"""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   					=    1_0
_A   					=    2_5_6




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




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

class lowerCamelCase		:






					'''simple docstring'''


					def __init__(						self			:				Any   , *,
					    _snake_case			:				float = 0.85   , )							->       List[Any]:
									SCREAMING_SNAKE_CASE__		  =				duplication_jaccard_threshold
									SCREAMING_SNAKE_CASE__		  =				NUM_PERM
									SCREAMING_SNAKE_CASE__		  =				MinHashLSH(threshold=self._duplication_jaccard_threshold   , num_perm=self._num_perm		)

									SCREAMING_SNAKE_CASE__		  =				defaultdict(_snake_case		)



					def 			lowerCAmelCase_					(						self			:				Any   , _snake_case			:				Tuple   , _snake_case			:				MinHash		)							->       None:
									SCREAMING_SNAKE_CASE__		  =				self._index.query(_snake_case		)
									if code_key in self._index.keys:
													print(F"""Duplicate key {code_key}"""		)
													return

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



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



					def 			lowerCAmelCase_					(						self			:				List[str]   , _snake_case			:				Union[str, Any]		)							->       None:
									SCREAMING_SNAKE_CASE__		  =				self.get_duplicate_clusters()
									with open(_snake_case   , "w"		) as f:
													json.dump(_snake_case   , _snake_case		)




def 						SCREAMING_SNAKE_CASE					(  __UpperCAmelCase       )							->   List[str]:
				SCREAMING_SNAKE_CASE__  ,       SCREAMING_SNAKE_CASE__		  =				element
				SCREAMING_SNAKE_CASE__		  =				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					(  __UpperCAmelCase       )							->   int:
				with mp.Pool() as pool:
								for data in pool.imap_unordered(
								    _compute_min_hash  ,					ThreadedIterator(__UpperCAmelCase  ,					max_queue_size=10_000       )  ,					chunksize=100  ,					):
												if data is not None:
																yield data




def 						SCREAMING_SNAKE_CASE					(  __UpperCAmelCase  ,					__UpperCAmelCase       )							->   List[Any]:
				SCREAMING_SNAKE_CASE__		  =				DuplicationIndex(duplication_jaccard_threshold=__UpperCAmelCase       )

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

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




def 						SCREAMING_SNAKE_CASE					(  __UpperCAmelCase  ,					__UpperCAmelCase       )							->   float:
				SCREAMING_SNAKE_CASE__		  =				get_tokens(__UpperCAmelCase       )
				SCREAMING_SNAKE_CASE__		  =				get_tokens(__UpperCAmelCase       )
				return len(tokensa & tokensa       ) / len(tokensa | tokensa       )


_A   					=    None




def 						SCREAMING_SNAKE_CASE					(  __UpperCAmelCase  ,					__UpperCAmelCase       )							->   int:
				SCREAMING_SNAKE_CASE__		  =				[]
				for elementa in cluster:
								SCREAMING_SNAKE_CASE__		  =				_shared_dataset[elementa["base_index"]]["content"]
								for elementa in extremes:
												SCREAMING_SNAKE_CASE__		  =				_shared_dataset[elementa["base_index"]]["content"]
												if jaccard_similarity(__UpperCAmelCase  ,					__UpperCAmelCase       ) >= jaccard_threshold:
																elementa["copies"] += 1
																break
								else:
												SCREAMING_SNAKE_CASE__		  =				1
												extremes.append(__UpperCAmelCase       )
				return extremes




def 						SCREAMING_SNAKE_CASE					(  __UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase       )							->   Union[str, Any]:
				global _shared_dataset
				SCREAMING_SNAKE_CASE__		  =				dataset
				SCREAMING_SNAKE_CASE__		  =				[]
				SCREAMING_SNAKE_CASE__		  =				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 						SCREAMING_SNAKE_CASE					(  __UpperCAmelCase  ,					__UpperCAmelCase = 0.85       )							->   Tuple[Type[Dataset], List[List[Dict]]]:
				SCREAMING_SNAKE_CASE__		  =				make_duplicate_clusters(__UpperCAmelCase  ,					__UpperCAmelCase       )
				SCREAMING_SNAKE_CASE__		  =				{x["base_index"] for cluster in duplicate_clusters for x in cluster}
				SCREAMING_SNAKE_CASE__		  =				{}
				SCREAMING_SNAKE_CASE__		  =				find_extremes(__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase       )
				for extremes in extremes_clusters:
								for element in extremes:
												SCREAMING_SNAKE_CASE__		  =				element
				SCREAMING_SNAKE_CASE__		  =				duplicate_indices - set(extreme_dict.keys()       )
				SCREAMING_SNAKE_CASE__		  =				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:
												SCREAMING_SNAKE_CASE__		  =				element["base_index"] in extreme_dict
												if element["is_extreme"]:
																SCREAMING_SNAKE_CASE__		  =				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





