

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

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

from ...test_tokenization_common import TokenizerTesterMixin


if is_torch_available():
       __A      		=     "pt"
elif is_tf_available():
       __A      		=     "tf"
else:
       __A      		=     "jax"



class   lowercase_   (  __lowercase       ,							unittest.TestCase					):
      UpperCamelCase_      :		Dict										=    ByTaTokenizer
      UpperCamelCase_      :		Dict										=    False



      def 						UpperCamelCase_		(      self				:    Tuple		)   ->					Tuple:
        super().setUp()
        _snake_case						=							ByTaTokenizer()
        tokenizer.save_pretrained(self.tmpdirname		)



      @cached_property
      def 						UpperCamelCase_		(      self				:    int		)   ->					Optional[Any]:
        return ByTaTokenizer.from_pretrained('''google/byt5-small'''		)



      def 						UpperCamelCase_		(      self				:    Union[str, Any]      , **A__				:    Optional[int]		)   ->					int:
        return self.tokenizer_class.from_pretrained(self.tmpdirname      , **_a		)



      def 						UpperCamelCase_		(      self				:    str      , A__				:    Optional[Any]      , A__				:    Optional[int]=False      , A__				:    Any=20      , A__				:    Tuple=5		)   ->					Union[str, Any]:
        # XXX The default common tokenizer tests assume that every ID is decodable on its own.
        # This assumption is invalid for ByT5 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.

        _snake_case						=							[]
        for i in range(len(_a		)		):
          try:
            _snake_case						=							tokenizer.decode([i]      , clean_up_tokenization_spaces=_a		)
          except UnicodeDecodeError:
            pass
          toks.append((i, tok)		)

        _snake_case						=							list(filter(lambda A__		: re.match(R'''^[ a-zA-Z]+$'''      , t[1]		)      , _a		)		)
        _snake_case						=							list(filter(lambda A__		: [t[0]] == tokenizer.encode(t[1]      , add_special_tokens=_a		)      , _a		)		)
        if max_length is not None and len(_a		) > max_length:
          _snake_case						=							toks[:max_length]
        if min_length is not None and len(_a		) < min_length and len(_a		) > 0:
          while len(_a		) < min_length:
            _snake_case						=							toks + toks
        # toks_str = [t[1] for t in toks]
        _snake_case						=							[t[0] for t in toks]

        # Ensure consistency
        _snake_case						=							tokenizer.decode(_a      , clean_up_tokenization_spaces=_a		)
        if " " not in output_txt and len(_a		) > 1:
          _snake_case						=							(
              tokenizer.decode([toks_ids[0]]      , clean_up_tokenization_spaces=_a		)
              + " "
              + tokenizer.decode(toks_ids[1:]      , clean_up_tokenization_spaces=_a		)
          )
        if with_prefix_space:
          _snake_case						=							" " + output_txt
        _snake_case						=							tokenizer.encode(_a      , add_special_tokens=_a		)
        return output_txt, output_ids



      def 						UpperCamelCase_		(      self				:    str		)   ->					Tuple:
        _snake_case						=							self.ta_base_tokenizer
        _snake_case						=							tokenizer(['''hi</s>''', '''I went to the gym</s>''', '''</s>''']		)
        _snake_case						=							tokenizer(['''hi''', '''I went to the gym''', '''''']		)
        self.assertListEqual(batch_with_eos_added['''input_ids''']      , batch_without_eos_added['''input_ids''']		)



      def 						UpperCamelCase_		(      self				:    int		)   ->					int:
        _snake_case						=							self.ta_base_tokenizer
        _snake_case						=							"Unicode €."
        _snake_case						=							tokenizer(_a		)
        _snake_case						=							[88, 113, 108, 102, 114, 103, 104, 35, 229, 133, 175, 49, 1]
        self.assertEqual(encoded['''input_ids''']      , _a		)

        # decoding
        _snake_case						=							tokenizer.decode(_a		)
        self.assertEqual(_a      , '''Unicode €.</s>'''		)

