


'''simple docstring'''
import pickle

import numpy as np
from matplotlib import pyplot as plt




class        _UpperCamelCase	:


				'''simple docstring'''


				def __init__(     self							,      a_							,      a_							,      a_							,      a_							,      a_							,      a_=0.2							,      a_=0.2							)    ->  List[str]:
					lowercase						:		Any   					=		bp_numa
					lowercase						:		Tuple   					=		bp_numa
					lowercase						:		Optional[Any]   					=		bp_numa
					lowercase						:		Union[str, Any]   					=		conva_get[:2]
					lowercase						:		Dict   					=		conva_get[2]
					lowercase						:		List[Any]   					=		size_pa
					lowercase						:		Any   					=		rate_w
					lowercase						:		List[Any]   					=		rate_t
					lowercase						:		List[Any]   					=		[
					    np.mat(-1 * np.random.rand(self.conva[0]							,      self.conva[0]							) + 0.5							)
					    for i in range(self.conva[1]							)
					]
					lowercase						:		Union[str, Any]   					=		np.mat(-1 * np.random.rand(self.num_bpa							,      self.num_bpa							) + 0.5							)
					lowercase						:		Union[str, Any]   					=		np.mat(-1 * np.random.rand(self.num_bpa							,      self.num_bpa							) + 0.5							)
					lowercase						:		Optional[Any]   					=		-2 * np.random.rand(self.conva[1]							) + 1
					lowercase						:		Optional[Any]   					=		-2 * np.random.rand(self.num_bpa							) + 1
					lowercase						:		Optional[Any]   					=		-2 * np.random.rand(self.num_bpa							) + 1

				def 	a__		(     self							,      a_							)    ->  Optional[Any]:
					# save model dict with pickle
					lowercase						:		Any   					=		{
					    "num_bp1": self.num_bpa,
					    "num_bp2": self.num_bpa,
					    "num_bp3": self.num_bpa,
					    "conv1": self.conva,
					    "step_conv1": self.step_conva,
					    "size_pooling1": self.size_poolinga,
					    "rate_weight": self.rate_weight,
					    "rate_thre": self.rate_thre,
					    "w_conv1": self.w_conva,
					    "wkj": self.wkj,
					    "vji": self.vji,
					    "thre_conv1": self.thre_conva,
					    "thre_bp2": self.thre_bpa,
					    "thre_bp3": self.thre_bpa,
					}
					with open(a_							,      "wb"							) as f:
						pickle.dump(a_							,      a_							)

					print(F'''Model saved： {save_path}'''							)

				@classmethod
				def 	a__		(     cls							,      a_							)    ->  Tuple:
					# read saved model
					with open(a_							,      "rb"							) as f:
						lowercase						:		Any   					=		pickle.load(a_							)  # noqa: S301

					lowercase						:		Optional[Any]   					=		model_dic.get("conv1"							)
					conv_get.append(model_dic.get("step_conv1"							)							)
					lowercase						:		List[Any]   					=		model_dic.get("size_pooling1"							)
					lowercase						:		str   					=		model_dic.get("num_bp1"							)
					lowercase						:		List[Any]   					=		model_dic.get("num_bp2"							)
					lowercase						:		List[Any]   					=		model_dic.get("num_bp3"							)
					lowercase						:		Tuple   					=		model_dic.get("rate_weight"							)
					lowercase						:		List[str]   					=		model_dic.get("rate_thre"							)
					# create model instance
					lowercase						:		Optional[Any]   					=		CNN(a_							,      a_							,      a_							,      a_							,      a_							,      a_							,      a_							)
					# modify model parameter
					lowercase						:		int   					=		model_dic.get("w_conv1"							)
					lowercase						:		Optional[int]   					=		model_dic.get("wkj"							)
					lowercase						:		Tuple   					=		model_dic.get("vji"							)
					lowercase						:		str   					=		model_dic.get("thre_conv1"							)
					lowercase						:		Optional[Any]   					=		model_dic.get("thre_bp2"							)
					lowercase						:		Any   					=		model_dic.get("thre_bp3"							)
					return conv_ins

				def 	a__		(     self							,      a_							)    ->  Any:
					return 1 / (1 + np.exp(-1 * x							))

				def 	a__		(     self							,      a_							)    ->  Union[str, Any]:
					return round(a_							,      3							)

