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import json
import os
import pickle
import shutil
import tempfile
from unittest import TestCase
from unittest.mock import patch
import numpy as np
from datasets import Dataset
from transformers import is_faiss_available
from transformers.models.bart.configuration_bart import BartConfig
from transformers.models.bart.tokenization_bart import BartTokenizer
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES as DPR_VOCAB_FILES_NAMES
from transformers.models.dpr.configuration_dpr import DPRConfig
from transformers.models.dpr.tokenization_dpr import DPRContextEncoderTokenizer, DPRQuestionEncoderTokenizer
from transformers.models.rag.configuration_rag import RagConfig
from transformers.models.rag.retrieval_rag import CustomHFIndex, RagRetriever
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES as BART_VOCAB_FILES_NAMES
from transformers.testing_utils import require_faiss, require_sentencepiece, require_tokenizers, require_torch
if is_faiss_available():
import faiss
@require_faiss
class UpperCAmelCase_ ( _A ):
'''simple docstring'''
def _lowercase ( self : Union[str, Any] ) -> List[Any]:
"""simple docstring"""
__magic_name__ = tempfile.mkdtemp()
__magic_name__ = 8
# DPR tok
__magic_name__ = [
"""[UNK]""",
"""[CLS]""",
"""[SEP]""",
"""[PAD]""",
"""[MASK]""",
"""want""",
"""##want""",
"""##ed""",
"""wa""",
"""un""",
"""runn""",
"""##ing""",
""",""",
"""low""",
"""lowest""",
]
__magic_name__ = os.path.join(self.tmpdirname , """dpr_tokenizer""" )
os.makedirs(UpperCamelCase__ , exist_ok=UpperCamelCase__ )
__magic_name__ = os.path.join(UpperCamelCase__ , DPR_VOCAB_FILES_NAMES["""vocab_file"""] )
with open(self.vocab_file , """w""" , encoding="""utf-8""" ) as vocab_writer:
vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens] ) )
# BART tok
__magic_name__ = [
"""l""",
"""o""",
"""w""",
"""e""",
"""r""",
"""s""",
"""t""",
"""i""",
"""d""",
"""n""",
"""\u0120""",
"""\u0120l""",
"""\u0120n""",
"""\u0120lo""",
"""\u0120low""",
"""er""",
"""\u0120lowest""",
"""\u0120newer""",
"""\u0120wider""",
"""<unk>""",
]
__magic_name__ = dict(zip(UpperCamelCase__ , range(len(UpperCamelCase__ ) ) ) )
__magic_name__ = ["""#version: 0.2""", """\u0120 l""", """\u0120l o""", """\u0120lo w""", """e r""", """"""]
__magic_name__ = {"""unk_token""": """<unk>"""}
__magic_name__ = os.path.join(self.tmpdirname , """bart_tokenizer""" )
os.makedirs(UpperCamelCase__ , exist_ok=UpperCamelCase__ )
__magic_name__ = os.path.join(UpperCamelCase__ , BART_VOCAB_FILES_NAMES["""vocab_file"""] )
__magic_name__ = os.path.join(UpperCamelCase__ , BART_VOCAB_FILES_NAMES["""merges_file"""] )
with open(self.vocab_file , """w""" , encoding="""utf-8""" ) as fp:
fp.write(json.dumps(UpperCamelCase__ ) + """\n""" )
with open(self.merges_file , """w""" , encoding="""utf-8""" ) as fp:
fp.write("""\n""".join(UpperCamelCase__ ) )
def _lowercase ( self : List[str] ) -> DPRQuestionEncoderTokenizer:
"""simple docstring"""
return DPRQuestionEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname , """dpr_tokenizer""" ) )
def _lowercase ( self : Tuple ) -> DPRContextEncoderTokenizer:
"""simple docstring"""
return DPRContextEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname , """dpr_tokenizer""" ) )
def _lowercase ( self : Any ) -> BartTokenizer:
"""simple docstring"""
return BartTokenizer.from_pretrained(os.path.join(self.tmpdirname , """bart_tokenizer""" ) )
def _lowercase ( self : Optional[int] ) -> Union[str, Any]:
"""simple docstring"""
shutil.rmtree(self.tmpdirname )
def _lowercase ( self : List[Any] ) -> Any:
"""simple docstring"""
__magic_name__ = Dataset.from_dict(
{
"""id""": ["""0""", """1"""],
"""text""": ["""foo""", """bar"""],
"""title""": ["""Foo""", """Bar"""],
"""embeddings""": [np.ones(self.retrieval_vector_size ), 2 * np.ones(self.retrieval_vector_size )],
} )
dataset.add_faiss_index("""embeddings""" , string_factory="""Flat""" , metric_type=faiss.METRIC_INNER_PRODUCT )
return dataset
def _lowercase ( self : Union[str, Any] ) -> str:
"""simple docstring"""
__magic_name__ = self.get_dummy_dataset()
__magic_name__ = RagConfig(
retrieval_vector_size=self.retrieval_vector_size , question_encoder=DPRConfig().to_dict() , generator=BartConfig().to_dict() , )
with patch("""transformers.models.rag.retrieval_rag.load_dataset""" ) as mock_load_dataset:
__magic_name__ = dataset
__magic_name__ = RagRetriever(
UpperCamelCase__ , question_encoder_tokenizer=self.get_dpr_tokenizer() , generator_tokenizer=self.get_bart_tokenizer() , )
return retriever
def _lowercase ( self : List[str] , UpperCamelCase__ : bool ) -> int:
"""simple docstring"""
__magic_name__ = self.get_dummy_dataset()
__magic_name__ = RagConfig(
retrieval_vector_size=self.retrieval_vector_size , question_encoder=DPRConfig().to_dict() , generator=BartConfig().to_dict() , index_name="""custom""" , )
if from_disk:
__magic_name__ = os.path.join(self.tmpdirname , """dataset""" )
__magic_name__ = os.path.join(self.tmpdirname , """index.faiss""" )
dataset.get_index("""embeddings""" ).save(os.path.join(self.tmpdirname , """index.faiss""" ) )
dataset.drop_index("""embeddings""" )
dataset.save_to_disk(os.path.join(self.tmpdirname , """dataset""" ) )
del dataset
__magic_name__ = RagRetriever(
UpperCamelCase__ , question_encoder_tokenizer=self.get_dpr_tokenizer() , generator_tokenizer=self.get_bart_tokenizer() , )
else:
__magic_name__ = RagRetriever(
UpperCamelCase__ , question_encoder_tokenizer=self.get_dpr_tokenizer() , generator_tokenizer=self.get_bart_tokenizer() , index=CustomHFIndex(config.retrieval_vector_size , UpperCamelCase__ ) , )
return retriever
def _lowercase ( self : List[str] ) -> int:
"""simple docstring"""
__magic_name__ = Dataset.from_dict(
{
"""id""": ["""0""", """1"""],
"""text""": ["""foo""", """bar"""],
"""title""": ["""Foo""", """Bar"""],
"""embeddings""": [np.ones(self.retrieval_vector_size + 1 ), 2 * np.ones(self.retrieval_vector_size + 1 )],
} )
dataset.add_faiss_index("""embeddings""" , string_factory="""Flat""" , metric_type=faiss.METRIC_INNER_PRODUCT )
__magic_name__ = os.path.join(self.tmpdirname , """hf_bert_base.hnswSQ8_correct_phi_128.c_index""" )
dataset.save_faiss_index("""embeddings""" , index_file_name + """.index.dpr""" )
pickle.dump(dataset["""id"""] , open(index_file_name + """.index_meta.dpr""" , """wb""" ) )
__magic_name__ = os.path.join(self.tmpdirname , """psgs_w100.tsv.pkl""" )
__magic_name__ = {sample["""id"""]: [sample["""text"""], sample["""title"""]] for sample in dataset}
pickle.dump(UpperCamelCase__ , open(UpperCamelCase__ , """wb""" ) )
__magic_name__ = RagConfig(
retrieval_vector_size=self.retrieval_vector_size , question_encoder=DPRConfig().to_dict() , generator=BartConfig().to_dict() , index_name="""legacy""" , index_path=self.tmpdirname , )
__magic_name__ = RagRetriever(
UpperCamelCase__ , question_encoder_tokenizer=self.get_dpr_tokenizer() , generator_tokenizer=self.get_bart_tokenizer() )
return retriever
def _lowercase ( self : List[str] ) -> str:
"""simple docstring"""
__magic_name__ = 1
__magic_name__ = self.get_dummy_canonical_hf_index_retriever()
__magic_name__ = np.array(
[np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )] , dtype=np.floataa )
__magic_name__ , __magic_name__ , __magic_name__ = retriever.retrieve(UpperCamelCase__ , n_docs=UpperCamelCase__ )
self.assertEqual(retrieved_doc_embeds.shape , (2, n_docs, self.retrieval_vector_size) )
self.assertEqual(len(UpperCamelCase__ ) , 2 )
self.assertEqual(sorted(doc_dicts[0] ) , ["""embeddings""", """id""", """text""", """title"""] )
self.assertEqual(len(doc_dicts[0]["""id"""] ) , UpperCamelCase__ )
self.assertEqual(doc_dicts[0]["""id"""][0] , """1""" ) # max inner product is reached with second doc
self.assertEqual(doc_dicts[1]["""id"""][0] , """0""" ) # max inner product is reached with first doc
self.assertListEqual(doc_ids.tolist() , [[1], [0]] )
def _lowercase ( self : Tuple ) -> Any:
"""simple docstring"""
__magic_name__ = self.get_dummy_canonical_hf_index_retriever()
with tempfile.TemporaryDirectory() as tmp_dirname:
with patch("""transformers.models.rag.retrieval_rag.load_dataset""" ) as mock_load_dataset:
__magic_name__ = self.get_dummy_dataset()
retriever.save_pretrained(UpperCamelCase__ )
__magic_name__ = RagRetriever.from_pretrained(UpperCamelCase__ )
self.assertIsInstance(UpperCamelCase__ , UpperCamelCase__ )
__magic_name__ = np.array(
[np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )] , dtype=np.floataa )
__magic_name__ = retriever.retrieve(UpperCamelCase__ , n_docs=1 )
self.assertTrue(out is not None )
def _lowercase ( self : List[str] ) -> str:
"""simple docstring"""
__magic_name__ = 1
__magic_name__ = self.get_dummy_custom_hf_index_retriever(from_disk=UpperCamelCase__ )
__magic_name__ = np.array(
[np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )] , dtype=np.floataa )
__magic_name__ , __magic_name__ , __magic_name__ = retriever.retrieve(UpperCamelCase__ , n_docs=UpperCamelCase__ )
self.assertEqual(retrieved_doc_embeds.shape , (2, n_docs, self.retrieval_vector_size) )
self.assertEqual(len(UpperCamelCase__ ) , 2 )
self.assertEqual(sorted(doc_dicts[0] ) , ["""embeddings""", """id""", """text""", """title"""] )
self.assertEqual(len(doc_dicts[0]["""id"""] ) , UpperCamelCase__ )
self.assertEqual(doc_dicts[0]["""id"""][0] , """1""" ) # max inner product is reached with second doc
self.assertEqual(doc_dicts[1]["""id"""][0] , """0""" ) # max inner product is reached with first doc
self.assertListEqual(doc_ids.tolist() , [[1], [0]] )
def _lowercase ( self : Any ) -> str:
"""simple docstring"""
__magic_name__ = self.get_dummy_custom_hf_index_retriever(from_disk=UpperCamelCase__ )
with tempfile.TemporaryDirectory() as tmp_dirname:
retriever.save_pretrained(UpperCamelCase__ )
__magic_name__ = RagRetriever.from_pretrained(UpperCamelCase__ )
self.assertIsInstance(UpperCamelCase__ , UpperCamelCase__ )
__magic_name__ = np.array(
[np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )] , dtype=np.floataa )
__magic_name__ = retriever.retrieve(UpperCamelCase__ , n_docs=1 )
self.assertTrue(out is not None )
def _lowercase ( self : Optional[Any] ) -> int:
"""simple docstring"""
__magic_name__ = 1
__magic_name__ = self.get_dummy_custom_hf_index_retriever(from_disk=UpperCamelCase__ )
__magic_name__ = np.array(
[np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )] , dtype=np.floataa )
__magic_name__ , __magic_name__ , __magic_name__ = retriever.retrieve(UpperCamelCase__ , n_docs=UpperCamelCase__ )
self.assertEqual(retrieved_doc_embeds.shape , (2, n_docs, self.retrieval_vector_size) )
self.assertEqual(len(UpperCamelCase__ ) , 2 )
self.assertEqual(sorted(doc_dicts[0] ) , ["""embeddings""", """id""", """text""", """title"""] )
self.assertEqual(len(doc_dicts[0]["""id"""] ) , UpperCamelCase__ )
self.assertEqual(doc_dicts[0]["""id"""][0] , """1""" ) # max inner product is reached with second doc
self.assertEqual(doc_dicts[1]["""id"""][0] , """0""" ) # max inner product is reached with first doc
self.assertListEqual(doc_ids.tolist() , [[1], [0]] )
def _lowercase ( self : str ) -> List[Any]:
"""simple docstring"""
__magic_name__ = self.get_dummy_custom_hf_index_retriever(from_disk=UpperCamelCase__ )
with tempfile.TemporaryDirectory() as tmp_dirname:
retriever.save_pretrained(UpperCamelCase__ )
__magic_name__ = RagRetriever.from_pretrained(UpperCamelCase__ )
self.assertIsInstance(UpperCamelCase__ , UpperCamelCase__ )
__magic_name__ = np.array(
[np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )] , dtype=np.floataa )
__magic_name__ = retriever.retrieve(UpperCamelCase__ , n_docs=1 )
self.assertTrue(out is not None )
def _lowercase ( self : List[Any] ) -> Optional[int]:
"""simple docstring"""
__magic_name__ = 1
__magic_name__ = self.get_dummy_legacy_index_retriever()
__magic_name__ = np.array(
[np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )] , dtype=np.floataa )
__magic_name__ , __magic_name__ , __magic_name__ = retriever.retrieve(UpperCamelCase__ , n_docs=UpperCamelCase__ )
self.assertEqual(retrieved_doc_embeds.shape , (2, n_docs, self.retrieval_vector_size) )
self.assertEqual(len(UpperCamelCase__ ) , 2 )
self.assertEqual(sorted(doc_dicts[0] ) , ["""text""", """title"""] )
self.assertEqual(len(doc_dicts[0]["""text"""] ) , UpperCamelCase__ )
self.assertEqual(doc_dicts[0]["""text"""][0] , """bar""" ) # max inner product is reached with second doc
self.assertEqual(doc_dicts[1]["""text"""][0] , """foo""" ) # max inner product is reached with first doc
self.assertListEqual(doc_ids.tolist() , [[1], [0]] )
def _lowercase ( self : Tuple ) -> List[Any]:
"""simple docstring"""
__magic_name__ = self.get_dummy_legacy_index_retriever()
with tempfile.TemporaryDirectory() as tmp_dirname:
retriever.save_pretrained(UpperCamelCase__ )
__magic_name__ = RagRetriever.from_pretrained(UpperCamelCase__ )
self.assertIsInstance(UpperCamelCase__ , UpperCamelCase__ )
__magic_name__ = np.array(
[np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )] , dtype=np.floataa )
__magic_name__ = retriever.retrieve(UpperCamelCase__ , n_docs=1 )
self.assertTrue(out is not None )
@require_torch
@require_tokenizers
@require_sentencepiece
def _lowercase ( self : str ) -> List[Any]:
"""simple docstring"""
import torch
__magic_name__ = 1
__magic_name__ = self.get_dummy_canonical_hf_index_retriever()
__magic_name__ = [[5, 7], [10, 11]]
__magic_name__ = np.array(
[np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )] , dtype=np.floataa )
__magic_name__ = retriever(UpperCamelCase__ , UpperCamelCase__ , prefix=retriever.config.generator.prefix , n_docs=UpperCamelCase__ )
__magic_name__ , __magic_name__ , __magic_name__ = (
out["""context_input_ids"""],
out["""context_attention_mask"""],
out["""retrieved_doc_embeds"""],
)
self.assertEqual(retrieved_doc_embeds.shape , (2, n_docs, self.retrieval_vector_size) )
self.assertIsInstance(UpperCamelCase__ , UpperCamelCase__ )
self.assertIsInstance(UpperCamelCase__ , UpperCamelCase__ )
self.assertIsInstance(UpperCamelCase__ , np.ndarray )
__magic_name__ = retriever(
UpperCamelCase__ , UpperCamelCase__ , prefix=retriever.config.generator.prefix , n_docs=UpperCamelCase__ , return_tensors="""pt""" , )
__magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ = ( # noqa: F841
out["""context_input_ids"""],
out["""context_attention_mask"""],
out["""retrieved_doc_embeds"""],
out["""doc_ids"""],
)
self.assertEqual(retrieved_doc_embeds.shape , (2, n_docs, self.retrieval_vector_size) )
self.assertIsInstance(UpperCamelCase__ , torch.Tensor )
self.assertIsInstance(UpperCamelCase__ , torch.Tensor )
self.assertIsInstance(UpperCamelCase__ , torch.Tensor )
@require_torch
@require_tokenizers
@require_sentencepiece
def _lowercase ( self : int ) -> List[Any]:
"""simple docstring"""
__magic_name__ = self.get_dpr_ctx_encoder_tokenizer()
__magic_name__ = 1
__magic_name__ = self.get_dummy_custom_hf_index_retriever(from_disk=UpperCamelCase__ )
retriever.set_ctx_encoder_tokenizer(UpperCamelCase__ )
__magic_name__ = [[5, 7], [10, 11]]
__magic_name__ = np.array(
[np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )] , dtype=np.floataa )
__magic_name__ = retriever(UpperCamelCase__ , UpperCamelCase__ , prefix=retriever.config.generator.prefix , n_docs=UpperCamelCase__ )
self.assertEqual(
len(UpperCamelCase__ ) , 6 ) # check whether the retriever output consist of 6 attributes including tokenized docs
self.assertEqual(
all(k in out for k in ("""tokenized_doc_ids""", """tokenized_doc_attention_mask""") ) , UpperCamelCase__ ) # check for doc token related keys in dictionary.
| 529 |
import warnings
from ...utils import logging
from .image_processing_beit import BeitImageProcessor
__lowerCAmelCase : int = logging.get_logger(__name__)
class UpperCAmelCase_ ( _A ):
'''simple docstring'''
def __init__( self : int , *UpperCamelCase__ : Optional[Any] , **UpperCamelCase__ : Optional[Any] ) -> None:
"""simple docstring"""
warnings.warn(
"""The class BeitFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"""
""" use BeitImageProcessor instead.""" , UpperCamelCase__ , )
super().__init__(*UpperCamelCase__ , **UpperCamelCase__ )
| 529 | 1 |
'''simple docstring'''
from __future__ import annotations
def _UpperCAmelCase ( __A : list[int] ): # This function is recursive
a_ : Any = len(__A )
# If the array contains only one element, we return it (it's the stop condition of
# recursion)
if array_length <= 1:
return array
# Else
a_ : Optional[int] = array[0]
a_ : Union[str, Any] = False
a_ : List[Any] = 1
a_ : list[int] = []
while not is_found and i < array_length:
if array[i] < pivot:
a_ : Optional[int] = True
a_ : Dict = [element for element in array[i:] if element >= array[i]]
a_ : Any = longest_subsequence(__A )
if len(__A ) > len(__A ):
a_ : Tuple = temp_array
else:
i += 1
a_ : str = [element for element in array[1:] if element >= pivot]
a_ : Optional[int] = [pivot, *longest_subsequence(__A )]
if len(__A ) > len(__A ):
return temp_array
else:
return longest_subseq
if __name__ == "__main__":
import doctest
doctest.testmod()
| 666 |
'''simple docstring'''
from math import pi, sqrt, tan
def _UpperCAmelCase ( __A : float ):
if side_length < 0:
raise ValueError('''surface_area_cube() only accepts non-negative values''' )
return 6 * side_length**2
def _UpperCAmelCase ( __A : float , __A : float , __A : float ):
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 ( __A : float ):
if radius < 0:
raise ValueError('''surface_area_sphere() only accepts non-negative values''' )
return 4 * pi * radius**2
def _UpperCAmelCase ( __A : float ):
if radius < 0:
raise ValueError('''surface_area_hemisphere() only accepts non-negative values''' )
return 3 * pi * radius**2
def _UpperCAmelCase ( __A : float , __A : float ):
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 ( __A : float , __A : float , __A : float ):
if radius_a < 0 or radius_a < 0 or height < 0:
raise ValueError(
'''surface_area_conical_frustum() only accepts non-negative values''' )
a_ : 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 ( __A : float , __A : float ):
if radius < 0 or height < 0:
raise ValueError('''surface_area_cylinder() only accepts non-negative values''' )
return 2 * pi * radius * (height + radius)
def _UpperCAmelCase ( __A : float , __A : float ):
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(__A , 2 ) * torus_radius * tube_radius
def _UpperCAmelCase ( __A : float , __A : float ):
if length < 0 or width < 0:
raise ValueError('''area_rectangle() only accepts non-negative values''' )
return length * width
def _UpperCAmelCase ( __A : float ):
if side_length < 0:
raise ValueError('''area_square() only accepts non-negative values''' )
return side_length**2
def _UpperCAmelCase ( __A : float , __A : float ):
if base < 0 or height < 0:
raise ValueError('''area_triangle() only accepts non-negative values''' )
return (base * height) / 2
def _UpperCAmelCase ( __A : float , __A : float , __A : float ):
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''' )
a_ : int = (sidea + sidea + sidea) / 2
a_ : Optional[Any] = sqrt(
semi_perimeter
* (semi_perimeter - sidea)
* (semi_perimeter - sidea)
* (semi_perimeter - sidea) )
return area
def _UpperCAmelCase ( __A : float , __A : float ):
if base < 0 or height < 0:
raise ValueError('''area_parallelogram() only accepts non-negative values''' )
return base * height
def _UpperCAmelCase ( __A : float , __A : float , __A : float ):
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 ( __A : float ):
if radius < 0:
raise ValueError('''area_circle() only accepts non-negative values''' )
return pi * radius**2
def _UpperCAmelCase ( __A : float , __A : float ):
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 ( __A : float , __A : float ):
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 ( __A : int , __A : float ):
if not isinstance(__A , __A ) 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) = }""")
| 666 | 1 |
'''simple docstring'''
import argparse
import collections
import os
import re
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_table.py
lowerCamelCase :Optional[Any] = '''src/transformers'''
lowerCamelCase :int = '''docs/source/en'''
lowerCamelCase :Union[str, Any] = '''.'''
def a ( lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ):
'''simple docstring'''
with open(__A , """r""" , encoding="""utf-8""" , newline="""\n""" ) as f:
A_ : int = f.readlines()
# Find the start prompt.
A_ : Dict = 0
while not lines[start_index].startswith(__A ):
start_index += 1
start_index += 1
A_ : Optional[Any] = start_index
while not lines[end_index].startswith(__A ):
end_index += 1
end_index -= 1
while len(lines[start_index] ) <= 1:
start_index += 1
while len(lines[end_index] ) <= 1:
end_index -= 1
end_index += 1
return "".join(lines[start_index:end_index] ), start_index, end_index, lines
# Add here suffixes that are used to identify models, separated by |
lowerCamelCase :str = '''Model|Encoder|Decoder|ForConditionalGeneration'''
# Regexes that match TF/Flax/PT model names.
lowerCamelCase :int = re.compile(R'''TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)''')
lowerCamelCase :List[str] = re.compile(R'''Flax(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)''')
# Will match any TF or Flax model too so need to be in an else branch afterthe two previous regexes.
lowerCamelCase :List[Any] = re.compile(R'''(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)''')
# This is to make sure the transformers module imported is the one in the repo.
lowerCamelCase :Optional[int] = direct_transformers_import(TRANSFORMERS_PATH)
def a ( lowerCamelCase__ ):
'''simple docstring'''
A_ : int = re.finditer(""".+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)""" , __A )
return [m.group(0 ) for m in matches]
def a ( lowerCamelCase__ , lowerCamelCase__ ):
'''simple docstring'''
A_ : List[str] = 2 if text == """✅""" or text == """❌""" else len(__A )
A_ : Dict = (width - text_length) // 2
A_ : Any = width - text_length - left_indent
return " " * left_indent + text + " " * right_indent
def a ( ):
'''simple docstring'''
A_ : Any = transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
A_ : int = {
name: config_maping_names[code]
for code, name in transformers_module.MODEL_NAMES_MAPPING.items()
if code in config_maping_names
}
A_ : List[Any] = {name: config.replace("""Config""" , """""" ) for name, config in model_name_to_config.items()}
# Dictionaries flagging if each model prefix has a slow/fast tokenizer, backend in PT/TF/Flax.
A_ : Tuple = collections.defaultdict(__A )
A_ : Optional[int] = collections.defaultdict(__A )
A_ : Union[str, Any] = collections.defaultdict(__A )
A_ : List[str] = collections.defaultdict(__A )
A_ : Tuple = collections.defaultdict(__A )
# Let's lookup through all transformers object (once).
for attr_name in dir(__A ):
A_ : List[str] = None
if attr_name.endswith("""Tokenizer""" ):
A_ : Dict = slow_tokenizers
A_ : Optional[Any] = attr_name[:-9]
elif attr_name.endswith("""TokenizerFast""" ):
A_ : List[Any] = fast_tokenizers
A_ : List[str] = attr_name[:-13]
elif _re_tf_models.match(__A ) is not None:
A_ : str = tf_models
A_ : List[str] = _re_tf_models.match(__A ).groups()[0]
elif _re_flax_models.match(__A ) is not None:
A_ : List[Any] = flax_models
A_ : str = _re_flax_models.match(__A ).groups()[0]
elif _re_pt_models.match(__A ) is not None:
A_ : List[str] = pt_models
A_ : List[Any] = _re_pt_models.match(__A ).groups()[0]
if lookup_dict is not None:
while len(__A ) > 0:
if attr_name in model_name_to_prefix.values():
A_ : Optional[Any] = True
break
# Try again after removing the last word in the name
A_ : List[str] = """""".join(camel_case_split(__A )[:-1] )
# Let's build that table!
A_ : Union[str, Any] = list(model_name_to_config.keys() )
model_names.sort(key=str.lower )
A_ : Any = ["""Model""", """Tokenizer slow""", """Tokenizer fast""", """PyTorch support""", """TensorFlow support""", """Flax Support"""]
# We'll need widths to properly display everything in the center (+2 is to leave one extra space on each side).
A_ : int = [len(__A ) + 2 for c in columns]
A_ : int = max([len(__A ) for name in model_names] ) + 2
# Build the table per se
A_ : List[Any] = """|""" + """|""".join([_center_text(__A , __A ) for c, w in zip(__A , __A )] ) + """|\n"""
# Use ":-----:" format to center-aligned table cell texts
table += "|" + "|".join([""":""" + """-""" * (w - 2) + """:""" for w in widths] ) + "|\n"
A_ : List[str] = {True: """✅""", False: """❌"""}
for name in model_names:
A_ : Optional[int] = model_name_to_prefix[name]
A_ : List[Any] = [
name,
check[slow_tokenizers[prefix]],
check[fast_tokenizers[prefix]],
check[pt_models[prefix]],
check[tf_models[prefix]],
check[flax_models[prefix]],
]
table += "|" + "|".join([_center_text(__A , __A ) for l, w in zip(__A , __A )] ) + "|\n"
return table
def a ( lowerCamelCase__=False ):
'''simple docstring'''
A_, A_, A_, A_ : int = _find_text_in_file(
filename=os.path.join(__A , """index.md""" ) , start_prompt="""<!--This table is updated automatically from the auto modules""" , end_prompt="""<!-- End table-->""" , )
A_ : int = get_model_table_from_auto_modules()
if current_table != new_table:
if overwrite:
with open(os.path.join(__A , """index.md""" ) , """w""" , encoding="""utf-8""" , newline="""\n""" ) as f:
f.writelines(lines[:start_index] + [new_table] + lines[end_index:] )
else:
raise ValueError(
"""The model table in the `index.md` has not been updated. Run `make fix-copies` to fix this.""" )
if __name__ == "__main__":
lowerCamelCase :List[str] = argparse.ArgumentParser()
parser.add_argument('''--fix_and_overwrite''', action='''store_true''', help='''Whether to fix inconsistencies.''')
lowerCamelCase :List[str] = parser.parse_args()
check_model_table(args.fix_and_overwrite) | 667 | def lowerCAmelCase_ ( __A, __A ) -> Optional[Any]:
'''simple docstring'''
UpperCAmelCase__ = [1]
for i in range(2, __A ):
factorials.append(factorials[-1] * i )
assert 0 <= k < factorials[-1] * n, "k out of bounds"
UpperCAmelCase__ = []
UpperCAmelCase__ = list(range(__A ) )
# Find permutation
while factorials:
UpperCAmelCase__ = factorials.pop()
UpperCAmelCase__ , UpperCAmelCase__ = divmod(__A, __A )
permutation.append(elements[number] )
elements.remove(elements[number] )
permutation.append(elements[0] )
return permutation
if __name__ == "__main__":
import doctest
doctest.testmod()
| 486 | 0 |
import argparse
import pathlib
import fairseq
import torch
from fairseq.models.roberta import RobertaModel as FairseqRobertaModel
from fairseq.modules import TransformerSentenceEncoderLayer
from packaging import version
from transformers import XLMRobertaConfig, XLMRobertaXLForMaskedLM, XLMRobertaXLForSequenceClassification
from transformers.models.bert.modeling_bert import (
BertIntermediate,
BertLayer,
BertOutput,
BertSelfAttention,
BertSelfOutput,
)
from transformers.models.roberta.modeling_roberta import RobertaAttention
from transformers.utils import logging
if version.parse(fairseq.__version__) < version.parse('''1.0.0a'''):
raise Exception('''requires fairseq >= 1.0.0a''')
logging.set_verbosity_info()
snake_case_ = logging.get_logger(__name__)
snake_case_ = '''Hello world! cécé herlolip'''
def A__ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> Optional[int]:
lowerCamelCase : Tuple =FairseqRobertaModel.from_pretrained(SCREAMING_SNAKE_CASE_ )
roberta.eval() # disable dropout
lowerCamelCase : Union[str, Any] =roberta.model.encoder.sentence_encoder
lowerCamelCase : Union[str, Any] =XLMRobertaConfig(
vocab_size=roberta_sent_encoder.embed_tokens.num_embeddings , hidden_size=roberta.cfg.model.encoder_embed_dim , num_hidden_layers=roberta.cfg.model.encoder_layers , num_attention_heads=roberta.cfg.model.encoder_attention_heads , intermediate_size=roberta.cfg.model.encoder_ffn_embed_dim , max_position_embeddings=5_1_4 , type_vocab_size=1 , layer_norm_eps=1E-5 , )
if classification_head:
lowerCamelCase : List[str] =roberta.model.classification_heads['''mnli'''].out_proj.weight.shape[0]
print('''Our RoBERTa config:''' , SCREAMING_SNAKE_CASE_ )
lowerCamelCase : Optional[int] =XLMRobertaXLForSequenceClassification(SCREAMING_SNAKE_CASE_ ) if classification_head else XLMRobertaXLForMaskedLM(SCREAMING_SNAKE_CASE_ )
model.eval()
# Now let's copy all the weights.
# Embeddings
lowerCamelCase : List[str] =roberta_sent_encoder.embed_tokens.weight
lowerCamelCase : List[str] =roberta_sent_encoder.embed_positions.weight
lowerCamelCase : Tuple =torch.zeros_like(
model.roberta.embeddings.token_type_embeddings.weight ) # just zero them out b/c RoBERTa doesn't use them.
lowerCamelCase : List[str] =roberta_sent_encoder.layer_norm.weight
lowerCamelCase : Tuple =roberta_sent_encoder.layer_norm.bias
for i in range(config.num_hidden_layers ):
# Encoder: start of layer
lowerCamelCase : BertLayer =model.roberta.encoder.layer[i]
lowerCamelCase : TransformerSentenceEncoderLayer =roberta_sent_encoder.layers[i]
lowerCamelCase : RobertaAttention =layer.attention
lowerCamelCase : List[Any] =roberta_layer.self_attn_layer_norm.weight
lowerCamelCase : Tuple =roberta_layer.self_attn_layer_norm.bias
# self attention
lowerCamelCase : BertSelfAttention =layer.attention.self
assert (
roberta_layer.self_attn.k_proj.weight.data.shape
== roberta_layer.self_attn.q_proj.weight.data.shape
== roberta_layer.self_attn.v_proj.weight.data.shape
== torch.Size((config.hidden_size, config.hidden_size) )
)
lowerCamelCase : Optional[int] =roberta_layer.self_attn.q_proj.weight
lowerCamelCase : str =roberta_layer.self_attn.q_proj.bias
lowerCamelCase : str =roberta_layer.self_attn.k_proj.weight
lowerCamelCase : Optional[Any] =roberta_layer.self_attn.k_proj.bias
lowerCamelCase : Union[str, Any] =roberta_layer.self_attn.v_proj.weight
lowerCamelCase : Dict =roberta_layer.self_attn.v_proj.bias
# self-attention output
lowerCamelCase : BertSelfOutput =layer.attention.output
assert self_output.dense.weight.shape == roberta_layer.self_attn.out_proj.weight.shape
lowerCamelCase : Any =roberta_layer.self_attn.out_proj.weight
lowerCamelCase : List[Any] =roberta_layer.self_attn.out_proj.bias
# this one is final layer norm
lowerCamelCase : Any =roberta_layer.final_layer_norm.weight
lowerCamelCase : Dict =roberta_layer.final_layer_norm.bias
# intermediate
lowerCamelCase : BertIntermediate =layer.intermediate
assert intermediate.dense.weight.shape == roberta_layer.fca.weight.shape
lowerCamelCase : Any =roberta_layer.fca.weight
lowerCamelCase : Any =roberta_layer.fca.bias
# output
lowerCamelCase : BertOutput =layer.output
assert bert_output.dense.weight.shape == roberta_layer.fca.weight.shape
lowerCamelCase : Any =roberta_layer.fca.weight
lowerCamelCase : Optional[int] =roberta_layer.fca.bias
# end of layer
if classification_head:
lowerCamelCase : List[Any] =roberta.model.classification_heads['''mnli'''].dense.weight
lowerCamelCase : Optional[Any] =roberta.model.classification_heads['''mnli'''].dense.bias
lowerCamelCase : Dict =roberta.model.classification_heads['''mnli'''].out_proj.weight
lowerCamelCase : List[Any] =roberta.model.classification_heads['''mnli'''].out_proj.bias
else:
# LM Head
lowerCamelCase : List[Any] =roberta.model.encoder.lm_head.dense.weight
lowerCamelCase : Optional[Any] =roberta.model.encoder.lm_head.dense.bias
lowerCamelCase : Dict =roberta.model.encoder.lm_head.layer_norm.weight
lowerCamelCase : List[str] =roberta.model.encoder.lm_head.layer_norm.bias
lowerCamelCase : Tuple =roberta.model.encoder.lm_head.weight
lowerCamelCase : int =roberta.model.encoder.lm_head.bias
# Let's check that we get the same results.
lowerCamelCase : torch.Tensor =roberta.encode(SCREAMING_SNAKE_CASE_ ).unsqueeze(0 ) # batch of size 1
lowerCamelCase : int =model(SCREAMING_SNAKE_CASE_ )[0]
if classification_head:
lowerCamelCase : Dict =roberta.model.classification_heads['''mnli'''](roberta.extract_features(SCREAMING_SNAKE_CASE_ ) )
else:
lowerCamelCase : str =roberta.model(SCREAMING_SNAKE_CASE_ )[0]
print(our_output.shape , their_output.shape )
lowerCamelCase : Dict =torch.max(torch.abs(our_output - their_output ) ).item()
print(F"max_absolute_diff = {max_absolute_diff}" ) # ~ 1e-7
lowerCamelCase : Optional[Any] =torch.allclose(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , atol=1E-3 )
print('''Do both models output the same tensors?''' , '''🔥''' if success else '''💩''' )
if not success:
raise Exception('''Something went wRoNg''' )
pathlib.Path(SCREAMING_SNAKE_CASE_ ).mkdir(parents=SCREAMING_SNAKE_CASE_ , exist_ok=SCREAMING_SNAKE_CASE_ )
print(F"Saving model to {pytorch_dump_folder_path}" )
model.save_pretrained(SCREAMING_SNAKE_CASE_ )
if __name__ == "__main__":
snake_case_ = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--roberta_checkpoint_path''', default=None, type=str, required=True, help='''Path the official PyTorch dump.'''
)
parser.add_argument(
'''--pytorch_dump_folder_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
)
parser.add_argument(
'''--classification_head''', action='''store_true''', help='''Whether to convert a final classification head.'''
)
snake_case_ = parser.parse_args()
convert_xlm_roberta_xl_checkpoint_to_pytorch(
args.roberta_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
)
| 262 |
import argparse
import re
from flax.traverse_util import flatten_dict, unflatten_dict
from tax import checkpoints
from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration
from transformers.modeling_flax_pytorch_utils import load_flax_weights_in_pytorch_model
from transformers.utils import logging
logging.set_verbosity_info()
# should not include what is already done by the `from_pt` argument
snake_case_ = {
'''/attention/''': '''/0/SelfAttention/''',
'''/self_attention/''': '''/0/SelfAttention/''',
'''/encoder_decoder_attention/''': '''/1/EncDecAttention/''',
'''value''': '''v''',
'''query''': '''q''',
'''key''': '''k''',
'''out''': '''o''',
'''pre_self_attention_layer_norm''': '''0/layer_norm''',
'''pre_cross_attention_layer_norm''': '''1/layer_norm''',
'''pre_attention_layer_norm''': '''0/layer_norm''', # previously 1, but seems wrong
'''token_embedder''': '''shared''',
'''encoder_norm''': '''final_layer_norm''',
'''decoder_norm''': '''final_layer_norm''',
'''relpos_bias/rel_embedding''': '''block/0/layer/0/SelfAttention/relative_attention_bias/weight''',
'''router/router_weights/w/''': '''router/classifier/''',
'''roer/roer_weights/w/''': '''router/classifier/''',
'''logits_dense''': '''lm_head''',
}
def A__ ( SCREAMING_SNAKE_CASE_ ) -> int:
# 1. in HF T5, we have block.{x}.layer.{y}. which corresponds to layer.{x} in
# the original model
lowerCamelCase : List[Any] =list(s_dict.keys() )
for key in keys:
lowerCamelCase : Dict =R'''.*/layers_(\d+)'''
lowerCamelCase : Optional[int] =key
if re.match(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
lowerCamelCase : Optional[Any] =re.sub(R'''layers_(\d+)''' , R'''block/\1/layer''' , SCREAMING_SNAKE_CASE_ )
lowerCamelCase : Dict =R'''(encoder|decoder)\/'''
if re.match(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
lowerCamelCase : Dict =re.match(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ).groups()
if groups[0] == "encoder":
lowerCamelCase : Dict =re.sub(R'''/mlp/''' , R'''/1/mlp/''' , SCREAMING_SNAKE_CASE_ )
lowerCamelCase : Optional[int] =re.sub(R'''/pre_mlp_layer_norm/''' , R'''/1/layer_norm/''' , SCREAMING_SNAKE_CASE_ )
elif groups[0] == "decoder":
lowerCamelCase : List[str] =re.sub(R'''/mlp/''' , R'''/2/mlp/''' , SCREAMING_SNAKE_CASE_ )
lowerCamelCase : str =re.sub(R'''/pre_mlp_layer_norm/''' , R'''/2/layer_norm/''' , SCREAMING_SNAKE_CASE_ )
# 2. Convert other classic mappings
for old_key, temp_key in MOE_LAYER_NAME_MAPPING.items():
if old_key in new_key:
lowerCamelCase : Dict =new_key.replace(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
print(F"{key} -> {new_key}" )
lowerCamelCase : Optional[Any] =s_dict.pop(SCREAMING_SNAKE_CASE_ )
if "encoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight" in s_dict:
lowerCamelCase : Dict =s_dict[
'''encoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight'''
].T
if "decoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight" in s_dict:
lowerCamelCase : Union[str, Any] =s_dict[
'''decoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight'''
].T
# 3. Take extra care of the EXPERTS layer
for key in list(s_dict.keys() ):
if "expert" in key:
lowerCamelCase : List[Any] =s_dict[key].shape[0]
lowerCamelCase : int =s_dict[key]
for idx in range(SCREAMING_SNAKE_CASE_ ):
lowerCamelCase : Tuple =expert_weihts[idx]
print(F"{key} -> {key.replace('expert/' , 'nested fstring' )}" )
s_dict.pop(SCREAMING_SNAKE_CASE_ )
return s_dict
snake_case_ = {
'''NUM_ENCODER_LAYERS''': '''num_layers''',
'''NUM_DECODER_LAYERS''': '''num_decoder_layers''',
'''NUM_HEADS''': '''num_heads''',
'''HEAD_DIM''': '''d_kv''',
'''EMBED_DIM''': '''d_model''',
'''MLP_DIM''': '''d_ff''',
'''NUM_SELECTED_EXPERTS''': '''num_selected_experts''',
'''NUM_ENCODER_SPARSE_LAYERS''': '''num_sparse_encoder_layers''',
'''NUM_DECODER_SPARSE_LAYERS''': '''num_sparse_decoder_layers''',
'''dense.MlpBlock.activations''': '''feed_forward_proj''',
}
def A__ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> Union[str, Any]:
# Convert a google style config to the hugging face fromat
import regex as re
with open(SCREAMING_SNAKE_CASE_ , '''r''' ) as f:
lowerCamelCase : str =f.read()
lowerCamelCase : Union[str, Any] =re.findall(R'''(.*) = ([0-9.]*)''' , SCREAMING_SNAKE_CASE_ )
lowerCamelCase : str ={}
for param, value in regex_match:
if param in GIN_TO_CONFIG_MAPPING and value != "":
lowerCamelCase : str =float(SCREAMING_SNAKE_CASE_ ) if '''.''' in value else int(SCREAMING_SNAKE_CASE_ )
lowerCamelCase : int =re.findall(R'''(.*activations) = \(\'(.*)\',\)''' , SCREAMING_SNAKE_CASE_ )[0]
lowerCamelCase : Any =str(activation[1] )
lowerCamelCase : Tuple =num_experts
lowerCamelCase : Any =SwitchTransformersConfig(**SCREAMING_SNAKE_CASE_ )
return config
def A__ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=None , SCREAMING_SNAKE_CASE_="./" , SCREAMING_SNAKE_CASE_=8 ) -> Dict:
# Initialise PyTorch model
print(F"Loading flax weights from : {flax_checkpoint_path}" )
lowerCamelCase : List[Any] =checkpoints.load_tax_checkpoint(SCREAMING_SNAKE_CASE_ )
if gin_file is not None:
lowerCamelCase : Dict =convert_gin_to_config(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
else:
lowerCamelCase : Optional[int] =SwitchTransformersConfig.from_pretrained(SCREAMING_SNAKE_CASE_ )
lowerCamelCase : Any =SwitchTransformersForConditionalGeneration(SCREAMING_SNAKE_CASE_ )
lowerCamelCase : int =flax_params['''target''']
lowerCamelCase : Optional[int] =flatten_dict(SCREAMING_SNAKE_CASE_ , sep='''/''' )
lowerCamelCase : str =rename_keys(SCREAMING_SNAKE_CASE_ )
lowerCamelCase : List[str] =unflatten_dict(SCREAMING_SNAKE_CASE_ , sep='''/''' )
# Load the flax params in the PT model
load_flax_weights_in_pytorch_model(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
print(F"Save PyTorch model to {pytorch_dump_path}" )
pt_model.save_pretrained(SCREAMING_SNAKE_CASE_ )
if __name__ == "__main__":
snake_case_ = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--switch_t5x_checkpoint_path''',
default=None,
type=str,
required=True,
help=(
'''The config json file corresponding to the pre-trained SwitchTransformers model. \nThis specifies the'''
''' model architecture. If not provided, a `gin_file` has to be provided.'''
),
)
parser.add_argument(
'''--gin_file''',
default=None,
type=str,
required=False,
help='''Path to the gin config file. If not provided, a `config_file` has to be passed ''',
)
parser.add_argument(
'''--config_name''', default=None, type=str, required=False, help='''Config name of SwitchTransformers model.'''
)
parser.add_argument(
'''--pytorch_dump_folder_path''', default=None, type=str, required=True, help='''Path to the output pytorch model.'''
)
parser.add_argument('''--num_experts''', default=8, type=int, required=False, help='''Number of experts''')
snake_case_ = parser.parse_args()
convert_flax_checkpoint_to_pytorch(
args.switch_tax_checkpoint_path,
args.config_name,
args.gin_file,
args.pytorch_dump_folder_path,
args.num_experts,
)
| 262 | 1 |
'''simple docstring'''
import argparse
import torch
from transformers import BertForMaskedLM
if __name__ == "__main__":
lowerCAmelCase : Optional[Any] = argparse.ArgumentParser(
description=(
"""Extraction some layers of the full BertForMaskedLM or RObertaForMaskedLM for Transfer Learned"""
""" Distillation"""
)
)
parser.add_argument("""--model_type""", default="""bert""", choices=["""bert"""])
parser.add_argument("""--model_name""", default="""bert-base-uncased""", type=str)
parser.add_argument("""--dump_checkpoint""", default="""serialization_dir/tf_bert-base-uncased_0247911.pth""", type=str)
parser.add_argument("""--vocab_transform""", action="""store_true""")
lowerCAmelCase : Optional[int] = parser.parse_args()
if args.model_type == "bert":
lowerCAmelCase : Any = BertForMaskedLM.from_pretrained(args.model_name)
lowerCAmelCase : Optional[int] = """bert"""
else:
raise ValueError("""args.model_type should be \"bert\".""")
lowerCAmelCase : str = model.state_dict()
lowerCAmelCase : str = {}
for w in ["word_embeddings", "position_embeddings"]:
lowerCAmelCase : Optional[int] = state_dict[F'''{prefix}.embeddings.{w}.weight''']
for w in ["weight", "bias"]:
lowerCAmelCase : Optional[int] = state_dict[F'''{prefix}.embeddings.LayerNorm.{w}''']
lowerCAmelCase : Union[str, Any] = 0
for teacher_idx in [0, 2, 4, 7, 9, 1_1]:
for w in ["weight", "bias"]:
lowerCAmelCase : str = state_dict[
F'''{prefix}.encoder.layer.{teacher_idx}.attention.self.query.{w}'''
]
lowerCAmelCase : List[Any] = state_dict[
F'''{prefix}.encoder.layer.{teacher_idx}.attention.self.key.{w}'''
]
lowerCAmelCase : str = state_dict[
F'''{prefix}.encoder.layer.{teacher_idx}.attention.self.value.{w}'''
]
lowerCAmelCase : Dict = state_dict[
F'''{prefix}.encoder.layer.{teacher_idx}.attention.output.dense.{w}'''
]
lowerCAmelCase : Union[str, Any] = state_dict[
F'''{prefix}.encoder.layer.{teacher_idx}.attention.output.LayerNorm.{w}'''
]
lowerCAmelCase : Union[str, Any] = state_dict[
F'''{prefix}.encoder.layer.{teacher_idx}.intermediate.dense.{w}'''
]
lowerCAmelCase : Union[str, Any] = state_dict[
F'''{prefix}.encoder.layer.{teacher_idx}.output.dense.{w}'''
]
lowerCAmelCase : Optional[Any] = state_dict[
F'''{prefix}.encoder.layer.{teacher_idx}.output.LayerNorm.{w}'''
]
std_idx += 1
lowerCAmelCase : Optional[Any] = state_dict["""cls.predictions.decoder.weight"""]
lowerCAmelCase : Tuple = state_dict["""cls.predictions.bias"""]
if args.vocab_transform:
for w in ["weight", "bias"]:
lowerCAmelCase : int = state_dict[F'''cls.predictions.transform.dense.{w}''']
lowerCAmelCase : Optional[int] = state_dict[F'''cls.predictions.transform.LayerNorm.{w}''']
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)
| 372 |
'''simple docstring'''
import copy
import inspect
import unittest
from transformers import PretrainedConfig, SwiftFormerConfig
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 SwiftFormerForImageClassification, SwiftFormerModel
from transformers.models.swiftformer.modeling_swiftformer import SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import ViTImageProcessor
class _UpperCamelCase :
'''simple docstring'''
def __init__( self , a_ , a_=1_3 , a_=3 , a_=True , a_=True , a_=0.1 , a_=0.1 , a_=2_2_4 , a_=1_0_0_0 , a_=[3, 3, 6, 4] , a_=[4_8, 5_6, 1_1_2, 2_2_0] , ) -> Union[str, Any]:
lowercase : Optional[Any] = parent
lowercase : List[Any] = batch_size
lowercase : Union[str, Any] = num_channels
lowercase : str = is_training
lowercase : Any = use_labels
lowercase : Optional[Any] = hidden_dropout_prob
lowercase : List[str] = attention_probs_dropout_prob
lowercase : Optional[Any] = num_labels
lowercase : str = image_size
lowercase : Dict = layer_depths
lowercase : List[str] = embed_dims
def a__ ( self ) -> Optional[Any]:
lowercase : int = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
lowercase : int = None
if self.use_labels:
lowercase : Optional[Any] = ids_tensor([self.batch_size] , self.num_labels )
lowercase : List[Any] = self.get_config()
return config, pixel_values, labels
def a__ ( self ) -> List[Any]:
return SwiftFormerConfig(
depths=self.layer_depths , embed_dims=self.embed_dims , mlp_ratio=4 , downsamples=[True, True, True, True] , hidden_act="gelu" , num_labels=self.num_labels , down_patch_size=3 , down_stride=2 , down_pad=1 , drop_rate=0.0 , drop_path_rate=0.0 , use_layer_scale=a_ , layer_scale_init_value=1e-5 , )
def a__ ( self , a_ , a_ , a_ ) -> Optional[Any]:
lowercase : Tuple = SwiftFormerModel(config=a_ )
model.to(a_ )
model.eval()
lowercase : Union[str, Any] = model(a_ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.embed_dims[-1], 7, 7) )
def a__ ( self , a_ , a_ , a_ ) -> Any:
lowercase : Dict = self.num_labels
lowercase : Optional[int] = SwiftFormerForImageClassification(a_ )
model.to(a_ )
model.eval()
lowercase : Tuple = model(a_ , labels=a_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
lowercase : Tuple = SwiftFormerForImageClassification(a_ )
model.to(a_ )
model.eval()
lowercase : List[str] = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
lowercase : str = model(a_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def a__ ( self ) -> Dict:
((lowercase) , (lowercase) , (lowercase)) : Dict = self.prepare_config_and_inputs()
lowercase : Dict = {"pixel_values": pixel_values}
return config, inputs_dict
@require_torch
class _UpperCamelCase ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , unittest.TestCase):
'''simple docstring'''
_snake_case = (SwiftFormerModel, SwiftFormerForImageClassification) if is_torch_available() else ()
_snake_case = (
{'''feature-extraction''': SwiftFormerModel, '''image-classification''': SwiftFormerForImageClassification}
if is_torch_available()
else {}
)
_snake_case = False
_snake_case = False
_snake_case = False
_snake_case = False
_snake_case = False
def a__ ( self ) -> str:
lowercase : Optional[int] = SwiftFormerModelTester(self )
lowercase : int = ConfigTester(
self , config_class=a_ , has_text_modality=a_ , hidden_size=3_7 , num_attention_heads=1_2 , num_hidden_layers=1_2 , )
def a__ ( self ) -> Any:
self.config_tester.run_common_tests()
@unittest.skip(reason="SwiftFormer does not use inputs_embeds" )
def a__ ( self ) -> int:
pass
def a__ ( self ) -> Optional[int]:
lowercase , lowercase : Optional[int] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowercase : Optional[int] = model_class(a_ )
lowercase : Tuple = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(a_ , nn.Linear ) )
def a__ ( self ) -> List[Any]:
lowercase , lowercase : Optional[Any] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowercase : str = model_class(a_ )
lowercase : int = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
lowercase : Union[str, Any] = [*signature.parameters.keys()]
lowercase : int = ["pixel_values"]
self.assertListEqual(arg_names[:1] , a_ )
def a__ ( self ) -> int:
lowercase : Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*a_ )
def a__ ( self ) -> Optional[int]:
lowercase : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*a_ )
@slow
def a__ ( self ) -> Dict:
for model_name in SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowercase : Optional[int] = SwiftFormerModel.from_pretrained(a_ )
self.assertIsNotNone(a_ )
@unittest.skip(reason="SwiftFormer does not output attentions" )
def a__ ( self ) -> Optional[Any]:
pass
def a__ ( self ) -> str:
def check_hidden_states_output(a_ , a_ , a_ ):
lowercase : Union[str, Any] = model_class(a_ )
model.to(a_ )
model.eval()
with torch.no_grad():
lowercase : List[str] = model(**self._prepare_for_class(a_ , a_ ) )
lowercase : str = outputs.hidden_states
lowercase : str = 8
self.assertEqual(len(a_ ) , a_ ) # TODO
# SwiftFormer's feature maps are of shape (batch_size, embed_dims, height, width)
# with the width and height being successively divided by 2, after every 2 blocks
for i in range(len(a_ ) ):
self.assertEqual(
hidden_states[i].shape , torch.Size(
[
self.model_tester.batch_size,
self.model_tester.embed_dims[i // 2],
(self.model_tester.image_size // 4) // 2 ** (i // 2),
(self.model_tester.image_size // 4) // 2 ** (i // 2),
] ) , )
lowercase , lowercase : Optional[Any] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowercase : Dict = True
check_hidden_states_output(a_ , a_ , a_ )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
lowercase : str = True
check_hidden_states_output(a_ , a_ , a_ )
def a__ ( self ) -> Optional[Any]:
def _config_zero_init(a_ ):
lowercase : str = copy.deepcopy(a_ )
for key in configs_no_init.__dict__.keys():
if "_range" in key or "_std" in key or "initializer_factor" in key or "layer_scale" in key:
setattr(a_ , a_ , 1e-1_0 )
if isinstance(getattr(a_ , a_ , a_ ) , a_ ):
lowercase : List[Any] = _config_zero_init(getattr(a_ , a_ ) )
setattr(a_ , a_ , a_ )
return configs_no_init
lowercase , lowercase : Any = self.model_tester.prepare_config_and_inputs_for_common()
lowercase : List[str] = _config_zero_init(a_ )
for model_class in self.all_model_classes:
lowercase : Tuple = model_class(config=a_ )
for name, param in model.named_parameters():
if param.requires_grad:
self.assertIn(
((param.data.mean() * 1e9) / 1e9).round().item() , [0.0, 1.0] , msg=F'''Parameter {name} of model {model_class} seems not properly initialized''' , )
@unittest.skip("Will be fixed soon by reducing the size of the model used for common tests." )
def a__ ( self ) -> Tuple:
pass
def _A ( ) -> List[str]:
lowercase : Optional[int] = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
return image
@require_torch
@require_vision
class _UpperCamelCase ( unittest.TestCase):
'''simple docstring'''
@cached_property
def a__ ( self ) -> List[Any]:
return ViTImageProcessor.from_pretrained("MBZUAI/swiftformer-xs" ) if is_vision_available() else None
@slow
def a__ ( self ) -> List[str]:
lowercase : Tuple = SwiftFormerForImageClassification.from_pretrained("MBZUAI/swiftformer-xs" ).to(a_ )
lowercase : Any = self.default_image_processor
lowercase : Optional[int] = prepare_img()
lowercase : Any = image_processor(images=a_ , return_tensors="pt" ).to(a_ )
# forward pass
with torch.no_grad():
lowercase : List[str] = model(**a_ )
# verify the logits
lowercase : str = torch.Size((1, 1_0_0_0) )
self.assertEqual(outputs.logits.shape , a_ )
lowercase : List[Any] = torch.tensor([[-2.1_7_0_3e0_0, 2.1_1_0_7e0_0, -2.0_8_1_1e0_0]] ).to(a_ )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , a_ , atol=1e-4 ) )
| 372 | 1 |
'''simple docstring'''
import argparse
import csv
import logging
import os
import random
import numpy as np
import torch
from torch.utils.data import DataLoader, RandomSampler, SequentialSampler, TensorDataset
from tqdm import tqdm, trange
from transformers import (
CONFIG_NAME,
WEIGHTS_NAME,
AdamW,
OpenAIGPTDoubleHeadsModel,
OpenAIGPTTokenizer,
get_linear_schedule_with_warmup,
)
logging.basicConfig(
format='%(asctime)s - %(levelname)s - %(name)s - %(message)s', datefmt='%m/%d/%Y %H:%M:%S', level=logging.INFO
)
lowerCAmelCase : Union[str, Any] = logging.getLogger(__name__)
def A_( A : List[Any] , A : List[str]) -> str:
UpperCamelCase = np.argmax(__UpperCamelCase , axis=1)
return np.sum(outputs == labels)
def A_( A : Optional[Any]) -> Optional[Any]:
with open(__UpperCamelCase , encoding='utf_8') as f:
UpperCamelCase = csv.reader(__UpperCamelCase)
UpperCamelCase = []
next(__UpperCamelCase) # skip the first line
for line in tqdm(__UpperCamelCase):
output.append((' '.join(line[1:5]), line[5], line[6], int(line[-1]) - 1))
return output
def A_( A : List[str] , A : List[Any] , A : Union[str, Any] , A : Union[str, Any] , A : Dict , A : Dict) -> Optional[int]:
UpperCamelCase = []
for dataset in encoded_datasets:
UpperCamelCase = len(__UpperCamelCase)
UpperCamelCase = np.zeros((n_batch, 2, input_len) , dtype=np.intaa)
UpperCamelCase = np.zeros((n_batch, 2) , dtype=np.intaa)
UpperCamelCase = np.full((n_batch, 2, input_len) , fill_value=-100 , dtype=np.intaa)
UpperCamelCase = np.zeros((n_batch,) , dtype=np.intaa)
for (
i,
(story, conta, conta, mc_label),
) in enumerate(__UpperCamelCase):
UpperCamelCase = [start_token] + story[:cap_length] + [delimiter_token] + conta[:cap_length] + [clf_token]
UpperCamelCase = [start_token] + story[:cap_length] + [delimiter_token] + conta[:cap_length] + [clf_token]
UpperCamelCase = with_conta
UpperCamelCase = with_conta
UpperCamelCase = len(__UpperCamelCase) - 1
UpperCamelCase = len(__UpperCamelCase) - 1
UpperCamelCase = with_conta
UpperCamelCase = with_conta
UpperCamelCase = mc_label
UpperCamelCase = (input_ids, mc_token_ids, lm_labels, mc_labels)
tensor_datasets.append(tuple(torch.tensor(__UpperCamelCase) for t in all_inputs))
return tensor_datasets
def A_( ) -> List[Any]:
UpperCamelCase = argparse.ArgumentParser()
parser.add_argument('--model_name' , type=__UpperCamelCase , default='openai-gpt' , help='pretrained model name')
parser.add_argument('--do_train' , action='store_true' , help='Whether to run training.')
parser.add_argument('--do_eval' , action='store_true' , help='Whether to run eval on the dev set.')
parser.add_argument(
'--output_dir' , default=__UpperCamelCase , type=__UpperCamelCase , required=__UpperCamelCase , help='The output directory where the model predictions and checkpoints will be written.' , )
parser.add_argument('--train_dataset' , type=__UpperCamelCase , default='')
parser.add_argument('--eval_dataset' , type=__UpperCamelCase , default='')
parser.add_argument('--seed' , type=__UpperCamelCase , default=42)
parser.add_argument('--num_train_epochs' , type=__UpperCamelCase , default=3)
parser.add_argument('--train_batch_size' , type=__UpperCamelCase , default=8)
parser.add_argument('--eval_batch_size' , type=__UpperCamelCase , default=16)
parser.add_argument('--adam_epsilon' , default=1E-8 , type=__UpperCamelCase , help='Epsilon for Adam optimizer.')
parser.add_argument('--max_grad_norm' , type=__UpperCamelCase , default=1)
parser.add_argument(
'--max_steps' , default=-1 , type=__UpperCamelCase , help=(
'If > 0: set total number of training steps to perform. Override num_train_epochs.'
) , )
parser.add_argument(
'--gradient_accumulation_steps' , type=__UpperCamelCase , default=1 , help='Number of updates steps to accumulate before performing a backward/update pass.' , )
parser.add_argument('--learning_rate' , type=__UpperCamelCase , default=6.25E-5)
parser.add_argument('--warmup_steps' , default=0 , type=__UpperCamelCase , help='Linear warmup over warmup_steps.')
parser.add_argument('--lr_schedule' , type=__UpperCamelCase , default='warmup_linear')
parser.add_argument('--weight_decay' , type=__UpperCamelCase , default=0.01)
parser.add_argument('--lm_coef' , type=__UpperCamelCase , default=0.9)
parser.add_argument('--n_valid' , type=__UpperCamelCase , default=374)
parser.add_argument('--server_ip' , type=__UpperCamelCase , default='' , help='Can be used for distant debugging.')
parser.add_argument('--server_port' , type=__UpperCamelCase , default='' , help='Can be used for distant debugging.')
UpperCamelCase = parser.parse_args()
print(__UpperCamelCase)
if args.server_ip and args.server_port:
# Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
import ptvsd
print('Waiting for debugger attach')
ptvsd.enable_attach(address=(args.server_ip, args.server_port) , redirect_output=__UpperCamelCase)
ptvsd.wait_for_attach()
random.seed(args.seed)
np.random.seed(args.seed)
torch.manual_seed(args.seed)
torch.cuda.manual_seed_all(args.seed)
UpperCamelCase = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
UpperCamelCase = torch.cuda.device_count()
logger.info('device: {}, n_gpu {}'.format(__UpperCamelCase , __UpperCamelCase))
if not args.do_train and not args.do_eval:
raise ValueError('At least one of `do_train` or `do_eval` must be True.')
if not os.path.exists(args.output_dir):
os.makedirs(args.output_dir)
# Load tokenizer and model
# This loading functions also add new tokens and embeddings called `special tokens`
# These new embeddings will be fine-tuned on the RocStories dataset
UpperCamelCase = ['_start_', '_delimiter_', '_classify_']
UpperCamelCase = OpenAIGPTTokenizer.from_pretrained(args.model_name)
tokenizer.add_tokens(__UpperCamelCase)
UpperCamelCase = tokenizer.convert_tokens_to_ids(__UpperCamelCase)
UpperCamelCase = OpenAIGPTDoubleHeadsModel.from_pretrained(args.model_name)
model.resize_token_embeddings(len(__UpperCamelCase))
model.to(__UpperCamelCase)
# Load and encode the datasets
def tokenize_and_encode(A : List[str]):
if isinstance(__UpperCamelCase , __UpperCamelCase):
return tokenizer.convert_tokens_to_ids(tokenizer.tokenize(__UpperCamelCase))
elif isinstance(__UpperCamelCase , __UpperCamelCase):
return obj
return [tokenize_and_encode(__UpperCamelCase) for o in obj]
logger.info('Encoding dataset...')
UpperCamelCase = load_rocstories_dataset(args.train_dataset)
UpperCamelCase = load_rocstories_dataset(args.eval_dataset)
UpperCamelCase = (train_dataset, eval_dataset)
UpperCamelCase = tokenize_and_encode(__UpperCamelCase)
# Compute the max input length for the Transformer
UpperCamelCase = model.config.n_positions // 2 - 2
UpperCamelCase = max(
len(story[:max_length]) + max(len(conta[:max_length]) , len(conta[:max_length])) + 3
for dataset in encoded_datasets
for story, conta, conta, _ in dataset)
UpperCamelCase = min(__UpperCamelCase , model.config.n_positions) # Max size of input for the pre-trained model
# Prepare inputs tensors and dataloaders
UpperCamelCase = pre_process_datasets(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase , *__UpperCamelCase)
UpperCamelCase , UpperCamelCase = tensor_datasets[0], tensor_datasets[1]
UpperCamelCase = TensorDataset(*__UpperCamelCase)
UpperCamelCase = RandomSampler(__UpperCamelCase)
UpperCamelCase = DataLoader(__UpperCamelCase , sampler=__UpperCamelCase , batch_size=args.train_batch_size)
UpperCamelCase = TensorDataset(*__UpperCamelCase)
UpperCamelCase = SequentialSampler(__UpperCamelCase)
UpperCamelCase = DataLoader(__UpperCamelCase , sampler=__UpperCamelCase , batch_size=args.eval_batch_size)
# Prepare optimizer
if args.do_train:
if args.max_steps > 0:
UpperCamelCase = args.max_steps
UpperCamelCase = args.max_steps // (len(__UpperCamelCase) // args.gradient_accumulation_steps) + 1
else:
UpperCamelCase = len(__UpperCamelCase) // args.gradient_accumulation_steps * args.num_train_epochs
UpperCamelCase = list(model.named_parameters())
UpperCamelCase = ['bias', 'LayerNorm.bias', 'LayerNorm.weight']
UpperCamelCase = [
{
'params': [p for n, p in param_optimizer if not any(nd in n for nd in no_decay)],
'weight_decay': args.weight_decay,
},
{'params': [p for n, p in param_optimizer if any(nd in n for nd in no_decay)], 'weight_decay': 0.0},
]
UpperCamelCase = AdamW(__UpperCamelCase , lr=args.learning_rate , eps=args.adam_epsilon)
UpperCamelCase = get_linear_schedule_with_warmup(
__UpperCamelCase , num_warmup_steps=args.warmup_steps , num_training_steps=__UpperCamelCase)
if args.do_train:
UpperCamelCase , UpperCamelCase , UpperCamelCase = 0, 0, None
model.train()
for _ in trange(int(args.num_train_epochs) , desc='Epoch'):
UpperCamelCase = 0
UpperCamelCase = 0
UpperCamelCase = tqdm(__UpperCamelCase , desc='Training')
for step, batch in enumerate(__UpperCamelCase):
UpperCamelCase = tuple(t.to(__UpperCamelCase) for t in batch)
UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase = batch
UpperCamelCase = model(__UpperCamelCase , mc_token_ids=__UpperCamelCase , lm_labels=__UpperCamelCase , mc_labels=__UpperCamelCase)
UpperCamelCase = args.lm_coef * losses[0] + losses[1]
loss.backward()
optimizer.step()
scheduler.step()
optimizer.zero_grad()
tr_loss += loss.item()
UpperCamelCase = (
loss.item() if exp_average_loss is None else 0.7 * exp_average_loss + 0.3 * loss.item()
)
nb_tr_steps += 1
UpperCamelCase = 'Training loss: {:.2e} lr: {:.2e}'.format(__UpperCamelCase , scheduler.get_lr()[0])
# Save a trained model
if args.do_train:
# Save a trained model, configuration and tokenizer
UpperCamelCase = model.module if hasattr(__UpperCamelCase , 'module') else model # Only save the model itself
# If we save using the predefined names, we can load using `from_pretrained`
UpperCamelCase = os.path.join(args.output_dir , __UpperCamelCase)
UpperCamelCase = os.path.join(args.output_dir , __UpperCamelCase)
torch.save(model_to_save.state_dict() , __UpperCamelCase)
model_to_save.config.to_json_file(__UpperCamelCase)
tokenizer.save_vocabulary(args.output_dir)
# Load a trained model and vocabulary that you have fine-tuned
UpperCamelCase = OpenAIGPTDoubleHeadsModel.from_pretrained(args.output_dir)
UpperCamelCase = OpenAIGPTTokenizer.from_pretrained(args.output_dir)
model.to(__UpperCamelCase)
if args.do_eval:
model.eval()
UpperCamelCase , UpperCamelCase = 0, 0
UpperCamelCase , UpperCamelCase = 0, 0
for batch in tqdm(__UpperCamelCase , desc='Evaluating'):
UpperCamelCase = tuple(t.to(__UpperCamelCase) for t in batch)
UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase = batch
with torch.no_grad():
UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase = model(
__UpperCamelCase , mc_token_ids=__UpperCamelCase , lm_labels=__UpperCamelCase , mc_labels=__UpperCamelCase)
UpperCamelCase = mc_logits.detach().cpu().numpy()
UpperCamelCase = mc_labels.to('cpu').numpy()
UpperCamelCase = accuracy(__UpperCamelCase , __UpperCamelCase)
eval_loss += mc_loss.mean().item()
eval_accuracy += tmp_eval_accuracy
nb_eval_examples += input_ids.size(0)
nb_eval_steps += 1
UpperCamelCase = eval_loss / nb_eval_steps
UpperCamelCase = eval_accuracy / nb_eval_examples
UpperCamelCase = tr_loss / nb_tr_steps if args.do_train else None
UpperCamelCase = {'eval_loss': eval_loss, 'eval_accuracy': eval_accuracy, 'train_loss': train_loss}
UpperCamelCase = os.path.join(args.output_dir , 'eval_results.txt')
with open(__UpperCamelCase , 'w') as writer:
logger.info('***** Eval results *****')
for key in sorted(result.keys()):
logger.info(' %s = %s' , __UpperCamelCase , str(result[key]))
writer.write('%s = %s\n' % (key, str(result[key])))
if __name__ == "__main__":
main()
| 708 |
'''simple docstring'''
import torch
from torch import nn
from transformers import CLIPPreTrainedModel, CLIPVisionModel
from ...models.attention import BasicTransformerBlock
from ...utils import logging
lowerCAmelCase : str = logging.get_logger(__name__) # pylint: disable=invalid-name
class SCREAMING_SNAKE_CASE__ ( snake_case_):
def __init__( self , A_ , A_=768 )-> Any:
'''simple docstring'''
super().__init__(A_ )
UpperCamelCase = proj_size
UpperCamelCase = CLIPVisionModel(A_ )
UpperCamelCase = PaintByExampleMapper(A_ )
UpperCamelCase = nn.LayerNorm(config.hidden_size )
UpperCamelCase = nn.Linear(config.hidden_size , self.proj_size )
# uncondition for scaling
UpperCamelCase = nn.Parameter(torch.randn((1, 1, self.proj_size) ) )
def UpperCAmelCase_ ( self , A_ , A_=False )-> Dict:
'''simple docstring'''
UpperCamelCase = self.model(pixel_values=A_ )
UpperCamelCase = clip_output.pooler_output
UpperCamelCase = self.mapper(latent_states[:, None] )
UpperCamelCase = self.final_layer_norm(A_ )
UpperCamelCase = self.proj_out(A_ )
if return_uncond_vector:
return latent_states, self.uncond_vector
return latent_states
class SCREAMING_SNAKE_CASE__ ( nn.Module):
def __init__( self , A_ )-> Tuple:
'''simple docstring'''
super().__init__()
UpperCamelCase = (config.num_hidden_layers + 1) // 5
UpperCamelCase = config.hidden_size
UpperCamelCase = 1
UpperCamelCase = nn.ModuleList(
[
BasicTransformerBlock(A_ , A_ , A_ , activation_fn='gelu' , attention_bias=A_ )
for _ in range(A_ )
] )
def UpperCAmelCase_ ( self , A_ )-> Dict:
'''simple docstring'''
for block in self.blocks:
UpperCamelCase = block(A_ )
return hidden_states
| 432 | 0 |
from ..utils import DummyObject, requires_backends
class SCREAMING_SNAKE_CASE__ ( metaclass=SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
__lowerCamelCase : Any = ["torch", "scipy"]
def __init__( self, *lowerCamelCase__, **lowerCamelCase__ ):
requires_backends(self, ["""torch""", """scipy"""] )
@classmethod
def _lowerCAmelCase ( cls, *lowerCamelCase__, **lowerCamelCase__ ):
requires_backends(cls, ["""torch""", """scipy"""] )
@classmethod
def _lowerCAmelCase ( cls, *lowerCamelCase__, **lowerCamelCase__ ):
requires_backends(cls, ["""torch""", """scipy"""] )
| 662 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
SCREAMING_SNAKE_CASE_:int = {
"""configuration_blenderbot""": [
"""BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""BlenderbotConfig""",
"""BlenderbotOnnxConfig""",
],
"""tokenization_blenderbot""": ["""BlenderbotTokenizer"""],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE_:Union[str, Any] = ["""BlenderbotTokenizerFast"""]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE_:Optional[int] = [
"""BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""BlenderbotForCausalLM""",
"""BlenderbotForConditionalGeneration""",
"""BlenderbotModel""",
"""BlenderbotPreTrainedModel""",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE_:Union[str, Any] = [
"""TFBlenderbotForConditionalGeneration""",
"""TFBlenderbotModel""",
"""TFBlenderbotPreTrainedModel""",
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE_:Any = [
"""FlaxBlenderbotForConditionalGeneration""",
"""FlaxBlenderbotModel""",
"""FlaxBlenderbotPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_blenderbot import (
BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP,
BlenderbotConfig,
BlenderbotOnnxConfig,
)
from .tokenization_blenderbot import BlenderbotTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_blenderbot_fast import BlenderbotTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_blenderbot import (
BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST,
BlenderbotForCausalLM,
BlenderbotForConditionalGeneration,
BlenderbotModel,
BlenderbotPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_blenderbot import (
TFBlenderbotForConditionalGeneration,
TFBlenderbotModel,
TFBlenderbotPreTrainedModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_blenderbot import (
FlaxBlenderbotForConditionalGeneration,
FlaxBlenderbotModel,
FlaxBlenderbotPreTrainedModel,
)
else:
import sys
SCREAMING_SNAKE_CASE_:Optional[Any] = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 662 | 1 |
'''simple docstring'''
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging
UpperCamelCase : Tuple = logging.get_logger(__name__)
UpperCamelCase : Optional[int] = {
"""google/umt5-small""": """https://huggingface.co/google/umt5-small/resolve/main/config.json""",
# See all umt5 models at https://huggingface.co/models?filter=umt5
}
class UpperCamelCase ( __UpperCAmelCase ):
"""simple docstring"""
A : Any = "umt5"
A : Union[str, Any] = ["past_key_values"]
def __init__( self : Dict , UpperCAmelCase_ : Dict=2_5_0_1_1_2 , UpperCAmelCase_ : Any=5_1_2 , UpperCAmelCase_ : List[str]=6_4 , UpperCAmelCase_ : Optional[int]=1_0_2_4 , UpperCAmelCase_ : List[str]=8 , UpperCAmelCase_ : str=None , UpperCAmelCase_ : int=6 , UpperCAmelCase_ : int=3_2 , UpperCAmelCase_ : Optional[Any]=1_2_8 , UpperCAmelCase_ : int=0.1 , UpperCAmelCase_ : str=1e-6 , UpperCAmelCase_ : Tuple=1.0 , UpperCAmelCase_ : List[Any]="gated-gelu" , UpperCAmelCase_ : Tuple=True , UpperCAmelCase_ : int=True , UpperCAmelCase_ : Dict="T5Tokenizer" , UpperCAmelCase_ : int=True , UpperCAmelCase_ : List[str]=0 , UpperCAmelCase_ : Dict=1 , UpperCAmelCase_ : Tuple=0 , **UpperCAmelCase_ : List[Any] , ):
"""simple docstring"""
super().__init__(
is_encoder_decoder=UpperCAmelCase_ , tokenizer_class=UpperCAmelCase_ , tie_word_embeddings=UpperCAmelCase_ , pad_token_id=UpperCAmelCase_ , eos_token_id=UpperCAmelCase_ , decoder_start_token_id=UpperCAmelCase_ , **UpperCAmelCase_ , )
a : Union[str, Any] = vocab_size
a : List[Any] = d_model
a : Optional[int] = d_kv
a : str = d_ff
a : Dict = num_layers
a : Tuple = (
num_decoder_layers if num_decoder_layers is not None else self.num_layers
) # default = symmetry
a : Union[str, Any] = num_heads
a : str = relative_attention_num_buckets
a : List[Any] = relative_attention_max_distance
a : str = dropout_rate
a : List[str] = layer_norm_epsilon
a : List[Any] = initializer_factor
a : int = feed_forward_proj
a : Union[str, Any] = use_cache
a : int = self.feed_forward_proj.split('-')
a : Optional[int] = act_info[-1]
a : Any = act_info[0] == 'gated'
if len(UpperCAmelCase_) > 1 and act_info[0] != "gated" or len(UpperCAmelCase_) > 2:
raise ValueError(
f"""`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer."""
'Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. '
'\'gated-gelu\' or \'relu\'')
if feed_forward_proj == "gated-gelu":
a : str = 'gelu_new'
@property
def SCREAMING_SNAKE_CASE_ ( self : Tuple):
"""simple docstring"""
return self.d_model
@property
def SCREAMING_SNAKE_CASE_ ( self : str):
"""simple docstring"""
return self.num_heads
@property
def SCREAMING_SNAKE_CASE_ ( self : List[Any]):
"""simple docstring"""
return self.num_layers
class UpperCamelCase ( __UpperCAmelCase ):
"""simple docstring"""
@property
# Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.inputs
def SCREAMING_SNAKE_CASE_ ( self : int):
"""simple docstring"""
a : Dict = {
'input_ids': {0: 'batch', 1: 'encoder_sequence'},
'attention_mask': {0: 'batch', 1: 'encoder_sequence'},
}
if self.use_past:
a : str = 'past_encoder_sequence + sequence'
a : int = {0: 'batch'}
a : Union[str, Any] = {0: 'batch', 1: 'past_decoder_sequence + sequence'}
else:
a : Optional[int] = {0: 'batch', 1: 'decoder_sequence'}
a : Dict = {0: 'batch', 1: 'decoder_sequence'}
if self.use_past:
self.fill_with_past_key_values_(UpperCAmelCase_ , direction='inputs')
return common_inputs
@property
# Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.default_onnx_opset
def SCREAMING_SNAKE_CASE_ ( self : Union[str, Any]):
"""simple docstring"""
return 1_3
@property
def SCREAMING_SNAKE_CASE_ ( self : Optional[int]):
"""simple docstring"""
return 5e-4
| 717 | '''simple docstring'''
import importlib
import os
import sys
# This is required to make the module import works (when the python process is running from the root of the repo)
sys.path.append(""".""")
def SCREAMING_SNAKE_CASE__ ( snake_case : Dict ) -> Optional[Any]:
"""simple docstring"""
a : Union[str, Any] = test_file.split(os.path.sep )
if components[0:2] != ["tests", "models"]:
raise ValueError(
'`test_file` should start with `tests/models/` (with `/` being the OS specific path separator). Got '
F"""{test_file} instead.""" )
a : int = components[-1]
if not test_fn.endswith('py' ):
raise ValueError(F"""`test_file` should be a python file. Got {test_fn} instead.""" )
if not test_fn.startswith('test_modeling_' ):
raise ValueError(
F"""`test_file` should point to a file name of the form `test_modeling_*.py`. Got {test_fn} instead.""" )
a : Tuple = components[:-1] + [test_fn.replace('.py' , '' )]
a : Union[str, Any] = '.'.join(snake_case )
return test_module_path
def SCREAMING_SNAKE_CASE__ ( snake_case : List[Any] ) -> Tuple:
"""simple docstring"""
a : List[str] = get_module_path(snake_case )
a : Tuple = importlib.import_module(snake_case )
return test_module
def SCREAMING_SNAKE_CASE__ ( snake_case : Union[str, Any] ) -> Optional[Any]:
"""simple docstring"""
a : Optional[int] = []
a : str = get_test_module(snake_case )
for attr in dir(snake_case ):
if attr.endswith('ModelTester' ):
tester_classes.append(getattr(snake_case , snake_case ) )
# sort with class names
return sorted(snake_case , key=lambda snake_case : x.__name__ )
def SCREAMING_SNAKE_CASE__ ( snake_case : Tuple ) -> str:
"""simple docstring"""
a : Dict = []
a : List[str] = get_test_module(snake_case )
for attr in dir(snake_case ):
a : int = getattr(snake_case , snake_case )
# (TF/Flax)ModelTesterMixin is also an attribute in specific model test module. Let's exclude them by checking
# `all_model_classes` is not empty (which also excludes other special classes).
a : Optional[Any] = getattr(snake_case , 'all_model_classes' , [] )
if len(snake_case ) > 0:
test_classes.append(snake_case )
# sort with class names
return sorted(snake_case , key=lambda snake_case : x.__name__ )
def SCREAMING_SNAKE_CASE__ ( snake_case : Any ) -> Union[str, Any]:
"""simple docstring"""
a : Dict = get_test_classes(snake_case )
a : List[str] = set()
for test_class in test_classes:
model_classes.update(test_class.all_model_classes )
# sort with class names
return sorted(snake_case , key=lambda snake_case : x.__name__ )
def SCREAMING_SNAKE_CASE__ ( snake_case : Optional[Any] ) -> str:
"""simple docstring"""
a : Dict = test_class()
if hasattr(snake_case , 'setUp' ):
test.setUp()
a : Union[str, Any] = None
if hasattr(snake_case , 'model_tester' ):
# `(TF/Flax)ModelTesterMixin` has this attribute default to `None`. Let's skip this case.
if test.model_tester is not None:
a : Tuple = test.model_tester.__class__
return model_tester
def SCREAMING_SNAKE_CASE__ ( snake_case : Dict , snake_case : Optional[Any] ) -> Optional[Any]:
"""simple docstring"""
a : Optional[int] = get_test_classes(snake_case )
a : str = []
for test_class in test_classes:
if model_class in test_class.all_model_classes:
target_test_classes.append(snake_case )
# sort with class names
return sorted(snake_case , key=lambda snake_case : x.__name__ )
def SCREAMING_SNAKE_CASE__ ( snake_case : Optional[Any] , snake_case : int ) -> Optional[int]:
"""simple docstring"""
a : Any = get_test_classes_for_model(snake_case , snake_case )
a : Tuple = []
for test_class in test_classes:
a : Any = get_model_tester_from_test_class(snake_case )
if tester_class is not None:
tester_classes.append(snake_case )
# sort with class names
return sorted(snake_case , key=lambda snake_case : x.__name__ )
def SCREAMING_SNAKE_CASE__ ( snake_case : Optional[int] ) -> str:
"""simple docstring"""
a : Dict = get_test_classes(snake_case )
a : Optional[Any] = {test_class: get_model_tester_from_test_class(snake_case ) for test_class in test_classes}
return test_tester_mapping
def SCREAMING_SNAKE_CASE__ ( snake_case : Optional[int] ) -> Union[str, Any]:
"""simple docstring"""
a : Dict = get_model_classes(snake_case )
a : Optional[Any] = {
model_class: get_test_classes_for_model(snake_case , snake_case ) for model_class in model_classes
}
return model_test_mapping
def SCREAMING_SNAKE_CASE__ ( snake_case : str ) -> Optional[Any]:
"""simple docstring"""
a : str = get_model_classes(snake_case )
a : List[Any] = {
model_class: get_tester_classes_for_model(snake_case , snake_case ) for model_class in model_classes
}
return model_to_tester_mapping
def SCREAMING_SNAKE_CASE__ ( snake_case : Any ) -> Union[str, Any]:
"""simple docstring"""
if isinstance(snake_case , snake_case ):
return o
elif isinstance(snake_case , snake_case ):
return o.__name__
elif isinstance(snake_case , (list, tuple) ):
return [to_json(snake_case ) for x in o]
elif isinstance(snake_case , snake_case ):
return {to_json(snake_case ): to_json(snake_case ) for k, v in o.items()}
else:
return o
| 610 | 0 |
'''simple docstring'''
import math
from typing import Any, Callable, List, Optional, Tuple, Union
import numpy as np
import torch
from ...models import TaFilmDecoder
from ...schedulers import DDPMScheduler
from ...utils import is_onnx_available, logging, randn_tensor
if is_onnx_available():
from ..onnx_utils import OnnxRuntimeModel
from ..pipeline_utils import AudioPipelineOutput, DiffusionPipeline
from .continous_encoder import SpectrogramContEncoder
from .notes_encoder import SpectrogramNotesEncoder
_UpperCAmelCase : Dict = logging.get_logger(__name__) # pylint: disable=invalid-name
_UpperCAmelCase : Any = 2_56
class lowercase_ ( UpperCamelCase_ ):
"""simple docstring"""
__lowerCAmelCase = ['melgan']
def __init__( self : str, UpperCamelCase__ : SpectrogramNotesEncoder, UpperCamelCase__ : SpectrogramContEncoder, UpperCamelCase__ : TaFilmDecoder, UpperCamelCase__ : DDPMScheduler, UpperCamelCase__ : OnnxRuntimeModel if is_onnx_available() else Any, ) -> None:
super().__init__()
# From MELGAN
_A = math.log(1e-5 ) # Matches MelGAN training.
_A = 4.0 # Largest value for most examples
_A = 1_28
self.register_modules(
notes_encoder=lowercase_, continuous_encoder=lowercase_, decoder=lowercase_, scheduler=lowercase_, melgan=lowercase_, )
def __UpperCAmelCase ( self : Optional[Any], UpperCamelCase__ : List[str], UpperCamelCase__ : List[Any]=(-1.0, 1.0), UpperCamelCase__ : Any=False ) -> List[str]:
_A = output_range
if clip:
_A = torch.clip(lowercase_, self.min_value, self.max_value )
# Scale to [0, 1].
_A = (features - self.min_value) / (self.max_value - self.min_value)
# Scale to [min_out, max_out].
return zero_one * (max_out - min_out) + min_out
def __UpperCAmelCase ( self : List[str], UpperCamelCase__ : str, UpperCamelCase__ : int=(-1.0, 1.0), UpperCamelCase__ : List[str]=False ) -> Union[str, Any]:
_A = input_range
_A = torch.clip(lowercase_, lowercase_, lowercase_ ) if clip else outputs
# Scale to [0, 1].
_A = (outputs - min_out) / (max_out - min_out)
# Scale to [self.min_value, self.max_value].
return zero_one * (self.max_value - self.min_value) + self.min_value
def __UpperCAmelCase ( self : Union[str, Any], UpperCamelCase__ : Optional[Any], UpperCamelCase__ : Tuple, UpperCamelCase__ : Union[str, Any] ) -> List[str]:
_A = input_tokens > 0
_A = self.notes_encoder(
encoder_input_tokens=lowercase_, encoder_inputs_mask=lowercase_ )
_A = self.continuous_encoder(
encoder_inputs=lowercase_, encoder_inputs_mask=lowercase_ )
return [(tokens_encoded, tokens_mask), (continuous_encoded, continuous_mask)]
def __UpperCAmelCase ( self : List[str], UpperCamelCase__ : Optional[Any], UpperCamelCase__ : str, UpperCamelCase__ : List[Any] ) -> Dict:
_A = noise_time
if not torch.is_tensor(lowercase_ ):
_A = torch.tensor([timesteps], dtype=torch.long, device=input_tokens.device )
elif torch.is_tensor(lowercase_ ) and len(timesteps.shape ) == 0:
_A = timesteps[None].to(input_tokens.device )
# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
_A = timesteps * torch.ones(input_tokens.shape[0], dtype=timesteps.dtype, device=timesteps.device )
_A = self.decoder(
encodings_and_masks=lowercase_, decoder_input_tokens=lowercase_, decoder_noise_time=lowercase_ )
return logits
@torch.no_grad()
def __call__( self : Dict, UpperCamelCase__ : List[List[int]], UpperCamelCase__ : Optional[torch.Generator] = None, UpperCamelCase__ : int = 1_00, UpperCamelCase__ : bool = True, UpperCamelCase__ : str = "numpy", UpperCamelCase__ : Optional[Callable[[int, int, torch.FloatTensor], None]] = None, UpperCamelCase__ : int = 1, ) -> Union[AudioPipelineOutput, Tuple]:
if (callback_steps is None) or (
callback_steps is not None and (not isinstance(lowercase_, lowercase_ ) or callback_steps <= 0)
):
raise ValueError(
f'`callback_steps` has to be a positive integer but is {callback_steps} of type'
f' {type(lowercase_ )}.' )
_A = np.zeros([1, TARGET_FEATURE_LENGTH, self.n_dims], dtype=np.floataa )
_A = np.zeros([1, 0, self.n_dims], np.floataa )
_A = torch.ones((1, TARGET_FEATURE_LENGTH), dtype=lowercase_, device=self.device )
for i, encoder_input_tokens in enumerate(lowercase_ ):
if i == 0:
_A = torch.from_numpy(pred_mel[:1].copy() ).to(
device=self.device, dtype=self.decoder.dtype )
# The first chunk has no previous context.
_A = torch.zeros((1, TARGET_FEATURE_LENGTH), dtype=lowercase_, device=self.device )
else:
# The full song pipeline does not feed in a context feature, so the mask
# will be all 0s after the feature converter. Because we know we're
# feeding in a full context chunk from the previous prediction, set it
# to all 1s.
_A = ones
_A = self.scale_features(
lowercase_, output_range=[-1.0, 1.0], clip=lowercase_ )
_A = self.encode(
input_tokens=torch.IntTensor([encoder_input_tokens] ).to(device=self.device ), continuous_inputs=lowercase_, continuous_mask=lowercase_, )
# Sample encoder_continuous_inputs shaped gaussian noise to begin loop
_A = randn_tensor(
shape=encoder_continuous_inputs.shape, generator=lowercase_, device=self.device, dtype=self.decoder.dtype, )
# set step values
self.scheduler.set_timesteps(lowercase_ )
# Denoising diffusion loop
for j, t in enumerate(self.progress_bar(self.scheduler.timesteps ) ):
_A = self.decode(
encodings_and_masks=lowercase_, input_tokens=lowercase_, noise_time=t / self.scheduler.config.num_train_timesteps, )
# Compute previous output: x_t -> x_t-1
_A = self.scheduler.step(lowercase_, lowercase_, lowercase_, generator=lowercase_ ).prev_sample
_A = self.scale_to_features(lowercase_, input_range=[-1.0, 1.0] )
_A = mel[:1]
_A = mel.cpu().float().numpy()
_A = np.concatenate([full_pred_mel, pred_mel[:1]], axis=1 )
# call the callback, if provided
if callback is not None and i % callback_steps == 0:
callback(lowercase_, lowercase_ )
logger.info('Generated segment', lowercase_ )
if output_type == "numpy" and not is_onnx_available():
raise ValueError(
'Cannot return output in \'np\' format if ONNX is not available. Make sure to have ONNX installed or set \'output_type\' to \'mel\'.' )
elif output_type == "numpy" and self.melgan is None:
raise ValueError(
'Cannot return output in \'np\' format if melgan component is not defined. Make sure to define `self.melgan` or set \'output_type\' to \'mel\'.' )
if output_type == "numpy":
_A = self.melgan(input_features=full_pred_mel.astype(np.floataa ) )
else:
_A = full_pred_mel
if not return_dict:
return (output,)
return AudioPipelineOutput(audios=lowercase_ )
| 107 |
import json
import os
import sys
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile
from huggingface_hub import HfFolder, Repository, create_repo, delete_repo
from requests.exceptions import HTTPError
import transformers
from transformers import (
CONFIG_MAPPING,
FEATURE_EXTRACTOR_MAPPING,
PROCESSOR_MAPPING,
TOKENIZER_MAPPING,
AutoConfig,
AutoFeatureExtractor,
AutoProcessor,
AutoTokenizer,
BertTokenizer,
ProcessorMixin,
WavaVecaConfig,
WavaVecaFeatureExtractor,
WavaVecaProcessor,
)
from transformers.testing_utils import TOKEN, USER, get_tests_dir, is_staging_test
from transformers.tokenization_utils import TOKENIZER_CONFIG_FILE
from transformers.utils import FEATURE_EXTRACTOR_NAME, is_tokenizers_available
sys.path.append(str(Path(__file__).parent.parent.parent.parent / 'utils'))
from test_module.custom_configuration import CustomConfig # noqa E402
from test_module.custom_feature_extraction import CustomFeatureExtractor # noqa E402
from test_module.custom_processing import CustomProcessor # noqa E402
from test_module.custom_tokenization import CustomTokenizer # noqa E402
lowerCamelCase = get_tests_dir('fixtures/dummy_feature_extractor_config.json')
lowerCamelCase = get_tests_dir('fixtures/vocab.json')
lowerCamelCase = get_tests_dir('fixtures')
class A ( unittest.TestCase ):
UpperCamelCase__ : Dict =['[UNK]', '[CLS]', '[SEP]', '[PAD]', '[MASK]', 'bla', 'blou']
def lowerCamelCase ( self : str ) -> Optional[Any]:
"""simple docstring"""
_lowerCamelCase : Dict =0
def lowerCamelCase ( self : str ) -> List[str]:
"""simple docstring"""
_lowerCamelCase : List[Any] =AutoProcessor.from_pretrained('facebook/wav2vec2-base-960h' )
self.assertIsInstance(lowercase_ , lowercase_ )
def lowerCamelCase ( self : int ) -> Tuple:
"""simple docstring"""
with tempfile.TemporaryDirectory() as tmpdirname:
_lowerCamelCase : int =WavaVecaConfig()
_lowerCamelCase : Dict =AutoProcessor.from_pretrained('facebook/wav2vec2-base-960h' )
# save in new folder
model_config.save_pretrained(lowercase_ )
processor.save_pretrained(lowercase_ )
_lowerCamelCase : Any =AutoProcessor.from_pretrained(lowercase_ )
self.assertIsInstance(lowercase_ , lowercase_ )
def lowerCamelCase ( self : str ) -> Union[str, Any]:
"""simple docstring"""
with tempfile.TemporaryDirectory() as tmpdirname:
# copy relevant files
copyfile(lowercase_ , os.path.join(lowercase_ , lowercase_ ) )
copyfile(lowercase_ , os.path.join(lowercase_ , 'vocab.json' ) )
_lowerCamelCase : Union[str, Any] =AutoProcessor.from_pretrained(lowercase_ )
self.assertIsInstance(lowercase_ , lowercase_ )
def lowerCamelCase ( self : Any ) -> Optional[Any]:
"""simple docstring"""
with tempfile.TemporaryDirectory() as tmpdirname:
_lowerCamelCase : List[Any] =WavaVecaFeatureExtractor()
_lowerCamelCase : List[str] =AutoTokenizer.from_pretrained('facebook/wav2vec2-base-960h' )
_lowerCamelCase : str =WavaVecaProcessor(lowercase_ , lowercase_ )
# save in new folder
processor.save_pretrained(lowercase_ )
# drop `processor_class` in tokenizer
with open(os.path.join(lowercase_ , lowercase_ ) , 'r' ) as f:
_lowerCamelCase : Optional[int] =json.load(lowercase_ )
config_dict.pop('processor_class' )
with open(os.path.join(lowercase_ , lowercase_ ) , 'w' ) as f:
f.write(json.dumps(lowercase_ ) )
_lowerCamelCase : Optional[int] =AutoProcessor.from_pretrained(lowercase_ )
self.assertIsInstance(lowercase_ , lowercase_ )
def lowerCamelCase ( self : Dict ) -> Optional[int]:
"""simple docstring"""
with tempfile.TemporaryDirectory() as tmpdirname:
_lowerCamelCase : Optional[Any] =WavaVecaFeatureExtractor()
_lowerCamelCase : Tuple =AutoTokenizer.from_pretrained('facebook/wav2vec2-base-960h' )
_lowerCamelCase : Dict =WavaVecaProcessor(lowercase_ , lowercase_ )
# save in new folder
processor.save_pretrained(lowercase_ )
# drop `processor_class` in feature extractor
with open(os.path.join(lowercase_ , lowercase_ ) , 'r' ) as f:
_lowerCamelCase : Union[str, Any] =json.load(lowercase_ )
config_dict.pop('processor_class' )
with open(os.path.join(lowercase_ , lowercase_ ) , 'w' ) as f:
f.write(json.dumps(lowercase_ ) )
_lowerCamelCase : Optional[int] =AutoProcessor.from_pretrained(lowercase_ )
self.assertIsInstance(lowercase_ , lowercase_ )
def lowerCamelCase ( self : List[str] ) -> List[str]:
"""simple docstring"""
with tempfile.TemporaryDirectory() as tmpdirname:
_lowerCamelCase : Optional[Any] =WavaVecaConfig(processor_class='Wav2Vec2Processor' )
model_config.save_pretrained(lowercase_ )
# copy relevant files
copyfile(lowercase_ , os.path.join(lowercase_ , 'vocab.json' ) )
# create emtpy sample processor
with open(os.path.join(lowercase_ , lowercase_ ) , 'w' ) as f:
f.write('{}' )
_lowerCamelCase : int =AutoProcessor.from_pretrained(lowercase_ )
self.assertIsInstance(lowercase_ , lowercase_ )
def lowerCamelCase ( self : str ) -> Optional[int]:
"""simple docstring"""
with self.assertRaises(lowercase_ ):
_lowerCamelCase : int =AutoProcessor.from_pretrained('hf-internal-testing/test_dynamic_processor' )
# If remote code is disabled, we can't load this config.
with self.assertRaises(lowercase_ ):
_lowerCamelCase : Union[str, Any] =AutoProcessor.from_pretrained(
'hf-internal-testing/test_dynamic_processor' , trust_remote_code=lowercase_ )
_lowerCamelCase : List[str] =AutoProcessor.from_pretrained('hf-internal-testing/test_dynamic_processor' , trust_remote_code=lowercase_ )
self.assertTrue(processor.special_attribute_present )
self.assertEqual(processor.__class__.__name__ , 'NewProcessor' )
_lowerCamelCase : int =processor.feature_extractor
self.assertTrue(feature_extractor.special_attribute_present )
self.assertEqual(feature_extractor.__class__.__name__ , 'NewFeatureExtractor' )
_lowerCamelCase : Optional[int] =processor.tokenizer
self.assertTrue(tokenizer.special_attribute_present )
if is_tokenizers_available():
self.assertEqual(tokenizer.__class__.__name__ , 'NewTokenizerFast' )
# Test we can also load the slow version
_lowerCamelCase : int =AutoProcessor.from_pretrained(
'hf-internal-testing/test_dynamic_processor' , trust_remote_code=lowercase_ , use_fast=lowercase_ )
_lowerCamelCase : Optional[int] =new_processor.tokenizer
self.assertTrue(new_tokenizer.special_attribute_present )
self.assertEqual(new_tokenizer.__class__.__name__ , 'NewTokenizer' )
else:
self.assertEqual(tokenizer.__class__.__name__ , 'NewTokenizer' )
def lowerCamelCase ( self : Optional[int] ) -> Optional[int]:
"""simple docstring"""
try:
AutoConfig.register('custom' , lowercase_ )
AutoFeatureExtractor.register(lowercase_ , lowercase_ )
AutoTokenizer.register(lowercase_ , slow_tokenizer_class=lowercase_ )
AutoProcessor.register(lowercase_ , lowercase_ )
# Trying to register something existing in the Transformers library will raise an error
with self.assertRaises(lowercase_ ):
AutoProcessor.register(lowercase_ , lowercase_ )
# Now that the config is registered, it can be used as any other config with the auto-API
_lowerCamelCase : str =CustomFeatureExtractor.from_pretrained(lowercase_ )
with tempfile.TemporaryDirectory() as tmp_dir:
_lowerCamelCase : str =os.path.join(lowercase_ , 'vocab.txt' )
with open(lowercase_ , 'w' , encoding='utf-8' ) as vocab_writer:
vocab_writer.write(''.join([x + '\n' for x in self.vocab_tokens] ) )
_lowerCamelCase : List[Any] =CustomTokenizer(lowercase_ )
_lowerCamelCase : Optional[int] =CustomProcessor(lowercase_ , lowercase_ )
with tempfile.TemporaryDirectory() as tmp_dir:
processor.save_pretrained(lowercase_ )
_lowerCamelCase : List[Any] =AutoProcessor.from_pretrained(lowercase_ )
self.assertIsInstance(lowercase_ , lowercase_ )
finally:
if "custom" in CONFIG_MAPPING._extra_content:
del CONFIG_MAPPING._extra_content["custom"]
if CustomConfig in FEATURE_EXTRACTOR_MAPPING._extra_content:
del FEATURE_EXTRACTOR_MAPPING._extra_content[CustomConfig]
if CustomConfig in TOKENIZER_MAPPING._extra_content:
del TOKENIZER_MAPPING._extra_content[CustomConfig]
if CustomConfig in PROCESSOR_MAPPING._extra_content:
del PROCESSOR_MAPPING._extra_content[CustomConfig]
def lowerCamelCase ( self : Union[str, Any] ) -> List[str]:
"""simple docstring"""
class A ( UpperCamelCase_ ):
UpperCamelCase__ : Optional[Any] =False
class A ( UpperCamelCase_ ):
UpperCamelCase__ : int =False
class A ( UpperCamelCase_ ):
UpperCamelCase__ : Union[str, Any] ='AutoFeatureExtractor'
UpperCamelCase__ : str ='AutoTokenizer'
UpperCamelCase__ : List[Any] =False
try:
AutoConfig.register('custom' , lowercase_ )
AutoFeatureExtractor.register(lowercase_ , lowercase_ )
AutoTokenizer.register(lowercase_ , slow_tokenizer_class=lowercase_ )
AutoProcessor.register(lowercase_ , lowercase_ )
# If remote code is not set, the default is to use local classes.
_lowerCamelCase : int =AutoProcessor.from_pretrained('hf-internal-testing/test_dynamic_processor' )
self.assertEqual(processor.__class__.__name__ , 'NewProcessor' )
self.assertFalse(processor.special_attribute_present )
self.assertFalse(processor.feature_extractor.special_attribute_present )
self.assertFalse(processor.tokenizer.special_attribute_present )
# If remote code is disabled, we load the local ones.
_lowerCamelCase : int =AutoProcessor.from_pretrained(
'hf-internal-testing/test_dynamic_processor' , trust_remote_code=lowercase_ )
self.assertEqual(processor.__class__.__name__ , 'NewProcessor' )
self.assertFalse(processor.special_attribute_present )
self.assertFalse(processor.feature_extractor.special_attribute_present )
self.assertFalse(processor.tokenizer.special_attribute_present )
# If remote is enabled, we load from the Hub.
_lowerCamelCase : str =AutoProcessor.from_pretrained(
'hf-internal-testing/test_dynamic_processor' , trust_remote_code=lowercase_ )
self.assertEqual(processor.__class__.__name__ , 'NewProcessor' )
self.assertTrue(processor.special_attribute_present )
self.assertTrue(processor.feature_extractor.special_attribute_present )
self.assertTrue(processor.tokenizer.special_attribute_present )
finally:
if "custom" in CONFIG_MAPPING._extra_content:
del CONFIG_MAPPING._extra_content["custom"]
if CustomConfig in FEATURE_EXTRACTOR_MAPPING._extra_content:
del FEATURE_EXTRACTOR_MAPPING._extra_content[CustomConfig]
if CustomConfig in TOKENIZER_MAPPING._extra_content:
del TOKENIZER_MAPPING._extra_content[CustomConfig]
if CustomConfig in PROCESSOR_MAPPING._extra_content:
del PROCESSOR_MAPPING._extra_content[CustomConfig]
def lowerCamelCase ( self : Optional[int] ) -> str:
"""simple docstring"""
_lowerCamelCase : List[Any] =AutoProcessor.from_pretrained('hf-internal-testing/tiny-random-bert' )
self.assertEqual(processor.__class__.__name__ , 'BertTokenizerFast' )
def lowerCamelCase ( self : Any ) -> Dict:
"""simple docstring"""
_lowerCamelCase : Any =AutoProcessor.from_pretrained('hf-internal-testing/tiny-random-convnext' )
self.assertEqual(processor.__class__.__name__ , 'ConvNextImageProcessor' )
@is_staging_test
class A ( unittest.TestCase ):
UpperCamelCase__ : List[Any] =['[UNK]', '[CLS]', '[SEP]', '[PAD]', '[MASK]', 'bla', 'blou']
@classmethod
def lowerCamelCase ( cls : int ) -> Optional[Any]:
"""simple docstring"""
_lowerCamelCase : Union[str, Any] =TOKEN
HfFolder.save_token(lowercase_ )
@classmethod
def lowerCamelCase ( cls : Optional[int] ) -> Union[str, Any]:
"""simple docstring"""
try:
delete_repo(token=cls._token , repo_id='test-processor' )
except HTTPError:
pass
try:
delete_repo(token=cls._token , repo_id='valid_org/test-processor-org' )
except HTTPError:
pass
try:
delete_repo(token=cls._token , repo_id='test-dynamic-processor' )
except HTTPError:
pass
def lowerCamelCase ( self : str ) -> int:
"""simple docstring"""
_lowerCamelCase : Tuple =WavaVecaProcessor.from_pretrained(lowercase_ )
with tempfile.TemporaryDirectory() as tmp_dir:
processor.save_pretrained(
os.path.join(lowercase_ , 'test-processor' ) , push_to_hub=lowercase_ , use_auth_token=self._token )
_lowerCamelCase : Union[str, Any] =WavaVecaProcessor.from_pretrained(F'''{USER}/test-processor''' )
for k, v in processor.feature_extractor.__dict__.items():
self.assertEqual(lowercase_ , getattr(new_processor.feature_extractor , lowercase_ ) )
self.assertDictEqual(new_processor.tokenizer.get_vocab() , processor.tokenizer.get_vocab() )
def lowerCamelCase ( self : str ) -> Tuple:
"""simple docstring"""
_lowerCamelCase : int =WavaVecaProcessor.from_pretrained(lowercase_ )
with tempfile.TemporaryDirectory() as tmp_dir:
processor.save_pretrained(
os.path.join(lowercase_ , 'test-processor-org' ) , push_to_hub=lowercase_ , use_auth_token=self._token , organization='valid_org' , )
_lowerCamelCase : str =WavaVecaProcessor.from_pretrained('valid_org/test-processor-org' )
for k, v in processor.feature_extractor.__dict__.items():
self.assertEqual(lowercase_ , getattr(new_processor.feature_extractor , lowercase_ ) )
self.assertDictEqual(new_processor.tokenizer.get_vocab() , processor.tokenizer.get_vocab() )
def lowerCamelCase ( self : List[Any] ) -> str:
"""simple docstring"""
CustomFeatureExtractor.register_for_auto_class()
CustomTokenizer.register_for_auto_class()
CustomProcessor.register_for_auto_class()
_lowerCamelCase : Optional[Any] =CustomFeatureExtractor.from_pretrained(lowercase_ )
with tempfile.TemporaryDirectory() as tmp_dir:
_lowerCamelCase : Dict =os.path.join(lowercase_ , 'vocab.txt' )
with open(lowercase_ , 'w' , encoding='utf-8' ) as vocab_writer:
vocab_writer.write(''.join([x + '\n' for x in self.vocab_tokens] ) )
_lowerCamelCase : Any =CustomTokenizer(lowercase_ )
_lowerCamelCase : List[Any] =CustomProcessor(lowercase_ , lowercase_ )
with tempfile.TemporaryDirectory() as tmp_dir:
create_repo(F'''{USER}/test-dynamic-processor''' , token=self._token )
_lowerCamelCase : List[str] =Repository(lowercase_ , clone_from=F'''{USER}/test-dynamic-processor''' , token=self._token )
processor.save_pretrained(lowercase_ )
# This has added the proper auto_map field to the feature extractor config
self.assertDictEqual(
processor.feature_extractor.auto_map , {
'AutoFeatureExtractor': 'custom_feature_extraction.CustomFeatureExtractor',
'AutoProcessor': 'custom_processing.CustomProcessor',
} , )
# This has added the proper auto_map field to the tokenizer config
with open(os.path.join(lowercase_ , 'tokenizer_config.json' ) ) as f:
_lowerCamelCase : Union[str, Any] =json.load(lowercase_ )
self.assertDictEqual(
tokenizer_config['auto_map'] , {
'AutoTokenizer': ['custom_tokenization.CustomTokenizer', None],
'AutoProcessor': 'custom_processing.CustomProcessor',
} , )
# The code has been copied from fixtures
self.assertTrue(os.path.isfile(os.path.join(lowercase_ , 'custom_feature_extraction.py' ) ) )
self.assertTrue(os.path.isfile(os.path.join(lowercase_ , 'custom_tokenization.py' ) ) )
self.assertTrue(os.path.isfile(os.path.join(lowercase_ , 'custom_processing.py' ) ) )
repo.push_to_hub()
_lowerCamelCase : Tuple =AutoProcessor.from_pretrained(F'''{USER}/test-dynamic-processor''' , trust_remote_code=lowercase_ )
# Can't make an isinstance check because the new_processor is from the CustomProcessor class of a dynamic module
self.assertEqual(new_processor.__class__.__name__ , 'CustomProcessor' )
| 464 | 0 |
def _A( UpperCamelCase__ : int ) -> bool:
'''simple docstring'''
if num < 0:
return False
__lowercase = num
__lowercase = 0
while num > 0:
__lowercase = rev_num * 10 + (num % 10)
num //= 10
return num_copy == rev_num
if __name__ == "__main__":
import doctest
doctest.testmod()
| 362 |
import unittest
import numpy as np
import timeout_decorator # noqa
from transformers import BlenderbotConfig, is_flax_available
from transformers.testing_utils import jax_device, require_flax, slow
from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor
if is_flax_available():
import os
# The slow tests are often failing with OOM error on GPU
# This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
# but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
UpperCAmelCase__ = "platform"
import jax
import jax.numpy as jnp
from transformers import BlenderbotTokenizer
from transformers.models.blenderbot.modeling_flax_blenderbot import (
FlaxBlenderbotForConditionalGeneration,
FlaxBlenderbotModel,
shift_tokens_right,
)
def _A( UpperCamelCase__ : Tuple , UpperCamelCase__ : Optional[int] , UpperCamelCase__ : Dict=None , UpperCamelCase__ : Optional[int]=None , UpperCamelCase__ : int=None , UpperCamelCase__ : Union[str, Any]=None , UpperCamelCase__ : Optional[Any]=None , UpperCamelCase__ : List[str]=None , ) -> int:
'''simple docstring'''
if attention_mask is None:
__lowercase = np.where(input_ids != config.pad_token_id , 1 , 0 )
if decoder_attention_mask is None:
__lowercase = np.where(decoder_input_ids != config.pad_token_id , 1 , 0 )
if head_mask is None:
__lowercase = np.ones((config.encoder_layers, config.encoder_attention_heads) )
if decoder_head_mask is None:
__lowercase = np.ones((config.decoder_layers, config.decoder_attention_heads) )
if cross_attn_head_mask is None:
__lowercase = np.ones((config.decoder_layers, config.decoder_attention_heads) )
return {
"input_ids": input_ids,
"decoder_input_ids": decoder_input_ids,
"attention_mask": attention_mask,
"decoder_attention_mask": attention_mask,
}
class a :
"""simple docstring"""
def __init__( self : Dict , lowerCamelCase__ : Dict , lowerCamelCase__ : Union[str, Any]=13 , lowerCamelCase__ : Union[str, Any]=7 , lowerCamelCase__ : Dict=True , lowerCamelCase__ : int=False , lowerCamelCase__ : Any=99 , lowerCamelCase__ : str=16 , lowerCamelCase__ : Union[str, Any]=2 , lowerCamelCase__ : Union[str, Any]=4 , lowerCamelCase__ : Tuple=4 , lowerCamelCase__ : Optional[Any]="gelu" , lowerCamelCase__ : int=0.1 , lowerCamelCase__ : Tuple=0.1 , lowerCamelCase__ : Any=32 , lowerCamelCase__ : List[str]=2 , lowerCamelCase__ : Optional[Any]=1 , lowerCamelCase__ : List[Any]=0 , lowerCamelCase__ : int=0.0_2 , ) -> Optional[int]:
"""simple docstring"""
__lowercase = parent
__lowercase = batch_size
__lowercase = seq_length
__lowercase = is_training
__lowercase = use_labels
__lowercase = vocab_size
__lowercase = hidden_size
__lowercase = num_hidden_layers
__lowercase = num_attention_heads
__lowercase = intermediate_size
__lowercase = hidden_act
__lowercase = hidden_dropout_prob
__lowercase = attention_probs_dropout_prob
__lowercase = max_position_embeddings
__lowercase = eos_token_id
__lowercase = pad_token_id
__lowercase = bos_token_id
__lowercase = initializer_range
def UpperCAmelCase_ ( self : int ) -> Any:
"""simple docstring"""
__lowercase = np.clip(ids_tensor([self.batch_size, self.seq_length - 1] , self.vocab_size ) , 3 , self.vocab_size )
__lowercase = np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1) , dtype=np.intaa )) , -1 )
__lowercase = shift_tokens_right(lowerCamelCase__ , 1 , 2 )
__lowercase = BlenderbotConfig(
vocab_size=self.vocab_size , d_model=self.hidden_size , encoder_layers=self.num_hidden_layers , decoder_layers=self.num_hidden_layers , encoder_attention_heads=self.num_attention_heads , decoder_attention_heads=self.num_attention_heads , encoder_ffn_dim=self.intermediate_size , decoder_ffn_dim=self.intermediate_size , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , eos_token_id=self.eos_token_id , bos_token_id=self.bos_token_id , pad_token_id=self.pad_token_id , initializer_range=self.initializer_range , use_cache=lowerCamelCase__ , )
__lowercase = prepare_blenderbot_inputs_dict(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ )
return config, inputs_dict
def UpperCAmelCase_ ( self : Dict ) -> Optional[int]:
"""simple docstring"""
__lowercase , __lowercase = self.prepare_config_and_inputs()
return config, inputs_dict
def UpperCAmelCase_ ( self : Optional[Any] , lowerCamelCase__ : str , lowerCamelCase__ : List[str] , lowerCamelCase__ : Tuple ) -> Any:
"""simple docstring"""
__lowercase = 20
__lowercase = model_class_name(lowerCamelCase__ )
__lowercase = model.encode(inputs_dict['''input_ids'''] )
__lowercase , __lowercase = (
inputs_dict['''decoder_input_ids'''],
inputs_dict['''decoder_attention_mask'''],
)
__lowercase = model.init_cache(decoder_input_ids.shape[0] , lowerCamelCase__ , lowerCamelCase__ )
__lowercase = jnp.ones((decoder_input_ids.shape[0], max_decoder_length) , dtype='''i4''' )
__lowercase = jnp.broadcast_to(
jnp.arange(decoder_input_ids.shape[-1] - 1 )[None, :] , (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1) , )
__lowercase = model.decode(
decoder_input_ids[:, :-1] , lowerCamelCase__ , decoder_attention_mask=lowerCamelCase__ , past_key_values=lowerCamelCase__ , decoder_position_ids=lowerCamelCase__ , )
__lowercase = jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]] , dtype='''i4''' )
__lowercase = model.decode(
decoder_input_ids[:, -1:] , lowerCamelCase__ , decoder_attention_mask=lowerCamelCase__ , past_key_values=outputs_cache.past_key_values , decoder_position_ids=lowerCamelCase__ , )
__lowercase = model.decode(lowerCamelCase__ , lowerCamelCase__ )
__lowercase = np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5]) ) )
self.parent.assertTrue(diff < 1e-3 , msg=f'Max diff is {diff}' )
def UpperCAmelCase_ ( self : int , lowerCamelCase__ : Optional[Any] , lowerCamelCase__ : str , lowerCamelCase__ : List[Any] ) -> Optional[Any]:
"""simple docstring"""
__lowercase = 20
__lowercase = model_class_name(lowerCamelCase__ )
__lowercase = model.encode(inputs_dict['''input_ids'''] )
__lowercase , __lowercase = (
inputs_dict['''decoder_input_ids'''],
inputs_dict['''decoder_attention_mask'''],
)
__lowercase = jnp.concatenate(
[
decoder_attention_mask,
jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1]) ),
] , axis=-1 , )
__lowercase = model.init_cache(decoder_input_ids.shape[0] , lowerCamelCase__ , lowerCamelCase__ )
__lowercase = jnp.broadcast_to(
jnp.arange(decoder_input_ids.shape[-1] - 1 )[None, :] , (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1) , )
__lowercase = model.decode(
decoder_input_ids[:, :-1] , lowerCamelCase__ , decoder_attention_mask=lowerCamelCase__ , past_key_values=lowerCamelCase__ , decoder_position_ids=lowerCamelCase__ , )
__lowercase = jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]] , dtype='''i4''' )
__lowercase = model.decode(
decoder_input_ids[:, -1:] , lowerCamelCase__ , past_key_values=outputs_cache.past_key_values , decoder_attention_mask=lowerCamelCase__ , decoder_position_ids=lowerCamelCase__ , )
__lowercase = model.decode(lowerCamelCase__ , lowerCamelCase__ , decoder_attention_mask=lowerCamelCase__ )
__lowercase = np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5]) ) )
self.parent.assertTrue(diff < 1e-3 , msg=f'Max diff is {diff}' )
@require_flax
class a ( unittest.TestCase ):
"""simple docstring"""
UpperCamelCase_ : List[Any] = 99
def UpperCAmelCase_ ( self : List[Any] ) -> Dict:
"""simple docstring"""
__lowercase = np.array(
[
[71, 82, 18, 33, 46, 91, 2],
[68, 34, 26, 58, 30, 82, 2],
[5, 97, 17, 39, 94, 40, 2],
[76, 83, 94, 25, 70, 78, 2],
[87, 59, 41, 35, 48, 66, 2],
[55, 13, 16, 58, 5, 2, 1], # note padding
[64, 27, 31, 51, 12, 75, 2],
[52, 64, 86, 17, 83, 39, 2],
[48, 61, 9, 24, 71, 82, 2],
[26, 1, 60, 48, 22, 13, 2],
[21, 5, 62, 28, 14, 76, 2],
[45, 98, 37, 86, 59, 48, 2],
[70, 70, 50, 9, 28, 0, 2],
] , dtype=np.intaa , )
__lowercase = input_ids.shape[0]
__lowercase = BlenderbotConfig(
vocab_size=self.vocab_size , d_model=24 , encoder_layers=2 , decoder_layers=2 , encoder_attention_heads=2 , decoder_attention_heads=2 , encoder_ffn_dim=32 , decoder_ffn_dim=32 , max_position_embeddings=48 , eos_token_id=2 , pad_token_id=1 , bos_token_id=0 , )
return config, input_ids, batch_size
def UpperCAmelCase_ ( self : Tuple ) -> Optional[int]:
"""simple docstring"""
__lowercase , __lowercase , __lowercase = self._get_config_and_data()
__lowercase = FlaxBlenderbotForConditionalGeneration(lowerCamelCase__ )
__lowercase = lm_model(input_ids=lowerCamelCase__ )
__lowercase = (batch_size, input_ids.shape[1], config.vocab_size)
self.assertEqual(outputs['''logits'''].shape , lowerCamelCase__ )
def UpperCAmelCase_ ( self : Union[str, Any] ) -> List[Any]:
"""simple docstring"""
__lowercase = BlenderbotConfig(
vocab_size=self.vocab_size , d_model=14 , encoder_layers=2 , decoder_layers=2 , encoder_attention_heads=2 , decoder_attention_heads=2 , encoder_ffn_dim=8 , decoder_ffn_dim=8 , max_position_embeddings=48 , )
__lowercase = FlaxBlenderbotForConditionalGeneration(lowerCamelCase__ )
__lowercase = np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]] , dtype=np.intaa )
__lowercase = np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]] , dtype=np.intaa )
__lowercase = lm_model(input_ids=lowerCamelCase__ , decoder_input_ids=lowerCamelCase__ )
__lowercase = (*summary.shape, config.vocab_size)
self.assertEqual(outputs['''logits'''].shape , lowerCamelCase__ )
def UpperCAmelCase_ ( self : List[str] ) -> List[Any]:
"""simple docstring"""
__lowercase = np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]] , dtype=np.intaa )
__lowercase = shift_tokens_right(lowerCamelCase__ , 1 , 2 )
__lowercase = np.equal(lowerCamelCase__ , 1 ).astype(np.floataa ).sum()
__lowercase = np.equal(lowerCamelCase__ , 1 ).astype(np.floataa ).sum()
self.assertEqual(shifted.shape , input_ids.shape )
self.assertEqual(lowerCamelCase__ , n_pad_before - 1 )
self.assertTrue(np.equal(shifted[:, 0] , 2 ).all() )
@require_flax
class a ( __SCREAMING_SNAKE_CASE , unittest.TestCase , __SCREAMING_SNAKE_CASE ):
"""simple docstring"""
UpperCamelCase_ : Optional[Any] = True
UpperCamelCase_ : List[str] = (
(
FlaxBlenderbotModel,
FlaxBlenderbotForConditionalGeneration,
)
if is_flax_available()
else ()
)
UpperCamelCase_ : Union[str, Any] = (FlaxBlenderbotForConditionalGeneration,) if is_flax_available() else ()
def UpperCAmelCase_ ( self : Optional[int] ) -> List[str]:
"""simple docstring"""
__lowercase = FlaxBlenderbotModelTester(self )
def UpperCAmelCase_ ( self : Any ) -> List[str]:
"""simple docstring"""
__lowercase , __lowercase = self.model_tester.prepare_config_and_inputs()
for model_class in self.all_model_classes:
self.model_tester.check_use_cache_forward(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ )
def UpperCAmelCase_ ( self : Optional[Any] ) -> Optional[Any]:
"""simple docstring"""
__lowercase , __lowercase = self.model_tester.prepare_config_and_inputs()
for model_class in self.all_model_classes:
self.model_tester.check_use_cache_forward_with_attn_mask(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ )
def UpperCAmelCase_ ( self : List[str] ) -> int:
"""simple docstring"""
__lowercase , __lowercase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__ ):
__lowercase = self._prepare_for_class(lowerCamelCase__ , lowerCamelCase__ )
__lowercase = model_class(lowerCamelCase__ )
@jax.jit
def encode_jitted(lowerCamelCase__ : List[str] , lowerCamelCase__ : Any=None , **lowerCamelCase__ : Dict ):
return model.encode(input_ids=lowerCamelCase__ , attention_mask=lowerCamelCase__ )
with self.subTest('''JIT Enabled''' ):
__lowercase = encode_jitted(**lowerCamelCase__ ).to_tuple()
with self.subTest('''JIT Disabled''' ):
with jax.disable_jit():
__lowercase = encode_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_ ( self : Dict ) -> Optional[Any]:
"""simple docstring"""
__lowercase , __lowercase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__ ):
__lowercase = model_class(lowerCamelCase__ )
__lowercase = model.encode(inputs_dict['''input_ids'''] , inputs_dict['''attention_mask'''] )
__lowercase = {
'''decoder_input_ids''': inputs_dict['''decoder_input_ids'''],
'''decoder_attention_mask''': inputs_dict['''decoder_attention_mask'''],
'''encoder_outputs''': encoder_outputs,
}
@jax.jit
def decode_jitted(lowerCamelCase__ : List[str] , lowerCamelCase__ : List[str] , lowerCamelCase__ : Tuple ):
return model.decode(
decoder_input_ids=lowerCamelCase__ , decoder_attention_mask=lowerCamelCase__ , encoder_outputs=lowerCamelCase__ , )
with self.subTest('''JIT Enabled''' ):
__lowercase = decode_jitted(**lowerCamelCase__ ).to_tuple()
with self.subTest('''JIT Disabled''' ):
with jax.disable_jit():
__lowercase = decode_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 )
@slow
def UpperCAmelCase_ ( self : int ) -> Union[str, Any]:
"""simple docstring"""
for model_class_name in self.all_model_classes:
__lowercase = model_class_name.from_pretrained('''facebook/blenderbot-400M-distill''' )
# FlaxBlenderbotForSequenceClassification expects eos token in input_ids
__lowercase = np.ones((1, 1) ) * model.config.eos_token_id
__lowercase = model(lowerCamelCase__ )
self.assertIsNotNone(lowerCamelCase__ )
@unittest.skipUnless(jax_device != '''cpu''' , '''3B test too slow on CPU.''' )
@slow
def UpperCAmelCase_ ( self : Tuple ) -> List[str]:
"""simple docstring"""
__lowercase = {'''num_beams''': 1, '''early_stopping''': True, '''min_length''': 15, '''max_length''': 25}
__lowercase = {'''skip_special_tokens''': True, '''clean_up_tokenization_spaces''': True}
__lowercase = FlaxBlenderbotForConditionalGeneration.from_pretrained('''facebook/blenderbot-3B''' , from_pt=lowerCamelCase__ )
__lowercase = BlenderbotTokenizer.from_pretrained('''facebook/blenderbot-3B''' )
__lowercase = ['''Sam''']
__lowercase = tokenizer(lowerCamelCase__ , return_tensors='''jax''' )
__lowercase = model.generate(**lowerCamelCase__ , **lowerCamelCase__ )
__lowercase = '''Sam is a great name. It means "sun" in Gaelic.'''
__lowercase = tokenizer.batch_decode(lowerCamelCase__ , **lowerCamelCase__ )
assert generated_txt[0].strip() == tgt_text
| 362 | 1 |
import argparse
import collections
import os
import re
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_table.py
_lowerCamelCase : Tuple = """src/transformers"""
_lowerCamelCase : Any = """docs/source/en"""
_lowerCamelCase : Dict = """."""
def SCREAMING_SNAKE_CASE ( lowercase_ , lowercase_ , lowercase_ ) -> Any:
"""simple docstring"""
with open(lowercase_ , '''r''' , encoding='''utf-8''' , newline='''\n''' ) as f:
A__ = f.readlines()
# Find the start prompt.
A__ = 0
while not lines[start_index].startswith(lowercase_ ):
start_index += 1
start_index += 1
A__ = start_index
while not lines[end_index].startswith(lowercase_ ):
end_index += 1
end_index -= 1
while len(lines[start_index] ) <= 1:
start_index += 1
while len(lines[end_index] ) <= 1:
end_index -= 1
end_index += 1
return "".join(lines[start_index:end_index] ), start_index, end_index, lines
# Add here suffixes that are used to identify models, separated by |
_lowerCamelCase : Optional[Any] = """Model|Encoder|Decoder|ForConditionalGeneration"""
# Regexes that match TF/Flax/PT model names.
_lowerCamelCase : Optional[Any] = re.compile(r"""TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)""")
_lowerCamelCase : Union[str, Any] = re.compile(r"""Flax(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)""")
# Will match any TF or Flax model too so need to be in an else branch afterthe two previous regexes.
_lowerCamelCase : str = re.compile(r"""(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)""")
# This is to make sure the transformers module imported is the one in the repo.
_lowerCamelCase : str = direct_transformers_import(TRANSFORMERS_PATH)
def SCREAMING_SNAKE_CASE ( lowercase_ ) -> Union[str, Any]:
"""simple docstring"""
A__ = re.finditer('''.+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)''' , lowercase_ )
return [m.group(0 ) for m in matches]
def SCREAMING_SNAKE_CASE ( lowercase_ , lowercase_ ) -> Optional[int]:
"""simple docstring"""
A__ = 2 if text == '''✅''' or text == '''❌''' else len(lowercase_ )
A__ = (width - text_length) // 2
A__ = width - text_length - left_indent
return " " * left_indent + text + " " * right_indent
def SCREAMING_SNAKE_CASE ( ) -> str:
"""simple docstring"""
A__ = transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
A__ = {
name: config_maping_names[code]
for code, name in transformers_module.MODEL_NAMES_MAPPING.items()
if code in config_maping_names
}
A__ = {name: config.replace('''Config''' , '''''' ) for name, config in model_name_to_config.items()}
# Dictionaries flagging if each model prefix has a slow/fast tokenizer, backend in PT/TF/Flax.
A__ = collections.defaultdict(lowercase_ )
A__ = collections.defaultdict(lowercase_ )
A__ = collections.defaultdict(lowercase_ )
A__ = collections.defaultdict(lowercase_ )
A__ = collections.defaultdict(lowercase_ )
# Let's lookup through all transformers object (once).
for attr_name in dir(lowercase_ ):
A__ = None
if attr_name.endswith('''Tokenizer''' ):
A__ = slow_tokenizers
A__ = attr_name[:-9]
elif attr_name.endswith('''TokenizerFast''' ):
A__ = fast_tokenizers
A__ = attr_name[:-13]
elif _re_tf_models.match(lowercase_ ) is not None:
A__ = tf_models
A__ = _re_tf_models.match(lowercase_ ).groups()[0]
elif _re_flax_models.match(lowercase_ ) is not None:
A__ = flax_models
A__ = _re_flax_models.match(lowercase_ ).groups()[0]
elif _re_pt_models.match(lowercase_ ) is not None:
A__ = pt_models
A__ = _re_pt_models.match(lowercase_ ).groups()[0]
if lookup_dict is not None:
while len(lowercase_ ) > 0:
if attr_name in model_name_to_prefix.values():
A__ = True
break
# Try again after removing the last word in the name
A__ = ''''''.join(camel_case_split(lowercase_ )[:-1] )
# Let's build that table!
A__ = list(model_name_to_config.keys() )
model_names.sort(key=str.lower )
A__ = ['''Model''', '''Tokenizer slow''', '''Tokenizer fast''', '''PyTorch support''', '''TensorFlow support''', '''Flax Support''']
# We'll need widths to properly display everything in the center (+2 is to leave one extra space on each side).
A__ = [len(lowercase_ ) + 2 for c in columns]
A__ = max([len(lowercase_ ) for name in model_names] ) + 2
# Build the table per se
A__ = '''|''' + '''|'''.join([_center_text(lowercase_ , lowercase_ ) for c, w in zip(lowercase_ , lowercase_ )] ) + '''|\n'''
# Use ":-----:" format to center-aligned table cell texts
table += "|" + "|".join([''':''' + '''-''' * (w - 2) + ''':''' for w in widths] ) + "|\n"
A__ = {True: '''✅''', False: '''❌'''}
for name in model_names:
A__ = model_name_to_prefix[name]
A__ = [
name,
check[slow_tokenizers[prefix]],
check[fast_tokenizers[prefix]],
check[pt_models[prefix]],
check[tf_models[prefix]],
check[flax_models[prefix]],
]
table += "|" + "|".join([_center_text(lowercase_ , lowercase_ ) for l, w in zip(lowercase_ , lowercase_ )] ) + "|\n"
return table
def SCREAMING_SNAKE_CASE ( lowercase_=False ) -> Optional[int]:
"""simple docstring"""
A__ , A__ , A__ , A__ = _find_text_in_file(
filename=os.path.join(lowercase_ , '''index.md''' ) , start_prompt='''<!--This table is updated automatically from the auto modules''' , end_prompt='''<!-- End table-->''' , )
A__ = get_model_table_from_auto_modules()
if current_table != new_table:
if overwrite:
with open(os.path.join(lowercase_ , '''index.md''' ) , '''w''' , encoding='''utf-8''' , newline='''\n''' ) as f:
f.writelines(lines[:start_index] + [new_table] + lines[end_index:] )
else:
raise ValueError(
'''The model table in the `index.md` has not been updated. Run `make fix-copies` to fix this.''' )
if __name__ == "__main__":
_lowerCamelCase : Optional[Any] = argparse.ArgumentParser()
parser.add_argument("""--fix_and_overwrite""", action="""store_true""", help="""Whether to fix inconsistencies.""")
