
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 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, logging


UpperCAmelCase     				=			logging.get_logger(__name__)







class 	A_       (      __lowerCamelCase       ):




	'''simple docstring'''


	_UpperCamelCase					:					Optional[Any]										=	["""pixel_values"""]





	def __init__(	self		,   snake_case = True		,   snake_case = None		,   snake_case = PILImageResampling.BILINEAR		,   snake_case = True		,   snake_case = 1 / 255		,   snake_case = True		,   snake_case = None		,   snake_case = None		,   **snake_case		,   ):
		super().__init__(**snake_case	)
		lowercase					  =   size if size is not None else {'height': 224, 'width': 224}
		lowercase					  =   get_size_dict(snake_case	)
		lowercase					  =   do_resize
		lowercase					  =   do_rescale
		lowercase					  =   do_normalize
		lowercase					  =   size
		lowercase					  =   resample
		lowercase					  =   rescale_factor
		lowercase					  =   image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
		lowercase					  =   image_std if image_std is not None else IMAGENET_STANDARD_STD





	def      SCREAMING_SNAKE_CASE__						(	self		,   snake_case		,   snake_case		,   snake_case = PILImageResampling.BILINEAR		,   snake_case = None		,   **snake_case		,   ):
		lowercase					  =   get_size_dict(snake_case	)
		if "height" not in size or "width" not in size:
			raise ValueError(F'''The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}'''	)
		return resize(
		    snake_case		,   size=(size['height'], size['width'])		,   resample=snake_case		,   data_format=snake_case		,   **snake_case	)





	def      SCREAMING_SNAKE_CASE__						(	self		,   snake_case		,   snake_case		,   snake_case = None		,   **snake_case	):
		return rescale(snake_case		,   scale=snake_case		,   data_format=snake_case		,   **snake_case	)





	def      SCREAMING_SNAKE_CASE__						(	self		,   snake_case		,   snake_case		,   snake_case		,   snake_case = None		,   **snake_case		,   ):
		return normalize(snake_case		,   mean=snake_case		,   std=snake_case		,   data_format=snake_case		,   **snake_case	)





	def      SCREAMING_SNAKE_CASE__						(	self		,   snake_case		,   snake_case = None		,   snake_case = None		,   snake_case = None		,   snake_case = None		,   snake_case = None		,   snake_case = None		,   snake_case = None		,   snake_case = None		,   snake_case = None		,   snake_case = ChannelDimension.FIRST		,   **snake_case		,   ):
		lowercase					  =   do_resize if do_resize is not None else self.do_resize
		lowercase					  =   do_rescale if do_rescale is not None else self.do_rescale
		lowercase					  =   do_normalize if do_normalize is not None else self.do_normalize
		lowercase					  =   resample if resample is not None else self.resample
		lowercase					  =   rescale_factor if rescale_factor is not None else self.rescale_factor
		lowercase					  =   image_mean if image_mean is not None else self.image_mean
		lowercase					  =   image_std if image_std is not None else self.image_std

		lowercase					  =   size if size is not None else self.size
		lowercase					  =   get_size_dict(snake_case	)

		lowercase					  =   make_list_of_images(snake_case	)

		if not valid_images(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.'	)

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

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

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

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

		lowercase					  =   [to_channel_dimension_format(snake_case		,   snake_case	) for image in images]

		lowercase					  =   {'pixel_values': images}
		return BatchFeature(data=snake_case		,   tensor_type=snake_case	)



import unittest
from timeit import timeit






def 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE			,							__SCREAMING_SNAKE_CASE							):
	lowercase					  =   first_num if first_num >= second_num else second_num
	lowercase					  =   max_num
	while (common_mult % first_num > 0) or (common_mult % second_num > 0):
		common_mult += max_num
	return common_mult






def 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE			,							__SCREAMING_SNAKE_CASE							):
	return b if a == 0 else greatest_common_divisor(b % a			,							__SCREAMING_SNAKE_CASE							)






def 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE			,							__SCREAMING_SNAKE_CASE							):
	return first_num // greatest_common_divisor(__SCREAMING_SNAKE_CASE			,							__SCREAMING_SNAKE_CASE							) * second_num






def 							UpperCAmelCase_		(							):
	lowercase					  =   (
	    'from __main__ import least_common_multiple_slow, least_common_multiple_fast'
	)
	print(
	    'least_common_multiple_slow():'			,							timeit('least_common_multiple_slow(1000, 999)'			,							setup=__SCREAMING_SNAKE_CASE							)			,							)
	print(
	    'least_common_multiple_fast():'			,							timeit('least_common_multiple_fast(1000, 999)'			,							setup=__SCREAMING_SNAKE_CASE							)			,							)







class 	A_       (      unittest.TestCase       ):




	'''simple docstring'''


	_UpperCamelCase					:					Any										=	(
	    (10, 20),
	    (13, 15),
	    (4, 31),
	    (10, 42),
	    (43, 34),
	    (5, 12),
	    (12, 25),
	    (10, 25),
	    (6, 9),
	)
	_UpperCamelCase					:					int										=	(20, 195, 124, 210, 1462, 60, 300, 50, 18)





	def      SCREAMING_SNAKE_CASE__						(	self	):
		for i, (first_num, second_num) in enumerate(self.test_inputs	):
			lowercase					  =   least_common_multiple_slow(snake_case		,   snake_case	)
			lowercase					  =   least_common_multiple_fast(snake_case		,   snake_case	)
			with self.subTest(i=snake_case	):
				self.assertEqual(snake_case		,   self.expected_results[i]	)
				self.assertEqual(snake_case		,   self.expected_results[i]	)


if __name__ == "__main__":
	benchmark()
	unittest.main()

