






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


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







def 		SCREAMING_SNAKE_CASE							( __lowerCAmelCase      )			->     Optional[MinHash]:

     if len(a__      ) < MIN_NUM_TOKENS:
          return None
     UpperCamelCase__      :       Dict	    =  MinHash(num_perm=a__      )
     for token in set(a__      ):
          min_hash.update(token.encode()      )
     return min_hash







def 		SCREAMING_SNAKE_CASE							( __lowerCAmelCase      )			->     Set[str]:
     return {t for t in NON_ALPHA.split(a__      ) if len(t.strip()      ) > 0}





class       __a  :







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


      '''simple docstring'''






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

      UpperCamelCase__      :       str	    =  defaultdict(lowercase_						)







 def  __lowercase (		self :       List[str]							,       SCREAMING_SNAKE_CASE :       Tuple							,       SCREAMING_SNAKE_CASE :       MinHash						):


      '''simple docstring'''






      UpperCamelCase__      :       List[Any]	    =  self._index.query(lowercase_						)
      if code_key in self._index.keys:
           print(F'Duplicate key {code_key}'						)
           return

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







 def  __lowercase (		self :       List[str]						):


      '''simple docstring'''






      UpperCamelCase__      :       Optional[Any]	    =  []
      for base, duplicates in self._duplicate_clusters.items():
           UpperCamelCase__      :       Union[str, Any]	    =  [base] + list(lowercase_						)
           # 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(lowercase_						)
      return duplicate_clusters







 def  __lowercase (		self :       int							,       SCREAMING_SNAKE_CASE :       Optional[int]						):


      '''simple docstring'''






      UpperCamelCase__      :       Optional[int]	    =  self.get_duplicate_clusters()
      with open(lowercase_							,       "w"						) as f:
           json.dump(lowercase_							,       lowercase_						)







def 		SCREAMING_SNAKE_CASE							( __lowerCAmelCase      )			->     Optional[Any]:
     UpperCamelCase__    ,					UpperCamelCase__      :       Any	    =  element
     UpperCamelCase__      :       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 		SCREAMING_SNAKE_CASE							( __lowerCAmelCase      )			->     Optional[int]:

     with mp.Pool() as pool:
          for data in pool.imap_unordered(
              _compute_min_hash ,   ThreadedIterator(a__ ,   max_queue_size=1_0000      ) ,   chunksize=100 ,   ):
               if data is not None:
                    yield data







def 		SCREAMING_SNAKE_CASE							( __lowerCAmelCase ,   __lowerCAmelCase      )			->     List[str]:
     UpperCamelCase__      :       List[Any]	    =  DuplicationIndex(duplication_jaccard_threshold=a__      )

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

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







def 		SCREAMING_SNAKE_CASE							( __lowerCAmelCase ,   __lowerCAmelCase      )			->     float:
     UpperCamelCase__      :       Optional[Any]	    =  get_tokens(a__      )
     UpperCamelCase__      :       Tuple	    =  get_tokens(a__      )
     return len(tokensa & tokensa      ) / len(tokensa | tokensa      )


lowerCamelCase     :							Union[str, Any]    =None







def 		SCREAMING_SNAKE_CASE							( __lowerCAmelCase ,   __lowerCAmelCase      )			->     Optional[Any]:
     UpperCamelCase__      :       Dict	    =  []
     for elementa in cluster:
          UpperCamelCase__      :       List[Any]	    =  _shared_dataset[elementa["base_index"]]["content"]
          for elementa in extremes:
               UpperCamelCase__      :       str	    =  _shared_dataset[elementa["base_index"]]["content"]
               if jaccard_similarity(a__ ,   a__      ) >= jaccard_threshold:
                    elementa["copies"] += 1
                    break
          else:
               UpperCamelCase__      :       str	    =  1
               extremes.append(a__      )
     return extremes







def 		SCREAMING_SNAKE_CASE							( __lowerCAmelCase ,   __lowerCAmelCase ,   __lowerCAmelCase      )			->     str:
     global _shared_dataset
     UpperCamelCase__      :       Any	    =  dataset
     UpperCamelCase__      :       str	    =  []
     UpperCamelCase__      :       Optional[int]	    =  partial(_find_cluster_extremes_shared ,   jaccard_threshold=a__      )
     with mp.Pool() as pool:
          for extremes in tqdm(
              pool.imap_unordered(
                  a__ ,   a__ ,   ) ,   total=len(a__      ) ,   ):
               extremes_list.append(a__      )
     return extremes_list







def 		SCREAMING_SNAKE_CASE							( __lowerCAmelCase ,   __lowerCAmelCase = 0.8_5      )			->     Tuple[Type[Dataset], List[List[Dict]]]:
     UpperCamelCase__      :       Optional[Any]	    =  make_duplicate_clusters(a__ ,   a__      )
     UpperCamelCase__      :       Union[str, Any]	    =  {x["base_index"] for cluster in duplicate_clusters for x in cluster}
     UpperCamelCase__      :       str	    =  {}
     UpperCamelCase__      :       List[Any]	    =  find_extremes(a__ ,   a__ ,   a__      )
     for extremes in extremes_clusters:
          for element in extremes:
               UpperCamelCase__      :       Optional[int]	    =  element
     UpperCamelCase__      :       Optional[Any]	    =  duplicate_indices - set(extreme_dict.keys()      )
     UpperCamelCase__      :       Any	    =  dataset.filter(lambda __lowerCAmelCase ,   __lowerCAmelCase      : idx not in remove_indices ,   with_indices=a__      )

