"""simple docstring"""




import flax.linen as nn
import jax
import jax.numpy as jnp
class 						__A							( nn.Module		):
     _UpperCamelCase							:			int
     _UpperCamelCase							:			jnp.dtype							=		jnp.floataa






     def     __A  (		self  ):
       _lowerCAmelCase						:       str  			=     nn.Conv(
           self.out_channels				,     kernel_size=(3, 3)				,     strides=(1, 1)				,     padding=((1, 1), (1, 1))				,     dtype=self.dtype				,     )






     def __call__(		self				,     a__  ):
       _lowerCAmelCase			,	_lowerCAmelCase			,	_lowerCAmelCase			,	_lowerCAmelCase						:       Tuple  			=     hidden_states.shape
       _lowerCAmelCase						:       List[Any]  			=     jax.image.resize(
           a__				,     shape=(batch, height * 2, width * 2, channels)				,     method="""nearest"""				,     )
       _lowerCAmelCase						:       str  			=     self.conv(a__  )
       return hidden_states







class 						__A							( nn.Module		):
     _UpperCamelCase							:			int
     _UpperCamelCase							:			jnp.dtype							=		jnp.floataa






     def     __A  (		self  ):
       _lowerCAmelCase						:       List[Any]  			=     nn.Conv(
           self.out_channels				,     kernel_size=(3, 3)				,     strides=(2, 2)				,     padding=((1, 1), (1, 1))				,     dtype=self.dtype				,     )






     def __call__(		self				,     a__  ):
       # pad = ((0, 0), (0, 1), (0, 1), (0, 0))  # pad height and width dim
       # hidden_states = jnp.pad(hidden_states, pad_width=pad)
       _lowerCAmelCase						:       Optional[int]  			=     self.conv(a__  )
       return hidden_states







class 						__A							( nn.Module		):
     _UpperCamelCase							:			int
     _UpperCamelCase							:			int							=		None
     _UpperCamelCase							:			float							=		0.0
     _UpperCamelCase							:			bool							=		None
     _UpperCamelCase							:			jnp.dtype							=		jnp.floataa






     def     __A  (		self  ):
       _lowerCAmelCase						:       Tuple  			=     self.in_channels if self.out_channels is None else self.out_channels

       _lowerCAmelCase						:       List[str]  			=     nn.GroupNorm(num_groups=32				,     epsilon=1e-5  )
       _lowerCAmelCase						:       Tuple  			=     nn.Conv(
           a__				,     kernel_size=(3, 3)				,     strides=(1, 1)				,     padding=((1, 1), (1, 1))				,     dtype=self.dtype				,     )

       _lowerCAmelCase						:       Optional[Any]  			=     nn.Dense(a__				,     dtype=self.dtype  )

       _lowerCAmelCase						:       Any  			=     nn.GroupNorm(num_groups=32				,     epsilon=1e-5  )
       _lowerCAmelCase						:       Any  			=     nn.Dropout(self.dropout_prob  )
       _lowerCAmelCase						:       Optional[Any]  			=     nn.Conv(
           a__				,     kernel_size=(3, 3)				,     strides=(1, 1)				,     padding=((1, 1), (1, 1))				,     dtype=self.dtype				,     )

       _lowerCAmelCase						:       Any  			=     self.in_channels != out_channels if self.use_nin_shortcut is None else self.use_nin_shortcut

       _lowerCAmelCase						:       str  			=     None
       if use_nin_shortcut:
         _lowerCAmelCase						:       Dict  			=     nn.Conv(
             a__				,     kernel_size=(1, 1)				,     strides=(1, 1)				,     padding="""VALID"""				,     dtype=self.dtype				,     )






     def __call__(		self				,     a__				,     a__				,     a__=True  ):
       _lowerCAmelCase						:       Union[str, Any]  			=     hidden_states
       _lowerCAmelCase						:       Union[str, Any]  			=     self.norma(a__  )
       _lowerCAmelCase						:       Union[str, Any]  			=     nn.swish(a__  )
       _lowerCAmelCase						:       Dict  			=     self.conva(a__  )

       _lowerCAmelCase						:       Any  			=     self.time_emb_proj(nn.swish(a__  )  )
       _lowerCAmelCase						:       int  			=     jnp.expand_dims(jnp.expand_dims(a__				,     1  )				,     1  )
       _lowerCAmelCase						:       Optional[Any]  			=     hidden_states + temb

