
'''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
__SCREAMING_SNAKE_CASE  =			"stas/tiny-wmt19-en-ru"




class 						__snake_case (						_a				, unittest.TestCase  ):
		a_        =				FSMTTokenizer
		a_        =				False




		def   _snake_case  (     self				:int)				-> str:






								'''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_    :  str				    =    dict(zip(_SCREAMING_SNAKE_CASE ,						range(len(_SCREAMING_SNAKE_CASE))))
								UpperCamelCase_    :  Optional[Any]				    =    ["""l o 123""", """lo w 1456""", """e r</w> 1789""", """"""]

								UpperCamelCase_    :  Any				    =    ["""en""", """ru"""]
								UpperCamelCase_    :  Optional[int]				    =    {
								    """langs""": self.langs,
								    """src_vocab_size""": 1_0,
								    """tgt_vocab_size""": 2_0,
								}

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




		@cached_property
		def   _snake_case  (     self				:str)				-> Optional[int]:






								'''simple docstring'''






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




		@cached_property
		def   _snake_case  (     self				:List[Any])				-> Optional[Any]:






								'''simple docstring'''






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




		def   _snake_case  (     self				:Any)				-> List[str]:






								'''simple docstring'''






								UpperCamelCase_    :  Any				    =    FSMTTokenizer.from_pretrained(_SCREAMING_SNAKE_CASE)
								self.assertListEqual([tokenizer.src_lang, tokenizer.tgt_lang] ,						["""en""", """ru"""])
								self.assertEqual(tokenizer.src_vocab_size ,						2_1)
								self.assertEqual(tokenizer.tgt_vocab_size ,						2_1)




		def   _snake_case  (     self				:Optional[Any])				-> Optional[Any]:






								'''simple docstring'''






								UpperCamelCase_    :  Tuple				    =    FSMTTokenizer(self.langs ,						self.src_vocab_file ,						self.tgt_vocab_file ,						self.merges_file)

								UpperCamelCase_    :  Optional[Any]				    =    """lower"""
								UpperCamelCase_    :  List[Any]				    =    ["""low""", """er</w>"""]
								UpperCamelCase_    :  Any				    =    tokenizer.tokenize(_SCREAMING_SNAKE_CASE)
								self.assertListEqual(_SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE)

								UpperCamelCase_    :  Union[str, Any]				    =    tokens + ["""<unk>"""]
								UpperCamelCase_    :  int				    =    [1_4, 1_5, 2_0]
								self.assertListEqual(tokenizer.convert_tokens_to_ids(_SCREAMING_SNAKE_CASE) ,						_SCREAMING_SNAKE_CASE)




		@slow
		def   _snake_case  (     self				:Tuple)				-> str:






								'''simple docstring'''






								UpperCamelCase_    :  str				    =    self.tokenizer_ru_en

								UpperCamelCase_    :  int				    =    tokenizer.encode("""sequence builders""" ,						add_special_tokens=_SCREAMING_SNAKE_CASE)
								UpperCamelCase_    :  Optional[Any]				    =    tokenizer.encode("""multi-sequence build""" ,						add_special_tokens=_SCREAMING_SNAKE_CASE)

								UpperCamelCase_    :  Optional[int]				    =    tokenizer.build_inputs_with_special_tokens(_SCREAMING_SNAKE_CASE)
								UpperCamelCase_    :  Tuple				    =    tokenizer.build_inputs_with_special_tokens(_SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE)

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




		@slow
		def   _snake_case  (     self				:Optional[int])				-> Optional[Any]:






								'''simple docstring'''






								UpperCamelCase_    :  List[Any]				    =    self.tokenizer_en_ru
								UpperCamelCase_    :  Optional[int]				    =    self.tokenizer_ru_en

