

'''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 DetrImageProcessor





class 		__SCREAMING_SNAKE_CASE							(			unittest.TestCase      ):







				def __init__(       self     :		Optional[int]		,					UpperCAmelCase__     :		Dict		,					UpperCAmelCase__     :		Optional[int]=7		,					UpperCAmelCase__     :		str=3		,					UpperCAmelCase__     :		str=30		,					UpperCAmelCase__     :		Tuple=400		,					UpperCAmelCase__     :		Tuple=True		,					UpperCAmelCase__     :		int=None		,					UpperCAmelCase__     :		Any=True		,					UpperCAmelCase__     :		Optional[int]=1 / 255		,					UpperCAmelCase__     :		Optional[Any]=True		,					UpperCAmelCase__     :		int=[0.5, 0.5, 0.5]		,					UpperCAmelCase__     :		Union[str, Any]=[0.5, 0.5, 0.5]		,					UpperCAmelCase__     :		Any=True		,					):
										'''simple docstring'''
										# by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
										lowercase :							Union[str, Any]    		=size if size is not None else {'''shortest_edge''': 18, '''longest_edge''': 1333}
										lowercase :							List[Any]    		=parent
										lowercase :							List[str]    		=batch_size
										lowercase :							Any    		=num_channels
										lowercase :							Tuple    		=min_resolution
										lowercase :							Union[str, Any]    		=max_resolution
										lowercase :							Tuple    		=do_resize
										lowercase :							Optional[Any]    		=size
										lowercase :							List[Any]    		=do_rescale
										lowercase :							Union[str, Any]    		=rescale_factor
										lowercase :							Dict    		=do_normalize
										lowercase :							int    		=image_mean
										lowercase :							Any    		=image_std
										lowercase :							Union[str, Any]    		=do_pad







				def  lowerCamelCase_	(       self     :		Any   ):
										'''simple docstring'''
										return {
										    "do_resize": self.do_resize,
										    "size": self.size,
										    "do_rescale": self.do_rescale,
										    "rescale_factor": self.rescale_factor,
										    "do_normalize": self.do_normalize,
										    "image_mean": self.image_mean,
										    "image_std": self.image_std,
										    "do_pad": self.do_pad,
										}







				def  lowerCamelCase_	(       self     :		Union[str, Any]		,					UpperCAmelCase__     :		List[Any]		,					UpperCAmelCase__     :		Any=False   ):
										'''simple docstring'''
										if not batched:
																lowercase :							List[Any]    		=image_inputs[0]
																if isinstance(UpperCAmelCase__		,					Image.Image   ):
																						lowercase		,	lowercase :							Dict    		=image.size
																else:
																						lowercase		,	lowercase :							Optional[int]    		=image.shape[1], image.shape[2]
																if w < h:
																						lowercase :							int    		=int(self.size['''shortest_edge'''] * h / w   )
																						lowercase :							Any    		=self.size['''shortest_edge''']
																elif w > h:
																						lowercase :							str    		=self.size['''shortest_edge''']
																						lowercase :							List[str]    		=int(self.size['''shortest_edge'''] * w / h   )
																else:
																						lowercase :							List[Any]    		=self.size['''shortest_edge''']
																						lowercase :							List[Any]    		=self.size['''shortest_edge''']

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

										return expected_height, expected_width





@require_torch
@require_vision
class 		__SCREAMING_SNAKE_CASE							(			lowercase__						,							unittest.TestCase      ):
				lowerCamelCase_		       =    DetrImageProcessor if is_vision_available() else None







				def  lowerCamelCase_	(       self     :		str   ):
										'''simple docstring'''
										lowercase :							Any    		=DetrImageProcessingTester(self   )







				@property
				def  lowerCamelCase_	(       self     :		str   ):
										'''simple docstring'''
										return self.image_processor_tester.prepare_image_processor_dict()







				def  lowerCamelCase_	(       self     :		Dict   ):
										'''simple docstring'''
										lowercase :							Optional[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_rescale'''   )   )
										self.assertTrue(hasattr(UpperCAmelCase__		,					'''rescale_factor'''   )   )
										self.assertTrue(hasattr(UpperCAmelCase__		,					'''do_resize'''   )   )
										self.assertTrue(hasattr(UpperCAmelCase__		,					'''size'''   )   )
										self.assertTrue(hasattr(UpperCAmelCase__		,					'''do_pad'''   )   )







