



'''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, is_vision_available, logging


if is_vision_available():
					import PIL


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






class 				lowercase__      (				lowercase  ):
	lowercase__    							=     ["""pixel_values"""]





	def __init__(					self   :							Optional[int]					,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]					,):







							'''simple docstring'''
							super().__init__(**lowerCamelCase__					)
							_UpperCamelCase   :						str												=  size if size is not None else {'shortest_edge': 384}
							_UpperCamelCase   :						Dict												=  get_size_dict(lowerCamelCase__					,default_to_square=lowerCamelCase__					)

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





	def       UpperCamelCase_    (					self   :							Dict					,lowerCamelCase__   :							np.ndarray					,lowerCamelCase__   :							Dict[str, int]					,lowerCamelCase__   :							float					,lowerCamelCase__   :							PILImageResampling = PILImageResampling.BICUBIC					,lowerCamelCase__   :							Optional[Union[str, ChannelDimension]] = None					,**lowerCamelCase__   :							Union[str, Any]					,):







							'''simple docstring'''
							_UpperCamelCase   :						Optional[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()}'					)
							_UpperCamelCase   :						List[Any]												=  size['shortest_edge']

							if shortest_edge < 384:
													# maintain same ratio, resizing shortest edge to shortest_edge/crop_pct
													_UpperCamelCase   :						List[Any]												=  int(shortest_edge / crop_pct					)
													_UpperCamelCase   :						Tuple												=  get_resize_output_image_size(lowerCamelCase__					,size=lowerCamelCase__					,default_to_square=lowerCamelCase__					)
													_UpperCamelCase   :						int												=  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       UpperCamelCase_    (					self   :							Union[str, Any]					,lowerCamelCase__   :							np.ndarray					,lowerCamelCase__   :							Union[int, float]					,lowerCamelCase__   :							Optional[Union[str, ChannelDimension]] = None					,**lowerCamelCase__   :							Any					,):







							'''simple docstring'''
							return rescale(lowerCamelCase__					,scale=lowerCamelCase__					,data_format=lowerCamelCase__					,**lowerCamelCase__					)





	def       UpperCamelCase_    (					self   :							Any					,lowerCamelCase__   :							np.ndarray					,lowerCamelCase__   :							Union[float, List[float]]					,lowerCamelCase__   :							Union[float, List[float]]					,lowerCamelCase__   :							Optional[Union[str, ChannelDimension]] = None					,**lowerCamelCase__   :							Optional[int]					,):







							'''simple docstring'''
							return normalize(lowerCamelCase__					,mean=lowerCamelCase__					,std=lowerCamelCase__					,data_format=lowerCamelCase__					,**lowerCamelCase__					)





	def       UpperCamelCase_    (					self   :							List[Any]					,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__   :							str					,):







							'''simple docstring'''
							_UpperCamelCase   :						Tuple												=  do_resize if do_resize is not None else self.do_resize
							_UpperCamelCase   :						List[str]												=  crop_pct if crop_pct is not None else self.crop_pct
							_UpperCamelCase   :						int												=  resample if resample is not None else self.resample
							_UpperCamelCase   :						List[str]												=  do_rescale if do_rescale is not None else self.do_rescale
							_UpperCamelCase   :						Tuple												=  rescale_factor if rescale_factor is not None else self.rescale_factor
							_UpperCamelCase   :						Any												=  do_normalize if do_normalize is not None else self.do_normalize
							_UpperCamelCase   :						Optional[Any]												=  image_mean if image_mean is not None else self.image_mean
							_UpperCamelCase   :						Tuple												=  image_std if image_std is not None else self.image_std

							_UpperCamelCase   :						List[Any]												=  size if size is not None else self.size
							_UpperCamelCase   :						Any												=  get_size_dict(lowerCamelCase__					,default_to_square=lowerCamelCase__					)

							_UpperCamelCase   :						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.
							_UpperCamelCase   :						Tuple												=  [to_numpy_array(lowerCamelCase__					) for image in images]

							if do_resize:
													_UpperCamelCase   :						List[Any]												=  [self.resize(image=lowerCamelCase__					,size=lowerCamelCase__					,crop_pct=lowerCamelCase__					,resample=lowerCamelCase__					) for image in images]

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

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

							_UpperCamelCase   :						Optional[Any]												=  [to_channel_dimension_format(lowerCamelCase__					,lowerCamelCase__					) for image in images]

							_UpperCamelCase   :						Tuple												=  {'pixel_values': images}
							return BatchFeature(data=lowerCamelCase__					,tensor_type=lowerCamelCase__					)







'''simple docstring'''
import os
from pathlib import Path






def A__       (	):
						from torch.utils.cpp_extension import load

						_UpperCamelCase   :						Optional[Any]												=  Path(UpperCAmelCase_				).resolve().parent.parent.parent / 'kernels' / 'deformable_detr'
						_UpperCamelCase   :						Tuple												=  [
						    root / filename
						    for filename in [
						        'vision.cpp',
						        os.path.join('cpu'    ,    'ms_deform_attn_cpu.cpp'				),
						        os.path.join('cuda'    ,    'ms_deform_attn_cuda.cu'				),
						    ]
						]

						load(
						    'MultiScaleDeformableAttention'    ,    UpperCAmelCase_    ,    with_cuda=UpperCAmelCase_    ,    extra_include_paths=[str(UpperCAmelCase_				)]    ,    extra_cflags=['-DWITH_CUDA=1']    ,    extra_cuda_cflags=[
						        '-DCUDA_HAS_FP16=1',
						        '-D__CUDA_NO_HALF_OPERATORS__',
						        '-D__CUDA_NO_HALF_CONVERSIONS__',
						        '-D__CUDA_NO_HALF2_OPERATORS__',
						    ]    ,    )

						import MultiScaleDeformableAttention as MSDA

						return MSDA


