


'''simple docstring'''







import unittest

import numpy as np
import requests

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

					from transformers.pytorch_utils import is_torch_greater_or_equal_than_1_11
else:
					snake_case_     :       Dict						=  False

if is_vision_available():
					from PIL import Image

					from transformers import PixaStructImageProcessor


class 					__a      (unittest.TestCase     ):

							def __init__(							self							:     Optional[int]       ,     __magic_name__							:     Optional[Any]       ,     __magic_name__							:     str=7       ,     __magic_name__							:     int=3       ,     __magic_name__							:     List[Any]=18       ,     __magic_name__							:     List[Any]=30       ,     __magic_name__							:     Union[str, Any]=4_00       ,     __magic_name__							:     Union[str, Any]=None       ,     __magic_name__							:     Tuple=True       ,     __magic_name__							:     List[str]=True       ,     __magic_name__							:     str=None       ,     )					->       str:







													"""simple docstring"""
													UpperCAmelCase_			:      List[Any]     						=  size if size is not None else {'''height''': 20, '''width''': 20}
													UpperCAmelCase_			:      List[str]     						=  parent
													UpperCAmelCase_			:      List[Any]     						=  batch_size
													UpperCAmelCase_			:      Dict     						=  num_channels
													UpperCAmelCase_			:      List[str]     						=  image_size
													UpperCAmelCase_			:      int     						=  min_resolution
													UpperCAmelCase_			:      int     						=  max_resolution
													UpperCAmelCase_			:      Tuple     						=  size
													UpperCAmelCase_			:      Tuple     						=  do_normalize
													UpperCAmelCase_			:      Union[str, Any]     						=  do_convert_rgb
													UpperCAmelCase_			:      Dict     						=  [5_12, 10_24, 20_48, 40_96]
													UpperCAmelCase_			:      Any     						=  patch_size if patch_size is not None else {'''height''': 16, '''width''': 16}

							def     UpperCAmelCase__	(							self							:     Optional[int]   )					->       Union[str, Any]:







													"""simple docstring"""
													return {"do_normalize": self.do_normalize, "do_convert_rgb": self.do_convert_rgb}



							def     UpperCAmelCase__	(							self							:     List[Any]   )					->       Tuple:







													"""simple docstring"""
													UpperCAmelCase_			:      Union[str, Any]     						=  '''https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/australia.jpg'''
													UpperCAmelCase_			:      int     						=  Image.open(requests.get(__magic_name__       ,     stream=__magic_name__   ).raw   ).convert('''RGB'''   )
													return raw_image





@unittest.skipIf(
    not is_torch_greater_or_equal_than_1_11       ,		reason="`Pix2StructImageProcessor` requires `torch>=1.11.0`."       ,		)
@require_torch
@require_vision
class 					__a      (lowerCamelCase       ,		unittest.TestCase     ):
							__a					:   Optional[Any]									=       PixaStructImageProcessor if is_vision_available() else None

							def     UpperCAmelCase__	(							self							:     Optional[int]   )					->       List[Any]:







													"""simple docstring"""
													UpperCAmelCase_			:      Any     						=  PixaStructImageProcessingTester(self   )

							@property
							def     UpperCAmelCase__	(							self							:     Union[str, Any]   )					->       str:







													"""simple docstring"""
													return self.image_processor_tester.prepare_image_processor_dict()

							def     UpperCAmelCase__	(							self							:     Union[str, Any]   )					->       List[Any]:







													"""simple docstring"""
													UpperCAmelCase_			:      int     						=  self.image_processing_class(**self.image_processor_dict   )
													self.assertTrue(hasattr(__magic_name__       ,     '''do_normalize'''   )   )
													self.assertTrue(hasattr(__magic_name__       ,     '''do_convert_rgb'''   )   )

							def     UpperCAmelCase__	(							self							:     Tuple   )					->       List[str]:







