




import unittest

import numpy as np
from datasets import load_dataset

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 BeitImageProcessor







class a (     unittest.TestCase  ):
						"""simple docstring"""

						def __init__( self :     Union[str, Any]				,							lowerCamelCase__ :     int				,							lowerCamelCase__ :     int=7				,							lowerCamelCase__ :     Optional[Any]=3				,							lowerCamelCase__ :     str=18				,							lowerCamelCase__ :     Union[str, Any]=30				,							lowerCamelCase__ :     int=400				,							lowerCamelCase__ :     str=True				,							lowerCamelCase__ :     str=None				,							lowerCamelCase__ :     List[str]=True				,							lowerCamelCase__ :     Tuple=None				,							lowerCamelCase__ :     Tuple=True				,							lowerCamelCase__ :     List[str]=[0.5, 0.5, 0.5]				,							lowerCamelCase__ :     Optional[Any]=[0.5, 0.5, 0.5]				,							lowerCamelCase__ :     List[str]=False				,							)    ->						Tuple:
													"""simple docstring"""


													__lowercase      =    size if size is not None else {'''height''': 20, '''width''': 20}
													__lowercase      =    crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}
													__lowercase      =    parent
													__lowercase      =    batch_size
													__lowercase      =    num_channels
													__lowercase      =    image_size
													__lowercase      =    min_resolution
													__lowercase      =    max_resolution
													__lowercase      =    do_resize
													__lowercase      =    size
													__lowercase      =    do_center_crop
													__lowercase      =    crop_size
													__lowercase      =    do_normalize
													__lowercase      =    image_mean
													__lowercase      =    image_std
													__lowercase      =    do_reduce_labels






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


													return {
													    "do_resize": self.do_resize,
													    "size": self.size,
													    "do_center_crop": self.do_center_crop,
													    "crop_size": self.crop_size,
													    "do_normalize": self.do_normalize,
													    "image_mean": self.image_mean,
													    "image_std": self.image_std,
													    "do_reduce_labels": self.do_reduce_labels,
													}







def 		_A(       )    -> List[str]:
							'''simple docstring'''


							__lowercase      =    load_dataset('''hf-internal-testing/fixtures_ade20k'''				,       split='''test'''				)

							__lowercase      =    Image.open(dataset[0]['''file''']				)
							__lowercase      =    Image.open(dataset[1]['''file''']				)

							return image, map







def 		_A(       )    -> int:
							'''simple docstring'''


							__lowercase      =    load_dataset('''hf-internal-testing/fixtures_ade20k'''				,       split='''test'''				)

							__lowercase      =    Image.open(ds[0]['''file''']				)
							__lowercase      =    Image.open(ds[1]['''file''']				)
							__lowercase      =    Image.open(ds[2]['''file''']				)
							__lowercase      =    Image.open(ds[3]['''file''']				)

							return [imagea, imagea], [mapa, mapa]







@require_torch
@require_vision
class a (     __SCREAMING_SNAKE_CASE       ,						unittest.TestCase  ):
						"""simple docstring"""

						UpperCamelCase_	:       List[Any]       	=		BeitImageProcessor if is_vision_available() else None






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


													__lowercase      =    BeitImageProcessingTester(self				)






						@property
						def     UpperCAmelCase_      ( self :     Any				)    ->						str:
													"""simple docstring"""


													return self.image_processor_tester.prepare_image_processor_dict()






						def     UpperCAmelCase_      ( self :     Tuple				)    ->						str:
													"""simple docstring"""


													__lowercase      =    self.image_processing_class(**self.image_processor_dict				)
													self.assertTrue(hasattr(lowerCamelCase__				,							'''do_resize'''				)				)
													self.assertTrue(hasattr(lowerCamelCase__				,							'''size'''				)				)
													self.assertTrue(hasattr(lowerCamelCase__				,							'''do_center_crop'''				)				)
													self.assertTrue(hasattr(lowerCamelCase__				,							'''center_crop'''				)				)
													self.assertTrue(hasattr(lowerCamelCase__				,							'''do_normalize'''				)				)
													self.assertTrue(hasattr(lowerCamelCase__				,							'''image_mean'''				)				)
													self.assertTrue(hasattr(lowerCamelCase__				,							'''image_std'''				)				)






						def     UpperCAmelCase_      ( self :     Tuple				)    ->						int:
													"""simple docstring"""


