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def UpperCamelCase ( snake_case__):
if isinstance(snake_case__ , snake_case__):
raise TypeError("'float' object cannot be interpreted as an integer")
if isinstance(snake_case__ , snake_case__):
raise TypeError("'str' object cannot be interpreted as an integer")
if num == 0:
return "0b0"
lowerCAmelCase_ : str = False
if num < 0:
lowerCAmelCase_ : Tuple = True
lowerCAmelCase_ : Dict = -num
lowerCAmelCase_ : list[int] = []
while num > 0:
binary.insert(0 , num % 2)
num >>= 1
if negative:
return "-0b" + "".join(str(snake_case__) for e in binary)
return "0b" + "".join(str(snake_case__) for e in binary)
if __name__ == "__main__":
import doctest
doctest.testmod()
| 683 |
from collections.abc import Iterable
from typing import Any
class __snake_case :
"""simple docstring"""
def __init__( self : Optional[Any] ,lowerCAmelCase__ : int | None = None ) -> List[str]:
'''simple docstring'''
lowerCAmelCase_ : Dict = value
lowerCAmelCase_ : Node | None = None # Added in order to delete a node easier
lowerCAmelCase_ : Node | None = None
lowerCAmelCase_ : Node | None = None
def __repr__( self : Union[str, Any] ) -> str:
'''simple docstring'''
from pprint import pformat
if self.left is None and self.right is None:
return str(self.value )
return pformat({f'''{self.value}''': (self.left, self.right)} ,indent=1 )
class __snake_case :
"""simple docstring"""
def __init__( self : Optional[Any] ,lowerCAmelCase__ : Node | None = None ) -> List[str]:
'''simple docstring'''
lowerCAmelCase_ : List[Any] = root
def __str__( self : Dict ) -> str:
'''simple docstring'''
return str(self.root )
def UpperCAmelCase_ ( self : List[Any] ,lowerCAmelCase__ : Node ,lowerCAmelCase__ : Node | None ) -> None:
'''simple docstring'''
if new_children is not None: # reset its kids
lowerCAmelCase_ : Optional[int] = node.parent
if node.parent is not None: # reset its parent
if self.is_right(lowerCAmelCase__ ): # If it is the right children
lowerCAmelCase_ : List[Any] = new_children
else:
lowerCAmelCase_ : List[Any] = new_children
else:
lowerCAmelCase_ : Any = new_children
def UpperCAmelCase_ ( self : Any ,lowerCAmelCase__ : Node ) -> bool:
'''simple docstring'''
if node.parent and node.parent.right:
return node == node.parent.right
return False
def UpperCAmelCase_ ( self : List[str] ) -> bool:
'''simple docstring'''
return self.root is None
def UpperCAmelCase_ ( self : List[Any] ,lowerCAmelCase__ : Union[str, Any] ) -> None:
'''simple docstring'''
lowerCAmelCase_ : str = Node(lowerCAmelCase__ ) # create a new Node
if self.empty(): # if Tree is empty
lowerCAmelCase_ : Optional[int] = new_node # set its root
else: # Tree is not empty
lowerCAmelCase_ : List[Any] = self.root # from root
if parent_node is None:
return
while True: # While we don't get to a leaf
if value < parent_node.value: # We go left
if parent_node.left is None:
lowerCAmelCase_ : Dict = new_node # We insert the new node in a leaf
break
else:
lowerCAmelCase_ : List[str] = parent_node.left
else:
if parent_node.right is None:
lowerCAmelCase_ : Dict = new_node
break
else:
lowerCAmelCase_ : str = parent_node.right
lowerCAmelCase_ : Optional[int] = parent_node
def UpperCAmelCase_ ( self : int ,*lowerCAmelCase__ : Tuple ) -> None:
'''simple docstring'''
for value in values:
self.__insert(lowerCAmelCase__ )
def UpperCAmelCase_ ( self : Optional[int] ,lowerCAmelCase__ : Optional[int] ) -> Node | None:
'''simple docstring'''
if self.empty():
raise IndexError("Warning: Tree is empty! please use another." )
else:
lowerCAmelCase_ : Dict = self.root
# use lazy evaluation here to avoid NoneType Attribute error
while node is not None and node.value is not value:
lowerCAmelCase_ : Union[str, Any] = node.left if value < node.value else node.right
return node
def UpperCAmelCase_ ( self : Dict ,lowerCAmelCase__ : Node | None = None ) -> Node | None:
'''simple docstring'''
if node is None:
if self.root is None:
return None
lowerCAmelCase_ : Dict = self.root
if not self.empty():
while node.right is not None:
lowerCAmelCase_ : Union[str, Any] = node.right
return node
def UpperCAmelCase_ ( self : Dict ,lowerCAmelCase__ : Node | None = None ) -> Node | None:
'''simple docstring'''
if node is None:
lowerCAmelCase_ : Dict = self.root
if self.root is None:
return None
if not self.empty():
lowerCAmelCase_ : Dict = self.root
while node.left is not None:
lowerCAmelCase_ : Union[str, Any] = node.left
return node
def UpperCAmelCase_ ( self : Union[str, Any] ,lowerCAmelCase__ : int ) -> None:
'''simple docstring'''
lowerCAmelCase_ : Dict = self.search(lowerCAmelCase__ ) # Look for the node with that label
if node is not None:
if node.left is None and node.right is None: # If it has no children
self.__reassign_nodes(lowerCAmelCase__ ,lowerCAmelCase__ )
elif node.left is None: # Has only right children
self.__reassign_nodes(lowerCAmelCase__ ,node.right )
elif node.right is None: # Has only left children
self.__reassign_nodes(lowerCAmelCase__ ,node.left )
else:
lowerCAmelCase_ : int = self.get_max(
node.left ) # Gets the max value of the left branch
self.remove(tmp_node.value ) # type: ignore
lowerCAmelCase_ : Any = (
tmp_node.value # type: ignore
) # Assigns the value to the node to delete and keep tree structure
def UpperCAmelCase_ ( self : Union[str, Any] ,lowerCAmelCase__ : Node | None ) -> Iterable:
'''simple docstring'''
if node is not None:
yield node # Preorder Traversal
yield from self.preorder_traverse(node.left )
yield from self.preorder_traverse(node.right )
def UpperCAmelCase_ ( self : Union[str, Any] ,lowerCAmelCase__ : Dict=None ) -> Any:
'''simple docstring'''
if traversal_function is None:
return self.preorder_traverse(self.root )
else:
return traversal_function(self.root )
def UpperCAmelCase_ ( self : str ,lowerCAmelCase__ : list ,lowerCAmelCase__ : Node | None ) -> None:
'''simple docstring'''
if node:
self.inorder(lowerCAmelCase__ ,node.left )
arr.append(node.value )
self.inorder(lowerCAmelCase__ ,node.right )
def UpperCAmelCase_ ( self : List[str] ,lowerCAmelCase__ : int ,lowerCAmelCase__ : Node ) -> int:
'''simple docstring'''
lowerCAmelCase_ : list[int] = []
self.inorder(lowerCAmelCase__ ,lowerCAmelCase__ ) # append all values to list using inorder traversal
return arr[k - 1]
def UpperCamelCase ( snake_case__):
lowerCAmelCase_ : Optional[Any] = []
if curr_node is not None:
lowerCAmelCase_ : Dict = postorder(curr_node.left) + postorder(curr_node.right) + [curr_node]
return node_list
def UpperCamelCase ( ):
lowerCAmelCase_ : Tuple = (8, 3, 6, 1, 10, 14, 13, 4, 7)
lowerCAmelCase_ : Tuple = BinarySearchTree()
for i in testlist:
t.insert(snake_case__)
# Prints all the elements of the list in order traversal
print(snake_case__)
if t.search(6) is not None:
print("The value 6 exists")
else:
print("The value 6 doesn't exist")
if t.search(-1) is not None:
print("The value -1 exists")
else:
print("The value -1 doesn't exist")
if not t.empty():
print("Max Value: " , t.get_max().value) # type: ignore
print("Min Value: " , t.get_min().value) # type: ignore
for i in testlist:
t.remove(snake_case__)
print(snake_case__)
if __name__ == "__main__":
import doctest
doctest.testmod(verbose=True)
| 683 | 1 |
import argparse
import os
from pathlib import Path
import torch
from bark.generation import _load_model as _bark_load_model
from huggingface_hub import hf_hub_download
from transformers import EncodecConfig, EncodecModel, set_seed
from transformers.models.bark.configuration_bark import (
BarkCoarseConfig,
BarkConfig,
BarkFineConfig,
BarkSemanticConfig,
)
from transformers.models.bark.generation_configuration_bark import (
BarkCoarseGenerationConfig,
BarkFineGenerationConfig,
BarkGenerationConfig,
BarkSemanticGenerationConfig,
)
from transformers.models.bark.modeling_bark import BarkCoarseModel, BarkFineModel, BarkModel, BarkSemanticModel
from transformers.utils import logging
logging.set_verbosity_info()
_lowercase = logging.get_logger(__name__)
set_seed(770)
_lowercase = {
'''c_attn''': '''att_proj''',
'''c_proj''': '''out_proj''',
'''c_fc''': '''in_proj''',
'''transformer.''': '''''',
'''h.''': '''layers.''',
'''ln_1''': '''layernorm_1''',
'''ln_2''': '''layernorm_2''',
'''ln_f''': '''layernorm_final''',
'''wpe''': '''position_embeds_layer''',
'''wte''': '''input_embeds_layer''',
}
_lowercase = {
'''text_small''': {
'''repo_id''': '''suno/bark''',
'''file_name''': '''text.pt''',
},
'''coarse_small''': {
'''repo_id''': '''suno/bark''',
'''file_name''': '''coarse.pt''',
},
'''fine_small''': {
'''repo_id''': '''suno/bark''',
'''file_name''': '''fine.pt''',
},
'''text''': {
'''repo_id''': '''suno/bark''',
'''file_name''': '''text_2.pt''',
},
'''coarse''': {
'''repo_id''': '''suno/bark''',
'''file_name''': '''coarse_2.pt''',
},
'''fine''': {
'''repo_id''': '''suno/bark''',
'''file_name''': '''fine_2.pt''',
},
}
_lowercase = os.path.dirname(os.path.abspath(__file__))
_lowercase = os.path.join(os.path.expanduser('''~'''), '''.cache''')
_lowercase = os.path.join(os.getenv('''XDG_CACHE_HOME''', default_cache_dir), '''suno''', '''bark_v0''')
def UpperCamelCase ( snake_case__ , snake_case__=False):
lowerCAmelCase_ : Union[str, Any] = model_type
if use_small:
key += "_small"
return os.path.join(snake_case__ , REMOTE_MODEL_PATHS[key]["file_name"])
def UpperCamelCase ( snake_case__ , snake_case__):
os.makedirs(snake_case__ , exist_ok=snake_case__)
hf_hub_download(repo_id=snake_case__ , filename=snake_case__ , local_dir=snake_case__)
def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__=False , snake_case__="text"):
if model_type == "text":
lowerCAmelCase_ : Optional[Any] = BarkSemanticModel
lowerCAmelCase_ : str = BarkSemanticConfig
lowerCAmelCase_ : Any = BarkSemanticGenerationConfig
elif model_type == "coarse":
lowerCAmelCase_ : Any = BarkCoarseModel
lowerCAmelCase_ : int = BarkCoarseConfig
lowerCAmelCase_ : List[Any] = BarkCoarseGenerationConfig
elif model_type == "fine":
lowerCAmelCase_ : Tuple = BarkFineModel
lowerCAmelCase_ : List[str] = BarkFineConfig
lowerCAmelCase_ : Optional[int] = BarkFineGenerationConfig
else:
raise NotImplementedError()
lowerCAmelCase_ : Optional[Any] = F'''{model_type}_small''' if use_small else model_type
lowerCAmelCase_ : int = REMOTE_MODEL_PATHS[model_key]
if not os.path.exists(snake_case__):
logger.info(F'''{model_type} model not found, downloading into `{CACHE_DIR}`.''')
_download(model_info["repo_id"] , model_info["file_name"])
lowerCAmelCase_ : Dict = torch.load(snake_case__ , map_location=snake_case__)
# this is a hack
lowerCAmelCase_ : Union[str, Any] = checkpoint["model_args"]
if "input_vocab_size" not in model_args:
lowerCAmelCase_ : Optional[int] = model_args["vocab_size"]
lowerCAmelCase_ : Any = model_args["vocab_size"]
del model_args["vocab_size"]
# convert Bark model arguments to HF Bark model arguments
lowerCAmelCase_ : Dict = model_args.pop("n_head")
lowerCAmelCase_ : Union[str, Any] = model_args.pop("n_embd")
lowerCAmelCase_ : str = model_args.pop("n_layer")
lowerCAmelCase_ : Union[str, Any] = ConfigClass(**checkpoint["model_args"])
lowerCAmelCase_ : Optional[Any] = ModelClass(config=snake_case__)
lowerCAmelCase_ : Dict = GenerationConfigClass()
lowerCAmelCase_ : str = model_generation_config
lowerCAmelCase_ : str = checkpoint["model"]
# fixup checkpoint
lowerCAmelCase_ : Union[str, Any] = "_orig_mod."
for k, v in list(state_dict.items()):
if k.startswith(snake_case__):
# replace part of the key with corresponding layer name in HF implementation
lowerCAmelCase_ : List[Any] = k[len(snake_case__) :]
for old_layer_name in new_layer_name_dict:
lowerCAmelCase_ : Tuple = new_k.replace(snake_case__ , new_layer_name_dict[old_layer_name])
lowerCAmelCase_ : List[str] = state_dict.pop(snake_case__)
lowerCAmelCase_ : Optional[Any] = set(state_dict.keys()) - set(model.state_dict().keys())
lowerCAmelCase_ : Optional[int] = {k for k in extra_keys if not k.endswith(".attn.bias")}
lowerCAmelCase_ : str = set(model.state_dict().keys()) - set(state_dict.keys())
lowerCAmelCase_ : List[Any] = {k for k in missing_keys if not k.endswith(".attn.bias")}
if len(snake_case__) != 0:
raise ValueError(F'''extra keys found: {extra_keys}''')
if len(snake_case__) != 0:
raise ValueError(F'''missing keys: {missing_keys}''')
model.load_state_dict(snake_case__ , strict=snake_case__)
lowerCAmelCase_ : Any = model.num_parameters(exclude_embeddings=snake_case__)
lowerCAmelCase_ : Dict = checkpoint["best_val_loss"].item()
logger.info(F'''model loaded: {round(n_params/1e6 , 1)}M params, {round(snake_case__ , 3)} loss''')
model.eval()
model.to(snake_case__)
del checkpoint, state_dict
return model
def UpperCamelCase ( snake_case__ , snake_case__=False , snake_case__="text"):
if model_type not in ("text", "coarse", "fine"):
raise NotImplementedError()
lowerCAmelCase_ : Optional[Any] = "cpu" # do conversion on cpu
lowerCAmelCase_ : int = _get_ckpt_path(snake_case__ , use_small=snake_case__)
lowerCAmelCase_ : Optional[int] = _load_model(snake_case__ , snake_case__ , model_type=snake_case__ , use_small=snake_case__)
# load bark initial model
lowerCAmelCase_ : Optional[Any] = _bark_load_model(snake_case__ , "cpu" , model_type=snake_case__ , use_small=snake_case__)
if model_type == "text":
lowerCAmelCase_ : int = bark_model["model"]
if model.num_parameters(exclude_embeddings=snake_case__) != bark_model.get_num_params():
raise ValueError("initial and new models don't have the same number of parameters")
# check if same output as the bark model
lowerCAmelCase_ : List[Any] = 5
lowerCAmelCase_ : List[Any] = 10
if model_type in ["text", "coarse"]:
lowerCAmelCase_ : Any = torch.randint(2_56 , (batch_size, sequence_length) , dtype=torch.int)
lowerCAmelCase_ : str = bark_model(snake_case__)[0]
lowerCAmelCase_ : List[str] = model(snake_case__)
# take last logits
lowerCAmelCase_ : Dict = output_new_model_total.logits[:, [-1], :]
else:
lowerCAmelCase_ : List[str] = 3
lowerCAmelCase_ : List[Any] = 8
lowerCAmelCase_ : Dict = torch.randint(2_56 , (batch_size, sequence_length, n_codes_total) , dtype=torch.int)
lowerCAmelCase_ : Optional[int] = model(snake_case__ , snake_case__)
lowerCAmelCase_ : Tuple = bark_model(snake_case__ , snake_case__)
lowerCAmelCase_ : List[str] = output_new_model_total.logits
# output difference should come from the difference of self-attention implementation design
if output_new_model.shape != output_old_model.shape:
raise ValueError("initial and new outputs don't have the same shape")
if (output_new_model - output_old_model).abs().max().item() > 1e-3:
raise ValueError("initial and new outputs are not equal")
Path(snake_case__).mkdir(exist_ok=snake_case__)
model.save_pretrained(snake_case__)
def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ , ):
lowerCAmelCase_ : Dict = os.path.join(snake_case__ , snake_case__)
lowerCAmelCase_ : List[Any] = BarkSemanticConfig.from_pretrained(os.path.join(snake_case__ , "config.json"))
lowerCAmelCase_ : Optional[int] = BarkCoarseConfig.from_pretrained(os.path.join(snake_case__ , "config.json"))
lowerCAmelCase_ : Tuple = BarkFineConfig.from_pretrained(os.path.join(snake_case__ , "config.json"))
lowerCAmelCase_ : Optional[int] = EncodecConfig.from_pretrained("facebook/encodec_24khz")
lowerCAmelCase_ : Dict = BarkSemanticModel.from_pretrained(snake_case__)
lowerCAmelCase_ : List[Any] = BarkCoarseModel.from_pretrained(snake_case__)
lowerCAmelCase_ : Tuple = BarkFineModel.from_pretrained(snake_case__)
lowerCAmelCase_ : str = EncodecModel.from_pretrained("facebook/encodec_24khz")
lowerCAmelCase_ : Any = BarkConfig.from_sub_model_configs(
snake_case__ , snake_case__ , snake_case__ , snake_case__)
lowerCAmelCase_ : str = BarkGenerationConfig.from_sub_model_configs(
semantic.generation_config , coarseAcoustic.generation_config , fineAcoustic.generation_config)
lowerCAmelCase_ : Tuple = BarkModel(snake_case__)
lowerCAmelCase_ : Optional[Any] = semantic
lowerCAmelCase_ : Union[str, Any] = coarseAcoustic
lowerCAmelCase_ : int = fineAcoustic
lowerCAmelCase_ : Tuple = codec
lowerCAmelCase_ : List[Any] = bark_generation_config
Path(snake_case__).mkdir(exist_ok=snake_case__)
bark.save_pretrained(snake_case__ , repo_id=snake_case__ , push_to_hub=snake_case__)
if __name__ == "__main__":
_lowercase = argparse.ArgumentParser()
# Required parameters
parser.add_argument('''model_type''', type=str, help='''text, coarse or fine.''')
parser.add_argument('''pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
parser.add_argument('''--is_small''', action='''store_true''', help='''convert the small version instead of the large.''')
_lowercase = parser.parse_args()
load_model(args.pytorch_dump_folder_path, model_type=args.model_type, use_small=args.is_small)
| 683 |
class __snake_case :
"""simple docstring"""
def __init__( self : Optional[int] ,lowerCAmelCase__ : str = "" ,lowerCAmelCase__ : bool = False ) -> None:
'''simple docstring'''
lowerCAmelCase_ : dict[str, RadixNode] = {}
# A node will be a leaf if the tree contains its word
lowerCAmelCase_ : int = is_leaf
lowerCAmelCase_ : Optional[Any] = prefix
def UpperCAmelCase_ ( self : str ,lowerCAmelCase__ : str ) -> tuple[str, str, str]:
'''simple docstring'''
lowerCAmelCase_ : Any = 0
for q, w in zip(self.prefix ,lowerCAmelCase__ ):
if q != w:
break
x += 1
return self.prefix[:x], self.prefix[x:], word[x:]
def UpperCAmelCase_ ( self : Optional[int] ,lowerCAmelCase__ : list[str] ) -> None:
'''simple docstring'''
for word in words:
self.insert(lowerCAmelCase__ )
def UpperCAmelCase_ ( self : Any ,lowerCAmelCase__ : str ) -> None:
'''simple docstring'''
if self.prefix == word:
lowerCAmelCase_ : Optional[Any] = True
# Case 2: The node has no edges that have a prefix to the word
# Solution: We create an edge from the current node to a new one
# containing the word
elif word[0] not in self.nodes:
lowerCAmelCase_ : List[Any] = RadixNode(prefix=lowerCAmelCase__ ,is_leaf=lowerCAmelCase__ )
else:
lowerCAmelCase_ : Tuple = self.nodes[word[0]]
lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ : List[str] = incoming_node.match(
lowerCAmelCase__ )
# Case 3: The node prefix is equal to the matching
# Solution: We insert remaining word on the next node
if remaining_prefix == "":
self.nodes[matching_string[0]].insert(lowerCAmelCase__ )
# Case 4: The word is greater equal to the matching
# Solution: Create a node in between both nodes, change
# prefixes and add the new node for the remaining word
else:
lowerCAmelCase_ : Optional[int] = remaining_prefix
lowerCAmelCase_ : Optional[int] = self.nodes[matching_string[0]]
lowerCAmelCase_ : List[Any] = RadixNode(lowerCAmelCase__ ,lowerCAmelCase__ )
lowerCAmelCase_ : Dict = aux_node
if remaining_word == "":
lowerCAmelCase_ : List[str] = True
else:
self.nodes[matching_string[0]].insert(lowerCAmelCase__ )
def UpperCAmelCase_ ( self : Tuple ,lowerCAmelCase__ : str ) -> bool:
'''simple docstring'''
lowerCAmelCase_ : Any = self.nodes.get(word[0] ,lowerCAmelCase__ )
if not incoming_node:
return False
else:
lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ : List[Any] = incoming_node.match(
lowerCAmelCase__ )
# If there is remaining prefix, the word can't be on the tree
if remaining_prefix != "":
return False
# This applies when the word and the prefix are equal
elif remaining_word == "":
return incoming_node.is_leaf
# We have word remaining so we check the next node
else:
return incoming_node.find(lowerCAmelCase__ )
def UpperCAmelCase_ ( self : Optional[Any] ,lowerCAmelCase__ : str ) -> bool:
'''simple docstring'''
lowerCAmelCase_ : int = self.nodes.get(word[0] ,lowerCAmelCase__ )
if not incoming_node:
return False
else:
lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ : Union[str, Any] = incoming_node.match(
lowerCAmelCase__ )
# If there is remaining prefix, the word can't be on the tree
if remaining_prefix != "":
return False
# We have word remaining so we check the next node
elif remaining_word != "":
return incoming_node.delete(lowerCAmelCase__ )
else:
# If it is not a leaf, we don't have to delete
if not incoming_node.is_leaf:
return False
else:
# We delete the nodes if no edges go from it
if len(incoming_node.nodes ) == 0:
del self.nodes[word[0]]
# We merge the current node with its only child
if len(self.nodes ) == 1 and not self.is_leaf:
lowerCAmelCase_ : str = list(self.nodes.values() )[0]
lowerCAmelCase_ : Tuple = merging_node.is_leaf
self.prefix += merging_node.prefix
lowerCAmelCase_ : Optional[int] = merging_node.nodes
# If there is more than 1 edge, we just mark it as non-leaf
elif len(incoming_node.nodes ) > 1:
lowerCAmelCase_ : Optional[Any] = False
# If there is 1 edge, we merge it with its child
else:
lowerCAmelCase_ : Tuple = list(incoming_node.nodes.values() )[0]
lowerCAmelCase_ : Union[str, Any] = merging_node.is_leaf
incoming_node.prefix += merging_node.prefix
lowerCAmelCase_ : str = merging_node.nodes
return True
def UpperCAmelCase_ ( self : Dict ,lowerCAmelCase__ : int = 0 ) -> None:
'''simple docstring'''
if self.prefix != "":
print("-" * height ,self.prefix ," (leaf)" if self.is_leaf else "" )
for value in self.nodes.values():
value.print_tree(height + 1 )
def UpperCamelCase ( ):
lowerCAmelCase_ : Dict = "banana bananas bandana band apple all beast".split()
lowerCAmelCase_ : List[Any] = RadixNode()
root.insert_many(snake_case__)
assert all(root.find(snake_case__) for word in words)
assert not root.find("bandanas")
assert not root.find("apps")
root.delete("all")
assert not root.find("all")
root.delete("banana")
assert not root.find("banana")
assert root.find("bananas")
return True
def UpperCamelCase ( ):
assert test_trie()
def UpperCamelCase ( ):
lowerCAmelCase_ : List[str] = RadixNode()
lowerCAmelCase_ : Optional[Any] = "banana bananas bandanas bandana band apple all beast".split()
root.insert_many(snake_case__)
print("Words:" , snake_case__)
print("Tree:")
root.print_tree()
if __name__ == "__main__":
main()
| 683 | 1 |
def UpperCamelCase ( snake_case__ , snake_case__):
return base * power(snake_case__ , (exponent - 1)) if exponent else 1
if __name__ == "__main__":
print('''Raise base to the power of exponent using recursion...''')
_lowercase = int(input('''Enter the base: ''').strip())
_lowercase = int(input('''Enter the exponent: ''').strip())
_lowercase = power(base, abs(exponent))
if exponent < 0: # power() does not properly deal w/ negative exponents
_lowercase = 1 / result
print(f"{base} to the power of {exponent} is {result}")
| 683 |
from __future__ import annotations
def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__ , ):
if (electron_conc, hole_conc, intrinsic_conc).count(0) != 1:
raise ValueError("You cannot supply more or less than 2 values")
elif electron_conc < 0:
raise ValueError("Electron concentration cannot be negative in a semiconductor")
elif hole_conc < 0:
raise ValueError("Hole concentration cannot be negative in a semiconductor")
elif intrinsic_conc < 0:
raise ValueError(
"Intrinsic concentration cannot be negative in a semiconductor")
elif electron_conc == 0:
return (
"electron_conc",
intrinsic_conc**2 / hole_conc,
)
elif hole_conc == 0:
return (
"hole_conc",
intrinsic_conc**2 / electron_conc,
)
elif intrinsic_conc == 0:
return (
"intrinsic_conc",
(electron_conc * hole_conc) ** 0.5,
)
else:
return (-1, -1)
if __name__ == "__main__":
import doctest
doctest.testmod()
| 683 | 1 |
import warnings
from contextlib import contextmanager
from ...processing_utils import ProcessorMixin
from .feature_extraction_wavaveca import WavaVecaFeatureExtractor
from .tokenization_wavaveca import WavaVecaCTCTokenizer
class __snake_case ( snake_case__ ):
"""simple docstring"""
UpperCamelCase_ = 'Wav2Vec2FeatureExtractor'
UpperCamelCase_ = 'AutoTokenizer'
def __init__( self : Optional[int] ,lowerCAmelCase__ : List[str] ,lowerCAmelCase__ : str ) -> Any:
'''simple docstring'''
super().__init__(lowerCAmelCase__ ,lowerCAmelCase__ )
lowerCAmelCase_ : Union[str, Any] = self.feature_extractor
lowerCAmelCase_ : Tuple = False
@classmethod
def UpperCAmelCase_ ( cls : Union[str, Any] ,lowerCAmelCase__ : List[str] ,**lowerCAmelCase__ : Optional[int] ) -> List[Any]:
'''simple docstring'''
try:
return super().from_pretrained(lowerCAmelCase__ ,**lowerCAmelCase__ )
except OSError:
warnings.warn(
f'''Loading a tokenizer inside {cls.__name__} from a config that does not'''
" include a `tokenizer_class` attribute is deprecated and will be "
"removed in v5. Please add `'tokenizer_class': 'Wav2Vec2CTCTokenizer'`"
" attribute to either your `config.json` or `tokenizer_config.json` "
"file to suppress this warning: " ,lowerCAmelCase__ ,)
lowerCAmelCase_ : Union[str, Any] = WavaVecaFeatureExtractor.from_pretrained(lowerCAmelCase__ ,**lowerCAmelCase__ )
lowerCAmelCase_ : int = WavaVecaCTCTokenizer.from_pretrained(lowerCAmelCase__ ,**lowerCAmelCase__ )
return cls(feature_extractor=lowerCAmelCase__ ,tokenizer=lowerCAmelCase__ )
def __call__( self : List[Any] ,*lowerCAmelCase__ : Optional[int] ,**lowerCAmelCase__ : str ) -> List[str]:
'''simple docstring'''
if self._in_target_context_manager:
return self.current_processor(*lowerCAmelCase__ ,**lowerCAmelCase__ )
if "raw_speech" in kwargs:
warnings.warn("Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead." )
lowerCAmelCase_ : List[Any] = kwargs.pop("raw_speech" )
else:
lowerCAmelCase_ : Union[str, Any] = kwargs.pop("audio" ,lowerCAmelCase__ )
lowerCAmelCase_ : Dict = kwargs.pop("sampling_rate" ,lowerCAmelCase__ )
lowerCAmelCase_ : List[Any] = kwargs.pop("text" ,lowerCAmelCase__ )
if len(lowerCAmelCase__ ) > 0:
lowerCAmelCase_ : str = args[0]
lowerCAmelCase_ : Union[str, Any] = args[1:]
if audio is None and text is None:
raise ValueError("You need to specify either an `audio` or `text` input to process." )
if audio is not None:
lowerCAmelCase_ : str = self.feature_extractor(lowerCAmelCase__ ,*lowerCAmelCase__ ,sampling_rate=lowerCAmelCase__ ,**lowerCAmelCase__ )
if text is not None:
lowerCAmelCase_ : Dict = self.tokenizer(lowerCAmelCase__ ,**lowerCAmelCase__ )
if text is None:
return inputs
elif audio is None:
return encodings
else:
lowerCAmelCase_ : int = encodings["input_ids"]
return inputs
def UpperCAmelCase_ ( self : str ,*lowerCAmelCase__ : List[Any] ,**lowerCAmelCase__ : Tuple ) -> int:
'''simple docstring'''
if self._in_target_context_manager:
return self.current_processor.pad(*lowerCAmelCase__ ,**lowerCAmelCase__ )
lowerCAmelCase_ : List[Any] = kwargs.pop("input_features" ,lowerCAmelCase__ )
lowerCAmelCase_ : Union[str, Any] = kwargs.pop("labels" ,lowerCAmelCase__ )
if len(lowerCAmelCase__ ) > 0:
lowerCAmelCase_ : List[str] = args[0]
lowerCAmelCase_ : str = args[1:]
if input_features is not None:
lowerCAmelCase_ : Optional[Any] = self.feature_extractor.pad(lowerCAmelCase__ ,*lowerCAmelCase__ ,**lowerCAmelCase__ )
if labels is not None:
lowerCAmelCase_ : Optional[int] = self.tokenizer.pad(lowerCAmelCase__ ,**lowerCAmelCase__ )
if labels is None:
return input_features
elif input_features is None:
return labels
else:
lowerCAmelCase_ : Any = labels["input_ids"]
return input_features
def UpperCAmelCase_ ( self : Optional[Any] ,*lowerCAmelCase__ : Optional[Any] ,**lowerCAmelCase__ : List[str] ) -> Optional[Any]:
'''simple docstring'''
return self.tokenizer.batch_decode(*lowerCAmelCase__ ,**lowerCAmelCase__ )
def UpperCAmelCase_ ( self : Union[str, Any] ,*lowerCAmelCase__ : Union[str, Any] ,**lowerCAmelCase__ : List[str] ) -> int:
'''simple docstring'''
return self.tokenizer.decode(*lowerCAmelCase__ ,**lowerCAmelCase__ )
@contextmanager
def UpperCAmelCase_ ( self : List[str] ) -> Tuple:
'''simple docstring'''
warnings.warn(
"`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your "
"labels by using the argument `text` of the regular `__call__` method (either in the same call as "
"your audio inputs, or in a separate call." )
lowerCAmelCase_ : int = True
lowerCAmelCase_ : int = self.tokenizer
yield
lowerCAmelCase_ : int = self.feature_extractor
lowerCAmelCase_ : Tuple = False
| 683 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_lowercase = {
'''configuration_git''': ['''GIT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GitConfig''', '''GitVisionConfig'''],
'''processing_git''': ['''GitProcessor'''],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowercase = [
'''GIT_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''GitForCausalLM''',
'''GitModel''',
'''GitPreTrainedModel''',
'''GitVisionModel''',
]
if TYPE_CHECKING:
from .configuration_git import GIT_PRETRAINED_CONFIG_ARCHIVE_MAP, GitConfig, GitVisionConfig
from .processing_git import GitProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_git import (
GIT_PRETRAINED_MODEL_ARCHIVE_LIST,
GitForCausalLM,
GitModel,
GitPreTrainedModel,
GitVisionModel,
)
else:
import sys
_lowercase = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 683 | 1 |
import unittest
from datasets import load_dataset
from transformers.pipelines import pipeline
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_torch, slow
@is_pipeline_test
@require_torch
class __snake_case ( unittest.TestCase ):
"""simple docstring"""
@require_torch
def UpperCAmelCase_ ( self : Tuple ) -> Tuple:
'''simple docstring'''
lowerCAmelCase_ : str = pipeline(
task="zero-shot-audio-classification" ,model="hf-internal-testing/tiny-clap-htsat-unfused" )
lowerCAmelCase_ : List[Any] = load_dataset("ashraq/esc50" )
lowerCAmelCase_ : Tuple = dataset["train"]["audio"][-1]["array"]
lowerCAmelCase_ : str = audio_classifier(lowerCAmelCase__ ,candidate_labels=["Sound of a dog", "Sound of vaccum cleaner"] )
self.assertEqual(
nested_simplify(lowerCAmelCase__ ) ,[{"score": 0.501, "label": "Sound of a dog"}, {"score": 0.499, "label": "Sound of vaccum cleaner"}] ,)
@unittest.skip("No models are available in TF" )
def UpperCAmelCase_ ( self : int ) -> List[str]:
'''simple docstring'''
pass
@slow
@require_torch
def UpperCAmelCase_ ( self : str ) -> Dict:
'''simple docstring'''
lowerCAmelCase_ : Any = pipeline(
task="zero-shot-audio-classification" ,model="laion/clap-htsat-unfused" ,)
# This is an audio of a dog
lowerCAmelCase_ : Optional[int] = load_dataset("ashraq/esc50" )
lowerCAmelCase_ : Optional[int] = dataset["train"]["audio"][-1]["array"]
lowerCAmelCase_ : int = audio_classifier(lowerCAmelCase__ ,candidate_labels=["Sound of a dog", "Sound of vaccum cleaner"] )
self.assertEqual(
nested_simplify(lowerCAmelCase__ ) ,[
{"score": 0.999, "label": "Sound of a dog"},
{"score": 0.001, "label": "Sound of vaccum cleaner"},
] ,)
lowerCAmelCase_ : str = audio_classifier([audio] * 5 ,candidate_labels=["Sound of a dog", "Sound of vaccum cleaner"] )
self.assertEqual(
nested_simplify(lowerCAmelCase__ ) ,[
[
{"score": 0.999, "label": "Sound of a dog"},
{"score": 0.001, "label": "Sound of vaccum cleaner"},
],
]
* 5 ,)
lowerCAmelCase_ : List[str] = audio_classifier(
[audio] * 5 ,candidate_labels=["Sound of a dog", "Sound of vaccum cleaner"] ,batch_size=5 )
self.assertEqual(
nested_simplify(lowerCAmelCase__ ) ,[
[
{"score": 0.999, "label": "Sound of a dog"},
{"score": 0.001, "label": "Sound of vaccum cleaner"},
],
]
* 5 ,)
@unittest.skip("No models are available in TF" )
def UpperCAmelCase_ ( self : Optional[int] ) -> Optional[Any]:
'''simple docstring'''
pass
| 683 |
from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments
def UpperCamelCase ( ):
lowerCAmelCase_ : List[str] = HfArgumentParser(snake_case__)
lowerCAmelCase_ : List[Any] = parser.parse_args_into_dataclasses()[0]
lowerCAmelCase_ : Optional[int] = TensorFlowBenchmark(args=snake_case__)
try:
lowerCAmelCase_ : Tuple = parser.parse_args_into_dataclasses()[0]
except ValueError as e:
lowerCAmelCase_ : Union[str, Any] = "Arg --no_{0} is no longer used, please use --no-{0} instead."
lowerCAmelCase_ : Tuple = " ".join(str(snake_case__).split(" ")[:-1])
lowerCAmelCase_ : Union[str, Any] = ""
lowerCAmelCase_ : Optional[Any] = eval(str(snake_case__).split(" ")[-1])
lowerCAmelCase_ : Tuple = []
for arg in depreciated_args:
# arg[2:] removes '--'
if arg[2:] in TensorFlowBenchmark.deprecated_args:
# arg[5:] removes '--no_'
full_error_msg += arg_error_msg.format(arg[5:])
else:
wrong_args.append(snake_case__)
if len(snake_case__) > 0:
lowerCAmelCase_ : Optional[Any] = full_error_msg + begin_error_msg + str(snake_case__)
raise ValueError(snake_case__)
benchmark.run()
if __name__ == "__main__":
main()
| 683 | 1 |
from __future__ import annotations
import unittest
from transformers import DistilBertConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers.models.distilbert.modeling_tf_distilbert import (
TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
TFDistilBertForMaskedLM,
TFDistilBertForMultipleChoice,
TFDistilBertForQuestionAnswering,
TFDistilBertForSequenceClassification,
TFDistilBertForTokenClassification,
TFDistilBertModel,
)
class __snake_case :
"""simple docstring"""
def __init__( self : Any ,lowerCAmelCase__ : List[Any] ,) -> List[str]:
'''simple docstring'''
lowerCAmelCase_ : Optional[Any] = parent
lowerCAmelCase_ : Optional[Any] = 13
lowerCAmelCase_ : Any = 7
lowerCAmelCase_ : int = True
lowerCAmelCase_ : Optional[int] = True
lowerCAmelCase_ : List[Any] = False
lowerCAmelCase_ : Dict = True
lowerCAmelCase_ : List[Any] = 99
lowerCAmelCase_ : List[str] = 32
lowerCAmelCase_ : str = 2
lowerCAmelCase_ : Optional[Any] = 4
lowerCAmelCase_ : str = 37
lowerCAmelCase_ : Union[str, Any] = "gelu"
lowerCAmelCase_ : List[Any] = 0.1
lowerCAmelCase_ : Optional[int] = 0.1
lowerCAmelCase_ : Tuple = 5_12
lowerCAmelCase_ : Optional[int] = 16
lowerCAmelCase_ : Tuple = 2
lowerCAmelCase_ : Optional[int] = 0.02
lowerCAmelCase_ : Tuple = 3
lowerCAmelCase_ : Tuple = 4
lowerCAmelCase_ : List[str] = None
def UpperCAmelCase_ ( self : str ) -> Any:
'''simple docstring'''
lowerCAmelCase_ : Tuple = ids_tensor([self.batch_size, self.seq_length] ,self.vocab_size )
lowerCAmelCase_ : Dict = None
if self.use_input_mask:
lowerCAmelCase_ : Optional[Any] = random_attention_mask([self.batch_size, self.seq_length] )
lowerCAmelCase_ : Optional[Any] = None
lowerCAmelCase_ : int = None
lowerCAmelCase_ : List[Any] = None
if self.use_labels:
lowerCAmelCase_ : Any = ids_tensor([self.batch_size] ,self.type_sequence_label_size )
lowerCAmelCase_ : List[str] = ids_tensor([self.batch_size, self.seq_length] ,self.num_labels )
lowerCAmelCase_ : Union[str, Any] = ids_tensor([self.batch_size] ,self.num_choices )
lowerCAmelCase_ : List[Any] = DistilBertConfig(
vocab_size=self.vocab_size ,dim=self.hidden_size ,n_layers=self.num_hidden_layers ,n_heads=self.num_attention_heads ,hidden_dim=self.intermediate_size ,hidden_act=self.hidden_act ,dropout=self.hidden_dropout_prob ,attention_dropout=self.attention_probs_dropout_prob ,max_position_embeddings=self.max_position_embeddings ,initializer_range=self.initializer_range ,)
return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
def UpperCAmelCase_ ( self : Tuple ,lowerCAmelCase__ : int ,lowerCAmelCase__ : int ,lowerCAmelCase__ : Dict ,lowerCAmelCase__ : Optional[Any] ,lowerCAmelCase__ : List[Any] ,lowerCAmelCase__ : List[str] ) -> Dict:
'''simple docstring'''
lowerCAmelCase_ : Optional[int] = TFDistilBertModel(config=lowerCAmelCase__ )
lowerCAmelCase_ : Optional[Any] = {"input_ids": input_ids, "attention_mask": input_mask}
lowerCAmelCase_ : Dict = model(lowerCAmelCase__ )
lowerCAmelCase_ : Optional[int] = [input_ids, input_mask]
lowerCAmelCase_ : List[Any] = model(lowerCAmelCase__ )
self.parent.assertEqual(result.last_hidden_state.shape ,(self.batch_size, self.seq_length, self.hidden_size) )
def UpperCAmelCase_ ( self : str ,lowerCAmelCase__ : Dict ,lowerCAmelCase__ : Optional[Any] ,lowerCAmelCase__ : Any ,lowerCAmelCase__ : Union[str, Any] ,lowerCAmelCase__ : Union[str, Any] ,lowerCAmelCase__ : Tuple ) -> Tuple:
'''simple docstring'''
lowerCAmelCase_ : Optional[int] = TFDistilBertForMaskedLM(config=lowerCAmelCase__ )
lowerCAmelCase_ : Dict = {"input_ids": input_ids, "attention_mask": input_mask}
lowerCAmelCase_ : List[str] = model(lowerCAmelCase__ )
self.parent.assertEqual(result.logits.shape ,(self.batch_size, self.seq_length, self.vocab_size) )
def UpperCAmelCase_ ( self : Union[str, Any] ,lowerCAmelCase__ : Any ,lowerCAmelCase__ : Optional[Any] ,lowerCAmelCase__ : Any ,lowerCAmelCase__ : Any ,lowerCAmelCase__ : int ,lowerCAmelCase__ : Tuple ) -> Union[str, Any]:
'''simple docstring'''
lowerCAmelCase_ : Dict = TFDistilBertForQuestionAnswering(config=lowerCAmelCase__ )
lowerCAmelCase_ : List[Any] = {
"input_ids": input_ids,
"attention_mask": input_mask,
}
lowerCAmelCase_ : Optional[int] = model(lowerCAmelCase__ )
self.parent.assertEqual(result.start_logits.shape ,(self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape ,(self.batch_size, self.seq_length) )
def UpperCAmelCase_ ( self : Union[str, Any] ,lowerCAmelCase__ : int ,lowerCAmelCase__ : int ,lowerCAmelCase__ : int ,lowerCAmelCase__ : Optional[int] ,lowerCAmelCase__ : Dict ,lowerCAmelCase__ : List[Any] ) -> Union[str, Any]:
'''simple docstring'''
lowerCAmelCase_ : Union[str, Any] = self.num_labels
lowerCAmelCase_ : int = TFDistilBertForSequenceClassification(lowerCAmelCase__ )
lowerCAmelCase_ : int = {"input_ids": input_ids, "attention_mask": input_mask}
lowerCAmelCase_ : Tuple = model(lowerCAmelCase__ )
self.parent.assertEqual(result.logits.shape ,(self.batch_size, self.num_labels) )
def UpperCAmelCase_ ( self : Any ,lowerCAmelCase__ : str ,lowerCAmelCase__ : Any ,lowerCAmelCase__ : List[str] ,lowerCAmelCase__ : List[str] ,lowerCAmelCase__ : int ,lowerCAmelCase__ : str ) -> Tuple:
'''simple docstring'''
lowerCAmelCase_ : Optional[Any] = self.num_choices
lowerCAmelCase_ : Dict = TFDistilBertForMultipleChoice(lowerCAmelCase__ )
lowerCAmelCase_ : Any = tf.tile(tf.expand_dims(lowerCAmelCase__ ,1 ) ,(1, self.num_choices, 1) )
lowerCAmelCase_ : str = tf.tile(tf.expand_dims(lowerCAmelCase__ ,1 ) ,(1, self.num_choices, 1) )
lowerCAmelCase_ : Optional[Any] = {
"input_ids": multiple_choice_inputs_ids,
"attention_mask": multiple_choice_input_mask,
}
lowerCAmelCase_ : Optional[int] = model(lowerCAmelCase__ )
self.parent.assertEqual(result.logits.shape ,(self.batch_size, self.num_choices) )
def UpperCAmelCase_ ( self : Optional[int] ,lowerCAmelCase__ : Optional[Any] ,lowerCAmelCase__ : List[Any] ,lowerCAmelCase__ : int ,lowerCAmelCase__ : str ,lowerCAmelCase__ : Optional[int] ,lowerCAmelCase__ : str ) -> Optional[Any]:
'''simple docstring'''
lowerCAmelCase_ : int = self.num_labels
lowerCAmelCase_ : Union[str, Any] = TFDistilBertForTokenClassification(lowerCAmelCase__ )
lowerCAmelCase_ : Any = {"input_ids": input_ids, "attention_mask": input_mask}
lowerCAmelCase_ : List[Any] = model(lowerCAmelCase__ )
self.parent.assertEqual(result.logits.shape ,(self.batch_size, self.seq_length, self.num_labels) )
def UpperCAmelCase_ ( self : Union[str, Any] ) -> List[str]:
'''simple docstring'''
lowerCAmelCase_ : Any = self.prepare_config_and_inputs()
((lowerCAmelCase_) , (lowerCAmelCase_) , (lowerCAmelCase_) , (lowerCAmelCase_) , (lowerCAmelCase_) , (lowerCAmelCase_)) : Optional[Any] = config_and_inputs
lowerCAmelCase_ : Any = {"input_ids": input_ids, "attention_mask": input_mask}
return config, inputs_dict
@require_tf
class __snake_case ( snake_case__ , snake_case__ , unittest.TestCase ):
"""simple docstring"""
UpperCamelCase_ = (
(
TFDistilBertModel,
TFDistilBertForMaskedLM,
TFDistilBertForQuestionAnswering,
TFDistilBertForSequenceClassification,
TFDistilBertForTokenClassification,
TFDistilBertForMultipleChoice,
)
if is_tf_available()
else None
)
UpperCamelCase_ = (
{
'feature-extraction': TFDistilBertModel,
'fill-mask': TFDistilBertForMaskedLM,
'question-answering': TFDistilBertForQuestionAnswering,
'text-classification': TFDistilBertForSequenceClassification,
'token-classification': TFDistilBertForTokenClassification,
'zero-shot': TFDistilBertForSequenceClassification,
}
if is_tf_available()
else {}
)
UpperCamelCase_ = False
UpperCamelCase_ = False
def UpperCAmelCase_ ( self : Tuple ) -> Tuple:
'''simple docstring'''
lowerCAmelCase_ : List[Any] = TFDistilBertModelTester(self )
lowerCAmelCase_ : Tuple = ConfigTester(self ,config_class=lowerCAmelCase__ ,dim=37 )
def UpperCAmelCase_ ( self : Optional[Any] ) -> Optional[int]:
'''simple docstring'''
self.config_tester.run_common_tests()
def UpperCAmelCase_ ( self : List[Any] ) -> Any:
'''simple docstring'''
lowerCAmelCase_ : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_model(*lowerCAmelCase__ )
def UpperCAmelCase_ ( self : List[str] ) -> Tuple:
'''simple docstring'''
lowerCAmelCase_ : List[str] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_for_masked_lm(*lowerCAmelCase__ )
def UpperCAmelCase_ ( self : List[Any] ) -> Optional[Any]:
'''simple docstring'''
lowerCAmelCase_ : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_for_question_answering(*lowerCAmelCase__ )
def UpperCAmelCase_ ( self : Tuple ) -> Union[str, Any]:
'''simple docstring'''
lowerCAmelCase_ : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_for_sequence_classification(*lowerCAmelCase__ )
def UpperCAmelCase_ ( self : int ) -> Tuple:
'''simple docstring'''
lowerCAmelCase_ : Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_for_multiple_choice(*lowerCAmelCase__ )
def UpperCAmelCase_ ( self : Optional[int] ) -> str:
'''simple docstring'''
lowerCAmelCase_ : int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_for_token_classification(*lowerCAmelCase__ )
@slow
def UpperCAmelCase_ ( self : Dict ) -> Tuple:
'''simple docstring'''
for model_name in list(TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1] ):
lowerCAmelCase_ : int = TFDistilBertModel.from_pretrained(lowerCAmelCase__ )
self.assertIsNotNone(lowerCAmelCase__ )
@require_tf
class __snake_case ( unittest.TestCase ):
"""simple docstring"""
@slow
def UpperCAmelCase_ ( self : List[Any] ) -> Dict:
'''simple docstring'''
lowerCAmelCase_ : int = TFDistilBertModel.from_pretrained("distilbert-base-uncased" )
lowerCAmelCase_ : str = tf.constant([[0, 1, 2, 3, 4, 5]] )
lowerCAmelCase_ : int = model(lowerCAmelCase__ )[0]
lowerCAmelCase_ : List[Any] = [1, 6, 7_68]
self.assertEqual(output.shape ,lowerCAmelCase__ )
lowerCAmelCase_ : List[str] = tf.constant(
[
[
[0.19_261_885, -0.13_732_955, 0.4_119_799],
[0.22_150_156, -0.07_422_661, 0.39_037_204],
[0.22_756_018, -0.0_896_414, 0.3_701_467],
]
] )
tf.debugging.assert_near(output[:, :3, :3] ,lowerCAmelCase__ ,atol=1e-4 )
| 683 |
_lowercase = {
0: '''0''',
1: '''1''',
2: '''2''',
3: '''3''',
4: '''4''',
5: '''5''',
6: '''6''',
7: '''7''',
8: '''8''',
9: '''9''',
10: '''a''',
11: '''b''',
12: '''c''',
13: '''d''',
14: '''e''',
15: '''f''',
}
def UpperCamelCase ( snake_case__):
assert type(snake_case__) in (int, float) and decimal == int(snake_case__)
lowerCAmelCase_ : Optional[Any] = int(snake_case__)
lowerCAmelCase_ : Tuple = ""
lowerCAmelCase_ : str = False
if decimal < 0:
lowerCAmelCase_ : Tuple = True
decimal *= -1
while decimal > 0:
lowerCAmelCase_ , lowerCAmelCase_ : Any = divmod(snake_case__ , 16)
lowerCAmelCase_ : Dict = values[remainder] + hexadecimal
lowerCAmelCase_ : List[str] = "0x" + hexadecimal
if negative:
lowerCAmelCase_ : Optional[Any] = "-" + hexadecimal
return hexadecimal
if __name__ == "__main__":
import doctest
doctest.testmod()
| 683 | 1 |
from __future__ import annotations
_lowercase = list[tuple[int, int]]
_lowercase = [
[0, 0, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0, 0], # 0 are free path whereas 1's are obstacles
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0],
[1, 0, 1, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 1, 0, 0],
]
_lowercase = ([-1, 0], [0, -1], [1, 0], [0, 1]) # up, left, down, right
class __snake_case :
"""simple docstring"""
def __init__( self : List[Any] ,lowerCAmelCase__ : int ,lowerCAmelCase__ : int ,lowerCAmelCase__ : int ,lowerCAmelCase__ : int ,lowerCAmelCase__ : float ,lowerCAmelCase__ : Node | None ,) -> List[Any]:
'''simple docstring'''
lowerCAmelCase_ : Any = pos_x
lowerCAmelCase_ : Tuple = pos_y
lowerCAmelCase_ : Union[str, Any] = (pos_y, pos_x)
lowerCAmelCase_ : Optional[int] = goal_x
lowerCAmelCase_ : Any = goal_y
lowerCAmelCase_ : int = g_cost
lowerCAmelCase_ : Optional[Any] = parent
lowerCAmelCase_ : Optional[Any] = self.calculate_heuristic()
def UpperCAmelCase_ ( self : Any ) -> float:
'''simple docstring'''
lowerCAmelCase_ : Dict = abs(self.pos_x - self.goal_x )
lowerCAmelCase_ : Any = abs(self.pos_y - self.goal_y )
return dx + dy
def __lt__( self : List[str] ,lowerCAmelCase__ : Dict ) -> bool:
'''simple docstring'''
return self.f_cost < other.f_cost
class __snake_case :
"""simple docstring"""
def __init__( self : Optional[Any] ,lowerCAmelCase__ : tuple[int, int] ,lowerCAmelCase__ : tuple[int, int] ) -> Tuple:
'''simple docstring'''
lowerCAmelCase_ : List[str] = Node(start[1] ,start[0] ,goal[1] ,goal[0] ,0 ,lowerCAmelCase__ )
lowerCAmelCase_ : List[Any] = Node(goal[1] ,goal[0] ,goal[1] ,goal[0] ,9_99_99 ,lowerCAmelCase__ )
lowerCAmelCase_ : List[Any] = [self.start]
lowerCAmelCase_ : list[Node] = []
lowerCAmelCase_ : Union[str, Any] = False
def UpperCAmelCase_ ( self : Any ) -> Path | None:
'''simple docstring'''
while self.open_nodes:
# Open Nodes are sorted using __lt__
self.open_nodes.sort()
lowerCAmelCase_ : Optional[int] = self.open_nodes.pop(0 )
if current_node.pos == self.target.pos:
lowerCAmelCase_ : Optional[Any] = True
return self.retrace_path(lowerCAmelCase__ )
self.closed_nodes.append(lowerCAmelCase__ )
lowerCAmelCase_ : Optional[Any] = self.get_successors(lowerCAmelCase__ )
for child_node in successors:
if child_node in self.closed_nodes:
continue
if child_node not in self.open_nodes:
self.open_nodes.append(lowerCAmelCase__ )
else:
# retrieve the best current path
lowerCAmelCase_ : List[str] = self.open_nodes.pop(self.open_nodes.index(lowerCAmelCase__ ) )
if child_node.g_cost < better_node.g_cost:
self.open_nodes.append(lowerCAmelCase__ )
else:
self.open_nodes.append(lowerCAmelCase__ )
if not self.reached:
return [self.start.pos]
return None
def UpperCAmelCase_ ( self : Tuple ,lowerCAmelCase__ : Node ) -> list[Node]:
'''simple docstring'''
lowerCAmelCase_ : Any = []
for action in delta:
lowerCAmelCase_ : Any = parent.pos_x + action[1]
lowerCAmelCase_ : Optional[Any] = parent.pos_y + action[0]
if not (0 <= pos_x <= len(grid[0] ) - 1 and 0 <= pos_y <= len(lowerCAmelCase__ ) - 1):
continue
if grid[pos_y][pos_x] != 0:
continue
successors.append(
Node(
lowerCAmelCase__ ,lowerCAmelCase__ ,self.target.pos_y ,self.target.pos_x ,parent.g_cost + 1 ,lowerCAmelCase__ ,) )
return successors
def UpperCAmelCase_ ( self : Any ,lowerCAmelCase__ : Node | None ) -> Path:
'''simple docstring'''
lowerCAmelCase_ : int = node
lowerCAmelCase_ : Tuple = []
while current_node is not None:
path.append((current_node.pos_y, current_node.pos_x) )
lowerCAmelCase_ : str = current_node.parent
path.reverse()
return path
if __name__ == "__main__":
_lowercase = (0, 0)
_lowercase = (len(grid) - 1, len(grid[0]) - 1)
for elem in grid:
print(elem)
print('''------''')
_lowercase = GreedyBestFirst(init, goal)
_lowercase = greedy_bf.search()
if path:
for pos_x, pos_y in path:
_lowercase = 2
for elem in grid:
print(elem)
| 683 |
from pathlib import Path
from typing import List
from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import get_tests_dir, is_tool_test
from transformers.tools.agent_types import AGENT_TYPE_MAPPING, AgentAudio, AgentImage, AgentText
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
_lowercase = ['''text''', '''image''', '''audio''']
def UpperCamelCase ( snake_case__):
lowerCAmelCase_ : int = []
for input_type in input_types:
if input_type == "text":
inputs.append("Text input")
elif input_type == "image":
inputs.append(
Image.open(Path(get_tests_dir("fixtures/tests_samples/COCO")) / "000000039769.png").resize((5_12, 5_12)))
elif input_type == "audio":
inputs.append(torch.ones(30_00))
elif isinstance(snake_case__ , snake_case__):
inputs.append(create_inputs(snake_case__))
else:
raise ValueError(F'''Invalid type requested: {input_type}''')
return inputs
def UpperCamelCase ( snake_case__):
lowerCAmelCase_ : List[Any] = []
for output in outputs:
if isinstance(snake_case__ , (str, AgentText)):
output_types.append("text")
elif isinstance(snake_case__ , (Image.Image, AgentImage)):
output_types.append("image")
elif isinstance(snake_case__ , (torch.Tensor, AgentAudio)):
output_types.append("audio")
else:
raise ValueError(F'''Invalid output: {output}''')
return output_types
@is_tool_test
class __snake_case :
"""simple docstring"""
def UpperCAmelCase_ ( self : int ) -> int:
'''simple docstring'''
self.assertTrue(hasattr(self.tool ,"inputs" ) )
self.assertTrue(hasattr(self.tool ,"outputs" ) )
lowerCAmelCase_ : List[Any] = self.tool.inputs
for _input in inputs:
if isinstance(_input ,lowerCAmelCase__ ):
for __input in _input:
self.assertTrue(__input in authorized_types )
else:
self.assertTrue(_input in authorized_types )
lowerCAmelCase_ : Any = self.tool.outputs
for _output in outputs:
self.assertTrue(_output in authorized_types )
def UpperCAmelCase_ ( self : List[Any] ) -> List[Any]:
'''simple docstring'''
lowerCAmelCase_ : Any = create_inputs(self.tool.inputs )
lowerCAmelCase_ : List[Any] = self.tool(*lowerCAmelCase__ )
# There is a single output
if len(self.tool.outputs ) == 1:
lowerCAmelCase_ : Optional[int] = [outputs]
self.assertListEqual(output_types(lowerCAmelCase__ ) ,self.tool.outputs )
def UpperCAmelCase_ ( self : int ) -> Any:
'''simple docstring'''
self.assertTrue(hasattr(self.tool ,"description" ) )
self.assertTrue(hasattr(self.tool ,"default_checkpoint" ) )
self.assertTrue(self.tool.description.startswith("This is a tool that" ) )
def UpperCAmelCase_ ( self : List[Any] ) -> Tuple:
'''simple docstring'''
lowerCAmelCase_ : Optional[Any] = create_inputs(self.tool.inputs )
lowerCAmelCase_ : List[Any] = self.tool(*lowerCAmelCase__ )
if not isinstance(lowerCAmelCase__ ,lowerCAmelCase__ ):
lowerCAmelCase_ : str = [outputs]
self.assertEqual(len(lowerCAmelCase__ ) ,len(self.tool.outputs ) )
for output, output_type in zip(lowerCAmelCase__ ,self.tool.outputs ):
lowerCAmelCase_ : Tuple = AGENT_TYPE_MAPPING[output_type]
self.assertTrue(isinstance(lowerCAmelCase__ ,lowerCAmelCase__ ) )
def UpperCAmelCase_ ( self : Any ) -> Tuple:
'''simple docstring'''
lowerCAmelCase_ : Tuple = create_inputs(self.tool.inputs )
lowerCAmelCase_ : List[Any] = []
for _input, input_type in zip(lowerCAmelCase__ ,self.tool.inputs ):
if isinstance(lowerCAmelCase__ ,lowerCAmelCase__ ):
_inputs.append([AGENT_TYPE_MAPPING[_input_type](_input ) for _input_type in input_type] )
else:
_inputs.append(AGENT_TYPE_MAPPING[input_type](_input ) )
# Should not raise an error
lowerCAmelCase_ : List[Any] = self.tool(*lowerCAmelCase__ )
if not isinstance(lowerCAmelCase__ ,lowerCAmelCase__ ):
lowerCAmelCase_ : int = [outputs]
self.assertEqual(len(lowerCAmelCase__ ) ,len(self.tool.outputs ) )
| 683 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowercase = logging.get_logger(__name__)
_lowercase = {'''openai-gpt''': '''https://huggingface.co/openai-gpt/resolve/main/config.json'''}
class __snake_case ( snake_case__ ):
"""simple docstring"""
UpperCamelCase_ = 'openai-gpt'
UpperCamelCase_ = {
'max_position_embeddings': 'n_positions',
'hidden_size': 'n_embd',
'num_attention_heads': 'n_head',
'num_hidden_layers': 'n_layer',
}
def __init__( self : Optional[int] ,lowerCAmelCase__ : Dict=4_04_78 ,lowerCAmelCase__ : Tuple=5_12 ,lowerCAmelCase__ : Dict=7_68 ,lowerCAmelCase__ : List[Any]=12 ,lowerCAmelCase__ : List[Any]=12 ,lowerCAmelCase__ : Optional[int]="gelu" ,lowerCAmelCase__ : Any=0.1 ,lowerCAmelCase__ : Optional[Any]=0.1 ,lowerCAmelCase__ : List[str]=0.1 ,lowerCAmelCase__ : Dict=1e-5 ,lowerCAmelCase__ : Union[str, Any]=0.02 ,lowerCAmelCase__ : Union[str, Any]="cls_index" ,lowerCAmelCase__ : Tuple=True ,lowerCAmelCase__ : Union[str, Any]=None ,lowerCAmelCase__ : List[str]=True ,lowerCAmelCase__ : int=0.1 ,**lowerCAmelCase__ : str ,) -> Dict:
'''simple docstring'''
lowerCAmelCase_ : Optional[Any] = vocab_size
lowerCAmelCase_ : Optional[int] = n_positions
lowerCAmelCase_ : Tuple = n_embd
lowerCAmelCase_ : Any = n_layer
lowerCAmelCase_ : Union[str, Any] = n_head
lowerCAmelCase_ : Optional[int] = afn
lowerCAmelCase_ : Optional[Any] = resid_pdrop
lowerCAmelCase_ : Union[str, Any] = embd_pdrop
lowerCAmelCase_ : Union[str, Any] = attn_pdrop
lowerCAmelCase_ : List[str] = layer_norm_epsilon
lowerCAmelCase_ : Any = initializer_range
lowerCAmelCase_ : str = summary_type
lowerCAmelCase_ : Union[str, Any] = summary_use_proj
lowerCAmelCase_ : Any = summary_activation
lowerCAmelCase_ : Dict = summary_first_dropout
lowerCAmelCase_ : Any = summary_proj_to_labels
super().__init__(**lowerCAmelCase__ )
| 683 |
import pytest
_lowercase = '''__dummy_dataset1__'''
_lowercase = '''
import json
import os
import datasets
REPO_URL = "https://huggingface.co/datasets/albertvillanova/tests-raw-jsonl/resolve/main/"
URLS = {"train": REPO_URL + "wikiann-bn-train.jsonl", "validation": REPO_URL + "wikiann-bn-validation.jsonl"}
class __DummyDataset1__(datasets.GeneratorBasedBuilder):
def _info(self):
features = datasets.Features(
{
"tokens": datasets.Sequence(datasets.Value("string")),
"ner_tags": datasets.Sequence(
datasets.features.ClassLabel(
names=[
"O",
"B-PER",
"I-PER",
"B-ORG",
"I-ORG",
"B-LOC",
"I-LOC",
]
)
),
"langs": datasets.Sequence(datasets.Value("string")),
"spans": datasets.Sequence(datasets.Value("string")),
}
)
return datasets.DatasetInfo(features=features)
def _split_generators(self, dl_manager):
dl_path = dl_manager.download(URLS)
return [
datasets.SplitGenerator(datasets.Split.TRAIN, gen_kwargs={"filepath": dl_path["train"]}),
datasets.SplitGenerator(datasets.Split.VALIDATION, gen_kwargs={"filepath": dl_path["validation"]}),
]
def _generate_examples(self, filepath):
with open(filepath, "r", encoding="utf-8") as f:
for i, line in enumerate(f):
yield i, json.loads(line)
'''
@pytest.fixture
def UpperCamelCase ( ):
return DATASET_LOADING_SCRIPT_NAME
@pytest.fixture
def UpperCamelCase ( ):
return DATASET_LOADING_SCRIPT_CODE
@pytest.fixture
def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__):
lowerCAmelCase_ : List[Any] = dataset_loading_script_name
lowerCAmelCase_ : List[str] = tmp_path / "datasets" / script_name
script_dir.mkdir(parents=snake_case__)
lowerCAmelCase_ : List[Any] = script_dir / F'''{script_name}.py'''
with open(snake_case__ , "w") as f:
f.write(snake_case__)
return str(snake_case__)
| 683 | 1 |
import argparse
import torch
from transformers import GPTaLMHeadModel, RobertaForMaskedLM
if __name__ == "__main__":
_lowercase = argparse.ArgumentParser(
description=(
'''Extraction some layers of the full RobertaForMaskedLM or GPT2LMHeadModel for Transfer Learned'''
''' Distillation'''
)
)
parser.add_argument('''--model_type''', default='''roberta''', choices=['''roberta''', '''gpt2'''])
parser.add_argument('''--model_name''', default='''roberta-large''', type=str)
parser.add_argument('''--dump_checkpoint''', default='''serialization_dir/tf_roberta_048131723.pth''', type=str)
parser.add_argument('''--vocab_transform''', action='''store_true''')
_lowercase = parser.parse_args()
if args.model_type == "roberta":
_lowercase = RobertaForMaskedLM.from_pretrained(args.model_name)
_lowercase = '''roberta'''
elif args.model_type == "gpt2":
_lowercase = GPTaLMHeadModel.from_pretrained(args.model_name)
_lowercase = '''transformer'''
_lowercase = model.state_dict()
_lowercase = {}
# Embeddings #
if args.model_type == "gpt2":
for param_name in ["wte.weight", "wpe.weight"]:
_lowercase = state_dict[f"{prefix}.{param_name}"]
else:
for w in ["word_embeddings", "position_embeddings", "token_type_embeddings"]:
_lowercase = f"{prefix}.embeddings.{w}.weight"
_lowercase = state_dict[param_name]
for w in ["weight", "bias"]:
_lowercase = f"{prefix}.embeddings.LayerNorm.{w}"
_lowercase = state_dict[param_name]
# Transformer Blocks #
_lowercase = 0
for teacher_idx in [0, 2, 4, 7, 9, 11]:
if args.model_type == "gpt2":
for layer in ["ln_1", "attn.c_attn", "attn.c_proj", "ln_2", "mlp.c_fc", "mlp.c_proj"]:
for w in ["weight", "bias"]:
_lowercase = state_dict[
f"{prefix}.h.{teacher_idx}.{layer}.{w}"
]
_lowercase = state_dict[f"{prefix}.h.{teacher_idx}.attn.bias"]
else:
for layer in [
"attention.self.query",
"attention.self.key",
"attention.self.value",
"attention.output.dense",
"attention.output.LayerNorm",
"intermediate.dense",
"output.dense",
"output.LayerNorm",
]:
for w in ["weight", "bias"]:
_lowercase = state_dict[
f"{prefix}.encoder.layer.{teacher_idx}.{layer}.{w}"
]
std_idx += 1
# Language Modeling Head ###s
if args.model_type == "roberta":
for layer in ["lm_head.decoder.weight", "lm_head.bias"]:
_lowercase = state_dict[f"{layer}"]
if args.vocab_transform:
for w in ["weight", "bias"]:
_lowercase = state_dict[f"lm_head.dense.{w}"]
_lowercase = state_dict[f"lm_head.layer_norm.{w}"]
elif args.model_type == "gpt2":
for w in ["weight", "bias"]:
_lowercase = state_dict[f"{prefix}.ln_f.{w}"]
_lowercase = state_dict['''lm_head.weight''']
print(f"N layers selected for distillation: {std_idx}")
print(f"Number of params transferred for distillation: {len(compressed_sd.keys())}")
print(f"Save transferred checkpoint to {args.dump_checkpoint}.")
torch.save(compressed_sd, args.dump_checkpoint)
| 683 |
import json
import os
import re
import unittest
from transformers import CodeGenTokenizer, CodeGenTokenizerFast
from transformers.models.codegen.tokenization_codegen import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class __snake_case ( snake_case__ , unittest.TestCase ):
"""simple docstring"""
UpperCamelCase_ = CodeGenTokenizer
UpperCamelCase_ = CodeGenTokenizerFast
UpperCamelCase_ = True
UpperCamelCase_ = {'add_prefix_space': True}
UpperCamelCase_ = False
def UpperCAmelCase_ ( self : str ) -> Tuple:
'''simple docstring'''
super().setUp()
# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
lowerCAmelCase_ : Optional[Any] = [
"l",
"o",
"w",
"e",
"r",
"s",
"t",
"i",
"d",
"n",
"\u0120",
"\u0120l",
"\u0120n",
"\u0120lo",
"\u0120low",
"er",
"\u0120lowest",
"\u0120newer",
"\u0120wider",
"<unk>",
"<|endoftext|>",
]
lowerCAmelCase_ : int = dict(zip(lowerCAmelCase__ ,range(len(lowerCAmelCase__ ) ) ) )
lowerCAmelCase_ : Dict = ["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
lowerCAmelCase_ : List[Any] = {"unk_token": "<unk>"}
lowerCAmelCase_ : List[Any] = os.path.join(self.tmpdirname ,VOCAB_FILES_NAMES["vocab_file"] )
lowerCAmelCase_ : Tuple = os.path.join(self.tmpdirname ,VOCAB_FILES_NAMES["merges_file"] )
with open(self.vocab_file ,"w" ,encoding="utf-8" ) as fp:
fp.write(json.dumps(lowerCAmelCase__ ) + "\n" )
with open(self.merges_file ,"w" ,encoding="utf-8" ) as fp:
fp.write("\n".join(lowerCAmelCase__ ) )
def UpperCAmelCase_ ( self : Optional[int] ,**lowerCAmelCase__ : str ) -> int:
'''simple docstring'''
kwargs.update(self.special_tokens_map )
return CodeGenTokenizer.from_pretrained(self.tmpdirname ,**lowerCAmelCase__ )
def UpperCAmelCase_ ( self : Optional[int] ,**lowerCAmelCase__ : Optional[Any] ) -> Tuple:
'''simple docstring'''
kwargs.update(self.special_tokens_map )
return CodeGenTokenizerFast.from_pretrained(self.tmpdirname ,**lowerCAmelCase__ )
def UpperCAmelCase_ ( self : Optional[int] ,lowerCAmelCase__ : str ) -> Optional[int]:
'''simple docstring'''
lowerCAmelCase_ : Tuple = "lower newer"
lowerCAmelCase_ : Tuple = "lower newer"
return input_text, output_text
def UpperCAmelCase_ ( self : Optional[Any] ) -> Any:
'''simple docstring'''
lowerCAmelCase_ : Optional[Any] = CodeGenTokenizer(self.vocab_file ,self.merges_file ,**self.special_tokens_map )
lowerCAmelCase_ : Dict = "lower newer"
lowerCAmelCase_ : Dict = ["\u0120low", "er", "\u0120", "n", "e", "w", "er"]
lowerCAmelCase_ : Union[str, Any] = tokenizer.tokenize(lowerCAmelCase__ ,add_prefix_space=lowerCAmelCase__ )
self.assertListEqual(lowerCAmelCase__ ,lowerCAmelCase__ )
lowerCAmelCase_ : Optional[Any] = tokens + [tokenizer.unk_token]
lowerCAmelCase_ : Union[str, Any] = [14, 15, 10, 9, 3, 2, 15, 19]
self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCAmelCase__ ) ,lowerCAmelCase__ )
def UpperCAmelCase_ ( self : List[str] ) -> Optional[Any]:
'''simple docstring'''
if not self.test_rust_tokenizer:
return
lowerCAmelCase_ : Tuple = self.get_tokenizer()
lowerCAmelCase_ : Optional[int] = self.get_rust_tokenizer(add_prefix_space=lowerCAmelCase__ )
lowerCAmelCase_ : Any = "lower newer"
# Testing tokenization
lowerCAmelCase_ : Tuple = tokenizer.tokenize(lowerCAmelCase__ ,add_prefix_space=lowerCAmelCase__ )
lowerCAmelCase_ : Any = rust_tokenizer.tokenize(lowerCAmelCase__ )
self.assertListEqual(lowerCAmelCase__ ,lowerCAmelCase__ )
# Testing conversion to ids without special tokens
lowerCAmelCase_ : str = tokenizer.encode(lowerCAmelCase__ ,add_special_tokens=lowerCAmelCase__ ,add_prefix_space=lowerCAmelCase__ )
lowerCAmelCase_ : Any = rust_tokenizer.encode(lowerCAmelCase__ ,add_special_tokens=lowerCAmelCase__ )
self.assertListEqual(lowerCAmelCase__ ,lowerCAmelCase__ )
# Testing conversion to ids with special tokens
lowerCAmelCase_ : int = self.get_rust_tokenizer(add_prefix_space=lowerCAmelCase__ )
lowerCAmelCase_ : str = tokenizer.encode(lowerCAmelCase__ ,add_prefix_space=lowerCAmelCase__ )
lowerCAmelCase_ : Optional[int] = rust_tokenizer.encode(lowerCAmelCase__ )
self.assertListEqual(lowerCAmelCase__ ,lowerCAmelCase__ )
# Testing the unknown token
lowerCAmelCase_ : Union[str, Any] = tokens + [rust_tokenizer.unk_token]
lowerCAmelCase_ : List[str] = [14, 15, 10, 9, 3, 2, 15, 19]
self.assertListEqual(rust_tokenizer.convert_tokens_to_ids(lowerCAmelCase__ ) ,lowerCAmelCase__ )
def UpperCAmelCase_ ( self : Optional[int] ,*lowerCAmelCase__ : List[str] ,**lowerCAmelCase__ : Optional[Any] ) -> List[str]:
'''simple docstring'''
pass
def UpperCAmelCase_ ( self : Tuple ,lowerCAmelCase__ : Any=15 ) -> str:
'''simple docstring'''
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})''' ):
lowerCAmelCase_ : Any = self.rust_tokenizer_class.from_pretrained(lowerCAmelCase__ ,**lowerCAmelCase__ )
# Simple input
lowerCAmelCase_ : int = "This is a simple input"
lowerCAmelCase_ : Dict = ["This is a simple input 1", "This is a simple input 2"]
lowerCAmelCase_ : str = ("This is a simple input", "This is a pair")
lowerCAmelCase_ : Optional[int] = [
("This is a simple input 1", "This is a simple input 2"),
("This is a simple pair 1", "This is a simple pair 2"),
]
# Simple input tests
self.assertRaises(lowerCAmelCase__ ,tokenizer_r.encode ,lowerCAmelCase__ ,max_length=lowerCAmelCase__ ,padding="max_length" )
# Simple input
self.assertRaises(lowerCAmelCase__ ,tokenizer_r.encode_plus ,lowerCAmelCase__ ,max_length=lowerCAmelCase__ ,padding="max_length" )
# Simple input
self.assertRaises(
lowerCAmelCase__ ,tokenizer_r.batch_encode_plus ,lowerCAmelCase__ ,max_length=lowerCAmelCase__ ,padding="max_length" ,)
# Pair input
self.assertRaises(lowerCAmelCase__ ,tokenizer_r.encode ,lowerCAmelCase__ ,max_length=lowerCAmelCase__ ,padding="max_length" )
# Pair input
self.assertRaises(lowerCAmelCase__ ,tokenizer_r.encode_plus ,lowerCAmelCase__ ,max_length=lowerCAmelCase__ ,padding="max_length" )
# Pair input
self.assertRaises(
lowerCAmelCase__ ,tokenizer_r.batch_encode_plus ,lowerCAmelCase__ ,max_length=lowerCAmelCase__ ,padding="max_length" ,)
def UpperCAmelCase_ ( self : Optional[int] ) -> Optional[int]:
'''simple docstring'''
lowerCAmelCase_ : Tuple = CodeGenTokenizer.from_pretrained(self.tmpdirname ,pad_token="<pad>" )
# Simple input
lowerCAmelCase_ : Dict = "This is a simple input"
lowerCAmelCase_ : List[str] = ["This is a simple input looooooooong", "This is a simple input"]
lowerCAmelCase_ : Any = ("This is a simple input", "This is a pair")
lowerCAmelCase_ : List[str] = [
("This is a simple input loooooong", "This is a simple input"),
("This is a simple pair loooooong", "This is a simple pair"),
]
lowerCAmelCase_ : Dict = tokenizer.pad_token_id
lowerCAmelCase_ : Union[str, Any] = tokenizer(lowerCAmelCase__ ,padding="max_length" ,max_length=30 ,return_tensors="np" )
lowerCAmelCase_ : Tuple = tokenizer(lowerCAmelCase__ ,padding=lowerCAmelCase__ ,truncate=lowerCAmelCase__ ,return_tensors="np" )
lowerCAmelCase_ : Any = tokenizer(*lowerCAmelCase__ ,padding="max_length" ,max_length=60 ,return_tensors="np" )
lowerCAmelCase_ : Optional[int] = tokenizer(lowerCAmelCase__ ,padding=lowerCAmelCase__ ,truncate=lowerCAmelCase__ ,return_tensors="np" )
# s
# test single string max_length padding
self.assertEqual(out_s["input_ids"].shape[-1] ,30 )
self.assertTrue(pad_token_id in out_s["input_ids"] )
self.assertTrue(0 in out_s["attention_mask"] )
# s2
# test automatic padding
self.assertEqual(out_sa["input_ids"].shape[-1] ,33 )
# long slice doesn't have padding
self.assertFalse(pad_token_id in out_sa["input_ids"][0] )
self.assertFalse(0 in out_sa["attention_mask"][0] )
# short slice does have padding
self.assertTrue(pad_token_id in out_sa["input_ids"][1] )
self.assertTrue(0 in out_sa["attention_mask"][1] )
# p
# test single pair max_length padding
self.assertEqual(out_p["input_ids"].shape[-1] ,60 )
self.assertTrue(pad_token_id in out_p["input_ids"] )
self.assertTrue(0 in out_p["attention_mask"] )
# p2
# test automatic padding pair
self.assertEqual(out_pa["input_ids"].shape[-1] ,52 )
# long slice pair doesn't have padding
self.assertFalse(pad_token_id in out_pa["input_ids"][0] )
self.assertFalse(0 in out_pa["attention_mask"][0] )
# short slice pair does have padding
self.assertTrue(pad_token_id in out_pa["input_ids"][1] )
self.assertTrue(0 in out_pa["attention_mask"][1] )
def UpperCAmelCase_ ( self : Union[str, Any] ) -> Any:
'''simple docstring'''
lowerCAmelCase_ : Any = "$$$"
lowerCAmelCase_ : List[str] = CodeGenTokenizer.from_pretrained(self.tmpdirname ,bos_token=lowerCAmelCase__ ,add_bos_token=lowerCAmelCase__ )
lowerCAmelCase_ : Dict = "This is a simple input"
lowerCAmelCase_ : Union[str, Any] = ["This is a simple input 1", "This is a simple input 2"]
lowerCAmelCase_ : int = tokenizer.bos_token_id
lowerCAmelCase_ : List[Any] = tokenizer(lowerCAmelCase__ )
lowerCAmelCase_ : Optional[Any] = tokenizer(lowerCAmelCase__ )
self.assertEqual(out_s.input_ids[0] ,lowerCAmelCase__ )
self.assertTrue(all(o[0] == bos_token_id for o in out_sa.input_ids ) )
lowerCAmelCase_ : List[str] = tokenizer.decode(out_s.input_ids )
lowerCAmelCase_ : Optional[Any] = tokenizer.batch_decode(out_sa.input_ids )
self.assertEqual(decode_s.split()[0] ,lowerCAmelCase__ )
self.assertTrue(all(d.split()[0] == bos_token for d in decode_sa ) )
@slow
def UpperCAmelCase_ ( self : Any ) -> Optional[int]:
'''simple docstring'''
lowerCAmelCase_ : Optional[Any] = CodeGenTokenizer.from_pretrained("Salesforce/codegen-350M-mono" )
lowerCAmelCase_ : str = "\nif len_a > len_b:\n result = a\nelse:\n result = b\n\n\n\n#"
lowerCAmelCase_ : int = "\nif len_a > len_b: result = a\nelse: result = b"
lowerCAmelCase_ : Dict = tokenizer.encode(lowerCAmelCase__ )
lowerCAmelCase_ : str = ["^#", re.escape("<|endoftext|>" ), "^'''", "^\"\"\"", "\n\n\n"]
lowerCAmelCase_ : Union[str, Any] = tokenizer.decode(lowerCAmelCase__ ,truncate_before_pattern=lowerCAmelCase__ )
self.assertEqual(lowerCAmelCase__ ,lowerCAmelCase__ )
def UpperCAmelCase_ ( self : Optional[Any] ) -> Optional[Any]:
'''simple docstring'''
pass
| 683 | 1 |
from __future__ import annotations
from dataclasses import dataclass
@dataclass
class __snake_case :
"""simple docstring"""
UpperCamelCase_ = 42
UpperCamelCase_ = None
UpperCamelCase_ = None
def UpperCamelCase ( snake_case__):
# Validation
def is_valid_tree(snake_case__) -> bool:
if node is None:
return True
if not isinstance(snake_case__ , snake_case__):
return False
try:
float(node.data)
except (TypeError, ValueError):
return False
return is_valid_tree(node.left) and is_valid_tree(node.right)
if not is_valid_tree(snake_case__):
raise ValueError(
"Each node should be type of TreeNode and data should be float.")
def is_binary_search_tree_recursive_check(
snake_case__ , snake_case__ , snake_case__) -> bool:
if node is None:
return True
return (
left_bound < node.data < right_bound
and is_binary_search_tree_recursive_check(node.left , snake_case__ , node.data)
and is_binary_search_tree_recursive_check(
node.right , node.data , snake_case__)
)
return is_binary_search_tree_recursive_check(snake_case__ , -float("inf") , float("inf"))
if __name__ == "__main__":
import doctest
doctest.testmod()
| 683 |
from __future__ import annotations
from random import random
class __snake_case :
"""simple docstring"""
def __init__( self : Optional[int] ,lowerCAmelCase__ : int | None = None ) -> int:
'''simple docstring'''
lowerCAmelCase_ : Dict = value
lowerCAmelCase_ : Any = random()
lowerCAmelCase_ : Node | None = None
lowerCAmelCase_ : Node | None = None
def __repr__( self : Any ) -> str:
'''simple docstring'''
from pprint import pformat
if self.left is None and self.right is None:
return f'''\'{self.value}: {self.prior:.5}\''''
else:
return pformat(
{f'''{self.value}: {self.prior:.5}''': (self.left, self.right)} ,indent=1 )
def __str__( self : str ) -> str:
'''simple docstring'''
lowerCAmelCase_ : List[Any] = str(self.value ) + " "
lowerCAmelCase_ : List[Any] = str(self.left or "" )
lowerCAmelCase_ : Union[str, Any] = str(self.right or "" )
return value + left + right
def UpperCamelCase ( snake_case__ , snake_case__):
if root is None: # None tree is split into 2 Nones
return None, None
elif root.value is None:
return None, None
else:
if value < root.value:
lowerCAmelCase_ , lowerCAmelCase_ : Any = split(root.left , snake_case__)
return left, root
else:
lowerCAmelCase_ , lowerCAmelCase_ : Optional[Any] = split(root.right , snake_case__)
return root, right
def UpperCamelCase ( snake_case__ , snake_case__):
if (not left) or (not right): # If one node is None, return the other
return left or right
elif left.prior < right.prior:
lowerCAmelCase_ : Dict = merge(left.right , snake_case__)
return left
else:
lowerCAmelCase_ : List[str] = merge(snake_case__ , right.left)
return right
def UpperCamelCase ( snake_case__ , snake_case__):
lowerCAmelCase_ : List[Any] = Node(snake_case__)
lowerCAmelCase_ , lowerCAmelCase_ : Tuple = split(snake_case__ , snake_case__)
return merge(merge(snake_case__ , snake_case__) , snake_case__)
def UpperCamelCase ( snake_case__ , snake_case__):
lowerCAmelCase_ , lowerCAmelCase_ : List[str] = split(snake_case__ , value - 1)
lowerCAmelCase_ , lowerCAmelCase_ : Union[str, Any] = split(snake_case__ , snake_case__)
return merge(snake_case__ , snake_case__)
def UpperCamelCase ( snake_case__):
if not root: # None
return
else:
inorder(root.left)
print(root.value , end=",")
inorder(root.right)
def UpperCamelCase ( snake_case__ , snake_case__):
for arg in args.split():
if arg[0] == "+":
lowerCAmelCase_ : List[str] = insert(snake_case__ , int(arg[1:]))
elif arg[0] == "-":
lowerCAmelCase_ : Optional[int] = erase(snake_case__ , int(arg[1:]))
else:
print("Unknown command")
return root
def UpperCamelCase ( ):
lowerCAmelCase_ : str = None
print(
"enter numbers to create a tree, + value to add value into treap, "
"- value to erase all nodes with value. 'q' to quit. ")
lowerCAmelCase_ : str = input()
while args != "q":
lowerCAmelCase_ : int = interact_treap(snake_case__ , snake_case__)
print(snake_case__)
lowerCAmelCase_ : str = input()
print("good by!")
if __name__ == "__main__":
import doctest
doctest.testmod()
main()
| 683 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_lowercase = {
'''configuration_xlm_roberta''': [
'''XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''XLMRobertaConfig''',
'''XLMRobertaOnnxConfig''',
],
}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowercase = ['''XLMRobertaTokenizer''']
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowercase = ['''XLMRobertaTokenizerFast''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowercase = [
'''XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''XLMRobertaForCausalLM''',
'''XLMRobertaForMaskedLM''',
'''XLMRobertaForMultipleChoice''',
'''XLMRobertaForQuestionAnswering''',
'''XLMRobertaForSequenceClassification''',
'''XLMRobertaForTokenClassification''',
'''XLMRobertaModel''',
'''XLMRobertaPreTrainedModel''',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowercase = [
'''TF_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''TFXLMRobertaForCausalLM''',
'''TFXLMRobertaForMaskedLM''',
'''TFXLMRobertaForMultipleChoice''',
'''TFXLMRobertaForQuestionAnswering''',
'''TFXLMRobertaForSequenceClassification''',
'''TFXLMRobertaForTokenClassification''',
'''TFXLMRobertaModel''',
'''TFXLMRobertaPreTrainedModel''',
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowercase = [
'''FLAX_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''FlaxXLMRobertaForMaskedLM''',
'''FlaxXLMRobertaForCausalLM''',
'''FlaxXLMRobertaForMultipleChoice''',
'''FlaxXLMRobertaForQuestionAnswering''',
'''FlaxXLMRobertaForSequenceClassification''',
'''FlaxXLMRobertaForTokenClassification''',
'''FlaxXLMRobertaModel''',
'''FlaxXLMRobertaPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_xlm_roberta import (
XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
XLMRobertaConfig,
XLMRobertaOnnxConfig,
)
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_xlm_roberta import XLMRobertaTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_xlm_roberta_fast import XLMRobertaTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_xlm_roberta import (
XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
XLMRobertaForCausalLM,
XLMRobertaForMaskedLM,
XLMRobertaForMultipleChoice,
XLMRobertaForQuestionAnswering,
XLMRobertaForSequenceClassification,
XLMRobertaForTokenClassification,
XLMRobertaModel,
XLMRobertaPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_xlm_roberta import (
TF_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
TFXLMRobertaForCausalLM,
TFXLMRobertaForMaskedLM,
TFXLMRobertaForMultipleChoice,
TFXLMRobertaForQuestionAnswering,
TFXLMRobertaForSequenceClassification,
TFXLMRobertaForTokenClassification,
TFXLMRobertaModel,
TFXLMRobertaPreTrainedModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_xlm_roberta import (
FLAX_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
FlaxXLMRobertaForCausalLM,
FlaxXLMRobertaForMaskedLM,
FlaxXLMRobertaForMultipleChoice,
FlaxXLMRobertaForQuestionAnswering,
FlaxXLMRobertaForSequenceClassification,
FlaxXLMRobertaForTokenClassification,
FlaxXLMRobertaModel,
FlaxXLMRobertaPreTrainedModel,
)
else:
import sys
_lowercase = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 683 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_funnel import FunnelTokenizer
_lowercase = logging.get_logger(__name__)
_lowercase = {'''vocab_file''': '''vocab.txt''', '''tokenizer_file''': '''tokenizer.json'''}
_lowercase = [
'''small''',
'''small-base''',
'''medium''',
'''medium-base''',
'''intermediate''',
'''intermediate-base''',
'''large''',
'''large-base''',
'''xlarge''',
'''xlarge-base''',
]
_lowercase = {
'''vocab_file''': {
'''funnel-transformer/small''': '''https://huggingface.co/funnel-transformer/small/resolve/main/vocab.txt''',
'''funnel-transformer/small-base''': '''https://huggingface.co/funnel-transformer/small-base/resolve/main/vocab.txt''',
'''funnel-transformer/medium''': '''https://huggingface.co/funnel-transformer/medium/resolve/main/vocab.txt''',
'''funnel-transformer/medium-base''': (
'''https://huggingface.co/funnel-transformer/medium-base/resolve/main/vocab.txt'''
),
'''funnel-transformer/intermediate''': (
'''https://huggingface.co/funnel-transformer/intermediate/resolve/main/vocab.txt'''
),
'''funnel-transformer/intermediate-base''': (
'''https://huggingface.co/funnel-transformer/intermediate-base/resolve/main/vocab.txt'''
),
'''funnel-transformer/large''': '''https://huggingface.co/funnel-transformer/large/resolve/main/vocab.txt''',
'''funnel-transformer/large-base''': '''https://huggingface.co/funnel-transformer/large-base/resolve/main/vocab.txt''',
'''funnel-transformer/xlarge''': '''https://huggingface.co/funnel-transformer/xlarge/resolve/main/vocab.txt''',
'''funnel-transformer/xlarge-base''': (
'''https://huggingface.co/funnel-transformer/xlarge-base/resolve/main/vocab.txt'''
),
},
'''tokenizer_file''': {
'''funnel-transformer/small''': '''https://huggingface.co/funnel-transformer/small/resolve/main/tokenizer.json''',
'''funnel-transformer/small-base''': (
'''https://huggingface.co/funnel-transformer/small-base/resolve/main/tokenizer.json'''
),
'''funnel-transformer/medium''': '''https://huggingface.co/funnel-transformer/medium/resolve/main/tokenizer.json''',
'''funnel-transformer/medium-base''': (
'''https://huggingface.co/funnel-transformer/medium-base/resolve/main/tokenizer.json'''
),
'''funnel-transformer/intermediate''': (
'''https://huggingface.co/funnel-transformer/intermediate/resolve/main/tokenizer.json'''
),
'''funnel-transformer/intermediate-base''': (
'''https://huggingface.co/funnel-transformer/intermediate-base/resolve/main/tokenizer.json'''
),
'''funnel-transformer/large''': '''https://huggingface.co/funnel-transformer/large/resolve/main/tokenizer.json''',
'''funnel-transformer/large-base''': (
'''https://huggingface.co/funnel-transformer/large-base/resolve/main/tokenizer.json'''
),
'''funnel-transformer/xlarge''': '''https://huggingface.co/funnel-transformer/xlarge/resolve/main/tokenizer.json''',
'''funnel-transformer/xlarge-base''': (
'''https://huggingface.co/funnel-transformer/xlarge-base/resolve/main/tokenizer.json'''
),
},
}
_lowercase = {f"funnel-transformer/{name}": 512 for name in _model_names}
_lowercase = {f"funnel-transformer/{name}": {'''do_lower_case''': True} for name in _model_names}
class __snake_case ( snake_case__ ):
"""simple docstring"""
UpperCamelCase_ = VOCAB_FILES_NAMES
UpperCamelCase_ = PRETRAINED_VOCAB_FILES_MAP
UpperCamelCase_ = PRETRAINED_INIT_CONFIGURATION
UpperCamelCase_ = FunnelTokenizer
UpperCamelCase_ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
UpperCamelCase_ = 2
def __init__( self : Optional[Any] ,lowerCAmelCase__ : Any=None ,lowerCAmelCase__ : Optional[int]=None ,lowerCAmelCase__ : Optional[Any]=True ,lowerCAmelCase__ : List[str]="<unk>" ,lowerCAmelCase__ : int="<sep>" ,lowerCAmelCase__ : Union[str, Any]="<pad>" ,lowerCAmelCase__ : List[str]="<cls>" ,lowerCAmelCase__ : Optional[int]="<mask>" ,lowerCAmelCase__ : Union[str, Any]="<s>" ,lowerCAmelCase__ : List[str]="</s>" ,lowerCAmelCase__ : Optional[int]=True ,lowerCAmelCase__ : Tuple=True ,lowerCAmelCase__ : Any=None ,lowerCAmelCase__ : List[Any]="##" ,**lowerCAmelCase__ : int ,) -> List[Any]:
'''simple docstring'''
super().__init__(
lowerCAmelCase__ ,tokenizer_file=lowerCAmelCase__ ,do_lower_case=lowerCAmelCase__ ,unk_token=lowerCAmelCase__ ,sep_token=lowerCAmelCase__ ,pad_token=lowerCAmelCase__ ,cls_token=lowerCAmelCase__ ,mask_token=lowerCAmelCase__ ,bos_token=lowerCAmelCase__ ,eos_token=lowerCAmelCase__ ,clean_text=lowerCAmelCase__ ,tokenize_chinese_chars=lowerCAmelCase__ ,strip_accents=lowerCAmelCase__ ,wordpieces_prefix=lowerCAmelCase__ ,**lowerCAmelCase__ ,)
lowerCAmelCase_ : str = json.loads(self.backend_tokenizer.normalizer.__getstate__() )
if (
normalizer_state.get("lowercase" ,lowerCAmelCase__ ) != do_lower_case
or normalizer_state.get("strip_accents" ,lowerCAmelCase__ ) != strip_accents
or normalizer_state.get("handle_chinese_chars" ,lowerCAmelCase__ ) != tokenize_chinese_chars
):
lowerCAmelCase_ : Optional[int] = getattr(lowerCAmelCase__ ,normalizer_state.pop("type" ) )
lowerCAmelCase_ : List[Any] = do_lower_case
lowerCAmelCase_ : List[str] = strip_accents
lowerCAmelCase_ : Any = tokenize_chinese_chars
lowerCAmelCase_ : List[Any] = normalizer_class(**lowerCAmelCase__ )
lowerCAmelCase_ : int = do_lower_case
def UpperCAmelCase_ ( self : Tuple ,lowerCAmelCase__ : int ,lowerCAmelCase__ : str=None ) -> List[Any]:
'''simple docstring'''
lowerCAmelCase_ : Union[str, Any] = [self.cls_token_id] + token_ids_a + [self.sep_token_id]
if token_ids_a:
output += token_ids_a + [self.sep_token_id]
return output
def UpperCAmelCase_ ( self : List[str] ,lowerCAmelCase__ : List[int] ,lowerCAmelCase__ : Optional[List[int]] = None ) -> List[int]:
'''simple docstring'''
lowerCAmelCase_ : str = [self.sep_token_id]
lowerCAmelCase_ : Optional[Any] = [self.cls_token_id]
if token_ids_a is None:
return len(cls ) * [self.cls_token_type_id] + len(token_ids_a + sep ) * [0]
return len(cls ) * [self.cls_token_type_id] + len(token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]
def UpperCAmelCase_ ( self : Any ,lowerCAmelCase__ : str ,lowerCAmelCase__ : Optional[str] = None ) -> Tuple[str]:
'''simple docstring'''
lowerCAmelCase_ : str = self._tokenizer.model.save(lowerCAmelCase__ ,name=lowerCAmelCase__ )
return tuple(lowerCAmelCase__ )
| 683 | 1 |
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import warnings
from typing import List
from unittest.mock import Mock
import torch
from torch.utils.data import DataLoader, IterableDataset, TensorDataset
from accelerate.accelerator import Accelerator
from accelerate.utils.dataclasses import DistributedType
class __snake_case ( snake_case__ ):
"""simple docstring"""
def __init__( self : Tuple ,lowerCAmelCase__ : Optional[int] ) -> int:
'''simple docstring'''
lowerCAmelCase_ : Tuple = data
def __iter__( self : Dict ) -> Union[str, Any]:
'''simple docstring'''
for element in self.data:
yield element
def UpperCamelCase ( snake_case__=True):
lowerCAmelCase_ : Dict = Accelerator(even_batches=snake_case__)
assert accelerator.num_processes == 2, "this script expects that two GPUs are available"
return accelerator
def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__ , snake_case__ = False):
if iterable:
lowerCAmelCase_ : str = DummyIterableDataset(torch.as_tensor(range(snake_case__)))
else:
lowerCAmelCase_ : Any = TensorDataset(torch.as_tensor(range(snake_case__)))
lowerCAmelCase_ : Optional[int] = DataLoader(snake_case__ , batch_size=snake_case__)
lowerCAmelCase_ : List[str] = accelerator.prepare(snake_case__)
return dl
def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ , ):
lowerCAmelCase_ : Tuple = create_dataloader(accelerator=snake_case__ , dataset_size=snake_case__ , batch_size=snake_case__)
lowerCAmelCase_ : List[str] = [len(batch[0]) for batch in dl]
if accelerator.process_index == 0:
assert batch_sizes == process_0_expected_batch_sizes
elif accelerator.process_index == 1:
assert batch_sizes == process_1_expected_batch_sizes
def UpperCamelCase ( ):
lowerCAmelCase_ : List[str] = create_accelerator()
# without padding, we would expect a different number of batches
verify_dataloader_batch_sizes(
snake_case__ , dataset_size=3 , batch_size=1 , process_0_expected_batch_sizes=[1, 1] , process_1_expected_batch_sizes=[1, 1] , )
# without padding, we would expect the same number of batches, but different sizes
verify_dataloader_batch_sizes(
snake_case__ , dataset_size=7 , batch_size=2 , process_0_expected_batch_sizes=[2, 2] , process_1_expected_batch_sizes=[2, 2] , )
def UpperCamelCase ( ):
lowerCAmelCase_ : Optional[int] = create_accelerator(even_batches=snake_case__)
verify_dataloader_batch_sizes(
snake_case__ , dataset_size=3 , batch_size=1 , process_0_expected_batch_sizes=[1, 1] , process_1_expected_batch_sizes=[1] , )
verify_dataloader_batch_sizes(
snake_case__ , dataset_size=7 , batch_size=2 , process_0_expected_batch_sizes=[2, 2] , process_1_expected_batch_sizes=[2, 1] , )
def UpperCamelCase ( ):
lowerCAmelCase_ : Dict = create_accelerator(even_batches=snake_case__)
lowerCAmelCase_ : str = torch.nn.Linear(1 , 1)
lowerCAmelCase_ : Optional[Any] = accelerator.prepare(snake_case__)
lowerCAmelCase_ : List[Any] = create_dataloader(snake_case__ , dataset_size=3 , batch_size=1)
lowerCAmelCase_ : List[str] = []
with accelerator.join_uneven_inputs([ddp_model]):
for batch_idx, batch in enumerate(snake_case__):
lowerCAmelCase_ : List[str] = ddp_model(batch[0].float())
lowerCAmelCase_ : List[str] = output.sum()
loss.backward()
batch_idxs.append(snake_case__)
accelerator.wait_for_everyone()
if accelerator.process_index == 0:
assert batch_idxs == [0, 1]
elif accelerator.process_index == 1:
assert batch_idxs == [0]
def UpperCamelCase ( snake_case__):
with warnings.catch_warnings(record=snake_case__) as w:
with accelerator.join_uneven_inputs([Mock()]):
pass
assert issubclass(w[-1].category , snake_case__)
assert "only supported for multi-GPU" in str(w[-1].message)
def UpperCamelCase ( ):
lowerCAmelCase_ : int = True
lowerCAmelCase_ : List[str] = False
lowerCAmelCase_ : int = create_accelerator(even_batches=snake_case__)
lowerCAmelCase_ : Union[str, Any] = torch.nn.Linear(1 , 1)
lowerCAmelCase_ : Dict = accelerator.prepare(snake_case__)
lowerCAmelCase_ : Tuple = create_dataloader(snake_case__ , dataset_size=3 , batch_size=1)
lowerCAmelCase_ : Tuple = create_dataloader(snake_case__ , dataset_size=3 , batch_size=1)
with accelerator.join_uneven_inputs([ddp_model] , even_batches=snake_case__):
lowerCAmelCase_ : int = train_dl.batch_sampler.even_batches
lowerCAmelCase_ : Tuple = valid_dl.batch_sampler.even_batches
assert train_dl_overridden_value == overridden_even_batches
assert valid_dl_overridden_value == overridden_even_batches
assert train_dl.batch_sampler.even_batches == default_even_batches
assert valid_dl.batch_sampler.even_batches == default_even_batches
def UpperCamelCase ( ):
lowerCAmelCase_ : Union[str, Any] = True
lowerCAmelCase_ : Dict = False
lowerCAmelCase_ : Dict = create_accelerator(even_batches=snake_case__)
lowerCAmelCase_ : int = torch.nn.Linear(1 , 1)
lowerCAmelCase_ : Union[str, Any] = accelerator.prepare(snake_case__)
create_dataloader(snake_case__ , dataset_size=3 , batch_size=1 , iterable=snake_case__)
lowerCAmelCase_ : List[str] = create_dataloader(snake_case__ , dataset_size=3 , batch_size=1)
with warnings.catch_warnings():
warnings.filterwarnings("ignore")
try:
with accelerator.join_uneven_inputs([ddp_model] , even_batches=snake_case__):
lowerCAmelCase_ : List[Any] = batch_dl.batch_sampler.even_batches
except AttributeError:
# ensure attribute error is not raised when processing iterable dl
raise AssertionError
assert batch_dl_overridden_value == overridden_even_batches
assert batch_dl.batch_sampler.even_batches == default_even_batches
def UpperCamelCase ( ):
lowerCAmelCase_ : List[str] = create_accelerator()
lowerCAmelCase_ : int = torch.nn.Linear(1 , 1)
lowerCAmelCase_ : Optional[Any] = accelerator.prepare(snake_case__)
create_dataloader(snake_case__ , dataset_size=3 , batch_size=1 , iterable=snake_case__)
with warnings.catch_warnings(record=snake_case__) as w:
with accelerator.join_uneven_inputs([ddp_model] , even_batches=snake_case__):
pass
assert issubclass(w[-1].category , snake_case__)
assert "only supported for map-style datasets" in str(w[-1].message)
def UpperCamelCase ( ):
lowerCAmelCase_ : Union[str, Any] = create_accelerator()
accelerator.print("Test that even_batches variable ensures uniform batches across processes")
test_default_ensures_even_batch_sizes()
accelerator.print("Run tests with even_batches disabled")
test_can_disable_even_batches()
accelerator.print("Test joining uneven inputs")
test_can_join_uneven_inputs()
accelerator.print("Test overriding even_batches when joining uneven inputs")
test_join_can_override_even_batches()
accelerator.print("Test overriding even_batches for mixed dataloader types")
test_join_can_override_for_mixed_type_dataloaders()
accelerator.print("Test overriding even_batches raises a warning for iterable dataloaders")
test_join_raises_warning_for_iterable_when_overriding_even_batches()
accelerator.print("Test join with non DDP distributed raises warning")
lowerCAmelCase_ : Any = accelerator.state.distributed_type
lowerCAmelCase_ : Union[str, Any] = DistributedType.FSDP
test_join_raises_warning_for_non_ddp_distributed(snake_case__)
lowerCAmelCase_ : Union[str, Any] = original_state
if __name__ == "__main__":
main()
| 683 |
# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run
import doctest
import sys
import warnings
from os.path import abspath, dirname, join
import _pytest
from transformers.testing_utils import HfDoctestModule, HfDocTestParser
# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install -e .[dev]' when switching between checkouts and running tests.
_lowercase = abspath(join(dirname(__file__), '''src'''))
sys.path.insert(1, git_repo_path)
# silence FutureWarning warnings in tests since often we can't act on them until
# they become normal warnings - i.e. the tests still need to test the current functionality
warnings.simplefilter(action='''ignore''', category=FutureWarning)
def UpperCamelCase ( snake_case__):
config.addinivalue_line(
"markers" , "is_pt_tf_cross_test: mark test to run only when PT and TF interactions are tested")
config.addinivalue_line(
"markers" , "is_pt_flax_cross_test: mark test to run only when PT and FLAX interactions are tested")
config.addinivalue_line("markers" , "is_pipeline_test: mark test to run only when pipelines are tested")
config.addinivalue_line("markers" , "is_staging_test: mark test to run only in the staging environment")
config.addinivalue_line("markers" , "accelerate_tests: mark test that require accelerate")
config.addinivalue_line("markers" , "tool_tests: mark the tool tests that are run on their specific schedule")
def UpperCamelCase ( snake_case__):
from transformers.testing_utils import pytest_addoption_shared
pytest_addoption_shared(snake_case__)
def UpperCamelCase ( snake_case__):
from transformers.testing_utils import pytest_terminal_summary_main
lowerCAmelCase_ : int = terminalreporter.config.getoption("--make-reports")
if make_reports:
pytest_terminal_summary_main(snake_case__ , id=snake_case__)
def UpperCamelCase ( snake_case__ , snake_case__):
# If no tests are collected, pytest exists with code 5, which makes the CI fail.
if exitstatus == 5:
lowerCAmelCase_ : List[Any] = 0
# Doctest custom flag to ignore output.
_lowercase = doctest.register_optionflag('''IGNORE_RESULT''')
_lowercase = doctest.OutputChecker
class __snake_case ( snake_case__ ):
"""simple docstring"""
def UpperCAmelCase_ ( self : List[str] ,lowerCAmelCase__ : int ,lowerCAmelCase__ : Optional[int] ,lowerCAmelCase__ : Tuple ) -> Any:
'''simple docstring'''
if IGNORE_RESULT & optionflags:
return True
return OutputChecker.check_output(self ,lowerCAmelCase__ ,lowerCAmelCase__ ,lowerCAmelCase__ )
_lowercase = CustomOutputChecker
_lowercase = HfDoctestModule
_lowercase = HfDocTestParser
| 683 | 1 |
import argparse
import logging
import os
import time
import timeit
import datasets
import numpy as np
import pycuda.autoinit # noqa: F401
import pycuda.driver as cuda
import tensorrt as trt
import torch
from absl import logging as absl_logging
from accelerate import Accelerator
from datasets import load_dataset, load_metric
from torch.utils.data import DataLoader
from utils_qa import postprocess_qa_predictions
import transformers
from transformers import AutoTokenizer, EvalPrediction, default_data_collator, set_seed
from transformers.trainer_pt_utils import nested_concat, nested_truncate
_lowercase = trt.Logger(trt.Logger.WARNING)
_lowercase = absl_logging.get_absl_logger()
absl_logger.setLevel(logging.WARNING)
_lowercase = logging.getLogger(__name__)
_lowercase = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--onnx_model_path''',
default=None,
type=str,
required=True,
help='''Path to ONNX model: ''',
)
parser.add_argument(
'''--output_dir''',
default=None,
type=str,
required=True,
help='''The output directory where the model checkpoints and predictions will be written.''',
)
# Other parameters
parser.add_argument(
'''--tokenizer_name''',
default='''''',
type=str,
required=True,
help='''Pretrained tokenizer name or path if not the same as model_name''',
)
parser.add_argument(
'''--version_2_with_negative''',
action='''store_true''',
help='''If true, the SQuAD examples contain some that do not have an answer.''',
)
parser.add_argument(
'''--null_score_diff_threshold''',
type=float,
default=0.0,
help='''If null_score - best_non_null is greater than the threshold predict null.''',
)
parser.add_argument(
'''--max_seq_length''',
default=384,
type=int,
help=(
'''The maximum total input sequence length after WordPiece tokenization. Sequences '''
'''longer than this will be truncated, and sequences shorter than this will be padded.'''
),
)
parser.add_argument(
'''--doc_stride''',
default=128,
type=int,
help='''When splitting up a long document into chunks, how much stride to take between chunks.''',
)
parser.add_argument('''--per_device_eval_batch_size''', default=8, type=int, help='''Batch size per GPU/CPU for evaluation.''')
parser.add_argument(
'''--n_best_size''',
default=20,
type=int,
help='''The total number of n-best predictions to generate in the nbest_predictions.json output file.''',
)
parser.add_argument(
'''--max_answer_length''',
default=30,
type=int,
help=(
'''The maximum length of an answer that can be generated. This is needed because the start '''
'''and end predictions are not conditioned on one another.'''
),
)
parser.add_argument('''--seed''', type=int, default=42, help='''random seed for initialization''')
parser.add_argument(
'''--dataset_name''',
type=str,
default=None,
required=True,
help='''The name of the dataset to use (via the datasets library).''',
)
parser.add_argument(
'''--dataset_config_name''',
type=str,
default=None,
help='''The configuration name of the dataset to use (via the datasets library).''',
)
parser.add_argument(
'''--preprocessing_num_workers''', type=int, default=4, help='''A csv or a json file containing the training data.'''
)
parser.add_argument('''--overwrite_cache''', action='''store_true''', help='''Overwrite the cached training and evaluation sets''')
parser.add_argument(
'''--fp16''',
action='''store_true''',
help='''Whether to use 16-bit (mixed) precision instead of 32-bit''',
)
parser.add_argument(
'''--int8''',
action='''store_true''',
help='''Whether to use INT8''',
)
_lowercase = parser.parse_args()
if args.tokenizer_name:
_lowercase = AutoTokenizer.from_pretrained(args.tokenizer_name, use_fast=True)
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.'''
)
logger.info('''Training/evaluation parameters %s''', args)
_lowercase = args.per_device_eval_batch_size
_lowercase = (args.eval_batch_size, args.max_seq_length)
# TRT Engine properties
_lowercase = True
_lowercase = '''temp_engine/bert-fp32.engine'''
if args.fpaa:
_lowercase = '''temp_engine/bert-fp16.engine'''
if args.inta:
_lowercase = '''temp_engine/bert-int8.engine'''
# import ONNX file
if not os.path.exists('''temp_engine'''):
os.makedirs('''temp_engine''')
_lowercase = 1 << (int)(trt.NetworkDefinitionCreationFlag.EXPLICIT_BATCH)
with trt.Builder(TRT_LOGGER) as builder, builder.create_network(EXPLICIT_BATCH) as network, trt.OnnxParser(
network, TRT_LOGGER
) as parser:
with open(args.onnx_model_path, '''rb''') as model:
if not parser.parse(model.read()):
for error in range(parser.num_errors):
print(parser.get_error(error))
# Query input names and shapes from parsed TensorRT network
_lowercase = [network.get_input(i) for i in range(network.num_inputs)]
_lowercase = [_input.name for _input in network_inputs] # ex: ["actual_input1"]
with builder.create_builder_config() as config:
_lowercase = 1 << 50
if STRICT_TYPES:
config.set_flag(trt.BuilderFlag.STRICT_TYPES)
if args.fpaa:
config.set_flag(trt.BuilderFlag.FPaa)
if args.inta:
config.set_flag(trt.BuilderFlag.INTa)
_lowercase = builder.create_optimization_profile()
config.add_optimization_profile(profile)
for i in range(len(input_names)):
profile.set_shape(input_names[i], INPUT_SHAPE, INPUT_SHAPE, INPUT_SHAPE)
_lowercase = builder.build_engine(network, config)
# serialize_engine and store in file (can be directly loaded and deserialized):
with open(engine_name, '''wb''') as f:
f.write(engine.serialize())
def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__):
lowerCAmelCase_ : List[Any] = np.asarray(inputs["input_ids"] , dtype=np.intaa)
lowerCAmelCase_ : Tuple = np.asarray(inputs["attention_mask"] , dtype=np.intaa)
lowerCAmelCase_ : Any = np.asarray(inputs["token_type_ids"] , dtype=np.intaa)
# Copy inputs
cuda.memcpy_htod_async(d_inputs[0] , input_ids.ravel() , snake_case__)
cuda.memcpy_htod_async(d_inputs[1] , attention_mask.ravel() , snake_case__)
cuda.memcpy_htod_async(d_inputs[2] , token_type_ids.ravel() , snake_case__)
# start time
lowerCAmelCase_ : str = time.time()
# Run inference
context.execute_async(
bindings=[int(snake_case__) for d_inp in d_inputs] + [int(snake_case__), int(snake_case__)] , stream_handle=stream.handle)
# Transfer predictions back from GPU
cuda.memcpy_dtoh_async(snake_case__ , snake_case__ , snake_case__)
cuda.memcpy_dtoh_async(snake_case__ , snake_case__ , snake_case__)
# Synchronize the stream and take time
stream.synchronize()
# end time
lowerCAmelCase_ : List[str] = time.time()
lowerCAmelCase_ : Optional[Any] = end_time - start_time
lowerCAmelCase_ : Dict = (h_outputa, h_outputa)
# print(outputs)
return outputs, infer_time
# Initialize the accelerator. We will let the accelerator handle device placement for us in this example.
_lowercase = Accelerator()
# Make one log on every process with the configuration for debugging.
logging.basicConfig(
format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''',
datefmt='''%m/%d/%Y %H:%M:%S''',
level=logging.INFO,
)
# Setup logging, we only want one process per machine to log things on the screen.
# accelerator.is_local_main_process is only True for one process per machine.
logger.setLevel(logging.INFO if accelerator.is_local_main_process else logging.ERROR)
if accelerator.is_local_main_process:
datasets.utils.logging.set_verbosity_warning()
transformers.utils.logging.set_verbosity_info()
else:
datasets.utils.logging.set_verbosity_error()
transformers.utils.logging.set_verbosity_error()
# If passed along, set the training seed now.
if args.seed is not None:
set_seed(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).
if args.dataset_name is not None:
# Downloading and loading a dataset from the hub.
_lowercase = load_dataset(args.dataset_name, args.dataset_config_name)
else:
raise ValueError('''Evaluation requires a dataset name''')
# 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.
# Preprocessing the datasets.
# Preprocessing is slighlty different for training and evaluation.
_lowercase = raw_datasets['''validation'''].column_names
_lowercase = '''question''' if '''question''' in column_names else column_names[0]
_lowercase = '''context''' if '''context''' in column_names else column_names[1]
_lowercase = '''answers''' if '''answers''' in column_names else column_names[2]
# Padding side determines if we do (question|context) or (context|question).
_lowercase = tokenizer.padding_side == '''right'''
if args.max_seq_length > tokenizer.model_max_length:
logger.warning(
f"The max_seq_length passed ({args.max_seq_length}) is larger than the maximum length for the"
f"model ({tokenizer.model_max_length}). Using max_seq_length={tokenizer.model_max_length}."
