"""simple docstring"""




import copy
from collections import OrderedDict
from typing import Dict, Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


__lowerCamelCase							 =     logging.get_logger(__name__)

__lowerCamelCase							 =     {
    "facebook/detr-resnet-50": "https://huggingface.co/facebook/detr-resnet-50/resolve/main/config.json",
    # See all DETR models at https://huggingface.co/models?filter=detr
}
class 			UpperCamelCase__(	__A       ):
						lowerCAmelCase__     :  Optional[int]  							=						'detr'
						lowerCAmelCase__     :  Optional[Any]  							=						['past_key_values']
						lowerCAmelCase__     :  Union[str, Any]  							=						{
						    'hidden_size': 'd_model',
						    'num_attention_heads': 'encoder_attention_heads',
						}





						def __init__(				self						,__UpperCAmelCase=True						,__UpperCAmelCase=None						,__UpperCAmelCase=3						,__UpperCAmelCase=1_00						,__UpperCAmelCase=6						,__UpperCAmelCase=20_48						,__UpperCAmelCase=8						,__UpperCAmelCase=6						,__UpperCAmelCase=20_48						,__UpperCAmelCase=8						,__UpperCAmelCase=0.0						,__UpperCAmelCase=0.0						,__UpperCAmelCase=True						,__UpperCAmelCase="relu"						,__UpperCAmelCase=2_56						,__UpperCAmelCase=0.1						,__UpperCAmelCase=0.0						,__UpperCAmelCase=0.0						,__UpperCAmelCase=0.0_2						,__UpperCAmelCase=1.0						,__UpperCAmelCase=False						,__UpperCAmelCase="sine"						,__UpperCAmelCase="resnet50"						,__UpperCAmelCase=True						,__UpperCAmelCase=False						,__UpperCAmelCase=1						,__UpperCAmelCase=5						,__UpperCAmelCase=2						,__UpperCAmelCase=1						,__UpperCAmelCase=1						,__UpperCAmelCase=5						,__UpperCAmelCase=2						,__UpperCAmelCase=0.1						,**__UpperCAmelCase						,)    -> Tuple:
													if backbone_config is not None and use_timm_backbone:
																				raise ValueError('You can\'t specify both `backbone_config` and `use_timm_backbone`.'   )

													if not use_timm_backbone:
																				if backbone_config is None:
																											logger.info('`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.'   )
																											A__		   =							CONFIG_MAPPING['resnet'](out_features=['stage4']   )
																				elif isinstance(__UpperCAmelCase						,__UpperCAmelCase   ):
																											A__		   =							backbone_config.get('model_type'   )
																											A__		   =							CONFIG_MAPPING[backbone_model_type]
																											A__		   =							config_class.from_dict(__UpperCAmelCase   )
																				# set timm attributes to None
																				A__   ,     A__   ,     A__		   =							None, None, None

													A__		   =							use_timm_backbone
													A__		   =							backbone_config
													A__		   =							num_channels
													A__		   =							num_queries
													A__		   =							d_model
													A__		   =							encoder_ffn_dim
													A__		   =							encoder_layers
													A__		   =							encoder_attention_heads
													A__		   =							decoder_ffn_dim
													A__		   =							decoder_layers
													A__		   =							decoder_attention_heads
													A__		   =							dropout
													A__		   =							attention_dropout
													A__		   =							activation_dropout
													A__		   =							activation_function
													A__		   =							init_std
													A__		   =							init_xavier_std
													A__		   =							encoder_layerdrop
													A__		   =							decoder_layerdrop
													A__		   =							encoder_layers
													A__		   =							auxiliary_loss
													A__		   =							position_embedding_type
													A__		   =							backbone
													A__		   =							use_pretrained_backbone
													A__		   =							dilation
													# Hungarian matcher
													A__		   =							class_cost
													A__		   =							bbox_cost
													A__		   =							giou_cost
													# Loss coefficients
													A__		   =							mask_loss_coefficient
													A__		   =							dice_loss_coefficient
													A__		   =							bbox_loss_coefficient
													A__		   =							giou_loss_coefficient
													A__		   =							eos_coefficient
													super().__init__(is_encoder_decoder=__UpperCAmelCase						,**__UpperCAmelCase   )





						@property
						def  snake_case__		(				self   )    -> int:
													return self.encoder_attention_heads





						@property
						def  snake_case__		(				self   )    -> int:
													return self.d_model





						@classmethod
						def  snake_case__		(				cls						,__UpperCAmelCase						,**__UpperCAmelCase   )    -> Optional[int]:
													return cls(backbone_config=__UpperCAmelCase						,**__UpperCAmelCase   )





						def  snake_case__		(				self   )    -> Dict[str, any]:
													A__		   =							copy.deepcopy(self.__dict__   )
													if output["backbone_config"] is not None:
																				A__		   =							self.backbone_config.to_dict()
													A__		   =							self.__class__.model_type
													return output






class 			UpperCamelCase__(	__A       ):
						lowerCAmelCase__     :  Optional[Any]  							=						version.parse('1.11'       )





						@property
						def  snake_case__		(				self   )    -> Mapping[str, Mapping[int, str]]:
													return OrderedDict(
													    [
													        ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
													        ('pixel_mask', {0: 'batch'}),
													    ]   )





						@property
						def  snake_case__		(				self   )    -> float:
													return 1e-5





						@property
						def  snake_case__		(				self   )    -> int:
													return 12






