


def        __lowerCAmelCase    (SCREAMING_SNAKE_CASE					)->  Dict:




      """simple docstring"""

      return int(SCREAMING_SNAKE_CASE					) if x - int(SCREAMING_SNAKE_CASE					) <= 0 else int(SCREAMING_SNAKE_CASE					) + 1


if __name__ == "__main__":
   import doctest

   doctest.testmod()







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 (
    get_resize_output_image_size,
    normalize,
    pad,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging
from ...utils.import_utils import is_vision_available


snake_case			 =						logging.get_logger(__name__)


if is_vision_available():
   import PIL


class        SCREAMING_SNAKE_CASE	(	lowerCAmelCase    ):
     '''simple docstring'''


     UpperCamelCase_							:						List[Any]								=		['''pixel_values''']


     def __init__( self				:		Tuple    ,       UpperCAmelCase_				:		bool = True    ,       UpperCAmelCase_				:		Dict[str, int] = None    ,       UpperCAmelCase_				:		PILImageResampling = PILImageResampling.BILINEAR    ,       UpperCAmelCase_				:		bool = True    ,       UpperCAmelCase_				:		bool = False    ,       UpperCAmelCase_				:		bool = True    ,       UpperCAmelCase_				:		bool = True    ,       UpperCAmelCase_				:		Union[int, float] = 1 / 255    ,       UpperCAmelCase_				:		bool = True    ,       UpperCAmelCase_				:		Optional[Union[float, List[float]]] = None    ,       UpperCAmelCase_				:		Optional[Union[float, List[float]]] = None    ,       **UpperCAmelCase_				:		List[str]    ,       ):
       super().__init__(**UpperCAmelCase_					)

       SCREAMING_SNAKE_CASE				:					Optional[int]		  =			size if size is not None else {"height": 2560, "width": 1920}
       if isinstance(UpperCAmelCase_    ,       (tuple, list)					):
         # The previous feature extractor size parameter was in (width, height) format
         SCREAMING_SNAKE_CASE				:					List[str]		  =			size[::-1]
       SCREAMING_SNAKE_CASE				:					Optional[int]		  =			get_size_dict(UpperCAmelCase_					)

       SCREAMING_SNAKE_CASE				:					int		  =			do_resize
       SCREAMING_SNAKE_CASE				:					int		  =			size
       SCREAMING_SNAKE_CASE				:					str		  =			resample
       SCREAMING_SNAKE_CASE				:					List[str]		  =			do_thumbnail
       SCREAMING_SNAKE_CASE				:					Optional[int]		  =			do_align_long_axis
       SCREAMING_SNAKE_CASE				:					Optional[Any]		  =			do_pad
       SCREAMING_SNAKE_CASE				:					Union[str, Any]		  =			do_rescale
       SCREAMING_SNAKE_CASE				:					Tuple		  =			rescale_factor
       SCREAMING_SNAKE_CASE				:					Dict		  =			do_normalize
       SCREAMING_SNAKE_CASE				:					Optional[Any]		  =			image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
       SCREAMING_SNAKE_CASE				:					Any		  =			image_std if image_std is not None else IMAGENET_STANDARD_STD


     def  _A				( self				:		List[Any]    ,       UpperCAmelCase_				:		np.ndarray    ,       UpperCAmelCase_				:		Dict[str, int]    ,       UpperCAmelCase_				:		Optional[Union[str, ChannelDimension]] = None					):
       SCREAMING_SNAKE_CASE  ,     SCREAMING_SNAKE_CASE				:					str		  =			get_image_size(UpperCAmelCase_					)
       SCREAMING_SNAKE_CASE  ,     SCREAMING_SNAKE_CASE				:					List[Any]		  =			size["height"], size["width"]

       if (output_width < output_height and input_width > input_height) or (
           output_width > output_height and input_width < input_height
       ):
         SCREAMING_SNAKE_CASE				:					List[Any]		  =			np.rotaa(UpperCAmelCase_    ,       3					)

       if data_format is not None:
         SCREAMING_SNAKE_CASE				:					Optional[int]		  =			to_channel_dimension_format(UpperCAmelCase_    ,       UpperCAmelCase_					)

       return image


     def  _A				( self				:		int    ,       UpperCAmelCase_				:		np.ndarray    ,       UpperCAmelCase_				:		Dict[str, int]    ,       UpperCAmelCase_				:		bool = False    ,       UpperCAmelCase_				:		Optional[Union[str, ChannelDimension]] = None    ,       ):
       SCREAMING_SNAKE_CASE  ,     SCREAMING_SNAKE_CASE				:					List[str]		  =			size["height"], size["width"]
       SCREAMING_SNAKE_CASE  ,     SCREAMING_SNAKE_CASE				:					Optional[int]		  =			get_image_size(UpperCAmelCase_					)

