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   :				List[Any]     			=						2_56





def 		__lowerCamelCase							(UpperCAmelCase__       :							List[str]					):
					if len(UpperCAmelCase__					) < MIN_NUM_TOKENS:
										return None
					SCREAMING_SNAKE_CASE           =    MinHash(num_perm=UpperCAmelCase__					)
					for token in set(UpperCAmelCase__					):
										min_hash.update(token.encode()					)
					return min_hash





def 		__lowerCamelCase							(UpperCAmelCase__       :							str					):
					return {t for t in NON_ALPHA.split(UpperCAmelCase__					) if len(t.strip()					) > 0}


class   lowercase      :




							def __init__(  self				:   List[Any]		,							*,
							    _UpperCamelCase				:   float = 0.8_5		,							)   ->			Optional[Any]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    duplication_jaccard_threshold
												SCREAMING_SNAKE_CASE           =    NUM_PERM
												SCREAMING_SNAKE_CASE           =    MinHashLSH(threshold=self._duplication_jaccard_threshold		,							num_perm=self._num_perm )

												SCREAMING_SNAKE_CASE           =    defaultdict(_UpperCamelCase )




							def 		__snake_case(  self				:   List[Any]		,							_UpperCamelCase				:   Tuple		,							_UpperCamelCase				:   MinHash )   ->			None:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    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 		__snake_case(  self				:   Optional[int] )   ->			List[List[Dict]]:



												'''simple docstring'''

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





							def 		__snake_case(  self				:   Dict		,							_UpperCamelCase				:   Any )   ->			None:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    self.get_duplicate_clusters()
												with open(_UpperCamelCase		,							"w" ) as f:
																	json.dump(_UpperCamelCase		,							_UpperCamelCase )





def 		__lowerCamelCase							(UpperCAmelCase__       :							Any					):
					SCREAMING_SNAKE_CASE     ,				SCREAMING_SNAKE_CASE           =    element
					SCREAMING_SNAKE_CASE           =    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 		__lowerCamelCase							(UpperCAmelCase__       :							Type[Dataset]					):
					with mp.Pool() as pool:
										for data in pool.imap_unordered(
										    _compute_min_hash ,	ThreadedIterator(UpperCAmelCase__ ,	max_queue_size=1_0_0_0_0					) ,	chunksize=1_0_0 ,	):
															if data is not None:
																				yield data





def 		__lowerCamelCase							(UpperCAmelCase__       :							Type[Dataset] ,	UpperCAmelCase__       :							float					):
					SCREAMING_SNAKE_CASE           =    DuplicationIndex(duplication_jaccard_threshold=UpperCAmelCase__					)

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

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





def 		__lowerCamelCase							(UpperCAmelCase__       :							str ,	UpperCAmelCase__       :							str					):
					SCREAMING_SNAKE_CASE           =    get_tokens(UpperCAmelCase__					)
					SCREAMING_SNAKE_CASE           =    get_tokens(UpperCAmelCase__					)
					return len(tokensa & tokensa					) / len(tokensa | tokensa					)


_lowerCamelCase   :				Optional[Any]     			=						None





def 		__lowerCamelCase							(UpperCAmelCase__       :							Union[str, Any] ,	UpperCAmelCase__       :							List[str]					):
					SCREAMING_SNAKE_CASE           =    []
					for elementa in cluster:
										SCREAMING_SNAKE_CASE           =    _shared_dataset[elementa["base_index"]]["content"]
										for elementa in extremes:
															SCREAMING_SNAKE_CASE           =    _shared_dataset[elementa["base_index"]]["content"]
															if jaccard_similarity(UpperCAmelCase__ ,	UpperCAmelCase__					) >= jaccard_threshold:
																				elementa["copies"] += 1
																				break
										else:
															SCREAMING_SNAKE_CASE           =    1
															extremes.append(UpperCAmelCase__					)
					return extremes





def 		__lowerCamelCase							(UpperCAmelCase__       :							List[Any] ,	UpperCAmelCase__       :							Tuple ,	UpperCAmelCase__       :							Union[str, Any]					):
					global _shared_dataset
					SCREAMING_SNAKE_CASE           =    dataset
					SCREAMING_SNAKE_CASE           =    []
					SCREAMING_SNAKE_CASE           =    partial(_find_cluster_extremes_shared ,	jaccard_threshold=UpperCAmelCase__					)
					with mp.Pool() as pool:
										for extremes in tqdm(
										    pool.imap_unordered(
										        UpperCAmelCase__ ,	UpperCAmelCase__ ,	) ,	total=len(UpperCAmelCase__					) ,	):
															extremes_list.append(UpperCAmelCase__					)
					return extremes_list





def 		__lowerCamelCase							(UpperCAmelCase__       :							Type[Dataset] ,	UpperCAmelCase__       :							float = 0.85					):
					SCREAMING_SNAKE_CASE           =    make_duplicate_clusters(UpperCAmelCase__ ,	UpperCAmelCase__					)
					SCREAMING_SNAKE_CASE           =    {x["base_index"] for cluster in duplicate_clusters for x in cluster}
					SCREAMING_SNAKE_CASE           =    {}
					SCREAMING_SNAKE_CASE           =    find_extremes(UpperCAmelCase__ ,	UpperCAmelCase__ ,	UpperCAmelCase__					)
					for extremes in extremes_clusters:
										for element in extremes:
															SCREAMING_SNAKE_CASE           =    element
					SCREAMING_SNAKE_CASE           =    duplicate_indices - set(extreme_dict.keys()					)
					SCREAMING_SNAKE_CASE           =    dataset.filter(lambda UpperCAmelCase__ ,	UpperCAmelCase__					: idx not in remove_indices ,	with_indices=UpperCAmelCase__					)

