





import warnings

from ...utils import logging
from .image_processing_deformable_detr import DeformableDetrImageProcessor


A_												=     logging.get_logger(__name__)

class SCREAMING_SNAKE_CASE_ (					lowercase_   ):





					"""simple docstring"""


					def __init__( self	,  *_lowerCAmelCase	,  **_lowerCAmelCase	):
										warnings.warn(
										    "The class DeformableDetrFeatureExtractor is deprecated and will be removed in version 5 of Transformers."
										    " Please use DeformableDetrImageProcessor instead."	,  __lowerCAmelCase	,  )
										super().__init__(*__lowerCAmelCase	,  **__lowerCAmelCase	)








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

A_												=     logging.get_logger(__name__)





def    __UpperCamelCase      (    a) ->List[List[ImageInput]]:
					if isinstance(a,     (list, tuple)) and isinstance(videos[0],     (list, tuple)) and is_valid_image(videos[0][0]):
										return videos

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

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

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







class SCREAMING_SNAKE_CASE_ (					lowercase_   ):





					"""simple docstring"""


					A__				 =    ["pixel_values"]





					def __init__( self	,  _lowerCAmelCase = True	,  _lowerCAmelCase = None	,  _lowerCAmelCase = PILImageResampling.BILINEAR	,  _lowerCAmelCase = True	,  _lowerCAmelCase = None	,  _lowerCAmelCase = True	,  _lowerCAmelCase = 1 / 255	,  _lowerCAmelCase = True	,  _lowerCAmelCase = True	,  _lowerCAmelCase = None	,  _lowerCAmelCase = None	,  **_lowerCAmelCase	,  ):
										super().__init__(**_lowerCAmelCase	)
										lowerCamelCase__       =       size if size is not None else {"shortest_edge": 256}
										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	,  param_name="crop_size"	)

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





					def 					__magic_name__ ( self	,  _lowerCAmelCase	,  _lowerCAmelCase	,  _lowerCAmelCase = PILImageResampling.BILINEAR	,  _lowerCAmelCase = None	,  **_lowerCAmelCase	,  ):
										lowerCamelCase__       =       get_size_dict(_lowerCAmelCase	,  default_to_square=_lowerCAmelCase	)
										if "shortest_edge" in size:
															lowerCamelCase__       =       get_resize_output_image_size(_lowerCAmelCase	,  size["shortest_edge"]	,  default_to_square=_lowerCAmelCase	)
										elif "height" in size and "width" in size:
															lowerCamelCase__       =       (size["height"], size["width"])
										else:
															raise ValueError(F"Size must have 'height' and 'width' or 'shortest_edge' as keys. Got {size.keys()}"	)
										return resize(_lowerCAmelCase	,  size=_lowerCAmelCase	,  resample=_lowerCAmelCase	,  data_format=_lowerCAmelCase	,  **_lowerCAmelCase	)





					def 					__magic_name__ ( self	,  _lowerCAmelCase	,  _lowerCAmelCase	,  _lowerCAmelCase = None	,  **_lowerCAmelCase	,  ):
										lowerCamelCase__       =       get_size_dict(_lowerCAmelCase	)
										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(_lowerCAmelCase	,  size=(size["height"], size["width"])	,  data_format=_lowerCAmelCase	,  **_lowerCAmelCase	)





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





					def 					__magic_name__ ( self	,  _lowerCAmelCase	,  _lowerCAmelCase	,  _lowerCAmelCase	,  _lowerCAmelCase = None	,  **_lowerCAmelCase	,  ):
										return normalize(_lowerCAmelCase	,  mean=_lowerCAmelCase	,  std=_lowerCAmelCase	,  data_format=_lowerCAmelCase	,  **_lowerCAmelCase	)





					def 					__magic_name__ ( 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 = 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.
										lowerCamelCase__       =       to_numpy_array(_lowerCAmelCase	)

										if do_resize:
															lowerCamelCase__       =       self.resize(image=_lowerCAmelCase	,  size=_lowerCAmelCase	,  resample=_lowerCAmelCase	)

										if do_center_crop:
															lowerCamelCase__       =       self.center_crop(_lowerCAmelCase	,  size=_lowerCAmelCase	)

										if do_rescale:
															lowerCamelCase__       =       self.rescale(image=_lowerCAmelCase	,  scale=_lowerCAmelCase	,  offset=_lowerCAmelCase	)

										if do_normalize:
															lowerCamelCase__       =       self.normalize(image=_lowerCAmelCase	,  mean=_lowerCAmelCase	,  std=_lowerCAmelCase	)

										lowerCamelCase__       =       to_channel_dimension_format(_lowerCAmelCase	,  _lowerCAmelCase	)
										return image






					def 					__magic_name__ ( 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__       =       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__       =       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__       =       offset if offset is not None else self.offset
										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__       =       size if size is not None else self.size
										lowerCamelCase__       =       get_size_dict(_lowerCAmelCase	,  default_to_square=_lowerCAmelCase	)
										lowerCamelCase__       =       crop_size if crop_size is not None else self.crop_size
										lowerCamelCase__       =       get_size_dict(_lowerCAmelCase	,  param_name="crop_size"	)

										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."	)

										lowerCamelCase__       =       make_batched(_lowerCAmelCase	)

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

										lowerCamelCase__       =       {"pixel_values": videos}
										return BatchFeature(data=_lowerCAmelCase	,  tensor_type=_lowerCAmelCase	)