"""simple docstring"""






# Logistic Regression from scratch

# In[62]:

# In[63]:

# importing all the required libraries

import numpy as np
from matplotlib import pyplot as plt
from sklearn import datasets




def 						SCREAMING_SNAKE_CASE					(  __UpperCAmelCase       )							->   List[str]:
				return 1 / (1 + np.exp(-z       ))




def 						SCREAMING_SNAKE_CASE					(  __UpperCAmelCase  ,					__UpperCAmelCase       )							->   Dict:
				return (-y * np.log(__UpperCAmelCase       ) - (1 - y) * np.log(1 - h       )).mean()




def 						SCREAMING_SNAKE_CASE					(  __UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase       )							->   Any:
				SCREAMING_SNAKE_CASE__		  =				np.dot(__UpperCAmelCase  ,					__UpperCAmelCase       )
				return np.sum(y * scores - np.log(1 + np.exp(__UpperCAmelCase       )       )       )




def 						SCREAMING_SNAKE_CASE					(  __UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase=70_000       )							->   Optional[Any]:
				SCREAMING_SNAKE_CASE__		  =				np.zeros(x.shape[1]       )

				for iterations in range(__UpperCAmelCase       ):
								SCREAMING_SNAKE_CASE__		  =				np.dot(__UpperCAmelCase  ,					__UpperCAmelCase       )
								SCREAMING_SNAKE_CASE__		  =				sigmoid_function(__UpperCAmelCase       )
								SCREAMING_SNAKE_CASE__		  =				np.dot(x.T  ,					h - y       ) / y.size
								SCREAMING_SNAKE_CASE__		  =				theta - alpha * gradient  # updating the weights
								SCREAMING_SNAKE_CASE__		  =				np.dot(__UpperCAmelCase  ,					__UpperCAmelCase       )
								SCREAMING_SNAKE_CASE__		  =				sigmoid_function(__UpperCAmelCase       )
								SCREAMING_SNAKE_CASE__		  =				cost_function(__UpperCAmelCase  ,					__UpperCAmelCase       )
								if iterations % 100 == 0:
												print(F"""loss: {j} \t"""       )  # printing the loss after every 100 iterations
				return theta


# In[68]:

if __name__ == "__main__":
						_A   					=    datasets.load_iris()
						_A   					=    iris.data[:, :2]
						_A   					=    (iris.target != 0) * 1

						_A   					=    0.1
						_A   					=    logistic_reg(alpha, x, y, max_iterations=7_0_0_0_0)
						print('theta: ', theta)  # printing the theta i.e our weights vector




						def 						SCREAMING_SNAKE_CASE					(  __UpperCAmelCase       )							->   Union[str, Any]:
										return sigmoid_function(
										    np.dot(__UpperCAmelCase  ,					__UpperCAmelCase       )       )  # predicting the value of probability from the logistic regression algorithm

						plt.figure(figsize=(1_0, 6))
						plt.scatter(x[y == 0][:, 0], x[y == 0][:, 1], color='b', label='0')
						plt.scatter(x[y == 1][:, 0], x[y == 1][:, 1], color='r', label='1')
						((_A)					,			(_A))   					=    (x[:, 0].min(), x[:, 0].max())
						((_A)					,			(_A))   					=    (x[:, 1].min(), x[:, 1].max())
						((_A)					,			(_A))   					=    np.meshgrid(np.linspace(xa_min, xa_max), np.linspace(xa_min, xa_max))
						_A   					=    np.c_[xxa.ravel(), xxa.ravel()]
						_A   					=    predict_prob(grid).reshape(xxa.shape)
						plt.contour(xxa, xxa, probs, [0.5], linewidths=1, colors='black')

						plt.legend()

						plt.show()