													"""simple docstring"""
													UpperCAmelCase_			:      Tuple     						=  self.image_processor_tester.prepare_dummy_image()

													UpperCAmelCase_			:      Dict     						=  self.image_processing_class(**self.image_processor_dict   )
													UpperCAmelCase_			:      Optional[Any]     						=  20_48

													UpperCAmelCase_			:      int     						=  image_processor(__magic_name__       ,     return_tensors='''pt'''       ,     max_patches=__magic_name__   )
													self.assertTrue(torch.allclose(inputs.flattened_patches.mean()       ,     torch.tensor(0.0_6_0_6   )       ,     atol=1E-3       ,     rtol=1E-3   )   )

							def     UpperCAmelCase__	(							self							:     List[str]   )					->       List[str]:







													"""simple docstring"""
													UpperCAmelCase_			:      Any     						=  self.image_processing_class(**self.image_processor_dict   )
													# create random PIL images
													UpperCAmelCase_			:      str     						=  prepare_image_inputs(self.image_processor_tester       ,     equal_resolution=__magic_name__   )
													for image in image_inputs:
																			self.assertIsInstance(__magic_name__       ,     Image.Image   )

													# Test not batched input
													UpperCAmelCase_			:      int     						=  (
													    (self.image_processor_tester.patch_size['''height'''] * self.image_processor_tester.patch_size['''width'''])
													    * self.image_processor_tester.num_channels
													) + 2

													for max_patch in self.image_processor_tester.max_patches:
																			# Test not batched input
																			UpperCAmelCase_			:      List[Any]     						=  image_processor(
																			    image_inputs[0]       ,     return_tensors='''pt'''       ,     max_patches=__magic_name__   ).flattened_patches
																			self.assertEqual(
																			    encoded_images.shape       ,     (1, max_patch, expected_hidden_dim)       ,     )

																			# Test batched
																			UpperCAmelCase_			:      Union[str, Any]     						=  image_processor(
																			    __magic_name__       ,     return_tensors='''pt'''       ,     max_patches=__magic_name__   ).flattened_patches
																			self.assertEqual(
																			    encoded_images.shape       ,     (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)       ,     )

							def     UpperCAmelCase__	(							self							:     str   )					->       Dict:







													"""simple docstring"""
													UpperCAmelCase_			:      Optional[int]     						=  self.image_processing_class(**self.image_processor_dict   )
													# create random PIL images
													UpperCAmelCase_			:      Dict     						=  prepare_image_inputs(self.image_processor_tester       ,     equal_resolution=__magic_name__   )
													for image in image_inputs:
																			self.assertIsInstance(__magic_name__       ,     Image.Image   )

													# Test not batched input
													UpperCAmelCase_			:      int     						=  (
													    (self.image_processor_tester.patch_size['''height'''] * self.image_processor_tester.patch_size['''width'''])
													    * self.image_processor_tester.num_channels
													) + 2

													UpperCAmelCase_			:      Optional[Any]     						=  True

													for max_patch in self.image_processor_tester.max_patches:
																			# Test not batched input
																			with self.assertRaises(__magic_name__   ):
																									UpperCAmelCase_			:      int     						=  image_processor(
																									    image_inputs[0]       ,     return_tensors='''pt'''       ,     max_patches=__magic_name__   ).flattened_patches

																			UpperCAmelCase_			:      Tuple     						=  '''Hello'''

																			UpperCAmelCase_			:      Optional[int]     						=  image_processor(
																			    image_inputs[0]       ,     return_tensors='''pt'''       ,     max_patches=__magic_name__       ,     header_text=__magic_name__   ).flattened_patches
																			self.assertEqual(
																			    encoded_images.shape       ,     (1, max_patch, expected_hidden_dim)       ,     )

