






import json
import os
import re
import shutil
import tempfile
import unittest
from typing import Tuple

from transformers import AddedToken, BatchEncoding, PerceiverTokenizer
from transformers.utils import cached_property, is_tf_available, is_torch_available

from ...test_tokenization_common import TokenizerTesterMixin


if is_torch_available():
		__UpperCAmelCase     							=						'pt'
elif is_tf_available():
		__UpperCAmelCase     							=						'tf'
else:
		__UpperCAmelCase     							=						'jax'



class   lowerCamelCase				(_snake_case			,  unittest.TestCase      ):







		'''simple docstring'''



		_snake_case   :   str							=  PerceiverTokenizer
		_snake_case   :   List[Any]							=  False



		def  __UpperCAmelCase      (							self       )     ->							Optional[int]:
							super().setUp()
							UpperCAmelCase_   :				int						=						PerceiverTokenizer()
							tokenizer.save_pretrained(self.tmpdirname       )



		@cached_property
		def  __UpperCAmelCase      (							self       )     ->							str:
							return PerceiverTokenizer.from_pretrained('deepmind/language-perceiver'       )



		def  __UpperCAmelCase      (							self    ,		**_UpperCamelCase       )     ->							PerceiverTokenizer:
							return self.tokenizer_class.from_pretrained(self.tmpdirname    ,		**_UpperCamelCase       )



		def  __UpperCAmelCase      (							self    ,		_UpperCamelCase    ,		_UpperCamelCase=False    ,		_UpperCamelCase=2_0    ,		_UpperCamelCase=5       )     ->							Tuple[str, list]:
							# XXX The default common tokenizer tests assume that every ID is decodable on its own.
							# This assumption is invalid for Perceiver because single bytes might not be
							# valid utf-8 (byte 128 for instance).
							# Here we're overriding the smallest possible method to provide
							# a clean sequence without making the same assumption.

							UpperCAmelCase_   :				List[str]						=						[]
							for i in range(len(_UpperCamelCase       )       ):
												try:
																	UpperCAmelCase_   :				Tuple						=						tokenizer.decode([i]    ,		clean_up_tokenization_spaces=_UpperCamelCase       )
												except UnicodeDecodeError:
																	pass
												toks.append((i, tok)       )

							UpperCAmelCase_   :				str						=						list(filter(lambda _UpperCamelCase       : re.match(r'^[ a-zA-Z]+$'    ,		t[1]       )    ,		_UpperCamelCase       )       )
							UpperCAmelCase_   :				Optional[Any]						=						list(filter(lambda _UpperCamelCase       : [t[0]] == tokenizer.encode(t[1]    ,		add_special_tokens=_UpperCamelCase       )    ,		_UpperCamelCase       )       )
							if max_length is not None and len(_UpperCamelCase       ) > max_length:
												UpperCAmelCase_   :				Optional[int]						=						toks[:max_length]
							if min_length is not None and len(_UpperCamelCase       ) < min_length and len(_UpperCamelCase       ) > 0:
												while len(_UpperCamelCase       ) < min_length:
																	UpperCAmelCase_   :				Dict						=						toks + toks
        # toks_str = [t[1] for t in toks]
							UpperCAmelCase_   :				List[Any]						=						[t[0] for t in toks]

							# Ensure consistency
							UpperCAmelCase_   :				List[Any]						=						tokenizer.decode(_UpperCamelCase    ,		clean_up_tokenization_spaces=_UpperCamelCase       )
							if " " not in output_txt and len(_UpperCamelCase       ) > 1:
												UpperCAmelCase_   :				List[str]						=						(
												    tokenizer.decode([toks_ids[0]]    ,		clean_up_tokenization_spaces=_UpperCamelCase       )
												    + ' '
												    + tokenizer.decode(toks_ids[1:]    ,		clean_up_tokenization_spaces=_UpperCamelCase       )
												)
							if with_prefix_space:
												UpperCAmelCase_   :				Any						=						' ' + output_txt
							UpperCAmelCase_   :				Union[str, Any]						=						tokenizer.encode(_UpperCamelCase    ,		add_special_tokens=_UpperCamelCase       )
							return output_txt, output_ids



