






"""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_							,						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"""



# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import argparse
import os

from accelerate.test_utils import execute_subprocess_async

def 			_snake_case    (					lowerCamelCase__     :   int=None  )    ->     Union[str, Any]:
							if subparsers is not None:
														lowerCamelCase_	:							List[Any]									=subparsers.add_parser("test"  )
							else:
														lowerCamelCase_	:							List[str]									=argparse.ArgumentParser("Accelerate test command"  )

							parser.add_argument(
							    "--config_file"    ,						default=lowerCamelCase__    ,						help=(
							        "The path to use to store the config file. Will default to a file named default_config.yaml in the cache "
							        "location, which is the content of the environment `HF_HOME` suffixed with 'accelerate', or if you don't have "
							        "such an environment variable, your cache directory ('~/.cache' or the content of `XDG_CACHE_HOME`) suffixed "
							        "with 'huggingface'."
							    )    ,						)

							if subparsers is not None:
														parser.set_defaults(func=lowerCamelCase__  )
							return parser

def 			_snake_case    (					lowerCamelCase__     :   List[Any]  )    ->     Any:
							lowerCamelCase_	:							Optional[Any]									=os.path.sep.join(__file__.split(os.path.sep  )[:-2] + ["test_utils", "scripts", "test_script.py"]  )

							if args.config_file is None:
														lowerCamelCase_	:							List[Any]									=script_name
							else:
														lowerCamelCase_	:							Union[str, Any]									=F"""--config_file={args.config_file} {script_name}"""

							lowerCamelCase_	:							List[str]									=["accelerate-launch"] + test_args.split()
							lowerCamelCase_	:							Tuple									=execute_subprocess_async(lowerCamelCase__    ,						env=os.environ.copy()  )
							if result.returncode == 0:
														print("Test is a success! You are ready for your distributed training!"  )

def 			_snake_case    (					)    ->     Tuple:
							lowerCamelCase_	:							Any									=test_command_parser()
							lowerCamelCase_	:							List[Any]									=parser.parse_args()
							test_command(lowerCamelCase__  )


if __name__ == "__main__":
						main()



