
"""simple docstring"""




from math import ceil




def 			_lowerCAmelCase		(		_UpperCamelCase			,					_UpperCamelCase    ):






  """simple docstring"""



  _lowercase:      Optional[int]   			=  list(range(0			,					_UpperCamelCase    )    )

  _lowercase:      Union[str, Any]   			=  [item for sublist in list(device_map.values()    ) for item in sublist]

  # Duplicate check
  _lowercase:      Union[str, Any]   			=  []
  for i in device_map_blocks:
    if device_map_blocks.count(_UpperCamelCase    ) > 1 and i not in duplicate_blocks:
      duplicate_blocks.append(_UpperCamelCase    )
    # Missing blocks
  _lowercase:      Optional[int]   			=  [i for i in blocks if i not in device_map_blocks]
  _lowercase:      List[str]   			=  [i for i in device_map_blocks if i not in blocks]

  if len(_UpperCamelCase    ) != 0:
    raise ValueError(
        '''Duplicate attention blocks specified in device_map. Attention blocks must be specified to one device.'''
        ''' These attention blocks were specified more than once: ''' + str(_UpperCamelCase    )    )
  if len(_UpperCamelCase    ) != 0:
    raise ValueError(
        '''There are attention blocks for this model that are not specified in the device_map. Add these attention '''
        '''blocks to a device on the device_map: ''' + str(_UpperCamelCase    )    )
  if len(_UpperCamelCase    ) != 0:
    raise ValueError(
        '''The device_map contains more attention blocks than this model has. Remove these from the device_map:'''
        + str(_UpperCamelCase    )    )







def 			_lowerCAmelCase		(		_UpperCamelCase			,					_UpperCamelCase    ):






  """simple docstring"""



  _lowercase:      Optional[Any]   			=  list(range(_UpperCamelCase    )    )
  _lowercase:      Tuple   			=  int(ceil(n_layers / len(_UpperCamelCase    )    )    )
  _lowercase:      Tuple   			=  [layers[i : i + n_blocks] for i in range(0			,					_UpperCamelCase			,					_UpperCamelCase    )]

  return dict(zip(_UpperCamelCase			,					_UpperCamelCase    )    )


"""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       __magic_name__							(							SCREAMING_SNAKE_CASE__			):
       UpperCamelCase_ =		'''microsoft/speecht5_tts'''
       UpperCamelCase_ =		(
           '''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_ =		'''text_reader'''
       UpperCamelCase_ =		SpeechTaProcessor
       UpperCamelCase_ =		SpeechTaForTextToSpeech
       UpperCamelCase_ =		SpeechTaHifiGan

       UpperCamelCase_ =		['''text''']
       UpperCamelCase_ =		['''audio''']
       def 				lowercase_				(  self							)  ->						Union[str, Any]:





         """simple docstring"""




         if self.post_processor is None:
           _lowercase:      Tuple   			=  '''microsoft/speecht5_hifigan'''
         super().setup()
       def 				lowercase_				(  self			,					A_			,					A_=None							)  ->						Optional[int]:





         """simple docstring"""




         _lowercase:      Dict   			=  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.'''							)

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

         return {"input_ids": inputs["input_ids"], "speaker_embeddings": speaker_embeddings}
       def 				lowercase_				(  self			,					A_							)  ->						Optional[int]:





         """simple docstring"""




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



       def 				lowercase_				(  self			,					A_							)  ->						Dict:





         """simple docstring"""




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