




import json
import multiprocessing as mp
import re
from collections import defaultdict
from functools import partial
from typing import Dict, List, Optional, Set, Tuple, Type

from datasets import Dataset
from datasketch import MinHash, MinHashLSH
from dpu_utils.utils.iterators import ThreadedIterator
from tqdm import tqdm


_UpperCAmelCase		:  Tuple	=   re.compile("[^A-Za-z_0-9]")
# parameters used in DuplicationIndex
_UpperCAmelCase		:  Tuple	=   10
_UpperCAmelCase		:  List[Any]	=   256



def 					UpperCAmelCase__     (   lowerCamelCase      ):
  if len(lowerCamelCase      ) < MIN_NUM_TOKENS:
    return None
  lowercase			:List[str]							=  MinHash(num_perm=lowerCamelCase      )
  for token in set(lowerCamelCase      ):
    min_hash.update(token.encode()      )
  return min_hash



def 					UpperCAmelCase__     (   lowerCamelCase      ):
  return {t for t in NON_ALPHA.split(lowerCamelCase      ) if len(t.strip()      ) > 0}




class       __lowerCAmelCase				:






       def __init__(      self:    List[str]				,    *,
           _lowerCAmelCase:    float = 0.85				,    ):
         lowercase			:List[str]							=  duplication_jaccard_threshold
         lowercase			:Dict							=  NUM_PERM
         lowercase			:Any							=  MinHashLSH(threshold=self._duplication_jaccard_threshold				,    num_perm=self._num_perm )

         lowercase			:int							=  defaultdict(_lowerCAmelCase )






       def     SCREAMING_SNAKE_CASE			(      self:    str				,    _lowerCAmelCase:    Tuple				,    _lowerCAmelCase:    MinHash ):
         lowercase			:List[str]							=  self._index.query(_lowerCAmelCase )
         if code_key in self._index.keys:
           print(F"Duplicate key {code_key}" )
           return

         self._index.insert(_lowerCAmelCase				,    _lowerCAmelCase )
         if len(_lowerCAmelCase ) > 0:
           for base_duplicate in close_duplicates:
             if base_duplicate in self._duplicate_clusters:
               self._duplicate_clusters[base_duplicate].add(_lowerCAmelCase )
               break
           else:
             self._duplicate_clusters[close_duplicates[0]].add(_lowerCAmelCase )






       def     SCREAMING_SNAKE_CASE			(      self:    List[str] ):
         lowercase			:Dict							=  []
         for base, duplicates in self._duplicate_clusters.items():
           lowercase			:str							=  [base] + list(_lowerCAmelCase )
           # reformat the cluster to be a list of dict
           lowercase			:Any							=  [{"base_index": el[0], "repo_name": el[1], "path": el[2]} for el in cluster]
           duplicate_clusters.append(_lowerCAmelCase )
         return duplicate_clusters






       def     SCREAMING_SNAKE_CASE			(      self:    Dict				,    _lowerCAmelCase:    str ):
         lowercase			:Any							=  self.get_duplicate_clusters()
         with open(_lowerCAmelCase				,    "w" ) as f:
           json.dump(_lowerCAmelCase				,    _lowerCAmelCase )







def 					UpperCAmelCase__     (   lowerCamelCase      ):
  lowercase		,			lowercase			:List[str]							=  element
  lowercase			:int							=  get_min_hash([t for t in NON_ALPHA.split(data["content"]      ) if len(t.strip()      ) > 0]      )
  if min_hash is not None:
    return (index, data["repo_name"], data["path"]), min_hash



def 					UpperCAmelCase__     (   lowerCamelCase      ):
  with mp.Pool() as pool:
    for data in pool.imap_unordered(
        _compute_min_hash,	ThreadedIterator(lowerCamelCase,	max_queue_size=10000      ),	chunksize=100,	):
      if data is not None:
        yield data



def 					UpperCAmelCase__     (   lowerCamelCase,	lowerCamelCase      ):
  lowercase			:List[str]							=  DuplicationIndex(duplication_jaccard_threshold=lowerCamelCase      )

  for filename, min_hash in tqdm(ThreadedIterator(minhash_iter(enumerate(lowerCamelCase      )      ),	max_queue_size=100      )      ):
    di.add(lowerCamelCase,	lowerCamelCase      )

