


"""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 _lowerCAmelCase       (   unittest.TestCase			):







   def __init__(			self		,			UpperCamelCase__		,			UpperCamelCase__=7		,			UpperCamelCase__=3		,			UpperCamelCase__=18		,			UpperCamelCase__=30		,			UpperCamelCase__=400		,			UpperCamelCase__=None		,			UpperCamelCase__=True		,			UpperCamelCase__=True		,			UpperCamelCase__=None		,			)			->	Union[str, Any]:

       '''simple docstring'''




       snake_case					:   List[str]         =					size if size is not None else {"height": 20, "width": 20}
       snake_case					:   Optional[Any]         =					parent
       snake_case					:   str         =					batch_size
       snake_case					:   Optional[int]         =					num_channels
       snake_case					:   str         =					image_size
       snake_case					:   int         =					min_resolution
       snake_case					:   str         =					max_resolution
       snake_case					:   Optional[int]         =					size
       snake_case					:   Dict         =					do_normalize
       snake_case					:   Optional[int]         =					do_convert_rgb
       snake_case					:   Union[str, Any]         =					[512, 1024, 2048, 4096]
       snake_case					:   List[Any]         =					patch_size if patch_size is not None else {"height": 16, "width": 16}







   def 				lowerCamelCase	(			self		)			->	str:

       '''simple docstring'''




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







   def 				lowerCamelCase	(			self		)			->	Any:

       '''simple docstring'''




       snake_case					:   List[str]         =					"https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/australia.jpg"
       snake_case					:   str         =					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			):
   __UpperCAmelCase				: Dict													=						PixaStructImageProcessor if is_vision_available() else None







   def 				lowerCamelCase	(			self		)			->	Tuple:

       '''simple docstring'''




       snake_case					:   Optional[int]         =					PixaStructImageProcessingTester(self		)







   @property
   def 				lowerCamelCase	(			self		)			->	Any:

       '''simple docstring'''




       return self.image_processor_tester.prepare_image_processor_dict()







   def 				lowerCamelCase	(			self		)			->	int:

       '''simple docstring'''




       snake_case					:   Optional[Any]         =					self.image_processing_class(**self.image_processor_dict		)
       self.assertTrue(hasattr(UpperCamelCase__		,			"do_normalize"		)		)
       self.assertTrue(hasattr(UpperCamelCase__		,			"do_convert_rgb"		)		)







   def 				lowerCamelCase	(			self		)			->	List[Any]:

       '''simple docstring'''




       snake_case					:   Tuple         =					self.image_processor_tester.prepare_dummy_image()

       snake_case					:   Union[str, Any]         =					self.image_processing_class(**self.image_processor_dict		)
       snake_case					:   str         =					2048

       snake_case					:   Union[str, Any]         =					image_processor(UpperCamelCase__		,			return_tensors="pt"		,			max_patches=UpperCamelCase__		)
       self.assertTrue(torch.allclose(inputs.flattened_patches.mean()		,			torch.tensor(0.0606		)		,			atol=1e-3		,			rtol=1e-3		)		)







   def 				lowerCamelCase	(			self		)			->	Any:

       '''simple docstring'''




       snake_case					:   str         =					self.image_processing_class(**self.image_processor_dict		)
       # create random PIL images
       snake_case					:   int         =					prepare_image_inputs(self.image_processor_tester		,			equal_resolution=UpperCamelCase__		)
       for image in image_inputs:
           self.assertIsInstance(UpperCamelCase__		,			Image.Image		)

       # Test not batched input
       snake_case					:   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
           snake_case					:   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
           snake_case					:   str         =					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 				lowerCamelCase	(			self		)			->	Tuple:

       '''simple docstring'''




       snake_case					:   int         =					self.image_processing_class(**self.image_processor_dict		)
       # create random PIL images
       snake_case					:   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
       snake_case					:   Optional[int]         =					(
           (self.image_processor_tester.patch_size["height"] * self.image_processor_tester.patch_size["width"])
           * self.image_processor_tester.num_channels
       ) + 2

       snake_case					:   List[Any]         =					True

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

           snake_case					:   Dict         =					"Hello"

           snake_case					:   Optional[Any]         =					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
           snake_case					:   Tuple         =					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 				lowerCamelCase	(			self		)			->	Any:

       '''simple docstring'''




       snake_case					:   List[str]         =					self.image_processing_class(**self.image_processor_dict		)
       # create random numpy tensors
       snake_case					:   Optional[int]         =					prepare_image_inputs(self.image_processor_tester		,			equal_resolution=UpperCamelCase__		,			numpify=UpperCamelCase__		)
       for image in image_inputs:
           self.assertIsInstance(UpperCamelCase__		,			np.ndarray		)

       snake_case					:   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
           snake_case					:   Optional[int]         =					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
           snake_case					:   Tuple         =					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 				lowerCamelCase	(			self		)			->	List[Any]:

       '''simple docstring'''




       snake_case					:   Optional[int]         =					self.image_processing_class(**self.image_processor_dict		)
       # create random PyTorch tensors
       snake_case					:   int         =					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
       snake_case					:   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
           snake_case					:   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
           snake_case					:   Tuple         =					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			):
   __UpperCAmelCase				: Tuple													=						PixaStructImageProcessor if is_vision_available() else None







   def 				lowerCamelCase	(			self		)			->	Dict:

       '''simple docstring'''




       snake_case					:   Any         =					PixaStructImageProcessingTester(self		,			num_channels=4		)
       snake_case					:   Optional[Any]         =					3







   @property
   def 				lowerCamelCase	(			self		)			->	List[Any]:

       '''simple docstring'''




       return self.image_processor_tester.prepare_image_processor_dict()







   def 				lowerCamelCase	(			self		)			->	List[Any]:

       '''simple docstring'''




       snake_case					:   Optional[int]         =					self.image_processing_class(**self.image_processor_dict		)
       self.assertTrue(hasattr(UpperCamelCase__		,			"do_normalize"		)		)
       self.assertTrue(hasattr(UpperCamelCase__		,			"do_convert_rgb"		)		)







   def 				lowerCamelCase	(			self		)			->	Dict:

       '''simple docstring'''




       snake_case					:   Optional[int]         =					self.image_processing_class(**self.image_processor_dict		)
       # create random PIL images
       snake_case					:   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
       snake_case					:   List[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
           snake_case					:   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
           snake_case					:   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)		,			)









"""simple docstring"""



from __future__ import annotations

from cmath import sqrt







def 						__lowerCAmelCase		(		lowercase						:						int		,							lowercase						:						int		,							lowercase						:						int				)			->		tuple[complex, complex]:


    """simple docstring"""





    if a == 0:
        raise ValueError("Coefficient 'a' must not be zero."				)
    snake_case					:   Dict         =					b * b - 4 * a * c

    snake_case					:   Tuple         =					(-b + sqrt(lowercase				)) / (2 * a)
    snake_case					:   Optional[int]         =					(-b - sqrt(lowercase				)) / (2 * a)

    return (
        root_a.real if not root_a.imag else root_a,
        root_a.real if not root_a.imag else root_a,
    )







def 						__lowerCAmelCase		(		)			->		List[Any]:


    """simple docstring"""





    snake_case      ,snake_case					:   Optional[Any]         =					quadratic_roots(a=5		,							b=6		,							c=1				)
    print(F'The solutions are: {solutiona} and {solutiona}'				)


if __name__ == "__main__":
  main()





