

"""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,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


_a							=							logging.get_logger(__name__)




class  _lowerCAmelCase  (     lowercase       ):







   """simple docstring"""







   __UpperCAmelCase     : Optional[Any]       		=     ["pixel_values"]


   def __init__(      self							:      int, UpperCAmelCase__							:      bool = True, UpperCAmelCase__							:      Optional[Dict[str, int]] = None, UpperCAmelCase__							:      PILImageResampling = PILImageResampling.BILINEAR, UpperCAmelCase__							:      bool = True, UpperCAmelCase__							:      Dict[str, int] = None, UpperCAmelCase__							:      bool = True, UpperCAmelCase__							:      Union[int, float] = 1 / 2_5_5, UpperCAmelCase__							:      bool = True, UpperCAmelCase__							:      Optional[Union[float, List[float]]] = None, UpperCAmelCase__							:      Optional[Union[float, List[float]]] = None, **UpperCAmelCase__							:      str, ):
      super().__init__(**UpperCAmelCase__     )
      __lowercase								=						size if size is not None else {"shortest_edge": 2_5_6}
      __lowercase								=						get_size_dict(UpperCAmelCase__, default_to_square=UpperCAmelCase__     )
      __lowercase								=						crop_size if crop_size is not None else {"height": 2_2_4, "width": 2_2_4}
      __lowercase								=						get_size_dict(UpperCAmelCase__     )
      __lowercase								=						do_resize
      __lowercase								=						size
      __lowercase								=						resample
      __lowercase								=						do_center_crop
      __lowercase								=						crop_size
      __lowercase								=						do_rescale
      __lowercase								=						rescale_factor
      __lowercase								=						do_normalize
      __lowercase								=						image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
      __lowercase								=						image_std if image_std is not None else IMAGENET_STANDARD_STD


   def  _lowercase			(      self							:      int, UpperCAmelCase__							:      np.ndarray, UpperCAmelCase__							:      Dict[str, int], UpperCAmelCase__							:      PILImageResampling = PILImageResampling.BICUBIC, UpperCAmelCase__							:      Optional[Union[str, ChannelDimension]] = None, **UpperCAmelCase__							:      Dict, ):
      __lowercase								=						get_size_dict(UpperCAmelCase__, default_to_square=UpperCAmelCase__     )
      if "shortest_edge" not in size:
         raise ValueError(F"""The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}"""     )
      __lowercase								=						get_resize_output_image_size(UpperCAmelCase__, size=size["shortest_edge"], default_to_square=UpperCAmelCase__     )
      return resize(UpperCAmelCase__, size=UpperCAmelCase__, resample=UpperCAmelCase__, data_format=UpperCAmelCase__, **UpperCAmelCase__     )


   def  _lowercase			(      self							:      Dict, UpperCAmelCase__							:      np.ndarray, UpperCAmelCase__							:      Dict[str, int], UpperCAmelCase__							:      Optional[Union[str, ChannelDimension]] = None, **UpperCAmelCase__							:      Dict, ):
      __lowercase								=						get_size_dict(UpperCAmelCase__     )
      return center_crop(UpperCAmelCase__, size=(size["height"], size["width"]), data_format=UpperCAmelCase__, **UpperCAmelCase__     )


   def  _lowercase			(      self							:      Optional[int], UpperCAmelCase__							:      np.ndarray, UpperCAmelCase__							:      float, UpperCAmelCase__							:      Optional[Union[str, ChannelDimension]] = None, **UpperCAmelCase__							:      Union[str, Any]     ):
      return rescale(UpperCAmelCase__, scale=UpperCAmelCase__, data_format=UpperCAmelCase__, **UpperCAmelCase__     )


   def  _lowercase			(      self							:      List[Any], UpperCAmelCase__							:      np.ndarray, UpperCAmelCase__							:      Union[float, List[float]], UpperCAmelCase__							:      Union[float, List[float]], UpperCAmelCase__							:      Optional[Union[str, ChannelDimension]] = None, **UpperCAmelCase__							:      int, ):
      return normalize(UpperCAmelCase__, mean=UpperCAmelCase__, std=UpperCAmelCase__, data_format=UpperCAmelCase__, **UpperCAmelCase__     )



   def  _lowercase			(      self							:      Any, UpperCAmelCase__							:      ImageInput, UpperCAmelCase__							:      Optional[bool] = None, UpperCAmelCase__							:      Dict[str, int] = None, UpperCAmelCase__							:      PILImageResampling = None, UpperCAmelCase__							:      bool = None, UpperCAmelCase__							:      Dict[str, int] = None, UpperCAmelCase__							:      Optional[bool] = None, UpperCAmelCase__							:      Optional[float] = None, UpperCAmelCase__							:      Optional[bool] = None, UpperCAmelCase__							:      Optional[Union[float, List[float]]] = None, UpperCAmelCase__							:      Optional[Union[float, List[float]]] = None, UpperCAmelCase__							:      Optional[Union[str, TensorType]] = None, UpperCAmelCase__							:      Union[str, ChannelDimension] = ChannelDimension.FIRST, **UpperCAmelCase__							:      Optional[int], ):
      __lowercase								=						do_resize if do_resize is not None else self.do_resize
      __lowercase								=						size if size is not None else self.size
      __lowercase								=						get_size_dict(UpperCAmelCase__, default_to_square=UpperCAmelCase__     )
      __lowercase								=						resample if resample is not None else self.resample
      __lowercase								=						do_center_crop if do_center_crop is not None else self.do_center_crop
      __lowercase								=						crop_size if crop_size is not None else self.crop_size
      __lowercase								=						get_size_dict(UpperCAmelCase__     )
      __lowercase								=						do_rescale if do_rescale is not None else self.do_rescale
      __lowercase								=						rescale_factor if rescale_factor is not None else self.rescale_factor
      __lowercase								=						do_normalize if do_normalize is not None else self.do_normalize
      __lowercase								=						image_mean if image_mean is not None else self.image_mean
      __lowercase								=						image_std if image_std is not None else self.image_std

      __lowercase								=						make_list_of_images(UpperCAmelCase__     )

      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."     )

      if do_resize and size is None:
         raise ValueError("Size 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.
      __lowercase								=						[to_numpy_array(UpperCAmelCase__     ) for image in images]

      if do_resize:
         __lowercase								=						[self.resize(image=UpperCAmelCase__, size=UpperCAmelCase__, resample=UpperCAmelCase__     ) for image in images]

      if do_center_crop:
         __lowercase								=						[self.center_crop(image=UpperCAmelCase__, size=UpperCAmelCase__     ) for image in images]

      if do_rescale:
         __lowercase								=						[self.rescale(image=UpperCAmelCase__, scale=UpperCAmelCase__     ) for image in images]

      if do_normalize:
         __lowercase								=						[self.normalize(image=UpperCAmelCase__, mean=UpperCAmelCase__, std=UpperCAmelCase__     ) for image in images]

      __lowercase								=						[to_channel_dimension_format(UpperCAmelCase__, UpperCAmelCase__     ) for image in images]

      __lowercase								=						{"pixel_values": images}
      return BatchFeature(data=UpperCAmelCase__, tensor_type=UpperCAmelCase__     )







"""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




