
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, is_vision_available, logging


if is_vision_available():
    import PIL


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 = None    ,    __SCREAMING_SNAKE_CASE = None    ,    __SCREAMING_SNAKE_CASE = PILImageResampling.BILINEAR    ,    __SCREAMING_SNAKE_CASE = True    ,    __SCREAMING_SNAKE_CASE = 1 / 2_5_5    ,    __SCREAMING_SNAKE_CASE = True    ,    __SCREAMING_SNAKE_CASE = None    ,    __SCREAMING_SNAKE_CASE = None    ,    **__SCREAMING_SNAKE_CASE    ,    )	-> None:




           """simple docstring"""







           super().__init__(**__SCREAMING_SNAKE_CASE	)
           UpperCamelCase__     : str											=					size if size is not None else {'''shortest_edge''': 3_8_4}
           UpperCamelCase__     : List[str]											=					get_size_dict(__SCREAMING_SNAKE_CASE    ,    default_to_square=__SCREAMING_SNAKE_CASE	)

           UpperCamelCase__     : int											=					do_resize
           UpperCamelCase__     : List[str]											=					size
           # Default value set here for backwards compatibility where the value in config is None
           UpperCamelCase__     : int											=					crop_pct if crop_pct is not None else 2_2_4 / 2_5_6
           UpperCamelCase__     : List[Any]											=					resample
           UpperCamelCase__     : List[Any]											=					do_rescale
           UpperCamelCase__     : int											=					rescale_factor
           UpperCamelCase__     : List[str]											=					do_normalize
           UpperCamelCase__     : int											=					image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
           UpperCamelCase__     : Union[str, Any]											=					image_std if image_std is not None else IMAGENET_STANDARD_STD






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




           """simple docstring"""







           UpperCamelCase__     : Any											=					get_size_dict(__SCREAMING_SNAKE_CASE    ,    default_to_square=__SCREAMING_SNAKE_CASE	)
           if "shortest_edge" not in size:
                 raise ValueError(F'''Size dictionary must contain \'shortest_edge\' key. Got {size.keys()}'''	)
           UpperCamelCase__     : int											=					size['''shortest_edge''']

           if shortest_edge < 3_8_4:
                 # maintain same ratio, resizing shortest edge to shortest_edge/crop_pct
                 UpperCamelCase__     : List[str]											=					int(shortest_edge / crop_pct	)
                 UpperCamelCase__     : Optional[int]											=					get_resize_output_image_size(__SCREAMING_SNAKE_CASE    ,    size=__SCREAMING_SNAKE_CASE    ,    default_to_square=__SCREAMING_SNAKE_CASE	)
                 UpperCamelCase__     : int											=					resize(image=__SCREAMING_SNAKE_CASE    ,    size=__SCREAMING_SNAKE_CASE    ,    resample=__SCREAMING_SNAKE_CASE    ,    data_format=__SCREAMING_SNAKE_CASE    ,    **__SCREAMING_SNAKE_CASE	)
                 # then crop to (shortest_edge, shortest_edge)
                 return center_crop(image=__SCREAMING_SNAKE_CASE    ,    size=(shortest_edge, shortest_edge)    ,    data_format=__SCREAMING_SNAKE_CASE    ,    **__SCREAMING_SNAKE_CASE	)
           else:
                 # warping (no cropping) when evaluated at 384 or larger
                 return resize(
                     __SCREAMING_SNAKE_CASE    ,    size=(shortest_edge, shortest_edge)    ,    resample=__SCREAMING_SNAKE_CASE    ,    data_format=__SCREAMING_SNAKE_CASE    ,    **__SCREAMING_SNAKE_CASE	)






     def  __SCREAMING_SNAKE_CASE      (				self    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE = None    ,    **__SCREAMING_SNAKE_CASE    ,    )	-> str:




           """simple docstring"""







           return rescale(__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    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE = None    ,    **__SCREAMING_SNAKE_CASE    ,    )	-> np.ndarray:




           """simple docstring"""







           return normalize(__SCREAMING_SNAKE_CASE    ,    mean=__SCREAMING_SNAKE_CASE    ,    std=__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 = 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    ,    )	-> PIL.Image.Image:




           """simple docstring"""







           UpperCamelCase__     : int											=					do_resize if do_resize is not None else self.do_resize
           UpperCamelCase__     : int											=					crop_pct if crop_pct is not None else self.crop_pct
           UpperCamelCase__     : List[Any]											=					resample if resample is not None else self.resample
           UpperCamelCase__     : Dict											=					do_rescale if do_rescale is not None else self.do_rescale
           UpperCamelCase__     : Union[str, Any]											=					rescale_factor if rescale_factor is not None else self.rescale_factor
           UpperCamelCase__     : Tuple											=					do_normalize if do_normalize is not None else self.do_normalize
           UpperCamelCase__     : Tuple											=					image_mean if image_mean is not None else self.image_mean
           UpperCamelCase__     : Tuple											=					image_std if image_std is not None else self.image_std

           UpperCamelCase__     : int											=					size if size is not None else self.size
           UpperCamelCase__     : Any											=					get_size_dict(__SCREAMING_SNAKE_CASE    ,    default_to_square=__SCREAMING_SNAKE_CASE	)

           UpperCamelCase__     : int											=					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 or resample is None:
                 raise ValueError('''Size and resample must be specified if do_resize is True.'''	)

           if do_resize and size["shortest_edge"] < 3_8_4 and crop_pct is None:
                 raise ValueError('''crop_pct must be specified if size < 384.'''	)

           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.
           UpperCamelCase__     : Tuple											=					[to_numpy_array(__SCREAMING_SNAKE_CASE	) for image in images]

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

           if do_rescale:
                 UpperCamelCase__     : Optional[Any]											=					[self.rescale(image=__SCREAMING_SNAKE_CASE    ,    scale=__SCREAMING_SNAKE_CASE	) for image in images]

           if do_normalize:
                 UpperCamelCase__     : Optional[int]											=					[self.normalize(image=__SCREAMING_SNAKE_CASE    ,    mean=__SCREAMING_SNAKE_CASE    ,    std=__SCREAMING_SNAKE_CASE	) for image in images]

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

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




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	)


