



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  lowerCamelCase__	(    a__):
     """simple docstring"""



     _A      =      'microsoft/speecht5_tts'
     _A      =      (
         '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      =      'text_reader'
     _A      =      SpeechTaProcessor
     _A      =      SpeechTaForTextToSpeech
     _A      =      SpeechTaHifiGan

     _A      =      ['text']
     _A      =      ['audio']







     def     _a   (self     ):






           '''simple docstring'''



           if self.post_processor is None:
                 lowerCamelCase             =		"microsoft/speecht5_hifigan"
           super().setup()







     def     _a   (self					,      __a					,      __a=None     ):






           '''simple docstring'''



           lowerCamelCase             =		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."     )

                 lowerCamelCase             =		load_dataset("Matthijs/cmu-arctic-xvectors"					,      split="validation"     )
                 lowerCamelCase             =		torch.tensor(embeddings_dataset[73_05]["xvector"]     ).unsqueeze(0     )

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







     def     _a   (self					,      __a     ):






           '''simple docstring'''



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







     def     _a   (self					,      __a     ):






           '''simple docstring'''



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






"""simple docstring"""
from __future__ import annotations

import requests

def 		_SCREAMING_SNAKE_CASE			(_UpperCAmelCase					:    str			):
 lowerCAmelCase    				= F'https://hacker-news.firebaseio.com/v0/item/{story_id}.json?print=pretty'
 return requests.get(_UpperCAmelCase			).json()

def 		_SCREAMING_SNAKE_CASE			(_UpperCAmelCase					:    int = 10			):
 lowerCAmelCase    				= 'https://hacker-news.firebaseio.com/v0/topstories.json?print=pretty'
 lowerCAmelCase    				= requests.get(_UpperCAmelCase			).json()[:max_stories]
 return [get_hackernews_story(_UpperCAmelCase			) for story_id in story_ids]

def 		_SCREAMING_SNAKE_CASE			(_UpperCAmelCase					:    int = 10			):
 lowerCAmelCase    				= hackernews_top_stories(_UpperCAmelCase			)
 return "\n".join('* [{title}]({url})'.format(**_UpperCAmelCase			) for story in stories			)


if __name__ == "__main__":
       print(hackernews_top_stories_as_markdown())





