




import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
						import torch

if is_vision_available():
						from PIL import Image

						from transformers import ViltImageProcessor



class       lowercase_		(unittest.TestCase   ):



						def __init__( self  :      List[Any]				,       snake_case__  :      Tuple				,       snake_case__  :      List[Any]=7				,       snake_case__  :      List[str]=3				,       snake_case__  :      Optional[Any]=18				,       snake_case__  :      Dict=30				,       snake_case__  :      str=4_00				,       snake_case__  :      List[str]=True				,       snake_case__  :      List[Any]=None				,       snake_case__  :      str=2				,       snake_case__  :      Union[str, Any]=True				,       snake_case__  :      Tuple=[0.5, 0.5, 0.5]				,       snake_case__  :      List[Any]=[0.5, 0.5, 0.5]				,       ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_							=       size if size is not None else {'shortest_edge': 30}
								SCREAMING_SNAKE_CASE_							=       parent
								SCREAMING_SNAKE_CASE_							=       batch_size
								SCREAMING_SNAKE_CASE_							=       num_channels
								SCREAMING_SNAKE_CASE_							=       image_size
								SCREAMING_SNAKE_CASE_							=       min_resolution
								SCREAMING_SNAKE_CASE_							=       max_resolution
								SCREAMING_SNAKE_CASE_							=       do_resize
								SCREAMING_SNAKE_CASE_							=       size
								SCREAMING_SNAKE_CASE_							=       size_divisor
								SCREAMING_SNAKE_CASE_							=       do_normalize
								SCREAMING_SNAKE_CASE_							=       image_mean
								SCREAMING_SNAKE_CASE_							=       image_std



						def   __a				( self  :      Optional[Any]     ):

								"""simple docstring"""

								return {
								    "image_mean": self.image_mean,
								    "image_std": self.image_std,
								    "do_normalize": self.do_normalize,
								    "do_resize": self.do_resize,
								    "size": self.size,
								    "size_divisor": self.size_divisor,
								}






						def   __a				( self  :      List[str]				,       snake_case__  :      Any				,       snake_case__  :      int=False     ):

								"""simple docstring"""

								if not batched:
										SCREAMING_SNAKE_CASE_							=       self.size['shortest_edge']
										SCREAMING_SNAKE_CASE_							=       image_inputs[0]
										if isinstance(snake_case__				,       Image.Image     ):
												SCREAMING_SNAKE_CASE_			,							SCREAMING_SNAKE_CASE_							=       image.size
										else:
												SCREAMING_SNAKE_CASE_			,							SCREAMING_SNAKE_CASE_							=       image.shape[1], image.shape[2]
										SCREAMING_SNAKE_CASE_							=       size / min(snake_case__				,       snake_case__     )
										if h < w:
												SCREAMING_SNAKE_CASE_			,							SCREAMING_SNAKE_CASE_							=       size, scale * w
										else:
												SCREAMING_SNAKE_CASE_			,							SCREAMING_SNAKE_CASE_							=       scale * h, size

										SCREAMING_SNAKE_CASE_							=       int((13_33 / 8_00) * size     )
										if max(snake_case__				,       snake_case__     ) > max_size:
												SCREAMING_SNAKE_CASE_							=       max_size / max(snake_case__				,       snake_case__     )
												SCREAMING_SNAKE_CASE_							=       newh * scale
												SCREAMING_SNAKE_CASE_							=       neww * scale

										SCREAMING_SNAKE_CASE_			,							SCREAMING_SNAKE_CASE_							=       int(newh + 0.5     ), int(neww + 0.5     )
										SCREAMING_SNAKE_CASE_			,							SCREAMING_SNAKE_CASE_							=       (
										    newh // self.size_divisor * self.size_divisor,
										    neww // self.size_divisor * self.size_divisor,
										)

								else:
										SCREAMING_SNAKE_CASE_							=       []
										for image in image_inputs:
												SCREAMING_SNAKE_CASE_			,							SCREAMING_SNAKE_CASE_							=       self.get_expected_values([image]     )
												expected_values.append((expected_height, expected_width)     )
										SCREAMING_SNAKE_CASE_							=       max(snake_case__				,       key=lambda snake_case__     : item[0]     )[0]
										SCREAMING_SNAKE_CASE_							=       max(snake_case__				,       key=lambda snake_case__     : item[1]     )[1]

								return expected_height, expected_width



@require_torch
@require_vision
class       lowercase_		(SCREAMING_SNAKE_CASE__       ,  unittest.TestCase   ):
						lowerCAmelCase__ 			=ViltImageProcessor if is_vision_available() else None



						def   __a				( self  :      Optional[int]     ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_							=       ViltImageProcessingTester(self     )



