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


def       lowercase_      (      _lowerCamelCase:		Union[str, Any]					)       ->					List[str]:



							'''simple docstring'''

							if len(_lowerCamelCase					) < MIN_NUM_TOKENS:
														return None
							__lowerCamelCase    :						Optional[Any]						      =   MinHash(num_perm=_lowerCamelCase					)
							for token in set(_lowerCamelCase					):
														min_hash.update(token.encode()					)
							return min_hash


def       lowercase_      (      _lowerCamelCase:		str					)       ->					Any:



							'''simple docstring'''

							return {t for t in NON_ALPHA.split(_lowerCamelCase					) if len(t.strip()					) > 0}
class        _snake_case   :






						def __init__(				self					:	Any							,    *,
						    UpperCAmelCase					:	float = 0.8_5							,    ):
													__lowerCamelCase    :						int						      =   duplication_jaccard_threshold
													__lowerCamelCase    :						Tuple						      =   NUM_PERM
													__lowerCamelCase    :						str						      =   MinHashLSH(threshold=self._duplication_jaccard_threshold							,    num_perm=self._num_perm			)

													__lowerCamelCase    :						Optional[int]						      =   defaultdict(lowerCamelCase__			)






						def 					lowerCamelCase__   (				self					:	List[Any]							,    UpperCAmelCase					:	Tuple							,    UpperCAmelCase					:	MinHash			):
													__lowerCamelCase    :						Tuple						      =   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					:	List[Any]			):
													__lowerCamelCase    :						int						      =   []
													for base, duplicates in self._duplicate_clusters.items():
																				__lowerCamelCase    :						Optional[Any]						      =   [base] + list(lowerCamelCase__			)
																				# reformat the cluster to be a list of dict
																				__lowerCamelCase    :						List[Any]						      =   [{"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					:	Dict							,    UpperCAmelCase					:	Optional[int]			):
													__lowerCamelCase    :						Tuple						      =   self.get_duplicate_clusters()
													with open(lowerCamelCase__							,    "w"			) as f:
																				json.dump(lowerCamelCase__							,    lowerCamelCase__			)


def       lowercase_      (      _lowerCamelCase:		List[str]					)       ->					Optional[Any]:



							'''simple docstring'''

							__lowerCamelCase      ,     __lowerCamelCase    :						List[str]						      =   element
							__lowerCamelCase    :						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       lowercase_      (      _lowerCamelCase:		List[Any]					)       ->					int:



							'''simple docstring'''

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


def       lowercase_      (      _lowerCamelCase:		List[str]  ,      _lowerCamelCase:		List[Any]					)       ->					List[Any]:



							'''simple docstring'''

							__lowerCamelCase    :						str						      =   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       lowercase_      (      _lowerCamelCase:		str  ,      _lowerCamelCase:		Tuple					)       ->					Optional[Any]:



							'''simple docstring'''

							__lowerCamelCase    :						Union[str, Any]						      =   get_tokens(_lowerCamelCase					)
							__lowerCamelCase    :						Union[str, Any]						      =   get_tokens(_lowerCamelCase					)
							return len(tokensa & tokensa					) / len(tokensa | tokensa					)


__A     = None


def       lowercase_      (      _lowerCamelCase:		Dict  ,      _lowerCamelCase:		Dict					)       ->					Any:



							'''simple docstring'''

							__lowerCamelCase    :						Tuple						      =   []
							for elementa in cluster:
														__lowerCamelCase    :						List[str]						      =   _shared_dataset[elementa["base_index"]]["content"]
														for elementa in extremes:
																					__lowerCamelCase    :						List[Any]						      =   _shared_dataset[elementa["base_index"]]["content"]
																					if jaccard_similarity(_lowerCamelCase  ,      _lowerCamelCase					) >= jaccard_threshold:
																												elementa["copies"] += 1
																												break
														else:
																					__lowerCamelCase    :						int						      =   1
																					extremes.append(_lowerCamelCase					)
							return extremes


def       lowercase_      (      _lowerCamelCase:		List[Any]  ,      _lowerCamelCase:		Dict  ,      _lowerCamelCase:		List[Any]					)       ->					List[Any]:



							'''simple docstring'''

							global _shared_dataset
							__lowerCamelCase    :						List[str]						      =   dataset
							__lowerCamelCase    :						int						      =   []
							__lowerCamelCase    :						List[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       lowercase_      (      _lowerCamelCase:		Union[str, Any]  ,      _lowerCamelCase:		Optional[Any] = 0.85					)       ->					Any:



							'''simple docstring'''

							__lowerCamelCase    :						Any						      =   make_duplicate_clusters(_lowerCamelCase  ,      _lowerCamelCase					)
							__lowerCamelCase    :						str						      =   {x["base_index"] for cluster in duplicate_clusters for x in cluster}
							__lowerCamelCase    :						Dict						      =   {}
							__lowerCamelCase    :						str						      =   find_extremes(_lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase					)
							for extremes in extremes_clusters:
														for element in extremes:
																					__lowerCamelCase    :						Union[str, Any]						      =   element
							__lowerCamelCase    :						Any						      =   duplicate_indices - set(extreme_dict.keys()					)
							__lowerCamelCase    :						List[str]						      =   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    :						Any						      =   element["base_index"] in extreme_dict
																					if element["is_extreme"]:
																												__lowerCamelCase    :						Optional[Any]						      =   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"""






from typing import TYPE_CHECKING

from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowerCAmelCase__  :  str						     =	{
    'configuration_mctct': ['MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MCTCTConfig'],
    'feature_extraction_mctct': ['MCTCTFeatureExtractor'],
    'processing_mctct': ['MCTCTProcessor'],
}


try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCAmelCase__  :  Any						     =	[
						    'MCTCT_PRETRAINED_MODEL_ARCHIVE_LIST',
						    'MCTCTForCTC',
						    'MCTCTModel',
						    'MCTCTPreTrainedModel',
						]


if TYPE_CHECKING:
						from .configuration_mctct import MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP, MCTCTConfig
						from .feature_extraction_mctct import MCTCTFeatureExtractor
						from .processing_mctct import MCTCTProcessor

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_mctct import MCTCT_PRETRAINED_MODEL_ARCHIVE_LIST, MCTCTForCTC, MCTCTModel, MCTCTPreTrainedModel

else:
						import sys

						lowerCAmelCase__  :  Any						     =	_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



