






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


__magic_name__   :  Any        =							re.compile('''[^A-Za-z_0-9]''')
# parameters used in DuplicationIndex
__magic_name__   :  str        =							10
__magic_name__   :  List[Any]        =							256
def 		lowercase__  (				_UpperCamelCase) ->			Optional[MinHash]:
      """simple docstring"""

      if len(_UpperCamelCase) < MIN_NUM_TOKENS:
            return None
      UpperCamelCase					       =  MinHash(num_perm=_UpperCamelCase)
      for token in set(_UpperCamelCase):
            min_hash.update(token.encode())
      return min_hash
def 		lowercase__  (				_UpperCamelCase) ->			Set[str]:
      """simple docstring"""

      return {t for t in NON_ALPHA.split(_UpperCamelCase) if len(t.strip()) > 0}





class 					A__     :

     '''simple docstring'''






     def __init__( self      :    Optional[int]   ,						*,
         _SCREAMING_SNAKE_CASE      :    float = 0.8_5   ,						):






           """simple docstring"""




           UpperCamelCase					       =  duplication_jaccard_threshold
           UpperCamelCase					       =  NUM_PERM
           UpperCamelCase					       =  MinHashLSH(threshold=self._duplication_jaccard_threshold   ,						num_perm=self._num_perm )

           UpperCamelCase					       =  defaultdict(_SCREAMING_SNAKE_CASE )







     def   _SCREAMING_SNAKE_CASE     ( self      :    Optional[Any]   ,						_SCREAMING_SNAKE_CASE      :    Tuple   ,						_SCREAMING_SNAKE_CASE      :    MinHash ):






           """simple docstring"""




           UpperCamelCase					       =  self._index.query(_SCREAMING_SNAKE_CASE )
           if code_key in self._index.keys:
                 print(f'Duplicate key {code_key}' )
                 return

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







     def   _SCREAMING_SNAKE_CASE     ( self      :    int ):






           """simple docstring"""




           UpperCamelCase					       =  []
           for base, duplicates in self._duplicate_clusters.items():
                 UpperCamelCase					       =  [base] + list(_SCREAMING_SNAKE_CASE )
                 # reformat the cluster to be a list of dict
                 UpperCamelCase					       =  [{'base_index': el[0], 'repo_name': el[1], 'path': el[2]} for el in cluster]
                 duplicate_clusters.append(_SCREAMING_SNAKE_CASE )
           return duplicate_clusters







     def   _SCREAMING_SNAKE_CASE     ( self      :    Optional[int]   ,						_SCREAMING_SNAKE_CASE      :    Any ):






           """simple docstring"""




           UpperCamelCase					       =  self.get_duplicate_clusters()
           with open(_SCREAMING_SNAKE_CASE   ,						'w' ) as f:
                 json.dump(_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE )

def 		lowercase__  (				_UpperCamelCase) ->			str:
      """simple docstring"""

      UpperCamelCase       ,  UpperCamelCase					       =  element
      UpperCamelCase					       =  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 		lowercase__  (				_UpperCamelCase) ->			List[Any]:
      """simple docstring"""

      with mp.Pool() as pool:
            for data in pool.imap_unordered(
                _compute_min_hash       ,							ThreadedIterator(_UpperCamelCase       ,							max_queue_size=1_00_00)       ,							chunksize=1_00       ,							):
                  if data is not None:
                        yield data
def 		lowercase__  (				_UpperCamelCase       ,							_UpperCamelCase) ->			List[Any]:
      """simple docstring"""

      UpperCamelCase					       =  DuplicationIndex(duplication_jaccard_threshold=_UpperCamelCase)

      for filename, min_hash in tqdm(ThreadedIterator(minhash_iter(enumerate(_UpperCamelCase))       ,							max_queue_size=1_00)):
            di.add(_UpperCamelCase       ,							_UpperCamelCase)

      # Returns a List[Cluster] where Cluster is List[str] with the filenames.
      return di.get_duplicate_clusters()
def 		lowercase__  (				_UpperCamelCase       ,							_UpperCamelCase) ->			float:
      """simple docstring"""

      UpperCamelCase					       =  get_tokens(_UpperCamelCase)
      UpperCamelCase					       =  get_tokens(_UpperCamelCase)
      return len(tokensa & tokensa) / len(tokensa | tokensa)


__magic_name__   :  str        =							None
def 		lowercase__  (				_UpperCamelCase       ,							_UpperCamelCase) ->			Union[str, Any]:
      """simple docstring"""

      UpperCamelCase					       =  []
      for elementa in cluster:
            UpperCamelCase					       =  _shared_dataset[elementa['base_index']]['content']
            for elementa in extremes:
                  UpperCamelCase					       =  _shared_dataset[elementa['base_index']]['content']
                  if jaccard_similarity(_UpperCamelCase       ,							_UpperCamelCase) >= jaccard_threshold:
                        elementa["copies"] += 1
                        break
            else:
                  UpperCamelCase					       =  1
                  extremes.append(_UpperCamelCase)
      return extremes
def 		lowercase__  (				_UpperCamelCase       ,							_UpperCamelCase       ,							_UpperCamelCase) ->			Tuple:
      """simple docstring"""

      global _shared_dataset
      UpperCamelCase					       =  dataset
      UpperCamelCase					       =  []
      UpperCamelCase					       =  partial(_find_cluster_extremes_shared       ,							jaccard_threshold=_UpperCamelCase)
      with mp.Pool() as pool:
            for extremes in tqdm(
                pool.imap_unordered(
                    _UpperCamelCase       ,							_UpperCamelCase       ,							)       ,							total=len(_UpperCamelCase)       ,							):
                  extremes_list.append(_UpperCamelCase)
      return extremes_list
def 		lowercase__  (				_UpperCamelCase       ,							_UpperCamelCase = 0.8_5) ->			Tuple[Type[Dataset], List[List[Dict]]]:
      """simple docstring"""

