






'''simple docstring'''







import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
			from .tokenization_fnet import FNetTokenizer
else:
			_A			:    str					  =None

_A			:    str					  =logging.get_logger(__name__)
_A			:    Any					  ={'''vocab_file''': '''spiece.model''', '''tokenizer_file''': '''tokenizer.json'''}

_A			:    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''',
    },
    '''tokenizer_file''': {
        '''google/fnet-base''': '''https://huggingface.co/google/fnet-base/resolve/main/tokenizer.json''',
        '''google/fnet-large''': '''https://huggingface.co/google/fnet-large/resolve/main/tokenizer.json''',
    },
}

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

_A			:    Optional[Any]					  ='''▁'''

class _lowercase			(       _lowercase	):
							a          = VOCAB_FILES_NAMES
							a          = PRETRAINED_VOCAB_FILES_MAP
							a          = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							a          = ["""input_ids""", """token_type_ids"""]
							a          = FNetTokenizer




							def __init__(							self:       Tuple    ,							UpperCamelCase__:       List[Any]=None    ,							UpperCamelCase__:       Union[str, Any]=None    ,							UpperCamelCase__:       Optional[Any]=False    ,							UpperCamelCase__:       Tuple=True    ,							UpperCamelCase__:       Any=True    ,							UpperCamelCase__:       List[str]="<unk>"    ,							UpperCamelCase__:       Union[str, Any]="[SEP]"    ,							UpperCamelCase__:       List[Any]="<pad>"    ,							UpperCamelCase__:       int="[CLS]"    ,							UpperCamelCase__:       Dict="[MASK]"    ,							**UpperCamelCase__:       Optional[Any]    ,							):
														# Mask token behave like a normal word, i.e. include the space before it and
														# is included in the raw text, there should be a match in a non-normalized sentence.
														lowerCamelCase__      :			Optional[int] 						= (
														    AddedToken(UpperCamelCase__    ,							lstrip=UpperCamelCase__    ,							rstrip=UpperCamelCase__    ,							normalized=UpperCamelCase__  )
														    if isinstance(UpperCamelCase__    ,							UpperCamelCase__  )
														    else mask_token
														)

														super().__init__(
														    UpperCamelCase__    ,							tokenizer_file=UpperCamelCase__    ,							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__    ,							**UpperCamelCase__    ,							)

														lowerCamelCase__      :			Dict 						= do_lower_case
														lowerCamelCase__      :			Union[str, Any] 						= remove_space
														lowerCamelCase__      :			Tuple 						= keep_accents
														lowerCamelCase__      :			List[str] 						= vocab_file
														lowerCamelCase__      :			int 						= False if not self.vocab_file else True




							def 	lowerCamelCase_       (							self:       Any    ,							UpperCamelCase__:       List[int]    ,							UpperCamelCase__:       Optional[List[int]] = None  ):
														lowerCamelCase__      :			Optional[Any] 						= [self.sep_token_id]
														lowerCamelCase__      :			int 						= [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 	lowerCamelCase_       (							self:       Union[str, Any]    ,							UpperCamelCase__:       List[int]    ,							UpperCamelCase__:       Optional[List[int]] = None  ):
														lowerCamelCase__      :			List[str] 						= [self.sep_token_id]
														lowerCamelCase__      :			Optional[Any] 						= [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 	lowerCamelCase_       (							self:       Tuple    ,							UpperCamelCase__:       str    ,							UpperCamelCase__:       Optional[str] = None  ):
														if not os.path.isdir(UpperCamelCase__  ):
																					logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''  )
																					return
														lowerCamelCase__      :			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__  ):
																					copyfile(self.vocab_file    ,							UpperCamelCase__  )

														return (out_vocab_file,)









'''simple docstring'''







from ...utils import is_torch_available, is_transformers_available


if is_transformers_available() and is_torch_available():
			from .pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings, VQDiffusionPipeline