_lowerCamelCase : Optional[Any] = parser.parse_args()
check_model_table(args.fix_and_overwrite)
| 87 | """simple docstring"""
import argparse
import re
import torch
from CLAP import create_model
from transformers import AutoFeatureExtractor, ClapConfig, ClapModel
__magic_name__ = {
"text_branch": "text_model",
"audio_branch": "audio_model.audio_encoder",
"attn": "attention.self",
"self.proj": "output.dense",
"attention.self_mask": "attn_mask",
"mlp.fc1": "intermediate.dense",
"mlp.fc2": "output.dense",
"norm1": "layernorm_before",
"norm2": "layernorm_after",
"bn0": "batch_norm",
}
__magic_name__ = AutoFeatureExtractor.from_pretrained("laion/clap-htsat-unfused", truncation="rand_trunc")
def _lowerCamelCase ( UpperCAmelCase__,UpperCAmelCase__=False ) -> Union[str, Any]:
'''simple docstring'''
a__ , a__ = create_model(
'HTSAT-tiny','roberta',UpperCAmelCase__,precision='fp32',device='cuda:0' if torch.cuda.is_available() else 'cpu',enable_fusion=UpperCAmelCase__,fusion_type='aff_2d' if enable_fusion else None,)
return model, model_cfg
def _lowerCamelCase ( UpperCAmelCase__ ) -> Optional[int]:
'''simple docstring'''
a__ = {}
a__ = R'.*sequential.(\d+).*'
a__ = R'.*_projection.(\d+).*'
for key, value in state_dict.items():
# check if any key needs to be modified
for key_to_modify, new_key in KEYS_TO_MODIFY_MAPPING.items():
if key_to_modify in key:
a__ = key.replace(UpperCAmelCase__,UpperCAmelCase__ )
if re.match(UpperCAmelCase__,UpperCAmelCase__ ):
# replace sequential layers with list
a__ = re.match(UpperCAmelCase__,UpperCAmelCase__ ).group(1 )
a__ = key.replace(f'''sequential.{sequential_layer}.''',f'''layers.{int(UpperCAmelCase__ )//3}.linear.''' )
elif re.match(UpperCAmelCase__,UpperCAmelCase__ ):
a__ = int(re.match(UpperCAmelCase__,UpperCAmelCase__ ).group(1 ) )
# Because in CLAP they use `nn.Sequential`...
a__ = 1 if projecton_layer == 0 else 2
a__ = key.replace(f'''_projection.{projecton_layer}.''',f'''_projection.linear{transformers_projection_layer}.''' )
if "audio" and "qkv" in key:
# split qkv into query key and value
a__ = value
a__ = mixed_qkv.size(0 ) // 3
a__ = mixed_qkv[:qkv_dim]
a__ = mixed_qkv[qkv_dim : qkv_dim * 2]
a__ = mixed_qkv[qkv_dim * 2 :]
a__ = query_layer
a__ = key_layer
a__ = value_layer
else:
a__ = value
return model_state_dict
def _lowerCamelCase ( UpperCAmelCase__,UpperCAmelCase__,UpperCAmelCase__,UpperCAmelCase__=False ) -> Any:
'''simple docstring'''
a__ , a__ = init_clap(UpperCAmelCase__,enable_fusion=UpperCAmelCase__ )
clap_model.eval()
a__ = clap_model.state_dict()
a__ = rename_state_dict(UpperCAmelCase__ )
a__ = ClapConfig()
a__ = enable_fusion
a__ = ClapModel(UpperCAmelCase__ )
# ignore the spectrogram embedding layer
model.load_state_dict(UpperCAmelCase__,strict=UpperCAmelCase__ )
model.save_pretrained(UpperCAmelCase__ )
transformers_config.save_pretrained(UpperCAmelCase__ )
if __name__ == "__main__":
__magic_name__ = argparse.ArgumentParser()
parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to fairseq checkpoint")
parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
parser.add_argument("--enable_fusion", action="store_true", help="Whether to enable fusion or not")
__magic_name__ = parser.parse_args()
convert_clap_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.enable_fusion)
| 232 | 0 |
import math
def _UpperCamelCase ( lowerCAmelCase_ ) ->bool:
assert isinstance(lowerCAmelCase_ , lowerCAmelCase_ ) and (
number >= 0
), "'number' must been an int and positive"
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or not number % 2:
# Negatives, 0, 1 and all even numbers are not primes
return False
UpperCAmelCase = range(3 , int(math.sqrt(lowerCAmelCase_ ) + 1 ) , 2 )
return not any(not number % i for i in odd_numbers )
def _UpperCamelCase ( lowerCAmelCase_ , lowerCAmelCase_=1 , **lowerCAmelCase_ ) ->str:
UpperCAmelCase = factor * value
UpperCAmelCase = value
while not is_prime(lowerCAmelCase_ ):
value += 1 if not ("desc" in kwargs and kwargs["desc"] is True) else -1
if value == first_value_val:
return next_prime(value + 1 , **lowerCAmelCase_ )
return value
| 627 |
from sklearn.metrics import fa_score, matthews_corrcoef
import datasets
from .record_evaluation import evaluate as evaluate_record
__a = """\
@article{wang2019superglue,
title={SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems},
author={Wang, Alex and Pruksachatkun, Yada and Nangia, Nikita and Singh, Amanpreet and Michael, Julian and Hill, Felix and Levy, Omer and Bowman, Samuel R},
journal={arXiv preprint arXiv:1905.00537},
year={2019}
}
"""
__a = """\
SuperGLUE (https://super.gluebenchmark.com/) is a new benchmark styled after
GLUE with a new set of more difficult language understanding tasks, improved
resources, and a new public leaderboard.
"""
__a = """
Compute SuperGLUE evaluation metric associated to each SuperGLUE dataset.
Args:
predictions: list of predictions to score. Depending on the SuperGlUE subset:
- for 'record': list of question-answer dictionaries with the following keys:
- 'idx': index of the question as specified by the dataset
- 'prediction_text': the predicted answer text
- for 'multirc': list of question-answer dictionaries with the following keys:
- 'idx': index of the question-answer pair as specified by the dataset
- 'prediction': the predicted answer label
- otherwise: list of predicted labels
references: list of reference labels. Depending on the SuperGLUE subset:
- for 'record': list of question-answers dictionaries with the following keys:
- 'idx': index of the question as specified by the dataset
- 'answers': list of possible answers
- otherwise: list of reference labels
Returns: depending on the SuperGLUE subset:
- for 'record':
- 'exact_match': Exact match between answer and gold answer
- 'f1': F1 score
- for 'multirc':
- 'exact_match': Exact match between answer and gold answer
- 'f1_m': Per-question macro-F1 score
- 'f1_a': Average F1 score over all answers
- for 'axb':
'matthews_correlation': Matthew Correlation
- for 'cb':
- 'accuracy': Accuracy
- 'f1': F1 score
- for all others:
- 'accuracy': Accuracy
Examples:
>>> super_glue_metric = datasets.load_metric('super_glue', 'copa') # any of [\"copa\", \"rte\", \"wic\", \"wsc\", \"wsc.fixed\", \"boolq\", \"axg\"]
>>> predictions = [0, 1]
>>> references = [0, 1]
>>> results = super_glue_metric.compute(predictions=predictions, references=references)
>>> print(results)
{'accuracy': 1.0}
>>> super_glue_metric = datasets.load_metric('super_glue', 'cb')
>>> predictions = [0, 1]
>>> references = [0, 1]
>>> results = super_glue_metric.compute(predictions=predictions, references=references)
>>> print(results)
{'accuracy': 1.0, 'f1': 1.0}
>>> super_glue_metric = datasets.load_metric('super_glue', 'record')
>>> predictions = [{'idx': {'passage': 0, 'query': 0}, 'prediction_text': 'answer'}]
>>> references = [{'idx': {'passage': 0, 'query': 0}, 'answers': ['answer', 'another_answer']}]
>>> results = super_glue_metric.compute(predictions=predictions, references=references)
>>> print(results)
{'exact_match': 1.0, 'f1': 1.0}
>>> super_glue_metric = datasets.load_metric('super_glue', 'multirc')
>>> predictions = [{'idx': {'answer': 0, 'paragraph': 0, 'question': 0}, 'prediction': 0}, {'idx': {'answer': 1, 'paragraph': 2, 'question': 3}, 'prediction': 1}]
>>> references = [0, 1]
>>> results = super_glue_metric.compute(predictions=predictions, references=references)
>>> print(results)
{'exact_match': 1.0, 'f1_m': 1.0, 'f1_a': 1.0}
>>> super_glue_metric = datasets.load_metric('super_glue', 'axb')
>>> references = [0, 1]
>>> predictions = [0, 1]
>>> results = super_glue_metric.compute(predictions=predictions, references=references)
>>> print(results)
{'matthews_correlation': 1.0}
"""
def _UpperCamelCase ( lowerCAmelCase_ , lowerCAmelCase_ ) ->List[str]:
return float((preds == labels).mean() )
def _UpperCamelCase ( lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_="binary" ) ->Union[str, Any]:
UpperCAmelCase = simple_accuracy(lowerCAmelCase_ , lowerCAmelCase_ )
UpperCAmelCase = float(fa_score(y_true=lowerCAmelCase_ , y_pred=lowerCAmelCase_ , average=lowerCAmelCase_ ) )
return {
"accuracy": acc,
"f1": fa,
}
def _UpperCamelCase ( lowerCAmelCase_ , lowerCAmelCase_ ) ->List[Any]:
UpperCAmelCase = {}
for id_pred, label in zip(lowerCAmelCase_ , lowerCAmelCase_ ):
UpperCAmelCase = F"""{id_pred['idx']['paragraph']}-{id_pred['idx']['question']}"""
UpperCAmelCase = id_pred["""prediction"""]
if question_id in question_map:
question_map[question_id].append((pred, label) )
else:
UpperCAmelCase = [(pred, label)]
UpperCAmelCase , UpperCAmelCase = [], []
for question, preds_labels in question_map.items():
UpperCAmelCase , UpperCAmelCase = zip(*lowerCAmelCase_ )
UpperCAmelCase = fa_score(y_true=lowerCAmelCase_ , y_pred=lowerCAmelCase_ , average="""macro""" )
fas.append(lowerCAmelCase_ )
UpperCAmelCase = int(sum(pred == label for pred, label in preds_labels ) == len(lowerCAmelCase_ ) )
ems.append(lowerCAmelCase_ )
UpperCAmelCase = float(sum(lowerCAmelCase_ ) / len(lowerCAmelCase_ ) )
UpperCAmelCase = sum(lowerCAmelCase_ ) / len(lowerCAmelCase_ )
UpperCAmelCase = float(fa_score(y_true=lowerCAmelCase_ , y_pred=[id_pred["""prediction"""] for id_pred in ids_preds] ) )
return {"exact_match": em, "f1_m": fa_m, "f1_a": fa_a}
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class __lowercase ( datasets.Metric ):
def _lowercase ( self : int ) -> Any:
"""simple docstring"""
if self.config_name not in [
"boolq",
"cb",
"copa",
"multirc",
"record",
"rte",
"wic",
"wsc",
"wsc.fixed",
"axb",
"axg",
]:
raise KeyError(
"""You should supply a configuration name selected in """
"""[\"boolq\", \"cb\", \"copa\", \"multirc\", \"record\", \"rte\", \"wic\", \"wsc\", \"wsc.fixed\", \"axb\", \"axg\",]""" )
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(self._get_feature_types() ) , codebase_urls=[] , reference_urls=[] , format="""numpy""" if not self.config_name == """record""" and not self.config_name == """multirc""" else None , )
def _lowercase ( self : Optional[Any] ) -> Any:
"""simple docstring"""
if self.config_name == "record":
return {
"predictions": {
"idx": {
"passage": datasets.Value("""int64""" ),
"query": datasets.Value("""int64""" ),
},
"prediction_text": datasets.Value("""string""" ),
},
"references": {
"idx": {
"passage": datasets.Value("""int64""" ),
"query": datasets.Value("""int64""" ),
},
"answers": datasets.Sequence(datasets.Value("""string""" ) ),
},
}
elif self.config_name == "multirc":
return {
"predictions": {
"idx": {
"answer": datasets.Value("""int64""" ),
"paragraph": datasets.Value("""int64""" ),
"question": datasets.Value("""int64""" ),
},
"prediction": datasets.Value("""int64""" ),
},
"references": datasets.Value("""int64""" ),
}
else:
return {
"predictions": datasets.Value("""int64""" ),
"references": datasets.Value("""int64""" ),
}
def _lowercase ( self : Dict , __lowerCamelCase : int , __lowerCamelCase : Optional[int] ) -> List[Any]:
"""simple docstring"""
if self.config_name == "axb":
return {"matthews_correlation": matthews_corrcoef(__lowerCamelCase , __lowerCamelCase )}
elif self.config_name == "cb":
return acc_and_fa(__lowerCamelCase , __lowerCamelCase , fa_avg="""macro""" )
elif self.config_name == "record":
UpperCAmelCase = [
{
"""qas""": [
{"""id""": ref["""idx"""]["""query"""], """answers""": [{"""text""": ans} for ans in ref["""answers"""]]}
for ref in references
]
}
]
UpperCAmelCase = {pred["""idx"""]["""query"""]: pred["""prediction_text"""] for pred in predictions}
return evaluate_record(__lowerCamelCase , __lowerCamelCase )[0]
elif self.config_name == "multirc":
return evaluate_multirc(__lowerCamelCase , __lowerCamelCase )
elif self.config_name in ["copa", "rte", "wic", "wsc", "wsc.fixed", "boolq", "axg"]:
return {"accuracy": simple_accuracy(__lowerCamelCase , __lowerCamelCase )}
else:
raise KeyError(
"""You should supply a configuration name selected in """
"""[\"boolq\", \"cb\", \"copa\", \"multirc\", \"record\", \"rte\", \"wic\", \"wsc\", \"wsc.fixed\", \"axb\", \"axg\",]""" )
| 627 | 1 |
'''simple docstring'''
import inspect
import unittest
import numpy as np
from tests.test_modeling_common import floats_tensor
from transformers import MaskaFormerConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import MaskaFormerForUniversalSegmentation, MaskaFormerModel
if is_vision_available():
from transformers import MaskaFormerImageProcessor
if is_vision_available():
from PIL import Image
class SCREAMING_SNAKE_CASE :
"""simple docstring"""
def __init__( self : List[str] , __lowerCAmelCase : Dict , __lowerCAmelCase : Tuple=2 , __lowerCAmelCase : List[Any]=True , __lowerCAmelCase : Optional[int]=False , __lowerCAmelCase : int=10 , __lowerCAmelCase : Optional[Any]=3 , __lowerCAmelCase : Any=32 * 8 , __lowerCAmelCase : Optional[Any]=32 * 8 , __lowerCAmelCase : List[Any]=4 , __lowerCAmelCase : Dict=64 , ):
"""simple docstring"""
_lowerCAmelCase = parent
_lowerCAmelCase = batch_size
_lowerCAmelCase = is_training
_lowerCAmelCase = use_auxiliary_loss
_lowerCAmelCase = num_queries
_lowerCAmelCase = num_channels
_lowerCAmelCase = min_size
_lowerCAmelCase = max_size
_lowerCAmelCase = num_labels
_lowerCAmelCase = hidden_dim
_lowerCAmelCase = hidden_dim
def a ( self : Tuple ):
"""simple docstring"""
_lowerCAmelCase = floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size] ).to(
__lowerCAmelCase )
_lowerCAmelCase = torch.ones([self.batch_size, self.min_size, self.max_size] , device=__lowerCAmelCase )
_lowerCAmelCase = (
torch.rand([self.batch_size, self.num_labels, self.min_size, self.max_size] , device=__lowerCAmelCase ) > 0.5
).float()
_lowerCAmelCase = (torch.rand((self.batch_size, self.num_labels) , device=__lowerCAmelCase ) > 0.5).long()
_lowerCAmelCase = self.get_config()
return config, pixel_values, pixel_mask, mask_labels, class_labels
def a ( self : int ):
"""simple docstring"""
_lowerCAmelCase = MaskaFormerConfig(
hidden_size=self.hidden_dim , )
_lowerCAmelCase = self.num_queries
_lowerCAmelCase = self.num_labels
_lowerCAmelCase = [1, 1, 1, 1]
_lowerCAmelCase = self.num_channels
_lowerCAmelCase = 64
_lowerCAmelCase = 128
_lowerCAmelCase = self.hidden_dim
_lowerCAmelCase = self.hidden_dim
_lowerCAmelCase = self.hidden_dim
return config
def a ( self : Union[str, Any] ):
"""simple docstring"""
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase = self.prepare_config_and_inputs()
_lowerCAmelCase = {'pixel_values': pixel_values, 'pixel_mask': pixel_mask}
return config, inputs_dict
def a ( self : Dict , __lowerCAmelCase : int , __lowerCAmelCase : Union[str, Any] ):
"""simple docstring"""
_lowerCAmelCase = output.encoder_hidden_states
_lowerCAmelCase = output.pixel_decoder_hidden_states
_lowerCAmelCase = output.transformer_decoder_hidden_states
self.parent.assertTrue(len(__lowerCAmelCase ) , len(config.backbone_config.depths ) )
self.parent.assertTrue(len(__lowerCAmelCase ) , len(config.backbone_config.depths ) )
self.parent.assertTrue(len(__lowerCAmelCase ) , config.decoder_layers )
def a ( self : Any , __lowerCAmelCase : Tuple , __lowerCAmelCase : Optional[int] , __lowerCAmelCase : Optional[int] , __lowerCAmelCase : Optional[int]=False ):
"""simple docstring"""
with torch.no_grad():
_lowerCAmelCase = MaskaFormerModel(config=__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.eval()
_lowerCAmelCase = model(pixel_values=__lowerCAmelCase , pixel_mask=__lowerCAmelCase )
_lowerCAmelCase = model(__lowerCAmelCase , output_hidden_states=__lowerCAmelCase )
self.parent.assertEqual(
output.transformer_decoder_last_hidden_state.shape , (self.batch_size, self.num_queries, self.hidden_dim) , )
# let's ensure the other two hidden state exists
self.parent.assertTrue(output.pixel_decoder_last_hidden_state is not None )
self.parent.assertTrue(output.encoder_last_hidden_state is not None )
if output_hidden_states:
self.check_output_hidden_state(__lowerCAmelCase , __lowerCAmelCase )
def a ( self : Any , __lowerCAmelCase : List[str] , __lowerCAmelCase : Dict , __lowerCAmelCase : str , __lowerCAmelCase : Optional[int] , __lowerCAmelCase : int ):
"""simple docstring"""
_lowerCAmelCase = MaskaFormerForUniversalSegmentation(config=__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.eval()
def comm_check_on_output(__lowerCAmelCase : List[str] ):
# let's still check that all the required stuff is there
self.parent.assertTrue(result.transformer_decoder_last_hidden_state is not None )
self.parent.assertTrue(result.pixel_decoder_last_hidden_state is not None )
self.parent.assertTrue(result.encoder_last_hidden_state is not None )
# okay, now we need to check the logits shape
# due to the encoder compression, masks have a //4 spatial size
self.parent.assertEqual(
result.masks_queries_logits.shape , (self.batch_size, self.num_queries, self.min_size // 4, self.max_size // 4) , )
# + 1 for null class
self.parent.assertEqual(
result.class_queries_logits.shape , (self.batch_size, self.num_queries, self.num_labels + 1) )
with torch.no_grad():
_lowerCAmelCase = model(pixel_values=__lowerCAmelCase , pixel_mask=__lowerCAmelCase )
_lowerCAmelCase = model(__lowerCAmelCase )
comm_check_on_output(__lowerCAmelCase )
_lowerCAmelCase = model(
pixel_values=__lowerCAmelCase , pixel_mask=__lowerCAmelCase , mask_labels=__lowerCAmelCase , class_labels=__lowerCAmelCase )
comm_check_on_output(__lowerCAmelCase )
self.parent.assertTrue(result.loss is not None )
self.parent.assertEqual(result.loss.shape , torch.Size([1] ) )
@require_torch
class SCREAMING_SNAKE_CASE ( __a , __a , unittest.TestCase ):
"""simple docstring"""
__A = (MaskaFormerModel, MaskaFormerForUniversalSegmentation) if is_torch_available() else ()
__A = {"feature-extraction": MaskaFormerModel} if is_torch_available() else {}
__A = False
__A = False
__A = False
__A = False
def a ( self : Tuple ):
"""simple docstring"""
_lowerCAmelCase = MaskaFormerModelTester(self )
_lowerCAmelCase = ConfigTester(self , config_class=__lowerCAmelCase , has_text_modality=__lowerCAmelCase )
def a ( self : Optional[int] ):
"""simple docstring"""
self.config_tester.run_common_tests()
def a ( self : Any ):
"""simple docstring"""
_lowerCAmelCase , _lowerCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.create_and_check_maskaformer_model(__lowerCAmelCase , **__lowerCAmelCase , output_hidden_states=__lowerCAmelCase )
def a ( self : Dict ):
"""simple docstring"""
_lowerCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_maskaformer_instance_segmentation_head_model(*__lowerCAmelCase )
@unittest.skip(reason='Mask2Former does not use inputs_embeds' )
def a ( self : List[Any] ):
"""simple docstring"""
pass
@unittest.skip(reason='Mask2Former does not have a get_input_embeddings method' )
def a ( self : Union[str, Any] ):
"""simple docstring"""
pass
@unittest.skip(reason='Mask2Former is not a generative model' )
def a ( self : Any ):
"""simple docstring"""
pass
@unittest.skip(reason='Mask2Former does not use token embeddings' )
def a ( self : int ):
"""simple docstring"""
pass
@require_torch_multi_gpu
@unittest.skip(
reason='Mask2Former has some layers using `add_module` which doesn\'t work well with `nn.DataParallel`' )
def a ( self : Union[str, Any] ):
"""simple docstring"""
pass
@unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.' )
def a ( self : Tuple ):
"""simple docstring"""
pass
def a ( self : Tuple ):
"""simple docstring"""
_lowerCAmelCase , _lowerCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_lowerCAmelCase = model_class(__lowerCAmelCase )
_lowerCAmelCase = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
_lowerCAmelCase = [*signature.parameters.keys()]
_lowerCAmelCase = ['pixel_values']
self.assertListEqual(arg_names[:1] , __lowerCAmelCase )
@slow
def a ( self : Union[str, Any] ):
"""simple docstring"""
for model_name in ["facebook/mask2former-swin-small-coco-instance"]:
_lowerCAmelCase = MaskaFormerModel.from_pretrained(__lowerCAmelCase )
self.assertIsNotNone(__lowerCAmelCase )
def a ( self : List[str] ):
"""simple docstring"""
_lowerCAmelCase = (self.model_tester.min_size,) * 2
_lowerCAmelCase = {
'pixel_values': torch.randn((2, 3, *size) , device=__lowerCAmelCase ),
'mask_labels': torch.randn((2, 10, *size) , device=__lowerCAmelCase ),
'class_labels': torch.zeros(2 , 10 , device=__lowerCAmelCase ).long(),
}
_lowerCAmelCase = self.model_tester.get_config()
_lowerCAmelCase = MaskaFormerForUniversalSegmentation(__lowerCAmelCase ).to(__lowerCAmelCase )
_lowerCAmelCase = model(**__lowerCAmelCase )
self.assertTrue(outputs.loss is not None )
def a ( self : Union[str, Any] ):
"""simple docstring"""
_lowerCAmelCase , _lowerCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.create_and_check_maskaformer_model(__lowerCAmelCase , **__lowerCAmelCase , output_hidden_states=__lowerCAmelCase )
def a ( self : List[str] ):
"""simple docstring"""
_lowerCAmelCase , _lowerCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_lowerCAmelCase = model_class(__lowerCAmelCase ).to(__lowerCAmelCase )
_lowerCAmelCase = model(**__lowerCAmelCase , output_attentions=__lowerCAmelCase )
self.assertTrue(outputs.attentions is not None )
def a ( self : int ):
"""simple docstring"""
if not self.model_tester.is_training:
return
_lowerCAmelCase = self.all_model_classes[1]
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase = self.model_tester.prepare_config_and_inputs()
_lowerCAmelCase = model_class(__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.train()
_lowerCAmelCase = model(__lowerCAmelCase , mask_labels=__lowerCAmelCase , class_labels=__lowerCAmelCase ).loss
loss.backward()
def a ( self : Dict ):
"""simple docstring"""
_lowerCAmelCase = self.all_model_classes[1]
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase = self.model_tester.prepare_config_and_inputs()
_lowerCAmelCase = True
_lowerCAmelCase = True
_lowerCAmelCase = model_class(__lowerCAmelCase ).to(__lowerCAmelCase )
model.train()
_lowerCAmelCase = model(__lowerCAmelCase , mask_labels=__lowerCAmelCase , class_labels=__lowerCAmelCase )
_lowerCAmelCase = outputs.encoder_hidden_states[0]
encoder_hidden_states.retain_grad()
_lowerCAmelCase = outputs.pixel_decoder_hidden_states[0]
pixel_decoder_hidden_states.retain_grad()
_lowerCAmelCase = outputs.transformer_decoder_hidden_states[0]
transformer_decoder_hidden_states.retain_grad()
_lowerCAmelCase = outputs.attentions[0]
attentions.retain_grad()
outputs.loss.backward(retain_graph=__lowerCAmelCase )
self.assertIsNotNone(encoder_hidden_states.grad )
self.assertIsNotNone(pixel_decoder_hidden_states.grad )
self.assertIsNotNone(transformer_decoder_hidden_states.grad )
self.assertIsNotNone(attentions.grad )
snake_case = 1e-4
def A_ ( ):
_lowerCAmelCase = Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
return image
@require_vision
@slow
class SCREAMING_SNAKE_CASE ( unittest.TestCase ):
"""simple docstring"""
@cached_property
def a ( self : List[Any] ):
"""simple docstring"""
return "facebook/mask2former-swin-small-coco-instance"
@cached_property
def a ( self : Tuple ):
"""simple docstring"""
return MaskaFormerImageProcessor.from_pretrained(self.model_checkpoints ) if is_vision_available() else None
def a ( self : Dict ):
"""simple docstring"""
_lowerCAmelCase = MaskaFormerModel.from_pretrained(self.model_checkpoints ).to(__lowerCAmelCase )
_lowerCAmelCase = self.default_image_processor
_lowerCAmelCase = prepare_img()
_lowerCAmelCase = image_processor(__lowerCAmelCase , return_tensors='pt' ).to(__lowerCAmelCase )
_lowerCAmelCase = inputs['pixel_values'].shape
# check size is divisible by 32
self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0 )
# check size
self.assertEqual(__lowerCAmelCase , (1, 3, 384, 384) )
with torch.no_grad():
_lowerCAmelCase = model(**__lowerCAmelCase )
_lowerCAmelCase = torch.tensor(
[[-0.2_7_9_0, -1.0_7_1_7, -1.1_6_6_8], [-0.5_1_2_8, -0.3_1_2_8, -0.4_9_8_7], [-0.5_8_3_2, 0.1_9_7_1, -0.0_1_9_7]] ).to(__lowerCAmelCase )
self.assertTrue(
torch.allclose(
outputs.encoder_last_hidden_state[0, 0, :3, :3] , __lowerCAmelCase , atol=__lowerCAmelCase ) )
_lowerCAmelCase = torch.tensor(
[[0.8_9_7_3, 1.1_8_4_7, 1.1_7_7_6], [1.1_9_3_4, 1.5_0_4_0, 1.5_1_2_8], [1.1_1_5_3, 1.4_4_8_6, 1.4_9_5_1]] ).to(__lowerCAmelCase )
self.assertTrue(
torch.allclose(
outputs.pixel_decoder_last_hidden_state[0, 0, :3, :3] , __lowerCAmelCase , atol=__lowerCAmelCase ) )
_lowerCAmelCase = torch.tensor(
[[2.1_1_5_2, 1.7_0_0_0, -0.8_6_0_3], [1.5_8_0_8, 1.8_0_0_4, -0.9_3_5_3], [1.6_0_4_3, 1.7_4_9_5, -0.5_9_9_9]] ).to(__lowerCAmelCase )
self.assertTrue(
torch.allclose(
outputs.transformer_decoder_last_hidden_state[0, :3, :3] , __lowerCAmelCase , atol=__lowerCAmelCase ) )
def a ( self : Dict ):
"""simple docstring"""
_lowerCAmelCase = MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints ).to(__lowerCAmelCase ).eval()
_lowerCAmelCase = self.default_image_processor
_lowerCAmelCase = prepare_img()
_lowerCAmelCase = image_processor(__lowerCAmelCase , return_tensors='pt' ).to(__lowerCAmelCase )
_lowerCAmelCase = inputs['pixel_values'].shape
# check size is divisible by 32
self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0 )
# check size
self.assertEqual(__lowerCAmelCase , (1, 3, 384, 384) )
with torch.no_grad():
_lowerCAmelCase = model(**__lowerCAmelCase )
# masks_queries_logits
_lowerCAmelCase = outputs.masks_queries_logits
self.assertEqual(
masks_queries_logits.shape , (1, model.config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4) )
_lowerCAmelCase = [
[-8.7_8_3_9, -9.0_0_5_6, -8.8_1_2_1],
[-7.4_1_0_4, -7.0_3_1_3, -6.5_4_0_1],
[-6.6_1_0_5, -6.3_4_2_7, -6.4_6_7_5],
]
_lowerCAmelCase = torch.tensor(__lowerCAmelCase ).to(__lowerCAmelCase )
self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3] , __lowerCAmelCase , atol=__lowerCAmelCase ) )
# class_queries_logits
_lowerCAmelCase = outputs.class_queries_logits
self.assertEqual(class_queries_logits.shape , (1, model.config.num_queries, model.config.num_labels + 1) )
_lowerCAmelCase = torch.tensor(
[
[1.8_3_2_4, -8.0_8_3_5, -4.1_9_2_2],
[0.8_4_5_0, -9.0_0_5_0, -3.6_0_5_3],
[0.3_0_4_5, -7.7_2_9_3, -3.0_2_7_5],
] ).to(__lowerCAmelCase )
self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3] , __lowerCAmelCase , atol=__lowerCAmelCase ) )
def a ( self : Optional[int] ):
"""simple docstring"""
_lowerCAmelCase = MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints ).to(__lowerCAmelCase ).eval()
_lowerCAmelCase = self.default_image_processor
_lowerCAmelCase = image_processor(
[np.zeros((3, 800, 1333) ), np.zeros((3, 800, 1333) )] , segmentation_maps=[np.zeros((384, 384) ).astype(np.floataa ), np.zeros((384, 384) ).astype(np.floataa )] , return_tensors='pt' , )
_lowerCAmelCase = inputs['pixel_values'].to(__lowerCAmelCase )
_lowerCAmelCase = [el.to(__lowerCAmelCase ) for el in inputs['mask_labels']]
_lowerCAmelCase = [el.to(__lowerCAmelCase ) for el in inputs['class_labels']]
with torch.no_grad():
_lowerCAmelCase = model(**__lowerCAmelCase )
self.assertTrue(outputs.loss is not None )
| 309 | '''simple docstring'''
import warnings
from typing import List, Optional, Union
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class SCREAMING_SNAKE_CASE ( __a ):
"""simple docstring"""
__A = ["image_processor", "tokenizer"]
__A = "FlavaImageProcessor"
__A = ("BertTokenizer", "BertTokenizerFast")
def __init__( self : Dict , __lowerCAmelCase : int=None , __lowerCAmelCase : Optional[Any]=None , **__lowerCAmelCase : int ):
"""simple docstring"""
_lowerCAmelCase = None
if "feature_extractor" in kwargs:
warnings.warn(
'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
' instead.' , __lowerCAmelCase , )
_lowerCAmelCase = kwargs.pop('feature_extractor' )
_lowerCAmelCase = image_processor if image_processor is not None else feature_extractor
if image_processor is None:
raise ValueError('You need to specify an `image_processor`.' )
if tokenizer is None:
raise ValueError('You need to specify a `tokenizer`.' )
super().__init__(__lowerCAmelCase , __lowerCAmelCase )
_lowerCAmelCase = self.image_processor
def __call__( self : Union[str, Any] , __lowerCAmelCase : Optional[ImageInput] = None , __lowerCAmelCase : Optional[Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]]] = None , __lowerCAmelCase : bool = True , __lowerCAmelCase : Union[bool, str, PaddingStrategy] = False , __lowerCAmelCase : Union[bool, str, TruncationStrategy] = False , __lowerCAmelCase : Optional[int] = None , __lowerCAmelCase : int = 0 , __lowerCAmelCase : Optional[int] = None , __lowerCAmelCase : Optional[bool] = None , __lowerCAmelCase : Optional[bool] = None , __lowerCAmelCase : Optional[bool] = None , __lowerCAmelCase : Optional[bool] = None , __lowerCAmelCase : bool = False , __lowerCAmelCase : bool = False , __lowerCAmelCase : bool = False , __lowerCAmelCase : bool = False , __lowerCAmelCase : bool = True , __lowerCAmelCase : Optional[Union[str, TensorType]] = None , **__lowerCAmelCase : Union[str, Any] , ):
"""simple docstring"""
if text is None and images is None:
raise ValueError('You have to specify either text or images. Both cannot be none.' )
if text is not None:
_lowerCAmelCase = self.tokenizer(
text=__lowerCAmelCase , add_special_tokens=__lowerCAmelCase , padding=__lowerCAmelCase , truncation=__lowerCAmelCase , max_length=__lowerCAmelCase , stride=__lowerCAmelCase , pad_to_multiple_of=__lowerCAmelCase , return_token_type_ids=__lowerCAmelCase , return_attention_mask=__lowerCAmelCase , return_overflowing_tokens=__lowerCAmelCase , return_special_tokens_mask=__lowerCAmelCase , return_offsets_mapping=__lowerCAmelCase , return_length=__lowerCAmelCase , verbose=__lowerCAmelCase , return_tensors=__lowerCAmelCase , **__lowerCAmelCase , )
if images is not None:
_lowerCAmelCase = self.image_processor(
__lowerCAmelCase , return_image_mask=__lowerCAmelCase , return_codebook_pixels=__lowerCAmelCase , return_tensors=__lowerCAmelCase , **__lowerCAmelCase , )
if text is not None and images is not None:
encoding.update(__lowerCAmelCase )
return encoding
elif text is not None:
return encoding
else:
return BatchEncoding(data=dict(**__lowerCAmelCase ) , tensor_type=__lowerCAmelCase )
def a ( self : Any , *__lowerCAmelCase : str , **__lowerCAmelCase : List[Any] ):
"""simple docstring"""
return self.tokenizer.batch_decode(*__lowerCAmelCase , **__lowerCAmelCase )
def a ( self : List[str] , *__lowerCAmelCase : List[str] , **__lowerCAmelCase : Optional[int] ):
"""simple docstring"""
return self.tokenizer.decode(*__lowerCAmelCase , **__lowerCAmelCase )
@property
def a ( self : List[str] ):
"""simple docstring"""
_lowerCAmelCase = self.tokenizer.model_input_names
_lowerCAmelCase = self.image_processor.model_input_names
return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names ) )
@property
def a ( self : Optional[int] ):
"""simple docstring"""
warnings.warn(
'`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.' , __lowerCAmelCase , )
return self.image_processor_class
@property
def a ( self : Any ):
"""simple docstring"""
warnings.warn(
'`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.' , __lowerCAmelCase , )
return self.image_processor
| 309 | 1 |
"""simple docstring"""
from collections.abc import Iterator, MutableMapping
from dataclasses import dataclass
from typing import Generic, TypeVar
A : Tuple = TypeVar("KEY")
A : Optional[Any] = TypeVar("VAL")
@dataclass(frozen=lowerCAmelCase__ ,slots=lowerCAmelCase__ )
class _UpperCamelCase ( Generic[KEY, VAL] ):
'''simple docstring'''
__UpperCAmelCase : KEY
__UpperCAmelCase : VAL
class _UpperCamelCase ( _Item ):
'''simple docstring'''
def __init__( self ):
super().__init__(__a , __a )
def __bool__( self ):
return False
A : Tuple = _DeletedItem()
class _UpperCamelCase ( MutableMapping[KEY, VAL] ):
'''simple docstring'''
def __init__( self , __a = 8 , __a = 0.7_5 ):
__lowerCAmelCase = initial_block_size
__lowerCAmelCase = [None] * initial_block_size
assert 0.0 < capacity_factor < 1.0
__lowerCAmelCase = capacity_factor
__lowerCAmelCase = 0
def snake_case ( self , __a ):
return hash(__a ) % len(self._buckets )
def snake_case ( self , __a ):
return (ind + 1) % len(self._buckets )
def snake_case ( self , __a , __a , __a ):
__lowerCAmelCase = self._buckets[ind]
if not stored:
__lowerCAmelCase = _Item(__a , __a )
self._len += 1
return True
elif stored.key == key:
__lowerCAmelCase = _Item(__a , __a )
return True
else:
return False
def snake_case ( self ):
__lowerCAmelCase = len(self._buckets ) * self._capacity_factor
return len(self ) >= int(__a )
def snake_case ( self ):
if len(self._buckets ) <= self._initial_block_size:
return False
__lowerCAmelCase = len(self._buckets ) * self._capacity_factor / 2
return len(self ) < limit
def snake_case ( self , __a ):
__lowerCAmelCase = self._buckets
__lowerCAmelCase = [None] * new_size
__lowerCAmelCase = 0
for item in old_buckets:
if item:
self._add_item(item.key , item.val )
def snake_case ( self ):
self._resize(len(self._buckets ) * 2 )
def snake_case ( self ):
self._resize(len(self._buckets ) // 2 )
def snake_case ( self , __a ):
__lowerCAmelCase = self._get_bucket_index(__a )
for _ in range(len(self._buckets ) ):
yield ind
__lowerCAmelCase = self._get_next_ind(__a )
def snake_case ( self , __a , __a ):
for ind in self._iterate_buckets(__a ):
if self._try_set(__a , __a , __a ):
break
def __setitem__( self , __a , __a ):
if self._is_full():
self._size_up()
self._add_item(__a , __a )
def __delitem__( self , __a ):
for ind in self._iterate_buckets(__a ):
__lowerCAmelCase = self._buckets[ind]
if item is None:
raise KeyError(__a )
if item is _deleted:
continue
if item.key == key:
__lowerCAmelCase = _deleted
self._len -= 1
break
if self._is_sparse():
self._size_down()
def __getitem__( self , __a ):
for ind in self._iterate_buckets(__a ):
__lowerCAmelCase = self._buckets[ind]
if item is None:
break
if item is _deleted:
continue
if item.key == key:
return item.val
raise KeyError(__a )
def __len__( self ):
return self._len
def __iter__( self ):
yield from (item.key for item in self._buckets if item)
def __repr__( self ):
__lowerCAmelCase = " ,".join(
f"{item.key}: {item.val}" for item in self._buckets if item )
return f"HashMap({val_string})"
| 282 |
"""simple docstring"""
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import SegformerImageProcessor, SwinConfig, UperNetConfig, UperNetForSemanticSegmentation
def _lowerCamelCase ( _UpperCamelCase ):
'''simple docstring'''
__lowerCAmelCase = 384
__lowerCAmelCase = 7
if "tiny" in model_name:
__lowerCAmelCase = 96
__lowerCAmelCase = (2, 2, 6, 2)
__lowerCAmelCase = (3, 6, 12, 24)
elif "small" in model_name:
__lowerCAmelCase = 96
__lowerCAmelCase = (2, 2, 18, 2)
__lowerCAmelCase = (3, 6, 12, 24)
elif "base" in model_name:
__lowerCAmelCase = 128
__lowerCAmelCase = (2, 2, 18, 2)
__lowerCAmelCase = (4, 8, 16, 32)
__lowerCAmelCase = 12
__lowerCAmelCase = 512
elif "large" in model_name:
__lowerCAmelCase = 192
__lowerCAmelCase = (2, 2, 18, 2)
__lowerCAmelCase = (6, 12, 24, 48)
__lowerCAmelCase = 12
__lowerCAmelCase = 768
# set label information
__lowerCAmelCase = 150
__lowerCAmelCase = "huggingface/label-files"
__lowerCAmelCase = "ade20k-id2label.json"
__lowerCAmelCase = json.load(open(hf_hub_download(_UpperCamelCase , _UpperCamelCase , repo_type="dataset" ) , "r" ) )
__lowerCAmelCase = {int(_UpperCamelCase ): v for k, v in idalabel.items()}
__lowerCAmelCase = {v: k for k, v in idalabel.items()}
__lowerCAmelCase = SwinConfig(
embed_dim=_UpperCamelCase , depths=_UpperCamelCase , num_heads=_UpperCamelCase , window_size=_UpperCamelCase , out_features=["stage1", "stage2", "stage3", "stage4"] , )
__lowerCAmelCase = UperNetConfig(
backbone_config=_UpperCamelCase , auxiliary_in_channels=_UpperCamelCase , num_labels=_UpperCamelCase , idalabel=_UpperCamelCase , labelaid=_UpperCamelCase , )
return config
def _lowerCamelCase ( _UpperCamelCase ):
'''simple docstring'''
__lowerCAmelCase = []
# fmt: off
# stem
rename_keys.append(("backbone.patch_embed.projection.weight", "backbone.embeddings.patch_embeddings.projection.weight") )
rename_keys.append(("backbone.patch_embed.projection.bias", "backbone.embeddings.patch_embeddings.projection.bias") )
rename_keys.append(("backbone.patch_embed.norm.weight", "backbone.embeddings.norm.weight") )
rename_keys.append(("backbone.patch_embed.norm.bias", "backbone.embeddings.norm.bias") )
# stages
for i in range(len(config.backbone_config.depths ) ):
for j in range(config.backbone_config.depths[i] ):
rename_keys.append((f"backbone.stages.{i}.blocks.{j}.norm1.weight", f"backbone.encoder.layers.{i}.blocks.{j}.layernorm_before.weight") )
rename_keys.append((f"backbone.stages.{i}.blocks.{j}.norm1.bias", f"backbone.encoder.layers.{i}.blocks.{j}.layernorm_before.bias") )
rename_keys.append((f"backbone.stages.{i}.blocks.{j}.attn.w_msa.relative_position_bias_table", f"backbone.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_bias_table") )
rename_keys.append((f"backbone.stages.{i}.blocks.{j}.attn.w_msa.relative_position_index", f"backbone.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_index") )
rename_keys.append((f"backbone.stages.{i}.blocks.{j}.attn.w_msa.proj.weight", f"backbone.encoder.layers.{i}.blocks.{j}.attention.output.dense.weight") )
rename_keys.append((f"backbone.stages.{i}.blocks.{j}.attn.w_msa.proj.bias", f"backbone.encoder.layers.{i}.blocks.{j}.attention.output.dense.bias") )
rename_keys.append((f"backbone.stages.{i}.blocks.{j}.norm2.weight", f"backbone.encoder.layers.{i}.blocks.{j}.layernorm_after.weight") )
rename_keys.append((f"backbone.stages.{i}.blocks.{j}.norm2.bias", f"backbone.encoder.layers.{i}.blocks.{j}.layernorm_after.bias") )
rename_keys.append((f"backbone.stages.{i}.blocks.{j}.ffn.layers.0.0.weight", f"backbone.encoder.layers.{i}.blocks.{j}.intermediate.dense.weight") )
rename_keys.append((f"backbone.stages.{i}.blocks.{j}.ffn.layers.0.0.bias", f"backbone.encoder.layers.{i}.blocks.{j}.intermediate.dense.bias") )
rename_keys.append((f"backbone.stages.{i}.blocks.{j}.ffn.layers.1.weight", f"backbone.encoder.layers.{i}.blocks.{j}.output.dense.weight") )
rename_keys.append((f"backbone.stages.{i}.blocks.{j}.ffn.layers.1.bias", f"backbone.encoder.layers.{i}.blocks.{j}.output.dense.bias") )
if i < 3:
rename_keys.append((f"backbone.stages.{i}.downsample.reduction.weight", f"backbone.encoder.layers.{i}.downsample.reduction.weight") )
rename_keys.append((f"backbone.stages.{i}.downsample.norm.weight", f"backbone.encoder.layers.{i}.downsample.norm.weight") )
rename_keys.append((f"backbone.stages.{i}.downsample.norm.bias", f"backbone.encoder.layers.{i}.downsample.norm.bias") )
rename_keys.append((f"backbone.norm{i}.weight", f"backbone.hidden_states_norms.stage{i+1}.weight") )
rename_keys.append((f"backbone.norm{i}.bias", f"backbone.hidden_states_norms.stage{i+1}.bias") )
# decode head
rename_keys.extend(
[
("decode_head.conv_seg.weight", "decode_head.classifier.weight"),
("decode_head.conv_seg.bias", "decode_head.classifier.bias"),
("auxiliary_head.conv_seg.weight", "auxiliary_head.classifier.weight"),
("auxiliary_head.conv_seg.bias", "auxiliary_head.classifier.bias"),
] )
# fmt: on
return rename_keys
def _lowerCamelCase ( _UpperCamelCase , _UpperCamelCase , _UpperCamelCase ):
'''simple docstring'''
__lowerCAmelCase = dct.pop(_UpperCamelCase )
__lowerCAmelCase = val
def _lowerCamelCase ( _UpperCamelCase , _UpperCamelCase ):
'''simple docstring'''
__lowerCAmelCase = [int(backbone_config.embed_dim * 2**i ) for i in range(len(backbone_config.depths ) )]
for i in range(len(backbone_config.depths ) ):
__lowerCAmelCase = num_features[i]
for j in range(backbone_config.depths[i] ):
# fmt: off
# read in weights + bias of input projection layer (in original implementation, this is a single matrix + bias)
__lowerCAmelCase = state_dict.pop(f"backbone.stages.{i}.blocks.{j}.attn.w_msa.qkv.weight" )
__lowerCAmelCase = state_dict.pop(f"backbone.stages.{i}.blocks.{j}.attn.w_msa.qkv.bias" )
# next, add query, keys and values (in that order) to the state dict
__lowerCAmelCase = in_proj_weight[:dim, :]
__lowerCAmelCase = in_proj_bias[: dim]
__lowerCAmelCase = in_proj_weight[
dim : dim * 2, :
]
__lowerCAmelCase = in_proj_bias[
dim : dim * 2
]
__lowerCAmelCase = in_proj_weight[
-dim :, :
]
__lowerCAmelCase = in_proj_bias[-dim :]
# fmt: on
def _lowerCamelCase ( _UpperCamelCase ):
'''simple docstring'''
__lowerCAmelCase , __lowerCAmelCase = x.shape
__lowerCAmelCase = x.reshape(_UpperCamelCase , 4 , in_channel // 4 )
__lowerCAmelCase = x[:, [0, 2, 1, 3], :].transpose(1 , 2 ).reshape(_UpperCamelCase , _UpperCamelCase )
return x
def _lowerCamelCase ( _UpperCamelCase ):
'''simple docstring'''
__lowerCAmelCase , __lowerCAmelCase = x.shape
__lowerCAmelCase = x.reshape(_UpperCamelCase , in_channel // 4 , 4 )
__lowerCAmelCase = x[:, :, [0, 2, 1, 3]].transpose(1 , 2 ).reshape(_UpperCamelCase , _UpperCamelCase )
return x
def _lowerCamelCase ( _UpperCamelCase ):
'''simple docstring'''
__lowerCAmelCase = x.shape[0]
__lowerCAmelCase = x.reshape(4 , in_channel // 4 )
__lowerCAmelCase = x[[0, 2, 1, 3], :].transpose(0 , 1 ).reshape(_UpperCamelCase )
return x
def _lowerCamelCase ( _UpperCamelCase ):
'''simple docstring'''
__lowerCAmelCase = x.shape[0]
__lowerCAmelCase = x.reshape(in_channel // 4 , 4 )
__lowerCAmelCase = x[:, [0, 2, 1, 3]].transpose(0 , 1 ).reshape(_UpperCamelCase )
return x
def _lowerCamelCase ( _UpperCamelCase , _UpperCamelCase , _UpperCamelCase ):
'''simple docstring'''
__lowerCAmelCase = {
"upernet-swin-tiny": "https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_tiny_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K/upernet_swin_tiny_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K_20210531_112542-e380ad3e.pth",
"upernet-swin-small": "https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_small_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K/upernet_swin_small_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K_20210526_192015-ee2fff1c.pth",
"upernet-swin-base": "https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_base_patch4_window12_512x512_160k_ade20k_pretrain_384x384_22K/upernet_swin_base_patch4_window12_512x512_160k_ade20k_pretrain_384x384_22K_20210531_125459-429057bf.pth",
"upernet-swin-large": "https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_large_patch4_window12_512x512_pretrain_384x384_22K_160k_ade20k/upernet_swin_large_patch4_window12_512x512_pretrain_384x384_22K_160k_ade20k_20220318_091743-9ba68901.pth",
}
__lowerCAmelCase = model_name_to_url[model_name]
__lowerCAmelCase = torch.hub.load_state_dict_from_url(_UpperCamelCase , map_location="cpu" , file_name=_UpperCamelCase )[
"state_dict"
]
for name, param in state_dict.items():
print(_UpperCamelCase , param.shape )
__lowerCAmelCase = get_upernet_config(_UpperCamelCase )
__lowerCAmelCase = UperNetForSemanticSegmentation(_UpperCamelCase )
model.eval()
# replace "bn" => "batch_norm"
for key in state_dict.copy().keys():
__lowerCAmelCase = state_dict.pop(_UpperCamelCase )
if "bn" in key:
__lowerCAmelCase = key.replace("bn" , "batch_norm" )
__lowerCAmelCase = val
# rename keys
__lowerCAmelCase = create_rename_keys(_UpperCamelCase )
for src, dest in rename_keys:
rename_key(_UpperCamelCase , _UpperCamelCase , _UpperCamelCase )
read_in_q_k_v(_UpperCamelCase , config.backbone_config )
# fix downsample parameters
for key, value in state_dict.items():
if "downsample" in key:
if "reduction" in key:
__lowerCAmelCase = reverse_correct_unfold_reduction_order(_UpperCamelCase )
if "norm" in key:
__lowerCAmelCase = reverse_correct_unfold_norm_order(_UpperCamelCase )
model.load_state_dict(_UpperCamelCase )
# verify on image
__lowerCAmelCase = "https://huggingface.co/datasets/hf-internal-testing/fixtures_ade20k/resolve/main/ADE_val_00000001.jpg"
__lowerCAmelCase = Image.open(requests.get(_UpperCamelCase , stream=_UpperCamelCase ).raw ).convert("RGB" )
__lowerCAmelCase = SegformerImageProcessor()
__lowerCAmelCase = processor(_UpperCamelCase , return_tensors="pt" ).pixel_values
with torch.no_grad():
__lowerCAmelCase = model(_UpperCamelCase )
__lowerCAmelCase = outputs.logits
print(logits.shape )
print("First values of logits:" , logits[0, 0, :3, :3] )
# assert values
if model_name == "upernet-swin-tiny":
__lowerCAmelCase = torch.tensor(
[[-7.59_58, -7.59_58, -7.43_02], [-7.59_58, -7.59_58, -7.43_02], [-7.47_97, -7.47_97, -7.30_68]] )
elif model_name == "upernet-swin-small":
__lowerCAmelCase = torch.tensor(
[[-7.19_21, -7.19_21, -6.95_32], [-7.19_21, -7.19_21, -6.95_32], [-7.09_08, -7.09_08, -6.85_34]] )
elif model_name == "upernet-swin-base":
__lowerCAmelCase = torch.tensor(
[[-6.58_51, -6.58_51, -6.43_30], [-6.58_51, -6.58_51, -6.43_30], [-6.47_63, -6.47_63, -6.32_54]] )
elif model_name == "upernet-swin-large":
__lowerCAmelCase = torch.tensor(
[[-7.52_97, -7.52_97, -7.38_02], [-7.52_97, -7.52_97, -7.38_02], [-7.40_44, -7.40_44, -7.25_86]] )
print("Logits:" , outputs.logits[0, 0, :3, :3] )
assert torch.allclose(outputs.logits[0, 0, :3, :3] , _UpperCamelCase , atol=1e-4 )
print("Looks ok!" )
if pytorch_dump_folder_path is not None:
print(f"Saving model {model_name} to {pytorch_dump_folder_path}" )
model.save_pretrained(_UpperCamelCase )
print(f"Saving processor to {pytorch_dump_folder_path}" )
processor.save_pretrained(_UpperCamelCase )
if push_to_hub:
print(f"Pushing model and processor for {model_name} to hub" )
model.push_to_hub(f"openmmlab/{model_name}" )
processor.push_to_hub(f"openmmlab/{model_name}" )
if __name__ == "__main__":
A : int = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--model_name",
default="upernet-swin-tiny",
type=str,
choices=[f'''upernet-swin-{size}''' for size in ["tiny", "small", "base", "large"]],
help="Name of the Swin + UperNet 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 or not to push the converted model to the 🤗 hub."
)
A : List[Any] = parser.parse_args()
convert_upernet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)
| 282 | 1 |
import unittest
from transformers import load_tool
from .test_tools_common import ToolTesterMixin
class _lowercase ( unittest.TestCase , snake_case_ ):
def SCREAMING_SNAKE_CASE__ ( self : Dict ) -> Dict:
"""simple docstring"""
UpperCamelCase_ : Optional[Any] = load_tool('text-classification' )
self.tool.setup()
UpperCamelCase_ : Union[str, Any] = load_tool('text-classification' , remote=snake_case )
def SCREAMING_SNAKE_CASE__ ( self : str ) -> Union[str, Any]:
"""simple docstring"""
UpperCamelCase_ : Union[str, Any] = self.tool('That\'s quite cool' , ['positive', 'negative'] )
self.assertEqual(snake_case , 'positive' )
def SCREAMING_SNAKE_CASE__ ( self : Dict ) -> str:
"""simple docstring"""
UpperCamelCase_ : Optional[Any] = self.remote_tool('That\'s quite cool' , ['positive', 'negative'] )
self.assertEqual(snake_case , 'positive' )
def SCREAMING_SNAKE_CASE__ ( self : str ) -> str:
"""simple docstring"""
UpperCamelCase_ : int = self.tool(text='That\'s quite cool' , labels=['positive', 'negative'] )
self.assertEqual(snake_case , 'positive' )
def SCREAMING_SNAKE_CASE__ ( self : Dict ) -> Optional[int]:
"""simple docstring"""
UpperCamelCase_ : List[Any] = self.remote_tool(text='That\'s quite cool' , labels=['positive', 'negative'] )
self.assertEqual(snake_case , 'positive' )
| 417 | import argparse
import requests
import torch
from PIL import Image
from torchvision.transforms import Compose, Normalize, Resize, ToTensor
from transformers import SwinaSRConfig, SwinaSRForImageSuperResolution, SwinaSRImageProcessor
def __lowercase ( lowerCamelCase : str ):
UpperCamelCase_ : Any = SwinaSRConfig()
if "Swin2SR_ClassicalSR_X4_64" in checkpoint_url:
UpperCamelCase_ : Union[str, Any] = 4
elif "Swin2SR_CompressedSR_X4_48" in checkpoint_url:
UpperCamelCase_ : Union[str, Any] = 4
UpperCamelCase_ : Union[str, Any] = 48
UpperCamelCase_ : List[Any] = 'pixelshuffle_aux'
elif "Swin2SR_Lightweight_X2_64" in checkpoint_url:
UpperCamelCase_ : Union[str, Any] = [6, 6, 6, 6]
UpperCamelCase_ : Union[str, Any] = 60
UpperCamelCase_ : List[str] = [6, 6, 6, 6]
UpperCamelCase_ : Tuple = 'pixelshuffledirect'
elif "Swin2SR_RealworldSR_X4_64_BSRGAN_PSNR" in checkpoint_url:
UpperCamelCase_ : Optional[int] = 4
UpperCamelCase_ : Dict = 'nearest+conv'
elif "Swin2SR_Jpeg_dynamic" in checkpoint_url:
UpperCamelCase_ : Any = 1
UpperCamelCase_ : List[Any] = 1
UpperCamelCase_ : List[str] = 126
UpperCamelCase_ : Dict = 7
UpperCamelCase_ : int = 2_5_5.0
UpperCamelCase_ : str = ''
return config
def __lowercase ( lowerCamelCase : Optional[int] , lowerCamelCase : Optional[Any] ):
if "patch_embed.proj" in name and "layers" not in name:
UpperCamelCase_ : Any = name.replace('patch_embed.proj' , 'embeddings.patch_embeddings.projection' )
if "patch_embed.norm" in name:
UpperCamelCase_ : Tuple = name.replace('patch_embed.norm' , 'embeddings.patch_embeddings.layernorm' )
if "layers" in name:
UpperCamelCase_ : List[str] = name.replace('layers' , 'encoder.stages' )
if "residual_group.blocks" in name:
UpperCamelCase_ : Optional[Any] = name.replace('residual_group.blocks' , 'layers' )
if "attn.proj" in name:
UpperCamelCase_ : List[Any] = name.replace('attn.proj' , 'attention.output.dense' )
if "attn" in name:
UpperCamelCase_ : Dict = name.replace('attn' , 'attention.self' )
if "norm1" in name:
UpperCamelCase_ : List[str] = name.replace('norm1' , 'layernorm_before' )
if "norm2" in name:
UpperCamelCase_ : Optional[Any] = name.replace('norm2' , 'layernorm_after' )
if "mlp.fc1" in name:
UpperCamelCase_ : List[str] = name.replace('mlp.fc1' , 'intermediate.dense' )
if "mlp.fc2" in name:
UpperCamelCase_ : Optional[Any] = name.replace('mlp.fc2' , 'output.dense' )
if "q_bias" in name:
UpperCamelCase_ : List[str] = name.replace('q_bias' , 'query.bias' )
if "k_bias" in name:
UpperCamelCase_ : str = name.replace('k_bias' , 'key.bias' )
if "v_bias" in name:
UpperCamelCase_ : Tuple = name.replace('v_bias' , 'value.bias' )
if "cpb_mlp" in name:
UpperCamelCase_ : Optional[int] = name.replace('cpb_mlp' , 'continuous_position_bias_mlp' )
if "patch_embed.proj" in name:
UpperCamelCase_ : Union[str, Any] = name.replace('patch_embed.proj' , 'patch_embed.projection' )
if name == "norm.weight":
UpperCamelCase_ : Dict = 'layernorm.weight'
if name == "norm.bias":
UpperCamelCase_ : List[str] = 'layernorm.bias'
if "conv_first" in name:
UpperCamelCase_ : Any = name.replace('conv_first' , 'first_convolution' )
if (
"upsample" in name
or "conv_before_upsample" in name
or "conv_bicubic" in name
or "conv_up" in name
or "conv_hr" in name
or "conv_last" in name
or "aux" in name
):
# heads
if "conv_last" in name:
UpperCamelCase_ : int = name.replace('conv_last' , 'final_convolution' )
if config.upsampler in ["pixelshuffle", "pixelshuffle_aux", "nearest+conv"]:
if "conv_before_upsample.0" in name:
UpperCamelCase_ : Union[str, Any] = name.replace('conv_before_upsample.0' , 'conv_before_upsample' )
if "upsample.0" in name:
UpperCamelCase_ : Optional[Any] = name.replace('upsample.0' , 'upsample.convolution_0' )
if "upsample.2" in name:
UpperCamelCase_ : Optional[Any] = name.replace('upsample.2' , 'upsample.convolution_1' )
UpperCamelCase_ : Dict = 'upsample.' + name
elif config.upsampler == "pixelshuffledirect":
UpperCamelCase_ : List[Any] = name.replace('upsample.0.weight' , 'upsample.conv.weight' )
UpperCamelCase_ : List[Any] = name.replace('upsample.0.bias' , 'upsample.conv.bias' )
else:
pass
else:
UpperCamelCase_ : Union[str, Any] = 'swin2sr.' + name
return name
def __lowercase ( lowerCamelCase : Dict , lowerCamelCase : str ):
for key in orig_state_dict.copy().keys():
UpperCamelCase_ : Any = orig_state_dict.pop(lowerCamelCase )
if "qkv" in key:
UpperCamelCase_ : List[str] = key.split('.' )
UpperCamelCase_ : Any = int(key_split[1] )
UpperCamelCase_ : Union[str, Any] = int(key_split[4] )
UpperCamelCase_ : str = config.embed_dim
if "weight" in key:
UpperCamelCase_ : str = val[:dim, :]
UpperCamelCase_ : Optional[int] = val[dim : dim * 2, :]
UpperCamelCase_ : Union[str, Any] = val[-dim:, :]
else:
UpperCamelCase_ : Dict = val[:dim]
UpperCamelCase_ : Tuple = val[dim : dim * 2]
UpperCamelCase_ : str = val[-dim:]
pass
else:
UpperCamelCase_ : Tuple = val
return orig_state_dict
def __lowercase ( lowerCamelCase : int , lowerCamelCase : int , lowerCamelCase : str ):
UpperCamelCase_ : int = get_config(lowerCamelCase )
UpperCamelCase_ : Dict = SwinaSRForImageSuperResolution(lowerCamelCase )
model.eval()
UpperCamelCase_ : Any = torch.hub.load_state_dict_from_url(lowerCamelCase , map_location='cpu' )
UpperCamelCase_ : str = convert_state_dict(lowerCamelCase , lowerCamelCase )
UpperCamelCase_, UpperCamelCase_ : List[Any] = model.load_state_dict(lowerCamelCase , strict=lowerCamelCase )
if len(lowerCamelCase ) > 0:
raise ValueError('Missing keys when converting: {}'.format(lowerCamelCase ) )
for key in unexpected_keys:
if not ("relative_position_index" in key or "relative_coords_table" in key or "self_mask" in key):
raise ValueError(F"Unexpected key {key} in state_dict" )
# verify values
UpperCamelCase_ : Tuple = 'https://github.com/mv-lab/swin2sr/blob/main/testsets/real-inputs/shanghai.jpg?raw=true'
UpperCamelCase_ : List[Any] = Image.open(requests.get(lowerCamelCase , stream=lowerCamelCase ).raw ).convert('RGB' )
UpperCamelCase_ : Dict = SwinaSRImageProcessor()
# pixel_values = processor(image, return_tensors="pt").pixel_values
UpperCamelCase_ : Union[str, Any] = 126 if 'Jpeg' in checkpoint_url else 256
UpperCamelCase_ : str = Compose(
[
Resize((image_size, image_size) ),
ToTensor(),
Normalize(mean=[0.4_8_5, 0.4_5_6, 0.4_0_6] , std=[0.2_2_9, 0.2_2_4, 0.2_2_5] ),
] )
UpperCamelCase_ : Union[str, Any] = transforms(lowerCamelCase ).unsqueeze(0 )
if config.num_channels == 1:
UpperCamelCase_ : List[str] = pixel_values[:, 0, :, :].unsqueeze(1 )
UpperCamelCase_ : Optional[Any] = model(lowerCamelCase )
# assert values
if "Swin2SR_ClassicalSR_X2_64" in checkpoint_url:
UpperCamelCase_ : int = torch.Size([1, 3, 512, 512] )
UpperCamelCase_ : List[str] = torch.tensor(
[[-0.7_0_8_7, -0.7_1_3_8, -0.6_7_2_1], [-0.8_3_4_0, -0.8_0_9_5, -0.7_2_9_8], [-0.9_1_4_9, -0.8_4_1_4, -0.7_9_4_0]] )
elif "Swin2SR_ClassicalSR_X4_64" in checkpoint_url:
UpperCamelCase_ : str = torch.Size([1, 3, 1024, 1024] )
UpperCamelCase_ : Optional[Any] = torch.tensor(
[[-0.7_7_7_5, -0.8_1_0_5, -0.8_9_3_3], [-0.7_7_6_4, -0.8_3_5_6, -0.9_2_2_5], [-0.7_9_7_6, -0.8_6_8_6, -0.9_5_7_9]] )
elif "Swin2SR_CompressedSR_X4_48" in checkpoint_url:
# TODO values didn't match exactly here
UpperCamelCase_ : List[str] = torch.Size([1, 3, 1024, 1024] )
UpperCamelCase_ : Tuple = torch.tensor(
[[-0.8_0_3_5, -0.7_5_0_4, -0.7_4_9_1], [-0.8_5_3_8, -0.8_1_2_4, -0.7_7_8_2], [-0.8_8_0_4, -0.8_6_5_1, -0.8_4_9_3]] )
elif "Swin2SR_Lightweight_X2_64" in checkpoint_url:
UpperCamelCase_ : Any = torch.Size([1, 3, 512, 512] )
UpperCamelCase_ : Tuple = torch.tensor(
[[-0.7_6_6_9, -0.8_6_6_2, -0.8_7_6_7], [-0.8_8_1_0, -0.9_9_6_2, -0.9_8_2_0], [-0.9_3_4_0, -1.0_3_2_2, -1.1_1_4_9]] )
elif "Swin2SR_RealworldSR_X4_64_BSRGAN_PSNR" in checkpoint_url:
UpperCamelCase_ : Optional[Any] = torch.Size([1, 3, 1024, 1024] )
UpperCamelCase_ : Tuple = torch.tensor(
[[-0.5_2_3_8, -0.5_5_5_7, -0.6_3_2_1], [-0.6_0_1_6, -0.5_9_0_3, -0.6_3_9_1], [-0.6_2_4_4, -0.6_3_3_4, -0.6_8_8_9]] )
assert (
outputs.reconstruction.shape == expected_shape
), F"Shape of reconstruction should be {expected_shape}, but is {outputs.reconstruction.shape}"
assert torch.allclose(outputs.reconstruction[0, 0, :3, :3] , lowerCamelCase , atol=1e-3 )
print('Looks ok!' )
UpperCamelCase_ : List[Any] = {
'https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_ClassicalSR_X2_64.pth': (
'swin2SR-classical-sr-x2-64'
),
'https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_ClassicalSR_X4_64.pth': (
'swin2SR-classical-sr-x4-64'
),
'https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_CompressedSR_X4_48.pth': (
'swin2SR-compressed-sr-x4-48'
),
'https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_Lightweight_X2_64.pth': (
'swin2SR-lightweight-x2-64'
),
'https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_RealworldSR_X4_64_BSRGAN_PSNR.pth': (
'swin2SR-realworld-sr-x4-64-bsrgan-psnr'
),
}
UpperCamelCase_ : List[str] = url_to_name[checkpoint_url]
if pytorch_dump_folder_path is not None:
print(F"Saving model {model_name} to {pytorch_dump_folder_path}" )
model.save_pretrained(lowerCamelCase )
print(F"Saving image processor to {pytorch_dump_folder_path}" )
processor.save_pretrained(lowerCamelCase )
if push_to_hub:
model.push_to_hub(F"caidas/{model_name}" )
processor.push_to_hub(F"caidas/{model_name}" )
if __name__ == "__main__":
a_ = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--checkpoint_url',
default='https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_ClassicalSR_X2_64.pth',
type=str,
help='URL of the original Swin2SR checkpoint 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 converted model to the hub.')
a_ = parser.parse_args()
convert_swinasr_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)
| 417 | 1 |
'''simple docstring'''
def __lowerCamelCase ( A__ = 1_000 ) -> int:
"""simple docstring"""
UpperCamelCase , UpperCamelCase = 1, 1
UpperCamelCase = []
for i in range(1 , n + 1 ):
UpperCamelCase = prev_numerator + 2 * prev_denominator
UpperCamelCase = prev_numerator + prev_denominator
if len(str(A__ ) ) > len(str(A__ ) ):
result.append(A__ )
UpperCamelCase = numerator
UpperCamelCase = denominator
return len(A__ )
if __name__ == "__main__":
print(f'''{solution() = }''')
| 714 |
'''simple docstring'''
import io
import math
from typing import Dict, Optional, Union
import numpy as np
from huggingface_hub import hf_hub_download
from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import convert_to_rgb, normalize, to_channel_dimension_format, to_pil_image
from ...image_utils import (
ChannelDimension,
ImageInput,
get_image_size,
infer_channel_dimension_format,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_torch_available, is_vision_available, logging
from ...utils.import_utils import requires_backends
if is_vision_available():
import textwrap
from PIL import Image, ImageDraw, ImageFont
if is_torch_available():
import torch
from transformers.pytorch_utils import is_torch_greater_or_equal_than_1_11
else:
_lowerCamelCase : Dict = False
_lowerCamelCase : int = logging.get_logger(__name__)
_lowerCamelCase : Dict = "ybelkada/fonts"
def __lowerCamelCase ( ) -> Optional[int]:
"""simple docstring"""
if is_torch_available() and not is_torch_greater_or_equal_than_1_11:
raise ImportError(
F"""You are using torch=={torch.__version__}, but torch>=1.11.0 is required to use """
'Pix2StructImageProcessor. Please upgrade torch.' )
def __lowerCamelCase ( A__ , A__ , A__ ) -> str:
"""simple docstring"""
requires_backends(A__ , ['torch'] )
_check_torch_version()
UpperCamelCase = image_tensor.unsqueeze(0 )
UpperCamelCase = torch.nn.functional.unfold(A__ , (patch_height, patch_width) , stride=(patch_height, patch_width) )
UpperCamelCase = patches.reshape(image_tensor.size(0 ) , image_tensor.size(1 ) , A__ , A__ , -1 )
UpperCamelCase = patches.permute(0 , 4 , 2 , 3 , 1 ).reshape(
image_tensor.size(2 ) // patch_height , image_tensor.size(3 ) // patch_width , image_tensor.size(1 ) * patch_height * patch_width , )
return patches.unsqueeze(0 )
def __lowerCamelCase ( A__ , A__ = 36 , A__ = "black" , A__ = "white" , A__ = 5 , A__ = 5 , A__ = 5 , A__ = 5 , A__ = None , A__ = None , ) -> Image.Image:
"""simple docstring"""
requires_backends(A__ , 'vision' )
# Add new lines so that each line is no more than 80 characters.
UpperCamelCase = textwrap.TextWrapper(width=80 )
UpperCamelCase = wrapper.wrap(text=A__ )
UpperCamelCase = '\n'.join(A__ )
if font_bytes is not None and font_path is None:
UpperCamelCase = io.BytesIO(A__ )
elif font_path is not None:
UpperCamelCase = font_path
else:
UpperCamelCase = hf_hub_download(A__ , 'Arial.TTF' )
UpperCamelCase = ImageFont.truetype(A__ , encoding='UTF-8' , size=A__ )
# Use a temporary canvas to determine the width and height in pixels when
# rendering the text.
UpperCamelCase = ImageDraw.Draw(Image.new('RGB' , (1, 1) , A__ ) )
UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase = temp_draw.textbbox((0, 0) , A__ , A__ )
# Create the actual image with a bit of padding around the text.
UpperCamelCase = text_width + left_padding + right_padding
UpperCamelCase = text_height + top_padding + bottom_padding
UpperCamelCase = Image.new('RGB' , (image_width, image_height) , A__ )
UpperCamelCase = ImageDraw.Draw(A__ )
draw.text(xy=(left_padding, top_padding) , text=A__ , fill=A__ , font=A__ )
return image
def __lowerCamelCase ( A__ , A__ , **A__ ) -> Optional[Any]:
"""simple docstring"""
requires_backends(A__ , 'vision' )
# Convert to PIL image if necessary
UpperCamelCase = to_pil_image(A__ )
UpperCamelCase = render_text(A__ , **A__ )
UpperCamelCase = max(header_image.width , image.width )
UpperCamelCase = int(image.height * (new_width / image.width) )
UpperCamelCase = int(header_image.height * (new_width / header_image.width) )
UpperCamelCase = Image.new('RGB' , (new_width, new_height + new_header_height) , 'white' )
new_image.paste(header_image.resize((new_width, new_header_height) ) , (0, 0) )
new_image.paste(image.resize((new_width, new_height) ) , (0, new_header_height) )
# Convert back to the original framework if necessary
UpperCamelCase = to_numpy_array(A__ )
if infer_channel_dimension_format(A__ ) == ChannelDimension.LAST:
UpperCamelCase = to_channel_dimension_format(A__ , ChannelDimension.LAST )
return new_image
class SCREAMING_SNAKE_CASE ( _a ):
"""simple docstring"""
_SCREAMING_SNAKE_CASE = ["""flattened_patches"""]
def __init__( self : Any , UpperCamelCase__ : bool = True , UpperCamelCase__ : bool = True , UpperCamelCase__ : Dict[str, int] = None , UpperCamelCase__ : int = 2_0_4_8 , UpperCamelCase__ : bool = False , **UpperCamelCase__ : Any , ):
"""simple docstring"""
super().__init__(**UpperCamelCase__ )
UpperCamelCase = patch_size if patch_size is not None else {'height': 1_6, 'width': 1_6}
UpperCamelCase = do_normalize
UpperCamelCase = do_convert_rgb
UpperCamelCase = max_patches
UpperCamelCase = is_vqa
def A ( self : int , UpperCamelCase__ : np.ndarray , UpperCamelCase__ : int , UpperCamelCase__ : dict , **UpperCamelCase__ : Union[str, Any] ):
"""simple docstring"""
requires_backends(self.extract_flattened_patches , 'torch' )
_check_torch_version()
# convert to torch
UpperCamelCase = to_channel_dimension_format(UpperCamelCase__ , ChannelDimension.FIRST )
UpperCamelCase = torch.from_numpy(UpperCamelCase__ )
UpperCamelCase , UpperCamelCase = patch_size['height'], patch_size['width']
UpperCamelCase , UpperCamelCase = get_image_size(UpperCamelCase__ )
# maximize scale s.t.
UpperCamelCase = math.sqrt(max_patches * (patch_height / image_height) * (patch_width / image_width) )
UpperCamelCase = max(min(math.floor(scale * image_height / patch_height ) , UpperCamelCase__ ) , 1 )
UpperCamelCase = max(min(math.floor(scale * image_width / patch_width ) , UpperCamelCase__ ) , 1 )
UpperCamelCase = max(num_feasible_rows * patch_height , 1 )
UpperCamelCase = max(num_feasible_cols * patch_width , 1 )
UpperCamelCase = torch.nn.functional.interpolate(
image.unsqueeze(0 ) , size=(resized_height, resized_width) , mode='bilinear' , align_corners=UpperCamelCase__ , antialias=UpperCamelCase__ , ).squeeze(0 )
# [1, rows, columns, patch_height * patch_width * image_channels]
UpperCamelCase = torch_extract_patches(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ )
UpperCamelCase = patches.shape
UpperCamelCase = patches_shape[1]
UpperCamelCase = patches_shape[2]
UpperCamelCase = patches_shape[3]
# [rows * columns, patch_height * patch_width * image_channels]
UpperCamelCase = patches.reshape([rows * columns, depth] )
# [rows * columns, 1]
UpperCamelCase = torch.arange(UpperCamelCase__ ).reshape([rows, 1] ).repeat(1 , UpperCamelCase__ ).reshape([rows * columns, 1] )
UpperCamelCase = torch.arange(UpperCamelCase__ ).reshape([1, columns] ).repeat(UpperCamelCase__ , 1 ).reshape([rows * columns, 1] )
# Offset by 1 so the ids do not contain zeros, which represent padding.
row_ids += 1
col_ids += 1
# Prepare additional patch features.
# [rows * columns, 1]
UpperCamelCase = row_ids.to(torch.floataa )
UpperCamelCase = col_ids.to(torch.floataa )
# [rows * columns, 2 + patch_height * patch_width * image_channels]
UpperCamelCase = torch.cat([row_ids, col_ids, patches] , -1 )
# [max_patches, 2 + patch_height * patch_width * image_channels]
UpperCamelCase = torch.nn.functional.pad(UpperCamelCase__ , [0, 0, 0, max_patches - (rows * columns)] ).float()
UpperCamelCase = to_numpy_array(UpperCamelCase__ )
return result
def A ( self : List[Any] , UpperCamelCase__ : np.ndarray , UpperCamelCase__ : Optional[Union[str, ChannelDimension]] = None , **UpperCamelCase__ : Tuple ):
"""simple docstring"""
if image.dtype == np.uinta:
UpperCamelCase = image.astype(np.floataa )
# take mean across the whole `image`
UpperCamelCase = np.mean(UpperCamelCase__ )
UpperCamelCase = np.std(UpperCamelCase__ )
UpperCamelCase = max(UpperCamelCase__ , 1.0 / math.sqrt(np.prod(image.shape ) ) )
return normalize(UpperCamelCase__ , mean=UpperCamelCase__ , std=UpperCamelCase__ , **UpperCamelCase__ )
def A ( self : Union[str, Any] , UpperCamelCase__ : ImageInput , UpperCamelCase__ : Optional[str] = None , UpperCamelCase__ : bool = None , UpperCamelCase__ : Optional[bool] = None , UpperCamelCase__ : Optional[int] = None , UpperCamelCase__ : Optional[Dict[str, int]] = None , UpperCamelCase__ : Optional[Union[str, TensorType]] = None , UpperCamelCase__ : ChannelDimension = ChannelDimension.FIRST , **UpperCamelCase__ : Any , ):
"""simple docstring"""
UpperCamelCase = do_normalize if do_normalize is not None else self.do_normalize
UpperCamelCase = do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb
UpperCamelCase = patch_size if patch_size is not None else self.patch_size
UpperCamelCase = max_patches if max_patches is not None else self.max_patches
UpperCamelCase = self.is_vqa
if kwargs.get('data_format' , UpperCamelCase__ ) is not None:
raise ValueError('data_format is not an accepted input as the outputs are ' )
UpperCamelCase = make_list_of_images(UpperCamelCase__ )
if not valid_images(UpperCamelCase__ ):
raise ValueError(
'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '
'torch.Tensor, tf.Tensor or jax.ndarray.' )
# PIL RGBA images are converted to RGB
if do_convert_rgb:
UpperCamelCase = [convert_to_rgb(UpperCamelCase__ ) for image in images]
# All transformations expect numpy arrays.
UpperCamelCase = [to_numpy_array(UpperCamelCase__ ) for image in images]
if is_vqa:
if header_text is None:
raise ValueError('A header text must be provided for VQA models.' )
UpperCamelCase = kwargs.pop('font_bytes' , UpperCamelCase__ )
UpperCamelCase = kwargs.pop('font_path' , UpperCamelCase__ )
if isinstance(UpperCamelCase__ , UpperCamelCase__ ):
UpperCamelCase = [header_text] * len(UpperCamelCase__ )
UpperCamelCase = [
render_header(UpperCamelCase__ , header_text[i] , font_bytes=UpperCamelCase__ , font_path=UpperCamelCase__ )
for i, image in enumerate(UpperCamelCase__ )
]
if do_normalize:
UpperCamelCase = [self.normalize(image=UpperCamelCase__ ) for image in images]
# convert to torch tensor and permute
UpperCamelCase = [
self.extract_flattened_patches(image=UpperCamelCase__ , max_patches=UpperCamelCase__ , patch_size=UpperCamelCase__ )
for image in images
]
# create attention mask in numpy
UpperCamelCase = [(image.sum(axis=-1 ) != 0).astype(np.floataa ) for image in images]
UpperCamelCase = BatchFeature(
data={'flattened_patches': images, 'attention_mask': attention_masks} , tensor_type=UpperCamelCase__ )
return encoded_outputs
| 324 | 0 |
import os
# Precomputes a list of the 100 first triangular numbers
UpperCamelCase = [int(0.5 * n * (n + 1)) for n in range(1, 101)]
def A ( ) -> Optional[Any]:
UpperCamelCase__ :Optional[int] = os.path.dirname(os.path.realpath(lowercase__ ) )
UpperCamelCase__ :Optional[Any] = os.path.join(lowercase__ , """words.txt""" )
UpperCamelCase__ :Optional[Any] = """"""
with open(lowercase__ ) as f:
UpperCamelCase__ :int = f.readline()
UpperCamelCase__ :Optional[int] = [word.strip("""\"""" ) for word in words.strip("""\r\n""" ).split(""",""" )]
UpperCamelCase__ :Union[str, Any] = [
word
for word in [sum(ord(lowercase__ ) - 64 for x in word ) for word in words]
if word in TRIANGULAR_NUMBERS
]
return len(lowercase__ )
if __name__ == "__main__":
print(solution()) | 45 | import io
import itertools
import json
from dataclasses import dataclass
from typing import Optional
import pyarrow as pa
import pyarrow.json as paj
import datasets
from datasets.table import table_cast
from datasets.utils.file_utils import readline
__lowercase = datasets.utils.logging.get_logger(__name__)
@dataclass
class lowerCamelCase_ ( datasets.BuilderConfig ):
'''simple docstring'''
a__ : Optional[datasets.Features] = None
a__ : str = "utf-8"
a__ : Optional[str] = None
a__ : Optional[str] = None
a__ : bool = True # deprecated
a__ : Optional[int] = None # deprecated
a__ : int = 1_0 << 2_0 # 10MB
a__ : Optional[bool] = None
class lowerCamelCase_ ( datasets.ArrowBasedBuilder ):
'''simple docstring'''
a__ : Optional[int] = JsonConfig
def UpperCamelCase__ ( self) -> int:
if self.config.block_size is not None:
logger.warning('''The JSON loader parameter `block_size` is deprecated. Please use `chunksize` instead''')
__UpperCamelCase :int = self.config.block_size
if self.config.use_threads is not True:
logger.warning(
'''The JSON loader parameter `use_threads` is deprecated and doesn\'t have any effect anymore.''')
if self.config.newlines_in_values is not None:
raise ValueError('''The JSON loader parameter `newlines_in_values` is no longer supported''')
return datasets.DatasetInfo(features=self.config.features)
def UpperCamelCase__ ( self , __lowercase) -> Optional[Any]:
if not self.config.data_files:
raise ValueError(f"""At least one data file must be specified, but got data_files={self.config.data_files}""")
__UpperCamelCase :Optional[Any] = dl_manager.download_and_extract(self.config.data_files)
if isinstance(__lowercase , (str, list, tuple)):
__UpperCamelCase :List[str] = data_files
if isinstance(__lowercase , __lowercase):
__UpperCamelCase :Optional[int] = [files]
__UpperCamelCase :Dict = [dl_manager.iter_files(__lowercase) for file in files]
return [datasets.SplitGenerator(name=datasets.Split.TRAIN , gen_kwargs={'''files''': files})]
__UpperCamelCase :str = []
for split_name, files in data_files.items():
if isinstance(__lowercase , __lowercase):
__UpperCamelCase :str = [files]
__UpperCamelCase :Tuple = [dl_manager.iter_files(__lowercase) for file in files]
splits.append(datasets.SplitGenerator(name=__lowercase , gen_kwargs={'''files''': files}))
return splits
def UpperCamelCase__ ( self , __lowercase) -> pa.Table:
if self.config.features is not None:
# adding missing columns
for column_name in set(self.config.features) - set(pa_table.column_names):
__UpperCamelCase :int = self.config.features.arrow_schema.field(__lowercase).type
__UpperCamelCase :Tuple = pa_table.append_column(__lowercase , pa.array([None] * len(__lowercase) , type=__lowercase))
# more expensive cast to support nested structures with keys in a different order
# allows str <-> int/float or str to Audio for example
__UpperCamelCase :List[str] = table_cast(__lowercase , self.config.features.arrow_schema)
return pa_table
def UpperCamelCase__ ( self , __lowercase) -> str:
for file_idx, file in enumerate(itertools.chain.from_iterable(__lowercase)):
# If the file is one json object and if we need to look at the list of items in one specific field
if self.config.field is not None:
with open(__lowercase , encoding=self.config.encoding , errors=self.config.encoding_errors) as f:
__UpperCamelCase :Optional[Any] = json.load(__lowercase)
# We keep only the field we are interested in
__UpperCamelCase :Union[str, Any] = dataset[self.config.field]
# We accept two format: a list of dicts or a dict of lists
if isinstance(__lowercase , (list, tuple)):
__UpperCamelCase :int = set().union(*[row.keys() for row in dataset])
__UpperCamelCase :Union[str, Any] = {col: [row.get(__lowercase) for row in dataset] for col in keys}
else:
__UpperCamelCase :List[Any] = dataset
__UpperCamelCase :Optional[int] = pa.Table.from_pydict(__lowercase)
yield file_idx, self._cast_table(__lowercase)
# If the file has one json object per line
else:
with open(__lowercase , '''rb''') as f:
__UpperCamelCase :List[str] = 0
# Use block_size equal to the chunk size divided by 32 to leverage multithreading
# Set a default minimum value of 16kB if the chunk size is really small
__UpperCamelCase :Optional[int] = max(self.config.chunksize // 32 , 16 << 10)
__UpperCamelCase :List[str] = (
self.config.encoding_errors if self.config.encoding_errors is not None else '''strict'''
)
while True:
__UpperCamelCase :List[str] = f.read(self.config.chunksize)
if not batch:
break
# Finish current line
try:
batch += f.readline()
except (AttributeError, io.UnsupportedOperation):
batch += readline(__lowercase)
# PyArrow only accepts utf-8 encoded bytes
if self.config.encoding != "utf-8":
__UpperCamelCase :Union[str, Any] = batch.decode(self.config.encoding , errors=__lowercase).encode('''utf-8''')
try:
while True:
try:
__UpperCamelCase :Optional[int] = paj.read_json(
io.BytesIO(__lowercase) , read_options=paj.ReadOptions(block_size=__lowercase))
break
except (pa.ArrowInvalid, pa.ArrowNotImplementedError) as e:
if (
isinstance(__lowercase , pa.ArrowInvalid)
and "straddling" not in str(__lowercase)
or block_size > len(__lowercase)
):
raise
else:
# Increase the block size in case it was too small.
# The block size will be reset for the next file.
logger.debug(
f"""Batch of {len(__lowercase)} bytes couldn't be parsed with block_size={block_size}. Retrying with block_size={block_size * 2}.""")
block_size *= 2
except pa.ArrowInvalid as e:
try:
with open(
__lowercase , encoding=self.config.encoding , errors=self.config.encoding_errors) as f:
__UpperCamelCase :Tuple = json.load(__lowercase)
except json.JSONDecodeError:
logger.error(f"""Failed to read file '{file}' with error {type(__lowercase)}: {e}""")
raise e
# If possible, parse the file as a list of json objects and exit the loop
if isinstance(__lowercase , __lowercase): # list is the only sequence type supported in JSON
try:
__UpperCamelCase :Optional[int] = set().union(*[row.keys() for row in dataset])
__UpperCamelCase :Optional[int] = {col: [row.get(__lowercase) for row in dataset] for col in keys}
__UpperCamelCase :int = pa.Table.from_pydict(__lowercase)
except (pa.ArrowInvalid, AttributeError) as e:
logger.error(f"""Failed to read file '{file}' with error {type(__lowercase)}: {e}""")
raise ValueError(f"""Not able to read records in the JSON file at {file}.""") from None
yield file_idx, self._cast_table(__lowercase)
break
else:
logger.error(f"""Failed to read file '{file}' with error {type(__lowercase)}: {e}""")
raise ValueError(
f"""Not able to read records in the JSON file at {file}. """
f"""You should probably indicate the field of the JSON file containing your records. """
f"""This JSON file contain the following fields: {str(list(dataset.keys()))}. """
f"""Select the correct one and provide it as `field='XXX'` to the dataset loading method. """) from None
# Uncomment for debugging (will print the Arrow table size and elements)
# logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
# logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
yield (file_idx, batch_idx), self._cast_table(__lowercase)
batch_idx += 1
| 167 | 0 |
"""simple docstring"""
import math
import unittest
from transformers import BioGptConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
BioGptForCausalLM,
BioGptForSequenceClassification,
BioGptForTokenClassification,
BioGptModel,
BioGptTokenizer,
)
from transformers.models.biogpt.modeling_biogpt import BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST
class lowerCamelCase__ :
def __init__( self ,A ,A=13 ,A=7 ,A=True ,A=True ,A=False ,A=True ,A=99 ,A=32 ,A=5 ,A=4 ,A=37 ,A="gelu" ,A=0.1 ,A=0.1 ,A=512 ,A=16 ,A=2 ,A=0.02 ,A=3 ,A=4 ,A=None ,):
UpperCAmelCase = parent
UpperCAmelCase = batch_size
UpperCAmelCase = seq_length
UpperCAmelCase = is_training
UpperCAmelCase = use_input_mask
UpperCAmelCase = use_token_type_ids
UpperCAmelCase = use_labels
UpperCAmelCase = vocab_size
UpperCAmelCase = hidden_size
UpperCAmelCase = num_hidden_layers
UpperCAmelCase = num_attention_heads
UpperCAmelCase = intermediate_size
UpperCAmelCase = hidden_act
UpperCAmelCase = hidden_dropout_prob
UpperCAmelCase = attention_probs_dropout_prob
UpperCAmelCase = max_position_embeddings
UpperCAmelCase = type_vocab_size
UpperCAmelCase = type_sequence_label_size
UpperCAmelCase = initializer_range
UpperCAmelCase = num_labels
UpperCAmelCase = num_choices
UpperCAmelCase = scope
def _UpperCamelCase ( self ):
UpperCAmelCase = ids_tensor([self.batch_size, self.seq_length] ,self.vocab_size )
UpperCAmelCase = None
if self.use_input_mask:
UpperCAmelCase = random_attention_mask([self.batch_size, self.seq_length] )
UpperCAmelCase = None
if self.use_token_type_ids:
UpperCAmelCase = ids_tensor([self.batch_size, self.seq_length] ,self.type_vocab_size )
UpperCAmelCase = None
UpperCAmelCase = None
UpperCAmelCase = None
if self.use_labels:
UpperCAmelCase = ids_tensor([self.batch_size] ,self.type_sequence_label_size )
UpperCAmelCase = ids_tensor([self.batch_size, self.seq_length] ,self.num_labels )
UpperCAmelCase = ids_tensor([self.batch_size] ,self.num_choices )
UpperCAmelCase = self.get_config()
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def _UpperCamelCase ( self ):
return BioGptConfig(
vocab_size=self.vocab_size ,hidden_size=self.hidden_size ,num_hidden_layers=self.num_hidden_layers ,num_attention_heads=self.num_attention_heads ,intermediate_size=self.intermediate_size ,hidden_act=self.hidden_act ,hidden_dropout_prob=self.hidden_dropout_prob ,attention_probs_dropout_prob=self.attention_probs_dropout_prob ,max_position_embeddings=self.max_position_embeddings ,type_vocab_size=self.type_vocab_size ,is_decoder=A ,initializer_range=self.initializer_range ,)
def _UpperCamelCase ( self ,A ,A ,A ,A ,A ,A ,A ):
UpperCAmelCase = BioGptModel(config=A )
model.to(A )
model.eval()
UpperCAmelCase = model(A ,attention_mask=A )
UpperCAmelCase = model(A )
self.parent.assertEqual(result.last_hidden_state.shape ,(self.batch_size, self.seq_length, self.hidden_size) )
def _UpperCamelCase ( self ,A ,A ,A ,A ,A ,A ,A ,A ,A ,):
UpperCAmelCase = BioGptForCausalLM(config=A )
model.to(A )
model.eval()
UpperCAmelCase = model(A ,attention_mask=A ,token_type_ids=A ,labels=A )
self.parent.assertEqual(result.logits.shape ,(self.batch_size, self.seq_length, self.vocab_size) )
def _UpperCamelCase ( self ,A ,A ,A ,A ,A ,*A ):
UpperCAmelCase = BioGptModel(config=A )
model.to(A )
model.eval()
# create attention mask
UpperCAmelCase = torch.ones(input_ids.shape ,dtype=torch.long ,device=A )
UpperCAmelCase = self.seq_length // 2
UpperCAmelCase = 0
# first forward pass
UpperCAmelCase , UpperCAmelCase = model(A ,attention_mask=A ).to_tuple()
# create hypothetical next token and extent to next_input_ids
UpperCAmelCase = ids_tensor((self.batch_size, 1) ,config.vocab_size )
# change a random masked slice from input_ids
UpperCAmelCase = ids_tensor((1,) ,A ).item() + 1
UpperCAmelCase = ids_tensor((self.batch_size, 1) ,config.vocab_size ).squeeze(-1 )
UpperCAmelCase = random_other_next_tokens
# append to next input_ids and attn_mask
UpperCAmelCase = torch.cat([input_ids, next_tokens] ,dim=-1 )
UpperCAmelCase = torch.cat(
[attn_mask, torch.ones((attn_mask.shape[0], 1) ,dtype=torch.long ,device=A )] ,dim=1 ,)
# get two different outputs
UpperCAmelCase = model(A ,attention_mask=A )["""last_hidden_state"""]
UpperCAmelCase = model(A ,past_key_values=A ,attention_mask=A )["""last_hidden_state"""]
# select random slice
UpperCAmelCase = ids_tensor((1,) ,output_from_past.shape[-1] ).item()
UpperCAmelCase = output_from_no_past[:, -1, random_slice_idx].detach()
UpperCAmelCase = output_from_past[:, 0, random_slice_idx].detach()
# test that outputs are equal for slice
self.parent.assertTrue(torch.allclose(A ,A ,atol=1e-3 ) )
def _UpperCamelCase ( self ,A ,A ,A ,A ,A ,*A ):
UpperCAmelCase = BioGptModel(config=A ).to(A ).eval()
UpperCAmelCase = torch.ones(input_ids.shape ,dtype=torch.long ,device=A )
# first forward pass
UpperCAmelCase = model(A ,attention_mask=A ,use_cache=A )
UpperCAmelCase , UpperCAmelCase = outputs.to_tuple()
# create hypothetical multiple next token and extent to next_input_ids
UpperCAmelCase = ids_tensor((self.batch_size, 3) ,config.vocab_size )
UpperCAmelCase = ids_tensor((self.batch_size, 3) ,2 )
# append to next input_ids and
UpperCAmelCase = torch.cat([input_ids, next_tokens] ,dim=-1 )
UpperCAmelCase = torch.cat([attention_mask, next_attn_mask] ,dim=-1 )
UpperCAmelCase = model(A ,attention_mask=A )["""last_hidden_state"""]
UpperCAmelCase = model(A ,attention_mask=A ,past_key_values=A )[
"""last_hidden_state"""
]
# select random slice
UpperCAmelCase = ids_tensor((1,) ,output_from_past.shape[-1] ).item()
UpperCAmelCase = output_from_no_past[:, -3:, random_slice_idx].detach()
UpperCAmelCase = output_from_past[:, :, random_slice_idx].detach()
self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1] )
# test that outputs are equal for slice
self.parent.assertTrue(torch.allclose(A ,A ,atol=1e-3 ) )
def _UpperCamelCase ( self ,A ,A ,A ,A ,A ,*A ,A=False ):
UpperCAmelCase = BioGptForCausalLM(A )
model.to(A )
if gradient_checkpointing:
model.gradient_checkpointing_enable()
UpperCAmelCase = model(A ,labels=A )
self.parent.assertEqual(result.loss.shape ,() )
self.parent.assertEqual(result.logits.shape ,(self.batch_size, self.seq_length, self.vocab_size) )
result.loss.backward()
def _UpperCamelCase ( self ,A ,*A ):
UpperCAmelCase = BioGptModel(A )
UpperCAmelCase = model.config.initializer_range / math.sqrt(2 * model.config.num_hidden_layers )
for key in model.state_dict().keys():
if "c_proj" in key and "weight" in key:
self.parent.assertLessEqual(abs(torch.std(model.state_dict()[key] ) - model_std ) ,0.001 )
self.parent.assertLessEqual(abs(torch.mean(model.state_dict()[key] ) - 0.0 ) ,0.01 )
def _UpperCamelCase ( self ,A ,A ,A ,A ,A ,*A ):
UpperCAmelCase = self.num_labels
UpperCAmelCase = BioGptForTokenClassification(A )
model.to(A )
model.eval()
UpperCAmelCase = model(A ,attention_mask=A ,token_type_ids=A )
self.parent.assertEqual(result.logits.shape ,(self.batch_size, self.seq_length, self.num_labels) )
def _UpperCamelCase ( self ):
UpperCAmelCase = self.prepare_config_and_inputs()
(
(
UpperCAmelCase
) , (
UpperCAmelCase
) , (
UpperCAmelCase
) , (
UpperCAmelCase
) , (
UpperCAmelCase
) , (
UpperCAmelCase
) , (
UpperCAmelCase
) ,
) = config_and_inputs
UpperCAmelCase = {"""input_ids""": input_ids, """attention_mask""": input_mask}
return config, inputs_dict
@require_torch
class lowerCamelCase__ ( snake_case , snake_case , snake_case , unittest.TestCase ):
SCREAMING_SNAKE_CASE = (
(BioGptModel, BioGptForCausalLM, BioGptForSequenceClassification, BioGptForTokenClassification)
if is_torch_available()
else ()
)
SCREAMING_SNAKE_CASE = (BioGptForCausalLM,) if is_torch_available() else ()
SCREAMING_SNAKE_CASE = (
{
'''feature-extraction''': BioGptModel,
'''text-classification''': BioGptForSequenceClassification,
'''text-generation''': BioGptForCausalLM,
'''token-classification''': BioGptForTokenClassification,
'''zero-shot''': BioGptForSequenceClassification,
}
if is_torch_available()
else {}
)
SCREAMING_SNAKE_CASE = False
def _UpperCamelCase ( self ):
UpperCAmelCase = BioGptModelTester(self )
UpperCAmelCase = ConfigTester(self ,config_class=A ,hidden_size=37 )
def _UpperCamelCase ( self ):
self.config_tester.run_common_tests()
def _UpperCamelCase ( self ):
UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*A )
def _UpperCamelCase ( self ):
UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
for type in ["absolute", "relative_key", "relative_key_query"]:
UpperCAmelCase = type
self.model_tester.create_and_check_model(*A )
def _UpperCamelCase ( self ):
UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_biogpt_model_attention_mask_past(*A )
def _UpperCamelCase ( self ):
UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_forward_and_backwards(*A ,gradient_checkpointing=A )
def _UpperCamelCase ( self ):
UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_biogpt_model_past_large_inputs(*A )
def _UpperCamelCase ( self ):
UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_biogpt_weight_initialization(*A )
def _UpperCamelCase ( self ):
UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_biogpt_for_token_classification(*A )
@slow
def _UpperCamelCase ( self ):
UpperCAmelCase = BioGptForCausalLM.from_pretrained("""microsoft/biogpt""" )
model.to(A )
UpperCAmelCase = BioGptTokenizer.from_pretrained("""microsoft/biogpt""" )
UpperCAmelCase = """left"""
# Define PAD Token = EOS Token = 50256
UpperCAmelCase = tokenizer.eos_token
UpperCAmelCase = model.config.eos_token_id
# use different length sentences to test batching
UpperCAmelCase = [
"""Hello, my dog is a little""",
"""Today, I""",
]
UpperCAmelCase = tokenizer(A ,return_tensors="""pt""" ,padding=A )
UpperCAmelCase = inputs["""input_ids"""].to(A )
UpperCAmelCase = model.generate(
input_ids=A ,attention_mask=inputs["""attention_mask"""].to(A ) ,)
UpperCAmelCase = tokenizer(sentences[0] ,return_tensors="""pt""" ).input_ids.to(A )
UpperCAmelCase = model.generate(input_ids=A )
UpperCAmelCase = inputs_non_padded.shape[-1] - inputs["""attention_mask"""][-1].long().sum().cpu().item()
UpperCAmelCase = tokenizer(sentences[1] ,return_tensors="""pt""" ).input_ids.to(A )
UpperCAmelCase = model.generate(input_ids=A ,max_length=model.config.max_length - num_paddings )
UpperCAmelCase = tokenizer.batch_decode(A ,skip_special_tokens=A )
UpperCAmelCase = tokenizer.decode(output_non_padded[0] ,skip_special_tokens=A )
UpperCAmelCase = tokenizer.decode(output_padded[0] ,skip_special_tokens=A )
UpperCAmelCase = [
"""Hello, my dog is a little bit bigger than a little bit.""",
"""Today, I have a good idea of how to use the information""",
]
self.assertListEqual(A ,A )
self.assertListEqual(A ,[non_padded_sentence, padded_sentence] )
@slow
def _UpperCamelCase ( self ):
for model_name in BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
UpperCAmelCase = BioGptModel.from_pretrained(A )
self.assertIsNotNone(A )
def _UpperCamelCase ( self ):
UpperCAmelCase , UpperCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
UpperCAmelCase = 3
UpperCAmelCase = input_dict["""input_ids"""]
UpperCAmelCase = input_ids.ne(1 ).to(A )
UpperCAmelCase = ids_tensor([self.model_tester.batch_size] ,self.model_tester.type_sequence_label_size )
UpperCAmelCase = BioGptForSequenceClassification(A )
model.to(A )
model.eval()
UpperCAmelCase = model(A ,attention_mask=A ,labels=A )
self.assertEqual(result.logits.shape ,(self.model_tester.batch_size, self.model_tester.num_labels) )
def _UpperCamelCase ( self ):
UpperCAmelCase , UpperCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
UpperCAmelCase = 3
UpperCAmelCase = """multi_label_classification"""
UpperCAmelCase = input_dict["""input_ids"""]
UpperCAmelCase = input_ids.ne(1 ).to(A )
UpperCAmelCase = ids_tensor(
[self.model_tester.batch_size, config.num_labels] ,self.model_tester.type_sequence_label_size ).to(torch.float )
UpperCAmelCase = BioGptForSequenceClassification(A )
model.to(A )
model.eval()
UpperCAmelCase = model(A ,attention_mask=A ,labels=A )
self.assertEqual(result.logits.shape ,(self.model_tester.batch_size, self.model_tester.num_labels) )
@require_torch
class lowerCamelCase__ ( unittest.TestCase ):
@slow
def _UpperCamelCase ( self ):
UpperCAmelCase = BioGptForCausalLM.from_pretrained("""microsoft/biogpt""" )
UpperCAmelCase = torch.tensor([[2, 4_805, 9, 656, 21]] )
UpperCAmelCase = model(A )[0]
UpperCAmelCase = 42_384
UpperCAmelCase = torch.Size((1, 5, vocab_size) )
self.assertEqual(output.shape ,A )
UpperCAmelCase = torch.tensor(
[[[-9.5236, -9.8918, 10.4557], [-11.0469, -9.6423, 8.1022], [-8.8664, -7.8826, 5.5325]]] )
self.assertTrue(torch.allclose(output[:, :3, :3] ,A ,atol=1e-4 ) )
@slow
def _UpperCamelCase ( self ):
UpperCAmelCase = BioGptTokenizer.from_pretrained("""microsoft/biogpt""" )
UpperCAmelCase = BioGptForCausalLM.from_pretrained("""microsoft/biogpt""" )
model.to(A )
torch.manual_seed(0 )
UpperCAmelCase = tokenizer("""COVID-19 is""" ,return_tensors="""pt""" ).to(A )
UpperCAmelCase = model.generate(
**A ,min_length=100 ,max_length=1_024 ,num_beams=5 ,early_stopping=A ,)
UpperCAmelCase = tokenizer.decode(output_ids[0] ,skip_special_tokens=A )
UpperCAmelCase = (
"""COVID-19 is a global pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the"""
""" causative agent of coronavirus disease 2019 (COVID-19), which has spread to more than 200 countries and"""
""" territories, including the United States (US), Canada, Australia, New Zealand, the United Kingdom (UK),"""
""" and the United States of America (USA), as of March 11, 2020, with more than 800,000 confirmed cases and"""
""" more than 800,000 deaths."""
)
self.assertEqual(A ,A )
| 74 |
"""simple docstring"""
from __future__ import annotations
from collections.abc import Generator
import requests
from bsa import BeautifulSoup
_UpperCamelCase = """https://www.indeed.co.in/jobs?q=mobile+app+development&l="""
def _a ( _snake_case = "mumbai" ):
"""simple docstring"""
UpperCAmelCase = BeautifulSoup(requests.get(url + location ).content , """html.parser""" )
# This attribute finds out all the specifics listed in a job
for job in soup.find_all("""div""" , attrs={"""data-tn-component""": """organicJob"""} ):
UpperCAmelCase = job.find("""a""" , attrs={"""data-tn-element""": """jobTitle"""} ).text.strip()
UpperCAmelCase = job.find("""span""" , {"""class""": """company"""} ).text.strip()
yield job_title, company_name
if __name__ == "__main__":
for i, job in enumerate(fetch_jobs("""Bangalore"""), 1):
print(F"""Job {i:>2} is {job[0]} at {job[1]}""")
| 74 | 1 |
"""simple docstring"""
from __future__ import annotations
def A__ ( __lowerCamelCase, __lowerCamelCase ):
"""simple docstring"""
_lowerCAmelCase = get_failure_array(__lowerCamelCase )
# 2) Step through text searching for pattern
_lowerCAmelCase = 0, 0 # index into text, pattern
while i < len(__lowerCamelCase ):
if pattern[j] == text[i]:
if j == (len(__lowerCamelCase ) - 1):
return True
j += 1
# if this is a prefix in our pattern
# just go back far enough to continue
elif j > 0:
_lowerCAmelCase = failure[j - 1]
continue
i += 1
return False
def A__ ( __lowerCamelCase ):
"""simple docstring"""
_lowerCAmelCase = [0]
_lowerCAmelCase = 0
_lowerCAmelCase = 1
while j < len(__lowerCamelCase ):
if pattern[i] == pattern[j]:
i += 1
elif i > 0:
_lowerCAmelCase = failure[i - 1]
continue
j += 1
failure.append(__lowerCamelCase )
return failure
if __name__ == "__main__":
# Test 1)
a__ : Optional[int] = """abc1abc12"""
a__ : Any = """alskfjaldsabc1abc1abc12k23adsfabcabc"""
a__ : Any = """alskfjaldsk23adsfabcabc"""
assert kmp(pattern, texta) and not kmp(pattern, texta)
# Test 2)
a__ : List[Any] = """ABABX"""
a__ : Tuple = """ABABZABABYABABX"""
assert kmp(pattern, text)
# Test 3)
a__ : List[Any] = """AAAB"""
a__ : str = """ABAAAAAB"""
assert kmp(pattern, text)
# Test 4)
a__ : List[str] = """abcdabcy"""
a__ : List[str] = """abcxabcdabxabcdabcdabcy"""
assert kmp(pattern, text)
# Test 5)
a__ : Tuple = """aabaabaaa"""
assert get_failure_array(pattern) == [0, 1, 0, 1, 2, 3, 4, 5, 2]
| 589 | '''simple docstring'''
from sklearn.metrics import fa_score
import datasets
lowerCAmelCase_ : int = """
The F1 score is the harmonic mean of the precision and recall. It can be computed with the equation:
F1 = 2 * (precision * recall) / (precision + recall)
"""
lowerCAmelCase_ : Optional[int] = """
Args:
predictions (`list` of `int`): Predicted labels.
references (`list` of `int`): Ground truth labels.
labels (`list` of `int`): The set of labels to include when `average` is not set to `'binary'`, and the order of the labels if `average` is `None`. Labels present in the data can be excluded, for example to calculate a multiclass average ignoring a majority negative class. Labels not present in the data will result in 0 components in a macro average. For multilabel targets, labels are column indices. By default, all labels in `predictions` and `references` are used in sorted order. Defaults to None.
pos_label (`int`): The class to be considered the positive class, in the case where `average` is set to `binary`. Defaults to 1.
average (`string`): This parameter is required for multiclass/multilabel targets. If set to `None`, the scores for each class are returned. Otherwise, this determines the type of averaging performed on the data. Defaults to `'binary'`.
