






"""simple docstring"""







from ...utils import is_note_seq_available, is_transformers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable


try:
  if not (is_transformers_available() and is_torch_available()):
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  from ...utils.dummy_torch_and_transformers_objects import *  # noqa F403
else:
  from .notes_encoder import SpectrogramNotesEncoder
  from .continous_encoder import SpectrogramContEncoder
  from .pipeline_spectrogram_diffusion import (
      SpectrogramContEncoder,
      SpectrogramDiffusionPipeline,
      TaFilmDecoder,
  )

try:
  if not (is_transformers_available() and is_torch_available() and is_note_seq_available()):
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  from ...utils.dummy_transformers_and_torch_and_note_seq_objects import *  # noqa F403
else:
  from .midi_utils import MidiProcessor











"""simple docstring"""







from typing import Dict, List, Optional, Union

import numpy as np

from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType

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 logging


if is_vision_available():
  import PIL

_UpperCamelCase					      =  logging.get_logger(__name__)

def _a			(		_snake_case							):







      """simple docstring"""






      if isinstance(_snake_case				,	(list, tuple)							) and isinstance(videos[0]				,	(list, tuple)							) and is_valid_image(videos[0][0]							):
            return videos

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

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

      raise ValueError(F'''Could not make batched video from {videos}'''							)







class  lowerCamelCase__ (	snake_case			):
    SCREAMING_SNAKE_CASE		=      ['''pixel_values''']






    def __init__(			self     ,A = True     ,A = None     ,A = PILImageResampling.BILINEAR     ,A = True     ,A = None     ,A = True     ,A = 1 / 255     ,A = True     ,A = True     ,A = None     ,A = None     ,**A     ,):
          super().__init__(**A )
          UpperCAmelCase          =	size if size is not None else {"""shortest_edge""": 256}
          UpperCAmelCase          =	get_size_dict(A     ,default_to_square=A )
          UpperCAmelCase          =	crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
          UpperCAmelCase          =	get_size_dict(A     ,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          =	offset
          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     _UpperCamelCase							(			self     ,A     ,A     ,A = PILImageResampling.BILINEAR     ,A = None     ,**A     ,):
          UpperCAmelCase          =	get_size_dict(A     ,default_to_square=A )
          if "shortest_edge" in size:
                UpperCAmelCase          =	get_resize_output_image_size(A     ,size["""shortest_edge"""]     ,default_to_square=A )
          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(A     ,size=A     ,resample=A     ,data_format=A     ,**A )






    def     _UpperCamelCase							(			self     ,A     ,A     ,A = None     ,**A     ,):
          UpperCAmelCase          =	get_size_dict(A )
          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(A     ,size=(size["""height"""], size["""width"""])     ,data_format=A     ,**A )






    def     _UpperCamelCase							(			self     ,A     ,A     ,A = True     ,A = None     ,**A     ,):
          UpperCAmelCase          =	image.astype(np.floataa )
          if offset:
                UpperCAmelCase          =	image - (scale / 2)
          return rescale(A     ,scale=A     ,data_format=A     ,**A )






    def     _UpperCamelCase							(			self     ,A     ,A     ,A     ,A = None     ,**A     ,):
          return normalize(A     ,mean=A     ,std=A     ,data_format=A     ,**A )






    def     _UpperCamelCase							(			self     ,A     ,A = None     ,A = None     ,A = None     ,A = None     ,A = None     ,A = None     ,A = None     ,A = None     ,A = None     ,A = None     ,A = None     ,A = ChannelDimension.FIRST     ,):
          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.""" )

          if offset and not do_rescale:
                raise ValueError("""For offset, do_rescale must also be set to True.""" )

          # All transformations expect numpy arrays.
          UpperCAmelCase          =	to_numpy_array(A )

          if do_resize:
                UpperCAmelCase          =	self.resize(image=A     ,size=A     ,resample=A )

          if do_center_crop:
                UpperCAmelCase          =	self.center_crop(A     ,size=A )

          if do_rescale:
                UpperCAmelCase          =	self.rescale(image=A     ,scale=A     ,offset=A )

          if do_normalize:
                UpperCAmelCase          =	self.normalize(image=A     ,mean=A     ,std=A )

          UpperCAmelCase          =	to_channel_dimension_format(A     ,A )
          return image






    def     _UpperCamelCase							(			self     ,A     ,A = None     ,A = None     ,A = None     ,A = None     ,A = None     ,A = None     ,A = None     ,A = None     ,A = None     ,A = None     ,A = None     ,A = None     ,A = ChannelDimension.FIRST     ,**A     ,):
          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          =	offset if offset is not None else self.offset
          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(A     ,default_to_square=A )
          UpperCAmelCase          =	crop_size if crop_size is not None else self.crop_size
          UpperCAmelCase          =	get_size_dict(A     ,param_name="""crop_size""" )

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

          UpperCAmelCase          =	make_batched(A )

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

          UpperCAmelCase          =	{"""pixel_values""": videos}
          return BatchFeature(data=A     ,tensor_type=A )



