
"""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__	(     _lowerCamelCase):
		A_						:	Dict           =     'microsoft/speecht5_tts'
		A_						:	Any           =     (
		    '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_						:	Tuple           =     'text_reader'
		A_						:	Tuple           =     SpeechTaProcessor
		A_						:	Optional[int]           =     SpeechTaForTextToSpeech
		A_						:	Tuple           =     SpeechTaHifiGan

		A_						:	Optional[int]           =     ['text']
		A_						:	int           =     ['audio']

		def 							__lowerCamelCase							(				self			):
				if self.post_processor is None:
						__lowerCAmelCase      :						Any						      =				'microsoft/speecht5_hifigan'
				super().setup()

		def 							__lowerCamelCase							(				self	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE=None			):
				__lowerCAmelCase      :						Dict						      =				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.'			)

						__lowerCAmelCase      :						Optional[Any]						      =				load_dataset('Matthijs/cmu-arctic-xvectors'	,		split='validation'			)
						__lowerCAmelCase      :						Dict						      =				torch.tensor(embeddings_dataset[73_05]['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(**_SCREAMING_SNAKE_CASE			)







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

"""simple docstring"""







# NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works

from ...utils import deprecate
from ..controlnet.pipeline_flax_controlnet import FlaxStableDiffusionControlNetPipeline  # noqa: F401


deprecate(
    """stable diffusion controlnet""",
    """0.22.0""",
    """Importing `FlaxStableDiffusionControlNetPipeline` from diffusers.pipelines.stable_diffusion.flax_pipeline_stable_diffusion_controlnet is deprecated. Please import `from diffusers import FlaxStableDiffusionControlNetPipeline` instead.""",
    standard_warn=False,
    stacklevel=3,
)