code: 
  
  
  import logging
  import random
  
  import ray
  
  from transformers import RagConfig, RagRetriever, RagTokenizer
  from transformers.models.rag.retrieval_rag import CustomHFIndex
  
  
  SCREAMING_SNAKE_CASE : Optional[Any]          =    logging.getLogger(__name__)
  
  
  
  
  
  class 					snake_case__  :
  
  
  
  
  
  	def __init__(  self							)		->			Optional[Any]:
  								"""simple docstring"""
  
  
  
  
  
  
  								a_       :   Union[str, Any]			=	False
  
  
  
  
  
  	def     A				(  self				,    UpperCamelCase_				,    UpperCamelCase_				,    UpperCamelCase_				,    UpperCamelCase_							)		->			Any:
  								"""simple docstring"""
  
  
  
  
  
  
  								if not self.initialized:
  															a_       :   Dict			=	RagRetriever(
  															    UpperCamelCase_				,    question_encoder_tokenizer=UpperCamelCase_				,    generator_tokenizer=UpperCamelCase_				,    index=UpperCamelCase_				,    init_retrieval=UpperCamelCase_				,    )
  															a_       :   str			=	True
  
  
  
  
  
  	def     A				(  self							)		->			Dict:
  								"""simple docstring"""
  
  
  
  
  
  
  								self.retriever.index.init_index()
  
  
  
  
  
  	def     A				(  self							)		->			Dict:
  								"""simple docstring"""
  
  
  
  
  
  
  								del self.retriever
  								a_       :   List[Any]			=	False
  
  
  
  
  
  	def     A				(  self				,    UpperCamelCase_				,    UpperCamelCase_							)		->			Optional[int]:
  								"""simple docstring"""
  
  
  
  
  
  
  								a_       :   str			=	self.retriever._main_retrieve(UpperCamelCase_				,    UpperCamelCase_							)
  								a_       :   int			=	self.retriever.index.get_doc_dicts(UpperCamelCase_							)
  								return doc_ids, retrieved_doc_embeds, doc_dicts
  
  
  
  
  
  class 					snake_case__  ( lowerCamelCase_       ):
  
  
  
  
  
  	def __init__(  self				,    UpperCamelCase_				,    UpperCamelCase_				,    UpperCamelCase_				,    UpperCamelCase_				,    UpperCamelCase_=None							)		->			int:
  								"""simple docstring"""
  
  
  
  
  
  
  								if index is not None and index.is_initialized() and len(UpperCamelCase_							) > 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__(
  								    UpperCamelCase_				,    question_encoder_tokenizer=UpperCamelCase_				,    generator_tokenizer=UpperCamelCase_				,    index=UpperCamelCase_				,    init_retrieval=UpperCamelCase_				,    )
  
  								a_       :   List[Any]			=	retrieval_workers
  								a_       :   Optional[int]			=	question_encoder_tokenizer
  								a_       :   List[Any]			=	generator_tokenizer
  								if len(self.retrieval_workers							) > 0:
  															ray.get(
  															    [
  															        worker.create_rag_retriever.remote(UpperCamelCase_				,    UpperCamelCase_				,    UpperCamelCase_				,    UpperCamelCase_							)
  															        for worker in self.retrieval_workers
  															    ]							)
  
  
  
  
  
  	def     A				(  self							)		->			Optional[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     A				(  self				,    UpperCamelCase_				,    UpperCamelCase_							)		->			int:
  								"""simple docstring"""
  
  
  
  
  
  
  								if len(self.retrieval_workers							) > 0:
  															# Select a random retrieval actor.
  															a_       :   List[str]			=	self.retrieval_workers[random.randint(0				,    len(self.retrieval_workers							) - 1							)]
  															a_       :   Optional[Any]			=	ray.get(
  															    random_worker.retrieve.remote(UpperCamelCase_				,    UpperCamelCase_							)							)
  								else:
  															a_       :   List[Any]			=	self._main_retrieve(UpperCamelCase_				,    UpperCamelCase_							)
  															a_       :   Union[str, Any]			=	self.index.get_doc_dicts(UpperCamelCase_							)
  								return retrieved_doc_embeds, doc_ids, doc_dicts
  
  
  
  
  
  	@classmethod
  	def     A				(  cls				,    UpperCamelCase_				,    UpperCamelCase_=None				,    **UpperCamelCase_							)		->			str:
  								"""simple docstring"""
  
