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




def 	UpperCamelCase__  (     SCREAMING_SNAKE_CASE_	:	List[str]       )  ->     Optional[MinHash]:
						if len(SCREAMING_SNAKE_CASE_       ) < MIN_NUM_TOKENS:
												return None
						_lowercase					       =					MinHash(num_perm=SCREAMING_SNAKE_CASE_       )
						for token in set(SCREAMING_SNAKE_CASE_       ):
												min_hash.update(token.encode()       )
						return min_hash




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



class    a_ :






			def __init__(      self				,			*,
			    __UpperCamelCase = 0.85				,			):
									_lowercase					       =					duplication_jaccard_threshold
									_lowercase					       =					NUM_PERM
									_lowercase					       =					MinHashLSH(threshold=self._duplication_jaccard_threshold				,			num_perm=self._num_perm  )

									_lowercase					       =					defaultdict(__UpperCamelCase  )






			def    UpperCamelCase_				(      self				,			__UpperCamelCase				,			__UpperCamelCase  ):
									_lowercase					       =					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  ):
									_lowercase					       =					[]
									for base, duplicates in self._duplicate_clusters.items():
															_lowercase					       =					[base] + list(__UpperCamelCase  )
															# reformat the cluster to be a list of dict
															_lowercase					       =					[{"""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				,			__UpperCamelCase  ):
									_lowercase					       =					self.get_duplicate_clusters()
									with open(__UpperCamelCase				,			"""w"""  ) as f:
															json.dump(__UpperCamelCase				,			__UpperCamelCase  )







def 	UpperCamelCase__  (     SCREAMING_SNAKE_CASE_	:	Optional[Any]       )  ->     Union[str, Any]:
						_lowercase       ,						_lowercase					       =					element
						_lowercase					       =					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__  (     SCREAMING_SNAKE_CASE_	:	Type[Dataset]       )  ->     Optional[Any]:
						with mp.Pool() as pool:
												for data in pool.imap_unordered(
												    _compute_min_hash		,   ThreadedIterator(SCREAMING_SNAKE_CASE_		,   max_queue_size=1_00_00       )		,   chunksize=1_00		,   ):
																		if data is not None:
																								yield data




def 	UpperCamelCase__  (     SCREAMING_SNAKE_CASE_	:	Type[Dataset]		,   SCREAMING_SNAKE_CASE_	:	float       )  ->     List[Any]:
						_lowercase					       =					DuplicationIndex(duplication_jaccard_threshold=SCREAMING_SNAKE_CASE_       )

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

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




def 	UpperCamelCase__  (     SCREAMING_SNAKE_CASE_	:	str		,   SCREAMING_SNAKE_CASE_	:	str       )  ->     float:
						_lowercase					       =					get_tokens(SCREAMING_SNAKE_CASE_       )
						_lowercase					       =					get_tokens(SCREAMING_SNAKE_CASE_       )
						return len(tokensa & tokensa       ) / len(tokensa | tokensa       )


A		: int								=     None




def 	UpperCamelCase__  (     SCREAMING_SNAKE_CASE_	:	List[str]		,   SCREAMING_SNAKE_CASE_	:	List[Any]       )  ->     Optional[Any]:
						_lowercase					       =					[]
						for elementa in cluster:
												_lowercase					       =					_shared_dataset[elementa["""base_index"""]]["""content"""]
												for elementa in extremes:
																		_lowercase					       =					_shared_dataset[elementa["""base_index"""]]["""content"""]
																		if jaccard_similarity(SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_       ) >= jaccard_threshold:
																								elementa["copies"] += 1
																								break
												else:
																		_lowercase					       =					1
																		extremes.append(SCREAMING_SNAKE_CASE_       )
						return extremes




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




def 	UpperCamelCase__  (     SCREAMING_SNAKE_CASE_	:	Type[Dataset]		,   SCREAMING_SNAKE_CASE_	:	float = 0.85       )  ->     Tuple[Type[Dataset], List[List[Dict]]]:
						_lowercase					       =					make_duplicate_clusters(SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_       )
						_lowercase					       =					{x["""base_index"""] for cluster in duplicate_clusters for x in cluster}
						_lowercase					       =					{}
						_lowercase					       =					find_extremes(SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_       )
						for extremes in extremes_clusters:
												for element in extremes:
																		_lowercase					       =					element
						_lowercase					       =					duplicate_indices - set(extreme_dict.keys()       )
						_lowercase					       =					dataset.filter(lambda SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_       : idx not in remove_indices		,   with_indices=SCREAMING_SNAKE_CASE_       )

						# update duplicate_clusters
						for cluster in duplicate_clusters:
												for element in cluster:
																		_lowercase					       =					element["""base_index"""] in extreme_dict
																		if element["is_extreme"]:
																								_lowercase					       =					extreme_dict[element["""base_index"""]]["""copies"""]

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

						return ds_filter, duplicate_clusters

import pandas as pd
from matplotlib import pyplot as plt
from sklearn.linear_model import LinearRegression

# Splitting the dataset into the Training set and Test set
from sklearn.model_selection import train_test_split

# Fitting Polynomial Regression to the dataset
from sklearn.preprocessing import PolynomialFeatures

# Importing the dataset
A		: Union[str, Any]								=     pd.read_csv(
    '''https://s3.us-west-2.amazonaws.com/public.gamelab.fun/dataset/'''
    '''position_salaries.csv'''
)
A		: Union[str, Any]								=     dataset.iloc[:, 1:2].values
A		: Dict								=     dataset.iloc[:, 2].values


A							,      A							,      A							,      A		: str								=     train_test_split(X, y, test_size=0.2, random_state=0)


A		: Union[str, Any]								=     PolynomialFeatures(degree=4)
A		: str								=     poly_reg.fit_transform(X)
A		: Union[str, Any]								=     LinearRegression()
pol_reg.fit(X_poly, y)




def 	UpperCamelCase__  (     )  ->     Any:
						plt.scatter(SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_		,   color="""red"""       )
						plt.plot(SCREAMING_SNAKE_CASE_		,   pol_reg.predict(poly_reg.fit_transform(SCREAMING_SNAKE_CASE_       )       )		,   color="""blue"""       )
						plt.title("""Truth or Bluff (Linear Regression)"""       )
						plt.xlabel("""Position level"""       )
						plt.ylabel("""Salary"""       )
						plt.show()


if __name__ == "__main__":
		viz_polymonial()

		# Predicting a new result with Polymonial Regression
		pol_reg.predict(poly_reg.fit_transform([[5.5]]))
		# output should be 132148.43750003