'''exact''': compute_em(predictions=snake_case__ , references=snake_case__ )} )
return result
| 348 | from unittest import TestCase
from datasets import Dataset
from minhash_deduplication import deduplicate_dataset, make_duplicate_clusters
def lowerCAmelCase_ ( )-> int:
'''simple docstring'''
UpperCAmelCase : str ={
'''repo_name''': ['''test_repo1''', '''test_repo2''', '''test_repo3'''],
'''path''': ['''test_1.py''', '''test_2.py''', '''unit_test.py'''],
'''content''': ['''a ''' * 20, '''a ''' * 30, '''b ''' * 7],
}
UpperCAmelCase : Union[str, Any] =Dataset.from_dict(__lowerCAmelCase )
return dataset
class __snake_case ( lowerCamelCase__ ):
def UpperCAmelCase__ ( self ) -> Tuple:
'''simple docstring'''
UpperCAmelCase : List[str] =get_dataset()
UpperCAmelCase : Optional[int] =make_duplicate_clusters(snake_case__ , 0.85 )
self.assertEqual(len(duplicate_clusters[0] ) , 2 )
def UpperCAmelCase__ ( self ) -> Tuple:
'''simple docstring'''
UpperCAmelCase : str =get_dataset()
UpperCAmelCase , UpperCAmelCase : Tuple =deduplicate_dataset(snake_case__ )
self.assertEqual(len(snake_case__ ) , 2 )
print(snake_case__ )
self.assertEqual(duplicate_clusters[0][0]['''copies'''] , 2 )
self.assertEqual(duplicate_clusters[0][0]['''is_extreme'''] , snake_case__ )
| 348 | 1 |
from typing import List, Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__snake_case = logging.get_logger(__name__)
__snake_case = {
'''huggingface/autoformer-tourism-monthly''': '''https://huggingface.co/huggingface/autoformer-tourism-monthly/resolve/main/config.json''',
}
class __snake_case ( lowerCamelCase__ ):
__lowerCamelCase : Dict = """autoformer"""
__lowerCamelCase : List[Any] = {
"""hidden_size""": """d_model""",
"""num_attention_heads""": """encoder_attention_heads""",
"""num_hidden_layers""": """encoder_layers""",
}
def __init__( self , snake_case__ = None , snake_case__ = None , snake_case__ = "student_t" , snake_case__ = "nll" , snake_case__ = 1 , snake_case__ = [1, 2, 3, 4, 5, 6, 7] , snake_case__ = True , snake_case__ = 0 , snake_case__ = 0 , snake_case__ = 0 , snake_case__ = 0 , snake_case__ = None , snake_case__ = None , snake_case__ = 64 , snake_case__ = 2 , snake_case__ = 2 , snake_case__ = 2 , snake_case__ = 2 , snake_case__ = 32 , snake_case__ = 32 , snake_case__ = "gelu" , snake_case__ = 0.1 , snake_case__ = 0.1 , snake_case__ = 0.1 , snake_case__ = 0.1 , snake_case__ = 0.1 , snake_case__ = 100 , snake_case__ = 0.02 , snake_case__ = True , snake_case__=True , snake_case__ = 10 , snake_case__ = 25 , snake_case__ = 3 , **snake_case__ , ) -> Any:
'''simple docstring'''
UpperCAmelCase : Dict =prediction_length
UpperCAmelCase : Tuple =context_length if context_length is not None else prediction_length
UpperCAmelCase : int =distribution_output
UpperCAmelCase : Any =loss
UpperCAmelCase : List[Any] =input_size
UpperCAmelCase : str =num_time_features
UpperCAmelCase : int =lags_sequence
UpperCAmelCase : str =scaling
UpperCAmelCase : Union[str, Any] =num_dynamic_real_features
UpperCAmelCase : List[str] =num_static_real_features
UpperCAmelCase : Union[str, Any] =num_static_categorical_features
if cardinality is not None and num_static_categorical_features > 0:
if len(snake_case__ ) != num_static_categorical_features:
raise ValueError(
'''The cardinality should be a list of the same length as `num_static_categorical_features`''' )
UpperCAmelCase : int =cardinality
else:
UpperCAmelCase : Tuple =[0]
if embedding_dimension is not None and num_static_categorical_features > 0:
if len(snake_case__ ) != num_static_categorical_features:
raise ValueError(
'''The embedding dimension should be a list of the same length as `num_static_categorical_features`''' )
UpperCAmelCase : Any =embedding_dimension
else:
UpperCAmelCase : Any =[min(50 , (cat + 1) // 2 ) for cat in self.cardinality]
UpperCAmelCase : List[Any] =num_parallel_samples
# Transformer architecture configuration
UpperCAmelCase : str =input_size * len(self.lags_sequence ) + self._number_of_features
UpperCAmelCase : Any =d_model
UpperCAmelCase : str =encoder_attention_heads
UpperCAmelCase : List[str] =decoder_attention_heads
UpperCAmelCase : int =encoder_ffn_dim
UpperCAmelCase : int =decoder_ffn_dim
UpperCAmelCase : Tuple =encoder_layers
UpperCAmelCase : Optional[Any] =decoder_layers
UpperCAmelCase : List[str] =dropout
UpperCAmelCase : Optional[int] =attention_dropout
UpperCAmelCase : Union[str, Any] =activation_dropout
UpperCAmelCase : int =encoder_layerdrop
UpperCAmelCase : Any =decoder_layerdrop
UpperCAmelCase : Union[str, Any] =activation_function
UpperCAmelCase : Dict =init_std
UpperCAmelCase : Any =use_cache
# Autoformer
UpperCAmelCase : Tuple =label_length
UpperCAmelCase : Union[str, Any] =moving_average
UpperCAmelCase : int =autocorrelation_factor
super().__init__(is_encoder_decoder=snake_case__ , **snake_case__ )
@property
def UpperCAmelCase__ ( self ) -> int:
'''simple docstring'''
return (
sum(self.embedding_dimension )
+ self.num_dynamic_real_features
+ self.num_time_features
+ self.num_static_real_features
+ self.input_size * 2 # the log1p(abs(loc)) and log(scale) features
)
| 348 | from typing import Callable, List, Optional, Tuple, Union
import torch
from transformers import CLIPTextModel, CLIPTokenizer
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin, TransformeraDModel, VQModel
from ...schedulers import VQDiffusionScheduler
from ...utils import logging
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
__snake_case = logging.get_logger(__name__) # pylint: disable=invalid-name
class __snake_case ( lowerCamelCase__ , lowerCamelCase__ ):
@register_to_config
def __init__( self , snake_case__ , snake_case__ = None , snake_case__ = None ) -> str:
'''simple docstring'''
super().__init__()
UpperCAmelCase : Optional[Any] =learnable
if self.learnable:
assert hidden_size is not None, "learnable=True requires `hidden_size` to be set"
assert length is not None, "learnable=True requires `length` to be set"
UpperCAmelCase : Any =torch.zeros(snake_case__ , snake_case__ )
else:
UpperCAmelCase : Union[str, Any] =None
UpperCAmelCase : Optional[int] =torch.nn.Parameter(snake_case__ )
class __snake_case ( lowerCamelCase__ ):
__lowerCamelCase : VQModel
__lowerCamelCase : CLIPTextModel
__lowerCamelCase : CLIPTokenizer
__lowerCamelCase : TransformeraDModel
__lowerCamelCase : LearnedClassifierFreeSamplingEmbeddings
__lowerCamelCase : VQDiffusionScheduler
def __init__( self , snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ , ) -> int:
'''simple docstring'''
super().__init__()
self.register_modules(
vqvae=snake_case__ , transformer=snake_case__ , text_encoder=snake_case__ , tokenizer=snake_case__ , scheduler=snake_case__ , learned_classifier_free_sampling_embeddings=snake_case__ , )
def UpperCAmelCase__ ( self , snake_case__ , snake_case__ , snake_case__ ) -> Optional[int]:
'''simple docstring'''
UpperCAmelCase : int =len(snake_case__ ) if isinstance(snake_case__ , snake_case__ ) else 1
# get prompt text embeddings
UpperCAmelCase : Optional[int] =self.tokenizer(
snake_case__ , padding='''max_length''' , max_length=self.tokenizer.model_max_length , return_tensors='''pt''' , )
UpperCAmelCase : int =text_inputs.input_ids
if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
UpperCAmelCase : List[str] =self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :] )
logger.warning(
'''The following part of your input was truncated because CLIP can only handle sequences up to'''
f''' {self.tokenizer.model_max_length} tokens: {removed_text}''' )
UpperCAmelCase : Optional[Any] =text_input_ids[:, : self.tokenizer.model_max_length]
UpperCAmelCase : List[Any] =self.text_encoder(text_input_ids.to(self.device ) )[0]
# NOTE: This additional step of normalizing the text embeddings is from VQ-Diffusion.
# While CLIP does normalize the pooled output of the text transformer when combining
# the image and text embeddings, CLIP does not directly normalize the last hidden state.
#
# CLIP normalizing the pooled output.
# https://github.com/huggingface/transformers/blob/d92e22d1f28324f513f3080e5c47c071a3916721/src/transformers/models/clip/modeling_clip.py#L1052-L1053
UpperCAmelCase : int =prompt_embeds / prompt_embeds.norm(dim=-1 , keepdim=snake_case__ )
# duplicate text embeddings for each generation per prompt
UpperCAmelCase : int =prompt_embeds.repeat_interleave(snake_case__ , dim=0 )
if do_classifier_free_guidance:
if self.learned_classifier_free_sampling_embeddings.learnable:
UpperCAmelCase : Optional[int] =self.learned_classifier_free_sampling_embeddings.embeddings
UpperCAmelCase : str =negative_prompt_embeds.unsqueeze(0 ).repeat(snake_case__ , 1 , 1 )
else:
UpperCAmelCase : str =[''''''] * batch_size
UpperCAmelCase : Tuple =text_input_ids.shape[-1]
UpperCAmelCase : Optional[Any] =self.tokenizer(
snake_case__ , padding='''max_length''' , max_length=snake_case__ , truncation=snake_case__ , return_tensors='''pt''' , )
UpperCAmelCase : Optional[Any] =self.text_encoder(uncond_input.input_ids.to(self.device ) )[0]
# See comment for normalizing text embeddings
UpperCAmelCase : Optional[int] =negative_prompt_embeds / negative_prompt_embeds.norm(dim=-1 , keepdim=snake_case__ )
# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
UpperCAmelCase : Optional[Any] =negative_prompt_embeds.shape[1]
UpperCAmelCase : Union[str, Any] =negative_prompt_embeds.repeat(1 , snake_case__ , 1 )
UpperCAmelCase : Optional[Any] =negative_prompt_embeds.view(batch_size * num_images_per_prompt , snake_case__ , -1 )
# For classifier free guidance, we need to do two forward passes.
# Here we concatenate the unconditional and text embeddings into a single batch
# to avoid doing two forward passes
UpperCAmelCase : int =torch.cat([negative_prompt_embeds, prompt_embeds] )
return prompt_embeds
@torch.no_grad()
def __call__( self , snake_case__ , snake_case__ = 100 , snake_case__ = 5.0 , snake_case__ = 1.0 , snake_case__ = 1 , snake_case__ = None , snake_case__ = None , snake_case__ = "pil" , snake_case__ = True , snake_case__ = None , snake_case__ = 1 , ) -> Union[ImagePipelineOutput, Tuple]:
'''simple docstring'''
if isinstance(snake_case__ , snake_case__ ):
UpperCAmelCase : Optional[int] =1
elif isinstance(snake_case__ , snake_case__ ):
UpperCAmelCase : Tuple =len(snake_case__ )
else:
raise ValueError(f'''`prompt` has to be of type `str` or `list` but is {type(snake_case__ )}''' )
UpperCAmelCase : Tuple =batch_size * num_images_per_prompt
UpperCAmelCase : List[str] =guidance_scale > 1.0
UpperCAmelCase : List[Any] =self._encode_prompt(snake_case__ , snake_case__ , snake_case__ )
if (callback_steps is None) or (
callback_steps is not None and (not isinstance(snake_case__ , snake_case__ ) or callback_steps <= 0)
):
raise ValueError(
f'''`callback_steps` has to be a positive integer but is {callback_steps} of type'''
f''' {type(snake_case__ )}.''' )
# get the initial completely masked latents unless the user supplied it
UpperCAmelCase : int =(batch_size, self.transformer.num_latent_pixels)
if latents is None:
UpperCAmelCase : Union[str, Any] =self.transformer.num_vector_embeds - 1
UpperCAmelCase : str =torch.full(snake_case__ , snake_case__ ).to(self.device )
else:
if latents.shape != latents_shape:
raise ValueError(f'''Unexpected latents shape, got {latents.shape}, expected {latents_shape}''' )
if (latents < 0).any() or (latents >= self.transformer.num_vector_embeds).any():
raise ValueError(
'''Unexpected latents value(s). All latents be valid embedding indices i.e. in the range 0,'''
f''' {self.transformer.num_vector_embeds - 1} (inclusive).''' )
UpperCAmelCase : Any =latents.to(self.device )
# set timesteps
self.scheduler.set_timesteps(snake_case__ , device=self.device )
UpperCAmelCase : Any =self.scheduler.timesteps.to(self.device )
UpperCAmelCase : Optional[int] =latents
for i, t in enumerate(self.progress_bar(snake_case__ ) ):
# expand the sample if we are doing classifier free guidance
UpperCAmelCase : Optional[Any] =torch.cat([sample] * 2 ) if do_classifier_free_guidance else sample
# predict the un-noised image
# model_output == `log_p_x_0`
UpperCAmelCase : Optional[int] =self.transformer(snake_case__ , encoder_hidden_states=snake_case__ , timestep=snake_case__ ).sample
if do_classifier_free_guidance:
UpperCAmelCase , UpperCAmelCase : str =model_output.chunk(2 )
UpperCAmelCase : Optional[int] =model_output_uncond + guidance_scale * (model_output_text - model_output_uncond)
model_output -= torch.logsumexp(snake_case__ , dim=1 , keepdim=snake_case__ )
UpperCAmelCase : Tuple =self.truncate(snake_case__ , snake_case__ )
# remove `log(0)`'s (`-inf`s)
UpperCAmelCase : Optional[Any] =model_output.clamp(-70 )
# compute the previous noisy sample x_t -> x_t-1
UpperCAmelCase : int =self.scheduler.step(snake_case__ , timestep=snake_case__ , sample=snake_case__ , generator=snake_case__ ).prev_sample
# call the callback, if provided
if callback is not None and i % callback_steps == 0:
callback(snake_case__ , snake_case__ , snake_case__ )
UpperCAmelCase : Optional[int] =self.vqvae.config.vq_embed_dim
UpperCAmelCase : Optional[Any] =(batch_size, self.transformer.height, self.transformer.width, embedding_channels)
UpperCAmelCase : Dict =self.vqvae.quantize.get_codebook_entry(snake_case__ , shape=snake_case__ )
UpperCAmelCase : Tuple =self.vqvae.decode(snake_case__ , force_not_quantize=snake_case__ ).sample
UpperCAmelCase : Union[str, Any] =(image / 2 + 0.5).clamp(0 , 1 )
UpperCAmelCase : Any =image.cpu().permute(0 , 2 , 3 , 1 ).numpy()
if output_type == "pil":
UpperCAmelCase : List[str] =self.numpy_to_pil(snake_case__ )
if not return_dict:
return (image,)
return ImagePipelineOutput(images=snake_case__ )
def UpperCAmelCase__ ( self , snake_case__ , snake_case__ ) -> torch.FloatTensor:
'''simple docstring'''
UpperCAmelCase , UpperCAmelCase : int =torch.sort(snake_case__ , 1 , descending=snake_case__ )
UpperCAmelCase : Union[str, Any] =torch.exp(snake_case__ )
UpperCAmelCase : Union[str, Any] =sorted_p_x_0.cumsum(dim=1 ) < truncation_rate
# Ensure that at least the largest probability is not zeroed out
UpperCAmelCase : Optional[Any] =torch.full_like(keep_mask[:, 0:1, :] , snake_case__ )
UpperCAmelCase : Tuple =torch.cat((all_true, keep_mask) , dim=1 )
UpperCAmelCase : int =keep_mask[:, :-1, :]
UpperCAmelCase : int =keep_mask.gather(1 , indices.argsort(1 ) )
UpperCAmelCase : Dict =log_p_x_0.clone()
UpperCAmelCase : List[Any] =-torch.inf # -inf = log(0)
return rv
| 348 | 1 |
import json
import logging
import math
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
from datasets import Dataset, load_dataset
import transformers
from transformers import (
CONFIG_MAPPING,
MODEL_FOR_MASKED_LM_MAPPING,
AutoConfig,
AutoModelForMaskedLM,
AutoTokenizer,
DataCollatorForWholeWordMask,
HfArgumentParser,
Trainer,
TrainingArguments,
set_seed,
)
from transformers.trainer_utils import get_last_checkpoint, is_main_process
__snake_case = logging.getLogger(__name__)
__snake_case = list(MODEL_FOR_MASKED_LM_MAPPING.keys())
__snake_case = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)
@dataclass
class __snake_case :
__lowerCamelCase : Optional[str] = field(
default=lowerCamelCase__ , metadata={
"""help""": (
"""The model checkpoint for weights initialization.Don't set if you want to train a model from scratch."""
)
} , )
__lowerCamelCase : Optional[str] = field(
default=lowerCamelCase__ , metadata={"""help""": """If training from scratch, pass a model type from the list: """ + """, """.join(lowerCamelCase__ )} , )
__lowerCamelCase : Optional[str] = field(
default=lowerCamelCase__ , metadata={
"""help""": (
"""Override some existing default config settings when a model is trained from scratch. Example: """
"""n_embd=10,resid_pdrop=0.2,scale_attn_weights=false,summary_type=cls_index"""
)
} , )
__lowerCamelCase : Optional[str] = field(
default=lowerCamelCase__ , metadata={"""help""": """Pretrained config name or path if not the same as model_name"""} )
__lowerCamelCase : Optional[str] = field(
default=lowerCamelCase__ , metadata={"""help""": """Pretrained tokenizer name or path if not the same as model_name"""} )
__lowerCamelCase : Optional[str] = field(
default=lowerCamelCase__ , metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""} , )
__lowerCamelCase : bool = field(
default=lowerCamelCase__ , metadata={"""help""": """Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."""} , )
__lowerCamelCase : str = field(
default="""main""" , metadata={"""help""": """The specific model version to use (can be a branch name, tag name or commit id)."""} , )
__lowerCamelCase : bool = field(
default=lowerCamelCase__ , metadata={
"""help""": (
"""Will use the token generated when running `huggingface-cli login` (necessary to use this script """
"""with private models)."""
)
} , )
def UpperCAmelCase__ ( self ) -> Dict:
'''simple docstring'''
if self.config_overrides is not None and (self.config_name is not None or self.model_name_or_path is not None):
raise ValueError(
'''--config_overrides can\'t be used in combination with --config_name or --model_name_or_path''' )
@dataclass
class __snake_case :
__lowerCamelCase : Optional[str] = field(
default=lowerCamelCase__ , metadata={"""help""": """The name of the dataset to use (via the datasets library)."""} )
__lowerCamelCase : Optional[str] = field(
default=lowerCamelCase__ , metadata={"""help""": """The configuration name of the dataset to use (via the datasets library)."""} )
__lowerCamelCase : Optional[str] = field(default=lowerCamelCase__ , metadata={"""help""": """The input training data file (a text file)."""} )
__lowerCamelCase : Optional[str] = field(
default=lowerCamelCase__ , metadata={"""help""": """An optional input evaluation data file to evaluate the perplexity on (a text file)."""} , )
__lowerCamelCase : Optional[str] = field(
default=lowerCamelCase__ , metadata={"""help""": """An optional input train ref data file for whole word masking in Chinese."""} , )
__lowerCamelCase : Optional[str] = field(
default=lowerCamelCase__ , metadata={"""help""": """An optional input validation ref data file for whole word masking in Chinese."""} , )
__lowerCamelCase : bool = field(
default=lowerCamelCase__ , metadata={"""help""": """Overwrite the cached training and evaluation sets"""} )
__lowerCamelCase : Optional[int] = field(
default=5 , metadata={
"""help""": """The percentage of the train set used as validation set in case there's no validation split"""
} , )
__lowerCamelCase : Optional[int] = field(
default=lowerCamelCase__ , metadata={
"""help""": (
"""The maximum total input sequence length after tokenization. Sequences longer """
"""than this will be truncated. Default to the max input length of the model."""
)
} , )
__lowerCamelCase : Optional[int] = field(
default=lowerCamelCase__ , metadata={"""help""": """The number of processes to use for the preprocessing."""} , )
__lowerCamelCase : float = field(
default=0.15 , metadata={"""help""": """Ratio of tokens to mask for masked language modeling loss"""} )
__lowerCamelCase : bool = field(
default=lowerCamelCase__ , metadata={
"""help""": (
"""Whether to pad all samples to `max_seq_length`. """
"""If False, will pad the samples dynamically when batching to the maximum length in the batch."""
)
} , )
def UpperCAmelCase__ ( self ) -> Optional[int]:
'''simple docstring'''
if self.train_file is not None:
UpperCAmelCase : List[Any] =self.train_file.split('''.''' )[-1]
assert extension in ["csv", "json", "txt"], "`train_file` should be a csv, a json or a txt file."
if self.validation_file is not None:
UpperCAmelCase : Any =self.validation_file.split('''.''' )[-1]
assert extension in ["csv", "json", "txt"], "`validation_file` should be a csv, a json or a txt file."
def lowerCAmelCase_ ( __lowerCAmelCase , __lowerCAmelCase )-> int:
'''simple docstring'''
with open(__lowerCAmelCase , '''r''' , encoding='''utf-8''' ) as f:
UpperCAmelCase : str =[json.loads(__lowerCAmelCase ) for line in f.read().splitlines() if (len(__lowerCAmelCase ) > 0 and not line.isspace())]
assert len(__lowerCAmelCase ) == len(__lowerCAmelCase )
UpperCAmelCase : List[Any] ={c: dataset[c] for c in dataset.column_names}
UpperCAmelCase : Any =refs
return Dataset.from_dict(__lowerCAmelCase )
def lowerCAmelCase_ ( )-> Optional[Any]:
'''simple docstring'''
UpperCAmelCase : List[Any] =HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
if len(sys.argv ) == 2 and sys.argv[1].endswith('''.json''' ):
# If we pass only one argument to the script and it's the path to a json file,
# let's parse it to get our arguments.
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase : str =parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
else:
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase : Any =parser.parse_args_into_dataclasses()
# Detecting last checkpoint.
UpperCAmelCase : Dict =None
if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir:
UpperCAmelCase : Optional[int] =get_last_checkpoint(training_args.output_dir )
if last_checkpoint is None and len(os.listdir(training_args.output_dir ) ) > 0:
raise ValueError(
f'''Output directory ({training_args.output_dir}) already exists and is not empty. '''
'''Use --overwrite_output_dir to overcome.''' )
elif last_checkpoint is not None:
logger.info(
f'''Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change '''
'''the `--output_dir` or add `--overwrite_output_dir` to train from scratch.''' )
# Setup logging
logging.basicConfig(
format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''' , datefmt='''%m/%d/%Y %H:%M:%S''' , handlers=[logging.StreamHandler(sys.stdout )] , )
logger.setLevel(logging.INFO if is_main_process(training_args.local_rank ) else logging.WARN )
# Log on each process the small summary:
logger.warning(
f'''Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}'''
+ f'''distributed training: {bool(training_args.local_rank != -1 )}, 16-bits training: {training_args.fpaa}''' )
# Set the verbosity to info of the Transformers logger (on main process only):
if is_main_process(training_args.local_rank ):
transformers.utils.logging.set_verbosity_info()
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
logger.info('''Training/evaluation parameters %s''' , __lowerCAmelCase )
# Set seed before initializing model.
set_seed(training_args.seed )
# Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below)
# or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/
# (the dataset will be downloaded automatically from the datasets Hub).
#
# For CSV/JSON files, this script will use the column called 'text' or the first column if no column called
# 'text' is found. You can easily tweak this behavior (see below).
#
# In distributed training, the load_dataset function guarantee that only one local process can concurrently
# download the dataset.
if data_args.dataset_name is not None:
# Downloading and loading a dataset from the hub.
UpperCAmelCase : Tuple =load_dataset(data_args.dataset_name , data_args.dataset_config_name )
if "validation" not in datasets.keys():
UpperCAmelCase : Tuple =load_dataset(
data_args.dataset_name , data_args.dataset_config_name , split=f'''train[:{data_args.validation_split_percentage}%]''' , )
UpperCAmelCase : Optional[int] =load_dataset(
data_args.dataset_name , data_args.dataset_config_name , split=f'''train[{data_args.validation_split_percentage}%:]''' , )
else:
UpperCAmelCase : List[str] ={}
if data_args.train_file is not None:
UpperCAmelCase : List[str] =data_args.train_file
if data_args.validation_file is not None:
UpperCAmelCase : Tuple =data_args.validation_file
UpperCAmelCase : int =data_args.train_file.split('''.''' )[-1]
if extension == "txt":
UpperCAmelCase : int ='''text'''
UpperCAmelCase : int =load_dataset(__lowerCAmelCase , data_files=__lowerCAmelCase )
# See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
# https://huggingface.co/docs/datasets/loading_datasets.html.
# Load pretrained model and tokenizer
#
# Distributed training:
# The .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
UpperCAmelCase : List[str] ={
'''cache_dir''': model_args.cache_dir,
'''revision''': model_args.model_revision,
'''use_auth_token''': True if model_args.use_auth_token else None,
}
if model_args.config_name:
UpperCAmelCase : str =AutoConfig.from_pretrained(model_args.config_name , **__lowerCAmelCase )
elif model_args.model_name_or_path:
UpperCAmelCase : Dict =AutoConfig.from_pretrained(model_args.model_name_or_path , **__lowerCAmelCase )
else:
UpperCAmelCase : int =CONFIG_MAPPING[model_args.model_type]()
logger.warning('''You are instantiating a new config instance from scratch.''' )
if model_args.config_overrides is not None:
logger.info(f'''Overriding config: {model_args.config_overrides}''' )
config.update_from_string(model_args.config_overrides )
logger.info(f'''New config: {config}''' )
UpperCAmelCase : Any ={
'''cache_dir''': model_args.cache_dir,
'''use_fast''': model_args.use_fast_tokenizer,
'''revision''': model_args.model_revision,
'''use_auth_token''': True if model_args.use_auth_token else None,
}
if model_args.tokenizer_name:
UpperCAmelCase : Any =AutoTokenizer.from_pretrained(model_args.tokenizer_name , **__lowerCAmelCase )
elif model_args.model_name_or_path:
UpperCAmelCase : str =AutoTokenizer.from_pretrained(model_args.model_name_or_path , **__lowerCAmelCase )
else:
raise ValueError(
'''You are instantiating a new tokenizer from scratch. This is not supported by this script.'''
'''You can do it from another script, save it, and load it from here, using --tokenizer_name.''' )
if model_args.model_name_or_path:
UpperCAmelCase : Dict =AutoModelForMaskedLM.from_pretrained(
model_args.model_name_or_path , from_tf=bool('''.ckpt''' in model_args.model_name_or_path ) , config=__lowerCAmelCase , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
else:
logger.info('''Training new model from scratch''' )
UpperCAmelCase : Optional[Any] =AutoModelForMaskedLM.from_config(__lowerCAmelCase )
model.resize_token_embeddings(len(__lowerCAmelCase ) )
# Preprocessing the datasets.
# First we tokenize all the texts.
if training_args.do_train:
UpperCAmelCase : str =datasets['''train'''].column_names
else:
UpperCAmelCase : Any =datasets['''validation'''].column_names
UpperCAmelCase : Optional[Any] ='''text''' if '''text''' in column_names else column_names[0]
UpperCAmelCase : Optional[Any] ='''max_length''' if data_args.pad_to_max_length else False
def tokenize_function(__lowerCAmelCase ):
# Remove empty lines
UpperCAmelCase : Dict =[line for line in examples['''text'''] if len(__lowerCAmelCase ) > 0 and not line.isspace()]
return tokenizer(examples['''text'''] , padding=__lowerCAmelCase , truncation=__lowerCAmelCase , max_length=data_args.max_seq_length )
UpperCAmelCase : Optional[int] =datasets.map(
__lowerCAmelCase , batched=__lowerCAmelCase , num_proc=data_args.preprocessing_num_workers , remove_columns=[text_column_name] , load_from_cache_file=not data_args.overwrite_cache , )
# Add the chinese references if provided
if data_args.train_ref_file is not None:
UpperCAmelCase : Tuple =add_chinese_references(tokenized_datasets['''train'''] , data_args.train_ref_file )
if data_args.validation_ref_file is not None:
UpperCAmelCase : int =add_chinese_references(
tokenized_datasets['''validation'''] , data_args.validation_ref_file )
# If we have ref files, need to avoid it removed by trainer
UpperCAmelCase : Optional[Any] =data_args.train_ref_file or data_args.validation_ref_file
if has_ref:
UpperCAmelCase : Tuple =False
# Data collator
# This one will take care of randomly masking the tokens.
UpperCAmelCase : List[Any] =DataCollatorForWholeWordMask(tokenizer=__lowerCAmelCase , mlm_probability=data_args.mlm_probability )
# Initialize our Trainer
UpperCAmelCase : List[Any] =Trainer(
model=__lowerCAmelCase , args=__lowerCAmelCase , train_dataset=tokenized_datasets['''train'''] if training_args.do_train else None , eval_dataset=tokenized_datasets['''validation'''] if training_args.do_eval else None , tokenizer=__lowerCAmelCase , data_collator=__lowerCAmelCase , )
# Training
if training_args.do_train:
if last_checkpoint is not None:
UpperCAmelCase : Union[str, Any] =last_checkpoint
elif model_args.model_name_or_path is not None and os.path.isdir(model_args.model_name_or_path ):
UpperCAmelCase : List[str] =model_args.model_name_or_path
else:
UpperCAmelCase : Dict =None
UpperCAmelCase : List[str] =trainer.train(resume_from_checkpoint=__lowerCAmelCase )
trainer.save_model() # Saves the tokenizer too for easy upload
UpperCAmelCase : List[Any] =os.path.join(training_args.output_dir , '''train_results.txt''' )
if trainer.is_world_process_zero():
with open(__lowerCAmelCase , '''w''' ) as writer:
logger.info('''***** Train results *****''' )
for key, value in sorted(train_result.metrics.items() ):
logger.info(f''' {key} = {value}''' )
writer.write(f'''{key} = {value}\n''' )
# Need to save the state, since Trainer.save_model saves only the tokenizer with the model
trainer.state.save_to_json(os.path.join(training_args.output_dir , '''trainer_state.json''' ) )
# Evaluation
UpperCAmelCase : str ={}
if training_args.do_eval:
logger.info('''*** Evaluate ***''' )
UpperCAmelCase : Optional[int] =trainer.evaluate()
UpperCAmelCase : Optional[Any] =math.exp(eval_output['''eval_loss'''] )
UpperCAmelCase : List[Any] =perplexity
UpperCAmelCase : Optional[Any] =os.path.join(training_args.output_dir , '''eval_results_mlm_wwm.txt''' )
if trainer.is_world_process_zero():
with open(__lowerCAmelCase , '''w''' ) as writer:
logger.info('''***** Eval results *****''' )
for key, value in sorted(results.items() ):
logger.info(f''' {key} = {value}''' )
writer.write(f'''{key} = {value}\n''' )
return results
def lowerCAmelCase_ ( __lowerCAmelCase )-> Optional[Any]:
'''simple docstring'''
main()
if __name__ == "__main__":
main()
| 348 | import unittest
import numpy as np
import torch
from diffusers import KarrasVePipeline, KarrasVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class __snake_case ( unittest.TestCase ):
@property
def UpperCAmelCase__ ( self ) -> int:
'''simple docstring'''
torch.manual_seed(0 )
UpperCAmelCase : Any =UNetaDModel(
block_out_channels=(32, 64) , layers_per_block=2 , sample_size=32 , in_channels=3 , out_channels=3 , down_block_types=('''DownBlock2D''', '''AttnDownBlock2D''') , up_block_types=('''AttnUpBlock2D''', '''UpBlock2D''') , )
return model
def UpperCAmelCase__ ( self ) -> Union[str, Any]:
'''simple docstring'''
UpperCAmelCase : Tuple =self.dummy_uncond_unet
UpperCAmelCase : Optional[int] =KarrasVeScheduler()
UpperCAmelCase : List[Any] =KarrasVePipeline(unet=snake_case__ , scheduler=snake_case__ )
pipe.to(snake_case__ )
pipe.set_progress_bar_config(disable=snake_case__ )
UpperCAmelCase : List[str] =torch.manual_seed(0 )
UpperCAmelCase : List[str] =pipe(num_inference_steps=2 , generator=snake_case__ , output_type='''numpy''' ).images
UpperCAmelCase : str =torch.manual_seed(0 )
UpperCAmelCase : str =pipe(num_inference_steps=2 , generator=snake_case__ , output_type='''numpy''' , return_dict=snake_case__ )[0]
UpperCAmelCase : Any =image[0, -3:, -3:, -1]
UpperCAmelCase : List[str] =image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 32, 32, 3)
UpperCAmelCase : int =np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1e-2
@slow
@require_torch
class __snake_case ( unittest.TestCase ):
def UpperCAmelCase__ ( self ) -> List[Any]:
'''simple docstring'''
UpperCAmelCase : Tuple ='''google/ncsnpp-celebahq-256'''
UpperCAmelCase : int =UNetaDModel.from_pretrained(snake_case__ )
UpperCAmelCase : Dict =KarrasVeScheduler()
UpperCAmelCase : Union[str, Any] =KarrasVePipeline(unet=snake_case__ , scheduler=snake_case__ )
pipe.to(snake_case__ )
pipe.set_progress_bar_config(disable=snake_case__ )
UpperCAmelCase : Any =torch.manual_seed(0 )
UpperCAmelCase : Tuple =pipe(num_inference_steps=20 , generator=snake_case__ , output_type='''numpy''' ).images
UpperCAmelCase : Optional[int] =image[0, -3:, -3:, -1]
assert image.shape == (1, 256, 256, 3)
UpperCAmelCase : Tuple =np.array([0.578, 0.5811, 0.5924, 0.5809, 0.587, 0.5886, 0.5861, 0.5802, 0.586] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
| 348 | 1 |
import copy
from typing import Any, Dict, List, Optional, Union
import numpy as np
import torch
from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import TensorType, logging
__snake_case = logging.get_logger(__name__)
class __snake_case ( lowerCamelCase__ ):
__lowerCamelCase : List[str] = ["""input_features""", """is_longer"""]
def __init__( self , snake_case__=64 , snake_case__=4_8000 , snake_case__=480 , snake_case__=10 , snake_case__=1024 , snake_case__=0.0 , snake_case__=False , snake_case__ = 0 , snake_case__ = 1_4000 , snake_case__ = None , snake_case__ = "fusion" , snake_case__ = "repeatpad" , **snake_case__ , ) -> Optional[Any]:
'''simple docstring'''
super().__init__(
feature_size=snake_case__ , sampling_rate=snake_case__ , padding_value=snake_case__ , return_attention_mask=snake_case__ , **snake_case__ , )
UpperCAmelCase : Tuple =top_db
UpperCAmelCase : Dict =truncation
UpperCAmelCase : int =padding
UpperCAmelCase : str =fft_window_size
UpperCAmelCase : str =(fft_window_size >> 1) + 1
UpperCAmelCase : Tuple =hop_length
UpperCAmelCase : List[Any] =max_length_s
UpperCAmelCase : List[Any] =max_length_s * sampling_rate
UpperCAmelCase : Any =sampling_rate
UpperCAmelCase : Dict =frequency_min
UpperCAmelCase : Union[str, Any] =frequency_max
UpperCAmelCase : str =mel_filter_bank(
num_frequency_bins=self.nb_frequency_bins , num_mel_filters=snake_case__ , min_frequency=snake_case__ , max_frequency=snake_case__ , sampling_rate=snake_case__ , norm=snake_case__ , mel_scale='''htk''' , )
UpperCAmelCase : List[str] =mel_filter_bank(
num_frequency_bins=self.nb_frequency_bins , num_mel_filters=snake_case__ , min_frequency=snake_case__ , max_frequency=snake_case__ , sampling_rate=snake_case__ , norm='''slaney''' , mel_scale='''slaney''' , )
def UpperCAmelCase__ ( self ) -> Dict[str, Any]:
'''simple docstring'''
UpperCAmelCase : Any =copy.deepcopy(self.__dict__ )
UpperCAmelCase : Union[str, Any] =self.__class__.__name__
if "mel_filters" in output:
del output["mel_filters"]
if "mel_filters_slaney" in output:
del output["mel_filters_slaney"]
return output
def UpperCAmelCase__ ( self , snake_case__ , snake_case__ = None ) -> np.ndarray:
'''simple docstring'''
UpperCAmelCase : List[Any] =spectrogram(
snake_case__ , window_function(self.fft_window_size , '''hann''' ) , frame_length=self.fft_window_size , hop_length=self.hop_length , power=2.0 , mel_filters=snake_case__ , log_mel='''dB''' , )
return log_mel_spectrogram.T
def UpperCAmelCase__ ( self , snake_case__ , snake_case__ , snake_case__ ) -> Optional[int]:
'''simple docstring'''
UpperCAmelCase : Tuple =np.array_split(list(range(0 , total_frames - chunk_frames + 1 ) ) , 3 )
if len(ranges[1] ) == 0:
# if the audio is too short, we just use the first chunk
UpperCAmelCase : Optional[Any] =[0]
if len(ranges[2] ) == 0:
# if the audio is too short, we just use the first chunk
UpperCAmelCase : Dict =[0]
# randomly choose index for each part
UpperCAmelCase : List[Any] =np.random.choice(ranges[0] )
UpperCAmelCase : Optional[Any] =np.random.choice(ranges[1] )
UpperCAmelCase : Tuple =np.random.choice(ranges[2] )
UpperCAmelCase : str =mel[idx_front : idx_front + chunk_frames, :]
UpperCAmelCase : str =mel[idx_middle : idx_middle + chunk_frames, :]
UpperCAmelCase : Dict =mel[idx_back : idx_back + chunk_frames, :]
UpperCAmelCase : str =torch.tensor(mel[None, None, :] )
UpperCAmelCase : Union[str, Any] =torch.nn.functional.interpolate(
snake_case__ , size=[chunk_frames, 64] , mode='''bilinear''' , align_corners=snake_case__ )
UpperCAmelCase : List[str] =mel_shrink[0][0].numpy()
UpperCAmelCase : Optional[Any] =np.stack([mel_shrink, mel_chunk_front, mel_chunk_middle, mel_chunk_back] , axis=0 )
return mel_fusion
def UpperCAmelCase__ ( self , snake_case__ , snake_case__ , snake_case__ , snake_case__ ) -> np.array:
'''simple docstring'''
if waveform.shape[0] > max_length:
if truncation == "rand_trunc":
UpperCAmelCase : Optional[Any] =True
# random crop to max_length (for compatibility) -> this should be handled by self.pad
UpperCAmelCase : Any =len(snake_case__ ) - max_length
UpperCAmelCase : Optional[int] =np.random.randint(0 , overflow + 1 )
UpperCAmelCase : Tuple =waveform[idx : idx + max_length]
UpperCAmelCase : List[str] =self._np_extract_fbank_features(snake_case__ , self.mel_filters_slaney )[None, :]
elif truncation == "fusion":
UpperCAmelCase : Tuple =self._np_extract_fbank_features(snake_case__ , self.mel_filters )
UpperCAmelCase : Union[str, Any] =max_length // self.hop_length + 1 # the +1 related to how the spectrogram is computed
UpperCAmelCase : Any =mel.shape[0]
if chunk_frames == total_frames:
