# 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_	:	str						="microsoft/speecht5_tts"
  SCREAMING_SNAKE_CASE_	:	int						=(
      "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_	:	Union[str, Any]						="text_reader"
  SCREAMING_SNAKE_CASE_	:	str						=SpeechTaProcessor
  SCREAMING_SNAKE_CASE_	:	Optional[Any]						=SpeechTaForTextToSpeech
  SCREAMING_SNAKE_CASE_	:	Dict						=SpeechTaHifiGan

  SCREAMING_SNAKE_CASE_	:	Dict						=["text"]
  SCREAMING_SNAKE_CASE_	:	Tuple						=["audio"]






  def 		_a					(self	)		->      Optional[Any]:





       '''simple docstring'''





       if self.post_processor is None:
            UpperCamelCase__										=      '''microsoft/speecht5_hifigan'''
       super().setup()






  def 		_a					(self   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_=None	)		->      Any:





       '''simple docstring'''





       UpperCamelCase__										=      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.'''	)

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

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






  def 		_a					(self   ,				SCREAMING_SNAKE_CASE_	)		->      Dict:





       '''simple docstring'''





       with torch.no_grad():
            return self.model.generate_speech(**SCREAMING_SNAKE_CASE_	)






  def 		_a					(self   ,				SCREAMING_SNAKE_CASE_	)		->      List[Any]:





       '''simple docstring'''





       with torch.no_grad():
            return self.post_processor(SCREAMING_SNAKE_CASE_	).cpu().detach()




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


__magic_name__								=logging.get_logger(__name__)

__magic_name__								={
    '''facebook/vit-mae-base''': '''https://huggingface.co/facebook/vit-mae-base/resolve/main/config.json''',
    # See all ViT MAE models at https://huggingface.co/models?filter=vit-mae
}



class     _A   (    __UpperCamelCase							):
  SCREAMING_SNAKE_CASE_	:	Dict						="vit_mae"






  def __init__(self   ,				SCREAMING_SNAKE_CASE_=768   ,				SCREAMING_SNAKE_CASE_=12   ,				SCREAMING_SNAKE_CASE_=12   ,				SCREAMING_SNAKE_CASE_=3072   ,				SCREAMING_SNAKE_CASE_="gelu"   ,				SCREAMING_SNAKE_CASE_=0.0   ,				SCREAMING_SNAKE_CASE_=0.0   ,				SCREAMING_SNAKE_CASE_=0.02   ,				SCREAMING_SNAKE_CASE_=1E-12   ,				SCREAMING_SNAKE_CASE_=224   ,				SCREAMING_SNAKE_CASE_=16   ,				SCREAMING_SNAKE_CASE_=3   ,				SCREAMING_SNAKE_CASE_=True   ,				SCREAMING_SNAKE_CASE_=16   ,				SCREAMING_SNAKE_CASE_=512   ,				SCREAMING_SNAKE_CASE_=8   ,				SCREAMING_SNAKE_CASE_=2048   ,				SCREAMING_SNAKE_CASE_=0.75   ,				SCREAMING_SNAKE_CASE_=False   ,				**SCREAMING_SNAKE_CASE_   ,				)		->      Optional[Any]:





       '''simple docstring'''





       super().__init__(**SCREAMING_SNAKE_CASE_	)

       UpperCamelCase__										=      hidden_size
       UpperCamelCase__										=      num_hidden_layers
       UpperCamelCase__										=      num_attention_heads
       UpperCamelCase__										=      intermediate_size
       UpperCamelCase__										=      hidden_act
       UpperCamelCase__										=      hidden_dropout_prob
       UpperCamelCase__										=      attention_probs_dropout_prob
       UpperCamelCase__										=      initializer_range
       UpperCamelCase__										=      layer_norm_eps
       UpperCamelCase__										=      image_size
       UpperCamelCase__										=      patch_size
       UpperCamelCase__										=      num_channels
       UpperCamelCase__										=      qkv_bias
       UpperCamelCase__										=      decoder_num_attention_heads
       UpperCamelCase__										=      decoder_hidden_size
       UpperCamelCase__										=      decoder_num_hidden_layers
       UpperCamelCase__										=      decoder_intermediate_size
       UpperCamelCase__										=      mask_ratio
       UpperCamelCase__										=      norm_pix_loss