- 'binary': Only report results for the class specified by `pos_label`. This is applicable only if the classes found in `predictions` and `references` are binary.
- 'micro': Calculate metrics globally by counting the total true positives, false negatives and false positives.
- 'macro': Calculate metrics for each label, and find their unweighted mean. This does not take label imbalance into account.
- 'weighted': Calculate metrics for each label, and find their average weighted by support (the number of true instances for each label). This alters `'macro'` to account for label imbalance. This option can result in an F-score that is not between precision and recall.
- 'samples': Calculate metrics for each instance, and find their average (only meaningful for multilabel classification).
sample_weight (`list` of `float`): Sample weights Defaults to None.
Returns:
f1 (`float` or `array` of `float`): F1 score or list of f1 scores, depending on the value passed to `average`. Minimum possible value is 0. Maximum possible value is 1. Higher f1 scores are better.
Examples:
Example 1-A simple binary example
>>> f1_metric = datasets.load_metric(\"f1\")
>>> results = f1_metric.compute(references=[0, 1, 0, 1, 0], predictions=[0, 0, 1, 1, 0])
>>> print(results)
{'f1': 0.5}
Example 2-The same simple binary example as in Example 1, but with `pos_label` set to `0`.
>>> f1_metric = datasets.load_metric(\"f1\")
>>> results = f1_metric.compute(references=[0, 1, 0, 1, 0], predictions=[0, 0, 1, 1, 0], pos_label=0)
>>> print(round(results['f1'], 2))
0.67
Example 3-The same simple binary example as in Example 1, but with `sample_weight` included.
>>> f1_metric = datasets.load_metric(\"f1\")
>>> results = f1_metric.compute(references=[0, 1, 0, 1, 0], predictions=[0, 0, 1, 1, 0], sample_weight=[0.9, 0.5, 3.9, 1.2, 0.3])
>>> print(round(results['f1'], 2))
0.35
Example 4-A multiclass example, with different values for the `average` input.
>>> predictions = [0, 2, 1, 0, 0, 1]
>>> references = [0, 1, 2, 0, 1, 2]
>>> results = f1_metric.compute(predictions=predictions, references=references, average=\"macro\")
>>> print(round(results['f1'], 2))
0.27
>>> results = f1_metric.compute(predictions=predictions, references=references, average=\"micro\")
>>> print(round(results['f1'], 2))
0.33
>>> results = f1_metric.compute(predictions=predictions, references=references, average=\"weighted\")
>>> print(round(results['f1'], 2))
0.27
>>> results = f1_metric.compute(predictions=predictions, references=references, average=None)
>>> print(results)
{'f1': array([0.8, 0. , 0. ])}
"""
lowerCAmelCase_ : Any = """
@article{scikit-learn,
title={Scikit-learn: Machine Learning in {P}ython},
author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.
and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.
and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and
Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},
journal={Journal of Machine Learning Research},
volume={12},
pages={2825--2830},
year={2011}
}
"""
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class SCREAMING_SNAKE_CASE ( datasets.Metric ):
'''simple docstring'''
def snake_case__ ( self : str ) ->Union[str, Any]:
'''simple docstring'''
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
"predictions": datasets.Sequence(datasets.Value("int32" ) ),
"references": datasets.Sequence(datasets.Value("int32" ) ),
}
if self.config_name == "multilabel"
else {
"predictions": datasets.Value("int32" ),
"references": datasets.Value("int32" ),
} ) , reference_urls=["https://scikit-learn.org/stable/modules/generated/sklearn.metrics.f1_score.html"] , )
def snake_case__ ( self : Optional[int] , lowercase__ : Any , lowercase__ : Tuple , lowercase__ : Optional[Any]=None , lowercase__ : List[str]=1 , lowercase__ : Optional[int]="binary" , lowercase__ : int=None ) ->int:
'''simple docstring'''
_UpperCamelCase : List[str] = fa_score(
lowercase__ , lowercase__ , labels=lowercase__ , pos_label=lowercase__ , average=lowercase__ , sample_weight=lowercase__ )
return {"f1": float(lowercase__ ) if score.size == 1 else score}
| 435 | 0 |
"""simple docstring"""
def lowerCamelCase_ ( __lowerCAmelCase = 100_0000 ) -> List[str]:
'''simple docstring'''
lowerCamelCase__ =1
lowerCamelCase__ =1
lowerCamelCase__ ={1: 1}
for inputa in range(2 , _lowerCAmelCase ):
lowerCamelCase__ =0
lowerCamelCase__ =inputa
while True:
if number in counters:
counter += counters[number]
break
if number % 2 == 0:
number //= 2
counter += 1
else:
lowerCamelCase__ =(3 * number) + 1
counter += 1
if inputa not in counters:
lowerCamelCase__ =counter
if counter > pre_counter:
lowerCamelCase__ =inputa
lowerCamelCase__ =counter
return largest_number
if __name__ == "__main__":
print(solution(int(input().strip())))
| 701 | """simple docstring"""
from collections.abc import Sequence
from queue import Queue
class __UpperCAmelCase :
def __init__( self , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase=None , _lowerCamelCase=None ):
lowerCamelCase__ =start
lowerCamelCase__ =end
lowerCamelCase__ =val
lowerCamelCase__ =(start + end) // 2
lowerCamelCase__ =left
lowerCamelCase__ =right
def __repr__( self ):
return F'''SegmentTreeNode(start={self.start}, end={self.end}, val={self.val})'''
class __UpperCAmelCase :
def __init__( self , _lowerCamelCase , _lowerCamelCase ):
lowerCamelCase__ =collection
lowerCamelCase__ =function
if self.collection:
lowerCamelCase__ =self._build_tree(0 , len(_lowerCamelCase ) - 1 )
def _a ( self , _lowerCamelCase , _lowerCamelCase ):
self._update_tree(self.root , _lowerCamelCase , _lowerCamelCase )
def _a ( self , _lowerCamelCase , _lowerCamelCase ):
return self._query_range(self.root , _lowerCamelCase , _lowerCamelCase )
def _a ( self , _lowerCamelCase , _lowerCamelCase ):
if start == end:
return SegmentTreeNode(_lowerCamelCase , _lowerCamelCase , self.collection[start] )
lowerCamelCase__ =(start + end) // 2
lowerCamelCase__ =self._build_tree(_lowerCamelCase , _lowerCamelCase )
lowerCamelCase__ =self._build_tree(mid + 1 , _lowerCamelCase )
return SegmentTreeNode(_lowerCamelCase , _lowerCamelCase , self.fn(left.val , right.val ) , _lowerCamelCase , _lowerCamelCase )
def _a ( self , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase ):
if node.start == i and node.end == i:
lowerCamelCase__ =val
return
if i <= node.mid:
self._update_tree(node.left , _lowerCamelCase , _lowerCamelCase )
else:
self._update_tree(node.right , _lowerCamelCase , _lowerCamelCase )
lowerCamelCase__ =self.fn(node.left.val , node.right.val )
def _a ( self , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase ):
if node.start == i and node.end == j:
return node.val
if i <= node.mid:
if j <= node.mid:
# range in left child tree
return self._query_range(node.left , _lowerCamelCase , _lowerCamelCase )
else:
# range in left child tree and right child tree
return self.fn(
self._query_range(node.left , _lowerCamelCase , node.mid ) , self._query_range(node.right , node.mid + 1 , _lowerCamelCase ) , )
else:
# range in right child tree
return self._query_range(node.right , _lowerCamelCase , _lowerCamelCase )
def _a ( self ):
if self.root is not None:
lowerCamelCase__ =Queue()
queue.put(self.root )
while not queue.empty():
lowerCamelCase__ =queue.get()
yield node
if node.left is not None:
queue.put(node.left )
if node.right is not None:
queue.put(node.right )
if __name__ == "__main__":
import operator
for fn in [operator.add, max, min]:
print('*' * 50)
a =SegmentTree([2, 1, 5, 3, 4], fn)
for node in arr.traverse():
print(node)
print()
arr.update(1, 5)
for node in arr.traverse():
print(node)
print()
print(arr.query_range(3, 4)) # 7
print(arr.query_range(2, 2)) # 5
print(arr.query_range(1, 3)) # 13
print()
| 132 | 0 |
import unittest
from transformers import BigBirdTokenizer, BigBirdTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
lowerCamelCase = '▁'
lowerCamelCase = get_tests_dir('fixtures/test_sentencepiece.model')
@require_sentencepiece
@require_tokenizers
class A ( UpperCamelCase_ , unittest.TestCase ):
UpperCamelCase__ : Optional[int] =BigBirdTokenizer
UpperCamelCase__ : str =BigBirdTokenizerFast
UpperCamelCase__ : Tuple =True
UpperCamelCase__ : Optional[Any] =True
def lowerCamelCase ( self : List[Any] ) -> Tuple:
"""simple docstring"""
super().setUp()
_lowerCamelCase : str =self.tokenizer_class(__A , keep_accents=__A )
tokenizer.save_pretrained(self.tmpdirname )
def lowerCamelCase ( self : List[Any] ) -> List[Any]:
"""simple docstring"""
_lowerCamelCase : str ='''<s>'''
_lowerCamelCase : Union[str, Any] =1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(__A ) , __A )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(__A ) , __A )
def lowerCamelCase ( self : Union[str, Any] ) -> Any:
"""simple docstring"""
_lowerCamelCase : str =list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] , '<unk>' )
self.assertEqual(vocab_keys[1] , '<s>' )
self.assertEqual(vocab_keys[-1] , '[MASK]' )
self.assertEqual(len(__A ) , 1004 )
def lowerCamelCase ( self : Any ) -> Optional[Any]:
"""simple docstring"""
self.assertEqual(self.get_tokenizer().vocab_size , 1000 )
def lowerCamelCase ( self : List[str] ) -> str:
"""simple docstring"""
if not self.test_rust_tokenizer:
return
_lowerCamelCase : Dict =self.get_tokenizer()
_lowerCamelCase : List[Any] =self.get_rust_tokenizer()
_lowerCamelCase : Union[str, Any] ='''I was born in 92000, and this is falsé.'''
_lowerCamelCase : List[Any] =tokenizer.tokenize(__A )
_lowerCamelCase : Optional[int] =rust_tokenizer.tokenize(__A )
self.assertListEqual(__A , __A )
_lowerCamelCase : Union[str, Any] =tokenizer.encode(__A , add_special_tokens=__A )
_lowerCamelCase : List[Any] =rust_tokenizer.encode(__A , add_special_tokens=__A )
self.assertListEqual(__A , __A )
_lowerCamelCase : Dict =self.get_rust_tokenizer()
_lowerCamelCase : int =tokenizer.encode(__A )
_lowerCamelCase : Dict =rust_tokenizer.encode(__A )
self.assertListEqual(__A , __A )
def lowerCamelCase ( self : List[str] ) -> str:
"""simple docstring"""
_lowerCamelCase : Optional[Any] =BigBirdTokenizer(__A , keep_accents=__A )
_lowerCamelCase : str =tokenizer.tokenize('This is a test' )
self.assertListEqual(__A , ['▁This', '▁is', '▁a', '▁t', 'est'] )
self.assertListEqual(
tokenizer.convert_tokens_to_ids(__A ) , [285, 46, 10, 170, 382] , )
_lowerCamelCase : List[Any] =tokenizer.tokenize('I was born in 92000, and this is falsé.' )
self.assertListEqual(
__A , [
SPIECE_UNDERLINE + 'I',
SPIECE_UNDERLINE + 'was',
SPIECE_UNDERLINE + 'b',
'or',
'n',
SPIECE_UNDERLINE + 'in',
SPIECE_UNDERLINE + '',
'9',
'2',
'0',
'0',
'0',
',',
SPIECE_UNDERLINE + 'and',
SPIECE_UNDERLINE + 'this',
SPIECE_UNDERLINE + 'is',
SPIECE_UNDERLINE + 'f',
'al',
's',
'é',
'.',
] , )
_lowerCamelCase : Dict =tokenizer.convert_tokens_to_ids(__A )
self.assertListEqual(
__A , [8, 21, 84, 55, 24, 19, 7, 0, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 0, 4] , )
_lowerCamelCase : Optional[Any] =tokenizer.convert_ids_to_tokens(__A )
self.assertListEqual(
__A , [
SPIECE_UNDERLINE + 'I',
SPIECE_UNDERLINE + 'was',
SPIECE_UNDERLINE + 'b',
'or',
'n',
SPIECE_UNDERLINE + 'in',
SPIECE_UNDERLINE + '',
'<unk>',
'2',
'0',
'0',
'0',
',',
SPIECE_UNDERLINE + 'and',
SPIECE_UNDERLINE + 'this',
SPIECE_UNDERLINE + 'is',
SPIECE_UNDERLINE + 'f',
'al',
's',
'<unk>',
'.',
] , )
@cached_property
def lowerCamelCase ( self : Optional[Any] ) -> Union[str, Any]:
"""simple docstring"""
return BigBirdTokenizer.from_pretrained('google/bigbird-roberta-base' )
@slow
def lowerCamelCase ( self : str ) -> Any:
"""simple docstring"""
_lowerCamelCase : str ='''Hello World!'''
_lowerCamelCase : Dict =[65, 1_8536, 2260, 101, 66]
self.assertListEqual(__A , self.big_tokenizer.encode(__A ) )
@slow
def lowerCamelCase ( self : List[str] ) -> Optional[int]:
"""simple docstring"""
_lowerCamelCase : Union[str, Any] =(
'''This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) " [ ] ! : - . Also we will'''
''' add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth'''
)
# fmt: off
_lowerCamelCase : List[Any] =[65, 871, 419, 358, 946, 991, 2521, 452, 358, 1357, 387, 7751, 3536, 112, 985, 456, 126, 865, 938, 5400, 5734, 458, 1368, 467, 786, 2462, 5246, 1159, 633, 865, 4519, 457, 582, 852, 2557, 427, 916, 508, 405, 3_4324, 497, 391, 408, 1_1342, 1244, 385, 100, 938, 985, 456, 574, 362, 1_2597, 3200, 3129, 1172, 66] # noqa: E231
# fmt: on
self.assertListEqual(__A , self.big_tokenizer.encode(__A ) )
@require_torch
@slow
def lowerCamelCase ( self : str ) -> int:
"""simple docstring"""
import torch
from transformers import BigBirdConfig, BigBirdModel
# Build sequence
_lowerCamelCase : Optional[int] =list(self.big_tokenizer.get_vocab().keys() )[:10]
_lowerCamelCase : Any =''' '''.join(__A )
_lowerCamelCase : int =self.big_tokenizer.encode_plus(__A , return_tensors='pt' , return_token_type_ids=__A )
_lowerCamelCase : Tuple =self.big_tokenizer.batch_encode_plus(
[sequence + ' ' + sequence] , return_tensors='pt' , return_token_type_ids=__A )
_lowerCamelCase : Union[str, Any] =BigBirdConfig(attention_type='original_full' )
_lowerCamelCase : Tuple =BigBirdModel(__A )
assert model.get_input_embeddings().weight.shape[0] >= self.big_tokenizer.vocab_size
with torch.no_grad():
model(**__A )
model(**__A )
@slow
def lowerCamelCase ( self : Optional[int] ) -> Tuple:
"""simple docstring"""
_lowerCamelCase : Any =BigBirdTokenizer.from_pretrained('google/bigbird-roberta-base' )
_lowerCamelCase : List[Any] =tokenizer.decode(tokenizer('Paris is the [MASK].' ).input_ids )
self.assertTrue(decoded_text == '[CLS] Paris is the[MASK].[SEP]' )
@slow
def lowerCamelCase ( self : Optional[int] ) -> Any:
"""simple docstring"""
_lowerCamelCase : Optional[int] ={'''input_ids''': [[65, 3_9286, 458, 3_6335, 2001, 456, 1_3073, 1_3266, 455, 113, 7746, 1741, 1_1157, 391, 1_3073, 1_3266, 455, 113, 3967, 3_5412, 113, 4936, 109, 3870, 2377, 113, 3_0084, 4_5720, 458, 134, 1_7496, 112, 503, 1_1672, 113, 118, 112, 5665, 1_3347, 3_8687, 112, 1496, 3_1389, 112, 3268, 4_7264, 134, 962, 112, 1_6377, 8035, 2_3130, 430, 1_2169, 1_5518, 2_8592, 458, 146, 4_1697, 109, 391, 1_2169, 1_5518, 1_6689, 458, 146, 4_1358, 109, 452, 726, 4034, 111, 763, 3_5412, 5082, 388, 1903, 111, 9051, 391, 2870, 4_8918, 1900, 1123, 550, 998, 112, 9586, 1_5985, 455, 391, 410, 2_2955, 3_7636, 114, 66], [65, 448, 1_7496, 419, 3663, 385, 763, 113, 2_7533, 2870, 3283, 1_3043, 1639, 2_4713, 523, 656, 2_4013, 1_8550, 2521, 517, 2_7014, 2_1244, 420, 1212, 1465, 391, 927, 4833, 388, 578, 1_1786, 114, 66, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [65, 484, 2169, 7687, 2_1932, 1_8146, 726, 363, 1_7032, 3391, 114, 66, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], '''attention_mask''': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]} # noqa: E501
# fmt: on
self.tokenizer_integration_test_util(
expected_encoding=__A , model_name='google/bigbird-roberta-base' , revision='215c99f1600e06f83acce68422f2035b2b5c3510' , )
| 464 |
from collections.abc import Sequence
def SCREAMING_SNAKE_CASE_ ( UpperCAmelCase_ : Sequence[int] | None = None ) -> int:
if nums is None or not nums:
raise ValueError('''Input sequence should not be empty''' )
SCREAMING_SNAKE_CASE_ : Tuple =nums[0]
for i in range(1 , len(UpperCAmelCase_ ) ):
SCREAMING_SNAKE_CASE_ : Any =nums[i]
SCREAMING_SNAKE_CASE_ : Optional[int] =max(UpperCAmelCase_ , ans + num , UpperCAmelCase_ )
return ans
if __name__ == "__main__":
import doctest
doctest.testmod()
# Try on a sample input from the user
_lowercase = int(input("""Enter number of elements : """).strip())
_lowercase = list(map(int, input("""\nEnter the numbers : """).strip().split()))[:n]
print(max_subsequence_sum(array))
| 443 | 0 |
'''simple docstring'''
import numpy as np
from cva import COLOR_BGR2GRAY, CV_8UC3, cvtColor, filteraD, imread, imshow, waitKey
def SCREAMING_SNAKE_CASE ( lowercase_ : int , lowercase_ : int , lowercase_ : int , lowercase_ : int , lowercase_ : int , lowercase_ : int ):
# prepare kernel
# the kernel size have to be odd
if (ksize % 2) == 0:
lowercase = ksize + 1
lowercase = np.zeros((ksize, ksize) , dtype=np.floataa )
# each value
for y in range(lowercase_ ):
for x in range(lowercase_ ):
# distance from center
lowercase = x - ksize // 2
lowercase = y - ksize // 2
# degree to radiant
lowercase = theta / 180 * np.pi
lowercase = np.cos(_theta )
lowercase = np.sin(_theta )
# get kernel x
lowercase = cos_theta * px + sin_theta * py
# get kernel y
lowercase = -sin_theta * px + cos_theta * py
# fill kernel
lowercase = np.exp(
-(_x**2 + gamma**2 * _y**2) / (2 * sigma**2) ) * np.cos(2 * np.pi * _x / lambd + psi )
return gabor
if __name__ == "__main__":
import doctest
doctest.testmod()
# read original image
lowercase_ : List[Any] = imread('''../image_data/lena.jpg''')
# turn image in gray scale value
lowercase_ : str = cvtColor(img, COLOR_BGR2GRAY)
# Apply multiple Kernel to detect edges
lowercase_ : Union[str, Any] = np.zeros(gray.shape[:2])
for theta in [0, 30, 60, 90, 120, 150]:
lowercase_ : Dict = gabor_filter_kernel(10, 8, theta, 10, 0, 0)
out += filteraD(gray, CV_8UC3, kernel_aa)
lowercase_ : Tuple = out / out.max() * 255
lowercase_ : Dict = out.astype(np.uinta)
imshow('''Original''', gray)
imshow('''Gabor filter with 20x20 mask and 6 directions''', out)
waitKey(0)
| 703 |
'''simple docstring'''
import json
import logging
import os
import socket
import git
import numpy as np
import torch
logging.basicConfig(
format='''%(asctime)s - %(levelname)s - %(name)s - PID: %(process)d - %(message)s''',
datefmt='''%m/%d/%Y %H:%M:%S''',
level=logging.INFO,
)
lowercase_ : Tuple = logging.getLogger(__name__)
def SCREAMING_SNAKE_CASE ( lowercase_ : str ):
lowercase = git.Repo(search_parent_directories=lowercase_ )
lowercase = {
"""repo_id""": str(lowercase_ ),
"""repo_sha""": str(repo.head.object.hexsha ),
"""repo_branch""": str(repo.active_branch ),
}
with open(os.path.join(lowercase_ , """git_log.json""" ) , """w""" ) as f:
json.dump(lowercase_ , lowercase_ , indent=4 )
def SCREAMING_SNAKE_CASE ( lowercase_ : str ):
if params.n_gpu <= 0:
lowercase = 0
lowercase = -1
lowercase = True
lowercase = False
return
assert torch.cuda.is_available()
logger.info("""Initializing GPUs""" )
if params.n_gpu > 1:
assert params.local_rank != -1
lowercase = int(os.environ["""WORLD_SIZE"""] )
lowercase = int(os.environ["""N_GPU_NODE"""] )
lowercase = int(os.environ["""RANK"""] )
# number of nodes / node ID
lowercase = params.world_size // params.n_gpu_per_node
lowercase = params.global_rank // params.n_gpu_per_node
lowercase = True
assert params.n_nodes == int(os.environ["""N_NODES"""] )
assert params.node_id == int(os.environ["""NODE_RANK"""] )
# local job (single GPU)
else:
assert params.local_rank == -1
lowercase = 1
lowercase = 0
lowercase = 0
lowercase = 0
lowercase = 1
lowercase = 1
lowercase = False
# sanity checks
assert params.n_nodes >= 1
assert 0 <= params.node_id < params.n_nodes
assert 0 <= params.local_rank <= params.global_rank < params.world_size
assert params.world_size == params.n_nodes * params.n_gpu_per_node
# define whether this is the master process / if we are in multi-node distributed mode
lowercase = params.node_id == 0 and params.local_rank == 0
lowercase = params.n_nodes > 1
# summary
lowercase = F"""--- Global rank: {params.global_rank} - """
logger.info(PREFIX + """Number of nodes: %i""" % params.n_nodes )
logger.info(PREFIX + """Node ID : %i""" % params.node_id )
logger.info(PREFIX + """Local rank : %i""" % params.local_rank )
logger.info(PREFIX + """World size : %i""" % params.world_size )
logger.info(PREFIX + """GPUs per node : %i""" % params.n_gpu_per_node )
logger.info(PREFIX + """Master : %s""" % str(params.is_master ) )
logger.info(PREFIX + """Multi-node : %s""" % str(params.multi_node ) )
logger.info(PREFIX + """Multi-GPU : %s""" % str(params.multi_gpu ) )
logger.info(PREFIX + """Hostname : %s""" % socket.gethostname() )
# set GPU device
torch.cuda.set_device(params.local_rank )
# initialize multi-GPU
if params.multi_gpu:
logger.info("""Initializing PyTorch distributed""" )
torch.distributed.init_process_group(
init_method="""env://""" , backend="""nccl""" , )
def SCREAMING_SNAKE_CASE ( lowercase_ : Optional[Any] ):
np.random.seed(args.seed )
torch.manual_seed(args.seed )
if args.n_gpu > 0:
torch.cuda.manual_seed_all(args.seed )
| 653 | 0 |
import doctest
import glob
import importlib
import inspect
import os
import re
from contextlib import contextmanager
from functools import wraps
from unittest.mock import patch
import numpy as np
import pytest
from absl.testing import parameterized
import datasets
from datasets import load_metric
from .utils import for_all_test_methods, local, slow
# mark all tests as integration
snake_case_ = pytest.mark.integration
snake_case_ = {'''comet'''}
snake_case_ = importlib.util.find_spec('''fairseq''') is not None
snake_case_ = {'''code_eval'''}
snake_case_ = os.name == '''nt'''
snake_case_ = {'''bertscore''', '''frugalscore''', '''perplexity'''}
snake_case_ = importlib.util.find_spec('''transformers''') is not None
def snake_case__ ( SCREAMING_SNAKE_CASE_ : Union[str, Any] ):
'''simple docstring'''
@wraps(SCREAMING_SNAKE_CASE_ )
def wrapper(self : Union[str, Any] , SCREAMING_SNAKE_CASE_ : List[str] ):
if not _has_fairseq and metric_name in REQUIRE_FAIRSEQ:
self.skipTest('"test requires Fairseq"' )
else:
test_case(self , SCREAMING_SNAKE_CASE_ )
return wrapper
def snake_case__ ( SCREAMING_SNAKE_CASE_ : List[Any] ):
'''simple docstring'''
@wraps(SCREAMING_SNAKE_CASE_ )
def wrapper(self : Optional[Any] , SCREAMING_SNAKE_CASE_ : Tuple ):
if not _has_transformers and metric_name in REQUIRE_TRANSFORMERS:
self.skipTest('"test requires transformers"' )
else:
test_case(self , SCREAMING_SNAKE_CASE_ )
return wrapper
def snake_case__ ( SCREAMING_SNAKE_CASE_ : int ):
'''simple docstring'''
@wraps(SCREAMING_SNAKE_CASE_ )
def wrapper(self : Dict , SCREAMING_SNAKE_CASE_ : List[str] ):
if _on_windows and metric_name in UNSUPPORTED_ON_WINDOWS:
self.skipTest('"test not supported on Windows"' )
else:
test_case(self , SCREAMING_SNAKE_CASE_ )
return wrapper
def snake_case__ ( ):
'''simple docstring'''
lowercase__ : Optional[Any] = [metric_dir.split(os.sep )[-2] for metric_dir in glob.glob('./metrics/*/' )]
return [{"testcase_name": x, "metric_name": x} for x in metrics if x != "gleu"] # gleu is unfinished
@parameterized.named_parameters(get_local_metric_names() )
@for_all_test_methods(
__snake_case , __snake_case , __snake_case )
@local
class SCREAMING_SNAKE_CASE__ (parameterized.TestCase ):
__lowerCamelCase : List[str] = {}
__lowerCamelCase : Tuple = None
@pytest.mark.filterwarnings('ignore:metric_module_factory is deprecated:FutureWarning')
@pytest.mark.filterwarnings('ignore:load_metric is deprecated:FutureWarning')
def snake_case_ ( self , a):
lowercase__ : str = '[...]'
lowercase__ : List[str] = importlib.import_module(
datasets.load.metric_module_factory(os.path.join('metrics' , a)).module_path)
lowercase__ : Dict = datasets.load.import_main_class(metric_module.__name__ , dataset=a)
# check parameters
lowercase__ : Union[str, Any] = inspect.signature(metric._compute).parameters
self.assertTrue(all(p.kind != p.VAR_KEYWORD for p in parameters.values())) # no **kwargs
# run doctest
with self.patch_intensive_calls(a , metric_module.__name__):
with self.use_local_metrics():
try:
lowercase__ : str = doctest.testmod(a , verbose=a , raise_on_error=a)
except doctest.UnexpectedException as e:
raise e.exc_info[1] # raise the exception that doctest caught
self.assertEqual(results.failed , 0)
self.assertGreater(results.attempted , 1)
@slow
def snake_case_ ( self , a):
lowercase__ : List[Any] = '[...]'
lowercase__ : Optional[int] = importlib.import_module(
datasets.load.metric_module_factory(os.path.join('metrics' , a)).module_path)
# run doctest
with self.use_local_metrics():
lowercase__ : Union[str, Any] = doctest.testmod(a , verbose=a , raise_on_error=a)
self.assertEqual(results.failed , 0)
self.assertGreater(results.attempted , 1)
@contextmanager
def snake_case_ ( self , a , a):
if metric_name in self.INTENSIVE_CALLS_PATCHER:
with self.INTENSIVE_CALLS_PATCHER[metric_name](a):
yield
else:
yield
@contextmanager
def snake_case_ ( self):
def load_local_metric(a , *a , **a):
return load_metric(os.path.join('metrics' , a) , *a , **a)
with patch('datasets.load_metric') as mock_load_metric:
lowercase__ : Tuple = load_local_metric
yield
@classmethod
def snake_case_ ( cls , a):
def wrapper(a):
lowercase__ : Optional[int] = contextmanager(a)
lowercase__ : Tuple = patcher
return patcher
return wrapper
@LocalMetricTest.register_intensive_calls_patcher('bleurt' )
def snake_case__ ( SCREAMING_SNAKE_CASE_ : Tuple ):
'''simple docstring'''
import tensorflow.compat.va as tf
from bleurt.score import Predictor
tf.flags.DEFINE_string('sv' , '' , '' ) # handle pytest cli flags
class SCREAMING_SNAKE_CASE__ (__snake_case ):
def snake_case_ ( self , a):
assert len(input_dict['input_ids']) == 2
return np.array([1.03, 1.04])
# mock predict_fn which is supposed to do a forward pass with a bleurt model
with patch('bleurt.score._create_predictor' ) as mock_create_predictor:
lowercase__ : Optional[Any] = MockedPredictor()
yield
@LocalMetricTest.register_intensive_calls_patcher('bertscore' )
def snake_case__ ( SCREAMING_SNAKE_CASE_ : Optional[Any] ):
'''simple docstring'''
import torch
def bert_cos_score_idf(SCREAMING_SNAKE_CASE_ : Optional[int] , SCREAMING_SNAKE_CASE_ : Optional[int] , *SCREAMING_SNAKE_CASE_ : List[str] , **SCREAMING_SNAKE_CASE_ : List[Any] ):
return torch.tensor([[1.0, 1.0, 1.0]] * len(SCREAMING_SNAKE_CASE_ ) )
# mock get_model which is supposed to do download a bert model
# mock bert_cos_score_idf which is supposed to do a forward pass with a bert model
with patch('bert_score.scorer.get_model' ), patch(
'bert_score.scorer.bert_cos_score_idf' ) as mock_bert_cos_score_idf:
lowercase__ : Any = bert_cos_score_idf
yield
@LocalMetricTest.register_intensive_calls_patcher('comet' )
def snake_case__ ( SCREAMING_SNAKE_CASE_ : str ):
'''simple docstring'''
def load_from_checkpoint(SCREAMING_SNAKE_CASE_ : Any ):
class SCREAMING_SNAKE_CASE__ :
def snake_case_ ( self , a , *a , **a):
assert len(a) == 2
lowercase__ : Any = [0.19, 0.92]
return scores, sum(a) / len(a)
return Model()
# mock load_from_checkpoint which is supposed to do download a bert model
# mock load_from_checkpoint which is supposed to do download a bert model
with patch('comet.download_model' ) as mock_download_model:
lowercase__ : Union[str, Any] = None
with patch('comet.load_from_checkpoint' ) as mock_load_from_checkpoint:
lowercase__ : int = load_from_checkpoint
yield
def snake_case__ ( ):
'''simple docstring'''
lowercase__ : Optional[Any] = load_metric(os.path.join('metrics' , 'seqeval' ) )
lowercase__ : List[str] = 'ERROR'
lowercase__ : Optional[Any] = f"""Scheme should be one of [IOB1, IOB2, IOE1, IOE2, IOBES, BILOU], got {wrong_scheme}"""
with pytest.raises(SCREAMING_SNAKE_CASE_ , match=re.escape(SCREAMING_SNAKE_CASE_ ) ):
metric.compute(predictions=[] , references=[] , scheme=SCREAMING_SNAKE_CASE_ )
| 164 |
def snake_case__ ( SCREAMING_SNAKE_CASE_ : int ):
'''simple docstring'''
lowercase__ : int = (1 + 24 * n) ** 0.5
return ((1 + root) / 6) % 1 == 0
def snake_case__ ( SCREAMING_SNAKE_CASE_ : int = 5_000 ):
'''simple docstring'''
lowercase__ : int = [(i * (3 * i - 1)) // 2 for i in range(1 , SCREAMING_SNAKE_CASE_ )]
for i, pentagonal_i in enumerate(SCREAMING_SNAKE_CASE_ ):
for j in range(SCREAMING_SNAKE_CASE_ , len(SCREAMING_SNAKE_CASE_ ) ):
lowercase__ : Tuple = pentagonal_nums[j]
lowercase__ : Union[str, Any] = pentagonal_i + pentagonal_j
lowercase__ : int = pentagonal_j - pentagonal_i
if is_pentagonal(SCREAMING_SNAKE_CASE_ ) and is_pentagonal(SCREAMING_SNAKE_CASE_ ):
return b
return -1
if __name__ == "__main__":
print(F'''{solution() = }''')
| 164 | 1 |
import os
from glob import glob
import imageio
import torch
import torchvision
import wandb
from img_processing import custom_to_pil, loop_post_process, preprocess, preprocess_vqgan
from loaders import load_vqgan
from PIL import Image
from torch import nn
from transformers import CLIPModel, CLIPTokenizerFast
from utils import get_device, get_timestamp, show_pil
class UpperCamelCase :
'''simple docstring'''
def __init__( self , A_ = "cpu" , A_ = "openai/clip-vit-large-patch14" ) -> None:
"""simple docstring"""
_lowerCamelCase = device
_lowerCamelCase = CLIPTokenizerFast.from_pretrained(A_ )
_lowerCamelCase = [0.48145466, 0.4578275, 0.40821073]
_lowerCamelCase = [0.26862954, 0.26130258, 0.27577711]
_lowerCamelCase = torchvision.transforms.Normalize(self.image_mean , self.image_std )
_lowerCamelCase = torchvision.transforms.Resize(2_24 )
_lowerCamelCase = torchvision.transforms.CenterCrop(2_24 )
def UpperCamelCase_ ( self , A_ ) -> int:
"""simple docstring"""
_lowerCamelCase = self.resize(A_ )
_lowerCamelCase = self.center_crop(A_ )
_lowerCamelCase = self.normalize(A_ )
return images
def __call__( self , A_=None , A_=None , **A_ ) -> Optional[Any]:
"""simple docstring"""
_lowerCamelCase = self.tokenizer(text=A_ , **A_ )
_lowerCamelCase = self.preprocess_img(A_ )
_lowerCamelCase = {key: value.to(self.device ) for (key, value) in encoding.items()}
return encoding
class UpperCamelCase ( nn.Module ):
'''simple docstring'''
def __init__( self , A_=10 , A_=0.01 , A_=None , A_=None , A_=None , A_=None , A_=None , A_=None , A_=False , A_=True , A_="image" , A_=True , A_=False , A_=False , A_=False , ) -> None:
"""simple docstring"""
super().__init__()
_lowerCamelCase = None
_lowerCamelCase = device if device else get_device()
if vqgan:
_lowerCamelCase = vqgan
else:
_lowerCamelCase = load_vqgan(self.device , conf_path=A_ , ckpt_path=A_ )
self.vqgan.eval()
if clip:
_lowerCamelCase = clip
else:
_lowerCamelCase = CLIPModel.from_pretrained('''openai/clip-vit-base-patch32''' )
self.clip.to(self.device )
_lowerCamelCase = ProcessorGradientFlow(device=self.device )
_lowerCamelCase = iterations
_lowerCamelCase = lr
_lowerCamelCase = log
_lowerCamelCase = make_grid
_lowerCamelCase = return_val
_lowerCamelCase = quantize
_lowerCamelCase = self.vqgan.decoder.z_shape
def UpperCamelCase_ ( self , A_=None , A_=None , A_=5 , A_=True ) -> Any:
"""simple docstring"""
_lowerCamelCase = []
if output_path is None:
_lowerCamelCase = '''./animation.gif'''
if input_path is None:
_lowerCamelCase = self.save_path
_lowerCamelCase = sorted(glob(input_path + '''/*''' ) )
if not len(A_ ):
raise ValueError(
'''No images found in save path, aborting (did you pass save_intermediate=True to the generate'''
''' function?)''' )
if len(A_ ) == 1:
print('''Only one image found in save path, (did you pass save_intermediate=True to the generate function?)''' )
_lowerCamelCase = total_duration / len(A_ )
_lowerCamelCase = [frame_duration] * len(A_ )
if extend_frames:
_lowerCamelCase = 1.5
_lowerCamelCase = 3
for file_name in paths:
if file_name.endswith('''.png''' ):
images.append(imageio.imread(A_ ) )
imageio.mimsave(A_ , A_ , duration=A_ )
print(F'gif saved to {output_path}' )
def UpperCamelCase_ ( self , A_=None , A_=None ) -> Union[str, Any]:
"""simple docstring"""
if not (path or img):
raise ValueError('''Input either path or tensor''' )
if img is not None:
raise NotImplementedError
_lowerCamelCase = preprocess(Image.open(A_ ) , target_image_size=2_56 ).to(self.device )
_lowerCamelCase = preprocess_vqgan(A_ )
_lowerCamelCase , *_lowerCamelCase = self.vqgan.encode(A_ )
return z
def UpperCamelCase_ ( self , A_ ) -> Optional[int]:
"""simple docstring"""
_lowerCamelCase = self.latent.detach().requires_grad_()
_lowerCamelCase = base_latent + transform_vector
if self.quantize:
_lowerCamelCase , *_lowerCamelCase = self.vqgan.quantize(A_ )
else:
_lowerCamelCase = trans_latent
return self.vqgan.decode(A_ )
def UpperCamelCase_ ( self , A_ , A_ , A_=None ) -> Any:
"""simple docstring"""
_lowerCamelCase = self.clip_preprocessor(text=A_ , images=A_ , return_tensors='''pt''' , padding=A_ )
_lowerCamelCase = self.clip(**A_ )
_lowerCamelCase = clip_outputs.logits_per_image
if weights is not None:
_lowerCamelCase = similarity_logits * weights
return similarity_logits.sum()
def UpperCamelCase_ ( self , A_ , A_ , A_ ) -> Dict:
"""simple docstring"""
_lowerCamelCase = self._get_clip_similarity(pos_prompts['''prompts'''] , A_ , weights=(1 / pos_prompts['''weights''']) )
if neg_prompts:
_lowerCamelCase = self._get_clip_similarity(neg_prompts['''prompts'''] , A_ , weights=neg_prompts['''weights'''] )
else:
_lowerCamelCase = torch.tensor([1] , device=self.device )
_lowerCamelCase = -torch.log(A_ ) + torch.log(A_ )
return loss
def UpperCamelCase_ ( self , A_ , A_ , A_ ) -> str:
"""simple docstring"""
_lowerCamelCase = torch.randn_like(self.latent , requires_grad=A_ , device=self.device )
_lowerCamelCase = torch.optim.Adam([vector] , lr=self.lr )
for i in range(self.iterations ):
optim.zero_grad()
_lowerCamelCase = self._add_vector(A_ )
_lowerCamelCase = loop_post_process(A_ )
_lowerCamelCase = self._get_CLIP_loss(A_ , A_ , A_ )
print('''CLIP loss''' , A_ )
if self.log:
wandb.log({'''CLIP Loss''': clip_loss} )
clip_loss.backward(retain_graph=A_ )
optim.step()
if self.return_val == "image":
yield custom_to_pil(transformed_img[0] )
else:
yield vector
def UpperCamelCase_ ( self , A_ , A_ , A_ ) -> Any:
"""simple docstring"""
wandb.init(reinit=A_ , project='''face-editor''' )
wandb.config.update({'''Positive Prompts''': positive_prompts} )
wandb.config.update({'''Negative Prompts''': negative_prompts} )
wandb.config.update({'''lr''': self.lr, '''iterations''': self.iterations} )
if image_path:
_lowerCamelCase = Image.open(A_ )
_lowerCamelCase = image.resize((2_56, 2_56) )
wandb.log('''Original Image''' , wandb.Image(A_ ) )
def UpperCamelCase_ ( self , A_ ) -> int:
"""simple docstring"""
if not prompts:
return []
_lowerCamelCase = []
_lowerCamelCase = []
if isinstance(A_ , A_ ):
_lowerCamelCase = [prompt.strip() for prompt in prompts.split('''|''' )]
for prompt in prompts:
if isinstance(A_ , (tuple, list) ):
_lowerCamelCase = prompt[0]
_lowerCamelCase = float(prompt[1] )
elif ":" in prompt:
_lowerCamelCase , _lowerCamelCase = prompt.split(''':''' )
_lowerCamelCase = float(A_ )
else:
_lowerCamelCase = prompt
_lowerCamelCase = 1.0
processed_prompts.append(A_ )
weights.append(A_ )
return {
"prompts": processed_prompts,
"weights": torch.tensor(A_ , device=self.device ),
}
def UpperCamelCase_ ( self , A_ , A_=None , A_=None , A_=True , A_=False , A_=True , A_=True , A_=None , ) -> str:
"""simple docstring"""
if image_path:
_lowerCamelCase = self._get_latent(A_ )
else:
_lowerCamelCase = torch.randn(self.latent_dim , device=self.device )
if self.log:
self._init_logging(A_ , A_ , A_ )
assert pos_prompts, "You must provide at least one positive prompt."
_lowerCamelCase = self.process_prompts(A_ )
_lowerCamelCase = self.process_prompts(A_ )
if save_final and save_path is None:
_lowerCamelCase = os.path.join('''./outputs/''' , '''_'''.join(pos_prompts['''prompts'''] ) )
if not os.path.exists(A_ ):
os.makedirs(A_ )
else:
_lowerCamelCase = save_path + '''_''' + get_timestamp()
os.makedirs(A_ )
_lowerCamelCase = save_path
_lowerCamelCase = self.vqgan.decode(self.latent )[0]
if show_intermediate:
print('''Original Image''' )
show_pil(custom_to_pil(A_ ) )
_lowerCamelCase = loop_post_process(A_ )
for iter, transformed_img in enumerate(self._optimize_CLIP(A_ , A_ , A_ ) ):
if show_intermediate:
show_pil(A_ )
if save_intermediate:
transformed_img.save(os.path.join(self.save_path , F'iter_{iter:03d}.png' ) )
if self.log:
wandb.log({'''Image''': wandb.Image(A_ )} )
if show_final:
show_pil(A_ )
if save_final:
transformed_img.save(os.path.join(self.save_path , F'iter_{iter:03d}_final.png' ) ) | 714 | from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
snake_case__ = {
'configuration_whisper': ['WHISPER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'WhisperConfig', 'WhisperOnnxConfig'],
'feature_extraction_whisper': ['WhisperFeatureExtractor'],
'processing_whisper': ['WhisperProcessor'],
'tokenization_whisper': ['WhisperTokenizer'],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
snake_case__ = ['WhisperTokenizerFast']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
snake_case__ = [
'WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST',
'WhisperForConditionalGeneration',
'WhisperModel',
'WhisperPreTrainedModel',
'WhisperForAudioClassification',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
snake_case__ = [
'TF_WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST',
'TFWhisperForConditionalGeneration',
'TFWhisperModel',
'TFWhisperPreTrainedModel',
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
snake_case__ = [
'FlaxWhisperForConditionalGeneration',
'FlaxWhisperModel',
'FlaxWhisperPreTrainedModel',
'FlaxWhisperForAudioClassification',
]
if TYPE_CHECKING:
from .configuration_whisper import WHISPER_PRETRAINED_CONFIG_ARCHIVE_MAP, WhisperConfig, WhisperOnnxConfig
from .feature_extraction_whisper import WhisperFeatureExtractor
from .processing_whisper import WhisperProcessor
from .tokenization_whisper import WhisperTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_whisper_fast import WhisperTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_whisper import (
WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST,
WhisperForAudioClassification,
WhisperForConditionalGeneration,
WhisperModel,
WhisperPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_whisper import (
TF_WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST,
TFWhisperForConditionalGeneration,
TFWhisperModel,
TFWhisperPreTrainedModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_whisper import (
FlaxWhisperForAudioClassification,
FlaxWhisperForConditionalGeneration,
FlaxWhisperModel,
FlaxWhisperPreTrainedModel,
)
else:
import sys
snake_case__ = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__) | 638 | 0 |
import os
import sys
import tempfile
import torch
from .state import AcceleratorState
from .utils import PrecisionType, PrepareForLaunch, is_mps_available, patch_environment
def UpperCamelCase_ ( __a , __a=() , __a=None , __a="no" , __a="29500" ) -> Any:
a__ : Union[str, Any] = False
a__ : List[str] = False
if any(key.startswith("KAGGLE" ) for key in os.environ.keys() ):
a__ : Optional[int] = True
elif "IPython" in sys.modules:
a__ : Union[str, Any] = '''google.colab''' in str(sys.modules["IPython"].get_ipython() )
try:
a__ : int = PrecisionType(mixed_precision.lower() )
except ValueError:
raise ValueError(
f'''Unknown mixed_precision mode: {args.mixed_precision.lower()}. Choose between {PrecisionType.list()}.''' )
if (in_colab or in_kaggle) and (os.environ.get("TPU_NAME" , __A ) is not None):
# TPU launch
import torch_xla.distributed.xla_multiprocessing as xmp
if len(AcceleratorState._shared_state ) > 0:
raise ValueError(
"To train on TPU in Colab or Kaggle Kernel, the `Accelerator` should only be initialized inside "
"your training function. Restart your notebook and make sure no cells initializes an "
"`Accelerator`." )
if num_processes is None:
a__ : List[Any] = 8
a__ : List[Any] = PrepareForLaunch(__A , distributed_type="TPU" )
print(f'''Launching a training on {num_processes} TPU cores.''' )
xmp.spawn(__A , args=__A , nprocs=__A , start_method="fork" )
elif in_colab:
# No need for a distributed launch otherwise as it's either CPU or one GPU.
if torch.cuda.is_available():
print("Launching training on one GPU." )
else:
print("Launching training on one CPU." )
function(*__A )
else:
if num_processes is None:
raise ValueError(
"You have to specify the number of GPUs you would like to use, add `num_processes=...` to your call." )
if num_processes > 1:
# Multi-GPU launch
from torch.multiprocessing import start_processes
from torch.multiprocessing.spawn import ProcessRaisedException
if len(AcceleratorState._shared_state ) > 0:
raise ValueError(
"To launch a multi-GPU training from your notebook, the `Accelerator` should only be initialized "
"inside your training function. Restart your notebook and make sure no cells initializes an "
"`Accelerator`." )
if torch.cuda.is_initialized():
raise ValueError(
"To launch a multi-GPU training from your notebook, you need to avoid running any instruction "
"using `torch.cuda` in any cell. Restart your notebook and make sure no cells use any CUDA "
"function." )
# torch.distributed will expect a few environment variable to be here. We set the ones common to each
# process here (the other ones will be set be the launcher).
with patch_environment(
world_size=__A , master_addr="127.0.01" , master_port=__A , mixed_precision=__A ):
a__ : str = PrepareForLaunch(__A , distributed_type="MULTI_GPU" )
print(f'''Launching training on {num_processes} GPUs.''' )
try:
start_processes(__A , args=__A , nprocs=__A , start_method="fork" )
except ProcessRaisedException as e:
if "Cannot re-initialize CUDA in forked subprocess" in e.args[0]:
raise RuntimeError(
"CUDA has been initialized before the `notebook_launcher` could create a forked subprocess. "
"This likely stems from an outside import causing issues once the `notebook_launcher()` is called. "
"Please review your imports and test them when running the `notebook_launcher()` to identify "
"which one is problematic." ) from e
else:
# No need for a distributed launch otherwise as it's either CPU, GPU or MPS.
if is_mps_available():
a__ : Dict = '''1'''
print("Launching training on MPS." )
elif torch.cuda.is_available():
print("Launching training on one GPU." )
else:
print("Launching training on CPU." )
function(*__A )
def UpperCamelCase_ ( __a , __a=() , __a=2 ) -> Any:
from torch.multiprocessing import start_processes
with tempfile.NamedTemporaryFile() as tmp_file:
# torch.distributed will expect a few environment variable to be here. We set the ones common to each
# process here (the other ones will be set be the launcher).
with patch_environment(
world_size=__A , master_addr="127.0.01" , master_port="29500" , accelerate_mixed_precision="no" , accelerate_debug_rdv_file=tmp_file.name , accelerate_use_cpu="yes" , ):
a__ : List[str] = PrepareForLaunch(__A , debug=__A )
start_processes(__A , args=__A , nprocs=__A , start_method="fork" )
| 37 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE = {
'junnyu/roformer_chinese_small': 'https://huggingface.co/junnyu/roformer_chinese_small/resolve/main/config.json',
'junnyu/roformer_chinese_base': 'https://huggingface.co/junnyu/roformer_chinese_base/resolve/main/config.json',
'junnyu/roformer_chinese_char_small': (
'https://huggingface.co/junnyu/roformer_chinese_char_small/resolve/main/config.json'
),
'junnyu/roformer_chinese_char_base': (
'https://huggingface.co/junnyu/roformer_chinese_char_base/resolve/main/config.json'
),
'junnyu/roformer_small_discriminator': (
'https://huggingface.co/junnyu/roformer_small_discriminator/resolve/main/config.json'
),
'junnyu/roformer_small_generator': (
'https://huggingface.co/junnyu/roformer_small_generator/resolve/main/config.json'
),
# See all RoFormer models at https://huggingface.co/models?filter=roformer
}
class A_ ( __lowercase ):
'''simple docstring'''
_SCREAMING_SNAKE_CASE : List[Any] = "roformer"
def __init__( self , _A=50000 , _A=None , _A=768 , _A=12 , _A=12 , _A=3072 , _A="gelu" , _A=0.1 , _A=0.1 , _A=1536 , _A=2 , _A=0.02 , _A=1e-12 , _A=0 , _A=False , _A=True , **_A , ) -> Optional[Any]:
"""simple docstring"""
super().__init__(pad_token_id=_A , **_A)
_UpperCAmelCase : Tuple = vocab_size
_UpperCAmelCase : Any = hidden_size if embedding_size is None else embedding_size
_UpperCAmelCase : Any = hidden_size
_UpperCAmelCase : int = num_hidden_layers
_UpperCAmelCase : Dict = num_attention_heads
_UpperCAmelCase : int = hidden_act
_UpperCAmelCase : Optional[Any] = intermediate_size
_UpperCAmelCase : str = hidden_dropout_prob
_UpperCAmelCase : List[Any] = attention_probs_dropout_prob
_UpperCAmelCase : Tuple = max_position_embeddings
_UpperCAmelCase : List[Any] = type_vocab_size
_UpperCAmelCase : Any = initializer_range
_UpperCAmelCase : Any = layer_norm_eps
_UpperCAmelCase : Optional[Any] = rotary_value
_UpperCAmelCase : int = use_cache
class A_ ( __lowercase ):
'''simple docstring'''
@property
def snake_case__ ( self) -> Mapping[str, Mapping[int, str]]:
"""simple docstring"""
if self.task == "multiple-choice":
_UpperCAmelCase : Optional[int] = {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
else:
_UpperCAmelCase : Optional[Any] = {0: '''batch''', 1: '''sequence'''}
_UpperCAmelCase : Dict = {0: '''batch''', 1: '''sequence'''}
return OrderedDict(
[
('''input_ids''', dynamic_axis),
('''attention_mask''', dynamic_axis),
('''token_type_ids''', dynamic_axis),
])
| 485 | 0 |
'''simple docstring'''
from __future__ import annotations
SCREAMING_SNAKE_CASE : Dict = [-10, -5, 0, 5, 5.1, 11, 13, 21, 3, 4, -21, -10, -5, -1, 0]
SCREAMING_SNAKE_CASE : Any = [-5, 0, 5, 5.1, 11, 13, 21, -1, 4, -1, -10, -5, -1, 0, -1]
def _UpperCamelCase ( lowerCAmelCase__: list[float] ) -> list[float]:
SCREAMING_SNAKE_CASE_ = []
SCREAMING_SNAKE_CASE_ = len(lowerCAmelCase__ )
for i in range(lowerCAmelCase__ ):
SCREAMING_SNAKE_CASE_ = -1
for j in range(i + 1 ,lowerCAmelCase__ ):
if arr[i] < arr[j]:
SCREAMING_SNAKE_CASE_ = arr[j]
break
result.append(lowerCAmelCase__ )
return result
def _UpperCamelCase ( lowerCAmelCase__: list[float] ) -> list[float]:
SCREAMING_SNAKE_CASE_ = []
for i, outer in enumerate(lowerCAmelCase__ ):
SCREAMING_SNAKE_CASE_ = -1
for inner in arr[i + 1 :]:
if outer < inner:
SCREAMING_SNAKE_CASE_ = inner
break
result.append(lowerCAmelCase__ )
return result
def _UpperCamelCase ( lowerCAmelCase__: list[float] ) -> list[float]:
SCREAMING_SNAKE_CASE_ = len(lowerCAmelCase__ )
SCREAMING_SNAKE_CASE_ = []
SCREAMING_SNAKE_CASE_ = [-1] * arr_size
for index in reversed(range(lowerCAmelCase__ ) ):
if stack:
while stack[-1] <= arr[index]:
stack.pop()
if not stack:
break
if stack:
SCREAMING_SNAKE_CASE_ = stack[-1]
stack.append(arr[index] )
return result
if __name__ == "__main__":
from doctest import testmod
from timeit import timeit
testmod()
print(next_greatest_element_slow(arr))
print(next_greatest_element_fast(arr))
print(next_greatest_element(arr))
SCREAMING_SNAKE_CASE : Any = (
"from __main__ import arr, next_greatest_element_slow, "
"next_greatest_element_fast, next_greatest_element"
)
print(
"next_greatest_element_slow():",
timeit("next_greatest_element_slow(arr)", setup=setup),
)
print(
"next_greatest_element_fast():",
timeit("next_greatest_element_fast(arr)", setup=setup),
)
print(
" next_greatest_element():",
timeit("next_greatest_element(arr)", setup=setup),
)
| 238 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
SCREAMING_SNAKE_CASE : Tuple = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE : Optional[int] = {
"facebook/xmod-base": "https://huggingface.co/facebook/xmod-base/resolve/main/config.json",
"facebook/xmod-large-prenorm": "https://huggingface.co/facebook/xmod-large-prenorm/resolve/main/config.json",
"facebook/xmod-base-13-125k": "https://huggingface.co/facebook/xmod-base-13-125k/resolve/main/config.json",
"facebook/xmod-base-30-125k": "https://huggingface.co/facebook/xmod-base-30-125k/resolve/main/config.json",
"facebook/xmod-base-30-195k": "https://huggingface.co/facebook/xmod-base-30-195k/resolve/main/config.json",
"facebook/xmod-base-60-125k": "https://huggingface.co/facebook/xmod-base-60-125k/resolve/main/config.json",
"facebook/xmod-base-60-265k": "https://huggingface.co/facebook/xmod-base-60-265k/resolve/main/config.json",
"facebook/xmod-base-75-125k": "https://huggingface.co/facebook/xmod-base-75-125k/resolve/main/config.json",
"facebook/xmod-base-75-269k": "https://huggingface.co/facebook/xmod-base-75-269k/resolve/main/config.json",
}
class snake_case ( lowercase_ ):
"""simple docstring"""
_a = """xmod"""
def __init__( self, _lowercase=30522, _lowercase=768, _lowercase=12, _lowercase=12, _lowercase=3072, _lowercase="gelu", _lowercase=0.1, _lowercase=0.1, _lowercase=512, _lowercase=2, _lowercase=0.02, _lowercase=1E-12, _lowercase=1, _lowercase=0, _lowercase=2, _lowercase="absolute", _lowercase=True, _lowercase=None, _lowercase=False, _lowercase=2, _lowercase=False, _lowercase=True, _lowercase=True, _lowercase=("en_XX",), _lowercase=None, **_lowercase, ) -> Optional[Any]:
super().__init__(pad_token_id=_lowercase, bos_token_id=_lowercase, eos_token_id=_lowercase, **_lowercase )
SCREAMING_SNAKE_CASE_ = vocab_size
SCREAMING_SNAKE_CASE_ = hidden_size
SCREAMING_SNAKE_CASE_ = num_hidden_layers
SCREAMING_SNAKE_CASE_ = num_attention_heads
SCREAMING_SNAKE_CASE_ = hidden_act
SCREAMING_SNAKE_CASE_ = intermediate_size
SCREAMING_SNAKE_CASE_ = hidden_dropout_prob
SCREAMING_SNAKE_CASE_ = attention_probs_dropout_prob
SCREAMING_SNAKE_CASE_ = max_position_embeddings
SCREAMING_SNAKE_CASE_ = type_vocab_size
SCREAMING_SNAKE_CASE_ = initializer_range
SCREAMING_SNAKE_CASE_ = layer_norm_eps
SCREAMING_SNAKE_CASE_ = position_embedding_type
SCREAMING_SNAKE_CASE_ = use_cache
SCREAMING_SNAKE_CASE_ = classifier_dropout
SCREAMING_SNAKE_CASE_ = pre_norm
SCREAMING_SNAKE_CASE_ = adapter_reduction_factor
SCREAMING_SNAKE_CASE_ = adapter_layer_norm
SCREAMING_SNAKE_CASE_ = adapter_reuse_layer_norm
SCREAMING_SNAKE_CASE_ = ln_before_adapter
SCREAMING_SNAKE_CASE_ = list(_lowercase )
SCREAMING_SNAKE_CASE_ = default_language
class snake_case ( lowercase_ ):
"""simple docstring"""
@property
def a__ ( self ) -> Mapping[str, Mapping[int, str]]:
if self.task == "multiple-choice":
SCREAMING_SNAKE_CASE_ = {0: 'batch', 1: 'choice', 2: 'sequence'}
else:
SCREAMING_SNAKE_CASE_ = {0: 'batch', 1: 'sequence'}
return OrderedDict(
[
('input_ids', dynamic_axis),
('attention_mask', dynamic_axis),
] )
| 238 | 1 |
'''simple docstring'''
def lowercase__( _UpperCamelCase : list )-> list:
"""simple docstring"""
if len(__lowercase ) < 2:
return collection
def circle_sort_util(_UpperCamelCase : list , _UpperCamelCase : int , _UpperCamelCase : int ) -> bool:
_UpperCamelCase = False
if low == high:
return swapped
_UpperCamelCase = low
_UpperCamelCase = high
while left < right:
if collection[left] > collection[right]:
_UpperCamelCase = (
collection[right],
collection[left],
)
_UpperCamelCase = True
left += 1
right -= 1
if left == right and collection[left] > collection[right + 1]:
_UpperCamelCase = (
collection[right + 1],
collection[left],
)
_UpperCamelCase = True
_UpperCamelCase = low + int((high - low) / 2 )
_UpperCamelCase = circle_sort_util(__lowercase , __lowercase , __lowercase )
_UpperCamelCase = circle_sort_util(__lowercase , mid + 1 , __lowercase )
return swapped or left_swap or right_swap
_UpperCamelCase = True
while is_not_sorted is True:
_UpperCamelCase = circle_sort_util(__lowercase , 0 , len(__lowercase ) - 1 )
return collection
if __name__ == "__main__":
snake_case_ : Any = input('''Enter numbers separated by a comma:\n''').strip()
snake_case_ : List[str] = [int(item) for item in user_input.split(''',''')]
print(circle_sort(unsorted))
| 138 |
def a__ (__lowercase :str , __lowercase :str ) -> float:
def get_matched_characters(__lowercase :str , __lowercase :str ) -> str:
_A : Union[str, Any] = []
_A : Dict = min(len(_stra ) , len(_stra ) ) // 2
for i, l in enumerate(_stra ):
_A : Tuple = int(max(0 , i - limit ) )
_A : Union[str, Any] = int(min(i + limit + 1 , len(_stra ) ) )
if l in _stra[left:right]:
matched.append(__lowercase )
_A : List[Any] = f"""{_stra[0:_stra.index(__lowercase )]} {_stra[_stra.index(__lowercase ) + 1:]}"""
return "".join(__lowercase )
# matching characters
_A : str = get_matched_characters(__lowercase , __lowercase )
_A : Optional[Any] = get_matched_characters(__lowercase , __lowercase )
_A : Any = len(__lowercase )
# transposition
_A : str = (
len([(ca, ca) for ca, ca in zip(__lowercase , __lowercase ) if ca != ca] ) // 2
)
if not match_count:
_A : Optional[int] = 0.0
else:
_A : str = (
1
/ 3
* (
match_count / len(__lowercase )
+ match_count / len(__lowercase )
+ (match_count - transpositions) / match_count
)
)
# common prefix up to 4 characters
_A : Any = 0
for ca, ca in zip(stra[:4] , stra[:4] ):
if ca == ca:
prefix_len += 1
else:
break
return jaro + 0.1 * prefix_len * (1 - jaro)
if __name__ == "__main__":
import doctest
doctest.testmod()
print(jaro_winkler('hello', 'world'))
| 206 | 0 |
from __future__ import annotations
def a_ ( lowerCAmelCase_ : dict, lowerCAmelCase_ : str ):
__lowerCAmelCase , __lowerCAmelCase = set(lowerCAmelCase_ ), [start]
while stack:
__lowerCAmelCase = stack.pop()
explored.add(lowerCAmelCase_ )
# Differences from BFS:
# 1) pop last element instead of first one
# 2) add adjacent elements to stack without exploring them
for adj in reversed(graph[v] ):
if adj not in explored:
stack.append(lowerCAmelCase_ )
return explored
_snake_case : Union[str, Any] = {
'A': ['B', 'C', 'D'],
'B': ['A', 'D', 'E'],
'C': ['A', 'F'],
'D': ['B', 'D'],
'E': ['B', 'F'],
'F': ['C', 'E', 'G'],
'G': ['F'],
}
if __name__ == "__main__":
import doctest
doctest.testmod()
print(depth_first_search(G, 'A'))
| 711 |
from __future__ import annotations
def a_ ( lowerCAmelCase_ : int | str ):
__lowerCAmelCase = str(lowerCAmelCase_ )
return n == n[::-1]
def a_ ( lowerCAmelCase_ : int = 100_0000 ):
__lowerCAmelCase = 0
for i in range(1, lowerCAmelCase_ ):
if is_palindrome(lowerCAmelCase_ ) and is_palindrome(bin(lowerCAmelCase_ ).split('b' )[1] ):
total += i
return total
if __name__ == "__main__":
print(solution(int(str(input().strip()))))
| 421 | 0 |
from typing import List
import datasets
from datasets.tasks import AudioClassification
from ..folder_based_builder import folder_based_builder
UpperCAmelCase__ : List[str] = datasets.utils.logging.get_logger(__name__)
class UpperCamelCase_ ( folder_based_builder.FolderBasedBuilderConfig ):
'''simple docstring'''
UpperCamelCase_ = None
UpperCamelCase_ = None
class UpperCamelCase_ ( folder_based_builder.FolderBasedBuilder ):
'''simple docstring'''
UpperCamelCase_ = datasets.Audio()
UpperCamelCase_ = """audio"""
UpperCamelCase_ = AudioFolderConfig
UpperCamelCase_ = 42 # definition at the bottom of the script
UpperCamelCase_ = AudioClassification(audio_column="""audio""" , label_column="""label""" )
UpperCAmelCase__ : List[Any] = [
'''.aiff''',
'''.au''',
'''.avr''',
'''.caf''',
'''.flac''',
'''.htk''',
'''.svx''',
'''.mat4''',
'''.mat5''',
'''.mpc2k''',
'''.ogg''',
'''.paf''',
'''.pvf''',
'''.raw''',
'''.rf64''',
'''.sd2''',
'''.sds''',
'''.ircam''',
'''.voc''',
'''.w64''',
'''.wav''',
'''.nist''',
'''.wavex''',
'''.wve''',
'''.xi''',
'''.mp3''',
'''.opus''',
]
UpperCAmelCase__ : List[Any] = AUDIO_EXTENSIONS
| 410 |
import math
def _lowercase ( __SCREAMING_SNAKE_CASE ) -> list[int]:
UpperCamelCase__ : Tuple = []
UpperCamelCase__ : int = 2
UpperCamelCase__ : str = int(math.sqrt(__SCREAMING_SNAKE_CASE ) ) # Size of every segment
UpperCamelCase__ : Optional[int] = [True] * (end + 1)
UpperCamelCase__ : Dict = []
while start <= end:
if temp[start] is True:
in_prime.append(__SCREAMING_SNAKE_CASE )
for i in range(start * start , end + 1 , __SCREAMING_SNAKE_CASE ):
UpperCamelCase__ : List[Any] = False
start += 1
prime += in_prime
UpperCamelCase__ : Union[str, Any] = end + 1
UpperCamelCase__ : Optional[Any] = min(2 * end , __SCREAMING_SNAKE_CASE )
while low <= n:
UpperCamelCase__ : Dict = [True] * (high - low + 1)
for each in in_prime:
UpperCamelCase__ : Any = math.floor(low / each ) * each
if t < low:
t += each
for j in range(__SCREAMING_SNAKE_CASE , high + 1 , __SCREAMING_SNAKE_CASE ):
UpperCamelCase__ : Optional[int] = False
for j in range(len(__SCREAMING_SNAKE_CASE ) ):
if temp[j] is True:
prime.append(j + low )
UpperCamelCase__ : Optional[int] = high + 1
UpperCamelCase__ : List[Any] = min(high + end , __SCREAMING_SNAKE_CASE )
return prime
print(sieve(10**6))
| 410 | 1 |
'''simple docstring'''
import unittest
import numpy as np
from transformers import is_flax_available
from transformers.testing_utils import require_flax
from ..test_modeling_flax_common import ids_tensor
if is_flax_available():
import jax
import jax.numpy as jnp
from transformers.generation import (
FlaxForcedBOSTokenLogitsProcessor,
FlaxForcedEOSTokenLogitsProcessor,
FlaxLogitsProcessorList,
FlaxMinLengthLogitsProcessor,
FlaxTemperatureLogitsWarper,
FlaxTopKLogitsWarper,
FlaxTopPLogitsWarper,
)
@require_flax
class lowerCAmelCase_( unittest.TestCase ):
'''simple docstring'''
def UpperCAmelCase_ ( self ,__UpperCAmelCase ,__UpperCAmelCase ) -> Any:
lowerCAmelCase__ : Union[str, Any] = jnp.ones((batch_size, length) ) / length
return scores
def UpperCAmelCase_ ( self ) -> Tuple:
lowerCAmelCase__ : Union[str, Any] = None
lowerCAmelCase__ : List[str] = 20
lowerCAmelCase__ : List[Any] = self._get_uniform_logits(batch_size=2 ,length=lowerCAmelCase_ )
# tweak scores to not be uniform anymore
lowerCAmelCase__ : Any = scores.at[1, 5].set((1 / length) + 0.1 ) # peak, 1st batch
lowerCAmelCase__ : Optional[Any] = scores.at[1, 10].set((1 / length) - 0.4 ) # valley, 1st batch
# compute softmax
lowerCAmelCase__ : int = jax.nn.softmax(lowerCAmelCase_ ,axis=-1 )
lowerCAmelCase__ : Optional[Any] = FlaxTemperatureLogitsWarper(temperature=0.5 )
lowerCAmelCase__ : Union[str, Any] = FlaxTemperatureLogitsWarper(temperature=1.3 )
lowerCAmelCase__ : Dict = jax.nn.softmax(temp_dist_warper_sharper(lowerCAmelCase_ ,scores.copy() ,cur_len=lowerCAmelCase_ ) ,axis=-1 )
lowerCAmelCase__ : List[Any] = jax.nn.softmax(temp_dist_warper_smoother(lowerCAmelCase_ ,scores.copy() ,cur_len=lowerCAmelCase_ ) ,axis=-1 )
# uniform distribution stays uniform
self.assertTrue(jnp.allclose(probs[0, :] ,warped_prob_sharp[0, :] ,atol=1E-3 ) )
self.assertTrue(jnp.allclose(probs[0, :] ,warped_prob_smooth[0, :] ,atol=1E-3 ) )
# sharp peaks get higher, valleys get lower
self.assertLess(probs[1, :].max() ,warped_prob_sharp[1, :].max() )
self.assertGreater(probs[1, :].min() ,warped_prob_sharp[1, :].min() )
# smooth peaks get lower, valleys get higher
self.assertGreater(probs[1, :].max() ,warped_prob_smooth[1, :].max() )
self.assertLess(probs[1, :].min() ,warped_prob_smooth[1, :].min() )
def UpperCAmelCase_ ( self ) -> str:
lowerCAmelCase__ : int = None
lowerCAmelCase__ : Tuple = 10
lowerCAmelCase__ : Union[str, Any] = 2
# create ramp distribution
lowerCAmelCase__ : Dict = np.broadcast_to(np.arange(lowerCAmelCase_ )[None, :] ,(batch_size, vocab_size) ).copy()
lowerCAmelCase__ : List[str] = ramp_logits[1:, : vocab_size // 2] + vocab_size
lowerCAmelCase__ : str = FlaxTopKLogitsWarper(3 )
lowerCAmelCase__ : Dict = top_k_warp(lowerCAmelCase_ ,lowerCAmelCase_ ,cur_len=lowerCAmelCase_ )
# check that correct tokens are filtered
self.assertListEqual(jnp.isinf(scores[0] ).tolist() ,7 * [True] + 3 * [False] )
self.assertListEqual(jnp.isinf(scores[1] ).tolist() ,2 * [True] + 3 * [False] + 5 * [True] )
# check special case
lowerCAmelCase__ : int = 5
lowerCAmelCase__ : Tuple = FlaxTopKLogitsWarper(top_k=1 ,filter_value=0.0 ,min_tokens_to_keep=3 )
lowerCAmelCase__ : Any = np.broadcast_to(np.arange(lowerCAmelCase_ )[None, :] ,(batch_size, length) ).copy()
lowerCAmelCase__ : Any = top_k_warp_safety_check(lowerCAmelCase_ ,lowerCAmelCase_ ,cur_len=lowerCAmelCase_ )
# min_tokens overwrites k: 3 tokens are kept => 2 tokens are nullified
self.assertListEqual((scores == 0.0).sum(axis=-1 ).tolist() ,[2, 2] )
def UpperCAmelCase_ ( self ) -> int:
lowerCAmelCase__ : List[Any] = None
lowerCAmelCase__ : str = 10
lowerCAmelCase__ : Dict = 2
# create distribution and take log (inverse to Softmax as taken in TopPLogitsWarper)
lowerCAmelCase__ : List[Any] = np.log(np.array([[0.3, 0.1, 0.1, 0.5], [0.1_5, 0.3, 0.3, 0.2_5]] ) )
lowerCAmelCase__ : List[str] = FlaxTopPLogitsWarper(0.8 )
lowerCAmelCase__ : Union[str, Any] = np.exp(top_p_warp(lowerCAmelCase_ ,lowerCAmelCase_ ,cur_len=lowerCAmelCase_ ) )
# dist should be filtered to keep min num values so that sum is >= top_p
# exp (-inf) => 0
lowerCAmelCase__ : Union[str, Any] = np.array([[0.3, 0.0, 0.0, 0.5], [0.0, 0.3, 0.3, 0.2_5]] )
self.assertTrue(np.allclose(lowerCAmelCase_ ,lowerCAmelCase_ ,atol=1E-3 ) )
# check edge cases with negative and extreme logits
lowerCAmelCase__ : Optional[int] = np.broadcast_to(np.arange(lowerCAmelCase_ )[None, :] ,(batch_size, vocab_size) ).copy() - (
vocab_size // 2
)
# make ramp_logits more extreme
lowerCAmelCase__ : List[Any] = ramp_logits[1] * 1_0_0.0
# make sure at least 2 tokens are kept
lowerCAmelCase__ : List[str] = FlaxTopPLogitsWarper(0.9 ,min_tokens_to_keep=2 ,filter_value=0.0 )
lowerCAmelCase__ : str = top_p_warp(lowerCAmelCase_ ,lowerCAmelCase_ ,cur_len=lowerCAmelCase_ )
# first batch should keep three tokens, second batch would keep only 1, but due to `min_tokens_to_keep=2` keeps 2.
self.assertListEqual((filtered_dist != 0.0).sum(axis=-1 ).tolist() ,[3, 2] )
def UpperCAmelCase_ ( self ) -> Optional[int]:
lowerCAmelCase__ : Optional[Any] = 20
lowerCAmelCase__ : Union[str, Any] = 4
lowerCAmelCase__ : List[Any] = 0
lowerCAmelCase__ : int = FlaxMinLengthLogitsProcessor(min_length=10 ,eos_token_id=lowerCAmelCase_ )
# check that min length is applied at length 5
lowerCAmelCase__ : Union[str, Any] = ids_tensor((batch_size, 20) ,vocab_size=20 )
lowerCAmelCase__ : Any = 5
lowerCAmelCase__ : int = self._get_uniform_logits(lowerCAmelCase_ ,lowerCAmelCase_ )
lowerCAmelCase__ : Optional[int] = min_dist_processor(lowerCAmelCase_ ,lowerCAmelCase_ ,cur_len=lowerCAmelCase_ )
self.assertListEqual(scores_before_min_length[:, eos_token_id].tolist() ,4 * [-float("""inf""" )] )
# check that min length is not applied anymore at length 15
lowerCAmelCase__ : Dict = self._get_uniform_logits(lowerCAmelCase_ ,lowerCAmelCase_ )
lowerCAmelCase__ : Any = 15
lowerCAmelCase__ : int = min_dist_processor(lowerCAmelCase_ ,lowerCAmelCase_ ,cur_len=lowerCAmelCase_ )
self.assertFalse(jnp.isinf(lowerCAmelCase_ ).any() )
def UpperCAmelCase_ ( self ) -> List[str]:
lowerCAmelCase__ : Any = 20
lowerCAmelCase__ : List[Any] = 4
lowerCAmelCase__ : Dict = 0
lowerCAmelCase__ : Dict = FlaxForcedBOSTokenLogitsProcessor(bos_token_id=lowerCAmelCase_ )
# check that all scores are -inf except the bos_token_id score
lowerCAmelCase__ : List[Any] = ids_tensor((batch_size, 1) ,vocab_size=20 )
lowerCAmelCase__ : Dict = 1
lowerCAmelCase__ : str = self._get_uniform_logits(lowerCAmelCase_ ,lowerCAmelCase_ )
lowerCAmelCase__ : List[str] = logits_processor(lowerCAmelCase_ ,lowerCAmelCase_ ,cur_len=lowerCAmelCase_ )
self.assertTrue(jnp.isneginf(scores[:, bos_token_id + 1 :] ).all() )
self.assertListEqual(scores[:, bos_token_id].tolist() ,4 * [0] ) # score for bos_token_id shold be zero
# check that bos_token_id is not forced if current length is greater than 1
lowerCAmelCase__ : List[Any] = 3
lowerCAmelCase__ : List[str] = self._get_uniform_logits(lowerCAmelCase_ ,lowerCAmelCase_ )
lowerCAmelCase__ : Optional[Any] = logits_processor(lowerCAmelCase_ ,lowerCAmelCase_ ,cur_len=lowerCAmelCase_ )
self.assertFalse(jnp.isinf(lowerCAmelCase_ ).any() )
def UpperCAmelCase_ ( self ) -> str:
lowerCAmelCase__ : str = 20
lowerCAmelCase__ : str = 4
lowerCAmelCase__ : Union[str, Any] = 0
lowerCAmelCase__ : Dict = 5
lowerCAmelCase__ : int = FlaxForcedEOSTokenLogitsProcessor(max_length=lowerCAmelCase_ ,eos_token_id=lowerCAmelCase_ )
# check that all scores are -inf except the eos_token_id when max_length is reached
lowerCAmelCase__ : Any = ids_tensor((batch_size, 4) ,vocab_size=20 )
lowerCAmelCase__ : Optional[Any] = 4
lowerCAmelCase__ : Any = self._get_uniform_logits(lowerCAmelCase_ ,lowerCAmelCase_ )
lowerCAmelCase__ : Tuple = logits_processor(lowerCAmelCase_ ,lowerCAmelCase_ ,cur_len=lowerCAmelCase_ )
self.assertTrue(jnp.isneginf(scores[:, eos_token_id + 1 :] ).all() )
self.assertListEqual(scores[:, eos_token_id].tolist() ,4 * [0] ) # score for eos_token_id should be zero
# check that eos_token_id is not forced if max_length is not reached
lowerCAmelCase__ : Dict = 3
lowerCAmelCase__ : Tuple = self._get_uniform_logits(lowerCAmelCase_ ,lowerCAmelCase_ )
lowerCAmelCase__ : Any = logits_processor(lowerCAmelCase_ ,lowerCAmelCase_ ,cur_len=lowerCAmelCase_ )
self.assertFalse(jnp.isinf(lowerCAmelCase_ ).any() )
def UpperCAmelCase_ ( self ) -> Tuple:
lowerCAmelCase__ : Tuple = 4
lowerCAmelCase__ : Any = 10
lowerCAmelCase__ : Tuple = 15
lowerCAmelCase__ : List[str] = 2
lowerCAmelCase__ : Any = 1
lowerCAmelCase__ : int = 15
# dummy input_ids and scores
lowerCAmelCase__ : Tuple = ids_tensor((batch_size, sequence_length) ,lowerCAmelCase_ )
lowerCAmelCase__ : Optional[int] = input_ids.copy()
lowerCAmelCase__ : Dict = self._get_uniform_logits(lowerCAmelCase_ ,lowerCAmelCase_ )
lowerCAmelCase__ : Tuple = scores.copy()
# instantiate all dist processors
lowerCAmelCase__ : Optional[int] = FlaxTemperatureLogitsWarper(temperature=0.5 )
lowerCAmelCase__ : Optional[int] = FlaxTopKLogitsWarper(3 )
lowerCAmelCase__ : List[str] = FlaxTopPLogitsWarper(0.8 )
# instantiate all logits processors
lowerCAmelCase__ : Any = FlaxMinLengthLogitsProcessor(min_length=10 ,eos_token_id=lowerCAmelCase_ )
lowerCAmelCase__ : List[str] = FlaxForcedBOSTokenLogitsProcessor(bos_token_id=lowerCAmelCase_ )
lowerCAmelCase__ : Dict = FlaxForcedEOSTokenLogitsProcessor(max_length=lowerCAmelCase_ ,eos_token_id=lowerCAmelCase_ )
lowerCAmelCase__ : Tuple = 10
# no processor list
lowerCAmelCase__ : List[Any] = temp_dist_warp(lowerCAmelCase_ ,lowerCAmelCase_ ,cur_len=lowerCAmelCase_ )
lowerCAmelCase__ : List[Any] = top_k_warp(lowerCAmelCase_ ,lowerCAmelCase_ ,cur_len=lowerCAmelCase_ )
lowerCAmelCase__ : Any = top_p_warp(lowerCAmelCase_ ,lowerCAmelCase_ ,cur_len=lowerCAmelCase_ )
lowerCAmelCase__ : int = min_dist_proc(lowerCAmelCase_ ,lowerCAmelCase_ ,cur_len=lowerCAmelCase_ )
lowerCAmelCase__ : Optional[Any] = bos_dist_proc(lowerCAmelCase_ ,lowerCAmelCase_ ,cur_len=lowerCAmelCase_ )
lowerCAmelCase__ : Tuple = eos_dist_proc(lowerCAmelCase_ ,lowerCAmelCase_ ,cur_len=lowerCAmelCase_ )
# with processor list
lowerCAmelCase__ : Any = FlaxLogitsProcessorList(
[temp_dist_warp, top_k_warp, top_p_warp, min_dist_proc, bos_dist_proc, eos_dist_proc] )
lowerCAmelCase__ : Dict = processor(lowerCAmelCase_ ,lowerCAmelCase_ ,cur_len=lowerCAmelCase_ )
# scores should be equal
self.assertTrue(jnp.allclose(lowerCAmelCase_ ,lowerCAmelCase_ ,atol=1E-3 ) )
# input_ids should never be changed
self.assertListEqual(input_ids.tolist() ,input_ids_comp.tolist() )
def UpperCAmelCase_ ( self ) -> List[Any]:
lowerCAmelCase__ : Optional[Any] = 4
lowerCAmelCase__ : Optional[int] = 10
lowerCAmelCase__ : Any = 15
lowerCAmelCase__ : Optional[Any] = 2
lowerCAmelCase__ : Union[str, Any] = 1
lowerCAmelCase__ : List[Any] = 15
# dummy input_ids and scores
lowerCAmelCase__ : Any = ids_tensor((batch_size, sequence_length) ,lowerCAmelCase_ )
lowerCAmelCase__ : List[str] = input_ids.copy()
lowerCAmelCase__ : List[Any] = self._get_uniform_logits(lowerCAmelCase_ ,lowerCAmelCase_ )
lowerCAmelCase__ : Union[str, Any] = scores.copy()
# instantiate all dist processors
lowerCAmelCase__ : Any = FlaxTemperatureLogitsWarper(temperature=0.5 )
lowerCAmelCase__ : Any = FlaxTopKLogitsWarper(3 )
lowerCAmelCase__ : Optional[int] = FlaxTopPLogitsWarper(0.8 )
# instantiate all logits processors
lowerCAmelCase__ : Dict = FlaxMinLengthLogitsProcessor(min_length=10 ,eos_token_id=lowerCAmelCase_ )
lowerCAmelCase__ : Dict = FlaxForcedBOSTokenLogitsProcessor(bos_token_id=lowerCAmelCase_ )
lowerCAmelCase__ : Optional[int] = FlaxForcedEOSTokenLogitsProcessor(max_length=lowerCAmelCase_ ,eos_token_id=lowerCAmelCase_ )
lowerCAmelCase__ : Optional[int] = 10
# no processor list
def run_no_processor_list(__UpperCAmelCase ,__UpperCAmelCase ,__UpperCAmelCase ):
lowerCAmelCase__ : List[str] = temp_dist_warp(lowerCAmelCase_ ,lowerCAmelCase_ ,cur_len=lowerCAmelCase_ )
lowerCAmelCase__ : Union[str, Any] = top_k_warp(lowerCAmelCase_ ,lowerCAmelCase_ ,cur_len=lowerCAmelCase_ )
lowerCAmelCase__ : str = top_p_warp(lowerCAmelCase_ ,lowerCAmelCase_ ,cur_len=lowerCAmelCase_ )
lowerCAmelCase__ : str = min_dist_proc(lowerCAmelCase_ ,lowerCAmelCase_ ,cur_len=lowerCAmelCase_ )
lowerCAmelCase__ : Optional[Any] = bos_dist_proc(lowerCAmelCase_ ,lowerCAmelCase_ ,cur_len=lowerCAmelCase_ )
lowerCAmelCase__ : List[Any] = eos_dist_proc(lowerCAmelCase_ ,lowerCAmelCase_ ,cur_len=lowerCAmelCase_ )
return scores
# with processor list
def run_processor_list(__UpperCAmelCase ,__UpperCAmelCase ,__UpperCAmelCase ):
lowerCAmelCase__ : Any = FlaxLogitsProcessorList(
[temp_dist_warp, top_k_warp, top_p_warp, min_dist_proc, bos_dist_proc, eos_dist_proc] )
lowerCAmelCase__ : Union[str, Any] = processor(lowerCAmelCase_ ,lowerCAmelCase_ ,cur_len=lowerCAmelCase_ )
return scores
lowerCAmelCase__ : Dict = jax.jit(lowerCAmelCase_ )
lowerCAmelCase__ : int = jax.jit(lowerCAmelCase_ )
lowerCAmelCase__ : str = jitted_run_no_processor_list(lowerCAmelCase_ ,lowerCAmelCase_ ,lowerCAmelCase_ )
lowerCAmelCase__ : str = jitted_run_processor_list(lowerCAmelCase_ ,lowerCAmelCase_ ,lowerCAmelCase_ )
# scores should be equal
self.assertTrue(jnp.allclose(lowerCAmelCase_ ,lowerCAmelCase_ ,atol=1E-3 ) )
# input_ids should never be changed
self.assertListEqual(input_ids.tolist() ,input_ids_comp.tolist() )
| 712 |
'''simple docstring'''
import unittest
import numpy as np
import torch
from diffusers import PNDMPipeline, PNDMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class lowerCAmelCase_( unittest.TestCase ):
'''simple docstring'''
@property
def UpperCAmelCase_ ( self ) -> List[str]:
torch.manual_seed(0 )
lowerCAmelCase__ : Any = UNetaDModel(
block_out_channels=(32, 64) ,layers_per_block=2 ,sample_size=32 ,in_channels=3 ,out_channels=3 ,down_block_types=("""DownBlock2D""", """AttnDownBlock2D""") ,up_block_types=("""AttnUpBlock2D""", """UpBlock2D""") ,)
return model
def UpperCAmelCase_ ( self ) -> Any:
lowerCAmelCase__ : List[str] = self.dummy_uncond_unet
lowerCAmelCase__ : Optional[Any] = PNDMScheduler()
lowerCAmelCase__ : List[Any] = PNDMPipeline(unet=__UpperCAmelCase ,scheduler=__UpperCAmelCase )
pndm.to(__UpperCAmelCase )
pndm.set_progress_bar_config(disable=__UpperCAmelCase )
lowerCAmelCase__ : Optional[int] = torch.manual_seed(0 )
lowerCAmelCase__ : int = pndm(generator=__UpperCAmelCase ,num_inference_steps=20 ,output_type="""numpy""" ).images
lowerCAmelCase__ : List[str] = torch.manual_seed(0 )
lowerCAmelCase__ : Any = pndm(generator=__UpperCAmelCase ,num_inference_steps=20 ,output_type="""numpy""" ,return_dict=__UpperCAmelCase )[0]
lowerCAmelCase__ : Dict = image[0, -3:, -3:, -1]
lowerCAmelCase__ : Union[str, Any] = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 32, 32, 3)
lowerCAmelCase__ : Union[str, Any] = np.array([1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1E-2
@slow
@require_torch
class lowerCAmelCase_( unittest.TestCase ):
'''simple docstring'''
def UpperCAmelCase_ ( self ) -> str:
lowerCAmelCase__ : Any = """google/ddpm-cifar10-32"""
lowerCAmelCase__ : str = UNetaDModel.from_pretrained(__UpperCAmelCase )
lowerCAmelCase__ : Tuple = PNDMScheduler()
lowerCAmelCase__ : Dict = PNDMPipeline(unet=__UpperCAmelCase ,scheduler=__UpperCAmelCase )
pndm.to(__UpperCAmelCase )
pndm.set_progress_bar_config(disable=__UpperCAmelCase )
lowerCAmelCase__ : Optional[Any] = torch.manual_seed(0 )
lowerCAmelCase__ : int = pndm(generator=__UpperCAmelCase ,output_type="""numpy""" ).images
lowerCAmelCase__ : List[str] = image[0, -3:, -3:, -1]
assert image.shape == (1, 32, 32, 3)
lowerCAmelCase__ : Optional[int] = np.array([0.1_5_6_4, 0.1_4_6_4_5, 0.1_4_0_6, 0.1_4_7_1_5, 0.1_2_4_2_5, 0.1_4_0_4_5, 0.1_3_1_1_5, 0.1_2_1_7_5, 0.1_2_5] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
| 160 | 0 |
import glob
import os
import random
from string import ascii_lowercase, digits
import cva
import numpy as np
# Parrameters
__lowerCamelCase : int = (7_20, 12_80) # Height, Width
__lowerCamelCase : Optional[Any] = (0.4, 0.6) # if height or width lower than this scale, drop it.
__lowerCamelCase : Optional[Any] = 1 / 1_00
__lowerCamelCase : Any = """"""
__lowerCamelCase : Dict = """"""
__lowerCamelCase : str = """"""
__lowerCamelCase : Optional[int] = 2_50
def A__ ( ):
'''simple docstring'''
snake_case__ , snake_case__ : List[Any] =get_dataset(_a , _a )
for index in range(_a ):
snake_case__ : Tuple =random.sample(range(len(_a ) ) , 4 )
snake_case__ , snake_case__ , snake_case__ : Any =update_image_and_anno(
_a , _a , _a , _a , _a , filter_scale=_a , )
# Get random string code: '7b7ad245cdff75241935e4dd860f3bad'
snake_case__ : int =random_chars(32 )
snake_case__ : List[str] =path.split(os.sep )[-1].rsplit(""".""" , 1 )[0]
snake_case__ : List[Any] =f"{OUTPUT_DIR}/{file_name}_MOSAIC_{letter_code}"
cva.imwrite(f"{file_root}.jpg" , _a , [cva.IMWRITE_JPEG_QUALITY, 85] )
print(f"Succeeded {index+1}/{NUMBER_IMAGES} with {file_name}" )
snake_case__ : int =[]
for anno in new_annos:
snake_case__ : Any =anno[3] - anno[1]
snake_case__ : Optional[int] =anno[4] - anno[2]
snake_case__ : int =anno[1] + width / 2
snake_case__ : Tuple =anno[2] + height / 2
snake_case__ : List[Any] =f"{anno[0]} {x_center} {y_center} {width} {height}"
annos_list.append(_a )
with open(f"{file_root}.txt" , """w""" ) as outfile:
outfile.write("""\n""".join(line for line in annos_list ) )
def A__ ( _a : str , _a : str ):
'''simple docstring'''
snake_case__ : Tuple =[]
snake_case__ : Dict =[]
for label_file in glob.glob(os.path.join(_a , """*.txt""" ) ):
snake_case__ : Optional[int] =label_file.split(os.sep )[-1].rsplit(""".""" , 1 )[0]
with open(_a ) as in_file:
snake_case__ : int =in_file.readlines()
snake_case__ : Optional[int] =os.path.join(_a , f"{label_name}.jpg" )
snake_case__ : List[str] =[]
for obj_list in obj_lists:
snake_case__ : int =obj_list.rstrip("""\n""" ).split(""" """ )
snake_case__ : Any =float(obj[1] ) - float(obj[3] ) / 2
snake_case__ : int =float(obj[2] ) - float(obj[4] ) / 2
snake_case__ : Dict =float(obj[1] ) + float(obj[3] ) / 2
snake_case__ : Union[str, Any] =float(obj[2] ) + float(obj[4] ) / 2
boxes.append([int(obj[0] ), xmin, ymin, xmax, ymax] )
if not boxes:
continue
img_paths.append(_a )
labels.append(_a )
return img_paths, labels
def A__ ( _a : list , _a : list , _a : list[int] , _a : tuple[int, int] , _a : tuple[float, float] , _a : float = 0.0 , ):
'''simple docstring'''
snake_case__ : Optional[int] =np.zeros([output_size[0], output_size[1], 3] , dtype=np.uinta )
snake_case__ : List[Any] =scale_range[0] + random.random() * (scale_range[1] - scale_range[0])
snake_case__ : Optional[int] =scale_range[0] + random.random() * (scale_range[1] - scale_range[0])
snake_case__ : str =int(scale_x * output_size[1] )
snake_case__ : Tuple =int(scale_y * output_size[0] )
snake_case__ : Optional[int] =[]
snake_case__ : List[str] =[]
for i, index in enumerate(_a ):
snake_case__ : Union[str, Any] =all_img_list[index]
path_list.append(_a )
snake_case__ : int =all_annos[index]
snake_case__ : Tuple =cva.imread(_a )
if i == 0: # top-left
snake_case__ : Tuple =cva.resize(_a , (divid_point_x, divid_point_y) )
snake_case__ : int =img
for bbox in img_annos:
snake_case__ : Dict =bbox[1] * scale_x
snake_case__ : str =bbox[2] * scale_y
snake_case__ : Tuple =bbox[3] * scale_x
snake_case__ : List[Any] =bbox[4] * scale_y
new_anno.append([bbox[0], xmin, ymin, xmax, ymax] )
elif i == 1: # top-right
snake_case__ : Optional[Any] =cva.resize(_a , (output_size[1] - divid_point_x, divid_point_y) )
snake_case__ : str =img
for bbox in img_annos:
snake_case__ : Any =scale_x + bbox[1] * (1 - scale_x)
snake_case__ : Dict =bbox[2] * scale_y
snake_case__ : List[Any] =scale_x + bbox[3] * (1 - scale_x)
snake_case__ : Optional[int] =bbox[4] * scale_y
new_anno.append([bbox[0], xmin, ymin, xmax, ymax] )
elif i == 2: # bottom-left
snake_case__ : List[Any] =cva.resize(_a , (divid_point_x, output_size[0] - divid_point_y) )
snake_case__ : Optional[Any] =img
for bbox in img_annos:
snake_case__ : int =bbox[1] * scale_x
snake_case__ : Optional[Any] =scale_y + bbox[2] * (1 - scale_y)
snake_case__ : Tuple =bbox[3] * scale_x
snake_case__ : Dict =scale_y + bbox[4] * (1 - scale_y)
new_anno.append([bbox[0], xmin, ymin, xmax, ymax] )
else: # bottom-right
snake_case__ : str =cva.resize(
_a , (output_size[1] - divid_point_x, output_size[0] - divid_point_y) )
snake_case__ : Optional[int] =img
for bbox in img_annos:
snake_case__ : List[Any] =scale_x + bbox[1] * (1 - scale_x)
snake_case__ : List[Any] =scale_y + bbox[2] * (1 - scale_y)
snake_case__ : Optional[int] =scale_x + bbox[3] * (1 - scale_x)
snake_case__ : List[str] =scale_y + bbox[4] * (1 - scale_y)
new_anno.append([bbox[0], xmin, ymin, xmax, ymax] )
# Remove bounding box small than scale of filter
if filter_scale > 0:
snake_case__ : Optional[Any] =[
anno
for anno in new_anno
if filter_scale < (anno[3] - anno[1]) and filter_scale < (anno[4] - anno[2])
]
return output_img, new_anno, path_list[0]
def A__ ( _a : int ):
'''simple docstring'''
assert number_char > 1, "The number of character should greater than 1"
snake_case__ : Union[str, Any] =ascii_lowercase + digits
return "".join(random.choice(_a ) for _ in range(_a ) )
if __name__ == "__main__":
main()
print("""DONE ✅""")
| 385 |
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
if is_tf_available():
import numpy as np
import tensorflow as tf
from transformers import TFCamembertModel
@require_tf
@require_sentencepiece
@require_tokenizers
class _lowercase ( unittest.TestCase ):
@slow
def lowercase__ ( self ):
snake_case__ : Union[str, Any] =TFCamembertModel.from_pretrained("""jplu/tf-camembert-base""" )
snake_case__ : List[str] =tf.convert_to_tensor(
[[5, 1_2_1, 1_1, 6_6_0, 1_6, 7_3_0, 2_5_5_4_3, 1_1_0, 8_3, 6]] , dtype=tf.intaa , ) # J'aime le camembert !"
snake_case__ : Dict =model(a )["""last_hidden_state"""]
snake_case__ : Any =tf.TensorShape((1, 1_0, 7_6_8) )
self.assertEqual(output.shape , a )
# compare the actual values for a slice.
snake_case__ : str =tf.convert_to_tensor(
[[[-0.0254, 0.0235, 0.1027], [0.0606, -0.1811, -0.0418], [-0.1561, -0.1127, 0.2687]]] , dtype=tf.floataa , )
# camembert = torch.hub.load('pytorch/fairseq', 'camembert.v0')
# camembert.eval()
# expected_slice = roberta.model.forward(input_ids)[0][:, :3, :3].detach()
self.assertTrue(np.allclose(output[:, :3, :3].numpy() , expected_slice.numpy() , atol=1e-4 ) )
| 385 | 1 |
"""simple docstring"""
from math import sqrt
def A__ ( UpperCamelCase ):
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(sqrt(lowerCAmelCase_ ) + 1 ) , 6 ):
if number % i == 0 or number % (i + 2) == 0:
return False
return True
def A__ ( UpperCamelCase = 10_001 ):
A = 0
A = 1
while count != nth and number < 3:
number += 1
if is_prime(lowerCAmelCase_ ):
count += 1
while count != nth:
number += 2
if is_prime(lowerCAmelCase_ ):
count += 1
return number
if __name__ == "__main__":
print(F"""{solution() = }""")
| 707 |
"""simple docstring"""
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,
)
_snake_case : Dict = {'configuration_mbart': ['MBART_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MBartConfig', 'MBartOnnxConfig']}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_snake_case : Any = ['MBartTokenizer']
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_snake_case : Dict = ['MBartTokenizerFast']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_snake_case : str = [
'MBART_PRETRAINED_MODEL_ARCHIVE_LIST',
'MBartForCausalLM',
'MBartForConditionalGeneration',
'MBartForQuestionAnswering',
'MBartForSequenceClassification',
'MBartModel',
'MBartPreTrainedModel',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_snake_case : List[Any] = [
'TFMBartForConditionalGeneration',
'TFMBartModel',
'TFMBartPreTrainedModel',
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_snake_case : str = [
'FlaxMBartForConditionalGeneration',
'FlaxMBartForQuestionAnswering',
'FlaxMBartForSequenceClassification',
'FlaxMBartModel',
'FlaxMBartPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_mbart import MBART_PRETRAINED_CONFIG_ARCHIVE_MAP, MBartConfig, MBartOnnxConfig
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_mbart import MBartTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_mbart_fast import MBartTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_mbart import (
MBART_PRETRAINED_MODEL_ARCHIVE_LIST,
MBartForCausalLM,
MBartForConditionalGeneration,
MBartForQuestionAnswering,
MBartForSequenceClassification,
MBartModel,
MBartPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_mbart import TFMBartForConditionalGeneration, TFMBartModel, TFMBartPreTrainedModel
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_mbart import (
FlaxMBartForConditionalGeneration,
FlaxMBartForQuestionAnswering,
FlaxMBartForSequenceClassification,
FlaxMBartModel,
FlaxMBartPreTrainedModel,
)
else:
import sys
_snake_case : str = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 524 | 0 |
'''simple docstring'''
A__ : Optional[int] =[
9_99,
8_00,
7_99,
6_00,
5_99,
5_00,
4_00,
3_99,
3_77,
3_55,
3_33,
3_11,
2_88,
2_66,
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2_22,
2_00,
1_99,
1_77,
1_55,
1_33,
1_11,
88,
66,
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22,
0,
]
A__ : Any =[
9_99,
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9_05,
8_82,
8_58,
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7_62,
7_15,
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2_85,
2_38,
1_90,
1_43,
1_42,
1_18,
95,
71,
47,
24,
0,
]
A__ : Dict =[
9_99,
9_88,
9_77,
9_66,
9_55,
9_44,
9_33,
9_22,
9_11,
9_00,
8_99,
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6_99,
6_50,
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2_00,
1_99,
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1_59,
1_40,
1_20,
1_00,
99,
88,
77,
66,
55,
44,
33,
22,
11,
0,
]
A__ : Optional[Any] =[
9_99,
9_95,
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9_89,
9_85,
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9_64,
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7_92,
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3_07,
2_64,
2_63,
2_20,
2_19,
1_76,
1_32,
88,
44,
0,
]
A__ : Optional[int] =[
9_99,
9_97,
9_95,
9_92,
9_90,
9_88,
9_86,
9_84,
9_81,
9_79,
9_77,
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8_99,
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8_91,
8_88,
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2_63,
2_42,
2_20,
2_19,
1_76,
1_75,
1_32,
1_31,
88,
44,
0,
]
A__ : List[Any] =[
9_99,
9_91,
9_82,
9_74,
9_66,
9_58,
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9_33,
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7_99,
7_00,
6_00,
5_00,
4_00,
3_00,
2_00,
1_00,
0,
]
A__ : List[str] =[
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9_85,
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9_64,
9_57,
9_49,
9_42,
9_35,
9_28,
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4_99,
4_00,
3_99,
3_00,
2_99,
2_00,
1_99,
1_00,
99,
0,
]
A__ : Dict =[
9_99,
9_96,
9_92,
9_89,
9_85,
9_82,
9_79,
9_75,
9_72,
9_68,
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]
| 207 |
'''simple docstring'''
import argparse
import ast
import logging
import os
import sys
import pandas as pd
import torch
from tqdm import tqdm
from transformers import BartForConditionalGeneration, RagRetriever, RagSequenceForGeneration, RagTokenForGeneration
from transformers import logging as transformers_logging
sys.path.append(os.path.join(os.getcwd())) # noqa: E402 # isort:skip
from utils_rag import exact_match_score, fa_score # noqa: E402 # isort:skip
A__ : Union[str, Any] =logging.getLogger(__name__)
logging.basicConfig(level=logging.INFO)
transformers_logging.set_verbosity_info()
def UpperCamelCase__ ( lowerCAmelCase ):
"""simple docstring"""
if "token" in model_name_or_path:
return "rag_token"
if "sequence" in model_name_or_path:
return "rag_sequence"
if "bart" in model_name_or_path:
return "bart"
return None
def UpperCamelCase__ ( lowerCAmelCase , lowerCAmelCase , lowerCAmelCase ):
"""simple docstring"""
return max(metric_fn(lowerCAmelCase , lowerCAmelCase ) for gt in ground_truths )
def UpperCamelCase__ ( lowerCAmelCase , lowerCAmelCase , lowerCAmelCase ):
"""simple docstring"""
_lowerCAmelCase = [line.strip() for line in open(lowerCAmelCase , """r""" ).readlines()]
_lowerCAmelCase = []
if args.gold_data_mode == "qa":
_lowerCAmelCase = pd.read_csv(lowerCAmelCase , sep="""\t""" , header=lowerCAmelCase )
for answer_list in data[1]:
_lowerCAmelCase = ast.literal_eval(lowerCAmelCase )
answers.append(lowerCAmelCase )
else:
_lowerCAmelCase = [line.strip() for line in open(lowerCAmelCase , """r""" ).readlines()]
_lowerCAmelCase = [[reference] for reference in references]
_lowerCAmelCase = _lowerCAmelCase = _lowerCAmelCase = 0
for prediction, ground_truths in zip(lowerCAmelCase , lowerCAmelCase ):
total += 1
em += metric_max_over_ground_truths(lowerCAmelCase , lowerCAmelCase , lowerCAmelCase )
fa += metric_max_over_ground_truths(lowerCAmelCase , lowerCAmelCase , lowerCAmelCase )
_lowerCAmelCase = 100.0 * em / total
_lowerCAmelCase = 100.0 * fa / total
logger.info(f"F1: {fa:.2f}" )
logger.info(f"EM: {em:.2f}" )
def UpperCamelCase__ ( lowerCAmelCase , lowerCAmelCase , lowerCAmelCase ):
"""simple docstring"""
_lowerCAmelCase = args.k
_lowerCAmelCase = [line.strip() for line in open(lowerCAmelCase , """r""" ).readlines()]
_lowerCAmelCase = [line.strip() for line in open(lowerCAmelCase , """r""" ).readlines()]
_lowerCAmelCase = _lowerCAmelCase = 0
for hypo, reference in zip(lowerCAmelCase , lowerCAmelCase ):
_lowerCAmelCase = set(hypo.split("""\t""" )[:k] )
_lowerCAmelCase = set(reference.split("""\t""" ) )
total += 1
em += len(hypo_provenance & ref_provenance ) / k
_lowerCAmelCase = 100.0 * em / total
logger.info(f"Precision@{k}: {em: .2f}" )
def UpperCamelCase__ ( lowerCAmelCase , lowerCAmelCase , lowerCAmelCase ):
"""simple docstring"""
def strip_title(lowerCAmelCase ):
if title.startswith("""\"""" ):
_lowerCAmelCase = title[1:]
if title.endswith("""\"""" ):
_lowerCAmelCase = title[:-1]
return title
_lowerCAmelCase = rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
lowerCAmelCase , return_tensors="""pt""" , padding=lowerCAmelCase , truncation=lowerCAmelCase , )["""input_ids"""].to(args.device )
_lowerCAmelCase = rag_model.rag.question_encoder(lowerCAmelCase )
_lowerCAmelCase = question_enc_outputs[0]
_lowerCAmelCase = rag_model.retriever(
lowerCAmelCase , question_enc_pool_output.cpu().detach().to(torch.floataa ).numpy() , prefix=rag_model.rag.generator.config.prefix , n_docs=rag_model.config.n_docs , return_tensors="""pt""" , )
_lowerCAmelCase = rag_model.retriever.index.get_doc_dicts(result.doc_ids )
_lowerCAmelCase = []
for docs in all_docs:
_lowerCAmelCase = [strip_title(lowerCAmelCase ) for title in docs["""title"""]]
provenance_strings.append("""\t""".join(lowerCAmelCase ) )
return provenance_strings
def UpperCamelCase__ ( lowerCAmelCase , lowerCAmelCase , lowerCAmelCase ):
"""simple docstring"""
with torch.no_grad():
_lowerCAmelCase = rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
lowerCAmelCase , return_tensors="""pt""" , padding=lowerCAmelCase , truncation=lowerCAmelCase )
_lowerCAmelCase = inputs_dict.input_ids.to(args.device )
_lowerCAmelCase = inputs_dict.attention_mask.to(args.device )
_lowerCAmelCase = rag_model.generate( # rag_model overwrites generate
lowerCAmelCase , attention_mask=lowerCAmelCase , num_beams=args.num_beams , min_length=args.min_length , max_length=args.max_length , early_stopping=lowerCAmelCase , num_return_sequences=1 , bad_words_ids=[[0, 0]] , )
_lowerCAmelCase = rag_model.retriever.generator_tokenizer.batch_decode(lowerCAmelCase , skip_special_tokens=lowerCAmelCase )
if args.print_predictions:
for q, a in zip(lowerCAmelCase , lowerCAmelCase ):
logger.info("""Q: {} - A: {}""".format(lowerCAmelCase , lowerCAmelCase ) )
return answers
def UpperCamelCase__ ( ):
"""simple docstring"""
_lowerCAmelCase = argparse.ArgumentParser()
parser.add_argument(
"""--model_type""" , choices=["""rag_sequence""", """rag_token""", """bart"""] , type=lowerCAmelCase , help=(
"""RAG model type: rag_sequence, rag_token or bart, if none specified, the type is inferred from the"""
""" model_name_or_path"""
) , )
parser.add_argument(
"""--index_name""" , default=lowerCAmelCase , choices=["""exact""", """compressed""", """legacy"""] , type=lowerCAmelCase , help="""RAG model retriever type""" , )
parser.add_argument(
"""--index_path""" , default=lowerCAmelCase , type=lowerCAmelCase , help="""Path to the retrieval index""" , )
parser.add_argument("""--n_docs""" , default=5 , type=lowerCAmelCase , help="""Number of retrieved docs""" )
parser.add_argument(
"""--model_name_or_path""" , default=lowerCAmelCase , type=lowerCAmelCase , required=lowerCAmelCase , help="""Path to pretrained checkpoints or model identifier from huggingface.co/models""" , )
parser.add_argument(
"""--eval_mode""" , choices=["""e2e""", """retrieval"""] , default="""e2e""" , type=lowerCAmelCase , help=(
"""Evaluation mode, e2e calculates exact match and F1 of the downstream task, retrieval calculates"""
""" precision@k."""
