






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     							=						False

if is_vision_available():
		from PIL import Image

		from transformers import PixaStructImageProcessor



class   lowerCamelCase				(unittest.TestCase      ):







		'''simple docstring'''



		def __init__(							self    ,		_UpperCamelCase    ,		_UpperCamelCase=7    ,		_UpperCamelCase=3    ,		_UpperCamelCase=1_8    ,		_UpperCamelCase=3_0    ,		_UpperCamelCase=4_0_0    ,		_UpperCamelCase=None    ,		_UpperCamelCase=True    ,		_UpperCamelCase=True    ,		_UpperCamelCase=None    ,		)     ->							Tuple:
							UpperCAmelCase_   :				Any						=						size if size is not None else {'height': 2_0, 'width': 2_0}
							UpperCAmelCase_   :				Optional[int]						=						parent
							UpperCAmelCase_   :				str						=						batch_size
							UpperCAmelCase_   :				int						=						num_channels
							UpperCAmelCase_   :				List[Any]						=						image_size
							UpperCAmelCase_   :				Dict						=						min_resolution
							UpperCAmelCase_   :				List[Any]						=						max_resolution
							UpperCAmelCase_   :				Optional[Any]						=						size
							UpperCAmelCase_   :				Union[str, Any]						=						do_normalize
							UpperCAmelCase_   :				List[Any]						=						do_convert_rgb
							UpperCAmelCase_   :				Tuple						=						[5_1_2, 1_0_2_4, 2_0_4_8, 4_0_9_6]
							UpperCAmelCase_   :				Optional[Any]						=						patch_size if patch_size is not None else {'height': 1_6, 'width': 1_6}



		def  __UpperCAmelCase      (							self       )     ->							Union[str, Any]:
							return {"do_normalize": self.do_normalize, "do_convert_rgb": self.do_convert_rgb}



		def  __UpperCAmelCase      (							self       )     ->							List[str]:
							UpperCAmelCase_   :				List[Any]						=						'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/australia.jpg'
							UpperCAmelCase_   :				int						=						Image.open(requests.get(_UpperCamelCase    ,		stream=_UpperCamelCase       ).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   lowerCamelCase				(_snake_case			,  unittest.TestCase      ):







		'''simple docstring'''



		_snake_case   :   Union[str, Any]							=  PixaStructImageProcessor if is_vision_available() else None



		def  __UpperCAmelCase      (							self       )     ->							Tuple:
							UpperCAmelCase_   :				Tuple						=						PixaStructImageProcessingTester(self       )



		@property
		def  __UpperCAmelCase      (							self       )     ->							Any:
							return self.image_processor_tester.prepare_image_processor_dict()



		def  __UpperCAmelCase      (							self       )     ->							Union[str, Any]:
							UpperCAmelCase_   :				List[Any]						=						self.image_processing_class(**self.image_processor_dict       )
							self.assertTrue(hasattr(_UpperCamelCase    ,		'do_normalize'       )       )
							self.assertTrue(hasattr(_UpperCamelCase    ,		'do_convert_rgb'       )       )



		def  __UpperCAmelCase      (							self       )     ->							str:
							UpperCAmelCase_   :				str						=						self.image_processor_tester.prepare_dummy_image()

							UpperCAmelCase_   :				Optional[Any]						=						self.image_processing_class(**self.image_processor_dict       )
							UpperCAmelCase_   :				str						=						2_0_4_8

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



		def  __UpperCAmelCase      (							self       )     ->							int:
							# Initialize image_processor
							UpperCAmelCase_   :				Tuple						=						self.image_processing_class(**self.image_processor_dict       )
							# create random PIL images
							UpperCAmelCase_   :				List[Any]						=						prepare_image_inputs(self.image_processor_tester    ,		equal_resolution=_UpperCamelCase       )
							for image in image_inputs:
												self.assertIsInstance(_UpperCamelCase    ,		Image.Image       )

							# Test not batched input
							UpperCAmelCase_   :				Optional[Any]						=						(
							    (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=_UpperCamelCase       ).flattened_patches
												self.assertEqual(
												    encoded_images.shape    ,		(1, max_patch, expected_hidden_dim)    ,		)