)
_lowercase = min(args.max_seq_length, tokenizer.model_max_length)
def UpperCamelCase ( snake_case__):
# Some of the questions have lots of whitespace on the left, which is not useful and will make the
# truncation of the context fail (the tokenized question will take a lots of space). So we remove that
# left whitespace
lowerCAmelCase_ : str = [q.lstrip() for q in examples[question_column_name]]
# Tokenize our examples with truncation and maybe padding, but keep the overflows using a stride. This results
# in one example possible giving several features when a context is long, each of those features having a
# context that overlaps a bit the context of the previous feature.
lowerCAmelCase_ : Union[str, Any] = tokenizer(
examples[question_column_name if pad_on_right else context_column_name] , examples[context_column_name if pad_on_right else question_column_name] , truncation="only_second" if pad_on_right else "only_first" , max_length=snake_case__ , stride=args.doc_stride , return_overflowing_tokens=snake_case__ , return_offsets_mapping=snake_case__ , padding="max_length" , )
# Since one example might give us several features if it has a long context, we need a map from a feature to
# its corresponding example. This key gives us just that.
lowerCAmelCase_ : List[str] = tokenized_examples.pop("overflow_to_sample_mapping")
# For evaluation, we will need to convert our predictions to substrings of the context, so we keep the
# corresponding example_id and we will store the offset mappings.
lowerCAmelCase_ : Optional[int] = []
for i in range(len(tokenized_examples["input_ids"])):
# Grab the sequence corresponding to that example (to know what is the context and what is the question).
lowerCAmelCase_ : Any = tokenized_examples.sequence_ids(snake_case__)
lowerCAmelCase_ : Any = 1 if pad_on_right else 0
# One example can give several spans, this is the index of the example containing this span of text.
lowerCAmelCase_ : Union[str, Any] = sample_mapping[i]
tokenized_examples["example_id"].append(examples["id"][sample_index])
# Set to None the offset_mapping that are not part of the context so it's easy to determine if a token
# position is part of the context or not.
lowerCAmelCase_ : List[str] = [
(o if sequence_ids[k] == context_index else None)
for k, o in enumerate(tokenized_examples["offset_mapping"][i])
]
return tokenized_examples
_lowercase = raw_datasets['''validation''']
# Validation Feature Creation
_lowercase = eval_examples.map(
prepare_validation_features,
batched=True,
num_proc=args.preprocessing_num_workers,
remove_columns=column_names,
load_from_cache_file=not args.overwrite_cache,
desc='''Running tokenizer on validation dataset''',
)
_lowercase = default_data_collator
_lowercase = eval_dataset.remove_columns(['''example_id''', '''offset_mapping'''])
_lowercase = DataLoader(
eval_dataset_for_model, collate_fn=data_collator, batch_size=args.per_device_eval_batch_size
)
def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__ , snake_case__="eval"):
# Post-processing: we match the start logits and end logits to answers in the original context.
lowerCAmelCase_ : Union[str, Any] = postprocess_qa_predictions(
examples=snake_case__ , features=snake_case__ , predictions=snake_case__ , version_2_with_negative=args.version_2_with_negative , n_best_size=args.n_best_size , max_answer_length=args.max_answer_length , null_score_diff_threshold=args.null_score_diff_threshold , output_dir=args.output_dir , prefix=snake_case__ , )
# Format the result to the format the metric expects.
if args.version_2_with_negative:
lowerCAmelCase_ : int = [
{"id": k, "prediction_text": v, "no_answer_probability": 0.0} for k, v in predictions.items()
]
else:
lowerCAmelCase_ : Tuple = [{"id": k, "prediction_text": v} for k, v in predictions.items()]
lowerCAmelCase_ : List[Any] = [{"id": ex["id"], "answers": ex[answer_column_name]} for ex in examples]
return EvalPrediction(predictions=snake_case__ , label_ids=snake_case__)
_lowercase = load_metric('''squad_v2''' if args.version_2_with_negative else '''squad''')
# Evaluation!
logger.info('''Loading ONNX model %s for evaluation''', args.onnx_model_path)
with open(engine_name, '''rb''') as f, trt.Runtime(TRT_LOGGER) as runtime, runtime.deserialize_cuda_engine(
f.read()
) as engine, engine.create_execution_context() as context:
# setup for TRT inferrence
for i in range(len(input_names)):
context.set_binding_shape(i, INPUT_SHAPE)
assert context.all_binding_shapes_specified
def UpperCamelCase ( snake_case__):
return trt.volume(engine.get_binding_shape(snake_case__)) * engine.get_binding_dtype(snake_case__).itemsize
# Allocate device memory for inputs and outputs.
_lowercase = [cuda.mem_alloc(binding_nbytes(binding)) for binding in engine if engine.binding_is_input(binding)]
# Allocate output buffer
_lowercase = cuda.pagelocked_empty(tuple(context.get_binding_shape(3)), dtype=np.floataa)
_lowercase = cuda.pagelocked_empty(tuple(context.get_binding_shape(4)), dtype=np.floataa)
_lowercase = cuda.mem_alloc(h_outputa.nbytes)
_lowercase = cuda.mem_alloc(h_outputa.nbytes)
# Create a stream in which to copy inputs/outputs and run inference.
_lowercase = cuda.Stream()
# Evaluation
logger.info('''***** Running Evaluation *****''')
logger.info(f" Num examples = {len(eval_dataset)}")
logger.info(f" Batch size = {args.per_device_eval_batch_size}")
_lowercase = 0.0
_lowercase = 0
_lowercase = timeit.default_timer()
_lowercase = None
for step, batch in enumerate(eval_dataloader):
_lowercase , _lowercase = model_infer(batch, context, d_inputs, h_outputa, h_outputa, d_outputa, d_outputa, stream)
total_time += infer_time
niter += 1
_lowercase , _lowercase = outputs
_lowercase = torch.tensor(start_logits)
_lowercase = torch.tensor(end_logits)
# necessary to pad predictions and labels for being gathered
_lowercase = accelerator.pad_across_processes(start_logits, dim=1, pad_index=-100)
_lowercase = accelerator.pad_across_processes(end_logits, dim=1, pad_index=-100)
_lowercase = (accelerator.gather(start_logits).cpu().numpy(), accelerator.gather(end_logits).cpu().numpy())
_lowercase = logits if all_preds is None else nested_concat(all_preds, logits, padding_index=-100)
if all_preds is not None:
_lowercase = nested_truncate(all_preds, len(eval_dataset))
_lowercase = timeit.default_timer() - start_time
logger.info(''' Evaluation done in total %f secs (%f sec per example)''', evalTime, evalTime / len(eval_dataset))
# Inference time from TRT
logger.info('''Average Inference Time = {:.3f} ms'''.format(total_time * 1000 / niter))
logger.info('''Total Inference Time = {:.3f} ms'''.format(total_time * 1000))
logger.info('''Total Number of Inference = %d''', niter)
_lowercase = post_processing_function(eval_examples, eval_dataset, all_preds)
_lowercase = metric.compute(predictions=prediction.predictions, references=prediction.label_ids)
logger.info(f"Evaluation metrics: {eval_metric}")
| 683 |
from __future__ import annotations
from collections.abc import Sequence
from typing import Literal
def UpperCamelCase ( snake_case__ , snake_case__):
lowerCAmelCase_ : Optional[int] = list(snake_case__)
lowerCAmelCase_ : Tuple = list(snake_case__)
lowerCAmelCase_ : List[str] = 0
for i in range(len(snake_case__)):
if lista[i] != lista[i]:
count += 1
lowerCAmelCase_ : Dict = "_"
if count > 1:
return False
else:
return "".join(snake_case__)
def UpperCamelCase ( snake_case__):
lowerCAmelCase_ : Union[str, Any] = []
while True:
lowerCAmelCase_ : Tuple = ["$"] * len(snake_case__)
lowerCAmelCase_ : Tuple = []
for i in range(len(snake_case__)):
for j in range(i + 1 , len(snake_case__)):
lowerCAmelCase_ : Optional[int] = compare_string(binary[i] , binary[j])
if k is False:
lowerCAmelCase_ : str = "*"
lowerCAmelCase_ : Tuple = "*"
temp.append("X")
for i in range(len(snake_case__)):
if checka[i] == "$":
pi.append(binary[i])
if len(snake_case__) == 0:
return pi
lowerCAmelCase_ : List[Any] = list(set(snake_case__))
def UpperCamelCase ( snake_case__ , snake_case__):
lowerCAmelCase_ : Optional[int] = []
for minterm in minterms:
lowerCAmelCase_ : Dict = ""
for _ in range(snake_case__):
lowerCAmelCase_ : Dict = str(minterm % 2) + string
minterm //= 2
temp.append(snake_case__)
return temp
def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__):
lowerCAmelCase_ : Optional[Any] = list(snake_case__)
lowerCAmelCase_ : Dict = list(snake_case__)
lowerCAmelCase_ : Dict = 0
for i in range(len(snake_case__)):
if lista[i] != lista[i]:
count_n += 1
return count_n == count
def UpperCamelCase ( snake_case__ , snake_case__):
lowerCAmelCase_ : Optional[Any] = []
lowerCAmelCase_ : Dict = [0] * len(snake_case__)
for i in range(len(chart[0])):
lowerCAmelCase_ : List[Any] = 0
lowerCAmelCase_ : int = -1
for j in range(len(snake_case__)):
if chart[j][i] == 1:
count += 1
lowerCAmelCase_ : Optional[int] = j
if count == 1:
lowerCAmelCase_ : Union[str, Any] = 1
for i in range(len(snake_case__)):
if select[i] == 1:
for j in range(len(chart[0])):
if chart[i][j] == 1:
for k in range(len(snake_case__)):
lowerCAmelCase_ : Tuple = 0
temp.append(prime_implicants[i])
while True:
lowerCAmelCase_ : Optional[Any] = 0
lowerCAmelCase_ : Dict = -1
lowerCAmelCase_ : Tuple = 0
for i in range(len(snake_case__)):
lowerCAmelCase_ : Dict = chart[i].count(1)
if count_n > max_n:
lowerCAmelCase_ : Optional[int] = count_n
lowerCAmelCase_ : Optional[Any] = i
if max_n == 0:
return temp
temp.append(prime_implicants[rem])
for i in range(len(chart[0])):
if chart[rem][i] == 1:
for j in range(len(snake_case__)):
lowerCAmelCase_ : Any = 0
def UpperCamelCase ( snake_case__ , snake_case__):
lowerCAmelCase_ : str = [[0 for x in range(len(snake_case__))] for x in range(len(snake_case__))]
for i in range(len(snake_case__)):
lowerCAmelCase_ : Optional[Any] = prime_implicants[i].count("_")
for j in range(len(snake_case__)):
if is_for_table(prime_implicants[i] , binary[j] , snake_case__):
lowerCAmelCase_ : Dict = 1
return chart
def UpperCamelCase ( ):
lowerCAmelCase_ : Optional[Any] = int(input("Enter the no. of variables\n"))
lowerCAmelCase_ : Tuple = [
float(snake_case__)
for x in input(
"Enter the decimal representation of Minterms 'Spaces Separated'\n").split()
]
lowerCAmelCase_ : Any = decimal_to_binary(snake_case__ , snake_case__)
lowerCAmelCase_ : Dict = check(snake_case__)
print("Prime Implicants are:")
print(snake_case__)
lowerCAmelCase_ : int = prime_implicant_chart(snake_case__ , snake_case__)
lowerCAmelCase_ : List[str] = selection(snake_case__ , snake_case__)
print("Essential Prime Implicants are:")
print(snake_case__)
if __name__ == "__main__":
import doctest
doctest.testmod()
main()
| 683 | 1 |
import torch
from diffusers import EulerDiscreteScheduler
from diffusers.utils import torch_device
from .test_schedulers import SchedulerCommonTest
class __snake_case ( snake_case__ ):
"""simple docstring"""
UpperCamelCase_ = (EulerDiscreteScheduler,)
UpperCamelCase_ = 1_0
def UpperCAmelCase_ ( self : Any ,**lowerCAmelCase__ : int ) -> Tuple:
'''simple docstring'''
lowerCAmelCase_ : int = {
"num_train_timesteps": 11_00,
"beta_start": 0.0_001,
"beta_end": 0.02,
"beta_schedule": "linear",
}
config.update(**lowerCAmelCase__ )
return config
def UpperCAmelCase_ ( self : str ) -> str:
'''simple docstring'''
for timesteps in [10, 50, 1_00, 10_00]:
self.check_over_configs(num_train_timesteps=lowerCAmelCase__ )
def UpperCAmelCase_ ( self : Tuple ) -> Union[str, Any]:
'''simple docstring'''
for beta_start, beta_end in zip([0.00_001, 0.0_001, 0.001] ,[0.0_002, 0.002, 0.02] ):
self.check_over_configs(beta_start=lowerCAmelCase__ ,beta_end=lowerCAmelCase__ )
def UpperCAmelCase_ ( self : int ) -> Optional[Any]:
'''simple docstring'''
for schedule in ["linear", "scaled_linear"]:
self.check_over_configs(beta_schedule=lowerCAmelCase__ )
def UpperCAmelCase_ ( self : Tuple ) -> int:
'''simple docstring'''
for prediction_type in ["epsilon", "v_prediction"]:
self.check_over_configs(prediction_type=lowerCAmelCase__ )
def UpperCAmelCase_ ( self : List[str] ) -> List[str]:
'''simple docstring'''
lowerCAmelCase_ : Tuple = self.scheduler_classes[0]
lowerCAmelCase_ : Tuple = self.get_scheduler_config()
lowerCAmelCase_ : str = scheduler_class(**lowerCAmelCase__ )
scheduler.set_timesteps(self.num_inference_steps )
lowerCAmelCase_ : List[str] = torch.manual_seed(0 )
lowerCAmelCase_ : Optional[Any] = self.dummy_model()
lowerCAmelCase_ : Any = self.dummy_sample_deter * scheduler.init_noise_sigma
lowerCAmelCase_ : List[Any] = sample.to(lowerCAmelCase__ )
for i, t in enumerate(scheduler.timesteps ):
lowerCAmelCase_ : Tuple = scheduler.scale_model_input(lowerCAmelCase__ ,lowerCAmelCase__ )
lowerCAmelCase_ : int = model(lowerCAmelCase__ ,lowerCAmelCase__ )
lowerCAmelCase_ : Optional[Any] = scheduler.step(lowerCAmelCase__ ,lowerCAmelCase__ ,lowerCAmelCase__ ,generator=lowerCAmelCase__ )
lowerCAmelCase_ : Optional[Any] = output.prev_sample
lowerCAmelCase_ : str = torch.sum(torch.abs(lowerCAmelCase__ ) )
lowerCAmelCase_ : Tuple = torch.mean(torch.abs(lowerCAmelCase__ ) )
assert abs(result_sum.item() - 10.0_807 ) < 1e-2
assert abs(result_mean.item() - 0.0_131 ) < 1e-3
def UpperCAmelCase_ ( self : List[str] ) -> str:
'''simple docstring'''
lowerCAmelCase_ : Tuple = self.scheduler_classes[0]
lowerCAmelCase_ : Optional[Any] = self.get_scheduler_config(prediction_type="v_prediction" )
lowerCAmelCase_ : List[str] = scheduler_class(**lowerCAmelCase__ )
scheduler.set_timesteps(self.num_inference_steps )
lowerCAmelCase_ : Optional[Any] = torch.manual_seed(0 )
lowerCAmelCase_ : Dict = self.dummy_model()
lowerCAmelCase_ : str = self.dummy_sample_deter * scheduler.init_noise_sigma
lowerCAmelCase_ : Any = sample.to(lowerCAmelCase__ )
for i, t in enumerate(scheduler.timesteps ):
lowerCAmelCase_ : Optional[Any] = scheduler.scale_model_input(lowerCAmelCase__ ,lowerCAmelCase__ )
lowerCAmelCase_ : Tuple = model(lowerCAmelCase__ ,lowerCAmelCase__ )
lowerCAmelCase_ : Union[str, Any] = scheduler.step(lowerCAmelCase__ ,lowerCAmelCase__ ,lowerCAmelCase__ ,generator=lowerCAmelCase__ )
lowerCAmelCase_ : Any = output.prev_sample
lowerCAmelCase_ : List[Any] = torch.sum(torch.abs(lowerCAmelCase__ ) )
lowerCAmelCase_ : List[str] = torch.mean(torch.abs(lowerCAmelCase__ ) )
assert abs(result_sum.item() - 0.0_002 ) < 1e-2
assert abs(result_mean.item() - 2.2_6_7_6e-0_6 ) < 1e-3
def UpperCAmelCase_ ( self : Tuple ) -> Tuple:
'''simple docstring'''
lowerCAmelCase_ : List[Any] = self.scheduler_classes[0]
lowerCAmelCase_ : Optional[Any] = self.get_scheduler_config()
lowerCAmelCase_ : Optional[int] = scheduler_class(**lowerCAmelCase__ )
scheduler.set_timesteps(self.num_inference_steps ,device=lowerCAmelCase__ )
lowerCAmelCase_ : Optional[int] = torch.manual_seed(0 )
lowerCAmelCase_ : Optional[Any] = self.dummy_model()
lowerCAmelCase_ : int = self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
lowerCAmelCase_ : List[str] = sample.to(lowerCAmelCase__ )
for t in scheduler.timesteps:
lowerCAmelCase_ : int = scheduler.scale_model_input(lowerCAmelCase__ ,lowerCAmelCase__ )
lowerCAmelCase_ : Dict = model(lowerCAmelCase__ ,lowerCAmelCase__ )
lowerCAmelCase_ : List[str] = scheduler.step(lowerCAmelCase__ ,lowerCAmelCase__ ,lowerCAmelCase__ ,generator=lowerCAmelCase__ )
lowerCAmelCase_ : Optional[Any] = output.prev_sample
lowerCAmelCase_ : str = torch.sum(torch.abs(lowerCAmelCase__ ) )
lowerCAmelCase_ : Tuple = torch.mean(torch.abs(lowerCAmelCase__ ) )
assert abs(result_sum.item() - 10.0_807 ) < 1e-2
assert abs(result_mean.item() - 0.0_131 ) < 1e-3
def UpperCAmelCase_ ( self : str ) -> Dict:
'''simple docstring'''
lowerCAmelCase_ : Optional[Any] = self.scheduler_classes[0]
lowerCAmelCase_ : List[Any] = self.get_scheduler_config()
lowerCAmelCase_ : List[Any] = scheduler_class(**lowerCAmelCase__ ,use_karras_sigmas=lowerCAmelCase__ )
scheduler.set_timesteps(self.num_inference_steps ,device=lowerCAmelCase__ )
lowerCAmelCase_ : Union[str, Any] = torch.manual_seed(0 )
lowerCAmelCase_ : List[Any] = self.dummy_model()
lowerCAmelCase_ : str = self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
lowerCAmelCase_ : Optional[Any] = sample.to(lowerCAmelCase__ )
for t in scheduler.timesteps:
lowerCAmelCase_ : List[str] = scheduler.scale_model_input(lowerCAmelCase__ ,lowerCAmelCase__ )
lowerCAmelCase_ : Optional[Any] = model(lowerCAmelCase__ ,lowerCAmelCase__ )
lowerCAmelCase_ : int = scheduler.step(lowerCAmelCase__ ,lowerCAmelCase__ ,lowerCAmelCase__ ,generator=lowerCAmelCase__ )
lowerCAmelCase_ : str = output.prev_sample
lowerCAmelCase_ : str = torch.sum(torch.abs(lowerCAmelCase__ ) )
lowerCAmelCase_ : Any = torch.mean(torch.abs(lowerCAmelCase__ ) )
assert abs(result_sum.item() - 124.52_299_499_511_719 ) < 1e-2
assert abs(result_mean.item() - 0.16_213_932_633_399_963 ) < 1e-3
| 683 |
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
_lowercase = logging.getLogger(__name__)
def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__ = None , snake_case__ = None , snake_case__ = None , snake_case__ = None , snake_case__ = None , snake_case__ = False , ):
lowerCAmelCase_ : List[Any] = bnb_quantization_config.load_in_abit
lowerCAmelCase_ : Optional[Any] = 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.")
lowerCAmelCase_ : List[str] = []
# custom device map
if isinstance(snake_case__ , snake_case__) and len(device_map.keys()) > 1:
lowerCAmelCase_ : Union[str, Any] = [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:
lowerCAmelCase_ : Union[str, Any] = get_keys_to_not_convert(snake_case__)
# add cpu modules to skip modules only for 4-bit modules
if load_in_abit:
bnb_quantization_config.skip_modules.extend(snake_case__)
lowerCAmelCase_ : Union[str, Any] = 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:
lowerCAmelCase_ : Optional[int] = []
lowerCAmelCase_ : int = bnb_quantization_config.keep_in_fpaa_modules
modules_to_not_convert.extend(snake_case__)
# compatibility with peft
lowerCAmelCase_ : Optional[int] = load_in_abit
lowerCAmelCase_ : List[str] = load_in_abit
lowerCAmelCase_ : Optional[int] = get_parameter_device(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.")
lowerCAmelCase_ : Union[str, Any] = replace_with_bnb_layers(snake_case__ , snake_case__ , modules_to_not_convert=snake_case__)
# convert param to the right dtype
lowerCAmelCase_ : Any = 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:
lowerCAmelCase_ : Optional[int] = name.replace(".weight" , "").replace(".bias" , "")
lowerCAmelCase_ : Optional[int] = getattr(snake_case__ , snake_case__ , snake_case__)
if param is not None:
param.to(torch.floataa)
elif torch.is_floating_point(snake_case__):
param.to(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():
lowerCAmelCase_ : str = replace_with_bnb_layers(
snake_case__ , snake_case__ , modules_to_not_convert=snake_case__)
lowerCAmelCase_ : Optional[int] = get_quantized_model_device_map(
snake_case__ , snake_case__ , snake_case__ , max_memory=snake_case__ , no_split_module_classes=snake_case__ , )
if offload_state_dict is None and device_map is not None and "disk" in device_map.values():
lowerCAmelCase_ : Optional[Any] = True
lowerCAmelCase_ : Optional[int] = any(x in list(device_map.values()) for x in ["cpu", "disk"])
load_checkpoint_in_model(
snake_case__ , snake_case__ , snake_case__ , dtype=bnb_quantization_config.torch_dtype , offload_folder=snake_case__ , offload_state_dict=snake_case__ , keep_in_fpaa_modules=bnb_quantization_config.keep_in_fpaa_modules , offload_abit_bnb=load_in_abit and offload , )
return dispatch_model(snake_case__ , device_map=snake_case__ , offload_dir=snake_case__)
def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__=None , snake_case__=None , snake_case__=None):
if device_map is None:
if torch.cuda.is_available():
lowerCAmelCase_ : Any = {"": 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(snake_case__ , 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'.")
lowerCAmelCase_ : Dict = {}
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)
})
lowerCAmelCase_ : List[str] = {}
lowerCAmelCase_ : Union[str, Any] = special_dtypes
lowerCAmelCase_ : Union[str, Any] = no_split_module_classes
lowerCAmelCase_ : Any = bnb_quantization_config.target_dtype
# get max_memory for each device.
if device_map != "sequential":
lowerCAmelCase_ : Tuple = get_balanced_memory(
snake_case__ , low_zero=(device_map == "balanced_low_0") , max_memory=snake_case__ , **snake_case__ , )
lowerCAmelCase_ : Tuple = max_memory
lowerCAmelCase_ : Optional[Any] = infer_auto_device_map(snake_case__ , **snake_case__)
if isinstance(snake_case__ , snake_case__):
# check if don't have any quantized module on the cpu
lowerCAmelCase_ : Union[str, Any] = bnb_quantization_config.skip_modules + bnb_quantization_config.keep_in_fpaa_modules
lowerCAmelCase_ : List[Any] = {
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(
"\n Some modules are dispatched on the CPU or the disk. Make sure you have enough GPU RAM to fit\n the quantized model. If you want to dispatch the model on the CPU or the disk while keeping\n these modules in `torch_dtype`, you need to pass a custom `device_map` to\n `load_and_quantize_model`. Check\n https://huggingface.co/docs/accelerate/main/en/usage_guides/quantization#offload-modules-to-cpu-and-disk\n for more details.\n ")
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 UpperCamelCase ( snake_case__ , snake_case__ , snake_case__=None , snake_case__=None):
if modules_to_not_convert is None:
lowerCAmelCase_ : List[str] = []
lowerCAmelCase_ , lowerCAmelCase_ : Tuple = _replace_with_bnb_layers(
snake_case__ , snake_case__ , snake_case__ , 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 UpperCamelCase ( snake_case__ , snake_case__ , snake_case__=None , snake_case__=None , ):
lowerCAmelCase_ : str = False
for name, module in model.named_children():
if current_key_name is None:
lowerCAmelCase_ : Optional[int] = []
current_key_name.append(snake_case__)
if isinstance(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`
lowerCAmelCase_ : Optional[int] = ".".join(snake_case__)
lowerCAmelCase_ : List[str] = 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:
lowerCAmelCase_ : List[Any] = False
break
if proceed:
# Load bnb module with empty weight and replace ``nn.Linear` module
if bnb_quantization_config.load_in_abit:
lowerCAmelCase_ : Tuple = bnb.nn.LinearabitLt(
module.in_features , module.out_features , module.bias is not None , has_fpaa_weights=snake_case__ , threshold=bnb_quantization_config.llm_inta_threshold , )
elif bnb_quantization_config.load_in_abit:
lowerCAmelCase_ : Dict = 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")
lowerCAmelCase_ : List[str] = module.weight.data
if module.bias is not None:
lowerCAmelCase_ : Any = module.bias.data
bnb_module.requires_grad_(snake_case__)
setattr(snake_case__ , snake_case__ , snake_case__)
lowerCAmelCase_ : List[str] = True
if len(list(module.children())) > 0:
lowerCAmelCase_ , lowerCAmelCase_ : List[Any] = _replace_with_bnb_layers(
snake_case__ , snake_case__ , snake_case__ , snake_case__)
lowerCAmelCase_ : Optional[int] = has_been_replaced | _has_been_replaced
# Remove the last key for recursion
current_key_name.pop(-1)
return model, has_been_replaced
def UpperCamelCase ( snake_case__):
# Create a copy of the model
with init_empty_weights():
lowerCAmelCase_ : List[Any] = deepcopy(snake_case__) # this has 0 cost since it is done inside `init_empty_weights` context manager`
lowerCAmelCase_ : Dict = find_tied_parameters(snake_case__)
# For compatibility with Accelerate < 0.18
if isinstance(snake_case__ , snake_case__):
lowerCAmelCase_ : List[str] = sum(list(tied_params.values()) , []) + list(tied_params.keys())
else:
lowerCAmelCase_ : Optional[Any] = sum(snake_case__ , [])
lowerCAmelCase_ : List[Any] = len(snake_case__) > 0
# Check if it is a base model
lowerCAmelCase_ : List[str] = False
if hasattr(snake_case__ , "base_model_prefix"):
lowerCAmelCase_ : Tuple = not hasattr(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
lowerCAmelCase_ : Union[str, Any] = list(model.named_children())
lowerCAmelCase_ : Optional[int] = [list_modules[-1][0]]
# add last module together with tied weights
lowerCAmelCase_ : Any = set(snake_case__) - set(snake_case__)
lowerCAmelCase_ : Tuple = list(set(snake_case__)) + list(snake_case__)
# remove ".weight" from the keys
lowerCAmelCase_ : List[str] = [".weight", ".bias"]
lowerCAmelCase_ : Tuple = []
for name in list_untouched:
for name_to_remove in names_to_remove:
if name_to_remove in name:
lowerCAmelCase_ : str = name.replace(snake_case__ , "")
filtered_module_names.append(snake_case__)
return filtered_module_names
def UpperCamelCase ( snake_case__):
for m in model.modules():
if isinstance(snake_case__ , bnb.nn.Linearabit):
return True
return False
def UpperCamelCase ( snake_case__):
return next(parameter.parameters()).device
def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__):
# 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(snake_case__ , snake_case__ , 0 , dtype=snake_case__ , value=snake_case__)
lowerCAmelCase_ : str = param_name
lowerCAmelCase_ : Tuple = model
if "." in tensor_name:
lowerCAmelCase_ : Dict = tensor_name.split(".")
for split in splits[:-1]:
lowerCAmelCase_ : Any = getattr(snake_case__ , snake_case__)
if new_module is None:
raise ValueError(F'''{module} has no attribute {split}.''')
lowerCAmelCase_ : Union[str, Any] = new_module
lowerCAmelCase_ : Any = splits[-1]
# offload weights
lowerCAmelCase_ : List[Any] = False
offload_weight(module._parameters[tensor_name] , snake_case__ , snake_case__ , index=snake_case__)
if hasattr(module._parameters[tensor_name] , "SCB"):
offload_weight(
module._parameters[tensor_name].SCB , param_name.replace("weight" , "SCB") , snake_case__ , index=snake_case__ , )
else:
offload_weight(snake_case__ , snake_case__ , snake_case__ , index=snake_case__)
offload_weight(snake_case__ , param_name.replace("weight" , "SCB") , snake_case__ , index=snake_case__)
set_module_tensor_to_device(snake_case__ , snake_case__ , "meta" , dtype=snake_case__ , value=torch.empty(*param.size()))
| 683 | 1 |
# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run
import doctest
import sys
import warnings
from os.path import abspath, dirname, join
import _pytest
from transformers.testing_utils import HfDoctestModule, HfDocTestParser
# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install -e .[dev]' when switching between checkouts and running tests.
_lowercase = abspath(join(dirname(__file__), '''src'''))
sys.path.insert(1, git_repo_path)
# silence FutureWarning warnings in tests since often we can't act on them until
# they become normal warnings - i.e. the tests still need to test the current functionality
warnings.simplefilter(action='''ignore''', category=FutureWarning)
def UpperCamelCase ( snake_case__):
config.addinivalue_line(
"markers" , "is_pt_tf_cross_test: mark test to run only when PT and TF interactions are tested")
config.addinivalue_line(
"markers" , "is_pt_flax_cross_test: mark test to run only when PT and FLAX interactions are tested")
config.addinivalue_line("markers" , "is_pipeline_test: mark test to run only when pipelines are tested")
config.addinivalue_line("markers" , "is_staging_test: mark test to run only in the staging environment")
config.addinivalue_line("markers" , "accelerate_tests: mark test that require accelerate")
config.addinivalue_line("markers" , "tool_tests: mark the tool tests that are run on their specific schedule")
def UpperCamelCase ( snake_case__):
from transformers.testing_utils import pytest_addoption_shared
pytest_addoption_shared(snake_case__)
def UpperCamelCase ( snake_case__):
from transformers.testing_utils import pytest_terminal_summary_main
lowerCAmelCase_ : int = terminalreporter.config.getoption("--make-reports")
if make_reports:
pytest_terminal_summary_main(snake_case__ , id=snake_case__)
def UpperCamelCase ( snake_case__ , snake_case__):
# If no tests are collected, pytest exists with code 5, which makes the CI fail.
if exitstatus == 5:
lowerCAmelCase_ : List[Any] = 0
# Doctest custom flag to ignore output.
_lowercase = doctest.register_optionflag('''IGNORE_RESULT''')
_lowercase = doctest.OutputChecker
class __snake_case ( snake_case__ ):
"""simple docstring"""
def UpperCAmelCase_ ( self : List[str] ,lowerCAmelCase__ : int ,lowerCAmelCase__ : Optional[int] ,lowerCAmelCase__ : Tuple ) -> Any:
'''simple docstring'''
if IGNORE_RESULT & optionflags:
return True
return OutputChecker.check_output(self ,lowerCAmelCase__ ,lowerCAmelCase__ ,lowerCAmelCase__ )
_lowercase = CustomOutputChecker
_lowercase = HfDoctestModule
_lowercase = HfDocTestParser
| 683 |
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
_lowercase = logging.get_logger(__name__)
class __snake_case ( snake_case__ ):
"""simple docstring"""
UpperCamelCase_ = ['input_features', 'is_longer']
def __init__( self : Optional[int] ,lowerCAmelCase__ : List[Any]=64 ,lowerCAmelCase__ : Any=4_80_00 ,lowerCAmelCase__ : Optional[Any]=4_80 ,lowerCAmelCase__ : List[str]=10 ,lowerCAmelCase__ : List[Any]=10_24 ,lowerCAmelCase__ : Union[str, Any]=0.0 ,lowerCAmelCase__ : Tuple=False ,lowerCAmelCase__ : float = 0 ,lowerCAmelCase__ : float = 1_40_00 ,lowerCAmelCase__ : int = None ,lowerCAmelCase__ : str = "fusion" ,lowerCAmelCase__ : str = "repeatpad" ,**lowerCAmelCase__ : Union[str, Any] ,) -> Union[str, Any]:
'''simple docstring'''
super().__init__(
feature_size=lowerCAmelCase__ ,sampling_rate=lowerCAmelCase__ ,padding_value=lowerCAmelCase__ ,return_attention_mask=lowerCAmelCase__ ,**lowerCAmelCase__ ,)
lowerCAmelCase_ : Optional[Any] = top_db
lowerCAmelCase_ : str = truncation
lowerCAmelCase_ : Tuple = padding
lowerCAmelCase_ : str = fft_window_size
lowerCAmelCase_ : Dict = (fft_window_size >> 1) + 1
lowerCAmelCase_ : Dict = hop_length
lowerCAmelCase_ : Any = max_length_s
lowerCAmelCase_ : int = max_length_s * sampling_rate
lowerCAmelCase_ : Optional[int] = sampling_rate
lowerCAmelCase_ : int = frequency_min
lowerCAmelCase_ : Optional[Any] = frequency_max
lowerCAmelCase_ : List[Any] = mel_filter_bank(
num_frequency_bins=self.nb_frequency_bins ,num_mel_filters=lowerCAmelCase__ ,min_frequency=lowerCAmelCase__ ,max_frequency=lowerCAmelCase__ ,sampling_rate=lowerCAmelCase__ ,norm=lowerCAmelCase__ ,mel_scale="htk" ,)
lowerCAmelCase_ : List[Any] = mel_filter_bank(
num_frequency_bins=self.nb_frequency_bins ,num_mel_filters=lowerCAmelCase__ ,min_frequency=lowerCAmelCase__ ,max_frequency=lowerCAmelCase__ ,sampling_rate=lowerCAmelCase__ ,norm="slaney" ,mel_scale="slaney" ,)
def UpperCAmelCase_ ( self : Dict ) -> Dict[str, Any]:
'''simple docstring'''
lowerCAmelCase_ : int = copy.deepcopy(self.__dict__ )
lowerCAmelCase_ : Optional[int] = 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 : Any ,lowerCAmelCase__ : np.array ,lowerCAmelCase__ : Optional[np.array] = None ) -> np.ndarray:
'''simple docstring'''
lowerCAmelCase_ : Optional[Any] = spectrogram(
lowerCAmelCase__ ,window_function(self.fft_window_size ,"hann" ) ,frame_length=self.fft_window_size ,hop_length=self.hop_length ,power=2.0 ,mel_filters=lowerCAmelCase__ ,log_mel="dB" ,)
return log_mel_spectrogram.T
def UpperCAmelCase_ ( self : Optional[int] ,lowerCAmelCase__ : Union[str, Any] ,lowerCAmelCase__ : Union[str, Any] ,lowerCAmelCase__ : Tuple ) -> Dict:
'''simple docstring'''
lowerCAmelCase_ : 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
lowerCAmelCase_ : List[Any] = [0]
if len(ranges[2] ) == 0:
# if the audio is too short, we just use the first chunk
lowerCAmelCase_ : List[Any] = [0]
# randomly choose index for each part
lowerCAmelCase_ : str = np.random.choice(ranges[0] )
lowerCAmelCase_ : Optional[Any] = np.random.choice(ranges[1] )
lowerCAmelCase_ : Any = np.random.choice(ranges[2] )
lowerCAmelCase_ : str = mel[idx_front : idx_front + chunk_frames, :]
lowerCAmelCase_ : Dict = mel[idx_middle : idx_middle + chunk_frames, :]
lowerCAmelCase_ : Optional[Any] = mel[idx_back : idx_back + chunk_frames, :]
lowerCAmelCase_ : List[str] = torch.tensor(mel[None, None, :] )
lowerCAmelCase_ : List[Any] = torch.nn.functional.interpolate(
lowerCAmelCase__ ,size=[chunk_frames, 64] ,mode="bilinear" ,align_corners=lowerCAmelCase__ )
lowerCAmelCase_ : Optional[int] = mel_shrink[0][0].numpy()
lowerCAmelCase_ : str = np.stack([mel_shrink, mel_chunk_front, mel_chunk_middle, mel_chunk_back] ,axis=0 )
return mel_fusion
def UpperCAmelCase_ ( self : Any ,lowerCAmelCase__ : np.array ,lowerCAmelCase__ : Union[str, Any] ,lowerCAmelCase__ : Optional[int] ,lowerCAmelCase__ : int ) -> np.array:
'''simple docstring'''
if waveform.shape[0] > max_length:
if truncation == "rand_trunc":
lowerCAmelCase_ : List[Any] = True
# random crop to max_length (for compatibility) -> this should be handled by self.pad
lowerCAmelCase_ : str = len(lowerCAmelCase__ ) - max_length
lowerCAmelCase_ : Any = np.random.randint(0 ,overflow + 1 )
lowerCAmelCase_ : Dict = waveform[idx : idx + max_length]
lowerCAmelCase_ : List[str] = self._np_extract_fbank_features(lowerCAmelCase__ ,self.mel_filters_slaney )[None, :]
elif truncation == "fusion":
lowerCAmelCase_ : Tuple = self._np_extract_fbank_features(lowerCAmelCase__ ,self.mel_filters )
lowerCAmelCase_ : str = max_length // self.hop_length + 1 # the +1 related to how the spectrogram is computed
lowerCAmelCase_ : List[str] = 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.
lowerCAmelCase_ : Dict = np.stack([mel, mel, mel, mel] ,axis=0 )
lowerCAmelCase_ : int = False
else:
lowerCAmelCase_ : str = self._random_mel_fusion(lowerCAmelCase__ ,lowerCAmelCase__ ,lowerCAmelCase__ )
lowerCAmelCase_ : Any = True
else:
raise NotImplementedError(f'''data_truncating {truncation} not implemented''' )
else:
lowerCAmelCase_ : Dict = 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":
lowerCAmelCase_ : List[Any] = int(max_length / len(lowerCAmelCase__ ) )
lowerCAmelCase_ : int = np.stack(np.tile(lowerCAmelCase__ ,n_repeat + 1 ) )[:max_length]
if padding == "repeatpad":
lowerCAmelCase_ : Optional[Any] = int(max_length / len(lowerCAmelCase__ ) )
lowerCAmelCase_ : Tuple = np.stack(np.tile(lowerCAmelCase__ ,lowerCAmelCase__ ) )
lowerCAmelCase_ : List[Any] = np.pad(lowerCAmelCase__ ,(0, max_length - waveform.shape[0]) ,mode="constant" ,constant_values=0 )
if truncation == "fusion":
lowerCAmelCase_ : int = self._np_extract_fbank_features(lowerCAmelCase__ ,self.mel_filters )
lowerCAmelCase_ : Tuple = np.stack([input_mel, input_mel, input_mel, input_mel] ,axis=0 )
else:
lowerCAmelCase_ : str = self._np_extract_fbank_features(lowerCAmelCase__ ,self.mel_filters_slaney )[None, :]
return input_mel, longer
def __call__( self : int ,lowerCAmelCase__ : Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]] ,lowerCAmelCase__ : str = None ,lowerCAmelCase__ : Optional[str] = None ,lowerCAmelCase__ : Optional[int] = None ,lowerCAmelCase__ : Optional[int] = None ,lowerCAmelCase__ : Optional[Union[str, TensorType]] = None ,**lowerCAmelCase__ : List[Any] ,) -> BatchFeature:
'''simple docstring'''
lowerCAmelCase_ : List[str] = truncation if truncation is not None else self.truncation
lowerCAmelCase_ : List[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." )
lowerCAmelCase_ : Dict = isinstance(lowerCAmelCase__ ,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}''' )
lowerCAmelCase_ : Dict = is_batched_numpy or (
isinstance(lowerCAmelCase__ ,(list, tuple) ) and (isinstance(raw_speech[0] ,(np.ndarray, tuple, list) ))
)
if is_batched:
lowerCAmelCase_ : List[str] = [np.asarray(lowerCAmelCase__ ,dtype=np.floataa ) for speech in raw_speech]
elif not is_batched and not isinstance(lowerCAmelCase__ ,np.ndarray ):
lowerCAmelCase_ : Tuple = np.asarray(lowerCAmelCase__ ,dtype=np.floataa )
elif isinstance(lowerCAmelCase__ ,np.ndarray ) and raw_speech.dtype is np.dtype(np.floataa ):
lowerCAmelCase_ : Any = raw_speech.astype(np.floataa )
# always return batch
if not is_batched:
lowerCAmelCase_ : Any = [np.asarray(lowerCAmelCase__ )]
# convert to mel spectrogram, truncate and pad if needed.
lowerCAmelCase_ : Optional[Any] = [
self._get_input_mel(lowerCAmelCase__ ,max_length if max_length else self.nb_max_samples ,lowerCAmelCase__ ,lowerCAmelCase__ )
for waveform in raw_speech
]
lowerCAmelCase_ : str = []
lowerCAmelCase_ : str = []
for mel, longer in padded_inputs:
input_mel.append(lowerCAmelCase__ )
is_longer.append(lowerCAmelCase__ )
if truncation == "fusion" and sum(lowerCAmelCase__ ) == 0:
# if no audio is longer than 10s, then randomly select one audio to be longer
lowerCAmelCase_ : Any = np.random.randint(0 ,len(lowerCAmelCase__ ) )
lowerCAmelCase_ : Dict = True
if isinstance(input_mel[0] ,lowerCAmelCase__ ):
lowerCAmelCase_ : Optional[int] = [np.asarray(lowerCAmelCase__ ,dtype=np.floataa ) for feature in input_mel]
# is_longer is a list of bool
lowerCAmelCase_ : List[Any] = [[longer] for longer in is_longer]
lowerCAmelCase_ : Optional[Any] = {"input_features": input_mel, "is_longer": is_longer}
lowerCAmelCase_ : Dict = BatchFeature(lowerCAmelCase__ )
if return_tensors is not None:
lowerCAmelCase_ : List[str] = input_features.convert_to_tensors(lowerCAmelCase__ )
return input_features
| 683 | 1 |
def UpperCamelCase ( snake_case__ , snake_case__):
lowerCAmelCase_ : Union[str, Any] = 1 # To kept the Calculated Value
# Since C(n, k) = C(n, n-k)
if k > (n - k):
lowerCAmelCase_ : Any = n - k
# Calculate C(n,k)
for i in range(snake_case__):
result *= n - i
result //= i + 1
return result
def UpperCamelCase ( snake_case__):
return binomial_coefficient(2 * node_count , snake_case__) // (node_count + 1)
def UpperCamelCase ( snake_case__):
if n < 0:
raise ValueError("factorial() not defined for negative values")
lowerCAmelCase_ : Union[str, Any] = 1
for i in range(1 , n + 1):
result *= i
return result
def UpperCamelCase ( snake_case__):
return catalan_number(snake_case__) * factorial(snake_case__)
if __name__ == "__main__":
_lowercase = int(input('''Enter the number of nodes: ''').strip() or 0)
if node_count <= 0:
raise ValueError('''We need some nodes to work with.''')
print(
f"Given {node_count} nodes, there are {binary_tree_count(node_count)} "
f"binary trees and {catalan_number(node_count)} binary search trees."
)
| 683 |
from multiprocessing import Lock, Pipe, Process
# lock used to ensure that two processes do not access a pipe at the same time
_lowercase = Lock()
def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__):
global process_lock
# we perform n swaps since after n swaps we know we are sorted
# we *could* stop early if we are sorted already, but it takes as long to
# find out we are sorted as it does to sort the list with this algorithm
for i in range(0 , 10):
if (i + position) % 2 == 0 and r_send is not None:
# send your value to your right neighbor
process_lock.acquire()
r_send[1].send(snake_case__)
process_lock.release()
# receive your right neighbor's value
process_lock.acquire()
lowerCAmelCase_ : Optional[Any] = rr_cv[0].recv()
process_lock.release()
# take the lower value since you are on the left
lowerCAmelCase_ : Any = min(snake_case__ , snake_case__)
elif (i + position) % 2 != 0 and l_send is not None:
# send your value to your left neighbor
process_lock.acquire()
l_send[1].send(snake_case__)
process_lock.release()
# receive your left neighbor's value
process_lock.acquire()
lowerCAmelCase_ : str = lr_cv[0].recv()
process_lock.release()
# take the higher value since you are on the right
lowerCAmelCase_ : Dict = max(snake_case__ , snake_case__)
# after all swaps are performed, send the values back to main
result_pipe[1].send(snake_case__)
def UpperCamelCase ( snake_case__):
lowerCAmelCase_ : Union[str, Any] = []
lowerCAmelCase_ : int = []
# initialize the list of pipes where the values will be retrieved
for _ in arr:
result_pipe.append(Pipe())
# creates the processes
# the first and last process only have one neighbor so they are made outside
# of the loop
lowerCAmelCase_ : Tuple = Pipe()
lowerCAmelCase_ : Optional[int] = Pipe()
process_array_.append(
Process(
target=snake_case__ , args=(0, arr[0], None, temp_rs, None, temp_rr, result_pipe[0]) , ))
lowerCAmelCase_ : int = temp_rs
lowerCAmelCase_ : List[Any] = temp_rr
for i in range(1 , len(snake_case__) - 1):
lowerCAmelCase_ : Dict = Pipe()
lowerCAmelCase_ : List[str] = Pipe()
process_array_.append(
Process(
target=snake_case__ , args=(i, arr[i], temp_ls, temp_rs, temp_lr, temp_rr, result_pipe[i]) , ))
lowerCAmelCase_ : Dict = temp_rs
lowerCAmelCase_ : Optional[Any] = temp_rr
process_array_.append(
Process(
target=snake_case__ , args=(
len(snake_case__) - 1,
arr[len(snake_case__) - 1],
temp_ls,
None,
temp_lr,
None,
result_pipe[len(snake_case__) - 1],
) , ))
# start the processes
for p in process_array_:
p.start()
# wait for the processes to end and write their values to the list
for p in range(0 , len(snake_case__)):
lowerCAmelCase_ : Union[str, Any] = result_pipe[p][0].recv()
process_array_[p].join()
return arr
def UpperCamelCase ( ):
lowerCAmelCase_ : Optional[Any] = list(range(10 , 0 , -1))
print("Initial List")
print(*snake_case__)
lowerCAmelCase_ : Tuple = odd_even_transposition(snake_case__)
print("Sorted List\n")
print(*snake_case__)
if __name__ == "__main__":
main()
| 683 | 1 |
from arguments import InitializationArguments
from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer, HfArgumentParser
# Configuration
_lowercase = HfArgumentParser(InitializationArguments)
_lowercase = parser.parse_args()
# Load codeparrot tokenizer trained for Python code tokenization
_lowercase = AutoTokenizer.from_pretrained(args.tokenizer_name)
# Config: "scale_attn_by_layer_idx" and "reorder_and_upcast_attn" are Mistral stability tweaks
_lowercase = {
'''vocab_size''': len(tokenizer),
'''scale_attn_by_inverse_layer_idx''': True,
'''reorder_and_upcast_attn''': True,
}
# Load model config (GPT-2 large in this case)
_lowercase = AutoConfig.from_pretrained(args.config_name, **config_kwargs)
# Initialize new model with config
_lowercase = AutoModelForCausalLM.from_config(config)
# Save model to the hub
model.save_pretrained(args.model_name, push_to_hub=args.push_to_hub)
| 683 |
from typing import Any
def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ , ):
_validation(
snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ , )
# Creates data structures and fill initial step
lowerCAmelCase_ : dict = {}
lowerCAmelCase_ : dict = {}
for state in states_space:
lowerCAmelCase_ : List[Any] = observations_space[0]
lowerCAmelCase_ : int = (
initial_probabilities[state] * emission_probabilities[state][observation]
)
lowerCAmelCase_ : Dict = None
# Fills the data structure with the probabilities of
# different transitions and pointers to previous states
for o in range(1 , len(snake_case__)):
lowerCAmelCase_ : List[Any] = observations_space[o]
lowerCAmelCase_ : Optional[Any] = observations_space[o - 1]
for state in states_space:
# Calculates the argmax for probability function
lowerCAmelCase_ : List[Any] = ""
lowerCAmelCase_ : Tuple = -1
for k_state in states_space:
lowerCAmelCase_ : int = (
probabilities[(k_state, prior_observation)]
* transition_probabilities[k_state][state]
* emission_probabilities[state][observation]
)
if probability > max_probability:
lowerCAmelCase_ : List[str] = probability
lowerCAmelCase_ : Optional[Any] = k_state
# Update probabilities and pointers dicts
lowerCAmelCase_ : Union[str, Any] = (
probabilities[(arg_max, prior_observation)]
* transition_probabilities[arg_max][state]
* emission_probabilities[state][observation]
)
lowerCAmelCase_ : Any = arg_max
# The final observation
lowerCAmelCase_ : List[Any] = observations_space[len(snake_case__) - 1]
# argmax for given final observation
lowerCAmelCase_ : List[str] = ""
lowerCAmelCase_ : List[str] = -1
for k_state in states_space:
lowerCAmelCase_ : List[str] = probabilities[(k_state, final_observation)]
if probability > max_probability:
lowerCAmelCase_ : List[str] = probability
lowerCAmelCase_ : Tuple = k_state
lowerCAmelCase_ : str = arg_max
# Process pointers backwards
lowerCAmelCase_ : int = last_state
lowerCAmelCase_ : int = []
for o in range(len(snake_case__) - 1 , -1 , -1):
result.append(snake_case__)
lowerCAmelCase_ : Optional[Any] = pointers[previous, observations_space[o]]
result.reverse()
return result
def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ , ):
_validate_not_empty(
snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ , )
_validate_lists(snake_case__ , snake_case__)
_validate_dicts(
snake_case__ , snake_case__ , snake_case__)
def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ , ):
if not all(
[
observations_space,
states_space,
initial_probabilities,
transition_probabilities,
emission_probabilities,
]):
raise ValueError("There's an empty parameter")
def UpperCamelCase ( snake_case__ , snake_case__):
_validate_list(snake_case__ , "observations_space")
_validate_list(snake_case__ , "states_space")
def UpperCamelCase ( snake_case__ , snake_case__):
if not isinstance(_object , snake_case__):
lowerCAmelCase_ : Optional[Any] = F'''{var_name} must be a list'''
raise ValueError(snake_case__)
else:
for x in _object:
if not isinstance(snake_case__ , snake_case__):
lowerCAmelCase_ : Optional[Any] = F'''{var_name} must be a list of strings'''
raise ValueError(snake_case__)
def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__ , ):
_validate_dict(snake_case__ , "initial_probabilities" , snake_case__)
_validate_nested_dict(snake_case__ , "transition_probabilities")
_validate_nested_dict(snake_case__ , "emission_probabilities")
def UpperCamelCase ( snake_case__ , snake_case__):
_validate_dict(_object , snake_case__ , snake_case__)
for x in _object.values():
_validate_dict(snake_case__ , snake_case__ , snake_case__ , snake_case__)
def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__ , snake_case__ = False):
if not isinstance(_object , snake_case__):
lowerCAmelCase_ : List[str] = F'''{var_name} must be a dict'''
raise ValueError(snake_case__)
if not all(isinstance(snake_case__ , snake_case__) for x in _object):
lowerCAmelCase_ : Dict = F'''{var_name} all keys must be strings'''
raise ValueError(snake_case__)
if not all(isinstance(snake_case__ , snake_case__) for x in _object.values()):
lowerCAmelCase_ : Union[str, Any] = "nested dictionary " if nested else ""
lowerCAmelCase_ : Any = F'''{var_name} {nested_text}all values must be {value_type.__name__}'''
raise ValueError(snake_case__)
if __name__ == "__main__":
from doctest import testmod
testmod()
| 683 | 1 |
import inspect
import unittest
from transformers import RegNetConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from transformers.utils import cached_property, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor
if is_flax_available():
import jax
import jax.numpy as jnp
from transformers.models.regnet.modeling_flax_regnet import FlaxRegNetForImageClassification, FlaxRegNetModel
if is_vision_available():
from PIL import Image
from transformers import AutoImageProcessor
class __snake_case ( unittest.TestCase ):
"""simple docstring"""
def __init__( self : int ,lowerCAmelCase__ : List[str] ,lowerCAmelCase__ : List[Any]=3 ,lowerCAmelCase__ : Any=32 ,lowerCAmelCase__ : Optional[Any]=3 ,lowerCAmelCase__ : Union[str, Any]=10 ,lowerCAmelCase__ : int=[10, 20, 30, 40] ,lowerCAmelCase__ : int=[1, 1, 2, 1] ,lowerCAmelCase__ : str=True ,lowerCAmelCase__ : Tuple=True ,lowerCAmelCase__ : Dict="relu" ,lowerCAmelCase__ : Optional[Any]=3 ,lowerCAmelCase__ : Dict=None ,) -> Dict:
'''simple docstring'''
lowerCAmelCase_ : List[Any] = parent
lowerCAmelCase_ : Optional[Any] = batch_size
lowerCAmelCase_ : Tuple = image_size
lowerCAmelCase_ : Tuple = num_channels
lowerCAmelCase_ : Optional[int] = embeddings_size
lowerCAmelCase_ : List[str] = hidden_sizes
lowerCAmelCase_ : Union[str, Any] = depths
lowerCAmelCase_ : int = is_training
lowerCAmelCase_ : int = use_labels
lowerCAmelCase_ : Dict = hidden_act
lowerCAmelCase_ : List[str] = num_labels
lowerCAmelCase_ : Tuple = scope
lowerCAmelCase_ : int = len(lowerCAmelCase__ )
def UpperCAmelCase_ ( self : Union[str, Any] ) -> str:
'''simple docstring'''
lowerCAmelCase_ : Tuple = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
lowerCAmelCase_ : Tuple = self.get_config()
return config, pixel_values
def UpperCAmelCase_ ( self : int ) -> Optional[Any]:
'''simple docstring'''
return RegNetConfig(
num_channels=self.num_channels ,embeddings_size=self.embeddings_size ,hidden_sizes=self.hidden_sizes ,depths=self.depths ,hidden_act=self.hidden_act ,num_labels=self.num_labels ,image_size=self.image_size ,)
def UpperCAmelCase_ ( self : Union[str, Any] ,lowerCAmelCase__ : Tuple ,lowerCAmelCase__ : Any ) -> Dict:
'''simple docstring'''
lowerCAmelCase_ : str = FlaxRegNetModel(config=lowerCAmelCase__ )
lowerCAmelCase_ : Optional[Any] = model(lowerCAmelCase__ )
# Output shape (b, c, h, w)
self.parent.assertEqual(
result.last_hidden_state.shape ,(self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32) ,)
def UpperCAmelCase_ ( self : int ,lowerCAmelCase__ : Optional[int] ,lowerCAmelCase__ : List[Any] ) -> Union[str, Any]:
'''simple docstring'''
lowerCAmelCase_ : Union[str, Any] = self.num_labels
lowerCAmelCase_ : Any = FlaxRegNetForImageClassification(config=lowerCAmelCase__ )
lowerCAmelCase_ : List[str] = model(lowerCAmelCase__ )
self.parent.assertEqual(result.logits.shape ,(self.batch_size, self.num_labels) )
def UpperCAmelCase_ ( self : Any ) -> str:
'''simple docstring'''
lowerCAmelCase_ : int = self.prepare_config_and_inputs()
lowerCAmelCase_ , lowerCAmelCase_ : Tuple = config_and_inputs
lowerCAmelCase_ : Dict = {"pixel_values": pixel_values}
return config, inputs_dict
@require_flax
class __snake_case ( snake_case__ , unittest.TestCase ):
"""simple docstring"""
UpperCamelCase_ = (FlaxRegNetModel, FlaxRegNetForImageClassification) if is_flax_available() else ()
UpperCamelCase_ = False
UpperCamelCase_ = False
UpperCamelCase_ = False
def UpperCAmelCase_ ( self : Optional[Any] ) -> None:
'''simple docstring'''
lowerCAmelCase_ : List[Any] = FlaxRegNetModelTester(self )
lowerCAmelCase_ : int = ConfigTester(self ,config_class=lowerCAmelCase__ ,has_text_modality=lowerCAmelCase__ )
def UpperCAmelCase_ ( self : str ) -> Optional[int]:
'''simple docstring'''
self.create_and_test_config_common_properties()
self.config_tester.create_and_test_config_to_json_string()
self.config_tester.create_and_test_config_to_json_file()
self.config_tester.create_and_test_config_from_and_save_pretrained()
self.config_tester.create_and_test_config_with_num_labels()
self.config_tester.check_config_can_be_init_without_params()
self.config_tester.check_config_arguments_init()
def UpperCAmelCase_ ( self : Optional[int] ) -> str:
'''simple docstring'''
return
def UpperCAmelCase_ ( self : List[str] ) -> List[Any]:
'''simple docstring'''
lowerCAmelCase_ : int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*lowerCAmelCase__ )
def UpperCAmelCase_ ( self : Dict ) -> Union[str, Any]:
'''simple docstring'''
lowerCAmelCase_ : int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*lowerCAmelCase__ )
@unittest.skip(reason="RegNet does not use inputs_embeds" )
def UpperCAmelCase_ ( self : Optional[int] ) -> Optional[Any]:
'''simple docstring'''
pass
@unittest.skip(reason="RegNet does not support input and output embeddings" )
def UpperCAmelCase_ ( self : List[Any] ) -> Union[str, Any]:
'''simple docstring'''
pass
def UpperCAmelCase_ ( self : Optional[int] ) -> Dict:
'''simple docstring'''
lowerCAmelCase_ , lowerCAmelCase_ : Tuple = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowerCAmelCase_ : Optional[Any] = model_class(lowerCAmelCase__ )
lowerCAmelCase_ : Optional[Any] = inspect.signature(model.__call__ )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
lowerCAmelCase_ : List[Any] = [*signature.parameters.keys()]
lowerCAmelCase_ : Union[str, Any] = ["pixel_values"]
self.assertListEqual(arg_names[:1] ,lowerCAmelCase__ )
def UpperCAmelCase_ ( self : Union[str, Any] ) -> Optional[int]:
'''simple docstring'''
def check_hidden_states_output(lowerCAmelCase__ : Union[str, Any] ,lowerCAmelCase__ : List[Any] ,lowerCAmelCase__ : Union[str, Any] ):
lowerCAmelCase_ : List[Any] = model_class(lowerCAmelCase__ )
lowerCAmelCase_ : Tuple = model(**self._prepare_for_class(lowerCAmelCase__ ,lowerCAmelCase__ ) )
lowerCAmelCase_ : Tuple = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
lowerCAmelCase_ : Any = self.model_tester.num_stages
self.assertEqual(len(lowerCAmelCase__ ) ,expected_num_stages + 1 )
lowerCAmelCase_ , lowerCAmelCase_ : Optional[int] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowerCAmelCase_ : List[str] = True
check_hidden_states_output(lowerCAmelCase__ ,lowerCAmelCase__ ,lowerCAmelCase__ )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
lowerCAmelCase_ : Dict = True
check_hidden_states_output(lowerCAmelCase__ ,lowerCAmelCase__ ,lowerCAmelCase__ )
def UpperCAmelCase_ ( self : Tuple ) -> Union[str, Any]:
'''simple docstring'''
lowerCAmelCase_ , lowerCAmelCase_ : Optional[int] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__ ):
lowerCAmelCase_ : Optional[Any] = self._prepare_for_class(lowerCAmelCase__ ,lowerCAmelCase__ )
lowerCAmelCase_ : str = model_class(lowerCAmelCase__ )
@jax.jit
def model_jitted(lowerCAmelCase__ : int ,**lowerCAmelCase__ : Optional[int] ):
return model(pixel_values=lowerCAmelCase__ ,**lowerCAmelCase__ )
with self.subTest("JIT Enabled" ):
lowerCAmelCase_ : int = model_jitted(**lowerCAmelCase__ ).to_tuple()
with self.subTest("JIT Disabled" ):
with jax.disable_jit():
lowerCAmelCase_ : Tuple = model_jitted(**lowerCAmelCase__ ).to_tuple()
self.assertEqual(len(lowerCAmelCase__ ) ,len(lowerCAmelCase__ ) )
for jitted_output, output in zip(lowerCAmelCase__ ,lowerCAmelCase__ ):
self.assertEqual(jitted_output.shape ,output.shape )
def UpperCamelCase ( ):
lowerCAmelCase_ : List[Any] = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
return image
@require_flax
class __snake_case ( unittest.TestCase ):
"""simple docstring"""
@cached_property
def UpperCAmelCase_ ( self : Tuple ) -> int:
'''simple docstring'''
return AutoImageProcessor.from_pretrained("facebook/regnet-y-040" ) if is_vision_available() else None
@slow
def UpperCAmelCase_ ( self : Optional[Any] ) -> Union[str, Any]:
'''simple docstring'''
lowerCAmelCase_ : Union[str, Any] = FlaxRegNetForImageClassification.from_pretrained("facebook/regnet-y-040" )
lowerCAmelCase_ : int = self.default_image_processor
lowerCAmelCase_ : List[str] = prepare_img()
lowerCAmelCase_ : int = image_processor(images=lowerCAmelCase__ ,return_tensors="np" )
lowerCAmelCase_ : List[Any] = model(**lowerCAmelCase__ )
# verify the logits
lowerCAmelCase_ : Any = (1, 10_00)
self.assertEqual(outputs.logits.shape ,lowerCAmelCase__ )
lowerCAmelCase_ : List[str] = jnp.array([-0.4_180, -1.5_051, -3.4_836] )
self.assertTrue(jnp.allclose(outputs.logits[0, :3] ,lowerCAmelCase__ ,atol=1e-4 ) )
| 683 |
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import torch
from ..models.speechta import SpeechTaForTextToSpeech, SpeechTaHifiGan, SpeechTaProcessor
from ..utils import is_datasets_available
from .base import PipelineTool
if is_datasets_available():
from datasets import load_dataset
class __snake_case ( snake_case__ ):
"""simple docstring"""
UpperCamelCase_ = 'microsoft/speecht5_tts'
UpperCamelCase_ = (
'This is a tool that reads an English text out loud. It takes an input named `text` which should contain the '
'text to read (in English) and returns a waveform object containing the sound.'
)
UpperCamelCase_ = 'text_reader'
UpperCamelCase_ = SpeechTaProcessor
UpperCamelCase_ = SpeechTaForTextToSpeech
UpperCamelCase_ = SpeechTaHifiGan
UpperCamelCase_ = ['text']
UpperCamelCase_ = ['audio']
def UpperCAmelCase_ ( self : Dict ) -> Any:
'''simple docstring'''
if self.post_processor is None:
lowerCAmelCase_ : Any = "microsoft/speecht5_hifigan"
super().setup()
def UpperCAmelCase_ ( self : List[Any] ,lowerCAmelCase__ : Optional[int] ,lowerCAmelCase__ : Optional[int]=None ) -> Tuple:
'''simple docstring'''
lowerCAmelCase_ : Any = self.pre_processor(text=lowerCAmelCase__ ,return_tensors="pt" ,truncation=lowerCAmelCase__ )
if speaker_embeddings is None:
if not is_datasets_available():
raise ImportError("Datasets needs to be installed if not passing speaker embeddings." )
lowerCAmelCase_ : str = load_dataset("Matthijs/cmu-arctic-xvectors" ,split="validation" )
lowerCAmelCase_ : List[Any] = torch.tensor(embeddings_dataset[73_05]["xvector"] ).unsqueeze(0 )
return {"input_ids": inputs["input_ids"], "speaker_embeddings": speaker_embeddings}
def UpperCAmelCase_ ( self : Dict ,lowerCAmelCase__ : Union[str, Any] ) -> Union[str, Any]:
'''simple docstring'''
with torch.no_grad():
return self.model.generate_speech(**lowerCAmelCase__ )
def UpperCAmelCase_ ( self : Union[str, Any] ,lowerCAmelCase__ : str ) -> Any:
'''simple docstring'''
with torch.no_grad():
return self.post_processor(lowerCAmelCase__ ).cpu().detach()
| 683 | 1 |
import argparse
import logging
import sys
from unittest.mock import patch
import run_glue_deebert
from transformers.testing_utils import TestCasePlus, get_gpu_count, require_torch_non_multi_gpu, slow
logging.basicConfig(level=logging.DEBUG)
_lowercase = logging.getLogger()
def UpperCamelCase ( ):
lowerCAmelCase_ : Any = argparse.ArgumentParser()
parser.add_argument("-f")
lowerCAmelCase_ : List[Any] = parser.parse_args()
return args.f
class __snake_case ( snake_case__ ):
"""simple docstring"""
def UpperCAmelCase_ ( self : Optional[Any] ) -> None:
'''simple docstring'''
lowerCAmelCase_ : Dict = logging.StreamHandler(sys.stdout )
logger.addHandler(lowerCAmelCase__ )
def UpperCAmelCase_ ( self : List[str] ,lowerCAmelCase__ : Tuple ) -> Union[str, Any]:
'''simple docstring'''
lowerCAmelCase_ : List[Any] = get_gpu_count()
if n_gpu > 1:
pass
# XXX: doesn't quite work with n_gpu > 1 https://github.com/huggingface/transformers/issues/10560
# script = f"{self.examples_dir_str}/research_projects/deebert/run_glue_deebert.py"
# distributed_args = f"-m torch.distributed.launch --nproc_per_node={n_gpu} {script}".split()
# cmd = [sys.executable] + distributed_args + args
# execute_subprocess_async(cmd, env=self.get_env())
# XXX: test the results - need to save them first into .json file
else:
args.insert(0 ,"run_glue_deebert.py" )
with patch.object(lowerCAmelCase__ ,"argv" ,lowerCAmelCase__ ):
lowerCAmelCase_ : List[str] = run_glue_deebert.main()
for value in result.values():
self.assertGreaterEqual(lowerCAmelCase__ ,0.666 )
@slow
@require_torch_non_multi_gpu
def UpperCAmelCase_ ( self : Optional[Any] ) -> Dict:
'''simple docstring'''
lowerCAmelCase_ : Optional[int] = "\n --model_type roberta\n --model_name_or_path roberta-base\n --task_name MRPC\n --do_train\n --do_eval\n --do_lower_case\n --data_dir ./tests/fixtures/tests_samples/MRPC/\n --max_seq_length 128\n --per_gpu_eval_batch_size=1\n --per_gpu_train_batch_size=8\n --learning_rate 2e-4\n --num_train_epochs 3\n --overwrite_output_dir\n --seed 42\n --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage\n --plot_data_dir ./examples/deebert/results/\n --save_steps 0\n --overwrite_cache\n --eval_after_first_stage\n ".split()
self.run_and_check(lowerCAmelCase__ )
lowerCAmelCase_ : Dict = "\n --model_type roberta\n --model_name_or_path ./examples/deebert/saved_models/roberta-base/MRPC/two_stage\n --task_name MRPC\n --do_eval\n --do_lower_case\n --data_dir ./tests/fixtures/tests_samples/MRPC/\n --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage\n --plot_data_dir ./examples/deebert/results/\n --max_seq_length 128\n --eval_each_highway\n --eval_highway\n --overwrite_cache\n --per_gpu_eval_batch_size=1\n ".split()
self.run_and_check(lowerCAmelCase__ )
lowerCAmelCase_ : Dict = "\n --model_type roberta\n --model_name_or_path ./examples/deebert/saved_models/roberta-base/MRPC/two_stage\n --task_name MRPC\n --do_eval\n --do_lower_case\n --data_dir ./tests/fixtures/tests_samples/MRPC/\n --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage\n --plot_data_dir ./examples/deebert/results/\n --max_seq_length 128\n --early_exit_entropy 0.1\n --eval_highway\n --overwrite_cache\n --per_gpu_eval_batch_size=1\n ".split()
self.run_and_check(lowerCAmelCase__ )
| 683 |
import argparse
import collections
import json
import os
import re
import string
import sys
import numpy as np
_lowercase = re.compile(r'''\b(a|an|the)\b''', re.UNICODE)
_lowercase = None
def UpperCamelCase ( ):
lowerCAmelCase_ : Optional[Any] = argparse.ArgumentParser("Official evaluation script for SQuAD version 2.0.")
parser.add_argument("data_file" , metavar="data.json" , help="Input data JSON file.")
parser.add_argument("pred_file" , metavar="pred.json" , help="Model predictions.")
parser.add_argument(
"--out-file" , "-o" , metavar="eval.json" , help="Write accuracy metrics to file (default is stdout).")
parser.add_argument(
"--na-prob-file" , "-n" , metavar="na_prob.json" , help="Model estimates of probability of no answer.")
parser.add_argument(
"--na-prob-thresh" , "-t" , type=snake_case__ , default=1.0 , help="Predict \"\" if no-answer probability exceeds this (default = 1.0)." , )
parser.add_argument(
"--out-image-dir" , "-p" , metavar="out_images" , default=snake_case__ , help="Save precision-recall curves to directory.")
parser.add_argument("--verbose" , "-v" , action="store_true")
if len(sys.argv) == 1:
parser.print_help()
sys.exit(1)
return parser.parse_args()
def UpperCamelCase ( snake_case__):
lowerCAmelCase_ : str = {}
for article in dataset:
for p in article["paragraphs"]:
for qa in p["qas"]:
lowerCAmelCase_ : Dict = bool(qa["answers"]["text"])
return qid_to_has_ans
def UpperCamelCase ( snake_case__):
def remove_articles(snake_case__):
return ARTICLES_REGEX.sub(" " , snake_case__)
def white_space_fix(snake_case__):
return " ".join(text.split())
def remove_punc(snake_case__):
lowerCAmelCase_ : Optional[int] = set(string.punctuation)
return "".join(ch for ch in text if ch not in exclude)
def lower(snake_case__):
return text.lower()
return white_space_fix(remove_articles(remove_punc(lower(snake_case__))))
def UpperCamelCase ( snake_case__):
if not s:
return []
return normalize_answer(snake_case__).split()
def UpperCamelCase ( snake_case__ , snake_case__):
return int(normalize_answer(snake_case__) == normalize_answer(snake_case__))
def UpperCamelCase ( snake_case__ , snake_case__):
lowerCAmelCase_ : Optional[int] = get_tokens(snake_case__)
lowerCAmelCase_ : Union[str, Any] = get_tokens(snake_case__)
lowerCAmelCase_ : Any = collections.Counter(snake_case__) & collections.Counter(snake_case__)
lowerCAmelCase_ : Dict = sum(common.values())
if len(snake_case__) == 0 or len(snake_case__) == 0:
# If either is no-answer, then F1 is 1 if they agree, 0 otherwise
return int(gold_toks == pred_toks)
if num_same == 0:
return 0
lowerCAmelCase_ : List[Any] = 1.0 * num_same / len(snake_case__)
lowerCAmelCase_ : int = 1.0 * num_same / len(snake_case__)
lowerCAmelCase_ : List[Any] = (2 * precision * recall) / (precision + recall)
return fa
def UpperCamelCase ( snake_case__ , snake_case__):
lowerCAmelCase_ : Tuple = {}
lowerCAmelCase_ : int = {}
for article in dataset:
for p in article["paragraphs"]:
for qa in p["qas"]:
lowerCAmelCase_ : int = qa["id"]
lowerCAmelCase_ : Any = [t for t in qa["answers"]["text"] if normalize_answer(snake_case__)]
if not gold_answers:
# For unanswerable questions, only correct answer is empty string
lowerCAmelCase_ : Any = [""]
if qid not in preds:
print(F'''Missing prediction for {qid}''')
continue
lowerCAmelCase_ : Tuple = preds[qid]
# Take max over all gold answers
lowerCAmelCase_ : Any = max(compute_exact(snake_case__ , snake_case__) for a in gold_answers)
lowerCAmelCase_ : Optional[Any] = max(compute_fa(snake_case__ , snake_case__) for a in gold_answers)
return exact_scores, fa_scores
def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__ , snake_case__):
lowerCAmelCase_ : Dict = {}
for qid, s in scores.items():
lowerCAmelCase_ : List[Any] = na_probs[qid] > na_prob_thresh
if pred_na:
lowerCAmelCase_ : List[str] = float(not qid_to_has_ans[qid])
else:
lowerCAmelCase_ : Union[str, Any] = s
return new_scores
def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__=None):
if not qid_list:
lowerCAmelCase_ : Any = len(snake_case__)
return collections.OrderedDict(
[
("exact", 100.0 * sum(exact_scores.values()) / total),
("f1", 100.0 * sum(fa_scores.values()) / total),
("total", total),
])
else:
lowerCAmelCase_ : Tuple = len(snake_case__)
return collections.OrderedDict(
[
("exact", 100.0 * sum(exact_scores[k] for k in qid_list) / total),
("f1", 100.0 * sum(fa_scores[k] for k in qid_list) / total),
("total", total),
])
def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__):
for k in new_eval:
lowerCAmelCase_ : Union[str, Any] = new_eval[k]
def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__ , snake_case__):
plt.step(snake_case__ , snake_case__ , color="b" , alpha=0.2 , where="post")
plt.fill_between(snake_case__ , snake_case__ , step="post" , alpha=0.2 , color="b")
plt.xlabel("Recall")
plt.ylabel("Precision")
plt.xlim([0.0, 1.05])
plt.ylim([0.0, 1.05])
plt.title(snake_case__)
plt.savefig(snake_case__)
plt.clf()
def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__=None , snake_case__=None):
lowerCAmelCase_ : List[Any] = sorted(snake_case__ , key=lambda snake_case__: na_probs[k])
lowerCAmelCase_ : Dict = 0.0
lowerCAmelCase_ : int = 1.0
lowerCAmelCase_ : List[str] = 0.0
lowerCAmelCase_ : Tuple = [1.0]
lowerCAmelCase_ : Tuple = [0.0]
lowerCAmelCase_ : Dict = 0.0
for i, qid in enumerate(snake_case__):
if qid_to_has_ans[qid]:
true_pos += scores[qid]
lowerCAmelCase_ : str = true_pos / float(i + 1)
lowerCAmelCase_ : Union[str, Any] = true_pos / float(snake_case__)
if i == len(snake_case__) - 1 or na_probs[qid] != na_probs[qid_list[i + 1]]:
# i.e., if we can put a threshold after this point
avg_prec += cur_p * (cur_r - recalls[-1])
precisions.append(snake_case__)
recalls.append(snake_case__)
if out_image:
plot_pr_curve(snake_case__ , snake_case__ , snake_case__ , snake_case__)
return {"ap": 100.0 * avg_prec}
def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__):
if out_image_dir and not os.path.exists(snake_case__):
os.makedirs(snake_case__)
lowerCAmelCase_ : Any = sum(1 for v in qid_to_has_ans.values() if v)
if num_true_pos == 0:
return
lowerCAmelCase_ : Any = make_precision_recall_eval(
snake_case__ , snake_case__ , snake_case__ , snake_case__ , out_image=os.path.join(snake_case__ , "pr_exact.png") , title="Precision-Recall curve for Exact Match score" , )
lowerCAmelCase_ : Dict = make_precision_recall_eval(
snake_case__ , snake_case__ , snake_case__ , snake_case__ , out_image=os.path.join(snake_case__ , "pr_f1.png") , title="Precision-Recall curve for F1 score" , )
lowerCAmelCase_ : Dict = {k: float(snake_case__) for k, v in qid_to_has_ans.items()}
lowerCAmelCase_ : str = make_precision_recall_eval(
snake_case__ , snake_case__ , snake_case__ , snake_case__ , out_image=os.path.join(snake_case__ , "pr_oracle.png") , title="Oracle Precision-Recall curve (binary task of HasAns vs. NoAns)" , )
merge_eval(snake_case__ , snake_case__ , "pr_exact")
merge_eval(snake_case__ , snake_case__ , "pr_f1")
merge_eval(snake_case__ , snake_case__ , "pr_oracle")
def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__ , snake_case__):
if not qid_list:
return
lowerCAmelCase_ : Optional[Any] = [na_probs[k] for k in qid_list]
lowerCAmelCase_ : Dict = np.ones_like(snake_case__) / float(len(snake_case__))
plt.hist(snake_case__ , weights=snake_case__ , bins=20 , range=(0.0, 1.0))
plt.xlabel("Model probability of no-answer")
plt.ylabel("Proportion of dataset")
plt.title(F'''Histogram of no-answer probability: {name}''')
plt.savefig(os.path.join(snake_case__ , F'''na_prob_hist_{name}.png'''))
plt.clf()
def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__ , snake_case__):
lowerCAmelCase_ : Dict = sum(1 for k in qid_to_has_ans if not qid_to_has_ans[k])
lowerCAmelCase_ : str = num_no_ans
lowerCAmelCase_ : List[str] = cur_score
lowerCAmelCase_ : List[Any] = 0.0
lowerCAmelCase_ : str = sorted(snake_case__ , key=lambda snake_case__: na_probs[k])
for i, qid in enumerate(snake_case__):
if qid not in scores:
continue
if qid_to_has_ans[qid]:
lowerCAmelCase_ : Union[str, Any] = scores[qid]
else:
if preds[qid]:
lowerCAmelCase_ : List[Any] = -1
else:
lowerCAmelCase_ : List[str] = 0
cur_score += diff
if cur_score > best_score:
lowerCAmelCase_ : Optional[Any] = cur_score
lowerCAmelCase_ : Optional[int] = na_probs[qid]
return 100.0 * best_score / len(snake_case__), best_thresh
def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__):
lowerCAmelCase_ , lowerCAmelCase_ : Optional[int] = find_best_thresh(snake_case__ , snake_case__ , snake_case__ , snake_case__)
lowerCAmelCase_ , lowerCAmelCase_ : Dict = find_best_thresh(snake_case__ , snake_case__ , snake_case__ , snake_case__)
lowerCAmelCase_ : List[str] = best_exact
lowerCAmelCase_ : List[str] = exact_thresh
lowerCAmelCase_ : Any = best_fa
lowerCAmelCase_ : List[str] = fa_thresh
def UpperCamelCase ( ):
with open(OPTS.data_file) as f:
lowerCAmelCase_ : Optional[int] = json.load(snake_case__)
lowerCAmelCase_ : List[Any] = dataset_json["data"]
with open(OPTS.pred_file) as f:
lowerCAmelCase_ : int = json.load(snake_case__)
if OPTS.na_prob_file:
with open(OPTS.na_prob_file) as f:
lowerCAmelCase_ : Optional[int] = json.load(snake_case__)
else:
lowerCAmelCase_ : List[Any] = {k: 0.0 for k in preds}
lowerCAmelCase_ : Tuple = make_qid_to_has_ans(snake_case__) # maps qid to True/False
lowerCAmelCase_ : Any = [k for k, v in qid_to_has_ans.items() if v]
lowerCAmelCase_ : List[str] = [k for k, v in qid_to_has_ans.items() if not v]
lowerCAmelCase_ , lowerCAmelCase_ : Dict = get_raw_scores(snake_case__ , snake_case__)
lowerCAmelCase_ : str = apply_no_ans_threshold(snake_case__ , snake_case__ , snake_case__ , OPTS.na_prob_thresh)
lowerCAmelCase_ : Dict = apply_no_ans_threshold(snake_case__ , snake_case__ , snake_case__ , OPTS.na_prob_thresh)
lowerCAmelCase_ : Union[str, Any] = make_eval_dict(snake_case__ , snake_case__)
if has_ans_qids:
lowerCAmelCase_ : str = make_eval_dict(snake_case__ , snake_case__ , qid_list=snake_case__)
merge_eval(snake_case__ , snake_case__ , "HasAns")
if no_ans_qids:
lowerCAmelCase_ : Union[str, Any] = make_eval_dict(snake_case__ , snake_case__ , qid_list=snake_case__)
merge_eval(snake_case__ , snake_case__ , "NoAns")
if OPTS.na_prob_file:
find_all_best_thresh(snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__)
if OPTS.na_prob_file and OPTS.out_image_dir:
run_precision_recall_analysis(snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ , OPTS.out_image_dir)
histogram_na_prob(snake_case__ , snake_case__ , OPTS.out_image_dir , "hasAns")
histogram_na_prob(snake_case__ , snake_case__ , OPTS.out_image_dir , "noAns")
if OPTS.out_file:
with open(OPTS.out_file , "w") as f:
json.dump(snake_case__ , snake_case__)
else:
print(json.dumps(snake_case__ , indent=2))
if __name__ == "__main__":
_lowercase = parse_args()
if OPTS.out_image_dir:
import matplotlib
matplotlib.use('''Agg''')
import matplotlib.pyplot as plt
main()
| 683 | 1 |
import json
import os
import re
import unittest
from transformers import CodeGenTokenizer, CodeGenTokenizerFast
from transformers.models.codegen.tokenization_codegen import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class __snake_case ( snake_case__ , unittest.TestCase ):
"""simple docstring"""
UpperCamelCase_ = CodeGenTokenizer
UpperCamelCase_ = CodeGenTokenizerFast
UpperCamelCase_ = True
UpperCamelCase_ = {'add_prefix_space': True}
UpperCamelCase_ = False
def UpperCAmelCase_ ( self : str ) -> Tuple:
'''simple docstring'''
super().setUp()
# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
lowerCAmelCase_ : Optional[Any] = [
"l",
"o",
"w",
"e",
"r",
"s",
"t",
"i",
"d",
"n",
"\u0120",
"\u0120l",
"\u0120n",
"\u0120lo",
"\u0120low",
"er",
"\u0120lowest",
"\u0120newer",
"\u0120wider",
"<unk>",
"<|endoftext|>",
]
lowerCAmelCase_ : int = dict(zip(lowerCAmelCase__ ,range(len(lowerCAmelCase__ ) ) ) )
lowerCAmelCase_ : Dict = ["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
lowerCAmelCase_ : List[Any] = {"unk_token": "<unk>"}
lowerCAmelCase_ : List[Any] = os.path.join(self.tmpdirname ,VOCAB_FILES_NAMES["vocab_file"] )
lowerCAmelCase_ : Tuple = os.path.join(self.tmpdirname ,VOCAB_FILES_NAMES["merges_file"] )
with open(self.vocab_file ,"w" ,encoding="utf-8" ) as fp:
fp.write(json.dumps(lowerCAmelCase__ ) + "\n" )
with open(self.merges_file ,"w" ,encoding="utf-8" ) as fp:
fp.write("\n".join(lowerCAmelCase__ ) )
def UpperCAmelCase_ ( self : Optional[int] ,**lowerCAmelCase__ : str ) -> int:
'''simple docstring'''
kwargs.update(self.special_tokens_map )
return CodeGenTokenizer.from_pretrained(self.tmpdirname ,**lowerCAmelCase__ )
def UpperCAmelCase_ ( self : Optional[int] ,**lowerCAmelCase__ : Optional[Any] ) -> Tuple:
'''simple docstring'''
kwargs.update(self.special_tokens_map )
return CodeGenTokenizerFast.from_pretrained(self.tmpdirname ,**lowerCAmelCase__ )
def UpperCAmelCase_ ( self : Optional[int] ,lowerCAmelCase__ : str ) -> Optional[int]:
'''simple docstring'''
lowerCAmelCase_ : Tuple = "lower newer"
lowerCAmelCase_ : Tuple = "lower newer"
return input_text, output_text
def UpperCAmelCase_ ( self : Optional[Any] ) -> Any:
'''simple docstring'''
lowerCAmelCase_ : Optional[Any] = CodeGenTokenizer(self.vocab_file ,self.merges_file ,**self.special_tokens_map )
lowerCAmelCase_ : Dict = "lower newer"
lowerCAmelCase_ : Dict = ["\u0120low", "er", "\u0120", "n", "e", "w", "er"]
lowerCAmelCase_ : Union[str, Any] = tokenizer.tokenize(lowerCAmelCase__ ,add_prefix_space=lowerCAmelCase__ )
self.assertListEqual(lowerCAmelCase__ ,lowerCAmelCase__ )
lowerCAmelCase_ : Optional[Any] = tokens + [tokenizer.unk_token]
lowerCAmelCase_ : Union[str, Any] = [14, 15, 10, 9, 3, 2, 15, 19]
self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCAmelCase__ ) ,lowerCAmelCase__ )
def UpperCAmelCase_ ( self : List[str] ) -> Optional[Any]:
'''simple docstring'''
if not self.test_rust_tokenizer:
return
lowerCAmelCase_ : Tuple = self.get_tokenizer()
lowerCAmelCase_ : Optional[int] = self.get_rust_tokenizer(add_prefix_space=lowerCAmelCase__ )
lowerCAmelCase_ : Any = "lower newer"
# Testing tokenization
lowerCAmelCase_ : Tuple = tokenizer.tokenize(lowerCAmelCase__ ,add_prefix_space=lowerCAmelCase__ )
lowerCAmelCase_ : Any = rust_tokenizer.tokenize(lowerCAmelCase__ )
self.assertListEqual(lowerCAmelCase__ ,lowerCAmelCase__ )
# Testing conversion to ids without special tokens
lowerCAmelCase_ : str = tokenizer.encode(lowerCAmelCase__ ,add_special_tokens=lowerCAmelCase__ ,add_prefix_space=lowerCAmelCase__ )
lowerCAmelCase_ : Any = rust_tokenizer.encode(lowerCAmelCase__ ,add_special_tokens=lowerCAmelCase__ )
self.assertListEqual(lowerCAmelCase__ ,lowerCAmelCase__ )
# Testing conversion to ids with special tokens
lowerCAmelCase_ : int = self.get_rust_tokenizer(add_prefix_space=lowerCAmelCase__ )
lowerCAmelCase_ : str = tokenizer.encode(lowerCAmelCase__ ,add_prefix_space=lowerCAmelCase__ )
lowerCAmelCase_ : Optional[int] = rust_tokenizer.encode(lowerCAmelCase__ )
self.assertListEqual(lowerCAmelCase__ ,lowerCAmelCase__ )
# Testing the unknown token
lowerCAmelCase_ : Union[str, Any] = tokens + [rust_tokenizer.unk_token]
lowerCAmelCase_ : List[str] = [14, 15, 10, 9, 3, 2, 15, 19]
self.assertListEqual(rust_tokenizer.convert_tokens_to_ids(lowerCAmelCase__ ) ,lowerCAmelCase__ )
def UpperCAmelCase_ ( self : Optional[int] ,*lowerCAmelCase__ : List[str] ,**lowerCAmelCase__ : Optional[Any] ) -> List[str]:
'''simple docstring'''
pass
def UpperCAmelCase_ ( self : Tuple ,lowerCAmelCase__ : Any=15 ) -> str:
'''simple docstring'''
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})''' ):
lowerCAmelCase_ : Any = self.rust_tokenizer_class.from_pretrained(lowerCAmelCase__ ,**lowerCAmelCase__ )
# Simple input
lowerCAmelCase_ : int = "This is a simple input"
lowerCAmelCase_ : Dict = ["This is a simple input 1", "This is a simple input 2"]
lowerCAmelCase_ : str = ("This is a simple input", "This is a pair")
lowerCAmelCase_ : Optional[int] = [
("This is a simple input 1", "This is a simple input 2"),
("This is a simple pair 1", "This is a simple pair 2"),
]
# Simple input tests
self.assertRaises(lowerCAmelCase__ ,tokenizer_r.encode ,lowerCAmelCase__ ,max_length=lowerCAmelCase__ ,padding="max_length" )
# Simple input
self.assertRaises(lowerCAmelCase__ ,tokenizer_r.encode_plus ,lowerCAmelCase__ ,max_length=lowerCAmelCase__ ,padding="max_length" )
# Simple input
self.assertRaises(
lowerCAmelCase__ ,tokenizer_r.batch_encode_plus ,lowerCAmelCase__ ,max_length=lowerCAmelCase__ ,padding="max_length" ,)
# Pair input
self.assertRaises(lowerCAmelCase__ ,tokenizer_r.encode ,lowerCAmelCase__ ,max_length=lowerCAmelCase__ ,padding="max_length" )
# Pair input
self.assertRaises(lowerCAmelCase__ ,tokenizer_r.encode_plus ,lowerCAmelCase__ ,max_length=lowerCAmelCase__ ,padding="max_length" )
# Pair input
self.assertRaises(
lowerCAmelCase__ ,tokenizer_r.batch_encode_plus ,lowerCAmelCase__ ,max_length=lowerCAmelCase__ ,padding="max_length" ,)
def UpperCAmelCase_ ( self : Optional[int] ) -> Optional[int]:
'''simple docstring'''
lowerCAmelCase_ : Tuple = CodeGenTokenizer.from_pretrained(self.tmpdirname ,pad_token="<pad>" )
# Simple input
lowerCAmelCase_ : Dict = "This is a simple input"
lowerCAmelCase_ : List[str] = ["This is a simple input looooooooong", "This is a simple input"]
lowerCAmelCase_ : Any = ("This is a simple input", "This is a pair")
lowerCAmelCase_ : List[str] = [
("This is a simple input loooooong", "This is a simple input"),
("This is a simple pair loooooong", "This is a simple pair"),
]
lowerCAmelCase_ : Dict = tokenizer.pad_token_id
lowerCAmelCase_ : Union[str, Any] = tokenizer(lowerCAmelCase__ ,padding="max_length" ,max_length=30 ,return_tensors="np" )
lowerCAmelCase_ : Tuple = tokenizer(lowerCAmelCase__ ,padding=lowerCAmelCase__ ,truncate=lowerCAmelCase__ ,return_tensors="np" )
lowerCAmelCase_ : Any = tokenizer(*lowerCAmelCase__ ,padding="max_length" ,max_length=60 ,return_tensors="np" )
lowerCAmelCase_ : Optional[int] = tokenizer(lowerCAmelCase__ ,padding=lowerCAmelCase__ ,truncate=lowerCAmelCase__ ,return_tensors="np" )
# s
# test single string max_length padding
self.assertEqual(out_s["input_ids"].shape[-1] ,30 )
self.assertTrue(pad_token_id in out_s["input_ids"] )
self.assertTrue(0 in out_s["attention_mask"] )
# s2
# test automatic padding
self.assertEqual(out_sa["input_ids"].shape[-1] ,33 )
# long slice doesn't have padding
self.assertFalse(pad_token_id in out_sa["input_ids"][0] )
self.assertFalse(0 in out_sa["attention_mask"][0] )
# short slice does have padding
self.assertTrue(pad_token_id in out_sa["input_ids"][1] )
self.assertTrue(0 in out_sa["attention_mask"][1] )
# p
# test single pair max_length padding
self.assertEqual(out_p["input_ids"].shape[-1] ,60 )
self.assertTrue(pad_token_id in out_p["input_ids"] )
self.assertTrue(0 in out_p["attention_mask"] )
# p2
# test automatic padding pair
self.assertEqual(out_pa["input_ids"].shape[-1] ,52 )
# long slice pair doesn't have padding
self.assertFalse(pad_token_id in out_pa["input_ids"][0] )
self.assertFalse(0 in out_pa["attention_mask"][0] )
# short slice pair does have padding
self.assertTrue(pad_token_id in out_pa["input_ids"][1] )
self.assertTrue(0 in out_pa["attention_mask"][1] )
def UpperCAmelCase_ ( self : Union[str, Any] ) -> Any:
'''simple docstring'''
lowerCAmelCase_ : Any = "$$$"
lowerCAmelCase_ : List[str] = CodeGenTokenizer.from_pretrained(self.tmpdirname ,bos_token=lowerCAmelCase__ ,add_bos_token=lowerCAmelCase__ )
lowerCAmelCase_ : Dict = "This is a simple input"
lowerCAmelCase_ : Union[str, Any] = ["This is a simple input 1", "This is a simple input 2"]
lowerCAmelCase_ : int = tokenizer.bos_token_id
lowerCAmelCase_ : List[Any] = tokenizer(lowerCAmelCase__ )
lowerCAmelCase_ : Optional[Any] = tokenizer(lowerCAmelCase__ )
self.assertEqual(out_s.input_ids[0] ,lowerCAmelCase__ )
self.assertTrue(all(o[0] == bos_token_id for o in out_sa.input_ids ) )
lowerCAmelCase_ : List[str] = tokenizer.decode(out_s.input_ids )
lowerCAmelCase_ : Optional[Any] = tokenizer.batch_decode(out_sa.input_ids )
self.assertEqual(decode_s.split()[0] ,lowerCAmelCase__ )
self.assertTrue(all(d.split()[0] == bos_token for d in decode_sa ) )
@slow
def UpperCAmelCase_ ( self : Any ) -> Optional[int]:
'''simple docstring'''
lowerCAmelCase_ : Optional[Any] = CodeGenTokenizer.from_pretrained("Salesforce/codegen-350M-mono" )
lowerCAmelCase_ : str = "\nif len_a > len_b:\n result = a\nelse:\n result = b\n\n\n\n#"
lowerCAmelCase_ : int = "\nif len_a > len_b: result = a\nelse: result = b"
lowerCAmelCase_ : Dict = tokenizer.encode(lowerCAmelCase__ )
lowerCAmelCase_ : str = ["^#", re.escape("<|endoftext|>" ), "^'''", "^\"\"\"", "\n\n\n"]
lowerCAmelCase_ : Union[str, Any] = tokenizer.decode(lowerCAmelCase__ ,truncate_before_pattern=lowerCAmelCase__ )
self.assertEqual(lowerCAmelCase__ ,lowerCAmelCase__ )
def UpperCAmelCase_ ( self : Optional[Any] ) -> Optional[Any]:
'''simple docstring'''
pass
| 683 |
from math import sqrt
def UpperCamelCase ( snake_case__):
lowerCAmelCase_ : Optional[int] = 0
for i in range(1 , int(sqrt(snake_case__) + 1)):
if n % i == 0 and i != sqrt(snake_case__):
total += i + n // i
elif i == sqrt(snake_case__):
total += i
return total - n
def UpperCamelCase ( snake_case__ = 1_00_00):
lowerCAmelCase_ : int = sum(
i
for i in range(1 , snake_case__)
if sum_of_divisors(sum_of_divisors(snake_case__)) == i and sum_of_divisors(snake_case__) != i)
return total
if __name__ == "__main__":
print(solution(int(str(input()).strip())))
| 683 | 1 |
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm import create_model
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform
from transformers import BitConfig, BitForImageClassification, BitImageProcessor
from transformers.image_utils import PILImageResampling
from transformers.utils import logging
logging.set_verbosity_info()
_lowercase = logging.get_logger(__name__)
def UpperCamelCase ( snake_case__):
lowerCAmelCase_ : Dict = "huggingface/label-files"
lowerCAmelCase_ : Tuple = "imagenet-1k-id2label.json"
lowerCAmelCase_ : Union[str, Any] = json.load(open(hf_hub_download(snake_case__ , snake_case__ , repo_type="dataset") , "r"))
lowerCAmelCase_ : List[str] = {int(snake_case__): v for k, v in idalabel.items()}
lowerCAmelCase_ : str = {v: k for k, v in idalabel.items()}
lowerCAmelCase_ : int = "std_conv" if "bit" in model_name else False
# note that when using BiT as backbone for ViT-hybrid checkpoints,
# one needs to additionally set config.layer_type = "bottleneck", config.stem_type = "same",
# config.conv_layer = "std_conv_same"
lowerCAmelCase_ : Optional[int] = BitConfig(
conv_layer=snake_case__ , num_labels=10_00 , idalabel=snake_case__ , labelaid=snake_case__ , )
return config
def UpperCamelCase ( snake_case__):
if "stem.conv" in name:
lowerCAmelCase_ : Union[str, Any] = name.replace("stem.conv" , "bit.embedder.convolution")
if "blocks" in name:
lowerCAmelCase_ : str = name.replace("blocks" , "layers")
if "head.fc" in name:
lowerCAmelCase_ : Optional[int] = name.replace("head.fc" , "classifier.1")
if name.startswith("norm"):
lowerCAmelCase_ : int = "bit." + name
if "bit" not in name and "classifier" not in name:
lowerCAmelCase_ : str = "bit.encoder." + name
return name
def UpperCamelCase ( ):
lowerCAmelCase_ : Tuple = "http://images.cocodataset.org/val2017/000000039769.jpg"
lowerCAmelCase_ : Optional[int] = Image.open(requests.get(snake_case__ , stream=snake_case__).raw)
return im
@torch.no_grad()
def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__=False):
lowerCAmelCase_ : str = get_config(snake_case__)
# load original model from timm
lowerCAmelCase_ : str = create_model(snake_case__ , pretrained=snake_case__)
timm_model.eval()
# load state_dict of original model
lowerCAmelCase_ : Dict = timm_model.state_dict()
for key in state_dict.copy().keys():
lowerCAmelCase_ : Optional[Any] = state_dict.pop(snake_case__)
lowerCAmelCase_ : List[str] = val.squeeze() if "head" in key else val
# load HuggingFace model
lowerCAmelCase_ : List[Any] = BitForImageClassification(snake_case__)
model.eval()
model.load_state_dict(snake_case__)
# create image processor
lowerCAmelCase_ : List[str] = create_transform(**resolve_data_config({} , model=snake_case__))
lowerCAmelCase_ : str = transform.transforms
lowerCAmelCase_ : str = {
"bilinear": PILImageResampling.BILINEAR,
"bicubic": PILImageResampling.BICUBIC,
"nearest": PILImageResampling.NEAREST,
}
lowerCAmelCase_ : Optional[int] = BitImageProcessor(
do_resize=snake_case__ , size={"shortest_edge": timm_transforms[0].size} , resample=pillow_resamplings[timm_transforms[0].interpolation.value] , do_center_crop=snake_case__ , crop_size={"height": timm_transforms[1].size[0], "width": timm_transforms[1].size[1]} , do_normalize=snake_case__ , image_mean=timm_transforms[-1].mean.tolist() , image_std=timm_transforms[-1].std.tolist() , )
lowerCAmelCase_ : Optional[int] = prepare_img()
lowerCAmelCase_ : Optional[int] = transform(snake_case__).unsqueeze(0)
lowerCAmelCase_ : str = processor(snake_case__ , return_tensors="pt").pixel_values
# verify pixel values
assert torch.allclose(snake_case__ , snake_case__)
# verify logits
with torch.no_grad():
lowerCAmelCase_ : Union[str, Any] = model(snake_case__)
lowerCAmelCase_ : List[str] = outputs.logits
print("Logits:" , logits[0, :3])
print("Predicted class:" , model.config.idalabel[logits.argmax(-1).item()])
lowerCAmelCase_ : Optional[int] = timm_model(snake_case__)
assert timm_logits.shape == outputs.logits.shape
assert torch.allclose(snake_case__ , outputs.logits , atol=1e-3)
print("Looks ok!")
if pytorch_dump_folder_path is not None:
Path(snake_case__).mkdir(exist_ok=snake_case__)
print(F'''Saving model {model_name} and processor to {pytorch_dump_folder_path}''')
model.save_pretrained(snake_case__)
processor.save_pretrained(snake_case__)
if push_to_hub:
print(F'''Pushing model {model_name} and processor to the hub''')
model.push_to_hub(F'''ybelkada/{model_name}''')
processor.push_to_hub(F'''ybelkada/{model_name}''')
if __name__ == "__main__":
_lowercase = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--model_name''',
default='''resnetv2_50x1_bitm''',
type=str,
help='''Name of the BiT 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.'''
)
parser.add_argument(
'''--push_to_hub''',
action='''store_true''',
help='''Whether to push the model to the hub.''',
)
_lowercase = parser.parse_args()
convert_bit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)
| 683 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_speech_available,
is_tf_available,
is_torch_available,
)
_lowercase = {
'''configuration_speech_to_text''': ['''SPEECH_TO_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''Speech2TextConfig'''],
'''processing_speech_to_text''': ['''Speech2TextProcessor'''],
}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowercase = ['''Speech2TextTokenizer''']
try:
if not is_speech_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowercase = ['''Speech2TextFeatureExtractor''']
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowercase = [
'''TF_SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''TFSpeech2TextForConditionalGeneration''',
'''TFSpeech2TextModel''',
'''TFSpeech2TextPreTrainedModel''',
]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowercase = [
'''SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''Speech2TextForConditionalGeneration''',
'''Speech2TextModel''',
'''Speech2TextPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_speech_to_text import SPEECH_TO_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP, SpeechaTextConfig
from .processing_speech_to_text import SpeechaTextProcessor
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_speech_to_text import SpeechaTextTokenizer
try:
if not is_speech_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_speech_to_text import SpeechaTextFeatureExtractor
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_speech_to_text import (
TF_SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
TFSpeechaTextForConditionalGeneration,
TFSpeechaTextModel,
TFSpeechaTextPreTrainedModel,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_speech_to_text import (
SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
SpeechaTextForConditionalGeneration,
SpeechaTextModel,
SpeechaTextPreTrainedModel,
)
else:
import sys
_lowercase = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 683 | 1 |
def UpperCamelCase ( snake_case__ = 1_00_00_00):
lowerCAmelCase_ : int = [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 , snake_case__):
phi[j] -= phi[j] // i
return sum(phi[2 : limit + 1])
if __name__ == "__main__":
print(solution())
| 683 |
import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
_lowercase = logging.get_logger(__name__)
_lowercase = {'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt'''}
_lowercase = {
'''vocab_file''': {
'''allenai/longformer-base-4096''': '''https://huggingface.co/allenai/longformer-base-4096/resolve/main/vocab.json''',
'''allenai/longformer-large-4096''': (
'''https://huggingface.co/allenai/longformer-large-4096/resolve/main/vocab.json'''
),
'''allenai/longformer-large-4096-finetuned-triviaqa''': (
'''https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/vocab.json'''
),
'''allenai/longformer-base-4096-extra.pos.embd.only''': (
'''https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/vocab.json'''
),
'''allenai/longformer-large-4096-extra.pos.embd.only''': (
'''https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/vocab.json'''
),
},
'''merges_file''': {
'''allenai/longformer-base-4096''': '''https://huggingface.co/allenai/longformer-base-4096/resolve/main/merges.txt''',
'''allenai/longformer-large-4096''': (
'''https://huggingface.co/allenai/longformer-large-4096/resolve/main/merges.txt'''
),
'''allenai/longformer-large-4096-finetuned-triviaqa''': (
'''https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/merges.txt'''
),
'''allenai/longformer-base-4096-extra.pos.embd.only''': (
'''https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/merges.txt'''
),
'''allenai/longformer-large-4096-extra.pos.embd.only''': (
'''https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/merges.txt'''
),
},
}
_lowercase = {
'''allenai/longformer-base-4096''': 4096,
'''allenai/longformer-large-4096''': 4096,
'''allenai/longformer-large-4096-finetuned-triviaqa''': 4096,
'''allenai/longformer-base-4096-extra.pos.embd.only''': 4096,
'''allenai/longformer-large-4096-extra.pos.embd.only''': 4096,
}
@lru_cache()
# Copied from transformers.models.roberta.tokenization_roberta.bytes_to_unicode
def UpperCamelCase ( ):
lowerCAmelCase_ : str = (
list(range(ord("!") , ord("~") + 1)) + list(range(ord("¡") , ord("¬") + 1)) + list(range(ord("®") , ord("ÿ") + 1))
)
lowerCAmelCase_ : Tuple = bs[:]
lowerCAmelCase_ : Dict = 0
for b in range(2**8):
if b not in bs:
bs.append(snake_case__)
cs.append(2**8 + n)
n += 1
lowerCAmelCase_ : Union[str, Any] = [chr(snake_case__) for n in cs]
return dict(zip(snake_case__ , snake_case__))
def UpperCamelCase ( snake_case__):
lowerCAmelCase_ : Optional[Any] = set()
lowerCAmelCase_ : List[Any] = word[0]
for char in word[1:]:
pairs.add((prev_char, char))
lowerCAmelCase_ : Union[str, Any] = char
return pairs
class __snake_case ( snake_case__ ):
"""simple docstring"""
UpperCamelCase_ = VOCAB_FILES_NAMES
UpperCamelCase_ = PRETRAINED_VOCAB_FILES_MAP
UpperCamelCase_ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
UpperCamelCase_ = ['input_ids', 'attention_mask']
def __init__( self : str ,lowerCAmelCase__ : Dict ,lowerCAmelCase__ : List[Any] ,lowerCAmelCase__ : Optional[Any]="replace" ,lowerCAmelCase__ : Dict="<s>" ,lowerCAmelCase__ : str="</s>" ,lowerCAmelCase__ : str="</s>" ,lowerCAmelCase__ : Optional[Any]="<s>" ,lowerCAmelCase__ : List[Any]="<unk>" ,lowerCAmelCase__ : Union[str, Any]="<pad>" ,lowerCAmelCase__ : int="<mask>" ,lowerCAmelCase__ : Any=False ,**lowerCAmelCase__ : int ,) -> Dict:
'''simple docstring'''
lowerCAmelCase_ : Optional[int] = AddedToken(lowerCAmelCase__ ,lstrip=lowerCAmelCase__ ,rstrip=lowerCAmelCase__ ) if isinstance(lowerCAmelCase__ ,lowerCAmelCase__ ) else bos_token
lowerCAmelCase_ : Tuple = AddedToken(lowerCAmelCase__ ,lstrip=lowerCAmelCase__ ,rstrip=lowerCAmelCase__ ) if isinstance(lowerCAmelCase__ ,lowerCAmelCase__ ) else eos_token
lowerCAmelCase_ : Dict = AddedToken(lowerCAmelCase__ ,lstrip=lowerCAmelCase__ ,rstrip=lowerCAmelCase__ ) if isinstance(lowerCAmelCase__ ,lowerCAmelCase__ ) else sep_token
lowerCAmelCase_ : int = AddedToken(lowerCAmelCase__ ,lstrip=lowerCAmelCase__ ,rstrip=lowerCAmelCase__ ) if isinstance(lowerCAmelCase__ ,lowerCAmelCase__ ) else cls_token
lowerCAmelCase_ : List[str] = AddedToken(lowerCAmelCase__ ,lstrip=lowerCAmelCase__ ,rstrip=lowerCAmelCase__ ) if isinstance(lowerCAmelCase__ ,lowerCAmelCase__ ) else unk_token
lowerCAmelCase_ : List[str] = AddedToken(lowerCAmelCase__ ,lstrip=lowerCAmelCase__ ,rstrip=lowerCAmelCase__ ) if isinstance(lowerCAmelCase__ ,lowerCAmelCase__ ) else pad_token
# Mask token behave like a normal word, i.e. include the space before it
lowerCAmelCase_ : Optional[Any] = AddedToken(lowerCAmelCase__ ,lstrip=lowerCAmelCase__ ,rstrip=lowerCAmelCase__ ) if isinstance(lowerCAmelCase__ ,lowerCAmelCase__ ) else mask_token
super().__init__(
errors=lowerCAmelCase__ ,bos_token=lowerCAmelCase__ ,eos_token=lowerCAmelCase__ ,unk_token=lowerCAmelCase__ ,sep_token=lowerCAmelCase__ ,cls_token=lowerCAmelCase__ ,pad_token=lowerCAmelCase__ ,mask_token=lowerCAmelCase__ ,add_prefix_space=lowerCAmelCase__ ,**lowerCAmelCase__ ,)
with open(lowerCAmelCase__ ,encoding="utf-8" ) as vocab_handle:
lowerCAmelCase_ : List[Any] = json.load(lowerCAmelCase__ )
lowerCAmelCase_ : Dict = {v: k for k, v in self.encoder.items()}
lowerCAmelCase_ : List[Any] = errors # how to handle errors in decoding
lowerCAmelCase_ : Optional[Any] = bytes_to_unicode()
lowerCAmelCase_ : int = {v: k for k, v in self.byte_encoder.items()}
with open(lowerCAmelCase__ ,encoding="utf-8" ) as merges_handle:
lowerCAmelCase_ : Union[str, Any] = merges_handle.read().split("\n" )[1:-1]
lowerCAmelCase_ : Dict = [tuple(merge.split() ) for merge in bpe_merges]
lowerCAmelCase_ : Dict = dict(zip(lowerCAmelCase__ ,range(len(lowerCAmelCase__ ) ) ) )
lowerCAmelCase_ : Any = {}
lowerCAmelCase_ : int = add_prefix_space
# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
lowerCAmelCase_ : Optional[Any] = re.compile(R"'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+" )
@property
def UpperCAmelCase_ ( self : Optional[int] ) -> Any:
'''simple docstring'''
return len(self.encoder )
def UpperCAmelCase_ ( self : Any ) -> Optional[int]:
'''simple docstring'''
return dict(self.encoder ,**self.added_tokens_encoder )
def UpperCAmelCase_ ( self : List[str] ,lowerCAmelCase__ : List[str] ) -> List[Any]:
'''simple docstring'''
if token in self.cache:
return self.cache[token]
lowerCAmelCase_ : Union[str, Any] = tuple(lowerCAmelCase__ )
lowerCAmelCase_ : Optional[Any] = get_pairs(lowerCAmelCase__ )
if not pairs:
return token
while True:
lowerCAmelCase_ : Dict = min(lowerCAmelCase__ ,key=lambda lowerCAmelCase__ : self.bpe_ranks.get(lowerCAmelCase__ ,float("inf" ) ) )
if bigram not in self.bpe_ranks:
break
lowerCAmelCase_ , lowerCAmelCase_ : Dict = bigram
lowerCAmelCase_ : Optional[Any] = []
lowerCAmelCase_ : Any = 0
while i < len(lowerCAmelCase__ ):
try:
lowerCAmelCase_ : Optional[int] = word.index(lowerCAmelCase__ ,lowerCAmelCase__ )
except ValueError:
new_word.extend(word[i:] )
break
else:
new_word.extend(word[i:j] )
lowerCAmelCase_ : Tuple = j
if word[i] == first and i < len(lowerCAmelCase__ ) - 1 and word[i + 1] == second:
new_word.append(first + second )
i += 2
else:
new_word.append(word[i] )
i += 1
lowerCAmelCase_ : Optional[Any] = tuple(lowerCAmelCase__ )
lowerCAmelCase_ : Union[str, Any] = new_word
if len(lowerCAmelCase__ ) == 1:
break
else:
lowerCAmelCase_ : Dict = get_pairs(lowerCAmelCase__ )
lowerCAmelCase_ : Tuple = " ".join(lowerCAmelCase__ )
lowerCAmelCase_ : Any = word
return word
def UpperCAmelCase_ ( self : Optional[int] ,lowerCAmelCase__ : Tuple ) -> Dict:
'''simple docstring'''
lowerCAmelCase_ : Dict = []
for token in re.findall(self.pat ,lowerCAmelCase__ ):
lowerCAmelCase_ : List[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(lowerCAmelCase__ ).split(" " ) )
return bpe_tokens
def UpperCAmelCase_ ( self : Tuple ,lowerCAmelCase__ : int ) -> Tuple:
'''simple docstring'''
return self.encoder.get(lowerCAmelCase__ ,self.encoder.get(self.unk_token ) )
def UpperCAmelCase_ ( self : List[str] ,lowerCAmelCase__ : Optional[Any] ) -> Union[str, Any]:
'''simple docstring'''
return self.decoder.get(lowerCAmelCase__ )
def UpperCAmelCase_ ( self : Tuple ,lowerCAmelCase__ : Dict ) -> Any:
'''simple docstring'''
lowerCAmelCase_ : Optional[int] = "".join(lowerCAmelCase__ )
lowerCAmelCase_ : Union[str, Any] = bytearray([self.byte_decoder[c] for c in text] ).decode("utf-8" ,errors=self.errors )
return text
def UpperCAmelCase_ ( self : Optional[Any] ,lowerCAmelCase__ : str ,lowerCAmelCase__ : Optional[str] = None ) -> Tuple[str]:
'''simple docstring'''
if not os.path.isdir(lowerCAmelCase__ ):
logger.error(f'''Vocabulary path ({save_directory}) should be a directory''' )
return
lowerCAmelCase_ : Optional[Any] = os.path.join(
lowerCAmelCase__ ,(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )
lowerCAmelCase_ : Tuple = os.path.join(
lowerCAmelCase__ ,(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"] )
with open(lowerCAmelCase__ ,"w" ,encoding="utf-8" ) as f:
f.write(json.dumps(self.encoder ,indent=2 ,sort_keys=lowerCAmelCase__ ,ensure_ascii=lowerCAmelCase__ ) + "\n" )
lowerCAmelCase_ : Tuple = 0
with open(lowerCAmelCase__ ,"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 lowerCAmelCase__ : 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!" )
lowerCAmelCase_ : Optional[Any] = token_index
writer.write(" ".join(lowerCAmelCase__ ) + "\n" )
index += 1
return vocab_file, merge_file
def UpperCAmelCase_ ( self : Union[str, Any] ,lowerCAmelCase__ : List[int] ,lowerCAmelCase__ : Optional[List[int]] = None ) -> List[int]:
'''simple docstring'''
if token_ids_a is None:
return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
lowerCAmelCase_ : List[Any] = [self.cls_token_id]
lowerCAmelCase_ : List[str] = [self.sep_token_id]
return cls + token_ids_a + sep + sep + token_ids_a + sep
def UpperCAmelCase_ ( self : Dict ,lowerCAmelCase__ : List[int] ,lowerCAmelCase__ : Optional[List[int]] = None ,lowerCAmelCase__ : bool = False ) -> List[int]:
'''simple docstring'''
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=lowerCAmelCase__ ,token_ids_a=lowerCAmelCase__ ,already_has_special_tokens=lowerCAmelCase__ )
if token_ids_a is None:
return [1] + ([0] * len(lowerCAmelCase__ )) + [1]
return [1] + ([0] * len(lowerCAmelCase__ )) + [1, 1] + ([0] * len(lowerCAmelCase__ )) + [1]
def UpperCAmelCase_ ( self : Any ,lowerCAmelCase__ : List[int] ,lowerCAmelCase__ : Optional[List[int]] = None ) -> List[int]:
'''simple docstring'''
lowerCAmelCase_ : List[str] = [self.sep_token_id]
lowerCAmelCase_ : 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 : Tuple ,lowerCAmelCase__ : List[str] ,lowerCAmelCase__ : Optional[int]=False ,**lowerCAmelCase__ : Optional[int] ) -> Any:
'''simple docstring'''
lowerCAmelCase_ : int = kwargs.pop("add_prefix_space" ,self.add_prefix_space )
if (is_split_into_words or add_prefix_space) and (len(lowerCAmelCase__ ) > 0 and not text[0].isspace()):
lowerCAmelCase_ : Union[str, Any] = " " + text
return (text, kwargs)
| 683 | 1 |
from __future__ import annotations
_lowercase = [True] * 1000001
_lowercase = 2
while i * i <= 1000000:
if seive[i]:
for j in range(i * i, 1000001, i):
_lowercase = False
i += 1
def UpperCamelCase ( snake_case__):
return seive[n]
def UpperCamelCase ( snake_case__):
return any(digit in "02468" for digit in str(snake_case__))
def UpperCamelCase ( snake_case__ = 1_00_00_00):
lowerCAmelCase_ : Union[str, Any] = [2] # result already includes the number 2.
