
# 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(				UpperCamelCase__       ):
     SCREAMING_SNAKE_CASE			    =    '''microsoft/speecht5_tts'''
     SCREAMING_SNAKE_CASE			    =    (
         '''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			    =    '''text_reader'''
     SCREAMING_SNAKE_CASE			    =    SpeechTaProcessor
     SCREAMING_SNAKE_CASE			    =    SpeechTaForTextToSpeech
     SCREAMING_SNAKE_CASE			    =    SpeechTaHifiGan

     SCREAMING_SNAKE_CASE			    =    ['''text''']
     SCREAMING_SNAKE_CASE			    =    ['''audio''']

     def    lowercase__      (	self					:     Dict   ):
            if self.post_processor is None:
                   lowerCamelCase_							  =					"""microsoft/speecht5_hifigan"""
            super().setup()

     def    lowercase__      (	self					:     str   ,					__UpperCamelCase					:     Union[str, Any]   ,					__UpperCamelCase					:     Tuple=None   ):
            lowerCamelCase_							  =					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_							  =					load_dataset("""Matthijs/cmu-arctic-xvectors"""   ,					split="""validation"""   )
                   lowerCamelCase_							  =					torch.tensor(embeddings_dataset[7_3_0_5]["""xvector"""]   ).unsqueeze(0   )

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

     def    lowercase__      (	self					:     Optional[Any]   ,					__UpperCamelCase					:     Union[str, Any]   ):
            with torch.no_grad():
                   return self.model.generate_speech(**_a   )



     def    lowercase__      (	self					:     str   ,					__UpperCamelCase					:     Optional[Any]   ):
            with torch.no_grad():
                   return self.post_processor(_a   ).cpu().detach()







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available


lowercase        =  {'''configuration_speech_encoder_decoder''': ['''SpeechEncoderDecoderConfig''']}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase        =  ['''SpeechEncoderDecoderModel''']

try:
		if not is_flax_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase        =  ['''FlaxSpeechEncoderDecoderModel''']

if TYPE_CHECKING:
		from .configuration_speech_encoder_decoder import SpeechEncoderDecoderConfig

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_speech_encoder_decoder import SpeechEncoderDecoderModel

		try:
				if not is_flax_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_flax_speech_encoder_decoder import FlaxSpeechEncoderDecoderModel

else:
		import sys

		lowercase        =  _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)





