


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,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


lowerCAmelCase__			:      Optional[Any]								=logging.get_logger(__name__)







class 	__lowercase    (__SCREAMING_SNAKE_CASE  ):





							"""simple docstring"""







							_UpperCAmelCase      =						["""pixel_values"""]






							def __init__(      self			,						lowerCAmelCase__ = True			,						lowerCAmelCase__ = None			,						lowerCAmelCase__ = PILImageResampling.BILINEAR			,						lowerCAmelCase__ = True			,						lowerCAmelCase__ = None			,						lowerCAmelCase__ = True			,						lowerCAmelCase__ = 1 / 2_5_5			,						lowerCAmelCase__ = True			,						lowerCAmelCase__ = None			,						lowerCAmelCase__ = None			,						**lowerCAmelCase__			,						):





										"""simple docstring"""




										super().__init__(**lowerCAmelCase__       )
										SCREAMING_SNAKE_CASE_		:     int					=					size if size is not None else {'shortest_edge': 2_5_6}
										SCREAMING_SNAKE_CASE_		:     Tuple					=					get_size_dict(lowerCAmelCase__			,						default_to_square=lowerCAmelCase__       )
										SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					crop_size if crop_size is not None else {'height': 2_2_4, 'width': 2_2_4}
										SCREAMING_SNAKE_CASE_		:     int					=					get_size_dict(lowerCAmelCase__       )
										SCREAMING_SNAKE_CASE_		:     List[str]					=					do_resize
										SCREAMING_SNAKE_CASE_		:     List[Any]					=					size
										SCREAMING_SNAKE_CASE_		:     Any					=					resample
										SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					do_center_crop
										SCREAMING_SNAKE_CASE_		:     Any					=					crop_size
										SCREAMING_SNAKE_CASE_		:     Optional[int]					=					do_rescale
										SCREAMING_SNAKE_CASE_		:     Tuple					=					rescale_factor
										SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					do_normalize
										SCREAMING_SNAKE_CASE_		:     Dict					=					image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
										SCREAMING_SNAKE_CASE_		:     Any					=					image_std if image_std is not None else IMAGENET_STANDARD_STD






							def 					UpperCamelCase__     (      self			,						lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__ = PILImageResampling.BICUBIC			,						lowerCAmelCase__ = None			,						**lowerCAmelCase__			,						):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					get_size_dict(lowerCAmelCase__			,						default_to_square=lowerCAmelCase__       )
										if "shortest_edge" not in size:
													raise ValueError(F'''The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}'''       )
										SCREAMING_SNAKE_CASE_		:     List[Any]					=					get_resize_output_image_size(lowerCAmelCase__			,						size=size['shortest_edge']			,						default_to_square=lowerCAmelCase__       )
										return resize(lowerCAmelCase__			,						size=lowerCAmelCase__			,						resample=lowerCAmelCase__			,						data_format=lowerCAmelCase__			,						**lowerCAmelCase__       )






							def 					UpperCamelCase__     (      self			,						lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__ = None			,						**lowerCAmelCase__			,						):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     List[str]					=					get_size_dict(lowerCAmelCase__       )
										return center_crop(lowerCAmelCase__			,						size=(size['height'], size['width'])			,						data_format=lowerCAmelCase__			,						**lowerCAmelCase__       )






							def 					UpperCamelCase__     (      self			,						lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__ = None			,						**lowerCAmelCase__       ):





										"""simple docstring"""




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






							def 					UpperCamelCase__     (      self			,						lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__ = None			,						**lowerCAmelCase__			,						):





										"""simple docstring"""




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






							def 					UpperCamelCase__     (      self			,						lowerCAmelCase__			,						lowerCAmelCase__ = None			,						lowerCAmelCase__ = None			,						lowerCAmelCase__ = None			,						lowerCAmelCase__ = None			,						lowerCAmelCase__ = None			,						lowerCAmelCase__ = None			,						lowerCAmelCase__ = None			,						lowerCAmelCase__ = None			,						lowerCAmelCase__ = None			,						lowerCAmelCase__ = None			,						lowerCAmelCase__ = None			,						lowerCAmelCase__ = ChannelDimension.FIRST			,						**lowerCAmelCase__			,						):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     List[Any]					=					do_resize if do_resize is not None else self.do_resize
										SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					size if size is not None else self.size
										SCREAMING_SNAKE_CASE_		:     int					=					get_size_dict(lowerCAmelCase__			,						default_to_square=lowerCAmelCase__       )
										SCREAMING_SNAKE_CASE_		:     List[str]					=					resample if resample is not None else self.resample
										SCREAMING_SNAKE_CASE_		:     Tuple					=					do_center_crop if do_center_crop is not None else self.do_center_crop
										SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					crop_size if crop_size is not None else self.crop_size
										SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					get_size_dict(lowerCAmelCase__       )
										SCREAMING_SNAKE_CASE_		:     List[str]					=					do_rescale if do_rescale is not None else self.do_rescale
										SCREAMING_SNAKE_CASE_		:     Tuple					=					rescale_factor if rescale_factor is not None else self.rescale_factor
										SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					do_normalize if do_normalize is not None else self.do_normalize
										SCREAMING_SNAKE_CASE_		:     str					=					image_mean if image_mean is not None else self.image_mean
										SCREAMING_SNAKE_CASE_		:     List[Any]					=					image_std if image_std is not None else self.image_std

										SCREAMING_SNAKE_CASE_		:     Any					=					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:
													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.'       )

										# All transformations expect numpy arrays.
										SCREAMING_SNAKE_CASE_		:     List[str]					=					[to_numpy_array(lowerCAmelCase__       ) for image in images]

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

										if do_center_crop:
													SCREAMING_SNAKE_CASE_		:     Tuple					=					[self.center_crop(image=lowerCAmelCase__			,						size=lowerCAmelCase__       ) for image in images]

										if do_rescale:
													SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					[self.rescale(image=lowerCAmelCase__			,						scale=lowerCAmelCase__       ) for image in images]

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

										SCREAMING_SNAKE_CASE_		:     Dict					=					[to_channel_dimension_format(lowerCAmelCase__			,						lowerCAmelCase__       ) for image in images]

										SCREAMING_SNAKE_CASE_		:     List[str]					=					{'pixel_values': images}
										return BatchFeature(data=lowerCAmelCase__			,						tensor_type=lowerCAmelCase__       )









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       )       )





