


"""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:
							a__       :							List[Any]        =  False

if is_vision_available():
							from PIL import Image

							from transformers import PixaStructImageProcessor




class 				UpperCamelCase_			(				unittest.TestCase):

	"""simple docstring"""





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




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




	def 						UpperCAmelCase_   (				self    :       Dict     ) ->			Union[str, Any]:
						__SCREAMING_SNAKE_CASE		     =       "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/australia.jpg"
						__SCREAMING_SNAKE_CASE		     =       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 				UpperCamelCase_			(				UpperCamelCase    ,						unittest.TestCase):

	"""simple docstring"""





	snake_case__       :						str					      =    PixaStructImageProcessor if is_vision_available() else None




	def 						UpperCAmelCase_   (				self    :       Union[str, Any]     ) ->			Optional[int]:
						__SCREAMING_SNAKE_CASE		     =       PixaStructImageProcessingTester(self     )




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




	def 						UpperCAmelCase_   (				self    :       List[str]     ) ->			List[Any]:
						__SCREAMING_SNAKE_CASE		     =       self.image_processing_class(**self.image_processor_dict     )
						self.assertTrue(hasattr(UpperCAmelCase__    ,    "do_normalize"     )     )
						self.assertTrue(hasattr(UpperCAmelCase__    ,    "do_convert_rgb"     )     )




	def 						UpperCAmelCase_   (				self    :       int     ) ->			Optional[Any]:
						__SCREAMING_SNAKE_CASE		     =       self.image_processor_tester.prepare_dummy_image()

						__SCREAMING_SNAKE_CASE		     =       self.image_processing_class(**self.image_processor_dict     )
						__SCREAMING_SNAKE_CASE		     =       2_0_4_8

						__SCREAMING_SNAKE_CASE		     =       image_processor(UpperCAmelCase__    ,    return_tensors="pt"    ,    max_patches=UpperCAmelCase__     )
						self.assertTrue(torch.allclose(inputs.flattened_patches.mean()    ,    torch.tensor(0.0_606     )    ,    atol=1E-3    ,    rtol=1E-3     )     )




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

						# Test not batched input
						__SCREAMING_SNAKE_CASE		     =       (
						    (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
											__SCREAMING_SNAKE_CASE		     =       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
											__SCREAMING_SNAKE_CASE		     =       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    :       str     ) ->			Any:
						# Initialize image_processor
						__SCREAMING_SNAKE_CASE		     =       self.image_processing_class(**self.image_processor_dict     )
						# create random PIL images
						__SCREAMING_SNAKE_CASE		     =       prepare_image_inputs(self.image_processor_tester    ,    equal_resolution=UpperCAmelCase__     )
						for image in image_inputs:
											self.assertIsInstance(UpperCAmelCase__    ,    Image.Image     )

						# Test not batched input
						__SCREAMING_SNAKE_CASE		     =       (
						    (self.image_processor_tester.patch_size["height"] * self.image_processor_tester.patch_size["width"])
						    * self.image_processor_tester.num_channels
						) + 2

						__SCREAMING_SNAKE_CASE		     =       True

						for max_patch in self.image_processor_tester.max_patches:
											# Test not batched input
											with self.assertRaises(UpperCAmelCase__     ):
																__SCREAMING_SNAKE_CASE		     =       image_processor(
																    image_inputs[0]    ,    return_tensors="pt"    ,    max_patches=UpperCAmelCase__     ).flattened_patches

											__SCREAMING_SNAKE_CASE		     =       "Hello"

											__SCREAMING_SNAKE_CASE		     =       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
											__SCREAMING_SNAKE_CASE		     =       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    :       Tuple     ) ->			Union[str, Any]:
						# Initialize image_processor
						__SCREAMING_SNAKE_CASE		     =       self.image_processing_class(**self.image_processor_dict     )
						# create random numpy tensors
						__SCREAMING_SNAKE_CASE		     =       prepare_image_inputs(self.image_processor_tester    ,    equal_resolution=UpperCAmelCase__    ,    numpify=UpperCAmelCase__     )
						for image in image_inputs:
											self.assertIsInstance(UpperCAmelCase__    ,    np.ndarray     )

						__SCREAMING_SNAKE_CASE		     =       (
						    (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
											__SCREAMING_SNAKE_CASE		     =       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
											__SCREAMING_SNAKE_CASE		     =       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]     ) ->			Tuple:
						# Initialize image_processor
						__SCREAMING_SNAKE_CASE		     =       self.image_processing_class(**self.image_processor_dict     )
						# create random PyTorch tensors
						__SCREAMING_SNAKE_CASE		     =       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
						__SCREAMING_SNAKE_CASE		     =       (
						    (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
											__SCREAMING_SNAKE_CASE		     =       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
											__SCREAMING_SNAKE_CASE		     =       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 				UpperCamelCase_			(				UpperCamelCase    ,						unittest.TestCase):

	"""simple docstring"""





	snake_case__       :						int					      =    PixaStructImageProcessor if is_vision_available() else None




	def 						UpperCAmelCase_   (				self    :       str     ) ->			List[str]:
						__SCREAMING_SNAKE_CASE		     =       PixaStructImageProcessingTester(self    ,    num_channels=4     )
						__SCREAMING_SNAKE_CASE		     =       3




	@property
	def 						UpperCAmelCase_   (				self    :       List[Any]     ) ->			Tuple:
						return self.image_processor_tester.prepare_image_processor_dict()




	def 						UpperCAmelCase_   (				self    :       Any     ) ->			Dict:
						__SCREAMING_SNAKE_CASE		     =       self.image_processing_class(**self.image_processor_dict     )
						self.assertTrue(hasattr(UpperCAmelCase__    ,    "do_normalize"     )     )
						self.assertTrue(hasattr(UpperCAmelCase__    ,    "do_convert_rgb"     )     )




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

						# Test not batched input
						__SCREAMING_SNAKE_CASE		     =       (
						    (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
											__SCREAMING_SNAKE_CASE		     =       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
											__SCREAMING_SNAKE_CASE		     =       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)    ,    )




"""simple docstring"""







from __future__ import annotations




def      UpperCAmelCase__ (lowerCAmelCase_     ):




					'''simple docstring'''


					if len(lowerCAmelCase_     ) < 2:
										raise ValueError("Monogons and Digons are not polygons in the Euclidean space"     )
					if any(i <= 0 for i in nums     ):
										raise ValueError("All values must be greater than 0"     )
					__SCREAMING_SNAKE_CASE		     =       nums.copy()
					copy_nums.sort()
					return copy_nums[-1] < sum(copy_nums[:-1]     )


if __name__ == "__main__":
		import doctest

		doctest.testmod()