# there is a corner case where the audio length is larger than max_length but smaller than max_length+hop_length.
# In this case, we just use the whole audio.
UpperCAmelCase : List[Any] =np.stack([mel, mel, mel, mel] , axis=0 )
UpperCAmelCase : List[str] =False
else:
UpperCAmelCase : Union[str, Any] =self._random_mel_fusion(snake_case__ , snake_case__ , snake_case__ )
UpperCAmelCase : Optional[Any] =True
else:
raise NotImplementedError(f'''data_truncating {truncation} not implemented''' )
else:
UpperCAmelCase : str =False
# only use repeat as a new possible value for padding. you repeat the audio before applying the usual max_length padding
if waveform.shape[0] < max_length:
if padding == "repeat":
UpperCAmelCase : Optional[Any] =int(max_length / len(snake_case__ ) )
UpperCAmelCase : Optional[Any] =np.stack(np.tile(snake_case__ , n_repeat + 1 ) )[:max_length]
if padding == "repeatpad":
UpperCAmelCase : List[Any] =int(max_length / len(snake_case__ ) )
UpperCAmelCase : Any =np.stack(np.tile(snake_case__ , snake_case__ ) )
UpperCAmelCase : str =np.pad(snake_case__ , (0, max_length - waveform.shape[0]) , mode='''constant''' , constant_values=0 )
if truncation == "fusion":
UpperCAmelCase : List[Any] =self._np_extract_fbank_features(snake_case__ , self.mel_filters )
UpperCAmelCase : int =np.stack([input_mel, input_mel, input_mel, input_mel] , axis=0 )
else:
UpperCAmelCase : Dict =self._np_extract_fbank_features(snake_case__ , self.mel_filters_slaney )[None, :]
return input_mel, longer
def __call__( self , snake_case__ , snake_case__ = None , snake_case__ = None , snake_case__ = None , snake_case__ = None , snake_case__ = None , **snake_case__ , ) -> BatchFeature:
'''simple docstring'''
UpperCAmelCase : str =truncation if truncation is not None else self.truncation
UpperCAmelCase : Optional[Any] =padding if padding else self.padding
if sampling_rate is not None:
if sampling_rate != self.sampling_rate:
raise ValueError(
f'''The model corresponding to this feature extractor: {self.__class__.__name__} was trained using a'''
f''' sampling rate of {self.sampling_rate}. Please make sure that the provided `raw_speech` input'''
f''' was sampled with {self.sampling_rate} and not {sampling_rate}.''' )
else:
logger.warning(
'''It is strongly recommended to pass the `sampling_rate` argument to this function. '''
'''Failing to do so can result in silent errors that might be hard to debug.''' )
UpperCAmelCase : Optional[int] =isinstance(snake_case__ , np.ndarray ) and len(raw_speech.shape ) > 1
if is_batched_numpy and len(raw_speech.shape ) > 2:
raise ValueError(f'''Only mono-channel audio is supported for input to {self}''' )
UpperCAmelCase : str =is_batched_numpy or (
isinstance(snake_case__ , (list, tuple) ) and (isinstance(raw_speech[0] , (np.ndarray, tuple, list) ))
)
if is_batched:
UpperCAmelCase : int =[np.asarray(snake_case__ , dtype=np.floataa ) for speech in raw_speech]
elif not is_batched and not isinstance(snake_case__ , np.ndarray ):
UpperCAmelCase : Optional[Any] =np.asarray(snake_case__ , dtype=np.floataa )
elif isinstance(snake_case__ , np.ndarray ) and raw_speech.dtype is np.dtype(np.floataa ):
UpperCAmelCase : Tuple =raw_speech.astype(np.floataa )
# always return batch
if not is_batched:
UpperCAmelCase : Dict =[np.asarray(snake_case__ )]
# convert to mel spectrogram, truncate and pad if needed.
UpperCAmelCase : Tuple =[
self._get_input_mel(snake_case__ , max_length if max_length else self.nb_max_samples , snake_case__ , snake_case__ )
for waveform in raw_speech
]
UpperCAmelCase : Tuple =[]
UpperCAmelCase : Optional[int] =[]
for mel, longer in padded_inputs:
input_mel.append(snake_case__ )
is_longer.append(snake_case__ )
if truncation == "fusion" and sum(snake_case__ ) == 0:
# if no audio is longer than 10s, then randomly select one audio to be longer
UpperCAmelCase : List[Any] =np.random.randint(0 , len(snake_case__ ) )
UpperCAmelCase : Union[str, Any] =True
if isinstance(input_mel[0] , snake_case__ ):
UpperCAmelCase : List[str] =[np.asarray(snake_case__ , dtype=np.floataa ) for feature in input_mel]
# is_longer is a list of bool
UpperCAmelCase : Tuple =[[longer] for longer in is_longer]
UpperCAmelCase : List[str] ={'''input_features''': input_mel, '''is_longer''': is_longer}
UpperCAmelCase : int =BatchFeature(snake_case__ )
if return_tensors is not None:
UpperCAmelCase : Optional[int] =input_features.convert_to_tensors(snake_case__ )
return input_features
| 348 | import qiskit
def lowerCAmelCase_ ( __lowerCAmelCase , __lowerCAmelCase )-> qiskit.result.counts.Counts:
'''simple docstring'''
UpperCAmelCase : Union[str, Any] =qiskit.Aer.get_backend('''aer_simulator''' )
UpperCAmelCase : List[str] =qiskit.QuantumCircuit(4 , 2 )
# encode inputs in qubits 0 and 1
if bita == 1:
qc_ha.x(0 )
if bita == 1:
qc_ha.x(1 )
qc_ha.barrier()
# use cnots to write XOR of the inputs on qubit2
qc_ha.cx(0 , 2 )
qc_ha.cx(1 , 2 )
# use ccx / toffoli gate to write AND of the inputs on qubit3
qc_ha.ccx(0 , 1 , 3 )
qc_ha.barrier()
# extract outputs
qc_ha.measure(2 , 0 ) # extract XOR value
qc_ha.measure(3 , 1 ) # extract AND value
# Execute the circuit on the qasm simulator
UpperCAmelCase : Dict =qiskit.execute(__lowerCAmelCase , __lowerCAmelCase , shots=10_00 )
# Return the histogram data of the results of the experiment
return job.result().get_counts(__lowerCAmelCase )
if __name__ == "__main__":
__snake_case = half_adder(1, 1)
print(f'Half Adder Output Qubit Counts: {counts}')
| 348 | 1 |
import numpy as np
from matplotlib import pyplot as plt
from sklearn.datasets import load_iris
from sklearn.metrics import ConfusionMatrixDisplay
from sklearn.model_selection import train_test_split
from xgboost import XGBClassifier
def lowerCAmelCase_ ( __lowerCAmelCase )-> tuple:
'''simple docstring'''
return (data["data"], data["target"])
def lowerCAmelCase_ ( __lowerCAmelCase , __lowerCAmelCase )-> XGBClassifier:
'''simple docstring'''
UpperCAmelCase : List[Any] =XGBClassifier()
classifier.fit(__lowerCAmelCase , __lowerCAmelCase )
return classifier
def lowerCAmelCase_ ( )-> None:
'''simple docstring'''
UpperCAmelCase : List[Any] =load_iris()
UpperCAmelCase , UpperCAmelCase : List[str] =data_handling(__lowerCAmelCase )
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase : Any =train_test_split(
__lowerCAmelCase , __lowerCAmelCase , test_size=0.25 )
UpperCAmelCase : Optional[int] =iris['''target_names''']
# Create an XGBoost Classifier from the training data
UpperCAmelCase : Union[str, Any] =xgboost(__lowerCAmelCase , __lowerCAmelCase )
# Display the confusion matrix of the classifier with both training and test sets
ConfusionMatrixDisplay.from_estimator(
__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , display_labels=__lowerCAmelCase , cmap='''Blues''' , normalize='''true''' , )
plt.title('''Normalized Confusion Matrix - IRIS Dataset''' )
plt.show()
if __name__ == "__main__":
import doctest
doctest.testmod(verbose=True)
main()
| 348 | from __future__ import annotations
import unittest
from transformers import BlenderbotConfig, BlenderbotTokenizer, is_tf_available
from transformers.testing_utils import require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import TFAutoModelForSeqaSeqLM, TFBlenderbotForConditionalGeneration, TFBlenderbotModel
@require_tf
class __snake_case :
__lowerCamelCase : str = BlenderbotConfig
__lowerCamelCase : Optional[Any] = {}
__lowerCamelCase : Optional[int] = """gelu"""
def __init__( self , snake_case__ , snake_case__=13 , snake_case__=7 , snake_case__=True , snake_case__=False , snake_case__=99 , snake_case__=32 , snake_case__=2 , snake_case__=4 , snake_case__=37 , snake_case__=0.1 , snake_case__=0.1 , snake_case__=20 , snake_case__=2 , snake_case__=1 , snake_case__=0 , ) -> Optional[Any]:
'''simple docstring'''
UpperCAmelCase : Union[str, Any] =parent
UpperCAmelCase : Optional[int] =batch_size
UpperCAmelCase : Dict =seq_length
UpperCAmelCase : Optional[Any] =is_training
UpperCAmelCase : List[str] =use_labels
UpperCAmelCase : List[Any] =vocab_size
UpperCAmelCase : Optional[int] =hidden_size
UpperCAmelCase : Tuple =num_hidden_layers
UpperCAmelCase : Any =num_attention_heads
UpperCAmelCase : Optional[int] =intermediate_size
UpperCAmelCase : str =hidden_dropout_prob
UpperCAmelCase : Optional[int] =attention_probs_dropout_prob
UpperCAmelCase : str =max_position_embeddings
UpperCAmelCase : List[Any] =eos_token_id
UpperCAmelCase : Optional[int] =pad_token_id
UpperCAmelCase : Tuple =bos_token_id
def UpperCAmelCase__ ( self ) -> Optional[Any]:
'''simple docstring'''
UpperCAmelCase : List[Any] =ids_tensor([self.batch_size, self.seq_length - 1] , self.vocab_size )
UpperCAmelCase : List[Any] =tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size ) , 1 )
UpperCAmelCase : Tuple =tf.concat([input_ids, eos_tensor] , axis=1 )
UpperCAmelCase : str =ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
UpperCAmelCase : Optional[Any] =self.config_cls(
vocab_size=self.vocab_size , d_model=self.hidden_size , encoder_layers=self.num_hidden_layers , decoder_layers=self.num_hidden_layers , encoder_attention_heads=self.num_attention_heads , decoder_attention_heads=self.num_attention_heads , encoder_ffn_dim=self.intermediate_size , decoder_ffn_dim=self.intermediate_size , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , eos_token_ids=[2] , bos_token_id=self.bos_token_id , pad_token_id=self.pad_token_id , decoder_start_token_id=self.pad_token_id , **self.config_updates , )
UpperCAmelCase : List[str] =prepare_blenderbot_inputs_dict(snake_case__ , snake_case__ , snake_case__ )
return config, inputs_dict
def UpperCAmelCase__ ( self , snake_case__ , snake_case__ ) -> int:
'''simple docstring'''
UpperCAmelCase : Union[str, Any] =TFBlenderbotModel(config=snake_case__ ).get_decoder()
UpperCAmelCase : Any =inputs_dict['''input_ids''']
UpperCAmelCase : str =input_ids[:1, :]
UpperCAmelCase : Tuple =inputs_dict['''attention_mask'''][:1, :]
UpperCAmelCase : Tuple =inputs_dict['''head_mask''']
UpperCAmelCase : List[Any] =1
# first forward pass
UpperCAmelCase : List[str] =model(snake_case__ , attention_mask=snake_case__ , head_mask=snake_case__ , use_cache=snake_case__ )
UpperCAmelCase , UpperCAmelCase : str =outputs.to_tuple()
# create hypothetical next token and extent to next_input_ids
UpperCAmelCase : Union[str, Any] =ids_tensor((self.batch_size, 3) , config.vocab_size )
UpperCAmelCase : List[Any] =tf.cast(ids_tensor((self.batch_size, 3) , 2 ) , tf.inta )
# append to next input_ids and
UpperCAmelCase : Tuple =tf.concat([input_ids, next_tokens] , axis=-1 )
UpperCAmelCase : int =tf.concat([attention_mask, next_attn_mask] , axis=-1 )
UpperCAmelCase : Optional[int] =model(snake_case__ , attention_mask=snake_case__ )[0]
UpperCAmelCase : str =model(snake_case__ , attention_mask=snake_case__ , past_key_values=snake_case__ )[0]
self.parent.assertEqual(next_tokens.shape[1] , output_from_past.shape[1] )
# select random slice
UpperCAmelCase : List[Any] =int(ids_tensor((1,) , output_from_past.shape[-1] ) )
UpperCAmelCase : List[Any] =output_from_no_past[:, -3:, random_slice_idx]
UpperCAmelCase : Dict =output_from_past[:, :, random_slice_idx]
# test that outputs are equal for slice
tf.debugging.assert_near(snake_case__ , snake_case__ , rtol=1e-3 )
def lowerCAmelCase_ ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase=None , __lowerCAmelCase=None , __lowerCAmelCase=None , __lowerCAmelCase=None , __lowerCAmelCase=None , )-> str:
'''simple docstring'''
if attention_mask is None:
UpperCAmelCase : int =tf.cast(tf.math.not_equal(__lowerCAmelCase , config.pad_token_id ) , tf.inta )
if decoder_attention_mask is None:
UpperCAmelCase : Tuple =tf.concat(
[
tf.ones(decoder_input_ids[:, :1].shape , dtype=tf.inta ),
tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:] , config.pad_token_id ) , tf.inta ),
] , axis=-1 , )
if head_mask is None:
UpperCAmelCase : str =tf.ones((config.encoder_layers, config.encoder_attention_heads) )
if decoder_head_mask is None:
UpperCAmelCase : Union[str, Any] =tf.ones((config.decoder_layers, config.decoder_attention_heads) )
if cross_attn_head_mask is None:
UpperCAmelCase : int =tf.ones((config.decoder_layers, config.decoder_attention_heads) )
return {
"input_ids": input_ids,
"decoder_input_ids": decoder_input_ids,
"attention_mask": attention_mask,
"decoder_attention_mask": decoder_attention_mask,
"head_mask": head_mask,
"decoder_head_mask": decoder_head_mask,
"cross_attn_head_mask": cross_attn_head_mask,
}
@require_tf
class __snake_case ( lowerCamelCase__ , lowerCamelCase__ , unittest.TestCase ):
__lowerCamelCase : List[str] = (TFBlenderbotForConditionalGeneration, TFBlenderbotModel) if is_tf_available() else ()
__lowerCamelCase : Dict = (TFBlenderbotForConditionalGeneration,) if is_tf_available() else ()
__lowerCamelCase : Dict = (
{
"""conversational""": TFBlenderbotForConditionalGeneration,
"""feature-extraction""": TFBlenderbotModel,
"""summarization""": TFBlenderbotForConditionalGeneration,
"""text2text-generation""": TFBlenderbotForConditionalGeneration,
"""translation""": TFBlenderbotForConditionalGeneration,
}
if is_tf_available()
else {}
)
__lowerCamelCase : Union[str, Any] = True
__lowerCamelCase : Union[str, Any] = False
__lowerCamelCase : Union[str, Any] = False
def UpperCAmelCase__ ( self ) -> Dict:
'''simple docstring'''
UpperCAmelCase : List[str] =TFBlenderbotModelTester(self )
UpperCAmelCase : List[Any] =ConfigTester(self , config_class=snake_case__ )
def UpperCAmelCase__ ( self ) -> Optional[Any]:
'''simple docstring'''
self.config_tester.run_common_tests()
def UpperCAmelCase__ ( self ) -> List[Any]:
'''simple docstring'''
UpperCAmelCase : int =self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.check_decoder_model_past_large_inputs(*snake_case__ )
@require_tokenizers
@require_tf
class __snake_case ( unittest.TestCase ):
__lowerCamelCase : List[str] = ["""My friends are cool but they eat too many carbs."""]
__lowerCamelCase : Dict = """facebook/blenderbot-400M-distill"""
@cached_property
def UpperCAmelCase__ ( self ) -> int:
'''simple docstring'''
return BlenderbotTokenizer.from_pretrained(self.model_name )
@cached_property
def UpperCAmelCase__ ( self ) -> int:
'''simple docstring'''
UpperCAmelCase : int =TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name )
return model
@slow
def UpperCAmelCase__ ( self ) -> Any:
'''simple docstring'''
UpperCAmelCase : Optional[int] =self.tokenizer(self.src_text , return_tensors='''tf''' )
UpperCAmelCase : Optional[int] =self.model.generate(
model_inputs.input_ids , )
UpperCAmelCase : str =self.tokenizer.batch_decode(generated_ids.numpy() , skip_special_tokens=snake_case__ )[0]
assert (
generated_words
== " That's unfortunate. Are they trying to lose weight or are they just trying to be healthier?"
)
| 348 | 1 |
from scipy.stats import spearmanr
import datasets
__snake_case = '''
The Spearman rank-order correlation coefficient is a measure of the
relationship between two datasets. Like other correlation coefficients,
this one varies between -1 and +1 with 0 implying no correlation.
Positive correlations imply that as data in dataset x increases, so
does data in dataset y. Negative correlations imply that as x increases,
y decreases. Correlations of -1 or +1 imply an exact monotonic relationship.
Unlike the Pearson correlation, the Spearman correlation does not
assume that both datasets are normally distributed.
The p-value roughly indicates the probability of an uncorrelated system
producing datasets that have a Spearman correlation at least as extreme
as the one computed from these datasets. The p-values are not entirely
reliable but are probably reasonable for datasets larger than 500 or so.
'''
__snake_case = '''
Args:
predictions (`List[float]`): Predicted labels, as returned by a model.
references (`List[float]`): Ground truth labels.
return_pvalue (`bool`): If `True`, returns the p-value. If `False`, returns
only the spearmanr score. Defaults to `False`.
Returns:
spearmanr (`float`): Spearman correlation coefficient.
p-value (`float`): p-value. **Note**: is only returned if `return_pvalue=True` is input.
Examples:
Example 1:
>>> spearmanr_metric = datasets.load_metric("spearmanr")
>>> results = spearmanr_metric.compute(references=[1, 2, 3, 4, 5], predictions=[10, 9, 2.5, 6, 4])
>>> print(results)
{\'spearmanr\': -0.7}
Example 2:
>>> spearmanr_metric = datasets.load_metric("spearmanr")
>>> results = spearmanr_metric.compute(references=[1, 2, 3, 4, 5],
... predictions=[10, 9, 2.5, 6, 4],
... return_pvalue=True)
>>> print(results[\'spearmanr\'])
-0.7
>>> print(round(results[\'spearmanr_pvalue\'], 2))
0.19
'''
__snake_case = r'''\
@book{kokoska2000crc,
title={CRC standard probability and statistics tables and formulae},
author={Kokoska, Stephen and Zwillinger, Daniel},
year={2000},
publisher={Crc Press}
}
@article{2020SciPy-NMeth,
author = {Virtanen, Pauli and Gommers, Ralf and Oliphant, Travis E. and
Haberland, Matt and Reddy, Tyler and Cournapeau, David and
Burovski, Evgeni and Peterson, Pearu and Weckesser, Warren and
Bright, Jonathan and {van der Walt}, St{\'e}fan J. and
Brett, Matthew and Wilson, Joshua and Millman, K. Jarrod and
Mayorov, Nikolay and Nelson, Andrew R. J. and Jones, Eric and
Kern, Robert and Larson, Eric and Carey, C J and
Polat, {\.I}lhan and Feng, Yu and Moore, Eric W. and
{VanderPlas}, Jake and Laxalde, Denis and Perktold, Josef and
Cimrman, Robert and Henriksen, Ian and Quintero, E. A. and
Harris, Charles R. and Archibald, Anne M. and
Ribeiro, Ant{\^o}nio H. and Pedregosa, Fabian and
{van Mulbregt}, Paul and {SciPy 1.0 Contributors}},
title = {{{SciPy} 1.0: Fundamental Algorithms for Scientific
Computing in Python}},
journal = {Nature Methods},
year = {2020},
volume = {17},
pages = {261--272},
adsurl = {https://rdcu.be/b08Wh},
doi = {10.1038/s41592-019-0686-2},
}
'''
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class __snake_case ( datasets.Metric ):
def UpperCAmelCase__ ( self ) -> int:
'''simple docstring'''
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
'''predictions''': datasets.Value('''float''' ),
'''references''': datasets.Value('''float''' ),
} ) , reference_urls=['''https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.spearmanr.html'''] , )
def UpperCAmelCase__ ( self , snake_case__ , snake_case__ , snake_case__=False ) -> int:
'''simple docstring'''
UpperCAmelCase : Any =spearmanr(snake_case__ , snake_case__ )
if return_pvalue:
return {"spearmanr": results[0], "spearmanr_pvalue": results[1]}
else:
return {"spearmanr": results[0]}
| 348 | import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__snake_case = logging.get_logger(__name__)
__snake_case = {
'''asapp/sew-d-tiny-100k''': '''https://huggingface.co/asapp/sew-d-tiny-100k/resolve/main/config.json''',
# See all SEW-D models at https://huggingface.co/models?filter=sew-d
}
class __snake_case ( lowerCamelCase__ ):
__lowerCamelCase : Optional[Any] = """sew-d"""
def __init__( self , snake_case__=32 , snake_case__=768 , snake_case__=12 , snake_case__=12 , snake_case__=3072 , snake_case__=2 , snake_case__=512 , snake_case__=256 , snake_case__=True , snake_case__=True , snake_case__=("p2c", "c2p") , snake_case__="layer_norm" , snake_case__="gelu_python" , snake_case__=0.1 , snake_case__=0.1 , snake_case__=0.1 , snake_case__=0.0 , snake_case__=0.1 , snake_case__=0.02 , snake_case__=1e-7 , snake_case__=1e-5 , snake_case__="group" , snake_case__="gelu" , snake_case__=(64, 128, 128, 128, 128, 256, 256, 256, 256, 512, 512, 512, 512) , snake_case__=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1) , snake_case__=(10, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1) , snake_case__=False , snake_case__=128 , snake_case__=16 , snake_case__=True , snake_case__=0.05 , snake_case__=10 , snake_case__=2 , snake_case__=0.0 , snake_case__=10 , snake_case__=0 , snake_case__="mean" , snake_case__=False , snake_case__=False , snake_case__=256 , snake_case__=0 , snake_case__=1 , snake_case__=2 , **snake_case__ , ) -> int:
'''simple docstring'''
super().__init__(**snake_case__ , pad_token_id=snake_case__ , bos_token_id=snake_case__ , eos_token_id=snake_case__ )
UpperCAmelCase : Union[str, Any] =hidden_size
UpperCAmelCase : Union[str, Any] =feat_extract_norm
UpperCAmelCase : Optional[Any] =feat_extract_activation
UpperCAmelCase : List[str] =list(snake_case__ )
UpperCAmelCase : int =list(snake_case__ )
UpperCAmelCase : List[str] =list(snake_case__ )
UpperCAmelCase : str =conv_bias
UpperCAmelCase : Tuple =num_conv_pos_embeddings
UpperCAmelCase : Dict =num_conv_pos_embedding_groups
UpperCAmelCase : str =len(self.conv_dim )
UpperCAmelCase : Dict =num_hidden_layers
UpperCAmelCase : Optional[int] =intermediate_size
UpperCAmelCase : List[Any] =squeeze_factor
UpperCAmelCase : str =max_position_embeddings
UpperCAmelCase : int =position_buckets
UpperCAmelCase : Optional[int] =share_att_key
UpperCAmelCase : Optional[int] =relative_attention
UpperCAmelCase : Tuple =norm_rel_ebd
UpperCAmelCase : List[Any] =list(snake_case__ )
UpperCAmelCase : Dict =hidden_act
UpperCAmelCase : Optional[int] =num_attention_heads
UpperCAmelCase : Any =hidden_dropout
UpperCAmelCase : str =attention_dropout
UpperCAmelCase : Union[str, Any] =activation_dropout
UpperCAmelCase : str =feat_proj_dropout
UpperCAmelCase : Union[str, Any] =final_dropout
UpperCAmelCase : Optional[int] =layer_norm_eps
UpperCAmelCase : str =feature_layer_norm_eps
UpperCAmelCase : str =initializer_range
UpperCAmelCase : Any =vocab_size
if (
(len(self.conv_stride ) != self.num_feat_extract_layers)
or (len(self.conv_kernel ) != self.num_feat_extract_layers)
or (len(self.conv_dim ) != self.num_feat_extract_layers)
):
raise ValueError(
'''Configuration for convolutional layers is incorrect.'''
'''It is required that `len(config.conv_dim)` == `len(config.conv_stride)` == `len(config.conv_kernel)`,'''
f'''but is `len(config.conv_dim) = {len(self.conv_dim )}`, `len(config.conv_stride)'''
f'''= {len(self.conv_stride )}`, `len(config.conv_kernel) = {len(self.conv_kernel )}`.''' )
# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
UpperCAmelCase : Union[str, Any] =apply_spec_augment
UpperCAmelCase : Optional[Any] =mask_time_prob
UpperCAmelCase : Tuple =mask_time_length
UpperCAmelCase : str =mask_time_min_masks
UpperCAmelCase : Optional[int] =mask_feature_prob
UpperCAmelCase : Optional[Any] =mask_feature_length
UpperCAmelCase : List[Any] =mask_feature_min_masks
# ctc loss
UpperCAmelCase : str =ctc_loss_reduction
UpperCAmelCase : Optional[int] =ctc_zero_infinity
# sequence classification
UpperCAmelCase : Union[str, Any] =use_weighted_layer_sum
UpperCAmelCase : int =classifier_proj_size
@property
def UpperCAmelCase__ ( self ) -> List[Any]:
'''simple docstring'''
return functools.reduce(operator.mul , self.conv_stride , 1 )
| 348 | 1 |
# A Bipartite Graph is a graph whose vertices can be divided into two independent sets,
# U and V such that every edge (u, v) either connects a vertex from U to V or a vertex
# from V to U. In other words, for every edge (u, v), either u belongs to U and v to V,
# or u belongs to V and v to U. We can also say that there is no edge that connects
# vertices of same set.
def lowerCAmelCase_ ( __lowerCAmelCase )-> Optional[Any]:
'''simple docstring'''
UpperCAmelCase : Any =[False] * len(__lowerCAmelCase )
UpperCAmelCase : Optional[Any] =[-1] * len(__lowerCAmelCase )
def dfs(__lowerCAmelCase , __lowerCAmelCase ):
UpperCAmelCase : int =True
UpperCAmelCase : Union[str, Any] =c
for u in graph[v]:
if not visited[u]:
dfs(__lowerCAmelCase , 1 - c )
for i in range(len(__lowerCAmelCase ) ):
if not visited[i]:
dfs(__lowerCAmelCase , 0 )
for i in range(len(__lowerCAmelCase ) ):
for j in graph[i]:
if color[i] == color[j]:
return False
return True
# Adjacency list of graph
__snake_case = {0: [1, 3], 1: [0, 2], 2: [1, 3], 3: [0, 2], 4: []}
print(check_bipartite_dfs(graph))
| 348 | import os
from argparse import ArgumentParser
from typing import List
import torch.utils.data
from datasets import Dataset, IterableDataset
from datasets.distributed import split_dataset_by_node
__snake_case = 4
__snake_case = 3
class __snake_case ( lowerCamelCase__ ):
pass
def lowerCAmelCase_ ( __lowerCAmelCase )-> List[str]:
'''simple docstring'''
for shard in shards:
for i in range(__lowerCAmelCase ):
yield {"i": i, "shard": shard}
def lowerCAmelCase_ ( )-> Optional[int]:
'''simple docstring'''
UpperCAmelCase : List[str] =int(os.environ['''RANK'''] )
UpperCAmelCase : Optional[Any] =int(os.environ['''WORLD_SIZE'''] )
UpperCAmelCase : List[Any] =ArgumentParser()
parser.add_argument('''--streaming''' , type=__lowerCAmelCase )
parser.add_argument('''--local_rank''' , type=__lowerCAmelCase )
parser.add_argument('''--num_workers''' , type=__lowerCAmelCase , default=0 )
UpperCAmelCase : Any =parser.parse_args()
UpperCAmelCase : List[str] =args.streaming
UpperCAmelCase : Tuple =args.num_workers
UpperCAmelCase : int ={'''shards''': [f'''shard_{shard_idx}''' for shard_idx in range(__lowerCAmelCase )]}
UpperCAmelCase : Optional[int] =IterableDataset.from_generator(__lowerCAmelCase , gen_kwargs=__lowerCAmelCase )
if not streaming:
UpperCAmelCase : List[Any] =Dataset.from_list(list(__lowerCAmelCase ) )
UpperCAmelCase : Dict =split_dataset_by_node(__lowerCAmelCase , rank=__lowerCAmelCase , world_size=__lowerCAmelCase )
UpperCAmelCase : List[Any] =torch.utils.data.DataLoader(__lowerCAmelCase , num_workers=__lowerCAmelCase )
UpperCAmelCase : Dict =NUM_SHARDS * NUM_ITEMS_PER_SHARD
UpperCAmelCase : str =full_size // world_size
expected_local_size += int(rank < (full_size % world_size) )
UpperCAmelCase : List[Any] =sum(1 for _ in dataloader )
if local_size != expected_local_size:
raise FailedTestError(f'''local_size {local_size} != expected_local_size {expected_local_size}''' )
if __name__ == "__main__":
main()
| 348 | 1 |
from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_herbert import HerbertTokenizer
__snake_case = logging.get_logger(__name__)
__snake_case = {'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', '''tokenizer_file''': '''tokenizer.json'''}
__snake_case = {
'''vocab_file''': {
'''allegro/herbert-base-cased''': '''https://huggingface.co/allegro/herbert-base-cased/resolve/main/vocab.json'''
},
'''merges_file''': {
'''allegro/herbert-base-cased''': '''https://huggingface.co/allegro/herbert-base-cased/resolve/main/merges.txt'''
},
}
__snake_case = {'''allegro/herbert-base-cased''': 5_14}
__snake_case = {}
class __snake_case ( lowerCamelCase__ ):
__lowerCamelCase : int = VOCAB_FILES_NAMES
__lowerCamelCase : int = PRETRAINED_VOCAB_FILES_MAP
__lowerCamelCase : Union[str, Any] = PRETRAINED_INIT_CONFIGURATION
__lowerCamelCase : Any = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
__lowerCamelCase : List[Any] = HerbertTokenizer
def __init__( self , snake_case__=None , snake_case__=None , snake_case__=None , snake_case__="<s>" , snake_case__="<unk>" , snake_case__="<pad>" , snake_case__="<mask>" , snake_case__="</s>" , **snake_case__ , ) -> int:
'''simple docstring'''
super().__init__(
snake_case__ , snake_case__ , tokenizer_file=snake_case__ , cls_token=snake_case__ , unk_token=snake_case__ , pad_token=snake_case__ , mask_token=snake_case__ , sep_token=snake_case__ , **snake_case__ , )
def UpperCAmelCase__ ( self , snake_case__ , snake_case__ = None ) -> List[int]:
'''simple docstring'''
UpperCAmelCase : int =[self.cls_token_id]
UpperCAmelCase : Any =[self.sep_token_id]
if token_ids_a is None:
return cls + token_ids_a + sep
return cls + token_ids_a + sep + token_ids_a + sep
def UpperCAmelCase__ ( self , snake_case__ , snake_case__ = None , snake_case__ = False ) -> List[int]:
'''simple docstring'''
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=snake_case__ , token_ids_a=snake_case__ , already_has_special_tokens=snake_case__ )
if token_ids_a is None:
return [1] + ([0] * len(snake_case__ )) + [1]
return [1] + ([0] * len(snake_case__ )) + [1] + ([0] * len(snake_case__ )) + [1]
def UpperCAmelCase__ ( self , snake_case__ , snake_case__ = None ) -> List[int]:
'''simple docstring'''
UpperCAmelCase : str =[self.sep_token_id]
UpperCAmelCase : int =[self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]
def UpperCAmelCase__ ( self , snake_case__ , snake_case__ = None ) -> Tuple[str]:
'''simple docstring'''
UpperCAmelCase : Any =self._tokenizer.model.save(snake_case__ , name=snake_case__ )
return tuple(snake_case__ )
| 348 | from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__snake_case = {'''configuration_opt''': ['''OPT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''OPTConfig''']}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__snake_case = [
'''OPT_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''OPTForCausalLM''',
'''OPTModel''',
'''OPTPreTrainedModel''',
'''OPTForSequenceClassification''',
'''OPTForQuestionAnswering''',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__snake_case = ['''TFOPTForCausalLM''', '''TFOPTModel''', '''TFOPTPreTrainedModel''']
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__snake_case = [
'''FlaxOPTForCausalLM''',
'''FlaxOPTModel''',
'''FlaxOPTPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_opt import OPT_PRETRAINED_CONFIG_ARCHIVE_MAP, OPTConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_opt import (
OPT_PRETRAINED_MODEL_ARCHIVE_LIST,
OPTForCausalLM,
OPTForQuestionAnswering,
OPTForSequenceClassification,
OPTModel,
OPTPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_opt import TFOPTForCausalLM, TFOPTModel, TFOPTPreTrainedModel
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_opt import FlaxOPTForCausalLM, FlaxOPTModel, FlaxOPTPreTrainedModel
else:
import sys
__snake_case = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 348 | 1 |
import json
import os
import tempfile
import transformers
import datasets
from utils import generate_example_dataset, get_duration
__snake_case = 50_00_00
__snake_case , __snake_case = os.path.split(__file__)
__snake_case = os.path.join(RESULTS_BASEPATH, '''results''', RESULTS_FILENAME.replace('''.py''', '''.json'''))
@get_duration
def lowerCAmelCase_ ( __lowerCAmelCase , **__lowerCAmelCase )-> int:
'''simple docstring'''
UpperCAmelCase : Optional[Any] =dataset.map(**__lowerCAmelCase )
@get_duration
def lowerCAmelCase_ ( __lowerCAmelCase , **__lowerCAmelCase )-> Tuple:
'''simple docstring'''
UpperCAmelCase : Union[str, Any] =dataset.filter(**__lowerCAmelCase )
def lowerCAmelCase_ ( )-> Optional[int]:
'''simple docstring'''
UpperCAmelCase : Any ={'''num examples''': SPEED_TEST_N_EXAMPLES}
with tempfile.TemporaryDirectory() as tmp_dir:
UpperCAmelCase : Dict =datasets.Features({'''text''': datasets.Value('''string''' ), '''numbers''': datasets.Value('''float32''' )} )
UpperCAmelCase : str =generate_example_dataset(
os.path.join(__lowerCAmelCase , '''dataset.arrow''' ) , __lowerCAmelCase , num_examples=__lowerCAmelCase )
UpperCAmelCase : Tuple =transformers.AutoTokenizer.from_pretrained('''bert-base-cased''' , use_fast=__lowerCAmelCase )
def tokenize(__lowerCAmelCase ):
return tokenizer(examples['''text'''] )
UpperCAmelCase : List[str] =map(__lowerCAmelCase )
UpperCAmelCase : Union[str, Any] =map(__lowerCAmelCase , batched=__lowerCAmelCase )
UpperCAmelCase : int =map(__lowerCAmelCase , function=lambda __lowerCAmelCase : None , batched=__lowerCAmelCase )
with dataset.formatted_as(type='''numpy''' ):
UpperCAmelCase : Optional[Any] =map(__lowerCAmelCase , function=lambda __lowerCAmelCase : None , batched=__lowerCAmelCase )
with dataset.formatted_as(type='''pandas''' ):
UpperCAmelCase : str =map(__lowerCAmelCase , function=lambda __lowerCAmelCase : None , batched=__lowerCAmelCase )
with dataset.formatted_as(type='''torch''' , columns='''numbers''' ):
UpperCAmelCase : str =map(__lowerCAmelCase , function=lambda __lowerCAmelCase : None , batched=__lowerCAmelCase )
with dataset.formatted_as(type='''tensorflow''' , columns='''numbers''' ):
UpperCAmelCase : Any =map(__lowerCAmelCase , function=lambda __lowerCAmelCase : None , batched=__lowerCAmelCase )
UpperCAmelCase : Dict =map(__lowerCAmelCase , function=__lowerCAmelCase , batched=__lowerCAmelCase )
UpperCAmelCase : Optional[int] =filter(__lowerCAmelCase )
# Activate later when tokenizer support batched inputs
# with dataset.formatted_as(type='numpy'):
# times[func.__name__ + " fast-tokenizer batched numpy"] = func(dataset, function=tokenize, batched=True)
with open(__lowerCAmelCase , '''wb''' ) as f:
f.write(json.dumps(__lowerCAmelCase ).encode('''utf-8''' ) )
if __name__ == "__main__": # useful to run the profiler
benchmark_map_filter()
| 348 | import tempfile
import unittest
import numpy as np
import transformers
from transformers import GPTaTokenizer, GPTJConfig, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, tooslow
from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import jax
import jax.numpy as jnp
from transformers.modeling_flax_pytorch_utils import (
convert_pytorch_state_dict_to_flax,
load_flax_weights_in_pytorch_model,
)
from transformers.models.gptj.modeling_flax_gptj import FlaxGPTJForCausalLM, FlaxGPTJModel
if is_torch_available():
import torch
class __snake_case :
def __init__( self , snake_case__ , snake_case__=14 , snake_case__=7 , snake_case__=True , snake_case__=True , snake_case__=False , snake_case__=True , snake_case__=99 , snake_case__=32 , snake_case__=4 , snake_case__=4 , snake_case__=4 , snake_case__=37 , snake_case__="gelu" , snake_case__=0.1 , snake_case__=0.1 , snake_case__=512 , snake_case__=0.02 , ) -> str:
'''simple docstring'''
UpperCAmelCase : str =parent
UpperCAmelCase : Tuple =batch_size
UpperCAmelCase : Optional[int] =seq_length
UpperCAmelCase : Optional[int] =is_training
UpperCAmelCase : Tuple =use_input_mask
UpperCAmelCase : List[Any] =use_token_type_ids
UpperCAmelCase : Optional[Any] =use_labels
UpperCAmelCase : Union[str, Any] =vocab_size
UpperCAmelCase : List[Any] =hidden_size
UpperCAmelCase : Optional[int] =rotary_dim
UpperCAmelCase : Union[str, Any] =num_hidden_layers
UpperCAmelCase : List[Any] =num_attention_heads
UpperCAmelCase : Dict =intermediate_size
UpperCAmelCase : Union[str, Any] =hidden_act
UpperCAmelCase : Any =hidden_dropout_prob
UpperCAmelCase : Dict =attention_probs_dropout_prob
UpperCAmelCase : Union[str, Any] =max_position_embeddings
UpperCAmelCase : str =initializer_range
UpperCAmelCase : Optional[int] =None
UpperCAmelCase : List[Any] =vocab_size - 1
UpperCAmelCase : Optional[Any] =vocab_size - 1
UpperCAmelCase : List[Any] =vocab_size - 1
def UpperCAmelCase__ ( self ) -> Optional[int]:
'''simple docstring'''
UpperCAmelCase : List[str] =ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
UpperCAmelCase : List[Any] =None
if self.use_input_mask:
UpperCAmelCase : Optional[Any] =random_attention_mask([self.batch_size, self.seq_length] )
UpperCAmelCase : Dict =GPTJConfig(
vocab_size=self.vocab_size , n_embd=self.hidden_size , n_layer=self.num_hidden_layers , n_head=self.num_attention_heads , n_positions=self.max_position_embeddings , use_cache=snake_case__ , bos_token_id=self.bos_token_id , eos_token_id=self.eos_token_id , pad_token_id=self.pad_token_id , rotary_dim=self.rotary_dim , )
return (config, input_ids, input_mask)
def UpperCAmelCase__ ( self ) -> Dict:
'''simple docstring'''
UpperCAmelCase : Tuple =self.prepare_config_and_inputs()
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase : Union[str, Any] =config_and_inputs
UpperCAmelCase : Tuple ={'''input_ids''': input_ids, '''attention_mask''': attention_mask}
return config, inputs_dict
def UpperCAmelCase__ ( self , snake_case__ , snake_case__ , snake_case__ , snake_case__ ) -> List[Any]:
'''simple docstring'''
UpperCAmelCase : Any =20