		def  __UpperCAmelCase      (							self       )     ->							Tuple:
							UpperCAmelCase_   :				List[str]						=						self.perceiver_tokenizer
							UpperCAmelCase_   :				Union[str, Any]						=						'Unicode €.'
							UpperCAmelCase_   :				List[str]						=						tokenizer(_UpperCamelCase       )
							UpperCAmelCase_   :				Optional[int]						=						[4, 9_1, 1_1_6, 1_1_1, 1_0_5, 1_1_7, 1_0_6, 1_0_7, 3_8, 2_3_2, 1_3_6, 1_7_8, 5_2, 5]
							self.assertEqual(encoded['input_ids']    ,		_UpperCamelCase       )

							# decoding
							UpperCAmelCase_   :				Optional[Any]						=						tokenizer.decode(_UpperCamelCase       )
							self.assertEqual(_UpperCamelCase    ,		'[CLS]Unicode €.[SEP]'       )

							UpperCAmelCase_   :				int						=						tokenizer('e è é ê ë'       )
							UpperCAmelCase_   :				Tuple						=						[4, 1_0_7, 3_8, 2_0_1, 1_7_4, 3_8, 2_0_1, 1_7_5, 3_8, 2_0_1, 1_7_6, 3_8, 2_0_1, 1_7_7, 5]
							self.assertEqual(encoded['input_ids']    ,		_UpperCamelCase       )
							# decoding
							UpperCAmelCase_   :				List[str]						=						tokenizer.decode(_UpperCamelCase       )
							self.assertEqual(_UpperCamelCase    ,		'[CLS]e è é ê ë[SEP]'       )

							# encode/decode, but with `encode` instead of `__call__`
							self.assertEqual(tokenizer.decode(tokenizer.encode('e è é ê ë'       )       )    ,		'[CLS]e è é ê ë[SEP]'       )



		def  __UpperCAmelCase      (							self       )     ->							Tuple:
							UpperCAmelCase_   :				Dict						=						self.perceiver_tokenizer
							UpperCAmelCase_   :				Dict						=						['A long paragraph for summarization.', 'Another paragraph for summarization.']
							# fmt: off
							UpperCAmelCase_   :				List[str]						=						[4, 7_1, 3_8, 1_1_4, 1_1_7, 1_1_6, 1_0_9, 3_8, 1_1_8, 1_0_3, 1_2_0, 1_0_3, 1_0_9, 1_2_0, 1_0_3, 1_1_8, 1_1_0, 3_8, 1_0_8, 1_1_7, 1_2_0, 3_8, 1_2_1, 1_2_3, 1_1_5, 1_1_5, 1_0_3, 1_2_0, 1_1_1, 1_2_8, 1_0_3, 1_2_2, 1_1_1, 1_1_7, 1_1_6, 5_2, 5, 0]
							# fmt: on
							UpperCAmelCase_   :				Tuple						=						tokenizer(_UpperCamelCase    ,		padding=_UpperCamelCase    ,		return_tensors=_UpperCamelCase       )
							self.assertIsInstance(_UpperCamelCase    ,		_UpperCamelCase       )

							if FRAMEWORK != "jax":
												UpperCAmelCase_   :				Union[str, Any]						=						list(batch.input_ids.numpy()[0]       )
							else:
												UpperCAmelCase_   :				Dict						=						list(batch.input_ids.tolist()[0]       )

							self.assertListEqual(_UpperCamelCase    ,		_UpperCamelCase       )

							self.assertEqual((2, 3_8)    ,		batch.input_ids.shape       )
							self.assertEqual((2, 3_8)    ,		batch.attention_mask.shape       )



