
"""simple docstring"""






import unittest

from transformers import BertGenerationTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_torch, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


__UpperCAmelCase       =  '▁'

__UpperCAmelCase       =  get_tests_dir('fixtures/test_sentencepiece.model')




@require_sentencepiece
class 			_SCREAMING_SNAKE_CASE					(			A__	, unittest.TestCase							):
			UpperCAmelCase_  :Dict				 =						BertGenerationTokenizer
			UpperCAmelCase_  :str				 =						False
			UpperCAmelCase_  :Union[str, Any]				 =						True






			def  __lowerCAmelCase (					self       )			->     List[Any]:
						super().setUp()

						lowerCAmelCase_    :Dict			     =  BertGenerationTokenizer(__A       ,   keep_accents=__A       )
						tokenizer.save_pretrained(self.tmpdirname       )






			def  __lowerCAmelCase (					self       )			->     Tuple:
						lowerCAmelCase_    :str			     =  """<s>"""
						lowerCAmelCase_    :Tuple			     =  1

						self.assertEqual(self.get_tokenizer()._convert_token_to_id(__A       )       ,   __A       )
						self.assertEqual(self.get_tokenizer()._convert_id_to_token(__A       )       ,   __A       )






			def  __lowerCAmelCase (					self       )			->     Optional[Any]:
						lowerCAmelCase_    :Optional[int]			     =  list(self.get_tokenizer().get_vocab().keys()       )

						self.assertEqual(vocab_keys[0]       ,   """<unk>"""       )
						self.assertEqual(vocab_keys[1]       ,   """<s>"""       )
						self.assertEqual(vocab_keys[-1]       ,   """<pad>"""       )
						self.assertEqual(len(__A       )       ,   1002       )






			def  __lowerCAmelCase (					self       )			->     str:
						self.assertEqual(self.get_tokenizer().vocab_size       ,   1000       )






			def  __lowerCAmelCase (					self       )			->     Tuple:
						lowerCAmelCase_    :str			     =  BertGenerationTokenizer(__A       ,   keep_accents=__A       )

						lowerCAmelCase_    :str			     =  tokenizer.tokenize("""This is a test"""       )
						self.assertListEqual(__A       ,   ["""▁This""", """▁is""", """▁a""", """▁t""", """est"""]       )

						self.assertListEqual(
						    tokenizer.convert_tokens_to_ids(__A       )       ,   [285, 46, 10, 170, 382]       ,   )

						lowerCAmelCase_    :Union[str, Any]			     =  tokenizer.tokenize("""I was born in 92000, and this is falsé."""       )
						self.assertListEqual(
						    __A       ,   [
						        SPIECE_UNDERLINE + """I""",
						        SPIECE_UNDERLINE + """was""",
						        SPIECE_UNDERLINE + """b""",
						        """or""",
						        """n""",
						        SPIECE_UNDERLINE + """in""",
						        SPIECE_UNDERLINE + """""",
						        """9""",
						        """2""",
						        """0""",
						        """0""",
						        """0""",
						        """,""",
						        SPIECE_UNDERLINE + """and""",
						        SPIECE_UNDERLINE + """this""",
						        SPIECE_UNDERLINE + """is""",
						        SPIECE_UNDERLINE + """f""",
						        """al""",
						        """s""",
						        """é""",
						        """.""",
						    ]       ,   )
						lowerCAmelCase_    :Tuple			     =  tokenizer.convert_tokens_to_ids(__A       )
						self.assertListEqual(
						    __A       ,   [8, 21, 84, 55, 24, 19, 7, 0, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 0, 4]       ,   )

						lowerCAmelCase_    :Dict			     =  tokenizer.convert_ids_to_tokens(__A       )
						self.assertListEqual(
						    __A       ,   [
						        SPIECE_UNDERLINE + """I""",
						        SPIECE_UNDERLINE + """was""",
						        SPIECE_UNDERLINE + """b""",
						        """or""",
						        """n""",
						        SPIECE_UNDERLINE + """in""",
						        SPIECE_UNDERLINE + """""",
						        """<unk>""",
						        """2""",
						        """0""",
						        """0""",
						        """0""",
						        """,""",
						        SPIECE_UNDERLINE + """and""",
						        SPIECE_UNDERLINE + """this""",
						        SPIECE_UNDERLINE + """is""",
						        SPIECE_UNDERLINE + """f""",
						        """al""",
						        """s""",
						        """<unk>""",
						        """.""",
						    ]       ,   )






