


"""simple docstring"""





from typing import List

import numpy as np


def    _snake_case  (  _snake_case :	dict	):
      lowerCAmelCase    :		Dict				=      {key: len(_UpperCamelCase	) for key, value in gen_kwargs.items() if isinstance(_UpperCamelCase       ,      _UpperCamelCase	)}
      if len(set(lists_lengths.values()	)	) > 1:
            raise RuntimeError(
                (
                    '''Sharding is ambiguous for this dataset: '''
                    + '''we found several data sources lists of different lengths, and we don\'t know over which list we should parallelize:\n'''
                    + '''\n'''.join(f'''\t- key {key} has length {length}''' for key, length in lists_lengths.items()	)
                    + '''\nTo fix this, check the \'gen_kwargs\' and make sure to use lists only for data sources, '''
                    + '''and use tuples otherwise. In the end there should only be one single list, or several lists with the same length.'''
                )	)
      lowerCAmelCase    :		Optional[Any]				=      max(lists_lengths.values()       ,      default=0	)
      return max(1       ,      _UpperCamelCase	)


def    _snake_case  (  _snake_case :	int       ,      _snake_case :	int	):
      lowerCAmelCase    :		int				=      []
      for group_idx in range(_UpperCamelCase	):
            lowerCAmelCase    :		Union[str, Any]				=      num_shards // max_num_jobs + (group_idx < (num_shards % max_num_jobs))
            if num_shards_to_add == 0:
                  break
            lowerCAmelCase    :		Any				=      shards_indices_per_group[-1].stop if shards_indices_per_group else 0
            lowerCAmelCase    :		Dict				=      range(_UpperCamelCase       ,      start + num_shards_to_add	)
            shards_indices_per_group.append(_UpperCamelCase	)
      return shards_indices_per_group


def    _snake_case  (  _snake_case :	dict       ,      _snake_case :	int	):
      lowerCAmelCase    :		str				=      _number_of_shards_in_gen_kwargs(_UpperCamelCase	)
      if num_shards == 1:
            return [dict(_UpperCamelCase	)]
      else:
            lowerCAmelCase    :		Union[str, Any]				=      _distribute_shards(num_shards=_UpperCamelCase       ,      max_num_jobs=_UpperCamelCase	)
            return [
                {
                    key: [value[shard_idx] for shard_idx in shard_indices_per_group[group_idx]]
                    if isinstance(_UpperCamelCase       ,      _UpperCamelCase	)
                    else value
                    for key, value in gen_kwargs.items()
                }
                for group_idx in range(len(_UpperCamelCase	)	)
            ]


def    _snake_case  (  _snake_case :	List[dict]	):
      return {
          key: [value for gen_kwargs in gen_kwargs_list for value in gen_kwargs[key]]
          if isinstance(gen_kwargs_list[0][key]       ,      _UpperCamelCase	)
          else gen_kwargs_list[0][key]
          for key in gen_kwargs_list[0]
      }






def    _snake_case  (  _snake_case :	np.random.Generator       ,      _snake_case :	dict	):
      lowerCAmelCase    :		List[Any]				=      {len(_UpperCamelCase	) for value in gen_kwargs.values() if isinstance(_UpperCamelCase       ,      _UpperCamelCase	)}
      lowerCAmelCase    :		int				=      {}
      for size in list_sizes:
            lowerCAmelCase    :		Any				=      list(range(_UpperCamelCase	)	)
            rng.shuffle(indices_per_size[size]	)
      # Now let's copy the gen_kwargs and shuffle the lists based on their sizes
      lowerCAmelCase    :		Dict				=      dict(_UpperCamelCase	)
      for key, value in shuffled_kwargs.items():
            if isinstance(_UpperCamelCase       ,      _UpperCamelCase	):
                  lowerCAmelCase    :		Union[str, Any]				=      [value[i] for i in indices_per_size[len(_UpperCamelCase	)]]
      return shuffled_kwargs





'''simple docstring'''
import copy

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


lowerCamelCase :      Dict					 =     logging.get_logger(__name__)

lowerCamelCase :      List[Any]					 =     {
    "ut/deta": "https://huggingface.co/ut/deta/resolve/main/config.json",
}







class 							A__    (       A__  ):
  A__		 =				'deta'
  A__		 =				{
      'hidden_size': 'd_model',
      'num_attention_heads': 'encoder_attention_heads',
  }





  def __init__(		self      :	Tuple							,					_a      :	Optional[int]=None							,					_a      :	int=900							,					_a      :	Optional[Any]=2048							,					_a      :	int=6							,					_a      :	Tuple=2048							,					_a      :	Optional[int]=8							,					_a      :	Any=6							,					_a      :	str=1024							,					_a      :	int=8							,					_a      :	int=0.0							,					_a      :	Optional[Any]=True							,					_a      :	Tuple="relu"							,					_a      :	Union[str, Any]=256							,					_a      :	Tuple=0.1							,					_a      :	str=0.0							,					_a      :	Dict=0.0							,					_a      :	Tuple=0.02							,					_a      :	Union[str, Any]=1.0							,					_a      :	Any=True							,					_a      :	Tuple=False							,					_a      :	List[Any]="sine"							,					_a      :	str=5							,					_a      :	List[Any]=4							,					_a      :	str=4							,					_a      :	Union[str, Any]=True							,					_a      :	Optional[int]=300							,					_a      :	Dict=True							,					_a      :	List[Any]=True							,					_a      :	List[Any]=1							,					_a      :	List[str]=5							,					_a      :	int=2							,					_a      :	Dict=1							,					_a      :	str=1							,					_a      :	Optional[Any]=5							,					_a      :	Union[str, Any]=2							,					_a      :	List[str]=0.1							,					_a      :	List[Any]=0.25							,					**_a      :	Union[str, Any]							,					)	->    List[str]:





       '''simple docstring'''






       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=['stage2', 'stage3', 'stage4'] )
       else:
            if isinstance(_a							,					_a ):
                 _SCREAMING_SNAKE_CASE							    =backbone_config.pop('model_type' )
                 _SCREAMING_SNAKE_CASE							    =CONFIG_MAPPING[backbone_model_type]
                 _SCREAMING_SNAKE_CASE							    =config_class.from_dict(_a )

       _SCREAMING_SNAKE_CASE							    =backbone_config
       _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
       # 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
       _SCREAMING_SNAKE_CASE							    =assign_first_stage
       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
       super().__init__(is_encoder_decoder=_a							,					**_a )





  @property
  def 						A  (		self      :	Dict )	->    int:





       '''simple docstring'''






       return self.encoder_attention_heads





  @property
  def 						A  (		self      :	List[Any] )	->    int:





       '''simple docstring'''






       return self.d_model





  def 						A  (		self      :	Optional[int] )	->    List[str]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =copy.deepcopy(self.__dict__ )
       _SCREAMING_SNAKE_CASE							    =self.backbone_config.to_dict()
       _SCREAMING_SNAKE_CASE							    =self.__class__.model_type
       return output
