


"""simple docstring"""


import os
import re
import shutil
import sys
import tempfile
import unittest

import black


A     :   Any			    =			os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, "utils"))

import check_copies  # noqa: E402


# This is the reference code that will be used in the tests.
# If BertLMPredictionHead is changed in modeling_bert.py, this code needs to be manually updated.
A     :   Dict			    =			"    def __init__(self, config):\n        super().__init__()\n        self.transform = BertPredictionHeadTransform(config)\n\n        # The output weights are the same as the input embeddings, but there is\n        # an output-only bias for each token.\n        self.decoder = nn.Linear(config.hidden_size, config.vocab_size, bias=False)\n\n        self.bias = nn.Parameter(torch.zeros(config.vocab_size))\n\n        # Need a link between the two variables so that the bias is correctly resized with `resize_token_embeddings`\n        self.decoder.bias = self.bias\n\n    def forward(self, hidden_states):\n        hidden_states = self.transform(hidden_states)\n        hidden_states = self.decoder(hidden_states)\n        return hidden_states\n"
class   _UpperCamelCase  (   unittest.TestCase		):
							'''simple docstring'''







							def 			snake_case				(     self	):
										__lowerCAmelCase				    =			tempfile.mkdtemp()
										os.makedirs(os.path.join(self.transformer_dir    ,   "models/bert/"	)	)
										__lowerCAmelCase				    =			self.transformer_dir
										shutil.copy(
										    os.path.join(__a    ,   "src/transformers/models/bert/modeling_bert.py"	)    ,   os.path.join(self.transformer_dir    ,   "models/bert/modeling_bert.py"	)    ,   )

							def 			snake_case				(     self	):
										__lowerCAmelCase				    =			"src/transformers"
										shutil.rmtree(self.transformer_dir	)

							def 			snake_case				(     self    ,   __a    ,   __a    ,   __a    ,   __a=None	):
										__lowerCAmelCase				    =			comment + f"\nclass {class_name}(nn.Module):\n" + class_code
										if overwrite_result is not None:
													__lowerCAmelCase				    =			comment + f"\nclass {class_name}(nn.Module):\n" + overwrite_result
										__lowerCAmelCase				    =			black.Mode(target_versions={black.TargetVersion.PYaa}    ,   line_length=1_19	)
										__lowerCAmelCase				    =			black.format_str(__a    ,   mode=__a	)
										__lowerCAmelCase				    =			os.path.join(self.transformer_dir    ,   "new_code.py"	)
										with open(__a    ,   "w"    ,   newline="\n"	) as f:
													f.write(__a	)
										if overwrite_result is None:
													self.assertTrue(len(check_copies.is_copy_consistent(__a	)	) == 0	)
										else:
													check_copies.is_copy_consistent(f.name    ,   overwrite=__a	)
													with open(__a    ,   "r"	) as f:
																self.assertTrue(f.read()    ,   __a	)

							def 			snake_case				(     self	):
										__lowerCAmelCase				    =			check_copies.find_code_in_transformers("models.bert.modeling_bert.BertLMPredictionHead"	)
										self.assertEqual(__a    ,   __a	)

							def 			snake_case				(     self	):
										# Base copy consistency
										self.check_copy_consistency(
										    "# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead"    ,   "BertLMPredictionHead"    ,   REFERENCE_CODE + "\n"    ,   )

										# With no empty line at the end
										self.check_copy_consistency(
										    "# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead"    ,   "BertLMPredictionHead"    ,   __a    ,   )

										# Copy consistency with rename
										self.check_copy_consistency(
										    "# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead with Bert->TestModel"    ,   "TestModelLMPredictionHead"    ,   re.sub("Bert"    ,   "TestModel"    ,   __a	)    ,   )

										# Copy consistency with a really long name
										__lowerCAmelCase				    =			"TestModelWithAReallyLongNameBecauseSomePeopleLikeThatForSomeReason"
										self.check_copy_consistency(
										    f"# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead with Bert->{long_class_name}"    ,   f"{long_class_name}LMPredictionHead"    ,   re.sub("Bert"    ,   __a    ,   __a	)    ,   )

										# Copy consistency with overwrite
										self.check_copy_consistency(
										    "# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead with Bert->TestModel"    ,   "TestModelLMPredictionHead"    ,   __a    ,   overwrite_result=re.sub("Bert"    ,   "TestModel"    ,   __a	)    ,   )



							def 			snake_case				(     self	):
										__lowerCAmelCase				    =			check_copies.LOCALIZED_READMES["README_zh-hans.md"]

