

import logging
import random

import ray

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


A__        =			logging.getLogger(__name__)







class    UpperCamelCase_			:


  """simple docstring"""



  def __init__(	self    :				Optional[int]	)					-> List[Any]:
      SCREAMING_SNAKE_CASE__					     =						False



  def      _UpperCamelCase    (	self    :				Union[str, Any]     ,  A_    :				Optional[Any]     ,  A_    :				Optional[Any]     ,  A_    :				Optional[Any]     ,  A_    :				str	)					-> Optional[Any]:
      if not self.initialized:
          SCREAMING_SNAKE_CASE__					     =						RagRetriever(
              A_     ,  question_encoder_tokenizer=A_     ,  generator_tokenizer=A_     ,  index=A_     ,  init_retrieval=A_     ,  )
          SCREAMING_SNAKE_CASE__					     =						True



  def      _UpperCamelCase    (	self    :				int	)					-> Optional[int]:
      self.retriever.index.init_index()



  def      _UpperCamelCase    (	self    :				Dict	)					-> Optional[Any]:
      # delete the old self.retriever object before assigning the new index
      del self.retriever
      SCREAMING_SNAKE_CASE__					     =						False







  def      _UpperCamelCase    (	self    :				Optional[int]     ,  A_    :				Union[str, Any]     ,  A_    :				Any	)					-> Union[str, Any]:
      SCREAMING_SNAKE_CASE__     ,		SCREAMING_SNAKE_CASE__					     =						self.retriever._main_retrieve(A_     ,  A_	)
      SCREAMING_SNAKE_CASE__					     =						self.retriever.index.get_doc_dicts(A_	)
      return doc_ids, retrieved_doc_embeds, doc_dicts







class    UpperCamelCase_			(  _snake_case			):


  """simple docstring"""



  def __init__(	self    :				Any     ,  A_    :				Any     ,  A_    :				Optional[Any]     ,  A_    :				Optional[Any]     ,  A_    :				Optional[Any]     ,  A_    :				Tuple=None	)					-> List[Any]:
      if index is not None and index.is_initialized() and len(A_	) > 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__(
          A_     ,  question_encoder_tokenizer=A_     ,  generator_tokenizer=A_     ,  index=A_     ,  init_retrieval=A_     ,  )

      SCREAMING_SNAKE_CASE__					     =						retrieval_workers
      SCREAMING_SNAKE_CASE__					     =						question_encoder_tokenizer
      SCREAMING_SNAKE_CASE__					     =						generator_tokenizer
      if len(self.retrieval_workers	) > 0:
          ray.get(
              [
                  worker.create_rag_retriever.remote(A_     ,  A_     ,  A_     ,  A_	)
                  for worker in self.retrieval_workers
              ]	)



  def      _UpperCamelCase    (	self    :				Dict	)					-> Union[str, Any]:
      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      _UpperCamelCase    (	self    :				Any     ,  A_    :				Any     ,  A_    :				int	)					-> Tuple:
      if len(self.retrieval_workers	) > 0:
          # Select a random retrieval actor.
          SCREAMING_SNAKE_CASE__					     =						self.retrieval_workers[random.randint(0     ,  len(self.retrieval_workers	) - 1	)]
          SCREAMING_SNAKE_CASE__     ,		SCREAMING_SNAKE_CASE__     ,		SCREAMING_SNAKE_CASE__					     =						ray.get(
              random_worker.retrieve.remote(A_     ,  A_	)	)
      else:
          SCREAMING_SNAKE_CASE__     ,		SCREAMING_SNAKE_CASE__					     =						self._main_retrieve(A_     ,  A_	)
          SCREAMING_SNAKE_CASE__					     =						self.index.get_doc_dicts(A_	)
      return retrieved_doc_embeds, doc_ids, doc_dicts



  @classmethod
  def      _UpperCamelCase    (	cls    :				Tuple     ,  A_    :				List[str]     ,  A_    :				Tuple=None     ,  **A_    :				int	)					-> List[str]:
      return super(A_     ,  cls	).get_tokenizers(A_     ,  A_     ,  **A_	)