        _snake_case						=							tokenizer('''e è é ê ë'''		)
        _snake_case						=							[104, 35, 198, 171, 35, 198, 172, 35, 198, 173, 35, 198, 174, 1]
        self.assertEqual(encoded['''input_ids''']      , _a		)
        # decoding
        _snake_case						=							tokenizer.decode(_a		)
        self.assertEqual(_a      , '''e è é ê ë</s>'''		)

        # encode/decode, but with `encode` instead of `__call__`
        self.assertEqual(tokenizer.decode(tokenizer.encode('''e è é ê ë'''		)		)      , '''e è é ê ë</s>'''		)



      def 						UpperCamelCase_		(      self				:    Dict		)   ->					Union[str, Any]:
        _snake_case						=							self.ta_base_tokenizer
        _snake_case						=							["A long paragraph for summarization.", "Another paragraph for summarization."]
        # fmt: off
        _snake_case						=							[68, 35, 111, 114, 113, 106, 35, 115, 100, 117, 100, 106, 117, 100, 115, 107, 35, 105, 114, 117, 35, 118, 120, 112, 112, 100, 117, 108, 125, 100, 119, 108, 114, 113, 49, 1, 0]
        # fmt: on
        _snake_case						=							tokenizer(_a      , padding=_a      , return_tensors=_a		)
        self.assertIsInstance(_a      , _a		)

        if FRAMEWORK != "jax":
          _snake_case						=							list(batch.input_ids.numpy()[0]		)
        else:
          _snake_case						=							list(batch.input_ids.tolist()[0]		)

        self.assertListEqual(_a      , _a		)

        self.assertEqual((2, 37)      , batch.input_ids.shape		)
        self.assertEqual((2, 37)      , batch.attention_mask.shape		)



      def 						UpperCamelCase_		(      self				:    List[str]		)   ->					Optional[int]:
        _snake_case						=							self.ta_base_tokenizer
        _snake_case						=							["A long paragraph for summarization.", "Another paragraph for summarization."]
        _snake_case						=							tokenizer(_a      , padding=_a      , return_tensors=_a		)
        # check if input_ids are returned and no decoder_input_ids
        self.assertIn('''input_ids'''      , _a		)
        self.assertIn('''attention_mask'''      , _a		)
        self.assertNotIn('''decoder_input_ids'''      , _a		)
        self.assertNotIn('''decoder_attention_mask'''      , _a		)



      def 						UpperCamelCase_		(      self				:    Optional[int]		)   ->					Any:
        _snake_case						=							self.ta_base_tokenizer
        _snake_case						=							[
            "Summary of the text.",
            "Another summary.",
        ]
        _snake_case						=							tokenizer(
            text_target=_a      , max_length=32      , padding='''max_length'''      , truncation=_a      , return_tensors=_a		)
        self.assertEqual(32      , targets['''input_ids'''].shape[1]		)



      def 						UpperCamelCase_		(      self				:    Any		)   ->					Any:
        _snake_case						=							self.ta_base_tokenizer
        _snake_case						=							["A long paragraph for summarization. </s>"]
        _snake_case						=							["Summary of the text. </s>"]
        # fmt: off
        _snake_case						=							[68, 35, 111, 114, 113, 106, 35, 115, 100, 117, 100, 106, 117, 100, 115, 107, 35, 105, 114, 117, 35, 118, 120, 112, 112, 100, 117, 108, 125, 100, 119, 108, 114, 113, 49, 35, 1]
        _snake_case						=							[86, 120, 112, 112, 100, 117, 124, 35, 114, 105, 35, 119, 107, 104, 35, 119, 104, 123, 119, 49, 35, 1]
        # fmt: on

