

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_       : str         =			'    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     _a						(unittest.TestCase				):


					'''simple docstring'''






					def   __A			( self			):
						A__							:    int        =		tempfile.mkdtemp()
						os.makedirs(os.path.join(self.transformer_dir    ,       """models/bert/"""			)			)
						A__							:    Dict        =		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   __A			( self			):
						A__							:    Any        =		"""src/transformers"""
						shutil.rmtree(self.transformer_dir			)






					def   __A			( self    ,       A__    ,       A__    ,       A__    ,       A__=None			):
						A__							:    Optional[int]        =		comment + F"""\nclass {class_name}(nn.Module):\n""" + class_code
						if overwrite_result is not None:
							A__							:    List[str]        =		comment + F"""\nclass {class_name}(nn.Module):\n""" + overwrite_result
						A__							:    Optional[int]        =		black.Mode(target_versions={black.TargetVersion.PYaa}    ,       line_length=119			)
						A__							:    Union[str, Any]        =		black.format_str(A__    ,       mode=A__			)
						A__							:    Union[str, Any]        =		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   __A			( self			):
						A__							:    str        =		check_copies.find_code_in_transformers("""models.bert.modeling_bert.BertLMPredictionHead"""			)
						self.assertEqual(A__    ,       A__			)






					def   __A			( self			):
						# 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"""    ,       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__							:    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"""    ,       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   __A			( self			):
						A__							:    Union[str, Any]        =		check_copies.LOCALIZED_READMES["""README_zh-hans.md"""]

						A__							:    Dict        =		(
						    """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__							:    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"""
						)
						A__							:    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。\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__							:    List[Any]        =		check_copies.convert_to_localized_md(
						    A__    ,       A__    ,       localized_readme["""format_model_list"""]			)

						self.assertFalse(A__			)
						self.assertEqual(A__    ,       A__			)

						A__   ,   A__							:    List[Any]        =		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__							:    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."""
						)
						A__							:    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"""
						)
						A__							:    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"""
						)

						A__   ,   A__							:    Optional[int]        =		check_copies.convert_to_localized_md(
						    A__    ,       A__    ,       localized_readme["""format_model_list"""]			)

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






import argparse

import torch

from transformers import GPTaConfig, GPTaModel, load_tf_weights_in_gpta
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()

def 						UpperCamelCase					(lowercase_: int    ,      lowercase_: Dict    ,      lowercase_: Tuple					)     ->      Any:
	# Construct model
	if gpta_config_file == "":
		A__							:    Dict        =		GPTaConfig()
	else:
		A__							:    List[Any]        =		GPTaConfig.from_json_file(lowercase_					)
	A__							:    Tuple        =		GPTaModel(lowercase_					)

	# Load weights from numpy
	load_tf_weights_in_gpta(lowercase_    ,      lowercase_    ,      lowercase_					)

	# Save pytorch-model
	A__							:    Optional[Any]        =		pytorch_dump_folder_path + """/""" + WEIGHTS_NAME
	A__							:    Optional[Any]        =		pytorch_dump_folder_path + """/""" + CONFIG_NAME
	print(f"""Save PyTorch model to {pytorch_weights_dump_path}"""					)
	torch.save(model.state_dict()    ,      lowercase_					)
	print(f"""Save configuration file to {pytorch_config_dump_path}"""					)
	with open(lowercase_    ,      """w"""    ,      encoding="""utf-8"""					) as f:
		f.write(config.to_json_string()					)


if __name__ == "__main__":
	A_       : Union[str, Any]         =			argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    '--gpt2_checkpoint_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
	)
	parser.add_argument(
	    '--pytorch_dump_folder_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
	)
	parser.add_argument(
	    '--gpt2_config_file',
	    default='',
	    type=str,
	    help=(
	        'An optional config json file corresponding to the pre-trained OpenAI model. \n'
	        'This specifies the model architecture.'
	    ),
	)
	A_       : str         =			parser.parse_args()
	convert_gpta_checkpoint_to_pytorch(args.gpta_checkpoint_path, args.gpta_config_file, args.pytorch_dump_folder_path)



