


import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


SCREAMING_SNAKE_CASE			: int				   =					"▁"

SCREAMING_SNAKE_CASE			: Union[str, Any]				   =					{"vocab_file": "spiece.model"}

SCREAMING_SNAKE_CASE			: Optional[int]				   =					{
    "vocab_file": {"google/pegasus-xsum": "https://huggingface.co/google/pegasus-xsum/resolve/main/spiece.model"}
}

SCREAMING_SNAKE_CASE			: List[Any]				   =					{
    "google/pegasus-xsum": 512,
}


SCREAMING_SNAKE_CASE			: int				   =					logging.get_logger(__name__)






class _lowerCamelCase(		_a  ):
       lowercase_					:      Tuple       					=     VOCAB_FILES_NAMES

       lowercase_					:      Union[str, Any]       					=     VOCAB_FILES_NAMES
       lowercase_					:      Dict       					=     PRETRAINED_VOCAB_FILES_MAP
       lowercase_					:      str       					=     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       lowercase_					:      List[Any]       					=     ["""input_ids""", """attention_mask"""]
       def __init__(   self, lowerCamelCase, lowerCamelCase="<pad>", lowerCamelCase="</s>", lowerCamelCase="<unk>", lowerCamelCase="<mask_2>", lowerCamelCase="<mask_1>", lowerCamelCase=None, lowerCamelCase=1_03, lowerCamelCase = None, **lowerCamelCase, )			->  None:




           """simple docstring"""

           _lowercase			:				str 						=						offset
           if additional_special_tokens is not None:
               if not isinstance(lowerCamelCase, lowerCamelCase):
                   raise TypeError(
                       F'''additional_special_tokens should be of type {type(lowerCamelCase)}, but is'''
                       F''' {type(lowerCamelCase)}''')

               _lowercase			:				Any 						=						(
                   ([mask_token_sent] + additional_special_tokens)
                   if mask_token_sent not in additional_special_tokens and mask_token_sent is not None
                   else additional_special_tokens
               )
               # fill additional tokens with ..., <unk_token_102> in case not all additional tokens are already taken
               additional_special_tokens_extended += [
                   F'''<unk_{i}>''' for i in range(len(lowerCamelCase), self.offset - 1)
               ]

               if len(set(lowerCamelCase)) != len(lowerCamelCase):
                   raise ValueError(
                       'Please make sure that the provided additional_special_tokens do not contain an incorrectly'
                       F''' shifted list of <unk_x> tokens. Found {additional_special_tokens_extended}.''')
               _lowercase			:				Optional[int] 						=						additional_special_tokens_extended
           else:
               _lowercase			:				str 						=						[mask_token_sent] if mask_token_sent is not None else []
               additional_special_tokens += [F'''<unk_{i}>''' for i in range(2, self.offset)]

           _lowercase			:				Optional[int] 						=						{} if sp_model_kwargs is None else sp_model_kwargs

           super().__init__(
               eos_token=lowerCamelCase, unk_token=lowerCamelCase, mask_token=lowerCamelCase, pad_token=lowerCamelCase, mask_token_sent=lowerCamelCase, offset=lowerCamelCase, additional_special_tokens=lowerCamelCase, sp_model_kwargs=self.sp_model_kwargs, **lowerCamelCase, )
           _lowercase			:				str 						=						mask_token_sent
           _lowercase			:				Optional[int] 						=						vocab_file
           _lowercase			:				Optional[int] 						=						spm.SentencePieceProcessor(**self.sp_model_kwargs)
           self.sp_model.Load(lowerCamelCase)

           # add special tokens to encoder dict
           _lowercase			:				Dict[int, str] 						=						{
               0: self.pad_token,
               1: self.eos_token,
           }

           if self.mask_token_sent is not None:
               self.encoder.update(
                   {
                       2: self.mask_token_sent,
                       3: self.mask_token,
                   })

           if self.offset > 0:
               # entries 2-104 are only used for pretraining and called <mask_1>, <mask_2>, unk_2, ...unk_102
               # mask_token_sent is already added to list -> so start at 1
               self.encoder.update({i + 3: additional_special_tokens[i] for i in range(1, self.offset - 1)})

           _lowercase			:				Dict[str, int] 						=						{v: k for k, v in self.encoder.items()}
       @property
       def      UpperCamelCase	(   self)			->  int:




           """simple docstring"""

           return len(self.sp_model) + self.offset
       def      UpperCamelCase	(   self)			->  Dict[str, int]:




           """simple docstring"""

           _lowercase			:				Union[str, Any] 						=						{self.convert_ids_to_tokens(lowerCamelCase): i for i in range(self.vocab_size)}
           vocab.update(self.added_tokens_encoder)
           return vocab
       def __getstate__(   self)			->  Any:




           """simple docstring"""

