'''simple docstring'''




import math

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

def 	UpperCamelCase_							(					_UpperCAmelCase  :      int = 3    )    ->    qiskit.result.counts.Counts:


       """simple docstring"""







       if isinstance(_UpperCAmelCase			,	_UpperCAmelCase    ):
              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(_UpperCAmelCase    ) != 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)."    )

       _UpperCAmelCase     :  Dict							      =    QuantumRegister(_UpperCAmelCase			,	"qr"    )
       _UpperCAmelCase     :  Any							      =    ClassicalRegister(_UpperCAmelCase			,	"cr"    )

       _UpperCAmelCase     :  int							      =    QuantumCircuit(_UpperCAmelCase			,	_UpperCAmelCase    )

       _UpperCAmelCase     :  List[Any]							      =    number_of_qubits

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

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

       # measure all the qubits
       quantum_circuit.measure(_UpperCAmelCase			,	_UpperCAmelCase    )
       # simulate with 10000 shots
       _UpperCAmelCase     :  str							      =    Aer.get_backend("qasm_simulator"    )
       _UpperCAmelCase     :  Dict							      =    execute(_UpperCAmelCase			,	_UpperCAmelCase			,	shots=10_000    )

       return job.result().get_counts(_UpperCAmelCase    )


if __name__ == "__main__":
  print(
      F'Total count for quantum fourier transform state is: \
    {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.
import torch

from ..models.speechta import SpeechTaForTextToSpeech, SpeechTaHifiGan, SpeechTaProcessor
from ..utils import is_datasets_available
from .base import PipelineTool


if is_datasets_available():
  from datasets import load_dataset

class __snake_case		(      lowerCAmelCase     ):
    _a		:						Union[str, Any]=				"microsoft/speecht5_tts"
    _a		:						Tuple=				(
        "This is a tool that reads an English text out loud. It takes an input named `text` which should contain the "
        "text to read (in English) and returns a waveform object containing the sound."
    )
    _a		:						Dict=				"text_reader"
    _a		:						Optional[Any]=				SpeechTaProcessor
    _a		:						Tuple=				SpeechTaForTextToSpeech
    _a		:						Optional[int]=				SpeechTaHifiGan

    _a		:						Union[str, Any]=				["text"]
    _a		:						Optional[int]=				["audio"]
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           if self.post_processor is None:
                  lowercase							:     Any       	=      """microsoft/speecht5_hifigan"""
           super().setup()
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case		,snake_case=None      ):



           '''simple docstring'''







           lowercase							:     int       	=      self.pre_processor(text=snake_case		,return_tensors="""pt"""		,truncation=snake_case      )

           if speaker_embeddings is None:
                  if not is_datasets_available():
                         raise ImportError("""Datasets needs to be installed if not passing speaker embeddings."""      )

                  lowercase							:     Tuple       	=      load_dataset("""Matthijs/cmu-arctic-xvectors"""		,split="""validation"""      )
                  lowercase							:     List[str]       	=      torch.tensor(embeddings_dataset[7305]["""xvector"""]      ).unsqueeze(0      )

           return {"input_ids": inputs["input_ids"], "speaker_embeddings": speaker_embeddings}
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case      ):



           '''simple docstring'''







           with torch.no_grad():
                  return self.model.generate_speech(**snake_case      )
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case      ):



           '''simple docstring'''







           with torch.no_grad():
                  return self.post_processor(snake_case      ).cpu().detach()
