



'''simple docstring'''







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

import sentencepiece as spm

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


lowercase__  :	Dict            =      logging.get_logger(__name__)
lowercase__  :	Optional[Any]            =      {"""vocab_file""": """spiece.model"""}

lowercase__  :	List[Any]            =      {
    """vocab_file""": {
        """google/fnet-base""": """https://huggingface.co/google/fnet-base/resolve/main/spiece.model""",
        """google/fnet-large""": """https://huggingface.co/google/fnet-large/resolve/main/spiece.model""",
    },
}

lowercase__  :	Optional[int]            =      {
    """google/fnet-base""": 5_12,
    """google/fnet-large""": 5_12,
}

lowercase__  :	int            =      """▁"""

class    lowerCamelCase_				(						__A	):






   '''simple docstring'''

   A			:  Optional[Any]					=						VOCAB_FILES_NAMES
   A			:  List[str]					=						PRETRAINED_VOCAB_FILES_MAP
   A			:  Optional[Any]					=						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   A			:  Dict					=						["input_ids", "token_type_ids"]


   def __init__(	self  ,    __UpperCamelCase  ,    __UpperCamelCase=False  ,    __UpperCamelCase=True  ,    __UpperCamelCase=True  ,    __UpperCamelCase="<unk>"  ,    __UpperCamelCase="[SEP]"  ,    __UpperCamelCase="<pad>"  ,    __UpperCamelCase="[CLS]"  ,    __UpperCamelCase="[MASK]"  ,    __UpperCamelCase = None  ,    **__UpperCamelCase  ,    ):
       UpperCamelCase__	     =      (
           AddedToken(A_  ,    lstrip=A_  ,    rstrip=A_  ,    normalized=A_			)
           if isinstance(A_  ,    A_			)
           else mask_token
       )

       UpperCamelCase__	     =      {} if sp_model_kwargs is None else sp_model_kwargs

       super().__init__(
           do_lower_case=A_  ,    remove_space=A_  ,    keep_accents=A_  ,    unk_token=A_  ,    sep_token=A_  ,    pad_token=A_  ,    cls_token=A_  ,    mask_token=A_  ,    sp_model_kwargs=self.sp_model_kwargs  ,    **A_  ,    )

       UpperCamelCase__	     =      do_lower_case
       UpperCamelCase__	     =      remove_space
       UpperCamelCase__	     =      keep_accents
       UpperCamelCase__	     =      vocab_file

       UpperCamelCase__	     =      spm.SentencePieceProcessor(**self.sp_model_kwargs			)
       self.sp_model.Load(A_			)


   @property
   def 						_UpperCamelCase							(	self			):
       return len(self.sp_model			)


   def 						_UpperCamelCase							(	self			):
       UpperCamelCase__	     =      {self.convert_ids_to_tokens(A_			): i for i in range(self.vocab_size			)}
       vocab.update(self.added_tokens_encoder			)
       return vocab


   def __getstate__(	self			):
       UpperCamelCase__	     =      self.__dict__.copy()
       UpperCamelCase__	     =      None
       return state


   def __setstate__(	self  ,    __UpperCamelCase			):
       UpperCamelCase__	     =      d

       # for backward compatibility
       if not hasattr(self  ,    "sp_model_kwargs"			):
           UpperCamelCase__	     =      {}

       UpperCamelCase__	     =      spm.SentencePieceProcessor(**self.sp_model_kwargs			)
       self.sp_model.Load(self.vocab_file			)


   def 						_UpperCamelCase							(	self  ,    __UpperCamelCase			):



       if self.remove_space:
           UpperCamelCase__	     =      " ".join(inputs.strip().split()			)
       else:
           UpperCamelCase__	     =      inputs
       UpperCamelCase__	     =      outputs.replace("``"  ,    "\""			).replace("\'\'"  ,    "\""			)

       if not self.keep_accents:
           UpperCamelCase__	     =      unicodedata.normalize("NFKD"  ,    A_			)
           UpperCamelCase__	     =      "".join([c for c in outputs if not unicodedata.combining(A_			)]			)
       if self.do_lower_case:
           UpperCamelCase__	     =      outputs.lower()

       return outputs


   def 						_UpperCamelCase							(	self  ,    __UpperCamelCase			):
       UpperCamelCase__	     =      self.preprocess_text(A_			)
       UpperCamelCase__	     =      self.sp_model.encode(A_  ,    out_type=A_			)
       UpperCamelCase__	     =      []
       for piece in pieces:
           if len(A_			) > 1 and piece[-1] == str(","			) and piece[-2].isdigit():
               UpperCamelCase__	     =      self.sp_model.EncodeAsPieces(piece[:-1].replace(A_  ,    ""			)			)
               if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
                   if len(cur_pieces[0]			) == 1:
                       UpperCamelCase__	     =      cur_pieces[1:]
                   else:
                       UpperCamelCase__	     =      cur_pieces[0][1:]
               cur_pieces.append(piece[-1]			)
               new_pieces.extend(A_			)
           else:
               new_pieces.append(A_			)

       return new_pieces


   def 						_UpperCamelCase							(	self  ,    __UpperCamelCase			):
       return self.sp_model.PieceToId(A_			)


