


'''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


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






def        __lowerCAmelCase       (__lowerCAmelCase      ):
		if len(__lowerCAmelCase      ) < MIN_NUM_TOKENS:
				return None
		_UpperCAmelCase						:				str	    =					MinHash(num_perm=__lowerCAmelCase      )
		for token in set(__lowerCAmelCase      ):
				min_hash.update(token.encode()      )
		return min_hash






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






class 		lowerCAmelCase__	:




			def __init__(						self     :   Tuple		,     *,
			    lowerCamelCase__     :   float = 0.8_5		,     )      ->Tuple:



					'''simple docstring'''







					_UpperCAmelCase						:				Tuple	    =					duplication_jaccard_threshold
					_UpperCAmelCase						:				Tuple	    =					NUM_PERM
					_UpperCAmelCase						:				str	    =					MinHashLSH(threshold=self._duplication_jaccard_threshold		,     num_perm=self._num_perm       )

					_UpperCAmelCase						:				List[Any]	    =					defaultdict(lowerCamelCase__       )




			def 							lowerCAmelCase__			(						self     :   Tuple		,     lowerCamelCase__     :   Tuple		,     lowerCamelCase__     :   MinHash       )      ->None:



					'''simple docstring'''







					_UpperCAmelCase						:				int	    =					self._index.query(lowerCamelCase__       )
					if code_key in self._index.keys:
							print(F"""Duplicate key {code_key}"""       )
							return

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




			def 							lowerCAmelCase__			(						self     :   str       )      ->List[List[Dict]]:



					'''simple docstring'''







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






			def 							lowerCAmelCase__			(						self     :   List[str]		,     lowerCamelCase__     :   Optional[Any]       )      ->None:



					'''simple docstring'''







					_UpperCAmelCase						:				Optional[Any]	    =					self.get_duplicate_clusters()
					with open(lowerCamelCase__		,     "w"       ) as f:
							json.dump(lowerCamelCase__		,     lowerCamelCase__       )






def        __lowerCAmelCase       (__lowerCAmelCase      ):
		_UpperCAmelCase    ,			_UpperCAmelCase						:				str	    =					element
		_UpperCAmelCase						:				Union[str, Any]	    =					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       (__lowerCAmelCase      ):
		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        __lowerCAmelCase       (__lowerCAmelCase					,       __lowerCAmelCase      ):
		_UpperCAmelCase						:				Dict	    =					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        __lowerCAmelCase       (__lowerCAmelCase					,       __lowerCAmelCase      ):
		_UpperCAmelCase						:				Any	    =					get_tokens(__lowerCAmelCase      )
		_UpperCAmelCase						:				Union[str, Any]	    =					get_tokens(__lowerCAmelCase      )
		return len(tokensa & tokensa      ) / len(tokensa | tokensa      )


lowerCamelCase__					=   None






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






def        __lowerCAmelCase       (__lowerCAmelCase					,       __lowerCAmelCase					,       __lowerCAmelCase      ):
		global _shared_dataset
		_UpperCAmelCase						:				List[str]	    =					dataset
		_UpperCAmelCase						:				Optional[int]	    =					[]
		_UpperCAmelCase						:				int	    =					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        __lowerCAmelCase       (__lowerCAmelCase					,       __lowerCAmelCase = 0.8_5      ):
		_UpperCAmelCase						:				List[Any]	    =					make_duplicate_clusters(__lowerCAmelCase					,       __lowerCAmelCase      )
		_UpperCAmelCase						:				int	    =					{x["base_index"] for cluster in duplicate_clusters for x in cluster}
		_UpperCAmelCase						:				Dict	    =					{}
		_UpperCAmelCase						:				int	    =					find_extremes(__lowerCAmelCase					,       __lowerCAmelCase					,       __lowerCAmelCase      )
		for extremes in extremes_clusters:
				for element in extremes:
						_UpperCAmelCase						:				Optional[int]	    =					element
		_UpperCAmelCase						:				str	    =					duplicate_indices - set(extreme_dict.keys()      )
		_UpperCAmelCase						:				Tuple	    =					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:
						_UpperCAmelCase						:				Tuple	    =					element["base_index"] in extreme_dict
						if element["is_extreme"]:
								_UpperCAmelCase						:				str	    =					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'''


import pytest

from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.text import TextDatasetReader

from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases






def        __lowerCAmelCase       (__lowerCAmelCase					,       __lowerCAmelCase      ):
		assert isinstance(__lowerCAmelCase					,       __lowerCAmelCase      )
		assert dataset.num_rows == 4
		assert dataset.num_columns == 1
		assert dataset.column_names == ["text"]
		for feature, expected_dtype in expected_features.items():
				assert dataset.features[feature].dtype == expected_dtype






@pytest.mark.parametrize("keep_in_memory"					,       [False, True]      )
def        __lowerCAmelCase       (__lowerCAmelCase					,       __lowerCAmelCase					,       __lowerCAmelCase      ):
		_UpperCAmelCase						:				Union[str, Any]	    =					tmp_path / "cache"
		_UpperCAmelCase						:				int	    =					{"text": "string"}
		with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
				_UpperCAmelCase						:				Union[str, Any]	    =					TextDatasetReader(__lowerCAmelCase					,       cache_dir=__lowerCAmelCase					,       keep_in_memory=__lowerCAmelCase      ).read()
		_check_text_dataset(__lowerCAmelCase					,       __lowerCAmelCase      )