  # Returns a List[Cluster] where Cluster is List[str] with the filenames.
  return di.get_duplicate_clusters()



def 					UpperCAmelCase__     (   lowerCamelCase,	lowerCamelCase      ):
  lowercase			:Dict							=  get_tokens(lowerCamelCase      )
  lowercase			:List[Any]							=  get_tokens(lowerCamelCase      )
  return len(tokensa & tokensa      ) / len(tokensa | tokensa      )


_UpperCAmelCase		:  Dict	=   None



def 					UpperCAmelCase__     (   lowerCamelCase,	lowerCamelCase      ):
  lowercase			:Any							=  []
  for elementa in cluster:
    lowercase			:Tuple							=  _shared_dataset[elementa["base_index"]]["content"]
    for elementa in extremes:
      lowercase			:Tuple							=  _shared_dataset[elementa["base_index"]]["content"]
      if jaccard_similarity(lowerCamelCase,	lowerCamelCase      ) >= jaccard_threshold:
        elementa["copies"] += 1
        break
    else:
      lowercase			:List[Any]							=  1
      extremes.append(lowerCamelCase      )
  return extremes



def 					UpperCAmelCase__     (   lowerCamelCase,	lowerCamelCase,	lowerCamelCase      ):
  global _shared_dataset
  lowercase			:List[Any]							=  dataset
  lowercase			:Optional[int]							=  []
  lowercase			:Dict							=  partial(_find_cluster_extremes_shared,	jaccard_threshold=lowerCamelCase      )
  with mp.Pool() as pool:
    for extremes in tqdm(
        pool.imap_unordered(
            lowerCamelCase,	lowerCamelCase,	),	total=len(lowerCamelCase      ),	):
      extremes_list.append(lowerCamelCase      )
  return extremes_list







def 					UpperCAmelCase__     (   lowerCamelCase,	lowerCamelCase = 0.85      ):
  lowercase			:Tuple							=  make_duplicate_clusters(lowerCamelCase,	lowerCamelCase      )
  lowercase			:Union[str, Any]							=  {x["base_index"] for cluster in duplicate_clusters for x in cluster}
  lowercase			:Dict							=  {}
  lowercase			:Any							=  find_extremes(lowerCamelCase,	lowerCamelCase,	lowerCamelCase      )
  for extremes in extremes_clusters:
    for element in extremes:
      lowercase			:Tuple							=  element
  lowercase			:Union[str, Any]							=  duplicate_indices - set(extreme_dict.keys()      )
  lowercase			:Union[str, Any]							=  dataset.filter(lambda lowerCamelCase,	lowerCamelCase      : idx not in remove_indices,	with_indices=lowerCamelCase      )

  # update duplicate_clusters
  for cluster in duplicate_clusters:
    for element in cluster:
      lowercase			:List[Any]							=  element["base_index"] in extreme_dict
      if element["is_extreme"]:
        lowercase			:List[str]							=  extreme_dict[element["base_index"]]["copies"]

  print(F"Original dataset size: {len(lowerCamelCase      )}"      )
  print(F"Number of duplicate clusters: {len(lowerCamelCase      )}"      )
  print(F"Files in duplicate cluster: {len(lowerCamelCase      )}"      )
  print(F"Unique files in duplicate cluster: {len(lowerCamelCase      )}"      )
  print(F"Filtered dataset size: {len(lowerCamelCase      )}"      )

  return ds_filter, duplicate_clusters









from typing import Union

import fire
import torch
from tqdm import tqdm



def 					UpperCAmelCase__     (   lowerCamelCase,	lowerCamelCase = "cpu",	lowerCamelCase = None      ):
  lowercase			:Optional[Any]							=  torch.load(lowerCamelCase,	map_location=lowerCamelCase      )
  for k, v in tqdm(state_dict.items()      ):
    if not isinstance(lowerCamelCase,	torch.Tensor      ):
      raise TypeError("FP16 conversion only works on paths that are saved state dicts, like pytorch_model.bin"      )
    lowercase			:List[Any]							=  v.half()
  if save_path is None:  # overwrite src_path
    lowercase			:Optional[Any]							=  src_path
  torch.save(lowerCamelCase,	lowerCamelCase      )


if __name__ == "__main__":
     fire.Fire(convert)