  
  
  
  
  
  								return super(UpperCamelCase_				,    cls							).get_tokenizers(UpperCamelCase_				,    UpperCamelCase_				,    **UpperCamelCase_							)
  
  
  
  
  
  	@classmethod
  	def     A				(  cls				,    UpperCamelCase_				,    UpperCamelCase_				,    UpperCamelCase_=None				,    **UpperCamelCase_							)		->			Optional[Any]:
  								"""simple docstring"""
  
  
  
  
  
  
  								a_       :   Union[str, Any]			=	kwargs.pop("""config"""				,    UpperCamelCase_							) or RagConfig.from_pretrained(UpperCamelCase_				,    **UpperCamelCase_							)
  								a_       :   Optional[int]			=	RagTokenizer.from_pretrained(UpperCamelCase_				,    config=UpperCamelCase_							)
  								a_       :   Optional[int]			=	rag_tokenizer.question_encoder
  								a_       :   List[str]			=	rag_tokenizer.generator
  
  								if indexed_dataset is not None:
  															a_       :   int			=	"custom"
  															a_       :   Dict			=	CustomHFIndex(config.retrieval_vector_size				,    UpperCamelCase_							)
  								else:
  															a_       :   int			=	cls._build_index(UpperCamelCase_							)
  
  								return cls(
  								    UpperCamelCase_				,    question_encoder_tokenizer=UpperCamelCase_				,    generator_tokenizer=UpperCamelCase_				,    retrieval_workers=UpperCamelCase_				,    index=UpperCamelCase_				,    )
  
  
  
  
  
  	def     A				(  self							)		->			Union[str, Any]:
  								"""simple docstring"""
  
  
  
  
  
  
  								logger.info("""re-loading the new dataset with embeddings"""							)
  								# access from the training loop
  
  								ray.get([worker.clear_object.remote() for worker in self.retrieval_workers]							)
  
  								# build the index object again
  								a_       :   Any			=	self._build_index(self.config							)
  
  								ray.get(
  								    [
  								        worker.create_rag_retriever.remote(
  								            self.config				,    self.question_encoder_tokenizer				,    self.generator_tokenizer				,    UpperCamelCase_							)
  								        for worker in self.retrieval_workers
  								    ]							)
  
  
  
style_context: 
  
  
  import copy
  from typing import Dict, Optional
  
  from ...configuration_utils import PretrainedConfig
  from ...utils import logging
  from ..auto import CONFIG_MAPPING
  
  
  SCREAMING_SNAKE_CASE : List[str]          =    logging.get_logger(__name__)
  
  SCREAMING_SNAKE_CASE : List[Any]          =    {
      "shi-labs/oneformer_ade20k_swin_tiny": (
          "https://huggingface.co/shi-labs/oneformer_ade20k_swin_tiny/blob/main/config.json"
      ),
      # See all OneFormer models at https://huggingface.co/models?filter=oneformer
  }
  
  
  
  
  
  class 					snake_case__  ( __A       ):
  	UpperCAmelCase   :							Tuple         = """oneformer"""
  	UpperCAmelCase   :							Union[str, Any]         = {"""hidden_size""": """hidden_dim"""}
  
  
  
  
  
  	def __init__(  self				,    UpperCamelCase_ = None				,    UpperCamelCase_ = 255				,    UpperCamelCase_ = 150				,    UpperCamelCase_ = 0.1				,    UpperCamelCase_ = 2.0				,    UpperCamelCase_ = 5.0				,    UpperCamelCase_ = 5.0				,    UpperCamelCase_ = 0.5				,    UpperCamelCase_ = 0.07				,    UpperCamelCase_ = 12544				,    UpperCamelCase_ = 3.0				,    UpperCamelCase_ = 0.75				,    UpperCamelCase_ = 0.02				,    UpperCamelCase_ = 1.0				,    UpperCamelCase_ = 1e-05				,    UpperCamelCase_ = False				,    UpperCamelCase_ = True				,    UpperCamelCase_ = True				,    UpperCamelCase_ = [4, 8, 16, 32]				,    UpperCamelCase_ = 77				,    UpperCamelCase_ = 256				,    UpperCamelCase_ = 77				,    UpperCamelCase_ = 6				,    UpperCamelCase_ = 49408				,    UpperCamelCase_ = 2				,    UpperCamelCase_ = 16				,    UpperCamelCase_ = 256				,    UpperCamelCase_ = 256				,    UpperCamelCase_ = 256				,    UpperCamelCase_ = 1024				,    UpperCamelCase_ = "GN"				,    UpperCamelCase_ = 6				,    UpperCamelCase_ = 10				,    UpperCamelCase_ = True				,    UpperCamelCase_ = 8				,    UpperCamelCase_ = 0.1				,    UpperCamelCase_ = 2048				,    UpperCamelCase_ = False				,    UpperCamelCase_ = False				,    UpperCamelCase_ = 2				,    UpperCamelCase_ = 4				,    **UpperCamelCase_				,    )		->			List[Any]:
  								"""simple docstring"""
  
