




import copy

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


__SCREAMING_SNAKE_CASE 						=logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE 						={
    """SenseTime/deformable-detr""": """https://huggingface.co/sensetime/deformable-detr/resolve/main/config.json""",
    # See all Deformable DETR models at https://huggingface.co/models?filter=deformable-detr
}






class  __magic_name__						(  __UpperCAmelCase):
				'''simple docstring'''







				SCREAMING_SNAKE_CASE__					:							Union[str, Any]	      =					"deformable_detr"
				SCREAMING_SNAKE_CASE__					:							Tuple	      =					{
				    "hidden_size": "d_model",
				    "num_attention_heads": "encoder_attention_heads",
				}


				def __init__(							self:   Union[str, Any]		,					_lowerCamelCase:   List[Any]=True		,					_lowerCamelCase:   Union[str, Any]=None		,					_lowerCamelCase:   str=3		,					_lowerCamelCase:   Optional[Any]=3_00		,					_lowerCamelCase:   Dict=10_24		,					_lowerCamelCase:   List[Any]=6		,					_lowerCamelCase:   Tuple=10_24		,					_lowerCamelCase:   Union[str, Any]=8		,					_lowerCamelCase:   Union[str, Any]=6		,					_lowerCamelCase:   List[str]=10_24		,					_lowerCamelCase:   Optional[Any]=8		,					_lowerCamelCase:   Any=0.0		,					_lowerCamelCase:   Union[str, Any]=True		,					_lowerCamelCase:   Optional[int]="relu"		,					_lowerCamelCase:   str=2_56		,					_lowerCamelCase:   str=0.1		,					_lowerCamelCase:   Union[str, Any]=0.0		,					_lowerCamelCase:   Optional[Any]=0.0		,					_lowerCamelCase:   Union[str, Any]=0.02		,					_lowerCamelCase:   Optional[Any]=1.0		,					_lowerCamelCase:   Any=True		,					_lowerCamelCase:   Any=False		,					_lowerCamelCase:   List[str]="sine"		,					_lowerCamelCase:   Any="resnet50"		,					_lowerCamelCase:   List[Any]=True		,					_lowerCamelCase:   Dict=False		,					_lowerCamelCase:   Optional[int]=4		,					_lowerCamelCase:   Dict=4		,					_lowerCamelCase:   List[str]=4		,					_lowerCamelCase:   Optional[int]=False		,					_lowerCamelCase:   Dict=3_00		,					_lowerCamelCase:   Optional[int]=False		,					_lowerCamelCase:   Tuple=1		,					_lowerCamelCase:   Optional[Any]=5		,					_lowerCamelCase:   Union[str, Any]=2		,					_lowerCamelCase:   int=1		,					_lowerCamelCase:   List[Any]=1		,					_lowerCamelCase:   Optional[int]=5		,					_lowerCamelCase:   int=2		,					_lowerCamelCase:   int=0.1		,					_lowerCamelCase:   Tuple=0.25		,					_lowerCamelCase:   List[str]=False		,					**_lowerCamelCase:   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.'''		)
																									SCREAMING_SNAKE_CASE_   	=    CONFIG_MAPPING['''resnet'''](out_features=['''stage4''']		)
																		elif isinstance(_lowerCamelCase		,					_lowerCamelCase		):
																									SCREAMING_SNAKE_CASE_   	=    backbone_config.get('''model_type'''		)
																									SCREAMING_SNAKE_CASE_   	=    CONFIG_MAPPING[backbone_model_type]
																									SCREAMING_SNAKE_CASE_   	=    config_class.from_dict(_lowerCamelCase		)
											SCREAMING_SNAKE_CASE_   	=    use_timm_backbone
											SCREAMING_SNAKE_CASE_   	=    backbone_config
											SCREAMING_SNAKE_CASE_   	=    num_channels
											SCREAMING_SNAKE_CASE_   	=    num_queries
											SCREAMING_SNAKE_CASE_   	=    max_position_embeddings
											SCREAMING_SNAKE_CASE_   	=    d_model
											SCREAMING_SNAKE_CASE_   	=    encoder_ffn_dim
											SCREAMING_SNAKE_CASE_   	=    encoder_layers
											SCREAMING_SNAKE_CASE_   	=    encoder_attention_heads
											SCREAMING_SNAKE_CASE_   	=    decoder_ffn_dim
											SCREAMING_SNAKE_CASE_   	=    decoder_layers
											SCREAMING_SNAKE_CASE_   	=    decoder_attention_heads
											SCREAMING_SNAKE_CASE_   	=    dropout
											SCREAMING_SNAKE_CASE_   	=    attention_dropout
											SCREAMING_SNAKE_CASE_   	=    activation_dropout
											SCREAMING_SNAKE_CASE_   	=    activation_function
											SCREAMING_SNAKE_CASE_   	=    init_std
											SCREAMING_SNAKE_CASE_   	=    init_xavier_std
											SCREAMING_SNAKE_CASE_   	=    encoder_layerdrop
											SCREAMING_SNAKE_CASE_   	=    auxiliary_loss
											SCREAMING_SNAKE_CASE_   	=    position_embedding_type
											SCREAMING_SNAKE_CASE_   	=    backbone
											SCREAMING_SNAKE_CASE_   	=    use_pretrained_backbone
											SCREAMING_SNAKE_CASE_   	=    dilation
											# deformable attributes
											SCREAMING_SNAKE_CASE_   	=    num_feature_levels
											SCREAMING_SNAKE_CASE_   	=    encoder_n_points
											SCREAMING_SNAKE_CASE_   	=    decoder_n_points
											SCREAMING_SNAKE_CASE_   	=    two_stage
											SCREAMING_SNAKE_CASE_   	=    two_stage_num_proposals
											SCREAMING_SNAKE_CASE_   	=    with_box_refine
											if two_stage is True and with_box_refine is False:
																		raise ValueError('''If two_stage is True, with_box_refine must be True.'''		)
											# Hungarian matcher
											SCREAMING_SNAKE_CASE_   	=    class_cost
											SCREAMING_SNAKE_CASE_   	=    bbox_cost
											SCREAMING_SNAKE_CASE_   	=    giou_cost
											# Loss coefficients
											SCREAMING_SNAKE_CASE_   	=    mask_loss_coefficient
											SCREAMING_SNAKE_CASE_   	=    dice_loss_coefficient
											SCREAMING_SNAKE_CASE_   	=    bbox_loss_coefficient
											SCREAMING_SNAKE_CASE_   	=    giou_loss_coefficient
											SCREAMING_SNAKE_CASE_   	=    eos_coefficient
											SCREAMING_SNAKE_CASE_   	=    focal_alpha
											SCREAMING_SNAKE_CASE_   	=    disable_custom_kernels
											super().__init__(is_encoder_decoder=_lowerCamelCase		,					**_lowerCamelCase		)


