





"""simple docstring"""



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""": 1024,
}

# 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 		__snake_case (		SCREAMING_SNAKE_CASE__					):





 """simple docstring"""


 _lowerCamelCase				=					VOCAB_FILES_NAMES
 _lowerCamelCase				=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 _lowerCamelCase				=					PRETRAINED_VOCAB_FILES_MAP
 _lowerCamelCase				=					["""input_ids""", """attention_mask"""]

 _lowerCamelCase				=					[]
 _lowerCamelCase				=					[]

 def __init__(   self ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase=None ,						__lowerCamelCase=None ,						__lowerCamelCase="<s>" ,						__lowerCamelCase="</s>" ,						__lowerCamelCase="</s>" ,						__lowerCamelCase="<pad>" ,						__lowerCamelCase="<unk>" ,						__lowerCamelCase="m2m100" ,						__lowerCamelCase = None ,						__lowerCamelCase=8 ,						**__lowerCamelCase ,						):



     '''simple docstring'''




     __A   :						str									=					{} if sp_model_kwargs is None else sp_model_kwargs

     __A   :						Any									=					language_codes
     __A   :						Tuple									=					FAIRSEQ_LANGUAGE_CODES[language_codes]
     __A   :						Optional[int]									=					{lang_code: F"""__{lang_code}__""" for lang_code in fairseq_language_code}

     __A   :						Optional[Any]									=					kwargs.get('''additional_special_tokens''' ,						[]  )
     kwargs["additional_special_tokens"] += [
         self.get_lang_token(__lowerCamelCase  )
         for lang_code in fairseq_language_code
         if self.get_lang_token(__lowerCamelCase  ) not in kwargs["additional_special_tokens"]
     ]

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

     __A   :						Optional[Any]									=					vocab_file
     __A   :						Union[str, Any]									=					load_json(__lowerCamelCase  )
     __A   :						str									=					{v: k for k, v in self.encoder.items()}
     __A   :						Optional[Any]									=					spm_file
     __A   :						Dict									=					load_spm(__lowerCamelCase ,						self.sp_model_kwargs  )

     __A   :						Optional[int]									=					len(self.encoder  )

     __A   :						List[str]									=					{
         self.get_lang_token(__lowerCamelCase  ): self.encoder_size + i for i, lang_code in enumerate(__lowerCamelCase  )
     }
     __A   :						Optional[int]									=					{lang_code: self.encoder_size + i for i, lang_code in enumerate(__lowerCamelCase  )}
     __A   :						Optional[Any]									=					{v: k for k, v in self.lang_token_to_id.items()}

     __A   :						List[Any]									=					src_lang if src_lang is not None else '''en'''
     __A   :						int									=					tgt_lang
     __A   :						Any									=					self.get_lang_id(self._src_lang  )
     self.set_src_lang_special_tokens(self._src_lang  )

     __A   :						List[str]									=					num_madeup_words

 @property
 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




     return len(self.encoder  ) + len(self.lang_token_to_id  )

 @property
 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




     return self._src_lang

 @src_lang.setter
 def  UpperCamelCase__(   self ,						__lowerCamelCase  ):



     '''simple docstring'''




     __A   :						List[Any]									=					new_src_lang
     self.set_src_lang_special_tokens(self._src_lang  )

 def  UpperCamelCase__(   self ,						__lowerCamelCase  ):



     '''simple docstring'''




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

 def  UpperCamelCase__(   self ,						__lowerCamelCase  ):



     '''simple docstring'''




     if token in self.lang_token_to_id:
         return self.lang_token_to_id[token]
     return self.encoder.get(__lowerCamelCase ,						self.encoder[self.unk_token]  )

 def  UpperCamelCase__(   self ,						__lowerCamelCase  ):



     '''simple docstring'''




     if index in self.id_to_lang_token:
         return self.id_to_lang_token[index]
     return self.decoder.get(__lowerCamelCase ,						self.unk_token  )

 def  UpperCamelCase__(   self ,						__lowerCamelCase  ):



     '''simple docstring'''




     __A   :						Any									=					[]
     __A   :						Any									=					''''''
     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(__lowerCamelCase  ) + token
             __A   :						Tuple									=					[]
         else:
             current_sub_tokens.append(__lowerCamelCase  )
     out_string += self.sp_model.decode(__lowerCamelCase  )
     return out_string.strip()

 def  UpperCamelCase__(   self ,						__lowerCamelCase ,						__lowerCamelCase = None ,						__lowerCamelCase = False  ):



     '''simple docstring'''




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

     __A   :						Optional[int]									=					[1] * len(self.prefix_tokens  )
     __A   :						Dict									=					[1] * len(self.suffix_tokens  )
     if token_ids_a is None:
         return prefix_ones + ([0] * len(__lowerCamelCase  )) + suffix_ones
     return prefix_ones + ([0] * len(__lowerCamelCase  )) + ([0] * len(__lowerCamelCase  )) + suffix_ones

 def  UpperCamelCase__(   self ,						__lowerCamelCase ,						__lowerCamelCase = 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  UpperCamelCase__(   self  ):