  
  
  
  
  
  								if backbone_config is None:
  															logger.info("""`backbone_config` is unset. Initializing the config with the default `Swin` backbone."""							)
  															a_       :   Tuple			=	CONFIG_MAPPING["""swin"""](
  															    image_size=224				,    in_channels=3				,    patch_size=4				,    embed_dim=96				,    depths=[2, 2, 6, 2]				,    num_heads=[3, 6, 12, 24]				,    window_size=7				,    drop_path_rate=0.3				,    use_absolute_embeddings=UpperCamelCase_				,    out_features=["""stage1""", """stage2""", """stage3""", """stage4"""]				,    )
  								elif isinstance(UpperCamelCase_				,    UpperCamelCase_							):
  															a_       :   str			=	backbone_config.get("""model_type"""							)
  															a_       :   Optional[Any]			=	CONFIG_MAPPING[backbone_model_type]
  															a_       :   List[Any]			=	config_class.from_dict(UpperCamelCase_							)
  
  								a_       :   str			=	backbone_config
  
  								a_       :   List[str]			=	ignore_value
  								a_       :   Optional[int]			=	num_queries
  								a_       :   Optional[Any]			=	no_object_weight
  								a_       :   List[Any]			=	class_weight
  								a_       :   Union[str, Any]			=	mask_weight
  								a_       :   Union[str, Any]			=	dice_weight
  								a_       :   Optional[Any]			=	contrastive_weight
  								a_       :   Any			=	contrastive_temperature
  								a_       :   Optional[int]			=	train_num_points
  								a_       :   Tuple			=	oversample_ratio
  								a_       :   str			=	importance_sample_ratio
  								a_       :   List[Any]			=	init_std
  								a_       :   int			=	init_xavier_std
  								a_       :   List[Any]			=	layer_norm_eps
  								a_       :   Optional[int]			=	is_training
  								a_       :   List[Any]			=	use_auxiliary_loss
  								a_       :   str			=	output_auxiliary_logits
  								a_       :   Dict			=	strides
  								a_       :   List[Any]			=	task_seq_len
  								a_       :   int			=	text_encoder_width
  								a_       :   List[Any]			=	text_encoder_context_length
  								a_       :   List[str]			=	text_encoder_num_layers
  								a_       :   Union[str, Any]			=	text_encoder_vocab_size
  								a_       :   Optional[Any]			=	text_encoder_proj_layers
  								a_       :   Optional[int]			=	text_encoder_n_ctx
  								a_       :   Optional[Any]			=	conv_dim
  								a_       :   str			=	mask_dim
  								a_       :   Optional[Any]			=	hidden_dim
  								a_       :   Any			=	encoder_feedforward_dim
  								a_       :   Dict			=	norm
  								a_       :   List[Any]			=	encoder_layers
  								a_       :   int			=	decoder_layers
  								a_       :   Any			=	use_task_norm
  								a_       :   Union[str, Any]			=	num_attention_heads
  								a_       :   List[str]			=	dropout
  								a_       :   List[Any]			=	dim_feedforward
  								a_       :   Union[str, Any]			=	pre_norm
  								a_       :   Dict			=	enforce_input_proj
  								a_       :   Tuple			=	query_dec_layers
  								a_       :   Any			=	common_stride
  								a_       :   List[str]			=	decoder_layers
  
  								super().__init__(**UpperCamelCase_							)
  
  
  
  
  
  	def     A				(  self							)		->			Dict[str, any]:
  								"""simple docstring"""
  
  
  
  
  
  
  								a_       :   Dict			=	copy.deepcopy(self.__dict__							)
  								a_       :   Any			=	self.backbone_config.to_dict()
  								a_       :   Union[str, Any]			=	self.__class__.model_type
  								return output
  
  
  