


'''simple docstring'''



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

import black


_lowercase      :      List[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.
_lowercase      :      int            =					"""    def __init__(self, config):
        super().__init__()
        self.transform = BertPredictionHeadTransform(config)

        # The output weights are the same as the input embeddings, but there is
        # an output-only bias for each token.
        self.decoder = nn.Linear(config.hidden_size, config.vocab_size, bias=False)

        self.bias = nn.Parameter(torch.zeros(config.vocab_size))

        # Need a link between the two variables so that the bias is correctly resized with `resize_token_embeddings`
        self.decoder.bias = self.bias

    def forward(self, hidden_states):
        hidden_states = self.transform(hidden_states)
        hidden_states = self.decoder(hidden_states)
        return hidden_states
"""






class 	UpperCamelCase__(       unittest.TestCase	):




				def 					a__(		self      :			Any				)->					Dict:

										"""simple docstring"""




										UpperCAmelCase					    =     tempfile.mkdtemp()
										os.makedirs(os.path.join(self.transformer_dir				,   '''models/bert/'''				)				)
										UpperCAmelCase					    =     self.transformer_dir
										shutil.copy(
										    os.path.join(lowerCAmelCase				,   '''src/transformers/models/bert/modeling_bert.py'''				)				,   os.path.join(self.transformer_dir				,   '''models/bert/modeling_bert.py'''				)				,   )




				def 					a__(		self      :			Tuple				)->					Optional[Any]:

										"""simple docstring"""




										UpperCAmelCase					    =     '''src/transformers'''
										shutil.rmtree(self.transformer_dir				)




				def 					a__(		self      :			Optional[Any]				,   lowerCAmelCase      :			Any				,   lowerCAmelCase      :			List[str]				,   lowerCAmelCase      :			int				,   lowerCAmelCase      :			List[str]=None				)->					Optional[Any]:

										"""simple docstring"""




										UpperCAmelCase					    =     comment + F"""\nclass {class_name}(nn.Module):\n""" + class_code
										if overwrite_result is not None:
																UpperCAmelCase					    =     comment + F"""\nclass {class_name}(nn.Module):\n""" + overwrite_result
										UpperCAmelCase					    =     black.Mode(target_versions={black.TargetVersion.PYaa}				,   line_length=119				)
										UpperCAmelCase					    =     black.format_str(lowerCAmelCase				,   mode=lowerCAmelCase				)
										UpperCAmelCase					    =     os.path.join(self.transformer_dir				,   '''new_code.py'''				)
										with open(lowerCAmelCase				,   '''w'''				,   newline='''\n'''				) as f:
																f.write(lowerCAmelCase				)
										if overwrite_result is None:
																self.assertTrue(len(check_copies.is_copy_consistent(lowerCAmelCase				)				) == 0				)
										else:
																check_copies.is_copy_consistent(f.name				,   overwrite=lowerCAmelCase				)
																with open(lowerCAmelCase				,   '''r'''				) as f:
																						self.assertTrue(f.read()				,   lowerCAmelCase				)




				def 					a__(		self      :			Tuple				)->					Optional[int]:

										"""simple docstring"""




										UpperCAmelCase					    =     check_copies.find_code_in_transformers('''models.bert.modeling_bert.BertLMPredictionHead'''				)
										self.assertEqual(lowerCAmelCase				,   lowerCAmelCase				)




				def 					a__(		self      :			List[Any]				)->					str:

										"""simple docstring"""




										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'''				,   lowerCAmelCase				,   )

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

										# Copy consistency with a really long name
										UpperCAmelCase					    =     '''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'''				,   lowerCAmelCase				,   lowerCAmelCase				)				,   )

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





				def 					a__(		self      :			Any				)->					Tuple:

										"""simple docstring"""




										UpperCAmelCase					    =     check_copies.LOCALIZED_READMES['''README_zh-hans.md''']

										UpperCAmelCase					    =     (
										    '''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.'''
										)
										UpperCAmelCase					    =     (
										    '''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'''
										)
										UpperCAmelCase					    =     (
										    '''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'''
										)

										UpperCAmelCase    ,    UpperCAmelCase					    =     check_copies.convert_to_localized_md(
										    lowerCAmelCase				,   lowerCAmelCase				,   localized_readme['''format_model_list''']				)

										self.assertFalse(lowerCAmelCase				)
										self.assertEqual(lowerCAmelCase				,   lowerCAmelCase				)

