





'''simple docstring'''






from typing import Dict, List, Optional, Tuple

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


snake_case_     			=						logging.get_logger(__name__)


class 	_lowercase    (				_UpperCAmelCase					):
   _UpperCamelCase									= ["input_ids", "attention_mask"]


   def __init__(			self     , _UpperCAmelCase="[PAD]"     , _UpperCAmelCase="[BOS]"     , _UpperCAmelCase="[EOS]"     , _UpperCAmelCase="[MASK]"     , _UpperCAmelCase="[CLS]"     , _UpperCAmelCase="[SEP]"     , _UpperCAmelCase=2_048     , **_UpperCAmelCase     , ):
       A							:							int				=    AddedToken(SCREAMING_SNAKE_CASE_     , lstrip=SCREAMING_SNAKE_CASE_     , rstrip=SCREAMING_SNAKE_CASE_       ) if isinstance(SCREAMING_SNAKE_CASE_     , SCREAMING_SNAKE_CASE_       ) else pad_token
       A							:							Tuple				=    AddedToken(SCREAMING_SNAKE_CASE_     , lstrip=SCREAMING_SNAKE_CASE_     , rstrip=SCREAMING_SNAKE_CASE_       ) if isinstance(SCREAMING_SNAKE_CASE_     , SCREAMING_SNAKE_CASE_       ) else bos_token
       A							:							Optional[int]				=    AddedToken(SCREAMING_SNAKE_CASE_     , lstrip=SCREAMING_SNAKE_CASE_     , rstrip=SCREAMING_SNAKE_CASE_       ) if isinstance(SCREAMING_SNAKE_CASE_     , SCREAMING_SNAKE_CASE_       ) else eos_token
       A							:							List[str]				=    AddedToken(SCREAMING_SNAKE_CASE_     , lstrip=SCREAMING_SNAKE_CASE_     , rstrip=SCREAMING_SNAKE_CASE_       ) if isinstance(SCREAMING_SNAKE_CASE_     , SCREAMING_SNAKE_CASE_       ) else mask_token
       A							:							Optional[Any]				=    AddedToken(SCREAMING_SNAKE_CASE_     , lstrip=SCREAMING_SNAKE_CASE_     , rstrip=SCREAMING_SNAKE_CASE_       ) if isinstance(SCREAMING_SNAKE_CASE_     , SCREAMING_SNAKE_CASE_       ) else cls_token
       A							:							int				=    AddedToken(SCREAMING_SNAKE_CASE_     , lstrip=SCREAMING_SNAKE_CASE_     , rstrip=SCREAMING_SNAKE_CASE_       ) if isinstance(SCREAMING_SNAKE_CASE_     , SCREAMING_SNAKE_CASE_       ) else sep_token

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

       A							:							Optional[int]				=    2**8  # utf is 8 bits

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


   def        snake_case					(			self       ):
       A							:							Any				=    self.special_tokens_encoder.copy()
       vocab.update(self.added_tokens_encoder       )
       for i in range(self._utf_vocab_size       ):
           A							:							Any				=    chr(SCREAMING_SNAKE_CASE_       )
           A							:							Tuple				=    i + len(self.special_tokens_encoder       )
       return vocab


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


   def        snake_case					(			self     , _UpperCAmelCase     , _UpperCAmelCase = None     , _UpperCAmelCase = False       ):
       if already_has_special_tokens:
           return super().get_special_tokens_mask(
               token_ids_a=SCREAMING_SNAKE_CASE_     , token_ids_a=SCREAMING_SNAKE_CASE_     , already_has_special_tokens=SCREAMING_SNAKE_CASE_       )

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


   def        snake_case					(			self     , _UpperCAmelCase     , _UpperCAmelCase = 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        snake_case					(			self     , _UpperCAmelCase       ):
       A							:							Optional[int]				=    [chr(SCREAMING_SNAKE_CASE_       ) for i in text.encode('''utf-8'''       )]
       return tokens


   def        snake_case					(			self     , _UpperCAmelCase       ):
       if token in self.special_tokens_encoder:
           A							:							Union[str, Any]				=    self.special_tokens_encoder[token]
       elif token in self.added_tokens_encoder:
           A							:							Tuple				=    self.added_tokens_encoder[token]
       elif len(SCREAMING_SNAKE_CASE_       ) != 1:
           A							:							Tuple				=    self.unk_token_id
       else:
           A							:							Tuple				=    ord(SCREAMING_SNAKE_CASE_       ) + self._num_special_tokens
       return token_id


   def        snake_case					(			self     , _UpperCAmelCase       ):
       if index in self.special_tokens_decoder:
           A							:							Union[str, Any]				=    self.special_tokens_decoder[index]
       elif index in self.added_tokens_decoder:
           A							:							Dict				=    self.added_tokens_decoder[index]
       else:
           A							:							Union[str, Any]				=    chr(index - self._num_special_tokens       )
       return token


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


   def        snake_case					(			self     , _UpperCAmelCase     , _UpperCAmelCase = None       ):
       return ()




import argparse

from transformers import CanineConfig, CanineModel, CanineTokenizer, load_tf_weights_in_canine
from transformers.utils import logging


logging.set_verbosity_info()



def      __lowerCAmelCase    (				snake_case						:     Union[str, Any]    ,   snake_case						:     Optional[int]			)							->				List[Any]:
   # Initialize PyTorch model
   __lowerCamelCase:					str	 =						CanineConfig()
   __lowerCamelCase:					Union[str, Any]	 =						CanineModel(snake_case			)
   model.eval()

   print(f'Building PyTorch model from configuration: {config}'			)

   # Load weights from tf checkpoint
   load_tf_weights_in_canine(snake_case    ,   snake_case    ,   snake_case			)

   # Save pytorch-model (weights and configuration)
   print(f'Save PyTorch model to {pytorch_dump_path}'			)
   model.save_pretrained(snake_case			)

   # Save tokenizer files
   __lowerCamelCase:					Optional[int]	 =						CanineTokenizer()
   print(f'Save tokenizer files to {pytorch_dump_path}'			)
   tokenizer.save_pretrained(snake_case			)


if __name__ == "__main__":
   _A		:					int          =							argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       '''--tf_checkpoint_path''',
       default=None,
       type=str,
       required=True,
       help='''Path to the TensorFlow checkpoint. Should end with model.ckpt''',
   )
   parser.add_argument(
       '''--pytorch_dump_path''',
       default=None,
       type=str,
       required=True,
       help='''Path to a folder where the PyTorch model will be placed.''',
   )
   _A		:					int          =							parser.parse_args()
   convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.pytorch_dump_path)


