

'''simple docstring'''






from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


lowerCAmelCase__   		=      logging.get_logger(__name__)


if is_vision_available():
 import PIL






class      lowercase_      (lowerCamelCase__ ):




  """simple docstring"""




  SCREAMING_SNAKE_CASE       :    str							     =     ['pixel_values']






  def __init__( self  :	str   ,lowercase__  :	bool = True   ,lowercase__  :	Dict[str, int] = None   ,lowercase__  :	PILImageResampling = PILImageResampling.BICUBIC   ,lowercase__  :	bool = True   ,lowercase__  :	Dict[str, int] = None   ,lowercase__  :	bool = True   ,lowercase__  :	Union[int, float] = 1 / 2_5_5   ,lowercase__  :	bool = True   ,lowercase__  :	Optional[Union[float, List[float]]] = None   ,lowercase__  :	Optional[Union[float, List[float]]] = None   ,lowercase__  :	bool = True   ,**lowercase__  :	Any   ,):
   super().__init__(**lowercase__     )
   __lowercase				    =					size if size is not None else {'''shortest_edge''': 2_2_4}
   __lowercase				    =					get_size_dict(lowercase__   ,default_to_square=lowercase__     )
   __lowercase				    =					crop_size if crop_size is not None else {'''height''': 2_2_4, '''width''': 2_2_4}
   __lowercase				    =					get_size_dict(lowercase__   ,default_to_square=lowercase__   ,param_name='''crop_size'''     )

   __lowercase				    =					do_resize
   __lowercase				    =					size
   __lowercase				    =					resample
   __lowercase				    =					do_center_crop
   __lowercase				    =					crop_size
   __lowercase				    =					do_rescale
   __lowercase				    =					rescale_factor
   __lowercase				    =					do_normalize
   __lowercase				    =					image_mean if image_mean is not None else OPENAI_CLIP_MEAN
   __lowercase				    =					image_std if image_std is not None else OPENAI_CLIP_STD
   __lowercase				    =					do_convert_rgb






  def    SCREAMING_SNAKE_CASE ( self  :	List[Any]   ,lowercase__  :	np.ndarray   ,lowercase__  :	Dict[str, int]   ,lowercase__  :	PILImageResampling = PILImageResampling.BICUBIC   ,lowercase__  :	Optional[Union[str, ChannelDimension]] = None   ,**lowercase__  :	Dict   ,):
   __lowercase				    =					get_size_dict(lowercase__   ,default_to_square=lowercase__     )
   if "shortest_edge" not in size:
    raise ValueError(F"The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}"     )
   __lowercase				    =					get_resize_output_image_size(lowercase__   ,size=size['''shortest_edge''']   ,default_to_square=lowercase__     )
   return resize(lowercase__   ,size=lowercase__   ,resample=lowercase__   ,data_format=lowercase__   ,**lowercase__     )






  def    SCREAMING_SNAKE_CASE ( self  :	List[str]   ,lowercase__  :	np.ndarray   ,lowercase__  :	Dict[str, int]   ,lowercase__  :	Optional[Union[str, ChannelDimension]] = None   ,**lowercase__  :	Optional[int]   ,):
   __lowercase				    =					get_size_dict(lowercase__     )
   if "height" not in size or "width" not in size:
    raise ValueError(F"The `size` parameter must contain the keys (height, width). Got {size.keys()}"     )
   return center_crop(lowercase__   ,size=(size['''height'''], size['''width'''])   ,data_format=lowercase__   ,**lowercase__     )






  def    SCREAMING_SNAKE_CASE ( self  :	int   ,lowercase__  :	np.ndarray   ,lowercase__  :	Union[int, float]   ,lowercase__  :	Optional[Union[str, ChannelDimension]] = None   ,**lowercase__  :	str   ,):
   return rescale(lowercase__   ,scale=lowercase__   ,data_format=lowercase__   ,**lowercase__     )






