




"""simple docstring"""


# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

from ..models.auto import AutoModelForVisionaSeq
from ..utils import requires_backends
from .base import PipelineTool


if TYPE_CHECKING:
     from PIL import Image






class  lowercase(					__a			):







 '''simple docstring'''





 lowercase__			 = "Salesforce/blip-image-captioning-base"
 lowercase__			 = (
     "This is a tool that generates a description of an image. It takes an input named `image` which should be the "
     "image to caption, and returns a text that contains the description in English."
 )
 lowercase__			 = "image_captioner"
 lowercase__			 = AutoModelForVisionaSeq

 lowercase__			 = ["image"]
 lowercase__			 = ["text"]


 def __init__(    self:		int,				*a_:		Optional[int],				**a_:		Tuple		):




   '''simple docstring'''



   requires_backends(self,				["""vision"""]		)
   super().__init__(*a_,				**a_		)


 def 			UpperCamelCase_       (    self:		Optional[int],				a_:		"Image"		):




   '''simple docstring'''



   return self.pre_processor(images=a_,				return_tensors="""pt"""		)


 def 			UpperCamelCase_       (    self:		List[Any],				a_:		Union[str, Any]		):




   '''simple docstring'''



   return self.model.generate(**a_		)


 def 			UpperCamelCase_       (    self:		List[str],				a_:		Tuple		):




   '''simple docstring'''



   return self.pre_processor.batch_decode(a_,				skip_special_tokens=a_		)[0].strip()








"""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)}'''
     )