for num in range(3 , limit + 1 , 2):
if is_prime(snake_case__) and not contains_an_even_digit(snake_case__):
lowerCAmelCase_ : Optional[Any] = str(snake_case__)
lowerCAmelCase_ : str = [int(str_num[j:] + str_num[:j]) for j in range(len(snake_case__))]
if all(is_prime(snake_case__) for i in list_nums):
result.append(snake_case__)
return result
def UpperCamelCase ( ):
return len(find_circular_primes())
if __name__ == "__main__":
print(f"{len(find_circular_primes()) = }")
| 683 |
from collections.abc import Iterable
from typing import Any
class __snake_case :
"""simple docstring"""
def __init__( self : Optional[Any] ,lowerCAmelCase__ : int | None = None ) -> List[str]:
'''simple docstring'''
lowerCAmelCase_ : Dict = value
lowerCAmelCase_ : Node | None = None # Added in order to delete a node easier
lowerCAmelCase_ : Node | None = None
lowerCAmelCase_ : Node | None = None
def __repr__( self : Union[str, Any] ) -> str:
'''simple docstring'''
from pprint import pformat
if self.left is None and self.right is None:
return str(self.value )
return pformat({f'''{self.value}''': (self.left, self.right)} ,indent=1 )
class __snake_case :
"""simple docstring"""
def __init__( self : Optional[Any] ,lowerCAmelCase__ : Node | None = None ) -> List[str]:
'''simple docstring'''
lowerCAmelCase_ : List[Any] = root
def __str__( self : Dict ) -> str:
'''simple docstring'''
return str(self.root )
def UpperCAmelCase_ ( self : List[Any] ,lowerCAmelCase__ : Node ,lowerCAmelCase__ : Node | None ) -> None:
'''simple docstring'''
if new_children is not None: # reset its kids
lowerCAmelCase_ : Optional[int] = node.parent
if node.parent is not None: # reset its parent
if self.is_right(lowerCAmelCase__ ): # If it is the right children
lowerCAmelCase_ : List[Any] = new_children
else:
lowerCAmelCase_ : List[Any] = new_children
else:
lowerCAmelCase_ : Any = new_children
def UpperCAmelCase_ ( self : Any ,lowerCAmelCase__ : Node ) -> bool:
'''simple docstring'''
if node.parent and node.parent.right:
return node == node.parent.right
return False
def UpperCAmelCase_ ( self : List[str] ) -> bool:
'''simple docstring'''
return self.root is None
def UpperCAmelCase_ ( self : List[Any] ,lowerCAmelCase__ : Union[str, Any] ) -> None:
'''simple docstring'''
lowerCAmelCase_ : str = Node(lowerCAmelCase__ ) # create a new Node
if self.empty(): # if Tree is empty
lowerCAmelCase_ : Optional[int] = new_node # set its root
else: # Tree is not empty
lowerCAmelCase_ : List[Any] = self.root # from root
if parent_node is None:
return
while True: # While we don't get to a leaf
if value < parent_node.value: # We go left
if parent_node.left is None:
lowerCAmelCase_ : Dict = new_node # We insert the new node in a leaf
break
else:
lowerCAmelCase_ : List[str] = parent_node.left
else:
if parent_node.right is None:
lowerCAmelCase_ : Dict = new_node
break
else:
lowerCAmelCase_ : str = parent_node.right
lowerCAmelCase_ : Optional[int] = parent_node
def UpperCAmelCase_ ( self : int ,*lowerCAmelCase__ : Tuple ) -> None:
'''simple docstring'''
for value in values:
self.__insert(lowerCAmelCase__ )
def UpperCAmelCase_ ( self : Optional[int] ,lowerCAmelCase__ : Optional[int] ) -> Node | None:
'''simple docstring'''
if self.empty():
raise IndexError("Warning: Tree is empty! please use another." )
else:
lowerCAmelCase_ : Dict = self.root
# use lazy evaluation here to avoid NoneType Attribute error
while node is not None and node.value is not value:
lowerCAmelCase_ : Union[str, Any] = node.left if value < node.value else node.right
return node
def UpperCAmelCase_ ( self : Dict ,lowerCAmelCase__ : Node | None = None ) -> Node | None:
'''simple docstring'''
if node is None:
if self.root is None:
return None
lowerCAmelCase_ : Dict = self.root
if not self.empty():
while node.right is not None:
lowerCAmelCase_ : Union[str, Any] = node.right
return node
def UpperCAmelCase_ ( self : Dict ,lowerCAmelCase__ : Node | None = None ) -> Node | None:
'''simple docstring'''
if node is None:
lowerCAmelCase_ : Dict = self.root
if self.root is None:
return None
if not self.empty():
lowerCAmelCase_ : Dict = self.root
while node.left is not None:
lowerCAmelCase_ : Union[str, Any] = node.left
return node
def UpperCAmelCase_ ( self : Union[str, Any] ,lowerCAmelCase__ : int ) -> None:
'''simple docstring'''
lowerCAmelCase_ : Dict = self.search(lowerCAmelCase__ ) # Look for the node with that label
if node is not None:
if node.left is None and node.right is None: # If it has no children
self.__reassign_nodes(lowerCAmelCase__ ,lowerCAmelCase__ )
elif node.left is None: # Has only right children
self.__reassign_nodes(lowerCAmelCase__ ,node.right )
elif node.right is None: # Has only left children
self.__reassign_nodes(lowerCAmelCase__ ,node.left )
else:
lowerCAmelCase_ : int = self.get_max(
node.left ) # Gets the max value of the left branch
self.remove(tmp_node.value ) # type: ignore
lowerCAmelCase_ : Any = (
tmp_node.value # type: ignore
) # Assigns the value to the node to delete and keep tree structure
def UpperCAmelCase_ ( self : Union[str, Any] ,lowerCAmelCase__ : Node | None ) -> Iterable:
'''simple docstring'''
if node is not None:
yield node # Preorder Traversal
yield from self.preorder_traverse(node.left )
yield from self.preorder_traverse(node.right )
def UpperCAmelCase_ ( self : Union[str, Any] ,lowerCAmelCase__ : Dict=None ) -> Any:
'''simple docstring'''
if traversal_function is None:
return self.preorder_traverse(self.root )
else:
return traversal_function(self.root )
def UpperCAmelCase_ ( self : str ,lowerCAmelCase__ : list ,lowerCAmelCase__ : Node | None ) -> None:
'''simple docstring'''
if node:
self.inorder(lowerCAmelCase__ ,node.left )
arr.append(node.value )
self.inorder(lowerCAmelCase__ ,node.right )
def UpperCAmelCase_ ( self : List[str] ,lowerCAmelCase__ : int ,lowerCAmelCase__ : Node ) -> int:
'''simple docstring'''
lowerCAmelCase_ : list[int] = []
self.inorder(lowerCAmelCase__ ,lowerCAmelCase__ ) # append all values to list using inorder traversal
return arr[k - 1]
def UpperCamelCase ( snake_case__):
lowerCAmelCase_ : Optional[Any] = []
if curr_node is not None:
lowerCAmelCase_ : Dict = postorder(curr_node.left) + postorder(curr_node.right) + [curr_node]
return node_list
def UpperCamelCase ( ):
lowerCAmelCase_ : Tuple = (8, 3, 6, 1, 10, 14, 13, 4, 7)
lowerCAmelCase_ : Tuple = BinarySearchTree()
for i in testlist:
t.insert(snake_case__)
# Prints all the elements of the list in order traversal
print(snake_case__)
if t.search(6) is not None:
print("The value 6 exists")
else:
print("The value 6 doesn't exist")
if t.search(-1) is not None:
print("The value -1 exists")
else:
print("The value -1 doesn't exist")
if not t.empty():
print("Max Value: " , t.get_max().value) # type: ignore
print("Min Value: " , t.get_min().value) # type: ignore
for i in testlist:
t.remove(snake_case__)
print(snake_case__)
if __name__ == "__main__":
import doctest
doctest.testmod(verbose=True)
| 683 | 1 |
from math import pi, sqrt, tan
def UpperCamelCase ( snake_case__):
if side_length < 0:
raise ValueError("surface_area_cube() only accepts non-negative values")
return 6 * side_length**2
def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__):
if length < 0 or breadth < 0 or height < 0:
raise ValueError("surface_area_cuboid() only accepts non-negative values")
return 2 * ((length * breadth) + (breadth * height) + (length * height))
def UpperCamelCase ( snake_case__):
if radius < 0:
raise ValueError("surface_area_sphere() only accepts non-negative values")
return 4 * pi * radius**2
def UpperCamelCase ( snake_case__):
if radius < 0:
raise ValueError("surface_area_hemisphere() only accepts non-negative values")
return 3 * pi * radius**2
def UpperCamelCase ( snake_case__ , snake_case__):
if radius < 0 or height < 0:
raise ValueError("surface_area_cone() only accepts non-negative values")
return pi * radius * (radius + (height**2 + radius**2) ** 0.5)
def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__):
if radius_a < 0 or radius_a < 0 or height < 0:
raise ValueError(
"surface_area_conical_frustum() only accepts non-negative values")
lowerCAmelCase_ : Union[str, Any] = (height**2 + (radius_a - radius_a) ** 2) ** 0.5
return pi * ((slant_height * (radius_a + radius_a)) + radius_a**2 + radius_a**2)
def UpperCamelCase ( snake_case__ , snake_case__):
if radius < 0 or height < 0:
raise ValueError("surface_area_cylinder() only accepts non-negative values")
return 2 * pi * radius * (height + radius)
def UpperCamelCase ( snake_case__ , snake_case__):
if torus_radius < 0 or tube_radius < 0:
raise ValueError("surface_area_torus() only accepts non-negative values")
if torus_radius < tube_radius:
raise ValueError(
"surface_area_torus() does not support spindle or self intersecting tori")
return 4 * pow(snake_case__ , 2) * torus_radius * tube_radius
def UpperCamelCase ( snake_case__ , snake_case__):
if length < 0 or width < 0:
raise ValueError("area_rectangle() only accepts non-negative values")
return length * width
def UpperCamelCase ( snake_case__):
if side_length < 0:
raise ValueError("area_square() only accepts non-negative values")
return side_length**2
def UpperCamelCase ( snake_case__ , snake_case__):
if base < 0 or height < 0:
raise ValueError("area_triangle() only accepts non-negative values")
return (base * height) / 2
def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__):
if sidea < 0 or sidea < 0 or sidea < 0:
raise ValueError("area_triangle_three_sides() only accepts non-negative values")
elif sidea + sidea < sidea or sidea + sidea < sidea or sidea + sidea < sidea:
raise ValueError("Given three sides do not form a triangle")
lowerCAmelCase_ : int = (sidea + sidea + sidea) / 2
lowerCAmelCase_ : Any = sqrt(
semi_perimeter
* (semi_perimeter - sidea)
* (semi_perimeter - sidea)
* (semi_perimeter - sidea))
return area
def UpperCamelCase ( snake_case__ , snake_case__):
if base < 0 or height < 0:
raise ValueError("area_parallelogram() only accepts non-negative values")
return base * height
def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__):
if basea < 0 or basea < 0 or height < 0:
raise ValueError("area_trapezium() only accepts non-negative values")
return 1 / 2 * (basea + basea) * height
def UpperCamelCase ( snake_case__):
if radius < 0:
raise ValueError("area_circle() only accepts non-negative values")
return pi * radius**2
def UpperCamelCase ( snake_case__ , snake_case__):
if radius_x < 0 or radius_y < 0:
raise ValueError("area_ellipse() only accepts non-negative values")
return pi * radius_x * radius_y
def UpperCamelCase ( snake_case__ , snake_case__):
if diagonal_a < 0 or diagonal_a < 0:
raise ValueError("area_rhombus() only accepts non-negative values")
return 1 / 2 * diagonal_a * diagonal_a
def UpperCamelCase ( snake_case__ , snake_case__):
if not isinstance(snake_case__ , snake_case__) or sides < 3:
raise ValueError(
"area_reg_polygon() only accepts integers greater than or \
equal to three as number of sides")
elif length < 0:
raise ValueError(
"area_reg_polygon() only accepts non-negative values as \
length of a side")
return (sides * length**2) / (4 * tan(pi / sides))
return (sides * length**2) / (4 * tan(pi / sides))
if __name__ == "__main__":
import doctest
doctest.testmod(verbose=True) # verbose so we can see methods missing tests
print('''[DEMO] Areas of various geometric shapes: \n''')
print(f"Rectangle: {area_rectangle(10, 20) = }")
print(f"Square: {area_square(10) = }")
print(f"Triangle: {area_triangle(10, 10) = }")
print(f"Triangle: {area_triangle_three_sides(5, 12, 13) = }")
print(f"Parallelogram: {area_parallelogram(10, 20) = }")
print(f"Rhombus: {area_rhombus(10, 20) = }")
print(f"Trapezium: {area_trapezium(10, 20, 30) = }")
print(f"Circle: {area_circle(20) = }")
print(f"Ellipse: {area_ellipse(10, 20) = }")
print('''\nSurface Areas of various geometric shapes: \n''')
print(f"Cube: {surface_area_cube(20) = }")
print(f"Cuboid: {surface_area_cuboid(10, 20, 30) = }")
print(f"Sphere: {surface_area_sphere(20) = }")
print(f"Hemisphere: {surface_area_hemisphere(20) = }")
print(f"Cone: {surface_area_cone(10, 20) = }")
print(f"Conical Frustum: {surface_area_conical_frustum(10, 20, 30) = }")
print(f"Cylinder: {surface_area_cylinder(10, 20) = }")
print(f"Torus: {surface_area_torus(20, 10) = }")
print(f"Equilateral Triangle: {area_reg_polygon(3, 10) = }")
print(f"Square: {area_reg_polygon(4, 10) = }")
print(f"Reqular Pentagon: {area_reg_polygon(5, 10) = }")
| 683 |
class __snake_case :
"""simple docstring"""
def __init__( self : Optional[int] ,lowerCAmelCase__ : str = "" ,lowerCAmelCase__ : bool = False ) -> None:
'''simple docstring'''
lowerCAmelCase_ : dict[str, RadixNode] = {}
# A node will be a leaf if the tree contains its word
lowerCAmelCase_ : int = is_leaf
lowerCAmelCase_ : Optional[Any] = prefix
def UpperCAmelCase_ ( self : str ,lowerCAmelCase__ : str ) -> tuple[str, str, str]:
'''simple docstring'''
lowerCAmelCase_ : Any = 0
for q, w in zip(self.prefix ,lowerCAmelCase__ ):
if q != w:
break
x += 1
return self.prefix[:x], self.prefix[x:], word[x:]
def UpperCAmelCase_ ( self : Optional[int] ,lowerCAmelCase__ : list[str] ) -> None:
'''simple docstring'''
for word in words:
self.insert(lowerCAmelCase__ )
def UpperCAmelCase_ ( self : Any ,lowerCAmelCase__ : str ) -> None:
'''simple docstring'''
if self.prefix == word:
lowerCAmelCase_ : Optional[Any] = True
# Case 2: The node has no edges that have a prefix to the word
# Solution: We create an edge from the current node to a new one
# containing the word
elif word[0] not in self.nodes:
lowerCAmelCase_ : List[Any] = RadixNode(prefix=lowerCAmelCase__ ,is_leaf=lowerCAmelCase__ )
else:
lowerCAmelCase_ : Tuple = self.nodes[word[0]]
lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ : List[str] = incoming_node.match(
lowerCAmelCase__ )
# Case 3: The node prefix is equal to the matching
# Solution: We insert remaining word on the next node
if remaining_prefix == "":
self.nodes[matching_string[0]].insert(lowerCAmelCase__ )
# Case 4: The word is greater equal to the matching
# Solution: Create a node in between both nodes, change
# prefixes and add the new node for the remaining word
else:
lowerCAmelCase_ : Optional[int] = remaining_prefix
lowerCAmelCase_ : Optional[int] = self.nodes[matching_string[0]]
lowerCAmelCase_ : List[Any] = RadixNode(lowerCAmelCase__ ,lowerCAmelCase__ )
lowerCAmelCase_ : Dict = aux_node
if remaining_word == "":
lowerCAmelCase_ : List[str] = True
else:
self.nodes[matching_string[0]].insert(lowerCAmelCase__ )
def UpperCAmelCase_ ( self : Tuple ,lowerCAmelCase__ : str ) -> bool:
'''simple docstring'''
lowerCAmelCase_ : Any = self.nodes.get(word[0] ,lowerCAmelCase__ )
if not incoming_node:
return False
else:
lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ : List[Any] = incoming_node.match(
lowerCAmelCase__ )
# If there is remaining prefix, the word can't be on the tree
if remaining_prefix != "":
return False
# This applies when the word and the prefix are equal
elif remaining_word == "":
return incoming_node.is_leaf
# We have word remaining so we check the next node
else:
return incoming_node.find(lowerCAmelCase__ )
def UpperCAmelCase_ ( self : Optional[Any] ,lowerCAmelCase__ : str ) -> bool:
'''simple docstring'''
lowerCAmelCase_ : int = self.nodes.get(word[0] ,lowerCAmelCase__ )
if not incoming_node:
return False
else:
lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ : Union[str, Any] = incoming_node.match(
lowerCAmelCase__ )
# If there is remaining prefix, the word can't be on the tree
if remaining_prefix != "":
return False
# We have word remaining so we check the next node
elif remaining_word != "":
return incoming_node.delete(lowerCAmelCase__ )
else:
# If it is not a leaf, we don't have to delete
if not incoming_node.is_leaf:
return False
else:
# We delete the nodes if no edges go from it
if len(incoming_node.nodes ) == 0:
del self.nodes[word[0]]
# We merge the current node with its only child
if len(self.nodes ) == 1 and not self.is_leaf:
lowerCAmelCase_ : str = list(self.nodes.values() )[0]
lowerCAmelCase_ : Tuple = merging_node.is_leaf
self.prefix += merging_node.prefix
lowerCAmelCase_ : Optional[int] = merging_node.nodes
# If there is more than 1 edge, we just mark it as non-leaf
elif len(incoming_node.nodes ) > 1:
lowerCAmelCase_ : Optional[Any] = False
# If there is 1 edge, we merge it with its child
else:
lowerCAmelCase_ : Tuple = list(incoming_node.nodes.values() )[0]
lowerCAmelCase_ : Union[str, Any] = merging_node.is_leaf
incoming_node.prefix += merging_node.prefix
lowerCAmelCase_ : str = merging_node.nodes
return True
def UpperCAmelCase_ ( self : Dict ,lowerCAmelCase__ : int = 0 ) -> None:
'''simple docstring'''
if self.prefix != "":
print("-" * height ,self.prefix ," (leaf)" if self.is_leaf else "" )
for value in self.nodes.values():
value.print_tree(height + 1 )
def UpperCamelCase ( ):
lowerCAmelCase_ : Dict = "banana bananas bandana band apple all beast".split()
lowerCAmelCase_ : List[Any] = RadixNode()
root.insert_many(snake_case__)
assert all(root.find(snake_case__) for word in words)
assert not root.find("bandanas")
assert not root.find("apps")
root.delete("all")
assert not root.find("all")
root.delete("banana")
assert not root.find("banana")
assert root.find("bananas")
return True
def UpperCamelCase ( ):
assert test_trie()
def UpperCamelCase ( ):
lowerCAmelCase_ : List[str] = RadixNode()
lowerCAmelCase_ : Optional[Any] = "banana bananas bandanas bandana band apple all beast".split()
root.insert_many(snake_case__)
print("Words:" , snake_case__)
print("Tree:")
root.print_tree()
if __name__ == "__main__":
main()
| 683 | 1 |
# Copyright 2022 The HuggingFace Team and The OpenBMB Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
_lowercase = {
'''configuration_cpmant''': ['''CPMANT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''CpmAntConfig'''],
'''tokenization_cpmant''': ['''CpmAntTokenizer'''],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowercase = [
'''CPMANT_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''CpmAntForCausalLM''',
'''CpmAntModel''',
'''CpmAntPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_cpmant import CPMANT_PRETRAINED_CONFIG_ARCHIVE_MAP, CpmAntConfig
from .tokenization_cpmant import CpmAntTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_cpmant import (
CPMANT_PRETRAINED_MODEL_ARCHIVE_LIST,
CpmAntForCausalLM,
CpmAntModel,
CpmAntPreTrainedModel,
)
else:
import sys
_lowercase = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 683 |
from __future__ import annotations
def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__ , ):
if (electron_conc, hole_conc, intrinsic_conc).count(0) != 1:
raise ValueError("You cannot supply more or less than 2 values")
elif electron_conc < 0:
raise ValueError("Electron concentration cannot be negative in a semiconductor")
elif hole_conc < 0:
raise ValueError("Hole concentration cannot be negative in a semiconductor")
elif intrinsic_conc < 0:
raise ValueError(
"Intrinsic concentration cannot be negative in a semiconductor")
elif electron_conc == 0:
return (
"electron_conc",
intrinsic_conc**2 / hole_conc,
)
elif hole_conc == 0:
return (
"hole_conc",
intrinsic_conc**2 / electron_conc,
)
elif intrinsic_conc == 0:
return (
"intrinsic_conc",
(electron_conc * hole_conc) ** 0.5,
)
else:
return (-1, -1)
if __name__ == "__main__":
import doctest
doctest.testmod()
| 683 | 1 |
import json
import sys
def UpperCamelCase ( snake_case__ , snake_case__):
with open(snake_case__ , encoding="utf-8") as f:
lowerCAmelCase_ : str = json.load(snake_case__)
lowerCAmelCase_ : Optional[Any] = ["<details>", "<summary>Show updated benchmarks!</summary>", " "]
for benchmark_name in sorted(snake_case__):
lowerCAmelCase_ : Optional[Any] = results[benchmark_name]
lowerCAmelCase_ : Union[str, Any] = benchmark_name.split("/")[-1]
output_md.append(F'''### Benchmark: {benchmark_file_name}''')
lowerCAmelCase_ : str = "| metric |"
lowerCAmelCase_ : Optional[int] = "|--------|"
lowerCAmelCase_ : Tuple = "| new / old (diff) |"
for metric_name in sorted(snake_case__):
lowerCAmelCase_ : Tuple = benchmark_res[metric_name]
lowerCAmelCase_ : Optional[int] = metric_vals["new"]
lowerCAmelCase_ : Dict = metric_vals.get("old" , snake_case__)
lowerCAmelCase_ : Tuple = metric_vals.get("diff" , snake_case__)
lowerCAmelCase_ : List[Any] = F''' {new_val:f}''' if isinstance(snake_case__ , (int, float)) else "None"
if old_val is not None:
val_str += F''' / {old_val:f}''' if isinstance(snake_case__ , (int, float)) else "None"
if dif_val is not None:
val_str += F''' ({dif_val:f})''' if isinstance(snake_case__ , (int, float)) else "None"
title += " " + metric_name + " |"
lines += "---|"
value += val_str + " |"
output_md += [title, lines, value, " "]
output_md.append("</details>")
with open(snake_case__ , "w" , encoding="utf-8") as f:
f.writelines("\n".join(snake_case__))
if __name__ == "__main__":
_lowercase = sys.argv[1]
_lowercase = sys.argv[2]
format_json_to_md(input_json_file, output_md_file)
| 683 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_lowercase = {
'''configuration_git''': ['''GIT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GitConfig''', '''GitVisionConfig'''],
'''processing_git''': ['''GitProcessor'''],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowercase = [
'''GIT_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''GitForCausalLM''',
'''GitModel''',
'''GitPreTrainedModel''',
'''GitVisionModel''',
]
if TYPE_CHECKING:
from .configuration_git import GIT_PRETRAINED_CONFIG_ARCHIVE_MAP, GitConfig, GitVisionConfig
from .processing_git import GitProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_git import (
GIT_PRETRAINED_MODEL_ARCHIVE_LIST,
GitForCausalLM,
GitModel,
GitPreTrainedModel,
GitVisionModel,
)
else:
import sys
_lowercase = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 683 | 1 |
import math
def UpperCamelCase ( snake_case__):
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
return False
# All primes number are in format of 6k +/- 1
for i in range(5 , int(math.sqrt(snake_case__) + 1) , 6):
if number % i == 0 or number % (i + 2) == 0:
return False
return True
def UpperCamelCase ( snake_case__ = 1_00_01):
try:
lowerCAmelCase_ : List[Any] = int(snake_case__)
except (TypeError, ValueError):
raise TypeError("Parameter nth must be int or castable to int.") from None
if nth <= 0:
raise ValueError("Parameter nth must be greater than or equal to one.")
lowerCAmelCase_ : list[int] = []
lowerCAmelCase_ : List[str] = 2
while len(snake_case__) < nth:
if is_prime(snake_case__):
primes.append(snake_case__)
num += 1
else:
num += 1
return primes[len(snake_case__) - 1]
if __name__ == "__main__":
print(f"{solution() = }")
| 683 |
from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments
def UpperCamelCase ( ):
lowerCAmelCase_ : List[str] = HfArgumentParser(snake_case__)
lowerCAmelCase_ : List[Any] = parser.parse_args_into_dataclasses()[0]
lowerCAmelCase_ : Optional[int] = TensorFlowBenchmark(args=snake_case__)
try:
lowerCAmelCase_ : Tuple = parser.parse_args_into_dataclasses()[0]
except ValueError as e:
lowerCAmelCase_ : Union[str, Any] = "Arg --no_{0} is no longer used, please use --no-{0} instead."
lowerCAmelCase_ : Tuple = " ".join(str(snake_case__).split(" ")[:-1])
lowerCAmelCase_ : Union[str, Any] = ""
lowerCAmelCase_ : Optional[Any] = eval(str(snake_case__).split(" ")[-1])
lowerCAmelCase_ : Tuple = []
for arg in depreciated_args:
# arg[2:] removes '--'
if arg[2:] in TensorFlowBenchmark.deprecated_args:
# arg[5:] removes '--no_'
full_error_msg += arg_error_msg.format(arg[5:])
else:
wrong_args.append(snake_case__)
if len(snake_case__) > 0:
lowerCAmelCase_ : Optional[Any] = full_error_msg + begin_error_msg + str(snake_case__)
raise ValueError(snake_case__)
benchmark.run()
if __name__ == "__main__":
main()
| 683 | 1 |
import argparse
from transformers import BigBirdConfig, BigBirdForPreTraining, BigBirdForQuestionAnswering, load_tf_weights_in_big_bird
from transformers.utils import logging
logging.set_verbosity_info()
def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__ , snake_case__):
# Initialise PyTorch model
lowerCAmelCase_ : Optional[Any] = BigBirdConfig.from_json_file(snake_case__)
print(F'''Building PyTorch model from configuration: {config}''')
if is_trivia_qa:
lowerCAmelCase_ : List[str] = BigBirdForQuestionAnswering(snake_case__)
else:
lowerCAmelCase_ : str = BigBirdForPreTraining(snake_case__)
# Load weights from tf checkpoint
load_tf_weights_in_big_bird(snake_case__ , snake_case__ , is_trivia_qa=snake_case__)
# Save pytorch-model
print(F'''Save PyTorch model to {pytorch_dump_path}''')
model.save_pretrained(snake_case__)
if __name__ == "__main__":
_lowercase = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--tf_checkpoint_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
)
parser.add_argument(
'''--big_bird_config_file''',
default=None,
type=str,
required=True,
help=(
'''The config json file corresponding to the pre-trained BERT model. \n'''
'''This specifies the model architecture.'''
),
)
parser.add_argument(
'''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
)
parser.add_argument(
'''--is_trivia_qa''', action='''store_true''', help='''Whether to convert a model with a trivia_qa head.'''
)
_lowercase = parser.parse_args()
convert_tf_checkpoint_to_pytorch(
args.tf_checkpoint_path, args.big_bird_config_file, args.pytorch_dump_path, args.is_trivia_qa
)
| 683 |
_lowercase = {
0: '''0''',
1: '''1''',
2: '''2''',
3: '''3''',
4: '''4''',
5: '''5''',
6: '''6''',
7: '''7''',
8: '''8''',
9: '''9''',
10: '''a''',
11: '''b''',
12: '''c''',
13: '''d''',
14: '''e''',
15: '''f''',
}
def UpperCamelCase ( snake_case__):
assert type(snake_case__) in (int, float) and decimal == int(snake_case__)
lowerCAmelCase_ : Optional[Any] = int(snake_case__)
lowerCAmelCase_ : Tuple = ""
lowerCAmelCase_ : str = False
if decimal < 0:
lowerCAmelCase_ : Tuple = True
decimal *= -1
while decimal > 0:
lowerCAmelCase_ , lowerCAmelCase_ : Any = divmod(snake_case__ , 16)
lowerCAmelCase_ : Dict = values[remainder] + hexadecimal
lowerCAmelCase_ : List[str] = "0x" + hexadecimal
if negative:
lowerCAmelCase_ : Optional[Any] = "-" + hexadecimal
return hexadecimal
if __name__ == "__main__":
import doctest
doctest.testmod()
| 683 | 1 |
import math
def UpperCamelCase ( snake_case__):
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
return False
# All primes number are in format of 6k +/- 1
for i in range(5 , int(math.sqrt(snake_case__) + 1) , 6):
if number % i == 0 or number % (i + 2) == 0:
return False
return True
def UpperCamelCase ( snake_case__ = 0.1):
lowerCAmelCase_ : Any = 3
lowerCAmelCase_ : List[str] = 3
while primes / (2 * j - 1) >= ratio:
for i in range(j * j + j + 1 , (j + 2) * (j + 2) , j + 1):
primes += is_prime(snake_case__)
j += 2
return j
if __name__ == "__main__":
import doctest
doctest.testmod()
| 683 |
from pathlib import Path
from typing import List
from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import get_tests_dir, is_tool_test
from transformers.tools.agent_types import AGENT_TYPE_MAPPING, AgentAudio, AgentImage, AgentText
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
_lowercase = ['''text''', '''image''', '''audio''']
def UpperCamelCase ( snake_case__):
lowerCAmelCase_ : int = []
for input_type in input_types:
if input_type == "text":
inputs.append("Text input")
elif input_type == "image":
inputs.append(
Image.open(Path(get_tests_dir("fixtures/tests_samples/COCO")) / "000000039769.png").resize((5_12, 5_12)))
elif input_type == "audio":
inputs.append(torch.ones(30_00))
elif isinstance(snake_case__ , snake_case__):
inputs.append(create_inputs(snake_case__))
else:
raise ValueError(F'''Invalid type requested: {input_type}''')
return inputs
def UpperCamelCase ( snake_case__):
lowerCAmelCase_ : List[Any] = []
for output in outputs:
if isinstance(snake_case__ , (str, AgentText)):
output_types.append("text")
elif isinstance(snake_case__ , (Image.Image, AgentImage)):
output_types.append("image")
elif isinstance(snake_case__ , (torch.Tensor, AgentAudio)):
output_types.append("audio")
else:
raise ValueError(F'''Invalid output: {output}''')
return output_types
@is_tool_test
class __snake_case :
"""simple docstring"""
def UpperCAmelCase_ ( self : int ) -> int:
'''simple docstring'''
self.assertTrue(hasattr(self.tool ,"inputs" ) )
self.assertTrue(hasattr(self.tool ,"outputs" ) )
lowerCAmelCase_ : List[Any] = self.tool.inputs
for _input in inputs:
if isinstance(_input ,lowerCAmelCase__ ):
for __input in _input:
self.assertTrue(__input in authorized_types )
else:
self.assertTrue(_input in authorized_types )
lowerCAmelCase_ : Any = self.tool.outputs
for _output in outputs:
self.assertTrue(_output in authorized_types )
def UpperCAmelCase_ ( self : List[Any] ) -> List[Any]:
'''simple docstring'''
lowerCAmelCase_ : Any = create_inputs(self.tool.inputs )
lowerCAmelCase_ : List[Any] = self.tool(*lowerCAmelCase__ )
# There is a single output
if len(self.tool.outputs ) == 1:
lowerCAmelCase_ : Optional[int] = [outputs]
self.assertListEqual(output_types(lowerCAmelCase__ ) ,self.tool.outputs )
def UpperCAmelCase_ ( self : int ) -> Any:
'''simple docstring'''
self.assertTrue(hasattr(self.tool ,"description" ) )
self.assertTrue(hasattr(self.tool ,"default_checkpoint" ) )
self.assertTrue(self.tool.description.startswith("This is a tool that" ) )
def UpperCAmelCase_ ( self : List[Any] ) -> Tuple:
'''simple docstring'''
lowerCAmelCase_ : Optional[Any] = create_inputs(self.tool.inputs )
lowerCAmelCase_ : List[Any] = self.tool(*lowerCAmelCase__ )
if not isinstance(lowerCAmelCase__ ,lowerCAmelCase__ ):
lowerCAmelCase_ : str = [outputs]
self.assertEqual(len(lowerCAmelCase__ ) ,len(self.tool.outputs ) )
for output, output_type in zip(lowerCAmelCase__ ,self.tool.outputs ):
lowerCAmelCase_ : Tuple = AGENT_TYPE_MAPPING[output_type]
self.assertTrue(isinstance(lowerCAmelCase__ ,lowerCAmelCase__ ) )
def UpperCAmelCase_ ( self : Any ) -> Tuple:
'''simple docstring'''
lowerCAmelCase_ : Tuple = create_inputs(self.tool.inputs )
lowerCAmelCase_ : List[Any] = []
for _input, input_type in zip(lowerCAmelCase__ ,self.tool.inputs ):
if isinstance(lowerCAmelCase__ ,lowerCAmelCase__ ):
_inputs.append([AGENT_TYPE_MAPPING[_input_type](_input ) for _input_type in input_type] )
else:
_inputs.append(AGENT_TYPE_MAPPING[input_type](_input ) )
# Should not raise an error
lowerCAmelCase_ : List[Any] = self.tool(*lowerCAmelCase__ )
if not isinstance(lowerCAmelCase__ ,lowerCAmelCase__ ):
lowerCAmelCase_ : int = [outputs]
self.assertEqual(len(lowerCAmelCase__ ) ,len(self.tool.outputs ) )
| 683 | 1 |
import inspect
import os
import sys
import unittest
import accelerate
from accelerate.test_utils import execute_subprocess_async, require_tpu
class __snake_case ( unittest.TestCase ):
"""simple docstring"""
def UpperCAmelCase_ ( self : List[str] ) -> List[str]:
'''simple docstring'''
lowerCAmelCase_ : Union[str, Any] = inspect.getfile(accelerate.test_utils )
lowerCAmelCase_ : Tuple = os.path.sep.join(mod_file.split(os.path.sep )[:-1] + ["scripts", "test_script.py"] )
lowerCAmelCase_ : List[Any] = os.path.sep.join(inspect.getfile(self.__class__ ).split(os.path.sep )[:-1] )
@require_tpu
def UpperCAmelCase_ ( self : Any ) -> Dict:
'''simple docstring'''
lowerCAmelCase_ : Tuple = f'''
{self.test_dir}/xla_spawn.py
--num_cores 8
{self.test_file_path}
'''.split()
lowerCAmelCase_ : str = [sys.executable] + distributed_args
execute_subprocess_async(lowerCAmelCase__ ,env=os.environ.copy() )
| 683 |
import pytest
_lowercase = '''__dummy_dataset1__'''
_lowercase = '''
import json
import os
import datasets
REPO_URL = "https://huggingface.co/datasets/albertvillanova/tests-raw-jsonl/resolve/main/"
URLS = {"train": REPO_URL + "wikiann-bn-train.jsonl", "validation": REPO_URL + "wikiann-bn-validation.jsonl"}
class __DummyDataset1__(datasets.GeneratorBasedBuilder):
def _info(self):
features = datasets.Features(
{
"tokens": datasets.Sequence(datasets.Value("string")),
"ner_tags": datasets.Sequence(
datasets.features.ClassLabel(
names=[
"O",
"B-PER",
"I-PER",
"B-ORG",
"I-ORG",
"B-LOC",
"I-LOC",
]
)
),
"langs": datasets.Sequence(datasets.Value("string")),
"spans": datasets.Sequence(datasets.Value("string")),
}
)
return datasets.DatasetInfo(features=features)
def _split_generators(self, dl_manager):
dl_path = dl_manager.download(URLS)
return [
datasets.SplitGenerator(datasets.Split.TRAIN, gen_kwargs={"filepath": dl_path["train"]}),
datasets.SplitGenerator(datasets.Split.VALIDATION, gen_kwargs={"filepath": dl_path["validation"]}),
]
def _generate_examples(self, filepath):
with open(filepath, "r", encoding="utf-8") as f:
for i, line in enumerate(f):
yield i, json.loads(line)
'''
@pytest.fixture
def UpperCamelCase ( ):
return DATASET_LOADING_SCRIPT_NAME
@pytest.fixture
def UpperCamelCase ( ):
return DATASET_LOADING_SCRIPT_CODE
@pytest.fixture
def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__):
lowerCAmelCase_ : List[Any] = dataset_loading_script_name
lowerCAmelCase_ : List[str] = tmp_path / "datasets" / script_name
script_dir.mkdir(parents=snake_case__)
lowerCAmelCase_ : List[Any] = script_dir / F'''{script_name}.py'''
with open(snake_case__ , "w") as f:
f.write(snake_case__)
return str(snake_case__)
| 683 | 1 |
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from transformers.generation import DisjunctiveConstraint
@require_torch
class __snake_case ( unittest.TestCase ):
"""simple docstring"""
def UpperCAmelCase_ ( self : Dict ) -> Any:
'''simple docstring'''
lowerCAmelCase_ : Union[str, Any] = [[1, 2, 4], [1, 2, 3, 4]]
lowerCAmelCase_ : Dict = DisjunctiveConstraint(lowerCAmelCase__ )
self.assertTrue(isinstance(dc.token_ids ,lowerCAmelCase__ ) )
with self.assertRaises(lowerCAmelCase__ ):
DisjunctiveConstraint(torch.LongTensor([[1, 2, 4], [1, 2, 3]] ) )
with self.assertRaises(lowerCAmelCase__ ):
DisjunctiveConstraint([torch.LongTensor([1, 2, 4] ), torch.LongTensor([1, 2, 3, 4, 5] )] )
def UpperCAmelCase_ ( self : Tuple ) -> List[str]:
'''simple docstring'''
lowerCAmelCase_ : Tuple = [[1, 2], [1, 2, 3, 4]]
with self.assertRaises(lowerCAmelCase__ ):
DisjunctiveConstraint(lowerCAmelCase__ ) # fails here
def UpperCAmelCase_ ( self : Optional[int] ) -> List[Any]:
'''simple docstring'''
lowerCAmelCase_ : List[str] = [[1, 2, 3], [1, 2, 4]]
lowerCAmelCase_ : str = DisjunctiveConstraint(lowerCAmelCase__ )
lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ : Union[str, Any] = dc.update(1 )
lowerCAmelCase_ : Tuple = stepped is True and completed is False and reset is False
self.assertTrue(lowerCAmelCase__ )
self.assertTrue(not dc.completed )
self.assertTrue(dc.current_seq == [1] )
lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ : List[str] = dc.update(2 )
lowerCAmelCase_ : Optional[Any] = stepped is True and completed is False and reset is False
self.assertTrue(lowerCAmelCase__ )
self.assertTrue(not dc.completed )
self.assertTrue(dc.current_seq == [1, 2] )
lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ : Any = dc.update(3 )
lowerCAmelCase_ : List[str] = stepped is True and completed is True and reset is False
self.assertTrue(lowerCAmelCase__ )
self.assertTrue(dc.completed ) # Completed!
self.assertTrue(dc.current_seq == [1, 2, 3] )
def UpperCAmelCase_ ( self : List[str] ) -> Dict:
'''simple docstring'''
lowerCAmelCase_ : Dict = [[1, 2, 3], [1, 2, 4, 5], [1, 2, 5]]
lowerCAmelCase_ : Any = DisjunctiveConstraint(lowerCAmelCase__ )
lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ : Tuple = dc.update(1 )
self.assertTrue(not dc.completed )
self.assertTrue(dc.current_seq == [1] )
lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ : List[Any] = dc.update(2 )
self.assertTrue(not dc.completed )
self.assertTrue(dc.current_seq == [1, 2] )
lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ : Optional[Any] = dc.update(4 )
self.assertTrue(not dc.completed )
self.assertTrue(dc.current_seq == [1, 2, 4] )
lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ : Optional[int] = dc.update(5 )
self.assertTrue(dc.completed ) # Completed!
self.assertTrue(dc.current_seq == [1, 2, 4, 5] )
dc.reset()
lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ : Optional[int] = dc.update(1 )
self.assertTrue(not dc.completed )
self.assertTrue(dc.remaining() == 3 )
self.assertTrue(dc.current_seq == [1] )
lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ : Tuple = dc.update(2 )
self.assertTrue(not dc.completed )
self.assertTrue(dc.remaining() == 2 )
self.assertTrue(dc.current_seq == [1, 2] )
lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ : Tuple = dc.update(5 )
self.assertTrue(dc.completed ) # Completed!
self.assertTrue(dc.remaining() == 0 )
self.assertTrue(dc.current_seq == [1, 2, 5] )
| 683 |
import json
import os
import re
import unittest
from transformers import CodeGenTokenizer, CodeGenTokenizerFast
from transformers.models.codegen.tokenization_codegen import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class __snake_case ( snake_case__ , unittest.TestCase ):
"""simple docstring"""
UpperCamelCase_ = CodeGenTokenizer
UpperCamelCase_ = CodeGenTokenizerFast
UpperCamelCase_ = True
UpperCamelCase_ = {'add_prefix_space': True}
UpperCamelCase_ = False
def UpperCAmelCase_ ( self : str ) -> Tuple:
'''simple docstring'''
super().setUp()
# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
lowerCAmelCase_ : Optional[Any] = [
"l",
"o",
"w",
"e",
"r",
"s",
"t",
"i",
"d",
"n",
"\u0120",
"\u0120l",
"\u0120n",
"\u0120lo",
"\u0120low",
"er",
"\u0120lowest",
"\u0120newer",
"\u0120wider",
"<unk>",
"<|endoftext|>",
]
lowerCAmelCase_ : int = dict(zip(lowerCAmelCase__ ,range(len(lowerCAmelCase__ ) ) ) )
lowerCAmelCase_ : Dict = ["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
lowerCAmelCase_ : List[Any] = {"unk_token": "<unk>"}
lowerCAmelCase_ : List[Any] = os.path.join(self.tmpdirname ,VOCAB_FILES_NAMES["vocab_file"] )
lowerCAmelCase_ : Tuple = os.path.join(self.tmpdirname ,VOCAB_FILES_NAMES["merges_file"] )
with open(self.vocab_file ,"w" ,encoding="utf-8" ) as fp:
fp.write(json.dumps(lowerCAmelCase__ ) + "\n" )
with open(self.merges_file ,"w" ,encoding="utf-8" ) as fp:
fp.write("\n".join(lowerCAmelCase__ ) )
def UpperCAmelCase_ ( self : Optional[int] ,**lowerCAmelCase__ : str ) -> int:
'''simple docstring'''
kwargs.update(self.special_tokens_map )
return CodeGenTokenizer.from_pretrained(self.tmpdirname ,**lowerCAmelCase__ )
def UpperCAmelCase_ ( self : Optional[int] ,**lowerCAmelCase__ : Optional[Any] ) -> Tuple:
'''simple docstring'''
kwargs.update(self.special_tokens_map )
return CodeGenTokenizerFast.from_pretrained(self.tmpdirname ,**lowerCAmelCase__ )
def UpperCAmelCase_ ( self : Optional[int] ,lowerCAmelCase__ : str ) -> Optional[int]:
'''simple docstring'''
lowerCAmelCase_ : Tuple = "lower newer"
lowerCAmelCase_ : Tuple = "lower newer"
return input_text, output_text
def UpperCAmelCase_ ( self : Optional[Any] ) -> Any:
'''simple docstring'''
lowerCAmelCase_ : Optional[Any] = CodeGenTokenizer(self.vocab_file ,self.merges_file ,**self.special_tokens_map )
lowerCAmelCase_ : Dict = "lower newer"
lowerCAmelCase_ : Dict = ["\u0120low", "er", "\u0120", "n", "e", "w", "er"]
lowerCAmelCase_ : Union[str, Any] = tokenizer.tokenize(lowerCAmelCase__ ,add_prefix_space=lowerCAmelCase__ )
self.assertListEqual(lowerCAmelCase__ ,lowerCAmelCase__ )
lowerCAmelCase_ : Optional[Any] = tokens + [tokenizer.unk_token]
lowerCAmelCase_ : Union[str, Any] = [14, 15, 10, 9, 3, 2, 15, 19]
self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCAmelCase__ ) ,lowerCAmelCase__ )
def UpperCAmelCase_ ( self : List[str] ) -> Optional[Any]:
'''simple docstring'''
if not self.test_rust_tokenizer:
return
lowerCAmelCase_ : Tuple = self.get_tokenizer()
lowerCAmelCase_ : Optional[int] = self.get_rust_tokenizer(add_prefix_space=lowerCAmelCase__ )
lowerCAmelCase_ : Any = "lower newer"
# Testing tokenization
lowerCAmelCase_ : Tuple = tokenizer.tokenize(lowerCAmelCase__ ,add_prefix_space=lowerCAmelCase__ )
lowerCAmelCase_ : Any = rust_tokenizer.tokenize(lowerCAmelCase__ )
self.assertListEqual(lowerCAmelCase__ ,lowerCAmelCase__ )
# Testing conversion to ids without special tokens
lowerCAmelCase_ : str = tokenizer.encode(lowerCAmelCase__ ,add_special_tokens=lowerCAmelCase__ ,add_prefix_space=lowerCAmelCase__ )
lowerCAmelCase_ : Any = rust_tokenizer.encode(lowerCAmelCase__ ,add_special_tokens=lowerCAmelCase__ )
self.assertListEqual(lowerCAmelCase__ ,lowerCAmelCase__ )
# Testing conversion to ids with special tokens
lowerCAmelCase_ : int = self.get_rust_tokenizer(add_prefix_space=lowerCAmelCase__ )
lowerCAmelCase_ : str = tokenizer.encode(lowerCAmelCase__ ,add_prefix_space=lowerCAmelCase__ )
lowerCAmelCase_ : Optional[int] = rust_tokenizer.encode(lowerCAmelCase__ )
self.assertListEqual(lowerCAmelCase__ ,lowerCAmelCase__ )
# Testing the unknown token
lowerCAmelCase_ : Union[str, Any] = tokens + [rust_tokenizer.unk_token]
lowerCAmelCase_ : List[str] = [14, 15, 10, 9, 3, 2, 15, 19]
self.assertListEqual(rust_tokenizer.convert_tokens_to_ids(lowerCAmelCase__ ) ,lowerCAmelCase__ )
def UpperCAmelCase_ ( self : Optional[int] ,*lowerCAmelCase__ : List[str] ,**lowerCAmelCase__ : Optional[Any] ) -> List[str]:
'''simple docstring'''
pass
def UpperCAmelCase_ ( self : Tuple ,lowerCAmelCase__ : Any=15 ) -> str:
'''simple docstring'''
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})''' ):
lowerCAmelCase_ : Any = self.rust_tokenizer_class.from_pretrained(lowerCAmelCase__ ,**lowerCAmelCase__ )
# Simple input
lowerCAmelCase_ : int = "This is a simple input"
lowerCAmelCase_ : Dict = ["This is a simple input 1", "This is a simple input 2"]
lowerCAmelCase_ : str = ("This is a simple input", "This is a pair")
lowerCAmelCase_ : Optional[int] = [
("This is a simple input 1", "This is a simple input 2"),
("This is a simple pair 1", "This is a simple pair 2"),
]
# Simple input tests
self.assertRaises(lowerCAmelCase__ ,tokenizer_r.encode ,lowerCAmelCase__ ,max_length=lowerCAmelCase__ ,padding="max_length" )
# Simple input
self.assertRaises(lowerCAmelCase__ ,tokenizer_r.encode_plus ,lowerCAmelCase__ ,max_length=lowerCAmelCase__ ,padding="max_length" )
# Simple input
self.assertRaises(
lowerCAmelCase__ ,tokenizer_r.batch_encode_plus ,lowerCAmelCase__ ,max_length=lowerCAmelCase__ ,padding="max_length" ,)
# Pair input
self.assertRaises(lowerCAmelCase__ ,tokenizer_r.encode ,lowerCAmelCase__ ,max_length=lowerCAmelCase__ ,padding="max_length" )
# Pair input
self.assertRaises(lowerCAmelCase__ ,tokenizer_r.encode_plus ,lowerCAmelCase__ ,max_length=lowerCAmelCase__ ,padding="max_length" )
# Pair input
self.assertRaises(
lowerCAmelCase__ ,tokenizer_r.batch_encode_plus ,lowerCAmelCase__ ,max_length=lowerCAmelCase__ ,padding="max_length" ,)
def UpperCAmelCase_ ( self : Optional[int] ) -> Optional[int]:
'''simple docstring'''
lowerCAmelCase_ : Tuple = CodeGenTokenizer.from_pretrained(self.tmpdirname ,pad_token="<pad>" )
# Simple input
lowerCAmelCase_ : Dict = "This is a simple input"
lowerCAmelCase_ : List[str] = ["This is a simple input looooooooong", "This is a simple input"]
lowerCAmelCase_ : Any = ("This is a simple input", "This is a pair")
lowerCAmelCase_ : List[str] = [
("This is a simple input loooooong", "This is a simple input"),
("This is a simple pair loooooong", "This is a simple pair"),
]
lowerCAmelCase_ : Dict = tokenizer.pad_token_id
lowerCAmelCase_ : Union[str, Any] = tokenizer(lowerCAmelCase__ ,padding="max_length" ,max_length=30 ,return_tensors="np" )
lowerCAmelCase_ : Tuple = tokenizer(lowerCAmelCase__ ,padding=lowerCAmelCase__ ,truncate=lowerCAmelCase__ ,return_tensors="np" )
lowerCAmelCase_ : Any = tokenizer(*lowerCAmelCase__ ,padding="max_length" ,max_length=60 ,return_tensors="np" )
lowerCAmelCase_ : Optional[int] = tokenizer(lowerCAmelCase__ ,padding=lowerCAmelCase__ ,truncate=lowerCAmelCase__ ,return_tensors="np" )
# s
# test single string max_length padding
self.assertEqual(out_s["input_ids"].shape[-1] ,30 )
self.assertTrue(pad_token_id in out_s["input_ids"] )
self.assertTrue(0 in out_s["attention_mask"] )
# s2
# test automatic padding
self.assertEqual(out_sa["input_ids"].shape[-1] ,33 )
# long slice doesn't have padding
self.assertFalse(pad_token_id in out_sa["input_ids"][0] )
self.assertFalse(0 in out_sa["attention_mask"][0] )
# short slice does have padding
self.assertTrue(pad_token_id in out_sa["input_ids"][1] )
self.assertTrue(0 in out_sa["attention_mask"][1] )
# p
# test single pair max_length padding
self.assertEqual(out_p["input_ids"].shape[-1] ,60 )
self.assertTrue(pad_token_id in out_p["input_ids"] )
self.assertTrue(0 in out_p["attention_mask"] )
# p2
# test automatic padding pair
self.assertEqual(out_pa["input_ids"].shape[-1] ,52 )
# long slice pair doesn't have padding
self.assertFalse(pad_token_id in out_pa["input_ids"][0] )
self.assertFalse(0 in out_pa["attention_mask"][0] )
# short slice pair does have padding
self.assertTrue(pad_token_id in out_pa["input_ids"][1] )
self.assertTrue(0 in out_pa["attention_mask"][1] )
def UpperCAmelCase_ ( self : Union[str, Any] ) -> Any:
'''simple docstring'''
lowerCAmelCase_ : Any = "$$$"
lowerCAmelCase_ : List[str] = CodeGenTokenizer.from_pretrained(self.tmpdirname ,bos_token=lowerCAmelCase__ ,add_bos_token=lowerCAmelCase__ )
lowerCAmelCase_ : Dict = "This is a simple input"
lowerCAmelCase_ : Union[str, Any] = ["This is a simple input 1", "This is a simple input 2"]
lowerCAmelCase_ : int = tokenizer.bos_token_id
lowerCAmelCase_ : List[Any] = tokenizer(lowerCAmelCase__ )
lowerCAmelCase_ : Optional[Any] = tokenizer(lowerCAmelCase__ )
self.assertEqual(out_s.input_ids[0] ,lowerCAmelCase__ )
self.assertTrue(all(o[0] == bos_token_id for o in out_sa.input_ids ) )
lowerCAmelCase_ : List[str] = tokenizer.decode(out_s.input_ids )
lowerCAmelCase_ : Optional[Any] = tokenizer.batch_decode(out_sa.input_ids )
self.assertEqual(decode_s.split()[0] ,lowerCAmelCase__ )
self.assertTrue(all(d.split()[0] == bos_token for d in decode_sa ) )
@slow
def UpperCAmelCase_ ( self : Any ) -> Optional[int]:
'''simple docstring'''
lowerCAmelCase_ : Optional[Any] = CodeGenTokenizer.from_pretrained("Salesforce/codegen-350M-mono" )
lowerCAmelCase_ : str = "\nif len_a > len_b:\n result = a\nelse:\n result = b\n\n\n\n#"
lowerCAmelCase_ : int = "\nif len_a > len_b: result = a\nelse: result = b"
lowerCAmelCase_ : Dict = tokenizer.encode(lowerCAmelCase__ )
lowerCAmelCase_ : str = ["^#", re.escape("<|endoftext|>" ), "^'''", "^\"\"\"", "\n\n\n"]
lowerCAmelCase_ : Union[str, Any] = tokenizer.decode(lowerCAmelCase__ ,truncate_before_pattern=lowerCAmelCase__ )
self.assertEqual(lowerCAmelCase__ ,lowerCAmelCase__ )
def UpperCAmelCase_ ( self : Optional[Any] ) -> Optional[Any]:
'''simple docstring'''
pass
| 683 | 1 |
import importlib.metadata
from typing import Union
from packaging.version import Version, parse
from .constants import STR_OPERATION_TO_FUNC
_lowercase = parse(importlib.metadata.version('''torch'''))
def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__):
if operation not in STR_OPERATION_TO_FUNC.keys():
raise ValueError(F'''`operation` must be one of {list(STR_OPERATION_TO_FUNC.keys())}, received {operation}''')
lowerCAmelCase_ : Dict = STR_OPERATION_TO_FUNC[operation]
if isinstance(snake_case__ , snake_case__):
lowerCAmelCase_ : Any = parse(importlib.metadata.version(snake_case__))
return operation(snake_case__ , parse(snake_case__))
def UpperCamelCase ( snake_case__ , snake_case__):
return compare_versions(snake_case__ , snake_case__ , snake_case__)
| 683 |
from __future__ import annotations
from random import random
class __snake_case :
"""simple docstring"""
def __init__( self : Optional[int] ,lowerCAmelCase__ : int | None = None ) -> int:
'''simple docstring'''
lowerCAmelCase_ : Dict = value
lowerCAmelCase_ : Any = random()
lowerCAmelCase_ : Node | None = None
lowerCAmelCase_ : Node | None = None
def __repr__( self : Any ) -> str:
'''simple docstring'''
from pprint import pformat
if self.left is None and self.right is None:
return f'''\'{self.value}: {self.prior:.5}\''''
else:
return pformat(
{f'''{self.value}: {self.prior:.5}''': (self.left, self.right)} ,indent=1 )
def __str__( self : str ) -> str:
'''simple docstring'''
lowerCAmelCase_ : List[Any] = str(self.value ) + " "
lowerCAmelCase_ : List[Any] = str(self.left or "" )
lowerCAmelCase_ : Union[str, Any] = str(self.right or "" )
return value + left + right
def UpperCamelCase ( snake_case__ , snake_case__):
if root is None: # None tree is split into 2 Nones
return None, None
elif root.value is None:
return None, None
else:
if value < root.value:
lowerCAmelCase_ , lowerCAmelCase_ : Any = split(root.left , snake_case__)
return left, root
else:
lowerCAmelCase_ , lowerCAmelCase_ : Optional[Any] = split(root.right , snake_case__)
return root, right
def UpperCamelCase ( snake_case__ , snake_case__):
if (not left) or (not right): # If one node is None, return the other
return left or right
elif left.prior < right.prior:
lowerCAmelCase_ : Dict = merge(left.right , snake_case__)
return left
else:
lowerCAmelCase_ : List[str] = merge(snake_case__ , right.left)
return right
def UpperCamelCase ( snake_case__ , snake_case__):
lowerCAmelCase_ : List[Any] = Node(snake_case__)
lowerCAmelCase_ , lowerCAmelCase_ : Tuple = split(snake_case__ , snake_case__)
return merge(merge(snake_case__ , snake_case__) , snake_case__)
def UpperCamelCase ( snake_case__ , snake_case__):
lowerCAmelCase_ , lowerCAmelCase_ : List[str] = split(snake_case__ , value - 1)
lowerCAmelCase_ , lowerCAmelCase_ : Union[str, Any] = split(snake_case__ , snake_case__)
return merge(snake_case__ , snake_case__)
def UpperCamelCase ( snake_case__):
if not root: # None
return
else:
inorder(root.left)
print(root.value , end=",")
inorder(root.right)
def UpperCamelCase ( snake_case__ , snake_case__):
for arg in args.split():
if arg[0] == "+":
lowerCAmelCase_ : List[str] = insert(snake_case__ , int(arg[1:]))
elif arg[0] == "-":
lowerCAmelCase_ : Optional[int] = erase(snake_case__ , int(arg[1:]))
else:
print("Unknown command")
return root
def UpperCamelCase ( ):
lowerCAmelCase_ : str = None
print(
"enter numbers to create a tree, + value to add value into treap, "
"- value to erase all nodes with value. 'q' to quit. ")
lowerCAmelCase_ : str = input()
while args != "q":
lowerCAmelCase_ : int = interact_treap(snake_case__ , snake_case__)
print(snake_case__)
lowerCAmelCase_ : str = input()
print("good by!")
if __name__ == "__main__":
import doctest
doctest.testmod()
main()
| 683 | 1 |
def UpperCamelCase ( snake_case__):
lowerCAmelCase_ : Optional[int] = []
lowerCAmelCase_ : int = []
lowerCAmelCase_ : Optional[int] = {
"^": 3,
"*": 2,
"/": 2,
"%": 2,
"+": 1,
"-": 1,
} # Priority of each operator
lowerCAmelCase_ : Union[str, Any] = len(snake_case__) if (len(snake_case__) > 7) else 7
# Print table header for output
print(
"Symbol".center(8) , "Stack".center(snake_case__) , "Postfix".center(snake_case__) , sep=" | " , )
print("-" * (print_width * 3 + 7))
for x in infix:
if x.isalpha() or x.isdigit():
post_fix.append(snake_case__) # if x is Alphabet / Digit, add it to Postfix
elif x == "(":
stack.append(snake_case__) # if x is "(" push to Stack
elif x == ")": # if x is ")" pop stack until "(" is encountered
while stack[-1] != "(":
post_fix.append(stack.pop()) # Pop stack & add the content to Postfix
stack.pop()
else:
if len(snake_case__) == 0:
stack.append(snake_case__) # If stack is empty, push x to stack
else: # while priority of x is not > priority of element in the stack
while len(snake_case__) > 0 and priority[x] <= priority[stack[-1]]:
post_fix.append(stack.pop()) # pop stack & add to Postfix
stack.append(snake_case__) # push x to stack
print(
x.center(8) , ("".join(snake_case__)).ljust(snake_case__) , ("".join(snake_case__)).ljust(snake_case__) , sep=" | " , ) # Output in tabular format
while len(snake_case__) > 0: # while stack is not empty
post_fix.append(stack.pop()) # pop stack & add to Postfix
print(
" ".center(8) , ("".join(snake_case__)).ljust(snake_case__) , ("".join(snake_case__)).ljust(snake_case__) , sep=" | " , ) # Output in tabular format
return "".join(snake_case__) # return Postfix as str
def UpperCamelCase ( snake_case__):
lowerCAmelCase_ : Union[str, Any] = list(infix[::-1]) # reverse the infix equation
for i in range(len(snake_case__)):
if infix[i] == "(":
lowerCAmelCase_ : str = ")" # change "(" to ")"
elif infix[i] == ")":
lowerCAmelCase_ : Optional[Any] = "(" # change ")" to "("
return (infix_2_postfix("".join(snake_case__)))[
::-1
] # call infix_2_postfix on Infix, return reverse of Postfix
if __name__ == "__main__":
_lowercase = input('''\nEnter an Infix Equation = ''') # Input an Infix equation
_lowercase = ''''''.join(Infix.split()) # Remove spaces from the input
print('''\n\t''', Infix, '''(Infix) -> ''', infix_2_prefix(Infix), '''(Prefix)''')
| 683 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_funnel import FunnelTokenizer
_lowercase = logging.get_logger(__name__)
_lowercase = {'''vocab_file''': '''vocab.txt''', '''tokenizer_file''': '''tokenizer.json'''}
_lowercase = [
'''small''',
'''small-base''',
'''medium''',
'''medium-base''',
'''intermediate''',
'''intermediate-base''',
'''large''',
'''large-base''',
'''xlarge''',
'''xlarge-base''',
]
_lowercase = {
'''vocab_file''': {
'''funnel-transformer/small''': '''https://huggingface.co/funnel-transformer/small/resolve/main/vocab.txt''',
'''funnel-transformer/small-base''': '''https://huggingface.co/funnel-transformer/small-base/resolve/main/vocab.txt''',
'''funnel-transformer/medium''': '''https://huggingface.co/funnel-transformer/medium/resolve/main/vocab.txt''',
'''funnel-transformer/medium-base''': (
'''https://huggingface.co/funnel-transformer/medium-base/resolve/main/vocab.txt'''
),
'''funnel-transformer/intermediate''': (
'''https://huggingface.co/funnel-transformer/intermediate/resolve/main/vocab.txt'''
),
'''funnel-transformer/intermediate-base''': (
'''https://huggingface.co/funnel-transformer/intermediate-base/resolve/main/vocab.txt'''
),
'''funnel-transformer/large''': '''https://huggingface.co/funnel-transformer/large/resolve/main/vocab.txt''',
'''funnel-transformer/large-base''': '''https://huggingface.co/funnel-transformer/large-base/resolve/main/vocab.txt''',
'''funnel-transformer/xlarge''': '''https://huggingface.co/funnel-transformer/xlarge/resolve/main/vocab.txt''',
'''funnel-transformer/xlarge-base''': (
'''https://huggingface.co/funnel-transformer/xlarge-base/resolve/main/vocab.txt'''
),
},
'''tokenizer_file''': {
'''funnel-transformer/small''': '''https://huggingface.co/funnel-transformer/small/resolve/main/tokenizer.json''',
'''funnel-transformer/small-base''': (
'''https://huggingface.co/funnel-transformer/small-base/resolve/main/tokenizer.json'''
),
'''funnel-transformer/medium''': '''https://huggingface.co/funnel-transformer/medium/resolve/main/tokenizer.json''',
'''funnel-transformer/medium-base''': (
'''https://huggingface.co/funnel-transformer/medium-base/resolve/main/tokenizer.json'''
),
'''funnel-transformer/intermediate''': (
'''https://huggingface.co/funnel-transformer/intermediate/resolve/main/tokenizer.json'''
),
'''funnel-transformer/intermediate-base''': (
'''https://huggingface.co/funnel-transformer/intermediate-base/resolve/main/tokenizer.json'''
),
'''funnel-transformer/large''': '''https://huggingface.co/funnel-transformer/large/resolve/main/tokenizer.json''',
'''funnel-transformer/large-base''': (
'''https://huggingface.co/funnel-transformer/large-base/resolve/main/tokenizer.json'''
),
'''funnel-transformer/xlarge''': '''https://huggingface.co/funnel-transformer/xlarge/resolve/main/tokenizer.json''',
'''funnel-transformer/xlarge-base''': (
'''https://huggingface.co/funnel-transformer/xlarge-base/resolve/main/tokenizer.json'''
),
},
}
_lowercase = {f"funnel-transformer/{name}": 512 for name in _model_names}
_lowercase = {f"funnel-transformer/{name}": {'''do_lower_case''': True} for name in _model_names}
class __snake_case ( snake_case__ ):
"""simple docstring"""
UpperCamelCase_ = VOCAB_FILES_NAMES
UpperCamelCase_ = PRETRAINED_VOCAB_FILES_MAP
UpperCamelCase_ = PRETRAINED_INIT_CONFIGURATION
UpperCamelCase_ = FunnelTokenizer
UpperCamelCase_ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
UpperCamelCase_ = 2
def __init__( self : Optional[Any] ,lowerCAmelCase__ : Any=None ,lowerCAmelCase__ : Optional[int]=None ,lowerCAmelCase__ : Optional[Any]=True ,lowerCAmelCase__ : List[str]="<unk>" ,lowerCAmelCase__ : int="<sep>" ,lowerCAmelCase__ : Union[str, Any]="<pad>" ,lowerCAmelCase__ : List[str]="<cls>" ,lowerCAmelCase__ : Optional[int]="<mask>" ,lowerCAmelCase__ : Union[str, Any]="<s>" ,lowerCAmelCase__ : List[str]="</s>" ,lowerCAmelCase__ : Optional[int]=True ,lowerCAmelCase__ : Tuple=True ,lowerCAmelCase__ : Any=None ,lowerCAmelCase__ : List[Any]="##" ,**lowerCAmelCase__ : int ,) -> List[Any]:
'''simple docstring'''
super().__init__(
lowerCAmelCase__ ,tokenizer_file=lowerCAmelCase__ ,do_lower_case=lowerCAmelCase__ ,unk_token=lowerCAmelCase__ ,sep_token=lowerCAmelCase__ ,pad_token=lowerCAmelCase__ ,cls_token=lowerCAmelCase__ ,mask_token=lowerCAmelCase__ ,bos_token=lowerCAmelCase__ ,eos_token=lowerCAmelCase__ ,clean_text=lowerCAmelCase__ ,tokenize_chinese_chars=lowerCAmelCase__ ,strip_accents=lowerCAmelCase__ ,wordpieces_prefix=lowerCAmelCase__ ,**lowerCAmelCase__ ,)
lowerCAmelCase_ : str = json.loads(self.backend_tokenizer.normalizer.__getstate__() )
if (
normalizer_state.get("lowercase" ,lowerCAmelCase__ ) != do_lower_case
or normalizer_state.get("strip_accents" ,lowerCAmelCase__ ) != strip_accents
or normalizer_state.get("handle_chinese_chars" ,lowerCAmelCase__ ) != tokenize_chinese_chars
):
lowerCAmelCase_ : Optional[int] = getattr(lowerCAmelCase__ ,normalizer_state.pop("type" ) )
lowerCAmelCase_ : List[Any] = do_lower_case
lowerCAmelCase_ : List[str] = strip_accents
lowerCAmelCase_ : Any = tokenize_chinese_chars
lowerCAmelCase_ : List[Any] = normalizer_class(**lowerCAmelCase__ )
lowerCAmelCase_ : int = do_lower_case
def UpperCAmelCase_ ( self : Tuple ,lowerCAmelCase__ : int ,lowerCAmelCase__ : str=None ) -> List[Any]:
'''simple docstring'''
lowerCAmelCase_ : Union[str, Any] = [self.cls_token_id] + token_ids_a + [self.sep_token_id]
if token_ids_a:
output += token_ids_a + [self.sep_token_id]
return output
def UpperCAmelCase_ ( self : List[str] ,lowerCAmelCase__ : List[int] ,lowerCAmelCase__ : Optional[List[int]] = None ) -> List[int]:
'''simple docstring'''
lowerCAmelCase_ : str = [self.sep_token_id]
lowerCAmelCase_ : Optional[Any] = [self.cls_token_id]
if token_ids_a is None:
return len(cls ) * [self.cls_token_type_id] + len(token_ids_a + sep ) * [0]
return len(cls ) * [self.cls_token_type_id] + len(token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]
def UpperCAmelCase_ ( self : Any ,lowerCAmelCase__ : str ,lowerCAmelCase__ : Optional[str] = None ) -> Tuple[str]:
'''simple docstring'''
lowerCAmelCase_ : str = self._tokenizer.model.save(lowerCAmelCase__ ,name=lowerCAmelCase__ )
return tuple(lowerCAmelCase__ )
| 683 | 1 |
import argparse
import os
import pickle
import sys
import torch
from transformers import TransfoXLConfig, TransfoXLLMHeadModel, load_tf_weights_in_transfo_xl
from transformers.models.transfo_xl import tokenization_transfo_xl as data_utils
from transformers.models.transfo_xl.tokenization_transfo_xl import CORPUS_NAME, VOCAB_FILES_NAMES
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
logging.set_verbosity_info()
# We do this to be able to load python 2 datasets pickles
# See e.g. https://stackoverflow.com/questions/2121874/python-pickling-after-changing-a-modules-directory/2121918#2121918
_lowercase = data_utils.TransfoXLTokenizer
_lowercase = data_utils.TransfoXLCorpus
_lowercase = data_utils
_lowercase = data_utils
def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__ , snake_case__):
if transfo_xl_dataset_file:
# Convert a pre-processed corpus (see original TensorFlow repo)
with open(snake_case__ , "rb") as fp:
lowerCAmelCase_ : List[Any] = pickle.load(snake_case__ , encoding="latin1")
# Save vocabulary and dataset cache as Dictionaries (should be better than pickles for the long-term)
lowerCAmelCase_ : List[str] = pytorch_dump_folder_path + "/" + VOCAB_FILES_NAMES["pretrained_vocab_file"]
print(F'''Save vocabulary to {pytorch_vocab_dump_path}''')
lowerCAmelCase_ : Tuple = corpus.vocab.__dict__
torch.save(snake_case__ , snake_case__)
lowerCAmelCase_ : int = corpus.__dict__
corpus_dict_no_vocab.pop("vocab" , snake_case__)
lowerCAmelCase_ : List[Any] = pytorch_dump_folder_path + "/" + CORPUS_NAME
print(F'''Save dataset to {pytorch_dataset_dump_path}''')
torch.save(snake_case__ , snake_case__)
if tf_checkpoint_path:
# Convert a pre-trained TensorFlow model
lowerCAmelCase_ : Tuple = os.path.abspath(snake_case__)
lowerCAmelCase_ : str = os.path.abspath(snake_case__)
print(F'''Converting Transformer XL checkpoint from {tf_path} with config at {config_path}.''')
# Initialise PyTorch model
if transfo_xl_config_file == "":
lowerCAmelCase_ : Tuple = TransfoXLConfig()
else:
lowerCAmelCase_ : Tuple = TransfoXLConfig.from_json_file(snake_case__)
print(F'''Building PyTorch model from configuration: {config}''')
lowerCAmelCase_ : Union[str, Any] = TransfoXLLMHeadModel(snake_case__)
lowerCAmelCase_ : List[str] = load_tf_weights_in_transfo_xl(snake_case__ , snake_case__ , snake_case__)
# Save pytorch-model
lowerCAmelCase_ : int = os.path.join(snake_case__ , snake_case__)
lowerCAmelCase_ : Optional[int] = os.path.join(snake_case__ , snake_case__)
print(F'''Save PyTorch model to {os.path.abspath(snake_case__)}''')
torch.save(model.state_dict() , snake_case__)
print(F'''Save configuration file to {os.path.abspath(snake_case__)}''')
with open(snake_case__ , "w" , encoding="utf-8") as f:
f.write(config.to_json_string())
if __name__ == "__main__":
_lowercase = argparse.ArgumentParser()
parser.add_argument(
'''--pytorch_dump_folder_path''',
default=None,
type=str,
required=True,
help='''Path to the folder to store the PyTorch model or dataset/vocab.''',
)
parser.add_argument(
'''--tf_checkpoint_path''',
default='''''',
type=str,
help='''An optional path to a TensorFlow checkpoint path to be converted.''',
)
parser.add_argument(
'''--transfo_xl_config_file''',
default='''''',
type=str,
help=(
'''An optional config json file corresponding to the pre-trained BERT model. \n'''
'''This specifies the model architecture.'''
),
)
parser.add_argument(
'''--transfo_xl_dataset_file''',
default='''''',
type=str,
help='''An optional dataset file to be converted in a vocabulary.''',
)
_lowercase = parser.parse_args()
convert_transfo_xl_checkpoint_to_pytorch(
args.tf_checkpoint_path,
args.transfo_xl_config_file,
args.pytorch_dump_folder_path,
args.transfo_xl_dataset_file,
)
| 683 |
# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run
import doctest
import sys
import warnings
from os.path import abspath, dirname, join
import _pytest
from transformers.testing_utils import HfDoctestModule, HfDocTestParser
# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install -e .[dev]' when switching between checkouts and running tests.
_lowercase = abspath(join(dirname(__file__), '''src'''))
sys.path.insert(1, git_repo_path)
# silence FutureWarning warnings in tests since often we can't act on them until
# they become normal warnings - i.e. the tests still need to test the current functionality
warnings.simplefilter(action='''ignore''', category=FutureWarning)
def UpperCamelCase ( snake_case__):
config.addinivalue_line(
"markers" , "is_pt_tf_cross_test: mark test to run only when PT and TF interactions are tested")
config.addinivalue_line(
"markers" , "is_pt_flax_cross_test: mark test to run only when PT and FLAX interactions are tested")
config.addinivalue_line("markers" , "is_pipeline_test: mark test to run only when pipelines are tested")
config.addinivalue_line("markers" , "is_staging_test: mark test to run only in the staging environment")
config.addinivalue_line("markers" , "accelerate_tests: mark test that require accelerate")
config.addinivalue_line("markers" , "tool_tests: mark the tool tests that are run on their specific schedule")
def UpperCamelCase ( snake_case__):
from transformers.testing_utils import pytest_addoption_shared
pytest_addoption_shared(snake_case__)
def UpperCamelCase ( snake_case__):
from transformers.testing_utils import pytest_terminal_summary_main
lowerCAmelCase_ : int = terminalreporter.config.getoption("--make-reports")
if make_reports:
pytest_terminal_summary_main(snake_case__ , id=snake_case__)
def UpperCamelCase ( snake_case__ , snake_case__):
# If no tests are collected, pytest exists with code 5, which makes the CI fail.
if exitstatus == 5:
lowerCAmelCase_ : List[Any] = 0
# Doctest custom flag to ignore output.
_lowercase = doctest.register_optionflag('''IGNORE_RESULT''')
_lowercase = doctest.OutputChecker
class __snake_case ( snake_case__ ):
"""simple docstring"""
def UpperCAmelCase_ ( self : List[str] ,lowerCAmelCase__ : int ,lowerCAmelCase__ : Optional[int] ,lowerCAmelCase__ : Tuple ) -> Any:
'''simple docstring'''
if IGNORE_RESULT & optionflags:
return True
return OutputChecker.check_output(self ,lowerCAmelCase__ ,lowerCAmelCase__ ,lowerCAmelCase__ )
_lowercase = CustomOutputChecker
_lowercase = HfDoctestModule
_lowercase = HfDocTestParser
| 683 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowercase = logging.get_logger(__name__)
_lowercase = {
'''microsoft/trocr-base-handwritten''': (
'''https://huggingface.co/microsoft/trocr-base-handwritten/resolve/main/config.json'''
),
# See all TrOCR models at https://huggingface.co/models?filter=trocr
}
class __snake_case ( snake_case__ ):
"""simple docstring"""
UpperCamelCase_ = 'trocr'
UpperCamelCase_ = ['past_key_values']
UpperCamelCase_ = {
'num_attention_heads': 'decoder_attention_heads',
'hidden_size': 'd_model',
'num_hidden_layers': 'decoder_layers',
}
def __init__( self : str ,lowerCAmelCase__ : List[str]=5_02_65 ,lowerCAmelCase__ : str=10_24 ,lowerCAmelCase__ : Optional[int]=12 ,lowerCAmelCase__ : Tuple=16 ,lowerCAmelCase__ : Dict=40_96 ,lowerCAmelCase__ : int="gelu" ,lowerCAmelCase__ : Union[str, Any]=5_12 ,lowerCAmelCase__ : Dict=0.1 ,lowerCAmelCase__ : Optional[Any]=0.0 ,lowerCAmelCase__ : List[str]=0.0 ,lowerCAmelCase__ : Optional[Any]=2 ,lowerCAmelCase__ : List[str]=0.02 ,lowerCAmelCase__ : Tuple=0.0 ,lowerCAmelCase__ : Optional[Any]=True ,lowerCAmelCase__ : Any=False ,lowerCAmelCase__ : Any=True ,lowerCAmelCase__ : Dict=True ,lowerCAmelCase__ : Optional[Any]=1 ,lowerCAmelCase__ : Optional[int]=0 ,lowerCAmelCase__ : Union[str, Any]=2 ,**lowerCAmelCase__ : Tuple ,) -> int:
'''simple docstring'''
lowerCAmelCase_ : Optional[int] = vocab_size
lowerCAmelCase_ : Tuple = d_model
lowerCAmelCase_ : List[Any] = decoder_layers
lowerCAmelCase_ : Optional[Any] = decoder_attention_heads
lowerCAmelCase_ : int = decoder_ffn_dim
lowerCAmelCase_ : Optional[Any] = activation_function
lowerCAmelCase_ : List[str] = max_position_embeddings
lowerCAmelCase_ : Optional[Any] = dropout
lowerCAmelCase_ : Tuple = attention_dropout
lowerCAmelCase_ : Any = activation_dropout
lowerCAmelCase_ : Optional[int] = init_std
lowerCAmelCase_ : Dict = decoder_layerdrop
lowerCAmelCase_ : int = use_cache
lowerCAmelCase_ : Dict = scale_embedding
lowerCAmelCase_ : Optional[Any] = use_learned_position_embeddings
lowerCAmelCase_ : Any = layernorm_embedding
super().__init__(
pad_token_id=lowerCAmelCase__ ,bos_token_id=lowerCAmelCase__ ,eos_token_id=lowerCAmelCase__ ,decoder_start_token_id=lowerCAmelCase__ ,**lowerCAmelCase__ ,)
| 683 |
from __future__ import annotations
from collections.abc import Sequence
from typing import Literal
def UpperCamelCase ( snake_case__ , snake_case__):
lowerCAmelCase_ : Optional[int] = list(snake_case__)
lowerCAmelCase_ : Tuple = list(snake_case__)
lowerCAmelCase_ : List[str] = 0
for i in range(len(snake_case__)):
if lista[i] != lista[i]:
count += 1
lowerCAmelCase_ : Dict = "_"
if count > 1:
return False
else:
return "".join(snake_case__)
def UpperCamelCase ( snake_case__):
lowerCAmelCase_ : Union[str, Any] = []
while True:
lowerCAmelCase_ : Tuple = ["$"] * len(snake_case__)
lowerCAmelCase_ : Tuple = []
for i in range(len(snake_case__)):
for j in range(i + 1 , len(snake_case__)):
lowerCAmelCase_ : Optional[int] = compare_string(binary[i] , binary[j])
if k is False:
lowerCAmelCase_ : str = "*"
lowerCAmelCase_ : Tuple = "*"
temp.append("X")
for i in range(len(snake_case__)):
if checka[i] == "$":
pi.append(binary[i])
if len(snake_case__) == 0:
return pi
lowerCAmelCase_ : List[Any] = list(set(snake_case__))
def UpperCamelCase ( snake_case__ , snake_case__):
lowerCAmelCase_ : Optional[int] = []
for minterm in minterms:
lowerCAmelCase_ : Dict = ""
for _ in range(snake_case__):
lowerCAmelCase_ : Dict = str(minterm % 2) + string
minterm //= 2
temp.append(snake_case__)
return temp
def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__):
lowerCAmelCase_ : Optional[Any] = list(snake_case__)
lowerCAmelCase_ : Dict = list(snake_case__)
lowerCAmelCase_ : Dict = 0
for i in range(len(snake_case__)):
if lista[i] != lista[i]:
count_n += 1
return count_n == count
def UpperCamelCase ( snake_case__ , snake_case__):
lowerCAmelCase_ : Optional[Any] = []
lowerCAmelCase_ : Dict = [0] * len(snake_case__)
for i in range(len(chart[0])):
lowerCAmelCase_ : List[Any] = 0
lowerCAmelCase_ : int = -1
for j in range(len(snake_case__)):
if chart[j][i] == 1:
count += 1
lowerCAmelCase_ : Optional[int] = j
if count == 1:
lowerCAmelCase_ : Union[str, Any] = 1
for i in range(len(snake_case__)):
if select[i] == 1:
for j in range(len(chart[0])):
if chart[i][j] == 1:
for k in range(len(snake_case__)):
lowerCAmelCase_ : Tuple = 0
temp.append(prime_implicants[i])
while True:
lowerCAmelCase_ : Optional[Any] = 0
lowerCAmelCase_ : Dict = -1
lowerCAmelCase_ : Tuple = 0
for i in range(len(snake_case__)):
lowerCAmelCase_ : Dict = chart[i].count(1)
if count_n > max_n:
lowerCAmelCase_ : Optional[int] = count_n
lowerCAmelCase_ : Optional[Any] = i
if max_n == 0:
return temp
temp.append(prime_implicants[rem])
for i in range(len(chart[0])):
if chart[rem][i] == 1:
for j in range(len(snake_case__)):
lowerCAmelCase_ : Any = 0
def UpperCamelCase ( snake_case__ , snake_case__):
lowerCAmelCase_ : str = [[0 for x in range(len(snake_case__))] for x in range(len(snake_case__))]
for i in range(len(snake_case__)):
lowerCAmelCase_ : Optional[Any] = prime_implicants[i].count("_")
for j in range(len(snake_case__)):
if is_for_table(prime_implicants[i] , binary[j] , snake_case__):
lowerCAmelCase_ : Dict = 1
return chart
def UpperCamelCase ( ):
lowerCAmelCase_ : Optional[Any] = int(input("Enter the no. of variables\n"))
lowerCAmelCase_ : Tuple = [
float(snake_case__)
for x in input(
"Enter the decimal representation of Minterms 'Spaces Separated'\n").split()
]
lowerCAmelCase_ : Any = decimal_to_binary(snake_case__ , snake_case__)
lowerCAmelCase_ : Dict = check(snake_case__)
print("Prime Implicants are:")
print(snake_case__)
lowerCAmelCase_ : int = prime_implicant_chart(snake_case__ , snake_case__)
lowerCAmelCase_ : List[str] = selection(snake_case__ , snake_case__)
print("Essential Prime Implicants are:")
print(snake_case__)
if __name__ == "__main__":
import doctest
doctest.testmod()
main()
| 683 | 1 |
import copy
import re
class __snake_case :
"""simple docstring"""
UpperCamelCase_ = 'hp'
UpperCamelCase_ = {}
UpperCamelCase_ = None
@classmethod
def UpperCAmelCase_ ( cls : List[str] ,lowerCAmelCase__ : List[str] ,lowerCAmelCase__ : Tuple ) -> Optional[Any]:
'''simple docstring'''
lowerCAmelCase_ : Dict = prefix
lowerCAmelCase_ : List[Any] = defaults
cls.build_naming_info()
@staticmethod
def UpperCAmelCase_ ( lowerCAmelCase__ : Tuple ,lowerCAmelCase__ : List[str] ) -> List[Any]:
'''simple docstring'''
if len(lowerCAmelCase__ ) == 0:
return ""
lowerCAmelCase_ : Dict = None
if any(char.isdigit() for char in word ):
raise Exception(f'''Parameters should not contain numbers: \'{word}\' contains a number''' )
if word in info["short_word"]:
return info["short_word"][word]
for prefix_len in range(1 ,len(lowerCAmelCase__ ) + 1 ):
lowerCAmelCase_ : Tuple = word[:prefix_len]
if prefix in info["reverse_short_word"]:
continue
else:
lowerCAmelCase_ : Any = prefix
break
if short_word is None:
# Paranoid fallback
def int_to_alphabetic(lowerCAmelCase__ : Union[str, Any] ):
lowerCAmelCase_ : str = ""
while integer != 0:
lowerCAmelCase_ : Dict = chr(ord("A" ) + integer % 10 ) + s
integer //= 10
return s
lowerCAmelCase_ : Tuple = 0
while True:
lowerCAmelCase_ : Optional[Any] = word + "#" + int_to_alphabetic(lowerCAmelCase__ )
if sword in info["reverse_short_word"]:
continue
else:
lowerCAmelCase_ : str = sword
break
lowerCAmelCase_ : Union[str, Any] = short_word
lowerCAmelCase_ : Dict = word
return short_word
@staticmethod
def UpperCAmelCase_ ( lowerCAmelCase__ : Dict ,lowerCAmelCase__ : List[str] ) -> int:
'''simple docstring'''
lowerCAmelCase_ : Optional[Any] = param_name.split("_" )
lowerCAmelCase_ : Dict = [TrialShortNamer.shortname_for_word(lowerCAmelCase__ ,lowerCAmelCase__ ) for word in words]
# We try to create a separatorless short name, but if there is a collision we have to fallback
# to a separated short name
lowerCAmelCase_ : Tuple = ["", "_"]
for separator in separators:
lowerCAmelCase_ : List[Any] = separator.join(lowerCAmelCase__ )
if shortname not in info["reverse_short_param"]:
lowerCAmelCase_ : List[str] = shortname
lowerCAmelCase_ : Optional[int] = param_name
return shortname
return param_name
@staticmethod
def UpperCAmelCase_ ( lowerCAmelCase__ : List[Any] ,lowerCAmelCase__ : str ) -> Tuple:
'''simple docstring'''
lowerCAmelCase_ : Dict = TrialShortNamer.shortname_for_key(lowerCAmelCase__ ,lowerCAmelCase__ )
lowerCAmelCase_ : Any = short_name
lowerCAmelCase_ : Union[str, Any] = param_name
@classmethod
def UpperCAmelCase_ ( cls : Any ) -> Optional[int]:
'''simple docstring'''
if cls.NAMING_INFO is not None:
return
lowerCAmelCase_ : Optional[int] = {
"short_word": {},
"reverse_short_word": {},
"short_param": {},
"reverse_short_param": {},
}
lowerCAmelCase_ : Optional[int] = list(cls.DEFAULTS.keys() )
for k in field_keys:
cls.add_new_param_name(lowerCAmelCase__ ,lowerCAmelCase__ )
lowerCAmelCase_ : List[str] = info
@classmethod
def UpperCAmelCase_ ( cls : Any ,lowerCAmelCase__ : Tuple ) -> str:
'''simple docstring'''
cls.build_naming_info()
assert cls.PREFIX is not None
lowerCAmelCase_ : Tuple = [copy.copy(cls.PREFIX )]
for k, v in params.items():
if k not in cls.DEFAULTS:
raise Exception(f'''You should provide a default value for the param name {k} with value {v}''' )
if v == cls.DEFAULTS[k]:
# The default value is not added to the name
continue
lowerCAmelCase_ : Optional[int] = cls.NAMING_INFO["short_param"][k]
if isinstance(lowerCAmelCase__ ,lowerCAmelCase__ ):
lowerCAmelCase_ : Any = 1 if v else 0
lowerCAmelCase_ : List[Any] = "" if isinstance(lowerCAmelCase__ ,(int, float) ) else "-"
lowerCAmelCase_ : Dict = f'''{key}{sep}{v}'''
name.append(lowerCAmelCase__ )
return "_".join(lowerCAmelCase__ )
@classmethod
def UpperCAmelCase_ ( cls : int ,lowerCAmelCase__ : Any ) -> List[str]:
'''simple docstring'''
lowerCAmelCase_ : List[str] = repr[len(cls.PREFIX ) + 1 :]
if repr == "":
lowerCAmelCase_ : Union[str, Any] = []
else:
lowerCAmelCase_ : Union[str, Any] = repr.split("_" )
lowerCAmelCase_ : Tuple = {}
for value in values:
if "-" in value:
lowerCAmelCase_ , lowerCAmelCase_ : Tuple = value.split("-" )
else:
lowerCAmelCase_ : List[Any] = re.sub("[0-9.]" ,"" ,lowerCAmelCase__ )
lowerCAmelCase_ : Optional[int] = float(re.sub("[^0-9.]" ,"" ,lowerCAmelCase__ ) )
lowerCAmelCase_ : str = cls.NAMING_INFO["reverse_short_param"][p_k]
lowerCAmelCase_ : int = p_v
for k in cls.DEFAULTS:
if k not in parameters:
lowerCAmelCase_ : List[Any] = cls.DEFAULTS[k]
return parameters
| 683 |
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
_lowercase = logging.getLogger(__name__)
def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__ = None , snake_case__ = None , snake_case__ = None , snake_case__ = None , snake_case__ = None , snake_case__ = False , ):
lowerCAmelCase_ : List[Any] = bnb_quantization_config.load_in_abit
lowerCAmelCase_ : Optional[Any] = 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.")
lowerCAmelCase_ : List[str] = []
# custom device map
if isinstance(snake_case__ , snake_case__) and len(device_map.keys()) > 1:
lowerCAmelCase_ : Union[str, Any] = [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:
lowerCAmelCase_ : Union[str, Any] = get_keys_to_not_convert(snake_case__)
# add cpu modules to skip modules only for 4-bit modules
if load_in_abit:
bnb_quantization_config.skip_modules.extend(snake_case__)
lowerCAmelCase_ : Union[str, Any] = 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:
lowerCAmelCase_ : Optional[int] = []
lowerCAmelCase_ : int = bnb_quantization_config.keep_in_fpaa_modules
modules_to_not_convert.extend(snake_case__)
# compatibility with peft
lowerCAmelCase_ : Optional[int] = load_in_abit
lowerCAmelCase_ : List[str] = load_in_abit
lowerCAmelCase_ : Optional[int] = get_parameter_device(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.")
lowerCAmelCase_ : Union[str, Any] = replace_with_bnb_layers(snake_case__ , snake_case__ , modules_to_not_convert=snake_case__)
# convert param to the right dtype
lowerCAmelCase_ : Any = 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:
lowerCAmelCase_ : Optional[int] = name.replace(".weight" , "").replace(".bias" , "")
lowerCAmelCase_ : Optional[int] = getattr(snake_case__ , snake_case__ , snake_case__)
if param is not None:
param.to(torch.floataa)
elif torch.is_floating_point(snake_case__):
param.to(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():
lowerCAmelCase_ : str = replace_with_bnb_layers(
snake_case__ , snake_case__ , modules_to_not_convert=snake_case__)
lowerCAmelCase_ : Optional[int] = get_quantized_model_device_map(
snake_case__ , snake_case__ , snake_case__ , max_memory=snake_case__ , no_split_module_classes=snake_case__ , )
if offload_state_dict is None and device_map is not None and "disk" in device_map.values():
lowerCAmelCase_ : Optional[Any] = True
lowerCAmelCase_ : Optional[int] = any(x in list(device_map.values()) for x in ["cpu", "disk"])
load_checkpoint_in_model(
snake_case__ , snake_case__ , snake_case__ , dtype=bnb_quantization_config.torch_dtype , offload_folder=snake_case__ , offload_state_dict=snake_case__ , keep_in_fpaa_modules=bnb_quantization_config.keep_in_fpaa_modules , offload_abit_bnb=load_in_abit and offload , )
return dispatch_model(snake_case__ , device_map=snake_case__ , offload_dir=snake_case__)
def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__=None , snake_case__=None , snake_case__=None):
if device_map is None:
if torch.cuda.is_available():
lowerCAmelCase_ : Any = {"": 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(snake_case__ , 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'.")
lowerCAmelCase_ : Dict = {}
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)
})
lowerCAmelCase_ : List[str] = {}
lowerCAmelCase_ : Union[str, Any] = special_dtypes
lowerCAmelCase_ : Union[str, Any] = no_split_module_classes
lowerCAmelCase_ : Any = bnb_quantization_config.target_dtype
# get max_memory for each device.
if device_map != "sequential":
lowerCAmelCase_ : Tuple = get_balanced_memory(
snake_case__ , low_zero=(device_map == "balanced_low_0") , max_memory=snake_case__ , **snake_case__ , )
lowerCAmelCase_ : Tuple = max_memory
lowerCAmelCase_ : Optional[Any] = infer_auto_device_map(snake_case__ , **snake_case__)
if isinstance(snake_case__ , snake_case__):
# check if don't have any quantized module on the cpu
lowerCAmelCase_ : Union[str, Any] = bnb_quantization_config.skip_modules + bnb_quantization_config.keep_in_fpaa_modules
lowerCAmelCase_ : List[Any] = {
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(
"\n Some modules are dispatched on the CPU or the disk. Make sure you have enough GPU RAM to fit\n the quantized model. If you want to dispatch the model on the CPU or the disk while keeping\n these modules in `torch_dtype`, you need to pass a custom `device_map` to\n `load_and_quantize_model`. Check\n https://huggingface.co/docs/accelerate/main/en/usage_guides/quantization#offload-modules-to-cpu-and-disk\n for more details.\n ")
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 UpperCamelCase ( snake_case__ , snake_case__ , snake_case__=None , snake_case__=None):
if modules_to_not_convert is None:
lowerCAmelCase_ : List[str] = []
lowerCAmelCase_ , lowerCAmelCase_ : Tuple = _replace_with_bnb_layers(
snake_case__ , snake_case__ , snake_case__ , 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 UpperCamelCase ( snake_case__ , snake_case__ , snake_case__=None , snake_case__=None , ):
lowerCAmelCase_ : str = False
for name, module in model.named_children():
if current_key_name is None:
lowerCAmelCase_ : Optional[int] = []
current_key_name.append(snake_case__)
if isinstance(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`
lowerCAmelCase_ : Optional[int] = ".".join(snake_case__)
lowerCAmelCase_ : List[str] = 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:
lowerCAmelCase_ : List[Any] = False
break
if proceed:
# Load bnb module with empty weight and replace ``nn.Linear` module
if bnb_quantization_config.load_in_abit:
lowerCAmelCase_ : Tuple = bnb.nn.LinearabitLt(
module.in_features , module.out_features , module.bias is not None , has_fpaa_weights=snake_case__ , threshold=bnb_quantization_config.llm_inta_threshold , )
elif bnb_quantization_config.load_in_abit:
lowerCAmelCase_ : Dict = 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")
lowerCAmelCase_ : List[str] = module.weight.data
if module.bias is not None:
lowerCAmelCase_ : Any = module.bias.data
bnb_module.requires_grad_(snake_case__)
setattr(snake_case__ , snake_case__ , snake_case__)
lowerCAmelCase_ : List[str] = True
if len(list(module.children())) > 0:
lowerCAmelCase_ , lowerCAmelCase_ : List[Any] = _replace_with_bnb_layers(
snake_case__ , snake_case__ , snake_case__ , snake_case__)
lowerCAmelCase_ : Optional[int] = has_been_replaced | _has_been_replaced
# Remove the last key for recursion
current_key_name.pop(-1)
return model, has_been_replaced
def UpperCamelCase ( snake_case__):
# Create a copy of the model
with init_empty_weights():
lowerCAmelCase_ : List[Any] = deepcopy(snake_case__) # this has 0 cost since it is done inside `init_empty_weights` context manager`
lowerCAmelCase_ : Dict = find_tied_parameters(snake_case__)
# For compatibility with Accelerate < 0.18
if isinstance(snake_case__ , snake_case__):
lowerCAmelCase_ : List[str] = sum(list(tied_params.values()) , []) + list(tied_params.keys())
else:
lowerCAmelCase_ : Optional[Any] = sum(snake_case__ , [])
lowerCAmelCase_ : List[Any] = len(snake_case__) > 0
# Check if it is a base model
lowerCAmelCase_ : List[str] = False
if hasattr(snake_case__ , "base_model_prefix"):
lowerCAmelCase_ : Tuple = not hasattr(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
lowerCAmelCase_ : Union[str, Any] = list(model.named_children())
lowerCAmelCase_ : Optional[int] = [list_modules[-1][0]]
# add last module together with tied weights
lowerCAmelCase_ : Any = set(snake_case__) - set(snake_case__)
lowerCAmelCase_ : Tuple = list(set(snake_case__)) + list(snake_case__)
# remove ".weight" from the keys
lowerCAmelCase_ : List[str] = [".weight", ".bias"]
lowerCAmelCase_ : Tuple = []
for name in list_untouched:
for name_to_remove in names_to_remove:
if name_to_remove in name:
lowerCAmelCase_ : str = name.replace(snake_case__ , "")
filtered_module_names.append(snake_case__)
return filtered_module_names
def UpperCamelCase ( snake_case__):
for m in model.modules():
if isinstance(snake_case__ , bnb.nn.Linearabit):
return True
return False
def UpperCamelCase ( snake_case__):
return next(parameter.parameters()).device
def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__):
# 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(snake_case__ , snake_case__ , 0 , dtype=snake_case__ , value=snake_case__)
lowerCAmelCase_ : str = param_name
lowerCAmelCase_ : Tuple = model
if "." in tensor_name:
lowerCAmelCase_ : Dict = tensor_name.split(".")
for split in splits[:-1]:
lowerCAmelCase_ : Any = getattr(snake_case__ , snake_case__)
if new_module is None:
raise ValueError(F'''{module} has no attribute {split}.''')
lowerCAmelCase_ : Union[str, Any] = new_module
lowerCAmelCase_ : Any = splits[-1]
# offload weights
lowerCAmelCase_ : List[Any] = False
offload_weight(module._parameters[tensor_name] , snake_case__ , snake_case__ , index=snake_case__)
if hasattr(module._parameters[tensor_name] , "SCB"):
offload_weight(
module._parameters[tensor_name].SCB , param_name.replace("weight" , "SCB") , snake_case__ , index=snake_case__ , )
else:
offload_weight(snake_case__ , snake_case__ , snake_case__ , index=snake_case__)
offload_weight(snake_case__ , param_name.replace("weight" , "SCB") , snake_case__ , index=snake_case__)
set_module_tensor_to_device(snake_case__ , snake_case__ , "meta" , dtype=snake_case__ , value=torch.empty(*param.size()))
| 683 | 1 |
def UpperCamelCase ( snake_case__):
return 1 if digit in (0, 1) else (digit * factorial(digit - 1))
def UpperCamelCase ( snake_case__):
lowerCAmelCase_ : Dict = 0
lowerCAmelCase_ : Any = number
while duplicate > 0:
lowerCAmelCase_ , lowerCAmelCase_ : str = divmod(snake_case__ , 10)
fact_sum += factorial(snake_case__)
return fact_sum == number
if __name__ == "__main__":
print('''Program to check whether a number is a Krisnamurthy Number or not.''')
_lowercase = int(input('''Enter number: ''').strip())
print(
f"{number} is {'' if krishnamurthy(number) else 'not '}a Krishnamurthy Number."
)
| 683 |
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
_lowercase = logging.get_logger(__name__)
class __snake_case ( snake_case__ ):
"""simple docstring"""
UpperCamelCase_ = ['input_features', 'is_longer']
def __init__( self : Optional[int] ,lowerCAmelCase__ : List[Any]=64 ,lowerCAmelCase__ : Any=4_80_00 ,lowerCAmelCase__ : Optional[Any]=4_80 ,lowerCAmelCase__ : List[str]=10 ,lowerCAmelCase__ : List[Any]=10_24 ,lowerCAmelCase__ : Union[str, Any]=0.0 ,lowerCAmelCase__ : Tuple=False ,lowerCAmelCase__ : float = 0 ,lowerCAmelCase__ : float = 1_40_00 ,lowerCAmelCase__ : int = None ,lowerCAmelCase__ : str = "fusion" ,lowerCAmelCase__ : str = "repeatpad" ,**lowerCAmelCase__ : Union[str, Any] ,) -> Union[str, Any]:
'''simple docstring'''
super().__init__(
feature_size=lowerCAmelCase__ ,sampling_rate=lowerCAmelCase__ ,padding_value=lowerCAmelCase__ ,return_attention_mask=lowerCAmelCase__ ,**lowerCAmelCase__ ,)
lowerCAmelCase_ : Optional[Any] = top_db
lowerCAmelCase_ : str = truncation
lowerCAmelCase_ : Tuple = padding
lowerCAmelCase_ : str = fft_window_size
lowerCAmelCase_ : Dict = (fft_window_size >> 1) + 1
lowerCAmelCase_ : Dict = hop_length
lowerCAmelCase_ : Any = max_length_s
lowerCAmelCase_ : int = max_length_s * sampling_rate
lowerCAmelCase_ : Optional[int] = sampling_rate
lowerCAmelCase_ : int = frequency_min
lowerCAmelCase_ : Optional[Any] = frequency_max
lowerCAmelCase_ : List[Any] = mel_filter_bank(
num_frequency_bins=self.nb_frequency_bins ,num_mel_filters=lowerCAmelCase__ ,min_frequency=lowerCAmelCase__ ,max_frequency=lowerCAmelCase__ ,sampling_rate=lowerCAmelCase__ ,norm=lowerCAmelCase__ ,mel_scale="htk" ,)
lowerCAmelCase_ : List[Any] = mel_filter_bank(
num_frequency_bins=self.nb_frequency_bins ,num_mel_filters=lowerCAmelCase__ ,min_frequency=lowerCAmelCase__ ,max_frequency=lowerCAmelCase__ ,sampling_rate=lowerCAmelCase__ ,norm="slaney" ,mel_scale="slaney" ,)
def UpperCAmelCase_ ( self : Dict ) -> Dict[str, Any]:
'''simple docstring'''
lowerCAmelCase_ : int = copy.deepcopy(self.__dict__ )
lowerCAmelCase_ : Optional[int] = 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 : Any ,lowerCAmelCase__ : np.array ,lowerCAmelCase__ : Optional[np.array] = None ) -> np.ndarray:
'''simple docstring'''
lowerCAmelCase_ : Optional[Any] = spectrogram(
lowerCAmelCase__ ,window_function(self.fft_window_size ,"hann" ) ,frame_length=self.fft_window_size ,hop_length=self.hop_length ,power=2.0 ,mel_filters=lowerCAmelCase__ ,log_mel="dB" ,)
return log_mel_spectrogram.T
def UpperCAmelCase_ ( self : Optional[int] ,lowerCAmelCase__ : Union[str, Any] ,lowerCAmelCase__ : Union[str, Any] ,lowerCAmelCase__ : Tuple ) -> Dict:
'''simple docstring'''
lowerCAmelCase_ : 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
lowerCAmelCase_ : List[Any] = [0]
if len(ranges[2] ) == 0:
# if the audio is too short, we just use the first chunk
lowerCAmelCase_ : List[Any] = [0]
# randomly choose index for each part
lowerCAmelCase_ : str = np.random.choice(ranges[0] )
lowerCAmelCase_ : Optional[Any] = np.random.choice(ranges[1] )
lowerCAmelCase_ : Any = np.random.choice(ranges[2] )
lowerCAmelCase_ : str = mel[idx_front : idx_front + chunk_frames, :]
lowerCAmelCase_ : Dict = mel[idx_middle : idx_middle + chunk_frames, :]
lowerCAmelCase_ : Optional[Any] = mel[idx_back : idx_back + chunk_frames, :]
lowerCAmelCase_ : List[str] = torch.tensor(mel[None, None, :] )
lowerCAmelCase_ : List[Any] = torch.nn.functional.interpolate(
lowerCAmelCase__ ,size=[chunk_frames, 64] ,mode="bilinear" ,align_corners=lowerCAmelCase__ )
lowerCAmelCase_ : Optional[int] = mel_shrink[0][0].numpy()
lowerCAmelCase_ : str = np.stack([mel_shrink, mel_chunk_front, mel_chunk_middle, mel_chunk_back] ,axis=0 )
return mel_fusion
def UpperCAmelCase_ ( self : Any ,lowerCAmelCase__ : np.array ,lowerCAmelCase__ : Union[str, Any] ,lowerCAmelCase__ : Optional[int] ,lowerCAmelCase__ : int ) -> np.array:
'''simple docstring'''
if waveform.shape[0] > max_length:
if truncation == "rand_trunc":
lowerCAmelCase_ : List[Any] = True
# random crop to max_length (for compatibility) -> this should be handled by self.pad
lowerCAmelCase_ : str = len(lowerCAmelCase__ ) - max_length
lowerCAmelCase_ : Any = np.random.randint(0 ,overflow + 1 )
lowerCAmelCase_ : Dict = waveform[idx : idx + max_length]
lowerCAmelCase_ : List[str] = self._np_extract_fbank_features(lowerCAmelCase__ ,self.mel_filters_slaney )[None, :]
elif truncation == "fusion":
lowerCAmelCase_ : Tuple = self._np_extract_fbank_features(lowerCAmelCase__ ,self.mel_filters )
lowerCAmelCase_ : str = max_length // self.hop_length + 1 # the +1 related to how the spectrogram is computed
lowerCAmelCase_ : List[str] = 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.
lowerCAmelCase_ : Dict = np.stack([mel, mel, mel, mel] ,axis=0 )
lowerCAmelCase_ : int = False
else:
lowerCAmelCase_ : str = self._random_mel_fusion(lowerCAmelCase__ ,lowerCAmelCase__ ,lowerCAmelCase__ )
lowerCAmelCase_ : Any = True
else:
raise NotImplementedError(f'''data_truncating {truncation} not implemented''' )
else:
lowerCAmelCase_ : Dict = 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":
lowerCAmelCase_ : List[Any] = int(max_length / len(lowerCAmelCase__ ) )
lowerCAmelCase_ : int = np.stack(np.tile(lowerCAmelCase__ ,n_repeat + 1 ) )[:max_length]
if padding == "repeatpad":
lowerCAmelCase_ : Optional[Any] = int(max_length / len(lowerCAmelCase__ ) )
lowerCAmelCase_ : Tuple = np.stack(np.tile(lowerCAmelCase__ ,lowerCAmelCase__ ) )
lowerCAmelCase_ : List[Any] = np.pad(lowerCAmelCase__ ,(0, max_length - waveform.shape[0]) ,mode="constant" ,constant_values=0 )
if truncation == "fusion":
lowerCAmelCase_ : int = self._np_extract_fbank_features(lowerCAmelCase__ ,self.mel_filters )
lowerCAmelCase_ : Tuple = np.stack([input_mel, input_mel, input_mel, input_mel] ,axis=0 )
else:
lowerCAmelCase_ : str = self._np_extract_fbank_features(lowerCAmelCase__ ,self.mel_filters_slaney )[None, :]
return input_mel, longer
def __call__( self : int ,lowerCAmelCase__ : Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]] ,lowerCAmelCase__ : str = None ,lowerCAmelCase__ : Optional[str] = None ,lowerCAmelCase__ : Optional[int] = None ,lowerCAmelCase__ : Optional[int] = None ,lowerCAmelCase__ : Optional[Union[str, TensorType]] = None ,**lowerCAmelCase__ : List[Any] ,) -> BatchFeature:
'''simple docstring'''
lowerCAmelCase_ : List[str] = truncation if truncation is not None else self.truncation
lowerCAmelCase_ : List[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." )
lowerCAmelCase_ : Dict = isinstance(lowerCAmelCase__ ,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}''' )
lowerCAmelCase_ : Dict = is_batched_numpy or (
isinstance(lowerCAmelCase__ ,(list, tuple) ) and (isinstance(raw_speech[0] ,(np.ndarray, tuple, list) ))
)
if is_batched:
lowerCAmelCase_ : List[str] = [np.asarray(lowerCAmelCase__ ,dtype=np.floataa ) for speech in raw_speech]
elif not is_batched and not isinstance(lowerCAmelCase__ ,np.ndarray ):
lowerCAmelCase_ : Tuple = np.asarray(lowerCAmelCase__ ,dtype=np.floataa )
elif isinstance(lowerCAmelCase__ ,np.ndarray ) and raw_speech.dtype is np.dtype(np.floataa ):
lowerCAmelCase_ : Any = raw_speech.astype(np.floataa )
# always return batch
if not is_batched:
lowerCAmelCase_ : Any = [np.asarray(lowerCAmelCase__ )]
# convert to mel spectrogram, truncate and pad if needed.
lowerCAmelCase_ : Optional[Any] = [
self._get_input_mel(lowerCAmelCase__ ,max_length if max_length else self.nb_max_samples ,lowerCAmelCase__ ,lowerCAmelCase__ )
for waveform in raw_speech
]
lowerCAmelCase_ : str = []
lowerCAmelCase_ : str = []
for mel, longer in padded_inputs:
input_mel.append(lowerCAmelCase__ )
is_longer.append(lowerCAmelCase__ )
if truncation == "fusion" and sum(lowerCAmelCase__ ) == 0:
# if no audio is longer than 10s, then randomly select one audio to be longer
lowerCAmelCase_ : Any = np.random.randint(0 ,len(lowerCAmelCase__ ) )
lowerCAmelCase_ : Dict = True
if isinstance(input_mel[0] ,lowerCAmelCase__ ):
lowerCAmelCase_ : Optional[int] = [np.asarray(lowerCAmelCase__ ,dtype=np.floataa ) for feature in input_mel]
# is_longer is a list of bool
lowerCAmelCase_ : List[Any] = [[longer] for longer in is_longer]
lowerCAmelCase_ : Optional[Any] = {"input_features": input_mel, "is_longer": is_longer}
lowerCAmelCase_ : Dict = BatchFeature(lowerCAmelCase__ )
if return_tensors is not None:
lowerCAmelCase_ : List[str] = input_features.convert_to_tensors(lowerCAmelCase__ )
return input_features
| 683 | 1 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_lowercase = logging.get_logger(__name__)
_lowercase = {
'''bert-base-uncased''': '''https://huggingface.co/bert-base-uncased/resolve/main/config.json''',
'''bert-large-uncased''': '''https://huggingface.co/bert-large-uncased/resolve/main/config.json''',
'''bert-base-cased''': '''https://huggingface.co/bert-base-cased/resolve/main/config.json''',
'''bert-large-cased''': '''https://huggingface.co/bert-large-cased/resolve/main/config.json''',
'''bert-base-multilingual-uncased''': '''https://huggingface.co/bert-base-multilingual-uncased/resolve/main/config.json''',
'''bert-base-multilingual-cased''': '''https://huggingface.co/bert-base-multilingual-cased/resolve/main/config.json''',
'''bert-base-chinese''': '''https://huggingface.co/bert-base-chinese/resolve/main/config.json''',
'''bert-base-german-cased''': '''https://huggingface.co/bert-base-german-cased/resolve/main/config.json''',
'''bert-large-uncased-whole-word-masking''': (
'''https://huggingface.co/bert-large-uncased-whole-word-masking/resolve/main/config.json'''
),
'''bert-large-cased-whole-word-masking''': (
'''https://huggingface.co/bert-large-cased-whole-word-masking/resolve/main/config.json'''
),
'''bert-large-uncased-whole-word-masking-finetuned-squad''': (
'''https://huggingface.co/bert-large-uncased-whole-word-masking-finetuned-squad/resolve/main/config.json'''
),
'''bert-large-cased-whole-word-masking-finetuned-squad''': (
'''https://huggingface.co/bert-large-cased-whole-word-masking-finetuned-squad/resolve/main/config.json'''
),
'''bert-base-cased-finetuned-mrpc''': '''https://huggingface.co/bert-base-cased-finetuned-mrpc/resolve/main/config.json''',
'''bert-base-german-dbmdz-cased''': '''https://huggingface.co/bert-base-german-dbmdz-cased/resolve/main/config.json''',
'''bert-base-german-dbmdz-uncased''': '''https://huggingface.co/bert-base-german-dbmdz-uncased/resolve/main/config.json''',
'''cl-tohoku/bert-base-japanese''': '''https://huggingface.co/cl-tohoku/bert-base-japanese/resolve/main/config.json''',
'''cl-tohoku/bert-base-japanese-whole-word-masking''': (
'''https://huggingface.co/cl-tohoku/bert-base-japanese-whole-word-masking/resolve/main/config.json'''
),
'''cl-tohoku/bert-base-japanese-char''': (
'''https://huggingface.co/cl-tohoku/bert-base-japanese-char/resolve/main/config.json'''
),
'''cl-tohoku/bert-base-japanese-char-whole-word-masking''': (
'''https://huggingface.co/cl-tohoku/bert-base-japanese-char-whole-word-masking/resolve/main/config.json'''
),
'''TurkuNLP/bert-base-finnish-cased-v1''': (
'''https://huggingface.co/TurkuNLP/bert-base-finnish-cased-v1/resolve/main/config.json'''
),
'''TurkuNLP/bert-base-finnish-uncased-v1''': (
'''https://huggingface.co/TurkuNLP/bert-base-finnish-uncased-v1/resolve/main/config.json'''
),
'''wietsedv/bert-base-dutch-cased''': '''https://huggingface.co/wietsedv/bert-base-dutch-cased/resolve/main/config.json''',
# See all BERT models at https://huggingface.co/models?filter=bert
}
class __snake_case ( snake_case__ ):
"""simple docstring"""
UpperCamelCase_ = 'bert'
def __init__( self : List[str] ,lowerCAmelCase__ : List[Any]=3_05_22 ,lowerCAmelCase__ : List[str]=7_68 ,lowerCAmelCase__ : Optional[int]=12 ,lowerCAmelCase__ : List[Any]=12 ,lowerCAmelCase__ : int=30_72 ,lowerCAmelCase__ : List[Any]="gelu" ,lowerCAmelCase__ : Tuple=0.1 ,lowerCAmelCase__ : Tuple=0.1 ,lowerCAmelCase__ : List[str]=5_12 ,lowerCAmelCase__ : Optional[int]=2 ,lowerCAmelCase__ : Any=0.02 ,lowerCAmelCase__ : Union[str, Any]=1e-1_2 ,lowerCAmelCase__ : List[Any]=0 ,lowerCAmelCase__ : Any="absolute" ,lowerCAmelCase__ : Any=True ,lowerCAmelCase__ : Optional[int]=None ,**lowerCAmelCase__ : Optional[Any] ,) -> str:
'''simple docstring'''
super().__init__(pad_token_id=lowerCAmelCase__ ,**lowerCAmelCase__ )
lowerCAmelCase_ : Any = vocab_size
lowerCAmelCase_ : Optional[Any] = hidden_size
lowerCAmelCase_ : Tuple = num_hidden_layers
lowerCAmelCase_ : int = num_attention_heads
lowerCAmelCase_ : str = hidden_act
lowerCAmelCase_ : List[str] = intermediate_size
lowerCAmelCase_ : Any = hidden_dropout_prob
lowerCAmelCase_ : str = attention_probs_dropout_prob
lowerCAmelCase_ : str = max_position_embeddings
lowerCAmelCase_ : int = type_vocab_size
lowerCAmelCase_ : str = initializer_range
lowerCAmelCase_ : Optional[int] = layer_norm_eps
lowerCAmelCase_ : Dict = position_embedding_type
lowerCAmelCase_ : Optional[Any] = use_cache
lowerCAmelCase_ : Tuple = classifier_dropout
class __snake_case ( snake_case__ ):
"""simple docstring"""
@property
def UpperCAmelCase_ ( self : Union[str, Any] ) -> Mapping[str, Mapping[int, str]]:
'''simple docstring'''
if self.task == "multiple-choice":
lowerCAmelCase_ : Any = {0: "batch", 1: "choice", 2: "sequence"}
else:
lowerCAmelCase_ : Union[str, Any] = {0: "batch", 1: "sequence"}
return OrderedDict(
[
("input_ids", dynamic_axis),
("attention_mask", dynamic_axis),
("token_type_ids", dynamic_axis),
] )
| 683 |
from multiprocessing import Lock, Pipe, Process
# lock used to ensure that two processes do not access a pipe at the same time
_lowercase = Lock()
def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__):
global process_lock
# we perform n swaps since after n swaps we know we are sorted
# we *could* stop early if we are sorted already, but it takes as long to
# find out we are sorted as it does to sort the list with this algorithm
for i in range(0 , 10):
if (i + position) % 2 == 0 and r_send is not None:
# send your value to your right neighbor
process_lock.acquire()
r_send[1].send(snake_case__)
process_lock.release()
# receive your right neighbor's value
process_lock.acquire()
lowerCAmelCase_ : Optional[Any] = rr_cv[0].recv()
process_lock.release()
# take the lower value since you are on the left
lowerCAmelCase_ : Any = min(snake_case__ , snake_case__)
elif (i + position) % 2 != 0 and l_send is not None:
# send your value to your left neighbor
process_lock.acquire()
l_send[1].send(snake_case__)
process_lock.release()
# receive your left neighbor's value
process_lock.acquire()
lowerCAmelCase_ : str = lr_cv[0].recv()
process_lock.release()
# take the higher value since you are on the right
lowerCAmelCase_ : Dict = max(snake_case__ , snake_case__)
# after all swaps are performed, send the values back to main
result_pipe[1].send(snake_case__)
def UpperCamelCase ( snake_case__):
lowerCAmelCase_ : Union[str, Any] = []
lowerCAmelCase_ : int = []
# initialize the list of pipes where the values will be retrieved
for _ in arr:
result_pipe.append(Pipe())
# creates the processes
# the first and last process only have one neighbor so they are made outside
# of the loop
lowerCAmelCase_ : Tuple = Pipe()
lowerCAmelCase_ : Optional[int] = Pipe()
process_array_.append(
Process(
target=snake_case__ , args=(0, arr[0], None, temp_rs, None, temp_rr, result_pipe[0]) , ))
lowerCAmelCase_ : int = temp_rs
lowerCAmelCase_ : List[Any] = temp_rr
for i in range(1 , len(snake_case__) - 1):
lowerCAmelCase_ : Dict = Pipe()
lowerCAmelCase_ : List[str] = Pipe()
process_array_.append(
Process(
target=snake_case__ , args=(i, arr[i], temp_ls, temp_rs, temp_lr, temp_rr, result_pipe[i]) , ))
lowerCAmelCase_ : Dict = temp_rs
lowerCAmelCase_ : Optional[Any] = temp_rr
process_array_.append(
Process(
target=snake_case__ , args=(
len(snake_case__) - 1,
arr[len(snake_case__) - 1],
temp_ls,
None,
temp_lr,
None,
result_pipe[len(snake_case__) - 1],
) , ))
# start the processes
for p in process_array_:
p.start()
# wait for the processes to end and write their values to the list
for p in range(0 , len(snake_case__)):
lowerCAmelCase_ : Union[str, Any] = result_pipe[p][0].recv()
process_array_[p].join()
return arr
def UpperCamelCase ( ):
lowerCAmelCase_ : Optional[Any] = list(range(10 , 0 , -1))
print("Initial List")
print(*snake_case__)
lowerCAmelCase_ : Tuple = odd_even_transposition(snake_case__)
print("Sorted List\n")
print(*snake_case__)
if __name__ == "__main__":
main()
| 683 | 1 |
from ..utils import is_flax_available, is_torch_available
if is_torch_available():
from .autoencoder_kl import AutoencoderKL
from .controlnet import ControlNetModel
from .dual_transformer_ad import DualTransformeraDModel
from .modeling_utils import ModelMixin
from .prior_transformer import PriorTransformer
from .ta_film_transformer import TaFilmDecoder
from .transformer_ad import TransformeraDModel
from .unet_ad import UNetaDModel
from .unet_ad import UNetaDModel
from .unet_ad_condition import UNetaDConditionModel
from .unet_ad_condition import UNetaDConditionModel
from .vq_model import VQModel
if is_flax_available():
from .controlnet_flax import FlaxControlNetModel
from .unet_ad_condition_flax import FlaxUNetaDConditionModel
from .vae_flax import FlaxAutoencoderKL
| 683 |
from typing import Any
def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ , ):
_validation(
snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ , )
# Creates data structures and fill initial step
lowerCAmelCase_ : dict = {}
lowerCAmelCase_ : dict = {}
for state in states_space:
lowerCAmelCase_ : List[Any] = observations_space[0]
lowerCAmelCase_ : int = (
initial_probabilities[state] * emission_probabilities[state][observation]
)
lowerCAmelCase_ : Dict = None
# Fills the data structure with the probabilities of
# different transitions and pointers to previous states
for o in range(1 , len(snake_case__)):
lowerCAmelCase_ : List[Any] = observations_space[o]
lowerCAmelCase_ : Optional[Any] = observations_space[o - 1]
for state in states_space:
# Calculates the argmax for probability function
lowerCAmelCase_ : List[Any] = ""
lowerCAmelCase_ : Tuple = -1
for k_state in states_space:
lowerCAmelCase_ : int = (
probabilities[(k_state, prior_observation)]
* transition_probabilities[k_state][state]
* emission_probabilities[state][observation]
)
if probability > max_probability:
lowerCAmelCase_ : List[str] = probability
lowerCAmelCase_ : Optional[Any] = k_state
# Update probabilities and pointers dicts
lowerCAmelCase_ : Union[str, Any] = (
probabilities[(arg_max, prior_observation)]
* transition_probabilities[arg_max][state]
* emission_probabilities[state][observation]
)
lowerCAmelCase_ : Any = arg_max
# The final observation
lowerCAmelCase_ : List[Any] = observations_space[len(snake_case__) - 1]
# argmax for given final observation
lowerCAmelCase_ : List[str] = ""
lowerCAmelCase_ : List[str] = -1
for k_state in states_space:
lowerCAmelCase_ : List[str] = probabilities[(k_state, final_observation)]
if probability > max_probability:
lowerCAmelCase_ : List[str] = probability
lowerCAmelCase_ : Tuple = k_state
lowerCAmelCase_ : str = arg_max
# Process pointers backwards
lowerCAmelCase_ : int = last_state
lowerCAmelCase_ : int = []
for o in range(len(snake_case__) - 1 , -1 , -1):
result.append(snake_case__)
lowerCAmelCase_ : Optional[Any] = pointers[previous, observations_space[o]]
result.reverse()
return result
def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ , ):
_validate_not_empty(
snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ , )
_validate_lists(snake_case__ , snake_case__)
_validate_dicts(
snake_case__ , snake_case__ , snake_case__)
def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ , ):
if not all(
[
observations_space,
states_space,
initial_probabilities,
transition_probabilities,
emission_probabilities,
]):
raise ValueError("There's an empty parameter")
def UpperCamelCase ( snake_case__ , snake_case__):
_validate_list(snake_case__ , "observations_space")
_validate_list(snake_case__ , "states_space")
def UpperCamelCase ( snake_case__ , snake_case__):
if not isinstance(_object , snake_case__):
lowerCAmelCase_ : Optional[Any] = F'''{var_name} must be a list'''
raise ValueError(snake_case__)
else:
for x in _object:
if not isinstance(snake_case__ , snake_case__):
lowerCAmelCase_ : Optional[Any] = F'''{var_name} must be a list of strings'''
raise ValueError(snake_case__)
def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__ , ):
_validate_dict(snake_case__ , "initial_probabilities" , snake_case__)
_validate_nested_dict(snake_case__ , "transition_probabilities")
_validate_nested_dict(snake_case__ , "emission_probabilities")
def UpperCamelCase ( snake_case__ , snake_case__):
_validate_dict(_object , snake_case__ , snake_case__)
for x in _object.values():
_validate_dict(snake_case__ , snake_case__ , snake_case__ , snake_case__)
def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__ , snake_case__ = False):
if not isinstance(_object , snake_case__):
lowerCAmelCase_ : List[str] = F'''{var_name} must be a dict'''
raise ValueError(snake_case__)
if not all(isinstance(snake_case__ , snake_case__) for x in _object):
lowerCAmelCase_ : Dict = F'''{var_name} all keys must be strings'''
raise ValueError(snake_case__)
if not all(isinstance(snake_case__ , snake_case__) for x in _object.values()):
lowerCAmelCase_ : Union[str, Any] = "nested dictionary " if nested else ""
lowerCAmelCase_ : Any = F'''{var_name} {nested_text}all values must be {value_type.__name__}'''
raise ValueError(snake_case__)
if __name__ == "__main__":
from doctest import testmod
testmod()
| 683 | 1 |
from __future__ import annotations
import math
def UpperCamelCase ( snake_case__ , snake_case__):
lowerCAmelCase_ : Dict = u
for i in range(1 , snake_case__):
lowerCAmelCase_ : Tuple = temp * (u - i)
return temp
def UpperCamelCase ( ):
lowerCAmelCase_ : Dict = int(input("enter the numbers of values: "))
lowerCAmelCase_ : list[list[float]] = []
for _ in range(snake_case__):
y.append([])
for i in range(snake_case__):
for j in range(snake_case__):
y[i].append(snake_case__)
lowerCAmelCase_ : int = 0
print("enter the values of parameters in a list: ")
lowerCAmelCase_ : Tuple = list(map(snake_case__ , input().split()))
print("enter the values of corresponding parameters: ")
for i in range(snake_case__):
lowerCAmelCase_ : int = float(input())
lowerCAmelCase_ : Tuple = int(input("enter the value to interpolate: "))
lowerCAmelCase_ : Tuple = (value - x[0]) / (x[1] - x[0])
# for calculating forward difference table
for i in range(1 , snake_case__):
for j in range(n - i):
lowerCAmelCase_ : List[str] = y[j + 1][i - 1] - y[j][i - 1]
lowerCAmelCase_ : List[Any] = y[0][0]
for i in range(1 , snake_case__):
summ += (ucal(snake_case__ , snake_case__) * y[0][i]) / math.factorial(snake_case__)
print(F'''the value at {value} is {summ}''')
if __name__ == "__main__":
main()
| 683 |
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import torch
from ..models.speechta import SpeechTaForTextToSpeech, SpeechTaHifiGan, SpeechTaProcessor
from ..utils import is_datasets_available
from .base import PipelineTool
if is_datasets_available():
from datasets import load_dataset
class __snake_case ( snake_case__ ):
"""simple docstring"""
UpperCamelCase_ = 'microsoft/speecht5_tts'
UpperCamelCase_ = (
'This is a tool that reads an English text out loud. It takes an input named `text` which should contain the '
'text to read (in English) and returns a waveform object containing the sound.'