UpperCAmelCase : Any =model_class_name(snake_case__ )
UpperCAmelCase : str =model.init_cache(input_ids.shape[0] , snake_case__ )
UpperCAmelCase : Any =jnp.ones((input_ids.shape[0], max_decoder_length) , dtype='''i4''' )
UpperCAmelCase : Optional[Any] =jnp.broadcast_to(
jnp.arange(input_ids.shape[-1] - 1 )[None, :] , (input_ids.shape[0], input_ids.shape[-1] - 1) )
UpperCAmelCase : Optional[Any] =model(
input_ids[:, :-1] , attention_mask=snake_case__ , past_key_values=snake_case__ , position_ids=snake_case__ , )
UpperCAmelCase : List[str] =jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]] , dtype='''i4''' )
UpperCAmelCase : Optional[Any] =model(
input_ids[:, -1:] , attention_mask=snake_case__ , past_key_values=outputs_cache.past_key_values , position_ids=snake_case__ , )
UpperCAmelCase : List[Any] =model(snake_case__ )
UpperCAmelCase : Any =np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5]) ) )
self.parent.assertTrue(diff < 1e-3 , msg=f'''Max diff is {diff}''' )
def UpperCAmelCase__ ( self , snake_case__ , snake_case__ , snake_case__ , snake_case__ ) -> List[Any]:
'''simple docstring'''
UpperCAmelCase : Dict =20
UpperCAmelCase : Dict =model_class_name(snake_case__ )
UpperCAmelCase : Tuple =jnp.concatenate(
[attention_mask, jnp.zeros((attention_mask.shape[0], max_decoder_length - attention_mask.shape[1]) )] , axis=-1 , )
UpperCAmelCase : Dict =model.init_cache(input_ids.shape[0] , snake_case__ )
UpperCAmelCase : int =jnp.broadcast_to(
jnp.arange(input_ids.shape[-1] - 1 )[None, :] , (input_ids.shape[0], input_ids.shape[-1] - 1) )
UpperCAmelCase : Optional[Any] =model(
input_ids[:, :-1] , attention_mask=snake_case__ , past_key_values=snake_case__ , position_ids=snake_case__ , )
UpperCAmelCase : Any =jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]] , dtype='''i4''' )
UpperCAmelCase : str =model(
input_ids[:, -1:] , past_key_values=outputs_cache.past_key_values , attention_mask=snake_case__ , position_ids=snake_case__ , )
UpperCAmelCase : Any =model(snake_case__ , attention_mask=snake_case__ )
UpperCAmelCase : Dict =np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5]) ) )
self.parent.assertTrue(diff < 1e-3 , msg=f'''Max diff is {diff}''' )
@require_flax
class __snake_case ( lowerCamelCase__ , lowerCamelCase__ , unittest.TestCase ):
__lowerCamelCase : Tuple = (FlaxGPTJModel, FlaxGPTJForCausalLM) if is_flax_available() else ()
__lowerCamelCase : Optional[Any] = (FlaxGPTJForCausalLM,) if is_flax_available() else ()
def UpperCAmelCase__ ( self ) -> int:
'''simple docstring'''
UpperCAmelCase : Union[str, Any] =FlaxGPTJModelTester(self )
def UpperCAmelCase__ ( self ) -> str:
'''simple docstring'''
for model_class_name in self.all_model_classes:
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase : Dict =self.model_tester.prepare_config_and_inputs()
self.model_tester.check_use_cache_forward(snake_case__ , snake_case__ , snake_case__ , snake_case__ )
def UpperCAmelCase__ ( self ) -> Dict:
'''simple docstring'''
for model_class_name in self.all_model_classes:
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase : int =self.model_tester.prepare_config_and_inputs()
self.model_tester.check_use_cache_forward_with_attn_mask(
snake_case__ , snake_case__ , snake_case__ , snake_case__ )
@tooslow
def UpperCAmelCase__ ( self ) -> Union[str, Any]:
'''simple docstring'''
UpperCAmelCase : Tuple =GPTaTokenizer.from_pretrained('''gpt2''' , pad_token='''<|endoftext|>''' , padding_side='''left''' )
UpperCAmelCase : Optional[Any] =tokenizer(['''Hello this is a long string''', '''Hey'''] , return_tensors='''np''' , padding=snake_case__ , truncation=snake_case__ )
UpperCAmelCase : Optional[int] =FlaxGPTJForCausalLM.from_pretrained('''EleutherAI/gpt-j-6B''' )
UpperCAmelCase : str =False
UpperCAmelCase : Union[str, Any] =model.config.eos_token_id
UpperCAmelCase : List[Any] =jax.jit(model.generate )
UpperCAmelCase : Dict =jit_generate(
inputs['''input_ids'''] , attention_mask=inputs['''attention_mask'''] , pad_token_id=tokenizer.pad_token_id ).sequences
UpperCAmelCase : Any =tokenizer.batch_decode(snake_case__ , skip_special_tokens=snake_case__ )
UpperCAmelCase : Tuple =[
'''Hello this is a long string of text.\n\nI\'m trying to get the text of the''',
'''Hey, I\'m a little late to the party. I\'m going to''',
]
self.assertListEqual(snake_case__ , snake_case__ )
@is_pt_flax_cross_test
def UpperCAmelCase__ ( self ) -> List[Any]:
'''simple docstring'''
UpperCAmelCase , UpperCAmelCase : List[str] =self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__ ):
# prepare inputs
UpperCAmelCase : Union[str, Any] =self._prepare_for_class(snake_case__ , snake_case__ )
UpperCAmelCase : List[str] ={k: torch.tensor(v.tolist() ) for k, v in prepared_inputs_dict.items()}
# load corresponding PyTorch class
UpperCAmelCase : Any =model_class.__name__[4:] # Skip the "Flax" at the beginning
UpperCAmelCase : Any =getattr(snake_case__ , snake_case__ )
UpperCAmelCase , UpperCAmelCase : Union[str, Any] =pt_inputs['''input_ids'''].shape
UpperCAmelCase : Tuple =np.random.randint(0 , seq_length - 1 , size=(batch_size,) )
for batch_idx, start_index in enumerate(snake_case__ ):
UpperCAmelCase : int =0
UpperCAmelCase : Optional[int] =1
UpperCAmelCase : Optional[int] =0
UpperCAmelCase : Union[str, Any] =1
UpperCAmelCase : List[str] =pt_model_class(snake_case__ ).eval()
UpperCAmelCase : Optional[int] =model_class(snake_case__ , dtype=jnp.floataa )
UpperCAmelCase : Any =convert_pytorch_state_dict_to_flax(pt_model.state_dict() , snake_case__ )
UpperCAmelCase : Union[str, Any] =fx_state
with torch.no_grad():
UpperCAmelCase : Any =pt_model(**snake_case__ ).to_tuple()
UpperCAmelCase : Dict =fx_model(**snake_case__ ).to_tuple()
self.assertEqual(len(snake_case__ ) , len(snake_case__ ) , '''Output lengths differ between Flax and PyTorch''' )
for fx_output, pt_output in zip(snake_case__ , snake_case__ ):
self.assert_almost_equals(fx_output[:, -1] , pt_output[:, -1].numpy() , 4e-2 )
with tempfile.TemporaryDirectory() as tmpdirname:
pt_model.save_pretrained(snake_case__ )
UpperCAmelCase : str =model_class.from_pretrained(snake_case__ , from_pt=snake_case__ )
UpperCAmelCase : int =fx_model_loaded(**snake_case__ ).to_tuple()
self.assertEqual(
len(snake_case__ ) , len(snake_case__ ) , '''Output lengths differ between Flax and PyTorch''' )
for fx_output_loaded, pt_output in zip(snake_case__ , snake_case__ ):
self.assert_almost_equals(fx_output_loaded[:, -1] , pt_output[:, -1].numpy() , 4e-2 )
@is_pt_flax_cross_test
def UpperCAmelCase__ ( self ) -> Optional[Any]:
'''simple docstring'''
UpperCAmelCase , UpperCAmelCase : Any =self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__ ):
# prepare inputs
UpperCAmelCase : Union[str, Any] =self._prepare_for_class(snake_case__ , snake_case__ )
UpperCAmelCase : Union[str, Any] ={k: torch.tensor(v.tolist() ) for k, v in prepared_inputs_dict.items()}
# load corresponding PyTorch class
UpperCAmelCase : int =model_class.__name__[4:] # Skip the "Flax" at the beginning
UpperCAmelCase : int =getattr(snake_case__ , snake_case__ )
UpperCAmelCase : Dict =pt_model_class(snake_case__ ).eval()
UpperCAmelCase : str =model_class(snake_case__ , dtype=jnp.floataa )
UpperCAmelCase : Optional[Any] =load_flax_weights_in_pytorch_model(snake_case__ , fx_model.params )
UpperCAmelCase , UpperCAmelCase : Optional[int] =pt_inputs['''input_ids'''].shape
UpperCAmelCase : Optional[int] =np.random.randint(0 , seq_length - 1 , size=(batch_size,) )
for batch_idx, start_index in enumerate(snake_case__ ):
UpperCAmelCase : str =0
UpperCAmelCase : Any =1
UpperCAmelCase : List[Any] =0
UpperCAmelCase : Tuple =1
# make sure weights are tied in PyTorch
pt_model.tie_weights()
with torch.no_grad():
UpperCAmelCase : Optional[Any] =pt_model(**snake_case__ ).to_tuple()
UpperCAmelCase : List[Any] =fx_model(**snake_case__ ).to_tuple()
self.assertEqual(len(snake_case__ ) , len(snake_case__ ) , '''Output lengths differ between Flax and PyTorch''' )
for fx_output, pt_output in zip(snake_case__ , snake_case__ ):
self.assert_almost_equals(fx_output[:, -1] , pt_output[:, -1].numpy() , 4e-2 )
with tempfile.TemporaryDirectory() as tmpdirname:
fx_model.save_pretrained(snake_case__ )
UpperCAmelCase : Tuple =pt_model_class.from_pretrained(snake_case__ , from_flax=snake_case__ )
with torch.no_grad():
UpperCAmelCase : Any =pt_model_loaded(**snake_case__ ).to_tuple()
self.assertEqual(
len(snake_case__ ) , len(snake_case__ ) , '''Output lengths differ between Flax and PyTorch''' )
for fx_output, pt_output in zip(snake_case__ , snake_case__ ):
self.assert_almost_equals(fx_output[:, -1] , pt_output[:, -1].numpy() , 4e-2 )
@tooslow
def UpperCAmelCase__ ( self ) -> List[str]:
'''simple docstring'''
for model_class_name in self.all_model_classes:
UpperCAmelCase : str =model_class_name.from_pretrained('''EleutherAI/gpt-j-6B''' )
UpperCAmelCase : Tuple =model(np.ones((1, 1) ) )
self.assertIsNotNone(snake_case__ )
| 348 | 1 |
import unittest
import numpy as np
from transformers import RobertaPreLayerNormConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_flax_available():
import jax.numpy as jnp
from transformers.models.roberta_prelayernorm.modeling_flax_roberta_prelayernorm import (
FlaxRobertaPreLayerNormForCausalLM,
FlaxRobertaPreLayerNormForMaskedLM,
FlaxRobertaPreLayerNormForMultipleChoice,
FlaxRobertaPreLayerNormForQuestionAnswering,
FlaxRobertaPreLayerNormForSequenceClassification,
FlaxRobertaPreLayerNormForTokenClassification,
FlaxRobertaPreLayerNormModel,
)
class __snake_case ( unittest.TestCase ):
def __init__( self , snake_case__ , snake_case__=13 , snake_case__=7 , snake_case__=True , snake_case__=True , snake_case__=True , snake_case__=True , snake_case__=99 , snake_case__=32 , snake_case__=5 , snake_case__=4 , snake_case__=37 , snake_case__="gelu" , snake_case__=0.1 , snake_case__=0.1 , snake_case__=512 , snake_case__=16 , snake_case__=2 , snake_case__=0.02 , snake_case__=4 , ) -> Optional[Any]:
'''simple docstring'''
UpperCAmelCase : Dict =parent
UpperCAmelCase : int =batch_size
UpperCAmelCase : str =seq_length
UpperCAmelCase : Optional[int] =is_training
UpperCAmelCase : str =use_attention_mask
UpperCAmelCase : List[Any] =use_token_type_ids
UpperCAmelCase : Any =use_labels
UpperCAmelCase : Union[str, Any] =vocab_size
UpperCAmelCase : int =hidden_size
UpperCAmelCase : str =num_hidden_layers
UpperCAmelCase : int =num_attention_heads
UpperCAmelCase : List[str] =intermediate_size
UpperCAmelCase : List[str] =hidden_act
UpperCAmelCase : int =hidden_dropout_prob
UpperCAmelCase : Dict =attention_probs_dropout_prob
UpperCAmelCase : Optional[Any] =max_position_embeddings
UpperCAmelCase : int =type_vocab_size
UpperCAmelCase : str =type_sequence_label_size
UpperCAmelCase : Union[str, Any] =initializer_range
UpperCAmelCase : List[str] =num_choices
def UpperCAmelCase__ ( self ) -> Dict:
'''simple docstring'''
UpperCAmelCase : Tuple =ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
UpperCAmelCase : str =None
if self.use_attention_mask:
UpperCAmelCase : Tuple =random_attention_mask([self.batch_size, self.seq_length] )
UpperCAmelCase : Optional[Any] =None
if self.use_token_type_ids:
UpperCAmelCase : str =ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
UpperCAmelCase : Dict =RobertaPreLayerNormConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , is_decoder=snake_case__ , initializer_range=self.initializer_range , )
return config, input_ids, token_type_ids, attention_mask
def UpperCAmelCase__ ( self ) -> Any:
'''simple docstring'''
UpperCAmelCase : Any =self.prepare_config_and_inputs()
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase : Optional[Any] =config_and_inputs
UpperCAmelCase : List[str] ={'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': attention_mask}
return config, inputs_dict
def UpperCAmelCase__ ( self ) -> Dict:
'''simple docstring'''
UpperCAmelCase : Union[str, Any] =self.prepare_config_and_inputs()
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase : Tuple =config_and_inputs
UpperCAmelCase : List[Any] =True
UpperCAmelCase : Tuple =floats_tensor([self.batch_size, self.seq_length, self.hidden_size] )
UpperCAmelCase : Tuple =ids_tensor([self.batch_size, self.seq_length] , vocab_size=2 )
return (
config,
input_ids,
token_type_ids,
encoder_hidden_states,
encoder_attention_mask,
)
@require_flax
# Copied from tests.models.roberta.test_modelling_flax_roberta.FlaxRobertaPreLayerNormModelTest with ROBERTA->ROBERTA_PRELAYERNORM,Roberta->RobertaPreLayerNorm,roberta-base->andreasmadsen/efficient_mlm_m0.40
class __snake_case ( lowerCamelCase__ , unittest.TestCase ):
__lowerCamelCase : Optional[Any] = True
__lowerCamelCase : List[str] = (
(
FlaxRobertaPreLayerNormModel,
FlaxRobertaPreLayerNormForCausalLM,
FlaxRobertaPreLayerNormForMaskedLM,
FlaxRobertaPreLayerNormForSequenceClassification,
FlaxRobertaPreLayerNormForTokenClassification,
FlaxRobertaPreLayerNormForMultipleChoice,
FlaxRobertaPreLayerNormForQuestionAnswering,
)
if is_flax_available()
else ()
)
def UpperCAmelCase__ ( self ) -> int:
'''simple docstring'''
UpperCAmelCase : List[Any] =FlaxRobertaPreLayerNormModelTester(self )
@slow
def UpperCAmelCase__ ( self ) -> Dict:
'''simple docstring'''
for model_class_name in self.all_model_classes:
UpperCAmelCase : Optional[int] =model_class_name.from_pretrained('''andreasmadsen/efficient_mlm_m0.40''' , from_pt=snake_case__ )
UpperCAmelCase : int =model(np.ones((1, 1) ) )
self.assertIsNotNone(snake_case__ )
@require_flax
class __snake_case ( unittest.TestCase ):
@slow
def UpperCAmelCase__ ( self ) -> Optional[Any]:
'''simple docstring'''
UpperCAmelCase : Any =FlaxRobertaPreLayerNormForMaskedLM.from_pretrained('''andreasmadsen/efficient_mlm_m0.40''' , from_pt=snake_case__ )
UpperCAmelCase : Tuple =np.array([[0, 3_1414, 232, 328, 740, 1140, 1_2695, 69, 4_6078, 1588, 2]] , dtype=jnp.intaa )
UpperCAmelCase : int =model(snake_case__ )[0]
UpperCAmelCase : List[Any] =[1, 11, 5_0265]
self.assertEqual(list(output.shape ) , snake_case__ )
# compare the actual values for a slice.
UpperCAmelCase : List[str] =np.array(
[[[40.4880, 18.0199, -5.2367], [-1.8877, -4.0885, 10.7085], [-2.2613, -5.6110, 7.2665]]] , dtype=np.floataa )
self.assertTrue(np.allclose(output[:, :3, :3] , snake_case__ , atol=1e-4 ) )
@slow
def UpperCAmelCase__ ( self ) -> Optional[Any]:
'''simple docstring'''
UpperCAmelCase : Union[str, Any] =FlaxRobertaPreLayerNormModel.from_pretrained('''andreasmadsen/efficient_mlm_m0.40''' , from_pt=snake_case__ )
UpperCAmelCase : Union[str, Any] =np.array([[0, 3_1414, 232, 328, 740, 1140, 1_2695, 69, 4_6078, 1588, 2]] , dtype=jnp.intaa )
UpperCAmelCase : int =model(snake_case__ )[0]
# compare the actual values for a slice.
UpperCAmelCase : Dict =np.array(
[[[0.0208, -0.0356, 0.0237], [-0.1569, -0.0411, -0.2626], [0.1879, 0.0125, -0.0089]]] , dtype=np.floataa )
self.assertTrue(np.allclose(output[:, :3, :3] , snake_case__ , atol=1e-4 ) )
| 348 | from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
__snake_case = {
'''configuration_bloom''': ['''BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''BloomConfig''', '''BloomOnnxConfig'''],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__snake_case = ['''BloomTokenizerFast''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__snake_case = [
'''BLOOM_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''BloomForCausalLM''',
'''BloomModel''',
'''BloomPreTrainedModel''',
'''BloomForSequenceClassification''',
'''BloomForTokenClassification''',
'''BloomForQuestionAnswering''',
]
if TYPE_CHECKING:
from .configuration_bloom import BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP, BloomConfig, BloomOnnxConfig
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_bloom_fast import BloomTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_bloom import (
BLOOM_PRETRAINED_MODEL_ARCHIVE_LIST,
BloomForCausalLM,
BloomForQuestionAnswering,
BloomForSequenceClassification,
BloomForTokenClassification,
BloomModel,
BloomPreTrainedModel,
)
else:
import sys
__snake_case = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 348 | 1 |
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
__snake_case = logging.get_logger(__name__)
__snake_case = {
'''microsoft/swin-tiny-patch4-window7-224''': (
'''https://huggingface.co/microsoft/swin-tiny-patch4-window7-224/resolve/main/config.json'''
),
# See all Swin models at https://huggingface.co/models?filter=swin
}
class __snake_case ( lowerCamelCase__ , lowerCamelCase__ ):
__lowerCamelCase : List[str] = """swin"""
__lowerCamelCase : int = {
"""num_attention_heads""": """num_heads""",
"""num_hidden_layers""": """num_layers""",
}
def __init__( self , snake_case__=224 , snake_case__=4 , snake_case__=3 , snake_case__=96 , snake_case__=[2, 2, 6, 2] , snake_case__=[3, 6, 12, 24] , snake_case__=7 , snake_case__=4.0 , snake_case__=True , snake_case__=0.0 , snake_case__=0.0 , snake_case__=0.1 , snake_case__="gelu" , snake_case__=False , snake_case__=0.02 , snake_case__=1e-5 , snake_case__=32 , snake_case__=None , snake_case__=None , **snake_case__ , ) -> Optional[int]:
'''simple docstring'''
super().__init__(**snake_case__ )
UpperCAmelCase : Union[str, Any] =image_size
UpperCAmelCase : Any =patch_size
UpperCAmelCase : List[str] =num_channels
UpperCAmelCase : Optional[int] =embed_dim
UpperCAmelCase : int =depths
UpperCAmelCase : str =len(snake_case__ )
UpperCAmelCase : int =num_heads
UpperCAmelCase : Optional[Any] =window_size
UpperCAmelCase : Any =mlp_ratio
UpperCAmelCase : List[str] =qkv_bias
UpperCAmelCase : Dict =hidden_dropout_prob
UpperCAmelCase : Union[str, Any] =attention_probs_dropout_prob
UpperCAmelCase : Optional[int] =drop_path_rate
UpperCAmelCase : Tuple =hidden_act
UpperCAmelCase : str =use_absolute_embeddings
UpperCAmelCase : Tuple =layer_norm_eps
UpperCAmelCase : List[Any] =initializer_range
UpperCAmelCase : Tuple =encoder_stride
# we set the hidden_size attribute in order to make Swin work with VisionEncoderDecoderModel
# this indicates the channel dimension after the last stage of the model
UpperCAmelCase : int =int(embed_dim * 2 ** (len(snake_case__ ) - 1) )
UpperCAmelCase : Union[str, Any] =['''stem'''] + [f'''stage{idx}''' for idx in range(1 , len(snake_case__ ) + 1 )]
UpperCAmelCase , UpperCAmelCase : Optional[Any] =get_aligned_output_features_output_indices(
out_features=snake_case__ , out_indices=snake_case__ , stage_names=self.stage_names )
class __snake_case ( lowerCamelCase__ ):
__lowerCamelCase : Dict = version.parse("""1.11""" )
@property
def UpperCAmelCase__ ( self ) -> Mapping[str, Mapping[int, str]]:
'''simple docstring'''
return OrderedDict(
[
('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
] )
@property
def UpperCAmelCase__ ( self ) -> float:
'''simple docstring'''
return 1e-4
| 348 | import os
from typing import Dict, List, Tuple, TypeVar, Union
__snake_case = TypeVar('''T''')
__snake_case = Union[List[T], Tuple[T, ...]]
__snake_case = Union[T, List[T], Dict[str, T]]
__snake_case = Union[str, bytes, os.PathLike]
| 348 | 1 |
def lowerCAmelCase_ ( __lowerCAmelCase = 10 )-> str:
'''simple docstring'''
if not isinstance(__lowerCAmelCase , __lowerCAmelCase ) or n < 0:
raise ValueError('''Invalid input''' )
UpperCAmelCase : List[Any] =10**n
UpperCAmelCase : Any =2_84_33 * (pow(2 , 7_83_04_57 , __lowerCAmelCase )) + 1
return str(number % modulus )
if __name__ == "__main__":
from doctest import testmod
testmod()
print(f'{solution(10) = }')
| 348 | import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_big_bird import BigBirdTokenizer
else:
__snake_case = None
__snake_case = logging.get_logger(__name__)
__snake_case = {'''vocab_file''': '''spiece.model''', '''tokenizer_file''': '''tokenizer.json'''}
__snake_case = {
'''vocab_file''': {
'''google/bigbird-roberta-base''': '''https://huggingface.co/google/bigbird-roberta-base/resolve/main/spiece.model''',
'''google/bigbird-roberta-large''': (
'''https://huggingface.co/google/bigbird-roberta-large/resolve/main/spiece.model'''
),
'''google/bigbird-base-trivia-itc''': (
'''https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/spiece.model'''
),
},
'''tokenizer_file''': {
'''google/bigbird-roberta-base''': (
'''https://huggingface.co/google/bigbird-roberta-base/resolve/main/tokenizer.json'''
),
'''google/bigbird-roberta-large''': (
'''https://huggingface.co/google/bigbird-roberta-large/resolve/main/tokenizer.json'''
),
'''google/bigbird-base-trivia-itc''': (
'''https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/tokenizer.json'''
),
},
}
__snake_case = {
'''google/bigbird-roberta-base''': 40_96,
'''google/bigbird-roberta-large''': 40_96,
'''google/bigbird-base-trivia-itc''': 40_96,
}
__snake_case = '''▁'''
class __snake_case ( lowerCamelCase__ ):
__lowerCamelCase : Dict = VOCAB_FILES_NAMES
__lowerCamelCase : List[Any] = PRETRAINED_VOCAB_FILES_MAP
__lowerCamelCase : Dict = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
__lowerCamelCase : List[str] = BigBirdTokenizer
__lowerCamelCase : Any = ["""input_ids""", """attention_mask"""]
__lowerCamelCase : List[int] = []
def __init__( self , snake_case__=None , snake_case__=None , snake_case__="<unk>" , snake_case__="<s>" , snake_case__="</s>" , snake_case__="<pad>" , snake_case__="[SEP]" , snake_case__="[MASK]" , snake_case__="[CLS]" , **snake_case__ , ) -> List[Any]:
'''simple docstring'''
UpperCAmelCase : Any =AddedToken(snake_case__ , lstrip=snake_case__ , rstrip=snake_case__ ) if isinstance(snake_case__ , snake_case__ ) else bos_token
UpperCAmelCase : Optional[int] =AddedToken(snake_case__ , lstrip=snake_case__ , rstrip=snake_case__ ) if isinstance(snake_case__ , snake_case__ ) else eos_token
UpperCAmelCase : List[str] =AddedToken(snake_case__ , lstrip=snake_case__ , rstrip=snake_case__ ) if isinstance(snake_case__ , snake_case__ ) else unk_token
UpperCAmelCase : Union[str, Any] =AddedToken(snake_case__ , lstrip=snake_case__ , rstrip=snake_case__ ) if isinstance(snake_case__ , snake_case__ ) else pad_token
UpperCAmelCase : int =AddedToken(snake_case__ , lstrip=snake_case__ , rstrip=snake_case__ ) if isinstance(snake_case__ , snake_case__ ) else cls_token
UpperCAmelCase : str =AddedToken(snake_case__ , lstrip=snake_case__ , rstrip=snake_case__ ) if isinstance(snake_case__ , snake_case__ ) else sep_token
# Mask token behave like a normal word, i.e. include the space before it
UpperCAmelCase : List[Any] =AddedToken(snake_case__ , lstrip=snake_case__ , rstrip=snake_case__ ) if isinstance(snake_case__ , snake_case__ ) else mask_token
super().__init__(
snake_case__ , tokenizer_file=snake_case__ , bos_token=snake_case__ , eos_token=snake_case__ , unk_token=snake_case__ , sep_token=snake_case__ , pad_token=snake_case__ , cls_token=snake_case__ , mask_token=snake_case__ , **snake_case__ , )
UpperCAmelCase : Tuple =vocab_file
UpperCAmelCase : Optional[int] =False if not self.vocab_file else True
def UpperCAmelCase__ ( self , snake_case__ , snake_case__ = None ) -> List[int]:
'''simple docstring'''
UpperCAmelCase : int =[self.sep_token_id]
UpperCAmelCase : Optional[int] =[self.cls_token_id]
if token_ids_a is None:
return cls + token_ids_a + sep
return cls + token_ids_a + sep + token_ids_a + sep
def UpperCAmelCase__ ( self , snake_case__ , snake_case__ = None , snake_case__ = False ) -> List[int]:
'''simple docstring'''
if already_has_special_tokens:
if token_ids_a is not None:
raise ValueError(
'''You should not supply a second sequence if the provided sequence of '''
'''ids is already formatted with special tokens for the model.''' )
return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]
if token_ids_a is None:
return [1] + ([0] * len(snake_case__ )) + [1]
return [1] + ([0] * len(snake_case__ )) + [1] + ([0] * len(snake_case__ )) + [1]
def UpperCAmelCase__ ( self , snake_case__ , snake_case__ = None ) -> List[int]:
'''simple docstring'''
UpperCAmelCase : Optional[Any] =[self.sep_token_id]
UpperCAmelCase : Optional[int] =[self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]
def UpperCAmelCase__ ( self , snake_case__ , snake_case__ = None ) -> Tuple[str]:
'''simple docstring'''
if not self.can_save_slow_tokenizer:
raise ValueError(
'''Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '''
'''tokenizer.''' )
if not os.path.isdir(snake_case__ ):
logger.error(f'''Vocabulary path ({save_directory}) should be a directory''' )
return
UpperCAmelCase : Optional[int] =os.path.join(
snake_case__ , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(snake_case__ ):
copyfile(self.vocab_file , snake_case__ )
return (out_vocab_file,)
| 348 | 1 |
import tempfile
import torch
from diffusers import PNDMScheduler
from .test_schedulers import SchedulerCommonTest
class __snake_case ( lowerCamelCase__ ):
__lowerCamelCase : Optional[Any] = (PNDMScheduler,)
__lowerCamelCase : Dict = (("""num_inference_steps""", 50),)
def UpperCAmelCase__ ( self , **snake_case__ ) -> Tuple:
'''simple docstring'''
UpperCAmelCase : List[Any] ={
'''num_train_timesteps''': 1000,
'''beta_start''': 0.0001,
'''beta_end''': 0.02,
'''beta_schedule''': '''linear''',
}
config.update(**snake_case__ )
return config
def UpperCAmelCase__ ( self , snake_case__=0 , **snake_case__ ) -> List[str]:
'''simple docstring'''
UpperCAmelCase : List[str] =dict(self.forward_default_kwargs )
UpperCAmelCase : Dict =kwargs.pop('''num_inference_steps''' , snake_case__ )
UpperCAmelCase : Any =self.dummy_sample
UpperCAmelCase : Optional[int] =0.1 * sample
UpperCAmelCase : Optional[Any] =[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
for scheduler_class in self.scheduler_classes:
UpperCAmelCase : List[str] =self.get_scheduler_config(**snake_case__ )
UpperCAmelCase : Dict =scheduler_class(**snake_case__ )
scheduler.set_timesteps(snake_case__ )
# copy over dummy past residuals
UpperCAmelCase : Optional[int] =dummy_past_residuals[:]
with tempfile.TemporaryDirectory() as tmpdirname:
scheduler.save_config(snake_case__ )
UpperCAmelCase : Optional[int] =scheduler_class.from_pretrained(snake_case__ )
new_scheduler.set_timesteps(snake_case__ )
# copy over dummy past residuals
UpperCAmelCase : int =dummy_past_residuals[:]
UpperCAmelCase : List[str] =scheduler.step_prk(snake_case__ , snake_case__ , snake_case__ , **snake_case__ ).prev_sample
UpperCAmelCase : List[str] =new_scheduler.step_prk(snake_case__ , snake_case__ , snake_case__ , **snake_case__ ).prev_sample
assert torch.sum(torch.abs(output - new_output ) ) < 1e-5, "Scheduler outputs are not identical"
UpperCAmelCase : Union[str, Any] =scheduler.step_plms(snake_case__ , snake_case__ , snake_case__ , **snake_case__ ).prev_sample
UpperCAmelCase : Optional[Any] =new_scheduler.step_plms(snake_case__ , snake_case__ , snake_case__ , **snake_case__ ).prev_sample
assert torch.sum(torch.abs(output - new_output ) ) < 1e-5, "Scheduler outputs are not identical"
def UpperCAmelCase__ ( self ) -> str:
'''simple docstring'''
pass
def UpperCAmelCase__ ( self , snake_case__=0 , **snake_case__ ) -> Optional[int]:
'''simple docstring'''
UpperCAmelCase : List[Any] =dict(self.forward_default_kwargs )
UpperCAmelCase : Tuple =kwargs.pop('''num_inference_steps''' , snake_case__ )
UpperCAmelCase : List[str] =self.dummy_sample
UpperCAmelCase : Tuple =0.1 * sample
UpperCAmelCase : Union[str, Any] =[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
for scheduler_class in self.scheduler_classes:
UpperCAmelCase : Optional[Any] =self.get_scheduler_config()
UpperCAmelCase : Any =scheduler_class(**snake_case__ )
scheduler.set_timesteps(snake_case__ )
# copy over dummy past residuals (must be after setting timesteps)
UpperCAmelCase : str =dummy_past_residuals[:]
with tempfile.TemporaryDirectory() as tmpdirname:
scheduler.save_config(snake_case__ )
UpperCAmelCase : Optional[int] =scheduler_class.from_pretrained(snake_case__ )
# copy over dummy past residuals
new_scheduler.set_timesteps(snake_case__ )
# copy over dummy past residual (must be after setting timesteps)
UpperCAmelCase : int =dummy_past_residuals[:]
UpperCAmelCase : List[str] =scheduler.step_prk(snake_case__ , snake_case__ , snake_case__ , **snake_case__ ).prev_sample
UpperCAmelCase : Optional[Any] =new_scheduler.step_prk(snake_case__ , snake_case__ , snake_case__ , **snake_case__ ).prev_sample
assert torch.sum(torch.abs(output - new_output ) ) < 1e-5, "Scheduler outputs are not identical"
UpperCAmelCase : str =scheduler.step_plms(snake_case__ , snake_case__ , snake_case__ , **snake_case__ ).prev_sample
UpperCAmelCase : Optional[Any] =new_scheduler.step_plms(snake_case__ , snake_case__ , snake_case__ , **snake_case__ ).prev_sample
assert torch.sum(torch.abs(output - new_output ) ) < 1e-5, "Scheduler outputs are not identical"
def UpperCAmelCase__ ( self , **snake_case__ ) -> Optional[int]:
'''simple docstring'''
UpperCAmelCase : Optional[int] =self.scheduler_classes[0]
UpperCAmelCase : List[str] =self.get_scheduler_config(**snake_case__ )
UpperCAmelCase : List[str] =scheduler_class(**snake_case__ )
UpperCAmelCase : str =10
UpperCAmelCase : List[Any] =self.dummy_model()
UpperCAmelCase : Optional[Any] =self.dummy_sample_deter
scheduler.set_timesteps(snake_case__ )
for i, t in enumerate(scheduler.prk_timesteps ):
UpperCAmelCase : int =model(snake_case__ , snake_case__ )
UpperCAmelCase : Dict =scheduler.step_prk(snake_case__ , snake_case__ , snake_case__ ).prev_sample
for i, t in enumerate(scheduler.plms_timesteps ):
UpperCAmelCase : Union[str, Any] =model(snake_case__ , snake_case__ )
UpperCAmelCase : Union[str, Any] =scheduler.step_plms(snake_case__ , snake_case__ , snake_case__ ).prev_sample
return sample
def UpperCAmelCase__ ( self ) -> Optional[int]:
'''simple docstring'''
UpperCAmelCase : List[str] =dict(self.forward_default_kwargs )
UpperCAmelCase : Any =kwargs.pop('''num_inference_steps''' , snake_case__ )
for scheduler_class in self.scheduler_classes:
UpperCAmelCase : str =self.get_scheduler_config()
UpperCAmelCase : str =scheduler_class(**snake_case__ )
UpperCAmelCase : List[str] =self.dummy_sample
UpperCAmelCase : str =0.1 * sample
if num_inference_steps is not None and hasattr(snake_case__ , '''set_timesteps''' ):
scheduler.set_timesteps(snake_case__ )
elif num_inference_steps is not None and not hasattr(snake_case__ , '''set_timesteps''' ):
UpperCAmelCase : Dict =num_inference_steps
# copy over dummy past residuals (must be done after set_timesteps)
UpperCAmelCase : Any =[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
UpperCAmelCase : List[Any] =dummy_past_residuals[:]
UpperCAmelCase : List[str] =scheduler.step_prk(snake_case__ , 0 , snake_case__ , **snake_case__ ).prev_sample
UpperCAmelCase : List[str] =scheduler.step_prk(snake_case__ , 1 , snake_case__ , **snake_case__ ).prev_sample
self.assertEqual(output_a.shape , sample.shape )
self.assertEqual(output_a.shape , output_a.shape )
UpperCAmelCase : Tuple =scheduler.step_plms(snake_case__ , 0 , snake_case__ , **snake_case__ ).prev_sample
UpperCAmelCase : List[Any] =scheduler.step_plms(snake_case__ , 1 , snake_case__ , **snake_case__ ).prev_sample
self.assertEqual(output_a.shape , sample.shape )
self.assertEqual(output_a.shape , output_a.shape )
def UpperCAmelCase__ ( self ) -> Union[str, Any]:
'''simple docstring'''
for timesteps in [100, 1000]:
self.check_over_configs(num_train_timesteps=snake_case__ )
def UpperCAmelCase__ ( self ) -> Any:
'''simple docstring'''
for steps_offset in [0, 1]:
self.check_over_configs(steps_offset=snake_case__ )
UpperCAmelCase : Any =self.scheduler_classes[0]
UpperCAmelCase : Optional[Any] =self.get_scheduler_config(steps_offset=1 )
UpperCAmelCase : Union[str, Any] =scheduler_class(**snake_case__ )
scheduler.set_timesteps(10 )
assert torch.equal(
scheduler.timesteps , torch.LongTensor(
[901, 851, 851, 801, 801, 751, 751, 701, 701, 651, 651, 601, 601, 501, 401, 301, 201, 101, 1] ) , )
def UpperCAmelCase__ ( self ) -> List[str]:
'''simple docstring'''
for beta_start, beta_end in zip([0.0001, 0.001] , [0.002, 0.02] ):
self.check_over_configs(beta_start=snake_case__ , beta_end=snake_case__ )
def UpperCAmelCase__ ( self ) -> Optional[int]:
'''simple docstring'''
for schedule in ["linear", "squaredcos_cap_v2"]:
self.check_over_configs(beta_schedule=snake_case__ )
def UpperCAmelCase__ ( self ) -> Optional[Any]:
'''simple docstring'''
for prediction_type in ["epsilon", "v_prediction"]:
self.check_over_configs(prediction_type=snake_case__ )
def UpperCAmelCase__ ( self ) -> Optional[Any]:
'''simple docstring'''
for t in [1, 5, 10]:
self.check_over_forward(time_step=snake_case__ )
def UpperCAmelCase__ ( self ) -> Optional[int]:
'''simple docstring'''
for t, num_inference_steps in zip([1, 5, 10] , [10, 50, 100] ):
self.check_over_forward(num_inference_steps=snake_case__ )
def UpperCAmelCase__ ( self ) -> Tuple:
'''simple docstring'''
UpperCAmelCase : str =27
for scheduler_class in self.scheduler_classes:
UpperCAmelCase : Dict =self.dummy_sample
UpperCAmelCase : Tuple =0.1 * sample
UpperCAmelCase : List[Any] =self.get_scheduler_config()
UpperCAmelCase : List[Any] =scheduler_class(**snake_case__ )
scheduler.set_timesteps(snake_case__ )
# before power of 3 fix, would error on first step, so we only need to do two
for i, t in enumerate(scheduler.prk_timesteps[:2] ):
UpperCAmelCase : int =scheduler.step_prk(snake_case__ , snake_case__ , snake_case__ ).prev_sample
def UpperCAmelCase__ ( self ) -> Dict:
'''simple docstring'''
with self.assertRaises(snake_case__ ):
UpperCAmelCase : Dict =self.scheduler_classes[0]
UpperCAmelCase : Tuple =self.get_scheduler_config()
UpperCAmelCase : Tuple =scheduler_class(**snake_case__ )
scheduler.step_plms(self.dummy_sample , 1 , self.dummy_sample ).prev_sample
def UpperCAmelCase__ ( self ) -> Optional[Any]:
'''simple docstring'''
UpperCAmelCase : List[str] =self.full_loop()
UpperCAmelCase : List[Any] =torch.sum(torch.abs(snake_case__ ) )
UpperCAmelCase : Dict =torch.mean(torch.abs(snake_case__ ) )
assert abs(result_sum.item() - 198.1318 ) < 1e-2
assert abs(result_mean.item() - 0.2580 ) < 1e-3
def UpperCAmelCase__ ( self ) -> Any:
'''simple docstring'''
UpperCAmelCase : Union[str, Any] =self.full_loop(prediction_type='''v_prediction''' )
UpperCAmelCase : Tuple =torch.sum(torch.abs(snake_case__ ) )
UpperCAmelCase : Union[str, Any] =torch.mean(torch.abs(snake_case__ ) )
assert abs(result_sum.item() - 67.3986 ) < 1e-2
assert abs(result_mean.item() - 0.0878 ) < 1e-3
def UpperCAmelCase__ ( self ) -> List[str]:
'''simple docstring'''
UpperCAmelCase : int =self.full_loop(set_alpha_to_one=snake_case__ , beta_start=0.01 )
UpperCAmelCase : Optional[Any] =torch.sum(torch.abs(snake_case__ ) )
UpperCAmelCase : Dict =torch.mean(torch.abs(snake_case__ ) )
assert abs(result_sum.item() - 230.0399 ) < 1e-2
assert abs(result_mean.item() - 0.2995 ) < 1e-3
def UpperCAmelCase__ ( self ) -> Tuple:
'''simple docstring'''
UpperCAmelCase : Dict =self.full_loop(set_alpha_to_one=snake_case__ , beta_start=0.01 )
UpperCAmelCase : List[str] =torch.sum(torch.abs(snake_case__ ) )
UpperCAmelCase : Any =torch.mean(torch.abs(snake_case__ ) )
assert abs(result_sum.item() - 186.9482 ) < 1e-2
assert abs(result_mean.item() - 0.2434 ) < 1e-3
| 348 | from collections.abc import Callable
from math import pi, sqrt
from random import uniform
from statistics import mean
def lowerCAmelCase_ ( __lowerCAmelCase )-> Optional[Any]:
'''simple docstring'''
def is_in_circle(__lowerCAmelCase , __lowerCAmelCase ) -> bool:
UpperCAmelCase : List[Any] =sqrt((x**2) + (y**2) )
# Our circle has a radius of 1, so a distance
# greater than 1 would land outside the circle.
return distance_from_centre <= 1
# The proportion of guesses that landed in the circle
UpperCAmelCase : List[Any] =mean(
int(is_in_circle(uniform(-1.0 , 1.0 ) , uniform(-1.0 , 1.0 ) ) )
for _ in range(__lowerCAmelCase ) )