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

					print(F"Original dataset size: {len(UpperCAmelCase__					)}"					)
					print(F"Number of duplicate clusters: {len(UpperCAmelCase__					)}"					)
					print(F"Files in duplicate cluster: {len(UpperCAmelCase__					)}"					)
					print(F"Unique files in duplicate cluster: {len(UpperCAmelCase__					)}"					)
					print(F"Filtered dataset size: {len(UpperCAmelCase__					)}"					)

					return ds_filter, duplicate_clusters





from typing import List, Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_herbert import HerbertTokenizer


_lowerCamelCase   :				Optional[Any]     			=						logging.get_logger(__name__)

_lowerCamelCase   :				Any     			=						{'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', '''tokenizer_file''': '''tokenizer.json'''}

_lowerCamelCase   :				int     			=						{
    '''vocab_file''': {
        '''allegro/herbert-base-cased''': '''https://huggingface.co/allegro/herbert-base-cased/resolve/main/vocab.json'''
    },
    '''merges_file''': {
        '''allegro/herbert-base-cased''': '''https://huggingface.co/allegro/herbert-base-cased/resolve/main/merges.txt'''
    },
}

_lowerCamelCase   :				Tuple     			=						{'''allegro/herbert-base-cased''': 5_14}
_lowerCamelCase   :				Optional[int]     			=						{}


class   lowercase      (       a       ):
							lowercase__  :    List[str]									=							VOCAB_FILES_NAMES
							lowercase__  :    str									=							PRETRAINED_VOCAB_FILES_MAP
							lowercase__  :    Tuple									=							PRETRAINED_INIT_CONFIGURATION
							lowercase__  :    Optional[int]									=							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							lowercase__  :    str									=							HerbertTokenizer




							def __init__(  self				:   Dict		,							_UpperCamelCase				:   Any=None		,							_UpperCamelCase				:   Any=None		,							_UpperCamelCase				:   Optional[int]=None		,							_UpperCamelCase				:   Optional[int]="<s>"		,							_UpperCamelCase				:   Union[str, Any]="<unk>"		,							_UpperCamelCase				:   List[str]="<pad>"		,							_UpperCamelCase				:   List[str]="<mask>"		,							_UpperCamelCase				:   Tuple="</s>"		,							**_UpperCamelCase				:   Any		,							)   ->			str:



												'''simple docstring'''

												super().__init__(
												    _UpperCamelCase		,							_UpperCamelCase		,							tokenizer_file=_UpperCamelCase		,							cls_token=_UpperCamelCase		,							unk_token=_UpperCamelCase		,							pad_token=_UpperCamelCase		,							mask_token=_UpperCamelCase		,							sep_token=_UpperCamelCase		,							**_UpperCamelCase		,							)




							def 		__snake_case(  self				:   Optional[Any]		,							_UpperCamelCase				:   List[int]		,							_UpperCamelCase				:   Optional[List[int]] = None )   ->			List[int]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    [self.cls_token_id]
												SCREAMING_SNAKE_CASE           =    [self.sep_token_id]
												if token_ids_a is None:
																	return cls + token_ids_a + sep

												return cls + token_ids_a + sep + token_ids_a + sep




							def 		__snake_case(  self				:   Any		,							_UpperCamelCase				:   List[int]		,							_UpperCamelCase				:   Optional[List[int]] = None		,							_UpperCamelCase				:   bool = False )   ->			List[int]:



												'''simple docstring'''

												if already_has_special_tokens:
																	return super().get_special_tokens_mask(
																	    token_ids_a=_UpperCamelCase		,							token_ids_a=_UpperCamelCase		,							already_has_special_tokens=_UpperCamelCase )

												if token_ids_a is None:
																	return [1] + ([0] * len(_UpperCamelCase )) + [1]
												return [1] + ([0] * len(_UpperCamelCase )) + [1] + ([0] * len(_UpperCamelCase )) + [1]




							def 		__snake_case(  self				:   Union[str, Any]		,							_UpperCamelCase				:   List[int]		,							_UpperCamelCase				:   Optional[List[int]] = None )   ->			List[int]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    [self.sep_token_id]
												SCREAMING_SNAKE_CASE           =    [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 ) * [0] + len(token_ids_a + sep ) * [1]





							def 		__snake_case(  self				:   str		,							_UpperCamelCase				:   str		,							_UpperCamelCase				:   Optional[str] = None )   ->			Tuple[str]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    self._tokenizer.model.save(_UpperCamelCase		,							name=_UpperCamelCase )
												return tuple(_UpperCamelCase )




