






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:
	_UpperCamelCase			:	str								=False

if is_vision_available():
	from PIL import Image

	from transformers import PixaStructImageProcessor






class 						UpperCAmelCase__ (							unittest.TestCase							):







		def __init__(					self       ,A__       ,A__=7       ,A__=3       ,A__=18       ,A__=30       ,A__=400       ,A__=None       ,A__=True       ,A__=True       ,A__=None       ,):
						_A	:				Tuple   =      size if size is not None else {'''height''': 20, '''width''': 20}
						_A	:				Optional[Any]   =      parent
						_A	:				str   =      batch_size
						_A	:				Any   =      num_channels
						_A	:				Optional[int]   =      image_size
						_A	:				Tuple   =      min_resolution
						_A	:				Tuple   =      max_resolution
						_A	:				Tuple   =      size
						_A	:				str   =      do_normalize
						_A	:				Union[str, Any]   =      do_convert_rgb
						_A	:				int   =      [512, 1024, 2048, 4096]
						_A	:				Union[str, Any]   =      patch_size if patch_size is not None else {'''height''': 16, '''width''': 16}







		def  A__      (					self		):
						return {"do_normalize": self.do_normalize, "do_convert_rgb": self.do_convert_rgb}







		def  A__      (					self		):
						_A	:				List[Any]   =      '''https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/australia.jpg'''
						_A	:				str   =      Image.open(requests.get(A__       ,stream=A__		).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 						UpperCAmelCase__ (							__snake_case     ,				unittest.TestCase							):
		__snake_case      :       List[Any]     =   PixaStructImageProcessor if is_vision_available() else None







		def  A__      (					self		):
						_A	:				Tuple   =      PixaStructImageProcessingTester(self		)







		@property
		def  A__      (					self		):
						return self.image_processor_tester.prepare_image_processor_dict()







		def  A__      (					self		):
						_A	:				Dict   =      self.image_processing_class(**self.image_processor_dict		)
						self.assertTrue(hasattr(A__       ,'''do_normalize'''		)		)
						self.assertTrue(hasattr(A__       ,'''do_convert_rgb'''		)		)







		def  A__      (					self		):
						_A	:				Union[str, Any]   =      self.image_processor_tester.prepare_dummy_image()

						_A	:				Optional[Any]   =      self.image_processing_class(**self.image_processor_dict		)
						_A	:				Dict   =      2048

						_A	:				str   =      image_processor(A__       ,return_tensors='''pt'''       ,max_patches=A__		)
						self.assertTrue(torch.allclose(inputs.flattened_patches.mean()       ,torch.tensor(0.06_06		)       ,atol=1E-3       ,rtol=1E-3		)		)







		def  A__      (					self		):
						# Initialize image_processor
						_A	:				List[str]   =      self.image_processing_class(**self.image_processor_dict		)
						# create random PIL images
						_A	:				Optional[Any]   =      prepare_image_inputs(self.image_processor_tester       ,equal_resolution=A__		)
						for image in image_inputs:
										self.assertIsInstance(A__       ,Image.Image		)

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

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







		def  A__      (					self		):
						# Initialize image_processor
						_A	:				Any   =      self.image_processing_class(**self.image_processor_dict		)
						# create random PIL images
						_A	:				Optional[Any]   =      prepare_image_inputs(self.image_processor_tester       ,equal_resolution=A__		)
						for image in image_inputs:
										self.assertIsInstance(A__       ,Image.Image		)

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

						_A	:				Dict   =      True

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

										_A	:				Optional[int]   =      '''Hello'''

										_A	:				Any   =      image_processor(
										    image_inputs[0]       ,return_tensors='''pt'''       ,max_patches=A__       ,header_text=A__		).flattened_patches
										self.assertEqual(
										    encoded_images.shape       ,(1, max_patch, expected_hidden_dim)       ,)

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







		def  A__      (					self		):
						# Initialize image_processor
						_A	:				int   =      self.image_processing_class(**self.image_processor_dict		)
						# create random numpy tensors
						_A	:				str   =      prepare_image_inputs(self.image_processor_tester       ,equal_resolution=A__       ,numpify=A__		)
						for image in image_inputs:
										self.assertIsInstance(A__       ,np.ndarray		)

						_A	:				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
										_A	:				List[str]   =      image_processor(
										    image_inputs[0]       ,return_tensors='''pt'''       ,max_patches=A__		).flattened_patches
										self.assertEqual(
										    encoded_images.shape       ,(1, max_patch, expected_hidden_dim)       ,)

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







		def  A__      (					self		):
						# Initialize image_processor
						_A	:				Optional[int]   =      self.image_processing_class(**self.image_processor_dict		)
						# create random PyTorch tensors
						_A	:				Dict   =      prepare_image_inputs(self.image_processor_tester       ,equal_resolution=A__       ,torchify=A__		)
						for image in image_inputs:
										self.assertIsInstance(A__       ,torch.Tensor		)

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

										# Test batched
										_A	:				Tuple   =      image_processor(
										    A__       ,return_tensors='''pt'''       ,max_patches=A__		).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 						UpperCAmelCase__ (							__snake_case     ,				unittest.TestCase							):
		__snake_case      :       Union[str, Any]     =   PixaStructImageProcessor if is_vision_available() else None







		def  A__      (					self		):
						_A	:				List[str]   =      PixaStructImageProcessingTester(self       ,num_channels=4		)
						_A	:				Any   =      3







		@property
		def  A__      (					self		):
						return self.image_processor_tester.prepare_image_processor_dict()







		def  A__      (					self		):
						_A	:				int   =      self.image_processing_class(**self.image_processor_dict		)
						self.assertTrue(hasattr(A__       ,'''do_normalize'''		)		)
						self.assertTrue(hasattr(A__       ,'''do_convert_rgb'''		)		)







		def  A__      (					self		):
						# Initialize image_processor
						_A	:				Optional[int]   =      self.image_processing_class(**self.image_processor_dict		)
						# create random PIL images
						_A	:				List[str]   =      prepare_image_inputs(self.image_processor_tester       ,equal_resolution=A__		)
						for image in image_inputs:
										self.assertIsInstance(A__       ,Image.Image		)

						# Test not batched input
						_A	:				Tuple   =      (
						    (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
										_A	:				Optional[int]   =      image_processor(
										    image_inputs[0]       ,return_tensors='''pt'''       ,max_patches=A__		).flattened_patches
										self.assertEqual(
										    encoded_images.shape       ,(1, max_patch, expected_hidden_dim)       ,)

										# Test batched
										_A	:				Any   =      image_processor(
										    A__       ,return_tensors='''pt'''       ,max_patches=A__		).flattened_patches
										self.assertEqual(
										    encoded_images.shape       ,(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)       ,)











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



def  a__ (__lowercase							:int					,  __lowercase							:int   )		-> int:
				while y:  # --> when y=0 then loop will terminate and return x as final GCD.
								_A  ,				_A	:				Union[str, Any]   =      y, x % y
				return abs(__lowercase   )



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


if __name__ == "__main__":
	main()