) , )
parser.add_argument("""--k""" , default=1 , type=lowerCAmelCase , help="""k for the precision@k calculation""" )
parser.add_argument(
"""--evaluation_set""" , default=lowerCAmelCase , type=lowerCAmelCase , required=lowerCAmelCase , help="""Path to a file containing evaluation samples""" , )
parser.add_argument(
"""--gold_data_path""" , default=lowerCAmelCase , type=lowerCAmelCase , required=lowerCAmelCase , help="""Path to a tab-separated file with gold samples""" , )
parser.add_argument(
"""--gold_data_mode""" , default="""qa""" , type=lowerCAmelCase , choices=["""qa""", """ans"""] , help=(
"""Format of the gold data file"""
"""qa - a single line in the following format: question [tab] answer_list"""
"""ans - a single line of the gold file contains the expected answer string"""
) , )
parser.add_argument(
"""--predictions_path""" , type=lowerCAmelCase , default="""predictions.txt""" , help="""Name of the predictions file, to be stored in the checkpoints directory""" , )
parser.add_argument(
"""--eval_all_checkpoints""" , action="""store_true""" , help="""Evaluate all checkpoints starting with the same prefix as model_name ending and ending with step number""" , )
parser.add_argument(
"""--eval_batch_size""" , default=8 , type=lowerCAmelCase , help="""Batch size per GPU/CPU for evaluation.""" , )
parser.add_argument(
"""--recalculate""" , help="""Recalculate predictions even if the prediction file exists""" , action="""store_true""" , )
parser.add_argument(
"""--num_beams""" , default=4 , type=lowerCAmelCase , help="""Number of beams to be used when generating answers""" , )
parser.add_argument("""--min_length""" , default=1 , type=lowerCAmelCase , help="""Min length of the generated answers""" )
parser.add_argument("""--max_length""" , default=50 , type=lowerCAmelCase , help="""Max length of the generated answers""" )
parser.add_argument(
"""--print_predictions""" , action="""store_true""" , help="""If True, prints predictions while evaluating.""" , )
parser.add_argument(
"""--print_docs""" , action="""store_true""" , help="""If True, prints docs retried while generating.""" , )
_lowerCAmelCase = parser.parse_args()
_lowerCAmelCase = torch.device("""cuda""" if torch.cuda.is_available() else """cpu""" )
return args
def UpperCamelCase__ ( lowerCAmelCase ):
"""simple docstring"""
_lowerCAmelCase = {}
if args.model_type is None:
_lowerCAmelCase = infer_model_type(args.model_name_or_path )
assert args.model_type is not None
if args.model_type.startswith("""rag""" ):
_lowerCAmelCase = RagTokenForGeneration if args.model_type == """rag_token""" else RagSequenceForGeneration
_lowerCAmelCase = args.n_docs
if args.index_name is not None:
_lowerCAmelCase = args.index_name
if args.index_path is not None:
_lowerCAmelCase = args.index_path
else:
_lowerCAmelCase = BartForConditionalGeneration
_lowerCAmelCase = (
[f.path for f in os.scandir(args.model_name_or_path ) if f.is_dir()]
if args.eval_all_checkpoints
else [args.model_name_or_path]
)
logger.info("""Evaluate the following checkpoints: %s""" , lowerCAmelCase )
_lowerCAmelCase = get_scores if args.eval_mode == """e2e""" else get_precision_at_k
_lowerCAmelCase = evaluate_batch_eae if args.eval_mode == """e2e""" else evaluate_batch_retrieval
for checkpoint in checkpoints:
if os.path.exists(args.predictions_path ) and (not args.recalculate):
logger.info("""Calculating metrics based on an existing predictions file: {}""".format(args.predictions_path ) )
score_fn(lowerCAmelCase , args.predictions_path , args.gold_data_path )
continue
logger.info("""***** Running evaluation for {} *****""".format(lowerCAmelCase ) )
logger.info(""" Batch size = %d""" , args.eval_batch_size )
logger.info(""" Predictions will be stored under {}""".format(args.predictions_path ) )
if args.model_type.startswith("""rag""" ):
_lowerCAmelCase = RagRetriever.from_pretrained(lowerCAmelCase , **lowerCAmelCase )
_lowerCAmelCase = model_class.from_pretrained(lowerCAmelCase , retriever=lowerCAmelCase , **lowerCAmelCase )
model.retriever.init_retrieval()
else:
_lowerCAmelCase = model_class.from_pretrained(lowerCAmelCase , **lowerCAmelCase )
model.to(args.device )
with open(args.evaluation_set , """r""" ) as eval_file, open(args.predictions_path , """w""" ) as preds_file:
_lowerCAmelCase = []
for line in tqdm(lowerCAmelCase ):
questions.append(line.strip() )
if len(lowerCAmelCase ) == args.eval_batch_size:
_lowerCAmelCase = evaluate_batch_fn(lowerCAmelCase , lowerCAmelCase , lowerCAmelCase )
preds_file.write("""\n""".join(lowerCAmelCase ) + """\n""" )
preds_file.flush()
_lowerCAmelCase = []
if len(lowerCAmelCase ) > 0:
_lowerCAmelCase = evaluate_batch_fn(lowerCAmelCase , lowerCAmelCase , lowerCAmelCase )
preds_file.write("""\n""".join(lowerCAmelCase ) )
preds_file.flush()
score_fn(lowerCAmelCase , args.predictions_path , args.gold_data_path )
if __name__ == "__main__":
A__ : Tuple =get_args()
main(args)
| 207 | 1 |
"""simple docstring"""
import argparse
import json
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelerator, DistributedType
from accelerate.utils.deepspeed import DummyOptim, DummyScheduler
A: List[str] = 1_6
A: Dict = 3_2
def _snake_case ( UpperCamelCase : Accelerator , UpperCamelCase : int = 16 , UpperCamelCase : str = "bert-base-cased" ):
UpperCAmelCase : Dict = AutoTokenizer.from_pretrained(UpperCamelCase )
UpperCAmelCase : str = load_dataset("""glue""" , """mrpc""" )
def tokenize_function(UpperCamelCase : List[Any] ):
# max_length=None => use the model max length (it's actually the default)
UpperCAmelCase : Tuple = tokenizer(examples["""sentence1"""] , examples["""sentence2"""] , truncation=UpperCamelCase , max_length=UpperCamelCase )
return outputs
# Apply the method we just defined to all the examples in all the splits of the dataset
UpperCAmelCase : Dict = datasets.map(
UpperCamelCase , batched=UpperCamelCase , remove_columns=["""idx""", """sentence1""", """sentence2"""] , load_from_cache_file=UpperCamelCase )
# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
# transformers library
UpperCAmelCase : Any = tokenized_datasets.rename_column("""label""" , """labels""" )
def collate_fn(UpperCamelCase : Union[str, Any] ):
# On TPU it's best to pad everything to the same length or training will be very slow.
if accelerator.distributed_type == DistributedType.TPU:
return tokenizer.pad(UpperCamelCase , padding="""max_length""" , max_length=128 , return_tensors="""pt""" )
return tokenizer.pad(UpperCamelCase , padding="""longest""" , return_tensors="""pt""" )
# Instantiate dataloaders.
UpperCAmelCase : List[str] = DataLoader(
tokenized_datasets["""train"""] , shuffle=UpperCamelCase , collate_fn=UpperCamelCase , batch_size=UpperCamelCase )
UpperCAmelCase : List[Any] = DataLoader(
tokenized_datasets["""validation"""] , shuffle=UpperCamelCase , collate_fn=UpperCamelCase , batch_size=UpperCamelCase )
return train_dataloader, eval_dataloader
def _snake_case ( UpperCamelCase : int , UpperCamelCase : List[str] ):
# Initialize accelerator
UpperCAmelCase : Any = Accelerator()
# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
UpperCAmelCase : Any = config["""lr"""]
UpperCAmelCase : Tuple = int(config["""num_epochs"""] )
UpperCAmelCase : Optional[Any] = int(config["""seed"""] )
UpperCAmelCase : List[str] = int(config["""batch_size"""] )
UpperCAmelCase : List[Any] = args.model_name_or_path
set_seed(UpperCamelCase )
UpperCAmelCase , UpperCAmelCase : Optional[int] = get_dataloaders(UpperCamelCase , UpperCamelCase , UpperCamelCase )
# Instantiate the model (we build the model here so that the seed also control new weights initialization)
UpperCAmelCase : Optional[int] = AutoModelForSequenceClassification.from_pretrained(UpperCamelCase , return_dict=UpperCamelCase )
# Instantiate optimizer
UpperCAmelCase : str = (
AdamW
if accelerator.state.deepspeed_plugin is None
or """optimizer""" not in accelerator.state.deepspeed_plugin.deepspeed_config
else DummyOptim
)
UpperCAmelCase : int = optimizer_cls(params=model.parameters() , lr=UpperCamelCase )
if accelerator.state.deepspeed_plugin is not None:
UpperCAmelCase : Optional[int] = accelerator.state.deepspeed_plugin.deepspeed_config[
"""gradient_accumulation_steps"""
]
else:
UpperCAmelCase : List[str] = 1
UpperCAmelCase : str = (len(UpperCamelCase ) * num_epochs) // gradient_accumulation_steps
# Instantiate scheduler
if (
accelerator.state.deepspeed_plugin is None
or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
):
UpperCAmelCase : str = get_linear_schedule_with_warmup(
optimizer=UpperCamelCase , num_warmup_steps=0 , num_training_steps=UpperCamelCase , )
else:
UpperCAmelCase : Any = DummyScheduler(UpperCamelCase , total_num_steps=UpperCamelCase , warmup_num_steps=0 )
# Prepare everything
# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
# prepare method.
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase : Optional[int] = accelerator.prepare(
UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase )
# We need to keep track of how many total steps we have iterated over
UpperCAmelCase : Any = 0
# We also need to keep track of the stating epoch so files are named properly
UpperCAmelCase : Dict = 0
# Now we train the model
UpperCAmelCase : Tuple = evaluate.load("""glue""" , """mrpc""" )
UpperCAmelCase : Any = 0
UpperCAmelCase : Optional[int] = {}
for epoch in range(UpperCamelCase , UpperCamelCase ):
model.train()
for step, batch in enumerate(UpperCamelCase ):
UpperCAmelCase : Union[str, Any] = model(**UpperCamelCase )
UpperCAmelCase : Dict = outputs.loss
UpperCAmelCase : Tuple = loss / gradient_accumulation_steps
accelerator.backward(UpperCamelCase )
if step % gradient_accumulation_steps == 0:
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
overall_step += 1
model.eval()
UpperCAmelCase : Optional[int] = 0
for step, batch in enumerate(UpperCamelCase ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
with torch.no_grad():
UpperCAmelCase : Optional[int] = model(**UpperCamelCase )
UpperCAmelCase : Any = outputs.logits.argmax(dim=-1 )
# It is slightly faster to call this once, than multiple times
UpperCAmelCase , UpperCAmelCase : Optional[Any] = accelerator.gather(
(predictions, batch["""labels"""]) ) # If we are in a multiprocess environment, the last batch has duplicates
if accelerator.use_distributed:
if step == len(UpperCamelCase ) - 1:
UpperCAmelCase : Union[str, Any] = predictions[: len(eval_dataloader.dataset ) - samples_seen]
UpperCAmelCase : Any = references[: len(eval_dataloader.dataset ) - samples_seen]
else:
samples_seen += references.shape[0]
metric.add_batch(
predictions=UpperCamelCase , references=UpperCamelCase , )
UpperCAmelCase : Tuple = metric.compute()
# Use accelerator.print to print only on the main process.
accelerator.print(F"epoch {epoch}:" , UpperCamelCase )
UpperCAmelCase : List[str] = eval_metric["""accuracy"""]
if best_performance < eval_metric["accuracy"]:
UpperCAmelCase : Any = eval_metric["""accuracy"""]
if args.performance_lower_bound is not None:
assert (
args.performance_lower_bound <= best_performance
), F"Best performance metric {best_performance} is lower than the lower bound {args.performance_lower_bound}"
accelerator.wait_for_everyone()
if accelerator.is_main_process:
with open(os.path.join(args.output_dir , """all_results.json""" ) , """w""" ) as f:
json.dump(UpperCamelCase , UpperCamelCase )
def _snake_case ( ):
UpperCAmelCase : int = argparse.ArgumentParser(description="""Simple example of training script tracking peak GPU memory usage.""" )
parser.add_argument(
"""--model_name_or_path""" , type=UpperCamelCase , default="""bert-base-cased""" , help="""Path to pretrained model or model identifier from huggingface.co/models.""" , required=UpperCamelCase , )
parser.add_argument(
"""--output_dir""" , type=UpperCamelCase , default=""".""" , help="""Optional save directory where all checkpoint folders will be stored. Default is the current working directory.""" , )
parser.add_argument(
"""--performance_lower_bound""" , type=UpperCamelCase , default=UpperCamelCase , help="""Optional lower bound for the performance metric. If set, the training will throw error when the performance metric drops below this value.""" , )
parser.add_argument(
"""--num_epochs""" , type=UpperCamelCase , default=3 , help="""Number of train epochs.""" , )
UpperCAmelCase : List[str] = parser.parse_args()
UpperCAmelCase : Optional[Any] = {"""lr""": 2e-5, """num_epochs""": args.num_epochs, """seed""": 42, """batch_size""": 16}
training_function(UpperCamelCase , UpperCamelCase )
if __name__ == "__main__":
main()
| 359 |
"""simple docstring"""
def _snake_case ( UpperCamelCase : int , UpperCamelCase : int ):
return 1 if input_a == input_a else 0
def _snake_case ( ):
assert xnor_gate(0 , 0 ) == 1
assert xnor_gate(0 , 1 ) == 0
assert xnor_gate(1 , 0 ) == 0
assert xnor_gate(1 , 1 ) == 1
if __name__ == "__main__":
print(xnor_gate(0, 0))
print(xnor_gate(0, 1))
print(xnor_gate(1, 0))
print(xnor_gate(1, 1))
| 359 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__SCREAMING_SNAKE_CASE : Optional[int] ={
'''configuration_megatron_bert''': ['''MEGATRON_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MegatronBertConfig'''],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__SCREAMING_SNAKE_CASE : int =[
'''MEGATRON_BERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''MegatronBertForCausalLM''',
'''MegatronBertForMaskedLM''',
'''MegatronBertForMultipleChoice''',
'''MegatronBertForNextSentencePrediction''',
'''MegatronBertForPreTraining''',
'''MegatronBertForQuestionAnswering''',
'''MegatronBertForSequenceClassification''',
'''MegatronBertForTokenClassification''',
'''MegatronBertModel''',
'''MegatronBertPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_megatron_bert import MEGATRON_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, MegatronBertConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_megatron_bert import (
MEGATRON_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
MegatronBertForCausalLM,
MegatronBertForMaskedLM,
MegatronBertForMultipleChoice,
MegatronBertForNextSentencePrediction,
MegatronBertForPreTraining,
MegatronBertForQuestionAnswering,
MegatronBertForSequenceClassification,
MegatronBertForTokenClassification,
MegatronBertModel,
MegatronBertPreTrainedModel,
)
else:
import sys
__SCREAMING_SNAKE_CASE : List[Any] =_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 428 |
__SCREAMING_SNAKE_CASE : Optional[Any] ='''Tobias Carryer'''
from time import time
class A_ :
def __init__( self : int , snake_case__ : List[Any] , snake_case__ : List[str] , snake_case__ : int , snake_case__ : int=int(time() ) ): # noqa: B008
lowercase = multiplier
lowercase = increment
lowercase = modulo
lowercase = seed
def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] ):
lowercase = (self.multiplier * self.seed + self.increment) % self.modulo
return self.seed
if __name__ == "__main__":
# Show the LCG in action.
__SCREAMING_SNAKE_CASE : Union[str, Any] =LinearCongruentialGenerator(1_664_525, 1_013_904_223, 2 << 31)
while True:
print(lcg.next_number())
| 428 | 1 |
import numpy as np
# Importing the Keras libraries and packages
import tensorflow as tf
from tensorflow.keras import layers, models
if __name__ == "__main__":
# Initialising the CNN
# (Sequential- Building the model layer by layer)
_SCREAMING_SNAKE_CASE : str = models.Sequential()
# Step 1 - Convolution
# Here 64,64 is the length & breadth of dataset images and 3 is for the RGB channel
# (3,3) is the kernel size (filter matrix)
classifier.add(
layers.ConvaD(32, (3, 3), input_shape=(64, 64, 3), activation='''relu''')
)
# Step 2 - Pooling
classifier.add(layers.MaxPoolingaD(pool_size=(2, 2)))
# Adding a second convolutional layer
classifier.add(layers.ConvaD(32, (3, 3), activation='''relu'''))
classifier.add(layers.MaxPoolingaD(pool_size=(2, 2)))
# Step 3 - Flattening
classifier.add(layers.Flatten())
# Step 4 - Full connection
classifier.add(layers.Dense(units=128, activation='''relu'''))
classifier.add(layers.Dense(units=1, activation='''sigmoid'''))
# Compiling the CNN
classifier.compile(
optimizer='''adam''', loss='''binary_crossentropy''', metrics=['''accuracy''']
)
# Part 2 - Fitting the CNN to the images
# Load Trained model weights
# from keras.models import load_model
# regressor=load_model('cnn.h5')
_SCREAMING_SNAKE_CASE : Optional[int] = tf.keras.preprocessing.image.ImageDataGenerator(
rescale=1.0 / 255, shear_range=0.2, zoom_range=0.2, horizontal_flip=True
)
_SCREAMING_SNAKE_CASE : Dict = tf.keras.preprocessing.image.ImageDataGenerator(rescale=1.0 / 255)
_SCREAMING_SNAKE_CASE : Union[str, Any] = train_datagen.flow_from_directory(
'''dataset/training_set''', target_size=(64, 64), batch_size=32, class_mode='''binary'''
)
_SCREAMING_SNAKE_CASE : Dict = test_datagen.flow_from_directory(
'''dataset/test_set''', target_size=(64, 64), batch_size=32, class_mode='''binary'''
)
classifier.fit_generator(
training_set, steps_per_epoch=5, epochs=30, validation_data=test_set
)
classifier.save('''cnn.h5''')
# Part 3 - Making new predictions
_SCREAMING_SNAKE_CASE : Tuple = tf.keras.preprocessing.image.load_img(
'''dataset/single_prediction/image.png''', target_size=(64, 64)
)
_SCREAMING_SNAKE_CASE : int = tf.keras.preprocessing.image.img_to_array(test_image)
_SCREAMING_SNAKE_CASE : Any = np.expand_dims(test_image, axis=0)
_SCREAMING_SNAKE_CASE : str = classifier.predict(test_image)
# training_set.class_indices
if result[0][0] == 0:
_SCREAMING_SNAKE_CASE : Optional[Any] = '''Normal'''
if result[0][0] == 1:
_SCREAMING_SNAKE_CASE : Tuple = '''Abnormality detected'''
| 708 |
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import TensorType, logging
if TYPE_CHECKING:
from ...onnx.config import PatchingSpec
from ...tokenization_utils_base import PreTrainedTokenizerBase
_SCREAMING_SNAKE_CASE : Any = logging.get_logger(__name__)
_SCREAMING_SNAKE_CASE : List[str] = {
'''allenai/longformer-base-4096''': '''https://huggingface.co/allenai/longformer-base-4096/resolve/main/config.json''',
'''allenai/longformer-large-4096''': '''https://huggingface.co/allenai/longformer-large-4096/resolve/main/config.json''',
'''allenai/longformer-large-4096-finetuned-triviaqa''': (
'''https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/config.json'''
),
'''allenai/longformer-base-4096-extra.pos.embd.only''': (
'''https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/config.json'''
),
'''allenai/longformer-large-4096-extra.pos.embd.only''': (
'''https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/config.json'''
),
}
class UpperCAmelCase__ ( A__ ):
"""simple docstring"""
a = "longformer"
def __init__( self : int , __lowerCamelCase : Union[List[int], int] = 512 , __lowerCamelCase : int = 2 , __lowerCamelCase : int = 1 , __lowerCamelCase : int = 0 , __lowerCamelCase : int = 2 , __lowerCamelCase : int = 3_0522 , __lowerCamelCase : int = 768 , __lowerCamelCase : int = 12 , __lowerCamelCase : int = 12 , __lowerCamelCase : int = 3072 , __lowerCamelCase : str = "gelu" , __lowerCamelCase : float = 0.1 , __lowerCamelCase : float = 0.1 , __lowerCamelCase : int = 512 , __lowerCamelCase : int = 2 , __lowerCamelCase : float = 0.02 , __lowerCamelCase : float = 1e-12 , __lowerCamelCase : bool = False , **__lowerCamelCase : Tuple , ) -> List[Any]:
super().__init__(pad_token_id=__lowerCamelCase , **__lowerCamelCase )
SCREAMING_SNAKE_CASE__ = attention_window
SCREAMING_SNAKE_CASE__ = sep_token_id
SCREAMING_SNAKE_CASE__ = bos_token_id
SCREAMING_SNAKE_CASE__ = eos_token_id
SCREAMING_SNAKE_CASE__ = vocab_size
SCREAMING_SNAKE_CASE__ = hidden_size
SCREAMING_SNAKE_CASE__ = num_hidden_layers
SCREAMING_SNAKE_CASE__ = num_attention_heads
SCREAMING_SNAKE_CASE__ = hidden_act
SCREAMING_SNAKE_CASE__ = intermediate_size
SCREAMING_SNAKE_CASE__ = hidden_dropout_prob
SCREAMING_SNAKE_CASE__ = attention_probs_dropout_prob
SCREAMING_SNAKE_CASE__ = max_position_embeddings
SCREAMING_SNAKE_CASE__ = type_vocab_size
SCREAMING_SNAKE_CASE__ = initializer_range
SCREAMING_SNAKE_CASE__ = layer_norm_eps
SCREAMING_SNAKE_CASE__ = onnx_export
class UpperCAmelCase__ ( A__ ):
"""simple docstring"""
def __init__( self : Dict , __lowerCamelCase : "PretrainedConfig" , __lowerCamelCase : str = "default" , __lowerCamelCase : "List[PatchingSpec]" = None ) -> List[str]:
super().__init__(__lowerCamelCase , __lowerCamelCase , __lowerCamelCase )
SCREAMING_SNAKE_CASE__ = True
@property
def lowercase_ ( self : Optional[Any] ) -> Mapping[str, Mapping[int, str]]:
if self.task == "multiple-choice":
SCREAMING_SNAKE_CASE__ = {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
else:
SCREAMING_SNAKE_CASE__ = {0: '''batch''', 1: '''sequence'''}
return OrderedDict(
[
('''input_ids''', dynamic_axis),
('''attention_mask''', dynamic_axis),
('''global_attention_mask''', dynamic_axis),
] )
@property
def lowercase_ ( self : Optional[int] ) -> Mapping[str, Mapping[int, str]]:
SCREAMING_SNAKE_CASE__ = super().outputs
if self.task == "default":
SCREAMING_SNAKE_CASE__ = {0: '''batch'''}
return outputs
@property
def lowercase_ ( self : Optional[int] ) -> float:
return 1e-4
@property
def lowercase_ ( self : Union[str, Any] ) -> int:
# needs to be >= 14 to support tril operator
return max(super().default_onnx_opset , 14 )
def lowercase_ ( self : Dict , __lowerCamelCase : "PreTrainedTokenizerBase" , __lowerCamelCase : int = -1 , __lowerCamelCase : int = -1 , __lowerCamelCase : bool = False , __lowerCamelCase : Optional[TensorType] = None , ) -> Mapping[str, Any]:
SCREAMING_SNAKE_CASE__ = super().generate_dummy_inputs(
preprocessor=__lowerCamelCase , batch_size=__lowerCamelCase , seq_length=__lowerCamelCase , is_pair=__lowerCamelCase , framework=__lowerCamelCase )
import torch
# for some reason, replacing this code by inputs["global_attention_mask"] = torch.randint(2, inputs["input_ids"].shape, dtype=torch.int64)
# makes the export fail randomly
SCREAMING_SNAKE_CASE__ = torch.zeros_like(inputs['''input_ids'''] )
# make every second token global
SCREAMING_SNAKE_CASE__ = 1
return inputs
| 472 | 0 |
import inspect
import re
from hashlib import shaaaa
from typing import Dict, List
from .arrow import arrow
from .audiofolder import audiofolder
from .csv import csv
from .imagefolder import imagefolder
from .json import json
from .pandas import pandas
from .parquet import parquet
from .sql import sql # noqa F401
from .text import text
def a__ ( lowercase__ ):
'''simple docstring'''
UpperCAmelCase_ =[]
for line in lines:
UpperCAmelCase_ =re.sub(R"#.*" , "" , lowercase__ ) # remove comments
if line:
filtered_lines.append(lowercase__ )
UpperCAmelCase_ ="\n".join(lowercase__ )
# Make a hash from all this code
UpperCAmelCase_ =full_str.encode("utf-8" )
return shaaaa(lowercase__ ).hexdigest()
# get importable module names and hash for caching
__lowercase : Tuple ={
"""csv""": (csv.__name__, _hash_python_lines(inspect.getsource(csv).splitlines())),
"""json""": (json.__name__, _hash_python_lines(inspect.getsource(json).splitlines())),
"""pandas""": (pandas.__name__, _hash_python_lines(inspect.getsource(pandas).splitlines())),
"""parquet""": (parquet.__name__, _hash_python_lines(inspect.getsource(parquet).splitlines())),
"""arrow""": (arrow.__name__, _hash_python_lines(inspect.getsource(arrow).splitlines())),
"""text""": (text.__name__, _hash_python_lines(inspect.getsource(text).splitlines())),
"""imagefolder""": (imagefolder.__name__, _hash_python_lines(inspect.getsource(imagefolder).splitlines())),
"""audiofolder""": (audiofolder.__name__, _hash_python_lines(inspect.getsource(audiofolder).splitlines())),
}
# Used to infer the module to use based on the data files extensions
__lowercase : Optional[int] ={
""".csv""": ("""csv""", {}),
""".tsv""": ("""csv""", {"""sep""": """\t"""}),
""".json""": ("""json""", {}),
""".jsonl""": ("""json""", {}),
""".parquet""": ("""parquet""", {}),
""".arrow""": ("""arrow""", {}),
""".txt""": ("""text""", {}),
}
_EXTENSION_TO_MODULE.update({ext: ("""imagefolder""", {}) for ext in imagefolder.ImageFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext.upper(): ("""imagefolder""", {}) for ext in imagefolder.ImageFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext: ("""audiofolder""", {}) for ext in audiofolder.AudioFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext.upper(): ("""audiofolder""", {}) for ext in audiofolder.AudioFolder.EXTENSIONS})
__lowercase : Dict ={"""imagefolder""", """audiofolder"""}
# Used to filter data files based on extensions given a module name
__lowercase : Dict[str, List[str]] ={}
for _ext, (_module, _) in _EXTENSION_TO_MODULE.items():
_MODULE_TO_EXTENSIONS.setdefault(_module, []).append(_ext)
_MODULE_TO_EXTENSIONS["imagefolder"].append(""".zip""")
_MODULE_TO_EXTENSIONS["audiofolder"].append(""".zip""")
| 54 |
import fire
from torch.utils.data import DataLoader
from tqdm import tqdm
from transformers import AutoTokenizer
from utils import SeqaSeqDataset, pickle_save
def a__ ( lowercase__ , lowercase__ , lowercase__=1_0_2_4 , lowercase__=1_0_2_4 , lowercase__=False , **lowercase__ ):
'''simple docstring'''
UpperCAmelCase_ =AutoTokenizer.from_pretrained(lowercase__ )
UpperCAmelCase_ =SeqaSeqDataset(lowercase__ , lowercase__ , lowercase__ , lowercase__ , type_path="train" , **lowercase__ )
UpperCAmelCase_ =tok.pad_token_id
def get_lens(lowercase__ ):
UpperCAmelCase_ =tqdm(
DataLoader(lowercase__ , batch_size=5_1_2 , num_workers=8 , shuffle=lowercase__ , collate_fn=ds.collate_fn ) , desc=str(ds.len_file ) , )
UpperCAmelCase_ =[]
for batch in dl:
UpperCAmelCase_ =batch["input_ids"].ne(lowercase__ ).sum(1 ).tolist()
UpperCAmelCase_ =batch["labels"].ne(lowercase__ ).sum(1 ).tolist()
if consider_target:
for src, tgt in zip(lowercase__ , lowercase__ ):
max_lens.append(max(lowercase__ , lowercase__ ) )
else:
max_lens.extend(lowercase__ )
return max_lens
UpperCAmelCase_ =get_lens(lowercase__ )
UpperCAmelCase_ =SeqaSeqDataset(lowercase__ , lowercase__ , lowercase__ , lowercase__ , type_path="val" , **lowercase__ )
UpperCAmelCase_ =get_lens(lowercase__ )
pickle_save(lowercase__ , train_ds.len_file )
pickle_save(lowercase__ , val_ds.len_file )
if __name__ == "__main__":
fire.Fire(save_len_file)
| 54 | 1 |
def __lowerCAmelCase ( lowercase : int , lowercase : int ) -> int:
"""simple docstring"""
while second != 0:
snake_case : str = first & second
first ^= second
snake_case : str = c << 1
return first
if __name__ == "__main__":
import doctest
doctest.testmod()
__snake_case = int(input("""Enter the first number: """).strip())
__snake_case = int(input("""Enter the second number: """).strip())
print(F'''{add(first, second) = }''')
| 715 |
"""simple docstring"""
import os
import unittest
from transformers import LayoutLMTokenizer, LayoutLMTokenizerFast
from transformers.models.layoutlm.tokenization_layoutlm import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class _lowerCAmelCase ( snake_case_ , unittest.TestCase ):
__UpperCAmelCase : List[Any] = LayoutLMTokenizer
__UpperCAmelCase : List[Any] = LayoutLMTokenizerFast
__UpperCAmelCase : List[Any] = True
__UpperCAmelCase : Dict = True
def lowerCamelCase ( self ) -> str:
'''simple docstring'''
super().setUp()
snake_case : Dict = [
"[UNK]",
"[CLS]",
"[SEP]",
"want",
"##want",
"##ed",
"wa",
"un",
"runn",
"##ing",
",",
"low",
"lowest",
]
snake_case : List[str] = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["vocab_file"] )
with open(self.vocab_file , "w" , encoding="utf-8" ) as vocab_writer:
vocab_writer.write("".join([x + "\n" for x in vocab_tokens] ) )
def lowerCamelCase ( self , **UpperCamelCase__ ) -> Dict:
'''simple docstring'''
return LayoutLMTokenizer.from_pretrained(self.tmpdirname , **UpperCamelCase__ )
def lowerCamelCase ( self , UpperCamelCase__ ) -> Tuple:
'''simple docstring'''
snake_case : str = "UNwant\u00E9d,running"
snake_case : Optional[int] = "unwanted, running"
return input_text, output_text
def lowerCamelCase ( self ) -> Any:
'''simple docstring'''
snake_case : List[str] = self.tokenizer_class(self.vocab_file )
snake_case : Optional[Any] = tokenizer.tokenize("UNwant\u00E9d,running" )
self.assertListEqual(UpperCamelCase__ , ["un", "##want", "##ed", ",", "runn", "##ing"] )
self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCamelCase__ ) , [7, 4, 5, 10, 8, 9] )
def lowerCamelCase ( self ) -> Dict:
'''simple docstring'''
pass
| 117 | 0 |
from __future__ import annotations
import unittest
from transformers import AutoTokenizer, PegasusConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import TFAutoModelForSeqaSeqLM, TFPegasusForConditionalGeneration, TFPegasusModel
@require_tf
class a__ :
_A = PegasusConfig
_A = {}
_A = "gelu"
def __init__( self : int , A_ : int , A_ : List[str]=13 , A_ : Optional[Any]=7 , A_ : Optional[Any]=True , A_ : Optional[int]=False , A_ : int=99 , A_ : List[Any]=32 , A_ : Optional[int]=2 , A_ : Tuple=4 , A_ : Optional[Any]=37 , A_ : List[Any]=0.1 , A_ : Any=0.1 , A_ : Optional[Any]=40 , A_ : str=2 , A_ : Optional[Any]=1 , A_ : Tuple=0 , ) -> Optional[int]:
"""simple docstring"""
lowerCamelCase_: str = parent
lowerCamelCase_: int = batch_size
lowerCamelCase_: Any = seq_length
lowerCamelCase_: Optional[int] = is_training
lowerCamelCase_: Union[str, Any] = use_labels
lowerCamelCase_: List[Any] = vocab_size
lowerCamelCase_: Dict = hidden_size
lowerCamelCase_: str = num_hidden_layers
lowerCamelCase_: List[str] = num_attention_heads
lowerCamelCase_: List[Any] = intermediate_size
lowerCamelCase_: List[Any] = hidden_dropout_prob
lowerCamelCase_: Any = attention_probs_dropout_prob
lowerCamelCase_: int = max_position_embeddings
lowerCamelCase_: int = eos_token_id
lowerCamelCase_: Optional[int] = pad_token_id
lowerCamelCase_: str = bos_token_id
def lowerCAmelCase ( self : List[Any] ) -> Any:
"""simple docstring"""
lowerCamelCase_: List[Any] = ids_tensor([self.batch_size, self.seq_length - 1] , self.vocab_size )
lowerCamelCase_: Optional[int] = tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size ) , 1 )
lowerCamelCase_: str = tf.concat([input_ids, eos_tensor] , axis=1 )
lowerCamelCase_: Optional[Any] = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
lowerCamelCase_: List[Any] = self.config_cls(
vocab_size=self.vocab_size , d_model=self.hidden_size , encoder_layers=self.num_hidden_layers , decoder_layers=self.num_hidden_layers , encoder_attention_heads=self.num_attention_heads , decoder_attention_heads=self.num_attention_heads , encoder_ffn_dim=self.intermediate_size , decoder_ffn_dim=self.intermediate_size , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , eos_token_ids=[2] , bos_token_id=self.bos_token_id , pad_token_id=self.pad_token_id , decoder_start_token_id=self.pad_token_id , **self.config_updates , )
lowerCamelCase_: List[Any] = prepare_pegasus_inputs_dict(A_ , A_ , A_ )
return config, inputs_dict
def lowerCAmelCase ( self : List[str] , A_ : Optional[Any] , A_ : str ) -> List[Any]:
"""simple docstring"""
lowerCamelCase_: Tuple = TFPegasusModel(config=A_ ).get_decoder()
lowerCamelCase_: Union[str, Any] = inputs_dict["""input_ids"""]
lowerCamelCase_: int = input_ids[:1, :]
lowerCamelCase_: List[Any] = inputs_dict["""attention_mask"""][:1, :]
lowerCamelCase_: Union[str, Any] = inputs_dict["""head_mask"""]
lowerCamelCase_: Tuple = 1
# first forward pass
lowerCamelCase_: Optional[int] = model(A_ , attention_mask=A_ , head_mask=A_ , use_cache=A_ )
lowerCamelCase_ , lowerCamelCase_: Optional[int] = outputs.to_tuple()
# create hypothetical next token and extent to next_input_ids
lowerCamelCase_: Optional[Any] = ids_tensor((self.batch_size, 3) , config.vocab_size )
lowerCamelCase_: Optional[int] = tf.cast(ids_tensor((self.batch_size, 3) , 2 ) , tf.inta )
# append to next input_ids and
lowerCamelCase_: Optional[Any] = tf.concat([input_ids, next_tokens] , axis=-1 )
lowerCamelCase_: Tuple = tf.concat([attention_mask, next_attn_mask] , axis=-1 )
lowerCamelCase_: int = model(A_ , attention_mask=A_ )[0]
lowerCamelCase_: Optional[int] = model(A_ , attention_mask=A_ , past_key_values=A_ )[0]
self.parent.assertEqual(next_tokens.shape[1] , output_from_past.shape[1] )
# select random slice
lowerCamelCase_: Optional[int] = int(ids_tensor((1,) , output_from_past.shape[-1] ) )
lowerCamelCase_: Union[str, Any] = output_from_no_past[:, -3:, random_slice_idx]
lowerCamelCase_: Optional[int] = output_from_past[:, :, random_slice_idx]
# test that outputs are equal for slice
tf.debugging.assert_near(A_ , A_ , rtol=1e-3 )
def UpperCAmelCase_ ( _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase=None , _UpperCAmelCase=None , _UpperCAmelCase=None , _UpperCAmelCase=None , _UpperCAmelCase=None , ):
if attention_mask is None:
lowerCamelCase_: Optional[int] = tf.cast(tf.math.not_equal(_UpperCAmelCase , config.pad_token_id ) , tf.inta )
if decoder_attention_mask is None:
lowerCamelCase_: List[str] = tf.concat(
[
tf.ones(decoder_input_ids[:, :1].shape , dtype=tf.inta ),
tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:] , config.pad_token_id ) , tf.inta ),
] , axis=-1 , )
if head_mask is None:
lowerCamelCase_: int = tf.ones((config.encoder_layers, config.encoder_attention_heads) )
if decoder_head_mask is None:
lowerCamelCase_: List[Any] = tf.ones((config.decoder_layers, config.decoder_attention_heads) )
if cross_attn_head_mask is None:
lowerCamelCase_: List[Any] = tf.ones((config.decoder_layers, config.decoder_attention_heads) )
return {
"input_ids": input_ids,
"decoder_input_ids": decoder_input_ids,
"attention_mask": attention_mask,
"decoder_attention_mask": decoder_attention_mask,
"head_mask": head_mask,
"decoder_head_mask": decoder_head_mask,
"cross_attn_head_mask": cross_attn_head_mask,
}
@require_tf
class a__ ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , unittest.TestCase ):
_A = (TFPegasusForConditionalGeneration, TFPegasusModel) if is_tf_available() else ()
_A = (TFPegasusForConditionalGeneration,) if is_tf_available() else ()
_A = (
{
"conversational": TFPegasusForConditionalGeneration,
"feature-extraction": TFPegasusModel,
"summarization": TFPegasusForConditionalGeneration,
"text2text-generation": TFPegasusForConditionalGeneration,
"translation": TFPegasusForConditionalGeneration,
}
if is_tf_available()
else {}
)
_A = True
_A = False
_A = False
def lowerCAmelCase ( self : Union[str, Any] ) -> Any:
"""simple docstring"""
lowerCamelCase_: Tuple = TFPegasusModelTester(self )
lowerCamelCase_: Optional[int] = ConfigTester(self , config_class=A_ )
def lowerCAmelCase ( self : Optional[Any] ) -> List[Any]:
"""simple docstring"""
self.config_tester.run_common_tests()
def lowerCAmelCase ( self : Dict ) -> Tuple:
"""simple docstring"""
lowerCamelCase_: List[str] = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.check_decoder_model_past_large_inputs(*A_ )
@require_sentencepiece
@require_tokenizers
@require_tf
class a__ ( unittest.TestCase ):
_A = [
" PG&E stated it scheduled the blackouts in response to forecasts for high winds amid dry conditions. The aim is to reduce the risk of wildfires. Nearly 800 thousand customers were scheduled to be affected by the shutoffs which were expected to last through at least midday tomorrow.",
" The London trio are up for best UK act and best album, as well as getting two nominations in the best song category.\"We got told like this morning 'Oh I think you're nominated'\", said Dappy.\"And I was like 'Oh yeah, which one?' And now we've got nominated for four awards. I mean, wow!\"Bandmate Fazer added: \"We thought it's best of us to come down and mingle with everyone and say hello to the cameras. And now we find we've got four nominations.\"The band have two shots at the best song prize, getting the nod for their Tynchy Stryder collaboration Number One, and single Strong Again.Their album Uncle B will also go up against records by the likes of Beyonce and Kanye West.N-Dubz picked up the best newcomer Mobo in 2007, but female member Tulisa said they wouldn't be too disappointed if they didn't win this time around.\"At the end of the day we're grateful to be where we are in our careers.\"If it don't happen then it don't happen - live to fight another day and keep on making albums and hits for the fans.\"Dappy also revealed they could be performing live several times on the night.The group will be doing Number One and also a possible rendition of the War Child single, I Got Soul.The charity song is a re-working of The Killers' All These Things That I've Done and is set to feature artists like Chipmunk, Ironik and Pixie Lott.This year's Mobos will be held outside of London for the first time, in Glasgow on 30 September.N-Dubz said they were looking forward to performing for their Scottish fans and boasted about their recent shows north of the border.\"We just done Edinburgh the other day,\" said Dappy.\"We smashed up an N-Dubz show over there. We done Aberdeen about three or four months ago - we smashed up that show over there! Everywhere we go we smash it up!\" ",
]
_A = [
"California's largest electricity provider has cut power to hundreds of thousands of customers in an effort to"
" reduce the risk of wildfires.",
"N-Dubz have revealed they\'re \"grateful\" to have been nominated for four Mobo Awards.",
] # differs slightly from pytorch, likely due to numerical differences in linear layers
_A = "google/pegasus-xsum"
@cached_property
def lowerCAmelCase ( self : Optional[int] ) -> Optional[int]:
"""simple docstring"""
return AutoTokenizer.from_pretrained(self.model_name )
@cached_property
def lowerCAmelCase ( self : Any ) -> Union[str, Any]:
"""simple docstring"""
lowerCamelCase_: Any = TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name )
return model
def lowerCAmelCase ( self : str , **A_ : Optional[int] ) -> int:
"""simple docstring"""
lowerCamelCase_: Union[str, Any] = self.translate_src_text(**A_ )
assert self.expected_text == generated_words
def lowerCAmelCase ( self : str , **A_ : int ) -> Optional[Any]:
"""simple docstring"""
lowerCamelCase_: List[Any] = self.tokenizer(self.src_text , **A_ , padding=A_ , return_tensors="""tf""" )
lowerCamelCase_: str = self.model.generate(
model_inputs.input_ids , attention_mask=model_inputs.attention_mask , num_beams=2 , use_cache=A_ , )
lowerCamelCase_: Tuple = self.tokenizer.batch_decode(generated_ids.numpy() , skip_special_tokens=A_ )
return generated_words
@slow
def lowerCAmelCase ( self : Optional[int] ) -> List[str]:
"""simple docstring"""
self._assert_generated_batch_equal_expected()
| 423 | import math
from collections import defaultdict
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput
def UpperCAmelCase_ ( _UpperCAmelCase , _UpperCAmelCase=0.9_9_9 , _UpperCAmelCase="cosine" , ):
if alpha_transform_type == "cosine":
def alpha_bar_fn(_UpperCAmelCase ):
return math.cos((t + 0.0_0_8) / 1.0_0_8 * math.pi / 2 ) ** 2
elif alpha_transform_type == "exp":
def alpha_bar_fn(_UpperCAmelCase ):
return math.exp(t * -1_2.0 )
else:
raise ValueError(f"""Unsupported alpha_tranform_type: {alpha_transform_type}""" )
lowerCamelCase_: Union[str, Any] = []
for i in range(_UpperCAmelCase ):
lowerCamelCase_: Tuple = i / num_diffusion_timesteps
lowerCamelCase_: Optional[Any] = (i + 1) / num_diffusion_timesteps
betas.append(min(1 - alpha_bar_fn(_UpperCAmelCase ) / alpha_bar_fn(_UpperCAmelCase ) , _UpperCAmelCase ) )
return torch.tensor(_UpperCAmelCase , dtype=torch.floataa )
class a__ ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
_A = [e.name for e in KarrasDiffusionSchedulers]
_A = 2
@register_to_config
def __init__( self : int , A_ : int = 10_00 , A_ : float = 0.00085 , A_ : float = 0.012 , A_ : str = "linear" , A_ : Optional[Union[np.ndarray, List[float]]] = None , A_ : str = "epsilon" , A_ : str = "linspace" , A_ : int = 0 , ) -> Any:
"""simple docstring"""
if trained_betas is not None:
lowerCamelCase_: Dict = torch.tensor(A_ , dtype=torch.floataa )
elif beta_schedule == "linear":
lowerCamelCase_: Tuple = torch.linspace(A_ , A_ , A_ , dtype=torch.floataa )
elif beta_schedule == "scaled_linear":
# this schedule is very specific to the latent diffusion model.
lowerCamelCase_: List[Any] = (
torch.linspace(beta_start**0.5 , beta_end**0.5 , A_ , dtype=torch.floataa ) ** 2
)
elif beta_schedule == "squaredcos_cap_v2":
# Glide cosine schedule
lowerCamelCase_: Tuple = betas_for_alpha_bar(A_ )
else:
raise NotImplementedError(f"""{beta_schedule} does is not implemented for {self.__class__}""" )
lowerCamelCase_: str = 1.0 - self.betas
lowerCamelCase_: Any = torch.cumprod(self.alphas , dim=0 )
# set all values
self.set_timesteps(A_ , A_ , A_ )
def lowerCAmelCase ( self : List[Any] , A_ : str , A_ : Union[str, Any]=None ) -> int:
"""simple docstring"""
if schedule_timesteps is None:
lowerCamelCase_: Union[str, Any] = self.timesteps
lowerCamelCase_: Tuple = (schedule_timesteps == timestep).nonzero()
# The sigma index that is taken for the **very** first `step`
# is always the second index (or the last index if there is only 1)
# This way we can ensure we don't accidentally skip a sigma in
# case we start in the middle of the denoising schedule (e.g. for image-to-image)
if len(self._index_counter ) == 0:
lowerCamelCase_: List[str] = 1 if len(A_ ) > 1 else 0
else:
lowerCamelCase_: int = timestep.cpu().item() if torch.is_tensor(A_ ) else timestep
lowerCamelCase_: List[Any] = self._index_counter[timestep_int]
return indices[pos].item()
@property
def lowerCAmelCase ( self : Any ) -> int:
"""simple docstring"""
# standard deviation of the initial noise distribution
if self.config.timestep_spacing in ["linspace", "trailing"]:
return self.sigmas.max()
return (self.sigmas.max() ** 2 + 1) ** 0.5
def lowerCAmelCase ( self : int , A_ : torch.FloatTensor , A_ : Union[float, torch.FloatTensor] , ) -> torch.FloatTensor:
"""simple docstring"""
lowerCamelCase_: List[Any] = self.index_for_timestep(A_ )
if self.state_in_first_order:
lowerCamelCase_: List[Any] = self.sigmas[step_index]
else:
lowerCamelCase_: Optional[Any] = self.sigmas_interpol[step_index]
lowerCamelCase_: Optional[int] = sample / ((sigma**2 + 1) ** 0.5)
return sample
def lowerCAmelCase ( self : Any , A_ : int , A_ : Union[str, torch.device] = None , A_ : Optional[int] = None , ) -> List[Any]:
"""simple docstring"""
lowerCamelCase_: List[str] = num_inference_steps
lowerCamelCase_: int = num_train_timesteps or self.config.num_train_timesteps
# "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
if self.config.timestep_spacing == "linspace":
lowerCamelCase_: int = np.linspace(0 , num_train_timesteps - 1 , A_ , dtype=A_ )[::-1].copy()
elif self.config.timestep_spacing == "leading":
lowerCamelCase_: Dict = num_train_timesteps // self.num_inference_steps
# creates integer timesteps by multiplying by ratio
# casting to int to avoid issues when num_inference_step is power of 3
lowerCamelCase_: List[Any] = (np.arange(0 , A_ ) * step_ratio).round()[::-1].copy().astype(A_ )
timesteps += self.config.steps_offset
elif self.config.timestep_spacing == "trailing":
lowerCamelCase_: Optional[int] = num_train_timesteps / self.num_inference_steps
# creates integer timesteps by multiplying by ratio
# casting to int to avoid issues when num_inference_step is power of 3
lowerCamelCase_: Any = (np.arange(A_ , 0 , -step_ratio )).round().copy().astype(A_ )
timesteps -= 1
else:
raise ValueError(
f"""{self.config.timestep_spacing} is not supported. Please make sure to choose one of 'linspace', 'leading' or 'trailing'.""" )
lowerCamelCase_: Dict = np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5 )
lowerCamelCase_: Any = torch.from_numpy(np.log(A_ ) ).to(A_ )
lowerCamelCase_: Union[str, Any] = np.interp(A_ , np.arange(0 , len(A_ ) ) , A_ )
lowerCamelCase_: List[str] = np.concatenate([sigmas, [0.0]] ).astype(np.floataa )
lowerCamelCase_: Tuple = torch.from_numpy(A_ ).to(device=A_ )
# interpolate sigmas
lowerCamelCase_: List[Any] = sigmas.log().lerp(sigmas.roll(1 ).log() , 0.5 ).exp()
lowerCamelCase_: List[Any] = torch.cat([sigmas[:1], sigmas[1:].repeat_interleave(2 ), sigmas[-1:]] )
lowerCamelCase_: int = torch.cat(
[sigmas_interpol[:1], sigmas_interpol[1:].repeat_interleave(2 ), sigmas_interpol[-1:]] )
if str(A_ ).startswith("""mps""" ):
# mps does not support float64
lowerCamelCase_: List[Any] = torch.from_numpy(A_ ).to(A_ , dtype=torch.floataa )
else:
lowerCamelCase_: Union[str, Any] = torch.from_numpy(A_ ).to(A_ )
# interpolate timesteps
lowerCamelCase_: Optional[int] = self.sigma_to_t(A_ ).to(A_ , dtype=timesteps.dtype )
lowerCamelCase_: Optional[int] = torch.stack((timesteps_interpol[1:-1, None], timesteps[1:, None]) , dim=-1 ).flatten()
lowerCamelCase_: str = torch.cat([timesteps[:1], interleaved_timesteps] )
lowerCamelCase_: Tuple = None
# for exp beta schedules, such as the one for `pipeline_shap_e.py`
# we need an index counter
lowerCamelCase_: List[str] = defaultdict(A_ )
def lowerCAmelCase ( self : Tuple , A_ : List[Any] ) -> Optional[Any]:
"""simple docstring"""
# get log sigma
lowerCamelCase_: List[Any] = sigma.log()
# get distribution
lowerCamelCase_: Union[str, Any] = log_sigma - self.log_sigmas[:, None]
# get sigmas range
lowerCamelCase_: Any = dists.ge(0 ).cumsum(dim=0 ).argmax(dim=0 ).clamp(max=self.log_sigmas.shape[0] - 2 )
lowerCamelCase_: int = low_idx + 1
lowerCamelCase_: Optional[Any] = self.log_sigmas[low_idx]
lowerCamelCase_: Dict = self.log_sigmas[high_idx]
# interpolate sigmas
lowerCamelCase_: int = (low - log_sigma) / (low - high)
lowerCamelCase_: Optional[int] = w.clamp(0 , 1 )
# transform interpolation to time range
lowerCamelCase_: Any = (1 - w) * low_idx + w * high_idx
lowerCamelCase_: Dict = t.view(sigma.shape )
return t
@property
def lowerCAmelCase ( self : Optional[Any] ) -> Union[str, Any]:
"""simple docstring"""
return self.sample is None
def lowerCAmelCase ( self : List[Any] , A_ : Union[torch.FloatTensor, np.ndarray] , A_ : Union[float, torch.FloatTensor] , A_ : Union[torch.FloatTensor, np.ndarray] , A_ : bool = True , ) -> Union[SchedulerOutput, Tuple]:
"""simple docstring"""
lowerCamelCase_: int = self.index_for_timestep(A_ )
# advance index counter by 1
lowerCamelCase_: List[str] = timestep.cpu().item() if torch.is_tensor(A_ ) else timestep
self._index_counter[timestep_int] += 1
if self.state_in_first_order:
lowerCamelCase_: List[str] = self.sigmas[step_index]
lowerCamelCase_: int = self.sigmas_interpol[step_index + 1]
lowerCamelCase_: int = self.sigmas[step_index + 1]
else:
# 2nd order / KDPM2's method
lowerCamelCase_: Union[str, Any] = self.sigmas[step_index - 1]
lowerCamelCase_: List[Any] = self.sigmas_interpol[step_index]
lowerCamelCase_: str = self.sigmas[step_index]
# currently only gamma=0 is supported. This usually works best anyways.
# We can support gamma in the future but then need to scale the timestep before
# passing it to the model which requires a change in API
lowerCamelCase_: List[str] = 0
lowerCamelCase_: int = sigma * (gamma + 1) # Note: sigma_hat == sigma for now
# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
if self.config.prediction_type == "epsilon":
lowerCamelCase_: str = sigma_hat if self.state_in_first_order else sigma_interpol
lowerCamelCase_: int = sample - sigma_input * model_output
elif self.config.prediction_type == "v_prediction":
lowerCamelCase_: Tuple = sigma_hat if self.state_in_first_order else sigma_interpol
lowerCamelCase_: Union[str, Any] = model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
sample / (sigma_input**2 + 1)
)
elif self.config.prediction_type == "sample":
raise NotImplementedError("""prediction_type not implemented yet: sample""" )
else:
raise ValueError(
f"""prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`""" )
if self.state_in_first_order:
# 2. Convert to an ODE derivative for 1st order
lowerCamelCase_: Optional[int] = (sample - pred_original_sample) / sigma_hat
# 3. delta timestep
lowerCamelCase_: Any = sigma_interpol - sigma_hat
# store for 2nd order step
lowerCamelCase_: str = sample
else:
# DPM-Solver-2
# 2. Convert to an ODE derivative for 2nd order
lowerCamelCase_: Dict = (sample - pred_original_sample) / sigma_interpol
# 3. delta timestep
lowerCamelCase_: List[Any] = sigma_next - sigma_hat
lowerCamelCase_: str = self.sample
lowerCamelCase_: Tuple = None
lowerCamelCase_: str = sample + derivative * dt
if not return_dict:
return (prev_sample,)
return SchedulerOutput(prev_sample=A_ )
def lowerCAmelCase ( self : Tuple , A_ : torch.FloatTensor , A_ : torch.FloatTensor , A_ : torch.FloatTensor , ) -> torch.FloatTensor:
"""simple docstring"""
# Make sure sigmas and timesteps have the same device and dtype as original_samples
lowerCamelCase_: List[str] = self.sigmas.to(device=original_samples.device , dtype=original_samples.dtype )
if original_samples.device.type == "mps" and torch.is_floating_point(A_ ):
# mps does not support float64
lowerCamelCase_: int = self.timesteps.to(original_samples.device , dtype=torch.floataa )
lowerCamelCase_: Dict = timesteps.to(original_samples.device , dtype=torch.floataa )
else:
lowerCamelCase_: int = self.timesteps.to(original_samples.device )
lowerCamelCase_: Dict = timesteps.to(original_samples.device )
lowerCamelCase_: Optional[Any] = [self.index_for_timestep(A_ , A_ ) for t in timesteps]
lowerCamelCase_: Tuple = sigmas[step_indices].flatten()
while len(sigma.shape ) < len(original_samples.shape ):
lowerCamelCase_: List[Any] = sigma.unsqueeze(-1 )
lowerCamelCase_: List[Any] = original_samples + noise * sigma
return noisy_samples
def __len__( self : Union[str, Any] ) -> Optional[Any]:
"""simple docstring"""
return self.config.num_train_timesteps
| 423 | 1 |
import unittest
import numpy as np
import torch
from diffusers import DDIMPipeline, DDIMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device
from ..pipeline_params import UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS, UNCONDITIONAL_IMAGE_GENERATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin
enable_full_determinism()
class A__ ( __SCREAMING_SNAKE_CASE , unittest.TestCase ):
lowerCamelCase__ : Tuple =DDIMPipeline
lowerCamelCase__ : List[Any] =UNCONDITIONAL_IMAGE_GENERATION_PARAMS
lowerCamelCase__ : List[Any] =PipelineTesterMixin.required_optional_params - {
"num_images_per_prompt",
"latents",
"callback",
"callback_steps",
}
lowerCamelCase__ : List[Any] =UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS
lowerCamelCase__ : List[Any] =False
def lowercase ( self ) -> List[Any]:
"""simple docstring"""
torch.manual_seed(0 )
__magic_name__ : Tuple = UNetaDModel(
block_out_channels=(32, 64) , layers_per_block=2 , sample_size=32 , in_channels=3 , out_channels=3 , down_block_types=('''DownBlock2D''', '''AttnDownBlock2D''') , up_block_types=('''AttnUpBlock2D''', '''UpBlock2D''') , )
__magic_name__ : List[Any] = DDIMScheduler()
__magic_name__ : Any = {'''unet''': unet, '''scheduler''': scheduler}
return components
def lowercase ( self , lowerCamelCase , lowerCamelCase=0 ) -> Tuple:
"""simple docstring"""
if str(lowerCamelCase ).startswith('''mps''' ):
__magic_name__ : List[str] = torch.manual_seed(lowerCamelCase )
else:
__magic_name__ : Union[str, Any] = torch.Generator(device=lowerCamelCase ).manual_seed(lowerCamelCase )
__magic_name__ : List[Any] = {
'''batch_size''': 1,
'''generator''': generator,
'''num_inference_steps''': 2,
'''output_type''': '''numpy''',
}
return inputs
def lowercase ( self ) -> Optional[int]:
"""simple docstring"""
__magic_name__ : Optional[Any] = '''cpu'''
__magic_name__ : str = self.get_dummy_components()
__magic_name__ : Optional[Any] = self.pipeline_class(**lowerCamelCase )
pipe.to(lowerCamelCase )
pipe.set_progress_bar_config(disable=lowerCamelCase )
__magic_name__ : Optional[Any] = self.get_dummy_inputs(lowerCamelCase )
__magic_name__ : str = pipe(**lowerCamelCase ).images
__magic_name__ : List[Any] = image[0, -3:, -3:, -1]
self.assertEqual(image.shape , (1, 32, 32, 3) )
__magic_name__ : Optional[int] = np.array(
[1.0_00e00, 5.7_17e-01, 4.7_17e-01, 1.0_00e00, 0.0_00e00, 1.0_00e00, 3.0_00e-04, 0.0_00e00, 9.0_00e-04] )
__magic_name__ : List[Any] = np.abs(image_slice.flatten() - expected_slice ).max()
self.assertLessEqual(lowerCamelCase , 1e-3 )
def lowercase ( self ) -> Dict:
"""simple docstring"""
super().test_dict_tuple_outputs_equivalent(expected_max_difference=3e-3 )
def lowercase ( self ) -> int:
"""simple docstring"""
super().test_save_load_local(expected_max_difference=3e-3 )
def lowercase ( self ) -> Any:
"""simple docstring"""
super().test_save_load_optional_components(expected_max_difference=3e-3 )
def lowercase ( self ) -> Union[str, Any]:
"""simple docstring"""
super().test_inference_batch_single_identical(expected_max_diff=3e-3 )
@slow
@require_torch_gpu
class A__ ( unittest.TestCase ):
def lowercase ( self ) -> List[Any]:
"""simple docstring"""
__magic_name__ : Union[str, Any] = '''google/ddpm-cifar10-32'''
__magic_name__ : str = UNetaDModel.from_pretrained(lowerCamelCase )
__magic_name__ : Optional[Any] = DDIMScheduler()
__magic_name__ : str = DDIMPipeline(unet=lowerCamelCase , scheduler=lowerCamelCase )
ddim.to(lowerCamelCase )
ddim.set_progress_bar_config(disable=lowerCamelCase )
__magic_name__ : Tuple = torch.manual_seed(0 )
__magic_name__ : List[str] = ddim(generator=lowerCamelCase , eta=0.0 , output_type='''numpy''' ).images
__magic_name__ : Optional[Any] = image[0, -3:, -3:, -1]
assert image.shape == (1, 32, 32, 3)
__magic_name__ : int = np.array([0.1_7_2_3, 0.1_6_1_7, 0.1_6_0_0, 0.1_6_2_6, 0.1_4_9_7, 0.1_5_1_3, 0.1_5_0_5, 0.1_4_4_2, 0.1_4_5_3] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
def lowercase ( self ) -> Optional[Any]:
"""simple docstring"""
__magic_name__ : List[str] = '''google/ddpm-ema-bedroom-256'''
__magic_name__ : Any = UNetaDModel.from_pretrained(lowerCamelCase )
__magic_name__ : List[Any] = DDIMScheduler.from_pretrained(lowerCamelCase )
__magic_name__ : Dict = DDIMPipeline(unet=lowerCamelCase , scheduler=lowerCamelCase )
ddpm.to(lowerCamelCase )
ddpm.set_progress_bar_config(disable=lowerCamelCase )
__magic_name__ : Dict = torch.manual_seed(0 )
__magic_name__ : List[str] = ddpm(generator=lowerCamelCase , output_type='''numpy''' ).images
__magic_name__ : Any = image[0, -3:, -3:, -1]
assert image.shape == (1, 256, 256, 3)
__magic_name__ : Dict = np.array([0.0_0_6_0, 0.0_2_0_1, 0.0_3_4_4, 0.0_0_2_4, 0.0_0_1_8, 0.0_0_0_2, 0.0_0_2_2, 0.0_0_0_0, 0.0_0_6_9] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
| 336 |
from io import BytesIO
from typing import List, Union
import requests
from ..utils import add_end_docstrings, is_decord_available, is_torch_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_decord_available():
import numpy as np
from decord import VideoReader
if is_torch_available():
from ..models.auto.modeling_auto import MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING
lowercase_ = logging.get_logger(__name__)
@add_end_docstrings(__SCREAMING_SNAKE_CASE )
class A__ ( __SCREAMING_SNAKE_CASE ):
def __init__( self , *lowerCamelCase , **lowerCamelCase ) -> Dict:
"""simple docstring"""
super().__init__(*lowerCamelCase , **lowerCamelCase )
requires_backends(self , '''decord''' )
self.check_model_type(lowerCamelCase )
def lowercase ( self , lowerCamelCase=None , lowerCamelCase=None , lowerCamelCase=None ) -> List[Any]:
"""simple docstring"""
__magic_name__ : List[str] = {}
if frame_sampling_rate is not None:
__magic_name__ : Optional[int] = frame_sampling_rate
if num_frames is not None:
__magic_name__ : Optional[Any] = num_frames
__magic_name__ : Union[str, Any] = {}
if top_k is not None:
__magic_name__ : Union[str, Any] = top_k
return preprocess_params, {}, postprocess_params
def __call__( self , lowerCamelCase , **lowerCamelCase ) -> List[Any]:
"""simple docstring"""
return super().__call__(lowerCamelCase , **lowerCamelCase )
def lowercase ( self , lowerCamelCase , lowerCamelCase=None , lowerCamelCase=1 ) -> int:
"""simple docstring"""
if num_frames is None:
__magic_name__ : Any = self.model.config.num_frames
if video.startswith('''http://''' ) or video.startswith('''https://''' ):
__magic_name__ : str = BytesIO(requests.get(lowerCamelCase ).content )
__magic_name__ : Optional[int] = VideoReader(lowerCamelCase )
videoreader.seek(0 )
__magic_name__ : Union[str, Any] = 0
__magic_name__ : Tuple = num_frames * frame_sampling_rate - 1
__magic_name__ : Tuple = np.linspace(lowerCamelCase , lowerCamelCase , num=lowerCamelCase , dtype=np.intaa )
__magic_name__ : Union[str, Any] = videoreader.get_batch(lowerCamelCase ).asnumpy()
__magic_name__ : List[str] = list(lowerCamelCase )
__magic_name__ : Tuple = self.image_processor(lowerCamelCase , return_tensors=self.framework )
return model_inputs
def lowercase ( self , lowerCamelCase ) -> str:
"""simple docstring"""
__magic_name__ : Union[str, Any] = self.model(**lowerCamelCase )
return model_outputs
def lowercase ( self , lowerCamelCase , lowerCamelCase=5 ) -> Optional[Any]:
"""simple docstring"""
if top_k > self.model.config.num_labels:
__magic_name__ : Dict = self.model.config.num_labels
if self.framework == "pt":
__magic_name__ : Tuple = model_outputs.logits.softmax(-1 )[0]
__magic_name__ , __magic_name__ : str = probs.topk(lowerCamelCase )
else:
raise ValueError(F'''Unsupported framework: {self.framework}''' )
__magic_name__ : List[str] = scores.tolist()
__magic_name__ : Tuple = ids.tolist()
return [{"score": score, "label": self.model.config.idalabel[_id]} for score, _id in zip(lowerCamelCase , lowerCamelCase )]
| 336 | 1 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from diffusers import (
DDIMScheduler,
KandinskyVaaInpaintPipeline,
KandinskyVaaPriorPipeline,
UNetaDConditionModel,
VQModel,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
enable_full_determinism()
class _a ( A__ , unittest.TestCase ):
"""simple docstring"""
snake_case =KandinskyVaaInpaintPipeline
snake_case =["""image_embeds""", """negative_image_embeds""", """image""", """mask_image"""]
snake_case =[
"""image_embeds""",
"""negative_image_embeds""",
"""image""",
"""mask_image""",
]
snake_case =[
"""generator""",
"""height""",
"""width""",
"""latents""",
"""guidance_scale""",
"""num_inference_steps""",
"""return_dict""",
"""guidance_scale""",
"""num_images_per_prompt""",
"""output_type""",
"""return_dict""",
]
snake_case =False
@property
def SCREAMING_SNAKE_CASE ( self ):
return 32
@property
def SCREAMING_SNAKE_CASE ( self ):
return 32
@property
def SCREAMING_SNAKE_CASE ( self ):
return self.time_input_dim
@property
def SCREAMING_SNAKE_CASE ( self ):
return self.time_input_dim * 4
@property
def SCREAMING_SNAKE_CASE ( self ):
return 100
@property
def SCREAMING_SNAKE_CASE ( self ):
torch.manual_seed(0 )
_UpperCAmelCase ={
"in_channels": 9,
# Out channels is double in channels because predicts mean and variance
"out_channels": 8,
"addition_embed_type": "image",
"down_block_types": ("ResnetDownsampleBlock2D", "SimpleCrossAttnDownBlock2D"),
"up_block_types": ("SimpleCrossAttnUpBlock2D", "ResnetUpsampleBlock2D"),
"mid_block_type": "UNetMidBlock2DSimpleCrossAttn",
"block_out_channels": (self.block_out_channels_a, self.block_out_channels_a * 2),
"layers_per_block": 1,
"encoder_hid_dim": self.text_embedder_hidden_size,
"encoder_hid_dim_type": "image_proj",
"cross_attention_dim": self.cross_attention_dim,
"attention_head_dim": 4,
"resnet_time_scale_shift": "scale_shift",
"class_embed_type": None,
}
_UpperCAmelCase =UNetaDConditionModel(**_snake_case )
return model
@property
def SCREAMING_SNAKE_CASE ( self ):
return {
"block_out_channels": [32, 64],
"down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
"in_channels": 3,
"latent_channels": 4,
"layers_per_block": 1,
"norm_num_groups": 8,
"norm_type": "spatial",
"num_vq_embeddings": 12,
"out_channels": 3,
"up_block_types": [
"AttnUpDecoderBlock2D",
"UpDecoderBlock2D",
],
"vq_embed_dim": 4,
}
@property
def SCREAMING_SNAKE_CASE ( self ):
torch.manual_seed(0 )
_UpperCAmelCase =VQModel(**self.dummy_movq_kwargs )
return model
def SCREAMING_SNAKE_CASE ( self ):
_UpperCAmelCase =self.dummy_unet
_UpperCAmelCase =self.dummy_movq
_UpperCAmelCase =DDIMScheduler(
num_train_timesteps=1000 , beta_schedule="linear" , beta_start=0.00_085 , beta_end=0.012 , clip_sample=_snake_case , set_alpha_to_one=_snake_case , steps_offset=1 , prediction_type="epsilon" , thresholding=_snake_case , )
_UpperCAmelCase ={
"unet": unet,
"scheduler": scheduler,
"movq": movq,
}
return components
def SCREAMING_SNAKE_CASE ( self , _snake_case , _snake_case=0 ):
_UpperCAmelCase =floats_tensor((1, self.text_embedder_hidden_size) , rng=random.Random(_snake_case ) ).to(_snake_case )
_UpperCAmelCase =floats_tensor((1, self.text_embedder_hidden_size) , rng=random.Random(seed + 1 ) ).to(
_snake_case )
# create init_image
_UpperCAmelCase =floats_tensor((1, 3, 64, 64) , rng=random.Random(_snake_case ) ).to(_snake_case )
_UpperCAmelCase =image.cpu().permute(0 , 2 , 3 , 1 )[0]
_UpperCAmelCase =Image.fromarray(np.uinta(_snake_case ) ).convert("RGB" ).resize((256, 256) )
# create mask
_UpperCAmelCase =np.ones((64, 64) , dtype=np.floataa )
_UpperCAmelCase =0
if str(_snake_case ).startswith("mps" ):
_UpperCAmelCase =torch.manual_seed(_snake_case )
else:
_UpperCAmelCase =torch.Generator(device=_snake_case ).manual_seed(_snake_case )
_UpperCAmelCase ={
"image": init_image,
"mask_image": mask,
"image_embeds": image_embeds,
"negative_image_embeds": negative_image_embeds,
"generator": generator,
"height": 64,
"width": 64,
"num_inference_steps": 2,
"guidance_scale": 4.0,
"output_type": "np",
}
return inputs
def SCREAMING_SNAKE_CASE ( self ):
_UpperCAmelCase ="cpu"
_UpperCAmelCase =self.get_dummy_components()
_UpperCAmelCase =self.pipeline_class(**_snake_case )
_UpperCAmelCase =pipe.to(_snake_case )
pipe.set_progress_bar_config(disable=_snake_case )
_UpperCAmelCase =pipe(**self.get_dummy_inputs(_snake_case ) )
_UpperCAmelCase =output.images
_UpperCAmelCase =pipe(
**self.get_dummy_inputs(_snake_case ) , return_dict=_snake_case , )[0]
_UpperCAmelCase =image[0, -3:, -3:, -1]
_UpperCAmelCase =image_from_tuple[0, -3:, -3:, -1]
print(F"image.shape {image.shape}" )
assert image.shape == (1, 64, 64, 3)
_UpperCAmelCase =np.array(
[0.50_775_903, 0.49_527_195, 0.48_824_543, 0.50_192_237, 0.48_644_906, 0.49_373_814, 0.4_780_598, 0.47_234_827, 0.48_327_848] )
assert (
np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
), F" expected_slice {expected_slice}, but got {image_slice.flatten()}"
assert (
np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1E-2
), F" expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}"
def SCREAMING_SNAKE_CASE ( self ):
super().test_inference_batch_single_identical(expected_max_diff=3E-3 )
@slow
@require_torch_gpu
class _a ( unittest.TestCase ):
"""simple docstring"""
def SCREAMING_SNAKE_CASE ( self ):
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def SCREAMING_SNAKE_CASE ( self ):
_UpperCAmelCase =load_numpy(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
"/kandinskyv22/kandinskyv22_inpaint_cat_with_hat_fp16.npy" )
_UpperCAmelCase =load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main" "/kandinsky/cat.png" )
_UpperCAmelCase =np.ones((768, 768) , dtype=np.floataa )
_UpperCAmelCase =0
_UpperCAmelCase ="a hat"
_UpperCAmelCase =KandinskyVaaPriorPipeline.from_pretrained(
"kandinsky-community/kandinsky-2-2-prior" , torch_dtype=torch.floataa )
pipe_prior.to(_snake_case )
_UpperCAmelCase =KandinskyVaaInpaintPipeline.from_pretrained(
"kandinsky-community/kandinsky-2-2-decoder-inpaint" , torch_dtype=torch.floataa )
_UpperCAmelCase =pipeline.to(_snake_case )
pipeline.set_progress_bar_config(disable=_snake_case )
_UpperCAmelCase =torch.Generator(device="cpu" ).manual_seed(0 )
_UpperCAmelCase , _UpperCAmelCase =pipe_prior(
_snake_case , generator=_snake_case , num_inference_steps=5 , negative_prompt="" , ).to_tuple()
_UpperCAmelCase =pipeline(
image=_snake_case , mask_image=_snake_case , image_embeds=_snake_case , negative_image_embeds=_snake_case , generator=_snake_case , num_inference_steps=100 , height=768 , width=768 , output_type="np" , )
_UpperCAmelCase =output.images[0]
assert image.shape == (768, 768, 3)
assert_mean_pixel_difference(_snake_case , _snake_case )
| 408 |
from ....configuration_utils import PretrainedConfig
from ....utils import logging
snake_case__ : List[str] = logging.get_logger(__name__)
# TODO: upload to AWS
snake_case__ : str = {
'yjernite/retribert-base-uncased': (
'https://huggingface.co/yjernite/retribert-base-uncased/resolve/main/config.json'
),
}
class _a ( A__ ):
"""simple docstring"""
snake_case ="""retribert"""
def __init__( self , _snake_case=3_0522 , _snake_case=768 , _snake_case=8 , _snake_case=12 , _snake_case=3072 , _snake_case="gelu" , _snake_case=0.1 , _snake_case=0.1 , _snake_case=512 , _snake_case=2 , _snake_case=0.02 , _snake_case=1E-1_2 , _snake_case=True , _snake_case=128 , _snake_case=0 , **_snake_case , ):
super().__init__(pad_token_id=_snake_case , **_snake_case )
_UpperCAmelCase =vocab_size
_UpperCAmelCase =hidden_size
_UpperCAmelCase =num_hidden_layers
_UpperCAmelCase =num_attention_heads
_UpperCAmelCase =hidden_act
_UpperCAmelCase =intermediate_size
_UpperCAmelCase =hidden_dropout_prob
_UpperCAmelCase =attention_probs_dropout_prob
_UpperCAmelCase =max_position_embeddings
_UpperCAmelCase =type_vocab_size
_UpperCAmelCase =initializer_range
_UpperCAmelCase =layer_norm_eps
_UpperCAmelCase =share_encoders
_UpperCAmelCase =projection_dim
| 408 | 1 |
'''simple docstring'''
import importlib
import json
import os
from collections import OrderedDict
from typing import Dict, Optional, Union
# Build the list of all feature extractors
from ...configuration_utils import PretrainedConfig
from ...dynamic_module_utils import get_class_from_dynamic_module, resolve_trust_remote_code
from ...feature_extraction_utils import FeatureExtractionMixin
from ...utils import CONFIG_NAME, FEATURE_EXTRACTOR_NAME, get_file_from_repo, logging
from .auto_factory import _LazyAutoMapping
from .configuration_auto import (
CONFIG_MAPPING_NAMES,
AutoConfig,
model_type_to_module_name,
replace_list_option_in_docstrings,
)
A__ = logging.get_logger(__name__)
A__ = OrderedDict(
[
("audio-spectrogram-transformer", "ASTFeatureExtractor"),
("beit", "BeitFeatureExtractor"),
("chinese_clip", "ChineseCLIPFeatureExtractor"),
("clap", "ClapFeatureExtractor"),
("clip", "CLIPFeatureExtractor"),
("clipseg", "ViTFeatureExtractor"),
("conditional_detr", "ConditionalDetrFeatureExtractor"),
("convnext", "ConvNextFeatureExtractor"),
("cvt", "ConvNextFeatureExtractor"),
("data2vec-audio", "Wav2Vec2FeatureExtractor"),
("data2vec-vision", "BeitFeatureExtractor"),
("deformable_detr", "DeformableDetrFeatureExtractor"),
("deit", "DeiTFeatureExtractor"),
("detr", "DetrFeatureExtractor"),
("dinat", "ViTFeatureExtractor"),
("donut-swin", "DonutFeatureExtractor"),
("dpt", "DPTFeatureExtractor"),
("encodec", "EncodecFeatureExtractor"),
("flava", "FlavaFeatureExtractor"),
("glpn", "GLPNFeatureExtractor"),
("groupvit", "CLIPFeatureExtractor"),
("hubert", "Wav2Vec2FeatureExtractor"),
("imagegpt", "ImageGPTFeatureExtractor"),
("layoutlmv2", "LayoutLMv2FeatureExtractor"),
("layoutlmv3", "LayoutLMv3FeatureExtractor"),
("levit", "LevitFeatureExtractor"),
("maskformer", "MaskFormerFeatureExtractor"),
("mctct", "MCTCTFeatureExtractor"),
("mobilenet_v1", "MobileNetV1FeatureExtractor"),
("mobilenet_v2", "MobileNetV2FeatureExtractor"),
("mobilevit", "MobileViTFeatureExtractor"),
("nat", "ViTFeatureExtractor"),
("owlvit", "OwlViTFeatureExtractor"),
("perceiver", "PerceiverFeatureExtractor"),
("poolformer", "PoolFormerFeatureExtractor"),
("regnet", "ConvNextFeatureExtractor"),
("resnet", "ConvNextFeatureExtractor"),
("segformer", "SegformerFeatureExtractor"),
("sew", "Wav2Vec2FeatureExtractor"),
("sew-d", "Wav2Vec2FeatureExtractor"),
("speech_to_text", "Speech2TextFeatureExtractor"),
("speecht5", "SpeechT5FeatureExtractor"),
("swiftformer", "ViTFeatureExtractor"),
("swin", "ViTFeatureExtractor"),
("swinv2", "ViTFeatureExtractor"),
("table-transformer", "DetrFeatureExtractor"),
("timesformer", "VideoMAEFeatureExtractor"),
("tvlt", "TvltFeatureExtractor"),
("unispeech", "Wav2Vec2FeatureExtractor"),
("unispeech-sat", "Wav2Vec2FeatureExtractor"),
("van", "ConvNextFeatureExtractor"),
("videomae", "VideoMAEFeatureExtractor"),
("vilt", "ViltFeatureExtractor"),
("vit", "ViTFeatureExtractor"),
("vit_mae", "ViTFeatureExtractor"),
("vit_msn", "ViTFeatureExtractor"),
("wav2vec2", "Wav2Vec2FeatureExtractor"),
("wav2vec2-conformer", "Wav2Vec2FeatureExtractor"),
("wavlm", "Wav2Vec2FeatureExtractor"),
("whisper", "WhisperFeatureExtractor"),
("xclip", "CLIPFeatureExtractor"),
("yolos", "YolosFeatureExtractor"),
]
)
A__ = _LazyAutoMapping(CONFIG_MAPPING_NAMES, FEATURE_EXTRACTOR_MAPPING_NAMES)
def _lowercase ( a_ : str ) -> Tuple:
'''simple docstring'''
for module_name, extractors in FEATURE_EXTRACTOR_MAPPING_NAMES.items():
if class_name in extractors:
__magic_name__ = model_type_to_module_name(a_ )
__magic_name__ = importlib.import_module(F'.{module_name}' ,'transformers.models' )
try:
return getattr(a_ ,a_ )
except AttributeError:
continue
for _, extractor in FEATURE_EXTRACTOR_MAPPING._extra_content.items():
if getattr(a_ ,'__name__' ,a_ ) == class_name:
return extractor
# We did not fine the class, but maybe it's because a dep is missing. In that case, the class will be in the main
# init and we return the proper dummy to get an appropriate error message.
__magic_name__ = importlib.import_module('transformers' )
if hasattr(a_ ,a_ ):
return getattr(a_ ,a_ )
return None
def _lowercase ( a_ : Union[str, os.PathLike] ,a_ : Optional[Union[str, os.PathLike]] = None ,a_ : bool = False ,a_ : bool = False ,a_ : Optional[Dict[str, str]] = None ,a_ : Optional[Union[bool, str]] = None ,a_ : Optional[str] = None ,a_ : bool = False ,**a_ : Tuple ,) -> List[str]:
'''simple docstring'''
__magic_name__ = get_file_from_repo(
a_ ,a_ ,cache_dir=a_ ,force_download=a_ ,resume_download=a_ ,proxies=a_ ,use_auth_token=a_ ,revision=a_ ,local_files_only=a_ ,)
if resolved_config_file is None:
logger.info(
'Could not locate the feature extractor configuration file, will try to use the model config instead.' )
return {}
with open(a_ ,encoding='utf-8' ) as reader:
return json.load(a_ )
class __UpperCamelCase :
def __init__( self: int ):
'''simple docstring'''
raise EnvironmentError(
'AutoFeatureExtractor is designed to be instantiated '
'using the `AutoFeatureExtractor.from_pretrained(pretrained_model_name_or_path)` method.' )
@classmethod
@replace_list_option_in_docstrings(__UpperCamelCase )
def _SCREAMING_SNAKE_CASE ( cls: str , __UpperCamelCase: int , **__UpperCamelCase: List[str] ):
'''simple docstring'''
__magic_name__ = kwargs.pop('config' , __UpperCamelCase )
__magic_name__ = kwargs.pop('trust_remote_code' , __UpperCamelCase )
__magic_name__ = True
__magic_name__, __magic_name__ = FeatureExtractionMixin.get_feature_extractor_dict(__UpperCamelCase , **__UpperCamelCase )
__magic_name__ = config_dict.get('feature_extractor_type' , __UpperCamelCase )
__magic_name__ = None
if "AutoFeatureExtractor" in config_dict.get('auto_map' , {} ):
__magic_name__ = config_dict['auto_map']['AutoFeatureExtractor']
# If we don't find the feature extractor class in the feature extractor config, let's try the model config.
if feature_extractor_class is None and feature_extractor_auto_map is None:
if not isinstance(__UpperCamelCase , __UpperCamelCase ):
__magic_name__ = AutoConfig.from_pretrained(__UpperCamelCase , **__UpperCamelCase )
# It could be in `config.feature_extractor_type``
__magic_name__ = getattr(__UpperCamelCase , 'feature_extractor_type' , __UpperCamelCase )
if hasattr(__UpperCamelCase , 'auto_map' ) and "AutoFeatureExtractor" in config.auto_map:
__magic_name__ = config.auto_map['AutoFeatureExtractor']
if feature_extractor_class is not None:
__magic_name__ = feature_extractor_class_from_name(__UpperCamelCase )
__magic_name__ = feature_extractor_auto_map is not None
__magic_name__ = feature_extractor_class is not None or type(__UpperCamelCase ) in FEATURE_EXTRACTOR_MAPPING
__magic_name__ = resolve_trust_remote_code(
__UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase )
if has_remote_code and trust_remote_code:
__magic_name__ = get_class_from_dynamic_module(
__UpperCamelCase , __UpperCamelCase , **__UpperCamelCase )
__magic_name__ = kwargs.pop('code_revision' , __UpperCamelCase )
if os.path.isdir(__UpperCamelCase ):
feature_extractor_class.register_for_auto_class()
return feature_extractor_class.from_dict(__UpperCamelCase , **__UpperCamelCase )
elif feature_extractor_class is not None:
return feature_extractor_class.from_dict(__UpperCamelCase , **__UpperCamelCase )
# Last try: we use the FEATURE_EXTRACTOR_MAPPING.
elif type(__UpperCamelCase ) in FEATURE_EXTRACTOR_MAPPING:
__magic_name__ = FEATURE_EXTRACTOR_MAPPING[type(__UpperCamelCase )]
return feature_extractor_class.from_dict(__UpperCamelCase , **__UpperCamelCase )
raise ValueError(
F'Unrecognized feature extractor in {pretrained_model_name_or_path}. Should have a '
F'`feature_extractor_type` key in its {FEATURE_EXTRACTOR_NAME} of {CONFIG_NAME}, or one of the following '
F'`model_type` keys in its {CONFIG_NAME}: {", ".join(c for c in FEATURE_EXTRACTOR_MAPPING_NAMES.keys() )}' )
@staticmethod
def _SCREAMING_SNAKE_CASE ( __UpperCamelCase: int , __UpperCamelCase: Optional[Any] ):
'''simple docstring'''
FEATURE_EXTRACTOR_MAPPING.register(__UpperCamelCase , __UpperCamelCase )
| 710 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
A__ = logging.get_logger(__name__)
A__ = {
"camembert-base": "https://huggingface.co/camembert-base/resolve/main/config.json",
"umberto-commoncrawl-cased-v1": (
"https://huggingface.co/Musixmatch/umberto-commoncrawl-cased-v1/resolve/main/config.json"
),
"umberto-wikipedia-uncased-v1": (
"https://huggingface.co/Musixmatch/umberto-wikipedia-uncased-v1/resolve/main/config.json"
),
}
class __UpperCamelCase ( SCREAMING_SNAKE_CASE ):
_lowercase : Optional[int] = "camembert"
def __init__( self: Optional[Any] , __UpperCamelCase: Any=3_05_22 , __UpperCamelCase: Tuple=7_68 , __UpperCamelCase: str=12 , __UpperCamelCase: Optional[int]=12 , __UpperCamelCase: List[str]=30_72 , __UpperCamelCase: Any="gelu" , __UpperCamelCase: Optional[int]=0.1 , __UpperCamelCase: Any=0.1 , __UpperCamelCase: str=5_12 , __UpperCamelCase: Dict=2 , __UpperCamelCase: str=0.02 , __UpperCamelCase: List[Any]=1E-12 , __UpperCamelCase: List[Any]=1 , __UpperCamelCase: Any=0 , __UpperCamelCase: Union[str, Any]=2 , __UpperCamelCase: Dict="absolute" , __UpperCamelCase: Any=True , __UpperCamelCase: Any=None , **__UpperCamelCase: Union[str, Any] , ):
'''simple docstring'''
super().__init__(pad_token_id=__UpperCamelCase , bos_token_id=__UpperCamelCase , eos_token_id=__UpperCamelCase , **__UpperCamelCase )
__magic_name__ = vocab_size
__magic_name__ = hidden_size
__magic_name__ = num_hidden_layers
__magic_name__ = num_attention_heads
__magic_name__ = hidden_act
__magic_name__ = intermediate_size
__magic_name__ = hidden_dropout_prob
__magic_name__ = attention_probs_dropout_prob
__magic_name__ = max_position_embeddings
__magic_name__ = type_vocab_size
__magic_name__ = initializer_range
__magic_name__ = layer_norm_eps
__magic_name__ = position_embedding_type
__magic_name__ = use_cache
__magic_name__ = classifier_dropout
class __UpperCamelCase ( SCREAMING_SNAKE_CASE ):
@property
def _SCREAMING_SNAKE_CASE ( self: Dict ):
'''simple docstring'''
if self.task == "multiple-choice":
__magic_name__ = {0: 'batch', 1: 'choice', 2: 'sequence'}
else:
__magic_name__ = {0: 'batch', 1: 'sequence'}
return OrderedDict(
[
('input_ids', dynamic_axis),
('attention_mask', dynamic_axis),
] )
| 184 | 0 |
from argparse import ArgumentParser
from ..pipelines import Pipeline, PipelineDataFormat, get_supported_tasks, pipeline
from ..utils import logging
from . import BaseTransformersCLICommand
__lowerCamelCase : Any = logging.get_logger(__name__) # pylint: disable=invalid-name
def SCREAMING_SNAKE_CASE ( snake_case_ : str ):
if not path:
return "pipe"
for ext in PipelineDataFormat.SUPPORTED_FORMATS:
if path.endswith(lowerCAmelCase__ ):
return ext
raise Exception(
F'''Unable to determine file format from file extension {path}. '''
F'''Please provide the format through --format {PipelineDataFormat.SUPPORTED_FORMATS}''' )
def SCREAMING_SNAKE_CASE ( snake_case_ : Optional[Any] ):
snake_case__ : List[Any] = pipeline(
task=args.task , model=args.model if args.model else None , config=args.config , tokenizer=args.tokenizer , device=args.device , )
snake_case__ : Union[str, Any] = try_infer_format_from_ext(args.input ) if args.format == "infer" else args.format
snake_case__ : Any = PipelineDataFormat.from_str(
format=lowerCAmelCase__ , output_path=args.output , input_path=args.input , column=args.column if args.column else nlp.default_input_names , overwrite=args.overwrite , )
return RunCommand(lowerCAmelCase__ , lowerCAmelCase__ )
class SCREAMING_SNAKE_CASE__ ( _UpperCamelCase ):
"""simple docstring"""
def __init__( self : int , __A : Pipeline , __A : PipelineDataFormat ):
snake_case__ : List[Any] = nlp
snake_case__ : str = reader
@staticmethod
def _lowercase ( __A : ArgumentParser ):
snake_case__ : List[str] = parser.add_parser("run" , help="Run a pipeline through the CLI" )
run_parser.add_argument("--task" , choices=get_supported_tasks() , help="Task to run" )
run_parser.add_argument("--input" , type=a_ , help="Path to the file to use for inference" )
run_parser.add_argument("--output" , type=a_ , help="Path to the file that will be used post to write results." )
run_parser.add_argument("--model" , type=a_ , help="Name or path to the model to instantiate." )
run_parser.add_argument("--config" , type=a_ , help="Name or path to the model's config to instantiate." )
run_parser.add_argument(
"--tokenizer" , type=a_ , help="Name of the tokenizer to use. (default: same as the model name)" )
run_parser.add_argument(
"--column" , type=a_ , help="Name of the column to use as input. (For multi columns input as QA use column1,columns2)" , )
run_parser.add_argument(
"--format" , type=a_ , default="infer" , choices=PipelineDataFormat.SUPPORTED_FORMATS , help="Input format to read from" , )
run_parser.add_argument(
"--device" , type=a_ , default=-1 , help="Indicate the device to run onto, -1 indicates CPU, >= 0 indicates GPU (default: -1)" , )
run_parser.add_argument("--overwrite" , action="store_true" , help="Allow overwriting the output file." )
run_parser.set_defaults(func=a_ )
def _lowercase ( self : int ):
snake_case__ : Tuple = self._nlp, []
for entry in self._reader:
snake_case__ : str = nlp(**a_ ) if self._reader.is_multi_columns else nlp(a_ )
if isinstance(a_ , a_ ):
outputs.append(a_ )
else:
outputs += output
# Saving data
if self._nlp.binary_output:
snake_case__ : Tuple = self._reader.save_binary(a_ )
logger.warning(f'''Current pipeline requires output to be in binary format, saving at {binary_path}''' )
else:
self._reader.save(a_ )
| 297 |
"""simple docstring"""
__UpperCAmelCase = [
[0, 16, 13, 0, 0, 0],
[0, 0, 10, 12, 0, 0],
[0, 4, 0, 0, 14, 0],
[0, 0, 9, 0, 0, 20],
[0, 0, 0, 7, 0, 4],
[0, 0, 0, 0, 0, 0],
]
def lowercase__ ( lowerCAmelCase__ : Dict , lowerCAmelCase__ : Optional[int] , lowerCAmelCase__ : List[str] , lowerCAmelCase__ : Tuple ) -> Dict:
'''simple docstring'''
# Return True if there is node that has not iterated.
a__ : int = [False] * len(lowerCAmelCase__ )
a__ : Optional[Any] = [s]
a__ : Optional[int] = True
while queue:
a__ : Any = queue.pop(0 )
for ind in range(len(graph[u] ) ):
if visited[ind] is False and graph[u][ind] > 0:
queue.append(lowerCAmelCase__ )
a__ : Any = True
a__ : Union[str, Any] = u
return visited[t]
def lowercase__ ( lowerCAmelCase__ : List[str] , lowerCAmelCase__ : List[Any] , lowerCAmelCase__ : Optional[Any] ) -> Any:
'''simple docstring'''
a__ : Any = [-1] * (len(lowerCAmelCase__ ))
a__ : Any = 0
a__ : List[str] = []
a__ : Tuple = [i[:] for i in graph] # Record original cut, copy.
while bfs(lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ ):
a__ : Any = float("Inf" )
a__ : Optional[int] = sink
while s != source:
# Find the minimum value in select path
a__ : Union[str, Any] = min(lowerCAmelCase__ , graph[parent[s]][s] )
a__ : List[str] = parent[s]
max_flow += path_flow
a__ : List[str] = sink
while v != source:
a__ : int = parent[v]
graph[u][v] -= path_flow
graph[v][u] += path_flow
a__ : List[str] = parent[v]
for i in range(len(lowerCAmelCase__ ) ):
for j in range(len(graph[0] ) ):
if graph[i][j] == 0 and temp[i][j] > 0:
res.append((i, j) )
return res
if __name__ == "__main__":
print(mincut(test_graph, source=0, sink=5)) | 642 | 0 |
"""simple docstring"""
import argparse
import json
from collections import OrderedDict
import torch
from huggingface_hub import cached_download, hf_hub_url
from transformers import AutoImageProcessor, CvtConfig, CvtForImageClassification
def _snake_case ( lowerCamelCase__ : Optional[int] ) -> List[str]:
lowerCamelCase_ : Dict =[]
embed.append(
(
F"""cvt.encoder.stages.{idx}.embedding.convolution_embeddings.projection.weight""",
F"""stage{idx}.patch_embed.proj.weight""",
) )
embed.append(
(
F"""cvt.encoder.stages.{idx}.embedding.convolution_embeddings.projection.bias""",
F"""stage{idx}.patch_embed.proj.bias""",
) )
embed.append(
(
F"""cvt.encoder.stages.{idx}.embedding.convolution_embeddings.normalization.weight""",
F"""stage{idx}.patch_embed.norm.weight""",
) )
embed.append(
(
F"""cvt.encoder.stages.{idx}.embedding.convolution_embeddings.normalization.bias""",
F"""stage{idx}.patch_embed.norm.bias""",
) )
return embed
def _snake_case ( lowerCamelCase__ : Tuple , lowerCamelCase__ : Dict ) -> List[str]:
lowerCamelCase_ : str =[]
attention_weights.append(
(
F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.convolution.weight""",
F"""stage{idx}.blocks.{cnt}.attn.conv_proj_q.conv.weight""",
) )
attention_weights.append(
(
F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.weight""",
F"""stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.weight""",
) )
attention_weights.append(
(
F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.bias""",
F"""stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.bias""",
) )
attention_weights.append(
(
F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.running_mean""",
F"""stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.running_mean""",
) )
attention_weights.append(
(
F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.running_var""",
F"""stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.running_var""",
) )
attention_weights.append(
(
F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.num_batches_tracked""",
F"""stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.num_batches_tracked""",
) )
attention_weights.append(
(
F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.convolution.weight""",
F"""stage{idx}.blocks.{cnt}.attn.conv_proj_k.conv.weight""",
) )
attention_weights.append(
(
F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.weight""",
F"""stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.weight""",
) )
attention_weights.append(
(
F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.bias""",
F"""stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.bias""",
) )
attention_weights.append(
(
F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.running_mean""",
F"""stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.running_mean""",
) )
attention_weights.append(
(
F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.running_var""",
F"""stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.running_var""",
) )
attention_weights.append(
(
F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.num_batches_tracked""",
F"""stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.num_batches_tracked""",
) )
attention_weights.append(
(
F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.convolution.weight""",
F"""stage{idx}.blocks.{cnt}.attn.conv_proj_v.conv.weight""",
) )
attention_weights.append(
(
F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.weight""",
F"""stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.weight""",
) )
attention_weights.append(
(
F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.bias""",
F"""stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.bias""",
) )
attention_weights.append(
(
F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.running_mean""",
F"""stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.running_mean""",
) )
attention_weights.append(
(
F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.running_var""",
F"""stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.running_var""",
) )
attention_weights.append(
(
F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.num_batches_tracked""",
F"""stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.num_batches_tracked""",
) )
attention_weights.append(
(
F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_query.weight""",
F"""stage{idx}.blocks.{cnt}.attn.proj_q.weight""",
) )
attention_weights.append(
(
F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_query.bias""",
F"""stage{idx}.blocks.{cnt}.attn.proj_q.bias""",
) )
attention_weights.append(
(
F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_key.weight""",
F"""stage{idx}.blocks.{cnt}.attn.proj_k.weight""",
) )
attention_weights.append(
(
F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_key.bias""",
F"""stage{idx}.blocks.{cnt}.attn.proj_k.bias""",
) )
attention_weights.append(
(
F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_value.weight""",
F"""stage{idx}.blocks.{cnt}.attn.proj_v.weight""",
) )
attention_weights.append(
(
F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_value.bias""",
F"""stage{idx}.blocks.{cnt}.attn.proj_v.bias""",
) )
attention_weights.append(
(
F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.output.dense.weight""",
F"""stage{idx}.blocks.{cnt}.attn.proj.weight""",
) )
attention_weights.append(
(
F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.output.dense.bias""",
F"""stage{idx}.blocks.{cnt}.attn.proj.bias""",
) )
attention_weights.append(
(F"""cvt.encoder.stages.{idx}.layers.{cnt}.intermediate.dense.weight""", F"""stage{idx}.blocks.{cnt}.mlp.fc1.weight""") )
attention_weights.append(
(F"""cvt.encoder.stages.{idx}.layers.{cnt}.intermediate.dense.bias""", F"""stage{idx}.blocks.{cnt}.mlp.fc1.bias""") )
attention_weights.append(
(F"""cvt.encoder.stages.{idx}.layers.{cnt}.output.dense.weight""", F"""stage{idx}.blocks.{cnt}.mlp.fc2.weight""") )
attention_weights.append(
(F"""cvt.encoder.stages.{idx}.layers.{cnt}.output.dense.bias""", F"""stage{idx}.blocks.{cnt}.mlp.fc2.bias""") )
attention_weights.append(
(F"""cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_before.weight""", F"""stage{idx}.blocks.{cnt}.norm1.weight""") )
attention_weights.append(
(F"""cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_before.bias""", F"""stage{idx}.blocks.{cnt}.norm1.bias""") )
attention_weights.append(
(F"""cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_after.weight""", F"""stage{idx}.blocks.{cnt}.norm2.weight""") )
attention_weights.append(
(F"""cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_after.bias""", F"""stage{idx}.blocks.{cnt}.norm2.bias""") )
return attention_weights
def _snake_case ( lowerCamelCase__ : Union[str, Any] ) -> Optional[Any]:
lowerCamelCase_ : int =[]
token.append((F"""cvt.encoder.stages.{idx}.cls_token""", "stage2.cls_token") )
return token
def _snake_case ( ) -> Optional[int]:
lowerCamelCase_ : int =[]
head.append(("layernorm.weight", "norm.weight") )
head.append(("layernorm.bias", "norm.bias") )
head.append(("classifier.weight", "head.weight") )
head.append(("classifier.bias", "head.bias") )
return head
def _snake_case ( lowerCamelCase__ : Optional[int] , lowerCamelCase__ : int , lowerCamelCase__ : List[Any] , lowerCamelCase__ : Dict ) -> Tuple:
lowerCamelCase_ : Tuple ='imagenet-1k-id2label.json'
lowerCamelCase_ : str =1_000
lowerCamelCase_ : Optional[int] ='huggingface/label-files'
lowerCamelCase_ : List[str] =num_labels
lowerCamelCase_ : List[Any] =json.load(open(cached_download(hf_hub_url(__lowercase , __lowercase , repo_type="dataset" ) ) , "r" ) )
lowerCamelCase_ : Union[str, Any] ={int(__lowercase ): v for k, v in idalabel.items()}
lowerCamelCase_ : str =idalabel
lowerCamelCase_ : List[Any] ={v: k for k, v in idalabel.items()}
lowerCamelCase_ : Tuple =CvtConfig(num_labels=__lowercase , idalabel=__lowercase , labelaid=__lowercase )
# For depth size 13 (13 = 1+2+10)
if cvt_model.rsplit("/" , 1 )[-1][4:6] == "13":
lowerCamelCase_ : str =[1, 2, 10]
# For depth size 21 (21 = 1+4+16)
elif cvt_model.rsplit("/" , 1 )[-1][4:6] == "21":
lowerCamelCase_ : Dict =[1, 4, 16]
# For wide cvt (similar to wide-resnet) depth size 24 (w24 = 2 + 2 20)
else:
lowerCamelCase_ : Tuple =[2, 2, 20]
lowerCamelCase_ : Optional[Any] =[3, 12, 16]
lowerCamelCase_ : Dict =[192, 768, 1_024]
lowerCamelCase_ : Dict =CvtForImageClassification(__lowercase )
lowerCamelCase_ : List[Any] =AutoImageProcessor.from_pretrained("facebook/convnext-base-224-22k-1k" )
lowerCamelCase_ : Dict =image_size
lowerCamelCase_ : Tuple =torch.load(__lowercase , map_location=torch.device("cpu" ) )
lowerCamelCase_ : List[Any] =OrderedDict()
lowerCamelCase_ : Optional[Any] =[]
for idx in range(len(config.depth ) ):
if config.cls_token[idx]:
lowerCamelCase_ : str =list_of_state_dict + cls_token(__lowercase )
lowerCamelCase_ : Optional[int] =list_of_state_dict + embeddings(__lowercase )
for cnt in range(config.depth[idx] ):
lowerCamelCase_ : Dict =list_of_state_dict + attention(__lowercase , __lowercase )
lowerCamelCase_ : str =list_of_state_dict + final()
for gg in list_of_state_dict:
print(__lowercase )
for i in range(len(__lowercase ) ):
lowerCamelCase_ : List[str] =original_weights[list_of_state_dict[i][1]]
model.load_state_dict(__lowercase )
model.save_pretrained(__lowercase )
image_processor.save_pretrained(__lowercase )
# Download the weights from zoo: https://1drv.ms/u/s!AhIXJn_J-blW9RzF3rMW7SsLHa8h?e=blQ0Al
if __name__ == "__main__":
A__ : str = argparse.ArgumentParser()
parser.add_argument(
'--cvt_model',
default='cvt-w24',
type=str,
help='Name of the cvt model you\'d like to convert.',
)
parser.add_argument(
'--image_size',
default=384,
type=int,
help='Input Image Size',
)
parser.add_argument(
'--cvt_file_name',
default=r'cvtmodels\CvT-w24-384x384-IN-22k.pth',
type=str,
help='Input Image Size',
)
parser.add_argument(
'--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
)
A__ : List[Any] = parser.parse_args()
convert_cvt_checkpoint(args.cvt_model, args.image_size, args.cvt_file_name, args.pytorch_dump_folder_path)
| 704 |
"""simple docstring"""
def _snake_case ( lowerCamelCase__ : str ) -> list:
lowerCamelCase_ : Union[str, Any] =[0] * len(lowerCamelCase__ )
for i in range(1 , len(lowerCamelCase__ ) ):
# use last results for better performance - dynamic programming
lowerCamelCase_ : List[str] =prefix_result[i - 1]
while j > 0 and input_string[i] != input_string[j]:
lowerCamelCase_ : List[str] =prefix_result[j - 1]
if input_string[i] == input_string[j]:
j += 1
lowerCamelCase_ : str =j
return prefix_result
def _snake_case ( lowerCamelCase__ : str ) -> int:
return max(prefix_function(lowerCamelCase__ ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 244 | 0 |
'''simple docstring'''
def lowerCamelCase ( ):
'''simple docstring'''
return [list(range(1_000 - i ,-1_000 - i ,-1 ) ) for i in range(1_000 )]
SCREAMING_SNAKE_CASE__ = generate_large_matrix()
SCREAMING_SNAKE_CASE__ = (
[[4, 3, 2, -1], [3, 2, 1, -1], [1, 1, -1, -2], [-1, -1, -2, -3]],
[[3, 2], [1, 0]],
[[7, 7, 6]],
[[7, 7, 6], [-1, -2, -3]],
grid,
)
def lowerCamelCase ( _snake_case : list[list[int]] ):
'''simple docstring'''
assert all(row == sorted(_snake_case ,reverse=_snake_case ) for row in grid )
assert all(list(_snake_case ) == sorted(_snake_case ,reverse=_snake_case ) for col in zip(*_snake_case ) )
def lowerCamelCase ( _snake_case : list[int] ):
'''simple docstring'''
lowercase__ = 0
lowercase__ = len(_snake_case ) - 1
# Edge cases such as no values or all numbers are negative.
if not array or array[0] < 0:
return 0
while right + 1 > left:
lowercase__ = (left + right) // 2
lowercase__ = array[mid]
# Num must be negative and the index must be greater than or equal to 0.
if num < 0 and array[mid - 1] >= 0:
return mid
if num >= 0:
lowercase__ = mid + 1
else:
lowercase__ = mid - 1
# No negative numbers so return the last index of the array + 1 which is the length.
return len(_snake_case )
def lowerCamelCase ( _snake_case : list[list[int]] ):
'''simple docstring'''
lowercase__ = 0
lowercase__ = len(grid[0] )
for i in range(len(_snake_case ) ):
lowercase__ = find_negative_index(grid[i][:bound] )
total += bound
return (len(_snake_case ) * len(grid[0] )) - total
def lowerCamelCase ( _snake_case : list[list[int]] ):
'''simple docstring'''
return len([number for row in grid for number in row if number < 0] )
def lowerCamelCase ( _snake_case : list[list[int]] ):
'''simple docstring'''
lowercase__ = 0
for row in grid:
for i, number in enumerate(_snake_case ):
if number < 0:
total += len(_snake_case ) - i
break
return total
def lowerCamelCase ( ):
'''simple docstring'''
from timeit import timeit
print("Running benchmarks" )
lowercase__ = (
"from __main__ import count_negatives_binary_search, "
"count_negatives_brute_force, count_negatives_brute_force_with_break, grid"
)
for func in (
"count_negatives_binary_search", # took 0.7727 seconds
"count_negatives_brute_force_with_break", # took 4.6505 seconds
"count_negatives_brute_force", # took 12.8160 seconds
):
lowercase__ = timeit(f'''{func}(grid=grid)''' ,setup=_snake_case ,number=500 )
print(f'''{func}() took {time:0.4f} seconds''' )
if __name__ == "__main__":
import doctest
doctest.testmod()
benchmark()
| 267 |
'''simple docstring'''
import pickle
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, XGLMTokenizer, XGLMTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
SCREAMING_SNAKE_CASE__ = get_tests_dir("fixtures/test_sentencepiece.model")
@require_sentencepiece
@require_tokenizers
class snake_case (UpperCamelCase , unittest.TestCase ):
lowerCAmelCase__ :List[Any] = XGLMTokenizer
lowerCAmelCase__ :Tuple = XGLMTokenizerFast
lowerCAmelCase__ :Any = True
lowerCAmelCase__ :List[Any] = True
def _a ( self ) -> Tuple:
super().setUp()
# We have a SentencePiece fixture for testing
lowercase__ = XGLMTokenizer(UpperCAmelCase_ ,keep_accents=UpperCAmelCase_ )
tokenizer.save_pretrained(self.tmpdirname )
def _a ( self ) -> Tuple:
lowercase__ = "<pad>"
lowercase__ = 1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(UpperCAmelCase_ ) ,UpperCAmelCase_ )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(UpperCAmelCase_ ) ,UpperCAmelCase_ )
def _a ( self ) -> Dict:
lowercase__ = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] ,"<s>" )
self.assertEqual(vocab_keys[1] ,"<pad>" )
self.assertEqual(len(UpperCAmelCase_ ) ,1_008 )
def _a ( self ) -> Union[str, Any]:
self.assertEqual(self.get_tokenizer().vocab_size ,1_008 )
def _a ( self ) -> Dict:
lowercase__ = XGLMTokenizer(UpperCAmelCase_ ,keep_accents=UpperCAmelCase_ )
lowercase__ = tokenizer.tokenize("This is a test" )
self.assertListEqual(UpperCAmelCase_ ,["▁This", "▁is", "▁a", "▁t", "est"] )
self.assertListEqual(
tokenizer.convert_tokens_to_ids(UpperCAmelCase_ ) ,[value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]] ,)
lowercase__ = tokenizer.tokenize("I was born in 92000, and this is falsé." )
self.assertListEqual(
UpperCAmelCase_ ,[
SPIECE_UNDERLINE + "I",
SPIECE_UNDERLINE + "was",
SPIECE_UNDERLINE + "b",
"or",
"n",
SPIECE_UNDERLINE + "in",
SPIECE_UNDERLINE + "",
"9",
"2",
"0",
"0",
"0",
",",
SPIECE_UNDERLINE + "and",
SPIECE_UNDERLINE + "this",
SPIECE_UNDERLINE + "is",
SPIECE_UNDERLINE + "f",
"al",
"s",
"é",
".",
] ,)
lowercase__ = tokenizer.convert_tokens_to_ids(UpperCAmelCase_ )
self.assertListEqual(
UpperCAmelCase_ ,[
value + tokenizer.fairseq_offset
for value in [8, 21, 84, 55, 24, 19, 7, 2, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 2, 4]
] ,)
lowercase__ = tokenizer.convert_ids_to_tokens(UpperCAmelCase_ )
self.assertListEqual(
UpperCAmelCase_ ,[
SPIECE_UNDERLINE + "I",
SPIECE_UNDERLINE + "was",
SPIECE_UNDERLINE + "b",
"or",
"n",
SPIECE_UNDERLINE + "in",
SPIECE_UNDERLINE + "",
"<unk>",
"2",
"0",
"0",
"0",
",",
SPIECE_UNDERLINE + "and",
SPIECE_UNDERLINE + "this",
SPIECE_UNDERLINE + "is",
SPIECE_UNDERLINE + "f",
"al",
"s",
"<unk>",
".",
] ,)
@cached_property
def _a ( self ) -> Optional[int]:
return XGLMTokenizer.from_pretrained("facebook/xglm-564M" )
def _a ( self ) -> int:
with tempfile.NamedTemporaryFile() as f:
shutil.copyfile(UpperCAmelCase_ ,f.name )
lowercase__ = XGLMTokenizer(f.name ,keep_accents=UpperCAmelCase_ )
lowercase__ = pickle.dumps(UpperCAmelCase_ )
pickle.loads(UpperCAmelCase_ )
def _a ( self ) -> Optional[int]:
if not self.test_rust_tokenizer:
return
lowercase__ = self.get_tokenizer()
lowercase__ = self.get_rust_tokenizer()
lowercase__ = "I was born in 92000, and this is falsé."
lowercase__ = tokenizer.tokenize(UpperCAmelCase_ )
lowercase__ = rust_tokenizer.tokenize(UpperCAmelCase_ )
self.assertListEqual(UpperCAmelCase_ ,UpperCAmelCase_ )
lowercase__ = tokenizer.encode(UpperCAmelCase_ ,add_special_tokens=UpperCAmelCase_ )
lowercase__ = rust_tokenizer.encode(UpperCAmelCase_ ,add_special_tokens=UpperCAmelCase_ )
self.assertListEqual(UpperCAmelCase_ ,UpperCAmelCase_ )
lowercase__ = self.get_rust_tokenizer()
lowercase__ = tokenizer.encode(UpperCAmelCase_ )
lowercase__ = rust_tokenizer.encode(UpperCAmelCase_ )
self.assertListEqual(UpperCAmelCase_ ,UpperCAmelCase_ )
@slow
def _a ( self ) -> List[str]:
lowercase__ = "Hello World!"
lowercase__ = [2, 31_227, 4_447, 35]
self.assertListEqual(UpperCAmelCase_ ,self.big_tokenizer.encode(UpperCAmelCase_ ) )
@slow
def _a ( self ) -> str:
lowercase__ = (
"This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) \" [ ] ! : - . Also we will"
" add words that should not exsist and be tokenized to unk, such as saoneuhaoesuth"
)
# fmt: off
lowercase__ = [2, 1_018, 67, 11, 1_988, 2_617, 5_631, 278, 11, 3_407, 48, 71_630, 28_085, 4, 3_234, 157, 13, 6, 5, 6, 4, 3_526, 768, 15, 659, 57, 298, 3_983, 864, 129, 21, 6, 5, 13_675, 377, 652, 7_580, 10_341, 155, 2_817, 422, 1_666, 7, 1_674, 53, 113, 202_277, 17_892, 33, 60, 87, 4, 3_234, 157, 61, 2_667, 52_376, 19, 88, 23, 735]
# fmt: on
self.assertListEqual(UpperCAmelCase_ ,self.big_tokenizer.encode(UpperCAmelCase_ ) )
@slow
def _a ( self ) -> str:
# fmt: off
lowercase__ = {
"input_ids": [[2, 108_825, 1_163, 15, 88_010, 473, 15_898, 157, 13_672, 1_857, 312, 8, 238_021, 1_163, 53, 13_672, 1_857, 312, 8, 53_283, 182_396, 8, 18_566, 16, 36_733, 4_101, 8, 230, 244_017, 122_553, 7, 15, 132_597, 4, 293, 12_511, 7_610, 4, 3_414, 132_597, 9, 4, 32_361, 362, 4, 734, 28_512, 32_569, 18, 4, 32_361, 26_096, 14_982, 73, 18_715, 21_433, 235_261, 15, 492, 12_427, 16, 53, 18_715, 21_433, 65_454, 15, 23_659, 563, 16, 278, 597, 2_843, 595, 7_931, 182_396, 64_186, 22, 886, 595, 132_981, 53, 25_540, 3_449, 43_982, 39_901, 5_951, 878, 330, 4, 27_694, 80_269, 312, 53, 6_517, 11_780, 611, 20_408, 5], [2, 6, 132_597, 67, 42_897, 33, 592, 8, 163_729, 25_540, 361, 136_997, 109_514, 173_230, 7, 501, 60, 102_913, 196, 5_631, 235, 63_243, 473, 6, 231_757, 74, 5_277, 7_905, 53, 3_095, 37_317, 22, 454, 183_874, 5], [2, 268, 31_298, 46_530, 6, 132_935, 43_831, 7, 597, 32, 24, 3_688, 9_865, 5]],
"attention_mask": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]
} # noqa: E501
# fmt: on
self.tokenizer_integration_test_util(
expected_encoding=UpperCAmelCase_ ,model_name="facebook/xglm-564M" ,padding=UpperCAmelCase_ ,)
| 267 | 1 |
from dataclasses import dataclass
from typing import Optional
import torch
from torch import nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .attention import BasicTransformerBlock
from .modeling_utils import ModelMixin
@dataclass
class lowercase_ (_UpperCAmelCase ):
A__ : torch.FloatTensor
class lowercase_ (_UpperCAmelCase, _UpperCAmelCase ):
@register_to_config
def __init__( self , a_ = 1_6 , a_ = 8_8 , a_ = None , a_ = None , a_ = 1 , a_ = 0.0 , a_ = 3_2 , a_ = None , a_ = False , a_ = None , a_ = "geglu" , a_ = True , a_ = True , ) ->int:
'''simple docstring'''
super().__init__()
_a = num_attention_heads
_a = attention_head_dim
_a = num_attention_heads * attention_head_dim
_a = in_channels
_a = torch.nn.GroupNorm(num_groups=a_ , num_channels=a_ , eps=1E-6 , affine=a_ )
_a = nn.Linear(a_ , a_ )
# 3. Define transformers blocks
_a = nn.ModuleList(
[
BasicTransformerBlock(
a_ , a_ , a_ , dropout=a_ , cross_attention_dim=a_ , activation_fn=a_ , attention_bias=a_ , double_self_attention=a_ , norm_elementwise_affine=a_ , )
for d in range(a_ )
] )
_a = nn.Linear(a_ , a_ )
def lowerCamelCase__ ( self , a_ , a_=None , a_=None , a_=None , a_=1 , a_=None , a_ = True , ) ->List[Any]:
'''simple docstring'''
_a , _a , _a , _a = hidden_states.shape
_a = batch_frames // num_frames
_a = hidden_states
_a = hidden_states[None, :].reshape(a_ , a_ , a_ , a_ , a_ )
_a = hidden_states.permute(0 , 2 , 1 , 3 , 4 )
_a = self.norm(a_ )
_a = hidden_states.permute(0 , 3 , 4 , 2 , 1 ).reshape(batch_size * height * width , a_ , a_ )
_a = self.proj_in(a_ )
# 2. Blocks
for block in self.transformer_blocks:
_a = block(
a_ , encoder_hidden_states=a_ , timestep=a_ , cross_attention_kwargs=a_ , class_labels=a_ , )
# 3. Output
_a = self.proj_out(a_ )
_a = (
hidden_states[None, None, :]
.reshape(a_ , a_ , a_ , a_ , a_ )
.permute(0 , 3 , 4 , 1 , 2 )
.contiguous()
)
_a = hidden_states.reshape(a_ , a_ , a_ , a_ )
_a = hidden_states + residual
if not return_dict:
return (output,)
return TransformerTemporalModelOutput(sample=a_ )
| 710 |
"""simple docstring"""
import torch
from diffusers import KDPMaDiscreteScheduler
from diffusers.utils import torch_device
from .test_schedulers import SchedulerCommonTest
class lowercase_ (_UpperCAmelCase ):
A__ : Tuple = (KDPMaDiscreteScheduler,)
A__ : Tuple = 10
def lowerCamelCase__ ( self , **a_ ) ->List[str]:
'''simple docstring'''
_a = {
"num_train_timesteps": 1_1_0_0,
"beta_start": 0.0_001,
"beta_end": 0.02,
"beta_schedule": "linear",
}
config.update(**a_ )
return config
def lowerCamelCase__ ( self ) ->Tuple:
'''simple docstring'''
for timesteps in [1_0, 5_0, 1_0_0, 1_0_0_0]:
self.check_over_configs(num_train_timesteps=a_ )
def lowerCamelCase__ ( self ) ->List[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=a_ , beta_end=a_ )
def lowerCamelCase__ ( self ) ->Any:
'''simple docstring'''
for schedule in ["linear", "scaled_linear"]:
self.check_over_configs(beta_schedule=a_ )
def lowerCamelCase__ ( self ) ->int:
'''simple docstring'''
for prediction_type in ["epsilon", "v_prediction"]:
self.check_over_configs(prediction_type=a_ )
def lowerCamelCase__ ( self ) ->Union[str, Any]:
'''simple docstring'''
_a = self.scheduler_classes[0]
_a = self.get_scheduler_config(prediction_type="v_prediction" )
_a = scheduler_class(**a_ )
scheduler.set_timesteps(self.num_inference_steps )
_a = self.dummy_model()
_a = self.dummy_sample_deter * scheduler.init_noise_sigma
_a = sample.to(a_ )
for i, t in enumerate(scheduler.timesteps ):
_a = scheduler.scale_model_input(a_ , a_ )
_a = model(a_ , a_ )
_a = scheduler.step(a_ , a_ , a_ )
_a = output.prev_sample
_a = torch.sum(torch.abs(a_ ) )
_a = torch.mean(torch.abs(a_ ) )
if torch_device in ["cpu", "mps"]:
assert abs(result_sum.item() - 4.6_934E-07 ) < 1E-2
assert abs(result_mean.item() - 6.1_112E-10 ) < 1E-3
else:
# CUDA
assert abs(result_sum.item() - 4.693_428_650_170_972E-07 ) < 1E-2
assert abs(result_mean.item() - 0.0_002 ) < 1E-3
def lowerCamelCase__ ( self ) ->Any:
'''simple docstring'''
if torch_device == "mps":
return
_a = self.scheduler_classes[0]
_a = self.get_scheduler_config()
_a = scheduler_class(**a_ )
scheduler.set_timesteps(self.num_inference_steps )
_a = self.dummy_model()
_a = self.dummy_sample_deter * scheduler.init_noise_sigma
_a = sample.to(a_ )
for i, t in enumerate(scheduler.timesteps ):
_a = scheduler.scale_model_input(a_ , a_ )
_a = model(a_ , a_ )
_a = scheduler.step(a_ , a_ , a_ )
_a = output.prev_sample
_a = torch.sum(torch.abs(a_ ) )
_a = torch.mean(torch.abs(a_ ) )
if torch_device in ["cpu", "mps"]:
assert abs(result_sum.item() - 20.4_125 ) < 1E-2
assert abs(result_mean.item() - 0.0_266 ) < 1E-3
else:
# CUDA
assert abs(result_sum.item() - 20.4_125 ) < 1E-2
assert abs(result_mean.item() - 0.0_266 ) < 1E-3
def lowerCamelCase__ ( self ) ->int:
'''simple docstring'''
if torch_device == "mps":
return
_a = self.scheduler_classes[0]
_a = self.get_scheduler_config()
_a = scheduler_class(**a_ )
scheduler.set_timesteps(self.num_inference_steps , device=a_ )
_a = self.dummy_model()
_a = self.dummy_sample_deter.to(a_ ) * scheduler.init_noise_sigma
for t in scheduler.timesteps:
_a = scheduler.scale_model_input(a_ , a_ )
_a = model(a_ , a_ )
_a = scheduler.step(a_ , a_ , a_ )
_a = output.prev_sample
_a = torch.sum(torch.abs(a_ ) )
_a = torch.mean(torch.abs(a_ ) )
if str(a_ ).startswith("cpu" ):
