



'''simple docstring'''
import json
import pathlib
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision, slow
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

if is_vision_available():
					from PIL import Image

					from transformers import DeformableDetrImageProcessor



class _A     (			unittest.TestCase			):





		def __init__(   self       ,				__UpperCAmelCase       ,				__UpperCAmelCase=7       ,				__UpperCAmelCase=3       ,				__UpperCAmelCase=30       ,				__UpperCAmelCase=400       ,				__UpperCAmelCase=True       ,				__UpperCAmelCase=None       ,				__UpperCAmelCase=True       ,				__UpperCAmelCase=[0.5, 0.5, 0.5]       ,				__UpperCAmelCase=[0.5, 0.5, 0.5]       ,				__UpperCAmelCase=True       ,				__UpperCAmelCase=1 / 255       ,				__UpperCAmelCase=True       ,				)				->   str:

					'''simple docstring'''
					__UpperCAmelCase      :					str   =   size if size is not None else {"""shortest_edge""": 18, """longest_edge""": 1_333}
					__UpperCAmelCase      :					Optional[int]   =   parent
					__UpperCAmelCase      :					List[str]   =   batch_size
					__UpperCAmelCase      :					List[str]   =   num_channels
					__UpperCAmelCase      :					Dict   =   min_resolution
					__UpperCAmelCase      :					List[str]   =   max_resolution
					__UpperCAmelCase      :					str   =   do_resize
					__UpperCAmelCase      :					int   =   size
					__UpperCAmelCase      :					Optional[Any]   =   do_normalize
					__UpperCAmelCase      :					List[Any]   =   image_mean
					__UpperCAmelCase      :					Optional[int]   =   image_std
					__UpperCAmelCase      :					Any   =   do_rescale
					__UpperCAmelCase      :					Any   =   rescale_factor
					__UpperCAmelCase      :					List[Any]   =   do_pad





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

					'''simple docstring'''
					return {
					    "do_resize": self.do_resize,
					    "size": self.size,
					    "do_normalize": self.do_normalize,
					    "image_mean": self.image_mean,
					    "image_std": self.image_std,
					    "do_rescale": self.do_rescale,
					    "rescale_factor": self.rescale_factor,
					    "do_pad": self.do_pad,
					}





		def 							__A				(   self       ,				__UpperCAmelCase       ,				__UpperCAmelCase=False )				->   Optional[Any]:

					'''simple docstring'''

					if not batched:
								__UpperCAmelCase      :					Union[str, Any]   =   image_inputs[0]
								if isinstance(UpperCAmelCase__       ,				Image.Image ):
											__UpperCAmelCase ,     __UpperCAmelCase      :					Union[str, Any]   =   image.size
								else:
											__UpperCAmelCase ,     __UpperCAmelCase      :					Optional[Any]   =   image.shape[1], image.shape[2]
								if w < h:
											__UpperCAmelCase      :					int   =   int(self.size["""shortest_edge"""] * h / w )
											__UpperCAmelCase      :					Tuple   =   self.size["""shortest_edge"""]
								elif w > h:
											__UpperCAmelCase      :					Union[str, Any]   =   self.size["""shortest_edge"""]
											__UpperCAmelCase      :					Optional[Any]   =   int(self.size["""shortest_edge"""] * w / h )
								else:
											__UpperCAmelCase      :					Optional[int]   =   self.size["""shortest_edge"""]
											__UpperCAmelCase      :					int   =   self.size["""shortest_edge"""]

					else:
								__UpperCAmelCase      :					Union[str, Any]   =   []
								for image in image_inputs:
											__UpperCAmelCase ,     __UpperCAmelCase      :					Any   =   self.get_expected_values([image] )
											expected_values.append((expected_height, expected_width) )
								__UpperCAmelCase      :					int   =   max(UpperCAmelCase__       ,				key=lambda __UpperCAmelCase : item[0] )[0]
								__UpperCAmelCase      :					List[str]   =   max(UpperCAmelCase__       ,				key=lambda __UpperCAmelCase : item[1] )[1]

					return expected_height, expected_width



@require_torch
@require_vision
class _A     (			UpperCAmelCase__		, unittest.TestCase			):
		_SCREAMING_SNAKE_CASE    :			Any  					=	DeformableDetrImageProcessor if is_vision_available() else None





		def 							__A				(   self )				->   Tuple:

					'''simple docstring'''
					__UpperCAmelCase      :					Dict   =   DeformableDetrImageProcessingTester(self )