      UpperCamelCase					       =  make_duplicate_clusters(_UpperCamelCase       ,							_UpperCamelCase)
      UpperCamelCase					       =  {x['base_index'] for cluster in duplicate_clusters for x in cluster}
      UpperCamelCase					       =  {}
      UpperCamelCase					       =  find_extremes(_UpperCamelCase       ,							_UpperCamelCase       ,							_UpperCamelCase)
      for extremes in extremes_clusters:
            for element in extremes:
                  UpperCamelCase					       =  element
      UpperCamelCase					       =  duplicate_indices - set(extreme_dict.keys())
      UpperCamelCase					       =  dataset.filter(lambda _UpperCamelCase       ,							_UpperCamelCase: idx not in remove_indices       ,							with_indices=_UpperCamelCase)

      # update duplicate_clusters
      for cluster in duplicate_clusters:
            for element in cluster:
                  UpperCamelCase					       =  element['base_index'] in extreme_dict
                  if element["is_extreme"]:
                        UpperCamelCase					       =  extreme_dict[element['base_index']]['copies']

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

      return ds_filter, duplicate_clusters











import argparse
from pathlib import Path

import torch

from transformers import OPTConfig, OPTModel
from transformers.utils import logging


logging.set_verbosity_info()
__magic_name__   :  str        =							logging.get_logger(__name__)
def 		lowercase__  (				_UpperCamelCase) ->			int:
      """simple docstring"""

      UpperCamelCase					       =  torch.load(_UpperCamelCase       ,							map_location='cpu')
      if "model" in sd.keys():
            UpperCamelCase					       =  torch.load(_UpperCamelCase       ,							map_location='cpu')['model']

      # pop unnecessary weights
      UpperCamelCase					       =  [
          'decoder.version',
          'decoder.output_projection.weight',
      ]
      for key in keys_to_delete:
            if key in sd:
                  sd.pop(_UpperCamelCase)

      UpperCamelCase					       =  {
          'decoder.project_in_dim.weight': 'decoder.project_in.weight',
          'decoder.project_out_dim.weight': 'decoder.project_out.weight',
          'decoder.layer_norm.weight': 'decoder.final_layer_norm.weight',
          'decoder.layer_norm.bias': 'decoder.final_layer_norm.bias',
      }
      for old_key, new_key in keys_to_rename.items():
            if old_key in sd:
                  UpperCamelCase					       =  sd.pop(_UpperCamelCase)

      UpperCamelCase					       =  list(sd.keys())
      for key in keys:
            if ".qkv_proj." in key:
                  UpperCamelCase					       =  sd[key]
                  # We split QKV in separate Q,K,V

                  UpperCamelCase					       =  key.replace('.qkv_proj.'       ,							'.q_proj.')
                  UpperCamelCase					       =  key.replace('.qkv_proj.'       ,							'.k_proj.')
                  UpperCamelCase					       =  key.replace('.qkv_proj.'       ,							'.v_proj.')

                  UpperCamelCase					       =  value.shape[0]
                  assert depth % 3 == 0
                  # `SequeuceParallelTransformerBlock` has QKV weight is separated in K,V,Q despite the naming:
                  # https://cs.github.com/facebookresearch/metaseq/blob/51871bd73cd04c038f239ea2a26db1d7f6b37927/metaseq/modules/sequence_parallel_transformer_layer.py#L97
                  UpperCamelCase       ,  UpperCamelCase       ,  UpperCamelCase					       =  torch.split(_UpperCamelCase       ,							depth // 3       ,							dim=0)

                  UpperCamelCase					       =  q
                  UpperCamelCase					       =  k
                  UpperCamelCase					       =  v
                  del sd[key]

      return sd
@torch.no_grad()
def 		lowercase__  (				_UpperCamelCase       ,							_UpperCamelCase       ,							_UpperCamelCase=None) ->			Optional[int]:
      """simple docstring"""

      UpperCamelCase					       =  load_checkpoint(_UpperCamelCase)

      if config is not None:
            UpperCamelCase					       =  OPTConfig.from_pretrained(_UpperCamelCase)
      else:
            UpperCamelCase					       =  OPTConfig()

      UpperCamelCase					       =  OPTModel(_UpperCamelCase).half().eval()
      model.load_state_dict(_UpperCamelCase)

      # Check results
      Path(_UpperCamelCase).mkdir(exist_ok=_UpperCamelCase)
      model.save_pretrained(_UpperCamelCase)


if __name__ == "__main__":
       __magic_name__   :  Any        =							argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           '''--fairseq_path''',
           type=str,
           help=(
               '''path to fairseq checkpoint in correct format. You can find all checkpoints in the correct format here:'''
               ''' https://huggingface.co/models?other=opt_metasq'''
           ),
       )
       parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
       parser.add_argument('''--hf_config''', default=None, type=str, help='''Define HF config.''')
       __magic_name__   :  str        =							parser.parse_args()
       convert_opt_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, config=args.hf_config)



