


'''simple docstring'''




import math
from enum import Enum
from typing import Optional, Union

from torch.optim import Optimizer
from torch.optim.lr_scheduler import LambdaLR

from .utils import logging


_lowerCAmelCase  :		str		     =	logging.get_logger(__name__)



class  lowerCAmelCase				(				a							):
	_lowerCamelCase   :					str			      = """linear"""
	_lowerCamelCase   :					str			      = """cosine"""
	_lowerCamelCase   :					Union[str, Any]			      = """cosine_with_restarts"""
	_lowerCamelCase   :					List[str]			      = """polynomial"""
	_lowerCamelCase   :					Tuple			      = """constant"""
	_lowerCamelCase   :					Optional[int]			      = """constant_with_warmup"""
	_lowerCamelCase   :					Optional[int]			      = """piecewise_constant"""


def 			__UpperCamelCase    (    _A    :				Optimizer					,   _A    :				int = -1				)    ->      List[Any]:




	"""simple docstring"""





	return LambdaLR(_A					,   lambda _A				: 1					,   last_epoch=_A				)

def 			__UpperCamelCase    (    _A    :				Optimizer					,   _A    :				int					,   _A    :				int = -1				)    ->      Tuple:




	"""simple docstring"""





	def lr_lambda(_A    :				int				):
		if current_step < num_warmup_steps:
			return float(_A				) / float(max(1.0					,   _A				)				)
		return 1.0

	return LambdaLR(_A					,   _A					,   last_epoch=_A				)

def 			__UpperCamelCase    (    _A    :				Optimizer					,   _A    :				str					,   _A    :				int = -1				)    ->      Union[str, Any]:




	"""simple docstring"""





	lowerCAmelCase					:    Dict      							= {}
	lowerCAmelCase					:    Dict      							= step_rules.split(','				)
	for rule_str in rule_list[:-1]:
		lowerCAmelCase							,   lowerCAmelCase					:    str      							= rule_str.split(':'				)
		lowerCAmelCase					:    int      							= int(_A				)
		lowerCAmelCase					:    List[Any]      							= float(_A				)
		lowerCAmelCase					:    Dict      							= value
	lowerCAmelCase					:    Union[str, Any]      							= float(rule_list[-1]				)

	def create_rules_function(_A    :				Any					,   _A    :				Any				):
		def rule_func(_A    :				int				) -> float:
			lowerCAmelCase					:    List[Any]      							= sorted(rules_dict.keys()				)
			for i, sorted_step in enumerate(_A				):
				if steps < sorted_step:
					return rules_dict[sorted_steps[i]]
			return last_lr_multiple

		return rule_func

	lowerCAmelCase					:    Optional[int]      							= create_rules_function(_A					,   _A				)

	return LambdaLR(_A					,   _A					,   last_epoch=_A				)

def 			__UpperCamelCase    (    _A    :				Dict					,   _A    :				Dict					,   _A    :				Dict					,   _A    :				Optional[int]=-1				)    ->      Dict:




	"""simple docstring"""





	def lr_lambda(_A    :				int				):
		if current_step < num_warmup_steps:
			return float(_A				) / float(max(1					,   _A				)				)
		return max(
		    0.0					,   float(num_training_steps - current_step				) / float(max(1					,   num_training_steps - num_warmup_steps				)				)				)

	return LambdaLR(_A					,   _A					,   _A				)

def 			__UpperCamelCase    (    _A    :				Optimizer					,   _A    :				int					,   _A    :				int					,   _A    :				float = 0.5					,   _A    :				int = -1				)    ->      Union[str, Any]:




	"""simple docstring"""





	def lr_lambda(_A    :				int				):
		if current_step < num_warmup_steps:
			return float(_A				) / float(max(1					,   _A				)				)
		lowerCAmelCase					:    str      							= float(current_step - num_warmup_steps				) / float(max(1					,   num_training_steps - num_warmup_steps				)				)
		return max(0.0					,   0.5 * (1.0 + math.cos(math.pi * float(_A				) * 2.0 * progress				))				)

	return LambdaLR(_A					,   _A					,   _A				)

def 			__UpperCamelCase    (    _A    :				Optimizer					,   _A    :				int					,   _A    :				int					,   _A    :				int = 1					,   _A    :				int = -1				)    ->      Optional[Any]:




	"""simple docstring"""





	def lr_lambda(_A    :				List[Any]				):
		if current_step < num_warmup_steps:
			return float(_A				) / float(max(1					,   _A				)				)
		lowerCAmelCase					:    Optional[int]      							= float(current_step - num_warmup_steps				) / float(max(1					,   num_training_steps - num_warmup_steps				)				)
		if progress >= 1.0:
			return 0.0
		return max(0.0					,   0.5 * (1.0 + math.cos(math.pi * ((float(_A				) * progress) % 1.0)				))				)

	return LambdaLR(_A					,   _A					,   _A				)

def 			__UpperCamelCase    (    _A    :				str					,   _A    :				str					,   _A    :				Optional[Any]					,   _A    :				Optional[int]=1e-7					,   _A    :				Optional[int]=1.0					,   _A    :				Union[str, Any]=-1				)    ->      str:




	"""simple docstring"""





	lowerCAmelCase					:    Optional[Any]      							= optimizer.defaults['lr']
	if not (lr_init > lr_end):
		raise ValueError(F"lr_end ({lr_end}) must be be smaller than initial lr ({lr_init})"				)

	def lr_lambda(_A    :				int				):
		if current_step < num_warmup_steps:
			return float(_A				) / float(max(1					,   _A				)				)
		elif current_step > num_training_steps:
			return lr_end / lr_init  # as LambdaLR multiplies by lr_init
		else:
			lowerCAmelCase					:    int      							= lr_init - lr_end
			lowerCAmelCase					:    Dict      							= num_training_steps - num_warmup_steps
			lowerCAmelCase					:    Dict      							= 1 - (current_step - num_warmup_steps) / decay_steps
			lowerCAmelCase					:    int      							= lr_range * pct_remaining**power + lr_end
			return decay / lr_init  # as LambdaLR multiplies by lr_init

	return LambdaLR(_A					,   _A					,   _A				)


_lowerCAmelCase  :		List[str]		     =	{
    SchedulerType.LINEAR: get_linear_schedule_with_warmup,
    SchedulerType.COSINE: get_cosine_schedule_with_warmup,
    SchedulerType.COSINE_WITH_RESTARTS: get_cosine_with_hard_restarts_schedule_with_warmup,
    SchedulerType.POLYNOMIAL: get_polynomial_decay_schedule_with_warmup,
    SchedulerType.CONSTANT: get_constant_schedule,
    SchedulerType.CONSTANT_WITH_WARMUP: get_constant_schedule_with_warmup,
    SchedulerType.PIECEWISE_CONSTANT: get_piecewise_constant_schedule,
}






def 			__UpperCamelCase    (    _A    :				Union[str, SchedulerType]					,   _A    :				Optimizer					,   _A    :				Optional[str] = None					,   _A    :				Optional[int] = None					,   _A    :				Optional[int] = None					,   _A    :				int = 1					,   _A    :				float = 1.0					,   _A    :				int = -1					,   )    ->      Optional[Any]:




	"""simple docstring"""





	lowerCAmelCase					:    Optional[Any]      							= SchedulerType(_A				)
	lowerCAmelCase					:    List[Any]      							= TYPE_TO_SCHEDULER_FUNCTION[name]
	if name == SchedulerType.CONSTANT:
		return schedule_func(_A					,   last_epoch=_A				)

	if name == SchedulerType.PIECEWISE_CONSTANT:
		return schedule_func(_A					,   step_rules=_A					,   last_epoch=_A				)

	# All other schedulers require `num_warmup_steps`
	if num_warmup_steps is None:
		raise ValueError(F"{name} requires `num_warmup_steps`, please provide that argument."				)

	if name == SchedulerType.CONSTANT_WITH_WARMUP:
		return schedule_func(_A					,   num_warmup_steps=_A					,   last_epoch=_A				)

	# All other schedulers require `num_training_steps`
	if num_training_steps is None:
		raise ValueError(F"{name} requires `num_training_steps`, please provide that argument."				)

	if name == SchedulerType.COSINE_WITH_RESTARTS:
		return schedule_func(
		    _A					,   num_warmup_steps=_A					,   num_training_steps=_A					,   num_cycles=_A					,   last_epoch=_A					,   )

	if name == SchedulerType.POLYNOMIAL:
		return schedule_func(
		    _A					,   num_warmup_steps=_A					,   num_training_steps=_A					,   power=_A					,   last_epoch=_A					,   )

	return schedule_func(
	    _A					,   num_warmup_steps=_A					,   num_training_steps=_A					,   last_epoch=_A				)




'''simple docstring'''




import copy

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


_lowerCAmelCase  :		Optional[Any]		     =	logging.get_logger(__name__)

