'''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


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



def 							UpperCamelCase__			(			a__      )     ->   Optional[int]:


						'''simple docstring'''






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



def 							UpperCamelCase__			(			a__      )     ->   Dict:


						'''simple docstring'''






						return {t for t in NON_ALPHA.split(__A      ) if len(t.strip()      ) > 0}
class   SCREAMING_SNAKE_CASE			:







		"""simple docstring"""






		def __init__(   self		,						*,
		    __A = 0.85		,						) ->     List[Any]:
								_lowerCAmelCase										=duplication_jaccard_threshold
								_lowerCAmelCase										=NUM_PERM
								_lowerCAmelCase										=MinHashLSH(threshold=self._duplication_jaccard_threshold		,						num_perm=self._num_perm				)

								_lowerCAmelCase										=defaultdict(_UpperCamelCase				)






		def 		UpperCamelCase__       (   self		,						__A		,						__A				) ->     None:
								_lowerCAmelCase										=self._index.query(_UpperCamelCase				)
								if code_key in self._index.keys:
														print(F'''Duplicate key {code_key}'''				)
														return

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






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






		def 		UpperCamelCase__       (   self		,						__A				) ->     None:
								_lowerCAmelCase										=self.get_duplicate_clusters()
								with open(_UpperCamelCase		,						'w'				) as f:
														json.dump(_UpperCamelCase		,						_UpperCamelCase				)



def 							UpperCamelCase__			(			a__      )     ->   Optional[Any]:


						'''simple docstring'''






						_lowerCAmelCase										=element
						_lowerCAmelCase										=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__			(			a__      )     ->   Any:


						'''simple docstring'''






						with mp.Pool() as pool:
												for data in pool.imap_unordered(
												    _compute_min_hash			,      ThreadedIterator(__A			,      max_queue_size=1_0_0_0_0      )			,      chunksize=1_0_0			,      ):
																		if data is not None:
																								yield data



def 							UpperCamelCase__			(			a__			,      a__      )     ->   Union[str, Any]:


						'''simple docstring'''






						_lowerCAmelCase										=DuplicationIndex(duplication_jaccard_threshold=__A      )

						for filename, min_hash in tqdm(ThreadedIterator(minhash_iter(enumerate(__A      )      )			,      max_queue_size=1_0_0      )      ):
												di.add(__A			,      __A      )

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



def 							UpperCamelCase__			(			a__			,      a__      )     ->   List[Any]:


						'''simple docstring'''






						_lowerCAmelCase										=get_tokens(__A      )
						_lowerCAmelCase										=get_tokens(__A      )
						return len(tokensa & tokensa      ) / len(tokensa | tokensa      )


lowercase_     		= None



def 							UpperCamelCase__			(			a__			,      a__      )     ->   Optional[int]:


						'''simple docstring'''






						_lowerCAmelCase										=[]
						for elementa in cluster:
												_lowerCAmelCase										=_shared_dataset[elementa["""base_index"""]]["""content"""]
												for elementa in extremes:
																		_lowerCAmelCase										=_shared_dataset[elementa["""base_index"""]]["""content"""]
																		if jaccard_similarity(__A			,      __A      ) >= jaccard_threshold:
																								elementa["copies"] += 1
																								break
												else:
																		_lowerCAmelCase										=1
																		extremes.append(__A      )
						return extremes



def 							UpperCamelCase__			(			a__			,      a__			,      a__      )     ->   Tuple:


						'''simple docstring'''






						global _shared_dataset
						_lowerCAmelCase										=dataset
						_lowerCAmelCase										=[]
						_lowerCAmelCase										=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 							UpperCamelCase__			(			a__			,      a__ = 0.85      )     ->   Optional[Any]:


						'''simple docstring'''






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

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






'''simple docstring'''

from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowercase_     		= logging.get_logger(__name__)

lowercase_     		= {
    '''facebook/data2vec-text-base''': '''https://huggingface.co/data2vec/resolve/main/config.json''',
}
class   SCREAMING_SNAKE_CASE			(						__lowercase):







		"""simple docstring"""
		lowercase   :      List[str]       =							'data2vec-text'






		def __init__(   self		,						__A=3_0522		,						__A=768		,						__A=12		,						__A=12		,						__A=3072		,						__A="gelu"		,						__A=0.1		,						__A=0.1		,						__A=512		,						__A=2		,						__A=0.02		,						__A=1E-12		,						__A=1		,						__A=0		,						__A=2		,						__A="absolute"		,						__A=True		,						__A=None		,						**__A		,						) ->     List[Any]:
								super().__init__(pad_token_id=__A		,						bos_token_id=__A		,						eos_token_id=__A		,						**__A				)

								_lowerCAmelCase										=vocab_size
								_lowerCAmelCase										=hidden_size
								_lowerCAmelCase										=num_hidden_layers
								_lowerCAmelCase										=num_attention_heads
								_lowerCAmelCase										=hidden_act
								_lowerCAmelCase										=intermediate_size
								_lowerCAmelCase										=hidden_dropout_prob
								_lowerCAmelCase										=attention_probs_dropout_prob
								_lowerCAmelCase										=max_position_embeddings
								_lowerCAmelCase										=type_vocab_size
								_lowerCAmelCase										=initializer_range
								_lowerCAmelCase										=layer_norm_eps
								_lowerCAmelCase										=position_embedding_type
								_lowerCAmelCase										=use_cache
								_lowerCAmelCase										=classifier_dropout

class   SCREAMING_SNAKE_CASE			(						__lowercase):







		"""simple docstring"""






		@property
		def 		UpperCamelCase__       (   self				) ->     Mapping[str, Mapping[int, str]]:
								if self.task == "multiple-choice":
														_lowerCAmelCase										={0: 'batch', 1: 'choice', 2: 'sequence'}
								else:
														_lowerCAmelCase										={0: 'batch', 1: 'sequence'}
								return OrderedDict(
								    [
								        ('input_ids', dynamic_axis),
								        ('attention_mask', dynamic_axis),
								    ]				)