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



		def  __UpperCAmelCase      (							self       )     ->							Optional[int]:
							# Initialize image_processor
							UpperCAmelCase_   :				Optional[Any]						=						self.image_processing_class(**self.image_processor_dict       )
							# create random PIL images
							UpperCAmelCase_   :				str						=						prepare_image_inputs(self.image_processor_tester    ,		equal_resolution=_UpperCamelCase       )
							for image in image_inputs:
												self.assertIsInstance(_UpperCamelCase    ,		Image.Image       )

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

							UpperCAmelCase_   :				Optional[int]						=						True

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

												UpperCAmelCase_   :				Optional[Any]						=						'Hello'

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

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



		def  __UpperCAmelCase      (							self       )     ->							int:
							# Initialize image_processor
							UpperCAmelCase_   :				Tuple						=						self.image_processing_class(**self.image_processor_dict       )
							# create random numpy tensors
							UpperCAmelCase_   :				List[Any]						=						prepare_image_inputs(self.image_processor_tester    ,		equal_resolution=_UpperCamelCase    ,		numpify=_UpperCamelCase       )
							for image in image_inputs:
												self.assertIsInstance(_UpperCamelCase    ,		np.ndarray       )

							UpperCAmelCase_   :				Any						=						(
							    (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_   :				str						=						image_processor(
												    image_inputs[0]    ,		return_tensors='pt'    ,		max_patches=_UpperCamelCase       ).flattened_patches
												self.assertEqual(
												    encoded_images.shape    ,		(1, max_patch, expected_hidden_dim)    ,		)

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



		def  __UpperCAmelCase      (							self       )     ->							Optional[Any]:
							# Initialize image_processor
							UpperCAmelCase_   :				List[str]						=						self.image_processing_class(**self.image_processor_dict       )
							# create random PyTorch tensors
							UpperCAmelCase_   :				List[Any]						=						prepare_image_inputs(self.image_processor_tester    ,		equal_resolution=_UpperCamelCase    ,		torchify=_UpperCamelCase       )
							for image in image_inputs:
												self.assertIsInstance(_UpperCamelCase    ,		torch.Tensor       )

							# Test not batched input
							UpperCAmelCase_   :				Any						=						(
							    (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=_UpperCamelCase       ).flattened_patches
												self.assertEqual(
												    encoded_images.shape    ,		(1, max_patch, expected_hidden_dim)    ,		)

												# Test batched
												UpperCAmelCase_   :				int						=						image_processor(
												    _UpperCamelCase    ,		return_tensors='pt'    ,		max_patches=_UpperCamelCase       ).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   lowerCamelCase				(_snake_case			,  unittest.TestCase      ):







		'''simple docstring'''



		_snake_case   :   str							=  PixaStructImageProcessor if is_vision_available() else None



		def  __UpperCAmelCase      (							self       )     ->							Any:
							UpperCAmelCase_   :				Union[str, Any]						=						PixaStructImageProcessingTester(self    ,		num_channels=4       )
							UpperCAmelCase_   :				Tuple						=						3



		@property
		def  __UpperCAmelCase      (							self       )     ->							Union[str, Any]:
							return self.image_processor_tester.prepare_image_processor_dict()



		def  __UpperCAmelCase      (							self       )     ->							Optional[int]:
							UpperCAmelCase_   :				Union[str, Any]						=						self.image_processing_class(**self.image_processor_dict       )
							self.assertTrue(hasattr(_UpperCamelCase    ,		'do_normalize'       )       )
							self.assertTrue(hasattr(_UpperCamelCase    ,		'do_convert_rgb'       )       )



		def  __UpperCAmelCase      (							self       )     ->							Dict:
							# Initialize image_processor
							UpperCAmelCase_   :				Dict						=						self.image_processing_class(**self.image_processor_dict       )
							# create random PIL images
							UpperCAmelCase_   :				List[str]						=						prepare_image_inputs(self.image_processor_tester    ,		equal_resolution=_UpperCamelCase       )
							for image in image_inputs:
												self.assertIsInstance(_UpperCamelCase    ,		Image.Image       )

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

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














from __future__ import annotations

from collections.abc import Callable

__UpperCAmelCase     							=						list[list[float | int]]




def 					lowercase__				(					__snake_case   :							Matrix					,	__snake_case   :							Matrix  ):


					'''simple docstring'''


