



import random
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():
			import PIL

			from transformers import FlavaImageProcessor
			from transformers.image_utils import PILImageResampling
			from transformers.models.flava.image_processing_flava import (
			    FLAVA_CODEBOOK_MEAN,
			    FLAVA_CODEBOOK_STD,
			    FLAVA_IMAGE_MEAN,
			    FLAVA_IMAGE_STD,
			)
else:
			UpperCamelCase__			=    UpperCamelCase__			=    UpperCamelCase__			=    UpperCamelCase__			=    None
class       _lowerCAmelCase (			unittest.TestCase      ):






			"""simple docstring"""





			def __init__(					self						:							Optional[Any]      ,  lowercase						:							Any      ,  lowercase						:							List[str]=7      ,  lowercase						:							Optional[Any]=3      ,  lowercase						:							Optional[int]=30      ,  lowercase						:							List[Any]=400      ,  lowercase						:							List[str]=True      ,  lowercase						:							int=None      ,  lowercase						:							Tuple=True      ,  lowercase						:							int=None      ,  lowercase						:							Optional[Any]=None      ,  lowercase						:							str=True      ,  lowercase						:							Any=1 / 255      ,  lowercase						:							Optional[Any]=True      ,  lowercase						:							Union[str, Any]=FLAVA_IMAGE_MEAN      ,  lowercase						:							Dict=FLAVA_IMAGE_STD      ,  lowercase						:							Optional[Any]=14      ,  lowercase						:							Tuple=75      ,  lowercase						:							Any=None      ,  lowercase						:							Union[str, Any]=16      ,  lowercase						:							Tuple=0.3      ,  lowercase						:							Union[str, Any]=None      ,  lowercase						:							Optional[Any]=True      ,  lowercase						:							Optional[int]=None      ,  lowercase						:							Tuple=None      ,  lowercase						:							List[Any]=True      ,  lowercase						:							List[str]=None      ,  lowercase						:							Optional[Any]=True      ,  lowercase						:							Optional[Any]=True      ,  lowercase						:							int=FLAVA_CODEBOOK_MEAN      ,  lowercase						:							Dict=FLAVA_CODEBOOK_STD      ,  )    ->       Dict:
									"""simple docstring"""







									__lowercase											=					size if size is not None else {"""height""": 224, """width""": 224}
									__lowercase											=					crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
									__lowercase											=					codebook_size if codebook_size is not None else {"""height""": 112, """width""": 112}
									__lowercase											=					codebook_crop_size if codebook_crop_size is not None else {"""height""": 112, """width""": 112}

									__lowercase											=					parent
									__lowercase											=					batch_size
									__lowercase											=					num_channels
									__lowercase											=					do_resize
									__lowercase											=					do_rescale
									__lowercase											=					rescale_factor
									__lowercase											=					min_resolution
									__lowercase											=					max_resolution
									__lowercase											=					size
									__lowercase											=					resample if resample is not None else PILImageResampling.BICUBIC
									__lowercase											=					do_normalize
									__lowercase											=					image_mean
									__lowercase											=					image_std
									__lowercase											=					do_center_crop
									__lowercase											=					crop_size

									__lowercase											=					input_size_patches
									__lowercase											=					total_mask_patches
									__lowercase											=					mask_group_max_patches
									__lowercase											=					mask_group_min_patches
									__lowercase											=					mask_group_min_aspect_ratio
									__lowercase											=					mask_group_max_aspect_ratio

									__lowercase											=					codebook_do_resize
									__lowercase											=					codebook_size
									__lowercase											=					codebook_resample if codebook_resample is not None else PILImageResampling.LANCZOS
									__lowercase											=					codebook_do_center_crop
									__lowercase											=					codebook_crop_size
									__lowercase											=					codebook_do_map_pixels
									__lowercase											=					codebook_do_normalize
									__lowercase											=					codebook_image_mean
									__lowercase											=					codebook_image_std