   def 						_UpperCamelCase							(	self  ,    __UpperCamelCase			):
       return self.sp_model.IdToPiece(A_			)


   def 						_UpperCamelCase							(	self  ,    __UpperCamelCase			):
       UpperCamelCase__	     =      []
       UpperCamelCase__	     =      ""
       UpperCamelCase__	     =      False
       for token in tokens:
           # make sure that special tokens are not decoded using sentencepiece model
           if token in self.all_special_tokens:
               if not prev_is_special:
                   out_string += " "
               out_string += self.sp_model.decode(A_			) + token
               UpperCamelCase__	     =      True
               UpperCamelCase__	     =      []
           else:
               current_sub_tokens.append(A_			)
               UpperCamelCase__	     =      False
       out_string += self.sp_model.decode(A_			)
       return out_string.strip()


   def 						_UpperCamelCase							(	self  ,    __UpperCamelCase  ,    __UpperCamelCase = False  ,    __UpperCamelCase = None  ,    __UpperCamelCase = True  ,    **__UpperCamelCase  ,    ):
       UpperCamelCase__	     =      kwargs.pop("use_source_tokenizer"  ,    A_			)

       UpperCamelCase__	     =      self.convert_ids_to_tokens(A_  ,    skip_special_tokens=A_			)

       # To avoid mixing byte-level and unicode for byte-level BPT
       # we need to build string separately for added tokens and byte-level tokens
       # cf. https://github.com/huggingface/transformers/issues/1133
       UpperCamelCase__	     =      []
       UpperCamelCase__	     =      []
       for token in filtered_tokens:
           if skip_special_tokens and token in self.all_special_ids:
               continue
           if token in self.added_tokens_encoder:
               if current_sub_text:
                   sub_texts.append(self.convert_tokens_to_string(A_			)			)
                   UpperCamelCase__	     =      []
               sub_texts.append(A_			)
           else:
               current_sub_text.append(A_			)
       if current_sub_text:
           sub_texts.append(self.convert_tokens_to_string(A_			)			)

       # Mimic the behavior of the Rust tokenizer:
       # No space after <unk>
       if spaces_between_special_tokens:
           UpperCamelCase__	     =      re.sub(R"(<unk>) "  ,    R"\1"  ,    " ".join(A_			)			)
       else:
           UpperCamelCase__	     =      "".join(A_			)

       UpperCamelCase__	     =      (
           clean_up_tokenization_spaces
           if clean_up_tokenization_spaces is not None
           else self.clean_up_tokenization_spaces
       )
       if clean_up_tokenization_spaces:
           UpperCamelCase__	     =      self.clean_up_tokenization(A_			)
           return clean_text
       else:
           return text


   def 						_UpperCamelCase							(	self  ,    __UpperCamelCase  ,    __UpperCamelCase = None			):
       UpperCamelCase__	     =      [self.sep_token_id]
       UpperCamelCase__	     =      [self.cls_token_id]
       if token_ids_a is None:
           return cls + token_ids_a + sep
       return cls + token_ids_a + sep + token_ids_a + sep


   def 						_UpperCamelCase							(	self  ,    __UpperCamelCase  ,    __UpperCamelCase = None  ,    __UpperCamelCase = 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_			)

       if token_ids_a is not None:
           return [1] + ([0] * len(A_			)) + [1] + ([0] * len(A_			)) + [1]
       return [1] + ([0] * len(A_			)) + [1]


   def 						_UpperCamelCase							(	self  ,    __UpperCamelCase  ,    __UpperCamelCase = None			):
       UpperCamelCase__	     =      [self.sep_token_id]
       UpperCamelCase__	     =      [self.cls_token_id]

       if token_ids_a is None:
           return len(cls + token_ids_a + sep			) * [0]
       return len(cls + token_ids_a + sep			) * [0] + len(token_ids_a + sep			) * [1]







   def 						_UpperCamelCase							(	self  ,    __UpperCamelCase  ,    __UpperCamelCase = None			):



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

       if os.path.abspath(self.vocab_file			) != os.path.abspath(A_			) and os.path.isfile(self.vocab_file			):
           copyfile(self.vocab_file  ,    A_			)
       elif not os.path.isfile(self.vocab_file			):
           with open(A_  ,    "wb"			) as fi:
               UpperCamelCase__	     =      self.sp_model.serialized_model_proto()
               fi.write(A_			)

       return (out_vocab_file,)









"""simple docstring"""






import argparse

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


logging.set_verbosity_info()


def 							A__ (     UpperCamelCase__       ,							UpperCamelCase__    ):


  '''simple docstring'''


  _SCREAMING_SNAKE_CASE        =			CanineConfig()
  _SCREAMING_SNAKE_CASE        =			CanineModel(UpperCamelCase__    )
  model.eval()

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

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

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

  # Save tokenizer files
  _SCREAMING_SNAKE_CASE        =			CanineTokenizer()
  print(F'''Save tokenizer files to {pytorch_dump_path}'''    )
  tokenizer.save_pretrained(UpperCamelCase__    )


if __name__ == "__main__":
    lowerCamelCase				:     Dict								=  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.""",
    )
    lowerCamelCase				:     str								=  parser.parse_args()
    convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.pytorch_dump_path)