)
UpperCamelCase_ = 'text_reader'
UpperCamelCase_ = SpeechTaProcessor
UpperCamelCase_ = SpeechTaForTextToSpeech
UpperCamelCase_ = SpeechTaHifiGan
UpperCamelCase_ = ['text']
UpperCamelCase_ = ['audio']
def UpperCAmelCase_ ( self : Dict ) -> Any:
'''simple docstring'''
if self.post_processor is None:
lowerCAmelCase_ : Any = "microsoft/speecht5_hifigan"
super().setup()
def UpperCAmelCase_ ( self : List[Any] ,lowerCAmelCase__ : Optional[int] ,lowerCAmelCase__ : Optional[int]=None ) -> Tuple:
'''simple docstring'''
lowerCAmelCase_ : Any = self.pre_processor(text=lowerCAmelCase__ ,return_tensors="pt" ,truncation=lowerCAmelCase__ )
if speaker_embeddings is None:
if not is_datasets_available():
raise ImportError("Datasets needs to be installed if not passing speaker embeddings." )
lowerCAmelCase_ : str = load_dataset("Matthijs/cmu-arctic-xvectors" ,split="validation" )
lowerCAmelCase_ : List[Any] = torch.tensor(embeddings_dataset[73_05]["xvector"] ).unsqueeze(0 )
return {"input_ids": inputs["input_ids"], "speaker_embeddings": speaker_embeddings}
def UpperCAmelCase_ ( self : Dict ,lowerCAmelCase__ : Union[str, Any] ) -> Union[str, Any]:
'''simple docstring'''
with torch.no_grad():
return self.model.generate_speech(**lowerCAmelCase__ )
def UpperCAmelCase_ ( self : Union[str, Any] ,lowerCAmelCase__ : str ) -> Any:
'''simple docstring'''
with torch.no_grad():
return self.post_processor(lowerCAmelCase__ ).cpu().detach()
| 683 | 1 |
import unittest
import numpy as np
from transformers.testing_utils import is_flaky, 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
if is_vision_available():
from PIL import Image
from transformers import DonutImageProcessor
class __snake_case ( unittest.TestCase ):
"""simple docstring"""
def __init__( self : Optional[int] ,lowerCAmelCase__ : str ,lowerCAmelCase__ : str=7 ,lowerCAmelCase__ : List[str]=3 ,lowerCAmelCase__ : Any=18 ,lowerCAmelCase__ : Dict=30 ,lowerCAmelCase__ : Optional[int]=4_00 ,lowerCAmelCase__ : int=True ,lowerCAmelCase__ : Tuple=None ,lowerCAmelCase__ : Optional[Any]=True ,lowerCAmelCase__ : Tuple=False ,lowerCAmelCase__ : Optional[Any]=True ,lowerCAmelCase__ : int=True ,lowerCAmelCase__ : Optional[int]=[0.5, 0.5, 0.5] ,lowerCAmelCase__ : List[str]=[0.5, 0.5, 0.5] ,) -> Any:
'''simple docstring'''
lowerCAmelCase_ : Optional[int] = parent
lowerCAmelCase_ : List[Any] = batch_size
lowerCAmelCase_ : Optional[Any] = num_channels
lowerCAmelCase_ : List[Any] = image_size
lowerCAmelCase_ : List[str] = min_resolution
lowerCAmelCase_ : Optional[int] = max_resolution
lowerCAmelCase_ : Tuple = do_resize
lowerCAmelCase_ : Dict = size if size is not None else {"height": 18, "width": 20}
lowerCAmelCase_ : Dict = do_thumbnail
lowerCAmelCase_ : List[str] = do_align_axis
lowerCAmelCase_ : Union[str, Any] = do_pad
lowerCAmelCase_ : Union[str, Any] = do_normalize
lowerCAmelCase_ : str = image_mean
lowerCAmelCase_ : Union[str, Any] = image_std
def UpperCAmelCase_ ( self : Dict ) -> Dict:
'''simple docstring'''
return {
"do_resize": self.do_resize,
"size": self.size,
"do_thumbnail": self.do_thumbnail,
"do_align_long_axis": self.do_align_axis,
"do_pad": self.do_pad,
"do_normalize": self.do_normalize,
"image_mean": self.image_mean,
"image_std": self.image_std,
}
@require_torch
@require_vision
class __snake_case ( snake_case__ , unittest.TestCase ):
"""simple docstring"""
UpperCamelCase_ = DonutImageProcessor if is_vision_available() else None
def UpperCAmelCase_ ( self : Optional[Any] ) -> int:
'''simple docstring'''
lowerCAmelCase_ : int = DonutImageProcessingTester(self )
@property
def UpperCAmelCase_ ( self : List[str] ) -> Tuple:
'''simple docstring'''
return self.image_processor_tester.prepare_image_processor_dict()
def UpperCAmelCase_ ( self : Optional[int] ) -> Union[str, Any]:
'''simple docstring'''
lowerCAmelCase_ : Tuple = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(lowerCAmelCase__ ,"do_resize" ) )
self.assertTrue(hasattr(lowerCAmelCase__ ,"size" ) )
self.assertTrue(hasattr(lowerCAmelCase__ ,"do_thumbnail" ) )
self.assertTrue(hasattr(lowerCAmelCase__ ,"do_align_long_axis" ) )
self.assertTrue(hasattr(lowerCAmelCase__ ,"do_pad" ) )
self.assertTrue(hasattr(lowerCAmelCase__ ,"do_normalize" ) )
self.assertTrue(hasattr(lowerCAmelCase__ ,"image_mean" ) )
self.assertTrue(hasattr(lowerCAmelCase__ ,"image_std" ) )
def UpperCAmelCase_ ( self : Optional[int] ) -> str:
'''simple docstring'''
lowerCAmelCase_ : List[Any] = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size ,{"height": 18, "width": 20} )
lowerCAmelCase_ : Dict = self.image_processing_class.from_dict(self.image_processor_dict ,size=42 )
self.assertEqual(image_processor.size ,{"height": 42, "width": 42} )
# Previous config had dimensions in (width, height) order
lowerCAmelCase_ : Union[str, Any] = self.image_processing_class.from_dict(self.image_processor_dict ,size=(42, 84) )
self.assertEqual(image_processor.size ,{"height": 84, "width": 42} )
def UpperCAmelCase_ ( self : Optional[Any] ) -> Any:
'''simple docstring'''
pass
@is_flaky()
def UpperCAmelCase_ ( self : int ) -> Dict:
'''simple docstring'''
lowerCAmelCase_ : Optional[Any] = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
lowerCAmelCase_ : Optional[Any] = prepare_image_inputs(self.image_processor_tester ,equal_resolution=lowerCAmelCase__ )
for image in image_inputs:
self.assertIsInstance(lowerCAmelCase__ ,Image.Image )
# Test not batched input
lowerCAmelCase_ : Tuple = image_processing(image_inputs[0] ,return_tensors="pt" ).pixel_values
self.assertEqual(
encoded_images.shape ,(
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.size["height"],
self.image_processor_tester.size["width"],
) ,)
# Test batched
lowerCAmelCase_ : Optional[int] = image_processing(lowerCAmelCase__ ,return_tensors="pt" ).pixel_values
self.assertEqual(
encoded_images.shape ,(
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.size["height"],
self.image_processor_tester.size["width"],
) ,)
@is_flaky()
def UpperCAmelCase_ ( self : Optional[int] ) -> Optional[Any]:
'''simple docstring'''
lowerCAmelCase_ : int = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
lowerCAmelCase_ : Tuple = prepare_image_inputs(self.image_processor_tester ,equal_resolution=lowerCAmelCase__ ,numpify=lowerCAmelCase__ )
for image in image_inputs:
self.assertIsInstance(lowerCAmelCase__ ,np.ndarray )
# Test not batched input
lowerCAmelCase_ : Optional[Any] = image_processing(image_inputs[0] ,return_tensors="pt" ).pixel_values
self.assertEqual(
encoded_images.shape ,(
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.size["height"],
self.image_processor_tester.size["width"],
) ,)
# Test batched
lowerCAmelCase_ : str = image_processing(lowerCAmelCase__ ,return_tensors="pt" ).pixel_values
self.assertEqual(
encoded_images.shape ,(
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.size["height"],
self.image_processor_tester.size["width"],
) ,)
@is_flaky()
def UpperCAmelCase_ ( self : Optional[Any] ) -> List[Any]:
'''simple docstring'''
lowerCAmelCase_ : int = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
lowerCAmelCase_ : List[str] = prepare_image_inputs(self.image_processor_tester ,equal_resolution=lowerCAmelCase__ ,torchify=lowerCAmelCase__ )
for image in image_inputs:
self.assertIsInstance(lowerCAmelCase__ ,torch.Tensor )
# Test not batched input
lowerCAmelCase_ : Optional[int] = image_processing(image_inputs[0] ,return_tensors="pt" ).pixel_values
self.assertEqual(
encoded_images.shape ,(
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.size["height"],
self.image_processor_tester.size["width"],
) ,)
# Test batched
lowerCAmelCase_ : Tuple = image_processing(lowerCAmelCase__ ,return_tensors="pt" ).pixel_values
self.assertEqual(
encoded_images.shape ,(
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.size["height"],
self.image_processor_tester.size["width"],
) ,)
| 683 |
import argparse
import collections
import json
import os
import re
import string
import sys
import numpy as np
_lowercase = re.compile(r'''\b(a|an|the)\b''', re.UNICODE)
_lowercase = None
def UpperCamelCase ( ):
lowerCAmelCase_ : Optional[Any] = argparse.ArgumentParser("Official evaluation script for SQuAD version 2.0.")
parser.add_argument("data_file" , metavar="data.json" , help="Input data JSON file.")
parser.add_argument("pred_file" , metavar="pred.json" , help="Model predictions.")
parser.add_argument(
"--out-file" , "-o" , metavar="eval.json" , help="Write accuracy metrics to file (default is stdout).")
parser.add_argument(
"--na-prob-file" , "-n" , metavar="na_prob.json" , help="Model estimates of probability of no answer.")
parser.add_argument(
"--na-prob-thresh" , "-t" , type=snake_case__ , default=1.0 , help="Predict \"\" if no-answer probability exceeds this (default = 1.0)." , )
parser.add_argument(
"--out-image-dir" , "-p" , metavar="out_images" , default=snake_case__ , help="Save precision-recall curves to directory.")
parser.add_argument("--verbose" , "-v" , action="store_true")
if len(sys.argv) == 1:
parser.print_help()
sys.exit(1)
return parser.parse_args()
def UpperCamelCase ( snake_case__):
lowerCAmelCase_ : str = {}
for article in dataset:
for p in article["paragraphs"]:
for qa in p["qas"]:
lowerCAmelCase_ : Dict = bool(qa["answers"]["text"])
return qid_to_has_ans
def UpperCamelCase ( snake_case__):
def remove_articles(snake_case__):
return ARTICLES_REGEX.sub(" " , snake_case__)
def white_space_fix(snake_case__):
return " ".join(text.split())
def remove_punc(snake_case__):
lowerCAmelCase_ : Optional[int] = set(string.punctuation)
return "".join(ch for ch in text if ch not in exclude)
def lower(snake_case__):
return text.lower()
return white_space_fix(remove_articles(remove_punc(lower(snake_case__))))
def UpperCamelCase ( snake_case__):
if not s:
return []
return normalize_answer(snake_case__).split()
def UpperCamelCase ( snake_case__ , snake_case__):
return int(normalize_answer(snake_case__) == normalize_answer(snake_case__))
def UpperCamelCase ( snake_case__ , snake_case__):
lowerCAmelCase_ : Optional[int] = get_tokens(snake_case__)
lowerCAmelCase_ : Union[str, Any] = get_tokens(snake_case__)
lowerCAmelCase_ : Any = collections.Counter(snake_case__) & collections.Counter(snake_case__)
lowerCAmelCase_ : Dict = sum(common.values())
if len(snake_case__) == 0 or len(snake_case__) == 0:
# If either is no-answer, then F1 is 1 if they agree, 0 otherwise
return int(gold_toks == pred_toks)
if num_same == 0:
return 0
lowerCAmelCase_ : List[Any] = 1.0 * num_same / len(snake_case__)
lowerCAmelCase_ : int = 1.0 * num_same / len(snake_case__)
lowerCAmelCase_ : List[Any] = (2 * precision * recall) / (precision + recall)
return fa
def UpperCamelCase ( snake_case__ , snake_case__):
lowerCAmelCase_ : Tuple = {}
lowerCAmelCase_ : int = {}
for article in dataset:
for p in article["paragraphs"]:
for qa in p["qas"]:
lowerCAmelCase_ : int = qa["id"]
lowerCAmelCase_ : Any = [t for t in qa["answers"]["text"] if normalize_answer(snake_case__)]
if not gold_answers:
# For unanswerable questions, only correct answer is empty string
lowerCAmelCase_ : Any = [""]
if qid not in preds:
print(F'''Missing prediction for {qid}''')
continue
lowerCAmelCase_ : Tuple = preds[qid]
# Take max over all gold answers
lowerCAmelCase_ : Any = max(compute_exact(snake_case__ , snake_case__) for a in gold_answers)
lowerCAmelCase_ : Optional[Any] = max(compute_fa(snake_case__ , snake_case__) for a in gold_answers)
return exact_scores, fa_scores
def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__ , snake_case__):
lowerCAmelCase_ : Dict = {}
for qid, s in scores.items():
lowerCAmelCase_ : List[Any] = na_probs[qid] > na_prob_thresh
if pred_na:
lowerCAmelCase_ : List[str] = float(not qid_to_has_ans[qid])
else:
lowerCAmelCase_ : Union[str, Any] = s
return new_scores
def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__=None):
if not qid_list:
lowerCAmelCase_ : Any = len(snake_case__)
return collections.OrderedDict(
[
("exact", 100.0 * sum(exact_scores.values()) / total),
("f1", 100.0 * sum(fa_scores.values()) / total),
("total", total),
])
else:
lowerCAmelCase_ : Tuple = len(snake_case__)
return collections.OrderedDict(
[
("exact", 100.0 * sum(exact_scores[k] for k in qid_list) / total),
("f1", 100.0 * sum(fa_scores[k] for k in qid_list) / total),
("total", total),
])
def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__):
for k in new_eval:
lowerCAmelCase_ : Union[str, Any] = new_eval[k]
def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__ , snake_case__):
plt.step(snake_case__ , snake_case__ , color="b" , alpha=0.2 , where="post")
plt.fill_between(snake_case__ , snake_case__ , step="post" , alpha=0.2 , color="b")
plt.xlabel("Recall")
plt.ylabel("Precision")
plt.xlim([0.0, 1.05])
plt.ylim([0.0, 1.05])
plt.title(snake_case__)
plt.savefig(snake_case__)
plt.clf()
def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__=None , snake_case__=None):
lowerCAmelCase_ : List[Any] = sorted(snake_case__ , key=lambda snake_case__: na_probs[k])
lowerCAmelCase_ : Dict = 0.0
lowerCAmelCase_ : int = 1.0
lowerCAmelCase_ : List[str] = 0.0
lowerCAmelCase_ : Tuple = [1.0]
lowerCAmelCase_ : Tuple = [0.0]
lowerCAmelCase_ : Dict = 0.0
for i, qid in enumerate(snake_case__):
if qid_to_has_ans[qid]:
true_pos += scores[qid]
lowerCAmelCase_ : str = true_pos / float(i + 1)
lowerCAmelCase_ : Union[str, Any] = true_pos / float(snake_case__)
if i == len(snake_case__) - 1 or na_probs[qid] != na_probs[qid_list[i + 1]]:
# i.e., if we can put a threshold after this point
avg_prec += cur_p * (cur_r - recalls[-1])
precisions.append(snake_case__)
recalls.append(snake_case__)
if out_image:
plot_pr_curve(snake_case__ , snake_case__ , snake_case__ , snake_case__)
return {"ap": 100.0 * avg_prec}
def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__):
if out_image_dir and not os.path.exists(snake_case__):
os.makedirs(snake_case__)
lowerCAmelCase_ : Any = sum(1 for v in qid_to_has_ans.values() if v)
if num_true_pos == 0:
return
lowerCAmelCase_ : Any = make_precision_recall_eval(
snake_case__ , snake_case__ , snake_case__ , snake_case__ , out_image=os.path.join(snake_case__ , "pr_exact.png") , title="Precision-Recall curve for Exact Match score" , )
lowerCAmelCase_ : Dict = make_precision_recall_eval(
snake_case__ , snake_case__ , snake_case__ , snake_case__ , out_image=os.path.join(snake_case__ , "pr_f1.png") , title="Precision-Recall curve for F1 score" , )
lowerCAmelCase_ : Dict = {k: float(snake_case__) for k, v in qid_to_has_ans.items()}
lowerCAmelCase_ : str = make_precision_recall_eval(
snake_case__ , snake_case__ , snake_case__ , snake_case__ , out_image=os.path.join(snake_case__ , "pr_oracle.png") , title="Oracle Precision-Recall curve (binary task of HasAns vs. NoAns)" , )
merge_eval(snake_case__ , snake_case__ , "pr_exact")
merge_eval(snake_case__ , snake_case__ , "pr_f1")
merge_eval(snake_case__ , snake_case__ , "pr_oracle")
def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__ , snake_case__):
if not qid_list:
return
lowerCAmelCase_ : Optional[Any] = [na_probs[k] for k in qid_list]
lowerCAmelCase_ : Dict = np.ones_like(snake_case__) / float(len(snake_case__))
plt.hist(snake_case__ , weights=snake_case__ , bins=20 , range=(0.0, 1.0))
plt.xlabel("Model probability of no-answer")
plt.ylabel("Proportion of dataset")
plt.title(F'''Histogram of no-answer probability: {name}''')
plt.savefig(os.path.join(snake_case__ , F'''na_prob_hist_{name}.png'''))
plt.clf()
def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__ , snake_case__):
lowerCAmelCase_ : Dict = sum(1 for k in qid_to_has_ans if not qid_to_has_ans[k])
lowerCAmelCase_ : str = num_no_ans
lowerCAmelCase_ : List[str] = cur_score
lowerCAmelCase_ : List[Any] = 0.0
lowerCAmelCase_ : str = sorted(snake_case__ , key=lambda snake_case__: na_probs[k])
for i, qid in enumerate(snake_case__):
if qid not in scores:
continue
if qid_to_has_ans[qid]:
lowerCAmelCase_ : Union[str, Any] = scores[qid]
else:
if preds[qid]:
lowerCAmelCase_ : List[Any] = -1
else:
lowerCAmelCase_ : List[str] = 0
cur_score += diff
if cur_score > best_score:
lowerCAmelCase_ : Optional[Any] = cur_score
lowerCAmelCase_ : Optional[int] = na_probs[qid]
return 100.0 * best_score / len(snake_case__), best_thresh
def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__):
lowerCAmelCase_ , lowerCAmelCase_ : Optional[int] = find_best_thresh(snake_case__ , snake_case__ , snake_case__ , snake_case__)
lowerCAmelCase_ , lowerCAmelCase_ : Dict = find_best_thresh(snake_case__ , snake_case__ , snake_case__ , snake_case__)
lowerCAmelCase_ : List[str] = best_exact
lowerCAmelCase_ : List[str] = exact_thresh
lowerCAmelCase_ : Any = best_fa
lowerCAmelCase_ : List[str] = fa_thresh
def UpperCamelCase ( ):
with open(OPTS.data_file) as f:
lowerCAmelCase_ : Optional[int] = json.load(snake_case__)
lowerCAmelCase_ : List[Any] = dataset_json["data"]
with open(OPTS.pred_file) as f:
lowerCAmelCase_ : int = json.load(snake_case__)
if OPTS.na_prob_file:
with open(OPTS.na_prob_file) as f:
lowerCAmelCase_ : Optional[int] = json.load(snake_case__)
else:
lowerCAmelCase_ : List[Any] = {k: 0.0 for k in preds}
lowerCAmelCase_ : Tuple = make_qid_to_has_ans(snake_case__) # maps qid to True/False
lowerCAmelCase_ : Any = [k for k, v in qid_to_has_ans.items() if v]
lowerCAmelCase_ : List[str] = [k for k, v in qid_to_has_ans.items() if not v]
lowerCAmelCase_ , lowerCAmelCase_ : Dict = get_raw_scores(snake_case__ , snake_case__)
lowerCAmelCase_ : str = apply_no_ans_threshold(snake_case__ , snake_case__ , snake_case__ , OPTS.na_prob_thresh)
lowerCAmelCase_ : Dict = apply_no_ans_threshold(snake_case__ , snake_case__ , snake_case__ , OPTS.na_prob_thresh)
lowerCAmelCase_ : Union[str, Any] = make_eval_dict(snake_case__ , snake_case__)
if has_ans_qids:
lowerCAmelCase_ : str = make_eval_dict(snake_case__ , snake_case__ , qid_list=snake_case__)
merge_eval(snake_case__ , snake_case__ , "HasAns")
if no_ans_qids:
lowerCAmelCase_ : Union[str, Any] = make_eval_dict(snake_case__ , snake_case__ , qid_list=snake_case__)
merge_eval(snake_case__ , snake_case__ , "NoAns")
if OPTS.na_prob_file:
find_all_best_thresh(snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__)
if OPTS.na_prob_file and OPTS.out_image_dir:
run_precision_recall_analysis(snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ , OPTS.out_image_dir)
histogram_na_prob(snake_case__ , snake_case__ , OPTS.out_image_dir , "hasAns")
histogram_na_prob(snake_case__ , snake_case__ , OPTS.out_image_dir , "noAns")
if OPTS.out_file:
with open(OPTS.out_file , "w") as f:
json.dump(snake_case__ , snake_case__)
else:
print(json.dumps(snake_case__ , indent=2))
if __name__ == "__main__":
_lowercase = parse_args()
if OPTS.out_image_dir:
import matplotlib
matplotlib.use('''Agg''')
import matplotlib.pyplot as plt
main()
| 683 | 1 |
import unittest
from transformers import MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING, AutoTokenizer, is_vision_available
from transformers.pipelines import pipeline
from transformers.pipelines.document_question_answering import apply_tesseract
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_detectrona,
require_pytesseract,
require_tf,
require_torch,
require_vision,
slow,
)
from .test_pipelines_common import ANY
if is_vision_available():
from PIL import Image
from transformers.image_utils import load_image
else:
class __snake_case :
"""simple docstring"""
@staticmethod
def UpperCAmelCase_ ( *lowerCAmelCase__ : Any ,**lowerCAmelCase__ : Dict ) -> Tuple:
'''simple docstring'''
pass
def UpperCamelCase ( snake_case__):
return None
# This is a pinned image from a specific revision of a document question answering space, hosted by HuggingFace,
# so we can expect it to be available.
_lowercase = (
'''https://huggingface.co/spaces/impira/docquery/resolve/2f6c96314dc84dfda62d40de9da55f2f5165d403/invoice.png'''
)
@is_pipeline_test
@require_torch
@require_vision
class __snake_case ( unittest.TestCase ):
"""simple docstring"""
UpperCamelCase_ = MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING
@require_pytesseract
@require_vision
def UpperCAmelCase_ ( self : List[Any] ,lowerCAmelCase__ : Any ,lowerCAmelCase__ : int ,lowerCAmelCase__ : str ) -> Union[str, Any]:
'''simple docstring'''
lowerCAmelCase_ : Dict = pipeline(
"document-question-answering" ,model=lowerCAmelCase__ ,tokenizer=lowerCAmelCase__ ,image_processor=lowerCAmelCase__ )
lowerCAmelCase_ : Optional[Any] = INVOICE_URL
lowerCAmelCase_ : List[Any] = list(zip(*apply_tesseract(load_image(lowerCAmelCase__ ) ,lowerCAmelCase__ ,"" ) ) )
lowerCAmelCase_ : List[Any] = "What is the placebo?"
lowerCAmelCase_ : Optional[int] = [
{
"image": load_image(lowerCAmelCase__ ),
"question": question,
},
{
"image": image,
"question": question,
},
{
"image": image,
"question": question,
"word_boxes": word_boxes,
},
]
return dqa_pipeline, examples
def UpperCAmelCase_ ( self : Dict ,lowerCAmelCase__ : int ,lowerCAmelCase__ : str ) -> str:
'''simple docstring'''
lowerCAmelCase_ : Dict = dqa_pipeline(lowerCAmelCase__ ,top_k=2 )
self.assertEqual(
lowerCAmelCase__ ,[
[
{"score": ANY(lowerCAmelCase__ ), "answer": ANY(lowerCAmelCase__ ), "start": ANY(lowerCAmelCase__ ), "end": ANY(lowerCAmelCase__ )},
{"score": ANY(lowerCAmelCase__ ), "answer": ANY(lowerCAmelCase__ ), "start": ANY(lowerCAmelCase__ ), "end": ANY(lowerCAmelCase__ )},
]
]
* 3 ,)
@require_torch
@require_detectrona
@require_pytesseract
def UpperCAmelCase_ ( self : List[str] ) -> Union[str, Any]:
'''simple docstring'''
lowerCAmelCase_ : Dict = pipeline("document-question-answering" ,model="hf-internal-testing/tiny-random-layoutlmv2" )
lowerCAmelCase_ : int = INVOICE_URL
lowerCAmelCase_ : int = "How many cats are there?"
lowerCAmelCase_ : List[str] = [
{"score": 0.0_001, "answer": "oy 2312/2019", "start": 38, "end": 39},
{"score": 0.0_001, "answer": "oy 2312/2019 DUE", "start": 38, "end": 40},
]
lowerCAmelCase_ : Optional[int] = dqa_pipeline(image=lowerCAmelCase__ ,question=lowerCAmelCase__ ,top_k=2 )
self.assertEqual(nested_simplify(lowerCAmelCase__ ,decimals=4 ) ,lowerCAmelCase__ )
lowerCAmelCase_ : List[str] = dqa_pipeline({"image": image, "question": question} ,top_k=2 )
self.assertEqual(nested_simplify(lowerCAmelCase__ ,decimals=4 ) ,lowerCAmelCase__ )
# This image does not detect ANY text in it, meaning layoutlmv2 should fail.
# Empty answer probably
lowerCAmelCase_ : Dict = "./tests/fixtures/tests_samples/COCO/000000039769.png"
lowerCAmelCase_ : Dict = dqa_pipeline(image=lowerCAmelCase__ ,question=lowerCAmelCase__ ,top_k=2 )
self.assertEqual(lowerCAmelCase__ ,[] )
# We can optionnally pass directly the words and bounding boxes
lowerCAmelCase_ : Optional[Any] = "./tests/fixtures/tests_samples/COCO/000000039769.png"
lowerCAmelCase_ : Any = []
lowerCAmelCase_ : int = []
lowerCAmelCase_ : List[Any] = dqa_pipeline(image=lowerCAmelCase__ ,question=lowerCAmelCase__ ,words=lowerCAmelCase__ ,boxes=lowerCAmelCase__ ,top_k=2 )
self.assertEqual(lowerCAmelCase__ ,[] )
@slow
@require_torch
@require_detectrona
@require_pytesseract
def UpperCAmelCase_ ( self : str ) -> str:
'''simple docstring'''
lowerCAmelCase_ : Tuple = pipeline(
"document-question-answering" ,model="tiennvcs/layoutlmv2-base-uncased-finetuned-docvqa" ,revision="9977165" ,)
lowerCAmelCase_ : Any = INVOICE_URL
lowerCAmelCase_ : Optional[Any] = "What is the invoice number?"
lowerCAmelCase_ : List[str] = dqa_pipeline(image=lowerCAmelCase__ ,question=lowerCAmelCase__ ,top_k=2 )
self.assertEqual(
nested_simplify(lowerCAmelCase__ ,decimals=4 ) ,[
{"score": 0.9_944, "answer": "us-001", "start": 16, "end": 16},
{"score": 0.0_009, "answer": "us-001", "start": 16, "end": 16},
] ,)
lowerCAmelCase_ : Dict = dqa_pipeline({"image": image, "question": question} ,top_k=2 )
self.assertEqual(
nested_simplify(lowerCAmelCase__ ,decimals=4 ) ,[
{"score": 0.9_944, "answer": "us-001", "start": 16, "end": 16},
{"score": 0.0_009, "answer": "us-001", "start": 16, "end": 16},
] ,)
lowerCAmelCase_ : List[str] = dqa_pipeline(
[{"image": image, "question": question}, {"image": image, "question": question}] ,top_k=2 )
self.assertEqual(
nested_simplify(lowerCAmelCase__ ,decimals=4 ) ,[
[
{"score": 0.9_944, "answer": "us-001", "start": 16, "end": 16},
{"score": 0.0_009, "answer": "us-001", "start": 16, "end": 16},
],
]
* 2 ,)
@slow
@require_torch
@require_detectrona
@require_pytesseract
def UpperCAmelCase_ ( self : Tuple ) -> Dict:
'''simple docstring'''
lowerCAmelCase_ : Optional[int] = pipeline(
"document-question-answering" ,model="tiennvcs/layoutlmv2-base-uncased-finetuned-docvqa" ,revision="9977165" ,max_seq_len=50 ,)
lowerCAmelCase_ : int = INVOICE_URL
lowerCAmelCase_ : Dict = "What is the invoice number?"
lowerCAmelCase_ : Dict = dqa_pipeline(image=lowerCAmelCase__ ,question=lowerCAmelCase__ ,top_k=2 )
self.assertEqual(
nested_simplify(lowerCAmelCase__ ,decimals=4 ) ,[
{"score": 0.9_974, "answer": "1110212019", "start": 23, "end": 23},
{"score": 0.9_948, "answer": "us-001", "start": 16, "end": 16},
] ,)
lowerCAmelCase_ : str = dqa_pipeline({"image": image, "question": question} ,top_k=2 )
self.assertEqual(
nested_simplify(lowerCAmelCase__ ,decimals=4 ) ,[
{"score": 0.9_974, "answer": "1110212019", "start": 23, "end": 23},
{"score": 0.9_948, "answer": "us-001", "start": 16, "end": 16},
] ,)
lowerCAmelCase_ : Any = dqa_pipeline(
[{"image": image, "question": question}, {"image": image, "question": question}] ,top_k=2 )
self.assertEqual(
nested_simplify(lowerCAmelCase__ ,decimals=4 ) ,[
[
{"score": 0.9_974, "answer": "1110212019", "start": 23, "end": 23},
{"score": 0.9_948, "answer": "us-001", "start": 16, "end": 16},
]
]
* 2 ,)
@slow
@require_torch
@require_pytesseract
@require_vision
def UpperCAmelCase_ ( self : Tuple ) -> Union[str, Any]:
'''simple docstring'''
lowerCAmelCase_ : Union[str, Any] = AutoTokenizer.from_pretrained(
"impira/layoutlm-document-qa" ,revision="3dc6de3" ,add_prefix_space=lowerCAmelCase__ )
lowerCAmelCase_ : Optional[int] = pipeline(
"document-question-answering" ,model="impira/layoutlm-document-qa" ,tokenizer=lowerCAmelCase__ ,revision="3dc6de3" ,)
lowerCAmelCase_ : str = INVOICE_URL
lowerCAmelCase_ : Tuple = "What is the invoice number?"
lowerCAmelCase_ : Tuple = dqa_pipeline(image=lowerCAmelCase__ ,question=lowerCAmelCase__ ,top_k=2 )
self.assertEqual(
nested_simplify(lowerCAmelCase__ ,decimals=4 ) ,[
{"score": 0.4_251, "answer": "us-001", "start": 16, "end": 16},
{"score": 0.0_819, "answer": "1110212019", "start": 23, "end": 23},
] ,)
lowerCAmelCase_ : Optional[Any] = dqa_pipeline({"image": image, "question": question} ,top_k=2 )
self.assertEqual(
nested_simplify(lowerCAmelCase__ ,decimals=4 ) ,[
{"score": 0.4_251, "answer": "us-001", "start": 16, "end": 16},
{"score": 0.0_819, "answer": "1110212019", "start": 23, "end": 23},
] ,)
lowerCAmelCase_ : str = dqa_pipeline(
[{"image": image, "question": question}, {"image": image, "question": question}] ,top_k=2 )
self.assertEqual(
nested_simplify(lowerCAmelCase__ ,decimals=4 ) ,[
[
{"score": 0.4_251, "answer": "us-001", "start": 16, "end": 16},
{"score": 0.0_819, "answer": "1110212019", "start": 23, "end": 23},
]
]
* 2 ,)
lowerCAmelCase_ : Tuple = list(zip(*apply_tesseract(load_image(lowerCAmelCase__ ) ,lowerCAmelCase__ ,"" ) ) )
# This model should also work if `image` is set to None
lowerCAmelCase_ : Optional[int] = dqa_pipeline({"image": None, "word_boxes": word_boxes, "question": question} ,top_k=2 )
self.assertEqual(
nested_simplify(lowerCAmelCase__ ,decimals=4 ) ,[
{"score": 0.4_251, "answer": "us-001", "start": 16, "end": 16},
{"score": 0.0_819, "answer": "1110212019", "start": 23, "end": 23},
] ,)
@slow
@require_torch
@require_pytesseract
@require_vision
def UpperCAmelCase_ ( self : Tuple ) -> int:
'''simple docstring'''
lowerCAmelCase_ : Dict = AutoTokenizer.from_pretrained(
"impira/layoutlm-document-qa" ,revision="3dc6de3" ,add_prefix_space=lowerCAmelCase__ )
lowerCAmelCase_ : Any = pipeline(
"document-question-answering" ,model="impira/layoutlm-document-qa" ,tokenizer=lowerCAmelCase__ ,revision="3dc6de3" ,max_seq_len=50 ,)
lowerCAmelCase_ : Any = INVOICE_URL
lowerCAmelCase_ : int = "What is the invoice number?"
lowerCAmelCase_ : Optional[int] = dqa_pipeline(image=lowerCAmelCase__ ,question=lowerCAmelCase__ ,top_k=2 )
self.assertEqual(
nested_simplify(lowerCAmelCase__ ,decimals=4 ) ,[
{"score": 0.9_999, "answer": "us-001", "start": 16, "end": 16},
{"score": 0.9_998, "answer": "us-001", "start": 16, "end": 16},
] ,)
lowerCAmelCase_ : List[str] = dqa_pipeline(
[{"image": image, "question": question}, {"image": image, "question": question}] ,top_k=2 )
self.assertEqual(
nested_simplify(lowerCAmelCase__ ,decimals=4 ) ,[
[
{"score": 0.9_999, "answer": "us-001", "start": 16, "end": 16},
{"score": 0.9_998, "answer": "us-001", "start": 16, "end": 16},
]
]
* 2 ,)
lowerCAmelCase_ : Optional[int] = list(zip(*apply_tesseract(load_image(lowerCAmelCase__ ) ,lowerCAmelCase__ ,"" ) ) )
# This model should also work if `image` is set to None
lowerCAmelCase_ : Union[str, Any] = dqa_pipeline({"image": None, "word_boxes": word_boxes, "question": question} ,top_k=2 )
self.assertEqual(
nested_simplify(lowerCAmelCase__ ,decimals=4 ) ,[
{"score": 0.9_999, "answer": "us-001", "start": 16, "end": 16},
{"score": 0.9_998, "answer": "us-001", "start": 16, "end": 16},
] ,)
@slow
@require_torch
def UpperCAmelCase_ ( self : int ) -> Any:
'''simple docstring'''
lowerCAmelCase_ : int = pipeline(
"document-question-answering" ,model="naver-clova-ix/donut-base-finetuned-docvqa" ,tokenizer=AutoTokenizer.from_pretrained("naver-clova-ix/donut-base-finetuned-docvqa" ) ,feature_extractor="naver-clova-ix/donut-base-finetuned-docvqa" ,)
lowerCAmelCase_ : int = INVOICE_URL
lowerCAmelCase_ : Optional[Any] = "What is the invoice number?"
lowerCAmelCase_ : List[Any] = dqa_pipeline(image=lowerCAmelCase__ ,question=lowerCAmelCase__ ,top_k=2 )
self.assertEqual(nested_simplify(lowerCAmelCase__ ,decimals=4 ) ,[{"answer": "us-001"}] )
@require_tf
@unittest.skip("Document question answering not implemented in TF" )
def UpperCAmelCase_ ( self : List[Any] ) -> Dict:
'''simple docstring'''
pass
| 683 |
from math import sqrt
def UpperCamelCase ( snake_case__):
lowerCAmelCase_ : Optional[int] = 0
for i in range(1 , int(sqrt(snake_case__) + 1)):
if n % i == 0 and i != sqrt(snake_case__):
total += i + n // i
elif i == sqrt(snake_case__):
total += i
return total - n
def UpperCamelCase ( snake_case__ = 1_00_00):
lowerCAmelCase_ : int = sum(
i
for i in range(1 , snake_case__)
if sum_of_divisors(sum_of_divisors(snake_case__)) == i and sum_of_divisors(snake_case__) != i)
return total
if __name__ == "__main__":
print(solution(int(str(input()).strip())))
| 683 | 1 |
from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union
import numpy as np
import PIL
from PIL import Image
from ...utils import BaseOutput, is_torch_available, is_transformers_available
@dataclass
class __snake_case ( snake_case__ ):
"""simple docstring"""
UpperCamelCase_ = 42
UpperCamelCase_ = 42
if is_transformers_available() and is_torch_available():
from .pipeline_semantic_stable_diffusion import SemanticStableDiffusionPipeline
| 683 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_speech_available,
is_tf_available,
is_torch_available,
)
_lowercase = {
'''configuration_speech_to_text''': ['''SPEECH_TO_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''Speech2TextConfig'''],
'''processing_speech_to_text''': ['''Speech2TextProcessor'''],
}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowercase = ['''Speech2TextTokenizer''']
try:
if not is_speech_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowercase = ['''Speech2TextFeatureExtractor''']
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowercase = [
'''TF_SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''TFSpeech2TextForConditionalGeneration''',
'''TFSpeech2TextModel''',
'''TFSpeech2TextPreTrainedModel''',
]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowercase = [
'''SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''Speech2TextForConditionalGeneration''',
'''Speech2TextModel''',
'''Speech2TextPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_speech_to_text import SPEECH_TO_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP, SpeechaTextConfig
from .processing_speech_to_text import SpeechaTextProcessor
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_speech_to_text import SpeechaTextTokenizer
try:
if not is_speech_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_speech_to_text import SpeechaTextFeatureExtractor
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_speech_to_text import (
TF_SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
TFSpeechaTextForConditionalGeneration,
TFSpeechaTextModel,
TFSpeechaTextPreTrainedModel,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_speech_to_text import (
SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
SpeechaTextForConditionalGeneration,
SpeechaTextModel,
SpeechaTextPreTrainedModel,
)
else:
import sys
_lowercase = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 683 | 1 |
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxSeqaSeqConfigWithPast
from ...utils import logging
if TYPE_CHECKING:
from ...feature_extraction_utils import FeatureExtractionMixin
from ...tokenization_utils_base import PreTrainedTokenizerBase
from ...utils import TensorType
_lowercase = logging.get_logger(__name__)
_lowercase = {
'''openai/whisper-base''': '''https://huggingface.co/openai/whisper-base/resolve/main/config.json''',
}
# fmt: off
_lowercase = [
1, 2, 7, 8, 9, 10, 14, 25,
26, 27, 28, 29, 31, 58, 59, 60, 61, 62,
63, 90, 91, 92, 93, 357, 366, 438, 532, 685,
705, 796, 930, 1058, 1220, 1267, 1279, 1303, 1343, 1377,
1391, 1635, 1782, 1875, 2162, 2361, 2488, 3467, 4008, 4211,
4600, 4808, 5299, 5855, 6329, 7203, 9609, 9959, 10563, 10786,
11420, 11709, 11907, 13163, 13697, 13700, 14808, 15306, 16410, 16791,
17992, 19203, 19510, 20724, 22305, 22935, 27007, 30109, 30420, 33409,
34949, 40283, 40493, 40549, 47282, 49146, 50257, 50359, 50360, 50361
]
_lowercase = [
1, 2, 7, 8, 9, 10, 14, 25,
26, 27, 28, 29, 31, 58, 59, 60, 61, 62,
63, 90, 91, 92, 93, 359, 503, 522, 542, 873,
893, 902, 918, 922, 931, 1350, 1853, 1982, 2460, 2627,
3246, 3253, 3268, 3536, 3846, 3961, 4183, 4667, 6585, 6647,
7273, 9061, 9383, 10428, 10929, 11938, 12033, 12331, 12562, 13793,
14157, 14635, 15265, 15618, 16553, 16604, 18362, 18956, 20075, 21675,
22520, 26130, 26161, 26435, 28279, 29464, 31650, 32302, 32470, 36865,
42863, 47425, 49870, 50254, 50258, 50360, 50361, 50362
]
class __snake_case ( snake_case__ ):
"""simple docstring"""
UpperCamelCase_ = 'whisper'
UpperCamelCase_ = ['past_key_values']
UpperCamelCase_ = {'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}
def __init__( self : Optional[Any] ,lowerCAmelCase__ : Union[str, Any]=5_18_65 ,lowerCAmelCase__ : Union[str, Any]=80 ,lowerCAmelCase__ : Optional[Any]=6 ,lowerCAmelCase__ : Optional[int]=4 ,lowerCAmelCase__ : Any=6 ,lowerCAmelCase__ : Dict=4 ,lowerCAmelCase__ : Dict=15_36 ,lowerCAmelCase__ : Tuple=15_36 ,lowerCAmelCase__ : Optional[Any]=0.0 ,lowerCAmelCase__ : List[str]=0.0 ,lowerCAmelCase__ : List[str]=5_02_57 ,lowerCAmelCase__ : str=True ,lowerCAmelCase__ : Union[str, Any]=True ,lowerCAmelCase__ : Tuple="gelu" ,lowerCAmelCase__ : List[Any]=2_56 ,lowerCAmelCase__ : Any=0.0 ,lowerCAmelCase__ : int=0.0 ,lowerCAmelCase__ : Tuple=0.0 ,lowerCAmelCase__ : str=0.02 ,lowerCAmelCase__ : Optional[Any]=False ,lowerCAmelCase__ : List[str]=15_00 ,lowerCAmelCase__ : List[Any]=4_48 ,lowerCAmelCase__ : Any=5_02_56 ,lowerCAmelCase__ : Optional[Any]=5_02_56 ,lowerCAmelCase__ : Optional[Any]=5_02_56 ,lowerCAmelCase__ : List[str]=None ,lowerCAmelCase__ : Dict=[2_20, 5_02_56] ,lowerCAmelCase__ : List[str]=False ,lowerCAmelCase__ : Tuple=2_56 ,lowerCAmelCase__ : List[str]=False ,lowerCAmelCase__ : Optional[int]=0.05 ,lowerCAmelCase__ : Dict=10 ,lowerCAmelCase__ : List[str]=2 ,lowerCAmelCase__ : Optional[Any]=0.0 ,lowerCAmelCase__ : Dict=10 ,lowerCAmelCase__ : Optional[int]=0 ,lowerCAmelCase__ : str=7 ,**lowerCAmelCase__ : Optional[int] ,) -> List[str]:
'''simple docstring'''
lowerCAmelCase_ : Optional[Any] = vocab_size
lowerCAmelCase_ : int = num_mel_bins
lowerCAmelCase_ : Dict = d_model
lowerCAmelCase_ : str = encoder_layers
lowerCAmelCase_ : Any = encoder_attention_heads
lowerCAmelCase_ : Dict = decoder_layers
lowerCAmelCase_ : int = decoder_attention_heads
lowerCAmelCase_ : Any = decoder_ffn_dim
lowerCAmelCase_ : List[str] = encoder_ffn_dim
lowerCAmelCase_ : int = dropout
lowerCAmelCase_ : Optional[int] = attention_dropout
lowerCAmelCase_ : Union[str, Any] = activation_dropout
lowerCAmelCase_ : List[str] = activation_function
lowerCAmelCase_ : Tuple = init_std
lowerCAmelCase_ : Dict = encoder_layerdrop
lowerCAmelCase_ : str = decoder_layerdrop
lowerCAmelCase_ : str = use_cache
lowerCAmelCase_ : Optional[Any] = encoder_layers
lowerCAmelCase_ : int = scale_embedding # scale factor will be sqrt(d_model) if True
lowerCAmelCase_ : Union[str, Any] = max_source_positions
lowerCAmelCase_ : int = max_target_positions
# Audio Classification-specific parameters. Feel free to ignore for other classes.
lowerCAmelCase_ : List[str] = classifier_proj_size
lowerCAmelCase_ : Optional[Any] = use_weighted_layer_sum
# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
lowerCAmelCase_ : List[str] = apply_spec_augment
lowerCAmelCase_ : Any = mask_time_prob
lowerCAmelCase_ : List[Any] = mask_time_length
lowerCAmelCase_ : Optional[Any] = mask_time_min_masks
lowerCAmelCase_ : Optional[Any] = mask_feature_prob
lowerCAmelCase_ : List[Any] = mask_feature_length
lowerCAmelCase_ : Optional[Any] = mask_feature_min_masks
lowerCAmelCase_ : List[str] = median_filter_width
super().__init__(
pad_token_id=lowerCAmelCase__ ,bos_token_id=lowerCAmelCase__ ,eos_token_id=lowerCAmelCase__ ,is_encoder_decoder=lowerCAmelCase__ ,decoder_start_token_id=lowerCAmelCase__ ,suppress_tokens=lowerCAmelCase__ ,begin_suppress_tokens=lowerCAmelCase__ ,**lowerCAmelCase__ ,)
class __snake_case ( snake_case__ ):
"""simple docstring"""
@property
def UpperCAmelCase_ ( self : int ) -> Mapping[str, Mapping[int, str]]:
'''simple docstring'''
lowerCAmelCase_ : Tuple = OrderedDict(
[
("input_features", {0: "batch", 1: "feature_size", 2: "encoder_sequence"}),
] )
if self.use_past:
lowerCAmelCase_ : Dict = {0: "batch"}
else:
lowerCAmelCase_ : Optional[int] = {0: "batch", 1: "decoder_sequence"}
if self.use_past:
self.fill_with_past_key_values_(lowerCAmelCase__ ,direction="inputs" )
return common_inputs
def UpperCAmelCase_ ( self : Tuple ,lowerCAmelCase__ : Union["PreTrainedTokenizerBase", "FeatureExtractionMixin"] ,lowerCAmelCase__ : int = -1 ,lowerCAmelCase__ : int = -1 ,lowerCAmelCase__ : bool = False ,lowerCAmelCase__ : Optional["TensorType"] = None ,lowerCAmelCase__ : int = 2_20_50 ,lowerCAmelCase__ : float = 5.0 ,lowerCAmelCase__ : int = 2_20 ,) -> Mapping[str, Any]:
'''simple docstring'''
lowerCAmelCase_ : List[str] = OrderedDict()
lowerCAmelCase_ : Optional[Any] = OnnxConfig.generate_dummy_inputs(
self ,preprocessor=preprocessor.feature_extractor ,batch_size=lowerCAmelCase__ ,framework=lowerCAmelCase__ ,sampling_rate=lowerCAmelCase__ ,time_duration=lowerCAmelCase__ ,frequency=lowerCAmelCase__ ,)
lowerCAmelCase_ : List[str] = encoder_inputs["input_features"].shape[2]
lowerCAmelCase_ : str = encoder_sequence_length // 2 if self.use_past else seq_length
lowerCAmelCase_ : Tuple = super().generate_dummy_inputs(
preprocessor.tokenizer ,lowerCAmelCase__ ,lowerCAmelCase__ ,lowerCAmelCase__ ,lowerCAmelCase__ )
lowerCAmelCase_ : List[str] = encoder_inputs.pop("input_features" )
lowerCAmelCase_ : List[str] = decoder_inputs.pop("decoder_input_ids" )
if "past_key_values" in decoder_inputs:
lowerCAmelCase_ : Optional[Any] = decoder_inputs.pop("past_key_values" )
return dummy_inputs
@property
def UpperCAmelCase_ ( self : Union[str, Any] ) -> float:
'''simple docstring'''
return 1e-3
| 683 |
import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
_lowercase = logging.get_logger(__name__)
_lowercase = {'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt'''}
_lowercase = {
'''vocab_file''': {
'''allenai/longformer-base-4096''': '''https://huggingface.co/allenai/longformer-base-4096/resolve/main/vocab.json''',
'''allenai/longformer-large-4096''': (
'''https://huggingface.co/allenai/longformer-large-4096/resolve/main/vocab.json'''
),
'''allenai/longformer-large-4096-finetuned-triviaqa''': (
'''https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/vocab.json'''
),
'''allenai/longformer-base-4096-extra.pos.embd.only''': (
'''https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/vocab.json'''
),
'''allenai/longformer-large-4096-extra.pos.embd.only''': (
'''https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/vocab.json'''
),
},
'''merges_file''': {
'''allenai/longformer-base-4096''': '''https://huggingface.co/allenai/longformer-base-4096/resolve/main/merges.txt''',
'''allenai/longformer-large-4096''': (
'''https://huggingface.co/allenai/longformer-large-4096/resolve/main/merges.txt'''
),
'''allenai/longformer-large-4096-finetuned-triviaqa''': (
'''https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/merges.txt'''
),
'''allenai/longformer-base-4096-extra.pos.embd.only''': (
'''https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/merges.txt'''
),
'''allenai/longformer-large-4096-extra.pos.embd.only''': (
'''https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/merges.txt'''
),
},
}
_lowercase = {
'''allenai/longformer-base-4096''': 4096,
'''allenai/longformer-large-4096''': 4096,
'''allenai/longformer-large-4096-finetuned-triviaqa''': 4096,
'''allenai/longformer-base-4096-extra.pos.embd.only''': 4096,
'''allenai/longformer-large-4096-extra.pos.embd.only''': 4096,
}
@lru_cache()
# Copied from transformers.models.roberta.tokenization_roberta.bytes_to_unicode
def UpperCamelCase ( ):
lowerCAmelCase_ : str = (
list(range(ord("!") , ord("~") + 1)) + list(range(ord("¡") , ord("¬") + 1)) + list(range(ord("®") , ord("ÿ") + 1))
)
lowerCAmelCase_ : Tuple = bs[:]
lowerCAmelCase_ : Dict = 0
for b in range(2**8):
if b not in bs:
bs.append(snake_case__)
cs.append(2**8 + n)
n += 1
lowerCAmelCase_ : Union[str, Any] = [chr(snake_case__) for n in cs]
return dict(zip(snake_case__ , snake_case__))
def UpperCamelCase ( snake_case__):
lowerCAmelCase_ : Optional[Any] = set()
lowerCAmelCase_ : List[Any] = word[0]
for char in word[1:]:
pairs.add((prev_char, char))
lowerCAmelCase_ : Union[str, Any] = char
return pairs
class __snake_case ( snake_case__ ):
"""simple docstring"""
UpperCamelCase_ = VOCAB_FILES_NAMES
UpperCamelCase_ = PRETRAINED_VOCAB_FILES_MAP
UpperCamelCase_ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
UpperCamelCase_ = ['input_ids', 'attention_mask']
def __init__( self : str ,lowerCAmelCase__ : Dict ,lowerCAmelCase__ : List[Any] ,lowerCAmelCase__ : Optional[Any]="replace" ,lowerCAmelCase__ : Dict="<s>" ,lowerCAmelCase__ : str="</s>" ,lowerCAmelCase__ : str="</s>" ,lowerCAmelCase__ : Optional[Any]="<s>" ,lowerCAmelCase__ : List[Any]="<unk>" ,lowerCAmelCase__ : Union[str, Any]="<pad>" ,lowerCAmelCase__ : int="<mask>" ,lowerCAmelCase__ : Any=False ,**lowerCAmelCase__ : int ,) -> Dict:
'''simple docstring'''
lowerCAmelCase_ : Optional[int] = AddedToken(lowerCAmelCase__ ,lstrip=lowerCAmelCase__ ,rstrip=lowerCAmelCase__ ) if isinstance(lowerCAmelCase__ ,lowerCAmelCase__ ) else bos_token
lowerCAmelCase_ : Tuple = AddedToken(lowerCAmelCase__ ,lstrip=lowerCAmelCase__ ,rstrip=lowerCAmelCase__ ) if isinstance(lowerCAmelCase__ ,lowerCAmelCase__ ) else eos_token
lowerCAmelCase_ : Dict = AddedToken(lowerCAmelCase__ ,lstrip=lowerCAmelCase__ ,rstrip=lowerCAmelCase__ ) if isinstance(lowerCAmelCase__ ,lowerCAmelCase__ ) else sep_token
lowerCAmelCase_ : int = AddedToken(lowerCAmelCase__ ,lstrip=lowerCAmelCase__ ,rstrip=lowerCAmelCase__ ) if isinstance(lowerCAmelCase__ ,lowerCAmelCase__ ) else cls_token
lowerCAmelCase_ : List[str] = AddedToken(lowerCAmelCase__ ,lstrip=lowerCAmelCase__ ,rstrip=lowerCAmelCase__ ) if isinstance(lowerCAmelCase__ ,lowerCAmelCase__ ) else unk_token
lowerCAmelCase_ : List[str] = AddedToken(lowerCAmelCase__ ,lstrip=lowerCAmelCase__ ,rstrip=lowerCAmelCase__ ) if isinstance(lowerCAmelCase__ ,lowerCAmelCase__ ) else pad_token
# Mask token behave like a normal word, i.e. include the space before it
lowerCAmelCase_ : Optional[Any] = AddedToken(lowerCAmelCase__ ,lstrip=lowerCAmelCase__ ,rstrip=lowerCAmelCase__ ) if isinstance(lowerCAmelCase__ ,lowerCAmelCase__ ) else mask_token
super().__init__(
errors=lowerCAmelCase__ ,bos_token=lowerCAmelCase__ ,eos_token=lowerCAmelCase__ ,unk_token=lowerCAmelCase__ ,sep_token=lowerCAmelCase__ ,cls_token=lowerCAmelCase__ ,pad_token=lowerCAmelCase__ ,mask_token=lowerCAmelCase__ ,add_prefix_space=lowerCAmelCase__ ,**lowerCAmelCase__ ,)
with open(lowerCAmelCase__ ,encoding="utf-8" ) as vocab_handle:
lowerCAmelCase_ : List[Any] = json.load(lowerCAmelCase__ )
lowerCAmelCase_ : Dict = {v: k for k, v in self.encoder.items()}
lowerCAmelCase_ : List[Any] = errors # how to handle errors in decoding
lowerCAmelCase_ : Optional[Any] = bytes_to_unicode()
lowerCAmelCase_ : int = {v: k for k, v in self.byte_encoder.items()}
with open(lowerCAmelCase__ ,encoding="utf-8" ) as merges_handle:
lowerCAmelCase_ : Union[str, Any] = merges_handle.read().split("\n" )[1:-1]
lowerCAmelCase_ : Dict = [tuple(merge.split() ) for merge in bpe_merges]
lowerCAmelCase_ : Dict = dict(zip(lowerCAmelCase__ ,range(len(lowerCAmelCase__ ) ) ) )
lowerCAmelCase_ : Any = {}
lowerCAmelCase_ : int = add_prefix_space
# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
lowerCAmelCase_ : Optional[Any] = re.compile(R"'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+" )
@property
def UpperCAmelCase_ ( self : Optional[int] ) -> Any:
'''simple docstring'''
return len(self.encoder )
def UpperCAmelCase_ ( self : Any ) -> Optional[int]:
'''simple docstring'''
return dict(self.encoder ,**self.added_tokens_encoder )
def UpperCAmelCase_ ( self : List[str] ,lowerCAmelCase__ : List[str] ) -> List[Any]:
'''simple docstring'''
if token in self.cache:
return self.cache[token]
lowerCAmelCase_ : Union[str, Any] = tuple(lowerCAmelCase__ )
lowerCAmelCase_ : Optional[Any] = get_pairs(lowerCAmelCase__ )
if not pairs:
return token
while True:
lowerCAmelCase_ : Dict = min(lowerCAmelCase__ ,key=lambda lowerCAmelCase__ : self.bpe_ranks.get(lowerCAmelCase__ ,float("inf" ) ) )
if bigram not in self.bpe_ranks:
break
lowerCAmelCase_ , lowerCAmelCase_ : Dict = bigram
lowerCAmelCase_ : Optional[Any] = []
lowerCAmelCase_ : Any = 0
while i < len(lowerCAmelCase__ ):
try:
lowerCAmelCase_ : Optional[int] = word.index(lowerCAmelCase__ ,lowerCAmelCase__ )
except ValueError:
new_word.extend(word[i:] )
break
else:
new_word.extend(word[i:j] )
lowerCAmelCase_ : Tuple = j
if word[i] == first and i < len(lowerCAmelCase__ ) - 1 and word[i + 1] == second:
new_word.append(first + second )
i += 2
else:
new_word.append(word[i] )
i += 1
lowerCAmelCase_ : Optional[Any] = tuple(lowerCAmelCase__ )
lowerCAmelCase_ : Union[str, Any] = new_word
if len(lowerCAmelCase__ ) == 1:
break
else:
lowerCAmelCase_ : Dict = get_pairs(lowerCAmelCase__ )
lowerCAmelCase_ : Tuple = " ".join(lowerCAmelCase__ )
lowerCAmelCase_ : Any = word
return word
def UpperCAmelCase_ ( self : Optional[int] ,lowerCAmelCase__ : Tuple ) -> Dict:
'''simple docstring'''
lowerCAmelCase_ : Dict = []
for token in re.findall(self.pat ,lowerCAmelCase__ ):
lowerCAmelCase_ : List[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(lowerCAmelCase__ ).split(" " ) )
return bpe_tokens
def UpperCAmelCase_ ( self : Tuple ,lowerCAmelCase__ : int ) -> Tuple:
'''simple docstring'''
return self.encoder.get(lowerCAmelCase__ ,self.encoder.get(self.unk_token ) )
def UpperCAmelCase_ ( self : List[str] ,lowerCAmelCase__ : Optional[Any] ) -> Union[str, Any]:
'''simple docstring'''
return self.decoder.get(lowerCAmelCase__ )
def UpperCAmelCase_ ( self : Tuple ,lowerCAmelCase__ : Dict ) -> Any:
'''simple docstring'''
lowerCAmelCase_ : Optional[int] = "".join(lowerCAmelCase__ )
lowerCAmelCase_ : Union[str, Any] = bytearray([self.byte_decoder[c] for c in text] ).decode("utf-8" ,errors=self.errors )
return text
def UpperCAmelCase_ ( self : Optional[Any] ,lowerCAmelCase__ : str ,lowerCAmelCase__ : Optional[str] = None ) -> Tuple[str]:
'''simple docstring'''
if not os.path.isdir(lowerCAmelCase__ ):
logger.error(f'''Vocabulary path ({save_directory}) should be a directory''' )
return
lowerCAmelCase_ : Optional[Any] = os.path.join(
lowerCAmelCase__ ,(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )
lowerCAmelCase_ : Tuple = os.path.join(
lowerCAmelCase__ ,(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"] )
with open(lowerCAmelCase__ ,"w" ,encoding="utf-8" ) as f:
f.write(json.dumps(self.encoder ,indent=2 ,sort_keys=lowerCAmelCase__ ,ensure_ascii=lowerCAmelCase__ ) + "\n" )
lowerCAmelCase_ : Tuple = 0
with open(lowerCAmelCase__ ,"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 lowerCAmelCase__ : 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!" )
lowerCAmelCase_ : Optional[Any] = token_index
writer.write(" ".join(lowerCAmelCase__ ) + "\n" )
index += 1
return vocab_file, merge_file
def UpperCAmelCase_ ( self : Union[str, Any] ,lowerCAmelCase__ : List[int] ,lowerCAmelCase__ : Optional[List[int]] = None ) -> List[int]:
'''simple docstring'''
if token_ids_a is None:
return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
lowerCAmelCase_ : List[Any] = [self.cls_token_id]
lowerCAmelCase_ : List[str] = [self.sep_token_id]
return cls + token_ids_a + sep + sep + token_ids_a + sep
def UpperCAmelCase_ ( self : Dict ,lowerCAmelCase__ : List[int] ,lowerCAmelCase__ : Optional[List[int]] = None ,lowerCAmelCase__ : bool = False ) -> List[int]:
'''simple docstring'''
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=lowerCAmelCase__ ,token_ids_a=lowerCAmelCase__ ,already_has_special_tokens=lowerCAmelCase__ )
if token_ids_a is None:
return [1] + ([0] * len(lowerCAmelCase__ )) + [1]
return [1] + ([0] * len(lowerCAmelCase__ )) + [1, 1] + ([0] * len(lowerCAmelCase__ )) + [1]
def UpperCAmelCase_ ( self : Any ,lowerCAmelCase__ : List[int] ,lowerCAmelCase__ : Optional[List[int]] = None ) -> List[int]:
'''simple docstring'''
lowerCAmelCase_ : List[str] = [self.sep_token_id]
lowerCAmelCase_ : 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 : Tuple ,lowerCAmelCase__ : List[str] ,lowerCAmelCase__ : Optional[int]=False ,**lowerCAmelCase__ : Optional[int] ) -> Any:
'''simple docstring'''
lowerCAmelCase_ : int = kwargs.pop("add_prefix_space" ,self.add_prefix_space )
if (is_split_into_words or add_prefix_space) and (len(lowerCAmelCase__ ) > 0 and not text[0].isspace()):
lowerCAmelCase_ : Union[str, Any] = " " + text
return (text, kwargs)
| 683 | 1 |
import copy
from typing import Dict, Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
from ..detr import DetrConfig
from ..swin import SwinConfig
_lowercase = {
'''facebook/maskformer-swin-base-ade''': (
'''https://huggingface.co/facebook/maskformer-swin-base-ade/blob/main/config.json'''
)
# See all MaskFormer models at https://huggingface.co/models?filter=maskformer
}
_lowercase = logging.get_logger(__name__)
class __snake_case ( snake_case__ ):
"""simple docstring"""
UpperCamelCase_ = 'maskformer'
UpperCamelCase_ = {'hidden_size': 'mask_feature_size'}
UpperCamelCase_ = ['resnet', 'swin']
UpperCamelCase_ = ['detr']
def __init__( self : Dict ,lowerCAmelCase__ : int = 2_56 ,lowerCAmelCase__ : int = 2_56 ,lowerCAmelCase__ : float = 0.1 ,lowerCAmelCase__ : bool = False ,lowerCAmelCase__ : Optional[Dict] = None ,lowerCAmelCase__ : Optional[Dict] = None ,lowerCAmelCase__ : float = 0.02 ,lowerCAmelCase__ : float = 1.0 ,lowerCAmelCase__ : float = 1.0 ,lowerCAmelCase__ : float = 1.0 ,lowerCAmelCase__ : float = 20.0 ,lowerCAmelCase__ : Optional[bool] = None ,**lowerCAmelCase__ : Union[str, Any] ,) -> str:
'''simple docstring'''
if backbone_config is None:
# fall back to https://huggingface.co/microsoft/swin-base-patch4-window12-384-in22k
lowerCAmelCase_ : int = SwinConfig(
image_size=3_84 ,in_channels=3 ,patch_size=4 ,embed_dim=1_28 ,depths=[2, 2, 18, 2] ,num_heads=[4, 8, 16, 32] ,window_size=12 ,drop_path_rate=0.3 ,out_features=["stage1", "stage2", "stage3", "stage4"] ,)
if isinstance(lowerCAmelCase__ ,lowerCAmelCase__ ):
lowerCAmelCase_ : List[Any] = backbone_config.pop("model_type" )
lowerCAmelCase_ : List[Any] = CONFIG_MAPPING[backbone_model_type]
lowerCAmelCase_ : int = config_class.from_dict(lowerCAmelCase__ )
# verify that the backbone is supported
if backbone_config.model_type not in self.backbones_supported:
logger.warning_once(
f'''Backbone {backbone_config.model_type} is not a supported model and may not be compatible with MaskFormer. '''
f'''Supported model types: {",".join(self.backbones_supported )}''' )
if decoder_config is None:
# fall back to https://huggingface.co/facebook/detr-resnet-50
lowerCAmelCase_ : List[str] = DetrConfig()
else:
# verify that the decoder is supported
lowerCAmelCase_ : Tuple = (
decoder_config.pop("model_type" ) if isinstance(lowerCAmelCase__ ,lowerCAmelCase__ ) else decoder_config.model_type
)
if decoder_type not in self.decoders_supported:
raise ValueError(
f'''Transformer Decoder {decoder_type} not supported, please use one of'''
f''' {",".join(self.decoders_supported )}''' )
if isinstance(lowerCAmelCase__ ,lowerCAmelCase__ ):
lowerCAmelCase_ : Optional[Any] = CONFIG_MAPPING[decoder_type]
lowerCAmelCase_ : List[Any] = config_class.from_dict(lowerCAmelCase__ )
lowerCAmelCase_ : List[Any] = backbone_config
lowerCAmelCase_ : str = decoder_config
# main feature dimension for the model
lowerCAmelCase_ : Tuple = fpn_feature_size
lowerCAmelCase_ : List[Any] = mask_feature_size
# initializer
lowerCAmelCase_ : Optional[int] = init_std
lowerCAmelCase_ : Optional[Any] = init_xavier_std
# Hungarian matcher && loss
lowerCAmelCase_ : Optional[int] = cross_entropy_weight
lowerCAmelCase_ : Tuple = dice_weight
lowerCAmelCase_ : str = mask_weight
lowerCAmelCase_ : List[str] = use_auxiliary_loss
lowerCAmelCase_ : Optional[int] = no_object_weight
lowerCAmelCase_ : Tuple = output_auxiliary_logits
lowerCAmelCase_ : Union[str, Any] = self.decoder_config.encoder_attention_heads
lowerCAmelCase_ : List[Any] = self.decoder_config.num_hidden_layers
super().__init__(**lowerCAmelCase__ )
@classmethod
def UpperCAmelCase_ ( cls : Optional[int] ,lowerCAmelCase__ : PretrainedConfig ,lowerCAmelCase__ : PretrainedConfig ,**lowerCAmelCase__ : Optional[Any] ) -> Optional[int]:
'''simple docstring'''
return cls(
backbone_config=lowerCAmelCase__ ,decoder_config=lowerCAmelCase__ ,**lowerCAmelCase__ ,)
def UpperCAmelCase_ ( self : Tuple ) -> Dict[str, any]:
'''simple docstring'''
lowerCAmelCase_ : Optional[int] = copy.deepcopy(self.__dict__ )
lowerCAmelCase_ : Any = self.backbone_config.to_dict()
lowerCAmelCase_ : Any = self.decoder_config.to_dict()
lowerCAmelCase_ : str = self.__class__.model_type
return output
| 683 |
from collections.abc import Iterable
from typing import Any
class __snake_case :
"""simple docstring"""
def __init__( self : Optional[Any] ,lowerCAmelCase__ : int | None = None ) -> List[str]:
'''simple docstring'''
lowerCAmelCase_ : Dict = value
lowerCAmelCase_ : Node | None = None # Added in order to delete a node easier
lowerCAmelCase_ : Node | None = None
lowerCAmelCase_ : Node | None = None
def __repr__( self : Union[str, Any] ) -> str:
'''simple docstring'''
from pprint import pformat
if self.left is None and self.right is None:
return str(self.value )
return pformat({f'''{self.value}''': (self.left, self.right)} ,indent=1 )
class __snake_case :
"""simple docstring"""
def __init__( self : Optional[Any] ,lowerCAmelCase__ : Node | None = None ) -> List[str]:
'''simple docstring'''
lowerCAmelCase_ : List[Any] = root
def __str__( self : Dict ) -> str:
'''simple docstring'''
return str(self.root )
def UpperCAmelCase_ ( self : List[Any] ,lowerCAmelCase__ : Node ,lowerCAmelCase__ : Node | None ) -> None:
'''simple docstring'''
if new_children is not None: # reset its kids
lowerCAmelCase_ : Optional[int] = node.parent
if node.parent is not None: # reset its parent
if self.is_right(lowerCAmelCase__ ): # If it is the right children
lowerCAmelCase_ : List[Any] = new_children
else:
lowerCAmelCase_ : List[Any] = new_children
else:
lowerCAmelCase_ : Any = new_children
def UpperCAmelCase_ ( self : Any ,lowerCAmelCase__ : Node ) -> bool:
'''simple docstring'''
if node.parent and node.parent.right:
return node == node.parent.right
return False
def UpperCAmelCase_ ( self : List[str] ) -> bool:
'''simple docstring'''
return self.root is None
def UpperCAmelCase_ ( self : List[Any] ,lowerCAmelCase__ : Union[str, Any] ) -> None:
'''simple docstring'''
lowerCAmelCase_ : str = Node(lowerCAmelCase__ ) # create a new Node
if self.empty(): # if Tree is empty
lowerCAmelCase_ : Optional[int] = new_node # set its root
else: # Tree is not empty
lowerCAmelCase_ : List[Any] = self.root # from root
if parent_node is None:
return
while True: # While we don't get to a leaf
if value < parent_node.value: # We go left
if parent_node.left is None:
lowerCAmelCase_ : Dict = new_node # We insert the new node in a leaf
break
else:
lowerCAmelCase_ : List[str] = parent_node.left
else:
if parent_node.right is None:
lowerCAmelCase_ : Dict = new_node
break
else:
lowerCAmelCase_ : str = parent_node.right
lowerCAmelCase_ : Optional[int] = parent_node
def UpperCAmelCase_ ( self : int ,*lowerCAmelCase__ : Tuple ) -> None:
'''simple docstring'''
for value in values:
self.__insert(lowerCAmelCase__ )
def UpperCAmelCase_ ( self : Optional[int] ,lowerCAmelCase__ : Optional[int] ) -> Node | None:
'''simple docstring'''
if self.empty():
raise IndexError("Warning: Tree is empty! please use another." )
else:
lowerCAmelCase_ : Dict = self.root
# use lazy evaluation here to avoid NoneType Attribute error
while node is not None and node.value is not value:
lowerCAmelCase_ : Union[str, Any] = node.left if value < node.value else node.right
return node
def UpperCAmelCase_ ( self : Dict ,lowerCAmelCase__ : Node | None = None ) -> Node | None:
'''simple docstring'''
if node is None:
if self.root is None:
return None
lowerCAmelCase_ : Dict = self.root
if not self.empty():
while node.right is not None:
lowerCAmelCase_ : Union[str, Any] = node.right
return node
def UpperCAmelCase_ ( self : Dict ,lowerCAmelCase__ : Node | None = None ) -> Node | None:
'''simple docstring'''
if node is None:
lowerCAmelCase_ : Dict = self.root
if self.root is None:
return None
if not self.empty():
lowerCAmelCase_ : Dict = self.root
while node.left is not None:
lowerCAmelCase_ : Union[str, Any] = node.left
return node
def UpperCAmelCase_ ( self : Union[str, Any] ,lowerCAmelCase__ : int ) -> None:
'''simple docstring'''
lowerCAmelCase_ : Dict = self.search(lowerCAmelCase__ ) # Look for the node with that label
if node is not None:
if node.left is None and node.right is None: # If it has no children
self.__reassign_nodes(lowerCAmelCase__ ,lowerCAmelCase__ )
elif node.left is None: # Has only right children
self.__reassign_nodes(lowerCAmelCase__ ,node.right )
elif node.right is None: # Has only left children
self.__reassign_nodes(lowerCAmelCase__ ,node.left )
else:
lowerCAmelCase_ : int = self.get_max(
node.left ) # Gets the max value of the left branch
self.remove(tmp_node.value ) # type: ignore
lowerCAmelCase_ : Any = (
tmp_node.value # type: ignore
) # Assigns the value to the node to delete and keep tree structure
def UpperCAmelCase_ ( self : Union[str, Any] ,lowerCAmelCase__ : Node | None ) -> Iterable:
'''simple docstring'''
if node is not None:
yield node # Preorder Traversal
yield from self.preorder_traverse(node.left )
yield from self.preorder_traverse(node.right )
def UpperCAmelCase_ ( self : Union[str, Any] ,lowerCAmelCase__ : Dict=None ) -> Any:
'''simple docstring'''
if traversal_function is None:
return self.preorder_traverse(self.root )
else:
return traversal_function(self.root )
def UpperCAmelCase_ ( self : str ,lowerCAmelCase__ : list ,lowerCAmelCase__ : Node | None ) -> None:
'''simple docstring'''
if node:
self.inorder(lowerCAmelCase__ ,node.left )
arr.append(node.value )
self.inorder(lowerCAmelCase__ ,node.right )
def UpperCAmelCase_ ( self : List[str] ,lowerCAmelCase__ : int ,lowerCAmelCase__ : Node ) -> int:
'''simple docstring'''
lowerCAmelCase_ : list[int] = []
self.inorder(lowerCAmelCase__ ,lowerCAmelCase__ ) # append all values to list using inorder traversal
return arr[k - 1]
def UpperCamelCase ( snake_case__):
lowerCAmelCase_ : Optional[Any] = []
if curr_node is not None:
lowerCAmelCase_ : Dict = postorder(curr_node.left) + postorder(curr_node.right) + [curr_node]
return node_list
def UpperCamelCase ( ):
lowerCAmelCase_ : Tuple = (8, 3, 6, 1, 10, 14, 13, 4, 7)
lowerCAmelCase_ : Tuple = BinarySearchTree()
for i in testlist:
t.insert(snake_case__)
# Prints all the elements of the list in order traversal
print(snake_case__)
if t.search(6) is not None:
print("The value 6 exists")
else:
print("The value 6 doesn't exist")
if t.search(-1) is not None:
print("The value -1 exists")
else:
print("The value -1 doesn't exist")
if not t.empty():
print("Max Value: " , t.get_max().value) # type: ignore
print("Min Value: " , t.get_min().value) # type: ignore
for i in testlist:
t.remove(snake_case__)
print(snake_case__)
if __name__ == "__main__":
import doctest
doctest.testmod(verbose=True)
| 683 | 1 |
def UpperCamelCase ( snake_case__):
lowerCAmelCase_ : Dict = [int(snake_case__) for i in ip_va_address.split(".") if i.isdigit()]
return len(snake_case__) == 4 and all(0 <= int(snake_case__) <= 2_54 for octet in octets)
if __name__ == "__main__":
_lowercase = input().strip()
_lowercase = '''valid''' if is_ip_va_address_valid(ip) else '''invalid'''
print(f"{ip} is a {valid_or_invalid} IP v4 address.")
| 683 |
class __snake_case :
"""simple docstring"""
def __init__( self : Optional[int] ,lowerCAmelCase__ : str = "" ,lowerCAmelCase__ : bool = False ) -> None:
'''simple docstring'''
lowerCAmelCase_ : dict[str, RadixNode] = {}
# A node will be a leaf if the tree contains its word
lowerCAmelCase_ : int = is_leaf
lowerCAmelCase_ : Optional[Any] = prefix
def UpperCAmelCase_ ( self : str ,lowerCAmelCase__ : str ) -> tuple[str, str, str]:
'''simple docstring'''
lowerCAmelCase_ : Any = 0
for q, w in zip(self.prefix ,lowerCAmelCase__ ):
if q != w:
break
x += 1
return self.prefix[:x], self.prefix[x:], word[x:]
def UpperCAmelCase_ ( self : Optional[int] ,lowerCAmelCase__ : list[str] ) -> None:
'''simple docstring'''
for word in words:
self.insert(lowerCAmelCase__ )
def UpperCAmelCase_ ( self : Any ,lowerCAmelCase__ : str ) -> None:
'''simple docstring'''
if self.prefix == word:
lowerCAmelCase_ : Optional[Any] = True
# Case 2: The node has no edges that have a prefix to the word
# Solution: We create an edge from the current node to a new one
# containing the word
elif word[0] not in self.nodes:
lowerCAmelCase_ : List[Any] = RadixNode(prefix=lowerCAmelCase__ ,is_leaf=lowerCAmelCase__ )
else:
lowerCAmelCase_ : Tuple = self.nodes[word[0]]
lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ : List[str] = incoming_node.match(
lowerCAmelCase__ )
# Case 3: The node prefix is equal to the matching
# Solution: We insert remaining word on the next node
if remaining_prefix == "":
self.nodes[matching_string[0]].insert(lowerCAmelCase__ )
# Case 4: The word is greater equal to the matching
# Solution: Create a node in between both nodes, change
# prefixes and add the new node for the remaining word
else:
lowerCAmelCase_ : Optional[int] = remaining_prefix
lowerCAmelCase_ : Optional[int] = self.nodes[matching_string[0]]
lowerCAmelCase_ : List[Any] = RadixNode(lowerCAmelCase__ ,lowerCAmelCase__ )
lowerCAmelCase_ : Dict = aux_node
if remaining_word == "":
lowerCAmelCase_ : List[str] = True
else:
self.nodes[matching_string[0]].insert(lowerCAmelCase__ )
def UpperCAmelCase_ ( self : Tuple ,lowerCAmelCase__ : str ) -> bool:
'''simple docstring'''
lowerCAmelCase_ : Any = self.nodes.get(word[0] ,lowerCAmelCase__ )
if not incoming_node:
return False
else:
lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ : List[Any] = incoming_node.match(
lowerCAmelCase__ )
# If there is remaining prefix, the word can't be on the tree
if remaining_prefix != "":
return False
# This applies when the word and the prefix are equal
elif remaining_word == "":
return incoming_node.is_leaf
# We have word remaining so we check the next node
else:
return incoming_node.find(lowerCAmelCase__ )
def UpperCAmelCase_ ( self : Optional[Any] ,lowerCAmelCase__ : str ) -> bool:
'''simple docstring'''
lowerCAmelCase_ : int = self.nodes.get(word[0] ,lowerCAmelCase__ )
if not incoming_node:
return False
else:
lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ : Union[str, Any] = incoming_node.match(
lowerCAmelCase__ )
# If there is remaining prefix, the word can't be on the tree
if remaining_prefix != "":
return False
# We have word remaining so we check the next node
elif remaining_word != "":
return incoming_node.delete(lowerCAmelCase__ )
else:
# If it is not a leaf, we don't have to delete
if not incoming_node.is_leaf:
return False
else:
# We delete the nodes if no edges go from it
if len(incoming_node.nodes ) == 0:
del self.nodes[word[0]]
# We merge the current node with its only child
if len(self.nodes ) == 1 and not self.is_leaf:
lowerCAmelCase_ : str = list(self.nodes.values() )[0]
lowerCAmelCase_ : Tuple = merging_node.is_leaf
self.prefix += merging_node.prefix
lowerCAmelCase_ : Optional[int] = merging_node.nodes
# If there is more than 1 edge, we just mark it as non-leaf
elif len(incoming_node.nodes ) > 1:
lowerCAmelCase_ : Optional[Any] = False
# If there is 1 edge, we merge it with its child
else:
lowerCAmelCase_ : Tuple = list(incoming_node.nodes.values() )[0]
lowerCAmelCase_ : Union[str, Any] = merging_node.is_leaf
incoming_node.prefix += merging_node.prefix
lowerCAmelCase_ : str = merging_node.nodes
return True
def UpperCAmelCase_ ( self : Dict ,lowerCAmelCase__ : int = 0 ) -> None:
'''simple docstring'''
if self.prefix != "":
print("-" * height ,self.prefix ," (leaf)" if self.is_leaf else "" )
for value in self.nodes.values():
value.print_tree(height + 1 )
def UpperCamelCase ( ):
lowerCAmelCase_ : Dict = "banana bananas bandana band apple all beast".split()
lowerCAmelCase_ : List[Any] = RadixNode()
root.insert_many(snake_case__)
assert all(root.find(snake_case__) for word in words)
assert not root.find("bandanas")
assert not root.find("apps")
root.delete("all")
assert not root.find("all")
root.delete("banana")
assert not root.find("banana")
assert root.find("bananas")
return True
def UpperCamelCase ( ):
assert test_trie()
def UpperCamelCase ( ):
lowerCAmelCase_ : List[str] = RadixNode()
lowerCAmelCase_ : Optional[Any] = "banana bananas bandanas bandana band apple all beast".split()
root.insert_many(snake_case__)
print("Words:" , snake_case__)
print("Tree:")
root.print_tree()
if __name__ == "__main__":
main()
| 683 | 1 |
def UpperCamelCase ( snake_case__ = "The quick brown fox jumps over the lazy dog" , ):
lowerCAmelCase_ : List[str] = set()
# Replace all the whitespace in our sentence
lowerCAmelCase_ : Tuple = input_str.replace(" " , "")
for alpha in input_str:
if "a" <= alpha.lower() <= "z":
frequency.add(alpha.lower())
return len(snake_case__) == 26
def UpperCamelCase ( snake_case__ = "The quick brown fox jumps over the lazy dog" , ):
lowerCAmelCase_ : Union[str, Any] = [False] * 26
for char in input_str:
if char.islower():
lowerCAmelCase_ : Any = True
elif char.isupper():
lowerCAmelCase_ : Union[str, Any] = True
return all(snake_case__)
def UpperCamelCase ( snake_case__ = "The quick brown fox jumps over the lazy dog" , ):
return len({char for char in input_str.lower() if char.isalpha()}) == 26
def UpperCamelCase ( ):
from timeit import timeit
lowerCAmelCase_ : str = "from __main__ import is_pangram, is_pangram_faster, is_pangram_fastest"
print(timeit("is_pangram()" , setup=snake_case__))
print(timeit("is_pangram_faster()" , setup=snake_case__))
print(timeit("is_pangram_fastest()" , setup=snake_case__))
# 5.348480500048026, 2.6477354579837993, 1.8470395830227062
# 5.036091582966037, 2.644472333951853, 1.8869528750656173
if __name__ == "__main__":
import doctest
doctest.testmod()
benchmark()
| 683 |
from __future__ import annotations
def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__ , ):
if (electron_conc, hole_conc, intrinsic_conc).count(0) != 1:
raise ValueError("You cannot supply more or less than 2 values")
elif electron_conc < 0:
raise ValueError("Electron concentration cannot be negative in a semiconductor")
elif hole_conc < 0:
raise ValueError("Hole concentration cannot be negative in a semiconductor")
elif intrinsic_conc < 0:
raise ValueError(
"Intrinsic concentration cannot be negative in a semiconductor")
elif electron_conc == 0:
return (
"electron_conc",
intrinsic_conc**2 / hole_conc,
)
elif hole_conc == 0:
return (
"hole_conc",
intrinsic_conc**2 / electron_conc,
)
elif intrinsic_conc == 0:
return (
"intrinsic_conc",
(electron_conc * hole_conc) ** 0.5,
)
else:
return (-1, -1)
if __name__ == "__main__":
import doctest
doctest.testmod()
| 683 | 1 |
from __future__ import annotations
from collections import namedtuple
from dataclasses import dataclass
@dataclass
class __snake_case :
"""simple docstring"""
UpperCamelCase_ = 42
UpperCamelCase_ = None
UpperCamelCase_ = None
_lowercase = namedtuple('''CoinsDistribResult''', '''moves excess''')
def UpperCamelCase ( snake_case__):
if root is None:
return 0
# Validation
def count_nodes(snake_case__) -> int:
if node is None:
return 0
return count_nodes(node.left) + count_nodes(node.right) + 1
def count_coins(snake_case__) -> int:
if node is None:
return 0
return count_coins(node.left) + count_coins(node.right) + node.data
if count_nodes(snake_case__) != count_coins(snake_case__):
raise ValueError("The nodes number should be same as the number of coins")
# Main calculation
def get_distrib(snake_case__) -> CoinsDistribResult:
if node is None:
return CoinsDistribResult(0 , 1)
lowerCAmelCase_ , lowerCAmelCase_ : int = get_distrib(node.left)
lowerCAmelCase_ , lowerCAmelCase_ : List[str] = get_distrib(node.right)
lowerCAmelCase_ : Optional[Any] = 1 - left_distrib_excess
lowerCAmelCase_ : Tuple = 1 - right_distrib_excess
lowerCAmelCase_ : List[Any] = (
left_distrib_moves
+ right_distrib_moves
+ abs(snake_case__)
+ abs(snake_case__)
)
lowerCAmelCase_ : Any = node.data - coins_to_left - coins_to_right
return CoinsDistribResult(snake_case__ , snake_case__)
return get_distrib(snake_case__)[0]
if __name__ == "__main__":
import doctest
doctest.testmod()
| 683 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_lowercase = {
'''configuration_git''': ['''GIT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GitConfig''', '''GitVisionConfig'''],
'''processing_git''': ['''GitProcessor'''],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowercase = [
'''GIT_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''GitForCausalLM''',
'''GitModel''',
'''GitPreTrainedModel''',
'''GitVisionModel''',
]
if TYPE_CHECKING:
from .configuration_git import GIT_PRETRAINED_CONFIG_ARCHIVE_MAP, GitConfig, GitVisionConfig
from .processing_git import GitProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_git import (
GIT_PRETRAINED_MODEL_ARCHIVE_LIST,
GitForCausalLM,
GitModel,
GitPreTrainedModel,
GitVisionModel,
)
else:
import sys
_lowercase = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 683 | 1 |
from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments
def UpperCamelCase ( ):
lowerCAmelCase_ : List[str] = HfArgumentParser(snake_case__)
lowerCAmelCase_ : List[Any] = parser.parse_args_into_dataclasses()[0]
lowerCAmelCase_ : Optional[int] = TensorFlowBenchmark(args=snake_case__)
try:
lowerCAmelCase_ : Tuple = parser.parse_args_into_dataclasses()[0]
except ValueError as e:
lowerCAmelCase_ : Union[str, Any] = "Arg --no_{0} is no longer used, please use --no-{0} instead."
lowerCAmelCase_ : Tuple = " ".join(str(snake_case__).split(" ")[:-1])
lowerCAmelCase_ : Union[str, Any] = ""
lowerCAmelCase_ : Optional[Any] = eval(str(snake_case__).split(" ")[-1])
lowerCAmelCase_ : Tuple = []
for arg in depreciated_args:
# arg[2:] removes '--'
if arg[2:] in TensorFlowBenchmark.deprecated_args:
# arg[5:] removes '--no_'
full_error_msg += arg_error_msg.format(arg[5:])
else:
wrong_args.append(snake_case__)
if len(snake_case__) > 0:
lowerCAmelCase_ : Optional[Any] = full_error_msg + begin_error_msg + str(snake_case__)
raise ValueError(snake_case__)
benchmark.run()
if __name__ == "__main__":
main()
| 683 |
from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments
def UpperCamelCase ( ):
lowerCAmelCase_ : List[str] = HfArgumentParser(snake_case__)
lowerCAmelCase_ : List[Any] = parser.parse_args_into_dataclasses()[0]
lowerCAmelCase_ : Optional[int] = TensorFlowBenchmark(args=snake_case__)
try:
lowerCAmelCase_ : Tuple = parser.parse_args_into_dataclasses()[0]
except ValueError as e:
lowerCAmelCase_ : Union[str, Any] = "Arg --no_{0} is no longer used, please use --no-{0} instead."
lowerCAmelCase_ : Tuple = " ".join(str(snake_case__).split(" ")[:-1])
lowerCAmelCase_ : Union[str, Any] = ""
lowerCAmelCase_ : Optional[Any] = eval(str(snake_case__).split(" ")[-1])
lowerCAmelCase_ : Tuple = []
for arg in depreciated_args:
# arg[2:] removes '--'
if arg[2:] in TensorFlowBenchmark.deprecated_args:
# arg[5:] removes '--no_'
full_error_msg += arg_error_msg.format(arg[5:])
else:
wrong_args.append(snake_case__)
if len(snake_case__) > 0:
lowerCAmelCase_ : Optional[Any] = full_error_msg + begin_error_msg + str(snake_case__)
raise ValueError(snake_case__)
benchmark.run()
if __name__ == "__main__":
main()
| 683 | 1 |
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Features, Value
from .base import TaskTemplate
@dataclass(frozen=snake_case__ )
class __snake_case ( snake_case__ ):
"""simple docstring"""
UpperCamelCase_ = field(default='language-modeling' , metadata={'include_in_asdict_even_if_is_default': True} )
UpperCamelCase_ = Features({'text': Value('string' )} )
UpperCamelCase_ = Features({} )
UpperCamelCase_ = "text"
@property
def UpperCAmelCase_ ( self : List[Any] ) -> Dict[str, str]:
'''simple docstring'''
return {self.text_column: "text"}
| 683 |
_lowercase = {
0: '''0''',
1: '''1''',
2: '''2''',
3: '''3''',
4: '''4''',
5: '''5''',
6: '''6''',
7: '''7''',
8: '''8''',
9: '''9''',
10: '''a''',
11: '''b''',
12: '''c''',
13: '''d''',
14: '''e''',
15: '''f''',
}
def UpperCamelCase ( snake_case__):
assert type(snake_case__) in (int, float) and decimal == int(snake_case__)
lowerCAmelCase_ : Optional[Any] = int(snake_case__)
lowerCAmelCase_ : Tuple = ""
lowerCAmelCase_ : str = False
if decimal < 0:
lowerCAmelCase_ : Tuple = True
decimal *= -1
while decimal > 0:
lowerCAmelCase_ , lowerCAmelCase_ : Any = divmod(snake_case__ , 16)
lowerCAmelCase_ : Dict = values[remainder] + hexadecimal
lowerCAmelCase_ : List[str] = "0x" + hexadecimal
if negative:
lowerCAmelCase_ : Optional[Any] = "-" + hexadecimal
return hexadecimal
if __name__ == "__main__":
import doctest
doctest.testmod()
| 683 | 1 |
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..bit import BitConfig
_lowercase = logging.get_logger(__name__)
_lowercase = {
'''Intel/dpt-large''': '''https://huggingface.co/Intel/dpt-large/resolve/main/config.json''',
# See all DPT models at https://huggingface.co/models?filter=dpt
}
class __snake_case ( snake_case__ ):
"""simple docstring"""
UpperCamelCase_ = 'dpt'
def __init__( self : List[Any] ,lowerCAmelCase__ : Dict=7_68 ,lowerCAmelCase__ : Optional[Any]=12 ,lowerCAmelCase__ : int=12 ,lowerCAmelCase__ : Dict=30_72 ,lowerCAmelCase__ : Optional[int]="gelu" ,lowerCAmelCase__ : List[Any]=0.0 ,lowerCAmelCase__ : str=0.0 ,lowerCAmelCase__ : Tuple=0.02 ,lowerCAmelCase__ : int=1e-1_2 ,lowerCAmelCase__ : Optional[Any]=3_84 ,lowerCAmelCase__ : int=16 ,lowerCAmelCase__ : List[Any]=3 ,lowerCAmelCase__ : Optional[int]=False ,lowerCAmelCase__ : Any=True ,lowerCAmelCase__ : Dict=[2, 5, 8, 11] ,lowerCAmelCase__ : List[Any]="project" ,lowerCAmelCase__ : Optional[int]=[4, 2, 1, 0.5] ,lowerCAmelCase__ : Any=[96, 1_92, 3_84, 7_68] ,lowerCAmelCase__ : Optional[Any]=2_56 ,lowerCAmelCase__ : Tuple=-1 ,lowerCAmelCase__ : Tuple=False ,lowerCAmelCase__ : Optional[Any]=True ,lowerCAmelCase__ : List[str]=0.4 ,lowerCAmelCase__ : Any=2_55 ,lowerCAmelCase__ : List[str]=0.1 ,lowerCAmelCase__ : Optional[Any]=[1, 10_24, 24, 24] ,lowerCAmelCase__ : str=[0, 1] ,lowerCAmelCase__ : Optional[int]=None ,**lowerCAmelCase__ : str ,) -> Dict:
'''simple docstring'''
super().__init__(**lowerCAmelCase__ )
lowerCAmelCase_ : Dict = hidden_size
lowerCAmelCase_ : Any = is_hybrid
if self.is_hybrid:
if backbone_config is None:
logger.info("Initializing the config with a `BiT` backbone." )
lowerCAmelCase_ : str = {
"global_padding": "same",
"layer_type": "bottleneck",
"depths": [3, 4, 9],
"out_features": ["stage1", "stage2", "stage3"],
"embedding_dynamic_padding": True,
}
lowerCAmelCase_ : int = BitConfig(**lowerCAmelCase__ )
elif isinstance(lowerCAmelCase__ ,lowerCAmelCase__ ):
logger.info("Initializing the config with a `BiT` backbone." )
lowerCAmelCase_ : Union[str, Any] = BitConfig(**lowerCAmelCase__ )
elif isinstance(lowerCAmelCase__ ,lowerCAmelCase__ ):
lowerCAmelCase_ : Dict = backbone_config
else:
raise ValueError(
f'''backbone_config must be a dictionary or a `PretrainedConfig`, got {backbone_config.__class__}.''' )
lowerCAmelCase_ : List[str] = backbone_featmap_shape
lowerCAmelCase_ : Union[str, Any] = neck_ignore_stages
if readout_type != "project":
raise ValueError("Readout type must be 'project' when using `DPT-hybrid` mode." )
else:
lowerCAmelCase_ : int = None
lowerCAmelCase_ : int = None
lowerCAmelCase_ : int = []
lowerCAmelCase_ : Dict = num_hidden_layers
lowerCAmelCase_ : Optional[Any] = num_attention_heads
lowerCAmelCase_ : str = intermediate_size
lowerCAmelCase_ : Union[str, Any] = hidden_act
lowerCAmelCase_ : List[str] = hidden_dropout_prob
lowerCAmelCase_ : int = attention_probs_dropout_prob
lowerCAmelCase_ : Any = initializer_range
lowerCAmelCase_ : List[Any] = layer_norm_eps
lowerCAmelCase_ : str = image_size
lowerCAmelCase_ : Tuple = patch_size
lowerCAmelCase_ : Optional[Any] = num_channels
lowerCAmelCase_ : int = qkv_bias
lowerCAmelCase_ : Optional[int] = backbone_out_indices
if readout_type not in ["ignore", "add", "project"]:
raise ValueError("Readout_type must be one of ['ignore', 'add', 'project']" )
lowerCAmelCase_ : Any = readout_type
lowerCAmelCase_ : str = reassemble_factors
lowerCAmelCase_ : List[str] = neck_hidden_sizes
lowerCAmelCase_ : int = fusion_hidden_size
lowerCAmelCase_ : List[str] = head_in_index
lowerCAmelCase_ : Any = use_batch_norm_in_fusion_residual
# auxiliary head attributes (semantic segmentation)
lowerCAmelCase_ : Dict = use_auxiliary_head
lowerCAmelCase_ : List[str] = auxiliary_loss_weight
lowerCAmelCase_ : Optional[Any] = semantic_loss_ignore_index
lowerCAmelCase_ : Tuple = semantic_classifier_dropout
def UpperCAmelCase_ ( self : str ) -> List[Any]:
'''simple docstring'''
lowerCAmelCase_ : int = copy.deepcopy(self.__dict__ )
if output["backbone_config"] is not None:
lowerCAmelCase_ : Optional[Any] = self.backbone_config.to_dict()
lowerCAmelCase_ : Dict = self.__class__.model_type
return output
| 683 |
from pathlib import Path
from typing import List
from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import get_tests_dir, is_tool_test
from transformers.tools.agent_types import AGENT_TYPE_MAPPING, AgentAudio, AgentImage, AgentText
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
_lowercase = ['''text''', '''image''', '''audio''']
def UpperCamelCase ( snake_case__):
lowerCAmelCase_ : int = []
for input_type in input_types:
if input_type == "text":
inputs.append("Text input")
elif input_type == "image":
inputs.append(
Image.open(Path(get_tests_dir("fixtures/tests_samples/COCO")) / "000000039769.png").resize((5_12, 5_12)))
elif input_type == "audio":
inputs.append(torch.ones(30_00))
elif isinstance(snake_case__ , snake_case__):
inputs.append(create_inputs(snake_case__))
else:
raise ValueError(F'''Invalid type requested: {input_type}''')
return inputs
def UpperCamelCase ( snake_case__):
lowerCAmelCase_ : List[Any] = []
for output in outputs:
if isinstance(snake_case__ , (str, AgentText)):
output_types.append("text")
elif isinstance(snake_case__ , (Image.Image, AgentImage)):
output_types.append("image")
elif isinstance(snake_case__ , (torch.Tensor, AgentAudio)):
output_types.append("audio")
else:
raise ValueError(F'''Invalid output: {output}''')
return output_types
@is_tool_test
class __snake_case :
"""simple docstring"""
def UpperCAmelCase_ ( self : int ) -> int:
'''simple docstring'''
self.assertTrue(hasattr(self.tool ,"inputs" ) )
self.assertTrue(hasattr(self.tool ,"outputs" ) )
lowerCAmelCase_ : List[Any] = self.tool.inputs
for _input in inputs:
if isinstance(_input ,lowerCAmelCase__ ):
for __input in _input:
self.assertTrue(__input in authorized_types )
else:
self.assertTrue(_input in authorized_types )
lowerCAmelCase_ : Any = self.tool.outputs
for _output in outputs:
self.assertTrue(_output in authorized_types )
def UpperCAmelCase_ ( self : List[Any] ) -> List[Any]:
'''simple docstring'''
lowerCAmelCase_ : Any = create_inputs(self.tool.inputs )
lowerCAmelCase_ : List[Any] = self.tool(*lowerCAmelCase__ )
# There is a single output
if len(self.tool.outputs ) == 1:
lowerCAmelCase_ : Optional[int] = [outputs]
self.assertListEqual(output_types(lowerCAmelCase__ ) ,self.tool.outputs )
def UpperCAmelCase_ ( self : int ) -> Any:
'''simple docstring'''
self.assertTrue(hasattr(self.tool ,"description" ) )
self.assertTrue(hasattr(self.tool ,"default_checkpoint" ) )
self.assertTrue(self.tool.description.startswith("This is a tool that" ) )
def UpperCAmelCase_ ( self : List[Any] ) -> Tuple:
'''simple docstring'''
lowerCAmelCase_ : Optional[Any] = create_inputs(self.tool.inputs )
lowerCAmelCase_ : List[Any] = self.tool(*lowerCAmelCase__ )
if not isinstance(lowerCAmelCase__ ,lowerCAmelCase__ ):
lowerCAmelCase_ : str = [outputs]
self.assertEqual(len(lowerCAmelCase__ ) ,len(self.tool.outputs ) )
for output, output_type in zip(lowerCAmelCase__ ,self.tool.outputs ):
lowerCAmelCase_ : Tuple = AGENT_TYPE_MAPPING[output_type]
self.assertTrue(isinstance(lowerCAmelCase__ ,lowerCAmelCase__ ) )
def UpperCAmelCase_ ( self : Any ) -> Tuple:
'''simple docstring'''
lowerCAmelCase_ : Tuple = create_inputs(self.tool.inputs )
lowerCAmelCase_ : List[Any] = []
for _input, input_type in zip(lowerCAmelCase__ ,self.tool.inputs ):
if isinstance(lowerCAmelCase__ ,lowerCAmelCase__ ):
_inputs.append([AGENT_TYPE_MAPPING[_input_type](_input ) for _input_type in input_type] )
else:
_inputs.append(AGENT_TYPE_MAPPING[input_type](_input ) )
# Should not raise an error
lowerCAmelCase_ : List[Any] = self.tool(*lowerCAmelCase__ )
if not isinstance(lowerCAmelCase__ ,lowerCAmelCase__ ):
lowerCAmelCase_ : int = [outputs]
self.assertEqual(len(lowerCAmelCase__ ) ,len(self.tool.outputs ) )
| 683 | 1 |
_lowercase = {
'''a''': '''AAAAA''',
'''b''': '''AAAAB''',
'''c''': '''AAABA''',
'''d''': '''AAABB''',
'''e''': '''AABAA''',
'''f''': '''AABAB''',
'''g''': '''AABBA''',
'''h''': '''AABBB''',
'''i''': '''ABAAA''',
'''j''': '''BBBAA''',
'''k''': '''ABAAB''',
'''l''': '''ABABA''',
'''m''': '''ABABB''',
'''n''': '''ABBAA''',
'''o''': '''ABBAB''',
'''p''': '''ABBBA''',
'''q''': '''ABBBB''',
'''r''': '''BAAAA''',
'''s''': '''BAAAB''',
'''t''': '''BAABA''',
'''u''': '''BAABB''',
'''v''': '''BBBAB''',
'''w''': '''BABAA''',
'''x''': '''BABAB''',
'''y''': '''BABBA''',
'''z''': '''BABBB''',
''' ''': ''' ''',
}
_lowercase = {value: key for key, value in encode_dict.items()}
def UpperCamelCase ( snake_case__):
lowerCAmelCase_ : Optional[int] = ""
for letter in word.lower():
if letter.isalpha() or letter == " ":
encoded += encode_dict[letter]
else:
raise Exception("encode() accepts only letters of the alphabet and spaces")
return encoded
def UpperCamelCase ( snake_case__):
if set(snake_case__) - {"A", "B", " "} != set():
raise Exception("decode() accepts only 'A', 'B' and spaces")
lowerCAmelCase_ : Optional[Any] = ""
for word in coded.split():
while len(snake_case__) != 0:
decoded += decode_dict[word[:5]]
lowerCAmelCase_ : Any = word[5:]
decoded += " "
return decoded.strip()
if __name__ == "__main__":
from doctest import testmod
testmod()
| 683 |
import pytest
_lowercase = '''__dummy_dataset1__'''
_lowercase = '''
import json
import os
import datasets
REPO_URL = "https://huggingface.co/datasets/albertvillanova/tests-raw-jsonl/resolve/main/"
URLS = {"train": REPO_URL + "wikiann-bn-train.jsonl", "validation": REPO_URL + "wikiann-bn-validation.jsonl"}
class __DummyDataset1__(datasets.GeneratorBasedBuilder):
def _info(self):
features = datasets.Features(
{
"tokens": datasets.Sequence(datasets.Value("string")),
"ner_tags": datasets.Sequence(
datasets.features.ClassLabel(
names=[
"O",
"B-PER",
"I-PER",
"B-ORG",
"I-ORG",
"B-LOC",
"I-LOC",
]
)
),
"langs": datasets.Sequence(datasets.Value("string")),
"spans": datasets.Sequence(datasets.Value("string")),
}
)
return datasets.DatasetInfo(features=features)
def _split_generators(self, dl_manager):
dl_path = dl_manager.download(URLS)
return [
datasets.SplitGenerator(datasets.Split.TRAIN, gen_kwargs={"filepath": dl_path["train"]}),
datasets.SplitGenerator(datasets.Split.VALIDATION, gen_kwargs={"filepath": dl_path["validation"]}),
]
def _generate_examples(self, filepath):
with open(filepath, "r", encoding="utf-8") as f:
for i, line in enumerate(f):
yield i, json.loads(line)
'''
@pytest.fixture
def UpperCamelCase ( ):
return DATASET_LOADING_SCRIPT_NAME
@pytest.fixture
def UpperCamelCase ( ):
return DATASET_LOADING_SCRIPT_CODE
@pytest.fixture
def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__):
lowerCAmelCase_ : List[Any] = dataset_loading_script_name
lowerCAmelCase_ : List[str] = tmp_path / "datasets" / script_name
script_dir.mkdir(parents=snake_case__)
lowerCAmelCase_ : List[Any] = script_dir / F'''{script_name}.py'''
with open(snake_case__ , "w") as f:
f.write(snake_case__)
return str(snake_case__)
| 683 | 1 |
from .imports import is_tqdm_available
if is_tqdm_available():
from tqdm.auto import tqdm as _tqdm
from ..state import PartialState
def UpperCamelCase ( snake_case__ = True , *snake_case__ , **snake_case__):
if not is_tqdm_available():
raise ImportError("Accelerate's `tqdm` module requires `tqdm` to be installed. Please run `pip install tqdm`.")
lowerCAmelCase_ : Union[str, Any] = False
if main_process_only:
lowerCAmelCase_ : str = PartialState().local_process_index == 0
return _tqdm(*snake_case__ , **snake_case__ , disable=snake_case__)
| 683 |
import json
import os
import re
import unittest
from transformers import CodeGenTokenizer, CodeGenTokenizerFast
from transformers.models.codegen.tokenization_codegen import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class __snake_case ( snake_case__ , unittest.TestCase ):
"""simple docstring"""
UpperCamelCase_ = CodeGenTokenizer
UpperCamelCase_ = CodeGenTokenizerFast
UpperCamelCase_ = True
UpperCamelCase_ = {'add_prefix_space': True}
UpperCamelCase_ = False
def UpperCAmelCase_ ( self : str ) -> Tuple:
'''simple docstring'''
super().setUp()
# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
lowerCAmelCase_ : Optional[Any] = [
"l",
"o",
"w",
"e",
"r",
"s",
"t",
"i",
"d",
"n",
"\u0120",
"\u0120l",
"\u0120n",
"\u0120lo",
"\u0120low",
"er",
"\u0120lowest",
"\u0120newer",
"\u0120wider",
"<unk>",
"<|endoftext|>",
]
lowerCAmelCase_ : int = dict(zip(lowerCAmelCase__ ,range(len(lowerCAmelCase__ ) ) ) )
lowerCAmelCase_ : Dict = ["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
lowerCAmelCase_ : List[Any] = {"unk_token": "<unk>"}
lowerCAmelCase_ : List[Any] = os.path.join(self.tmpdirname ,VOCAB_FILES_NAMES["vocab_file"] )
lowerCAmelCase_ : Tuple = os.path.join(self.tmpdirname ,VOCAB_FILES_NAMES["merges_file"] )
with open(self.vocab_file ,"w" ,encoding="utf-8" ) as fp:
fp.write(json.dumps(lowerCAmelCase__ ) + "\n" )
with open(self.merges_file ,"w" ,encoding="utf-8" ) as fp:
fp.write("\n".join(lowerCAmelCase__ ) )
def UpperCAmelCase_ ( self : Optional[int] ,**lowerCAmelCase__ : str ) -> int:
'''simple docstring'''
kwargs.update(self.special_tokens_map )
return CodeGenTokenizer.from_pretrained(self.tmpdirname ,**lowerCAmelCase__ )
def UpperCAmelCase_ ( self : Optional[int] ,**lowerCAmelCase__ : Optional[Any] ) -> Tuple:
'''simple docstring'''
kwargs.update(self.special_tokens_map )
return CodeGenTokenizerFast.from_pretrained(self.tmpdirname ,**lowerCAmelCase__ )
def UpperCAmelCase_ ( self : Optional[int] ,lowerCAmelCase__ : str ) -> Optional[int]:
'''simple docstring'''
lowerCAmelCase_ : Tuple = "lower newer"
lowerCAmelCase_ : Tuple = "lower newer"
return input_text, output_text
def UpperCAmelCase_ ( self : Optional[Any] ) -> Any:
'''simple docstring'''
lowerCAmelCase_ : Optional[Any] = CodeGenTokenizer(self.vocab_file ,self.merges_file ,**self.special_tokens_map )
lowerCAmelCase_ : Dict = "lower newer"
lowerCAmelCase_ : Dict = ["\u0120low", "er", "\u0120", "n", "e", "w", "er"]
lowerCAmelCase_ : Union[str, Any] = tokenizer.tokenize(lowerCAmelCase__ ,add_prefix_space=lowerCAmelCase__ )
self.assertListEqual(lowerCAmelCase__ ,lowerCAmelCase__ )
lowerCAmelCase_ : Optional[Any] = tokens + [tokenizer.unk_token]
lowerCAmelCase_ : Union[str, Any] = [14, 15, 10, 9, 3, 2, 15, 19]
self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCAmelCase__ ) ,lowerCAmelCase__ )
def UpperCAmelCase_ ( self : List[str] ) -> Optional[Any]:
'''simple docstring'''
if not self.test_rust_tokenizer:
return
lowerCAmelCase_ : Tuple = self.get_tokenizer()
lowerCAmelCase_ : Optional[int] = self.get_rust_tokenizer(add_prefix_space=lowerCAmelCase__ )
lowerCAmelCase_ : Any = "lower newer"
# Testing tokenization
lowerCAmelCase_ : Tuple = tokenizer.tokenize(lowerCAmelCase__ ,add_prefix_space=lowerCAmelCase__ )
lowerCAmelCase_ : Any = rust_tokenizer.tokenize(lowerCAmelCase__ )
self.assertListEqual(lowerCAmelCase__ ,lowerCAmelCase__ )
# Testing conversion to ids without special tokens
lowerCAmelCase_ : str = tokenizer.encode(lowerCAmelCase__ ,add_special_tokens=lowerCAmelCase__ ,add_prefix_space=lowerCAmelCase__ )
lowerCAmelCase_ : Any = rust_tokenizer.encode(lowerCAmelCase__ ,add_special_tokens=lowerCAmelCase__ )
self.assertListEqual(lowerCAmelCase__ ,lowerCAmelCase__ )
# Testing conversion to ids with special tokens
lowerCAmelCase_ : int = self.get_rust_tokenizer(add_prefix_space=lowerCAmelCase__ )
lowerCAmelCase_ : str = tokenizer.encode(lowerCAmelCase__ ,add_prefix_space=lowerCAmelCase__ )
lowerCAmelCase_ : Optional[int] = rust_tokenizer.encode(lowerCAmelCase__ )
self.assertListEqual(lowerCAmelCase__ ,lowerCAmelCase__ )
# Testing the unknown token
lowerCAmelCase_ : Union[str, Any] = tokens + [rust_tokenizer.unk_token]
lowerCAmelCase_ : List[str] = [14, 15, 10, 9, 3, 2, 15, 19]
self.assertListEqual(rust_tokenizer.convert_tokens_to_ids(lowerCAmelCase__ ) ,lowerCAmelCase__ )
def UpperCAmelCase_ ( self : Optional[int] ,*lowerCAmelCase__ : List[str] ,**lowerCAmelCase__ : Optional[Any] ) -> List[str]:
'''simple docstring'''
pass
def UpperCAmelCase_ ( self : Tuple ,lowerCAmelCase__ : Any=15 ) -> str:
'''simple docstring'''
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})''' ):
lowerCAmelCase_ : Any = self.rust_tokenizer_class.from_pretrained(lowerCAmelCase__ ,**lowerCAmelCase__ )
# Simple input
lowerCAmelCase_ : int = "This is a simple input"
lowerCAmelCase_ : Dict = ["This is a simple input 1", "This is a simple input 2"]
lowerCAmelCase_ : str = ("This is a simple input", "This is a pair")
lowerCAmelCase_ : Optional[int] = [
("This is a simple input 1", "This is a simple input 2"),
("This is a simple pair 1", "This is a simple pair 2"),
]
# Simple input tests
self.assertRaises(lowerCAmelCase__ ,tokenizer_r.encode ,lowerCAmelCase__ ,max_length=lowerCAmelCase__ ,padding="max_length" )
# Simple input
self.assertRaises(lowerCAmelCase__ ,tokenizer_r.encode_plus ,lowerCAmelCase__ ,max_length=lowerCAmelCase__ ,padding="max_length" )
# Simple input
self.assertRaises(
lowerCAmelCase__ ,tokenizer_r.batch_encode_plus ,lowerCAmelCase__ ,max_length=lowerCAmelCase__ ,padding="max_length" ,)
# Pair input
self.assertRaises(lowerCAmelCase__ ,tokenizer_r.encode ,lowerCAmelCase__ ,max_length=lowerCAmelCase__ ,padding="max_length" )
# Pair input
self.assertRaises(lowerCAmelCase__ ,tokenizer_r.encode_plus ,lowerCAmelCase__ ,max_length=lowerCAmelCase__ ,padding="max_length" )
# Pair input
self.assertRaises(
lowerCAmelCase__ ,tokenizer_r.batch_encode_plus ,lowerCAmelCase__ ,max_length=lowerCAmelCase__ ,padding="max_length" ,)
def UpperCAmelCase_ ( self : Optional[int] ) -> Optional[int]:
'''simple docstring'''
lowerCAmelCase_ : Tuple = CodeGenTokenizer.from_pretrained(self.tmpdirname ,pad_token="<pad>" )
# Simple input
lowerCAmelCase_ : Dict = "This is a simple input"
lowerCAmelCase_ : List[str] = ["This is a simple input looooooooong", "This is a simple input"]
lowerCAmelCase_ : Any = ("This is a simple input", "This is a pair")
lowerCAmelCase_ : List[str] = [
("This is a simple input loooooong", "This is a simple input"),
("This is a simple pair loooooong", "This is a simple pair"),
]
lowerCAmelCase_ : Dict = tokenizer.pad_token_id
lowerCAmelCase_ : Union[str, Any] = tokenizer(lowerCAmelCase__ ,padding="max_length" ,max_length=30 ,return_tensors="np" )
lowerCAmelCase_ : Tuple = tokenizer(lowerCAmelCase__ ,padding=lowerCAmelCase__ ,truncate=lowerCAmelCase__ ,return_tensors="np" )
lowerCAmelCase_ : Any = tokenizer(*lowerCAmelCase__ ,padding="max_length" ,max_length=60 ,return_tensors="np" )
lowerCAmelCase_ : Optional[int] = tokenizer(lowerCAmelCase__ ,padding=lowerCAmelCase__ ,truncate=lowerCAmelCase__ ,return_tensors="np" )
# s
# test single string max_length padding
self.assertEqual(out_s["input_ids"].shape[-1] ,30 )
self.assertTrue(pad_token_id in out_s["input_ids"] )
self.assertTrue(0 in out_s["attention_mask"] )
# s2
# test automatic padding
self.assertEqual(out_sa["input_ids"].shape[-1] ,33 )
# long slice doesn't have padding
self.assertFalse(pad_token_id in out_sa["input_ids"][0] )
self.assertFalse(0 in out_sa["attention_mask"][0] )
# short slice does have padding
self.assertTrue(pad_token_id in out_sa["input_ids"][1] )
self.assertTrue(0 in out_sa["attention_mask"][1] )
# p
# test single pair max_length padding
self.assertEqual(out_p["input_ids"].shape[-1] ,60 )
self.assertTrue(pad_token_id in out_p["input_ids"] )
self.assertTrue(0 in out_p["attention_mask"] )
# p2
# test automatic padding pair
self.assertEqual(out_pa["input_ids"].shape[-1] ,52 )
# long slice pair doesn't have padding
self.assertFalse(pad_token_id in out_pa["input_ids"][0] )
self.assertFalse(0 in out_pa["attention_mask"][0] )
# short slice pair does have padding
self.assertTrue(pad_token_id in out_pa["input_ids"][1] )
self.assertTrue(0 in out_pa["attention_mask"][1] )
def UpperCAmelCase_ ( self : Union[str, Any] ) -> Any:
'''simple docstring'''
lowerCAmelCase_ : Any = "$$$"
lowerCAmelCase_ : List[str] = CodeGenTokenizer.from_pretrained(self.tmpdirname ,bos_token=lowerCAmelCase__ ,add_bos_token=lowerCAmelCase__ )
lowerCAmelCase_ : Dict = "This is a simple input"
lowerCAmelCase_ : Union[str, Any] = ["This is a simple input 1", "This is a simple input 2"]
lowerCAmelCase_ : int = tokenizer.bos_token_id
lowerCAmelCase_ : List[Any] = tokenizer(lowerCAmelCase__ )
lowerCAmelCase_ : Optional[Any] = tokenizer(lowerCAmelCase__ )
self.assertEqual(out_s.input_ids[0] ,lowerCAmelCase__ )
self.assertTrue(all(o[0] == bos_token_id for o in out_sa.input_ids ) )
lowerCAmelCase_ : List[str] = tokenizer.decode(out_s.input_ids )
lowerCAmelCase_ : Optional[Any] = tokenizer.batch_decode(out_sa.input_ids )
self.assertEqual(decode_s.split()[0] ,lowerCAmelCase__ )
self.assertTrue(all(d.split()[0] == bos_token for d in decode_sa ) )
@slow
def UpperCAmelCase_ ( self : Any ) -> Optional[int]:
'''simple docstring'''
lowerCAmelCase_ : Optional[Any] = CodeGenTokenizer.from_pretrained("Salesforce/codegen-350M-mono" )
lowerCAmelCase_ : str = "\nif len_a > len_b:\n result = a\nelse:\n result = b\n\n\n\n#"
lowerCAmelCase_ : int = "\nif len_a > len_b: result = a\nelse: result = b"
lowerCAmelCase_ : Dict = tokenizer.encode(lowerCAmelCase__ )
lowerCAmelCase_ : str = ["^#", re.escape("<|endoftext|>" ), "^'''", "^\"\"\"", "\n\n\n"]
lowerCAmelCase_ : Union[str, Any] = tokenizer.decode(lowerCAmelCase__ ,truncate_before_pattern=lowerCAmelCase__ )
self.assertEqual(lowerCAmelCase__ ,lowerCAmelCase__ )
def UpperCAmelCase_ ( self : Optional[Any] ) -> Optional[Any]:
'''simple docstring'''
pass
| 683 | 1 |
import io
import os
import unicodedata
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
_lowercase = logging.get_logger(__name__)
_lowercase = '''▁'''
_lowercase = {'''vocab_file''': '''vocab.txt''', '''sentencepiece_model_ckpt''': '''sentencepiece.bpe.model'''}
_lowercase = {
'''sentencepiece_model_file''': '''sentencepiece.bpe.model''',
'''vocab_file''': '''vocab.txt''',
}
_lowercase = {
'''vocab_file''': {
'''ernie-m-base''': '''https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/vocab.txt''',
'''ernie-m-large''': '''https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/vocab.txt''',
},
'''sentencepiece_model_file''': {
'''ernie-m-base''': '''https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/sentencepiece.bpe.model''',
'''ernie-m-large''': '''https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/sentencepiece.bpe.model''',
},
}
_lowercase = {
'''ernie-m-base''': 514,
'''ernie-m-large''': 514,
}
_lowercase = {
'''ernie-m-base''': {'''do_lower_case''': False},
'''ernie-m-large''': {'''do_lower_case''': False},
}
class __snake_case ( snake_case__ ):
"""simple docstring"""
UpperCamelCase_ = ["input_ids"]
UpperCamelCase_ = VOCAB_FILES_NAMES
UpperCamelCase_ = PRETRAINED_INIT_CONFIGURATION
UpperCamelCase_ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
UpperCamelCase_ = PRETRAINED_VOCAB_FILES_MAP
UpperCamelCase_ = RESOURCE_FILES_NAMES
def __init__( self : Union[str, Any] ,lowerCAmelCase__ : Any ,lowerCAmelCase__ : Optional[Any]=None ,lowerCAmelCase__ : Optional[int]=False ,lowerCAmelCase__ : Optional[int]="utf8" ,lowerCAmelCase__ : Tuple="[UNK]" ,lowerCAmelCase__ : Optional[int]="[SEP]" ,lowerCAmelCase__ : Any="[PAD]" ,lowerCAmelCase__ : Optional[Any]="[CLS]" ,lowerCAmelCase__ : Tuple="[MASK]" ,lowerCAmelCase__ : Optional[Dict[str, Any]] = None ,**lowerCAmelCase__ : List[Any] ,) -> None:
'''simple docstring'''
lowerCAmelCase_ : str = {} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
do_lower_case=lowerCAmelCase__ ,unk_token=lowerCAmelCase__ ,sep_token=lowerCAmelCase__ ,pad_token=lowerCAmelCase__ ,cls_token=lowerCAmelCase__ ,mask_token=lowerCAmelCase__ ,vocab_file=lowerCAmelCase__ ,encoding=lowerCAmelCase__ ,sp_model_kwargs=self.sp_model_kwargs ,**lowerCAmelCase__ ,)
lowerCAmelCase_ : Any = do_lower_case
lowerCAmelCase_ : List[str] = sentencepiece_model_ckpt
lowerCAmelCase_ : Any = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(lowerCAmelCase__ )
# to mimic paddlenlp.transformers.ernie_m.tokenizer.ErnieMTokenizer functioning
if vocab_file is not None:
lowerCAmelCase_ : List[Any] = self.load_vocab(filepath=lowerCAmelCase__ )
else:
lowerCAmelCase_ : List[str] = {self.sp_model.id_to_piece(lowerCAmelCase__ ): id for id in range(self.sp_model.get_piece_size() )}
lowerCAmelCase_ : List[str] = {v: k for k, v in self.vocab.items()}
def UpperCAmelCase_ ( self : List[Any] ,lowerCAmelCase__ : Optional[int] ) -> int:
'''simple docstring'''
if text is None:
return None
lowerCAmelCase_ : Dict = self.tokenize(lowerCAmelCase__ )
lowerCAmelCase_ , lowerCAmelCase_ : List[Any] = "", []
for i, ch in enumerate(lowerCAmelCase__ ):
if ch in self.SP_CHAR_MAPPING:
lowerCAmelCase_ : str = self.SP_CHAR_MAPPING.get(lowerCAmelCase__ )
else:
lowerCAmelCase_ : List[Any] = unicodedata.normalize("NFKC" ,lowerCAmelCase__ )
if self.is_whitespace(lowerCAmelCase__ ):
continue
normalized_text += ch
char_mapping.extend([i] * len(lowerCAmelCase__ ) )
lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ : int = normalized_text, [], 0
if self.do_lower_case:
lowerCAmelCase_ : int = text.lower()
for token in split_tokens:
if token[:1] == "▁":
lowerCAmelCase_ : List[Any] = token[1:]
lowerCAmelCase_ : Dict = text[offset:].index(lowerCAmelCase__ ) + offset
lowerCAmelCase_ : Tuple = start + len(lowerCAmelCase__ )
token_mapping.append((char_mapping[start], char_mapping[end - 1] + 1) )
lowerCAmelCase_ : str = end
return token_mapping
@property
def UpperCAmelCase_ ( self : Optional[int] ) -> Optional[int]:
'''simple docstring'''
return len(self.vocab )
def UpperCAmelCase_ ( self : Any ) -> Dict:
'''simple docstring'''
return dict(self.vocab ,**self.added_tokens_encoder )
def __getstate__( self : Union[str, Any] ) -> Tuple:
'''simple docstring'''
lowerCAmelCase_ : Optional[Any] = self.__dict__.copy()
lowerCAmelCase_ : List[Any] = None
return state
def __setstate__( self : Optional[int] ,lowerCAmelCase__ : Optional[Any] ) -> int:
'''simple docstring'''
lowerCAmelCase_ : List[Any] = d
# for backward compatibility
if not hasattr(self ,"sp_model_kwargs" ):
lowerCAmelCase_ : List[Any] = {}
lowerCAmelCase_ : Optional[Any] = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(self.sentencepiece_model_ckpt )
def UpperCAmelCase_ ( self : str ,lowerCAmelCase__ : Optional[Any] ) -> Optional[int]:
'''simple docstring'''
return "".join((self.SP_CHAR_MAPPING.get(lowerCAmelCase__ ,lowerCAmelCase__ ) for c in text) )
def UpperCAmelCase_ ( self : List[Any] ,lowerCAmelCase__ : List[Any] ,lowerCAmelCase__ : List[str]=False ,lowerCAmelCase__ : int=64 ,lowerCAmelCase__ : Union[str, Any]=0.1 ) -> Optional[Any]:
'''simple docstring'''
if self.sp_model_kwargs.get("enable_sampling" ) is True:
lowerCAmelCase_ : Union[str, Any] = True
if self.sp_model_kwargs.get("alpha" ) is not None:
lowerCAmelCase_ : Tuple = self.sp_model_kwargs.get("alpha" )
if self.sp_model_kwargs.get("nbest_size" ) is not None:
lowerCAmelCase_ : Any = self.sp_model_kwargs.get("nbest_size" )
if not enable_sampling:
lowerCAmelCase_ : Dict = self.sp_model.EncodeAsPieces(lowerCAmelCase__ )
else:
lowerCAmelCase_ : Optional[int] = self.sp_model.SampleEncodeAsPieces(lowerCAmelCase__ ,lowerCAmelCase__ ,lowerCAmelCase__ )
lowerCAmelCase_ : List[Any] = []
for pi, piece in enumerate(lowerCAmelCase__ ):
if piece == SPIECE_UNDERLINE:
if not pieces[pi + 1].startswith(lowerCAmelCase__ ) and pi != 0:
new_pieces.append(lowerCAmelCase__ )
continue
else:
continue
lowerCAmelCase_ : Any = 0
for i, chunk in enumerate(lowerCAmelCase__ ):
if chunk == SPIECE_UNDERLINE:
continue
if self.is_ch_char(lowerCAmelCase__ ) or self.is_punct(lowerCAmelCase__ ):
if i > lst_i and piece[lst_i:i] != SPIECE_UNDERLINE:
new_pieces.append(piece[lst_i:i] )
new_pieces.append(lowerCAmelCase__ )
lowerCAmelCase_ : Tuple = i + 1
elif chunk.isdigit() and i > 0 and not piece[i - 1].isdigit():
if i > lst_i and piece[lst_i:i] != SPIECE_UNDERLINE:
new_pieces.append(piece[lst_i:i] )
lowerCAmelCase_ : int = i
elif not chunk.isdigit() and i > 0 and piece[i - 1].isdigit():
if i > lst_i and piece[lst_i:i] != SPIECE_UNDERLINE:
new_pieces.append(piece[lst_i:i] )
lowerCAmelCase_ : int = i
if len(lowerCAmelCase__ ) > lst_i:
new_pieces.append(piece[lst_i:] )
return new_pieces
def UpperCAmelCase_ ( self : str ,lowerCAmelCase__ : Dict ) -> int:
'''simple docstring'''
lowerCAmelCase_ : Tuple = "".join(lowerCAmelCase__ ).replace(lowerCAmelCase__ ," " ).strip()
return out_string
def UpperCAmelCase_ ( self : Optional[Any] ,lowerCAmelCase__ : str ) -> int:
'''simple docstring'''
lowerCAmelCase_ : Tuple = self.convert_ids_to_tokens(lowerCAmelCase__ )
lowerCAmelCase_ : Union[str, Any] = "".join(lowerCAmelCase__ ).replace(lowerCAmelCase__ ," " ).strip()
return out_string
def UpperCAmelCase_ ( self : Any ,lowerCAmelCase__ : Optional[int] ) -> int:
'''simple docstring'''
return self.vocab.get(lowerCAmelCase__ ,self.vocab.get(self.unk_token ) )
def UpperCAmelCase_ ( self : int ,lowerCAmelCase__ : List[Any] ) -> List[Any]:
'''simple docstring'''
return self.reverse_vocab.get(lowerCAmelCase__ ,self.unk_token )
def UpperCAmelCase_ ( self : Tuple ,lowerCAmelCase__ : Optional[int] ,lowerCAmelCase__ : Optional[Any]=None ) -> List[str]:
'''simple docstring'''
if token_ids_a is None:
return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
lowerCAmelCase_ : Union[str, Any] = [self.cls_token_id]
lowerCAmelCase_ : List[Any] = [self.sep_token_id]
return _cls + token_ids_a + _sep + _sep + token_ids_a + _sep
def UpperCAmelCase_ ( self : List[str] ,lowerCAmelCase__ : str ,lowerCAmelCase__ : str=None ) -> Any:
'''simple docstring'''
if offset_mapping_a is None:
return [(0, 0)] + offset_mapping_a + [(0, 0)]
return [(0, 0)] + offset_mapping_a + [(0, 0), (0, 0)] + offset_mapping_a + [(0, 0)]
def UpperCAmelCase_ ( self : List[Any] ,lowerCAmelCase__ : Dict ,lowerCAmelCase__ : List[Any]=None ,lowerCAmelCase__ : List[str]=False ) -> List[str]:
'''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 not None:
return [1] + ([0] * len(lowerCAmelCase__ )) + [1, 1] + ([0] * len(lowerCAmelCase__ )) + [1]
return [1] + ([0] * len(lowerCAmelCase__ )) + [1]
def UpperCAmelCase_ ( self : List[str] ,lowerCAmelCase__ : List[int] ,lowerCAmelCase__ : Optional[List[int]] = None ) -> List[int]:
'''simple docstring'''
if token_ids_a is None:
# [CLS] X [SEP]
return (len(lowerCAmelCase__ ) + 2) * [0]
# [CLS] A [SEP] [SEP] B [SEP]
return [0] * (len(lowerCAmelCase__ ) + 1) + [1] * (len(lowerCAmelCase__ ) + 3)
def UpperCAmelCase_ ( self : List[Any] ,lowerCAmelCase__ : Any ) -> List[str]:
'''simple docstring'''
if "\u4e00" <= char <= "\u9fff":
return True
return False
def UpperCAmelCase_ ( self : Optional[Any] ,lowerCAmelCase__ : Optional[Any] ) -> Union[str, Any]:
'''simple docstring'''
if ("a" <= char <= "z") or ("A" <= char <= "Z"):
return True
return False
def UpperCAmelCase_ ( self : int ,lowerCAmelCase__ : Optional[int] ) -> Optional[int]:
'''simple docstring'''
if char in ",;:.?!~，；：。？！《》【】":
return True
return False
def UpperCAmelCase_ ( self : Any ,lowerCAmelCase__ : Dict ) -> Optional[int]:
'''simple docstring'''
if char == " " or char == "\t" or char == "\n" or char == "\r":
return True
if len(lowerCAmelCase__ ) == 1:
lowerCAmelCase_ : List[Any] = unicodedata.category(lowerCAmelCase__ )
if cat == "Zs":
return True
return False
def UpperCAmelCase_ ( self : Tuple ,lowerCAmelCase__ : List[Any] ) -> List[str]:
'''simple docstring'''
lowerCAmelCase_ : Optional[Any] = {}
with io.open(lowerCAmelCase__ ,"r" ,encoding="utf-8" ) as f:
for index, line in enumerate(lowerCAmelCase__ ):
lowerCAmelCase_ : List[Any] = line.rstrip("\n" )
lowerCAmelCase_ : Optional[Any] = int(lowerCAmelCase__ )
return token_to_idx
def UpperCAmelCase_ ( self : List[str] ,lowerCAmelCase__ : str ,lowerCAmelCase__ : Optional[str] = None ) -> Tuple[str]:
'''simple docstring'''
lowerCAmelCase_ : Tuple = 0
if os.path.isdir(lowerCAmelCase__ ):
lowerCAmelCase_ : Tuple = os.path.join(
lowerCAmelCase__ ,(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )
else:
lowerCAmelCase_ : Tuple = (filename_prefix + "-" if filename_prefix else "") + save_directory
with open(lowerCAmelCase__ ,"w" ,encoding="utf-8" ) as writer:
for token, token_index in sorted(self.vocab.items() ,key=lambda lowerCAmelCase__ : kv[1] ):
if index != token_index:
logger.warning(
f'''Saving vocabulary to {vocab_file}: vocabulary indices are not consecutive.'''
