
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


__a         =   logging.get_logger(__name__)


if is_vision_available():
						import PIL
class 					lowercase__(							UpperCAmelCase				):



	"""simple docstring"""




	a     :Tuple       	=      ['pixel_values']
	def __init__(			self    :     str			,				SCREAMING_SNAKE_CASE_    :     bool = True			,				SCREAMING_SNAKE_CASE_    :     Dict[str, int] = None			,				SCREAMING_SNAKE_CASE_    :     PILImageResampling = PILImageResampling.BICUBIC			,				SCREAMING_SNAKE_CASE_    :     bool = True			,				SCREAMING_SNAKE_CASE_    :     Dict[str, int] = None			,				SCREAMING_SNAKE_CASE_    :     bool = True			,				SCREAMING_SNAKE_CASE_    :     Union[int, float] = 1 / 2_5_5			,				SCREAMING_SNAKE_CASE_    :     bool = True			,				SCREAMING_SNAKE_CASE_    :     Optional[Union[float, List[float]]] = None			,				SCREAMING_SNAKE_CASE_    :     Optional[Union[float, List[float]]] = None			,				SCREAMING_SNAKE_CASE_    :     bool = True			,				**SCREAMING_SNAKE_CASE_    :     Optional[int]			,				)			->	None:
					super().__init__(**SCREAMING_SNAKE_CASE_			)
					lowercase_	    =   size if size is not None else {'''shortest_edge''': 2_2_4}
					lowercase_	    =   get_size_dict(SCREAMING_SNAKE_CASE_			,				default_to_square=SCREAMING_SNAKE_CASE_			)
					lowercase_	    =   crop_size if crop_size is not None else {'''height''': 2_2_4, '''width''': 2_2_4}
					lowercase_	    =   get_size_dict(SCREAMING_SNAKE_CASE_			,				default_to_square=SCREAMING_SNAKE_CASE_			,				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 		_lowercase    (			self    :     Optional[int]			,				SCREAMING_SNAKE_CASE_    :     np.ndarray			,				SCREAMING_SNAKE_CASE_    :     Dict[str, int]			,				SCREAMING_SNAKE_CASE_    :     PILImageResampling = PILImageResampling.BICUBIC			,				SCREAMING_SNAKE_CASE_    :     Optional[Union[str, ChannelDimension]] = None			,				**SCREAMING_SNAKE_CASE_    :     Union[str, Any]			,				)			->	np.ndarray:
					lowercase_	    =   get_size_dict(SCREAMING_SNAKE_CASE_			,				default_to_square=SCREAMING_SNAKE_CASE_			)
					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(SCREAMING_SNAKE_CASE_			,				size=size['''shortest_edge''']			,				default_to_square=SCREAMING_SNAKE_CASE_			)
					return resize(SCREAMING_SNAKE_CASE_			,				size=SCREAMING_SNAKE_CASE_			,				resample=SCREAMING_SNAKE_CASE_			,				data_format=SCREAMING_SNAKE_CASE_			,				**SCREAMING_SNAKE_CASE_			)
	def 		_lowercase    (			self    :     Optional[Any]			,				SCREAMING_SNAKE_CASE_    :     np.ndarray			,				SCREAMING_SNAKE_CASE_    :     Dict[str, int]			,				SCREAMING_SNAKE_CASE_    :     Optional[Union[str, ChannelDimension]] = None			,				**SCREAMING_SNAKE_CASE_    :     Dict			,				)			->	np.ndarray:
					lowercase_	    =   get_size_dict(SCREAMING_SNAKE_CASE_			)
					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(SCREAMING_SNAKE_CASE_			,				size=(size['''height'''], size['''width'''])			,				data_format=SCREAMING_SNAKE_CASE_			,				**SCREAMING_SNAKE_CASE_			)
	def 		_lowercase    (			self    :     Tuple			,				SCREAMING_SNAKE_CASE_    :     np.ndarray			,				SCREAMING_SNAKE_CASE_    :     Union[int, float]			,				SCREAMING_SNAKE_CASE_    :     Optional[Union[str, ChannelDimension]] = None			,				**SCREAMING_SNAKE_CASE_    :     Optional[Any]			,				)			->	List[str]:
					return rescale(SCREAMING_SNAKE_CASE_			,				scale=SCREAMING_SNAKE_CASE_			,				data_format=SCREAMING_SNAKE_CASE_			,				**SCREAMING_SNAKE_CASE_			)
	def 		_lowercase    (			self    :     int			,				SCREAMING_SNAKE_CASE_    :     np.ndarray			,				SCREAMING_SNAKE_CASE_    :     Union[float, List[float]]			,				SCREAMING_SNAKE_CASE_    :     Union[float, List[float]]			,				SCREAMING_SNAKE_CASE_    :     Optional[Union[str, ChannelDimension]] = None			,				**SCREAMING_SNAKE_CASE_    :     Tuple			,				)			->	np.ndarray:
					return normalize(SCREAMING_SNAKE_CASE_			,				mean=SCREAMING_SNAKE_CASE_			,				std=SCREAMING_SNAKE_CASE_			,				data_format=SCREAMING_SNAKE_CASE_			,				**SCREAMING_SNAKE_CASE_			)