			@cached_property
			def  __lowerCAmelCase (					self       )			->     Dict:
						return BertGenerationTokenizer.from_pretrained("""google/bert_for_seq_generation_L-24_bbc_encoder"""       )






			@slow
			def  __lowerCAmelCase (					self       )			->     Optional[Any]:
						lowerCAmelCase_    :Dict			     =  """Hello World!"""
						lowerCAmelCase_    :Optional[Any]			     =  [1_8536, 2260, 101]

						self.assertListEqual(__A       ,   self.big_tokenizer.encode(__A       )       )






			@slow
			def  __lowerCAmelCase (					self       )			->     Dict:
						lowerCAmelCase_    :List[Any]			     =  (
						    """This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) \" [ ] ! : - . Also we will"""
						    """ add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth"""
						)
						lowerCAmelCase_    :Tuple			     =  [
						    871,
						    419,
						    358,
						    946,
						    991,
						    2521,
						    452,
						    358,
						    1357,
						    387,
						    7751,
						    3536,
						    112,
						    985,
						    456,
						    126,
						    865,
						    938,
						    5400,
						    5734,
						    458,
						    1368,
						    467,
						    786,
						    2462,
						    5246,
						    1159,
						    633,
						    865,
						    4519,
						    457,
						    582,
						    852,
						    2557,
						    427,
						    916,
						    508,
						    405,
						    3_4324,
						    497,
						    391,
						    408,
						    1_1342,
						    1244,
						    385,
						    100,
						    938,
						    985,
						    456,
						    574,
						    362,
						    1_2597,
						    3200,
						    3129,
						    1172,
						]

						self.assertListEqual(__A       ,   self.big_tokenizer.encode(__A       )       )






			@require_torch
			@slow
			def  __lowerCAmelCase (					self       )			->     Optional[Any]:
						import torch

						from transformers import BertGenerationConfig, BertGenerationEncoder

						# Build sequence
						lowerCAmelCase_    :Tuple			     =  list(self.big_tokenizer.get_vocab().keys()       )[:10]
						lowerCAmelCase_    :str			     =  """ """.join(__A       )
						lowerCAmelCase_    :Tuple			     =  self.big_tokenizer.encode_plus(__A       ,   return_tensors="""pt"""       ,   return_token_type_ids=__A       )
						lowerCAmelCase_    :Any			     =  self.big_tokenizer.batch_encode_plus(
						    [sequence + """ """ + sequence]       ,   return_tensors="""pt"""       ,   return_token_type_ids=__A       )

						lowerCAmelCase_    :int			     =  BertGenerationConfig()
						lowerCAmelCase_    :Tuple			     =  BertGenerationEncoder(__A       )

						assert model.get_input_embeddings().weight.shape[0] >= self.big_tokenizer.vocab_size

						with torch.no_grad():
									model(**__A       )
									model(**__A       )