				def 	a__		(     self							,      a_							,      a_							,      a_							,      a_							,      a_							)    ->  List[Any]:
					# convolution process
					lowercase						:		int   					=		convs[0]
					lowercase						:		int   					=		convs[1]
					lowercase						:		Dict   					=		np.shape(a_							)[0]
					# get the data slice of original image data, data_focus
					lowercase						:		List[Any]   					=		[]
					for i_focus in range(0							,      size_data - size_conv + 1							,      a_							):
						for j_focus in range(0							,      size_data - size_conv + 1							,      a_							):
							lowercase						:		str   					=		data[
							    i_focus : i_focus + size_conv, j_focus : j_focus + size_conv
							]
							data_focus.append(a_							)
        # calculate the feature map of every single kernel, and saved as list of matrix
					lowercase						:		str   					=		[]
					lowercase						:		Optional[int]   					=		int((size_data - size_conv) / conv_step + 1							)
					for i_map in range(a_							):
						lowercase						:		Dict   					=		[]
						for i_focus in range(len(a_							)							):
							lowercase						:		Tuple   					=		(
							    np.sum(np.multiply(data_focus[i_focus]							,      w_convs[i_map]							)							)
							    - thre_convs[i_map]
							)
							featuremap.append(self.sig(a_							)							)
						lowercase						:		Union[str, Any]   					=		np.asmatrix(a_							).reshape(
						    a_							,      a_							)
						data_featuremap.append(a_							)

					# expanding the data slice to One dimenssion
					lowercase						:		Optional[Any]   					=		[]
					for each_focus in data_focus:
						focusa_list.extend(self.Expand_Mat(a_							)							)
					lowercase						:		Tuple   					=		np.asarray(a_							)
					return focus_list, data_featuremap

				def 	a__		(     self							,      a_							,      a_							,      a_="average_pool"							)    ->  Dict:
					# pooling process
					lowercase						:		Union[str, Any]   					=		len(featuremaps[0]							)
					lowercase						:		str   					=		int(size_map / size_pooling							)
					lowercase						:		Tuple   					=		[]
					for i_map in range(len(a_							)							):
						lowercase						:		str   					=		featuremaps[i_map]
						lowercase						:		Any   					=		[]
						for i_focus in range(0							,      a_							,      a_							):
							for j_focus in range(0							,      a_							,      a_							):
								lowercase						:		List[str]   					=		feature_map[
								    i_focus : i_focus + size_pooling,
								    j_focus : j_focus + size_pooling,
								]
								if pooling_type == "average_pool":
									# average pooling
									map_pooled.append(np.average(a_							)							)
								elif pooling_type == "max_pooling":
									# max pooling
									map_pooled.append(np.max(a_							)							)
						lowercase						:		List[str]   					=		np.asmatrix(a_							).reshape(a_							,      a_							)
						featuremap_pooled.append(a_							)
					return featuremap_pooled

				def 	a__		(     self							,      a_							)    ->  List[str]:
					# expanding three dimension data to one dimension list
					lowercase						:		Dict   					=		[]
					for i in range(len(a_							)							):
						lowercase						:		Any   					=		np.shape(data[i]							)
						lowercase						:		str   					=		data[i].reshape(1							,      shapes[0] * shapes[1]							)
						lowercase						:		Union[str, Any]   					=		data_listed.getA().tolist()[0]
						data_expanded.extend(a_							)
					lowercase						:		int   					=		np.asarray(a_							)
					return data_expanded

				def 	a__		(     self							,      a_							)    ->  Dict:
					# expanding matrix to one dimension list
					lowercase						:		int   					=		np.asarray(a_							)
					lowercase						:		str   					=		np.shape(a_							)
					lowercase						:		Union[str, Any]   					=		data_mat.reshape(1							,      shapes[0] * shapes[1]							)
					return data_expanded

				def 	a__		(     self							,      a_							,      a_							,      a_							,      a_							,      a_							)    ->  List[str]:
					lowercase						:		List[Any]   					=		[]
					lowercase						:		Optional[int]   					=		0
					for i_map in range(a_							):
						lowercase						:		Dict   					=		np.ones((size_map, size_map)							)
						for i in range(0							,      a_							,      a_							):
							for j in range(0							,      a_							,      a_							):
								lowercase						:		Dict   					=		pd_pool[
								    i_pool
								]
								lowercase						:		Union[str, Any]   					=		i_pool + 1
						lowercase						:		Optional[int]   					=		np.multiply(
						    a_							,      np.multiply(out_map[i_map]							,      (1 - out_map[i_map])							)							)
						pd_all.append(a_							)
					return pd_all

