



'''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


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


def 				snake_case_  (      _lowerCAmelCase						:		List[str]						)   ->     Optional[Any]:





							if len(SCREAMING_SNAKE_CASE__						) < MIN_NUM_TOKENS:
														return None
							UpperCAmelCase				: Union[str, Any]											=	MinHash(num_perm=SCREAMING_SNAKE_CASE__						)
							for token in set(SCREAMING_SNAKE_CASE__						):
														min_hash.update(token.encode()						)
							return min_hash


def 				snake_case_  (      _lowerCAmelCase						:		str						)   ->     Tuple:
							return {t for t in NON_ALPHA.split(SCREAMING_SNAKE_CASE__						) if len(t.strip()						) > 0}

class        SCREAMING_SNAKE_CASE:







	"""simple docstring"""






	def __init__(		self    :  List[str]	,    *,
	    __snake_case    :  int = 0.85	,    )    ->	str:
								UpperCAmelCase				: Tuple											=	duplication_jaccard_threshold
								UpperCAmelCase				: Optional[Any]											=	NUM_PERM
								UpperCAmelCase				: Tuple											=	MinHashLSH(threshold=self._duplication_jaccard_threshold	,    num_perm=self._num_perm	)

								UpperCAmelCase				: List[Any]											=	defaultdict(lowercase__	)







	def   A			(		self    :  Dict	,    __snake_case    :  Dict	,    __snake_case    :  Tuple	)    ->	str:
								UpperCAmelCase				: int											=	self._index.query(lowercase__	)
								if code_key in self._index.keys:
															print(F"""Duplicate key {code_key}"""	)
															return

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







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







	def   A			(		self    :  Dict	,    __snake_case    :  Any	)    ->	Dict:
								UpperCAmelCase				: int											=	self.get_duplicate_clusters()
								with open(lowercase__	,    '''w'''	) as f:
															json.dump(lowercase__	,    lowercase__	)


def 				snake_case_  (      _lowerCAmelCase						:		List[str]						)   ->     List[str]:
							UpperCAmelCase				: str											=	element
							UpperCAmelCase				: 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]						)   ->     Dict:





							with mp.Pool() as pool:
														for data in pool.imap_unordered(
														    _compute_min_hash     ,   ThreadedIterator(SCREAMING_SNAKE_CASE__     ,   max_queue_size=10000						)     ,   chunksize=100     ,   ):
																					if data is not None:
																												yield data


def 				snake_case_  (      _lowerCAmelCase						:		Type[Dataset]     ,   _lowerCAmelCase						:		float						)   ->     Optional[Any]:
							UpperCAmelCase				: int											=	DuplicationIndex(duplication_jaccard_threshold=SCREAMING_SNAKE_CASE__						)

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

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


def 				snake_case_  (      _lowerCAmelCase						:		str     ,   _lowerCAmelCase						:		str						)   ->     Union[str, Any]:
							UpperCAmelCase				: int											=	get_tokens(SCREAMING_SNAKE_CASE__						)
							UpperCAmelCase				: Tuple											=	get_tokens(SCREAMING_SNAKE_CASE__						)
							return len(tokensa & tokensa						) / len(tokensa | tokensa						)


UpperCamelCase__:     List[Any]										=    None


def 				snake_case_  (      _lowerCAmelCase						:		Tuple     ,   _lowerCAmelCase						:		List[str]						)   ->     Optional[Any]:
							UpperCAmelCase				: Optional[Any]											=	[]
							for elementa in cluster:
														UpperCAmelCase				: Union[str, Any]											=	_shared_dataset[elementa["""base_index"""]]["""content"""]
														for elementa in extremes:
																					UpperCAmelCase				: Any											=	_shared_dataset[elementa["""base_index"""]]["""content"""]
																					if jaccard_similarity(SCREAMING_SNAKE_CASE__     ,   SCREAMING_SNAKE_CASE__						) >= jaccard_threshold:
																												elementa["copies"] += 1
																												break
														else:
																					UpperCAmelCase				: Union[str, Any]											=	1
																					extremes.append(SCREAMING_SNAKE_CASE__						)
							return extremes


def 				snake_case_  (      _lowerCAmelCase						:		Optional[Any]     ,   _lowerCAmelCase						:		str     ,   _lowerCAmelCase						:		Union[str, Any]						)   ->     Any:
							global _shared_dataset
							UpperCAmelCase				: str											=	dataset
							UpperCAmelCase				: int											=	[]
							UpperCAmelCase				: Optional[int]											=	partial(_find_cluster_extremes_shared     ,   jaccard_threshold=SCREAMING_SNAKE_CASE__						)
							with mp.Pool() as pool:
														for extremes in tqdm(
														    pool.imap_unordered(
														        SCREAMING_SNAKE_CASE__     ,   SCREAMING_SNAKE_CASE__     ,   )     ,   total=len(SCREAMING_SNAKE_CASE__						)     ,   ):
																					extremes_list.append(SCREAMING_SNAKE_CASE__						)
							return extremes_list




