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 convert_to_rgb, 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


if is_vision_available():
     import PIL


_SCREAMING_SNAKE_CASE 		=      logging.get_logger(__name__)
class SCREAMING_SNAKE_CASE_		(      __SCREAMING_SNAKE_CASE		):
   __lowerCAmelCase           =				["""pixel_values"""]







   def __init__(     self						:						List[str]   ,   lowerCamelCase_						:						str = True   ,   lowerCamelCase_						:						Union[str, Any] = None   ,   lowerCamelCase_						:						Dict = PILImageResampling.BICUBIC   ,   lowerCamelCase_						:						int = True   ,   lowerCamelCase_						:						Any = 1 / 255   ,   lowerCamelCase_						:						List[str] = True   ,   lowerCamelCase_						:						Dict = None   ,   lowerCamelCase_						:						Optional[int] = None   ,   lowerCamelCase_						:						str = True   ,   **lowerCamelCase_						:						str   ,   ):





    """simple docstring"""


    super().__init__(**lowerCAmelCase__				)
    UpperCamelCase			  =      size if size is not None else {'height': 384, 'width': 384}
    UpperCamelCase			  =      get_size_dict(lowerCAmelCase__   ,   default_to_square=lowerCAmelCase__				)

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







   def 						lowerCamelCase_	(     self						:						List[Any]   ,   lowerCamelCase_						:						List[Any]   ,   lowerCamelCase_						:						Dict   ,   lowerCamelCase_						:						Tuple = PILImageResampling.BICUBIC   ,   lowerCamelCase_						:						Optional[Any] = None   ,   **lowerCamelCase_						:						int   ,   ):





    """simple docstring"""


    UpperCamelCase			  =      get_size_dict(lowerCAmelCase__   ,   default_to_square=lowerCAmelCase__				)
    if "height" not in size or "width" not in size:
     raise ValueError(f"""The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}"""				)
    UpperCamelCase			  =      (size['height'], size['width'])
    return resize(lowerCAmelCase__   ,   size=lowerCAmelCase__   ,   resample=lowerCAmelCase__   ,   data_format=lowerCAmelCase__   ,   **lowerCAmelCase__				)







   def 						lowerCamelCase_	(     self						:						Any   ,   lowerCamelCase_						:						int   ,   lowerCamelCase_						:						str   ,   lowerCamelCase_						:						Optional[Any] = None   ,   **lowerCamelCase_						:						str   ,   ):





    """simple docstring"""


    return rescale(lowerCAmelCase__   ,   scale=lowerCAmelCase__   ,   data_format=lowerCAmelCase__   ,   **lowerCAmelCase__				)







   def 						lowerCamelCase_	(     self						:						Dict   ,   lowerCamelCase_						:						str   ,   lowerCamelCase_						:						List[str]   ,   lowerCamelCase_						:						List[Any]   ,   lowerCamelCase_						:						str = None   ,   **lowerCamelCase_						:						List[Any]   ,   ):





    """simple docstring"""


    return normalize(lowerCAmelCase__   ,   mean=lowerCAmelCase__   ,   std=lowerCAmelCase__   ,   data_format=lowerCAmelCase__   ,   **lowerCAmelCase__				)







   def 						lowerCamelCase_	(     self						:						str   ,   lowerCamelCase_						:						Optional[Any]   ,   lowerCamelCase_						:						List[str] = None   ,   lowerCamelCase_						:						Optional[int] = None   ,   lowerCamelCase_						:						Optional[Any] = None   ,   lowerCamelCase_						:						List[str] = None   ,   lowerCamelCase_						:						Any = None   ,   lowerCamelCase_						:						Dict = None   ,   lowerCamelCase_						:						Union[str, Any] = None   ,   lowerCamelCase_						:						Dict = None   ,   lowerCamelCase_						:						int = None   ,   lowerCamelCase_						:						Union[str, Any] = None   ,   lowerCamelCase_						:						str = ChannelDimension.FIRST   ,   **lowerCamelCase_						:						List[Any]   ,   ):





    """simple docstring"""


    UpperCamelCase			  =      do_resize if do_resize is not None else self.do_resize
    UpperCamelCase			  =      resample if resample is not None else self.resample
    UpperCamelCase			  =      do_rescale if do_rescale is not None else self.do_rescale
    UpperCamelCase			  =      rescale_factor if rescale_factor is not None else self.rescale_factor
    UpperCamelCase			  =      do_normalize if do_normalize is not None else self.do_normalize
    UpperCamelCase			  =      image_mean if image_mean is not None else self.image_mean
    UpperCamelCase			  =      image_std if image_std is not None else self.image_std
    UpperCamelCase			  =      do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

    UpperCamelCase			  =      size if size is not None else self.size
    UpperCamelCase			  =      get_size_dict(lowerCAmelCase__   ,   default_to_square=lowerCAmelCase__				)

    UpperCamelCase			  =      make_list_of_images(lowerCAmelCase__				)

    if not valid_images(lowerCAmelCase__				):
     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 or resample is None:
     raise ValueError("""Size and resample must be specified if do_resize 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:
     UpperCamelCase			  =      [convert_to_rgb(lowerCAmelCase__				) for image in images]

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

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

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

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

    UpperCamelCase			  =      [to_channel_dimension_format(lowerCAmelCase__   ,   lowerCAmelCase__				) for image in images]

    UpperCamelCase			  =      BatchFeature(data={"""pixel_values""": images}   ,   tensor_type=lowerCAmelCase__				)

    return encoded_outputs




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       )       )