" Please check that the vocabulary is not corrupted!" )
lowerCAmelCase_ : Optional[Any] = token_index
writer.write(token + "\n" )
index += 1
lowerCAmelCase_ : List[str] = os.path.join(lowerCAmelCase__ ,"sentencepiece.bpe.model" )
with open(lowerCAmelCase__ ,"wb" ) as fi:
lowerCAmelCase_ : int = self.sp_model.serialized_model_proto()
fi.write(lowerCAmelCase__ )
return (vocab_file,)
| 683 |
from __future__ import annotations
from random import random
class __snake_case :
"""simple docstring"""
def __init__( self : Optional[int] ,lowerCAmelCase__ : int | None = None ) -> int:
'''simple docstring'''
lowerCAmelCase_ : Dict = value
lowerCAmelCase_ : Any = random()
lowerCAmelCase_ : Node | None = None
lowerCAmelCase_ : Node | None = None
def __repr__( self : Any ) -> str:
'''simple docstring'''
from pprint import pformat
if self.left is None and self.right is None:
return f'''\'{self.value}: {self.prior:.5}\''''
else:
return pformat(
{f'''{self.value}: {self.prior:.5}''': (self.left, self.right)} ,indent=1 )
def __str__( self : str ) -> str:
'''simple docstring'''
lowerCAmelCase_ : List[Any] = str(self.value ) + " "
lowerCAmelCase_ : List[Any] = str(self.left or "" )
lowerCAmelCase_ : Union[str, Any] = str(self.right or "" )
return value + left + right
def UpperCamelCase ( snake_case__ , snake_case__):
if root is None: # None tree is split into 2 Nones
return None, None
elif root.value is None:
return None, None
else:
if value < root.value:
lowerCAmelCase_ , lowerCAmelCase_ : Any = split(root.left , snake_case__)
return left, root
else:
lowerCAmelCase_ , lowerCAmelCase_ : Optional[Any] = split(root.right , snake_case__)
return root, right
def UpperCamelCase ( snake_case__ , snake_case__):
if (not left) or (not right): # If one node is None, return the other
return left or right
elif left.prior < right.prior:
lowerCAmelCase_ : Dict = merge(left.right , snake_case__)
return left
else:
lowerCAmelCase_ : List[str] = merge(snake_case__ , right.left)
return right
def UpperCamelCase ( snake_case__ , snake_case__):
lowerCAmelCase_ : List[Any] = Node(snake_case__)
lowerCAmelCase_ , lowerCAmelCase_ : Tuple = split(snake_case__ , snake_case__)
return merge(merge(snake_case__ , snake_case__) , snake_case__)
def UpperCamelCase ( snake_case__ , snake_case__):
lowerCAmelCase_ , lowerCAmelCase_ : List[str] = split(snake_case__ , value - 1)
lowerCAmelCase_ , lowerCAmelCase_ : Union[str, Any] = split(snake_case__ , snake_case__)
return merge(snake_case__ , snake_case__)
def UpperCamelCase ( snake_case__):
if not root: # None
return
else:
inorder(root.left)
print(root.value , end=",")
inorder(root.right)
def UpperCamelCase ( snake_case__ , snake_case__):
for arg in args.split():
if arg[0] == "+":
lowerCAmelCase_ : List[str] = insert(snake_case__ , int(arg[1:]))
elif arg[0] == "-":
lowerCAmelCase_ : Optional[int] = erase(snake_case__ , int(arg[1:]))
else:
print("Unknown command")
return root
def UpperCamelCase ( ):
lowerCAmelCase_ : str = None
print(
"enter numbers to create a tree, + value to add value into treap, "
"- value to erase all nodes with value. 'q' to quit. ")
lowerCAmelCase_ : str = input()
while args != "q":
lowerCAmelCase_ : int = interact_treap(snake_case__ , snake_case__)
print(snake_case__)
lowerCAmelCase_ : str = input()
print("good by!")
if __name__ == "__main__":
import doctest
doctest.testmod()
main()
| 683 | 1 |
import numpy as np
def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__):
lowerCAmelCase_ : List[Any] = int(np.ceil((x_end - xa) / h))
lowerCAmelCase_ : int = np.zeros((n + 1,))
lowerCAmelCase_ : Optional[int] = ya
lowerCAmelCase_ : Optional[Any] = xa
for k in range(snake_case__):
lowerCAmelCase_ : int = f(snake_case__ , y[k])
lowerCAmelCase_ : Any = f(x + 0.5 * h , y[k] + 0.5 * h * ka)
lowerCAmelCase_ : Dict = f(x + 0.5 * h , y[k] + 0.5 * h * ka)
lowerCAmelCase_ : Tuple = f(x + h , y[k] + h * ka)
lowerCAmelCase_ : Optional[int] = y[k] + (1 / 6) * h * (ka + 2 * ka + 2 * ka + ka)
x += h
return y
if __name__ == "__main__":
import doctest
doctest.testmod()
| 683 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_funnel import FunnelTokenizer
_lowercase = logging.get_logger(__name__)
_lowercase = {'''vocab_file''': '''vocab.txt''', '''tokenizer_file''': '''tokenizer.json'''}
_lowercase = [
'''small''',
'''small-base''',
'''medium''',
'''medium-base''',
'''intermediate''',
'''intermediate-base''',
'''large''',
'''large-base''',
'''xlarge''',
'''xlarge-base''',
]
_lowercase = {
'''vocab_file''': {
'''funnel-transformer/small''': '''https://huggingface.co/funnel-transformer/small/resolve/main/vocab.txt''',
'''funnel-transformer/small-base''': '''https://huggingface.co/funnel-transformer/small-base/resolve/main/vocab.txt''',
'''funnel-transformer/medium''': '''https://huggingface.co/funnel-transformer/medium/resolve/main/vocab.txt''',
'''funnel-transformer/medium-base''': (
'''https://huggingface.co/funnel-transformer/medium-base/resolve/main/vocab.txt'''
),
'''funnel-transformer/intermediate''': (
'''https://huggingface.co/funnel-transformer/intermediate/resolve/main/vocab.txt'''
),
'''funnel-transformer/intermediate-base''': (
'''https://huggingface.co/funnel-transformer/intermediate-base/resolve/main/vocab.txt'''
),
'''funnel-transformer/large''': '''https://huggingface.co/funnel-transformer/large/resolve/main/vocab.txt''',
'''funnel-transformer/large-base''': '''https://huggingface.co/funnel-transformer/large-base/resolve/main/vocab.txt''',
'''funnel-transformer/xlarge''': '''https://huggingface.co/funnel-transformer/xlarge/resolve/main/vocab.txt''',
'''funnel-transformer/xlarge-base''': (
'''https://huggingface.co/funnel-transformer/xlarge-base/resolve/main/vocab.txt'''
),
},
'''tokenizer_file''': {
'''funnel-transformer/small''': '''https://huggingface.co/funnel-transformer/small/resolve/main/tokenizer.json''',
'''funnel-transformer/small-base''': (
'''https://huggingface.co/funnel-transformer/small-base/resolve/main/tokenizer.json'''
),
'''funnel-transformer/medium''': '''https://huggingface.co/funnel-transformer/medium/resolve/main/tokenizer.json''',
'''funnel-transformer/medium-base''': (
'''https://huggingface.co/funnel-transformer/medium-base/resolve/main/tokenizer.json'''
),
'''funnel-transformer/intermediate''': (
'''https://huggingface.co/funnel-transformer/intermediate/resolve/main/tokenizer.json'''
),
'''funnel-transformer/intermediate-base''': (
'''https://huggingface.co/funnel-transformer/intermediate-base/resolve/main/tokenizer.json'''
),
'''funnel-transformer/large''': '''https://huggingface.co/funnel-transformer/large/resolve/main/tokenizer.json''',
'''funnel-transformer/large-base''': (
'''https://huggingface.co/funnel-transformer/large-base/resolve/main/tokenizer.json'''
),
'''funnel-transformer/xlarge''': '''https://huggingface.co/funnel-transformer/xlarge/resolve/main/tokenizer.json''',
'''funnel-transformer/xlarge-base''': (
'''https://huggingface.co/funnel-transformer/xlarge-base/resolve/main/tokenizer.json'''
),
},
}
_lowercase = {f"funnel-transformer/{name}": 512 for name in _model_names}
_lowercase = {f"funnel-transformer/{name}": {'''do_lower_case''': True} for name in _model_names}
class __snake_case ( snake_case__ ):
"""simple docstring"""
UpperCamelCase_ = VOCAB_FILES_NAMES
UpperCamelCase_ = PRETRAINED_VOCAB_FILES_MAP
UpperCamelCase_ = PRETRAINED_INIT_CONFIGURATION
UpperCamelCase_ = FunnelTokenizer
UpperCamelCase_ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
UpperCamelCase_ = 2
def __init__( self : Optional[Any] ,lowerCAmelCase__ : Any=None ,lowerCAmelCase__ : Optional[int]=None ,lowerCAmelCase__ : Optional[Any]=True ,lowerCAmelCase__ : List[str]="<unk>" ,lowerCAmelCase__ : int="<sep>" ,lowerCAmelCase__ : Union[str, Any]="<pad>" ,lowerCAmelCase__ : List[str]="<cls>" ,lowerCAmelCase__ : Optional[int]="<mask>" ,lowerCAmelCase__ : Union[str, Any]="<s>" ,lowerCAmelCase__ : List[str]="</s>" ,lowerCAmelCase__ : Optional[int]=True ,lowerCAmelCase__ : Tuple=True ,lowerCAmelCase__ : Any=None ,lowerCAmelCase__ : List[Any]="##" ,**lowerCAmelCase__ : int ,) -> List[Any]:
'''simple docstring'''
super().__init__(
lowerCAmelCase__ ,tokenizer_file=lowerCAmelCase__ ,do_lower_case=lowerCAmelCase__ ,unk_token=lowerCAmelCase__ ,sep_token=lowerCAmelCase__ ,pad_token=lowerCAmelCase__ ,cls_token=lowerCAmelCase__ ,mask_token=lowerCAmelCase__ ,bos_token=lowerCAmelCase__ ,eos_token=lowerCAmelCase__ ,clean_text=lowerCAmelCase__ ,tokenize_chinese_chars=lowerCAmelCase__ ,strip_accents=lowerCAmelCase__ ,wordpieces_prefix=lowerCAmelCase__ ,**lowerCAmelCase__ ,)
lowerCAmelCase_ : str = json.loads(self.backend_tokenizer.normalizer.__getstate__() )
if (
normalizer_state.get("lowercase" ,lowerCAmelCase__ ) != do_lower_case
or normalizer_state.get("strip_accents" ,lowerCAmelCase__ ) != strip_accents
or normalizer_state.get("handle_chinese_chars" ,lowerCAmelCase__ ) != tokenize_chinese_chars
):
lowerCAmelCase_ : Optional[int] = getattr(lowerCAmelCase__ ,normalizer_state.pop("type" ) )
lowerCAmelCase_ : List[Any] = do_lower_case
lowerCAmelCase_ : List[str] = strip_accents
lowerCAmelCase_ : Any = tokenize_chinese_chars
lowerCAmelCase_ : List[Any] = normalizer_class(**lowerCAmelCase__ )
lowerCAmelCase_ : int = do_lower_case
def UpperCAmelCase_ ( self : Tuple ,lowerCAmelCase__ : int ,lowerCAmelCase__ : str=None ) -> List[Any]:
'''simple docstring'''
lowerCAmelCase_ : Union[str, Any] = [self.cls_token_id] + token_ids_a + [self.sep_token_id]
if token_ids_a:
output += token_ids_a + [self.sep_token_id]
return output
def UpperCAmelCase_ ( self : List[str] ,lowerCAmelCase__ : List[int] ,lowerCAmelCase__ : Optional[List[int]] = None ) -> List[int]:
'''simple docstring'''
lowerCAmelCase_ : str = [self.sep_token_id]
lowerCAmelCase_ : Optional[Any] = [self.cls_token_id]
if token_ids_a is None:
return len(cls ) * [self.cls_token_type_id] + len(token_ids_a + sep ) * [0]
return len(cls ) * [self.cls_token_type_id] + len(token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]
def UpperCAmelCase_ ( self : Any ,lowerCAmelCase__ : str ,lowerCAmelCase__ : Optional[str] = None ) -> Tuple[str]:
'''simple docstring'''
lowerCAmelCase_ : str = self._tokenizer.model.save(lowerCAmelCase__ ,name=lowerCAmelCase__ )
return tuple(lowerCAmelCase__ )
| 683 | 1 |
_lowercase = {
"joule": 1.0,
"kilojoule": 1000,
"megajoule": 1000000,
"gigajoule": 1000000000,
"wattsecond": 1.0,
"watthour": 3600,
"kilowatthour": 3600000,
"newtonmeter": 1.0,
"calorie_nutr": 4186.8,
"kilocalorie_nutr": 4186800.00,
"electronvolt": 1.602176634E-19,
"britishthermalunit_it": 1055.05585,
"footpound": 1.355_818,
}
def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__):
if to_type not in ENERGY_CONVERSION or from_type not in ENERGY_CONVERSION:
lowerCAmelCase_ : Any = (
F'''Incorrect \'from_type\' or \'to_type\' value: {from_type!r}, {to_type!r}\n'''
F'''Valid values are: {", ".join(snake_case__)}'''
)
raise ValueError(snake_case__)
return value * ENERGY_CONVERSION[from_type] / ENERGY_CONVERSION[to_type]
if __name__ == "__main__":
import doctest
doctest.testmod()
| 683 |
# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run
import doctest
import sys
import warnings
from os.path import abspath, dirname, join
import _pytest
from transformers.testing_utils import HfDoctestModule, HfDocTestParser
# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install -e .[dev]' when switching between checkouts and running tests.
_lowercase = abspath(join(dirname(__file__), '''src'''))
sys.path.insert(1, git_repo_path)
# silence FutureWarning warnings in tests since often we can't act on them until
# they become normal warnings - i.e. the tests still need to test the current functionality
warnings.simplefilter(action='''ignore''', category=FutureWarning)
def UpperCamelCase ( snake_case__):
config.addinivalue_line(
"markers" , "is_pt_tf_cross_test: mark test to run only when PT and TF interactions are tested")
config.addinivalue_line(
"markers" , "is_pt_flax_cross_test: mark test to run only when PT and FLAX interactions are tested")
config.addinivalue_line("markers" , "is_pipeline_test: mark test to run only when pipelines are tested")
config.addinivalue_line("markers" , "is_staging_test: mark test to run only in the staging environment")
config.addinivalue_line("markers" , "accelerate_tests: mark test that require accelerate")
config.addinivalue_line("markers" , "tool_tests: mark the tool tests that are run on their specific schedule")
def UpperCamelCase ( snake_case__):
from transformers.testing_utils import pytest_addoption_shared
pytest_addoption_shared(snake_case__)
def UpperCamelCase ( snake_case__):
from transformers.testing_utils import pytest_terminal_summary_main
lowerCAmelCase_ : int = terminalreporter.config.getoption("--make-reports")
if make_reports:
pytest_terminal_summary_main(snake_case__ , id=snake_case__)
def UpperCamelCase ( snake_case__ , snake_case__):
# If no tests are collected, pytest exists with code 5, which makes the CI fail.
if exitstatus == 5:
lowerCAmelCase_ : List[Any] = 0
# Doctest custom flag to ignore output.
_lowercase = doctest.register_optionflag('''IGNORE_RESULT''')
_lowercase = doctest.OutputChecker
class __snake_case ( snake_case__ ):
"""simple docstring"""
def UpperCAmelCase_ ( self : List[str] ,lowerCAmelCase__ : int ,lowerCAmelCase__ : Optional[int] ,lowerCAmelCase__ : Tuple ) -> Any:
'''simple docstring'''
if IGNORE_RESULT & optionflags:
return True
return OutputChecker.check_output(self ,lowerCAmelCase__ ,lowerCAmelCase__ ,lowerCAmelCase__ )
_lowercase = CustomOutputChecker
_lowercase = HfDoctestModule
_lowercase = HfDocTestParser
| 683 | 1 |
import enum
import os
from hashlib import shaaaa
from typing import Optional
from .. import config
from .logging import get_logger
_lowercase = get_logger(__name__)
class __snake_case ( enum.Enum ):
"""simple docstring"""
UpperCamelCase_ = 'all_checks'
UpperCamelCase_ = 'basic_checks'
UpperCamelCase_ = 'no_checks'
class __snake_case ( snake_case__ ):
"""simple docstring"""
class __snake_case ( snake_case__ ):
"""simple docstring"""
class __snake_case ( snake_case__ ):
"""simple docstring"""
class __snake_case ( snake_case__ ):
"""simple docstring"""
def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__=None):
if expected_checksums is None:
logger.info("Unable to verify checksums.")
return
if len(set(snake_case__) - set(snake_case__)) > 0:
raise ExpectedMoreDownloadedFiles(str(set(snake_case__) - set(snake_case__)))
if len(set(snake_case__) - set(snake_case__)) > 0:
raise UnexpectedDownloadedFile(str(set(snake_case__) - set(snake_case__)))
lowerCAmelCase_ : str = [url for url in expected_checksums if expected_checksums[url] != recorded_checksums[url]]
lowerCAmelCase_ : Optional[Any] = " for " + verification_name if verification_name is not None else ""
if len(snake_case__) > 0:
raise NonMatchingChecksumError(
F'''Checksums didn\'t match{for_verification_name}:\n'''
F'''{bad_urls}\n'''
"Set `verification_mode='no_checks'` to skip checksums verification and ignore this error")
logger.info("All the checksums matched successfully" + for_verification_name)
class __snake_case ( snake_case__ ):
"""simple docstring"""
class __snake_case ( snake_case__ ):
"""simple docstring"""
class __snake_case ( snake_case__ ):
"""simple docstring"""
class __snake_case ( snake_case__ ):
"""simple docstring"""
def UpperCamelCase ( snake_case__ , snake_case__):
if expected_splits is None:
logger.info("Unable to verify splits sizes.")
return
if len(set(snake_case__) - set(snake_case__)) > 0:
raise ExpectedMoreSplits(str(set(snake_case__) - set(snake_case__)))
if len(set(snake_case__) - set(snake_case__)) > 0:
raise UnexpectedSplits(str(set(snake_case__) - set(snake_case__)))
lowerCAmelCase_ : Union[str, Any] = [
{"expected": expected_splits[name], "recorded": recorded_splits[name]}
for name in expected_splits
if expected_splits[name].num_examples != recorded_splits[name].num_examples
]
if len(snake_case__) > 0:
raise NonMatchingSplitsSizesError(str(snake_case__))
logger.info("All the splits matched successfully.")
def UpperCamelCase ( snake_case__ , snake_case__ = True):
if record_checksum:
lowerCAmelCase_ : Optional[Any] = shaaaa()
with open(snake_case__ , "rb") as f:
for chunk in iter(lambda: f.read(1 << 20) , b""):
m.update(snake_case__)
lowerCAmelCase_ : Optional[int] = m.hexdigest()
else:
lowerCAmelCase_ : str = None
return {"num_bytes": os.path.getsize(snake_case__), "checksum": checksum}
def UpperCamelCase ( snake_case__):
if dataset_size and config.IN_MEMORY_MAX_SIZE:
return dataset_size < config.IN_MEMORY_MAX_SIZE
else:
return False
| 683 |
from __future__ import annotations
from collections.abc import Sequence
from typing import Literal
def UpperCamelCase ( snake_case__ , snake_case__):
lowerCAmelCase_ : Optional[int] = list(snake_case__)
lowerCAmelCase_ : Tuple = list(snake_case__)
lowerCAmelCase_ : List[str] = 0
for i in range(len(snake_case__)):
if lista[i] != lista[i]:
count += 1
lowerCAmelCase_ : Dict = "_"
if count > 1:
return False
else:
return "".join(snake_case__)
def UpperCamelCase ( snake_case__):
lowerCAmelCase_ : Union[str, Any] = []
while True:
lowerCAmelCase_ : Tuple = ["$"] * len(snake_case__)
lowerCAmelCase_ : Tuple = []
for i in range(len(snake_case__)):
for j in range(i + 1 , len(snake_case__)):
lowerCAmelCase_ : Optional[int] = compare_string(binary[i] , binary[j])
if k is False:
lowerCAmelCase_ : str = "*"
lowerCAmelCase_ : Tuple = "*"
temp.append("X")
for i in range(len(snake_case__)):
if checka[i] == "$":
pi.append(binary[i])
if len(snake_case__) == 0:
return pi
lowerCAmelCase_ : List[Any] = list(set(snake_case__))
def UpperCamelCase ( snake_case__ , snake_case__):
lowerCAmelCase_ : Optional[int] = []
for minterm in minterms:
lowerCAmelCase_ : Dict = ""
for _ in range(snake_case__):
lowerCAmelCase_ : Dict = str(minterm % 2) + string
minterm //= 2
temp.append(snake_case__)
return temp
def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__):
lowerCAmelCase_ : Optional[Any] = list(snake_case__)
lowerCAmelCase_ : Dict = list(snake_case__)
lowerCAmelCase_ : Dict = 0
for i in range(len(snake_case__)):
if lista[i] != lista[i]:
count_n += 1
return count_n == count
def UpperCamelCase ( snake_case__ , snake_case__):
lowerCAmelCase_ : Optional[Any] = []
lowerCAmelCase_ : Dict = [0] * len(snake_case__)
for i in range(len(chart[0])):
lowerCAmelCase_ : List[Any] = 0
lowerCAmelCase_ : int = -1
for j in range(len(snake_case__)):
if chart[j][i] == 1:
count += 1
lowerCAmelCase_ : Optional[int] = j
if count == 1:
lowerCAmelCase_ : Union[str, Any] = 1
for i in range(len(snake_case__)):
if select[i] == 1:
for j in range(len(chart[0])):
if chart[i][j] == 1:
for k in range(len(snake_case__)):
lowerCAmelCase_ : Tuple = 0
temp.append(prime_implicants[i])
while True:
lowerCAmelCase_ : Optional[Any] = 0
lowerCAmelCase_ : Dict = -1
lowerCAmelCase_ : Tuple = 0
for i in range(len(snake_case__)):
lowerCAmelCase_ : Dict = chart[i].count(1)
if count_n > max_n:
lowerCAmelCase_ : Optional[int] = count_n
lowerCAmelCase_ : Optional[Any] = i
if max_n == 0:
return temp
temp.append(prime_implicants[rem])
for i in range(len(chart[0])):
if chart[rem][i] == 1:
for j in range(len(snake_case__)):
lowerCAmelCase_ : Any = 0
def UpperCamelCase ( snake_case__ , snake_case__):
lowerCAmelCase_ : str = [[0 for x in range(len(snake_case__))] for x in range(len(snake_case__))]
for i in range(len(snake_case__)):
lowerCAmelCase_ : Optional[Any] = prime_implicants[i].count("_")
for j in range(len(snake_case__)):
if is_for_table(prime_implicants[i] , binary[j] , snake_case__):
lowerCAmelCase_ : Dict = 1
return chart
def UpperCamelCase ( ):
lowerCAmelCase_ : Optional[Any] = int(input("Enter the no. of variables\n"))
lowerCAmelCase_ : Tuple = [
float(snake_case__)
for x in input(
"Enter the decimal representation of Minterms 'Spaces Separated'\n").split()
]
lowerCAmelCase_ : Any = decimal_to_binary(snake_case__ , snake_case__)
lowerCAmelCase_ : Dict = check(snake_case__)
print("Prime Implicants are:")
print(snake_case__)
lowerCAmelCase_ : int = prime_implicant_chart(snake_case__ , snake_case__)
lowerCAmelCase_ : List[str] = selection(snake_case__ , snake_case__)
print("Essential Prime Implicants are:")
print(snake_case__)
if __name__ == "__main__":
import doctest
doctest.testmod()
main()
| 683 | 1 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_funnel import FunnelTokenizer
_lowercase = logging.get_logger(__name__)
_lowercase = {'''vocab_file''': '''vocab.txt''', '''tokenizer_file''': '''tokenizer.json'''}
_lowercase = [
'''small''',
'''small-base''',
'''medium''',
'''medium-base''',
'''intermediate''',
'''intermediate-base''',
'''large''',
'''large-base''',
'''xlarge''',
'''xlarge-base''',
]
_lowercase = {
'''vocab_file''': {
'''funnel-transformer/small''': '''https://huggingface.co/funnel-transformer/small/resolve/main/vocab.txt''',
'''funnel-transformer/small-base''': '''https://huggingface.co/funnel-transformer/small-base/resolve/main/vocab.txt''',
'''funnel-transformer/medium''': '''https://huggingface.co/funnel-transformer/medium/resolve/main/vocab.txt''',
'''funnel-transformer/medium-base''': (
'''https://huggingface.co/funnel-transformer/medium-base/resolve/main/vocab.txt'''
),
'''funnel-transformer/intermediate''': (
'''https://huggingface.co/funnel-transformer/intermediate/resolve/main/vocab.txt'''
),
'''funnel-transformer/intermediate-base''': (
'''https://huggingface.co/funnel-transformer/intermediate-base/resolve/main/vocab.txt'''
),
'''funnel-transformer/large''': '''https://huggingface.co/funnel-transformer/large/resolve/main/vocab.txt''',
'''funnel-transformer/large-base''': '''https://huggingface.co/funnel-transformer/large-base/resolve/main/vocab.txt''',
'''funnel-transformer/xlarge''': '''https://huggingface.co/funnel-transformer/xlarge/resolve/main/vocab.txt''',
'''funnel-transformer/xlarge-base''': (
'''https://huggingface.co/funnel-transformer/xlarge-base/resolve/main/vocab.txt'''
),
},
'''tokenizer_file''': {
'''funnel-transformer/small''': '''https://huggingface.co/funnel-transformer/small/resolve/main/tokenizer.json''',
'''funnel-transformer/small-base''': (
'''https://huggingface.co/funnel-transformer/small-base/resolve/main/tokenizer.json'''
),
'''funnel-transformer/medium''': '''https://huggingface.co/funnel-transformer/medium/resolve/main/tokenizer.json''',
'''funnel-transformer/medium-base''': (
'''https://huggingface.co/funnel-transformer/medium-base/resolve/main/tokenizer.json'''
),
'''funnel-transformer/intermediate''': (
'''https://huggingface.co/funnel-transformer/intermediate/resolve/main/tokenizer.json'''
),
'''funnel-transformer/intermediate-base''': (
'''https://huggingface.co/funnel-transformer/intermediate-base/resolve/main/tokenizer.json'''
),
'''funnel-transformer/large''': '''https://huggingface.co/funnel-transformer/large/resolve/main/tokenizer.json''',
'''funnel-transformer/large-base''': (
'''https://huggingface.co/funnel-transformer/large-base/resolve/main/tokenizer.json'''
),
'''funnel-transformer/xlarge''': '''https://huggingface.co/funnel-transformer/xlarge/resolve/main/tokenizer.json''',
'''funnel-transformer/xlarge-base''': (
'''https://huggingface.co/funnel-transformer/xlarge-base/resolve/main/tokenizer.json'''
),
},
}
_lowercase = {f"funnel-transformer/{name}": 512 for name in _model_names}
_lowercase = {f"funnel-transformer/{name}": {'''do_lower_case''': True} for name in _model_names}
class __snake_case ( snake_case__ ):
"""simple docstring"""
UpperCamelCase_ = VOCAB_FILES_NAMES
UpperCamelCase_ = PRETRAINED_VOCAB_FILES_MAP
UpperCamelCase_ = PRETRAINED_INIT_CONFIGURATION
UpperCamelCase_ = FunnelTokenizer
UpperCamelCase_ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
UpperCamelCase_ = 2
def __init__( self : Optional[Any] ,lowerCAmelCase__ : Any=None ,lowerCAmelCase__ : Optional[int]=None ,lowerCAmelCase__ : Optional[Any]=True ,lowerCAmelCase__ : List[str]="<unk>" ,lowerCAmelCase__ : int="<sep>" ,lowerCAmelCase__ : Union[str, Any]="<pad>" ,lowerCAmelCase__ : List[str]="<cls>" ,lowerCAmelCase__ : Optional[int]="<mask>" ,lowerCAmelCase__ : Union[str, Any]="<s>" ,lowerCAmelCase__ : List[str]="</s>" ,lowerCAmelCase__ : Optional[int]=True ,lowerCAmelCase__ : Tuple=True ,lowerCAmelCase__ : Any=None ,lowerCAmelCase__ : List[Any]="##" ,**lowerCAmelCase__ : int ,) -> List[Any]:
'''simple docstring'''
super().__init__(
lowerCAmelCase__ ,tokenizer_file=lowerCAmelCase__ ,do_lower_case=lowerCAmelCase__ ,unk_token=lowerCAmelCase__ ,sep_token=lowerCAmelCase__ ,pad_token=lowerCAmelCase__ ,cls_token=lowerCAmelCase__ ,mask_token=lowerCAmelCase__ ,bos_token=lowerCAmelCase__ ,eos_token=lowerCAmelCase__ ,clean_text=lowerCAmelCase__ ,tokenize_chinese_chars=lowerCAmelCase__ ,strip_accents=lowerCAmelCase__ ,wordpieces_prefix=lowerCAmelCase__ ,**lowerCAmelCase__ ,)
lowerCAmelCase_ : str = json.loads(self.backend_tokenizer.normalizer.__getstate__() )
if (
normalizer_state.get("lowercase" ,lowerCAmelCase__ ) != do_lower_case
or normalizer_state.get("strip_accents" ,lowerCAmelCase__ ) != strip_accents
or normalizer_state.get("handle_chinese_chars" ,lowerCAmelCase__ ) != tokenize_chinese_chars
):
lowerCAmelCase_ : Optional[int] = getattr(lowerCAmelCase__ ,normalizer_state.pop("type" ) )
lowerCAmelCase_ : List[Any] = do_lower_case
lowerCAmelCase_ : List[str] = strip_accents
lowerCAmelCase_ : Any = tokenize_chinese_chars
lowerCAmelCase_ : List[Any] = normalizer_class(**lowerCAmelCase__ )
lowerCAmelCase_ : int = do_lower_case
def UpperCAmelCase_ ( self : Tuple ,lowerCAmelCase__ : int ,lowerCAmelCase__ : str=None ) -> List[Any]:
'''simple docstring'''
lowerCAmelCase_ : Union[str, Any] = [self.cls_token_id] + token_ids_a + [self.sep_token_id]
if token_ids_a:
output += token_ids_a + [self.sep_token_id]
return output
def UpperCAmelCase_ ( self : List[str] ,lowerCAmelCase__ : List[int] ,lowerCAmelCase__ : Optional[List[int]] = None ) -> List[int]:
'''simple docstring'''
lowerCAmelCase_ : str = [self.sep_token_id]
lowerCAmelCase_ : Optional[Any] = [self.cls_token_id]
if token_ids_a is None:
return len(cls ) * [self.cls_token_type_id] + len(token_ids_a + sep ) * [0]
return len(cls ) * [self.cls_token_type_id] + len(token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]
def UpperCAmelCase_ ( self : Any ,lowerCAmelCase__ : str ,lowerCAmelCase__ : Optional[str] = None ) -> Tuple[str]:
'''simple docstring'''
lowerCAmelCase_ : str = self._tokenizer.model.save(lowerCAmelCase__ ,name=lowerCAmelCase__ )
return tuple(lowerCAmelCase__ )
| 683 |
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
_lowercase = logging.getLogger(__name__)
def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__ = None , snake_case__ = None , snake_case__ = None , snake_case__ = None , snake_case__ = None , snake_case__ = False , ):
lowerCAmelCase_ : List[Any] = bnb_quantization_config.load_in_abit
lowerCAmelCase_ : Optional[Any] = 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.")
lowerCAmelCase_ : List[str] = []
# custom device map
if isinstance(snake_case__ , snake_case__) and len(device_map.keys()) > 1:
lowerCAmelCase_ : Union[str, Any] = [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:
lowerCAmelCase_ : Union[str, Any] = get_keys_to_not_convert(snake_case__)
# add cpu modules to skip modules only for 4-bit modules
if load_in_abit:
bnb_quantization_config.skip_modules.extend(snake_case__)
lowerCAmelCase_ : Union[str, Any] = 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:
lowerCAmelCase_ : Optional[int] = []
lowerCAmelCase_ : int = bnb_quantization_config.keep_in_fpaa_modules
modules_to_not_convert.extend(snake_case__)
# compatibility with peft
lowerCAmelCase_ : Optional[int] = load_in_abit
lowerCAmelCase_ : List[str] = load_in_abit
lowerCAmelCase_ : Optional[int] = get_parameter_device(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.")
lowerCAmelCase_ : Union[str, Any] = replace_with_bnb_layers(snake_case__ , snake_case__ , modules_to_not_convert=snake_case__)
# convert param to the right dtype
lowerCAmelCase_ : Any = 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:
lowerCAmelCase_ : Optional[int] = name.replace(".weight" , "").replace(".bias" , "")
lowerCAmelCase_ : Optional[int] = getattr(snake_case__ , snake_case__ , snake_case__)
if param is not None:
param.to(torch.floataa)
elif torch.is_floating_point(snake_case__):
param.to(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():
lowerCAmelCase_ : str = replace_with_bnb_layers(
snake_case__ , snake_case__ , modules_to_not_convert=snake_case__)
lowerCAmelCase_ : Optional[int] = get_quantized_model_device_map(
snake_case__ , snake_case__ , snake_case__ , max_memory=snake_case__ , no_split_module_classes=snake_case__ , )
if offload_state_dict is None and device_map is not None and "disk" in device_map.values():
lowerCAmelCase_ : Optional[Any] = True
lowerCAmelCase_ : Optional[int] = any(x in list(device_map.values()) for x in ["cpu", "disk"])
load_checkpoint_in_model(
snake_case__ , snake_case__ , snake_case__ , dtype=bnb_quantization_config.torch_dtype , offload_folder=snake_case__ , offload_state_dict=snake_case__ , keep_in_fpaa_modules=bnb_quantization_config.keep_in_fpaa_modules , offload_abit_bnb=load_in_abit and offload , )
return dispatch_model(snake_case__ , device_map=snake_case__ , offload_dir=snake_case__)
def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__=None , snake_case__=None , snake_case__=None):
if device_map is None:
if torch.cuda.is_available():
lowerCAmelCase_ : Any = {"": 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(snake_case__ , 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'.")
lowerCAmelCase_ : Dict = {}
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)
})
lowerCAmelCase_ : List[str] = {}
lowerCAmelCase_ : Union[str, Any] = special_dtypes
lowerCAmelCase_ : Union[str, Any] = no_split_module_classes
lowerCAmelCase_ : Any = bnb_quantization_config.target_dtype
# get max_memory for each device.
if device_map != "sequential":
lowerCAmelCase_ : Tuple = get_balanced_memory(
snake_case__ , low_zero=(device_map == "balanced_low_0") , max_memory=snake_case__ , **snake_case__ , )
lowerCAmelCase_ : Tuple = max_memory
lowerCAmelCase_ : Optional[Any] = infer_auto_device_map(snake_case__ , **snake_case__)
if isinstance(snake_case__ , snake_case__):
# check if don't have any quantized module on the cpu
lowerCAmelCase_ : Union[str, Any] = bnb_quantization_config.skip_modules + bnb_quantization_config.keep_in_fpaa_modules
lowerCAmelCase_ : List[Any] = {
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(
"\n Some modules are dispatched on the CPU or the disk. Make sure you have enough GPU RAM to fit\n the quantized model. If you want to dispatch the model on the CPU or the disk while keeping\n these modules in `torch_dtype`, you need to pass a custom `device_map` to\n `load_and_quantize_model`. Check\n https://huggingface.co/docs/accelerate/main/en/usage_guides/quantization#offload-modules-to-cpu-and-disk\n for more details.\n ")
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 UpperCamelCase ( snake_case__ , snake_case__ , snake_case__=None , snake_case__=None):
if modules_to_not_convert is None:
lowerCAmelCase_ : List[str] = []
lowerCAmelCase_ , lowerCAmelCase_ : Tuple = _replace_with_bnb_layers(
snake_case__ , snake_case__ , snake_case__ , 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 UpperCamelCase ( snake_case__ , snake_case__ , snake_case__=None , snake_case__=None , ):
lowerCAmelCase_ : str = False
for name, module in model.named_children():
if current_key_name is None:
lowerCAmelCase_ : Optional[int] = []
current_key_name.append(snake_case__)
if isinstance(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`
lowerCAmelCase_ : Optional[int] = ".".join(snake_case__)
lowerCAmelCase_ : List[str] = 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:
lowerCAmelCase_ : List[Any] = False
break
if proceed:
# Load bnb module with empty weight and replace ``nn.Linear` module
if bnb_quantization_config.load_in_abit:
lowerCAmelCase_ : Tuple = bnb.nn.LinearabitLt(
module.in_features , module.out_features , module.bias is not None , has_fpaa_weights=snake_case__ , threshold=bnb_quantization_config.llm_inta_threshold , )
elif bnb_quantization_config.load_in_abit:
lowerCAmelCase_ : Dict = 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")
lowerCAmelCase_ : List[str] = module.weight.data
if module.bias is not None:
lowerCAmelCase_ : Any = module.bias.data
bnb_module.requires_grad_(snake_case__)
setattr(snake_case__ , snake_case__ , snake_case__)
lowerCAmelCase_ : List[str] = True
if len(list(module.children())) > 0:
lowerCAmelCase_ , lowerCAmelCase_ : List[Any] = _replace_with_bnb_layers(
snake_case__ , snake_case__ , snake_case__ , snake_case__)
lowerCAmelCase_ : Optional[int] = has_been_replaced | _has_been_replaced
# Remove the last key for recursion
current_key_name.pop(-1)
return model, has_been_replaced
def UpperCamelCase ( snake_case__):
# Create a copy of the model
with init_empty_weights():
lowerCAmelCase_ : List[Any] = deepcopy(snake_case__) # this has 0 cost since it is done inside `init_empty_weights` context manager`
lowerCAmelCase_ : Dict = find_tied_parameters(snake_case__)
# For compatibility with Accelerate < 0.18
if isinstance(snake_case__ , snake_case__):
lowerCAmelCase_ : List[str] = sum(list(tied_params.values()) , []) + list(tied_params.keys())
else:
lowerCAmelCase_ : Optional[Any] = sum(snake_case__ , [])
lowerCAmelCase_ : List[Any] = len(snake_case__) > 0
# Check if it is a base model
lowerCAmelCase_ : List[str] = False
if hasattr(snake_case__ , "base_model_prefix"):
lowerCAmelCase_ : Tuple = not hasattr(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
lowerCAmelCase_ : Union[str, Any] = list(model.named_children())
lowerCAmelCase_ : Optional[int] = [list_modules[-1][0]]
# add last module together with tied weights
lowerCAmelCase_ : Any = set(snake_case__) - set(snake_case__)
lowerCAmelCase_ : Tuple = list(set(snake_case__)) + list(snake_case__)
# remove ".weight" from the keys
lowerCAmelCase_ : List[str] = [".weight", ".bias"]
lowerCAmelCase_ : Tuple = []
for name in list_untouched:
for name_to_remove in names_to_remove:
if name_to_remove in name:
lowerCAmelCase_ : str = name.replace(snake_case__ , "")
filtered_module_names.append(snake_case__)
return filtered_module_names
def UpperCamelCase ( snake_case__):
for m in model.modules():
if isinstance(snake_case__ , bnb.nn.Linearabit):
return True
return False
def UpperCamelCase ( snake_case__):
return next(parameter.parameters()).device
def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__):
# 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(snake_case__ , snake_case__ , 0 , dtype=snake_case__ , value=snake_case__)
lowerCAmelCase_ : str = param_name
lowerCAmelCase_ : Tuple = model
if "." in tensor_name:
lowerCAmelCase_ : Dict = tensor_name.split(".")
for split in splits[:-1]:
lowerCAmelCase_ : Any = getattr(snake_case__ , snake_case__)
if new_module is None:
raise ValueError(F'''{module} has no attribute {split}.''')
lowerCAmelCase_ : Union[str, Any] = new_module
lowerCAmelCase_ : Any = splits[-1]
# offload weights
lowerCAmelCase_ : List[Any] = False
offload_weight(module._parameters[tensor_name] , snake_case__ , snake_case__ , index=snake_case__)
if hasattr(module._parameters[tensor_name] , "SCB"):
offload_weight(
module._parameters[tensor_name].SCB , param_name.replace("weight" , "SCB") , snake_case__ , index=snake_case__ , )
else:
offload_weight(snake_case__ , snake_case__ , snake_case__ , index=snake_case__)
offload_weight(snake_case__ , param_name.replace("weight" , "SCB") , snake_case__ , index=snake_case__)
set_module_tensor_to_device(snake_case__ , snake_case__ , "meta" , dtype=snake_case__ , value=torch.empty(*param.size()))
| 683 | 1 |
import argparse
from collections import OrderedDict
from pathlib import Path
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from torchvision.transforms import functional as F
from transformers import DetrImageProcessor, TableTransformerConfig, TableTransformerForObjectDetection
from transformers.utils import logging
logging.set_verbosity_info()
_lowercase = logging.get_logger(__name__)
# here we list all keys to be renamed (original name on the left, our name on the right)
_lowercase = []
for i in range(6):
# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
rename_keys.append(
(f"transformer.encoder.layers.{i}.self_attn.out_proj.weight", f"encoder.layers.{i}.self_attn.out_proj.weight")
)
rename_keys.append(
(f"transformer.encoder.layers.{i}.self_attn.out_proj.bias", f"encoder.layers.{i}.self_attn.out_proj.bias")
)
rename_keys.append((f"transformer.encoder.layers.{i}.linear1.weight", f"encoder.layers.{i}.fc1.weight"))
rename_keys.append((f"transformer.encoder.layers.{i}.linear1.bias", f"encoder.layers.{i}.fc1.bias"))
rename_keys.append((f"transformer.encoder.layers.{i}.linear2.weight", f"encoder.layers.{i}.fc2.weight"))
rename_keys.append((f"transformer.encoder.layers.{i}.linear2.bias", f"encoder.layers.{i}.fc2.bias"))
rename_keys.append(
(f"transformer.encoder.layers.{i}.norm1.weight", f"encoder.layers.{i}.self_attn_layer_norm.weight")
)
rename_keys.append((f"transformer.encoder.layers.{i}.norm1.bias", f"encoder.layers.{i}.self_attn_layer_norm.bias"))
rename_keys.append((f"transformer.encoder.layers.{i}.norm2.weight", f"encoder.layers.{i}.final_layer_norm.weight"))
rename_keys.append((f"transformer.encoder.layers.{i}.norm2.bias", f"encoder.layers.{i}.final_layer_norm.bias"))
# decoder layers: 2 times output projection, 2 feedforward neural networks and 3 layernorms
rename_keys.append(
(f"transformer.decoder.layers.{i}.self_attn.out_proj.weight", f"decoder.layers.{i}.self_attn.out_proj.weight")
)
rename_keys.append(
(f"transformer.decoder.layers.{i}.self_attn.out_proj.bias", f"decoder.layers.{i}.self_attn.out_proj.bias")
)
rename_keys.append(
(
f"transformer.decoder.layers.{i}.multihead_attn.out_proj.weight",
f"decoder.layers.{i}.encoder_attn.out_proj.weight",
)
)
rename_keys.append(
(
f"transformer.decoder.layers.{i}.multihead_attn.out_proj.bias",
f"decoder.layers.{i}.encoder_attn.out_proj.bias",
)
)
rename_keys.append((f"transformer.decoder.layers.{i}.linear1.weight", f"decoder.layers.{i}.fc1.weight"))
rename_keys.append((f"transformer.decoder.layers.{i}.linear1.bias", f"decoder.layers.{i}.fc1.bias"))
rename_keys.append((f"transformer.decoder.layers.{i}.linear2.weight", f"decoder.layers.{i}.fc2.weight"))
rename_keys.append((f"transformer.decoder.layers.{i}.linear2.bias", f"decoder.layers.{i}.fc2.bias"))
rename_keys.append(
(f"transformer.decoder.layers.{i}.norm1.weight", f"decoder.layers.{i}.self_attn_layer_norm.weight")
)
rename_keys.append((f"transformer.decoder.layers.{i}.norm1.bias", f"decoder.layers.{i}.self_attn_layer_norm.bias"))
rename_keys.append(
(f"transformer.decoder.layers.{i}.norm2.weight", f"decoder.layers.{i}.encoder_attn_layer_norm.weight")
)
rename_keys.append(
(f"transformer.decoder.layers.{i}.norm2.bias", f"decoder.layers.{i}.encoder_attn_layer_norm.bias")
)
rename_keys.append((f"transformer.decoder.layers.{i}.norm3.weight", f"decoder.layers.{i}.final_layer_norm.weight"))
rename_keys.append((f"transformer.decoder.layers.{i}.norm3.bias", f"decoder.layers.{i}.final_layer_norm.bias"))
# convolutional projection + query embeddings + layernorm of encoder + layernorm of decoder + class and bounding box heads
rename_keys.extend(
[
('''input_proj.weight''', '''input_projection.weight'''),
('''input_proj.bias''', '''input_projection.bias'''),
('''query_embed.weight''', '''query_position_embeddings.weight'''),
('''transformer.encoder.norm.weight''', '''encoder.layernorm.weight'''),
('''transformer.encoder.norm.bias''', '''encoder.layernorm.bias'''),
('''transformer.decoder.norm.weight''', '''decoder.layernorm.weight'''),
('''transformer.decoder.norm.bias''', '''decoder.layernorm.bias'''),
('''class_embed.weight''', '''class_labels_classifier.weight'''),
('''class_embed.bias''', '''class_labels_classifier.bias'''),
('''bbox_embed.layers.0.weight''', '''bbox_predictor.layers.0.weight'''),
('''bbox_embed.layers.0.bias''', '''bbox_predictor.layers.0.bias'''),
('''bbox_embed.layers.1.weight''', '''bbox_predictor.layers.1.weight'''),
('''bbox_embed.layers.1.bias''', '''bbox_predictor.layers.1.bias'''),
('''bbox_embed.layers.2.weight''', '''bbox_predictor.layers.2.weight'''),
('''bbox_embed.layers.2.bias''', '''bbox_predictor.layers.2.bias'''),
]
)
def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__):
lowerCAmelCase_ : Union[str, Any] = state_dict.pop(snake_case__)
lowerCAmelCase_ : Dict = val
def UpperCamelCase ( snake_case__):
lowerCAmelCase_ : Union[str, Any] = OrderedDict()
for key, value in state_dict.items():
if "backbone.0.body" in key:
lowerCAmelCase_ : Optional[Any] = key.replace("backbone.0.body" , "backbone.conv_encoder.model")
lowerCAmelCase_ : int = value
else:
lowerCAmelCase_ : Any = value
return new_state_dict
def UpperCamelCase ( snake_case__):
lowerCAmelCase_ : List[str] = ""
# first: transformer encoder
for i in range(6):
# read in weights + bias of input projection layer (in PyTorch's MultiHeadAttention, this is a single matrix + bias)
lowerCAmelCase_ : Dict = state_dict.pop(F'''{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight''')
lowerCAmelCase_ : Any = state_dict.pop(F'''{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_bias''')
# next, add query, keys and values (in that order) to the state dict
lowerCAmelCase_ : List[str] = in_proj_weight[:2_56, :]
lowerCAmelCase_ : Union[str, Any] = in_proj_bias[:2_56]
lowerCAmelCase_ : Tuple = in_proj_weight[2_56:5_12, :]
lowerCAmelCase_ : int = in_proj_bias[2_56:5_12]
lowerCAmelCase_ : Optional[int] = in_proj_weight[-2_56:, :]
lowerCAmelCase_ : List[Any] = in_proj_bias[-2_56:]
# next: transformer decoder (which is a bit more complex because it also includes cross-attention)
for i in range(6):
# read in weights + bias of input projection layer of self-attention
lowerCAmelCase_ : str = state_dict.pop(F'''{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight''')
lowerCAmelCase_ : Union[str, Any] = state_dict.pop(F'''{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_bias''')
# next, add query, keys and values (in that order) to the state dict
lowerCAmelCase_ : Optional[int] = in_proj_weight[:2_56, :]
lowerCAmelCase_ : Tuple = in_proj_bias[:2_56]
lowerCAmelCase_ : Dict = in_proj_weight[2_56:5_12, :]
lowerCAmelCase_ : Optional[int] = in_proj_bias[2_56:5_12]
lowerCAmelCase_ : Dict = in_proj_weight[-2_56:, :]
lowerCAmelCase_ : Dict = in_proj_bias[-2_56:]
# read in weights + bias of input projection layer of cross-attention
lowerCAmelCase_ : Tuple = state_dict.pop(
F'''{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight''')
lowerCAmelCase_ : Union[str, Any] = state_dict.pop(F'''{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_bias''')
# next, add query, keys and values (in that order) of cross-attention to the state dict
lowerCAmelCase_ : Union[str, Any] = in_proj_weight_cross_attn[:2_56, :]
lowerCAmelCase_ : Any = in_proj_bias_cross_attn[:2_56]
lowerCAmelCase_ : Tuple = in_proj_weight_cross_attn[2_56:5_12, :]
lowerCAmelCase_ : Tuple = in_proj_bias_cross_attn[2_56:5_12]
lowerCAmelCase_ : Any = in_proj_weight_cross_attn[-2_56:, :]
lowerCAmelCase_ : Optional[int] = in_proj_bias_cross_attn[-2_56:]
def UpperCamelCase ( snake_case__ , snake_case__):
lowerCAmelCase_ , lowerCAmelCase_ : str = image.size
lowerCAmelCase_ : List[str] = max(snake_case__ , snake_case__)
lowerCAmelCase_ : Any = 8_00 if "detection" in checkpoint_url else 10_00
lowerCAmelCase_ : Union[str, Any] = target_max_size / current_max_size
lowerCAmelCase_ : Any = image.resize((int(round(scale * width)), int(round(scale * height))))
return resized_image
def UpperCamelCase ( snake_case__):
lowerCAmelCase_ : Dict = F.to_tensor(snake_case__)
lowerCAmelCase_ : Tuple = F.normalize(snake_case__ , mean=[0.485, 0.456, 0.406] , std=[0.229, 0.224, 0.225])
return image
@torch.no_grad()
def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__):
logger.info("Converting model...")
# load original state dict
lowerCAmelCase_ : Tuple = torch.hub.load_state_dict_from_url(snake_case__ , map_location="cpu")
# rename keys
for src, dest in rename_keys:
rename_key(snake_case__ , snake_case__ , snake_case__)
lowerCAmelCase_ : Optional[Any] = rename_backbone_keys(snake_case__)
# query, key and value matrices need special treatment
read_in_q_k_v(snake_case__)
# important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
lowerCAmelCase_ : Any = "model."
for key in state_dict.copy().keys():
if not key.startswith("class_labels_classifier") and not key.startswith("bbox_predictor"):
lowerCAmelCase_ : str = state_dict.pop(snake_case__)
lowerCAmelCase_ : Optional[int] = val
# create HuggingFace model and load state dict
lowerCAmelCase_ : Dict = TableTransformerConfig(
backbone="resnet18" , mask_loss_coefficient=1 , dice_loss_coefficient=1 , ce_loss_coefficient=1 , bbox_loss_coefficient=5 , giou_loss_coefficient=2 , eos_coefficient=0.4 , class_cost=1 , bbox_cost=5 , giou_cost=2 , )
if "detection" in checkpoint_url:
lowerCAmelCase_ : Optional[int] = 15
lowerCAmelCase_ : Dict = 2
lowerCAmelCase_ : Union[str, Any] = {0: "table", 1: "table rotated"}
lowerCAmelCase_ : str = idalabel
lowerCAmelCase_ : Optional[int] = {v: k for k, v in idalabel.items()}
else:
lowerCAmelCase_ : str = 1_25
lowerCAmelCase_ : Optional[Any] = 6
lowerCAmelCase_ : List[str] = {
0: "table",
1: "table column",
2: "table row",
3: "table column header",
4: "table projected row header",
5: "table spanning cell",
}
lowerCAmelCase_ : List[str] = idalabel
lowerCAmelCase_ : List[Any] = {v: k for k, v in idalabel.items()}
lowerCAmelCase_ : List[Any] = DetrImageProcessor(
format="coco_detection" , max_size=8_00 if "detection" in checkpoint_url else 10_00)
lowerCAmelCase_ : List[str] = TableTransformerForObjectDetection(snake_case__)
model.load_state_dict(snake_case__)
model.eval()
# verify our conversion
lowerCAmelCase_ : Optional[int] = "example_pdf.png" if "detection" in checkpoint_url else "example_table.png"
lowerCAmelCase_ : int = hf_hub_download(repo_id="nielsr/example-pdf" , repo_type="dataset" , filename=snake_case__)
lowerCAmelCase_ : Optional[int] = Image.open(snake_case__).convert("RGB")
lowerCAmelCase_ : Union[str, Any] = normalize(resize(snake_case__ , snake_case__)).unsqueeze(0)
lowerCAmelCase_ : Union[str, Any] = model(snake_case__)
if "detection" in checkpoint_url:
lowerCAmelCase_ : str = (1, 15, 3)
lowerCAmelCase_ : Dict = torch.tensor(
[[-6.7_897, -16.9_985, 6.7_937], [-8.0_186, -22.2_192, 6.9_677], [-7.3_117, -21.0_708, 7.4_055]])
lowerCAmelCase_ : List[str] = torch.tensor([[0.4_867, 0.1_767, 0.6_732], [0.6_718, 0.4_479, 0.3_830], [0.4_716, 0.1_760, 0.6_364]])
else:
lowerCAmelCase_ : Tuple = (1, 1_25, 7)
lowerCAmelCase_ : Any = torch.tensor(
[[-18.1_430, -8.3_214, 4.8_274], [-18.4_685, -7.1_361, -4.2_667], [-26.3_693, -9.3_429, -4.9_962]])
lowerCAmelCase_ : Any = torch.tensor([[0.4_983, 0.5_595, 0.9_440], [0.4_916, 0.6_315, 0.5_954], [0.6_108, 0.8_637, 0.1_135]])
assert outputs.logits.shape == expected_shape
assert torch.allclose(outputs.logits[0, :3, :3] , snake_case__ , atol=1e-4)
assert torch.allclose(outputs.pred_boxes[0, :3, :3] , snake_case__ , atol=1e-4)
print("Looks ok!")
if pytorch_dump_folder_path is not None:
# Save model and image processor
logger.info(F'''Saving PyTorch model and image processor to {pytorch_dump_folder_path}...''')
Path(snake_case__).mkdir(exist_ok=snake_case__)
model.save_pretrained(snake_case__)
image_processor.save_pretrained(snake_case__)
if push_to_hub:
# Push model to HF hub
logger.info("Pushing model to the hub...")
lowerCAmelCase_ : List[Any] = (
"microsoft/table-transformer-detection"
if "detection" in checkpoint_url
else "microsoft/table-transformer-structure-recognition"
)
model.push_to_hub(snake_case__)
image_processor.push_to_hub(snake_case__)
if __name__ == "__main__":
_lowercase = argparse.ArgumentParser()
parser.add_argument(
'''--checkpoint_url''',
default='''https://pubtables1m.blob.core.windows.net/model/pubtables1m_detection_detr_r18.pth''',
type=str,
choices=[
'''https://pubtables1m.blob.core.windows.net/model/pubtables1m_detection_detr_r18.pth''',
'''https://pubtables1m.blob.core.windows.net/model/pubtables1m_structure_detr_r18.pth''',
],
help='''URL of the Table Transformer checkpoint you\'d like to convert.''',
)
parser.add_argument(
'''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the folder to output PyTorch model.'''
)
parser.add_argument(
'''--push_to_hub''', action='''store_true''', help='''Whether or not to push the converted model to the 🤗 hub.'''
)
_lowercase = parser.parse_args()
convert_table_transformer_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)
| 683 |
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
_lowercase = logging.get_logger(__name__)
class __snake_case ( snake_case__ ):
"""simple docstring"""
UpperCamelCase_ = ['input_features', 'is_longer']
def __init__( self : Optional[int] ,lowerCAmelCase__ : List[Any]=64 ,lowerCAmelCase__ : Any=4_80_00 ,lowerCAmelCase__ : Optional[Any]=4_80 ,lowerCAmelCase__ : List[str]=10 ,lowerCAmelCase__ : List[Any]=10_24 ,lowerCAmelCase__ : Union[str, Any]=0.0 ,lowerCAmelCase__ : Tuple=False ,lowerCAmelCase__ : float = 0 ,lowerCAmelCase__ : float = 1_40_00 ,lowerCAmelCase__ : int = None ,lowerCAmelCase__ : str = "fusion" ,lowerCAmelCase__ : str = "repeatpad" ,**lowerCAmelCase__ : Union[str, Any] ,) -> Union[str, Any]:
'''simple docstring'''
super().__init__(
feature_size=lowerCAmelCase__ ,sampling_rate=lowerCAmelCase__ ,padding_value=lowerCAmelCase__ ,return_attention_mask=lowerCAmelCase__ ,**lowerCAmelCase__ ,)
lowerCAmelCase_ : Optional[Any] = top_db
lowerCAmelCase_ : str = truncation
lowerCAmelCase_ : Tuple = padding
lowerCAmelCase_ : str = fft_window_size
lowerCAmelCase_ : Dict = (fft_window_size >> 1) + 1
lowerCAmelCase_ : Dict = hop_length
lowerCAmelCase_ : Any = max_length_s
lowerCAmelCase_ : int = max_length_s * sampling_rate
lowerCAmelCase_ : Optional[int] = sampling_rate
lowerCAmelCase_ : int = frequency_min
lowerCAmelCase_ : Optional[Any] = frequency_max
lowerCAmelCase_ : List[Any] = mel_filter_bank(
num_frequency_bins=self.nb_frequency_bins ,num_mel_filters=lowerCAmelCase__ ,min_frequency=lowerCAmelCase__ ,max_frequency=lowerCAmelCase__ ,sampling_rate=lowerCAmelCase__ ,norm=lowerCAmelCase__ ,mel_scale="htk" ,)
lowerCAmelCase_ : List[Any] = mel_filter_bank(
num_frequency_bins=self.nb_frequency_bins ,num_mel_filters=lowerCAmelCase__ ,min_frequency=lowerCAmelCase__ ,max_frequency=lowerCAmelCase__ ,sampling_rate=lowerCAmelCase__ ,norm="slaney" ,mel_scale="slaney" ,)
def UpperCAmelCase_ ( self : Dict ) -> Dict[str, Any]:
'''simple docstring'''
lowerCAmelCase_ : int = copy.deepcopy(self.__dict__ )
lowerCAmelCase_ : Optional[int] = 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 : Any ,lowerCAmelCase__ : np.array ,lowerCAmelCase__ : Optional[np.array] = None ) -> np.ndarray:
'''simple docstring'''
lowerCAmelCase_ : Optional[Any] = spectrogram(
lowerCAmelCase__ ,window_function(self.fft_window_size ,"hann" ) ,frame_length=self.fft_window_size ,hop_length=self.hop_length ,power=2.0 ,mel_filters=lowerCAmelCase__ ,log_mel="dB" ,)
return log_mel_spectrogram.T
def UpperCAmelCase_ ( self : Optional[int] ,lowerCAmelCase__ : Union[str, Any] ,lowerCAmelCase__ : Union[str, Any] ,lowerCAmelCase__ : Tuple ) -> Dict:
'''simple docstring'''
lowerCAmelCase_ : 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
lowerCAmelCase_ : List[Any] = [0]
if len(ranges[2] ) == 0:
# if the audio is too short, we just use the first chunk
lowerCAmelCase_ : List[Any] = [0]
# randomly choose index for each part
lowerCAmelCase_ : str = np.random.choice(ranges[0] )
lowerCAmelCase_ : Optional[Any] = np.random.choice(ranges[1] )
lowerCAmelCase_ : Any = np.random.choice(ranges[2] )
lowerCAmelCase_ : str = mel[idx_front : idx_front + chunk_frames, :]
lowerCAmelCase_ : Dict = mel[idx_middle : idx_middle + chunk_frames, :]
lowerCAmelCase_ : Optional[Any] = mel[idx_back : idx_back + chunk_frames, :]
lowerCAmelCase_ : List[str] = torch.tensor(mel[None, None, :] )
lowerCAmelCase_ : List[Any] = torch.nn.functional.interpolate(
lowerCAmelCase__ ,size=[chunk_frames, 64] ,mode="bilinear" ,align_corners=lowerCAmelCase__ )
lowerCAmelCase_ : Optional[int] = mel_shrink[0][0].numpy()
lowerCAmelCase_ : str = np.stack([mel_shrink, mel_chunk_front, mel_chunk_middle, mel_chunk_back] ,axis=0 )
return mel_fusion
def UpperCAmelCase_ ( self : Any ,lowerCAmelCase__ : np.array ,lowerCAmelCase__ : Union[str, Any] ,lowerCAmelCase__ : Optional[int] ,lowerCAmelCase__ : int ) -> np.array:
'''simple docstring'''
if waveform.shape[0] > max_length:
if truncation == "rand_trunc":
lowerCAmelCase_ : List[Any] = True
# random crop to max_length (for compatibility) -> this should be handled by self.pad
lowerCAmelCase_ : str = len(lowerCAmelCase__ ) - max_length
lowerCAmelCase_ : Any = np.random.randint(0 ,overflow + 1 )
lowerCAmelCase_ : Dict = waveform[idx : idx + max_length]
lowerCAmelCase_ : List[str] = self._np_extract_fbank_features(lowerCAmelCase__ ,self.mel_filters_slaney )[None, :]
elif truncation == "fusion":
lowerCAmelCase_ : Tuple = self._np_extract_fbank_features(lowerCAmelCase__ ,self.mel_filters )
lowerCAmelCase_ : str = max_length // self.hop_length + 1 # the +1 related to how the spectrogram is computed
lowerCAmelCase_ : List[str] = 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.
lowerCAmelCase_ : Dict = np.stack([mel, mel, mel, mel] ,axis=0 )
lowerCAmelCase_ : int = False
else:
lowerCAmelCase_ : str = self._random_mel_fusion(lowerCAmelCase__ ,lowerCAmelCase__ ,lowerCAmelCase__ )
lowerCAmelCase_ : Any = True
else:
raise NotImplementedError(f'''data_truncating {truncation} not implemented''' )
else:
lowerCAmelCase_ : Dict = 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":
lowerCAmelCase_ : List[Any] = int(max_length / len(lowerCAmelCase__ ) )
lowerCAmelCase_ : int = np.stack(np.tile(lowerCAmelCase__ ,n_repeat + 1 ) )[:max_length]
if padding == "repeatpad":
lowerCAmelCase_ : Optional[Any] = int(max_length / len(lowerCAmelCase__ ) )
lowerCAmelCase_ : Tuple = np.stack(np.tile(lowerCAmelCase__ ,lowerCAmelCase__ ) )
lowerCAmelCase_ : List[Any] = np.pad(lowerCAmelCase__ ,(0, max_length - waveform.shape[0]) ,mode="constant" ,constant_values=0 )
if truncation == "fusion":
lowerCAmelCase_ : int = self._np_extract_fbank_features(lowerCAmelCase__ ,self.mel_filters )
lowerCAmelCase_ : Tuple = np.stack([input_mel, input_mel, input_mel, input_mel] ,axis=0 )
else:
lowerCAmelCase_ : str = self._np_extract_fbank_features(lowerCAmelCase__ ,self.mel_filters_slaney )[None, :]
return input_mel, longer
def __call__( self : int ,lowerCAmelCase__ : Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]] ,lowerCAmelCase__ : str = None ,lowerCAmelCase__ : Optional[str] = None ,lowerCAmelCase__ : Optional[int] = None ,lowerCAmelCase__ : Optional[int] = None ,lowerCAmelCase__ : Optional[Union[str, TensorType]] = None ,**lowerCAmelCase__ : List[Any] ,) -> BatchFeature:
'''simple docstring'''
lowerCAmelCase_ : List[str] = truncation if truncation is not None else self.truncation
lowerCAmelCase_ : List[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." )
lowerCAmelCase_ : Dict = isinstance(lowerCAmelCase__ ,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}''' )
lowerCAmelCase_ : Dict = is_batched_numpy or (
isinstance(lowerCAmelCase__ ,(list, tuple) ) and (isinstance(raw_speech[0] ,(np.ndarray, tuple, list) ))
)
if is_batched:
lowerCAmelCase_ : List[str] = [np.asarray(lowerCAmelCase__ ,dtype=np.floataa ) for speech in raw_speech]
elif not is_batched and not isinstance(lowerCAmelCase__ ,np.ndarray ):
lowerCAmelCase_ : Tuple = np.asarray(lowerCAmelCase__ ,dtype=np.floataa )
elif isinstance(lowerCAmelCase__ ,np.ndarray ) and raw_speech.dtype is np.dtype(np.floataa ):
lowerCAmelCase_ : Any = raw_speech.astype(np.floataa )
# always return batch
if not is_batched:
lowerCAmelCase_ : Any = [np.asarray(lowerCAmelCase__ )]
# convert to mel spectrogram, truncate and pad if needed.
lowerCAmelCase_ : Optional[Any] = [
self._get_input_mel(lowerCAmelCase__ ,max_length if max_length else self.nb_max_samples ,lowerCAmelCase__ ,lowerCAmelCase__ )
for waveform in raw_speech
]
lowerCAmelCase_ : str = []
lowerCAmelCase_ : str = []
for mel, longer in padded_inputs:
input_mel.append(lowerCAmelCase__ )
is_longer.append(lowerCAmelCase__ )
if truncation == "fusion" and sum(lowerCAmelCase__ ) == 0:
# if no audio is longer than 10s, then randomly select one audio to be longer
lowerCAmelCase_ : Any = np.random.randint(0 ,len(lowerCAmelCase__ ) )
lowerCAmelCase_ : Dict = True
if isinstance(input_mel[0] ,lowerCAmelCase__ ):
lowerCAmelCase_ : Optional[int] = [np.asarray(lowerCAmelCase__ ,dtype=np.floataa ) for feature in input_mel]
# is_longer is a list of bool
lowerCAmelCase_ : List[Any] = [[longer] for longer in is_longer]
lowerCAmelCase_ : Optional[Any] = {"input_features": input_mel, "is_longer": is_longer}
lowerCAmelCase_ : Dict = BatchFeature(lowerCAmelCase__ )
if return_tensors is not None:
lowerCAmelCase_ : List[str] = input_features.convert_to_tensors(lowerCAmelCase__ )
return input_features
| 683 | 1 |
from typing import List, Union
from ..utils import (
add_end_docstrings,
is_tf_available,
is_torch_available,
is_vision_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_image
if is_tf_available():
import tensorflow as tf
from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
from ..tf_utils import stable_softmax
if is_torch_available():
from ..models.auto.modeling_auto import MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
_lowercase = logging.get_logger(__name__)
@add_end_docstrings(snake_case__ )
class __snake_case ( snake_case__ ):
"""simple docstring"""
def __init__( self : List[Any] ,*lowerCAmelCase__ : List[Any] ,**lowerCAmelCase__ : str ) -> Tuple:
'''simple docstring'''
super().__init__(*lowerCAmelCase__ ,**lowerCAmelCase__ )
requires_backends(self ,"vision" )
self.check_model_type(
TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
if self.framework == "tf"
else MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING )
def UpperCAmelCase_ ( self : List[str] ,lowerCAmelCase__ : int=None ) -> int:
'''simple docstring'''
lowerCAmelCase_ : Any = {}
if top_k is not None:
lowerCAmelCase_ : Optional[int] = top_k
return {}, {}, postprocess_params
def __call__( self : List[str] ,lowerCAmelCase__ : Union[str, List[str], "Image.Image", List["Image.Image"]] ,**lowerCAmelCase__ : Dict ) -> Union[str, Any]:
'''simple docstring'''
return super().__call__(lowerCAmelCase__ ,**lowerCAmelCase__ )
def UpperCAmelCase_ ( self : Any ,lowerCAmelCase__ : Tuple ) -> Tuple:
'''simple docstring'''
lowerCAmelCase_ : int = load_image(lowerCAmelCase__ )
lowerCAmelCase_ : int = self.image_processor(images=lowerCAmelCase__ ,return_tensors=self.framework )
return model_inputs
def UpperCAmelCase_ ( self : Dict ,lowerCAmelCase__ : List[str] ) -> List[Any]:
'''simple docstring'''
lowerCAmelCase_ : Optional[Any] = self.model(**lowerCAmelCase__ )
return model_outputs
def UpperCAmelCase_ ( self : int ,lowerCAmelCase__ : List[Any] ,lowerCAmelCase__ : str=5 ) -> Optional[Any]:
'''simple docstring'''
if top_k > self.model.config.num_labels:
lowerCAmelCase_ : List[str] = self.model.config.num_labels
if self.framework == "pt":
lowerCAmelCase_ : int = model_outputs.logits.softmax(-1 )[0]
lowerCAmelCase_ , lowerCAmelCase_ : List[str] = probs.topk(lowerCAmelCase__ )
elif self.framework == "tf":
lowerCAmelCase_ : Any = stable_softmax(model_outputs.logits ,axis=-1 )[0]
lowerCAmelCase_ : List[str] = tf.math.top_k(lowerCAmelCase__ ,k=lowerCAmelCase__ )
lowerCAmelCase_ , lowerCAmelCase_ : List[str] = topk.values.numpy(), topk.indices.numpy()
else:
raise ValueError(f'''Unsupported framework: {self.framework}''' )
lowerCAmelCase_ : int = scores.tolist()
lowerCAmelCase_ : Tuple = ids.tolist()
return [{"score": score, "label": self.model.config.idalabel[_id]} for score, _id in zip(lowerCAmelCase__ ,lowerCAmelCase__ )]
| 683 |
from multiprocessing import Lock, Pipe, Process
# lock used to ensure that two processes do not access a pipe at the same time
_lowercase = Lock()
def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__):
global process_lock
# we perform n swaps since after n swaps we know we are sorted
# we *could* stop early if we are sorted already, but it takes as long to
# find out we are sorted as it does to sort the list with this algorithm
for i in range(0 , 10):
if (i + position) % 2 == 0 and r_send is not None:
# send your value to your right neighbor
process_lock.acquire()
r_send[1].send(snake_case__)
process_lock.release()
# receive your right neighbor's value
process_lock.acquire()
lowerCAmelCase_ : Optional[Any] = rr_cv[0].recv()
process_lock.release()
# take the lower value since you are on the left
lowerCAmelCase_ : Any = min(snake_case__ , snake_case__)
elif (i + position) % 2 != 0 and l_send is not None:
# send your value to your left neighbor
process_lock.acquire()
l_send[1].send(snake_case__)
process_lock.release()
# receive your left neighbor's value
process_lock.acquire()
lowerCAmelCase_ : str = lr_cv[0].recv()
process_lock.release()
# take the higher value since you are on the right
lowerCAmelCase_ : Dict = max(snake_case__ , snake_case__)
# after all swaps are performed, send the values back to main
result_pipe[1].send(snake_case__)
def UpperCamelCase ( snake_case__):
lowerCAmelCase_ : Union[str, Any] = []
lowerCAmelCase_ : int = []
# initialize the list of pipes where the values will be retrieved
for _ in arr:
result_pipe.append(Pipe())
# creates the processes
# the first and last process only have one neighbor so they are made outside
# of the loop
lowerCAmelCase_ : Tuple = Pipe()
lowerCAmelCase_ : Optional[int] = Pipe()
process_array_.append(
Process(
target=snake_case__ , args=(0, arr[0], None, temp_rs, None, temp_rr, result_pipe[0]) , ))
lowerCAmelCase_ : int = temp_rs
lowerCAmelCase_ : List[Any] = temp_rr
for i in range(1 , len(snake_case__) - 1):
lowerCAmelCase_ : Dict = Pipe()
lowerCAmelCase_ : List[str] = Pipe()
process_array_.append(
Process(
target=snake_case__ , args=(i, arr[i], temp_ls, temp_rs, temp_lr, temp_rr, result_pipe[i]) , ))
lowerCAmelCase_ : Dict = temp_rs
lowerCAmelCase_ : Optional[Any] = temp_rr
process_array_.append(
Process(
target=snake_case__ , args=(
len(snake_case__) - 1,
arr[len(snake_case__) - 1],
temp_ls,
None,
temp_lr,
None,
result_pipe[len(snake_case__) - 1],
) , ))
# start the processes
for p in process_array_:
p.start()
# wait for the processes to end and write their values to the list
for p in range(0 , len(snake_case__)):
lowerCAmelCase_ : Union[str, Any] = result_pipe[p][0].recv()
process_array_[p].join()
return arr
def UpperCamelCase ( ):
lowerCAmelCase_ : Optional[Any] = list(range(10 , 0 , -1))
print("Initial List")
print(*snake_case__)
lowerCAmelCase_ : Tuple = odd_even_transposition(snake_case__)
print("Sorted List\n")
print(*snake_case__)
if __name__ == "__main__":
main()
| 683 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
_lowercase = {
'''configuration_blip''': [
'''BLIP_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''BlipConfig''',
'''BlipTextConfig''',
'''BlipVisionConfig''',
],
'''processing_blip''': ['''BlipProcessor'''],
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowercase = ['''BlipImageProcessor''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowercase = [
'''BLIP_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''BlipModel''',
'''BlipPreTrainedModel''',
'''BlipForConditionalGeneration''',
'''BlipForQuestionAnswering''',
'''BlipVisionModel''',
'''BlipTextModel''',
'''BlipForImageTextRetrieval''',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowercase = [
'''TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''TFBlipModel''',
'''TFBlipPreTrainedModel''',
'''TFBlipForConditionalGeneration''',
'''TFBlipForQuestionAnswering''',
'''TFBlipVisionModel''',
'''TFBlipTextModel''',
'''TFBlipForImageTextRetrieval''',
]
if TYPE_CHECKING:
from .configuration_blip import BLIP_PRETRAINED_CONFIG_ARCHIVE_MAP, BlipConfig, BlipTextConfig, BlipVisionConfig
from .processing_blip import BlipProcessor
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .image_processing_blip import BlipImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_blip import (
BLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
BlipForConditionalGeneration,
BlipForImageTextRetrieval,
BlipForQuestionAnswering,
BlipModel,
BlipPreTrainedModel,
BlipTextModel,
BlipVisionModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_blip import (
TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
TFBlipForConditionalGeneration,
TFBlipForImageTextRetrieval,
TFBlipForQuestionAnswering,
TFBlipModel,
TFBlipPreTrainedModel,
TFBlipTextModel,
TFBlipVisionModel,
)
else:
import sys
_lowercase = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 683 |
from typing import Any
def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ , ):
_validation(
snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ , )
# Creates data structures and fill initial step
lowerCAmelCase_ : dict = {}
lowerCAmelCase_ : dict = {}
for state in states_space:
lowerCAmelCase_ : List[Any] = observations_space[0]
lowerCAmelCase_ : int = (
initial_probabilities[state] * emission_probabilities[state][observation]
)
lowerCAmelCase_ : Dict = None
# Fills the data structure with the probabilities of
# different transitions and pointers to previous states
for o in range(1 , len(snake_case__)):
lowerCAmelCase_ : List[Any] = observations_space[o]
lowerCAmelCase_ : Optional[Any] = observations_space[o - 1]
for state in states_space:
# Calculates the argmax for probability function
lowerCAmelCase_ : List[Any] = ""
lowerCAmelCase_ : Tuple = -1
for k_state in states_space:
lowerCAmelCase_ : int = (
probabilities[(k_state, prior_observation)]
* transition_probabilities[k_state][state]
* emission_probabilities[state][observation]
)
if probability > max_probability:
lowerCAmelCase_ : List[str] = probability
lowerCAmelCase_ : Optional[Any] = k_state
# Update probabilities and pointers dicts
lowerCAmelCase_ : Union[str, Any] = (
probabilities[(arg_max, prior_observation)]
* transition_probabilities[arg_max][state]
* emission_probabilities[state][observation]
)
lowerCAmelCase_ : Any = arg_max
# The final observation
lowerCAmelCase_ : List[Any] = observations_space[len(snake_case__) - 1]
# argmax for given final observation
lowerCAmelCase_ : List[str] = ""
lowerCAmelCase_ : List[str] = -1
for k_state in states_space:
lowerCAmelCase_ : List[str] = probabilities[(k_state, final_observation)]
if probability > max_probability:
lowerCAmelCase_ : List[str] = probability
lowerCAmelCase_ : Tuple = k_state
lowerCAmelCase_ : str = arg_max
# Process pointers backwards
lowerCAmelCase_ : int = last_state
lowerCAmelCase_ : int = []
for o in range(len(snake_case__) - 1 , -1 , -1):
result.append(snake_case__)
lowerCAmelCase_ : Optional[Any] = pointers[previous, observations_space[o]]
result.reverse()
return result
def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ , ):
_validate_not_empty(
snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ , )
_validate_lists(snake_case__ , snake_case__)
_validate_dicts(
snake_case__ , snake_case__ , snake_case__)
def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ , ):
if not all(
[
observations_space,
states_space,
initial_probabilities,
transition_probabilities,
emission_probabilities,
]):
raise ValueError("There's an empty parameter")
def UpperCamelCase ( snake_case__ , snake_case__):
_validate_list(snake_case__ , "observations_space")
_validate_list(snake_case__ , "states_space")
def UpperCamelCase ( snake_case__ , snake_case__):
if not isinstance(_object , snake_case__):
lowerCAmelCase_ : Optional[Any] = F'''{var_name} must be a list'''
raise ValueError(snake_case__)
else:
for x in _object:
if not isinstance(snake_case__ , snake_case__):
lowerCAmelCase_ : Optional[Any] = F'''{var_name} must be a list of strings'''
raise ValueError(snake_case__)
def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__ , ):
_validate_dict(snake_case__ , "initial_probabilities" , snake_case__)
_validate_nested_dict(snake_case__ , "transition_probabilities")
_validate_nested_dict(snake_case__ , "emission_probabilities")
def UpperCamelCase ( snake_case__ , snake_case__):
_validate_dict(_object , snake_case__ , snake_case__)
for x in _object.values():
_validate_dict(snake_case__ , snake_case__ , snake_case__ , snake_case__)
def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__ , snake_case__ = False):
if not isinstance(_object , snake_case__):
lowerCAmelCase_ : List[str] = F'''{var_name} must be a dict'''
raise ValueError(snake_case__)
if not all(isinstance(snake_case__ , snake_case__) for x in _object):
lowerCAmelCase_ : Dict = F'''{var_name} all keys must be strings'''
raise ValueError(snake_case__)
if not all(isinstance(snake_case__ , snake_case__) for x in _object.values()):
lowerCAmelCase_ : Union[str, Any] = "nested dictionary " if nested else ""
lowerCAmelCase_ : Any = F'''{var_name} {nested_text}all values must be {value_type.__name__}'''
raise ValueError(snake_case__)
if __name__ == "__main__":
from doctest import testmod
testmod()
| 683 | 1 |
import copy
import inspect
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
from transformers import TimesformerConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, 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, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import (
MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING,
TimesformerForVideoClassification,
TimesformerModel,
)
from transformers.models.timesformer.modeling_timesformer import TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from transformers import VideoMAEImageProcessor
class __snake_case :
"""simple docstring"""
def __init__( self : Optional[int] ,lowerCAmelCase__ : Union[str, Any] ,lowerCAmelCase__ : str=13 ,lowerCAmelCase__ : List[Any]=10 ,lowerCAmelCase__ : Optional[int]=3 ,lowerCAmelCase__ : Optional[Any]=2 ,lowerCAmelCase__ : Optional[int]=2 ,lowerCAmelCase__ : Dict=True ,lowerCAmelCase__ : Optional[int]=True ,lowerCAmelCase__ : Any=32 ,lowerCAmelCase__ : Union[str, Any]=5 ,lowerCAmelCase__ : Dict=4 ,lowerCAmelCase__ : Optional[int]=37 ,lowerCAmelCase__ : str="gelu" ,lowerCAmelCase__ : int=0.1 ,lowerCAmelCase__ : int=0.1 ,lowerCAmelCase__ : List[Any]=10 ,lowerCAmelCase__ : int=0.02 ,lowerCAmelCase__ : str="divided_space_time" ,lowerCAmelCase__ : int=None ,) -> Union[str, Any]:
'''simple docstring'''
lowerCAmelCase_ : Optional[Any] = parent
lowerCAmelCase_ : List[Any] = batch_size
lowerCAmelCase_ : Optional[int] = image_size
lowerCAmelCase_ : int = num_channels
lowerCAmelCase_ : Optional[int] = patch_size
lowerCAmelCase_ : Any = num_frames
lowerCAmelCase_ : str = is_training
lowerCAmelCase_ : Union[str, Any] = use_labels
lowerCAmelCase_ : List[str] = hidden_size
lowerCAmelCase_ : Optional[Any] = num_hidden_layers
lowerCAmelCase_ : List[str] = num_attention_heads
lowerCAmelCase_ : List[str] = intermediate_size
lowerCAmelCase_ : Union[str, Any] = hidden_act
lowerCAmelCase_ : Optional[int] = hidden_dropout_prob
lowerCAmelCase_ : Tuple = attention_probs_dropout_prob
lowerCAmelCase_ : Optional[int] = attention_type
lowerCAmelCase_ : Tuple = initializer_range
lowerCAmelCase_ : Union[str, Any] = scope
lowerCAmelCase_ : Any = num_labels
# in TimeSformer, the number of spatial tokens equals num_frames * num_patches per frame + 1 CLS token
lowerCAmelCase_ : List[str] = (image_size // patch_size) ** 2
lowerCAmelCase_ : int = (num_frames) * self.num_patches_per_frame + 1
def UpperCAmelCase_ ( self : int ) -> List[Any]:
'''simple docstring'''
lowerCAmelCase_ : List[Any] = floats_tensor(
[self.batch_size, self.num_frames, self.num_channels, self.image_size, self.image_size] )
lowerCAmelCase_ : str = None
if self.use_labels:
lowerCAmelCase_ : Tuple = ids_tensor([self.batch_size] ,self.num_labels )
lowerCAmelCase_ : Optional[int] = self.get_config()
return config, pixel_values, labels
def UpperCAmelCase_ ( self : str ) -> str:
'''simple docstring'''
lowerCAmelCase_ : Optional[Any] = TimesformerConfig(
image_size=self.image_size ,patch_size=self.patch_size ,num_channels=self.num_channels ,num_frames=self.num_frames ,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 ,initializer_range=self.initializer_range ,attention_type=self.attention_type ,)
lowerCAmelCase_ : List[Any] = self.num_labels
return config
def UpperCAmelCase_ ( self : Any ,lowerCAmelCase__ : Union[str, Any] ,lowerCAmelCase__ : Any ,lowerCAmelCase__ : Any ) -> List[str]:
'''simple docstring'''
lowerCAmelCase_ : Union[str, Any] = TimesformerModel(config=lowerCAmelCase__ )
model.to(lowerCAmelCase__ )
model.eval()
lowerCAmelCase_ : List[Any] = model(lowerCAmelCase__ )
self.parent.assertEqual(result.last_hidden_state.shape ,(self.batch_size, self.seq_length, self.hidden_size) )
def UpperCAmelCase_ ( self : List[Any] ,lowerCAmelCase__ : Optional[Any] ,lowerCAmelCase__ : List[Any] ,lowerCAmelCase__ : int ) -> Optional[int]:
'''simple docstring'''
lowerCAmelCase_ : Any = TimesformerForVideoClassification(lowerCAmelCase__ )
model.to(lowerCAmelCase__ )
model.eval()
lowerCAmelCase_ : Tuple = model(lowerCAmelCase__ )
# verify the logits shape
lowerCAmelCase_ : int = torch.Size((self.batch_size, self.num_labels) )
self.parent.assertEqual(result.logits.shape ,lowerCAmelCase__ )
def UpperCAmelCase_ ( self : Any ) -> List[str]:
'''simple docstring'''
lowerCAmelCase_ : Any = self.prepare_config_and_inputs()
lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ : Union[str, Any] = config_and_inputs
lowerCAmelCase_ : Any = {"pixel_values": pixel_values}
return config, inputs_dict
@require_torch
class __snake_case ( snake_case__ , snake_case__ , unittest.TestCase ):
"""simple docstring"""
UpperCamelCase_ = (TimesformerModel, TimesformerForVideoClassification) if is_torch_available() else ()
UpperCamelCase_ = (
{'feature-extraction': TimesformerModel, 'video-classification': TimesformerForVideoClassification}
if is_torch_available()
else {}
)
UpperCamelCase_ = False
UpperCamelCase_ = False
UpperCamelCase_ = False
UpperCamelCase_ = False
def UpperCAmelCase_ ( self : List[Any] ) -> str:
'''simple docstring'''
lowerCAmelCase_ : str = TimesformerModelTester(self )
lowerCAmelCase_ : Dict = ConfigTester(
self ,config_class=lowerCAmelCase__ ,has_text_modality=lowerCAmelCase__ ,hidden_size=37 )
def UpperCAmelCase_ ( self : str ,lowerCAmelCase__ : str ,lowerCAmelCase__ : Tuple ,lowerCAmelCase__ : Dict=False ) -> List[Any]:
'''simple docstring'''
lowerCAmelCase_ : List[str] = copy.deepcopy(lowerCAmelCase__ )
if return_labels:
if model_class in get_values(lowerCAmelCase__ ):
lowerCAmelCase_ : Tuple = torch.zeros(
self.model_tester.batch_size ,dtype=torch.long ,device=lowerCAmelCase__ )
return inputs_dict
def UpperCAmelCase_ ( self : Dict ) -> Optional[int]:
'''simple docstring'''
self.config_tester.run_common_tests()
@unittest.skip(reason="TimeSformer does not use inputs_embeds" )
def UpperCAmelCase_ ( self : Dict ) -> Optional[int]:
'''simple docstring'''
pass
def UpperCAmelCase_ ( self : Tuple ) -> Optional[int]:
'''simple docstring'''
lowerCAmelCase_ , lowerCAmelCase_ : List[str] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowerCAmelCase_ : List[str] = model_class(lowerCAmelCase__ )
self.assertIsInstance(model.get_input_embeddings() ,(nn.Module) )
lowerCAmelCase_ : int = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(lowerCAmelCase__ ,nn.Linear ) )
def UpperCAmelCase_ ( self : List[Any] ) -> List[str]:
'''simple docstring'''
lowerCAmelCase_ , lowerCAmelCase_ : Union[str, Any] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowerCAmelCase_ : Tuple = model_class(lowerCAmelCase__ )
lowerCAmelCase_ : List[str] = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
lowerCAmelCase_ : Optional[Any] = [*signature.parameters.keys()]
lowerCAmelCase_ : List[Any] = ["pixel_values"]
self.assertListEqual(arg_names[:1] ,lowerCAmelCase__ )
def UpperCAmelCase_ ( self : Any ) -> List[str]:
'''simple docstring'''
lowerCAmelCase_ : int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*lowerCAmelCase__ )
def UpperCAmelCase_ ( self : Tuple ) -> List[str]:
'''simple docstring'''
lowerCAmelCase_ : List[str] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_video_classification(*lowerCAmelCase__ )
@slow
def UpperCAmelCase_ ( self : str ) -> int:
'''simple docstring'''
for model_name in TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowerCAmelCase_ : List[str] = TimesformerModel.from_pretrained(lowerCAmelCase__ )
self.assertIsNotNone(lowerCAmelCase__ )
def UpperCAmelCase_ ( self : Optional[int] ) -> Optional[Any]:
'''simple docstring'''
if not self.has_attentions:
pass
else:
lowerCAmelCase_ , lowerCAmelCase_ : Optional[int] = self.model_tester.prepare_config_and_inputs_for_common()
lowerCAmelCase_ : Union[str, Any] = True
for model_class in self.all_model_classes:
lowerCAmelCase_ : Optional[int] = self.model_tester.seq_length
lowerCAmelCase_ : Optional[Any] = self.model_tester.num_frames
lowerCAmelCase_ : Any = True
lowerCAmelCase_ : List[Any] = False
lowerCAmelCase_ : List[str] = True
lowerCAmelCase_ : Optional[Any] = model_class(lowerCAmelCase__ )
model.to(lowerCAmelCase__ )
model.eval()
with torch.no_grad():
lowerCAmelCase_ : List[str] = model(**self._prepare_for_class(lowerCAmelCase__ ,lowerCAmelCase__ ) )
lowerCAmelCase_ : str = outputs.attentions
self.assertEqual(len(lowerCAmelCase__ ) ,self.model_tester.num_hidden_layers )
# check that output_attentions also work using config
del inputs_dict["output_attentions"]
lowerCAmelCase_ : Union[str, Any] = True
lowerCAmelCase_ : Optional[int] = model_class(lowerCAmelCase__ )
model.to(lowerCAmelCase__ )
model.eval()
with torch.no_grad():
lowerCAmelCase_ : Optional[Any] = model(**self._prepare_for_class(lowerCAmelCase__ ,lowerCAmelCase__ ) )
lowerCAmelCase_ : Dict = outputs.attentions
self.assertEqual(len(lowerCAmelCase__ ) ,self.model_tester.num_hidden_layers )
# attentions has shape (batch_size x num_frames) x num_heads x (num_patches per frame + 1) x (num_patches per frame + 1)
self.assertListEqual(
list(attentions[0].shape[-3:] ) ,[self.model_tester.num_attention_heads, seq_len // num_frames + 1, seq_len // num_frames + 1] ,)
lowerCAmelCase_ : List[str] = len(lowerCAmelCase__ )
# Check attention is always last and order is fine
lowerCAmelCase_ : List[Any] = True
lowerCAmelCase_ : List[Any] = True
lowerCAmelCase_ : Optional[Any] = model_class(lowerCAmelCase__ )
model.to(lowerCAmelCase__ )
model.eval()
with torch.no_grad():
lowerCAmelCase_ : Dict = model(**self._prepare_for_class(lowerCAmelCase__ ,lowerCAmelCase__ ) )
self.assertEqual(out_len + 1 ,len(lowerCAmelCase__ ) )
lowerCAmelCase_ : Dict = outputs.attentions
self.assertEqual(len(lowerCAmelCase__ ) ,self.model_tester.num_hidden_layers )
# attentions has shape (batch_size x num_frames) x num_heads x (num_patches per frame + 1) x (num_patches per frame + 1)
self.assertListEqual(
list(self_attentions[0].shape[-3:] ) ,[self.model_tester.num_attention_heads, seq_len // num_frames + 1, seq_len // num_frames + 1] ,)
def UpperCAmelCase_ ( self : List[Any] ) -> Dict:
'''simple docstring'''
def check_hidden_states_output(lowerCAmelCase__ : Union[str, Any] ,lowerCAmelCase__ : List[str] ,lowerCAmelCase__ : Optional[Any] ):
lowerCAmelCase_ : Tuple = model_class(lowerCAmelCase__ )
model.to(lowerCAmelCase__ )
model.eval()
with torch.no_grad():
lowerCAmelCase_ : Union[str, Any] = model(**self._prepare_for_class(lowerCAmelCase__ ,lowerCAmelCase__ ) )
lowerCAmelCase_ : List[Any] = outputs.hidden_states
lowerCAmelCase_ : Tuple = self.model_tester.num_hidden_layers + 1
self.assertEqual(len(lowerCAmelCase__ ) ,lowerCAmelCase__ )
lowerCAmelCase_ : int = self.model_tester.seq_length
self.assertListEqual(
list(hidden_states[0].shape[-2:] ) ,[seq_length, self.model_tester.hidden_size] ,)
lowerCAmelCase_ , lowerCAmelCase_ : Any = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowerCAmelCase_ : List[str] = True
check_hidden_states_output(lowerCAmelCase__ ,lowerCAmelCase__ ,lowerCAmelCase__ )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
lowerCAmelCase_ : Dict = True
check_hidden_states_output(lowerCAmelCase__ ,lowerCAmelCase__ ,lowerCAmelCase__ )
def UpperCamelCase ( ):
lowerCAmelCase_ : Dict = hf_hub_download(
repo_id="hf-internal-testing/spaghetti-video" , filename="eating_spaghetti.npy" , repo_type="dataset")
lowerCAmelCase_ : Any = np.load(snake_case__)
return list(snake_case__)
@require_torch
@require_vision
class __snake_case ( unittest.TestCase ):
"""simple docstring"""
@cached_property
def UpperCAmelCase_ ( self : Any ) -> List[Any]:
'''simple docstring'''
return (
VideoMAEImageProcessor(image_mean=[0.5, 0.5, 0.5] ,image_std=[0.5, 0.5, 0.5] )
if is_vision_available()
else None
)
@slow
def UpperCAmelCase_ ( self : Dict ) -> int:
'''simple docstring'''
lowerCAmelCase_ : Optional[Any] = TimesformerForVideoClassification.from_pretrained("facebook/timesformer-base-finetuned-k400" ).to(
lowerCAmelCase__ )
lowerCAmelCase_ : Dict = self.default_image_processor
lowerCAmelCase_ : str = prepare_video()
lowerCAmelCase_ : int = image_processor(video[:8] ,return_tensors="pt" ).to(lowerCAmelCase__ )
# forward pass
with torch.no_grad():
lowerCAmelCase_ : int = model(**lowerCAmelCase__ )
# verify the logits
lowerCAmelCase_ : int = torch.Size((1, 4_00) )
self.assertEqual(outputs.logits.shape ,lowerCAmelCase__ )
lowerCAmelCase_ : List[Any] = torch.tensor([-0.3_016, -0.7_713, -0.4_205] ).to(lowerCAmelCase__ )
self.assertTrue(torch.allclose(outputs.logits[0, :3] ,lowerCAmelCase__ ,atol=1e-4 ) )
| 683 |
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import torch
from ..models.speechta import SpeechTaForTextToSpeech, SpeechTaHifiGan, SpeechTaProcessor
from ..utils import is_datasets_available
from .base import PipelineTool
if is_datasets_available():
from datasets import load_dataset
class __snake_case ( snake_case__ ):
"""simple docstring"""
UpperCamelCase_ = 'microsoft/speecht5_tts'
UpperCamelCase_ = (
'This is a tool that reads an English text out loud. It takes an input named `text` which should contain the '
'text to read (in English) and returns a waveform object containing the sound.'
)
UpperCamelCase_ = 'text_reader'
UpperCamelCase_ = SpeechTaProcessor
UpperCamelCase_ = SpeechTaForTextToSpeech
UpperCamelCase_ = SpeechTaHifiGan
UpperCamelCase_ = ['text']
UpperCamelCase_ = ['audio']
def UpperCAmelCase_ ( self : Dict ) -> Any:
'''simple docstring'''
if self.post_processor is None:
lowerCAmelCase_ : Any = "microsoft/speecht5_hifigan"
super().setup()
def UpperCAmelCase_ ( self : List[Any] ,lowerCAmelCase__ : Optional[int] ,lowerCAmelCase__ : Optional[int]=None ) -> Tuple:
'''simple docstring'''
lowerCAmelCase_ : Any = self.pre_processor(text=lowerCAmelCase__ ,return_tensors="pt" ,truncation=lowerCAmelCase__ )
if speaker_embeddings is None:
if not is_datasets_available():
raise ImportError("Datasets needs to be installed if not passing speaker embeddings." )
lowerCAmelCase_ : str = load_dataset("Matthijs/cmu-arctic-xvectors" ,split="validation" )
lowerCAmelCase_ : List[Any] = torch.tensor(embeddings_dataset[73_05]["xvector"] ).unsqueeze(0 )
return {"input_ids": inputs["input_ids"], "speaker_embeddings": speaker_embeddings}
def UpperCAmelCase_ ( self : Dict ,lowerCAmelCase__ : Union[str, Any] ) -> Union[str, Any]:
'''simple docstring'''
with torch.no_grad():
return self.model.generate_speech(**lowerCAmelCase__ )
def UpperCAmelCase_ ( self : Union[str, Any] ,lowerCAmelCase__ : str ) -> Any:
'''simple docstring'''
with torch.no_grad():
return self.post_processor(lowerCAmelCase__ ).cpu().detach()
| 683 | 1 |
import qiskit
def UpperCamelCase ( snake_case__ , snake_case__):
lowerCAmelCase_ : List[Any] = qiskit.Aer.get_backend("aer_simulator")
# Create a Quantum Circuit acting on the q register
lowerCAmelCase_ : Tuple = qiskit.QuantumCircuit(snake_case__ , snake_case__)
# Apply X (NOT) Gate to Qubits 0 & 1
circuit.x(0)
circuit.x(1)
# Map the quantum measurement to the classical bits
circuit.measure([0, 1] , [0, 1])
# Execute the circuit on the qasm simulator
lowerCAmelCase_ : Tuple = qiskit.execute(snake_case__ , snake_case__ , shots=10_00)
# Return the histogram data of the results of the experiment.
return job.result().get_counts(snake_case__)
if __name__ == "__main__":
_lowercase = single_qubit_measure(2, 2)
print(f"Total count for various states are: {counts}")
| 683 |
import argparse
import collections
import json
import os
import re
import string
import sys
import numpy as np
_lowercase = re.compile(r'''\b(a|an|the)\b''', re.UNICODE)
_lowercase = None
def UpperCamelCase ( ):
lowerCAmelCase_ : Optional[Any] = argparse.ArgumentParser("Official evaluation script for SQuAD version 2.0.")
parser.add_argument("data_file" , metavar="data.json" , help="Input data JSON file.")
parser.add_argument("pred_file" , metavar="pred.json" , help="Model predictions.")
parser.add_argument(
"--out-file" , "-o" , metavar="eval.json" , help="Write accuracy metrics to file (default is stdout).")
parser.add_argument(
"--na-prob-file" , "-n" , metavar="na_prob.json" , help="Model estimates of probability of no answer.")
parser.add_argument(
"--na-prob-thresh" , "-t" , type=snake_case__ , default=1.0 , help="Predict \"\" if no-answer probability exceeds this (default = 1.0)." , )
parser.add_argument(
"--out-image-dir" , "-p" , metavar="out_images" , default=snake_case__ , help="Save precision-recall curves to directory.")
parser.add_argument("--verbose" , "-v" , action="store_true")
if len(sys.argv) == 1:
parser.print_help()
sys.exit(1)
return parser.parse_args()
def UpperCamelCase ( snake_case__):
lowerCAmelCase_ : str = {}
for article in dataset:
for p in article["paragraphs"]:
for qa in p["qas"]:
lowerCAmelCase_ : Dict = bool(qa["answers"]["text"])
return qid_to_has_ans
def UpperCamelCase ( snake_case__):
def remove_articles(snake_case__):
return ARTICLES_REGEX.sub(" " , snake_case__)
def white_space_fix(snake_case__):
return " ".join(text.split())
def remove_punc(snake_case__):
lowerCAmelCase_ : Optional[int] = set(string.punctuation)
return "".join(ch for ch in text if ch not in exclude)
def lower(snake_case__):
return text.lower()
return white_space_fix(remove_articles(remove_punc(lower(snake_case__))))
def UpperCamelCase ( snake_case__):
if not s:
return []
return normalize_answer(snake_case__).split()
def UpperCamelCase ( snake_case__ , snake_case__):
return int(normalize_answer(snake_case__) == normalize_answer(snake_case__))
def UpperCamelCase ( snake_case__ , snake_case__):
lowerCAmelCase_ : Optional[int] = get_tokens(snake_case__)
lowerCAmelCase_ : Union[str, Any] = get_tokens(snake_case__)
lowerCAmelCase_ : Any = collections.Counter(snake_case__) & collections.Counter(snake_case__)
lowerCAmelCase_ : Dict = sum(common.values())
if len(snake_case__) == 0 or len(snake_case__) == 0:
# If either is no-answer, then F1 is 1 if they agree, 0 otherwise
return int(gold_toks == pred_toks)
if num_same == 0:
return 0
lowerCAmelCase_ : List[Any] = 1.0 * num_same / len(snake_case__)
lowerCAmelCase_ : int = 1.0 * num_same / len(snake_case__)
lowerCAmelCase_ : List[Any] = (2 * precision * recall) / (precision + recall)
return fa
def UpperCamelCase ( snake_case__ , snake_case__):
lowerCAmelCase_ : Tuple = {}
lowerCAmelCase_ : int = {}
for article in dataset:
for p in article["paragraphs"]:
for qa in p["qas"]:
lowerCAmelCase_ : int = qa["id"]
lowerCAmelCase_ : Any = [t for t in qa["answers"]["text"] if normalize_answer(snake_case__)]
if not gold_answers:
# For unanswerable questions, only correct answer is empty string
lowerCAmelCase_ : Any = [""]
if qid not in preds:
print(F'''Missing prediction for {qid}''')
continue
lowerCAmelCase_ : Tuple = preds[qid]
# Take max over all gold answers
lowerCAmelCase_ : Any = max(compute_exact(snake_case__ , snake_case__) for a in gold_answers)
lowerCAmelCase_ : Optional[Any] = max(compute_fa(snake_case__ , snake_case__) for a in gold_answers)
return exact_scores, fa_scores
def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__ , snake_case__):
lowerCAmelCase_ : Dict = {}
for qid, s in scores.items():
lowerCAmelCase_ : List[Any] = na_probs[qid] > na_prob_thresh
if pred_na:
lowerCAmelCase_ : List[str] = float(not qid_to_has_ans[qid])
else:
lowerCAmelCase_ : Union[str, Any] = s
return new_scores
def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__=None):
if not qid_list:
lowerCAmelCase_ : Any = len(snake_case__)
return collections.OrderedDict(
[
("exact", 100.0 * sum(exact_scores.values()) / total),
("f1", 100.0 * sum(fa_scores.values()) / total),
("total", total),
])
else:
lowerCAmelCase_ : Tuple = len(snake_case__)
return collections.OrderedDict(
[
("exact", 100.0 * sum(exact_scores[k] for k in qid_list) / total),
("f1", 100.0 * sum(fa_scores[k] for k in qid_list) / total),
("total", total),
])
def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__):
for k in new_eval:
lowerCAmelCase_ : Union[str, Any] = new_eval[k]
def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__ , snake_case__):
plt.step(snake_case__ , snake_case__ , color="b" , alpha=0.2 , where="post")
plt.fill_between(snake_case__ , snake_case__ , step="post" , alpha=0.2 , color="b")
plt.xlabel("Recall")
plt.ylabel("Precision")
plt.xlim([0.0, 1.05])
plt.ylim([0.0, 1.05])
plt.title(snake_case__)
plt.savefig(snake_case__)
plt.clf()
def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__=None , snake_case__=None):
lowerCAmelCase_ : List[Any] = sorted(snake_case__ , key=lambda snake_case__: na_probs[k])
lowerCAmelCase_ : Dict = 0.0
lowerCAmelCase_ : int = 1.0
lowerCAmelCase_ : List[str] = 0.0
lowerCAmelCase_ : Tuple = [1.0]
lowerCAmelCase_ : Tuple = [0.0]
lowerCAmelCase_ : Dict = 0.0
for i, qid in enumerate(snake_case__):
if qid_to_has_ans[qid]:
true_pos += scores[qid]
lowerCAmelCase_ : str = true_pos / float(i + 1)
lowerCAmelCase_ : Union[str, Any] = true_pos / float(snake_case__)
if i == len(snake_case__) - 1 or na_probs[qid] != na_probs[qid_list[i + 1]]:
# i.e., if we can put a threshold after this point
avg_prec += cur_p * (cur_r - recalls[-1])
precisions.append(snake_case__)
recalls.append(snake_case__)
if out_image:
plot_pr_curve(snake_case__ , snake_case__ , snake_case__ , snake_case__)
return {"ap": 100.0 * avg_prec}
def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__):
if out_image_dir and not os.path.exists(snake_case__):
os.makedirs(snake_case__)
lowerCAmelCase_ : Any = sum(1 for v in qid_to_has_ans.values() if v)
if num_true_pos == 0:
return
lowerCAmelCase_ : Any = make_precision_recall_eval(
snake_case__ , snake_case__ , snake_case__ , snake_case__ , out_image=os.path.join(snake_case__ , "pr_exact.png") , title="Precision-Recall curve for Exact Match score" , )
lowerCAmelCase_ : Dict = make_precision_recall_eval(
snake_case__ , snake_case__ , snake_case__ , snake_case__ , out_image=os.path.join(snake_case__ , "pr_f1.png") , title="Precision-Recall curve for F1 score" , )
lowerCAmelCase_ : Dict = {k: float(snake_case__) for k, v in qid_to_has_ans.items()}
lowerCAmelCase_ : str = make_precision_recall_eval(
snake_case__ , snake_case__ , snake_case__ , snake_case__ , out_image=os.path.join(snake_case__ , "pr_oracle.png") , title="Oracle Precision-Recall curve (binary task of HasAns vs. NoAns)" , )
merge_eval(snake_case__ , snake_case__ , "pr_exact")
merge_eval(snake_case__ , snake_case__ , "pr_f1")
merge_eval(snake_case__ , snake_case__ , "pr_oracle")
def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__ , snake_case__):
if not qid_list:
return
lowerCAmelCase_ : Optional[Any] = [na_probs[k] for k in qid_list]
lowerCAmelCase_ : Dict = np.ones_like(snake_case__) / float(len(snake_case__))
plt.hist(snake_case__ , weights=snake_case__ , bins=20 , range=(0.0, 1.0))
plt.xlabel("Model probability of no-answer")
plt.ylabel("Proportion of dataset")
plt.title(F'''Histogram of no-answer probability: {name}''')
plt.savefig(os.path.join(snake_case__ , F'''na_prob_hist_{name}.png'''))
plt.clf()
def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__ , snake_case__):
lowerCAmelCase_ : Dict = sum(1 for k in qid_to_has_ans if not qid_to_has_ans[k])
lowerCAmelCase_ : str = num_no_ans
lowerCAmelCase_ : List[str] = cur_score
lowerCAmelCase_ : List[Any] = 0.0
lowerCAmelCase_ : str = sorted(snake_case__ , key=lambda snake_case__: na_probs[k])
for i, qid in enumerate(snake_case__):
if qid not in scores:
continue
if qid_to_has_ans[qid]:
lowerCAmelCase_ : Union[str, Any] = scores[qid]
else:
if preds[qid]:
lowerCAmelCase_ : List[Any] = -1
else:
lowerCAmelCase_ : List[str] = 0
cur_score += diff
if cur_score > best_score:
lowerCAmelCase_ : Optional[Any] = cur_score
lowerCAmelCase_ : Optional[int] = na_probs[qid]
return 100.0 * best_score / len(snake_case__), best_thresh
def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__):
lowerCAmelCase_ , lowerCAmelCase_ : Optional[int] = find_best_thresh(snake_case__ , snake_case__ , snake_case__ , snake_case__)
lowerCAmelCase_ , lowerCAmelCase_ : Dict = find_best_thresh(snake_case__ , snake_case__ , snake_case__ , snake_case__)
lowerCAmelCase_ : List[str] = best_exact
lowerCAmelCase_ : List[str] = exact_thresh
lowerCAmelCase_ : Any = best_fa
lowerCAmelCase_ : List[str] = fa_thresh
def UpperCamelCase ( ):
with open(OPTS.data_file) as f:
lowerCAmelCase_ : Optional[int] = json.load(snake_case__)
lowerCAmelCase_ : List[Any] = dataset_json["data"]
with open(OPTS.pred_file) as f:
lowerCAmelCase_ : int = json.load(snake_case__)
if OPTS.na_prob_file:
with open(OPTS.na_prob_file) as f:
lowerCAmelCase_ : Optional[int] = json.load(snake_case__)
else:
lowerCAmelCase_ : List[Any] = {k: 0.0 for k in preds}
lowerCAmelCase_ : Tuple = make_qid_to_has_ans(snake_case__) # maps qid to True/False
lowerCAmelCase_ : Any = [k for k, v in qid_to_has_ans.items() if v]
lowerCAmelCase_ : List[str] = [k for k, v in qid_to_has_ans.items() if not v]
lowerCAmelCase_ , lowerCAmelCase_ : Dict = get_raw_scores(snake_case__ , snake_case__)
lowerCAmelCase_ : str = apply_no_ans_threshold(snake_case__ , snake_case__ , snake_case__ , OPTS.na_prob_thresh)
lowerCAmelCase_ : Dict = apply_no_ans_threshold(snake_case__ , snake_case__ , snake_case__ , OPTS.na_prob_thresh)
lowerCAmelCase_ : Union[str, Any] = make_eval_dict(snake_case__ , snake_case__)
if has_ans_qids:
lowerCAmelCase_ : str = make_eval_dict(snake_case__ , snake_case__ , qid_list=snake_case__)
merge_eval(snake_case__ , snake_case__ , "HasAns")
if no_ans_qids:
lowerCAmelCase_ : Union[str, Any] = make_eval_dict(snake_case__ , snake_case__ , qid_list=snake_case__)
merge_eval(snake_case__ , snake_case__ , "NoAns")
if OPTS.na_prob_file:
find_all_best_thresh(snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__)
if OPTS.na_prob_file and OPTS.out_image_dir:
run_precision_recall_analysis(snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ , OPTS.out_image_dir)
histogram_na_prob(snake_case__ , snake_case__ , OPTS.out_image_dir , "hasAns")
histogram_na_prob(snake_case__ , snake_case__ , OPTS.out_image_dir , "noAns")
if OPTS.out_file:
with open(OPTS.out_file , "w") as f:
json.dump(snake_case__ , snake_case__)
else:
print(json.dumps(snake_case__ , indent=2))
if __name__ == "__main__":
_lowercase = parse_args()
if OPTS.out_image_dir:
import matplotlib
matplotlib.use('''Agg''')
import matplotlib.pyplot as plt
main()
| 683 | 1 |
from __future__ import annotations
from decimal import Decimal
from numpy import array
def UpperCamelCase ( snake_case__):
lowerCAmelCase_ : int = Decimal
# Check if the provided matrix has 2 rows and 2 columns
# since this implementation only works for 2x2 matrices
if len(snake_case__) == 2 and len(matrix[0]) == 2 and len(matrix[1]) == 2:
# Calculate the determinant of the matrix
lowerCAmelCase_ : Dict = float(
d(matrix[0][0]) * d(matrix[1][1]) - d(matrix[1][0]) * d(matrix[0][1]))
if determinant == 0:
raise ValueError("This matrix has no inverse.")