  def    SCREAMING_SNAKE_CASE ( self  :	Union[str, Any]   ,lowercase__  :	np.ndarray   ,lowercase__  :	Union[float, List[float]]   ,lowercase__  :	Union[float, List[float]]   ,lowercase__  :	Optional[Union[str, ChannelDimension]] = None   ,**lowercase__  :	List[Any]   ,):
   return normalize(lowercase__   ,mean=lowercase__   ,std=lowercase__   ,data_format=lowercase__   ,**lowercase__     )






  def    SCREAMING_SNAKE_CASE ( self  :	int   ,lowercase__  :	ImageInput   ,lowercase__  :	bool = None   ,lowercase__  :	Dict[str, int] = None   ,lowercase__  :	PILImageResampling = None   ,lowercase__  :	bool = None   ,lowercase__  :	int = None   ,lowercase__  :	bool = None   ,lowercase__  :	float = None   ,lowercase__  :	bool = None   ,lowercase__  :	Optional[Union[float, List[float]]] = None   ,lowercase__  :	Optional[Union[float, List[float]]] = None   ,lowercase__  :	bool = None   ,lowercase__  :	Optional[Union[str, TensorType]] = None   ,lowercase__  :	Optional[ChannelDimension] = ChannelDimension.FIRST   ,**lowercase__  :	Dict   ,):
   __lowercase				    =					do_resize if do_resize is not None else self.do_resize
   __lowercase				    =					size if size is not None else self.size
   __lowercase				    =					get_size_dict(lowercase__   ,param_name='''size'''   ,default_to_square=lowercase__     )
   __lowercase				    =					resample if resample is not None else self.resample
   __lowercase				    =					do_center_crop if do_center_crop is not None else self.do_center_crop
   __lowercase				    =					crop_size if crop_size is not None else self.crop_size
   __lowercase				    =					get_size_dict(lowercase__   ,param_name='''crop_size'''   ,default_to_square=lowercase__     )
   __lowercase				    =					do_rescale if do_rescale is not None else self.do_rescale
   __lowercase				    =					rescale_factor if rescale_factor is not None else self.rescale_factor
   __lowercase				    =					do_normalize if do_normalize is not None else self.do_normalize
   __lowercase				    =					image_mean if image_mean is not None else self.image_mean
   __lowercase				    =					image_std if image_std is not None else self.image_std
   __lowercase				    =					do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

   __lowercase				    =					make_list_of_images(lowercase__     )

   if not valid_images(lowercase__     ):
    raise ValueError(
        '''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
        '''torch.Tensor, tf.Tensor or jax.ndarray.'''     )

   if do_resize and size is None:
    raise ValueError('''Size must be specified if do_resize is True.'''     )

   if do_center_crop and crop_size is None:
    raise ValueError('''Crop size must be specified if do_center_crop is True.'''     )

   if do_rescale and rescale_factor is None:
    raise ValueError('''Rescale factor must be specified if do_rescale is True.'''     )

   if do_normalize and (image_mean is None or image_std is None):
    raise ValueError('''Image mean and std must be specified if do_normalize is True.'''     )

   # PIL RGBA images are converted to RGB
   if do_convert_rgb:
    __lowercase				    =					[convert_to_rgb(lowercase__     ) for image in images]

   # All transformations expect numpy arrays.
   __lowercase				    =					[to_numpy_array(lowercase__     ) for image in images]

   if do_resize:
    __lowercase				    =					[self.resize(image=lowercase__   ,size=lowercase__   ,resample=lowercase__     ) for image in images]

   if do_center_crop:
    __lowercase				    =					[self.center_crop(image=lowercase__   ,size=lowercase__     ) for image in images]

   if do_rescale:
    __lowercase				    =					[self.rescale(image=lowercase__   ,scale=lowercase__     ) for image in images]

   if do_normalize:
    __lowercase				    =					[self.normalize(image=lowercase__   ,mean=lowercase__   ,std=lowercase__     ) for image in images]