					UpperCAmelCase_   :				int						=						len(__snake_case  )
					UpperCAmelCase_   :				Matrix						=						[[0 for _ in range(size + 1  )] for _ in range(__snake_case  )]
					UpperCAmelCase_   :				int
					UpperCAmelCase_   :				int
					UpperCAmelCase_   :				int
					UpperCAmelCase_   :				int
					UpperCAmelCase_   :				int
					UpperCAmelCase_   :				float

					for row in range(__snake_case  ):
										for col in range(__snake_case  ):
															UpperCAmelCase_   :				Dict						=						matrix[row][col]

										UpperCAmelCase_   :				Union[str, Any]						=						vector[row][0]

					UpperCAmelCase_   :				Union[str, Any]						=						0
					UpperCAmelCase_   :				Union[str, Any]						=						0
					while row < size and col < size:
										# pivoting
										UpperCAmelCase_   :				Optional[Any]						=						max((abs(augmented[rowa][col]  ), rowa) for rowa in range(__snake_case					,	__snake_case  )  )[
										    1
										]
										if augmented[pivot_row][col] == 0:
															col += 1
															continue
										else:
															UpperCAmelCase_	,  UpperCAmelCase_   :				Tuple						=						augmented[pivot_row], augmented[row]

										for rowa in range(row + 1					,	__snake_case  ):
															UpperCAmelCase_   :				Optional[int]						=						augmented[rowa][col] / augmented[row][col]
															UpperCAmelCase_   :				List[str]						=						0
															for cola in range(col + 1					,	size + 1  ):
																				augmented[rowa][cola] -= augmented[row][cola] * ratio

										row += 1
										col += 1

					# back substitution
					for col in range(1					,	__snake_case  ):
										for row in range(__snake_case  ):
															UpperCAmelCase_   :				Union[str, Any]						=						augmented[row][col] / augmented[col][col]
															for cola in range(__snake_case					,	size + 1  ):
																				augmented[row][cola] -= augmented[col][cola] * ratio

    # round to get rid of numbers like 2.000000000000004
					return [
					    [round(augmented[row][size] / augmented[row][row]					,	10  )] for row in range(__snake_case  )
					]




def 					lowercase__				(					__snake_case   :							list[int]  ):


					'''simple docstring'''


					UpperCAmelCase_   :				int						=						len(__snake_case  )
					UpperCAmelCase_   :				Matrix						=						[[0 for _ in range(__snake_case  )] for _ in range(__snake_case  )]
					UpperCAmelCase_   :				Matrix						=						[[0] for _ in range(__snake_case  )]
					UpperCAmelCase_   :				Matrix
					UpperCAmelCase_   :				int
					UpperCAmelCase_   :				int
					UpperCAmelCase_   :				int

					for x_val, y_val in enumerate(__snake_case  ):
										for col in range(__snake_case  ):
															UpperCAmelCase_   :				int						=						(x_val + 1) ** (size - col - 1)
										UpperCAmelCase_   :				int						=						y_val

					UpperCAmelCase_   :				List[str]						=						solve(__snake_case					,	__snake_case  )

					def interpolated_func(__snake_case   :							int  ) -> int:
										return sum(
										    round(coeffs[x_val][0]  ) * (var ** (size - x_val - 1))
										    for x_val in range(__snake_case  )  )

					return interpolated_func




def 					lowercase__				(					__snake_case   :							int  ):


					'''simple docstring'''


					return (
					    1
					    - variable
					    + variable**2
					    - variable**3
					    + variable**4
					    - variable**5
					    + variable**6
					    - variable**7
					    + variable**8
					    - variable**9
					    + variable**10
					)




def 					lowercase__				(					__snake_case   :							Callable[[int], int] = question_function					,	__snake_case   :							int = 10  ):


					'''simple docstring'''


					UpperCAmelCase_   :				list[int]						=						[func(__snake_case  ) for x_val in range(1					,	order + 1  )]

					UpperCAmelCase_   :				list[Callable[[int], int]]						=						[
					    interpolate(data_points[:max_coeff]  ) for max_coeff in range(1					,	order + 1  )
					]

					UpperCAmelCase_   :				int						=						0
					UpperCAmelCase_   :				Callable[[int], int]
					UpperCAmelCase_   :				int

					for poly in polynomials:
										UpperCAmelCase_   :				Optional[int]						=						1
										while func(__snake_case  ) == poly(__snake_case  ):
															x_val += 1

										ret += poly(__snake_case  )

					return ret


if __name__ == "__main__":
		print(F'{solution() = }')






