



"""simple docstring"""







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, 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


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







class       UpperCamelCase__   (	__UpperCAmelCase  ):




      """simple docstring"""




      _SCREAMING_SNAKE_CASE  				=     ["""pixel_values"""]



      def __init__(      self				:      Optional[int]    ,			SCREAMING_SNAKE_CASE_				:      List[str] = True    ,			SCREAMING_SNAKE_CASE_				:      Optional[Any] = None    ,			SCREAMING_SNAKE_CASE_				:      Optional[Any] = PIL.Image.BICUBIC    ,			SCREAMING_SNAKE_CASE_				:      Union[str, Any] = True    ,			SCREAMING_SNAKE_CASE_				:      List[str] = None    ,			SCREAMING_SNAKE_CASE_				:      int = 1 / 2_5_5    ,			SCREAMING_SNAKE_CASE_				:      int = True    ,			SCREAMING_SNAKE_CASE_				:      str = True    ,			SCREAMING_SNAKE_CASE_				:      List[str] = None    ,			SCREAMING_SNAKE_CASE_				:      Union[str, Any] = None    ,			**SCREAMING_SNAKE_CASE_				:      List[str]    ,			):
        super().__init__(**UpperCamelCase__       )
        lowerCAmelCase_   :				int								= size if size is not None else {'height': 2_5_6, 'width': 2_5_6}
        lowerCAmelCase_   :				Tuple								= get_size_dict(UpperCamelCase__       )
        lowerCAmelCase_   :				Union[str, Any]								= crop_size if crop_size is not None else {'height': 2_2_4, 'width': 2_2_4}
        lowerCAmelCase_   :				Union[str, Any]								= get_size_dict(UpperCamelCase__    ,			param_name='crop_size'       )

        lowerCAmelCase_   :				Optional[Any]								= do_resize
        lowerCAmelCase_   :				Optional[int]								= size
        lowerCAmelCase_   :				List[Any]								= resample
        lowerCAmelCase_   :				Any								= do_center_crop
        lowerCAmelCase_   :				Optional[int]								= crop_size
        lowerCAmelCase_   :				int								= do_rescale
        lowerCAmelCase_   :				Union[str, Any]								= rescale_factor
        lowerCAmelCase_   :				Union[str, Any]								= do_normalize
        lowerCAmelCase_   :				Any								= image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
        lowerCAmelCase_   :				int								= image_std if image_std is not None else IMAGENET_STANDARD_STD



      def      SCREAMING_SNAKE_CASE__ (      self				:      Optional[Any]    ,			SCREAMING_SNAKE_CASE_				:      List[str]    ,			SCREAMING_SNAKE_CASE_				:      Union[str, Any]    ,			SCREAMING_SNAKE_CASE_				:      Optional[Any] = PIL.Image.BICUBIC    ,			SCREAMING_SNAKE_CASE_				:      Optional[Any] = None    ,			**SCREAMING_SNAKE_CASE_				:      int    ,			):
        lowerCAmelCase_   :				Dict								= get_size_dict(UpperCamelCase__       )
        if "height" not in size or "width" not in size:
          raise ValueError(F"The size dictionary must have keys 'height' and 'width'. Got {size.keys()}"       )
        return resize(
            UpperCamelCase__    ,			size=(size['height'], size['width'])    ,			resample=UpperCamelCase__    ,			data_format=UpperCamelCase__    ,			**UpperCamelCase__       )



      def      SCREAMING_SNAKE_CASE__ (      self				:      int    ,			SCREAMING_SNAKE_CASE_				:      Tuple    ,			SCREAMING_SNAKE_CASE_				:      Union[str, Any]    ,			SCREAMING_SNAKE_CASE_				:      Optional[int] = None    ,			**SCREAMING_SNAKE_CASE_				:      Union[str, Any]    ,			):
        lowerCAmelCase_   :				List[Any]								= get_size_dict(UpperCamelCase__       )
        if "height" not in size or "width" not in size:
          raise ValueError(F"The size dictionary must have keys 'height' and 'width'. Got {size.keys()}"       )
        return center_crop(UpperCamelCase__    ,			size=(size['height'], size['width'])    ,			data_format=UpperCamelCase__    ,			**UpperCamelCase__       )



