


"""simple docstring"""






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

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


UpperCamelCase__				:List[Any]			   =	logging.get_logger(__name__)

UpperCamelCase__				:List[str]			   =	{"""vocab_file""": """sentencepiece.bpe.model"""}

UpperCamelCase__				:List[str]			   =	{
    """vocab_file""": {
        """moussaKam/mbarthez""": """https://huggingface.co/moussaKam/mbarthez/resolve/main/sentencepiece.bpe.model""",
        """moussaKam/barthez""": """https://huggingface.co/moussaKam/barthez/resolve/main/sentencepiece.bpe.model""",
        """moussaKam/barthez-orangesum-title""": (
            """https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/sentencepiece.bpe.model"""
        ),
    },
}

UpperCamelCase__				:Dict			   =	{
    """moussaKam/mbarthez""": 1_024,
    """moussaKam/barthez""": 1_024,
    """moussaKam/barthez-orangesum-title""": 1_024,
}

UpperCamelCase__				:List[str]			   =	"""▁"""






class      A(  lowerCamelCase__		):

		"""simple docstring"""





		A    =							VOCAB_FILES_NAMES
		A    =							PRETRAINED_VOCAB_FILES_MAP
		A    =							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		A    =							["input_ids", "attention_mask"]
		def __init__( self					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__="<s>"					,				SCREAMING_SNAKE_CASE__="</s>"					,				SCREAMING_SNAKE_CASE__="</s>"					,				SCREAMING_SNAKE_CASE__="<s>"					,				SCREAMING_SNAKE_CASE__="<unk>"					,				SCREAMING_SNAKE_CASE__="<pad>"					,				SCREAMING_SNAKE_CASE__="<mask>"					,				SCREAMING_SNAKE_CASE__ = None					,				**SCREAMING_SNAKE_CASE__					,				)      ->	None:

					"""simple docstring"""



					_UpperCamelCase					:int					     =	AddedToken(SCREAMING_SNAKE_CASE__					,				lstrip=SCREAMING_SNAKE_CASE__					,				rstrip=SCREAMING_SNAKE_CASE__   ) if isinstance(SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__   ) else mask_token

					_UpperCamelCase					:Optional[int]					     =	{} if sp_model_kwargs is None else sp_model_kwargs

					super().__init__(
					    bos_token=SCREAMING_SNAKE_CASE__					,				eos_token=SCREAMING_SNAKE_CASE__					,				unk_token=SCREAMING_SNAKE_CASE__					,				sep_token=SCREAMING_SNAKE_CASE__					,				cls_token=SCREAMING_SNAKE_CASE__					,				pad_token=SCREAMING_SNAKE_CASE__					,				mask_token=SCREAMING_SNAKE_CASE__					,				sp_model_kwargs=self.sp_model_kwargs					,				**SCREAMING_SNAKE_CASE__					,				)

					_UpperCamelCase					:Union[str, Any]					     =	vocab_file
					_UpperCamelCase					:Union[str, Any]					     =	spm.SentencePieceProcessor(**self.sp_model_kwargs   )
					self.sp_model.Load(str(SCREAMING_SNAKE_CASE__   )   )

					_UpperCamelCase					:Dict					     =	{'''<s>''': 0, '''<pad>''': 1, '''</s>''': 2, '''<unk>''': 3}

					_UpperCamelCase					:str					     =	len(self.sp_model   ) - 1
					_UpperCamelCase					:Dict					     =	{v: k for k, v in self.fairseq_tokens_to_ids.items()}
		def   _UpperCamelCase( self					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__ = None   )      ->	List[int]:

					"""simple docstring"""



					if token_ids_a is None:
								return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
					_UpperCamelCase					:int					     =	[self.cls_token_id]
					_UpperCamelCase					:Optional[int]					     =	[self.sep_token_id]
					return cls + token_ids_a + sep + sep + token_ids_a + sep
		def   _UpperCamelCase( self					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__ = None					,				SCREAMING_SNAKE_CASE__ = False   )      ->	List[int]:

					"""simple docstring"""



					if already_has_special_tokens:
								return super().get_special_tokens_mask(
								    token_ids_a=SCREAMING_SNAKE_CASE__					,				token_ids_a=SCREAMING_SNAKE_CASE__					,				already_has_special_tokens=SCREAMING_SNAKE_CASE__   )

					if token_ids_a is None:
								return [1] + ([0] * len(SCREAMING_SNAKE_CASE__   )) + [1]
					return [1] + ([0] * len(SCREAMING_SNAKE_CASE__   )) + [1, 1] + ([0] * len(SCREAMING_SNAKE_CASE__   )) + [1]
		def   _UpperCamelCase( self					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__ = None   )      ->	List[int]:

					"""simple docstring"""



					_UpperCamelCase					:List[str]					     =	[self.sep_token_id]
					_UpperCamelCase					:Any					     =	[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 + sep + token_ids_a + sep   ) * [0]
		@property
		def   _UpperCamelCase( self   )      ->	Optional[int]:

					"""simple docstring"""



					return len(self.sp_model   )
		def   _UpperCamelCase( self   )      ->	Tuple:

					"""simple docstring"""



					_UpperCamelCase					:List[Any]					     =	{self.convert_ids_to_tokens(SCREAMING_SNAKE_CASE__   ): i for i in range(self.vocab_size   )}
					vocab.update(self.added_tokens_encoder   )
					return vocab
		def   _UpperCamelCase( self					,				SCREAMING_SNAKE_CASE__   )      ->	List[str]:

					"""simple docstring"""



					return self.sp_model.encode(SCREAMING_SNAKE_CASE__					,				out_type=SCREAMING_SNAKE_CASE__   )
		def   _UpperCamelCase( self					,				SCREAMING_SNAKE_CASE__   )      ->	int:

