

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_       : Optional[int]         =			re.compile('[^A-Za-z_0-9]')
# parameters used in DuplicationIndex
A_       : Dict         =			10
A_       : Dict         =			256

def 						UpperCamelCase					(lowercase_: List[str]					)     ->      Optional[MinHash]:
	if len(lowercase_					) < MIN_NUM_TOKENS:
		return None
	A__							:    Union[str, Any]        =		MinHash(num_perm=lowercase_					)
	for token in set(lowercase_					):
		min_hash.update(token.encode()					)
	return min_hash

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



class     _a						:


					'''simple docstring'''






					def __init__( self    ,       *,
					    A__ = 0.8_5    ,       ):
						A__							:    Optional[int]        =		duplication_jaccard_threshold
						A__							:    Tuple        =		NUM_PERM
						A__							:    Tuple        =		MinHashLSH(threshold=self._duplication_jaccard_threshold    ,       num_perm=self._num_perm			)

						A__							:    str        =		defaultdict(A__			)






					def   __A			( self    ,       A__    ,       A__			):
						A__							:    List[Any]        =		self._index.query(A__			)
						if code_key in self._index.keys:
							print(F"""Duplicate key {code_key}"""			)
							return

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






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






					def   __A			( self    ,       A__			):
						A__							:    Union[str, Any]        =		self.get_duplicate_clusters()
						with open(A__    ,       """w"""			) as f:
							json.dump(A__    ,       A__			)



def 						UpperCamelCase					(lowercase_: int					)     ->      Tuple:
	A__   ,   A__							:    Dict        =		element
	A__							:    List[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 						UpperCamelCase					(lowercase_: Type[Dataset]					)     ->      List[Any]:
	with mp.Pool() as pool:
		for data in pool.imap_unordered(
		    _compute_min_hash    ,      ThreadedIterator(lowercase_    ,      max_queue_size=10000					)    ,      chunksize=100    ,      ):
			if data is not None:
				yield data

def 						UpperCamelCase					(lowercase_: Type[Dataset]    ,      lowercase_: float					)     ->      Optional[Any]:
	A__							:    List[str]        =		DuplicationIndex(duplication_jaccard_threshold=lowercase_					)

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

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

def 						UpperCamelCase					(lowercase_: str    ,      lowercase_: str					)     ->      float:
	A__							:    Any        =		get_tokens(lowercase_					)
	A__							:    Optional[int]        =		get_tokens(lowercase_					)
	return len(tokensa & tokensa					) / len(tokensa | tokensa					)


A_       : Optional[int]         =			None

def 						UpperCamelCase					(lowercase_: List[str]    ,      lowercase_: str					)     ->      List[str]:
	A__							:    Any        =		[]
	for elementa in cluster:
		A__							:    int        =		_shared_dataset[elementa["""base_index"""]]["""content"""]
		for elementa in extremes:
			A__							:    Dict        =		_shared_dataset[elementa["""base_index"""]]["""content"""]
			if jaccard_similarity(lowercase_    ,      lowercase_					) >= jaccard_threshold:
				elementa["copies"] += 1
				break
		else:
			A__							:    Union[str, Any]        =		1
			extremes.append(lowercase_					)
	return extremes

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

	# update duplicate_clusters
	for cluster in duplicate_clusters:
		for element in cluster:
			A__							:    Union[str, Any]        =		element["""base_index"""] in extreme_dict
			if element["is_extreme"]:
				A__							:    Dict        =		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






import warnings

from ...utils import logging
from .image_processing_donut import DonutImageProcessor


A_       : Union[str, Any]         =			logging.get_logger(__name__)



class     _a						(__magic_name__				):


					'''simple docstring'''






					def __init__( self    ,       *A__    ,       **A__			):
						warnings.warn(
						    """The class DonutFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"""
						    """ use DonutImageProcessor instead."""    ,       A__    ,       )
						super().__init__(*A__    ,       **A__			)