        _snake_case						=							tokenizer(_a      , text_target=_a		)

        self.assertEqual(_a      , batch['''input_ids'''][0]		)
        self.assertEqual(_a      , batch['''labels'''][0]		)



      def 						UpperCamelCase_		(      self				:    int		)   ->					Optional[Any]:
        # safety check on max_len default value so we are sure the test works
        _snake_case						=							self.get_tokenizers()
        for tokenizer in tokenizers:
          with self.subTest(f"""{tokenizer.__class__.__name__}"""		):
            self.assertNotEqual(tokenizer.model_max_length      , 42		)

        # Now let's start the test
        _snake_case						=							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
            _snake_case						=							tempfile.mkdtemp()

            _snake_case						=							" He is very happy, UNwant\u00E9d,running"
            _snake_case						=							tokenizer.encode(_a      , add_special_tokens=_a		)
            tokenizer.save_pretrained(_a		)

            _snake_case						=							tokenizer.__class__.from_pretrained(_a		)
            _snake_case						=							after_tokenizer.encode(_a      , add_special_tokens=_a		)
            self.assertListEqual(_a      , _a		)

            shutil.rmtree(_a		)

        _snake_case						=							self.get_tokenizers(model_max_length=42		)
        for tokenizer in tokenizers:
          with self.subTest(f"""{tokenizer.__class__.__name__}"""		):
            # Isolate this from the other tests because we save additional tokens/etc
            _snake_case						=							tempfile.mkdtemp()

            _snake_case						=							" He is very happy, UNwant\u00E9d,running"
            tokenizer.add_tokens(['''bim''', '''bambam''']		)
            _snake_case						=							tokenizer.additional_special_tokens
            additional_special_tokens.append('''new_additional_special_token'''		)
            tokenizer.add_special_tokens({'''additional_special_tokens''': additional_special_tokens}		)
            _snake_case						=							tokenizer.encode(_a      , add_special_tokens=_a		)
            tokenizer.save_pretrained(_a		)

            _snake_case						=							tokenizer.__class__.from_pretrained(_a		)
            _snake_case						=							after_tokenizer.encode(_a      , add_special_tokens=_a		)
            self.assertListEqual(_a      , _a		)
            self.assertIn('''new_additional_special_token'''      , after_tokenizer.additional_special_tokens		)
            self.assertEqual(after_tokenizer.model_max_length      , 42		)

            _snake_case						=							tokenizer.__class__.from_pretrained(_a      , model_max_length=43		)
            self.assertEqual(tokenizer.model_max_length      , 43		)

            shutil.rmtree(_a		)



      def 						UpperCamelCase_		(      self				:    Dict		)   ->					List[str]:
        _snake_case						=							[]
        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(_a		)

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

            with open(os.path.join(_a      , '''tokenizer_config.json'''		)      , encoding='''utf-8'''		) as json_file:
              _snake_case						=							json.load(_a		)

            _snake_case						=							[f"""<extra_id_{i}>""" for i in range(125		)]

            _snake_case						=							added_tokens_extra_ids + [
                "an_additional_special_token"
            ]
            _snake_case						=							added_tokens_extra_ids + [
                "an_additional_special_token"
            ]

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

            # 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
            _snake_case						=							tokenizer_class.from_pretrained(
                _a      , )
            self.assertIn(
                '''an_additional_special_token'''      , tokenizer_without_change_in_init.additional_special_tokens		)
            # self.assertIn("an_additional_special_token",tokenizer_without_change_in_init.get_vocab()) # ByT5Tokenization no vocab
            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
            _snake_case						=							added_tokens_extra_ids + [AddedToken('''a_new_additional_special_token'''      , lstrip=_a		)]
            _snake_case						=							tokenizer_class.from_pretrained(
                _a      , additional_special_tokens=_a      , )

            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				:    Optional[int]		)   ->					List[str]:
        _snake_case						=							[]
        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(_a		)

            _snake_case						=							tokenizer_class.from_pretrained(_a		)

            self.assertTrue(tokenizer.decode([255]		) == ''''''		)



      def 						UpperCamelCase_		(      self				:    int		)   ->					Tuple:
        pass



      def 						UpperCamelCase_		(      self				:    Optional[Any]		)   ->					Dict:
        pass



      def 						UpperCamelCase_		(      self				:    Tuple		)   ->					str:
        pass



