
import os
import unicodedata
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


lowercase__	:Tuple										=					logging.get_logger(__name__)
lowercase__	:str										=					{"vocab_file": "spiece.model"}

lowercase__	:List[str]										=					{
    "vocab_file": {
        "albert-base-v1": "https://huggingface.co/albert-base-v1/resolve/main/spiece.model",
        "albert-large-v1": "https://huggingface.co/albert-large-v1/resolve/main/spiece.model",
        "albert-xlarge-v1": "https://huggingface.co/albert-xlarge-v1/resolve/main/spiece.model",
        "albert-xxlarge-v1": "https://huggingface.co/albert-xxlarge-v1/resolve/main/spiece.model",
        "albert-base-v2": "https://huggingface.co/albert-base-v2/resolve/main/spiece.model",
        "albert-large-v2": "https://huggingface.co/albert-large-v2/resolve/main/spiece.model",
        "albert-xlarge-v2": "https://huggingface.co/albert-xlarge-v2/resolve/main/spiece.model",
        "albert-xxlarge-v2": "https://huggingface.co/albert-xxlarge-v2/resolve/main/spiece.model",
    }
}

lowercase__	:int										=					{
    "albert-base-v1": 512,
    "albert-large-v1": 512,
    "albert-xlarge-v1": 512,
    "albert-xxlarge-v1": 512,
    "albert-base-v2": 512,
    "albert-large-v2": 512,
    "albert-xlarge-v2": 512,
    "albert-xxlarge-v2": 512,
}

lowercase__	:Optional[Any]										=					"▁"







class        lowercase	(      SCREAMING_SNAKE_CASE__	):
							lowercase_ : Optional[int]       =VOCAB_FILES_NAMES
							lowercase_ : List[str]       =PRETRAINED_VOCAB_FILES_MAP
							lowercase_ : Optional[Any]       =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES




							def __init__(       self     ,A__     ,A__=True     ,A__=True     ,A__=False     ,A__="[CLS]"     ,A__="[SEP]"     ,A__="<unk>"     ,A__="[SEP]"     ,A__="<pad>"     ,A__="[CLS]"     ,A__="[MASK]"     ,A__ = None     ,**A__     ,):
												# Mask token behave like a normal word, i.e. include the space before it and
												# is included in the raw text, there should be a match in a non-normalized sentence.
												lowercase					=		(
												    AddedToken(A__     ,lstrip=A__     ,rstrip=A__     ,normalized=A__)
												    if isinstance(A__     ,A__)
												    else mask_token
												)

												lowercase					=		{} if sp_model_kwargs is None else sp_model_kwargs

												super().__init__(
												    do_lower_case=A__     ,remove_space=A__     ,keep_accents=A__     ,bos_token=A__     ,eos_token=A__     ,unk_token=A__     ,sep_token=A__     ,pad_token=A__     ,cls_token=A__     ,mask_token=A__     ,sp_model_kwargs=self.sp_model_kwargs     ,**A__     ,)

												lowercase					=		do_lower_case
												lowercase					=		remove_space
												lowercase					=		keep_accents
												lowercase					=		vocab_file

												lowercase					=		spm.SentencePieceProcessor(**self.sp_model_kwargs)
												self.sp_model.Load(A__)




							@property
							def 				A__   (       self):
												return len(self.sp_model)




							def 				A__   (       self):
												lowercase					=		{self.convert_ids_to_tokens(A__): i for i in range(self.vocab_size)}
												vocab.update(self.added_tokens_encoder)
												return vocab




							def __getstate__(       self):
												lowercase					=		self.__dict__.copy()
												lowercase					=		None
												return state




							def __setstate__(       self     ,A__):
												lowercase					=		d

												# for backward compatibility
												if not hasattr(self     ,'''sp_model_kwargs'''):
																	lowercase					=		{}

												lowercase					=		spm.SentencePieceProcessor(**self.sp_model_kwargs)
												self.sp_model.Load(self.vocab_file)




							def 				A__   (       self     ,A__):
												if self.remove_space:
																	lowercase					=		''' '''.join(inputs.strip().split())
												else:
																	lowercase					=		inputs
												lowercase					=		outputs.replace('''``'''     ,'''"''').replace('''\'\''''     ,'''"''')

												if not self.keep_accents:
																	lowercase					=		unicodedata.normalize('''NFKD'''     ,A__)
																	lowercase					=		''''''.join([c for c in outputs if not unicodedata.combining(A__)])
												if self.do_lower_case:
																	lowercase					=		outputs.lower()

												return outputs




							def 				A__   (       self     ,A__):
												lowercase					=		self.preprocess_text(A__)
												lowercase					=		self.sp_model.encode(A__     ,out_type=A__)
												lowercase					=		[]
												for piece in pieces:
																	if len(A__) > 1 and piece[-1] == str(''',''') and piece[-2].isdigit():
																						lowercase					=		self.sp_model.EncodeAsPieces(piece[:-1].replace(A__     ,''''''))
																						if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
																											if len(cur_pieces[0]) == 1:
																																lowercase					=		cur_pieces[1:]
																											else:
																																lowercase					=		cur_pieces[0][1:]
																						cur_pieces.append(piece[-1])
																						new_pieces.extend(A__)
																	else:
																						new_pieces.append(A__)

												return new_pieces




							def 				A__   (       self     ,A__):
												return self.sp_model.PieceToId(A__)




							def 				A__   (       self     ,A__):
												return self.sp_model.IdToPiece(A__)




							def 				A__   (       self     ,A__):
												lowercase					=		[]
												lowercase					=		''''''
												lowercase					=		False
												for token in tokens:
																	# make sure that special tokens are not decoded using sentencepiece model
																	if token in self.all_special_tokens:
																						if not prev_is_special:
																											out_string += " "
																						out_string += self.sp_model.decode(A__) + token
																						lowercase					=		True
																						lowercase					=		[]
																	else:
																						current_sub_tokens.append(A__)
																						lowercase					=		False
												out_string += self.sp_model.decode(A__)
												return out_string.strip()




							def 				A__   (       self     ,A__     ,A__ = None):
												lowercase					=		[self.sep_token_id]
												lowercase					=		[self.cls_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 				A__   (       self     ,A__     ,A__ = None     ,A__ = False):

