


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							:	Optional[int]					      =				logging.get_logger(__name__)

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







class 	snake_case_       (lowerCamelCase_				):
					UpperCAmelCase__ :   str   				=      '''conditional_detr'''
					UpperCAmelCase__ :   Tuple   				=      ['''past_key_values''']
					UpperCAmelCase__ :   List[str]   				=      {
					    '''hidden_size''': '''d_model''',
					    '''num_attention_heads''': '''encoder_attention_heads''',
					}
					def __init__(				self       :List[Any]      ,__snake_case       :Union[str, Any]=True      ,__snake_case       :int=None      ,__snake_case       :Dict=3      ,__snake_case       :str=3_00      ,__snake_case       :Union[str, Any]=6      ,__snake_case       :Any=20_48      ,__snake_case       :str=8      ,__snake_case       :Tuple=6      ,__snake_case       :Union[str, Any]=20_48      ,__snake_case       :Any=8      ,__snake_case       :Dict=0.0      ,__snake_case       :Dict=0.0      ,__snake_case       :Optional[Any]=True      ,__snake_case       :Optional[int]="relu"      ,__snake_case       :Any=2_56      ,__snake_case       :int=0.1      ,__snake_case       :List[Any]=0.0      ,__snake_case       :Dict=0.0      ,__snake_case       :List[Any]=0.02      ,__snake_case       :List[str]=1.0      ,__snake_case       :str=False      ,__snake_case       :Optional[Any]="sine"      ,__snake_case       :int="resnet50"      ,__snake_case       :Optional[Any]=True      ,__snake_case       :Any=False      ,__snake_case       :Optional[Any]=2      ,__snake_case       :Optional[int]=5      ,__snake_case       :Optional[int]=2      ,__snake_case       :Tuple=1      ,__snake_case       :int=1      ,__snake_case       :Any=2      ,__snake_case       :Tuple=5      ,__snake_case       :int=2      ,__snake_case       :Dict=0.25      ,**__snake_case       :Any      ,) ->   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(__snake_case      ,__snake_case				):
																										a__					=					backbone_config.get('model_type'				)
																										a__					=					CONFIG_MAPPING[backbone_model_type]
																										a__					=					config_class.from_dict(__snake_case				)

												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=__snake_case      ,**__snake_case				)
					@property
					def 			lowerCamelCase__(				self       :Dict				) ->   int:
												return self.encoder_attention_heads
					@property
					def 			lowerCamelCase__(				self       :str				) ->   int:
												return self.d_model



					def 			lowerCamelCase__(				self       :List[str]				) ->   Optional[int]:
												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 	snake_case_       (lowerCamelCase_				):
					UpperCAmelCase__ :   str   				=      version.parse('''1.11'''				)
					@property
					def 			lowerCamelCase__(				self       :int				) ->   Mapping[str, Mapping[int, str]]:
												return OrderedDict(
												    [
												        ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
												        ('pixel_mask', {0: 'batch'}),
												    ]				)
					@property
					def 			lowerCamelCase__(				self       :int				) ->   float:
												return 1E-5



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








from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


snake_case							:	Optional[int]					      =				{
    '''configuration_clipseg''': [
        '''CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''CLIPSegConfig''',
        '''CLIPSegTextConfig''',
        '''CLIPSegVisionConfig''',
    ],
    '''processing_clipseg''': ['''CLIPSegProcessor'''],
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						snake_case							:	List[str]					      =				[
						    '''CLIPSEG_PRETRAINED_MODEL_ARCHIVE_LIST''',
						    '''CLIPSegModel''',
						    '''CLIPSegPreTrainedModel''',
						    '''CLIPSegTextModel''',
						    '''CLIPSegVisionModel''',
						    '''CLIPSegForImageSegmentation''',
						]

if TYPE_CHECKING:
						from .configuration_clipseg import (
						    CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP,
						    CLIPSegConfig,
						    CLIPSegTextConfig,
						    CLIPSegVisionConfig,
						)
						from .processing_clipseg import CLIPSegProcessor

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_clipseg import (
												    CLIPSEG_PRETRAINED_MODEL_ARCHIVE_LIST,
												    CLIPSegForImageSegmentation,
												    CLIPSegModel,
												    CLIPSegPreTrainedModel,
												    CLIPSegTextModel,
												    CLIPSegVisionModel,
												)

else:
						import sys

						snake_case							:	Optional[int]					      =				_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




