
"""simple docstring"""
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


_a   :      List[Any]       =				{
    """configuration_markuplm""": ["""MARKUPLM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MarkupLMConfig"""],
    """feature_extraction_markuplm""": ["""MarkupLMFeatureExtractor"""],
    """processing_markuplm""": ["""MarkupLMProcessor"""],
    """tokenization_markuplm""": ["""MarkupLMTokenizer"""],
}

try:
    if not is_tokenizers_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _a   :      List[str]       =				["""MarkupLMTokenizerFast"""]

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _a   :      Any       =				[
        """MARKUPLM_PRETRAINED_MODEL_ARCHIVE_LIST""",
        """MarkupLMForQuestionAnswering""",
        """MarkupLMForSequenceClassification""",
        """MarkupLMForTokenClassification""",
        """MarkupLMModel""",
        """MarkupLMPreTrainedModel""",
    ]


if TYPE_CHECKING:
    from .configuration_markuplm import MARKUPLM_PRETRAINED_CONFIG_ARCHIVE_MAP, MarkupLMConfig
    from .feature_extraction_markuplm import MarkupLMFeatureExtractor
    from .processing_markuplm import MarkupLMProcessor
    from .tokenization_markuplm import MarkupLMTokenizer

    try:
        if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_markuplm_fast import MarkupLMTokenizerFast

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_markuplm import (
            MARKUPLM_PRETRAINED_MODEL_ARCHIVE_LIST,
            MarkupLMForQuestionAnswering,
            MarkupLMForSequenceClassification,
            MarkupLMForTokenClassification,
            MarkupLMModel,
            MarkupLMPreTrainedModel,
        )


else:
    import sys

    _a   :      int       =				_LazyModule(__name__, globals()["""__file__"""], _import_structure)


'''simple docstring'''




import logging
import random

import ray

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


__snake_case  : Optional[int]		       =    logging.getLogger(__name__)




class       __UpperCAmelCase							:





       '''simple docstring'''



       def __init__( self					)						->	List[str]:
           A_      =  False

       def    __A			( self       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE					)						->	str:
           if not self.initialized:
               A_      =  RagRetriever(
                   _SCREAMING_SNAKE_CASE       ,	question_encoder_tokenizer=_SCREAMING_SNAKE_CASE       ,	generator_tokenizer=_SCREAMING_SNAKE_CASE       ,	index=_SCREAMING_SNAKE_CASE       ,	init_retrieval=_SCREAMING_SNAKE_CASE       ,	)
               A_      =  True

       def    __A			( self					)						->	List[Any]:
           self.retriever.index.init_index()

       def    __A			( self					)						->	Dict:
           # delete the old self.retriever object before assigning the new index
           del self.retriever
           A_      =  False

       def    __A			( self       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE					)						->	Optional[int]:
           A_  ,A_      =  self.retriever._main_retrieve(_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE					)
           A_      =  self.retriever.index.get_doc_dicts(_SCREAMING_SNAKE_CASE					)
           return doc_ids, retrieved_doc_embeds, doc_dicts




class       __UpperCAmelCase							(							_UpperCamelCase	):





       '''simple docstring'''



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

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

       def    __A			( self					)						->	List[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    __A			( self       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE					)						->	Optional[int]:
           if len(self.retrieval_workers					) > 0:
               # Select a random retrieval actor.
               A_      =  self.retrieval_workers[random.randint(0       ,	len(self.retrieval_workers					) - 1					)]
               A_  ,A_  ,A_      =  ray.get(
                   random_worker.retrieve.remote(_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE					)					)
           else:
               A_  ,A_      =  self._main_retrieve(_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE					)
               A_      =  self.index.get_doc_dicts(_SCREAMING_SNAKE_CASE					)
           return retrieved_doc_embeds, doc_ids, doc_dicts

       @classmethod
       def    __A			( cls       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE=None       ,	**_SCREAMING_SNAKE_CASE					)						->	List[Any]:
           return super(_SCREAMING_SNAKE_CASE       ,	cls					).get_tokenizers(_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	**_SCREAMING_SNAKE_CASE					)

       @classmethod
       def    __A			( cls       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE=None       ,	**_SCREAMING_SNAKE_CASE					)						->	int:
           A_      =  kwargs.pop('''config'''       ,	_SCREAMING_SNAKE_CASE					) or RagConfig.from_pretrained(_SCREAMING_SNAKE_CASE       ,	**_SCREAMING_SNAKE_CASE					)
           A_      =  RagTokenizer.from_pretrained(_SCREAMING_SNAKE_CASE       ,	config=_SCREAMING_SNAKE_CASE					)
           A_      =  rag_tokenizer.question_encoder
           A_      =  rag_tokenizer.generator

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

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

       def    __A			( self					)						->	Optional[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
           A_      =  self._build_index(self.config					)

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






