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


lowerCAmelCase__	:  Any						=  logging.get_logger(__name__)
lowerCAmelCase__	:  Dict						=  {'''vocab_file''': '''spiece.model'''}

lowerCAmelCase__	:  List[str]						=  {
    '''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''',
    },
}

lowerCAmelCase__	:  Tuple						=  {
    '''google/fnet-base''': 5_12,
    '''google/fnet-large''': 5_12,
}

lowerCAmelCase__	:  List[str]						=  '''▁'''





class 					__snake_case    (     _lowerCamelCase      ):
					__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       ,			)	->    None:



									'''simple docstring'''







									snake_case__   :      List[Any]   				= (
									    AddedToken(__UpperCamelCase       ,			lstrip=__UpperCamelCase       ,			rstrip=__UpperCamelCase       ,			normalized=__UpperCamelCase						)
									    if isinstance(__UpperCamelCase       ,			__UpperCamelCase						)
									    else mask_token
									)

									snake_case__   :      Union[str, 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       ,			)

									snake_case__   :      List[Any]   				= do_lower_case
									snake_case__   :      Union[str, Any]   				= remove_space
									snake_case__   :      int   				= keep_accents
									snake_case__   :      Dict   				= vocab_file

									snake_case__   :      Optional[int]   				= spm.SentencePieceProcessor(**self.sp_model_kwargs						)
									self.sp_model.Load(__UpperCamelCase						)




					@property
					def      __a     (  self						)	->    Optional[Any]:



									'''simple docstring'''







									return len(self.sp_model						)




					def      __a     (  self						)	->    Any:



									'''simple docstring'''







									snake_case__   :      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						)	->    List[str]:



									'''simple docstring'''







									snake_case__   :      Dict   				= self.__dict__.copy()
									snake_case__   :      int   				= None
									return state




					def __setstate__(  self       ,			__UpperCamelCase						)	->    str:



									'''simple docstring'''







									snake_case__   :      Any   				= d

									# for backward compatibility
									if not hasattr(self       ,			'sp_model_kwargs'						):
													snake_case__   :      List[str]   				= {}

									snake_case__   :      str   				= spm.SentencePieceProcessor(**self.sp_model_kwargs						)
									self.sp_model.Load(self.vocab_file						)




					def      __a     (  self       ,			__UpperCamelCase						)	->    Dict:



									'''simple docstring'''







									if self.remove_space:
													snake_case__   :      Optional[int]   				= ' '.join(inputs.strip().split()						)
									else:
													snake_case__   :      Optional[int]   				= inputs
									snake_case__   :      Any   				= outputs.replace('``'       ,			'"'						).replace('\'\''       ,			'"'						)

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

									return outputs




					def      __a     (  self       ,			__UpperCamelCase						)	->    List[str]:



									'''simple docstring'''







									snake_case__   :      Tuple   				= self.preprocess_text(__UpperCamelCase						)
									snake_case__   :      Any   				= self.sp_model.encode(__UpperCamelCase       ,			out_type=__UpperCamelCase						)
									snake_case__   :      str   				= []
									for piece in pieces:
													if len(__UpperCamelCase						) > 1 and piece[-1] == str(','						) and piece[-2].isdigit():
																	snake_case__   :      int   				= 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:
																									snake_case__   :      Tuple   				= cur_pieces[1:]
																					else:
																									snake_case__   :      Dict   				= cur_pieces[0][1:]
																	cur_pieces.append(piece[-1]						)
																	new_pieces.extend(__UpperCamelCase						)
													else:
																	new_pieces.append(__UpperCamelCase						)

									return new_pieces




					def      __a     (  self       ,			__UpperCamelCase						)	->    Any:



									'''simple docstring'''







									return self.sp_model.PieceToId(__UpperCamelCase						)




					def      __a     (  self       ,			__UpperCamelCase						)	->    Any:



									'''simple docstring'''







									return self.sp_model.IdToPiece(__UpperCamelCase						)




					def      __a     (  self       ,			__UpperCamelCase						)	->    int:



