

from .integrations import (
    is_optuna_available,
    is_ray_available,
    is_sigopt_available,
    is_wandb_available,
    run_hp_search_optuna,
    run_hp_search_ray,
    run_hp_search_sigopt,
    run_hp_search_wandb,
)
from .trainer_utils import (
    HPSearchBackend,
    default_hp_space_optuna,
    default_hp_space_ray,
    default_hp_space_sigopt,
    default_hp_space_wandb,
)
from .utils import logging


_UpperCamelCase 			=		logging.get_logger(__name__)







class   __lowercase      :
 _UpperCamelCase			  =  42
 _UpperCamelCase			  =  None
 @staticmethod
 def 	UpperCamelCase__  (							)					->int:



       '''simple docstring'''





       raise NotImplementedError
 def 	UpperCamelCase__  (							self      ,							A_      ,							A_      ,							A_      ,							**A_					)					->List[str]:



       '''simple docstring'''





       raise NotImplementedError
 def 	UpperCamelCase__  (							self      ,							A_					)					->List[str]:



       '''simple docstring'''





       raise NotImplementedError
 def 	UpperCamelCase__  (							self					)					->Optional[int]:



       '''simple docstring'''





       if not self.is_available():
             raise RuntimeError(
                 f"""You picked the {self.name} backend, but it is not installed. Run {self.pip_install()}."""					)





 @classmethod
 def 	UpperCamelCase__  (							cls					)					->int:



       '''simple docstring'''





       return f"""`pip install {cls.pip_package or cls.name}`"""







class   __lowercase      (_UpperCAmelCase ):
 _UpperCamelCase			  =  """optuna"""
 @staticmethod
 def 	UpperCamelCase__  (							)					->List[str]:



       '''simple docstring'''





       return is_optuna_available()
 def 	UpperCamelCase__  (							self      ,							A_      ,							A_      ,							A_      ,							**A_					)					->Any:



       '''simple docstring'''





       return run_hp_search_optuna(A_      ,							A_      ,							A_      ,							**A_					)





 def 	UpperCamelCase__  (							self      ,							A_					)					->Optional[int]:



       '''simple docstring'''





       return default_hp_space_optuna(A_					)







class   __lowercase      (_UpperCAmelCase ):
 _UpperCamelCase			  =  """ray"""
 _UpperCamelCase			  =  """'ray[tune]'"""
 @staticmethod
 def 	UpperCamelCase__  (							)					->List[Any]:



       '''simple docstring'''





       return is_ray_available()
 def 	UpperCamelCase__  (							self      ,							A_      ,							A_      ,							A_      ,							**A_					)					->Dict:



       '''simple docstring'''





       return run_hp_search_ray(A_      ,							A_      ,							A_      ,							**A_					)





 def 	UpperCamelCase__  (							self      ,							A_					)					->Dict:



       '''simple docstring'''





       return default_hp_space_ray(A_					)







class   __lowercase      (_UpperCAmelCase ):
 _UpperCamelCase			  =  """sigopt"""
 @staticmethod
 def 	UpperCamelCase__  (							)					->Dict:



       '''simple docstring'''





       return is_sigopt_available()
 def 	UpperCamelCase__  (							self      ,							A_      ,							A_      ,							A_      ,							**A_					)					->List[Any]:



       '''simple docstring'''





       return run_hp_search_sigopt(A_      ,							A_      ,							A_      ,							**A_					)





 def 	UpperCamelCase__  (							self      ,							A_					)					->Dict:



       '''simple docstring'''





       return default_hp_space_sigopt(A_					)







class   __lowercase      (_UpperCAmelCase ):
 _UpperCamelCase			  =  """wandb"""
 @staticmethod
 def 	UpperCamelCase__  (							)					->Any:



       '''simple docstring'''





       return is_wandb_available()
 def 	UpperCamelCase__  (							self      ,							A_      ,							A_      ,							A_      ,							**A_					)					->Union[str, Any]:



       '''simple docstring'''





       return run_hp_search_wandb(A_      ,							A_      ,							A_      ,							**A_					)





 def 	UpperCamelCase__  (							self      ,							A_					)					->List[str]:



       '''simple docstring'''





       return default_hp_space_wandb(A_					)



_UpperCamelCase 			=		{
    HPSearchBackend(backend.name): backend for backend in [OptunaBackend, RayTuneBackend, SigOptBackend, WandbBackend]
}







def 		_lowercase (     ):
      __lowerCAmelCase					:						List[str]					=					[backend for backend in ALL_HYPERPARAMETER_SEARCH_BACKENDS.values() if backend.is_available()]
      if len(lowercase__						) > 0:
            __lowerCAmelCase					:						Tuple					=					available_backends[0].name
            if len(lowercase__						) > 1:
                  logger.info(
                      f"""{len(lowercase__						)} hyperparameter search backends available. Using {name} as the default."""						)
            return name
      raise RuntimeError(
          '''No hyperparameter search backend available.\n'''
          + '''\n'''.join(
              f""" - To install {backend.name} run {backend.pip_install()}"""
              for backend in ALL_HYPERPARAMETER_SEARCH_BACKENDS.values()						)						)