# The ratio of the area for circle to square is pi/4.
UpperCAmelCase : Dict =proportion * 4
print(f'''The estimated value of pi is {pi_estimate}''' )
print(f'''The numpy value of pi is {pi}''' )
print(f'''The total error is {abs(pi - pi_estimate )}''' )
def lowerCAmelCase_ ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase = 0.0 , __lowerCAmelCase = 1.0 , )-> float:
'''simple docstring'''
return mean(
function_to_integrate(uniform(__lowerCAmelCase , __lowerCAmelCase ) ) for _ in range(__lowerCAmelCase ) ) * (max_value - min_value)
def lowerCAmelCase_ ( __lowerCAmelCase , __lowerCAmelCase = 0.0 , __lowerCAmelCase = 1.0 )-> None:
'''simple docstring'''
def identity_function(__lowerCAmelCase ) -> float:
return x
UpperCAmelCase : List[Any] =area_under_curve_estimator(
__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
UpperCAmelCase : Dict =(max_value * max_value - min_value * min_value) / 2
print('''******************''' )
print(f'''Estimating area under y=x where x varies from {min_value} to {max_value}''' )
print(f'''Estimated value is {estimated_value}''' )
print(f'''Expected value is {expected_value}''' )
print(f'''Total error is {abs(estimated_value - expected_value )}''' )
print('''******************''' )
def lowerCAmelCase_ ( __lowerCAmelCase )-> None:
'''simple docstring'''
def function_to_integrate(__lowerCAmelCase ) -> float:
return sqrt(4.0 - x * x )
UpperCAmelCase : Dict =area_under_curve_estimator(
__lowerCAmelCase , __lowerCAmelCase , 0.0 , 2.0 )
print('''******************''' )
print('''Estimating pi using area_under_curve_estimator''' )
print(f'''Estimated value is {estimated_value}''' )
print(f'''Expected value is {pi}''' )
print(f'''Total error is {abs(estimated_value - pi )}''' )
print('''******************''' )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 348 | 1 |
import json
import os
from datetime import date
from pathlib import Path
from tabulate import DataRow, TableFormat, tabulate
__snake_case = TableFormat(
lineabove=None,
linebelowheader=None,
linebetweenrows=None,
linebelow=None,
headerrow=DataRow('''''', '''|''', '''|'''),
datarow=DataRow('''''', '''|''', '''|'''),
padding=1,
with_header_hide=None,
)
__snake_case = []
__snake_case = []
__snake_case = {'''type''': '''section''', '''text''': {'''type''': '''plain_text''', '''text''': '''No failed tests! 🤗''', '''emoji''': True}}
__snake_case = [
{
'''type''': '''header''',
'''text''': {
'''type''': '''plain_text''',
'''text''': f'🤗 Accelerate nightly {os.environ.get("TEST_TYPE", "")} test results',
'''emoji''': True,
},
}
]
__snake_case = 0
for log in Path().glob('''*.log'''):
__snake_case = 0
with open(log, '''r''') as f:
for line in f:
__snake_case = json.loads(line)
if line.get('''nodeid''', '''''') != "":
__snake_case = line['''nodeid''']
if line.get('''duration''', None) is not None:
__snake_case = f'{line["duration"]:.4f}'
if line.get('''outcome''', '''''') == "failed":
section_num_failed += 1
failed.append([test, duration, log.name.split('''_''')[0]])
total_num_failed += 1
group_info.append([str(log), section_num_failed, failed])
__snake_case = []
log.unlink()
__snake_case = ''''''
__snake_case = []
if total_num_failed > 0:
for name, num_failed, failed_tests in group_info:
if num_failed > 0:
if num_failed == 1:
message += f"*{name[1:]}: {num_failed} failed test*\n"
else:
message += f"*{name[1:]}: {num_failed} failed tests*\n"
__snake_case = []
__snake_case = {}
for test in failed_tests:
__snake_case = test[0].split('''::''')
__snake_case = data[0].split('''/''')[-1]
if data[0] not in filesafailed:
__snake_case = [data[1:]]
else:
filesafailed[data[0]] += [data[1:]]
failed_table.append(data)
__snake_case = [test[0] for test in failed_table]
__snake_case = list(set(files))
# Count number of instances in failed_tests
__snake_case = []
for file in individual_files:
table.append([file, len(filesafailed[file])])
__snake_case = tabulate(
table,
headers=['''Test Location''', '''Num Failed'''],
tablefmt=hf_table_format,
stralign='''right''',
)
message += f"\n```\n{failed_table}\n```"
all_filesafailed.append(filesafailed)
if len(message) > 30_00:
__snake_case = '''Too many failed tests, please see the full report in the Action results.'''
__snake_case = len(err) + 10
__snake_case = message[: 30_00 - offset] + f'\n...\n```\n{err}'
print(f'### {message}')
else:
__snake_case = '''No failed tests! 🤗'''
print(f'## {message}')
payload.append(no_error_payload)
if os.environ.get('''TEST_TYPE''', '''''') != "":
from slack_sdk import WebClient
__snake_case = WebClient(token=os.environ['''SLACK_API_TOKEN'''])
if message != "No failed tests! 🤗":
__snake_case = {
'''type''': '''section''',
'''text''': {
'''type''': '''mrkdwn''',
'''text''': message,
},
}
payload.append(md_report)
__snake_case = {
'''type''': '''section''',
'''text''': {
'''type''': '''mrkdwn''',
'''text''': '''*For more details:*''',
},
'''accessory''': {
'''type''': '''button''',
'''text''': {
'''type''': '''plain_text''',
'''text''': '''Check Action results''',
'''emoji''': True,
},
'''url''': f'https://github.com/{os.environ["GITHUB_REPOSITORY"]}/actions/runs/{os.environ["GITHUB_RUN_ID"]}',
},
}
payload.append(action_button)
__snake_case = {
'''type''': '''context''',
'''elements''': [
{
'''type''': '''plain_text''',
'''text''': f'Nightly {os.environ.get("TEST_TYPE")} test results for {date.today()}',
}
],
}
payload.append(date_report)
__snake_case = client.chat_postMessage(channel='''#accelerate-ci-daily''', text=message, blocks=payload)
__snake_case = response.data['''ts''']
for failed_file in all_filesafailed:
for test_location, test_failures in failed_file.items():
# Keep only the first instance of the test name
__snake_case = ''''''
for i, row in enumerate(test_failures):
if row[0] != test_class:
__snake_case = row[0]
else:
__snake_case = ''''''
__snake_case = {
'''type''': '''section''',
'''text''': {
'''type''': '''mrkdwn''',
'''text''': f'Test location: {test_location}\n```\n{tabulate(test_failures, headers=["Class", "Test"], tablefmt=hf_table_format, stralign="right")}\n```',
},
}
client.chat_postMessage(
channel='''#accelerate-ci-daily''',
thread_ts=ts,
blocks=[payload],
)
| 348 | from __future__ import annotations
import unittest
import numpy as np
from transformers import BlipTextConfig
from transformers.testing_utils import require_tf, slow
from transformers.utils import is_tf_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
if is_tf_available():
import tensorflow as tf
from transformers import TFBlipTextModel
from transformers.models.blip.modeling_tf_blip import TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST
class __snake_case :
def __init__( self , snake_case__ , snake_case__=12 , snake_case__=7 , snake_case__=True , snake_case__=True , snake_case__=True , snake_case__=99 , snake_case__=32 , snake_case__=32 , snake_case__=2 , snake_case__=4 , snake_case__=37 , snake_case__=0.1 , snake_case__=0.1 , snake_case__=512 , snake_case__=0.02 , snake_case__=0 , snake_case__=None , ) -> Tuple:
'''simple docstring'''
UpperCAmelCase : List[Any] =parent
UpperCAmelCase : Optional[int] =batch_size
UpperCAmelCase : List[Any] =seq_length
UpperCAmelCase : Optional[int] =is_training
UpperCAmelCase : Union[str, Any] =use_input_mask
UpperCAmelCase : Tuple =use_labels
UpperCAmelCase : Union[str, Any] =vocab_size
UpperCAmelCase : Tuple =hidden_size
UpperCAmelCase : Dict =projection_dim
UpperCAmelCase : Optional[int] =num_hidden_layers
UpperCAmelCase : Dict =num_attention_heads
UpperCAmelCase : int =intermediate_size
UpperCAmelCase : Any =dropout
UpperCAmelCase : Union[str, Any] =attention_dropout
UpperCAmelCase : Union[str, Any] =max_position_embeddings
UpperCAmelCase : List[str] =initializer_range
UpperCAmelCase : str =scope
UpperCAmelCase : str =bos_token_id
def UpperCAmelCase__ ( self ) -> int:
'''simple docstring'''
UpperCAmelCase : int =ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
UpperCAmelCase : int =None
if self.use_input_mask:
UpperCAmelCase : Union[str, Any] =random_attention_mask([self.batch_size, self.seq_length] )
if input_mask is not None:
UpperCAmelCase : Optional[int] =input_mask.numpy()
UpperCAmelCase , UpperCAmelCase : List[Any] =input_mask.shape
UpperCAmelCase : Optional[Any] =np.random.randint(1 , seq_length - 1 , size=(batch_size,) )
for batch_idx, start_index in enumerate(snake_case__ ):
UpperCAmelCase : List[Any] =1
UpperCAmelCase : Tuple =0
UpperCAmelCase : List[Any] =self.get_config()
return config, input_ids, tf.convert_to_tensor(snake_case__ )
def UpperCAmelCase__ ( self ) -> Union[str, Any]:
'''simple docstring'''
return BlipTextConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , projection_dim=self.projection_dim , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , dropout=self.dropout , attention_dropout=self.attention_dropout , max_position_embeddings=self.max_position_embeddings , initializer_range=self.initializer_range , bos_token_id=self.bos_token_id , )
def UpperCAmelCase__ ( self , snake_case__ , snake_case__ , snake_case__ ) -> Dict:
'''simple docstring'''
UpperCAmelCase : Tuple =TFBlipTextModel(config=snake_case__ )
UpperCAmelCase : List[Any] =model(snake_case__ , attention_mask=snake_case__ , training=snake_case__ )
UpperCAmelCase : str =model(snake_case__ , training=snake_case__ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
self.parent.assertEqual(result.pooler_output.shape , (self.batch_size, self.hidden_size) )
def UpperCAmelCase__ ( self ) -> Optional[Any]:
'''simple docstring'''
UpperCAmelCase : List[str] =self.prepare_config_and_inputs()
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase : List[Any] =config_and_inputs
UpperCAmelCase : Optional[int] ={'''input_ids''': input_ids, '''attention_mask''': input_mask}
return config, inputs_dict
@require_tf
class __snake_case ( lowerCamelCase__ , unittest.TestCase ):
__lowerCamelCase : Optional[int] = (TFBlipTextModel,) if is_tf_available() else ()
__lowerCamelCase : Dict = False
__lowerCamelCase : Optional[Any] = False
__lowerCamelCase : Dict = False
def UpperCAmelCase__ ( self ) -> Optional[Any]:
'''simple docstring'''
UpperCAmelCase : str =BlipTextModelTester(self )
UpperCAmelCase : Optional[int] =ConfigTester(self , config_class=snake_case__ , hidden_size=37 )
def UpperCAmelCase__ ( self ) -> Optional[int]:
'''simple docstring'''
self.config_tester.run_common_tests()
def UpperCAmelCase__ ( self ) -> List[Any]:
'''simple docstring'''
UpperCAmelCase : Any =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*snake_case__ )
def UpperCAmelCase__ ( self ) -> Optional[int]:
'''simple docstring'''
pass
def UpperCAmelCase__ ( self ) -> Union[str, Any]:
'''simple docstring'''
pass
@unittest.skip(reason='''Blip does not use inputs_embeds''' )
def UpperCAmelCase__ ( self ) -> str:
'''simple docstring'''
pass
@unittest.skip(reason='''BlipTextModel has no base class and is not available in MODEL_MAPPING''' )
def UpperCAmelCase__ ( self ) -> Optional[int]:
'''simple docstring'''
pass
@unittest.skip(reason='''BlipTextModel has no base class and is not available in MODEL_MAPPING''' )
def UpperCAmelCase__ ( self ) -> str:
'''simple docstring'''
pass
@slow
def UpperCAmelCase__ ( self ) -> Tuple:
'''simple docstring'''
for model_name in TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
UpperCAmelCase : Optional[Any] =TFBlipTextModel.from_pretrained(snake_case__ )
self.assertIsNotNone(snake_case__ )
def UpperCAmelCase__ ( self , snake_case__=True ) -> Any:
'''simple docstring'''
super().test_pt_tf_model_equivalence(allow_missing_keys=snake_case__ )
| 348 | 1 |
import requests
__snake_case = '''https://newsapi.org/v1/articles?source=bbc-news&sortBy=top&apiKey='''
def lowerCAmelCase_ ( __lowerCAmelCase )-> None:
'''simple docstring'''
UpperCAmelCase : List[str] =requests.get(_NEWS_API + bbc_news_api_key ).json()
# each article in the list is a dict
for i, article in enumerate(bbc_news_page['''articles'''] , 1 ):
print(f'''{i}.) {article['title']}''' )
if __name__ == "__main__":
fetch_bbc_news(bbc_news_api_key='''<Your BBC News API key goes here>''')
| 348 | import torch
import torch.nn as nn
from transformers import CLIPConfig, CLIPVisionModel, PreTrainedModel
from ...utils import logging
__snake_case = logging.get_logger(__name__)
def lowerCAmelCase_ ( __lowerCAmelCase , __lowerCAmelCase )-> str:
'''simple docstring'''
UpperCAmelCase : Dict =nn.functional.normalize(__lowerCAmelCase )
UpperCAmelCase : Tuple =nn.functional.normalize(__lowerCAmelCase )
return torch.mm(__lowerCAmelCase , normalized_text_embeds.t() )
class __snake_case ( lowerCamelCase__ ):
__lowerCamelCase : List[str] = CLIPConfig
__lowerCamelCase : List[Any] = ["""CLIPEncoderLayer"""]
def __init__( self , snake_case__ ) -> Dict:
'''simple docstring'''
super().__init__(snake_case__ )
UpperCAmelCase : Dict =CLIPVisionModel(config.vision_config )
UpperCAmelCase : Optional[Any] =nn.Linear(config.vision_config.hidden_size , config.projection_dim , bias=snake_case__ )
UpperCAmelCase : int =nn.Parameter(torch.ones(17 , config.projection_dim ) , requires_grad=snake_case__ )
UpperCAmelCase : List[str] =nn.Parameter(torch.ones(3 , config.projection_dim ) , requires_grad=snake_case__ )
UpperCAmelCase : str =nn.Parameter(torch.ones(17 ) , requires_grad=snake_case__ )
UpperCAmelCase : Optional[int] =nn.Parameter(torch.ones(3 ) , requires_grad=snake_case__ )
@torch.no_grad()
def UpperCAmelCase__ ( self , snake_case__ , snake_case__ ) -> Tuple:
'''simple docstring'''
UpperCAmelCase : Union[str, Any] =self.vision_model(snake_case__ )[1] # pooled_output
UpperCAmelCase : Optional[Any] =self.visual_projection(snake_case__ )
# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
UpperCAmelCase : List[str] =cosine_distance(snake_case__ , self.special_care_embeds ).cpu().float().numpy()
UpperCAmelCase : Optional[Any] =cosine_distance(snake_case__ , self.concept_embeds ).cpu().float().numpy()
UpperCAmelCase : Tuple =[]
UpperCAmelCase : Dict =image_embeds.shape[0]
for i in range(snake_case__ ):
UpperCAmelCase : str ={'''special_scores''': {}, '''special_care''': [], '''concept_scores''': {}, '''bad_concepts''': []}
# increase this value to create a stronger `nfsw` filter
# at the cost of increasing the possibility of filtering benign images
UpperCAmelCase : str =0.0
for concept_idx in range(len(special_cos_dist[0] ) ):
UpperCAmelCase : Optional[Any] =special_cos_dist[i][concept_idx]
UpperCAmelCase : Union[str, Any] =self.special_care_embeds_weights[concept_idx].item()
UpperCAmelCase : str =round(concept_cos - concept_threshold + adjustment , 3 )
if result_img["special_scores"][concept_idx] > 0:
result_img["special_care"].append({concept_idx, result_img['''special_scores'''][concept_idx]} )
UpperCAmelCase : int =0.01
for concept_idx in range(len(cos_dist[0] ) ):
UpperCAmelCase : Any =cos_dist[i][concept_idx]
UpperCAmelCase : Optional[int] =self.concept_embeds_weights[concept_idx].item()
UpperCAmelCase : int =round(concept_cos - concept_threshold + adjustment , 3 )
if result_img["concept_scores"][concept_idx] > 0:
result_img["bad_concepts"].append(snake_case__ )
result.append(snake_case__ )
UpperCAmelCase : Optional[int] =[len(res['''bad_concepts'''] ) > 0 for res in result]
return images, has_nsfw_concepts
@torch.no_grad()
def UpperCAmelCase__ ( self , snake_case__ , snake_case__ ) -> Tuple:
'''simple docstring'''
UpperCAmelCase : Any =self.vision_model(snake_case__ )[1] # pooled_output
UpperCAmelCase : List[str] =self.visual_projection(snake_case__ )
UpperCAmelCase : Any =cosine_distance(snake_case__ , self.special_care_embeds )
UpperCAmelCase : Optional[Any] =cosine_distance(snake_case__ , self.concept_embeds )
# increase this value to create a stronger `nsfw` filter
# at the cost of increasing the possibility of filtering benign images
UpperCAmelCase : Optional[Any] =0.0
UpperCAmelCase : Any =special_cos_dist - self.special_care_embeds_weights + adjustment
# special_scores = special_scores.round(decimals=3)
UpperCAmelCase : str =torch.any(special_scores > 0 , dim=1 )
UpperCAmelCase : List[Any] =special_care * 0.01
UpperCAmelCase : Union[str, Any] =special_adjustment.unsqueeze(1 ).expand(-1 , cos_dist.shape[1] )
UpperCAmelCase : List[Any] =(cos_dist - self.concept_embeds_weights) + special_adjustment
# concept_scores = concept_scores.round(decimals=3)
UpperCAmelCase : str =torch.any(concept_scores > 0 , dim=1 )
return images, has_nsfw_concepts
| 348 | 1 |
from sklearn.metrics import matthews_corrcoef
import datasets
__snake_case = '''
Compute the Matthews correlation coefficient (MCC)
The Matthews correlation coefficient is used in machine learning as a
measure of the quality of binary and multiclass classifications. It takes
into account true and false positives and negatives and is generally
regarded as a balanced measure which can be used even if the classes are of
very different sizes. The MCC is in essence a correlation coefficient value
between -1 and +1. A coefficient of +1 represents a perfect prediction, 0
an average random prediction and -1 an inverse prediction. The statistic
is also known as the phi coefficient. [source: Wikipedia]
'''
__snake_case = '''
Args:
predictions (list of int): Predicted labels, as returned by a model.
references (list of int): Ground truth labels.
sample_weight (list of int, float, or bool): Sample weights. Defaults to `None`.
Returns:
matthews_correlation (dict containing float): Matthews correlation.
Examples:
Example 1, a basic example with only predictions and references as inputs:
>>> matthews_metric = datasets.load_metric("matthews_correlation")
>>> results = matthews_metric.compute(references=[1, 3, 2, 0, 3, 2],
... predictions=[1, 2, 2, 0, 3, 3])
>>> print(round(results[\'matthews_correlation\'], 2))
0.54
Example 2, the same example as above, but also including sample weights:
>>> matthews_metric = datasets.load_metric("matthews_correlation")
>>> results = matthews_metric.compute(references=[1, 3, 2, 0, 3, 2],
... predictions=[1, 2, 2, 0, 3, 3],
... sample_weight=[0.5, 3, 1, 1, 1, 2])
>>> print(round(results[\'matthews_correlation\'], 2))
0.1
Example 3, the same example as above, but with sample weights that cause a negative correlation:
>>> matthews_metric = datasets.load_metric("matthews_correlation")
>>> results = matthews_metric.compute(references=[1, 3, 2, 0, 3, 2],
... predictions=[1, 2, 2, 0, 3, 3],
... sample_weight=[0.5, 1, 0, 0, 0, 1])
>>> print(round(results[\'matthews_correlation\'], 2))
-0.25
'''
__snake_case = '''\
@article{scikit-learn,
title={Scikit-learn: Machine Learning in {P}ython},
author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.
and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.
and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and
Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},
journal={Journal of Machine Learning Research},
volume={12},
pages={2825--2830},
year={2011}
}
'''
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class __snake_case ( datasets.Metric ):
def UpperCAmelCase__ ( self ) -> Union[str, Any]:
'''simple docstring'''
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
'''predictions''': datasets.Value('''int32''' ),
'''references''': datasets.Value('''int32''' ),
} ) , reference_urls=[
'''https://scikit-learn.org/stable/modules/generated/sklearn.metrics.matthews_corrcoef.html'''
] , )
def UpperCAmelCase__ ( self , snake_case__ , snake_case__ , snake_case__=None ) -> Optional[int]:
'''simple docstring'''
return {
"matthews_correlation": float(matthews_corrcoef(snake_case__ , snake_case__ , sample_weight=snake_case__ ) ),
}
| 348 | import argparse
import intel_extension_for_pytorch as ipex
import torch
from diffusers import DPMSolverMultistepScheduler, StableDiffusionPipeline
__snake_case = argparse.ArgumentParser('''Stable Diffusion script with intel optimization''', add_help=False)
parser.add_argument('''--dpm''', action='''store_true''', help='''Enable DPMSolver or not''')
parser.add_argument('''--steps''', default=None, type=int, help='''Num inference steps''')
__snake_case = parser.parse_args()
__snake_case = '''cpu'''
__snake_case = '''a lovely <dicoo> in red dress and hat, in the snowly and brightly night, with many brighly buildings'''
__snake_case = '''path-to-your-trained-model'''
__snake_case = StableDiffusionPipeline.from_pretrained(model_id)
if args.dpm:
__snake_case = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
__snake_case = pipe.to(device)
# to channels last
__snake_case = pipe.unet.to(memory_format=torch.channels_last)
__snake_case = pipe.vae.to(memory_format=torch.channels_last)
__snake_case = pipe.text_encoder.to(memory_format=torch.channels_last)
if pipe.requires_safety_checker:
__snake_case = pipe.safety_checker.to(memory_format=torch.channels_last)
# optimize with ipex
__snake_case = torch.randn(2, 4, 64, 64)
__snake_case = torch.rand(1) * 9_99
__snake_case = torch.randn(2, 77, 7_68)
__snake_case = (sample, timestep, encoder_hidden_status)
try:
__snake_case = ipex.optimize(pipe.unet.eval(), dtype=torch.bfloataa, inplace=True, sample_input=input_example)
except Exception:
__snake_case = ipex.optimize(pipe.unet.eval(), dtype=torch.bfloataa, inplace=True)
__snake_case = ipex.optimize(pipe.vae.eval(), dtype=torch.bfloataa, inplace=True)
__snake_case = ipex.optimize(pipe.text_encoder.eval(), dtype=torch.bfloataa, inplace=True)
if pipe.requires_safety_checker:
__snake_case = ipex.optimize(pipe.safety_checker.eval(), dtype=torch.bfloataa, inplace=True)
# compute
__snake_case = 6_66
__snake_case = torch.Generator(device).manual_seed(seed)
__snake_case = {'''generator''': generator}
if args.steps is not None:
__snake_case = args.steps
with torch.cpu.amp.autocast(enabled=True, dtype=torch.bfloataa):
__snake_case = pipe(prompt, **generate_kwargs).images[0]
# save image
image.save('''generated.png''')
| 348 | 1 |
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import ClassLabel, Features, Value
from .base import TaskTemplate
@dataclass(frozen=lowerCamelCase__ )
class __snake_case ( lowerCamelCase__ ):
# `task` is not a ClassVar since we want it to be part of the `asdict` output for JSON serialization
__lowerCamelCase : str = field(default="""text-classification""" , metadata={"""include_in_asdict_even_if_is_default""": True} )
__lowerCamelCase : ClassVar[Features] = Features({"""text""": Value("""string""" )} )
__lowerCamelCase : ClassVar[Features] = Features({"""labels""": ClassLabel} )
__lowerCamelCase : str = "text"
__lowerCamelCase : str = "labels"
def UpperCAmelCase__ ( self , snake_case__ ) -> Union[str, Any]:
'''simple docstring'''
if self.label_column not in features:
raise ValueError(f'''Column {self.label_column} is not present in features.''' )
if not isinstance(features[self.label_column] , snake_case__ ):
raise ValueError(f'''Column {self.label_column} is not a ClassLabel.''' )
UpperCAmelCase : Optional[Any] =copy.deepcopy(self )
UpperCAmelCase : Dict =self.label_schema.copy()
UpperCAmelCase : Union[str, Any] =features[self.label_column]
UpperCAmelCase : Optional[int] =label_schema
return task_template
@property
def UpperCAmelCase__ ( self ) -> Dict[str, str]:
'''simple docstring'''
return {
self.text_column: "text",
self.label_column: "labels",
}
| 348 | __snake_case = '''Input must be a string of 8 numbers plus letter'''
__snake_case = '''TRWAGMYFPDXBNJZSQVHLCKE'''
def lowerCAmelCase_ ( __lowerCAmelCase )-> bool:
'''simple docstring'''
if not isinstance(__lowerCAmelCase , __lowerCAmelCase ):
UpperCAmelCase : Optional[Any] =f'''Expected string as input, found {type(__lowerCAmelCase ).__name__}'''
raise TypeError(__lowerCAmelCase )
UpperCAmelCase : List[Any] =spanish_id.replace('''-''' , '''''' ).upper()
if len(__lowerCAmelCase ) != 9:
raise ValueError(__lowerCAmelCase )
try:
UpperCAmelCase : int =int(spanish_id_clean[0:8] )
UpperCAmelCase : Optional[int] =spanish_id_clean[8]
except ValueError as ex:
raise ValueError(__lowerCAmelCase ) from ex
if letter.isdigit():
raise ValueError(__lowerCAmelCase )
return letter == LOOKUP_LETTERS[number % 23]
if __name__ == "__main__":
import doctest
doctest.testmod()
| 348 | 1 |
import unittest
from transformers import is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, require_torch, slow
if is_flax_available():
import optax
from flax.training.common_utils import onehot
from transformers import AutoTokenizer, FlaxMTaForConditionalGeneration
from transformers.models.ta.modeling_flax_ta import shift_tokens_right
@require_torch
@require_sentencepiece
@require_tokenizers
@require_flax
class __snake_case ( unittest.TestCase ):
@slow
def UpperCAmelCase__ ( self ) -> Union[str, Any]:
'''simple docstring'''
UpperCAmelCase : Any =FlaxMTaForConditionalGeneration.from_pretrained('''google/mt5-small''' )
UpperCAmelCase : Tuple =AutoTokenizer.from_pretrained('''google/mt5-small''' )
UpperCAmelCase : List[str] =tokenizer('''Hello there''' , return_tensors='''np''' ).input_ids
UpperCAmelCase : List[Any] =tokenizer('''Hi I am''' , return_tensors='''np''' ).input_ids
UpperCAmelCase : Union[str, Any] =shift_tokens_right(snake_case__ , model.config.pad_token_id , model.config.decoder_start_token_id )
UpperCAmelCase : List[str] =model(snake_case__ , decoder_input_ids=snake_case__ ).logits
UpperCAmelCase : Any =optax.softmax_cross_entropy(snake_case__ , onehot(snake_case__ , logits.shape[-1] ) ).mean()
UpperCAmelCase : Union[str, Any] =-(labels.shape[-1] * loss.item())
UpperCAmelCase : List[str] =-84.9127
self.assertTrue(abs(mtf_score - EXPECTED_SCORE ) < 1e-4 )
| 348 | def lowerCAmelCase_ ( __lowerCAmelCase , __lowerCAmelCase )-> str:
'''simple docstring'''
if number < 0 or shift_amount < 0:
raise ValueError('''both inputs must be positive integers''' )
UpperCAmelCase : Dict =str(bin(__lowerCAmelCase ) )
binary_number += "0" * shift_amount
return binary_number
def lowerCAmelCase_ ( __lowerCAmelCase , __lowerCAmelCase )-> str:
'''simple docstring'''
if number < 0 or shift_amount < 0:
raise ValueError('''both inputs must be positive integers''' )
UpperCAmelCase : Any =str(bin(__lowerCAmelCase ) )[2:]
if shift_amount >= len(__lowerCAmelCase ):
return "0b0"
UpperCAmelCase : Optional[Any] =binary_number[: len(__lowerCAmelCase ) - shift_amount]
return "0b" + shifted_binary_number
def lowerCAmelCase_ ( __lowerCAmelCase , __lowerCAmelCase )-> str:
'''simple docstring'''
if number >= 0: # Get binary representation of positive number
UpperCAmelCase : Optional[Any] ='''0''' + str(bin(__lowerCAmelCase ) ).strip('''-''' )[2:]
else: # Get binary (2's complement) representation of negative number
UpperCAmelCase : int =len(bin(__lowerCAmelCase )[3:] ) # Find 2's complement of number
UpperCAmelCase : Any =bin(abs(__lowerCAmelCase ) - (1 << binary_number_length) )[3:]
UpperCAmelCase : Optional[Any] =(
'''1''' + '''0''' * (binary_number_length - len(__lowerCAmelCase )) + binary_number
)
if shift_amount >= len(__lowerCAmelCase ):
return "0b" + binary_number[0] * len(__lowerCAmelCase )
return (
"0b"
+ binary_number[0] * shift_amount
+ binary_number[: len(__lowerCAmelCase ) - shift_amount]
)
if __name__ == "__main__":
import doctest
doctest.testmod()
| 348 | 1 |
import argparse
import torch
from torch import nn
from transformers import SpeechaTextConfig, SpeechaTextForConditionalGeneration
def lowerCAmelCase_ ( __lowerCAmelCase )-> str:
'''simple docstring'''
UpperCAmelCase : Any =[
'''encoder.version''',
'''decoder.version''',
'''model.encoder.version''',
'''model.decoder.version''',
'''decoder.output_projection.weight''',
'''_float_tensor''',
'''encoder.embed_positions._float_tensor''',
'''decoder.embed_positions._float_tensor''',
]
for k in ignore_keys:
state_dict.pop(__lowerCAmelCase , __lowerCAmelCase )
def lowerCAmelCase_ ( __lowerCAmelCase )-> List[str]:
'''simple docstring'''
UpperCAmelCase : List[str] =list(s_dict.keys() )
for key in keys:
if "transformer_layers" in key:
UpperCAmelCase : int =s_dict.pop(__lowerCAmelCase )
elif "subsample" in key:
UpperCAmelCase : Any =s_dict.pop(__lowerCAmelCase )
def lowerCAmelCase_ ( __lowerCAmelCase )-> List[Any]:
'''simple docstring'''
UpperCAmelCase , UpperCAmelCase : List[str] =emb.weight.shape
UpperCAmelCase : Optional[Any] =nn.Linear(__lowerCAmelCase , __lowerCAmelCase , bias=__lowerCAmelCase )
UpperCAmelCase : List[str] =emb.weight.data
return lin_layer
def lowerCAmelCase_ ( __lowerCAmelCase , __lowerCAmelCase )-> int:
'''simple docstring'''
UpperCAmelCase : Tuple =torch.load(__lowerCAmelCase , map_location='''cpu''' )
UpperCAmelCase : Dict =mam_aaa['''args''']
UpperCAmelCase : Union[str, Any] =mam_aaa['''model''']
UpperCAmelCase : Tuple =state_dict['''decoder.output_projection.weight''']
remove_ignore_keys_(__lowerCAmelCase )
rename_keys(__lowerCAmelCase )
UpperCAmelCase : str =state_dict['''decoder.embed_tokens.weight'''].shape[0]
UpperCAmelCase : Tuple =args.share_decoder_input_output_embed
UpperCAmelCase : str =[int(__lowerCAmelCase ) for i in args.conv_kernel_sizes.split(''',''' )]
UpperCAmelCase : Optional[int] =SpeechaTextConfig(
vocab_size=__lowerCAmelCase , max_source_positions=args.max_source_positions , max_target_positions=args.max_target_positions , encoder_layers=args.encoder_layers , decoder_layers=args.decoder_layers , encoder_attention_heads=args.encoder_attention_heads , decoder_attention_heads=args.decoder_attention_heads , encoder_ffn_dim=args.encoder_ffn_embed_dim , decoder_ffn_dim=args.decoder_ffn_embed_dim , d_model=args.encoder_embed_dim , dropout=args.dropout , attention_dropout=args.attention_dropout , activation_dropout=args.activation_dropout , activation_function='''relu''' , num_conv_layers=len(__lowerCAmelCase ) , conv_channels=args.conv_channels , conv_kernel_sizes=__lowerCAmelCase , input_feat_per_channel=args.input_feat_per_channel , input_channels=args.input_channels , tie_word_embeddings=__lowerCAmelCase , num_beams=5 , max_length=2_00 , use_cache=__lowerCAmelCase , decoder_start_token_id=2 , early_stopping=__lowerCAmelCase , )
UpperCAmelCase : List[Any] =SpeechaTextForConditionalGeneration(__lowerCAmelCase )
UpperCAmelCase , UpperCAmelCase : Tuple =model.model.load_state_dict(__lowerCAmelCase , strict=__lowerCAmelCase )
if len(__lowerCAmelCase ) > 0 and not set(__lowerCAmelCase ) <= {
"encoder.embed_positions.weights",
"decoder.embed_positions.weights",
}:
raise ValueError(
'''Only `encoder.embed_positions.weights` and `decoder.embed_positions.weights` are allowed to be missing,'''
f''' but all the following weights are missing {missing}''' )
if tie_embeds:
UpperCAmelCase : Union[str, Any] =make_linear_from_emb(model.model.decoder.embed_tokens )
else:
UpperCAmelCase : Union[str, Any] =lm_head_weights
model.save_pretrained(__lowerCAmelCase )
if __name__ == "__main__":
__snake_case = argparse.ArgumentParser()
# Required parameters
parser.add_argument('''--fairseq_path''', type=str, help='''Path to the fairseq model (.pt) file.''')
parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
__snake_case = parser.parse_args()
convert_fairseq_sat_checkpoint_to_tfms(args.fairseq_path, args.pytorch_dump_folder_path)
| 348 | from dataclasses import asdict, dataclass
from typing import Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__snake_case = logging.get_logger(__name__)
# TODO Update this
__snake_case = {
'''facebook/esm-1b''': '''https://huggingface.co/facebook/esm-1b/resolve/main/config.json''',
# See all ESM models at https://huggingface.co/models?filter=esm
}
class __snake_case ( lowerCamelCase__ ):
__lowerCamelCase : Tuple = """esm"""
def __init__( self , snake_case__=None , snake_case__=None , snake_case__=None , snake_case__=768 , snake_case__=12 , snake_case__=12 , snake_case__=3072 , snake_case__=0.1 , snake_case__=0.1 , snake_case__=1026 , snake_case__=0.02 , snake_case__=1e-12 , snake_case__="absolute" , snake_case__=True , snake_case__=None , snake_case__=False , snake_case__=False , snake_case__=None , snake_case__=None , **snake_case__ , ) -> Union[str, Any]:
'''simple docstring'''
super().__init__(pad_token_id=snake_case__ , mask_token_id=snake_case__ , **snake_case__ )
UpperCAmelCase : List[str] =vocab_size
UpperCAmelCase : str =hidden_size
UpperCAmelCase : List[Any] =num_hidden_layers
UpperCAmelCase : Optional[Any] =num_attention_heads
UpperCAmelCase : str =intermediate_size
UpperCAmelCase : Any =hidden_dropout_prob
UpperCAmelCase : int =attention_probs_dropout_prob
UpperCAmelCase : Dict =max_position_embeddings
UpperCAmelCase : List[str] =initializer_range
UpperCAmelCase : Union[str, Any] =layer_norm_eps
UpperCAmelCase : Dict =position_embedding_type
UpperCAmelCase : Optional[Any] =use_cache
UpperCAmelCase : int =emb_layer_norm_before
UpperCAmelCase : List[str] =token_dropout
UpperCAmelCase : Optional[Any] =is_folding_model
if is_folding_model:
if esmfold_config is None:
logger.info('''No esmfold_config supplied for folding model, using default values.''' )
UpperCAmelCase : Optional[Any] =EsmFoldConfig()
elif isinstance(snake_case__ , snake_case__ ):
UpperCAmelCase : Optional[int] =EsmFoldConfig(**snake_case__ )
UpperCAmelCase : Tuple =esmfold_config
if vocab_list is None:
logger.warning('''No vocab_list supplied for folding model, assuming the ESM-2 vocabulary!''' )
UpperCAmelCase : Any =get_default_vocab_list()
else:
UpperCAmelCase : Tuple =vocab_list
else:
UpperCAmelCase : Optional[int] =None
UpperCAmelCase : Union[str, Any] =None
if self.esmfold_config is not None and getattr(self.esmfold_config , '''use_esm_attn_map''' , snake_case__ ):
raise ValueError('''The HuggingFace port of ESMFold does not support use_esm_attn_map at this time!''' )
def UpperCAmelCase__ ( self ) -> List[Any]:
'''simple docstring'''
UpperCAmelCase : Union[str, Any] =super().to_dict()
if isinstance(self.esmfold_config , snake_case__ ):
UpperCAmelCase : str =self.esmfold_config.to_dict()
return output
@dataclass
class __snake_case :
__lowerCamelCase : str = None
__lowerCamelCase : bool = True
__lowerCamelCase : bool = False
__lowerCamelCase : bool = False
__lowerCamelCase : bool = False
__lowerCamelCase : float = 0
__lowerCamelCase : bool = True
__lowerCamelCase : bool = False
__lowerCamelCase : int = 128
__lowerCamelCase : "TrunkConfig" = None
def UpperCAmelCase__ ( self ) -> Union[str, Any]:
'''simple docstring'''
if self.trunk is None:
UpperCAmelCase : str =TrunkConfig()
elif isinstance(self.trunk , snake_case__ ):
UpperCAmelCase : Optional[int] =TrunkConfig(**self.trunk )
def UpperCAmelCase__ ( self ) -> Any:
'''simple docstring'''
UpperCAmelCase : Optional[Any] =asdict(self )
UpperCAmelCase : Any =self.trunk.to_dict()
return output
@dataclass
class __snake_case :
__lowerCamelCase : int = 48
__lowerCamelCase : int = 1024
__lowerCamelCase : int = 128
__lowerCamelCase : int = 32
__lowerCamelCase : int = 32
__lowerCamelCase : int = 32
__lowerCamelCase : float = 0
__lowerCamelCase : float = 0
__lowerCamelCase : bool = False
__lowerCamelCase : int = 4
__lowerCamelCase : Optional[int] = 128
__lowerCamelCase : "StructureModuleConfig" = None
def UpperCAmelCase__ ( self ) -> List[str]:
'''simple docstring'''
if self.structure_module is None:
UpperCAmelCase : Any =StructureModuleConfig()
elif isinstance(self.structure_module , snake_case__ ):
UpperCAmelCase : str =StructureModuleConfig(**self.structure_module )
if self.max_recycles <= 0:
raise ValueError(f'''`max_recycles` should be positive, got {self.max_recycles}.''' )
if self.sequence_state_dim % self.sequence_state_dim != 0:
raise ValueError(
'''`sequence_state_dim` should be a round multiple of `sequence_state_dim`, got'''
f''' {self.sequence_state_dim} and {self.sequence_state_dim}.''' )
if self.pairwise_state_dim % self.pairwise_state_dim != 0:
raise ValueError(
'''`pairwise_state_dim` should be a round multiple of `pairwise_state_dim`, got'''
f''' {self.pairwise_state_dim} and {self.pairwise_state_dim}.''' )
UpperCAmelCase : Optional[int] =self.sequence_state_dim // self.sequence_head_width
UpperCAmelCase : Any =self.pairwise_state_dim // self.pairwise_head_width
if self.sequence_state_dim != sequence_num_heads * self.sequence_head_width:
raise ValueError(
'''`sequence_state_dim` should be equal to `sequence_num_heads * sequence_head_width, got'''
f''' {self.sequence_state_dim} != {sequence_num_heads} * {self.sequence_head_width}.''' )
if self.pairwise_state_dim != pairwise_num_heads * self.pairwise_head_width:
raise ValueError(
'''`pairwise_state_dim` should be equal to `pairwise_num_heads * pairwise_head_width, got'''
f''' {self.pairwise_state_dim} != {pairwise_num_heads} * {self.pairwise_head_width}.''' )
if self.pairwise_state_dim % 2 != 0:
raise ValueError(f'''`pairwise_state_dim` should be even, got {self.pairwise_state_dim}.''' )
if self.dropout >= 0.4:
raise ValueError(f'''`dropout` should not be greater than 0.4, got {self.dropout}.''' )
def UpperCAmelCase__ ( self ) -> List[Any]:
'''simple docstring'''
UpperCAmelCase : Union[str, Any] =asdict(self )
UpperCAmelCase : Tuple =self.structure_module.to_dict()
return output
@dataclass
class __snake_case :
__lowerCamelCase : int = 384
__lowerCamelCase : int = 128
__lowerCamelCase : int = 16
__lowerCamelCase : int = 128
__lowerCamelCase : int = 12
__lowerCamelCase : int = 4
__lowerCamelCase : int = 8
__lowerCamelCase : float = 0.1
__lowerCamelCase : int = 8
__lowerCamelCase : int = 1
__lowerCamelCase : int = 2
__lowerCamelCase : int = 7
__lowerCamelCase : int = 10
__lowerCamelCase : float = 1E-8
__lowerCamelCase : float = 1E5
def UpperCAmelCase__ ( self ) -> Union[str, Any]:
'''simple docstring'''
return asdict(self )
def lowerCAmelCase_ ( )-> Tuple:
'''simple docstring'''
return (
"<cls>",
"<pad>",
"<eos>",
"<unk>",
"L",
"A",
"G",
"V",
"S",
"E",
"R",
"T",
"I",
"D",
"P",
"K",
"Q",
"N",
"F",
"Y",
"M",
"H",
"W",
"C",
"X",
"B",
"U",
"Z",
"O",
".",
"-",
"<null_1>",
"<mask>",
)
| 348 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
__snake_case = {
'''configuration_bloom''': ['''BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''BloomConfig''', '''BloomOnnxConfig'''],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__snake_case = ['''BloomTokenizerFast''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__snake_case = [
'''BLOOM_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''BloomForCausalLM''',
'''BloomModel''',
'''BloomPreTrainedModel''',
'''BloomForSequenceClassification''',
'''BloomForTokenClassification''',
'''BloomForQuestionAnswering''',
]
if TYPE_CHECKING:
from .configuration_bloom import BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP, BloomConfig, BloomOnnxConfig
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_bloom_fast import BloomTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_bloom import (
BLOOM_PRETRAINED_MODEL_ARCHIVE_LIST,
BloomForCausalLM,
BloomForQuestionAnswering,
BloomForSequenceClassification,
BloomForTokenClassification,
BloomModel,
BloomPreTrainedModel,
)
else:
import sys
__snake_case = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 348 | import torch
from diffusers import KDPMaDiscreteScheduler
from diffusers.utils import torch_device
from .test_schedulers import SchedulerCommonTest
class __snake_case ( lowerCamelCase__ ):
__lowerCamelCase : Optional[int] = (KDPMaDiscreteScheduler,)
__lowerCamelCase : List[str] = 10
def UpperCAmelCase__ ( self , **snake_case__ ) -> str:
'''simple docstring'''
UpperCAmelCase : int ={
'''num_train_timesteps''': 1100,
'''beta_start''': 0.0001,
'''beta_end''': 0.02,
'''beta_schedule''': '''linear''',
}
config.update(**snake_case__ )
return config
def UpperCAmelCase__ ( self ) -> Tuple:
'''simple docstring'''
for timesteps in [10, 50, 100, 1000]:
self.check_over_configs(num_train_timesteps=snake_case__ )
def UpperCAmelCase__ ( self ) -> Optional[int]:
'''simple docstring'''
for beta_start, beta_end in zip([0.0_0001, 0.0001, 0.001] , [0.0002, 0.002, 0.02] ):
self.check_over_configs(beta_start=snake_case__ , beta_end=snake_case__ )
def UpperCAmelCase__ ( self ) -> str:
'''simple docstring'''
for schedule in ["linear", "scaled_linear"]:
self.check_over_configs(beta_schedule=snake_case__ )
def UpperCAmelCase__ ( self ) -> Dict:
'''simple docstring'''
for prediction_type in ["epsilon", "v_prediction"]:
self.check_over_configs(prediction_type=snake_case__ )
def UpperCAmelCase__ ( self ) -> str:
'''simple docstring'''
UpperCAmelCase : Optional[Any] =self.scheduler_classes[0]
UpperCAmelCase : Optional[int] =self.get_scheduler_config(prediction_type='''v_prediction''' )
UpperCAmelCase : Optional[Any] =scheduler_class(**snake_case__ )
scheduler.set_timesteps(self.num_inference_steps )
UpperCAmelCase : str =self.dummy_model()
UpperCAmelCase : Optional[Any] =self.dummy_sample_deter * scheduler.init_noise_sigma
UpperCAmelCase : Union[str, Any] =sample.to(snake_case__ )
for i, t in enumerate(scheduler.timesteps ):
UpperCAmelCase : str =scheduler.scale_model_input(snake_case__ , snake_case__ )
UpperCAmelCase : Any =model(snake_case__ , snake_case__ )
UpperCAmelCase : Union[str, Any] =scheduler.step(snake_case__ , snake_case__ , snake_case__ )
UpperCAmelCase : int =output.prev_sample
UpperCAmelCase : Dict =torch.sum(torch.abs(snake_case__ ) )
UpperCAmelCase : Optional[Any] =torch.mean(torch.abs(snake_case__ ) )
if torch_device in ["cpu", "mps"]:
assert abs(result_sum.item() - 4.69_34e-07 ) < 1e-2
assert abs(result_mean.item() - 6.11_12e-10 ) < 1e-3
else:
# CUDA
assert abs(result_sum.item() - 4.6_93_42_86_50_17_09_72e-07 ) < 1e-2
assert abs(result_mean.item() - 0.0002 ) < 1e-3
def UpperCAmelCase__ ( self ) -> int:
'''simple docstring'''
if torch_device == "mps":
return
UpperCAmelCase : Any =self.scheduler_classes[0]
UpperCAmelCase : Optional[int] =self.get_scheduler_config()
UpperCAmelCase : Optional[Any] =scheduler_class(**snake_case__ )
scheduler.set_timesteps(self.num_inference_steps )
UpperCAmelCase : Optional[int] =self.dummy_model()
UpperCAmelCase : Union[str, Any] =self.dummy_sample_deter * scheduler.init_noise_sigma
UpperCAmelCase : str =sample.to(snake_case__ )
for i, t in enumerate(scheduler.timesteps ):
UpperCAmelCase : Dict =scheduler.scale_model_input(snake_case__ , snake_case__ )
UpperCAmelCase : Union[str, Any] =model(snake_case__ , snake_case__ )
UpperCAmelCase : List[str] =scheduler.step(snake_case__ , snake_case__ , snake_case__ )
UpperCAmelCase : Optional[int] =output.prev_sample
UpperCAmelCase : Any =torch.sum(torch.abs(snake_case__ ) )
UpperCAmelCase : Union[str, Any] =torch.mean(torch.abs(snake_case__ ) )
if torch_device in ["cpu", "mps"]:
assert abs(result_sum.item() - 20.4125 ) < 1e-2
assert abs(result_mean.item() - 0.0266 ) < 1e-3
else:
# CUDA
assert abs(result_sum.item() - 20.4125 ) < 1e-2
assert abs(result_mean.item() - 0.0266 ) < 1e-3
def UpperCAmelCase__ ( self ) -> str:
'''simple docstring'''
if torch_device == "mps":
return
UpperCAmelCase : List[Any] =self.scheduler_classes[0]
UpperCAmelCase : Dict =self.get_scheduler_config()
UpperCAmelCase : List[str] =scheduler_class(**snake_case__ )
scheduler.set_timesteps(self.num_inference_steps , device=snake_case__ )
UpperCAmelCase : int =self.dummy_model()
UpperCAmelCase : Tuple =self.dummy_sample_deter.to(snake_case__ ) * scheduler.init_noise_sigma
for t in scheduler.timesteps:
UpperCAmelCase : Optional[Any] =scheduler.scale_model_input(snake_case__ , snake_case__ )
UpperCAmelCase : int =model(snake_case__ , snake_case__ )
UpperCAmelCase : str =scheduler.step(snake_case__ , snake_case__ , snake_case__ )
UpperCAmelCase : List[str] =output.prev_sample
UpperCAmelCase : List[str] =torch.sum(torch.abs(snake_case__ ) )
UpperCAmelCase : Dict =torch.mean(torch.abs(snake_case__ ) )
if str(snake_case__ ).startswith('''cpu''' ):
# The following sum varies between 148 and 156 on mps. Why?
assert abs(result_sum.item() - 20.4125 ) < 1e-2
assert abs(result_mean.item() - 0.0266 ) < 1e-3
else:
# CUDA
assert abs(result_sum.item() - 20.4125 ) < 1e-2
assert abs(result_mean.item() - 0.0266 ) < 1e-3
| 348 | 1 |
def lowerCAmelCase_ ( __lowerCAmelCase )-> str:
'''simple docstring'''
return "".join([hex(__lowerCAmelCase )[2:].zfill(2 ).upper() for byte in list(__lowerCAmelCase )] )
def lowerCAmelCase_ ( __lowerCAmelCase )-> bytes:
'''simple docstring'''
if (len(__lowerCAmelCase ) % 2) != 0:
raise ValueError(
'''Base16 encoded data is invalid:
Data does not have an even number of hex digits.''' )
# Check the character set - the standard base16 alphabet
# is uppercase according to RFC3548 section 6
if not set(__lowerCAmelCase ) <= set('''0123456789ABCDEF''' ):
raise ValueError(
'''Base16 encoded data is invalid:
Data is not uppercase hex or it contains invalid characters.''' )
# For every two hexadecimal digits (= a byte), turn it into an integer.
# Then, string the result together into bytes, and return it.
return bytes(int(data[i] + data[i + 1] , 16 ) for i in range(0 , len(__lowerCAmelCase ) , 2 ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 348 | import unittest
from transformers import is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, require_torch, slow
if is_flax_available():
import optax
from flax.training.common_utils import onehot
from transformers import AutoTokenizer, FlaxMTaForConditionalGeneration
from transformers.models.ta.modeling_flax_ta import shift_tokens_right
@require_torch
@require_sentencepiece
@require_tokenizers
@require_flax
class __snake_case ( unittest.TestCase ):
@slow
def UpperCAmelCase__ ( self ) -> Union[str, Any]:
'''simple docstring'''
UpperCAmelCase : Any =FlaxMTaForConditionalGeneration.from_pretrained('''google/mt5-small''' )
UpperCAmelCase : Tuple =AutoTokenizer.from_pretrained('''google/mt5-small''' )
UpperCAmelCase : List[str] =tokenizer('''Hello there''' , return_tensors='''np''' ).input_ids
UpperCAmelCase : List[Any] =tokenizer('''Hi I am''' , return_tensors='''np''' ).input_ids
UpperCAmelCase : Union[str, Any] =shift_tokens_right(snake_case__ , model.config.pad_token_id , model.config.decoder_start_token_id )
UpperCAmelCase : List[str] =model(snake_case__ , decoder_input_ids=snake_case__ ).logits
UpperCAmelCase : Any =optax.softmax_cross_entropy(snake_case__ , onehot(snake_case__ , logits.shape[-1] ) ).mean()
UpperCAmelCase : Union[str, Any] =-(labels.shape[-1] * loss.item())
UpperCAmelCase : List[str] =-84.9127
self.assertTrue(abs(mtf_score - EXPECTED_SCORE ) < 1e-4 )
| 348 | 1 |
import os
import tempfile
import unittest
from pathlib import Path
from transformers import AutoConfig, is_tf_available
from transformers.testing_utils import require_tf
if is_tf_available():
import tensorflow as tf
from transformers import TensorFlowBenchmark, TensorFlowBenchmarkArguments
@require_tf
class __snake_case ( unittest.TestCase ):
def UpperCAmelCase__ ( self , snake_case__ ) -> Optional[Any]:
'''simple docstring'''
for model_result in results.values():
for batch_size, sequence_length in zip(model_result['''bs'''] , model_result['''ss'''] ):
UpperCAmelCase : Optional[int] =model_result['''result'''][batch_size][sequence_length]
self.assertIsNotNone(snake_case__ )
def UpperCAmelCase__ ( self ) -> Tuple:
'''simple docstring'''
UpperCAmelCase : Tuple ='''sshleifer/tiny-gpt2'''
UpperCAmelCase : Union[str, Any] =TensorFlowBenchmarkArguments(
models=[MODEL_ID] , training=snake_case__ , inference=snake_case__ , sequence_lengths=[8] , batch_sizes=[1] , eager_mode=snake_case__ , multi_process=snake_case__ , )
UpperCAmelCase : List[Any] =TensorFlowBenchmark(snake_case__ )
UpperCAmelCase : Union[str, Any] =benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
def UpperCAmelCase__ ( self ) -> Optional[Any]:
'''simple docstring'''
UpperCAmelCase : List[Any] ='''sgugger/tiny-distilbert-classification'''
UpperCAmelCase : List[str] =TensorFlowBenchmarkArguments(
models=[MODEL_ID] , training=snake_case__ , inference=snake_case__ , sequence_lengths=[8] , batch_sizes=[1] , multi_process=snake_case__ , only_pretrain_model=snake_case__ , )
UpperCAmelCase : Optional[int] =TensorFlowBenchmark(snake_case__ )
UpperCAmelCase : Tuple =benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
def UpperCAmelCase__ ( self ) -> Tuple:
'''simple docstring'''
UpperCAmelCase : Tuple ='''sshleifer/tiny-gpt2'''
UpperCAmelCase : str =TensorFlowBenchmarkArguments(
models=[MODEL_ID] , training=snake_case__ , inference=snake_case__ , sequence_lengths=[8] , batch_sizes=[1] , multi_process=snake_case__ , )
UpperCAmelCase : Any =TensorFlowBenchmark(snake_case__ )
UpperCAmelCase : Tuple =benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
def UpperCAmelCase__ ( self ) -> Union[str, Any]:
'''simple docstring'''
UpperCAmelCase : Union[str, Any] ='''sshleifer/tiny-gpt2'''
UpperCAmelCase : int =AutoConfig.from_pretrained(snake_case__ )
UpperCAmelCase : List[str] =TensorFlowBenchmarkArguments(
models=[MODEL_ID] , training=snake_case__ , inference=snake_case__ , sequence_lengths=[8] , batch_sizes=[1] , eager_mode=snake_case__ , multi_process=snake_case__ , )
UpperCAmelCase : Optional[int] =TensorFlowBenchmark(snake_case__ , [config] )
UpperCAmelCase : str =benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
def UpperCAmelCase__ ( self ) -> int:
'''simple docstring'''
UpperCAmelCase : Dict ='''sshleifer/tiny-gpt2'''
UpperCAmelCase : Tuple =AutoConfig.from_pretrained(snake_case__ )
UpperCAmelCase : List[Any] =TensorFlowBenchmarkArguments(
models=[MODEL_ID] , training=snake_case__ , inference=snake_case__ , sequence_lengths=[8] , batch_sizes=[1] , multi_process=snake_case__ , )
UpperCAmelCase : Optional[int] =TensorFlowBenchmark(snake_case__ , [config] )
UpperCAmelCase : str =benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
def UpperCAmelCase__ ( self ) -> Dict:
'''simple docstring'''
UpperCAmelCase : Any ='''sshleifer/tiny-gpt2'''
UpperCAmelCase : Optional[Any] =TensorFlowBenchmarkArguments(
models=[MODEL_ID] , training=snake_case__ , inference=snake_case__ , sequence_lengths=[8] , batch_sizes=[1] , multi_process=snake_case__ , )
UpperCAmelCase : Dict =TensorFlowBenchmark(snake_case__ )
UpperCAmelCase : Tuple =benchmark.run()
self.check_results_dict_not_empty(results.time_train_result )
self.check_results_dict_not_empty(results.memory_train_result )
def UpperCAmelCase__ ( self ) -> str:
'''simple docstring'''
UpperCAmelCase : Union[str, Any] ='''sshleifer/tiny-gpt2'''
UpperCAmelCase : str =AutoConfig.from_pretrained(snake_case__ )
UpperCAmelCase : Tuple =TensorFlowBenchmarkArguments(
models=[MODEL_ID] , training=snake_case__ , inference=snake_case__ , sequence_lengths=[8] , batch_sizes=[1] , multi_process=snake_case__ , )
UpperCAmelCase : Optional[int] =TensorFlowBenchmark(snake_case__ , [config] )
UpperCAmelCase : List[Any] =benchmark.run()
self.check_results_dict_not_empty(results.time_train_result )
self.check_results_dict_not_empty(results.memory_train_result )
def UpperCAmelCase__ ( self ) -> List[Any]:
'''simple docstring'''
UpperCAmelCase : int ='''patrickvonplaten/t5-tiny-random'''
UpperCAmelCase : List[str] =AutoConfig.from_pretrained(snake_case__ )
UpperCAmelCase : Tuple =TensorFlowBenchmarkArguments(
models=[MODEL_ID] , training=snake_case__ , inference=snake_case__ , sequence_lengths=[8] , batch_sizes=[1] , multi_process=snake_case__ , )
UpperCAmelCase : str =TensorFlowBenchmark(snake_case__ , configs=[config] )
UpperCAmelCase : Tuple =benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
@unittest.skipIf(is_tf_available() and len(tf.config.list_physical_devices('''GPU''' ) ) == 0 , '''Cannot do xla on CPU.''' )
def UpperCAmelCase__ ( self ) -> Tuple:
'''simple docstring'''
UpperCAmelCase : Tuple ='''sshleifer/tiny-gpt2'''
UpperCAmelCase : Union[str, Any] =TensorFlowBenchmarkArguments(
models=[MODEL_ID] , training=snake_case__ , inference=snake_case__ , sequence_lengths=[8] , batch_sizes=[1] , use_xla=snake_case__ , multi_process=snake_case__ , )
UpperCAmelCase : Dict =TensorFlowBenchmark(snake_case__ )
UpperCAmelCase : int =benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
def UpperCAmelCase__ ( self ) -> Tuple:
'''simple docstring'''
UpperCAmelCase : Any ='''sshleifer/tiny-gpt2'''
with tempfile.TemporaryDirectory() as tmp_dir:
UpperCAmelCase : Optional[int] =TensorFlowBenchmarkArguments(
models=[MODEL_ID] , inference=snake_case__ , save_to_csv=snake_case__ , sequence_lengths=[8] , batch_sizes=[1] , inference_time_csv_file=os.path.join(snake_case__ , '''inf_time.csv''' ) , inference_memory_csv_file=os.path.join(snake_case__ , '''inf_mem.csv''' ) , env_info_csv_file=os.path.join(snake_case__ , '''env.csv''' ) , multi_process=snake_case__ , )
UpperCAmelCase : Union[str, Any] =TensorFlowBenchmark(snake_case__ )
benchmark.run()
self.assertTrue(Path(os.path.join(snake_case__ , '''inf_time.csv''' ) ).exists() )
self.assertTrue(Path(os.path.join(snake_case__ , '''inf_mem.csv''' ) ).exists() )
self.assertTrue(Path(os.path.join(snake_case__ , '''env.csv''' ) ).exists() )
def UpperCAmelCase__ ( self ) -> int:
'''simple docstring'''
UpperCAmelCase : Tuple ='''sshleifer/tiny-gpt2'''
def _check_summary_is_not_empty(snake_case__ ):
self.assertTrue(hasattr(snake_case__ , '''sequential''' ) )
self.assertTrue(hasattr(snake_case__ , '''cumulative''' ) )
self.assertTrue(hasattr(snake_case__ , '''current''' ) )
self.assertTrue(hasattr(snake_case__ , '''total''' ) )
with tempfile.TemporaryDirectory() as tmp_dir:
UpperCAmelCase : Union[str, Any] =TensorFlowBenchmarkArguments(
models=[MODEL_ID] , inference=snake_case__ , sequence_lengths=[8] , batch_sizes=[1] , log_filename=os.path.join(snake_case__ , '''log.txt''' ) , log_print=snake_case__ , trace_memory_line_by_line=snake_case__ , eager_mode=snake_case__ , multi_process=snake_case__ , )
UpperCAmelCase : Union[str, Any] =TensorFlowBenchmark(snake_case__ )
UpperCAmelCase : Any =benchmark.run()
_check_summary_is_not_empty(result.inference_summary )
self.assertTrue(Path(os.path.join(snake_case__ , '''log.txt''' ) ).exists() )
| 348 | import unittest
import numpy as np
from diffusers import LMSDiscreteScheduler, OnnxStableDiffusionInpaintPipeline
from diffusers.utils.testing_utils import (
is_onnx_available,
load_image,
nightly,
require_onnxruntime,
require_torch_gpu,
)
from ..test_pipelines_onnx_common import OnnxPipelineTesterMixin
if is_onnx_available():
import onnxruntime as ort
class __snake_case ( lowerCamelCase__ , unittest.TestCase ):
# FIXME: add fast tests
pass
@nightly
@require_onnxruntime
@require_torch_gpu
class __snake_case ( unittest.TestCase ):
@property
def UpperCAmelCase__ ( self ) -> List[str]:
'''simple docstring'''
return (
"CUDAExecutionProvider",
{
"gpu_mem_limit": "15000000000", # 15GB
"arena_extend_strategy": "kSameAsRequested",
},
)
@property
def UpperCAmelCase__ ( self ) -> int:
'''simple docstring'''
UpperCAmelCase : List[Any] =ort.SessionOptions()
UpperCAmelCase : Optional[int] =False
return options
def UpperCAmelCase__ ( self ) -> Dict:
'''simple docstring'''
UpperCAmelCase : int =load_image(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
'''/in_paint/overture-creations-5sI6fQgYIuo.png''' )
UpperCAmelCase : Optional[Any] =load_image(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
'''/in_paint/overture-creations-5sI6fQgYIuo_mask.png''' )
UpperCAmelCase : List[str] =OnnxStableDiffusionInpaintPipeline.from_pretrained(
'''runwayml/stable-diffusion-inpainting''' , revision='''onnx''' , safety_checker=snake_case__ , feature_extractor=snake_case__ , provider=self.gpu_provider , sess_options=self.gpu_options , )
pipe.set_progress_bar_config(disable=snake_case__ )
UpperCAmelCase : Dict ='''A red cat sitting on a park bench'''
UpperCAmelCase : int =np.random.RandomState(0 )
UpperCAmelCase : Any =pipe(
prompt=snake_case__ , image=snake_case__ , mask_image=snake_case__ , guidance_scale=7.5 , num_inference_steps=10 , generator=snake_case__ , output_type='''np''' , )
UpperCAmelCase : Dict =output.images
UpperCAmelCase : Optional[int] =images[0, 255:258, 255:258, -1]
assert images.shape == (1, 512, 512, 3)
UpperCAmelCase : Tuple =np.array([0.2514, 0.3007, 0.3517, 0.1790, 0.2382, 0.3167, 0.1944, 0.2273, 0.2464] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-3
def UpperCAmelCase__ ( self ) -> Optional[Any]:
'''simple docstring'''
UpperCAmelCase : List[str] =load_image(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
'''/in_paint/overture-creations-5sI6fQgYIuo.png''' )
UpperCAmelCase : Tuple =load_image(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
'''/in_paint/overture-creations-5sI6fQgYIuo_mask.png''' )
UpperCAmelCase : List[str] =LMSDiscreteScheduler.from_pretrained(
'''runwayml/stable-diffusion-inpainting''' , subfolder='''scheduler''' , revision='''onnx''' )
UpperCAmelCase : int =OnnxStableDiffusionInpaintPipeline.from_pretrained(
'''runwayml/stable-diffusion-inpainting''' , revision='''onnx''' , scheduler=snake_case__ , safety_checker=snake_case__ , feature_extractor=snake_case__ , provider=self.gpu_provider , sess_options=self.gpu_options , )
pipe.set_progress_bar_config(disable=snake_case__ )
UpperCAmelCase : Union[str, Any] ='''A red cat sitting on a park bench'''
UpperCAmelCase : int =np.random.RandomState(0 )
UpperCAmelCase : str =pipe(
prompt=snake_case__ , image=snake_case__ , mask_image=snake_case__ , guidance_scale=7.5 , num_inference_steps=20 , generator=snake_case__ , output_type='''np''' , )
UpperCAmelCase : Dict =output.images
UpperCAmelCase : int =images[0, 255:258, 255:258, -1]
assert images.shape == (1, 512, 512, 3)
UpperCAmelCase : Union[str, Any] =np.array([0.0086, 0.0077, 0.0083, 0.0093, 0.0107, 0.0139, 0.0094, 0.0097, 0.0125] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-3
| 348 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__snake_case = {
'''configuration_blenderbot_small''': [
'''BLENDERBOT_SMALL_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''BlenderbotSmallConfig''',
'''BlenderbotSmallOnnxConfig''',
],
'''tokenization_blenderbot_small''': ['''BlenderbotSmallTokenizer'''],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__snake_case = ['''BlenderbotSmallTokenizerFast''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__snake_case = [
'''BLENDERBOT_SMALL_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''BlenderbotSmallForCausalLM''',
'''BlenderbotSmallForConditionalGeneration''',
'''BlenderbotSmallModel''',
'''BlenderbotSmallPreTrainedModel''',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__snake_case = [
'''TFBlenderbotSmallForConditionalGeneration''',
'''TFBlenderbotSmallModel''',
'''TFBlenderbotSmallPreTrainedModel''',
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__snake_case = [
'''FlaxBlenderbotSmallForConditionalGeneration''',
'''FlaxBlenderbotSmallModel''',
'''FlaxBlenderbotSmallPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_blenderbot_small import (
BLENDERBOT_SMALL_PRETRAINED_CONFIG_ARCHIVE_MAP,
BlenderbotSmallConfig,
BlenderbotSmallOnnxConfig,
)
from .tokenization_blenderbot_small import BlenderbotSmallTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_blenderbot_small_fast import BlenderbotSmallTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_blenderbot_small import (
BLENDERBOT_SMALL_PRETRAINED_MODEL_ARCHIVE_LIST,
BlenderbotSmallForCausalLM,
BlenderbotSmallForConditionalGeneration,
BlenderbotSmallModel,
BlenderbotSmallPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_blenderbot_small import (
TFBlenderbotSmallForConditionalGeneration,
TFBlenderbotSmallModel,
TFBlenderbotSmallPreTrainedModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_blenderbot_small import (
FlaxBlenderbotSmallForConditionalGeneration,
FlaxBlenderbotSmallModel,
FlaxBlenderbotSmallPreTrainedModel,
)
else:
import sys
__snake_case = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 348 | from unittest import TestCase
from datasets import Dataset
from minhash_deduplication import deduplicate_dataset, make_duplicate_clusters
def lowerCAmelCase_ ( )-> int:
'''simple docstring'''
UpperCAmelCase : str ={
'''repo_name''': ['''test_repo1''', '''test_repo2''', '''test_repo3'''],
'''path''': ['''test_1.py''', '''test_2.py''', '''unit_test.py'''],
'''content''': ['''a ''' * 20, '''a ''' * 30, '''b ''' * 7],
}
UpperCAmelCase : Union[str, Any] =Dataset.from_dict(__lowerCAmelCase )
return dataset
class __snake_case ( lowerCamelCase__ ):
def UpperCAmelCase__ ( self ) -> Tuple:
'''simple docstring'''
UpperCAmelCase : List[str] =get_dataset()
UpperCAmelCase : Optional[int] =make_duplicate_clusters(snake_case__ , 0.85 )
self.assertEqual(len(duplicate_clusters[0] ) , 2 )
def UpperCAmelCase__ ( self ) -> Tuple:
'''simple docstring'''
UpperCAmelCase : str =get_dataset()
UpperCAmelCase , UpperCAmelCase : Tuple =deduplicate_dataset(snake_case__ )
self.assertEqual(len(snake_case__ ) , 2 )
print(snake_case__ )
self.assertEqual(duplicate_clusters[0][0]['''copies'''] , 2 )
self.assertEqual(duplicate_clusters[0][0]['''is_extreme'''] , snake_case__ )
| 348 | 1 |
import unittest
import numpy as np
from diffusers import LMSDiscreteScheduler, OnnxStableDiffusionInpaintPipeline
from diffusers.utils.testing_utils import (
is_onnx_available,
load_image,
nightly,
require_onnxruntime,
require_torch_gpu,
)
from ..test_pipelines_onnx_common import OnnxPipelineTesterMixin
if is_onnx_available():
import onnxruntime as ort
class __snake_case ( lowerCamelCase__ , unittest.TestCase ):
# FIXME: add fast tests
pass
@nightly
@require_onnxruntime
@require_torch_gpu
class __snake_case ( unittest.TestCase ):
@property
def UpperCAmelCase__ ( self ) -> List[str]:
'''simple docstring'''
return (
"CUDAExecutionProvider",
{
"gpu_mem_limit": "15000000000", # 15GB
"arena_extend_strategy": "kSameAsRequested",
},
)
@property
def UpperCAmelCase__ ( self ) -> int:
'''simple docstring'''
UpperCAmelCase : List[Any] =ort.SessionOptions()
UpperCAmelCase : Optional[int] =False
return options
def UpperCAmelCase__ ( self ) -> Dict:
'''simple docstring'''
UpperCAmelCase : int =load_image(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
'''/in_paint/overture-creations-5sI6fQgYIuo.png''' )
UpperCAmelCase : Optional[Any] =load_image(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
'''/in_paint/overture-creations-5sI6fQgYIuo_mask.png''' )
UpperCAmelCase : List[str] =OnnxStableDiffusionInpaintPipeline.from_pretrained(
'''runwayml/stable-diffusion-inpainting''' , revision='''onnx''' , safety_checker=snake_case__ , feature_extractor=snake_case__ , provider=self.gpu_provider , sess_options=self.gpu_options , )
pipe.set_progress_bar_config(disable=snake_case__ )
UpperCAmelCase : Dict ='''A red cat sitting on a park bench'''
UpperCAmelCase : int =np.random.RandomState(0 )
UpperCAmelCase : Any =pipe(
prompt=snake_case__ , image=snake_case__ , mask_image=snake_case__ , guidance_scale=7.5 , num_inference_steps=10 , generator=snake_case__ , output_type='''np''' , )
UpperCAmelCase : Dict =output.images
UpperCAmelCase : Optional[int] =images[0, 255:258, 255:258, -1]
assert images.shape == (1, 512, 512, 3)
UpperCAmelCase : Tuple =np.array([0.2514, 0.3007, 0.3517, 0.1790, 0.2382, 0.3167, 0.1944, 0.2273, 0.2464] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-3
def UpperCAmelCase__ ( self ) -> Optional[Any]:
'''simple docstring'''
UpperCAmelCase : List[str] =load_image(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
'''/in_paint/overture-creations-5sI6fQgYIuo.png''' )
UpperCAmelCase : Tuple =load_image(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
'''/in_paint/overture-creations-5sI6fQgYIuo_mask.png''' )
UpperCAmelCase : List[str] =LMSDiscreteScheduler.from_pretrained(
'''runwayml/stable-diffusion-inpainting''' , subfolder='''scheduler''' , revision='''onnx''' )
UpperCAmelCase : int =OnnxStableDiffusionInpaintPipeline.from_pretrained(
'''runwayml/stable-diffusion-inpainting''' , revision='''onnx''' , scheduler=snake_case__ , safety_checker=snake_case__ , feature_extractor=snake_case__ , provider=self.gpu_provider , sess_options=self.gpu_options , )
pipe.set_progress_bar_config(disable=snake_case__ )
UpperCAmelCase : Union[str, Any] ='''A red cat sitting on a park bench'''
UpperCAmelCase : int =np.random.RandomState(0 )
UpperCAmelCase : str =pipe(
prompt=snake_case__ , image=snake_case__ , mask_image=snake_case__ , guidance_scale=7.5 , num_inference_steps=20 , generator=snake_case__ , output_type='''np''' , )
UpperCAmelCase : Dict =output.images
UpperCAmelCase : int =images[0, 255:258, 255:258, -1]
assert images.shape == (1, 512, 512, 3)
UpperCAmelCase : Union[str, Any] =np.array([0.0086, 0.0077, 0.0083, 0.0093, 0.0107, 0.0139, 0.0094, 0.0097, 0.0125] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-3
| 348 | from typing import Callable, List, Optional, Tuple, Union
import torch
from transformers import CLIPTextModel, CLIPTokenizer
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin, TransformeraDModel, VQModel
from ...schedulers import VQDiffusionScheduler
from ...utils import logging
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
__snake_case = logging.get_logger(__name__) # pylint: disable=invalid-name
class __snake_case ( lowerCamelCase__ , lowerCamelCase__ ):
@register_to_config
def __init__( self , snake_case__ , snake_case__ = None , snake_case__ = None ) -> str:
'''simple docstring'''
super().__init__()
UpperCAmelCase : Optional[Any] =learnable
if self.learnable:
assert hidden_size is not None, "learnable=True requires `hidden_size` to be set"
assert length is not None, "learnable=True requires `length` to be set"
UpperCAmelCase : Any =torch.zeros(snake_case__ , snake_case__ )
else:
UpperCAmelCase : Union[str, Any] =None
UpperCAmelCase : Optional[int] =torch.nn.Parameter(snake_case__ )
class __snake_case ( lowerCamelCase__ ):
__lowerCamelCase : VQModel
__lowerCamelCase : CLIPTextModel
__lowerCamelCase : CLIPTokenizer
__lowerCamelCase : TransformeraDModel
__lowerCamelCase : LearnedClassifierFreeSamplingEmbeddings
__lowerCamelCase : VQDiffusionScheduler
def __init__( self , snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ , ) -> int:
'''simple docstring'''
super().__init__()
self.register_modules(
vqvae=snake_case__ , transformer=snake_case__ , text_encoder=snake_case__ , tokenizer=snake_case__ , scheduler=snake_case__ , learned_classifier_free_sampling_embeddings=snake_case__ , )
def UpperCAmelCase__ ( self , snake_case__ , snake_case__ , snake_case__ ) -> Optional[int]:
'''simple docstring'''
UpperCAmelCase : int =len(snake_case__ ) if isinstance(snake_case__ , snake_case__ ) else 1
# get prompt text embeddings
UpperCAmelCase : Optional[int] =self.tokenizer(
snake_case__ , padding='''max_length''' , max_length=self.tokenizer.model_max_length , return_tensors='''pt''' , )
UpperCAmelCase : int =text_inputs.input_ids
if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
UpperCAmelCase : List[str] =self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :] )
logger.warning(
'''The following part of your input was truncated because CLIP can only handle sequences up to'''
f''' {self.tokenizer.model_max_length} tokens: {removed_text}''' )
UpperCAmelCase : Optional[Any] =text_input_ids[:, : self.tokenizer.model_max_length]
UpperCAmelCase : List[Any] =self.text_encoder(text_input_ids.to(self.device ) )[0]