@pytest.mark.parametrize(
    "features"					,       [
        None,
        {"text": "string"},
        {"text": "int32"},
        {"text": "float32"},
    ]					,       )
def        __lowerCAmelCase       (__lowerCAmelCase					,       __lowerCAmelCase					,       __lowerCAmelCase      ):
		_UpperCAmelCase						:				List[Any]	    =					tmp_path / "cache"
		_UpperCAmelCase						:				Any	    =					{"text": "string"}
		_UpperCAmelCase						:				Optional[Any]	    =					features.copy() if features else default_expected_features
		_UpperCAmelCase						:				Union[str, Any]	    =					(
		    Features({feature: Value(__lowerCAmelCase      ) for feature, dtype in features.items()}      ) if features is not None else None
		)
		_UpperCAmelCase						:				Union[str, Any]	    =					TextDatasetReader(__lowerCAmelCase					,       features=__lowerCAmelCase					,       cache_dir=__lowerCAmelCase      ).read()
		_check_text_dataset(__lowerCAmelCase					,       __lowerCAmelCase      )






@pytest.mark.parametrize("split"					,       [None, NamedSplit("train"      ), "train", "test"]      )
def        __lowerCAmelCase       (__lowerCAmelCase					,       __lowerCAmelCase					,       __lowerCAmelCase      ):
		_UpperCAmelCase						:				Any	    =					tmp_path / "cache"
		_UpperCAmelCase						:				Dict	    =					{"text": "string"}
		_UpperCAmelCase						:				Union[str, Any]	    =					TextDatasetReader(__lowerCAmelCase					,       cache_dir=__lowerCAmelCase					,       split=__lowerCAmelCase      ).read()
		_check_text_dataset(__lowerCAmelCase					,       __lowerCAmelCase      )
		assert dataset.split == split if split else "train"






@pytest.mark.parametrize("path_type"					,       [str, list]      )
def        __lowerCAmelCase       (__lowerCAmelCase					,       __lowerCAmelCase					,       __lowerCAmelCase      ):
		if issubclass(__lowerCAmelCase					,       __lowerCAmelCase      ):
				_UpperCAmelCase						:				Union[str, Any]	    =					text_path
		elif issubclass(__lowerCAmelCase					,       __lowerCAmelCase      ):
				_UpperCAmelCase						:				int	    =					[text_path]
		_UpperCAmelCase						:				List[Any]	    =					tmp_path / "cache"
		_UpperCAmelCase						:				Union[str, Any]	    =					{"text": "string"}
		_UpperCAmelCase						:				Optional[int]	    =					TextDatasetReader(__lowerCAmelCase					,       cache_dir=__lowerCAmelCase      ).read()
		_check_text_dataset(__lowerCAmelCase					,       __lowerCAmelCase      )






def        __lowerCAmelCase       (__lowerCAmelCase					,       __lowerCAmelCase					,       __lowerCAmelCase=("train",)      ):
		assert isinstance(__lowerCAmelCase					,       __lowerCAmelCase      )
		for split in splits:
				_UpperCAmelCase						:				List[str]	    =					dataset_dict[split]
				assert dataset.num_rows == 4
				assert dataset.num_columns == 1
				assert dataset.column_names == ["text"]
				for feature, expected_dtype in expected_features.items():
						assert dataset.features[feature].dtype == expected_dtype






@pytest.mark.parametrize("keep_in_memory"					,       [False, True]      )
def        __lowerCAmelCase       (__lowerCAmelCase					,       __lowerCAmelCase					,       __lowerCAmelCase      ):
		_UpperCAmelCase						:				List[Any]	    =					tmp_path / "cache"
		_UpperCAmelCase						:				Tuple	    =					{"text": "string"}
		with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
				_UpperCAmelCase						:				Any	    =					TextDatasetReader({"train": text_path}					,       cache_dir=__lowerCAmelCase					,       keep_in_memory=__lowerCAmelCase      ).read()
		_check_text_datasetdict(__lowerCAmelCase					,       __lowerCAmelCase      )






@pytest.mark.parametrize(
    "features"					,       [
        None,
        {"text": "string"},
        {"text": "int32"},
        {"text": "float32"},
    ]					,       )
def        __lowerCAmelCase       (__lowerCAmelCase					,       __lowerCAmelCase					,       __lowerCAmelCase      ):
		_UpperCAmelCase						:				List[Any]	    =					tmp_path / "cache"
		# CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
		_UpperCAmelCase						:				List[Any]	    =					{"text": "string"}
		_UpperCAmelCase						:				List[str]	    =					features.copy() if features else default_expected_features
		_UpperCAmelCase						:				Optional[int]	    =					(
		    Features({feature: Value(__lowerCAmelCase      ) for feature, dtype in features.items()}      ) if features is not None else None
		)
		_UpperCAmelCase						:				Tuple	    =					TextDatasetReader({"train": text_path}					,       features=__lowerCAmelCase					,       cache_dir=__lowerCAmelCase      ).read()
		_check_text_datasetdict(__lowerCAmelCase					,       __lowerCAmelCase      )






@pytest.mark.parametrize("split"					,       [None, NamedSplit("train"      ), "train", "test"]      )
def        __lowerCAmelCase       (__lowerCAmelCase					,       __lowerCAmelCase					,       __lowerCAmelCase      ):
		if split:
				_UpperCAmelCase						:				int	    =					{split: text_path}
		else:
				_UpperCAmelCase						:				Tuple	    =					"train"
				_UpperCAmelCase						:				List[str]	    =					{"train": text_path, "test": text_path}
		_UpperCAmelCase						:				Optional[Any]	    =					tmp_path / "cache"
		_UpperCAmelCase						:				Optional[int]	    =					{"text": "string"}
		_UpperCAmelCase						:				int	    =					TextDatasetReader(__lowerCAmelCase					,       cache_dir=__lowerCAmelCase      ).read()
		_check_text_datasetdict(__lowerCAmelCase					,       __lowerCAmelCase					,       splits=list(path.keys()      )      )
		assert all(dataset[split].split == split for split in path.keys()      )