										__lowerCAmelCase				    =			(
										    "1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (from Google Research and the"
										    " Toyota Technological Institute at Chicago) released with the paper [ALBERT: A Lite BERT for"
										    " Self-supervised Learning of Language Representations](https://arxiv.org/abs/1909.11942), by Zhenzhong"
										    " Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut.\n1."
										    " **[DistilBERT](https://huggingface.co/transformers/model_doc/distilbert.html)** (from HuggingFace),"
										    " released together with the paper [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and"
										    " lighter](https://arxiv.org/abs/1910.01108) by Victor Sanh, Lysandre Debut and Thomas Wolf. The same"
										    " method has been applied to compress GPT2 into"
										    " [DistilGPT2](https://github.com/huggingface/transformers/tree/main/examples/distillation), RoBERTa into"
										    " [DistilRoBERTa](https://github.com/huggingface/transformers/tree/main/examples/distillation),"
										    " Multilingual BERT into"
										    " [DistilmBERT](https://github.com/huggingface/transformers/tree/main/examples/distillation) and a German"
										    " version of DistilBERT.\n1. **[ELECTRA](https://huggingface.co/transformers/model_doc/electra.html)**"
										    " (from Google Research/Stanford University) released with the paper [ELECTRA: Pre-training text encoders"
										    " as discriminators rather than generators](https://arxiv.org/abs/2003.10555) by Kevin Clark, Minh-Thang"
										    " Luong, Quoc V. Le, Christopher D. Manning."
										)
										__lowerCAmelCase				    =			(
										    "1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (来自 Google Research and the"
										    " Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of"
										    " Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian"
										    " Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n"
										)
										__lowerCAmelCase				    =			(
										    "1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (来自 Google Research and the"
										    " Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of"
										    " Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian"
										    " Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n1."
										    " **[DistilBERT](https://huggingface.co/transformers/model_doc/distilbert.html)** (来自 HuggingFace) 伴随论文"
										    " [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and"
										    " lighter](https://arxiv.org/abs/1910.01108) 由 Victor Sanh, Lysandre Debut and Thomas Wolf 发布。 The same"
										    " method has been applied to compress GPT2 into"
										    " [DistilGPT2](https://github.com/huggingface/transformers/tree/main/examples/distillation), RoBERTa into"
										    " [DistilRoBERTa](https://github.com/huggingface/transformers/tree/main/examples/distillation),"
										    " Multilingual BERT into"
										    " [DistilmBERT](https://github.com/huggingface/transformers/tree/main/examples/distillation) and a German"
										    " version of DistilBERT.\n1. **[ELECTRA](https://huggingface.co/transformers/model_doc/electra.html)** (来自"
										    " Google Research/Stanford University) 伴随论文 [ELECTRA: Pre-training text encoders as discriminators rather"
										    " than generators](https://arxiv.org/abs/2003.10555) 由 Kevin Clark, Minh-Thang Luong, Quoc V. Le,"
										    " Christopher D. Manning 发布。\n"
										)

										__lowerCAmelCase    ,	__lowerCAmelCase				    =			check_copies.convert_to_localized_md(
										    __a    ,   __a    ,   localized_readme["format_model_list"]	)

										self.assertFalse(__a	)
										self.assertEqual(__a    ,   __a	)

										__lowerCAmelCase    ,	__lowerCAmelCase				    =			check_copies.convert_to_localized_md(
										    __a    ,   __a    ,   localized_readme["format_model_list"]	)

										# Check whether the number of models is equal to README.md after conversion.
										self.assertTrue(__a	)

										__lowerCAmelCase				    =			(
										    "1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (from Google Research and the"
										    " Toyota Technological Institute at Chicago) released with the paper [ALBERT: A Lite BERT for"
										    " Self-supervised Learning of Language Representations](https://arxiv.org/abs/1909.11942), by Zhenzhong"
										    " Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut."
										)
										__lowerCAmelCase				    =			(
										    "1. **[ALBERT](https://huggingface.co/transformers/main/model_doc/albert.html)** (来自 Google Research and"
										    " the Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of"
										    " Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian"
										    " Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n"
										)
										__lowerCAmelCase				    =			(
										    "1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (来自 Google Research and the"
										    " Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of"
										    " Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian"
										    " Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n"
										)

										__lowerCAmelCase    ,	__lowerCAmelCase				    =			check_copies.convert_to_localized_md(
										    __a    ,   __a    ,   localized_readme["format_model_list"]	)

										# Check if the model link is synchronized.
										self.assertEqual(__a    ,   __a	)







import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import Audio, Features, Value
from .base import TaskTemplate



@dataclass(frozen=UpperCAmelCase_	)
class   __lowercase				( UpperCAmelCase_	):




			"""simple docstring"""







			_UpperCAmelCase   :							str										=     field(default='''automatic-speech-recognition'''		,       metadata={'''include_in_asdict_even_if_is_default''': True}	)
			_UpperCAmelCase   :							ClassVar[Features]										=     Features({'''audio''': Audio()}	)
			_UpperCAmelCase   :							ClassVar[Features]										=     Features({'''transcription''': Value('''string'''	)}	)
			_UpperCAmelCase   :							str										=     "audio"
			_UpperCAmelCase   :							str										=     "transcription"



			def        _SCREAMING_SNAKE_CASE			( self  :     Optional[Any]   ,						lowerCAmelCase__  :     int):
									if self.audio_column not in features:
															raise ValueError(F"Column {self.audio_column} is not present in features.")
									if not isinstance(features[self.audio_column]   ,						lowerCAmelCase__):
															raise ValueError(F"Column {self.audio_column} is not an Audio type.")
									SCREAMING_SNAKE_CASE_: Tuple  		=       copy.deepcopy(self)
									SCREAMING_SNAKE_CASE_: Optional[int]  		=       self.input_schema.copy()
									SCREAMING_SNAKE_CASE_: Dict  		=       features[self.audio_column]
									SCREAMING_SNAKE_CASE_: int  		=       input_schema
									return task_template




			@property
			def        _SCREAMING_SNAKE_CASE			( self  :     int):
									return {self.audio_column: "audio", self.transcription_column: "transcription"}






