



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


UpperCAmelCase							=       logging.get_logger(__name__)


if is_vision_available():
	import PIL

class 			__snake_case   ( SCREAMING_SNAKE_CASE):







							'''simple docstring'''






							UpperCamelCase__					:   Optional[int]       =       ["""pixel_values"""]


							def __init__(				self	,	a_ = True	,	a_ = None	,	a_ = PILImageResampling.BILINEAR	,	a_ = True	,	a_ = False	,	a_ = True	,	a_ = True	,	a_ = 1 / 255	,	a_ = True	,	a_ = None	,	a_ = None	,	**a_	,	):
											super().__init__(**a_		)

											a__					   =		size if size is not None else {"""height""": 2_560, """width""": 1_920}
											if isinstance(a_	,	(tuple, list)		):
															# The previous feature extractor size parameter was in (width, height) format
															a__					   =		size[::-1]
											a__					   =		get_size_dict(a_		)

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


							def _a   (				self	,	a_	,	a_	,	a_ = None		):
											a__   ,      a__					   =		get_image_size(a_		)
											a__   ,      a__					   =		size["""height"""], size["""width"""]

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

											if data_format is not None:
															a__					   =		to_channel_dimension_format(a_	,	a_		)

											return image


							def _a   (				self	,	a_	,	a_	,	a_ = False	,	a_ = None	,	):
											a__   ,      a__					   =		size["""height"""], size["""width"""]
											a__   ,      a__					   =		get_image_size(a_		)

											a__					   =		output_width - input_width
											a__					   =		output_height - input_height

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

											a__					   =		delta_height - pad_top
											a__					   =		delta_width - pad_left

											a__					   =		((pad_top, pad_bottom), (pad_left, pad_right))
											return pad(a_	,	a_	,	data_format=a_		)


							def _a   (				self	,	*a_	,	**a_		):
											logger.info("""pad is deprecated and will be removed in version 4.27. Please use pad_image instead."""		)
											return self.pad_image(*a_	,	**a_		)


							def _a   (				self	,	a_	,	a_	,	a_ = PILImageResampling.BICUBIC	,	a_ = None	,	**a_	,	):
											a__   ,      a__					   =		get_image_size(a_		)
											a__   ,      a__					   =		size["""height"""], size["""width"""]

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

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

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

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


							def _a   (				self	,	a_	,	a_	,	a_ = PILImageResampling.BICUBIC	,	a_ = None	,	**a_	,	):
											a__					   =		get_size_dict(a_		)
											a__					   =		min(size["""height"""]	,	size["""width"""]		)
											a__					   =		get_resize_output_image_size(a_	,	size=a_	,	default_to_square=a_		)
											a__					   =		resize(a_	,	size=a_	,	resample=a_	,	data_format=a_	,	**a_		)
											return resized_image


							def _a   (				self	,	a_	,	a_	,	a_ = None	,	**a_	,	):
											return rescale(a_	,	scale=a_	,	data_format=a_	,	**a_		)


							def _a   (				self	,	a_	,	a_	,	a_	,	a_ = None	,	**a_	,	):
											return normalize(a_	,	mean=a_	,	std=a_	,	data_format=a_	,	**a_		)







							def _a   (				self	,	a_	,	a_ = None	,	a_ = None	,	a_ = None	,	a_ = None	,	a_ = None	,	a_ = None	,	a_ = False	,	a_ = None	,	a_ = None	,	a_ = None	,	a_ = None	,	a_ = None	,	a_ = None	,	a_ = ChannelDimension.FIRST	,	**a_	,	):
											a__					   =		do_resize if do_resize is not None else self.do_resize
											a__					   =		size if size is not None else self.size
											if isinstance(a_	,	(tuple, list)		):
															# Previous feature extractor had size in (width, height) format
															a__					   =		size[::-1]
											a__					   =		get_size_dict(a_		)
											a__					   =		resample if resample is not None else self.resample
											a__					   =		do_thumbnail if do_thumbnail is not None else self.do_thumbnail
											a__					   =		do_align_long_axis if do_align_long_axis is not None else self.do_align_long_axis
											a__					   =		do_pad if do_pad is not None else self.do_pad
											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__					   =		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_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.
											a__					   =		[to_numpy_array(a_		) for image in images]

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

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

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

											if do_pad:
															a__					   =		[self.pad_image(image=a_	,	size=a_	,	random_padding=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_		)






from math import gcd

def       A_   (						__a  :					int = 12_000		):

				"""simple docstring"""
				a__					   =		0
				for d in range(max_d + 1		):
								for n in range(d // 3 + 1			,		(d + 1) // 2		):
												if gcd(__a			,		__a		) == 1:
																fractions_number += 1
				return fractions_number


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

