



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


A__       =			logging.get_logger(__name__)
A__       =			{"""vocab_file""": """spiece.model"""}

A__       =			{
    """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""",
    },
}

A__       =			{
    """google/fnet-base""": 5_12,
    """google/fnet-large""": 5_12,
}

A__       =			"""▁"""





class    __lowerCAmelCase					(	lowerCamelCase__			):
				__lowerCamelCase					=							VOCAB_FILES_NAMES
				__lowerCamelCase					=							PRETRAINED_VOCAB_FILES_MAP
				__lowerCamelCase					=							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				__lowerCamelCase					=							['''input_ids''', '''token_type_ids''']




				def __init__(				self		,							_snake_case		,							_snake_case=False		,							_snake_case=True		,							_snake_case=True		,							_snake_case="<unk>"		,							_snake_case="[SEP]"		,							_snake_case="<pad>"		,							_snake_case="[CLS]"		,							_snake_case="[MASK]"		,							_snake_case = None		,							**_snake_case		,							):



									"""simple docstring"""





									_lowerCAmelCase         =       (
									    AddedToken(_snake_case		,							lstrip=_snake_case		,							rstrip=_snake_case		,							normalized=_snake_case						)
									    if isinstance(_snake_case		,							_snake_case						)
									    else mask_token
									)

									_lowerCAmelCase         =       {} if sp_model_kwargs is None else sp_model_kwargs

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

									_lowerCAmelCase         =       do_lower_case
									_lowerCAmelCase         =       remove_space
									_lowerCAmelCase         =       keep_accents
									_lowerCAmelCase         =       vocab_file

									_lowerCAmelCase         =       spm.SentencePieceProcessor(**self.sp_model_kwargs						)
									self.sp_model.Load(_snake_case						)




				@property
				def 				snake_case  (				self						):



									"""simple docstring"""





									return len(self.sp_model						)




				def 				snake_case  (				self						):



									"""simple docstring"""





									_lowerCAmelCase         =       {self.convert_ids_to_tokens(_snake_case						): i for i in range(self.vocab_size						)}
									vocab.update(self.added_tokens_encoder						)
									return vocab




				def __getstate__(				self						):



									"""simple docstring"""





									_lowerCAmelCase         =       self.__dict__.copy()
									_lowerCAmelCase         =       None
									return state




				def __setstate__(				self		,							_snake_case						):



									"""simple docstring"""





									_lowerCAmelCase         =       d

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

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




				def 				snake_case  (				self		,							_snake_case						):



									"""simple docstring"""





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

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

									return outputs




				def 				snake_case  (				self		,							_snake_case						):



									"""simple docstring"""





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

									return new_pieces




				def 				snake_case  (				self		,							_snake_case						):



									"""simple docstring"""





									return self.sp_model.PieceToId(_snake_case						)




				def 				snake_case  (				self		,							_snake_case						):



									"""simple docstring"""





									return self.sp_model.IdToPiece(_snake_case						)




				def 				snake_case  (				self		,							_snake_case						):



									"""simple docstring"""





									_lowerCAmelCase         =       []
									_lowerCAmelCase         =       """"""
									_lowerCAmelCase         =       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(_snake_case						) + token
																			_lowerCAmelCase         =       True
																			_lowerCAmelCase         =       []
														else:
																			current_sub_tokens.append(_snake_case						)
																			_lowerCAmelCase         =       False
									out_string += self.sp_model.decode(_snake_case						)
									return out_string.strip()




				def 				snake_case  (				self		,							_snake_case		,							_snake_case = False		,							_snake_case = None		,							_snake_case = True		,							**_snake_case		,							):



									"""simple docstring"""





									_lowerCAmelCase         =       kwargs.pop("""use_source_tokenizer"""		,							_snake_case						)

									_lowerCAmelCase         =       self.convert_ids_to_tokens(_snake_case		,							skip_special_tokens=_snake_case						)

									# 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
									_lowerCAmelCase         =       []
									_lowerCAmelCase         =       []
									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(_snake_case						)						)
																								_lowerCAmelCase         =       []
																			sub_texts.append(_snake_case						)
														else:
																			current_sub_text.append(_snake_case						)
									if current_sub_text:
														sub_texts.append(self.convert_tokens_to_string(_snake_case						)						)

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

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




				def 				snake_case  (				self		,							_snake_case		,							_snake_case = None						):



									"""simple docstring"""





									_lowerCAmelCase         =       [self.sep_token_id]
									_lowerCAmelCase         =       [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 				snake_case  (				self		,							_snake_case		,							_snake_case = None		,							_snake_case = False						):



									"""simple docstring"""





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

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




				def 				snake_case  (				self		,							_snake_case		,							_snake_case = None						):



									"""simple docstring"""





									_lowerCAmelCase         =       [self.sep_token_id]
									_lowerCAmelCase         =       [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 				snake_case  (				self		,							_snake_case		,							_snake_case = None						):



									"""simple docstring"""





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

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

									return (out_vocab_file,)






"""simple docstring"""
from collections import defaultdict







def       UpperCamelCase_					(     lowerCamelCase							:					str   )						->     dict:



       """simple docstring"""







       __magic_name__			:		defaultdict[str, int]        =     defaultdict(lowerCamelCase   )
       # Creating a dictionary containing count of each word
       for word in sentence.split():
              occurrence[word] += 1
       return occurrence


if __name__ == "__main__":
 for word, count in word_occurrence("""INPUT STRING""").items():
  print(F"""{word}: {count}""")
