


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, is_vision_available, logging


if is_vision_available():
			import PIL


_snake_case					:		Union[str, Any]      =    logging.get_logger(__name__)

class     a		(_lowerCAmelCase				):




							"""simple docstring"""


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

							def __init__(							self	:  Optional[Any]   ,     lowerCamelCase	:  bool = True   ,     lowerCamelCase	:  Dict[str, int] = None   ,     lowerCamelCase	:  float = None   ,     lowerCamelCase	:  PILImageResampling = PILImageResampling.BILINEAR   ,     lowerCamelCase	:  bool = True   ,     lowerCamelCase	:  Union[int, float] = 1 / 255   ,     lowerCamelCase	:  bool = True   ,     lowerCamelCase	:  Optional[Union[float, List[float]]] = None   ,     lowerCamelCase	:  Optional[Union[float, List[float]]] = None   ,     **lowerCamelCase	:  Optional[Any]   ,     )     ->   None:
														super().__init__(**lowerCamelCase    )
														__snake_case		:      Optional[Any]										=       size if size is not None else {"shortest_edge": 384}
														__snake_case		:      Any										=       get_size_dict(lowerCamelCase   ,     default_to_square=lowerCamelCase    )

														__snake_case		:      str										=       do_resize
														__snake_case		:      List[str]										=       size
														# Default value set here for backwards compatibility where the value in config is None
														__snake_case		:      Dict										=       crop_pct if crop_pct is not None else 224 / 256
														__snake_case		:      List[str]										=       resample
														__snake_case		:      List[str]										=       do_rescale
														__snake_case		:      List[Any]										=       rescale_factor
														__snake_case		:      Any										=       do_normalize
														__snake_case		:      Tuple										=       image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
														__snake_case		:      Any										=       image_std if image_std is not None else IMAGENET_STANDARD_STD

							def    __snake_case (							self	:  str   ,     lowerCamelCase	:  np.ndarray   ,     lowerCamelCase	:  Dict[str, int]   ,     lowerCamelCase	:  float   ,     lowerCamelCase	:  PILImageResampling = PILImageResampling.BICUBIC   ,     lowerCamelCase	:  Optional[Union[str, ChannelDimension]] = None   ,     **lowerCamelCase	:  Union[str, Any]   ,     )     ->   np.ndarray:
														__snake_case		:      List[Any]										=       get_size_dict(lowerCamelCase   ,     default_to_square=lowerCamelCase    )
														if "shortest_edge" not in size:
																					raise ValueError(F'Size dictionary must contain \'shortest_edge\' key. Got {size.keys()}'    )
														__snake_case		:      Tuple										=       size["shortest_edge"]

														if shortest_edge < 384:
																					# maintain same ratio, resizing shortest edge to shortest_edge/crop_pct
																					__snake_case		:      Optional[Any]										=       int(shortest_edge / crop_pct    )
																					__snake_case		:      Optional[int]										=       get_resize_output_image_size(lowerCamelCase   ,     size=lowerCamelCase   ,     default_to_square=lowerCamelCase    )
																					__snake_case		:      List[str]										=       resize(image=lowerCamelCase   ,     size=lowerCamelCase   ,     resample=lowerCamelCase   ,     data_format=lowerCamelCase   ,     **lowerCamelCase    )
																					# then crop to (shortest_edge, shortest_edge)
																					return center_crop(image=lowerCamelCase   ,     size=(shortest_edge, shortest_edge)   ,     data_format=lowerCamelCase   ,     **lowerCamelCase    )
														else:
																					# warping (no cropping) when evaluated at 384 or larger
																					return resize(
																					    lowerCamelCase   ,     size=(shortest_edge, shortest_edge)   ,     resample=lowerCamelCase   ,     data_format=lowerCamelCase   ,     **lowerCamelCase    )

							def    __snake_case (							self	:  List[Any]   ,     lowerCamelCase	:  np.ndarray   ,     lowerCamelCase	:  Union[int, float]   ,     lowerCamelCase	:  Optional[Union[str, ChannelDimension]] = None   ,     **lowerCamelCase	:  Dict   ,     )     ->   str:
														return rescale(lowerCamelCase   ,     scale=lowerCamelCase   ,     data_format=lowerCamelCase   ,     **lowerCamelCase    )

