





"""simple docstring"""





import unittest

from transformers import MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING, is_vision_available, 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    _lowerCAmelCase  :
         """simple docstring"""




         @staticmethod
         def 	snake_case		(       *__UpperCAmelCase       ,					**__UpperCAmelCase ):



           '''simple docstring'''

           pass






@is_pipeline_test
@require_vision
@require_torch
class    _lowerCAmelCase  (	unittest.TestCase			):
       """simple docstring"""




       __magic_name__	:int						    =   MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING






       def 	snake_case		(       self       ,					__UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase ):



         '''simple docstring'''

         lowerCAmelCase__     :List[Any]											=				pipeline(
             'zero-shot-object-detection'       ,					model='hf-internal-testing/tiny-random-owlvit-object-detection' )

         lowerCAmelCase__     :int											=				[
             {
                 'image': './tests/fixtures/tests_samples/COCO/000000039769.png',
                 'candidate_labels': ['cat', 'remote', 'couch'],
             }
         ]
         return object_detector, examples






       def 	snake_case		(       self       ,					__UpperCAmelCase       ,					__UpperCAmelCase ):



         '''simple docstring'''

         lowerCAmelCase__     :Union[str, Any]											=				object_detector(examples[0]       ,					threshold=0.0 )

         lowerCAmelCase__     :Optional[Any]											=				len(__UpperCAmelCase )
         self.assertGreater(__UpperCAmelCase       ,					0 )
         self.assertEqual(
             __UpperCAmelCase       ,					[
                 {
                     'score': ANY(__UpperCAmelCase ),
                     'label': ANY(__UpperCAmelCase ),
                     'box': {'xmin': ANY(__UpperCAmelCase ), 'ymin': ANY(__UpperCAmelCase ), 'xmax': ANY(__UpperCAmelCase ), 'ymax': ANY(__UpperCAmelCase )},
                 }
                 for i in range(__UpperCAmelCase )
             ]       ,					)






       @require_tf
       @unittest.skip('Zero Shot Object Detection not implemented in TF' )
       def 	snake_case		(       self ):



         '''simple docstring'''

         pass






       @require_torch
       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__     :int											=				pipeline(
             'zero-shot-object-detection'       ,					model='hf-internal-testing/tiny-random-owlvit-object-detection' )

         lowerCAmelCase__     :str											=				object_detector(
             './tests/fixtures/tests_samples/COCO/000000039769.png'       ,					candidate_labels=['cat', 'remote', 'couch']       ,					threshold=0.64       ,					)

         self.assertEqual(
             nested_simplify(__UpperCAmelCase       ,					decimals=4 )       ,					[
                 {'score': 0.72_35, 'label': 'cat', 'box': {'xmin': 2_0_4, 'ymin': 1_6_7, 'xmax': 2_3_2, 'ymax': 1_9_0}},
                 {'score': 0.72_18, 'label': 'remote', 'box': {'xmin': 2_0_4, 'ymin': 1_6_7, 'xmax': 2_3_2, 'ymax': 1_9_0}},
                 {'score': 0.71_84, 'label': 'couch', 'box': {'xmin': 2_0_4, 'ymin': 1_6_7, 'xmax': 2_3_2, 'ymax': 1_9_0}},
                 {'score': 0.67_48, 'label': 'remote', 'box': {'xmin': 5_7_1, 'ymin': 8_3, 'xmax': 5_9_8, 'ymax': 1_0_3}},
                 {'score': 0.66_56, 'label': 'cat', 'box': {'xmin': 5_7_1, 'ymin': 8_3, 'xmax': 5_9_8, 'ymax': 1_0_3}},
                 {'score': 0.66_14, 'label': 'couch', 'box': {'xmin': 5_7_1, 'ymin': 8_3, 'xmax': 5_9_8, 'ymax': 1_0_3}},
                 {'score': 0.64_56, 'label': 'remote', 'box': {'xmin': 4_9_4, 'ymin': 1_0_5, 'xmax': 5_2_1, 'ymax': 1_2_7}},
                 {'score': 0.6_42, 'label': 'remote', 'box': {'xmin': 6_7, 'ymin': 2_7_4, 'xmax': 9_3, 'ymax': 2_9_7}},
                 {'score': 0.64_19, 'label': 'cat', 'box': {'xmin': 4_9_4, 'ymin': 1_0_5, 'xmax': 5_2_1, 'ymax': 1_2_7}},
             ]       ,					)

