




'''simple docstring'''






# 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        lowercase_      (						lowerCAmelCase_							):
     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 			_lowerCAmelCase      (     self     :					int			):
           if self.post_processor is None:
                 snake_case__       :   Union[str, Any]	      =    'microsoft/speecht5_hifigan'
           super().setup()





     def 			_lowerCAmelCase      (     self     :					Union[str, Any]     ,							__lowerCamelCase     :					Any     ,							__lowerCamelCase     :					Tuple=None			):
           snake_case__       :   Tuple	      =    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.'			)

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

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





     def 			_lowerCAmelCase      (     self     :					Any     ,							__lowerCamelCase     :					str			):
           with torch.no_grad():
                 return self.model.generate_speech(**__lowerCamelCase			)





     def 			_lowerCAmelCase      (     self     :					Any     ,							__lowerCamelCase     :					Tuple			):
           with torch.no_grad():
                 return self.post_processor(__lowerCamelCase			).cpu().detach()






'''simple docstring'''






from .configuration_bert_masked import MaskedBertConfig
from .modeling_bert_masked import (
    MaskedBertForMultipleChoice,
    MaskedBertForQuestionAnswering,
    MaskedBertForSequenceClassification,
    MaskedBertForTokenClassification,
    MaskedBertModel,
)
from .modules import *