# The following sum varies between 148 and 156 on mps. Why?
assert abs(result_sum.item() - 20.4_125 ) < 1E-2
assert abs(result_mean.item() - 0.0_266 ) < 1E-3
else:
# CUDA
assert abs(result_sum.item() - 20.4_125 ) < 1E-2
assert abs(result_mean.item() - 0.0_266 ) < 1E-3
| 612 | 0 |
import json
from typing import List, Optional, Tuple
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_bart import BartTokenizer
a_ :List[Any] = logging.get_logger(__name__)
a_ :Tuple = {"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}
# See all BART models at https://huggingface.co/models?filter=bart
a_ :Union[str, Any] = {
"vocab_file": {
"facebook/bart-base": "https://huggingface.co/facebook/bart-base/resolve/main/vocab.json",
"facebook/bart-large": "https://huggingface.co/facebook/bart-large/resolve/main/vocab.json",
"facebook/bart-large-mnli": "https://huggingface.co/facebook/bart-large-mnli/resolve/main/vocab.json",
"facebook/bart-large-cnn": "https://huggingface.co/facebook/bart-large-cnn/resolve/main/vocab.json",
"facebook/bart-large-xsum": "https://huggingface.co/facebook/bart-large-xsum/resolve/main/vocab.json",
"yjernite/bart_eli5": "https://huggingface.co/yjernite/bart_eli5/resolve/main/vocab.json",
},
"merges_file": {
"facebook/bart-base": "https://huggingface.co/facebook/bart-base/resolve/main/merges.txt",
"facebook/bart-large": "https://huggingface.co/facebook/bart-large/resolve/main/merges.txt",
"facebook/bart-large-mnli": "https://huggingface.co/facebook/bart-large-mnli/resolve/main/merges.txt",
"facebook/bart-large-cnn": "https://huggingface.co/facebook/bart-large-cnn/resolve/main/merges.txt",
"facebook/bart-large-xsum": "https://huggingface.co/facebook/bart-large-xsum/resolve/main/merges.txt",
"yjernite/bart_eli5": "https://huggingface.co/yjernite/bart_eli5/resolve/main/merges.txt",
},
"tokenizer_file": {
"facebook/bart-base": "https://huggingface.co/facebook/bart-base/resolve/main/tokenizer.json",
"facebook/bart-large": "https://huggingface.co/facebook/bart-large/resolve/main/tokenizer.json",
"facebook/bart-large-mnli": "https://huggingface.co/facebook/bart-large-mnli/resolve/main/tokenizer.json",
"facebook/bart-large-cnn": "https://huggingface.co/facebook/bart-large-cnn/resolve/main/tokenizer.json",
"facebook/bart-large-xsum": "https://huggingface.co/facebook/bart-large-xsum/resolve/main/tokenizer.json",
"yjernite/bart_eli5": "https://huggingface.co/yjernite/bart_eli5/resolve/main/tokenizer.json",
},
}
a_ :Dict = {
"facebook/bart-base": 1_024,
"facebook/bart-large": 1_024,
"facebook/bart-large-mnli": 1_024,
"facebook/bart-large-cnn": 1_024,
"facebook/bart-large-xsum": 1_024,
"yjernite/bart_eli5": 1_024,
}
class snake_case__ ( __SCREAMING_SNAKE_CASE ):
"""simple docstring"""
_SCREAMING_SNAKE_CASE = VOCAB_FILES_NAMES
_SCREAMING_SNAKE_CASE = PRETRAINED_VOCAB_FILES_MAP
_SCREAMING_SNAKE_CASE = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
_SCREAMING_SNAKE_CASE = ["""input_ids""", """attention_mask"""]
_SCREAMING_SNAKE_CASE = BartTokenizer
def __init__( self : Optional[int], _snake_case : List[Any]=None, _snake_case : List[Any]=None, _snake_case : Optional[Any]=None, _snake_case : Optional[int]="replace", _snake_case : List[str]="<s>", _snake_case : Any="</s>", _snake_case : List[Any]="</s>", _snake_case : Any="<s>", _snake_case : Tuple="<unk>", _snake_case : Optional[int]="<pad>", _snake_case : List[str]="<mask>", _snake_case : Dict=False, _snake_case : List[str]=True, **_snake_case : Any, ) ->Optional[Any]:
super().__init__(
_lowercase, _lowercase, tokenizer_file=_lowercase, errors=_lowercase, bos_token=_lowercase, eos_token=_lowercase, sep_token=_lowercase, cls_token=_lowercase, unk_token=_lowercase, pad_token=_lowercase, mask_token=_lowercase, add_prefix_space=_lowercase, trim_offsets=_lowercase, **_lowercase, )
snake_case__ : Tuple = json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__() )
if pre_tok_state.get('add_prefix_space', _lowercase ) != add_prefix_space:
snake_case__ : str = getattr(_lowercase, pre_tok_state.pop('type' ) )
snake_case__ : Dict = add_prefix_space
snake_case__ : Optional[int] = pre_tok_class(**_lowercase )
snake_case__ : str = add_prefix_space
# the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
snake_case__ : Union[str, Any] = 'post_processor'
snake_case__ : str = getattr(self.backend_tokenizer, _lowercase, _lowercase )
if tokenizer_component_instance:
snake_case__ : Dict = 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:
snake_case__ : Union[str, Any] = tuple(state['sep'] )
if "cls" in state:
snake_case__ : List[Any] = tuple(state['cls'] )
snake_case__ : List[Any] = False
if state.get('add_prefix_space', _lowercase ) != add_prefix_space:
snake_case__ : Optional[int] = add_prefix_space
snake_case__ : Optional[int] = True
if state.get('trim_offsets', _lowercase ) != trim_offsets:
snake_case__ : int = trim_offsets
snake_case__ : int = True
if changes_to_apply:
snake_case__ : Tuple = getattr(_lowercase, state.pop('type' ) )
snake_case__ : List[str] = component_class(**_lowercase )
setattr(self.backend_tokenizer, _lowercase, _lowercase )
@property
def lowercase_ ( self : Optional[Any] ) ->Union[str, Any]:
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 lowercase_ ( self : Any, _snake_case : str ) ->Tuple:
snake_case__ : str = AddedToken(_lowercase, lstrip=_lowercase, rstrip=_lowercase ) if isinstance(_lowercase, _lowercase ) else value
snake_case__ : int = value
def lowercase_ ( self : Any, *_snake_case : Tuple, **_snake_case : str ) ->Union[str, Any]:
snake_case__ : Optional[Any] = kwargs.get('is_split_into_words', _lowercase )
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(*_lowercase, **_lowercase )
def lowercase_ ( self : Tuple, *_snake_case : List[Any], **_snake_case : List[Any] ) ->List[str]:
snake_case__ : Dict = kwargs.get('is_split_into_words', _lowercase )
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(*_lowercase, **_lowercase )
def lowercase_ ( self : Union[str, Any], _snake_case : str, _snake_case : Optional[str] = None ) ->Optional[int]:
snake_case__ : List[Any] = self._tokenizer.model.save(_lowercase, name=_lowercase )
return tuple(_lowercase )
def lowercase_ ( self : Tuple, _snake_case : Optional[int], _snake_case : Optional[int]=None ) ->Union[str, Any]:
snake_case__ : Optional[int] = [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 lowercase_ ( self : Tuple, _snake_case : List[int], _snake_case : Optional[List[int]] = None ) ->Optional[int]:
snake_case__ : List[Any] = [self.sep_token_id]
snake_case__ : Optional[Any] = [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]
| 478 |
from typing import List, Optional, Tuple, Union
import torch
from ...models import UNetaDModel
from ...schedulers import ScoreSdeVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class lowercase__ ( __SCREAMING_SNAKE_CASE ):
A__= 42
A__= 42
def __init__( self : Tuple , _lowercase : UNetaDModel , _lowercase : ScoreSdeVeScheduler ):
"""simple docstring"""
super().__init__()
self.register_modules(unet=_lowercase , scheduler=_lowercase )
@torch.no_grad()
def __call__( self : Dict , _lowercase : int = 1 , _lowercase : int = 20_00 , _lowercase : Optional[Union[torch.Generator, List[torch.Generator]]] = None , _lowercase : Optional[str] = "pil" , _lowercase : bool = True , **_lowercase : Any , ):
"""simple docstring"""
UpperCAmelCase__ = self.unet.config.sample_size
UpperCAmelCase__ = (batch_size, 3, img_size, img_size)
UpperCAmelCase__ = self.unet
UpperCAmelCase__ = randn_tensor(_lowercase , generator=_lowercase ) * self.scheduler.init_noise_sigma
UpperCAmelCase__ = sample.to(self.device )
self.scheduler.set_timesteps(_lowercase )
self.scheduler.set_sigmas(_lowercase )
for i, t in enumerate(self.progress_bar(self.scheduler.timesteps ) ):
UpperCAmelCase__ = self.scheduler.sigmas[i] * torch.ones(shape[0] , device=self.device )
# correction step
for _ in range(self.scheduler.config.correct_steps ):
UpperCAmelCase__ = self.unet(_lowercase , _lowercase ).sample
UpperCAmelCase__ = self.scheduler.step_correct(_lowercase , _lowercase , generator=_lowercase ).prev_sample
# prediction step
UpperCAmelCase__ = model(_lowercase , _lowercase ).sample
UpperCAmelCase__ = self.scheduler.step_pred(_lowercase , _lowercase , _lowercase , generator=_lowercase )
UpperCAmelCase__ , UpperCAmelCase__ = output.prev_sample, output.prev_sample_mean
UpperCAmelCase__ = sample_mean.clamp(0 , 1 )
UpperCAmelCase__ = sample.cpu().permute(0 , 2 , 3 , 1 ).numpy()
if output_type == "pil":
UpperCAmelCase__ = self.numpy_to_pil(_lowercase )
if not return_dict:
return (sample,)
return ImagePipelineOutput(images=_lowercase )
| 475 | 0 |
'''simple docstring'''
import inspect
import unittest
from transformers import ConvNextConfig
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_backbone_common import BackboneTesterMixin
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 ConvNextBackbone, ConvNextForImageClassification, ConvNextModel
from transformers.models.convnext.modeling_convnext import CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import AutoImageProcessor
class __magic_name__ :
def __init__( self , snake_case , snake_case=1_3 , snake_case=3_2 , snake_case=3 , snake_case=4 , snake_case=[1_0, 2_0, 3_0, 4_0] , snake_case=[2, 2, 3, 2] , snake_case=True , snake_case=True , snake_case=3_7 , snake_case="gelu" , snake_case=1_0 , snake_case=0.02 , snake_case=["stage2", "stage3", "stage4"] , snake_case=[2, 3, 4] , snake_case=None , ) -> Optional[Any]:
'''simple docstring'''
_UpperCAmelCase : Dict =parent
_UpperCAmelCase : int =batch_size
_UpperCAmelCase : Union[str, Any] =image_size
_UpperCAmelCase : List[str] =num_channels
_UpperCAmelCase : Dict =num_stages
_UpperCAmelCase : int =hidden_sizes
_UpperCAmelCase : Tuple =depths
_UpperCAmelCase : str =is_training
_UpperCAmelCase : Any =use_labels
_UpperCAmelCase : List[Any] =intermediate_size
_UpperCAmelCase : Any =hidden_act
_UpperCAmelCase : int =num_labels
_UpperCAmelCase : int =initializer_range
_UpperCAmelCase : Optional[int] =out_features
_UpperCAmelCase : Dict =out_indices
_UpperCAmelCase : int =scope
def lowerCAmelCase ( self) -> List[Any]:
'''simple docstring'''
_UpperCAmelCase : Any =floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])
_UpperCAmelCase : int =None
if self.use_labels:
_UpperCAmelCase : Union[str, Any] =ids_tensor([self.batch_size] , self.num_labels)
_UpperCAmelCase : Optional[int] =self.get_config()
return config, pixel_values, labels
def lowerCAmelCase ( self) -> Dict:
'''simple docstring'''
return ConvNextConfig(
num_channels=self.num_channels , hidden_sizes=self.hidden_sizes , depths=self.depths , num_stages=self.num_stages , hidden_act=self.hidden_act , is_decoder=snake_case , initializer_range=self.initializer_range , out_features=self.out_features , out_indices=self.out_indices , num_labels=self.num_labels , )
def lowerCAmelCase ( self , snake_case , snake_case , snake_case) -> str:
'''simple docstring'''
_UpperCAmelCase : Optional[int] =ConvNextModel(config=snake_case)
model.to(snake_case)
model.eval()
_UpperCAmelCase : List[str] =model(snake_case)
# expected last hidden states: B, C, H // 32, W // 32
self.parent.assertEqual(
result.last_hidden_state.shape , (self.batch_size, self.hidden_sizes[-1], self.image_size // 3_2, self.image_size // 3_2) , )
def lowerCAmelCase ( self , snake_case , snake_case , snake_case) -> int:
'''simple docstring'''
_UpperCAmelCase : Optional[Any] =ConvNextForImageClassification(snake_case)
model.to(snake_case)
model.eval()
_UpperCAmelCase : List[Any] =model(snake_case , labels=snake_case)
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels))
def lowerCAmelCase ( self , snake_case , snake_case , snake_case) -> Tuple:
'''simple docstring'''
_UpperCAmelCase : int =ConvNextBackbone(config=snake_case)
model.to(snake_case)
model.eval()
_UpperCAmelCase : Dict =model(snake_case)
# verify hidden states
self.parent.assertEqual(len(result.feature_maps) , len(config.out_features))
self.parent.assertListEqual(list(result.feature_maps[0].shape) , [self.batch_size, self.hidden_sizes[1], 4, 4])
# verify channels
self.parent.assertEqual(len(model.channels) , len(config.out_features))
self.parent.assertListEqual(model.channels , config.hidden_sizes[1:])
# verify backbone works with out_features=None
_UpperCAmelCase : str =None
_UpperCAmelCase : int =ConvNextBackbone(config=snake_case)
model.to(snake_case)
model.eval()
_UpperCAmelCase : Union[str, Any] =model(snake_case)
# verify feature maps
self.parent.assertEqual(len(result.feature_maps) , 1)
self.parent.assertListEqual(list(result.feature_maps[0].shape) , [self.batch_size, self.hidden_sizes[-1], 1, 1])
# verify channels
self.parent.assertEqual(len(model.channels) , 1)
self.parent.assertListEqual(model.channels , [config.hidden_sizes[-1]])
def lowerCAmelCase ( self) -> Union[str, Any]:
'''simple docstring'''
_UpperCAmelCase : Optional[Any] =self.prepare_config_and_inputs()
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase : int =config_and_inputs
_UpperCAmelCase : Dict ={'pixel_values': pixel_values}
return config, inputs_dict
@require_torch
class __magic_name__ ( lowercase_ ,lowercase_ ,unittest.TestCase ):
UpperCAmelCase =(
(
ConvNextModel,
ConvNextForImageClassification,
ConvNextBackbone,
)
if is_torch_available()
else ()
)
UpperCAmelCase =(
{'''feature-extraction''': ConvNextModel, '''image-classification''': ConvNextForImageClassification}
if is_torch_available()
else {}
)
UpperCAmelCase =True
UpperCAmelCase =False
UpperCAmelCase =False
UpperCAmelCase =False
UpperCAmelCase =False
def lowerCAmelCase ( self) -> Dict:
'''simple docstring'''
_UpperCAmelCase : Any =ConvNextModelTester(self)
_UpperCAmelCase : Union[str, Any] =ConfigTester(self , config_class=snake_case , has_text_modality=snake_case , hidden_size=3_7)
def lowerCAmelCase ( self) -> List[Any]:
'''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 lowerCAmelCase ( self) -> str:
'''simple docstring'''
return
@unittest.skip(reason='ConvNext does not use inputs_embeds')
def lowerCAmelCase ( self) -> Optional[Any]:
'''simple docstring'''
pass
@unittest.skip(reason='ConvNext does not support input and output embeddings')
def lowerCAmelCase ( self) -> List[str]:
'''simple docstring'''
pass
@unittest.skip(reason='ConvNext does not use feedforward chunking')
def lowerCAmelCase ( self) -> Any:
'''simple docstring'''
pass
def lowerCAmelCase ( self) -> Optional[Any]:
'''simple docstring'''
_UpperCAmelCase , _UpperCAmelCase : Optional[Any] =self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_UpperCAmelCase : Union[str, Any] =model_class(snake_case)
_UpperCAmelCase : Union[str, Any] =inspect.signature(model.forward)
# signature.parameters is an OrderedDict => so arg_names order is deterministic
_UpperCAmelCase : str =[*signature.parameters.keys()]
_UpperCAmelCase : str =['pixel_values']
self.assertListEqual(arg_names[:1] , snake_case)
def lowerCAmelCase ( self) -> Dict:
'''simple docstring'''
_UpperCAmelCase : Any =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*snake_case)
def lowerCAmelCase ( self) -> Tuple:
'''simple docstring'''
_UpperCAmelCase : int =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_backbone(*snake_case)
def lowerCAmelCase ( self) -> str:
'''simple docstring'''
def check_hidden_states_output(snake_case , snake_case , snake_case):
_UpperCAmelCase : str =model_class(snake_case)
model.to(snake_case)
model.eval()
with torch.no_grad():
_UpperCAmelCase : Any =model(**self._prepare_for_class(snake_case , snake_case))
_UpperCAmelCase : Any =outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
_UpperCAmelCase : Any =self.model_tester.num_stages
self.assertEqual(len(snake_case) , expected_num_stages + 1)
# ConvNext's feature maps are of shape (batch_size, num_channels, height, width)
self.assertListEqual(
list(hidden_states[0].shape[-2:]) , [self.model_tester.image_size // 4, self.model_tester.image_size // 4] , )
_UpperCAmelCase , _UpperCAmelCase : int =self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_UpperCAmelCase : List[Any] =True
check_hidden_states_output(snake_case , snake_case , snake_case)
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
_UpperCAmelCase : List[Any] =True
check_hidden_states_output(snake_case , snake_case , snake_case)
def lowerCAmelCase ( self) -> List[str]:
'''simple docstring'''
_UpperCAmelCase : int =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*snake_case)
@slow
def lowerCAmelCase ( self) -> str:
'''simple docstring'''
for model_name in CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_UpperCAmelCase : str =ConvNextModel.from_pretrained(snake_case)
self.assertIsNotNone(snake_case)
def lowerCamelCase__ ( ):
'''simple docstring'''
_UpperCAmelCase : Optional[int] =Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
return image
@require_torch
@require_vision
class __magic_name__ ( unittest.TestCase ):
@cached_property
def lowerCAmelCase ( self) -> List[str]:
'''simple docstring'''
return AutoImageProcessor.from_pretrained('facebook/convnext-tiny-224') if is_vision_available() else None
@slow
def lowerCAmelCase ( self) -> Any:
'''simple docstring'''
_UpperCAmelCase : List[Any] =ConvNextForImageClassification.from_pretrained('facebook/convnext-tiny-224').to(snake_case)
_UpperCAmelCase : Dict =self.default_image_processor
_UpperCAmelCase : List[str] =prepare_img()
_UpperCAmelCase : str =image_processor(images=snake_case , return_tensors='pt').to(snake_case)
# forward pass
with torch.no_grad():
_UpperCAmelCase : str =model(**snake_case)
# verify the logits
_UpperCAmelCase : Union[str, Any] =torch.Size((1, 1_0_0_0))
self.assertEqual(outputs.logits.shape , snake_case)
_UpperCAmelCase : str =torch.tensor([-0.02_60, -0.47_39, 0.19_11]).to(snake_case)
self.assertTrue(torch.allclose(outputs.logits[0, :3] , snake_case , atol=1E-4))
@require_torch
class __magic_name__ ( unittest.TestCase ,lowercase_ ):
UpperCAmelCase =(ConvNextBackbone,) if is_torch_available() else ()
UpperCAmelCase =ConvNextConfig
UpperCAmelCase =False
def lowerCAmelCase ( self) -> Dict:
'''simple docstring'''
_UpperCAmelCase : str =ConvNextModelTester(self)
| 720 |
'''simple docstring'''
import argparse
import requests
import torch
# pip3 install salesforce-lavis
# I'm actually installing a slightly modified version: pip3 install git+https://github.com/nielsrogge/LAVIS.git@fix_lavis
from lavis.models import load_model_and_preprocess
from PIL import Image
from transformers import (
AutoTokenizer,
BlipaConfig,
BlipaForConditionalGeneration,
BlipaProcessor,
BlipaVisionConfig,
BlipImageProcessor,
OPTConfig,
TaConfig,
)
from transformers.utils.constants import OPENAI_CLIP_MEAN, OPENAI_CLIP_STD
def lowerCamelCase__ ( ):
'''simple docstring'''
_UpperCAmelCase : int ='https://storage.googleapis.com/sfr-vision-language-research/LAVIS/assets/merlion.png'
_UpperCAmelCase : Optional[int] =Image.open(requests.get(__lowerCamelCase , stream=__lowerCamelCase ).raw ).convert('RGB' )
return image
def lowerCamelCase__ ( __lowerCamelCase : Any ):
'''simple docstring'''
_UpperCAmelCase : int =[]
# fmt: off
# vision encoder
rename_keys.append(('visual_encoder.cls_token', 'vision_model.embeddings.class_embedding') )
rename_keys.append(('visual_encoder.pos_embed', 'vision_model.embeddings.position_embedding') )
rename_keys.append(('visual_encoder.patch_embed.proj.weight', 'vision_model.embeddings.patch_embedding.weight') )
rename_keys.append(('visual_encoder.patch_embed.proj.bias', 'vision_model.embeddings.patch_embedding.bias') )
rename_keys.append(('ln_vision.weight', 'vision_model.post_layernorm.weight') )
rename_keys.append(('ln_vision.bias', 'vision_model.post_layernorm.bias') )
for i in range(config.vision_config.num_hidden_layers ):
rename_keys.append((f"visual_encoder.blocks.{i}.norm1.weight", f"vision_model.encoder.layers.{i}.layer_norm1.weight") )
rename_keys.append((f"visual_encoder.blocks.{i}.norm1.bias", f"vision_model.encoder.layers.{i}.layer_norm1.bias") )
rename_keys.append((f"visual_encoder.blocks.{i}.norm2.weight", f"vision_model.encoder.layers.{i}.layer_norm2.weight") )
rename_keys.append((f"visual_encoder.blocks.{i}.norm2.bias", f"vision_model.encoder.layers.{i}.layer_norm2.bias") )
rename_keys.append((f"visual_encoder.blocks.{i}.attn.qkv.weight", f"vision_model.encoder.layers.{i}.self_attn.qkv.weight") )
rename_keys.append((f"visual_encoder.blocks.{i}.attn.proj.weight", f"vision_model.encoder.layers.{i}.self_attn.projection.weight",) )
rename_keys.append((f"visual_encoder.blocks.{i}.attn.proj.bias", f"vision_model.encoder.layers.{i}.self_attn.projection.bias") )
rename_keys.append((f"visual_encoder.blocks.{i}.mlp.fc1.weight", f"vision_model.encoder.layers.{i}.mlp.fc1.weight") )
rename_keys.append((f"visual_encoder.blocks.{i}.mlp.fc1.bias", f"vision_model.encoder.layers.{i}.mlp.fc1.bias") )
rename_keys.append((f"visual_encoder.blocks.{i}.mlp.fc2.weight", f"vision_model.encoder.layers.{i}.mlp.fc2.weight") )
rename_keys.append((f"visual_encoder.blocks.{i}.mlp.fc2.bias", f"vision_model.encoder.layers.{i}.mlp.fc2.bias") )
# QFormer
rename_keys.append(('Qformer.bert.embeddings.LayerNorm.weight', 'qformer.layernorm.weight') )
rename_keys.append(('Qformer.bert.embeddings.LayerNorm.bias', 'qformer.layernorm.bias') )
# fmt: on
return rename_keys
def lowerCamelCase__ ( __lowerCamelCase : Union[str, Any] , __lowerCamelCase : int , __lowerCamelCase : int ):
'''simple docstring'''
_UpperCAmelCase : Optional[Any] =dct.pop(__lowerCamelCase )
_UpperCAmelCase : Dict =val
def lowerCamelCase__ ( __lowerCamelCase : Union[str, Any] , __lowerCamelCase : List[str] ):
'''simple docstring'''
for i in range(config.vision_config.num_hidden_layers ):
# read in original q and v biases
_UpperCAmelCase : str =state_dict.pop(f"visual_encoder.blocks.{i}.attn.q_bias" )
_UpperCAmelCase : Tuple =state_dict.pop(f"visual_encoder.blocks.{i}.attn.v_bias" )
# next, set bias in the state dict
_UpperCAmelCase : Optional[Any] =torch.cat((q_bias, torch.zeros_like(__lowerCamelCase , requires_grad=__lowerCamelCase ), v_bias) )
_UpperCAmelCase : int =qkv_bias
def lowerCamelCase__ ( __lowerCamelCase : List[str] , __lowerCamelCase : int ):
'''simple docstring'''
_UpperCAmelCase : Union[str, Any] =3_6_4 if 'coco' in model_name else 2_2_4
_UpperCAmelCase : Optional[int] =BlipaVisionConfig(image_size=__lowerCamelCase ).to_dict()
# make sure the models have proper bos_token_id and eos_token_id set (important for generation)
# seems like flan-T5 models don't have bos_token_id properly set?
if "opt-2.7b" in model_name:
_UpperCAmelCase : Tuple =OPTConfig.from_pretrained('facebook/opt-2.7b' , eos_token_id=__lowerCamelCase ).to_dict()
elif "opt-6.7b" in model_name:
_UpperCAmelCase : Dict =OPTConfig.from_pretrained('facebook/opt-6.7b' , eos_token_id=__lowerCamelCase ).to_dict()
elif "t5-xl" in model_name:
_UpperCAmelCase : Union[str, Any] =TaConfig.from_pretrained('google/flan-t5-xl' , dense_act_fn='gelu' , bos_token_id=1 ).to_dict()
elif "t5-xxl" in model_name:
_UpperCAmelCase : Dict =TaConfig.from_pretrained('google/flan-t5-xxl' , dense_act_fn='gelu' , bos_token_id=1 ).to_dict()
_UpperCAmelCase : str =BlipaConfig(vision_config=__lowerCamelCase , text_config=__lowerCamelCase )
return config, image_size
@torch.no_grad()
def lowerCamelCase__ ( __lowerCamelCase : Union[str, Any] , __lowerCamelCase : Dict=None , __lowerCamelCase : Dict=False ):
'''simple docstring'''
_UpperCAmelCase : str =(
AutoTokenizer.from_pretrained('facebook/opt-2.7b' )
if 'opt' in model_name
else AutoTokenizer.from_pretrained('google/flan-t5-xl' )
)
_UpperCAmelCase : Tuple =tokenizer('\n' , add_special_tokens=__lowerCamelCase ).input_ids[0]
_UpperCAmelCase , _UpperCAmelCase : List[str] =get_blipa_config(__lowerCamelCase , eos_token_id=__lowerCamelCase )
_UpperCAmelCase : Optional[int] =BlipaForConditionalGeneration(__lowerCamelCase ).eval()
_UpperCAmelCase : int ={
'blip2-opt-2.7b': ('blip2_opt', 'pretrain_opt2.7b'),
'blip2-opt-6.7b': ('blip2_opt', 'pretrain_opt6.7b'),
'blip2-opt-2.7b-coco': ('blip2_opt', 'caption_coco_opt2.7b'),
'blip2-opt-6.7b-coco': ('blip2_opt', 'caption_coco_opt6.7b'),
'blip2-flan-t5-xl': ('blip2_t5', 'pretrain_flant5xl'),
'blip2-flan-t5-xl-coco': ('blip2_t5', 'caption_coco_flant5xl'),
'blip2-flan-t5-xxl': ('blip2_t5', 'pretrain_flant5xxl'),
}
_UpperCAmelCase , _UpperCAmelCase : Tuple =model_name_to_original[model_name]
# load original model
print('Loading original model...' )
_UpperCAmelCase : Optional[int] ='cuda' if torch.cuda.is_available() else 'cpu'
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase : Union[str, Any] =load_model_and_preprocess(
name=__lowerCamelCase , model_type=__lowerCamelCase , is_eval=__lowerCamelCase , device=__lowerCamelCase )
original_model.eval()
print('Done!' )
# update state dict keys
_UpperCAmelCase : List[Any] =original_model.state_dict()
_UpperCAmelCase : Optional[Any] =create_rename_keys(__lowerCamelCase )
for src, dest in rename_keys:
rename_key(__lowerCamelCase , __lowerCamelCase , __lowerCamelCase )
# some keys can be renamed efficiently
for key, val in state_dict.copy().items():
_UpperCAmelCase : Optional[Any] =state_dict.pop(__lowerCamelCase )
if key.startswith('Qformer.bert' ):
_UpperCAmelCase : Tuple =key.replace('Qformer.bert' , 'qformer' )
if "attention.self" in key:
_UpperCAmelCase : Optional[Any] =key.replace('self' , 'attention' )
if "opt_proj" in key:
_UpperCAmelCase : List[str] =key.replace('opt_proj' , 'language_projection' )
if "t5_proj" in key:
_UpperCAmelCase : Tuple =key.replace('t5_proj' , 'language_projection' )
if key.startswith('opt' ):
_UpperCAmelCase : Optional[Any] =key.replace('opt' , 'language' )
if key.startswith('t5' ):
_UpperCAmelCase : Dict =key.replace('t5' , 'language' )
_UpperCAmelCase : Any =val
# read in qv biases
read_in_q_v_bias(__lowerCamelCase , __lowerCamelCase )
_UpperCAmelCase , _UpperCAmelCase : List[Any] =hf_model.load_state_dict(__lowerCamelCase , strict=__lowerCamelCase )
assert len(__lowerCamelCase ) == 0
assert unexpected_keys == ["qformer.embeddings.position_ids"]
_UpperCAmelCase : Union[str, Any] =load_demo_image()
_UpperCAmelCase : str =vis_processors['eval'](__lowerCamelCase ).unsqueeze(0 ).to(__lowerCamelCase )
_UpperCAmelCase : Any =tokenizer(['\n'] , return_tensors='pt' ).input_ids.to(__lowerCamelCase )
# create processor
_UpperCAmelCase : str =BlipImageProcessor(
size={'height': image_size, 'width': image_size} , image_mean=__lowerCamelCase , image_std=__lowerCamelCase )
_UpperCAmelCase : Union[str, Any] =BlipaProcessor(image_processor=__lowerCamelCase , tokenizer=__lowerCamelCase )
_UpperCAmelCase : str =processor(images=__lowerCamelCase , return_tensors='pt' ).pixel_values.to(__lowerCamelCase )
# make sure processor creates exact same pixel values
assert torch.allclose(__lowerCamelCase , __lowerCamelCase )
original_model.to(__lowerCamelCase )
hf_model.to(__lowerCamelCase )
with torch.no_grad():
if "opt" in model_name:
_UpperCAmelCase : Dict =original_model({'image': original_pixel_values, 'text_input': ['']} ).logits
_UpperCAmelCase : int =hf_model(__lowerCamelCase , __lowerCamelCase ).logits
else:
_UpperCAmelCase : Tuple =original_model(
{'image': original_pixel_values, 'text_input': ['\n'], 'text_output': ['\n']} ).logits
_UpperCAmelCase : Union[str, Any] =input_ids.masked_fill(input_ids == tokenizer.pad_token_id , -1_0_0 )
_UpperCAmelCase : Any =hf_model(__lowerCamelCase , __lowerCamelCase , labels=__lowerCamelCase ).logits
assert original_logits.shape == logits.shape
print('First values of original logits:' , original_logits[0, :3, :3] )
print('First values of HF logits:' , logits[0, :3, :3] )
# assert values
if model_name == "blip2-flan-t5-xl":
_UpperCAmelCase : Dict =torch.tensor(
[[-41.58_50, -4.44_40, -8.99_22], [-47.43_22, -5.91_43, -1.73_40]] , device=__lowerCamelCase )
assert torch.allclose(logits[0, :3, :3] , __lowerCamelCase , atol=1e-4 )
elif model_name == "blip2-flan-t5-xl-coco":
_UpperCAmelCase : Optional[Any] =torch.tensor(
[[-57.01_09, -9.89_67, -12.62_80], [-68.65_78, -12.71_91, -10.50_65]] , device=__lowerCamelCase )
else:
# cast to same type
_UpperCAmelCase : List[str] =logits.dtype
assert torch.allclose(original_logits.to(__lowerCamelCase ) , __lowerCamelCase , atol=1e-2 )
print('Looks ok!' )
print('Generating a caption...' )
_UpperCAmelCase : str =''
_UpperCAmelCase : Tuple =tokenizer(__lowerCamelCase , return_tensors='pt' ).input_ids.to(__lowerCamelCase )
_UpperCAmelCase : Any =original_model.generate({'image': original_pixel_values} )
_UpperCAmelCase : List[str] =hf_model.generate(
__lowerCamelCase , __lowerCamelCase , do_sample=__lowerCamelCase , num_beams=5 , max_length=3_0 , min_length=1 , top_p=0.9 , repetition_penalty=1.0 , length_penalty=1.0 , temperature=1 , )
print('Original generation:' , __lowerCamelCase )
_UpperCAmelCase : List[Any] =input_ids.shape[1]
_UpperCAmelCase : Optional[int] =processor.batch_decode(outputs[:, prompt_length:] , skip_special_tokens=__lowerCamelCase )
_UpperCAmelCase : Optional[Any] =[text.strip() for text in output_text]
print('HF generation:' , __lowerCamelCase )
if pytorch_dump_folder_path is not None:
processor.save_pretrained(__lowerCamelCase )
hf_model.save_pretrained(__lowerCamelCase )
if push_to_hub:
processor.push_to_hub(f"nielsr/{model_name}" )
hf_model.push_to_hub(f"nielsr/{model_name}" )
if __name__ == "__main__":
lowercase =argparse.ArgumentParser()
lowercase =[
'blip2-opt-2.7b',
'blip2-opt-6.7b',
'blip2-opt-2.7b-coco',
'blip2-opt-6.7b-coco',
'blip2-flan-t5-xl',
'blip2-flan-t5-xl-coco',
'blip2-flan-t5-xxl',
]
parser.add_argument(
'--model_name',
default='blip2-opt-2.7b',
choices=choices,
type=str,
help='Path to hf config.json of model to convert',
)
parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
parser.add_argument(
'--push_to_hub',
action='store_true',
help='Whether to push the model and processor to the hub after converting',
)
lowercase =parser.parse_args()
convert_blipa_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)
| 331 | 0 |
def lowerCAmelCase_ ( __a ) -> bool:
"""simple docstring"""
lowerCamelCase__: Tuple =[int(__a ) for i in ip_va_address.split("." ) if i.isdigit()]
return len(__a ) == 4 and all(0 <= int(__a ) <= 254 for octet in octets )
if __name__ == "__main__":
__A = input().strip()
__A = "valid" if is_ip_va_address_valid(ip) else "invalid"
print(f'{ip} is a {valid_or_invalid} IP v4 address.')
| 59 |
class UpperCamelCase_ :
'''simple docstring'''
def __init__( self , a ) -> Tuple:
snake_case_ = n
snake_case_ = [None] * self.n
snake_case_ = 0 # index of the first element
snake_case_ = 0
snake_case_ = 0
def __len__( self ) -> int:
return self.size
def _UpperCamelCase ( self ) -> bool:
return self.size == 0
def _UpperCamelCase ( self ) -> List[Any]:
return False if self.is_empty() else self.array[self.front]
def _UpperCamelCase ( self , a ) -> Dict:
if self.size >= self.n:
raise Exception('QUEUE IS FULL' )
snake_case_ = data
snake_case_ = (self.rear + 1) % self.n
self.size += 1
return self
def _UpperCamelCase ( self ) -> List[Any]:
if self.size == 0:
raise Exception('UNDERFLOW' )
snake_case_ = self.array[self.front]
snake_case_ = None
snake_case_ = (self.front + 1) % self.n
self.size -= 1
return temp
| 198 | 0 |
"""simple docstring"""
from itertools import permutations
def __UpperCAmelCase ( __UpperCamelCase ):
if num[3] % 2 != 0:
return False
if (num[2] + num[3] + num[4]) % 3 != 0:
return False
if num[5] % 5 != 0:
return False
__lowercase : Union[str, Any] = [7, 11, 13, 17]
for i, test in enumerate(__UpperCamelCase ):
if (num[i + 4] * 1_00 + num[i + 5] * 10 + num[i + 6]) % test != 0:
return False
return True
def __UpperCAmelCase ( __UpperCamelCase = 10 ):
return sum(
int(''''''.join(map(__UpperCamelCase , __UpperCamelCase ) ) )
for num in permutations(range(__UpperCamelCase ) )
if is_substring_divisible(__UpperCamelCase ) )
if __name__ == "__main__":
print(F"{solution() = }")
| 523 |
"""simple docstring"""
def __UpperCAmelCase ( __UpperCamelCase ):
assert column_title.isupper()
__lowercase : Optional[Any] = 0
__lowercase : Union[str, Any] = len(__UpperCamelCase ) - 1
__lowercase : Union[str, Any] = 0
while index >= 0:
__lowercase : List[Any] = (ord(column_title[index] ) - 64) * pow(26 , __UpperCamelCase )
answer += value
power += 1
index -= 1
return answer
if __name__ == "__main__":
from doctest import testmod
testmod()
| 523 | 1 |
'''simple docstring'''
from typing import List
import numpy as np
def lowercase__( _UpperCamelCase : dict )-> int:
"""simple docstring"""
_UpperCamelCase = {key: len(_UpperCamelCase ) for key, value in gen_kwargs.items() if isinstance(_UpperCamelCase , _UpperCamelCase )}
if len(set(lists_lengths.values() ) ) > 1:
raise RuntimeError(
(
"Sharding is ambiguous for this dataset: "
+ "we found several data sources lists of different lengths, and we don't know over which list we should parallelize:\n"
+ "\n".join(f"\t- key {key} has length {length}" for key, length in lists_lengths.items() )
+ "\nTo fix this, check the 'gen_kwargs' and make sure to use lists only for data sources, "
+ "and use tuples otherwise. In the end there should only be one single list, or several lists with the same length."
) )
_UpperCamelCase = max(lists_lengths.values() , default=0 )
return max(1 , _UpperCamelCase )
def lowercase__( _UpperCamelCase : int , _UpperCamelCase : int )-> List[range]:
"""simple docstring"""
_UpperCamelCase = []
for group_idx in range(_UpperCamelCase ):
_UpperCamelCase = num_shards // max_num_jobs + (group_idx < (num_shards % max_num_jobs))
if num_shards_to_add == 0:
break
_UpperCamelCase = shards_indices_per_group[-1].stop if shards_indices_per_group else 0
_UpperCamelCase = range(_UpperCamelCase , start + num_shards_to_add )
shards_indices_per_group.append(_UpperCamelCase )
return shards_indices_per_group
def lowercase__( _UpperCamelCase : dict , _UpperCamelCase : int )-> List[dict]:
"""simple docstring"""
_UpperCamelCase = _number_of_shards_in_gen_kwargs(_UpperCamelCase )
if num_shards == 1:
return [dict(_UpperCamelCase )]
else:
_UpperCamelCase = _distribute_shards(num_shards=_UpperCamelCase , max_num_jobs=_UpperCamelCase )
return [
{
key: [value[shard_idx] for shard_idx in shard_indices_per_group[group_idx]]
if isinstance(_UpperCamelCase , _UpperCamelCase )
else value
for key, value in gen_kwargs.items()
}
for group_idx in range(len(_UpperCamelCase ) )
]
def lowercase__( _UpperCamelCase : List[dict] )-> dict:
"""simple docstring"""
return {
key: [value for gen_kwargs in gen_kwargs_list for value in gen_kwargs[key]]
if isinstance(gen_kwargs_list[0][key] , _UpperCamelCase )
else gen_kwargs_list[0][key]
for key in gen_kwargs_list[0]
}
def lowercase__( _UpperCamelCase : np.random.Generator , _UpperCamelCase : dict )-> dict:
"""simple docstring"""
_UpperCamelCase = {len(_UpperCamelCase ) for value in gen_kwargs.values() if isinstance(_UpperCamelCase , _UpperCamelCase )}
_UpperCamelCase = {}
for size in list_sizes:
_UpperCamelCase = list(range(_UpperCamelCase ) )
rng.shuffle(indices_per_size[size] )
# Now let's copy the gen_kwargs and shuffle the lists based on their sizes
_UpperCamelCase = dict(_UpperCamelCase )
for key, value in shuffled_kwargs.items():
if isinstance(_UpperCamelCase , _UpperCamelCase ):
_UpperCamelCase = [value[i] for i in indices_per_size[len(_UpperCamelCase )]]
return shuffled_kwargs
| 138 |
'''simple docstring'''
def lowercase__( _UpperCamelCase : str )-> str:
"""simple docstring"""
return " ".join(
"".join(word[::-1] ) if len(_UpperCamelCase ) > 4 else word for word in sentence.split() )
if __name__ == "__main__":
import doctest
doctest.testmod()
print(reverse_long_words('''Hey wollef sroirraw'''))
| 138 | 1 |
from typing import Optional
import torch
import torch.utils.checkpoint
from torch import Tensor, nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward
from ...modeling_outputs import (
BaseModelOutputWithNoAttention,
BaseModelOutputWithPoolingAndNoAttention,
ImageClassifierOutputWithNoAttention,
)
from ...modeling_utils import PreTrainedModel
from ...utils import logging
from .configuration_regnet import RegNetConfig
__lowerCAmelCase =logging.get_logger(__name__)
# General docstring
__lowerCAmelCase ='RegNetConfig'
# Base docstring
__lowerCAmelCase ='facebook/regnet-y-040'
__lowerCAmelCase =[1, 1088, 7, 7]
# Image classification docstring
__lowerCAmelCase ='facebook/regnet-y-040'
__lowerCAmelCase ='tabby, tabby cat'
__lowerCAmelCase =[
'facebook/regnet-y-040',
# See all regnet models at https://huggingface.co/models?filter=regnet
]
class __magic_name__ ( nn.Module):
def __init__( self : Optional[int] ,__SCREAMING_SNAKE_CASE : Tuple ,__SCREAMING_SNAKE_CASE : int ,__SCREAMING_SNAKE_CASE : Any = 3 ,__SCREAMING_SNAKE_CASE : List[Any] = 1 ,__SCREAMING_SNAKE_CASE : Optional[Any] = 1 ,__SCREAMING_SNAKE_CASE : str = "relu" ,):
super().__init__()
UpperCAmelCase = nn.Convad(
_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,kernel_size=_SCREAMING_SNAKE_CASE ,stride=_SCREAMING_SNAKE_CASE ,padding=kernel_size // 2 ,groups=_SCREAMING_SNAKE_CASE ,bias=_SCREAMING_SNAKE_CASE ,)
UpperCAmelCase = nn.BatchNormad(_SCREAMING_SNAKE_CASE )
UpperCAmelCase = ACTaFN[activation] if activation is not None else nn.Identity()
def _UpperCAmelCase ( self : List[str] ,__SCREAMING_SNAKE_CASE : Union[str, Any] ):
UpperCAmelCase = self.convolution(_SCREAMING_SNAKE_CASE )
UpperCAmelCase = self.normalization(_SCREAMING_SNAKE_CASE )
UpperCAmelCase = self.activation(_SCREAMING_SNAKE_CASE )
return hidden_state
class __magic_name__ ( nn.Module):
def __init__( self : Union[str, Any] ,__SCREAMING_SNAKE_CASE : Any ):
super().__init__()
UpperCAmelCase = RegNetConvLayer(
config.num_channels ,config.embedding_size ,kernel_size=3 ,stride=2 ,activation=config.hidden_act )
UpperCAmelCase = config.num_channels
def _UpperCAmelCase ( self : str ,__SCREAMING_SNAKE_CASE : List[Any] ):
UpperCAmelCase = pixel_values.shape[1]
if num_channels != self.num_channels:
raise ValueError(
"Make sure that the channel dimension of the pixel values match with the one set in the configuration." )
UpperCAmelCase = self.embedder(_SCREAMING_SNAKE_CASE )
return hidden_state
class __magic_name__ ( nn.Module):
def __init__( self : List[Any] ,__SCREAMING_SNAKE_CASE : int ,__SCREAMING_SNAKE_CASE : List[str] ,__SCREAMING_SNAKE_CASE : Any = 2 ):
super().__init__()
UpperCAmelCase = nn.Convad(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,kernel_size=1 ,stride=_SCREAMING_SNAKE_CASE ,bias=_SCREAMING_SNAKE_CASE )
UpperCAmelCase = nn.BatchNormad(_SCREAMING_SNAKE_CASE )
def _UpperCAmelCase ( self : Optional[Any] ,__SCREAMING_SNAKE_CASE : Any ):
UpperCAmelCase = self.convolution(_SCREAMING_SNAKE_CASE )
UpperCAmelCase = self.normalization(_SCREAMING_SNAKE_CASE )
return hidden_state
class __magic_name__ ( nn.Module):
def __init__( self : Dict ,__SCREAMING_SNAKE_CASE : Any ,__SCREAMING_SNAKE_CASE : Dict ):
super().__init__()
UpperCAmelCase = nn.AdaptiveAvgPoolad((1, 1) )
UpperCAmelCase = nn.Sequential(
nn.Convad(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,kernel_size=1 ) ,nn.ReLU() ,nn.Convad(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,kernel_size=1 ) ,nn.Sigmoid() ,)
def _UpperCAmelCase ( self : List[Any] ,__SCREAMING_SNAKE_CASE : Tuple ):
# b c h w -> b c 1 1
UpperCAmelCase = self.pooler(_SCREAMING_SNAKE_CASE )
UpperCAmelCase = self.attention(_SCREAMING_SNAKE_CASE )
UpperCAmelCase = hidden_state * attention
return hidden_state
class __magic_name__ ( nn.Module):
def __init__( self : Union[str, Any] ,__SCREAMING_SNAKE_CASE : str ,__SCREAMING_SNAKE_CASE : List[Any] ,__SCREAMING_SNAKE_CASE : Tuple ,__SCREAMING_SNAKE_CASE : Optional[Any] = 1 ):
super().__init__()
UpperCAmelCase = in_channels != out_channels or stride != 1
UpperCAmelCase = max(1 ,out_channels // config.groups_width )
UpperCAmelCase = (
RegNetShortCut(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,stride=_SCREAMING_SNAKE_CASE ) if should_apply_shortcut else nn.Identity()
)
UpperCAmelCase = nn.Sequential(
RegNetConvLayer(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,kernel_size=1 ,activation=config.hidden_act ) ,RegNetConvLayer(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,stride=_SCREAMING_SNAKE_CASE ,groups=_SCREAMING_SNAKE_CASE ,activation=config.hidden_act ) ,RegNetConvLayer(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,kernel_size=1 ,activation=_SCREAMING_SNAKE_CASE ) ,)
UpperCAmelCase = ACTaFN[config.hidden_act]
def _UpperCAmelCase ( self : List[str] ,__SCREAMING_SNAKE_CASE : List[Any] ):
UpperCAmelCase = hidden_state
UpperCAmelCase = self.layer(_SCREAMING_SNAKE_CASE )
UpperCAmelCase = self.shortcut(_SCREAMING_SNAKE_CASE )
hidden_state += residual
UpperCAmelCase = self.activation(_SCREAMING_SNAKE_CASE )
return hidden_state
class __magic_name__ ( nn.Module):
def __init__( self : Dict ,__SCREAMING_SNAKE_CASE : str ,__SCREAMING_SNAKE_CASE : Dict ,__SCREAMING_SNAKE_CASE : List[Any] ,__SCREAMING_SNAKE_CASE : Union[str, Any] = 1 ):
super().__init__()
UpperCAmelCase = in_channels != out_channels or stride != 1
UpperCAmelCase = max(1 ,out_channels // config.groups_width )
UpperCAmelCase = (
RegNetShortCut(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,stride=_SCREAMING_SNAKE_CASE ) if should_apply_shortcut else nn.Identity()
)
UpperCAmelCase = nn.Sequential(
RegNetConvLayer(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,kernel_size=1 ,activation=config.hidden_act ) ,RegNetConvLayer(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,stride=_SCREAMING_SNAKE_CASE ,groups=_SCREAMING_SNAKE_CASE ,activation=config.hidden_act ) ,RegNetSELayer(_SCREAMING_SNAKE_CASE ,reduced_channels=int(round(in_channels / 4 ) ) ) ,RegNetConvLayer(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,kernel_size=1 ,activation=_SCREAMING_SNAKE_CASE ) ,)
UpperCAmelCase = ACTaFN[config.hidden_act]
def _UpperCAmelCase ( self : int ,__SCREAMING_SNAKE_CASE : List[str] ):
UpperCAmelCase = hidden_state
UpperCAmelCase = self.layer(_SCREAMING_SNAKE_CASE )
UpperCAmelCase = self.shortcut(_SCREAMING_SNAKE_CASE )
hidden_state += residual
UpperCAmelCase = self.activation(_SCREAMING_SNAKE_CASE )
return hidden_state
class __magic_name__ ( nn.Module):
def __init__( self : str ,__SCREAMING_SNAKE_CASE : int ,__SCREAMING_SNAKE_CASE : Any ,__SCREAMING_SNAKE_CASE : str ,__SCREAMING_SNAKE_CASE : List[str] = 2 ,__SCREAMING_SNAKE_CASE : Any = 2 ,):
super().__init__()
UpperCAmelCase = RegNetXLayer if config.layer_type == "x" else RegNetYLayer
UpperCAmelCase = nn.Sequential(
# downsampling is done in the first layer with stride of 2
layer(
_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,stride=_SCREAMING_SNAKE_CASE ,) ,*[layer(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ) for _ in range(depth - 1 )] ,)
def _UpperCAmelCase ( self : int ,__SCREAMING_SNAKE_CASE : Union[str, Any] ):
UpperCAmelCase = self.layers(_SCREAMING_SNAKE_CASE )
return hidden_state
class __magic_name__ ( nn.Module):
def __init__( self : List[str] ,__SCREAMING_SNAKE_CASE : Any ):
super().__init__()
UpperCAmelCase = nn.ModuleList([] )
# based on `downsample_in_first_stage`, the first layer of the first stage may or may not downsample the input
self.stages.append(
RegNetStage(
_SCREAMING_SNAKE_CASE ,config.embedding_size ,config.hidden_sizes[0] ,stride=2 if config.downsample_in_first_stage else 1 ,depth=config.depths[0] ,) )
UpperCAmelCase = zip(config.hidden_sizes ,config.hidden_sizes[1:] )
for (in_channels, out_channels), depth in zip(_SCREAMING_SNAKE_CASE ,config.depths[1:] ):
self.stages.append(RegNetStage(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,depth=_SCREAMING_SNAKE_CASE ) )
def _UpperCAmelCase ( self : List[Any] ,__SCREAMING_SNAKE_CASE : int ,__SCREAMING_SNAKE_CASE : List[str] = False ,__SCREAMING_SNAKE_CASE : int = True ):
UpperCAmelCase = () if output_hidden_states else None
for stage_module in self.stages:
if output_hidden_states:
UpperCAmelCase = hidden_states + (hidden_state,)
UpperCAmelCase = stage_module(_SCREAMING_SNAKE_CASE )
if output_hidden_states:
UpperCAmelCase = hidden_states + (hidden_state,)
if not return_dict:
return tuple(v for v in [hidden_state, hidden_states] if v is not None )
return BaseModelOutputWithNoAttention(last_hidden_state=_SCREAMING_SNAKE_CASE ,hidden_states=_SCREAMING_SNAKE_CASE )
class __magic_name__ ( lowerCAmelCase__):
_UpperCAmelCase : Any = RegNetConfig
_UpperCAmelCase : List[str] = "regnet"
_UpperCAmelCase : Optional[int] = "pixel_values"
_UpperCAmelCase : Tuple = True
def _UpperCAmelCase ( self : Dict ,__SCREAMING_SNAKE_CASE : Tuple ):
if isinstance(_SCREAMING_SNAKE_CASE ,nn.Convad ):
nn.init.kaiming_normal_(module.weight ,mode="fan_out" ,nonlinearity="relu" )
elif isinstance(_SCREAMING_SNAKE_CASE ,(nn.BatchNormad, nn.GroupNorm) ):
nn.init.constant_(module.weight ,1 )
nn.init.constant_(module.bias ,0 )
def _UpperCAmelCase ( self : Tuple ,__SCREAMING_SNAKE_CASE : Any ,__SCREAMING_SNAKE_CASE : List[str]=False ):
if isinstance(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ):
UpperCAmelCase = value
__lowerCAmelCase =R'\n This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it\n as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and\n behavior.\n\n Parameters:\n config ([`RegNetConfig`]): Model configuration class with all the parameters of the model.\n Initializing with a config file does not load the weights associated with the model, only the\n configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.\n'
__lowerCAmelCase =R'\n Args:\n pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):\n Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See\n [`ConvNextImageProcessor.__call__`] for details.\n\n output_hidden_states (`bool`, *optional*):\n Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for\n more detail.\n return_dict (`bool`, *optional*):\n Whether or not to return a [`~file_utils.ModelOutput`] instead of a plain tuple.\n'
@add_start_docstrings(
'The bare RegNet model outputting raw features without any specific head on top.' , lowerCAmelCase__ , )
# Copied from transformers.models.resnet.modeling_resnet.ResNetModel with RESNET->REGNET,ResNet->RegNet
class __magic_name__ ( lowerCAmelCase__):
def __init__( self : List[str] ,__SCREAMING_SNAKE_CASE : Optional[int] ):
super().__init__(_SCREAMING_SNAKE_CASE )
UpperCAmelCase = config
UpperCAmelCase = RegNetEmbeddings(_SCREAMING_SNAKE_CASE )
UpperCAmelCase = RegNetEncoder(_SCREAMING_SNAKE_CASE )
UpperCAmelCase = nn.AdaptiveAvgPoolad((1, 1) )
# Initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(_SCREAMING_SNAKE_CASE )
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC ,output_type=_SCREAMING_SNAKE_CASE ,config_class=_CONFIG_FOR_DOC ,modality="vision" ,expected_output=_EXPECTED_OUTPUT_SHAPE ,)
def _UpperCAmelCase ( self : Any ,__SCREAMING_SNAKE_CASE : List[Any] ,__SCREAMING_SNAKE_CASE : Dict = None ,__SCREAMING_SNAKE_CASE : Dict = None ):
UpperCAmelCase = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
UpperCAmelCase = return_dict if return_dict is not None else self.config.use_return_dict
UpperCAmelCase = self.embedder(_SCREAMING_SNAKE_CASE )
UpperCAmelCase = self.encoder(
_SCREAMING_SNAKE_CASE ,output_hidden_states=_SCREAMING_SNAKE_CASE ,return_dict=_SCREAMING_SNAKE_CASE )
UpperCAmelCase = encoder_outputs[0]
UpperCAmelCase = self.pooler(_SCREAMING_SNAKE_CASE )
if not return_dict:
return (last_hidden_state, pooled_output) + encoder_outputs[1:]
return BaseModelOutputWithPoolingAndNoAttention(
last_hidden_state=_SCREAMING_SNAKE_CASE ,pooler_output=_SCREAMING_SNAKE_CASE ,hidden_states=encoder_outputs.hidden_states ,)
@add_start_docstrings(
'\n RegNet Model with an image classification head on top (a linear layer on top of the pooled features), e.g. for\n ImageNet.\n ' , lowerCAmelCase__ , )
# Copied from transformers.models.resnet.modeling_resnet.ResNetForImageClassification with RESNET->REGNET,ResNet->RegNet,resnet->regnet
class __magic_name__ ( lowerCAmelCase__):
def __init__( self : Dict ,__SCREAMING_SNAKE_CASE : List[Any] ):
super().__init__(_SCREAMING_SNAKE_CASE )
UpperCAmelCase = config.num_labels
UpperCAmelCase = RegNetModel(_SCREAMING_SNAKE_CASE )
# classification head
UpperCAmelCase = nn.Sequential(
nn.Flatten() ,nn.Linear(config.hidden_sizes[-1] ,config.num_labels ) if config.num_labels > 0 else nn.Identity() ,)
# initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(_SCREAMING_SNAKE_CASE )
@add_code_sample_docstrings(
checkpoint=_IMAGE_CLASS_CHECKPOINT ,output_type=_SCREAMING_SNAKE_CASE ,config_class=_CONFIG_FOR_DOC ,expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT ,)
def _UpperCAmelCase ( self : Tuple ,__SCREAMING_SNAKE_CASE : Any = None ,__SCREAMING_SNAKE_CASE : Optional[int] = None ,__SCREAMING_SNAKE_CASE : Any = None ,__SCREAMING_SNAKE_CASE : str = None ,):
UpperCAmelCase = return_dict if return_dict is not None else self.config.use_return_dict
UpperCAmelCase = self.regnet(_SCREAMING_SNAKE_CASE ,output_hidden_states=_SCREAMING_SNAKE_CASE ,return_dict=_SCREAMING_SNAKE_CASE )
UpperCAmelCase = outputs.pooler_output if return_dict else outputs[1]
UpperCAmelCase = self.classifier(_SCREAMING_SNAKE_CASE )
UpperCAmelCase = None
if labels is not None:
if self.config.problem_type is None:
if self.num_labels == 1:
UpperCAmelCase = "regression"
elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
UpperCAmelCase = "single_label_classification"
else:
UpperCAmelCase = "multi_label_classification"
if self.config.problem_type == "regression":
UpperCAmelCase = MSELoss()
if self.num_labels == 1:
UpperCAmelCase = loss_fct(logits.squeeze() ,labels.squeeze() )
else:
UpperCAmelCase = loss_fct(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE )
elif self.config.problem_type == "single_label_classification":
UpperCAmelCase = CrossEntropyLoss()
UpperCAmelCase = loss_fct(logits.view(-1 ,self.num_labels ) ,labels.view(-1 ) )
elif self.config.problem_type == "multi_label_classification":
UpperCAmelCase = BCEWithLogitsLoss()
UpperCAmelCase = loss_fct(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE )
if not return_dict:
UpperCAmelCase = (logits,) + outputs[2:]
return (loss,) + output if loss is not None else output
return ImageClassifierOutputWithNoAttention(loss=_SCREAMING_SNAKE_CASE ,logits=_SCREAMING_SNAKE_CASE ,hidden_states=outputs.hidden_states )
| 709 |
# 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 argparse
import os
from accelerate.utils import ComputeEnvironment
from .cluster import get_cluster_input
from .config_args import cache_dir, default_config_file, default_yaml_config_file, load_config_from_file # noqa: F401
from .config_utils import _ask_field, _ask_options, _convert_compute_environment # noqa: F401
from .sagemaker import get_sagemaker_input
__lowerCAmelCase ="Launches a series of prompts to create and save a `default_config.yaml` configuration file for your training system. Should always be ran first on your machine"
def __UpperCamelCase ( ):
"""simple docstring"""
UpperCAmelCase = _ask_options(
"In which compute environment are you running?" , ["This machine", "AWS (Amazon SageMaker)"] , _convert_compute_environment , )
if compute_environment == ComputeEnvironment.AMAZON_SAGEMAKER:
UpperCAmelCase = get_sagemaker_input()
else:
UpperCAmelCase = get_cluster_input()
return config
def __UpperCamelCase ( _lowerCAmelCase=None ):
"""simple docstring"""
if subparsers is not None:
UpperCAmelCase = subparsers.add_parser("config" , description=_lowerCAmelCase )
else:
UpperCAmelCase = argparse.ArgumentParser("Accelerate config command" , description=_lowerCAmelCase )
parser.add_argument(
"--config_file" , default=_lowerCAmelCase , help=(
"The path to use to store the config file. Will default to a file named default_config.yaml in the cache "
"location, which is the content of the environment `HF_HOME` suffixed with 'accelerate', or if you don't have "
"such an environment variable, your cache directory ('~/.cache' or the content of `XDG_CACHE_HOME`) suffixed "
"with 'huggingface'."
) , )
if subparsers is not None:
parser.set_defaults(func=_lowerCAmelCase )
return parser
def __UpperCamelCase ( _lowerCAmelCase ):
"""simple docstring"""
UpperCAmelCase = get_user_input()
if args.config_file is not None:
UpperCAmelCase = args.config_file
else:
if not os.path.isdir(_lowerCAmelCase ):
os.makedirs(_lowerCAmelCase )
UpperCAmelCase = default_yaml_config_file
if config_file.endswith(".json" ):
config.to_json_file(_lowerCAmelCase )
else:
config.to_yaml_file(_lowerCAmelCase )
print(F'''accelerate configuration saved at {config_file}''' )
def __UpperCamelCase ( ):
"""simple docstring"""
UpperCAmelCase = config_command_parser()
UpperCAmelCase = parser.parse_args()
config_command(_lowerCAmelCase )
if __name__ == "__main__":
main()
| 405 | 0 |
def lowerCAmelCase_ ( __A, __A, __A ) -> float:
'''simple docstring'''
return round(float(moles / volume ) * nfactor )
def lowerCAmelCase_ ( __A, __A, __A ) -> float:
'''simple docstring'''
return round(float((moles * 0.0821 * temperature) / (volume) ) )
def lowerCAmelCase_ ( __A, __A, __A ) -> float:
'''simple docstring'''
return round(float((moles * 0.0821 * temperature) / (pressure) ) )
def lowerCAmelCase_ ( __A, __A, __A ) -> float:
'''simple docstring'''
return round(float((pressure * volume) / (0.0821 * moles) ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 486 | import warnings
from typing import List, Optional, Union
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class A ( UpperCAmelCase_ ):
__UpperCAmelCase : Any = ['image_processor', 'tokenizer']
__UpperCAmelCase : List[str] = 'FlavaImageProcessor'
__UpperCAmelCase : Dict = ('BertTokenizer', 'BertTokenizerFast')
def __init__(self : int , __UpperCAmelCase : Dict=None , __UpperCAmelCase : Union[str, Any]=None , **__UpperCAmelCase : Dict ) -> List[Any]:
"""simple docstring"""
UpperCAmelCase__ = None
if "feature_extractor" in kwargs:
warnings.warn(
"The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
" instead." , __UpperCAmelCase , )
UpperCAmelCase__ = kwargs.pop("feature_extractor" )
UpperCAmelCase__ = image_processor if image_processor is not None else feature_extractor
if image_processor is None:
raise ValueError("You need to specify an `image_processor`." )
if tokenizer is None:
raise ValueError("You need to specify a `tokenizer`." )
super().__init__(__UpperCAmelCase , __UpperCAmelCase )
UpperCAmelCase__ = self.image_processor
def __call__(self : Optional[int] , __UpperCAmelCase : Optional[ImageInput] = None , __UpperCAmelCase : Optional[Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]]] = None , __UpperCAmelCase : bool = True , __UpperCAmelCase : Union[bool, str, PaddingStrategy] = False , __UpperCAmelCase : Union[bool, str, TruncationStrategy] = False , __UpperCAmelCase : Optional[int] = None , __UpperCAmelCase : int = 0 , __UpperCAmelCase : Optional[int] = None , __UpperCAmelCase : Optional[bool] = None , __UpperCAmelCase : Optional[bool] = None , __UpperCAmelCase : Optional[bool] = None , __UpperCAmelCase : Optional[bool] = None , __UpperCAmelCase : bool = False , __UpperCAmelCase : bool = False , __UpperCAmelCase : bool = False , __UpperCAmelCase : bool = False , __UpperCAmelCase : bool = True , __UpperCAmelCase : Optional[Union[str, TensorType]] = None , **__UpperCAmelCase : Any , ) -> Union[str, Any]:
"""simple docstring"""
if text is None and images is None:
raise ValueError("You have to specify either text or images. Both cannot be none." )
if text is not None:
UpperCAmelCase__ = self.tokenizer(
text=__UpperCAmelCase , add_special_tokens=__UpperCAmelCase , padding=__UpperCAmelCase , truncation=__UpperCAmelCase , max_length=__UpperCAmelCase , stride=__UpperCAmelCase , pad_to_multiple_of=__UpperCAmelCase , return_token_type_ids=__UpperCAmelCase , return_attention_mask=__UpperCAmelCase , return_overflowing_tokens=__UpperCAmelCase , return_special_tokens_mask=__UpperCAmelCase , return_offsets_mapping=__UpperCAmelCase , return_length=__UpperCAmelCase , verbose=__UpperCAmelCase , return_tensors=__UpperCAmelCase , **__UpperCAmelCase , )
if images is not None:
UpperCAmelCase__ = self.image_processor(
__UpperCAmelCase , return_image_mask=__UpperCAmelCase , return_codebook_pixels=__UpperCAmelCase , return_tensors=__UpperCAmelCase , **__UpperCAmelCase , )
if text is not None and images is not None:
encoding.update(__UpperCAmelCase )
return encoding
elif text is not None:
return encoding
else:
return BatchEncoding(data=dict(**__UpperCAmelCase ) , tensor_type=__UpperCAmelCase )
def lowercase_ (self : Optional[Any] , *__UpperCAmelCase : str , **__UpperCAmelCase : Tuple ) -> int:
"""simple docstring"""
return self.tokenizer.batch_decode(*__UpperCAmelCase , **__UpperCAmelCase )
def lowercase_ (self : int , *__UpperCAmelCase : List[str] , **__UpperCAmelCase : Union[str, Any] ) -> Any:
"""simple docstring"""
return self.tokenizer.decode(*__UpperCAmelCase , **__UpperCAmelCase )
@property
def lowercase_ (self : Tuple ) -> Dict:
"""simple docstring"""
UpperCAmelCase__ = self.tokenizer.model_input_names
UpperCAmelCase__ = self.image_processor.model_input_names
return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names ) )
@property
def lowercase_ (self : Any ) -> Optional[int]:
"""simple docstring"""
warnings.warn(
"`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead." , __UpperCAmelCase , )
return self.image_processor_class
@property
def lowercase_ (self : str ) -> Tuple:
"""simple docstring"""
warnings.warn(
"`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead." , __UpperCAmelCase , )
return self.image_processor
| 486 | 1 |
import argparse
import os
import gluonnlp as nlp
import mxnet as mx
import numpy as np
import torch
from gluonnlp.base import get_home_dir
from gluonnlp.model.bert import BERTEncoder
from gluonnlp.model.utils import _load_vocab
from gluonnlp.vocab import Vocab
from packaging import version
from torch import nn
from transformers import BertConfig, BertForMaskedLM, BertModel, RobertaTokenizer
from transformers.models.bert.modeling_bert import (
BertIntermediate,
BertLayer,
BertOutput,
BertSelfAttention,
BertSelfOutput,
)
from transformers.utils import logging
if version.parse(nlp.__version__) != version.parse('0.8.3'):
raise Exception('requires gluonnlp == 0.8.3')
if version.parse(mx.__version__) != version.parse('1.5.0'):
raise Exception('requires mxnet == 1.5.0')
logging.set_verbosity_info()
__A : Any = logging.get_logger(__name__)
__A : str = 'The Nymphenburg Palace is a beautiful palace in Munich!'
def __UpperCamelCase ( _A : str , _A : str ) ->List[str]:
"""simple docstring"""
lowerCamelCase_ ={
"""attention_cell""": """multi_head""",
"""num_layers""": 4,
"""units""": 1024,
"""hidden_size""": 768,
"""max_length""": 512,
"""num_heads""": 8,
"""scaled""": True,
"""dropout""": 0.1,
"""use_residual""": True,
"""embed_size""": 1024,
"""embed_dropout""": 0.1,
"""word_embed""": None,
"""layer_norm_eps""": 1E-5,
"""token_type_vocab_size""": 2,
}
lowerCamelCase_ =bort_4_8_768_1024_hparams
# Let's construct the original Bort model here
# Taken from official BERT implementation, see:
# https://github.com/alexa/bort/blob/master/bort/bort.py
lowerCamelCase_ =BERTEncoder(
attention_cell=predefined_args["""attention_cell"""] , num_layers=predefined_args["""num_layers"""] , units=predefined_args["""units"""] , hidden_size=predefined_args["""hidden_size"""] , max_length=predefined_args["""max_length"""] , num_heads=predefined_args["""num_heads"""] , scaled=predefined_args["""scaled"""] , dropout=predefined_args["""dropout"""] , output_attention=_A , output_all_encodings=_A , use_residual=predefined_args["""use_residual"""] , activation=predefined_args.get("""activation""" , """gelu""" ) , layer_norm_eps=predefined_args.get("""layer_norm_eps""" , _A ) , )
# Vocab information needs to be fetched first
# It's the same as RoBERTa, so RobertaTokenizer can be used later
lowerCamelCase_ ="""openwebtext_ccnews_stories_books_cased"""
# Specify download folder to Gluonnlp's vocab
lowerCamelCase_ =os.path.join(get_home_dir() , """models""" )
lowerCamelCase_ =_load_vocab(_A , _A , _A , cls=_A )
lowerCamelCase_ =nlp.model.BERTModel(
_A , len(_A ) , units=predefined_args["""units"""] , embed_size=predefined_args["""embed_size"""] , embed_dropout=predefined_args["""embed_dropout"""] , word_embed=predefined_args["""word_embed"""] , use_pooler=_A , use_token_type_embed=_A , token_type_vocab_size=predefined_args["""token_type_vocab_size"""] , use_classifier=_A , use_decoder=_A , )
original_bort.load_parameters(_A , cast_dtype=_A , ignore_extra=_A )
lowerCamelCase_ =original_bort._collect_params_with_prefix()
# Build our config 🤗
lowerCamelCase_ ={
"""architectures""": ["""BertForMaskedLM"""],
"""attention_probs_dropout_prob""": predefined_args["""dropout"""],
"""hidden_act""": """gelu""",
"""hidden_dropout_prob""": predefined_args["""dropout"""],
"""hidden_size""": predefined_args["""embed_size"""],
"""initializer_range""": 0.0_2,
"""intermediate_size""": predefined_args["""hidden_size"""],
"""layer_norm_eps""": predefined_args["""layer_norm_eps"""],
"""max_position_embeddings""": predefined_args["""max_length"""],
"""model_type""": """bort""",
"""num_attention_heads""": predefined_args["""num_heads"""],
"""num_hidden_layers""": predefined_args["""num_layers"""],
"""pad_token_id""": 1, # 2 = BERT, 1 = RoBERTa
"""type_vocab_size""": 1, # 2 = BERT, 1 = RoBERTa
"""vocab_size""": len(_A ),
}
lowerCamelCase_ =BertConfig.from_dict(_A )
lowerCamelCase_ =BertForMaskedLM(_A )
hf_bort_model.eval()
# Parameter mapping table (Gluonnlp to Transformers)
# * denotes layer index
#
# | Gluon Parameter | Transformers Parameter
# | -------------------------------------------------------------- | ----------------------
# | `encoder.layer_norm.beta` | `bert.embeddings.LayerNorm.bias`
# | `encoder.layer_norm.gamma` | `bert.embeddings.LayerNorm.weight`
# | `encoder.position_weight` | `bert.embeddings.position_embeddings.weight`
# | `word_embed.0.weight` | `bert.embeddings.word_embeddings.weight`
# | `encoder.transformer_cells.*.attention_cell.proj_key.bias` | `bert.encoder.layer.*.attention.self.key.bias`
# | `encoder.transformer_cells.*.attention_cell.proj_key.weight` | `bert.encoder.layer.*.attention.self.key.weight`
# | `encoder.transformer_cells.*.attention_cell.proj_query.bias` | `bert.encoder.layer.*.attention.self.query.bias`
# | `encoder.transformer_cells.*.attention_cell.proj_query.weight` | `bert.encoder.layer.*.attention.self.query.weight`
# | `encoder.transformer_cells.*.attention_cell.proj_value.bias` | `bert.encoder.layer.*.attention.self.value.bias`
# | `encoder.transformer_cells.*.attention_cell.proj_value.weight` | `bert.encoder.layer.*.attention.self.value.weight`
# | `encoder.transformer_cells.*.ffn.ffn_2.bias` | `bert.encoder.layer.*.attention.output.dense.bias`
# | `encoder.transformer_cells.*.ffn.ffn_2.weight` | `bert.encoder.layer.*.attention.output.dense.weight`
# | `encoder.transformer_cells.*.layer_norm.beta` | `bert.encoder.layer.*.attention.output.LayerNorm.bias`
# | `encoder.transformer_cells.*.layer_norm.gamma` | `bert.encoder.layer.*.attention.output.LayerNorm.weight`
# | `encoder.transformer_cells.*.ffn.ffn_1.bias` | `bert.encoder.layer.*.intermediate.dense.bias`
# | `encoder.transformer_cells.*.ffn.ffn_1.weight` | `bert.encoder.layer.*.intermediate.dense.weight`
# | `encoder.transformer_cells.*.ffn.layer_norm.beta` | `bert.encoder.layer.*.output.LayerNorm.bias`
# | `encoder.transformer_cells.*.ffn.layer_norm.gamma` | `bert.encoder.layer.*.output.LayerNorm.weight`
# | `encoder.transformer_cells.*.proj.bias` | `bert.encoder.layer.*.output.dense.bias`
# | `encoder.transformer_cells.*.proj.weight` | `bert.encoder.layer.*.output.dense.weight`
# Helper function to convert MXNET Arrays to PyTorch
def to_torch(_A : Dict ) -> nn.Parameter:
return nn.Parameter(torch.FloatTensor(mx_array.data().asnumpy() ) )
# Check param shapes and map new HF param back
def check_and_map_params(_A : List[str] , _A : Any ):
lowerCamelCase_ =hf_param.shape
lowerCamelCase_ =to_torch(params[gluon_param] )
lowerCamelCase_ =gluon_param.shape
assert (
shape_hf == shape_gluon
), f'The gluon parameter {gluon_param} has shape {shape_gluon}, but expects shape {shape_hf} for Transformers'
return gluon_param
lowerCamelCase_ =check_and_map_params(
hf_bort_model.bert.embeddings.word_embeddings.weight , """word_embed.0.weight""" )
lowerCamelCase_ =check_and_map_params(
hf_bort_model.bert.embeddings.position_embeddings.weight , """encoder.position_weight""" )
lowerCamelCase_ =check_and_map_params(
hf_bort_model.bert.embeddings.LayerNorm.bias , """encoder.layer_norm.beta""" )
lowerCamelCase_ =check_and_map_params(
hf_bort_model.bert.embeddings.LayerNorm.weight , """encoder.layer_norm.gamma""" )
# Inspired by RoBERTa conversion script, we just zero them out (Bort does not use them)
lowerCamelCase_ =torch.zeros_like(
hf_bort_model.bert.embeddings.token_type_embeddings.weight.data )
for i in range(hf_bort_config.num_hidden_layers ):
lowerCamelCase_ =hf_bort_model.bert.encoder.layer[i]
# self attention
lowerCamelCase_ =layer.attention.self
lowerCamelCase_ =check_and_map_params(
self_attn.key.bias.data , f'encoder.transformer_cells.{i}.attention_cell.proj_key.bias' )
lowerCamelCase_ =check_and_map_params(
self_attn.key.weight.data , f'encoder.transformer_cells.{i}.attention_cell.proj_key.weight' )
lowerCamelCase_ =check_and_map_params(
self_attn.query.bias.data , f'encoder.transformer_cells.{i}.attention_cell.proj_query.bias' )
lowerCamelCase_ =check_and_map_params(
self_attn.query.weight.data , f'encoder.transformer_cells.{i}.attention_cell.proj_query.weight' )
lowerCamelCase_ =check_and_map_params(
self_attn.value.bias.data , f'encoder.transformer_cells.{i}.attention_cell.proj_value.bias' )
lowerCamelCase_ =check_and_map_params(
self_attn.value.weight.data , f'encoder.transformer_cells.{i}.attention_cell.proj_value.weight' )
# self attention output
lowerCamelCase_ =layer.attention.output
lowerCamelCase_ =check_and_map_params(
self_output.dense.bias , f'encoder.transformer_cells.{i}.proj.bias' )
lowerCamelCase_ =check_and_map_params(
self_output.dense.weight , f'encoder.transformer_cells.{i}.proj.weight' )
lowerCamelCase_ =check_and_map_params(
self_output.LayerNorm.bias , f'encoder.transformer_cells.{i}.layer_norm.beta' )
lowerCamelCase_ =check_and_map_params(
self_output.LayerNorm.weight , f'encoder.transformer_cells.{i}.layer_norm.gamma' )
# intermediate
lowerCamelCase_ =layer.intermediate
lowerCamelCase_ =check_and_map_params(
intermediate.dense.bias , f'encoder.transformer_cells.{i}.ffn.ffn_1.bias' )
lowerCamelCase_ =check_and_map_params(
intermediate.dense.weight , f'encoder.transformer_cells.{i}.ffn.ffn_1.weight' )
# output
lowerCamelCase_ =layer.output
lowerCamelCase_ =check_and_map_params(
bert_output.dense.bias , f'encoder.transformer_cells.{i}.ffn.ffn_2.bias' )
lowerCamelCase_ =check_and_map_params(
bert_output.dense.weight , f'encoder.transformer_cells.{i}.ffn.ffn_2.weight' )
lowerCamelCase_ =check_and_map_params(
bert_output.LayerNorm.bias , f'encoder.transformer_cells.{i}.ffn.layer_norm.beta' )
lowerCamelCase_ =check_and_map_params(
bert_output.LayerNorm.weight , f'encoder.transformer_cells.{i}.ffn.layer_norm.gamma' )
# Save space and energy 🎄
hf_bort_model.half()
# Compare output of both models
lowerCamelCase_ =RobertaTokenizer.from_pretrained("""roberta-base""" )
lowerCamelCase_ =tokenizer.encode_plus(_A )["""input_ids"""]
# Get gluon output
lowerCamelCase_ =mx.nd.array([input_ids] )
lowerCamelCase_ =original_bort(inputs=_A , token_types=[] )
# Get Transformer output (save and reload model again)
hf_bort_model.save_pretrained(_A )
lowerCamelCase_ =BertModel.from_pretrained(_A )
hf_bort_model.eval()
lowerCamelCase_ =tokenizer.encode_plus(_A , return_tensors="""pt""" )
lowerCamelCase_ =hf_bort_model(**_A )[0]
lowerCamelCase_ =output_gluon[0].asnumpy()
lowerCamelCase_ =output_hf[0].detach().numpy()
lowerCamelCase_ =np.max(np.abs(hf_layer - gluon_layer ) ).item()
lowerCamelCase_ =np.allclose(_A , _A , atol=1E-3 )
if success:
print("""✔️ Both model do output the same tensors""" )
else:
print("""❌ Both model do **NOT** output the same tensors""" )
print("""Absolute difference is:""" , _A )
if __name__ == "__main__":
__A : Dict = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--bort_checkpoint_path', default=None, type=str, required=True, help='Path the official Bort params file.'
)
parser.add_argument(
'--pytorch_dump_folder_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
)
__A : List[str] = parser.parse_args()
convert_bort_checkpoint_to_pytorch(args.bort_checkpoint_path, args.pytorch_dump_folder_path)
| 75 |
from ..utils import DummyObject, requires_backends
class _SCREAMING_SNAKE_CASE ( metaclass=lowerCAmelCase__):
_UpperCamelCase:List[Any] = ["torch", "torchsde"]
def __init__( self , *_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE )-> List[Any]:
requires_backends(self , ["""torch""", """torchsde"""] )
@classmethod
def _snake_case ( cls , *_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE )-> Union[str, Any]:
requires_backends(cls , ["""torch""", """torchsde"""] )
@classmethod
def _snake_case ( cls , *_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE )-> str:
requires_backends(cls , ["""torch""", """torchsde"""] )
| 75 | 1 |
import unittest
import numpy as np
from transformers import AlbertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import jax.numpy as jnp
from transformers.models.albert.modeling_flax_albert import (
FlaxAlbertForMaskedLM,
FlaxAlbertForMultipleChoice,
FlaxAlbertForPreTraining,
FlaxAlbertForQuestionAnswering,
FlaxAlbertForSequenceClassification,
FlaxAlbertForTokenClassification,
FlaxAlbertModel,
)
class UpperCAmelCase ( unittest.TestCase ):
'''simple docstring'''
def __init__( self : str ,A : List[str] ,A : List[Any]=13 ,A : Optional[int]=7 ,A : Tuple=True ,A : Tuple=True ,A : Tuple=True ,A : Optional[Any]=True ,A : Optional[int]=99 ,A : Optional[Any]=32 ,A : int=5 ,A : List[Any]=4 ,A : Union[str, Any]=37 ,A : int="gelu" ,A : int=0.1 ,A : List[Any]=0.1 ,A : List[str]=5_12 ,A : int=16 ,A : str=2 ,A : int=0.02 ,A : Optional[Any]=4 ,):
__A = parent
__A = batch_size
__A = seq_length
__A = is_training
__A = use_attention_mask
__A = use_token_type_ids
__A = use_labels
__A = vocab_size
__A = hidden_size
__A = num_hidden_layers
__A = num_attention_heads
__A = intermediate_size
__A = hidden_act
__A = hidden_dropout_prob
__A = attention_probs_dropout_prob
__A = max_position_embeddings
__A = type_vocab_size
__A = type_sequence_label_size
__A = initializer_range
__A = num_choices
def UpperCamelCase_ ( self : Dict ):
__A = ids_tensor([self.batch_size, self.seq_length] ,self.vocab_size )
__A = None
if self.use_attention_mask:
__A = random_attention_mask([self.batch_size, self.seq_length] )
__A = None
if self.use_token_type_ids:
__A = ids_tensor([self.batch_size, self.seq_length] ,self.type_vocab_size )
__A = AlbertConfig(
vocab_size=self.vocab_size ,hidden_size=self.hidden_size ,num_hidden_layers=self.num_hidden_layers ,num_attention_heads=self.num_attention_heads ,intermediate_size=self.intermediate_size ,hidden_act=self.hidden_act ,hidden_dropout_prob=self.hidden_dropout_prob ,attention_probs_dropout_prob=self.attention_probs_dropout_prob ,max_position_embeddings=self.max_position_embeddings ,type_vocab_size=self.type_vocab_size ,is_decoder=A ,initializer_range=self.initializer_range ,)
return config, input_ids, token_type_ids, attention_mask
def UpperCamelCase_ ( self : Dict ):
__A = self.prepare_config_and_inputs()
__A , __A , __A , __A = config_and_inputs
__A = {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": attention_mask}
return config, inputs_dict
@require_flax
class UpperCAmelCase ( __SCREAMING_SNAKE_CASE , unittest.TestCase ):
'''simple docstring'''
snake_case_ = (
(
FlaxAlbertModel,
FlaxAlbertForPreTraining,
FlaxAlbertForMaskedLM,
FlaxAlbertForMultipleChoice,
FlaxAlbertForQuestionAnswering,
FlaxAlbertForSequenceClassification,
FlaxAlbertForTokenClassification,
FlaxAlbertForQuestionAnswering,
)
if is_flax_available()
else ()
)
def UpperCamelCase_ ( self : List[Any] ):
__A = FlaxAlbertModelTester(self )
@slow
def UpperCamelCase_ ( self : List[Any] ):
for model_class_name in self.all_model_classes:
__A = model_class_name.from_pretrained("albert-base-v2" )
__A = model(np.ones((1, 1) ) )
self.assertIsNotNone(A )
@require_flax
class UpperCAmelCase ( unittest.TestCase ):
'''simple docstring'''
@slow
def UpperCamelCase_ ( self : Optional[Any] ):
__A = FlaxAlbertModel.from_pretrained("albert-base-v2" )
__A = np.array([[0, 3_45, 2_32, 3_28, 7_40, 1_40, 16_95, 69, 60_78, 15_88, 2]] )
__A = np.array([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]] )
__A = model(A ,attention_mask=A )[0]
__A = (1, 11, 7_68)
self.assertEqual(output.shape ,A )
__A = np.array(
[[[-0.65_13, 1.50_35, -0.27_66], [-0.65_15, 1.50_46, -0.27_80], [-0.65_12, 1.50_49, -0.27_84]]] )
self.assertTrue(jnp.allclose(output[:, 1:4, 1:4] ,A ,atol=1E-4 ) )
| 55 | from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowerCamelCase__ = {
'''configuration_longformer''': [
'''LONGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''LongformerConfig''',
'''LongformerOnnxConfig''',
],
'''tokenization_longformer''': ['''LongformerTokenizer'''],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCamelCase__ = ['''LongformerTokenizerFast''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCamelCase__ = [
'''LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''LongformerForMaskedLM''',
'''LongformerForMultipleChoice''',
'''LongformerForQuestionAnswering''',
'''LongformerForSequenceClassification''',
'''LongformerForTokenClassification''',
'''LongformerModel''',
'''LongformerPreTrainedModel''',
'''LongformerSelfAttention''',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCamelCase__ = [
'''TF_LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''TFLongformerForMaskedLM''',
'''TFLongformerForMultipleChoice''',
'''TFLongformerForQuestionAnswering''',
'''TFLongformerForSequenceClassification''',
'''TFLongformerForTokenClassification''',
'''TFLongformerModel''',
'''TFLongformerPreTrainedModel''',
'''TFLongformerSelfAttention''',
]
if TYPE_CHECKING:
from .configuration_longformer import (
LONGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
LongformerConfig,
LongformerOnnxConfig,
)
from .tokenization_longformer import LongformerTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_longformer_fast import LongformerTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_longformer import (
LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
LongformerForMaskedLM,
LongformerForMultipleChoice,
LongformerForQuestionAnswering,
LongformerForSequenceClassification,
LongformerForTokenClassification,
LongformerModel,
LongformerPreTrainedModel,
LongformerSelfAttention,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_longformer import (
TF_LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
TFLongformerForMaskedLM,
TFLongformerForMultipleChoice,
TFLongformerForQuestionAnswering,
TFLongformerForSequenceClassification,
TFLongformerForTokenClassification,
TFLongformerModel,
TFLongformerPreTrainedModel,
TFLongformerSelfAttention,
)
else:
import sys
lowerCamelCase__ = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 547 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
a__ : Any = {
"""configuration_whisper""": ["""WHISPER_PRETRAINED_CONFIG_ARCHIVE_MAP""", """WhisperConfig""", """WhisperOnnxConfig"""],
"""feature_extraction_whisper""": ["""WhisperFeatureExtractor"""],
"""processing_whisper""": ["""WhisperProcessor"""],
"""tokenization_whisper""": ["""WhisperTokenizer"""],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a__ : Tuple = ["""WhisperTokenizerFast"""]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a__ : Tuple = [
"""WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""WhisperForConditionalGeneration""",
"""WhisperModel""",
"""WhisperPreTrainedModel""",
"""WhisperForAudioClassification""",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a__ : Union[str, Any] = [
"""TF_WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""TFWhisperForConditionalGeneration""",
"""TFWhisperModel""",
"""TFWhisperPreTrainedModel""",
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a__ : Optional[Any] = [
"""FlaxWhisperForConditionalGeneration""",
"""FlaxWhisperModel""",
"""FlaxWhisperPreTrainedModel""",
"""FlaxWhisperForAudioClassification""",
]
if TYPE_CHECKING:
from .configuration_whisper import WHISPER_PRETRAINED_CONFIG_ARCHIVE_MAP, WhisperConfig, WhisperOnnxConfig
from .feature_extraction_whisper import WhisperFeatureExtractor
from .processing_whisper import WhisperProcessor
from .tokenization_whisper import WhisperTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_whisper_fast import WhisperTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_whisper import (
WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST,
WhisperForAudioClassification,
WhisperForConditionalGeneration,
WhisperModel,
WhisperPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_whisper import (
TF_WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST,
TFWhisperForConditionalGeneration,
TFWhisperModel,
TFWhisperPreTrainedModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_whisper import (
FlaxWhisperForAudioClassification,
FlaxWhisperForConditionalGeneration,
FlaxWhisperModel,
FlaxWhisperPreTrainedModel,
)
else:
import sys
a__ : Tuple = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 702 |
"""simple docstring"""
from __future__ import annotations
import numpy as np
def A__ ( __lowerCamelCase ):
"""simple docstring"""
return np.maximum(0, __lowerCamelCase )
if __name__ == "__main__":
print(np.array(relu([-1, 0, 5]))) # --> [0, 0, 5]
| 309 | 0 |
"""simple docstring"""
import os
from tempfile import TemporaryDirectory
from unittest import TestCase
import pytest
from absl.testing import parameterized
from datasets import config
from datasets.arrow_reader import HF_GCP_BASE_URL
from datasets.builder import DatasetBuilder
from datasets.dataset_dict import IterableDatasetDict
from datasets.iterable_dataset import IterableDataset
from datasets.load import dataset_module_factory, import_main_class
from datasets.utils.file_utils import cached_path
_lowercase = [
{'''dataset''': '''wikipedia''', '''config_name''': '''20220301.de'''},
{'''dataset''': '''wikipedia''', '''config_name''': '''20220301.en'''},
{'''dataset''': '''wikipedia''', '''config_name''': '''20220301.fr'''},
{'''dataset''': '''wikipedia''', '''config_name''': '''20220301.frr'''},
{'''dataset''': '''wikipedia''', '''config_name''': '''20220301.it'''},
{'''dataset''': '''wikipedia''', '''config_name''': '''20220301.simple'''},
{'''dataset''': '''snli''', '''config_name''': '''plain_text'''},
{'''dataset''': '''eli5''', '''config_name''': '''LFQA_reddit'''},
{'''dataset''': '''wiki40b''', '''config_name''': '''en'''},
{'''dataset''': '''wiki_dpr''', '''config_name''': '''psgs_w100.nq.compressed'''},
{'''dataset''': '''wiki_dpr''', '''config_name''': '''psgs_w100.nq.no_index'''},
{'''dataset''': '''wiki_dpr''', '''config_name''': '''psgs_w100.multiset.no_index'''},
{'''dataset''': '''natural_questions''', '''config_name''': '''default'''},
]
def _snake_case ( snake_case__ : str=True ):
if with_config:
return [
{
"testcase_name": d["dataset"] + "/" + d["config_name"],
"dataset": d["dataset"],
"config_name": d["config_name"],
}
for d in DATASETS_ON_HF_GCP
]
else:
return [
{"testcase_name": dataset, "dataset": dataset} for dataset in {d["dataset"] for d in DATASETS_ON_HF_GCP}
]
@parameterized.named_parameters(list_datasets_on_hf_gcp_parameters(with_config=_lowercase ) )
class lowerCAmelCase_ ( _lowercase ):
'''simple docstring'''
_lowerCamelCase: Optional[Any] = None
_lowerCamelCase: str = None
def _SCREAMING_SNAKE_CASE ( self : Tuple ,A_ : int ,A_ : Tuple ) -> Any:
with TemporaryDirectory() as tmp_dir:
A = dataset_module_factory(A_ ,cache_dir=A_ )
A = import_main_class(dataset_module.module_path ,dataset=A_ )
A = builder_cls(
cache_dir=A_ ,config_name=A_ ,hash=dataset_module.hash ,)
A = '/'.join(
[
HF_GCP_BASE_URL,
builder_instance._relative_data_dir(with_hash=A_ ).replace(os.sep ,'/' ),
config.DATASET_INFO_FILENAME,
] )
A = cached_path(A_ ,cache_dir=A_ )
self.assertTrue(os.path.exists(A_ ) )
@pytest.mark.integration
def _snake_case ( snake_case__ : Optional[int] ):
A = tmp_path_factory.mktemp('test_hf_gcp' ) / 'test_wikipedia_simple'
A = dataset_module_factory('wikipedia' , cache_dir=snake_case__ )
A = import_main_class(dataset_module.module_path )
A = builder_cls(
cache_dir=snake_case__ , config_name='20220301.frr' , hash=dataset_module.hash , )
# use the HF cloud storage, not the original download_and_prepare that uses apache-beam
A = None
builder_instance.download_and_prepare()
A = builder_instance.as_dataset()
assert ds
@pytest.mark.integration
def _snake_case ( snake_case__ : List[Any] ):
A = dataset_module_factory('wikipedia' , cache_dir=snake_case__ )
A = import_main_class(dataset_module.module_path , dataset=snake_case__ )
A = builder_cls(
cache_dir=snake_case__ , config_name='20220301.frr' , hash=dataset_module.hash , )
A = builder_instance.as_streaming_dataset()
assert ds
assert isinstance(snake_case__ , snake_case__ )
assert "train" in ds
assert isinstance(ds['train'] , snake_case__ )
assert next(iter(ds['train'] ) ) | 91 |
'''simple docstring'''
import unittest
import numpy as np
from transformers import RobertaPreLayerNormConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_flax_available():
import jax.numpy as jnp
from transformers.models.roberta_prelayernorm.modeling_flax_roberta_prelayernorm import (
FlaxRobertaPreLayerNormForCausalLM,
FlaxRobertaPreLayerNormForMaskedLM,
FlaxRobertaPreLayerNormForMultipleChoice,
FlaxRobertaPreLayerNormForQuestionAnswering,
FlaxRobertaPreLayerNormForSequenceClassification,
FlaxRobertaPreLayerNormForTokenClassification,
FlaxRobertaPreLayerNormModel,
)
class A__ ( unittest.TestCase ):
def __init__( self :List[Any] , SCREAMING_SNAKE_CASE :Union[str, Any] , SCREAMING_SNAKE_CASE :Any=1_3 , SCREAMING_SNAKE_CASE :Any=7 , SCREAMING_SNAKE_CASE :Any=True , SCREAMING_SNAKE_CASE :int=True , SCREAMING_SNAKE_CASE :Optional[int]=True , SCREAMING_SNAKE_CASE :List[str]=True , SCREAMING_SNAKE_CASE :Optional[Any]=9_9 , SCREAMING_SNAKE_CASE :Tuple=3_2 , SCREAMING_SNAKE_CASE :Union[str, Any]=5 , SCREAMING_SNAKE_CASE :List[str]=4 , SCREAMING_SNAKE_CASE :int=3_7 , SCREAMING_SNAKE_CASE :Optional[Any]="gelu" , SCREAMING_SNAKE_CASE :Optional[int]=0.1 , SCREAMING_SNAKE_CASE :List[Any]=0.1 , SCREAMING_SNAKE_CASE :Dict=5_1_2 , SCREAMING_SNAKE_CASE :List[Any]=1_6 , SCREAMING_SNAKE_CASE :Union[str, Any]=2 , SCREAMING_SNAKE_CASE :List[Any]=0.02 , SCREAMING_SNAKE_CASE :int=4 , ) -> Tuple:
'''simple docstring'''
_a : Optional[Any] =parent
_a : List[str] =batch_size
_a : List[str] =seq_length
_a : List[Any] =is_training
_a : Optional[int] =use_attention_mask
_a : List[Any] =use_token_type_ids
_a : List[Any] =use_labels
_a : Optional[Any] =vocab_size
_a : str =hidden_size
_a : List[Any] =num_hidden_layers
_a : List[Any] =num_attention_heads
_a : Union[str, Any] =intermediate_size
_a : int =hidden_act
_a : List[str] =hidden_dropout_prob
_a : Optional[int] =attention_probs_dropout_prob
_a : Dict =max_position_embeddings
_a : Any =type_vocab_size
_a : str =type_sequence_label_size
_a : str =initializer_range
_a : List[str] =num_choices
def __UpperCAmelCase ( self :Union[str, Any] ) -> Dict:
'''simple docstring'''
_a : str =ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
_a : Dict =None
if self.use_attention_mask:
_a : Any =random_attention_mask([self.batch_size, self.seq_length] )
_a : Optional[int] =None
if self.use_token_type_ids:
_a : Any =ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
_a : Union[str, Any] =RobertaPreLayerNormConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , is_decoder=SCREAMING_SNAKE_CASE , initializer_range=self.initializer_range , )
return config, input_ids, token_type_ids, attention_mask
def __UpperCAmelCase ( self :Optional[Any] ) -> int:
'''simple docstring'''
_a : Tuple =self.prepare_config_and_inputs()
_a , _a , _a , _a : List[Any] =config_and_inputs
_a : Optional[int] ={"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": attention_mask}
return config, inputs_dict
def __UpperCAmelCase ( self :int ) -> str:
'''simple docstring'''
_a : List[Any] =self.prepare_config_and_inputs()
_a , _a , _a , _a : Optional[int] =config_and_inputs
_a : Tuple =True
_a : Optional[Any] =floats_tensor([self.batch_size, self.seq_length, self.hidden_size] )
_a : Optional[int] =ids_tensor([self.batch_size, self.seq_length] , vocab_size=2 )
return (
config,
input_ids,
token_type_ids,
encoder_hidden_states,
encoder_attention_mask,
)
@require_flax
# Copied from tests.models.roberta.test_modelling_flax_roberta.FlaxRobertaPreLayerNormModelTest with ROBERTA->ROBERTA_PRELAYERNORM,Roberta->RobertaPreLayerNorm,roberta-base->andreasmadsen/efficient_mlm_m0.40
class A__ ( UpperCAmelCase__ , unittest.TestCase ):
__UpperCamelCase : Union[str, Any] = True
__UpperCamelCase : Dict = (
(
FlaxRobertaPreLayerNormModel,
FlaxRobertaPreLayerNormForCausalLM,
FlaxRobertaPreLayerNormForMaskedLM,
FlaxRobertaPreLayerNormForSequenceClassification,
FlaxRobertaPreLayerNormForTokenClassification,
FlaxRobertaPreLayerNormForMultipleChoice,
FlaxRobertaPreLayerNormForQuestionAnswering,
)
if is_flax_available()
else ()
)
def __UpperCAmelCase ( self :List[str] ) -> Optional[int]:
'''simple docstring'''
_a : Union[str, Any] =FlaxRobertaPreLayerNormModelTester(self )
@slow
def __UpperCAmelCase ( self :str ) -> int:
'''simple docstring'''
for model_class_name in self.all_model_classes:
_a : Optional[int] =model_class_name.from_pretrained("""andreasmadsen/efficient_mlm_m0.40""" , from_pt=SCREAMING_SNAKE_CASE )
_a : Dict =model(np.ones((1, 1) ) )
self.assertIsNotNone(SCREAMING_SNAKE_CASE )
@require_flax
class A__ ( unittest.TestCase ):
@slow
def __UpperCAmelCase ( self :Any ) -> str:
'''simple docstring'''
_a : str =FlaxRobertaPreLayerNormForMaskedLM.from_pretrained("""andreasmadsen/efficient_mlm_m0.40""" , from_pt=SCREAMING_SNAKE_CASE )
_a : List[Any] =np.array([[0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9, 4_6_0_7_8, 1_5_8_8, 2]] , dtype=jnp.intaa )
_a : Dict =model(SCREAMING_SNAKE_CASE )[0]
_a : List[Any] =[1, 1_1, 5_0_2_6_5]
self.assertEqual(list(output.shape ) , SCREAMING_SNAKE_CASE )
# compare the actual values for a slice.
_a : Any =np.array(
[[[40.4_880, 18.0_199, -5.2_367], [-1.8_877, -4.0_885, 10.7_085], [-2.2_613, -5.6_110, 7.2_665]]] , dtype=np.floataa )
self.assertTrue(np.allclose(output[:, :3, :3] , SCREAMING_SNAKE_CASE , atol=1e-4 ) )
@slow
def __UpperCAmelCase ( self :int ) -> int:
'''simple docstring'''
_a : Union[str, Any] =FlaxRobertaPreLayerNormModel.from_pretrained("""andreasmadsen/efficient_mlm_m0.40""" , from_pt=SCREAMING_SNAKE_CASE )
_a : Any =np.array([[0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9, 4_6_0_7_8, 1_5_8_8, 2]] , dtype=jnp.intaa )
_a : Optional[int] =model(SCREAMING_SNAKE_CASE )[0]
# compare the actual values for a slice.
_a : str =np.array(
[[[0.0_208, -0.0_356, 0.0_237], [-0.1_569, -0.0_411, -0.2_626], [0.1_879, 0.0_125, -0.0_089]]] , dtype=np.floataa )
self.assertTrue(np.allclose(output[:, :3, :3] , SCREAMING_SNAKE_CASE , atol=1e-4 ) )
| 694 | 0 |
'''simple docstring'''
import os
from bleurt import score # From: git+https://github.com/google-research/bleurt.git
import datasets
a_ :Optional[Any] = datasets.logging.get_logger(__name__)
a_ :List[str] = "\\n@inproceedings{bleurt,\n title={BLEURT: Learning Robust Metrics for Text Generation},\n author={Thibault Sellam and Dipanjan Das and Ankur P. Parikh},\n booktitle={ACL},\n year={2020},\n url={https://arxiv.org/abs/2004.04696}\n}\n"
a_ :List[str] = "\\nBLEURT a learnt evaluation metric for Natural Language Generation. It is built using multiple phases of transfer learning starting from a pretrained BERT model (Devlin et al. 2018)\nand then employing another pre-training phrase using synthetic data. Finally it is trained on WMT human annotations. You may run BLEURT out-of-the-box or fine-tune\nit for your specific application (the latter is expected to perform better).\n\nSee the project's README at https://github.com/google-research/bleurt#readme for more information.\n"
a_ :Optional[int] = "\nBLEURT score.\n\nArgs:\n `predictions` (list of str): prediction/candidate sentences\n `references` (list of str): reference sentences\n `checkpoint` BLEURT checkpoint. Will default to BLEURT-tiny if None.\n\nReturns:\n 'scores': List of scores.\nExamples:\n\n >>> predictions = [\"hello there\", \"general kenobi\"]\n >>> references = [\"hello there\", \"general kenobi\"]\n >>> bleurt = datasets.load_metric(\"bleurt\")\n >>> results = bleurt.compute(predictions=predictions, references=references)\n >>> print([round(v, 2) for v in results[\"scores\"]])\n [1.03, 1.04]\n"
a_ :Optional[int] = {
"bleurt-tiny-128": "https://storage.googleapis.com/bleurt-oss/bleurt-tiny-128.zip",
"bleurt-tiny-512": "https://storage.googleapis.com/bleurt-oss/bleurt-tiny-512.zip",
"bleurt-base-128": "https://storage.googleapis.com/bleurt-oss/bleurt-base-128.zip",
"bleurt-base-512": "https://storage.googleapis.com/bleurt-oss/bleurt-base-512.zip",
"bleurt-large-128": "https://storage.googleapis.com/bleurt-oss/bleurt-large-128.zip",
"bleurt-large-512": "https://storage.googleapis.com/bleurt-oss/bleurt-large-512.zip",
"BLEURT-20-D3": "https://storage.googleapis.com/bleurt-oss-21/BLEURT-20-D3.zip",
"BLEURT-20-D6": "https://storage.googleapis.com/bleurt-oss-21/BLEURT-20-D6.zip",
"BLEURT-20-D12": "https://storage.googleapis.com/bleurt-oss-21/BLEURT-20-D12.zip",
"BLEURT-20": "https://storage.googleapis.com/bleurt-oss-21/BLEURT-20.zip",
}
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class snake_case__ ( datasets.Metric ):
"""simple docstring"""
def lowercase_ ( self : str ) ->int:
return datasets.MetricInfo(
description=_DESCRIPTION, citation=_CITATION, homepage='https://github.com/google-research/bleurt', inputs_description=_KWARGS_DESCRIPTION, features=datasets.Features(
{
'predictions': datasets.Value('string', id='sequence' ),
'references': datasets.Value('string', id='sequence' ),
} ), codebase_urls=['https://github.com/google-research/bleurt'], reference_urls=['https://github.com/google-research/bleurt', 'https://arxiv.org/abs/2004.04696'], )
def lowercase_ ( self : Union[str, Any], _snake_case : List[Any] ) ->Any:
# check that config name specifies a valid BLEURT model
if self.config_name == "default":
logger.warning(
'Using default BLEURT-Base checkpoint for sequence maximum length 128. '
'You can use a bigger model for better results with e.g.: datasets.load_metric(\'bleurt\', \'bleurt-large-512\').' )
snake_case__ : Optional[Any] = 'bleurt-base-128'
if self.config_name.lower() in CHECKPOINT_URLS:
snake_case__ : Union[str, Any] = self.config_name.lower()
elif self.config_name.upper() in CHECKPOINT_URLS:
snake_case__ : Optional[Any] = self.config_name.upper()
else:
raise KeyError(
F'''{self.config_name} model not found. You should supply the name of a model checkpoint for bleurt in {CHECKPOINT_URLS.keys()}''' )
# download the model checkpoint specified by self.config_name and set up the scorer
snake_case__ : List[str] = dl_manager.download_and_extract(CHECKPOINT_URLS[checkpoint_name] )
snake_case__ : List[str] = score.BleurtScorer(os.path.join(_snake_case, _snake_case ) )
def lowercase_ ( self : Tuple, _snake_case : Union[str, Any], _snake_case : Tuple ) ->Any:
snake_case__ : Union[str, Any] = self.scorer.score(references=_snake_case, candidates=_snake_case )
return {"scores": scores}
| 700 |
import argparse
import collections
import torch
from flax import traverse_util
from tax import checkpoints
from transformers import TaConfig, TaEncoderModel, TaForConditionalGeneration
from transformers.utils import logging
logging.set_verbosity_info()
def lowercase_ (A : Optional[int] , A : Tuple , A : List[Any] , A : List[str]="attention" ):
snake_case__ : str = params[F'''{prefix}/layers_{i}/{layer_name}/key/kernel''']
snake_case__ : List[str] = params[F'''{prefix}/layers_{i}/{layer_name}/out/kernel''']
snake_case__ : Optional[int] = params[F'''{prefix}/layers_{i}/{layer_name}/query/kernel''']
snake_case__ : Union[str, Any] = params[F'''{prefix}/layers_{i}/{layer_name}/value/kernel''']
return k, o, q, v
def lowercase_ (A : Tuple , A : Union[str, Any] , A : int , A : Any=False ):
if split_mlp_wi:
snake_case__ : Dict = params[F'''{prefix}/layers_{i}/mlp/wi_0/kernel''']
snake_case__ : List[str] = params[F'''{prefix}/layers_{i}/mlp/wi_1/kernel''']
snake_case__ : Optional[Any] = (wi_a, wi_a)
else:
snake_case__ : Optional[Any] = params[F'''{prefix}/layers_{i}/mlp/wi/kernel''']
snake_case__ : Tuple = params[F'''{prefix}/layers_{i}/mlp/wo/kernel''']
return wi, wo
def lowercase_ (A : Optional[int] , A : Dict , A : Any , A : List[str] ):
return params[F'''{prefix}/layers_{i}/{layer_name}/scale''']
def lowercase_ (A : dict , *, A : int , A : bool ):
snake_case__ : Dict = traverse_util.flatten_dict(variables['target'] )
snake_case__ : Union[str, Any] = {'/'.join(A ): v for k, v in old.items()}
# v1.1 models have a gated GeLU with wi_0 and wi_1 instead of wi
snake_case__ : Union[str, Any] = 'encoder/layers_0/mlp/wi_0/kernel' in old
print('Split MLP:' , A )
snake_case__ : int = collections.OrderedDict()
# Shared embeddings.
snake_case__ : Dict = old['token_embedder/embedding']
# Encoder.
for i in range(A ):
# Block i, layer 0 (Self Attention).
snake_case__ : Dict = tax_layer_norm_lookup(A , A , 'encoder' , 'pre_attention_layer_norm' )
snake_case__ , snake_case__ , snake_case__ , snake_case__ : Dict = tax_attention_lookup(A , A , 'encoder' , 'attention' )
snake_case__ : Optional[Any] = layer_norm
snake_case__ : Union[str, Any] = k.T
snake_case__ : List[Any] = o.T
snake_case__ : Any = q.T
snake_case__ : Union[str, Any] = v.T
# Block i, layer 1 (MLP).
snake_case__ : List[str] = tax_layer_norm_lookup(A , A , 'encoder' , 'pre_mlp_layer_norm' )
snake_case__ , snake_case__ : Dict = tax_mlp_lookup(A , A , 'encoder' , A )
snake_case__ : Optional[int] = layer_norm
if split_mlp_wi:
snake_case__ : Union[str, Any] = wi[0].T
snake_case__ : int = wi[1].T
else:
snake_case__ : Optional[int] = wi.T
snake_case__ : Tuple = wo.T
snake_case__ : Optional[int] = old[
'encoder/relpos_bias/rel_embedding'
].T
snake_case__ : str = old['encoder/encoder_norm/scale']
if not is_encoder_only:
# Decoder.
for i in range(A ):
# Block i, layer 0 (Self Attention).
snake_case__ : List[str] = tax_layer_norm_lookup(A , A , 'decoder' , 'pre_self_attention_layer_norm' )
snake_case__ , snake_case__ , snake_case__ , snake_case__ : List[str] = tax_attention_lookup(A , A , 'decoder' , 'self_attention' )
snake_case__ : int = layer_norm
snake_case__ : List[Any] = k.T
snake_case__ : Tuple = o.T
snake_case__ : Any = q.T
snake_case__ : List[str] = v.T
# Block i, layer 1 (Cross Attention).
snake_case__ : List[str] = tax_layer_norm_lookup(A , A , 'decoder' , 'pre_cross_attention_layer_norm' )
snake_case__ , snake_case__ , snake_case__ , snake_case__ : int = tax_attention_lookup(A , A , 'decoder' , 'encoder_decoder_attention' )
snake_case__ : List[Any] = layer_norm
snake_case__ : Union[str, Any] = k.T
snake_case__ : List[str] = o.T
snake_case__ : List[str] = q.T
snake_case__ : List[Any] = v.T
# Block i, layer 2 (MLP).
snake_case__ : Any = tax_layer_norm_lookup(A , A , 'decoder' , 'pre_mlp_layer_norm' )
snake_case__ , snake_case__ : Tuple = tax_mlp_lookup(A , A , 'decoder' , A )
snake_case__ : List[str] = layer_norm
if split_mlp_wi:
snake_case__ : Any = wi[0].T
snake_case__ : str = wi[1].T
else:
snake_case__ : Optional[int] = wi.T
snake_case__ : int = wo.T
snake_case__ : Dict = old['decoder/decoder_norm/scale']
snake_case__ : int = old[
'decoder/relpos_bias/rel_embedding'
].T
# LM Head (only in v1.1 checkpoints, in v1.0 embeddings are used instead)
if "decoder/logits_dense/kernel" in old:
snake_case__ : int = old['decoder/logits_dense/kernel'].T
return new
def lowercase_ (A : List[Any] , A : bool ):
snake_case__ : Dict = 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:
snake_case__ : Any = state_dict['shared.weight']
if not is_encoder_only:
if "decoder.embed_tokens.weight" not in state_dict:
snake_case__ : Optional[int] = 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.' )
snake_case__ : List[Any] = state_dict['shared.weight']
return state_dict
def lowercase_ (A : Union[str, Any] , A : Any , A : Union[str, Any] , A : Tuple ):
snake_case__ : Optional[Any] = checkpoints.load_tax_checkpoint(A )
snake_case__ : List[str] = convert_tax_to_pytorch(A , num_layers=config.num_layers , is_encoder_only=A )
snake_case__ : Optional[int] = make_state_dict(A , A )
model.load_state_dict(A , strict=A )
def lowercase_ (A : List[str] , A : Union[str, Any] , A : Optional[Any] , A : bool = False ):
snake_case__ : str = TaConfig.from_json_file(A )
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:
snake_case__ : List[str] = TaEncoderModel(A )
else:
snake_case__ : str = TaForConditionalGeneration(A )
# Load weights from tf checkpoint
load_tax_weights_in_ta(A , A , A , A )
# Save pytorch-model
print(F'''Save PyTorch model to {pytorch_dump_path}''' )
model.save_pretrained(A )
# Verify that we can load the checkpoint.
model.from_pretrained(A )
print('Done' )
if __name__ == "__main__":
a_ :Tuple = 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
)
a_ :List[Any] = parser.parse_args()
convert_tax_checkpoint_to_pytorch(
args.tax_checkpoint_path, args.config_file, args.pytorch_dump_path, args.is_encoder_only
)
| 243 | 0 |
from pathlib import Path
import torch
from ...utils import is_npu_available, is_xpu_available
from .config_args import ClusterConfig, default_json_config_file
from .config_utils import SubcommandHelpFormatter
a_ : List[Any] = 'Create a default config file for Accelerate with only a few flags set.'
def __a ( __UpperCAmelCase="no" , __UpperCAmelCase = default_json_config_file , __UpperCAmelCase = False ):
a__ = Path(lowerCAmelCase__ )
path.parent.mkdir(parents=lowerCAmelCase__ , exist_ok=lowerCAmelCase__ )
if path.exists():
print(
f"Configuration already exists at {save_location}, will not override. Run `accelerate config` manually or pass a different `save_location`." )
return False
a__ = mixed_precision.lower()
if mixed_precision not in ["no", "fp16", "bf16", "fp8"]:
raise ValueError(
f"`mixed_precision` should be one of \'no\', \'fp16\', \'bf16\', or \'fp8\'. Received {mixed_precision}" )
a__ = {
'''compute_environment''': '''LOCAL_MACHINE''',
'''mixed_precision''': mixed_precision,
}
if torch.cuda.is_available():
a__ = torch.cuda.device_count()
a__ = num_gpus
a__ = False
if num_gpus > 1:
a__ = '''MULTI_GPU'''
else:
a__ = '''NO'''
elif is_xpu_available() and use_xpu:
a__ = torch.xpu.device_count()
a__ = num_xpus
a__ = False
if num_xpus > 1:
a__ = '''MULTI_XPU'''
else:
a__ = '''NO'''
elif is_npu_available():
a__ = torch.npu.device_count()
a__ = num_npus
a__ = False
if num_npus > 1:
a__ = '''MULTI_NPU'''
else:
a__ = '''NO'''
else:
a__ = 0
a__ = True
a__ = 1
a__ = '''NO'''
a__ = ClusterConfig(**lowerCAmelCase__ )
config.to_json_file(lowerCAmelCase__ )
return path
def __a ( __UpperCAmelCase , __UpperCAmelCase ):
a__ = parser.add_parser('''default''' , parents=lowerCAmelCase__ , help=lowerCAmelCase__ , formatter_class=lowerCAmelCase__ )
parser.add_argument(
'''--config_file''' , default=lowerCAmelCase__ , help=(
'''The path to use to store the config file. Will default to a file named default_config.yaml in the cache '''
'''location, which is the content of the environment `HF_HOME` suffixed with \'accelerate\', or if you don\'t have '''
'''such an environment variable, your cache directory (\'~/.cache\' or the content of `XDG_CACHE_HOME`) suffixed '''
'''with \'huggingface\'.'''
) , dest='''save_location''' , )
parser.add_argument(
'''--mixed_precision''' , choices=['''no''', '''fp16''', '''bf16'''] , type=lowerCAmelCase__ , help='''Whether or not to use mixed precision training. '''
'''Choose between FP16 and BF16 (bfloat16) training. '''
'''BF16 training is only supported on Nvidia Ampere GPUs and PyTorch 1.10 or later.''' , default='''no''' , )
parser.set_defaults(func=lowerCAmelCase__ )
return parser
def __a ( __UpperCAmelCase ):
a__ = write_basic_config(args.mixed_precision , args.save_location )
if config_file:
print(f"accelerate configuration saved at {config_file}" )
| 194 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowercase_ = {
"configuration_mctct": ["MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP", "MCTCTConfig"],
"feature_extraction_mctct": ["MCTCTFeatureExtractor"],
"processing_mctct": ["MCTCTProcessor"],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase_ = [
"MCTCT_PRETRAINED_MODEL_ARCHIVE_LIST",
"MCTCTForCTC",
"MCTCTModel",
"MCTCTPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_mctct import MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP, MCTCTConfig
from .feature_extraction_mctct import MCTCTFeatureExtractor
from .processing_mctct import MCTCTProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_mctct import MCTCT_PRETRAINED_MODEL_ARCHIVE_LIST, MCTCTForCTC, MCTCTModel, MCTCTPreTrainedModel
else:
import sys
lowercase_ = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 695 | 0 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import Iterator
from typing import Any
class A :
def __init__( self : Any , lowerCAmelCase_ : Any ) -> Tuple:
"""simple docstring"""
_a = data
_a = None
class A :
def __init__( self : List[Any] ) -> Union[str, Any]:
"""simple docstring"""
_a = None
_a = None
def __iter__( self : Optional[int] ) -> Iterator[Any]:
"""simple docstring"""
_a = self.head
while self.head:
yield node.data
_a = node.next
if node == self.head:
break
def __len__( self : Any ) -> int:
"""simple docstring"""
return sum(1 for _ in self )
def __repr__( self : Tuple ) -> Dict:
"""simple docstring"""
return "->".join(str(lowerCAmelCase_ ) for item in iter(self ) )
def __lowerCAmelCase ( self : Tuple , lowerCAmelCase_ : Any ) -> None:
"""simple docstring"""
self.insert_nth(len(self ) , lowerCAmelCase_ )
def __lowerCAmelCase ( self : Optional[int] , lowerCAmelCase_ : Any ) -> None:
"""simple docstring"""
self.insert_nth(0 , lowerCAmelCase_ )
def __lowerCAmelCase ( self : Dict , lowerCAmelCase_ : int , lowerCAmelCase_ : Any ) -> None:
"""simple docstring"""
if index < 0 or index > len(self ):
raise IndexError('''list index out of range.''' )
_a = Node(lowerCAmelCase_ )
if self.head is None:
_a = new_node # first node points itself
_a = _a = new_node
elif index == 0: # insert at head
_a = self.head
_a = _a = new_node
else:
_a = self.head
for _ in range(index - 1 ):
_a = temp.next
_a = temp.next
_a = new_node
if index == len(self ) - 1: # insert at tail
_a = new_node
def __lowerCAmelCase ( self : Tuple ) -> Optional[Any]:
"""simple docstring"""
return self.delete_nth(0 )
def __lowerCAmelCase ( self : List[Any] ) -> Any:
"""simple docstring"""
return self.delete_nth(len(self ) - 1 )
def __lowerCAmelCase ( self : List[str] , lowerCAmelCase_ : int = 0 ) -> Any:
"""simple docstring"""
if not 0 <= index < len(self ):
raise IndexError('''list index out of range.''' )
_a = self.head
if self.head == self.tail: # just one node
_a = _a = None
elif index == 0: # delete head node
_a = self.tail.next.next
_a = self.head.next
else:
_a = self.head
for _ in range(index - 1 ):
_a = temp.next
_a = temp.next
_a = temp.next.next
if index == len(self ) - 1: # delete at tail
_a = temp
return delete_node.data
def __lowerCAmelCase ( self : Union[str, Any] ) -> bool:
"""simple docstring"""
return len(self ) == 0
def snake_case_ ():
'''simple docstring'''
_a = CircularLinkedList()
assert len(UpperCamelCase ) == 0
assert circular_linked_list.is_empty() is True
assert str(UpperCamelCase ) == ""
try:
circular_linked_list.delete_front()
raise AssertionError # This should not happen
except IndexError:
assert True # This should happen
try:
circular_linked_list.delete_tail()
raise AssertionError # This should not happen
except IndexError:
assert True # This should happen
try:
circular_linked_list.delete_nth(-1 )
raise AssertionError
except IndexError:
assert True
try:
circular_linked_list.delete_nth(0 )
raise AssertionError
except IndexError:
assert True
assert circular_linked_list.is_empty() is True
for i in range(5 ):
assert len(UpperCamelCase ) == i
circular_linked_list.insert_nth(UpperCamelCase , i + 1 )
assert str(UpperCamelCase ) == "->".join(str(UpperCamelCase ) for i in range(1 , 6 ) )
circular_linked_list.insert_tail(6 )
assert str(UpperCamelCase ) == "->".join(str(UpperCamelCase ) for i in range(1 , 7 ) )
circular_linked_list.insert_head(0 )
assert str(UpperCamelCase ) == "->".join(str(UpperCamelCase ) for i in range(0 , 7 ) )
assert circular_linked_list.delete_front() == 0
assert circular_linked_list.delete_tail() == 6
assert str(UpperCamelCase ) == "->".join(str(UpperCamelCase ) for i in range(1 , 6 ) )
assert circular_linked_list.delete_nth(2 ) == 3
circular_linked_list.insert_nth(2 , 3 )
assert str(UpperCamelCase ) == "->".join(str(UpperCamelCase ) for i in range(1 , 6 ) )
assert circular_linked_list.is_empty() is False
if __name__ == "__main__":
import doctest
doctest.testmod()
| 377 |
'''simple docstring'''
import os
import sys
from contextlib import contextmanager
# Windows only
if os.name == "nt":
import ctypes
import msvcrt # noqa
class A ( ctypes.Structure ):
# _fields is a specific attr expected by ctypes
lowercase_ = [('size', ctypes.c_int), ('visible', ctypes.c_byte)]
def snake_case_ ():
'''simple docstring'''
if os.name == "nt":
_a = CursorInfo()
_a = ctypes.windll.kernelaa.GetStdHandle(-11 )
ctypes.windll.kernelaa.GetConsoleCursorInfo(UpperCamelCase , ctypes.byref(UpperCamelCase ) )
_a = False
ctypes.windll.kernelaa.SetConsoleCursorInfo(UpperCamelCase , ctypes.byref(UpperCamelCase ) )
elif os.name == "posix":
sys.stdout.write('''\033[?25l''' )
sys.stdout.flush()
def snake_case_ ():
'''simple docstring'''
if os.name == "nt":
_a = CursorInfo()
_a = ctypes.windll.kernelaa.GetStdHandle(-11 )
ctypes.windll.kernelaa.GetConsoleCursorInfo(UpperCamelCase , ctypes.byref(UpperCamelCase ) )
_a = True
ctypes.windll.kernelaa.SetConsoleCursorInfo(UpperCamelCase , ctypes.byref(UpperCamelCase ) )
elif os.name == "posix":
sys.stdout.write('''\033[?25h''' )
sys.stdout.flush()
@contextmanager
def snake_case_ ():
'''simple docstring'''
try:
hide_cursor()
yield
finally:
show_cursor()
| 377 | 1 |
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCamelCase__ = logging.get_logger(__name__)
lowerCamelCase__ = {
'''asapp/sew-tiny-100k''': '''https://huggingface.co/asapp/sew-tiny-100k/resolve/main/config.json''',
# See all SEW models at https://huggingface.co/models?filter=sew
}
class _lowerCAmelCase ( __UpperCamelCase ):
"""simple docstring"""
lowerCAmelCase__ ='''sew'''
def __init__( self , __SCREAMING_SNAKE_CASE=32 , __SCREAMING_SNAKE_CASE=768 , __SCREAMING_SNAKE_CASE=12 , __SCREAMING_SNAKE_CASE=12 , __SCREAMING_SNAKE_CASE=3072 , __SCREAMING_SNAKE_CASE=2 , __SCREAMING_SNAKE_CASE="gelu" , __SCREAMING_SNAKE_CASE=0.1 , __SCREAMING_SNAKE_CASE=0.1 , __SCREAMING_SNAKE_CASE=0.1 , __SCREAMING_SNAKE_CASE=0.0 , __SCREAMING_SNAKE_CASE=0.1 , __SCREAMING_SNAKE_CASE=0.1 , __SCREAMING_SNAKE_CASE=0.02 , __SCREAMING_SNAKE_CASE=1e-5 , __SCREAMING_SNAKE_CASE="group" , __SCREAMING_SNAKE_CASE="gelu" , __SCREAMING_SNAKE_CASE=(64, 128, 128, 128, 128, 256, 256, 256, 256, 512, 512, 512, 512) , __SCREAMING_SNAKE_CASE=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1) , __SCREAMING_SNAKE_CASE=(10, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1) , __SCREAMING_SNAKE_CASE=False , __SCREAMING_SNAKE_CASE=128 , __SCREAMING_SNAKE_CASE=16 , __SCREAMING_SNAKE_CASE=True , __SCREAMING_SNAKE_CASE=0.05 , __SCREAMING_SNAKE_CASE=10 , __SCREAMING_SNAKE_CASE=2 , __SCREAMING_SNAKE_CASE=0.0 , __SCREAMING_SNAKE_CASE=10 , __SCREAMING_SNAKE_CASE=0 , __SCREAMING_SNAKE_CASE="mean" , __SCREAMING_SNAKE_CASE=False , __SCREAMING_SNAKE_CASE=False , __SCREAMING_SNAKE_CASE=256 , __SCREAMING_SNAKE_CASE=0 , __SCREAMING_SNAKE_CASE=1 , __SCREAMING_SNAKE_CASE=2 , **__SCREAMING_SNAKE_CASE , ) -> int:
"""simple docstring"""
super().__init__(**__SCREAMING_SNAKE_CASE , pad_token_id=__SCREAMING_SNAKE_CASE , bos_token_id=__SCREAMING_SNAKE_CASE , eos_token_id=__SCREAMING_SNAKE_CASE )
snake_case__ : str =hidden_size
snake_case__ : Union[str, Any] =feat_extract_norm
snake_case__ : List[str] =feat_extract_activation
snake_case__ : Union[str, Any] =list(__SCREAMING_SNAKE_CASE )
snake_case__ : Any =list(__SCREAMING_SNAKE_CASE )
snake_case__ : List[str] =list(__SCREAMING_SNAKE_CASE )
snake_case__ : str =conv_bias
snake_case__ : Dict =num_conv_pos_embeddings
snake_case__ : Tuple =num_conv_pos_embedding_groups
snake_case__ : List[Any] =len(self.conv_dim )
snake_case__ : Tuple =num_hidden_layers
snake_case__ : Union[str, Any] =intermediate_size
snake_case__ : Optional[Any] =squeeze_factor
snake_case__ : List[str] =hidden_act
snake_case__ : List[Any] =num_attention_heads
snake_case__ : List[str] =hidden_dropout
snake_case__ : Any =attention_dropout
snake_case__ : List[str] =activation_dropout
snake_case__ : int =feat_proj_dropout
snake_case__ : Union[str, Any] =final_dropout
snake_case__ : str =layerdrop
snake_case__ : Optional[Any] =layer_norm_eps
snake_case__ : Any =initializer_range
snake_case__ : Any =vocab_size
if (
(len(self.conv_stride ) != self.num_feat_extract_layers)
or (len(self.conv_kernel ) != self.num_feat_extract_layers)
or (len(self.conv_dim ) != self.num_feat_extract_layers)
):
raise ValueError(
'''Configuration for convolutional layers is incorrect.'''
