
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__						:List[str]					    =       logging.get_logger(__name__)
lowercase__						:List[str]					    =       {"vocab_file": "spiece.model"}

lowercase__						:Optional[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__						:List[Any]					    =       {
    "google/fnet-base": 512,
    "google/fnet-large": 512,
}

lowercase__						:str					    =       "▁"







class 							lowercase						(   __lowerCamelCase						):
							lowercase_			:			Optional[int]			   =VOCAB_FILES_NAMES
							lowercase_			:			Optional[int]			   =PRETRAINED_VOCAB_FILES_MAP
							lowercase_			:			str			   =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							lowercase_			:			Optional[Any]			   =['''input_ids''', '''token_type_ids''']




							def __init__(							self  ,A__  ,A__=False  ,A__=True  ,A__=True  ,A__="<unk>"  ,A__="[SEP]"  ,A__="<pad>"  ,A__="[CLS]"  ,A__="[MASK]"  ,A__ = None  ,**A__  ,):
												# 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.
												lowercase		      =		(
												    AddedToken(__lowerCAmelCase  ,lstrip=__lowerCAmelCase  ,rstrip=__lowerCAmelCase  ,normalized=__lowerCAmelCase)
												    if isinstance(__lowerCAmelCase  ,__lowerCAmelCase)
												    else mask_token
												)

												lowercase		      =		{} if sp_model_kwargs is None else sp_model_kwargs

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

												lowercase		      =		do_lower_case
												lowercase		      =		remove_space
												lowercase		      =		keep_accents
												lowercase		      =		vocab_file

												lowercase		      =		spm.SentencePieceProcessor(**self.sp_model_kwargs)
												self.sp_model.Load(__lowerCAmelCase)




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




							def 					A__ (							self):
												lowercase		      =		{self.convert_ids_to_tokens(__lowerCAmelCase): i for i in range(self.vocab_size)}
												vocab.update(self.added_tokens_encoder)
												return vocab




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




							def __setstate__(							self  ,A__):
												lowercase		      =		d

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

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




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

												if not self.keep_accents:
																	lowercase		      =		unicodedata.normalize('''NFKD'''  ,__lowerCAmelCase)
																	lowercase		      =		''''''.join([c for c in outputs if not unicodedata.combining(__lowerCAmelCase)])
												if self.do_lower_case:
																	lowercase		      =		outputs.lower()

												return outputs




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

												return new_pieces




							def 					A__ (							self  ,A__):
												return self.sp_model.PieceToId(__lowerCAmelCase)




							def 					A__ (							self  ,A__):
												return self.sp_model.IdToPiece(__lowerCAmelCase)




							def 					A__ (							self  ,A__):
												lowercase		      =		[]
												lowercase		      =		''''''
												lowercase		      =		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(__lowerCAmelCase) + token
																						lowercase		      =		True
																						lowercase		      =		[]
																	else:
																						current_sub_tokens.append(__lowerCAmelCase)
																						lowercase		      =		False
												out_string += self.sp_model.decode(__lowerCAmelCase)
												return out_string.strip()




							def 					A__ (							self  ,A__  ,A__ = False  ,A__ = None  ,A__ = True  ,**A__  ,):
												lowercase		      =		kwargs.pop('''use_source_tokenizer'''  ,__lowerCAmelCase)

												lowercase		      =		self.convert_ids_to_tokens(__lowerCAmelCase  ,skip_special_tokens=__lowerCAmelCase)

												# 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
												lowercase		      =		[]
												lowercase		      =		[]
												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(__lowerCAmelCase))
																											lowercase		      =		[]
																						sub_texts.append(__lowerCAmelCase)
																	else:
																						current_sub_text.append(__lowerCAmelCase)
												if current_sub_text:
																	sub_texts.append(self.convert_tokens_to_string(__lowerCAmelCase))

												# Mimic the behavior of the Rust tokenizer:
												# No space after <unk>
												if spaces_between_special_tokens:
																	lowercase		      =		re.sub(r'''(<unk>) '''  ,r'''\1'''  ,''' '''.join(__lowerCAmelCase))
												else:
																	lowercase		      =		''''''.join(__lowerCAmelCase)

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




							def 					A__ (							self  ,A__  ,A__ = None):
												lowercase		      =		[self.sep_token_id]
												lowercase		      =		[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  ,A__  ,A__ = None  ,A__ = False):

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

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




							def 					A__ (							self  ,A__  ,A__ = None):
												lowercase		      =		[self.sep_token_id]
												lowercase		      =		[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  ,A__  ,A__ = None):
												if not os.path.isdir(__lowerCAmelCase):
																	logger.error(f'Vocabulary path ({save_directory}) should be a directory')
																	return
												lowercase		      =		os.path.join(
												    __lowerCAmelCase  ,(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''])

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

												return (out_vocab_file,)





'''simple docstring'''

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


lowerCAmelCase_			:			Tuple    	=   {"configuration_vilt": ["VILT_PRETRAINED_CONFIG_ARCHIVE_MAP", "ViltConfig"]}

try:
       if not is_vision_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCAmelCase_			:			Optional[int]    	=   ["ViltFeatureExtractor"]
       lowerCAmelCase_			:			str    	=   ["ViltImageProcessor"]
       lowerCAmelCase_			:			Any    	=   ["ViltProcessor"]

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCAmelCase_			:			Any    	=   [
           "VILT_PRETRAINED_MODEL_ARCHIVE_LIST",
           "ViltForImageAndTextRetrieval",
           "ViltForImagesAndTextClassification",
           "ViltForTokenClassification",
           "ViltForMaskedLM",
           "ViltForQuestionAnswering",
           "ViltLayer",
           "ViltModel",
           "ViltPreTrainedModel",
       ]


if TYPE_CHECKING:
       from .configuration_vilt import VILT_PRETRAINED_CONFIG_ARCHIVE_MAP, ViltConfig

       try:
              if not is_vision_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .feature_extraction_vilt import ViltFeatureExtractor
              from .image_processing_vilt import ViltImageProcessor
              from .processing_vilt import ViltProcessor

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_vilt import (
                  VILT_PRETRAINED_MODEL_ARCHIVE_LIST,
                  ViltForImageAndTextRetrieval,
                  ViltForImagesAndTextClassification,
                  ViltForMaskedLM,
                  ViltForQuestionAnswering,
                  ViltForTokenClassification,
                  ViltLayer,
                  ViltModel,
                  ViltPreTrainedModel,
              )


else:
       import sys

       lowerCAmelCase_			:			str    	=   _LazyModule(__name__, globals()["__file__"], _import_structure)






