from typing import Dict, List, Optional, Tuple

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


__lowerCamelCase :     Any          =  logging.get_logger(__name__)






class 		a__						( A__ ):
					A     	=							['input_ids', 'attention_mask']

					def __init__(   self				:	Optional[int],_A				:	Optional[Any]="[PAD]",_A				:	Any="[BOS]",_A				:	Optional[Any]="[EOS]",_A				:	str="[MASK]",_A				:	List[Any]="[CLS]",_A				:	str="[SEP]",_A				:	int=2048,**_A				:	Tuple,):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		str    				=   AddedToken(_A,lstrip=_A,rstrip=_A   ) if isinstance(_A,_A   ) else pad_token
							SCREAMING_SNAKE_CASE_  :		Dict    				=   AddedToken(_A,lstrip=_A,rstrip=_A   ) if isinstance(_A,_A   ) else bos_token
							SCREAMING_SNAKE_CASE_  :		Optional[int]    				=   AddedToken(_A,lstrip=_A,rstrip=_A   ) if isinstance(_A,_A   ) else eos_token
							SCREAMING_SNAKE_CASE_  :		Dict    				=   AddedToken(_A,lstrip=_A,rstrip=_A   ) if isinstance(_A,_A   ) else mask_token
							SCREAMING_SNAKE_CASE_  :		int    				=   AddedToken(_A,lstrip=_A,rstrip=_A   ) if isinstance(_A,_A   ) else cls_token
							SCREAMING_SNAKE_CASE_  :		str    				=   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,)

							SCREAMING_SNAKE_CASE_  :		int    				=   2**8  # utf is 8 bits

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

					def     __UpperCamelCase       (   self				:	Any   ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		str    				=   self.special_tokens_encoder.copy()
							vocab.update(self.added_tokens_encoder   )
							for i in range(self._utf_vocab_size   ):
									SCREAMING_SNAKE_CASE_  :		str    				=   chr(_A   )
									SCREAMING_SNAKE_CASE_  :		Union[str, Any]    				=   i + len(self.special_tokens_encoder   )
							return vocab

					@property
					def     __UpperCamelCase       (   self				:	Optional[int]   ):







							"""simple docstring"""






							return self._utf_vocab_size + self._num_special_tokens

					def     __UpperCamelCase       (   self				:	Optional[Any],_A				:	List[int],_A				:	Optional[List[int]] = None,_A				:	bool = False   ):







							"""simple docstring"""






							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     __UpperCamelCase       (   self				:	str,_A				:	List[int],_A				:	Optional[List[int]] = None   ):







							"""simple docstring"""






							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     __UpperCamelCase       (   self				:	Optional[Any],_A				:	str   ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Optional[Any]    				=   [chr(_A   ) for i in text.encode("utf-8"   )]
							return tokens

					def     __UpperCamelCase       (   self				:	Optional[Any],_A				:	str   ):







							"""simple docstring"""






							if token in self.special_tokens_encoder:
									SCREAMING_SNAKE_CASE_  :		int    				=   self.special_tokens_encoder[token]
							elif token in self.added_tokens_encoder:
									SCREAMING_SNAKE_CASE_  :		Optional[Any]    				=   self.added_tokens_encoder[token]
							elif len(_A   ) != 1:
									SCREAMING_SNAKE_CASE_  :		int    				=   self.unk_token_id
							else:
									SCREAMING_SNAKE_CASE_  :		Any    				=   ord(_A   ) + self._num_special_tokens
							return token_id

					def     __UpperCamelCase       (   self				:	Union[str, Any],_A				:	Dict   ):







							"""simple docstring"""






							if index in self.special_tokens_decoder:
									SCREAMING_SNAKE_CASE_  :		Tuple    				=   self.special_tokens_decoder[index]
							elif index in self.added_tokens_decoder:
									SCREAMING_SNAKE_CASE_  :		Optional[Any]    				=   self.added_tokens_decoder[index]
							else:
									SCREAMING_SNAKE_CASE_  :		Union[str, Any]    				=   chr(index - self._num_special_tokens   )
							return token

					def     __UpperCamelCase       (   self				:	Optional[Any],_A				:	str   ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Optional[int]    				=   B""
							for token in tokens:
									if token in self.special_tokens_decoder:
											SCREAMING_SNAKE_CASE_  :		List[str]    				=   self.special_tokens_decoder[token].encode("utf-8"   )
									elif token in self.added_tokens_decoder:
											SCREAMING_SNAKE_CASE_  :		Union[str, Any]    				=   self.special_tokens_decoder[token].encode("utf-8"   )
									elif token in self.special_tokens_encoder:
											SCREAMING_SNAKE_CASE_  :		str    				=   token.encode("utf-8"   )
									elif token in self.added_tokens_encoder:
											SCREAMING_SNAKE_CASE_  :		Optional[int]    				=   token.encode("utf-8"   )
									else:
											SCREAMING_SNAKE_CASE_  :		Any    				=   bytes([ord(_A   )]   )
									bstring += tok_string
							SCREAMING_SNAKE_CASE_  :		int    				=   bstring.decode("utf-8",errors="replace"   )
							return string






					def     __UpperCamelCase       (   self				:	Dict,_A				:	str,_A				:	Optional[str] = None   ):







							"""simple docstring"""






							return ()

import argparse

from transformers import ConvBertConfig, ConvBertModel, TFConvBertModel, load_tf_weights_in_convbert
from transformers.utils import logging


logging.set_verbosity_info()




def 				_snake_case     (       lowerCAmelCase							:       Dict       ,			lowerCAmelCase							:       int       ,			lowerCAmelCase							:       Union[str, Any]     ):
		"""simple docstring"""







		SCREAMING_SNAKE_CASE_  :		List[Any]    				=   ConvBertConfig.from_json_file(lowerCAmelCase     )
		SCREAMING_SNAKE_CASE_  :		Tuple    				=   ConvBertModel(lowerCAmelCase     )

		SCREAMING_SNAKE_CASE_  :		Dict    				=   load_tf_weights_in_convbert(lowerCAmelCase       ,			lowerCAmelCase       ,			lowerCAmelCase     )
		model.save_pretrained(lowerCAmelCase     )

		SCREAMING_SNAKE_CASE_  :		Union[str, Any]    				=   TFConvBertModel.from_pretrained(lowerCAmelCase       ,			from_pt=lowerCAmelCase     )
		tf_model.save_pretrained(lowerCAmelCase     )


if __name__ == "__main__":
		__lowerCamelCase :     Optional[Any]          =  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(
		    '''--convbert_config_file''',
		    default=None,
		    type=str,
		    required=True,
		    help=(
		        '''The config json file corresponding to the pre-trained ConvBERT 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.'''
		)
		__lowerCamelCase :     Optional[int]          =  parser.parse_args()
		convert_orig_tfa_checkpoint_to_pytorch(args.tf_checkpoint_path, args.convbert_config_file, args.pytorch_dump_path)
