




'''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


lowerCamelCase_			=						logging.get_logger(__name__)

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







class 		lowercase_   (			A       ):


   """simple docstring"""

   lowerCamelCase_      =						'''conditional_detr'''
   lowerCamelCase_      =						['''past_key_values''']
   lowerCamelCase_      =						{
       '''hidden_size''': '''d_model''',
       '''num_attention_heads''': '''encoder_attention_heads''',
   }



   def __init__(   self :     List[Any]  ,				__lowerCamelCase :     str=True  ,				__lowerCamelCase :     List[str]=None  ,				__lowerCamelCase :     Tuple=3  ,				__lowerCamelCase :     List[Any]=3_0_0  ,				__lowerCamelCase :     Optional[int]=6  ,				__lowerCamelCase :     str=2_0_4_8  ,				__lowerCamelCase :     List[Any]=8  ,				__lowerCamelCase :     Tuple=6  ,				__lowerCamelCase :     Tuple=2_0_4_8  ,				__lowerCamelCase :     str=8  ,				__lowerCamelCase :     str=0.0  ,				__lowerCamelCase :     List[str]=0.0  ,				__lowerCamelCase :     Optional[Any]=True  ,				__lowerCamelCase :     Tuple="relu"  ,				__lowerCamelCase :     Union[str, Any]=2_5_6  ,				__lowerCamelCase :     Tuple=0.1  ,				__lowerCamelCase :     Any=0.0  ,				__lowerCamelCase :     Union[str, Any]=0.0  ,				__lowerCamelCase :     str=0.0_2  ,				__lowerCamelCase :     Union[str, Any]=1.0  ,				__lowerCamelCase :     Optional[int]=False  ,				__lowerCamelCase :     Dict="sine"  ,				__lowerCamelCase :     Union[str, Any]="resnet50"  ,				__lowerCamelCase :     List[Any]=True  ,				__lowerCamelCase :     Dict=False  ,				__lowerCamelCase :     Dict=2  ,				__lowerCamelCase :     List[Any]=5  ,				__lowerCamelCase :     int=2  ,				__lowerCamelCase :     str=1  ,				__lowerCamelCase :     str=1  ,				__lowerCamelCase :     Tuple=2  ,				__lowerCamelCase :     Dict=5  ,				__lowerCamelCase :     List[Any]=2  ,				__lowerCamelCase :     Optional[int]=0.2_5  ,				**__lowerCamelCase :     Dict  ,				):

      """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."   )
            _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	=				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	=				decoder_layerdrop
      _SCREAMING_SNAKE_CASE	=				encoder_layers
      _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
      # 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	=				cls_loss_coefficient
      _SCREAMING_SNAKE_CASE	=				bbox_loss_coefficient
      _SCREAMING_SNAKE_CASE	=				giou_loss_coefficient
      _SCREAMING_SNAKE_CASE	=				focal_alpha
      super().__init__(is_encoder_decoder=__lowerCamelCase  ,				**__lowerCamelCase   )



   @property
   def 							lowerCAmelCase_      (   self :     List[Any]   ):

      """simple docstring"""
      return self.encoder_attention_heads



   @property
   def 							lowerCAmelCase_      (   self :     Union[str, Any]   ):

      """simple docstring"""
      return self.d_model



   def 							lowerCAmelCase_      (   self :     List[Any]   ):

      """simple docstring"""
      _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







class 		lowercase_   (			A       ):


   """simple docstring"""

   lowerCamelCase_      =						version.parse('''1.11'''       )



   @property
   def 							lowerCAmelCase_      (   self :     int   ):

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



   @property
   def 							lowerCAmelCase_      (   self :     Tuple   ):

      """simple docstring"""
      return 1e-5



   @property
   def 							lowerCAmelCase_      (   self :     Tuple   ):

      """simple docstring"""
      return 1_2







'''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_			=						logging.get_logger(__name__)







class 		lowercase_   (			A       ):


