

# 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  _A		(  __magic_name__):
  SCREAMING_SNAKE_CASE    :      List[str]       =  '''microsoft/speecht5_tts'''
  SCREAMING_SNAKE_CASE    :      Dict       =  (
      '''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.'''
  )
  SCREAMING_SNAKE_CASE    :      Optional[Any]       =  '''text_reader'''
  SCREAMING_SNAKE_CASE    :      List[Any]       =  SpeechTaProcessor
  SCREAMING_SNAKE_CASE    :      Any       =  SpeechTaForTextToSpeech
  SCREAMING_SNAKE_CASE    :      Dict       =  SpeechTaHifiGan

  SCREAMING_SNAKE_CASE    :      Tuple       =  ['''text''']
  SCREAMING_SNAKE_CASE    :      Optional[Any]       =  ['''audio''']

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        if self.post_processor is None:
              SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     'microsoft/speecht5_hifigan'
        super().setup()

  def  UpperCAmelCase    (				self ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE=None					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  Tuple           =     self.pre_processor(text=_SCREAMING_SNAKE_CASE ,					return_tensors='pt' ,					truncation=_SCREAMING_SNAKE_CASE					)

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

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

        return {"input_ids": inputs["input_ids"], "speaker_embeddings": speaker_embeddings}

  def  UpperCAmelCase    (				self ,					_SCREAMING_SNAKE_CASE					):





        """simple docstring"""







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



  def  UpperCAmelCase    (				self ,					_SCREAMING_SNAKE_CASE					):





        """simple docstring"""







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









def    A_  (							a  ):

      """simple docstring"""



      if upper_limit < 0:
            raise ValueError('Limit for the Catalan sequence must be ≥ 0'  )

      SCREAMING_SNAKE_CASE_				:  List[str]           =     [0] * (upper_limit + 1)

      # Base case: C(0) = C(1) = 1
      SCREAMING_SNAKE_CASE_				:  Any           =     1
      if upper_limit > 0:
            SCREAMING_SNAKE_CASE_				:  int           =     1

      # Recurrence relation: C(i) = sum(C(j).C(i-j-1)), from j = 0 to i
      for i in range(2					,   upper_limit + 1  ):
            for j in range(a  ):
                  catalan_list[i] += catalan_list[j] * catalan_list[i - j - 1]

      return catalan_list


if __name__ == "__main__":
    print('\n********* Catalan Numbers Using Dynamic Programming ************\n')
    print('\n*** Enter -1 at any time to quit ***')
    print('\nEnter the upper limit (≥ 0) for the Catalan number sequence: ', end='')
    try:
        while True:
            lowerCAmelCase     :   Union[str, Any]						     =  int(input().strip())
            if N < 0:
                print('\n********* Goodbye!! ************')
                break
            else:
                print(F'The Catalan numbers from 0 through {N} are:')
                print(catalan_numbers(N))
                print('Try another upper limit for the sequence: ', end='')
    except (NameError, ValueError):
        print('\n********* Invalid input, goodbye! ************\n')

    import doctest

    doctest.testmod()






