





'''simple docstring'''







import json
import os
import unittest

from transformers.models.fsmt.tokenization_fsmt import VOCAB_FILES_NAMES, FSMTTokenizer
from transformers.testing_utils import slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


# using a different tiny model than the one used for default params defined in init to ensure proper testing
snake_case_  :							str            =	"stas/tiny-wmt19-en-ru"


class        __a   (lowerCamelCase							,		unittest.TestCase  ):
							__a       : Any          =							FSMTTokenizer
							__a       : List[Any]          =							False

							def      UpperCAmelCase__						(				self				:  str )       ->							Dict:







													"""simple docstring"""
													super().setUp()

													# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
													UpperCAmelCase_      :   Optional[int]      =	[
													    '''l''',
													    '''o''',
													    '''w''',
													    '''e''',
													    '''r''',
													    '''s''',
													    '''t''',
													    '''i''',
													    '''d''',
													    '''n''',
													    '''w</w>''',
													    '''r</w>''',
													    '''t</w>''',
													    '''lo''',
													    '''low''',
													    '''er</w>''',
													    '''low</w>''',
													    '''lowest</w>''',
													    '''newer</w>''',
													    '''wider</w>''',
													    '''<unk>''',
													]
													UpperCAmelCase_      :   Optional[int]      =	dict(zip(__magic_name__							,  range(len(__magic_name__ ) ) ) )
													UpperCAmelCase_      :   Any      =	['''l o 123''', '''lo w 1456''', '''e r</w> 1789''', '''''']

													UpperCAmelCase_      :   str      =	['''en''', '''ru''']
													UpperCAmelCase_      :   Tuple      =	{
													    '''langs''': self.langs,
													    '''src_vocab_size''': 10,
													    '''tgt_vocab_size''': 20,
													}

													UpperCAmelCase_      :   Tuple      =	os.path.join(self.tmpdirname							,  VOCAB_FILES_NAMES['''src_vocab_file'''] )
													UpperCAmelCase_      :   Any      =	os.path.join(self.tmpdirname							,  VOCAB_FILES_NAMES['''tgt_vocab_file'''] )
													UpperCAmelCase_      :   Optional[Any]      =	os.path.join(self.tmpdirname							,  '''tokenizer_config.json''' )
													UpperCAmelCase_      :   Union[str, Any]      =	os.path.join(self.tmpdirname							,  VOCAB_FILES_NAMES['''merges_file'''] )
													with open(self.src_vocab_file							,  '''w''' ) as fp:
																			fp.write(json.dumps(__magic_name__ ) )
													with open(self.tgt_vocab_file							,  '''w''' ) as fp:
																			fp.write(json.dumps(__magic_name__ ) )
													with open(self.merges_file							,  '''w''' ) as fp:
																			fp.write('''\n'''.join(__magic_name__ ) )
													with open(__magic_name__							,  '''w''' ) as fp:
																			fp.write(json.dumps(__magic_name__ ) )

							@cached_property
							def      UpperCAmelCase__						(				self				:  Union[str, Any] )       ->							int:







													"""simple docstring"""
													return FSMTTokenizer.from_pretrained('''facebook/wmt19-ru-en''' )

							@cached_property
							def      UpperCAmelCase__						(				self				:  str )       ->							str:







													"""simple docstring"""
													return FSMTTokenizer.from_pretrained('''facebook/wmt19-en-ru''' )

							def      UpperCAmelCase__						(				self				:  Optional[Any] )       ->							int:







													"""simple docstring"""
													UpperCAmelCase_      :   Optional[Any]      =	FSMTTokenizer.from_pretrained(__magic_name__ )
													self.assertListEqual([tokenizer.src_lang, tokenizer.tgt_lang]							,  ['''en''', '''ru'''] )
													self.assertEqual(tokenizer.src_vocab_size							,  21 )
													self.assertEqual(tokenizer.tgt_vocab_size							,  21 )

							def      UpperCAmelCase__						(				self				:  Dict )       ->							Any:







													"""simple docstring"""
													UpperCAmelCase_      :   List[Any]      =	FSMTTokenizer(self.langs							,  self.src_vocab_file							,  self.tgt_vocab_file							,  self.merges_file )

													UpperCAmelCase_      :   Tuple      =	'''lower'''
													UpperCAmelCase_      :   str      =	['''low''', '''er</w>''']
													UpperCAmelCase_      :   str      =	tokenizer.tokenize(__magic_name__ )
													self.assertListEqual(__magic_name__							,  __magic_name__ )