# NOTE: This additional step of normalizing the text embeddings is from VQ-Diffusion.
# While CLIP does normalize the pooled output of the text transformer when combining
# the image and text embeddings, CLIP does not directly normalize the last hidden state.
#
# CLIP normalizing the pooled output.
# https://github.com/huggingface/transformers/blob/d92e22d1f28324f513f3080e5c47c071a3916721/src/transformers/models/clip/modeling_clip.py#L1052-L1053
UpperCAmelCase : int =prompt_embeds / prompt_embeds.norm(dim=-1 , keepdim=snake_case__ )
# duplicate text embeddings for each generation per prompt
UpperCAmelCase : int =prompt_embeds.repeat_interleave(snake_case__ , dim=0 )
if do_classifier_free_guidance:
if self.learned_classifier_free_sampling_embeddings.learnable:
UpperCAmelCase : Optional[int] =self.learned_classifier_free_sampling_embeddings.embeddings
UpperCAmelCase : str =negative_prompt_embeds.unsqueeze(0 ).repeat(snake_case__ , 1 , 1 )
else:
UpperCAmelCase : str =[''''''] * batch_size
UpperCAmelCase : Tuple =text_input_ids.shape[-1]
UpperCAmelCase : Optional[Any] =self.tokenizer(
snake_case__ , padding='''max_length''' , max_length=snake_case__ , truncation=snake_case__ , return_tensors='''pt''' , )
UpperCAmelCase : Optional[Any] =self.text_encoder(uncond_input.input_ids.to(self.device ) )[0]
# See comment for normalizing text embeddings
UpperCAmelCase : Optional[int] =negative_prompt_embeds / negative_prompt_embeds.norm(dim=-1 , keepdim=snake_case__ )
# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
UpperCAmelCase : Optional[Any] =negative_prompt_embeds.shape[1]
UpperCAmelCase : Union[str, Any] =negative_prompt_embeds.repeat(1 , snake_case__ , 1 )
UpperCAmelCase : Optional[Any] =negative_prompt_embeds.view(batch_size * num_images_per_prompt , snake_case__ , -1 )
# For classifier free guidance, we need to do two forward passes.
# Here we concatenate the unconditional and text embeddings into a single batch
# to avoid doing two forward passes
UpperCAmelCase : int =torch.cat([negative_prompt_embeds, prompt_embeds] )
return prompt_embeds
@torch.no_grad()
def __call__( self , snake_case__ , snake_case__ = 100 , snake_case__ = 5.0 , snake_case__ = 1.0 , snake_case__ = 1 , snake_case__ = None , snake_case__ = None , snake_case__ = "pil" , snake_case__ = True , snake_case__ = None , snake_case__ = 1 , ) -> Union[ImagePipelineOutput, Tuple]:
'''simple docstring'''
if isinstance(snake_case__ , snake_case__ ):
UpperCAmelCase : Optional[int] =1
elif isinstance(snake_case__ , snake_case__ ):
UpperCAmelCase : Tuple =len(snake_case__ )
else:
raise ValueError(f'''`prompt` has to be of type `str` or `list` but is {type(snake_case__ )}''' )
UpperCAmelCase : Tuple =batch_size * num_images_per_prompt
UpperCAmelCase : List[str] =guidance_scale > 1.0
UpperCAmelCase : List[Any] =self._encode_prompt(snake_case__ , snake_case__ , snake_case__ )
if (callback_steps is None) or (
callback_steps is not None and (not isinstance(snake_case__ , snake_case__ ) or callback_steps <= 0)
):
raise ValueError(
f'''`callback_steps` has to be a positive integer but is {callback_steps} of type'''
f''' {type(snake_case__ )}.''' )
# get the initial completely masked latents unless the user supplied it
UpperCAmelCase : int =(batch_size, self.transformer.num_latent_pixels)
if latents is None:
UpperCAmelCase : Union[str, Any] =self.transformer.num_vector_embeds - 1
UpperCAmelCase : str =torch.full(snake_case__ , snake_case__ ).to(self.device )
else:
if latents.shape != latents_shape:
raise ValueError(f'''Unexpected latents shape, got {latents.shape}, expected {latents_shape}''' )
if (latents < 0).any() or (latents >= self.transformer.num_vector_embeds).any():
raise ValueError(
'''Unexpected latents value(s). All latents be valid embedding indices i.e. in the range 0,'''
f''' {self.transformer.num_vector_embeds - 1} (inclusive).''' )
UpperCAmelCase : Any =latents.to(self.device )
# set timesteps
self.scheduler.set_timesteps(snake_case__ , device=self.device )
UpperCAmelCase : Any =self.scheduler.timesteps.to(self.device )
UpperCAmelCase : Optional[int] =latents
for i, t in enumerate(self.progress_bar(snake_case__ ) ):
# expand the sample if we are doing classifier free guidance
UpperCAmelCase : Optional[Any] =torch.cat([sample] * 2 ) if do_classifier_free_guidance else sample
# predict the un-noised image
# model_output == `log_p_x_0`
UpperCAmelCase : Optional[int] =self.transformer(snake_case__ , encoder_hidden_states=snake_case__ , timestep=snake_case__ ).sample
if do_classifier_free_guidance:
UpperCAmelCase , UpperCAmelCase : str =model_output.chunk(2 )
UpperCAmelCase : Optional[int] =model_output_uncond + guidance_scale * (model_output_text - model_output_uncond)
model_output -= torch.logsumexp(snake_case__ , dim=1 , keepdim=snake_case__ )
UpperCAmelCase : Tuple =self.truncate(snake_case__ , snake_case__ )
# remove `log(0)`'s (`-inf`s)
UpperCAmelCase : Optional[Any] =model_output.clamp(-70 )
# compute the previous noisy sample x_t -> x_t-1
UpperCAmelCase : int =self.scheduler.step(snake_case__ , timestep=snake_case__ , sample=snake_case__ , generator=snake_case__ ).prev_sample
# call the callback, if provided
if callback is not None and i % callback_steps == 0:
callback(snake_case__ , snake_case__ , snake_case__ )
UpperCAmelCase : Optional[int] =self.vqvae.config.vq_embed_dim
UpperCAmelCase : Optional[Any] =(batch_size, self.transformer.height, self.transformer.width, embedding_channels)
UpperCAmelCase : Dict =self.vqvae.quantize.get_codebook_entry(snake_case__ , shape=snake_case__ )
UpperCAmelCase : Tuple =self.vqvae.decode(snake_case__ , force_not_quantize=snake_case__ ).sample
UpperCAmelCase : Union[str, Any] =(image / 2 + 0.5).clamp(0 , 1 )
UpperCAmelCase : Any =image.cpu().permute(0 , 2 , 3 , 1 ).numpy()
if output_type == "pil":
UpperCAmelCase : List[str] =self.numpy_to_pil(snake_case__ )
if not return_dict:
return (image,)
return ImagePipelineOutput(images=snake_case__ )
def UpperCAmelCase__ ( self , snake_case__ , snake_case__ ) -> torch.FloatTensor:
'''simple docstring'''
UpperCAmelCase , UpperCAmelCase : int =torch.sort(snake_case__ , 1 , descending=snake_case__ )
UpperCAmelCase : Union[str, Any] =torch.exp(snake_case__ )
UpperCAmelCase : Union[str, Any] =sorted_p_x_0.cumsum(dim=1 ) < truncation_rate
# Ensure that at least the largest probability is not zeroed out
UpperCAmelCase : Optional[Any] =torch.full_like(keep_mask[:, 0:1, :] , snake_case__ )
UpperCAmelCase : Tuple =torch.cat((all_true, keep_mask) , dim=1 )
UpperCAmelCase : int =keep_mask[:, :-1, :]
UpperCAmelCase : int =keep_mask.gather(1 , indices.argsort(1 ) )
UpperCAmelCase : Dict =log_p_x_0.clone()
UpperCAmelCase : List[Any] =-torch.inf # -inf = log(0)
return rv
| 348 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
__snake_case = {'''configuration_fnet''': ['''FNET_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''FNetConfig''']}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__snake_case = ['''FNetTokenizer''']
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__snake_case = ['''FNetTokenizerFast''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__snake_case = [
'''FNET_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''FNetForMaskedLM''',
'''FNetForMultipleChoice''',
'''FNetForNextSentencePrediction''',
'''FNetForPreTraining''',
'''FNetForQuestionAnswering''',
'''FNetForSequenceClassification''',
'''FNetForTokenClassification''',
'''FNetLayer''',
'''FNetModel''',
'''FNetPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_fnet import FNET_PRETRAINED_CONFIG_ARCHIVE_MAP, FNetConfig
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_fnet import FNetTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_fnet_fast import FNetTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_fnet import (
FNET_PRETRAINED_MODEL_ARCHIVE_LIST,
FNetForMaskedLM,
FNetForMultipleChoice,
FNetForNextSentencePrediction,
FNetForPreTraining,
FNetForQuestionAnswering,
FNetForSequenceClassification,
FNetForTokenClassification,
FNetLayer,
FNetModel,
FNetPreTrainedModel,
)
else:
import sys
__snake_case = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 348 | import unittest
import numpy as np
import torch
from diffusers import KarrasVePipeline, KarrasVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class __snake_case ( unittest.TestCase ):
@property
def UpperCAmelCase__ ( self ) -> int:
'''simple docstring'''
torch.manual_seed(0 )
UpperCAmelCase : Any =UNetaDModel(
block_out_channels=(32, 64) , layers_per_block=2 , sample_size=32 , in_channels=3 , out_channels=3 , down_block_types=('''DownBlock2D''', '''AttnDownBlock2D''') , up_block_types=('''AttnUpBlock2D''', '''UpBlock2D''') , )
return model
def UpperCAmelCase__ ( self ) -> Union[str, Any]:
'''simple docstring'''
UpperCAmelCase : Tuple =self.dummy_uncond_unet
UpperCAmelCase : Optional[int] =KarrasVeScheduler()
UpperCAmelCase : List[Any] =KarrasVePipeline(unet=snake_case__ , scheduler=snake_case__ )
pipe.to(snake_case__ )
pipe.set_progress_bar_config(disable=snake_case__ )
UpperCAmelCase : List[str] =torch.manual_seed(0 )
UpperCAmelCase : List[str] =pipe(num_inference_steps=2 , generator=snake_case__ , output_type='''numpy''' ).images
UpperCAmelCase : str =torch.manual_seed(0 )
UpperCAmelCase : str =pipe(num_inference_steps=2 , generator=snake_case__ , output_type='''numpy''' , return_dict=snake_case__ )[0]
UpperCAmelCase : Any =image[0, -3:, -3:, -1]
UpperCAmelCase : List[str] =image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 32, 32, 3)
UpperCAmelCase : int =np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1e-2
@slow
@require_torch
class __snake_case ( unittest.TestCase ):
def UpperCAmelCase__ ( self ) -> List[Any]:
'''simple docstring'''
UpperCAmelCase : Tuple ='''google/ncsnpp-celebahq-256'''
UpperCAmelCase : int =UNetaDModel.from_pretrained(snake_case__ )
UpperCAmelCase : Dict =KarrasVeScheduler()
UpperCAmelCase : Union[str, Any] =KarrasVePipeline(unet=snake_case__ , scheduler=snake_case__ )
pipe.to(snake_case__ )
pipe.set_progress_bar_config(disable=snake_case__ )
UpperCAmelCase : Any =torch.manual_seed(0 )
UpperCAmelCase : Tuple =pipe(num_inference_steps=20 , generator=snake_case__ , output_type='''numpy''' ).images
UpperCAmelCase : Optional[int] =image[0, -3:, -3:, -1]
assert image.shape == (1, 256, 256, 3)
UpperCAmelCase : Tuple =np.array([0.578, 0.5811, 0.5924, 0.5809, 0.587, 0.5886, 0.5861, 0.5802, 0.586] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
| 348 | 1 |
__snake_case = '''Input must be a string of 8 numbers plus letter'''
__snake_case = '''TRWAGMYFPDXBNJZSQVHLCKE'''
def lowerCAmelCase_ ( __lowerCAmelCase )-> bool:
'''simple docstring'''
if not isinstance(__lowerCAmelCase , __lowerCAmelCase ):
UpperCAmelCase : Optional[Any] =f'''Expected string as input, found {type(__lowerCAmelCase ).__name__}'''
raise TypeError(__lowerCAmelCase )
UpperCAmelCase : List[Any] =spanish_id.replace('''-''' , '''''' ).upper()
if len(__lowerCAmelCase ) != 9:
raise ValueError(__lowerCAmelCase )
try:
UpperCAmelCase : int =int(spanish_id_clean[0:8] )
UpperCAmelCase : Optional[int] =spanish_id_clean[8]
except ValueError as ex:
raise ValueError(__lowerCAmelCase ) from ex
if letter.isdigit():
raise ValueError(__lowerCAmelCase )
return letter == LOOKUP_LETTERS[number % 23]
if __name__ == "__main__":
import doctest
doctest.testmod()
| 348 | import qiskit
def lowerCAmelCase_ ( __lowerCAmelCase , __lowerCAmelCase )-> qiskit.result.counts.Counts:
'''simple docstring'''
UpperCAmelCase : Union[str, Any] =qiskit.Aer.get_backend('''aer_simulator''' )
UpperCAmelCase : List[str] =qiskit.QuantumCircuit(4 , 2 )
# encode inputs in qubits 0 and 1
if bita == 1:
qc_ha.x(0 )
if bita == 1:
qc_ha.x(1 )
qc_ha.barrier()
# use cnots to write XOR of the inputs on qubit2
qc_ha.cx(0 , 2 )
qc_ha.cx(1 , 2 )
# use ccx / toffoli gate to write AND of the inputs on qubit3
qc_ha.ccx(0 , 1 , 3 )
qc_ha.barrier()
# extract outputs
qc_ha.measure(2 , 0 ) # extract XOR value
qc_ha.measure(3 , 1 ) # extract AND value
# Execute the circuit on the qasm simulator
UpperCAmelCase : Dict =qiskit.execute(__lowerCAmelCase , __lowerCAmelCase , shots=10_00 )
# Return the histogram data of the results of the experiment
return job.result().get_counts(__lowerCAmelCase )
if __name__ == "__main__":
__snake_case = half_adder(1, 1)
print(f'Half Adder Output Qubit Counts: {counts}')
| 348 | 1 |
import os
import time
import warnings
from dataclasses import dataclass, field
from enum import Enum
from typing import List, Optional, Union
import torch
from filelock import FileLock
from torch.utils.data import Dataset
from ...tokenization_utils_base import PreTrainedTokenizerBase
from ...utils import logging
from ..processors.glue import glue_convert_examples_to_features, glue_output_modes, glue_processors
from ..processors.utils import InputFeatures
__snake_case = logging.get_logger(__name__)
@dataclass
class __snake_case :
__lowerCamelCase : str = field(metadata={"""help""": """The name of the task to train on: """ + """, """.join(glue_processors.keys() )} )
__lowerCamelCase : str = field(
metadata={"""help""": """The input data dir. Should contain the .tsv files (or other data files) for the task."""} )
__lowerCamelCase : int = field(
default=128 , metadata={
"""help""": (
"""The maximum total input sequence length after tokenization. Sequences longer """
"""than this will be truncated, sequences shorter will be padded."""
)
} , )
__lowerCamelCase : bool = field(
default=lowerCamelCase__ , metadata={"""help""": """Overwrite the cached training and evaluation sets"""} )
def UpperCAmelCase__ ( self ) -> int:
'''simple docstring'''
UpperCAmelCase : List[str] =self.task_name.lower()
class __snake_case ( lowerCamelCase__ ):
__lowerCamelCase : List[str] = """train"""
__lowerCamelCase : Union[str, Any] = """dev"""
__lowerCamelCase : List[Any] = """test"""
class __snake_case ( lowerCamelCase__ ):
__lowerCamelCase : GlueDataTrainingArguments
__lowerCamelCase : str
__lowerCamelCase : List[InputFeatures]
def __init__( self , snake_case__ , snake_case__ , snake_case__ = None , snake_case__ = Split.train , snake_case__ = None , ) -> str:
'''simple docstring'''
warnings.warn(
'''This dataset will be removed from the library soon, preprocessing should be handled with the 🤗 Datasets '''
'''library. You can have a look at this example script for pointers: '''
'''https://github.com/huggingface/transformers/blob/main/examples/pytorch/text-classification/run_glue.py''' , snake_case__ , )
UpperCAmelCase : Dict =args
UpperCAmelCase : List[str] =glue_processors[args.task_name]()
UpperCAmelCase : Dict =glue_output_modes[args.task_name]
if isinstance(snake_case__ , snake_case__ ):
try:
UpperCAmelCase : str =Split[mode]
except KeyError:
raise KeyError('''mode is not a valid split name''' )
# Load data features from cache or dataset file
UpperCAmelCase : Any =os.path.join(
cache_dir if cache_dir is not None else args.data_dir , f'''cached_{mode.value}_{tokenizer.__class__.__name__}_{args.max_seq_length}_{args.task_name}''' , )
UpperCAmelCase : Any =self.processor.get_labels()
if args.task_name in ["mnli", "mnli-mm"] and tokenizer.__class__.__name__ in (
"RobertaTokenizer",
"RobertaTokenizerFast",
"XLMRobertaTokenizer",
"BartTokenizer",
"BartTokenizerFast",
):
# HACK(label indices are swapped in RoBERTa pretrained model)
UpperCAmelCase , UpperCAmelCase : Tuple =label_list[2], label_list[1]
UpperCAmelCase : str =label_list
# Make sure only the first process in distributed training processes the dataset,
# and the others will use the cache.
UpperCAmelCase : List[str] =cached_features_file + '''.lock'''
with FileLock(snake_case__ ):
if os.path.exists(snake_case__ ) and not args.overwrite_cache:
UpperCAmelCase : List[Any] =time.time()
UpperCAmelCase : Optional[int] =torch.load(snake_case__ )
logger.info(
f'''Loading features from cached file {cached_features_file} [took %.3f s]''' , time.time() - start )
else:
logger.info(f'''Creating features from dataset file at {args.data_dir}''' )
if mode == Split.dev:
UpperCAmelCase : str =self.processor.get_dev_examples(args.data_dir )
elif mode == Split.test:
UpperCAmelCase : Any =self.processor.get_test_examples(args.data_dir )
else:
UpperCAmelCase : Union[str, Any] =self.processor.get_train_examples(args.data_dir )
if limit_length is not None:
UpperCAmelCase : int =examples[:limit_length]
UpperCAmelCase : Optional[Any] =glue_convert_examples_to_features(
snake_case__ , snake_case__ , max_length=args.max_seq_length , label_list=snake_case__ , output_mode=self.output_mode , )
UpperCAmelCase : Optional[Any] =time.time()
torch.save(self.features , snake_case__ )
# ^ This seems to take a lot of time so I want to investigate why and how we can improve.
logger.info(
f'''Saving features into cached file {cached_features_file} [took {time.time() - start:.3f} s]''' )
def __len__( self ) -> int:
'''simple docstring'''
return len(self.features )
def __getitem__( self , snake_case__ ) -> InputFeatures:
'''simple docstring'''
return self.features[i]
def UpperCAmelCase__ ( self ) -> List[str]:
'''simple docstring'''
return self.label_list
| 348 | from __future__ import annotations
import unittest
from transformers import BlenderbotConfig, BlenderbotTokenizer, is_tf_available
from transformers.testing_utils import require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import TFAutoModelForSeqaSeqLM, TFBlenderbotForConditionalGeneration, TFBlenderbotModel
@require_tf
class __snake_case :
__lowerCamelCase : str = BlenderbotConfig
__lowerCamelCase : Optional[Any] = {}
__lowerCamelCase : Optional[int] = """gelu"""
def __init__( self , snake_case__ , snake_case__=13 , snake_case__=7 , snake_case__=True , snake_case__=False , snake_case__=99 , snake_case__=32 , snake_case__=2 , snake_case__=4 , snake_case__=37 , snake_case__=0.1 , snake_case__=0.1 , snake_case__=20 , snake_case__=2 , snake_case__=1 , snake_case__=0 , ) -> Optional[Any]:
'''simple docstring'''
UpperCAmelCase : Union[str, Any] =parent
UpperCAmelCase : Optional[int] =batch_size
UpperCAmelCase : Dict =seq_length
UpperCAmelCase : Optional[Any] =is_training
UpperCAmelCase : List[str] =use_labels
UpperCAmelCase : List[Any] =vocab_size
UpperCAmelCase : Optional[int] =hidden_size
UpperCAmelCase : Tuple =num_hidden_layers
UpperCAmelCase : Any =num_attention_heads
UpperCAmelCase : Optional[int] =intermediate_size
UpperCAmelCase : str =hidden_dropout_prob
UpperCAmelCase : Optional[int] =attention_probs_dropout_prob
UpperCAmelCase : str =max_position_embeddings
UpperCAmelCase : List[Any] =eos_token_id
UpperCAmelCase : Optional[int] =pad_token_id
UpperCAmelCase : Tuple =bos_token_id
def UpperCAmelCase__ ( self ) -> Optional[Any]:
'''simple docstring'''
UpperCAmelCase : List[Any] =ids_tensor([self.batch_size, self.seq_length - 1] , self.vocab_size )
UpperCAmelCase : List[Any] =tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size ) , 1 )
UpperCAmelCase : Tuple =tf.concat([input_ids, eos_tensor] , axis=1 )
UpperCAmelCase : str =ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
UpperCAmelCase : Optional[Any] =self.config_cls(
vocab_size=self.vocab_size , d_model=self.hidden_size , encoder_layers=self.num_hidden_layers , decoder_layers=self.num_hidden_layers , encoder_attention_heads=self.num_attention_heads , decoder_attention_heads=self.num_attention_heads , encoder_ffn_dim=self.intermediate_size , decoder_ffn_dim=self.intermediate_size , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , eos_token_ids=[2] , bos_token_id=self.bos_token_id , pad_token_id=self.pad_token_id , decoder_start_token_id=self.pad_token_id , **self.config_updates , )
UpperCAmelCase : List[str] =prepare_blenderbot_inputs_dict(snake_case__ , snake_case__ , snake_case__ )
return config, inputs_dict
def UpperCAmelCase__ ( self , snake_case__ , snake_case__ ) -> int:
'''simple docstring'''
UpperCAmelCase : Union[str, Any] =TFBlenderbotModel(config=snake_case__ ).get_decoder()
UpperCAmelCase : Any =inputs_dict['''input_ids''']
UpperCAmelCase : str =input_ids[:1, :]
UpperCAmelCase : Tuple =inputs_dict['''attention_mask'''][:1, :]
UpperCAmelCase : Tuple =inputs_dict['''head_mask''']
UpperCAmelCase : List[Any] =1
# first forward pass
UpperCAmelCase : List[str] =model(snake_case__ , attention_mask=snake_case__ , head_mask=snake_case__ , use_cache=snake_case__ )
UpperCAmelCase , UpperCAmelCase : str =outputs.to_tuple()
# create hypothetical next token and extent to next_input_ids
UpperCAmelCase : Union[str, Any] =ids_tensor((self.batch_size, 3) , config.vocab_size )
UpperCAmelCase : List[Any] =tf.cast(ids_tensor((self.batch_size, 3) , 2 ) , tf.inta )
# append to next input_ids and
UpperCAmelCase : Tuple =tf.concat([input_ids, next_tokens] , axis=-1 )
UpperCAmelCase : int =tf.concat([attention_mask, next_attn_mask] , axis=-1 )
UpperCAmelCase : Optional[int] =model(snake_case__ , attention_mask=snake_case__ )[0]
UpperCAmelCase : str =model(snake_case__ , attention_mask=snake_case__ , past_key_values=snake_case__ )[0]
self.parent.assertEqual(next_tokens.shape[1] , output_from_past.shape[1] )
# select random slice
UpperCAmelCase : List[Any] =int(ids_tensor((1,) , output_from_past.shape[-1] ) )
UpperCAmelCase : List[Any] =output_from_no_past[:, -3:, random_slice_idx]
UpperCAmelCase : Dict =output_from_past[:, :, random_slice_idx]
# test that outputs are equal for slice
tf.debugging.assert_near(snake_case__ , snake_case__ , rtol=1e-3 )
def lowerCAmelCase_ ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase=None , __lowerCAmelCase=None , __lowerCAmelCase=None , __lowerCAmelCase=None , __lowerCAmelCase=None , )-> str:
'''simple docstring'''
if attention_mask is None:
UpperCAmelCase : int =tf.cast(tf.math.not_equal(__lowerCAmelCase , config.pad_token_id ) , tf.inta )
if decoder_attention_mask is None:
UpperCAmelCase : Tuple =tf.concat(
[
tf.ones(decoder_input_ids[:, :1].shape , dtype=tf.inta ),
tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:] , config.pad_token_id ) , tf.inta ),
] , axis=-1 , )
if head_mask is None:
UpperCAmelCase : str =tf.ones((config.encoder_layers, config.encoder_attention_heads) )
if decoder_head_mask is None:
UpperCAmelCase : Union[str, Any] =tf.ones((config.decoder_layers, config.decoder_attention_heads) )
if cross_attn_head_mask is None:
UpperCAmelCase : int =tf.ones((config.decoder_layers, config.decoder_attention_heads) )
return {
"input_ids": input_ids,
"decoder_input_ids": decoder_input_ids,
"attention_mask": attention_mask,
"decoder_attention_mask": decoder_attention_mask,
"head_mask": head_mask,
"decoder_head_mask": decoder_head_mask,
"cross_attn_head_mask": cross_attn_head_mask,
}
@require_tf
class __snake_case ( lowerCamelCase__ , lowerCamelCase__ , unittest.TestCase ):
__lowerCamelCase : List[str] = (TFBlenderbotForConditionalGeneration, TFBlenderbotModel) if is_tf_available() else ()
__lowerCamelCase : Dict = (TFBlenderbotForConditionalGeneration,) if is_tf_available() else ()
__lowerCamelCase : Dict = (
{
"""conversational""": TFBlenderbotForConditionalGeneration,
"""feature-extraction""": TFBlenderbotModel,
"""summarization""": TFBlenderbotForConditionalGeneration,
"""text2text-generation""": TFBlenderbotForConditionalGeneration,
"""translation""": TFBlenderbotForConditionalGeneration,
}
if is_tf_available()
else {}
)
__lowerCamelCase : Union[str, Any] = True
__lowerCamelCase : Union[str, Any] = False
__lowerCamelCase : Union[str, Any] = False
def UpperCAmelCase__ ( self ) -> Dict:
'''simple docstring'''
UpperCAmelCase : List[str] =TFBlenderbotModelTester(self )
UpperCAmelCase : List[Any] =ConfigTester(self , config_class=snake_case__ )
def UpperCAmelCase__ ( self ) -> Optional[Any]:
'''simple docstring'''
self.config_tester.run_common_tests()
def UpperCAmelCase__ ( self ) -> List[Any]:
'''simple docstring'''
UpperCAmelCase : int =self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.check_decoder_model_past_large_inputs(*snake_case__ )
@require_tokenizers
@require_tf
class __snake_case ( unittest.TestCase ):
__lowerCamelCase : List[str] = ["""My friends are cool but they eat too many carbs."""]
__lowerCamelCase : Dict = """facebook/blenderbot-400M-distill"""
@cached_property
def UpperCAmelCase__ ( self ) -> int:
'''simple docstring'''
return BlenderbotTokenizer.from_pretrained(self.model_name )
@cached_property
def UpperCAmelCase__ ( self ) -> int:
'''simple docstring'''
UpperCAmelCase : int =TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name )
return model
@slow
def UpperCAmelCase__ ( self ) -> Any:
'''simple docstring'''
UpperCAmelCase : Optional[int] =self.tokenizer(self.src_text , return_tensors='''tf''' )
UpperCAmelCase : Optional[int] =self.model.generate(
model_inputs.input_ids , )
UpperCAmelCase : str =self.tokenizer.batch_decode(generated_ids.numpy() , skip_special_tokens=snake_case__ )[0]
assert (
generated_words
== " That's unfortunate. Are they trying to lose weight or are they just trying to be healthier?"
)
| 348 | 1 |
import unittest
from transformers import RoFormerTokenizer, RoFormerTokenizerFast
from transformers.testing_utils import require_rjieba, require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_rjieba
@require_tokenizers
class __snake_case ( lowerCamelCase__ , unittest.TestCase ):
__lowerCamelCase : Union[str, Any] = RoFormerTokenizer
__lowerCamelCase : Union[str, Any] = RoFormerTokenizerFast
__lowerCamelCase : Any = True
__lowerCamelCase : Optional[Any] = True
def UpperCAmelCase__ ( self ) -> Optional[int]:
'''simple docstring'''
super().setUp()
def UpperCAmelCase__ ( self , **snake_case__ ) -> Any:
'''simple docstring'''
return self.tokenizer_class.from_pretrained('''junnyu/roformer_chinese_base''' , **snake_case__ )
def UpperCAmelCase__ ( self , **snake_case__ ) -> Optional[Any]:
'''simple docstring'''
return self.rust_tokenizer_class.from_pretrained('''junnyu/roformer_chinese_base''' , **snake_case__ )
def UpperCAmelCase__ ( self ) -> List[Any]:
'''simple docstring'''
UpperCAmelCase : Union[str, Any] ='''永和服装饰品有限公司,今天天气非常好'''
UpperCAmelCase : Optional[Any] ='''永和 服装 饰品 有限公司 , 今 天 天 气 非常 好'''
return input_text, output_text
def UpperCAmelCase__ ( self ) -> List[str]:
'''simple docstring'''
UpperCAmelCase : Dict =self.get_tokenizer()
UpperCAmelCase , UpperCAmelCase : List[str] =self.get_chinese_input_output_texts()
UpperCAmelCase : Optional[int] =tokenizer.tokenize(snake_case__ )
self.assertListEqual(snake_case__ , output_text.split() )
UpperCAmelCase : Union[str, Any] =tokens + [tokenizer.unk_token]
UpperCAmelCase : Optional[int] =[2_2943, 2_1332, 3_4431, 4_5904, 117, 306, 1231, 1231, 2653, 3_3994, 1266, 100]
self.assertListEqual(tokenizer.convert_tokens_to_ids(snake_case__ ) , snake_case__ )
def UpperCAmelCase__ ( self ) -> List[Any]:
'''simple docstring'''
UpperCAmelCase : List[Any] =self.get_rust_tokenizer()
UpperCAmelCase , UpperCAmelCase : Dict =self.get_chinese_input_output_texts()
UpperCAmelCase : Union[str, Any] =tokenizer.tokenize(snake_case__ )
self.assertListEqual(snake_case__ , output_text.split() )
UpperCAmelCase : Optional[Any] =tokens + [tokenizer.unk_token]
UpperCAmelCase : Union[str, Any] =[2_2943, 2_1332, 3_4431, 4_5904, 117, 306, 1231, 1231, 2653, 3_3994, 1266, 100]
self.assertListEqual(tokenizer.convert_tokens_to_ids(snake_case__ ) , snake_case__ )
def UpperCAmelCase__ ( self ) -> Any:
'''simple docstring'''
pass
def UpperCAmelCase__ ( self ) -> Optional[Any]:
'''simple docstring'''
pass
def UpperCAmelCase__ ( self ) -> List[str]:
'''simple docstring'''
pass
| 348 | import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__snake_case = logging.get_logger(__name__)
__snake_case = {
'''asapp/sew-d-tiny-100k''': '''https://huggingface.co/asapp/sew-d-tiny-100k/resolve/main/config.json''',
# See all SEW-D models at https://huggingface.co/models?filter=sew-d
}
class __snake_case ( lowerCamelCase__ ):
__lowerCamelCase : Optional[Any] = """sew-d"""
def __init__( self , snake_case__=32 , snake_case__=768 , snake_case__=12 , snake_case__=12 , snake_case__=3072 , snake_case__=2 , snake_case__=512 , snake_case__=256 , snake_case__=True , snake_case__=True , snake_case__=("p2c", "c2p") , snake_case__="layer_norm" , snake_case__="gelu_python" , snake_case__=0.1 , snake_case__=0.1 , snake_case__=0.1 , snake_case__=0.0 , snake_case__=0.1 , snake_case__=0.02 , snake_case__=1e-7 , snake_case__=1e-5 , snake_case__="group" , snake_case__="gelu" , snake_case__=(64, 128, 128, 128, 128, 256, 256, 256, 256, 512, 512, 512, 512) , snake_case__=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1) , snake_case__=(10, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1) , snake_case__=False , snake_case__=128 , snake_case__=16 , snake_case__=True , snake_case__=0.05 , snake_case__=10 , snake_case__=2 , snake_case__=0.0 , snake_case__=10 , snake_case__=0 , snake_case__="mean" , snake_case__=False , snake_case__=False , snake_case__=256 , snake_case__=0 , snake_case__=1 , snake_case__=2 , **snake_case__ , ) -> int:
'''simple docstring'''
super().__init__(**snake_case__ , pad_token_id=snake_case__ , bos_token_id=snake_case__ , eos_token_id=snake_case__ )
UpperCAmelCase : Union[str, Any] =hidden_size
UpperCAmelCase : Union[str, Any] =feat_extract_norm
UpperCAmelCase : Optional[Any] =feat_extract_activation
UpperCAmelCase : List[str] =list(snake_case__ )
UpperCAmelCase : int =list(snake_case__ )
UpperCAmelCase : List[str] =list(snake_case__ )
UpperCAmelCase : str =conv_bias
UpperCAmelCase : Tuple =num_conv_pos_embeddings
UpperCAmelCase : Dict =num_conv_pos_embedding_groups
UpperCAmelCase : str =len(self.conv_dim )
UpperCAmelCase : Dict =num_hidden_layers
UpperCAmelCase : Optional[int] =intermediate_size
UpperCAmelCase : List[Any] =squeeze_factor
UpperCAmelCase : str =max_position_embeddings
UpperCAmelCase : int =position_buckets
UpperCAmelCase : Optional[int] =share_att_key
UpperCAmelCase : Optional[int] =relative_attention
UpperCAmelCase : Tuple =norm_rel_ebd
UpperCAmelCase : List[Any] =list(snake_case__ )
UpperCAmelCase : Dict =hidden_act
UpperCAmelCase : Optional[int] =num_attention_heads
UpperCAmelCase : Any =hidden_dropout
UpperCAmelCase : str =attention_dropout
UpperCAmelCase : Union[str, Any] =activation_dropout
UpperCAmelCase : str =feat_proj_dropout
UpperCAmelCase : Union[str, Any] =final_dropout
UpperCAmelCase : Optional[int] =layer_norm_eps
UpperCAmelCase : str =feature_layer_norm_eps
UpperCAmelCase : str =initializer_range
UpperCAmelCase : Any =vocab_size
if (
(len(self.conv_stride ) != self.num_feat_extract_layers)
or (len(self.conv_kernel ) != self.num_feat_extract_layers)
or (len(self.conv_dim ) != self.num_feat_extract_layers)
):
raise ValueError(
'''Configuration for convolutional layers is incorrect.'''