         lowerCAmelCase__     :Any											=				object_detector(
             [
                 {
                     'image': './tests/fixtures/tests_samples/COCO/000000039769.png',
                     'candidate_labels': ['cat', 'remote', 'couch'],
                 }
             ]       ,					threshold=0.64       ,					)

         self.assertEqual(
             nested_simplify(__UpperCAmelCase       ,					decimals=4 )       ,					[
                 [
                     {'score': 0.72_35, 'label': 'cat', 'box': {'xmin': 2_0_4, 'ymin': 1_6_7, 'xmax': 2_3_2, 'ymax': 1_9_0}},
                     {'score': 0.72_18, 'label': 'remote', 'box': {'xmin': 2_0_4, 'ymin': 1_6_7, 'xmax': 2_3_2, 'ymax': 1_9_0}},
                     {'score': 0.71_84, 'label': 'couch', 'box': {'xmin': 2_0_4, 'ymin': 1_6_7, 'xmax': 2_3_2, 'ymax': 1_9_0}},
                     {'score': 0.67_48, 'label': 'remote', 'box': {'xmin': 5_7_1, 'ymin': 8_3, 'xmax': 5_9_8, 'ymax': 1_0_3}},
                     {'score': 0.66_56, 'label': 'cat', 'box': {'xmin': 5_7_1, 'ymin': 8_3, 'xmax': 5_9_8, 'ymax': 1_0_3}},
                     {'score': 0.66_14, 'label': 'couch', 'box': {'xmin': 5_7_1, 'ymin': 8_3, 'xmax': 5_9_8, 'ymax': 1_0_3}},
                     {'score': 0.64_56, 'label': 'remote', 'box': {'xmin': 4_9_4, 'ymin': 1_0_5, 'xmax': 5_2_1, 'ymax': 1_2_7}},
                     {'score': 0.6_42, 'label': 'remote', 'box': {'xmin': 6_7, 'ymin': 2_7_4, 'xmax': 9_3, 'ymax': 2_9_7}},
                     {'score': 0.64_19, 'label': 'cat', 'box': {'xmin': 4_9_4, 'ymin': 1_0_5, 'xmax': 5_2_1, 'ymax': 1_2_7}},
                 ]
             ]       ,					)






       @require_torch
       @slow
       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__     :int											=				pipeline('zero-shot-object-detection' )

         lowerCAmelCase__     :str											=				object_detector(
             'http://images.cocodataset.org/val2017/000000039769.jpg'       ,					candidate_labels=['cat', 'remote', 'couch']       ,					)
         self.assertEqual(
             nested_simplify(__UpperCAmelCase       ,					decimals=4 )       ,					[
                 {'score': 0.28_68, 'label': 'cat', 'box': {'xmin': 3_2_4, 'ymin': 2_0, 'xmax': 6_4_0, 'ymax': 3_7_3}},
                 {'score': 0.2_77, 'label': 'remote', 'box': {'xmin': 4_0, 'ymin': 7_2, 'xmax': 1_7_7, 'ymax': 1_1_5}},
                 {'score': 0.25_37, 'label': 'cat', 'box': {'xmin': 1, 'ymin': 5_5, 'xmax': 3_1_5, 'ymax': 4_7_2}},
                 {'score': 0.14_74, 'label': 'remote', 'box': {'xmin': 3_3_5, 'ymin': 7_4, 'xmax': 3_7_1, 'ymax': 1_8_7}},
                 {'score': 0.12_08, 'label': 'couch', 'box': {'xmin': 4, 'ymin': 0, 'xmax': 6_4_2, 'ymax': 4_7_6}},
             ]       ,					)

