
'''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:
      lowercase			:				str    		=			False

if is_vision_available():
      from PIL import Image

      from transformers import PixaStructImageProcessor





class  A				(							unittest.TestCase						):





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






         """simple docstring"""






         A       :   Any					=						size if size is not None else {'''height''': 20, '''width''': 20}
         A       :   List[Any]					=						parent
         A       :   Dict					=						batch_size
         A       :   Optional[Any]					=						num_channels
         A       :   str					=						image_size
         A       :   List[Any]					=						min_resolution
         A       :   Optional[int]					=						max_resolution
         A       :   Union[str, Any]					=						size
         A       :   Tuple					=						do_normalize
         A       :   Tuple					=						do_convert_rgb
         A       :   Union[str, Any]					=						[512, 1024, 2048, 4096]
         A       :   Optional[int]					=						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	) ->				int:






         """simple docstring"""






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





     def 				__lowerCAmelCase							(							self	) ->				Optional[Any]:






         """simple docstring"""






         A       :   Optional[int]					=						PixaStructImageProcessingTester(self	)





     @property
     def 				__lowerCAmelCase							(							self	) ->				Tuple:






         """simple docstring"""






         return self.image_processor_tester.prepare_image_processor_dict()





     def 				__lowerCAmelCase							(							self	) ->				Tuple:






         """simple docstring"""






         A       :   List[str]					=						self.image_processing_class(**self.image_processor_dict	)
         self.assertTrue(hasattr(SCREAMING_SNAKE_CASE						,			'''do_normalize'''	)	)
         self.assertTrue(hasattr(SCREAMING_SNAKE_CASE						,			'''do_convert_rgb'''	)	)





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






         """simple docstring"""






         A       :   Dict					=						self.image_processor_tester.prepare_dummy_image()

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

         A       :   Tuple					=						image_processor(SCREAMING_SNAKE_CASE						,			return_tensors='''pt'''						,			max_patches=SCREAMING_SNAKE_CASE	)
         self.assertTrue(torch.allclose(inputs.flattened_patches.mean()						,			torch.tensor(0.0_606	)						,			atol=1e-3						,			rtol=1e-3	)	)





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






         """simple docstring"""






         A       :   Optional[int]					=						self.image_processing_class(**self.image_processor_dict	)
         # create random PIL images
         A       :   int					=						prepare_image_inputs(self.image_processor_tester						,			equal_resolution=SCREAMING_SNAKE_CASE	)
         for image in image_inputs:
             self.assertIsInstance(SCREAMING_SNAKE_CASE						,			Image.Image	)

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

             # Test batched
             A       :   Union[str, Any]					=						image_processor(
                 SCREAMING_SNAKE_CASE						,			return_tensors='''pt'''						,			max_patches=SCREAMING_SNAKE_CASE	).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"""






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

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

         A       :   Optional[int]					=						True

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

             A       :   Any					=						'''Hello'''

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

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





     def 				__lowerCAmelCase							(							self	) ->				Dict:






         """simple docstring"""






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

         A       :   Tuple					=						(
             (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=SCREAMING_SNAKE_CASE	).flattened_patches
             self.assertEqual(
                 encoded_images.shape						,			(1, max_patch, expected_hidden_dim)						,			)

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





     def 				__lowerCAmelCase							(							self	) ->				List[str]:






         """simple docstring"""






         A       :   str					=						self.image_processing_class(**self.image_processor_dict	)
         # create random PyTorch tensors
         A       :   Optional[Any]					=						prepare_image_inputs(self.image_processor_tester						,			equal_resolution=SCREAMING_SNAKE_CASE						,			torchify=SCREAMING_SNAKE_CASE	)
         for image in image_inputs:
             self.assertIsInstance(SCREAMING_SNAKE_CASE						,			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       :   Optional[Any]					=						image_processor(
                 image_inputs[0]						,			return_tensors='''pt'''						,			max_patches=SCREAMING_SNAKE_CASE	).flattened_patches
             self.assertEqual(
                 encoded_images.shape						,			(1, max_patch, expected_hidden_dim)						,			)

             # Test batched
             A       :   Dict					=						image_processor(
                 SCREAMING_SNAKE_CASE						,			return_tensors='''pt'''						,			max_patches=SCREAMING_SNAKE_CASE	).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  A				(							__snake_case    ,			unittest.TestCase						):
     __magic_name__						=		PixaStructImageProcessor if is_vision_available() else None





     def 				__lowerCAmelCase							(							self	) ->				str:






         """simple docstring"""






         A       :   Union[str, Any]					=						PixaStructImageProcessingTester(self						,			num_channels=4	)
         A       :   Optional[Any]					=						3





     @property
     def 				__lowerCAmelCase							(							self	) ->				Tuple:






         """simple docstring"""






         return self.image_processor_tester.prepare_image_processor_dict()





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






         """simple docstring"""






         A       :   str					=						self.image_processing_class(**self.image_processor_dict	)
         self.assertTrue(hasattr(SCREAMING_SNAKE_CASE						,			'''do_normalize'''	)	)
         self.assertTrue(hasattr(SCREAMING_SNAKE_CASE						,			'''do_convert_rgb'''	)	)





     def 				__lowerCAmelCase							(							self	) ->				Optional[Any]:






         """simple docstring"""






         A       :   List[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=SCREAMING_SNAKE_CASE	)
         for image in image_inputs:
             self.assertIsInstance(SCREAMING_SNAKE_CASE						,			Image.Image	)

         # 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 - 1)
         ) + 2

         for max_patch in self.image_processor_tester.max_patches:
             # Test not batched input
             A       :   Any					=						image_processor(
                 image_inputs[0]						,			return_tensors='''pt'''						,			max_patches=SCREAMING_SNAKE_CASE	).flattened_patches
             self.assertEqual(
                 encoded_images.shape						,			(1, max_patch, expected_hidden_dim)						,			)

             # Test batched
             A       :   int					=						image_processor(
                 SCREAMING_SNAKE_CASE						,			return_tensors='''pt'''						,			max_patches=SCREAMING_SNAKE_CASE	).flattened_patches
             self.assertEqual(
                 encoded_images.shape						,			(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)						,			)






'''simple docstring'''




import requests
from bsa import BeautifulSoup







def 		lowerCAmelCase_				(   snake_case__		,				snake_case__						):




    '''simple docstring'''



    A       :   str					=						BeautifulSoup(requests.get(snake_case__		,				params=snake_case__						).content		,				'''html.parser'''						)
    A       :   Dict					=						soup.find('''div'''		,				attrs={'''class''': '''gs_ri'''}						)
    A       :   Optional[int]					=						div.find('''div'''		,				attrs={'''class''': '''gs_fl'''}						).find_all('''a'''						)
    return anchors[2].get_text()


if __name__ == "__main__":
      lowercase			:				str    		=			{
          'title': (
              'Precisely geometry controlled microsupercapacitors for ultrahigh areal '
              'capacitance, volumetric capacitance, and energy density'
          ),
          'journal': 'Chem. Mater.',
          'volume': 30,
          'pages': '3979-3990',
          'year': 20_18,
          'hl': 'en',
      }
      print(get_citation('https://scholar.google.com/scholar_lookup', params=params))