		@property
		def 							__A				(   self )				->   Any:

					'''simple docstring'''
					return self.image_processor_tester.prepare_image_processor_dict()





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

					'''simple docstring'''
					__UpperCAmelCase      :					Union[str, Any]   =   self.image_processing_class(**self.image_processor_dict )
					self.assertTrue(hasattr(UpperCAmelCase__       ,				"""image_mean""" ) )
					self.assertTrue(hasattr(UpperCAmelCase__       ,				"""image_std""" ) )
					self.assertTrue(hasattr(UpperCAmelCase__       ,				"""do_normalize""" ) )
					self.assertTrue(hasattr(UpperCAmelCase__       ,				"""do_resize""" ) )
					self.assertTrue(hasattr(UpperCAmelCase__       ,				"""do_rescale""" ) )
					self.assertTrue(hasattr(UpperCAmelCase__       ,				"""do_pad""" ) )
					self.assertTrue(hasattr(UpperCAmelCase__       ,				"""size""" ) )





		def 							__A				(   self )				->   int:

					'''simple docstring'''
					__UpperCAmelCase      :					Tuple   =   self.image_processing_class.from_dict(self.image_processor_dict )
					self.assertEqual(image_processor.size       ,				{"""shortest_edge""": 18, """longest_edge""": 1_333} )
					self.assertEqual(image_processor.do_pad       ,				UpperCAmelCase__ )

					__UpperCAmelCase      :					Any   =   self.image_processing_class.from_dict(
					    self.image_processor_dict       ,				size=42       ,				max_size=84       ,				pad_and_return_pixel_mask=UpperCAmelCase__ )
					self.assertEqual(image_processor.size       ,				{"""shortest_edge""": 42, """longest_edge""": 84} )
					self.assertEqual(image_processor.do_pad       ,				UpperCAmelCase__ )





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

					'''simple docstring'''
					pass





		def 							__A				(   self )				->   Dict:

					'''simple docstring'''
					__UpperCAmelCase      :					Optional[int]   =   self.image_processing_class(**self.image_processor_dict )
					# create random PIL images
					__UpperCAmelCase      :					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[int]   =   image_processing(image_inputs[0]       ,				return_tensors="""pt""" ).pixel_values

					__UpperCAmelCase ,     __UpperCAmelCase      :					Dict   =   self.image_processor_tester.get_expected_values(UpperCAmelCase__ )

					self.assertEqual(
					    encoded_images.shape       ,				(1, self.image_processor_tester.num_channels, expected_height, expected_width)       ,				)

					# Test batched
					__UpperCAmelCase ,     __UpperCAmelCase      :					Any   =   self.image_processor_tester.get_expected_values(UpperCAmelCase__       ,				batched=UpperCAmelCase__ )

					__UpperCAmelCase      :					Optional[int]   =   image_processing(UpperCAmelCase__       ,				return_tensors="""pt""" ).pixel_values
					self.assertEqual(
					    encoded_images.shape       ,				(
					        self.image_processor_tester.batch_size,
					        self.image_processor_tester.num_channels,
					        expected_height,
					        expected_width,
					    )       ,				)





		def 							__A				(   self )				->   Optional[int]:

					'''simple docstring'''
					__UpperCAmelCase      :					Optional[Any]   =   self.image_processing_class(**self.image_processor_dict )
					# create random numpy tensors
					__UpperCAmelCase      :					int   =   prepare_image_inputs(self.image_processor_tester       ,				equal_resolution=UpperCAmelCase__       ,				numpify=UpperCAmelCase__ )
					for image in image_inputs:
								self.assertIsInstance(UpperCAmelCase__       ,				np.ndarray )

					# Test not batched input
					__UpperCAmelCase      :					List[str]   =   image_processing(image_inputs[0]       ,				return_tensors="""pt""" ).pixel_values

					__UpperCAmelCase ,     __UpperCAmelCase      :					str   =   self.image_processor_tester.get_expected_values(UpperCAmelCase__ )

					self.assertEqual(
					    encoded_images.shape       ,				(1, self.image_processor_tester.num_channels, expected_height, expected_width)       ,				)

					# Test batched
					__UpperCAmelCase      :					int   =   image_processing(UpperCAmelCase__       ,				return_tensors="""pt""" ).pixel_values

