
import logging
import random

import ray

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


lowercase  :					Optional[Any]						    =							logging.getLogger(__name__)

class __snake_case		:
    def __init__(  self      ):



           '''simple docstring'''







           lowercase							:     Optional[int]       	=      False
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case		,snake_case		,snake_case		,snake_case      ):



           '''simple docstring'''







           if not self.initialized:
                  lowercase							:     int       	=      RagRetriever(
                      snake_case		,question_encoder_tokenizer=snake_case		,generator_tokenizer=snake_case		,index=snake_case		,init_retrieval=snake_case		,)
                  lowercase							:     Dict       	=      True
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           self.retriever.index.init_index()
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case		,snake_case      ):



           '''simple docstring'''







           lowercase	,			lowercase							:     List[Any]       	=      self.retriever._main_retrieve(snake_case		,snake_case      )
           return doc_ids, retrieved_doc_embeds






class __snake_case		(      lowerCAmelCase     ):
    def __init__(  self		,snake_case		,snake_case		,snake_case		,snake_case		,snake_case=None      ):



           '''simple docstring'''







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



           '''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       _SCREAMING_SNAKE_CASE       (  self		,snake_case		,snake_case      ):



           '''simple docstring'''







           if len(self.retrieval_workers      ) > 0:
                  # Select a random retrieval actor.
                  lowercase							:     Tuple       	=      self.retrieval_workers[random.randint(0		,len(self.retrieval_workers      ) - 1      )]
                  lowercase	,			lowercase							:     Dict       	=      ray.get(random_worker.retrieve.remote(snake_case		,snake_case      )      )
           else:
                  lowercase	,			lowercase							:     Optional[Any]       	=      self._main_retrieve(snake_case		,snake_case      )
           return retrieved_doc_embeds, doc_ids, self.index.get_doc_dicts(snake_case      )
    @classmethod
    def       _SCREAMING_SNAKE_CASE       (  cls		,snake_case		,snake_case=None		,**snake_case      ):



           '''simple docstring'''







           return super(snake_case		,cls      ).get_tokenizers(snake_case		,snake_case		,**snake_case      )
    @classmethod
    def       _SCREAMING_SNAKE_CASE       (  cls		,snake_case		,snake_case		,snake_case=None		,**snake_case      ):



           '''simple docstring'''







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


from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowercase  :					List[str]						    =							logging.get_logger(__name__)

lowercase  :					Any						    =							{
    """uclanlp/visualbert-vqa""": """https://huggingface.co/uclanlp/visualbert-vqa/resolve/main/config.json""",
    """uclanlp/visualbert-vqa-pre""": """https://huggingface.co/uclanlp/visualbert-vqa-pre/resolve/main/config.json""",
    """uclanlp/visualbert-vqa-coco-pre""": (
        """https://huggingface.co/uclanlp/visualbert-vqa-coco-pre/resolve/main/config.json"""
    ),
    """uclanlp/visualbert-vcr""": """https://huggingface.co/uclanlp/visualbert-vcr/resolve/main/config.json""",
    """uclanlp/visualbert-vcr-pre""": """https://huggingface.co/uclanlp/visualbert-vcr-pre/resolve/main/config.json""",
    """uclanlp/visualbert-vcr-coco-pre""": (
        """https://huggingface.co/uclanlp/visualbert-vcr-coco-pre/resolve/main/config.json"""
    ),
    """uclanlp/visualbert-nlvr2""": """https://huggingface.co/uclanlp/visualbert-nlvr2/resolve/main/config.json""",
    """uclanlp/visualbert-nlvr2-pre""": """https://huggingface.co/uclanlp/visualbert-nlvr2-pre/resolve/main/config.json""",
    """uclanlp/visualbert-nlvr2-coco-pre""": (
        """https://huggingface.co/uclanlp/visualbert-nlvr2-coco-pre/resolve/main/config.json"""
    )
    # See all VisualBERT models at https://huggingface.co/models?filter=visual_bert
}

class __snake_case		(      lowerCAmelCase     ):
    _a		:						Union[str, Any]=				"visual_bert"
    def __init__(  self		,snake_case=30522		,snake_case=768		,snake_case=512		,snake_case=12		,snake_case=12		,snake_case=3072		,snake_case="gelu"		,snake_case=0.1		,snake_case=0.1		,snake_case=512		,snake_case=2		,snake_case=0.02		,snake_case=1e-12		,snake_case=False		,snake_case=True		,snake_case=1		,snake_case=0		,snake_case=2		,**snake_case		,):



           '''simple docstring'''







           super().__init__(pad_token_id=snake_case		,bos_token_id=snake_case		,eos_token_id=snake_case		,**snake_case      )

           lowercase							:     Tuple       	=      vocab_size
           lowercase							:     int       	=      max_position_embeddings
           lowercase							:     Optional[Any]       	=      hidden_size
           lowercase							:     int       	=      visual_embedding_dim
           lowercase							:     Tuple       	=      num_hidden_layers
           lowercase							:     str       	=      num_attention_heads
           lowercase							:     Optional[Any]       	=      intermediate_size
           lowercase							:     str       	=      hidden_act
           lowercase							:     Tuple       	=      hidden_dropout_prob
           lowercase							:     List[Any]       	=      attention_probs_dropout_prob
           lowercase							:     Union[str, Any]       	=      initializer_range
           lowercase							:     int       	=      type_vocab_size
           lowercase							:     Union[str, Any]       	=      layer_norm_eps
           lowercase							:     Union[str, Any]       	=      bypass_transformer
           lowercase							:     int       	=      special_visual_initialize
