





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


A   : List[Any]			=				{
    '''configuration_conditional_detr''': [
        '''CONDITIONAL_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''ConditionalDetrConfig''',
        '''ConditionalDetrOnnxConfig''',
    ]
}

try:
					if not is_vision_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					A   : Optional[Any]			=				['''ConditionalDetrFeatureExtractor''']
					A   : int			=				['''ConditionalDetrImageProcessor''']

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					A   : List[str]			=				[
					    '''CONDITIONAL_DETR_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''ConditionalDetrForObjectDetection''',
					    '''ConditionalDetrForSegmentation''',
					    '''ConditionalDetrModel''',
					    '''ConditionalDetrPreTrainedModel''',
					]


if TYPE_CHECKING:
					from .configuration_conditional_detr import (
					    CONDITIONAL_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP,
					    ConditionalDetrConfig,
					    ConditionalDetrOnnxConfig,
					)

					try:
										if not is_vision_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .feature_extraction_conditional_detr import ConditionalDetrFeatureExtractor
										from .image_processing_conditional_detr import ConditionalDetrImageProcessor

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_conditional_detr import (
										    CONDITIONAL_DETR_PRETRAINED_MODEL_ARCHIVE_LIST,
										    ConditionalDetrForObjectDetection,
										    ConditionalDetrForSegmentation,
										    ConditionalDetrModel,
										    ConditionalDetrPreTrainedModel,
										)

else:
					import sys

					A   : Dict			=				_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)










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_DEFAULT_MEAN,
    IMAGENET_DEFAULT_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


A   : int			=				logging.get_logger(__name__)


class   A      (SCREAMING_SNAKE_CASE   ):
			'''simple docstring'''




			__lowerCamelCase     :	Optional[Any]          =   ['''pixel_values''']






			def __init__(      self       :   Dict							,     __lowerCAmelCase       :   bool = True							,     __lowerCAmelCase       :   Dict[str, int] = None							,     __lowerCAmelCase       :   int = 0.9							,     __lowerCAmelCase       :   PILImageResampling = PILImageResampling.BICUBIC							,     __lowerCAmelCase       :   bool = True							,     __lowerCAmelCase       :   Dict[str, int] = None							,     __lowerCAmelCase       :   Union[int, float] = 1 / 2_55							,     __lowerCAmelCase       :   bool = True							,     __lowerCAmelCase       :   bool = True							,     __lowerCAmelCase       :   Optional[Union[float, List[float]]] = None							,     __lowerCAmelCase       :   Optional[Union[float, List[float]]] = None							,     **__lowerCAmelCase       :   Optional[int]							,     )     ->     None:







					"""simple docstring"""






					super().__init__(**__lowerCAmelCase       )
					A__  				=       size if size is not None else {"""shortest_edge""": 2_24}
					A__  				=       get_size_dict(__lowerCAmelCase							,     default_to_square=__lowerCAmelCase       )
					A__  				=       crop_size if crop_size is not None else {"""height""": 2_24, """width""": 2_24}
					A__  				=       get_size_dict(__lowerCAmelCase							,     param_name="""crop_size"""       )

					A__  				=       do_resize
					A__  				=       size
					A__  				=       crop_pct
					A__  				=       resample
					A__  				=       do_center_crop
					A__  				=       crop_size
					A__  				=       do_rescale
					A__  				=       rescale_factor
					A__  				=       do_normalize
					A__  				=       image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
					A__  				=       image_std if image_std is not None else IMAGENET_DEFAULT_STD






			def        a_	(      self       :   int							,     __lowerCAmelCase       :   np.ndarray							,     __lowerCAmelCase       :   Dict[str, int]							,     __lowerCAmelCase       :   Optional[float] = None							,     __lowerCAmelCase       :   PILImageResampling = PILImageResampling.BICUBIC							,     __lowerCAmelCase       :   Optional[Union[str, ChannelDimension]] = None							,     **__lowerCAmelCase       :   int							,     )     ->     np.ndarray:







					"""simple docstring"""






					A__  				=       get_size_dict(__lowerCAmelCase							,     default_to_square=__lowerCAmelCase       )
					if "shortest_edge" not in size and ("height" not in size or "width" not in size):
							raise ValueError(f'size must contain \'height\' and \'width\' or \'shortest_edge\' as keys. Got {size.keys()}'       )
					if crop_pct is not None:
							if "shortest_edge" in size:
									A__  				=       int(size["""shortest_edge"""] / crop_pct       )
							elif "height" in size and "width" in size:
									if size["height"] == size["width"]:
											A__  				=       int(size["""height"""] / crop_pct       )
									else:
											A__  				=       (int(size["""height"""] / crop_pct       ), int(size["""width"""] / crop_pct       ))
							else:
									raise ValueError("""Invalid size for resize: {}""".format(__lowerCAmelCase       )       )