     '''simple docstring'''




     __A   :						Optional[int]									=					{self.convert_ids_to_tokens(__lowerCamelCase  ): i for i in range(self.vocab_size  )}
     vocab.update(self.added_tokens_encoder  )
     return vocab

 def __getstate__(   self  ):



     '''simple docstring'''




     __A   :						str									=					self.__dict__.copy()
     __A   :						Dict									=					None
     return state

 def __setstate__(   self ,						__lowerCamelCase  ):



     '''simple docstring'''




     __A   :						List[str]									=					d

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

     __A   :						Optional[Any]									=					load_spm(self.spm_file ,						self.sp_model_kwargs  )

 def  UpperCamelCase__(   self ,						__lowerCamelCase ,						__lowerCamelCase = None  ):



     '''simple docstring'''




     __A   :						int									=					Path(__lowerCamelCase  )
     if not save_dir.is_dir():
         raise OSError(F"""{save_directory} should be a directory"""  )
     __A   :						int									=					save_dir / (
         (filename_prefix + '''-''' if filename_prefix else '''''') + self.vocab_files_names['''vocab_file''']
     )
     __A   :						Union[str, Any]									=					save_dir / (
         (filename_prefix + '''-''' if filename_prefix else '''''') + self.vocab_files_names['''spm_file''']
     )

     save_json(self.encoder ,						__lowerCamelCase  )

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

     return (str(__lowerCamelCase  ), str(__lowerCamelCase  ))

 def  UpperCamelCase__(   self ,						__lowerCamelCase ,						__lowerCamelCase = "en" ,						__lowerCamelCase = None ,						__lowerCamelCase = "ro" ,						**__lowerCamelCase ,						):



     '''simple docstring'''




     __A   :						List[str]									=					src_lang
     __A   :						List[Any]									=					tgt_lang
     self.set_src_lang_special_tokens(self.src_lang  )
     return super().prepare_seqaseq_batch(__lowerCamelCase ,						__lowerCamelCase ,						**__lowerCamelCase  )

 def  UpperCamelCase__(   self ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase ,						**__lowerCamelCase  ):



     '''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'''  )
     __A   :						List[str]									=					src_lang
     __A   :						Optional[Any]									=					self(__lowerCamelCase ,						add_special_tokens=__lowerCamelCase ,						**__lowerCamelCase  )
     __A   :						int									=					self.get_lang_id(__lowerCamelCase  )
     __A   :						str									=					tgt_lang_id
     return inputs

 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




     self.set_src_lang_special_tokens(self.src_lang  )

 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




     self.set_tgt_lang_special_tokens(self.tgt_lang  )

 def  UpperCamelCase__(   self ,						__lowerCamelCase  ):



     '''simple docstring'''




     __A   :						List[Any]									=					self.get_lang_token(__lowerCamelCase  )
     __A   :						Union[str, Any]									=					self.lang_token_to_id[lang_token]
     __A   :						int									=					[self.cur_lang_id]
     __A   :						List[str]									=					[self.eos_token_id]

 def  UpperCamelCase__(   self ,						__lowerCamelCase  ):



     '''simple docstring'''




     __A   :						int									=					self.get_lang_token(__lowerCamelCase  )
     __A   :						List[str]									=					self.lang_token_to_id[lang_token]
     __A   :						Optional[int]									=					[self.cur_lang_id]
     __A   :						Optional[int]									=					[self.eos_token_id]

 def  UpperCamelCase__(   self ,						__lowerCamelCase  ):



     '''simple docstring'''




     return self.lang_code_to_token[lang]







 def  UpperCamelCase__(   self ,						__lowerCamelCase  ):



     '''simple docstring'''




     __A   :						str									=					self.get_lang_token(__lowerCamelCase  )
     return self.lang_token_to_id[lang_token]







def 		__lowercase					(       snake_case_  :  str    ,snake_case_  :  Dict[str, Any]						)       ->sentencepiece.SentencePieceProcessor:



    '''simple docstring'''







    __A   :						Optional[Any]									=					sentencepiece.SentencePieceProcessor(**snake_case_						)
    spm.Load(str(snake_case_						)						)
    return spm







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



    '''simple docstring'''







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







def 		__lowercase					(       snake_case_  :  Dict    ,snake_case_  :  str						)       ->None:



    '''simple docstring'''







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








"""simple docstring"""



import unittest

from transformers import BarthezTokenizer, BarthezTokenizerFast, BatchEncoding
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow

from ...test_tokenization_common import TokenizerTesterMixin






@require_tokenizers
@require_sentencepiece
@slow  # see https://github.com/huggingface/transformers/issues/11457
class 		__snake_case (		SCREAMING_SNAKE_CASE__							,							unittest.TestCase					):





 """simple docstring"""


 _lowerCamelCase				=					BarthezTokenizer
 _lowerCamelCase				=					BarthezTokenizerFast
 _lowerCamelCase				=					True
 _lowerCamelCase				=					True

