



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   			=1_0
__A   			=2_5_6

def        a						(      _UpperCAmelCase							:					List[str]						):







		'''simple docstring'''





		if len(_UpperCAmelCase						) < MIN_NUM_TOKENS:
				return None
		__UpperCAmelCase    :     List[str]													=	MinHash(num_perm=_UpperCAmelCase						)
		for token in set(_UpperCAmelCase						):
				min_hash.update(token.encode()						)
		return min_hash

def        a						(      _UpperCAmelCase							:					str						):







		'''simple docstring'''





		return {t for t in NON_ALPHA.split(_UpperCAmelCase						) if len(t.strip()						) > 0}

class 		UpperCAmelCase__			:




			'''simple docstring'''






			def __init__(   self							: Tuple       ,       *,
			    a_							: float = 0.8_5       ,       ):
					'''simple docstring'''





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

					__UpperCAmelCase    :     Any													=	defaultdict(a_ )




			def  snake_case__     (   self							: str       ,       a_							: Tuple       ,       a_							: MinHash ):
					'''simple docstring'''





					__UpperCAmelCase    :     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  snake_case__     (   self							: Optional[Any] ):
					'''simple docstring'''





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




			def  snake_case__     (   self							: Tuple       ,       a_							: List[str] ):
					'''simple docstring'''





					__UpperCAmelCase    :     Dict													=	self.get_duplicate_clusters()
					with open(a_       ,       '''w''' ) as f:
							json.dump(a_       ,       a_ )

def        a						(      _UpperCAmelCase							:					Optional[int]						):







		'''simple docstring'''





		__UpperCAmelCase							,					__UpperCAmelCase    :     Tuple													=	element
		__UpperCAmelCase    :     List[str]													=	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						(      _UpperCAmelCase							:					Type[Dataset]						):







		'''simple docstring'''





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

def        a						(      _UpperCAmelCase							:					Type[Dataset] ,   _UpperCAmelCase							:					float						):







		'''simple docstring'''





		__UpperCAmelCase    :     List[Any]													=	DuplicationIndex(duplication_jaccard_threshold=_UpperCAmelCase						)

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

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

def        a						(      _UpperCAmelCase							:					str ,   _UpperCAmelCase							:					str						):







		'''simple docstring'''





		__UpperCAmelCase    :     Dict													=	get_tokens(_UpperCAmelCase						)
		__UpperCAmelCase    :     List[Any]													=	get_tokens(_UpperCAmelCase						)
		return len(tokensa & tokensa						) / len(tokensa | tokensa						)


__A   			=None

def        a						(      _UpperCAmelCase							:					int ,   _UpperCAmelCase							:					List[Any]						):







		'''simple docstring'''





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

def        a						(      _UpperCAmelCase							:					Optional[Any] ,   _UpperCAmelCase							:					str ,   _UpperCAmelCase							:					Optional[int]						):







		'''simple docstring'''





		global _shared_dataset
		__UpperCAmelCase    :     str													=	dataset
		__UpperCAmelCase    :     List[str]													=	[]
		__UpperCAmelCase    :     List[str]													=	partial(_find_cluster_extremes_shared ,   jaccard_threshold=_UpperCAmelCase						)
		with mp.Pool() as pool:
				for extremes in tqdm(
				    pool.imap_unordered(
				        _UpperCAmelCase ,   _UpperCAmelCase ,   ) ,   total=len(_UpperCAmelCase						) ,   ):
						extremes_list.append(_UpperCAmelCase						)
		return extremes_list

def        a						(      _UpperCAmelCase							:					Type[Dataset] ,   _UpperCAmelCase							:					float = 0.85						):







		'''simple docstring'''





		__UpperCAmelCase    :     List[str]													=	make_duplicate_clusters(_UpperCAmelCase ,   _UpperCAmelCase						)
		__UpperCAmelCase    :     str													=	{x['''base_index'''] for cluster in duplicate_clusters for x in cluster}
		__UpperCAmelCase    :     List[str]													=	{}
		__UpperCAmelCase    :     Any													=	find_extremes(_UpperCAmelCase ,   _UpperCAmelCase ,   _UpperCAmelCase						)
		for extremes in extremes_clusters:
				for element in extremes:
						__UpperCAmelCase    :     Dict													=	element
		__UpperCAmelCase    :     List[str]													=	duplicate_indices - set(extreme_dict.keys()						)
		__UpperCAmelCase    :     Union[str, Any]													=	dataset.filter(lambda _UpperCAmelCase ,   _UpperCAmelCase						: idx not in remove_indices ,   with_indices=_UpperCAmelCase						)

