


'''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,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


lowercase__				     =		logging.get_logger(__name__)


if is_vision_available():
			import PIL





class 						__SCREAMING_SNAKE_CASE					(      UpperCamelCase_ ):
					UpperCAmelCase 					=				['''pixel_values''']






					def __init__(			self     ,							__UpperCamelCase = True     ,							__UpperCamelCase = None     ,							__UpperCamelCase = PILImageResampling.BICUBIC     ,							__UpperCamelCase = True     ,							__UpperCamelCase = None     ,							__UpperCamelCase = True     ,							__UpperCamelCase = 1 / 255     ,							__UpperCamelCase = True     ,							__UpperCamelCase = None     ,							__UpperCamelCase = None     ,							__UpperCamelCase = True     ,							**__UpperCamelCase     ,							)       ->    None:
										super().__init__(**__a		)
										_a						=		size if size is not None else {'shortest_edge': 224}
										_a						=		get_size_dict(__a     ,							default_to_square=__a		)
										_a						=		crop_size if crop_size is not None else {'height': 224, 'width': 224}
										_a						=		get_size_dict(__a     ,							default_to_square=__a     ,							param_name="crop_size"		)

										_a						=		do_resize
										_a						=		size
										_a						=		resample
										_a						=		do_center_crop
										_a						=		crop_size
										_a						=		do_rescale
										_a						=		rescale_factor
										_a						=		do_normalize
										_a						=		image_mean if image_mean is not None else OPENAI_CLIP_MEAN
										_a						=		image_std if image_std is not None else OPENAI_CLIP_STD
										_a						=		do_convert_rgb






					def  a_					(			self     ,							__UpperCamelCase     ,							__UpperCamelCase     ,							__UpperCamelCase = PILImageResampling.BICUBIC     ,							__UpperCamelCase = None     ,							**__UpperCamelCase     ,							)       ->    np.ndarray:
										_a						=		get_size_dict(__a     ,							default_to_square=__a		)
										if "shortest_edge" not in size:
															raise ValueError(f"The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}"		)
										_a						=		get_resize_output_image_size(__a     ,							size=size["shortest_edge"]     ,							default_to_square=__a		)
										return resize(__a     ,							size=__a     ,							resample=__a     ,							data_format=__a     ,							**__a		)






					def  a_					(			self     ,							__UpperCamelCase     ,							__UpperCamelCase     ,							__UpperCamelCase = None     ,							**__UpperCamelCase     ,							)       ->    np.ndarray:
										_a						=		get_size_dict(__a		)
										if "height" not in size or "width" not in size:
															raise ValueError(f"The `size` parameter must contain the keys (height, width). Got {size.keys()}"		)
										return center_crop(__a     ,							size=(size["height"], size["width"])     ,							data_format=__a     ,							**__a		)






					def  a_					(			self     ,							__UpperCamelCase     ,							__UpperCamelCase     ,							__UpperCamelCase = None     ,							**__UpperCamelCase     ,							)       ->    Union[str, Any]:
										return rescale(__a     ,							scale=__a     ,							data_format=__a     ,							**__a		)






					def  a_					(			self     ,							__UpperCamelCase     ,							__UpperCamelCase     ,							__UpperCamelCase     ,							__UpperCamelCase = None     ,							**__UpperCamelCase     ,							)       ->    np.ndarray:
										return normalize(__a     ,							mean=__a     ,							std=__a     ,							data_format=__a     ,							**__a		)






					def  a_					(			self     ,							__UpperCamelCase     ,							__UpperCamelCase = None     ,							__UpperCamelCase = None     ,							__UpperCamelCase = None     ,							__UpperCamelCase = None     ,							__UpperCamelCase = None     ,							__UpperCamelCase = None     ,							__UpperCamelCase = None     ,							__UpperCamelCase = None     ,							__UpperCamelCase = None     ,							__UpperCamelCase = None     ,							__UpperCamelCase = None     ,							__UpperCamelCase = None     ,							__UpperCamelCase = ChannelDimension.FIRST     ,							**__UpperCamelCase     ,							)       ->    PIL.Image.Image:
										_a						=		do_resize if do_resize is not None else self.do_resize
										_a						=		size if size is not None else self.size
										_a						=		get_size_dict(__a     ,							param_name="size"     ,							default_to_square=__a		)
										_a						=		resample if resample is not None else self.resample
										_a						=		do_center_crop if do_center_crop is not None else self.do_center_crop
										_a						=		crop_size if crop_size is not None else self.crop_size
										_a						=		get_size_dict(__a     ,							param_name="crop_size"     ,							default_to_square=__a		)
										_a						=		do_rescale if do_rescale is not None else self.do_rescale
										_a						=		rescale_factor if rescale_factor is not None else self.rescale_factor
										_a						=		do_normalize if do_normalize is not None else self.do_normalize
										_a						=		image_mean if image_mean is not None else self.image_mean
										_a						=		image_std if image_std is not None else self.image_std
										_a						=		do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

										_a						=		make_list_of_images(__a		)

										if not valid_images(__a		):
															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_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."		)

										# PIL RGBA images are converted to RGB
										if do_convert_rgb:
															_a						=		[convert_to_rgb(__a		) for image in images]

										# All transformations expect numpy arrays.
										_a						=		[to_numpy_array(__a		) for image in images]

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

										if do_center_crop:
															_a						=		[self.center_crop(image=__a     ,							size=__a		) for image in images]

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

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

										_a						=		[to_channel_dimension_format(__a     ,							__a		) for image in images]

										_a						=		{'pixel_values': images}
										return BatchFeature(data=__a     ,							tensor_type=__a		)





'''simple docstring'''

def __UpperCamelCase   (			__lowerCamelCase		: int = 400_0000				)      ->     int:







     '''simple docstring'''






     _a						=		[]
     _a			,			_a						=		0, 1
     while b <= n:
          if b % 2 == 0:
               even_fibs.append(__lowerCamelCase				)
          _a			,			_a						=		b, a + b
     return sum(__lowerCamelCase				)


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