			def 		snake_case__      (					self						:							Tuple )    ->       str:
									"""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,
									    "resample": self.resample,
									    "do_rescale": self.do_rescale,
									    "rescale_factor": self.rescale_factor,
									    "do_center_crop": self.do_center_crop,
									    "crop_size": self.crop_size,
									    "input_size_patches": self.input_size_patches,
									    "total_mask_patches": self.total_mask_patches,
									    "mask_group_max_patches": self.mask_group_max_patches,
									    "mask_group_min_patches": self.mask_group_min_patches,
									    "mask_group_min_aspect_ratio": self.mask_group_min_aspect_ratio,
									    "mask_group_max_aspect_ratio": self.mask_group_min_aspect_ratio,
									    "codebook_do_resize": self.codebook_do_resize,
									    "codebook_size": self.codebook_size,
									    "codebook_resample": self.codebook_resample,
									    "codebook_do_center_crop": self.codebook_do_center_crop,
									    "codebook_crop_size": self.codebook_crop_size,
									    "codebook_do_map_pixels": self.codebook_do_map_pixels,
									    "codebook_do_normalize": self.codebook_do_normalize,
									    "codebook_image_mean": self.codebook_image_mean,
									    "codebook_image_std": self.codebook_image_std,
									}





			def 		snake_case__      (					self						:							Union[str, Any] )    ->       List[str]:
									"""simple docstring"""







									return (self.size["height"], self.size["width"])





			def 		snake_case__      (					self						:							Optional[int] )    ->       Union[str, Any]:
									"""simple docstring"""







									return (
									    (self.input_size_patches, self.input_size_patches)
									    if not isinstance(self.input_size_patches      ,  lowercase )
									    else self.input_size_patches
									)







			def 		snake_case__      (					self						:							str )    ->       Optional[int]:
									"""simple docstring"""







									return (self.codebook_size["height"], self.codebook_size["width"])





@require_torch
@require_vision
class       _lowerCAmelCase (			_UpperCAmelCase     ,       unittest.TestCase      ):






			"""simple docstring"""





			lowercase__   :	Optional[int]  			=      FlavaImageProcessor if is_vision_available() else None
			lowercase__   :	List[str]  			=      None





			def 		snake_case__      (					self						:							Any )    ->       List[Any]:
									"""simple docstring"""







									__lowercase											=					FlavaImageProcessingTester(self )





			@property
			def 		snake_case__      (					self						:							Union[str, Any] )    ->       str:
									"""simple docstring"""







									return self.image_processor_tester.prepare_image_processor_dict()





			def 		snake_case__      (					self						:							Dict )    ->       Union[str, Any]:
									"""simple docstring"""







									__lowercase											=					self.image_processing_class(**self.image_processor_dict )
									self.assertTrue(hasattr(lowercase      ,  """image_mean""" ) )
									self.assertTrue(hasattr(lowercase      ,  """image_std""" ) )
									self.assertTrue(hasattr(lowercase      ,  """do_normalize""" ) )
									self.assertTrue(hasattr(lowercase      ,  """do_resize""" ) )
									self.assertTrue(hasattr(lowercase      ,  """resample""" ) )
									self.assertTrue(hasattr(lowercase      ,  """crop_size""" ) )
									self.assertTrue(hasattr(lowercase      ,  """do_center_crop""" ) )
									self.assertTrue(hasattr(lowercase      ,  """do_rescale""" ) )
									self.assertTrue(hasattr(lowercase      ,  """rescale_factor""" ) )
									self.assertTrue(hasattr(lowercase      ,  """masking_generator""" ) )
									self.assertTrue(hasattr(lowercase      ,  """codebook_do_resize""" ) )
									self.assertTrue(hasattr(lowercase      ,  """codebook_size""" ) )
									self.assertTrue(hasattr(lowercase      ,  """codebook_resample""" ) )
									self.assertTrue(hasattr(lowercase      ,  """codebook_do_center_crop""" ) )
									self.assertTrue(hasattr(lowercase      ,  """codebook_crop_size""" ) )
									self.assertTrue(hasattr(lowercase      ,  """codebook_do_map_pixels""" ) )
									self.assertTrue(hasattr(lowercase      ,  """codebook_do_normalize""" ) )
									self.assertTrue(hasattr(lowercase      ,  """codebook_image_mean""" ) )
									self.assertTrue(hasattr(lowercase      ,  """codebook_image_std""" ) )





			def 		snake_case__      (					self						:							Any )    ->       int:
									"""simple docstring"""







