



"""simple docstring"""





from typing import Dict, Iterable, 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_DEFAULT_MEAN,
    IMAGENET_DEFAULT_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  _lowerCAmelCase     (					SCREAMING_SNAKE_CASE__		):





		"""simple docstring"""






		_lowerCamelCase      	=							['''pixel_values''']





		def __init__(       self				,     _lowercase = True				,     _lowercase = None				,     _lowercase = PILImageResampling.BICUBIC				,     _lowercase = True				,     _lowercase = None				,     _lowercase = True				,     _lowercase = 1 / 2_5_5				,     _lowercase = True				,     _lowercase = IMAGENET_DEFAULT_MEAN				,     _lowercase = IMAGENET_DEFAULT_STD				,     **_lowercase				,     )			->    None:







								'''simple docstring'''

								super().__init__(**_lowercase )
								snake_case_ :			Tuple            =      size if size is not None else {"""shortest_edge""": 2_2_4}
								snake_case_ :			Tuple            =      get_size_dict(_lowercase				,     default_to_square=_lowercase )
								snake_case_ :			Tuple            =      crop_size if crop_size is not None else {"""height""": 2_2_4, """width""": 2_2_4}
								snake_case_ :			Optional[int]            =      get_size_dict(_lowercase				,     param_name="""crop_size""" )

								snake_case_ :			Union[str, Any]            =      do_resize
								snake_case_ :			List[Any]            =      size
								snake_case_ :			Optional[Any]            =      resample
								snake_case_ :			Tuple            =      do_center_crop
								snake_case_ :			Optional[Any]            =      crop_size
								snake_case_ :			List[str]            =      do_rescale
								snake_case_ :			int            =      rescale_factor
								snake_case_ :			Tuple            =      do_normalize
								snake_case_ :			str            =      image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
								snake_case_ :			Any            =      image_std if image_std is not None else IMAGENET_DEFAULT_STD





		def  UpperCAmelCase__				(       self				,     _lowercase				,     _lowercase				,     _lowercase = PILImageResampling.BICUBIC				,     _lowercase = None				,     **_lowercase				,     )			->    np.ndarray:







								'''simple docstring'''

								snake_case_ :			List[Any]            =      get_size_dict(_lowercase				,     default_to_square=_lowercase )
								# size_dict is a dict with either keys "height" and "width" or "shortest_edge"
								if "shortest_edge" in size:
														snake_case_ :			Optional[Any]            =      int((2_5_6 / 2_2_4) * size["""shortest_edge"""] )
														snake_case_ :			Union[str, Any]            =      get_resize_output_image_size(_lowercase				,     size=_lowercase				,     default_to_square=_lowercase )
														snake_case_ :			Tuple            =      {"""height""": output_size[0], """width""": output_size[1]}
								if "height" not in size_dict or "width" not in size_dict:
														raise ValueError(
														    f'Size dict must have keys \'height\' and \'width\' or \'shortest_edge\'. Got {size_dict.keys()}' )
								return resize(
								    _lowercase				,     size=(size_dict["""height"""], size_dict["""width"""])				,     resample=_lowercase				,     data_format=_lowercase				,     **_lowercase )





		def  UpperCAmelCase__				(       self				,     _lowercase				,     _lowercase				,     _lowercase = None				,     **_lowercase				,     )			->    np.ndarray:







								'''simple docstring'''

								snake_case_ :			str            =      get_size_dict(_lowercase )
								if "height" not in size or "width" not in size:
														raise ValueError(f'Size dict must have keys \'height\' and \'width\'. Got {size.keys()}' )
								return center_crop(_lowercase				,     size=(size["""height"""], size["""width"""])				,     data_format=_lowercase				,     **_lowercase )





		def  UpperCAmelCase__				(       self				,     _lowercase				,     _lowercase				,     _lowercase = None				,     **_lowercase				,     )			->    np.ndarray:







								'''simple docstring'''

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





		def  UpperCAmelCase__				(       self				,     _lowercase				,     _lowercase				,     _lowercase				,     _lowercase = None				,     **_lowercase				,     )			->    np.ndarray:







								'''simple docstring'''

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





		def  UpperCAmelCase__				(       self				,     _lowercase				,     _lowercase = None				,     _lowercase = None				,     _lowercase = None				,     _lowercase = None				,     _lowercase = None				,     _lowercase = None				,     _lowercase = None				,     _lowercase = None				,     _lowercase = None				,     _lowercase = None				,     _lowercase = None				,     _lowercase = ChannelDimension.FIRST				,     **_lowercase				,     )			->    BatchFeature:







								'''simple docstring'''

								snake_case_ :			List[str]            =      do_resize if do_resize is not None else self.do_resize
								snake_case_ :			str            =      resample if resample is not None else self.resample
								snake_case_ :			Dict            =      do_center_crop if do_center_crop is not None else self.do_center_crop
								snake_case_ :			Optional[int]            =      do_rescale if do_rescale is not None else self.do_rescale
								snake_case_ :			Any            =      rescale_factor if rescale_factor is not None else self.rescale_factor
								snake_case_ :			List[str]            =      do_normalize if do_normalize is not None else self.do_normalize
								snake_case_ :			Optional[int]            =      image_mean if image_mean is not None else self.image_mean
								snake_case_ :			Any            =      image_std if image_std is not None else self.image_std

								snake_case_ :			Optional[int]            =      size if size is not None else self.size
								snake_case_ :			List[str]            =      get_size_dict(_lowercase				,     default_to_square=_lowercase )
								snake_case_ :			Any            =      crop_size if crop_size is not None else self.crop_size
								snake_case_ :			Any            =      get_size_dict(_lowercase				,     param_name="""crop_size""" )

								snake_case_ :			Optional[Any]            =      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.""" )

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

								if do_resize:
														snake_case_ :			Any            =      [self.resize(_lowercase				,     _lowercase				,     _lowercase ) for image in images]

								if do_center_crop:
														snake_case_ :			Dict            =      [self.center_crop(_lowercase				,     _lowercase ) for image in images]

								if do_rescale:
														snake_case_ :			str            =      [self.rescale(_lowercase				,     _lowercase ) for image in images]

								if do_normalize:
														snake_case_ :			List[str]            =      [self.normalize(_lowercase				,     _lowercase				,     _lowercase ) for image in images]

								snake_case_ :			List[str]            =      [to_channel_dimension_format(_lowercase				,     _lowercase ) for image in images]

								snake_case_ :			List[Any]            =      {"""pixel_values""": images}
								return BatchFeature(data=_lowercase				,     tensor_type=_lowercase )





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       )       )





