
"""simple docstring"""






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


__SCREAMING_SNAKE_CASE							=     re.compile('[^A-Za-z_0-9]')
# parameters used in DuplicationIndex
__SCREAMING_SNAKE_CASE							=     10
__SCREAMING_SNAKE_CASE							=     256




def       A_  (				__lowercase	):
 if len(__lowercase	) < MIN_NUM_TOKENS:
  return None
 UpperCamelCase_     :					Dict   	=MinHash(num_perm=__lowercase	)
 for token in set(__lowercase	):
  min_hash.update(token.encode()	)
 return min_hash




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





class 				a__					:



     def __init__(							self :str      ,  *,
         _lowerCamelCase :float = 0.85      ,  ):







      '''simple docstring'''



      UpperCamelCase_     :					Optional[int]   	=duplication_jaccard_threshold
      UpperCamelCase_     :					Tuple   	=NUM_PERM
      UpperCamelCase_     :					Optional[int]   	=MinHashLSH(threshold=self._duplication_jaccard_threshold      ,  num_perm=self._num_perm   )

      UpperCamelCase_     :					List[str]   	=defaultdict(_lowerCamelCase   )



     def  lowerCamelCase_						(							self :Dict      ,  _lowerCamelCase :Tuple      ,  _lowerCamelCase :MinHash   ):







      '''simple docstring'''



      UpperCamelCase_     :					Any   	=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  lowerCamelCase_						(							self :int   ):







      '''simple docstring'''



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




     def  lowerCamelCase_						(							self :Union[str, Any]      ,  _lowerCamelCase :List[str]   ):







      '''simple docstring'''



      UpperCamelCase_     :					str   	=self.get_duplicate_clusters()
      with open(_lowerCamelCase      ,  'w'   ) as f:
       json.dump(_lowerCamelCase      ,  _lowerCamelCase   )




def       A_  (				__lowercase	):
 UpperCamelCase_	,     UpperCamelCase_     :					int   	=element
 UpperCamelCase_     :					Union[str, Any]   	=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       A_  (				__lowercase	):
 with mp.Pool() as pool:
  for data in pool.imap_unordered(
      _compute_min_hash						,	ThreadedIterator(__lowercase						,	max_queue_size=1_00_00	)						,	chunksize=1_00						,	):
   if data is not None:
    yield data




def       A_  (				__lowercase						,	__lowercase	):
 UpperCamelCase_     :					Any   	=DuplicationIndex(duplication_jaccard_threshold=__lowercase	)

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

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




def       A_  (				__lowercase						,	__lowercase	):
 UpperCamelCase_     :					List[Any]   	=get_tokens(__lowercase	)
 UpperCamelCase_     :					str   	=get_tokens(__lowercase	)
 return len(tokensa & tokensa	) / len(tokensa | tokensa	)


__SCREAMING_SNAKE_CASE							=     None




def       A_  (				__lowercase						,	__lowercase	):
 UpperCamelCase_     :					Tuple   	=[]
 for elementa in cluster:
  UpperCamelCase_     :					Optional[Any]   	=_shared_dataset[elementa['base_index']]['content']
  for elementa in extremes:
   UpperCamelCase_     :					List[str]   	=_shared_dataset[elementa['base_index']]['content']
   if jaccard_similarity(__lowercase						,	__lowercase	) >= jaccard_threshold:
    elementa["copies"] += 1
    break
  else:
   UpperCamelCase_     :					Any   	=1
   extremes.append(__lowercase	)
 return extremes




def       A_  (				__lowercase						,	__lowercase						,	__lowercase	):
 global _shared_dataset
 UpperCamelCase_     :					str   	=dataset
 UpperCamelCase_     :					str   	=[]
 UpperCamelCase_     :					int   	=partial(_find_cluster_extremes_shared						,	jaccard_threshold=__lowercase	)
 with mp.Pool() as pool:
  for extremes in tqdm(
      pool.imap_unordered(
          __lowercase						,	__lowercase						,	)						,	total=len(__lowercase	)						,	):
   extremes_list.append(__lowercase	)
 return extremes_list





def       A_  (				__lowercase						,	__lowercase = 0.85	):
 UpperCamelCase_     :					Optional[int]   	=make_duplicate_clusters(__lowercase						,	__lowercase	)
 UpperCamelCase_     :					Tuple   	={x['base_index'] for cluster in duplicate_clusters for x in cluster}
 UpperCamelCase_     :					str   	={}
 UpperCamelCase_     :					Optional[int]   	=find_extremes(__lowercase						,	__lowercase						,	__lowercase	)
 for extremes in extremes_clusters:
  for element in extremes:
   UpperCamelCase_     :					List[Any]   	=element
 UpperCamelCase_     :					Union[str, Any]   	=duplicate_indices - set(extreme_dict.keys()	)
 UpperCamelCase_     :					Tuple   	=dataset.filter(lambda __lowercase						,	__lowercase	: idx not in remove_indices						,	with_indices=__lowercase	)