					"""simple docstring"""



					if token in self.fairseq_tokens_to_ids:
								return self.fairseq_tokens_to_ids[token]
					_UpperCamelCase					:Tuple					     =	self.sp_model.PieceToId(SCREAMING_SNAKE_CASE__   )

					return spm_id if spm_id else self.unk_token_id
		def   _UpperCamelCase( self					,				SCREAMING_SNAKE_CASE__   )      ->	str:

					"""simple docstring"""



					if index in self.fairseq_ids_to_tokens:
								return self.fairseq_ids_to_tokens[index]
					return self.sp_model.IdToPiece(SCREAMING_SNAKE_CASE__   )
		def   _UpperCamelCase( self					,				SCREAMING_SNAKE_CASE__   )      ->	List[Any]:

					"""simple docstring"""



					_UpperCamelCase					:List[str]					     =	[]
					_UpperCamelCase					:Optional[int]					     =	''''''
					_UpperCamelCase					:List[Any]					     =	False
					for token in tokens:
								# make sure that special tokens are not decoded using sentencepiece model
								if token in self.all_special_tokens:
											if not prev_is_special:
														out_string += " "
											out_string += self.sp_model.decode(SCREAMING_SNAKE_CASE__   ) + token
											_UpperCamelCase					:List[str]					     =	True
											_UpperCamelCase					:int					     =	[]
								else:
											current_sub_tokens.append(SCREAMING_SNAKE_CASE__   )
											_UpperCamelCase					:Union[str, Any]					     =	False
					out_string += self.sp_model.decode(SCREAMING_SNAKE_CASE__   )
					return out_string.strip()
		def __getstate__( self   )      ->	Dict:

					"""simple docstring"""



					_UpperCamelCase					:List[Any]					     =	self.__dict__.copy()
					_UpperCamelCase					:Dict					     =	None
					return state
		def __setstate__( self					,				SCREAMING_SNAKE_CASE__   )      ->	Optional[Any]:

					"""simple docstring"""



					_UpperCamelCase					:Optional[int]					     =	d

					# for backward compatibility
					if not hasattr(self					,				'''sp_model_kwargs'''   ):
								_UpperCamelCase					:Tuple					     =	{}

					_UpperCamelCase					:Optional[int]					     =	spm.SentencePieceProcessor(**self.sp_model_kwargs   )
					self.sp_model.Load(self.vocab_file   )





		def   _UpperCamelCase( self					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__ = None   )      ->	Tuple[str]:

					"""simple docstring"""



					if not os.path.isdir(SCREAMING_SNAKE_CASE__   ):
								logger.error(f"Vocabulary path ({save_directory}) should be a directory"   )
								return
					_UpperCamelCase					:Tuple					     =	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__   ) and os.path.isfile(self.vocab_file   ):
								copyfile(self.vocab_file					,				SCREAMING_SNAKE_CASE__   )
					elif not os.path.isfile(self.vocab_file   ):
								with open(SCREAMING_SNAKE_CASE__					,				'''wb'''   ) as fi:
											_UpperCamelCase					:List[str]					     =	self.sp_model.serialized_model_proto()
											fi.write(SCREAMING_SNAKE_CASE__   )

					return (out_vocab_file,)





"""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)
UpperCamelCase__				:str			   =	logging.getLogger()





def 				A_       ( snake_case__       ,					snake_case__		)					->  Optional[Any]:
			_UpperCamelCase					:Optional[int]					     =	'''\n'''.join(snake_case__		)
			Path(snake_case__		).open('''w'''		).writelines(snake_case__		)


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

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






class      A(  lowerCamelCase__		):

		"""simple docstring"""





		def   _UpperCamelCase( self					,				SCREAMING_SNAKE_CASE__   )      ->	List[Any]:

					"""simple docstring"""



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

					_UpperCamelCase					:str					     =	str(Path(self.get_auto_remove_tmp_dir()   ) / '''scores.json'''   )
					_UpperCamelCase					:Any					     =	'''translation_en_to_de''' if model == T5_TINY else '''summarization'''
					_UpperCamelCase					:Union[str, Any]					     =	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   _UpperCamelCase( self   )      ->	Optional[int]:

					"""simple docstring"""



					self.run_eval_tester(SCREAMING_SNAKE_CASE__   )
		@parameterized.expand([BART_TINY, MBART_TINY]   )
		@slow
		def   _UpperCamelCase( self					,				SCREAMING_SNAKE_CASE__   )      ->	List[str]:

					"""simple docstring"""



					self.run_eval_tester(SCREAMING_SNAKE_CASE__   )





		@parameterized.expand([T5_TINY, MBART_TINY]   )
		@slow
		def   _UpperCamelCase( self					,				SCREAMING_SNAKE_CASE__   )      ->	Optional[Any]:

					"""simple docstring"""



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

					_UpperCamelCase					:Union[str, Any]					     =	{
					    '''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!''',
					    ],
					}

					_UpperCamelCase					:str					     =	Path(self.get_auto_remove_tmp_dir()   )
					_UpperCamelCase					:Optional[int]					     =	str(tmp_dir / '''scores.json'''   )
					_UpperCamelCase					:int					     =	str(tmp_dir / '''val.target'''   )
					_dump_articles(SCREAMING_SNAKE_CASE__					,				text['''en''']   )
					_dump_articles(SCREAMING_SNAKE_CASE__					,				text['''de''']   )
					_UpperCamelCase					:List[str]					     =	'''translation_en_to_de''' if model == T5_TINY else '''summarization'''
					_UpperCamelCase					: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()
								_UpperCamelCase					:Union[str, Any]					     =	[''' num_beams | length_penalty''', model, '''Best score args''']
								_UpperCamelCase					:List[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__   )   )

