"""simple docstring"""
import copy

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


lowercase__					       =	logging.get_logger(__name__)

lowercase__					       =	{
    'ut/deta': 'https://huggingface.co/ut/deta/resolve/main/config.json',
}




class    __snake_case       (     __lowerCAmelCase				):
      a__        =		"""deta"""
      a__        =		{
          """hidden_size""": """d_model""",
          """num_attention_heads""": """encoder_attention_heads""",
      }



      def __init__(						self    ,			lowercase=None    ,			lowercase=9_00    ,			lowercase=20_48    ,			lowercase=6    ,			lowercase=20_48    ,			lowercase=8    ,			lowercase=6    ,			lowercase=10_24    ,			lowercase=8    ,			lowercase=0.0    ,			lowercase=True    ,			lowercase="relu"    ,			lowercase=2_56    ,			lowercase=0.1    ,			lowercase=0.0    ,			lowercase=0.0    ,			lowercase=0.02    ,			lowercase=1.0    ,			lowercase=True    ,			lowercase=False    ,			lowercase="sine"    ,			lowercase=5    ,			lowercase=4    ,			lowercase=4    ,			lowercase=True    ,			lowercase=3_00    ,			lowercase=True    ,			lowercase=True    ,			lowercase=1    ,			lowercase=5    ,			lowercase=2    ,			lowercase=1    ,			lowercase=1    ,			lowercase=5    ,			lowercase=2    ,			lowercase=0.1    ,			lowercase=0.25    ,			**lowercase    ,			)	->			Dict:





        '''simple docstring'''





        if backbone_config is None:
          logger.info('`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.')
          a__:     Optional[Any]										=	CONFIG_MAPPING['resnet'](out_features=['stage2', 'stage3', 'stage4'])
        else:
          if isinstance(lowercase    ,			lowercase):
            a__:     Optional[int]										=	backbone_config.pop('model_type')
            a__:     Optional[int]										=	CONFIG_MAPPING[backbone_model_type]
            a__:     List[Any]										=	config_class.from_dict(lowercase)

        a__:     Any										=	backbone_config
        a__:     Union[str, Any]										=	num_queries
        a__:     int										=	max_position_embeddings
        a__:     Tuple										=	d_model
        a__:     List[str]										=	encoder_ffn_dim
        a__:     int										=	encoder_layers
        a__:     Any										=	encoder_attention_heads
        a__:     Any										=	decoder_ffn_dim
        a__:     Any										=	decoder_layers
        a__:     int										=	decoder_attention_heads
        a__:     Any										=	dropout
        a__:     Optional[Any]										=	attention_dropout
        a__:     int										=	activation_dropout
        a__:     Tuple										=	activation_function
        a__:     int										=	init_std
        a__:     Dict										=	init_xavier_std
        a__:     int										=	encoder_layerdrop
        a__:     Any										=	auxiliary_loss
        a__:     str										=	position_embedding_type
        # deformable attributes
        a__:     Tuple										=	num_feature_levels
        a__:     Union[str, Any]										=	encoder_n_points
        a__:     Dict										=	decoder_n_points
        a__:     Tuple										=	two_stage
        a__:     List[str]										=	two_stage_num_proposals
        a__:     List[Any]										=	with_box_refine
        a__:     List[str]										=	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
        a__:     int										=	class_cost
        a__:     Dict										=	bbox_cost
        a__:     Optional[int]										=	giou_cost
        # Loss coefficients
        a__:     Union[str, Any]										=	mask_loss_coefficient
        a__:     Union[str, Any]										=	dice_loss_coefficient
        a__:     List[str]										=	bbox_loss_coefficient
        a__:     str										=	giou_loss_coefficient
        a__:     int										=	eos_coefficient
        a__:     Tuple										=	focal_alpha
        super().__init__(is_encoder_decoder=lowercase    ,			**lowercase)