				@property
				def     _A							(							self:   List[Any]		):
											return self.encoder_attention_heads


				@property
				def     _A							(							self:   int		):
											return self.d_model





				def     _A							(							self:   int		):
											SCREAMING_SNAKE_CASE_   	=    copy.deepcopy(self.__dict__		)
											if self.backbone_config is not None:
																		SCREAMING_SNAKE_CASE_   	=    self.backbone_config.to_dict()
											SCREAMING_SNAKE_CASE_   	=    self.__class__.model_type
											return output








import unittest

from diffusers import FlaxAutoencoderKL
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import require_flax

from .test_modeling_common_flax import FlaxModelTesterMixin


if is_flax_available():
			import jax






@require_flax
class  __magic_name__						(  __UpperCAmelCase    ,  unittest.TestCase):
				'''simple docstring'''







				SCREAMING_SNAKE_CASE__					:							str	      =					FlaxAutoencoderKL


				@property
				def     _A							(							self:   Any		):
											SCREAMING_SNAKE_CASE_   	=    4
											SCREAMING_SNAKE_CASE_   	=    3
											SCREAMING_SNAKE_CASE_   	=    (32, 32)

											SCREAMING_SNAKE_CASE_   	=    jax.random.PRNGKey(0		)
											SCREAMING_SNAKE_CASE_   	=    jax.random.uniform(_lowerCamelCase		,					((batch_size, num_channels) + sizes)		)

											return {"sample": image, "prng_key": prng_key}





				def     _A							(							self:   List[str]		):
											SCREAMING_SNAKE_CASE_   	=    {
											    '''block_out_channels''': [32, 64],
											    '''in_channels''': 3,
											    '''out_channels''': 3,
											    '''down_block_types''': ['''DownEncoderBlock2D''', '''DownEncoderBlock2D'''],
											    '''up_block_types''': ['''UpDecoderBlock2D''', '''UpDecoderBlock2D'''],
											    '''latent_channels''': 4,
											}
											SCREAMING_SNAKE_CASE_   	=    self.dummy_input
											return init_dict, inputs_dict