													__lowercase      =    self.image_processing_class.from_dict(self.image_processor_dict				)
													self.assertEqual(image_processor.size				,							{'''height''': 20, '''width''': 20}				)
													self.assertEqual(image_processor.crop_size				,							{'''height''': 18, '''width''': 18}				)
													self.assertEqual(image_processor.do_reduce_labels				,							lowerCamelCase__				)

													__lowercase      =    self.image_processing_class.from_dict(
													    self.image_processor_dict				,							size=42				,							crop_size=84				,							reduce_labels=lowerCamelCase__				)
													self.assertEqual(image_processor.size				,							{'''height''': 42, '''width''': 42}				)
													self.assertEqual(image_processor.crop_size				,							{'''height''': 84, '''width''': 84}				)
													self.assertEqual(image_processor.do_reduce_labels				,							lowerCamelCase__				)






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


													pass






						def     UpperCAmelCase_      ( self :     Tuple				)    ->						List[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=lowerCamelCase__				)
													for image in image_inputs:
																				self.assertIsInstance(lowerCamelCase__				,							Image.Image				)

													# Test not batched input
													__lowercase      =    image_processing(image_inputs[0]				,							return_tensors='''pt'''				).pixel_values
													self.assertEqual(
													    encoded_images.shape				,							(
													        1,
													        self.image_processor_tester.num_channels,
													        self.image_processor_tester.crop_size['''height'''],
													        self.image_processor_tester.crop_size['''width'''],
													    )				,							)

													# Test batched
													__lowercase      =    image_processing(lowerCamelCase__				,							return_tensors='''pt'''				).pixel_values
													self.assertEqual(
													    encoded_images.shape				,							(
													        self.image_processor_tester.batch_size,
													        self.image_processor_tester.num_channels,
													        self.image_processor_tester.crop_size['''height'''],
													        self.image_processor_tester.crop_size['''width'''],
													    )				,							)






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


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

													# Test not batched input
													__lowercase      =    image_processing(image_inputs[0]				,							return_tensors='''pt'''				).pixel_values
													self.assertEqual(
													    encoded_images.shape				,							(
													        1,
													        self.image_processor_tester.num_channels,
													        self.image_processor_tester.crop_size['''height'''],
													        self.image_processor_tester.crop_size['''width'''],
													    )				,							)

													# Test batched
													__lowercase      =    image_processing(lowerCamelCase__				,							return_tensors='''pt'''				).pixel_values
													self.assertEqual(
													    encoded_images.shape				,							(
													        self.image_processor_tester.batch_size,
													        self.image_processor_tester.num_channels,
													        self.image_processor_tester.crop_size['''height'''],
													        self.image_processor_tester.crop_size['''width'''],
													    )				,							)






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


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

													# Test not batched input
													__lowercase      =    image_processing(image_inputs[0]				,							return_tensors='''pt'''				).pixel_values
													self.assertEqual(
													    encoded_images.shape				,							(
													        1,
													        self.image_processor_tester.num_channels,
													        self.image_processor_tester.crop_size['''height'''],
													        self.image_processor_tester.crop_size['''width'''],
													    )				,							)

													# Test batched
													__lowercase      =    image_processing(lowerCamelCase__				,							return_tensors='''pt'''				).pixel_values
													self.assertEqual(
													    encoded_images.shape				,							(
													        self.image_processor_tester.batch_size,
													        self.image_processor_tester.num_channels,
													        self.image_processor_tester.crop_size['''height'''],
													        self.image_processor_tester.crop_size['''width'''],
													    )				,							)






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


													__lowercase      =    self.image_processing_class(**self.image_processor_dict				)
													# create random PyTorch tensors
													__lowercase      =    prepare_image_inputs(self.image_processor_tester				,							equal_resolution=lowerCamelCase__				,							torchify=lowerCamelCase__				)
													__lowercase      =    []
													for image in image_inputs:
																				self.assertIsInstance(lowerCamelCase__				,							torch.Tensor				)
																				maps.append(torch.zeros(image.shape[-2:]				).long()				)

