



"""simple docstring"""





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__     (					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    :						List[str]					)				->		Union[str, Any]:


             '''simple docstring'''




             if self.post_processor is None:
                   UpperCAmelCase_    				=							"microsoft/speecht5_hifigan"
             super().setup()



       def   lowercase__       (							self    :						Any	,       _UpperCAmelCase    :						str	,       _UpperCAmelCase    :						Optional[Any]=None					)				->		List[str]:


             '''simple docstring'''




             UpperCAmelCase_    				=							self.pre_processor(text=_UpperCAmelCase	,       return_tensors="pt"	,       truncation=_UpperCAmelCase					)

             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   lowercase__       (							self    :						List[Any]	,       _UpperCAmelCase    :						List[str]					)				->		Dict:


             '''simple docstring'''




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







       def   lowercase__       (							self    :						Tuple	,       _UpperCAmelCase    :						Optional[int]					)				->		List[str]:


             '''simple docstring'''




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









"""simple docstring"""





from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    is_valid_image,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
  import PIL


lowerCamelCase      =		logging.get_logger(__name__)
def      a__			(   lowerCAmelCase__   ):
      if isinstance(lowerCAmelCase__			,    (list, tuple)   ) and isinstance(videos[0]			,    (list, tuple)   ) and is_valid_image(videos[0][0]   ):
            return videos

      elif isinstance(lowerCAmelCase__			,    (list, tuple)   ) and is_valid_image(videos[0]   ):
            return [videos]

      elif is_valid_image(lowerCAmelCase__   ):
            return [[videos]]

      raise ValueError(f"""Could not make batched video from {videos}"""   )


class    lowercase__     (					SCREAMING_SNAKE_CASE   ):







       '''simple docstring'''


       UpperCamelCase		=  ['''pixel_values''']



       def __init__(							self    :						Tuple	,       _UpperCAmelCase    :						bool = True	,       _UpperCAmelCase    :						Dict[str, int] = None	,       _UpperCAmelCase    :						PILImageResampling = PILImageResampling.BILINEAR	,       _UpperCAmelCase    :						bool = True	,       _UpperCAmelCase    :						Dict[str, int] = None	,       _UpperCAmelCase    :						bool = True	,       _UpperCAmelCase    :						Union[int, float] = 1 / 255	,       _UpperCAmelCase    :						bool = True	,       _UpperCAmelCase    :						Optional[Union[float, List[float]]] = None	,       _UpperCAmelCase    :						Optional[Union[float, List[float]]] = None	,       **_UpperCAmelCase    :						Any	,       )				->		None:


             '''simple docstring'''




             super().__init__(**_UpperCAmelCase					)
             UpperCAmelCase_    				=							size if size is not None else {"shortest_edge": 224}
             UpperCAmelCase_    				=							get_size_dict(_UpperCAmelCase	,       default_to_square=_UpperCAmelCase					)
             UpperCAmelCase_    				=							crop_size if crop_size is not None else {"height": 224, "width": 224}
             UpperCAmelCase_    				=							get_size_dict(_UpperCAmelCase	,       param_name="crop_size"					)

             UpperCAmelCase_    				=							do_resize
             UpperCAmelCase_    				=							size
             UpperCAmelCase_    				=							do_center_crop
             UpperCAmelCase_    				=							crop_size
             UpperCAmelCase_    				=							resample
             UpperCAmelCase_    				=							do_rescale
             UpperCAmelCase_    				=							rescale_factor
             UpperCAmelCase_    				=							do_normalize
             UpperCAmelCase_    				=							image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
             UpperCAmelCase_    				=							image_std if image_std is not None else IMAGENET_STANDARD_STD



       def   lowercase__       (							self    :						Tuple	,       _UpperCAmelCase    :						np.ndarray	,       _UpperCAmelCase    :						Dict[str, int]	,       _UpperCAmelCase    :						PILImageResampling = PILImageResampling.BILINEAR	,       _UpperCAmelCase    :						Optional[Union[str, ChannelDimension]] = None	,       **_UpperCAmelCase    :						Tuple	,       )				->		np.ndarray:


             '''simple docstring'''




