


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


snake_case__     							= logging.get_logger(__name__)
snake_case__     							= {'vocab_file': 'spiece.model'}

snake_case__     							= {
    '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',
    },
}

snake_case__     							= {
    'google/fnet-base': 512,
    'google/fnet-large': 512,
}

snake_case__     							= '▁'



class     snake_case__  (      _A  ):
				__lowerCAmelCase		=      VOCAB_FILES_NAMES
				__lowerCAmelCase		=      PRETRAINED_VOCAB_FILES_MAP
				__lowerCAmelCase		=      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				__lowerCAmelCase		=      ['''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     ,      ):




										"""simple docstring"""




										__magic_name__				:							str				=      (
										    AddedToken(_UpperCAmelCase     ,      lstrip=_UpperCAmelCase     ,      rstrip=_UpperCAmelCase     ,      normalized=_UpperCAmelCase      )
										    if isinstance(_UpperCAmelCase     ,      _UpperCAmelCase      )
										    else mask_token
										)

										__magic_name__				:							List[Any]				=      {} if sp_model_kwargs is None else sp_model_kwargs

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

										__magic_name__				:							Any				=      do_lower_case
										__magic_name__				:							Any				=      remove_space
										__magic_name__				:							Tuple				=      keep_accents
										__magic_name__				:							Dict				=      vocab_file

										__magic_name__				:							Dict				=      spm.SentencePieceProcessor(**self.sp_model_kwargs      )
										self.sp_model.Load(_UpperCAmelCase      )
				@property
				def  UpperCamelCase_						(  self      ):




										"""simple docstring"""




										return len(self.sp_model      )
				def  UpperCamelCase_						(  self      ):




										"""simple docstring"""




										__magic_name__				:							Any				=      {self.convert_ids_to_tokens(_UpperCAmelCase      ): i for i in range(self.vocab_size      )}
										vocab.update(self.added_tokens_encoder      )
										return vocab
				def __getstate__(  self      ):




										"""simple docstring"""




										__magic_name__				:							Union[str, Any]				=      self.__dict__.copy()
										__magic_name__				:							Union[str, Any]				=      None
										return state
				def __setstate__(  self     ,      _UpperCAmelCase      ):




										"""simple docstring"""




										__magic_name__				:							Optional[Any]				=      d

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

										__magic_name__				:							Tuple				=      spm.SentencePieceProcessor(**self.sp_model_kwargs      )
										self.sp_model.Load(self.vocab_file      )
				def  UpperCamelCase_						(  self     ,      _UpperCAmelCase      ):




										"""simple docstring"""




										if self.remove_space:
																__magic_name__				:							Dict				=      ' '.join(inputs.strip().split()      )
										else:
																__magic_name__				:							str				=      inputs
										__magic_name__				:							List[str]				=      outputs.replace('``'     ,      '"'      ).replace('\'\''     ,      '"'      )

										if not self.keep_accents:
																__magic_name__				:							Union[str, Any]				=      unicodedata.normalize('NFKD'     ,      _UpperCAmelCase      )
																__magic_name__				:							Tuple				=      ''.join([c for c in outputs if not unicodedata.combining(_UpperCAmelCase      )]      )
										if self.do_lower_case:
																__magic_name__				:							List[str]				=      outputs.lower()

										return outputs
				def  UpperCamelCase_						(  self     ,      _UpperCAmelCase      ):




										"""simple docstring"""




										__magic_name__				:							Tuple				=      self.preprocess_text(_UpperCAmelCase      )
										__magic_name__				:							Optional[int]				=      self.sp_model.encode(_UpperCAmelCase     ,      out_type=_UpperCAmelCase      )
										__magic_name__				:							str				=      []
										for piece in pieces:
																if len(_UpperCAmelCase      ) > 1 and piece[-1] == str(','      ) and piece[-2].isdigit():
																						__magic_name__				:							Optional[Any]				=      self.sp_model.EncodeAsPieces(piece[:-1].replace(_UpperCAmelCase     ,      ''      )      )
																						if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
																												if len(cur_pieces[0]      ) == 1:
																																		__magic_name__				:							Union[str, Any]				=      cur_pieces[1:]
																												else:
																																		__magic_name__				:							Optional[int]				=      cur_pieces[0][1:]
																						cur_pieces.append(piece[-1]      )
																						new_pieces.extend(_UpperCAmelCase      )
																else:
																						new_pieces.append(_UpperCAmelCase      )

										return new_pieces
				def  UpperCamelCase_						(  self     ,      _UpperCAmelCase      ):




										"""simple docstring"""




										return self.sp_model.PieceToId(_UpperCAmelCase      )
				def  UpperCamelCase_						(  self     ,      _UpperCAmelCase      ):




										"""simple docstring"""




										return self.sp_model.IdToPiece(_UpperCAmelCase      )
				def  UpperCamelCase_						(  self     ,      _UpperCAmelCase      ):




										"""simple docstring"""




										__magic_name__				:							Tuple				=      []
										__magic_name__				:							List[Any]				=      ''
										__magic_name__				:							List[Any]				=      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(_UpperCAmelCase      ) + token
																						__magic_name__				:							int				=      True
																						__magic_name__				:							int				=      []
																else:
																						current_sub_tokens.append(_UpperCAmelCase      )
																						__magic_name__				:							Tuple				=      False
										out_string += self.sp_model.decode(_UpperCAmelCase      )
										return out_string.strip()
				def  UpperCamelCase_						(  self     ,      _UpperCAmelCase     ,      _UpperCAmelCase = False     ,      _UpperCAmelCase = None     ,      _UpperCAmelCase = True     ,      **_UpperCAmelCase     ,      ):




