


import copy
from collections import OrderedDict
from typing import Mapping

from packaging import version

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


_A		       =						logging.get_logger(__name__)

_A		       =						{
    'microsoft/conditional-detr-resnet-50': (
        'https://huggingface.co/microsoft/conditional-detr-resnet-50/resolve/main/config.json'
    ),
}






class     UpperCAmelCase__       (					A_		):



    """simple docstring"""






    UpperCAmelCase__						:		List[Any]      =  "conditional_detr"
    UpperCAmelCase__						:		Optional[Any]      =  ["past_key_values"]
    UpperCAmelCase__						:		int      =  {
        "hidden_size": "d_model",
        "num_attention_heads": "encoder_attention_heads",
    }






    def __init__(			self					,   A_=True					,   A_=None					,   A_=3					,   A_=300					,   A_=6					,   A_=2048					,   A_=8					,   A_=6					,   A_=2048					,   A_=8					,   A_=0.0					,   A_=0.0					,   A_=True					,   A_="relu"					,   A_=256					,   A_=0.1					,   A_=0.0					,   A_=0.0					,   A_=0.02					,   A_=1.0					,   A_=False					,   A_="sine"					,   A_="resnet50"					,   A_=True					,   A_=False					,   A_=2					,   A_=5					,   A_=2					,   A_=1					,   A_=1					,   A_=2					,   A_=5					,   A_=2					,   A_=0.25					,   **A_					,   )  ->       Dict:
         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.'    )
                   __UpperCamelCase    							=CONFIG_MAPPING['resnet'](out_features=['stage4']    )
              elif isinstance(A_					,   A_    ):
                   __UpperCamelCase    							=backbone_config.get('model_type'    )
                   __UpperCamelCase    							=CONFIG_MAPPING[backbone_model_type]
                   __UpperCamelCase    							=config_class.from_dict(A_    )

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






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






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






    def _a					(			self    )  ->       Optional[Any]:
         __UpperCamelCase    							=copy.deepcopy(self.__dict__    )
         if self.backbone_config is not None:
              __UpperCamelCase    							=self.backbone_config.to_dict()
         __UpperCamelCase    							=self.__class__.model_type
         return output






class     UpperCAmelCase__       (					A_		):



    """simple docstring"""






    UpperCAmelCase__						:		Optional[int]      =  version.parse("1.11"		)






    @property
    def _a					(			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 _a					(			self    )  ->       float:
         return 1E-5






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







import PIL.Image
import PIL.ImageOps
from packaging import version
from PIL import Image


if version.parse(version.parse(PIL.__version__).base_version) >= version.parse('9.1.0'):
		lowercase  :					int													=       {
		    'linear': PIL.Image.Resampling.BILINEAR,
		    'bilinear': PIL.Image.Resampling.BILINEAR,
		    'bicubic': PIL.Image.Resampling.BICUBIC,
		    'lanczos': PIL.Image.Resampling.LANCZOS,
		    'nearest': PIL.Image.Resampling.NEAREST,
		}
else:
		lowercase  :					Any													=       {
		    'linear': PIL.Image.LINEAR,
		    'bilinear': PIL.Image.BILINEAR,
		    'bicubic': PIL.Image.BICUBIC,
		    'lanczos': PIL.Image.LANCZOS,
		    'nearest': PIL.Image.NEAREST,
		}




def 	_SCREAMING_SNAKE_CASE					(		_lowerCamelCase :			int)					->    Any:






							'''simple docstring'''





							__UpperCamelCase	:   Union[str, Any]			   =					(images / 2 + 0.5).clamp(0						,  1)
							__UpperCamelCase	:   int			   =					images.cpu().permute(0						,  2						,  3						,  1).float().numpy()
							__UpperCamelCase	:   Dict			   =					numpy_to_pil(_lowerCamelCase)
							return images




def 	_SCREAMING_SNAKE_CASE					(		_lowerCamelCase :			Optional[Any])					->    Optional[Any]:






							'''simple docstring'''





							if images.ndim == 3:
														__UpperCamelCase	:   Optional[int]			   =					images[None, ...]
							__UpperCamelCase	:   Dict			   =					(images * 255).round().astype("uint8")
							if images.shape[-1] == 1:
														# special case for grayscale (single channel) images
														__UpperCamelCase	:   int			   =					[Image.fromarray(image.squeeze()						,  mode="L") for image in images]
							else:
														__UpperCamelCase	:   Union[str, Any]			   =					[Image.fromarray(_lowerCamelCase) for image in images]

							return pil_images