									__lowercase											=					self.image_processing_class.from_dict(self.image_processor_dict )
									self.assertEqual(image_processor.size      ,  {"""height""": 224, """width""": 224} )
									self.assertEqual(image_processor.crop_size      ,  {"""height""": 224, """width""": 224} )
									self.assertEqual(image_processor.codebook_size      ,  {"""height""": 112, """width""": 112} )
									self.assertEqual(image_processor.codebook_crop_size      ,  {"""height""": 112, """width""": 112} )

									__lowercase											=					self.image_processing_class.from_dict(
									    self.image_processor_dict      ,  size=42      ,  crop_size=84      ,  codebook_size=33      ,  codebook_crop_size=66 )
									self.assertEqual(image_processor.size      ,  {"""height""": 42, """width""": 42} )
									self.assertEqual(image_processor.crop_size      ,  {"""height""": 84, """width""": 84} )
									self.assertEqual(image_processor.codebook_size      ,  {"""height""": 33, """width""": 33} )
									self.assertEqual(image_processor.codebook_crop_size      ,  {"""height""": 66, """width""": 66} )





			def 		snake_case__      (					self						:							str )    ->       Any:
									"""simple docstring"""







									pass





			def 		snake_case__      (					self						:							List[Any] )    ->       str:
									"""simple docstring"""







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

									# Test not batched input
									__lowercase											=					image_processing(image_inputs[0]      ,  return_tensors="""pt""" )

									# Test no bool masked pos
									self.assertFalse("""bool_masked_pos""" in encoded_images )

									__lowercase						,      __lowercase											=					self.image_processor_tester.get_expected_image_size()

									self.assertEqual(
									    encoded_images.pixel_values.shape      ,  (1, self.image_processor_tester.num_channels, expected_height, expected_width)      ,  )

									# Test batched
									__lowercase											=					image_processing(lowercase      ,  return_tensors="""pt""" )
									__lowercase						,      __lowercase											=					self.image_processor_tester.get_expected_image_size()

									# Test no bool masked pos
									self.assertFalse("""bool_masked_pos""" in encoded_images )

									self.assertEqual(
									    encoded_images.pixel_values.shape      ,  (
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_channels,
									        expected_height,
									        expected_width,
									    )      ,  )





			def 		snake_case__      (					self						:							List[Any]      ,  lowercase						:							Dict      ,  lowercase						:							int )    ->       List[str]:
									"""simple docstring"""







									__lowercase											=					self.image_processing_class(**self.image_processor_dict )
									# create random tensors
									__lowercase											=					prepare_image_inputs(self.image_processor_tester      ,  equal_resolution=lowercase      ,  **lowercase )
									for image in image_inputs:
															self.assertIsInstance(lowercase      ,  lowercase )

									# Test not batched input
									__lowercase											=					image_processing(image_inputs[0]      ,  return_tensors="""pt""" )

									__lowercase						,      __lowercase											=					self.image_processor_tester.get_expected_image_size()
									self.assertEqual(
									    encoded_images.pixel_values.shape      ,  (1, self.image_processor_tester.num_channels, expected_height, expected_width)      ,  )

									__lowercase											=					image_processing(lowercase      ,  return_image_mask=lowercase      ,  return_tensors="""pt""" )

									__lowercase						,      __lowercase											=					self.image_processor_tester.get_expected_image_size()
									self.assertEqual(
									    encoded_images.pixel_values.shape      ,  (
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_channels,
									        expected_height,
									        expected_width,
									    )      ,  )

									__lowercase						,      __lowercase											=					self.image_processor_tester.get_expected_mask_size()
									self.assertEqual(
									    encoded_images.bool_masked_pos.shape      ,  (
									        self.image_processor_tester.batch_size,
									        expected_height,
									        expected_width,
									    )      ,  )

									# Test batched
									__lowercase											=					image_processing(lowercase      ,  return_tensors="""pt""" ).pixel_values

									__lowercase						,      __lowercase											=					self.image_processor_tester.get_expected_image_size()
									self.assertEqual(
									    encoded_images.shape      ,  (
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_channels,
									        expected_height,
									        expected_width,
									    )      ,  )

									# Test masking
									__lowercase											=					image_processing(lowercase      ,  return_image_mask=lowercase      ,  return_tensors="""pt""" )

