


'''simple docstring'''

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      snake_case    (    _SCREAMING_SNAKE_CASE			):





  """simple docstring"""




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






  def __init__(    self			,  UpperCamelCase			,  UpperCamelCase     ):







   """simple docstring"""
   lowerCamelCase_      =  False
   super().__init__(_lowerCAmelCase			,  _lowerCAmelCase     )






  def __call__(    self			,  UpperCamelCase=None			,  UpperCamelCase = None			,  UpperCamelCase = True			,  UpperCamelCase = False			,  UpperCamelCase = None			,  UpperCamelCase = None			,  UpperCamelCase = 2048			,  UpperCamelCase = 0			,  UpperCamelCase = None			,  UpperCamelCase = None			,  UpperCamelCase = False			,  UpperCamelCase = False			,  UpperCamelCase = False			,  UpperCamelCase = False			,  UpperCamelCase = False			,  UpperCamelCase = True			,  UpperCamelCase = None			,  **UpperCamelCase			,  ):







   """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:
    lowerCamelCase_      =  self.tokenizer
    lowerCamelCase_      =  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
    lowerCamelCase_      =  self.image_processor(
        _lowerCAmelCase			,  return_tensors=_lowerCAmelCase			,  max_patches=_lowerCAmelCase			,  **_lowerCAmelCase     )
   else:
    # add pixel_values and bbox
    lowerCamelCase_      =  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:
    lowerCamelCase_      =  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:
     lowerCamelCase_      =  text_encoding.pop("attention_mask"     )
    if "input_ids" in text_encoding:
     lowerCamelCase_      =  text_encoding.pop("input_ids"     )
   else:
    lowerCamelCase_      =  None

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

   return encoding_image_processor






  def   snake_case		(    self			,  *UpperCamelCase			,  **UpperCamelCase     ):







   """simple docstring"""
   return self.tokenizer.batch_decode(*_lowerCAmelCase			,  **_lowerCAmelCase     )






  def   snake_case		(    self			,  *UpperCamelCase			,  **UpperCamelCase     ):







   """simple docstring"""
   return self.tokenizer.decode(*_lowerCAmelCase			,  **_lowerCAmelCase     )







  @property
  def   snake_case		(    self     ):







   """simple docstring"""
   lowerCamelCase_      =  self.tokenizer.model_input_names
   lowerCamelCase_      =  self.image_processor.model_input_names
   return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names     )     )









import warnings

from ...utils import logging
from .image_processing_imagegpt import ImageGPTImageProcessor


lowerCamelCase__ :   List[str]					=							logging.get_logger(__name__)





class        lowerCamelCase_							(      _SCREAMING_SNAKE_CASE					):


 '''simple docstring'''







 def __init__(							self  :		str				,				*_lowerCAmelCase  :		Optional[Any]				,				**_lowerCAmelCase  :		Optional[Any]						):
       warnings.warn(
           'The class ImageGPTFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'
           ' Please use ImageGPTImageProcessor instead.'				,				_lowerCAmelCase				,				)
       super().__init__(*_lowerCAmelCase				,				**_lowerCAmelCase						)