


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
								    ]							)






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