           _lowercase			:				Optional[Any] 						=						self.__dict__.copy()
           _lowercase			:				Union[str, Any] 						=						None
           return state
       def __setstate__(   self, lowerCamelCase)			->  str:




           """simple docstring"""

           _lowercase			:				Dict 						=						d

           # for backward compatibility
           if not hasattr(self, 'sp_model_kwargs'):
               _lowercase			:				Any 						=						{}

           _lowercase			:				Any 						=						spm.SentencePieceProcessor(**self.sp_model_kwargs)
           self.sp_model.Load(self.vocab_file)
       def      UpperCamelCase	(   self, lowerCamelCase)			->  List[str]:




           """simple docstring"""

           return self.sp_model.encode(lowerCamelCase, out_type=lowerCamelCase)
       def      UpperCamelCase	(   self, lowerCamelCase)			->  int:




           """simple docstring"""

           if token in self.decoder:
               return self.decoder[token]
           elif token in self.added_tokens_decoder:
               return self.added_tokens_decoder[token]
           _lowercase			:				Any 						=						self.sp_model.piece_to_id(lowerCamelCase)
           return sp_id + self.offset
       def      UpperCamelCase	(   self, lowerCamelCase)			->  str:




           """simple docstring"""

           if index in self.encoder:
               return self.encoder[index]
           elif index in self.added_tokens_encoder:
               return self.added_tokens_encoder[index]
           else:
               _lowercase			:				Tuple 						=						self.sp_model.IdToPiece(index - self.offset)
           return token
       def      UpperCamelCase	(   self, lowerCamelCase)			->  List[str]:




           """simple docstring"""

           _lowercase			:				Union[str, Any] 						=						[]
           _lowercase			:				Optional[int] 						=						''
           for token in tokens:
               # make sure that special tokens are not decoded using sentencepiece model
               if token in self.all_special_tokens:
                   out_string += self.sp_model.decode(lowerCamelCase) + token
                   _lowercase			:				List[str] 						=						[]
               else:
                   current_sub_tokens.append(lowerCamelCase)
           out_string += self.sp_model.decode(lowerCamelCase)
           return out_string.strip()
       def      UpperCamelCase	(   self, lowerCamelCase=False)			->  Optional[Any]:




           """simple docstring"""

           return 1
       def      UpperCamelCase	(   self, lowerCamelCase)			->  str:




           """simple docstring"""

           _lowercase			:				str 						=						set(self.all_special_ids)  # call it once instead of inside list comp
           all_special_ids.remove(self.unk_token_id)  # <unk> is only sometimes special

           return [1 if x in all_special_ids else 0 for x in seq]
       def      UpperCamelCase	(   self, lowerCamelCase, lowerCamelCase = None, lowerCamelCase = False)			->  List[int]:




           """simple docstring"""

           if already_has_special_tokens:
               return self._special_token_mask(lowerCamelCase)
           elif token_ids_a is None:
               return self._special_token_mask(lowerCamelCase) + [1]
           else:
               return self._special_token_mask(token_ids_a + token_ids_a) + [1]
       def      UpperCamelCase	(   self, lowerCamelCase, lowerCamelCase=None)			->  List[int]:




           """simple docstring"""

           if token_ids_a is None:
               return token_ids_a + [self.eos_token_id]
           # We don't expect to process pairs, but leave the pair logic for API consistency
           return token_ids_a + token_ids_a + [self.eos_token_id]




       def      UpperCamelCase	(   self, lowerCamelCase, lowerCamelCase = None)			->  Tuple[str]:




           """simple docstring"""

           if not os.path.isdir(lowerCamelCase):
               logger.error(F'''Vocabulary path ({save_directory}) should be a directory''')
               return
           _lowercase			:				Tuple 						=						os.path.join(
               lowerCamelCase, (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'])

           if os.path.abspath(self.vocab_file) != os.path.abspath(lowerCamelCase) and os.path.isfile(self.vocab_file):
               copyfile(self.vocab_file, lowerCamelCase)
           elif not os.path.isfile(self.vocab_file):
               with open(lowerCamelCase, 'wb') as fi:
                   _lowercase			:				Tuple 						=						self.sp_model.serialized_model_proto()
                   fi.write(lowerCamelCase)

           return (out_vocab_file,)






import argparse

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


logging.set_verbosity_info()



def 							UpperCamelCase_( lowerCamelCase_				,					lowerCamelCase_				,					lowerCamelCase_					)       ->		Optional[int]:
    # Initialise PyTorch model
    _lowercase			:				Optional[int] 						=						TaConfig.from_json_file(lowerCamelCase_					)
    print(F'''Building PyTorch model from configuration: {config}'''					)
    _lowercase			:				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__":
 SCREAMING_SNAKE_CASE			: Optional[int]				   =					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."
 )
 SCREAMING_SNAKE_CASE			: Any				   =					parser.parse_args()
 convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)