'''It is required that `len(config.conv_dim)` == `len(config.conv_stride)` == `len(config.conv_kernel)`,'''
f'''but is `len(config.conv_dim) = {len(self.conv_dim )}`, `len(config.conv_stride)'''
f'''= {len(self.conv_stride )}`, `len(config.conv_kernel) = {len(self.conv_kernel )}`.''' )
# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
UpperCAmelCase : Union[str, Any] =apply_spec_augment
UpperCAmelCase : Optional[Any] =mask_time_prob
UpperCAmelCase : Tuple =mask_time_length
UpperCAmelCase : str =mask_time_min_masks
UpperCAmelCase : Optional[int] =mask_feature_prob
UpperCAmelCase : Optional[Any] =mask_feature_length
UpperCAmelCase : List[Any] =mask_feature_min_masks
# ctc loss
UpperCAmelCase : str =ctc_loss_reduction
UpperCAmelCase : Optional[int] =ctc_zero_infinity
# sequence classification
UpperCAmelCase : Union[str, Any] =use_weighted_layer_sum
UpperCAmelCase : int =classifier_proj_size
@property
def UpperCAmelCase__ ( self ) -> List[Any]:
'''simple docstring'''
return functools.reduce(operator.mul , self.conv_stride , 1 )
| 348 | 1 |
import argparse
from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection
from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline
if __name__ == "__main__":
__snake_case = argparse.ArgumentParser()
parser.add_argument('''--dump_path''', default=None, type=str, required=True, help='''Path to the output model.''')
parser.add_argument(
'''--txt2img_unclip''',
default='''kakaobrain/karlo-v1-alpha''',
type=str,
required=False,
help='''The pretrained txt2img unclip.''',
)
__snake_case = parser.parse_args()
__snake_case = UnCLIPPipeline.from_pretrained(args.txtaimg_unclip)
__snake_case = CLIPImageProcessor()
__snake_case = CLIPVisionModelWithProjection.from_pretrained('''openai/clip-vit-large-patch14''')
__snake_case = UnCLIPImageVariationPipeline(
decoder=txtaimg.decoder,
text_encoder=txtaimg.text_encoder,
tokenizer=txtaimg.tokenizer,
text_proj=txtaimg.text_proj,
feature_extractor=feature_extractor,
image_encoder=image_encoder,
super_res_first=txtaimg.super_res_first,
super_res_last=txtaimg.super_res_last,
decoder_scheduler=txtaimg.decoder_scheduler,
super_res_scheduler=txtaimg.super_res_scheduler,
)
imgaimg.save_pretrained(args.dump_path)
| 348 | import os
from argparse import ArgumentParser
from typing import List
import torch.utils.data
from datasets import Dataset, IterableDataset
from datasets.distributed import split_dataset_by_node
__snake_case = 4
__snake_case = 3
class __snake_case ( lowerCamelCase__ ):
pass
def lowerCAmelCase_ ( __lowerCAmelCase )-> List[str]:
'''simple docstring'''
for shard in shards:
for i in range(__lowerCAmelCase ):
yield {"i": i, "shard": shard}
def lowerCAmelCase_ ( )-> Optional[int]:
'''simple docstring'''
UpperCAmelCase : List[str] =int(os.environ['''RANK'''] )
UpperCAmelCase : Optional[Any] =int(os.environ['''WORLD_SIZE'''] )
UpperCAmelCase : List[Any] =ArgumentParser()
parser.add_argument('''--streaming''' , type=__lowerCAmelCase )
parser.add_argument('''--local_rank''' , type=__lowerCAmelCase )
parser.add_argument('''--num_workers''' , type=__lowerCAmelCase , default=0 )
UpperCAmelCase : Any =parser.parse_args()
UpperCAmelCase : List[str] =args.streaming
UpperCAmelCase : Tuple =args.num_workers
UpperCAmelCase : int ={'''shards''': [f'''shard_{shard_idx}''' for shard_idx in range(__lowerCAmelCase )]}
UpperCAmelCase : Optional[int] =IterableDataset.from_generator(__lowerCAmelCase , gen_kwargs=__lowerCAmelCase )
if not streaming:
UpperCAmelCase : List[Any] =Dataset.from_list(list(__lowerCAmelCase ) )
UpperCAmelCase : Dict =split_dataset_by_node(__lowerCAmelCase , rank=__lowerCAmelCase , world_size=__lowerCAmelCase )
UpperCAmelCase : List[Any] =torch.utils.data.DataLoader(__lowerCAmelCase , num_workers=__lowerCAmelCase )
UpperCAmelCase : Dict =NUM_SHARDS * NUM_ITEMS_PER_SHARD
UpperCAmelCase : str =full_size // world_size
expected_local_size += int(rank < (full_size % world_size) )
UpperCAmelCase : List[Any] =sum(1 for _ in dataloader )
if local_size != expected_local_size:
raise FailedTestError(f'''local_size {local_size} != expected_local_size {expected_local_size}''' )
if __name__ == "__main__":
main()
| 348 | 1 |
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import TensorType, logging
if TYPE_CHECKING:
from ...onnx.config import PatchingSpec
from ...tokenization_utils_base import PreTrainedTokenizerBase
__snake_case = logging.get_logger(__name__)
__snake_case = {
'''allenai/longformer-base-4096''': '''https://huggingface.co/allenai/longformer-base-4096/resolve/main/config.json''',
'''allenai/longformer-large-4096''': '''https://huggingface.co/allenai/longformer-large-4096/resolve/main/config.json''',
'''allenai/longformer-large-4096-finetuned-triviaqa''': (
'''https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/config.json'''
),
'''allenai/longformer-base-4096-extra.pos.embd.only''': (
'''https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/config.json'''
),
'''allenai/longformer-large-4096-extra.pos.embd.only''': (
'''https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/config.json'''
),
}
class __snake_case ( lowerCamelCase__ ):
__lowerCamelCase : int = """longformer"""
def __init__( self , snake_case__ = 512 , snake_case__ = 2 , snake_case__ = 1 , snake_case__ = 0 , snake_case__ = 2 , snake_case__ = 3_0522 , snake_case__ = 768 , snake_case__ = 12 , snake_case__ = 12 , snake_case__ = 3072 , snake_case__ = "gelu" , snake_case__ = 0.1 , snake_case__ = 0.1 , snake_case__ = 512 , snake_case__ = 2 , snake_case__ = 0.02 , snake_case__ = 1e-12 , snake_case__ = False , **snake_case__ , ) -> Any:
'''simple docstring'''
super().__init__(pad_token_id=snake_case__ , **snake_case__ )
UpperCAmelCase : Union[str, Any] =attention_window
UpperCAmelCase : Optional[Any] =sep_token_id
UpperCAmelCase : int =bos_token_id
UpperCAmelCase : int =eos_token_id
UpperCAmelCase : Optional[int] =vocab_size
UpperCAmelCase : Optional[int] =hidden_size
UpperCAmelCase : Any =num_hidden_layers
UpperCAmelCase : Dict =num_attention_heads
UpperCAmelCase : Union[str, Any] =hidden_act
UpperCAmelCase : str =intermediate_size
UpperCAmelCase : List[str] =hidden_dropout_prob
UpperCAmelCase : Tuple =attention_probs_dropout_prob
UpperCAmelCase : Any =max_position_embeddings
UpperCAmelCase : int =type_vocab_size
UpperCAmelCase : str =initializer_range
UpperCAmelCase : Optional[Any] =layer_norm_eps
UpperCAmelCase : List[str] =onnx_export
class __snake_case ( lowerCamelCase__ ):
def __init__( self , snake_case__ , snake_case__ = "default" , snake_case__ = None ) -> List[str]:
'''simple docstring'''
super().__init__(snake_case__ , snake_case__ , snake_case__ )
UpperCAmelCase : List[Any] =True
@property
def UpperCAmelCase__ ( self ) -> Mapping[str, Mapping[int, str]]:
'''simple docstring'''
if self.task == "multiple-choice":
UpperCAmelCase : str ={0: '''batch''', 1: '''choice''', 2: '''sequence'''}
else:
UpperCAmelCase : Optional[Any] ={0: '''batch''', 1: '''sequence'''}
return OrderedDict(
[
('''input_ids''', dynamic_axis),
('''attention_mask''', dynamic_axis),
('''global_attention_mask''', dynamic_axis),
] )
@property
def UpperCAmelCase__ ( self ) -> Mapping[str, Mapping[int, str]]:
'''simple docstring'''
UpperCAmelCase : Any =super().outputs
if self.task == "default":
UpperCAmelCase : Union[str, Any] ={0: '''batch'''}
return outputs
@property
def UpperCAmelCase__ ( self ) -> float:
'''simple docstring'''
return 1e-4
@property
def UpperCAmelCase__ ( self ) -> int:
'''simple docstring'''
return max(super().default_onnx_opset , 14 )
def UpperCAmelCase__ ( self , snake_case__ , snake_case__ = -1 , snake_case__ = -1 , snake_case__ = False , snake_case__ = None , ) -> Mapping[str, Any]:
'''simple docstring'''
UpperCAmelCase : Any =super().generate_dummy_inputs(
preprocessor=snake_case__ , batch_size=snake_case__ , seq_length=snake_case__ , is_pair=snake_case__ , framework=snake_case__ )
import torch
# for some reason, replacing this code by inputs["global_attention_mask"] = torch.randint(2, inputs["input_ids"].shape, dtype=torch.int64)
# makes the export fail randomly
UpperCAmelCase : List[Any] =torch.zeros_like(inputs['''input_ids'''] )
# make every second token global
UpperCAmelCase : List[Any] =1
return inputs
| 348 | from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__snake_case = {'''configuration_opt''': ['''OPT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''OPTConfig''']}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__snake_case = [
'''OPT_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''OPTForCausalLM''',
'''OPTModel''',
'''OPTPreTrainedModel''',
'''OPTForSequenceClassification''',
'''OPTForQuestionAnswering''',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__snake_case = ['''TFOPTForCausalLM''', '''TFOPTModel''', '''TFOPTPreTrainedModel''']
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__snake_case = [
'''FlaxOPTForCausalLM''',
'''FlaxOPTModel''',
'''FlaxOPTPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_opt import OPT_PRETRAINED_CONFIG_ARCHIVE_MAP, OPTConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_opt import (
OPT_PRETRAINED_MODEL_ARCHIVE_LIST,
OPTForCausalLM,
OPTForQuestionAnswering,
OPTForSequenceClassification,
OPTModel,
OPTPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_opt import TFOPTForCausalLM, TFOPTModel, TFOPTPreTrainedModel
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_opt import FlaxOPTForCausalLM, FlaxOPTModel, FlaxOPTPreTrainedModel
else:
import sys
__snake_case = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 348 | 1 |
import itertools
import json
import linecache
import os
import pickle
import re
import socket
import string
from collections import Counter
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List
import git
import torch
from torch.utils.data import Dataset
from transformers import BartTokenizer, RagTokenizer, TaTokenizer
def lowerCAmelCase_ ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase=True , __lowerCAmelCase="pt" )-> str:
'''simple docstring'''
UpperCAmelCase : Any ={'''add_prefix_space''': True} if isinstance(__lowerCAmelCase , __lowerCAmelCase ) and not line.startswith(''' ''' ) else {}
UpperCAmelCase : Optional[int] =padding_side
return tokenizer(
[line] , max_length=__lowerCAmelCase , padding='''max_length''' if pad_to_max_length else None , truncation=__lowerCAmelCase , return_tensors=__lowerCAmelCase , add_special_tokens=__lowerCAmelCase , **__lowerCAmelCase , )
def lowerCAmelCase_ ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase=None , )-> str:
'''simple docstring'''
UpperCAmelCase : Tuple =input_ids.ne(__lowerCAmelCase ).any(dim=0 )
if attention_mask is None:
return input_ids[:, keep_column_mask]
else:
return (input_ids[:, keep_column_mask], attention_mask[:, keep_column_mask])
class __snake_case ( lowerCamelCase__ ):
def __init__( self , snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__="train" , snake_case__=None , snake_case__=None , snake_case__=None , snake_case__="" , ) -> int:
'''simple docstring'''
super().__init__()
UpperCAmelCase : Dict =Path(snake_case__ ).joinpath(type_path + '''.source''' )
UpperCAmelCase : int =Path(snake_case__ ).joinpath(type_path + '''.target''' )
UpperCAmelCase : Dict =self.get_char_lens(self.src_file )
UpperCAmelCase : Any =max_source_length
UpperCAmelCase : Dict =max_target_length
assert min(self.src_lens ) > 0, f'''found empty line in {self.src_file}'''
UpperCAmelCase : List[str] =tokenizer
UpperCAmelCase : List[Any] =prefix
if n_obs is not None:
UpperCAmelCase : Union[str, Any] =self.src_lens[:n_obs]
UpperCAmelCase : List[Any] =src_lang
UpperCAmelCase : Union[str, Any] =tgt_lang
def __len__( self ) -> Dict:
'''simple docstring'''
return len(self.src_lens )
def __getitem__( self , snake_case__ ) -> Dict[str, torch.Tensor]:
'''simple docstring'''
UpperCAmelCase : int =index + 1 # linecache starts at 1
UpperCAmelCase : Union[str, Any] =self.prefix + linecache.getline(str(self.src_file ) , snake_case__ ).rstrip('''\n''' )
UpperCAmelCase : List[Any] =linecache.getline(str(self.tgt_file ) , snake_case__ ).rstrip('''\n''' )
assert source_line, f'''empty source line for index {index}'''
assert tgt_line, f'''empty tgt line for index {index}'''
# Need to add eos token manually for T5
if isinstance(self.tokenizer , snake_case__ ):
source_line += self.tokenizer.eos_token
tgt_line += self.tokenizer.eos_token
# Pad source and target to the right
UpperCAmelCase : Dict =(
self.tokenizer.question_encoder if isinstance(self.tokenizer , snake_case__ ) else self.tokenizer
)
UpperCAmelCase : str =self.tokenizer.generator if isinstance(self.tokenizer , snake_case__ ) else self.tokenizer
UpperCAmelCase : Union[str, Any] =encode_line(snake_case__ , snake_case__ , self.max_source_length , '''right''' )
UpperCAmelCase : Any =encode_line(snake_case__ , snake_case__ , self.max_target_length , '''right''' )
UpperCAmelCase : Union[str, Any] =source_inputs['''input_ids'''].squeeze()
UpperCAmelCase : str =target_inputs['''input_ids'''].squeeze()
UpperCAmelCase : Dict =source_inputs['''attention_mask'''].squeeze()
return {
"input_ids": source_ids,
"attention_mask": src_mask,
"decoder_input_ids": target_ids,
}
@staticmethod
def UpperCAmelCase__ ( snake_case__ ) -> Tuple:
'''simple docstring'''
return [len(snake_case__ ) for x in Path(snake_case__ ).open().readlines()]
def UpperCAmelCase__ ( self , snake_case__ ) -> Dict[str, torch.Tensor]:
'''simple docstring'''
UpperCAmelCase : int =torch.stack([x['''input_ids'''] for x in batch] )
UpperCAmelCase : Tuple =torch.stack([x['''attention_mask'''] for x in batch] )
UpperCAmelCase : List[Any] =torch.stack([x['''decoder_input_ids'''] for x in batch] )
UpperCAmelCase : Union[str, Any] =(
self.tokenizer.generator.pad_token_id
if isinstance(self.tokenizer , snake_case__ )
else self.tokenizer.pad_token_id
)
UpperCAmelCase : Union[str, Any] =(
self.tokenizer.question_encoder.pad_token_id
if isinstance(self.tokenizer , snake_case__ )
else self.tokenizer.pad_token_id
)
UpperCAmelCase : Tuple =trim_batch(snake_case__ , snake_case__ )
UpperCAmelCase , UpperCAmelCase : Any =trim_batch(snake_case__ , snake_case__ , attention_mask=snake_case__ )
UpperCAmelCase : Optional[Any] ={
'''input_ids''': source_ids,
'''attention_mask''': source_mask,
'''decoder_input_ids''': y,
}
return batch
__snake_case = getLogger(__name__)
def lowerCAmelCase_ ( __lowerCAmelCase )-> str:
'''simple docstring'''
return list(itertools.chain.from_iterable(__lowerCAmelCase ) )
def lowerCAmelCase_ ( __lowerCAmelCase )-> None:
'''simple docstring'''
UpperCAmelCase : str =get_git_info()
save_json(__lowerCAmelCase , os.path.join(__lowerCAmelCase , '''git_log.json''' ) )
def lowerCAmelCase_ ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase=4 , **__lowerCAmelCase )-> Any:
'''simple docstring'''
with open(__lowerCAmelCase , '''w''' ) as f:
json.dump(__lowerCAmelCase , __lowerCAmelCase , indent=__lowerCAmelCase , **__lowerCAmelCase )
def lowerCAmelCase_ ( __lowerCAmelCase )-> List[Any]:
'''simple docstring'''
with open(__lowerCAmelCase ) as f:
return json.load(__lowerCAmelCase )
def lowerCAmelCase_ ( )-> str:
'''simple docstring'''
UpperCAmelCase : List[str] =git.Repo(search_parent_directories=__lowerCAmelCase )
UpperCAmelCase : Union[str, Any] ={
'''repo_id''': str(__lowerCAmelCase ),
'''repo_sha''': str(repo.head.object.hexsha ),
'''repo_branch''': str(repo.active_branch ),
'''hostname''': str(socket.gethostname() ),
}
return repo_infos
def lowerCAmelCase_ ( __lowerCAmelCase , __lowerCAmelCase )-> List:
'''simple docstring'''
return list(map(__lowerCAmelCase , __lowerCAmelCase ) )
def lowerCAmelCase_ ( __lowerCAmelCase , __lowerCAmelCase )-> Any:
'''simple docstring'''
with open(__lowerCAmelCase , '''wb''' ) as f:
return pickle.dump(__lowerCAmelCase , __lowerCAmelCase )
def lowerCAmelCase_ ( __lowerCAmelCase )-> Tuple:
'''simple docstring'''
def remove_articles(__lowerCAmelCase ):
return re.sub(R'''\b(a|an|the)\b''' , ''' ''' , __lowerCAmelCase )
def white_space_fix(__lowerCAmelCase ):
return " ".join(text.split() )
def remove_punc(__lowerCAmelCase ):
UpperCAmelCase : List[str] =set(string.punctuation )
return "".join(ch for ch in text if ch not in exclude )
def lower(__lowerCAmelCase ):
return text.lower()
return white_space_fix(remove_articles(remove_punc(lower(__lowerCAmelCase ) ) ) )
def lowerCAmelCase_ ( __lowerCAmelCase , __lowerCAmelCase )-> Dict:
'''simple docstring'''
UpperCAmelCase : int =normalize_answer(__lowerCAmelCase ).split()
UpperCAmelCase : Union[str, Any] =normalize_answer(__lowerCAmelCase ).split()
UpperCAmelCase : Tuple =Counter(__lowerCAmelCase ) & Counter(__lowerCAmelCase )
UpperCAmelCase : Union[str, Any] =sum(common.values() )
if num_same == 0:
return 0
UpperCAmelCase : int =1.0 * num_same / len(__lowerCAmelCase )
UpperCAmelCase : Dict =1.0 * num_same / len(__lowerCAmelCase )
UpperCAmelCase : List[str] =(2 * precision * recall) / (precision + recall)
return fa
def lowerCAmelCase_ ( __lowerCAmelCase , __lowerCAmelCase )-> List[Any]:
'''simple docstring'''
return normalize_answer(__lowerCAmelCase ) == normalize_answer(__lowerCAmelCase )
def lowerCAmelCase_ ( __lowerCAmelCase , __lowerCAmelCase )-> Dict:
'''simple docstring'''
assert len(__lowerCAmelCase ) == len(__lowerCAmelCase )
UpperCAmelCase : Union[str, Any] =0
for hypo, pred in zip(__lowerCAmelCase , __lowerCAmelCase ):
em += exact_match_score(__lowerCAmelCase , __lowerCAmelCase )
if len(__lowerCAmelCase ) > 0:
em /= len(__lowerCAmelCase )
return {"em": em}
def lowerCAmelCase_ ( __lowerCAmelCase )-> Tuple:
'''simple docstring'''
return model_prefix.startswith('''rag''' )
def lowerCAmelCase_ ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )-> List[str]:
'''simple docstring'''
UpperCAmelCase : List[Any] ={p: p for p in extra_params}
# T5 models don't have `dropout` param, they have `dropout_rate` instead
UpperCAmelCase : List[str] ='''dropout_rate'''
for p in extra_params:
if getattr(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ):
if not hasattr(__lowerCAmelCase , __lowerCAmelCase ) and not hasattr(__lowerCAmelCase , equivalent_param[p] ):
logger.info('''config doesn\'t have a `{}` attribute'''.format(__lowerCAmelCase ) )
delattr(__lowerCAmelCase , __lowerCAmelCase )
continue
UpperCAmelCase : Union[str, Any] =p if hasattr(__lowerCAmelCase , __lowerCAmelCase ) else equivalent_param[p]
setattr(__lowerCAmelCase , __lowerCAmelCase , getattr(__lowerCAmelCase , __lowerCAmelCase ) )
delattr(__lowerCAmelCase , __lowerCAmelCase )
return hparams, config
| 348 | import tempfile
import unittest
import numpy as np
import transformers
from transformers import GPTaTokenizer, GPTJConfig, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, tooslow
from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import jax
import jax.numpy as jnp
from transformers.modeling_flax_pytorch_utils import (
convert_pytorch_state_dict_to_flax,
load_flax_weights_in_pytorch_model,
)
from transformers.models.gptj.modeling_flax_gptj import FlaxGPTJForCausalLM, FlaxGPTJModel
if is_torch_available():
import torch
class __snake_case :
def __init__( self , snake_case__ , snake_case__=14 , snake_case__=7 , snake_case__=True , snake_case__=True , snake_case__=False , snake_case__=True , snake_case__=99 , snake_case__=32 , snake_case__=4 , snake_case__=4 , snake_case__=4 , snake_case__=37 , snake_case__="gelu" , snake_case__=0.1 , snake_case__=0.1 , snake_case__=512 , snake_case__=0.02 , ) -> str:
'''simple docstring'''
UpperCAmelCase : str =parent
UpperCAmelCase : Tuple =batch_size
UpperCAmelCase : Optional[int] =seq_length
UpperCAmelCase : Optional[int] =is_training
UpperCAmelCase : Tuple =use_input_mask
UpperCAmelCase : List[Any] =use_token_type_ids
UpperCAmelCase : Optional[Any] =use_labels
UpperCAmelCase : Union[str, Any] =vocab_size
UpperCAmelCase : List[Any] =hidden_size
UpperCAmelCase : Optional[int] =rotary_dim
UpperCAmelCase : Union[str, Any] =num_hidden_layers
UpperCAmelCase : List[Any] =num_attention_heads
UpperCAmelCase : Dict =intermediate_size
UpperCAmelCase : Union[str, Any] =hidden_act
UpperCAmelCase : Any =hidden_dropout_prob
UpperCAmelCase : Dict =attention_probs_dropout_prob
UpperCAmelCase : Union[str, Any] =max_position_embeddings
UpperCAmelCase : str =initializer_range
UpperCAmelCase : Optional[int] =None
UpperCAmelCase : List[Any] =vocab_size - 1
UpperCAmelCase : Optional[Any] =vocab_size - 1
UpperCAmelCase : List[Any] =vocab_size - 1
def UpperCAmelCase__ ( self ) -> Optional[int]:
'''simple docstring'''
UpperCAmelCase : List[str] =ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
UpperCAmelCase : List[Any] =None
if self.use_input_mask:
UpperCAmelCase : Optional[Any] =random_attention_mask([self.batch_size, self.seq_length] )
UpperCAmelCase : Dict =GPTJConfig(
vocab_size=self.vocab_size , n_embd=self.hidden_size , n_layer=self.num_hidden_layers , n_head=self.num_attention_heads , n_positions=self.max_position_embeddings , use_cache=snake_case__ , bos_token_id=self.bos_token_id , eos_token_id=self.eos_token_id , pad_token_id=self.pad_token_id , rotary_dim=self.rotary_dim , )
return (config, input_ids, input_mask)
def UpperCAmelCase__ ( self ) -> Dict:
'''simple docstring'''
UpperCAmelCase : Tuple =self.prepare_config_and_inputs()
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase : Union[str, Any] =config_and_inputs
UpperCAmelCase : Tuple ={'''input_ids''': input_ids, '''attention_mask''': attention_mask}
return config, inputs_dict
def UpperCAmelCase__ ( self , snake_case__ , snake_case__ , snake_case__ , snake_case__ ) -> List[Any]:
'''simple docstring'''
UpperCAmelCase : Any =20
UpperCAmelCase : Any =model_class_name(snake_case__ )
UpperCAmelCase : str =model.init_cache(input_ids.shape[0] , snake_case__ )
UpperCAmelCase : Any =jnp.ones((input_ids.shape[0], max_decoder_length) , dtype='''i4''' )
UpperCAmelCase : Optional[Any] =jnp.broadcast_to(
jnp.arange(input_ids.shape[-1] - 1 )[None, :] , (input_ids.shape[0], input_ids.shape[-1] - 1) )
UpperCAmelCase : Optional[Any] =model(
input_ids[:, :-1] , attention_mask=snake_case__ , past_key_values=snake_case__ , position_ids=snake_case__ , )
UpperCAmelCase : List[str] =jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]] , dtype='''i4''' )
UpperCAmelCase : Optional[Any] =model(
input_ids[:, -1:] , attention_mask=snake_case__ , past_key_values=outputs_cache.past_key_values , position_ids=snake_case__ , )
UpperCAmelCase : List[Any] =model(snake_case__ )
UpperCAmelCase : Any =np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5]) ) )
self.parent.assertTrue(diff < 1e-3 , msg=f'''Max diff is {diff}''' )
def UpperCAmelCase__ ( self , snake_case__ , snake_case__ , snake_case__ , snake_case__ ) -> List[Any]:
'''simple docstring'''
UpperCAmelCase : Dict =20
UpperCAmelCase : Dict =model_class_name(snake_case__ )
UpperCAmelCase : Tuple =jnp.concatenate(
[attention_mask, jnp.zeros((attention_mask.shape[0], max_decoder_length - attention_mask.shape[1]) )] , axis=-1 , )
UpperCAmelCase : Dict =model.init_cache(input_ids.shape[0] , snake_case__ )
UpperCAmelCase : int =jnp.broadcast_to(
jnp.arange(input_ids.shape[-1] - 1 )[None, :] , (input_ids.shape[0], input_ids.shape[-1] - 1) )
UpperCAmelCase : Optional[Any] =model(
input_ids[:, :-1] , attention_mask=snake_case__ , past_key_values=snake_case__ , position_ids=snake_case__ , )
UpperCAmelCase : Any =jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]] , dtype='''i4''' )
UpperCAmelCase : str =model(
input_ids[:, -1:] , past_key_values=outputs_cache.past_key_values , attention_mask=snake_case__ , position_ids=snake_case__ , )
UpperCAmelCase : Any =model(snake_case__ , attention_mask=snake_case__ )
UpperCAmelCase : Dict =np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5]) ) )
self.parent.assertTrue(diff < 1e-3 , msg=f'''Max diff is {diff}''' )
@require_flax
class __snake_case ( lowerCamelCase__ , lowerCamelCase__ , unittest.TestCase ):
__lowerCamelCase : Tuple = (FlaxGPTJModel, FlaxGPTJForCausalLM) if is_flax_available() else ()
__lowerCamelCase : Optional[Any] = (FlaxGPTJForCausalLM,) if is_flax_available() else ()
def UpperCAmelCase__ ( self ) -> int:
'''simple docstring'''
UpperCAmelCase : Union[str, Any] =FlaxGPTJModelTester(self )
def UpperCAmelCase__ ( self ) -> str:
'''simple docstring'''
for model_class_name in self.all_model_classes:
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase : Dict =self.model_tester.prepare_config_and_inputs()
self.model_tester.check_use_cache_forward(snake_case__ , snake_case__ , snake_case__ , snake_case__ )
def UpperCAmelCase__ ( self ) -> Dict:
'''simple docstring'''
for model_class_name in self.all_model_classes:
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase : int =self.model_tester.prepare_config_and_inputs()
self.model_tester.check_use_cache_forward_with_attn_mask(
snake_case__ , snake_case__ , snake_case__ , snake_case__ )
@tooslow
def UpperCAmelCase__ ( self ) -> Union[str, Any]:
'''simple docstring'''
UpperCAmelCase : Tuple =GPTaTokenizer.from_pretrained('''gpt2''' , pad_token='''<|endoftext|>''' , padding_side='''left''' )
UpperCAmelCase : Optional[Any] =tokenizer(['''Hello this is a long string''', '''Hey'''] , return_tensors='''np''' , padding=snake_case__ , truncation=snake_case__ )
UpperCAmelCase : Optional[int] =FlaxGPTJForCausalLM.from_pretrained('''EleutherAI/gpt-j-6B''' )
UpperCAmelCase : str =False
UpperCAmelCase : Union[str, Any] =model.config.eos_token_id
UpperCAmelCase : List[Any] =jax.jit(model.generate )
UpperCAmelCase : Dict =jit_generate(
inputs['''input_ids'''] , attention_mask=inputs['''attention_mask'''] , pad_token_id=tokenizer.pad_token_id ).sequences
UpperCAmelCase : Any =tokenizer.batch_decode(snake_case__ , skip_special_tokens=snake_case__ )
UpperCAmelCase : Tuple =[
'''Hello this is a long string of text.\n\nI\'m trying to get the text of the''',
'''Hey, I\'m a little late to the party. I\'m going to''',
]
self.assertListEqual(snake_case__ , snake_case__ )
@is_pt_flax_cross_test
def UpperCAmelCase__ ( self ) -> List[Any]:
'''simple docstring'''
UpperCAmelCase , UpperCAmelCase : List[str] =self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__ ):
# prepare inputs
UpperCAmelCase : Union[str, Any] =self._prepare_for_class(snake_case__ , snake_case__ )
UpperCAmelCase : List[str] ={k: torch.tensor(v.tolist() ) for k, v in prepared_inputs_dict.items()}
# load corresponding PyTorch class
UpperCAmelCase : Any =model_class.__name__[4:] # Skip the "Flax" at the beginning
UpperCAmelCase : Any =getattr(snake_case__ , snake_case__ )
UpperCAmelCase , UpperCAmelCase : Union[str, Any] =pt_inputs['''input_ids'''].shape
UpperCAmelCase : Tuple =np.random.randint(0 , seq_length - 1 , size=(batch_size,) )
for batch_idx, start_index in enumerate(snake_case__ ):
UpperCAmelCase : int =0
UpperCAmelCase : Optional[int] =1
UpperCAmelCase : Optional[int] =0
UpperCAmelCase : Union[str, Any] =1
UpperCAmelCase : List[str] =pt_model_class(snake_case__ ).eval()
UpperCAmelCase : Optional[int] =model_class(snake_case__ , dtype=jnp.floataa )
UpperCAmelCase : Any =convert_pytorch_state_dict_to_flax(pt_model.state_dict() , snake_case__ )
UpperCAmelCase : Union[str, Any] =fx_state
with torch.no_grad():
UpperCAmelCase : Any =pt_model(**snake_case__ ).to_tuple()
UpperCAmelCase : Dict =fx_model(**snake_case__ ).to_tuple()
self.assertEqual(len(snake_case__ ) , len(snake_case__ ) , '''Output lengths differ between Flax and PyTorch''' )
for fx_output, pt_output in zip(snake_case__ , snake_case__ ):
self.assert_almost_equals(fx_output[:, -1] , pt_output[:, -1].numpy() , 4e-2 )
with tempfile.TemporaryDirectory() as tmpdirname:
pt_model.save_pretrained(snake_case__ )
UpperCAmelCase : str =model_class.from_pretrained(snake_case__ , from_pt=snake_case__ )
UpperCAmelCase : int =fx_model_loaded(**snake_case__ ).to_tuple()
self.assertEqual(
len(snake_case__ ) , len(snake_case__ ) , '''Output lengths differ between Flax and PyTorch''' )
for fx_output_loaded, pt_output in zip(snake_case__ , snake_case__ ):
self.assert_almost_equals(fx_output_loaded[:, -1] , pt_output[:, -1].numpy() , 4e-2 )
@is_pt_flax_cross_test
def UpperCAmelCase__ ( self ) -> Optional[Any]:
'''simple docstring'''
UpperCAmelCase , UpperCAmelCase : Any =self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__ ):
# prepare inputs
UpperCAmelCase : Union[str, Any] =self._prepare_for_class(snake_case__ , snake_case__ )
UpperCAmelCase : Union[str, Any] ={k: torch.tensor(v.tolist() ) for k, v in prepared_inputs_dict.items()}
# load corresponding PyTorch class
UpperCAmelCase : int =model_class.__name__[4:] # Skip the "Flax" at the beginning
UpperCAmelCase : int =getattr(snake_case__ , snake_case__ )
UpperCAmelCase : Dict =pt_model_class(snake_case__ ).eval()
UpperCAmelCase : str =model_class(snake_case__ , dtype=jnp.floataa )
UpperCAmelCase : Optional[Any] =load_flax_weights_in_pytorch_model(snake_case__ , fx_model.params )
UpperCAmelCase , UpperCAmelCase : Optional[int] =pt_inputs['''input_ids'''].shape
UpperCAmelCase : Optional[int] =np.random.randint(0 , seq_length - 1 , size=(batch_size,) )
for batch_idx, start_index in enumerate(snake_case__ ):
UpperCAmelCase : str =0
UpperCAmelCase : Any =1
UpperCAmelCase : List[Any] =0
UpperCAmelCase : Tuple =1
# make sure weights are tied in PyTorch
pt_model.tie_weights()
with torch.no_grad():
UpperCAmelCase : Optional[Any] =pt_model(**snake_case__ ).to_tuple()
UpperCAmelCase : List[Any] =fx_model(**snake_case__ ).to_tuple()
self.assertEqual(len(snake_case__ ) , len(snake_case__ ) , '''Output lengths differ between Flax and PyTorch''' )
for fx_output, pt_output in zip(snake_case__ , snake_case__ ):
self.assert_almost_equals(fx_output[:, -1] , pt_output[:, -1].numpy() , 4e-2 )
with tempfile.TemporaryDirectory() as tmpdirname:
fx_model.save_pretrained(snake_case__ )
UpperCAmelCase : Tuple =pt_model_class.from_pretrained(snake_case__ , from_flax=snake_case__ )
with torch.no_grad():
UpperCAmelCase : Any =pt_model_loaded(**snake_case__ ).to_tuple()
self.assertEqual(
len(snake_case__ ) , len(snake_case__ ) , '''Output lengths differ between Flax and PyTorch''' )
for fx_output, pt_output in zip(snake_case__ , snake_case__ ):
self.assert_almost_equals(fx_output[:, -1] , pt_output[:, -1].numpy() , 4e-2 )
@tooslow
def UpperCAmelCase__ ( self ) -> List[str]:
'''simple docstring'''
for model_class_name in self.all_model_classes:
UpperCAmelCase : str =model_class_name.from_pretrained('''EleutherAI/gpt-j-6B''' )
UpperCAmelCase : Tuple =model(np.ones((1, 1) ) )
self.assertIsNotNone(snake_case__ )
| 348 | 1 |
import argparse
import json
import os
import fairseq
import torch
from torch import nn
from transformers import (
SpeechaTextaConfig,
SpeechaTextaForCausalLM,
SpeechaTextaTokenizer,
SpeechEncoderDecoderConfig,
SpeechEncoderDecoderModel,
WavaVecaConfig,
WavaVecaFeatureExtractor,
WavaVecaModel,
logging,
)
logging.set_verbosity_info()
__snake_case = logging.get_logger(__name__)
__snake_case = {
'''post_extract_proj''': '''feature_projection.projection''',
'''encoder.pos_conv.0''': '''encoder.pos_conv_embed.conv''',
'''self_attn.k_proj''': '''encoder.layers.*.attention.k_proj''',
'''self_attn.v_proj''': '''encoder.layers.*.attention.v_proj''',
'''self_attn.q_proj''': '''encoder.layers.*.attention.q_proj''',
'''self_attn.out_proj''': '''encoder.layers.*.attention.out_proj''',
'''self_attn_layer_norm''': '''encoder.layers.*.layer_norm''',
'''fc1''': '''encoder.layers.*.feed_forward.intermediate_dense''',
'''fc2''': '''encoder.layers.*.feed_forward.output_dense''',
'''final_layer_norm''': '''encoder.layers.*.final_layer_norm''',
'''encoder.layer_norm''': '''encoder.layer_norm''',
'''w2v_model.layer_norm''': '''feature_projection.layer_norm''',
'''quantizer.weight_proj''': '''quantizer.weight_proj''',
'''quantizer.vars''': '''quantizer.codevectors''',
'''project_q''': '''project_q''',
'''final_proj''': '''project_hid''',
'''w2v_encoder.proj''': '''lm_head''',
'''mask_emb''': '''masked_spec_embed''',
}
__snake_case = [
'''lm_head''',
'''quantizer.weight_proj''',
'''quantizer.codevectors''',
'''project_q''',
'''project_hid''',
]
def lowerCAmelCase_ ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )-> List[str]:
'''simple docstring'''
for attribute in key.split('''.''' ):
UpperCAmelCase : Tuple =getattr(__lowerCAmelCase , __lowerCAmelCase )
if weight_type is not None:
UpperCAmelCase : List[Any] =getattr(__lowerCAmelCase , __lowerCAmelCase ).shape
else:
UpperCAmelCase : str =hf_pointer.shape
assert hf_shape == value.shape, (
f'''Shape of hf {key + '.' + weight_type if weight_type is not None else ''} is {hf_shape}, but should be'''
f''' {value.shape} for {full_name}'''
)
if weight_type == "weight":
UpperCAmelCase : str =value
elif weight_type == "weight_g":
UpperCAmelCase : str =value
elif weight_type == "weight_v":
UpperCAmelCase : Dict =value
elif weight_type == "bias":
UpperCAmelCase : List[str] =value
else:
UpperCAmelCase : Union[str, Any] =value
logger.info(f'''{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}.''' )
def lowerCAmelCase_ ( __lowerCAmelCase , __lowerCAmelCase )-> List[str]:
'''simple docstring'''
UpperCAmelCase : Tuple =[]
UpperCAmelCase : List[str] =fairseq_model.state_dict()
UpperCAmelCase : List[str] =hf_model.feature_extractor
# if encoder has different dim to decoder -> use proj_weight
UpperCAmelCase : List[Any] =None
for name, value in fairseq_dict.items():
UpperCAmelCase : Dict =False
if "conv_layers" in name:
load_conv_layer(
__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , hf_model.config.feat_extract_norm == '''group''' , )
UpperCAmelCase : Optional[Any] =True
elif name.split('''.''' )[0] == "proj":
UpperCAmelCase : List[Any] =fairseq_model.proj
UpperCAmelCase : str =True
else:
for key, mapped_key in MAPPING.items():
if key in name or key.split('''w2v_model.''' )[-1] == name.split('''.''' )[0]:
UpperCAmelCase : Optional[int] =True
if "*" in mapped_key:
UpperCAmelCase : List[Any] =name.split(__lowerCAmelCase )[0].split('''.''' )[-2]
UpperCAmelCase : Any =mapped_key.replace('''*''' , __lowerCAmelCase )
if "weight_g" in name:
UpperCAmelCase : Union[str, Any] ='''weight_g'''
elif "weight_v" in name:
UpperCAmelCase : List[str] ='''weight_v'''
elif "bias" in name:
UpperCAmelCase : Any ='''bias'''
elif "weight" in name:
UpperCAmelCase : int ='''weight'''
else:
UpperCAmelCase : int =None
set_recursively(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
continue
if not is_used:
unused_weights.append(__lowerCAmelCase )
logger.warning(f'''Unused weights: {unused_weights}''' )
return proj_weight
def lowerCAmelCase_ ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )-> int:
'''simple docstring'''
UpperCAmelCase : int =full_name.split('''conv_layers.''' )[-1]
UpperCAmelCase : Tuple =name.split('''.''' )
UpperCAmelCase : Any =int(items[0] )
UpperCAmelCase : Dict =int(items[1] )
if type_id == 0:
if "bias" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
f'''{full_name} has size {value.shape}, but'''
f''' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.'''
)
UpperCAmelCase : Union[str, Any] =value
logger.info(f'''Feat extract conv layer {layer_id} was initialized from {full_name}.''' )
elif "weight" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
f'''{full_name} has size {value.shape}, but'''
f''' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.'''
)
UpperCAmelCase : Any =value
logger.info(f'''Feat extract conv layer {layer_id} was initialized from {full_name}.''' )
elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
if "bias" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
f'''{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was'''
" found."
)
UpperCAmelCase : Any =value
logger.info(f'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.''' )
elif "weight" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
f'''{full_name} has size {value.shape}, but'''
f''' {feature_extractor[layer_id].layer_norm.weight.data.shape} was found.'''