													UpperCAmelCase_      :   List[Any]      =	tokens + ['''<unk>''']
													UpperCAmelCase_      :   int      =	[14, 15, 20]
													self.assertListEqual(tokenizer.convert_tokens_to_ids(__magic_name__ )							,  __magic_name__ )

							@slow
							def      UpperCAmelCase__						(				self				:  Tuple )       ->							Union[str, Any]:







													"""simple docstring"""
													UpperCAmelCase_      :   Any      =	self.tokenizer_ru_en

													UpperCAmelCase_      :   List[Any]      =	tokenizer.encode('''sequence builders'''							,  add_special_tokens=__magic_name__ )
													UpperCAmelCase_      :   Dict      =	tokenizer.encode('''multi-sequence build'''							,  add_special_tokens=__magic_name__ )

													UpperCAmelCase_      :   Union[str, Any]      =	tokenizer.build_inputs_with_special_tokens(__magic_name__ )
													UpperCAmelCase_      :   List[Any]      =	tokenizer.build_inputs_with_special_tokens(__magic_name__							,  __magic_name__ )

													assert encoded_sentence == text + [2]
													assert encoded_pair == text + [2] + text_a + [2]

							@slow
							def      UpperCAmelCase__						(				self				:  Tuple )       ->							Any:







													"""simple docstring"""
													UpperCAmelCase_      :   Dict      =	self.tokenizer_en_ru
													UpperCAmelCase_      :   Tuple      =	self.tokenizer_ru_en

													UpperCAmelCase_      :   Dict      =	[
													    [
													        '''Here\'s a little song I wrote. Don\'t worry, be happy.''',
													        [24_70, 39, 11, 23_49, 72_22, 70, 59_79, 7, 84_50, 10_50, 1_31_60, 5, 26, 64_45, 7, 2],
													    ],
													    ['''This is it. No more. I\'m done!''', [1_32, 21, 37, 7, 14_34, 86, 7, 70, 64_76, 13_05, 4_27, 2]],
													]

													# if data needs to be recreated or added, run:
													# import torch
													# model = torch.hub.load("pytorch/fairseq", "transformer.wmt19.en-ru", checkpoint_file="model4.pt", tokenizer="moses", bpe="fastbpe")
													# for src_text, _ in targets: print(f"""[\n"{src_text}",\n {model.encode(src_text).tolist()}\n],""")

													for src_text, tgt_input_ids in targets:
																			UpperCAmelCase_      :   int      =	tokenizer_enc.encode(__magic_name__							,  return_tensors=__magic_name__ )
																			self.assertListEqual(__magic_name__							,  __magic_name__ )

																			# and decode backward, using the reversed languages model
																			UpperCAmelCase_      :   List[str]      =	tokenizer_dec.decode(__magic_name__							,  skip_special_tokens=__magic_name__ )
																			self.assertEqual(__magic_name__							,  __magic_name__ )

							@slow
							def      UpperCAmelCase__						(				self				:  Tuple )       ->							Tuple:







													"""simple docstring"""
													UpperCAmelCase_      :   List[Any]      =	FSMTTokenizer.from_pretrained('''facebook/wmt19-ru-en'''							,  do_lower_case=__magic_name__ )
													UpperCAmelCase_      :   Union[str, Any]      =	tokenizer.tokenize('''USA is United States of America''' )
													UpperCAmelCase_      :   Union[str, Any]      =	['''us''', '''a</w>''', '''is</w>''', '''un''', '''i''', '''ted</w>''', '''st''', '''ates</w>''', '''of</w>''', '''am''', '''er''', '''ica</w>''']
													self.assertListEqual(__magic_name__							,  __magic_name__ )

							@unittest.skip('''FSMTConfig.__init__  requires non-optional args''' )
							def      UpperCAmelCase__						(				self				:  int )       ->							Optional[Any]:







													"""simple docstring"""
													pass



							@unittest.skip('''FSMTConfig.__init__  requires non-optional args''' )
							def      UpperCAmelCase__						(				self				:  Optional[Any] )       ->							Union[str, Any]:







													"""simple docstring"""
													pass










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

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


__SCREAMING_SNAKE_CASE     =	logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE     =	{"""vocab_file""": """spiece.model""", """tokenizer_file""": """tokenizer.json"""}