							A__  				=       get_resize_output_image_size(__lowerCAmelCase							,     size=__lowerCAmelCase							,     default_to_square=__lowerCAmelCase       )
					else:
							if "shortest_edge" in size:
									A__  				=       get_resize_output_image_size(__lowerCAmelCase							,     size=size["""shortest_edge"""]							,     default_to_square=__lowerCAmelCase       )
							elif "height" in size and "width" in size:
									A__  				=       (size["""height"""], size["""width"""])
							else:
									raise ValueError("""Invalid size for resize: {}""".format(__lowerCAmelCase       )       )

					return resize(__lowerCAmelCase							,     size=__lowerCAmelCase							,     resample=__lowerCAmelCase							,     data_format=__lowerCAmelCase							,     **__lowerCAmelCase       )






			def        a_	(      self       :   Optional[Any]							,     __lowerCAmelCase       :   np.ndarray							,     __lowerCAmelCase       :   Dict[str, int]							,     __lowerCAmelCase       :   Optional[Union[str, ChannelDimension]] = None							,     **__lowerCAmelCase       :   Optional[Any]							,     )     ->     np.ndarray:







					"""simple docstring"""






					A__  				=       get_size_dict(__lowerCAmelCase       )
					if "height" not in size or "width" not in size:
							raise ValueError(f'size must contain \'height\' and \'width\' as keys. Got {size.keys()}'       )
					return center_crop(__lowerCAmelCase							,     size=(size["""height"""], size["""width"""])							,     data_format=__lowerCAmelCase							,     **__lowerCAmelCase       )






			def        a_	(      self       :   str							,     __lowerCAmelCase       :   np.ndarray							,     __lowerCAmelCase       :   Union[int, float]							,     __lowerCAmelCase       :   Optional[Union[str, ChannelDimension]] = None							,     **__lowerCAmelCase       :   Dict							,     )     ->     List[str]:







					"""simple docstring"""






					return rescale(__lowerCAmelCase							,     scale=__lowerCAmelCase							,     data_format=__lowerCAmelCase							,     **__lowerCAmelCase       )






			def        a_	(      self       :   int							,     __lowerCAmelCase       :   np.ndarray							,     __lowerCAmelCase       :   Union[float, List[float]]							,     __lowerCAmelCase       :   Union[float, List[float]]							,     __lowerCAmelCase       :   Optional[Union[str, ChannelDimension]] = None							,     **__lowerCAmelCase       :   int							,     )     ->     np.ndarray:







					"""simple docstring"""






					return normalize(__lowerCAmelCase							,     mean=__lowerCAmelCase							,     std=__lowerCAmelCase							,     data_format=__lowerCAmelCase							,     **__lowerCAmelCase       )







			def        a_	(      self       :   Optional[Any]							,     __lowerCAmelCase       :   ImageInput							,     __lowerCAmelCase       :   bool = None							,     __lowerCAmelCase       :   Dict[str, int] = None							,     __lowerCAmelCase       :   int = None							,     __lowerCAmelCase       :   PILImageResampling = None							,     __lowerCAmelCase       :   bool = None							,     __lowerCAmelCase       :   Dict[str, int] = 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       :   Tuple							,     )     ->     PIL.Image.Image:







					"""simple docstring"""






					A__  				=       do_resize if do_resize is not None else self.do_resize
					A__  				=       crop_pct if crop_pct is not None else self.crop_pct
					A__  				=       resample if resample is not None else self.resample
					A__  				=       do_center_crop if do_center_crop is not None else self.do_center_crop
					A__  				=       do_rescale if do_rescale is not None else self.do_rescale
					A__  				=       rescale_factor if rescale_factor is not None else self.rescale_factor
					A__  				=       do_normalize if do_normalize is not None else self.do_normalize
					A__  				=       image_mean if image_mean is not None else self.image_mean
					A__  				=       image_std if image_std is not None else self.image_std

					A__  				=       size if size is not None else self.size
					A__  				=       get_size_dict(__lowerCAmelCase							,     default_to_square=__lowerCAmelCase       )
					A__  				=       crop_size if crop_size is not None else self.crop_size
					A__  				=       get_size_dict(__lowerCAmelCase							,     param_name="""crop_size"""       )

					A__  				=       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_center_crop and crop_pct is None:
							raise ValueError("""Crop_pct 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.
					A__  				=       [to_numpy_array(__lowerCAmelCase       ) for image in images]

					if do_resize:
							A__  				=       [self.resize(image=__lowerCAmelCase							,     size=__lowerCAmelCase							,     crop_pct=__lowerCAmelCase							,     resample=__lowerCAmelCase       ) for image in images]

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

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

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

					A__  				=       [to_channel_dimension_format(__lowerCAmelCase							,     __lowerCAmelCase       ) for image in images]

					A__  				=       {"""pixel_values""": images}
					return BatchFeature(data=__lowerCAmelCase							,     tensor_type=__lowerCAmelCase       )