	def 		_lowercase    (			self    :     int			,				SCREAMING_SNAKE_CASE_    :     ImageInput			,				SCREAMING_SNAKE_CASE_    :     bool = None			,				SCREAMING_SNAKE_CASE_    :     Dict[str, int] = None			,				SCREAMING_SNAKE_CASE_    :     PILImageResampling = None			,				SCREAMING_SNAKE_CASE_    :     bool = None			,				SCREAMING_SNAKE_CASE_    :     int = None			,				SCREAMING_SNAKE_CASE_    :     bool = None			,				SCREAMING_SNAKE_CASE_    :     float = None			,				SCREAMING_SNAKE_CASE_    :     bool = None			,				SCREAMING_SNAKE_CASE_    :     Optional[Union[float, List[float]]] = None			,				SCREAMING_SNAKE_CASE_    :     Optional[Union[float, List[float]]] = None			,				SCREAMING_SNAKE_CASE_    :     bool = None			,				SCREAMING_SNAKE_CASE_    :     Optional[Union[str, TensorType]] = None			,				SCREAMING_SNAKE_CASE_    :     Optional[ChannelDimension] = ChannelDimension.FIRST			,				**SCREAMING_SNAKE_CASE_    :     str			,				)			->	PIL.Image.Image:
					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(SCREAMING_SNAKE_CASE_			,				param_name='''size'''			,				default_to_square=SCREAMING_SNAKE_CASE_			)
					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(SCREAMING_SNAKE_CASE_			,				param_name='''crop_size'''			,				default_to_square=SCREAMING_SNAKE_CASE_			)
					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(SCREAMING_SNAKE_CASE_			)

					if not valid_images(SCREAMING_SNAKE_CASE_			):
									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(SCREAMING_SNAKE_CASE_			) for image in images]

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

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

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

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

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

					lowercase_	    =   [to_channel_dimension_format(SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			) for image in images]

					lowercase_	    =   {'''pixel_values''': images}
					return BatchFeature(data=SCREAMING_SNAKE_CASE_			,				tensor_type=SCREAMING_SNAKE_CASE_			)






import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging


__a         =   logging.get_logger(__name__)
class 					lowercase__(							UpperCAmelCase				):



	"""simple docstring"""




	a     :List[str]       	=      'encoder-decoder'
	a     :Any       	=      True
	def __init__(			self    :     List[Any]			,				**SCREAMING_SNAKE_CASE_    :     List[Any]			)			->	Optional[int]:
					super().__init__(**SCREAMING_SNAKE_CASE_			)
					assert (
					    "encoder" in kwargs and "decoder" in kwargs
					), "Config has to be initialized with encoder and decoder config"
					lowercase_	    =   kwargs.pop('''encoder'''			)
					lowercase_	    =   encoder_config.pop('''model_type'''			)
					lowercase_	    =   kwargs.pop('''decoder'''			)
					lowercase_	    =   decoder_config.pop('''model_type'''			)

					from ..auto.configuration_auto import AutoConfig

					lowercase_	    =   AutoConfig.for_model(SCREAMING_SNAKE_CASE_			,				**SCREAMING_SNAKE_CASE_			)
					lowercase_	    =   AutoConfig.for_model(SCREAMING_SNAKE_CASE_			,				**SCREAMING_SNAKE_CASE_			)
					lowercase_	    =   True
	@classmethod
	def 		_lowercase    (			cls    :     Optional[int]			,				SCREAMING_SNAKE_CASE_    :     PretrainedConfig			,				SCREAMING_SNAKE_CASE_    :     PretrainedConfig			,				**SCREAMING_SNAKE_CASE_    :     Optional[int]			)			->	PretrainedConfig:
					logger.info('''Set `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config'''			)
					lowercase_	    =   True
					lowercase_	    =   True

					return cls(encoder=encoder_config.to_dict()			,				decoder=decoder_config.to_dict()			,				**SCREAMING_SNAKE_CASE_			)




	def 		_lowercase    (			self    :     Tuple			)			->	List[Any]:
					lowercase_	    =   copy.deepcopy(self.__dict__			)
					lowercase_	    =   self.encoder.to_dict()
					lowercase_	    =   self.decoder.to_dict()
					lowercase_	    =   self.__class__.model_type
					return output




