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:
     _SCREAMING_SNAKE_CASE 		=      None

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

_SCREAMING_SNAKE_CASE 		=      {
    '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',
    },
}

_SCREAMING_SNAKE_CASE 		=      {
    'google/fnet-base': 5_1_2,
    'google/fnet-large': 5_1_2,
}

_SCREAMING_SNAKE_CASE 		=      '▁'
class SCREAMING_SNAKE_CASE_		(      __SCREAMING_SNAKE_CASE		):
   __lowerCAmelCase           =				VOCAB_FILES_NAMES
   __lowerCAmelCase           =				PRETRAINED_VOCAB_FILES_MAP
   __lowerCAmelCase           =				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   __lowerCAmelCase           =				["""input_ids""", """token_type_ids"""]
   __lowerCAmelCase           =				FNetTokenizer







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





    """simple docstring"""


    UpperCamelCase			  =      (
        AddedToken(lowerCAmelCase__   ,   lstrip=lowerCAmelCase__   ,   rstrip=lowerCAmelCase__   ,   normalized=lowerCAmelCase__				)
        if isinstance(lowerCAmelCase__   ,   lowerCAmelCase__				)
        else mask_token
    )

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

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







   def 						lowerCamelCase_	(     self						:						int   ,   lowerCamelCase_						:						Tuple   ,   lowerCamelCase_						:						Union[str, Any] = None				):





    """simple docstring"""


    UpperCamelCase			  =      [self.sep_token_id]
    UpperCamelCase			  =      [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]   ,   lowerCamelCase_						:						str   ,   lowerCamelCase_						:						Dict = None				):





    """simple docstring"""


    UpperCamelCase			  =      [self.sep_token_id]
    UpperCamelCase			  =      [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						:						Any   ,   lowerCamelCase_						:						List[Any]   ,   lowerCamelCase_						:						Any = None				):





    """simple docstring"""


    if not os.path.isdir(lowerCAmelCase__				):
     logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""				)
     return
    UpperCamelCase			  =      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__				):
     copyfile(self.vocab_file   ,   lowerCAmelCase__				)

    return (out_vocab_file,)




from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType







class 	__lowercase    (__SCREAMING_SNAKE_CASE  ):





							"""simple docstring"""







							_UpperCAmelCase      =						["""image_processor""", """tokenizer"""]
							_UpperCAmelCase      =						"""Pix2StructImageProcessor"""
							_UpperCAmelCase      =						("""T5Tokenizer""", """T5TokenizerFast""")






							def __init__(      self			,						lowerCAmelCase__			,						lowerCAmelCase__       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     Dict					=					False
										super().__init__(lowerCAmelCase__			,						lowerCAmelCase__       )






							def __call__(      self			,						lowerCAmelCase__=None			,						lowerCAmelCase__ = None			,						lowerCAmelCase__ = True			,						lowerCAmelCase__ = False			,						lowerCAmelCase__ = None			,						lowerCAmelCase__ = None			,						lowerCAmelCase__ = 2_0_4_8			,						lowerCAmelCase__ = 0			,						lowerCAmelCase__ = None			,						lowerCAmelCase__ = None			,						lowerCAmelCase__ = False			,						lowerCAmelCase__ = False			,						lowerCAmelCase__ = False			,						lowerCAmelCase__ = False			,						lowerCAmelCase__ = False			,						lowerCAmelCase__ = True			,						lowerCAmelCase__ = None			,						**lowerCAmelCase__			,						):





										"""simple docstring"""




										if images is None and text is None:
													raise ValueError('You have to specify either images or text.'       )

										# Get only text
										if images is None and not self.image_processor.is_vqa:
													SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					self.tokenizer
													SCREAMING_SNAKE_CASE_		:     str					=					self.tokenizer(
													    text=lowerCAmelCase__			,						add_special_tokens=lowerCAmelCase__			,						padding=lowerCAmelCase__			,						truncation=lowerCAmelCase__			,						max_length=lowerCAmelCase__			,						stride=lowerCAmelCase__			,						pad_to_multiple_of=lowerCAmelCase__			,						return_attention_mask=lowerCAmelCase__			,						return_overflowing_tokens=lowerCAmelCase__			,						return_special_tokens_mask=lowerCAmelCase__			,						return_offsets_mapping=lowerCAmelCase__			,						return_token_type_ids=lowerCAmelCase__			,						return_length=lowerCAmelCase__			,						verbose=lowerCAmelCase__			,						return_tensors=lowerCAmelCase__			,						**lowerCAmelCase__			,						)
													return text_encoding

										if not self.image_processor.is_vqa:
													# add pixel_values
													SCREAMING_SNAKE_CASE_		:     List[Any]					=					self.image_processor(
													    lowerCAmelCase__			,						return_tensors=lowerCAmelCase__			,						max_patches=lowerCAmelCase__			,						**lowerCAmelCase__       )
										else:
													# add pixel_values and bbox
													SCREAMING_SNAKE_CASE_		:     Any					=					self.image_processor(
													    lowerCAmelCase__			,						return_tensors=lowerCAmelCase__			,						max_patches=lowerCAmelCase__			,						header_text=lowerCAmelCase__			,						**lowerCAmelCase__       )

										if text is not None and not self.image_processor.is_vqa:
													SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					self.tokenizer(
													    text=lowerCAmelCase__			,						add_special_tokens=lowerCAmelCase__			,						padding=lowerCAmelCase__			,						truncation=lowerCAmelCase__			,						max_length=lowerCAmelCase__			,						stride=lowerCAmelCase__			,						pad_to_multiple_of=lowerCAmelCase__			,						return_attention_mask=lowerCAmelCase__			,						return_overflowing_tokens=lowerCAmelCase__			,						return_special_tokens_mask=lowerCAmelCase__			,						return_offsets_mapping=lowerCAmelCase__			,						return_token_type_ids=lowerCAmelCase__			,						return_length=lowerCAmelCase__			,						verbose=lowerCAmelCase__			,						return_tensors=lowerCAmelCase__			,						**lowerCAmelCase__			,						)

													if "attention_mask" in text_encoding:
																SCREAMING_SNAKE_CASE_		:     Any					=					text_encoding.pop('attention_mask'       )
													if "input_ids" in text_encoding:
																SCREAMING_SNAKE_CASE_		:     List[str]					=					text_encoding.pop('input_ids'       )
										else:
													SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					None

										if text_encoding is not None:
													encoding_image_processor.update(lowerCAmelCase__       )

										return encoding_image_processor






							def 					UpperCamelCase__     (      self			,						*lowerCAmelCase__			,						**lowerCAmelCase__       ):





										"""simple docstring"""




										return self.tokenizer.batch_decode(*lowerCAmelCase__			,						**lowerCAmelCase__       )






							def 					UpperCamelCase__     (      self			,						*lowerCAmelCase__			,						**lowerCAmelCase__       ):





										"""simple docstring"""




										return self.tokenizer.decode(*lowerCAmelCase__			,						**lowerCAmelCase__       )






							@property
							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     List[str]					=					self.tokenizer.model_input_names
										SCREAMING_SNAKE_CASE_		:     Optional[int]					=					self.image_processor.model_input_names
										return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names       )       )





