

"""simple docstring"""




import hashlib
import unittest

from transformers import MODEL_FOR_DEPTH_ESTIMATION_MAPPING, is_torch_available, is_vision_available
from transformers.pipelines import DepthEstimationPipeline, pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_timm,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_torch_available():
				import torch

if is_vision_available():
				from PIL import Image
else:
				class 			_UpperCAmelCase					:



					@staticmethod
					def     a   (   *_lowercase			:    List[Any]       ,  **_lowercase			:    Any		):
						pass





def 	lowercase__		(    snake_case_					:List[Any]  ):
	__UpperCAmelCase							=      hashlib.mda(image.tobytes()  )
	return m.hexdigest()




@is_pipeline_test
@require_vision
@require_timm
@require_torch
class 			_UpperCAmelCase					(				unittest.TestCase    ):
	a__						:      Tuple						       = MODEL_FOR_DEPTH_ESTIMATION_MAPPING



	def     a   (   self			:    str       ,  _lowercase			:    str       ,  _lowercase			:    Union[str, Any]       ,  _lowercase			:    Dict		):
		__UpperCAmelCase							=      DepthEstimationPipeline(model=UpperCamelCase__       ,  image_processor=UpperCamelCase__		)
		return depth_estimator, [
		    "./tests/fixtures/tests_samples/COCO/000000039769.png",
		    "./tests/fixtures/tests_samples/COCO/000000039769.png",
		]



	def     a   (   self			:    Optional[Any]       ,  _lowercase			:    Union[str, Any]       ,  _lowercase			:    str		):
		__UpperCAmelCase							=      depth_estimator('''./tests/fixtures/tests_samples/COCO/000000039769.png'''		)
		self.assertEqual({'''predicted_depth''': ANY(torch.Tensor		), '''depth''': ANY(Image.Image		)}       ,  UpperCamelCase__		)
		import datasets

		__UpperCAmelCase							=      datasets.load_dataset('''hf-internal-testing/fixtures_image_utils'''       ,  '''image'''       ,  split='''test'''		)
		__UpperCAmelCase							=      depth_estimator(
		    [
		        Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''		),
		        '''http://images.cocodataset.org/val2017/000000039769.jpg''',
		        # RGBA
		        dataset[0]['''file'''],
		        # LA
		        dataset[1]['''file'''],
		        # L
		        dataset[2]['''file'''],
		    ]		)
		self.assertEqual(
		    [
		        {'''predicted_depth''': ANY(torch.Tensor		), '''depth''': ANY(Image.Image		)},
		        {'''predicted_depth''': ANY(torch.Tensor		), '''depth''': ANY(Image.Image		)},
		        {'''predicted_depth''': ANY(torch.Tensor		), '''depth''': ANY(Image.Image		)},
		        {'''predicted_depth''': ANY(torch.Tensor		), '''depth''': ANY(Image.Image		)},
		        {'''predicted_depth''': ANY(torch.Tensor		), '''depth''': ANY(Image.Image		)},
		    ]       ,  UpperCamelCase__       ,  )



	@require_tf
	@unittest.skip('''Depth estimation is not implemented in TF'''		)
	def     a   (   self			:    Union[str, Any]		):
		pass



	@slow
	@require_torch
	def     a   (   self			:    str		):
		__UpperCAmelCase							=      '''Intel/dpt-large'''
		__UpperCAmelCase							=      pipeline('''depth-estimation'''       ,  model=UpperCamelCase__		)
		__UpperCAmelCase							=      depth_estimator('''http://images.cocodataset.org/val2017/000000039769.jpg'''		)
		__UpperCAmelCase							=      hashimage(outputs['''depth''']		)

		# This seems flaky.
		# self.assertEqual(outputs["depth"], "1a39394e282e9f3b0741a90b9f108977")
		self.assertEqual(nested_simplify(outputs['''predicted_depth'''].max().item()		)       ,  29.304		)
		self.assertEqual(nested_simplify(outputs['''predicted_depth'''].min().item()		)       ,  2.662		)



	@require_torch
	def     a   (   self			:    Optional[Any]		):
		self.skipTest('''There is not hf-internal-testing tiny model for either GLPN nor DPT'''		)






import math

import numpy as np
import qiskit
from qiskit import Aer, ClassicalRegister, QuantumCircuit, QuantumRegister, execute





def __UpperCamelCase			(       _A = 3  ):
						if isinstance(_A ,  _A  ):
												raise TypeError('''number of qubits must be a integer.'''  )
						if number_of_qubits <= 0:
												raise ValueError('''number of qubits must be > 0.'''  )
						if math.floor(_A  ) != number_of_qubits:
												raise ValueError('''number of qubits must be exact integer.'''  )
						if number_of_qubits > 10:
												raise ValueError('''number of qubits too large to simulate(>10).'''  )

						lowerCAmelCase_      			=  QuantumRegister(_A ,  '''qr'''  )
						lowerCAmelCase_      			=  ClassicalRegister(_A ,  '''cr'''  )

						lowerCAmelCase_      			=  QuantumCircuit(_A ,  _A  )

						lowerCAmelCase_      			=  number_of_qubits

						for i in range(_A  ):
												quantum_circuit.h(number_of_qubits - i - 1  )
												counter -= 1
												for j in range(_A  ):
																		quantum_circuit.cp(np.pi / 2 ** (counter - j) ,  _A ,  _A  )

						for k in range(number_of_qubits // 2  ):
												quantum_circuit.swap(_A ,  number_of_qubits - k - 1  )

						# measure all the qubits
						quantum_circuit.measure(_A ,  _A  )
						# simulate with 10000 shots
						lowerCAmelCase_      			=  Aer.get_backend('''qasm_simulator'''  )
						lowerCAmelCase_      			=  execute(_A ,  _A ,  shots=10000  )

						return job.result().get_counts(_A  )


if __name__ == "__main__":
						print(
						    f"Total count for quantum fourier transform state is: \
    {quantum_fourier_transform(3)}"
						)




