

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

def 						UpperCamelCase (lowercase_:			List[str] )					->				Optional[MinHash]:
 if len(lowercase_ ) < MIN_NUM_TOKENS:
  return None
 A__							:   Optional[int]					       =		MinHash(num_perm=lowercase_ )
 for token in set(lowercase_ ):
  min_hash.update(token.encode() )
 return min_hash

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



class    _a						:


     '''simple docstring'''






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

      A__							:   Dict					       =		defaultdict(A__      )






     def   __A      (			self   ,	A__   ,	A__      ):
      A__							:   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      ):
      A__							:   Optional[Any]					       =		[]
      for base, duplicates in self._duplicate_clusters.items():
       A__							:   Dict					       =		[base] + list(A__      )
       # reformat the cluster to be a list of dict
       A__							:   Union[str, 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__      ):
      A__							:   List[Any]					       =		self.get_duplicate_clusters()
      with open(A__   ,	"""w"""      ) as f:
       json.dump(A__   ,	A__      )



def 						UpperCamelCase (lowercase_:			List[Any] )					->				Union[str, Any]:
 A__							:   Tuple					       =		element
 A__							:   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 						UpperCamelCase (lowercase_:			Type[Dataset] )					->				Union[str, Any]:
 with mp.Pool() as pool:
  for data in pool.imap_unordered(
      _compute_min_hash   ,				ThreadedIterator(lowercase_   ,				max_queue_size=10000 )   ,				chunksize=100   ,				):
   if data is not None:
    yield data

def 						UpperCamelCase (lowercase_:			Type[Dataset]   ,				lowercase_:			float )					->				Tuple:
 A__							:   Tuple					       =		DuplicationIndex(duplication_jaccard_threshold=lowercase_ )

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

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

def 						UpperCamelCase (lowercase_:			str   ,				lowercase_:			str )					->				float:
 A__							:   Union[str, Any]					       =		get_tokens(lowercase_ )
 A__							:   Union[str, Any]					       =		get_tokens(lowercase_ )
 return len(tokensa & tokensa ) / len(tokensa | tokensa )


A_	:			Union[str, Any]  	=      None

def 						UpperCamelCase (lowercase_:			List[str]   ,				lowercase_:			Optional[Any] )					->				int:
 A__							:   Any					       =		[]
 for elementa in cluster:
  A__							:   List[Any]					       =		_shared_dataset[elementa["""base_index"""]]["""content"""]
  for elementa in extremes:
   A__							:   str					       =		_shared_dataset[elementa["""base_index"""]]["""content"""]
   if jaccard_similarity(lowercase_   ,				lowercase_ ) >= jaccard_threshold:
    elementa["copies"] += 1
    break
  else:
   A__							:   List[str]					       =		1
   extremes.append(lowercase_ )
 return extremes

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






from typing import TYPE_CHECKING

from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable


A_	:			List[Any]  	=      {'configuration_gpt_neox': ['GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GPTNeoXConfig']}

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	A_	:			Optional[Any]  	=      ['GPTNeoXTokenizerFast']

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	A_	:			Optional[int]  	=      [
	    'GPT_NEOX_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'GPTNeoXForCausalLM',
	    'GPTNeoXForQuestionAnswering',
	    'GPTNeoXForSequenceClassification',
	    'GPTNeoXForTokenClassification',
	    'GPTNeoXLayer',
	    'GPTNeoXModel',
	    'GPTNeoXPreTrainedModel',
	]


if TYPE_CHECKING:
	from .configuration_gpt_neox import GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoXConfig

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_gpt_neox_fast import GPTNeoXTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_gpt_neox import (
		    GPT_NEOX_PRETRAINED_MODEL_ARCHIVE_LIST,
		    GPTNeoXForCausalLM,
		    GPTNeoXForQuestionAnswering,
		    GPTNeoXForSequenceClassification,
		    GPTNeoXForTokenClassification,
		    GPTNeoXLayer,
		    GPTNeoXModel,
		    GPTNeoXPreTrainedModel,
		)


else:
	import sys

	A_	:			Any  	=      _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



