


import logging
import random

import ray

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


SCREAMING_SNAKE_CASE		:int     						=   logging.getLogger(__name__)


class 			__lowerCAmelCase   :






  """simple docstring"""





  def __init__(   self				:				int     )      ->       str:




     """simple docstring"""
     snake_case_    			=    False



  def     lowerCAmelCase__     (   self				:				Optional[Any]			, _lowerCAmelCase				:				Optional[Any]			, _lowerCAmelCase				:				str			, _lowerCAmelCase				:				Optional[Any]			, _lowerCAmelCase				:				Union[str, Any]     )      ->       int:




     """simple docstring"""
     if not self.initialized:
        snake_case_    			=    RagRetriever(
            _lowerCAmelCase			, question_encoder_tokenizer=_lowerCAmelCase			, generator_tokenizer=_lowerCAmelCase			, index=_lowerCAmelCase			, init_retrieval=_lowerCAmelCase			, )
        snake_case_    			=    True



  def     lowerCAmelCase__     (   self				:				List[Any]     )      ->       int:




     """simple docstring"""
     self.retriever.index.init_index()



  def     lowerCAmelCase__     (   self				:				List[Any]			, _lowerCAmelCase				:				Optional[int]			, _lowerCAmelCase				:				Dict     )      ->       Optional[Any]:




     """simple docstring"""
     snake_case_			,			snake_case_    			=    self.retriever._main_retrieve(_lowerCAmelCase			, _lowerCAmelCase     )
     return doc_ids, retrieved_doc_embeds






class 			__lowerCAmelCase   (       __SCREAMING_SNAKE_CASE    ):






  """simple docstring"""





  def __init__(   self				:				Tuple			, _lowerCAmelCase				:				int			, _lowerCAmelCase				:				Union[str, Any]			, _lowerCAmelCase				:				List[Any]			, _lowerCAmelCase				:				Tuple			, _lowerCAmelCase				:				List[str]=None     )      ->       Any:




     """simple docstring"""
     if index is not None and index.is_initialized() and len(_lowerCAmelCase     ) > 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__(
         _lowerCAmelCase			, question_encoder_tokenizer=_lowerCAmelCase			, generator_tokenizer=_lowerCAmelCase			, index=_lowerCAmelCase			, init_retrieval=_lowerCAmelCase			, )
     snake_case_    			=    retrieval_workers
     if len(self.retrieval_workers     ) > 0:
        ray.get(
            [
                worker.create_rag_retriever.remote(_lowerCAmelCase			, _lowerCAmelCase			, _lowerCAmelCase			, _lowerCAmelCase     )
                for worker in self.retrieval_workers
            ]     )



  def     lowerCAmelCase__     (   self				:				str     )      ->       str:




     """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     lowerCAmelCase__     (   self				:				Optional[Any]			, _lowerCAmelCase				:				List[Any]			, _lowerCAmelCase				:				List[Any]     )      ->       int:




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



  @classmethod
  def     lowerCAmelCase__     (   cls				:				str			, _lowerCAmelCase				:				List[Any]			, _lowerCAmelCase				:				int=None			, **_lowerCAmelCase				:				Optional[Any]     )      ->       str:




     """simple docstring"""
     return super(_lowerCAmelCase			, cls     ).get_tokenizers(_lowerCAmelCase			, _lowerCAmelCase			, **_lowerCAmelCase     )



  @classmethod
  def     lowerCAmelCase__     (   cls				:				Any			, _lowerCAmelCase				:				List[str]			, _lowerCAmelCase				:				Optional[Any]			, _lowerCAmelCase				:				List[str]=None			, **_lowerCAmelCase				:				List[str]     )      ->       Any:




     """simple docstring"""
     snake_case_    			=    kwargs.pop("config"			, _lowerCAmelCase     ) or RagConfig.from_pretrained(_lowerCAmelCase			, **_lowerCAmelCase     )
     snake_case_    			=    RagTokenizer.from_pretrained(_lowerCAmelCase			, config=_lowerCAmelCase     )
     snake_case_    			=    rag_tokenizer.question_encoder
     snake_case_    			=    rag_tokenizer.generator
     if indexed_dataset is not None:
        snake_case_    			=    "custom"
        snake_case_    			=    CustomHFIndex(config.retrieval_vector_size			, _lowerCAmelCase     )
     else:
        snake_case_    			=    cls._build_index(_lowerCAmelCase     )
     return cls(
         _lowerCAmelCase			, question_encoder_tokenizer=_lowerCAmelCase			, generator_tokenizer=_lowerCAmelCase			, retrieval_workers=_lowerCAmelCase			, index=_lowerCAmelCase			, )






import argparse
import os
import shutil

import torch
from emmental.modules import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer


def 						UpperCAmelCase			(	a_						)		->	List[str]:







