



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

import black


__snake_case    :		Dict  =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.
__snake_case    :		int  ='    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 	lowerCamelCase__      (							unittest.TestCase):




		'''simple docstring'''



		def 		lowerCAmelCase__				(self			)					->      List[str]:

						"""simple docstring"""






						lowerCAmelCase__					:    List[Any]												=					tempfile.mkdtemp()
						os.makedirs(os.path.join(self.transformer_dir		,'''models/bert/'''			)			)
						lowerCAmelCase__					:    List[Any]												=					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 		lowerCAmelCase__				(self			)					->      Optional[Any]:

						"""simple docstring"""






						lowerCAmelCase__					:    List[str]												=					'''src/transformers'''
						shutil.rmtree(self.transformer_dir			)



		def 		lowerCAmelCase__				(self		,__lowerCamelCase		,__lowerCamelCase		,__lowerCamelCase		,__lowerCamelCase=None			)					->      Tuple:

						"""simple docstring"""






						lowerCAmelCase__					:    Tuple												=					comment + f"""\nclass {class_name}(nn.Module):\n""" + class_code
						if overwrite_result is not None:
										lowerCAmelCase__					:    str												=					comment + f"""\nclass {class_name}(nn.Module):\n""" + overwrite_result
						lowerCAmelCase__					:    Dict												=					black.Mode(target_versions={black.TargetVersion.PYaa}		,line_length=1_19			)
						lowerCAmelCase__					:    Tuple												=					black.format_str(__lowerCamelCase		,mode=__lowerCamelCase			)
						lowerCAmelCase__					:    Any												=					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 		lowerCAmelCase__				(self			)					->      str:

						"""simple docstring"""






						lowerCAmelCase__					:    Any												=					check_copies.find_code_in_transformers('''models.bert.modeling_bert.BertLMPredictionHead'''			)
						self.assertEqual(__lowerCamelCase		,__lowerCamelCase			)



		def 		lowerCAmelCase__				(self			)					->      Optional[Any]:

						"""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
						lowerCAmelCase__					:    Any												=					'''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 		lowerCAmelCase__				(self			)					->      int:

						"""simple docstring"""






						lowerCAmelCase__					:    Union[str, Any]												=					check_copies.LOCALIZED_READMES['''README_zh-hans.md''']

						lowerCAmelCase__					:    Dict												=					(
						    '''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__					:    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'''
						)
						lowerCAmelCase__					:    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。\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__					:    Dict												=					check_copies.convert_to_localized_md(
						    __lowerCamelCase		,__lowerCamelCase		,localized_readme['''format_model_list''']			)

						self.assertFalse(__lowerCamelCase			)
						self.assertEqual(__lowerCamelCase		,__lowerCamelCase			)

						lowerCAmelCase__						,		lowerCAmelCase__					:    List[str]												=					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			)

						lowerCAmelCase__					:    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.'''
						)
						lowerCAmelCase__					:    Any												=					(
						    '''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__					:    List[str]												=					(
						    '''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__					:    Any												=					check_copies.convert_to_localized_md(
						    __lowerCamelCase		,__lowerCamelCase		,localized_readme['''format_model_list''']			)

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






from typing import TYPE_CHECKING

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


__snake_case    :		str  ={
    'configuration_conditional_detr': [
        'CONDITIONAL_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'ConditionalDetrConfig',
        'ConditionalDetrOnnxConfig',
    ]
}

try:
							if not is_vision_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__snake_case    :		Tuple  =['ConditionalDetrFeatureExtractor']
							__snake_case    :		Union[str, Any]  =['ConditionalDetrImageProcessor']

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__snake_case    :		List[str]  =[
							    'CONDITIONAL_DETR_PRETRAINED_MODEL_ARCHIVE_LIST',
							    'ConditionalDetrForObjectDetection',
							    'ConditionalDetrForSegmentation',
							    'ConditionalDetrModel',
							    'ConditionalDetrPreTrainedModel',
							]


if TYPE_CHECKING:
							from .configuration_conditional_detr import (
							    CONDITIONAL_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP,
							    ConditionalDetrConfig,
							    ConditionalDetrOnnxConfig,
							)

							try:
														if not is_vision_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .feature_extraction_conditional_detr import ConditionalDetrFeatureExtractor
														from .image_processing_conditional_detr import ConditionalDetrImageProcessor

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_conditional_detr import (
														    CONDITIONAL_DETR_PRETRAINED_MODEL_ARCHIVE_LIST,
														    ConditionalDetrForObjectDetection,
														    ConditionalDetrForSegmentation,
														    ConditionalDetrModel,
														    ConditionalDetrPreTrainedModel,
														)

else:
							import sys

							__snake_case    :		str  =_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)

