"""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,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


a							=logging.get_logger(__name__)


if is_vision_available():
	import PIL






class    __UpperCAmelCase						(	__lowerCAmelCase	):
				A__      :   Union[str, Any]  							=       ['''pixel_values''']

				def __init__(      self	, _lowerCamelCase = True	, _lowerCamelCase = None	, _lowerCamelCase = PILImageResampling.BICUBIC	, _lowerCamelCase = True	, _lowerCamelCase = None	, _lowerCamelCase = True	, _lowerCamelCase = 1 / 255	, _lowerCamelCase = True	, _lowerCamelCase = None	, _lowerCamelCase = None	, _lowerCamelCase = True	, **_lowerCamelCase	, ):
										super().__init__(**_lowerCamelCase      )
										lowerCamelCase__					=size if size is not None else {"shortest_edge": 224}
										lowerCamelCase__					=get_size_dict(_lowerCamelCase	, default_to_square=_lowerCamelCase      )
										lowerCamelCase__					=crop_size if crop_size is not None else {"height": 224, "width": 224}
										lowerCamelCase__					=get_size_dict(_lowerCamelCase      )

										lowerCamelCase__					=do_resize
										lowerCamelCase__					=size
										lowerCamelCase__					=resample
										lowerCamelCase__					=do_center_crop
										lowerCamelCase__					=crop_size
										lowerCamelCase__					=do_rescale
										lowerCamelCase__					=rescale_factor
										lowerCamelCase__					=do_normalize
										lowerCamelCase__					=image_mean if image_mean is not None else OPENAI_CLIP_MEAN
										lowerCamelCase__					=image_std if image_std is not None else OPENAI_CLIP_STD
										lowerCamelCase__					=do_convert_rgb

				def 		_a	(      self	, _lowerCamelCase	, _lowerCamelCase	, _lowerCamelCase = PILImageResampling.BICUBIC	, _lowerCamelCase = None	, **_lowerCamelCase	, ):
										lowerCamelCase__					=get_size_dict(_lowerCamelCase	, default_to_square=_lowerCamelCase      )
										lowerCamelCase__					=get_resize_output_image_size(
										    _lowerCamelCase	, size=(size["height"], size["width"])	, default_to_square=_lowerCamelCase      )
										return resize(_lowerCamelCase	, size=_lowerCamelCase	, resample=_lowerCamelCase	, data_format=_lowerCamelCase	, **_lowerCamelCase      )

				def 		_a	(      self	, _lowerCamelCase	, _lowerCamelCase	, _lowerCamelCase = None	, **_lowerCamelCase	, ):
										lowerCamelCase__					=get_size_dict(_lowerCamelCase      )
										return center_crop(_lowerCamelCase	, size=(size["height"], size["width"])	, data_format=_lowerCamelCase	, **_lowerCamelCase      )

				def 		_a	(      self	, _lowerCamelCase	, _lowerCamelCase	, _lowerCamelCase = None	, **_lowerCamelCase	, ):
										return rescale(_lowerCamelCase	, scale=_lowerCamelCase	, data_format=_lowerCamelCase	, **_lowerCamelCase      )

				def 		_a	(      self	, _lowerCamelCase	, _lowerCamelCase	, _lowerCamelCase	, _lowerCamelCase = None	, **_lowerCamelCase	, ):
										return normalize(_lowerCamelCase	, mean=_lowerCamelCase	, std=_lowerCamelCase	, data_format=_lowerCamelCase	, **_lowerCamelCase      )






				def 		_a	(      self	, _lowerCamelCase	, _lowerCamelCase = None	, _lowerCamelCase = None	, _lowerCamelCase = None	, _lowerCamelCase = None	, _lowerCamelCase = None	, _lowerCamelCase = None	, _lowerCamelCase = None	, _lowerCamelCase = None	, _lowerCamelCase = None	, _lowerCamelCase = None	, _lowerCamelCase = None	, _lowerCamelCase = None	, _lowerCamelCase = ChannelDimension.FIRST	, **_lowerCamelCase	, ):
										lowerCamelCase__					=do_resize if do_resize is not None else self.do_resize
										lowerCamelCase__					=size if size is not None else self.size
										lowerCamelCase__					=get_size_dict(_lowerCamelCase	, default_to_square=_lowerCamelCase      )
										lowerCamelCase__					=resample if resample is not None else self.resample
										lowerCamelCase__					=do_center_crop if do_center_crop is not None else self.do_center_crop
										lowerCamelCase__					=crop_size if crop_size is not None else self.crop_size
										lowerCamelCase__					=get_size_dict(_lowerCamelCase      )
										lowerCamelCase__					=do_rescale if do_rescale is not None else self.do_rescale
										lowerCamelCase__					=rescale_factor if rescale_factor is not None else self.rescale_factor
										lowerCamelCase__					=do_normalize if do_normalize is not None else self.do_normalize
										lowerCamelCase__					=image_mean if image_mean is not None else self.image_mean
										lowerCamelCase__					=image_std if image_std is not None else self.image_std
										lowerCamelCase__					=do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

										lowerCamelCase__					=make_list_of_images(_lowerCamelCase      )

										if not valid_images(_lowerCamelCase      ):
																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."      )

										# PIL RGBA images are converted to RGB
										if do_convert_rgb:
																lowerCamelCase__					=[convert_to_rgb(_lowerCamelCase      ) for image in images]

										# All transformations expect numpy arrays.
										lowerCamelCase__					=[to_numpy_array(_lowerCamelCase      ) for image in images]

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

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

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

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

										lowerCamelCase__					=[to_channel_dimension_format(_lowerCamelCase	, _lowerCamelCase      ) for image in images]

										lowerCamelCase__					={"pixel_values": images}
										return BatchFeature(data=_lowerCamelCase	, tensor_type=_lowerCamelCase      )



"""simple docstring"""


from ..utils import DummyObject, requires_backends






class    __UpperCAmelCase						(	metaclass=__lowerCAmelCase	):
				A__      :   List[Any]  							=       ['''torch''', '''librosa''']

				def __init__(      self	, *_lowerCamelCase	, **_lowerCamelCase      ):
										requires_backends(self	, ["torch", "librosa"]      )

				@classmethod
				def 		_a	(      cls	, *_lowerCamelCase	, **_lowerCamelCase      ):
										requires_backends(cls	, ["torch", "librosa"]      )






				@classmethod
				def 		_a	(      cls	, *_lowerCamelCase	, **_lowerCamelCase      ):
										requires_backends(cls	, ["torch", "librosa"]      )






class    __UpperCAmelCase						(	metaclass=__lowerCAmelCase	):
				A__      :   List[str]  							=       ['''torch''', '''librosa''']

				def __init__(      self	, *_lowerCamelCase	, **_lowerCamelCase      ):
										requires_backends(self	, ["torch", "librosa"]      )

				@classmethod
				def 		_a	(      cls	, *_lowerCamelCase	, **_lowerCamelCase      ):
										requires_backends(cls	, ["torch", "librosa"]      )






				@classmethod
				def 		_a	(      cls	, *_lowerCamelCase	, **_lowerCamelCase      ):
										requires_backends(cls	, ["torch", "librosa"]      )