			@slow
			def  __lowerCAmelCase (					self       )			->     List[Any]:
						# fmt: off
						lowerCAmelCase_    :Tuple			     =  {"""input_ids""": [[3_9286, 458, 3_6335, 2001, 456, 1_3073, 1_3266, 455, 113, 7746, 1741, 1_1157, 391, 1_3073, 1_3266, 455, 113, 3967, 3_5412, 113, 4936, 109, 3870, 2377, 113, 3_0084, 4_5720, 458, 134, 1_7496, 112, 503, 1_1672, 113, 118, 112, 5665, 1_3347, 3_8687, 112, 1496, 3_1389, 112, 3268, 4_7264, 134, 962, 112, 1_6377, 8035, 2_3130, 430, 1_2169, 1_5518, 2_8592, 458, 146, 4_1697, 109, 391, 1_2169, 1_5518, 1_6689, 458, 146, 4_1358, 109, 452, 726, 4034, 111, 763, 3_5412, 5082, 388, 1903, 111, 9051, 391, 2870, 4_8918, 1900, 1123, 550, 998, 112, 9586, 1_5985, 455, 391, 410, 2_2955, 3_7636, 114], [448, 1_7496, 419, 3663, 385, 763, 113, 2_7533, 2870, 3283, 1_3043, 1639, 2_4713, 523, 656, 2_4013, 1_8550, 2521, 517, 2_7014, 2_1244, 420, 1212, 1465, 391, 927, 4833, 388, 578, 1_1786, 114, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [484, 2169, 7687, 2_1932, 1_8146, 726, 363, 1_7032, 3391, 114, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], """attention_mask""": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
						# fmt: on

						self.tokenizer_integration_test_util(
						    expected_encoding=__A       ,   model_name="""google/bert_for_seq_generation_L-24_bbc_encoder"""       ,   revision="""c817d1fd1be2ffa69431227a1fe320544943d4db"""       ,   )








'''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, BatchEncoding, PreTrainedTokenizer
from ...utils import logging


_lowercase :  str    							=       logging.get_logger(__name__)

_lowercase :  List[Any]    							=       "▁"

_lowercase :  List[Any]    							=       {"vocab_file": "sentencepiece.bpe.model"}

_lowercase :  Optional[int]    							=       {
    "vocab_file": {
        "facebook/mbart-large-en-ro": (
            "https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/sentencepiece.bpe.model"
        ),
        "facebook/mbart-large-cc25": (
            "https://huggingface.co/facebook/mbart-large-cc25/resolve/main/sentencepiece.bpe.model"
        ),
    }
}

_lowercase :  str    							=       {
    "facebook/mbart-large-en-ro": 1_0_2_4,
    "facebook/mbart-large-cc25": 1_0_2_4,
}

# fmt: off
_lowercase :  List[Any]    							=       ["ar_AR", "cs_CZ", "de_DE", "en_XX", "es_XX", "et_EE", "fi_FI", "fr_XX", "gu_IN", "hi_IN", "it_IT", "ja_XX", "kk_KZ", "ko_KR", "lt_LT", "lv_LV", "my_MM", "ne_NP", "nl_XX", "ro_RO", "ru_RU", "si_LK", "tr_TR", "vi_VN", "zh_CN"]






class  lowerCAmelCase__   (  lowerCamelCase_     ):
					lowerCAmelCase_        =     VOCAB_FILES_NAMES
					lowerCAmelCase_        =     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					lowerCAmelCase_        =     PRETRAINED_VOCAB_FILES_MAP
					lowerCAmelCase_        =     ['''input_ids''', '''attention_mask''']

					lowerCAmelCase_        =     []
					lowerCAmelCase_        =     []






					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				,							__SCREAMING_SNAKE_CASE=None				,							__SCREAMING_SNAKE_CASE = None				,							__SCREAMING_SNAKE_CASE=None				,							**__SCREAMING_SNAKE_CASE				,							):


											"""simple docstring"""





											lowercase_	:     Any													=       AddedToken(__SCREAMING_SNAKE_CASE				,							lstrip=__SCREAMING_SNAKE_CASE				,							rstrip=__SCREAMING_SNAKE_CASE		) if isinstance(__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE		) else mask_token

											lowercase_	:     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				,							tokenizer_file=__SCREAMING_SNAKE_CASE				,							src_lang=__SCREAMING_SNAKE_CASE				,							tgt_lang=__SCREAMING_SNAKE_CASE				,							additional_special_tokens=__SCREAMING_SNAKE_CASE				,							sp_model_kwargs=self.sp_model_kwargs				,							**__SCREAMING_SNAKE_CASE				,							)

											lowercase_	:     Optional[Any]													=       spm.SentencePieceProcessor(**self.sp_model_kwargs		)
											self.sp_model.Load(str(__SCREAMING_SNAKE_CASE		)		)
											lowercase_	:     List[str]													=       vocab_file