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



      def 						UpperCamelCase_		(      self				:    Union[str, Any]		)   ->					List[str]:
        # The default common tokenizer tests uses invalid tokens for ByT5 that can only accept one-character strings
        # and special added tokens as tokens
        _snake_case						=							self.get_tokenizers(fast=_a      , do_lower_case=_a		)
        for tokenizer in tokenizers:
          with self.subTest(f"""{tokenizer.__class__.__name__}"""		):
            _snake_case						=							["t", "h", "i", "s", " ", "i", "s", " ", "a", " ", "t", "e", "x", "t", "</s>"]
            _snake_case						=							tokenizer.convert_tokens_to_string(_a		)

            self.assertIsInstance(_a      , _a		)







      def 						UpperCamelCase_		(      self				:    Any		)   ->					Optional[Any]:
        _snake_case						=							self.get_tokenizers()
        for tokenizer in tokenizers:
          with self.subTest(f"""{tokenizer.__class__.__name__}"""		):
            _snake_case						=							[
                "bos_token",
                "eos_token",
                "unk_token",
                "sep_token",
                "pad_token",
                "cls_token",
                "mask_token",
            ]

            _snake_case						=							0
            _snake_case						=							tokenizer.convert_ids_to_tokens(
                _a      , skip_special_tokens=_a		)

            for attr in attributes_list:
              setattr(_a      , attr + '''_id'''      , _a		)
              self.assertEqual(getattr(_a      , _a		)      , _a		)
              self.assertEqual(getattr(_a      , attr + '''_id'''		)      , _a		)

              setattr(_a      , attr + '''_id'''      , _a		)
              self.assertEqual(getattr(_a      , _a		)      , _a		)
              self.assertEqual(getattr(_a      , attr + '''_id'''		)      , _a		)

            setattr(_a      , '''additional_special_tokens_ids'''      , []		)
            self.assertListEqual(getattr(_a      , '''additional_special_tokens'''		)      , []		)
            self.assertListEqual(getattr(_a      , '''additional_special_tokens_ids'''		)      , []		)

            setattr(_a      , '''additional_special_tokens_ids'''      , [token_id_to_test_setters]		)
            self.assertListEqual(getattr(_a      , '''additional_special_tokens'''		)      , [token_to_test_setters]		)
            self.assertListEqual(getattr(_a      , '''additional_special_tokens_ids'''		)      , [token_id_to_test_setters]		)




import json
import os
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union

import sentencepiece

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


__A      		=     logging.get_logger(__name__)

__A      		=     '''▁'''

__A      		=     {
    '''vocab_file''': '''vocab.json''',
    '''spm_file''': '''sentencepiece.bpe.model''',
    '''tokenizer_config_file''': '''tokenizer_config.json''',
}

__A      		=     {
    '''vocab_file''': {
        '''facebook/m2m100_418M''': '''https://huggingface.co/facebook/m2m100_418M/resolve/main/vocab.json''',
        '''facebook/m2m100_1.2B''': '''https://huggingface.co/facebook/m2m100_1.2B/resolve/main/vocab.json''',
    },
    '''spm_file''': {
        '''facebook/m2m100_418M''': '''https://huggingface.co/facebook/m2m100_418M/resolve/main/sentencepiece.bpe.model''',
        '''facebook/m2m100_1.2B''': '''https://huggingface.co/facebook/m2m100_1.2B/resolve/main/sentencepiece.bpe.model''',
    },
    '''tokenizer_config_file''': {
        '''facebook/m2m100_418M''': '''https://huggingface.co/facebook/m2m100_418M/resolve/main/tokenizer_config.json''',
        '''facebook/m2m100_1.2B''': '''https://huggingface.co/facebook/m2m100_1.2B/resolve/main/tokenizer_config.json''',
    },
}

