



'''simple docstring'''

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

import black


__lowerCAmelCase		     =  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.
__lowerCAmelCase		     =  '''    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       __magic_name__		(       unittest.TestCase  ):







							def 				__lowercase     (				self   :			Any						):
									_a	: int								=				tempfile.mkdtemp()
									os.makedirs(os.path.join(self.transformer_dir      ,'models/bert/'						)						)
									_a	: Any								=				self.transformer_dir
									shutil.copy(
									    os.path.join(_UpperCAmelCase      ,'src/transformers/models/bert/modeling_bert.py'						)      ,os.path.join(self.transformer_dir      ,'models/bert/modeling_bert.py'						)      ,)







							def 				__lowercase     (				self   :			Optional[int]						):
									_a	: Any								=				'src/transformers'
									shutil.rmtree(self.transformer_dir						)







							def 				__lowercase     (				self   :			Tuple      ,_UpperCAmelCase   :			Any      ,_UpperCAmelCase   :			Optional[int]      ,_UpperCAmelCase   :			Union[str, Any]      ,_UpperCAmelCase   :			str=None						):
									_a	: str								=				comment + F"""\nclass {class_name}(nn.Module):\n""" + class_code
									if overwrite_result is not None:
											_a	: List[str]								=				comment + F"""\nclass {class_name}(nn.Module):\n""" + overwrite_result
									_a	: Any								=				black.Mode(target_versions={black.TargetVersion.PYaa}      ,line_length=119						)
									_a	: str								=				black.format_str(_UpperCAmelCase      ,mode=_UpperCAmelCase						)
									_a	: List[Any]								=				os.path.join(self.transformer_dir      ,'new_code.py'						)
									with open(_UpperCAmelCase      ,'w'      ,newline='\n'						) as f:
											f.write(_UpperCAmelCase						)
									if overwrite_result is None:
											self.assertTrue(len(check_copies.is_copy_consistent(_UpperCAmelCase						)						) == 0						)
									else:
											check_copies.is_copy_consistent(f.name      ,overwrite=_UpperCAmelCase						)
											with open(_UpperCAmelCase      ,'r'						) as f:
													self.assertTrue(f.read()      ,_UpperCAmelCase						)







							def 				__lowercase     (				self   :			List[str]						):
									_a	: Dict								=				check_copies.find_code_in_transformers('models.bert.modeling_bert.BertLMPredictionHead'						)
									self.assertEqual(_UpperCAmelCase      ,_UpperCAmelCase						)







							def 				__lowercase     (				self   :			Tuple						):
									# 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'      ,_UpperCAmelCase      ,)

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

									# Copy consistency with a really long name
									_a	: int								=				'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'      ,_UpperCAmelCase      ,_UpperCAmelCase						)      ,)

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







							def 				__lowercase     (				self   :			List[Any]						):
									_a	: Optional[Any]								=				check_copies.LOCALIZED_READMES['README_zh-hans.md']

									_a	: List[Any]								=				(
									    '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.'
									)
									_a	: Optional[Any]								=				(
									    '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'
									)
									_a	: List[Any]								=				(
									    '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'
									)

									_a			,   _a	: List[str]								=				check_copies.convert_to_localized_md(
									    _UpperCAmelCase      ,_UpperCAmelCase      ,localized_readme['format_model_list']						)

									self.assertFalse(_UpperCAmelCase						)
									self.assertEqual(_UpperCAmelCase      ,_UpperCAmelCase						)

									_a			,   _a	: List[str]								=				check_copies.convert_to_localized_md(
									    _UpperCAmelCase      ,_UpperCAmelCase      ,localized_readme['format_model_list']						)

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

									_a	: Optional[Any]								=				(
									    '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.'
									)
									_a	: str								=				(
									    '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'
									)
									_a	: Optional[int]								=				(
									    '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'
									)

									_a			,   _a	: Dict								=				check_copies.convert_to_localized_md(
									    _UpperCAmelCase      ,_UpperCAmelCase      ,localized_readme['format_model_list']						)

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









'''simple docstring'''

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tensorflow_text_available, is_torch_available


__lowerCAmelCase		     =  {
    '''configuration_ernie''': ['''ERNIE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ErnieConfig''', '''ErnieOnnxConfig'''],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__lowerCAmelCase		     =  [
							    '''ERNIE_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''ErnieForCausalLM''',
							    '''ErnieForMaskedLM''',
							    '''ErnieForMultipleChoice''',
							    '''ErnieForNextSentencePrediction''',
							    '''ErnieForPreTraining''',
							    '''ErnieForQuestionAnswering''',
							    '''ErnieForSequenceClassification''',
							    '''ErnieForTokenClassification''',
							    '''ErnieModel''',
							    '''ErniePreTrainedModel''',
							]

if TYPE_CHECKING:
							from .configuration_ernie import ERNIE_PRETRAINED_CONFIG_ARCHIVE_MAP, ErnieConfig, ErnieOnnxConfig

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_ernie import (
														    ERNIE_PRETRAINED_MODEL_ARCHIVE_LIST,
														    ErnieForCausalLM,
														    ErnieForMaskedLM,
														    ErnieForMultipleChoice,
														    ErnieForNextSentencePrediction,
														    ErnieForPreTraining,
														    ErnieForQuestionAnswering,
														    ErnieForSequenceClassification,
														    ErnieForTokenClassification,
														    ErnieModel,
														    ErniePreTrainedModel,
														)

else:
							import sys

							__lowerCAmelCase		     =  _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




