


"""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__				:Any			   =	re.compile("""[^A-Za-z_0-9]""")
# parameters used in DuplicationIndex
UpperCamelCase__				:int			   =	10
UpperCamelCase__				:Dict			   =	256





def 				A_       ( snake_case__		)					->  Optional[MinHash]:

			if len(lowercase_		) < MIN_NUM_TOKENS:
						return None
			_UpperCamelCase					:Dict					     =	MinHash(num_perm=lowercase_		)
			for token in set(lowercase_		):
						min_hash.update(token.encode()		)
			return min_hash





def 				A_       ( snake_case__		)					->  Set[str]:
			return {t for t in NON_ALPHA.split(lowercase_		) if len(t.strip()		) > 0}






class      A:

		"""simple docstring"""





		def __init__( self					,				*,
		    SCREAMING_SNAKE_CASE__ = 0.8_5					,				)      ->	int:

					"""simple docstring"""



					_UpperCamelCase					:List[Any]					     =	duplication_jaccard_threshold
					_UpperCamelCase					:List[str]					     =	NUM_PERM
					_UpperCamelCase					:int					     =	MinHashLSH(threshold=self._duplication_jaccard_threshold					,				num_perm=self._num_perm   )

					_UpperCamelCase					:Optional[int]					     =	defaultdict(UpperCAmelCase__   )
		def   _UpperCamelCase( self					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__   )      ->	None:

					"""simple docstring"""



					_UpperCamelCase					:List[Any]					     =	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   _UpperCamelCase( self   )      ->	List[List[Dict]]:

					"""simple docstring"""



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





		def   _UpperCamelCase( self					,				SCREAMING_SNAKE_CASE__   )      ->	None:

					"""simple docstring"""



					_UpperCamelCase					:int					     =	self.get_duplicate_clusters()
					with open(UpperCAmelCase__					,				'''w'''   ) as f:
								json.dump(UpperCAmelCase__					,				UpperCAmelCase__   )





def 				A_       ( snake_case__		)					->  Optional[Any]:
			_UpperCamelCase     ,      _UpperCamelCase					:List[str]					     =	element
			_UpperCamelCase					:Optional[int]					     =	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 				A_       ( snake_case__		)					->  List[Any]:

			with mp.Pool() as pool:
						for data in pool.imap_unordered(
						    _compute_min_hash       ,					ThreadedIterator(lowercase_       ,					max_queue_size=1_00_00		)       ,					chunksize=1_00       ,					):
									if data is not None:
												yield data





def 				A_       ( snake_case__       ,					snake_case__		)					->  Dict:
			_UpperCamelCase					:List[str]					     =	DuplicationIndex(duplication_jaccard_threshold=lowercase_		)

			for filename, min_hash in tqdm(ThreadedIterator(minhash_iter(enumerate(lowercase_		)		)       ,					max_queue_size=1_00		)		):
						di.add(lowercase_       ,					lowercase_		)

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





def 				A_       ( snake_case__       ,					snake_case__		)					->  float:
			_UpperCamelCase					:Optional[Any]					     =	get_tokens(lowercase_		)
			_UpperCamelCase					:Optional[Any]					     =	get_tokens(lowercase_		)
			return len(tokensa & tokensa		) / len(tokensa | tokensa		)


UpperCamelCase__				:Any			   =	None





def 				A_       ( snake_case__       ,					snake_case__		)					->  Optional[int]:
			_UpperCamelCase					:Tuple					     =	[]
			for elementa in cluster:
						_UpperCamelCase					:Union[str, Any]					     =	_shared_dataset[elementa['''base_index''']]['''content''']
						for elementa in extremes:
									_UpperCamelCase					:Optional[Any]					     =	_shared_dataset[elementa['''base_index''']]['''content''']
									if jaccard_similarity(lowercase_       ,					lowercase_		) >= jaccard_threshold:
												elementa["copies"] += 1
												break
						else:
									_UpperCamelCase					:Any					     =	1
									extremes.append(lowercase_		)
			return extremes





