



'''simple docstring'''






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

import black


A_							: List[str]						 =	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_							: Optional[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 							lowercase       (  unittest.TestCase				):


 """simple docstring"""


 def _snake_case (							self							)		->   List[Any]:
        _UpperCAmelCase  :						List[str]			     = tempfile.mkdtemp()
        os.makedirs(os.path.join(self.transformer_dir  ,"""models/bert/"""							)							)
        _UpperCAmelCase  :						Optional[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 _snake_case (							self							)		->   Optional[Any]:
        _UpperCAmelCase  :						Any			     = """src/transformers"""
        shutil.rmtree(self.transformer_dir							)







 def _snake_case (							self  ,a_  ,a_  ,a_  ,a_=None							)		->   List[Any]:
        _UpperCAmelCase  :						List[str]			     = comment + f'''\nclass {class_name}(nn.Module):\n''' + class_code
        if overwrite_result is not None:
               _UpperCAmelCase  :						Union[str, Any]			     = comment + f'''\nclass {class_name}(nn.Module):\n''' + overwrite_result
        _UpperCAmelCase  :						List[str]			     = black.Mode(target_versions={black.TargetVersion.PYaa}  ,line_length=119							)
        _UpperCAmelCase  :						List[str]			     = black.format_str(lowerCAmelCase_  ,mode=lowerCAmelCase_							)
        _UpperCAmelCase  :						int			     = 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 _snake_case (							self							)		->   Dict:
        _UpperCAmelCase  :						Any			     = check_copies.find_code_in_transformers("""models.bert.modeling_bert.BertLMPredictionHead"""							)
        self.assertEqual(lowerCAmelCase_  ,lowerCAmelCase_							)







 def _snake_case (							self							)		->   Dict:
        # 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"""  ,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  :						List[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 _snake_case (							self							)		->   Dict:
        _UpperCAmelCase  :						Tuple			     = check_copies.LOCALIZED_READMES["""README_zh-hans.md"""]

        _UpperCAmelCase  :						Tuple			     = (
            """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  :						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"""
        )
        _UpperCAmelCase  :						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"""
        )

        _UpperCAmelCase     ,_UpperCAmelCase  :						Any			     = check_copies.convert_to_localized_md(
            lowerCAmelCase_  ,lowerCAmelCase_  ,localized_readme["""format_model_list"""]							)

        self.assertFalse(lowerCAmelCase_							)
        self.assertEqual(lowerCAmelCase_  ,lowerCAmelCase_							)

        _UpperCAmelCase     ,_UpperCAmelCase  :						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_							)

        _UpperCAmelCase  :						List[str]			     = (
            """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  :						List[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"""
        )
        _UpperCAmelCase  :						Union[str, 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"""
        )

        _UpperCAmelCase     ,_UpperCAmelCase  :						Optional[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 collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


_SCREAMING_SNAKE_CASE										=						logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE										=						{
    'microsoft/resnet-50': 'https://huggingface.co/microsoft/resnet-50/blob/main/config.json',
}



class 				a							(			__lowerCAmelCase       ,			__lowerCAmelCase       ):







 """simple docstring"""






 lowerCamelCase	:Any    			=		'''resnet'''
 lowerCamelCase	:Any    			=		['''basic''', '''bottleneck''']



 def __init__(					self		,				lowerCAmelCase_=3		,				lowerCAmelCase_=64		,				lowerCAmelCase_=[2_56, 5_12, 10_24, 20_48]		,				lowerCAmelCase_=[3, 4, 6, 3]		,				lowerCAmelCase_="bottleneck"		,				lowerCAmelCase_="relu"		,				lowerCAmelCase_=False		,				lowerCAmelCase_=None		,				lowerCAmelCase_=None		,				**lowerCAmelCase_		,				)  ->					Union[str, Any]:
        super().__init__(**lowerCAmelCase_							)
        if layer_type not in self.layer_types:
               raise ValueError(F'''layer_type={layer_type} is not one of {','.join(self.layer_types							)}'''							)
        _A        =  num_channels
        _A        =  embedding_size
        _A        =  hidden_sizes
        _A        =  depths
        _A        =  layer_type
        _A        =  hidden_act
        _A        =  downsample_in_first_stage
        _A        =  ["""stem"""] + [F'''stage{idx}''' for idx in range(1		,				len(lowerCAmelCase_							) + 1							)]
        _A   ,  _A        =  get_aligned_output_features_output_indices(
            out_features=lowerCAmelCase_		,				out_indices=lowerCAmelCase_		,				stage_names=self.stage_names							)



class 				a							(			__lowerCAmelCase       ):







 """simple docstring"""






 lowerCamelCase	:Optional[Any]    			=		version.parse('''1.11'''       )



 @property
 def      UpperCAmelCase							(					self							)  ->					Mapping[str, Mapping[int, str]]:
        return OrderedDict(
            [
                ("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
            ]							)



 @property
 def      UpperCAmelCase							(					self							)  ->					float:
        return 1E-3




