





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     A__   (  lowerCamelCase       ,      lowerCamelCase       ,      lowerCamelCase  )					->		Union[str, Any]:
				# Construct model
				if gpta_config_file == "":
								UpperCamelCase_:							Union[str, Any]		=  GPTaConfig()
				else:
								UpperCamelCase_:							Optional[Any]		=  GPTaConfig.from_json_file(lowerCamelCase  )
				UpperCamelCase_:							List[Any]		=  GPTaModel(lowerCamelCase  )

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

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


if __name__ == "__main__":
							lowerCamelCase_  :	int 				=					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."""
							    ),
							)
							lowerCamelCase_  :	List[str] 				=					parser.parse_args()
							convert_gpta_checkpoint_to_pytorch(args.gpta_checkpoint_path, args.gpta_config_file, args.pytorch_dump_folder_path)








import unittest

from transformers import RoFormerTokenizer, RoFormerTokenizerFast
from transformers.testing_utils import require_rjieba, require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin

@require_rjieba
@require_tokenizers
class     _UpperCamelCase			(   _A       ,    unittest.TestCase					):






			'''simple docstring'''

			__UpperCamelCase :  Union[str, Any]    =	RoFormerTokenizer
			__UpperCamelCase :  Union[str, Any]    =	RoFormerTokenizerFast
			__UpperCamelCase :  Optional[Any]    =	True
			__UpperCamelCase :  Tuple    =	True

			def  lowerCAmelCase__		(    self	:    Optional[Any] ):
							super().setUp()

			def  lowerCAmelCase__		(    self	:    List[str]  ,		**snake_case_	:    Union[str, Any] ):
							return self.tokenizer_class.from_pretrained("""junnyu/roformer_chinese_base"""  ,		**snake_case_ )

			def  lowerCAmelCase__		(    self	:    Dict  ,		**snake_case_	:    Union[str, Any] ):
							return self.rust_tokenizer_class.from_pretrained("""junnyu/roformer_chinese_base"""  ,		**snake_case_ )

			def  lowerCAmelCase__		(    self	:    Any ):
							UpperCamelCase_:							int		=  """永和服装饰品有限公司,今天天气非常好"""
							UpperCamelCase_:							int		=  """永和 服装 饰品 有限公司 , 今 天 天 气 非常 好"""
							return input_text, output_text

			def  lowerCAmelCase__		(    self	:    Dict ):
							UpperCamelCase_:							Dict		=  self.get_tokenizer()
							UpperCamelCase_,				UpperCamelCase_:							Optional[int]		=  self.get_chinese_input_output_texts()
							UpperCamelCase_:							Optional[int]		=  tokenizer.tokenize(snake_case_ )

							self.assertListEqual(snake_case_  ,		output_text.split() )

							UpperCamelCase_:							Dict		=  tokens + [tokenizer.unk_token]
							UpperCamelCase_:							int		=  [2_2943, 2_1332, 3_4431, 4_5904, 117, 306, 1231, 1231, 2653, 3_3994, 1266, 100]
							self.assertListEqual(tokenizer.convert_tokens_to_ids(snake_case_ )  ,		snake_case_ )

			def  lowerCAmelCase__		(    self	:    Dict ):
							UpperCamelCase_:							Optional[int]		=  self.get_rust_tokenizer()
							UpperCamelCase_,				UpperCamelCase_:							Any		=  self.get_chinese_input_output_texts()
							UpperCamelCase_:							int		=  tokenizer.tokenize(snake_case_ )
							self.assertListEqual(snake_case_  ,		output_text.split() )
							UpperCamelCase_:							Union[str, Any]		=  tokens + [tokenizer.unk_token]
							UpperCamelCase_:							Optional[Any]		=  [2_2943, 2_1332, 3_4431, 4_5904, 117, 306, 1231, 1231, 2653, 3_3994, 1266, 100]
							self.assertListEqual(tokenizer.convert_tokens_to_ids(snake_case_ )  ,		snake_case_ )

			def  lowerCAmelCase__		(    self	:    Dict ):
							pass

			def  lowerCAmelCase__		(    self	:    Tuple ):
							pass




			def  lowerCAmelCase__		(    self	:    Union[str, Any] ):
							pass