																			# Test batched
																			UpperCAmelCase_			:      Tuple     						=  image_processor(
																			    __magic_name__       ,     return_tensors='''pt'''       ,     max_patches=__magic_name__       ,     header_text=__magic_name__   ).flattened_patches
																			self.assertEqual(
																			    encoded_images.shape       ,     (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)       ,     )

							def     UpperCAmelCase__	(							self							:     List[Any]   )					->       int:







													"""simple docstring"""
													UpperCAmelCase_			:      Optional[int]     						=  self.image_processing_class(**self.image_processor_dict   )
													# create random numpy tensors
													UpperCAmelCase_			:      Optional[Any]     						=  prepare_image_inputs(self.image_processor_tester       ,     equal_resolution=__magic_name__       ,     numpify=__magic_name__   )
													for image in image_inputs:
																			self.assertIsInstance(__magic_name__       ,     np.ndarray   )

													UpperCAmelCase_			:      List[str]     						=  (
													    (self.image_processor_tester.patch_size['''height'''] * self.image_processor_tester.patch_size['''width'''])
													    * self.image_processor_tester.num_channels
													) + 2

													for max_patch in self.image_processor_tester.max_patches:
																			# Test not batched input
																			UpperCAmelCase_			:      Optional[Any]     						=  image_processor(
																			    image_inputs[0]       ,     return_tensors='''pt'''       ,     max_patches=__magic_name__   ).flattened_patches
																			self.assertEqual(
																			    encoded_images.shape       ,     (1, max_patch, expected_hidden_dim)       ,     )

																			# Test batched
																			UpperCAmelCase_			:      int     						=  image_processor(
																			    __magic_name__       ,     return_tensors='''pt'''       ,     max_patches=__magic_name__   ).flattened_patches
																			self.assertEqual(
																			    encoded_images.shape       ,     (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)       ,     )



							def     UpperCAmelCase__	(							self							:     str   )					->       Optional[int]:







													"""simple docstring"""
													UpperCAmelCase_			:      Tuple     						=  self.image_processing_class(**self.image_processor_dict   )
													# create random PyTorch tensors
													UpperCAmelCase_			:      Optional[Any]     						=  prepare_image_inputs(self.image_processor_tester       ,     equal_resolution=__magic_name__       ,     torchify=__magic_name__   )
													for image in image_inputs:
																			self.assertIsInstance(__magic_name__       ,     torch.Tensor   )

													# Test not batched input
													UpperCAmelCase_			:      str     						=  (
													    (self.image_processor_tester.patch_size['''height'''] * self.image_processor_tester.patch_size['''width'''])
													    * self.image_processor_tester.num_channels
													) + 2

													for max_patch in self.image_processor_tester.max_patches:
																			# Test not batched input
																			UpperCAmelCase_			:      Union[str, Any]     						=  image_processor(
																			    image_inputs[0]       ,     return_tensors='''pt'''       ,     max_patches=__magic_name__   ).flattened_patches
																			self.assertEqual(
																			    encoded_images.shape       ,     (1, max_patch, expected_hidden_dim)       ,     )

																			# Test batched
																			UpperCAmelCase_			:      Optional[int]     						=  image_processor(
																			    __magic_name__       ,     return_tensors='''pt'''       ,     max_patches=__magic_name__   ).flattened_patches
																			self.assertEqual(
																			    encoded_images.shape       ,     (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)       ,     )





@unittest.skipIf(
    not is_torch_greater_or_equal_than_1_11       ,		reason="`Pix2StructImageProcessor` requires `torch>=1.11.0`."       ,		)
@require_torch
@require_vision
class 					__a      (lowerCamelCase       ,		unittest.TestCase     ):
							__a					:   Optional[int]									=       PixaStructImageProcessor if is_vision_available() else None

							def     UpperCAmelCase__	(							self							:     Tuple   )					->       Optional[int]:







													"""simple docstring"""
													UpperCAmelCase_			:      List[str]     						=  PixaStructImageProcessingTester(self       ,     num_channels=4   )
													UpperCAmelCase_			:      Optional[int]     						=  3