													# Test not batched input
													__lowercase      =    image_processing(image_inputs[0]				,							maps[0]				,							return_tensors='''pt'''				)
													self.assertEqual(
													    encoding['''pixel_values'''].shape				,							(
													        1,
													        self.image_processor_tester.num_channels,
													        self.image_processor_tester.crop_size['''height'''],
													        self.image_processor_tester.crop_size['''width'''],
													    )				,							)
													self.assertEqual(
													    encoding['''labels'''].shape				,							(
													        1,
													        self.image_processor_tester.crop_size['''height'''],
													        self.image_processor_tester.crop_size['''width'''],
													    )				,							)
													self.assertEqual(encoding['''labels'''].dtype				,							torch.long				)
													self.assertTrue(encoding['''labels'''].min().item() >= 0				)
													self.assertTrue(encoding['''labels'''].max().item() <= 255				)

													# Test batched
													__lowercase      =    image_processing(lowerCamelCase__				,							lowerCamelCase__				,							return_tensors='''pt'''				)
													self.assertEqual(
													    encoding['''pixel_values'''].shape				,							(
													        self.image_processor_tester.batch_size,
													        self.image_processor_tester.num_channels,
													        self.image_processor_tester.crop_size['''height'''],
													        self.image_processor_tester.crop_size['''width'''],
													    )				,							)
													self.assertEqual(
													    encoding['''labels'''].shape				,							(
													        self.image_processor_tester.batch_size,
													        self.image_processor_tester.crop_size['''height'''],
													        self.image_processor_tester.crop_size['''width'''],
													    )				,							)
													self.assertEqual(encoding['''labels'''].dtype				,							torch.long				)
													self.assertTrue(encoding['''labels'''].min().item() >= 0				)
													self.assertTrue(encoding['''labels'''].max().item() <= 255				)

													# Test not batched input (PIL images)
													__lowercase     ,     __lowercase      =    prepare_semantic_single_inputs()

													__lowercase      =    image_processing(lowerCamelCase__				,							lowerCamelCase__				,							return_tensors='''pt'''				)
													self.assertEqual(
													    encoding['''pixel_values'''].shape				,							(
													        1,
													        self.image_processor_tester.num_channels,
													        self.image_processor_tester.crop_size['''height'''],
													        self.image_processor_tester.crop_size['''width'''],
													    )				,							)
													self.assertEqual(
													    encoding['''labels'''].shape				,							(
													        1,
													        self.image_processor_tester.crop_size['''height'''],
													        self.image_processor_tester.crop_size['''width'''],
													    )				,							)
													self.assertEqual(encoding['''labels'''].dtype				,							torch.long				)
													self.assertTrue(encoding['''labels'''].min().item() >= 0				)
													self.assertTrue(encoding['''labels'''].max().item() <= 255				)

													# Test batched input (PIL images)
													__lowercase     ,     __lowercase      =    prepare_semantic_batch_inputs()

													__lowercase      =    image_processing(lowerCamelCase__				,							lowerCamelCase__				,							return_tensors='''pt'''				)
													self.assertEqual(
													    encoding['''pixel_values'''].shape				,							(
													        2,
													        self.image_processor_tester.num_channels,
													        self.image_processor_tester.crop_size['''height'''],
													        self.image_processor_tester.crop_size['''width'''],
													    )				,							)
													self.assertEqual(
													    encoding['''labels'''].shape				,							(
													        2,
													        self.image_processor_tester.crop_size['''height'''],
													        self.image_processor_tester.crop_size['''width'''],
													    )				,							)
													self.assertEqual(encoding['''labels'''].dtype				,							torch.long				)
													self.assertTrue(encoding['''labels'''].min().item() >= 0				)
													self.assertTrue(encoding['''labels'''].max().item() <= 255				)






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


													__lowercase      =    self.image_processing_class(**self.image_processor_dict				)

													# ADE20k has 150 classes, and the background is included, so labels should be between 0 and 150
													__lowercase     ,     __lowercase      =    prepare_semantic_single_inputs()
													__lowercase      =    image_processing(lowerCamelCase__				,							lowerCamelCase__				,							return_tensors='''pt'''				)
													self.assertTrue(encoding['''labels'''].min().item() >= 0				)
													self.assertTrue(encoding['''labels'''].max().item() <= 150				)