         lowerCAmelCase__     :Any											=				object_detector(
             [
                 {
                     'image': 'http://images.cocodataset.org/val2017/000000039769.jpg',
                     'candidate_labels': ['cat', 'remote', 'couch'],
                 },
                 {
                     'image': 'http://images.cocodataset.org/val2017/000000039769.jpg',
                     'candidate_labels': ['cat', 'remote', 'couch'],
                 },
             ]       ,					)
         self.assertEqual(
             nested_simplify(__UpperCAmelCase       ,					decimals=4 )       ,					[
                 [
                     {'score': 0.28_68, 'label': 'cat', 'box': {'xmin': 3_2_4, 'ymin': 2_0, 'xmax': 6_4_0, 'ymax': 3_7_3}},
                     {'score': 0.2_77, 'label': 'remote', 'box': {'xmin': 4_0, 'ymin': 7_2, 'xmax': 1_7_7, 'ymax': 1_1_5}},
                     {'score': 0.25_37, 'label': 'cat', 'box': {'xmin': 1, 'ymin': 5_5, 'xmax': 3_1_5, 'ymax': 4_7_2}},
                     {'score': 0.14_74, 'label': 'remote', 'box': {'xmin': 3_3_5, 'ymin': 7_4, 'xmax': 3_7_1, 'ymax': 1_8_7}},
                     {'score': 0.12_08, 'label': 'couch', 'box': {'xmin': 4, 'ymin': 0, 'xmax': 6_4_2, 'ymax': 4_7_6}},
                 ],
                 [
                     {'score': 0.28_68, 'label': 'cat', 'box': {'xmin': 3_2_4, 'ymin': 2_0, 'xmax': 6_4_0, 'ymax': 3_7_3}},
                     {'score': 0.2_77, 'label': 'remote', 'box': {'xmin': 4_0, 'ymin': 7_2, 'xmax': 1_7_7, 'ymax': 1_1_5}},
                     {'score': 0.25_37, 'label': 'cat', 'box': {'xmin': 1, 'ymin': 5_5, 'xmax': 3_1_5, 'ymax': 4_7_2}},
                     {'score': 0.14_74, 'label': 'remote', 'box': {'xmin': 3_3_5, 'ymin': 7_4, 'xmax': 3_7_1, 'ymax': 1_8_7}},
                     {'score': 0.12_08, 'label': 'couch', 'box': {'xmin': 4, 'ymin': 0, 'xmax': 6_4_2, 'ymax': 4_7_6}},
                 ],
             ]       ,					)






       @require_tf
       @unittest.skip('Zero Shot Object Detection not implemented in TF' )
       def 	snake_case		(       self ):



         '''simple docstring'''

         pass






       @require_torch
       @slow
       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__     :str											=				0.2
         lowerCAmelCase__     :Optional[Any]											=				pipeline('zero-shot-object-detection' )

         lowerCAmelCase__     :Optional[Any]											=				object_detector(
             'http://images.cocodataset.org/val2017/000000039769.jpg'       ,					candidate_labels=['cat', 'remote', 'couch']       ,					threshold=__UpperCAmelCase       ,					)
         self.assertEqual(
             nested_simplify(__UpperCAmelCase       ,					decimals=4 )       ,					[
                 {'score': 0.28_68, 'label': 'cat', 'box': {'xmin': 3_2_4, 'ymin': 2_0, 'xmax': 6_4_0, 'ymax': 3_7_3}},
                 {'score': 0.2_77, 'label': 'remote', 'box': {'xmin': 4_0, 'ymin': 7_2, 'xmax': 1_7_7, 'ymax': 1_1_5}},
                 {'score': 0.25_37, 'label': 'cat', 'box': {'xmin': 1, 'ymin': 5_5, 'xmax': 3_1_5, 'ymax': 4_7_2}},
             ]       ,					)






       @require_torch
       @slow
       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__     :Optional[Any]											=				2
         lowerCAmelCase__     :Tuple											=				pipeline('zero-shot-object-detection' )

         lowerCAmelCase__     :str											=				object_detector(
             'http://images.cocodataset.org/val2017/000000039769.jpg'       ,					candidate_labels=['cat', 'remote', 'couch']       ,					top_k=__UpperCAmelCase       ,					)
         self.assertEqual(
             nested_simplify(__UpperCAmelCase       ,					decimals=4 )       ,					[
                 {'score': 0.28_68, 'label': 'cat', 'box': {'xmin': 3_2_4, 'ymin': 2_0, 'xmax': 6_4_0, 'ymax': 3_7_3}},
                 {'score': 0.2_77, 'label': 'remote', 'box': {'xmin': 4_0, 'ymin': 7_2, 'xmax': 1_7_7, 'ymax': 1_1_5}},
             ]       ,					)





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)}'
   )



