



'''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     UpperCAmelCase_       (       _a	):
    """simple docstring"""







    lowercase					=		"microsoft/speecht5_tts"
    lowercase					=		(
        "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."
    )
    lowercase					=		"text_reader"
    lowercase					=		SpeechTaProcessor
    lowercase					=		SpeechTaForTextToSpeech
    lowercase					=		SpeechTaHifiGan

    lowercase					=		["text"]
    lowercase					=		["audio"]




    def lowerCamelCase       (    self					:     str						):
          if self.post_processor is None:
                snake_case__  :    str    						=      """microsoft/speecht5_hifigan"""
          super().setup()




    def lowerCamelCase       (    self					:     Any ,     snake_case_					:     Optional[Any] ,     snake_case_					:     List[Any]=None						):
          snake_case__  :    Dict    						=      self.pre_processor(text=snake_case_ ,     return_tensors="""pt""" ,     truncation=snake_case_						)

          if speaker_embeddings is None:
                if not is_datasets_available():
                      raise ImportError("""Datasets needs to be installed if not passing speaker embeddings."""						)

                snake_case__  :    int    						=      load_dataset("""Matthijs/cmu-arctic-xvectors""" ,     split="""validation"""						)
                snake_case__  :    Dict    						=      torch.tensor(embeddings_dataset[7_305]["""xvector"""]						).unsqueeze(0						)

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




    def lowerCamelCase       (    self					:     Union[str, Any] ,     snake_case_					:     str						):
          with torch.no_grad():
                return self.model.generate_speech(**snake_case_						)




    def lowerCamelCase       (    self					:     List[Any] ,     snake_case_					:     int						):
          with torch.no_grad():
                return self.post_processor(snake_case_						).cpu().detach()





'''simple docstring'''





from PIL import Image





def 						__snake_case(				_lowerCAmelCase     ,   _lowerCAmelCase		)		-> Image:

      def brightness(_lowerCAmelCase		) -> float:
            return 128 + level + (c - 128)

      if not -255.0 <= level <= 255.0:
            raise ValueError("""level must be between -255.0 (black) and 255.0 (white)"""		)
      return img.point(_lowerCAmelCase		)


if __name__ == "__main__":
       # Load image
       with Image.open("image_data/lena.jpg") as img:
              # Change brightness to 100
              __a          =		change_brightness(img, 100)
              brigt_img.save("image_data/lena_brightness.png", format="png")