						@property
						def   __a				( self  :      Optional[Any]     ):

								"""simple docstring"""

								return self.image_processor_tester.prepare_image_processor_dict()



						def   __a				( self  :      Tuple     ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_							=       self.image_processing_class(**self.image_processor_dict     )
								self.assertTrue(hasattr(snake_case__				,       'image_mean'     )     )
								self.assertTrue(hasattr(snake_case__				,       'image_std'     )     )
								self.assertTrue(hasattr(snake_case__				,       'do_normalize'     )     )
								self.assertTrue(hasattr(snake_case__				,       'do_resize'     )     )
								self.assertTrue(hasattr(snake_case__				,       'size'     )     )
								self.assertTrue(hasattr(snake_case__				,       'size_divisor'     )     )



						def   __a				( self  :      List[Any]     ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_							=       self.image_processing_class.from_dict(self.image_processor_dict     )
								self.assertEqual(image_processor.size				,       {'shortest_edge': 30}     )

								SCREAMING_SNAKE_CASE_							=       self.image_processing_class.from_dict(self.image_processor_dict				,       size=42     )
								self.assertEqual(image_processor.size				,       {'shortest_edge': 42}     )



						def   __a				( self  :      List[Any]     ):

								"""simple docstring"""

								pass



						def   __a				( self  :      Dict     ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_							=       self.image_processing_class(**self.image_processor_dict     )
								# create random PIL images
								SCREAMING_SNAKE_CASE_							=       prepare_image_inputs(self.image_processor_tester				,       equal_resolution=snake_case__     )
								for image in image_inputs:
										self.assertIsInstance(snake_case__				,       Image.Image     )

								# Test not batched input
								SCREAMING_SNAKE_CASE_							=       image_processing(image_inputs[0]				,       return_tensors='pt'     ).pixel_values

								SCREAMING_SNAKE_CASE_			,							SCREAMING_SNAKE_CASE_							=       self.image_processor_tester.get_expected_values(snake_case__     )
								self.assertEqual(
								    encoded_images.shape				,       (1, self.image_processor_tester.num_channels, expected_height, expected_width)				,       )

								# Test batched
								SCREAMING_SNAKE_CASE_							=       image_processing(snake_case__				,       return_tensors='pt'     ).pixel_values

								SCREAMING_SNAKE_CASE_			,							SCREAMING_SNAKE_CASE_							=       self.image_processor_tester.get_expected_values(snake_case__				,       batched=snake_case__     )
								self.assertEqual(
								    encoded_images.shape				,       (
								        self.image_processor_tester.batch_size,
								        self.image_processor_tester.num_channels,
								        expected_height,
								        expected_width,
								    )				,       )



						def   __a				( self  :      List[str]     ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_							=       self.image_processing_class(**self.image_processor_dict     )
								# create random numpy tensors
								SCREAMING_SNAKE_CASE_							=       prepare_image_inputs(self.image_processor_tester				,       equal_resolution=snake_case__				,       numpify=snake_case__     )
								for image in image_inputs:
										self.assertIsInstance(snake_case__				,       np.ndarray     )

								# Test not batched input
								SCREAMING_SNAKE_CASE_							=       image_processing(image_inputs[0]				,       return_tensors='pt'     ).pixel_values

								SCREAMING_SNAKE_CASE_			,							SCREAMING_SNAKE_CASE_							=       self.image_processor_tester.get_expected_values(snake_case__     )
								self.assertEqual(
								    encoded_images.shape				,       (1, self.image_processor_tester.num_channels, expected_height, expected_width)				,       )

								# Test batched
								SCREAMING_SNAKE_CASE_							=       image_processing(snake_case__				,       return_tensors='pt'     ).pixel_values

								SCREAMING_SNAKE_CASE_			,							SCREAMING_SNAKE_CASE_							=       self.image_processor_tester.get_expected_values(snake_case__				,       batched=snake_case__     )
								self.assertEqual(
								    encoded_images.shape				,       (
								        self.image_processor_tester.batch_size,
								        self.image_processor_tester.num_channels,
								        expected_height,
								        expected_width,
								    )				,       )






						def   __a				( self  :      str     ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_							=       self.image_processing_class(**self.image_processor_dict     )
								# create random PyTorch tensors
								SCREAMING_SNAKE_CASE_							=       prepare_image_inputs(self.image_processor_tester				,       equal_resolution=snake_case__				,       torchify=snake_case__     )
								for image in image_inputs:
										self.assertIsInstance(snake_case__				,       torch.Tensor     )

