


"""simple docstring"""






import math

import numpy as np
import qiskit
from qiskit import Aer, ClassicalRegister, QuantumCircuit, QuantumRegister, execute
def 					A_      (      _lowerCAmelCase   :    int = 3				):






     """simple docstring"""

     if isinstance(_lowerCAmelCase,   _lowerCAmelCase				):
          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(_lowerCAmelCase				) != 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(_lowerCAmelCase,   '''qr'''				)
     _a					= ClassicalRegister(_lowerCAmelCase,   '''cr'''				)

     _a					= QuantumCircuit(_lowerCAmelCase,   _lowerCAmelCase				)

     _a					= number_of_qubits

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

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

     # measure all the qubits
     quantum_circuit.measure(_lowerCAmelCase,   _lowerCAmelCase				)
     # simulate with 10000 shots
     _a					= Aer.get_backend('''qasm_simulator'''				)
     _a					= execute(_lowerCAmelCase,   _lowerCAmelCase,   shots=1_00_00				)

     return job.result().get_counts(_lowerCAmelCase				)


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






# 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 ..models.auto import AutoModelForSeqaSeqLM, AutoTokenizer
from .base import PipelineTool







class 				__lowerCAmelCase       (    UpperCamelCase__):
      _lowercase				:					Optional[Any]						=			"""philschmid/bart-large-cnn-samsum"""
      _lowercase				:					List[Any]						=			(
          """This is a tool that summarizes an English text. It takes an input `text` containing the text to summarize, """
          """and returns a summary of the text."""
      )
      _lowercase				:					Any						=			"""summarizer"""
      _lowercase				:					Any						=			AutoTokenizer
      _lowercase				:					str						=			AutoModelForSeqaSeqLM

      _lowercase				:					Optional[int]						=			["""text"""]
      _lowercase				:					Optional[int]						=			["""text"""]







      def    _lowercase  (    self		,      lowerCAmelCase__							)   ->						Tuple:



        '''simple docstring'''




        return self.pre_processor(lowerCAmelCase__		,      return_tensors="pt"		,      truncation=lowerCAmelCase__							)







      def    _lowercase  (    self		,      lowerCAmelCase__							)   ->						Union[str, Any]:



        '''simple docstring'''




        return self.model.generate(**lowerCAmelCase__							)[0]







      def    _lowercase  (    self		,      lowerCAmelCase__							)   ->						Any:



        '''simple docstring'''




        return self.pre_processor.decode(lowerCAmelCase__		,      skip_special_tokens=lowerCAmelCase__		,      clean_up_tokenization_spaces=lowerCAmelCase__							)






