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

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


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

_lowerCamelCase	:    Optional[int]          =      {'''vocab_file''': '''sentencepiece.model'''}

_lowerCamelCase	:    str          =      {
    '''vocab_file''': {
        '''google/rembert''': '''https://huggingface.co/google/rembert/resolve/main/sentencepiece.model''',
    },
}

_lowerCamelCase	:    Optional[int]          =      {
    '''google/rembert''': 256,
}




class        SCREAMING_SNAKE_CASE__					(     UpperCAmelCase ):





	'''simple docstring'''



	_UpperCAmelCase	:		List[Any]          = VOCAB_FILES_NAMES
	_UpperCAmelCase	:		Optional[Any]          = PRETRAINED_VOCAB_FILES_MAP
	_UpperCAmelCase	:		List[Any]          = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES



	def __init__(  self     :   Optional[int]      ,  lowercase     :   int      ,  lowercase     :   Any=False      ,  lowercase     :   str=True      ,  lowercase     :   Union[str, Any]=True      ,  lowercase     :   Optional[int]="[CLS]"      ,  lowercase     :   str="[SEP]"      ,  lowercase     :   Optional[Any]="[UNK]"      ,  lowercase     :   Optional[Any]="[SEP]"      ,  lowercase     :   List[Any]="[PAD]"      ,  lowercase     :   Dict="[CLS]"      ,  lowercase     :   List[str]="[MASK]"      ,  **lowercase     :   str      ,  ):


				'''simple docstring'''


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

				_snake_case          =      do_lower_case
				_snake_case          =      remove_space
				_snake_case          =      keep_accents
				_snake_case          =      vocab_file

				_snake_case          =      spm.SentencePieceProcessor()
				self.sp_model.Load(lowercase     )



	@property
	def 			A							(  self     :   List[Any]     ):


				'''simple docstring'''


				return len(self.sp_model     )



	def 			A							(  self     :   Any     ):


				'''simple docstring'''


				_snake_case          =      {self.convert_ids_to_tokens(lowercase     ): i for i in range(self.vocab_size     )}
				vocab.update(self.added_tokens_encoder     )
				return vocab



	def __getstate__(  self     :   Union[str, Any]     ):


				'''simple docstring'''


				_snake_case          =      self.__dict__.copy()
				_snake_case          =      None
				return state



	def __setstate__(  self     :   Optional[int]      ,  lowercase     :   List[str]     ):


				'''simple docstring'''


				_snake_case          =      d
				_snake_case          =      spm.SentencePieceProcessor()
				self.sp_model.Load(self.vocab_file     )



	def 			A							(  self     :   str      ,  lowercase     :   Union[str, Any]      ,  lowercase     :   Any=False     ):


				'''simple docstring'''


				_snake_case          =      self.sp_model.EncodeAsPieces(lowercase     )
				return pieces



	def 			A							(  self     :   Tuple      ,  lowercase     :   int     ):


				'''simple docstring'''


				return self.sp_model.PieceToId(lowercase     )



	def 			A							(  self     :   List[str]      ,  lowercase     :   List[Any]     ):


				'''simple docstring'''


				return self.sp_model.IdToPiece(lowercase     )



	def 			A							(  self     :   Any      ,  lowercase     :   Optional[int]     ):


				'''simple docstring'''


				_snake_case          =      self.sp_model.decode_pieces(lowercase     )
				return out_string



	def 			A							(  self     :   Dict      ,  lowercase     :   List[int]      ,  lowercase     :   Optional[List[int]] = None     ):


				'''simple docstring'''


				_snake_case          =      [self.sep_token_id]
				_snake_case          =      [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 			A							(  self     :   int      ,  lowercase     :   List[int]      ,  lowercase     :   Optional[List[int]] = None      ,  lowercase     :   bool = False     ):


				'''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(lowercase     )) + [1] + ([0] * len(lowercase     )) + [1]
				return [1] + ([0] * len(lowercase     )) + [1]



	def 			A							(  self     :   Optional[Any]      ,  lowercase     :   List[int]      ,  lowercase     :   Optional[List[int]] = None     ):


				'''simple docstring'''


				_snake_case          =      [self.sep_token_id]
				_snake_case          =      [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 			A							(  self     :   Union[str, Any]      ,  lowercase     :   str      ,  lowercase     :   Optional[str] = None     ):


				'''simple docstring'''


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

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

				return (out_vocab_file,)

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)
_lowerCamelCase	:    Union[str, Any]          =      logging.getLogger()






def 	a_		(	__lowercase       :     Path	, __lowercase       :     list			)			->  Tuple:
			_snake_case          =      '\n'.join(__lowercase			)
			Path(__lowercase			).open('w'			).writelines(__lowercase			)


_lowerCamelCase	:    Any          =      '''patrickvonplaten/t5-tiny-random'''
_lowerCamelCase	:    List[Any]          =      '''sshleifer/bart-tiny-random'''
_lowerCamelCase	:    List[Any]          =      '''sshleifer/tiny-mbart'''

_lowerCamelCase	:    Union[str, Any]          =      logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
logging.disable(logging.CRITICAL)  # remove noisy download output from tracebacks




class        SCREAMING_SNAKE_CASE__					(     UpperCAmelCase ):





	'''simple docstring'''



	def 			A							(  self     :   Optional[int]      ,  lowercase     :   int     ):


				'''simple docstring'''


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

				_snake_case          =      str(Path(self.get_auto_remove_tmp_dir()     ) / 'scores.json'     )
				_snake_case          =      'translation_en_to_de' if model == T5_TINY else 'summarization'
				_snake_case          =      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(lowercase      ,  'argv'      ,  lowercase     ):
							run_generate()
							assert Path(lowercase     ).exists()
							# os.remove(Path(output_file_name))



	def 			A							(  self     :   List[Any]     ):


				'''simple docstring'''


				self.run_eval_tester(lowercase     )



	@parameterized.expand([BART_TINY, MBART_TINY]     )
	@slow
	def 			A							(  self     :   Any      ,  lowercase     :   int     ):


				'''simple docstring'''


				self.run_eval_tester(lowercase     )





	@parameterized.expand([T5_TINY, MBART_TINY]     )
	@slow
	def 			A							(  self     :   Any      ,  lowercase     :   str     ):


				'''simple docstring'''


				_snake_case          =      Path(self.get_auto_remove_tmp_dir()     ) / 'utest_input.source'
				_snake_case          =      input_file_name.parent / 'utest_output.txt'
				assert not output_file_name.exists()

				_snake_case          =      {
				    '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!',
				    ],
				}

				_snake_case          =      Path(self.get_auto_remove_tmp_dir()     )
				_snake_case          =      str(tmp_dir / 'scores.json'     )
				_snake_case          =      str(tmp_dir / 'val.target'     )
				_dump_articles(lowercase      ,  text['en']     )
				_dump_articles(lowercase      ,  text['de']     )
				_snake_case          =      'translation_en_to_de' if model == T5_TINY else 'summarization'
				_snake_case          =      f'''
            run_eval_search.py
            {model}
            {str(lowercase     )}
            {str(lowercase     )}
            --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(lowercase      ,  'argv'      ,  lowercase     ):
							with CaptureStdout() as cs:
										run_search()
							_snake_case          =      [' num_beams | length_penalty', model, 'Best score args']
							_snake_case          =      ['Info']
							if "translation" in task:
										expected_strings.append('bleu'     )
							else:
										expected_strings.extend(lowercase     )
							for w in expected_strings:
										assert w in cs.out
							for w in un_expected_strings:
										assert w not in cs.out
							assert Path(lowercase     ).exists()
							os.remove(Path(lowercase     )     )