							def    __snake_case (							self	:  Any   ,     lowerCamelCase	:  np.ndarray   ,     lowerCamelCase	:  Union[float, List[float]]   ,     lowerCamelCase	:  Union[float, List[float]]   ,     lowerCamelCase	:  Optional[Union[str, ChannelDimension]] = None   ,     **lowerCamelCase	:  Union[str, Any]   ,     )     ->   np.ndarray:
														return normalize(lowerCamelCase   ,     mean=lowerCamelCase   ,     std=lowerCamelCase   ,     data_format=lowerCamelCase   ,     **lowerCamelCase    )






							def    __snake_case (							self	:  int   ,     lowerCamelCase	:  ImageInput   ,     lowerCamelCase	:  bool = None   ,     lowerCamelCase	:  Dict[str, int] = None   ,     lowerCamelCase	:  float = None   ,     lowerCamelCase	:  PILImageResampling = None   ,     lowerCamelCase	:  bool = None   ,     lowerCamelCase	:  float = None   ,     lowerCamelCase	:  bool = None   ,     lowerCamelCase	:  Optional[Union[float, List[float]]] = None   ,     lowerCamelCase	:  Optional[Union[float, List[float]]] = None   ,     lowerCamelCase	:  Optional[Union[str, TensorType]] = None   ,     lowerCamelCase	:  ChannelDimension = ChannelDimension.FIRST   ,     **lowerCamelCase	:  List[str]   ,     )     ->   PIL.Image.Image:
														__snake_case		:      Any										=       do_resize if do_resize is not None else self.do_resize
														__snake_case		:      str										=       crop_pct if crop_pct is not None else self.crop_pct
														__snake_case		:      Any										=       resample if resample is not None else self.resample
														__snake_case		:      Optional[int]										=       do_rescale if do_rescale is not None else self.do_rescale
														__snake_case		:      List[str]										=       rescale_factor if rescale_factor is not None else self.rescale_factor
														__snake_case		:      Union[str, Any]										=       do_normalize if do_normalize is not None else self.do_normalize
														__snake_case		:      List[str]										=       image_mean if image_mean is not None else self.image_mean
														__snake_case		:      int										=       image_std if image_std is not None else self.image_std

														__snake_case		:      str										=       size if size is not None else self.size
														__snake_case		:      int										=       get_size_dict(lowerCamelCase   ,     default_to_square=lowerCamelCase    )

														__snake_case		:      List[str]										=       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 or resample is None:
																					raise ValueError("Size and resample must be specified if do_resize is True."    )

														if do_resize and size["shortest_edge"] < 384 and crop_pct is None:
																					raise ValueError("crop_pct must be specified if size < 384."    )

														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.
														__snake_case		:      int										=       [to_numpy_array(lowerCamelCase    ) for image in images]

														if do_resize:
																					__snake_case		:      List[str]										=       [self.resize(image=lowerCamelCase   ,     size=lowerCamelCase   ,     crop_pct=lowerCamelCase   ,     resample=lowerCamelCase    ) for image in images]

														if do_rescale:
																					__snake_case		:      Optional[Any]										=       [self.rescale(image=lowerCamelCase   ,     scale=lowerCamelCase    ) for image in images]

														if do_normalize:
																					__snake_case		:      Dict										=       [self.normalize(image=lowerCamelCase   ,     mean=lowerCamelCase   ,     std=lowerCamelCase    ) for image in images]

														__snake_case		:      List[Any]										=       [to_channel_dimension_format(lowerCamelCase   ,     lowerCamelCase    ) for image in images]

														__snake_case		:      Union[str, Any]										=       {"pixel_values": images}
														return BatchFeature(data=lowerCamelCase   ,     tensor_type=lowerCamelCase    )





from ..utils import DummyObject, requires_backends

class     a		(metaclass=_lowerCAmelCase				):




							"""simple docstring"""


							__UpperCAmelCase   :							int					  =  ["speech"]

							def __init__(							self	:  List[Any]   ,     *lowerCamelCase	:  List[Any]   ,     **lowerCamelCase	:  Optional[Any]    )     ->   Dict:
														requires_backends(self   ,     ["speech"]    )

class     a		(metaclass=_lowerCAmelCase				):




							"""simple docstring"""


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

							def __init__(							self	:  int   ,     *lowerCamelCase	:  List[Any]   ,     **lowerCamelCase	:  List[Any]    )     ->   Optional[int]:
														requires_backends(self   ,     ["speech"]    )