								# Test not batched input
								SCREAMING_SNAKE_CASE_							=       image_processing(image_inputs[0]				,       return_tensors='pt'     ).pixel_values

								SCREAMING_SNAKE_CASE_			,							SCREAMING_SNAKE_CASE_							=       self.image_processor_tester.get_expected_values(snake_case__     )
								self.assertEqual(
								    encoded_images.shape				,       (1, self.image_processor_tester.num_channels, expected_height, expected_width)				,       )

								# Test batched
								SCREAMING_SNAKE_CASE_							=       image_processing(snake_case__				,       return_tensors='pt'     ).pixel_values

								SCREAMING_SNAKE_CASE_			,							SCREAMING_SNAKE_CASE_							=       self.image_processor_tester.get_expected_values(snake_case__				,       batched=snake_case__     )
								self.assertEqual(
								    encoded_images.shape				,       (
								        self.image_processor_tester.batch_size,
								        self.image_processor_tester.num_channels,
								        expected_height,
								        expected_width,
								    )				,       )





from __future__ import annotations


def 				_a       (				lowerCAmelCase   )->  list[int]:
		if not list_of_ints:
				return []

		if min(lowerCAmelCase   ) < 0:
				raise ValueError('All numbers must be positive'   )

		SCREAMING_SNAKE_CASE_							=       max(len(bin(lowerCAmelCase   )[2:]   ) for x in list_of_ints   )
		return _msd_radix_sort(lowerCAmelCase						,	lowerCAmelCase   )


def 				_a       (				lowerCAmelCase						,	lowerCAmelCase   )->  list[int]:
		if bit_position == 0 or len(lowerCAmelCase   ) in [0, 1]:
				return list_of_ints

		SCREAMING_SNAKE_CASE_							=       []
		SCREAMING_SNAKE_CASE_							=       []
		# Split numbers based on bit at bit_position from the right
		for number in list_of_ints:
				if (number >> (bit_position - 1)) & 1:
						# number has a one at bit bit_position
						ones.append(lowerCAmelCase   )
				else:
						# number has a zero at bit bit_position
						zeros.append(lowerCAmelCase   )

    # recursively split both lists further
		SCREAMING_SNAKE_CASE_							=       _msd_radix_sort(lowerCAmelCase						,	bit_position - 1   )
		SCREAMING_SNAKE_CASE_							=       _msd_radix_sort(lowerCAmelCase						,	bit_position - 1   )

		# recombine lists
		SCREAMING_SNAKE_CASE_							=       zeros
		res.extend(lowerCAmelCase   )

		return res


def 				_a       (				lowerCAmelCase   )->  Dict:
		SCREAMING_SNAKE_CASE_							=       len(lowerCAmelCase   )
		if not list_of_ints or length == 1:
				return

		if min(lowerCAmelCase   ) < 0:
				raise ValueError('All numbers must be positive'   )

		SCREAMING_SNAKE_CASE_							=       max(len(bin(lowerCAmelCase   )[2:]   ) for x in list_of_ints   )
		_msd_radix_sort_inplace(lowerCAmelCase						,	lowerCAmelCase						,	0						,	lowerCAmelCase   )


def 				_a       (				lowerCAmelCase						,	lowerCAmelCase						,	lowerCAmelCase						,	lowerCAmelCase   )->  Optional[int]:
		if bit_position == 0 or end_index - begin_index <= 1:
				return

		bit_position -= 1

		SCREAMING_SNAKE_CASE_							=       begin_index
		SCREAMING_SNAKE_CASE_							=       end_index - 1
		while i <= j:
				SCREAMING_SNAKE_CASE_							=       False
				if not (list_of_ints[i] >> bit_position) & 1:
						# found zero at the beginning
						i += 1
						SCREAMING_SNAKE_CASE_							=       True
				if (list_of_ints[j] >> bit_position) & 1:
						# found one at the end
						j -= 1
						SCREAMING_SNAKE_CASE_							=       True

				if changed:
						continue

				SCREAMING_SNAKE_CASE_			,							SCREAMING_SNAKE_CASE_							=       list_of_ints[j], list_of_ints[i]
				j -= 1
				if j != i:
						i += 1

		_msd_radix_sort_inplace(lowerCAmelCase						,	lowerCAmelCase						,	lowerCAmelCase						,	lowerCAmelCase   )
		_msd_radix_sort_inplace(lowerCAmelCase						,	lowerCAmelCase						,	lowerCAmelCase						,	lowerCAmelCase   )


if __name__ == "__main__":
						import doctest

						doctest.testmod()