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


_UpperCamelCase 			=		logging.get_logger(__name__)

_UpperCamelCase 			=		{
    "microsoft/table-transformer-detection": (
        "https://huggingface.co/microsoft/table-transformer-detection/resolve/main/config.json"
    ),
}







class   __lowercase      (_UpperCAmelCase ):
 _UpperCamelCase			  =  """table-transformer"""
 _UpperCamelCase			  =  ["""past_key_values"""]
 _UpperCamelCase			  =  {
     """hidden_size""": """d_model""",
     """num_attention_heads""": """encoder_attention_heads""",
 }
 def __init__(							self      ,							A_=True      ,							A_=None      ,							A_=3      ,							A_=100      ,							A_=6      ,							A_=2048      ,							A_=8      ,							A_=6      ,							A_=2048      ,							A_=8      ,							A_=0.0      ,							A_=0.0      ,							A_=True      ,							A_="relu"      ,							A_=256      ,							A_=0.1      ,							A_=0.0      ,							A_=0.0      ,							A_=0.02      ,							A_=1.0      ,							A_=False      ,							A_="sine"      ,							A_="resnet50"      ,							A_=True      ,							A_=False      ,							A_=1      ,							A_=5      ,							A_=2      ,							A_=1      ,							A_=1      ,							A_=5      ,							A_=2      ,							A_=0.1      ,							**A_      ,							)					->Any:



       '''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.'''					)
                   __lowerCAmelCase					:						Optional[Any]					=					CONFIG_MAPPING['''resnet'''](out_features=['''stage4''']					)
             elif isinstance(A_      ,							A_					):
                   __lowerCAmelCase					:						int					=					backbone_config.get('''model_type'''					)
                   __lowerCAmelCase					:						List[str]					=					CONFIG_MAPPING[backbone_model_type]
                   __lowerCAmelCase					:						Any					=					config_class.from_dict(A_					)
             # set timm attributes to None
             __lowerCAmelCase,							__lowerCAmelCase,							__lowerCAmelCase					:						List[str]					=					None, None, None

       __lowerCAmelCase					:						Tuple					=					use_timm_backbone
       __lowerCAmelCase					:						Optional[Any]					=					backbone_config
       __lowerCAmelCase					:						List[str]					=					num_channels
       __lowerCAmelCase					:						Tuple					=					num_queries
       __lowerCAmelCase					:						int					=					d_model
       __lowerCAmelCase					:						List[Any]					=					encoder_ffn_dim
       __lowerCAmelCase					:						Optional[int]					=					encoder_layers
       __lowerCAmelCase					:						List[str]					=					encoder_attention_heads
       __lowerCAmelCase					:						str					=					decoder_ffn_dim
       __lowerCAmelCase					:						Union[str, Any]					=					decoder_layers
       __lowerCAmelCase					:						Any					=					decoder_attention_heads
       __lowerCAmelCase					:						Optional[int]					=					dropout
       __lowerCAmelCase					:						Any					=					attention_dropout
       __lowerCAmelCase					:						Tuple					=					activation_dropout
       __lowerCAmelCase					:						Optional[Any]					=					activation_function
       __lowerCAmelCase					:						List[str]					=					init_std
       __lowerCAmelCase					:						Tuple					=					init_xavier_std
       __lowerCAmelCase					:						Any					=					encoder_layerdrop
       __lowerCAmelCase					:						List[Any]					=					decoder_layerdrop
       __lowerCAmelCase					:						Optional[Any]					=					encoder_layers
       __lowerCAmelCase					:						Optional[Any]					=					auxiliary_loss
       __lowerCAmelCase					:						Optional[Any]					=					position_embedding_type
       __lowerCAmelCase					:						Tuple					=					backbone
       __lowerCAmelCase					:						Any					=					use_pretrained_backbone
       __lowerCAmelCase					:						int					=					dilation
       # Hungarian matcher
       __lowerCAmelCase					:						Dict					=					class_cost
       __lowerCAmelCase					:						List[str]					=					bbox_cost
       __lowerCAmelCase					:						int					=					giou_cost
       # Loss coefficients
       __lowerCAmelCase					:						Optional[Any]					=					mask_loss_coefficient
       __lowerCAmelCase					:						Tuple					=					dice_loss_coefficient
       __lowerCAmelCase					:						int					=					bbox_loss_coefficient
       __lowerCAmelCase					:						List[Any]					=					giou_loss_coefficient
       __lowerCAmelCase					:						int					=					eos_coefficient
       super().__init__(is_encoder_decoder=A_      ,							**A_					)
 @property
 def 	UpperCamelCase__  (							self					)					->int:



       '''simple docstring'''





       return self.encoder_attention_heads





 @property
 def 	UpperCamelCase__  (							self					)					->int:



       '''simple docstring'''





       return self.d_model







class   __lowercase      (_UpperCAmelCase ):
 _UpperCamelCase			  =  version.parse("""1.11""" )
 @property
 def 	UpperCamelCase__  (							self					)					->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 	UpperCamelCase__  (							self					)					->float:



       '''simple docstring'''





       return 1e-5





 @property
 def 	UpperCamelCase__  (							self					)					->int:



       '''simple docstring'''





       return 12


