
"""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
)
_SCREAMING_SNAKE_CASE	       =      logging.getLogger(__name__)
def   __UpperCamelCase   (					)  ->     str:







							"""simple docstring"""


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

							logger.info(F'''Loading Tokenizer ({args.tokenizer_name})''' )
							if args.tokenizer_type == "bert":
														__snake_case     			=       BertTokenizer.from_pretrained(args.tokenizer_name )
														__snake_case     			=       tokenizer.special_tokens_map["cls_token"]  # `[CLS]`
														__snake_case     			=       tokenizer.special_tokens_map["sep_token"]  # `[SEP]`
							elif args.tokenizer_type == "roberta":
														__snake_case     			=       RobertaTokenizer.from_pretrained(args.tokenizer_name )
														__snake_case     			=       tokenizer.special_tokens_map["cls_token"]  # `<s>`
														__snake_case     			=       tokenizer.special_tokens_map["sep_token"]  # `</s>`
							elif args.tokenizer_type == "gpt2":
														__snake_case     			=       GPTaTokenizer.from_pretrained(args.tokenizer_name )
														__snake_case     			=       tokenizer.special_tokens_map["bos_token"]  # `<|endoftext|>`
														__snake_case     			=       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:
														__snake_case     			=       fp.readlines()

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

							__snake_case     			=       []
							__snake_case     			=       0
							__snake_case     			=       1_00_00
							__snake_case     			=       time.time()
							for text in data:
														__snake_case     			=       F'''{bos} {text.strip()} {sep}'''
														__snake_case     			=       tokenizer.encode(SCREAMING_SNAKE_CASE							,    add_special_tokens=SCREAMING_SNAKE_CASE )
														rslt.append(SCREAMING_SNAKE_CASE )

														iter += 1
														if iter % interval == 0:
																					__snake_case     			=       time.time()
																					logger.info(F'''{iter} examples processed. - {(end-start):.2f}s/{interval}expl''' )
																					__snake_case     			=       time.time()
							logger.info("Finished binarization" )
							logger.info(F'''{len(SCREAMING_SNAKE_CASE )} examples processed.''' )

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


if __name__ == "__main__":
		main()


"""simple docstring"""







from heapq import heappop, heappush

import numpy as np
def   __UpperCamelCase   (					SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE							,    )  ->     tuple[float | int, list[tuple[int, int]]]:







							"""simple docstring"""


							__snake_case			,   __snake_case     			=       grid.shape
							__snake_case     			=       [-1, 1, 0, 0]
							__snake_case     			=       [0, 0, -1, 1]
							if allow_diagonal:
														dx += [-1, -1, 1, 1]
														dy += [-1, 1, -1, 1]

							__snake_case			,   __snake_case     			=       [(0, source)], set()
							__snake_case     			=       np.full((rows, cols)							,    np.inf )
							__snake_case     			=       0
							__snake_case     			=       np.empty((rows, cols)							,    dtype=SCREAMING_SNAKE_CASE )
							__snake_case     			=       None

							while queue:
														((__snake_case)			,   (__snake_case))     			=       heappop(SCREAMING_SNAKE_CASE )
														if (x, y) in visited:
																					continue
														visited.add((x, y) )

														if (x, y) == destination:
																					__snake_case     			=       []
																					while (x, y) != source:
																												path.append((x, y) )
																												__snake_case			,   __snake_case     			=       predecessors[x, y]
																					path.append(SCREAMING_SNAKE_CASE )  # add the source manually
																					path.reverse()
																					return matrix[destination], path

														for i in range(len(SCREAMING_SNAKE_CASE ) ):
																					__snake_case			,   __snake_case     			=       x + dx[i], y + dy[i]
																					if 0 <= nx < rows and 0 <= ny < cols:
																												__snake_case     			=       grid[nx][ny]
																												if next_node == 1 and matrix[nx, ny] > dist + 1:
																																			heappush(SCREAMING_SNAKE_CASE							,    (dist + 1, (nx, ny)) )
																																			__snake_case     			=       dist + 1
																																			__snake_case     			=       (x, y)

							return np.inf, []


if __name__ == "__main__":
		import doctest

		doctest.testmod()