       SCREAMING_SNAKE_CASE				:					Union[str, Any]		  =			output_width - input_width
       SCREAMING_SNAKE_CASE				:					Union[str, Any]		  =			output_height - input_height

       if random_padding:
         SCREAMING_SNAKE_CASE				:					int		  =			np.random.randint(low=0    ,       high=delta_height + 1					)
         SCREAMING_SNAKE_CASE				:					Optional[int]		  =			np.random.randint(low=0    ,       high=delta_width + 1					)
       else:
         SCREAMING_SNAKE_CASE				:					List[str]		  =			delta_height // 2
         SCREAMING_SNAKE_CASE				:					Union[str, Any]		  =			delta_width // 2

       SCREAMING_SNAKE_CASE				:					Any		  =			delta_height - pad_top
       SCREAMING_SNAKE_CASE				:					Optional[int]		  =			delta_width - pad_left

       SCREAMING_SNAKE_CASE				:					Dict		  =			((pad_top, pad_bottom), (pad_left, pad_right))
       return pad(UpperCAmelCase_    ,       UpperCAmelCase_    ,       data_format=UpperCAmelCase_					)


     def  _A				( self				:		Optional[Any]    ,       *UpperCAmelCase_				:		int    ,       **UpperCAmelCase_				:		Union[str, Any]					):
       logger.info("pad is deprecated and will be removed in version 4.27. Please use pad_image instead."					)
       return self.pad_image(*UpperCAmelCase_    ,       **UpperCAmelCase_					)


     def  _A				( self				:		List[Any]    ,       UpperCAmelCase_				:		np.ndarray    ,       UpperCAmelCase_				:		Dict[str, int]    ,       UpperCAmelCase_				:		PILImageResampling = PILImageResampling.BICUBIC    ,       UpperCAmelCase_				:		Optional[Union[str, ChannelDimension]] = None    ,       **UpperCAmelCase_				:		int    ,       ):
       SCREAMING_SNAKE_CASE  ,     SCREAMING_SNAKE_CASE				:					Union[str, Any]		  =			get_image_size(UpperCAmelCase_					)
       SCREAMING_SNAKE_CASE  ,     SCREAMING_SNAKE_CASE				:					int		  =			size["height"], size["width"]

       # We always resize to the smallest of either the input or output size.
       SCREAMING_SNAKE_CASE				:					Any		  =			min(UpperCAmelCase_    ,       UpperCAmelCase_					)
       SCREAMING_SNAKE_CASE				:					List[str]		  =			min(UpperCAmelCase_    ,       UpperCAmelCase_					)

       if height == input_height and width == input_width:
         return image

       if input_height > input_width:
         SCREAMING_SNAKE_CASE				:					Tuple		  =			int(input_width * height / input_height					)
       elif input_width > input_height:
         SCREAMING_SNAKE_CASE				:					Optional[Any]		  =			int(input_height * width / input_width					)

       return resize(
           UpperCAmelCase_    ,       size=(height, width)    ,       resample=UpperCAmelCase_    ,       reducing_gap=2.0    ,       data_format=UpperCAmelCase_    ,       **UpperCAmelCase_					)


     def  _A				( self				:		List[Any]    ,       UpperCAmelCase_				:		np.ndarray    ,       UpperCAmelCase_				:		Dict[str, int]    ,       UpperCAmelCase_				:		PILImageResampling = PILImageResampling.BICUBIC    ,       UpperCAmelCase_				:		Optional[Union[str, ChannelDimension]] = None    ,       **UpperCAmelCase_				:		Optional[int]    ,       ):
       SCREAMING_SNAKE_CASE				:					Optional[Any]		  =			get_size_dict(UpperCAmelCase_					)
       SCREAMING_SNAKE_CASE				:					Union[str, Any]		  =			min(size["height"]    ,       size["width"]					)
       SCREAMING_SNAKE_CASE				:					Tuple		  =			get_resize_output_image_size(UpperCAmelCase_    ,       size=UpperCAmelCase_    ,       default_to_square=UpperCAmelCase_					)
       SCREAMING_SNAKE_CASE				:					Union[str, Any]		  =			resize(UpperCAmelCase_    ,       size=UpperCAmelCase_    ,       resample=UpperCAmelCase_    ,       data_format=UpperCAmelCase_    ,       **UpperCAmelCase_					)
       return resized_image


     def  _A				( self				:		Dict    ,       UpperCAmelCase_				:		np.ndarray    ,       UpperCAmelCase_				:		Union[int, float]    ,       UpperCAmelCase_				:		Optional[Union[str, ChannelDimension]] = None    ,       **UpperCAmelCase_				:		str    ,       ):
       return rescale(UpperCAmelCase_    ,       scale=UpperCAmelCase_    ,       data_format=UpperCAmelCase_    ,       **UpperCAmelCase_					)


     def  _A				( self				:		Tuple    ,       UpperCAmelCase_				:		np.ndarray    ,       UpperCAmelCase_				:		Union[float, List[float]]    ,       UpperCAmelCase_				:		Union[float, List[float]]    ,       UpperCAmelCase_				:		Optional[Union[str, ChannelDimension]] = None    ,       **UpperCAmelCase_				:		List[Any]    ,       ):
       return normalize(UpperCAmelCase_    ,       mean=UpperCAmelCase_    ,       std=UpperCAmelCase_    ,       data_format=UpperCAmelCase_    ,       **UpperCAmelCase_					)