             UpperCAmelCase_    				=							get_size_dict(_UpperCAmelCase	,       default_to_square=_UpperCAmelCase					)
             if "shortest_edge" in size:
                   UpperCAmelCase_    				=							get_resize_output_image_size(_UpperCAmelCase	,       size["shortest_edge"]	,       default_to_square=_UpperCAmelCase					)
             elif "height" in size and "width" in size:
                   UpperCAmelCase_    				=							(size["height"], size["width"])
             else:
                   raise ValueError(F"""Size must have 'height' and 'width' or 'shortest_edge' as keys. Got {size.keys()}"""					)
             return resize(_UpperCAmelCase	,       size=_UpperCAmelCase	,       resample=_UpperCAmelCase	,       data_format=_UpperCAmelCase	,       **_UpperCAmelCase					)



       def   lowercase__       (							self    :						Union[str, Any]	,       _UpperCAmelCase    :						np.ndarray	,       _UpperCAmelCase    :						Dict[str, int]	,       _UpperCAmelCase    :						Optional[Union[str, ChannelDimension]] = None	,       **_UpperCAmelCase    :						Union[str, Any]	,       )				->		np.ndarray:


             '''simple docstring'''




             UpperCAmelCase_    				=							get_size_dict(_UpperCAmelCase					)
             if "height" not in size or "width" not in size:
                   raise ValueError(F"""Size must have 'height' and 'width' as keys. Got {size.keys()}"""					)
             return center_crop(_UpperCAmelCase	,       size=(size["height"], size["width"])	,       data_format=_UpperCAmelCase	,       **_UpperCAmelCase					)



       def   lowercase__       (							self    :						List[Any]	,       _UpperCAmelCase    :						np.ndarray	,       _UpperCAmelCase    :						Union[int, float]	,       _UpperCAmelCase    :						Optional[Union[str, ChannelDimension]] = None	,       **_UpperCAmelCase    :						str	,       )				->		List[str]:


             '''simple docstring'''




             return rescale(_UpperCAmelCase	,       scale=_UpperCAmelCase	,       data_format=_UpperCAmelCase	,       **_UpperCAmelCase					)



       def   lowercase__       (							self    :						Optional[Any]	,       _UpperCAmelCase    :						np.ndarray	,       _UpperCAmelCase    :						Union[float, List[float]]	,       _UpperCAmelCase    :						Union[float, List[float]]	,       _UpperCAmelCase    :						Optional[Union[str, ChannelDimension]] = None	,       **_UpperCAmelCase    :						Tuple	,       )				->		np.ndarray:


             '''simple docstring'''




             return normalize(_UpperCAmelCase	,       mean=_UpperCAmelCase	,       std=_UpperCAmelCase	,       data_format=_UpperCAmelCase	,       **_UpperCAmelCase					)



       def   lowercase__       (							self    :						Any	,       _UpperCAmelCase    :						ImageInput	,       _UpperCAmelCase    :						bool = None	,       _UpperCAmelCase    :						Dict[str, int] = None	,       _UpperCAmelCase    :						PILImageResampling = None	,       _UpperCAmelCase    :						bool = None	,       _UpperCAmelCase    :						Dict[str, int] = None	,       _UpperCAmelCase    :						bool = None	,       _UpperCAmelCase    :						float = None	,       _UpperCAmelCase    :						bool = None	,       _UpperCAmelCase    :						Optional[Union[float, List[float]]] = None	,       _UpperCAmelCase    :						Optional[Union[float, List[float]]] = None	,       _UpperCAmelCase    :						Optional[ChannelDimension] = ChannelDimension.FIRST	,       )				->		np.ndarray:


             '''simple docstring'''




             if do_resize and size is None or resample is None:
                   raise ValueError("Size and resample must be specified if do_resize is True."					)

             if do_center_crop and crop_size is None:
                   raise ValueError("Crop size must be specified if do_center_crop is True."					)

             if do_rescale and rescale_factor is None:
                   raise ValueError("Rescale factor must be specified if do_rescale is True."					)

             if do_normalize and (image_mean is None or image_std is None):
                   raise ValueError("Image mean and std must be specified if do_normalize is True."					)