													__lowercase      =    True
													__lowercase      =    image_processing(lowerCamelCase__				,							lowerCamelCase__				,							return_tensors='''pt'''				)
													self.assertTrue(encoding['''labels'''].min().item() >= 0				)
													self.assertTrue(encoding['''labels'''].max().item() <= 255				)












import itertools
import string
from collections.abc import Generator, Iterable







def 		_A(       UpperCamelCase__							:			Iterable[str]				,       UpperCamelCase__							:			int				)    -> Generator[tuple[str, ...], None, None]:
							'''simple docstring'''


							__lowercase      =    iter(UpperCamelCase__				)
							while True:
														__lowercase      =    tuple(itertools.islice(UpperCamelCase__				,       UpperCamelCase__				)				)
														if not chunk:
																					return
														yield chunk







def 		_A(       UpperCamelCase__							:			str				)    -> str:
							'''simple docstring'''


							__lowercase      =    ''''''.join([c.upper() for c in dirty if c in string.ascii_letters]				)
							__lowercase      =    ''''''

							if len(UpperCamelCase__				) < 2:
														return dirty

							for i in range(len(UpperCamelCase__				) - 1				):
														clean += dirty[i]

														if dirty[i] == dirty[i + 1]:
																					clean += "X"

							clean += dirty[-1]

							if len(UpperCamelCase__				) & 1:
														clean += "X"

							return clean







def 		_A(       UpperCamelCase__							:			str				)    -> list[str]:
							'''simple docstring'''


							__lowercase      =    '''ABCDEFGHIKLMNOPQRSTUVWXYZ'''
							# we're using a list instead of a '2d' array because it makes the math
							# for setting up the table and doing the actual encoding/decoding simpler
							__lowercase      =    []

							# copy key chars into the table if they are in `alphabet` ignoring duplicates
							for char in key.upper():
														if char not in table and char in alphabet:
																					table.append(UpperCamelCase__				)

    # fill the rest of the table in with the remaining alphabet chars
							for char in alphabet:
														if char not in table:
																					table.append(UpperCamelCase__				)

							return table







def 		_A(       UpperCamelCase__							:			str				,       UpperCamelCase__							:			str				)    -> str:
							'''simple docstring'''


							__lowercase      =    generate_table(UpperCamelCase__				)
							__lowercase      =    prepare_input(UpperCamelCase__				)
							__lowercase      =    ''''''

							# https://en.wikipedia.org/wiki/Playfair_cipher#Description
							for chara, chara in chunker(UpperCamelCase__				,       2				):
														__lowercase     ,     __lowercase      =    divmod(table.index(UpperCamelCase__				)				,       5				)
														__lowercase     ,     __lowercase      =    divmod(table.index(UpperCamelCase__				)				,       5				)

														if rowa == rowa:
																					ciphertext += table[rowa * 5 + (cola + 1) % 5]
																					ciphertext += table[rowa * 5 + (cola + 1) % 5]
														elif cola == cola:
																					ciphertext += table[((rowa + 1) % 5) * 5 + cola]
																					ciphertext += table[((rowa + 1) % 5) * 5 + cola]
														else:  # rectangle
																					ciphertext += table[rowa * 5 + cola]
																					ciphertext += table[rowa * 5 + cola]

							return ciphertext







def 		_A(       UpperCamelCase__							:			str				,       UpperCamelCase__							:			str				)    -> str:
							'''simple docstring'''


							__lowercase      =    generate_table(UpperCamelCase__				)
							__lowercase      =    ''''''

							# https://en.wikipedia.org/wiki/Playfair_cipher#Description
							for chara, chara in chunker(UpperCamelCase__				,       2				):
														__lowercase     ,     __lowercase      =    divmod(table.index(UpperCamelCase__				)				,       5				)
														__lowercase     ,     __lowercase      =    divmod(table.index(UpperCamelCase__				)				,       5				)

														if rowa == rowa:
																					plaintext += table[rowa * 5 + (cola - 1) % 5]
																					plaintext += table[rowa * 5 + (cola - 1) % 5]
														elif cola == cola:
																					plaintext += table[((rowa - 1) % 5) * 5 + cola]
																					plaintext += table[((rowa - 1) % 5) * 5 + cola]
														else:  # rectangle
																					plaintext += table[rowa * 5 + cola]
																					plaintext += table[rowa * 5 + cola]

							return plaintext