      @property
      def   lowerCamelCase_   (						self)	->			int:





        '''simple docstring'''





        return self.encoder_attention_heads



      @property
      def   lowerCamelCase_   (						self)	->			int:





        '''simple docstring'''





        return self.d_model



      def   lowerCamelCase_   (						self)	->			Tuple:





        '''simple docstring'''





        a__:     List[Any]										=	copy.deepcopy(self.__dict__)
        a__:     Tuple										=	self.backbone_config.to_dict()
        a__:     Optional[Any]										=	self.__class__.model_type
        return output



"""simple docstring"""
import logging
import random

import ray

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


lowercase__					       =	logging.getLogger(__name__)




class    __snake_case       :



      def __init__(						self)	->			Optional[int]:





        '''simple docstring'''





        a__:     Optional[Any]										=	False



      def   lowerCamelCase_   (						self    ,			lowercase    ,			lowercase    ,			lowercase    ,			lowercase)	->			str:





        '''simple docstring'''





        if not self.initialized:
          a__:     Optional[int]										=	RagRetriever(
              lowercase    ,			question_encoder_tokenizer=lowercase    ,			generator_tokenizer=lowercase    ,			index=lowercase    ,			init_retrieval=lowercase    ,			)
          a__:     Optional[int]										=	True



      def   lowerCamelCase_   (						self)	->			int:





        '''simple docstring'''





        self.retriever.index.init_index()



      def   lowerCamelCase_   (						self    ,			lowercase    ,			lowercase)	->			Union[str, Any]:





        '''simple docstring'''





        a__							,    a__:     str										=	self.retriever._main_retrieve(lowercase    ,			lowercase)
        return doc_ids, retrieved_doc_embeds







class    __snake_case       (     __lowerCAmelCase				):



      def __init__(						self    ,			lowercase    ,			lowercase    ,			lowercase    ,			lowercase    ,			lowercase=None)	->			int:





        '''simple docstring'''





        if index is not None and index.is_initialized() and len(lowercase) > 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__(
            lowercase    ,			question_encoder_tokenizer=lowercase    ,			generator_tokenizer=lowercase    ,			index=lowercase    ,			init_retrieval=lowercase    ,			)
        a__:     Any										=	retrieval_workers
        if len(self.retrieval_workers) > 0:
          ray.get(
              [
                  worker.create_rag_retriever.remote(lowercase    ,			lowercase    ,			lowercase    ,			lowercase)
                  for worker in self.retrieval_workers
              ])



      def   lowerCamelCase_   (						self)	->			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    ,			lowercase    ,			lowercase)	->			Union[str, Any]:





        '''simple docstring'''





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



      @classmethod
      def   lowerCamelCase_   (						cls    ,			lowercase    ,			lowercase=None    ,			**lowercase)	->			Tuple:





        '''simple docstring'''





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



      @classmethod
      def   lowerCamelCase_   (						cls    ,			lowercase    ,			lowercase    ,			lowercase=None    ,			**lowercase)	->			Union[str, Any]:





        '''simple docstring'''





        a__:     Optional[int]										=	kwargs.pop('config'    ,			lowercase) or RagConfig.from_pretrained(lowercase    ,			**lowercase)
        a__:     Union[str, Any]										=	RagTokenizer.from_pretrained(lowercase    ,			config=lowercase)
        a__:     int										=	rag_tokenizer.question_encoder
        a__:     Any										=	rag_tokenizer.generator
        if indexed_dataset is not None:
          a__:     List[Any]										=	'custom'
          a__:     Optional[Any]										=	CustomHFIndex(config.retrieval_vector_size    ,			lowercase)
        else:
          a__:     Dict										=	cls._build_index(lowercase)
        return cls(
            lowercase    ,			question_encoder_tokenizer=lowercase    ,			generator_tokenizer=lowercase    ,			retrieval_workers=lowercase    ,			index=lowercase    ,			)