'''It is required that `len(config.conv_dim)` == `len(config.conv_stride)` == `len(config.conv_kernel)`,'''
f'''but is `len(config.conv_dim) = {len(self.conv_dim )}`, `len(config.conv_stride)'''
f'''= {len(self.conv_stride )}`, `len(config.conv_kernel) = {len(self.conv_kernel )}`.''' )
# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
snake_case__ : Optional[int] =apply_spec_augment
snake_case__ : str =mask_time_prob
snake_case__ : Tuple =mask_time_length
snake_case__ : List[str] =mask_time_min_masks
snake_case__ : int =mask_feature_prob
snake_case__ : Optional[int] =mask_feature_length
snake_case__ : List[Any] =mask_feature_min_masks
# ctc loss
snake_case__ : List[str] =ctc_loss_reduction
snake_case__ : Dict =ctc_zero_infinity
# sequence classification
snake_case__ : Any =use_weighted_layer_sum
snake_case__ : Any =classifier_proj_size
@property
def UpperCAmelCase ( self ) -> Union[str, Any]:
"""simple docstring"""
return functools.reduce(operator.mul , self.conv_stride , 1 )
| 381 |
import argparse
import os
import re
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_dummies.py
lowerCamelCase__ = '''src/diffusers'''
# Matches is_xxx_available()
lowerCamelCase__ = re.compile(r'''is\_([a-z_]*)_available\(\)''')
# Matches from xxx import bla
lowerCamelCase__ = re.compile(r'''\s+from\s+\S*\s+import\s+([^\(\s].*)\n''')
lowerCamelCase__ = '''
{0} = None
'''
lowerCamelCase__ = '''
class {0}(metaclass=DummyObject):
_backends = {1}
def __init__(self, *args, **kwargs):
requires_backends(self, {1})
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, {1})
@classmethod
def from_pretrained(cls, *args, **kwargs):
requires_backends(cls, {1})
'''
lowerCamelCase__ = '''
def {0}(*args, **kwargs):
requires_backends({0}, {1})
'''
def lowercase_ ( SCREAMING_SNAKE_CASE : List[str] ):
"""simple docstring"""
snake_case__ : Tuple =_re_backend.findall(SCREAMING_SNAKE_CASE )
if len(SCREAMING_SNAKE_CASE ) == 0:
return None
return "_and_".join(SCREAMING_SNAKE_CASE )
def lowercase_ ( ):
"""simple docstring"""
with open(os.path.join(SCREAMING_SNAKE_CASE , '''__init__.py''' ) , '''r''' , encoding='''utf-8''' , newline='''\n''' ) as f:
snake_case__ : int =f.readlines()
# Get to the point we do the actual imports for type checking
snake_case__ : Optional[Any] =0
snake_case__ : Any ={}
# Go through the end of the file
while line_index < len(SCREAMING_SNAKE_CASE ):
# If the line contains is_backend_available, we grab all objects associated with the `else` block
snake_case__ : List[str] =find_backend(lines[line_index] )
if backend is not None:
while not lines[line_index].startswith('''else:''' ):
line_index += 1
line_index += 1
snake_case__ : List[Any] =[]
# Until we unindent, add backend objects to the list
while line_index < len(SCREAMING_SNAKE_CASE ) and len(lines[line_index] ) > 1:
snake_case__ : List[str] =lines[line_index]
snake_case__ : Any =_re_single_line_import.search(SCREAMING_SNAKE_CASE )
if single_line_import_search is not None:
objects.extend(single_line_import_search.groups()[0].split(''', ''' ) )
elif line.startswith(''' ''' * 8 ):
objects.append(line[8:-2] )
line_index += 1
if len(SCREAMING_SNAKE_CASE ) > 0:
snake_case__ : List[Any] =objects
else:
line_index += 1
return backend_specific_objects
def lowercase_ ( SCREAMING_SNAKE_CASE : Union[str, Any] , SCREAMING_SNAKE_CASE : List[str] ):
"""simple docstring"""
if name.isupper():
return DUMMY_CONSTANT.format(SCREAMING_SNAKE_CASE )
elif name.islower():
return DUMMY_FUNCTION.format(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
else:
return DUMMY_CLASS.format(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
def lowercase_ ( SCREAMING_SNAKE_CASE : str=None ):
"""simple docstring"""
if backend_specific_objects is None:
snake_case__ : int =read_init()
# For special correspondence backend to module name as used in the function requires_modulename
snake_case__ : Dict ={}
for backend, objects in backend_specific_objects.items():
snake_case__ : str ='''[''' + ''', '''.join(F'''"{b}"''' for b in backend.split('''_and_''' ) ) + ''']'''
snake_case__ : List[Any] ='''# This file is autogenerated by the command `make fix-copies`, do not edit.\n'''
dummy_file += "from ..utils import DummyObject, requires_backends\n\n"
dummy_file += "\n".join([create_dummy_object(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) for o in objects] )
snake_case__ : int =dummy_file
return dummy_files
def lowercase_ ( SCREAMING_SNAKE_CASE : Optional[int]=False ):
"""simple docstring"""
snake_case__ : Dict =create_dummy_files()
# For special correspondence backend to shortcut as used in utils/dummy_xxx_objects.py
snake_case__ : int ={'''torch''': '''pt'''}
# Locate actual dummy modules and read their content.
snake_case__ : List[Any] =os.path.join(SCREAMING_SNAKE_CASE , '''utils''' )
snake_case__ : str ={
backend: os.path.join(SCREAMING_SNAKE_CASE , F'''dummy_{short_names.get(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )}_objects.py''' )
for backend in dummy_files.keys()
}
snake_case__ : Tuple ={}
for backend, file_path in dummy_file_paths.items():
if os.path.isfile(SCREAMING_SNAKE_CASE ):
with open(SCREAMING_SNAKE_CASE , '''r''' , encoding='''utf-8''' , newline='''\n''' ) as f:
snake_case__ : Optional[int] =f.read()
else:
snake_case__ : Union[str, Any] =''''''
for backend in dummy_files.keys():
if dummy_files[backend] != actual_dummies[backend]:
if overwrite:
print(
F'''Updating diffusers.utils.dummy_{short_names.get(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )}_objects.py as the main '''
'''__init__ has new objects.''' )
with open(dummy_file_paths[backend] , '''w''' , encoding='''utf-8''' , newline='''\n''' ) as f:
f.write(dummy_files[backend] )
else:
raise ValueError(
'''The main __init__ has objects that are not present in '''
F'''diffusers.utils.dummy_{short_names.get(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )}_objects.py. Run `make fix-copies` '''
'''to fix this.''' )
if __name__ == "__main__":
lowerCamelCase__ = argparse.ArgumentParser()
parser.add_argument('''--fix_and_overwrite''', action='''store_true''', help='''Whether to fix inconsistencies.''')
lowerCamelCase__ = parser.parse_args()
check_dummies(args.fix_and_overwrite)
| 381 | 1 |
"""simple docstring"""
def lowerCamelCase_ ( _lowerCamelCase ):
lowerCamelCase__ : Union[str, Any] = 1
for i in range(1 , num + 1 ):
fact *= i
return fact
def lowerCamelCase_ ( _lowerCamelCase ):
lowerCamelCase__ : Optional[Any] = 0
while number > 0:
lowerCamelCase__ : List[str] = number % 10
sum_of_digits += last_digit
lowerCamelCase__ : str = number // 10 # Removing the last_digit from the given number
return sum_of_digits
def lowerCamelCase_ ( _lowerCamelCase = 100 ):
lowerCamelCase__ : Union[str, Any] = factorial(_lowerCamelCase )
lowerCamelCase__ : List[Any] = split_and_add(_lowerCamelCase )
return result
if __name__ == "__main__":
print(solution(int(input("Enter the Number: ").strip())))
| 696 |
"""simple docstring"""
def lowerCamelCase_ ( _lowerCamelCase , _lowerCamelCase ):
if mass < 0:
raise ValueError('The mass of a body cannot be negative' )
return 0.5 * mass * abs(_lowerCamelCase ) * abs(_lowerCamelCase )
if __name__ == "__main__":
import doctest
doctest.testmod(verbose=True)
| 696 | 1 |
'''simple docstring'''
from __future__ import annotations
import sys
from collections import deque
from typing import Generic, TypeVar
_snake_case : str = TypeVar('T')
class A ( Generic[T] ):
lowercase_ = 42 # Cache store of keys
lowercase_ = 42 # References of the keys in cache
lowercase_ = 10 # Maximum capacity of cache
def __init__( self : str , lowerCAmelCase_ : str ) -> Optional[Any]:
"""simple docstring"""
_a = deque()
_a = set()
if not n:
_a = sys.maxsize
elif n < 0:
raise ValueError('''n should be an integer greater than 0.''' )
else:
_a = n
def __lowerCAmelCase ( self : Any , lowerCAmelCase_ : Any ) -> Union[str, Any]:
"""simple docstring"""
if x not in self.key_reference:
if len(self.dq_store ) == LRUCache._MAX_CAPACITY:
_a = self.dq_store.pop()
self.key_reference.remove(_lowerCAmelCase )
else:
self.dq_store.remove(_lowerCAmelCase )
self.dq_store.appendleft(_lowerCAmelCase )
self.key_reference.add(_lowerCAmelCase )
def __lowerCAmelCase ( self : int ) -> List[str]:
"""simple docstring"""
for k in self.dq_store:
print(_lowerCAmelCase )
def __repr__( self : List[str] ) -> Dict:
"""simple docstring"""
return F'LRUCache({self._MAX_CAPACITY}) => {list(self.dq_store )}'
if __name__ == "__main__":
import doctest
doctest.testmod()
_snake_case : LRUCache[str | int] = LRUCache(4)
lru_cache.refer('A')
lru_cache.refer(2)
lru_cache.refer(3)
lru_cache.refer('A')
lru_cache.refer(4)
lru_cache.refer(5)
lru_cache.display()
print(lru_cache)
assert str(lru_cache) == "LRUCache(4) => [5, 4, 'A', 3]"
| 22 |
from typing import Callable, Optional
from .. import Features
from ..packaged_modules.generator.generator import Generator
from .abc import AbstractDatasetInputStream
class a ( __lowercase ):
def __init__( self , _lowerCAmelCase , _lowerCAmelCase = None , _lowerCAmelCase = None , _lowerCAmelCase = False , _lowerCAmelCase = False , _lowerCAmelCase = None , _lowerCAmelCase = None , **_lowerCAmelCase , ):
"""simple docstring"""
super().__init__(
features=_lowerCAmelCase , cache_dir=_lowerCAmelCase , keep_in_memory=_lowerCAmelCase , streaming=_lowerCAmelCase , num_proc=_lowerCAmelCase , **_lowerCAmelCase , )
__SCREAMING_SNAKE_CASE: Any = Generator(
cache_dir=_lowerCAmelCase , features=_lowerCAmelCase , generator=_lowerCAmelCase , gen_kwargs=_lowerCAmelCase , **_lowerCAmelCase , )
def snake_case_ ( self ):
"""simple docstring"""
if self.streaming:
__SCREAMING_SNAKE_CASE: List[str] = self.builder.as_streaming_dataset(split='''train''' )
# Build regular (map-style) dataset
else:
__SCREAMING_SNAKE_CASE: str = None
__SCREAMING_SNAKE_CASE: List[Any] = None
__SCREAMING_SNAKE_CASE: Tuple = None
__SCREAMING_SNAKE_CASE: Optional[Any] = None
self.builder.download_and_prepare(
download_config=_lowerCAmelCase , download_mode=_lowerCAmelCase , verification_mode=_lowerCAmelCase , base_path=_lowerCAmelCase , num_proc=self.num_proc , )
__SCREAMING_SNAKE_CASE: List[str] = self.builder.as_dataset(
split='''train''' , verification_mode=_lowerCAmelCase , in_memory=self.keep_in_memory )
return dataset
| 202 | 0 |
'''simple docstring'''
import os
from dataclasses import dataclass, field
from io import BytesIO
from typing import TYPE_CHECKING, Any, ClassVar, Dict, Optional, Union
import numpy as np
import pyarrow as pa
from .. import config
from ..download.streaming_download_manager import xopen, xsplitext
from ..table import array_cast
from ..utils.py_utils import no_op_if_value_is_null, string_to_dict
if TYPE_CHECKING:
from .features import FeatureType
__UpperCamelCase , __UpperCamelCase , __UpperCamelCase : Tuple = False, False, False
@dataclass
class lowerCamelCase__ :
"""simple docstring"""
__magic_name__ = None
__magic_name__ = True
__magic_name__ = True
__magic_name__ = None
# Automatically constructed
__magic_name__ = "dict"
__magic_name__ = pa.struct({"""bytes""": pa.binary(), """path""": pa.string()} )
__magic_name__ = field(default="""Audio""" , init=__SCREAMING_SNAKE_CASE , repr=__SCREAMING_SNAKE_CASE )
def __call__( self ) -> int:
return self.pa_type
def _lowerCamelCase ( self , UpperCAmelCase__ ) -> Any:
try:
import soundfile as sf # soundfile is a dependency of librosa, needed to decode audio files.
except ImportError as err:
raise ImportError('''To support encoding audio data, please install \'soundfile\'.''' ) from err
if isinstance(_a , _a ):
return {"bytes": None, "path": value}
elif isinstance(_a , _a ):
return {"bytes": value, "path": None}
elif "array" in value:
# convert the audio array to wav bytes
_A : Optional[Any] = BytesIO()
sf.write(_a , value['''array'''] , value['''sampling_rate'''] , format='''wav''' )
return {"bytes": buffer.getvalue(), "path": None}
elif value.get('''path''' ) is not None and os.path.isfile(value['''path'''] ):
# we set "bytes": None to not duplicate the data if they're already available locally
if value["path"].endswith('''pcm''' ):
# "PCM" only has raw audio bytes
if value.get('''sampling_rate''' ) is None:
# At least, If you want to convert "PCM-byte" to "WAV-byte", you have to know sampling rate
raise KeyError('''To use PCM files, please specify a \'sampling_rate\' in Audio object''' )
if value.get('''bytes''' ):
# If we already had PCM-byte, we don`t have to make "read file, make bytes" (just use it!)
_A : Tuple = np.frombuffer(value['''bytes'''] , dtype=np.intaa ).astype(np.floataa ) / 3_2_7_6_7
else:
_A : Optional[Any] = np.memmap(value['''path'''] , dtype='''h''' , mode='''r''' ).astype(np.floataa ) / 3_2_7_6_7
_A : Any = BytesIO(bytes() )
sf.write(_a , _a , value['''sampling_rate'''] , format='''wav''' )
return {"bytes": buffer.getvalue(), "path": None}
else:
return {"bytes": None, "path": value.get('''path''' )}
elif value.get('''bytes''' ) is not None or value.get('''path''' ) is not None:
# store the audio bytes, and path is used to infer the audio format using the file extension
return {"bytes": value.get('''bytes''' ), "path": value.get('''path''' )}
else:
raise ValueError(
F"""An audio sample should have one of \'path\' or \'bytes\' but they are missing or None in {value}.""" )
def _lowerCamelCase ( self , UpperCAmelCase__ , UpperCAmelCase__ = None ) -> Optional[Any]:
if not self.decode:
raise RuntimeError('''Decoding is disabled for this feature. Please use Audio(decode=True) instead.''' )
_A , _A : Optional[int] = (value['''path'''], BytesIO(value['''bytes'''] )) if value['''bytes'''] is not None else (value['''path'''], None)
if path is None and file is None:
raise ValueError(F"""An audio sample should have one of \'path\' or \'bytes\' but both are None in {value}.""" )
try:
import librosa
import soundfile as sf
except ImportError as err:
raise ImportError('''To support decoding audio files, please install \'librosa\' and \'soundfile\'.''' ) from err
_A : Optional[Any] = xsplitext(_a )[1][1:].lower() if path is not None else None
if not config.IS_OPUS_SUPPORTED and audio_format == "opus":
raise RuntimeError(
'''Decoding \'opus\' files requires system library \'libsndfile\'>=1.0.31, '''
'''You can try to update `soundfile` python library: `pip install "soundfile>=0.12.1"`. ''' )
elif not config.IS_MP3_SUPPORTED and audio_format == "mp3":
raise RuntimeError(
'''Decoding \'mp3\' files requires system library \'libsndfile\'>=1.1.0, '''
'''You can try to update `soundfile` python library: `pip install "soundfile>=0.12.1"`. ''' )
if file is None:
_A : Dict = token_per_repo_id or {}
_A : Union[str, Any] = path.split('''::''' )[-1]
try:
_A : Optional[int] = string_to_dict(_a , config.HUB_DATASETS_URL )['''repo_id''']
_A : Union[str, Any] = token_per_repo_id[repo_id]
except (ValueError, KeyError):
_A : List[Any] = None
with xopen(_a , '''rb''' , use_auth_token=_a ) as f:
_A , _A : List[Any] = sf.read(_a )
else:
_A , _A : Optional[Any] = sf.read(_a )
_A : Optional[Any] = array.T
if self.mono:
_A : Tuple = librosa.to_mono(_a )
if self.sampling_rate and self.sampling_rate != sampling_rate:
_A : List[str] = librosa.resample(_a , orig_sr=_a , target_sr=self.sampling_rate )
_A : Optional[Any] = self.sampling_rate
return {"path": path, "array": array, "sampling_rate": sampling_rate}
def _lowerCamelCase ( self ) -> Optional[Any]:
from .features import Value
if self.decode:
raise ValueError('''Cannot flatten a decoded Audio feature.''' )
return {
"bytes": Value('''binary''' ),
"path": Value('''string''' ),
}
def _lowerCamelCase ( self , UpperCAmelCase__ ) -> List[str]:
if pa.types.is_string(storage.type ):
_A : str = pa.array([None] * len(_a ) , type=pa.binary() )
_A : List[Any] = pa.StructArray.from_arrays([bytes_array, storage] , ['''bytes''', '''path'''] , mask=storage.is_null() )
elif pa.types.is_binary(storage.type ):
_A : Optional[int] = pa.array([None] * len(_a ) , type=pa.string() )
_A : str = pa.StructArray.from_arrays([storage, path_array] , ['''bytes''', '''path'''] , mask=storage.is_null() )
elif pa.types.is_struct(storage.type ) and storage.type.get_all_field_indices('''array''' ):
_A : Tuple = pa.array([Audio().encode_example(_a ) if x is not None else None for x in storage.to_pylist()] )
elif pa.types.is_struct(storage.type ):
if storage.type.get_field_index('''bytes''' ) >= 0:
_A : Optional[Any] = storage.field('''bytes''' )
else:
_A : Optional[Any] = pa.array([None] * len(_a ) , type=pa.binary() )
if storage.type.get_field_index('''path''' ) >= 0:
_A : Any = storage.field('''path''' )
else:
_A : Dict = pa.array([None] * len(_a ) , type=pa.string() )
_A : List[str] = pa.StructArray.from_arrays([bytes_array, path_array] , ['''bytes''', '''path'''] , mask=storage.is_null() )
return array_cast(_a , self.pa_type )
def _lowerCamelCase ( self , UpperCAmelCase__ ) -> int:
@no_op_if_value_is_null
def path_to_bytes(UpperCAmelCase__ ):
with xopen(_a , '''rb''' ) as f:
_A : Optional[Any] = f.read()
return bytes_
_A : Optional[int] = pa.array(
[
(path_to_bytes(x['''path'''] ) if x['''bytes'''] is None else x['''bytes''']) if x is not None else None
for x in storage.to_pylist()
] , type=pa.binary() , )
_A : Union[str, Any] = pa.array(
[os.path.basename(_a ) if path is not None else None for path in storage.field('''path''' ).to_pylist()] , type=pa.string() , )
_A : Optional[int] = pa.StructArray.from_arrays([bytes_array, path_array] , ['''bytes''', '''path'''] , mask=bytes_array.is_null() )
return array_cast(_a , self.pa_type )
| 721 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
__UpperCamelCase : str = {
'''albert-base-v1''': '''https://huggingface.co/albert-base-v1/resolve/main/config.json''',
'''albert-large-v1''': '''https://huggingface.co/albert-large-v1/resolve/main/config.json''',
'''albert-xlarge-v1''': '''https://huggingface.co/albert-xlarge-v1/resolve/main/config.json''',
'''albert-xxlarge-v1''': '''https://huggingface.co/albert-xxlarge-v1/resolve/main/config.json''',
'''albert-base-v2''': '''https://huggingface.co/albert-base-v2/resolve/main/config.json''',
'''albert-large-v2''': '''https://huggingface.co/albert-large-v2/resolve/main/config.json''',
'''albert-xlarge-v2''': '''https://huggingface.co/albert-xlarge-v2/resolve/main/config.json''',
'''albert-xxlarge-v2''': '''https://huggingface.co/albert-xxlarge-v2/resolve/main/config.json''',
}
class lowerCamelCase__ ( snake_case_ ):
"""simple docstring"""
__magic_name__ = """albert"""
def __init__( self , UpperCAmelCase__=3_0_0_0_0 , UpperCAmelCase__=1_2_8 , UpperCAmelCase__=4_0_9_6 , UpperCAmelCase__=1_2 , UpperCAmelCase__=1 , UpperCAmelCase__=6_4 , UpperCAmelCase__=1_6_3_8_4 , UpperCAmelCase__=1 , UpperCAmelCase__="gelu_new" , UpperCAmelCase__=0 , UpperCAmelCase__=0 , UpperCAmelCase__=5_1_2 , UpperCAmelCase__=2 , UpperCAmelCase__=0.0_2 , UpperCAmelCase__=1e-12 , UpperCAmelCase__=0.1 , UpperCAmelCase__="absolute" , UpperCAmelCase__=0 , UpperCAmelCase__=2 , UpperCAmelCase__=3 , **UpperCAmelCase__ , ) -> Tuple:
super().__init__(pad_token_id=UpperCAmelCase__ , bos_token_id=UpperCAmelCase__ , eos_token_id=UpperCAmelCase__ , **UpperCAmelCase__ )
_A : Optional[Any] = vocab_size
_A : Optional[int] = embedding_size
_A : str = hidden_size
_A : Union[str, Any] = num_hidden_layers
_A : Optional[int] = num_hidden_groups
_A : Optional[Any] = num_attention_heads
_A : Tuple = inner_group_num
_A : Tuple = hidden_act
_A : List[Any] = intermediate_size
_A : str = hidden_dropout_prob
_A : str = attention_probs_dropout_prob
_A : List[str] = max_position_embeddings
_A : List[Any] = type_vocab_size
_A : Any = initializer_range
_A : Tuple = layer_norm_eps
_A : Dict = classifier_dropout_prob
_A : Union[str, Any] = position_embedding_type
class lowerCamelCase__ ( snake_case_ ):
"""simple docstring"""
@property
def _lowerCamelCase ( self ) -> Mapping[str, Mapping[int, str]]:
if self.task == "multiple-choice":
_A : Dict = {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
else:
_A : Any = {0: '''batch''', 1: '''sequence'''}
return OrderedDict(
[
('''input_ids''', dynamic_axis),
('''attention_mask''', dynamic_axis),
('''token_type_ids''', dynamic_axis),
] )
| 417 | 0 |
import datasets
from .nmt_bleu import compute_bleu # From: https://github.com/tensorflow/nmt/blob/master/nmt/scripts/bleu.py
a_ : Dict = '\\n@INPROCEEDINGS{Papineni02bleu:a,\n author = {Kishore Papineni and Salim Roukos and Todd Ward and Wei-jing Zhu},\n title = {BLEU: a Method for Automatic Evaluation of Machine Translation},\n booktitle = {},\n year = {2002},\n pages = {311--318}\n}\n@inproceedings{lin-och-2004-orange,\n title = "{ORANGE}: a Method for Evaluating Automatic Evaluation Metrics for Machine Translation",\n author = "Lin, Chin-Yew and\n Och, Franz Josef",\n booktitle = "{COLING} 2004: Proceedings of the 20th International Conference on Computational Linguistics",\n month = "aug 23{--}aug 27",\n year = "2004",\n address = "Geneva, Switzerland",\n publisher = "COLING",\n url = "https://www.aclweb.org/anthology/C04-1072",\n pages = "501--507",\n}\n'
a_ : Dict = '\\nBLEU (bilingual evaluation understudy) is an algorithm for evaluating the quality of text which has been machine-translated from one natural language to another.\nQuality is considered to be the correspondence between a machine\'s output and that of a human: "the closer a machine translation is to a professional human translation,\nthe better it is" – this is the central idea behind BLEU. BLEU was one of the first metrics to claim a high correlation with human judgements of quality, and\nremains one of the most popular automated and inexpensive metrics.\n\nScores are calculated for individual translated segments—generally sentences—by comparing them with a set of good quality reference translations.\nThose scores are then averaged over the whole corpus to reach an estimate of the translation\'s overall quality. Intelligibility or grammatical correctness\nare not taken into account[citation needed].\n\nBLEU\'s output is always a number between 0 and 1. This value indicates how similar the candidate text is to the reference texts, with values closer to 1\nrepresenting more similar texts. Few human translations will attain a score of 1, since this would indicate that the candidate is identical to one of the\nreference translations. For this reason, it is not necessary to attain a score of 1. Because there are more opportunities to match, adding additional\nreference translations will increase the BLEU score.\n'
a_ : int = '\nComputes BLEU score of translated segments against one or more references.\nArgs:\n predictions: list of translations to score.\n Each translation should be tokenized into a list of tokens.\n references: list of lists of references for each translation.\n Each reference should be tokenized into a list of tokens.\n max_order: Maximum n-gram order to use when computing BLEU score.\n smooth: Whether or not to apply Lin et al. 2004 smoothing.\nReturns:\n \'bleu\': bleu score,\n \'precisions\': geometric mean of n-gram precisions,\n \'brevity_penalty\': brevity penalty,\n \'length_ratio\': ratio of lengths,\n \'translation_length\': translation_length,\n \'reference_length\': reference_length\nExamples:\n\n >>> predictions = [\n ... ["hello", "there", "general", "kenobi"], # tokenized prediction of the first sample\n ... ["foo", "bar", "foobar"] # tokenized prediction of the second sample\n ... ]\n >>> references = [\n ... [["hello", "there", "general", "kenobi"], ["hello", "there", "!"]], # tokenized references for the first sample (2 references)\n ... [["foo", "bar", "foobar"]] # tokenized references for the second sample (1 reference)\n ... ]\n >>> bleu = datasets.load_metric("bleu")\n >>> results = bleu.compute(predictions=predictions, references=references)\n >>> print(results["bleu"])\n 1.0\n'
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION)
class lowerCamelCase__ ( datasets.Metric):
"""simple docstring"""
def _a (self ):
'''simple docstring'''
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
"predictions": datasets.Sequence(datasets.Value("string" , id="token" ) , id="sequence" ),
"references": datasets.Sequence(
datasets.Sequence(datasets.Value("string" , id="token" ) , id="sequence" ) , id="references" ),
} ) , codebase_urls=["https://github.com/tensorflow/nmt/blob/master/nmt/scripts/bleu.py"] , reference_urls=[
"https://en.wikipedia.org/wiki/BLEU",
"https://towardsdatascience.com/evaluating-text-output-in-nlp-bleu-at-your-own-risk-e8609665a213",
] , )
def _a (self , __a , __a , __a=4 , __a=False ):
'''simple docstring'''
lowerCamelCase = compute_bleu(
reference_corpus=__a , translation_corpus=__a , max_order=__a , smooth=__a )
((lowerCamelCase) , (lowerCamelCase) , (lowerCamelCase) , (lowerCamelCase) , (lowerCamelCase) , (lowerCamelCase)) = score
return {
"bleu": bleu,
"precisions": precisions,
"brevity_penalty": bp,
"length_ratio": ratio,
"translation_length": translation_length,
"reference_length": reference_length,
} | 623 |
from __future__ import annotations
from math import pow, sqrt
def __lowercase( UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ ):
"""simple docstring"""
if (resistance, reactance, impedance).count(0 ) != 1:
raise ValueError("One and only one argument must be 0" )
if resistance == 0:
return {"resistance": sqrt(pow(UpperCAmelCase__ , 2 ) - pow(UpperCAmelCase__ , 2 ) )}
elif reactance == 0:
return {"reactance": sqrt(pow(UpperCAmelCase__ , 2 ) - pow(UpperCAmelCase__ , 2 ) )}
elif impedance == 0:
return {"impedance": sqrt(pow(UpperCAmelCase__ , 2 ) + pow(UpperCAmelCase__ , 2 ) )}
else:
raise ValueError("Exactly one argument must be 0" )
if __name__ == "__main__":
import doctest
doctest.testmod() | 623 | 1 |
import math
import unittest
def __snake_case ( _UpperCAmelCase ):
"""simple docstring"""
assert isinstance(_UpperCAmelCase , _UpperCAmelCase ) and (
number >= 0
), "'number' must been an int and positive"
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(_UpperCAmelCase ) + 1 ) , 6 ):
if number % i == 0 or number % (i + 2) == 0:
return False
return True
class a__ ( unittest.TestCase ):
"""simple docstring"""
def __UpperCAmelCase ( self :List[str] ):
self.assertTrue(is_prime(2 ) )
self.assertTrue(is_prime(3 ) )
self.assertTrue(is_prime(5 ) )
self.assertTrue(is_prime(7 ) )
self.assertTrue(is_prime(11 ) )
self.assertTrue(is_prime(13 ) )
self.assertTrue(is_prime(17 ) )
self.assertTrue(is_prime(19 ) )
self.assertTrue(is_prime(23 ) )
self.assertTrue(is_prime(29 ) )
def __UpperCAmelCase ( self :int ):
with self.assertRaises(lowercase__ ):
is_prime(-19 )
self.assertFalse(
is_prime(0 ) , 'Zero doesn\'t have any positive factors, primes must have exactly two.' , )
self.assertFalse(
is_prime(1 ) , 'One only has 1 positive factor, primes must have exactly two.' , )
self.assertFalse(is_prime(2 * 2 ) )
self.assertFalse(is_prime(2 * 3 ) )
self.assertFalse(is_prime(3 * 3 ) )
self.assertFalse(is_prime(3 * 5 ) )
self.assertFalse(is_prime(3 * 5 * 7 ) )
if __name__ == "__main__":
unittest.main()
| 314 |
from queue import PriorityQueue
from typing import Any
import numpy as np
def __snake_case ( _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , ):
"""simple docstring"""
for nxt, d in graph[v]:
if nxt in visited_forward:
continue
lowercase = cst_fwd.get(_UpperCAmelCase , np.inf )
lowercase = cst_fwd[v] + d
if new_cost_f < old_cost_f:
queue.put((new_cost_f, nxt) )
lowercase = new_cost_f
lowercase = v
if nxt in visited_backward:
if cst_fwd[v] + d + cst_bwd[nxt] < shortest_distance:
lowercase = cst_fwd[v] + d + cst_bwd[nxt]
return shortest_distance
def __snake_case ( _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase ):
"""simple docstring"""
lowercase = -1
lowercase = set()
lowercase = set()
lowercase = {source: 0}
lowercase = {destination: 0}
lowercase = {source: None}
lowercase = {destination: None}
lowercase = PriorityQueue()
lowercase = PriorityQueue()
lowercase = np.inf
queue_forward.put((0, source) )
queue_backward.put((0, destination) )
if source == destination:
return 0
while not queue_forward.empty() and not queue_backward.empty():
lowercase , lowercase = queue_forward.get()
visited_forward.add(_UpperCAmelCase )
lowercase , lowercase = queue_backward.get()
visited_backward.add(_UpperCAmelCase )
lowercase = pass_and_relaxation(
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , )
lowercase = pass_and_relaxation(
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , )
if cst_fwd[v_fwd] + cst_bwd[v_bwd] >= shortest_distance:
break
if shortest_distance != np.inf:
lowercase = shortest_distance
return shortest_path_distance
__magic_name__ = {
'''B''': [['''C''', 1]],
'''C''': [['''D''', 1]],
'''D''': [['''F''', 1]],
'''E''': [['''B''', 1], ['''G''', 2]],
'''F''': [],
'''G''': [['''F''', 1]],
}
__magic_name__ = {
'''B''': [['''E''', 1]],
'''C''': [['''B''', 1]],
'''D''': [['''C''', 1]],
'''F''': [['''D''', 1], ['''G''', 1]],
'''E''': [[None, np.inf]],
'''G''': [['''E''', 2]],
}
if __name__ == "__main__":
import doctest
doctest.testmod()
| 314 | 1 |
import argparse
import json
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelerator, DistributedType
from accelerate.utils.deepspeed import DummyOptim, DummyScheduler
__A = 16
__A = 32
def __a ( lowerCAmelCase_ : Accelerator ,lowerCAmelCase_ : int = 16 ,lowerCAmelCase_ : str = "bert-base-cased" ) -> List[str]:
'''simple docstring'''
UpperCAmelCase_= AutoTokenizer.from_pretrained(lowerCAmelCase_ )
UpperCAmelCase_= load_dataset("""glue""" ,"""mrpc""" )
def tokenize_function(lowerCAmelCase_ : Optional[int] ):
# max_length=None => use the model max length (it's actually the default)
UpperCAmelCase_= tokenizer(examples["""sentence1"""] ,examples["""sentence2"""] ,truncation=lowerCAmelCase_ ,max_length=lowerCAmelCase_ )
return outputs
# Apply the method we just defined to all the examples in all the splits of the dataset
UpperCAmelCase_= datasets.map(
lowerCAmelCase_ ,batched=lowerCAmelCase_ ,remove_columns=["""idx""", """sentence1""", """sentence2"""] ,load_from_cache_file=lowerCAmelCase_ )
# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
# transformers library
UpperCAmelCase_= tokenized_datasets.rename_column("""label""" ,"""labels""" )
def collate_fn(lowerCAmelCase_ : List[str] ):
# On TPU it's best to pad everything to the same length or training will be very slow.
if accelerator.distributed_type == DistributedType.TPU:
return tokenizer.pad(lowerCAmelCase_ ,padding="""max_length""" ,max_length=1_28 ,return_tensors="""pt""" )
return tokenizer.pad(lowerCAmelCase_ ,padding="""longest""" ,return_tensors="""pt""" )
# Instantiate dataloaders.
UpperCAmelCase_= DataLoader(
tokenized_datasets["""train"""] ,shuffle=lowerCAmelCase_ ,collate_fn=lowerCAmelCase_ ,batch_size=lowerCAmelCase_ )
UpperCAmelCase_= DataLoader(
tokenized_datasets["""validation"""] ,shuffle=lowerCAmelCase_ ,collate_fn=lowerCAmelCase_ ,batch_size=lowerCAmelCase_ )
return train_dataloader, eval_dataloader
def __a ( lowerCAmelCase_ : Dict ,lowerCAmelCase_ : str ) -> str:
'''simple docstring'''
UpperCAmelCase_= Accelerator()
# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
UpperCAmelCase_= config["""lr"""]
UpperCAmelCase_= int(config["""num_epochs"""] )
UpperCAmelCase_= int(config["""seed"""] )
UpperCAmelCase_= int(config["""batch_size"""] )
UpperCAmelCase_= args.model_name_or_path
set_seed(lowerCAmelCase_ )
UpperCAmelCase_, UpperCAmelCase_= get_dataloaders(lowerCAmelCase_ ,lowerCAmelCase_ ,lowerCAmelCase_ )
# Instantiate the model (we build the model here so that the seed also control new weights initialization)
UpperCAmelCase_= AutoModelForSequenceClassification.from_pretrained(lowerCAmelCase_ ,return_dict=lowerCAmelCase_ )
# Instantiate optimizer
UpperCAmelCase_= (
AdamW
if accelerator.state.deepspeed_plugin is None
or """optimizer""" not in accelerator.state.deepspeed_plugin.deepspeed_config
else DummyOptim
)
UpperCAmelCase_= optimizer_cls(params=model.parameters() ,lr=lowerCAmelCase_ )
if accelerator.state.deepspeed_plugin is not None:
UpperCAmelCase_= accelerator.state.deepspeed_plugin.deepspeed_config[
"""gradient_accumulation_steps"""
]
else:
UpperCAmelCase_= 1
UpperCAmelCase_= (len(lowerCAmelCase_ ) * num_epochs) // gradient_accumulation_steps
# Instantiate scheduler
if (
accelerator.state.deepspeed_plugin is None
or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
):
UpperCAmelCase_= get_linear_schedule_with_warmup(
optimizer=lowerCAmelCase_ ,num_warmup_steps=0 ,num_training_steps=lowerCAmelCase_ ,)
else:
UpperCAmelCase_= DummyScheduler(lowerCAmelCase_ ,total_num_steps=lowerCAmelCase_ ,warmup_num_steps=0 )
# Prepare everything
# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
# prepare method.
UpperCAmelCase_, UpperCAmelCase_, UpperCAmelCase_, UpperCAmelCase_, UpperCAmelCase_= accelerator.prepare(
lowerCAmelCase_ ,lowerCAmelCase_ ,lowerCAmelCase_ ,lowerCAmelCase_ ,lowerCAmelCase_ )
# We need to keep track of how many total steps we have iterated over
UpperCAmelCase_= 0
# We also need to keep track of the stating epoch so files are named properly
UpperCAmelCase_= 0
# Now we train the model
UpperCAmelCase_= evaluate.load("""glue""" ,"""mrpc""" )
UpperCAmelCase_= 0
UpperCAmelCase_= {}
for epoch in range(lowerCAmelCase_ ,lowerCAmelCase_ ):
model.train()
for step, batch in enumerate(lowerCAmelCase_ ):
UpperCAmelCase_= model(**lowerCAmelCase_ )
UpperCAmelCase_= outputs.loss
UpperCAmelCase_= loss / gradient_accumulation_steps
accelerator.backward(lowerCAmelCase_ )
if step % gradient_accumulation_steps == 0:
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
overall_step += 1
model.eval()
UpperCAmelCase_= 0
for step, batch in enumerate(lowerCAmelCase_ ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
with torch.no_grad():
UpperCAmelCase_= model(**lowerCAmelCase_ )
UpperCAmelCase_= outputs.logits.argmax(dim=-1 )
# It is slightly faster to call this once, than multiple times
UpperCAmelCase_, UpperCAmelCase_= accelerator.gather(
(predictions, batch["""labels"""]) ) # If we are in a multiprocess environment, the last batch has duplicates
if accelerator.use_distributed:
if step == len(lowerCAmelCase_ ) - 1:
UpperCAmelCase_= predictions[: len(eval_dataloader.dataset ) - samples_seen]
UpperCAmelCase_= references[: len(eval_dataloader.dataset ) - samples_seen]
else:
samples_seen += references.shape[0]
metric.add_batch(
predictions=lowerCAmelCase_ ,references=lowerCAmelCase_ ,)
UpperCAmelCase_= metric.compute()
# Use accelerator.print to print only on the main process.
accelerator.print(F"""epoch {epoch}:""" ,lowerCAmelCase_ )
UpperCAmelCase_= eval_metric["""accuracy"""]
if best_performance < eval_metric["accuracy"]:
UpperCAmelCase_= eval_metric["""accuracy"""]
if args.performance_lower_bound is not None:
assert (
args.performance_lower_bound <= best_performance
), F"""Best performance metric {best_performance} is lower than the lower bound {args.performance_lower_bound}"""
accelerator.wait_for_everyone()
if accelerator.is_main_process:
with open(os.path.join(args.output_dir ,"""all_results.json""" ) ,"""w""" ) as f:
json.dump(lowerCAmelCase_ ,lowerCAmelCase_ )
def __a ( ) -> List[Any]:
'''simple docstring'''
UpperCAmelCase_= argparse.ArgumentParser(description="""Simple example of training script tracking peak GPU memory usage.""" )
parser.add_argument(
"""--model_name_or_path""" ,type=lowerCAmelCase_ ,default="""bert-base-cased""" ,help="""Path to pretrained model or model identifier from huggingface.co/models.""" ,required=lowerCAmelCase_ ,)
parser.add_argument(
"""--output_dir""" ,type=lowerCAmelCase_ ,default=""".""" ,help="""Optional save directory where all checkpoint folders will be stored. Default is the current working directory.""" ,)
parser.add_argument(
"""--performance_lower_bound""" ,type=lowerCAmelCase_ ,default=lowerCAmelCase_ ,help="""Optional lower bound for the performance metric. If set, the training will throw error when the performance metric drops below this value.""" ,)
parser.add_argument(
"""--num_epochs""" ,type=lowerCAmelCase_ ,default=3 ,help="""Number of train epochs.""" ,)
UpperCAmelCase_= parser.parse_args()
UpperCAmelCase_= {"""lr""": 2E-5, """num_epochs""": args.num_epochs, """seed""": 42, """batch_size""": 16}
training_function(lowerCAmelCase_ ,lowerCAmelCase_ )
if __name__ == "__main__":
main()
| 593 |
from argparse import ArgumentParser, Namespace
from ..utils import logging
from . import BaseTransformersCLICommand
def __a ( lowerCAmelCase_ : Namespace ) -> Optional[int]:
'''simple docstring'''
return ConvertCommand(
args.model_type ,args.tf_checkpoint ,args.pytorch_dump_output ,args.config ,args.finetuning_task_name )
__A = '''
transformers can only be used from the commandline to convert TensorFlow models in PyTorch, In that case, it requires
TensorFlow to be installed. Please see https://www.tensorflow.org/install/ for installation instructions.
'''
class lowercase ( snake_case__):
"""simple docstring"""
@staticmethod
def _SCREAMING_SNAKE_CASE ( __UpperCAmelCase : ArgumentParser ) -> Union[str, Any]:
UpperCAmelCase_= parser.add_parser(
"""convert""" , help="""CLI tool to run convert model from original author checkpoints to Transformers PyTorch checkpoints.""" , )
train_parser.add_argument("""--model_type""" , type=__UpperCAmelCase , required=__UpperCAmelCase , help="""Model's type.""" )
train_parser.add_argument(
"""--tf_checkpoint""" , type=__UpperCAmelCase , required=__UpperCAmelCase , help="""TensorFlow checkpoint path or folder.""" )
train_parser.add_argument(
"""--pytorch_dump_output""" , type=__UpperCAmelCase , required=__UpperCAmelCase , help="""Path to the PyTorch saved model output.""" )
train_parser.add_argument("""--config""" , type=__UpperCAmelCase , default="""""" , help="""Configuration file path or folder.""" )
train_parser.add_argument(
"""--finetuning_task_name""" , type=__UpperCAmelCase , default=__UpperCAmelCase , help="""Optional fine-tuning task name if the TF model was a finetuned model.""" , )
train_parser.set_defaults(func=__UpperCAmelCase )
def __init__( self : Tuple , __UpperCAmelCase : str , __UpperCAmelCase : str , __UpperCAmelCase : str , __UpperCAmelCase : str , __UpperCAmelCase : str , *__UpperCAmelCase : Any , ) -> Optional[Any]:
UpperCAmelCase_= logging.get_logger("""transformers-cli/converting""" )
self._logger.info(F"""Loading model {model_type}""" )
UpperCAmelCase_= model_type
UpperCAmelCase_= tf_checkpoint
UpperCAmelCase_= pytorch_dump_output
UpperCAmelCase_= config
UpperCAmelCase_= finetuning_task_name
def _SCREAMING_SNAKE_CASE ( self : List[Any] ) -> Optional[Any]:
if self._model_type == "albert":
try:
from ..models.albert.convert_albert_original_tf_checkpoint_to_pytorch import (
convert_tf_checkpoint_to_pytorch,
)
except ImportError:
raise ImportError(__UpperCAmelCase )
convert_tf_checkpoint_to_pytorch(self._tf_checkpoint , self._config , self._pytorch_dump_output )
elif self._model_type == "bert":
try:
from ..models.bert.convert_bert_original_tf_checkpoint_to_pytorch import (
convert_tf_checkpoint_to_pytorch,
)
except ImportError:
raise ImportError(__UpperCAmelCase )
convert_tf_checkpoint_to_pytorch(self._tf_checkpoint , self._config , self._pytorch_dump_output )
elif self._model_type == "funnel":
try:
from ..models.funnel.convert_funnel_original_tf_checkpoint_to_pytorch import (
convert_tf_checkpoint_to_pytorch,
)
except ImportError:
raise ImportError(__UpperCAmelCase )
convert_tf_checkpoint_to_pytorch(self._tf_checkpoint , self._config , self._pytorch_dump_output )
elif self._model_type == "t5":
try:
from ..models.ta.convert_ta_original_tf_checkpoint_to_pytorch import convert_tf_checkpoint_to_pytorch
except ImportError:
raise ImportError(__UpperCAmelCase )
convert_tf_checkpoint_to_pytorch(self._tf_checkpoint , self._config , self._pytorch_dump_output )
elif self._model_type == "gpt":
from ..models.openai.convert_openai_original_tf_checkpoint_to_pytorch import (
convert_openai_checkpoint_to_pytorch,
)
convert_openai_checkpoint_to_pytorch(self._tf_checkpoint , self._config , self._pytorch_dump_output )
elif self._model_type == "transfo_xl":
try:
from ..models.transfo_xl.convert_transfo_xl_original_tf_checkpoint_to_pytorch import (
convert_transfo_xl_checkpoint_to_pytorch,
)
except ImportError:
raise ImportError(__UpperCAmelCase )
if "ckpt" in self._tf_checkpoint.lower():
UpperCAmelCase_= self._tf_checkpoint
UpperCAmelCase_= """"""
else:
UpperCAmelCase_= self._tf_checkpoint
UpperCAmelCase_= """"""
convert_transfo_xl_checkpoint_to_pytorch(
__UpperCAmelCase , self._config , self._pytorch_dump_output , __UpperCAmelCase )
elif self._model_type == "gpt2":
try:
from ..models.gpta.convert_gpta_original_tf_checkpoint_to_pytorch import (
convert_gpta_checkpoint_to_pytorch,
)
except ImportError:
raise ImportError(__UpperCAmelCase )
convert_gpta_checkpoint_to_pytorch(self._tf_checkpoint , self._config , self._pytorch_dump_output )
elif self._model_type == "xlnet":
try:
from ..models.xlnet.convert_xlnet_original_tf_checkpoint_to_pytorch import (
convert_xlnet_checkpoint_to_pytorch,
)
except ImportError:
raise ImportError(__UpperCAmelCase )
convert_xlnet_checkpoint_to_pytorch(
self._tf_checkpoint , self._config , self._pytorch_dump_output , self._finetuning_task_name )
elif self._model_type == "xlm":
from ..models.xlm.convert_xlm_original_pytorch_checkpoint_to_pytorch import (
convert_xlm_checkpoint_to_pytorch,
)
convert_xlm_checkpoint_to_pytorch(self._tf_checkpoint , self._pytorch_dump_output )
elif self._model_type == "lxmert":
from ..models.lxmert.convert_lxmert_original_tf_checkpoint_to_pytorch import (
convert_lxmert_checkpoint_to_pytorch,
)
convert_lxmert_checkpoint_to_pytorch(self._tf_checkpoint , self._pytorch_dump_output )
elif self._model_type == "rembert":
from ..models.rembert.convert_rembert_tf_checkpoint_to_pytorch import (
convert_rembert_tf_checkpoint_to_pytorch,
)
convert_rembert_tf_checkpoint_to_pytorch(self._tf_checkpoint , self._config , self._pytorch_dump_output )
else:
raise ValueError(
"""--model_type should be selected in the list [bert, gpt, gpt2, t5, transfo_xl, xlnet, xlm, lxmert]""" )
| 593 | 1 |
'''simple docstring'''
def lowerCamelCase_ ( lowercase__):
if number < 0:
raise ValueError("number must not be negative")
return number & (number - 1) == 0
if __name__ == "__main__":
import doctest
doctest.testmod()
| 187 |
'''simple docstring'''
import logging
import sys
from dataclasses import dataclass, field
from typing import Any, Dict, List, Optional, Union
import librosa
import torch
from datasets import DatasetDict, load_dataset
from packaging import version
from torch import nn
from transformers import (
HfArgumentParser,
Trainer,
TrainingArguments,
WavaVecaConfig,
WavaVecaFeatureExtractor,
WavaVecaForPreTraining,
is_apex_available,
trainer_utils,
)
from transformers.models.wavaveca.modeling_wavaveca import _compute_mask_indices
if is_apex_available():
from apex import amp
if version.parse(version.parse(torch.__version__).base_version) >= version.parse("""1.6"""):
__A : List[Any] = True
from torch.cuda.amp import autocast
__A : List[Any] = logging.getLogger(__name__)
@dataclass
class lowercase :
'''simple docstring'''
lowerCAmelCase__ = field(
metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"} )
lowerCAmelCase__ = field(
default=_lowerCamelCase , metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"} , )
lowerCAmelCase__ = field(
default=_lowerCamelCase , metadata={"help": "Whether to freeze the feature extractor layers of the model."} )
lowerCAmelCase__ = field(
default=_lowerCamelCase , metadata={"help": "Whether to log verbose messages or not."} , )
lowerCAmelCase__ = field(
default=2.0 , metadata={"help": "Maximum temperature for gumbel softmax."} )
lowerCAmelCase__ = field(
default=0.5 , metadata={"help": "Minimum temperature for gumbel softmax."} )
lowerCAmelCase__ = field(
default=0.999995 , metadata={"help": "Decay of gumbel temperature during training."} )
def lowerCamelCase_ ( lowercase__ , lowercase__):
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s" , datefmt="%m/%d/%Y %H:%M:%S" , handlers=[logging.StreamHandler(sys.stdout)] , )
lowerCamelCase__ = logging.WARNING
if model_args.verbose_logging:
lowerCamelCase__ = logging.DEBUG
elif trainer_utils.is_main_process(training_args.local_rank):
lowerCamelCase__ = logging.INFO
logger.setLevel(lowercase__)
@dataclass
class lowercase :
'''simple docstring'''
lowerCAmelCase__ = field(
default=_lowerCamelCase , metadata={"help": "The name of the dataset to use (via the datasets library)."} )
lowerCAmelCase__ = field(
default=_lowerCamelCase , metadata={"help": "The configuration name of the dataset to use (via the datasets library)."} )
lowerCAmelCase__ = field(
default="train" , metadata={
"help": "The name of the training data set split to use (via the datasets library). Defaults to 'train'"
} , )
lowerCAmelCase__ = field(
default="validation" , metadata={
"help": (
"The name of the validation data set split to use (via the datasets library). Defaults to 'validation'"
)
} , )
lowerCAmelCase__ = field(
default="file" , metadata={"help": "Column in the dataset that contains speech file path. Defaults to 'file'"} , )
lowerCAmelCase__ = field(
default=_lowerCamelCase , metadata={"help": "Overwrite the cached preprocessed datasets or not."} )
lowerCAmelCase__ = field(
default=1 , metadata={
"help": "The percentage of the train set used as validation set in case there's no validation split"
} , )
lowerCAmelCase__ = field(
default=_lowerCamelCase , metadata={"help": "The number of processes to use for the preprocessing."} , )
lowerCAmelCase__ = field(
default=20.0 , metadata={"help": "Filter audio files that are longer than `max_duration_in_seconds` seconds"} )
@dataclass
class lowercase :
'''simple docstring'''
lowerCAmelCase__ = 42
lowerCAmelCase__ = 42
lowerCAmelCase__ = "longest"
lowerCAmelCase__ = None
lowerCAmelCase__ = None
def __call__( self : Optional[int] , __lowerCamelCase : List[Dict[str, Union[List[int], torch.Tensor]]] ) -> Dict[str, torch.Tensor]:
'''simple docstring'''
lowerCamelCase__ = self.feature_extractor.pad(
__lowerCamelCase , max_length=self.max_length , padding=self.padding , pad_to_multiple_of=self.pad_to_multiple_of , return_tensors="pt" , )
lowerCamelCase__ = self.model._get_feat_extract_output_lengths(batch["input_values"].shape[-1] )
lowerCamelCase__ = batch["input_values"].shape[0]
# make sure that no loss is computed on padded inputs
if batch["attention_mask"] is not None:
# compute real output lengths according to convolution formula
lowerCamelCase__ = self.model._get_feat_extract_output_lengths(batch["attention_mask"].sum(-1 ) ).to(
torch.long )
lowerCamelCase__ = torch.zeros(
(batch_size, mask_indices_seq_length) , dtype=torch.long , device=batch["input_values"].device )
# these two operations makes sure that all values
# before the output lengths indices are attended to
lowerCamelCase__ = 1
lowerCamelCase__ = attention_mask.flip([-1] ).cumsum(-1 ).flip([-1] ).bool()
# sample randomly masked indices
lowerCamelCase__ = _compute_mask_indices(
(batch_size, mask_indices_seq_length) , self.model.config.mask_time_prob , self.model.config.mask_time_length , attention_mask=__lowerCamelCase , min_masks=2 , )
return batch
class lowercase ( _lowerCamelCase ):
'''simple docstring'''
def __init__( self : str , *__lowerCamelCase : Union[str, Any] , __lowerCamelCase : int=1 , __lowerCamelCase : Optional[Any]=0 , __lowerCamelCase : str=1.0 , **__lowerCamelCase : Optional[Any] ) -> str:
'''simple docstring'''
super().__init__(*__lowerCamelCase , **__lowerCamelCase )
lowerCamelCase__ = 0
lowerCamelCase__ = max_gumbel_temp
lowerCamelCase__ = min_gumbel_temp
lowerCamelCase__ = gumbel_temp_decay
def a__ ( self : Tuple , __lowerCamelCase : nn.Module , __lowerCamelCase : Dict[str, Union[torch.Tensor, Any]] ) -> torch.Tensor:
'''simple docstring'''
model.train()
lowerCamelCase__ = self._prepare_inputs(__lowerCamelCase )
if self.use_amp:
with autocast():
lowerCamelCase__ = self.compute_loss(__lowerCamelCase , __lowerCamelCase )
else:
lowerCamelCase__ = self.compute_loss(__lowerCamelCase , __lowerCamelCase )
if self.args.n_gpu > 1 or self.deepspeed:
if model.module.config.ctc_loss_reduction == "mean":
lowerCamelCase__ = loss.mean()
elif model.module.config.ctc_loss_reduction == "sum":
lowerCamelCase__ = loss.sum() / (inputs["mask_time_indices"]).sum()
else:
raise ValueError(f'''{model.config.ctc_loss_reduction} is not valid. Choose one of [\'mean\', \'sum\']''' )
if self.args.gradient_accumulation_steps > 1:
lowerCamelCase__ = loss / self.args.gradient_accumulation_steps
if self.use_amp:
self.scaler.scale(__lowerCamelCase ).backward()
elif self.use_apex:
with amp.scale_loss(__lowerCamelCase , self.optimizer ) as scaled_loss:
scaled_loss.backward()
elif self.deepspeed:
self.deepspeed.backward(__lowerCamelCase )
else:
loss.backward()
self.num_update_step += 1
# make sure gumbel softmax temperature is decayed
if self.args.n_gpu > 1 or self.deepspeed:
model.module.set_gumbel_temperature(
max(self.max_gumbel_temp * self.gumbel_temp_decay**self.num_update_step , self.min_gumbel_temp ) )
else:
model.set_gumbel_temperature(
max(self.max_gumbel_temp * self.gumbel_temp_decay**self.num_update_step , self.min_gumbel_temp ) )
return loss.detach()
def lowerCamelCase_ ( ):
# See all possible arguments in src/transformers/training_args.py
# or by passing the --help flag to this script.
# We now keep distinct sets of args, for a cleaner separation of concerns.
lowerCamelCase__ = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments))
lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ = parser.parse_args_into_dataclasses()
configure_logger(lowercase__ , lowercase__)
# Downloading and loading a dataset from the hub.
lowerCamelCase__ = load_dataset(data_args.dataset_name , data_args.dataset_config_name , cache_dir=model_args.cache_dir)
if "validation" not in datasets.keys():
# make sure only "validation" and "train" keys remain"
lowerCamelCase__ = DatasetDict()
lowerCamelCase__ = load_dataset(
data_args.dataset_name , data_args.dataset_config_name , split=F'''{data_args.train_split_name}[:{data_args.validation_split_percentage}%]''' , cache_dir=model_args.cache_dir , )
lowerCamelCase__ = load_dataset(
data_args.dataset_name , data_args.dataset_config_name , split=F'''{data_args.train_split_name}[{data_args.validation_split_percentage}%:]''' , cache_dir=model_args.cache_dir , )
else:
# make sure only "validation" and "train" keys remain"
lowerCamelCase__ = DatasetDict()
lowerCamelCase__ = load_dataset(
data_args.dataset_name , data_args.dataset_config_name , split="validation" , cache_dir=model_args.cache_dir , )
lowerCamelCase__ = load_dataset(
data_args.dataset_name , data_args.dataset_config_name , split=F'''{data_args.train_split_name}''' , cache_dir=model_args.cache_dir , )
# only normalized-inputs-training is supported
lowerCamelCase__ = WavaVecaFeatureExtractor.from_pretrained(
model_args.model_name_or_path , cache_dir=model_args.cache_dir , do_normalize=lowercase__)
def prepare_dataset(lowercase__):
# check that all files have the correct sampling rate
lowerCamelCase__ , lowerCamelCase__ = librosa.load(batch[data_args.speech_file_column] , sr=feature_extractor.sampling_rate)
return batch
# load audio files into numpy arrays
lowerCamelCase__ = datasets.map(
lowercase__ , num_proc=data_args.preprocessing_num_workers , remove_columns=datasets["train"].column_names)
# filter audio files that are too long
lowerCamelCase__ = vectorized_datasets.filter(
lambda lowercase__: len(data["speech"]) < int(data_args.max_duration_in_seconds * feature_extractor.sampling_rate))
def normalize(lowercase__):
return feature_extractor(batch["speech"] , sampling_rate=feature_extractor.sampling_rate)
# normalize and transform to `BatchFeatures`
lowerCamelCase__ = vectorized_datasets.map(
lowercase__ , batched=lowercase__ , num_proc=data_args.preprocessing_num_workers , load_from_cache_file=not data_args.overwrite_cache , remove_columns=vectorized_datasets["train"].column_names , )
# pretraining is only supported for "newer" stable layer norm architecture
# apply_spec_augment has to be True, mask_feature_prob has to be 0.0
lowerCamelCase__ = WavaVecaConfig.from_pretrained(
model_args.model_name_or_path , cache_dir=model_args.cache_dir , gradient_checkpointing=training_args.gradient_checkpointing , )
if not config.do_stable_layer_norm or config.feat_extract_norm != "layer":
raise ValueError(
"PreTraining is only supported for ``config.do_stable_layer_norm=True`` and"
" ``config.feat_extract_norm='layer'")
lowerCamelCase__ = WavaVecaForPreTraining(lowercase__)
lowerCamelCase__ = DataCollatorForWavaVecaPretraining(model=lowercase__ , feature_extractor=lowercase__)
lowerCamelCase__ = WavaVecaPreTrainer(
model=lowercase__ , data_collator=lowercase__ , args=lowercase__ , train_dataset=vectorized_datasets["train"] , eval_dataset=vectorized_datasets["validation"] , tokenizer=lowercase__ , max_gumbel_temp=model_args.max_gumbel_temperature , min_gumbel_temp=model_args.min_gumbel_temperature , gumbel_temp_decay=model_args.gumbel_temperature_decay , )
trainer.train()
if __name__ == "__main__":
main()
| 187 | 1 |
"""simple docstring"""
import argparse
import torch
from transformers import GPTaLMHeadModel, RobertaForMaskedLM
if __name__ == "__main__":
__lowerCamelCase = 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")
__lowerCamelCase = parser.parse_args()
if args.model_type == "roberta":
__lowerCamelCase = RobertaForMaskedLM.from_pretrained(args.model_name)
__lowerCamelCase = "roberta"
elif args.model_type == "gpt2":
__lowerCamelCase = GPTaLMHeadModel.from_pretrained(args.model_name)
__lowerCamelCase = "transformer"
__lowerCamelCase = model.state_dict()
__lowerCamelCase = {}
# Embeddings #
if args.model_type == "gpt2":
for param_name in ["wte.weight", "wpe.weight"]:
__lowerCamelCase = state_dict[f"""{prefix}.{param_name}"""]
else:
for w in ["word_embeddings", "position_embeddings", "token_type_embeddings"]:
__lowerCamelCase = f"""{prefix}.embeddings.{w}.weight"""
__lowerCamelCase = state_dict[param_name]
for w in ["weight", "bias"]:
__lowerCamelCase = f"""{prefix}.embeddings.LayerNorm.{w}"""
__lowerCamelCase = state_dict[param_name]
# Transformer Blocks #
__lowerCamelCase = 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"]:
__lowerCamelCase = state_dict[
f"""{prefix}.h.{teacher_idx}.{layer}.{w}"""
]
__lowerCamelCase = 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"]:
__lowerCamelCase = 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"]:
__lowerCamelCase = state_dict[f"""{layer}"""]
if args.vocab_transform:
for w in ["weight", "bias"]:
__lowerCamelCase = state_dict[f"""lm_head.dense.{w}"""]
__lowerCamelCase = state_dict[f"""lm_head.layer_norm.{w}"""]
elif args.model_type == "gpt2":
for w in ["weight", "bias"]:
__lowerCamelCase = state_dict[f"""{prefix}.ln_f.{w}"""]
__lowerCamelCase = 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)
| 490 |
"""simple docstring"""
from collections import defaultdict
from graphs.minimum_spanning_tree_prims import prisms_algorithm as mst
def lowercase ( ) -> int:
__magic_name__ , __magic_name__ = 9, 14 # noqa: F841
__magic_name__ = [
[0, 1, 4],
[0, 7, 8],
[1, 2, 8],
[7, 8, 7],
[7, 6, 1],
[2, 8, 2],
[8, 6, 6],
[2, 3, 7],
[2, 5, 4],
[6, 5, 2],
[3, 5, 14],
[3, 4, 9],
[5, 4, 10],
[1, 7, 11],
]
__magic_name__ = defaultdict(__UpperCamelCase )
for nodea, nodea, cost in edges:
adjancency[nodea].append([nodea, cost] )
adjancency[nodea].append([nodea, cost] )
__magic_name__ = mst(__UpperCamelCase )
__magic_name__ = [
[7, 6, 1],
[2, 8, 2],
[6, 5, 2],
[0, 1, 4],
[2, 5, 4],
[2, 3, 7],
[0, 7, 8],
[3, 4, 9],
]
for answer in expected:
__magic_name__ = tuple(answer[:2] )
__magic_name__ = tuple(edge[::-1] )
assert edge in result or reverse in result
| 490 | 1 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_UpperCamelCase = logging.get_logger(__name__)
_UpperCamelCase = {
"""funnel-transformer/small""": """https://huggingface.co/funnel-transformer/small/resolve/main/config.json""",
"""funnel-transformer/small-base""": """https://huggingface.co/funnel-transformer/small-base/resolve/main/config.json""",
"""funnel-transformer/medium""": """https://huggingface.co/funnel-transformer/medium/resolve/main/config.json""",
"""funnel-transformer/medium-base""": """https://huggingface.co/funnel-transformer/medium-base/resolve/main/config.json""",
"""funnel-transformer/intermediate""": (
"""https://huggingface.co/funnel-transformer/intermediate/resolve/main/config.json"""
),
"""funnel-transformer/intermediate-base""": (
"""https://huggingface.co/funnel-transformer/intermediate-base/resolve/main/config.json"""
),
"""funnel-transformer/large""": """https://huggingface.co/funnel-transformer/large/resolve/main/config.json""",
"""funnel-transformer/large-base""": """https://huggingface.co/funnel-transformer/large-base/resolve/main/config.json""",
"""funnel-transformer/xlarge""": """https://huggingface.co/funnel-transformer/xlarge/resolve/main/config.json""",
"""funnel-transformer/xlarge-base""": """https://huggingface.co/funnel-transformer/xlarge-base/resolve/main/config.json""",
}
class lowerCamelCase__ ( snake_case ):
SCREAMING_SNAKE_CASE = '''funnel'''
SCREAMING_SNAKE_CASE = {
'''hidden_size''': '''d_model''',
'''num_attention_heads''': '''n_head''',
}
def __init__( self ,A=30_522 ,A=[4, 4, 4] ,A=None ,A=2 ,A=768 ,A=12 ,A=64 ,A=3_072 ,A="gelu_new" ,A=0.1 ,A=0.1 ,A=0.0 ,A=0.1 ,A=None ,A=1e-9 ,A="mean" ,A="relative_shift" ,A=True ,A=True ,A=True ,**A ,):
UpperCAmelCase = vocab_size
UpperCAmelCase = block_sizes
UpperCAmelCase = [1] * len(A ) if block_repeats is None else block_repeats
assert len(A ) == len(
self.block_repeats ), "`block_sizes` and `block_repeats` should have the same length."
UpperCAmelCase = num_decoder_layers
UpperCAmelCase = d_model
UpperCAmelCase = n_head
UpperCAmelCase = d_head
UpperCAmelCase = d_inner
UpperCAmelCase = hidden_act
UpperCAmelCase = hidden_dropout
UpperCAmelCase = attention_dropout
UpperCAmelCase = activation_dropout
UpperCAmelCase = initializer_range
UpperCAmelCase = initializer_std
UpperCAmelCase = layer_norm_eps
assert pooling_type in [
"mean",
"max",
], F'''Got {pooling_type} for `pooling_type` but only \'mean\' and \'max\' are supported.'''
UpperCAmelCase = pooling_type
assert attention_type in [
"relative_shift",
"factorized",
], F'''Got {attention_type} for `attention_type` but only \'relative_shift\' and \'factorized\' are supported.'''
UpperCAmelCase = attention_type
UpperCAmelCase = separate_cls
UpperCAmelCase = truncate_seq
UpperCAmelCase = pool_q_only
super().__init__(**A )
@property
def _UpperCamelCase ( self ):
return sum(self.block_sizes )
@num_hidden_layers.setter
def _UpperCamelCase ( self ,A ):
raise NotImplementedError(
"""This model does not support the setting of `num_hidden_layers`. Please set `block_sizes`.""" )
@property
def _UpperCamelCase ( self ):
return len(self.block_sizes )
@num_blocks.setter
def _UpperCamelCase ( self ,A ):
raise NotImplementedError("""This model does not support the setting of `num_blocks`. Please set `block_sizes`.""" )
| 74 |
"""simple docstring"""
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, 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 numpy as np
import tensorflow as tf
from transformers import (
TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
FlaubertConfig,
TFFlaubertForMultipleChoice,
TFFlaubertForQuestionAnsweringSimple,
TFFlaubertForSequenceClassification,
TFFlaubertForTokenClassification,
TFFlaubertModel,
TFFlaubertWithLMHeadModel,
)
class lowerCamelCase__ :
def __init__( self ,A ,):
UpperCAmelCase = parent
UpperCAmelCase = 13
UpperCAmelCase = 7
UpperCAmelCase = True
UpperCAmelCase = True
UpperCAmelCase = True
UpperCAmelCase = True
UpperCAmelCase = True
UpperCAmelCase = False
UpperCAmelCase = False
UpperCAmelCase = False
UpperCAmelCase = 2
UpperCAmelCase = 99
UpperCAmelCase = 0
UpperCAmelCase = 32
UpperCAmelCase = 2
UpperCAmelCase = 4
UpperCAmelCase = 0.1
UpperCAmelCase = 0.1
UpperCAmelCase = 512
UpperCAmelCase = 16
UpperCAmelCase = 2
UpperCAmelCase = 0.02
UpperCAmelCase = 3
UpperCAmelCase = 4
UpperCAmelCase = """last"""
UpperCAmelCase = True
UpperCAmelCase = None
UpperCAmelCase = 0
def _UpperCamelCase ( self ):
UpperCAmelCase = ids_tensor([self.batch_size, self.seq_length] ,self.vocab_size )
UpperCAmelCase = random_attention_mask([self.batch_size, self.seq_length] ,dtype=tf.floataa )
UpperCAmelCase = None
if self.use_input_lengths:
UpperCAmelCase = (
ids_tensor([self.batch_size] ,vocab_size=2 ) + self.seq_length - 2
) # small variation of seq_length
UpperCAmelCase = None
if self.use_token_type_ids:
UpperCAmelCase = ids_tensor([self.batch_size, self.seq_length] ,self.n_langs )
UpperCAmelCase = None
UpperCAmelCase = None
UpperCAmelCase = None
if self.use_labels:
UpperCAmelCase = ids_tensor([self.batch_size] ,self.type_sequence_label_size )
UpperCAmelCase = ids_tensor([self.batch_size, self.seq_length] ,self.num_labels )
UpperCAmelCase = ids_tensor([self.batch_size] ,2 ,dtype=tf.floataa )
UpperCAmelCase = ids_tensor([self.batch_size] ,self.num_choices )
UpperCAmelCase = FlaubertConfig(
vocab_size=self.vocab_size ,n_special=self.n_special ,emb_dim=self.hidden_size ,n_layers=self.num_hidden_layers ,n_heads=self.num_attention_heads ,dropout=self.hidden_dropout_prob ,attention_dropout=self.attention_probs_dropout_prob ,gelu_activation=self.gelu_activation ,sinusoidal_embeddings=self.sinusoidal_embeddings ,asm=self.asm ,causal=self.causal ,n_langs=self.n_langs ,max_position_embeddings=self.max_position_embeddings ,initializer_range=self.initializer_range ,summary_type=self.summary_type ,use_proj=self.use_proj ,bos_token_id=self.bos_token_id ,)
return (
config,
input_ids,
token_type_ids,
input_lengths,
sequence_labels,
token_labels,
is_impossible_labels,
choice_labels,
input_mask,
)
def _UpperCamelCase ( self ,A ,A ,A ,A ,A ,A ,A ,A ,A ,):
UpperCAmelCase = TFFlaubertModel(config=A )
UpperCAmelCase = {"""input_ids""": input_ids, """lengths""": input_lengths, """langs""": token_type_ids}
UpperCAmelCase = model(A )
UpperCAmelCase = [input_ids, input_mask]
UpperCAmelCase = model(A )
self.parent.assertEqual(result.last_hidden_state.shape ,(self.batch_size, self.seq_length, self.hidden_size) )
def _UpperCamelCase ( self ,A ,A ,A ,A ,A ,A ,A ,A ,A ,):
UpperCAmelCase = TFFlaubertWithLMHeadModel(A )
UpperCAmelCase = {"""input_ids""": input_ids, """lengths""": input_lengths, """langs""": token_type_ids}
UpperCAmelCase = model(A )
self.parent.assertEqual(result.logits.shape ,(self.batch_size, self.seq_length, self.vocab_size) )
def _UpperCamelCase ( self ,A ,A ,A ,A ,A ,A ,A ,A ,A ,):
UpperCAmelCase = TFFlaubertForQuestionAnsweringSimple(A )
UpperCAmelCase = {"""input_ids""": input_ids, """lengths""": input_lengths}
UpperCAmelCase = model(A )
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 ,A ,A ,A ,A ,A ,A ,A ,A ,A ,):
UpperCAmelCase = TFFlaubertForSequenceClassification(A )
UpperCAmelCase = {"""input_ids""": input_ids, """lengths""": input_lengths}
UpperCAmelCase = model(A )
self.parent.assertEqual(result.logits.shape ,(self.batch_size, self.type_sequence_label_size) )
def _UpperCamelCase ( self ,A ,A ,A ,A ,A ,A ,A ,A ,A ,):
UpperCAmelCase = self.num_labels
UpperCAmelCase = TFFlaubertForTokenClassification(config=A )
UpperCAmelCase = {"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}
UpperCAmelCase = model(A )
self.parent.assertEqual(result.logits.shape ,(self.batch_size, self.seq_length, self.num_labels) )
def _UpperCamelCase ( self ,A ,A ,A ,A ,A ,A ,A ,A ,A ,):
UpperCAmelCase = self.num_choices
UpperCAmelCase = TFFlaubertForMultipleChoice(config=A )
UpperCAmelCase = tf.tile(tf.expand_dims(A ,1 ) ,(1, self.num_choices, 1) )
UpperCAmelCase = tf.tile(tf.expand_dims(A ,1 ) ,(1, self.num_choices, 1) )
UpperCAmelCase = tf.tile(tf.expand_dims(A ,1 ) ,(1, self.num_choices, 1) )
UpperCAmelCase = {
"""input_ids""": multiple_choice_inputs_ids,
"""attention_mask""": multiple_choice_input_mask,
"""token_type_ids""": multiple_choice_token_type_ids,
}
UpperCAmelCase = model(A )
self.parent.assertEqual(result.logits.shape ,(self.batch_size, self.num_choices) )
def _UpperCamelCase ( self ):
UpperCAmelCase = self.prepare_config_and_inputs()
(
(
UpperCAmelCase
) , (
UpperCAmelCase
) , (
UpperCAmelCase
) , (
UpperCAmelCase
) , (
UpperCAmelCase
) , (
UpperCAmelCase
) , (
UpperCAmelCase
) , (
UpperCAmelCase
) , (
UpperCAmelCase
) ,
) = config_and_inputs
UpperCAmelCase = {
"""input_ids""": input_ids,
"""token_type_ids""": token_type_ids,
"""langs""": token_type_ids,
"""lengths""": input_lengths,
}
return config, inputs_dict
@require_tf
class lowerCamelCase__ ( snake_case , snake_case , unittest.TestCase ):
SCREAMING_SNAKE_CASE = (
(
TFFlaubertModel,
TFFlaubertWithLMHeadModel,
TFFlaubertForSequenceClassification,
TFFlaubertForQuestionAnsweringSimple,
TFFlaubertForTokenClassification,
TFFlaubertForMultipleChoice,
)
if is_tf_available()
else ()
)
SCREAMING_SNAKE_CASE = (
(TFFlaubertWithLMHeadModel,) if is_tf_available() else ()
) # TODO (PVP): Check other models whether language generation is also applicable
SCREAMING_SNAKE_CASE = (
{
'''feature-extraction''': TFFlaubertModel,
'''fill-mask''': TFFlaubertWithLMHeadModel,
'''question-answering''': TFFlaubertForQuestionAnsweringSimple,
'''text-classification''': TFFlaubertForSequenceClassification,
'''token-classification''': TFFlaubertForTokenClassification,
'''zero-shot''': TFFlaubertForSequenceClassification,
}
if is_tf_available()
else {}
)
SCREAMING_SNAKE_CASE = False
SCREAMING_SNAKE_CASE = False
def _UpperCamelCase ( self ,A ,A ,A ,A ,A ):
if (
pipeline_test_casse_name == "QAPipelineTests"
and tokenizer_name is not None
and not tokenizer_name.endswith("""Fast""" )
):
# `QAPipelineTests` fails for a few models when the slower tokenizer are used.
# (The slower tokenizers were never used for pipeline tests before the pipeline testing rework)
# TODO: check (and possibly fix) the `QAPipelineTests` with slower tokenizer
return True
return False
def _UpperCamelCase ( self ):
UpperCAmelCase = TFFlaubertModelTester(self )
UpperCAmelCase = ConfigTester(self ,config_class=A ,emb_dim=37 )
def _UpperCamelCase ( self ):
self.config_tester.run_common_tests()
def _UpperCamelCase ( self ):
UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_flaubert_model(*A )
def _UpperCamelCase ( self ):
UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_flaubert_lm_head(*A )
def _UpperCamelCase ( self ):
UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_flaubert_qa(*A )
def _UpperCamelCase ( self ):
UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_flaubert_sequence_classif(*A )
def _UpperCamelCase ( self ):
UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_flaubert_for_token_classification(*A )
def _UpperCamelCase ( self ):
UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_flaubert_for_multiple_choice(*A )
@slow
def _UpperCamelCase ( self ):
for model_name in TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
UpperCAmelCase = TFFlaubertModel.from_pretrained(A )
self.assertIsNotNone(A )
@require_tf
@require_sentencepiece
@require_tokenizers
class lowerCamelCase__ ( unittest.TestCase ):
@slow
def _UpperCamelCase ( self ):
UpperCAmelCase = TFFlaubertModel.from_pretrained("""jplu/tf-flaubert-small-cased""" )
UpperCAmelCase = tf.convert_to_tensor(
[[0, 158, 735, 2_592, 1_424, 6_727, 82, 1]] ,dtype=tf.intaa ,) # "J'aime flaubert !"
UpperCAmelCase = model(A )[0]
UpperCAmelCase = tf.TensorShape((1, 8, 512) )
self.assertEqual(output.shape ,A )
# compare the actual values for a slice.
UpperCAmelCase = tf.convert_to_tensor(
[
[
[-1.8768773, -1.566555, 0.27072418],
[-1.6920038, -0.5873505, 1.9329599],
[-2.9563985, -1.6993835, 1.7972052],
]
] ,dtype=tf.floataa ,)
self.assertTrue(np.allclose(output[:, :3, :3].numpy() ,expected_slice.numpy() ,atol=1e-4 ) )
| 74 | 1 |
'''simple docstring'''
import random
import unittest
import torch
from diffusers import IFImgaImgSuperResolutionPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device
from ..pipeline_params import TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin
@skip_mps
class __snake_case ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , unittest.TestCase ):
'''simple docstring'''
lowerCamelCase__ = IFImgaImgSuperResolutionPipeline
lowerCamelCase__ = TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'''width''', '''height'''}
lowerCamelCase__ = TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({'''original_image'''} )
lowerCamelCase__ = PipelineTesterMixin.required_optional_params - {'''latents'''}
def __UpperCamelCase ( self ):
return self._get_superresolution_dummy_components()
def __UpperCamelCase ( self , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE=0 ):
if str(__SCREAMING_SNAKE_CASE ).startswith("""mps""" ):
snake_case__ : List[Any] = torch.manual_seed(__SCREAMING_SNAKE_CASE )
else:
snake_case__ : Tuple = torch.Generator(device=__SCREAMING_SNAKE_CASE ).manual_seed(__SCREAMING_SNAKE_CASE )
snake_case__ : Tuple = floats_tensor((1, 3, 3_2, 3_2) , rng=random.Random(__SCREAMING_SNAKE_CASE ) ).to(__SCREAMING_SNAKE_CASE )
snake_case__ : Union[str, Any] = floats_tensor((1, 3, 1_6, 1_6) , rng=random.Random(__SCREAMING_SNAKE_CASE ) ).to(__SCREAMING_SNAKE_CASE )
snake_case__ : int = {
"""prompt""": """A painting of a squirrel eating a burger""",
"""image""": image,
"""original_image""": original_image,
"""generator""": generator,
"""num_inference_steps""": 2,
"""output_type""": """numpy""",
}
return inputs
@unittest.skipIf(
torch_device != """cuda""" or not is_xformers_available() , reason="""XFormers attention is only available with CUDA and `xformers` installed""" , )
def __UpperCamelCase ( self ):
self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3 )
def __UpperCamelCase ( self ):
self._test_save_load_optional_components()
@unittest.skipIf(torch_device != """cuda""" , reason="""float16 requires CUDA""" )
def __UpperCamelCase ( self ):
# Due to non-determinism in save load of the hf-internal-testing/tiny-random-t5 text encoder
super().test_save_load_floataa(expected_max_diff=1e-1 )
def __UpperCamelCase ( self ):
self._test_attention_slicing_forward_pass(expected_max_diff=1e-2 )
def __UpperCamelCase ( self ):
self._test_save_load_local()
def __UpperCamelCase ( self ):
self._test_inference_batch_single_identical(
expected_max_diff=1e-2 , )
| 38 |
'''simple docstring'''
def UpperCamelCase__ ( __magic_name__ : List[Any] ) -> Tuple:
'''simple docstring'''
if not head:
return True
# split the list to two parts
snake_case__ , snake_case__ : Dict = head.next, head
while fast and fast.next:
snake_case__ : Any = fast.next.next
snake_case__ : int = slow.next
snake_case__ : Dict = slow.next
snake_case__ : List[str] = None # Don't forget here! But forget still works!
# reverse the second part
snake_case__ : Tuple = None
while second:
snake_case__ : Tuple = second.next
snake_case__ : Any = node
snake_case__ : str = second
snake_case__ : Optional[Any] = nxt
# compare two parts
# second part has the same or one less node
while node:
if node.val != head.val:
return False
snake_case__ : List[Any] = node.next
snake_case__ : int = head.next
return True
def UpperCamelCase__ ( __magic_name__ : Any ) -> Optional[Any]:
'''simple docstring'''
if not head or not head.next:
return True
# 1. Get the midpoint (slow)
snake_case__ : List[Any] = head
while fast and fast.next:
snake_case__ , snake_case__ : Any = fast.next.next, slow.next
# 2. Push the second half into the stack
snake_case__ : Tuple = [slow.val]
while slow.next:
snake_case__ : Optional[Any] = slow.next
stack.append(slow.val )
# 3. Comparison
while stack:
if stack.pop() != cur.val:
return False
snake_case__ : str = cur.next
return True
def UpperCamelCase__ ( __magic_name__ : Optional[Any] ) -> Tuple:
'''simple docstring'''
if not head or not head.next:
return True
snake_case__ : int = {}
snake_case__ : Union[str, Any] = 0
while head:
if head.val in d:
d[head.val].append(__magic_name__ )
else:
snake_case__ : Tuple = [pos]
snake_case__ : Optional[Any] = head.next
pos += 1
snake_case__ : int = pos - 1
snake_case__ : str = 0
for v in d.values():
if len(__magic_name__ ) % 2 != 0:
middle += 1
else:
snake_case__ : List[str] = 0
for i in range(0 , len(__magic_name__ ) ):
if v[i] + v[len(__magic_name__ ) - 1 - step] != checksum:
return False
step += 1
if middle > 1:
return False
return True
| 38 | 1 |
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
a = logging.get_logger(__name__)
a = {"""vocab_file""": """sentencepiece.bpe.model"""}
a = {
"""vocab_file""": {
"""camembert-base""": """https://huggingface.co/camembert-base/resolve/main/sentencepiece.bpe.model""",
}
}
a = {
"""camembert-base""": 512,
}
a = """▁"""
class UpperCAmelCase_ (snake_case__ ):
"""simple docstring"""
lowerCamelCase : int = VOCAB_FILES_NAMES
lowerCamelCase : Tuple = PRETRAINED_VOCAB_FILES_MAP
lowerCamelCase : int = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowerCamelCase : int = ['input_ids', 'attention_mask']
def __init__( self: List[Any] , _UpperCAmelCase: Union[str, Any] , _UpperCAmelCase: str="<s>" , _UpperCAmelCase: List[Any]="</s>" , _UpperCAmelCase: Any="</s>" , _UpperCAmelCase: int="<s>" , _UpperCAmelCase: str="<unk>" , _UpperCAmelCase: Any="<pad>" , _UpperCAmelCase: Any="<mask>" , _UpperCAmelCase: Union[str, Any]=["<s>NOTUSED", "</s>NOTUSED"] , _UpperCAmelCase: Optional[Dict[str, Any]] = None , **_UpperCAmelCase: Optional[int] , ):
# Mask token behave like a normal word, i.e. include the space before it
_lowerCAmelCase :Union[str, Any] = AddedToken(_UpperCAmelCase , lstrip=_UpperCAmelCase , rstrip=_UpperCAmelCase ) if isinstance(_UpperCAmelCase , _UpperCAmelCase ) else mask_token
_lowerCAmelCase :Union[str, Any] = {} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
bos_token=_UpperCAmelCase , eos_token=_UpperCAmelCase , unk_token=_UpperCAmelCase , sep_token=_UpperCAmelCase , cls_token=_UpperCAmelCase , pad_token=_UpperCAmelCase , mask_token=_UpperCAmelCase , additional_special_tokens=_UpperCAmelCase , sp_model_kwargs=self.sp_model_kwargs , **_UpperCAmelCase , )
_lowerCAmelCase :Optional[Any] = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(str(_UpperCAmelCase ) )
_lowerCAmelCase :Dict = vocab_file
# HACK: These tokens were added by fairseq but don't seem to be actually used when duplicated in the actual
# sentencepiece vocabulary (this is the case for <s> and </s>
_lowerCAmelCase :int = {'<s>NOTUSED': 0, '<pad>': 1, '</s>NOTUSED': 2, '<unk>': 3}
_lowerCAmelCase :List[Any] = len(self.fairseq_tokens_to_ids )
_lowerCAmelCase :List[str] = len(self.sp_model ) + len(self.fairseq_tokens_to_ids )
_lowerCAmelCase :str = {v: k for k, v in self.fairseq_tokens_to_ids.items()}
def SCREAMING_SNAKE_CASE__ ( self: Dict , _UpperCAmelCase: List[int] , _UpperCAmelCase: Optional[List[int]] = None ):
if token_ids_a is None:
return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
_lowerCAmelCase :List[str] = [self.cls_token_id]
_lowerCAmelCase :Dict = [self.sep_token_id]
return cls + token_ids_a + sep + sep + token_ids_a + sep
def SCREAMING_SNAKE_CASE__ ( self: str , _UpperCAmelCase: List[int] , _UpperCAmelCase: Optional[List[int]] = None , _UpperCAmelCase: bool = False ):
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=_UpperCAmelCase , token_ids_a=_UpperCAmelCase , already_has_special_tokens=_UpperCAmelCase )
if token_ids_a is None:
return [1] + ([0] * len(_UpperCAmelCase )) + [1]
return [1] + ([0] * len(_UpperCAmelCase )) + [1, 1] + ([0] * len(_UpperCAmelCase )) + [1]
def SCREAMING_SNAKE_CASE__ ( self: Optional[int] , _UpperCAmelCase: List[int] , _UpperCAmelCase: Optional[List[int]] = None ):
_lowerCAmelCase :List[Any] = [self.sep_token_id]
_lowerCAmelCase :Optional[Any] = [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]
@property
def SCREAMING_SNAKE_CASE__ ( self: Dict ):
return len(self.fairseq_tokens_to_ids ) + len(self.sp_model )
def SCREAMING_SNAKE_CASE__ ( self: Optional[int] ):
_lowerCAmelCase :List[str] = {self.convert_ids_to_tokens(_UpperCAmelCase ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def SCREAMING_SNAKE_CASE__ ( self: Union[str, Any] , _UpperCAmelCase: str ):
return self.sp_model.encode(_UpperCAmelCase , out_type=_UpperCAmelCase )
def SCREAMING_SNAKE_CASE__ ( self: Dict , _UpperCAmelCase: int ):
if token in self.fairseq_tokens_to_ids:
return self.fairseq_tokens_to_ids[token]
elif self.sp_model.PieceToId(_UpperCAmelCase ) == 0:
# Convert sentence piece unk token to fairseq unk token index
return self.unk_token_id
return self.fairseq_offset + self.sp_model.PieceToId(_UpperCAmelCase )
def SCREAMING_SNAKE_CASE__ ( self: List[str] , _UpperCAmelCase: Union[str, Any] ):
if index in self.fairseq_ids_to_tokens:
return self.fairseq_ids_to_tokens[index]
return self.sp_model.IdToPiece(index - self.fairseq_offset )
def SCREAMING_SNAKE_CASE__ ( self: Optional[Any] , _UpperCAmelCase: str ):
_lowerCAmelCase :Union[str, Any] = []
_lowerCAmelCase :str = ''
_lowerCAmelCase :Tuple = False
for token in tokens:
# make sure that special tokens are not decoded using sentencepiece model
if token in self.all_special_tokens:
if not prev_is_special:
out_string += " "
out_string += self.sp_model.decode(_UpperCAmelCase ) + token
_lowerCAmelCase :Any = True
_lowerCAmelCase :List[str] = []
else:
current_sub_tokens.append(_UpperCAmelCase )
_lowerCAmelCase :Optional[Any] = False
out_string += self.sp_model.decode(_UpperCAmelCase )
return out_string.strip()
def __getstate__( self: Dict ):
_lowerCAmelCase :Any = self.__dict__.copy()
_lowerCAmelCase :List[Any] = None
return state
def __setstate__( self: Optional[int] , _UpperCAmelCase: str ):
_lowerCAmelCase :List[str] = d
# for backward compatibility
if not hasattr(self , 'sp_model_kwargs' ):
_lowerCAmelCase :str = {}
_lowerCAmelCase :Dict = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(self.vocab_file )
def SCREAMING_SNAKE_CASE__ ( self: List[str] , _UpperCAmelCase: str , _UpperCAmelCase: Optional[str] = None ):
if not os.path.isdir(_UpperCAmelCase ):
logger.error(f"""Vocabulary path ({save_directory}) should be a directory""" )
return
_lowerCAmelCase :List[Any] = os.path.join(
_UpperCAmelCase , (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(_UpperCAmelCase ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file , _UpperCAmelCase )
elif not os.path.isfile(self.vocab_file ):
with open(_UpperCAmelCase , 'wb' ) as fi:
_lowerCAmelCase :Union[str, Any] = self.sp_model.serialized_model_proto()
fi.write(_UpperCAmelCase )
return (out_vocab_file,) | 382 |
from math import sqrt
def UpperCamelCase_( __magic_name__ : int = 1000000 ):
"""simple docstring"""
_lowerCAmelCase :int = 0
_lowerCAmelCase :int = 0
_lowerCAmelCase :int
while num_cuboids <= limit:
max_cuboid_size += 1
for sum_shortest_sides in range(2 , 2 * max_cuboid_size + 1 ):
if sqrt(sum_shortest_sides**2 + max_cuboid_size**2 ).is_integer():
num_cuboids += (
min(__magic_name__ , sum_shortest_sides // 2 )
- max(1 , sum_shortest_sides - max_cuboid_size )
+ 1
)
return max_cuboid_size
if __name__ == "__main__":
print(F'''{solution() = }''') | 382 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_speech_available,
is_tf_available,
is_torch_available,
)
SCREAMING_SNAKE_CASE_ = {
'''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:
SCREAMING_SNAKE_CASE_ = ['''Speech2TextTokenizer''']
try:
if not is_speech_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE_ = ['''Speech2TextFeatureExtractor''']
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE_ = [
'''TF_SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''TFSpeech2TextForConditionalGeneration''',
'''TFSpeech2TextModel''',
'''TFSpeech2TextPreTrainedModel''',
]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE_ = [
'''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
SCREAMING_SNAKE_CASE_ = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 465 |
"""simple docstring"""
from __future__ import annotations
from statistics import mean
def lowercase (_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ):
__lowerCAmelCase = [0] * no_of_processes
__lowerCAmelCase = [0] * no_of_processes
# Initialize remaining_time to waiting_time.
for i in range(_lowerCAmelCase ):
__lowerCAmelCase = burst_time[i]
__lowerCAmelCase = []
__lowerCAmelCase = 0
__lowerCAmelCase = 0
# When processes are not completed,
# A process whose arrival time has passed \
# and has remaining execution time is put into the ready_process.
# The shortest process in the ready_process, target_process is executed.
while completed != no_of_processes:
__lowerCAmelCase = []
__lowerCAmelCase = -1
for i in range(_lowerCAmelCase ):
if (arrival_time[i] <= total_time) and (remaining_time[i] > 0):
ready_process.append(_lowerCAmelCase )
if len(_lowerCAmelCase ) > 0:
__lowerCAmelCase = ready_process[0]
for i in ready_process:
if remaining_time[i] < remaining_time[target_process]:
__lowerCAmelCase = i
total_time += burst_time[target_process]
completed += 1
__lowerCAmelCase = 0
__lowerCAmelCase = (
total_time - arrival_time[target_process] - burst_time[target_process]
)
else:
total_time += 1
return waiting_time
def lowercase (_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ):
__lowerCAmelCase = [0] * no_of_processes
for i in range(_lowerCAmelCase ):
__lowerCAmelCase = burst_time[i] + waiting_time[i]
return turn_around_time
if __name__ == "__main__":
print('''[TEST CASE 01]''')
SCREAMING_SNAKE_CASE_ = 4
SCREAMING_SNAKE_CASE_ = [2, 5, 3, 7]
SCREAMING_SNAKE_CASE_ = [0, 0, 0, 0]
SCREAMING_SNAKE_CASE_ = calculate_waitingtime(arrival_time, burst_time, no_of_processes)
SCREAMING_SNAKE_CASE_ = calculate_turnaroundtime(
burst_time, no_of_processes, waiting_time
)
# Printing the Result
print('''PID\tBurst Time\tArrival Time\tWaiting Time\tTurnaround Time''')
for i, process_id in enumerate(list(range(1, 5))):
print(
F"{process_id}\t{burst_time[i]}\t\t\t{arrival_time[i]}\t\t\t\t"
F"{waiting_time[i]}\t\t\t\t{turn_around_time[i]}"
)
print(F"\nAverage waiting time = {mean(waiting_time):.5f}")
print(F"Average turnaround time = {mean(turn_around_time):.5f}")
| 465 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
_UpperCamelCase = {
"configuration_falcon": ["FALCON_PRETRAINED_CONFIG_ARCHIVE_MAP", "FalconConfig"],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_UpperCamelCase = [
"FALCON_PRETRAINED_MODEL_ARCHIVE_LIST",
"FalconForCausalLM",
"FalconModel",
"FalconPreTrainedModel",
"FalconForSequenceClassification",
"FalconForTokenClassification",
"FalconForQuestionAnswering",
]
if TYPE_CHECKING:
from .configuration_falcon import FALCON_PRETRAINED_CONFIG_ARCHIVE_MAP, FalconConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_falcon import (
FALCON_PRETRAINED_MODEL_ARCHIVE_LIST,
FalconForCausalLM,
FalconForQuestionAnswering,
FalconForSequenceClassification,
FalconForTokenClassification,
FalconModel,
FalconPreTrainedModel,
)
else:
import sys
_UpperCamelCase = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 705 |
"""simple docstring"""
from typing import Optional, Tuple, Union
import tensorflow as tf
from ...activations_tf import ACTaFN
from ...file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward
from ...modeling_tf_outputs import (
TFBaseModelOutputWithNoAttention,
TFBaseModelOutputWithPoolingAndNoAttention,
TFSequenceClassifierOutput,
)
from ...modeling_tf_utils import TFPreTrainedModel, TFSequenceClassificationLoss, keras_serializable, unpack_inputs
from ...tf_utils import shape_list
from ...utils import logging
from .configuration_regnet import RegNetConfig
_UpperCamelCase = logging.get_logger(__name__)
# General docstring
_UpperCamelCase = 'RegNetConfig'
# Base docstring
_UpperCamelCase = 'facebook/regnet-y-040'
_UpperCamelCase = [1, 1088, 7, 7]
# Image classification docstring
_UpperCamelCase = 'facebook/regnet-y-040'
_UpperCamelCase = 'tabby, tabby cat'
_UpperCamelCase = [
'facebook/regnet-y-040',
# See all regnet models at https://huggingface.co/models?filter=regnet
]
class SCREAMING_SNAKE_CASE_ ( tf.keras.layers.Layer ):
"""simple docstring"""
def __init__( self :Dict , __lowercase :int , __lowercase :int = 3 , __lowercase :int = 1 , __lowercase :int = 1 , __lowercase :Optional[str] = "relu" , **__lowercase :int , ):
super().__init__(**__lowercase )
# The padding and conv has been verified in
# https://colab.research.google.com/gist/sayakpaul/854bc10eeaf21c9ee2119e0b9f3841a7/scratchpad.ipynb
__lowerCamelCase : int =tf.keras.layers.ZeroPaddingaD(padding=kernel_size // 2 )
__lowerCamelCase : str =tf.keras.layers.ConvaD(
filters=__lowercase , kernel_size=__lowercase , strides=__lowercase , padding='''VALID''' , groups=__lowercase , use_bias=__lowercase , name='''convolution''' , )
__lowerCamelCase : str =tf.keras.layers.BatchNormalization(epsilon=1e-5 , momentum=0.9 , name='''normalization''' )
__lowerCamelCase : Optional[int] =ACTaFN[activation] if activation is not None else tf.identity
def __lowercase ( self :Optional[int] , __lowercase :Any ):
__lowerCamelCase : str =self.convolution(self.padding(__lowercase ) )
__lowerCamelCase : Optional[int] =self.normalization(__lowercase )
__lowerCamelCase : Any =self.activation(__lowercase )
return hidden_state
class SCREAMING_SNAKE_CASE_ ( tf.keras.layers.Layer ):
"""simple docstring"""
def __init__( self :Union[str, Any] , __lowercase :RegNetConfig , **__lowercase :Any ):
super().__init__(**__lowercase )
__lowerCamelCase : Tuple =config.num_channels
__lowerCamelCase : Union[str, Any] =TFRegNetConvLayer(
out_channels=config.embedding_size , kernel_size=3 , stride=2 , activation=config.hidden_act , name='''embedder''' , )
def __lowercase ( self :int , __lowercase :List[str] ):
__lowerCamelCase : int =shape_list(__lowercase )[1]
if tf.executing_eagerly() and num_channels != self.num_channels:
raise ValueError(
'''Make sure that the channel dimension of the pixel values match with the one set in the configuration.''' )
# When running on CPU, `tf.keras.layers.Conv2D` doesn't support `NCHW` format.
# So change the input format from `NCHW` to `NHWC`.
# shape = (batch_size, in_height, in_width, in_channels=num_channels)
__lowerCamelCase : Union[str, Any] =tf.transpose(__lowercase , perm=(0, 2, 3, 1) )
__lowerCamelCase : Optional[int] =self.embedder(__lowercase )
return hidden_state
class SCREAMING_SNAKE_CASE_ ( tf.keras.layers.Layer ):
"""simple docstring"""
def __init__( self :List[Any] , __lowercase :int , __lowercase :int = 2 , **__lowercase :Optional[int] ):
super().__init__(**__lowercase )
__lowerCamelCase : int =tf.keras.layers.ConvaD(
filters=__lowercase , kernel_size=1 , strides=__lowercase , use_bias=__lowercase , name='''convolution''' )
__lowerCamelCase : List[str] =tf.keras.layers.BatchNormalization(epsilon=1e-5 , momentum=0.9 , name='''normalization''' )
def __lowercase ( self :Optional[Any] , __lowercase :tf.Tensor , __lowercase :bool = False ):
return self.normalization(self.convolution(__lowercase ) , training=__lowercase )
class SCREAMING_SNAKE_CASE_ ( tf.keras.layers.Layer ):
"""simple docstring"""
def __init__( self :Dict , __lowercase :int , __lowercase :int , **__lowercase :List[str] ):
super().__init__(**__lowercase )
__lowerCamelCase : int =tf.keras.layers.GlobalAveragePoolingaD(keepdims=__lowercase , name='''pooler''' )
__lowerCamelCase : int =[
tf.keras.layers.ConvaD(filters=__lowercase , kernel_size=1 , activation='''relu''' , name='''attention.0''' ),
tf.keras.layers.ConvaD(filters=__lowercase , kernel_size=1 , activation='''sigmoid''' , name='''attention.2''' ),
]
def __lowercase ( self :Dict , __lowercase :Union[str, Any] ):
# [batch_size, h, w, num_channels] -> [batch_size, 1, 1, num_channels]
__lowerCamelCase : Any =self.pooler(__lowercase )
for layer_module in self.attention:
__lowerCamelCase : Any =layer_module(__lowercase )
__lowerCamelCase : Dict =hidden_state * pooled
return hidden_state
class SCREAMING_SNAKE_CASE_ ( tf.keras.layers.Layer ):
"""simple docstring"""
def __init__( self :Optional[int] , __lowercase :RegNetConfig , __lowercase :int , __lowercase :int , __lowercase :int = 1 , **__lowercase :str ):
super().__init__(**__lowercase )
__lowerCamelCase : Dict =in_channels != out_channels or stride != 1
__lowerCamelCase : int =max(1 , out_channels // config.groups_width )
__lowerCamelCase : List[str] =(
TFRegNetShortCut(__lowercase , stride=__lowercase , name='''shortcut''' )
if should_apply_shortcut
else tf.keras.layers.Activation('''linear''' , name='''shortcut''' )
)
# `self.layers` instead of `self.layer` because that is a reserved argument.
__lowerCamelCase : str =[
TFRegNetConvLayer(__lowercase , kernel_size=1 , activation=config.hidden_act , name='''layer.0''' ),
TFRegNetConvLayer(
__lowercase , stride=__lowercase , groups=__lowercase , activation=config.hidden_act , name='''layer.1''' ),
TFRegNetConvLayer(__lowercase , kernel_size=1 , activation=__lowercase , name='''layer.2''' ),
]
__lowerCamelCase : Optional[int] =ACTaFN[config.hidden_act]
def __lowercase ( self :int , __lowercase :Optional[int] ):
__lowerCamelCase : List[Any] =hidden_state
for layer_module in self.layers:
__lowerCamelCase : str =layer_module(__lowercase )
__lowerCamelCase : List[Any] =self.shortcut(__lowercase )
hidden_state += residual
__lowerCamelCase : Optional[int] =self.activation(__lowercase )
return hidden_state
class SCREAMING_SNAKE_CASE_ ( tf.keras.layers.Layer ):
"""simple docstring"""
def __init__( self :Union[str, Any] , __lowercase :RegNetConfig , __lowercase :int , __lowercase :int , __lowercase :int = 1 , **__lowercase :List[str] ):
super().__init__(**__lowercase )
__lowerCamelCase : Optional[Any] =in_channels != out_channels or stride != 1
__lowerCamelCase : Optional[Any] =max(1 , out_channels // config.groups_width )
__lowerCamelCase : Dict =(
TFRegNetShortCut(__lowercase , stride=__lowercase , name='''shortcut''' )
if should_apply_shortcut
else tf.keras.layers.Activation('''linear''' , name='''shortcut''' )
)
__lowerCamelCase : Union[str, Any] =[
TFRegNetConvLayer(__lowercase , kernel_size=1 , activation=config.hidden_act , name='''layer.0''' ),
TFRegNetConvLayer(
__lowercase , stride=__lowercase , groups=__lowercase , activation=config.hidden_act , name='''layer.1''' ),
TFRegNetSELayer(__lowercase , reduced_channels=int(round(in_channels / 4 ) ) , name='''layer.2''' ),
TFRegNetConvLayer(__lowercase , kernel_size=1 , activation=__lowercase , name='''layer.3''' ),
]
__lowerCamelCase : Tuple =ACTaFN[config.hidden_act]
def __lowercase ( self :Tuple , __lowercase :Tuple ):
__lowerCamelCase : List[Any] =hidden_state
for layer_module in self.layers:
__lowerCamelCase : int =layer_module(__lowercase )
__lowerCamelCase : List[str] =self.shortcut(__lowercase )
hidden_state += residual
__lowerCamelCase : List[str] =self.activation(__lowercase )
return hidden_state
class SCREAMING_SNAKE_CASE_ ( tf.keras.layers.Layer ):
"""simple docstring"""
def __init__( self :int , __lowercase :RegNetConfig , __lowercase :int , __lowercase :int , __lowercase :int = 2 , __lowercase :int = 2 , **__lowercase :Union[str, Any] ):
super().__init__(**__lowercase )
__lowerCamelCase : List[str] =TFRegNetXLayer if config.layer_type == '''x''' else TFRegNetYLayer
__lowerCamelCase : List[Any] =[
# downsampling is done in the first layer with stride of 2
layer(__lowercase , __lowercase , __lowercase , stride=__lowercase , name='''layers.0''' ),
*[layer(__lowercase , __lowercase , __lowercase , name=f'layers.{i+1}' ) for i in range(depth - 1 )],
]
def __lowercase ( self :int , __lowercase :List[str] ):
for layer_module in self.layers:
__lowerCamelCase : int =layer_module(__lowercase )
return hidden_state
class SCREAMING_SNAKE_CASE_ ( tf.keras.layers.Layer ):
"""simple docstring"""
def __init__( self :List[Any] , __lowercase :RegNetConfig , **__lowercase :List[str] ):
super().__init__(**__lowercase )
__lowerCamelCase : Optional[int] =[]
# based on `downsample_in_first_stage`, the first layer of the first stage may or may not downsample the input
self.stages.append(
TFRegNetStage(
__lowercase , config.embedding_size , config.hidden_sizes[0] , stride=2 if config.downsample_in_first_stage else 1 , depth=config.depths[0] , name='''stages.0''' , ) )
__lowerCamelCase : Any =zip(config.hidden_sizes , config.hidden_sizes[1:] )
for i, ((in_channels, out_channels), depth) in enumerate(zip(__lowercase , config.depths[1:] ) ):
self.stages.append(TFRegNetStage(__lowercase , __lowercase , __lowercase , depth=__lowercase , name=f'stages.{i+1}' ) )
def __lowercase ( self :str , __lowercase :tf.Tensor , __lowercase :bool = False , __lowercase :bool = True ):
__lowerCamelCase : Optional[Any] =() if output_hidden_states else None
for stage_module in self.stages:
if output_hidden_states:
__lowerCamelCase : Dict =hidden_states + (hidden_state,)
__lowerCamelCase : List[Any] =stage_module(__lowercase )
if output_hidden_states:
__lowerCamelCase : Union[str, Any] =hidden_states + (hidden_state,)
if not return_dict:
return tuple(v for v in [hidden_state, hidden_states] if v is not None )
return TFBaseModelOutputWithNoAttention(last_hidden_state=__lowercase , hidden_states=__lowercase )
@keras_serializable
class SCREAMING_SNAKE_CASE_ ( tf.keras.layers.Layer ):
"""simple docstring"""
__snake_case : Optional[int] = RegNetConfig
def __init__( self :List[Any] , __lowercase :Dict , **__lowercase :Union[str, Any] ):
super().__init__(**__lowercase )
__lowerCamelCase : int =config
__lowerCamelCase : List[str] =TFRegNetEmbeddings(__lowercase , name='''embedder''' )
__lowerCamelCase : List[str] =TFRegNetEncoder(__lowercase , name='''encoder''' )
__lowerCamelCase : List[Any] =tf.keras.layers.GlobalAveragePoolingaD(keepdims=__lowercase , name='''pooler''' )
@unpack_inputs
def __lowercase ( self :List[Any] , __lowercase :tf.Tensor , __lowercase :Optional[bool] = None , __lowercase :Optional[bool] = None , __lowercase :bool = False , ):
__lowerCamelCase : Union[str, Any] =(
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
__lowerCamelCase : Tuple =return_dict if return_dict is not None else self.config.use_return_dict
__lowerCamelCase : Tuple =self.embedder(__lowercase , training=__lowercase )
__lowerCamelCase : Optional[Any] =self.encoder(
__lowercase , output_hidden_states=__lowercase , return_dict=__lowercase , training=__lowercase )
__lowerCamelCase : str =encoder_outputs[0]
__lowerCamelCase : Tuple =self.pooler(__lowercase )
# Change to NCHW output format have uniformity in the modules
__lowerCamelCase : int =tf.transpose(__lowercase , perm=(0, 3, 1, 2) )
__lowerCamelCase : Any =tf.transpose(__lowercase , perm=(0, 3, 1, 2) )
# Change the other hidden state outputs to NCHW as well
if output_hidden_states:
__lowerCamelCase : str =tuple([tf.transpose(__lowercase , perm=(0, 3, 1, 2) ) for h in encoder_outputs[1]] )
if not return_dict:
return (last_hidden_state, pooled_output) + encoder_outputs[1:]
return TFBaseModelOutputWithPoolingAndNoAttention(
last_hidden_state=__lowercase , pooler_output=__lowercase , hidden_states=hidden_states if output_hidden_states else encoder_outputs.hidden_states , )
class SCREAMING_SNAKE_CASE_ ( snake_case__ ):
"""simple docstring"""
__snake_case : Optional[int] = RegNetConfig
__snake_case : int = """regnet"""
__snake_case : int = """pixel_values"""
@property
def __lowercase ( self :List[str] ):
return {"pixel_values": tf.TensorSpec(shape=(None, self.config.num_channels, 224, 224) , dtype=tf.floataa )}
_UpperCamelCase = r'\n Parameters:\n This model is a Tensorflow\n [tf.keras.layers.Layer](https://www.tensorflow.org/api_docs/python/tf/keras/layers/Layer) sub-class. Use it as a\n regular Tensorflow Module and refer to the Tensorflow documentation for all matter related to general usage and\n behavior.\n config ([`RegNetConfig`]): Model configuration class with all the parameters of the model.\n Initializing with a config file does not load the weights associated with the model, only the\n configuration. Check out the [`~TFPreTrainedModel.from_pretrained`] method to load the model weights.\n'
_UpperCamelCase = r'\n Args:\n pixel_values (`tf.Tensor` of shape `(batch_size, num_channels, height, width)`):\n Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See\n [`ConveNextImageProcessor.__call__`] for details.\n output_hidden_states (`bool`, *optional*):\n Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for\n more detail.\n return_dict (`bool`, *optional*):\n Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.\n'
@add_start_docstrings(
"""The bare RegNet model outputting raw features without any specific head on top.""" , snake_case__ , )
class SCREAMING_SNAKE_CASE_ ( snake_case__ ):
"""simple docstring"""
def __init__( self :Union[str, Any] , __lowercase :RegNetConfig , *__lowercase :List[str] , **__lowercase :int ):
super().__init__(__lowercase , *__lowercase , **__lowercase )
__lowerCamelCase : Tuple =TFRegNetMainLayer(__lowercase , name='''regnet''' )
@unpack_inputs
@add_start_docstrings_to_model_forward(__lowercase )
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC , output_type=__lowercase , config_class=_CONFIG_FOR_DOC , modality='''vision''' , expected_output=_EXPECTED_OUTPUT_SHAPE , )
def __lowercase ( self :Optional[Any] , __lowercase :tf.Tensor , __lowercase :Optional[bool] = None , __lowercase :Optional[bool] = None , __lowercase :Optional[int]=False , ):
__lowerCamelCase : List[Any] =(
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
__lowerCamelCase : Optional[int] =return_dict if return_dict is not None else self.config.use_return_dict
__lowerCamelCase : Dict =self.regnet(
pixel_values=__lowercase , output_hidden_states=__lowercase , return_dict=__lowercase , training=__lowercase , )
if not return_dict:
return (outputs[0],) + outputs[1:]
return TFBaseModelOutputWithPoolingAndNoAttention(
last_hidden_state=outputs.last_hidden_state , pooler_output=outputs.pooler_output , hidden_states=outputs.hidden_states , )
@add_start_docstrings(
"""
RegNet Model with an image classification head on top (a linear layer on top of the pooled features), e.g. for
ImageNet.