def 				A_       ( snake_case__       ,					snake_case__       ,					snake_case__		)					->  List[str]:
			global _shared_dataset
			_UpperCamelCase					:int					     =	dataset
			_UpperCamelCase					:Optional[Any]					     =	[]
			_UpperCamelCase					:List[Any]					     =	partial(_find_cluster_extremes_shared       ,					jaccard_threshold=lowercase_		)
			with mp.Pool() as pool:
						for extremes in tqdm(
						    pool.imap_unordered(
						        lowercase_       ,					lowercase_       ,					)       ,					total=len(lowercase_		)       ,					):
									extremes_list.append(lowercase_		)
			return extremes_list





def 				A_       ( snake_case__       ,					snake_case__ = 0.85		)					->  Tuple[Type[Dataset], List[List[Dict]]]:
			_UpperCamelCase					:List[Any]					     =	make_duplicate_clusters(lowercase_       ,					lowercase_		)
			_UpperCamelCase					:Tuple					     =	{x['''base_index'''] for cluster in duplicate_clusters for x in cluster}
			_UpperCamelCase					:int					     =	{}
			_UpperCamelCase					:List[str]					     =	find_extremes(lowercase_       ,					lowercase_       ,					lowercase_		)
			for extremes in extremes_clusters:
						for element in extremes:
									_UpperCamelCase					:List[str]					     =	element
			_UpperCamelCase					:Tuple					     =	duplicate_indices - set(extreme_dict.keys()		)
			_UpperCamelCase					:Union[str, Any]					     =	dataset.filter(lambda snake_case__       ,					snake_case__		: idx not in remove_indices       ,					with_indices=lowercase_		)

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

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

			return ds_filter, duplicate_clusters







# 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.utils import ComputeEnvironment

from .cluster import get_cluster_input
from .config_args import cache_dir, default_config_file, default_yaml_config_file, load_config_from_file  # noqa: F401
from .config_utils import _ask_field, _ask_options, _convert_compute_environment  # noqa: F401
from .sagemaker import get_sagemaker_input


_lowerCamelCase      :						Optional[Any]							=  """Launches a series of prompts to create and save a `default_config.yaml` configuration file for your training system. Should always be ran first on your machine"""







def SCREAMING_SNAKE_CASE							(			)  ->			Dict:



       """simple docstring"""

       A__ 					=       _ask_options(
           '''In which compute environment are you running?'''					,							['''This machine''', '''AWS (Amazon SageMaker)''']					,							_convert_compute_environment					,							)
       if compute_environment == ComputeEnvironment.AMAZON_SAGEMAKER:
              A__ 					=       get_sagemaker_input()
       else:
              A__ 					=       get_cluster_input()
       return config







def SCREAMING_SNAKE_CASE							(			lowercase_=None		)  ->			List[Any]:



       """simple docstring"""

       if subparsers is not None:
              A__ 					=       subparsers.add_parser('''config'''					,							description=lowercase_		)
       else:
              A__ 					=       argparse.ArgumentParser('''Accelerate config command'''					,							description=lowercase_		)

       parser.add_argument(
           '''--config_file'''					,							default=lowercase_					,							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=lowercase_		)
       return parser







def SCREAMING_SNAKE_CASE							(			lowercase_		)  ->			Any:



       """simple docstring"""

       A__ 					=       get_user_input()
       if args.config_file is not None:
              A__ 					=       args.config_file
       else:
              if not os.path.isdir(lowercase_		):
                     os.makedirs(lowercase_		)
              A__ 					=       default_yaml_config_file

       if config_file.endswith('''.json'''		):
              config.to_json_file(lowercase_		)
       else:
              config.to_yaml_file(lowercase_		)
       print(f"""accelerate configuration saved at {config_file}"""		)







def SCREAMING_SNAKE_CASE							(			)  ->			Union[str, Any]:



       """simple docstring"""

       A__ 					=       config_command_parser()
       A__ 					=       parser.parse_args()
       config_command(lowercase_		)


if __name__ == "__main__":
  main()


