

from __future__ import annotations

from typing import Any


class        _SCREAMING_SNAKE_CASE	:







						def __init__(       self			,		_SCREAMING_SNAKE_CASE				)->				None:
													lowerCamelCase_				     =num_of_nodes
													lowerCamelCase_				     =[]
													lowerCamelCase_				     ={}







						def     _snake_case							(       self			,		_SCREAMING_SNAKE_CASE			,		_SCREAMING_SNAKE_CASE			,		_SCREAMING_SNAKE_CASE				)->				None:
													self.m_edges.append([u_node, v_node, weight]				)







						def     _snake_case							(       self			,		_SCREAMING_SNAKE_CASE				)->				int:

													if self.m_component[u_node] == u_node:
																				return u_node
													return self.find_component(self.m_component[u_node]				)







						def     _snake_case							(       self			,		_SCREAMING_SNAKE_CASE				)->				None:

													if self.m_component[u_node] != u_node:
																				for k in self.m_component:
																											lowerCamelCase_				     =self.find_component(lowerCAmelCase__				)







						def     _snake_case							(       self			,		_SCREAMING_SNAKE_CASE			,		_SCREAMING_SNAKE_CASE			,		_SCREAMING_SNAKE_CASE				)->				None:

													if component_size[u_node] <= component_size[v_node]:
																				lowerCamelCase_				     =v_node
																				component_size[v_node] += component_size[u_node]
																				self.set_component(lowerCAmelCase__				)

													elif component_size[u_node] >= component_size[v_node]:
																				lowerCamelCase_				     =self.find_component(lowerCAmelCase__				)
																				component_size[u_node] += component_size[v_node]
																				self.set_component(lowerCAmelCase__				)







						def     _snake_case							(       self				)->				None:
													lowerCamelCase_				     =[]
													lowerCamelCase_				     =0

													lowerCamelCase_				     =[-1] * self.m_num_of_nodes

													# A list of components (initialized to all of the nodes)
													for node in range(self.m_num_of_nodes				):
																				self.m_component.update({node: node}				)
																				component_size.append(1				)

													lowerCamelCase_				     =self.m_num_of_nodes

													while num_of_components > 1:
																				for edge in self.m_edges:
																											lowerCamelCase_				     =edge

																											lowerCamelCase_				     =self.m_component[u]
																											lowerCamelCase_				     =self.m_component[v]

																											if u_component != v_component:

																																		for component in (u_component, v_component):
																																									if (
																																									    minimum_weight_edge[component] == -1
																																									    or minimum_weight_edge[component][2] > w
																																									):
																																																lowerCamelCase_				     =[u, v, w]

																				for edge in minimum_weight_edge:
																											if isinstance(lowerCAmelCase__			,		lowerCAmelCase__				):
																																		lowerCamelCase_				     =edge

																																		lowerCamelCase_				     =self.m_component[u]
																																		lowerCamelCase_				     =self.m_component[v]

																																		if u_component != v_component:
																																									mst_weight += w
																																									self.union(lowerCAmelCase__			,		lowerCAmelCase__			,		lowerCAmelCase__				)
																																									print(f'Added edge [{u} - {v}]\nAdded weight: {w}\n'				)
																																									num_of_components -= 1

																				lowerCamelCase_				     =[-1] * self.m_num_of_nodes
													print(f'The total weight of the minimal spanning tree is: {mst_weight}'				)




def     __UpperCamelCase			(	)							->None:



							"""simple docstring"""








if __name__ == "__main__":
	import doctest

	doctest.testmod()







"""simple docstring"""




import argparse
import logging
import pickle
import random
import time

import numpy as np

from transformers import BertTokenizer, GPTaTokenizer, RobertaTokenizer


logging.basicConfig(
    format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''', datefmt='''%m/%d/%Y %H:%M:%S''', level=logging.INFO
)
a						: Union[str, Any]     	=			logging.getLogger(__name__)






def       _SCREAMING_SNAKE_CASE		(	) ->Tuple:






	'''simple docstring'''






	a      :		Dict	=				argparse.ArgumentParser(
	    description="Preprocess the data to avoid re-doing it several times by (tokenization + token_to_ids)."					)
	parser.add_argument("--file_path"    ,       type=_lowercase    ,       default="data/dump.txt"    ,       help="The path to the data."					)
	parser.add_argument("--tokenizer_type"    ,       type=_lowercase    ,       default="bert"    ,       choices=["bert", "roberta", "gpt2"]					)
	parser.add_argument("--tokenizer_name"    ,       type=_lowercase    ,       default="bert-base-uncased"    ,       help="The tokenizer to use."					)
	parser.add_argument("--dump_file"    ,       type=_lowercase    ,       default="data/dump"    ,       help="The dump file prefix."					)
	a      :		Dict	=				parser.parse_args()

	logger.info(F"""Loading Tokenizer ({args.tokenizer_name})"""					)
	if args.tokenizer_type == "bert":
		a      :		Optional[Any]	=				BertTokenizer.from_pretrained(args.tokenizer_name					)
		a      :		str	=				tokenizer.special_tokens_map["cls_token"]  # `[CLS]`
		a      :		List[str]	=				tokenizer.special_tokens_map["sep_token"]  # `[SEP]`
	elif args.tokenizer_type == "roberta":
		a      :		Tuple	=				RobertaTokenizer.from_pretrained(args.tokenizer_name					)
		a      :		Union[str, Any]	=				tokenizer.special_tokens_map["cls_token"]  # `<s>`
		a      :		str	=				tokenizer.special_tokens_map["sep_token"]  # `</s>`
	elif args.tokenizer_type == "gpt2":
		a      :		List[Any]	=				GPTaTokenizer.from_pretrained(args.tokenizer_name					)
		a      :		Optional[int]	=				tokenizer.special_tokens_map["bos_token"]  # `<|endoftext|>`
		a      :		List[Any]	=				tokenizer.special_tokens_map["eos_token"]  # `<|endoftext|>`

	logger.info(F"""Loading text from {args.file_path}"""					)
	with open(args.file_path    ,       "r"    ,       encoding="utf8"					) as fp:
		a      :		List[Any]	=				fp.readlines()

	logger.info("Start encoding"					)
	logger.info(F"""{len(_lowercase					)} examples to process."""					)

	a      :		Optional[Any]	=				[]
	a      :		Optional[Any]	=				0
	a      :		int	=				1_0000
	a      :		Dict	=				time.time()
	for text in data:
		a      :		List[Any]	=				F"""{bos} {text.strip()} {sep}"""
		a      :		Optional[int]	=				tokenizer.encode(_lowercase    ,       add_special_tokens=_lowercase					)
		rslt.append(_lowercase					)

		iter += 1
		if iter % interval == 0:
			a      :		Optional[Any]	=				time.time()
			logger.info(F"""{iter} examples processed. - {(end-start):.2f}s/{interval}expl"""					)
			a      :		Optional[Any]	=				time.time()
	logger.info("Finished binarization"					)
	logger.info(F"""{len(_lowercase					)} examples processed."""					)

	a      :		Optional[int]	=				F"""{args.dump_file}.{args.tokenizer_name}.pickle"""
	a      :		Tuple	=				tokenizer.vocab_size
	if vocab_size < (1 << 16):
		a      :		Optional[int]	=				[np.uintaa(_lowercase					) for d in rslt]
	else:
		a      :		Optional[Any]	=				[np.intaa(_lowercase					) for d in rslt]
	random.shuffle(rslt_					)
	logger.info(F"""Dump to {dp_file}"""					)
	with open(_lowercase    ,       "wb"					) as handle:
		pickle.dump(rslt_    ,       _lowercase    ,       protocol=pickle.HIGHEST_PROTOCOL					)


if __name__ == "__main__":
		main()


