


'''simple docstring'''
import logging
import random

import ray

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


UpperCamelCase__    					=      logging.getLogger(__name__)




class 			lowerCamelCase_   :






      def __init__(					self				:    List[Any] ):


          '''simple docstring'''


          UpperCAmelCase__			:		str													=				False






      def lowercase_							(					self				:    List[str]						,						_A				:    Union[str, Any]						,						_A				:    Union[str, Any]						,						_A				:    List[Any]						,						_A				:    int ):


          '''simple docstring'''


          if not self.initialized:
              UpperCAmelCase__			:		List[Any]													=				RagRetriever(
                  _A						,						question_encoder_tokenizer=_A						,						generator_tokenizer=_A						,						index=_A						,						init_retrieval=_A						,						)
              UpperCAmelCase__			:		List[Any]													=				True






      def lowercase_							(					self				:    Tuple ):


          '''simple docstring'''


          self.retriever.index.init_index()






      def lowercase_							(					self				:    Any						,						_A				:    Union[str, Any]						,						_A				:    Dict ):


          '''simple docstring'''


          UpperCAmelCase__						,						UpperCAmelCase__			:		Tuple													=				self.retriever._main_retrieve(_A						,						_A )
          return doc_ids, retrieved_doc_embeds




class 			lowerCamelCase_   (	__a      ):






      def __init__(					self				:    List[Any]						,						_A				:    List[Any]						,						_A				:    int						,						_A				:    str						,						_A				:    Any						,						_A				:    Any=None ):


          '''simple docstring'''


          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						,						)
          UpperCAmelCase__			:		Optional[Any]													=				retrieval_workers
          if len(self.retrieval_workers ) > 0:
              ray.get(
                  [
                      worker.create_rag_retriever.remote(_A						,						_A						,						_A						,						_A )
                      for worker in self.retrieval_workers
                  ] )






      def lowercase_							(					self				:    Optional[int] ):


          '''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 lowercase_							(					self				:    List[str]						,						_A				:    Optional[int]						,						_A				:    Union[str, Any] ):


          '''simple docstring'''


          if len(self.retrieval_workers ) > 0:
              # Select a random retrieval actor.
              UpperCAmelCase__			:		Any													=				self.retrieval_workers[random.randint(0						,						len(self.retrieval_workers ) - 1 )]
              UpperCAmelCase__						,						UpperCAmelCase__			:		List[Any]													=				ray.get(random_worker.retrieve.remote(_A						,						_A ) )
          else:
              UpperCAmelCase__						,						UpperCAmelCase__			:		Union[str, Any]													=				self._main_retrieve(_A						,						_A )
          return retrieved_doc_embeds, doc_ids, self.index.get_doc_dicts(_A )






      @classmethod
      def lowercase_							(					cls				:    List[Any]						,						_A				:    Optional[int]						,						_A				:    Dict=None						,						**_A				:    str ):


          '''simple docstring'''


          return super(_A						,						cls ).get_tokenizers(_A						,						_A						,						**_A )






      @classmethod
      def lowercase_							(					cls				:    Union[str, Any]						,						_A				:    List[Any]						,						_A				:    str						,						_A				:    Union[str, Any]=None						,						**_A				:    Tuple ):


          '''simple docstring'''


          UpperCAmelCase__			:		List[str]													=				kwargs.pop('''config'''						,						_A ) or RagConfig.from_pretrained(_A						,						**_A )
          UpperCAmelCase__			:		Union[str, Any]													=				RagTokenizer.from_pretrained(_A						,						config=_A )
          UpperCAmelCase__			:		List[Any]													=				rag_tokenizer.question_encoder
          UpperCAmelCase__			:		Optional[Any]													=				rag_tokenizer.generator
          if indexed_dataset is not None:
              UpperCAmelCase__			:		Optional[Any]													=				'''custom'''
              UpperCAmelCase__			:		Any													=				CustomHFIndex(config.retrieval_vector_size						,						_A )
          else:
              UpperCAmelCase__			:		List[str]													=				cls._build_index(_A )
          return cls(
              _A						,						question_encoder_tokenizer=_A						,						generator_tokenizer=_A						,						retrieval_workers=_A						,						index=_A						,						)




'''simple docstring'''
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


UpperCamelCase__    					=      {
    '''configuration_rembert''': ['''REMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''RemBertConfig''', '''RemBertOnnxConfig''']
}

try:
  if not is_sentencepiece_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCamelCase__    					=      ['''RemBertTokenizer''']

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCamelCase__    					=      ['''RemBertTokenizerFast''']

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCamelCase__    					=      [
      '''REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
      '''RemBertForCausalLM''',
      '''RemBertForMaskedLM''',
      '''RemBertForMultipleChoice''',
      '''RemBertForQuestionAnswering''',
      '''RemBertForSequenceClassification''',
      '''RemBertForTokenClassification''',
      '''RemBertLayer''',
      '''RemBertModel''',
      '''RemBertPreTrainedModel''',
      '''load_tf_weights_in_rembert''',
  ]


try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCamelCase__    					=      [
      '''TF_REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
      '''TFRemBertForCausalLM''',
      '''TFRemBertForMaskedLM''',
      '''TFRemBertForMultipleChoice''',
      '''TFRemBertForQuestionAnswering''',
      '''TFRemBertForSequenceClassification''',
      '''TFRemBertForTokenClassification''',
      '''TFRemBertLayer''',
      '''TFRemBertModel''',
      '''TFRemBertPreTrainedModel''',
  ]


if TYPE_CHECKING:
  from .configuration_rembert import REMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RemBertConfig, RemBertOnnxConfig

  try:
    if not is_sentencepiece_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_rembert import RemBertTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_rembert_fast import RemBertTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_rembert import (
        REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
        RemBertForCausalLM,
        RemBertForMaskedLM,
        RemBertForMultipleChoice,
        RemBertForQuestionAnswering,
        RemBertForSequenceClassification,
        RemBertForTokenClassification,
        RemBertLayer,
        RemBertModel,
        RemBertPreTrainedModel,
        load_tf_weights_in_rembert,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_rembert import (
        TF_REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFRemBertForCausalLM,
        TFRemBertForMaskedLM,
        TFRemBertForMultipleChoice,
        TFRemBertForQuestionAnswering,
        TFRemBertForSequenceClassification,
        TFRemBertForTokenClassification,
        TFRemBertLayer,
        TFRemBertModel,
        TFRemBertPreTrainedModel,
    )


else:
  import sys

  UpperCamelCase__    					=      _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