								UpperCamelCase_    :  Union[str, Any]				    =    [
								    [
								        """Here's a little song I wrote. Don't worry, be happy.""",
								        [2_4_7_0, 3_9, 1_1, 2_3_4_9, 7_2_2_2, 7_0, 5_9_7_9, 7, 8_4_5_0, 1_0_5_0, 1_3_1_6_0, 5, 2_6, 6_4_4_5, 7, 2],
								    ],
								    ["""This is it. No more. I'm done!""", [1_3_2, 2_1, 3_7, 7, 1_4_3_4, 8_6, 7, 7_0, 6_4_7_6, 1_3_0_5, 4_2_7, 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(_SCREAMING_SNAKE_CASE ,						return_tensors=_SCREAMING_SNAKE_CASE)
														self.assertListEqual(_SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE)

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




		@slow
		def   _snake_case  (     self				:Tuple)				-> List[str]:






								'''simple docstring'''






								UpperCamelCase_    :  Any				    =    FSMTTokenizer.from_pretrained("""facebook/wmt19-ru-en""" ,						do_lower_case=_SCREAMING_SNAKE_CASE)
								UpperCamelCase_    :  Dict				    =    tokenizer.tokenize("""USA is United States of America""")
								UpperCamelCase_    :  Optional[Any]				    =    ["""us""", """a</w>""", """is</w>""", """un""", """i""", """ted</w>""", """st""", """ates</w>""", """of</w>""", """am""", """er""", """ica</w>"""]
								self.assertListEqual(_SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE)




		@unittest.skip("""FSMTConfig.__init__  requires non-optional args""")
		def   _snake_case  (     self				:List[str])				-> int:






								'''simple docstring'''






								pass





		@unittest.skip("""FSMTConfig.__init__  requires non-optional args""")
		def   _snake_case  (     self				:Tuple)				-> Dict:






								'''simple docstring'''






								pass






'''simple docstring'''


import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_mpnet import MPNetTokenizer


__SCREAMING_SNAKE_CASE  =			logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE  =			{"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}

__SCREAMING_SNAKE_CASE  =			{
    "vocab_file": {
        "microsoft/mpnet-base": "https://huggingface.co/microsoft/mpnet-base/resolve/main/vocab.txt",
    },
    "tokenizer_file": {
        "microsoft/mpnet-base": "https://huggingface.co/microsoft/mpnet-base/resolve/main/tokenizer.json",
    },
}

__SCREAMING_SNAKE_CASE  =			{
    "microsoft/mpnet-base": 512,
}

__SCREAMING_SNAKE_CASE  =			{
    "microsoft/mpnet-base": {"do_lower_case": True},
}




class 						__snake_case (						_a  ):
		a_        =				VOCAB_FILES_NAMES
		a_        =				PRETRAINED_VOCAB_FILES_MAP
		a_        =				PRETRAINED_INIT_CONFIGURATION
		a_        =				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		a_        =				MPNetTokenizer
		a_        =				["input_ids", "attention_mask"]




		def __init__(     self				:Tuple ,						_SCREAMING_SNAKE_CASE				:Dict=None ,						_SCREAMING_SNAKE_CASE				:List[Any]=None ,						_SCREAMING_SNAKE_CASE				:Optional[Any]=True ,						_SCREAMING_SNAKE_CASE				:Any="<s>" ,						_SCREAMING_SNAKE_CASE				:int="</s>" ,						_SCREAMING_SNAKE_CASE				:Optional[Any]="</s>" ,						_SCREAMING_SNAKE_CASE				:Any="<s>" ,						_SCREAMING_SNAKE_CASE				:Optional[int]="[UNK]" ,						_SCREAMING_SNAKE_CASE				:List[Any]="<pad>" ,						_SCREAMING_SNAKE_CASE				:int="<mask>" ,						_SCREAMING_SNAKE_CASE				:Union[str, Any]=True ,						_SCREAMING_SNAKE_CASE				:Union[str, Any]=None ,						**_SCREAMING_SNAKE_CASE				:Optional[int] ,						)				-> Optional[Any]:






								'''simple docstring'''






								super().__init__(
								    _SCREAMING_SNAKE_CASE ,						tokenizer_file=_SCREAMING_SNAKE_CASE ,						do_lower_case=_SCREAMING_SNAKE_CASE ,						bos_token=_SCREAMING_SNAKE_CASE ,						eos_token=_SCREAMING_SNAKE_CASE ,						sep_token=_SCREAMING_SNAKE_CASE ,						cls_token=_SCREAMING_SNAKE_CASE ,						unk_token=_SCREAMING_SNAKE_CASE ,						pad_token=_SCREAMING_SNAKE_CASE ,						mask_token=_SCREAMING_SNAKE_CASE ,						tokenize_chinese_chars=_SCREAMING_SNAKE_CASE ,						strip_accents=_SCREAMING_SNAKE_CASE ,						**_SCREAMING_SNAKE_CASE ,						)

								UpperCamelCase_    :  Dict				    =    json.loads(self.backend_tokenizer.normalizer.__getstate__())
								if (
								    pre_tok_state.get("""lowercase""" ,						_SCREAMING_SNAKE_CASE) != do_lower_case
								    or pre_tok_state.get("""strip_accents""" ,						_SCREAMING_SNAKE_CASE) != strip_accents
								):
														UpperCamelCase_    :  Any				    =    getattr(_SCREAMING_SNAKE_CASE ,						pre_tok_state.pop("""type"""))
														UpperCamelCase_    :  Optional[int]				    =    do_lower_case
														UpperCamelCase_    :  str				    =    strip_accents
														UpperCamelCase_    :  str				    =    pre_tok_class(**_SCREAMING_SNAKE_CASE)

								UpperCamelCase_    :  List[str]				    =    do_lower_case




		@property
		def   _snake_case  (     self				:Optional[int])				-> str:






								'''simple docstring'''






								if self._mask_token is None:
														if self.verbose:
																				logger.error("""Using mask_token, but it is not set yet.""")
														return None
								return str(self._mask_token)




		@mask_token.setter
		def   _snake_case  (     self				:Optional[Any] ,						_SCREAMING_SNAKE_CASE				:List[Any])				-> int:






								'''simple docstring'''






								UpperCamelCase_    :  Tuple				    =    AddedToken(_SCREAMING_SNAKE_CASE ,						lstrip=_SCREAMING_SNAKE_CASE ,						rstrip=_SCREAMING_SNAKE_CASE) if isinstance(_SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE) else value
								UpperCamelCase_    :  int				    =    value




		def   _snake_case  (     self				:str ,						_SCREAMING_SNAKE_CASE				:int ,						_SCREAMING_SNAKE_CASE				:Any=None)				-> str:






								'''simple docstring'''






								UpperCamelCase_    :  Tuple				    =    [self.bos_token_id] + token_ids_a + [self.eos_token_id]
								if token_ids_a is None:
														return output

								return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]




		def   _snake_case  (     self				:Optional[Any] ,						_SCREAMING_SNAKE_CASE				:List[int] ,						_SCREAMING_SNAKE_CASE				:Optional[List[int]] = None)				-> List[int]:






								'''simple docstring'''






								UpperCamelCase_    :  Optional[Any]				    =    [self.sep_token_id]
								UpperCamelCase_    :  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 + sep + token_ids_a + sep) * [0]





		def   _snake_case  (     self				:List[str] ,						_SCREAMING_SNAKE_CASE				:str ,						_SCREAMING_SNAKE_CASE				:Optional[str] = None)				-> Tuple[str]:






								'''simple docstring'''






								UpperCamelCase_    :  List[str]				    =    self._tokenizer.model.save(_SCREAMING_SNAKE_CASE ,						name=_SCREAMING_SNAKE_CASE)
								return tuple(_SCREAMING_SNAKE_CASE)