				def 	a__		(     self							,      a_							,      a_							,      a_							,      a_							,      a_							,      a_=bool							)    ->  Tuple:
					# model traning
					print("----------------------Start Training-------------------------"							)
					print((" - - Shape: Train_Data  ", np.shape(a_							))							)
					print((" - - Shape: Teach_Data  ", np.shape(a_							))							)
					lowercase						:		int   					=		0
					lowercase						:		int   					=		[]
					lowercase						:		Union[str, Any]   					=		1_0_0_0_0
					while rp < n_repeat and mse >= error_accuracy:
						lowercase						:		Tuple   					=		0
						print(F'''-------------Learning Time {rp}--------------'''							)
						for p in range(len(a_							)							):
							# print('------------Learning Image: %d--------------'%p)
							lowercase						:		List[Any]   					=		np.asmatrix(datas_train[p]							)
							lowercase						:		Optional[int]   					=		np.asarray(datas_teach[p]							)
							lowercase					,		lowercase						:		List[str]   					=		self.convolute(
							    a_							,      self.conva							,      self.w_conva							,      self.thre_conva							,      conv_step=self.step_conva							,      )
							lowercase						:		Optional[int]   					=		self.pooling(a_							,      self.size_poolinga							)
							lowercase						:		Tuple   					=		np.shape(a_							)
							lowercase						:		List[Any]   					=		self._expand(a_							)
							lowercase						:		Tuple   					=		data_bp_input

							lowercase						:		Union[str, Any]   					=		np.dot(a_							,      self.vji.T							) - self.thre_bpa
							lowercase						:		Any   					=		self.sig(a_							)
							lowercase						:		int   					=		np.dot(a_							,      self.wkj.T							) - self.thre_bpa
							lowercase						:		Tuple   					=		self.sig(a_							)

							# --------------Model Leaning ------------------------
							# calculate error and gradient---------------
							lowercase						:		Optional[int]   					=		np.multiply(
							    (data_teach - bp_outa)							,      np.multiply(a_							,      (1 - bp_outa)							)							)
							lowercase						:		int   					=		np.multiply(
							    np.dot(a_							,      self.wkj							)							,      np.multiply(a_							,      (1 - bp_outa)							)							)
							lowercase						:		Union[str, Any]   					=		np.dot(a_							,      self.vji							)

							lowercase						:		List[str]   					=		pd_i_all / (self.size_poolinga * self.size_poolinga)
							lowercase						:		Tuple   					=		pd_conva_pooled.T.getA().tolist()
							lowercase						:		List[Any]   					=		self._calculate_gradient_from_pool(
							    a_							,      a_							,      shape_featuremapa[0]							,      shape_featuremapa[1]							,      self.size_poolinga							,      )
							# weight and threshold learning process---------
							# convolution layer
							for k_conv in range(self.conva[1]							):
								lowercase						:		Tuple   					=		self._expand_mat(pd_conva_all[k_conv]							)
								lowercase						:		Union[str, Any]   					=		self.rate_weight * np.dot(a_							,      a_							)

								lowercase						:		List[str]   					=		self.w_conva[k_conv] + delta_w.reshape(
								    (self.conva[0], self.conva[0])							)

								lowercase						:		Union[str, Any]   					=		(
								    self.thre_conva[k_conv]
								    - np.sum(pd_conva_all[k_conv]							) * self.rate_thre
								)
							# all connected layer
							lowercase						:		Dict   					=		self.wkj + pd_k_all.T * bp_outa * self.rate_weight
							lowercase						:		Any   					=		self.vji + pd_j_all.T * bp_outa * self.rate_weight
							lowercase						:		Optional[Any]   					=		self.thre_bpa - pd_k_all * self.rate_thre
							lowercase						:		List[str]   					=		self.thre_bpa - pd_j_all * self.rate_thre
							# calculate the sum error of all single image
							lowercase						:		Optional[Any]   					=		np.sum(abs(data_teach - bp_outa							)							)
							error_count += errors
							# print('   ----Teach      ',data_teach)
							# print('   ----BP_output  ',bp_out3)
						lowercase						:		Dict   					=		rp + 1
						lowercase						:		Optional[int]   					=		error_count / patterns
						all_mse.append(a_							)

					def draw_error():
						lowercase						:		List[Any]   					=		[error_accuracy for i in range(int(n_repeat * 1.2							)							)]
						plt.plot(a_							,      "+-"							)
						plt.plot(a_							,      "r--"							)
						plt.xlabel("Learning Times"							)
						plt.ylabel("All_mse"							)
						plt.grid(a_							,      alpha=0.5							)
						plt.show()

					print("------------------Training Complished---------------------"							)
					print((" - - Training epoch: ", rp, F'''     - - Mse: {mse:.6f}''')							)
					if draw_e:
						draw_error()
					return mse