     # update duplicate_clusters
     for cluster in duplicate_clusters:
          for element in cluster:
               UpperCamelCase__      :       Union[str, Any]	    =  element["base_index"] in extreme_dict
               if element["is_extreme"]:
                    UpperCamelCase__      :       str	    =  extreme_dict[element["base_index"]]["copies"]

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

     return ds_filter, duplicate_clusters







from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCamelCase     :							Optional[int]    =logging.get_logger(__name__)

lowerCamelCase     :							Optional[Any]    ={
    '''edbeeching/decision-transformer-gym-hopper-medium''': (
        '''https://huggingface.co/edbeeching/decision-transformer-gym-hopper-medium/resolve/main/config.json'''
    ),
    # See all DecisionTransformer models at https://huggingface.co/models?filter=decision_transformer
}





class       __a  (   A__	):
 _lowerCAmelCase	:	str				=			'''decision_transformer'''
 _lowerCAmelCase	:	Optional[Any]				=			['''past_key_values''']
 _lowerCAmelCase	:	Optional[int]				=			{
     '''max_position_embeddings''': '''n_positions''',
     '''num_attention_heads''': '''n_head''',
     '''num_hidden_layers''': '''n_layer''',
 }







 def __init__(		self :       List[Any]							,       SCREAMING_SNAKE_CASE :       Optional[int]=17							,       SCREAMING_SNAKE_CASE :       Optional[int]=4							,       SCREAMING_SNAKE_CASE :       Optional[int]=1_28							,       SCREAMING_SNAKE_CASE :       Dict=40_96							,       SCREAMING_SNAKE_CASE :       Union[str, Any]=True							,       SCREAMING_SNAKE_CASE :       Tuple=1							,       SCREAMING_SNAKE_CASE :       Optional[int]=10_24							,       SCREAMING_SNAKE_CASE :       Any=3							,       SCREAMING_SNAKE_CASE :       str=1							,       SCREAMING_SNAKE_CASE :       Optional[int]=None							,       SCREAMING_SNAKE_CASE :       Dict="relu"							,       SCREAMING_SNAKE_CASE :       Optional[int]=0.1							,       SCREAMING_SNAKE_CASE :       str=0.1							,       SCREAMING_SNAKE_CASE :       str=0.1							,       SCREAMING_SNAKE_CASE :       Union[str, Any]=1e-5							,       SCREAMING_SNAKE_CASE :       Optional[int]=0.0_2							,       SCREAMING_SNAKE_CASE :       Optional[Any]=True							,       SCREAMING_SNAKE_CASE :       Tuple=True							,       SCREAMING_SNAKE_CASE :       int=5_02_56							,       SCREAMING_SNAKE_CASE :       List[Any]=5_02_56							,       SCREAMING_SNAKE_CASE :       Any=False							,       SCREAMING_SNAKE_CASE :       List[Any]=False							,       **SCREAMING_SNAKE_CASE :       Optional[int]							,       ):


      '''simple docstring'''






      UpperCamelCase__      :       List[str]	    =  state_dim
      UpperCamelCase__      :       Optional[int]	    =  act_dim
      UpperCamelCase__      :       Dict	    =  hidden_size
      UpperCamelCase__      :       Any	    =  max_ep_len
      UpperCamelCase__      :       Optional[Any]	    =  action_tanh
      UpperCamelCase__      :       str	    =  vocab_size
      UpperCamelCase__      :       List[str]	    =  n_positions
      UpperCamelCase__      :       Tuple	    =  n_layer
      UpperCamelCase__      :       Union[str, Any]	    =  n_head
      UpperCamelCase__      :       Dict	    =  n_inner
      UpperCamelCase__      :       int	    =  activation_function
      UpperCamelCase__      :       List[str]	    =  resid_pdrop
      UpperCamelCase__      :       Optional[int]	    =  embd_pdrop
      UpperCamelCase__      :       Optional[Any]	    =  attn_pdrop
      UpperCamelCase__      :       Optional[Any]	    =  layer_norm_epsilon
      UpperCamelCase__      :       int	    =  initializer_range
      UpperCamelCase__      :       Dict	    =  scale_attn_weights
      UpperCamelCase__      :       Tuple	    =  use_cache
      UpperCamelCase__      :       List[str]	    =  scale_attn_by_inverse_layer_idx
      UpperCamelCase__      :       Union[str, Any]	    =  reorder_and_upcast_attn

      UpperCamelCase__      :       Optional[Any]	    =  bos_token_id
      UpperCamelCase__      :       Optional[int]	    =  eos_token_id

      super().__init__(bos_token_id=SCREAMING_SNAKE_CASE							,       eos_token_id=SCREAMING_SNAKE_CASE							,       **SCREAMING_SNAKE_CASE						)