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

												if token_ids_a is not None:
																	return [1] + ([0] * len(A__)) + [1] + ([0] * len(A__)) + [1]
												return [1] + ([0] * len(A__)) + [1]




							def 				A__   (       self     ,A__     ,A__ = None):
												lowercase					=		[self.sep_token_id]
												lowercase					=		[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 				A__   (       self     ,A__     ,A__ = None):
												if not os.path.isdir(A__):
																	logger.error(f'Vocabulary path ({save_directory}) should be a directory')
																	return
												lowercase					=		os.path.join(
												    A__     ,(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''])

												if os.path.abspath(self.vocab_file) != os.path.abspath(A__) and os.path.isfile(self.vocab_file):
																	copyfile(self.vocab_file     ,A__)
												elif not os.path.isfile(self.vocab_file):
																	with open(A__     ,'''wb''') as fi:
																						lowercase					=		self.sp_model.serialized_model_proto()
																						fi.write(A__)

												return (out_vocab_file,)






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



def  UpperCamelCase					(							lowerCAmelCase__	):
					'''simple docstring'''
					if len(lowerCAmelCase__	) < MIN_NUM_TOKENS:
										return None
					lowercase					=		MinHash(num_perm=lowerCAmelCase__	)
					for token in set(lowerCAmelCase__	):
										min_hash.update(token.encode()	)
					return min_hash



def  UpperCamelCase					(							lowerCAmelCase__	):
					'''simple docstring'''
					return {t for t in NON_ALPHA.split(lowerCAmelCase__	) if len(t.strip()	) > 0}







class        lowercase	:




							def __init__(       self     ,*,
							    A__ = 0.85     ,):
												lowercase					=		duplication_jaccard_threshold
												lowercase					=		NUM_PERM
												lowercase					=		MinHashLSH(threshold=self._duplication_jaccard_threshold     ,num_perm=self._num_perm)

												lowercase					=		defaultdict(A__)




							def 				A__   (       self     ,A__     ,A__):
												lowercase					=		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):
												lowercase					=		[]
												for base, duplicates in self._duplicate_clusters.items():
																	lowercase					=		[base] + list(A__)
																	# reformat the cluster to be a list of dict
																	lowercase					=		[{'''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__):
												lowercase					=		self.get_duplicate_clusters()
												with open(A__     ,'''w''') as f:
																	json.dump(A__     ,A__)



def  UpperCamelCase					(							lowerCAmelCase__	):
					'''simple docstring'''
					lowercase			,		lowercase					=		element
					lowercase					=		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					(							lowerCAmelCase__	):
					'''simple docstring'''
					with mp.Pool() as pool:
										for data in pool.imap_unordered(
										    _compute_min_hash				,    ThreadedIterator(lowerCAmelCase__				,    max_queue_size=1_0000	)				,    chunksize=100				,    ):
															if data is not None:
																				yield data



def  UpperCamelCase					(							lowerCAmelCase__				,    lowerCAmelCase__	):
					'''simple docstring'''
					lowercase					=		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  UpperCamelCase					(							lowerCAmelCase__				,    lowerCAmelCase__	):
					'''simple docstring'''
					lowercase					=		get_tokens(lowerCAmelCase__	)
					lowercase					=		get_tokens(lowerCAmelCase__	)
					return len(tokensa & tokensa	) / len(tokensa | tokensa	)


lowercase__	:List[Any]										=					None



def  UpperCamelCase					(							lowerCAmelCase__				,    lowerCAmelCase__	):
					'''simple docstring'''
					lowercase					=		[]
					for elementa in cluster:
										lowercase					=		_shared_dataset[elementa['''base_index''']]['''content''']
										for elementa in extremes:
															lowercase					=		_shared_dataset[elementa['''base_index''']]['''content''']
															if jaccard_similarity(lowerCAmelCase__				,    lowerCAmelCase__	) >= jaccard_threshold:
																				elementa["copies"] += 1
																				break
										else:
															lowercase					=		1
															extremes.append(lowerCAmelCase__	)
					return extremes



def  UpperCamelCase					(							lowerCAmelCase__				,    lowerCAmelCase__				,    lowerCAmelCase__	):
					'''simple docstring'''
					global _shared_dataset
					lowercase					=		dataset
					lowercase					=		[]
					lowercase					=		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  UpperCamelCase					(							lowerCAmelCase__				,    lowerCAmelCase__ = 0.85	):
					'''simple docstring'''
					lowercase					=		make_duplicate_clusters(lowerCAmelCase__				,    lowerCAmelCase__	)
					lowercase					=		{x['''base_index'''] for cluster in duplicate_clusters for x in cluster}
					lowercase					=		{}
					lowercase					=		find_extremes(lowerCAmelCase__				,    lowerCAmelCase__				,    lowerCAmelCase__	)
					for extremes in extremes_clusters:
										for element in extremes:
															lowercase					=		element
					lowercase					=		duplicate_indices - set(extreme_dict.keys()	)
					lowercase					=		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:
															lowercase					=		element['''base_index'''] in extreme_dict
															if element["is_extreme"]:
																				lowercase					=		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