 # update duplicate_clusters
 for cluster in duplicate_clusters:
  for element in cluster:
   UpperCamelCase_     :					List[str]   	=element['base_index'] in extreme_dict
   if element["is_extreme"]:
    UpperCamelCase_     :					int   	=extreme_dict[element['base_index']]['copies']

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

 return ds_filter, duplicate_clusters


"""simple docstring"""






# We ignore warnings about stepping the scheduler since we step it ourselves during gradient accumulation

import warnings

from .state import AcceleratorState, GradientState


warnings.filterwarnings('ignore', category=UserWarning, module='torch.optim.lr_scheduler')





class 				a__					:



     def __init__(							self :List[str]      ,  _lowerCamelCase :Any      ,  _lowerCamelCase :Optional[Any]      ,  _lowerCamelCase :bool = True      ,  _lowerCamelCase :bool = False   ):







      '''simple docstring'''



      UpperCamelCase_     :					List[Any]   	=scheduler
      UpperCamelCase_     :					Any   	=optimizers if isinstance(_lowerCamelCase      ,  (list, tuple)   ) else [optimizers]
      UpperCamelCase_     :					Dict   	=split_batches
      UpperCamelCase_     :					str   	=step_with_optimizer
      UpperCamelCase_     :					Dict   	=GradientState()



     def  lowerCamelCase_						(							self :Optional[int]      ,  *_lowerCamelCase :str      ,  **_lowerCamelCase :Union[str, Any]   ):







      '''simple docstring'''



      if not self.step_with_optimizer:
       # No link between scheduler and optimizer -> just step
       self.scheduler.step(*_lowerCamelCase      ,  **_lowerCamelCase   )
       return

      # Otherwise, first make sure the optimizer was stepped.
      if not self.gradient_state.sync_gradients:
       if self.gradient_state.adjust_scheduler:
        self.scheduler._step_count += 1
       return

      for opt in self.optimizers:
       if opt.step_was_skipped:
        return
      if self.split_batches:
       # Split batches -> the training dataloader batch size is not changed so one step per training step
       self.scheduler.step(*_lowerCamelCase      ,  **_lowerCamelCase   )
      else:
       # Otherwise the training dataloader batch size was multiplied by `num_processes`, so we need to do
       # num_processes steps per training step
       UpperCamelCase_     :					Optional[Any]   	=AcceleratorState().num_processes
       for _ in range(_lowerCamelCase   ):
        # Special case when using OneCycle and `drop_last` was not used
        if hasattr(self.scheduler      ,  'total_steps'   ):
         if self.scheduler._step_count <= self.scheduler.total_steps:
          self.scheduler.step(*_lowerCamelCase      ,  **_lowerCamelCase   )
        else:
         self.scheduler.step(*_lowerCamelCase      ,  **_lowerCamelCase   )



     def  lowerCamelCase_						(							self :Dict   ):







      '''simple docstring'''



      return self.scheduler.get_last_lr()



     def  lowerCamelCase_						(							self :Tuple   ):







      '''simple docstring'''



      return self.scheduler.state_dict()



     def  lowerCamelCase_						(							self :Any      ,  _lowerCamelCase :Any   ):







      '''simple docstring'''



      self.scheduler.load_state_dict(_lowerCamelCase   )



     def  lowerCamelCase_						(							self :Any   ):







      '''simple docstring'''



      return self.scheduler.get_lr()




     def  lowerCamelCase_						(							self :Optional[int]      ,  *_lowerCamelCase :Union[str, Any]      ,  **_lowerCamelCase :Optional[int]   ):







      '''simple docstring'''



      return self.scheduler.print_lr(*_lowerCamelCase      ,  **_lowerCamelCase   )