				def  lowerCamelCase_	(       self     :		int   ):
										'''simple docstring'''
										lowercase :							str    		=self.image_processing_class.from_dict(self.image_processor_dict   )
										self.assertEqual(image_processor.size		,					{'''shortest_edge''': 18, '''longest_edge''': 1333}   )
										self.assertEqual(image_processor.do_pad		,					UpperCAmelCase__   )

										lowercase :							Optional[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  lowerCamelCase_	(       self     :		int   ):
										'''simple docstring'''
										pass







				def  lowerCamelCase_	(       self     :		Union[str, Any]   ):
										'''simple docstring'''
										# Initialize image_processing
										lowercase :							int    		=self.image_processing_class(**self.image_processor_dict   )
										# create random PIL images
										lowercase :							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
										lowercase :							Tuple    		=image_processing(image_inputs[0]		,					return_tensors='''pt'''   ).pixel_values

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

										lowercase :							Union[str, Any]    		=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  lowerCamelCase_	(       self     :		Union[str, Any]   ):
										'''simple docstring'''
										# Initialize image_processing
										lowercase :							str    		=self.image_processing_class(**self.image_processor_dict   )
										# create random numpy tensors
										lowercase :							Tuple    		=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
										lowercase :							Any    		=image_processing(image_inputs[0]		,					return_tensors='''pt'''   ).pixel_values

										lowercase		,	lowercase :							Optional[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
										lowercase :							List[Any]    		=image_processing(UpperCAmelCase__		,					return_tensors='''pt'''   ).pixel_values

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

										lowercase		,	lowercase :							Optional[Any]    		=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
										lowercase :							Dict    		=image_processing(UpperCAmelCase__		,					return_tensors='''pt'''   ).pixel_values

										lowercase		,	lowercase :							Optional[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,
										    )		,					)







				@slow
				def  lowerCamelCase_	(       self     :		Optional[Any]   ):
										'''simple docstring'''
										# prepare image and target
										lowercase :							Union[str, Any]    		=Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''   )
										with open('''./tests/fixtures/tests_samples/COCO/coco_annotations.txt'''		,					'''r'''   ) as f:
																lowercase :							str    		=json.loads(f.read()   )

										lowercase :							Dict    		={'''image_id''': 39769, '''annotations''': target}

										# encode them
										lowercase :							Union[str, Any]    		=DetrImageProcessor.from_pretrained('''facebook/detr-resnet-50'''   )
										lowercase :							Optional[int]    		=image_processing(images=UpperCAmelCase__		,					annotations=UpperCAmelCase__		,					return_tensors='''pt'''   )

										# verify pixel values
										lowercase :							int    		=torch.Size([1, 3, 800, 1066]   )
										self.assertEqual(encoding['''pixel_values'''].shape		,					UpperCAmelCase__   )

										lowercase :							List[str]    		=torch.tensor([0.27_96, 0.31_38, 0.34_81]   )
										self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3]		,					UpperCAmelCase__		,					atol=1E-4   )   )

										# verify area
										lowercase :							Any    		=torch.tensor([58_87.96_00, 1_12_50.20_61, 48_93_53.84_38, 83_71_22.75_00, 14_79_67.51_56, 16_57_32.34_38]   )
										self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area''']		,					UpperCAmelCase__   )   )
										# verify boxes
										lowercase :							Any    		=torch.Size([6, 4]   )
										self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape		,					UpperCAmelCase__   )
										lowercase :							List[str]    		=torch.tensor([0.55_03, 0.27_65, 0.06_04, 0.22_15]   )
										self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0]		,					UpperCAmelCase__		,					atol=1E-3   )   )
										# verify image_id
										lowercase :							int    		=torch.tensor([39769]   )
										self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']		,					UpperCAmelCase__   )   )
										# verify is_crowd
										lowercase :							Optional[Any]    		=torch.tensor([0, 0, 0, 0, 0, 0]   )
										self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']		,					UpperCAmelCase__   )   )
										# verify class_labels
										lowercase :							Union[str, Any]    		=torch.tensor([75, 75, 63, 65, 17, 17]   )
										self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']		,					UpperCAmelCase__   )   )
										# verify orig_size
										lowercase :							Optional[int]    		=torch.tensor([480, 640]   )
										self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']		,					UpperCAmelCase__   )   )
										# verify size
										lowercase :							Any    		=torch.tensor([800, 1066]   )
										self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']		,					UpperCAmelCase__   )   )