  @classmethod
  def      _UpperCamelCase    (	cls    :				str     ,  A_    :				int     ,  A_    :				Tuple     ,  A_    :				Dict=None     ,  **A_    :				str	)					-> Union[str, Any]:
      SCREAMING_SNAKE_CASE__					     =						kwargs.pop("config"     ,  A_	) or RagConfig.from_pretrained(A_     ,  **A_	)
      SCREAMING_SNAKE_CASE__					     =						RagTokenizer.from_pretrained(A_     ,  config=A_	)
      SCREAMING_SNAKE_CASE__					     =						rag_tokenizer.question_encoder
      SCREAMING_SNAKE_CASE__					     =						rag_tokenizer.generator

      if indexed_dataset is not None:
          SCREAMING_SNAKE_CASE__					     =						"custom"
          SCREAMING_SNAKE_CASE__					     =						CustomHFIndex(config.retrieval_vector_size     ,  A_	)
      else:
          SCREAMING_SNAKE_CASE__					     =						cls._build_index(A_	)

      return cls(
          A_     ,  question_encoder_tokenizer=A_     ,  generator_tokenizer=A_     ,  retrieval_workers=A_     ,  index=A_     ,  )







  def      _UpperCamelCase    (	self    :				List[str]	)					-> int:
      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
      SCREAMING_SNAKE_CASE__					     =						self._build_index(self.config	)

      ray.get(
          [
              worker.create_rag_retriever.remote(
                  self.config     ,  self.question_encoder_tokenizer     ,  self.generator_tokenizer     ,  A_	)
              for worker in self.retrieval_workers
          ]	)





from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


A__        =			logging.get_logger(__name__)

A__        =			{
    "facebook/convnext-tiny-224": "https://huggingface.co/facebook/convnext-tiny-224/resolve/main/config.json",
    # See all ConvNeXT models at https://huggingface.co/models?filter=convnext
}







class    UpperCamelCase_			(  _snake_case		,     _snake_case			):


  """simple docstring"""
  SCREAMING_SNAKE_CASE__				: List[str]										=      """convnext"""



  def __init__(	self    :				Optional[Any]     ,  A_    :				Any=3     ,  A_    :				Tuple=4     ,  A_    :				Optional[int]=4     ,  A_    :				Dict=None     ,  A_    :				List[Any]=None     ,  A_    :				Union[str, Any]="gelu"     ,  A_    :				Optional[Any]=0.02     ,  A_    :				Union[str, Any]=1E-1_2     ,  A_    :				Dict=1E-6     ,  A_    :				List[str]=0.0     ,  A_    :				int=224     ,  A_    :				Optional[Any]=None     ,  A_    :				List[str]=None     ,  **A_    :				Union[str, Any]     ,  )					-> Tuple:
      super().__init__(**A_	)

      SCREAMING_SNAKE_CASE__					     =						num_channels
      SCREAMING_SNAKE_CASE__					     =						patch_size
      SCREAMING_SNAKE_CASE__					     =						num_stages
      SCREAMING_SNAKE_CASE__					     =						[96, 192, 384, 768] if hidden_sizes is None else hidden_sizes
      SCREAMING_SNAKE_CASE__					     =						[3, 3, 9, 3] if depths is None else depths
      SCREAMING_SNAKE_CASE__					     =						hidden_act
      SCREAMING_SNAKE_CASE__					     =						initializer_range
      SCREAMING_SNAKE_CASE__					     =						layer_norm_eps
      SCREAMING_SNAKE_CASE__					     =						layer_scale_init_value
      SCREAMING_SNAKE_CASE__					     =						drop_path_rate
      SCREAMING_SNAKE_CASE__					     =						image_size
      SCREAMING_SNAKE_CASE__					     =						["stem"] + [f"stage{idx}" for idx in range(1     ,  len(self.depths	) + 1	)]
      SCREAMING_SNAKE_CASE__     ,		SCREAMING_SNAKE_CASE__					     =						get_aligned_output_features_output_indices(
          out_features=A_     ,  out_indices=A_     ,  stage_names=self.stage_names	)







class    UpperCamelCase_			(  _snake_case			):


  """simple docstring"""
  SCREAMING_SNAKE_CASE__				: Dict										=      version.parse("""1.11"""			)



  @property
  def      _UpperCamelCase    (	self    :				str	)					-> Mapping[str, Mapping[int, str]]:
      return OrderedDict(
          [
              ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
          ]	)







  @property
  def      _UpperCamelCase    (	self    :				Tuple	)					-> float:
      return 1E-5


