



'''simple docstring'''
import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding






class 							lowercase__	(							lowercase   ):
	lowercase__  				=			["""image_processor""", """tokenizer"""]
	lowercase__  				=			"""ViTImageProcessor"""
	lowercase__  				=			("""CLIPTokenizer""", """CLIPTokenizerFast""")





	def __init__( self					:				Optional[int] ,lowerCamelCase__					:				Union[str, Any]=None ,lowerCamelCase__					:				Optional[Any]=None ,**lowerCamelCase__					:				Optional[Any] ):







							'''simple docstring'''
							_UpperCamelCase					:		int 				=   None
							if "feature_extractor" in kwargs:
													warnings.warn(
													    'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
													    ' instead.' ,lowerCamelCase__ ,)
													_UpperCamelCase					:		List[Any] 				=   kwargs.pop('feature_extractor' )

							_UpperCamelCase					:		Tuple 				=   image_processor if image_processor is not None else feature_extractor
							if image_processor is None:
													raise ValueError('You need to specify an `image_processor`.' )
							if tokenizer is None:
													raise ValueError('You need to specify a `tokenizer`.' )

							super().__init__(lowerCamelCase__ ,lowerCamelCase__ )





	def __call__( self					:				List[Any] ,lowerCamelCase__					:				List[Any]=None ,lowerCamelCase__					:				Any=None ,lowerCamelCase__					:				str=None ,lowerCamelCase__					:				List[Any]=None ,**lowerCamelCase__					:				List[str] ):







							'''simple docstring'''
							if text is None and visual_prompt is None and images is None:
													raise ValueError('You have to specify either text, visual prompt or images.' )

							if text is not None and visual_prompt is not None:
													raise ValueError('You have to specify exactly one type of prompt. Either text or visual prompt.' )

							if text is not None:
													_UpperCamelCase					:		Optional[int] 				=   self.tokenizer(lowerCamelCase__ ,return_tensors=lowerCamelCase__ ,**lowerCamelCase__ )

							if visual_prompt is not None:
													_UpperCamelCase					:		Any 				=   self.image_processor(lowerCamelCase__ ,return_tensors=lowerCamelCase__ ,**lowerCamelCase__ )

							if images is not None:
													_UpperCamelCase					:		Union[str, Any] 				=   self.image_processor(lowerCamelCase__ ,return_tensors=lowerCamelCase__ ,**lowerCamelCase__ )

							if visual_prompt is not None and images is not None:
													_UpperCamelCase					:		Dict 				=   {
													    'pixel_values': image_features.pixel_values,
													    'conditional_pixel_values': prompt_features.pixel_values,
													}
													return encoding
							elif text is not None and images is not None:
													_UpperCamelCase					:		Optional[int] 				=   image_features.pixel_values
													return encoding
							elif text is not None:
													return encoding
							elif visual_prompt is not None:
													_UpperCamelCase					:		Dict 				=   {
													    'conditional_pixel_values': prompt_features.pixel_values,
													}
													return encoding
							else:
													return BatchEncoding(data=dict(**lowerCamelCase__ ) ,tensor_type=lowerCamelCase__ )





	def 	UpperCamelCase_  ( self					:				Tuple ,*lowerCamelCase__					:				Dict ,**lowerCamelCase__					:				int ):







							'''simple docstring'''
							return self.tokenizer.batch_decode(*lowerCamelCase__ ,**lowerCamelCase__ )





	def 	UpperCamelCase_  ( self					:				Tuple ,*lowerCamelCase__					:				Any ,**lowerCamelCase__					:				int ):







							'''simple docstring'''
							return self.tokenizer.decode(*lowerCamelCase__ ,**lowerCamelCase__ )





	@property
	def 	UpperCamelCase_  ( self					:				Union[str, Any] ):







							'''simple docstring'''
							warnings.warn(
							    '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.' ,lowerCamelCase__ ,)
							return self.image_processor_class





	@property
	def 	UpperCamelCase_  ( self					:				List[str] ):







							'''simple docstring'''
							warnings.warn(
							    '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.' ,lowerCamelCase__ ,)
							return self.image_processor








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:
 lowerCamelCase				:		List[Any]     =   None

lowerCamelCase				:		int     =   logging.get_logger(__name__)
lowerCamelCase				:		Optional[int]     =   {"vocab_file": "spiece.model", "tokenizer_file": "tokenizer.json"}

lowerCamelCase				:		Optional[int]     =   {
    "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",
    },
}

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

lowerCamelCase				:		Any     =   "▁"







class   A(						UpperCamelCase   ):



 '''simple docstring'''


 UpperCamelCase						   =						VOCAB_FILES_NAMES
 UpperCamelCase						   =						PRETRAINED_VOCAB_FILES_MAP
 UpperCamelCase						   =						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 UpperCamelCase						   =						['''input_ids''', '''token_type_ids''']
 UpperCamelCase						   =						FNetTokenizer

 def __init__(				self					:  Optional[int]						,							A_					:  Any=None						,							A_					:  int=None						,							A_					:  int=False						,							A_					:  Optional[int]=True						,							A_					:  List[Any]=True						,							A_					:  Tuple="<unk>"						,							A_					:  Optional[int]="[SEP]"						,							A_					:  List[Any]="<pad>"						,							A_					:  Optional[int]="[CLS]"						,							A_					:  Optional[Any]="[MASK]"						,							**A_					:  Dict						,							)   ->	List[str]:

       """simple docstring"""






       lowerCamelCase_		     =    (
           AddedToken(A_						,							lstrip=A_						,							rstrip=A_						,							normalized=A_  )
           if isinstance(A_						,							A_  )
           else mask_token
       )

       super().__init__(
           A_						,							tokenizer_file=A_						,							do_lower_case=A_						,							remove_space=A_						,							keep_accents=A_						,							unk_token=A_						,							sep_token=A_						,							pad_token=A_						,							cls_token=A_						,							mask_token=A_						,							**A_						,							)

       lowerCamelCase_		     =    do_lower_case
       lowerCamelCase_		     =    remove_space
       lowerCamelCase_		     =    keep_accents
       lowerCamelCase_		     =    vocab_file
       lowerCamelCase_		     =    False if not self.vocab_file else True

 def 				a__ (				self					:  List[Any]						,							A_					:  List[int]						,							A_					:  Optional[List[int]] = None  )   ->	List[int]:

       """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 				a__ (				self					:  Tuple						,							A_					:  List[int]						,							A_					:  Optional[List[int]] = None  )   ->	List[int]:

       """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 				a__ (				self					:  Dict						,							A_					:  str						,							A_					:  Optional[str] = None  )   ->	Tuple[str]:

       """simple docstring"""






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

       if os.path.abspath(self.vocab_file  ) != os.path.abspath(A_  ):
             copyfile(self.vocab_file						,							A_  )

       return (out_vocab_file,)



