




'''simple docstring'''


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


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

_snake_case : Optional[int]  				=						{
    'microsoft/conditional-detr-resnet-50': (
        'https://huggingface.co/microsoft/conditional-detr-resnet-50/resolve/main/config.json'
    ),
}

class      A       (						_a       ):
							lowercase_							     =							'conditional_detr'
							lowercase_							     =							['past_key_values']
							lowercase_							     =							{
							    'hidden_size': 'd_model',
							    'num_attention_heads': 'encoder_attention_heads',
							}







							def __init__(    self			:    Dict				,     lowerCAmelCase_			:    str=True				,     lowerCAmelCase_			:    Optional[int]=None				,     lowerCAmelCase_			:    List[str]=3				,     lowerCAmelCase_			:    List[str]=3_00				,     lowerCAmelCase_			:    Tuple=6				,     lowerCAmelCase_			:    Dict=20_48				,     lowerCAmelCase_			:    int=8				,     lowerCAmelCase_			:    Union[str, Any]=6				,     lowerCAmelCase_			:    Optional[int]=20_48				,     lowerCAmelCase_			:    Tuple=8				,     lowerCAmelCase_			:    List[str]=0.0				,     lowerCAmelCase_			:    Dict=0.0				,     lowerCAmelCase_			:    int=True				,     lowerCAmelCase_			:    Tuple="relu"				,     lowerCAmelCase_			:    List[str]=2_56				,     lowerCAmelCase_			:    List[Any]=0.1				,     lowerCAmelCase_			:    str=0.0				,     lowerCAmelCase_			:    Tuple=0.0				,     lowerCAmelCase_			:    Optional[int]=0.0_2				,     lowerCAmelCase_			:    List[Any]=1.0				,     lowerCAmelCase_			:    Union[str, Any]=False				,     lowerCAmelCase_			:    int="sine"				,     lowerCAmelCase_			:    Optional[Any]="resnet50"				,     lowerCAmelCase_			:    Union[str, Any]=True				,     lowerCAmelCase_			:    List[str]=False				,     lowerCAmelCase_			:    Dict=2				,     lowerCAmelCase_			:    Any=5				,     lowerCAmelCase_			:    Dict=2				,     lowerCAmelCase_			:    List[Any]=1				,     lowerCAmelCase_			:    str=1				,     lowerCAmelCase_			:    List[Any]=2				,     lowerCAmelCase_			:    str=5				,     lowerCAmelCase_			:    Optional[Any]=2				,     lowerCAmelCase_			:    int=0.2_5				,     **lowerCAmelCase_			:    Optional[Any]				,     )     ->				List[str]:


														"""simple docstring"""


														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(lowerCAmelCase_				,     lowerCAmelCase_   ):
																												_a      	=							backbone_config.get('''model_type'''   )
																												_a      	=							CONFIG_MAPPING[backbone_model_type]
																												_a      	=							config_class.from_dict(lowerCAmelCase_   )

														_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      	=							cls_loss_coefficient
														_a      	=							bbox_loss_coefficient
														_a      	=							giou_loss_coefficient
														_a      	=							focal_alpha
														super().__init__(is_encoder_decoder=lowerCAmelCase_				,     **lowerCAmelCase_   )







							@property
							def   __lowerCAmelCase     (    self			:    List[Any]   )     ->				int:


														"""simple docstring"""


														return self.encoder_attention_heads







							@property
							def   __lowerCAmelCase     (    self			:    Tuple   )     ->				int:


														"""simple docstring"""


														return self.d_model







							def   __lowerCAmelCase     (    self			:    Tuple   )     ->				int:


														"""simple docstring"""


														_a      	=							copy.deepcopy(self.__dict__   )
														if self.backbone_config is not None:
																					_a      	=							self.backbone_config.to_dict()
														_a      	=							self.__class__.model_type
														return output



class      A       (						_a       ):
							lowercase_							     =							version.parse('1.11'       )







							@property
							def   __lowerCAmelCase     (    self			:    int   )     ->				Mapping[str, Mapping[int, str]]:


														"""simple docstring"""


														return OrderedDict(
														    [
														        ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
														        ('''pixel_mask''', {0: '''batch'''}),
														    ]   )







							@property
							def   __lowerCAmelCase     (    self			:    str   )     ->				float:


														"""simple docstring"""


														return 1e-5







							@property
							def   __lowerCAmelCase     (    self			:    str   )     ->				int:


														"""simple docstring"""


														return 12







'''simple docstring'''


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'):
				_snake_case : Tuple  				=						{
				    '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:
				_snake_case : Any  				=						{
				    'linear': PIL.Image.LINEAR,
				    'bilinear': PIL.Image.BILINEAR,
				    'bicubic': PIL.Image.BICUBIC,
				    'lanczos': PIL.Image.LANCZOS,
				    'nearest': PIL.Image.NEAREST,
				}
def 						snake_case_       (UpperCamelCase			:    Optional[int] ):





							'''simple docstring'''


							_a      	=							(images / 2 + 0.5).clamp(0					,		1 )
							_a      	=							images.cpu().permute(0					,		2					,		3					,		1 ).float().numpy()
							_a      	=							numpy_to_pil(UpperCamelCase )
							return images

def 						snake_case_       (UpperCamelCase			:    str ):





							'''simple docstring'''


							if images.ndim == 3:
														_a      	=							images[None, ...]
							_a      	=							(images * 255).round().astype('''uint8''' )
							if images.shape[-1] == 1:
														# special case for grayscale (single channel) images
														_a      	=							[Image.fromarray(image.squeeze()					,		mode='''L''' ) for image in images]
							else:
														_a      	=							[Image.fromarray(UpperCamelCase ) for image in images]

							return pil_images

