

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






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






class 	A_		:
							def __init__(		self		:      List[str]       ,*,
							    SCREAMING_SNAKE_CASE__		:      float = 0.85       ,):
								__lowerCamelCase    :    Tuple 	=			duplication_jaccard_threshold
								__lowerCamelCase    :    int 	=			NUM_PERM
								__lowerCamelCase    :    int 	=			MinHashLSH(threshold=self._duplication_jaccard_threshold       ,num_perm=self._num_perm)

								__lowerCamelCase    :    Dict 	=			defaultdict(SCREAMING_SNAKE_CASE__)
							def    lowerCAmelCase					(		self		:      Tuple       ,SCREAMING_SNAKE_CASE__		:      Tuple       ,SCREAMING_SNAKE_CASE__		:      MinHash):
								__lowerCamelCase    :    Optional[Any] 	=			self._index.query(SCREAMING_SNAKE_CASE__)
								if code_key in self._index.keys:
									print(F"Duplicate key {code_key}")
									return

								self._index.insert(SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__)
								if len(SCREAMING_SNAKE_CASE__) > 0:
									for base_duplicate in close_duplicates:
										if base_duplicate in self._duplicate_clusters:
											self._duplicate_clusters[base_duplicate].add(SCREAMING_SNAKE_CASE__)
											break
									else:
										self._duplicate_clusters[close_duplicates[0]].add(SCREAMING_SNAKE_CASE__)
							def    lowerCAmelCase					(		self		:      Union[str, Any]):
								__lowerCamelCase    :    Union[str, Any] 	=			[]
								for base, duplicates in self._duplicate_clusters.items():
									__lowerCamelCase    :    str 	=			[base] + list(SCREAMING_SNAKE_CASE__)
									# reformat the cluster to be a list of dict
									__lowerCamelCase    :    Tuple 	=			[{'base_index': el[0], 'repo_name': el[1], 'path': el[2]} for el in cluster]
									duplicate_clusters.append(SCREAMING_SNAKE_CASE__)
								return duplicate_clusters


							def    lowerCAmelCase					(		self		:      Tuple       ,SCREAMING_SNAKE_CASE__		:      Union[str, Any]):
								__lowerCamelCase    :    Tuple 	=			self.get_duplicate_clusters()
								with open(SCREAMING_SNAKE_CASE__       ,'w') as f:
									json.dump(SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__)






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__      )  ->					int:
	__lowerCamelCase	,			__lowerCamelCase    :    List[str] 	=			element
	__lowerCamelCase    :    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        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__      )  ->					Union[str, Any]:
	with mp.Pool() as pool:
		for data in pool.imap_unordered(
		    _compute_min_hash     ,			ThreadedIterator(lowerCamelCase__     ,			max_queue_size=1_0_0_0_0      )     ,			chunksize=1_0_0     ,			):
			if data is not None:
				yield data






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__     ,			lowerCamelCase__      )  ->					List[str]:
	__lowerCamelCase    :    List[str] 	=			DuplicationIndex(duplication_jaccard_threshold=lowerCamelCase__      )

	for filename, min_hash in tqdm(ThreadedIterator(minhash_iter(enumerate(lowerCamelCase__      )      )     ,			max_queue_size=1_0_0      )      ):
		di.add(lowerCamelCase__     ,			lowerCamelCase__      )

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






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__     ,			lowerCamelCase__      )  ->					float:
	__lowerCamelCase    :    Optional[int] 	=			get_tokens(lowerCamelCase__      )
	__lowerCamelCase    :    Tuple 	=			get_tokens(lowerCamelCase__      )
	return len(tokensa & tokensa      ) / len(tokensa | tokensa      )


a		=None






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__     ,			lowerCamelCase__      )  ->					List[str]:
	__lowerCamelCase    :    Any 	=			[]
	for elementa in cluster:
		__lowerCamelCase    :    List[Any] 	=			_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    :    List[Any] 	=			1
			extremes.append(lowerCamelCase__      )
	return extremes