										"""simple docstring"""




										__magic_name__				:							Tuple				=      kwargs.pop('use_source_tokenizer'     ,      _UpperCAmelCase      )

										__magic_name__				:							List[str]				=      self.convert_ids_to_tokens(_UpperCAmelCase     ,      skip_special_tokens=_UpperCAmelCase      )

										# 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
										__magic_name__				:							Optional[int]				=      []
										__magic_name__				:							Optional[int]				=      []
										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(_UpperCAmelCase      )      )
																												__magic_name__				:							str				=      []
																						sub_texts.append(_UpperCAmelCase      )
																else:
																						current_sub_text.append(_UpperCAmelCase      )
										if current_sub_text:
																sub_texts.append(self.convert_tokens_to_string(_UpperCAmelCase      )      )

										# Mimic the behavior of the Rust tokenizer:
										# No space after <unk>
										if spaces_between_special_tokens:
																__magic_name__				:							List[Any]				=      re.sub(R'(<unk>) '     ,      R'\1'     ,      ' '.join(_UpperCAmelCase      )      )
										else:
																__magic_name__				:							Optional[int]				=      ''.join(_UpperCAmelCase      )

										__magic_name__				:							Dict				=      (
										    clean_up_tokenization_spaces
										    if clean_up_tokenization_spaces is not None
										    else self.clean_up_tokenization_spaces
										)
										if clean_up_tokenization_spaces:
																__magic_name__				:							Union[str, Any]				=      self.clean_up_tokenization(_UpperCAmelCase      )
																return clean_text
										else:
																return text
				def  UpperCamelCase_						(  self     ,      _UpperCAmelCase     ,      _UpperCAmelCase = None      ):




										"""simple docstring"""




										__magic_name__				:							List[Any]				=      [self.sep_token_id]
										__magic_name__				:							List[Any]				=      [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      ):




										"""simple docstring"""




										if already_has_special_tokens:
																return super().get_special_tokens_mask(
																    token_ids_a=_UpperCAmelCase     ,      token_ids_a=_UpperCAmelCase     ,      already_has_special_tokens=_UpperCAmelCase      )

										if token_ids_a is not None:
																return [1] + ([0] * len(_UpperCAmelCase      )) + [1] + ([0] * len(_UpperCAmelCase      )) + [1]
										return [1] + ([0] * len(_UpperCAmelCase      )) + [1]
				def  UpperCamelCase_						(  self     ,      _UpperCAmelCase     ,      _UpperCAmelCase = None      ):




										"""simple docstring"""




										__magic_name__				:							Optional[int]				=      [self.sep_token_id]
										__magic_name__				:							Dict				=      [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      ):




										"""simple docstring"""




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

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

										return (out_vocab_file,)






import argparse

from . import (
    BertConfig,
    BertGenerationConfig,
    BertGenerationDecoder,
    BertGenerationEncoder,
    load_tf_weights_in_bert_generation,
    logging,
)


logging.set_verbosity_info()
def  __SCREAMING_SNAKE_CASE       (				lowercase__  :Optional[int]      ,						lowercase__  :List[Any]      ,						lowercase__  :str      ,						lowercase__  :List[str]      ,						lowercase__  :List[Any]				)       ->  List[Any]:
						# Initialise PyTorch model
						__magic_name__				:							Any				=      BertConfig.from_pretrained(
						    'bert-large-cased'      ,						vocab_size=lowercase__      ,						max_position_embeddings=5_1_2      ,						is_decoder=lowercase__      ,						add_cross_attention=lowercase__      ,						)
						__magic_name__				:							Optional[int]				=      bert_config.to_dict()
						del bert_config_dict["type_vocab_size"]
						__magic_name__				:							Optional[int]				=      BertGenerationConfig(**lowercase__				)
						if is_encoder:
												__magic_name__				:							Dict				=      BertGenerationEncoder(lowercase__				)
						else:
												__magic_name__				:							str				=      BertGenerationDecoder(lowercase__				)
						print(f"Building PyTorch model from configuration: {config}"				)

						# Load weights from tf checkpoint
						load_tf_weights_in_bert_generation(
						    lowercase__      ,						lowercase__      ,						model_class='bert'      ,						is_encoder_named_decoder=lowercase__      ,						is_encoder=lowercase__      ,						)

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


if __name__ == "__main__":
			snake_case__     							= argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    '--tf_hub_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
			)
			parser.add_argument(
			    '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
			)
			parser.add_argument(
			    '--is_encoder_named_decoder',
			    action='store_true',
			    help='If decoder has to be renamed to encoder in PyTorch model.',
			)
			parser.add_argument('--is_encoder', action='store_true', help='If model is an encoder.')
			parser.add_argument('--vocab_size', default=5_0358, type=int, help='Vocab size of model')
			snake_case__     							= parser.parse_args()
			convert_tf_checkpoint_to_pytorch(
			    args.tf_hub_path,
			    args.pytorch_dump_path,
			    args.is_encoder_named_decoder,
			    args.vocab_size,
			    is_encoder=args.is_encoder,
			)