def 				snake_case_  (      _lowerCAmelCase						:		Type[Dataset]     ,   _lowerCAmelCase						:		float = 0.8_5						)   ->     List[str]:
							UpperCAmelCase				: List[str]											=	make_duplicate_clusters(SCREAMING_SNAKE_CASE__     ,   SCREAMING_SNAKE_CASE__						)
							UpperCAmelCase				: str											=	{x["""base_index"""] for cluster in duplicate_clusters for x in cluster}
							UpperCAmelCase				: str											=	{}
							UpperCAmelCase				: Dict											=	find_extremes(SCREAMING_SNAKE_CASE__     ,   SCREAMING_SNAKE_CASE__     ,   SCREAMING_SNAKE_CASE__						)
							for extremes in extremes_clusters:
														for element in extremes:
																					UpperCAmelCase				: int											=	element
							UpperCAmelCase				: Optional[int]											=	duplicate_indices - set(extreme_dict.keys()						)
							UpperCAmelCase				: List[Any]											=	dataset.filter(lambda _lowerCAmelCase     ,   _lowerCAmelCase						: idx not in remove_indices     ,   with_indices=SCREAMING_SNAKE_CASE__						)

							# update duplicate_clusters
							for cluster in duplicate_clusters:
														for element in cluster:
																					UpperCAmelCase				: List[Any]											=	element["""base_index"""] in extreme_dict
																					if element["is_extreme"]:
																												UpperCAmelCase				: str											=	extreme_dict[element["""base_index"""]]["""copies"""]

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

							return ds_filter, duplicate_clusters










'''simple docstring'''






import tempfile
import unittest

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from transformers.testing_utils import (
    is_torch_available,
    require_optimum,
    require_torch,
    slow,
)


if is_torch_available():
						import torch

@require_torch
@require_optimum
@slow
class        SCREAMING_SNAKE_CASE(							unittest.TestCase    ):







	"""simple docstring"""






	def   A			(		self    :  Union[str, Any]	)    ->	Any:
								UpperCAmelCase				: Optional[int]											=	'''hf-internal-testing/tiny-random-t5'''
								UpperCAmelCase				: int											=	AutoTokenizer.from_pretrained(__snake_case	)
								UpperCAmelCase				: Tuple											=	AutoModelForSeqaSeqLM.from_pretrained(__snake_case	)

								UpperCAmelCase				: Optional[Any]											=	tokenizer('''This is me'''	,    return_tensors='''pt'''	)

								UpperCAmelCase				: Union[str, Any]											=	model.to_bettertransformer()

								self.assertTrue(any('''BetterTransformer''' in mod.__class__.__name__ for _, mod in model.named_modules()	)	)

								UpperCAmelCase				: Dict											=	model.generate(**__snake_case	)

								UpperCAmelCase				: Union[str, Any]											=	model.reverse_bettertransformer()

								self.assertFalse(any('''BetterTransformer''' in mod.__class__.__name__ for _, mod in model.named_modules()	)	)

								with tempfile.TemporaryDirectory() as tmpdirname:
															model.save_pretrained(__snake_case	)

															UpperCAmelCase				: Optional[int]											=	AutoModelForSeqaSeqLM.from_pretrained(__snake_case	)

															self.assertFalse(
															    any('''BetterTransformer''' in mod.__class__.__name__ for _, mod in model_reloaded.named_modules()	)	)

															UpperCAmelCase				: Dict											=	model_reloaded.generate(**__snake_case	)
															self.assertTrue(torch.allclose(__snake_case	,    __snake_case	)	)







	def   A			(		self    :  Optional[int]	)    ->	List[Any]:
								UpperCAmelCase				: Dict											=	'''hf-internal-testing/tiny-random-t5'''
								UpperCAmelCase				: Optional[int]											=	AutoModelForSeqaSeqLM.from_pretrained(__snake_case	)

								UpperCAmelCase				: List[Any]											=	model.to_bettertransformer()

								with tempfile.TemporaryDirectory() as tmpdirname:
															with self.assertRaises(__snake_case	):
																						model.save_pretrained(__snake_case	)

															UpperCAmelCase				: Any											=	model.reverse_bettertransformer()
															model.save_pretrained(__snake_case	)





