

"""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_        =       False

if is_vision_available():
	from PIL import Image

	from transformers import PixaStructImageProcessor







class       snake_case__  ( unittest.TestCase     ):







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





								'''simple docstring'''



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







			def 			__lowerCAmelCase				(			self			: List[Any]  ):





								'''simple docstring'''



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







			def 			__lowerCAmelCase				(			self			: Optional[Any]  ):





								'''simple docstring'''



								UpperCAmelCase			:   List[str]      =				"https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/australia.jpg"
								UpperCAmelCase			:   Optional[Any]      =				Image.open(requests.get(lowercase			,  stream=lowercase  ).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       snake_case__  ( lowerCAmelCase_					,  unittest.TestCase     ):
			SCREAMING_SNAKE_CASE__     				=						PixaStructImageProcessor if is_vision_available() else None







			def 			__lowerCAmelCase				(			self			: Optional[int]  ):





								'''simple docstring'''



								UpperCAmelCase			:   Any      =				PixaStructImageProcessingTester(self  )







			@property
			def 			__lowerCAmelCase				(			self			: List[str]  ):





								'''simple docstring'''



								return self.image_processor_tester.prepare_image_processor_dict()







			def 			__lowerCAmelCase				(			self			: int  ):





								'''simple docstring'''



								UpperCAmelCase			:   Union[str, Any]      =				self.image_processing_class(**self.image_processor_dict  )
								self.assertTrue(hasattr(lowercase			,  "do_normalize"  )  )
								self.assertTrue(hasattr(lowercase			,  "do_convert_rgb"  )  )







			def 			__lowerCAmelCase				(			self			: Union[str, Any]  ):





								'''simple docstring'''



								UpperCAmelCase			:   int      =				self.image_processor_tester.prepare_dummy_image()

								UpperCAmelCase			:   Tuple      =				self.image_processing_class(**self.image_processor_dict  )
								UpperCAmelCase			:   int      =				20_48

								UpperCAmelCase			:   List[str]      =				image_processor(lowercase			,  return_tensors="pt"			,  max_patches=lowercase  )
								self.assertTrue(torch.allclose(inputs.flattened_patches.mean()			,  torch.tensor(0.0_6_0_6  )			,  atol=1E-3			,  rtol=1E-3  )  )







			def 			__lowerCAmelCase				(			self			: str  ):





								'''simple docstring'''



								UpperCAmelCase			:   List[str]      =				self.image_processing_class(**self.image_processor_dict  )
								# create random PIL images
								UpperCAmelCase			:   List[Any]      =				prepare_image_inputs(self.image_processor_tester			,  equal_resolution=lowercase  )
								for image in image_inputs:
													self.assertIsInstance(lowercase			,  Image.Image  )

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

													# Test batched
													UpperCAmelCase			:   str      =				image_processor(
													    lowercase			,  return_tensors="pt"			,  max_patches=lowercase  ).flattened_patches
													self.assertEqual(
													    encoded_images.shape			,  (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)			,  )







			def 			__lowerCAmelCase				(			self			: Tuple  ):





								'''simple docstring'''



								UpperCAmelCase			:   Union[str, Any]      =				self.image_processing_class(**self.image_processor_dict  )
								# create random PIL images
								UpperCAmelCase			:   int      =				prepare_image_inputs(self.image_processor_tester			,  equal_resolution=lowercase  )
								for image in image_inputs:
													self.assertIsInstance(lowercase			,  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(lowercase  ):
																		UpperCAmelCase			:   Optional[Any]      =				image_processor(
																		    image_inputs[0]			,  return_tensors="pt"			,  max_patches=lowercase  ).flattened_patches

													UpperCAmelCase			:   Tuple      =				"Hello"

													UpperCAmelCase			:   Dict      =				image_processor(
													    image_inputs[0]			,  return_tensors="pt"			,  max_patches=lowercase			,  header_text=lowercase  ).flattened_patches
													self.assertEqual(
													    encoded_images.shape			,  (1, max_patch, expected_hidden_dim)			,  )

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







			def 			__lowerCAmelCase				(			self			: Dict  ):





								'''simple docstring'''



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

								UpperCAmelCase			:   Optional[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			:   Tuple      =				image_processor(
													    image_inputs[0]			,  return_tensors="pt"			,  max_patches=lowercase  ).flattened_patches
													self.assertEqual(
													    encoded_images.shape			,  (1, max_patch, expected_hidden_dim)			,  )

													# Test batched
													UpperCAmelCase			:   Tuple      =				image_processor(
													    lowercase			,  return_tensors="pt"			,  max_patches=lowercase  ).flattened_patches
													self.assertEqual(
													    encoded_images.shape			,  (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)			,  )







			def 			__lowerCAmelCase				(			self			: Union[str, Any]  ):





								'''simple docstring'''



								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=lowercase			,  torchify=lowercase  )
								for image in image_inputs:
													self.assertIsInstance(lowercase			,  torch.Tensor  )

								# Test not batched input
								UpperCAmelCase			:   Dict      =				(
								    (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=lowercase  ).flattened_patches
													self.assertEqual(
													    encoded_images.shape			,  (1, max_patch, expected_hidden_dim)			,  )

													# Test batched
													UpperCAmelCase			:   List[Any]      =				image_processor(
													    lowercase			,  return_tensors="pt"			,  max_patches=lowercase  ).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       snake_case__  ( lowerCAmelCase_					,  unittest.TestCase     ):
			SCREAMING_SNAKE_CASE__     				=						PixaStructImageProcessor if is_vision_available() else None







			def 			__lowerCAmelCase				(			self			: Union[str, Any]  ):





								'''simple docstring'''



								UpperCAmelCase			:   Tuple      =				PixaStructImageProcessingTester(self			,  num_channels=4  )
								UpperCAmelCase			:   int      =				3







			@property
			def 			__lowerCAmelCase				(			self			: List[str]  ):





								'''simple docstring'''



								return self.image_processor_tester.prepare_image_processor_dict()







			def 			__lowerCAmelCase				(			self			: Any  ):





								'''simple docstring'''



								UpperCAmelCase			:   Dict      =				self.image_processing_class(**self.image_processor_dict  )
								self.assertTrue(hasattr(lowercase			,  "do_normalize"  )  )
								self.assertTrue(hasattr(lowercase			,  "do_convert_rgb"  )  )







			def 			__lowerCAmelCase				(			self			: Any  ):





								'''simple docstring'''



								UpperCAmelCase			:   Union[str, Any]      =				self.image_processing_class(**self.image_processor_dict  )
								# create random PIL images
								UpperCAmelCase			:   List[str]      =				prepare_image_inputs(self.image_processor_tester			,  equal_resolution=lowercase  )
								for image in image_inputs:
													self.assertIsInstance(lowercase			,  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			:   Union[str, Any]      =				image_processor(
													    image_inputs[0]			,  return_tensors="pt"			,  max_patches=lowercase  ).flattened_patches
													self.assertEqual(
													    encoded_images.shape			,  (1, max_patch, expected_hidden_dim)			,  )

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





"""simple docstring"""






import os



def   lowercase_	(    ):




					'''simple docstring'''






					UpperCAmelCase			:   Union[str, Any]      =				os.path.join(os.path.dirname(_lowercase	)							,      "num.txt"	)
					with open(_lowercase	) as file_hand:
										return str(sum(int(_lowercase	) for line in file_hand	)	)[:10]


if __name__ == "__main__":
	print(solution())