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__     ,			lowerCamelCase__     ,			lowerCamelCase__      )  ->					Optional[int]:
	global _shared_dataset
	__lowerCamelCase    :    str 	=			dataset
	__lowerCamelCase    :    int 	=			[]
	__lowerCamelCase    :    Optional[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        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__     ,			lowerCamelCase__ = 0.85      )  ->					Tuple[Type[Dataset], List[List[Dict]]]:
	__lowerCamelCase    :    Optional[int] 	=			make_duplicate_clusters(lowerCamelCase__     ,			lowerCamelCase__      )
	__lowerCamelCase    :    Tuple 	=			{x['base_index'] for cluster in duplicate_clusters for x in cluster}
	__lowerCamelCase    :    Optional[int] 	=			{}
	__lowerCamelCase    :    int 	=			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    :    Dict 	=			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    :    Optional[Any] 	=			element['base_index'] in extreme_dict
			if element["is_extreme"]:
				__lowerCamelCase    :    List[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







from collections.abc import Callable
from math import pi, sqrt
from random import uniform
from statistics import mean






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__      )  ->					List[Any]:

	# A local function to see if a dot lands in the circle.
	def is_in_circle(lowerCamelCase__     ,			lowerCamelCase__      ) -> bool:
		__lowerCamelCase    :    Dict 	=			sqrt((x**2) + (y**2)      )
		# Our circle has a radius of 1, so a distance
		# greater than 1 would land outside the circle.
		return distance_from_centre <= 1

	# The proportion of guesses that landed in the circle
	__lowerCamelCase    :    Optional[int] 	=			mean(
	    int(is_in_circle(uniform(-1.0     ,			1.0      )     ,			uniform(-1.0     ,			1.0      )      )      )
	    for _ in range(lowerCamelCase__      )      )
	# The ratio of the area for circle to square is pi/4.
	__lowerCamelCase    :    Any 	=			proportion * 4
	print(F"The estimated value of pi is {pi_estimate}"      )
	print(F"The numpy value of pi is {pi}"      )
	print(F"The total error is {abs(pi - pi_estimate      )}"      )






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__     ,			lowerCamelCase__     ,			lowerCamelCase__ = 0.0     ,			lowerCamelCase__ = 1.0     ,			)  ->					float:
	return mean(
	    function_to_integrate(uniform(lowerCamelCase__     ,			lowerCamelCase__      )      ) for _ in range(lowerCamelCase__      )      ) * (max_value - min_value)






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__     ,			lowerCamelCase__ = 0.0     ,			lowerCamelCase__ = 1.0      )  ->					None:

	def identity_function(lowerCamelCase__      ) -> float:
		return x

	__lowerCamelCase    :    str 	=			area_under_curve_estimator(
	    lowerCamelCase__     ,			lowerCamelCase__     ,			lowerCamelCase__     ,			lowerCamelCase__      )
	__lowerCamelCase    :    int 	=			(max_value * max_value - min_value * min_value) / 2

	print('******************'      )
	print(F"Estimating area under y=x where x varies from {min_value} to {max_value}"      )
	print(F"Estimated value is {estimated_value}"      )
	print(F"Expected value is {expected_value}"      )
	print(F"Total error is {abs(estimated_value - expected_value      )}"      )
	print('******************'      )






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__      )  ->					None:

	def function_to_integrate(lowerCamelCase__      ) -> float:
		return sqrt(4.0 - x * x      )

	__lowerCamelCase    :    Any 	=			area_under_curve_estimator(
	    lowerCamelCase__     ,			lowerCamelCase__     ,			0.0     ,			2.0      )

	print('******************'      )
	print('Estimating pi using area_under_curve_estimator'      )
	print(F"Estimated value is {estimated_value}"      )
	print(F"Expected value is {pi}"      )
	print(F"Total error is {abs(estimated_value - pi      )}"      )
	print('******************'      )


if __name__ == "__main__":
					import doctest

					doctest.testmod()




