


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

import black


SCREAMING_SNAKE_CASE			: List[Any]				   =					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.
SCREAMING_SNAKE_CASE			: Optional[Any]				   =					"    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  ):
       def      UpperCamelCase	(   self)			->  str:




           """simple docstring"""

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




           """simple docstring"""

           _lowercase			:				Optional[Any] 						=						'src/transformers'
           shutil.rmtree(self.transformer_dir)
       def      UpperCamelCase	(   self, lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase=None)			->  str:




           """simple docstring"""

           _lowercase			:				Optional[Any] 						=						comment + F'''\nclass {class_name}(nn.Module):\n''' + class_code
           if overwrite_result is not None:
               _lowercase			:				Optional[int] 						=						comment + F'''\nclass {class_name}(nn.Module):\n''' + overwrite_result
           _lowercase			:				Dict 						=						black.Mode(target_versions={black.TargetVersion.PYaa}, line_length=1_19)
           _lowercase			:				Union[str, Any] 						=						black.format_str(lowerCamelCase, mode=lowerCamelCase)
           _lowercase			:				Optional[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      UpperCamelCase	(   self)			->  Dict:




           """simple docstring"""

           _lowercase			:				Tuple 						=						check_copies.find_code_in_transformers('models.bert.modeling_bert.BertLMPredictionHead')
           self.assertEqual(lowerCamelCase, lowerCamelCase)
       def      UpperCamelCase	(   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
           _lowercase			:				Tuple 						=						'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      UpperCamelCase	(   self)			->  Any:




           """simple docstring"""

           _lowercase			:				Tuple 						=						check_copies.LOCALIZED_READMES['README_zh-hans.md']

           _lowercase			:				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.'
           )
           _lowercase			:				Tuple 						=						(
               '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'
           )
           _lowercase			:				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'
           )

           _lowercase						,							_lowercase			:				List[Any] 						=						check_copies.convert_to_localized_md(
               lowerCamelCase, lowerCamelCase, localized_readme['format_model_list'])

           self.assertFalse(lowerCamelCase)
           self.assertEqual(lowerCamelCase, lowerCamelCase)

           _lowercase						,							_lowercase			:				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)

           _lowercase			:				Union[str, 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.'
           )
           _lowercase			:				Tuple 						=						(
               '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'
           )
           _lowercase			:				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'
           )

           _lowercase						,							_lowercase			:				Dict 						=						check_copies.convert_to_localized_md(
               lowerCamelCase, lowerCamelCase, localized_readme['format_model_list'])

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






import argparse

from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
from transformers.utils import logging


logging.set_verbosity_info()



def 							UpperCamelCase_( lowerCamelCase_				,					lowerCamelCase_				,					lowerCamelCase_					)       ->		Optional[int]:
    # Initialise PyTorch model
    _lowercase			:				Optional[int] 						=						TaConfig.from_json_file(lowerCamelCase_					)
    print(F'''Building PyTorch model from configuration: {config}'''					)
    _lowercase			:				Union[str, Any] 						=						TaForConditionalGeneration(lowerCamelCase_					)

    # Load weights from tf checkpoint
    load_tf_weights_in_ta(lowerCamelCase_				,					lowerCamelCase_				,					lowerCamelCase_					)

    # Save pytorch-model
    print(F'''Save PyTorch model to {pytorch_dump_path}'''					)
    model.save_pretrained(lowerCamelCase_					)


if __name__ == "__main__":
 SCREAMING_SNAKE_CASE			: Optional[int]				   =					argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     "--tf_checkpoint_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
 )
 parser.add_argument(
     "--config_file",
     default=None,
     type=str,
     required=True,
     help=(
         "The config json file corresponding to the pre-trained T5 model. \nThis specifies the model architecture."
     ),
 )
 parser.add_argument(
     "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
 )
 SCREAMING_SNAKE_CASE			: Any				   =					parser.parse_args()
 convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)


