


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  __SCREAMING_SNAKE_CASE      (	__UpperCAmelCase      ):
      A :							List[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 :							Optional[int]										=   'text_reader'
      A :							Any										=   SpeechTaProcessor
      A :							Optional[int]										=   SpeechTaForTextToSpeech
      A :							Union[str, Any]										=   SpeechTaHifiGan

      A :							Optional[Any]										=   ['text']
      A :							Optional[int]										=   ['audio']


      def 				__lowerCamelCase	(							self						):
            if self.post_processor is None:
                  lowercase	:      List[str]          =						'''microsoft/speecht5_hifigan'''
            super().setup()


      def 				__lowerCamelCase	(							self							,  SCREAMING_SNAKE_CASE__							,  SCREAMING_SNAKE_CASE__=None						):
            lowercase	:      str          =						self.pre_processor(text=lowerCamelCase__							,  return_tensors='''pt'''							,  truncation=lowerCamelCase__						)

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

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

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


      def 				__lowerCamelCase	(							self							,  SCREAMING_SNAKE_CASE__						):
            with torch.no_grad():
                  return self.model.generate_speech(**lowerCamelCase__						)






      def 				__lowerCamelCase	(							self							,  SCREAMING_SNAKE_CASE__						):
            with torch.no_grad():
                  return self.post_processor(lowerCamelCase__						).cpu().detach()


"""simple docstring"""






from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCAmelCase__  :  int						     =	logging.get_logger(__name__)

lowerCAmelCase__  :  str						     =	{
    'facebook/xglm-564M': 'https://huggingface.co/facebook/xglm-564M/resolve/main/config.json',
    # See all XGLM models at https://huggingface.co/models?filter=xglm
}
class 				snake_case       ( __UpperCAmelCase				):




					"""simple docstring"""

					snake_case__				    = "xglm"
					snake_case__				    = ["past_key_values"]

					snake_case__				    = {
					    "num_attention_heads": "attention_heads",
					    "hidden_size": "d_model",
					    "num_hidden_layers": "num_layers",
					}







					def __init__(				self   :	Any    ,lowerCamelCase__   :	Any=256_008    ,lowerCamelCase__   :	Optional[Any]=2_048    ,lowerCamelCase__   :	List[str]=1_024    ,lowerCamelCase__   :	List[str]=4_096    ,lowerCamelCase__   :	Tuple=24    ,lowerCamelCase__   :	Optional[int]=16    ,lowerCamelCase__   :	int="gelu"    ,lowerCamelCase__   :	Dict=0.1    ,lowerCamelCase__   :	int=0.1    ,lowerCamelCase__   :	List[Any]=0.0    ,lowerCamelCase__   :	List[str]=0.0    ,lowerCamelCase__   :	Optional[Any]=0.0_2    ,lowerCamelCase__   :	List[str]=True    ,lowerCamelCase__   :	Optional[Any]=True    ,lowerCamelCase__   :	str=2    ,lowerCamelCase__   :	Dict=1    ,lowerCamelCase__   :	Optional[int]=0    ,lowerCamelCase__   :	Tuple=2    ,**lowerCamelCase__   :	List[Any]    ,):
									UpperCAmelCase__             =							vocab_size
									UpperCAmelCase__             =							max_position_embeddings
									UpperCAmelCase__             =							d_model
									UpperCAmelCase__             =							ffn_dim
									UpperCAmelCase__             =							num_layers
									UpperCAmelCase__             =							attention_heads
									UpperCAmelCase__             =							activation_function
									UpperCAmelCase__             =							dropout
									UpperCAmelCase__             =							attention_dropout
									UpperCAmelCase__             =							activation_dropout
									UpperCAmelCase__             =							layerdrop
									UpperCAmelCase__             =							init_std
									UpperCAmelCase__             =							scale_embedding  # scale factor will be sqrt(d_model) if True
									UpperCAmelCase__             =							use_cache

									super().__init__(
									    pad_token_id=lowerCamelCase__    ,bos_token_id=lowerCamelCase__    ,eos_token_id=lowerCamelCase__    ,decoder_start_token_id=lowerCamelCase__    ,**lowerCamelCase__    ,)