""" , snake_case__ , )
class SCREAMING_SNAKE_CASE_ ( snake_case__ , snake_case__ ):
"""simple docstring"""
def __init__( self :Union[str, Any] , __lowercase :RegNetConfig , *__lowercase :List[Any] , **__lowercase :Dict ):
super().__init__(__lowercase , *__lowercase , **__lowercase )
__lowerCamelCase : Optional[int] =config.num_labels
__lowerCamelCase : Optional[int] =TFRegNetMainLayer(__lowercase , name='''regnet''' )
# classification head
__lowerCamelCase : Union[str, Any] =[
tf.keras.layers.Flatten(),
tf.keras.layers.Dense(config.num_labels , name='''classifier.1''' ) if config.num_labels > 0 else tf.identity,
]
@unpack_inputs
@add_start_docstrings_to_model_forward(__lowercase )
@add_code_sample_docstrings(
checkpoint=_IMAGE_CLASS_CHECKPOINT , output_type=__lowercase , config_class=_CONFIG_FOR_DOC , expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT , )
def __lowercase ( self :List[Any] , __lowercase :tf.Tensor = None , __lowercase :tf.Tensor = None , __lowercase :bool = None , __lowercase :bool = None , __lowercase :int=False , ):
__lowerCamelCase : str =(
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
__lowerCamelCase : Optional[int] =return_dict if return_dict is not None else self.config.use_return_dict
__lowerCamelCase : str =self.regnet(
__lowercase , output_hidden_states=__lowercase , return_dict=__lowercase , training=__lowercase )
__lowerCamelCase : Any =outputs.pooler_output if return_dict else outputs[1]
__lowerCamelCase : List[str] =self.classifier[0](__lowercase )
__lowerCamelCase : str =self.classifier[1](__lowercase )
__lowerCamelCase : str =None if labels is None else self.hf_compute_loss(labels=__lowercase , logits=__lowercase )
if not return_dict:
__lowerCamelCase : Optional[int] =(logits,) + outputs[2:]
return ((loss,) + output) if loss is not None else output
return TFSequenceClassifierOutput(loss=__lowercase , logits=__lowercase , hidden_states=outputs.hidden_states )
| 363 | 0 |
'''simple docstring'''
# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING
import numpy as np
import pyarrow as pa
from .. import config
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter
if TYPE_CHECKING:
import torch
class a__( TensorFormatter[Mapping, '''torch.Tensor''', Mapping] ):
'''simple docstring'''
def __init__( self , __lowerCAmelCase=None , **__lowerCAmelCase):
"""simple docstring"""
super().__init__(features=__lowerCAmelCase)
lowerCAmelCase = torch_tensor_kwargs
import torch # noqa import torch at initialization
def a_ ( self , __lowerCAmelCase):
"""simple docstring"""
import torch
if isinstance(__lowerCAmelCase , __lowerCAmelCase) and column:
if all(
isinstance(__lowerCAmelCase , torch.Tensor) and x.shape == column[0].shape and x.dtype == column[0].dtype
for x in column):
return torch.stack(__lowerCAmelCase)
return column
def a_ ( self , __lowerCAmelCase):
"""simple docstring"""
import torch
if isinstance(__lowerCAmelCase , (str, bytes, type(__lowerCAmelCase))):
return value
elif isinstance(__lowerCAmelCase , (np.character, np.ndarray)) and np.issubdtype(value.dtype , np.character):
return value.tolist()
lowerCAmelCase = {}
if isinstance(__lowerCAmelCase , (np.number, np.ndarray)) and np.issubdtype(value.dtype , np.integer):
lowerCAmelCase = {"""dtype""": torch.intaa}
elif isinstance(__lowerCAmelCase , (np.number, np.ndarray)) and np.issubdtype(value.dtype , np.floating):
lowerCAmelCase = {"""dtype""": torch.floataa}
elif config.PIL_AVAILABLE and "PIL" in sys.modules:
import PIL.Image
if isinstance(__lowerCAmelCase , PIL.Image.Image):
lowerCAmelCase = np.asarray(__lowerCAmelCase)
return torch.tensor(__lowerCAmelCase , **{**default_dtype, **self.torch_tensor_kwargs})
def a_ ( self , __lowerCAmelCase):
"""simple docstring"""
import torch
# support for torch, tf, jax etc.
if hasattr(__lowerCAmelCase , """__array__""") and not isinstance(__lowerCAmelCase , torch.Tensor):
lowerCAmelCase = data_struct.__array__()
# support for nested types like struct of list of struct
if isinstance(__lowerCAmelCase , np.ndarray):
if data_struct.dtype == object: # torch tensors cannot be instantied from an array of objects
return self._consolidate([self.recursive_tensorize(__lowerCAmelCase) for substruct in data_struct])
elif isinstance(__lowerCAmelCase , (list, tuple)):
return self._consolidate([self.recursive_tensorize(__lowerCAmelCase) for substruct in data_struct])
return self._tensorize(__lowerCAmelCase)
def a_ ( self , __lowerCAmelCase):
"""simple docstring"""
return map_nested(self._recursive_tensorize , __lowerCAmelCase , map_list=__lowerCAmelCase)
def a_ ( self , __lowerCAmelCase):
"""simple docstring"""
lowerCAmelCase = self.numpy_arrow_extractor().extract_row(__lowerCAmelCase)
lowerCAmelCase = self.python_features_decoder.decode_row(__lowerCAmelCase)
return self.recursive_tensorize(__lowerCAmelCase)
def a_ ( self , __lowerCAmelCase):
"""simple docstring"""
lowerCAmelCase = self.numpy_arrow_extractor().extract_column(__lowerCAmelCase)
lowerCAmelCase = self.python_features_decoder.decode_column(__lowerCAmelCase , pa_table.column_names[0])
lowerCAmelCase = self.recursive_tensorize(__lowerCAmelCase)
lowerCAmelCase = self._consolidate(__lowerCAmelCase)
return column
def a_ ( self , __lowerCAmelCase):
"""simple docstring"""
lowerCAmelCase = self.numpy_arrow_extractor().extract_batch(__lowerCAmelCase)
lowerCAmelCase = self.python_features_decoder.decode_batch(__lowerCAmelCase)
lowerCAmelCase = self.recursive_tensorize(__lowerCAmelCase)
for column_name in batch:
lowerCAmelCase = self._consolidate(batch[column_name])
return batch
| 370 | '''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast
from diffusers import DDIMScheduler, KandinskyImgaImgPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
enable_full_determinism()
class a__( lowerCAmelCase__ , unittest.TestCase ):
'''simple docstring'''
UpperCAmelCase_ : List[str] = KandinskyImgaImgPipeline
UpperCAmelCase_ : Union[str, Any] = ['''prompt''', '''image_embeds''', '''negative_image_embeds''', '''image''']
UpperCAmelCase_ : List[str] = [
'''prompt''',
'''negative_prompt''',
'''image_embeds''',
'''negative_image_embeds''',
'''image''',
]
UpperCAmelCase_ : Dict = [
'''generator''',
'''height''',
'''width''',
'''strength''',
'''guidance_scale''',
'''negative_prompt''',
'''num_inference_steps''',
'''return_dict''',
'''guidance_scale''',
'''num_images_per_prompt''',
'''output_type''',
'''return_dict''',
]
UpperCAmelCase_ : str = False
@property
def a_ ( self):
"""simple docstring"""
return 32
@property
def a_ ( self):
"""simple docstring"""
return 32
@property
def a_ ( self):
"""simple docstring"""
return self.time_input_dim
@property
def a_ ( self):
"""simple docstring"""
return self.time_input_dim * 4
@property
def a_ ( self):
"""simple docstring"""
return 100
@property
def a_ ( self):
"""simple docstring"""
lowerCAmelCase = XLMRobertaTokenizerFast.from_pretrained("""YiYiXu/tiny-random-mclip-base""")
return tokenizer
@property
def a_ ( self):
"""simple docstring"""
torch.manual_seed(0)
lowerCAmelCase = MCLIPConfig(
numDims=self.cross_attention_dim , transformerDimensions=self.text_embedder_hidden_size , hidden_size=self.text_embedder_hidden_size , intermediate_size=37 , num_attention_heads=4 , num_hidden_layers=5 , vocab_size=1005 , )
lowerCAmelCase = MultilingualCLIP(__lowerCAmelCase)
lowerCAmelCase = text_encoder.eval()
return text_encoder
@property
def a_ ( self):
"""simple docstring"""
torch.manual_seed(0)
lowerCAmelCase = {
"""in_channels""": 4,
# Out channels is double in channels because predicts mean and variance
"""out_channels""": 8,
"""addition_embed_type""": """text_image""",
"""down_block_types""": ("""ResnetDownsampleBlock2D""", """SimpleCrossAttnDownBlock2D"""),
"""up_block_types""": ("""SimpleCrossAttnUpBlock2D""", """ResnetUpsampleBlock2D"""),
"""mid_block_type""": """UNetMidBlock2DSimpleCrossAttn""",
"""block_out_channels""": (self.block_out_channels_a, self.block_out_channels_a * 2),
"""layers_per_block""": 1,
"""encoder_hid_dim""": self.text_embedder_hidden_size,
"""encoder_hid_dim_type""": """text_image_proj""",
"""cross_attention_dim""": self.cross_attention_dim,
"""attention_head_dim""": 4,
"""resnet_time_scale_shift""": """scale_shift""",
"""class_embed_type""": None,
}
lowerCAmelCase = UNetaDConditionModel(**__lowerCAmelCase)
return model
@property
def a_ ( self):
"""simple docstring"""
return {
"block_out_channels": [32, 64],
"down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
"in_channels": 3,
"latent_channels": 4,
"layers_per_block": 1,
"norm_num_groups": 8,
"norm_type": "spatial",
"num_vq_embeddings": 12,
"out_channels": 3,
"up_block_types": [
"AttnUpDecoderBlock2D",
"UpDecoderBlock2D",
],
"vq_embed_dim": 4,
}
@property
def a_ ( self):
"""simple docstring"""
torch.manual_seed(0)
lowerCAmelCase = VQModel(**self.dummy_movq_kwargs)
return model
def a_ ( self):
"""simple docstring"""
lowerCAmelCase = self.dummy_text_encoder
lowerCAmelCase = self.dummy_tokenizer
lowerCAmelCase = self.dummy_unet
lowerCAmelCase = self.dummy_movq
lowerCAmelCase = {
"""num_train_timesteps""": 1000,
"""beta_schedule""": """linear""",
"""beta_start""": 0.00085,
"""beta_end""": 0.012,
"""clip_sample""": False,
"""set_alpha_to_one""": False,
"""steps_offset""": 0,
"""prediction_type""": """epsilon""",
"""thresholding""": False,
}
lowerCAmelCase = DDIMScheduler(**__lowerCAmelCase)
lowerCAmelCase = {
"""text_encoder""": text_encoder,
"""tokenizer""": tokenizer,
"""unet""": unet,
"""scheduler""": scheduler,
"""movq""": movq,
}
return components
def a_ ( self , __lowerCAmelCase , __lowerCAmelCase=0):
"""simple docstring"""
lowerCAmelCase = floats_tensor((1, self.cross_attention_dim) , rng=random.Random(__lowerCAmelCase)).to(__lowerCAmelCase)
lowerCAmelCase = floats_tensor((1, self.cross_attention_dim) , rng=random.Random(seed + 1)).to(__lowerCAmelCase)
# create init_image
lowerCAmelCase = floats_tensor((1, 3, 64, 64) , rng=random.Random(__lowerCAmelCase)).to(__lowerCAmelCase)
lowerCAmelCase = image.cpu().permute(0 , 2 , 3 , 1)[0]
lowerCAmelCase = Image.fromarray(np.uinta(__lowerCAmelCase)).convert("""RGB""").resize((256, 256))
if str(__lowerCAmelCase).startswith("""mps"""):
lowerCAmelCase = torch.manual_seed(__lowerCAmelCase)
else:
lowerCAmelCase = torch.Generator(device=__lowerCAmelCase).manual_seed(__lowerCAmelCase)
lowerCAmelCase = {
"""prompt""": """horse""",
"""image""": init_image,
"""image_embeds""": image_embeds,
"""negative_image_embeds""": negative_image_embeds,
"""generator""": generator,
"""height""": 64,
"""width""": 64,
"""num_inference_steps""": 10,
"""guidance_scale""": 7.0,
"""strength""": 0.2,
"""output_type""": """np""",
}
return inputs
def a_ ( self):
"""simple docstring"""
lowerCAmelCase = """cpu"""
lowerCAmelCase = self.get_dummy_components()
lowerCAmelCase = self.pipeline_class(**__lowerCAmelCase)
lowerCAmelCase = pipe.to(__lowerCAmelCase)
pipe.set_progress_bar_config(disable=__lowerCAmelCase)
lowerCAmelCase = pipe(**self.get_dummy_inputs(__lowerCAmelCase))
lowerCAmelCase = output.images
lowerCAmelCase = pipe(
**self.get_dummy_inputs(__lowerCAmelCase) , return_dict=__lowerCAmelCase , )[0]
lowerCAmelCase = image[0, -3:, -3:, -1]
lowerCAmelCase = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
lowerCAmelCase = np.array(
[0.61474943, 0.6073539, 0.43308544, 0.5928269, 0.47493595, 0.46755973, 0.4613838, 0.45368797, 0.50119233])
assert (
np.abs(image_slice.flatten() - expected_slice).max() < 1E-2
), f" expected_slice {expected_slice}, but got {image_slice.flatten()}"
assert (
np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1E-2
), f" expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}"
@slow
@require_torch_gpu
class a__( unittest.TestCase ):
'''simple docstring'''
def a_ ( self):
"""simple docstring"""
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def a_ ( self):
"""simple docstring"""
lowerCAmelCase = load_numpy(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
"""/kandinsky/kandinsky_img2img_frog.npy""")
lowerCAmelCase = load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main""" """/kandinsky/cat.png""")
lowerCAmelCase = """A red cartoon frog, 4k"""
lowerCAmelCase = KandinskyPriorPipeline.from_pretrained(
"""kandinsky-community/kandinsky-2-1-prior""" , torch_dtype=torch.floataa)
pipe_prior.to(__lowerCAmelCase)
lowerCAmelCase = KandinskyImgaImgPipeline.from_pretrained(
"""kandinsky-community/kandinsky-2-1""" , torch_dtype=torch.floataa)
lowerCAmelCase = pipeline.to(__lowerCAmelCase)
pipeline.set_progress_bar_config(disable=__lowerCAmelCase)
lowerCAmelCase = torch.Generator(device="""cpu""").manual_seed(0)
lowerCAmelCase , lowerCAmelCase = pipe_prior(
__lowerCAmelCase , generator=__lowerCAmelCase , num_inference_steps=5 , negative_prompt="""""" , ).to_tuple()
lowerCAmelCase = pipeline(
__lowerCAmelCase , image=__lowerCAmelCase , image_embeds=__lowerCAmelCase , negative_image_embeds=__lowerCAmelCase , generator=__lowerCAmelCase , num_inference_steps=100 , height=768 , width=768 , strength=0.2 , output_type="""np""" , )
lowerCAmelCase = output.images[0]
assert image.shape == (768, 768, 3)
assert_mean_pixel_difference(__lowerCAmelCase , __lowerCAmelCase)
| 370 | 1 |
from ..utils import DummyObject, requires_backends
class _snake_case ( metaclass=A__ ):
_lowercase : List[str] = ['''torch''', '''torchsde''']
def __init__( self , *a , **a) -> Optional[int]:
requires_backends(self , ['torch', 'torchsde'])
@classmethod
def SCREAMING_SNAKE_CASE__ ( cls , *a , **a) -> List[str]:
requires_backends(cls , ['torch', 'torchsde'])
@classmethod
def SCREAMING_SNAKE_CASE__ ( cls , *a , **a) -> List[Any]:
requires_backends(cls , ['torch', 'torchsde'])
| 444 |
import os
# Precomputes a list of the 100 first triangular numbers
a_ : str = [int(0.5 * n * (n + 1)) for n in range(1, 1_01)]
def lowerCamelCase__ ():
SCREAMING_SNAKE_CASE = os.path.dirname(os.path.realpath(_UpperCAmelCase))
SCREAMING_SNAKE_CASE = os.path.join(_UpperCAmelCase , 'words.txt')
SCREAMING_SNAKE_CASE = ''
with open(_UpperCAmelCase) as f:
SCREAMING_SNAKE_CASE = f.readline()
SCREAMING_SNAKE_CASE = [word.strip('"') for word in words.strip('\r\n').split(',')]
SCREAMING_SNAKE_CASE = [
word
for word in [sum(ord(_UpperCAmelCase) - 64 for x in word) for word in words]
if word in TRIANGULAR_NUMBERS
]
return len(_UpperCAmelCase)
if __name__ == "__main__":
print(solution())
| 444 | 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_retribert import RetriBertTokenizer
__A =logging.get_logger(__name__)
__A ={'''vocab_file''': '''vocab.txt''', '''tokenizer_file''': '''tokenizer.json'''}
__A ={
'''vocab_file''': {
'''yjernite/retribert-base-uncased''': (
'''https://huggingface.co/yjernite/retribert-base-uncased/resolve/main/vocab.txt'''
),
},
'''tokenizer_file''': {
'''yjernite/retribert-base-uncased''': (
'''https://huggingface.co/yjernite/retribert-base-uncased/resolve/main/tokenizer.json'''
),
},
}
__A ={
'''yjernite/retribert-base-uncased''': 5_1_2,
}
__A ={
'''yjernite/retribert-base-uncased''': {'''do_lower_case''': True},
}
class _SCREAMING_SNAKE_CASE ( snake_case_ ):
lowerCAmelCase__ = VOCAB_FILES_NAMES
lowerCAmelCase__ = PRETRAINED_VOCAB_FILES_MAP
lowerCAmelCase__ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowerCAmelCase__ = PRETRAINED_INIT_CONFIGURATION
lowerCAmelCase__ = RetriBertTokenizer
lowerCAmelCase__ = ['input_ids', 'attention_mask']
def __init__( self , lowercase=None , lowercase=None , lowercase=True , lowercase="[UNK]" , lowercase="[SEP]" , lowercase="[PAD]" , lowercase="[CLS]" , lowercase="[MASK]" , lowercase=True , lowercase=None , **lowercase , ) -> str:
super().__init__(
lowercase , tokenizer_file=lowercase , do_lower_case=lowercase , unk_token=lowercase , sep_token=lowercase , pad_token=lowercase , cls_token=lowercase , mask_token=lowercase , tokenize_chinese_chars=lowercase , strip_accents=lowercase , **lowercase , )
lowerCamelCase_ = json.loads(self.backend_tokenizer.normalizer.__getstate__() )
if (
normalizer_state.get("lowercase" , lowercase ) != do_lower_case
or normalizer_state.get("strip_accents" , lowercase ) != strip_accents
or normalizer_state.get("handle_chinese_chars" , lowercase ) != tokenize_chinese_chars
):
lowerCamelCase_ = getattr(lowercase , normalizer_state.pop("type" ) )
lowerCamelCase_ = do_lower_case
lowerCamelCase_ = strip_accents
lowerCamelCase_ = tokenize_chinese_chars
lowerCamelCase_ = normalizer_class(**lowercase )
lowerCamelCase_ = do_lower_case
def SCREAMING_SNAKE_CASE_( self , lowercase , lowercase=None ) -> Optional[int]:
lowerCamelCase_ = [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 SCREAMING_SNAKE_CASE_( self , lowercase , lowercase = None ) -> List[int]:
lowerCamelCase_ = [self.sep_token_id]
lowerCamelCase_ = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]
def SCREAMING_SNAKE_CASE_( self , lowercase , lowercase = None ) -> Tuple[str]:
lowerCamelCase_ = self._tokenizer.model.save(lowercase , name=lowercase )
return tuple(lowercase )
| 463 |
import inspect
import unittest
from transformers import ConvNextConfig
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_backbone_common import BackboneTesterMixin
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 ConvNextBackbone, ConvNextForImageClassification, ConvNextModel
from transformers.models.convnext.modeling_convnext import CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import AutoImageProcessor
class _SCREAMING_SNAKE_CASE :
def __init__( self , lowercase , lowercase=13 , lowercase=32 , lowercase=3 , lowercase=4 , lowercase=[10, 20, 30, 40] , lowercase=[2, 2, 3, 2] , lowercase=True , lowercase=True , lowercase=37 , lowercase="gelu" , lowercase=10 , lowercase=0.0_2 , lowercase=["stage2", "stage3", "stage4"] , lowercase=[2, 3, 4] , lowercase=None , ) -> Any:
lowerCamelCase_ = parent
lowerCamelCase_ = batch_size
lowerCamelCase_ = image_size
lowerCamelCase_ = num_channels
lowerCamelCase_ = num_stages
lowerCamelCase_ = hidden_sizes
lowerCamelCase_ = depths
lowerCamelCase_ = is_training
lowerCamelCase_ = use_labels
lowerCamelCase_ = intermediate_size
lowerCamelCase_ = hidden_act
lowerCamelCase_ = num_labels
lowerCamelCase_ = initializer_range
lowerCamelCase_ = out_features
lowerCamelCase_ = out_indices
lowerCamelCase_ = scope
def SCREAMING_SNAKE_CASE_( self ) -> Tuple:
lowerCamelCase_ = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
lowerCamelCase_ = None
if self.use_labels:
lowerCamelCase_ = ids_tensor([self.batch_size] , self.num_labels )
lowerCamelCase_ = self.get_config()
return config, pixel_values, labels
def SCREAMING_SNAKE_CASE_( self ) -> Dict:
return ConvNextConfig(
num_channels=self.num_channels , hidden_sizes=self.hidden_sizes , depths=self.depths , num_stages=self.num_stages , hidden_act=self.hidden_act , is_decoder=lowercase , initializer_range=self.initializer_range , out_features=self.out_features , out_indices=self.out_indices , num_labels=self.num_labels , )
def SCREAMING_SNAKE_CASE_( self , lowercase , lowercase , lowercase ) -> Tuple:
lowerCamelCase_ = ConvNextModel(config=lowercase )
model.to(lowercase )
model.eval()
lowerCamelCase_ = model(lowercase )
# expected last hidden states: B, C, H // 32, W // 32
self.parent.assertEqual(
result.last_hidden_state.shape , (self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32) , )
def SCREAMING_SNAKE_CASE_( self , lowercase , lowercase , lowercase ) -> str:
lowerCamelCase_ = ConvNextForImageClassification(lowercase )
model.to(lowercase )
model.eval()
lowerCamelCase_ = model(lowercase , labels=lowercase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def SCREAMING_SNAKE_CASE_( self , lowercase , lowercase , lowercase ) -> str:
lowerCamelCase_ = ConvNextBackbone(config=lowercase )
model.to(lowercase )
model.eval()
lowerCamelCase_ = model(lowercase )
# verify hidden states
self.parent.assertEqual(len(result.feature_maps ) , len(config.out_features ) )
self.parent.assertListEqual(list(result.feature_maps[0].shape ) , [self.batch_size, self.hidden_sizes[1], 4, 4] )
# verify channels
self.parent.assertEqual(len(model.channels ) , len(config.out_features ) )
self.parent.assertListEqual(model.channels , config.hidden_sizes[1:] )
# verify backbone works with out_features=None
lowerCamelCase_ = None
lowerCamelCase_ = ConvNextBackbone(config=lowercase )
model.to(lowercase )
model.eval()
lowerCamelCase_ = model(lowercase )
# verify feature maps
self.parent.assertEqual(len(result.feature_maps ) , 1 )
self.parent.assertListEqual(list(result.feature_maps[0].shape ) , [self.batch_size, self.hidden_sizes[-1], 1, 1] )
# verify channels
self.parent.assertEqual(len(model.channels ) , 1 )
self.parent.assertListEqual(model.channels , [config.hidden_sizes[-1]] )
def SCREAMING_SNAKE_CASE_( self ) -> Optional[Any]:
lowerCamelCase_ = self.prepare_config_and_inputs()
lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ = config_and_inputs
lowerCamelCase_ = {"pixel_values": pixel_values}
return config, inputs_dict
@require_torch
class _SCREAMING_SNAKE_CASE ( snake_case_ , snake_case_ , unittest.TestCase ):
lowerCAmelCase__ = (
(
ConvNextModel,
ConvNextForImageClassification,
ConvNextBackbone,
)
if is_torch_available()
else ()
)
lowerCAmelCase__ = (
{'feature-extraction': ConvNextModel, 'image-classification': ConvNextForImageClassification}
if is_torch_available()
else {}
)
lowerCAmelCase__ = True
lowerCAmelCase__ = False
lowerCAmelCase__ = False
lowerCAmelCase__ = False
lowerCAmelCase__ = False
def SCREAMING_SNAKE_CASE_( self ) -> Optional[int]:
lowerCamelCase_ = ConvNextModelTester(self )
lowerCamelCase_ = ConfigTester(self , config_class=lowercase , has_text_modality=lowercase , hidden_size=37 )
def SCREAMING_SNAKE_CASE_( self ) -> Union[str, Any]:
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 SCREAMING_SNAKE_CASE_( self ) -> Optional[Any]:
return
@unittest.skip(reason="ConvNext does not use inputs_embeds" )
def SCREAMING_SNAKE_CASE_( self ) -> List[str]:
pass
@unittest.skip(reason="ConvNext does not support input and output embeddings" )
def SCREAMING_SNAKE_CASE_( self ) -> List[Any]:
pass
@unittest.skip(reason="ConvNext does not use feedforward chunking" )
def SCREAMING_SNAKE_CASE_( self ) -> Tuple:
pass
def SCREAMING_SNAKE_CASE_( self ) -> Union[str, Any]:
lowerCamelCase_ , lowerCamelCase_ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowerCamelCase_ = model_class(lowercase )
lowerCamelCase_ = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
lowerCamelCase_ = [*signature.parameters.keys()]
lowerCamelCase_ = ["pixel_values"]
self.assertListEqual(arg_names[:1] , lowercase )
def SCREAMING_SNAKE_CASE_( self ) -> Dict:
lowerCamelCase_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*lowercase )
def SCREAMING_SNAKE_CASE_( self ) -> Dict:
lowerCamelCase_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_backbone(*lowercase )
def SCREAMING_SNAKE_CASE_( self ) -> Optional[Any]:
def check_hidden_states_output(lowercase , lowercase , lowercase ):
lowerCamelCase_ = model_class(lowercase )
model.to(lowercase )
model.eval()
with torch.no_grad():
lowerCamelCase_ = model(**self._prepare_for_class(lowercase , lowercase ) )
lowerCamelCase_ = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
lowerCamelCase_ = self.model_tester.num_stages
self.assertEqual(len(lowercase ) , expected_num_stages + 1 )
# ConvNext's feature maps are of shape (batch_size, num_channels, height, width)
self.assertListEqual(
list(hidden_states[0].shape[-2:] ) , [self.model_tester.image_size // 4, self.model_tester.image_size // 4] , )
lowerCamelCase_ , lowerCamelCase_ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowerCamelCase_ = True
check_hidden_states_output(lowercase , lowercase , lowercase )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
lowerCamelCase_ = True
check_hidden_states_output(lowercase , lowercase , lowercase )
def SCREAMING_SNAKE_CASE_( self ) -> str:
lowerCamelCase_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*lowercase )
@slow
def SCREAMING_SNAKE_CASE_( self ) -> Dict:
for model_name in CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowerCamelCase_ = ConvNextModel.from_pretrained(lowercase )
self.assertIsNotNone(lowercase )
def lowerCamelCase_ ( ):
lowerCamelCase_ = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
return image
@require_torch
@require_vision
class _SCREAMING_SNAKE_CASE ( unittest.TestCase ):
@cached_property
def SCREAMING_SNAKE_CASE_( self ) -> List[str]:
return AutoImageProcessor.from_pretrained("facebook/convnext-tiny-224" ) if is_vision_available() else None
@slow
def SCREAMING_SNAKE_CASE_( self ) -> List[Any]:
lowerCamelCase_ = ConvNextForImageClassification.from_pretrained("facebook/convnext-tiny-224" ).to(lowercase )
lowerCamelCase_ = self.default_image_processor
lowerCamelCase_ = prepare_img()
lowerCamelCase_ = image_processor(images=lowercase , return_tensors="pt" ).to(lowercase )
# forward pass
with torch.no_grad():
lowerCamelCase_ = model(**lowercase )
# verify the logits
lowerCamelCase_ = torch.Size((1, 1000) )
self.assertEqual(outputs.logits.shape , lowercase )
lowerCamelCase_ = torch.tensor([-0.0_2_6_0, -0.4_7_3_9, 0.1_9_1_1] ).to(lowercase )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , lowercase , atol=1e-4 ) )
@require_torch
class _SCREAMING_SNAKE_CASE ( unittest.TestCase , snake_case_ ):
lowerCAmelCase__ = (ConvNextBackbone,) if is_torch_available() else ()
lowerCAmelCase__ = ConvNextConfig
lowerCAmelCase__ = False
def SCREAMING_SNAKE_CASE_( self ) -> List[Any]:
lowerCamelCase_ = ConvNextModelTester(self )
| 463 | 1 |
import gc
import unittest
import numpy as np
import torch
from torch.backends.cuda import sdp_kernel
from diffusers import (
CMStochasticIterativeScheduler,
ConsistencyModelPipeline,
UNetaDModel,
)
from diffusers.utils import randn_tensor, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_a, require_torch_gpu
from ..pipeline_params import UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS, UNCONDITIONAL_IMAGE_GENERATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin
enable_full_determinism()
class __SCREAMING_SNAKE_CASE ( lowercase__ , unittest.TestCase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ = ConsistencyModelPipeline
SCREAMING_SNAKE_CASE_ = UNCONDITIONAL_IMAGE_GENERATION_PARAMS
SCREAMING_SNAKE_CASE_ = UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS
# Override required_optional_params to remove num_images_per_prompt
SCREAMING_SNAKE_CASE_ = frozenset(
[
'num_inference_steps',
'generator',
'latents',
'output_type',
'return_dict',
'callback',
'callback_steps',
] )
@property
def __lowerCamelCase( self ):
"""simple docstring"""
_snake_case : Dict = UNetaDModel.from_pretrained(
"""diffusers/consistency-models-test""" , subfolder="""test_unet""" , )
return unet
@property
def __lowerCamelCase( self ):
"""simple docstring"""
_snake_case : Union[str, Any] = UNetaDModel.from_pretrained(
"""diffusers/consistency-models-test""" , subfolder="""test_unet_class_cond""" , )
return unet
def __lowerCamelCase( self , SCREAMING_SNAKE_CASE__=False ):
"""simple docstring"""
if class_cond:
_snake_case : Tuple = self.dummy_cond_unet
else:
_snake_case : Dict = self.dummy_uncond_unet
# Default to CM multistep sampler
_snake_case : Optional[Any] = CMStochasticIterativeScheduler(
num_train_timesteps=40 , sigma_min=0.002 , sigma_max=80.0 , )
_snake_case : List[Any] = {
'unet': unet,
'scheduler': scheduler,
}
return components
def __lowerCamelCase( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__=0 ):
"""simple docstring"""
if str(SCREAMING_SNAKE_CASE__ ).startswith("""mps""" ):
_snake_case : List[str] = torch.manual_seed(SCREAMING_SNAKE_CASE__ )
else:
_snake_case : str = torch.Generator(device=SCREAMING_SNAKE_CASE__ ).manual_seed(SCREAMING_SNAKE_CASE__ )
_snake_case : str = {
'batch_size': 1,
'num_inference_steps': None,
'timesteps': [22, 0],
'generator': generator,
'output_type': 'np',
}
return inputs
def __lowerCamelCase( self ):
"""simple docstring"""
_snake_case : Any = 'cpu' # ensure determinism for the device-dependent torch.Generator
_snake_case : Optional[Any] = self.get_dummy_components()
_snake_case : List[Any] = ConsistencyModelPipeline(**SCREAMING_SNAKE_CASE__ )
_snake_case : Tuple = pipe.to(SCREAMING_SNAKE_CASE__ )
pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__ )
_snake_case : str = self.get_dummy_inputs(SCREAMING_SNAKE_CASE__ )
_snake_case : str = pipe(**SCREAMING_SNAKE_CASE__ ).images
assert image.shape == (1, 32, 32, 3)
_snake_case : str = image[0, -3:, -3:, -1]
_snake_case : Tuple = np.array([0.3572, 0.6273, 0.4031, 0.3961, 0.4321, 0.5730, 0.5266, 0.4780, 0.5004] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-3
def __lowerCamelCase( self ):
"""simple docstring"""
_snake_case : int = 'cpu' # ensure determinism for the device-dependent torch.Generator
_snake_case : Tuple = self.get_dummy_components(class_cond=SCREAMING_SNAKE_CASE__ )
_snake_case : Union[str, Any] = ConsistencyModelPipeline(**SCREAMING_SNAKE_CASE__ )
_snake_case : Tuple = pipe.to(SCREAMING_SNAKE_CASE__ )
pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__ )
_snake_case : List[Any] = self.get_dummy_inputs(SCREAMING_SNAKE_CASE__ )
_snake_case : int = 0
_snake_case : Union[str, Any] = pipe(**SCREAMING_SNAKE_CASE__ ).images
assert image.shape == (1, 32, 32, 3)
_snake_case : Tuple = image[0, -3:, -3:, -1]
_snake_case : str = np.array([0.3572, 0.6273, 0.4031, 0.3961, 0.4321, 0.5730, 0.5266, 0.4780, 0.5004] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-3
def __lowerCamelCase( self ):
"""simple docstring"""
_snake_case : Tuple = 'cpu' # ensure determinism for the device-dependent torch.Generator
_snake_case : Union[str, Any] = self.get_dummy_components()
_snake_case : Tuple = ConsistencyModelPipeline(**SCREAMING_SNAKE_CASE__ )
_snake_case : Dict = pipe.to(SCREAMING_SNAKE_CASE__ )
pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__ )
_snake_case : Union[str, Any] = self.get_dummy_inputs(SCREAMING_SNAKE_CASE__ )
_snake_case : Optional[Any] = 1
_snake_case : Dict = None
_snake_case : List[Any] = pipe(**SCREAMING_SNAKE_CASE__ ).images
assert image.shape == (1, 32, 32, 3)
_snake_case : Tuple = image[0, -3:, -3:, -1]
_snake_case : Optional[Any] = np.array([0.5004, 0.5004, 0.4994, 0.5008, 0.4976, 0.5018, 0.4990, 0.4982, 0.4987] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-3
def __lowerCamelCase( self ):
"""simple docstring"""
_snake_case : Dict = 'cpu' # ensure determinism for the device-dependent torch.Generator
_snake_case : Optional[int] = self.get_dummy_components(class_cond=SCREAMING_SNAKE_CASE__ )
_snake_case : List[Any] = ConsistencyModelPipeline(**SCREAMING_SNAKE_CASE__ )
_snake_case : Optional[Any] = pipe.to(SCREAMING_SNAKE_CASE__ )
pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__ )
_snake_case : Tuple = self.get_dummy_inputs(SCREAMING_SNAKE_CASE__ )
_snake_case : Dict = 1
_snake_case : Tuple = None
_snake_case : Optional[Any] = 0
_snake_case : str = pipe(**SCREAMING_SNAKE_CASE__ ).images
assert image.shape == (1, 32, 32, 3)
_snake_case : Union[str, Any] = image[0, -3:, -3:, -1]
_snake_case : Dict = np.array([0.5004, 0.5004, 0.4994, 0.5008, 0.4976, 0.5018, 0.4990, 0.4982, 0.4987] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-3
@slow
@require_torch_gpu
class __SCREAMING_SNAKE_CASE ( unittest.TestCase ):
"""simple docstring"""
def __lowerCamelCase( self ):
"""simple docstring"""
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def __lowerCamelCase( self , SCREAMING_SNAKE_CASE__=0 , SCREAMING_SNAKE_CASE__=False , SCREAMING_SNAKE_CASE__="cpu" , SCREAMING_SNAKE_CASE__=torch.floataa , SCREAMING_SNAKE_CASE__=(1, 3, 64, 64) ):
"""simple docstring"""
_snake_case : Union[str, Any] = torch.manual_seed(SCREAMING_SNAKE_CASE__ )
_snake_case : List[Any] = {
'num_inference_steps': None,
'timesteps': [22, 0],
'class_labels': 0,
'generator': generator,
'output_type': 'np',
}
if get_fixed_latents:
_snake_case : Optional[int] = self.get_fixed_latents(seed=SCREAMING_SNAKE_CASE__ , device=SCREAMING_SNAKE_CASE__ , dtype=SCREAMING_SNAKE_CASE__ , shape=SCREAMING_SNAKE_CASE__ )
_snake_case : Tuple = latents
return inputs
def __lowerCamelCase( self , SCREAMING_SNAKE_CASE__=0 , SCREAMING_SNAKE_CASE__="cpu" , SCREAMING_SNAKE_CASE__=torch.floataa , SCREAMING_SNAKE_CASE__=(1, 3, 64, 64) ):
"""simple docstring"""
if type(SCREAMING_SNAKE_CASE__ ) == str:
_snake_case : str = torch.device(SCREAMING_SNAKE_CASE__ )
_snake_case : List[str] = torch.Generator(device=SCREAMING_SNAKE_CASE__ ).manual_seed(SCREAMING_SNAKE_CASE__ )
_snake_case : Any = randn_tensor(SCREAMING_SNAKE_CASE__ , generator=SCREAMING_SNAKE_CASE__ , device=SCREAMING_SNAKE_CASE__ , dtype=SCREAMING_SNAKE_CASE__ )
return latents
def __lowerCamelCase( self ):
"""simple docstring"""
_snake_case : int = UNetaDModel.from_pretrained("""diffusers/consistency_models""" , subfolder="""diffusers_cd_imagenet64_l2""" )
_snake_case : List[str] = CMStochasticIterativeScheduler(
num_train_timesteps=40 , sigma_min=0.002 , sigma_max=80.0 , )
_snake_case : List[Any] = ConsistencyModelPipeline(unet=SCREAMING_SNAKE_CASE__ , scheduler=SCREAMING_SNAKE_CASE__ )
pipe.to(torch_device=SCREAMING_SNAKE_CASE__ )
pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__ )
_snake_case : Optional[Any] = self.get_inputs()
_snake_case : Dict = pipe(**SCREAMING_SNAKE_CASE__ ).images
assert image.shape == (1, 64, 64, 3)
_snake_case : List[str] = image[0, -3:, -3:, -1]
_snake_case : Union[str, Any] = np.array([0.0888, 0.0881, 0.0666, 0.0479, 0.0292, 0.0195, 0.0201, 0.0163, 0.0254] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 2e-2
def __lowerCamelCase( self ):
"""simple docstring"""
_snake_case : Optional[int] = UNetaDModel.from_pretrained("""diffusers/consistency_models""" , subfolder="""diffusers_cd_imagenet64_l2""" )
_snake_case : Any = CMStochasticIterativeScheduler(
num_train_timesteps=40 , sigma_min=0.002 , sigma_max=80.0 , )
_snake_case : Optional[int] = ConsistencyModelPipeline(unet=SCREAMING_SNAKE_CASE__ , scheduler=SCREAMING_SNAKE_CASE__ )
pipe.to(torch_device=SCREAMING_SNAKE_CASE__ )
pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__ )
_snake_case : List[str] = self.get_inputs()
_snake_case : Union[str, Any] = 1
_snake_case : List[str] = None
_snake_case : List[str] = pipe(**SCREAMING_SNAKE_CASE__ ).images
assert image.shape == (1, 64, 64, 3)
_snake_case : Optional[int] = image[0, -3:, -3:, -1]
_snake_case : Union[str, Any] = np.array([0.0340, 0.0152, 0.0063, 0.0267, 0.0221, 0.0107, 0.0416, 0.0186, 0.0217] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 2e-2
@require_torch_a
def __lowerCamelCase( self ):
"""simple docstring"""
_snake_case : int = UNetaDModel.from_pretrained("""diffusers/consistency_models""" , subfolder="""diffusers_cd_imagenet64_l2""" )
_snake_case : List[Any] = CMStochasticIterativeScheduler(
num_train_timesteps=40 , sigma_min=0.002 , sigma_max=80.0 , )
_snake_case : Tuple = ConsistencyModelPipeline(unet=SCREAMING_SNAKE_CASE__ , scheduler=SCREAMING_SNAKE_CASE__ )
pipe.to(torch_device=SCREAMING_SNAKE_CASE__ , torch_dtype=torch.floataa )
pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__ )
_snake_case : str = self.get_inputs(get_fixed_latents=SCREAMING_SNAKE_CASE__ , device=SCREAMING_SNAKE_CASE__ )
# Ensure usage of flash attention in torch 2.0
with sdp_kernel(enable_flash=SCREAMING_SNAKE_CASE__ , enable_math=SCREAMING_SNAKE_CASE__ , enable_mem_efficient=SCREAMING_SNAKE_CASE__ ):
_snake_case : Dict = pipe(**SCREAMING_SNAKE_CASE__ ).images
assert image.shape == (1, 64, 64, 3)
_snake_case : str = image[0, -3:, -3:, -1]
_snake_case : str = np.array([0.1875, 0.1428, 0.1289, 0.2151, 0.2092, 0.1477, 0.1877, 0.1641, 0.1353] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-3
@require_torch_a
def __lowerCamelCase( self ):
"""simple docstring"""
_snake_case : Optional[Any] = UNetaDModel.from_pretrained("""diffusers/consistency_models""" , subfolder="""diffusers_cd_imagenet64_l2""" )
_snake_case : List[Any] = CMStochasticIterativeScheduler(
num_train_timesteps=40 , sigma_min=0.002 , sigma_max=80.0 , )
_snake_case : Dict = ConsistencyModelPipeline(unet=SCREAMING_SNAKE_CASE__ , scheduler=SCREAMING_SNAKE_CASE__ )
pipe.to(torch_device=SCREAMING_SNAKE_CASE__ , torch_dtype=torch.floataa )
pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__ )
_snake_case : Any = self.get_inputs(get_fixed_latents=SCREAMING_SNAKE_CASE__ , device=SCREAMING_SNAKE_CASE__ )
_snake_case : List[str] = 1
_snake_case : str = None
# Ensure usage of flash attention in torch 2.0
with sdp_kernel(enable_flash=SCREAMING_SNAKE_CASE__ , enable_math=SCREAMING_SNAKE_CASE__ , enable_mem_efficient=SCREAMING_SNAKE_CASE__ ):
_snake_case : List[str] = pipe(**SCREAMING_SNAKE_CASE__ ).images
assert image.shape == (1, 64, 64, 3)
_snake_case : Dict = image[0, -3:, -3:, -1]
_snake_case : Optional[int] = np.array([0.1663, 0.1948, 0.2275, 0.1680, 0.1204, 0.1245, 0.1858, 0.1338, 0.2095] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-3
| 703 |
from __future__ import annotations
import unittest
from transformers import BlenderbotSmallConfig, BlenderbotSmallTokenizer, is_tf_available
from transformers.testing_utils import require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import TFAutoModelForSeqaSeqLM, TFBlenderbotSmallForConditionalGeneration, TFBlenderbotSmallModel
@require_tf
class __SCREAMING_SNAKE_CASE :
"""simple docstring"""
SCREAMING_SNAKE_CASE_ = BlenderbotSmallConfig
SCREAMING_SNAKE_CASE_ = {}
SCREAMING_SNAKE_CASE_ = 'gelu'
def __init__( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__=13 , SCREAMING_SNAKE_CASE__=7 , SCREAMING_SNAKE_CASE__=True , SCREAMING_SNAKE_CASE__=False , SCREAMING_SNAKE_CASE__=99 , SCREAMING_SNAKE_CASE__=32 , SCREAMING_SNAKE_CASE__=2 , SCREAMING_SNAKE_CASE__=4 , SCREAMING_SNAKE_CASE__=37 , SCREAMING_SNAKE_CASE__=0.1 , SCREAMING_SNAKE_CASE__=0.1 , SCREAMING_SNAKE_CASE__=20 , SCREAMING_SNAKE_CASE__=2 , SCREAMING_SNAKE_CASE__=1 , SCREAMING_SNAKE_CASE__=0 , ):
"""simple docstring"""
_snake_case : Any = parent
_snake_case : str = batch_size
_snake_case : Optional[Any] = seq_length
_snake_case : Union[str, Any] = is_training
_snake_case : int = use_labels
_snake_case : int = vocab_size
_snake_case : Tuple = hidden_size
_snake_case : Any = num_hidden_layers
_snake_case : Optional[int] = num_attention_heads
_snake_case : List[str] = intermediate_size
_snake_case : int = hidden_dropout_prob
_snake_case : str = attention_probs_dropout_prob
_snake_case : str = max_position_embeddings
_snake_case : Optional[Any] = eos_token_id
_snake_case : Dict = pad_token_id
_snake_case : Dict = bos_token_id
def __lowerCamelCase( self ):
"""simple docstring"""
_snake_case : Optional[Any] = ids_tensor([self.batch_size, self.seq_length - 1] , self.vocab_size )
_snake_case : Union[str, Any] = tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size ) , 1 )
_snake_case : Tuple = tf.concat([input_ids, eos_tensor] , axis=1 )
_snake_case : Union[str, Any] = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
_snake_case : int = self.config_cls(
vocab_size=self.vocab_size , d_model=self.hidden_size , encoder_layers=self.num_hidden_layers , decoder_layers=self.num_hidden_layers , encoder_attention_heads=self.num_attention_heads , decoder_attention_heads=self.num_attention_heads , encoder_ffn_dim=self.intermediate_size , decoder_ffn_dim=self.intermediate_size , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , eos_token_ids=[2] , bos_token_id=self.bos_token_id , pad_token_id=self.pad_token_id , decoder_start_token_id=self.pad_token_id , **self.config_updates , )
_snake_case : Dict = prepare_blenderbot_small_inputs_dict(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
return config, inputs_dict
def __lowerCamelCase( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
"""simple docstring"""
_snake_case : Dict = TFBlenderbotSmallModel(config=SCREAMING_SNAKE_CASE__ ).get_decoder()
_snake_case : Any = inputs_dict["""input_ids"""]
_snake_case : Optional[int] = input_ids[:1, :]
_snake_case : Any = inputs_dict["""attention_mask"""][:1, :]
_snake_case : Dict = inputs_dict["""head_mask"""]
_snake_case : Dict = 1
# first forward pass
_snake_case : str = model(SCREAMING_SNAKE_CASE__ , attention_mask=SCREAMING_SNAKE_CASE__ , head_mask=SCREAMING_SNAKE_CASE__ , use_cache=SCREAMING_SNAKE_CASE__ )
_snake_case , _snake_case : Tuple = outputs.to_tuple()
# create hypothetical next token and extent to next_input_ids
_snake_case : Any = ids_tensor((self.batch_size, 3) , config.vocab_size )
_snake_case : Dict = tf.cast(ids_tensor((self.batch_size, 3) , 2 ) , tf.inta )
# append to next input_ids and
_snake_case : Union[str, Any] = tf.concat([input_ids, next_tokens] , axis=-1 )
_snake_case : int = tf.concat([attention_mask, next_attn_mask] , axis=-1 )
_snake_case : Dict = model(SCREAMING_SNAKE_CASE__ , attention_mask=SCREAMING_SNAKE_CASE__ )[0]
_snake_case : Dict = model(SCREAMING_SNAKE_CASE__ , attention_mask=SCREAMING_SNAKE_CASE__ , past_key_values=SCREAMING_SNAKE_CASE__ )[0]
self.parent.assertEqual(next_tokens.shape[1] , output_from_past.shape[1] )
# select random slice
_snake_case : List[str] = int(ids_tensor((1,) , output_from_past.shape[-1] ) )
_snake_case : List[Any] = output_from_no_past[:, -3:, random_slice_idx]
_snake_case : List[str] = output_from_past[:, :, random_slice_idx]
# test that outputs are equal for slice
tf.debugging.assert_near(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , rtol=1e-3 )
def UpperCAmelCase ( A__ , A__ , A__ , A__=None , A__=None , A__=None , A__=None , A__=None , ) -> Union[str, Any]:
if attention_mask is None:
_snake_case : str = tf.cast(tf.math.not_equal(A__ , config.pad_token_id ) , tf.inta )
if decoder_attention_mask is None:
_snake_case : Optional[Any] = tf.concat(
[
tf.ones(decoder_input_ids[:, :1].shape , dtype=tf.inta ),
tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:] , config.pad_token_id ) , tf.inta ),
] , axis=-1 , )
if head_mask is None:
_snake_case : Optional[Any] = tf.ones((config.encoder_layers, config.encoder_attention_heads) )
if decoder_head_mask is None:
_snake_case : Optional[Any] = tf.ones((config.decoder_layers, config.decoder_attention_heads) )
if cross_attn_head_mask is None:
_snake_case : List[Any] = tf.ones((config.decoder_layers, config.decoder_attention_heads) )
return {
"input_ids": input_ids,
"decoder_input_ids": decoder_input_ids,
"attention_mask": attention_mask,
"decoder_attention_mask": decoder_attention_mask,
"head_mask": head_mask,
"decoder_head_mask": decoder_head_mask,
"cross_attn_head_mask": cross_attn_head_mask,
}
@require_tf
class __SCREAMING_SNAKE_CASE ( lowercase__ , lowercase__ , unittest.TestCase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ = (
(TFBlenderbotSmallForConditionalGeneration, TFBlenderbotSmallModel) if is_tf_available() else ()
)
SCREAMING_SNAKE_CASE_ = (TFBlenderbotSmallForConditionalGeneration,) if is_tf_available() else ()
SCREAMING_SNAKE_CASE_ = (
{
'conversational': TFBlenderbotSmallForConditionalGeneration,
'feature-extraction': TFBlenderbotSmallModel,
'summarization': TFBlenderbotSmallForConditionalGeneration,
'text2text-generation': TFBlenderbotSmallForConditionalGeneration,
'translation': TFBlenderbotSmallForConditionalGeneration,
}
if is_tf_available()
else {}
)
SCREAMING_SNAKE_CASE_ = True
SCREAMING_SNAKE_CASE_ = False
SCREAMING_SNAKE_CASE_ = False
def __lowerCamelCase( self ):
"""simple docstring"""
_snake_case : int = TFBlenderbotSmallModelTester(self )
_snake_case : List[Any] = ConfigTester(self , config_class=SCREAMING_SNAKE_CASE__ )
def __lowerCamelCase( self ):
"""simple docstring"""
self.config_tester.run_common_tests()
def __lowerCamelCase( self ):
"""simple docstring"""
_snake_case : int = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.check_decoder_model_past_large_inputs(*SCREAMING_SNAKE_CASE__ )
@require_tokenizers
@require_tf
class __SCREAMING_SNAKE_CASE ( unittest.TestCase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ = [
'Social anxiety\nWow, I am never shy. Do you have anxiety?\nYes. I end up sweating and blushing and feel like '
' i\'m going to throw up.\nand why is that?'
]
SCREAMING_SNAKE_CASE_ = 'facebook/blenderbot_small-90M'
@cached_property
def __lowerCamelCase( self ):
"""simple docstring"""
return BlenderbotSmallTokenizer.from_pretrained("""facebook/blenderbot-90M""" )
@cached_property
def __lowerCamelCase( self ):
"""simple docstring"""
_snake_case : Union[str, Any] = TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name )
return model
@slow
def __lowerCamelCase( self ):
"""simple docstring"""
_snake_case : Tuple = self.tokenizer(self.src_text , return_tensors="""tf""" )
_snake_case : List[str] = self.model.generate(
model_inputs.input_ids , attention_mask=model_inputs.attention_mask , num_beams=2 , use_cache=SCREAMING_SNAKE_CASE__ , )
_snake_case : Optional[int] = self.tokenizer.batch_decode(generated_ids.numpy() , skip_special_tokens=SCREAMING_SNAKE_CASE__ )[0]
assert generated_words in (
"i don't know. i just feel like i'm going to throw up. it's not fun.",
"i'm not sure. i just feel like i've been feeling like i have to be in a certain place",
"i'm not sure. i just feel like i've been in a bad situation.",
)
| 519 | 0 |
"""simple docstring"""
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import ConvNextConfig, SegformerImageProcessor, UperNetConfig, UperNetForSemanticSegmentation
def UpperCAmelCase ( A__: Optional[int] ) -> Dict:
__lowerCamelCase : Optional[Any] = 384
if "tiny" in model_name:
__lowerCamelCase : int = [3, 3, 9, 3]
__lowerCamelCase : int = [96, 192, 384, 768]
if "small" in model_name:
__lowerCamelCase : Optional[int] = [3, 3, 27, 3]
__lowerCamelCase : List[str] = [96, 192, 384, 768]
if "base" in model_name:
__lowerCamelCase : Dict = [3, 3, 27, 3]
__lowerCamelCase : int = [128, 256, 512, 1024]
__lowerCamelCase : List[str] = 512
if "large" in model_name:
__lowerCamelCase : Optional[Any] = [3, 3, 27, 3]
__lowerCamelCase : Tuple = [192, 384, 768, 1536]
__lowerCamelCase : Any = 768
if "xlarge" in model_name:
__lowerCamelCase : Tuple = [3, 3, 27, 3]
__lowerCamelCase : Optional[int] = [256, 512, 1024, 2048]
__lowerCamelCase : int = 1024
# set label information
__lowerCamelCase : Optional[Any] = 150
__lowerCamelCase : List[Any] = 'huggingface/label-files'
__lowerCamelCase : Optional[Any] = 'ade20k-id2label.json'
__lowerCamelCase : Dict = json.load(open(hf_hub_download(A__ , A__ , repo_type='dataset' ) , 'r' ) )
__lowerCamelCase : Union[str, Any] = {int(A__ ): v for k, v in idalabel.items()}
__lowerCamelCase : Any = {v: k for k, v in idalabel.items()}
__lowerCamelCase : Optional[int] = ConvNextConfig(
depths=A__ , hidden_sizes=A__ , out_features=['stage1', 'stage2', 'stage3', 'stage4'] )
__lowerCamelCase : Union[str, Any] = UperNetConfig(
backbone_config=A__ , auxiliary_in_channels=A__ , num_labels=A__ , idalabel=A__ , labelaid=A__ , )
return config
def UpperCAmelCase ( A__: Any ) -> List[Any]:
__lowerCamelCase : Any = []
# fmt: off
# stem
rename_keys.append(('backbone.downsample_layers.0.0.weight', 'backbone.embeddings.patch_embeddings.weight') )
rename_keys.append(('backbone.downsample_layers.0.0.bias', 'backbone.embeddings.patch_embeddings.bias') )
rename_keys.append(('backbone.downsample_layers.0.1.weight', 'backbone.embeddings.layernorm.weight') )
rename_keys.append(('backbone.downsample_layers.0.1.bias', 'backbone.embeddings.layernorm.bias') )
# stages
for i in range(len(config.backbone_config.depths ) ):
for j in range(config.backbone_config.depths[i] ):
rename_keys.append((f'''backbone.stages.{i}.{j}.gamma''', f'''backbone.encoder.stages.{i}.layers.{j}.layer_scale_parameter''') )
rename_keys.append((f'''backbone.stages.{i}.{j}.depthwise_conv.weight''', f'''backbone.encoder.stages.{i}.layers.{j}.dwconv.weight''') )
rename_keys.append((f'''backbone.stages.{i}.{j}.depthwise_conv.bias''', f'''backbone.encoder.stages.{i}.layers.{j}.dwconv.bias''') )
rename_keys.append((f'''backbone.stages.{i}.{j}.norm.weight''', f'''backbone.encoder.stages.{i}.layers.{j}.layernorm.weight''') )
rename_keys.append((f'''backbone.stages.{i}.{j}.norm.bias''', f'''backbone.encoder.stages.{i}.layers.{j}.layernorm.bias''') )
rename_keys.append((f'''backbone.stages.{i}.{j}.pointwise_conv1.weight''', f'''backbone.encoder.stages.{i}.layers.{j}.pwconv1.weight''') )
rename_keys.append((f'''backbone.stages.{i}.{j}.pointwise_conv1.bias''', f'''backbone.encoder.stages.{i}.layers.{j}.pwconv1.bias''') )
rename_keys.append((f'''backbone.stages.{i}.{j}.pointwise_conv2.weight''', f'''backbone.encoder.stages.{i}.layers.{j}.pwconv2.weight''') )
rename_keys.append((f'''backbone.stages.{i}.{j}.pointwise_conv2.bias''', f'''backbone.encoder.stages.{i}.layers.{j}.pwconv2.bias''') )
if i > 0:
rename_keys.append((f'''backbone.downsample_layers.{i}.0.weight''', f'''backbone.encoder.stages.{i}.downsampling_layer.0.weight''') )
rename_keys.append((f'''backbone.downsample_layers.{i}.0.bias''', f'''backbone.encoder.stages.{i}.downsampling_layer.0.bias''') )
rename_keys.append((f'''backbone.downsample_layers.{i}.1.weight''', f'''backbone.encoder.stages.{i}.downsampling_layer.1.weight''') )
rename_keys.append((f'''backbone.downsample_layers.{i}.1.bias''', f'''backbone.encoder.stages.{i}.downsampling_layer.1.bias''') )
rename_keys.append((f'''backbone.norm{i}.weight''', f'''backbone.hidden_states_norms.stage{i+1}.weight''') )
rename_keys.append((f'''backbone.norm{i}.bias''', f'''backbone.hidden_states_norms.stage{i+1}.bias''') )
# decode head
rename_keys.extend(
[
('decode_head.conv_seg.weight', 'decode_head.classifier.weight'),
('decode_head.conv_seg.bias', 'decode_head.classifier.bias'),
('auxiliary_head.conv_seg.weight', 'auxiliary_head.classifier.weight'),
('auxiliary_head.conv_seg.bias', 'auxiliary_head.classifier.bias'),
] )
# fmt: on
return rename_keys
def UpperCAmelCase ( A__: Dict , A__: Any , A__: str ) -> str:
__lowerCamelCase : Union[str, Any] = dct.pop(A__ )
__lowerCamelCase : str = val
def UpperCAmelCase ( A__: Tuple , A__: Optional[Any] , A__: str ) -> List[str]:
__lowerCamelCase : Union[str, Any] = {
'upernet-convnext-tiny': 'https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_tiny_fp16_512x512_160k_ade20k/upernet_convnext_tiny_fp16_512x512_160k_ade20k_20220227_124553-cad485de.pth',
'upernet-convnext-small': 'https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_small_fp16_512x512_160k_ade20k/upernet_convnext_small_fp16_512x512_160k_ade20k_20220227_131208-1b1e394f.pth',
'upernet-convnext-base': 'https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_base_fp16_512x512_160k_ade20k/upernet_convnext_base_fp16_512x512_160k_ade20k_20220227_181227-02a24fc6.pth',
'upernet-convnext-large': 'https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_large_fp16_640x640_160k_ade20k/upernet_convnext_large_fp16_640x640_160k_ade20k_20220226_040532-e57aa54d.pth',
'upernet-convnext-xlarge': 'https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_xlarge_fp16_640x640_160k_ade20k/upernet_convnext_xlarge_fp16_640x640_160k_ade20k_20220226_080344-95fc38c2.pth',
}
__lowerCamelCase : int = model_name_to_url[model_name]
__lowerCamelCase : Optional[Any] = torch.hub.load_state_dict_from_url(A__ , map_location='cpu' )['state_dict']
__lowerCamelCase : List[Any] = get_upernet_config(A__ )
__lowerCamelCase : str = UperNetForSemanticSegmentation(A__ )
model.eval()
# replace "bn" => "batch_norm"
for key in state_dict.copy().keys():
__lowerCamelCase : Dict = state_dict.pop(A__ )
if "bn" in key:
__lowerCamelCase : Dict = key.replace('bn' , 'batch_norm' )
__lowerCamelCase : Optional[int] = val
# rename keys
__lowerCamelCase : str = create_rename_keys(A__ )
for src, dest in rename_keys:
rename_key(A__ , A__ , A__ )
model.load_state_dict(A__ )
# verify on image
__lowerCamelCase : List[Any] = 'https://huggingface.co/datasets/hf-internal-testing/fixtures_ade20k/resolve/main/ADE_val_00000001.jpg'
__lowerCamelCase : Optional[Any] = Image.open(requests.get(A__ , stream=A__ ).raw ).convert('RGB' )
__lowerCamelCase : int = SegformerImageProcessor()
__lowerCamelCase : Any = processor(A__ , return_tensors='pt' ).pixel_values
with torch.no_grad():
__lowerCamelCase : Any = model(A__ )
if model_name == "upernet-convnext-tiny":
__lowerCamelCase : Dict = torch.tensor(
[[-8.8_110, -8.8_110, -8.6_521], [-8.8_110, -8.8_110, -8.6_521], [-8.7_746, -8.7_746, -8.6_130]] )
elif model_name == "upernet-convnext-small":
__lowerCamelCase : List[str] = torch.tensor(
[[-8.8_236, -8.8_236, -8.6_771], [-8.8_236, -8.8_236, -8.6_771], [-8.7_638, -8.7_638, -8.6_240]] )
elif model_name == "upernet-convnext-base":
__lowerCamelCase : Tuple = torch.tensor(
[[-8.8_558, -8.8_558, -8.6_905], [-8.8_558, -8.8_558, -8.6_905], [-8.7_669, -8.7_669, -8.6_021]] )
elif model_name == "upernet-convnext-large":
__lowerCamelCase : str = torch.tensor(
[[-8.6_660, -8.6_660, -8.6_210], [-8.6_660, -8.6_660, -8.6_210], [-8.6_310, -8.6_310, -8.5_964]] )
elif model_name == "upernet-convnext-xlarge":
__lowerCamelCase : Optional[Any] = torch.tensor(
[[-8.4_980, -8.4_980, -8.3_977], [-8.4_980, -8.4_980, -8.3_977], [-8.4_379, -8.4_379, -8.3_412]] )
print('Logits:' , outputs.logits[0, 0, :3, :3] )
assert torch.allclose(outputs.logits[0, 0, :3, :3] , A__ , atol=1E-4 )
print('Looks ok!' )
if pytorch_dump_folder_path is not None:
print(f'''Saving model {model_name} to {pytorch_dump_folder_path}''' )
model.save_pretrained(A__ )
print(f'''Saving processor to {pytorch_dump_folder_path}''' )
processor.save_pretrained(A__ )
if push_to_hub:
print(f'''Pushing model and processor for {model_name} to hub''' )
model.push_to_hub(f'''openmmlab/{model_name}''' )
processor.push_to_hub(f'''openmmlab/{model_name}''' )
if __name__ == "__main__":
a_ : Optional[Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--model_name''',
default='''upernet-convnext-tiny''',
type=str,
choices=[F"""upernet-convnext-{size}""" for size in ['''tiny''', '''small''', '''base''', '''large''', '''xlarge''']],
help='''Name of the ConvNext UperNet 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 or not to push the converted model to the 🤗 hub.'''
)
a_ : Optional[Any] = parser.parse_args()
convert_upernet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)
| 594 |
"""simple docstring"""
import unittest
from transformers import DebertaVaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
DebertaVaForMaskedLM,
DebertaVaForMultipleChoice,
DebertaVaForQuestionAnswering,
DebertaVaForSequenceClassification,
DebertaVaForTokenClassification,
DebertaVaModel,
)
from transformers.models.deberta_va.modeling_deberta_va import DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST
class __lowercase( lowercase__ ):
'''simple docstring'''
def __init__( self , __a , __a=13 , __a=7 , __a=True , __a=True , __a=True , __a=True , __a=99 , __a=32 , __a=5 , __a=4 , __a=37 , __a="gelu" , __a=0.1 , __a=0.1 , __a=512 , __a=16 , __a=2 , __a=0.02 , __a=False , __a=True , __a="None" , __a=3 , __a=4 , __a=None , ):
__lowerCamelCase : List[str] = parent
__lowerCamelCase : Optional[int] = batch_size
__lowerCamelCase : Union[str, Any] = seq_length
__lowerCamelCase : Optional[int] = is_training
__lowerCamelCase : List[str] = use_input_mask
__lowerCamelCase : Dict = use_token_type_ids
__lowerCamelCase : Dict = use_labels
__lowerCamelCase : Union[str, Any] = vocab_size
__lowerCamelCase : List[Any] = hidden_size
__lowerCamelCase : int = num_hidden_layers
__lowerCamelCase : int = num_attention_heads
__lowerCamelCase : int = intermediate_size
__lowerCamelCase : Dict = hidden_act
__lowerCamelCase : Optional[int] = hidden_dropout_prob
__lowerCamelCase : Any = attention_probs_dropout_prob
__lowerCamelCase : List[str] = max_position_embeddings
__lowerCamelCase : Tuple = type_vocab_size
__lowerCamelCase : List[Any] = type_sequence_label_size
__lowerCamelCase : Optional[Any] = initializer_range
__lowerCamelCase : Any = num_labels
__lowerCamelCase : Union[str, Any] = num_choices
__lowerCamelCase : int = relative_attention
__lowerCamelCase : Tuple = position_biased_input
__lowerCamelCase : int = pos_att_type
__lowerCamelCase : Dict = scope
def snake_case_ ( self ):
__lowerCamelCase : Tuple = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
__lowerCamelCase : Dict = None
if self.use_input_mask:
__lowerCamelCase : List[Any] = ids_tensor([self.batch_size, self.seq_length] , vocab_size=2 )
__lowerCamelCase : Dict = None
if self.use_token_type_ids:
__lowerCamelCase : Optional[Any] = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
__lowerCamelCase : Any = None
__lowerCamelCase : Optional[int] = None
__lowerCamelCase : Union[str, Any] = None
if self.use_labels:
__lowerCamelCase : Dict = ids_tensor([self.batch_size] , self.type_sequence_label_size )
__lowerCamelCase : Tuple = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
__lowerCamelCase : Optional[Any] = ids_tensor([self.batch_size] , self.num_choices )
__lowerCamelCase : List[str] = self.get_config()
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def snake_case_ ( self ):
return DebertaVaConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , initializer_range=self.initializer_range , relative_attention=self.relative_attention , position_biased_input=self.position_biased_input , pos_att_type=self.pos_att_type , )
def snake_case_ ( self , __a ):
self.parent.assertListEqual(list(result.loss.size() ) , [] )
def snake_case_ ( self , __a , __a , __a , __a , __a , __a , __a ):
__lowerCamelCase : Optional[int] = DebertaVaModel(config=__a )
model.to(__a )
model.eval()
__lowerCamelCase : int = model(__a , attention_mask=__a , token_type_ids=__a )[0]
__lowerCamelCase : Optional[int] = model(__a , token_type_ids=__a )[0]
__lowerCamelCase : Optional[Any] = model(__a )[0]
self.parent.assertListEqual(list(sequence_output.size() ) , [self.batch_size, self.seq_length, self.hidden_size] )
def snake_case_ ( self , __a , __a , __a , __a , __a , __a , __a ):
__lowerCamelCase : List[str] = DebertaVaForMaskedLM(config=__a )
model.to(__a )
model.eval()
__lowerCamelCase : List[str] = model(__a , attention_mask=__a , token_type_ids=__a , labels=__a )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def snake_case_ ( self , __a , __a , __a , __a , __a , __a , __a ):
__lowerCamelCase : str = self.num_labels
__lowerCamelCase : Tuple = DebertaVaForSequenceClassification(__a )
model.to(__a )
model.eval()
__lowerCamelCase : Optional[int] = model(__a , attention_mask=__a , token_type_ids=__a , labels=__a )
self.parent.assertListEqual(list(result.logits.size() ) , [self.batch_size, self.num_labels] )
self.check_loss_output(__a )
def snake_case_ ( self , __a , __a , __a , __a , __a , __a , __a ):
__lowerCamelCase : Optional[Any] = self.num_labels
__lowerCamelCase : List[str] = DebertaVaForTokenClassification(config=__a )
model.to(__a )
model.eval()
__lowerCamelCase : List[Any] = model(__a , attention_mask=__a , token_type_ids=__a , labels=__a )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def snake_case_ ( self , __a , __a , __a , __a , __a , __a , __a ):
__lowerCamelCase : Optional[int] = DebertaVaForQuestionAnswering(config=__a )
model.to(__a )
model.eval()
__lowerCamelCase : List[str] = model(
__a , attention_mask=__a , token_type_ids=__a , start_positions=__a , end_positions=__a , )
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 snake_case_ ( self , __a , __a , __a , __a , __a , __a , __a ):
__lowerCamelCase : Optional[Any] = DebertaVaForMultipleChoice(config=__a )
model.to(__a )
model.eval()
__lowerCamelCase : str = input_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
__lowerCamelCase : Dict = token_type_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
__lowerCamelCase : int = input_mask.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
__lowerCamelCase : str = model(
__a , attention_mask=__a , token_type_ids=__a , labels=__a , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def snake_case_ ( self ):
__lowerCamelCase : Tuple = self.prepare_config_and_inputs()
(
(
__lowerCamelCase
) , (
__lowerCamelCase
) , (
__lowerCamelCase
) , (
__lowerCamelCase
) , (
__lowerCamelCase
) , (
__lowerCamelCase
) , (
__lowerCamelCase
) ,
) : int = config_and_inputs
__lowerCamelCase : Tuple = {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
return config, inputs_dict
@require_torch
class __lowercase( lowercase__ , lowercase__ , unittest.TestCase ):
'''simple docstring'''
__a : str = (
(
DebertaVaModel,
DebertaVaForMaskedLM,
DebertaVaForSequenceClassification,
DebertaVaForTokenClassification,
DebertaVaForQuestionAnswering,
DebertaVaForMultipleChoice,
)
if is_torch_available()
else ()
)
__a : Optional[int] = (
{
'feature-extraction': DebertaVaModel,
'fill-mask': DebertaVaForMaskedLM,
'question-answering': DebertaVaForQuestionAnswering,
'text-classification': DebertaVaForSequenceClassification,
'token-classification': DebertaVaForTokenClassification,
'zero-shot': DebertaVaForSequenceClassification,
}
if is_torch_available()
else {}
)
__a : Tuple = True
__a : List[Any] = False
__a : Any = False
__a : Tuple = False
__a : Tuple = False
def snake_case_ ( self ):
__lowerCamelCase : Optional[int] = DebertaVaModelTester(self )
__lowerCamelCase : Union[str, Any] = ConfigTester(self , config_class=__a , hidden_size=37 )
def snake_case_ ( self ):
self.config_tester.run_common_tests()
def snake_case_ ( self ):
__lowerCamelCase : Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_deberta_model(*__a )
def snake_case_ ( self ):
__lowerCamelCase : int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_deberta_for_sequence_classification(*__a )
def snake_case_ ( self ):
__lowerCamelCase : int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_deberta_for_masked_lm(*__a )
def snake_case_ ( self ):
__lowerCamelCase : str = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_deberta_for_question_answering(*__a )
def snake_case_ ( self ):
__lowerCamelCase : List[str] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_deberta_for_token_classification(*__a )
def snake_case_ ( self ):
__lowerCamelCase : Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_deberta_for_multiple_choice(*__a )
@slow
def snake_case_ ( self ):
for model_name in DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__lowerCamelCase : List[str] = DebertaVaModel.from_pretrained(__a )
self.assertIsNotNone(__a )
@require_torch
@require_sentencepiece
@require_tokenizers
class __lowercase( unittest.TestCase ):
'''simple docstring'''
@unittest.skip(reason='Model not available yet' )
def snake_case_ ( self ):
pass
@slow
def snake_case_ ( self ):
__lowerCamelCase : Optional[int] = DebertaVaModel.from_pretrained('microsoft/deberta-v2-xlarge' )
__lowerCamelCase : Dict = torch.tensor([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]] )
__lowerCamelCase : Optional[int] = torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]] )
with torch.no_grad():
__lowerCamelCase : Union[str, Any] = model(__a , attention_mask=__a )[0]
# compare the actual values for a slice.
__lowerCamelCase : Optional[int] = torch.tensor(
[[[0.2_356, 0.1_948, 0.0_369], [-0.1_063, 0.3_586, -0.5_152], [-0.6_399, -0.0_259, -0.2_525]]] )
self.assertTrue(torch.allclose(output[:, 1:4, 1:4] , __a , atol=1E-4 ) , f'''{output[:, 1:4, 1:4]}''' )
| 594 | 1 |
'''simple docstring'''
from dataclasses import dataclass, field
from typing import Optional
from transformers import AutoConfig, AutoImageProcessor, AutoTokenizer, FlaxVisionEncoderDecoderModel, HfArgumentParser
@dataclass
class _lowerCAmelCase :
"""simple docstring"""
lowerCamelCase = field(
metadata={'''help''': '''The output directory where the model will be written.'''}, )
lowerCamelCase = field(
metadata={
'''help''': (
'''The encoder model checkpoint for weights initialization.'''
'''Don\'t set if you want to train an encoder model from scratch.'''
)
}, )
lowerCamelCase = field(
metadata={
'''help''': (
'''The decoder model checkpoint for weights initialization.'''
'''Don\'t set if you want to train a decoder model from scratch.'''
)
}, )
lowerCamelCase = field(
default=__A, metadata={'''help''': '''Pretrained encoder config name or path if not the same as encoder_model_name'''} )
lowerCamelCase = field(
default=__A, metadata={'''help''': '''Pretrained decoder config name or path if not the same as decoder_model_name'''} )
def UpperCAmelCase ( ) -> List[str]:
"""simple docstring"""
A_ : Union[str, Any] = HfArgumentParser((ModelArguments,) )
(A_ ) : str = parser.parse_args_into_dataclasses()
# Load pretrained model and tokenizer
# Use explicit specified encoder config
if model_args.encoder_config_name:
A_ : List[str] = AutoConfig.from_pretrained(model_args.encoder_config_name )
# Use pretrained encoder model's config
else:
A_ : str = AutoConfig.from_pretrained(model_args.encoder_model_name_or_path )
# Use explicit specified decoder config
if model_args.decoder_config_name:
A_ : Optional[Any] = AutoConfig.from_pretrained(model_args.decoder_config_name )
# Use pretrained decoder model's config
else:
A_ : Union[str, Any] = AutoConfig.from_pretrained(model_args.decoder_model_name_or_path )
# necessary for `from_encoder_decoder_pretrained` when `decoder_config` is passed
A_ : str = True
A_ : Optional[Any] = True
A_ : Union[str, Any] = FlaxVisionEncoderDecoderModel.from_encoder_decoder_pretrained(
encoder_pretrained_model_name_or_path=model_args.encoder_model_name_or_path , decoder_pretrained_model_name_or_path=model_args.decoder_model_name_or_path , encoder_config=a_ , decoder_config=a_ , )
# GPT2 only has bos/eos tokens but not decoder_start/pad tokens
A_ : Any = decoder_config.decoder_start_token_id
A_ : List[Any] = decoder_config.pad_token_id
if decoder_start_token_id is None:
A_ : Optional[Any] = decoder_config.bos_token_id
if pad_token_id is None:
A_ : List[Any] = decoder_config.eos_token_id
# This is necessary to make Flax's generate() work
A_ : Any = decoder_config.eos_token_id
A_ : Union[str, Any] = decoder_start_token_id
A_ : Any = pad_token_id
A_ : Dict = AutoImageProcessor.from_pretrained(model_args.encoder_model_name_or_path )
A_ : Optional[int] = AutoTokenizer.from_pretrained(model_args.decoder_model_name_or_path )
A_ : Optional[int] = tokenizer.convert_ids_to_tokens(model.config.pad_token_id )
model.save_pretrained(model_args.output_dir )
image_processor.save_pretrained(model_args.output_dir )
tokenizer.save_pretrained(model_args.output_dir )
if __name__ == "__main__":
main()
| 706 |
'''simple docstring'''
import importlib.util
import json
import os
import warnings
from dataclasses import dataclass, field
import torch
from ..training_args import TrainingArguments
from ..utils import cached_property, is_sagemaker_dp_enabled, logging
UpperCamelCase__ : str = logging.get_logger(__name__)
def UpperCAmelCase ( ) -> Optional[int]:
"""simple docstring"""
A_ : Optional[Any] = os.getenv("""SM_HP_MP_PARAMETERS""" , """{}""" )
try:
# Parse it and check the field "partitions" is included, it is required for model parallel.
A_ : Any = json.loads(a_ )
if "partitions" not in smp_options:
return False
except json.JSONDecodeError:
return False
# Get the sagemaker specific framework parameters from mpi_options variable.
A_ : Dict = os.getenv("""SM_FRAMEWORK_PARAMS""" , """{}""" )
try:
# Parse it and check the field "sagemaker_distributed_dataparallel_enabled".
A_ : int = json.loads(a_ )
if not mpi_options.get("""sagemaker_mpi_enabled""" , a_ ):
return False
except json.JSONDecodeError:
return False
# Lastly, check if the `smdistributed` module is present.
return importlib.util.find_spec("""smdistributed""" ) is not None
if is_sagemaker_model_parallel_available():
import smdistributed.modelparallel.torch as smp
smp.init()
@dataclass
class _lowerCAmelCase ( __A ):
"""simple docstring"""
lowerCamelCase = field(
default='''''', metadata={'''help''': '''Used by the SageMaker launcher to send mp-specific args. Ignored in SageMakerTrainer'''}, )
def UpperCAmelCase_ ( self ) -> Optional[int]:
super().__post_init__()
warnings.warn(
"""`SageMakerTrainingArguments` is deprecated and will be removed in v5 of Transformers. You can use """
"""`TrainingArguments` instead.""" , _lowerCamelCase , )
@cached_property
def UpperCAmelCase_ ( self ) -> "torch.device":
logger.info("""PyTorch: setting up devices""" )
if torch.distributed.is_available() and torch.distributed.is_initialized() and self.local_rank == -1:
logger.warning(
"""torch.distributed process group is initialized, but local_rank == -1. """
"""In order to use Torch DDP, launch your script with `python -m torch.distributed.launch""" )
if self.no_cuda:
A_ : Union[str, Any] = torch.device("""cpu""" )
A_ : Any = 0
elif is_sagemaker_model_parallel_available():
A_ : Dict = smp.local_rank()
A_ : Union[str, Any] = torch.device("""cuda""" , _lowerCamelCase )
A_ : int = 1
elif is_sagemaker_dp_enabled():
import smdistributed.dataparallel.torch.torch_smddp # noqa: F401
torch.distributed.init_process_group(backend="""smddp""" , timeout=self.ddp_timeout_delta )
A_ : str = int(os.getenv("""SMDATAPARALLEL_LOCAL_RANK""" ) )
A_ : Any = torch.device("""cuda""" , self.local_rank )
A_ : List[str] = 1
elif self.local_rank == -1:
# if n_gpu is > 1 we'll use nn.DataParallel.
# If you only want to use a specific subset of GPUs use `CUDA_VISIBLE_DEVICES=0`
# Explicitly set CUDA to the first (index 0) CUDA device, otherwise `set_device` will
# trigger an error that a device index is missing. Index 0 takes into account the
# GPUs available in the environment, so `CUDA_VISIBLE_DEVICES=1,2` with `cuda:0`
# will use the first GPU in that env, i.e. GPU#1
A_ : Optional[Any] = torch.device("""cuda:0""" if torch.cuda.is_available() else """cpu""" )
# Sometimes the line in the postinit has not been run before we end up here, so just checking we're not at
# the default value.
A_ : Optional[int] = torch.cuda.device_count()
else:
# Here, we'll use torch.distributed.
# Initializes the distributed backend which will take care of synchronizing nodes/GPUs
if not torch.distributed.is_initialized():
torch.distributed.init_process_group(backend="""nccl""" , timeout=self.ddp_timeout_delta )
A_ : Any = torch.device("""cuda""" , self.local_rank )
A_ : str = 1
if device.type == "cuda":
torch.cuda.set_device(_lowerCamelCase )
return device
@property
def UpperCAmelCase_ ( self ) -> Optional[int]:
if is_sagemaker_model_parallel_available():
return smp.dp_size()
return super().world_size
@property
def UpperCAmelCase_ ( self ) -> int:
return not is_sagemaker_model_parallel_available()
@property
def UpperCAmelCase_ ( self ) -> Tuple:
return False
| 385 | 0 |
'''simple docstring'''
def lowercase_ ( __A : int ) -> bool:
"""simple docstring"""
return number & 1 == 0
if __name__ == "__main__":
import doctest
doctest.testmod()
| 94 |
'''simple docstring'''
from __future__ import annotations
def lowercase_ ( __A : list[list[int]] ) -> int:
"""simple docstring"""
for i in range(1 , len(matrix[0] ) ):
matrix[0][i] += matrix[0][i - 1]
# preprocessing the first column
for i in range(1 , len(__A ) ):
matrix[i][0] += matrix[i - 1][0]
# updating the path cost for current position
for i in range(1 , len(__A ) ):
for j in range(1 , len(matrix[0] ) ):
matrix[i][j] += min(matrix[i - 1][j] , matrix[i][j - 1] )
return matrix[-1][-1]
if __name__ == "__main__":
import doctest
doctest.testmod()
| 94 | 1 |
'''simple docstring'''
import numpy as np
import torch
import tqdm
from ...models.unet_ad import UNetaDModel
from ...pipelines import DiffusionPipeline
from ...utils import randn_tensor
from ...utils.dummy_pt_objects import DDPMScheduler
class lowerCAmelCase__ ( UpperCAmelCase_ ):
'''simple docstring'''
def __init__( self : Optional[int] , a__ : UNetaDModel , a__ : UNetaDModel , a__ : DDPMScheduler , a__ : Dict , ):
super().__init__()
UpperCAmelCase = value_function
UpperCAmelCase = unet
UpperCAmelCase = scheduler
UpperCAmelCase = env
UpperCAmelCase = env.get_dataset()
UpperCAmelCase = {}
for key in self.data.keys():
try:
UpperCAmelCase = self.data[key].mean()
except: # noqa: E722
pass
UpperCAmelCase = {}
for key in self.data.keys():
try:
UpperCAmelCase = self.data[key].std()
except: # noqa: E722
pass
UpperCAmelCase = env.observation_space.shape[0]
UpperCAmelCase = env.action_space.shape[0]
def __snake_case ( self : Union[str, Any] , a__ : Tuple , a__ : Optional[int] ):
return (x_in - self.means[key]) / self.stds[key]
def __snake_case ( self : str , a__ : List[Any] , a__ : Dict ):
return x_in * self.stds[key] + self.means[key]
def __snake_case ( self : Dict , a__ : int ):
if type(a__ ) is dict:
return {k: self.to_torch(a__ ) for k, v in x_in.items()}
elif torch.is_tensor(a__ ):
return x_in.to(self.unet.device )
return torch.tensor(a__ , device=self.unet.device )
def __snake_case ( self : Tuple , a__ : List[str] , a__ : Union[str, Any] , a__ : str ):
for key, val in cond.items():
UpperCAmelCase = val.clone()
return x_in
def __snake_case ( self : Dict , a__ : Optional[Any] , a__ : Optional[Any] , a__ : Tuple , a__ : int ):
UpperCAmelCase = x.shape[0]
UpperCAmelCase = None
for i in tqdm.tqdm(self.scheduler.timesteps ):
# create batch of timesteps to pass into model
UpperCAmelCase = torch.full((batch_size,) , a__ , device=self.unet.device , dtype=torch.long )
for _ in range(a__ ):
with torch.enable_grad():
x.requires_grad_()
# permute to match dimension for pre-trained models
UpperCAmelCase = self.value_function(x.permute(0 , 2 , 1 ) , a__ ).sample
UpperCAmelCase = torch.autograd.grad([y.sum()] , [x] )[0]
UpperCAmelCase = self.scheduler._get_variance(a__ )
UpperCAmelCase = torch.exp(0.5 * posterior_variance )
UpperCAmelCase = model_std * grad
UpperCAmelCase = 0
UpperCAmelCase = x.detach()
UpperCAmelCase = x + scale * grad
UpperCAmelCase = self.reset_xa(a__ , a__ , self.action_dim )
UpperCAmelCase = self.unet(x.permute(0 , 2 , 1 ) , a__ ).sample.permute(0 , 2 , 1 )
# TODO: verify deprecation of this kwarg
UpperCAmelCase = self.scheduler.step(a__ , a__ , a__ , predict_epsilon=a__ )['''prev_sample''']
# apply conditions to the trajectory (set the initial state)
UpperCAmelCase = self.reset_xa(a__ , a__ , self.action_dim )
UpperCAmelCase = self.to_torch(a__ )
return x, y
def __call__( self : List[str] , a__ : List[str] , a__ : Dict=64 , a__ : str=32 , a__ : List[str]=2 , a__ : Any=0.1 ):
# normalize the observations and create batch dimension
UpperCAmelCase = self.normalize(a__ , '''observations''' )
UpperCAmelCase = obs[None].repeat(a__ , axis=0 )
UpperCAmelCase = {0: self.to_torch(a__ )}
UpperCAmelCase = (batch_size, planning_horizon, self.state_dim + self.action_dim)
# generate initial noise and apply our conditions (to make the trajectories start at current state)
UpperCAmelCase = randn_tensor(a__ , device=self.unet.device )
UpperCAmelCase = self.reset_xa(a__ , a__ , self.action_dim )
UpperCAmelCase = self.to_torch(a__ )
# run the diffusion process
UpperCAmelCase, UpperCAmelCase = self.run_diffusion(a__ , a__ , a__ , a__ )
# sort output trajectories by value
UpperCAmelCase = y.argsort(0 , descending=a__ ).squeeze()
UpperCAmelCase = x[sorted_idx]
UpperCAmelCase = sorted_values[:, :, : self.action_dim]
UpperCAmelCase = actions.detach().cpu().numpy()
UpperCAmelCase = self.de_normalize(a__ , key='''actions''' )
# select the action with the highest value
if y is not None:
UpperCAmelCase = 0
else:
# if we didn't run value guiding, select a random action
UpperCAmelCase = np.random.randint(0 , a__ )
UpperCAmelCase = denorm_actions[selected_index, 0]
return denorm_actions
| 570 |
'''simple docstring'''
from typing import List, Optional, Union
import torch
from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
is_accelerate_available,
is_accelerate_version,
logging,
randn_tensor,
replace_example_docstring,
)
a__ : List[Any] = logging.get_logger(__name__) # pylint: disable=invalid-name
a__ : List[str] = '\n Examples:\n ```py\n >>> import torch\n >>> import numpy as np\n\n >>> from diffusers import KandinskyV22PriorPipeline, KandinskyV22ControlnetPipeline\n >>> from transformers import pipeline\n >>> from diffusers.utils import load_image\n\n\n >>> def make_hint(image, depth_estimator):\n ... image = depth_estimator(image)["depth"]\n ... image = np.array(image)\n ... image = image[:, :, None]\n ... image = np.concatenate([image, image, image], axis=2)\n ... detected_map = torch.from_numpy(image).float() / 255.0\n ... hint = detected_map.permute(2, 0, 1)\n ... return hint\n\n\n >>> depth_estimator = pipeline("depth-estimation")\n\n >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(\n ... "kandinsky-community/kandinsky-2-2-prior", torch_dtype=torch.float16\n ... )\n >>> pipe_prior = pipe_prior.to("cuda")\n\n >>> pipe = KandinskyV22ControlnetPipeline.from_pretrained(\n ... "kandinsky-community/kandinsky-2-2-controlnet-depth", torch_dtype=torch.float16\n ... )\n >>> pipe = pipe.to("cuda")\n\n\n >>> img = load_image(\n ... "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"\n ... "/kandinsky/cat.png"\n ... ).resize((768, 768))\n\n >>> hint = make_hint(img, depth_estimator).unsqueeze(0).half().to("cuda")\n\n >>> prompt = "A robot, 4k photo"\n >>> negative_prior_prompt = "lowres, text, error, cropped, worst quality, low quality, jpeg artifacts, ugly, duplicate, morbid, mutilated, out of frame, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, mutation, deformed, blurry, dehydrated, bad anatomy, bad proportions, extra limbs, cloned face, disfigured, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, fused fingers, too many fingers, long neck, username, watermark, signature"\n\n >>> generator = torch.Generator(device="cuda").manual_seed(43)\n\n >>> image_emb, zero_image_emb = pipe_prior(\n ... prompt=prompt, negative_prompt=negative_prior_prompt, generator=generator\n ... ).to_tuple()\n\n >>> images = pipe(\n ... image_embeds=image_emb,\n ... negative_image_embeds=zero_image_emb,\n ... hint=hint,\n ... num_inference_steps=50,\n ... generator=generator,\n ... height=768,\n ... width=768,\n ... ).images\n\n >>> images[0].save("robot_cat.png")\n ```\n'
def __snake_case ( SCREAMING_SNAKE_CASE_ : List[str] , SCREAMING_SNAKE_CASE_ : Union[str, Any] , SCREAMING_SNAKE_CASE_ : List[str]=8 ) -> str:
"""simple docstring"""
UpperCAmelCase = height // scale_factor**2
if height % scale_factor**2 != 0:
new_height += 1
UpperCAmelCase = width // scale_factor**2
if width % scale_factor**2 != 0:
new_width += 1
return new_height * scale_factor, new_width * scale_factor
class lowerCAmelCase__ ( UpperCAmelCase_ ):
'''simple docstring'''
def __init__( self : Tuple , a__ : UNetaDConditionModel , a__ : DDPMScheduler , a__ : VQModel , ):
super().__init__()
self.register_modules(
unet=a__ , scheduler=a__ , movq=a__ , )
UpperCAmelCase = 2 ** (len(self.movq.config.block_out_channels ) - 1)
def __snake_case ( self : str , a__ : Union[str, Any] , a__ : List[str] , a__ : int , a__ : Optional[Any] , a__ : List[Any] , a__ : Union[str, Any] ):
if latents is None:
UpperCAmelCase = randn_tensor(a__ , generator=a__ , device=a__ , dtype=a__ )
else:
if latents.shape != shape:
raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {shape}" )
UpperCAmelCase = latents.to(a__ )
UpperCAmelCase = latents * scheduler.init_noise_sigma
return latents
def __snake_case ( self : Optional[Any] , a__ : Union[str, Any]=0 ):
if is_accelerate_available():
from accelerate import cpu_offload
else:
raise ImportError('''Please install accelerate via `pip install accelerate`''' )
UpperCAmelCase = torch.device(f"cuda:{gpu_id}" )
UpperCAmelCase = [
self.unet,
self.movq,
]
for cpu_offloaded_model in models:
if cpu_offloaded_model is not None:
cpu_offload(a__ , a__ )
def __snake_case ( self : Union[str, Any] , a__ : List[str]=0 ):
if is_accelerate_available() and is_accelerate_version('''>=''' , '''0.17.0.dev0''' ):
from accelerate import cpu_offload_with_hook
else:
raise ImportError('''`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.''' )
UpperCAmelCase = torch.device(f"cuda:{gpu_id}" )
if self.device.type != "cpu":
self.to('''cpu''' , silence_dtype_warnings=a__ )
torch.cuda.empty_cache() # otherwise we don't see the memory savings (but they probably exist)
UpperCAmelCase = None
for cpu_offloaded_model in [self.unet, self.movq]:
UpperCAmelCase, UpperCAmelCase = cpu_offload_with_hook(a__ , a__ , prev_module_hook=a__ )
# We'll offload the last model manually.
UpperCAmelCase = hook
@property
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
def __snake_case ( self : List[Any] ):
if not hasattr(self.unet , '''_hf_hook''' ):
return self.device
for module in self.unet.modules():
if (
hasattr(a__ , '''_hf_hook''' )
and hasattr(module._hf_hook , '''execution_device''' )
and module._hf_hook.execution_device is not None
):
return torch.device(module._hf_hook.execution_device )
return self.device
@torch.no_grad()
@replace_example_docstring(a__ )
def __call__( self : Union[str, Any] , a__ : Union[torch.FloatTensor, List[torch.FloatTensor]] , a__ : Union[torch.FloatTensor, List[torch.FloatTensor]] , a__ : torch.FloatTensor , a__ : int = 512 , a__ : int = 512 , a__ : int = 100 , a__ : float = 4.0 , a__ : int = 1 , a__ : Optional[Union[torch.Generator, List[torch.Generator]]] = None , a__ : Optional[torch.FloatTensor] = None , a__ : Optional[str] = "pil" , a__ : bool = True , ):
UpperCAmelCase = self._execution_device
UpperCAmelCase = guidance_scale > 1.0
if isinstance(a__ , a__ ):
UpperCAmelCase = torch.cat(a__ , dim=0 )
if isinstance(a__ , a__ ):
UpperCAmelCase = torch.cat(a__ , dim=0 )
if isinstance(a__ , a__ ):
UpperCAmelCase = torch.cat(a__ , dim=0 )
UpperCAmelCase = image_embeds.shape[0] * num_images_per_prompt
if do_classifier_free_guidance:
UpperCAmelCase = image_embeds.repeat_interleave(a__ , dim=0 )
UpperCAmelCase = negative_image_embeds.repeat_interleave(a__ , dim=0 )
UpperCAmelCase = hint.repeat_interleave(a__ , dim=0 )
UpperCAmelCase = torch.cat([negative_image_embeds, image_embeds] , dim=0 ).to(dtype=self.unet.dtype , device=a__ )
UpperCAmelCase = torch.cat([hint, hint] , dim=0 ).to(dtype=self.unet.dtype , device=a__ )
self.scheduler.set_timesteps(a__ , device=a__ )
UpperCAmelCase = self.scheduler.timesteps
UpperCAmelCase = self.movq.config.latent_channels
UpperCAmelCase, UpperCAmelCase = downscale_height_and_width(a__ , a__ , self.movq_scale_factor )
# create initial latent
UpperCAmelCase = self.prepare_latents(
(batch_size, num_channels_latents, height, width) , image_embeds.dtype , a__ , a__ , a__ , self.scheduler , )
for i, t in enumerate(self.progress_bar(a__ ) ):
# expand the latents if we are doing classifier free guidance
UpperCAmelCase = torch.cat([latents] * 2 ) if do_classifier_free_guidance else latents
UpperCAmelCase = {'''image_embeds''': image_embeds, '''hint''': hint}
UpperCAmelCase = self.unet(
sample=a__ , timestep=a__ , encoder_hidden_states=a__ , added_cond_kwargs=a__ , return_dict=a__ , )[0]
if do_classifier_free_guidance:
UpperCAmelCase, UpperCAmelCase = noise_pred.split(latents.shape[1] , dim=1 )
UpperCAmelCase, UpperCAmelCase = noise_pred.chunk(2 )
UpperCAmelCase, UpperCAmelCase = variance_pred.chunk(2 )
UpperCAmelCase = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
UpperCAmelCase = torch.cat([noise_pred, variance_pred_text] , dim=1 )
if not (
hasattr(self.scheduler.config , '''variance_type''' )
and self.scheduler.config.variance_type in ["learned", "learned_range"]
):
UpperCAmelCase, UpperCAmelCase = noise_pred.split(latents.shape[1] , dim=1 )
# compute the previous noisy sample x_t -> x_t-1
UpperCAmelCase = self.scheduler.step(
a__ , a__ , a__ , generator=a__ , )[0]
# post-processing
UpperCAmelCase = self.movq.decode(a__ , force_not_quantize=a__ )['''sample''']
if output_type not in ["pt", "np", "pil"]:
raise ValueError(f"Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}" )
if output_type in ["np", "pil"]:
UpperCAmelCase = image * 0.5 + 0.5
UpperCAmelCase = image.clamp(0 , 1 )
UpperCAmelCase = image.cpu().permute(0 , 2 , 3 , 1 ).float().numpy()
if output_type == "pil":
UpperCAmelCase = self.numpy_to_pil(a__ )
if not return_dict:
return (image,)
return ImagePipelineOutput(images=a__ )
| 570 | 1 |
import re
def __UpperCamelCase ( A ):
UpperCamelCase__ = re.compile(r'''^(\+91[\-\s]?)?[0]?(91)?[789]\d{9}$''' )
if match := re.search(A , A ):
return match.string == phone
return False
if __name__ == "__main__":
print(indian_phone_validator('''+918827897895'''))
| 415 | def __UpperCamelCase ( A ):
if len(A ) < 2:
return collection
def circle_sort_util(A , A , A ) -> bool:
UpperCamelCase__ = False
if low == high:
return swapped
UpperCamelCase__ = low
UpperCamelCase__ = high
while left < right:
if collection[left] > collection[right]:
UpperCamelCase__ , UpperCamelCase__ = (
collection[right],
collection[left],
)
UpperCamelCase__ = True
left += 1
right -= 1
if left == right and collection[left] > collection[right + 1]:
UpperCamelCase__ , UpperCamelCase__ = (
collection[right + 1],
collection[left],
)
UpperCamelCase__ = True
UpperCamelCase__ = low + int((high - low) / 2 )
UpperCamelCase__ = circle_sort_util(A , A , A )
UpperCamelCase__ = circle_sort_util(A , mid + 1 , A )
return swapped or left_swap or right_swap
UpperCamelCase__ = True
while is_not_sorted is True:
UpperCamelCase__ = circle_sort_util(A , 0 , len(A ) - 1 )
return collection
if __name__ == "__main__":
__magic_name__ =input('''Enter numbers separated by a comma:\n''').strip()
__magic_name__ =[int(item) for item in user_input.split(''',''')]
print(circle_sort(unsorted))
| 415 | 1 |
'''simple docstring'''
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import AutoImageProcessor, ViTImageProcessor
from transformers.testing_utils import TOKEN, USER, get_tests_dir, is_staging_test
sys.path.append(str(Path(__file__).parent.parent / 'utils'))
from test_module.custom_image_processing import CustomImageProcessor # noqa E402
lowerCamelCase__ = get_tests_dir('fixtures')
class _lowerCAmelCase ( unittest.TestCase ):
'''simple docstring'''
def __lowercase ( self : List[Any] ) -> str:
'''simple docstring'''
_lowercase : Union[str, Any] = mock.Mock()
_lowercase : List[Any] = 500
_lowercase : Tuple = {}
_lowercase : Tuple = HTTPError
_lowercase : str = {}
# Download this model to make sure it's in the cache.
_lowercase : List[str] = ViTImageProcessor.from_pretrained('''hf-internal-testing/tiny-random-vit''' )
# Under the mock environment we get a 500 error when trying to reach the model.
with mock.patch('''requests.Session.request''' , return_value=UpperCamelCase_ ) as mock_head:
_lowercase : int = ViTImageProcessor.from_pretrained('''hf-internal-testing/tiny-random-vit''' )
# This check we did call the fake head request
mock_head.assert_called()
def __lowercase ( self : List[Any] ) -> Optional[int]:
'''simple docstring'''
_lowercase : List[Any] = ViTImageProcessor.from_pretrained(
'''https://huggingface.co/hf-internal-testing/tiny-random-vit/resolve/main/preprocessor_config.json''' )
def __lowercase ( self : Union[str, Any] ) -> List[Any]:
'''simple docstring'''
with self.assertRaises(UpperCamelCase_ ):
# config is in subfolder, the following should not work without specifying the subfolder
_lowercase : Tuple = AutoImageProcessor.from_pretrained('''hf-internal-testing/stable-diffusion-all-variants''' )
_lowercase : Optional[int] = AutoImageProcessor.from_pretrained(
'''hf-internal-testing/stable-diffusion-all-variants''' , subfolder='''feature_extractor''' )
self.assertIsNotNone(UpperCamelCase_ )
@is_staging_test
class _lowerCAmelCase ( unittest.TestCase ):
'''simple docstring'''
@classmethod
def __lowercase ( cls : List[str] ) -> Optional[Any]:
'''simple docstring'''
_lowercase : List[Any] = TOKEN
HfFolder.save_token(UpperCamelCase_ )
@classmethod
def __lowercase ( cls : str ) -> str:
'''simple docstring'''
try:
delete_repo(token=cls._token , repo_id='''test-image-processor''' )
except HTTPError:
pass
try:
delete_repo(token=cls._token , repo_id='''valid_org/test-image-processor-org''' )
except HTTPError:
pass
try:
delete_repo(token=cls._token , repo_id='''test-dynamic-image-processor''' )
except HTTPError:
pass
def __lowercase ( self : Any ) -> Optional[Any]:
'''simple docstring'''
_lowercase : Union[str, Any] = ViTImageProcessor.from_pretrained(UpperCamelCase_ )
image_processor.push_to_hub('''test-image-processor''' , use_auth_token=self._token )
_lowercase : Tuple = ViTImageProcessor.from_pretrained(F"{USER}/test-image-processor" )
for k, v in image_processor.__dict__.items():
self.assertEqual(UpperCamelCase_ , getattr(UpperCamelCase_ , UpperCamelCase_ ) )
# Reset repo
delete_repo(token=self._token , repo_id='''test-image-processor''' )
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
image_processor.save_pretrained(
UpperCamelCase_ , repo_id='''test-image-processor''' , push_to_hub=UpperCamelCase_ , use_auth_token=self._token )
_lowercase : Tuple = ViTImageProcessor.from_pretrained(F"{USER}/test-image-processor" )
for k, v in image_processor.__dict__.items():
self.assertEqual(UpperCamelCase_ , getattr(UpperCamelCase_ , UpperCamelCase_ ) )
def __lowercase ( self : Any ) -> Tuple:
'''simple docstring'''
_lowercase : Dict = ViTImageProcessor.from_pretrained(UpperCamelCase_ )
image_processor.push_to_hub('''valid_org/test-image-processor''' , use_auth_token=self._token )
_lowercase : Tuple = ViTImageProcessor.from_pretrained('''valid_org/test-image-processor''' )
for k, v in image_processor.__dict__.items():
self.assertEqual(UpperCamelCase_ , getattr(UpperCamelCase_ , UpperCamelCase_ ) )
# Reset repo
delete_repo(token=self._token , repo_id='''valid_org/test-image-processor''' )
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
image_processor.save_pretrained(
UpperCamelCase_ , repo_id='''valid_org/test-image-processor-org''' , push_to_hub=UpperCamelCase_ , use_auth_token=self._token )
_lowercase : Tuple = ViTImageProcessor.from_pretrained('''valid_org/test-image-processor-org''' )
for k, v in image_processor.__dict__.items():
self.assertEqual(UpperCamelCase_ , getattr(UpperCamelCase_ , UpperCamelCase_ ) )
def __lowercase ( self : Union[str, Any] ) -> Optional[int]:
'''simple docstring'''
CustomImageProcessor.register_for_auto_class()
_lowercase : Any = CustomImageProcessor.from_pretrained(UpperCamelCase_ )
image_processor.push_to_hub('''test-dynamic-image-processor''' , use_auth_token=self._token )
# This has added the proper auto_map field to the config
self.assertDictEqual(
image_processor.auto_map , {'''AutoImageProcessor''': '''custom_image_processing.CustomImageProcessor'''} , )
_lowercase : Union[str, Any] = AutoImageProcessor.from_pretrained(
F"{USER}/test-dynamic-image-processor" , trust_remote_code=UpperCamelCase_ )
# Can't make an isinstance check because the new_image_processor is from the CustomImageProcessor class of a dynamic module
self.assertEqual(new_image_processor.__class__.__name__ , '''CustomImageProcessor''' )
| 411 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_clip import CLIPImageProcessor
lowerCamelCase__ = logging.get_logger(__name__)
class _lowerCAmelCase ( __A ):
'''simple docstring'''
def __init__( self : Optional[int] , *UpperCamelCase_ : Union[str, Any] , **UpperCamelCase_ : Optional[int] ) -> None:
'''simple docstring'''
warnings.warn(
'''The class CLIPFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'''
''' use CLIPImageProcessor instead.''' , UpperCamelCase_ , )
super().__init__(*UpperCamelCase_ , **UpperCamelCase_ )
| 411 | 1 |
def lowerCamelCase__ ():
return [
a * b * (1000 - a - b)
for a in range(1 , 999)
for b in range(_UpperCAmelCase , 999)
if (a * a + b * b == (1000 - a - b) ** 2)
][0]
if __name__ == "__main__":
print(f"""{solution() = }""")
| 73 |
from torch import nn
def SCREAMING_SNAKE_CASE__ ( UpperCamelCase__: str ):
if act_fn in ["swish", "silu"]:
return nn.SiLU()
elif act_fn == "mish":
return nn.Mish()
elif act_fn == "gelu":
return nn.GELU()
else:
raise ValueError(f'''Unsupported activation function: {act_fn}''' ) | 6 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
_A = {
"configuration_bloom": ["BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP", "BloomConfig", "BloomOnnxConfig"],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_A = ["BloomTokenizerFast"]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_A = [
"BLOOM_PRETRAINED_MODEL_ARCHIVE_LIST",
"BloomForCausalLM",
"BloomModel",
"BloomPreTrainedModel",
"BloomForSequenceClassification",
"BloomForTokenClassification",
"BloomForQuestionAnswering",
]
if TYPE_CHECKING:
from .configuration_bloom import BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP, BloomConfig, BloomOnnxConfig
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_bloom_fast import BloomTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_bloom import (
BLOOM_PRETRAINED_MODEL_ARCHIVE_LIST,
BloomForCausalLM,
BloomForQuestionAnswering,
BloomForSequenceClassification,
BloomForTokenClassification,
BloomModel,
BloomPreTrainedModel,
)
else:
import sys
_A = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 721 |
from ...processing_utils import ProcessorMixin
class _lowerCAmelCase ( __a ):
_lowercase ='''SpeechT5FeatureExtractor'''
_lowercase ='''SpeechT5Tokenizer'''
def __init__( self , _UpperCamelCase , _UpperCamelCase ) -> int:
super().__init__(_UpperCamelCase , _UpperCamelCase )
def __call__( self , *_UpperCamelCase , **_UpperCamelCase ) -> Optional[int]:
lowerCAmelCase_ = kwargs.pop("audio" , _UpperCamelCase )
lowerCAmelCase_ = kwargs.pop("text" , _UpperCamelCase )
lowerCAmelCase_ = kwargs.pop("text_target" , _UpperCamelCase )
lowerCAmelCase_ = kwargs.pop("audio_target" , _UpperCamelCase )
lowerCAmelCase_ = kwargs.pop("sampling_rate" , _UpperCamelCase )
if audio is not None and text is not None:
raise ValueError(
"Cannot process both `audio` and `text` inputs. Did you mean `audio_target` or `text_target`?" )
if audio_target is not None and text_target is not None:
raise ValueError(
"Cannot process both `audio_target` and `text_target` inputs. Did you mean `audio` or `text`?" )
if audio is None and audio_target is None and text is None and text_target is None:
raise ValueError(
"You need to specify either an `audio`, `audio_target`, `text`, or `text_target` input to process." )
if audio is not None:
lowerCAmelCase_ = self.feature_extractor(_UpperCamelCase , *_UpperCamelCase , sampling_rate=_UpperCamelCase , **_UpperCamelCase )
elif text is not None:
lowerCAmelCase_ = self.tokenizer(_UpperCamelCase , **_UpperCamelCase )
else:
lowerCAmelCase_ = None
if audio_target is not None:
lowerCAmelCase_ = self.feature_extractor(audio_target=_UpperCamelCase , *_UpperCamelCase , sampling_rate=_UpperCamelCase , **_UpperCamelCase )
lowerCAmelCase_ = targets["input_values"]
elif text_target is not None:
lowerCAmelCase_ = self.tokenizer(_UpperCamelCase , **_UpperCamelCase )
lowerCAmelCase_ = targets["input_ids"]
else:
lowerCAmelCase_ = None
if inputs is None:
return targets
if targets is not None:
lowerCAmelCase_ = labels
lowerCAmelCase_ = targets.get("attention_mask" )
if decoder_attention_mask is not None:
lowerCAmelCase_ = decoder_attention_mask
return inputs
def __a ( self , *_UpperCamelCase , **_UpperCamelCase ) -> str:
lowerCAmelCase_ = kwargs.pop("input_values" , _UpperCamelCase )
lowerCAmelCase_ = kwargs.pop("input_ids" , _UpperCamelCase )
lowerCAmelCase_ = kwargs.pop("labels" , _UpperCamelCase )
if input_values is not None and input_ids is not None:
raise ValueError("Cannot process both `input_values` and `input_ids` inputs." )
if input_values is None and input_ids is None and labels is None:
raise ValueError(
"You need to specify either an `input_values`, `input_ids`, or `labels` input to be padded." )
if input_values is not None:
lowerCAmelCase_ = self.feature_extractor.pad(_UpperCamelCase , *_UpperCamelCase , **_UpperCamelCase )
elif input_ids is not None:
lowerCAmelCase_ = self.tokenizer.pad(_UpperCamelCase , **_UpperCamelCase )
else:
lowerCAmelCase_ = None
if labels is not None:
if "input_ids" in labels or (isinstance(_UpperCamelCase , _UpperCamelCase ) and "input_ids" in labels[0]):
lowerCAmelCase_ = self.tokenizer.pad(_UpperCamelCase , **_UpperCamelCase )
lowerCAmelCase_ = targets["input_ids"]
else:
lowerCAmelCase_ = self.feature_extractor.feature_size
lowerCAmelCase_ = self.feature_extractor.num_mel_bins
lowerCAmelCase_ = self.feature_extractor.pad(_UpperCamelCase , *_UpperCamelCase , **_UpperCamelCase )
lowerCAmelCase_ = feature_size_hack
lowerCAmelCase_ = targets["input_values"]
else:
lowerCAmelCase_ = None
if inputs is None:
return targets
if targets is not None:
lowerCAmelCase_ = labels
lowerCAmelCase_ = targets.get("attention_mask" )
if decoder_attention_mask is not None:
lowerCAmelCase_ = decoder_attention_mask
return inputs
def __a ( self , *_UpperCamelCase , **_UpperCamelCase ) -> List[Any]:
return self.tokenizer.batch_decode(*_UpperCamelCase , **_UpperCamelCase )
def __a ( self , *_UpperCamelCase , **_UpperCamelCase ) -> Optional[Any]:
return self.tokenizer.decode(*_UpperCamelCase , **_UpperCamelCase )
| 279 | 0 |
import logging
from transformers import PretrainedConfig
__magic_name__ : Optional[int] = logging.getLogger(__name__)
__magic_name__ : Optional[int] = {
'''bertabs-finetuned-cnndm''': '''https://huggingface.co/remi/bertabs-finetuned-cnndm-extractive-abstractive-summarization/resolve/main/config.json''',
}
class A__ ( __snake_case ):
'''simple docstring'''
snake_case__ = """bertabs"""
def __init__( self : Any , _SCREAMING_SNAKE_CASE : Optional[int]=3_0522 , _SCREAMING_SNAKE_CASE : Dict=512 , _SCREAMING_SNAKE_CASE : Tuple=6 , _SCREAMING_SNAKE_CASE : str=512 , _SCREAMING_SNAKE_CASE : Tuple=8 , _SCREAMING_SNAKE_CASE : Optional[int]=512 , _SCREAMING_SNAKE_CASE : Tuple=0.2 , _SCREAMING_SNAKE_CASE : Dict=6 , _SCREAMING_SNAKE_CASE : Optional[int]=768 , _SCREAMING_SNAKE_CASE : List[Any]=8 , _SCREAMING_SNAKE_CASE : Any=2048 , _SCREAMING_SNAKE_CASE : Optional[Any]=0.2 , **_SCREAMING_SNAKE_CASE : List[str] , ):
"""simple docstring"""
super().__init__(**__lowerCAmelCase )
UpperCamelCase = vocab_size
UpperCamelCase = max_pos
UpperCamelCase = enc_layers
UpperCamelCase = enc_hidden_size
UpperCamelCase = enc_heads
UpperCamelCase = enc_ff_size
UpperCamelCase = enc_dropout
UpperCamelCase = dec_layers
UpperCamelCase = dec_hidden_size
UpperCamelCase = dec_heads
UpperCamelCase = dec_ff_size
UpperCamelCase = dec_dropout
| 280 |
# 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.
from argparse import ArgumentParser
from accelerate.commands.config import get_config_parser
from accelerate.commands.env import env_command_parser
from accelerate.commands.launch import launch_command_parser
from accelerate.commands.test import test_command_parser
from accelerate.commands.tpu import tpu_command_parser
def lowerCAmelCase__() -> str:
'''simple docstring'''
lowerCamelCase__ = ArgumentParser('''Accelerate CLI tool''' ,usage='''accelerate <command> [<args>]''' ,allow_abbrev=__snake_case )
lowerCamelCase__ = parser.add_subparsers(help='''accelerate command helpers''' )
# Register commands
get_config_parser(subparsers=__snake_case )
env_command_parser(subparsers=__snake_case )
launch_command_parser(subparsers=__snake_case )
tpu_command_parser(subparsers=__snake_case )
test_command_parser(subparsers=__snake_case )
# Let's go
lowerCamelCase__ = parser.parse_args()
if not hasattr(__snake_case ,'''func''' ):
parser.print_help()
exit(1 )
# Run
args.func(__snake_case )
if __name__ == "__main__":
main()
| 481 | 0 |
"""simple docstring"""
from __future__ import annotations
import random
import unittest
from transformers import TransfoXLConfig, 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
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import (
TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
TFTransfoXLForSequenceClassification,
TFTransfoXLLMHeadModel,
TFTransfoXLModel,
)
class __lowerCAmelCase :
"""simple docstring"""
def __init__( self : int , _snake_case : Tuple , ) -> Tuple:
"""simple docstring"""
A_ = parent
A_ = 13
A_ = 7
A_ = 30
A_ = self.seq_length + self.mem_len
A_ = 15
A_ = True
A_ = True
A_ = 99
A_ = [10, 50, 80]
A_ = 32
A_ = 32
A_ = 4
A_ = 8
A_ = 128
A_ = 2
A_ = 2
A_ = None
A_ = 1
A_ = 0
A_ = 3
A_ = self.vocab_size - 1
A_ = 0.0_1
def lowerCamelCase__ ( self : Optional[int] ) -> Dict:
"""simple docstring"""
A_ = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
A_ = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
A_ = None
if self.use_labels:
A_ = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
A_ = TransfoXLConfig(
vocab_size=self.vocab_size , mem_len=self.mem_len , clamp_len=self.clamp_len , cutoffs=self.cutoffs , d_model=self.hidden_size , d_embed=self.d_embed , n_head=self.num_attention_heads , d_head=self.d_head , d_inner=self.d_inner , div_val=self.div_val , n_layer=self.num_hidden_layers , eos_token_id=self.eos_token_id , pad_token_id=self.vocab_size - 1 , init_range=self.init_range , num_labels=self.num_labels , )
return (config, input_ids_a, input_ids_a, lm_labels)
def lowerCamelCase__ ( self : List[Any] ) -> Optional[int]:
"""simple docstring"""
random.seed(self.seed )
tf.random.set_seed(self.seed )
def lowerCamelCase__ ( self : Optional[int] , _snake_case : Union[str, Any] , _snake_case : Any , _snake_case : Tuple , _snake_case : Any ) -> List[Any]:
"""simple docstring"""
A_ = TFTransfoXLModel(UpperCAmelCase__ )
A_ = model(UpperCAmelCase__ ).to_tuple()
A_ = {'''input_ids''': input_ids_a, '''mems''': mems_a}
A_ = model(UpperCAmelCase__ ).to_tuple()
self.parent.assertEqual(hidden_states_a.shape , (self.batch_size, self.seq_length, self.hidden_size) )
self.parent.assertEqual(hidden_states_a.shape , (self.batch_size, self.seq_length, self.hidden_size) )
self.parent.assertListEqual(
[mem.shape for mem in mems_a] , [(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers , )
self.parent.assertListEqual(
[mem.shape for mem in mems_a] , [(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers , )
def lowerCamelCase__ ( self : Tuple , _snake_case : int , _snake_case : str , _snake_case : Any , _snake_case : Tuple ) -> int:
"""simple docstring"""
A_ = TFTransfoXLLMHeadModel(UpperCAmelCase__ )
A_ = model(UpperCAmelCase__ ).to_tuple()
A_ = {'''input_ids''': input_ids_a, '''labels''': lm_labels}
A_ = model(UpperCAmelCase__ ).to_tuple()
A_ = model([input_ids_a, mems_a] ).to_tuple()
A_ = {'''input_ids''': input_ids_a, '''mems''': mems_a, '''labels''': lm_labels}
A_ = model(UpperCAmelCase__ ).to_tuple()
self.parent.assertEqual(lm_logits_a.shape , (self.batch_size, self.seq_length, self.vocab_size) )
self.parent.assertListEqual(
[mem.shape for mem in mems_a] , [(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers , )
self.parent.assertEqual(lm_logits_a.shape , (self.batch_size, self.seq_length, self.vocab_size) )
self.parent.assertListEqual(
[mem.shape for mem in mems_a] , [(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers , )
def lowerCamelCase__ ( self : int , _snake_case : str , _snake_case : List[Any] , _snake_case : Optional[int] , _snake_case : int ) -> Union[str, Any]:
"""simple docstring"""
A_ = TFTransfoXLForSequenceClassification(UpperCAmelCase__ )
A_ = model(UpperCAmelCase__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def lowerCamelCase__ ( self : str ) -> List[Any]:
"""simple docstring"""
A_ = self.prepare_config_and_inputs()
(A_) = config_and_inputs
A_ = {'''input_ids''': input_ids_a}
return config, inputs_dict
@require_tf
class __lowerCAmelCase ( lowercase__ , lowercase__ , unittest.TestCase ):
"""simple docstring"""
snake_case = (
(TFTransfoXLModel, TFTransfoXLLMHeadModel, TFTransfoXLForSequenceClassification) if is_tf_available() else ()
)
snake_case = () if is_tf_available() else ()
snake_case = (
{
"feature-extraction": TFTransfoXLModel,
"text-classification": TFTransfoXLForSequenceClassification,
"text-generation": TFTransfoXLLMHeadModel,
"zero-shot": TFTransfoXLForSequenceClassification,
}
if is_tf_available()
else {}
)
# TODO: add this test when TFTransfoXLLMHead has a linear output layer implemented
snake_case = False
snake_case = False
snake_case = False
snake_case = False
def lowerCamelCase__ ( self : Optional[int] , _snake_case : List[str] , _snake_case : Union[str, Any] , _snake_case : Any , _snake_case : Tuple , _snake_case : Union[str, Any] ) -> Tuple:
"""simple docstring"""
if pipeline_test_casse_name == "TextGenerationPipelineTests":
# Get `ValueError: AttributeError: 'NoneType' object has no attribute 'new_ones'` or `AssertionError`.
# `TransfoXLConfig` was never used in pipeline tests: cannot create a simple
# tokenizer.
return True
return False
def lowerCamelCase__ ( self : List[Any] ) -> Optional[Any]:
"""simple docstring"""
A_ = TFTransfoXLModelTester(self )
A_ = ConfigTester(self , config_class=UpperCAmelCase__ , d_embed=37 )
def lowerCamelCase__ ( self : int ) -> Tuple:
"""simple docstring"""
self.config_tester.run_common_tests()
def lowerCamelCase__ ( self : int ) -> Optional[Any]:
"""simple docstring"""
self.model_tester.set_seed()
A_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_transfo_xl_model(*UpperCAmelCase__ )
def lowerCamelCase__ ( self : Optional[Any] ) -> Dict:
"""simple docstring"""
self.model_tester.set_seed()
A_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_transfo_xl_lm_head(*UpperCAmelCase__ )
def lowerCamelCase__ ( self : Any ) -> Union[str, Any]:
"""simple docstring"""
A_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_transfo_xl_for_sequence_classification(*UpperCAmelCase__ )
def lowerCamelCase__ ( self : Optional[int] ) -> Any:
"""simple docstring"""
A_ = self.model_tester.prepare_config_and_inputs_for_common()
A_ = [TFTransfoXLForSequenceClassification]
for model_class in self.all_model_classes:
A_ = model_class(UpperCAmelCase__ )
assert isinstance(model.get_input_embeddings() , tf.keras.layers.Layer )
if model_class in list_other_models_with_output_ebd:
A_ = model.get_output_embeddings()
assert isinstance(UpperCAmelCase__ , tf.keras.layers.Layer )
A_ = model.get_bias()
assert name is None
else:
A_ = model.get_output_embeddings()
assert x is None
A_ = model.get_bias()
assert name is None
def lowerCamelCase__ ( self : Union[str, Any] ) -> Dict:
"""simple docstring"""
pass
@slow
def lowerCamelCase__ ( self : int ) -> Optional[Any]:
"""simple docstring"""
for model_name in TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
A_ = TFTransfoXLModel.from_pretrained(UpperCAmelCase__ )
self.assertIsNotNone(UpperCAmelCase__ )
@unittest.skip(reason="This model doesn\'t play well with fit() due to not returning a single loss." )
def lowerCamelCase__ ( self : List[Any] ) -> Tuple:
"""simple docstring"""
pass
@require_tf
class __lowerCAmelCase ( unittest.TestCase ):
"""simple docstring"""
@unittest.skip("Skip test until #12651 is resolved." )
@slow
def lowerCamelCase__ ( self : int ) -> List[str]:
"""simple docstring"""
A_ = TFTransfoXLLMHeadModel.from_pretrained("transfo-xl-wt103" )
# fmt: off
A_ = tf.convert_to_tensor([[33,1_297,2,1,1_009,4,1_109,11_739,4_762,358,5,25,245,22,1_706,17,20_098,5,3_215,21,37,1_110,3,13,1_041,4,24,603,490,2,71_477,20_098,104_447,2,20_961,1,2_604,4,1,329,3,6_224,831,16_002,2,8,603,78_967,29_546,23,803,20,25,416,5,8,232,4,277,6,1_855,4_601,3,29_546,54,8,3_609,5,57_211,49,4,1,277,18,8,1_755,15_691,3,341,25,416,693,42_573,71,17,401,94,31,17_919,2,29_546,7_873,18,1,435,23,11_011,755,5,5_167,3,7_983,98,84,2,29_546,3_267,8,3_609,4,1,4_865,1_075,2,6_087,71,6,346,8,5_854,3,29_546,824,1_400,1_868,2,19,160,2,311,8,5_496,2,20_920,17,25,15_097,3,24,24,0]] , dtype=tf.intaa ) # noqa: E231
# fmt: on
# In 1991 , the remains of Russian Tsar Nicholas II and his family
# ( except for Alexei and Maria ) are discovered .
# The voice of Nicholas's young son , Tsarevich Alexei Nikolaevich , narrates the
# remainder of the story . 1883 Western Siberia ,
# a young Grigori Rasputin is asked by his father and a group of men to perform magic .
# Rasputin has a vision and denounces one of the men as a horse thief . Although his
# father initially slaps him for making such an accusation , Rasputin watches as the
# man is chased outside and beaten . Twenty years later , Rasputin sees a vision of
# the Virgin Mary , prompting him to become a priest . Rasputin quickly becomes famous ,
# with people , even a bishop , begging for his blessing . <eod> </s> <eos>
# fmt: off
A_ = [33,1_297,2,1,1_009,4,1_109,11_739,4_762,358,5,25,245,22,1_706,17,20_098,5,3_215,21,37,1_110,3,13,1_041,4,24,603,490,2,71_477,20_098,104_447,2,20_961,1,2_604,4,1,329,3,6_224,831,16_002,2,8,603,78_967,29_546,23,803,20,25,416,5,8,232,4,277,6,1_855,4_601,3,29_546,54,8,3_609,5,57_211,49,4,1,277,18,8,1_755,15_691,3,341,25,416,693,42_573,71,17,401,94,31,17_919,2,29_546,7_873,18,1,435,23,11_011,755,5,5_167,3,7_983,98,84,2,29_546,3_267,8,3_609,4,1,4_865,1_075,2,6_087,71,6,346,8,5_854,3,29_546,824,1_400,1_868,2,19,160,2,311,8,5_496,2,20_920,17,25,15_097,3,24,24,0,33,1,1_857,2,1,1_009,4,1_109,11_739,4_762,358,5,25,245,28,1_110,3,13,1_041,4,24,603,490,2,71_477,20_098,104_447,2,20_961,1,2_604,4,1,329,3,0] # noqa: E231
# fmt: on
# In 1991, the remains of Russian Tsar Nicholas II and his family (
# except for Alexei and Maria ) are discovered. The voice of young son,
# Tsarevich Alexei Nikolaevich, narrates the remainder of the story.
# 1883 Western Siberia, a young Grigori Rasputin is asked by his father
# and a group of men to perform magic. Rasputin has a vision and
# denounces one of the men as a horse thief. Although his father initially
# slaps him for making such an accusation, Rasputin watches as the man
# is chased outside and beaten. Twenty years later, Rasputin sees a vision
# of the Virgin Mary, prompting him to become a priest.
# Rasputin quickly becomes famous, with people, even a bishop, begging for
# his blessing. <unk> <unk> <eos> In the 1990s, the remains of Russian Tsar
# Nicholas II and his family were discovered. The voice of <unk> young son,
# Tsarevich Alexei Nikolaevich, narrates the remainder of the story.<eos>
A_ = model.generate(UpperCAmelCase__ , max_length=200 , do_sample=UpperCAmelCase__ )
self.assertListEqual(output_ids[0].numpy().tolist() , UpperCAmelCase__ )
| 700 |
"""simple docstring"""
from argparse import ArgumentParser, Namespace
from ..utils import logging
from . import BaseTransformersCLICommand
def A_ (__a ):
'''simple docstring'''
return ConvertCommand(
args.model_type , args.tf_checkpoint , args.pytorch_dump_output , args.config , args.finetuning_task_name )
UpperCamelCase_ : Tuple = '''
transformers can only be used from the commandline to convert TensorFlow models in PyTorch, In that case, it requires
TensorFlow to be installed. Please see https://www.tensorflow.org/install/ for installation instructions.