									__lowercase						,      __lowercase											=					self.image_processor_tester.get_expected_image_size()
									self.assertEqual(
									    encoded_images.pixel_values.shape      ,  (
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_channels,
									        expected_height,
									        expected_width,
									    )      ,  )

									__lowercase						,      __lowercase											=					self.image_processor_tester.get_expected_mask_size()
									self.assertEqual(
									    encoded_images.bool_masked_pos.shape      ,  (
									        self.image_processor_tester.batch_size,
									        expected_height,
									        expected_width,
									    )      ,  )





			def 		snake_case__      (					self						:							int )    ->       int:
									"""simple docstring"""







									self._test_call_framework(np.ndarray      ,  prepare_kwargs={"""numpify""": True} )





			def 		snake_case__      (					self						:							List[Any] )    ->       Optional[Any]:
									"""simple docstring"""







									self._test_call_framework(torch.Tensor      ,  prepare_kwargs={"""torchify""": True} )





			def 		snake_case__      (					self						:							int )    ->       Any:
									"""simple docstring"""







									random.seed(1_234 )
									__lowercase											=					self.image_processing_class(**self.image_processor_dict )
									__lowercase											=					prepare_image_inputs(self.image_processor_tester      ,  equal_resolution=lowercase      ,  torchify=lowercase )

									# Test not batched input
									__lowercase											=					image_processing(image_inputs[0]      ,  return_image_mask=lowercase      ,  return_tensors="""pt""" )
									self.assertEqual(encoded_images.bool_masked_pos.sum().item()      ,  75 )







			def 		snake_case__      (					self						:							Optional[int] )    ->       Any:
									"""simple docstring"""







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

									# Test not batched input
									__lowercase											=					image_processing(image_inputs[0]      ,  return_codebook_pixels=lowercase      ,  return_tensors="""pt""" )
									__lowercase						,      __lowercase											=					self.image_processor_tester.get_expected_codebook_image_size()
									self.assertEqual(
									    encoded_images.codebook_pixel_values.shape      ,  (1, self.image_processor_tester.num_channels, expected_height, expected_width)      ,  )

									# Test batched
									__lowercase											=					image_processing(lowercase      ,  return_codebook_pixels=lowercase      ,  return_tensors="""pt""" )
									__lowercase						,      __lowercase											=					self.image_processor_tester.get_expected_codebook_image_size()
									self.assertEqual(
									    encoded_images.codebook_pixel_values.shape      ,  (
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_channels,
									        expected_height,
									        expected_width,
									    )      ,  )










from __future__ import annotations






def 			UpperCAmelCase__     (     lowercase__ )			->   list[int]:
						if not list_of_ints:
												return []

						if min(lowercase__ ) < 0:
												raise ValueError("""All numbers must be positive""" )

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






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

						__lowercase											=					[]
						__lowercase											=					[]
						# 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(lowercase__ )
												else:
																		# number has a zero at bit bit_position
																		zeros.append(lowercase__ )

    # recursively split both lists further
						__lowercase											=					_msd_radix_sort(lowercase__    ,							bit_position - 1 )
						__lowercase											=					_msd_radix_sort(lowercase__    ,							bit_position - 1 )

						# recombine lists
						__lowercase											=					zeros
						res.extend(lowercase__ )

						return res






def 			UpperCAmelCase__     (     lowercase__ )			->   List[Any]:
						__lowercase											=					len(lowercase__ )
						if not list_of_ints or length == 1:
												return

						if min(lowercase__ ) < 0:
												raise ValueError("""All numbers must be positive""" )

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






def 			UpperCAmelCase__     (     lowercase__    ,							lowercase__    ,							lowercase__    ,							lowercase__ )			->   Optional[Any]:
						if bit_position == 0 or end_index - begin_index <= 1:
												return

						bit_position -= 1

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

												if changed:
																		continue

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

						_msd_radix_sort_inplace(lowercase__    ,							lowercase__    ,							lowercase__    ,							lowercase__ )
						_msd_radix_sort_inplace(lowercase__    ,							lowercase__    ,							lowercase__    ,							lowercase__ )


if __name__ == "__main__":
			import doctest

			doctest.testmod()