				@slow
				def  lowerCamelCase_	(       self     :		Dict   ):
										'''simple docstring'''
										# prepare image, target and masks_path
										lowercase :							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:
																lowercase :							Tuple    		=json.loads(f.read()   )

										lowercase :							Dict    		={'''file_name''': '''000000039769.png''', '''image_id''': 39769, '''segments_info''': target}

										lowercase :							Any    		=pathlib.Path('''./tests/fixtures/tests_samples/COCO/coco_panoptic'''   )

										# encode them
										lowercase :							Optional[Any]    		=DetrImageProcessor.from_pretrained('''facebook/detr-resnet-50-panoptic'''   )
										lowercase :							List[str]    		=image_processing(images=UpperCAmelCase__		,					annotations=UpperCAmelCase__		,					masks_path=UpperCAmelCase__		,					return_tensors='''pt'''   )

										# verify pixel values
										lowercase :							Union[str, Any]    		=torch.Size([1, 3, 800, 1066]   )
										self.assertEqual(encoding['''pixel_values'''].shape		,					UpperCAmelCase__   )

										lowercase :							int    		=torch.tensor([0.27_96, 0.31_38, 0.34_81]   )
										self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3]		,					UpperCAmelCase__		,					atol=1E-4   )   )

										# verify area
										lowercase :							Any    		=torch.tensor([14_79_79.68_75, 16_55_27.04_69, 48_46_38.59_38, 1_12_92.93_75, 58_79.65_62, 76_34.11_47]   )
										self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area''']		,					UpperCAmelCase__   )   )
										# verify boxes
										lowercase :							int    		=torch.Size([6, 4]   )
										self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape		,					UpperCAmelCase__   )
										lowercase :							int    		=torch.tensor([0.26_25, 0.54_37, 0.46_88, 0.86_25]   )
										self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0]		,					UpperCAmelCase__		,					atol=1E-3   )   )
										# verify image_id
										lowercase :							List[Any]    		=torch.tensor([39769]   )
										self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']		,					UpperCAmelCase__   )   )
										# verify is_crowd
										lowercase :							Optional[int]    		=torch.tensor([0, 0, 0, 0, 0, 0]   )
										self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']		,					UpperCAmelCase__   )   )
										# verify class_labels
										lowercase :							Optional[Any]    		=torch.tensor([17, 17, 63, 75, 75, 93]   )
										self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']		,					UpperCAmelCase__   )   )
										# verify masks
										lowercase :							str    		=822873
										self.assertEqual(encoding['''labels'''][0]['''masks'''].sum().item()		,					UpperCAmelCase__   )
										# verify orig_size
										lowercase :							Any    		=torch.tensor([480, 640]   )
										self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']		,					UpperCAmelCase__   )   )
										# verify size
										lowercase :							str    		=torch.tensor([800, 1066]   )
										self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']		,					UpperCAmelCase__   )   )











"""simple docstring"""






import qiskit







def 	_snake_case     (							lowercase__ = 2						):
				_lowerCamelCase	:     Optional[Any]       =       qubits

				# Using Aer's simulator
				_lowerCamelCase	:     Dict       =       qiskit.Aer.get_backend('aer_simulator'						)

				# Creating a Quantum Circuit acting on the q register
				_lowerCamelCase	:     Dict       =       qiskit.QuantumCircuit(lowercase__				,       lowercase__						)

				# Adding a H gate on qubit 0 (now q0 in superposition)
				circuit.h(0						)

				for i in range(1				,       lowercase__						):
								# Adding CX (CNOT) gate
								circuit.cx(i - 1				,       lowercase__						)

				# Mapping the quantum measurement to the classical bits
				circuit.measure(list(range(lowercase__						)						)				,       list(range(lowercase__						)						)						)

				# Now measuring any one qubit would affect other qubits to collapse
				# their super position and have same state as the measured one.

				# Executing the circuit on the simulator
				_lowerCamelCase	:     Optional[Any]       =       qiskit.execute(lowercase__				,       lowercase__				,       shots=1000						)

				return job.result().get_counts(lowercase__						)


if __name__ == "__main__":
			print(F"Total count for various states are: {quantum_entanglement(3)}")