             # All transformations expect numpy arrays.
             UpperCAmelCase_    				=							to_numpy_array(_UpperCAmelCase					)

             if do_resize:
                   UpperCAmelCase_    				=							self.resize(image=_UpperCAmelCase	,       size=_UpperCAmelCase	,       resample=_UpperCAmelCase					)

             if do_center_crop:
                   UpperCAmelCase_    				=							self.center_crop(_UpperCAmelCase	,       size=_UpperCAmelCase					)

             if do_rescale:
                   UpperCAmelCase_    				=							self.rescale(image=_UpperCAmelCase	,       scale=_UpperCAmelCase					)

             if do_normalize:
                   UpperCAmelCase_    				=							self.normalize(image=_UpperCAmelCase	,       mean=_UpperCAmelCase	,       std=_UpperCAmelCase					)

             UpperCAmelCase_    				=							to_channel_dimension_format(_UpperCAmelCase	,       _UpperCAmelCase					)
             return image







       def   lowercase__       (							self    :						Optional[Any]	,       _UpperCAmelCase    :						ImageInput	,       _UpperCAmelCase    :						bool = None	,       _UpperCAmelCase    :						Dict[str, int] = None	,       _UpperCAmelCase    :						PILImageResampling = None	,       _UpperCAmelCase    :						bool = None	,       _UpperCAmelCase    :						Dict[str, int] = None	,       _UpperCAmelCase    :						bool = None	,       _UpperCAmelCase    :						float = None	,       _UpperCAmelCase    :						bool = None	,       _UpperCAmelCase    :						Optional[Union[float, List[float]]] = None	,       _UpperCAmelCase    :						Optional[Union[float, List[float]]] = None	,       _UpperCAmelCase    :						Optional[Union[str, TensorType]] = None	,       _UpperCAmelCase    :						ChannelDimension = ChannelDimension.FIRST	,       **_UpperCAmelCase    :						int	,       )				->		PIL.Image.Image:


             '''simple docstring'''




             UpperCAmelCase_    				=							do_resize if do_resize is not None else self.do_resize
             UpperCAmelCase_    				=							resample if resample is not None else self.resample
             UpperCAmelCase_    				=							do_center_crop if do_center_crop is not None else self.do_center_crop
             UpperCAmelCase_    				=							do_rescale if do_rescale is not None else self.do_rescale
             UpperCAmelCase_    				=							rescale_factor if rescale_factor is not None else self.rescale_factor
             UpperCAmelCase_    				=							do_normalize if do_normalize is not None else self.do_normalize
             UpperCAmelCase_    				=							image_mean if image_mean is not None else self.image_mean
             UpperCAmelCase_    				=							image_std if image_std is not None else self.image_std

             UpperCAmelCase_    				=							size if size is not None else self.size
             UpperCAmelCase_    				=							get_size_dict(_UpperCAmelCase	,       default_to_square=_UpperCAmelCase					)
             UpperCAmelCase_    				=							crop_size if crop_size is not None else self.crop_size
             UpperCAmelCase_    				=							get_size_dict(_UpperCAmelCase	,       param_name="crop_size"					)

             if not valid_images(_UpperCAmelCase					):
                   raise ValueError(
                       "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
                       "torch.Tensor, tf.Tensor or jax.ndarray."					)

             UpperCAmelCase_    				=							make_batched(_UpperCAmelCase					)

             UpperCAmelCase_    				=							[
                 [
                     self._preprocess_image(
                         image=_UpperCAmelCase	,       do_resize=_UpperCAmelCase	,       size=_UpperCAmelCase	,       resample=_UpperCAmelCase	,       do_center_crop=_UpperCAmelCase	,       crop_size=_UpperCAmelCase	,       do_rescale=_UpperCAmelCase	,       rescale_factor=_UpperCAmelCase	,       do_normalize=_UpperCAmelCase	,       image_mean=_UpperCAmelCase	,       image_std=_UpperCAmelCase	,       data_format=_UpperCAmelCase	,       )
                     for img in video
                 ]
                 for video in videos
             ]

             UpperCAmelCase_    				=							{"pixel_values": videos}
             return BatchFeature(data=_UpperCAmelCase	,       tensor_type=_UpperCAmelCase					)