      def      SCREAMING_SNAKE_CASE__ (      self				:      Dict    ,			SCREAMING_SNAKE_CASE_				:      Optional[Any]    ,			SCREAMING_SNAKE_CASE_				:      Tuple    ,			SCREAMING_SNAKE_CASE_				:      List[Any] = None    ,			**SCREAMING_SNAKE_CASE_				:      List[str]    ,			):
        return rescale(UpperCamelCase__    ,			scale=UpperCamelCase__    ,			data_format=UpperCamelCase__    ,			**UpperCamelCase__       )



      def      SCREAMING_SNAKE_CASE__ (      self				:      Optional[int]    ,			SCREAMING_SNAKE_CASE_				:      Any    ,			SCREAMING_SNAKE_CASE_				:      str    ,			SCREAMING_SNAKE_CASE_				:      Optional[Any]    ,			SCREAMING_SNAKE_CASE_				:      List[str] = None    ,			**SCREAMING_SNAKE_CASE_				:      Union[str, Any]    ,			):
        return normalize(UpperCamelCase__    ,			mean=UpperCamelCase__    ,			std=UpperCamelCase__    ,			data_format=UpperCamelCase__    ,			**UpperCamelCase__       )





      def      SCREAMING_SNAKE_CASE__ (      self				:      Tuple    ,			SCREAMING_SNAKE_CASE_				:      Tuple    ,			SCREAMING_SNAKE_CASE_				:      Tuple = None    ,			SCREAMING_SNAKE_CASE_				:      Optional[Any] = None    ,			SCREAMING_SNAKE_CASE_				:      Dict=None    ,			SCREAMING_SNAKE_CASE_				:      str = None    ,			SCREAMING_SNAKE_CASE_				:      str = None    ,			SCREAMING_SNAKE_CASE_				:      int = None    ,			SCREAMING_SNAKE_CASE_				:      Optional[int] = None    ,			SCREAMING_SNAKE_CASE_				:      Optional[int] = None    ,			SCREAMING_SNAKE_CASE_				:      List[str] = None    ,			SCREAMING_SNAKE_CASE_				:      Optional[int] = None    ,			SCREAMING_SNAKE_CASE_				:      int = None    ,			SCREAMING_SNAKE_CASE_				:      Any = ChannelDimension.FIRST    ,			**SCREAMING_SNAKE_CASE_				:      Any    ,			):
        lowerCAmelCase_   :				Any								= do_resize if do_resize is not None else self.do_resize
        lowerCAmelCase_   :				int								= resample if resample is not None else self.resample
        lowerCAmelCase_   :				Optional[Any]								= do_center_crop if do_center_crop is not None else self.do_center_crop
        lowerCAmelCase_   :				Optional[int]								= do_rescale if do_rescale is not None else self.do_rescale
        lowerCAmelCase_   :				int								= rescale_factor if rescale_factor is not None else self.rescale_factor
        lowerCAmelCase_   :				List[str]								= do_normalize if do_normalize is not None else self.do_normalize
        lowerCAmelCase_   :				Tuple								= image_mean if image_mean is not None else self.image_mean
        lowerCAmelCase_   :				Dict								= image_std if image_std is not None else self.image_std

        lowerCAmelCase_   :				Optional[Any]								= size if size is not None else self.size
        lowerCAmelCase_   :				Optional[int]								= get_size_dict(UpperCamelCase__       )
        lowerCAmelCase_   :				Union[str, Any]								= crop_size if crop_size is not None else self.crop_size
        lowerCAmelCase_   :				Optional[int]								= get_size_dict(UpperCamelCase__    ,			param_name='crop_size'       )

        lowerCAmelCase_   :				Dict								= make_list_of_images(UpperCamelCase__       )

        if not valid_images(UpperCamelCase__       ):
          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_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.
        lowerCAmelCase_   :				Dict								= [to_numpy_array(UpperCamelCase__       ) for image in images]

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

        if do_center_crop:
          lowerCAmelCase_   :				Optional[Any]								= [self.center_crop(image=UpperCamelCase__    ,			size=UpperCamelCase__       ) for image in images]

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

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

        lowerCAmelCase_   :				Tuple								= [to_channel_dimension_format(UpperCamelCase__    ,			UpperCamelCase__       ) for image in images]

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











from functools import lru_cache





@lru_cache
def __UpperCamelCase			(       _A  ):
						if num < 0:
												raise ValueError('''Number should not be negative.'''  )

						return 1 if num in (0, 1) else num * factorial(num - 1  )


if __name__ == "__main__":
						import doctest

						doctest.testmod()




