

"""simple docstring"""




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_		(	__a    ):
							_UpperCAmelCase   :  Any										=				"""microsoft/speecht5_tts"""
							_UpperCAmelCase   :  List[str]										=				(
							    """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."""
							)
							_UpperCAmelCase   :  int										=				"""text_reader"""
							_UpperCAmelCase   :  Tuple										=				SpeechTaProcessor
							_UpperCAmelCase   :  Optional[Any]										=				SpeechTaForTextToSpeech
							_UpperCAmelCase   :  Optional[int]										=				SpeechTaHifiGan

							_UpperCAmelCase   :  List[Any]										=				["""text"""]
							_UpperCAmelCase   :  Optional[Any]										=				["""audio"""]
							def    lowerCAmelCase					(		self		:      List[Any]):
								if self.post_processor is None:
									__lowerCamelCase    :    Optional[int] 	=			'microsoft/speecht5_hifigan'
								super().setup()
							def    lowerCAmelCase					(		self		:      int       ,SCREAMING_SNAKE_CASE__		:      Dict       ,SCREAMING_SNAKE_CASE__		:      Optional[Any]=None):
								__lowerCamelCase    :    int 	=			self.pre_processor(text=a__       ,return_tensors='pt'       ,truncation=a__)

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

									__lowerCamelCase    :    Union[str, Any] 	=			load_dataset('Matthijs/cmu-arctic-xvectors'       ,split='validation')
									__lowerCamelCase    :    List[Any] 	=			torch.tensor(embeddings_dataset[7_3_0_5]['xvector']).unsqueeze(0)

								return {"input_ids": inputs["input_ids"], "speaker_embeddings": speaker_embeddings}
							def    lowerCAmelCase					(		self		:      Union[str, Any]       ,SCREAMING_SNAKE_CASE__		:      Any):
								with torch.no_grad():
									return self.model.generate_speech(**a__)


							def    lowerCAmelCase					(		self		:      Any       ,SCREAMING_SNAKE_CASE__		:      str):
								with torch.no_grad():
									return self.post_processor(a__).cpu().detach()







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


a		=logging.get_logger(__name__)

a		={
    """sayakpaul/vit-msn-base""": """https://huggingface.co/sayakpaul/vit-msn-base/resolve/main/config.json""",
    # See all ViT MSN models at https://huggingface.co/models?filter=vit_msn
}






class 	A_		(	SCREAMING_SNAKE_CASE    ):
							_UpperCAmelCase   :  str										=				'''vit_msn'''
							def __init__(		self		:      Any       ,SCREAMING_SNAKE_CASE__		:      int=7_6_8       ,SCREAMING_SNAKE_CASE__		:      Optional[int]=1_2       ,SCREAMING_SNAKE_CASE__		:      Optional[Any]=1_2       ,SCREAMING_SNAKE_CASE__		:      Tuple=3_0_7_2       ,SCREAMING_SNAKE_CASE__		:      str="gelu"       ,SCREAMING_SNAKE_CASE__		:      Any=0.0       ,SCREAMING_SNAKE_CASE__		:      str=0.0       ,SCREAMING_SNAKE_CASE__		:      Any=0.02       ,SCREAMING_SNAKE_CASE__		:      int=1E-06       ,SCREAMING_SNAKE_CASE__		:      Optional[int]=2_2_4       ,SCREAMING_SNAKE_CASE__		:      Dict=1_6       ,SCREAMING_SNAKE_CASE__		:      int=3       ,SCREAMING_SNAKE_CASE__		:      List[str]=True       ,**SCREAMING_SNAKE_CASE__		:      str       ,):
								super().__init__(**SCREAMING_SNAKE_CASE__)

								__lowerCamelCase    :    List[Any] 	=			hidden_size
								__lowerCamelCase    :    Tuple 	=			num_hidden_layers
								__lowerCamelCase    :    Union[str, Any] 	=			num_attention_heads
								__lowerCamelCase    :    Dict 	=			intermediate_size
								__lowerCamelCase    :    List[Any] 	=			hidden_act
								__lowerCamelCase    :    Optional[Any] 	=			hidden_dropout_prob
								__lowerCamelCase    :    str 	=			attention_probs_dropout_prob
								__lowerCamelCase    :    List[str] 	=			initializer_range
								__lowerCamelCase    :    Optional[int] 	=			layer_norm_eps
								__lowerCamelCase    :    Tuple 	=			image_size
								__lowerCamelCase    :    Union[str, Any] 	=			patch_size
								__lowerCamelCase    :    Optional[Any] 	=			num_channels
								__lowerCamelCase    :    List[str] 	=			qkv_bias




