





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


A   : int			=			{
    '''configuration_swiftformer''': [
        '''SWIFTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''SwiftFormerConfig''',
        '''SwiftFormerOnnxConfig''',
    ]
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					A   : Tuple			=			[
					    '''SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''SwiftFormerForImageClassification''',
					    '''SwiftFormerModel''',
					    '''SwiftFormerPreTrainedModel''',
					]

if TYPE_CHECKING:
					from .configuration_swiftformer import (
					    SWIFTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
					    SwiftFormerConfig,
					    SwiftFormerOnnxConfig,
					)

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_swiftformer import (
										    SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
										    SwiftFormerForImageClassification,
										    SwiftFormerModel,
										    SwiftFormerPreTrainedModel,
										)

else:
					import sys

					A   : List[Any]			=			_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)










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

import black


A   : 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.
A   : Optional[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   A      (unittest.TestCase   ):
			'''simple docstring'''




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







					"""simple docstring"""






					A__  				=       tempfile.mkdtemp()
					os.makedirs(os.path.join(self.transformer_dir							,     """models/bert/"""       )       )
					A__  				=       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       :   str       )     ->     Optional[int]:







					"""simple docstring"""






					A__  				=       """src/transformers"""
					shutil.rmtree(self.transformer_dir       )






			def        a_	(      self       :   List[str]							,     __lowerCAmelCase       :   List[Any]							,     __lowerCAmelCase       :   Optional[int]							,     __lowerCAmelCase       :   int							,     __lowerCAmelCase       :   Tuple=None       )     ->     Dict:







					"""simple docstring"""






					A__  				=       comment + f'\nclass {class_name}(nn.Module):\n' + class_code
					if overwrite_result is not None:
							A__  				=       comment + f'\nclass {class_name}(nn.Module):\n' + overwrite_result
					A__  				=       black.Mode(target_versions={black.TargetVersion.PYaa}							,     line_length=1_19       )
					A__  				=       black.format_str(__lowerCAmelCase							,     mode=__lowerCAmelCase       )
					A__  				=       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       )     ->     List[Any]:







					"""simple docstring"""






					A__  				=       check_copies.find_code_in_transformers("""models.bert.modeling_bert.BertLMPredictionHead"""       )
					self.assertEqual(__lowerCAmelCase							,     __lowerCAmelCase       )






			def        a_	(      self       :   Tuple       )     ->     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
					A__  				=       """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       :   Optional[int]       )     ->     Optional[int]:







					"""simple docstring"""






					A__  				=       check_copies.LOCALIZED_READMES["""README_zh-hans.md"""]

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

					self.assertFalse(__lowerCAmelCase       )
					self.assertEqual(__lowerCAmelCase							,     __lowerCAmelCase       )

					A__				,      A__  				=       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       )

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

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