		def  __UpperCAmelCase      (							self       )     ->							Tuple:
							UpperCAmelCase_   :				Optional[int]						=						self.perceiver_tokenizer
							UpperCAmelCase_   :				Optional[Any]						=						['A long paragraph for summarization.', 'Another paragraph for summarization.']
							UpperCAmelCase_   :				str						=						tokenizer(_UpperCamelCase    ,		padding=_UpperCamelCase    ,		return_tensors=_UpperCamelCase       )
							# check if input_ids are returned and no decoder_input_ids
							self.assertIn('input_ids'    ,		_UpperCamelCase       )
							self.assertIn('attention_mask'    ,		_UpperCamelCase       )
							self.assertNotIn('decoder_input_ids'    ,		_UpperCamelCase       )
							self.assertNotIn('decoder_attention_mask'    ,		_UpperCamelCase       )



		def  __UpperCAmelCase      (							self       )     ->							Optional[Any]:
							UpperCAmelCase_   :				Any						=						self.perceiver_tokenizer
							UpperCAmelCase_   :				Tuple						=						[
							    'Summary of the text.',
							    'Another summary.',
							]
							UpperCAmelCase_   :				str						=						tokenizer(
							    text_target=_UpperCamelCase    ,		max_length=3_2    ,		padding='max_length'    ,		truncation=_UpperCamelCase    ,		return_tensors=_UpperCamelCase       )
							self.assertEqual(3_2    ,		targets['input_ids'].shape[1]       )



		def  __UpperCAmelCase      (							self       )     ->							List[str]:
							# safety check on max_len default value so we are sure the test works
							UpperCAmelCase_   :				int						=						self.get_tokenizers()
							for tokenizer in tokenizers:
												with self.subTest(f"{tokenizer.__class__.__name__}"       ):
																	self.assertNotEqual(tokenizer.model_max_length    ,		4_2       )

        # Now let's start the test
							UpperCAmelCase_   :				int						=						self.get_tokenizers()
							for tokenizer in tokenizers:
												with self.subTest(f"{tokenizer.__class__.__name__}"       ):
																	# Isolate this from the other tests because we save additional tokens/etc
																	UpperCAmelCase_   :				Dict						=						tempfile.mkdtemp()

																	UpperCAmelCase_   :				Any						=						' He is very happy, UNwant\u00E9d,running'
																	UpperCAmelCase_   :				Dict						=						tokenizer.encode(_UpperCamelCase    ,		add_special_tokens=_UpperCamelCase       )
																	tokenizer.save_pretrained(_UpperCamelCase       )

																	UpperCAmelCase_   :				Dict						=						tokenizer.__class__.from_pretrained(_UpperCamelCase       )
																	UpperCAmelCase_   :				List[str]						=						after_tokenizer.encode(_UpperCamelCase    ,		add_special_tokens=_UpperCamelCase       )
																	self.assertListEqual(_UpperCamelCase    ,		_UpperCamelCase       )

																	shutil.rmtree(_UpperCamelCase       )

							UpperCAmelCase_   :				str						=						self.get_tokenizers(model_max_length=4_2       )
							for tokenizer in tokenizers:
												with self.subTest(f"{tokenizer.__class__.__name__}"       ):
																	# Isolate this from the other tests because we save additional tokens/etc
																	UpperCAmelCase_   :				Dict						=						tempfile.mkdtemp()

																	UpperCAmelCase_   :				Any						=						' He is very happy, UNwant\u00E9d,running'
																	tokenizer.add_tokens(['bim', 'bambam']       )
																	UpperCAmelCase_   :				Tuple						=						tokenizer.additional_special_tokens
																	additional_special_tokens.append('new_additional_special_token'       )
																	tokenizer.add_special_tokens({'additional_special_tokens': additional_special_tokens}       )
																	UpperCAmelCase_   :				Optional[int]						=						tokenizer.encode(_UpperCamelCase    ,		add_special_tokens=_UpperCamelCase       )
																	tokenizer.save_pretrained(_UpperCamelCase       )

