"""simple docstring"""





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

import black


__A  =				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  =				'''    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  _snake_case     (					unittest.TestCase		):






      def       lowerCamelCase__     (       self      :			int    ):
             __lowerCamelCase					:		Tuple		   =     tempfile.mkdtemp()
             os.makedirs(os.path.join(self.transformer_dir							,					"models/bert/"    )    )
             __lowerCamelCase					:		Optional[int]		   =     self.transformer_dir
             shutil.copy(
                 os.path.join(_snake_case							,					"src/transformers/models/bert/modeling_bert.py"    )							,					os.path.join(self.transformer_dir							,					"models/bert/modeling_bert.py"    )							,					)






      def       lowerCamelCase__     (       self      :			Union[str, Any]    ):
             __lowerCamelCase					:		Optional[int]		   =     "src/transformers"
             shutil.rmtree(self.transformer_dir    )






      def       lowerCamelCase__     (       self      :			List[str]							,					UpperCAmelCase      :			Tuple							,					UpperCAmelCase      :			int							,					UpperCAmelCase      :			Tuple							,					UpperCAmelCase      :			int=None    ):
             __lowerCamelCase					:		Dict		   =     comment + F"""\nclass {class_name}(nn.Module):\n""" + class_code
             if overwrite_result is not None:
                    __lowerCamelCase					:		Tuple		   =     comment + F"""\nclass {class_name}(nn.Module):\n""" + overwrite_result
             __lowerCamelCase					:		Optional[int]		   =     black.Mode(target_versions={black.TargetVersion.PYaa}							,					line_length=119    )
             __lowerCamelCase					:		List[Any]		   =     black.format_str(_snake_case							,					mode=_snake_case    )
             __lowerCamelCase					:		Optional[Any]		   =     os.path.join(self.transformer_dir							,					"new_code.py"    )
             with open(_snake_case							,					"w"							,					newline="\n"    ) as f:
                    f.write(_snake_case    )
             if overwrite_result is None:
                    self.assertTrue(len(check_copies.is_copy_consistent(_snake_case    )    ) == 0    )
             else:
                    check_copies.is_copy_consistent(f.name							,					overwrite=_snake_case    )
                    with open(_snake_case							,					"r"    ) as f:
                           self.assertTrue(f.read()							,					_snake_case    )






      def       lowerCamelCase__     (       self      :			str    ):
             __lowerCamelCase					:		Tuple		   =     check_copies.find_code_in_transformers("models.bert.modeling_bert.BertLMPredictionHead"    )
             self.assertEqual(_snake_case							,					_snake_case    )






      def       lowerCamelCase__     (       self      :			List[Any]    ):
             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"							,					_snake_case							,					)

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

             # Copy consistency with a really long name
             __lowerCamelCase					:		int		   =     "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"							,					_snake_case							,					_snake_case    )							,					)

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







      def       lowerCamelCase__     (       self      :			List[str]    ):
             __lowerCamelCase					:		Any		   =     check_copies.LOCALIZED_READMES["README_zh-hans.md"]

             __lowerCamelCase					:		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."
             )
             __lowerCamelCase					:		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					:		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。\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					:		Optional[Any]		   =     check_copies.convert_to_localized_md(
                 _snake_case							,					_snake_case							,					localized_readme["format_model_list"]    )

             self.assertFalse(_snake_case    )
             self.assertEqual(_snake_case							,					_snake_case    )

             __lowerCamelCase   ,  __lowerCamelCase					:		Union[str, Any]		   =     check_copies.convert_to_localized_md(
                 _snake_case							,					_snake_case							,					localized_readme["format_model_list"]    )

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

             __lowerCamelCase					:		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."
             )
             __lowerCamelCase					:		Optional[int]		   =     (
                 "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					:		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"
             )

             __lowerCamelCase   ,  __lowerCamelCase					:		List[Any]		   =     check_copies.convert_to_localized_md(
                 _snake_case							,					_snake_case							,					localized_readme["format_model_list"]    )

             # Check if the model link is synchronized.
             self.assertEqual(_snake_case							,					_snake_case    )
"""simple docstring"""





import argparse

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


logging.set_verbosity_info()


def    lowercase_   (   _lowerCamelCase:	Dict						,   _lowerCamelCase:	str						,   _lowerCamelCase:	Dict      ) ->      List[str]:



							'''simple docstring'''

							__lowerCamelCase					:		List[Any]		   =     TaConfig.from_json_file(_lowerCamelCase      )
							print(F"""Building PyTorch model from configuration: {config}"""      )
							__lowerCamelCase					:		Optional[int]		   =     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__":
				__A  =				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.'''
				)
				__A  =				parser.parse_args()
				convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)