




import logging
import random

import ray

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


__A						:		str          =						logging.getLogger(__name__)

class 		lowercase_       :




					def __init__(   self:		Union[str, Any]):


									'''simple docstring'''


									__lowerCAmelCase					=  False




					def     _lowercase						(   self:		str,     _lowercase:		Tuple,     _lowercase:		List[str],     _lowercase:		List[str],     _lowercase:		Union[str, Any]):


									'''simple docstring'''


									if not self.initialized:
													__lowerCAmelCase					=  RagRetriever(
													    _lowercase,     question_encoder_tokenizer=_lowercase,     generator_tokenizer=_lowercase,     index=_lowercase,     init_retrieval=_lowercase,     )
													__lowerCAmelCase					=  True




					def     _lowercase						(   self:		str):


									'''simple docstring'''


									self.retriever.index.init_index()




					def     _lowercase						(   self:		List[str]):


									'''simple docstring'''


									del self.retriever
									__lowerCAmelCase					=  False







					def     _lowercase						(   self:		Any,     _lowercase:		Optional[int],     _lowercase:		Dict):


									'''simple docstring'''


									__lowerCAmelCase	,				__lowerCAmelCase					=  self.retriever._main_retrieve(_lowercase,     _lowercase)
									__lowerCAmelCase					=  self.retriever.index.get_doc_dicts(_lowercase)
									return doc_ids, retrieved_doc_embeds, doc_dicts





class 		lowercase_       (	lowerCAmelCase__				):




					def __init__(   self:		str,     _lowercase:		List[Any],     _lowercase:		str,     _lowercase:		Dict,     _lowercase:		List[str],     _lowercase:		str=None):


									'''simple docstring'''


									if index is not None and index.is_initialized() and len(_lowercase) > 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__(
									    _lowercase,     question_encoder_tokenizer=_lowercase,     generator_tokenizer=_lowercase,     index=_lowercase,     init_retrieval=_lowercase,     )

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




					def     _lowercase						(   self:		Tuple):


									'''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:		Optional[Any],     _lowercase:		Optional[Any],     _lowercase:		List[str]):


									'''simple docstring'''


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




					@classmethod
					def     _lowercase						(   cls:		Union[str, Any],     _lowercase:		Optional[int],     _lowercase:		Any=None,     **_lowercase:		str):


									'''simple docstring'''


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




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


									'''simple docstring'''


									__lowerCAmelCase					=  kwargs.pop("""config""",     _lowercase) or RagConfig.from_pretrained(_lowercase,     **_lowercase)
									__lowerCAmelCase					=  RagTokenizer.from_pretrained(_lowercase,     config=_lowercase)
									__lowerCAmelCase					=  rag_tokenizer.question_encoder
									__lowerCAmelCase					=  rag_tokenizer.generator

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

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







					def     _lowercase						(   self:		int):


									'''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
									__lowerCAmelCase					=  self._build_index(self.config)

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







"""simple docstring"""

import argparse

from . import (
    BertConfig,
    BertGenerationConfig,
    BertGenerationDecoder,
    BertGenerationEncoder,
    load_tf_weights_in_bert_generation,
    logging,
)


logging.set_verbosity_info()






def 				A		(							snake_case :Dict							,   snake_case :int							,   snake_case :List[str]							,   snake_case :int							,   snake_case :List[str]						) ->      List[str]:
 # Initialise PyTorch model
 __UpperCamelCase							    =			BertConfig.from_pretrained(
     'bert-large-cased'							,   vocab_size=snake_case							,   max_position_embeddings=5_1_2							,   is_decoder=snake_case							,   add_cross_attention=snake_case							,   )
 __UpperCamelCase							    =			bert_config.to_dict()
 del bert_config_dict["type_vocab_size"]
 __UpperCamelCase							    =			BertGenerationConfig(**snake_case						)
 if is_encoder:
  __UpperCamelCase							    =			BertGenerationEncoder(snake_case						)
 else:
  __UpperCamelCase							    =			BertGenerationDecoder(snake_case						)
 print(f'Building PyTorch model from configuration: {config}'						)

 # Load weights from tf checkpoint
 load_tf_weights_in_bert_generation(
     snake_case							,   snake_case							,   model_class='bert'							,   is_encoder_named_decoder=snake_case							,   is_encoder=snake_case							,   )

 # Save pytorch-model
 print(f'Save PyTorch model and config to {pytorch_dump_path}'						)
 model.save_pretrained(snake_case						)


if __name__ == "__main__":
      UpperCamelCase      :							List[str]          =					argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          "--tf_hub_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
      )
      parser.add_argument(
          "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
      )
      parser.add_argument(
          "--is_encoder_named_decoder",
          action="store_true",
          help="If decoder has to be renamed to encoder in PyTorch model.",
      )
      parser.add_argument("--is_encoder", action="store_true", help="If model is an encoder.")
      parser.add_argument("--vocab_size", default=5_0_3_5_8, type=int, help="Vocab size of model")
      UpperCamelCase      :							Dict          =					parser.parse_args()
      convert_tf_checkpoint_to_pytorch(
          args.tf_hub_path,
          args.pytorch_dump_path,
          args.is_encoder_named_decoder,
          args.vocab_size,
          is_encoder=args.is_encoder,
      )