																	UpperCAmelCase_   :				str						=						tokenizer.__class__.from_pretrained(_UpperCamelCase       )
																	UpperCAmelCase_   :				List[Any]						=						after_tokenizer.encode(_UpperCamelCase    ,		add_special_tokens=_UpperCamelCase       )
																	self.assertListEqual(_UpperCamelCase    ,		_UpperCamelCase       )
																	self.assertIn('new_additional_special_token'    ,		after_tokenizer.additional_special_tokens       )
																	self.assertEqual(after_tokenizer.model_max_length    ,		4_2       )

																	UpperCAmelCase_   :				str						=						tokenizer.__class__.from_pretrained(_UpperCamelCase    ,		model_max_length=4_3       )
																	self.assertEqual(tokenizer.model_max_length    ,		4_3       )

																	shutil.rmtree(_UpperCamelCase       )



		def  __UpperCAmelCase      (							self       )     ->							int:
							UpperCAmelCase_   :				Tuple						=						[]
							if self.test_slow_tokenizer:
												tokenizer_list.append((self.tokenizer_class, self.get_tokenizer())       )

							if self.test_rust_tokenizer:
												tokenizer_list.append((self.rust_tokenizer_class, self.get_rust_tokenizer())       )

							for tokenizer_class, tokenizer_utils in tokenizer_list:
												with tempfile.TemporaryDirectory() as tmp_dir:
																	tokenizer_utils.save_pretrained(_UpperCamelCase       )

																	with open(os.path.join(_UpperCamelCase    ,		'special_tokens_map.json'       )    ,		encoding='utf-8'       ) as json_file:
																						UpperCAmelCase_   :				int						=						json.load(_UpperCamelCase       )

																	with open(os.path.join(_UpperCamelCase    ,		'tokenizer_config.json'       )    ,		encoding='utf-8'       ) as json_file:
																						UpperCAmelCase_   :				Union[str, Any]						=						json.load(_UpperCamelCase       )

																	UpperCAmelCase_   :				Union[str, Any]						=						[f"<extra_id_{i}>" for i in range(1_2_5       )]

																	UpperCAmelCase_   :				List[str]						=						added_tokens_extra_ids + [
																	    'an_additional_special_token'
																	]
																	UpperCAmelCase_   :				Optional[int]						=						added_tokens_extra_ids + [
																	    'an_additional_special_token'
																	]

																	with open(os.path.join(_UpperCamelCase    ,		'special_tokens_map.json'       )    ,		'w'    ,		encoding='utf-8'       ) as outfile:
																						json.dump(_UpperCamelCase    ,		_UpperCamelCase       )
																	with open(os.path.join(_UpperCamelCase    ,		'tokenizer_config.json'       )    ,		'w'    ,		encoding='utf-8'       ) as outfile:
																						json.dump(_UpperCamelCase    ,		_UpperCamelCase       )

																	# the following checks allow us to verify that our test works as expected, i.e. that the tokenizer takes
																	# into account the new value of additional_special_tokens given in the "tokenizer_config.json" and
																	# "special_tokens_map.json" files
																	UpperCAmelCase_   :				Union[str, Any]						=						tokenizer_class.from_pretrained(
																	    _UpperCamelCase    ,		)
																	self.assertIn(
																	    'an_additional_special_token'    ,		tokenizer_without_change_in_init.additional_special_tokens       )
																	self.assertEqual(
																	    ['an_additional_special_token']    ,		tokenizer_without_change_in_init.convert_ids_to_tokens(
																	        tokenizer_without_change_in_init.convert_tokens_to_ids(['an_additional_special_token']       )       )    ,		)

																	# Now we test that we can change the value of additional_special_tokens in the from_pretrained
																	UpperCAmelCase_   :				Tuple						=						added_tokens_extra_ids + [AddedToken('a_new_additional_special_token'    ,		lstrip=_UpperCamelCase       )]
																	UpperCAmelCase_   :				List[Any]						=						tokenizer_class.from_pretrained(
																	    _UpperCamelCase    ,		additional_special_tokens=_UpperCamelCase    ,		)

