



import json
import os
import tempfile

from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available


if is_torch_available():
							import numpy as np
							import torch

if is_vision_available():
							from PIL import Image







def 							A     (						__UpperCAmelCase				,   __UpperCAmelCase=False				,   __UpperCAmelCase=False				,   __UpperCAmelCase=False      )			->			List[str]:



						'''simple docstring'''






						assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

						UpperCAmelCase_					=	[]
						for i in range(image_processor_tester.batch_size      ):
												if equal_resolution:
																		UpperCAmelCase_					=	UpperCAmelCase_					=	image_processor_tester.max_resolution
												else:
																		# To avoid getting image width/height 0
																		UpperCAmelCase_					=	image_processor_tester.min_resolution
																		if getattr(__UpperCAmelCase				,   '''size_divisor'''				,   __UpperCAmelCase      ):
																								# If `size_divisor` is defined, the image needs to have width/size >= `size_divisor`
																								UpperCAmelCase_					=	max(image_processor_tester.size_divisor				,   __UpperCAmelCase      )
																		UpperCAmelCase_				, UpperCAmelCase_					=	np.random.choice(np.arange(__UpperCAmelCase				,   image_processor_tester.max_resolution      )				,   2      )
												image_inputs.append(
												    np.random.randint(255				,   size=(image_processor_tester.num_channels, width, height)				,   dtype=np.uinta      )      )

						if not numpify and not torchify:
												# PIL expects the channel dimension as last dimension
												UpperCAmelCase_					=	[Image.fromarray(np.moveaxis(__UpperCAmelCase				,   0				,   -1      )      ) for image in image_inputs]

						if torchify:
												UpperCAmelCase_					=	[torch.from_numpy(__UpperCAmelCase      ) for image in image_inputs]

						return image_inputs







def 							A     (						__UpperCAmelCase				,   __UpperCAmelCase=10				,   __UpperCAmelCase=10				,   __UpperCAmelCase=False				,   __UpperCAmelCase=False      )			->			List[str]:



						'''simple docstring'''






						UpperCAmelCase_					=	[]
						for i in range(image_processor_tester.num_frames      ):
												video.append(np.random.randint(255				,   size=(image_processor_tester.num_channels, width, height)				,   dtype=np.uinta      )      )

						if not numpify and not torchify:
												# PIL expects the channel dimension as last dimension
												UpperCAmelCase_					=	[Image.fromarray(np.moveaxis(__UpperCAmelCase				,   0				,   -1      )      ) for frame in video]

						if torchify:
												UpperCAmelCase_					=	[torch.from_numpy(__UpperCAmelCase      ) for frame in video]

						return video







def 							A     (						__UpperCAmelCase				,   __UpperCAmelCase=False				,   __UpperCAmelCase=False				,   __UpperCAmelCase=False      )			->			Dict:



						'''simple docstring'''






						assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

						UpperCAmelCase_					=	[]
						for i in range(image_processor_tester.batch_size      ):
												if equal_resolution:
																		UpperCAmelCase_					=	UpperCAmelCase_					=	image_processor_tester.max_resolution
												else:
																		UpperCAmelCase_				, UpperCAmelCase_					=	np.random.choice(
																		    np.arange(image_processor_tester.min_resolution				,   image_processor_tester.max_resolution      )				,   2      )
																		UpperCAmelCase_					=	prepare_video(
																		    image_processor_tester=__UpperCAmelCase				,   width=__UpperCAmelCase				,   height=__UpperCAmelCase				,   numpify=__UpperCAmelCase				,   torchify=__UpperCAmelCase				,   )
												video_inputs.append(__UpperCAmelCase      )

						return video_inputs







class 				a_    :
						UpperCamelCase__ :		Union[str, Any]										=None

						def       __a			(       self      :List[str])						->   Optional[Any]:
												UpperCAmelCase_					=	self.image_processing_class(**self.image_processor_dict)
												UpperCAmelCase_					=	json.loads(image_processor.to_json_string())
												for key, value in self.image_processor_dict.items():
																		self.assertEqual(obj[key]  ,		_lowercase)