							@property
							def     UpperCAmelCase__	(							self							:     Optional[int]   )					->       Dict:







													"""simple docstring"""
													return self.image_processor_tester.prepare_image_processor_dict()

							def     UpperCAmelCase__	(							self							:     Optional[int]   )					->       Any:







													"""simple docstring"""
													UpperCAmelCase_			:      Any     						=  self.image_processing_class(**self.image_processor_dict   )
													self.assertTrue(hasattr(__magic_name__       ,     '''do_normalize'''   )   )
													self.assertTrue(hasattr(__magic_name__       ,     '''do_convert_rgb'''   )   )



							def     UpperCAmelCase__	(							self							:     List[str]   )					->       Optional[Any]:







													"""simple docstring"""
													UpperCAmelCase_			:      Dict     						=  self.image_processing_class(**self.image_processor_dict   )
													# create random PIL images
													UpperCAmelCase_			:      str     						=  prepare_image_inputs(self.image_processor_tester       ,     equal_resolution=__magic_name__   )
													for image in image_inputs:
																			self.assertIsInstance(__magic_name__       ,     Image.Image   )

													# Test not batched input
													UpperCAmelCase_			:      Union[str, Any]     						=  (
													    (self.image_processor_tester.patch_size['''height'''] * self.image_processor_tester.patch_size['''width'''])
													    * (self.image_processor_tester.num_channels - 1)
													) + 2

													for max_patch in self.image_processor_tester.max_patches:
																			# Test not batched input
																			UpperCAmelCase_			:      List[str]     						=  image_processor(
																			    image_inputs[0]       ,     return_tensors='''pt'''       ,     max_patches=__magic_name__   ).flattened_patches
																			self.assertEqual(
																			    encoded_images.shape       ,     (1, max_patch, expected_hidden_dim)       ,     )

																			# Test batched
																			UpperCAmelCase_			:      Tuple     						=  image_processor(
																			    __magic_name__       ,     return_tensors='''pt'''       ,     max_patches=__magic_name__   ).flattened_patches
																			self.assertEqual(
																			    encoded_images.shape       ,     (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)       ,     )












'''simple docstring'''







def        lowerCamelCase_      (  SCREAMING_SNAKE_CASE__      :			int,			SCREAMING_SNAKE_CASE__      :			int   ) ->	int:
						return abs(SCREAMING_SNAKE_CASE__   ) if a == 0 else greatest_common_divisor(b % a,			SCREAMING_SNAKE_CASE__   )





def        lowerCamelCase_      (  SCREAMING_SNAKE_CASE__      :			int,			SCREAMING_SNAKE_CASE__      :			int   ) ->	int:
						while y:  # --> when y=0 then loop will terminate and return x as final GCD.
												UpperCAmelCase_						,       UpperCAmelCase_			:      Optional[Any]     						=  y, x % y
						return abs(SCREAMING_SNAKE_CASE__   )





def        lowerCamelCase_      (  ) ->	Optional[int]:
						try:
												UpperCAmelCase_			:      Optional[Any]     						=  input('''Enter two integers separated by comma (,): '''   ).split(''','''   )
												UpperCAmelCase_			:      Optional[int]     						=  int(nums[0]   )
												UpperCAmelCase_			:      List[Any]     						=  int(nums[1]   )
												print(
												    F"""greatest_common_divisor({num_a}, {num_a}) = """
												    F"""{greatest_common_divisor(SCREAMING_SNAKE_CASE__,			SCREAMING_SNAKE_CASE__   )}"""   )
												print(F"""By iterative gcd({num_a}, {num_a}) = {gcd_by_iterative(SCREAMING_SNAKE_CASE__,			SCREAMING_SNAKE_CASE__   )}"""   )
						except (IndexError, UnboundLocalError, ValueError):
												print('''Wrong input'''   )


if __name__ == "__main__":
					main()





