


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 :   Any							      =				logging.getLogger(__name__)



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

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

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

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

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

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


if __name__ == "__main__":
	main()






import logging
import os
import sys
from pathlib import Path
from unittest.mock import patch

from parameterized import parameterized
from run_eval import run_generate
from run_eval_search import run_search

from transformers.testing_utils import CaptureStdout, TestCasePlus, slow
from utils import ROUGE_KEYS


logging.basicConfig(level=logging.DEBUG)
SCREAMING_SNAKE_CASE :   Optional[Any]							      =				logging.getLogger()



def        UpperCamelCase_(   lowerCamelCase_							,				lowerCamelCase_				)					->		Optional[Any]:
    _lowercase :							List[Any]   	=	'\n'.join(lowerCamelCase_				)
    Path(lowerCamelCase_				).open('w'				).writelines(lowerCamelCase_				)


SCREAMING_SNAKE_CASE :   str							      =				"patrickvonplaten/t5-tiny-random"
SCREAMING_SNAKE_CASE :   List[Any]							      =				"sshleifer/bart-tiny-random"
SCREAMING_SNAKE_CASE :   int							      =				"sshleifer/tiny-mbart"

SCREAMING_SNAKE_CASE :   Optional[int]							      =				logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
logging.disable(logging.CRITICAL)  # remove noisy download output from tracebacks






class _lowerCamelCase(		_a     ):
       def     UpperCamelCase  (      self,   lowerCamelCase) ->     Dict:




           """simple docstring"""

           _lowercase :							int   	=	Path(self.get_auto_remove_tmp_dir()) / 'utest_input.source'
           _lowercase :							str   	=	input_file_name.parent / 'utest_output.txt'
           assert not output_file_name.exists()
           _lowercase :							str   	=	[' New York (CNN)When Liana Barrientos was 23 years old, she got married in Westchester County.']
           _dump_articles(lowerCamelCase,   lowerCamelCase)

           _lowercase :							List[str]   	=	str(Path(self.get_auto_remove_tmp_dir()) / 'scores.json')
           _lowercase :							Optional[int]   	=	'translation_en_to_de' if model == T5_TINY else 'summarization'
           _lowercase :							Any   	=	F'''
            run_eval_search.py
            {model}
            {input_file_name}
            {output_file_name}
            --score_path {score_path}
            --task {task}
            --num_beams 2
            --length_penalty 2.0
            '''.split()

           with patch.object(lowerCamelCase,   'argv',   lowerCamelCase):
               run_generate()
               assert Path(lowerCamelCase).exists()
               # os.remove(Path(output_file_name))
       def     UpperCamelCase  (      self) ->     Dict:




           """simple docstring"""

           self.run_eval_tester(lowerCamelCase)
       @parameterized.expand([BART_TINY, MBART_TINY])
       @slow
       def     UpperCamelCase  (      self,   lowerCamelCase) ->     int:




           """simple docstring"""

           self.run_eval_tester(lowerCamelCase)




       @parameterized.expand([T5_TINY, MBART_TINY])
       @slow
       def     UpperCamelCase  (      self,   lowerCamelCase) ->     List[str]:




           """simple docstring"""

           _lowercase :							str   	=	Path(self.get_auto_remove_tmp_dir()) / 'utest_input.source'
           _lowercase :							Any   	=	input_file_name.parent / 'utest_output.txt'
           assert not output_file_name.exists()

           _lowercase :							List[str]   	=	{
               'en': ['Machine learning is great, isn\'t it?', 'I like to eat bananas', 'Tomorrow is another great day!'],
               'de': [
                   'Maschinelles Lernen ist großartig, oder?',
                   'Ich esse gerne Bananen',
                   'Morgen ist wieder ein toller Tag!',
               ],
           }

           _lowercase :							Optional[Any]   	=	Path(self.get_auto_remove_tmp_dir())
           _lowercase :							Optional[Any]   	=	str(tmp_dir / 'scores.json')
           _lowercase :							str   	=	str(tmp_dir / 'val.target')
           _dump_articles(lowerCamelCase,   text['en'])
           _dump_articles(lowerCamelCase,   text['de'])
           _lowercase :							Tuple   	=	'translation_en_to_de' if model == T5_TINY else 'summarization'
           _lowercase :							Tuple   	=	F'''
            run_eval_search.py
            {model}
            {str(lowerCamelCase)}
            {str(lowerCamelCase)}
            --score_path {score_path}
            --reference_path {reference_path}
            --task {task}
            '''.split()
           testargs.extend(['--search', 'num_beams=1:2 length_penalty=0.9:1.0'])

           with patch.object(lowerCamelCase,   'argv',   lowerCamelCase):
               with CaptureStdout() as cs:
                   run_search()
               _lowercase :							Dict   	=	[' num_beams | length_penalty', model, 'Best score args']
               _lowercase :							Optional[Any]   	=	['Info']
               if "translation" in task:
                   expected_strings.append('bleu')
               else:
                   expected_strings.extend(lowerCamelCase)
               for w in expected_strings:
                   assert w in cs.out
               for w in un_expected_strings:
                   assert w not in cs.out
               assert Path(lowerCamelCase).exists()
               os.remove(Path(lowerCamelCase))


