




"""simple docstring"""




import unittest

from transformers import MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING, is_vision_available
from transformers.pipelines import pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_vision_available():
			from PIL import Image
else:

			class   UpperCAmelCase_    :
					@staticmethod
					def 	_lowerCamelCase							(						*UpperCamelCase_				,						**UpperCamelCase_       )						->       List[Any]:
								pass

@is_pipeline_test
@require_torch
@require_vision
class   UpperCAmelCase_    (			unittest.TestCase		):
		UpperCamelCase							=MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING
		def 	_lowerCamelCase							(						self				,						UpperCamelCase_				,						UpperCamelCase_				,						UpperCamelCase_       )						->       int:
					__lowercase			:  Optional[int]								=       pipeline('''visual-question-answering'''				,						model='''hf-internal-testing/tiny-vilt-random-vqa'''       )
					__lowercase			:  Dict								=       [
					    {
					        '''image''': Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''       ),
					        '''question''': '''How many cats are there?''',
					    },
					    {
					        '''image''': '''./tests/fixtures/tests_samples/COCO/000000039769.png''',
					        '''question''': '''How many cats are there?''',
					    },
					]
					return vqa_pipeline, examples
		def 	_lowerCamelCase							(						self				,						UpperCamelCase_				,						UpperCamelCase_       )						->       Tuple:
					__lowercase			:  Dict								=       vqa_pipeline(__lowerCAmelCase				,						top_k=1       )
					self.assertEqual(
					    __lowerCAmelCase				,						[
					        [{'''score''': ANY(__lowerCAmelCase       ), '''answer''': ANY(__lowerCAmelCase       )}],
					        [{'''score''': ANY(__lowerCAmelCase       ), '''answer''': ANY(__lowerCAmelCase       )}],
					    ]				,						)
		@require_torch
		def 	_lowerCamelCase							(						self       )						->       Any:
					__lowercase			:  List[Any]								=       pipeline('''visual-question-answering'''				,						model='''hf-internal-testing/tiny-vilt-random-vqa'''       )
					__lowercase			:  Optional[Any]								=       '''./tests/fixtures/tests_samples/COCO/000000039769.png'''
					__lowercase			:  Tuple								=       '''How many cats are there?'''

					__lowercase			:  Optional[Any]								=       vqa_pipeline(image=__lowerCAmelCase				,						question='''How many cats are there?'''				,						top_k=2       )
					self.assertEqual(
					    __lowerCAmelCase				,						[{'''score''': ANY(__lowerCAmelCase       ), '''answer''': ANY(__lowerCAmelCase       )}, {'''score''': ANY(__lowerCAmelCase       ), '''answer''': ANY(__lowerCAmelCase       )}]       )

					__lowercase			:  List[Any]								=       vqa_pipeline({'''image''': image, '''question''': question}				,						top_k=2       )
					self.assertEqual(
					    __lowerCAmelCase				,						[{'''score''': ANY(__lowerCAmelCase       ), '''answer''': ANY(__lowerCAmelCase       )}, {'''score''': ANY(__lowerCAmelCase       ), '''answer''': ANY(__lowerCAmelCase       )}]       )
		@slow
		@require_torch
		def 	_lowerCamelCase							(						self       )						->       List[str]:
					__lowercase			:  str								=       pipeline('''visual-question-answering'''				,						model='''dandelin/vilt-b32-finetuned-vqa'''       )
					__lowercase			:  List[str]								=       '''./tests/fixtures/tests_samples/COCO/000000039769.png'''
					__lowercase			:  Union[str, Any]								=       '''How many cats are there?'''

					__lowercase			:  Dict								=       vqa_pipeline(image=__lowerCAmelCase				,						question=__lowerCAmelCase				,						top_k=2       )
					self.assertEqual(
					    nested_simplify(__lowerCAmelCase				,						decimals=4       )				,						[{'''score''': 0.8_7_9_9, '''answer''': '''2'''}, {'''score''': 0.2_9_6, '''answer''': '''1'''}]       )

					__lowercase			:  Tuple								=       vqa_pipeline({'''image''': image, '''question''': question}				,						top_k=2       )
					self.assertEqual(
					    nested_simplify(__lowerCAmelCase				,						decimals=4       )				,						[{'''score''': 0.8_7_9_9, '''answer''': '''2'''}, {'''score''': 0.2_9_6, '''answer''': '''1'''}]       )

					__lowercase			:  Any								=       vqa_pipeline(
					    [{'''image''': image, '''question''': question}, {'''image''': image, '''question''': question}]				,						top_k=2       )
					self.assertEqual(
					    nested_simplify(__lowerCAmelCase				,						decimals=4       )				,						[[{'''score''': 0.8_7_9_9, '''answer''': '''2'''}, {'''score''': 0.2_9_6, '''answer''': '''1'''}]] * 2				,						)
		@require_tf
		@unittest.skip('''Visual question answering not implemented in TF'''       )
		def 	_lowerCamelCase							(						self       )						->       List[Any]:
					pass








import math

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

def 	SCREAMING_SNAKE_CASE_							(	_snake_case	:int = 3				)  ->     qiskit.result.counts.Counts:
   if isinstance(_snake_case	,   _snake_case				):
      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(_snake_case				) != 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).'''				)

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

   _A      =       QuantumCircuit(_snake_case	,   _snake_case				)

   _A      =       number_of_qubits

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

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

   # measure all the qubits
   quantum_circuit.measure(_snake_case	,   _snake_case				)
   # simulate with 10000 shots
   _A      =       Aer.get_backend('''qasm_simulator'''				)
   _A      =       execute(_snake_case	,   _snake_case	,   shots=10_000				)

   return job.result().get_counts(_snake_case				)


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