											# Original fairseq vocab and spm vocab must be "aligned":
											# Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
											# -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
											# fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
											# spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

											# Mimic fairseq token-to-id alignment for the first 4 token
											lowercase_	:     Tuple													=       {'''<s>''': 0, '''<pad>''': 1, '''</s>''': 2, '''<unk>''': 3}

											# The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
											lowercase_	:     str													=       1

											lowercase_	:     str													=       len(self.sp_model		)
											lowercase_	:     List[Any]													=       {
											    code: self.sp_model_size + i + self.fairseq_offset for i, code in enumerate(__SCREAMING_SNAKE_CASE		)
											}
											lowercase_	:     Union[str, Any]													=       {v: k for k, v in self.lang_code_to_id.items()}
											lowercase_	:     List[Any]													=       len(self.sp_model		) + len(self.lang_code_to_id		) + self.fairseq_offset

											self.fairseq_tokens_to_ids.update(self.lang_code_to_id		)
											lowercase_	:     Union[str, Any]													=       {v: k for k, v in self.fairseq_tokens_to_ids.items()}
											lowercase_	:     Optional[Any]													=       list(self.lang_code_to_id.keys()		)

											if additional_special_tokens is not None:
																	# Only add those special tokens if they are not already there.
																	self._additional_special_tokens.extend(
																	    [t for t in additional_special_tokens if t not in self._additional_special_tokens]		)

											lowercase_	:     Optional[Any]													=       src_lang if src_lang is not None else '''en_XX'''
											lowercase_	:     str													=       self.lang_code_to_id[self._src_lang]
											lowercase_	:     Optional[Any]													=       tgt_lang
											self.set_src_lang_special_tokens(self._src_lang		)






					def __getstate__(   self		):


											"""simple docstring"""





											lowercase_	:     Optional[int]													=       self.__dict__.copy()
											lowercase_	:     Dict													=       None
											lowercase_	:     Any													=       self.sp_model.serialized_model_proto()
											return state






					def __setstate__(   self				,							__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											lowercase_	:     Optional[Any]													=       d

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

											lowercase_	:     Optional[Any]													=       spm.SentencePieceProcessor(**self.sp_model_kwargs		)
											self.sp_model.LoadFromSerializedProto(self.sp_model_proto		)






					@property
					def 	_snake_case    (   self		):


											"""simple docstring"""





											return len(self.sp_model		) + len(self.lang_code_to_id		) + self.fairseq_offset + 1  # Plus 1 for the mask token






					@property
					def 	_snake_case    (   self		):


											"""simple docstring"""





											return self._src_lang






					@src_lang.setter
					def 	_snake_case    (   self				,							__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											lowercase_	:     Tuple													=       new_src_lang
											self.set_src_lang_special_tokens(self._src_lang		)






					def 	_snake_case    (   self				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE = None				,							__SCREAMING_SNAKE_CASE = False		):


											"""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		)

											lowercase_	:     Optional[Any]													=       [1] * len(self.prefix_tokens		)
											lowercase_	:     Tuple													=       [1] * len(self.suffix_tokens		)
											if token_ids_a is None:
																	return prefix_ones + ([0] * len(__SCREAMING_SNAKE_CASE		)) + suffix_ones
											return prefix_ones + ([0] * len(__SCREAMING_SNAKE_CASE		)) + ([0] * len(__SCREAMING_SNAKE_CASE		)) + suffix_ones






					def 	_snake_case    (   self				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE = None		):


