
'''simple docstring'''




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)
lowercase			:				Optional[int]    		=			logging.getLogger()







def 		lowerCAmelCase_				(   snake_case__		,				snake_case__						):




    '''simple docstring'''



    A       :   List[Any]					=						'''\n'''.join(snake_case__						)
    Path(snake_case__						).open('''w'''						).writelines(snake_case__						)


lowercase			:				int    		=			'patrickvonplaten/t5-tiny-random'
lowercase			:				Union[str, Any]    		=			'sshleifer/bart-tiny-random'
lowercase			:				Optional[int]    		=			'sshleifer/tiny-mbart'

lowercase			:				str    		=			logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
logging.disable(logging.CRITICAL)  # remove noisy download output from tracebacks







class  A				(							__snake_case						):





     def 				__lowerCAmelCase							(							self						,			SCREAMING_SNAKE_CASE	) ->				Dict:






         """simple docstring"""






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

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

         with patch.object(SCREAMING_SNAKE_CASE						,			'''argv'''						,			SCREAMING_SNAKE_CASE	):
             run_generate()
             assert Path(SCREAMING_SNAKE_CASE	).exists()
             # os.remove(Path(output_file_name))





     def 				__lowerCAmelCase							(							self	) ->				int:






         """simple docstring"""






         self.run_eval_tester(SCREAMING_SNAKE_CASE	)





     @parameterized.expand([BART_TINY, MBART_TINY]	)
     @slow
     def 				__lowerCAmelCase							(							self						,			SCREAMING_SNAKE_CASE	) ->				Optional[Any]:






         """simple docstring"""






         self.run_eval_tester(SCREAMING_SNAKE_CASE	)





     @parameterized.expand([T5_TINY, MBART_TINY]	)
     @slow
     def 				__lowerCAmelCase							(							self						,			SCREAMING_SNAKE_CASE	) ->				Dict:






         """simple docstring"""






         A       :   List[str]					=						Path(self.get_auto_remove_tmp_dir()	) / '''utest_input.source'''
         A       :   Optional[Any]					=						input_file_name.parent / '''utest_output.txt'''
         assert not output_file_name.exists()

         A       :   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!''',
             ],
         }

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

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






'''simple docstring'''




import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
      from .tokenization_rembert import RemBertTokenizer
else:
      lowercase			:				Optional[Any]    		=			None

lowercase			:				Tuple    		=			logging.get_logger(__name__)
lowercase			:				Dict    		=			{'vocab_file': 'sentencepiece.model', 'tokenizer_file': 'tokenizer.json'}

lowercase			:				Tuple    		=			{
    'vocab_file': {
        'google/rembert': 'https://huggingface.co/google/rembert/resolve/main/sentencepiece.model',
    },
    'tokenizer_file': {
        'google/rembert': 'https://huggingface.co/google/rembert/resolve/main/tokenizer.json',
    },
}

lowercase			:				List[str]    		=			{
    'google/rembert': 2_56,
}

lowercase			:				Dict    		=			'▁'





class  A				(							__snake_case						):
     __magic_name__						=		VOCAB_FILES_NAMES
     __magic_name__						=		PRETRAINED_VOCAB_FILES_MAP
     __magic_name__						=		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     __magic_name__						=		RemBertTokenizer





     def __init__(							self						,			SCREAMING_SNAKE_CASE=None						,			SCREAMING_SNAKE_CASE=None						,			SCREAMING_SNAKE_CASE=True						,			SCREAMING_SNAKE_CASE=True						,			SCREAMING_SNAKE_CASE=False						,			SCREAMING_SNAKE_CASE="[CLS]"						,			SCREAMING_SNAKE_CASE="[SEP]"						,			SCREAMING_SNAKE_CASE="<unk>"						,			SCREAMING_SNAKE_CASE="[SEP]"						,			SCREAMING_SNAKE_CASE="<pad>"						,			SCREAMING_SNAKE_CASE="[CLS]"						,			SCREAMING_SNAKE_CASE="[MASK]"						,			**SCREAMING_SNAKE_CASE						,			) ->				List[str]:






         """simple docstring"""






         A       :   Optional[Any]					=						AddedToken(SCREAMING_SNAKE_CASE						,			lstrip=SCREAMING_SNAKE_CASE						,			rstrip=SCREAMING_SNAKE_CASE	) if isinstance(SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE	) else mask_token

         super().__init__(
             SCREAMING_SNAKE_CASE						,			tokenizer_file=SCREAMING_SNAKE_CASE						,			do_lower_case=SCREAMING_SNAKE_CASE						,			remove_space=SCREAMING_SNAKE_CASE						,			keep_accents=SCREAMING_SNAKE_CASE						,			bos_token=SCREAMING_SNAKE_CASE						,			eos_token=SCREAMING_SNAKE_CASE						,			unk_token=SCREAMING_SNAKE_CASE						,			sep_token=SCREAMING_SNAKE_CASE						,			pad_token=SCREAMING_SNAKE_CASE						,			cls_token=SCREAMING_SNAKE_CASE						,			mask_token=SCREAMING_SNAKE_CASE						,			**SCREAMING_SNAKE_CASE						,			)

         A       :   List[Any]					=						do_lower_case
         A       :   str					=						remove_space
         A       :   int					=						keep_accents
         A       :   Union[str, Any]					=						vocab_file
         A       :   List[Any]					=						False if not self.vocab_file else True





     def 				__lowerCAmelCase							(							self						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE = None	) ->				List[int]:






         """simple docstring"""






         A       :   List[Any]					=						[self.sep_token_id]
         A       :   Tuple					=						[self.cls_token_id]
         if token_ids_a is None:
             return cls + token_ids_a + sep
         return cls + token_ids_a + sep + token_ids_a + sep





     def 				__lowerCAmelCase							(							self						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE = None						,			SCREAMING_SNAKE_CASE = False	) ->				List[int]:






         """simple docstring"""






         if already_has_special_tokens:
             if token_ids_a is not None:
                 raise ValueError(
                     '''You should not supply a second sequence if the provided sequence of '''
                     '''ids is already formatted with special tokens for the model.'''	)
             return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]

         if token_ids_a is not None:
             return [1] + ([0] * len(SCREAMING_SNAKE_CASE	)) + [1] + ([0] * len(SCREAMING_SNAKE_CASE	)) + [1]
         return [1] + ([0] * len(SCREAMING_SNAKE_CASE	)) + [1]





     def 				__lowerCAmelCase							(							self						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE = None	) ->				List[int]:






         """simple docstring"""






         A       :   Tuple					=						[self.sep_token_id]
         A       :   Dict					=						[self.cls_token_id]

         if token_ids_a is None:
             return len(cls + token_ids_a + sep	) * [0]
         return len(cls + token_ids_a + sep	) * [0] + len(token_ids_a + sep	) * [1]





     def 				__lowerCAmelCase							(							self						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE = None	) ->				Tuple[str]:






         """simple docstring"""






         if not os.path.isdir(SCREAMING_SNAKE_CASE	):
             logger.error('''Vocabulary path ({}) should be a directory'''.format(SCREAMING_SNAKE_CASE	)	)
             return
         A       :   Any					=						os.path.join(
             SCREAMING_SNAKE_CASE						,			(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']	)

         if os.path.abspath(self.vocab_file	) != os.path.abspath(SCREAMING_SNAKE_CASE	):
             copyfile(self.vocab_file						,			SCREAMING_SNAKE_CASE	)

         return (out_vocab_file,)