																	self.assertIn('a_new_additional_special_token'    ,		tokenizer.additional_special_tokens       )
																	self.assertEqual(
																	    ['a_new_additional_special_token']    ,		tokenizer.convert_ids_to_tokens(
																	        tokenizer.convert_tokens_to_ids(['a_new_additional_special_token']       )       )    ,		)



		def  __UpperCAmelCase      (							self       )     ->							Tuple:
							UpperCAmelCase_   :				Dict						=						self.perceiver_tokenizer
							self.assertEqual(tokenizer.decode([1_7_8]       )    ,		'�'       )



		def  __UpperCAmelCase      (							self       )     ->							str:
							pass



		def  __UpperCAmelCase      (							self       )     ->							Dict:
							pass



		def  __UpperCAmelCase      (							self       )     ->							Dict:
							pass



		def  __UpperCAmelCase      (							self       )     ->							Any:
							pass



		def  __UpperCAmelCase      (							self       )     ->							List[str]:
							# The default common tokenizer tests uses invalid tokens for Perceiver that can only accept one-character
							# strings and special added tokens as tokens
							UpperCAmelCase_   :				Union[str, Any]						=						self.get_tokenizers(fast=_UpperCamelCase    ,		do_lower_case=_UpperCamelCase       )
							for tokenizer in tokenizers:
												with self.subTest(f"{tokenizer.__class__.__name__}"       ):
																	UpperCAmelCase_   :				int						=						['[CLS]', 't', 'h', 'i', 's', ' ', 'i', 's', ' ', 'a', ' ', 't', 'e', 's', 't', '[SEP]']
																	UpperCAmelCase_   :				Dict						=						tokenizer.convert_tokens_to_string(_UpperCamelCase       )

																	self.assertIsInstance(_UpperCamelCase    ,		_UpperCamelCase       )














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


__UpperCAmelCase     							=						logging.get_logger(__name__)

__UpperCAmelCase     							=						'▁'

__UpperCAmelCase     							=						{'vocab_file': 'sentencepiece.bpe.model'}

__UpperCAmelCase     							=						{
    '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'
        ),
    }
}

__UpperCAmelCase     							=						{
    'facebook/mbart-large-en-ro': 1024,
    'facebook/mbart-large-cc25': 1024,
}

# fmt: off
__UpperCAmelCase     							=						['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				(_snake_case      ):







		'''simple docstring'''



		_snake_case   :   List[Any]							=  VOCAB_FILES_NAMES
		_snake_case   :   Union[str, Any]							=  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		_snake_case   :   Tuple							=  PRETRAINED_VOCAB_FILES_MAP
		_snake_case   :   Any							=  ['''input_ids''', '''attention_mask''']

		_snake_case   :   List[int]							=  []
		_snake_case   :   List[int]							=  []



		def __init__(							self    ,		_UpperCamelCase    ,		_UpperCamelCase="<s>"    ,		_UpperCamelCase="</s>"    ,		_UpperCamelCase="</s>"    ,		_UpperCamelCase="<s>"    ,		_UpperCamelCase="<unk>"    ,		_UpperCamelCase="<pad>"    ,		_UpperCamelCase="<mask>"    ,		_UpperCamelCase=None    ,		_UpperCamelCase=None    ,		_UpperCamelCase=None    ,		_UpperCamelCase = None    ,		_UpperCamelCase=None    ,		**_UpperCamelCase    ,		)     ->							List[str]:
							# Mask token behave like a normal word, i.e. include the space before it
							UpperCAmelCase_   :				List[Any]						=						AddedToken(_UpperCamelCase    ,		lstrip=_UpperCamelCase    ,		rstrip=_UpperCamelCase       ) if isinstance(_UpperCamelCase    ,		_UpperCamelCase       ) else mask_token

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

							super().__init__(
							    bos_token=_UpperCamelCase    ,		eos_token=_UpperCamelCase    ,		unk_token=_UpperCamelCase    ,		sep_token=_UpperCamelCase    ,		cls_token=_UpperCamelCase    ,		pad_token=_UpperCamelCase    ,		mask_token=_UpperCamelCase    ,		tokenizer_file=_UpperCamelCase    ,		src_lang=_UpperCamelCase    ,		tgt_lang=_UpperCamelCase    ,		additional_special_tokens=_UpperCamelCase    ,		sp_model_kwargs=self.sp_model_kwargs    ,		**_UpperCamelCase    ,		)