# Creates a copy of the matrix with swapped positions of the elements
lowerCAmelCase_ : int = [[0.0, 0.0], [0.0, 0.0]]
lowerCAmelCase_ , lowerCAmelCase_ : Dict = matrix[1][1], matrix[0][0]
lowerCAmelCase_ , lowerCAmelCase_ : List[str] = -matrix[1][0], -matrix[0][1]
# Calculate the inverse of the matrix
return [
[(float(d(snake_case__)) / determinant) or 0.0 for n in row] for row in swapped_matrix
]
elif (
len(snake_case__) == 3
and len(matrix[0]) == 3
and len(matrix[1]) == 3
and len(matrix[2]) == 3
):
# Calculate the determinant of the matrix using Sarrus rule
lowerCAmelCase_ : int = float(
(
(d(matrix[0][0]) * d(matrix[1][1]) * d(matrix[2][2]))
+ (d(matrix[0][1]) * d(matrix[1][2]) * d(matrix[2][0]))
+ (d(matrix[0][2]) * d(matrix[1][0]) * d(matrix[2][1]))
)
- (
(d(matrix[0][2]) * d(matrix[1][1]) * d(matrix[2][0]))
+ (d(matrix[0][1]) * d(matrix[1][0]) * d(matrix[2][2]))
+ (d(matrix[0][0]) * d(matrix[1][2]) * d(matrix[2][1]))
))
if determinant == 0:
raise ValueError("This matrix has no inverse.")
# Creating cofactor matrix
lowerCAmelCase_ : Tuple = [
[d(0.0), d(0.0), d(0.0)],
[d(0.0), d(0.0), d(0.0)],
[d(0.0), d(0.0), d(0.0)],
]
lowerCAmelCase_ : Optional[int] = (d(matrix[1][1]) * d(matrix[2][2])) - (
d(matrix[1][2]) * d(matrix[2][1])
)
lowerCAmelCase_ : Dict = -(
(d(matrix[1][0]) * d(matrix[2][2])) - (d(matrix[1][2]) * d(matrix[2][0]))
)
lowerCAmelCase_ : Union[str, Any] = (d(matrix[1][0]) * d(matrix[2][1])) - (
d(matrix[1][1]) * d(matrix[2][0])
)
lowerCAmelCase_ : List[str] = -(
(d(matrix[0][1]) * d(matrix[2][2])) - (d(matrix[0][2]) * d(matrix[2][1]))
)
lowerCAmelCase_ : List[str] = (d(matrix[0][0]) * d(matrix[2][2])) - (
d(matrix[0][2]) * d(matrix[2][0])
)
lowerCAmelCase_ : Any = -(
(d(matrix[0][0]) * d(matrix[2][1])) - (d(matrix[0][1]) * d(matrix[2][0]))
)
lowerCAmelCase_ : Any = (d(matrix[0][1]) * d(matrix[1][2])) - (
d(matrix[0][2]) * d(matrix[1][1])
)
lowerCAmelCase_ : Any = -(
(d(matrix[0][0]) * d(matrix[1][2])) - (d(matrix[0][2]) * d(matrix[1][0]))
)
lowerCAmelCase_ : List[str] = (d(matrix[0][0]) * d(matrix[1][1])) - (
d(matrix[0][1]) * d(matrix[1][0])
)
# Transpose the cofactor matrix (Adjoint matrix)
lowerCAmelCase_ : Optional[Any] = array(snake_case__)
for i in range(3):
for j in range(3):
lowerCAmelCase_ : Union[str, Any] = cofactor_matrix[j][i]
# Inverse of the matrix using the formula (1/determinant) * adjoint matrix
lowerCAmelCase_ : Any = array(snake_case__)
for i in range(3):
for j in range(3):
inverse_matrix[i][j] /= d(snake_case__)
# Calculate the inverse of the matrix
return [[float(d(snake_case__)) or 0.0 for n in row] for row in inverse_matrix]
raise ValueError("Please provide a matrix of size 2x2 or 3x3.")
| 683 |
from math import sqrt
def UpperCamelCase ( snake_case__):
lowerCAmelCase_ : Optional[int] = 0
for i in range(1 , int(sqrt(snake_case__) + 1)):
if n % i == 0 and i != sqrt(snake_case__):
total += i + n // i
elif i == sqrt(snake_case__):
total += i
return total - n
def UpperCamelCase ( snake_case__ = 1_00_00):
lowerCAmelCase_ : int = sum(
i
for i in range(1 , snake_case__)
if sum_of_divisors(sum_of_divisors(snake_case__)) == i and sum_of_divisors(snake_case__) != i)
return total
if __name__ == "__main__":
print(solution(int(str(input()).strip())))
| 683 | 1 |
def UpperCamelCase ( snake_case__):
lowerCAmelCase_ : list[list[float]] = []
for data in source_data:
for i, el in enumerate(snake_case__):
if len(snake_case__) < i + 1:
data_lists.append([])
data_lists[i].append(float(snake_case__))
return data_lists
def UpperCamelCase ( snake_case__ , snake_case__):
lowerCAmelCase_ : list[list[float]] = []
for dlist, weight in zip(snake_case__ , snake_case__):
lowerCAmelCase_ : Dict = min(snake_case__)
lowerCAmelCase_ : str = max(snake_case__)
lowerCAmelCase_ : list[float] = []
# for weight 0 score is 1 - actual score
if weight == 0:
for item in dlist:
try:
score.append(1 - ((item - mind) / (maxd - mind)))
except ZeroDivisionError:
score.append(1)
elif weight == 1:
for item in dlist:
try:
score.append((item - mind) / (maxd - mind))
except ZeroDivisionError:
score.append(0)
# weight not 0 or 1
else:
lowerCAmelCase_ : List[Any] = F'''Invalid weight of {weight:f} provided'''
raise ValueError(snake_case__)
score_lists.append(snake_case__)
return score_lists
def UpperCamelCase ( snake_case__):
lowerCAmelCase_ : list[float] = [0 for i in range(len(score_lists[0]))]
for slist in score_lists:
for j, ele in enumerate(snake_case__):
lowerCAmelCase_ : Optional[Any] = final_scores[j] + ele
return final_scores
def UpperCamelCase ( snake_case__ , snake_case__):
lowerCAmelCase_ : int = get_data(snake_case__)
lowerCAmelCase_ : int = calculate_each_score(snake_case__ , snake_case__)
lowerCAmelCase_ : Optional[Any] = generate_final_scores(snake_case__)
# append scores to source data
for i, ele in enumerate(snake_case__):
source_data[i].append(snake_case__)
return source_data
| 683 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_speech_available,
is_tf_available,
is_torch_available,
)
_lowercase = {
'''configuration_speech_to_text''': ['''SPEECH_TO_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''Speech2TextConfig'''],
'''processing_speech_to_text''': ['''Speech2TextProcessor'''],
}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowercase = ['''Speech2TextTokenizer''']
try:
if not is_speech_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowercase = ['''Speech2TextFeatureExtractor''']
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowercase = [
'''TF_SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''TFSpeech2TextForConditionalGeneration''',
'''TFSpeech2TextModel''',
'''TFSpeech2TextPreTrainedModel''',
]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowercase = [
'''SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''Speech2TextForConditionalGeneration''',
'''Speech2TextModel''',
'''Speech2TextPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_speech_to_text import SPEECH_TO_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP, SpeechaTextConfig
from .processing_speech_to_text import SpeechaTextProcessor
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_speech_to_text import SpeechaTextTokenizer
try:
if not is_speech_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_speech_to_text import SpeechaTextFeatureExtractor
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_speech_to_text import (
TF_SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
TFSpeechaTextForConditionalGeneration,
TFSpeechaTextModel,
TFSpeechaTextPreTrainedModel,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_speech_to_text import (
SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
SpeechaTextForConditionalGeneration,
SpeechaTextModel,
SpeechaTextPreTrainedModel,
)
else:
import sys
_lowercase = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 683 | 1 |
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
_lowercase = logging.getLogger(__name__)
def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__ = None , snake_case__ = None , snake_case__ = None , snake_case__ = None , snake_case__ = None , snake_case__ = False , ):
lowerCAmelCase_ : List[Any] = bnb_quantization_config.load_in_abit
lowerCAmelCase_ : Optional[Any] = 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.")
lowerCAmelCase_ : List[str] = []
# custom device map
if isinstance(snake_case__ , snake_case__) and len(device_map.keys()) > 1:
lowerCAmelCase_ : Union[str, Any] = [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:
lowerCAmelCase_ : Union[str, Any] = get_keys_to_not_convert(snake_case__)
# add cpu modules to skip modules only for 4-bit modules
if load_in_abit:
bnb_quantization_config.skip_modules.extend(snake_case__)
lowerCAmelCase_ : Union[str, Any] = 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:
lowerCAmelCase_ : Optional[int] = []
lowerCAmelCase_ : int = bnb_quantization_config.keep_in_fpaa_modules
modules_to_not_convert.extend(snake_case__)
# compatibility with peft
lowerCAmelCase_ : Optional[int] = load_in_abit
lowerCAmelCase_ : List[str] = load_in_abit
lowerCAmelCase_ : Optional[int] = get_parameter_device(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.")
lowerCAmelCase_ : Union[str, Any] = replace_with_bnb_layers(snake_case__ , snake_case__ , modules_to_not_convert=snake_case__)
# convert param to the right dtype
lowerCAmelCase_ : Any = 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:
lowerCAmelCase_ : Optional[int] = name.replace(".weight" , "").replace(".bias" , "")
lowerCAmelCase_ : Optional[int] = getattr(snake_case__ , snake_case__ , snake_case__)
if param is not None:
param.to(torch.floataa)
elif torch.is_floating_point(snake_case__):
param.to(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():
lowerCAmelCase_ : str = replace_with_bnb_layers(
snake_case__ , snake_case__ , modules_to_not_convert=snake_case__)
lowerCAmelCase_ : Optional[int] = get_quantized_model_device_map(
snake_case__ , snake_case__ , snake_case__ , max_memory=snake_case__ , no_split_module_classes=snake_case__ , )
if offload_state_dict is None and device_map is not None and "disk" in device_map.values():
lowerCAmelCase_ : Optional[Any] = True
lowerCAmelCase_ : Optional[int] = any(x in list(device_map.values()) for x in ["cpu", "disk"])
load_checkpoint_in_model(
snake_case__ , snake_case__ , snake_case__ , dtype=bnb_quantization_config.torch_dtype , offload_folder=snake_case__ , offload_state_dict=snake_case__ , keep_in_fpaa_modules=bnb_quantization_config.keep_in_fpaa_modules , offload_abit_bnb=load_in_abit and offload , )
return dispatch_model(snake_case__ , device_map=snake_case__ , offload_dir=snake_case__)
def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__=None , snake_case__=None , snake_case__=None):
if device_map is None:
if torch.cuda.is_available():
lowerCAmelCase_ : Any = {"": 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(snake_case__ , 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'.")
lowerCAmelCase_ : Dict = {}
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)
})
lowerCAmelCase_ : List[str] = {}
lowerCAmelCase_ : Union[str, Any] = special_dtypes
lowerCAmelCase_ : Union[str, Any] = no_split_module_classes
lowerCAmelCase_ : Any = bnb_quantization_config.target_dtype
# get max_memory for each device.
if device_map != "sequential":
lowerCAmelCase_ : Tuple = get_balanced_memory(
snake_case__ , low_zero=(device_map == "balanced_low_0") , max_memory=snake_case__ , **snake_case__ , )
lowerCAmelCase_ : Tuple = max_memory
lowerCAmelCase_ : Optional[Any] = infer_auto_device_map(snake_case__ , **snake_case__)
if isinstance(snake_case__ , snake_case__):
# check if don't have any quantized module on the cpu
lowerCAmelCase_ : Union[str, Any] = bnb_quantization_config.skip_modules + bnb_quantization_config.keep_in_fpaa_modules
lowerCAmelCase_ : List[Any] = {
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(
"\n Some modules are dispatched on the CPU or the disk. Make sure you have enough GPU RAM to fit\n the quantized model. If you want to dispatch the model on the CPU or the disk while keeping\n these modules in `torch_dtype`, you need to pass a custom `device_map` to\n `load_and_quantize_model`. Check\n https://huggingface.co/docs/accelerate/main/en/usage_guides/quantization#offload-modules-to-cpu-and-disk\n for more details.\n ")
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 UpperCamelCase ( snake_case__ , snake_case__ , snake_case__=None , snake_case__=None):
if modules_to_not_convert is None:
lowerCAmelCase_ : List[str] = []
lowerCAmelCase_ , lowerCAmelCase_ : Tuple = _replace_with_bnb_layers(
snake_case__ , snake_case__ , snake_case__ , 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 UpperCamelCase ( snake_case__ , snake_case__ , snake_case__=None , snake_case__=None , ):
lowerCAmelCase_ : str = False
for name, module in model.named_children():
if current_key_name is None:
lowerCAmelCase_ : Optional[int] = []
current_key_name.append(snake_case__)
if isinstance(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`
lowerCAmelCase_ : Optional[int] = ".".join(snake_case__)
lowerCAmelCase_ : List[str] = 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:
lowerCAmelCase_ : List[Any] = False
break
if proceed:
# Load bnb module with empty weight and replace ``nn.Linear` module
if bnb_quantization_config.load_in_abit:
lowerCAmelCase_ : Tuple = bnb.nn.LinearabitLt(
module.in_features , module.out_features , module.bias is not None , has_fpaa_weights=snake_case__ , threshold=bnb_quantization_config.llm_inta_threshold , )
elif bnb_quantization_config.load_in_abit:
lowerCAmelCase_ : Dict = 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")
lowerCAmelCase_ : List[str] = module.weight.data
if module.bias is not None:
lowerCAmelCase_ : Any = module.bias.data
bnb_module.requires_grad_(snake_case__)
setattr(snake_case__ , snake_case__ , snake_case__)
lowerCAmelCase_ : List[str] = True
if len(list(module.children())) > 0:
lowerCAmelCase_ , lowerCAmelCase_ : List[Any] = _replace_with_bnb_layers(
snake_case__ , snake_case__ , snake_case__ , snake_case__)
lowerCAmelCase_ : Optional[int] = has_been_replaced | _has_been_replaced
# Remove the last key for recursion
current_key_name.pop(-1)
return model, has_been_replaced
def UpperCamelCase ( snake_case__):
# Create a copy of the model
with init_empty_weights():
lowerCAmelCase_ : List[Any] = deepcopy(snake_case__) # this has 0 cost since it is done inside `init_empty_weights` context manager`
lowerCAmelCase_ : Dict = find_tied_parameters(snake_case__)
# For compatibility with Accelerate < 0.18
if isinstance(snake_case__ , snake_case__):
lowerCAmelCase_ : List[str] = sum(list(tied_params.values()) , []) + list(tied_params.keys())
else:
lowerCAmelCase_ : Optional[Any] = sum(snake_case__ , [])
lowerCAmelCase_ : List[Any] = len(snake_case__) > 0
# Check if it is a base model
lowerCAmelCase_ : List[str] = False
if hasattr(snake_case__ , "base_model_prefix"):
lowerCAmelCase_ : Tuple = not hasattr(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
lowerCAmelCase_ : Union[str, Any] = list(model.named_children())
lowerCAmelCase_ : Optional[int] = [list_modules[-1][0]]
# add last module together with tied weights
lowerCAmelCase_ : Any = set(snake_case__) - set(snake_case__)
lowerCAmelCase_ : Tuple = list(set(snake_case__)) + list(snake_case__)
# remove ".weight" from the keys
lowerCAmelCase_ : List[str] = [".weight", ".bias"]
lowerCAmelCase_ : Tuple = []
for name in list_untouched:
for name_to_remove in names_to_remove:
if name_to_remove in name:
lowerCAmelCase_ : str = name.replace(snake_case__ , "")
filtered_module_names.append(snake_case__)
return filtered_module_names
def UpperCamelCase ( snake_case__):
for m in model.modules():
if isinstance(snake_case__ , bnb.nn.Linearabit):
return True
return False
def UpperCamelCase ( snake_case__):
return next(parameter.parameters()).device
def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__):
# 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(snake_case__ , snake_case__ , 0 , dtype=snake_case__ , value=snake_case__)
lowerCAmelCase_ : str = param_name
lowerCAmelCase_ : Tuple = model
if "." in tensor_name:
lowerCAmelCase_ : Dict = tensor_name.split(".")
for split in splits[:-1]:
lowerCAmelCase_ : Any = getattr(snake_case__ , snake_case__)
if new_module is None:
raise ValueError(F'''{module} has no attribute {split}.''')
lowerCAmelCase_ : Union[str, Any] = new_module
lowerCAmelCase_ : Any = splits[-1]
# offload weights
lowerCAmelCase_ : List[Any] = False
offload_weight(module._parameters[tensor_name] , snake_case__ , snake_case__ , index=snake_case__)
if hasattr(module._parameters[tensor_name] , "SCB"):
offload_weight(
module._parameters[tensor_name].SCB , param_name.replace("weight" , "SCB") , snake_case__ , index=snake_case__ , )
else:
offload_weight(snake_case__ , snake_case__ , snake_case__ , index=snake_case__)
offload_weight(snake_case__ , param_name.replace("weight" , "SCB") , snake_case__ , index=snake_case__)
set_module_tensor_to_device(snake_case__ , snake_case__ , "meta" , dtype=snake_case__ , value=torch.empty(*param.size()))
| 683 |
import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
_lowercase = logging.get_logger(__name__)
_lowercase = {'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt'''}
_lowercase = {
'''vocab_file''': {
'''allenai/longformer-base-4096''': '''https://huggingface.co/allenai/longformer-base-4096/resolve/main/vocab.json''',
'''allenai/longformer-large-4096''': (
'''https://huggingface.co/allenai/longformer-large-4096/resolve/main/vocab.json'''
),
'''allenai/longformer-large-4096-finetuned-triviaqa''': (
'''https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/vocab.json'''
),
'''allenai/longformer-base-4096-extra.pos.embd.only''': (
'''https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/vocab.json'''
),
'''allenai/longformer-large-4096-extra.pos.embd.only''': (
'''https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/vocab.json'''
),
},
'''merges_file''': {
'''allenai/longformer-base-4096''': '''https://huggingface.co/allenai/longformer-base-4096/resolve/main/merges.txt''',
'''allenai/longformer-large-4096''': (
'''https://huggingface.co/allenai/longformer-large-4096/resolve/main/merges.txt'''
),
'''allenai/longformer-large-4096-finetuned-triviaqa''': (
'''https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/merges.txt'''
),
'''allenai/longformer-base-4096-extra.pos.embd.only''': (
'''https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/merges.txt'''
),
'''allenai/longformer-large-4096-extra.pos.embd.only''': (
'''https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/merges.txt'''
),
},
}
_lowercase = {
'''allenai/longformer-base-4096''': 4096,
'''allenai/longformer-large-4096''': 4096,
'''allenai/longformer-large-4096-finetuned-triviaqa''': 4096,
'''allenai/longformer-base-4096-extra.pos.embd.only''': 4096,
'''allenai/longformer-large-4096-extra.pos.embd.only''': 4096,
}
@lru_cache()
# Copied from transformers.models.roberta.tokenization_roberta.bytes_to_unicode
def UpperCamelCase ( ):
lowerCAmelCase_ : str = (
list(range(ord("!") , ord("~") + 1)) + list(range(ord("¡") , ord("¬") + 1)) + list(range(ord("®") , ord("ÿ") + 1))
)
lowerCAmelCase_ : Tuple = bs[:]
lowerCAmelCase_ : Dict = 0
for b in range(2**8):
if b not in bs:
bs.append(snake_case__)
cs.append(2**8 + n)
n += 1
lowerCAmelCase_ : Union[str, Any] = [chr(snake_case__) for n in cs]
return dict(zip(snake_case__ , snake_case__))
def UpperCamelCase ( snake_case__):
lowerCAmelCase_ : Optional[Any] = set()
lowerCAmelCase_ : List[Any] = word[0]
for char in word[1:]:
pairs.add((prev_char, char))
lowerCAmelCase_ : Union[str, Any] = char
return pairs
class __snake_case ( snake_case__ ):
"""simple docstring"""
UpperCamelCase_ = VOCAB_FILES_NAMES
UpperCamelCase_ = PRETRAINED_VOCAB_FILES_MAP
UpperCamelCase_ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
UpperCamelCase_ = ['input_ids', 'attention_mask']
def __init__( self : str ,lowerCAmelCase__ : Dict ,lowerCAmelCase__ : List[Any] ,lowerCAmelCase__ : Optional[Any]="replace" ,lowerCAmelCase__ : Dict="<s>" ,lowerCAmelCase__ : str="</s>" ,lowerCAmelCase__ : str="</s>" ,lowerCAmelCase__ : Optional[Any]="<s>" ,lowerCAmelCase__ : List[Any]="<unk>" ,lowerCAmelCase__ : Union[str, Any]="<pad>" ,lowerCAmelCase__ : int="<mask>" ,lowerCAmelCase__ : Any=False ,**lowerCAmelCase__ : int ,) -> Dict:
'''simple docstring'''
lowerCAmelCase_ : Optional[int] = AddedToken(lowerCAmelCase__ ,lstrip=lowerCAmelCase__ ,rstrip=lowerCAmelCase__ ) if isinstance(lowerCAmelCase__ ,lowerCAmelCase__ ) else bos_token
lowerCAmelCase_ : Tuple = AddedToken(lowerCAmelCase__ ,lstrip=lowerCAmelCase__ ,rstrip=lowerCAmelCase__ ) if isinstance(lowerCAmelCase__ ,lowerCAmelCase__ ) else eos_token
lowerCAmelCase_ : Dict = AddedToken(lowerCAmelCase__ ,lstrip=lowerCAmelCase__ ,rstrip=lowerCAmelCase__ ) if isinstance(lowerCAmelCase__ ,lowerCAmelCase__ ) else sep_token
lowerCAmelCase_ : int = AddedToken(lowerCAmelCase__ ,lstrip=lowerCAmelCase__ ,rstrip=lowerCAmelCase__ ) if isinstance(lowerCAmelCase__ ,lowerCAmelCase__ ) else cls_token
lowerCAmelCase_ : List[str] = AddedToken(lowerCAmelCase__ ,lstrip=lowerCAmelCase__ ,rstrip=lowerCAmelCase__ ) if isinstance(lowerCAmelCase__ ,lowerCAmelCase__ ) else unk_token
lowerCAmelCase_ : List[str] = AddedToken(lowerCAmelCase__ ,lstrip=lowerCAmelCase__ ,rstrip=lowerCAmelCase__ ) if isinstance(lowerCAmelCase__ ,lowerCAmelCase__ ) else pad_token
# Mask token behave like a normal word, i.e. include the space before it
lowerCAmelCase_ : Optional[Any] = AddedToken(lowerCAmelCase__ ,lstrip=lowerCAmelCase__ ,rstrip=lowerCAmelCase__ ) if isinstance(lowerCAmelCase__ ,lowerCAmelCase__ ) else mask_token
super().__init__(
errors=lowerCAmelCase__ ,bos_token=lowerCAmelCase__ ,eos_token=lowerCAmelCase__ ,unk_token=lowerCAmelCase__ ,sep_token=lowerCAmelCase__ ,cls_token=lowerCAmelCase__ ,pad_token=lowerCAmelCase__ ,mask_token=lowerCAmelCase__ ,add_prefix_space=lowerCAmelCase__ ,**lowerCAmelCase__ ,)
with open(lowerCAmelCase__ ,encoding="utf-8" ) as vocab_handle:
lowerCAmelCase_ : List[Any] = json.load(lowerCAmelCase__ )
lowerCAmelCase_ : Dict = {v: k for k, v in self.encoder.items()}
lowerCAmelCase_ : List[Any] = errors # how to handle errors in decoding
lowerCAmelCase_ : Optional[Any] = bytes_to_unicode()
lowerCAmelCase_ : int = {v: k for k, v in self.byte_encoder.items()}
with open(lowerCAmelCase__ ,encoding="utf-8" ) as merges_handle:
lowerCAmelCase_ : Union[str, Any] = merges_handle.read().split("\n" )[1:-1]
lowerCAmelCase_ : Dict = [tuple(merge.split() ) for merge in bpe_merges]
lowerCAmelCase_ : Dict = dict(zip(lowerCAmelCase__ ,range(len(lowerCAmelCase__ ) ) ) )
lowerCAmelCase_ : Any = {}
lowerCAmelCase_ : int = add_prefix_space
# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
lowerCAmelCase_ : Optional[Any] = re.compile(R"'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+" )
@property
def UpperCAmelCase_ ( self : Optional[int] ) -> Any:
'''simple docstring'''
return len(self.encoder )
def UpperCAmelCase_ ( self : Any ) -> Optional[int]:
'''simple docstring'''
return dict(self.encoder ,**self.added_tokens_encoder )
def UpperCAmelCase_ ( self : List[str] ,lowerCAmelCase__ : List[str] ) -> List[Any]:
'''simple docstring'''
if token in self.cache:
return self.cache[token]
lowerCAmelCase_ : Union[str, Any] = tuple(lowerCAmelCase__ )
lowerCAmelCase_ : Optional[Any] = get_pairs(lowerCAmelCase__ )
if not pairs:
return token
while True:
lowerCAmelCase_ : Dict = min(lowerCAmelCase__ ,key=lambda lowerCAmelCase__ : self.bpe_ranks.get(lowerCAmelCase__ ,float("inf" ) ) )
if bigram not in self.bpe_ranks:
break
lowerCAmelCase_ , lowerCAmelCase_ : Dict = bigram
lowerCAmelCase_ : Optional[Any] = []
lowerCAmelCase_ : Any = 0
while i < len(lowerCAmelCase__ ):
try:
lowerCAmelCase_ : Optional[int] = word.index(lowerCAmelCase__ ,lowerCAmelCase__ )
except ValueError:
new_word.extend(word[i:] )
break
else:
new_word.extend(word[i:j] )
lowerCAmelCase_ : Tuple = j
if word[i] == first and i < len(lowerCAmelCase__ ) - 1 and word[i + 1] == second:
new_word.append(first + second )
i += 2
else:
new_word.append(word[i] )
i += 1
lowerCAmelCase_ : Optional[Any] = tuple(lowerCAmelCase__ )
lowerCAmelCase_ : Union[str, Any] = new_word
if len(lowerCAmelCase__ ) == 1:
break
else:
lowerCAmelCase_ : Dict = get_pairs(lowerCAmelCase__ )
lowerCAmelCase_ : Tuple = " ".join(lowerCAmelCase__ )
lowerCAmelCase_ : Any = word
return word
def UpperCAmelCase_ ( self : Optional[int] ,lowerCAmelCase__ : Tuple ) -> Dict:
'''simple docstring'''
lowerCAmelCase_ : Dict = []
for token in re.findall(self.pat ,lowerCAmelCase__ ):
lowerCAmelCase_ : List[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(lowerCAmelCase__ ).split(" " ) )
return bpe_tokens
def UpperCAmelCase_ ( self : Tuple ,lowerCAmelCase__ : int ) -> Tuple:
'''simple docstring'''
return self.encoder.get(lowerCAmelCase__ ,self.encoder.get(self.unk_token ) )
def UpperCAmelCase_ ( self : List[str] ,lowerCAmelCase__ : Optional[Any] ) -> Union[str, Any]:
'''simple docstring'''
return self.decoder.get(lowerCAmelCase__ )
def UpperCAmelCase_ ( self : Tuple ,lowerCAmelCase__ : Dict ) -> Any:
'''simple docstring'''
lowerCAmelCase_ : Optional[int] = "".join(lowerCAmelCase__ )
lowerCAmelCase_ : Union[str, Any] = bytearray([self.byte_decoder[c] for c in text] ).decode("utf-8" ,errors=self.errors )
return text
def UpperCAmelCase_ ( self : Optional[Any] ,lowerCAmelCase__ : str ,lowerCAmelCase__ : Optional[str] = None ) -> Tuple[str]:
'''simple docstring'''
if not os.path.isdir(lowerCAmelCase__ ):
logger.error(f'''Vocabulary path ({save_directory}) should be a directory''' )
return
lowerCAmelCase_ : Optional[Any] = os.path.join(
lowerCAmelCase__ ,(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )
lowerCAmelCase_ : Tuple = os.path.join(
lowerCAmelCase__ ,(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"] )
with open(lowerCAmelCase__ ,"w" ,encoding="utf-8" ) as f:
f.write(json.dumps(self.encoder ,indent=2 ,sort_keys=lowerCAmelCase__ ,ensure_ascii=lowerCAmelCase__ ) + "\n" )
lowerCAmelCase_ : Tuple = 0
with open(lowerCAmelCase__ ,"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 lowerCAmelCase__ : 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!" )
lowerCAmelCase_ : Optional[Any] = token_index
writer.write(" ".join(lowerCAmelCase__ ) + "\n" )
index += 1
return vocab_file, merge_file
def UpperCAmelCase_ ( self : Union[str, Any] ,lowerCAmelCase__ : List[int] ,lowerCAmelCase__ : Optional[List[int]] = None ) -> List[int]:
'''simple docstring'''
if token_ids_a is None:
return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
lowerCAmelCase_ : List[Any] = [self.cls_token_id]
lowerCAmelCase_ : List[str] = [self.sep_token_id]
return cls + token_ids_a + sep + sep + token_ids_a + sep
def UpperCAmelCase_ ( self : Dict ,lowerCAmelCase__ : List[int] ,lowerCAmelCase__ : Optional[List[int]] = None ,lowerCAmelCase__ : bool = False ) -> List[int]:
'''simple docstring'''
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=lowerCAmelCase__ ,token_ids_a=lowerCAmelCase__ ,already_has_special_tokens=lowerCAmelCase__ )
if token_ids_a is None:
return [1] + ([0] * len(lowerCAmelCase__ )) + [1]
return [1] + ([0] * len(lowerCAmelCase__ )) + [1, 1] + ([0] * len(lowerCAmelCase__ )) + [1]
def UpperCAmelCase_ ( self : Any ,lowerCAmelCase__ : List[int] ,lowerCAmelCase__ : Optional[List[int]] = None ) -> List[int]:
'''simple docstring'''
lowerCAmelCase_ : List[str] = [self.sep_token_id]
lowerCAmelCase_ : 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 : Tuple ,lowerCAmelCase__ : List[str] ,lowerCAmelCase__ : Optional[int]=False ,**lowerCAmelCase__ : Optional[int] ) -> Any:
'''simple docstring'''
lowerCAmelCase_ : int = kwargs.pop("add_prefix_space" ,self.add_prefix_space )
if (is_split_into_words or add_prefix_space) and (len(lowerCAmelCase__ ) > 0 and not text[0].isspace()):
lowerCAmelCase_ : Union[str, Any] = " " + text
return (text, kwargs)
| 683 | 1 |
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import CONFIG_MAPPING
_lowercase = logging.get_logger(__name__)
class __snake_case ( snake_case__ ):
"""simple docstring"""
UpperCamelCase_ = 'upernet'
def __init__( self : Dict ,lowerCAmelCase__ : int=None ,lowerCAmelCase__ : Tuple=5_12 ,lowerCAmelCase__ : str=0.02 ,lowerCAmelCase__ : List[str]=[1, 2, 3, 6] ,lowerCAmelCase__ : str=True ,lowerCAmelCase__ : Union[str, Any]=0.4 ,lowerCAmelCase__ : Tuple=3_84 ,lowerCAmelCase__ : Dict=2_56 ,lowerCAmelCase__ : Dict=1 ,lowerCAmelCase__ : int=False ,lowerCAmelCase__ : Tuple=2_55 ,**lowerCAmelCase__ : Optional[int] ,) -> Tuple:
'''simple docstring'''
super().__init__(**lowerCAmelCase__ )
if backbone_config is None:
logger.info("`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone." )
lowerCAmelCase_ : List[Any] = CONFIG_MAPPING["resnet"](out_features=["stage1", "stage2", "stage3", "stage4"] )
elif isinstance(lowerCAmelCase__ ,lowerCAmelCase__ ):
lowerCAmelCase_ : Dict = backbone_config.get("model_type" )
lowerCAmelCase_ : List[Any] = CONFIG_MAPPING[backbone_model_type]
lowerCAmelCase_ : str = config_class.from_dict(lowerCAmelCase__ )
lowerCAmelCase_ : List[str] = backbone_config
lowerCAmelCase_ : List[str] = hidden_size
lowerCAmelCase_ : Optional[Any] = initializer_range
lowerCAmelCase_ : Union[str, Any] = pool_scales
lowerCAmelCase_ : Any = use_auxiliary_head
lowerCAmelCase_ : int = auxiliary_loss_weight
lowerCAmelCase_ : Union[str, Any] = auxiliary_in_channels
lowerCAmelCase_ : List[Any] = auxiliary_channels
lowerCAmelCase_ : Union[str, Any] = auxiliary_num_convs
lowerCAmelCase_ : Optional[int] = auxiliary_concat_input
lowerCAmelCase_ : int = loss_ignore_index
def UpperCAmelCase_ ( self : int ) -> Dict:
'''simple docstring'''
lowerCAmelCase_ : Dict = copy.deepcopy(self.__dict__ )
lowerCAmelCase_ : List[str] = self.backbone_config.to_dict()
lowerCAmelCase_ : Optional[int] = self.__class__.model_type
return output
| 683 |
from collections.abc import Iterable
from typing import Any
class __snake_case :
"""simple docstring"""
def __init__( self : Optional[Any] ,lowerCAmelCase__ : int | None = None ) -> List[str]:
'''simple docstring'''
lowerCAmelCase_ : Dict = value
lowerCAmelCase_ : Node | None = None # Added in order to delete a node easier
lowerCAmelCase_ : Node | None = None
lowerCAmelCase_ : Node | None = None
def __repr__( self : Union[str, Any] ) -> str:
'''simple docstring'''
from pprint import pformat
if self.left is None and self.right is None:
return str(self.value )
return pformat({f'''{self.value}''': (self.left, self.right)} ,indent=1 )
class __snake_case :
"""simple docstring"""
def __init__( self : Optional[Any] ,lowerCAmelCase__ : Node | None = None ) -> List[str]:
'''simple docstring'''
lowerCAmelCase_ : List[Any] = root
def __str__( self : Dict ) -> str:
'''simple docstring'''
return str(self.root )
def UpperCAmelCase_ ( self : List[Any] ,lowerCAmelCase__ : Node ,lowerCAmelCase__ : Node | None ) -> None:
'''simple docstring'''
if new_children is not None: # reset its kids
lowerCAmelCase_ : Optional[int] = node.parent
if node.parent is not None: # reset its parent
if self.is_right(lowerCAmelCase__ ): # If it is the right children
lowerCAmelCase_ : List[Any] = new_children
else:
lowerCAmelCase_ : List[Any] = new_children
else:
lowerCAmelCase_ : Any = new_children
def UpperCAmelCase_ ( self : Any ,lowerCAmelCase__ : Node ) -> bool:
'''simple docstring'''
if node.parent and node.parent.right:
return node == node.parent.right
return False
def UpperCAmelCase_ ( self : List[str] ) -> bool:
'''simple docstring'''
return self.root is None
def UpperCAmelCase_ ( self : List[Any] ,lowerCAmelCase__ : Union[str, Any] ) -> None:
'''simple docstring'''
lowerCAmelCase_ : str = Node(lowerCAmelCase__ ) # create a new Node
if self.empty(): # if Tree is empty
lowerCAmelCase_ : Optional[int] = new_node # set its root
else: # Tree is not empty
lowerCAmelCase_ : List[Any] = self.root # from root
if parent_node is None:
return
while True: # While we don't get to a leaf
if value < parent_node.value: # We go left
if parent_node.left is None:
lowerCAmelCase_ : Dict = new_node # We insert the new node in a leaf
break
else:
lowerCAmelCase_ : List[str] = parent_node.left
else:
if parent_node.right is None:
lowerCAmelCase_ : Dict = new_node
break
else:
lowerCAmelCase_ : str = parent_node.right
lowerCAmelCase_ : Optional[int] = parent_node
def UpperCAmelCase_ ( self : int ,*lowerCAmelCase__ : Tuple ) -> None:
'''simple docstring'''
for value in values:
self.__insert(lowerCAmelCase__ )
def UpperCAmelCase_ ( self : Optional[int] ,lowerCAmelCase__ : Optional[int] ) -> Node | None:
'''simple docstring'''
if self.empty():
raise IndexError("Warning: Tree is empty! please use another." )
else:
lowerCAmelCase_ : Dict = self.root
# use lazy evaluation here to avoid NoneType Attribute error
while node is not None and node.value is not value:
lowerCAmelCase_ : Union[str, Any] = node.left if value < node.value else node.right
return node
def UpperCAmelCase_ ( self : Dict ,lowerCAmelCase__ : Node | None = None ) -> Node | None:
'''simple docstring'''
if node is None:
if self.root is None:
return None
lowerCAmelCase_ : Dict = self.root
if not self.empty():
while node.right is not None:
lowerCAmelCase_ : Union[str, Any] = node.right
return node
def UpperCAmelCase_ ( self : Dict ,lowerCAmelCase__ : Node | None = None ) -> Node | None:
'''simple docstring'''
if node is None:
lowerCAmelCase_ : Dict = self.root
if self.root is None:
return None
if not self.empty():
lowerCAmelCase_ : Dict = self.root
while node.left is not None:
lowerCAmelCase_ : Union[str, Any] = node.left
return node
def UpperCAmelCase_ ( self : Union[str, Any] ,lowerCAmelCase__ : int ) -> None:
'''simple docstring'''
lowerCAmelCase_ : Dict = self.search(lowerCAmelCase__ ) # Look for the node with that label
if node is not None:
if node.left is None and node.right is None: # If it has no children
self.__reassign_nodes(lowerCAmelCase__ ,lowerCAmelCase__ )
elif node.left is None: # Has only right children
self.__reassign_nodes(lowerCAmelCase__ ,node.right )
elif node.right is None: # Has only left children
self.__reassign_nodes(lowerCAmelCase__ ,node.left )
else:
lowerCAmelCase_ : int = self.get_max(
node.left ) # Gets the max value of the left branch
self.remove(tmp_node.value ) # type: ignore
lowerCAmelCase_ : Any = (
tmp_node.value # type: ignore
) # Assigns the value to the node to delete and keep tree structure
def UpperCAmelCase_ ( self : Union[str, Any] ,lowerCAmelCase__ : Node | None ) -> Iterable:
'''simple docstring'''
if node is not None:
yield node # Preorder Traversal
yield from self.preorder_traverse(node.left )
yield from self.preorder_traverse(node.right )
def UpperCAmelCase_ ( self : Union[str, Any] ,lowerCAmelCase__ : Dict=None ) -> Any:
'''simple docstring'''
if traversal_function is None:
return self.preorder_traverse(self.root )
else:
return traversal_function(self.root )
def UpperCAmelCase_ ( self : str ,lowerCAmelCase__ : list ,lowerCAmelCase__ : Node | None ) -> None:
'''simple docstring'''
if node:
self.inorder(lowerCAmelCase__ ,node.left )
arr.append(node.value )
self.inorder(lowerCAmelCase__ ,node.right )
def UpperCAmelCase_ ( self : List[str] ,lowerCAmelCase__ : int ,lowerCAmelCase__ : Node ) -> int:
'''simple docstring'''
lowerCAmelCase_ : list[int] = []
self.inorder(lowerCAmelCase__ ,lowerCAmelCase__ ) # append all values to list using inorder traversal
return arr[k - 1]
def UpperCamelCase ( snake_case__):
lowerCAmelCase_ : Optional[Any] = []
if curr_node is not None:
lowerCAmelCase_ : Dict = postorder(curr_node.left) + postorder(curr_node.right) + [curr_node]
return node_list
def UpperCamelCase ( ):
lowerCAmelCase_ : Tuple = (8, 3, 6, 1, 10, 14, 13, 4, 7)
lowerCAmelCase_ : Tuple = BinarySearchTree()
for i in testlist:
t.insert(snake_case__)
# Prints all the elements of the list in order traversal
print(snake_case__)
if t.search(6) is not None:
print("The value 6 exists")
else:
print("The value 6 doesn't exist")
if t.search(-1) is not None:
print("The value -1 exists")
else:
print("The value -1 doesn't exist")
if not t.empty():
print("Max Value: " , t.get_max().value) # type: ignore
print("Min Value: " , t.get_min().value) # type: ignore
for i in testlist:
t.remove(snake_case__)
print(snake_case__)
if __name__ == "__main__":
import doctest
doctest.testmod(verbose=True)
| 683 | 1 |
import copy
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 ..auto import CONFIG_MAPPING
_lowercase = logging.get_logger(__name__)
_lowercase = {
'''microsoft/conditional-detr-resnet-50''': (
'''https://huggingface.co/microsoft/conditional-detr-resnet-50/resolve/main/config.json'''
),
}
class __snake_case ( snake_case__ ):
"""simple docstring"""
UpperCamelCase_ = 'conditional_detr'
UpperCamelCase_ = ['past_key_values']
UpperCamelCase_ = {
'hidden_size': 'd_model',
'num_attention_heads': 'encoder_attention_heads',
}
def __init__( self : Union[str, Any] ,lowerCAmelCase__ : Optional[int]=True ,lowerCAmelCase__ : Dict=None ,lowerCAmelCase__ : Optional[int]=3 ,lowerCAmelCase__ : int=3_00 ,lowerCAmelCase__ : List[Any]=6 ,lowerCAmelCase__ : int=20_48 ,lowerCAmelCase__ : str=8 ,lowerCAmelCase__ : Tuple=6 ,lowerCAmelCase__ : str=20_48 ,lowerCAmelCase__ : str=8 ,lowerCAmelCase__ : Union[str, Any]=0.0 ,lowerCAmelCase__ : Optional[Any]=0.0 ,lowerCAmelCase__ : Optional[Any]=True ,lowerCAmelCase__ : str="relu" ,lowerCAmelCase__ : List[str]=2_56 ,lowerCAmelCase__ : Dict=0.1 ,lowerCAmelCase__ : Optional[int]=0.0 ,lowerCAmelCase__ : Dict=0.0 ,lowerCAmelCase__ : str=0.02 ,lowerCAmelCase__ : List[str]=1.0 ,lowerCAmelCase__ : Any=False ,lowerCAmelCase__ : int="sine" ,lowerCAmelCase__ : int="resnet50" ,lowerCAmelCase__ : Tuple=True ,lowerCAmelCase__ : Tuple=False ,lowerCAmelCase__ : Optional[int]=2 ,lowerCAmelCase__ : List[str]=5 ,lowerCAmelCase__ : Any=2 ,lowerCAmelCase__ : Dict=1 ,lowerCAmelCase__ : Any=1 ,lowerCAmelCase__ : Optional[int]=2 ,lowerCAmelCase__ : Tuple=5 ,lowerCAmelCase__ : Any=2 ,lowerCAmelCase__ : Union[str, Any]=0.25 ,**lowerCAmelCase__ : Optional[int] ,) -> Optional[int]:
'''simple docstring'''
if backbone_config is not None and use_timm_backbone:
raise ValueError("You can't specify both `backbone_config` and `use_timm_backbone`." )
if not use_timm_backbone:
if backbone_config is None:
logger.info("`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone." )
lowerCAmelCase_ : Dict = CONFIG_MAPPING["resnet"](out_features=["stage4"] )
elif isinstance(lowerCAmelCase__ ,lowerCAmelCase__ ):
lowerCAmelCase_ : Dict = backbone_config.get("model_type" )
lowerCAmelCase_ : Tuple = CONFIG_MAPPING[backbone_model_type]
lowerCAmelCase_ : Tuple = config_class.from_dict(lowerCAmelCase__ )
lowerCAmelCase_ : Optional[int] = use_timm_backbone
lowerCAmelCase_ : Optional[int] = backbone_config
lowerCAmelCase_ : Union[str, Any] = num_channels
lowerCAmelCase_ : int = num_queries
lowerCAmelCase_ : Union[str, Any] = d_model
lowerCAmelCase_ : Tuple = encoder_ffn_dim
lowerCAmelCase_ : Union[str, Any] = encoder_layers
lowerCAmelCase_ : List[Any] = encoder_attention_heads
lowerCAmelCase_ : Optional[Any] = decoder_ffn_dim
lowerCAmelCase_ : Optional[int] = decoder_layers
lowerCAmelCase_ : Tuple = decoder_attention_heads
lowerCAmelCase_ : Tuple = dropout
lowerCAmelCase_ : List[Any] = attention_dropout
lowerCAmelCase_ : int = activation_dropout
lowerCAmelCase_ : Optional[int] = activation_function
lowerCAmelCase_ : Tuple = init_std
lowerCAmelCase_ : Optional[Any] = init_xavier_std
lowerCAmelCase_ : List[Any] = encoder_layerdrop
lowerCAmelCase_ : List[str] = decoder_layerdrop
lowerCAmelCase_ : int = encoder_layers
lowerCAmelCase_ : List[Any] = auxiliary_loss
lowerCAmelCase_ : int = position_embedding_type
lowerCAmelCase_ : Tuple = backbone
lowerCAmelCase_ : Dict = use_pretrained_backbone
lowerCAmelCase_ : str = dilation
# Hungarian matcher
lowerCAmelCase_ : List[str] = class_cost
lowerCAmelCase_ : Union[str, Any] = bbox_cost
lowerCAmelCase_ : Dict = giou_cost
# Loss coefficients
lowerCAmelCase_ : Tuple = mask_loss_coefficient
lowerCAmelCase_ : str = dice_loss_coefficient
lowerCAmelCase_ : Dict = cls_loss_coefficient
lowerCAmelCase_ : str = bbox_loss_coefficient
lowerCAmelCase_ : Optional[int] = giou_loss_coefficient
lowerCAmelCase_ : Optional[Any] = focal_alpha
super().__init__(is_encoder_decoder=lowerCAmelCase__ ,**lowerCAmelCase__ )
@property
def UpperCAmelCase_ ( self : str ) -> int:
'''simple docstring'''
return self.encoder_attention_heads
@property
def UpperCAmelCase_ ( self : str ) -> int:
'''simple docstring'''
return self.d_model
def UpperCAmelCase_ ( self : Optional[int] ) -> str:
'''simple docstring'''
lowerCAmelCase_ : int = copy.deepcopy(self.__dict__ )
if self.backbone_config is not None:
lowerCAmelCase_ : Optional[Any] = self.backbone_config.to_dict()
lowerCAmelCase_ : Any = self.__class__.model_type
return output
class __snake_case ( snake_case__ ):
"""simple docstring"""
UpperCamelCase_ = version.parse('1.11' )
@property
def UpperCAmelCase_ ( self : Optional[int] ) -> Mapping[str, Mapping[int, str]]:
'''simple docstring'''
return OrderedDict(
[
("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
("pixel_mask", {0: "batch"}),
] )
@property
def UpperCAmelCase_ ( self : int ) -> float:
'''simple docstring'''
return 1e-5
@property
def UpperCAmelCase_ ( self : Optional[int] ) -> int:
'''simple docstring'''
return 12
| 683 |
class __snake_case :
"""simple docstring"""
def __init__( self : Optional[int] ,lowerCAmelCase__ : str = "" ,lowerCAmelCase__ : bool = False ) -> None:
'''simple docstring'''
lowerCAmelCase_ : dict[str, RadixNode] = {}
# A node will be a leaf if the tree contains its word
lowerCAmelCase_ : int = is_leaf
lowerCAmelCase_ : Optional[Any] = prefix
def UpperCAmelCase_ ( self : str ,lowerCAmelCase__ : str ) -> tuple[str, str, str]:
'''simple docstring'''
lowerCAmelCase_ : Any = 0
for q, w in zip(self.prefix ,lowerCAmelCase__ ):
if q != w:
break
x += 1
return self.prefix[:x], self.prefix[x:], word[x:]
def UpperCAmelCase_ ( self : Optional[int] ,lowerCAmelCase__ : list[str] ) -> None:
'''simple docstring'''
for word in words:
self.insert(lowerCAmelCase__ )
def UpperCAmelCase_ ( self : Any ,lowerCAmelCase__ : str ) -> None:
'''simple docstring'''
if self.prefix == word:
lowerCAmelCase_ : Optional[Any] = True
# Case 2: The node has no edges that have a prefix to the word
# Solution: We create an edge from the current node to a new one
# containing the word
elif word[0] not in self.nodes:
lowerCAmelCase_ : List[Any] = RadixNode(prefix=lowerCAmelCase__ ,is_leaf=lowerCAmelCase__ )
else:
lowerCAmelCase_ : Tuple = self.nodes[word[0]]
lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ : List[str] = incoming_node.match(
lowerCAmelCase__ )
# Case 3: The node prefix is equal to the matching
# Solution: We insert remaining word on the next node
if remaining_prefix == "":
self.nodes[matching_string[0]].insert(lowerCAmelCase__ )
# Case 4: The word is greater equal to the matching
# Solution: Create a node in between both nodes, change
# prefixes and add the new node for the remaining word
else:
lowerCAmelCase_ : Optional[int] = remaining_prefix
lowerCAmelCase_ : Optional[int] = self.nodes[matching_string[0]]
lowerCAmelCase_ : List[Any] = RadixNode(lowerCAmelCase__ ,lowerCAmelCase__ )
lowerCAmelCase_ : Dict = aux_node
if remaining_word == "":
lowerCAmelCase_ : List[str] = True
else:
self.nodes[matching_string[0]].insert(lowerCAmelCase__ )
def UpperCAmelCase_ ( self : Tuple ,lowerCAmelCase__ : str ) -> bool:
'''simple docstring'''
lowerCAmelCase_ : Any = self.nodes.get(word[0] ,lowerCAmelCase__ )
if not incoming_node:
return False
else:
lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ : List[Any] = incoming_node.match(
lowerCAmelCase__ )
# If there is remaining prefix, the word can't be on the tree
if remaining_prefix != "":
return False
# This applies when the word and the prefix are equal
elif remaining_word == "":
return incoming_node.is_leaf
# We have word remaining so we check the next node
else:
return incoming_node.find(lowerCAmelCase__ )
def UpperCAmelCase_ ( self : Optional[Any] ,lowerCAmelCase__ : str ) -> bool:
'''simple docstring'''
lowerCAmelCase_ : int = self.nodes.get(word[0] ,lowerCAmelCase__ )
if not incoming_node:
return False
else:
lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ : Union[str, Any] = incoming_node.match(
lowerCAmelCase__ )
# If there is remaining prefix, the word can't be on the tree
if remaining_prefix != "":
return False
# We have word remaining so we check the next node
elif remaining_word != "":
return incoming_node.delete(lowerCAmelCase__ )
else:
# If it is not a leaf, we don't have to delete
if not incoming_node.is_leaf:
return False
else:
# We delete the nodes if no edges go from it
if len(incoming_node.nodes ) == 0:
del self.nodes[word[0]]
# We merge the current node with its only child
if len(self.nodes ) == 1 and not self.is_leaf:
lowerCAmelCase_ : str = list(self.nodes.values() )[0]
lowerCAmelCase_ : Tuple = merging_node.is_leaf
self.prefix += merging_node.prefix
lowerCAmelCase_ : Optional[int] = merging_node.nodes
# If there is more than 1 edge, we just mark it as non-leaf
elif len(incoming_node.nodes ) > 1:
lowerCAmelCase_ : Optional[Any] = False
# If there is 1 edge, we merge it with its child
else:
lowerCAmelCase_ : Tuple = list(incoming_node.nodes.values() )[0]
lowerCAmelCase_ : Union[str, Any] = merging_node.is_leaf
incoming_node.prefix += merging_node.prefix
lowerCAmelCase_ : str = merging_node.nodes
return True
def UpperCAmelCase_ ( self : Dict ,lowerCAmelCase__ : int = 0 ) -> None:
'''simple docstring'''
if self.prefix != "":
print("-" * height ,self.prefix ," (leaf)" if self.is_leaf else "" )
for value in self.nodes.values():
value.print_tree(height + 1 )
def UpperCamelCase ( ):
lowerCAmelCase_ : Dict = "banana bananas bandana band apple all beast".split()
lowerCAmelCase_ : List[Any] = RadixNode()
root.insert_many(snake_case__)
assert all(root.find(snake_case__) for word in words)
assert not root.find("bandanas")
assert not root.find("apps")
root.delete("all")
assert not root.find("all")
root.delete("banana")
assert not root.find("banana")
assert root.find("bananas")
return True
def UpperCamelCase ( ):
assert test_trie()
def UpperCamelCase ( ):
lowerCAmelCase_ : List[str] = RadixNode()
lowerCAmelCase_ : Optional[Any] = "banana bananas bandanas bandana band apple all beast".split()
root.insert_many(snake_case__)
print("Words:" , snake_case__)
print("Tree:")
root.print_tree()
if __name__ == "__main__":
main()
| 683 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowercase = logging.get_logger(__name__)
_lowercase = {
'''tanreinama/GPTSAN-2.8B-spout_is_uniform''': (
'''https://huggingface.co/tanreinama/GPTSAN-2.8B-spout_is_uniform/resolve/main/config.json'''
),
}
class __snake_case ( snake_case__ ):
"""simple docstring"""
UpperCamelCase_ = 'gptsan-japanese'
UpperCamelCase_ = [
'past_key_values',
]
UpperCamelCase_ = {
'hidden_size': 'd_model',
'num_attention_heads': 'num_heads',
'num_hidden_layers': 'num_layers',
}
def __init__( self : int ,lowerCAmelCase__ : List[Any]=3_60_00 ,lowerCAmelCase__ : Union[str, Any]=12_80 ,lowerCAmelCase__ : int=10_24 ,lowerCAmelCase__ : Optional[int]=81_92 ,lowerCAmelCase__ : Optional[Any]=40_96 ,lowerCAmelCase__ : str=1_28 ,lowerCAmelCase__ : Union[str, Any]=10 ,lowerCAmelCase__ : Optional[Any]=0 ,lowerCAmelCase__ : str=16 ,lowerCAmelCase__ : List[Any]=16 ,lowerCAmelCase__ : Dict=1_28 ,lowerCAmelCase__ : Dict=0.0 ,lowerCAmelCase__ : List[str]=1e-5 ,lowerCAmelCase__ : Any=False ,lowerCAmelCase__ : List[str]=0.0 ,lowerCAmelCase__ : Optional[int]="float32" ,lowerCAmelCase__ : Optional[int]=False ,lowerCAmelCase__ : int=False ,lowerCAmelCase__ : Tuple=False ,lowerCAmelCase__ : List[str]=0.002 ,lowerCAmelCase__ : Tuple=False ,lowerCAmelCase__ : Tuple=True ,lowerCAmelCase__ : Optional[int]=3_59_98 ,lowerCAmelCase__ : List[Any]=3_59_95 ,lowerCAmelCase__ : Dict=3_59_99 ,**lowerCAmelCase__ : Optional[int] ,) -> Optional[Any]:
'''simple docstring'''
lowerCAmelCase_ : str = vocab_size
lowerCAmelCase_ : Any = max_position_embeddings
lowerCAmelCase_ : str = d_model
lowerCAmelCase_ : List[str] = d_ff
lowerCAmelCase_ : Tuple = d_ext
lowerCAmelCase_ : Optional[Any] = d_spout
lowerCAmelCase_ : Optional[int] = num_switch_layers
lowerCAmelCase_ : Optional[int] = num_ext_layers
lowerCAmelCase_ : Tuple = num_switch_layers + num_ext_layers
lowerCAmelCase_ : List[Any] = num_heads
lowerCAmelCase_ : Tuple = num_experts
lowerCAmelCase_ : Optional[int] = expert_capacity
lowerCAmelCase_ : Optional[int] = dropout_rate
lowerCAmelCase_ : int = layer_norm_epsilon
lowerCAmelCase_ : List[str] = router_bias
lowerCAmelCase_ : Optional[Any] = router_jitter_noise
lowerCAmelCase_ : Optional[Any] = router_dtype
lowerCAmelCase_ : Any = router_ignore_padding_tokens
lowerCAmelCase_ : int = output_hidden_states
lowerCAmelCase_ : Tuple = output_attentions
lowerCAmelCase_ : List[str] = initializer_factor
lowerCAmelCase_ : Dict = output_router_logits
lowerCAmelCase_ : Optional[int] = use_cache
super().__init__(
separator_token_id=lowerCAmelCase__ ,pad_token_id=lowerCAmelCase__ ,eos_token_id=lowerCAmelCase__ ,**lowerCAmelCase__ ,)
| 683 |
from __future__ import annotations
def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__ , ):
if (electron_conc, hole_conc, intrinsic_conc).count(0) != 1:
raise ValueError("You cannot supply more or less than 2 values")
elif electron_conc < 0:
raise ValueError("Electron concentration cannot be negative in a semiconductor")
elif hole_conc < 0:
raise ValueError("Hole concentration cannot be negative in a semiconductor")
elif intrinsic_conc < 0:
raise ValueError(
"Intrinsic concentration cannot be negative in a semiconductor")
elif electron_conc == 0:
return (
"electron_conc",
intrinsic_conc**2 / hole_conc,
)
elif hole_conc == 0:
return (
"hole_conc",
intrinsic_conc**2 / electron_conc,
)
elif intrinsic_conc == 0:
return (
"intrinsic_conc",
(electron_conc * hole_conc) ** 0.5,
)
else:
return (-1, -1)
if __name__ == "__main__":
import doctest
doctest.testmod()
| 683 | 1 |
_lowercase = [0, 2, 4, 6, 8]
_lowercase = [1, 3, 5, 7, 9]
def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__ , snake_case__):
if remaining_length == 0:
if digits[0] == 0 or digits[-1] == 0:
return 0
for i in range(length // 2 - 1 , -1 , -1):
remainder += digits[i] + digits[length - i - 1]
if remainder % 2 == 0:
return 0
remainder //= 10
return 1
if remaining_length == 1:
if remainder % 2 == 0:
return 0
lowerCAmelCase_ : Tuple = 0
for digit in range(10):
lowerCAmelCase_ : Optional[int] = digit
result += reversible_numbers(
0 , (remainder + 2 * digit) // 10 , snake_case__ , snake_case__)
return result
lowerCAmelCase_ : str = 0
for digita in range(10):
lowerCAmelCase_ : int = digita
if (remainder + digita) % 2 == 0:
lowerCAmelCase_ : str = ODD_DIGITS
else:
lowerCAmelCase_ : str = EVEN_DIGITS
for digita in other_parity_digits:
lowerCAmelCase_ : Dict = digita
result += reversible_numbers(
remaining_length - 2 , (remainder + digita + digita) // 10 , snake_case__ , snake_case__ , )
return result
def UpperCamelCase ( snake_case__ = 9):
lowerCAmelCase_ : Tuple = 0
for length in range(1 , max_power + 1):
result += reversible_numbers(snake_case__ , 0 , [0] * length , snake_case__)
return result
if __name__ == "__main__":
print(f"{solution() = }")
| 683 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_lowercase = {
'''configuration_git''': ['''GIT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GitConfig''', '''GitVisionConfig'''],
'''processing_git''': ['''GitProcessor'''],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowercase = [
'''GIT_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''GitForCausalLM''',
'''GitModel''',
'''GitPreTrainedModel''',
'''GitVisionModel''',
]
if TYPE_CHECKING:
from .configuration_git import GIT_PRETRAINED_CONFIG_ARCHIVE_MAP, GitConfig, GitVisionConfig
from .processing_git import GitProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_git import (
GIT_PRETRAINED_MODEL_ARCHIVE_LIST,
GitForCausalLM,
GitModel,
GitPreTrainedModel,
GitVisionModel,
)
else:
import sys
_lowercase = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 683 | 1 |
def UpperCamelCase ( snake_case__ , snake_case__):
return price * (1 + tax_rate)
if __name__ == "__main__":
print(f"{price_plus_tax(100, 0.25) = }")
print(f"{price_plus_tax(125.50, 0.05) = }")
| 683 |
from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments
def UpperCamelCase ( ):
lowerCAmelCase_ : List[str] = HfArgumentParser(snake_case__)
lowerCAmelCase_ : List[Any] = parser.parse_args_into_dataclasses()[0]
lowerCAmelCase_ : Optional[int] = TensorFlowBenchmark(args=snake_case__)
try:
lowerCAmelCase_ : Tuple = parser.parse_args_into_dataclasses()[0]
except ValueError as e:
lowerCAmelCase_ : Union[str, Any] = "Arg --no_{0} is no longer used, please use --no-{0} instead."
lowerCAmelCase_ : Tuple = " ".join(str(snake_case__).split(" ")[:-1])
lowerCAmelCase_ : Union[str, Any] = ""
lowerCAmelCase_ : Optional[Any] = eval(str(snake_case__).split(" ")[-1])
lowerCAmelCase_ : Tuple = []
for arg in depreciated_args:
# arg[2:] removes '--'
if arg[2:] in TensorFlowBenchmark.deprecated_args:
# arg[5:] removes '--no_'
full_error_msg += arg_error_msg.format(arg[5:])
else:
wrong_args.append(snake_case__)
if len(snake_case__) > 0:
lowerCAmelCase_ : Optional[Any] = full_error_msg + begin_error_msg + str(snake_case__)
raise ValueError(snake_case__)
benchmark.run()
if __name__ == "__main__":
main()
| 683 | 1 |
import json
import os
from typing import Dict, List, Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
_lowercase = logging.get_logger(__name__)
_lowercase = {
'''vocab_file''': '''vocab.json''',
'''tokenizer_config_file''': '''tokenizer_config.json''',
'''merges_file''': '''merges.txt''',
}
_lowercase = {
'''vocab_file''': {
'''facebook/s2t-wav2vec2-large-en-de''': (
'''https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/vocab.json'''
),
},
'''tokenizer_config_file''': {
'''facebook/s2t-wav2vec2-large-en-de''': (
'''https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/tokenizer_config.json'''
),
},
'''merges_file''': {
'''facebook/s2t-wav2vec2-large-en-de''': (
'''https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/merges.txt'''
),
},
}
_lowercase = '''</w>'''
_lowercase = '''@@ '''
def UpperCamelCase ( snake_case__):
lowerCAmelCase_ : List[str] = set()
lowerCAmelCase_ : Dict = word[0]
for char in word[1:]:
pairs.add((prev_char, char))
lowerCAmelCase_ : Tuple = char
return pairs
# Speech2Text2 has no max input length
_lowercase = {'''facebook/s2t-wav2vec2-large-en-de''': 1024}
class __snake_case ( snake_case__ ):
"""simple docstring"""
UpperCamelCase_ = VOCAB_FILES_NAMES
UpperCamelCase_ = PRETRAINED_VOCAB_FILES_MAP
UpperCamelCase_ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
UpperCamelCase_ = ['input_ids', 'attention_mask']
def __init__( self : Union[str, Any] ,lowerCAmelCase__ : Union[str, Any] ,lowerCAmelCase__ : Optional[Any]="<s>" ,lowerCAmelCase__ : Optional[Any]="<pad>" ,lowerCAmelCase__ : Optional[int]="</s>" ,lowerCAmelCase__ : Union[str, Any]="<unk>" ,lowerCAmelCase__ : Optional[Any]=False ,lowerCAmelCase__ : Dict=None ,**lowerCAmelCase__ : Any ,) -> Tuple:
'''simple docstring'''
super().__init__(
unk_token=lowerCAmelCase__ ,bos_token=lowerCAmelCase__ ,eos_token=lowerCAmelCase__ ,pad_token=lowerCAmelCase__ ,do_lower_case=lowerCAmelCase__ ,**lowerCAmelCase__ ,)
lowerCAmelCase_ : List[Any] = do_lower_case
with open(lowerCAmelCase__ ,encoding="utf-8" ) as vocab_handle:
lowerCAmelCase_ : Any = json.load(lowerCAmelCase__ )
lowerCAmelCase_ : Optional[Any] = {v: k for k, v in self.encoder.items()}
if merges_file is None:
logger.info(f'''No merges files provided. {self.__class__.__name__} can only be used for decoding.''' )
lowerCAmelCase_ : List[Any] = None
lowerCAmelCase_ : str = None
else:
with open(lowerCAmelCase__ ,encoding="utf-8" ) as merges_handle:
lowerCAmelCase_ : Union[str, Any] = merges_handle.read().split("\n" )[:-1]
lowerCAmelCase_ : Dict = [tuple(merge.split()[:2] ) for merge in merges]
lowerCAmelCase_ : Tuple = dict(zip(lowerCAmelCase__ ,range(len(lowerCAmelCase__ ) ) ) )
lowerCAmelCase_ : Union[str, Any] = {}
@property
def UpperCAmelCase_ ( self : Union[str, Any] ) -> int:
'''simple docstring'''
return len(self.decoder )
def UpperCAmelCase_ ( self : str ) -> Dict:
'''simple docstring'''
return dict(self.encoder ,**self.added_tokens_encoder )
def UpperCAmelCase_ ( self : Union[str, Any] ,lowerCAmelCase__ : Dict ) -> Optional[Any]:
'''simple docstring'''
lowerCAmelCase_ : Any = tuple(token[:-1] ) + (token[-1] + BPE_TOKEN_MERGES,)
if token in self.cache:
return self.cache[token]
lowerCAmelCase_ : Any = get_pairs(lowerCAmelCase__ )
if not pairs:
return token
while True:
lowerCAmelCase_ : Optional[int] = min(lowerCAmelCase__ ,key=lambda lowerCAmelCase__ : self.bpe_ranks.get(lowerCAmelCase__ ,float("inf" ) ) )
if bigram not in self.bpe_ranks:
break
lowerCAmelCase_ , lowerCAmelCase_ : List[str] = bigram
lowerCAmelCase_ : List[Any] = []
lowerCAmelCase_ : List[str] = 0
while i < len(lowerCAmelCase__ ):
try:
lowerCAmelCase_ : Union[str, Any] = word.index(lowerCAmelCase__ ,lowerCAmelCase__ )
except ValueError:
new_word.extend(word[i:] )
break
else:
new_word.extend(word[i:j] )
lowerCAmelCase_ : Optional[Any] = j
if word[i] == first and i < len(lowerCAmelCase__ ) - 1 and word[i + 1] == second:
new_word.append(first + second )
i += 2
else:
new_word.append(word[i] )
i += 1
lowerCAmelCase_ : Tuple = tuple(lowerCAmelCase__ )
lowerCAmelCase_ : str = new_word
if len(lowerCAmelCase__ ) == 1:
break
else:
lowerCAmelCase_ : str = get_pairs(lowerCAmelCase__ )
lowerCAmelCase_ : List[Any] = " ".join(lowerCAmelCase__ )
if word == "\n " + BPE_TOKEN_MERGES:
lowerCAmelCase_ : Union[str, Any] = "\n" + BPE_TOKEN_MERGES
if word.endswith(lowerCAmelCase__ ):
lowerCAmelCase_ : List[Any] = word.replace(lowerCAmelCase__ ,"" )
lowerCAmelCase_ : str = word.replace(" " ,lowerCAmelCase__ )
lowerCAmelCase_ : Optional[int] = word
return word
def UpperCAmelCase_ ( self : Optional[Any] ,lowerCAmelCase__ : str ) -> Dict:
'''simple docstring'''
if self.bpe_ranks is None:
raise ValueError(
"This tokenizer was instantiated without a `merges.txt` file, so"
" that it can only be used for decoding, not for encoding."
"Make sure to provide `merges.txt` file at instantiation to enable "
"encoding." )
if self.do_lower_case:
lowerCAmelCase_ : Union[str, Any] = text.lower()
lowerCAmelCase_ : Optional[int] = text.split()
lowerCAmelCase_ : List[Any] = []
for token in text:
if token:
split_tokens.extend(list(self.bpe(lowerCAmelCase__ ).split(" " ) ) )
return split_tokens
def UpperCAmelCase_ ( self : int ,lowerCAmelCase__ : str ) -> int:
'''simple docstring'''
return self.encoder.get(lowerCAmelCase__ ,self.encoder.get(self.unk_token ) )
def UpperCAmelCase_ ( self : str ,lowerCAmelCase__ : int ) -> str:
'''simple docstring'''
lowerCAmelCase_ : Tuple = self.decoder.get(lowerCAmelCase__ ,self.unk_token )
return result
def UpperCAmelCase_ ( self : Union[str, Any] ,lowerCAmelCase__ : List[str] ) -> str:
'''simple docstring'''
lowerCAmelCase_ : Any = " ".join(lowerCAmelCase__ )
# make sure @@ tokens are concatenated
lowerCAmelCase_ : str = "".join(string.split(lowerCAmelCase__ ) )
return string
def UpperCAmelCase_ ( self : Tuple ,lowerCAmelCase__ : str ,lowerCAmelCase__ : Optional[str] = None ) -> Tuple[str]:
'''simple docstring'''
if not os.path.isdir(lowerCAmelCase__ ):
logger.error(f'''Vocabulary path ({save_directory}) should be a directory''' )
return
lowerCAmelCase_ : List[Any] = os.path.join(
lowerCAmelCase__ ,(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )
lowerCAmelCase_ : Tuple = os.path.join(
lowerCAmelCase__ ,(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"] )
with open(lowerCAmelCase__ ,"w" ,encoding="utf-8" ) as f:
f.write(json.dumps(self.encoder ,indent=2 ,sort_keys=lowerCAmelCase__ ,ensure_ascii=lowerCAmelCase__ ) + "\n" )
lowerCAmelCase_ : Dict = 0
if self.bpe_ranks is None:
return (vocab_file,)
with open(lowerCAmelCase__ ,"w" ,encoding="utf-8" ) as writer:
for bpe_tokens, token_index in sorted(self.bpe_ranks.items() ,key=lambda lowerCAmelCase__ : kv[1] ):
if index != token_index:
logger.warning(
f'''Saving vocabulary to {merges_file}: BPE merge indices are not consecutive.'''
" Please check that the tokenizer is not corrupted!" )
lowerCAmelCase_ : int = token_index
writer.write(" ".join(lowerCAmelCase__ ) + "\n" )
index += 1
return (vocab_file, merges_file)
| 683 |
_lowercase = {
0: '''0''',
1: '''1''',
2: '''2''',
3: '''3''',
4: '''4''',
5: '''5''',
6: '''6''',
7: '''7''',
8: '''8''',
9: '''9''',
10: '''a''',
11: '''b''',
12: '''c''',
13: '''d''',
14: '''e''',
15: '''f''',
}
def UpperCamelCase ( snake_case__):
assert type(snake_case__) in (int, float) and decimal == int(snake_case__)
lowerCAmelCase_ : Optional[Any] = int(snake_case__)
lowerCAmelCase_ : Tuple = ""
lowerCAmelCase_ : str = False
if decimal < 0:
lowerCAmelCase_ : Tuple = True
decimal *= -1
while decimal > 0:
lowerCAmelCase_ , lowerCAmelCase_ : Any = divmod(snake_case__ , 16)
lowerCAmelCase_ : Dict = values[remainder] + hexadecimal
lowerCAmelCase_ : List[str] = "0x" + hexadecimal
if negative:
lowerCAmelCase_ : Optional[Any] = "-" + hexadecimal
return hexadecimal
if __name__ == "__main__":
import doctest
doctest.testmod()
| 683 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
_lowercase = logging.get_logger(__name__)
_lowercase = {
'''facebook/convnextv2-tiny-1k-224''': '''https://huggingface.co/facebook/convnextv2-tiny-1k-224/resolve/main/config.json''',
}
class __snake_case ( snake_case__ , snake_case__ ):
"""simple docstring"""
UpperCamelCase_ = 'convnextv2'
def __init__( self : Optional[int] ,lowerCAmelCase__ : Optional[Any]=3 ,lowerCAmelCase__ : List[Any]=4 ,lowerCAmelCase__ : Tuple=4 ,lowerCAmelCase__ : Tuple=None ,lowerCAmelCase__ : List[str]=None ,lowerCAmelCase__ : List[str]="gelu" ,lowerCAmelCase__ : List[str]=0.02 ,lowerCAmelCase__ : int=1e-1_2 ,lowerCAmelCase__ : Tuple=0.0 ,lowerCAmelCase__ : Dict=2_24 ,lowerCAmelCase__ : Union[str, Any]=None ,lowerCAmelCase__ : Optional[Any]=None ,**lowerCAmelCase__ : str ,) -> Optional[Any]:
'''simple docstring'''
super().__init__(**lowerCAmelCase__ )
lowerCAmelCase_ : Tuple = num_channels
lowerCAmelCase_ : str = patch_size
lowerCAmelCase_ : List[Any] = num_stages
lowerCAmelCase_ : List[Any] = [96, 1_92, 3_84, 7_68] if hidden_sizes is None else hidden_sizes
lowerCAmelCase_ : Optional[int] = [3, 3, 9, 3] if depths is None else depths
lowerCAmelCase_ : int = hidden_act
lowerCAmelCase_ : Any = initializer_range
lowerCAmelCase_ : List[Any] = layer_norm_eps
lowerCAmelCase_ : Union[str, Any] = drop_path_rate
lowerCAmelCase_ : int = image_size
lowerCAmelCase_ : Dict = ["stem"] + [f'''stage{idx}''' for idx in range(1 ,len(self.depths ) + 1 )]
lowerCAmelCase_ , lowerCAmelCase_ : Any = get_aligned_output_features_output_indices(
out_features=lowerCAmelCase__ ,out_indices=lowerCAmelCase__ ,stage_names=self.stage_names )
| 683 |
from pathlib import Path
from typing import List
from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import get_tests_dir, is_tool_test
from transformers.tools.agent_types import AGENT_TYPE_MAPPING, AgentAudio, AgentImage, AgentText
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
_lowercase = ['''text''', '''image''', '''audio''']
def UpperCamelCase ( snake_case__):
lowerCAmelCase_ : int = []
for input_type in input_types:
if input_type == "text":
inputs.append("Text input")
elif input_type == "image":
inputs.append(
Image.open(Path(get_tests_dir("fixtures/tests_samples/COCO")) / "000000039769.png").resize((5_12, 5_12)))
elif input_type == "audio":
inputs.append(torch.ones(30_00))
elif isinstance(snake_case__ , snake_case__):
inputs.append(create_inputs(snake_case__))
else:
raise ValueError(F'''Invalid type requested: {input_type}''')
return inputs
def UpperCamelCase ( snake_case__):
lowerCAmelCase_ : List[Any] = []
for output in outputs:
if isinstance(snake_case__ , (str, AgentText)):
output_types.append("text")
elif isinstance(snake_case__ , (Image.Image, AgentImage)):
output_types.append("image")
elif isinstance(snake_case__ , (torch.Tensor, AgentAudio)):
output_types.append("audio")
else:
raise ValueError(F'''Invalid output: {output}''')
return output_types
@is_tool_test
class __snake_case :
"""simple docstring"""
def UpperCAmelCase_ ( self : int ) -> int:
'''simple docstring'''
self.assertTrue(hasattr(self.tool ,"inputs" ) )
self.assertTrue(hasattr(self.tool ,"outputs" ) )
lowerCAmelCase_ : List[Any] = self.tool.inputs
for _input in inputs:
if isinstance(_input ,lowerCAmelCase__ ):
for __input in _input:
self.assertTrue(__input in authorized_types )
else:
self.assertTrue(_input in authorized_types )
lowerCAmelCase_ : Any = self.tool.outputs
for _output in outputs:
self.assertTrue(_output in authorized_types )
def UpperCAmelCase_ ( self : List[Any] ) -> List[Any]:
'''simple docstring'''
lowerCAmelCase_ : Any = create_inputs(self.tool.inputs )
lowerCAmelCase_ : List[Any] = self.tool(*lowerCAmelCase__ )
# There is a single output
if len(self.tool.outputs ) == 1:
lowerCAmelCase_ : Optional[int] = [outputs]
self.assertListEqual(output_types(lowerCAmelCase__ ) ,self.tool.outputs )
def UpperCAmelCase_ ( self : int ) -> Any:
'''simple docstring'''
self.assertTrue(hasattr(self.tool ,"description" ) )
self.assertTrue(hasattr(self.tool ,"default_checkpoint" ) )
self.assertTrue(self.tool.description.startswith("This is a tool that" ) )
def UpperCAmelCase_ ( self : List[Any] ) -> Tuple:
'''simple docstring'''
lowerCAmelCase_ : Optional[Any] = create_inputs(self.tool.inputs )
lowerCAmelCase_ : List[Any] = self.tool(*lowerCAmelCase__ )
if not isinstance(lowerCAmelCase__ ,lowerCAmelCase__ ):
lowerCAmelCase_ : str = [outputs]
self.assertEqual(len(lowerCAmelCase__ ) ,len(self.tool.outputs ) )
for output, output_type in zip(lowerCAmelCase__ ,self.tool.outputs ):
lowerCAmelCase_ : Tuple = AGENT_TYPE_MAPPING[output_type]
self.assertTrue(isinstance(lowerCAmelCase__ ,lowerCAmelCase__ ) )
def UpperCAmelCase_ ( self : Any ) -> Tuple:
'''simple docstring'''
lowerCAmelCase_ : Tuple = create_inputs(self.tool.inputs )
lowerCAmelCase_ : List[Any] = []
for _input, input_type in zip(lowerCAmelCase__ ,self.tool.inputs ):
if isinstance(lowerCAmelCase__ ,lowerCAmelCase__ ):
_inputs.append([AGENT_TYPE_MAPPING[_input_type](_input ) for _input_type in input_type] )
else:
_inputs.append(AGENT_TYPE_MAPPING[input_type](_input ) )
# Should not raise an error
lowerCAmelCase_ : List[Any] = self.tool(*lowerCAmelCase__ )
if not isinstance(lowerCAmelCase__ ,lowerCAmelCase__ ):
lowerCAmelCase_ : int = [outputs]
self.assertEqual(len(lowerCAmelCase__ ) ,len(self.tool.outputs ) )
| 683 | 1 |
from typing import List, Optional, Union
import numpy as np
import tensorflow as tf
from .utils import logging
_lowercase = logging.get_logger(__name__)
def UpperCamelCase ( snake_case__):
if isinstance(snake_case__ , np.ndarray):
return list(tensor.shape)
lowerCAmelCase_ : str = tf.shape(snake_case__)
if tensor.shape == tf.TensorShape(snake_case__):
return dynamic
lowerCAmelCase_ : Union[str, Any] = tensor.shape.as_list()
return [dynamic[i] if s is None else s for i, s in enumerate(snake_case__)]
def UpperCamelCase ( snake_case__ , snake_case__ = None , snake_case__ = None):
return tf.nn.softmax(logits=logits + 1e-9 , axis=snake_case__ , name=snake_case__)
def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__ , snake_case__=1e-5 , snake_case__=-1):
# This is a very simplified functional layernorm, designed to duplicate
# the functionality of PyTorch nn.functional.layer_norm when this is needed to port
# models in Transformers.
if weight.shape.rank != 1 or bias.shape.rank != 1 or not isinstance(snake_case__ , snake_case__):
raise NotImplementedError("Only 1D weight and bias tensors are supported for now, with only a single axis.")
# Get mean and variance on the axis to be normalized
lowerCAmelCase_ , lowerCAmelCase_ : str = tf.nn.moments(snake_case__ , axes=[axis] , keepdims=snake_case__)
if axis != -1:
# Reshape scale and weight to have the same rank as inputs, but with 1 dimensions
# on every dimension except axis
lowerCAmelCase_ : Optional[Any] = [1] * inputs.shape.rank
lowerCAmelCase_ : Any = shape_list(snake_case__)[axis]
lowerCAmelCase_ : List[str] = tf.reshape(snake_case__ , snake_case__)
lowerCAmelCase_ : Dict = tf.reshape(snake_case__ , snake_case__)
# Compute layer normalization using the batch_normalization
# function.
lowerCAmelCase_ : List[Any] = tf.nn.batch_normalization(
snake_case__ , snake_case__ , snake_case__ , offset=snake_case__ , scale=snake_case__ , variance_epsilon=snake_case__ , )
return outputs
def UpperCamelCase ( snake_case__ , snake_case__=0 , snake_case__=-1):
# Replicates the behavior of torch.flatten in TF
# If end_dim or start_dim is negative, count them from the end
if end_dim < 0:
end_dim += input.shape.rank
if start_dim < 0:
start_dim += input.shape.rank
if start_dim == end_dim:
return input
lowerCAmelCase_ : Dict = tf.shape(snake_case__)
lowerCAmelCase_ : Dict = tf.math.reduce_prod(in_shape[start_dim : end_dim + 1])
lowerCAmelCase_ : str = tf.concat([in_shape[:start_dim], [flattened_dim], in_shape[end_dim + 1 :]] , axis=0)
return tf.reshape(snake_case__ , snake_case__)
def UpperCamelCase ( snake_case__):
if not isinstance(snake_case__ , tf.Tensor):
lowerCAmelCase_ : Optional[int] = tf.convert_to_tensor(snake_case__) # Catches stray NumPy inputs
if encoder_attention_mask.shape.rank == 3:
lowerCAmelCase_ : int = encoder_attention_mask[:, None, :, :]
if encoder_attention_mask.shape.rank == 2:
lowerCAmelCase_ : List[str] = encoder_attention_mask[:, None, None, :]
# T5 has a mask that can compare sequence ids, we can simulate this here with this transposition
# Cf. https://github.com/tensorflow/mesh/blob/8d2465e9bc93129b913b5ccc6a59aa97abd96ec6/mesh_tensorflow
# /transformer/transformer_layers.py#L270
# encoder_extended_attention_mask = (encoder_extended_attention_mask ==
# encoder_extended_attention_mask.transpose(-1, -2))
lowerCAmelCase_ : List[str] = (
tf.cast(1 , encoder_attention_mask.dtype) - encoder_extended_attention_mask
) * encoder_extended_attention_mask.dtype.min
return encoder_extended_attention_mask
def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__ = "input_ids"):
tf.debugging.assert_less(
snake_case__ , tf.cast(snake_case__ , dtype=tensor.dtype) , message=(
F'''The maximum value of {tensor_name} ({tf.math.reduce_max(snake_case__)}) must be smaller than the embedding '''
F'''layer\'s input dimension ({embed_dim}). The likely cause is some problem at tokenization time.'''
) , )
def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__):
lowerCAmelCase_ : Tuple = 6_45_12
# Check that no item in `data` is larger than `HDF5_OBJECT_HEADER_LIMIT`
# because in that case even chunking the array would not make the saving
# possible.
lowerCAmelCase_ : str = [x for x in data if len(snake_case__) > HDF5_OBJECT_HEADER_LIMIT]
# Expecting this to never be true.
if bad_attributes:
raise RuntimeError(
"The following attributes cannot be saved to HDF5 file because "
F'''they are larger than {HDF5_OBJECT_HEADER_LIMIT} '''
F'''bytes: {bad_attributes}''')
lowerCAmelCase_ : List[Any] = np.asarray(snake_case__)
lowerCAmelCase_ : Any = 1
lowerCAmelCase_ : str = np.array_split(snake_case__ , snake_case__)
# This will never loop forever thanks to the test above.
while any(x.nbytes > HDF5_OBJECT_HEADER_LIMIT for x in chunked_data):
num_chunks += 1
lowerCAmelCase_ : Optional[int] = np.array_split(snake_case__ , snake_case__)
if num_chunks > 1:
for chunk_id, chunk_data in enumerate(snake_case__):
lowerCAmelCase_ : Optional[int] = chunk_data
else:
lowerCAmelCase_ : List[str] = data
def UpperCamelCase ( snake_case__ , snake_case__):
if name in group.attrs:
lowerCAmelCase_ : Tuple = [n.decode("utf8") if hasattr(snake_case__ , "decode") else n for n in group.attrs[name]]
else:
lowerCAmelCase_ : List[Any] = []
lowerCAmelCase_ : List[str] = 0
while "%s%d" % (name, chunk_id) in group.attrs:
data.extend(
[n.decode("utf8") if hasattr(snake_case__ , "decode") else n for n in group.attrs["%s%d" % (name, chunk_id)]])
chunk_id += 1
return data
def UpperCamelCase ( snake_case__):
def _expand_single_ad_tensor(snake_case__):
if isinstance(snake_case__ , tf.Tensor) and t.shape.rank == 1:
return tf.expand_dims(snake_case__ , axis=-1)
return t
return tf.nest.map_structure(_expand_single_ad_tensor , snake_case__)
| 683 |
import pytest
_lowercase = '''__dummy_dataset1__'''
_lowercase = '''
import json
import os
import datasets
REPO_URL = "https://huggingface.co/datasets/albertvillanova/tests-raw-jsonl/resolve/main/"
URLS = {"train": REPO_URL + "wikiann-bn-train.jsonl", "validation": REPO_URL + "wikiann-bn-validation.jsonl"}
class __DummyDataset1__(datasets.GeneratorBasedBuilder):
def _info(self):
features = datasets.Features(
{
"tokens": datasets.Sequence(datasets.Value("string")),
"ner_tags": datasets.Sequence(
datasets.features.ClassLabel(
names=[
"O",
"B-PER",
"I-PER",
"B-ORG",
"I-ORG",
"B-LOC",
"I-LOC",
]
)
),
"langs": datasets.Sequence(datasets.Value("string")),
"spans": datasets.Sequence(datasets.Value("string")),
}
)
return datasets.DatasetInfo(features=features)
def _split_generators(self, dl_manager):
dl_path = dl_manager.download(URLS)
return [
datasets.SplitGenerator(datasets.Split.TRAIN, gen_kwargs={"filepath": dl_path["train"]}),
datasets.SplitGenerator(datasets.Split.VALIDATION, gen_kwargs={"filepath": dl_path["validation"]}),
]
def _generate_examples(self, filepath):
with open(filepath, "r", encoding="utf-8") as f:
for i, line in enumerate(f):
yield i, json.loads(line)
'''
@pytest.fixture
def UpperCamelCase ( ):
return DATASET_LOADING_SCRIPT_NAME
@pytest.fixture
def UpperCamelCase ( ):
return DATASET_LOADING_SCRIPT_CODE
@pytest.fixture
def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__):
lowerCAmelCase_ : List[Any] = dataset_loading_script_name
lowerCAmelCase_ : List[str] = tmp_path / "datasets" / script_name
script_dir.mkdir(parents=snake_case__)
lowerCAmelCase_ : List[Any] = script_dir / F'''{script_name}.py'''
with open(snake_case__ , "w") as f:
f.write(snake_case__)
return str(snake_case__)
| 683 | 1 |
def UpperCamelCase ( snake_case__ = 10_00):
return sum(2 * a * ((a - 1) // 2) for a in range(3 , n + 1))
if __name__ == "__main__":
print(solution())
| 683 |
import json
import os
import re
import unittest
from transformers import CodeGenTokenizer, CodeGenTokenizerFast
from transformers.models.codegen.tokenization_codegen import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class __snake_case ( snake_case__ , unittest.TestCase ):
"""simple docstring"""
UpperCamelCase_ = CodeGenTokenizer
UpperCamelCase_ = CodeGenTokenizerFast
UpperCamelCase_ = True
UpperCamelCase_ = {'add_prefix_space': True}
UpperCamelCase_ = False
def UpperCAmelCase_ ( self : str ) -> Tuple:
'''simple docstring'''
super().setUp()
# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
lowerCAmelCase_ : Optional[Any] = [
"l",
"o",
"w",
"e",
"r",
"s",
"t",
"i",
"d",
"n",
"\u0120",
"\u0120l",
"\u0120n",
"\u0120lo",
"\u0120low",
"er",
"\u0120lowest",
"\u0120newer",
"\u0120wider",
"<unk>",
"<|endoftext|>",
]
lowerCAmelCase_ : int = dict(zip(lowerCAmelCase__ ,range(len(lowerCAmelCase__ ) ) ) )
lowerCAmelCase_ : Dict = ["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
lowerCAmelCase_ : List[Any] = {"unk_token": "<unk>"}
lowerCAmelCase_ : List[Any] = os.path.join(self.tmpdirname ,VOCAB_FILES_NAMES["vocab_file"] )
lowerCAmelCase_ : Tuple = os.path.join(self.tmpdirname ,VOCAB_FILES_NAMES["merges_file"] )
with open(self.vocab_file ,"w" ,encoding="utf-8" ) as fp:
fp.write(json.dumps(lowerCAmelCase__ ) + "\n" )
with open(self.merges_file ,"w" ,encoding="utf-8" ) as fp:
fp.write("\n".join(lowerCAmelCase__ ) )
def UpperCAmelCase_ ( self : Optional[int] ,**lowerCAmelCase__ : str ) -> int:
'''simple docstring'''
kwargs.update(self.special_tokens_map )
return CodeGenTokenizer.from_pretrained(self.tmpdirname ,**lowerCAmelCase__ )
def UpperCAmelCase_ ( self : Optional[int] ,**lowerCAmelCase__ : Optional[Any] ) -> Tuple:
'''simple docstring'''
kwargs.update(self.special_tokens_map )
return CodeGenTokenizerFast.from_pretrained(self.tmpdirname ,**lowerCAmelCase__ )
def UpperCAmelCase_ ( self : Optional[int] ,lowerCAmelCase__ : str ) -> Optional[int]:
'''simple docstring'''
lowerCAmelCase_ : Tuple = "lower newer"
lowerCAmelCase_ : Tuple = "lower newer"
return input_text, output_text
def UpperCAmelCase_ ( self : Optional[Any] ) -> Any:
'''simple docstring'''
lowerCAmelCase_ : Optional[Any] = CodeGenTokenizer(self.vocab_file ,self.merges_file ,**self.special_tokens_map )
lowerCAmelCase_ : Dict = "lower newer"
lowerCAmelCase_ : Dict = ["\u0120low", "er", "\u0120", "n", "e", "w", "er"]
lowerCAmelCase_ : Union[str, Any] = tokenizer.tokenize(lowerCAmelCase__ ,add_prefix_space=lowerCAmelCase__ )
self.assertListEqual(lowerCAmelCase__ ,lowerCAmelCase__ )
lowerCAmelCase_ : Optional[Any] = tokens + [tokenizer.unk_token]
lowerCAmelCase_ : Union[str, Any] = [14, 15, 10, 9, 3, 2, 15, 19]
self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCAmelCase__ ) ,lowerCAmelCase__ )
def UpperCAmelCase_ ( self : List[str] ) -> Optional[Any]:
'''simple docstring'''
if not self.test_rust_tokenizer:
return
lowerCAmelCase_ : Tuple = self.get_tokenizer()
lowerCAmelCase_ : Optional[int] = self.get_rust_tokenizer(add_prefix_space=lowerCAmelCase__ )
lowerCAmelCase_ : Any = "lower newer"
# Testing tokenization
lowerCAmelCase_ : Tuple = tokenizer.tokenize(lowerCAmelCase__ ,add_prefix_space=lowerCAmelCase__ )
lowerCAmelCase_ : Any = rust_tokenizer.tokenize(lowerCAmelCase__ )
self.assertListEqual(lowerCAmelCase__ ,lowerCAmelCase__ )
# Testing conversion to ids without special tokens
lowerCAmelCase_ : str = tokenizer.encode(lowerCAmelCase__ ,add_special_tokens=lowerCAmelCase__ ,add_prefix_space=lowerCAmelCase__ )
lowerCAmelCase_ : Any = rust_tokenizer.encode(lowerCAmelCase__ ,add_special_tokens=lowerCAmelCase__ )
self.assertListEqual(lowerCAmelCase__ ,lowerCAmelCase__ )
# Testing conversion to ids with special tokens
lowerCAmelCase_ : int = self.get_rust_tokenizer(add_prefix_space=lowerCAmelCase__ )
lowerCAmelCase_ : str = tokenizer.encode(lowerCAmelCase__ ,add_prefix_space=lowerCAmelCase__ )
lowerCAmelCase_ : Optional[int] = rust_tokenizer.encode(lowerCAmelCase__ )
self.assertListEqual(lowerCAmelCase__ ,lowerCAmelCase__ )
# Testing the unknown token
lowerCAmelCase_ : Union[str, Any] = tokens + [rust_tokenizer.unk_token]
lowerCAmelCase_ : List[str] = [14, 15, 10, 9, 3, 2, 15, 19]
self.assertListEqual(rust_tokenizer.convert_tokens_to_ids(lowerCAmelCase__ ) ,lowerCAmelCase__ )
def UpperCAmelCase_ ( self : Optional[int] ,*lowerCAmelCase__ : List[str] ,**lowerCAmelCase__ : Optional[Any] ) -> List[str]:
'''simple docstring'''
pass
def UpperCAmelCase_ ( self : Tuple ,lowerCAmelCase__ : Any=15 ) -> str:
'''simple docstring'''
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})''' ):
lowerCAmelCase_ : Any = self.rust_tokenizer_class.from_pretrained(lowerCAmelCase__ ,**lowerCAmelCase__ )
# Simple input
lowerCAmelCase_ : int = "This is a simple input"
lowerCAmelCase_ : Dict = ["This is a simple input 1", "This is a simple input 2"]
lowerCAmelCase_ : str = ("This is a simple input", "This is a pair")
lowerCAmelCase_ : Optional[int] = [
("This is a simple input 1", "This is a simple input 2"),
("This is a simple pair 1", "This is a simple pair 2"),
]
# Simple input tests
self.assertRaises(lowerCAmelCase__ ,tokenizer_r.encode ,lowerCAmelCase__ ,max_length=lowerCAmelCase__ ,padding="max_length" )
# Simple input
self.assertRaises(lowerCAmelCase__ ,tokenizer_r.encode_plus ,lowerCAmelCase__ ,max_length=lowerCAmelCase__ ,padding="max_length" )
# Simple input
self.assertRaises(
lowerCAmelCase__ ,tokenizer_r.batch_encode_plus ,lowerCAmelCase__ ,max_length=lowerCAmelCase__ ,padding="max_length" ,)
# Pair input
self.assertRaises(lowerCAmelCase__ ,tokenizer_r.encode ,lowerCAmelCase__ ,max_length=lowerCAmelCase__ ,padding="max_length" )
# Pair input
self.assertRaises(lowerCAmelCase__ ,tokenizer_r.encode_plus ,lowerCAmelCase__ ,max_length=lowerCAmelCase__ ,padding="max_length" )
# Pair input
self.assertRaises(
lowerCAmelCase__ ,tokenizer_r.batch_encode_plus ,lowerCAmelCase__ ,max_length=lowerCAmelCase__ ,padding="max_length" ,)
def UpperCAmelCase_ ( self : Optional[int] ) -> Optional[int]:
'''simple docstring'''
lowerCAmelCase_ : Tuple = CodeGenTokenizer.from_pretrained(self.tmpdirname ,pad_token="<pad>" )
# Simple input
lowerCAmelCase_ : Dict = "This is a simple input"
lowerCAmelCase_ : List[str] = ["This is a simple input looooooooong", "This is a simple input"]
lowerCAmelCase_ : Any = ("This is a simple input", "This is a pair")
lowerCAmelCase_ : List[str] = [
("This is a simple input loooooong", "This is a simple input"),
("This is a simple pair loooooong", "This is a simple pair"),
]
lowerCAmelCase_ : Dict = tokenizer.pad_token_id
lowerCAmelCase_ : Union[str, Any] = tokenizer(lowerCAmelCase__ ,padding="max_length" ,max_length=30 ,return_tensors="np" )
lowerCAmelCase_ : Tuple = tokenizer(lowerCAmelCase__ ,padding=lowerCAmelCase__ ,truncate=lowerCAmelCase__ ,return_tensors="np" )
lowerCAmelCase_ : Any = tokenizer(*lowerCAmelCase__ ,padding="max_length" ,max_length=60 ,return_tensors="np" )
lowerCAmelCase_ : Optional[int] = tokenizer(lowerCAmelCase__ ,padding=lowerCAmelCase__ ,truncate=lowerCAmelCase__ ,return_tensors="np" )
# s
# test single string max_length padding
self.assertEqual(out_s["input_ids"].shape[-1] ,30 )
self.assertTrue(pad_token_id in out_s["input_ids"] )
self.assertTrue(0 in out_s["attention_mask"] )
# s2
# test automatic padding
self.assertEqual(out_sa["input_ids"].shape[-1] ,33 )
# long slice doesn't have padding
self.assertFalse(pad_token_id in out_sa["input_ids"][0] )
self.assertFalse(0 in out_sa["attention_mask"][0] )
# short slice does have padding
self.assertTrue(pad_token_id in out_sa["input_ids"][1] )
self.assertTrue(0 in out_sa["attention_mask"][1] )
# p
# test single pair max_length padding
self.assertEqual(out_p["input_ids"].shape[-1] ,60 )
self.assertTrue(pad_token_id in out_p["input_ids"] )
self.assertTrue(0 in out_p["attention_mask"] )
# p2
# test automatic padding pair
self.assertEqual(out_pa["input_ids"].shape[-1] ,52 )
# long slice pair doesn't have padding
self.assertFalse(pad_token_id in out_pa["input_ids"][0] )
self.assertFalse(0 in out_pa["attention_mask"][0] )
# short slice pair does have padding
self.assertTrue(pad_token_id in out_pa["input_ids"][1] )
self.assertTrue(0 in out_pa["attention_mask"][1] )
def UpperCAmelCase_ ( self : Union[str, Any] ) -> Any:
'''simple docstring'''
lowerCAmelCase_ : Any = "$$$"
lowerCAmelCase_ : List[str] = CodeGenTokenizer.from_pretrained(self.tmpdirname ,bos_token=lowerCAmelCase__ ,add_bos_token=lowerCAmelCase__ )
lowerCAmelCase_ : Dict = "This is a simple input"
lowerCAmelCase_ : Union[str, Any] = ["This is a simple input 1", "This is a simple input 2"]
lowerCAmelCase_ : int = tokenizer.bos_token_id
lowerCAmelCase_ : List[Any] = tokenizer(lowerCAmelCase__ )
lowerCAmelCase_ : Optional[Any] = tokenizer(lowerCAmelCase__ )
self.assertEqual(out_s.input_ids[0] ,lowerCAmelCase__ )
self.assertTrue(all(o[0] == bos_token_id for o in out_sa.input_ids ) )
lowerCAmelCase_ : List[str] = tokenizer.decode(out_s.input_ids )
lowerCAmelCase_ : Optional[Any] = tokenizer.batch_decode(out_sa.input_ids )
self.assertEqual(decode_s.split()[0] ,lowerCAmelCase__ )
self.assertTrue(all(d.split()[0] == bos_token for d in decode_sa ) )
@slow
def UpperCAmelCase_ ( self : Any ) -> Optional[int]:
'''simple docstring'''
lowerCAmelCase_ : Optional[Any] = CodeGenTokenizer.from_pretrained("Salesforce/codegen-350M-mono" )
lowerCAmelCase_ : str = "\nif len_a > len_b:\n result = a\nelse:\n result = b\n\n\n\n#"
lowerCAmelCase_ : int = "\nif len_a > len_b: result = a\nelse: result = b"
lowerCAmelCase_ : Dict = tokenizer.encode(lowerCAmelCase__ )
lowerCAmelCase_ : str = ["^#", re.escape("<|endoftext|>" ), "^'''", "^\"\"\"", "\n\n\n"]
lowerCAmelCase_ : Union[str, Any] = tokenizer.decode(lowerCAmelCase__ ,truncate_before_pattern=lowerCAmelCase__ )
self.assertEqual(lowerCAmelCase__ ,lowerCAmelCase__ )
def UpperCAmelCase_ ( self : Optional[Any] ) -> Optional[Any]:
'''simple docstring'''
pass
| 683 | 1 |
from datetime import datetime as dt
import os
from github import Github
_lowercase = [
'''good first issue''',
'''good second issue''',
'''good difficult issue''',
'''feature request''',
'''new model''',
'''wip''',
]
def UpperCamelCase ( ):
lowerCAmelCase_ : int = Github(os.environ["GITHUB_TOKEN"])
lowerCAmelCase_ : str = g.get_repo("huggingface/transformers")
lowerCAmelCase_ : List[str] = repo.get_issues(state="open")
for issue in open_issues:
lowerCAmelCase_ : int = sorted([comment for comment in issue.get_comments()] , key=lambda snake_case__: i.created_at , reverse=snake_case__)
lowerCAmelCase_ : Optional[Any] = comments[0] if len(snake_case__) > 0 else None
if (
last_comment is not None
and last_comment.user.login == "github-actions[bot]"
and (dt.utcnow() - issue.updated_at).days > 7
and (dt.utcnow() - issue.created_at).days >= 30
and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels())
):
# print(f"Would close issue {issue.number} since it has been 7 days of inactivity since bot mention.")
issue.edit(state="closed")
elif (
(dt.utcnow() - issue.updated_at).days > 23
and (dt.utcnow() - issue.created_at).days >= 30
and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels())
):
# print(f"Would add stale comment to {issue.number}")
issue.create_comment(
"This issue has been automatically marked as stale because it has not had "
"recent activity. If you think this still needs to be addressed "
"please comment on this thread.\n\nPlease note that issues that do not follow the "
"[contributing guidelines](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md) "
"are likely to be ignored.")
if __name__ == "__main__":
main()
| 683 |
from __future__ import annotations
from random import random
class __snake_case :
"""simple docstring"""
def __init__( self : Optional[int] ,lowerCAmelCase__ : int | None = None ) -> int:
'''simple docstring'''
lowerCAmelCase_ : Dict = value
lowerCAmelCase_ : Any = random()
lowerCAmelCase_ : Node | None = None
lowerCAmelCase_ : Node | None = None
def __repr__( self : Any ) -> str:
'''simple docstring'''
from pprint import pformat
if self.left is None and self.right is None:
return f'''\'{self.value}: {self.prior:.5}\''''
else:
return pformat(
{f'''{self.value}: {self.prior:.5}''': (self.left, self.right)} ,indent=1 )
def __str__( self : str ) -> str:
'''simple docstring'''
lowerCAmelCase_ : List[Any] = str(self.value ) + " "
lowerCAmelCase_ : List[Any] = str(self.left or "" )
lowerCAmelCase_ : Union[str, Any] = str(self.right or "" )
return value + left + right
def UpperCamelCase ( snake_case__ , snake_case__):
if root is None: # None tree is split into 2 Nones
return None, None
elif root.value is None:
return None, None
else:
if value < root.value:
lowerCAmelCase_ , lowerCAmelCase_ : Any = split(root.left , snake_case__)
return left, root
else:
lowerCAmelCase_ , lowerCAmelCase_ : Optional[Any] = split(root.right , snake_case__)
return root, right
def UpperCamelCase ( snake_case__ , snake_case__):
if (not left) or (not right): # If one node is None, return the other
return left or right
elif left.prior < right.prior:
lowerCAmelCase_ : Dict = merge(left.right , snake_case__)
return left
else:
lowerCAmelCase_ : List[str] = merge(snake_case__ , right.left)
return right
def UpperCamelCase ( snake_case__ , snake_case__):
lowerCAmelCase_ : List[Any] = Node(snake_case__)
lowerCAmelCase_ , lowerCAmelCase_ : Tuple = split(snake_case__ , snake_case__)
return merge(merge(snake_case__ , snake_case__) , snake_case__)
def UpperCamelCase ( snake_case__ , snake_case__):
lowerCAmelCase_ , lowerCAmelCase_ : List[str] = split(snake_case__ , value - 1)
lowerCAmelCase_ , lowerCAmelCase_ : Union[str, Any] = split(snake_case__ , snake_case__)
return merge(snake_case__ , snake_case__)
def UpperCamelCase ( snake_case__):
if not root: # None
return
else:
inorder(root.left)
print(root.value , end=",")
inorder(root.right)
def UpperCamelCase ( snake_case__ , snake_case__):
for arg in args.split():
if arg[0] == "+":
lowerCAmelCase_ : List[str] = insert(snake_case__ , int(arg[1:]))
elif arg[0] == "-":
lowerCAmelCase_ : Optional[int] = erase(snake_case__ , int(arg[1:]))
else:
print("Unknown command")
return root
def UpperCamelCase ( ):
lowerCAmelCase_ : str = None
print(
"enter numbers to create a tree, + value to add value into treap, "
"- value to erase all nodes with value. 'q' to quit. ")
lowerCAmelCase_ : str = input()
while args != "q":
lowerCAmelCase_ : int = interact_treap(snake_case__ , snake_case__)
print(snake_case__)
lowerCAmelCase_ : str = input()
print("good by!")
if __name__ == "__main__":
import doctest
doctest.testmod()
main()
| 683 | 1 |
from multiprocessing import Lock, Pipe, Process
# lock used to ensure that two processes do not access a pipe at the same time
_lowercase = Lock()
def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__):
global process_lock
# we perform n swaps since after n swaps we know we are sorted
# we *could* stop early if we are sorted already, but it takes as long to
# find out we are sorted as it does to sort the list with this algorithm
for i in range(0 , 10):
if (i + position) % 2 == 0 and r_send is not None:
# send your value to your right neighbor
process_lock.acquire()
r_send[1].send(snake_case__)
process_lock.release()
# receive your right neighbor's value
process_lock.acquire()
lowerCAmelCase_ : Optional[Any] = rr_cv[0].recv()
process_lock.release()
# take the lower value since you are on the left
lowerCAmelCase_ : Any = min(snake_case__ , snake_case__)
elif (i + position) % 2 != 0 and l_send is not None:
# send your value to your left neighbor
process_lock.acquire()
l_send[1].send(snake_case__)
process_lock.release()
# receive your left neighbor's value
process_lock.acquire()
lowerCAmelCase_ : str = lr_cv[0].recv()
process_lock.release()
# take the higher value since you are on the right
lowerCAmelCase_ : Dict = max(snake_case__ , snake_case__)
# after all swaps are performed, send the values back to main
result_pipe[1].send(snake_case__)
def UpperCamelCase ( snake_case__):
lowerCAmelCase_ : Union[str, Any] = []
lowerCAmelCase_ : int = []
# initialize the list of pipes where the values will be retrieved
for _ in arr:
result_pipe.append(Pipe())
# creates the processes
# the first and last process only have one neighbor so they are made outside
# of the loop
lowerCAmelCase_ : Tuple = Pipe()
lowerCAmelCase_ : Optional[int] = Pipe()
process_array_.append(
Process(
target=snake_case__ , args=(0, arr[0], None, temp_rs, None, temp_rr, result_pipe[0]) , ))
lowerCAmelCase_ : int = temp_rs
lowerCAmelCase_ : List[Any] = temp_rr
for i in range(1 , len(snake_case__) - 1):
lowerCAmelCase_ : Dict = Pipe()
lowerCAmelCase_ : List[str] = Pipe()
process_array_.append(
Process(
target=snake_case__ , args=(i, arr[i], temp_ls, temp_rs, temp_lr, temp_rr, result_pipe[i]) , ))
lowerCAmelCase_ : Dict = temp_rs
lowerCAmelCase_ : Optional[Any] = temp_rr
process_array_.append(
Process(
target=snake_case__ , args=(
len(snake_case__) - 1,
arr[len(snake_case__) - 1],
temp_ls,
None,
temp_lr,
None,
result_pipe[len(snake_case__) - 1],
) , ))
# start the processes
for p in process_array_:
p.start()
# wait for the processes to end and write their values to the list
for p in range(0 , len(snake_case__)):
lowerCAmelCase_ : Union[str, Any] = result_pipe[p][0].recv()
process_array_[p].join()
return arr
def UpperCamelCase ( ):
lowerCAmelCase_ : Optional[Any] = list(range(10 , 0 , -1))
print("Initial List")
print(*snake_case__)
lowerCAmelCase_ : Tuple = odd_even_transposition(snake_case__)
print("Sorted List\n")
print(*snake_case__)
if __name__ == "__main__":
main()
| 683 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_funnel import FunnelTokenizer
_lowercase = logging.get_logger(__name__)
_lowercase = {'''vocab_file''': '''vocab.txt''', '''tokenizer_file''': '''tokenizer.json'''}
_lowercase = [
'''small''',
'''small-base''',
'''medium''',
'''medium-base''',
'''intermediate''',
'''intermediate-base''',
'''large''',
'''large-base''',
'''xlarge''',
'''xlarge-base''',
]
_lowercase = {
'''vocab_file''': {
'''funnel-transformer/small''': '''https://huggingface.co/funnel-transformer/small/resolve/main/vocab.txt''',
'''funnel-transformer/small-base''': '''https://huggingface.co/funnel-transformer/small-base/resolve/main/vocab.txt''',
'''funnel-transformer/medium''': '''https://huggingface.co/funnel-transformer/medium/resolve/main/vocab.txt''',
'''funnel-transformer/medium-base''': (
'''https://huggingface.co/funnel-transformer/medium-base/resolve/main/vocab.txt'''
),
'''funnel-transformer/intermediate''': (
'''https://huggingface.co/funnel-transformer/intermediate/resolve/main/vocab.txt'''
),
'''funnel-transformer/intermediate-base''': (
'''https://huggingface.co/funnel-transformer/intermediate-base/resolve/main/vocab.txt'''
),
'''funnel-transformer/large''': '''https://huggingface.co/funnel-transformer/large/resolve/main/vocab.txt''',
'''funnel-transformer/large-base''': '''https://huggingface.co/funnel-transformer/large-base/resolve/main/vocab.txt''',
'''funnel-transformer/xlarge''': '''https://huggingface.co/funnel-transformer/xlarge/resolve/main/vocab.txt''',
'''funnel-transformer/xlarge-base''': (
'''https://huggingface.co/funnel-transformer/xlarge-base/resolve/main/vocab.txt'''
),
},
'''tokenizer_file''': {
'''funnel-transformer/small''': '''https://huggingface.co/funnel-transformer/small/resolve/main/tokenizer.json''',
'''funnel-transformer/small-base''': (
'''https://huggingface.co/funnel-transformer/small-base/resolve/main/tokenizer.json'''
),
'''funnel-transformer/medium''': '''https://huggingface.co/funnel-transformer/medium/resolve/main/tokenizer.json''',
'''funnel-transformer/medium-base''': (
'''https://huggingface.co/funnel-transformer/medium-base/resolve/main/tokenizer.json'''
),
'''funnel-transformer/intermediate''': (
'''https://huggingface.co/funnel-transformer/intermediate/resolve/main/tokenizer.json'''
),
'''funnel-transformer/intermediate-base''': (
'''https://huggingface.co/funnel-transformer/intermediate-base/resolve/main/tokenizer.json'''
),
'''funnel-transformer/large''': '''https://huggingface.co/funnel-transformer/large/resolve/main/tokenizer.json''',
'''funnel-transformer/large-base''': (
'''https://huggingface.co/funnel-transformer/large-base/resolve/main/tokenizer.json'''
),
'''funnel-transformer/xlarge''': '''https://huggingface.co/funnel-transformer/xlarge/resolve/main/tokenizer.json''',
'''funnel-transformer/xlarge-base''': (
'''https://huggingface.co/funnel-transformer/xlarge-base/resolve/main/tokenizer.json'''
),
},
}
_lowercase = {f"funnel-transformer/{name}": 512 for name in _model_names}
_lowercase = {f"funnel-transformer/{name}": {'''do_lower_case''': True} for name in _model_names}
class __snake_case ( snake_case__ ):
"""simple docstring"""
UpperCamelCase_ = VOCAB_FILES_NAMES
UpperCamelCase_ = PRETRAINED_VOCAB_FILES_MAP
UpperCamelCase_ = PRETRAINED_INIT_CONFIGURATION
UpperCamelCase_ = FunnelTokenizer
UpperCamelCase_ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
UpperCamelCase_ = 2
def __init__( self : Optional[Any] ,lowerCAmelCase__ : Any=None ,lowerCAmelCase__ : Optional[int]=None ,lowerCAmelCase__ : Optional[Any]=True ,lowerCAmelCase__ : List[str]="<unk>" ,lowerCAmelCase__ : int="<sep>" ,lowerCAmelCase__ : Union[str, Any]="<pad>" ,lowerCAmelCase__ : List[str]="<cls>" ,lowerCAmelCase__ : Optional[int]="<mask>" ,lowerCAmelCase__ : Union[str, Any]="<s>" ,lowerCAmelCase__ : List[str]="</s>" ,lowerCAmelCase__ : Optional[int]=True ,lowerCAmelCase__ : Tuple=True ,lowerCAmelCase__ : Any=None ,lowerCAmelCase__ : List[Any]="##" ,**lowerCAmelCase__ : int ,) -> List[Any]:
'''simple docstring'''
super().__init__(
lowerCAmelCase__ ,tokenizer_file=lowerCAmelCase__ ,do_lower_case=lowerCAmelCase__ ,unk_token=lowerCAmelCase__ ,sep_token=lowerCAmelCase__ ,pad_token=lowerCAmelCase__ ,cls_token=lowerCAmelCase__ ,mask_token=lowerCAmelCase__ ,bos_token=lowerCAmelCase__ ,eos_token=lowerCAmelCase__ ,clean_text=lowerCAmelCase__ ,tokenize_chinese_chars=lowerCAmelCase__ ,strip_accents=lowerCAmelCase__ ,wordpieces_prefix=lowerCAmelCase__ ,**lowerCAmelCase__ ,)
lowerCAmelCase_ : str = json.loads(self.backend_tokenizer.normalizer.__getstate__() )
if (
normalizer_state.get("lowercase" ,lowerCAmelCase__ ) != do_lower_case
or normalizer_state.get("strip_accents" ,lowerCAmelCase__ ) != strip_accents
or normalizer_state.get("handle_chinese_chars" ,lowerCAmelCase__ ) != tokenize_chinese_chars
):
lowerCAmelCase_ : Optional[int] = getattr(lowerCAmelCase__ ,normalizer_state.pop("type" ) )
lowerCAmelCase_ : List[Any] = do_lower_case
lowerCAmelCase_ : List[str] = strip_accents
lowerCAmelCase_ : Any = tokenize_chinese_chars
lowerCAmelCase_ : List[Any] = normalizer_class(**lowerCAmelCase__ )
lowerCAmelCase_ : int = do_lower_case
def UpperCAmelCase_ ( self : Tuple ,lowerCAmelCase__ : int ,lowerCAmelCase__ : str=None ) -> List[Any]:
'''simple docstring'''
lowerCAmelCase_ : Union[str, Any] = [self.cls_token_id] + token_ids_a + [self.sep_token_id]
if token_ids_a:
output += token_ids_a + [self.sep_token_id]
return output
def UpperCAmelCase_ ( self : List[str] ,lowerCAmelCase__ : List[int] ,lowerCAmelCase__ : Optional[List[int]] = None ) -> List[int]:
'''simple docstring'''
lowerCAmelCase_ : str = [self.sep_token_id]
lowerCAmelCase_ : Optional[Any] = [self.cls_token_id]
if token_ids_a is None:
return len(cls ) * [self.cls_token_type_id] + len(token_ids_a + sep ) * [0]
return len(cls ) * [self.cls_token_type_id] + len(token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]
def UpperCAmelCase_ ( self : Any ,lowerCAmelCase__ : str ,lowerCAmelCase__ : Optional[str] = None ) -> Tuple[str]:
'''simple docstring'''
lowerCAmelCase_ : str = self._tokenizer.model.save(lowerCAmelCase__ ,name=lowerCAmelCase__ )
return tuple(lowerCAmelCase__ )
| 683 | 1 |
def UpperCamelCase ( ):
lowerCAmelCase_ : Tuple = []
lowerCAmelCase_ : int = 1
while len(snake_case__) < 1e6:
constant.append(str(snake_case__))
i += 1
lowerCAmelCase_ : List[Any] = "".join(snake_case__)
return (
int(constant[0])
* int(constant[9])
* int(constant[99])
* int(constant[9_99])
* int(constant[99_99])
* int(constant[9_99_99])
* int(constant[99_99_99])
)
if __name__ == "__main__":
print(solution())
| 683 |
# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run
import doctest
import sys
import warnings
from os.path import abspath, dirname, join
import _pytest
from transformers.testing_utils import HfDoctestModule, HfDocTestParser
# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install -e .[dev]' when switching between checkouts and running tests.
