






"""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__	:    int			 = re.compile('[^A-Za-z_0-9]')
# parameters used in DuplicationIndex
A__	:    Optional[Any]			 = 10
A__	:    Tuple			 = 256

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

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







class        lowercase__      :


			def __init__(				self					:  List[str]       ,	*,
			    snake_case__					:  float = 0.85       ,	):
										lowerCamelCase_	:							Union[str, Any]									=duplication_jaccard_threshold
										lowerCamelCase_	:							Tuple									=NUM_PERM
										lowerCamelCase_	:							str									=MinHashLSH(threshold=self._duplication_jaccard_threshold       ,	num_perm=self._num_perm )

										lowerCamelCase_	:							Union[str, Any]									=defaultdict(snake_case__ )


			def      UpperCAmelCase__						(				self					:  Tuple       ,	snake_case__					:  Tuple       ,	snake_case__					:  MinHash ):
										lowerCamelCase_	:							List[Any]									=self._index.query(snake_case__ )
										if code_key in self._index.keys:
																	print(F"""Duplicate key {code_key}""" )
																	return

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


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


			def      UpperCAmelCase__						(				self					:  Tuple       ,	snake_case__					:  List[Any] ):
										lowerCamelCase_	:							Dict									=self.get_duplicate_clusters()
										with open(snake_case__       ,	"w" ) as f:
																	json.dump(snake_case__       ,	snake_case__ )


def 			_snake_case    (					lowerCamelCase__     :   str  )    ->     List[str]:
							lowerCamelCase_	:							List[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 			_snake_case    (					lowerCamelCase__     :   Type[Dataset]  )    ->     Union[str, Any]:
							with mp.Pool() as pool:
														for data in pool.imap_unordered(
														    _compute_min_hash    ,						ThreadedIterator(lowerCamelCase__    ,						max_queue_size=10_000  )    ,						chunksize=100    ,						):
																					if data is not None:
																												yield data

def 			_snake_case    (					lowerCamelCase__     :   Type[Dataset]    ,						lowerCamelCase__     :   float  )    ->     Optional[int]:
							lowerCamelCase_	:							List[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 			_snake_case    (					lowerCamelCase__     :   str    ,						lowerCamelCase__     :   str  )    ->     float:
							lowerCamelCase_	:							str									=get_tokens(lowerCamelCase__  )
							lowerCamelCase_	:							List[str]									=get_tokens(lowerCamelCase__  )
							return len(tokensa & tokensa  ) / len(tokensa | tokensa  )


A__	:    Union[str, Any]			 = None

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

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











"""simple docstring"""



from typing import List, Optional

from tokenizers import ByteLevelBPETokenizer

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderbot_small import BlenderbotSmallTokenizer


A__	:    Dict			 = logging.get_logger(__name__)

A__	:    Dict			 = {
    'vocab_file': 'vocab.json',
    'merges_file': 'merges.txt',
    'tokenizer_config_file': 'tokenizer_config.json',
}

A__	:    List[Any]			 = {
    'vocab_file': {
        'facebook/blenderbot_small-90M': 'https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/vocab.json'
    },
    'merges_file': {
        'facebook/blenderbot_small-90M': 'https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/merges.txt'
    },
    'tokenizer_config_file': {
        'facebook/blenderbot_small-90M': (
            'https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/tokenizer_config.json'
        )
    },
}

A__	:    Optional[int]			 = {
    'facebook/blenderbot_small-90M': 512,
}







class        lowercase__      (							snake_case__       ):
			_UpperCAmelCase		:Optional[int]             =			VOCAB_FILES_NAMES
			_UpperCAmelCase		:Tuple             =			PRETRAINED_VOCAB_FILES_MAP
			_UpperCAmelCase		:Any             =			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			_UpperCAmelCase		:Tuple             =			BlenderbotSmallTokenizer


			def __init__(				self					:  Tuple       ,	snake_case__					:  Optional[Any]=None       ,	snake_case__					:  str=None       ,	snake_case__					:  Any="<|endoftext|>"       ,	snake_case__					:  Tuple="<|endoftext|>"       ,	snake_case__					:  Tuple="<|endoftext|>"       ,	snake_case__					:  str=False       ,	snake_case__					:  int=True       ,	**snake_case__					:  Tuple       ,	):
										super().__init__(
										    ByteLevelBPETokenizer(
										        vocab=snake_case__       ,	merges=snake_case__       ,	add_prefix_space=snake_case__       ,	trim_offsets=snake_case__       ,	)       ,	bos_token=snake_case__       ,	eos_token=snake_case__       ,	unk_token=snake_case__       ,	**snake_case__       ,	)
										lowerCamelCase_	:							Optional[int]									=add_prefix_space


			def      UpperCAmelCase__						(				self					:  Tuple       ,	snake_case__					:  Optional[Any]       ,	snake_case__					:  List[str]=None ):
										lowerCamelCase_	:							Optional[Any]									=[self.bos_token_id] + token_ids_a + [self.eos_token_id]
										if token_ids_a is None:
																	return output

										return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]


			def      UpperCAmelCase__						(				self					:  Tuple       ,	snake_case__					:  List[int]       ,	snake_case__					:  Optional[List[int]] = None ):
										lowerCamelCase_	:							int									=[self.sep_token_id]
										lowerCamelCase_	:							List[Any]									=[self.cls_token_id]

										if token_ids_a is None:
																	return len(cls + token_ids_a + sep ) * [0]
										return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]