							UpperCAmelCase_   :				Any						=						spm.SentencePieceProcessor(**self.sp_model_kwargs       )
							self.sp_model.Load(str(_UpperCamelCase       )       )
							UpperCAmelCase_   :				int						=						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
							UpperCAmelCase_   :				List[str]						=						{'<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
							UpperCAmelCase_   :				str						=						1

							UpperCAmelCase_   :				Optional[int]						=						len(self.sp_model       )
							UpperCAmelCase_   :				str						=						{
							    code: self.sp_model_size + i + self.fairseq_offset for i, code in enumerate(_UpperCamelCase       )
							}
							UpperCAmelCase_   :				int						=						{v: k for k, v in self.lang_code_to_id.items()}
							UpperCAmelCase_   :				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       )
							UpperCAmelCase_   :				Tuple						=						{v: k for k, v in self.fairseq_tokens_to_ids.items()}
							UpperCAmelCase_   :				int						=						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]       )

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



		def __getstate__(							self       )     ->							Dict:
							UpperCAmelCase_   :				Optional[int]						=						self.__dict__.copy()
							UpperCAmelCase_   :				str						=						None
							UpperCAmelCase_   :				str						=						self.sp_model.serialized_model_proto()
							return state



		def __setstate__(							self    ,		_UpperCamelCase       )     ->							Dict:
							UpperCAmelCase_   :				Union[str, Any]						=						d

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

							UpperCAmelCase_   :				Any						=						spm.SentencePieceProcessor(**self.sp_model_kwargs       )
							self.sp_model.LoadFromSerializedProto(self.sp_model_proto       )



		@property
		def  __UpperCAmelCase      (							self       )     ->							Tuple:
							return len(self.sp_model       ) + len(self.lang_code_to_id       ) + self.fairseq_offset + 1  # Plus 1 for the mask token



		@property
		def  __UpperCAmelCase      (							self       )     ->							str:
							return self._src_lang



		@src_lang.setter
		def  __UpperCAmelCase      (							self    ,		_UpperCamelCase       )     ->							None:
							UpperCAmelCase_   :				Tuple						=						new_src_lang
							self.set_src_lang_special_tokens(self._src_lang       )



		def  __UpperCAmelCase      (							self    ,		_UpperCamelCase    ,		_UpperCamelCase = None    ,		_UpperCamelCase = False       )     ->							List[int]:

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

							UpperCAmelCase_   :				Tuple						=						[1] * len(self.prefix_tokens       )
							UpperCAmelCase_   :				Dict						=						[1] * len(self.suffix_tokens       )
							if token_ids_a is None:
												return prefix_ones + ([0] * len(_UpperCamelCase       )) + suffix_ones
							return prefix_ones + ([0] * len(_UpperCamelCase       )) + ([0] * len(_UpperCamelCase       )) + suffix_ones



		def  __UpperCAmelCase      (							self    ,		_UpperCamelCase    ,		_UpperCamelCase = None       )     ->							List[int]:
							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  __UpperCAmelCase      (							self    ,		_UpperCamelCase    ,		_UpperCamelCase = None       )     ->							List[int]:
							UpperCAmelCase_   :				int						=						[self.sep_token_id]
							UpperCAmelCase_   :				Optional[int]						=						[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  __UpperCAmelCase      (							self    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		**_UpperCamelCase       )     ->							int:
							if src_lang is None or tgt_lang is None:
												raise ValueError('Translation requires a `src_lang` and a `tgt_lang` for this model'       )
							UpperCAmelCase_   :				Optional[int]						=						src_lang
							UpperCAmelCase_   :				Dict						=						self(_UpperCamelCase    ,		add_special_tokens=_UpperCamelCase    ,		return_tensors=_UpperCamelCase    ,		**_UpperCamelCase       )
							UpperCAmelCase_   :				int						=						self.convert_tokens_to_ids(_UpperCamelCase       )
							UpperCAmelCase_   :				Any						=						tgt_lang_id
							return inputs



