
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


lowercase__				:				str						=      logging.get_logger(__name__)


if is_vision_available():
				import PIL






class 							SCREAMING_SNAKE_CASE__     (				SCREAMING_SNAKE_CASE_     ):

							"""simple docstring"""






							_snake_case  		=	['pixel_values']



							def __init__(	self  ,						SCREAMING_SNAKE_CASE_ = True  ,						SCREAMING_SNAKE_CASE_ = None  ,						SCREAMING_SNAKE_CASE_ = PILImageResampling.BILINEAR  ,						SCREAMING_SNAKE_CASE_ = True  ,						SCREAMING_SNAKE_CASE_ = False  ,						SCREAMING_SNAKE_CASE_ = True  ,						SCREAMING_SNAKE_CASE_ = True  ,						SCREAMING_SNAKE_CASE_ = 1 / 255  ,						SCREAMING_SNAKE_CASE_ = True  ,						SCREAMING_SNAKE_CASE_ = None  ,						SCREAMING_SNAKE_CASE_ = None  ,						**SCREAMING_SNAKE_CASE_  ,						)-> None:





													'''simple docstring'''






													super().__init__(**SCREAMING_SNAKE_CASE_      )

													__UpperCamelCase  	=      size if size is not None else {'''height''': 2560, '''width''': 1920}
													if isinstance(SCREAMING_SNAKE_CASE_  ,						(tuple, list)      ):
																			# The previous feature extractor size parameter was in (width, height) format
																			__UpperCamelCase  	=      size[::-1]
													__UpperCamelCase  	=      get_size_dict(SCREAMING_SNAKE_CASE_      )

													__UpperCamelCase  	=      do_resize
													__UpperCamelCase  	=      size
													__UpperCamelCase  	=      resample
													__UpperCamelCase  	=      do_thumbnail
													__UpperCamelCase  	=      do_align_long_axis
													__UpperCamelCase  	=      do_pad
													__UpperCamelCase  	=      do_rescale
													__UpperCamelCase  	=      rescale_factor
													__UpperCamelCase  	=      do_normalize
													__UpperCamelCase  	=      image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
													__UpperCamelCase  	=      image_std if image_std is not None else IMAGENET_STANDARD_STD



							def      A__		(	self  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_ = None      )-> np.ndarray:





													'''simple docstring'''






													__UpperCamelCase	,				__UpperCamelCase  	=      get_image_size(SCREAMING_SNAKE_CASE_      )
													__UpperCamelCase	,				__UpperCamelCase  	=      size['''height'''], size['''width''']

													if (output_width < output_height and input_width > input_height) or (
													    output_width > output_height and input_width < input_height
													):
																			__UpperCamelCase  	=      np.rotaa(SCREAMING_SNAKE_CASE_  ,						3      )

													if data_format is not None:
																			__UpperCamelCase  	=      to_channel_dimension_format(SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_      )

													return image



							def      A__		(	self  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_ = False  ,						SCREAMING_SNAKE_CASE_ = None  ,						)-> np.ndarray:





													'''simple docstring'''






													__UpperCamelCase	,				__UpperCamelCase  	=      size['''height'''], size['''width''']
													__UpperCamelCase	,				__UpperCamelCase  	=      get_image_size(SCREAMING_SNAKE_CASE_      )

													__UpperCamelCase  	=      output_width - input_width
													__UpperCamelCase  	=      output_height - input_height

													if random_padding:
																			__UpperCamelCase  	=      np.random.randint(low=0  ,						high=delta_height + 1      )
																			__UpperCamelCase  	=      np.random.randint(low=0  ,						high=delta_width + 1      )
													else:
																			__UpperCamelCase  	=      delta_height // 2
																			__UpperCamelCase  	=      delta_width // 2

													__UpperCamelCase  	=      delta_height - pad_top
													__UpperCamelCase  	=      delta_width - pad_left

													__UpperCamelCase  	=      ((pad_top, pad_bottom), (pad_left, pad_right))
													return pad(SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						data_format=SCREAMING_SNAKE_CASE_      )



							def      A__		(	self  ,						*SCREAMING_SNAKE_CASE_  ,						**SCREAMING_SNAKE_CASE_      )-> Optional[int]:





													'''simple docstring'''






													logger.info('''pad is deprecated and will be removed in version 4.27. Please use pad_image instead.'''      )
													return self.pad_image(*SCREAMING_SNAKE_CASE_  ,						**SCREAMING_SNAKE_CASE_      )



