

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


lowerCAmelCase_			      =      logging.get_logger(__name__)

lowerCAmelCase_			      =      '''▁'''

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

lowerCAmelCase_			      =      {
    '''vocab_file''': {
        '''facebook/mbart-large-50-one-to-many-mmt''': (
            '''https://huggingface.co/facebook/mbart-large-50-one-to-many-mmt/resolve/main/sentencepiece.bpe.model'''
        ),
    }
}

lowerCAmelCase_			      =      {
    '''facebook/mbart-large-50-one-to-many-mmt''': 10_24,
}

# fmt: off
lowerCAmelCase_			      =      ['''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''', '''af_ZA''', '''az_AZ''', '''bn_IN''', '''fa_IR''', '''he_IL''', '''hr_HR''', '''id_ID''', '''ka_GE''', '''km_KH''', '''mk_MK''', '''ml_IN''', '''mn_MN''', '''mr_IN''', '''pl_PL''', '''ps_AF''', '''pt_XX''', '''sv_SE''', '''sw_KE''', '''ta_IN''', '''te_IN''', '''th_TH''', '''tl_XX''', '''uk_UA''', '''ur_PK''', '''xh_ZA''', '''gl_ES''', '''sl_SI''']





class       snake_case_  (						__A   ):







			'''simple docstring'''

			SCREAMING_SNAKE_CASE	:  Dict		     =							VOCAB_FILES_NAMES
			SCREAMING_SNAKE_CASE	:  List[Any]		     =							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			SCREAMING_SNAKE_CASE	:  Dict		     =							PRETRAINED_VOCAB_FILES_MAP
			SCREAMING_SNAKE_CASE	:  Dict		     =							["input_ids", "attention_mask"]

			SCREAMING_SNAKE_CASE	:  List[int]		     =							[]
			SCREAMING_SNAKE_CASE	:  List[int]		     =							[]







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

									snake_case_  =      {} if sp_model_kwargs is None else sp_model_kwargs

									snake_case_  =      kwargs.get('''additional_special_tokens'''				,  []				)
									kwargs["additional_special_tokens"] += [
									    code for code in FAIRSEQ_LANGUAGE_CODES if code not in kwargs["additional_special_tokens"]
									]

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

									snake_case_  =      spm.SentencePieceProcessor(**self.sp_model_kwargs				)
									self.sp_model.Load(str(_UpperCamelCase				)				)
									snake_case_  =      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
									snake_case_  =      {'''<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
									snake_case_  =      1

									snake_case_  =      len(self.sp_model				)
									snake_case_  =      {
									    code: self.sp_model_size + i + self.fairseq_offset for i, code in enumerate(_UpperCamelCase				)
									}
									snake_case_  =      {v: k for k, v in self.lang_code_to_id.items()}
									snake_case_  =      len(self.sp_model				) + len(self.lang_code_to_id				) + self.fairseq_offset

									self.fairseq_tokens_to_ids.update(self.lang_code_to_id				)
									snake_case_  =      {v: k for k, v in self.fairseq_tokens_to_ids.items()}

									snake_case_  =      src_lang if src_lang is not None else '''en_XX'''
									snake_case_  =      self.lang_code_to_id[self._src_lang]
									snake_case_  =      tgt_lang
									self.set_src_lang_special_tokens(self._src_lang				)







			@property
			def 							snake_case__(	self					:							Optional[int]				) ->int:
									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					:							Optional[int]				) ->str:
									return self._src_lang







			@src_lang.setter
			def 							snake_case__(	self					:							Any				,  _UpperCamelCase					:							str				) ->None:
									snake_case_  =      new_src_lang
									self.set_src_lang_special_tokens(self._src_lang				)







			def __getstate__(	self					:							List[str]				) ->Dict:
									snake_case_  =      self.__dict__.copy()
									snake_case_  =      None
									return state







			def __setstate__(	self					:							List[Any]				,  _UpperCamelCase					:							Dict				) ->None:
									snake_case_  =      d

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

									snake_case_  =      spm.SentencePieceProcessor(**self.sp_model_kwargs				)
									self.sp_model.Load(self.vocab_file				)







			def 							snake_case__(	self					:							Any				) ->Dict:
									snake_case_  =      {self.convert_ids_to_tokens(_UpperCamelCase				): i for i in range(self.vocab_size				)}
									vocab.update(self.added_tokens_encoder				)
									return vocab







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







			def 							snake_case__(	self					:							Optional[int]				,  _UpperCamelCase					:							str				) ->int:
									if token in self.fairseq_tokens_to_ids:
															return self.fairseq_tokens_to_ids[token]
									snake_case_  =      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 							snake_case__(	self					:							List[Any]				,  _UpperCamelCase					:							int				) ->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 							snake_case__(	self					:							str				,  _UpperCamelCase					:							Optional[int]				) ->Any:
									snake_case_  =      []
									snake_case_  =      ''''''
									snake_case_  =      False
									for token in tokens:
															# make sure that special tokens are not decoded using sentencepiece model
															if token in self.all_special_tokens:
																					if not prev_is_special:
																											out_string += " "
																					out_string += self.sp_model.decode(_UpperCamelCase				) + token
																					snake_case_  =      True
																					snake_case_  =      []
															else:
																					current_sub_tokens.append(_UpperCamelCase				)
																					snake_case_  =      False
									out_string += self.sp_model.decode(_UpperCamelCase				)
									return out_string.strip()







			def 							snake_case__(	self					:							Union[str, Any]				,  _UpperCamelCase					:							str				,  _UpperCamelCase					:							Optional[str] = None				) ->Tuple[str]:
									if not os.path.isdir(_UpperCamelCase				):
															logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''				)
															return
									snake_case_  =      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:
																					snake_case_  =      self.sp_model.serialized_model_proto()
																					fi.write(_UpperCamelCase				)

									return (out_vocab_file,)







			def 							snake_case__(	self					:							Any				,  _UpperCamelCase					:							List[int]				,  _UpperCamelCase					:							Optional[List[int]] = None				,  _UpperCamelCase					:							bool = 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				)