					__UpperCAmelCase ,     __UpperCAmelCase      :					List[Any]   =   self.image_processor_tester.get_expected_values(UpperCAmelCase__       ,				batched=UpperCAmelCase__ )

					self.assertEqual(
					    encoded_images.shape       ,				(
					        self.image_processor_tester.batch_size,
					        self.image_processor_tester.num_channels,
					        expected_height,
					        expected_width,
					    )       ,				)





		def 							__A				(   self )				->   Tuple:

					'''simple docstring'''
					__UpperCAmelCase      :					List[Any]   =   self.image_processing_class(**self.image_processor_dict )
					# create random PyTorch tensors
					__UpperCAmelCase      :					List[str]   =   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      :					int   =   image_processing(image_inputs[0]       ,				return_tensors="""pt""" ).pixel_values

					__UpperCAmelCase ,     __UpperCAmelCase      :					int   =   self.image_processor_tester.get_expected_values(UpperCAmelCase__ )

					self.assertEqual(
					    encoded_images.shape       ,				(1, self.image_processor_tester.num_channels, expected_height, expected_width)       ,				)

					# Test batched
					__UpperCAmelCase      :					List[Any]   =   image_processing(UpperCAmelCase__       ,				return_tensors="""pt""" ).pixel_values

					__UpperCAmelCase ,     __UpperCAmelCase      :					List[str]   =   self.image_processor_tester.get_expected_values(UpperCAmelCase__       ,				batched=UpperCAmelCase__ )

					self.assertEqual(
					    encoded_images.shape       ,				(
					        self.image_processor_tester.batch_size,
					        self.image_processor_tester.num_channels,
					        expected_height,
					        expected_width,
					    )       ,				)





		@slow
		def 							__A				(   self )				->   List[str]:

					'''simple docstring'''
					__UpperCAmelCase      :					Dict   =   Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
					with open("""./tests/fixtures/tests_samples/COCO/coco_annotations.txt"""       ,				"""r""" ) as f:
								__UpperCAmelCase      :					Any   =   json.loads(f.read() )

					__UpperCAmelCase      :					str   =   {"""image_id""": 39_769, """annotations""": target}

					# encode them
					__UpperCAmelCase      :					List[str]   =   DeformableDetrImageProcessor()
					__UpperCAmelCase      :					List[Any]   =   image_processing(images=UpperCAmelCase__       ,				annotations=UpperCAmelCase__       ,				return_tensors="""pt""" )

					# verify pixel values
					__UpperCAmelCase      :					Any   =   torch.Size([1, 3, 800, 1_066] )
					self.assertEqual(encoding["""pixel_values"""].shape       ,				UpperCAmelCase__ )

					__UpperCAmelCase      :					Dict   =   torch.tensor([0.2796, 0.3138, 0.3481] )
					self.assertTrue(torch.allclose(encoding["""pixel_values"""][0, 0, 0, :3]       ,				UpperCAmelCase__       ,				atol=1E-4 ) )

					# verify area
					__UpperCAmelCase      :					str   =   torch.tensor([5887.9600, 1_1250.2061, 48_9353.8438, 83_7122.7500, 14_7967.5156, 16_5732.3438] )
					self.assertTrue(torch.allclose(encoding["""labels"""][0]["""area"""]       ,				UpperCAmelCase__ ) )
					# verify boxes
					__UpperCAmelCase      :					str   =   torch.Size([6, 4] )
					self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape       ,				UpperCAmelCase__ )
					__UpperCAmelCase      :					Optional[Any]   =   torch.tensor([0.5503, 0.2765, 0.0604, 0.2215] )
					self.assertTrue(torch.allclose(encoding["""labels"""][0]["""boxes"""][0]       ,				UpperCAmelCase__       ,				atol=1E-3 ) )
					# verify image_id
					__UpperCAmelCase      :					Optional[Any]   =   torch.tensor([39_769] )
					self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""]       ,				UpperCAmelCase__ ) )
					# verify is_crowd
					__UpperCAmelCase      :					Optional[Any]   =   torch.tensor([0, 0, 0, 0, 0, 0] )
					self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""]       ,				UpperCAmelCase__ ) )
					# verify class_labels
					__UpperCAmelCase      :					Dict   =   torch.tensor([75, 75, 63, 65, 17, 17] )
					self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""]       ,				UpperCAmelCase__ ) )
					# verify orig_size
					__UpperCAmelCase      :					Optional[Any]   =   torch.tensor([480, 640] )
					self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""]       ,				UpperCAmelCase__ ) )
					# verify size
					__UpperCAmelCase      :					str   =   torch.tensor([800, 1_066] )
					self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""]       ,				UpperCAmelCase__ ) )