)
UpperCAmelCase : Optional[int] =value
logger.info(f'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.''' )
else:
unused_weights.append(__lowerCAmelCase )
def lowerCAmelCase_ ( __lowerCAmelCase )-> Tuple:
'''simple docstring'''
UpperCAmelCase , UpperCAmelCase : Union[str, Any] =emb.weight.shape
UpperCAmelCase : Optional[Any] =nn.Linear(__lowerCAmelCase , __lowerCAmelCase , bias=__lowerCAmelCase )
UpperCAmelCase : List[str] =emb.weight.data
return lin_layer
def lowerCAmelCase_ ( __lowerCAmelCase )-> List[str]:
'''simple docstring'''
with open(__lowerCAmelCase , '''r''' , encoding='''utf-8''' ) as f:
UpperCAmelCase : Any =f.readlines()
UpperCAmelCase : Optional[Any] =[line.split(''' ''' )[0] for line in lines]
UpperCAmelCase : str =len(__lowerCAmelCase )
UpperCAmelCase : Optional[int] ={
'''<s>''': 0,
'''<pad>''': 1,
'''</s>''': 2,
'''<unk>''': 3,
}
vocab_dict.update(dict(zip(__lowerCAmelCase , range(4 , num_words + 4 ) ) ) )
return vocab_dict
@torch.no_grad()
def lowerCAmelCase_ ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , )-> Union[str, Any]:
'''simple docstring'''
UpperCAmelCase : List[Any] =WavaVecaConfig.from_pretrained(__lowerCAmelCase )
UpperCAmelCase : str =SpeechaTextaConfig.from_pretrained(
__lowerCAmelCase , vocab_size=__lowerCAmelCase , decoder_layers=__lowerCAmelCase , do_stable_layer_norm=__lowerCAmelCase )
UpperCAmelCase : List[Any] =WavaVecaFeatureExtractor(
feature_size=1 , sampling_rate=1_60_00 , padding_value=0 , do_normalize=__lowerCAmelCase , return_attention_mask=__lowerCAmelCase , )
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase : Any =fairseq.checkpoint_utils.load_model_ensemble_and_task(
[checkpoint_path] , arg_overrides={'''data''': '''/'''.join(dict_path.split('''/''' )[:-1] )} )
UpperCAmelCase : List[Any] =model[0].eval()
# set weights for wav2vec2 encoder
UpperCAmelCase : int =WavaVecaModel(__lowerCAmelCase )
UpperCAmelCase : Optional[Any] =recursively_load_weights_wavaveca(model.encoder , __lowerCAmelCase )
UpperCAmelCase : Optional[int] =SpeechaTextaForCausalLM(__lowerCAmelCase )
UpperCAmelCase , UpperCAmelCase : int =hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict() , strict=__lowerCAmelCase )
# set output linear layer
unexpected_keys.remove('''embed_out''' )
UpperCAmelCase : str =nn.Parameter(model.decoder.embed_out.detach() )
# layer norm is init to identity matrix so leaving it is fine
logger.warning(f'''The following keys are missing when loading the decoder weights: {missing_keys}''' )
logger.warning(f'''The following keys are unexpected when loading the decoder weights: {unexpected_keys}''' )
UpperCAmelCase : Union[str, Any] =SpeechEncoderDecoderModel(encoder=__lowerCAmelCase , decoder=__lowerCAmelCase )
UpperCAmelCase : List[str] =False
# add projection layer
UpperCAmelCase : Union[str, Any] =nn.Parameter(projection_layer.weight )
UpperCAmelCase : Optional[Any] =nn.Parameter(projection_layer.bias )
UpperCAmelCase : Union[str, Any] =create_vocab_dict(__lowerCAmelCase )
with open(os.path.join(__lowerCAmelCase , '''vocab.json''' ) , '''w''' ) as fp:
json.dump(__lowerCAmelCase , __lowerCAmelCase )
UpperCAmelCase : Dict =SpeechaTextaTokenizer(os.path.join(__lowerCAmelCase , '''vocab.json''' ) )
tokenizer.save_pretrained(__lowerCAmelCase )
UpperCAmelCase : Tuple =hf_wavavec.config.to_dict()
UpperCAmelCase : int =tokenizer.pad_token_id
UpperCAmelCase : Any =tokenizer.bos_token_id
UpperCAmelCase : str =tokenizer.eos_token_id
UpperCAmelCase : Union[str, Any] ='''speech_to_text_2'''
UpperCAmelCase : Any ='''wav2vec2'''
UpperCAmelCase : Any =SpeechEncoderDecoderConfig.from_dict(__lowerCAmelCase )
hf_wavavec.save_pretrained(__lowerCAmelCase )
feature_extractor.save_pretrained(__lowerCAmelCase )
if __name__ == "__main__":
__snake_case = argparse.ArgumentParser()
parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
parser.add_argument('''--checkpoint_path''', default=None, type=str, help='''Path to fairseq checkpoint''')
parser.add_argument('''--dict_path''', default=None, type=str, help='''Path to dict of fine-tuned model''')
parser.add_argument(
'''--encoder_config_path''',
default='''facebook/wav2vec2-large-lv60''',
type=str,
help='''Path to hf encoder wav2vec2 checkpoint config''',
)
parser.add_argument(
'''--decoder_config_path''',
default='''facebook/s2t-small-mustc-en-fr-st''',
type=str,
help='''Path to hf decoder s2t checkpoint config''',
)
parser.add_argument('''--vocab_size''', default=1_02_24, type=int, help='''Vocab size of decoder''')
parser.add_argument('''--num_decoder_layers''', default=7, type=int, help='''Number of decoder layers''')
__snake_case = parser.parse_args()
convert_wavaveca_checkpoint(
args.checkpoint_path,
args.pytorch_dump_folder_path,
args.dict_path,
encoder_config_path=args.encoder_config_path,
decoder_config_path=args.decoder_config_path,
vocab_size=args.vocab_size,
num_decoder_layers=args.num_decoder_layers,
)
| 348 | from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
__snake_case = {
'''configuration_bloom''': ['''BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''BloomConfig''', '''BloomOnnxConfig'''],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__snake_case = ['''BloomTokenizerFast''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__snake_case = [
'''BLOOM_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''BloomForCausalLM''',
'''BloomModel''',
'''BloomPreTrainedModel''',
'''BloomForSequenceClassification''',
'''BloomForTokenClassification''',
'''BloomForQuestionAnswering''',
]
if TYPE_CHECKING:
from .configuration_bloom import BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP, BloomConfig, BloomOnnxConfig
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_bloom_fast import BloomTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_bloom import (
BLOOM_PRETRAINED_MODEL_ARCHIVE_LIST,
BloomForCausalLM,
BloomForQuestionAnswering,
BloomForSequenceClassification,
BloomForTokenClassification,
BloomModel,
BloomPreTrainedModel,
)
else:
import sys
__snake_case = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 348 | 1 |
from __future__ import annotations
def lowerCAmelCase_ ( __lowerCAmelCase )-> bool:
'''simple docstring'''
if len(__lowerCAmelCase ) < 2:
raise ValueError('''Monogons and Digons are not polygons in the Euclidean space''' )
if any(i <= 0 for i in nums ):
raise ValueError('''All values must be greater than 0''' )
UpperCAmelCase : List[str] =nums.copy()
copy_nums.sort()
return copy_nums[-1] < sum(copy_nums[:-1] )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 348 | import os
from typing import Dict, List, Tuple, TypeVar, Union
__snake_case = TypeVar('''T''')
__snake_case = Union[List[T], Tuple[T, ...]]
__snake_case = Union[T, List[T], Dict[str, T]]
__snake_case = Union[str, bytes, os.PathLike]
| 348 | 1 |
def lowerCAmelCase_ ( __lowerCAmelCase , __lowerCAmelCase )-> List[Any]:
'''simple docstring'''
print('''\nThe shortest path matrix using Floyd Warshall algorithm\n''' )
for i in range(__lowerCAmelCase ):
for j in range(__lowerCAmelCase ):
if dist[i][j] != float('''inf''' ):
print(int(dist[i][j] ) , end='''\t''' )
else:
print('''INF''' , end='''\t''' )
print()
def lowerCAmelCase_ ( __lowerCAmelCase , __lowerCAmelCase )-> List[str]:
'''simple docstring'''
UpperCAmelCase : Union[str, Any] =[[float('''inf''' ) for _ in range(__lowerCAmelCase )] for _ in range(__lowerCAmelCase )]
for i in range(__lowerCAmelCase ):
for j in range(__lowerCAmelCase ):
UpperCAmelCase : int =graph[i][j]
# check vertex k against all other vertices (i, j)
for k in range(__lowerCAmelCase ):
# looping through rows of graph array
for i in range(__lowerCAmelCase ):
# looping through columns of graph array
for j in range(__lowerCAmelCase ):
if (
dist[i][k] != float('''inf''' )
and dist[k][j] != float('''inf''' )
and dist[i][k] + dist[k][j] < dist[i][j]
):
UpperCAmelCase : List[Any] =dist[i][k] + dist[k][j]
_print_dist(__lowerCAmelCase , __lowerCAmelCase )
return dist, v
if __name__ == "__main__":
__snake_case = int(input('''Enter number of vertices: '''))
__snake_case = int(input('''Enter number of edges: '''))
__snake_case = [[float('''inf''') for i in range(v)] for j in range(v)]
for i in range(v):
__snake_case = 0.0
# src and dst are indices that must be within the array size graph[e][v]
# failure to follow this will result in an error
for i in range(e):
print('''\nEdge ''', i + 1)
__snake_case = int(input('''Enter source:'''))
__snake_case = int(input('''Enter destination:'''))
__snake_case = float(input('''Enter weight:'''))
__snake_case = weight
floyd_warshall(graph, v)
# Example Input
# Enter number of vertices: 3
# Enter number of edges: 2
# # generated graph from vertex and edge inputs
# [[inf, inf, inf], [inf, inf, inf], [inf, inf, inf]]
# [[0.0, inf, inf], [inf, 0.0, inf], [inf, inf, 0.0]]
# specify source, destination and weight for edge #1
# Edge 1
# Enter source:1
# Enter destination:2
# Enter weight:2
# specify source, destination and weight for edge #2
# Edge 2
# Enter source:2
# Enter destination:1
# Enter weight:1
# # Expected Output from the vertice, edge and src, dst, weight inputs!!
# 0 INF INF
# INF 0 2
# INF 1 0
| 348 | import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_big_bird import BigBirdTokenizer
else:
__snake_case = None
__snake_case = logging.get_logger(__name__)
__snake_case = {'''vocab_file''': '''spiece.model''', '''tokenizer_file''': '''tokenizer.json'''}
__snake_case = {
'''vocab_file''': {
'''google/bigbird-roberta-base''': '''https://huggingface.co/google/bigbird-roberta-base/resolve/main/spiece.model''',
'''google/bigbird-roberta-large''': (
'''https://huggingface.co/google/bigbird-roberta-large/resolve/main/spiece.model'''
),
'''google/bigbird-base-trivia-itc''': (
'''https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/spiece.model'''
),
},
'''tokenizer_file''': {
'''google/bigbird-roberta-base''': (
'''https://huggingface.co/google/bigbird-roberta-base/resolve/main/tokenizer.json'''
),
'''google/bigbird-roberta-large''': (
'''https://huggingface.co/google/bigbird-roberta-large/resolve/main/tokenizer.json'''
),
'''google/bigbird-base-trivia-itc''': (
'''https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/tokenizer.json'''
),
},
}
__snake_case = {
'''google/bigbird-roberta-base''': 40_96,
'''google/bigbird-roberta-large''': 40_96,
'''google/bigbird-base-trivia-itc''': 40_96,
}
__snake_case = '''▁'''
class __snake_case ( lowerCamelCase__ ):
__lowerCamelCase : Dict = VOCAB_FILES_NAMES
__lowerCamelCase : List[Any] = PRETRAINED_VOCAB_FILES_MAP
__lowerCamelCase : Dict = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
__lowerCamelCase : List[str] = BigBirdTokenizer
__lowerCamelCase : Any = ["""input_ids""", """attention_mask"""]
__lowerCamelCase : List[int] = []
def __init__( self , snake_case__=None , snake_case__=None , snake_case__="<unk>" , snake_case__="<s>" , snake_case__="</s>" , snake_case__="<pad>" , snake_case__="[SEP]" , snake_case__="[MASK]" , snake_case__="[CLS]" , **snake_case__ , ) -> List[Any]:
'''simple docstring'''
UpperCAmelCase : Any =AddedToken(snake_case__ , lstrip=snake_case__ , rstrip=snake_case__ ) if isinstance(snake_case__ , snake_case__ ) else bos_token
UpperCAmelCase : Optional[int] =AddedToken(snake_case__ , lstrip=snake_case__ , rstrip=snake_case__ ) if isinstance(snake_case__ , snake_case__ ) else eos_token
UpperCAmelCase : List[str] =AddedToken(snake_case__ , lstrip=snake_case__ , rstrip=snake_case__ ) if isinstance(snake_case__ , snake_case__ ) else unk_token
UpperCAmelCase : Union[str, Any] =AddedToken(snake_case__ , lstrip=snake_case__ , rstrip=snake_case__ ) if isinstance(snake_case__ , snake_case__ ) else pad_token
UpperCAmelCase : int =AddedToken(snake_case__ , lstrip=snake_case__ , rstrip=snake_case__ ) if isinstance(snake_case__ , snake_case__ ) else cls_token
UpperCAmelCase : str =AddedToken(snake_case__ , lstrip=snake_case__ , rstrip=snake_case__ ) if isinstance(snake_case__ , snake_case__ ) else sep_token
# Mask token behave like a normal word, i.e. include the space before it
UpperCAmelCase : List[Any] =AddedToken(snake_case__ , lstrip=snake_case__ , rstrip=snake_case__ ) if isinstance(snake_case__ , snake_case__ ) else mask_token
super().__init__(
snake_case__ , tokenizer_file=snake_case__ , bos_token=snake_case__ , eos_token=snake_case__ , unk_token=snake_case__ , sep_token=snake_case__ , pad_token=snake_case__ , cls_token=snake_case__ , mask_token=snake_case__ , **snake_case__ , )
UpperCAmelCase : Tuple =vocab_file
UpperCAmelCase : Optional[int] =False if not self.vocab_file else True
def UpperCAmelCase__ ( self , snake_case__ , snake_case__ = None ) -> List[int]:
'''simple docstring'''
UpperCAmelCase : int =[self.sep_token_id]
UpperCAmelCase : Optional[int] =[self.cls_token_id]
if token_ids_a is None:
return cls + token_ids_a + sep
return cls + token_ids_a + sep + token_ids_a + sep
def UpperCAmelCase__ ( self , snake_case__ , snake_case__ = None , snake_case__ = False ) -> List[int]:
'''simple docstring'''
if already_has_special_tokens:
if token_ids_a is not None:
raise ValueError(
'''You should not supply a second sequence if the provided sequence of '''
'''ids is already formatted with special tokens for the model.''' )
return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]
if token_ids_a is None:
return [1] + ([0] * len(snake_case__ )) + [1]
return [1] + ([0] * len(snake_case__ )) + [1] + ([0] * len(snake_case__ )) + [1]
def UpperCAmelCase__ ( self , snake_case__ , snake_case__ = None ) -> List[int]:
'''simple docstring'''
UpperCAmelCase : Optional[Any] =[self.sep_token_id]
UpperCAmelCase : Optional[int] =[self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]
def UpperCAmelCase__ ( self , snake_case__ , snake_case__ = None ) -> Tuple[str]:
'''simple docstring'''
if not self.can_save_slow_tokenizer:
raise ValueError(
'''Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '''
'''tokenizer.''' )
if not os.path.isdir(snake_case__ ):
logger.error(f'''Vocabulary path ({save_directory}) should be a directory''' )
return
UpperCAmelCase : Optional[int] =os.path.join(
snake_case__ , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(snake_case__ ):
copyfile(self.vocab_file , snake_case__ )
return (out_vocab_file,)
| 348 | 1 |
import json
import sys
def lowerCAmelCase_ ( __lowerCAmelCase , __lowerCAmelCase )-> str:
'''simple docstring'''
with open(__lowerCAmelCase , encoding='''utf-8''' ) as f:
UpperCAmelCase : str =json.load(__lowerCAmelCase )
UpperCAmelCase : List[Any] =['''<details>''', '''<summary>Show updated benchmarks!</summary>''', ''' ''']
for benchmark_name in sorted(__lowerCAmelCase ):
UpperCAmelCase : Any =results[benchmark_name]
UpperCAmelCase : str =benchmark_name.split('''/''' )[-1]
output_md.append(f'''### Benchmark: {benchmark_file_name}''' )
UpperCAmelCase : List[str] ='''| metric |'''
UpperCAmelCase : Any ='''|--------|'''
UpperCAmelCase : Dict ='''| new / old (diff) |'''
for metric_name in sorted(__lowerCAmelCase ):
UpperCAmelCase : int =benchmark_res[metric_name]
UpperCAmelCase : str =metric_vals['''new''']
UpperCAmelCase : Optional[Any] =metric_vals.get('''old''' , __lowerCAmelCase )
UpperCAmelCase : Any =metric_vals.get('''diff''' , __lowerCAmelCase )
UpperCAmelCase : int =f''' {new_val:f}''' if isinstance(__lowerCAmelCase , (int, float) ) else '''None'''
if old_val is not None:
val_str += f''' / {old_val:f}''' if isinstance(__lowerCAmelCase , (int, float) ) else "None"
if dif_val is not None:
val_str += f''' ({dif_val:f})''' if isinstance(__lowerCAmelCase , (int, float) ) else "None"
title += " " + metric_name + " |"
lines += "---|"
value += val_str + " |"
output_md += [title, lines, value, " "]
output_md.append('''</details>''' )
with open(__lowerCAmelCase , '''w''' , encoding='''utf-8''' ) as f:
f.writelines('''\n'''.join(__lowerCAmelCase ) )
if __name__ == "__main__":
__snake_case = sys.argv[1]
__snake_case = sys.argv[2]
format_json_to_md(input_json_file, output_md_file)
| 348 | from collections.abc import Callable
from math import pi, sqrt
from random import uniform
from statistics import mean
def lowerCAmelCase_ ( __lowerCAmelCase )-> Optional[Any]:
'''simple docstring'''
def is_in_circle(__lowerCAmelCase , __lowerCAmelCase ) -> bool:
UpperCAmelCase : List[Any] =sqrt((x**2) + (y**2) )
# Our circle has a radius of 1, so a distance
# greater than 1 would land outside the circle.
return distance_from_centre <= 1
# The proportion of guesses that landed in the circle
UpperCAmelCase : List[Any] =mean(
int(is_in_circle(uniform(-1.0 , 1.0 ) , uniform(-1.0 , 1.0 ) ) )
for _ in range(__lowerCAmelCase ) )
# The ratio of the area for circle to square is pi/4.
UpperCAmelCase : Dict =proportion * 4
print(f'''The estimated value of pi is {pi_estimate}''' )
print(f'''The numpy value of pi is {pi}''' )
print(f'''The total error is {abs(pi - pi_estimate )}''' )
def lowerCAmelCase_ ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase = 0.0 , __lowerCAmelCase = 1.0 , )-> float:
'''simple docstring'''
return mean(
function_to_integrate(uniform(__lowerCAmelCase , __lowerCAmelCase ) ) for _ in range(__lowerCAmelCase ) ) * (max_value - min_value)
def lowerCAmelCase_ ( __lowerCAmelCase , __lowerCAmelCase = 0.0 , __lowerCAmelCase = 1.0 )-> None:
'''simple docstring'''
def identity_function(__lowerCAmelCase ) -> float:
return x
UpperCAmelCase : List[Any] =area_under_curve_estimator(
__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
UpperCAmelCase : Dict =(max_value * max_value - min_value * min_value) / 2
print('''******************''' )
print(f'''Estimating area under y=x where x varies from {min_value} to {max_value}''' )
print(f'''Estimated value is {estimated_value}''' )
print(f'''Expected value is {expected_value}''' )
print(f'''Total error is {abs(estimated_value - expected_value )}''' )
print('''******************''' )
def lowerCAmelCase_ ( __lowerCAmelCase )-> None:
'''simple docstring'''
def function_to_integrate(__lowerCAmelCase ) -> float:
return sqrt(4.0 - x * x )
UpperCAmelCase : Dict =area_under_curve_estimator(
__lowerCAmelCase , __lowerCAmelCase , 0.0 , 2.0 )
print('''******************''' )
print('''Estimating pi using area_under_curve_estimator''' )
print(f'''Estimated value is {estimated_value}''' )
print(f'''Expected value is {pi}''' )
print(f'''Total error is {abs(estimated_value - pi )}''' )
print('''******************''' )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 348 | 1 |
from typing import Callable, List, Optional, Tuple, Union
import torch
from transformers import CLIPTextModel, CLIPTokenizer
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin, TransformeraDModel, VQModel
from ...schedulers import VQDiffusionScheduler
from ...utils import logging
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
__snake_case = logging.get_logger(__name__) # pylint: disable=invalid-name
class __snake_case ( lowerCamelCase__ , lowerCamelCase__ ):
@register_to_config
def __init__( self , snake_case__ , snake_case__ = None , snake_case__ = None ) -> str:
'''simple docstring'''
super().__init__()
UpperCAmelCase : Optional[Any] =learnable
if self.learnable:
assert hidden_size is not None, "learnable=True requires `hidden_size` to be set"
assert length is not None, "learnable=True requires `length` to be set"
UpperCAmelCase : Any =torch.zeros(snake_case__ , snake_case__ )
else:
UpperCAmelCase : Union[str, Any] =None
UpperCAmelCase : Optional[int] =torch.nn.Parameter(snake_case__ )
class __snake_case ( lowerCamelCase__ ):
__lowerCamelCase : VQModel
__lowerCamelCase : CLIPTextModel
__lowerCamelCase : CLIPTokenizer
__lowerCamelCase : TransformeraDModel
__lowerCamelCase : LearnedClassifierFreeSamplingEmbeddings
__lowerCamelCase : VQDiffusionScheduler
def __init__( self , snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ , ) -> int:
'''simple docstring'''
super().__init__()
self.register_modules(
vqvae=snake_case__ , transformer=snake_case__ , text_encoder=snake_case__ , tokenizer=snake_case__ , scheduler=snake_case__ , learned_classifier_free_sampling_embeddings=snake_case__ , )
def UpperCAmelCase__ ( self , snake_case__ , snake_case__ , snake_case__ ) -> Optional[int]:
'''simple docstring'''
UpperCAmelCase : int =len(snake_case__ ) if isinstance(snake_case__ , snake_case__ ) else 1
# get prompt text embeddings
UpperCAmelCase : Optional[int] =self.tokenizer(
snake_case__ , padding='''max_length''' , max_length=self.tokenizer.model_max_length , return_tensors='''pt''' , )
UpperCAmelCase : int =text_inputs.input_ids
if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
UpperCAmelCase : List[str] =self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :] )
logger.warning(
'''The following part of your input was truncated because CLIP can only handle sequences up to'''
f''' {self.tokenizer.model_max_length} tokens: {removed_text}''' )
UpperCAmelCase : Optional[Any] =text_input_ids[:, : self.tokenizer.model_max_length]
UpperCAmelCase : List[Any] =self.text_encoder(text_input_ids.to(self.device ) )[0]
# NOTE: This additional step of normalizing the text embeddings is from VQ-Diffusion.
# While CLIP does normalize the pooled output of the text transformer when combining
# the image and text embeddings, CLIP does not directly normalize the last hidden state.
#
# CLIP normalizing the pooled output.
# https://github.com/huggingface/transformers/blob/d92e22d1f28324f513f3080e5c47c071a3916721/src/transformers/models/clip/modeling_clip.py#L1052-L1053
UpperCAmelCase : int =prompt_embeds / prompt_embeds.norm(dim=-1 , keepdim=snake_case__ )
# duplicate text embeddings for each generation per prompt
UpperCAmelCase : int =prompt_embeds.repeat_interleave(snake_case__ , dim=0 )
if do_classifier_free_guidance:
if self.learned_classifier_free_sampling_embeddings.learnable:
UpperCAmelCase : Optional[int] =self.learned_classifier_free_sampling_embeddings.embeddings
UpperCAmelCase : str =negative_prompt_embeds.unsqueeze(0 ).repeat(snake_case__ , 1 , 1 )
else:
UpperCAmelCase : str =[''''''] * batch_size
UpperCAmelCase : Tuple =text_input_ids.shape[-1]
UpperCAmelCase : Optional[Any] =self.tokenizer(
snake_case__ , padding='''max_length''' , max_length=snake_case__ , truncation=snake_case__ , return_tensors='''pt''' , )
UpperCAmelCase : Optional[Any] =self.text_encoder(uncond_input.input_ids.to(self.device ) )[0]
# See comment for normalizing text embeddings
UpperCAmelCase : Optional[int] =negative_prompt_embeds / negative_prompt_embeds.norm(dim=-1 , keepdim=snake_case__ )
# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
UpperCAmelCase : Optional[Any] =negative_prompt_embeds.shape[1]
UpperCAmelCase : Union[str, Any] =negative_prompt_embeds.repeat(1 , snake_case__ , 1 )
UpperCAmelCase : Optional[Any] =negative_prompt_embeds.view(batch_size * num_images_per_prompt , snake_case__ , -1 )
# For classifier free guidance, we need to do two forward passes.
# Here we concatenate the unconditional and text embeddings into a single batch
# to avoid doing two forward passes
UpperCAmelCase : int =torch.cat([negative_prompt_embeds, prompt_embeds] )
return prompt_embeds
@torch.no_grad()
def __call__( self , snake_case__ , snake_case__ = 100 , snake_case__ = 5.0 , snake_case__ = 1.0 , snake_case__ = 1 , snake_case__ = None , snake_case__ = None , snake_case__ = "pil" , snake_case__ = True , snake_case__ = None , snake_case__ = 1 , ) -> Union[ImagePipelineOutput, Tuple]:
'''simple docstring'''
if isinstance(snake_case__ , snake_case__ ):
UpperCAmelCase : Optional[int] =1
elif isinstance(snake_case__ , snake_case__ ):
UpperCAmelCase : Tuple =len(snake_case__ )
else:
raise ValueError(f'''`prompt` has to be of type `str` or `list` but is {type(snake_case__ )}''' )
UpperCAmelCase : Tuple =batch_size * num_images_per_prompt
UpperCAmelCase : List[str] =guidance_scale > 1.0
UpperCAmelCase : List[Any] =self._encode_prompt(snake_case__ , snake_case__ , snake_case__ )
if (callback_steps is None) or (
callback_steps is not None and (not isinstance(snake_case__ , snake_case__ ) or callback_steps <= 0)
):
raise ValueError(
f'''`callback_steps` has to be a positive integer but is {callback_steps} of type'''
f''' {type(snake_case__ )}.''' )
# get the initial completely masked latents unless the user supplied it
UpperCAmelCase : int =(batch_size, self.transformer.num_latent_pixels)
if latents is None:
UpperCAmelCase : Union[str, Any] =self.transformer.num_vector_embeds - 1
UpperCAmelCase : str =torch.full(snake_case__ , snake_case__ ).to(self.device )
else:
if latents.shape != latents_shape:
raise ValueError(f'''Unexpected latents shape, got {latents.shape}, expected {latents_shape}''' )
if (latents < 0).any() or (latents >= self.transformer.num_vector_embeds).any():
raise ValueError(
'''Unexpected latents value(s). All latents be valid embedding indices i.e. in the range 0,'''
f''' {self.transformer.num_vector_embeds - 1} (inclusive).''' )
UpperCAmelCase : Any =latents.to(self.device )
# set timesteps
self.scheduler.set_timesteps(snake_case__ , device=self.device )
UpperCAmelCase : Any =self.scheduler.timesteps.to(self.device )
UpperCAmelCase : Optional[int] =latents
for i, t in enumerate(self.progress_bar(snake_case__ ) ):
# expand the sample if we are doing classifier free guidance
UpperCAmelCase : Optional[Any] =torch.cat([sample] * 2 ) if do_classifier_free_guidance else sample
# predict the un-noised image
# model_output == `log_p_x_0`
UpperCAmelCase : Optional[int] =self.transformer(snake_case__ , encoder_hidden_states=snake_case__ , timestep=snake_case__ ).sample
if do_classifier_free_guidance:
UpperCAmelCase , UpperCAmelCase : str =model_output.chunk(2 )
UpperCAmelCase : Optional[int] =model_output_uncond + guidance_scale * (model_output_text - model_output_uncond)
model_output -= torch.logsumexp(snake_case__ , dim=1 , keepdim=snake_case__ )
UpperCAmelCase : Tuple =self.truncate(snake_case__ , snake_case__ )
# remove `log(0)`'s (`-inf`s)
UpperCAmelCase : Optional[Any] =model_output.clamp(-70 )
# compute the previous noisy sample x_t -> x_t-1
UpperCAmelCase : int =self.scheduler.step(snake_case__ , timestep=snake_case__ , sample=snake_case__ , generator=snake_case__ ).prev_sample
# call the callback, if provided
if callback is not None and i % callback_steps == 0:
callback(snake_case__ , snake_case__ , snake_case__ )
UpperCAmelCase : Optional[int] =self.vqvae.config.vq_embed_dim
UpperCAmelCase : Optional[Any] =(batch_size, self.transformer.height, self.transformer.width, embedding_channels)
UpperCAmelCase : Dict =self.vqvae.quantize.get_codebook_entry(snake_case__ , shape=snake_case__ )
UpperCAmelCase : Tuple =self.vqvae.decode(snake_case__ , force_not_quantize=snake_case__ ).sample
UpperCAmelCase : Union[str, Any] =(image / 2 + 0.5).clamp(0 , 1 )
UpperCAmelCase : Any =image.cpu().permute(0 , 2 , 3 , 1 ).numpy()
if output_type == "pil":
UpperCAmelCase : List[str] =self.numpy_to_pil(snake_case__ )
if not return_dict:
return (image,)
return ImagePipelineOutput(images=snake_case__ )
def UpperCAmelCase__ ( self , snake_case__ , snake_case__ ) -> torch.FloatTensor:
'''simple docstring'''
UpperCAmelCase , UpperCAmelCase : int =torch.sort(snake_case__ , 1 , descending=snake_case__ )
UpperCAmelCase : Union[str, Any] =torch.exp(snake_case__ )
UpperCAmelCase : Union[str, Any] =sorted_p_x_0.cumsum(dim=1 ) < truncation_rate
# Ensure that at least the largest probability is not zeroed out
UpperCAmelCase : Optional[Any] =torch.full_like(keep_mask[:, 0:1, :] , snake_case__ )
UpperCAmelCase : Tuple =torch.cat((all_true, keep_mask) , dim=1 )
UpperCAmelCase : int =keep_mask[:, :-1, :]
UpperCAmelCase : int =keep_mask.gather(1 , indices.argsort(1 ) )
UpperCAmelCase : Dict =log_p_x_0.clone()
UpperCAmelCase : List[Any] =-torch.inf # -inf = log(0)
return rv
| 348 | from __future__ import annotations
import unittest
import numpy as np
from transformers import BlipTextConfig
from transformers.testing_utils import require_tf, slow
from transformers.utils import is_tf_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
if is_tf_available():
import tensorflow as tf
from transformers import TFBlipTextModel
from transformers.models.blip.modeling_tf_blip import TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST
class __snake_case :
def __init__( self , snake_case__ , snake_case__=12 , snake_case__=7 , snake_case__=True , snake_case__=True , snake_case__=True , snake_case__=99 , snake_case__=32 , snake_case__=32 , snake_case__=2 , snake_case__=4 , snake_case__=37 , snake_case__=0.1 , snake_case__=0.1 , snake_case__=512 , snake_case__=0.02 , snake_case__=0 , snake_case__=None , ) -> Tuple:
'''simple docstring'''
UpperCAmelCase : List[Any] =parent
UpperCAmelCase : Optional[int] =batch_size
UpperCAmelCase : List[Any] =seq_length
UpperCAmelCase : Optional[int] =is_training
UpperCAmelCase : Union[str, Any] =use_input_mask
UpperCAmelCase : Tuple =use_labels
UpperCAmelCase : Union[str, Any] =vocab_size
UpperCAmelCase : Tuple =hidden_size
UpperCAmelCase : Dict =projection_dim
UpperCAmelCase : Optional[int] =num_hidden_layers
UpperCAmelCase : Dict =num_attention_heads
UpperCAmelCase : int =intermediate_size
UpperCAmelCase : Any =dropout
UpperCAmelCase : Union[str, Any] =attention_dropout
UpperCAmelCase : Union[str, Any] =max_position_embeddings
UpperCAmelCase : List[str] =initializer_range
UpperCAmelCase : str =scope
UpperCAmelCase : str =bos_token_id
def UpperCAmelCase__ ( self ) -> int:
'''simple docstring'''
UpperCAmelCase : int =ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
UpperCAmelCase : int =None
if self.use_input_mask:
UpperCAmelCase : Union[str, Any] =random_attention_mask([self.batch_size, self.seq_length] )
if input_mask is not None:
UpperCAmelCase : Optional[int] =input_mask.numpy()
UpperCAmelCase , UpperCAmelCase : List[Any] =input_mask.shape
UpperCAmelCase : Optional[Any] =np.random.randint(1 , seq_length - 1 , size=(batch_size,) )
for batch_idx, start_index in enumerate(snake_case__ ):
UpperCAmelCase : List[Any] =1
UpperCAmelCase : Tuple =0
UpperCAmelCase : List[Any] =self.get_config()
return config, input_ids, tf.convert_to_tensor(snake_case__ )
def UpperCAmelCase__ ( self ) -> Union[str, Any]:
'''simple docstring'''
return BlipTextConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , projection_dim=self.projection_dim , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , dropout=self.dropout , attention_dropout=self.attention_dropout , max_position_embeddings=self.max_position_embeddings , initializer_range=self.initializer_range , bos_token_id=self.bos_token_id , )
def UpperCAmelCase__ ( self , snake_case__ , snake_case__ , snake_case__ ) -> Dict:
'''simple docstring'''
UpperCAmelCase : Tuple =TFBlipTextModel(config=snake_case__ )
UpperCAmelCase : List[Any] =model(snake_case__ , attention_mask=snake_case__ , training=snake_case__ )
UpperCAmelCase : str =model(snake_case__ , training=snake_case__ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
self.parent.assertEqual(result.pooler_output.shape , (self.batch_size, self.hidden_size) )
def UpperCAmelCase__ ( self ) -> Optional[Any]:
'''simple docstring'''
UpperCAmelCase : List[str] =self.prepare_config_and_inputs()
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase : List[Any] =config_and_inputs
UpperCAmelCase : Optional[int] ={'''input_ids''': input_ids, '''attention_mask''': input_mask}
return config, inputs_dict
@require_tf
class __snake_case ( lowerCamelCase__ , unittest.TestCase ):
__lowerCamelCase : Optional[int] = (TFBlipTextModel,) if is_tf_available() else ()
__lowerCamelCase : Dict = False
__lowerCamelCase : Optional[Any] = False
__lowerCamelCase : Dict = False
def UpperCAmelCase__ ( self ) -> Optional[Any]:
'''simple docstring'''
UpperCAmelCase : str =BlipTextModelTester(self )
UpperCAmelCase : Optional[int] =ConfigTester(self , config_class=snake_case__ , hidden_size=37 )
def UpperCAmelCase__ ( self ) -> Optional[int]:
'''simple docstring'''
self.config_tester.run_common_tests()
def UpperCAmelCase__ ( self ) -> List[Any]:
'''simple docstring'''
UpperCAmelCase : Any =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*snake_case__ )
def UpperCAmelCase__ ( self ) -> Optional[int]:
'''simple docstring'''
pass
def UpperCAmelCase__ ( self ) -> Union[str, Any]:
'''simple docstring'''
pass
@unittest.skip(reason='''Blip does not use inputs_embeds''' )
def UpperCAmelCase__ ( self ) -> str:
'''simple docstring'''
pass
@unittest.skip(reason='''BlipTextModel has no base class and is not available in MODEL_MAPPING''' )
def UpperCAmelCase__ ( self ) -> Optional[int]:
'''simple docstring'''
pass
@unittest.skip(reason='''BlipTextModel has no base class and is not available in MODEL_MAPPING''' )
def UpperCAmelCase__ ( self ) -> str:
'''simple docstring'''
pass
@slow
def UpperCAmelCase__ ( self ) -> Tuple:
'''simple docstring'''
for model_name in TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
UpperCAmelCase : Optional[Any] =TFBlipTextModel.from_pretrained(snake_case__ )
self.assertIsNotNone(snake_case__ )
def UpperCAmelCase__ ( self , snake_case__=True ) -> Any:
'''simple docstring'''
super().test_pt_tf_model_equivalence(allow_missing_keys=snake_case__ )
| 348 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
__snake_case = {
'''configuration_conditional_detr''': [
'''CONDITIONAL_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''ConditionalDetrConfig''',
'''ConditionalDetrOnnxConfig''',
]
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__snake_case = ['''ConditionalDetrFeatureExtractor''']
__snake_case = ['''ConditionalDetrImageProcessor''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__snake_case = [
'''CONDITIONAL_DETR_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''ConditionalDetrForObjectDetection''',
'''ConditionalDetrForSegmentation''',
'''ConditionalDetrModel''',
'''ConditionalDetrPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_conditional_detr import (
CONDITIONAL_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP,
ConditionalDetrConfig,
ConditionalDetrOnnxConfig,
)
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_conditional_detr import ConditionalDetrFeatureExtractor
from .image_processing_conditional_detr import ConditionalDetrImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_conditional_detr import (
CONDITIONAL_DETR_PRETRAINED_MODEL_ARCHIVE_LIST,
ConditionalDetrForObjectDetection,
ConditionalDetrForSegmentation,
ConditionalDetrModel,
ConditionalDetrPreTrainedModel,
)
else:
import sys
__snake_case = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 348 | import torch
import torch.nn as nn
from transformers import CLIPConfig, CLIPVisionModel, PreTrainedModel
from ...utils import logging
__snake_case = logging.get_logger(__name__)
def lowerCAmelCase_ ( __lowerCAmelCase , __lowerCAmelCase )-> str:
'''simple docstring'''
UpperCAmelCase : Dict =nn.functional.normalize(__lowerCAmelCase )
UpperCAmelCase : Tuple =nn.functional.normalize(__lowerCAmelCase )
return torch.mm(__lowerCAmelCase , normalized_text_embeds.t() )
class __snake_case ( lowerCamelCase__ ):
__lowerCamelCase : List[str] = CLIPConfig
__lowerCamelCase : List[Any] = ["""CLIPEncoderLayer"""]
def __init__( self , snake_case__ ) -> Dict:
'''simple docstring'''
super().__init__(snake_case__ )
UpperCAmelCase : Dict =CLIPVisionModel(config.vision_config )
UpperCAmelCase : Optional[Any] =nn.Linear(config.vision_config.hidden_size , config.projection_dim , bias=snake_case__ )
UpperCAmelCase : int =nn.Parameter(torch.ones(17 , config.projection_dim ) , requires_grad=snake_case__ )
UpperCAmelCase : List[str] =nn.Parameter(torch.ones(3 , config.projection_dim ) , requires_grad=snake_case__ )
UpperCAmelCase : str =nn.Parameter(torch.ones(17 ) , requires_grad=snake_case__ )
UpperCAmelCase : Optional[int] =nn.Parameter(torch.ones(3 ) , requires_grad=snake_case__ )
@torch.no_grad()
def UpperCAmelCase__ ( self , snake_case__ , snake_case__ ) -> Tuple:
'''simple docstring'''
UpperCAmelCase : Union[str, Any] =self.vision_model(snake_case__ )[1] # pooled_output
UpperCAmelCase : Optional[Any] =self.visual_projection(snake_case__ )
# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
UpperCAmelCase : List[str] =cosine_distance(snake_case__ , self.special_care_embeds ).cpu().float().numpy()
UpperCAmelCase : Optional[Any] =cosine_distance(snake_case__ , self.concept_embeds ).cpu().float().numpy()
UpperCAmelCase : Tuple =[]
UpperCAmelCase : Dict =image_embeds.shape[0]
for i in range(snake_case__ ):
UpperCAmelCase : str ={'''special_scores''': {}, '''special_care''': [], '''concept_scores''': {}, '''bad_concepts''': []}
# increase this value to create a stronger `nfsw` filter
# at the cost of increasing the possibility of filtering benign images
UpperCAmelCase : str =0.0
for concept_idx in range(len(special_cos_dist[0] ) ):
UpperCAmelCase : Optional[Any] =special_cos_dist[i][concept_idx]
UpperCAmelCase : Union[str, Any] =self.special_care_embeds_weights[concept_idx].item()
UpperCAmelCase : str =round(concept_cos - concept_threshold + adjustment , 3 )
if result_img["special_scores"][concept_idx] > 0:
result_img["special_care"].append({concept_idx, result_img['''special_scores'''][concept_idx]} )
UpperCAmelCase : int =0.01
for concept_idx in range(len(cos_dist[0] ) ):
UpperCAmelCase : Any =cos_dist[i][concept_idx]
UpperCAmelCase : Optional[int] =self.concept_embeds_weights[concept_idx].item()
UpperCAmelCase : int =round(concept_cos - concept_threshold + adjustment , 3 )
if result_img["concept_scores"][concept_idx] > 0:
result_img["bad_concepts"].append(snake_case__ )
result.append(snake_case__ )
UpperCAmelCase : Optional[int] =[len(res['''bad_concepts'''] ) > 0 for res in result]
return images, has_nsfw_concepts
@torch.no_grad()
def UpperCAmelCase__ ( self , snake_case__ , snake_case__ ) -> Tuple:
'''simple docstring'''
UpperCAmelCase : Any =self.vision_model(snake_case__ )[1] # pooled_output
UpperCAmelCase : List[str] =self.visual_projection(snake_case__ )
UpperCAmelCase : Any =cosine_distance(snake_case__ , self.special_care_embeds )
UpperCAmelCase : Optional[Any] =cosine_distance(snake_case__ , self.concept_embeds )
# increase this value to create a stronger `nsfw` filter
# at the cost of increasing the possibility of filtering benign images
UpperCAmelCase : Optional[Any] =0.0
UpperCAmelCase : Any =special_cos_dist - self.special_care_embeds_weights + adjustment
# special_scores = special_scores.round(decimals=3)
UpperCAmelCase : str =torch.any(special_scores > 0 , dim=1 )
UpperCAmelCase : List[Any] =special_care * 0.01
UpperCAmelCase : Union[str, Any] =special_adjustment.unsqueeze(1 ).expand(-1 , cos_dist.shape[1] )
UpperCAmelCase : List[Any] =(cos_dist - self.concept_embeds_weights) + special_adjustment
# concept_scores = concept_scores.round(decimals=3)
UpperCAmelCase : str =torch.any(concept_scores > 0 , dim=1 )
return images, has_nsfw_concepts
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