   __lowercase				    =					[to_channel_dimension_format(lowercase__   ,lowercase__     ) for image in images]

   __lowercase				    =					{'''pixel_values''': images}
   return BatchFeature(data=lowercase__   ,tensor_type=lowercase__     )



"""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 					lowerCAmelCase_					(   _lowercase     ):



						'''simple docstring'''


						_lowerCamelCase:     List[str]   =     ['''image_processor''', '''tokenizer''']
						_lowerCamelCase:     Optional[int]   =     '''Pix2StructImageProcessor'''
						_lowerCamelCase:     Dict   =     ('''T5Tokenizer''', '''T5TokenizerFast''')


						def __init__(			self  :   Optional[int]	,A_  :   List[str]	,A_  :   Optional[int]   )  -> int:
							A     = False
							super().__init__(A_	,A_   )


						def __call__(			self  :   Any	,A_  :   List[str]=None	,A_  :   Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None	,A_  :   bool = True	,A_  :   Union[bool, str, PaddingStrategy] = False	,A_  :   Union[bool, str, TruncationStrategy] = None	,A_  :   Optional[int] = None	,A_  :   Optional[int] = 2048	,A_  :   int = 0	,A_  :   Optional[int] = None	,A_  :   Optional[bool] = None	,A_  :   bool = False	,A_  :   bool = False	,A_  :   bool = False	,A_  :   bool = False	,A_  :   bool = False	,A_  :   bool = True	,A_  :   Optional[Union[str, TensorType]] = None	,**A_  :   Tuple	,)  -> BatchEncoding:
							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:
								A     = self.tokenizer
								A     = self.tokenizer(
								    text=A_	,add_special_tokens=A_	,padding=A_	,truncation=A_	,max_length=A_	,stride=A_	,pad_to_multiple_of=A_	,return_attention_mask=A_	,return_overflowing_tokens=A_	,return_special_tokens_mask=A_	,return_offsets_mapping=A_	,return_token_type_ids=A_	,return_length=A_	,verbose=A_	,return_tensors=A_	,**A_	,)
								return text_encoding

							if not self.image_processor.is_vqa:
								# add pixel_values
								A     = self.image_processor(
								    A_	,return_tensors=A_	,max_patches=A_	,**A_   )
							else:
								# add pixel_values and bbox
								A     = self.image_processor(
								    A_	,return_tensors=A_	,max_patches=A_	,header_text=A_	,**A_   )

							if text is not None and not self.image_processor.is_vqa:
								A     = self.tokenizer(
								    text=A_	,add_special_tokens=A_	,padding=A_	,truncation=A_	,max_length=A_	,stride=A_	,pad_to_multiple_of=A_	,return_attention_mask=A_	,return_overflowing_tokens=A_	,return_special_tokens_mask=A_	,return_offsets_mapping=A_	,return_token_type_ids=A_	,return_length=A_	,verbose=A_	,return_tensors=A_	,**A_	,)

								if "attention_mask" in text_encoding:
									A     = text_encoding.pop('attention_mask'   )
								if "input_ids" in text_encoding:
									A     = text_encoding.pop('input_ids'   )
							else:
								A     = None

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

							return encoding_image_processor


						def     _SCREAMING_SNAKE_CASE   (			self  :   Optional[int]	,*A_  :   Optional[Any]	,**A_  :   Dict   )  -> Union[str, Any]:
							return self.tokenizer.batch_decode(*A_	,**A_   )


						def     _SCREAMING_SNAKE_CASE   (			self  :   List[str]	,*A_  :   Tuple	,**A_  :   List[str]   )  -> Any:
							return self.tokenizer.decode(*A_	,**A_   )




						@property
						def     _SCREAMING_SNAKE_CASE   (			self  :   List[str]   )  -> Any:
							A     = self.tokenizer.model_input_names
							A     = self.image_processor.model_input_names
							return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names   )   )