		@slow
		def 							__A				(   self )				->   Optional[int]:

					'''simple docstring'''
					__UpperCAmelCase      :					List[str]   =   Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
					with open("""./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt"""       ,				"""r""" ) as f:
								__UpperCAmelCase      :					List[str]   =   json.loads(f.read() )

					__UpperCAmelCase      :					str   =   {"""file_name""": """000000039769.png""", """image_id""": 39_769, """segments_info""": target}

					__UpperCAmelCase      :					Optional[Any]   =   pathlib.Path("""./tests/fixtures/tests_samples/COCO/coco_panoptic""" )

					# encode them
					__UpperCAmelCase      :					Dict   =   DeformableDetrImageProcessor(format="""coco_panoptic""" )
					__UpperCAmelCase      :					List[str]   =   image_processing(images=UpperCAmelCase__       ,				annotations=UpperCAmelCase__       ,				masks_path=UpperCAmelCase__       ,				return_tensors="""pt""" )

					# verify pixel values
					__UpperCAmelCase      :					Any   =   torch.Size([1, 3, 800, 1_066] )
					self.assertEqual(encoding["""pixel_values"""].shape       ,				UpperCAmelCase__ )

					__UpperCAmelCase      :					int   =   torch.tensor([0.2796, 0.3138, 0.3481] )
					self.assertTrue(torch.allclose(encoding["""pixel_values"""][0, 0, 0, :3]       ,				UpperCAmelCase__       ,				atol=1E-4 ) )

					# verify area
					__UpperCAmelCase      :					Optional[Any]   =   torch.tensor([14_7979.6875, 16_5527.0469, 48_4638.5938, 1_1292.9375, 5879.6562, 7634.1147] )
					self.assertTrue(torch.allclose(encoding["""labels"""][0]["""area"""]       ,				UpperCAmelCase__ ) )
					# verify boxes
					__UpperCAmelCase      :					Tuple   =   torch.Size([6, 4] )
					self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape       ,				UpperCAmelCase__ )
					__UpperCAmelCase      :					Dict   =   torch.tensor([0.2625, 0.5437, 0.4688, 0.8625] )
					self.assertTrue(torch.allclose(encoding["""labels"""][0]["""boxes"""][0]       ,				UpperCAmelCase__       ,				atol=1E-3 ) )
					# verify image_id
					__UpperCAmelCase      :					List[Any]   =   torch.tensor([39_769] )
					self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""]       ,				UpperCAmelCase__ ) )
					# verify is_crowd
					__UpperCAmelCase      :					str   =   torch.tensor([0, 0, 0, 0, 0, 0] )
					self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""]       ,				UpperCAmelCase__ ) )
					# verify class_labels
					__UpperCAmelCase      :					Tuple   =   torch.tensor([17, 17, 63, 75, 75, 93] )
					self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""]       ,				UpperCAmelCase__ ) )
					# verify masks
					__UpperCAmelCase      :					List[Any]   =   822_873
					self.assertEqual(encoding["""labels"""][0]["""masks"""].sum().item()       ,				UpperCAmelCase__ )
					# verify orig_size
					__UpperCAmelCase      :					List[str]   =   torch.tensor([480, 640] )
					self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""]       ,				UpperCAmelCase__ ) )
					# verify size
					__UpperCAmelCase      :					Optional[int]   =   torch.tensor([800, 1_066] )
					self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""]       ,				UpperCAmelCase__ ) )










import requests
from bsa import BeautifulSoup







def SCREAMING_SNAKE_CASE				(       lowercase_ ,				lowercase_						)   ->       str:



       """simple docstring"""

       A__       =    BeautifulSoup(requests.get(lowercase_ ,				params=lowercase_						).content ,				'''html.parser'''						)
       A__       =    soup.find('''div''' ,				attrs={'''class''': '''gs_ri'''}						)
       A__       =    div.find('''div''' ,				attrs={'''class''': '''gs_fl'''}						).find_all('''a'''						)
       return anchors[2].get_text()


if __name__ == "__main__":
  _lowerCamelCase	:		Optional[Any]							       =   {
      """title""": (
          """Precisely geometry controlled microsupercapacitors for ultrahigh areal """
          """capacitance, volumetric capacitance, and energy density"""
      ),
      """journal""": """Chem. Mater.""",
      """volume""": 30,
      """pages""": """3979-3990""",
      """year""": 2018,
      """hl""": """en""",
  }
  print(get_citation("""https://scholar.google.com/scholar_lookup""", params=params))