		def  __UpperCAmelCase      (							self       )     ->							str:
							UpperCAmelCase_   :				str						=						{self.convert_ids_to_tokens(_UpperCamelCase       ): i for i in range(self.vocab_size       )}
							vocab.update(self.added_tokens_encoder       )
							return vocab



		def  __UpperCAmelCase      (							self    ,		_UpperCamelCase       )     ->							List[str]:
							return self.sp_model.encode(_UpperCamelCase    ,		out_type=_UpperCamelCase       )



		def  __UpperCAmelCase      (							self    ,		_UpperCamelCase       )     ->							Tuple:
							if token in self.fairseq_tokens_to_ids:
												return self.fairseq_tokens_to_ids[token]
							UpperCAmelCase_   :				Dict						=						self.sp_model.PieceToId(_UpperCamelCase       )

							# 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  __UpperCAmelCase      (							self    ,		_UpperCamelCase       )     ->							str:
							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  __UpperCAmelCase      (							self    ,		_UpperCamelCase       )     ->							Optional[int]:
							UpperCAmelCase_   :				Optional[int]						=						''.join(_UpperCamelCase       ).replace(_UpperCamelCase    ,		' '       ).strip()
							return out_string



		def  __UpperCAmelCase      (							self    ,		_UpperCamelCase    ,		_UpperCamelCase = None       )     ->							Tuple[str]:
							if not os.path.isdir(_UpperCamelCase       ):
												logger.error(f"Vocabulary path ({save_directory}) should be a directory"       )
												return
							UpperCAmelCase_   :				List[Any]						=						os.path.join(
							    _UpperCamelCase    ,		(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']       )

							if os.path.abspath(self.vocab_file       ) != os.path.abspath(_UpperCamelCase       ) and os.path.isfile(self.vocab_file       ):
												copyfile(self.vocab_file    ,		_UpperCamelCase       )
							elif not os.path.isfile(self.vocab_file       ):
												with open(_UpperCamelCase    ,		'wb'       ) as fi:
																	UpperCAmelCase_   :				Optional[Any]						=						self.sp_model.serialized_model_proto()
																	fi.write(_UpperCamelCase       )

							return (out_vocab_file,)



		def  __UpperCAmelCase      (							self    ,		_UpperCamelCase    ,		_UpperCamelCase = "en_XX"    ,		_UpperCamelCase = None    ,		_UpperCamelCase = "ro_RO"    ,		**_UpperCamelCase    ,		)     ->							BatchEncoding:
							UpperCAmelCase_   :				Union[str, Any]						=						src_lang
							UpperCAmelCase_   :				Dict						=						tgt_lang
							return super().prepare_seqaseq_batch(_UpperCamelCase    ,		_UpperCamelCase    ,		**_UpperCamelCase       )



		def  __UpperCAmelCase      (							self       )     ->							Union[str, Any]:
							return self.set_src_lang_special_tokens(self.src_lang       )



		def  __UpperCAmelCase      (							self       )     ->							Optional[Any]:
							return self.set_tgt_lang_special_tokens(self.tgt_lang       )



		def  __UpperCAmelCase      (							self    ,		_UpperCamelCase       )     ->							None:
							UpperCAmelCase_   :				Any						=						self.lang_code_to_id[src_lang]
							UpperCAmelCase_   :				Optional[Any]						=						[]
							UpperCAmelCase_   :				str						=						[self.eos_token_id, self.cur_lang_code]



		def  __UpperCAmelCase      (							self    ,		_UpperCamelCase       )     ->							None:
							UpperCAmelCase_   :				Any						=						self.lang_code_to_id[lang]
							UpperCAmelCase_   :				Dict						=						[]
							UpperCAmelCase_   :				int						=						[self.eos_token_id, self.cur_lang_code]