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





													'''simple docstring'''






													__UpperCamelCase	,				__UpperCamelCase  	=      get_image_size(SCREAMING_SNAKE_CASE_      )
													__UpperCamelCase	,				__UpperCamelCase  	=      size['''height'''], size['''width''']

													# We always resize to the smallest of either the input or output size.
													__UpperCamelCase  	=      min(SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_      )
													__UpperCamelCase  	=      min(SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_      )

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

													if input_height > input_width:
																			__UpperCamelCase  	=      int(input_width * height / input_height      )
													elif input_width > input_height:
																			__UpperCamelCase  	=      int(input_height * width / input_width      )

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



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





													'''simple docstring'''






													__UpperCamelCase  	=      get_size_dict(SCREAMING_SNAKE_CASE_      )
													__UpperCamelCase  	=      min(size['''height''']  ,						size['''width''']      )
													__UpperCamelCase  	=      get_resize_output_image_size(SCREAMING_SNAKE_CASE_  ,						size=SCREAMING_SNAKE_CASE_  ,						default_to_square=SCREAMING_SNAKE_CASE_      )
													__UpperCamelCase  	=      resize(SCREAMING_SNAKE_CASE_  ,						size=SCREAMING_SNAKE_CASE_  ,						resample=SCREAMING_SNAKE_CASE_  ,						data_format=SCREAMING_SNAKE_CASE_  ,						**SCREAMING_SNAKE_CASE_      )
													return resized_image



							def      A__		(	self  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_ = None  ,						**SCREAMING_SNAKE_CASE_  ,						)-> List[str]:





													'''simple docstring'''






													return rescale(SCREAMING_SNAKE_CASE_  ,						scale=SCREAMING_SNAKE_CASE_  ,						data_format=SCREAMING_SNAKE_CASE_  ,						**SCREAMING_SNAKE_CASE_      )



							def      A__		(	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      A__		(	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_ = False  ,						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  	=      do_resize if do_resize is not None else self.do_resize
													__UpperCamelCase  	=      size if size is not None else self.size
													if isinstance(SCREAMING_SNAKE_CASE_  ,						(tuple, list)      ):
																			# Previous feature extractor had size in (width, height) format
																			__UpperCamelCase  	=      size[::-1]
													__UpperCamelCase  	=      get_size_dict(SCREAMING_SNAKE_CASE_      )
													__UpperCamelCase  	=      resample if resample is not None else self.resample
													__UpperCamelCase  	=      do_thumbnail if do_thumbnail is not None else self.do_thumbnail
													__UpperCamelCase  	=      do_align_long_axis if do_align_long_axis is not None else self.do_align_long_axis
													__UpperCamelCase  	=      do_pad if do_pad is not None else self.do_pad
													__UpperCamelCase  	=      do_rescale if do_rescale is not None else self.do_rescale
													__UpperCamelCase  	=      rescale_factor if rescale_factor is not None else self.rescale_factor
													__UpperCamelCase  	=      do_normalize if do_normalize is not None else self.do_normalize
													__UpperCamelCase  	=      image_mean if image_mean is not None else self.image_mean
													__UpperCamelCase  	=      image_std if image_std is not None else self.image_std

													__UpperCamelCase  	=      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_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.
													__UpperCamelCase  	=      [to_numpy_array(SCREAMING_SNAKE_CASE_      ) for image in images]

													if do_align_long_axis:
																			__UpperCamelCase  	=      [self.align_long_axis(SCREAMING_SNAKE_CASE_  ,						size=SCREAMING_SNAKE_CASE_      ) for image in images]

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

													if do_thumbnail:
																			__UpperCamelCase  	=      [self.thumbnail(image=SCREAMING_SNAKE_CASE_  ,						size=SCREAMING_SNAKE_CASE_      ) for image in images]

													if do_pad:
																			__UpperCamelCase  	=      [self.pad_image(image=SCREAMING_SNAKE_CASE_  ,						size=SCREAMING_SNAKE_CASE_  ,						random_padding=SCREAMING_SNAKE_CASE_      ) for image in images]

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

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

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

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











def        __SCREAMING_SNAKE_CASE	(				__lowercase:		int = 10_00):



  '''simple docstring'''






  __lowerCAmelCase								=					0
  for i in range(__lowercase):
    if i % 3 == 0 or i % 5 == 0:
      result += i
  return result


if __name__ == "__main__":
      print(f"""{solution() = }""")