__A      		=     {
    '''facebook/m2m100_418M''': 10_24,
}

# fmt: off
__A      		=     {
    '''m2m100''': ['''af''', '''am''', '''ar''', '''ast''', '''az''', '''ba''', '''be''', '''bg''', '''bn''', '''br''', '''bs''', '''ca''', '''ceb''', '''cs''', '''cy''', '''da''', '''de''', '''el''', '''en''', '''es''', '''et''', '''fa''', '''ff''', '''fi''', '''fr''', '''fy''', '''ga''', '''gd''', '''gl''', '''gu''', '''ha''', '''he''', '''hi''', '''hr''', '''ht''', '''hu''', '''hy''', '''id''', '''ig''', '''ilo''', '''is''', '''it''', '''ja''', '''jv''', '''ka''', '''kk''', '''km''', '''kn''', '''ko''', '''lb''', '''lg''', '''ln''', '''lo''', '''lt''', '''lv''', '''mg''', '''mk''', '''ml''', '''mn''', '''mr''', '''ms''', '''my''', '''ne''', '''nl''', '''no''', '''ns''', '''oc''', '''or''', '''pa''', '''pl''', '''ps''', '''pt''', '''ro''', '''ru''', '''sd''', '''si''', '''sk''', '''sl''', '''so''', '''sq''', '''sr''', '''ss''', '''su''', '''sv''', '''sw''', '''ta''', '''th''', '''tl''', '''tn''', '''tr''', '''uk''', '''ur''', '''uz''', '''vi''', '''wo''', '''xh''', '''yi''', '''yo''', '''zh''', '''zu'''],
    '''wmt21''': ['''en''', '''ha''', '''is''', '''ja''', '''cs''', '''ru''', '''zh''', '''de''']
}



class   lowercase_   (  __lowercase					):
      UpperCamelCase_      :		str										=    VOCAB_FILES_NAMES
      UpperCamelCase_      :		Optional[int]										=    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      UpperCamelCase_      :		Dict										=    PRETRAINED_VOCAB_FILES_MAP
      UpperCamelCase_      :		Union[str, Any]										=    ["input_ids", "attention_mask"]

      UpperCamelCase_      :		List[int]										=    []
      UpperCamelCase_      :		List[int]										=    []



      def __init__(      self				:    str      , A__				:    str      , A__				:    Optional[Any]      , A__				:    Union[str, Any]=None      , A__				:    Dict=None      , A__				:    Any="<s>"      , A__				:    Union[str, Any]="</s>"      , A__				:    Tuple="</s>"      , A__				:    Dict="<pad>"      , A__				:    List[Any]="<unk>"      , A__				:    str="m2m100"      , A__				:    Optional[Dict[str, Any]] = None      , A__				:    List[Any]=8      , **A__				:    Union[str, Any]      , )   ->					None:
        _snake_case						=							{} if sp_model_kwargs is None else sp_model_kwargs

        _snake_case						=							language_codes
        _snake_case						=							FAIRSEQ_LANGUAGE_CODES[language_codes]
        _snake_case						=							{lang_code: f"""__{lang_code}__""" for lang_code in fairseq_language_code}

        _snake_case						=							kwargs.get('''additional_special_tokens'''      , []		)
        kwargs["additional_special_tokens"] += [
            self.get_lang_token(A__		)
            for lang_code in fairseq_language_code
            if self.get_lang_token(A__		) not in kwargs["additional_special_tokens"]
        ]

        super().__init__(
            src_lang=A__      , tgt_lang=A__      , bos_token=A__      , eos_token=A__      , sep_token=A__      , unk_token=A__      , pad_token=A__      , language_codes=A__      , sp_model_kwargs=self.sp_model_kwargs      , num_madeup_words=A__      , **A__      , )

        _snake_case						=							vocab_file
        _snake_case						=							load_json(A__		)
        _snake_case						=							{v: k for k, v in self.encoder.items()}
        _snake_case						=							spm_file
        _snake_case						=							load_spm(A__      , self.sp_model_kwargs		)

