


"""simple docstring"""


from pathlib import Path
from typing import List

from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import get_tests_dir, is_tool_test
from transformers.tools.agent_types import AGENT_TYPE_MAPPING, AgentAudio, AgentImage, AgentText


if is_torch_available():
     import torch

if is_vision_available():
     from PIL import Image


A_      =    ['''text''', '''image''', '''audio''']


def  UpperCAmelCase__	(snake_case__							: List[str]   ):
  """simple docstring"""

  _snake_case							:		Tuple								=  []

  for input_type in input_types:
    if input_type == "text":
      inputs.append("""Text input"""   )
    elif input_type == "image":
      inputs.append(
          Image.open(Path(get_tests_dir("""fixtures/tests_samples/COCO"""   )   ) / """000000039769.png"""   ).resize((5_12, 5_12)   )   )
    elif input_type == "audio":
      inputs.append(torch.ones(30_00   )   )
    elif isinstance(snake_case__		,       snake_case__   ):
      inputs.append(create_inputs(snake_case__   )   )
    else:
      raise ValueError(F"Invalid type requested: {input_type}"   )

  return inputs


def  UpperCAmelCase__	(snake_case__							: List   ):
  """simple docstring"""

  _snake_case							:		int								=  []

  for output in outputs:
    if isinstance(snake_case__		,       (str, AgentText)   ):
      output_types.append("""text"""   )
    elif isinstance(snake_case__		,       (Image.Image, AgentImage)   ):
      output_types.append("""image"""   )
    elif isinstance(snake_case__		,       (torch.Tensor, AgentAudio)   ):
      output_types.append("""audio"""   )
    else:
      raise ValueError(F"Invalid output: {output}"   )

  return output_types






@is_tool_test
class  lowercase:







 '''simple docstring'''





 def 			UpperCamelCase_       (    self:		Dict		):




   '''simple docstring'''



   self.assertTrue(hasattr(self.tool,				"""inputs"""		)		)
   self.assertTrue(hasattr(self.tool,				"""outputs"""		)		)

   _snake_case							:		Optional[Any]								=  self.tool.inputs
   for _input in inputs:
     if isinstance(_input,				a_		):
       for __input in _input:
         self.assertTrue(__input in authorized_types		)
     else:
       self.assertTrue(_input in authorized_types		)

   _snake_case							:		Optional[Any]								=  self.tool.outputs
   for _output in outputs:
     self.assertTrue(_output in authorized_types		)


 def 			UpperCamelCase_       (    self:		str		):




   '''simple docstring'''



   _snake_case							:		Any								=  create_inputs(self.tool.inputs		)
   _snake_case							:		str								=  self.tool(*a_		)

   # There is a single output
   if len(self.tool.outputs		) == 1:
     _snake_case							:		List[str]								=  [outputs]

   self.assertListEqual(output_types(a_		),				self.tool.outputs		)


 def 			UpperCamelCase_       (    self:		Optional[Any]		):




   '''simple docstring'''



   self.assertTrue(hasattr(self.tool,				"""description"""		)		)
   self.assertTrue(hasattr(self.tool,				"""default_checkpoint"""		)		)
   self.assertTrue(self.tool.description.startswith("""This is a tool that"""		)		)


 def 			UpperCamelCase_       (    self:		Any		):




   '''simple docstring'''



   _snake_case							:		List[str]								=  create_inputs(self.tool.inputs		)
   _snake_case							:		List[str]								=  self.tool(*a_		)

   if not isinstance(a_,				a_		):
     _snake_case							:		Dict								=  [outputs]

   self.assertEqual(len(a_		),				len(self.tool.outputs		)		)

   for output, output_type in zip(a_,				self.tool.outputs		):
     _snake_case							:		Optional[Any]								=  AGENT_TYPE_MAPPING[output_type]
     self.assertTrue(isinstance(a_,				a_		)		)


 def 			UpperCamelCase_       (    self:		Any		):




   '''simple docstring'''



   _snake_case							:		Union[str, Any]								=  create_inputs(self.tool.inputs		)

   _snake_case							:		int								=  []

   for _input, input_type in zip(a_,				self.tool.inputs		):
     if isinstance(a_,				a_		):
       _inputs.append([AGENT_TYPE_MAPPING[_input_type](_input		) for _input_type in input_type]		)
     else:
       _inputs.append(AGENT_TYPE_MAPPING[input_type](_input		)		)

        # Should not raise an error
   _snake_case							:		List[str]								=  self.tool(*a_		)

   if not isinstance(a_,				a_		):
     _snake_case							:		List[str]								=  [outputs]

   self.assertEqual(len(a_		),				len(self.tool.outputs		)		)








"""simple docstring"""


import math

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


def  UpperCAmelCase__	(snake_case__							: int = 3   ):
  """simple docstring"""

  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)."""   )

  _snake_case							:		Optional[int]								=  QuantumRegister(snake_case__		,       """qr"""   )
  _snake_case							:		List[Any]								=  ClassicalRegister(snake_case__		,       """cr"""   )

  _snake_case							:		Optional[int]								=  QuantumCircuit(snake_case__		,       snake_case__   )

  _snake_case							:		Dict								=  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
  _snake_case							:		Optional[int]								=  Aer.get_backend("""qasm_simulator"""   )
  _snake_case							:		Optional[int]								=  execute(snake_case__		,       snake_case__		,       shots=1_00_00   )

  return job.result().get_counts(snake_case__   )


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