       _lowerCAmelCase						:       Optional[Any]  			=     self.norma(a__  )
       _lowerCAmelCase						:       int  			=     nn.swish(a__  )
       _lowerCAmelCase						:       Optional[int]  			=     self.dropout(a__				,     a__  )
       _lowerCAmelCase						:       List[Any]  			=     self.conva(a__  )

       if self.conv_shortcut is not None:
         _lowerCAmelCase						:       Tuple  			=     self.conv_shortcut(a__  )

       return hidden_states + residual





"""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


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





def 							SCREAMING_SNAKE_CASE     (			_lowerCamelCase						:       List[str]	)       ->      Optional[MinHash]:
  if len(_lowerCamelCase	) < MIN_NUM_TOKENS:
    return None
  _lowerCAmelCase						:       Optional[Any]  			=     MinHash(num_perm=_lowerCamelCase	)
  for token in set(_lowerCamelCase	):
    min_hash.update(token.encode()	)
  return min_hash





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






     def __init__(		self				,     *,
         a__ = 0.8_5				,     ):
       _lowerCAmelCase						:       List[Any]  			=     duplication_jaccard_threshold
       _lowerCAmelCase						:       Union[str, Any]  			=     NUM_PERM
       _lowerCAmelCase						:       Optional[int]  			=     MinHashLSH(threshold=self._duplication_jaccard_threshold				,     num_perm=self._num_perm  )

       _lowerCAmelCase						:       Optional[int]  			=     defaultdict(a__  )






     def     __A  (		self				,     a__				,     a__  ):
       _lowerCAmelCase						:       Optional[Any]  			=     self._index.query(a__  )
       if code_key in self._index.keys:
         print(F"Duplicate key {code_key}"  )
         return

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






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






     def     __A  (		self				,     a__  ):
       _lowerCAmelCase						:       Dict  			=     self.get_duplicate_clusters()
       with open(a__				,     """w"""  ) as f:
         json.dump(a__				,     a__  )







def 							SCREAMING_SNAKE_CASE     (			_lowerCamelCase						:       Optional[Any]	)       ->      Tuple:
  _lowerCAmelCase			,	_lowerCAmelCase						:       Union[str, Any]  			=     element
  _lowerCAmelCase						:       Tuple  			=     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						:       Type[Dataset]	)       ->      Optional[Any]:
  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 							SCREAMING_SNAKE_CASE     (			_lowerCamelCase						:       Type[Dataset]			,_lowerCamelCase						:       float	)       ->      List[str]:
  _lowerCAmelCase						:       Optional[Any]  			=     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 							SCREAMING_SNAKE_CASE     (			_lowerCamelCase						:       str			,_lowerCamelCase						:       str	)       ->      float:
  _lowerCAmelCase						:       Any  			=     get_tokens(_lowerCamelCase	)
  _lowerCAmelCase						:       Optional[int]  			=     get_tokens(_lowerCamelCase	)
  return len(tokensa & tokensa	) / len(tokensa | tokensa	)


_a      :		str           =   None





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





def 							SCREAMING_SNAKE_CASE     (			_lowerCamelCase						:       List[Any]			,_lowerCamelCase						:       Optional[Any]			,_lowerCamelCase						:       Any	)       ->      str:
  global _shared_dataset
  _lowerCAmelCase						:       Tuple  			=     dataset
  _lowerCAmelCase						:       Optional[Any]  			=     []
  _lowerCAmelCase						:       Optional[Any]  			=     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 							SCREAMING_SNAKE_CASE     (			_lowerCamelCase						:       Type[Dataset]			,_lowerCamelCase						:       float = 0.85	)       ->      Tuple[Type[Dataset], List[List[Dict]]]:
  _lowerCAmelCase						:       Tuple  			=     make_duplicate_clusters(_lowerCamelCase			,_lowerCamelCase	)
  _lowerCAmelCase						:       str  			=     {x["""base_index"""] for cluster in duplicate_clusters for x in cluster}
  _lowerCAmelCase						:       Optional[int]  			=     {}
  _lowerCAmelCase						:       Tuple  			=     find_extremes(_lowerCamelCase			,_lowerCamelCase			,_lowerCamelCase	)
  for extremes in extremes_clusters:
    for element in extremes:
      _lowerCAmelCase						:       Union[str, Any]  			=     element
  _lowerCAmelCase						:       List[Any]  			=     duplicate_indices - set(extreme_dict.keys()	)
  _lowerCAmelCase						:       List[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:
      _lowerCAmelCase						:       Tuple  			=     element["""base_index"""] in extreme_dict
      if element["is_extreme"]:
        _lowerCAmelCase						:       Dict  			=     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