				def 	a__		(     self							,      a_							)    ->  Optional[Any]:
					# model predict
					lowercase						:		List[Any]   					=		[]
					print("-------------------Start Testing-------------------------"							)
					print((" - - Shape: Test_Data  ", np.shape(a_							))							)
					for p in range(len(a_							)							):
						lowercase						:		Optional[int]   					=		np.asmatrix(datas_test[p]							)
						lowercase					,		lowercase						:		Optional[int]   					=		self.convolute(
						    a_							,      self.conva							,      self.w_conva							,      self.thre_conva							,      conv_step=self.step_conva							,      )
						lowercase						:		Optional[Any]   					=		self.pooling(a_							,      self.size_poolinga							)
						lowercase						:		Any   					=		self._expand(a_							)

						lowercase						:		Optional[int]   					=		data_bp_input
						lowercase						:		Optional[int]   					=		bp_outa * self.vji.T - self.thre_bpa
						lowercase						:		List[Any]   					=		self.sig(a_							)
						lowercase						:		List[str]   					=		bp_outa * self.wkj.T - self.thre_bpa
						lowercase						:		Union[str, Any]   					=		self.sig(a_							)
						produce_out.extend(bp_outa.getA().tolist()							)
					lowercase						:		List[str]   					=		[list(map(self.do_round							,      a_							)							) for each in produce_out]
					return np.asarray(a_							)


				def 	a__		(     self							,      a_							)    ->  Union[str, Any]:
					# return the data of image after convoluting process so we can check it out
					lowercase						:		Optional[Any]   					=		np.asmatrix(a_							)
					lowercase					,		lowercase						:		Optional[int]   					=		self.convolute(
					    a_							,      self.conva							,      self.w_conva							,      self.thre_conva							,      conv_step=self.step_conva							,      )
					lowercase						:		Tuple   					=		self.pooling(a_							,      self.size_poolinga							)

					return data_conveda, data_pooleda


if __name__ == "__main__":
					pass






'''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


lowerCAmelCase   : List[str]             =				re.compile("""[^A-Za-z_0-9]""")
# parameters used in DuplicationIndex
lowerCAmelCase   : Union[str, Any]             =				1_0
lowerCAmelCase   : Optional[Any]             =				2_5_6




def 	_A  (       A							)  ->				Optional[MinHash]:
	if len(A							) < MIN_NUM_TOKENS:
		return None
	lowercase						:		List[Any]   					=		MinHash(num_perm=A							)
	for token in set(A							):
		min_hash.update(token.encode()							)
	return min_hash




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




class        _UpperCamelCase	:


				'''simple docstring'''


				def __init__(     self							,      *,
				    a_ = 0.85							,      )    ->  List[str]:
					lowercase						:		Any   					=		duplication_jaccard_threshold
					lowercase						:		str   					=		NUM_PERM
					lowercase						:		Union[str, Any]   					=		MinHashLSH(threshold=self._duplication_jaccard_threshold							,      num_perm=self._num_perm							)

					lowercase						:		Any   					=		defaultdict(a_							)

				def 	a__		(     self							,      a_							,      a_							)    ->  None:
					lowercase						:		Dict   					=		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							)    ->  List[List[Dict]]:
					lowercase						:		str   					=		[]
					for base, duplicates in self._duplicate_clusters.items():
						lowercase						:		str   					=		[base] + list(a_							)
						# reformat the cluster to be a list of dict
						lowercase						:		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_							)    ->  None:
					lowercase						:		Tuple   					=		self.get_duplicate_clusters()
					with open(a_							,      "w"							) as f:
						json.dump(a_							,      a_							)




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




def 	_A  (       A							,A							)  ->				List[str]:
	lowercase						:		Dict   					=		DuplicationIndex(duplication_jaccard_threshold=A							)

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

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




def 	_A  (       A							,A							)  ->				float:
	lowercase						:		int   					=		get_tokens(A							)
	lowercase						:		Dict   					=		get_tokens(A							)
	return len(tokensa & tokensa							) / len(tokensa | tokensa							)


lowerCAmelCase   : List[str]             =				None




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




def 	_A  (       A							,A							,A							)  ->				Optional[Any]:
	global _shared_dataset
	lowercase						:		Dict   					=		dataset
	lowercase						:		int   					=		[]
	lowercase						:		int   					=		partial(_find_cluster_extremes_shared							,jaccard_threshold=A							)
	with mp.Pool() as pool:
		for extremes in tqdm(
		    pool.imap_unordered(
		        A							,A							,)							,total=len(A							)							,):
			extremes_list.append(A							)
	return extremes_list






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

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

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

	return ds_filter, duplicate_clusters