   """simple docstring"""

   lowerCamelCase_      =						'''linear'''
   lowerCamelCase_      =						'''cosine'''
   lowerCamelCase_      =						'''cosine_with_restarts'''
   lowerCamelCase_      =						'''polynomial'''
   lowerCamelCase_      =						'''constant'''
   lowerCamelCase_      =						'''constant_with_warmup'''
   lowerCamelCase_      =						'''piecewise_constant'''


def 			SCREAMING_SNAKE_CASE_			(							__A    :			Optimizer	,					__A    :			int = -1  )			->			Optional[int]:
   return LambdaLR(__A	,					lambda __A  : 1	,					last_epoch=__A  )
def 			SCREAMING_SNAKE_CASE_			(							__A    :			Optimizer	,					__A    :			int	,					__A    :			int = -1  )			->			Dict:

   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 			SCREAMING_SNAKE_CASE_			(							__A    :			Optimizer	,					__A    :			str	,					__A    :			int = -1  )			->			Tuple:
   _SCREAMING_SNAKE_CASE	=				{}
   _SCREAMING_SNAKE_CASE	=				step_rules.split(","  )
   for rule_str in rule_list[:-1]:
      _SCREAMING_SNAKE_CASE,	_SCREAMING_SNAKE_CASE	=				rule_str.split(":"  )
      _SCREAMING_SNAKE_CASE	=				int(__A  )
      _SCREAMING_SNAKE_CASE	=				float(__A  )
      _SCREAMING_SNAKE_CASE	=				value
   _SCREAMING_SNAKE_CASE	=				float(rule_list[-1]  )

   def create_rules_function(__A    :			Tuple	,					__A    :			List[Any]  ):
      def rule_func(__A    :			int  ) -> float:
         _SCREAMING_SNAKE_CASE	=				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

   _SCREAMING_SNAKE_CASE	=				create_rules_function(__A	,					__A  )

   return LambdaLR(__A	,					__A	,					last_epoch=__A  )
def 			SCREAMING_SNAKE_CASE_			(							__A    :			Union[str, Any]	,					__A    :			Optional[Any]	,					__A    :			List[str]	,					__A    :			Union[str, Any]=-1  )			->			str:

   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 			SCREAMING_SNAKE_CASE_			(							__A    :			Optimizer	,					__A    :			int	,					__A    :			int	,					__A    :			float = 0.5	,					__A    :			int = -1  )			->			Any:

   def lr_lambda(__A    :			int  ):
      if current_step < num_warmup_steps:
         return float(__A  ) / float(max(1	,					__A  )  )
      _SCREAMING_SNAKE_CASE	=				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 			SCREAMING_SNAKE_CASE_			(							__A    :			Optimizer	,					__A    :			int	,					__A    :			int	,					__A    :			int = 1	,					__A    :			int = -1  )			->			str:

   def lr_lambda(__A    :			Optional[int]  ):
      if current_step < num_warmup_steps:
         return float(__A  ) / float(max(1	,					__A  )  )
      _SCREAMING_SNAKE_CASE	=				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 			SCREAMING_SNAKE_CASE_			(							__A    :			Optional[int]	,					__A    :			Optional[Any]	,					__A    :			List[str]	,					__A    :			Any=1e-7	,					__A    :			Optional[Any]=1.0	,					__A    :			List[Any]=-1  )			->			Tuple:
   _SCREAMING_SNAKE_CASE	=				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:
         _SCREAMING_SNAKE_CASE	=				lr_init - lr_end
         _SCREAMING_SNAKE_CASE	=				num_training_steps - num_warmup_steps
         _SCREAMING_SNAKE_CASE	=				1 - (current_step - num_warmup_steps) / decay_steps
         _SCREAMING_SNAKE_CASE	=				lr_range * pct_remaining**power + lr_end
         return decay / lr_init  # as LambdaLR multiplies by lr_init

   return LambdaLR(__A	,					__A	,					__A  )


lowerCamelCase_			=						{
    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 			SCREAMING_SNAKE_CASE_			(							__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]:
   _SCREAMING_SNAKE_CASE	=				SchedulerType(__A  )
   _SCREAMING_SNAKE_CASE	=				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  )