_lowercase = abspath(join(dirname(__file__), '''src'''))
sys.path.insert(1, git_repo_path)
# silence FutureWarning warnings in tests since often we can't act on them until
# they become normal warnings - i.e. the tests still need to test the current functionality
warnings.simplefilter(action='''ignore''', category=FutureWarning)
def UpperCamelCase ( snake_case__):
config.addinivalue_line(
"markers" , "is_pt_tf_cross_test: mark test to run only when PT and TF interactions are tested")
config.addinivalue_line(
"markers" , "is_pt_flax_cross_test: mark test to run only when PT and FLAX interactions are tested")
config.addinivalue_line("markers" , "is_pipeline_test: mark test to run only when pipelines are tested")
config.addinivalue_line("markers" , "is_staging_test: mark test to run only in the staging environment")
config.addinivalue_line("markers" , "accelerate_tests: mark test that require accelerate")
config.addinivalue_line("markers" , "tool_tests: mark the tool tests that are run on their specific schedule")
def UpperCamelCase ( snake_case__):
from transformers.testing_utils import pytest_addoption_shared
pytest_addoption_shared(snake_case__)
def UpperCamelCase ( snake_case__):
from transformers.testing_utils import pytest_terminal_summary_main
lowerCAmelCase_ : int = terminalreporter.config.getoption("--make-reports")
if make_reports:
pytest_terminal_summary_main(snake_case__ , id=snake_case__)
def UpperCamelCase ( snake_case__ , snake_case__):
# If no tests are collected, pytest exists with code 5, which makes the CI fail.
if exitstatus == 5:
lowerCAmelCase_ : List[Any] = 0
# Doctest custom flag to ignore output.
_lowercase = doctest.register_optionflag('''IGNORE_RESULT''')
_lowercase = doctest.OutputChecker
class __snake_case ( snake_case__ ):
"""simple docstring"""
def UpperCAmelCase_ ( self : List[str] ,lowerCAmelCase__ : int ,lowerCAmelCase__ : Optional[int] ,lowerCAmelCase__ : Tuple ) -> Any:
'''simple docstring'''
if IGNORE_RESULT & optionflags:
return True
return OutputChecker.check_output(self ,lowerCAmelCase__ ,lowerCAmelCase__ ,lowerCAmelCase__ )
_lowercase = CustomOutputChecker
_lowercase = HfDoctestModule
_lowercase = HfDocTestParser
| 683 | 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_mvp import MvpTokenizer
_lowercase = logging.get_logger(__name__)
_lowercase = {'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', '''tokenizer_file''': '''tokenizer.json'''}
# See all MVP models at https://huggingface.co/models?filter=mvp
_lowercase = {
'''vocab_file''': {
'''RUCAIBox/mvp''': '''https://huggingface.co/RUCAIBox/mvp/resolve/main/vocab.json''',
},
'''added_tokens.json''': {
'''RUCAIBox/mvp''': '''https://huggingface.co/RUCAIBox/mvp/resolve/main/added_tokens.json''',
},
'''merges_file''': {
'''RUCAIBox/mvp''': '''https://huggingface.co/RUCAIBox/mvp/resolve/main/merges.txt''',
},
'''tokenizer_file''': {
'''RUCAIBox/mvp''': '''https://huggingface.co/RUCAIBox/mvp/resolve/main/tokenizer.json''',
},
}
_lowercase = {
'''RUCAIBox/mvp''': 1024,
}
class __snake_case ( snake_case__ ):
"""simple docstring"""
UpperCamelCase_ = VOCAB_FILES_NAMES
UpperCamelCase_ = PRETRAINED_VOCAB_FILES_MAP
UpperCamelCase_ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
UpperCamelCase_ = ['input_ids', 'attention_mask']
UpperCamelCase_ = MvpTokenizer
def __init__( self : Any ,lowerCAmelCase__ : Dict=None ,lowerCAmelCase__ : int=None ,lowerCAmelCase__ : Any=None ,lowerCAmelCase__ : Union[str, Any]="replace" ,lowerCAmelCase__ : Union[str, Any]="<s>" ,lowerCAmelCase__ : List[str]="</s>" ,lowerCAmelCase__ : Optional[int]="</s>" ,lowerCAmelCase__ : List[Any]="<s>" ,lowerCAmelCase__ : Optional[Any]="<unk>" ,lowerCAmelCase__ : Union[str, Any]="<pad>" ,lowerCAmelCase__ : Tuple="<mask>" ,lowerCAmelCase__ : Any=False ,lowerCAmelCase__ : Optional[Any]=True ,**lowerCAmelCase__ : Optional[Any] ,) -> Union[str, Any]:
'''simple docstring'''
super().__init__(
lowerCAmelCase__ ,lowerCAmelCase__ ,tokenizer_file=lowerCAmelCase__ ,errors=lowerCAmelCase__ ,bos_token=lowerCAmelCase__ ,eos_token=lowerCAmelCase__ ,sep_token=lowerCAmelCase__ ,cls_token=lowerCAmelCase__ ,unk_token=lowerCAmelCase__ ,pad_token=lowerCAmelCase__ ,mask_token=lowerCAmelCase__ ,add_prefix_space=lowerCAmelCase__ ,trim_offsets=lowerCAmelCase__ ,**lowerCAmelCase__ ,)
lowerCAmelCase_ : Optional[Any] = json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__() )
if pre_tok_state.get("add_prefix_space" ,lowerCAmelCase__ ) != add_prefix_space:
lowerCAmelCase_ : List[str] = getattr(lowerCAmelCase__ ,pre_tok_state.pop("type" ) )
lowerCAmelCase_ : Tuple = add_prefix_space
lowerCAmelCase_ : List[Any] = pre_tok_class(**lowerCAmelCase__ )
lowerCAmelCase_ : Union[str, Any] = add_prefix_space
# the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
lowerCAmelCase_ : Optional[Any] = "post_processor"
lowerCAmelCase_ : Union[str, Any] = getattr(self.backend_tokenizer ,lowerCAmelCase__ ,lowerCAmelCase__ )
if tokenizer_component_instance:
lowerCAmelCase_ : List[Any] = 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:
lowerCAmelCase_ : int = tuple(state["sep"] )
if "cls" in state:
lowerCAmelCase_ : List[str] = tuple(state["cls"] )
lowerCAmelCase_ : List[Any] = False
if state.get("add_prefix_space" ,lowerCAmelCase__ ) != add_prefix_space:
lowerCAmelCase_ : Tuple = add_prefix_space
lowerCAmelCase_ : Tuple = True
if state.get("trim_offsets" ,lowerCAmelCase__ ) != trim_offsets:
lowerCAmelCase_ : str = trim_offsets
lowerCAmelCase_ : int = True
if changes_to_apply:
lowerCAmelCase_ : List[str] = getattr(lowerCAmelCase__ ,state.pop("type" ) )
lowerCAmelCase_ : Dict = component_class(**lowerCAmelCase__ )
setattr(self.backend_tokenizer ,lowerCAmelCase__ ,lowerCAmelCase__ )
@property
def UpperCAmelCase_ ( self : Any ) -> 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 : Tuple ,lowerCAmelCase__ : Dict ) -> Tuple:
'''simple docstring'''
lowerCAmelCase_ : Tuple = AddedToken(lowerCAmelCase__ ,lstrip=lowerCAmelCase__ ,rstrip=lowerCAmelCase__ ) if isinstance(lowerCAmelCase__ ,lowerCAmelCase__ ) else value
lowerCAmelCase_ : Optional[int] = value
def UpperCAmelCase_ ( self : int ,*lowerCAmelCase__ : int ,**lowerCAmelCase__ : Optional[Any] ) -> BatchEncoding:
'''simple docstring'''
lowerCAmelCase_ : Dict = kwargs.get("is_split_into_words" ,lowerCAmelCase__ )
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(*lowerCAmelCase__ ,**lowerCAmelCase__ )
def UpperCAmelCase_ ( self : Union[str, Any] ,*lowerCAmelCase__ : List[str] ,**lowerCAmelCase__ : Union[str, Any] ) -> BatchEncoding:
'''simple docstring'''
lowerCAmelCase_ : List[str] = kwargs.get("is_split_into_words" ,lowerCAmelCase__ )
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(*lowerCAmelCase__ ,**lowerCAmelCase__ )
def UpperCAmelCase_ ( self : str ,lowerCAmelCase__ : str ,lowerCAmelCase__ : Optional[str] = None ) -> Tuple[str]:
'''simple docstring'''
lowerCAmelCase_ : str = self._tokenizer.model.save(lowerCAmelCase__ ,name=lowerCAmelCase__ )
return tuple(lowerCAmelCase__ )
def UpperCAmelCase_ ( self : Union[str, Any] ,lowerCAmelCase__ : Optional[Any] ,lowerCAmelCase__ : Dict=None ) -> Union[str, Any]:
'''simple docstring'''
lowerCAmelCase_ : 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 : List[str] ,lowerCAmelCase__ : List[int] ,lowerCAmelCase__ : Optional[List[int]] = None ) -> List[int]:
'''simple docstring'''
lowerCAmelCase_ : int = [self.sep_token_id]
lowerCAmelCase_ : str = [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]
| 683 |
from __future__ import annotations
from collections.abc import Sequence
from typing import Literal
def UpperCamelCase ( snake_case__ , snake_case__):
lowerCAmelCase_ : Optional[int] = list(snake_case__)
lowerCAmelCase_ : Tuple = list(snake_case__)
lowerCAmelCase_ : List[str] = 0
for i in range(len(snake_case__)):
if lista[i] != lista[i]:
count += 1
lowerCAmelCase_ : Dict = "_"
if count > 1:
return False
else:
return "".join(snake_case__)
def UpperCamelCase ( snake_case__):
lowerCAmelCase_ : Union[str, Any] = []
while True:
lowerCAmelCase_ : Tuple = ["$"] * len(snake_case__)
lowerCAmelCase_ : Tuple = []
for i in range(len(snake_case__)):
for j in range(i + 1 , len(snake_case__)):
lowerCAmelCase_ : Optional[int] = compare_string(binary[i] , binary[j])
if k is False:
lowerCAmelCase_ : str = "*"
lowerCAmelCase_ : Tuple = "*"
temp.append("X")
for i in range(len(snake_case__)):
if checka[i] == "$":
pi.append(binary[i])
if len(snake_case__) == 0:
return pi
lowerCAmelCase_ : List[Any] = list(set(snake_case__))
def UpperCamelCase ( snake_case__ , snake_case__):
lowerCAmelCase_ : Optional[int] = []
for minterm in minterms:
lowerCAmelCase_ : Dict = ""
for _ in range(snake_case__):
lowerCAmelCase_ : Dict = str(minterm % 2) + string
minterm //= 2
temp.append(snake_case__)
return temp
def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__):
lowerCAmelCase_ : Optional[Any] = list(snake_case__)
lowerCAmelCase_ : Dict = list(snake_case__)
lowerCAmelCase_ : Dict = 0
for i in range(len(snake_case__)):
if lista[i] != lista[i]:
count_n += 1
return count_n == count
def UpperCamelCase ( snake_case__ , snake_case__):
lowerCAmelCase_ : Optional[Any] = []
lowerCAmelCase_ : Dict = [0] * len(snake_case__)
for i in range(len(chart[0])):
lowerCAmelCase_ : List[Any] = 0
lowerCAmelCase_ : int = -1
for j in range(len(snake_case__)):
if chart[j][i] == 1:
count += 1
lowerCAmelCase_ : Optional[int] = j
if count == 1:
lowerCAmelCase_ : Union[str, Any] = 1
for i in range(len(snake_case__)):
if select[i] == 1:
for j in range(len(chart[0])):
if chart[i][j] == 1:
for k in range(len(snake_case__)):
lowerCAmelCase_ : Tuple = 0
temp.append(prime_implicants[i])
while True:
lowerCAmelCase_ : Optional[Any] = 0
lowerCAmelCase_ : Dict = -1
lowerCAmelCase_ : Tuple = 0
for i in range(len(snake_case__)):
lowerCAmelCase_ : Dict = chart[i].count(1)
if count_n > max_n:
lowerCAmelCase_ : Optional[int] = count_n
lowerCAmelCase_ : Optional[Any] = i
if max_n == 0:
return temp
temp.append(prime_implicants[rem])
for i in range(len(chart[0])):
if chart[rem][i] == 1:
for j in range(len(snake_case__)):
lowerCAmelCase_ : Any = 0
def UpperCamelCase ( snake_case__ , snake_case__):
lowerCAmelCase_ : str = [[0 for x in range(len(snake_case__))] for x in range(len(snake_case__))]
for i in range(len(snake_case__)):
lowerCAmelCase_ : Optional[Any] = prime_implicants[i].count("_")
for j in range(len(snake_case__)):
if is_for_table(prime_implicants[i] , binary[j] , snake_case__):
lowerCAmelCase_ : Dict = 1
return chart
def UpperCamelCase ( ):
lowerCAmelCase_ : Optional[Any] = int(input("Enter the no. of variables\n"))
lowerCAmelCase_ : Tuple = [
float(snake_case__)
for x in input(
"Enter the decimal representation of Minterms 'Spaces Separated'\n").split()
]
lowerCAmelCase_ : Any = decimal_to_binary(snake_case__ , snake_case__)
lowerCAmelCase_ : Dict = check(snake_case__)
print("Prime Implicants are:")
print(snake_case__)
lowerCAmelCase_ : int = prime_implicant_chart(snake_case__ , snake_case__)
lowerCAmelCase_ : List[str] = selection(snake_case__ , snake_case__)
print("Essential Prime Implicants are:")
print(snake_case__)
if __name__ == "__main__":
import doctest
doctest.testmod()
main()
| 683 | 1 |
from __future__ import annotations
import typing
from collections import Counter
def UpperCamelCase ( snake_case__):
lowerCAmelCase_ : typing.Counter[int] = Counter()
for base in range(1 , max_perimeter + 1):
for perpendicular in range(snake_case__ , max_perimeter + 1):
lowerCAmelCase_ : Union[str, Any] = (base * base + perpendicular * perpendicular) ** 0.5
if hypotenuse == int(snake_case__):
lowerCAmelCase_ : List[str] = int(base + perpendicular + hypotenuse)
if perimeter > max_perimeter:
continue
triplets[perimeter] += 1
return triplets
def UpperCamelCase ( snake_case__ = 10_00):
lowerCAmelCase_ : Any = pythagorean_triple(snake_case__)
return triplets.most_common(1)[0][0]
if __name__ == "__main__":
print(f"Perimeter {solution()} has maximum solutions")
| 683 |
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
_lowercase = logging.getLogger(__name__)
def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__ = None , snake_case__ = None , snake_case__ = None , snake_case__ = None , snake_case__ = None , snake_case__ = False , ):
lowerCAmelCase_ : List[Any] = bnb_quantization_config.load_in_abit
lowerCAmelCase_ : Optional[Any] = 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.")
lowerCAmelCase_ : List[str] = []
# custom device map
if isinstance(snake_case__ , snake_case__) and len(device_map.keys()) > 1:
lowerCAmelCase_ : Union[str, Any] = [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:
lowerCAmelCase_ : Union[str, Any] = get_keys_to_not_convert(snake_case__)
# add cpu modules to skip modules only for 4-bit modules
if load_in_abit:
bnb_quantization_config.skip_modules.extend(snake_case__)
lowerCAmelCase_ : Union[str, Any] = 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:
lowerCAmelCase_ : Optional[int] = []
lowerCAmelCase_ : int = bnb_quantization_config.keep_in_fpaa_modules
modules_to_not_convert.extend(snake_case__)
# compatibility with peft
lowerCAmelCase_ : Optional[int] = load_in_abit
lowerCAmelCase_ : List[str] = load_in_abit
lowerCAmelCase_ : Optional[int] = get_parameter_device(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.")
lowerCAmelCase_ : Union[str, Any] = replace_with_bnb_layers(snake_case__ , snake_case__ , modules_to_not_convert=snake_case__)
# convert param to the right dtype
lowerCAmelCase_ : Any = 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:
lowerCAmelCase_ : Optional[int] = name.replace(".weight" , "").replace(".bias" , "")
lowerCAmelCase_ : Optional[int] = getattr(snake_case__ , snake_case__ , snake_case__)
if param is not None:
param.to(torch.floataa)
elif torch.is_floating_point(snake_case__):
param.to(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():
lowerCAmelCase_ : str = replace_with_bnb_layers(
snake_case__ , snake_case__ , modules_to_not_convert=snake_case__)
lowerCAmelCase_ : Optional[int] = get_quantized_model_device_map(
snake_case__ , snake_case__ , snake_case__ , max_memory=snake_case__ , no_split_module_classes=snake_case__ , )
if offload_state_dict is None and device_map is not None and "disk" in device_map.values():
lowerCAmelCase_ : Optional[Any] = True
lowerCAmelCase_ : Optional[int] = any(x in list(device_map.values()) for x in ["cpu", "disk"])
load_checkpoint_in_model(
snake_case__ , snake_case__ , snake_case__ , dtype=bnb_quantization_config.torch_dtype , offload_folder=snake_case__ , offload_state_dict=snake_case__ , keep_in_fpaa_modules=bnb_quantization_config.keep_in_fpaa_modules , offload_abit_bnb=load_in_abit and offload , )
return dispatch_model(snake_case__ , device_map=snake_case__ , offload_dir=snake_case__)
def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__=None , snake_case__=None , snake_case__=None):
if device_map is None:
if torch.cuda.is_available():
lowerCAmelCase_ : Any = {"": 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(snake_case__ , 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'.")
lowerCAmelCase_ : Dict = {}
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)
})
lowerCAmelCase_ : List[str] = {}
lowerCAmelCase_ : Union[str, Any] = special_dtypes
lowerCAmelCase_ : Union[str, Any] = no_split_module_classes
lowerCAmelCase_ : Any = bnb_quantization_config.target_dtype
# get max_memory for each device.
if device_map != "sequential":
lowerCAmelCase_ : Tuple = get_balanced_memory(
snake_case__ , low_zero=(device_map == "balanced_low_0") , max_memory=snake_case__ , **snake_case__ , )
lowerCAmelCase_ : Tuple = max_memory
lowerCAmelCase_ : Optional[Any] = infer_auto_device_map(snake_case__ , **snake_case__)
if isinstance(snake_case__ , snake_case__):
# check if don't have any quantized module on the cpu
lowerCAmelCase_ : Union[str, Any] = bnb_quantization_config.skip_modules + bnb_quantization_config.keep_in_fpaa_modules
lowerCAmelCase_ : List[Any] = {
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(
"\n Some modules are dispatched on the CPU or the disk. Make sure you have enough GPU RAM to fit\n the quantized model. If you want to dispatch the model on the CPU or the disk while keeping\n these modules in `torch_dtype`, you need to pass a custom `device_map` to\n `load_and_quantize_model`. Check\n https://huggingface.co/docs/accelerate/main/en/usage_guides/quantization#offload-modules-to-cpu-and-disk\n for more details.\n ")
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 UpperCamelCase ( snake_case__ , snake_case__ , snake_case__=None , snake_case__=None):
if modules_to_not_convert is None:
lowerCAmelCase_ : List[str] = []
lowerCAmelCase_ , lowerCAmelCase_ : Tuple = _replace_with_bnb_layers(
snake_case__ , snake_case__ , snake_case__ , 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 UpperCamelCase ( snake_case__ , snake_case__ , snake_case__=None , snake_case__=None , ):
lowerCAmelCase_ : str = False
for name, module in model.named_children():
if current_key_name is None:
lowerCAmelCase_ : Optional[int] = []
current_key_name.append(snake_case__)
if isinstance(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`
lowerCAmelCase_ : Optional[int] = ".".join(snake_case__)
lowerCAmelCase_ : List[str] = 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:
lowerCAmelCase_ : List[Any] = False
break
if proceed:
# Load bnb module with empty weight and replace ``nn.Linear` module
if bnb_quantization_config.load_in_abit:
lowerCAmelCase_ : Tuple = bnb.nn.LinearabitLt(
module.in_features , module.out_features , module.bias is not None , has_fpaa_weights=snake_case__ , threshold=bnb_quantization_config.llm_inta_threshold , )
elif bnb_quantization_config.load_in_abit:
lowerCAmelCase_ : Dict = 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")
lowerCAmelCase_ : List[str] = module.weight.data
if module.bias is not None:
lowerCAmelCase_ : Any = module.bias.data
bnb_module.requires_grad_(snake_case__)
setattr(snake_case__ , snake_case__ , snake_case__)
lowerCAmelCase_ : List[str] = True
if len(list(module.children())) > 0:
lowerCAmelCase_ , lowerCAmelCase_ : List[Any] = _replace_with_bnb_layers(
snake_case__ , snake_case__ , snake_case__ , snake_case__)
lowerCAmelCase_ : Optional[int] = has_been_replaced | _has_been_replaced
# Remove the last key for recursion
current_key_name.pop(-1)
return model, has_been_replaced
def UpperCamelCase ( snake_case__):
# Create a copy of the model
with init_empty_weights():
lowerCAmelCase_ : List[Any] = deepcopy(snake_case__) # this has 0 cost since it is done inside `init_empty_weights` context manager`
lowerCAmelCase_ : Dict = find_tied_parameters(snake_case__)
# For compatibility with Accelerate < 0.18
if isinstance(snake_case__ , snake_case__):
lowerCAmelCase_ : List[str] = sum(list(tied_params.values()) , []) + list(tied_params.keys())
else:
lowerCAmelCase_ : Optional[Any] = sum(snake_case__ , [])
lowerCAmelCase_ : List[Any] = len(snake_case__) > 0
# Check if it is a base model
lowerCAmelCase_ : List[str] = False
if hasattr(snake_case__ , "base_model_prefix"):
lowerCAmelCase_ : Tuple = not hasattr(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
lowerCAmelCase_ : Union[str, Any] = list(model.named_children())
lowerCAmelCase_ : Optional[int] = [list_modules[-1][0]]
# add last module together with tied weights
lowerCAmelCase_ : Any = set(snake_case__) - set(snake_case__)
lowerCAmelCase_ : Tuple = list(set(snake_case__)) + list(snake_case__)
# remove ".weight" from the keys
lowerCAmelCase_ : List[str] = [".weight", ".bias"]
lowerCAmelCase_ : Tuple = []
for name in list_untouched:
for name_to_remove in names_to_remove:
if name_to_remove in name:
lowerCAmelCase_ : str = name.replace(snake_case__ , "")
filtered_module_names.append(snake_case__)
return filtered_module_names
def UpperCamelCase ( snake_case__):
for m in model.modules():
if isinstance(snake_case__ , bnb.nn.Linearabit):
return True
return False
def UpperCamelCase ( snake_case__):
return next(parameter.parameters()).device
def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__):
# 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(snake_case__ , snake_case__ , 0 , dtype=snake_case__ , value=snake_case__)
lowerCAmelCase_ : str = param_name
lowerCAmelCase_ : Tuple = model
if "." in tensor_name:
lowerCAmelCase_ : Dict = tensor_name.split(".")
for split in splits[:-1]:
lowerCAmelCase_ : Any = getattr(snake_case__ , snake_case__)
if new_module is None:
raise ValueError(F'''{module} has no attribute {split}.''')
lowerCAmelCase_ : Union[str, Any] = new_module
lowerCAmelCase_ : Any = splits[-1]
# offload weights
lowerCAmelCase_ : List[Any] = False
offload_weight(module._parameters[tensor_name] , snake_case__ , snake_case__ , index=snake_case__)
if hasattr(module._parameters[tensor_name] , "SCB"):
offload_weight(
module._parameters[tensor_name].SCB , param_name.replace("weight" , "SCB") , snake_case__ , index=snake_case__ , )
else:
offload_weight(snake_case__ , snake_case__ , snake_case__ , index=snake_case__)
offload_weight(snake_case__ , param_name.replace("weight" , "SCB") , snake_case__ , index=snake_case__)
set_module_tensor_to_device(snake_case__ , snake_case__ , "meta" , dtype=snake_case__ , value=torch.empty(*param.size()))
| 683 | 1 |
from __future__ import annotations
from random import random
class __snake_case :
"""simple docstring"""
def __init__( self : Optional[int] ,lowerCAmelCase__ : int | None = None ) -> int:
'''simple docstring'''
lowerCAmelCase_ : Dict = value
lowerCAmelCase_ : Any = random()
lowerCAmelCase_ : Node | None = None
lowerCAmelCase_ : Node | None = None
def __repr__( self : Any ) -> str:
'''simple docstring'''
from pprint import pformat
if self.left is None and self.right is None:
return f'''\'{self.value}: {self.prior:.5}\''''
else:
return pformat(
{f'''{self.value}: {self.prior:.5}''': (self.left, self.right)} ,indent=1 )
def __str__( self : str ) -> str:
'''simple docstring'''
lowerCAmelCase_ : List[Any] = str(self.value ) + " "
lowerCAmelCase_ : List[Any] = str(self.left or "" )
lowerCAmelCase_ : Union[str, Any] = str(self.right or "" )
return value + left + right
def UpperCamelCase ( snake_case__ , snake_case__):
if root is None: # None tree is split into 2 Nones
return None, None
elif root.value is None:
return None, None
else:
if value < root.value:
lowerCAmelCase_ , lowerCAmelCase_ : Any = split(root.left , snake_case__)
return left, root
else:
lowerCAmelCase_ , lowerCAmelCase_ : Optional[Any] = split(root.right , snake_case__)
return root, right
def UpperCamelCase ( snake_case__ , snake_case__):
if (not left) or (not right): # If one node is None, return the other
return left or right
elif left.prior < right.prior:
lowerCAmelCase_ : Dict = merge(left.right , snake_case__)
return left
else:
lowerCAmelCase_ : List[str] = merge(snake_case__ , right.left)
return right
def UpperCamelCase ( snake_case__ , snake_case__):
lowerCAmelCase_ : List[Any] = Node(snake_case__)
lowerCAmelCase_ , lowerCAmelCase_ : Tuple = split(snake_case__ , snake_case__)
return merge(merge(snake_case__ , snake_case__) , snake_case__)
def UpperCamelCase ( snake_case__ , snake_case__):
lowerCAmelCase_ , lowerCAmelCase_ : List[str] = split(snake_case__ , value - 1)
lowerCAmelCase_ , lowerCAmelCase_ : Union[str, Any] = split(snake_case__ , snake_case__)
return merge(snake_case__ , snake_case__)
def UpperCamelCase ( snake_case__):
if not root: # None
return
else:
inorder(root.left)
print(root.value , end=",")
inorder(root.right)
def UpperCamelCase ( snake_case__ , snake_case__):
for arg in args.split():
if arg[0] == "+":
lowerCAmelCase_ : List[str] = insert(snake_case__ , int(arg[1:]))
elif arg[0] == "-":
lowerCAmelCase_ : Optional[int] = erase(snake_case__ , int(arg[1:]))
else:
print("Unknown command")
return root
def UpperCamelCase ( ):
lowerCAmelCase_ : str = None
print(
"enter numbers to create a tree, + value to add value into treap, "
"- value to erase all nodes with value. 'q' to quit. ")
lowerCAmelCase_ : str = input()
while args != "q":
lowerCAmelCase_ : int = interact_treap(snake_case__ , snake_case__)
print(snake_case__)
lowerCAmelCase_ : str = input()
print("good by!")
if __name__ == "__main__":
import doctest
doctest.testmod()
main()
| 683 |
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
_lowercase = logging.get_logger(__name__)
class __snake_case ( snake_case__ ):
"""simple docstring"""
UpperCamelCase_ = ['input_features', 'is_longer']
def __init__( self : Optional[int] ,lowerCAmelCase__ : List[Any]=64 ,lowerCAmelCase__ : Any=4_80_00 ,lowerCAmelCase__ : Optional[Any]=4_80 ,lowerCAmelCase__ : List[str]=10 ,lowerCAmelCase__ : List[Any]=10_24 ,lowerCAmelCase__ : Union[str, Any]=0.0 ,lowerCAmelCase__ : Tuple=False ,lowerCAmelCase__ : float = 0 ,lowerCAmelCase__ : float = 1_40_00 ,lowerCAmelCase__ : int = None ,lowerCAmelCase__ : str = "fusion" ,lowerCAmelCase__ : str = "repeatpad" ,**lowerCAmelCase__ : Union[str, Any] ,) -> Union[str, Any]:
'''simple docstring'''
super().__init__(
feature_size=lowerCAmelCase__ ,sampling_rate=lowerCAmelCase__ ,padding_value=lowerCAmelCase__ ,return_attention_mask=lowerCAmelCase__ ,**lowerCAmelCase__ ,)
lowerCAmelCase_ : Optional[Any] = top_db
lowerCAmelCase_ : str = truncation
lowerCAmelCase_ : Tuple = padding
lowerCAmelCase_ : str = fft_window_size
lowerCAmelCase_ : Dict = (fft_window_size >> 1) + 1
lowerCAmelCase_ : Dict = hop_length
lowerCAmelCase_ : Any = max_length_s
lowerCAmelCase_ : int = max_length_s * sampling_rate
lowerCAmelCase_ : Optional[int] = sampling_rate
lowerCAmelCase_ : int = frequency_min
lowerCAmelCase_ : Optional[Any] = frequency_max
lowerCAmelCase_ : List[Any] = mel_filter_bank(
num_frequency_bins=self.nb_frequency_bins ,num_mel_filters=lowerCAmelCase__ ,min_frequency=lowerCAmelCase__ ,max_frequency=lowerCAmelCase__ ,sampling_rate=lowerCAmelCase__ ,norm=lowerCAmelCase__ ,mel_scale="htk" ,)
lowerCAmelCase_ : List[Any] = mel_filter_bank(
num_frequency_bins=self.nb_frequency_bins ,num_mel_filters=lowerCAmelCase__ ,min_frequency=lowerCAmelCase__ ,max_frequency=lowerCAmelCase__ ,sampling_rate=lowerCAmelCase__ ,norm="slaney" ,mel_scale="slaney" ,)
def UpperCAmelCase_ ( self : Dict ) -> Dict[str, Any]:
'''simple docstring'''
lowerCAmelCase_ : int = copy.deepcopy(self.__dict__ )
lowerCAmelCase_ : Optional[int] = 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 : Any ,lowerCAmelCase__ : np.array ,lowerCAmelCase__ : Optional[np.array] = None ) -> np.ndarray:
'''simple docstring'''
lowerCAmelCase_ : Optional[Any] = spectrogram(
lowerCAmelCase__ ,window_function(self.fft_window_size ,"hann" ) ,frame_length=self.fft_window_size ,hop_length=self.hop_length ,power=2.0 ,mel_filters=lowerCAmelCase__ ,log_mel="dB" ,)
return log_mel_spectrogram.T
def UpperCAmelCase_ ( self : Optional[int] ,lowerCAmelCase__ : Union[str, Any] ,lowerCAmelCase__ : Union[str, Any] ,lowerCAmelCase__ : Tuple ) -> Dict:
'''simple docstring'''
lowerCAmelCase_ : 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
lowerCAmelCase_ : List[Any] = [0]
if len(ranges[2] ) == 0:
# if the audio is too short, we just use the first chunk
lowerCAmelCase_ : List[Any] = [0]
# randomly choose index for each part
lowerCAmelCase_ : str = np.random.choice(ranges[0] )
lowerCAmelCase_ : Optional[Any] = np.random.choice(ranges[1] )
lowerCAmelCase_ : Any = np.random.choice(ranges[2] )
lowerCAmelCase_ : str = mel[idx_front : idx_front + chunk_frames, :]
lowerCAmelCase_ : Dict = mel[idx_middle : idx_middle + chunk_frames, :]
lowerCAmelCase_ : Optional[Any] = mel[idx_back : idx_back + chunk_frames, :]
lowerCAmelCase_ : List[str] = torch.tensor(mel[None, None, :] )
lowerCAmelCase_ : List[Any] = torch.nn.functional.interpolate(
lowerCAmelCase__ ,size=[chunk_frames, 64] ,mode="bilinear" ,align_corners=lowerCAmelCase__ )
lowerCAmelCase_ : Optional[int] = mel_shrink[0][0].numpy()
lowerCAmelCase_ : str = np.stack([mel_shrink, mel_chunk_front, mel_chunk_middle, mel_chunk_back] ,axis=0 )
return mel_fusion
def UpperCAmelCase_ ( self : Any ,lowerCAmelCase__ : np.array ,lowerCAmelCase__ : Union[str, Any] ,lowerCAmelCase__ : Optional[int] ,lowerCAmelCase__ : int ) -> np.array:
'''simple docstring'''
if waveform.shape[0] > max_length:
if truncation == "rand_trunc":
lowerCAmelCase_ : List[Any] = True
# random crop to max_length (for compatibility) -> this should be handled by self.pad
lowerCAmelCase_ : str = len(lowerCAmelCase__ ) - max_length
lowerCAmelCase_ : Any = np.random.randint(0 ,overflow + 1 )
lowerCAmelCase_ : Dict = waveform[idx : idx + max_length]
lowerCAmelCase_ : List[str] = self._np_extract_fbank_features(lowerCAmelCase__ ,self.mel_filters_slaney )[None, :]
elif truncation == "fusion":
lowerCAmelCase_ : Tuple = self._np_extract_fbank_features(lowerCAmelCase__ ,self.mel_filters )
lowerCAmelCase_ : str = max_length // self.hop_length + 1 # the +1 related to how the spectrogram is computed
lowerCAmelCase_ : List[str] = 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.
lowerCAmelCase_ : Dict = np.stack([mel, mel, mel, mel] ,axis=0 )
lowerCAmelCase_ : int = False
else:
lowerCAmelCase_ : str = self._random_mel_fusion(lowerCAmelCase__ ,lowerCAmelCase__ ,lowerCAmelCase__ )
lowerCAmelCase_ : Any = True
else:
raise NotImplementedError(f'''data_truncating {truncation} not implemented''' )
else:
lowerCAmelCase_ : Dict = 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":
lowerCAmelCase_ : List[Any] = int(max_length / len(lowerCAmelCase__ ) )
lowerCAmelCase_ : int = np.stack(np.tile(lowerCAmelCase__ ,n_repeat + 1 ) )[:max_length]
if padding == "repeatpad":
lowerCAmelCase_ : Optional[Any] = int(max_length / len(lowerCAmelCase__ ) )
lowerCAmelCase_ : Tuple = np.stack(np.tile(lowerCAmelCase__ ,lowerCAmelCase__ ) )
lowerCAmelCase_ : List[Any] = np.pad(lowerCAmelCase__ ,(0, max_length - waveform.shape[0]) ,mode="constant" ,constant_values=0 )
if truncation == "fusion":
lowerCAmelCase_ : int = self._np_extract_fbank_features(lowerCAmelCase__ ,self.mel_filters )
lowerCAmelCase_ : Tuple = np.stack([input_mel, input_mel, input_mel, input_mel] ,axis=0 )
else:
lowerCAmelCase_ : str = self._np_extract_fbank_features(lowerCAmelCase__ ,self.mel_filters_slaney )[None, :]
return input_mel, longer
def __call__( self : int ,lowerCAmelCase__ : Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]] ,lowerCAmelCase__ : str = None ,lowerCAmelCase__ : Optional[str] = None ,lowerCAmelCase__ : Optional[int] = None ,lowerCAmelCase__ : Optional[int] = None ,lowerCAmelCase__ : Optional[Union[str, TensorType]] = None ,**lowerCAmelCase__ : List[Any] ,) -> BatchFeature:
'''simple docstring'''
lowerCAmelCase_ : List[str] = truncation if truncation is not None else self.truncation
lowerCAmelCase_ : List[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." )
lowerCAmelCase_ : Dict = isinstance(lowerCAmelCase__ ,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}''' )
lowerCAmelCase_ : Dict = is_batched_numpy or (
isinstance(lowerCAmelCase__ ,(list, tuple) ) and (isinstance(raw_speech[0] ,(np.ndarray, tuple, list) ))
)
if is_batched:
lowerCAmelCase_ : List[str] = [np.asarray(lowerCAmelCase__ ,dtype=np.floataa ) for speech in raw_speech]
elif not is_batched and not isinstance(lowerCAmelCase__ ,np.ndarray ):
lowerCAmelCase_ : Tuple = np.asarray(lowerCAmelCase__ ,dtype=np.floataa )
elif isinstance(lowerCAmelCase__ ,np.ndarray ) and raw_speech.dtype is np.dtype(np.floataa ):
lowerCAmelCase_ : Any = raw_speech.astype(np.floataa )
# always return batch
if not is_batched:
lowerCAmelCase_ : Any = [np.asarray(lowerCAmelCase__ )]
# convert to mel spectrogram, truncate and pad if needed.
lowerCAmelCase_ : Optional[Any] = [
self._get_input_mel(lowerCAmelCase__ ,max_length if max_length else self.nb_max_samples ,lowerCAmelCase__ ,lowerCAmelCase__ )
for waveform in raw_speech
]
lowerCAmelCase_ : str = []
lowerCAmelCase_ : str = []
for mel, longer in padded_inputs:
input_mel.append(lowerCAmelCase__ )
is_longer.append(lowerCAmelCase__ )
if truncation == "fusion" and sum(lowerCAmelCase__ ) == 0:
# if no audio is longer than 10s, then randomly select one audio to be longer
lowerCAmelCase_ : Any = np.random.randint(0 ,len(lowerCAmelCase__ ) )
lowerCAmelCase_ : Dict = True
if isinstance(input_mel[0] ,lowerCAmelCase__ ):
lowerCAmelCase_ : Optional[int] = [np.asarray(lowerCAmelCase__ ,dtype=np.floataa ) for feature in input_mel]
# is_longer is a list of bool
lowerCAmelCase_ : List[Any] = [[longer] for longer in is_longer]
lowerCAmelCase_ : Optional[Any] = {"input_features": input_mel, "is_longer": is_longer}
lowerCAmelCase_ : Dict = BatchFeature(lowerCAmelCase__ )
if return_tensors is not None:
lowerCAmelCase_ : List[str] = input_features.convert_to_tensors(lowerCAmelCase__ )
return input_features
| 683 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
_lowercase = {
'''configuration_mobilevit''': ['''MOBILEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MobileViTConfig''', '''MobileViTOnnxConfig'''],
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowercase = ['''MobileViTFeatureExtractor''']
_lowercase = ['''MobileViTImageProcessor''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowercase = [
'''MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''MobileViTForImageClassification''',
'''MobileViTForSemanticSegmentation''',
'''MobileViTModel''',
'''MobileViTPreTrainedModel''',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowercase = [
'''TF_MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''TFMobileViTForImageClassification''',
'''TFMobileViTForSemanticSegmentation''',
'''TFMobileViTModel''',
'''TFMobileViTPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_mobilevit import MOBILEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP, MobileViTConfig, MobileViTOnnxConfig
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_mobilevit import MobileViTFeatureExtractor
from .image_processing_mobilevit import MobileViTImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_mobilevit import (
MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
MobileViTForImageClassification,
MobileViTForSemanticSegmentation,
MobileViTModel,
MobileViTPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_mobilevit import (
TF_MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
TFMobileViTForImageClassification,
TFMobileViTForSemanticSegmentation,
TFMobileViTModel,
TFMobileViTPreTrainedModel,
)
else:
import sys
_lowercase = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 683 |
from multiprocessing import Lock, Pipe, Process
# lock used to ensure that two processes do not access a pipe at the same time
_lowercase = Lock()
def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__):
global process_lock
# we perform n swaps since after n swaps we know we are sorted
# we *could* stop early if we are sorted already, but it takes as long to
# find out we are sorted as it does to sort the list with this algorithm
for i in range(0 , 10):
if (i + position) % 2 == 0 and r_send is not None:
# send your value to your right neighbor
process_lock.acquire()
r_send[1].send(snake_case__)
process_lock.release()
# receive your right neighbor's value
process_lock.acquire()
lowerCAmelCase_ : Optional[Any] = rr_cv[0].recv()
process_lock.release()
# take the lower value since you are on the left
lowerCAmelCase_ : Any = min(snake_case__ , snake_case__)
elif (i + position) % 2 != 0 and l_send is not None:
# send your value to your left neighbor
process_lock.acquire()
l_send[1].send(snake_case__)
process_lock.release()
# receive your left neighbor's value
process_lock.acquire()
lowerCAmelCase_ : str = lr_cv[0].recv()
process_lock.release()
# take the higher value since you are on the right
lowerCAmelCase_ : Dict = max(snake_case__ , snake_case__)
# after all swaps are performed, send the values back to main
result_pipe[1].send(snake_case__)
def UpperCamelCase ( snake_case__):
lowerCAmelCase_ : Union[str, Any] = []
lowerCAmelCase_ : int = []
# initialize the list of pipes where the values will be retrieved
for _ in arr:
result_pipe.append(Pipe())
# creates the processes
# the first and last process only have one neighbor so they are made outside
# of the loop
lowerCAmelCase_ : Tuple = Pipe()
lowerCAmelCase_ : Optional[int] = Pipe()
process_array_.append(
Process(
target=snake_case__ , args=(0, arr[0], None, temp_rs, None, temp_rr, result_pipe[0]) , ))
lowerCAmelCase_ : int = temp_rs
lowerCAmelCase_ : List[Any] = temp_rr
for i in range(1 , len(snake_case__) - 1):
lowerCAmelCase_ : Dict = Pipe()
lowerCAmelCase_ : List[str] = Pipe()
process_array_.append(
Process(
target=snake_case__ , args=(i, arr[i], temp_ls, temp_rs, temp_lr, temp_rr, result_pipe[i]) , ))
lowerCAmelCase_ : Dict = temp_rs
lowerCAmelCase_ : Optional[Any] = temp_rr
process_array_.append(
Process(
target=snake_case__ , args=(
len(snake_case__) - 1,
arr[len(snake_case__) - 1],
temp_ls,
None,
temp_lr,
None,
result_pipe[len(snake_case__) - 1],
) , ))
# start the processes
for p in process_array_:
p.start()
# wait for the processes to end and write their values to the list
for p in range(0 , len(snake_case__)):
lowerCAmelCase_ : Union[str, Any] = result_pipe[p][0].recv()
process_array_[p].join()
return arr
def UpperCamelCase ( ):
lowerCAmelCase_ : Optional[Any] = list(range(10 , 0 , -1))
print("Initial List")
print(*snake_case__)
lowerCAmelCase_ : Tuple = odd_even_transposition(snake_case__)
print("Sorted List\n")
print(*snake_case__)
if __name__ == "__main__":
main()
| 683 | 1 |
_lowercase = range(2, 20 + 1)
_lowercase = [10**k for k in range(ks[-1] + 1)]
_lowercase = {}
def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__ , snake_case__):
lowerCAmelCase_ : Dict = sum(a_i[j] for j in range(snake_case__ , len(snake_case__)))
lowerCAmelCase_ : List[Any] = sum(a_i[j] * base[j] for j in range(min(len(snake_case__) , snake_case__)))
lowerCAmelCase_ , lowerCAmelCase_ : Tuple = 0, 0
lowerCAmelCase_ : Dict = n - i
lowerCAmelCase_ : Union[str, Any] = memo.get(snake_case__)
if sub_memo is not None:
lowerCAmelCase_ : Tuple = sub_memo.get(snake_case__)
if jumps is not None and len(snake_case__) > 0:
# find and make the largest jump without going over
lowerCAmelCase_ : str = -1
for _k in range(len(snake_case__) - 1 , -1 , -1):
if jumps[_k][2] <= k and jumps[_k][1] <= max_dn:
lowerCAmelCase_ : Optional[Any] = _k
break
if max_jump >= 0:
lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ : List[str] = jumps[max_jump]
# since the difference between jumps is cached, add c
lowerCAmelCase_ : List[Any] = diff + c
for j in range(min(snake_case__ , len(snake_case__))):
lowerCAmelCase_ , lowerCAmelCase_ : int = divmod(snake_case__ , 10)
if new_c > 0:
add(snake_case__ , snake_case__ , snake_case__)
else:
lowerCAmelCase_ : Optional[int] = []
else:
lowerCAmelCase_ : Tuple = {c: []}
lowerCAmelCase_ : List[Any] = sub_memo
if dn >= max_dn or c + diff >= base[k]:
return diff, dn
if k > ks[0]:
while True:
# keep doing smaller jumps
lowerCAmelCase_ , lowerCAmelCase_ : Union[str, Any] = next_term(snake_case__ , k - 1 , i + dn , 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
lowerCAmelCase_ , lowerCAmelCase_ : str = compute(snake_case__ , snake_case__ , i + dn , snake_case__)
diff += _diff
dn += terms_jumped
lowerCAmelCase_ : Union[str, Any] = sub_memo[c]
# keep jumps sorted by # of terms skipped
lowerCAmelCase_ : List[Any] = 0
while j < len(snake_case__):
if jumps[j][1] > dn:
break
j += 1
# cache the jump for this value digitsum(b) and c
sub_memo[c].insert(snake_case__ , (diff, dn, k))
return (diff, dn)
def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__ , snake_case__):
if i >= n:
return 0, i
if k > len(snake_case__):
a_i.extend([0 for _ in range(k - len(snake_case__))])
# note: a_i -> b * 10^k + c
# ds_b -> digitsum(b)
# ds_c -> digitsum(c)
lowerCAmelCase_ : Dict = i
lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ : Any = 0, 0, 0
for j in range(len(snake_case__)):
if j >= k:
ds_b += a_i[j]
else:
ds_c += a_i[j]
while i < n:
i += 1
lowerCAmelCase_ : Union[str, Any] = ds_c + ds_b
diff += addend
lowerCAmelCase_ : int = 0
for j in range(snake_case__):
lowerCAmelCase_ : int = a_i[j] + addend
lowerCAmelCase_ , lowerCAmelCase_ : List[Any] = divmod(snake_case__ , 10)
ds_c += a_i[j]
if addend > 0:
break
if addend > 0:
add(snake_case__ , snake_case__ , snake_case__)
return diff, i - start_i
def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__):
for j in range(snake_case__ , len(snake_case__)):
lowerCAmelCase_ : Tuple = digits[j] + addend
if s >= 10:
lowerCAmelCase_ , lowerCAmelCase_ : int = divmod(snake_case__ , 10)
lowerCAmelCase_ : int = addend // 10 + quotient
else:
lowerCAmelCase_ : Optional[Any] = s
lowerCAmelCase_ : Optional[int] = addend // 10
if addend == 0:
break
while addend > 0:
lowerCAmelCase_ , lowerCAmelCase_ : Tuple = divmod(snake_case__ , 10)
digits.append(snake_case__)
def UpperCamelCase ( snake_case__ = 10**15):
lowerCAmelCase_ : Union[str, Any] = [1]
lowerCAmelCase_ : List[str] = 1
lowerCAmelCase_ : Tuple = 0
while True:
lowerCAmelCase_ , lowerCAmelCase_ : int = next_term(snake_case__ , 20 , i + dn , snake_case__)
dn += terms_jumped
if dn == n - i:
break
lowerCAmelCase_ : List[Any] = 0
for j in range(len(snake_case__)):
a_n += digits[j] * 10**j
return a_n
if __name__ == "__main__":
print(f"{solution() = }")
| 683 |
from typing import Any
def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ , ):
_validation(
snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ , )
# Creates data structures and fill initial step
lowerCAmelCase_ : dict = {}
lowerCAmelCase_ : dict = {}
for state in states_space:
lowerCAmelCase_ : List[Any] = observations_space[0]
lowerCAmelCase_ : int = (
initial_probabilities[state] * emission_probabilities[state][observation]
)
lowerCAmelCase_ : Dict = None
# Fills the data structure with the probabilities of
# different transitions and pointers to previous states
for o in range(1 , len(snake_case__)):
lowerCAmelCase_ : List[Any] = observations_space[o]
lowerCAmelCase_ : Optional[Any] = observations_space[o - 1]
for state in states_space:
# Calculates the argmax for probability function
lowerCAmelCase_ : List[Any] = ""
lowerCAmelCase_ : Tuple = -1
for k_state in states_space:
lowerCAmelCase_ : int = (
probabilities[(k_state, prior_observation)]
* transition_probabilities[k_state][state]
* emission_probabilities[state][observation]
)
if probability > max_probability:
lowerCAmelCase_ : List[str] = probability
lowerCAmelCase_ : Optional[Any] = k_state
# Update probabilities and pointers dicts
lowerCAmelCase_ : Union[str, Any] = (
probabilities[(arg_max, prior_observation)]
* transition_probabilities[arg_max][state]
* emission_probabilities[state][observation]
)
lowerCAmelCase_ : Any = arg_max
# The final observation
lowerCAmelCase_ : List[Any] = observations_space[len(snake_case__) - 1]
# argmax for given final observation
lowerCAmelCase_ : List[str] = ""
lowerCAmelCase_ : List[str] = -1
for k_state in states_space:
lowerCAmelCase_ : List[str] = probabilities[(k_state, final_observation)]
if probability > max_probability:
lowerCAmelCase_ : List[str] = probability
lowerCAmelCase_ : Tuple = k_state
lowerCAmelCase_ : str = arg_max
# Process pointers backwards
lowerCAmelCase_ : int = last_state
lowerCAmelCase_ : int = []
for o in range(len(snake_case__) - 1 , -1 , -1):
result.append(snake_case__)
lowerCAmelCase_ : Optional[Any] = pointers[previous, observations_space[o]]
result.reverse()
return result
def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ , ):
_validate_not_empty(
snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ , )
_validate_lists(snake_case__ , snake_case__)
_validate_dicts(
snake_case__ , snake_case__ , snake_case__)
def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ , ):
if not all(
[
observations_space,
states_space,
initial_probabilities,
transition_probabilities,
emission_probabilities,
]):
raise ValueError("There's an empty parameter")
def UpperCamelCase ( snake_case__ , snake_case__):
_validate_list(snake_case__ , "observations_space")
_validate_list(snake_case__ , "states_space")
def UpperCamelCase ( snake_case__ , snake_case__):
if not isinstance(_object , snake_case__):
lowerCAmelCase_ : Optional[Any] = F'''{var_name} must be a list'''
raise ValueError(snake_case__)
else:
for x in _object:
if not isinstance(snake_case__ , snake_case__):
lowerCAmelCase_ : Optional[Any] = F'''{var_name} must be a list of strings'''
raise ValueError(snake_case__)
def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__ , ):
_validate_dict(snake_case__ , "initial_probabilities" , snake_case__)
_validate_nested_dict(snake_case__ , "transition_probabilities")
_validate_nested_dict(snake_case__ , "emission_probabilities")
def UpperCamelCase ( snake_case__ , snake_case__):
_validate_dict(_object , snake_case__ , snake_case__)
for x in _object.values():
_validate_dict(snake_case__ , snake_case__ , snake_case__ , snake_case__)
def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__ , snake_case__ = False):
if not isinstance(_object , snake_case__):
lowerCAmelCase_ : List[str] = F'''{var_name} must be a dict'''
raise ValueError(snake_case__)
if not all(isinstance(snake_case__ , snake_case__) for x in _object):
lowerCAmelCase_ : Dict = F'''{var_name} all keys must be strings'''
raise ValueError(snake_case__)
if not all(isinstance(snake_case__ , snake_case__) for x in _object.values()):
lowerCAmelCase_ : Union[str, Any] = "nested dictionary " if nested else ""
lowerCAmelCase_ : Any = F'''{var_name} {nested_text}all values must be {value_type.__name__}'''
raise ValueError(snake_case__)
if __name__ == "__main__":
from doctest import testmod
testmod()
| 683 | 1 |
import argparse
import io
import requests
import torch
from omegaconf import OmegaConf
from diffusers import AutoencoderKL
from diffusers.pipelines.stable_diffusion.convert_from_ckpt import (
assign_to_checkpoint,
conv_attn_to_linear,
create_vae_diffusers_config,
renew_vae_attention_paths,
renew_vae_resnet_paths,
)
def UpperCamelCase ( snake_case__ , snake_case__):
lowerCAmelCase_ : str = checkpoint
lowerCAmelCase_ : List[Any] = {}
lowerCAmelCase_ : List[Any] = vae_state_dict["encoder.conv_in.weight"]
lowerCAmelCase_ : Optional[int] = vae_state_dict["encoder.conv_in.bias"]
lowerCAmelCase_ : Optional[int] = vae_state_dict["encoder.conv_out.weight"]
lowerCAmelCase_ : List[str] = vae_state_dict["encoder.conv_out.bias"]
lowerCAmelCase_ : Union[str, Any] = vae_state_dict["encoder.norm_out.weight"]
lowerCAmelCase_ : Dict = vae_state_dict["encoder.norm_out.bias"]
lowerCAmelCase_ : Optional[int] = vae_state_dict["decoder.conv_in.weight"]
lowerCAmelCase_ : Dict = vae_state_dict["decoder.conv_in.bias"]
lowerCAmelCase_ : Union[str, Any] = vae_state_dict["decoder.conv_out.weight"]
lowerCAmelCase_ : Optional[int] = vae_state_dict["decoder.conv_out.bias"]
lowerCAmelCase_ : Tuple = vae_state_dict["decoder.norm_out.weight"]
lowerCAmelCase_ : Union[str, Any] = vae_state_dict["decoder.norm_out.bias"]
lowerCAmelCase_ : Dict = vae_state_dict["quant_conv.weight"]
lowerCAmelCase_ : List[Any] = vae_state_dict["quant_conv.bias"]
lowerCAmelCase_ : Union[str, Any] = vae_state_dict["post_quant_conv.weight"]
lowerCAmelCase_ : Any = vae_state_dict["post_quant_conv.bias"]
# Retrieves the keys for the encoder down blocks only
lowerCAmelCase_ : Optional[int] = len({".".join(layer.split(".")[:3]) for layer in vae_state_dict if "encoder.down" in layer})
lowerCAmelCase_ : Any = {
layer_id: [key for key in vae_state_dict if F'''down.{layer_id}''' in key] for layer_id in range(snake_case__)
}
# Retrieves the keys for the decoder up blocks only
lowerCAmelCase_ : List[str] = len({".".join(layer.split(".")[:3]) for layer in vae_state_dict if "decoder.up" in layer})
lowerCAmelCase_ : Any = {
layer_id: [key for key in vae_state_dict if F'''up.{layer_id}''' in key] for layer_id in range(snake_case__)
}
for i in range(snake_case__):
lowerCAmelCase_ : Any = [key for key in down_blocks[i] if F'''down.{i}''' in key and F'''down.{i}.downsample''' not in key]
if F'''encoder.down.{i}.downsample.conv.weight''' in vae_state_dict:
lowerCAmelCase_ : Any = vae_state_dict.pop(
F'''encoder.down.{i}.downsample.conv.weight''')
lowerCAmelCase_ : Union[str, Any] = vae_state_dict.pop(
F'''encoder.down.{i}.downsample.conv.bias''')
lowerCAmelCase_ : Tuple = renew_vae_resnet_paths(snake_case__)
lowerCAmelCase_ : Tuple = {"old": F'''down.{i}.block''', "new": F'''down_blocks.{i}.resnets'''}
assign_to_checkpoint(snake_case__ , snake_case__ , snake_case__ , additional_replacements=[meta_path] , config=snake_case__)
lowerCAmelCase_ : Dict = [key for key in vae_state_dict if "encoder.mid.block" in key]
lowerCAmelCase_ : List[Any] = 2
for i in range(1 , num_mid_res_blocks + 1):
lowerCAmelCase_ : List[str] = [key for key in mid_resnets if F'''encoder.mid.block_{i}''' in key]
lowerCAmelCase_ : Optional[int] = renew_vae_resnet_paths(snake_case__)
lowerCAmelCase_ : Optional[Any] = {"old": F'''mid.block_{i}''', "new": F'''mid_block.resnets.{i - 1}'''}
assign_to_checkpoint(snake_case__ , snake_case__ , snake_case__ , additional_replacements=[meta_path] , config=snake_case__)
lowerCAmelCase_ : int = [key for key in vae_state_dict if "encoder.mid.attn" in key]
lowerCAmelCase_ : List[str] = renew_vae_attention_paths(snake_case__)
lowerCAmelCase_ : List[Any] = {"old": "mid.attn_1", "new": "mid_block.attentions.0"}
assign_to_checkpoint(snake_case__ , snake_case__ , snake_case__ , additional_replacements=[meta_path] , config=snake_case__)
conv_attn_to_linear(snake_case__)
for i in range(snake_case__):
lowerCAmelCase_ : Optional[int] = num_up_blocks - 1 - i
lowerCAmelCase_ : int = [
key for key in up_blocks[block_id] if F'''up.{block_id}''' in key and F'''up.{block_id}.upsample''' not in key
]
if F'''decoder.up.{block_id}.upsample.conv.weight''' in vae_state_dict:
lowerCAmelCase_ : Union[str, Any] = vae_state_dict[
F'''decoder.up.{block_id}.upsample.conv.weight'''
]
lowerCAmelCase_ : Tuple = vae_state_dict[
F'''decoder.up.{block_id}.upsample.conv.bias'''
]
lowerCAmelCase_ : List[Any] = renew_vae_resnet_paths(snake_case__)
lowerCAmelCase_ : Tuple = {"old": F'''up.{block_id}.block''', "new": F'''up_blocks.{i}.resnets'''}
assign_to_checkpoint(snake_case__ , snake_case__ , snake_case__ , additional_replacements=[meta_path] , config=snake_case__)
lowerCAmelCase_ : List[Any] = [key for key in vae_state_dict if "decoder.mid.block" in key]
lowerCAmelCase_ : Optional[Any] = 2
for i in range(1 , num_mid_res_blocks + 1):
lowerCAmelCase_ : int = [key for key in mid_resnets if F'''decoder.mid.block_{i}''' in key]
lowerCAmelCase_ : List[Any] = renew_vae_resnet_paths(snake_case__)
lowerCAmelCase_ : Optional[int] = {"old": F'''mid.block_{i}''', "new": F'''mid_block.resnets.{i - 1}'''}
assign_to_checkpoint(snake_case__ , snake_case__ , snake_case__ , additional_replacements=[meta_path] , config=snake_case__)
lowerCAmelCase_ : Dict = [key for key in vae_state_dict if "decoder.mid.attn" in key]
lowerCAmelCase_ : Tuple = renew_vae_attention_paths(snake_case__)
lowerCAmelCase_ : Any = {"old": "mid.attn_1", "new": "mid_block.attentions.0"}
assign_to_checkpoint(snake_case__ , snake_case__ , snake_case__ , additional_replacements=[meta_path] , config=snake_case__)
conv_attn_to_linear(snake_case__)
return new_checkpoint
def UpperCamelCase ( snake_case__ , snake_case__ , ):
# Only support V1
lowerCAmelCase_ : Tuple = requests.get(
" https://raw.githubusercontent.com/CompVis/stable-diffusion/main/configs/stable-diffusion/v1-inference.yaml")
lowerCAmelCase_ : Dict = io.BytesIO(r.content)
lowerCAmelCase_ : Tuple = OmegaConf.load(snake_case__)
lowerCAmelCase_ : Any = 5_12
lowerCAmelCase_ : Dict = "cuda" if torch.cuda.is_available() else "cpu"
if checkpoint_path.endswith("safetensors"):
from safetensors import safe_open
lowerCAmelCase_ : Dict = {}
with safe_open(snake_case__ , framework="pt" , device="cpu") as f:
for key in f.keys():
lowerCAmelCase_ : Union[str, Any] = f.get_tensor(snake_case__)
else:
lowerCAmelCase_ : Union[str, Any] = torch.load(snake_case__ , map_location=snake_case__)["state_dict"]
# Convert the VAE model.
lowerCAmelCase_ : List[Any] = create_vae_diffusers_config(snake_case__ , image_size=snake_case__)
lowerCAmelCase_ : Tuple = custom_convert_ldm_vae_checkpoint(snake_case__ , snake_case__)
lowerCAmelCase_ : Union[str, Any] = AutoencoderKL(**snake_case__)
vae.load_state_dict(snake_case__)
vae.save_pretrained(snake_case__)
if __name__ == "__main__":
_lowercase = argparse.ArgumentParser()
parser.add_argument('''--vae_pt_path''', default=None, type=str, required=True, help='''Path to the VAE.pt to convert.''')
parser.add_argument('''--dump_path''', default=None, type=str, required=True, help='''Path to the VAE.pt to convert.''')
_lowercase = parser.parse_args()
vae_pt_to_vae_diffuser(args.vae_pt_path, args.dump_path)
| 683 |
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import torch
from ..models.speechta import SpeechTaForTextToSpeech, SpeechTaHifiGan, SpeechTaProcessor
from ..utils import is_datasets_available
from .base import PipelineTool
if is_datasets_available():
from datasets import load_dataset
class __snake_case ( snake_case__ ):
"""simple docstring"""
UpperCamelCase_ = 'microsoft/speecht5_tts'
UpperCamelCase_ = (
'This is a tool that reads an English text out loud. It takes an input named `text` which should contain the '
'text to read (in English) and returns a waveform object containing the sound.'
)
UpperCamelCase_ = 'text_reader'
UpperCamelCase_ = SpeechTaProcessor
UpperCamelCase_ = SpeechTaForTextToSpeech
UpperCamelCase_ = SpeechTaHifiGan
UpperCamelCase_ = ['text']
UpperCamelCase_ = ['audio']
def UpperCAmelCase_ ( self : Dict ) -> Any:
'''simple docstring'''
if self.post_processor is None:
lowerCAmelCase_ : Any = "microsoft/speecht5_hifigan"
super().setup()
def UpperCAmelCase_ ( self : List[Any] ,lowerCAmelCase__ : Optional[int] ,lowerCAmelCase__ : Optional[int]=None ) -> Tuple:
'''simple docstring'''
lowerCAmelCase_ : Any = self.pre_processor(text=lowerCAmelCase__ ,return_tensors="pt" ,truncation=lowerCAmelCase__ )
if speaker_embeddings is None:
if not is_datasets_available():
raise ImportError("Datasets needs to be installed if not passing speaker embeddings." )
lowerCAmelCase_ : str = load_dataset("Matthijs/cmu-arctic-xvectors" ,split="validation" )
lowerCAmelCase_ : List[Any] = torch.tensor(embeddings_dataset[73_05]["xvector"] ).unsqueeze(0 )
return {"input_ids": inputs["input_ids"], "speaker_embeddings": speaker_embeddings}
def UpperCAmelCase_ ( self : Dict ,lowerCAmelCase__ : Union[str, Any] ) -> Union[str, Any]:
'''simple docstring'''
with torch.no_grad():
return self.model.generate_speech(**lowerCAmelCase__ )
def UpperCAmelCase_ ( self : Union[str, Any] ,lowerCAmelCase__ : str ) -> Any:
'''simple docstring'''
with torch.no_grad():
return self.post_processor(lowerCAmelCase__ ).cpu().detach()
| 683 | 1 |
import torch
import torch.nn as nn
from transformers import CLIPConfig, CLIPVisionModel, PreTrainedModel
from ...utils import logging
_lowercase = logging.get_logger(__name__)
def UpperCamelCase ( snake_case__ , snake_case__):
lowerCAmelCase_ : List[Any] = nn.functional.normalize(snake_case__)
lowerCAmelCase_ : Optional[Any] = nn.functional.normalize(snake_case__)
return torch.mm(snake_case__ , normalized_text_embeds.t())
class __snake_case ( snake_case__ ):
"""simple docstring"""
UpperCamelCase_ = CLIPConfig
UpperCamelCase_ = ['CLIPEncoderLayer']
def __init__( self : Tuple ,lowerCAmelCase__ : CLIPConfig ) -> Optional[int]:
'''simple docstring'''
super().__init__(lowerCAmelCase__ )
lowerCAmelCase_ : Any = CLIPVisionModel(config.vision_config )
lowerCAmelCase_ : Dict = nn.Linear(config.vision_config.hidden_size ,config.projection_dim ,bias=lowerCAmelCase__ )
lowerCAmelCase_ : int = nn.Parameter(torch.ones(17 ,config.projection_dim ) ,requires_grad=lowerCAmelCase__ )
lowerCAmelCase_ : Tuple = nn.Parameter(torch.ones(3 ,config.projection_dim ) ,requires_grad=lowerCAmelCase__ )
lowerCAmelCase_ : int = nn.Parameter(torch.ones(17 ) ,requires_grad=lowerCAmelCase__ )
lowerCAmelCase_ : Optional[Any] = nn.Parameter(torch.ones(3 ) ,requires_grad=lowerCAmelCase__ )
@torch.no_grad()
def UpperCAmelCase_ ( self : int ,lowerCAmelCase__ : Union[str, Any] ,lowerCAmelCase__ : Optional[int] ) -> List[str]:
'''simple docstring'''
lowerCAmelCase_ : str = self.vision_model(lowerCAmelCase__ )[1] # pooled_output
lowerCAmelCase_ : Optional[Any] = self.visual_projection(lowerCAmelCase__ )
# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
lowerCAmelCase_ : Union[str, Any] = cosine_distance(lowerCAmelCase__ ,self.special_care_embeds ).cpu().float().numpy()
lowerCAmelCase_ : Optional[Any] = cosine_distance(lowerCAmelCase__ ,self.concept_embeds ).cpu().float().numpy()
lowerCAmelCase_ : Optional[Any] = []
lowerCAmelCase_ : Union[str, Any] = image_embeds.shape[0]
for i in range(lowerCAmelCase__ ):
lowerCAmelCase_ : Any = {"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
lowerCAmelCase_ : Tuple = 0.0
for concept_idx in range(len(special_cos_dist[0] ) ):
lowerCAmelCase_ : int = special_cos_dist[i][concept_idx]
lowerCAmelCase_ : Dict = self.special_care_embeds_weights[concept_idx].item()
lowerCAmelCase_ : List[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]} )
lowerCAmelCase_ : Union[str, Any] = 0.01
for concept_idx in range(len(cos_dist[0] ) ):
lowerCAmelCase_ : int = cos_dist[i][concept_idx]
lowerCAmelCase_ : Optional[int] = self.concept_embeds_weights[concept_idx].item()
lowerCAmelCase_ : Optional[Any] = round(concept_cos - concept_threshold + adjustment ,3 )
if result_img["concept_scores"][concept_idx] > 0:
result_img["bad_concepts"].append(lowerCAmelCase__ )
result.append(lowerCAmelCase__ )
lowerCAmelCase_ : Dict = [len(res["bad_concepts"] ) > 0 for res in result]
return images, has_nsfw_concepts
@torch.no_grad()
def UpperCAmelCase_ ( self : Union[str, Any] ,lowerCAmelCase__ : torch.FloatTensor ,lowerCAmelCase__ : torch.FloatTensor ) -> Union[str, Any]:
'''simple docstring'''
lowerCAmelCase_ : Tuple = self.vision_model(lowerCAmelCase__ )[1] # pooled_output
lowerCAmelCase_ : Optional[int] = self.visual_projection(lowerCAmelCase__ )
lowerCAmelCase_ : Tuple = cosine_distance(lowerCAmelCase__ ,self.special_care_embeds )
lowerCAmelCase_ : Tuple = cosine_distance(lowerCAmelCase__ ,self.concept_embeds )
# increase this value to create a stronger `nsfw` filter
# at the cost of increasing the possibility of filtering benign images
lowerCAmelCase_ : List[Any] = 0.0
lowerCAmelCase_ : Union[str, Any] = special_cos_dist - self.special_care_embeds_weights + adjustment
# special_scores = special_scores.round(decimals=3)
lowerCAmelCase_ : Optional[int] = torch.any(special_scores > 0 ,dim=1 )
lowerCAmelCase_ : str = special_care * 0.01
lowerCAmelCase_ : Optional[Any] = special_adjustment.unsqueeze(1 ).expand(-1 ,cos_dist.shape[1] )
lowerCAmelCase_ : Tuple = (cos_dist - self.concept_embeds_weights) + special_adjustment
# concept_scores = concept_scores.round(decimals=3)
lowerCAmelCase_ : Tuple = torch.any(concept_scores > 0 ,dim=1 )
return images, has_nsfw_concepts
| 683 |
import argparse
import collections
import json
import os
import re
import string
import sys
import numpy as np
_lowercase = re.compile(r'''\b(a|an|the)\b''', re.UNICODE)
_lowercase = None
def UpperCamelCase ( ):
lowerCAmelCase_ : Optional[Any] = argparse.ArgumentParser("Official evaluation script for SQuAD version 2.0.")
parser.add_argument("data_file" , metavar="data.json" , help="Input data JSON file.")
parser.add_argument("pred_file" , metavar="pred.json" , help="Model predictions.")
parser.add_argument(
"--out-file" , "-o" , metavar="eval.json" , help="Write accuracy metrics to file (default is stdout).")
parser.add_argument(
"--na-prob-file" , "-n" , metavar="na_prob.json" , help="Model estimates of probability of no answer.")
parser.add_argument(
"--na-prob-thresh" , "-t" , type=snake_case__ , default=1.0 , help="Predict \"\" if no-answer probability exceeds this (default = 1.0)." , )
parser.add_argument(
"--out-image-dir" , "-p" , metavar="out_images" , default=snake_case__ , help="Save precision-recall curves to directory.")
parser.add_argument("--verbose" , "-v" , action="store_true")
if len(sys.argv) == 1:
parser.print_help()
sys.exit(1)
return parser.parse_args()
def UpperCamelCase ( snake_case__):
lowerCAmelCase_ : str = {}
for article in dataset:
for p in article["paragraphs"]:
for qa in p["qas"]:
lowerCAmelCase_ : Dict = bool(qa["answers"]["text"])
return qid_to_has_ans
def UpperCamelCase ( snake_case__):
def remove_articles(snake_case__):
return ARTICLES_REGEX.sub(" " , snake_case__)
def white_space_fix(snake_case__):
return " ".join(text.split())
def remove_punc(snake_case__):
lowerCAmelCase_ : Optional[int] = set(string.punctuation)
return "".join(ch for ch in text if ch not in exclude)
def lower(snake_case__):
return text.lower()
return white_space_fix(remove_articles(remove_punc(lower(snake_case__))))
def UpperCamelCase ( snake_case__):
if not s:
return []
return normalize_answer(snake_case__).split()
def UpperCamelCase ( snake_case__ , snake_case__):
return int(normalize_answer(snake_case__) == normalize_answer(snake_case__))
def UpperCamelCase ( snake_case__ , snake_case__):
lowerCAmelCase_ : Optional[int] = get_tokens(snake_case__)
lowerCAmelCase_ : Union[str, Any] = get_tokens(snake_case__)
lowerCAmelCase_ : Any = collections.Counter(snake_case__) & collections.Counter(snake_case__)
lowerCAmelCase_ : Dict = sum(common.values())
if len(snake_case__) == 0 or len(snake_case__) == 0:
# If either is no-answer, then F1 is 1 if they agree, 0 otherwise
return int(gold_toks == pred_toks)
if num_same == 0:
return 0
lowerCAmelCase_ : List[Any] = 1.0 * num_same / len(snake_case__)
lowerCAmelCase_ : int = 1.0 * num_same / len(snake_case__)
lowerCAmelCase_ : List[Any] = (2 * precision * recall) / (precision + recall)
return fa
def UpperCamelCase ( snake_case__ , snake_case__):
lowerCAmelCase_ : Tuple = {}
lowerCAmelCase_ : int = {}
for article in dataset:
for p in article["paragraphs"]:
for qa in p["qas"]:
lowerCAmelCase_ : int = qa["id"]
lowerCAmelCase_ : Any = [t for t in qa["answers"]["text"] if normalize_answer(snake_case__)]
if not gold_answers:
# For unanswerable questions, only correct answer is empty string
lowerCAmelCase_ : Any = [""]
if qid not in preds:
print(F'''Missing prediction for {qid}''')
continue
lowerCAmelCase_ : Tuple = preds[qid]
# Take max over all gold answers
lowerCAmelCase_ : Any = max(compute_exact(snake_case__ , snake_case__) for a in gold_answers)
lowerCAmelCase_ : Optional[Any] = max(compute_fa(snake_case__ , snake_case__) for a in gold_answers)
return exact_scores, fa_scores
def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__ , snake_case__):
lowerCAmelCase_ : Dict = {}
for qid, s in scores.items():
lowerCAmelCase_ : List[Any] = na_probs[qid] > na_prob_thresh
if pred_na:
lowerCAmelCase_ : List[str] = float(not qid_to_has_ans[qid])
else:
lowerCAmelCase_ : Union[str, Any] = s
return new_scores
def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__=None):
if not qid_list:
lowerCAmelCase_ : Any = len(snake_case__)
return collections.OrderedDict(
[
("exact", 100.0 * sum(exact_scores.values()) / total),
("f1", 100.0 * sum(fa_scores.values()) / total),
("total", total),
])
else:
lowerCAmelCase_ : Tuple = len(snake_case__)
return collections.OrderedDict(
[
("exact", 100.0 * sum(exact_scores[k] for k in qid_list) / total),
("f1", 100.0 * sum(fa_scores[k] for k in qid_list) / total),
("total", total),
])
def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__):
for k in new_eval:
lowerCAmelCase_ : Union[str, Any] = new_eval[k]
def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__ , snake_case__):
plt.step(snake_case__ , snake_case__ , color="b" , alpha=0.2 , where="post")
plt.fill_between(snake_case__ , snake_case__ , step="post" , alpha=0.2 , color="b")
plt.xlabel("Recall")
plt.ylabel("Precision")
plt.xlim([0.0, 1.05])
plt.ylim([0.0, 1.05])
plt.title(snake_case__)
plt.savefig(snake_case__)
plt.clf()
def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__=None , snake_case__=None):
lowerCAmelCase_ : List[Any] = sorted(snake_case__ , key=lambda snake_case__: na_probs[k])
lowerCAmelCase_ : Dict = 0.0
lowerCAmelCase_ : int = 1.0
lowerCAmelCase_ : List[str] = 0.0
lowerCAmelCase_ : Tuple = [1.0]
lowerCAmelCase_ : Tuple = [0.0]
lowerCAmelCase_ : Dict = 0.0
for i, qid in enumerate(snake_case__):
if qid_to_has_ans[qid]:
true_pos += scores[qid]
lowerCAmelCase_ : str = true_pos / float(i + 1)
lowerCAmelCase_ : Union[str, Any] = true_pos / float(snake_case__)
if i == len(snake_case__) - 1 or na_probs[qid] != na_probs[qid_list[i + 1]]:
# i.e., if we can put a threshold after this point
avg_prec += cur_p * (cur_r - recalls[-1])
precisions.append(snake_case__)
recalls.append(snake_case__)
if out_image:
plot_pr_curve(snake_case__ , snake_case__ , snake_case__ , snake_case__)
return {"ap": 100.0 * avg_prec}
def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__):
if out_image_dir and not os.path.exists(snake_case__):
os.makedirs(snake_case__)
lowerCAmelCase_ : Any = sum(1 for v in qid_to_has_ans.values() if v)
if num_true_pos == 0:
return
lowerCAmelCase_ : Any = make_precision_recall_eval(
snake_case__ , snake_case__ , snake_case__ , snake_case__ , out_image=os.path.join(snake_case__ , "pr_exact.png") , title="Precision-Recall curve for Exact Match score" , )
lowerCAmelCase_ : Dict = make_precision_recall_eval(
snake_case__ , snake_case__ , snake_case__ , snake_case__ , out_image=os.path.join(snake_case__ , "pr_f1.png") , title="Precision-Recall curve for F1 score" , )
lowerCAmelCase_ : Dict = {k: float(snake_case__) for k, v in qid_to_has_ans.items()}
lowerCAmelCase_ : str = make_precision_recall_eval(
snake_case__ , snake_case__ , snake_case__ , snake_case__ , out_image=os.path.join(snake_case__ , "pr_oracle.png") , title="Oracle Precision-Recall curve (binary task of HasAns vs. NoAns)" , )
merge_eval(snake_case__ , snake_case__ , "pr_exact")
merge_eval(snake_case__ , snake_case__ , "pr_f1")
merge_eval(snake_case__ , snake_case__ , "pr_oracle")
def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__ , snake_case__):
if not qid_list:
return
lowerCAmelCase_ : Optional[Any] = [na_probs[k] for k in qid_list]
lowerCAmelCase_ : Dict = np.ones_like(snake_case__) / float(len(snake_case__))
plt.hist(snake_case__ , weights=snake_case__ , bins=20 , range=(0.0, 1.0))
plt.xlabel("Model probability of no-answer")
plt.ylabel("Proportion of dataset")
plt.title(F'''Histogram of no-answer probability: {name}''')
plt.savefig(os.path.join(snake_case__ , F'''na_prob_hist_{name}.png'''))
plt.clf()
def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__ , snake_case__):
lowerCAmelCase_ : Dict = sum(1 for k in qid_to_has_ans if not qid_to_has_ans[k])
lowerCAmelCase_ : str = num_no_ans
lowerCAmelCase_ : List[str] = cur_score
lowerCAmelCase_ : List[Any] = 0.0
lowerCAmelCase_ : str = sorted(snake_case__ , key=lambda snake_case__: na_probs[k])
for i, qid in enumerate(snake_case__):
if qid not in scores:
continue
if qid_to_has_ans[qid]:
lowerCAmelCase_ : Union[str, Any] = scores[qid]
else:
if preds[qid]:
lowerCAmelCase_ : List[Any] = -1
else:
lowerCAmelCase_ : List[str] = 0
cur_score += diff
if cur_score > best_score:
lowerCAmelCase_ : Optional[Any] = cur_score
lowerCAmelCase_ : Optional[int] = na_probs[qid]
return 100.0 * best_score / len(snake_case__), best_thresh
def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__):
lowerCAmelCase_ , lowerCAmelCase_ : Optional[int] = find_best_thresh(snake_case__ , snake_case__ , snake_case__ , snake_case__)
lowerCAmelCase_ , lowerCAmelCase_ : Dict = find_best_thresh(snake_case__ , snake_case__ , snake_case__ , snake_case__)
lowerCAmelCase_ : List[str] = best_exact
lowerCAmelCase_ : List[str] = exact_thresh
lowerCAmelCase_ : Any = best_fa
lowerCAmelCase_ : List[str] = fa_thresh
def UpperCamelCase ( ):
with open(OPTS.data_file) as f:
lowerCAmelCase_ : Optional[int] = json.load(snake_case__)
lowerCAmelCase_ : List[Any] = dataset_json["data"]
with open(OPTS.pred_file) as f:
lowerCAmelCase_ : int = json.load(snake_case__)
if OPTS.na_prob_file:
with open(OPTS.na_prob_file) as f:
lowerCAmelCase_ : Optional[int] = json.load(snake_case__)
else:
lowerCAmelCase_ : List[Any] = {k: 0.0 for k in preds}
lowerCAmelCase_ : Tuple = make_qid_to_has_ans(snake_case__) # maps qid to True/False
lowerCAmelCase_ : Any = [k for k, v in qid_to_has_ans.items() if v]
lowerCAmelCase_ : List[str] = [k for k, v in qid_to_has_ans.items() if not v]
lowerCAmelCase_ , lowerCAmelCase_ : Dict = get_raw_scores(snake_case__ , snake_case__)
lowerCAmelCase_ : str = apply_no_ans_threshold(snake_case__ , snake_case__ , snake_case__ , OPTS.na_prob_thresh)
lowerCAmelCase_ : Dict = apply_no_ans_threshold(snake_case__ , snake_case__ , snake_case__ , OPTS.na_prob_thresh)
lowerCAmelCase_ : Union[str, Any] = make_eval_dict(snake_case__ , snake_case__)
if has_ans_qids:
lowerCAmelCase_ : str = make_eval_dict(snake_case__ , snake_case__ , qid_list=snake_case__)
merge_eval(snake_case__ , snake_case__ , "HasAns")
if no_ans_qids:
lowerCAmelCase_ : Union[str, Any] = make_eval_dict(snake_case__ , snake_case__ , qid_list=snake_case__)
merge_eval(snake_case__ , snake_case__ , "NoAns")
if OPTS.na_prob_file:
find_all_best_thresh(snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__)
if OPTS.na_prob_file and OPTS.out_image_dir:
run_precision_recall_analysis(snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ , OPTS.out_image_dir)
histogram_na_prob(snake_case__ , snake_case__ , OPTS.out_image_dir , "hasAns")
histogram_na_prob(snake_case__ , snake_case__ , OPTS.out_image_dir , "noAns")
if OPTS.out_file:
with open(OPTS.out_file , "w") as f:
json.dump(snake_case__ , snake_case__)
else:
print(json.dumps(snake_case__ , indent=2))
if __name__ == "__main__":
_lowercase = parse_args()
if OPTS.out_image_dir:
import matplotlib
matplotlib.use('''Agg''')
import matplotlib.pyplot as plt
main()
| 683 | 1 |
import json
import os
from typing import Optional, Tuple
import regex as re
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
_lowercase = logging.get_logger(__name__)
_lowercase = {
'''vocab_file''': '''vocab.json''',
'''merges_file''': '''merges.txt''',
}
_lowercase = {
'''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'''},
}
_lowercase = {
'''ctrl''': 256,
}
_lowercase = {
'''Pregnancy''': 168629,
'''Christianity''': 7675,
'''Explain''': 106423,
'''Fitness''': 63440,
'''Saving''': 63163,
'''Ask''': 27171,
'''Ass''': 95985,
'''Joke''': 163509,
'''Questions''': 45622,
'''Thoughts''': 49605,
'''Retail''': 52342,
'''Feminism''': 164338,
'''Writing''': 11992,
'''Atheism''': 192263,
'''Netflix''': 48616,
'''Computing''': 39639,
'''Opinion''': 43213,
'''Alone''': 44967,
'''Funny''': 58917,
'''Gaming''': 40358,
'''Human''': 4088,
'''India''': 1331,
'''Joker''': 77138,
'''Diet''': 36206,
'''Legal''': 11859,
'''Norman''': 4939,
'''Tip''': 72689,
'''Weight''': 52343,
'''Movies''': 46273,
'''Running''': 23425,
'''Science''': 2090,
'''Horror''': 37793,
'''Confession''': 60572,
'''Finance''': 12250,
'''Politics''': 16360,
'''Scary''': 191985,
'''Support''': 12654,
'''Technologies''': 32516,
'''Teenage''': 66160,
'''Event''': 32769,
'''Learned''': 67460,
'''Notion''': 182770,
'''Wikipedia''': 37583,
'''Books''': 6665,
'''Extract''': 76050,
'''Confessions''': 102701,
'''Conspiracy''': 75932,
'''Links''': 63674,
'''Narcissus''': 150425,
'''Relationship''': 54766,
'''Relationships''': 134796,
'''Reviews''': 41671,
'''News''': 4256,
'''Translation''': 26820,
'''multilingual''': 128406,
}
def UpperCamelCase ( snake_case__):
lowerCAmelCase_ : Tuple = set()
lowerCAmelCase_ : Optional[Any] = word[0]
for char in word[1:]:
pairs.add((prev_char, char))
lowerCAmelCase_ : str = char
lowerCAmelCase_ : Union[str, Any] = set(snake_case__)
return pairs
class __snake_case ( snake_case__ ):
"""simple docstring"""
UpperCamelCase_ = VOCAB_FILES_NAMES
UpperCamelCase_ = PRETRAINED_VOCAB_FILES_MAP
UpperCamelCase_ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
UpperCamelCase_ = CONTROL_CODES
def __init__( self : Optional[int] ,lowerCAmelCase__ : Any ,lowerCAmelCase__ : Any ,lowerCAmelCase__ : Any="<unk>" ,**lowerCAmelCase__ : int ) -> List[str]:
'''simple docstring'''
super().__init__(unk_token=lowerCAmelCase__ ,**lowerCAmelCase__ )
with open(lowerCAmelCase__ ,encoding="utf-8" ) as vocab_handle:
lowerCAmelCase_ : Dict = json.load(lowerCAmelCase__ )
lowerCAmelCase_ : List[Any] = {v: k for k, v in self.encoder.items()}
with open(lowerCAmelCase__ ,encoding="utf-8" ) as merges_handle:
lowerCAmelCase_ : Optional[Any] = merges_handle.read().split("\n" )[1:-1]
lowerCAmelCase_ : Optional[int] = [tuple(merge.split() ) for merge in merges]
lowerCAmelCase_ : List[str] = dict(zip(lowerCAmelCase__ ,range(len(lowerCAmelCase__ ) ) ) )
lowerCAmelCase_ : int = {}
@property
def UpperCAmelCase_ ( self : Tuple ) -> List[str]:
'''simple docstring'''
return len(self.encoder )
def UpperCAmelCase_ ( self : List[Any] ) -> Any:
'''simple docstring'''
return dict(self.encoder ,**self.added_tokens_encoder )
def UpperCAmelCase_ ( self : Dict ,lowerCAmelCase__ : Tuple ) -> int:
'''simple docstring'''
if token in self.cache:
return self.cache[token]
lowerCAmelCase_ : Any = tuple(lowerCAmelCase__ )
lowerCAmelCase_ : Optional[Any] = tuple(list(word[:-1] ) + [word[-1] + "</w>"] )
lowerCAmelCase_ : List[Any] = get_pairs(lowerCAmelCase__ )
if not pairs:
return token
while True:
lowerCAmelCase_ : Optional[int] = min(lowerCAmelCase__ ,key=lambda lowerCAmelCase__ : self.bpe_ranks.get(lowerCAmelCase__ ,float("inf" ) ) )
if bigram not in self.bpe_ranks:
break
lowerCAmelCase_ , lowerCAmelCase_ : Optional[Any] = bigram
lowerCAmelCase_ : List[str] = []
lowerCAmelCase_ : Any = 0
while i < len(lowerCAmelCase__ ):
try:
lowerCAmelCase_ : Optional[Any] = word.index(lowerCAmelCase__ ,lowerCAmelCase__ )
except ValueError:
new_word.extend(word[i:] )
break
else:
new_word.extend(word[i:j] )
lowerCAmelCase_ : str = j
if word[i] == first and i < len(lowerCAmelCase__ ) - 1 and word[i + 1] == second:
new_word.append(first + second )
i += 2
else:
new_word.append(word[i] )
i += 1
lowerCAmelCase_ : List[Any] = tuple(lowerCAmelCase__ )
lowerCAmelCase_ : Optional[Any] = new_word
if len(lowerCAmelCase__ ) == 1:
break
else:
lowerCAmelCase_ : int = get_pairs(lowerCAmelCase__ )
lowerCAmelCase_ : Any = "@@ ".join(lowerCAmelCase__ )
lowerCAmelCase_ : Any = word[:-4]
lowerCAmelCase_ : Optional[Any] = word
return word
def UpperCAmelCase_ ( self : Tuple ,lowerCAmelCase__ : Tuple ) -> Optional[int]:
'''simple docstring'''
lowerCAmelCase_ : List[str] = []
lowerCAmelCase_ : Optional[Any] = re.findall(R"\S+\n?" ,lowerCAmelCase__ )
for token in words:
split_tokens.extend(list(self.bpe(lowerCAmelCase__ ).split(" " ) ) )
return split_tokens
def UpperCAmelCase_ ( self : Tuple ,lowerCAmelCase__ : Optional[Any] ) -> Union[str, Any]:
'''simple docstring'''
return self.encoder.get(lowerCAmelCase__ ,self.encoder.get(self.unk_token ) )
def UpperCAmelCase_ ( self : Union[str, Any] ,lowerCAmelCase__ : Optional[int] ) -> Any:
'''simple docstring'''
return self.decoder.get(lowerCAmelCase__ ,self.unk_token )
def UpperCAmelCase_ ( self : str ,lowerCAmelCase__ : str ) -> int:
'''simple docstring'''
lowerCAmelCase_ : List[str] = " ".join(lowerCAmelCase__ ).replace("@@ " ,"" ).strip()
return out_string
def UpperCAmelCase_ ( self : int ,lowerCAmelCase__ : str ,lowerCAmelCase__ : Optional[str] = None ) -> Tuple[str]:
'''simple docstring'''
if not os.path.isdir(lowerCAmelCase__ ):
logger.error(f'''Vocabulary path ({save_directory}) should be a directory''' )
return
lowerCAmelCase_ : Optional[int] = os.path.join(
lowerCAmelCase__ ,(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )
lowerCAmelCase_ : Tuple = os.path.join(
lowerCAmelCase__ ,(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"] )
with open(lowerCAmelCase__ ,"w" ,encoding="utf-8" ) as f:
f.write(json.dumps(self.encoder ,indent=2 ,sort_keys=lowerCAmelCase__ ,ensure_ascii=lowerCAmelCase__ ) + "\n" )
lowerCAmelCase_ : Optional[int] = 0
with open(lowerCAmelCase__ ,"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 lowerCAmelCase__ : 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!" )
lowerCAmelCase_ : int = token_index
writer.write(" ".join(lowerCAmelCase__ ) + "\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)
| 683 |
from math import sqrt
def UpperCamelCase ( snake_case__):
lowerCAmelCase_ : Optional[int] = 0
for i in range(1 , int(sqrt(snake_case__) + 1)):
if n % i == 0 and i != sqrt(snake_case__):
total += i + n // i
elif i == sqrt(snake_case__):
total += i
return total - n
def UpperCamelCase ( snake_case__ = 1_00_00):
lowerCAmelCase_ : int = sum(
i
for i in range(1 , snake_case__)
if sum_of_divisors(sum_of_divisors(snake_case__)) == i and sum_of_divisors(snake_case__) != i)
return total
if __name__ == "__main__":
print(solution(int(str(input()).strip())))
| 683 | 1 |
import argparse
import collections
import numpy as np
import torch
from flax import traverse_util
from tax import checkpoints
from transformers import MTaConfig, UMTaEncoderModel, UMTaForConditionalGeneration
from transformers.utils import logging
logging.set_verbosity_info()
def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__):
return params[F'''{prefix}/{prefix}/relpos_bias/rel_embedding'''][:, i, :]
def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__ , snake_case__="attention"):
lowerCAmelCase_ : List[str] = np.ascontiguousarray(params[F'''{prefix}/{prefix}/{layer_name}/key/kernel'''][:, i, :, :])
lowerCAmelCase_ : Any = k_tmp.reshape(k_tmp.shape[0] , k_tmp.shape[1] * k_tmp.shape[2])
lowerCAmelCase_ : Dict = np.ascontiguousarray(params[F'''{prefix}/{prefix}/{layer_name}/out/kernel'''][:, i, :, :])
lowerCAmelCase_ : Dict = o_tmp.reshape(o_tmp.shape[0] * o_tmp.shape[1] , o_tmp.shape[2])
lowerCAmelCase_ : Dict = np.ascontiguousarray(params[F'''{prefix}/{prefix}/{layer_name}/query/kernel'''][:, i, :, :])
lowerCAmelCase_ : List[str] = q_tmp.reshape(q_tmp.shape[0] , q_tmp.shape[1] * q_tmp.shape[2])
lowerCAmelCase_ : Optional[int] = np.ascontiguousarray(params[F'''{prefix}/{prefix}/{layer_name}/value/kernel'''][:, i, :, :])
lowerCAmelCase_ : Tuple = v_tmp.reshape(v_tmp.shape[0] , v_tmp.shape[1] * v_tmp.shape[2])
return k, o, q, v
def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__ , snake_case__=False):
if split_mlp_wi:
lowerCAmelCase_ : Dict = params[F'''{prefix}/{prefix}/mlp/wi_0/kernel'''][:, i, :]
lowerCAmelCase_ : Optional[Any] = params[F'''{prefix}/{prefix}/mlp/wi_1/kernel'''][:, i, :]
lowerCAmelCase_ : Union[str, Any] = (wi_a, wi_a)
else:
lowerCAmelCase_ : Any = params[F'''{prefix}/{prefix}/mlp/wi/kernel'''][:, i, :]
lowerCAmelCase_ : List[str] = params[F'''{prefix}/{prefix}/mlp/wo/kernel'''][:, i, :]
return wi, wo
def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__ , snake_case__):
return params[F'''{prefix}/{prefix}/{layer_name}/scale'''][:, i]
def UpperCamelCase ( snake_case__ , *, snake_case__ , snake_case__ , snake_case__ = False):
lowerCAmelCase_ : Any = traverse_util.flatten_dict(variables["target"])
lowerCAmelCase_ : Dict = {"/".join(snake_case__): v for k, v in old.items()}
# v1.1 models have a gated GeLU with wi_0 and wi_1 instead of wi
lowerCAmelCase_ : str = "encoder/encoder/mlp/wi_0/kernel" in old
print("Split MLP:" , snake_case__)
lowerCAmelCase_ : Any = collections.OrderedDict()
# Shared embeddings.
lowerCAmelCase_ : List[Any] = old["token_embedder/embedding"]
# Encoder.
for i in range(snake_case__):
# Block i, layer 0 (Self Attention).
lowerCAmelCase_ : Dict = tax_layer_norm_lookup(snake_case__ , snake_case__ , "encoder" , "pre_attention_layer_norm")
lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ : Dict = tax_attention_lookup(snake_case__ , snake_case__ , "encoder" , "attention")
lowerCAmelCase_ : str = layer_norm
lowerCAmelCase_ : Any = k.T
lowerCAmelCase_ : str = o.T
lowerCAmelCase_ : Union[str, Any] = q.T
lowerCAmelCase_ : Union[str, Any] = v.T
# Block i, layer 1 (MLP).
lowerCAmelCase_ : Optional[Any] = tax_layer_norm_lookup(snake_case__ , snake_case__ , "encoder" , "pre_mlp_layer_norm")
lowerCAmelCase_ , lowerCAmelCase_ : Tuple = tax_mlp_lookup(snake_case__ , snake_case__ , "encoder" , snake_case__)
lowerCAmelCase_ : int = layer_norm
if split_mlp_wi:
lowerCAmelCase_ : List[str] = wi[0].T
lowerCAmelCase_ : List[str] = wi[1].T
else:
lowerCAmelCase_ : List[Any] = wi.T
lowerCAmelCase_ : Optional[Any] = wo.T
if scalable_attention:
# convert the rel_embedding of each layer
lowerCAmelCase_ : List[str] = tax_relpos_bias_lookup(
snake_case__ , snake_case__ , "encoder").T
lowerCAmelCase_ : List[str] = old["encoder/encoder_norm/scale"]
if not scalable_attention:
lowerCAmelCase_ : List[Any] = tax_relpos_bias_lookup(
snake_case__ , 0 , "encoder").T
lowerCAmelCase_ : Optional[int] = tax_relpos_bias_lookup(
snake_case__ , 0 , "decoder").T
if not is_encoder_only:
# Decoder.
for i in range(snake_case__):
# Block i, layer 0 (Self Attention).
lowerCAmelCase_ : Dict = tax_layer_norm_lookup(snake_case__ , snake_case__ , "decoder" , "pre_self_attention_layer_norm")
lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ : Optional[Any] = tax_attention_lookup(snake_case__ , snake_case__ , "decoder" , "self_attention")
lowerCAmelCase_ : List[str] = layer_norm
lowerCAmelCase_ : Union[str, Any] = k.T
lowerCAmelCase_ : Optional[Any] = o.T
lowerCAmelCase_ : Union[str, Any] = q.T
lowerCAmelCase_ : Dict = v.T
# Block i, layer 1 (Cross Attention).
lowerCAmelCase_ : int = tax_layer_norm_lookup(snake_case__ , snake_case__ , "decoder" , "pre_cross_attention_layer_norm")
lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ : int = tax_attention_lookup(snake_case__ , snake_case__ , "decoder" , "encoder_decoder_attention")
lowerCAmelCase_ : int = layer_norm
lowerCAmelCase_ : Union[str, Any] = k.T
lowerCAmelCase_ : str = o.T
lowerCAmelCase_ : Dict = q.T
lowerCAmelCase_ : int = v.T
# Block i, layer 2 (MLP).
lowerCAmelCase_ : Optional[int] = tax_layer_norm_lookup(snake_case__ , snake_case__ , "decoder" , "pre_mlp_layer_norm")
lowerCAmelCase_ , lowerCAmelCase_ : Optional[int] = tax_mlp_lookup(snake_case__ , snake_case__ , "decoder" , snake_case__)
lowerCAmelCase_ : str = layer_norm
if split_mlp_wi:
lowerCAmelCase_ : Union[str, Any] = wi[0].T
lowerCAmelCase_ : int = wi[1].T
else:
lowerCAmelCase_ : Tuple = wi.T
lowerCAmelCase_ : Tuple = wo.T
if scalable_attention:
# convert the rel_embedding of each layer
lowerCAmelCase_ : List[Any] = tax_relpos_bias_lookup(snake_case__ , snake_case__ , "decoder").T
lowerCAmelCase_ : Tuple = old["decoder/decoder_norm/scale"]
# LM Head (only in v1.1 checkpoints, in v1.0 embeddings are used instead)
if "decoder/logits_dense/kernel" in old:
lowerCAmelCase_ : Union[str, Any] = old["decoder/logits_dense/kernel"].T
return new
def UpperCamelCase ( snake_case__ , snake_case__):
lowerCAmelCase_ : Tuple = collections.OrderedDict([(k, torch.from_numpy(v.copy())) for (k, v) in converted_params.items()])
# Add what is missing.
if "encoder.embed_tokens.weight" not in state_dict:
lowerCAmelCase_ : Optional[int] = state_dict["shared.weight"]
if not is_encoder_only:
if "decoder.embed_tokens.weight" not in state_dict:
lowerCAmelCase_ : Dict = state_dict["shared.weight"]
if "lm_head.weight" not in state_dict: # For old 1.0 models.
print("Using shared word embeddings as lm_head.")
lowerCAmelCase_ : Any = state_dict["shared.weight"]
return state_dict
def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__):
lowerCAmelCase_ : Tuple = checkpoints.load_tax_checkpoint(snake_case__)
lowerCAmelCase_ : List[str] = convert_tax_to_pytorch(
snake_case__ , num_layers=config.num_layers , is_encoder_only=snake_case__ , scalable_attention=snake_case__)
lowerCAmelCase_ : Dict = make_state_dict(snake_case__ , snake_case__)
model.load_state_dict(snake_case__ , strict=snake_case__)
def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__ , snake_case__ = False , snake_case__ = False , ):
lowerCAmelCase_ : Optional[int] = MTaConfig.from_json_file(snake_case__)
print(F'''Building PyTorch model from configuration: {config}''')
# Non-v1.1 checkpoints could also use T5Model, but this works for all.
# The v1.0 checkpoints will simply have an LM head that is the word embeddings.
if is_encoder_only:
lowerCAmelCase_ : List[Any] = UMTaEncoderModel(snake_case__)
else:
lowerCAmelCase_ : Optional[int] = UMTaForConditionalGeneration(snake_case__)
# Load weights from tf checkpoint
load_tax_weights_in_ta(snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__)
# Save pytorch-model
print(F'''Save PyTorch model to {pytorch_dump_path}''')
model.save_pretrained(snake_case__)
# Verify that we can load the checkpoint.
model.from_pretrained(snake_case__)
print("Done")
if __name__ == "__main__":
_lowercase = argparse.ArgumentParser(description='''Converts a native T5X checkpoint into a PyTorch checkpoint.''')
# Required parameters
parser.add_argument(
'''--t5x_checkpoint_path''', default=None, type=str, required=True, help='''Path to the T5X checkpoint.'''
