




'''simple docstring'''




import argparse
import os

import torch
from emmental.modules import ThresholdBinarizer, TopKBinarizer

def    _UpperCamelCase      (				SCREAMING_SNAKE_CASE__			:      Optional[Any]       ):
 '''simple docstring'''





 UpperCAmelCase__										=    args.serialization_dir
 UpperCAmelCase__										=    args.pruning_method
 UpperCAmelCase__										=    args.threshold

 UpperCAmelCase__										=    torch.load(os.path.join(SCREAMING_SNAKE_CASE__       ,						"""pytorch_model.bin"""       )       ,						map_location="""cpu"""       )

 UpperCAmelCase__										=    0  # Number of remaining (not pruned) params in the encoder
 UpperCAmelCase__										=    0  # Number of params in the encoder

 print("""name""".ljust(60       ,						""" """       )       ,						"""Remaining Weights %"""       ,						"""Remaining Weight"""       )
 for name, param in st.items():
  if "encoder" not in name:
   continue

  if "mask_scores" in name:
   if pruning_method == "topK":
    UpperCAmelCase__										=    TopKBinarizer.apply(SCREAMING_SNAKE_CASE__       ,						SCREAMING_SNAKE_CASE__       ).sum().item()
   elif pruning_method == "sigmoied_threshold":
    UpperCAmelCase__										=    ThresholdBinarizer.apply(SCREAMING_SNAKE_CASE__       ,						SCREAMING_SNAKE_CASE__       ,						SCREAMING_SNAKE_CASE__       ).sum().item()
   elif pruning_method == "l0":
    UpperCAmelCase__							,			UpperCAmelCase__										=    -0.1, 1.1
    UpperCAmelCase__										=    torch.sigmoid(SCREAMING_SNAKE_CASE__       )
    UpperCAmelCase__										=    s * (r - l) + l
    UpperCAmelCase__										=    s_bar.clamp(min=0.0       ,						max=1.0       )
    UpperCAmelCase__										=    (mask > 0.0).sum().item()
   else:
    raise ValueError("""Unknown pruning method"""       )
   remaining_count += mask_ones
   print(name.ljust(60       ,						""" """       )       ,						str(round(100 * mask_ones / param.numel()       ,						3       )       ).ljust(20       ,						""" """       )       ,						str(SCREAMING_SNAKE_CASE__       )       )
  else:
   encoder_count += param.numel()
   if "bias" in name or "LayerNorm" in name:
    remaining_count += param.numel()

 print(""""""       )
 print("""Remaining Weights (global) %: """       ,						100 * remaining_count / encoder_count       )


if __name__ == "__main__":
    UpperCAmelCase_       				=      argparse.ArgumentParser()

    parser.add_argument(
        '--pruning_method',
        choices=['l0', 'topK', 'sigmoied_threshold'],
        type=str,
        required=True,
        help=(
            'Pruning Method (l0 = L0 regularization, topK = Movement pruning, sigmoied_threshold = Soft movement'
            ' pruning)'
        ),
    )
    parser.add_argument(
        '--threshold',
        type=float,
        required=False,
        help=(
            'For `topK`, it is the level of remaining weights (in %) in the fine-pruned model.'
            'For `sigmoied_threshold`, it is the threshold \tau against which the (sigmoied) scores are compared.'
            'Not needed for `l0`'
        ),
    )
    parser.add_argument(
        '--serialization_dir',
        type=str,
        required=True,
        help='Folder containing the model that was previously fine-pruned',
    )

    UpperCAmelCase_       				=      parser.parse_args()

    main(args)






'''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_   :







  '''simple docstring'''







  def __init__(						self    :	int ):






   """simple docstring"""






   UpperCAmelCase__										=    False


  def 							SCREAMING_SNAKE_CASE__			(						self    :	Any						,		_UpperCAmelCase    :	Optional[Any]						,		_UpperCAmelCase    :	str						,		_UpperCAmelCase    :	Tuple						,		_UpperCAmelCase    :	Optional[Any] ):






   """simple docstring"""






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


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






   """simple docstring"""






   self.retriever.index.init_index()


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






   """simple docstring"""






   del self.retriever
   UpperCAmelCase__										=    False



  def 							SCREAMING_SNAKE_CASE__			(						self    :	Any						,		_UpperCAmelCase    :	Union[str, Any]						,		_UpperCAmelCase    :	Optional[int] ):






   """simple docstring"""






   UpperCAmelCase__							,			UpperCAmelCase__										=    self.retriever._main_retrieve(_UpperCAmelCase						,		_UpperCAmelCase )
   UpperCAmelCase__										=    self.retriever.index.get_doc_dicts(_UpperCAmelCase )
   return doc_ids, retrieved_doc_embeds, doc_dicts

class 						lowerCAmelCase_   ( lowerCamelCase_   ):







  '''simple docstring'''







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






   """simple docstring"""






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

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


  def 							SCREAMING_SNAKE_CASE__			(						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 							SCREAMING_SNAKE_CASE__			(						self    :	Tuple						,		_UpperCAmelCase    :	str						,		_UpperCAmelCase    :	Dict ):






   """simple docstring"""






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


  @classmethod
  def 							SCREAMING_SNAKE_CASE__			(						cls    :	str						,		_UpperCAmelCase    :	Optional[Any]						,		_UpperCAmelCase    :	str=None						,		**_UpperCAmelCase    :	Tuple ):






   """simple docstring"""






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


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






   """simple docstring"""






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

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

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



  def 							SCREAMING_SNAKE_CASE__			(						self    :	Dict ):






   """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
   UpperCAmelCase__										=    self._build_index(self.config )

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