						def       __a			(       self      :Optional[int])						->   List[str]:
												UpperCAmelCase_					=	self.image_processing_class(**self.image_processor_dict)

												with tempfile.TemporaryDirectory() as tmpdirname:
																		UpperCAmelCase_					=	os.path.join(_lowercase  ,		'''image_processor.json''')
																		image_processor_first.to_json_file(_lowercase)
																		UpperCAmelCase_					=	self.image_processing_class.from_json_file(_lowercase)

												self.assertEqual(image_processor_second.to_dict()  ,		image_processor_first.to_dict())

						def       __a			(       self      :Optional[int])						->   List[Any]:
												UpperCAmelCase_					=	self.image_processing_class(**self.image_processor_dict)

												with tempfile.TemporaryDirectory() as tmpdirname:
																		UpperCAmelCase_					=	image_processor_first.save_pretrained(_lowercase)[0]
																		check_json_file_has_correct_format(_lowercase)
																		UpperCAmelCase_					=	self.image_processing_class.from_pretrained(_lowercase)

												self.assertEqual(image_processor_second.to_dict()  ,		image_processor_first.to_dict())

						def       __a			(       self      :Any)						->   Optional[Any]:
												UpperCAmelCase_					=	self.image_processing_class()
												self.assertIsNotNone(_lowercase)




						@require_torch
						@require_vision
						def       __a			(       self      :List[str])						->   Union[str, Any]:
												if self.test_cast_dtype is not None:
																		# Initialize image_processor
																		UpperCAmelCase_					=	self.image_processing_class(**self.image_processor_dict)

																		# create random PyTorch tensors
																		UpperCAmelCase_					=	prepare_image_inputs(self.image_processor_tester  ,		equal_resolution=_lowercase  ,		torchify=_lowercase)

																		UpperCAmelCase_					=	image_processor(_lowercase  ,		return_tensors='''pt''')
																		# for layoutLM compatiblity
																		self.assertEqual(encoding.pixel_values.device  ,		torch.device('''cpu'''))
																		self.assertEqual(encoding.pixel_values.dtype  ,		torch.floataa)

																		UpperCAmelCase_					=	image_processor(_lowercase  ,		return_tensors='''pt''').to(torch.floataa)
																		self.assertEqual(encoding.pixel_values.device  ,		torch.device('''cpu'''))
																		self.assertEqual(encoding.pixel_values.dtype  ,		torch.floataa)

																		UpperCAmelCase_					=	image_processor(_lowercase  ,		return_tensors='''pt''').to('''cpu'''  ,		torch.bfloataa)
																		self.assertEqual(encoding.pixel_values.device  ,		torch.device('''cpu'''))
																		self.assertEqual(encoding.pixel_values.dtype  ,		torch.bfloataa)

																		with self.assertRaises(_lowercase):
																								UpperCAmelCase_					=	image_processor(_lowercase  ,		return_tensors='''pt''').to(torch.bfloataa  ,		'''cpu''')

																		# Try with text + image feature
																		UpperCAmelCase_					=	image_processor(_lowercase  ,		return_tensors='''pt''')
																		encoding.update({'''input_ids''': torch.LongTensor([[1, 2, 3], [4, 5, 6]])})
																		UpperCAmelCase_					=	encoding.to(torch.floataa)

																		self.assertEqual(encoding.pixel_values.device  ,		torch.device('''cpu'''))
																		self.assertEqual(encoding.pixel_values.dtype  ,		torch.floataa)
																		self.assertEqual(encoding.input_ids.dtype  ,		torch.long)










import numpy as np
import qiskit







def 							A     (						__UpperCAmelCase				,   __UpperCAmelCase      )			->			qiskit.QuantumCircuit:



						'''simple docstring'''






						UpperCAmelCase_					=	qiskit.QuantumCircuit(num_qubits + 1      )

						# First, let's deal with the case in which oracle is balanced
						if case == "balanced":
												# First generate a random number that tells us which CNOTs to
												# wrap in X-gates:
												UpperCAmelCase_					=	np.random.randint(1				,   2**num_qubits      )
												# Next, format 'b' as a binary string of length 'n', padded with zeros:
												UpperCAmelCase_					=	format(__UpperCAmelCase				,   f"0{num_qubits}b"      )
												# Next, we place the first X-gates. Each digit in our binary string
												# corresponds to a qubit, if the digit is 0, we do nothing, if it's 1
												# we apply an X-gate to that qubit:
												for index, bit in enumerate(__UpperCAmelCase      ):
																		if bit == "1":
																								oracle_qc.x(__UpperCAmelCase      )
        # Do the controlled-NOT gates for each qubit, using the output qubit
        # as the target:
												for index in range(__UpperCAmelCase      ):
																		oracle_qc.cx(__UpperCAmelCase				,   __UpperCAmelCase      )
												# Next, place the final X-gates
												for index, bit in enumerate(__UpperCAmelCase      ):
																		if bit == "1":
																								oracle_qc.x(__UpperCAmelCase      )

    # Case in which oracle is constant
						if case == "constant":
												# First decide what the fixed output of the oracle will be
												# (either always 0 or always 1)
												UpperCAmelCase_					=	np.random.randint(2      )
												if output == 1:
																		oracle_qc.x(__UpperCAmelCase      )

						UpperCAmelCase_					=	oracle_qc.to_gate()
						UpperCAmelCase_					=	'''Oracle'''  # To show when we display the circuit
						return oracle_gate







def 							A     (						__UpperCAmelCase				,   __UpperCAmelCase      )			->			qiskit.QuantumCircuit:



						'''simple docstring'''






						UpperCAmelCase_					=	qiskit.QuantumCircuit(num_qubits + 1				,   __UpperCAmelCase      )
						# Set up the output qubit:
						dj_circuit.x(__UpperCAmelCase      )
						dj_circuit.h(__UpperCAmelCase      )
						# And set up the input register:
						for qubit in range(__UpperCAmelCase      ):
												dj_circuit.h(__UpperCAmelCase      )
						# Let's append the oracle gate to our circuit:
						dj_circuit.append(__UpperCAmelCase				,   range(num_qubits + 1      )      )
						# Finally, perform the H-gates again and measure:
						for qubit in range(__UpperCAmelCase      ):
												dj_circuit.h(__UpperCAmelCase      )

						for i in range(__UpperCAmelCase      ):
												dj_circuit.measure(__UpperCAmelCase				,   __UpperCAmelCase      )

						return dj_circuit







def 							A     (						__UpperCAmelCase				,   __UpperCAmelCase      )			->			qiskit.result.counts.Counts:



						'''simple docstring'''






						UpperCAmelCase_					=	qiskit.Aer.get_backend('''aer_simulator'''      )

						UpperCAmelCase_					=	dj_oracle(__UpperCAmelCase				,   __UpperCAmelCase      )
						UpperCAmelCase_					=	dj_algorithm(__UpperCAmelCase				,   __UpperCAmelCase      )

						# Execute the circuit on the simulator
						UpperCAmelCase_					=	qiskit.execute(__UpperCAmelCase				,   __UpperCAmelCase				,   shots=1000      )

						# Return the histogram data of the results of the experiment.
						return job.result().get_counts(__UpperCAmelCase      )


if __name__ == "__main__":
							print(f"Deutsch Jozsa - Constant Oracle: {deutsch_jozsa('constant', 3)}")
							print(f"Deutsch Jozsa - Balanced Oracle: {deutsch_jozsa('balanced', 3)}")