_lowerCAmelCase  :		Dict		     =	{
    '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  lowerCAmelCase				(				a							):
	_lowerCamelCase   :					Any			      = """deformable_detr"""
	_lowerCamelCase   :					List[str]			      = {
	    """hidden_size""": """d_model""",
	    """num_attention_heads""": """encoder_attention_heads""",
	}



	def __init__(					self ,					snake_case__=True ,					snake_case__=None ,					snake_case__=3 ,					snake_case__=300 ,					snake_case__=1024 ,					snake_case__=6 ,					snake_case__=1024 ,					snake_case__=8 ,					snake_case__=6 ,					snake_case__=1024 ,					snake_case__=8 ,					snake_case__=0.0 ,					snake_case__=True ,					snake_case__="relu" ,					snake_case__=256 ,					snake_case__=0.1 ,					snake_case__=0.0 ,					snake_case__=0.0 ,					snake_case__=0.0_2 ,					snake_case__=1.0 ,					snake_case__=True ,					snake_case__=False ,					snake_case__="sine" ,					snake_case__="resnet50" ,					snake_case__=True ,					snake_case__=False ,					snake_case__=4 ,					snake_case__=4 ,					snake_case__=4 ,					snake_case__=False ,					snake_case__=300 ,					snake_case__=False ,					snake_case__=1 ,					snake_case__=5 ,					snake_case__=2 ,					snake_case__=1 ,					snake_case__=1 ,					snake_case__=5 ,					snake_case__=2 ,					snake_case__=0.1 ,					snake_case__=0.2_5 ,					snake_case__=False ,					**snake_case__ ,					):
		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.'    )
				lowerCAmelCase					:    Optional[int]      							= CONFIG_MAPPING['resnet'](out_features=['stage4']    )
			elif isinstance(snake_case__ ,					snake_case__    ):
				lowerCAmelCase					:    List[str]      							= backbone_config.get('model_type'    )
				lowerCAmelCase					:    str      							= CONFIG_MAPPING[backbone_model_type]
				lowerCAmelCase					:    Optional[Any]      							= config_class.from_dict(snake_case__    )
		lowerCAmelCase					:    Union[str, Any]      							= use_timm_backbone
		lowerCAmelCase					:    List[Any]      							= backbone_config
		lowerCAmelCase					:    Any      							= num_channels
		lowerCAmelCase					:    Tuple      							= num_queries
		lowerCAmelCase					:    Dict      							= max_position_embeddings
		lowerCAmelCase					:    int      							= d_model
		lowerCAmelCase					:    List[str]      							= encoder_ffn_dim
		lowerCAmelCase					:    List[str]      							= encoder_layers
		lowerCAmelCase					:    int      							= encoder_attention_heads
		lowerCAmelCase					:    str      							= decoder_ffn_dim
		lowerCAmelCase					:    str      							= decoder_layers
		lowerCAmelCase					:    Dict      							= decoder_attention_heads
		lowerCAmelCase					:    str      							= dropout
		lowerCAmelCase					:    List[str]      							= attention_dropout
		lowerCAmelCase					:    Union[str, Any]      							= activation_dropout
		lowerCAmelCase					:    str      							= activation_function
		lowerCAmelCase					:    Any      							= init_std
		lowerCAmelCase					:    Any      							= init_xavier_std
		lowerCAmelCase					:    Dict      							= encoder_layerdrop
		lowerCAmelCase					:    int      							= auxiliary_loss
		lowerCAmelCase					:    Optional[Any]      							= position_embedding_type
		lowerCAmelCase					:    List[str]      							= backbone
		lowerCAmelCase					:    int      							= use_pretrained_backbone
		lowerCAmelCase					:    int      							= dilation
		# deformable attributes
		lowerCAmelCase					:    List[str]      							= num_feature_levels
		lowerCAmelCase					:    List[str]      							= encoder_n_points
		lowerCAmelCase					:    Union[str, Any]      							= decoder_n_points
		lowerCAmelCase					:    Tuple      							= two_stage
		lowerCAmelCase					:    Dict      							= two_stage_num_proposals
		lowerCAmelCase					:    Union[str, Any]      							= 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
		lowerCAmelCase					:    Union[str, Any]      							= class_cost
		lowerCAmelCase					:    Dict      							= bbox_cost
		lowerCAmelCase					:    List[Any]      							= giou_cost
		# Loss coefficients
		lowerCAmelCase					:    Dict      							= mask_loss_coefficient
		lowerCAmelCase					:    Any      							= dice_loss_coefficient
		lowerCAmelCase					:    str      							= bbox_loss_coefficient
		lowerCAmelCase					:    Tuple      							= giou_loss_coefficient
		lowerCAmelCase					:    List[str]      							= eos_coefficient
		lowerCAmelCase					:    Any      							= focal_alpha
		lowerCAmelCase					:    Dict      							= disable_custom_kernels
		super().__init__(is_encoder_decoder=snake_case__ ,					**snake_case__    )



	@property
	def 				lowercase     (					self    ):
		return self.encoder_attention_heads



	@property
	def 				lowercase     (					self    ):
		return self.d_model



	def 				lowercase     (					self    ):
		lowerCAmelCase					:    Union[str, Any]      							= copy.deepcopy(self.__dict__    )
		if self.backbone_config is not None:
			lowerCAmelCase					:    List[Any]      							= self.backbone_config.to_dict()
		lowerCAmelCase					:    str      							= self.__class__.model_type
		return output