										UpperCAmelCase    ,    UpperCAmelCase					    =     check_copies.convert_to_localized_md(
										    lowerCAmelCase				,   lowerCAmelCase				,   localized_readme['''format_model_list''']				)

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

										UpperCAmelCase					    =     (
										    '''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.'''
										)
										UpperCAmelCase					    =     (
										    '''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'''
										)
										UpperCAmelCase					    =     (
										    '''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'''
										)

										UpperCAmelCase    ,    UpperCAmelCase					    =     check_copies.convert_to_localized_md(
										    lowerCAmelCase				,   lowerCAmelCase				,   localized_readme['''format_model_list''']				)

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









'''simple docstring'''



from dataclasses import dataclass, field
from typing import TYPE_CHECKING, Any, ClassVar, Dict, List, Optional, Union

import pyarrow as pa


if TYPE_CHECKING:
	from .features import FeatureType






@dataclass
class 	UpperCamelCase__:
				__magic_name__    :				List[str]
				__magic_name__    :				Optional[str]  		=	None
				# Automatically constructed
				__magic_name__    :				ClassVar[str]  		=	"dict"
				__magic_name__    :				ClassVar[Any]  		=	None
				__magic_name__    :				str  		=	field(default="Translation"						,    init=lowerCAmelCase						,    repr=lowerCAmelCase	)




				def __call__(		self      :			Union[str, Any]				)->					str:

										"""simple docstring"""




										return pa.struct({lang: pa.string() for lang in sorted(self.languages				)}				)





				def 					a__(		self      :			int				)->					Union["FeatureType", Dict[str, "FeatureType"]]:

										"""simple docstring"""




										from .features import Value

										return {k: Value('''string'''				) for k in sorted(self.languages				)}







@dataclass
class 	UpperCamelCase__:
				__magic_name__    :				Optional[List]  		=	None
				__magic_name__    :				Optional[int]  		=	None
				__magic_name__    :				Optional[str]  		=	None
				# Automatically constructed
				__magic_name__    :				ClassVar[str]  		=	"dict"
				__magic_name__    :				ClassVar[Any]  		=	None
				__magic_name__    :				str  		=	field(default="TranslationVariableLanguages"						,    init=lowerCAmelCase						,    repr=lowerCAmelCase	)




				def 					a__(		self      :			Union[str, Any]				)->					Optional[Any]:

										"""simple docstring"""




										UpperCAmelCase					    =     sorted(set(self.languages				)				) if self.languages else None
										UpperCAmelCase					    =     len(self.languages				) if self.languages else None




				def __call__(		self      :			int				)->					Optional[Any]:

										"""simple docstring"""




										return pa.struct({'''language''': pa.list_(pa.string()				), '''translation''': pa.list_(pa.string()				)}				)




				def 					a__(		self      :			Optional[int]				,   lowerCAmelCase      :			Dict				)->					Tuple:

										"""simple docstring"""




										UpperCAmelCase					    =     set(self.languages				)
										if self.languages and set(lowerCAmelCase				) - lang_set:
																raise ValueError(
																    F"""Some languages in example ({", ".join(sorted(set(lowerCAmelCase				) - lang_set				)				)}) are not in valid set ({", ".join(lowerCAmelCase				)})."""				)

										# Convert dictionary into tuples, splitting out cases where there are
										# multiple translations for a single language.
										UpperCAmelCase					    =     []
										for lang, text in translation_dict.items():
																if isinstance(lowerCAmelCase				,   lowerCAmelCase				):
																						translation_tuples.append((lang, text)				)
																else:
																						translation_tuples.extend([(lang, el) for el in text]				)

        # Ensure translations are in ascending order by language code.
										UpperCAmelCase    ,    UpperCAmelCase					    =     zip(*sorted(lowerCAmelCase				)				)

										return {"language": languages, "translation": translations}





				def 					a__(		self      :			Any				)->					Union["FeatureType", Dict[str, "FeatureType"]]:

										"""simple docstring"""




										from .features import Sequence, Value

										return {
										    "language": Sequence(Value('''string'''				)				),
										    "translation": Sequence(Value('''string'''				)				),
										}





