
import copy

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


lowerCamelCase  =logging.get_logger(__name__)




class 					_lowerCamelCase			(   UpperCamelCase_			):





     """simple docstring"""







     SCREAMING_SNAKE_CASE_		 =	'''encoder-decoder'''
     SCREAMING_SNAKE_CASE_		 =	True






     def __init__(				self    ,    **__SCREAMING_SNAKE_CASE	)	-> Optional[Any]:




           """simple docstring"""







           super().__init__(**__SCREAMING_SNAKE_CASE	)
           assert (
               "encoder" in kwargs and "decoder" in kwargs
           ), "Config has to be initialized with encoder and decoder config"
           UpperCamelCase__     : List[Any]											=					kwargs.pop('''encoder'''	)
           UpperCamelCase__     : Optional[Any]											=					encoder_config.pop('''model_type'''	)
           UpperCamelCase__     : Optional[Any]											=					kwargs.pop('''decoder'''	)
           UpperCamelCase__     : Any											=					decoder_config.pop('''model_type'''	)

           from ..auto.configuration_auto import AutoConfig

           UpperCamelCase__     : Union[str, Any]											=					AutoConfig.for_model(__SCREAMING_SNAKE_CASE    ,    **__SCREAMING_SNAKE_CASE	)
           UpperCamelCase__     : Union[str, Any]											=					AutoConfig.for_model(__SCREAMING_SNAKE_CASE    ,    **__SCREAMING_SNAKE_CASE	)
           UpperCamelCase__     : List[str]											=					True






     @classmethod
     def  __SCREAMING_SNAKE_CASE      (				cls    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE    ,    **__SCREAMING_SNAKE_CASE	)	-> PretrainedConfig:




           """simple docstring"""







           logger.info('''Set `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config'''	)
           UpperCamelCase__     : str											=					True
           UpperCamelCase__     : Any											=					True

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






     def  __SCREAMING_SNAKE_CASE      (				self	)	-> Union[str, Any]:




           """simple docstring"""







           UpperCamelCase__     : Tuple											=					copy.deepcopy(self.__dict__	)
           UpperCamelCase__     : int											=					self.encoder.to_dict()
           UpperCamelCase__     : List[str]											=					self.decoder.to_dict()
           UpperCamelCase__     : Optional[Any]											=					self.__class__.model_type
           return output




from typing import List, Optional, Union

import numpy as np
import PIL.Image

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
    ChannelDimension,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


lowerCamelCase  =logging.get_logger(__name__)




class 					_lowerCamelCase			(   UpperCamelCase_			):





     """simple docstring"""







     SCREAMING_SNAKE_CASE_		 =	['''pixel_values''']






     def __init__(				self    ,    __SCREAMING_SNAKE_CASE = True    ,    __SCREAMING_SNAKE_CASE = 3_2    ,    __SCREAMING_SNAKE_CASE=PILImageResampling.BILINEAR    ,    __SCREAMING_SNAKE_CASE = True    ,    **__SCREAMING_SNAKE_CASE    ,    )	-> None:




           """simple docstring"""







           UpperCamelCase__     : Dict											=					do_resize
           UpperCamelCase__     : Tuple											=					do_rescale
           UpperCamelCase__     : Dict											=					size_divisor
           UpperCamelCase__     : str											=					resample
           super().__init__(**__SCREAMING_SNAKE_CASE	)






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




           """simple docstring"""







           UpperCamelCase__					,UpperCamelCase__     : Optional[Any]											=					get_image_size(__SCREAMING_SNAKE_CASE	)
           # Rounds the height and width down to the closest multiple of size_divisor
           UpperCamelCase__     : List[Any]											=					height // size_divisor * size_divisor
           UpperCamelCase__     : Dict											=					width // size_divisor * size_divisor
           UpperCamelCase__     : str											=					resize(__SCREAMING_SNAKE_CASE    ,    (new_h, new_w)    ,    resample=__SCREAMING_SNAKE_CASE    ,    data_format=__SCREAMING_SNAKE_CASE    ,    **__SCREAMING_SNAKE_CASE	)
           return image






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




           """simple docstring"""







           return rescale(image=__SCREAMING_SNAKE_CASE    ,    scale=__SCREAMING_SNAKE_CASE    ,    data_format=__SCREAMING_SNAKE_CASE    ,    **__SCREAMING_SNAKE_CASE	)






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




           """simple docstring"""







           UpperCamelCase__     : Dict											=					do_resize if do_resize is not None else self.do_resize
           UpperCamelCase__     : List[Any]											=					do_rescale if do_rescale is not None else self.do_rescale
           UpperCamelCase__     : Union[str, Any]											=					size_divisor if size_divisor is not None else self.size_divisor
           UpperCamelCase__     : List[str]											=					resample if resample is not None else self.resample

           if do_resize and size_divisor is None:
                 raise ValueError('''size_divisor is required for resizing'''	)

           UpperCamelCase__     : List[Any]											=					make_list_of_images(__SCREAMING_SNAKE_CASE	)

           if not valid_images(__SCREAMING_SNAKE_CASE	):
                 raise ValueError('''Invalid image(s)'''	)

           # All transformations expect numpy arrays.
           UpperCamelCase__     : Dict											=					[to_numpy_array(__SCREAMING_SNAKE_CASE	) for img in images]

           if do_resize:
                 UpperCamelCase__     : Optional[int]											=					[self.resize(__SCREAMING_SNAKE_CASE    ,    size_divisor=__SCREAMING_SNAKE_CASE    ,    resample=__SCREAMING_SNAKE_CASE	) for image in images]

           if do_rescale:
                 UpperCamelCase__     : Union[str, Any]											=					[self.rescale(__SCREAMING_SNAKE_CASE    ,    scale=1 / 2_5_5	) for image in images]

           UpperCamelCase__     : List[str]											=					[to_channel_dimension_format(__SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE	) for image in images]

           UpperCamelCase__     : Optional[Any]											=					{'''pixel_values''': images}
           return BatchFeature(data=__SCREAMING_SNAKE_CASE    ,    tensor_type=__SCREAMING_SNAKE_CASE	)


