



from typing import Dict, List, Optional, Tuple

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


UpperCAmelCase							=       logging.get_logger(__name__)

class 			__snake_case   ( SCREAMING_SNAKE_CASE):







							'''simple docstring'''






							UpperCamelCase__					:   Tuple       =       ["""input_ids""", """attention_mask"""]


							def __init__(				self	,	a_="[PAD]"	,	a_="[BOS]"	,	a_="[EOS]"	,	a_="[MASK]"	,	a_="[CLS]"	,	a_="[SEP]"	,	a_=2_048	,	**a_	,	):
											a__					   =		AddedToken(a_	,	lstrip=a_	,	rstrip=a_		) if isinstance(a_	,	a_		) else pad_token
											a__					   =		AddedToken(a_	,	lstrip=a_	,	rstrip=a_		) if isinstance(a_	,	a_		) else bos_token
											a__					   =		AddedToken(a_	,	lstrip=a_	,	rstrip=a_		) if isinstance(a_	,	a_		) else eos_token
											a__					   =		AddedToken(a_	,	lstrip=a_	,	rstrip=a_		) if isinstance(a_	,	a_		) else mask_token
											a__					   =		AddedToken(a_	,	lstrip=a_	,	rstrip=a_		) if isinstance(a_	,	a_		) else cls_token
											a__					   =		AddedToken(a_	,	lstrip=a_	,	rstrip=a_		) if isinstance(a_	,	a_		) else sep_token

											super().__init__(
											    pad_token=a_	,	bos_token=a_	,	eos_token=a_	,	mask_token=a_	,	cls_token=a_	,	sep_token=a_	,	model_max_length=a_	,	**a_	,	)

											a__					   =		2**8  # utf is 8 bits

											# define special tokens dict
											a__					   =		{
											    self.pad_token: 0,
											    self.bos_token: 1,
											    self.eos_token: 2,
											    self.mask_token: 3,
											    self.cls_token: 4,
											    self.sep_token: 5,
											}
											a__					   =		len(self.special_tokens_encoder		)
											a__					   =		{v: k for k, v in self.special_tokens_encoder.items()}


							def _a   (				self		):
											a__					   =		self.special_tokens_encoder.copy()
											vocab.update(self.added_tokens_encoder		)
											for i in range(self._utf_vocab_size		):
															a__					   =		chr(a_		)
															a__					   =		i + len(self.special_tokens_encoder		)
											return vocab


							@property
							def _a   (				self		):
											return self._utf_vocab_size + self._num_special_tokens


							def _a   (				self	,	a_	,	a_ = None	,	a_ = False		):
											if already_has_special_tokens:
															return super().get_special_tokens_mask(
															    token_ids_a=a_	,	token_ids_a=a_	,	already_has_special_tokens=a_		)

											# normal case: some special tokens
											if token_ids_a is None:
															return [1] + [0] * len(a_		) + [1]
											return [1] + ([0] * len(a_		)) + [1] + ([0] * len(a_		)) + [1]


							def _a   (				self	,	a_	,	a_ = None		):
											if token_ids_a is None:
															return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
											else:
															return [self.cls_token_id] + token_ids_a + [self.sep_token_id] + token_ids_a + [self.sep_token_id]


							def _a   (				self	,	a_		):
											a__					   =		[chr(a_		) for i in text.encode("""utf-8"""		)]
											return tokens


							def _a   (				self	,	a_		):
											if token in self.special_tokens_encoder:
															a__					   =		self.special_tokens_encoder[token]
											elif token in self.added_tokens_encoder:
															a__					   =		self.added_tokens_encoder[token]
											elif len(a_		) != 1:
															a__					   =		self.unk_token_id
											else:
															a__					   =		ord(a_		) + self._num_special_tokens
											return token_id


							def _a   (				self	,	a_		):
											if index in self.special_tokens_decoder:
															a__					   =		self.special_tokens_decoder[index]
											elif index in self.added_tokens_decoder:
															a__					   =		self.added_tokens_decoder[index]
											else:
															a__					   =		chr(index - self._num_special_tokens		)
											return token


							def _a   (				self	,	a_		):
											a__					   =		B""""""
											for token in tokens:
															if token in self.special_tokens_decoder:
																			a__					   =		self.special_tokens_decoder[token].encode("""utf-8"""		)
															elif token in self.added_tokens_decoder:
																			a__					   =		self.special_tokens_decoder[token].encode("""utf-8"""		)
															elif token in self.special_tokens_encoder:
																			a__					   =		token.encode("""utf-8"""		)
															elif token in self.added_tokens_encoder:
																			a__					   =		token.encode("""utf-8"""		)
															else:
																			a__					   =		bytes([ord(a_		)]		)
															bstring += tok_string
											a__					   =		bstring.decode("""utf-8"""	,	errors="""replace"""		)
											return string







							def _a   (				self	,	a_	,	a_ = None		):
											return ()



'''simple docstring'''





import argparse

import torch

from transformers import RoFormerConfig, RoFormerForMaskedLM, load_tf_weights_in_roformer
from transformers.utils import logging


logging.set_verbosity_info()





def 							snake_case				(		UpperCAmelCase   ,	UpperCAmelCase   ,	UpperCAmelCase						)->      Union[str, Any]:







      """simple docstring"""
      # Initialise PyTorch model
      __A													=      RoFormerConfig.from_json_file(UpperCAmelCase						)
      print(f'Building PyTorch model from configuration: {config}'						)
      __A													=      RoFormerForMaskedLM(UpperCAmelCase						)

      # Load weights from tf checkpoint
      load_tf_weights_in_roformer(UpperCAmelCase   ,	UpperCAmelCase   ,	UpperCAmelCase						)

      # Save pytorch-model
      print(f'Save PyTorch model to {pytorch_dump_path}'						)
      torch.save(model.state_dict()   ,	UpperCAmelCase   ,	_use_new_zipfile_serialization=UpperCAmelCase						)


if __name__ == "__main__":
 a__		: Tuple  							=							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(
     "--bert_config_file",
     default=None,
     type=str,
     required=True,
     help=(
         "The config json file corresponding to the pre-trained BERT model. \n"
         "This specifies the model architecture."
     ),
 )
 parser.add_argument(
     "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
 )
 a__		: Union[str, Any]  							=							parser.parse_args()
 convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)