__SCREAMING_SNAKE_CASE     =	{
    """vocab_file""": {
        """TsinghuaAI/CPM-Generate""": """https://huggingface.co/TsinghuaAI/CPM-Generate/resolve/main/spiece.model""",
    },
    """tokenizer_file""": {
        """TsinghuaAI/CPM-Generate""": """https://huggingface.co/TsinghuaAI/CPM-Generate/resolve/main/tokenizer.json""",
    },
}

class 						lowerCamelCase_     (			_A    ):






						'''simple docstring'''







						def __init__(						self   :      Dict						,						__lowerCamelCase   :      str=None						,						__lowerCamelCase   :      int=None						,						__lowerCamelCase   :      Optional[Any]=False						,						__lowerCamelCase   :      List[Any]=True						,						__lowerCamelCase   :      int=False						,						__lowerCamelCase   :      Optional[int]="<s>"						,						__lowerCamelCase   :      Union[str, Any]="</s>"						,						__lowerCamelCase   :      Dict="<unk>"						,						__lowerCamelCase   :      List[str]="<sep>"						,						__lowerCamelCase   :      Any="<pad>"						,						__lowerCamelCase   :      Optional[Any]="<cls>"						,						__lowerCamelCase   :      Union[str, Any]="<mask>"						,						__lowerCamelCase   :      List[str]=["<eop>", "<eod>"]						,						**__lowerCamelCase   :      Optional[Any]						,						)   ->					Optional[int]:
									A				:       Any														=    AddedToken(__lowerCamelCase						,						lstrip=__lowerCamelCase						,						rstrip=__lowerCamelCase			) if isinstance(__lowerCamelCase						,						__lowerCamelCase			) else mask_token

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

									A				:       str														=    3
									A				:       str														=    do_lower_case
									A				:       int														=    remove_space
									A				:       Optional[int]														=    keep_accents
									A				:       Tuple														=    vocab_file
									A				:       int														=    False if not self.vocab_file else True

									try:
												import jieba
									except ModuleNotFoundError as error:
												raise error.__class__(
												    "You need to install jieba to use CpmTokenizer or CpmTokenizerFast. "
												    "See https://pypi.org/project/jieba/ for installation."			)
									A				:       str														=    jieba
									A				:       Optional[Any]														=    str.maketrans(" \n"						,						"\u2582\u2583"			)







						def       SCREAMING_SNAKE_CASE__		(						self   :      List[str]						,						__lowerCamelCase   :      List[int]						,						__lowerCamelCase   :      Optional[List[int]] = None			)   ->					List[int]:
									A				:       Dict														=    [self.sep_token_id]
									A				:       Tuple														=    [self.cls_token_id]
									if token_ids_a is None:
												return token_ids_a + sep + cls
									return token_ids_a + sep + token_ids_a + sep + cls







						def       SCREAMING_SNAKE_CASE__		(						self   :      int						,						__lowerCamelCase   :      List[int]						,						__lowerCamelCase   :      Optional[List[int]] = None			)   ->					List[int]:
									A				:       Tuple														=    [self.sep_token_id]
									A				:       Dict														=    [2]

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







						def       SCREAMING_SNAKE_CASE__		(						self   :      int						,						__lowerCamelCase   :      str						,						__lowerCamelCase   :      Optional[str] = None			)   ->					Tuple[str]:
									if not self.can_save_slow_tokenizer:
												raise ValueError(
												    "Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
												    "tokenizer."			)

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

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

									return (out_vocab_file,)







						def       SCREAMING_SNAKE_CASE__		(						self   :      Any						,						__lowerCamelCase   :      Dict						,						*__lowerCamelCase   :      List[str]						,						**__lowerCamelCase   :      Tuple			)   ->					str:
									A				:       Union[str, Any]														=    [
									    " ".join([x.translate(self.translator			) for x in self.jieba.cut(__lowerCamelCase						,						cut_all=__lowerCamelCase			)]			)
									    for text in batch_text_or_text_pairs
									]
									return super()._batch_encode_plus(__lowerCamelCase						,						*__lowerCamelCase						,						**__lowerCamelCase			)







						def       SCREAMING_SNAKE_CASE__		(						self   :      Any						,						*__lowerCamelCase   :      Optional[Any]						,						**__lowerCamelCase   :      Optional[int]			)   ->					Tuple:
									A				:       Optional[Any]														=    super()._decode(*__lowerCamelCase						,						**__lowerCamelCase			)
									A				:       Tuple														=    text.replace(" "						,						""			).replace("\u2582"						,						" "			).replace("\u2583"						,						"\n"			)
									return text