        _snake_case						=							len(self.encoder		)

        _snake_case						=							{
            self.get_lang_token(A__		): self.encoder_size + i for i, lang_code in enumerate(A__		)
        }
        _snake_case						=							{lang_code: self.encoder_size + i for i, lang_code in enumerate(A__		)}
        _snake_case						=							{v: k for k, v in self.lang_token_to_id.items()}

        _snake_case						=							src_lang if src_lang is not None else '''en'''
        _snake_case						=							tgt_lang
        _snake_case						=							self.get_lang_id(self._src_lang		)
        self.set_src_lang_special_tokens(self._src_lang		)

        _snake_case						=							num_madeup_words



      @property
      def 						UpperCamelCase_		(      self				:    int		)   ->					int:
        return len(self.encoder		) + len(self.lang_token_to_id		)



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



      @src_lang.setter
      def 						UpperCamelCase_		(      self				:    List[str]      , A__				:    str		)   ->					None:
        _snake_case						=							new_src_lang
        self.set_src_lang_special_tokens(self._src_lang		)



      def 						UpperCamelCase_		(      self				:    Any      , A__				:    str		)   ->					List[str]:
        return self.sp_model.encode(A__      , out_type=A__		)



      def 						UpperCamelCase_		(      self				:    Optional[int]      , A__				:    Dict		)   ->					str:
        if token in self.lang_token_to_id:
          return self.lang_token_to_id[token]
        return self.encoder.get(A__      , self.encoder[self.unk_token]		)



      def 						UpperCamelCase_		(      self				:    Union[str, Any]      , A__				:    int		)   ->					str:
        if index in self.id_to_lang_token:
          return self.id_to_lang_token[index]
        return self.decoder.get(A__      , self.unk_token		)



      def 						UpperCamelCase_		(      self				:    Optional[int]      , A__				:    Optional[int]		)   ->					List[Any]:
        _snake_case						=							[]
        _snake_case						=							''''''
        for token in tokens:
          # make sure that special tokens are not decoded using sentencepiece model
          if token in self.all_special_tokens:
            out_string += self.sp_model.decode(A__		) + token
            _snake_case						=							[]
          else:
            current_sub_tokens.append(A__		)
        out_string += self.sp_model.decode(A__		)
        return out_string.strip()



      def 						UpperCamelCase_		(      self				:    str      , A__				:    List[int]      , A__				:    Optional[List[int]] = None      , A__				:    bool = False		)   ->					List[int]:

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

        _snake_case						=							[1] * len(self.prefix_tokens		)
        _snake_case						=							[1] * len(self.suffix_tokens		)
        if token_ids_a is None:
          return prefix_ones + ([0] * len(A__		)) + suffix_ones
        return prefix_ones + ([0] * len(A__		)) + ([0] * len(A__		)) + suffix_ones



      def 						UpperCamelCase_		(      self				:    Tuple      , A__				:    List[int]      , A__				:    Optional[List[int]] = 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				:    str		)   ->					Dict:
        _snake_case						=							{self.convert_ids_to_tokens(A__		): i for i in range(self.vocab_size		)}
        vocab.update(self.added_tokens_encoder		)
        return vocab



      def __getstate__(      self				:    str		)   ->					Dict:
        _snake_case						=							self.__dict__.copy()
        _snake_case						=							None
        return state



      def __setstate__(      self				:    Union[str, Any]      , A__				:    Dict		)   ->					None:
        _snake_case						=							d