		# 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    :     Dict													=	extreme_dict[element['''base_index''']]['''copies''']

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

		return ds_filter, duplicate_clusters










import numpy as np

class 		UpperCAmelCase__			:




			'''simple docstring'''






			def __init__(   self							: List[Any] ):
					'''simple docstring'''





					__UpperCAmelCase    :     Optional[Any]													=	(0, 0)
					__UpperCAmelCase    :     List[str]													=	None
					__UpperCAmelCase    :     int													=	0
					__UpperCAmelCase    :     Union[str, Any]													=	0
					__UpperCAmelCase    :     List[Any]													=	0




			def __eq__(   self							: Optional[int]       ,       a_							: List[Any] ):
					'''simple docstring'''





					return self.position == cell.position




			def  snake_case__     (   self							: List[Any] ):
					'''simple docstring'''





					print(self.position )

class 		UpperCAmelCase__			:




			'''simple docstring'''






			def __init__(   self							: str       ,       a_							: Dict=(5, 5) ):
					'''simple docstring'''





					__UpperCAmelCase    :     str													=	np.zeros(a_ )
					__UpperCAmelCase    :     List[Any]													=	world_size[0]
					__UpperCAmelCase    :     List[str]													=	world_size[1]




			def  snake_case__     (   self							: Tuple ):
					'''simple docstring'''





					print(self.w )




			def  snake_case__     (   self							: Union[str, Any]       ,       a_							: Tuple ):
					'''simple docstring'''





					__UpperCAmelCase    :     Optional[Any]													=	[
					    (-1, -1),
					    (-1, 0),
					    (-1, 1),
					    (0, -1),
					    (0, 1),
					    (1, -1),
					    (1, 0),
					    (1, 1),
					]
					__UpperCAmelCase    :     str													=	cell.position[0]
					__UpperCAmelCase    :     List[Any]													=	cell.position[1]
					__UpperCAmelCase    :     str													=	[]
					for n in neughbour_cord:
							__UpperCAmelCase    :     Optional[Any]													=	current_x + n[0]
							__UpperCAmelCase    :     Any													=	current_y + n[1]
							if 0 <= x < self.world_x_limit and 0 <= y < self.world_y_limit:
									__UpperCAmelCase    :     int													=	Cell()
									__UpperCAmelCase    :     List[str]													=	(x, y)
									__UpperCAmelCase    :     List[Any]													=	cell
									neighbours.append(a_ )
					return neighbours

def        a						(      _UpperCAmelCase							:					List[Any] ,   _UpperCAmelCase							:					Any ,   _UpperCAmelCase							:					int						):







		'''simple docstring'''





		__UpperCAmelCase    :     List[Any]													=	[]
		__UpperCAmelCase    :     Optional[Any]													=	[]
		_open.append(_UpperCAmelCase						)

		while _open:
				__UpperCAmelCase    :     str													=	np.argmin([n.f for n in _open]						)
				__UpperCAmelCase    :     Optional[Any]													=	_open[min_f]
				_closed.append(_open.pop(_UpperCAmelCase						)						)
				if current == goal:
						break
				for n in world.get_neigbours(_UpperCAmelCase						):
						for c in _closed:
								if c == n:
										continue
						__UpperCAmelCase    :     Optional[Any]													=	current.g + 1
						__UpperCAmelCase							,					__UpperCAmelCase    :     Union[str, Any]													=	n.position
						__UpperCAmelCase							,					__UpperCAmelCase    :     Tuple													=	goal.position
						__UpperCAmelCase    :     Union[str, Any]													=	(ya - ya) ** 2 + (xa - xa) ** 2
						__UpperCAmelCase    :     Optional[Any]													=	n.h + n.g

						for c in _open:
								if c == n and c.f < n.f:
										continue
						_open.append(_UpperCAmelCase						)
		__UpperCAmelCase    :     str													=	[]
		while current.parent is not None:
				path.append(current.position						)
				__UpperCAmelCase    :     int													=	current.parent
		path.append(current.position						)
		return path[::-1]


if __name__ == "__main__":
				__A   			=Gridworld()
				# Start position and goal
				__A   			=Cell()
				__A   			=(0, 0)
				__A   			=Cell()
				__A   			=(4, 4)
				print(f'''path from {start.position} to {goal.position}''')
				__A   			=astar(world, start, goal)
				#   Just for visual reasons.
				for i in s:
								__A   			=1
				print(world.w)