 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




     super().setUp()

     __A   :						List[Any]									=					BarthezTokenizerFast.from_pretrained('''moussaKam/mbarthez'''  )
     tokenizer.save_pretrained(self.tmpdirname  )
     tokenizer.save_pretrained(self.tmpdirname ,						legacy_format=__lowerCamelCase  )
     __A   :						Any									=					tokenizer

 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




     __A   :						Any									=					'''<pad>'''
     __A   :						Union[str, Any]									=					1

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

 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




     __A   :						Optional[Any]									=					list(self.get_tokenizer().get_vocab().keys()  )

     self.assertEqual(vocab_keys[0] ,						'''<s>'''  )
     self.assertEqual(vocab_keys[1] ,						'''<pad>'''  )
     self.assertEqual(vocab_keys[-1] ,						'''<mask>'''  )
     self.assertEqual(len(__lowerCamelCase  ) ,						10_1122  )

 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




     self.assertEqual(self.get_tokenizer().vocab_size ,						10_1122  )

 @require_torch
 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




     __A   :						List[Any]									=					['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
     __A   :						Any									=					[0, 57, 3018, 7_0307, 91, 2]

     __A   :						List[Any]									=					self.tokenizer(
         __lowerCamelCase ,						max_length=len(__lowerCamelCase  ) ,						padding=__lowerCamelCase ,						truncation=__lowerCamelCase ,						return_tensors='''pt'''  )
     self.assertIsInstance(__lowerCamelCase ,						__lowerCamelCase  )

     self.assertEqual((2, 6) ,						batch.input_ids.shape  )
     self.assertEqual((2, 6) ,						batch.attention_mask.shape  )
     __A   :						Any									=					batch.input_ids.tolist()[0]
     self.assertListEqual(__lowerCamelCase ,						__lowerCamelCase  )

 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




     if not self.test_rust_tokenizer:
         return

     __A   :						Tuple									=					self.get_tokenizer()
     __A   :						Dict									=					self.get_rust_tokenizer()

     __A   :						Union[str, Any]									=					'''I was born in 92000, and this is falsé.'''

     __A   :						List[Any]									=					tokenizer.tokenize(__lowerCamelCase  )
     __A   :						Optional[int]									=					rust_tokenizer.tokenize(__lowerCamelCase  )
     self.assertListEqual(__lowerCamelCase ,						__lowerCamelCase  )

     __A   :						str									=					tokenizer.encode(__lowerCamelCase ,						add_special_tokens=__lowerCamelCase  )
     __A   :						Union[str, Any]									=					rust_tokenizer.encode(__lowerCamelCase ,						add_special_tokens=__lowerCamelCase  )
     self.assertListEqual(__lowerCamelCase ,						__lowerCamelCase  )

     __A   :						str									=					self.get_rust_tokenizer()
     __A   :						Optional[int]									=					tokenizer.encode(__lowerCamelCase  )
     __A   :						Any									=					rust_tokenizer.encode(__lowerCamelCase  )
     self.assertListEqual(__lowerCamelCase ,						__lowerCamelCase  )







 @slow
 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




     __A   :						List[str]									=					{'''input_ids''': [[0, 490, 1_4328, 4507, 354, 47, 4_3669, 95, 25, 7_8117, 2_0215, 1_9779, 190, 22, 400, 4, 3_5343, 8_0310, 603, 86, 2_4937, 105, 3_3438, 9_4762, 196, 3_9642, 7, 15, 1_5933, 173, 2, 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, 1_0534, 87, 25, 66, 3358, 196, 5_5289, 8, 8_2961, 81, 2204, 7_5203, 7, 15, 763, 1_2956, 216, 178, 1_4328, 9595, 1377, 6_9693, 7, 448, 7_1021, 196, 1_8106, 1437, 1_3974, 108, 9083, 4, 4_9315, 7, 39, 86, 1326, 2793, 4_6333, 4, 448, 196, 7_4588, 7, 4_9315, 7, 39, 21, 822, 3_8470, 74, 21, 6_6723, 6_2480, 8, 2_2050, 5, 2]], '''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, 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, 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]]}  # noqa: E501
     # fmt: on

     # moussaKam/mbarthez is a french model. So we also use french texts.
     __A   :						Union[str, Any]									=					[
         '''Le transformeur est un modèle d\'apprentissage profond introduit en 2017, '''
         '''utilisé principalement dans le domaine du traitement automatique des langues (TAL).''',
         '''À l\'instar des réseaux de neurones récurrents (RNN), les transformeurs sont conçus '''
         '''pour gérer des données séquentielles, telles que le langage naturel, pour des tâches '''
         '''telles que la traduction et la synthèse de texte.''',
     ]

     self.tokenizer_integration_test_util(
         expected_encoding=__lowerCamelCase ,						model_name='''moussaKam/mbarthez''' ,						revision='''c2e4ecbca5e3cd2c37fe1ac285ca4fbdf1366fb6''' ,						sequences=__lowerCamelCase ,						)




