
import os
import unittest

from transformers.models.bartpho.tokenization_bartpho import VOCAB_FILES_NAMES, BartphoTokenizer
from transformers.testing_utils import get_tests_dir

from ...test_tokenization_common import TokenizerTesterMixin


lowerCamelCase_  :	List[str] 				=					get_tests_dir("""fixtures/test_sentencepiece_bpe.model""")

class     _UpperCamelCase			(   _A       ,    unittest.TestCase					):






			'''simple docstring'''

			__UpperCamelCase :  List[str]    =	BartphoTokenizer
			__UpperCamelCase :  Any    =	False
			__UpperCamelCase :  Any    =	True

			def  lowerCAmelCase__		(    self	:    Any ):
							super().setUp()

							UpperCamelCase_:							Union[str, Any]		=  ["""▁This""", """▁is""", """▁a""", """▁t""", """est"""]
							UpperCamelCase_:							Union[str, Any]		=  dict(zip(snake_case_  ,		range(len(snake_case_ ) ) ) )
							UpperCamelCase_:							Union[str, Any]		=  {"""unk_token""": """<unk>"""}

							UpperCamelCase_:							Union[str, Any]		=  os.path.join(self.tmpdirname  ,		VOCAB_FILES_NAMES["""monolingual_vocab_file"""] )
							with open(self.monolingual_vocab_file  ,		"""w"""  ,		encoding="""utf-8""" ) as fp:
											for token in vocab_tokens:
															fp.write(f'''{token} {vocab_tokens[token]}\n''' )

							UpperCamelCase_:							str		=  BartphoTokenizer(snake_case_  ,		self.monolingual_vocab_file  ,		**self.special_tokens_map )
							tokenizer.save_pretrained(self.tmpdirname )

			def  lowerCAmelCase__		(    self	:    Dict  ,		**snake_case_	:    Optional[Any] ):
							kwargs.update(self.special_tokens_map )
							return BartphoTokenizer.from_pretrained(self.tmpdirname  ,		**snake_case_ )

			def  lowerCAmelCase__		(    self	:    Dict  ,		snake_case_	:    str ):
							UpperCamelCase_:							str		=  """This is a là test"""
							UpperCamelCase_:							str		=  """This is a<unk><unk> test"""
							return input_text, output_text




			def  lowerCAmelCase__		(    self	:    Tuple ):
							UpperCamelCase_:							Optional[int]		=  BartphoTokenizer(snake_case_  ,		self.monolingual_vocab_file  ,		**self.special_tokens_map )
							UpperCamelCase_:							Optional[Any]		=  """This is a là test"""
							UpperCamelCase_:							Optional[int]		=  """▁This ▁is ▁a ▁l à ▁t est""".split()
							UpperCamelCase_:							Dict		=  tokenizer.tokenize(snake_case_ )
							self.assertListEqual(snake_case_  ,		snake_case_ )

							UpperCamelCase_:							Dict		=  tokens + [tokenizer.unk_token]
							UpperCamelCase_:							str		=  [4, 5, 6, 3, 3, 7, 8, 3]
							self.assertListEqual(tokenizer.convert_tokens_to_ids(snake_case_ )  ,		snake_case_ )








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)






