



import logging
import random

import ray

from transformers import RagConfig, RagRetriever, RagTokenizer
from transformers.models.rag.retrieval_rag import CustomHFIndex


__snake_case						:List[Any]      		=     logging.getLogger(__name__)

class     _A       :



       def __init__(		self							:     List[str]):


           '''simple docstring'''

           __a						       =				False



       def 		_lowerCamelCase			(		self							:     Any     ,		__SCREAMING_SNAKE_CASE							:     Optional[Any]     ,		__SCREAMING_SNAKE_CASE							:     Optional[Any]     ,		__SCREAMING_SNAKE_CASE							:     List[str]     ,		__SCREAMING_SNAKE_CASE							:     Union[str, Any]):


           '''simple docstring'''

           if not self.initialized:
               __a						       =				RagRetriever(
                   __SCREAMING_SNAKE_CASE     ,		question_encoder_tokenizer=__SCREAMING_SNAKE_CASE     ,		generator_tokenizer=__SCREAMING_SNAKE_CASE     ,		index=__SCREAMING_SNAKE_CASE     ,		init_retrieval=__SCREAMING_SNAKE_CASE     ,		)
               __a						       =				True



       def 		_lowerCamelCase			(		self							:     List[str]):


           '''simple docstring'''

           self.retriever.index.init_index()





       def 		_lowerCamelCase			(		self							:     Tuple     ,		__SCREAMING_SNAKE_CASE							:     Union[str, Any]     ,		__SCREAMING_SNAKE_CASE							:     Union[str, Any]):


           '''simple docstring'''

           __a       ,   __a						       =				self.retriever._main_retrieve(__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE)
           return doc_ids, retrieved_doc_embeds


class     _A       (	__UpperCAmelCase    ):



       def __init__(		self							:     Optional[Any]     ,		__SCREAMING_SNAKE_CASE							:     List[Any]     ,		__SCREAMING_SNAKE_CASE							:     List[str]     ,		__SCREAMING_SNAKE_CASE							:     List[str]     ,		__SCREAMING_SNAKE_CASE							:     int     ,		__SCREAMING_SNAKE_CASE							:     Union[str, Any]=None):


           '''simple docstring'''

           if index is not None and index.is_initialized() and len(__SCREAMING_SNAKE_CASE) > 0:
               raise ValueError(
                   '''When using Ray for distributed fine-tuning, '''
                   '''you\'ll need to provide the paths instead, '''
                   '''as the dataset and the index are loaded '''
                   '''separately. More info in examples/rag/use_own_knowledge_dataset.py ''')
           super().__init__(
               __SCREAMING_SNAKE_CASE     ,		question_encoder_tokenizer=__SCREAMING_SNAKE_CASE     ,		generator_tokenizer=__SCREAMING_SNAKE_CASE     ,		index=__SCREAMING_SNAKE_CASE     ,		init_retrieval=__SCREAMING_SNAKE_CASE     ,		)
           __a						       =				retrieval_workers
           if len(self.retrieval_workers) > 0:
               ray.get(
                   [
                       worker.create_rag_retriever.remote(__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE)
                       for worker in self.retrieval_workers
                   ])



       def 		_lowerCamelCase			(		self							:     List[Any]):


           '''simple docstring'''

           logger.info('''initializing retrieval''')

           if len(self.retrieval_workers) > 0:
               ray.get([worker.init_retrieval.remote() for worker in self.retrieval_workers])
           else:
               # Non-distributed training. Load index into this same process.
               self.index.init_index()



       def 		_lowerCamelCase			(		self							:     Dict     ,		__SCREAMING_SNAKE_CASE							:     Optional[int]     ,		__SCREAMING_SNAKE_CASE							:     List[Any]):


           '''simple docstring'''

           if len(self.retrieval_workers) > 0:
               # Select a random retrieval actor.
               __a						       =				self.retrieval_workers[random.randint(0     ,		len(self.retrieval_workers) - 1)]
               __a       ,   __a						       =				ray.get(random_worker.retrieve.remote(__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE))
           else:
               __a       ,   __a						       =				self._main_retrieve(__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE)
           return retrieved_doc_embeds, doc_ids, self.index.get_doc_dicts(__SCREAMING_SNAKE_CASE)



       @classmethod
       def 		_lowerCamelCase			(		cls							:     Any     ,		__SCREAMING_SNAKE_CASE							:     Tuple     ,		__SCREAMING_SNAKE_CASE							:     Tuple=None     ,		**__SCREAMING_SNAKE_CASE							:     Optional[int]):


           '''simple docstring'''

           return super(__SCREAMING_SNAKE_CASE     ,		cls).get_tokenizers(__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE     ,		**__SCREAMING_SNAKE_CASE)





       @classmethod
       def 		_lowerCamelCase			(		cls							:     Tuple     ,		__SCREAMING_SNAKE_CASE							:     Optional[int]     ,		__SCREAMING_SNAKE_CASE							:     str     ,		__SCREAMING_SNAKE_CASE							:     str=None     ,		**__SCREAMING_SNAKE_CASE							:     List[Any]):


           '''simple docstring'''

           __a						       =				kwargs.pop('''config'''     ,		__SCREAMING_SNAKE_CASE) or RagConfig.from_pretrained(__SCREAMING_SNAKE_CASE     ,		**__SCREAMING_SNAKE_CASE)
           __a						       =				RagTokenizer.from_pretrained(__SCREAMING_SNAKE_CASE     ,		config=__SCREAMING_SNAKE_CASE)
           __a						       =				rag_tokenizer.question_encoder
           __a						       =				rag_tokenizer.generator
           if indexed_dataset is not None:
               __a						       =				'''custom'''
               __a						       =				CustomHFIndex(config.retrieval_vector_size     ,		__SCREAMING_SNAKE_CASE)
           else:
               __a						       =				cls._build_index(__SCREAMING_SNAKE_CASE)
           return cls(
               __SCREAMING_SNAKE_CASE     ,		question_encoder_tokenizer=__SCREAMING_SNAKE_CASE     ,		generator_tokenizer=__SCREAMING_SNAKE_CASE     ,		retrieval_workers=__SCREAMING_SNAKE_CASE     ,		index=__SCREAMING_SNAKE_CASE     ,		)








'''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