									'''simple docstring'''







									snake_case__   :      int   				= []
									snake_case__   :      Optional[int]   				= ''
									snake_case__   :      Dict   				= 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
																	snake_case__   :      Tuple   				= True
																	snake_case__   :      Dict   				= []
													else:
																	current_sub_tokens.append(__UpperCamelCase						)
																	snake_case__   :      Any   				= False
									out_string += self.sp_model.decode(__UpperCamelCase						)
									return out_string.strip()




					def      __a     (  self       ,			__UpperCamelCase       ,			__UpperCamelCase = False       ,			__UpperCamelCase = None       ,			__UpperCamelCase = True       ,			**__UpperCamelCase       ,			)	->    str:



									'''simple docstring'''







									snake_case__   :      str   				= kwargs.pop('use_source_tokenizer'       ,			__UpperCamelCase						)

									snake_case__   :      int   				= 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
									snake_case__   :      Union[str, Any]   				= []
									snake_case__   :      Optional[Any]   				= []
									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						)						)
																					snake_case__   :      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:
													snake_case__   :      int   				= re.sub(r'(<unk>) '       ,			r'\1'       ,			' '.join(__UpperCamelCase						)						)
									else:
													snake_case__   :      List[str]   				= ''.join(__UpperCamelCase						)

									snake_case__   :      List[Any]   				= (
									    clean_up_tokenization_spaces
									    if clean_up_tokenization_spaces is not None
									    else self.clean_up_tokenization_spaces
									)
									if clean_up_tokenization_spaces:
													snake_case__   :      int   				= self.clean_up_tokenization(__UpperCamelCase						)
													return clean_text
									else:
													return text




					def      __a     (  self       ,			__UpperCamelCase       ,			__UpperCamelCase = None						)	->    List[int]:



									'''simple docstring'''







									snake_case__   :      Any   				= [self.sep_token_id]
									snake_case__   :      Union[str, 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      __a     (  self       ,			__UpperCamelCase       ,			__UpperCamelCase = None       ,			__UpperCamelCase = False						)	->    List[int]:



									'''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      __a     (  self       ,			__UpperCamelCase       ,			__UpperCamelCase = None						)	->    List[int]:



									'''simple docstring'''







									snake_case__   :      Any   				= [self.sep_token_id]
									snake_case__   :      int   				= [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      __a     (  self       ,			__UpperCamelCase       ,			__UpperCamelCase = None						)	->    Tuple[str]:



									'''simple docstring'''







									if not os.path.isdir(__UpperCamelCase						):
													logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""						)
													return
									snake_case__   :      List[Any]   				= 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:
																	snake_case__   :      List[Any]   				= self.sp_model.serialized_model_proto()
																	fi.write(__UpperCamelCase						)

									return (out_vocab_file,)



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowerCAmelCase__	:  int						=  {
    '''configuration_switch_transformers''': [
        '''SWITCH_TRANSFORMERS_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''SwitchTransformersConfig''',
        '''SwitchTransformersOnnxConfig''',
    ]
}

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				lowerCAmelCase__	:  List[Any]						=  [
				    '''SWITCH_TRANSFORMERS_PRETRAINED_MODEL_ARCHIVE_LIST''',
				    '''SwitchTransformersEncoderModel''',
				    '''SwitchTransformersForConditionalGeneration''',
				    '''SwitchTransformersModel''',
				    '''SwitchTransformersPreTrainedModel''',
				    '''SwitchTransformersTop1Router''',
				    '''SwitchTransformersSparseMLP''',
				]


if TYPE_CHECKING:
				from .configuration_switch_transformers import (
				    SWITCH_TRANSFORMERS_PRETRAINED_CONFIG_ARCHIVE_MAP,
				    SwitchTransformersConfig,
				    SwitchTransformersOnnxConfig,
				)

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_switch_transformers import (
								    SWITCH_TRANSFORMERS_PRETRAINED_MODEL_ARCHIVE_LIST,
								    SwitchTransformersEncoderModel,
								    SwitchTransformersForConditionalGeneration,
								    SwitchTransformersModel,
								    SwitchTransformersPreTrainedModel,
								    SwitchTransformersSparseMLP,
								    SwitchTransformersTopaRouter,
								)


else:
				import sys

				lowerCAmelCase__	:  Union[str, Any]						=  _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)


