



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				(     __snake_case ):






   '''simple docstring'''






   A__      : List[str]       =						["input_ids", "attention_mask"]


   def __init__(    self:     Optional[Any]  ,lowerCamelCase_:     Any="[PAD]"  ,lowerCamelCase_:     str="[BOS]"  ,lowerCamelCase_:     str="[EOS]"  ,lowerCamelCase_:     str="[MASK]"  ,lowerCamelCase_:     Dict="[CLS]"  ,lowerCamelCase_:     Optional[Any]="[SEP]"  ,lowerCamelCase_:     Optional[int]=2048  ,**lowerCamelCase_:     int  ,)	->      None:
       UpperCAmelCase_     :       Dict  		=					AddedToken(lowerCamelCase_  ,lstrip=lowerCamelCase_  ,rstrip=lowerCamelCase_							) if isinstance(lowerCamelCase_  ,lowerCamelCase_							) else pad_token
       UpperCAmelCase_     :       str  		=					AddedToken(lowerCamelCase_  ,lstrip=lowerCamelCase_  ,rstrip=lowerCamelCase_							) if isinstance(lowerCamelCase_  ,lowerCamelCase_							) else bos_token
       UpperCAmelCase_     :       Tuple  		=					AddedToken(lowerCamelCase_  ,lstrip=lowerCamelCase_  ,rstrip=lowerCamelCase_							) if isinstance(lowerCamelCase_  ,lowerCamelCase_							) else eos_token
       UpperCAmelCase_     :       List[Any]  		=					AddedToken(lowerCamelCase_  ,lstrip=lowerCamelCase_  ,rstrip=lowerCamelCase_							) if isinstance(lowerCamelCase_  ,lowerCamelCase_							) else mask_token
       UpperCAmelCase_     :       Optional[Any]  		=					AddedToken(lowerCamelCase_  ,lstrip=lowerCamelCase_  ,rstrip=lowerCamelCase_							) if isinstance(lowerCamelCase_  ,lowerCamelCase_							) else cls_token
       UpperCAmelCase_     :       Union[str, Any]  		=					AddedToken(lowerCamelCase_  ,lstrip=lowerCamelCase_  ,rstrip=lowerCamelCase_							) if isinstance(lowerCamelCase_  ,lowerCamelCase_							) else sep_token

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

       UpperCAmelCase_     :       List[str]  		=					2**8  # utf is 8 bits

       # define special tokens dict
       UpperCAmelCase_     :       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,
       }
       UpperCAmelCase_     :       Any  		=					len(self.special_tokens_encoder							)
       UpperCAmelCase_     :       Dict[int, str]  		=					{v: k for k, v in self.special_tokens_encoder.items()}


   def       A__    (    self:     Dict							)	->      Dict[str, int]:
       UpperCAmelCase_     :       List[str]  		=					self.special_tokens_encoder.copy()
       vocab.update(self.added_tokens_encoder							)
       for i in range(self._utf_vocab_size							):
           UpperCAmelCase_     :       Union[str, Any]  		=					chr(lowerCamelCase_							)
           UpperCAmelCase_     :       int  		=					i + len(self.special_tokens_encoder							)
       return vocab


   @property
   def       A__    (    self:     List[str]							)	->      Optional[Any]:
       return self._utf_vocab_size + self._num_special_tokens


   def       A__    (    self:     Tuple  ,lowerCamelCase_:     List[int]  ,lowerCamelCase_:     Optional[List[int]] = None  ,lowerCamelCase_:     bool = False							)	->      List[int]:
       if already_has_special_tokens:
           return super().get_special_tokens_mask(
               token_ids_a=lowerCamelCase_  ,token_ids_a=lowerCamelCase_  ,already_has_special_tokens=lowerCamelCase_							)