'''
class __lowerCAmelCase ( _lowercase ):
"""simple docstring"""
@staticmethod
def lowerCamelCase__ ( _snake_case : ArgumentParser ) -> Union[str, Any]:
"""simple docstring"""
A_ = parser.add_parser(
"convert" , help="CLI tool to run convert model from original author checkpoints to Transformers PyTorch checkpoints." , )
train_parser.add_argument("--model_type" , type=_snake_case , required=_snake_case , help="Model's type." )
train_parser.add_argument(
"--tf_checkpoint" , type=_snake_case , required=_snake_case , help="TensorFlow checkpoint path or folder." )
train_parser.add_argument(
"--pytorch_dump_output" , type=_snake_case , required=_snake_case , help="Path to the PyTorch saved model output." )
train_parser.add_argument("--config" , type=_snake_case , default="" , help="Configuration file path or folder." )
train_parser.add_argument(
"--finetuning_task_name" , type=_snake_case , default=_snake_case , help="Optional fine-tuning task name if the TF model was a finetuned model." , )
train_parser.set_defaults(func=_snake_case )
def __init__( self : str , _snake_case : str , _snake_case : str , _snake_case : str , _snake_case : str , _snake_case : str , *_snake_case : Optional[int] , ) -> Union[str, Any]:
"""simple docstring"""
A_ = logging.get_logger("transformers-cli/converting" )
self._logger.info(F'Loading model {model_type}' )
A_ = model_type
A_ = tf_checkpoint
A_ = pytorch_dump_output
A_ = config
A_ = finetuning_task_name
def lowerCamelCase__ ( self : Tuple ) -> Dict:
"""simple docstring"""
if self._model_type == "albert":
try:
from ..models.albert.convert_albert_original_tf_checkpoint_to_pytorch import (
convert_tf_checkpoint_to_pytorch,
)
except ImportError:
raise ImportError(_snake_case )
convert_tf_checkpoint_to_pytorch(self._tf_checkpoint , self._config , self._pytorch_dump_output )
elif self._model_type == "bert":
try:
from ..models.bert.convert_bert_original_tf_checkpoint_to_pytorch import (
convert_tf_checkpoint_to_pytorch,
)
except ImportError:
raise ImportError(_snake_case )
convert_tf_checkpoint_to_pytorch(self._tf_checkpoint , self._config , self._pytorch_dump_output )
elif self._model_type == "funnel":
try:
from ..models.funnel.convert_funnel_original_tf_checkpoint_to_pytorch import (
convert_tf_checkpoint_to_pytorch,
)
except ImportError:
raise ImportError(_snake_case )
convert_tf_checkpoint_to_pytorch(self._tf_checkpoint , self._config , self._pytorch_dump_output )
elif self._model_type == "t5":
try:
from ..models.ta.convert_ta_original_tf_checkpoint_to_pytorch import convert_tf_checkpoint_to_pytorch
except ImportError:
raise ImportError(_snake_case )
convert_tf_checkpoint_to_pytorch(self._tf_checkpoint , self._config , self._pytorch_dump_output )
elif self._model_type == "gpt":
from ..models.openai.convert_openai_original_tf_checkpoint_to_pytorch import (
convert_openai_checkpoint_to_pytorch,
)
convert_openai_checkpoint_to_pytorch(self._tf_checkpoint , self._config , self._pytorch_dump_output )
elif self._model_type == "transfo_xl":
try:
from ..models.transfo_xl.convert_transfo_xl_original_tf_checkpoint_to_pytorch import (
convert_transfo_xl_checkpoint_to_pytorch,
)
except ImportError:
raise ImportError(_snake_case )
if "ckpt" in self._tf_checkpoint.lower():
A_ = self._tf_checkpoint
A_ = ""
else:
A_ = self._tf_checkpoint
A_ = ""
convert_transfo_xl_checkpoint_to_pytorch(
_snake_case , self._config , self._pytorch_dump_output , _snake_case )
elif self._model_type == "gpt2":
try:
from ..models.gpta.convert_gpta_original_tf_checkpoint_to_pytorch import (
convert_gpta_checkpoint_to_pytorch,
)
except ImportError:
raise ImportError(_snake_case )
convert_gpta_checkpoint_to_pytorch(self._tf_checkpoint , self._config , self._pytorch_dump_output )
elif self._model_type == "xlnet":
try:
from ..models.xlnet.convert_xlnet_original_tf_checkpoint_to_pytorch import (
convert_xlnet_checkpoint_to_pytorch,
)
except ImportError:
raise ImportError(_snake_case )
convert_xlnet_checkpoint_to_pytorch(
self._tf_checkpoint , self._config , self._pytorch_dump_output , self._finetuning_task_name )
elif self._model_type == "xlm":
from ..models.xlm.convert_xlm_original_pytorch_checkpoint_to_pytorch import (
convert_xlm_checkpoint_to_pytorch,
)
convert_xlm_checkpoint_to_pytorch(self._tf_checkpoint , self._pytorch_dump_output )
elif self._model_type == "lxmert":
from ..models.lxmert.convert_lxmert_original_tf_checkpoint_to_pytorch import (
convert_lxmert_checkpoint_to_pytorch,
)
convert_lxmert_checkpoint_to_pytorch(self._tf_checkpoint , self._pytorch_dump_output )
elif self._model_type == "rembert":
from ..models.rembert.convert_rembert_tf_checkpoint_to_pytorch import (
convert_rembert_tf_checkpoint_to_pytorch,
)
convert_rembert_tf_checkpoint_to_pytorch(self._tf_checkpoint , self._config , self._pytorch_dump_output )
else:
raise ValueError(
"--model_type should be selected in the list [bert, gpt, gpt2, t5, transfo_xl, xlnet, xlm, lxmert]" )
| 482 | 0 |
from __future__ import annotations
from sys import maxsize
from typing import Generic, TypeVar
__lowerCamelCase : Union[str, Any] = TypeVar("""T""")
def A__ ( _a : int ):
'''simple docstring'''
return (position - 1) // 2
def A__ ( _a : int ):
'''simple docstring'''
return (2 * position) + 1
def A__ ( _a : int ):
'''simple docstring'''
return (2 * position) + 2
class _lowercase ( Generic[T] ):
def __init__( self ):
snake_case__ : list[tuple[T, int]] =[]
snake_case__ : dict[T, int] ={}
snake_case__ : int =0
def __len__( self ):
return self.elements
def __repr__( self ):
return str(self.heap )
def lowercase__ ( self ):
# Check if the priority queue is empty
return self.elements == 0
def lowercase__ ( self , a , a ):
# Add an element with given priority to the queue
self.heap.append((elem, weight) )
snake_case__ : Tuple =self.elements
self.elements += 1
self._bubble_up(a )
def lowercase__ ( self ):
# Remove and return the element with lowest weight (highest priority)
if self.elements > 1:
self._swap_nodes(0 , self.elements - 1 )
snake_case__ , snake_case__ : Any =self.heap.pop()
del self.position_map[elem]
self.elements -= 1
if self.elements > 0:
snake_case__ , snake_case__ : Tuple =self.heap[0]
self._bubble_down(a )
return elem
def lowercase__ ( self , a , a ):
# Update the weight of the given key
snake_case__ : Optional[int] =self.position_map[elem]
snake_case__ : Union[str, Any] =(elem, weight)
if position > 0:
snake_case__ : List[Any] =get_parent_position(a )
snake_case__ , snake_case__ : Union[str, Any] =self.heap[parent_position]
if parent_weight > weight:
self._bubble_up(a )
else:
self._bubble_down(a )
else:
self._bubble_down(a )
def lowercase__ ( self , a ):
# Place a node at the proper position (upward movement) [to be used internally
# only]
snake_case__ : List[Any] =self.position_map[elem]
if curr_pos == 0:
return None
snake_case__ : List[str] =get_parent_position(a )
snake_case__ , snake_case__ : Dict =self.heap[curr_pos]
snake_case__ , snake_case__ : Tuple =self.heap[parent_position]
if parent_weight > weight:
self._swap_nodes(a , a )
return self._bubble_up(a )
return None
def lowercase__ ( self , a ):
# Place a node at the proper position (downward movement) [to be used
# internally only]
snake_case__ : Dict =self.position_map[elem]
snake_case__ , snake_case__ : Tuple =self.heap[curr_pos]
snake_case__ : Optional[int] =get_child_left_position(a )
snake_case__ : Union[str, Any] =get_child_right_position(a )
if child_left_position < self.elements and child_right_position < self.elements:
snake_case__ , snake_case__ : str =self.heap[child_left_position]
snake_case__ , snake_case__ : Any =self.heap[child_right_position]
if child_right_weight < child_left_weight and child_right_weight < weight:
self._swap_nodes(a , a )
return self._bubble_down(a )
if child_left_position < self.elements:
snake_case__ , snake_case__ : List[str] =self.heap[child_left_position]
if child_left_weight < weight:
self._swap_nodes(a , a )
return self._bubble_down(a )
else:
return None
if child_right_position < self.elements:
snake_case__ , snake_case__ : Tuple =self.heap[child_right_position]
if child_right_weight < weight:
self._swap_nodes(a , a )
return self._bubble_down(a )
return None
def lowercase__ ( self , a , a ):
# Swap the nodes at the given positions
snake_case__ : Any =self.heap[nodea_pos][0]
snake_case__ : int =self.heap[nodea_pos][0]
snake_case__ , snake_case__ : List[str] =(
self.heap[nodea_pos],
self.heap[nodea_pos],
)
snake_case__ : Optional[Any] =nodea_pos
snake_case__ : str =nodea_pos
class _lowercase ( Generic[T] ):
def __init__( self ):
snake_case__ : dict[T, dict[T, int]] ={}
snake_case__ : int =0
def __repr__( self ):
return str(self.connections )
def __len__( self ):
return self.nodes
def lowercase__ ( self , a ):
# Add a node in the graph if it is not in the graph
if node not in self.connections:
snake_case__ : int ={}
self.nodes += 1
def lowercase__ ( self , a , a , a ):
# Add an edge between 2 nodes in the graph
self.add_node(a )
self.add_node(a )
snake_case__ : List[str] =weight
snake_case__ : Dict =weight
def A__ ( _a : GraphUndirectedWeighted[T] , ):
'''simple docstring'''
snake_case__ : dict[T, int] ={node: maxsize for node in graph.connections}
snake_case__ : dict[T, T | None] ={node: None for node in graph.connections}
snake_case__ : MinPriorityQueue[T] =MinPriorityQueue()
for node, weight in dist.items():
priority_queue.push(_a , _a )
if priority_queue.is_empty():
return dist, parent
# initialization
snake_case__ : Dict =priority_queue.extract_min()
snake_case__ : Any =0
for neighbour in graph.connections[node]:
if dist[neighbour] > dist[node] + graph.connections[node][neighbour]:
snake_case__ : Optional[Any] =dist[node] + graph.connections[node][neighbour]
priority_queue.update_key(_a , dist[neighbour] )
snake_case__ : List[Any] =node
# running prim's algorithm
while not priority_queue.is_empty():
snake_case__ : int =priority_queue.extract_min()
for neighbour in graph.connections[node]:
if dist[neighbour] > dist[node] + graph.connections[node][neighbour]:
snake_case__ : Union[str, Any] =dist[node] + graph.connections[node][neighbour]
priority_queue.update_key(_a , dist[neighbour] )
snake_case__ : Tuple =node
return dist, parent
| 385 |
import os
from itertools import chain
from random import randrange, shuffle
import pytest
from .sola import PokerHand
__lowerCamelCase : List[str] = (
"""4S 3H 2C 7S 5H""",
"""9D 8H 2C 6S 7H""",
"""2D 6D 9D TH 7D""",
"""TC 8C 2S JH 6C""",
"""JH 8S TH AH QH""",
"""TS KS 5S 9S AC""",
"""KD 6S 9D TH AD""",
"""KS 8D 4D 9S 4S""", # pair
"""8C 4S KH JS 4D""", # pair
"""QH 8H KD JH 8S""", # pair
"""KC 4H KS 2H 8D""", # pair
"""KD 4S KC 3H 8S""", # pair
"""AH 8S AS KC JH""", # pair
"""3H 4C 4H 3S 2H""", # 2 pairs
"""5S 5D 2C KH KH""", # 2 pairs
"""3C KH 5D 5S KH""", # 2 pairs
"""AS 3C KH AD KH""", # 2 pairs
"""7C 7S 3S 7H 5S""", # 3 of a kind
"""7C 7S KH 2H 7H""", # 3 of a kind
"""AC KH QH AH AS""", # 3 of a kind
"""2H 4D 3C AS 5S""", # straight (low ace)
"""3C 5C 4C 2C 6H""", # straight
"""6S 8S 7S 5H 9H""", # straight
"""JS QS 9H TS KH""", # straight
"""QC KH TS JS AH""", # straight (high ace)
"""8C 9C 5C 3C TC""", # flush
"""3S 8S 9S 5S KS""", # flush
"""4C 5C 9C 8C KC""", # flush
"""JH 8H AH KH QH""", # flush
"""3D 2H 3H 2C 2D""", # full house
"""2H 2C 3S 3H 3D""", # full house
"""KH KC 3S 3H 3D""", # full house
"""JC 6H JS JD JH""", # 4 of a kind
"""JC 7H JS JD JH""", # 4 of a kind
"""JC KH JS JD JH""", # 4 of a kind
"""2S AS 4S 5S 3S""", # straight flush (low ace)
"""2D 6D 3D 4D 5D""", # straight flush
"""5C 6C 3C 7C 4C""", # straight flush
"""JH 9H TH KH QH""", # straight flush
"""JH AH TH KH QH""", # royal flush (high ace straight flush)
)
__lowerCamelCase : Optional[Any] = (
("""2H 3H 4H 5H 6H""", """KS AS TS QS JS""", """Loss"""),
("""2H 3H 4H 5H 6H""", """AS AD AC AH JD""", """Win"""),
("""AS AH 2H AD AC""", """JS JD JC JH 3D""", """Win"""),
("""2S AH 2H AS AC""", """JS JD JC JH AD""", """Loss"""),
("""2S AH 2H AS AC""", """2H 3H 5H 6H 7H""", """Win"""),
("""AS 3S 4S 8S 2S""", """2H 3H 5H 6H 7H""", """Win"""),
("""2H 3H 5H 6H 7H""", """2S 3H 4H 5S 6C""", """Win"""),
("""2S 3H 4H 5S 6C""", """3D 4C 5H 6H 2S""", """Tie"""),
("""2S 3H 4H 5S 6C""", """AH AC 5H 6H AS""", """Win"""),
("""2S 2H 4H 5S 4C""", """AH AC 5H 6H AS""", """Loss"""),
("""2S 2H 4H 5S 4C""", """AH AC 5H 6H 7S""", """Win"""),
("""6S AD 7H 4S AS""", """AH AC 5H 6H 7S""", """Loss"""),
("""2S AH 4H 5S KC""", """AH AC 5H 6H 7S""", """Loss"""),
("""2S 3H 6H 7S 9C""", """7H 3C TH 6H 9S""", """Loss"""),
("""4S 5H 6H TS AC""", """3S 5H 6H TS AC""", """Win"""),
("""2S AH 4H 5S 6C""", """AD 4C 5H 6H 2C""", """Tie"""),
("""AS AH 3H AD AC""", """AS AH 2H AD AC""", """Win"""),
("""AH AC 5H 5C QS""", """AH AC 5H 5C KS""", """Loss"""),
("""AH AC 5H 5C QS""", """KH KC 5H 5C QS""", """Win"""),
("""7C 7S KH 2H 7H""", """3C 3S AH 2H 3H""", """Win"""),
("""3C 3S AH 2H 3H""", """7C 7S KH 2H 7H""", """Loss"""),
("""6H 5H 4H 3H 2H""", """5H 4H 3H 2H AH""", """Win"""),
("""5H 4H 3H 2H AH""", """5H 4H 3H 2H AH""", """Tie"""),
("""5H 4H 3H 2H AH""", """6H 5H 4H 3H 2H""", """Loss"""),
("""AH AD KS KC AC""", """AH KD KH AC KC""", """Win"""),
("""2H 4D 3C AS 5S""", """2H 4D 3C 6S 5S""", """Loss"""),
("""2H 3S 3C 3H 2S""", """3S 3C 2S 2H 2D""", """Win"""),
("""4D 6D 5D 2D JH""", """3S 8S 3H TC KH""", """Loss"""),
("""4S 6C 8S 3S 7S""", """AD KS 2D 7D 7C""", """Loss"""),
("""6S 4C 7H 8C 3H""", """5H JC AH 9D 9C""", """Loss"""),
("""9D 9H JH TC QH""", """3C 2S JS 5C 7H""", """Win"""),
("""2H TC 8S AD 9S""", """4H TS 7H 2C 5C""", """Win"""),
("""9D 3S 2C 7S 7C""", """JC TD 3C TC 9H""", """Loss"""),
)
__lowerCamelCase : List[str] = (
("""2H 3H 4H 5H 6H""", True),
("""AS AH 2H AD AC""", False),
("""2H 3H 5H 6H 7H""", True),
("""KS AS TS QS JS""", True),
("""8H 9H QS JS TH""", False),
("""AS 3S 4S 8S 2S""", True),
)
__lowerCamelCase : Tuple = (
("""2H 3H 4H 5H 6H""", True),
("""AS AH 2H AD AC""", False),
("""2H 3H 5H 6H 7H""", False),
("""KS AS TS QS JS""", True),
("""8H 9H QS JS TH""", True),
)
__lowerCamelCase : List[Any] = (
("""2H 4D 3C AS 5S""", True, [5, 4, 3, 2, 14]),
("""2H 5D 3C AS 5S""", False, [14, 5, 5, 3, 2]),
("""JH QD KC AS TS""", False, [14, 13, 12, 11, 10]),
("""9D 3S 2C 7S 7C""", False, [9, 7, 7, 3, 2]),
)
__lowerCamelCase : Any = (
("""JH AH TH KH QH""", 0),
("""JH 9H TH KH QH""", 0),
("""JC KH JS JD JH""", 7),
("""KH KC 3S 3H 3D""", 6),
("""8C 9C 5C 3C TC""", 0),
("""JS QS 9H TS KH""", 0),
("""7C 7S KH 2H 7H""", 3),
("""3C KH 5D 5S KH""", 2),
("""QH 8H KD JH 8S""", 1),
("""2D 6D 9D TH 7D""", 0),
)
__lowerCamelCase : Tuple = (
("""JH AH TH KH QH""", 23),
("""JH 9H TH KH QH""", 22),
("""JC KH JS JD JH""", 21),
("""KH KC 3S 3H 3D""", 20),
("""8C 9C 5C 3C TC""", 19),
("""JS QS 9H TS KH""", 18),
("""7C 7S KH 2H 7H""", 17),
("""3C KH 5D 5S KH""", 16),
("""QH 8H KD JH 8S""", 15),
("""2D 6D 9D TH 7D""", 14),
)
def A__ ( ):
'''simple docstring'''
snake_case__ , snake_case__ : Any =randrange(len(_a ) ), randrange(len(_a ) )
snake_case__ : Tuple =["""Loss""", """Tie""", """Win"""][(play >= oppo) + (play > oppo)]
snake_case__ , snake_case__ : Union[str, Any] =SORTED_HANDS[play], SORTED_HANDS[oppo]
return hand, other, expected
def A__ ( _a : int = 100 ):
'''simple docstring'''
return (generate_random_hand() for _ in range(_a ))
@pytest.mark.parametrize("""hand, expected""" , _a )
def A__ ( _a : List[Any] , _a : Any ):
'''simple docstring'''
assert PokerHand(_a )._is_flush() == expected
@pytest.mark.parametrize("""hand, expected""" , _a )
def A__ ( _a : Union[str, Any] , _a : int ):
'''simple docstring'''
assert PokerHand(_a )._is_straight() == expected
@pytest.mark.parametrize("""hand, expected, card_values""" , _a )
def A__ ( _a : Optional[int] , _a : Tuple , _a : Tuple ):
'''simple docstring'''
snake_case__ : Any =PokerHand(_a )
assert player._is_five_high_straight() == expected
assert player._card_values == card_values
@pytest.mark.parametrize("""hand, expected""" , _a )
def A__ ( _a : Any , _a : Tuple ):
'''simple docstring'''
assert PokerHand(_a )._is_same_kind() == expected
@pytest.mark.parametrize("""hand, expected""" , _a )
def A__ ( _a : Union[str, Any] , _a : Tuple ):
'''simple docstring'''
assert PokerHand(_a )._hand_type == expected
@pytest.mark.parametrize("""hand, other, expected""" , _a )
def A__ ( _a : str , _a : Tuple , _a : Union[str, Any] ):
'''simple docstring'''
assert PokerHand(_a ).compare_with(PokerHand(_a ) ) == expected
@pytest.mark.parametrize("""hand, other, expected""" , generate_random_hands() )
def A__ ( _a : Any , _a : Optional[Any] , _a : str ):
'''simple docstring'''
assert PokerHand(_a ).compare_with(PokerHand(_a ) ) == expected
def A__ ( ):
'''simple docstring'''
snake_case__ : str =[PokerHand(_a ) for hand in SORTED_HANDS]
snake_case__ : List[str] =poker_hands.copy()
shuffle(_a )
snake_case__ : Any =chain(sorted(_a ) )
for index, hand in enumerate(_a ):
assert hand == poker_hands[index]
def A__ ( ):
'''simple docstring'''
snake_case__ : Tuple =[PokerHand("""2D AC 3H 4H 5S""" ), PokerHand("""2S 3H 4H 5S 6C""" )]
pokerhands.sort(reverse=_a )
assert pokerhands[0].__str__() == "2S 3H 4H 5S 6C"
def A__ ( ):
'''simple docstring'''
snake_case__ : Optional[int] =PokerHand("""2C 4S AS 3D 5C""" )
snake_case__ : Optional[Any] =True
snake_case__ : Any =[5, 4, 3, 2, 14]
for _ in range(10 ):
assert pokerhand._is_five_high_straight() == expected
assert pokerhand._card_values == expected_card_values
def A__ ( ):
'''simple docstring'''
snake_case__ : Tuple =0
snake_case__ : int =os.path.abspath(os.path.dirname(_a ) )
snake_case__ : List[Any] =os.path.join(_a , """poker_hands.txt""" )
with open(_a ) as file_hand:
for line in file_hand:
snake_case__ : List[Any] =line[:14].strip()
snake_case__ : Any =line[15:].strip()
snake_case__ , snake_case__ : str =PokerHand(_a ), PokerHand(_a )
snake_case__ : Optional[Any] =player.compare_with(_a )
if output == "Win":
answer += 1
assert answer == 376
| 385 | 1 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
UpperCAmelCase = logging.get_logger(__name__)
UpperCAmelCase = {
'''google/bigbird-roberta-base''': '''https://huggingface.co/google/bigbird-roberta-base/resolve/main/config.json''',
'''google/bigbird-roberta-large''': '''https://huggingface.co/google/bigbird-roberta-large/resolve/main/config.json''',
'''google/bigbird-base-trivia-itc''': '''https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/config.json''',
# See all BigBird models at https://huggingface.co/models?filter=big_bird
}
class A_ ( __lowerCamelCase ):
'''simple docstring'''
_UpperCamelCase : Dict = """big_bird"""
def __init__( self , snake_case=5_0358 , snake_case=768 , snake_case=12 , snake_case=12 , snake_case=3072 , snake_case="gelu_new" , snake_case=0.1 , snake_case=0.1 , snake_case=4096 , snake_case=2 , snake_case=0.02 , snake_case=1E-12 , snake_case=True , snake_case=0 , snake_case=1 , snake_case=2 , snake_case=66 , snake_case="block_sparse" , snake_case=True , snake_case=False , snake_case=64 , snake_case=3 , snake_case=None , **snake_case , ):
super().__init__(
pad_token_id=snake_case , bos_token_id=snake_case , eos_token_id=snake_case , sep_token_id=snake_case , **snake_case , )
lowercase = vocab_size
lowercase = max_position_embeddings
lowercase = hidden_size
lowercase = num_hidden_layers
lowercase = num_attention_heads
lowercase = intermediate_size
lowercase = hidden_act
lowercase = hidden_dropout_prob
lowercase = attention_probs_dropout_prob
lowercase = initializer_range
lowercase = type_vocab_size
lowercase = layer_norm_eps
lowercase = use_cache
lowercase = rescale_embeddings
lowercase = attention_type
lowercase = use_bias
lowercase = block_size
lowercase = num_random_blocks
lowercase = classifier_dropout
class A_ ( __lowerCamelCase ):
'''simple docstring'''
@property
def SCREAMING_SNAKE_CASE__ ( self ):
if self.task == "multiple-choice":
lowercase = {0: 'batch', 1: 'choice', 2: 'sequence'}
else:
lowercase = {0: 'batch', 1: 'sequence'}
return OrderedDict(
[
('input_ids', dynamic_axis),
('attention_mask', dynamic_axis),
] )
| 565 |
from bisect import bisect
from itertools import accumulate
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
lowercase = sorted(zip(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ) , key=lambda __SCREAMING_SNAKE_CASE : x[0] / x[1] , reverse=__SCREAMING_SNAKE_CASE )
lowercase , lowercase = [i[0] for i in r], [i[1] for i in r]
lowercase = list(accumulate(__SCREAMING_SNAKE_CASE ) )
lowercase = bisect(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
return (
0
if k == 0
else sum(vl[:k] ) + (w - acc[k - 1]) * (vl[k]) / (wt[k])
if k != n
else sum(vl[:k] )
)
if __name__ == "__main__":
import doctest
doctest.testmod()
| 565 | 1 |
"""simple docstring"""
from typing import List, Optional, Tuple, Union
import torch
from torch import nn
from torch.nn import CrossEntropyLoss
from ... import AutoBackbone
from ...modeling_outputs import SemanticSegmenterOutput
from ...modeling_utils import PreTrainedModel
from ...utils import add_start_docstrings, add_start_docstrings_to_model_forward, replace_return_docstrings
from ...utils.backbone_utils import BackboneMixin
from .configuration_upernet import UperNetConfig
SCREAMING_SNAKE_CASE_ = [
'openmmlab/upernet-convnext-tiny',
# See all UperNet models at https://huggingface.co/models?filter=upernet
]
# General docstring
SCREAMING_SNAKE_CASE_ = 'UperNetConfig'
class snake_case_ ( nn.Module ):
"""simple docstring"""
def __init__( self , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ = 0 , lowerCamelCase_ = False , lowerCamelCase_ = 1 , ) -> None:
super().__init__()
UpperCamelCase = nn.Convad(
in_channels=lowerCamelCase_ , out_channels=lowerCamelCase_ , kernel_size=lowerCamelCase_ , padding=lowerCamelCase_ , bias=lowerCamelCase_ , dilation=lowerCamelCase_ , )
UpperCamelCase = nn.BatchNormad(lowerCamelCase_)
UpperCamelCase = nn.ReLU()
def UpperCAmelCase__ ( self , lowerCamelCase_) -> torch.Tensor:
UpperCamelCase = self.conv(lowerCamelCase_)
UpperCamelCase = self.batch_norm(lowerCamelCase_)
UpperCamelCase = self.activation(lowerCamelCase_)
return output
class snake_case_ ( nn.Module ):
"""simple docstring"""
def __init__( self , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_) -> None:
super().__init__()
UpperCamelCase = [
nn.AdaptiveAvgPoolad(lowerCamelCase_),
UperNetConvModule(lowerCamelCase_ , lowerCamelCase_ , kernel_size=1),
]
for i, layer in enumerate(self.layers):
self.add_module(str(lowerCamelCase_) , lowerCamelCase_)
def UpperCAmelCase__ ( self , lowerCamelCase_) -> torch.Tensor:
UpperCamelCase = input
for layer in self.layers:
UpperCamelCase = layer(lowerCamelCase_)
return hidden_state
class snake_case_ ( nn.Module ):
"""simple docstring"""
def __init__( self , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_) -> None:
super().__init__()
UpperCamelCase = pool_scales
UpperCamelCase = align_corners
UpperCamelCase = in_channels
UpperCamelCase = channels
UpperCamelCase = []
for i, pool_scale in enumerate(lowerCamelCase_):
UpperCamelCase = UperNetPyramidPoolingBlock(pool_scale=lowerCamelCase_ , in_channels=lowerCamelCase_ , channels=lowerCamelCase_)
self.blocks.append(lowerCamelCase_)
self.add_module(str(lowerCamelCase_) , lowerCamelCase_)
def UpperCAmelCase__ ( self , lowerCamelCase_) -> List[torch.Tensor]:
UpperCamelCase = []
for ppm in self.blocks:
UpperCamelCase = ppm(lowerCamelCase_)
UpperCamelCase = nn.functional.interpolate(
lowerCamelCase_ , size=x.size()[2:] , mode='''bilinear''' , align_corners=self.align_corners)
ppm_outs.append(lowerCamelCase_)
return ppm_outs
class snake_case_ ( nn.Module ):
"""simple docstring"""
def __init__( self , lowerCamelCase_ , lowerCamelCase_) -> Any:
super().__init__()
UpperCamelCase = config
UpperCamelCase = config.pool_scales # e.g. (1, 2, 3, 6)
UpperCamelCase = in_channels
UpperCamelCase = config.hidden_size
UpperCamelCase = False
UpperCamelCase = nn.Convad(self.channels , config.num_labels , kernel_size=1)
# PSP Module
UpperCamelCase = UperNetPyramidPoolingModule(
self.pool_scales , self.in_channels[-1] , self.channels , align_corners=self.align_corners , )
UpperCamelCase = UperNetConvModule(
self.in_channels[-1] + len(self.pool_scales) * self.channels , self.channels , kernel_size=3 , padding=1 , )
# FPN Module
UpperCamelCase = nn.ModuleList()
UpperCamelCase = nn.ModuleList()
for in_channels in self.in_channels[:-1]: # skip the top layer
UpperCamelCase = UperNetConvModule(lowerCamelCase_ , self.channels , kernel_size=1)
UpperCamelCase = UperNetConvModule(self.channels , self.channels , kernel_size=3 , padding=1)
self.lateral_convs.append(lowerCamelCase_)
self.fpn_convs.append(lowerCamelCase_)
UpperCamelCase = UperNetConvModule(
len(self.in_channels) * self.channels , self.channels , kernel_size=3 , padding=1 , )
def UpperCAmelCase__ ( self) -> Union[str, Any]:
self.apply(self._init_weights)
def UpperCAmelCase__ ( self , lowerCamelCase_) -> Union[str, Any]:
if isinstance(lowerCamelCase_ , nn.Convad):
module.weight.data.normal_(mean=0.0 , std=self.config.initializer_range)
if module.bias is not None:
module.bias.data.zero_()
def UpperCAmelCase__ ( self , lowerCamelCase_) -> Tuple:
UpperCamelCase = inputs[-1]
UpperCamelCase = [x]
psp_outs.extend(self.psp_modules(lowerCamelCase_))
UpperCamelCase = torch.cat(lowerCamelCase_ , dim=1)
UpperCamelCase = self.bottleneck(lowerCamelCase_)
return output
def UpperCAmelCase__ ( self , lowerCamelCase_) -> torch.Tensor:
# build laterals
UpperCamelCase = [lateral_conv(encoder_hidden_states[i]) for i, lateral_conv in enumerate(self.lateral_convs)]
laterals.append(self.psp_forward(lowerCamelCase_))
# build top-down path
UpperCamelCase = len(lowerCamelCase_)
for i in range(used_backbone_levels - 1 , 0 , -1):
UpperCamelCase = laterals[i - 1].shape[2:]
UpperCamelCase = laterals[i - 1] + nn.functional.interpolate(
laterals[i] , size=lowerCamelCase_ , mode='''bilinear''' , align_corners=self.align_corners)
# build outputs
UpperCamelCase = [self.fpn_convs[i](laterals[i]) for i in range(used_backbone_levels - 1)]
# append psp feature
fpn_outs.append(laterals[-1])
for i in range(used_backbone_levels - 1 , 0 , -1):
UpperCamelCase = nn.functional.interpolate(
fpn_outs[i] , size=fpn_outs[0].shape[2:] , mode='''bilinear''' , align_corners=self.align_corners)
UpperCamelCase = torch.cat(lowerCamelCase_ , dim=1)
UpperCamelCase = self.fpn_bottleneck(lowerCamelCase_)
UpperCamelCase = self.classifier(lowerCamelCase_)
return output
class snake_case_ ( nn.Module ):
"""simple docstring"""
def __init__( self , lowerCamelCase_ , lowerCamelCase_ = 2 , lowerCamelCase_ = 3 , lowerCamelCase_ = 1) -> None:
super().__init__()
UpperCamelCase = config
UpperCamelCase = config.auxiliary_in_channels
UpperCamelCase = config.auxiliary_channels
UpperCamelCase = config.auxiliary_num_convs
UpperCamelCase = config.auxiliary_concat_input
UpperCamelCase = in_index
UpperCamelCase = (kernel_size // 2) * dilation
UpperCamelCase = []
convs.append(
UperNetConvModule(
self.in_channels , self.channels , kernel_size=lowerCamelCase_ , padding=lowerCamelCase_ , dilation=lowerCamelCase_))
for i in range(self.num_convs - 1):
convs.append(
UperNetConvModule(
self.channels , self.channels , kernel_size=lowerCamelCase_ , padding=lowerCamelCase_ , dilation=lowerCamelCase_))
if self.num_convs == 0:
UpperCamelCase = nn.Identity()
else:
UpperCamelCase = nn.Sequential(*lowerCamelCase_)
if self.concat_input:
UpperCamelCase = UperNetConvModule(
self.in_channels + self.channels , self.channels , kernel_size=lowerCamelCase_ , padding=kernel_size // 2)
UpperCamelCase = nn.Convad(self.channels , config.num_labels , kernel_size=1)
def UpperCAmelCase__ ( self) -> Optional[int]:
self.apply(self._init_weights)
def UpperCAmelCase__ ( self , lowerCamelCase_) -> Any:
if isinstance(lowerCamelCase_ , nn.Convad):
module.weight.data.normal_(mean=0.0 , std=self.config.initializer_range)
if module.bias is not None:
module.bias.data.zero_()
def UpperCAmelCase__ ( self , lowerCamelCase_) -> torch.Tensor:
# just take the relevant feature maps
UpperCamelCase = encoder_hidden_states[self.in_index]
UpperCamelCase = self.convs(lowerCamelCase_)
if self.concat_input:
UpperCamelCase = self.conv_cat(torch.cat([hidden_states, output] , dim=1))
UpperCamelCase = self.classifier(lowerCamelCase_)
return output
class snake_case_ ( lowerCamelCase_ ):
"""simple docstring"""
A_ = UperNetConfig
A_ = '''pixel_values'''
A_ = True
def UpperCAmelCase__ ( self , lowerCamelCase_) -> List[str]:
if isinstance(lowerCamelCase_ , lowerCamelCase_):
module.backbone.init_weights()
module.decode_head.init_weights()
module.auxiliary_head.init_weights()
def UpperCAmelCase__ ( self) -> List[Any]:
self.backbone.init_weights()
self.decode_head.init_weights()
self.auxiliary_head.init_weights()
def UpperCAmelCase__ ( self , lowerCamelCase_ , lowerCamelCase_=False) -> str:
if isinstance(lowerCamelCase_ , lowerCamelCase_):
UpperCamelCase = value
SCREAMING_SNAKE_CASE_ = R'\n Parameters:\n This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class. Use\n it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and\n behavior.\n config ([`UperNetConfig`]): Model configuration class with all the parameters of the model.\n Initializing with a config file does not load the weights associated with the model, only the\n configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.\n'
SCREAMING_SNAKE_CASE_ = R'\n Args:\n pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):\n Pixel values. Padding will be ignored by default should you provide it. Pixel values can be obtained using\n [`AutoImageProcessor`]. See [`SegformerImageProcessor.__call__`] for details.\n output_attentions (`bool`, *optional*):\n Whether or not to return the attentions tensors of all attention layers in case the backbone has them. See\n `attentions` under returned tensors for more detail.\n output_hidden_states (`bool`, *optional*):\n Whether or not to return the hidden states of all layers of the backbone. See `hidden_states` under\n returned tensors for more detail.\n return_dict (`bool`, *optional*):\n Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.\n'
@add_start_docstrings(
'''UperNet framework leveraging any vision backbone e.g. for ADE20k, CityScapes.''' , lowerCamelCase_ , )
class snake_case_ ( lowerCamelCase_ ):
"""simple docstring"""
def __init__( self , lowerCamelCase_) -> int:
super().__init__(lowerCamelCase_)
UpperCamelCase = AutoBackbone.from_config(config.backbone_config)
# Semantic segmentation head(s)
UpperCamelCase = UperNetHead(lowerCamelCase_ , in_channels=self.backbone.channels)
UpperCamelCase = UperNetFCNHead(lowerCamelCase_) if config.use_auxiliary_head else None
# Initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(UPERNET_INPUTS_DOCSTRING.format('''batch_size, sequence_length'''))
@replace_return_docstrings(output_type=lowerCamelCase_ , config_class=_CONFIG_FOR_DOC)
def UpperCAmelCase__ ( self , lowerCamelCase_ = None , lowerCamelCase_ = None , lowerCamelCase_ = None , lowerCamelCase_ = None , lowerCamelCase_ = None , ) -> Union[tuple, SemanticSegmenterOutput]:
UpperCamelCase = return_dict if return_dict is not None else self.config.use_return_dict
UpperCamelCase = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
UpperCamelCase = output_attentions if output_attentions is not None else self.config.output_attentions
UpperCamelCase = self.backbone.forward_with_filtered_kwargs(
lowerCamelCase_ , output_hidden_states=lowerCamelCase_ , output_attentions=lowerCamelCase_)
UpperCamelCase = outputs.feature_maps
UpperCamelCase = self.decode_head(lowerCamelCase_)
UpperCamelCase = nn.functional.interpolate(lowerCamelCase_ , size=pixel_values.shape[2:] , mode='''bilinear''' , align_corners=lowerCamelCase_)
UpperCamelCase = None
if self.auxiliary_head is not None:
UpperCamelCase = self.auxiliary_head(lowerCamelCase_)
UpperCamelCase = nn.functional.interpolate(
lowerCamelCase_ , size=pixel_values.shape[2:] , mode='''bilinear''' , align_corners=lowerCamelCase_)
UpperCamelCase = None
if labels is not None:
if self.config.num_labels == 1:
raise ValueError('''The number of labels should be greater than one''')
else:
# compute weighted loss
UpperCamelCase = CrossEntropyLoss(ignore_index=self.config.loss_ignore_index)
UpperCamelCase = loss_fct(lowerCamelCase_ , lowerCamelCase_)
UpperCamelCase = loss_fct(lowerCamelCase_ , lowerCamelCase_)
UpperCamelCase = main_loss + self.config.auxiliary_loss_weight * auxiliary_loss
if not return_dict:
if output_hidden_states:
UpperCamelCase = (logits,) + outputs[1:]
else:
UpperCamelCase = (logits,) + outputs[2:]
return ((loss,) + output) if loss is not None else output
return SemanticSegmenterOutput(
loss=lowerCamelCase_ , logits=lowerCamelCase_ , hidden_states=outputs.hidden_states , attentions=outputs.attentions , ) | 34 |
"""simple docstring"""
import os
import unicodedata
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import SPIECE_UNDERLINE, logging
SCREAMING_SNAKE_CASE_ = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE_ = {'vocab_file': 'spiece.model'}
SCREAMING_SNAKE_CASE_ = {
'vocab_file': {
'xlnet-base-cased': 'https://huggingface.co/xlnet-base-cased/resolve/main/spiece.model',
'xlnet-large-cased': 'https://huggingface.co/xlnet-large-cased/resolve/main/spiece.model',
}
}
SCREAMING_SNAKE_CASE_ = {
'xlnet-base-cased': None,
'xlnet-large-cased': None,
}
# Segments (not really needed)
SCREAMING_SNAKE_CASE_ = 0
SCREAMING_SNAKE_CASE_ = 1
SCREAMING_SNAKE_CASE_ = 2
SCREAMING_SNAKE_CASE_ = 3
SCREAMING_SNAKE_CASE_ = 4
class snake_case_ ( lowerCamelCase_ ):
"""simple docstring"""
A_ = VOCAB_FILES_NAMES
A_ = PRETRAINED_VOCAB_FILES_MAP
A_ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
A_ = '''left'''
def __init__( self , lowerCamelCase_ , lowerCamelCase_=False , lowerCamelCase_=True , lowerCamelCase_=False , lowerCamelCase_="<s>" , lowerCamelCase_="</s>" , lowerCamelCase_="<unk>" , lowerCamelCase_="<sep>" , lowerCamelCase_="<pad>" , lowerCamelCase_="<cls>" , lowerCamelCase_="<mask>" , lowerCamelCase_=["<eop>", "<eod>"] , lowerCamelCase_ = None , **lowerCamelCase_ , ) -> None:
# Mask token behave like a normal word, i.e. include the space before it
UpperCamelCase = AddedToken(lowerCamelCase_ , lstrip=lowerCamelCase_ , rstrip=lowerCamelCase_) if isinstance(lowerCamelCase_ , lowerCamelCase_) else mask_token
UpperCamelCase = {} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
do_lower_case=lowerCamelCase_ , remove_space=lowerCamelCase_ , keep_accents=lowerCamelCase_ , bos_token=lowerCamelCase_ , eos_token=lowerCamelCase_ , unk_token=lowerCamelCase_ , sep_token=lowerCamelCase_ , pad_token=lowerCamelCase_ , cls_token=lowerCamelCase_ , mask_token=lowerCamelCase_ , additional_special_tokens=lowerCamelCase_ , sp_model_kwargs=self.sp_model_kwargs , **lowerCamelCase_ , )
UpperCamelCase = 3
UpperCamelCase = do_lower_case
UpperCamelCase = remove_space
UpperCamelCase = keep_accents
UpperCamelCase = vocab_file
UpperCamelCase = spm.SentencePieceProcessor(**self.sp_model_kwargs)
self.sp_model.Load(lowerCamelCase_)
@property
def UpperCAmelCase__ ( self) -> List[str]:
return len(self.sp_model)
def UpperCAmelCase__ ( self) -> Tuple:
UpperCamelCase = {self.convert_ids_to_tokens(lowerCamelCase_): i for i in range(self.vocab_size)}
vocab.update(self.added_tokens_encoder)
return vocab
def __getstate__( self) -> Any:
UpperCamelCase = self.__dict__.copy()
UpperCamelCase = None
return state
def __setstate__( self , lowerCamelCase_) -> str:
UpperCamelCase = d
# for backward compatibility
if not hasattr(self , '''sp_model_kwargs'''):
UpperCamelCase = {}
UpperCamelCase = spm.SentencePieceProcessor(**self.sp_model_kwargs)
self.sp_model.Load(self.vocab_file)
def UpperCAmelCase__ ( self , lowerCamelCase_) -> Union[str, Any]:
if self.remove_space:
UpperCamelCase = ''' '''.join(inputs.strip().split())
else:
UpperCamelCase = inputs
UpperCamelCase = outputs.replace('''``''' , '''"''').replace('''\'\'''' , '''"''')
if not self.keep_accents:
UpperCamelCase = unicodedata.normalize('''NFKD''' , lowerCamelCase_)
UpperCamelCase = ''''''.join([c for c in outputs if not unicodedata.combining(lowerCamelCase_)])
if self.do_lower_case:
UpperCamelCase = outputs.lower()
return outputs
def UpperCAmelCase__ ( self , lowerCamelCase_) -> List[str]:
UpperCamelCase = self.preprocess_text(lowerCamelCase_)
UpperCamelCase = self.sp_model.encode(lowerCamelCase_ , out_type=lowerCamelCase_)
UpperCamelCase = []
for piece in pieces:
if len(lowerCamelCase_) > 1 and piece[-1] == str(''',''') and piece[-2].isdigit():
UpperCamelCase = self.sp_model.EncodeAsPieces(piece[:-1].replace(lowerCamelCase_ , ''''''))
if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
if len(cur_pieces[0]) == 1:
UpperCamelCase = cur_pieces[1:]
else:
UpperCamelCase = cur_pieces[0][1:]
cur_pieces.append(piece[-1])
new_pieces.extend(lowerCamelCase_)
else:
new_pieces.append(lowerCamelCase_)
return new_pieces
def UpperCAmelCase__ ( self , lowerCamelCase_) -> int:
return self.sp_model.PieceToId(lowerCamelCase_)
def UpperCAmelCase__ ( self , lowerCamelCase_) -> Optional[int]:
return self.sp_model.IdToPiece(lowerCamelCase_)
def UpperCAmelCase__ ( self , lowerCamelCase_) -> Dict:
UpperCamelCase = ''''''.join(lowerCamelCase_).replace(lowerCamelCase_ , ''' ''').strip()
return out_string
def UpperCAmelCase__ ( self , lowerCamelCase_ , lowerCamelCase_ = False , lowerCamelCase_ = None , lowerCamelCase_ = True , **lowerCamelCase_ , ) -> str:
UpperCamelCase = kwargs.pop('''use_source_tokenizer''' , lowerCamelCase_)
UpperCamelCase = self.convert_ids_to_tokens(lowerCamelCase_ , skip_special_tokens=lowerCamelCase_)
# To avoid mixing byte-level and unicode for byte-level BPT
# we need to build string separately for added tokens and byte-level tokens
# cf. https://github.com/huggingface/transformers/issues/1133
UpperCamelCase = []
UpperCamelCase = []
for token in filtered_tokens:
if skip_special_tokens and token in self.all_special_ids:
continue
if token in self.added_tokens_encoder:
if current_sub_text:
sub_texts.append(self.convert_tokens_to_string(lowerCamelCase_))
UpperCamelCase = []
sub_texts.append(lowerCamelCase_)
else:
current_sub_text.append(lowerCamelCase_)
if current_sub_text:
sub_texts.append(self.convert_tokens_to_string(lowerCamelCase_))
# Mimic the behavior of the Rust tokenizer:
# By default, there are no spaces between special tokens
UpperCamelCase = ''''''.join(lowerCamelCase_)
UpperCamelCase = (
clean_up_tokenization_spaces
if clean_up_tokenization_spaces is not None
else self.clean_up_tokenization_spaces
)
if clean_up_tokenization_spaces:
UpperCamelCase = self.clean_up_tokenization(lowerCamelCase_)
return clean_text
else:
return text
def UpperCAmelCase__ ( self , lowerCamelCase_ , lowerCamelCase_ = None) -> List[int]:
UpperCamelCase = [self.sep_token_id]
UpperCamelCase = [self.cls_token_id]
if token_ids_a is None:
return token_ids_a + sep + cls
return token_ids_a + sep + token_ids_a + sep + cls
def UpperCAmelCase__ ( self , lowerCamelCase_ , lowerCamelCase_ = None , lowerCamelCase_ = False) -> List[int]:
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 ([0] * len(lowerCamelCase_)) + [1] + ([0] * len(lowerCamelCase_)) + [1, 1]
return ([0] * len(lowerCamelCase_)) + [1, 1]
def UpperCAmelCase__ ( self , lowerCamelCase_ , lowerCamelCase_ = None) -> List[int]:
UpperCamelCase = [self.sep_token_id]
UpperCamelCase = [2]
if token_ids_a is None:
return len(token_ids_a + sep) * [0] + cls_segment_id
return len(token_ids_a + sep) * [0] + len(token_ids_a + sep) * [1] + cls_segment_id
def UpperCAmelCase__ ( self , lowerCamelCase_ , lowerCamelCase_ = None) -> Tuple[str]:
if not os.path.isdir(lowerCamelCase_):
logger.error(F'Vocabulary path ({save_directory}) should be a directory')
return
UpperCamelCase = os.path.join(
lowerCamelCase_ , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''])
if os.path.abspath(self.vocab_file) != os.path.abspath(lowerCamelCase_) and os.path.isfile(self.vocab_file):
copyfile(self.vocab_file , lowerCamelCase_)
elif not os.path.isfile(self.vocab_file):
with open(lowerCamelCase_ , '''wb''') as fi:
UpperCamelCase = self.sp_model.serialized_model_proto()
fi.write(lowerCamelCase_)
return (out_vocab_file,) | 34 | 1 |
SCREAMING_SNAKE_CASE : Union[str, Any] = """\n# Installazione di Transformers\n! pip install transformers datasets\n# Per installare dalla fonte invece dell'ultima versione rilasciata, commenta il comando sopra e\n# rimuovi la modalità commento al comando seguente.\n# ! pip install git+https://github.com/huggingface/transformers.git\n"""
SCREAMING_SNAKE_CASE : Dict = [{"""type""": """code""", """content""": INSTALL_CONTENT}]
SCREAMING_SNAKE_CASE : List[Any] = {
"""{processor_class}""": """FakeProcessorClass""",
"""{model_class}""": """FakeModelClass""",
"""{object_class}""": """FakeObjectClass""",
}
| 707 | import argparse
import re
from typing import Dict
import torch
from datasets import Audio, Dataset, load_dataset, load_metric
from transformers import AutoFeatureExtractor, pipeline
def __A ( _A , _A ):
"""simple docstring"""
__a = args.log_outputs
__a = "_".join(args.dataset.split("/" ) + [args.config, args.split] )
# load metric
__a = load_metric("wer" )
__a = load_metric("cer" )
# compute metrics
__a = wer.compute(references=result["target"] , predictions=result["prediction"] )
__a = cer.compute(references=result["target"] , predictions=result["prediction"] )
# print & log results
__a = f"""WER: {wer_result}\nCER: {cer_result}"""
print(_A )
with open(f"""{dataset_id}_eval_results.txt""" , "w" ) as f:
f.write(_A )
# log all results in text file. Possibly interesting for analysis
if log_outputs is not None:
__a = f"""log_{dataset_id}_predictions.txt"""
__a = f"""log_{dataset_id}_targets.txt"""
with open(_A , "w" ) as p, open(_A , "w" ) as t:
# mapping function to write output
def write_to_file(_A , _A ):
p.write(f"""{i}""" + "\n" )
p.write(batch["prediction"] + "\n" )
t.write(f"""{i}""" + "\n" )
t.write(batch["target"] + "\n" )
result.map(_A , with_indices=_A )
def __A ( _A ):
"""simple docstring"""
__a = "[,?.!\-\;\:\"“%‘”�—’…–]" # noqa: W605 IMPORTANT: this should correspond to the chars that were ignored during training
__a = re.sub(_A , "" , text.lower() )
# In addition, we can normalize the target text, e.g. removing new lines characters etc...
# note that order is important here!
__a = ["\n\n", "\n", " ", " "]
for t in token_sequences_to_ignore:
__a = " ".join(text.split(_A ) )
return text
def __A ( _A ):
"""simple docstring"""
__a = load_dataset(args.dataset , args.config , split=args.split , use_auth_token=_A )
# for testing: only process the first two examples as a test
# dataset = dataset.select(range(10))
# load processor
__a = AutoFeatureExtractor.from_pretrained(args.model_id )
__a = feature_extractor.sampling_rate
# resample audio
__a = dataset.cast_column("audio" , Audio(sampling_rate=_A ) )
# load eval pipeline
if args.device is None:
__a = 0 if torch.cuda.is_available() else -1
__a = pipeline("automatic-speech-recognition" , model=args.model_id , device=args.device )
# map function to decode audio
def map_to_pred(_A ):
__a = asr(
batch["audio"]["array"] , chunk_length_s=args.chunk_length_s , stride_length_s=args.stride_length_s )
__a = prediction["text"]
__a = normalize_text(batch["sentence"] )
return batch
# run inference on all examples
__a = dataset.map(_A , remove_columns=dataset.column_names )
# compute and log_results
# do not change function below
log_results(_A , _A )
if __name__ == "__main__":
SCREAMING_SNAKE_CASE : Any = argparse.ArgumentParser()
parser.add_argument(
"""--model_id""", type=str, required=True, help="""Model identifier. Should be loadable with 🤗 Transformers"""
)
parser.add_argument(
"""--dataset""",
type=str,
required=True,
help="""Dataset name to evaluate the `model_id`. Should be loadable with 🤗 Datasets""",
)
parser.add_argument(
"""--config""", type=str, required=True, help="""Config of the dataset. *E.g.* `'en'` for Common Voice"""
)
parser.add_argument("""--split""", type=str, required=True, help="""Split of the dataset. *E.g.* `'test'`""")
parser.add_argument(
"""--chunk_length_s""", type=float, default=None, help="""Chunk length in seconds. Defaults to 5 seconds."""
)
parser.add_argument(
"""--stride_length_s""", type=float, default=None, help="""Stride of the audio chunks. Defaults to 1 second."""
)
parser.add_argument(
"""--log_outputs""", action="""store_true""", help="""If defined, write outputs to log file for analysis."""
)
parser.add_argument(
"""--device""",
type=int,
default=None,
help="""The device to run the pipeline on. -1 for CPU (default), 0 for the first GPU and so on.""",
)
SCREAMING_SNAKE_CASE : Optional[Any] = parser.parse_args()
main(args)
| 525 | 0 |
class _a :
'''simple docstring'''
def __init__( self , __UpperCAmelCase ):
__A : Optional[Any] = n
__A : Optional[int] = [None] * self.n
__A : Optional[int] = 0 # index of the first element
__A : Any = 0
__A : Any = 0
def __len__( self ):
return self.size
def __UpperCAmelCase( self ):
return self.size == 0
def __UpperCAmelCase( self ):
return False if self.is_empty() else self.array[self.front]
def __UpperCAmelCase( self , __UpperCAmelCase ):
if self.size >= self.n:
raise Exception("QUEUE IS FULL" )
__A : Tuple = data
__A : List[Any] = (self.rear + 1) % self.n
self.size += 1
return self
def __UpperCAmelCase( self ):
if self.size == 0:
raise Exception("UNDERFLOW" )
__A : List[Any] = self.array[self.front]
__A : str = None
__A : Optional[Any] = (self.front + 1) % self.n
self.size -= 1
return temp
| 520 | from pickle import UnpicklingError
import jax
import jax.numpy as jnp
import numpy as np
from flax.serialization import from_bytes
from flax.traverse_util import flatten_dict
from ..utils import logging
UpperCamelCase = logging.get_logger(__name__)
def lowerCamelCase_ ( _lowercase , _lowercase ) -> Optional[int]:
try:
with open(_lowercase , "rb" ) as flax_state_f:
__A : int = from_bytes(_lowercase , flax_state_f.read() )
except UnpicklingError as e:
try:
with open(_lowercase ) as f:
if f.read().startswith("version" ):
raise OSError(
"You seem to have cloned a repository without having git-lfs installed. Please"
" install git-lfs and run `git lfs install` followed by `git lfs pull` in the"
" folder you cloned." )
else:
raise ValueError from e
except (UnicodeDecodeError, ValueError):
raise EnvironmentError(F"Unable to convert {model_file} to Flax deserializable object. " )
return load_flax_weights_in_pytorch_model(_lowercase , _lowercase )
def lowerCamelCase_ ( _lowercase , _lowercase ) -> Optional[Any]:
try:
import torch # noqa: F401
except ImportError:
logger.error(
"Loading Flax weights in PyTorch requires both PyTorch and Flax to be installed. Please see"
" https://pytorch.org/ and https://flax.readthedocs.io/en/latest/installation.html for installation"
" instructions." )
raise
# check if we have bf16 weights
__A : Dict = flatten_dict(jax.tree_util.tree_map(lambda _lowercase : x.dtype == jnp.bfloataa , _lowercase ) ).values()
if any(_lowercase ):
# convert all weights to fp32 if they are bf16 since torch.from_numpy can-not handle bf16
# and bf16 is not fully supported in PT yet.
logger.warning(
"Found ``bfloat16`` weights in Flax model. Casting all ``bfloat16`` weights to ``float32`` "
"before loading those in PyTorch model." )
__A : Any = jax.tree_util.tree_map(
lambda _lowercase : params.astype(np.floataa ) if params.dtype == jnp.bfloataa else params , _lowercase )
__A : Union[str, Any] = ""
__A : List[Any] = flatten_dict(_lowercase , sep="." )
__A : str = pt_model.state_dict()
# keep track of unexpected & missing keys
__A : Tuple = []
__A : List[Any] = set(pt_model_dict.keys() )
for flax_key_tuple, flax_tensor in flax_state_dict.items():
__A : List[Any] = flax_key_tuple.split("." )
if flax_key_tuple_array[-1] == "kernel" and flax_tensor.ndim == 4:
__A : Dict = flax_key_tuple_array[:-1] + ["weight"]
__A : List[str] = jnp.transpose(_lowercase , (3, 2, 0, 1) )
elif flax_key_tuple_array[-1] == "kernel":
__A : str = flax_key_tuple_array[:-1] + ["weight"]
__A : List[str] = flax_tensor.T
elif flax_key_tuple_array[-1] == "scale":
__A : Optional[int] = flax_key_tuple_array[:-1] + ["weight"]
if "time_embedding" not in flax_key_tuple_array:
for i, flax_key_tuple_string in enumerate(_lowercase ):
__A : List[Any] = (
flax_key_tuple_string.replace("_0" , ".0" )
.replace("_1" , ".1" )
.replace("_2" , ".2" )
.replace("_3" , ".3" )
.replace("_4" , ".4" )
.replace("_5" , ".5" )
.replace("_6" , ".6" )
.replace("_7" , ".7" )
.replace("_8" , ".8" )
.replace("_9" , ".9" )
)
__A : Optional[int] = ".".join(_lowercase )
if flax_key in pt_model_dict:
if flax_tensor.shape != pt_model_dict[flax_key].shape:
raise ValueError(
F"Flax checkpoint seems to be incorrect. Weight {flax_key_tuple} was expected "
F"to be of shape {pt_model_dict[flax_key].shape}, but is {flax_tensor.shape}." )
else:
# add weight to pytorch dict
__A : str = np.asarray(_lowercase ) if not isinstance(_lowercase , np.ndarray ) else flax_tensor
__A : Union[str, Any] = torch.from_numpy(_lowercase )
# remove from missing keys
missing_keys.remove(_lowercase )
else:
# weight is not expected by PyTorch model
unexpected_keys.append(_lowercase )
pt_model.load_state_dict(_lowercase )
# re-transform missing_keys to list
__A : Optional[Any] = list(_lowercase )
if len(_lowercase ) > 0:
logger.warning(
"Some weights of the Flax model were not used when initializing the PyTorch model"
F" {pt_model.__class__.__name__}: {unexpected_keys}\n- This IS expected if you are initializing"
F" {pt_model.__class__.__name__} from a Flax model trained on another task or with another architecture"
" (e.g. initializing a BertForSequenceClassification model from a FlaxBertForPreTraining model).\n- This"
F" IS NOT expected if you are initializing {pt_model.__class__.__name__} from a Flax model that you expect"
" to be exactly identical (e.g. initializing a BertForSequenceClassification model from a"
" FlaxBertForSequenceClassification model)." )
if len(_lowercase ) > 0:
logger.warning(
F"Some weights of {pt_model.__class__.__name__} were not initialized from the Flax model and are newly"
F" initialized: {missing_keys}\nYou should probably TRAIN this model on a down-stream task to be able to"
" use it for predictions and inference." )
return pt_model
| 520 | 1 |
"""simple docstring"""
import os
import tempfile
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from torch import nn
from transformers import (
Adafactor,
AdamW,
get_constant_schedule,
get_constant_schedule_with_warmup,
get_cosine_schedule_with_warmup,
get_cosine_with_hard_restarts_schedule_with_warmup,
get_inverse_sqrt_schedule,
get_linear_schedule_with_warmup,
get_polynomial_decay_schedule_with_warmup,
)
def __UpperCAmelCase ( __lowerCamelCase , __lowerCamelCase=10 ) -> Any:
lowercase__ : int = []
for _ in range(__lowerCamelCase ):
lrs.append(scheduler.get_lr()[0] )
scheduler.step()
return lrs
def __UpperCAmelCase ( __lowerCamelCase , __lowerCamelCase=10 ) -> Optional[int]:
lowercase__ : Dict = []
for step in range(__lowerCamelCase ):
lrs.append(scheduler.get_lr()[0] )
scheduler.step()
if step == num_steps // 2:
with tempfile.TemporaryDirectory() as tmpdirname:
lowercase__ : Optional[Any] = os.path.join(__lowerCamelCase , '''schedule.bin''' )
torch.save(scheduler.state_dict() , __lowerCamelCase )
lowercase__ : int = torch.load(__lowerCamelCase )
scheduler.load_state_dict(__lowerCamelCase )
return lrs
@require_torch
class __A ( unittest.TestCase ):
def UpperCAmelCase ( self : str ,_snake_case : List[str] ,_snake_case : str ,_snake_case : Any ) -> List[Any]:
"""simple docstring"""
self.assertEqual(len(_snake_case ) ,len(_snake_case ) )
for a, b in zip(_snake_case ,_snake_case ):
self.assertAlmostEqual(_snake_case ,_snake_case ,delta=_snake_case )
def UpperCAmelCase ( self : List[str] ) -> str:
"""simple docstring"""
lowercase__ : List[Any] = torch.tensor([0.1, -0.2, -0.1] ,requires_grad=_snake_case )
lowercase__ : Any = torch.tensor([0.4, 0.2, -0.5] )
lowercase__ : Tuple = nn.MSELoss()
# No warmup, constant schedule, no gradient clipping
lowercase__ : Union[str, Any] = AdamW(params=[w] ,lr=2e-1 ,weight_decay=0.0 )
for _ in range(100 ):
lowercase__ : Tuple = criterion(_snake_case ,_snake_case )
loss.backward()
optimizer.step()
w.grad.detach_() # No zero_grad() function on simple tensors. we do it ourselves.
w.grad.zero_()
self.assertListAlmostEqual(w.tolist() ,[0.4, 0.2, -0.5] ,tol=1e-2 )
def UpperCAmelCase ( self : List[str] ) -> List[str]:
"""simple docstring"""
lowercase__ : str = torch.tensor([0.1, -0.2, -0.1] ,requires_grad=_snake_case )
lowercase__ : Union[str, Any] = torch.tensor([0.4, 0.2, -0.5] )
lowercase__ : int = nn.MSELoss()
# No warmup, constant schedule, no gradient clipping
lowercase__ : str = Adafactor(
params=[w] ,lr=1e-2 ,eps=(1e-30, 1e-3) ,clip_threshold=1.0 ,decay_rate=-0.8 ,betaa=_snake_case ,weight_decay=0.0 ,relative_step=_snake_case ,scale_parameter=_snake_case ,warmup_init=_snake_case ,)
for _ in range(1_000 ):
lowercase__ : Tuple = criterion(_snake_case ,_snake_case )
loss.backward()
optimizer.step()
w.grad.detach_() # No zero_grad() function on simple tensors. we do it ourselves.
w.grad.zero_()
self.assertListAlmostEqual(w.tolist() ,[0.4, 0.2, -0.5] ,tol=1e-2 )
@require_torch
class __A ( unittest.TestCase ):
lowerCAmelCase : List[str] = nn.Linear(5_0 ,5_0 ) if is_torch_available() else None
lowerCAmelCase : Optional[Any] = AdamW(m.parameters() ,lr=10.0 ) if is_torch_available() else None
lowerCAmelCase : List[str] = 1_0
def UpperCAmelCase ( self : Tuple ,_snake_case : List[Any] ,_snake_case : Union[str, Any] ,_snake_case : Any ,_snake_case : Optional[Any]=None ) -> str:
"""simple docstring"""
self.assertEqual(len(_snake_case ) ,len(_snake_case ) )
for a, b in zip(_snake_case ,_snake_case ):
self.assertAlmostEqual(_snake_case ,_snake_case ,delta=_snake_case ,msg=_snake_case )
def UpperCAmelCase ( self : Optional[Any] ) -> Any:
"""simple docstring"""
lowercase__ : Any = {'''num_warmup_steps''': 2, '''num_training_steps''': 10}
# schedulers doct format
# function: (sched_args_dict, expected_learning_rates)
lowercase__ : str = {
get_constant_schedule: ({}, [10.0] * self.num_steps),
get_constant_schedule_with_warmup: (
{'''num_warmup_steps''': 4},
[0.0, 2.5, 5.0, 7.5, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0],
),
get_linear_schedule_with_warmup: (
{**common_kwargs},
[0.0, 5.0, 10.0, 8.75, 7.5, 6.25, 5.0, 3.75, 2.5, 1.25],
),
get_cosine_schedule_with_warmup: (
{**common_kwargs},
[0.0, 5.0, 10.0, 9.61, 8.53, 6.91, 5.0, 3.08, 1.46, 0.38],
),
get_cosine_with_hard_restarts_schedule_with_warmup: (
{**common_kwargs, '''num_cycles''': 2},
[0.0, 5.0, 10.0, 8.53, 5.0, 1.46, 10.0, 8.53, 5.0, 1.46],
),
get_polynomial_decay_schedule_with_warmup: (
{**common_kwargs, '''power''': 2.0, '''lr_end''': 1e-7},
[0.0, 5.0, 10.0, 7.656, 5.625, 3.906, 2.5, 1.406, 0.625, 0.156],
),
get_inverse_sqrt_schedule: (
{'''num_warmup_steps''': 2},
[0.0, 5.0, 10.0, 8.165, 7.071, 6.325, 5.774, 5.345, 5.0, 4.714],
),
}
for scheduler_func, data in scheds.items():
lowercase__ : int = data
lowercase__ : List[Any] = scheduler_func(self.optimizer ,**_snake_case )
self.assertEqual(len([scheduler.get_lr()[0]] ) ,1 )
lowercase__ : List[str] = unwrap_schedule(_snake_case ,self.num_steps )
self.assertListAlmostEqual(
_snake_case ,_snake_case ,tol=1e-2 ,msg=f"""failed for {scheduler_func} in normal scheduler""" ,)
lowercase__ : Union[str, Any] = scheduler_func(self.optimizer ,**_snake_case )
if scheduler_func.__name__ != "get_constant_schedule":
LambdaScheduleWrapper.wrap_scheduler(_snake_case ) # wrap to test picklability of the schedule
lowercase__ : List[str] = unwrap_and_save_reload_schedule(_snake_case ,self.num_steps )
self.assertListEqual(_snake_case ,_snake_case ,msg=f"""failed for {scheduler_func} in save and reload""" )
class __A :
def __init__( self : Optional[int] ,_snake_case : List[Any] ) -> Optional[int]:
"""simple docstring"""
lowercase__ : List[str] = fn
def __call__( self : Optional[Any] ,*_snake_case : Optional[int] ,**_snake_case : Optional[Any] ) -> str:
"""simple docstring"""
return self.fn(*_snake_case ,**_snake_case )
@classmethod
def UpperCAmelCase ( self : Dict ,_snake_case : Any ) -> Optional[int]:
"""simple docstring"""
lowercase__ : Optional[Any] = list(map(self ,scheduler.lr_lambdas ) )
| 717 |
"""simple docstring"""
import os
import socket
from contextlib import contextmanager
import torch
from ..commands.config.default import write_basic_config # noqa: F401
from ..state import PartialState
from .dataclasses import DistributedType
from .imports import is_deepspeed_available, is_tpu_available
from .transformer_engine import convert_model
from .versions import is_torch_version
if is_deepspeed_available():
from deepspeed import DeepSpeedEngine
if is_tpu_available(check_device=False):
import torch_xla.core.xla_model as xm
def __UpperCAmelCase ( __lowerCamelCase ) -> Optional[int]:
if is_torch_version('''<''' , '''2.0.0''' ) or not hasattr(__lowerCamelCase , '''_dynamo''' ):
return False
return isinstance(__lowerCamelCase , torch._dynamo.eval_frame.OptimizedModule )
def __UpperCAmelCase ( __lowerCamelCase , __lowerCamelCase = True ) -> Optional[Any]:
lowercase__ : List[Any] = (torch.nn.parallel.DistributedDataParallel, torch.nn.DataParallel)
lowercase__ : str = is_compiled_module(__lowerCamelCase )
if is_compiled:
lowercase__ : int = model
lowercase__ : int = model._orig_mod
if is_deepspeed_available():
options += (DeepSpeedEngine,)
while isinstance(__lowerCamelCase , __lowerCamelCase ):
lowercase__ : Union[str, Any] = model.module
if not keep_fpaa_wrapper:
lowercase__ : List[Any] = getattr(__lowerCamelCase , '''forward''' )
lowercase__ : Any = model.__dict__.pop('''_original_forward''' , __lowerCamelCase )
if original_forward is not None:
while hasattr(__lowerCamelCase , '''__wrapped__''' ):
lowercase__ : Optional[int] = forward.__wrapped__
if forward == original_forward:
break
lowercase__ : Dict = forward
if getattr(__lowerCamelCase , '''_converted_to_transformer_engine''' , __lowerCamelCase ):
convert_model(__lowerCamelCase , to_transformer_engine=__lowerCamelCase )
if is_compiled:
lowercase__ : Optional[Any] = model
lowercase__ : Tuple = compiled_model
return model
def __UpperCAmelCase ( ) -> int:
PartialState().wait_for_everyone()
def __UpperCAmelCase ( __lowerCamelCase , __lowerCamelCase ) -> str:
if PartialState().distributed_type == DistributedType.TPU:
xm.save(__lowerCamelCase , __lowerCamelCase )
elif PartialState().local_process_index == 0:
torch.save(__lowerCamelCase , __lowerCamelCase )
@contextmanager
def __UpperCAmelCase ( **__lowerCamelCase ) -> Optional[int]:
for key, value in kwargs.items():
lowercase__ : Optional[int] = str(__lowerCamelCase )
yield
for key in kwargs:
if key.upper() in os.environ:
del os.environ[key.upper()]
def __UpperCAmelCase ( __lowerCamelCase ) -> Union[str, Any]:
if not hasattr(__lowerCamelCase , '''__qualname__''' ) and not hasattr(__lowerCamelCase , '''__name__''' ):
lowercase__ : Tuple = getattr(__lowerCamelCase , '''__class__''' , __lowerCamelCase )
if hasattr(__lowerCamelCase , '''__qualname__''' ):
return obj.__qualname__
if hasattr(__lowerCamelCase , '''__name__''' ):
return obj.__name__
return str(__lowerCamelCase )
def __UpperCAmelCase ( __lowerCamelCase , __lowerCamelCase ) -> int:
for key, value in source.items():
if isinstance(__lowerCamelCase , __lowerCamelCase ):
lowercase__ : int = destination.setdefault(__lowerCamelCase , {} )
merge_dicts(__lowerCamelCase , __lowerCamelCase )
else:
lowercase__ : Optional[int] = value
return destination
def __UpperCAmelCase ( __lowerCamelCase = None ) -> bool:
if port is None:
lowercase__ : List[Any] = 2_95_00
with socket.socket(socket.AF_INET , socket.SOCK_STREAM ) as s:
return s.connect_ex(('''localhost''', port) ) == 0
| 122 | 0 |
'''simple docstring'''
import unittest
from typing import Tuple
import torch
from diffusers.utils import floats_tensor, randn_tensor, torch_all_close, torch_device
from diffusers.utils.testing_utils import require_torch
@require_torch
class __SCREAMING_SNAKE_CASE :
@property
def __UpperCamelCase ( self ) ->Tuple:
'''simple docstring'''
return self.get_dummy_input()
@property
def __UpperCamelCase ( self ) ->Optional[int]:
'''simple docstring'''
if self.block_type == "down":
return (4, 32, 16, 16)
elif self.block_type == "mid":
return (4, 32, 32, 32)
elif self.block_type == "up":
return (4, 32, 64, 64)
raise ValueError(F"""\'{self.block_type}\' is not a supported block_type. Set it to \'up\', \'mid\', or \'down\'.""" )
def __UpperCamelCase ( self , lowerCamelCase=True , lowerCamelCase=False , lowerCamelCase=False , lowerCamelCase=False , ) ->str:
'''simple docstring'''
__a = 4
__a = 32
__a = (32, 32)
__a = torch.manual_seed(0 )
__a = torch.device(lowerCamelCase )
__a = (batch_size, num_channels) + sizes
__a = randn_tensor(lowerCamelCase , generator=lowerCamelCase , device=lowerCamelCase )
__a = {'''hidden_states''': hidden_states}
if include_temb:
__a = 128
__a = randn_tensor((batch_size, temb_channels) , generator=lowerCamelCase , device=lowerCamelCase )
if include_res_hidden_states_tuple:
__a = torch.manual_seed(1 )
__a = (randn_tensor(lowerCamelCase , generator=lowerCamelCase , device=lowerCamelCase ),)
if include_encoder_hidden_states:
__a = floats_tensor((batch_size, 32, 32) ).to(lowerCamelCase )
if include_skip_sample:
__a = randn_tensor(((batch_size, 3) + sizes) , generator=lowerCamelCase , device=lowerCamelCase )
return dummy_input
def __UpperCamelCase ( self ) ->Optional[Any]:
'''simple docstring'''
__a = {
'''in_channels''': 32,
'''out_channels''': 32,
'''temb_channels''': 128,
}
if self.block_type == "up":
__a = 32
if self.block_type == "mid":
init_dict.pop('out_channels' )
__a = self.dummy_input
return init_dict, inputs_dict
def __UpperCamelCase ( self , lowerCamelCase ) ->List[str]:
'''simple docstring'''
__a = self.prepare_init_args_and_inputs_for_common()
__a = self.block_class(**lowerCamelCase )
unet_block.to(lowerCamelCase )
unet_block.eval()
with torch.no_grad():
__a = unet_block(**lowerCamelCase )
if isinstance(lowerCamelCase , lowerCamelCase ):
__a = output[0]
self.assertEqual(output.shape , self.output_shape )
__a = output[0, -1, -3:, -3:]
__a = torch.tensor(lowerCamelCase ).to(lowerCamelCase )
assert torch_all_close(output_slice.flatten() , lowerCamelCase , atol=5e-3 )
@unittest.skipIf(torch_device == 'mps' , 'Training is not supported in mps' )
def __UpperCamelCase ( self ) ->Optional[int]:
'''simple docstring'''
__a = self.prepare_init_args_and_inputs_for_common()
__a = self.block_class(**lowerCamelCase )
model.to(lowerCamelCase )
model.train()
__a = model(**lowerCamelCase )
if isinstance(lowerCamelCase , lowerCamelCase ):
__a = output[0]
__a = torch.device(lowerCamelCase )
__a = randn_tensor(output.shape , device=lowerCamelCase )
__a = torch.nn.functional.mse_loss(lowerCamelCase , lowerCamelCase )
loss.backward() | 448 | from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available() and is_transformers_version('>=', '4.25.0')):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_torch_and_transformers_objects import UnCLIPImageVariationPipeline, UnCLIPPipeline
else:
from .pipeline_unclip import UnCLIPPipeline
from .pipeline_unclip_image_variation import UnCLIPImageVariationPipeline
from .text_proj import UnCLIPTextProjModel
| 64 | 0 |
'''simple docstring'''
from math import sqrt
def a_ ( _UpperCAmelCase : int ) -> bool:
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(sqrt(_UpperCAmelCase ) + 1 ) ,6 ):
if number % i == 0 or number % (i + 2) == 0:
return False
return True
def a_ ( _UpperCAmelCase : int = 1_00_01 ) -> int:
__snake_case : List[Any] = 0
__snake_case : Optional[int] = 1
while count != nth and number < 3:
number += 1
if is_prime(_UpperCAmelCase ):
count += 1
while count != nth:
number += 2
if is_prime(_UpperCAmelCase ):
count += 1
return number
if __name__ == "__main__":
print(F"""{solution() = }""")
| 124 |
'''simple docstring'''
A__ : str = '''ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/'''
def a_ ( _UpperCAmelCase : bytes ) -> bytes:
# Make sure the supplied data is a bytes-like object
if not isinstance(_UpperCAmelCase ,_UpperCAmelCase ):
__snake_case : int = f'''a bytes-like object is required, not \'{data.__class__.__name__}\''''
raise TypeError(_UpperCAmelCase )
__snake_case : Optional[int] = ''.join(bin(_UpperCAmelCase )[2:].zfill(8 ) for byte in data )
__snake_case : Optional[int] = len(_UpperCAmelCase ) % 6 != 0
if padding_needed:
# The padding that will be added later
__snake_case : Dict = b'=' * ((6 - len(_UpperCAmelCase ) % 6) // 2)
# Append binary_stream with arbitrary binary digits (0's by default) to make its
# length a multiple of 6.
binary_stream += "0" * (6 - len(_UpperCAmelCase ) % 6)
else:
__snake_case : List[Any] = b''
# Encode every 6 binary digits to their corresponding Base64 character
return (
"".join(
B64_CHARSET[int(binary_stream[index : index + 6] ,2 )]
for index in range(0 ,len(_UpperCAmelCase ) ,6 ) ).encode()
+ padding
)
def a_ ( _UpperCAmelCase : str ) -> bytes:
# Make sure encoded_data is either a string or a bytes-like object
if not isinstance(_UpperCAmelCase ,_UpperCAmelCase ) and not isinstance(_UpperCAmelCase ,_UpperCAmelCase ):
__snake_case : List[Any] = (
'argument should be a bytes-like object or ASCII string, '
f'''not \'{encoded_data.__class__.__name__}\''''
)
raise TypeError(_UpperCAmelCase )
# In case encoded_data is a bytes-like object, make sure it contains only
# ASCII characters so we convert it to a string object
if isinstance(_UpperCAmelCase ,_UpperCAmelCase ):
try:
__snake_case : Optional[int] = encoded_data.decode('utf-8' )
except UnicodeDecodeError:
raise ValueError('base64 encoded data should only contain ASCII characters' )
__snake_case : List[Any] = encoded_data.count('=' )
# Check if the encoded string contains non base64 characters
if padding:
assert all(
char in B64_CHARSET for char in encoded_data[:-padding] ), "Invalid base64 character(s) found."
else:
assert all(
char in B64_CHARSET for char in encoded_data ), "Invalid base64 character(s) found."
# Check the padding
assert len(_UpperCAmelCase ) % 4 == 0 and padding < 3, "Incorrect padding"
if padding:
# Remove padding if there is one
__snake_case : Any = encoded_data[:-padding]
__snake_case : str = ''.join(
bin(B64_CHARSET.index(_UpperCAmelCase ) )[2:].zfill(6 ) for char in encoded_data )[: -padding * 2]
else:
__snake_case : str = ''.join(
bin(B64_CHARSET.index(_UpperCAmelCase ) )[2:].zfill(6 ) for char in encoded_data )
__snake_case : Tuple = [
int(binary_stream[index : index + 8] ,2 )
for index in range(0 ,len(_UpperCAmelCase ) ,8 )
]
return bytes(_UpperCAmelCase )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 124 | 1 |
import os
from pickle import UnpicklingError
from typing import Dict, Tuple
import jax
import jax.numpy as jnp
import numpy as np
from flax.serialization import from_bytes
from flax.traverse_util import flatten_dict, unflatten_dict
import transformers
from .utils import logging
SCREAMING_SNAKE_CASE_ = logging.get_logger(__name__)
def __SCREAMING_SNAKE_CASE ( lowerCAmelCase: Dict , lowerCAmelCase: Optional[int] , lowerCAmelCase: int , lowerCAmelCase: str=False ) -> Any:
try:
import torch # noqa: F401
except ImportError:
logger.error(
"Loading a PyTorch model in Flax, requires both PyTorch and Flax to be installed. Please see"
" https://pytorch.org/ and https://flax.readthedocs.io/en/latest/installation.html for installation"
" instructions." )
raise
if not is_sharded:
_UpperCAmelCase : Dict = os.path.abspath(lowerCAmelCase )
logger.info(F'Loading PyTorch weights from {pt_path}' )
_UpperCAmelCase : Dict = torch.load(lowerCAmelCase , map_location="cpu" )
logger.info(F'PyTorch checkpoint contains {sum(t.numel() for t in pt_state_dict.values() ):,} parameters.' )
_UpperCAmelCase : int = convert_pytorch_state_dict_to_flax(lowerCAmelCase , lowerCAmelCase )
else:
# model is sharded and pytorch_checkpoint_path already contains the list of .pt shard files
_UpperCAmelCase : List[Any] = convert_pytorch_sharded_state_dict_to_flax(lowerCAmelCase , lowerCAmelCase )
return flax_state_dict
def __SCREAMING_SNAKE_CASE ( lowerCAmelCase: Tuple[str] , lowerCAmelCase: np.ndarray , lowerCAmelCase: Dict[str, jnp.ndarray] , lowerCAmelCase: str , ) -> (Tuple[str], np.ndarray):
def is_key_or_prefix_key_in_dict(lowerCAmelCase: Tuple[str] ) -> bool:
return len(set(lowerCAmelCase ) & {key, (model_prefix,) + key} ) > 0
# layer norm
_UpperCAmelCase : Any = pt_tuple_key[:-1] + ("scale",)
if pt_tuple_key[-1] in ["weight", "gamma"] and is_key_or_prefix_key_in_dict(lowerCAmelCase ):
return renamed_pt_tuple_key, pt_tensor
# batch norm layer mean
_UpperCAmelCase : int = pt_tuple_key[:-1] + ("mean",)
if pt_tuple_key[-1] == "running_mean" and not is_key_or_prefix_key_in_dict(lowerCAmelCase ):
return renamed_pt_tuple_key, pt_tensor
# batch norm layer var
_UpperCAmelCase : List[str] = pt_tuple_key[:-1] + ("var",)
if pt_tuple_key[-1] == "running_var" and not is_key_or_prefix_key_in_dict(lowerCAmelCase ):
return renamed_pt_tuple_key, pt_tensor
# embedding
_UpperCAmelCase : List[Any] = pt_tuple_key[:-1] + ("embedding",)
if pt_tuple_key[-1] == "weight" and is_key_or_prefix_key_in_dict(lowerCAmelCase ):
return renamed_pt_tuple_key, pt_tensor
# conv layer
_UpperCAmelCase : Optional[int] = pt_tuple_key[:-1] + ("kernel",)
if pt_tuple_key[-1] == "weight" and pt_tensor.ndim == 4 and not is_key_or_prefix_key_in_dict(lowerCAmelCase ):
_UpperCAmelCase : List[str] = pt_tensor.transpose(2 , 3 , 1 , 0 )
return renamed_pt_tuple_key, pt_tensor
# linear layer
_UpperCAmelCase : Dict = pt_tuple_key[:-1] + ("kernel",)
if pt_tuple_key[-1] == "weight" and not is_key_or_prefix_key_in_dict(lowerCAmelCase ):
_UpperCAmelCase : int = pt_tensor.T
return renamed_pt_tuple_key, pt_tensor
# old PyTorch layer norm weight
_UpperCAmelCase : List[str] = pt_tuple_key[:-1] + ("weight",)
if pt_tuple_key[-1] == "gamma":
return renamed_pt_tuple_key, pt_tensor
# old PyTorch layer norm bias
_UpperCAmelCase : List[str] = pt_tuple_key[:-1] + ("bias",)
if pt_tuple_key[-1] == "beta":
return renamed_pt_tuple_key, pt_tensor
# New `weight_norm` from https://github.com/huggingface/transformers/pull/24030
_UpperCAmelCase : List[Any] = None
if pt_tuple_key[-3::2] == ("parametrizations", "original0"):
_UpperCAmelCase : str = pt_tuple_key[-2] + "_g"
elif pt_tuple_key[-3::2] == ("parametrizations", "original1"):
_UpperCAmelCase : str = pt_tuple_key[-2] + "_v"
if name is not None:
_UpperCAmelCase : str = pt_tuple_key[:-3] + (name,)
return renamed_pt_tuple_key, pt_tensor
return pt_tuple_key, pt_tensor
def __SCREAMING_SNAKE_CASE ( lowerCAmelCase: Optional[int] , lowerCAmelCase: Optional[int] ) -> Optional[int]:
# convert pytorch tensor to numpy
_UpperCAmelCase : Optional[Any] = {k: v.numpy() for k, v in pt_state_dict.items()}
_UpperCAmelCase : List[str] = flax_model.base_model_prefix
# use params dict if the model contains batch norm layers
if "params" in flax_model.params:
_UpperCAmelCase : int = flax_model.params["params"]
else:
_UpperCAmelCase : str = flax_model.params
_UpperCAmelCase : Tuple = flatten_dict(lowerCAmelCase )
# add batch_stats keys,values to dict
if "batch_stats" in flax_model.params:
_UpperCAmelCase : List[Any] = flatten_dict(flax_model.params["batch_stats"] )
random_flax_state_dict.update(lowerCAmelCase )
_UpperCAmelCase : List[Any] = {}
_UpperCAmelCase : Any = (model_prefix not in flax_model_params) and (
model_prefix in {k.split("." )[0] for k in pt_state_dict.keys()}
)
_UpperCAmelCase : Optional[int] = (model_prefix in flax_model_params) and (
model_prefix not in {k.split("." )[0] for k in pt_state_dict.keys()}
)
# Need to change some parameters name to match Flax names
for pt_key, pt_tensor in pt_state_dict.items():
_UpperCAmelCase : str = tuple(pt_key.split("." ) )
# remove base model prefix if necessary
_UpperCAmelCase : Optional[Any] = pt_tuple_key[0] == model_prefix
if load_model_with_head_into_base_model and has_base_model_prefix:
_UpperCAmelCase : Any = pt_tuple_key[1:]
# Correctly rename weight parameters
_UpperCAmelCase , _UpperCAmelCase : str = rename_key_and_reshape_tensor(
lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase )
# add model prefix if necessary
_UpperCAmelCase : Dict = (model_prefix,) + flax_key in random_flax_state_dict
if load_base_model_into_model_with_head and require_base_model_prefix:
_UpperCAmelCase : Dict = (model_prefix,) + flax_key
if flax_key in random_flax_state_dict:
if flax_tensor.shape != random_flax_state_dict[flax_key].shape:
raise ValueError(
F'PyTorch checkpoint seems to be incorrect. Weight {pt_key} was expected to be of shape '
F'{random_flax_state_dict[flax_key].shape}, but is {flax_tensor.shape}.' )
# add batch stats if the model contains batchnorm layers
if "batch_stats" in flax_model.params:
if "mean" in flax_key[-1] or "var" in flax_key[-1]:
_UpperCAmelCase : List[str] = jnp.asarray(lowerCAmelCase )
continue
# remove num_batches_tracked key
if "num_batches_tracked" in flax_key[-1]:
flax_state_dict.pop(lowerCAmelCase , lowerCAmelCase )
continue
# also add unexpected weight so that warning is thrown
_UpperCAmelCase : Optional[Any] = jnp.asarray(lowerCAmelCase )
else:
# also add unexpected weight so that warning is thrown
_UpperCAmelCase : List[Any] = jnp.asarray(lowerCAmelCase )
return unflatten_dict(lowerCAmelCase )
def __SCREAMING_SNAKE_CASE ( lowerCAmelCase: List[str] , lowerCAmelCase: List[str] ) -> Optional[int]:
import torch
# Load the index
_UpperCAmelCase : List[Any] = {}
for shard_file in shard_filenames:
# load using msgpack utils
_UpperCAmelCase : List[str] = torch.load(lowerCAmelCase )
_UpperCAmelCase : Union[str, Any] = {k: v.numpy() for k, v in pt_state_dict.items()}
_UpperCAmelCase : List[str] = flax_model.base_model_prefix
# use params dict if the model contains batch norm layers and then add batch_stats keys,values to dict
if "batch_stats" in flax_model.params:
_UpperCAmelCase : List[str] = flax_model.params["params"]
_UpperCAmelCase : Tuple = flatten_dict(lowerCAmelCase )
random_flax_state_dict.update(flatten_dict(flax_model.params["batch_stats"] ) )
else:
_UpperCAmelCase : int = flax_model.params
_UpperCAmelCase : List[str] = flatten_dict(lowerCAmelCase )
_UpperCAmelCase : Dict = (model_prefix not in flax_model_params) and (
model_prefix in {k.split("." )[0] for k in pt_state_dict.keys()}
)
_UpperCAmelCase : Dict = (model_prefix in flax_model_params) and (
model_prefix not in {k.split("." )[0] for k in pt_state_dict.keys()}
)
# Need to change some parameters name to match Flax names
for pt_key, pt_tensor in pt_state_dict.items():
_UpperCAmelCase : Dict = tuple(pt_key.split("." ) )
# remove base model prefix if necessary
_UpperCAmelCase : List[Any] = pt_tuple_key[0] == model_prefix
if load_model_with_head_into_base_model and has_base_model_prefix:
_UpperCAmelCase : Any = pt_tuple_key[1:]
# Correctly rename weight parameters
_UpperCAmelCase , _UpperCAmelCase : List[Any] = rename_key_and_reshape_tensor(
lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase )
# add model prefix if necessary
_UpperCAmelCase : Dict = (model_prefix,) + flax_key in random_flax_state_dict
if load_base_model_into_model_with_head and require_base_model_prefix:
_UpperCAmelCase : Optional[Any] = (model_prefix,) + flax_key
if flax_key in random_flax_state_dict:
if flax_tensor.shape != random_flax_state_dict[flax_key].shape:
raise ValueError(
F'PyTorch checkpoint seems to be incorrect. Weight {pt_key} was expected to be of shape '
F'{random_flax_state_dict[flax_key].shape}, but is {flax_tensor.shape}.' )
# add batch stats if the model contains batchnorm layers
if "batch_stats" in flax_model.params:
if "mean" in flax_key[-1]:
_UpperCAmelCase : Tuple = jnp.asarray(lowerCAmelCase )
continue
if "var" in flax_key[-1]:
_UpperCAmelCase : Dict = jnp.asarray(lowerCAmelCase )
continue
# remove num_batches_tracked key
if "num_batches_tracked" in flax_key[-1]:
flax_state_dict.pop(lowerCAmelCase , lowerCAmelCase )
continue
# also add unexpected weight so that warning is thrown
_UpperCAmelCase : int = jnp.asarray(lowerCAmelCase )
else:
# also add unexpected weight so that warning is thrown
_UpperCAmelCase : int = jnp.asarray(lowerCAmelCase )
return unflatten_dict(lowerCAmelCase )
def __SCREAMING_SNAKE_CASE ( lowerCAmelCase: List[Any] , lowerCAmelCase: str ) -> Optional[Any]:
_UpperCAmelCase : List[str] = os.path.abspath(lowerCAmelCase )
logger.info(F'Loading Flax weights from {flax_checkpoint_path}' )
# import correct flax class
_UpperCAmelCase : Any = getattr(lowerCAmelCase , "Flax" + model.__class__.__name__ )
# load flax weight dict
with open(lowerCAmelCase , "rb" ) as state_f:
try:
_UpperCAmelCase : str = from_bytes(lowerCAmelCase , state_f.read() )
except UnpicklingError:
raise EnvironmentError(F'Unable to convert {flax_checkpoint_path} to Flax deserializable object. ' )
return load_flax_weights_in_pytorch_model(lowerCAmelCase , lowerCAmelCase )
def __SCREAMING_SNAKE_CASE ( lowerCAmelCase: Optional[int] , lowerCAmelCase: int ) -> Union[str, Any]:
try:
import torch # noqa: F401
except ImportError:
logger.error(
"Loading a Flax weights in PyTorch, requires both PyTorch and Flax to be installed. Please see"
" https://pytorch.org/ and https://flax.readthedocs.io/en/latest/installation.html for installation"
" instructions." )
raise
# check if we have bf16 weights
_UpperCAmelCase : int = flatten_dict(jax.tree_util.tree_map(lambda lowerCAmelCase : x.dtype == jnp.bfloataa , lowerCAmelCase ) ).values()
if any(lowerCAmelCase ):
# convert all weights to fp32 if the are bf16 since torch.from_numpy can-not handle bf16
# and bf16 is not fully supported in PT yet.
logger.warning(
"Found ``bfloat16`` weights in Flax model. Casting all ``bfloat16`` weights to ``float32`` "
"before loading those in PyTorch model." )
_UpperCAmelCase : Union[str, Any] = jax.tree_util.tree_map(
lambda lowerCAmelCase : params.astype(np.floataa ) if params.dtype == jnp.bfloataa else params , lowerCAmelCase )
_UpperCAmelCase : Tuple = flatten_dict(lowerCAmelCase )
_UpperCAmelCase : Optional[Any] = pt_model.state_dict()
_UpperCAmelCase : Tuple = (pt_model.base_model_prefix in flax_state) and (
pt_model.base_model_prefix not in {k.split("." )[0] for k in pt_model_dict.keys()}
)
_UpperCAmelCase : Optional[Any] = (pt_model.base_model_prefix not in flax_state) and (
pt_model.base_model_prefix in {k.split("." )[0] for k in pt_model_dict.keys()}
)
# keep track of unexpected & missing keys
_UpperCAmelCase : List[Any] = []
_UpperCAmelCase : Union[str, Any] = set(pt_model_dict.keys() )
for flax_key_tuple, flax_tensor in flax_state_dict.items():
_UpperCAmelCase : str = flax_key_tuple[0] == pt_model.base_model_prefix
_UpperCAmelCase : Union[str, Any] = ".".join((pt_model.base_model_prefix,) + flax_key_tuple ) in pt_model_dict
# adapt flax_key to prepare for loading from/to base model only
if load_model_with_head_into_base_model and has_base_model_prefix:
_UpperCAmelCase : str = flax_key_tuple[1:]
elif load_base_model_into_model_with_head and require_base_model_prefix:
_UpperCAmelCase : Any = (pt_model.base_model_prefix,) + flax_key_tuple
# rename flax weights to PyTorch format
if flax_key_tuple[-1] == "kernel" and flax_tensor.ndim == 4 and ".".join(lowerCAmelCase ) not in pt_model_dict:
# conv layer
_UpperCAmelCase : Any = flax_key_tuple[:-1] + ("weight",)
_UpperCAmelCase : Dict = jnp.transpose(lowerCAmelCase , (3, 2, 0, 1) )
elif flax_key_tuple[-1] == "kernel" and ".".join(lowerCAmelCase ) not in pt_model_dict:
# linear layer
_UpperCAmelCase : Optional[int] = flax_key_tuple[:-1] + ("weight",)
_UpperCAmelCase : Tuple = flax_tensor.T
elif flax_key_tuple[-1] in ["scale", "embedding"]:
_UpperCAmelCase : List[str] = flax_key_tuple[:-1] + ("weight",)
# adding batch stats from flax batch norm to pt
elif "mean" in flax_key_tuple[-1]:
_UpperCAmelCase : int = flax_key_tuple[:-1] + ("running_mean",)
elif "var" in flax_key_tuple[-1]:
_UpperCAmelCase : Any = flax_key_tuple[:-1] + ("running_var",)
if "batch_stats" in flax_state:
_UpperCAmelCase : Tuple = ".".join(flax_key_tuple[1:] ) # Remove the params/batch_stats header
else:
_UpperCAmelCase : List[str] = ".".join(lowerCAmelCase )
# We also need to look at `pt_model_dict` and see if there are keys requiring further transformation.
_UpperCAmelCase : Optional[Any] = {}
# New `weight_norm` from https://github.com/huggingface/transformers/pull/24030
for key in pt_model_dict:
_UpperCAmelCase : Union[str, Any] = key.split("." )
_UpperCAmelCase : Any = None
if key_components[-3::2] == ["parametrizations", "original0"]:
_UpperCAmelCase : Tuple = key_components[-2] + "_g"
elif key_components[-3::2] == ["parametrizations", "original1"]:
_UpperCAmelCase : int = key_components[-2] + "_v"
if name is not None:
_UpperCAmelCase : Union[str, Any] = key_components[:-3] + [name]
_UpperCAmelCase : Tuple = ".".join(lowerCAmelCase )
_UpperCAmelCase : Dict = key
if flax_key in special_pt_names:
_UpperCAmelCase : List[Any] = special_pt_names[flax_key]
if flax_key in pt_model_dict:
if flax_tensor.shape != pt_model_dict[flax_key].shape:
raise ValueError(
F'Flax checkpoint seems to be incorrect. Weight {flax_key_tuple} was expected '
F'to be of shape {pt_model_dict[flax_key].shape}, but is {flax_tensor.shape}.' )
else:
# add weight to pytorch dict
_UpperCAmelCase : str = np.asarray(lowerCAmelCase ) if not isinstance(lowerCAmelCase , np.ndarray ) else flax_tensor
_UpperCAmelCase : Optional[Any] = torch.from_numpy(lowerCAmelCase )
# remove from missing keys
missing_keys.remove(lowerCAmelCase )
else:
# weight is not expected by PyTorch model
unexpected_keys.append(lowerCAmelCase )
pt_model.load_state_dict(lowerCAmelCase )
# re-transform missing_keys to list
_UpperCAmelCase : Optional[Any] = list(lowerCAmelCase )
if len(lowerCAmelCase ) > 0:
logger.warning(
"Some weights of the Flax model were not used when initializing the PyTorch model"
F' {pt_model.__class__.__name__}: {unexpected_keys}\n- This IS expected if you are initializing'
F' {pt_model.__class__.__name__} from a Flax model trained on another task or with another architecture'
" (e.g. initializing a BertForSequenceClassification model from a FlaxBertForPreTraining model).\n- This"
F' IS NOT expected if you are initializing {pt_model.__class__.__name__} from a Flax model that you expect'
" to be exactly identical (e.g. initializing a BertForSequenceClassification model from a"
" FlaxBertForSequenceClassification model)." )
else:
logger.warning(F'All Flax model weights were used when initializing {pt_model.__class__.__name__}.\n' )
if len(lowerCAmelCase ) > 0:
logger.warning(
F'Some weights of {pt_model.__class__.__name__} were not initialized from the Flax model and are newly'
F' initialized: {missing_keys}\nYou should probably TRAIN this model on a down-stream task to be able to'
" use it for predictions and inference." )
else:
logger.warning(
F'All the weights of {pt_model.__class__.__name__} were initialized from the Flax model.\n'
"If your task is similar to the task the model of the checkpoint was trained on, "
F'you can already use {pt_model.__class__.__name__} for predictions without further training.' )
return pt_model
| 300 |
import doctest
from collections import deque
import numpy as np
class a :
def __init__( self ):
'''simple docstring'''
_UpperCAmelCase : Optional[Any] = [2, 1, 2, -1]
_UpperCAmelCase : Dict = [1, 2, 3, 4]
def _UpperCAmelCase ( self ):
'''simple docstring'''
_UpperCAmelCase : Union[str, Any] = len(self.first_signal )
_UpperCAmelCase : Optional[Any] = len(self.second_signal )
_UpperCAmelCase : int = max(A_ , A_ )
# create a zero matrix of max_length x max_length
_UpperCAmelCase : int = [[0] * max_length for i in range(A_ )]
# fills the smaller signal with zeros to make both signals of same length
if length_first_signal < length_second_signal:
self.first_signal += [0] * (max_length - length_first_signal)
elif length_first_signal > length_second_signal:
self.second_signal += [0] * (max_length - length_second_signal)
for i in range(A_ ):
_UpperCAmelCase : Any = deque(self.second_signal )
rotated_signal.rotate(A_ )
for j, item in enumerate(A_ ):
matrix[i][j] += item
# multiply the matrix with the first signal
_UpperCAmelCase : Any = np.matmul(np.transpose(A_ ) , np.transpose(self.first_signal ) )
# rounding-off to two decimal places
return [round(A_ , 2 ) for i in final_signal]
if __name__ == "__main__":
doctest.testmod()
| 300 | 1 |
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class __SCREAMING_SNAKE_CASE ( A_):
"""simple docstring"""
__UpperCAmelCase = '''ClapFeatureExtractor'''
__UpperCAmelCase = ('''RobertaTokenizer''', '''RobertaTokenizerFast''')
def __init__( self , _UpperCAmelCase , _UpperCAmelCase ):
super().__init__(_UpperCAmelCase , _UpperCAmelCase )
def __call__( self , _UpperCAmelCase=None , _UpperCAmelCase=None , _UpperCAmelCase=None , **_UpperCAmelCase ):
__snake_case : List[Any] = kwargs.pop('sampling_rate' , _UpperCAmelCase )
if text is None and audios is None:
raise ValueError('You have to specify either text or audios. Both cannot be none.' )
if text is not None:
__snake_case : str = self.tokenizer(_UpperCAmelCase , return_tensors=_UpperCAmelCase , **_UpperCAmelCase )
if audios is not None:
__snake_case : str = self.feature_extractor(
_UpperCAmelCase , sampling_rate=_UpperCAmelCase , return_tensors=_UpperCAmelCase , **_UpperCAmelCase )
if text is not None and audios is not None:
__snake_case : int = audio_features.input_features
return encoding
elif text is not None:
return encoding
else:
return BatchEncoding(data=dict(**_UpperCAmelCase ) , tensor_type=_UpperCAmelCase )
def lowercase_ ( self , *_UpperCAmelCase , **_UpperCAmelCase ):
return self.tokenizer.batch_decode(*_UpperCAmelCase , **_UpperCAmelCase )
def lowercase_ ( self , *_UpperCAmelCase , **_UpperCAmelCase ):
return self.tokenizer.decode(*_UpperCAmelCase , **_UpperCAmelCase )
@property
def lowercase_ ( self ):
__snake_case : int = self.tokenizer.model_input_names
__snake_case : List[str] = self.feature_extractor.model_input_names
return list(dict.fromkeys(tokenizer_input_names + feature_extractor_input_names ) )
| 708 | from __future__ import annotations
__magic_name__ = [
[-1, 0], # left
[0, -1], # down
[1, 0], # right
[0, 1], # up
]
def UpperCAmelCase__( __UpperCAmelCase : list[list[int]] , __UpperCAmelCase : list[int] , __UpperCAmelCase : list[int] , __UpperCAmelCase : int , __UpperCAmelCase : list[list[int]] , ):
__snake_case : Optional[int] = [
[0 for col in range(len(grid[0] ) )] for row in range(len(__UpperCAmelCase ) )
] # the reference grid
__snake_case : List[str] = 1
__snake_case : str = [
[0 for col in range(len(grid[0] ) )] for row in range(len(__UpperCAmelCase ) )
] # the action grid
__snake_case : Dict = init[0]
__snake_case : List[str] = init[1]
__snake_case : Optional[Any] = 0
__snake_case : Union[str, Any] = g + heuristic[x][y] # cost from starting cell to destination cell
__snake_case : Any = [[f, g, x, y]]
__snake_case : List[str] = False # flag that is set when search is complete
__snake_case : str = False # flag set if we can't find expand
while not found and not resign:
if len(__UpperCAmelCase ) == 0:
raise ValueError('Algorithm is unable to find solution' )
else: # to choose the least costliest action so as to move closer to the goal
cell.sort()
cell.reverse()
__snake_case : List[Any] = cell.pop()
__snake_case : Optional[int] = next_cell[2]
__snake_case : int = next_cell[3]
__snake_case : Optional[Any] = next_cell[1]
if x == goal[0] and y == goal[1]:
__snake_case : Union[str, Any] = True
else:
for i in range(len(__UpperCAmelCase ) ): # to try out different valid actions
__snake_case : Tuple = x + DIRECTIONS[i][0]
__snake_case : Tuple = y + DIRECTIONS[i][1]
if xa >= 0 and xa < len(__UpperCAmelCase ) and ya >= 0 and ya < len(grid[0] ):
if closed[xa][ya] == 0 and grid[xa][ya] == 0:
__snake_case : List[str] = g + cost
__snake_case : Optional[Any] = ga + heuristic[xa][ya]
cell.append([fa, ga, xa, ya] )
__snake_case : Dict = 1
__snake_case : Any = i
__snake_case : Tuple = []
__snake_case : Dict = goal[0]
__snake_case : Optional[int] = goal[1]
invpath.append([x, y] ) # we get the reverse path from here
while x != init[0] or y != init[1]:
__snake_case : Tuple = x - DIRECTIONS[action[x][y]][0]
__snake_case : Optional[Any] = y - DIRECTIONS[action[x][y]][1]
__snake_case : Tuple = xa
__snake_case : List[str] = ya
invpath.append([x, y] )
__snake_case : Dict = []
for i in range(len(__UpperCAmelCase ) ):
path.append(invpath[len(__UpperCAmelCase ) - 1 - i] )
return path, action
if __name__ == "__main__":
__magic_name__ = [
[0, 1, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0], # 0 are free path whereas 1's are obstacles
[0, 1, 0, 0, 0, 0],
[0, 1, 0, 0, 1, 0],
[0, 0, 0, 0, 1, 0],
]
__magic_name__ = [0, 0]
# all coordinates are given in format [y,x]
__magic_name__ = [len(grid) - 1, len(grid[0]) - 1]
__magic_name__ = 1
# the cost map which pushes the path closer to the goal
__magic_name__ = [[0 for row in range(len(grid[0]))] for col in range(len(grid))]
for i in range(len(grid)):
for j in range(len(grid[0])):
__magic_name__ = abs(i - goal[0]) + abs(j - goal[1])
if grid[i][j] == 1:
# added extra penalty in the heuristic map
__magic_name__ = 99
__magic_name__ , __magic_name__ = search(grid, init, goal, cost, heuristic)
print('''ACTION MAP''')
for i in range(len(action)):
print(action[i])
for i in range(len(path)):
print(path[i])
| 679 | 0 |
"""simple docstring"""
import json
import os
import unittest
from transformers import BatchEncoding, MvpTokenizer, MvpTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin, filter_roberta_detectors
@require_tokenizers
class UpperCamelCase ( __SCREAMING_SNAKE_CASE , unittest.TestCase ):
A__ = MvpTokenizer
A__ = MvpTokenizerFast
A__ = True
A__ = filter_roberta_detectors
def __SCREAMING_SNAKE_CASE ( self ):
"""simple docstring"""
super().setUp()
_SCREAMING_SNAKE_CASE : Optional[Any] = [
"l",
"o",
"w",
"e",
"r",
"s",
"t",
"i",
"d",
"n",
"\u0120",
"\u0120l",
"\u0120n",
"\u0120lo",
"\u0120low",
"er",
"\u0120lowest",
"\u0120newer",
"\u0120wider",
"<unk>",
]
_SCREAMING_SNAKE_CASE : Tuple = dict(zip(snake_case__ , range(len(snake_case__ ) ) ) )
_SCREAMING_SNAKE_CASE : Dict = ["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
_SCREAMING_SNAKE_CASE : int = {"unk_token": "<unk>"}
_SCREAMING_SNAKE_CASE : str = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["vocab_file"] )
_SCREAMING_SNAKE_CASE : Optional[int] = 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(snake_case__ ) + "\n" )
with open(self.merges_file , "w" , encoding="utf-8" ) as fp:
fp.write("\n".join(snake_case__ ) )
def __SCREAMING_SNAKE_CASE ( self , **snake_case__ ):
"""simple docstring"""
kwargs.update(self.special_tokens_map )
return self.tokenizer_class.from_pretrained(self.tmpdirname , **snake_case__ )
def __SCREAMING_SNAKE_CASE ( self , **snake_case__ ):
"""simple docstring"""
kwargs.update(self.special_tokens_map )
return self.rust_tokenizer_class.from_pretrained(self.tmpdirname , **snake_case__ )
def __SCREAMING_SNAKE_CASE ( self , snake_case__ ):
"""simple docstring"""
return "lower newer", "lower newer"
@cached_property
def __SCREAMING_SNAKE_CASE ( self ):
"""simple docstring"""
return MvpTokenizer.from_pretrained("RUCAIBox/mvp" )
@cached_property
def __SCREAMING_SNAKE_CASE ( self ):
"""simple docstring"""
return MvpTokenizerFast.from_pretrained("RUCAIBox/mvp" )
@require_torch
def __SCREAMING_SNAKE_CASE ( self ):
"""simple docstring"""
_SCREAMING_SNAKE_CASE : Union[str, Any] = ["A long paragraph for summarization.", "Another paragraph for summarization."]
_SCREAMING_SNAKE_CASE : Dict = [0, 250, 251, 17818, 13, 39186, 1938, 4, 2]
for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
_SCREAMING_SNAKE_CASE : List[str] = tokenizer(snake_case__ , max_length=len(snake_case__ ) , padding=snake_case__ , return_tensors="pt" )
self.assertIsInstance(snake_case__ , snake_case__ )
self.assertEqual((2, 9) , batch.input_ids.shape )
self.assertEqual((2, 9) , batch.attention_mask.shape )
_SCREAMING_SNAKE_CASE : int = batch.input_ids.tolist()[0]
self.assertListEqual(snake_case__ , snake_case__ )
# Test that special tokens are reset
@require_torch
def __SCREAMING_SNAKE_CASE ( self ):
"""simple docstring"""
_SCREAMING_SNAKE_CASE : str = ["A long paragraph for summarization.", "Another paragraph for summarization."]
for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
_SCREAMING_SNAKE_CASE : List[str] = tokenizer(snake_case__ , padding=snake_case__ , return_tensors="pt" )
# check if input_ids are returned and no labels
self.assertIn("input_ids" , snake_case__ )
self.assertIn("attention_mask" , snake_case__ )
self.assertNotIn("labels" , snake_case__ )
self.assertNotIn("decoder_attention_mask" , snake_case__ )
@require_torch
def __SCREAMING_SNAKE_CASE ( self ):
"""simple docstring"""
_SCREAMING_SNAKE_CASE : Any = [
"Summary of the text.",
"Another summary.",
]
for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
_SCREAMING_SNAKE_CASE : str = tokenizer(text_target=snake_case__ , max_length=32 , padding="max_length" , return_tensors="pt" )
self.assertEqual(32 , targets["input_ids"].shape[1] )
@require_torch
def __SCREAMING_SNAKE_CASE ( self ):
"""simple docstring"""
for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
_SCREAMING_SNAKE_CASE : int = tokenizer(
["I am a small frog" * 1024, "I am a small frog"] , padding=snake_case__ , truncation=snake_case__ , return_tensors="pt" )
self.assertIsInstance(snake_case__ , snake_case__ )
self.assertEqual(batch.input_ids.shape , (2, 1024) )
@require_torch
def __SCREAMING_SNAKE_CASE ( self ):
"""simple docstring"""
_SCREAMING_SNAKE_CASE : str = ["A long paragraph for summarization."]
_SCREAMING_SNAKE_CASE : Any = [
"Summary of the text.",
]
for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
_SCREAMING_SNAKE_CASE : Any = tokenizer(snake_case__ , text_target=snake_case__ , return_tensors="pt" )
_SCREAMING_SNAKE_CASE : Union[str, Any] = inputs["input_ids"]
_SCREAMING_SNAKE_CASE : Any = inputs["labels"]
self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item() )
self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item() )
self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item() )
self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item() )
def __SCREAMING_SNAKE_CASE ( self ):
"""simple docstring"""
pass
def __SCREAMING_SNAKE_CASE ( self ):
"""simple docstring"""
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})''' ):
_SCREAMING_SNAKE_CASE : Any = self.rust_tokenizer_class.from_pretrained(snake_case__ , **snake_case__ )
_SCREAMING_SNAKE_CASE : Optional[Any] = self.tokenizer_class.from_pretrained(snake_case__ , **snake_case__ )
_SCREAMING_SNAKE_CASE : str = "A, <mask> AllenNLP sentence."
_SCREAMING_SNAKE_CASE : Tuple = tokenizer_r.encode_plus(snake_case__ , add_special_tokens=snake_case__ , return_token_type_ids=snake_case__ )
_SCREAMING_SNAKE_CASE : str = tokenizer_p.encode_plus(snake_case__ , add_special_tokens=snake_case__ , return_token_type_ids=snake_case__ )
# token_type_ids should put 0 everywhere
self.assertEqual(sum(tokens_r["token_type_ids"] ) , sum(tokens_p["token_type_ids"] ) )
# attention_mask should put 1 everywhere, so sum over length should be 1
self.assertEqual(
sum(tokens_r["attention_mask"] ) / len(tokens_r["attention_mask"] ) , sum(tokens_p["attention_mask"] ) / len(tokens_p["attention_mask"] ) , )
_SCREAMING_SNAKE_CASE : List[Any] = tokenizer_r.convert_ids_to_tokens(tokens_r["input_ids"] )
_SCREAMING_SNAKE_CASE : int = tokenizer_p.convert_ids_to_tokens(tokens_p["input_ids"] )
# Rust correctly handles the space before the mask while python doesnt
self.assertSequenceEqual(tokens_p["input_ids"] , [0, 250, 6, 50264, 3823, 487, 21992, 3645, 4, 2] )
self.assertSequenceEqual(tokens_r["input_ids"] , [0, 250, 6, 50264, 3823, 487, 21992, 3645, 4, 2] )
self.assertSequenceEqual(
snake_case__ , ["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"] )
self.assertSequenceEqual(
snake_case__ , ["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"] )
| 572 |
"""simple docstring"""
import argparse
import json
from pathlib import Path
import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import AutoImageProcessor, SwinvaConfig, SwinvaForImageClassification
def _lowerCAmelCase ( lowerCamelCase__ : str ) -> Any:
_SCREAMING_SNAKE_CASE : Any = SwinvaConfig()
_SCREAMING_SNAKE_CASE : List[str] = swinva_name.split("_" )
_SCREAMING_SNAKE_CASE : Dict = name_split[1]
if "to" in name_split[3]:
_SCREAMING_SNAKE_CASE : Union[str, Any] = int(name_split[3][-3:] )
else:
_SCREAMING_SNAKE_CASE : Tuple = int(name_split[3] )
if "to" in name_split[2]:
_SCREAMING_SNAKE_CASE : str = int(name_split[2][-2:] )
else:
_SCREAMING_SNAKE_CASE : Union[str, Any] = int(name_split[2][6:] )
if model_size == "tiny":
_SCREAMING_SNAKE_CASE : List[Any] = 9_6
_SCREAMING_SNAKE_CASE : List[Any] = (2, 2, 6, 2)
_SCREAMING_SNAKE_CASE : List[str] = (3, 6, 1_2, 2_4)
elif model_size == "small":
_SCREAMING_SNAKE_CASE : Optional[int] = 9_6
_SCREAMING_SNAKE_CASE : Optional[Any] = (2, 2, 1_8, 2)
_SCREAMING_SNAKE_CASE : List[str] = (3, 6, 1_2, 2_4)
elif model_size == "base":
_SCREAMING_SNAKE_CASE : Union[str, Any] = 1_2_8
_SCREAMING_SNAKE_CASE : List[str] = (2, 2, 1_8, 2)
_SCREAMING_SNAKE_CASE : List[str] = (4, 8, 1_6, 3_2)
else:
_SCREAMING_SNAKE_CASE : Dict = 1_9_2
_SCREAMING_SNAKE_CASE : Optional[Any] = (2, 2, 1_8, 2)
_SCREAMING_SNAKE_CASE : List[str] = (6, 1_2, 2_4, 4_8)
if "to" in swinva_name:
_SCREAMING_SNAKE_CASE : Optional[int] = (1_2, 1_2, 1_2, 6)
if ("22k" in swinva_name) and ("to" not in swinva_name):
_SCREAMING_SNAKE_CASE : Tuple = 2_1_8_4_1
_SCREAMING_SNAKE_CASE : Optional[int] = "huggingface/label-files"
_SCREAMING_SNAKE_CASE : int = "imagenet-22k-id2label.json"
_SCREAMING_SNAKE_CASE : str = json.load(open(hf_hub_download(lowerCamelCase__, lowerCamelCase__, repo_type="dataset" ), "r" ) )
_SCREAMING_SNAKE_CASE : Optional[int] = {int(lowerCamelCase__ ): v for k, v in idalabel.items()}
_SCREAMING_SNAKE_CASE : int = idalabel
_SCREAMING_SNAKE_CASE : List[str] = {v: k for k, v in idalabel.items()}
else:
_SCREAMING_SNAKE_CASE : Optional[Any] = 1_0_0_0
_SCREAMING_SNAKE_CASE : Dict = "huggingface/label-files"
_SCREAMING_SNAKE_CASE : Any = "imagenet-1k-id2label.json"
_SCREAMING_SNAKE_CASE : Tuple = json.load(open(hf_hub_download(lowerCamelCase__, lowerCamelCase__, repo_type="dataset" ), "r" ) )
_SCREAMING_SNAKE_CASE : Dict = {int(lowerCamelCase__ ): v for k, v in idalabel.items()}
_SCREAMING_SNAKE_CASE : int = idalabel
_SCREAMING_SNAKE_CASE : List[Any] = {v: k for k, v in idalabel.items()}
_SCREAMING_SNAKE_CASE : List[str] = img_size
_SCREAMING_SNAKE_CASE : List[Any] = num_classes
_SCREAMING_SNAKE_CASE : List[Any] = embed_dim
_SCREAMING_SNAKE_CASE : List[Any] = depths
_SCREAMING_SNAKE_CASE : List[Any] = num_heads
_SCREAMING_SNAKE_CASE : int = window_size
return config
def _lowerCAmelCase ( lowerCamelCase__ : Dict ) -> List[str]:
if "patch_embed.proj" in name:
_SCREAMING_SNAKE_CASE : List[str] = name.replace("patch_embed.proj", "embeddings.patch_embeddings.projection" )
if "patch_embed.norm" in name:
_SCREAMING_SNAKE_CASE : List[str] = name.replace("patch_embed.norm", "embeddings.norm" )
if "layers" in name:
_SCREAMING_SNAKE_CASE : Optional[Any] = "encoder." + name
if "attn.proj" in name:
_SCREAMING_SNAKE_CASE : List[Any] = name.replace("attn.proj", "attention.output.dense" )
if "attn" in name:
_SCREAMING_SNAKE_CASE : str = name.replace("attn", "attention.self" )
if "norm1" in name:
_SCREAMING_SNAKE_CASE : List[str] = name.replace("norm1", "layernorm_before" )
if "norm2" in name:
_SCREAMING_SNAKE_CASE : str = name.replace("norm2", "layernorm_after" )
if "mlp.fc1" in name:
_SCREAMING_SNAKE_CASE : Dict = name.replace("mlp.fc1", "intermediate.dense" )
if "mlp.fc2" in name:
_SCREAMING_SNAKE_CASE : Tuple = name.replace("mlp.fc2", "output.dense" )
if "q_bias" in name:
_SCREAMING_SNAKE_CASE : Dict = name.replace("q_bias", "query.bias" )
if "k_bias" in name:
_SCREAMING_SNAKE_CASE : Optional[int] = name.replace("k_bias", "key.bias" )
if "v_bias" in name:
_SCREAMING_SNAKE_CASE : int = name.replace("v_bias", "value.bias" )
if "cpb_mlp" in name:
_SCREAMING_SNAKE_CASE : Optional[Any] = name.replace("cpb_mlp", "continuous_position_bias_mlp" )
if name == "norm.weight":
_SCREAMING_SNAKE_CASE : str = "layernorm.weight"
if name == "norm.bias":
_SCREAMING_SNAKE_CASE : Tuple = "layernorm.bias"
if "head" in name:
_SCREAMING_SNAKE_CASE : str = name.replace("head", "classifier" )
else:
_SCREAMING_SNAKE_CASE : Tuple = "swinv2." + name
return name
def _lowerCAmelCase ( lowerCamelCase__ : Any, lowerCamelCase__ : int ) -> Tuple:
for key in orig_state_dict.copy().keys():
_SCREAMING_SNAKE_CASE : List[str] = orig_state_dict.pop(lowerCamelCase__ )
if "mask" in key:
continue
elif "qkv" in key:
_SCREAMING_SNAKE_CASE : Any = key.split("." )
_SCREAMING_SNAKE_CASE : Optional[Any] = int(key_split[1] )
_SCREAMING_SNAKE_CASE : Union[str, Any] = int(key_split[3] )
_SCREAMING_SNAKE_CASE : Tuple = model.swinva.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size
if "weight" in key:
_SCREAMING_SNAKE_CASE : Optional[int] = val[:dim, :]
_SCREAMING_SNAKE_CASE : Tuple = val[dim : dim * 2, :]
_SCREAMING_SNAKE_CASE : List[str] = val[-dim:, :]
else:
_SCREAMING_SNAKE_CASE : List[Any] = val[:dim]
_SCREAMING_SNAKE_CASE : str = val[
dim : dim * 2
]
_SCREAMING_SNAKE_CASE : Union[str, Any] = val[-dim:]
else:
_SCREAMING_SNAKE_CASE : Optional[Any] = val
return orig_state_dict
def _lowerCAmelCase ( lowerCamelCase__ : List[Any], lowerCamelCase__ : str ) -> Optional[Any]:
_SCREAMING_SNAKE_CASE : Any = timm.create_model(lowerCamelCase__, pretrained=lowerCamelCase__ )
timm_model.eval()
_SCREAMING_SNAKE_CASE : Tuple = get_swinva_config(lowerCamelCase__ )
_SCREAMING_SNAKE_CASE : int = SwinvaForImageClassification(lowerCamelCase__ )
model.eval()
_SCREAMING_SNAKE_CASE : int = convert_state_dict(timm_model.state_dict(), lowerCamelCase__ )
model.load_state_dict(lowerCamelCase__ )
_SCREAMING_SNAKE_CASE : List[str] = "http://images.cocodataset.org/val2017/000000039769.jpg"
_SCREAMING_SNAKE_CASE : str = AutoImageProcessor.from_pretrained("microsoft/{}".format(swinva_name.replace("_", "-" ) ) )
_SCREAMING_SNAKE_CASE : List[Any] = Image.open(requests.get(lowerCamelCase__, stream=lowerCamelCase__ ).raw )
_SCREAMING_SNAKE_CASE : Dict = image_processor(images=lowerCamelCase__, return_tensors="pt" )
_SCREAMING_SNAKE_CASE : Tuple = timm_model(inputs["pixel_values"] )
_SCREAMING_SNAKE_CASE : Dict = model(**lowerCamelCase__ ).logits
assert torch.allclose(lowerCamelCase__, lowerCamelCase__, atol=1E-3 )
print(f'''Saving model {swinva_name} to {pytorch_dump_folder_path}''' )
model.save_pretrained(lowerCamelCase__ )
print(f'''Saving image processor to {pytorch_dump_folder_path}''' )
image_processor.save_pretrained(lowerCamelCase__ )
model.push_to_hub(
repo_path_or_name=Path(lowerCamelCase__, lowerCamelCase__ ), organization="nandwalritik", commit_message="Add model", )
if __name__ == "__main__":
lowercase_ : Union[str, Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--swinv2_name''',
default='''swinv2_tiny_patch4_window8_256''',
type=str,
help='''Name of the Swinv2 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.'''
)
lowercase_ : Optional[int] = parser.parse_args()
convert_swinva_checkpoint(args.swinva_name, args.pytorch_dump_folder_path)
| 572 | 1 |
'''simple docstring'''
def a ( ):
'''simple docstring'''
return [
a * b * (10_00 - a - b)
for a in range(1 , 9_99 )
for b in range(lowerCamelCase__ , 9_99 )
if (a * a + b * b == (10_00 - a - b) ** 2)
][0]
if __name__ == "__main__":
print(F"{solution() = }") | 712 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowerCamelCase :Any = logging.get_logger(__name__)
lowerCamelCase :Any = {
'''microsoft/beit-base-patch16-224-pt22k''': (
'''https://huggingface.co/microsoft/beit-base-patch16-224-pt22k/resolve/main/config.json'''
),
# See all BEiT models at https://huggingface.co/models?filter=beit
}
class _lowerCAmelCase ( __UpperCAmelCase ):
__SCREAMING_SNAKE_CASE : Optional[Any] = 'beit'
def __init__(self , lowercase=8192 , lowercase=768 , lowercase=12 , lowercase=12 , lowercase=3072 , lowercase="gelu" , lowercase=0.0 , lowercase=0.0 , lowercase=0.02 , lowercase=1E-12 , lowercase=224 , lowercase=16 , lowercase=3 , lowercase=False , lowercase=False , lowercase=False , lowercase=False , lowercase=0.1 , lowercase=0.1 , lowercase=True , lowercase=[3, 5, 7, 11] , lowercase=[1, 2, 3, 6] , lowercase=True , lowercase=0.4 , lowercase=256 , lowercase=1 , lowercase=False , lowercase=255 , **lowercase , ):
super().__init__(**lowercase )
A_ : Union[str, Any] = vocab_size
A_ : List[str] = hidden_size
A_ : Optional[int] = num_hidden_layers
A_ : Tuple = num_attention_heads
A_ : List[Any] = intermediate_size
A_ : Optional[int] = hidden_act
A_ : str = hidden_dropout_prob
A_ : Any = attention_probs_dropout_prob
A_ : Dict = initializer_range
A_ : str = layer_norm_eps
A_ : Any = image_size
A_ : int = patch_size
A_ : List[str] = num_channels
A_ : Any = use_mask_token
A_ : Dict = use_absolute_position_embeddings
A_ : List[Any] = use_relative_position_bias
A_ : Tuple = use_shared_relative_position_bias
A_ : Optional[int] = layer_scale_init_value
A_ : Tuple = drop_path_rate
A_ : Dict = use_mean_pooling
# decode head attributes (semantic segmentation)
A_ : Tuple = out_indices
A_ : Union[str, Any] = pool_scales
# auxiliary head attributes (semantic segmentation)
A_ : Optional[int] = use_auxiliary_head
A_ : Union[str, Any] = auxiliary_loss_weight
A_ : Tuple = auxiliary_channels
A_ : List[Any] = auxiliary_num_convs
A_ : Dict = auxiliary_concat_input
A_ : Optional[Any] = semantic_loss_ignore_index
class _lowerCAmelCase ( __UpperCAmelCase ):
__SCREAMING_SNAKE_CASE : Optional[Any] = version.parse('1.11' )
@property
def _a (self ):
return OrderedDict(
[
("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
] )
@property
def _a (self ):
return 1E-4 | 686 | 0 |
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