     def  _A				( self				:		List[Any]    ,       UpperCAmelCase_				:		ImageInput    ,       UpperCAmelCase_				:		bool = None    ,       UpperCAmelCase_				:		Dict[str, int] = None    ,       UpperCAmelCase_				:		PILImageResampling = None    ,       UpperCAmelCase_				:		bool = None    ,       UpperCAmelCase_				:		bool = None    ,       UpperCAmelCase_				:		bool = None    ,       UpperCAmelCase_				:		bool = False    ,       UpperCAmelCase_				:		bool = None    ,       UpperCAmelCase_				:		float = None    ,       UpperCAmelCase_				:		bool = None    ,       UpperCAmelCase_				:		Optional[Union[float, List[float]]] = None    ,       UpperCAmelCase_				:		Optional[Union[float, List[float]]] = None    ,       UpperCAmelCase_				:		Optional[Union[str, TensorType]] = None    ,       UpperCAmelCase_				:		Optional[ChannelDimension] = ChannelDimension.FIRST    ,       **UpperCAmelCase_				:		Union[str, Any]    ,       ):
       SCREAMING_SNAKE_CASE				:					int		  =			do_resize if do_resize is not None else self.do_resize
       SCREAMING_SNAKE_CASE				:					str		  =			size if size is not None else self.size
       if isinstance(UpperCAmelCase_    ,       (tuple, list)					):
         # Previous feature extractor had size in (width, height) format
         SCREAMING_SNAKE_CASE				:					str		  =			size[::-1]
       SCREAMING_SNAKE_CASE				:					Tuple		  =			get_size_dict(UpperCAmelCase_					)
       SCREAMING_SNAKE_CASE				:					int		  =			resample if resample is not None else self.resample
       SCREAMING_SNAKE_CASE				:					Union[str, Any]		  =			do_thumbnail if do_thumbnail is not None else self.do_thumbnail
       SCREAMING_SNAKE_CASE				:					Optional[int]		  =			do_align_long_axis if do_align_long_axis is not None else self.do_align_long_axis
       SCREAMING_SNAKE_CASE				:					int		  =			do_pad if do_pad is not None else self.do_pad
       SCREAMING_SNAKE_CASE				:					str		  =			do_rescale if do_rescale is not None else self.do_rescale
       SCREAMING_SNAKE_CASE				:					int		  =			rescale_factor if rescale_factor is not None else self.rescale_factor
       SCREAMING_SNAKE_CASE				:					Tuple		  =			do_normalize if do_normalize is not None else self.do_normalize
       SCREAMING_SNAKE_CASE				:					Optional[Any]		  =			image_mean if image_mean is not None else self.image_mean
       SCREAMING_SNAKE_CASE				:					List[str]		  =			image_std if image_std is not None else self.image_std

       SCREAMING_SNAKE_CASE				:					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:
         raise ValueError("Size must be specified if do_resize is True."					)

       if do_rescale and rescale_factor is None:
         raise ValueError("Rescale factor must be specified if do_rescale is True."					)

       if do_pad and size is None:
         raise ValueError("Size must be specified if do_pad 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.
       SCREAMING_SNAKE_CASE				:					int		  =			[to_numpy_array(UpperCAmelCase_					) for image in images]

       if do_align_long_axis:
         SCREAMING_SNAKE_CASE				:					Union[str, Any]		  =			[self.align_long_axis(UpperCAmelCase_    ,       size=UpperCAmelCase_					) for image in images]

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

       if do_thumbnail:
         SCREAMING_SNAKE_CASE				:					Optional[int]		  =			[self.thumbnail(image=UpperCAmelCase_    ,       size=UpperCAmelCase_					) for image in images]

       if do_pad:
         SCREAMING_SNAKE_CASE				:					Optional[Any]		  =			[self.pad_image(image=UpperCAmelCase_    ,       size=UpperCAmelCase_    ,       random_padding=UpperCAmelCase_					) for image in images]

       if do_rescale:
         SCREAMING_SNAKE_CASE				:					Union[str, Any]		  =			[self.rescale(image=UpperCAmelCase_    ,       scale=UpperCAmelCase_					) for image in images]

       if do_normalize:
         SCREAMING_SNAKE_CASE				:					Any		  =			[self.normalize(image=UpperCAmelCase_    ,       mean=UpperCAmelCase_    ,       std=UpperCAmelCase_					) for image in images]

       SCREAMING_SNAKE_CASE				:					str		  =			[to_channel_dimension_format(UpperCAmelCase_    ,       UpperCAmelCase_					) for image in images]

       SCREAMING_SNAKE_CASE				:					Tuple		  =			{"pixel_values": images}
       return BatchFeature(data=UpperCAmelCase_    ,       tensor_type=UpperCAmelCase_					)