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


   def       A__    (    self:     int  ,lowerCamelCase_:     List[int]  ,lowerCamelCase_:     Optional[List[int]] = None							)	->      List[int]:
       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:     List[str]  ,lowerCamelCase_:     str							)	->      List[str]:
       UpperCAmelCase_     :       Any  		=					[chr(lowerCamelCase_							) for i in text.encode("""utf-8"""							)]
       return tokens


   def       A__    (    self:     str  ,lowerCamelCase_:     int							)	->      Optional[Any]:
       if token in self.special_tokens_encoder:
           UpperCAmelCase_     :       Optional[Any]  		=					self.special_tokens_encoder[token]
       elif token in self.added_tokens_encoder:
           UpperCAmelCase_     :       int  		=					self.added_tokens_encoder[token]
       elif len(lowerCamelCase_							) != 1:
           UpperCAmelCase_     :       Any  		=					self.unk_token_id
       else:
           UpperCAmelCase_     :       str  		=					ord(lowerCamelCase_							) + self._num_special_tokens
       return token_id


   def       A__    (    self:     Tuple  ,lowerCamelCase_:     List[str]							)	->      Dict:
       if index in self.special_tokens_decoder:
           UpperCAmelCase_     :       int  		=					self.special_tokens_decoder[index]
       elif index in self.added_tokens_decoder:
           UpperCAmelCase_     :       Any  		=					self.added_tokens_decoder[index]
       else:
           UpperCAmelCase_     :       Any  		=					chr(index - self._num_special_tokens							)
       return token


   def       A__    (    self:     Any  ,lowerCamelCase_:     Optional[Any]							)	->      Optional[Any]:
       UpperCAmelCase_     :       Tuple  		=					b""""""
       for token in tokens:
           if token in self.special_tokens_decoder:
               UpperCAmelCase_     :       int  		=					self.special_tokens_decoder[token].encode("""utf-8"""							)
           elif token in self.added_tokens_decoder:
               UpperCAmelCase_     :       Dict  		=					self.special_tokens_decoder[token].encode("""utf-8"""							)
           elif token in self.special_tokens_encoder:
               UpperCAmelCase_     :       Dict  		=					token.encode("""utf-8"""							)
           elif token in self.added_tokens_encoder:
               UpperCAmelCase_     :       Optional[Any]  		=					token.encode("""utf-8"""							)
           else:
               UpperCAmelCase_     :       List[str]  		=					bytes([ord(lowerCamelCase_							)]							)
           bstring += tok_string
       UpperCAmelCase_     :       Dict  		=					bstring.decode("""utf-8"""  ,errors="""replace"""							)
       return string






   def       A__    (    self:     Tuple  ,lowerCamelCase_:     str  ,lowerCamelCase_:     Optional[str] = None							)	->      Tuple[str]:
       return ()



"""simple docstring"""
import argparse

from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
from transformers.utils import logging


logging.set_verbosity_info()







def       UpperCamelCase_					(     lowerCamelCase							:					List[str]     , lowerCamelCase							:					Optional[int]     , lowerCamelCase							:					List[str]   )						->     Optional[int]:



       """simple docstring"""







       __magic_name__			:		int        =     TaConfig.from_json_file(lowerCamelCase   )
       print(f"""Building PyTorch model from configuration: {config}"""   )
       __magic_name__			:		Union[str, Any]        =     TaForConditionalGeneration(lowerCamelCase   )

       # Load weights from tf checkpoint
       load_tf_weights_in_ta(lowerCamelCase     , lowerCamelCase     , lowerCamelCase   )

       # Save pytorch-model
       print(f"""Save PyTorch model to {pytorch_dump_path}"""   )
       model.save_pretrained(lowerCamelCase   )


if __name__ == "__main__":
 A									=						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(
     """--config_file""",
     default=None,
     type=str,
     required=True,
     help=(
         """The config json file corresponding to the pre-trained T5 model. \nThis specifies the model architecture."""
     ),
 )
 parser.add_argument(
     """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
 )
 A									=						parser.parse_args()
 convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)