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

        _snake_case						=							load_spm(self.spm_file      , self.sp_model_kwargs		)



      def 						UpperCamelCase_		(      self				:    Any      , A__				:    str      , A__				:    Optional[str] = None		)   ->					Tuple[str]:
        _snake_case						=							Path(A__		)
        if not save_dir.is_dir():
          raise OSError(f"""{save_directory} should be a directory"""		)
        _snake_case						=							save_dir / (
            (filename_prefix + '''-''' if filename_prefix else '''''') + self.vocab_files_names['''vocab_file''']
        )
        _snake_case						=							save_dir / (
            (filename_prefix + '''-''' if filename_prefix else '''''') + self.vocab_files_names['''spm_file''']
        )

        save_json(self.encoder      , A__		)

        if os.path.abspath(self.spm_file		) != os.path.abspath(A__		) and os.path.isfile(self.spm_file		):
          copyfile(self.spm_file      , A__		)
        elif not os.path.isfile(self.spm_file		):
          with open(A__      , '''wb'''		) as fi:
            _snake_case						=							self.sp_model.serialized_model_proto()
            fi.write(A__		)

        return (str(A__		), str(A__		))



      def 						UpperCamelCase_		(      self				:    Optional[int]      , A__				:    List[str]      , A__				:    str = "en"      , A__				:    Optional[List[str]] = None      , A__				:    str = "ro"      , **A__				:    List[Any]      , )   ->					BatchEncoding:
        _snake_case						=							src_lang
        _snake_case						=							tgt_lang
        self.set_src_lang_special_tokens(self.src_lang		)
        return super().prepare_seqaseq_batch(A__      , A__      , **A__		)



      def 						UpperCamelCase_		(      self				:    List[str]      , A__				:    int      , A__				:    Optional[str]      , A__				:    Optional[str]      , **A__				:    Union[str, Any]		)   ->					Tuple:
        if src_lang is None or tgt_lang is None:
          raise ValueError('''Translation requires a `src_lang` and a `tgt_lang` for this model'''		)
        _snake_case						=							src_lang
        _snake_case						=							self(A__      , add_special_tokens=A__      , **A__		)
        _snake_case						=							self.get_lang_id(A__		)
        _snake_case						=							tgt_lang_id
        return inputs



      def 						UpperCamelCase_		(      self				:    Dict		)   ->					Optional[Any]:
        self.set_src_lang_special_tokens(self.src_lang		)



      def 						UpperCamelCase_		(      self				:    Optional[Any]		)   ->					Dict:
        self.set_tgt_lang_special_tokens(self.tgt_lang		)



      def 						UpperCamelCase_		(      self				:    List[Any]      , A__				:    str		)   ->					None:
        _snake_case						=							self.get_lang_token(A__		)
        _snake_case						=							self.lang_token_to_id[lang_token]
        _snake_case						=							[self.cur_lang_id]
        _snake_case						=							[self.eos_token_id]



      def 						UpperCamelCase_		(      self				:    List[str]      , A__				:    str		)   ->					None:
        _snake_case						=							self.get_lang_token(A__		)
        _snake_case						=							self.lang_token_to_id[lang_token]
        _snake_case						=							[self.cur_lang_id]
        _snake_case						=							[self.eos_token_id]



      def 						UpperCamelCase_		(      self				:    Dict      , A__				:    str		)   ->					str:
        return self.lang_code_to_token[lang]







      def 						UpperCamelCase_		(      self				:    Tuple      , A__				:    str		)   ->					int:
        _snake_case						=							self.get_lang_token(A__		)
        return self.lang_token_to_id[lang_token]



def 				snake_case_(_UpperCamelCase			,	_UpperCamelCase					)							->     sentencepiece.SentencePieceProcessor:



  """simple docstring"""






  _snake_case						=							sentencepiece.SentencePieceProcessor(**_UpperCamelCase					)
  spm.Load(str(_UpperCamelCase					)					)
  return spm



def 				snake_case_(_UpperCamelCase					)							->     Union[Dict, List]:



  """simple docstring"""






  with open(_UpperCamelCase			,	'''r'''					) as f:
    return json.load(_UpperCamelCase					)



def 				snake_case_(_UpperCamelCase			,	_UpperCamelCase					)							->     None:



  """simple docstring"""






  with open(_UpperCamelCase			,	'''w'''					) as f:
    json.dump(_UpperCamelCase			,	_UpperCamelCase			,	indent=2					)