)
parser.add_argument(
'''--config_file''',
default=None,
type=str,
required=True,
help='''The config json file corresponding to the pre-trained T5 model.\nThis specifies the model architecture.''',
)
parser.add_argument(
'''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
)
parser.add_argument(
'''--is_encoder_only''', action='''store_true''', help='''Check if the model is encoder-decoder model''', default=False
)
parser.add_argument(
'''--scalable_attention''',
action='''store_true''',
help='''Whether the model uses scaled attention (umt5 model)''',
default=False,
)
_lowercase = parser.parse_args()
convert_tax_checkpoint_to_pytorch(
args.tax_checkpoint_path,
args.config_file,
args.pytorch_dump_path,
args.is_encoder_only,
args.scalable_attention,
)
| 683 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_speech_available,
is_tf_available,
is_torch_available,
)
_lowercase = {
'''configuration_speech_to_text''': ['''SPEECH_TO_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''Speech2TextConfig'''],
'''processing_speech_to_text''': ['''Speech2TextProcessor'''],
}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowercase = ['''Speech2TextTokenizer''']
try:
if not is_speech_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowercase = ['''Speech2TextFeatureExtractor''']
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowercase = [
'''TF_SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''TFSpeech2TextForConditionalGeneration''',
'''TFSpeech2TextModel''',
'''TFSpeech2TextPreTrainedModel''',
]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowercase = [
'''SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''Speech2TextForConditionalGeneration''',
'''Speech2TextModel''',
'''Speech2TextPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_speech_to_text import SPEECH_TO_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP, SpeechaTextConfig
from .processing_speech_to_text import SpeechaTextProcessor
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_speech_to_text import SpeechaTextTokenizer
try:
if not is_speech_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_speech_to_text import SpeechaTextFeatureExtractor
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_speech_to_text import (
TF_SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
TFSpeechaTextForConditionalGeneration,
TFSpeechaTextModel,
TFSpeechaTextPreTrainedModel,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_speech_to_text import (
SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
SpeechaTextForConditionalGeneration,
SpeechaTextModel,
SpeechaTextPreTrainedModel,
)
else:
import sys
_lowercase = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 683 | 1 |
import pytest
_lowercase = '''__dummy_dataset1__'''
_lowercase = '''
import json
import os
import datasets
REPO_URL = "https://huggingface.co/datasets/albertvillanova/tests-raw-jsonl/resolve/main/"
URLS = {"train": REPO_URL + "wikiann-bn-train.jsonl", "validation": REPO_URL + "wikiann-bn-validation.jsonl"}
class __DummyDataset1__(datasets.GeneratorBasedBuilder):
def _info(self):
features = datasets.Features(
{
"tokens": datasets.Sequence(datasets.Value("string")),
"ner_tags": datasets.Sequence(
datasets.features.ClassLabel(
names=[
"O",
"B-PER",
"I-PER",
"B-ORG",
"I-ORG",
"B-LOC",
"I-LOC",
]
)
),
"langs": datasets.Sequence(datasets.Value("string")),
"spans": datasets.Sequence(datasets.Value("string")),
}
)
return datasets.DatasetInfo(features=features)
def _split_generators(self, dl_manager):
dl_path = dl_manager.download(URLS)
return [
datasets.SplitGenerator(datasets.Split.TRAIN, gen_kwargs={"filepath": dl_path["train"]}),
datasets.SplitGenerator(datasets.Split.VALIDATION, gen_kwargs={"filepath": dl_path["validation"]}),
]
def _generate_examples(self, filepath):
with open(filepath, "r", encoding="utf-8") as f:
for i, line in enumerate(f):
yield i, json.loads(line)
'''
@pytest.fixture
def UpperCamelCase ( ):
return DATASET_LOADING_SCRIPT_NAME
@pytest.fixture
def UpperCamelCase ( ):
return DATASET_LOADING_SCRIPT_CODE
@pytest.fixture
def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__):
lowerCAmelCase_ : List[Any] = dataset_loading_script_name
lowerCAmelCase_ : List[str] = tmp_path / "datasets" / script_name
script_dir.mkdir(parents=snake_case__)
lowerCAmelCase_ : List[Any] = script_dir / F'''{script_name}.py'''
with open(snake_case__ , "w") as f:
f.write(snake_case__)
return str(snake_case__)
| 683 |
import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
_lowercase = logging.get_logger(__name__)
_lowercase = {'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt'''}
_lowercase = {
'''vocab_file''': {
'''allenai/longformer-base-4096''': '''https://huggingface.co/allenai/longformer-base-4096/resolve/main/vocab.json''',
'''allenai/longformer-large-4096''': (
'''https://huggingface.co/allenai/longformer-large-4096/resolve/main/vocab.json'''
),
'''allenai/longformer-large-4096-finetuned-triviaqa''': (
'''https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/vocab.json'''
),
'''allenai/longformer-base-4096-extra.pos.embd.only''': (
'''https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/vocab.json'''
),
'''allenai/longformer-large-4096-extra.pos.embd.only''': (
'''https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/vocab.json'''
),
},
'''merges_file''': {
'''allenai/longformer-base-4096''': '''https://huggingface.co/allenai/longformer-base-4096/resolve/main/merges.txt''',
'''allenai/longformer-large-4096''': (
'''https://huggingface.co/allenai/longformer-large-4096/resolve/main/merges.txt'''
),
'''allenai/longformer-large-4096-finetuned-triviaqa''': (
'''https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/merges.txt'''
),
'''allenai/longformer-base-4096-extra.pos.embd.only''': (
'''https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/merges.txt'''
),
'''allenai/longformer-large-4096-extra.pos.embd.only''': (
'''https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/merges.txt'''
),
},
}
_lowercase = {
'''allenai/longformer-base-4096''': 4096,
'''allenai/longformer-large-4096''': 4096,
'''allenai/longformer-large-4096-finetuned-triviaqa''': 4096,
'''allenai/longformer-base-4096-extra.pos.embd.only''': 4096,
'''allenai/longformer-large-4096-extra.pos.embd.only''': 4096,
}
@lru_cache()
# Copied from transformers.models.roberta.tokenization_roberta.bytes_to_unicode
def UpperCamelCase ( ):
lowerCAmelCase_ : str = (
list(range(ord("!") , ord("~") + 1)) + list(range(ord("¡") , ord("¬") + 1)) + list(range(ord("®") , ord("ÿ") + 1))
)
lowerCAmelCase_ : Tuple = bs[:]
lowerCAmelCase_ : Dict = 0
for b in range(2**8):
if b not in bs:
bs.append(snake_case__)
cs.append(2**8 + n)
n += 1
lowerCAmelCase_ : Union[str, Any] = [chr(snake_case__) for n in cs]
return dict(zip(snake_case__ , snake_case__))
def UpperCamelCase ( snake_case__):
lowerCAmelCase_ : Optional[Any] = set()
lowerCAmelCase_ : List[Any] = word[0]
for char in word[1:]:
pairs.add((prev_char, char))
lowerCAmelCase_ : Union[str, Any] = char
return pairs
class __snake_case ( snake_case__ ):
"""simple docstring"""
UpperCamelCase_ = VOCAB_FILES_NAMES
UpperCamelCase_ = PRETRAINED_VOCAB_FILES_MAP
UpperCamelCase_ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
UpperCamelCase_ = ['input_ids', 'attention_mask']
def __init__( self : str ,lowerCAmelCase__ : Dict ,lowerCAmelCase__ : List[Any] ,lowerCAmelCase__ : Optional[Any]="replace" ,lowerCAmelCase__ : Dict="<s>" ,lowerCAmelCase__ : str="</s>" ,lowerCAmelCase__ : str="</s>" ,lowerCAmelCase__ : Optional[Any]="<s>" ,lowerCAmelCase__ : List[Any]="<unk>" ,lowerCAmelCase__ : Union[str, Any]="<pad>" ,lowerCAmelCase__ : int="<mask>" ,lowerCAmelCase__ : Any=False ,**lowerCAmelCase__ : int ,) -> Dict:
'''simple docstring'''
lowerCAmelCase_ : Optional[int] = AddedToken(lowerCAmelCase__ ,lstrip=lowerCAmelCase__ ,rstrip=lowerCAmelCase__ ) if isinstance(lowerCAmelCase__ ,lowerCAmelCase__ ) else bos_token
lowerCAmelCase_ : Tuple = AddedToken(lowerCAmelCase__ ,lstrip=lowerCAmelCase__ ,rstrip=lowerCAmelCase__ ) if isinstance(lowerCAmelCase__ ,lowerCAmelCase__ ) else eos_token
lowerCAmelCase_ : Dict = AddedToken(lowerCAmelCase__ ,lstrip=lowerCAmelCase__ ,rstrip=lowerCAmelCase__ ) if isinstance(lowerCAmelCase__ ,lowerCAmelCase__ ) else sep_token
lowerCAmelCase_ : int = AddedToken(lowerCAmelCase__ ,lstrip=lowerCAmelCase__ ,rstrip=lowerCAmelCase__ ) if isinstance(lowerCAmelCase__ ,lowerCAmelCase__ ) else cls_token
lowerCAmelCase_ : List[str] = AddedToken(lowerCAmelCase__ ,lstrip=lowerCAmelCase__ ,rstrip=lowerCAmelCase__ ) if isinstance(lowerCAmelCase__ ,lowerCAmelCase__ ) else unk_token
lowerCAmelCase_ : List[str] = AddedToken(lowerCAmelCase__ ,lstrip=lowerCAmelCase__ ,rstrip=lowerCAmelCase__ ) if isinstance(lowerCAmelCase__ ,lowerCAmelCase__ ) else pad_token
# Mask token behave like a normal word, i.e. include the space before it
lowerCAmelCase_ : Optional[Any] = AddedToken(lowerCAmelCase__ ,lstrip=lowerCAmelCase__ ,rstrip=lowerCAmelCase__ ) if isinstance(lowerCAmelCase__ ,lowerCAmelCase__ ) else mask_token
super().__init__(
errors=lowerCAmelCase__ ,bos_token=lowerCAmelCase__ ,eos_token=lowerCAmelCase__ ,unk_token=lowerCAmelCase__ ,sep_token=lowerCAmelCase__ ,cls_token=lowerCAmelCase__ ,pad_token=lowerCAmelCase__ ,mask_token=lowerCAmelCase__ ,add_prefix_space=lowerCAmelCase__ ,**lowerCAmelCase__ ,)
with open(lowerCAmelCase__ ,encoding="utf-8" ) as vocab_handle:
lowerCAmelCase_ : List[Any] = json.load(lowerCAmelCase__ )
lowerCAmelCase_ : Dict = {v: k for k, v in self.encoder.items()}
lowerCAmelCase_ : List[Any] = errors # how to handle errors in decoding
lowerCAmelCase_ : Optional[Any] = bytes_to_unicode()
lowerCAmelCase_ : int = {v: k for k, v in self.byte_encoder.items()}
with open(lowerCAmelCase__ ,encoding="utf-8" ) as merges_handle:
lowerCAmelCase_ : Union[str, Any] = merges_handle.read().split("\n" )[1:-1]
lowerCAmelCase_ : Dict = [tuple(merge.split() ) for merge in bpe_merges]
lowerCAmelCase_ : Dict = dict(zip(lowerCAmelCase__ ,range(len(lowerCAmelCase__ ) ) ) )
lowerCAmelCase_ : Any = {}
lowerCAmelCase_ : int = add_prefix_space
# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
lowerCAmelCase_ : Optional[Any] = re.compile(R"'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+" )
@property
def UpperCAmelCase_ ( self : Optional[int] ) -> Any:
'''simple docstring'''
return len(self.encoder )
def UpperCAmelCase_ ( self : Any ) -> Optional[int]:
'''simple docstring'''
return dict(self.encoder ,**self.added_tokens_encoder )
def UpperCAmelCase_ ( self : List[str] ,lowerCAmelCase__ : List[str] ) -> List[Any]:
'''simple docstring'''
if token in self.cache:
return self.cache[token]
lowerCAmelCase_ : Union[str, Any] = tuple(lowerCAmelCase__ )
lowerCAmelCase_ : Optional[Any] = get_pairs(lowerCAmelCase__ )
if not pairs:
return token
while True:
lowerCAmelCase_ : Dict = min(lowerCAmelCase__ ,key=lambda lowerCAmelCase__ : self.bpe_ranks.get(lowerCAmelCase__ ,float("inf" ) ) )
if bigram not in self.bpe_ranks:
break
lowerCAmelCase_ , lowerCAmelCase_ : Dict = bigram
lowerCAmelCase_ : Optional[Any] = []
lowerCAmelCase_ : Any = 0
while i < len(lowerCAmelCase__ ):
try:
lowerCAmelCase_ : Optional[int] = word.index(lowerCAmelCase__ ,lowerCAmelCase__ )
except ValueError:
new_word.extend(word[i:] )
break
else:
new_word.extend(word[i:j] )
lowerCAmelCase_ : Tuple = j
if word[i] == first and i < len(lowerCAmelCase__ ) - 1 and word[i + 1] == second:
new_word.append(first + second )
i += 2
else:
new_word.append(word[i] )
i += 1
lowerCAmelCase_ : Optional[Any] = tuple(lowerCAmelCase__ )
lowerCAmelCase_ : Union[str, Any] = new_word
if len(lowerCAmelCase__ ) == 1:
break
else:
lowerCAmelCase_ : Dict = get_pairs(lowerCAmelCase__ )
lowerCAmelCase_ : Tuple = " ".join(lowerCAmelCase__ )
lowerCAmelCase_ : Any = word
return word
def UpperCAmelCase_ ( self : Optional[int] ,lowerCAmelCase__ : Tuple ) -> Dict:
'''simple docstring'''
lowerCAmelCase_ : Dict = []
for token in re.findall(self.pat ,lowerCAmelCase__ ):
lowerCAmelCase_ : List[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(lowerCAmelCase__ ).split(" " ) )
return bpe_tokens
def UpperCAmelCase_ ( self : Tuple ,lowerCAmelCase__ : int ) -> Tuple:
'''simple docstring'''
return self.encoder.get(lowerCAmelCase__ ,self.encoder.get(self.unk_token ) )
def UpperCAmelCase_ ( self : List[str] ,lowerCAmelCase__ : Optional[Any] ) -> Union[str, Any]:
'''simple docstring'''
return self.decoder.get(lowerCAmelCase__ )
def UpperCAmelCase_ ( self : Tuple ,lowerCAmelCase__ : Dict ) -> Any:
'''simple docstring'''
lowerCAmelCase_ : Optional[int] = "".join(lowerCAmelCase__ )
lowerCAmelCase_ : Union[str, Any] = bytearray([self.byte_decoder[c] for c in text] ).decode("utf-8" ,errors=self.errors )
return text
def UpperCAmelCase_ ( self : Optional[Any] ,lowerCAmelCase__ : str ,lowerCAmelCase__ : Optional[str] = None ) -> Tuple[str]:
'''simple docstring'''
if not os.path.isdir(lowerCAmelCase__ ):
logger.error(f'''Vocabulary path ({save_directory}) should be a directory''' )
return
lowerCAmelCase_ : Optional[Any] = os.path.join(
lowerCAmelCase__ ,(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )
lowerCAmelCase_ : Tuple = os.path.join(
lowerCAmelCase__ ,(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"] )
with open(lowerCAmelCase__ ,"w" ,encoding="utf-8" ) as f:
f.write(json.dumps(self.encoder ,indent=2 ,sort_keys=lowerCAmelCase__ ,ensure_ascii=lowerCAmelCase__ ) + "\n" )
lowerCAmelCase_ : Tuple = 0
with open(lowerCAmelCase__ ,"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 lowerCAmelCase__ : 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!" )
lowerCAmelCase_ : Optional[Any] = token_index
writer.write(" ".join(lowerCAmelCase__ ) + "\n" )
index += 1
return vocab_file, merge_file
def UpperCAmelCase_ ( self : Union[str, Any] ,lowerCAmelCase__ : List[int] ,lowerCAmelCase__ : Optional[List[int]] = None ) -> List[int]:
'''simple docstring'''
if token_ids_a is None:
return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
lowerCAmelCase_ : List[Any] = [self.cls_token_id]
lowerCAmelCase_ : List[str] = [self.sep_token_id]
return cls + token_ids_a + sep + sep + token_ids_a + sep
def UpperCAmelCase_ ( self : Dict ,lowerCAmelCase__ : List[int] ,lowerCAmelCase__ : Optional[List[int]] = None ,lowerCAmelCase__ : bool = False ) -> List[int]:
'''simple docstring'''
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=lowerCAmelCase__ ,token_ids_a=lowerCAmelCase__ ,already_has_special_tokens=lowerCAmelCase__ )
if token_ids_a is None:
return [1] + ([0] * len(lowerCAmelCase__ )) + [1]
return [1] + ([0] * len(lowerCAmelCase__ )) + [1, 1] + ([0] * len(lowerCAmelCase__ )) + [1]
def UpperCAmelCase_ ( self : Any ,lowerCAmelCase__ : List[int] ,lowerCAmelCase__ : Optional[List[int]] = None ) -> List[int]:
'''simple docstring'''
lowerCAmelCase_ : List[str] = [self.sep_token_id]
lowerCAmelCase_ : 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 : Tuple ,lowerCAmelCase__ : List[str] ,lowerCAmelCase__ : Optional[int]=False ,**lowerCAmelCase__ : Optional[int] ) -> Any:
'''simple docstring'''
lowerCAmelCase_ : int = kwargs.pop("add_prefix_space" ,self.add_prefix_space )
if (is_split_into_words or add_prefix_space) and (len(lowerCAmelCase__ ) > 0 and not text[0].isspace()):
lowerCAmelCase_ : Union[str, Any] = " " + text
return (text, kwargs)
| 683 | 1 |
from manim import *
class __snake_case ( snake_case__ ):
"""simple docstring"""
def UpperCAmelCase_ ( self : List[str] ) -> Union[str, Any]:
'''simple docstring'''
lowerCAmelCase_ : Any = Rectangle(height=0.5 ,width=0.5 )
lowerCAmelCase_ : Any = Rectangle(height=0.46 ,width=0.46 ).set_stroke(width=0 )
lowerCAmelCase_ : Optional[int] = [mem.copy() for i in range(6 )]
lowerCAmelCase_ : List[Any] = [mem.copy() for i in range(6 )]
lowerCAmelCase_ : str = VGroup(*lowerCAmelCase__ ).arrange(lowerCAmelCase__ ,buff=0 )
lowerCAmelCase_ : Optional[Any] = VGroup(*lowerCAmelCase__ ).arrange(lowerCAmelCase__ ,buff=0 )
lowerCAmelCase_ : int = VGroup(lowerCAmelCase__ ,lowerCAmelCase__ ).arrange(lowerCAmelCase__ ,buff=0 )
lowerCAmelCase_ : str = Text("CPU" ,font_size=24 )
lowerCAmelCase_ : str = Group(lowerCAmelCase__ ,lowerCAmelCase__ ).arrange(lowerCAmelCase__ ,buff=0.5 ,aligned_edge=lowerCAmelCase__ )
cpu.move_to([-2.5, -0.5, 0] )
self.add(lowerCAmelCase__ )
lowerCAmelCase_ : Optional[int] = [mem.copy() for i in range(1 )]
lowerCAmelCase_ : Dict = VGroup(*lowerCAmelCase__ ).arrange(lowerCAmelCase__ ,buff=0 )
lowerCAmelCase_ : Tuple = Text("GPU" ,font_size=24 )
lowerCAmelCase_ : Union[str, Any] = Group(lowerCAmelCase__ ,lowerCAmelCase__ ).arrange(lowerCAmelCase__ ,buff=0.5 ,aligned_edge=lowerCAmelCase__ )
gpu.align_to(lowerCAmelCase__ ,lowerCAmelCase__ )
gpu.set_x(gpu.get_x() - 1 )
self.add(lowerCAmelCase__ )
lowerCAmelCase_ : Optional[Any] = [mem.copy() for i in range(6 )]
lowerCAmelCase_ : List[Any] = VGroup(*lowerCAmelCase__ ).arrange(lowerCAmelCase__ ,buff=0 )
lowerCAmelCase_ : str = Text("Model" ,font_size=24 )
lowerCAmelCase_ : List[str] = Group(lowerCAmelCase__ ,lowerCAmelCase__ ).arrange(lowerCAmelCase__ ,buff=0.5 ,aligned_edge=lowerCAmelCase__ )
model.move_to([3, -1.0, 0] )
self.play(
Create(lowerCAmelCase__ ,run_time=1 ) ,Create(lowerCAmelCase__ ,run_time=1 ) ,Create(lowerCAmelCase__ ,run_time=1 ) ,)
lowerCAmelCase_ : str = MarkupText(
f'''First, an empty model skeleton is loaded\ninto <span fgcolor=\'{YELLOW}\'>memory</span> without using much RAM.''' ,font_size=24 ,)
lowerCAmelCase_ : List[str] = Square(side_length=2.2 )
key.move_to([-5, 2, 0] )
lowerCAmelCase_ : List[Any] = MarkupText(
f'''<b>Key:</b>\n\n<span fgcolor=\'{YELLOW}\'>●</span> Empty Model''' ,font_size=18 ,)
key_text.move_to([-5, 2.4, 0] )
step_a.move_to([2, 2, 0] )
self.play(Write(lowerCAmelCase__ ,run_time=2.5 ) ,Write(lowerCAmelCase__ ) ,Write(lowerCAmelCase__ ) )
self.add(lowerCAmelCase__ )
lowerCAmelCase_ : Any = []
lowerCAmelCase_ : Dict = []
lowerCAmelCase_ : Dict = []
for i, rect in enumerate(lowerCAmelCase__ ):
lowerCAmelCase_ : Union[str, Any] = Rectangle(height=0.46 ,width=0.46 ).set_stroke(width=0.0 ).set_fill(lowerCAmelCase__ ,opacity=0.7 )
cpu_target.move_to(lowerCAmelCase__ )
cpu_target.generate_target()
lowerCAmelCase_ : List[str] = 0.46 / 4
lowerCAmelCase_ : Tuple = 0.46 / 3
if i == 0:
cpu_target.target.next_to(cpu_left_col_base[0].get_corner(DOWN + LEFT ) ,buff=0.02 ,direction=lowerCAmelCase__ )
cpu_target.target.set_x(cpu_target.target.get_x() + 0.1 )
elif i == 3:
cpu_target.target.next_to(cpu_targs[0].target ,direction=lowerCAmelCase__ ,buff=0.0 )
else:
cpu_target.target.next_to(cpu_targs[i - 1].target ,direction=lowerCAmelCase__ ,buff=0.0 )
cpu_targs.append(lowerCAmelCase__ )
first_animations.append(rect.animate(run_time=0.5 ).set_stroke(lowerCAmelCase__ ) )
second_animations.append(MoveToTarget(lowerCAmelCase__ ,run_time=1.5 ) )
self.play(*lowerCAmelCase__ )
self.play(*lowerCAmelCase__ )
self.wait()
| 683 |
from collections.abc import Iterable
from typing import Any
class __snake_case :
"""simple docstring"""
def __init__( self : Optional[Any] ,lowerCAmelCase__ : int | None = None ) -> List[str]:
'''simple docstring'''
lowerCAmelCase_ : Dict = value
lowerCAmelCase_ : Node | None = None # Added in order to delete a node easier
lowerCAmelCase_ : Node | None = None
lowerCAmelCase_ : Node | None = None
def __repr__( self : Union[str, Any] ) -> str:
'''simple docstring'''
from pprint import pformat
if self.left is None and self.right is None:
return str(self.value )
return pformat({f'''{self.value}''': (self.left, self.right)} ,indent=1 )
class __snake_case :
"""simple docstring"""
def __init__( self : Optional[Any] ,lowerCAmelCase__ : Node | None = None ) -> List[str]:
'''simple docstring'''
lowerCAmelCase_ : List[Any] = root
def __str__( self : Dict ) -> str:
'''simple docstring'''
return str(self.root )
def UpperCAmelCase_ ( self : List[Any] ,lowerCAmelCase__ : Node ,lowerCAmelCase__ : Node | None ) -> None:
'''simple docstring'''
if new_children is not None: # reset its kids
lowerCAmelCase_ : Optional[int] = node.parent
if node.parent is not None: # reset its parent
if self.is_right(lowerCAmelCase__ ): # If it is the right children
lowerCAmelCase_ : List[Any] = new_children
else:
lowerCAmelCase_ : List[Any] = new_children
else:
lowerCAmelCase_ : Any = new_children
def UpperCAmelCase_ ( self : Any ,lowerCAmelCase__ : Node ) -> bool:
'''simple docstring'''
if node.parent and node.parent.right:
return node == node.parent.right
return False
def UpperCAmelCase_ ( self : List[str] ) -> bool:
'''simple docstring'''
return self.root is None
def UpperCAmelCase_ ( self : List[Any] ,lowerCAmelCase__ : Union[str, Any] ) -> None:
'''simple docstring'''
lowerCAmelCase_ : str = Node(lowerCAmelCase__ ) # create a new Node
if self.empty(): # if Tree is empty
lowerCAmelCase_ : Optional[int] = new_node # set its root
else: # Tree is not empty
lowerCAmelCase_ : List[Any] = self.root # from root
if parent_node is None:
return
while True: # While we don't get to a leaf
if value < parent_node.value: # We go left
if parent_node.left is None:
lowerCAmelCase_ : Dict = new_node # We insert the new node in a leaf
break
else:
lowerCAmelCase_ : List[str] = parent_node.left
else:
if parent_node.right is None:
lowerCAmelCase_ : Dict = new_node
break
else:
lowerCAmelCase_ : str = parent_node.right
lowerCAmelCase_ : Optional[int] = parent_node
def UpperCAmelCase_ ( self : int ,*lowerCAmelCase__ : Tuple ) -> None:
'''simple docstring'''
for value in values:
self.__insert(lowerCAmelCase__ )
def UpperCAmelCase_ ( self : Optional[int] ,lowerCAmelCase__ : Optional[int] ) -> Node | None:
'''simple docstring'''
if self.empty():
raise IndexError("Warning: Tree is empty! please use another." )
else:
lowerCAmelCase_ : Dict = self.root
# use lazy evaluation here to avoid NoneType Attribute error
while node is not None and node.value is not value:
lowerCAmelCase_ : Union[str, Any] = node.left if value < node.value else node.right
return node
def UpperCAmelCase_ ( self : Dict ,lowerCAmelCase__ : Node | None = None ) -> Node | None:
'''simple docstring'''
if node is None:
if self.root is None:
return None
lowerCAmelCase_ : Dict = self.root
if not self.empty():
while node.right is not None:
lowerCAmelCase_ : Union[str, Any] = node.right
return node
def UpperCAmelCase_ ( self : Dict ,lowerCAmelCase__ : Node | None = None ) -> Node | None:
'''simple docstring'''
if node is None:
lowerCAmelCase_ : Dict = self.root
if self.root is None:
return None
if not self.empty():
lowerCAmelCase_ : Dict = self.root
while node.left is not None:
lowerCAmelCase_ : Union[str, Any] = node.left
return node
def UpperCAmelCase_ ( self : Union[str, Any] ,lowerCAmelCase__ : int ) -> None:
'''simple docstring'''
lowerCAmelCase_ : Dict = self.search(lowerCAmelCase__ ) # Look for the node with that label
if node is not None:
if node.left is None and node.right is None: # If it has no children
self.__reassign_nodes(lowerCAmelCase__ ,lowerCAmelCase__ )
elif node.left is None: # Has only right children
self.__reassign_nodes(lowerCAmelCase__ ,node.right )
elif node.right is None: # Has only left children
self.__reassign_nodes(lowerCAmelCase__ ,node.left )
else:
lowerCAmelCase_ : int = self.get_max(
node.left ) # Gets the max value of the left branch
self.remove(tmp_node.value ) # type: ignore
lowerCAmelCase_ : Any = (
tmp_node.value # type: ignore
) # Assigns the value to the node to delete and keep tree structure
def UpperCAmelCase_ ( self : Union[str, Any] ,lowerCAmelCase__ : Node | None ) -> Iterable:
'''simple docstring'''
if node is not None:
yield node # Preorder Traversal
yield from self.preorder_traverse(node.left )
yield from self.preorder_traverse(node.right )
def UpperCAmelCase_ ( self : Union[str, Any] ,lowerCAmelCase__ : Dict=None ) -> Any:
'''simple docstring'''
if traversal_function is None:
return self.preorder_traverse(self.root )
else:
return traversal_function(self.root )
def UpperCAmelCase_ ( self : str ,lowerCAmelCase__ : list ,lowerCAmelCase__ : Node | None ) -> None:
'''simple docstring'''
if node:
self.inorder(lowerCAmelCase__ ,node.left )
arr.append(node.value )
self.inorder(lowerCAmelCase__ ,node.right )
def UpperCAmelCase_ ( self : List[str] ,lowerCAmelCase__ : int ,lowerCAmelCase__ : Node ) -> int:
'''simple docstring'''
lowerCAmelCase_ : list[int] = []
self.inorder(lowerCAmelCase__ ,lowerCAmelCase__ ) # append all values to list using inorder traversal
return arr[k - 1]
def UpperCamelCase ( snake_case__):
lowerCAmelCase_ : Optional[Any] = []
if curr_node is not None:
lowerCAmelCase_ : Dict = postorder(curr_node.left) + postorder(curr_node.right) + [curr_node]
return node_list
def UpperCamelCase ( ):
lowerCAmelCase_ : Tuple = (8, 3, 6, 1, 10, 14, 13, 4, 7)
lowerCAmelCase_ : Tuple = BinarySearchTree()
for i in testlist:
t.insert(snake_case__)
# Prints all the elements of the list in order traversal
print(snake_case__)
if t.search(6) is not None:
print("The value 6 exists")
else:
print("The value 6 doesn't exist")
if t.search(-1) is not None:
print("The value -1 exists")
else:
print("The value -1 doesn't exist")
if not t.empty():
print("Max Value: " , t.get_max().value) # type: ignore
print("Min Value: " , t.get_min().value) # type: ignore
for i in testlist:
t.remove(snake_case__)
print(snake_case__)
if __name__ == "__main__":
import doctest
doctest.testmod(verbose=True)
| 683 | 1 |
import torch
from diffusers import DPMSolverSDEScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import require_torchsde
from .test_schedulers import SchedulerCommonTest
@require_torchsde
class __snake_case ( snake_case__ ):
"""simple docstring"""
UpperCamelCase_ = (DPMSolverSDEScheduler,)
UpperCamelCase_ = 1_0
def UpperCAmelCase_ ( self : List[Any] ,**lowerCAmelCase__ : Tuple ) -> Optional[int]:
'''simple docstring'''
lowerCAmelCase_ : Optional[Any] = {
"num_train_timesteps": 11_00,
"beta_start": 0.0_001,
"beta_end": 0.02,
"beta_schedule": "linear",
"noise_sampler_seed": 0,
}
config.update(**lowerCAmelCase__ )
return config
def UpperCAmelCase_ ( self : Optional[Any] ) -> Optional[Any]:
'''simple docstring'''
for timesteps in [10, 50, 1_00, 10_00]:
self.check_over_configs(num_train_timesteps=lowerCAmelCase__ )
def UpperCAmelCase_ ( self : Optional[Any] ) -> Optional[Any]:
'''simple docstring'''
for beta_start, beta_end in zip([0.00_001, 0.0_001, 0.001] ,[0.0_002, 0.002, 0.02] ):
self.check_over_configs(beta_start=lowerCAmelCase__ ,beta_end=lowerCAmelCase__ )
def UpperCAmelCase_ ( self : Dict ) -> Tuple:
'''simple docstring'''
for schedule in ["linear", "scaled_linear"]:
self.check_over_configs(beta_schedule=lowerCAmelCase__ )
def UpperCAmelCase_ ( self : Dict ) -> Optional[int]:
'''simple docstring'''
for prediction_type in ["epsilon", "v_prediction"]:
self.check_over_configs(prediction_type=lowerCAmelCase__ )
def UpperCAmelCase_ ( self : int ) -> Union[str, Any]:
'''simple docstring'''
lowerCAmelCase_ : Dict = self.scheduler_classes[0]
lowerCAmelCase_ : Union[str, Any] = self.get_scheduler_config()
lowerCAmelCase_ : Any = scheduler_class(**lowerCAmelCase__ )
scheduler.set_timesteps(self.num_inference_steps )
lowerCAmelCase_ : Any = self.dummy_model()
lowerCAmelCase_ : Union[str, Any] = self.dummy_sample_deter * scheduler.init_noise_sigma
lowerCAmelCase_ : int = sample.to(lowerCAmelCase__ )
for i, t in enumerate(scheduler.timesteps ):
lowerCAmelCase_ : Optional[int] = scheduler.scale_model_input(lowerCAmelCase__ ,lowerCAmelCase__ )
lowerCAmelCase_ : Tuple = model(lowerCAmelCase__ ,lowerCAmelCase__ )
lowerCAmelCase_ : Optional[Any] = scheduler.step(lowerCAmelCase__ ,lowerCAmelCase__ ,lowerCAmelCase__ )
lowerCAmelCase_ : Any = output.prev_sample
lowerCAmelCase_ : Union[str, Any] = torch.sum(torch.abs(lowerCAmelCase__ ) )
lowerCAmelCase_ : int = torch.mean(torch.abs(lowerCAmelCase__ ) )
if torch_device in ["mps"]:
assert abs(result_sum.item() - 167.47_821_044_921_875 ) < 1e-2
assert abs(result_mean.item() - 0.2_178_705_964_565_277 ) < 1e-3
elif torch_device in ["cuda"]:
assert abs(result_sum.item() - 171.59_352_111_816_406 ) < 1e-2
assert abs(result_mean.item() - 0.22_342_906_892_299_652 ) < 1e-3
else:
assert abs(result_sum.item() - 162.52_383_422_851_562 ) < 1e-2
assert abs(result_mean.item() - 0.211_619_570_851_326 ) < 1e-3
def UpperCAmelCase_ ( self : Union[str, Any] ) -> Any:
'''simple docstring'''
lowerCAmelCase_ : Optional[Any] = self.scheduler_classes[0]
lowerCAmelCase_ : Optional[Any] = self.get_scheduler_config(prediction_type="v_prediction" )
lowerCAmelCase_ : Optional[int] = scheduler_class(**lowerCAmelCase__ )
scheduler.set_timesteps(self.num_inference_steps )
lowerCAmelCase_ : int = self.dummy_model()
lowerCAmelCase_ : int = self.dummy_sample_deter * scheduler.init_noise_sigma
lowerCAmelCase_ : Any = sample.to(lowerCAmelCase__ )
for i, t in enumerate(scheduler.timesteps ):
lowerCAmelCase_ : Optional[int] = scheduler.scale_model_input(lowerCAmelCase__ ,lowerCAmelCase__ )
lowerCAmelCase_ : List[str] = model(lowerCAmelCase__ ,lowerCAmelCase__ )
lowerCAmelCase_ : List[str] = scheduler.step(lowerCAmelCase__ ,lowerCAmelCase__ ,lowerCAmelCase__ )
lowerCAmelCase_ : List[str] = output.prev_sample
lowerCAmelCase_ : List[Any] = torch.sum(torch.abs(lowerCAmelCase__ ) )
lowerCAmelCase_ : Union[str, Any] = torch.mean(torch.abs(lowerCAmelCase__ ) )
if torch_device in ["mps"]:
assert abs(result_sum.item() - 124.77_149_200_439_453 ) < 1e-2
assert abs(result_mean.item() - 0.16_226_289_014_816_284 ) < 1e-3
elif torch_device in ["cuda"]:
assert abs(result_sum.item() - 128.1_663_360_595_703 ) < 1e-2
assert abs(result_mean.item() - 0.16_688_326_001_167_297 ) < 1e-3
else:
assert abs(result_sum.item() - 119.8_487_548_828_125 ) < 1e-2
assert abs(result_mean.item() - 0.1_560_530_662_536_621 ) < 1e-3
def UpperCAmelCase_ ( self : List[Any] ) -> int:
'''simple docstring'''
lowerCAmelCase_ : str = self.scheduler_classes[0]
lowerCAmelCase_ : Optional[int] = self.get_scheduler_config()
lowerCAmelCase_ : Dict = scheduler_class(**lowerCAmelCase__ )
scheduler.set_timesteps(self.num_inference_steps ,device=lowerCAmelCase__ )
lowerCAmelCase_ : Union[str, Any] = self.dummy_model()
lowerCAmelCase_ : List[Any] = self.dummy_sample_deter.to(lowerCAmelCase__ ) * scheduler.init_noise_sigma
for t in scheduler.timesteps:
lowerCAmelCase_ : Optional[int] = scheduler.scale_model_input(lowerCAmelCase__ ,lowerCAmelCase__ )
lowerCAmelCase_ : Optional[Any] = model(lowerCAmelCase__ ,lowerCAmelCase__ )
lowerCAmelCase_ : List[str] = scheduler.step(lowerCAmelCase__ ,lowerCAmelCase__ ,lowerCAmelCase__ )
lowerCAmelCase_ : List[str] = output.prev_sample
lowerCAmelCase_ : List[Any] = torch.sum(torch.abs(lowerCAmelCase__ ) )
lowerCAmelCase_ : Union[str, Any] = torch.mean(torch.abs(lowerCAmelCase__ ) )
if torch_device in ["mps"]:
assert abs(result_sum.item() - 167.46_957_397_460_938 ) < 1e-2
assert abs(result_mean.item() - 0.21_805_934_607_982_635 ) < 1e-3
elif torch_device in ["cuda"]:
assert abs(result_sum.item() - 171.59_353_637_695_312 ) < 1e-2
assert abs(result_mean.item() - 0.22_342_908_382_415_771 ) < 1e-3
else:
assert abs(result_sum.item() - 162.52_383_422_851_562 ) < 1e-2
assert abs(result_mean.item() - 0.211_619_570_851_326 ) < 1e-3
def UpperCAmelCase_ ( self : str ) -> Union[str, Any]:
'''simple docstring'''
lowerCAmelCase_ : Optional[int] = self.scheduler_classes[0]
lowerCAmelCase_ : int = self.get_scheduler_config()
lowerCAmelCase_ : List[str] = scheduler_class(**lowerCAmelCase__ ,use_karras_sigmas=lowerCAmelCase__ )
scheduler.set_timesteps(self.num_inference_steps ,device=lowerCAmelCase__ )
lowerCAmelCase_ : Optional[Any] = self.dummy_model()
lowerCAmelCase_ : int = self.dummy_sample_deter.to(lowerCAmelCase__ ) * scheduler.init_noise_sigma
lowerCAmelCase_ : List[str] = sample.to(lowerCAmelCase__ )
for t in scheduler.timesteps:
lowerCAmelCase_ : Any = scheduler.scale_model_input(lowerCAmelCase__ ,lowerCAmelCase__ )
lowerCAmelCase_ : int = model(lowerCAmelCase__ ,lowerCAmelCase__ )
lowerCAmelCase_ : int = scheduler.step(lowerCAmelCase__ ,lowerCAmelCase__ ,lowerCAmelCase__ )
lowerCAmelCase_ : Tuple = output.prev_sample
lowerCAmelCase_ : Dict = torch.sum(torch.abs(lowerCAmelCase__ ) )
lowerCAmelCase_ : List[str] = torch.mean(torch.abs(lowerCAmelCase__ ) )
if torch_device in ["mps"]:
assert abs(result_sum.item() - 176.66_974_135_742_188 ) < 1e-2
assert abs(result_mean.item() - 0.23_003_872_730_981_811 ) < 1e-2
elif torch_device in ["cuda"]:
assert abs(result_sum.item() - 177.63_653_564_453_125 ) < 1e-2
assert abs(result_mean.item() - 0.23_003_872_730_981_811 ) < 1e-2
else:
assert abs(result_sum.item() - 170.3_135_223_388_672 ) < 1e-2
assert abs(result_mean.item() - 0.23_003_872_730_981_811 ) < 1e-2
| 683 |
class __snake_case :
"""simple docstring"""
def __init__( self : Optional[int] ,lowerCAmelCase__ : str = "" ,lowerCAmelCase__ : bool = False ) -> None:
'''simple docstring'''
lowerCAmelCase_ : dict[str, RadixNode] = {}
# A node will be a leaf if the tree contains its word
lowerCAmelCase_ : int = is_leaf
lowerCAmelCase_ : Optional[Any] = prefix
def UpperCAmelCase_ ( self : str ,lowerCAmelCase__ : str ) -> tuple[str, str, str]:
'''simple docstring'''
lowerCAmelCase_ : Any = 0
for q, w in zip(self.prefix ,lowerCAmelCase__ ):
if q != w:
break
x += 1
return self.prefix[:x], self.prefix[x:], word[x:]
def UpperCAmelCase_ ( self : Optional[int] ,lowerCAmelCase__ : list[str] ) -> None:
'''simple docstring'''
for word in words:
self.insert(lowerCAmelCase__ )
def UpperCAmelCase_ ( self : Any ,lowerCAmelCase__ : str ) -> None:
'''simple docstring'''
if self.prefix == word:
lowerCAmelCase_ : Optional[Any] = True
# Case 2: The node has no edges that have a prefix to the word
# Solution: We create an edge from the current node to a new one
# containing the word
elif word[0] not in self.nodes:
lowerCAmelCase_ : List[Any] = RadixNode(prefix=lowerCAmelCase__ ,is_leaf=lowerCAmelCase__ )
else:
lowerCAmelCase_ : Tuple = self.nodes[word[0]]
lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ : List[str] = incoming_node.match(
lowerCAmelCase__ )
# Case 3: The node prefix is equal to the matching
# Solution: We insert remaining word on the next node
if remaining_prefix == "":
self.nodes[matching_string[0]].insert(lowerCAmelCase__ )
# Case 4: The word is greater equal to the matching
# Solution: Create a node in between both nodes, change
# prefixes and add the new node for the remaining word
else:
lowerCAmelCase_ : Optional[int] = remaining_prefix
lowerCAmelCase_ : Optional[int] = self.nodes[matching_string[0]]
lowerCAmelCase_ : List[Any] = RadixNode(lowerCAmelCase__ ,lowerCAmelCase__ )
lowerCAmelCase_ : Dict = aux_node
if remaining_word == "":
lowerCAmelCase_ : List[str] = True
else:
self.nodes[matching_string[0]].insert(lowerCAmelCase__ )
def UpperCAmelCase_ ( self : Tuple ,lowerCAmelCase__ : str ) -> bool:
'''simple docstring'''
lowerCAmelCase_ : Any = self.nodes.get(word[0] ,lowerCAmelCase__ )
if not incoming_node:
return False
else:
lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ : List[Any] = incoming_node.match(
lowerCAmelCase__ )
# If there is remaining prefix, the word can't be on the tree
if remaining_prefix != "":
return False
# This applies when the word and the prefix are equal
elif remaining_word == "":
return incoming_node.is_leaf
# We have word remaining so we check the next node
else:
return incoming_node.find(lowerCAmelCase__ )
def UpperCAmelCase_ ( self : Optional[Any] ,lowerCAmelCase__ : str ) -> bool:
'''simple docstring'''
lowerCAmelCase_ : int = self.nodes.get(word[0] ,lowerCAmelCase__ )
if not incoming_node:
return False
else:
lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ : Union[str, Any] = incoming_node.match(
lowerCAmelCase__ )
# If there is remaining prefix, the word can't be on the tree
if remaining_prefix != "":
return False
# We have word remaining so we check the next node
elif remaining_word != "":
return incoming_node.delete(lowerCAmelCase__ )
else:
# If it is not a leaf, we don't have to delete
if not incoming_node.is_leaf:
return False
else:
# We delete the nodes if no edges go from it
if len(incoming_node.nodes ) == 0:
del self.nodes[word[0]]
# We merge the current node with its only child
if len(self.nodes ) == 1 and not self.is_leaf:
lowerCAmelCase_ : str = list(self.nodes.values() )[0]
lowerCAmelCase_ : Tuple = merging_node.is_leaf
self.prefix += merging_node.prefix
lowerCAmelCase_ : Optional[int] = merging_node.nodes
# If there is more than 1 edge, we just mark it as non-leaf
elif len(incoming_node.nodes ) > 1:
lowerCAmelCase_ : Optional[Any] = False
# If there is 1 edge, we merge it with its child
else:
lowerCAmelCase_ : Tuple = list(incoming_node.nodes.values() )[0]
lowerCAmelCase_ : Union[str, Any] = merging_node.is_leaf
incoming_node.prefix += merging_node.prefix
lowerCAmelCase_ : str = merging_node.nodes
return True
def UpperCAmelCase_ ( self : Dict ,lowerCAmelCase__ : int = 0 ) -> None:
'''simple docstring'''
if self.prefix != "":
print("-" * height ,self.prefix ," (leaf)" if self.is_leaf else "" )
for value in self.nodes.values():
value.print_tree(height + 1 )
def UpperCamelCase ( ):
lowerCAmelCase_ : Dict = "banana bananas bandana band apple all beast".split()
lowerCAmelCase_ : List[Any] = RadixNode()
root.insert_many(snake_case__)
assert all(root.find(snake_case__) for word in words)
assert not root.find("bandanas")
assert not root.find("apps")
root.delete("all")
assert not root.find("all")
root.delete("banana")
assert not root.find("banana")
assert root.find("bananas")
return True
def UpperCamelCase ( ):
assert test_trie()
def UpperCamelCase ( ):
lowerCAmelCase_ : List[str] = RadixNode()
lowerCAmelCase_ : Optional[Any] = "banana bananas bandanas bandana band apple all beast".split()
root.insert_many(snake_case__)
print("Words:" , snake_case__)
print("Tree:")
root.print_tree()
if __name__ == "__main__":
main()
| 683 | 1 |
import logging
import os
import sys
from pathlib import Path
from unittest.mock import patch
from parameterized import parameterized
from run_eval import run_generate
from run_eval_search import run_search
from transformers.testing_utils import CaptureStdout, TestCasePlus, slow
from utils import ROUGE_KEYS
logging.basicConfig(level=logging.DEBUG)
_lowercase = logging.getLogger()
def UpperCamelCase ( snake_case__ , snake_case__):
lowerCAmelCase_ : Tuple = "\n".join(snake_case__)
Path(snake_case__).open("w").writelines(snake_case__)
_lowercase = '''patrickvonplaten/t5-tiny-random'''
_lowercase = '''sshleifer/bart-tiny-random'''
_lowercase = '''sshleifer/tiny-mbart'''
_lowercase = logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
logging.disable(logging.CRITICAL) # remove noisy download output from tracebacks
class __snake_case ( snake_case__ ):
"""simple docstring"""
def UpperCAmelCase_ ( self : int ,lowerCAmelCase__ : Union[str, Any] ) -> Union[str, Any]:
'''simple docstring'''
lowerCAmelCase_ : Dict = Path(self.get_auto_remove_tmp_dir() ) / "utest_input.source"
lowerCAmelCase_ : Optional[Any] = input_file_name.parent / "utest_output.txt"
assert not output_file_name.exists()
lowerCAmelCase_ : List[Any] = [" New York (CNN)When Liana Barrientos was 23 years old, she got married in Westchester County."]
_dump_articles(lowerCAmelCase__ ,lowerCAmelCase__ )
lowerCAmelCase_ : str = str(Path(self.get_auto_remove_tmp_dir() ) / "scores.json" )
lowerCAmelCase_ : str = "translation_en_to_de" if model == T5_TINY else "summarization"
lowerCAmelCase_ : int = f'''
run_eval_search.py
{model}
{input_file_name}
{output_file_name}
--score_path {score_path}
--task {task}
--num_beams 2
--length_penalty 2.0
'''.split()
with patch.object(lowerCAmelCase__ ,"argv" ,lowerCAmelCase__ ):
run_generate()
assert Path(lowerCAmelCase__ ).exists()
# os.remove(Path(output_file_name))
def UpperCAmelCase_ ( self : Tuple ) -> Tuple:
'''simple docstring'''
self.run_eval_tester(lowerCAmelCase__ )
@parameterized.expand([BART_TINY, MBART_TINY] )
@slow
def UpperCAmelCase_ ( self : Optional[int] ,lowerCAmelCase__ : Any ) -> List[Any]:
'''simple docstring'''
self.run_eval_tester(lowerCAmelCase__ )
@parameterized.expand([T5_TINY, MBART_TINY] )
@slow
def UpperCAmelCase_ ( self : List[str] ,lowerCAmelCase__ : List[str] ) -> Optional[Any]:
'''simple docstring'''
lowerCAmelCase_ : Optional[int] = Path(self.get_auto_remove_tmp_dir() ) / "utest_input.source"
lowerCAmelCase_ : Optional[int] = input_file_name.parent / "utest_output.txt"
assert not output_file_name.exists()
lowerCAmelCase_ : Dict = {
"en": ["Machine learning is great, isn't it?", "I like to eat bananas", "Tomorrow is another great day!"],
"de": [
"Maschinelles Lernen ist großartig, oder?",
"Ich esse gerne Bananen",
"Morgen ist wieder ein toller Tag!",
],
}
lowerCAmelCase_ : str = Path(self.get_auto_remove_tmp_dir() )
lowerCAmelCase_ : Union[str, Any] = str(tmp_dir / "scores.json" )
lowerCAmelCase_ : Union[str, Any] = str(tmp_dir / "val.target" )
_dump_articles(lowerCAmelCase__ ,text["en"] )
_dump_articles(lowerCAmelCase__ ,text["de"] )
lowerCAmelCase_ : int = "translation_en_to_de" if model == T5_TINY else "summarization"
lowerCAmelCase_ : str = f'''
run_eval_search.py
{model}
{str(lowerCAmelCase__ )}
{str(lowerCAmelCase__ )}
--score_path {score_path}
--reference_path {reference_path}
--task {task}
'''.split()
testargs.extend(["--search", "num_beams=1:2 length_penalty=0.9:1.0"] )
with patch.object(lowerCAmelCase__ ,"argv" ,lowerCAmelCase__ ):
with CaptureStdout() as cs:
run_search()
lowerCAmelCase_ : Dict = [" num_beams | length_penalty", model, "Best score args"]
lowerCAmelCase_ : Any = ["Info"]
if "translation" in task:
expected_strings.append("bleu" )
else:
expected_strings.extend(lowerCAmelCase__ )
for w in expected_strings:
assert w in cs.out
for w in un_expected_strings:
assert w not in cs.out
assert Path(lowerCAmelCase__ ).exists()
os.remove(Path(lowerCAmelCase__ ) )
| 683 |
from __future__ import annotations
def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__ , ):
if (electron_conc, hole_conc, intrinsic_conc).count(0) != 1:
raise ValueError("You cannot supply more or less than 2 values")
elif electron_conc < 0:
raise ValueError("Electron concentration cannot be negative in a semiconductor")
elif hole_conc < 0:
raise ValueError("Hole concentration cannot be negative in a semiconductor")
elif intrinsic_conc < 0:
raise ValueError(
"Intrinsic concentration cannot be negative in a semiconductor")
elif electron_conc == 0:
return (
"electron_conc",
intrinsic_conc**2 / hole_conc,
)
elif hole_conc == 0:
return (
"hole_conc",
intrinsic_conc**2 / electron_conc,
)
elif intrinsic_conc == 0:
return (
"intrinsic_conc",
(electron_conc * hole_conc) ** 0.5,
)
else:
return (-1, -1)
if __name__ == "__main__":
import doctest
doctest.testmod()
| 683 | 1 |
# Author: OMKAR PATHAK, Nwachukwu Chidiebere
# Use a Python dictionary to construct the graph.
from __future__ import annotations
from pprint import pformat
from typing import Generic, TypeVar
_lowercase = TypeVar('''T''')
class __snake_case ( Generic[T] ):
"""simple docstring"""
def __init__( self : Union[str, Any] ,lowerCAmelCase__ : bool = True ) -> None:
'''simple docstring'''
lowerCAmelCase_ : dict[T, list[T]] = {} # dictionary of lists
lowerCAmelCase_ : Optional[int] = directed
def UpperCAmelCase_ ( self : int ,lowerCAmelCase__ : T ,lowerCAmelCase__ : T ) -> GraphAdjacencyList[T]:
'''simple docstring'''
if not self.directed: # For undirected graphs
# if both source vertex and destination vertex are both present in the
# adjacency list, add destination vertex to source vertex list of adjacent
# vertices and add source vertex to destination vertex list of adjacent
# vertices.
if source_vertex in self.adj_list and destination_vertex in self.adj_list:
self.adj_list[source_vertex].append(lowerCAmelCase__ )
self.adj_list[destination_vertex].append(lowerCAmelCase__ )
# if only source vertex is present in adjacency list, add destination vertex
# to source vertex list of adjacent vertices, then create a new vertex with
# destination vertex as key and assign a list containing the source vertex
# as it's first adjacent vertex.
elif source_vertex in self.adj_list:
self.adj_list[source_vertex].append(lowerCAmelCase__ )
lowerCAmelCase_ : Optional[int] = [source_vertex]
# if only destination vertex is present in adjacency list, add source vertex
# to destination vertex list of adjacent vertices, then create a new vertex
# with source vertex as key and assign a list containing the source vertex
# as it's first adjacent vertex.
elif destination_vertex in self.adj_list:
self.adj_list[destination_vertex].append(lowerCAmelCase__ )
lowerCAmelCase_ : List[str] = [destination_vertex]
# if both source vertex and destination vertex are not present in adjacency
# list, create a new vertex with source vertex as key and assign a list
# containing the destination vertex as it's first adjacent vertex also
# create a new vertex with destination vertex as key and assign a list
# containing the source vertex as it's first adjacent vertex.
else:
lowerCAmelCase_ : Any = [destination_vertex]
lowerCAmelCase_ : Optional[Any] = [source_vertex]
else: # For directed graphs
# if both source vertex and destination vertex are present in adjacency
# list, add destination vertex to source vertex list of adjacent vertices.
if source_vertex in self.adj_list and destination_vertex in self.adj_list:
self.adj_list[source_vertex].append(lowerCAmelCase__ )
# if only source vertex is present in adjacency list, add destination
# vertex to source vertex list of adjacent vertices and create a new vertex
# with destination vertex as key, which has no adjacent vertex
elif source_vertex in self.adj_list:
self.adj_list[source_vertex].append(lowerCAmelCase__ )
lowerCAmelCase_ : int = []
# if only destination vertex is present in adjacency list, create a new
# vertex with source vertex as key and assign a list containing destination
# vertex as first adjacent vertex
elif destination_vertex in self.adj_list:
lowerCAmelCase_ : List[Any] = [destination_vertex]
# if both source vertex and destination vertex are not present in adjacency
# list, create a new vertex with source vertex as key and a list containing
# destination vertex as it's first adjacent vertex. Then create a new vertex
# with destination vertex as key, which has no adjacent vertex
else:
lowerCAmelCase_ : List[Any] = [destination_vertex]
lowerCAmelCase_ : Union[str, Any] = []
return self
def __repr__( self : List[Any] ) -> str:
'''simple docstring'''
return pformat(self.adj_list )
| 683 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_lowercase = {
'''configuration_git''': ['''GIT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GitConfig''', '''GitVisionConfig'''],
'''processing_git''': ['''GitProcessor'''],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowercase = [
'''GIT_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''GitForCausalLM''',
'''GitModel''',
'''GitPreTrainedModel''',
'''GitVisionModel''',
]
if TYPE_CHECKING:
from .configuration_git import GIT_PRETRAINED_CONFIG_ARCHIVE_MAP, GitConfig, GitVisionConfig
from .processing_git import GitProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_git import (
GIT_PRETRAINED_MODEL_ARCHIVE_LIST,
GitForCausalLM,
GitModel,
GitPreTrainedModel,
GitVisionModel,
)
else:
import sys
_lowercase = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 683 | 1 |
import inspect
import unittest
from transformers import MobileNetVaConfig
from transformers.testing_utils import require_torch, 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, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import MobileNetVaForImageClassification, MobileNetVaModel
from transformers.models.mobilenet_va.modeling_mobilenet_va import MOBILENET_V1_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import MobileNetVaImageProcessor
class __snake_case ( snake_case__ ):
"""simple docstring"""
def UpperCAmelCase_ ( self : int ) -> Dict:
'''simple docstring'''
lowerCAmelCase_ : int = self.config_class(**self.inputs_dict )
self.parent.assertTrue(hasattr(lowerCAmelCase__ ,"tf_padding" ) )
self.parent.assertTrue(hasattr(lowerCAmelCase__ ,"depth_multiplier" ) )
class __snake_case :
"""simple docstring"""
def __init__( self : List[Any] ,lowerCAmelCase__ : Dict ,lowerCAmelCase__ : int=13 ,lowerCAmelCase__ : List[Any]=3 ,lowerCAmelCase__ : str=32 ,lowerCAmelCase__ : List[str]=0.25 ,lowerCAmelCase__ : int=8 ,lowerCAmelCase__ : Optional[Any]=True ,lowerCAmelCase__ : Any=10_24 ,lowerCAmelCase__ : Optional[Any]=32 ,lowerCAmelCase__ : Tuple="relu6" ,lowerCAmelCase__ : Dict=0.1 ,lowerCAmelCase__ : Tuple=0.02 ,lowerCAmelCase__ : int=True ,lowerCAmelCase__ : Optional[int]=True ,lowerCAmelCase__ : Any=10 ,lowerCAmelCase__ : Optional[int]=None ,) -> Optional[Any]:
'''simple docstring'''
lowerCAmelCase_ : Dict = parent
lowerCAmelCase_ : List[str] = batch_size
lowerCAmelCase_ : Optional[int] = num_channels
lowerCAmelCase_ : Any = image_size
lowerCAmelCase_ : int = depth_multiplier
lowerCAmelCase_ : Optional[Any] = min_depth
lowerCAmelCase_ : List[str] = tf_padding
lowerCAmelCase_ : Optional[Any] = int(last_hidden_size * depth_multiplier )
lowerCAmelCase_ : Optional[Any] = output_stride
lowerCAmelCase_ : Optional[Any] = hidden_act
lowerCAmelCase_ : str = classifier_dropout_prob
lowerCAmelCase_ : Tuple = use_labels
lowerCAmelCase_ : List[Any] = is_training
lowerCAmelCase_ : Optional[Any] = num_labels
lowerCAmelCase_ : Optional[Any] = initializer_range
lowerCAmelCase_ : List[str] = scope
def UpperCAmelCase_ ( self : Union[str, Any] ) -> Tuple:
'''simple docstring'''
lowerCAmelCase_ : List[Any] = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
lowerCAmelCase_ : List[str] = None
lowerCAmelCase_ : List[Any] = None
if self.use_labels:
lowerCAmelCase_ : List[str] = ids_tensor([self.batch_size] ,self.num_labels )
lowerCAmelCase_ : str = ids_tensor([self.batch_size, self.image_size, self.image_size] ,self.num_labels )
lowerCAmelCase_ : int = self.get_config()
return config, pixel_values, labels, pixel_labels
def UpperCAmelCase_ ( self : Tuple ) -> Optional[int]:
'''simple docstring'''
return MobileNetVaConfig(
num_channels=self.num_channels ,image_size=self.image_size ,depth_multiplier=self.depth_multiplier ,min_depth=self.min_depth ,tf_padding=self.tf_padding ,hidden_act=self.hidden_act ,classifier_dropout_prob=self.classifier_dropout_prob ,initializer_range=self.initializer_range ,)
def UpperCAmelCase_ ( self : Any ,lowerCAmelCase__ : List[Any] ,lowerCAmelCase__ : str ,lowerCAmelCase__ : str ,lowerCAmelCase__ : Optional[int] ) -> int:
'''simple docstring'''
lowerCAmelCase_ : List[str] = MobileNetVaModel(config=lowerCAmelCase__ )
model.to(lowerCAmelCase__ )
model.eval()
lowerCAmelCase_ : Dict = model(lowerCAmelCase__ )
self.parent.assertEqual(
result.last_hidden_state.shape ,(
self.batch_size,
self.last_hidden_size,
self.image_size // self.output_stride,
self.image_size // self.output_stride,
) ,)
def UpperCAmelCase_ ( self : Dict ,lowerCAmelCase__ : List[Any] ,lowerCAmelCase__ : int ,lowerCAmelCase__ : List[Any] ,lowerCAmelCase__ : Tuple ) -> Dict:
'''simple docstring'''
lowerCAmelCase_ : str = self.num_labels
lowerCAmelCase_ : str = MobileNetVaForImageClassification(lowerCAmelCase__ )
model.to(lowerCAmelCase__ )
model.eval()
lowerCAmelCase_ : Tuple = model(lowerCAmelCase__ ,labels=lowerCAmelCase__ )
self.parent.assertEqual(result.logits.shape ,(self.batch_size, self.num_labels) )
def UpperCAmelCase_ ( self : Optional[Any] ) -> List[str]:
'''simple docstring'''
lowerCAmelCase_ : Optional[Any] = self.prepare_config_and_inputs()
lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ : int = config_and_inputs
lowerCAmelCase_ : Optional[int] = {"pixel_values": pixel_values}
return config, inputs_dict
@require_torch
class __snake_case ( snake_case__ , snake_case__ , unittest.TestCase ):
"""simple docstring"""
UpperCamelCase_ = (MobileNetVaModel, MobileNetVaForImageClassification) if is_torch_available() else ()
UpperCamelCase_ = (
{'feature-extraction': MobileNetVaModel, 'image-classification': MobileNetVaForImageClassification}
if is_torch_available()
else {}
)
UpperCamelCase_ = False
UpperCamelCase_ = False
UpperCamelCase_ = False
UpperCamelCase_ = False
def UpperCAmelCase_ ( self : str ) -> List[str]:
'''simple docstring'''
lowerCAmelCase_ : int = MobileNetVaModelTester(self )
lowerCAmelCase_ : Union[str, Any] = MobileNetVaConfigTester(self ,config_class=lowerCAmelCase__ ,has_text_modality=lowerCAmelCase__ )
def UpperCAmelCase_ ( self : List[str] ) -> List[Any]:
'''simple docstring'''
self.config_tester.run_common_tests()
@unittest.skip(reason="MobileNetV1 does not use inputs_embeds" )
def UpperCAmelCase_ ( self : Optional[Any] ) -> Tuple:
'''simple docstring'''
pass
@unittest.skip(reason="MobileNetV1 does not support input and output embeddings" )
def UpperCAmelCase_ ( self : Dict ) -> Dict:
'''simple docstring'''
pass
@unittest.skip(reason="MobileNetV1 does not output attentions" )
def UpperCAmelCase_ ( self : str ) -> List[Any]:
'''simple docstring'''
pass
def UpperCAmelCase_ ( self : List[str] ) -> str:
'''simple docstring'''
lowerCAmelCase_ , lowerCAmelCase_ : str = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowerCAmelCase_ : Dict = model_class(lowerCAmelCase__ )
lowerCAmelCase_ : List[Any] = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
lowerCAmelCase_ : Tuple = [*signature.parameters.keys()]
lowerCAmelCase_ : Optional[Any] = ["pixel_values"]
self.assertListEqual(arg_names[:1] ,lowerCAmelCase__ )
def UpperCAmelCase_ ( self : str ) -> Optional[int]:
'''simple docstring'''
lowerCAmelCase_ : int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*lowerCAmelCase__ )
def UpperCAmelCase_ ( self : str ) -> Dict:
'''simple docstring'''
def check_hidden_states_output(lowerCAmelCase__ : str ,lowerCAmelCase__ : List[str] ,lowerCAmelCase__ : Union[str, Any] ):
lowerCAmelCase_ : Optional[int] = model_class(lowerCAmelCase__ )
model.to(lowerCAmelCase__ )
model.eval()
with torch.no_grad():
lowerCAmelCase_ : Optional[Any] = model(**self._prepare_for_class(lowerCAmelCase__ ,lowerCAmelCase__ ) )
lowerCAmelCase_ : Dict = outputs.hidden_states
lowerCAmelCase_ : List[str] = 26
self.assertEqual(len(lowerCAmelCase__ ) ,lowerCAmelCase__ )
lowerCAmelCase_ , lowerCAmelCase_ : int = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowerCAmelCase_ : List[str] = True
check_hidden_states_output(lowerCAmelCase__ ,lowerCAmelCase__ ,lowerCAmelCase__ )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
lowerCAmelCase_ : str = True
check_hidden_states_output(lowerCAmelCase__ ,lowerCAmelCase__ ,lowerCAmelCase__ )
def UpperCAmelCase_ ( self : List[str] ) -> Dict:
'''simple docstring'''
lowerCAmelCase_ : int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*lowerCAmelCase__ )
@slow
def UpperCAmelCase_ ( self : Tuple ) -> Tuple:
'''simple docstring'''
for model_name in MOBILENET_V1_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowerCAmelCase_ : Tuple = MobileNetVaModel.from_pretrained(lowerCAmelCase__ )
self.assertIsNotNone(lowerCAmelCase__ )
def UpperCamelCase ( ):
lowerCAmelCase_ : Optional[Any] = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
return image
@require_torch
@require_vision
class __snake_case ( unittest.TestCase ):
"""simple docstring"""
@cached_property
def UpperCAmelCase_ ( self : Dict ) -> Optional[Any]:
'''simple docstring'''
return (
MobileNetVaImageProcessor.from_pretrained("google/mobilenet_v1_1.0_224" ) if is_vision_available() else None
)
@slow
def UpperCAmelCase_ ( self : Optional[Any] ) -> Union[str, Any]:
'''simple docstring'''
lowerCAmelCase_ : Tuple = MobileNetVaForImageClassification.from_pretrained("google/mobilenet_v1_1.0_224" ).to(lowerCAmelCase__ )
lowerCAmelCase_ : Optional[Any] = self.default_image_processor
lowerCAmelCase_ : Union[str, Any] = prepare_img()
lowerCAmelCase_ : Any = image_processor(images=lowerCAmelCase__ ,return_tensors="pt" ).to(lowerCAmelCase__ )
# forward pass
with torch.no_grad():
lowerCAmelCase_ : List[str] = model(**lowerCAmelCase__ )
# verify the logits
lowerCAmelCase_ : str = torch.Size((1, 10_01) )
self.assertEqual(outputs.logits.shape ,lowerCAmelCase__ )
lowerCAmelCase_ : Tuple = torch.tensor([-4.1_739, -1.1_233, 3.1_205] ).to(lowerCAmelCase__ )
self.assertTrue(torch.allclose(outputs.logits[0, :3] ,lowerCAmelCase__ ,atol=1e-4 ) )
| 683 |
from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments
def UpperCamelCase ( ):
lowerCAmelCase_ : List[str] = HfArgumentParser(snake_case__)
lowerCAmelCase_ : List[Any] = parser.parse_args_into_dataclasses()[0]
lowerCAmelCase_ : Optional[int] = TensorFlowBenchmark(args=snake_case__)
try:
lowerCAmelCase_ : Tuple = parser.parse_args_into_dataclasses()[0]
except ValueError as e:
lowerCAmelCase_ : Union[str, Any] = "Arg --no_{0} is no longer used, please use --no-{0} instead."
lowerCAmelCase_ : Tuple = " ".join(str(snake_case__).split(" ")[:-1])
lowerCAmelCase_ : Union[str, Any] = ""
lowerCAmelCase_ : Optional[Any] = eval(str(snake_case__).split(" ")[-1])
lowerCAmelCase_ : Tuple = []
for arg in depreciated_args:
# arg[2:] removes '--'
if arg[2:] in TensorFlowBenchmark.deprecated_args:
# arg[5:] removes '--no_'
full_error_msg += arg_error_msg.format(arg[5:])
else:
wrong_args.append(snake_case__)
if len(snake_case__) > 0:
lowerCAmelCase_ : Optional[Any] = full_error_msg + begin_error_msg + str(snake_case__)
raise ValueError(snake_case__)
benchmark.run()
if __name__ == "__main__":
main()
| 683 | 1 |
import collections
import os
from typing import List, Optional, Tuple
from transformers.utils import is_jieba_available, requires_backends
if is_jieba_available():
import jieba
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
_lowercase = logging.get_logger(__name__)
_lowercase = {'''vocab_file''': '''vocab.txt'''}
_lowercase = {
'''vocab_file''': {
'''openbmb/cpm-ant-10b''': '''https://huggingface.co/openbmb/cpm-ant-10b/blob/main/vocab.txt''',
},
}
_lowercase = {
'''openbmb/cpm-ant-10b''': 1024,
}
def UpperCamelCase ( snake_case__):
lowerCAmelCase_ : Any = collections.OrderedDict()
with open(snake_case__ , "r" , encoding="utf-8") as reader:
lowerCAmelCase_ : Any = reader.readlines()
for index, token in enumerate(snake_case__):
lowerCAmelCase_ : Optional[int] = token.rstrip("\n")
lowerCAmelCase_ : Union[str, Any] = index
return vocab
class __snake_case ( snake_case__ ):
"""simple docstring"""
def __init__( self : Any ,lowerCAmelCase__ : str ,lowerCAmelCase__ : Any="<unk>" ,lowerCAmelCase__ : Union[str, Any]=2_00 ) -> Optional[Any]:
'''simple docstring'''
lowerCAmelCase_ : List[Any] = vocab
lowerCAmelCase_ : Any = unk_token
lowerCAmelCase_ : List[str] = max_input_chars_per_word
def UpperCAmelCase_ ( self : Dict ,lowerCAmelCase__ : Dict ) -> Optional[int]:
'''simple docstring'''
lowerCAmelCase_ : str = list(lowerCAmelCase__ )
if len(lowerCAmelCase__ ) > self.max_input_chars_per_word:
return [self.unk_token]
lowerCAmelCase_ : Tuple = 0
lowerCAmelCase_ : Union[str, Any] = []
while start < len(lowerCAmelCase__ ):
lowerCAmelCase_ : List[Any] = len(lowerCAmelCase__ )
lowerCAmelCase_ : str = None
while start < end:
lowerCAmelCase_ : int = "".join(chars[start:end] )
if substr in self.vocab:
lowerCAmelCase_ : Dict = substr
break
end -= 1
if cur_substr is None:
sub_tokens.append(self.unk_token )
start += 1
else:
sub_tokens.append(lowerCAmelCase__ )
lowerCAmelCase_ : Any = end
return sub_tokens
class __snake_case ( snake_case__ ):
"""simple docstring"""
UpperCamelCase_ = VOCAB_FILES_NAMES
UpperCamelCase_ = PRETRAINED_VOCAB_FILES_MAP
UpperCamelCase_ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
UpperCamelCase_ = ['input_ids', 'attention_mask']
UpperCamelCase_ = False
def __init__( self : Union[str, Any] ,lowerCAmelCase__ : Tuple ,lowerCAmelCase__ : List[str]="<d>" ,lowerCAmelCase__ : Union[str, Any]="</d>" ,lowerCAmelCase__ : str="<s>" ,lowerCAmelCase__ : List[Any]="</s>" ,lowerCAmelCase__ : str="<pad>" ,lowerCAmelCase__ : List[str]="<unk>" ,lowerCAmelCase__ : int="</n>" ,lowerCAmelCase__ : Optional[int]="</_>" ,lowerCAmelCase__ : List[Any]="left" ,**lowerCAmelCase__ : Tuple ,) -> Optional[int]:
'''simple docstring'''
requires_backends(self ,["jieba"] )
super().__init__(
bod_token=lowerCAmelCase__ ,eod_token=lowerCAmelCase__ ,bos_token=lowerCAmelCase__ ,eos_token=lowerCAmelCase__ ,pad_token=lowerCAmelCase__ ,unk_token=lowerCAmelCase__ ,line_token=lowerCAmelCase__ ,space_token=lowerCAmelCase__ ,padding_side=lowerCAmelCase__ ,**lowerCAmelCase__ ,)
lowerCAmelCase_ : List[str] = bod_token
lowerCAmelCase_ : Tuple = eod_token
lowerCAmelCase_ : Tuple = load_vocab(lowerCAmelCase__ )
lowerCAmelCase_ : Union[str, Any] = self.encoder[space_token]
lowerCAmelCase_ : Optional[Any] = self.encoder[line_token]
del self.encoder[space_token]
del self.encoder[line_token]
lowerCAmelCase_ : Tuple = collections.OrderedDict(sorted(self.encoder.items() ,key=lambda lowerCAmelCase__ : x[1] ) )
lowerCAmelCase_ : List[Any] = {v: k for k, v in self.encoder.items()}
lowerCAmelCase_ : Union[str, Any] = WordpieceTokenizer(vocab=self.encoder ,unk_token=self.unk_token )
@property
def UpperCAmelCase_ ( self : Dict ) -> Any:
'''simple docstring'''
return self.encoder[self.bod_token]
@property
def UpperCAmelCase_ ( self : Union[str, Any] ) -> List[Any]:
'''simple docstring'''
return self.encoder[self.eod_token]
@property
def UpperCAmelCase_ ( self : Optional[Any] ) -> Optional[Any]:
'''simple docstring'''
return self.encoder["\n"]
@property
def UpperCAmelCase_ ( self : List[Any] ) -> int:
'''simple docstring'''
return len(self.encoder )
def UpperCAmelCase_ ( self : str ) -> Dict:
'''simple docstring'''
return dict(self.encoder ,**self.added_tokens_encoder )
def UpperCAmelCase_ ( self : List[Any] ,lowerCAmelCase__ : Dict ) -> str:
'''simple docstring'''
lowerCAmelCase_ : Tuple = []
for x in jieba.cut(lowerCAmelCase__ ,cut_all=lowerCAmelCase__ ):
output_tokens.extend(self.wordpiece_tokenizer.tokenize(lowerCAmelCase__ ) )
return output_tokens
def UpperCAmelCase_ ( self : Optional[Any] ,lowerCAmelCase__ : Any ,**lowerCAmelCase__ : Union[str, Any] ) -> Optional[int]:
'''simple docstring'''
lowerCAmelCase_ : int = [i for i in token_ids if i >= 0]
lowerCAmelCase_ : List[Any] = [
x for x in token_ids if x != self.pad_token_id and x != self.eos_token_id and x != self.bos_token_id
]
return super()._decode(lowerCAmelCase__ ,**lowerCAmelCase__ )
def UpperCAmelCase_ ( self : Dict ,lowerCAmelCase__ : Optional[Any] ) -> Optional[Any]:
'''simple docstring'''
return token in self.encoder
def UpperCAmelCase_ ( self : str ,lowerCAmelCase__ : List[str] ) -> str:
'''simple docstring'''
return "".join(lowerCAmelCase__ )
def UpperCAmelCase_ ( self : Optional[Any] ,lowerCAmelCase__ : Tuple ) -> Dict:
'''simple docstring'''
return self.encoder.get(lowerCAmelCase__ ,self.encoder.get(self.unk_token ) )
def UpperCAmelCase_ ( self : List[Any] ,lowerCAmelCase__ : List[str] ) -> str:
'''simple docstring'''
return self.decoder.get(lowerCAmelCase__ ,self.unk_token )
def UpperCAmelCase_ ( self : Any ,lowerCAmelCase__ : str ,lowerCAmelCase__ : Optional[str] = None ) -> Tuple[str]:
'''simple docstring'''
if os.path.isdir(lowerCAmelCase__ ):
lowerCAmelCase_ : List[str] = os.path.join(
lowerCAmelCase__ ,(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )
else:
lowerCAmelCase_ : List[str] = (filename_prefix + "-" if filename_prefix else "") + save_directory
lowerCAmelCase_ : str = 0
if " " in self.encoder:
lowerCAmelCase_ : str = self.encoder[" "]
del self.encoder[" "]
if "\n" in self.encoder:
lowerCAmelCase_ : List[str] = self.encoder["\n"]
del self.encoder["\n"]
lowerCAmelCase_ : str = collections.OrderedDict(sorted(self.encoder.items() ,key=lambda lowerCAmelCase__ : x[1] ) )
with open(lowerCAmelCase__ ,"w" ,encoding="utf-8" ) as writer:
for token, token_index in self.encoder.items():
if index != token_index:
logger.warning(
f'''Saving vocabulary to {vocab_file}: vocabulary indices are not consecutive.'''
" Please check that the vocabulary is not corrupted!" )
lowerCAmelCase_ : Dict = token_index
writer.write(token + "\n" )
index += 1
return (vocab_file,)
def UpperCAmelCase_ ( self : Dict ,lowerCAmelCase__ : List[int] ,lowerCAmelCase__ : List[int] = None ) -> List[int]:
'''simple docstring'''
if token_ids_a is None:
return [self.bos_token_id] + token_ids_a
return [self.bos_token_id] + token_ids_a + [self.bos_token_id] + token_ids_a
def UpperCAmelCase_ ( self : Optional[Any] ,lowerCAmelCase__ : List[int] ,lowerCAmelCase__ : Optional[List[int]] = None ,lowerCAmelCase__ : bool = False ) -> List[int]:
'''simple docstring'''
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=lowerCAmelCase__ ,token_ids_a=lowerCAmelCase__ ,already_has_special_tokens=lowerCAmelCase__ )
if token_ids_a is not None:
return [1] + ([0] * len(lowerCAmelCase__ )) + [1] + ([0] * len(lowerCAmelCase__ ))
return [1] + ([0] * len(lowerCAmelCase__ ))
| 683 |
_lowercase = {
0: '''0''',
1: '''1''',
2: '''2''',
3: '''3''',
4: '''4''',
5: '''5''',
6: '''6''',
7: '''7''',
8: '''8''',
9: '''9''',
10: '''a''',
11: '''b''',
12: '''c''',
13: '''d''',
14: '''e''',
15: '''f''',
}
def UpperCamelCase ( snake_case__):
assert type(snake_case__) in (int, float) and decimal == int(snake_case__)
lowerCAmelCase_ : Optional[Any] = int(snake_case__)
lowerCAmelCase_ : Tuple = ""
lowerCAmelCase_ : str = False
if decimal < 0:
lowerCAmelCase_ : Tuple = True
decimal *= -1
while decimal > 0:
lowerCAmelCase_ , lowerCAmelCase_ : Any = divmod(snake_case__ , 16)
lowerCAmelCase_ : Dict = values[remainder] + hexadecimal
lowerCAmelCase_ : List[str] = "0x" + hexadecimal
if negative:
lowerCAmelCase_ : Optional[Any] = "-" + hexadecimal
return hexadecimal
if __name__ == "__main__":
import doctest
doctest.testmod()
| 683 | 1 |
# Copyright 2022 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import argparse
import os
import platform
import numpy as np
import psutil
import torch
from accelerate import __version__ as version
from accelerate.commands.config import default_config_file, load_config_from_file
from ..utils import is_npu_available, is_xpu_available
def UpperCamelCase ( snake_case__=None):
if subparsers is not None:
lowerCAmelCase_ : Optional[int] = subparsers.add_parser("env")
else:
lowerCAmelCase_ : List[Any] = argparse.ArgumentParser("Accelerate env command")
parser.add_argument(
"--config_file" , default=snake_case__ , help="The config file to use for the default values in the launching script.")
if subparsers is not None:
parser.set_defaults(func=snake_case__)
return parser
def UpperCamelCase ( snake_case__):
lowerCAmelCase_ : List[Any] = torch.__version__
lowerCAmelCase_ : Optional[Any] = torch.cuda.is_available()
lowerCAmelCase_ : str = is_xpu_available()
lowerCAmelCase_ : str = is_npu_available()
lowerCAmelCase_ : List[Any] = "Not found"
# Get the default from the config file.
if args.config_file is not None or os.path.isfile(snake_case__):
lowerCAmelCase_ : Tuple = load_config_from_file(args.config_file).to_dict()
lowerCAmelCase_ : Tuple = {
"`Accelerate` version": version,
"Platform": platform.platform(),
"Python version": platform.python_version(),
"Numpy version": np.__version__,
"PyTorch version (GPU?)": F'''{pt_version} ({pt_cuda_available})''',
"PyTorch XPU available": str(snake_case__),
"PyTorch NPU available": str(snake_case__),
"System RAM": F'''{psutil.virtual_memory().total / 10_24 ** 3:.2f} GB''',
}
if pt_cuda_available:
lowerCAmelCase_ : Optional[int] = torch.cuda.get_device_name()
print("\nCopy-and-paste the text below in your GitHub issue\n")
print("\n".join([F'''- {prop}: {val}''' for prop, val in info.items()]))
print("- `Accelerate` default config:" if args.config_file is None else "- `Accelerate` config passed:")
lowerCAmelCase_ : int = (
"\n".join([F'''\t- {prop}: {val}''' for prop, val in accelerate_config.items()])
if isinstance(snake_case__ , snake_case__)
else F'''\t{accelerate_config}'''
)
print(snake_case__)
lowerCAmelCase_ : List[Any] = accelerate_config
return info
def UpperCamelCase ( ):
lowerCAmelCase_ : Union[str, Any] = env_command_parser()
lowerCAmelCase_ : Optional[int] = parser.parse_args()
env_command(snake_case__)
return 0
if __name__ == "__main__":
raise SystemExit(main())
| 683 |
from pathlib import Path
from typing import List
from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import get_tests_dir, is_tool_test
from transformers.tools.agent_types import AGENT_TYPE_MAPPING, AgentAudio, AgentImage, AgentText
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
_lowercase = ['''text''', '''image''', '''audio''']
def UpperCamelCase ( snake_case__):
lowerCAmelCase_ : int = []
for input_type in input_types:
if input_type == "text":
inputs.append("Text input")
elif input_type == "image":
inputs.append(
Image.open(Path(get_tests_dir("fixtures/tests_samples/COCO")) / "000000039769.png").resize((5_12, 5_12)))
elif input_type == "audio":
inputs.append(torch.ones(30_00))
elif isinstance(snake_case__ , snake_case__):
inputs.append(create_inputs(snake_case__))
else:
raise ValueError(F'''Invalid type requested: {input_type}''')
return inputs
def UpperCamelCase ( snake_case__):
lowerCAmelCase_ : List[Any] = []
for output in outputs:
if isinstance(snake_case__ , (str, AgentText)):
output_types.append("text")
elif isinstance(snake_case__ , (Image.Image, AgentImage)):
output_types.append("image")
elif isinstance(snake_case__ , (torch.Tensor, AgentAudio)):
output_types.append("audio")
else:
raise ValueError(F'''Invalid output: {output}''')
return output_types
@is_tool_test
class __snake_case :
"""simple docstring"""
def UpperCAmelCase_ ( self : int ) -> int:
'''simple docstring'''
self.assertTrue(hasattr(self.tool ,"inputs" ) )
self.assertTrue(hasattr(self.tool ,"outputs" ) )
lowerCAmelCase_ : List[Any] = self.tool.inputs
for _input in inputs:
if isinstance(_input ,lowerCAmelCase__ ):
for __input in _input:
self.assertTrue(__input in authorized_types )
else:
self.assertTrue(_input in authorized_types )
lowerCAmelCase_ : Any = self.tool.outputs
for _output in outputs:
self.assertTrue(_output in authorized_types )
def UpperCAmelCase_ ( self : List[Any] ) -> List[Any]:
'''simple docstring'''
lowerCAmelCase_ : Any = create_inputs(self.tool.inputs )
lowerCAmelCase_ : List[Any] = self.tool(*lowerCAmelCase__ )
# There is a single output
if len(self.tool.outputs ) == 1:
lowerCAmelCase_ : Optional[int] = [outputs]
self.assertListEqual(output_types(lowerCAmelCase__ ) ,self.tool.outputs )
def UpperCAmelCase_ ( self : int ) -> Any:
'''simple docstring'''
self.assertTrue(hasattr(self.tool ,"description" ) )
self.assertTrue(hasattr(self.tool ,"default_checkpoint" ) )
self.assertTrue(self.tool.description.startswith("This is a tool that" ) )
def UpperCAmelCase_ ( self : List[Any] ) -> Tuple:
'''simple docstring'''
lowerCAmelCase_ : Optional[Any] = create_inputs(self.tool.inputs )
lowerCAmelCase_ : List[Any] = self.tool(*lowerCAmelCase__ )
if not isinstance(lowerCAmelCase__ ,lowerCAmelCase__ ):
lowerCAmelCase_ : str = [outputs]
self.assertEqual(len(lowerCAmelCase__ ) ,len(self.tool.outputs ) )
for output, output_type in zip(lowerCAmelCase__ ,self.tool.outputs ):
lowerCAmelCase_ : Tuple = AGENT_TYPE_MAPPING[output_type]
self.assertTrue(isinstance(lowerCAmelCase__ ,lowerCAmelCase__ ) )
def UpperCAmelCase_ ( self : Any ) -> Tuple:
'''simple docstring'''
lowerCAmelCase_ : Tuple = create_inputs(self.tool.inputs )
lowerCAmelCase_ : List[Any] = []
for _input, input_type in zip(lowerCAmelCase__ ,self.tool.inputs ):
if isinstance(lowerCAmelCase__ ,lowerCAmelCase__ ):
_inputs.append([AGENT_TYPE_MAPPING[_input_type](_input ) for _input_type in input_type] )
else:
_inputs.append(AGENT_TYPE_MAPPING[input_type](_input ) )
# Should not raise an error
lowerCAmelCase_ : List[Any] = self.tool(*lowerCAmelCase__ )
if not isinstance(lowerCAmelCase__ ,lowerCAmelCase__ ):
lowerCAmelCase_ : int = [outputs]
self.assertEqual(len(lowerCAmelCase__ ) ,len(self.tool.outputs ) )
| 683 | 1 |
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_lowercase = {
'''configuration_xmod''': [
'''XMOD_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''XmodConfig''',
'''XmodOnnxConfig''',
],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowercase = [
'''XMOD_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''XmodForCausalLM''',
'''XmodForMaskedLM''',
'''XmodForMultipleChoice''',
'''XmodForQuestionAnswering''',
'''XmodForSequenceClassification''',
'''XmodForTokenClassification''',
'''XmodModel''',
'''XmodPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_xmod import XMOD_PRETRAINED_CONFIG_ARCHIVE_MAP, XmodConfig, XmodOnnxConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_xmod import (
XMOD_PRETRAINED_MODEL_ARCHIVE_LIST,
XmodForCausalLM,
XmodForMaskedLM,
XmodForMultipleChoice,
XmodForQuestionAnswering,
XmodForSequenceClassification,
XmodForTokenClassification,
XmodModel,
XmodPreTrainedModel,
)
else:
import sys
_lowercase = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 683 |
import pytest
_lowercase = '''__dummy_dataset1__'''
_lowercase = '''
import json
import os
import datasets
REPO_URL = "https://huggingface.co/datasets/albertvillanova/tests-raw-jsonl/resolve/main/"
URLS = {"train": REPO_URL + "wikiann-bn-train.jsonl", "validation": REPO_URL + "wikiann-bn-validation.jsonl"}
class __DummyDataset1__(datasets.GeneratorBasedBuilder):
def _info(self):
features = datasets.Features(
{
"tokens": datasets.Sequence(datasets.Value("string")),
"ner_tags": datasets.Sequence(
datasets.features.ClassLabel(
names=[
"O",
"B-PER",
"I-PER",
"B-ORG",
"I-ORG",
"B-LOC",
"I-LOC",
]
)
),
"langs": datasets.Sequence(datasets.Value("string")),
"spans": datasets.Sequence(datasets.Value("string")),
}
)
return datasets.DatasetInfo(features=features)
def _split_generators(self, dl_manager):
dl_path = dl_manager.download(URLS)
return [
datasets.SplitGenerator(datasets.Split.TRAIN, gen_kwargs={"filepath": dl_path["train"]}),
datasets.SplitGenerator(datasets.Split.VALIDATION, gen_kwargs={"filepath": dl_path["validation"]}),
]
def _generate_examples(self, filepath):
with open(filepath, "r", encoding="utf-8") as f:
for i, line in enumerate(f):
yield i, json.loads(line)
'''
@pytest.fixture
def UpperCamelCase ( ):
return DATASET_LOADING_SCRIPT_NAME
@pytest.fixture
def UpperCamelCase ( ):
return DATASET_LOADING_SCRIPT_CODE
@pytest.fixture
def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__):
lowerCAmelCase_ : List[Any] = dataset_loading_script_name
lowerCAmelCase_ : List[str] = tmp_path / "datasets" / script_name
script_dir.mkdir(parents=snake_case__)
lowerCAmelCase_ : List[Any] = script_dir / F'''{script_name}.py'''
with open(snake_case__ , "w") as f:
f.write(snake_case__)
return str(snake_case__)
| 683 | 1 |
from urllib.parse import quote
import pytest
from datasets.utils.hub import hf_hub_url
@pytest.mark.parametrize("repo_id" , ["canonical_dataset_name", "org-name/dataset-name"])
@pytest.mark.parametrize("path" , ["filename.csv", "filename with blanks.csv"])
@pytest.mark.parametrize("revision" , [None, "v2"])
def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__):
lowerCAmelCase_ : Tuple = hf_hub_url(repo_id=snake_case__ , path=snake_case__ , revision=snake_case__)
assert url == F'''https://huggingface.co/datasets/{repo_id}/resolve/{revision or "main"}/{quote(snake_case__)}'''
| 683 |
import json
import os
import re
import unittest
from transformers import CodeGenTokenizer, CodeGenTokenizerFast
from transformers.models.codegen.tokenization_codegen import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class __snake_case ( snake_case__ , unittest.TestCase ):
"""simple docstring"""
UpperCamelCase_ = CodeGenTokenizer
UpperCamelCase_ = CodeGenTokenizerFast
UpperCamelCase_ = True
UpperCamelCase_ = {'add_prefix_space': True}
UpperCamelCase_ = False
def UpperCAmelCase_ ( self : str ) -> Tuple:
'''simple docstring'''
super().setUp()
# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
lowerCAmelCase_ : Optional[Any] = [
"l",
"o",
"w",
"e",
"r",
"s",
"t",
"i",
"d",
"n",
"\u0120",
"\u0120l",
"\u0120n",
"\u0120lo",
"\u0120low",
"er",
"\u0120lowest",
"\u0120newer",
"\u0120wider",
"<unk>",
"<|endoftext|>",
]
lowerCAmelCase_ : int = dict(zip(lowerCAmelCase__ ,range(len(lowerCAmelCase__ ) ) ) )
lowerCAmelCase_ : Dict = ["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
lowerCAmelCase_ : List[Any] = {"unk_token": "<unk>"}
lowerCAmelCase_ : List[Any] = os.path.join(self.tmpdirname ,VOCAB_FILES_NAMES["vocab_file"] )
lowerCAmelCase_ : Tuple = os.path.join(self.tmpdirname ,VOCAB_FILES_NAMES["merges_file"] )
with open(self.vocab_file ,"w" ,encoding="utf-8" ) as fp:
fp.write(json.dumps(lowerCAmelCase__ ) + "\n" )
with open(self.merges_file ,"w" ,encoding="utf-8" ) as fp:
fp.write("\n".join(lowerCAmelCase__ ) )
def UpperCAmelCase_ ( self : Optional[int] ,**lowerCAmelCase__ : str ) -> int:
'''simple docstring'''
kwargs.update(self.special_tokens_map )
return CodeGenTokenizer.from_pretrained(self.tmpdirname ,**lowerCAmelCase__ )
def UpperCAmelCase_ ( self : Optional[int] ,**lowerCAmelCase__ : Optional[Any] ) -> Tuple:
'''simple docstring'''
kwargs.update(self.special_tokens_map )
return CodeGenTokenizerFast.from_pretrained(self.tmpdirname ,**lowerCAmelCase__ )
def UpperCAmelCase_ ( self : Optional[int] ,lowerCAmelCase__ : str ) -> Optional[int]:
'''simple docstring'''
lowerCAmelCase_ : Tuple = "lower newer"
lowerCAmelCase_ : Tuple = "lower newer"
return input_text, output_text
def UpperCAmelCase_ ( self : Optional[Any] ) -> Any:
'''simple docstring'''
lowerCAmelCase_ : Optional[Any] = CodeGenTokenizer(self.vocab_file ,self.merges_file ,**self.special_tokens_map )
lowerCAmelCase_ : Dict = "lower newer"
lowerCAmelCase_ : Dict = ["\u0120low", "er", "\u0120", "n", "e", "w", "er"]
lowerCAmelCase_ : Union[str, Any] = tokenizer.tokenize(lowerCAmelCase__ ,add_prefix_space=lowerCAmelCase__ )
self.assertListEqual(lowerCAmelCase__ ,lowerCAmelCase__ )
lowerCAmelCase_ : Optional[Any] = tokens + [tokenizer.unk_token]
lowerCAmelCase_ : Union[str, Any] = [14, 15, 10, 9, 3, 2, 15, 19]
self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCAmelCase__ ) ,lowerCAmelCase__ )
def UpperCAmelCase_ ( self : List[str] ) -> Optional[Any]:
'''simple docstring'''
if not self.test_rust_tokenizer:
return
lowerCAmelCase_ : Tuple = self.get_tokenizer()
lowerCAmelCase_ : Optional[int] = self.get_rust_tokenizer(add_prefix_space=lowerCAmelCase__ )
lowerCAmelCase_ : Any = "lower newer"
# Testing tokenization
lowerCAmelCase_ : Tuple = tokenizer.tokenize(lowerCAmelCase__ ,add_prefix_space=lowerCAmelCase__ )
lowerCAmelCase_ : Any = rust_tokenizer.tokenize(lowerCAmelCase__ )
self.assertListEqual(lowerCAmelCase__ ,lowerCAmelCase__ )
# Testing conversion to ids without special tokens
lowerCAmelCase_ : str = tokenizer.encode(lowerCAmelCase__ ,add_special_tokens=lowerCAmelCase__ ,add_prefix_space=lowerCAmelCase__ )
lowerCAmelCase_ : Any = rust_tokenizer.encode(lowerCAmelCase__ ,add_special_tokens=lowerCAmelCase__ )
self.assertListEqual(lowerCAmelCase__ ,lowerCAmelCase__ )
# Testing conversion to ids with special tokens
lowerCAmelCase_ : int = self.get_rust_tokenizer(add_prefix_space=lowerCAmelCase__ )
lowerCAmelCase_ : str = tokenizer.encode(lowerCAmelCase__ ,add_prefix_space=lowerCAmelCase__ )
lowerCAmelCase_ : Optional[int] = rust_tokenizer.encode(lowerCAmelCase__ )
self.assertListEqual(lowerCAmelCase__ ,lowerCAmelCase__ )
# Testing the unknown token
lowerCAmelCase_ : Union[str, Any] = tokens + [rust_tokenizer.unk_token]
lowerCAmelCase_ : List[str] = [14, 15, 10, 9, 3, 2, 15, 19]
self.assertListEqual(rust_tokenizer.convert_tokens_to_ids(lowerCAmelCase__ ) ,lowerCAmelCase__ )
def UpperCAmelCase_ ( self : Optional[int] ,*lowerCAmelCase__ : List[str] ,**lowerCAmelCase__ : Optional[Any] ) -> List[str]:
'''simple docstring'''
pass
def UpperCAmelCase_ ( self : Tuple ,lowerCAmelCase__ : Any=15 ) -> str:
'''simple docstring'''
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})''' ):
lowerCAmelCase_ : Any = self.rust_tokenizer_class.from_pretrained(lowerCAmelCase__ ,**lowerCAmelCase__ )
# Simple input
lowerCAmelCase_ : int = "This is a simple input"
lowerCAmelCase_ : Dict = ["This is a simple input 1", "This is a simple input 2"]
lowerCAmelCase_ : str = ("This is a simple input", "This is a pair")
lowerCAmelCase_ : Optional[int] = [
("This is a simple input 1", "This is a simple input 2"),
("This is a simple pair 1", "This is a simple pair 2"),
]
# Simple input tests
self.assertRaises(lowerCAmelCase__ ,tokenizer_r.encode ,lowerCAmelCase__ ,max_length=lowerCAmelCase__ ,padding="max_length" )
# Simple input
self.assertRaises(lowerCAmelCase__ ,tokenizer_r.encode_plus ,lowerCAmelCase__ ,max_length=lowerCAmelCase__ ,padding="max_length" )
# Simple input
self.assertRaises(
lowerCAmelCase__ ,tokenizer_r.batch_encode_plus ,lowerCAmelCase__ ,max_length=lowerCAmelCase__ ,padding="max_length" ,)
# Pair input
self.assertRaises(lowerCAmelCase__ ,tokenizer_r.encode ,lowerCAmelCase__ ,max_length=lowerCAmelCase__ ,padding="max_length" )
# Pair input
self.assertRaises(lowerCAmelCase__ ,tokenizer_r.encode_plus ,lowerCAmelCase__ ,max_length=lowerCAmelCase__ ,padding="max_length" )
# Pair input
self.assertRaises(
lowerCAmelCase__ ,tokenizer_r.batch_encode_plus ,lowerCAmelCase__ ,max_length=lowerCAmelCase__ ,padding="max_length" ,)
def UpperCAmelCase_ ( self : Optional[int] ) -> Optional[int]:
'''simple docstring'''
lowerCAmelCase_ : Tuple = CodeGenTokenizer.from_pretrained(self.tmpdirname ,pad_token="<pad>" )
# Simple input
lowerCAmelCase_ : Dict = "This is a simple input"
lowerCAmelCase_ : List[str] = ["This is a simple input looooooooong", "This is a simple input"]
lowerCAmelCase_ : Any = ("This is a simple input", "This is a pair")
lowerCAmelCase_ : List[str] = [
("This is a simple input loooooong", "This is a simple input"),
("This is a simple pair loooooong", "This is a simple pair"),
]
lowerCAmelCase_ : Dict = tokenizer.pad_token_id
lowerCAmelCase_ : Union[str, Any] = tokenizer(lowerCAmelCase__ ,padding="max_length" ,max_length=30 ,return_tensors="np" )
lowerCAmelCase_ : Tuple = tokenizer(lowerCAmelCase__ ,padding=lowerCAmelCase__ ,truncate=lowerCAmelCase__ ,return_tensors="np" )
lowerCAmelCase_ : Any = tokenizer(*lowerCAmelCase__ ,padding="max_length" ,max_length=60 ,return_tensors="np" )
lowerCAmelCase_ : Optional[int] = tokenizer(lowerCAmelCase__ ,padding=lowerCAmelCase__ ,truncate=lowerCAmelCase__ ,return_tensors="np" )
# s
# test single string max_length padding
self.assertEqual(out_s["input_ids"].shape[-1] ,30 )
self.assertTrue(pad_token_id in out_s["input_ids"] )
self.assertTrue(0 in out_s["attention_mask"] )
# s2
# test automatic padding
self.assertEqual(out_sa["input_ids"].shape[-1] ,33 )
# long slice doesn't have padding
self.assertFalse(pad_token_id in out_sa["input_ids"][0] )
self.assertFalse(0 in out_sa["attention_mask"][0] )
# short slice does have padding
self.assertTrue(pad_token_id in out_sa["input_ids"][1] )
self.assertTrue(0 in out_sa["attention_mask"][1] )
# p
# test single pair max_length padding
self.assertEqual(out_p["input_ids"].shape[-1] ,60 )
self.assertTrue(pad_token_id in out_p["input_ids"] )
self.assertTrue(0 in out_p["attention_mask"] )
# p2
# test automatic padding pair
self.assertEqual(out_pa["input_ids"].shape[-1] ,52 )
# long slice pair doesn't have padding
self.assertFalse(pad_token_id in out_pa["input_ids"][0] )
self.assertFalse(0 in out_pa["attention_mask"][0] )
# short slice pair does have padding
self.assertTrue(pad_token_id in out_pa["input_ids"][1] )
self.assertTrue(0 in out_pa["attention_mask"][1] )
def UpperCAmelCase_ ( self : Union[str, Any] ) -> Any:
'''simple docstring'''
lowerCAmelCase_ : Any = "$$$"
lowerCAmelCase_ : List[str] = CodeGenTokenizer.from_pretrained(self.tmpdirname ,bos_token=lowerCAmelCase__ ,add_bos_token=lowerCAmelCase__ )
lowerCAmelCase_ : Dict = "This is a simple input"
lowerCAmelCase_ : Union[str, Any] = ["This is a simple input 1", "This is a simple input 2"]
lowerCAmelCase_ : int = tokenizer.bos_token_id
lowerCAmelCase_ : List[Any] = tokenizer(lowerCAmelCase__ )
lowerCAmelCase_ : Optional[Any] = tokenizer(lowerCAmelCase__ )
self.assertEqual(out_s.input_ids[0] ,lowerCAmelCase__ )
self.assertTrue(all(o[0] == bos_token_id for o in out_sa.input_ids ) )
lowerCAmelCase_ : List[str] = tokenizer.decode(out_s.input_ids )
lowerCAmelCase_ : Optional[Any] = tokenizer.batch_decode(out_sa.input_ids )
self.assertEqual(decode_s.split()[0] ,lowerCAmelCase__ )
self.assertTrue(all(d.split()[0] == bos_token for d in decode_sa ) )
@slow
def UpperCAmelCase_ ( self : Any ) -> Optional[int]:
'''simple docstring'''
lowerCAmelCase_ : Optional[Any] = CodeGenTokenizer.from_pretrained("Salesforce/codegen-350M-mono" )
lowerCAmelCase_ : str = "\nif len_a > len_b:\n result = a\nelse:\n result = b\n\n\n\n#"
lowerCAmelCase_ : int = "\nif len_a > len_b: result = a\nelse: result = b"
lowerCAmelCase_ : Dict = tokenizer.encode(lowerCAmelCase__ )
lowerCAmelCase_ : str = ["^#", re.escape("<|endoftext|>" ), "^'''", "^\"\"\"", "\n\n\n"]
lowerCAmelCase_ : Union[str, Any] = tokenizer.decode(lowerCAmelCase__ ,truncate_before_pattern=lowerCAmelCase__ )
self.assertEqual(lowerCAmelCase__ ,lowerCAmelCase__ )
def UpperCAmelCase_ ( self : Optional[Any] ) -> Optional[Any]:
'''simple docstring'''
pass
| 683 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_speech_available,
is_tf_available,
is_torch_available,
)
_lowercase = {
'''configuration_speech_to_text''': ['''SPEECH_TO_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''Speech2TextConfig'''],
'''processing_speech_to_text''': ['''Speech2TextProcessor'''],
}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowercase = ['''Speech2TextTokenizer''']
try:
if not is_speech_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowercase = ['''Speech2TextFeatureExtractor''']
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowercase = [
'''TF_SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''TFSpeech2TextForConditionalGeneration''',
'''TFSpeech2TextModel''',
'''TFSpeech2TextPreTrainedModel''',
]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowercase = [
'''SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''Speech2TextForConditionalGeneration''',
'''Speech2TextModel''',
'''Speech2TextPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_speech_to_text import SPEECH_TO_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP, SpeechaTextConfig
from .processing_speech_to_text import SpeechaTextProcessor
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_speech_to_text import SpeechaTextTokenizer
try:
if not is_speech_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_speech_to_text import SpeechaTextFeatureExtractor
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_speech_to_text import (
TF_SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
TFSpeechaTextForConditionalGeneration,
TFSpeechaTextModel,
TFSpeechaTextPreTrainedModel,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_speech_to_text import (
SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
SpeechaTextForConditionalGeneration,
SpeechaTextModel,
SpeechaTextPreTrainedModel,
)
else:
import sys
_lowercase = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 683 |
from __future__ import annotations
from random import random
class __snake_case :
"""simple docstring"""
def __init__( self : Optional[int] ,lowerCAmelCase__ : int | None = None ) -> int:
'''simple docstring'''
lowerCAmelCase_ : Dict = value
lowerCAmelCase_ : Any = random()
lowerCAmelCase_ : Node | None = None
lowerCAmelCase_ : Node | None = None
def __repr__( self : Any ) -> str:
'''simple docstring'''
from pprint import pformat
if self.left is None and self.right is None:
return f'''\'{self.value}: {self.prior:.5}\''''
else:
return pformat(
{f'''{self.value}: {self.prior:.5}''': (self.left, self.right)} ,indent=1 )
def __str__( self : str ) -> str:
'''simple docstring'''
lowerCAmelCase_ : List[Any] = str(self.value ) + " "
lowerCAmelCase_ : List[Any] = str(self.left or "" )
lowerCAmelCase_ : Union[str, Any] = str(self.right or "" )
return value + left + right
def UpperCamelCase ( snake_case__ , snake_case__):
if root is None: # None tree is split into 2 Nones
return None, None
elif root.value is None:
return None, None
else:
if value < root.value:
lowerCAmelCase_ , lowerCAmelCase_ : Any = split(root.left , snake_case__)
return left, root
else:
lowerCAmelCase_ , lowerCAmelCase_ : Optional[Any] = split(root.right , snake_case__)
return root, right
def UpperCamelCase ( snake_case__ , snake_case__):
if (not left) or (not right): # If one node is None, return the other
return left or right
elif left.prior < right.prior:
lowerCAmelCase_ : Dict = merge(left.right , snake_case__)
return left
else:
lowerCAmelCase_ : List[str] = merge(snake_case__ , right.left)
return right
def UpperCamelCase ( snake_case__ , snake_case__):
lowerCAmelCase_ : List[Any] = Node(snake_case__)
lowerCAmelCase_ , lowerCAmelCase_ : Tuple = split(snake_case__ , snake_case__)
return merge(merge(snake_case__ , snake_case__) , snake_case__)
def UpperCamelCase ( snake_case__ , snake_case__):
lowerCAmelCase_ , lowerCAmelCase_ : List[str] = split(snake_case__ , value - 1)
lowerCAmelCase_ , lowerCAmelCase_ : Union[str, Any] = split(snake_case__ , snake_case__)
return merge(snake_case__ , snake_case__)
def UpperCamelCase ( snake_case__):
if not root: # None
return
else:
inorder(root.left)
print(root.value , end=",")
inorder(root.right)
def UpperCamelCase ( snake_case__ , snake_case__):
for arg in args.split():
if arg[0] == "+":
lowerCAmelCase_ : List[str] = insert(snake_case__ , int(arg[1:]))
elif arg[0] == "-":
lowerCAmelCase_ : Optional[int] = erase(snake_case__ , int(arg[1:]))
else:
print("Unknown command")
return root
def UpperCamelCase ( ):
lowerCAmelCase_ : str = None
print(
"enter numbers to create a tree, + value to add value into treap, "
"- value to erase all nodes with value. 'q' to quit. ")
lowerCAmelCase_ : str = input()
while args != "q":
lowerCAmelCase_ : int = interact_treap(snake_case__ , snake_case__)
print(snake_case__)
lowerCAmelCase_ : str = input()
print("good by!")
if __name__ == "__main__":
import doctest
doctest.testmod()
main()
| 683 | 1 |
import datasets
import faiss
import numpy as np
import streamlit as st
import torch
from elasticsearch import Elasticsearch
from elia_utils import (
embed_questions_for_retrieval,
make_qa_sas_model,
qa_sas_generate,
query_es_index,
query_qa_dense_index,
)
import transformers
from transformers import AutoModel, AutoModelForSeqaSeqLM, AutoTokenizer
_lowercase = '''bart'''
_lowercase = True
@st.cache(allow_output_mutation=snake_case__)
def UpperCamelCase ( ):
if LOAD_DENSE_INDEX:
lowerCAmelCase_ : int = AutoTokenizer.from_pretrained("yjernite/retribert-base-uncased")
lowerCAmelCase_ : List[Any] = AutoModel.from_pretrained("yjernite/retribert-base-uncased").to("cuda:0")
lowerCAmelCase_ : Union[str, Any] = qar_model.eval()
else:
lowerCAmelCase_ , lowerCAmelCase_ : Union[str, Any] = (None, None)
if MODEL_TYPE == "bart":
lowerCAmelCase_ : str = AutoTokenizer.from_pretrained("yjernite/bart_eli5")
lowerCAmelCase_ : Union[str, Any] = AutoModelForSeqaSeqLM.from_pretrained("yjernite/bart_eli5").to("cuda:0")
lowerCAmelCase_ : Union[str, Any] = torch.load("seq2seq_models/eli5_bart_model_blm_2.pth")
sas_model.load_state_dict(save_dict["model"])
lowerCAmelCase_ : int = sas_model.eval()
else:
lowerCAmelCase_ , lowerCAmelCase_ : Tuple = make_qa_sas_model(
model_name="t5-small" , from_file="seq2seq_models/eli5_t5_model_1024_4.pth" , device="cuda:0")
return (qar_tokenizer, qar_model, sas_tokenizer, sas_model)
@st.cache(allow_output_mutation=snake_case__)
def UpperCamelCase ( ):
if LOAD_DENSE_INDEX:
lowerCAmelCase_ : Union[str, Any] = faiss.StandardGpuResources()
lowerCAmelCase_ : int = datasets.load_dataset(path="wiki_snippets" , name="wiki40b_en_100_0")["train"]
lowerCAmelCase_ : int = np.memmap(
"wiki40b_passages_reps_32_l-8_h-768_b-512-512.dat" , dtype="float32" , mode="r" , shape=(wikiaab_passages.num_rows, 1_28) , )
lowerCAmelCase_ : Optional[Any] = faiss.IndexFlatIP(1_28)
lowerCAmelCase_ : List[str] = faiss.index_cpu_to_gpu(snake_case__ , 1 , snake_case__)
wikiaab_gpu_index_flat.add(snake_case__) # TODO fix for larger GPU
else:
lowerCAmelCase_ , lowerCAmelCase_ : Any = (None, None)
lowerCAmelCase_ : int = Elasticsearch([{"host": "localhost", "port": "9200"}])
return (wikiaab_passages, wikiaab_gpu_index_flat, es_client)
@st.cache(allow_output_mutation=snake_case__)
def UpperCamelCase ( ):
lowerCAmelCase_ : Tuple = datasets.load_dataset("eli5" , name="LFQA_reddit")
lowerCAmelCase_ : List[str] = elia["train_eli5"]
lowerCAmelCase_ : int = np.memmap(
"eli5_questions_reps.dat" , dtype="float32" , mode="r" , shape=(elia_train.num_rows, 1_28))
lowerCAmelCase_ : Optional[int] = faiss.IndexFlatIP(1_28)
eli5_train_q_index.add(snake_case__)
return (elia_train, eli5_train_q_index)
_lowercase , _lowercase , _lowercase = load_indexes()
_lowercase , _lowercase , _lowercase , _lowercase = load_models()
_lowercase , _lowercase = load_train_data()
def UpperCamelCase ( snake_case__ , snake_case__=10):
lowerCAmelCase_ : Optional[int] = embed_questions_for_retrieval([question] , snake_case__ , snake_case__)
lowerCAmelCase_ , lowerCAmelCase_ : Any = eli5_train_q_index.search(snake_case__ , snake_case__)
lowerCAmelCase_ : int = [elia_train[int(snake_case__)] for i in I[0]]
return nn_examples
def UpperCamelCase ( snake_case__ , snake_case__="wiki40b" , snake_case__="dense" , snake_case__=10):
if source == "none":
lowerCAmelCase_ , lowerCAmelCase_ : Dict = (" <P> ".join(["" for _ in range(11)]).strip(), [])
else:
if method == "dense":
lowerCAmelCase_ , lowerCAmelCase_ : int = query_qa_dense_index(
snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__)
else:
lowerCAmelCase_ , lowerCAmelCase_ : Optional[int] = query_es_index(
snake_case__ , snake_case__ , index_name="english_wiki40b_snippets_100w" , n_results=snake_case__ , )
lowerCAmelCase_ : Union[str, Any] = [
(res["article_title"], res["section_title"].strip(), res["score"], res["passage_text"]) for res in hit_lst
]
lowerCAmelCase_ : Tuple = "question: {} context: {}".format(snake_case__ , snake_case__)
return question_doc, support_list
@st.cache(
hash_funcs={
torch.Tensor: (lambda snake_case__: None),
transformers.models.bart.tokenization_bart.BartTokenizer: (lambda snake_case__: None),
})
def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__ , snake_case__=64 , snake_case__=2_56 , snake_case__=False , snake_case__=2 , snake_case__=0.95 , snake_case__=0.8):
with torch.no_grad():
lowerCAmelCase_ : Optional[int] = qa_sas_generate(
snake_case__ , snake_case__ , snake_case__ , num_answers=1 , num_beams=snake_case__ , min_len=snake_case__ , max_len=snake_case__ , do_sample=snake_case__ , temp=snake_case__ , top_p=snake_case__ , top_k=snake_case__ , max_input_length=10_24 , device="cuda:0" , )[0]
return (answer, support_list)
st.title('''Long Form Question Answering with ELI5''')
# Start sidebar
_lowercase = '''<img src=\'https://huggingface.co/front/assets/huggingface_logo.svg\'>'''
_lowercase = '''
<html>
<head>
<style>
.img-container {
padding-left: 90px;
padding-right: 90px;
padding-top: 50px;
padding-bottom: 50px;
background-color: #f0f3f9;
}
</style>
</head>
<body>
<span class="img-container"> <!-- Inline parent element -->
%s
</span>
</body>
</html>
''' % (
header_html,
)
st.sidebar.markdown(
header_full,
unsafe_allow_html=True,
)
# Long Form QA with ELI5 and Wikipedia
_lowercase = '''
This demo presents a model trained to [provide long-form answers to open-domain questions](https://yjernite.github.io/lfqa.html).
First, a document retriever fetches a set of relevant Wikipedia passages given the question from the [Wiki40b](https://research.google/pubs/pub49029/) dataset,
a pre-processed fixed snapshot of Wikipedia.
'''
st.sidebar.markdown(description, unsafe_allow_html=True)
_lowercase = [
'''Answer the question''',
'''View the retrieved document only''',
'''View the most similar ELI5 question and answer''',
'''Show me everything, please!''',
]
_lowercase = st.sidebar.checkbox('''Demo options''')
if demo_options:
_lowercase = st.sidebar.selectbox(
'''''',
action_list,
index=3,
)
_lowercase = action_list.index(action_st)
_lowercase = st.sidebar.selectbox(
'''''',
['''Show full text of passages''', '''Show passage section titles'''],
index=0,
)
_lowercase = show_type == '''Show full text of passages'''
else:
_lowercase = 3
_lowercase = True
_lowercase = st.sidebar.checkbox('''Retrieval options''')
if retrieval_options:
_lowercase = '''
### Information retriever options
The **sparse** retriever uses ElasticSearch, while the **dense** retriever uses max-inner-product search between a question and passage embedding
trained using the [ELI5](https://arxiv.org/abs/1907.09190) questions-answer pairs.
The answer is then generated by sequence to sequence model which takes the question and retrieved document as input.
'''
st.sidebar.markdown(retriever_info)
_lowercase = st.sidebar.selectbox('''Which Wikipedia format should the model use?''', ['''wiki40b''', '''none'''])
_lowercase = st.sidebar.selectbox('''Which Wikipedia indexer should the model use?''', ['''dense''', '''sparse''', '''mixed'''])
else:
_lowercase = '''wiki40b'''
_lowercase = '''dense'''
_lowercase = '''beam'''
_lowercase = 2
_lowercase = 64
_lowercase = 256
_lowercase = None
_lowercase = None
_lowercase = st.sidebar.checkbox('''Generation options''')
if generate_options:
_lowercase = '''
### Answer generation options
The sequence-to-sequence model was initialized with [BART](https://huggingface.co/facebook/bart-large)
weights and fine-tuned on the ELI5 QA pairs and retrieved documents. You can use the model for greedy decoding with
**beam** search, or **sample** from the decoder\'s output probabilities.
'''
st.sidebar.markdown(generate_info)
_lowercase = st.sidebar.selectbox('''Would you like to use beam search or sample an answer?''', ['''beam''', '''sampled'''])
_lowercase = st.sidebar.slider(
'''Minimum generation length''', min_value=8, max_value=256, value=64, step=8, format=None, key=None
)
_lowercase = st.sidebar.slider(
'''Maximum generation length''', min_value=64, max_value=512, value=256, step=16, format=None, key=None
)
if sampled == "beam":
_lowercase = st.sidebar.slider('''Beam size''', min_value=1, max_value=8, value=2, step=None, format=None, key=None)
else:
_lowercase = st.sidebar.slider(
'''Nucleus sampling p''', min_value=0.1, max_value=1.0, value=0.95, step=0.01, format=None, key=None
)
_lowercase = st.sidebar.slider(
'''Temperature''', min_value=0.1, max_value=1.0, value=0.7, step=0.01, format=None, key=None
)
_lowercase = None
# start main text
_lowercase = [
'''<MY QUESTION>''',
'''How do people make chocolate?''',
'''Why do we get a fever when we are sick?''',
'''How can different animals perceive different colors?''',
'''What is natural language processing?''',
'''What\'s the best way to treat a sunburn?''',
'''What exactly are vitamins ?''',
'''How does nuclear energy provide electricity?''',
'''What\'s the difference between viruses and bacteria?''',
'''Why are flutes classified as woodwinds when most of them are made out of metal ?''',
'''Why do people like drinking coffee even though it tastes so bad?''',
'''What happens when wine ages? How does it make the wine taste better?''',
'''If an animal is an herbivore, where does it get the protein that it needs to survive if it only eats grass?''',
'''How can we set a date to the beginning or end of an artistic period? Doesn\'t the change happen gradually?''',
'''How does New Zealand have so many large bird predators?''',
]
_lowercase = st.selectbox(
'''What would you like to ask? ---- select <MY QUESTION> to enter a new query''',
questions_list,
index=1,
)
if question_s == "<MY QUESTION>":
_lowercase = st.text_input('''Enter your question here:''', '''''')
else:
_lowercase = question_s
if st.button('''Show me!'''):
if action in [0, 1, 3]:
if index_type == "mixed":
_lowercase , _lowercase = make_support(question, source=wiki_source, method='''dense''', n_results=10)
_lowercase , _lowercase = make_support(question, source=wiki_source, method='''sparse''', n_results=10)
_lowercase = []
for res_d, res_s in zip(support_list_dense, support_list_sparse):
if tuple(res_d) not in support_list:
support_list += [tuple(res_d)]
if tuple(res_s) not in support_list:
support_list += [tuple(res_s)]
_lowercase = support_list[:10]
_lowercase = '''<P> ''' + ''' <P> '''.join([res[-1] for res in support_list])
else:
_lowercase , _lowercase = make_support(question, source=wiki_source, method=index_type, n_results=10)
if action in [0, 3]:
_lowercase , _lowercase = answer_question(
question_doc,
sas_model,
sas_tokenizer,
min_len=min_len,
max_len=int(max_len),
sampling=(sampled == '''sampled'''),
n_beams=n_beams,
top_p=top_p,
temp=temp,
)
st.markdown('''### The model generated answer is:''')
st.write(answer)
if action in [0, 1, 3] and wiki_source != "none":
st.markdown('''--- \n ### The model is drawing information from the following Wikipedia passages:''')
for i, res in enumerate(support_list):
_lowercase = '''https://en.wikipedia.org/wiki/{}'''.format(res[0].replace(''' ''', '''_'''))
_lowercase = res[1].strip()
if sec_titles == "":
_lowercase = '''[{}]({})'''.format(res[0], wiki_url)
else:
_lowercase = sec_titles.split(''' & ''')
_lowercase = ''' & '''.join(
['''[{}]({}#{})'''.format(sec.strip(), wiki_url, sec.strip().replace(''' ''', '''_''')) for sec in sec_list]
)
st.markdown(
'''{0:02d} - **Article**: {1:<18} <br> _Section_: {2}'''.format(i + 1, res[0], sections),
unsafe_allow_html=True,
)
if show_passages:
st.write(
'''> <span style="font-family:arial; font-size:10pt;">''' + res[-1] + '''</span>''', unsafe_allow_html=True
)
if action in [2, 3]:
_lowercase = find_nearest_training(question)
_lowercase = nn_train_list[0]
st.markdown(
'''--- \n ### The most similar question in the ELI5 training set was: \n\n {}'''.format(train_exple['''title'''])
)
_lowercase = [
'''{}. {}'''.format(i + 1, ''' \n'''.join([line.strip() for line in ans.split('''\n''') if line.strip() != '''''']))
for i, (ans, sc) in enumerate(zip(train_exple['''answers''']['''text'''], train_exple['''answers''']['''score''']))
if i == 0 or sc > 2
]
st.markdown('''##### Its answers were: \n\n {}'''.format('''\n'''.join(answers_st)))
_lowercase = '''
---
**Disclaimer**
*The intent of this app is to provide some (hopefully entertaining) insights into the behavior of a current LFQA system.
Evaluating biases of such a model and ensuring factual generations are still very much open research problems.
Therefore, until some significant progress is achieved, we caution against using the generated answers for practical purposes.*
'''
st.sidebar.markdown(disclaimer, unsafe_allow_html=True)
| 683 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_funnel import FunnelTokenizer
_lowercase = logging.get_logger(__name__)
_lowercase = {'''vocab_file''': '''vocab.txt''', '''tokenizer_file''': '''tokenizer.json'''}
_lowercase = [
'''small''',
'''small-base''',
'''medium''',
'''medium-base''',
'''intermediate''',
'''intermediate-base''',
'''large''',
'''large-base''',
'''xlarge''',
'''xlarge-base''',
]
_lowercase = {
'''vocab_file''': {
'''funnel-transformer/small''': '''https://huggingface.co/funnel-transformer/small/resolve/main/vocab.txt''',
'''funnel-transformer/small-base''': '''https://huggingface.co/funnel-transformer/small-base/resolve/main/vocab.txt''',
'''funnel-transformer/medium''': '''https://huggingface.co/funnel-transformer/medium/resolve/main/vocab.txt''',
'''funnel-transformer/medium-base''': (
'''https://huggingface.co/funnel-transformer/medium-base/resolve/main/vocab.txt'''
),
'''funnel-transformer/intermediate''': (
'''https://huggingface.co/funnel-transformer/intermediate/resolve/main/vocab.txt'''
),
'''funnel-transformer/intermediate-base''': (
'''https://huggingface.co/funnel-transformer/intermediate-base/resolve/main/vocab.txt'''
),
'''funnel-transformer/large''': '''https://huggingface.co/funnel-transformer/large/resolve/main/vocab.txt''',
'''funnel-transformer/large-base''': '''https://huggingface.co/funnel-transformer/large-base/resolve/main/vocab.txt''',
'''funnel-transformer/xlarge''': '''https://huggingface.co/funnel-transformer/xlarge/resolve/main/vocab.txt''',
'''funnel-transformer/xlarge-base''': (
'''https://huggingface.co/funnel-transformer/xlarge-base/resolve/main/vocab.txt'''
),
},
'''tokenizer_file''': {
'''funnel-transformer/small''': '''https://huggingface.co/funnel-transformer/small/resolve/main/tokenizer.json''',
'''funnel-transformer/small-base''': (
'''https://huggingface.co/funnel-transformer/small-base/resolve/main/tokenizer.json'''
),
'''funnel-transformer/medium''': '''https://huggingface.co/funnel-transformer/medium/resolve/main/tokenizer.json''',
'''funnel-transformer/medium-base''': (
'''https://huggingface.co/funnel-transformer/medium-base/resolve/main/tokenizer.json'''
),
'''funnel-transformer/intermediate''': (
'''https://huggingface.co/funnel-transformer/intermediate/resolve/main/tokenizer.json'''
),
'''funnel-transformer/intermediate-base''': (
'''https://huggingface.co/funnel-transformer/intermediate-base/resolve/main/tokenizer.json'''
),
'''funnel-transformer/large''': '''https://huggingface.co/funnel-transformer/large/resolve/main/tokenizer.json''',
'''funnel-transformer/large-base''': (
'''https://huggingface.co/funnel-transformer/large-base/resolve/main/tokenizer.json'''
),
'''funnel-transformer/xlarge''': '''https://huggingface.co/funnel-transformer/xlarge/resolve/main/tokenizer.json''',
'''funnel-transformer/xlarge-base''': (
'''https://huggingface.co/funnel-transformer/xlarge-base/resolve/main/tokenizer.json'''
),
},
}
_lowercase = {f"funnel-transformer/{name}": 512 for name in _model_names}
_lowercase = {f"funnel-transformer/{name}": {'''do_lower_case''': True} for name in _model_names}
class __snake_case ( snake_case__ ):
"""simple docstring"""
UpperCamelCase_ = VOCAB_FILES_NAMES
UpperCamelCase_ = PRETRAINED_VOCAB_FILES_MAP
UpperCamelCase_ = PRETRAINED_INIT_CONFIGURATION
UpperCamelCase_ = FunnelTokenizer
UpperCamelCase_ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
UpperCamelCase_ = 2
def __init__( self : Optional[Any] ,lowerCAmelCase__ : Any=None ,lowerCAmelCase__ : Optional[int]=None ,lowerCAmelCase__ : Optional[Any]=True ,lowerCAmelCase__ : List[str]="<unk>" ,lowerCAmelCase__ : int="<sep>" ,lowerCAmelCase__ : Union[str, Any]="<pad>" ,lowerCAmelCase__ : List[str]="<cls>" ,lowerCAmelCase__ : Optional[int]="<mask>" ,lowerCAmelCase__ : Union[str, Any]="<s>" ,lowerCAmelCase__ : List[str]="</s>" ,lowerCAmelCase__ : Optional[int]=True ,lowerCAmelCase__ : Tuple=True ,lowerCAmelCase__ : Any=None ,lowerCAmelCase__ : List[Any]="##" ,**lowerCAmelCase__ : int ,) -> List[Any]:
'''simple docstring'''
super().__init__(
lowerCAmelCase__ ,tokenizer_file=lowerCAmelCase__ ,do_lower_case=lowerCAmelCase__ ,unk_token=lowerCAmelCase__ ,sep_token=lowerCAmelCase__ ,pad_token=lowerCAmelCase__ ,cls_token=lowerCAmelCase__ ,mask_token=lowerCAmelCase__ ,bos_token=lowerCAmelCase__ ,eos_token=lowerCAmelCase__ ,clean_text=lowerCAmelCase__ ,tokenize_chinese_chars=lowerCAmelCase__ ,strip_accents=lowerCAmelCase__ ,wordpieces_prefix=lowerCAmelCase__ ,**lowerCAmelCase__ ,)
lowerCAmelCase_ : str = json.loads(self.backend_tokenizer.normalizer.__getstate__() )
if (
normalizer_state.get("lowercase" ,lowerCAmelCase__ ) != do_lower_case
or normalizer_state.get("strip_accents" ,lowerCAmelCase__ ) != strip_accents
or normalizer_state.get("handle_chinese_chars" ,lowerCAmelCase__ ) != tokenize_chinese_chars
):
lowerCAmelCase_ : Optional[int] = getattr(lowerCAmelCase__ ,normalizer_state.pop("type" ) )
lowerCAmelCase_ : List[Any] = do_lower_case
lowerCAmelCase_ : List[str] = strip_accents
lowerCAmelCase_ : Any = tokenize_chinese_chars
lowerCAmelCase_ : List[Any] = normalizer_class(**lowerCAmelCase__ )
lowerCAmelCase_ : int = do_lower_case
def UpperCAmelCase_ ( self : Tuple ,lowerCAmelCase__ : int ,lowerCAmelCase__ : str=None ) -> List[Any]:
'''simple docstring'''
lowerCAmelCase_ : Union[str, Any] = [self.cls_token_id] + token_ids_a + [self.sep_token_id]
if token_ids_a:
output += token_ids_a + [self.sep_token_id]
return output
def UpperCAmelCase_ ( self : List[str] ,lowerCAmelCase__ : List[int] ,lowerCAmelCase__ : Optional[List[int]] = None ) -> List[int]:
'''simple docstring'''
lowerCAmelCase_ : str = [self.sep_token_id]
lowerCAmelCase_ : Optional[Any] = [self.cls_token_id]
if token_ids_a is None:
return len(cls ) * [self.cls_token_type_id] + len(token_ids_a + sep ) * [0]
return len(cls ) * [self.cls_token_type_id] + len(token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]
def UpperCAmelCase_ ( self : Any ,lowerCAmelCase__ : str ,lowerCAmelCase__ : Optional[str] = None ) -> Tuple[str]:
'''simple docstring'''
lowerCAmelCase_ : str = self._tokenizer.model.save(lowerCAmelCase__ ,name=lowerCAmelCase__ )
return tuple(lowerCAmelCase__ )
| 683 | 1 |
import pytest
from datasets import inspect_metric, list_metrics, load_metric
@pytest.fixture
def UpperCamelCase ( snake_case__):
monkeypatch.setattr("datasets.utils.deprecation_utils._emitted_deprecation_warnings" , set())
@pytest.fixture
def UpperCamelCase ( snake_case__):
class __snake_case :
"""simple docstring"""
def __init__( self : Any ,lowerCAmelCase__ : Tuple ) -> int:
'''simple docstring'''
lowerCAmelCase_ : Optional[Any] = metric_id
class __snake_case :
"""simple docstring"""
UpperCamelCase_ = [MetricMock(snake_case__ ) for metric_id in ['accuracy', 'mse', 'precision', 'codeparrot/apps_metric']]
def UpperCAmelCase_ ( self : List[Any] ) -> int:
'''simple docstring'''
return self._metrics
monkeypatch.setattr("datasets.inspect.huggingface_hub" , HfhMock())
@pytest.mark.parametrize(
"func, args" , [(load_metric, ("metrics/mse",)), (list_metrics, ()), (inspect_metric, ("metrics/mse", "tmp_path"))])
def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__):
if "tmp_path" in args:
lowerCAmelCase_ : List[Any] = tuple(arg if arg != "tmp_path" else tmp_path for arg in args)
with pytest.warns(snake_case__ , match="https://huggingface.co/docs/evaluate"):
func(*snake_case__)
| 683 |
# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run
import doctest
import sys
import warnings
from os.path import abspath, dirname, join
import _pytest
from transformers.testing_utils import HfDoctestModule, HfDocTestParser
# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install -e .[dev]' when switching between checkouts and running tests.
_lowercase = abspath(join(dirname(__file__), '''src'''))
sys.path.insert(1, git_repo_path)
# silence FutureWarning warnings in tests since often we can't act on them until
# they become normal warnings - i.e. the tests still need to test the current functionality
warnings.simplefilter(action='''ignore''', category=FutureWarning)
def UpperCamelCase ( snake_case__):
config.addinivalue_line(
"markers" , "is_pt_tf_cross_test: mark test to run only when PT and TF interactions are tested")
config.addinivalue_line(
"markers" , "is_pt_flax_cross_test: mark test to run only when PT and FLAX interactions are tested")
config.addinivalue_line("markers" , "is_pipeline_test: mark test to run only when pipelines are tested")
config.addinivalue_line("markers" , "is_staging_test: mark test to run only in the staging environment")
config.addinivalue_line("markers" , "accelerate_tests: mark test that require accelerate")
config.addinivalue_line("markers" , "tool_tests: mark the tool tests that are run on their specific schedule")
def UpperCamelCase ( snake_case__):
from transformers.testing_utils import pytest_addoption_shared
pytest_addoption_shared(snake_case__)
def UpperCamelCase ( snake_case__):
from transformers.testing_utils import pytest_terminal_summary_main
lowerCAmelCase_ : int = terminalreporter.config.getoption("--make-reports")
if make_reports:
pytest_terminal_summary_main(snake_case__ , id=snake_case__)
def UpperCamelCase ( snake_case__ , snake_case__):
# If no tests are collected, pytest exists with code 5, which makes the CI fail.
if exitstatus == 5:
lowerCAmelCase_ : List[Any] = 0
# Doctest custom flag to ignore output.
_lowercase = doctest.register_optionflag('''IGNORE_RESULT''')
_lowercase = doctest.OutputChecker
class __snake_case ( snake_case__ ):
"""simple docstring"""
def UpperCAmelCase_ ( self : List[str] ,lowerCAmelCase__ : int ,lowerCAmelCase__ : Optional[int] ,lowerCAmelCase__ : Tuple ) -> Any:
'''simple docstring'''
if IGNORE_RESULT & optionflags:
return True
return OutputChecker.check_output(self ,lowerCAmelCase__ ,lowerCAmelCase__ ,lowerCAmelCase__ )
_lowercase = CustomOutputChecker
_lowercase = HfDoctestModule
_lowercase = HfDocTestParser
| 683 | 1 |
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