				"""simple docstring"""



				__A   			= args.pruning_method
				__A   			= args.threshold

				__A   			= args.model_name_or_path.rstrip("/"						)
				__A   			= args.target_model_path

				print(F'''Load fine-pruned model from {model_name_or_path}'''						)
				__A   			= torch.load(os.path.join(a_		,   "pytorch_model.bin"						)						)
				__A   			= {}

				for name, tensor in model.items():
								if "embeddings" in name or "LayerNorm" in name or "pooler" in name:
												__A   			= tensor
												print(F'''Copied layer {name}'''						)
								elif "classifier" in name or "qa_output" in name:
												__A   			= tensor
												print(F'''Copied layer {name}'''						)
								elif "bias" in name:
												__A   			= tensor
												print(F'''Copied layer {name}'''						)
								else:
												if pruning_method == "magnitude":
																__A   			= MagnitudeBinarizer.apply(inputs=a_		,   threshold=a_						)
																__A   			= tensor * mask
																print(F'''Pruned layer {name}'''						)
												elif pruning_method == "topK":
																if "mask_scores" in name:
																				continue
																__A   			= name[:-6]
																__A   			= model[F'''{prefix_}mask_scores''']
																__A   			= TopKBinarizer.apply(a_		,   a_						)
																__A   			= tensor * mask
																print(F'''Pruned layer {name}'''						)
												elif pruning_method == "sigmoied_threshold":
																if "mask_scores" in name:
																				continue
																__A   			= name[:-6]
																__A   			= model[F'''{prefix_}mask_scores''']
																__A   			= ThresholdBinarizer.apply(a_		,   a_		,   a_						)
																__A   			= tensor * mask
																print(F'''Pruned layer {name}'''						)
												elif pruning_method == "l0":
																if "mask_scores" in name:
																				continue
																__A   			= name[:-6]
																__A   			= model[F'''{prefix_}mask_scores''']
																__A			,							__A   			= -0.1, 1.1
																__A   			= torch.sigmoid(a_						)
																__A   			= s * (r - l) + l
																__A   			= s_bar.clamp(min=0.0		,   max=1.0						)
																__A   			= tensor * mask
																print(F'''Pruned layer {name}'''						)
												else:
																raise ValueError("Unknown pruning method"						)

				if target_model_path is None:
								__A   			= os.path.join(
								    os.path.dirname(a_						)		,   F'''bertarized_{os.path.basename(a_						)}'''						)

				if not os.path.isdir(a_						):
								shutil.copytree(a_		,   a_						)
								print(F'''\nCreated folder {target_model_path}'''						)

				torch.save(a_		,   os.path.join(a_		,   "pytorch_model.bin"						)						)
				print("\nPruned model saved! See you later!"						)


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE							:Tuple							 =				argparse.ArgumentParser()

					parser.add_argument(
					    '--pruning_method',
					    choices=['l0', 'magnitude', 'topK', 'sigmoied_threshold'],
					    type=str,
					    required=True,
					    help=(
					        'Pruning Method (l0 = L0 regularization, magnitude = Magnitude pruning, topK = Movement pruning,'
					        ' sigmoied_threshold = Soft movement pruning)'
					    ),
					)
					parser.add_argument(
					    '--threshold',
					    type=float,
					    required=False,
					    help=(
					        'For `magnitude` and `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(
					    '--model_name_or_path',
					    type=str,
					    required=True,
					    help='Folder containing the model that was previously fine-pruned',
					)
					parser.add_argument(
					    '--target_model_path',
					    default=None,
					    type=str,
					    required=False,
					    help='Folder containing the model that was previously fine-pruned',
					)

					SCREAMING_SNAKE_CASE							:str							 =				parser.parse_args()

					main(args)
