


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

import black


A_     :       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_     :       Dict   							="""    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 							lowercase_					(			unittest.TestCase):






							"""simple docstring"""







							def     lowercase__  ( self		):







													"""simple docstring"""


													a_		      =		tempfile.mkdtemp()
													os.makedirs(os.path.join(self.transformer_dir      ,     """models/bert/"""		)		)
													a_		      =		self.transformer_dir
													shutil.copy(
													    os.path.join(__a      ,     """src/transformers/models/bert/modeling_bert.py"""		)      ,     os.path.join(self.transformer_dir      ,     """models/bert/modeling_bert.py"""		)      ,     )


							def     lowercase__  ( self		):







													"""simple docstring"""


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


							def     lowercase__  ( self      ,     _UpperCAmelCase      ,     _UpperCAmelCase      ,     _UpperCAmelCase      ,     _UpperCAmelCase=None		):







													"""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=119		)
													a_		      =		black.format_str(__a      ,     mode=__a		)
													a_		      =		os.path.join(self.transformer_dir      ,     """new_code.py"""		)
													with open(__a      ,     """w"""      ,     newline="""\n"""		) as f:
																			f.write(__a		)
													if overwrite_result is None:
																			self.assertTrue(len(check_copies.is_copy_consistent(__a		)		) == 0		)
													else:
																			check_copies.is_copy_consistent(f.name      ,     overwrite=__a		)
																			with open(__a      ,     """r"""		) as f:
																									self.assertTrue(f.read()      ,     __a		)


							def     lowercase__  ( self		):







													"""simple docstring"""


													a_		      =		check_copies.find_code_in_transformers("""models.bert.modeling_bert.BertLMPredictionHead"""		)
													self.assertEqual(__a      ,     __a		)


							def     lowercase__  ( self		):







													"""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"""      ,     __a      ,     )

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

													# 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"""      ,     __a      ,     __a		)      ,     )

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






							def     lowercase__  ( self		):







													"""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_		      =		check_copies.convert_to_localized_md(
													    __a      ,     __a      ,     localized_readme["""format_model_list"""]		)

													self.assertFalse(__a		)
													self.assertEqual(__a      ,     __a		)

													a_		      =		check_copies.convert_to_localized_md(
													    __a      ,     __a      ,     localized_readme["""format_model_list"""]		)

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

													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_		      =		check_copies.convert_to_localized_md(
													    __a      ,     __a      ,     localized_readme["""format_model_list"""]		)

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





'''simple docstring'''







from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowercase      :   Optional[int]      =       logging.get_logger(__name__)

lowercase      :   Optional[Any]      =       {
    """google/pegasus-large""": """https://huggingface.co/google/pegasus-large/resolve/main/config.json""",
    # See all PEGASUS models at https://huggingface.co/models?filter=pegasus
}







class 					_a (a__	):





     '''simple docstring'''



     lowerCAmelCase_  :     Any          =					"""pegasus"""
     lowerCAmelCase_  :     Dict          =					["""past_key_values"""]
     lowerCAmelCase_  :     str          =					{"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}







     def __init__(				self							,__a=50_265							,__a=1_024							,__a=12							,__a=4_096							,__a=16							,__a=12							,__a=4_096							,__a=16							,__a=0.0							,__a=0.0							,__a=True							,__a=True							,__a="gelu"							,__a=1_024							,__a=0.1							,__a=0.0							,__a=0.0							,__a=0.02							,__a=0							,__a=False							,__a=0							,__a=1							,__a=1							,**__a							,)      ->				int:
          snake_case			:      List[Any]        =			vocab_size
          snake_case			:      Optional[Any]        =			max_position_embeddings
          snake_case			:      List[Any]        =			d_model
          snake_case			:      str        =			encoder_ffn_dim
          snake_case			:      List[Any]        =			encoder_layers
          snake_case			:      Optional[Any]        =			encoder_attention_heads
          snake_case			:      Union[str, Any]        =			decoder_ffn_dim
          snake_case			:      Tuple        =			decoder_layers
          snake_case			:      Union[str, Any]        =			decoder_attention_heads
          snake_case			:      Union[str, Any]        =			dropout
          snake_case			:      int        =			attention_dropout
          snake_case			:      int        =			activation_dropout
          snake_case			:      Optional[Any]        =			activation_function
          snake_case			:      Tuple        =			init_std
          snake_case			:      Union[str, Any]        =			encoder_layerdrop
          snake_case			:      int        =			decoder_layerdrop
          snake_case			:      Dict        =			use_cache
          snake_case			:      Dict        =			encoder_layers
          snake_case			:      List[Any]        =			scale_embedding  # scale factor will be sqrt(d_model) if True
          super().__init__(
              pad_token_id=__a							,eos_token_id=__a							,is_encoder_decoder=__a							,decoder_start_token_id=__a							,forced_eos_token_id=__a							,**__a							,)







     @property
     def 			snake_case_      (				self							)      ->				int:
          return self.encoder_attention_heads







     @property
     def 			snake_case_      (				self							)      ->				int:
          return self.d_model