"""simple docstring"""




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

__lowerCamelCase							 =     logging.get_logger(__name__)






def      UpperCAmelCase    (		UpperCamelCase__ ):






							"""simple docstring"""





							if isinstance(UpperCamelCase__				,			(list, tuple) ) and isinstance(videos[0]				,			(list, tuple) ) and is_valid_image(videos[0][0] ):
														return videos

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

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

							raise ValueError(F'''Could not make batched video from {videos}''' )


class 			UpperCamelCase__(	__A       ):
						lowerCAmelCase__     :  List[Any]  							=						['pixel_values']





						def __init__(				self						,__UpperCAmelCase = True						,__UpperCAmelCase = None						,__UpperCAmelCase = PILImageResampling.BILINEAR						,__UpperCAmelCase = True						,__UpperCAmelCase = None						,__UpperCAmelCase = True						,__UpperCAmelCase = 1 / 2_55						,__UpperCAmelCase = True						,__UpperCAmelCase = True						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,**__UpperCAmelCase						,)    -> None:
													super().__init__(**__UpperCAmelCase   )
													A__		   =							size if size is not None else {'shortest_edge': 2_56}
													A__		   =							get_size_dict(__UpperCAmelCase						,default_to_square=__UpperCAmelCase   )
													A__		   =							crop_size if crop_size is not None else {'height': 2_24, 'width': 2_24}
													A__		   =							get_size_dict(__UpperCAmelCase						,param_name='crop_size'   )

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





						def  snake_case__		(				self						,__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase = PILImageResampling.BILINEAR						,__UpperCAmelCase = None						,**__UpperCAmelCase						,)    -> np.ndarray:
													A__		   =							get_size_dict(__UpperCAmelCase						,default_to_square=__UpperCAmelCase   )
													if "shortest_edge" in size:
																				A__		   =							get_resize_output_image_size(__UpperCAmelCase						,size['shortest_edge']						,default_to_square=__UpperCAmelCase   )
													elif "height" in size and "width" in size:
																				A__		   =							(size['height'], size['width'])
													else:
																				raise ValueError(f'''Size must have \'height\' and \'width\' or \'shortest_edge\' as keys. Got {size.keys()}'''   )
													return resize(__UpperCAmelCase						,size=__UpperCAmelCase						,resample=__UpperCAmelCase						,data_format=__UpperCAmelCase						,**__UpperCAmelCase   )





						def  snake_case__		(				self						,__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase = None						,**__UpperCAmelCase						,)    -> np.ndarray:
													A__		   =							get_size_dict(__UpperCAmelCase   )
													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(__UpperCAmelCase						,size=(size['height'], size['width'])						,data_format=__UpperCAmelCase						,**__UpperCAmelCase   )





						def  snake_case__		(				self						,__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase = True						,__UpperCAmelCase = None						,**__UpperCAmelCase						,)    -> Optional[Any]:
													A__		   =							image.astype(np.floataa   )
													if offset:
																				A__		   =							image - (scale / 2)
													return rescale(__UpperCAmelCase						,scale=__UpperCAmelCase						,data_format=__UpperCAmelCase						,**__UpperCAmelCase   )





						def  snake_case__		(				self						,__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase = None						,**__UpperCAmelCase						,)    -> np.ndarray:
													return normalize(__UpperCAmelCase						,mean=__UpperCAmelCase						,std=__UpperCAmelCase						,data_format=__UpperCAmelCase						,**__UpperCAmelCase   )





						def  snake_case__		(				self						,__UpperCAmelCase						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,__UpperCAmelCase = ChannelDimension.FIRST						,)    -> np.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_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.
													A__		   =							to_numpy_array(__UpperCAmelCase   )

													if do_resize:
																				A__		   =							self.resize(image=__UpperCAmelCase						,size=__UpperCAmelCase						,resample=__UpperCAmelCase   )

													if do_center_crop:
																				A__		   =							self.center_crop(__UpperCAmelCase						,size=__UpperCAmelCase   )

													if do_rescale:
																				A__		   =							self.rescale(image=__UpperCAmelCase						,scale=__UpperCAmelCase						,offset=__UpperCAmelCase   )

													if do_normalize:
																				A__		   =							self.normalize(image=__UpperCAmelCase						,mean=__UpperCAmelCase						,std=__UpperCAmelCase   )

													A__		   =							to_channel_dimension_format(__UpperCAmelCase						,__UpperCAmelCase   )
													return image





						def  snake_case__		(				self						,__UpperCAmelCase						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,__UpperCAmelCase = ChannelDimension.FIRST						,**__UpperCAmelCase						,)    -> PIL.Image.Image:
													A__		   =							do_resize if do_resize is not None else self.do_resize
													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__		   =							offset if offset is not None else self.offset
													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(__UpperCAmelCase						,default_to_square=__UpperCAmelCase   )
													A__		   =							crop_size if crop_size is not None else self.crop_size
													A__		   =							get_size_dict(__UpperCAmelCase						,param_name='crop_size'   )

													if not valid_images(__UpperCAmelCase   ):
																				raise ValueError(
																				    'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '
																				    'torch.Tensor, tf.Tensor or jax.ndarray.'   )

													A__		   =							make_batched(__UpperCAmelCase   )

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

													A__		   =							{'pixel_values': videos}
													return BatchFeature(data=__UpperCAmelCase						,tensor_type=__UpperCAmelCase   )