											"""simple docstring"""





											if token_ids_a is None:
																	return self.prefix_tokens + token_ids_a + self.suffix_tokens
											# We don't expect to process pairs, but leave the pair logic for API consistency
											return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens






					def 	_snake_case    (   self				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE = None		):


											"""simple docstring"""





											lowercase_	:     Optional[int]													=       [self.sep_token_id]
											lowercase_	:     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]






					def 	_snake_case    (   self				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							**__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											if src_lang is None or tgt_lang is None:
																	raise ValueError('''Translation requires a `src_lang` and a `tgt_lang` for this model'''		)
											lowercase_	:     Optional[Any]													=       src_lang
											lowercase_	:     Dict													=       self(__SCREAMING_SNAKE_CASE				,							add_special_tokens=__SCREAMING_SNAKE_CASE				,							return_tensors=__SCREAMING_SNAKE_CASE				,							**__SCREAMING_SNAKE_CASE		)
											lowercase_	:     Optional[int]													=       self.convert_tokens_to_ids(__SCREAMING_SNAKE_CASE		)
											lowercase_	:     Optional[Any]													=       tgt_lang_id
											return inputs






					def 	_snake_case    (   self		):


											"""simple docstring"""





											lowercase_	:     str													=       {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 	_snake_case    (   self				,							__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											return self.sp_model.encode(__SCREAMING_SNAKE_CASE				,							out_type=__SCREAMING_SNAKE_CASE		)






					def 	_snake_case    (   self				,							__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											if token in self.fairseq_tokens_to_ids:
																	return self.fairseq_tokens_to_ids[token]
											lowercase_	:     Any													=       self.sp_model.PieceToId(__SCREAMING_SNAKE_CASE		)

											# Need to return unknown token if the SP model returned 0
											return spm_id + self.fairseq_offset if spm_id else self.unk_token_id






					def 	_snake_case    (   self				,							__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											if index in self.fairseq_ids_to_tokens:
																	return self.fairseq_ids_to_tokens[index]
											return self.sp_model.IdToPiece(index - self.fairseq_offset		)






					def 	_snake_case    (   self				,							__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											lowercase_	:     int													=       ''''''.join(__SCREAMING_SNAKE_CASE		).replace(__SCREAMING_SNAKE_CASE				,							''' '''		).strip()
											return out_string






					def 	_snake_case    (   self				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE = None		):


											"""simple docstring"""





											if not os.path.isdir(__SCREAMING_SNAKE_CASE		):
																	logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''		)
																	return
											lowercase_	:     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:
																							lowercase_	:     List[str]													=       self.sp_model.serialized_model_proto()
																							fi.write(__SCREAMING_SNAKE_CASE		)

											return (out_vocab_file,)






					def 	_snake_case    (   self				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE = "en_XX"				,							__SCREAMING_SNAKE_CASE = None				,							__SCREAMING_SNAKE_CASE = "ro_RO"				,							**__SCREAMING_SNAKE_CASE				,							):


											"""simple docstring"""





											lowercase_	:     List[str]													=       src_lang
											lowercase_	:     int													=       tgt_lang
											return super().prepare_seqaseq_batch(__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							**__SCREAMING_SNAKE_CASE		)






					def 	_snake_case    (   self		):


											"""simple docstring"""





											return self.set_src_lang_special_tokens(self.src_lang		)






					def 	_snake_case    (   self		):


											"""simple docstring"""





											return self.set_tgt_lang_special_tokens(self.tgt_lang		)






					def 	_snake_case    (   self				,							__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											lowercase_	:     Dict													=       self.lang_code_to_id[src_lang]
											lowercase_	:     Optional[Any]													=       []
											lowercase_	:     List[str]													=       [self.eos_token_id, self.cur_lang_code]






					def 	_snake_case    (   self				,							__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											lowercase_	:     List[Any]													=       self.lang_code_to_id[lang]
											lowercase_	:     Dict													=       []
											lowercase_	:     Union[str, Any]													=       [self.eos_token_id, self.cur_lang_code]