									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(_UpperCamelCase				)) + suffix_ones
									return prefix_ones + ([0] * len(_UpperCamelCase				)) + ([0] * len(_UpperCamelCase				)) + suffix_ones







			def 							snake_case__(	self					:							Dict				,  _UpperCamelCase					:							List[int]				,  _UpperCamelCase					:							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 							snake_case__(	self					:							List[Any]				,  _UpperCamelCase					:							Any				,  _UpperCamelCase					:							str				,  _UpperCamelCase					:							Optional[str]				,  _UpperCamelCase					:							Optional[str]				,  **_UpperCamelCase					:							Dict				) ->List[str]:
									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(_UpperCamelCase				,  add_special_tokens=_UpperCamelCase				,  return_tensors=_UpperCamelCase				,  **_UpperCamelCase				)
									snake_case_  =      self.convert_tokens_to_ids(_UpperCamelCase				)
									snake_case_  =      tgt_lang_id
									return inputs







			def 							snake_case__(	self					:							Optional[Any]				,  _UpperCamelCase					:							List[str]				,  _UpperCamelCase					:							str = "en_XX"				,  _UpperCamelCase					:							Optional[List[str]] = None				,  _UpperCamelCase					:							str = "ro_RO"				,  **_UpperCamelCase					:							int				,  ) ->BatchEncoding:
									snake_case_  =      src_lang
									snake_case_  =      tgt_lang
									return super().prepare_seqaseq_batch(_UpperCamelCase				,  _UpperCamelCase				,  **_UpperCamelCase				)







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







			def 							snake_case__(	self					:							List[str]				) ->str:
									return self.set_tgt_lang_special_tokens(self.tgt_lang				)







			def 							snake_case__(	self					:							Optional[Any]				,  _UpperCamelCase					:							str				) ->None:
									snake_case_  =      self.lang_code_to_id[src_lang]
									snake_case_  =      [self.cur_lang_code_id]
									snake_case_  =      [self.eos_token_id]







			def 							snake_case__(	self					:							List[str]				,  _UpperCamelCase					:							str				) ->None:
									snake_case_  =      self.lang_code_to_id[tgt_lang]
									snake_case_  =      [self.cur_lang_code_id]
									snake_case_  =      [self.eos_token_id]




'''simple docstring'''





import json
import os
import unittest

from transformers import DebertaTokenizer, DebertaTokenizerFast
from transformers.models.deberta.tokenization_deberta import VOCAB_FILES_NAMES
from transformers.testing_utils import slow

from ...test_tokenization_common import TokenizerTesterMixin



class  snake_case__						(    SCREAMING_SNAKE_CASE_  ,     unittest.TestCase				):
 A__				= DebertaTokenizer
 A__				= True
 A__				= DebertaTokenizerFast
 def A_   (   self						:							Optional[int]  )      ->   str:







       '''simple docstring'''
       super().setUp()

       # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
       __snake_case :   Union[str, Any]													=    [
           'l',
           'o',
           'w',
           'e',
           'r',
           's',
           't',
           'i',
           'd',
           'n',
           '\u0120',
           '\u0120l',
           '\u0120n',
           '\u0120lo',
           '\u0120low',
           'er',
           '\u0120lowest',
           '\u0120newer',
           '\u0120wider',
           '[UNK]',
       ]
       __snake_case :   List[str]													=    dict(zip(__a      ,     range(len(__a  )  )  )  )
       __snake_case :   int													=    ['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', '']
       __snake_case :   Any													=    {'unk_token': '[UNK]'}

       __snake_case :   Union[str, Any]													=    os.path.join(self.tmpdirname      ,     VOCAB_FILES_NAMES['vocab_file']  )
       __snake_case :   Dict													=    os.path.join(self.tmpdirname      ,     VOCAB_FILES_NAMES['merges_file']  )
       with open(self.vocab_file      ,     'w'      ,     encoding='utf-8'  ) as fp:
             fp.write(json.dumps(__a  ) + '\n'  )
       with open(self.merges_file      ,     'w'      ,     encoding='utf-8'  ) as fp:
             fp.write('\n'.join(__a  )  )
 def A_   (   self						:							Optional[int]      ,     **__a						:							List[Any]  )      ->   Optional[Any]:







       '''simple docstring'''
       kwargs.update(self.special_tokens_map  )
       return self.tokenizer_class.from_pretrained(self.tmpdirname      ,     **__a  )
 def A_   (   self						:							Optional[int]      ,     __a						:							Dict  )      ->   List[str]:







       '''simple docstring'''
       __snake_case :   Union[str, Any]													=    'lower newer'
       __snake_case :   List[Any]													=    'lower newer'
       return input_text, output_text
 def A_   (   self						:							Dict  )      ->   Union[str, Any]:







       '''simple docstring'''
       __snake_case :   str													=    self.get_tokenizer()
       __snake_case :   List[str]													=    'lower newer'
       __snake_case :   Tuple													=    ['l', 'o', 'w', 'er', '\u0120', 'n', 'e', 'w', 'er']
       __snake_case :   Optional[int]													=    tokenizer.tokenize(__a  )
       self.assertListEqual(__a      ,     __a  )

       __snake_case :   str													=    tokens + [tokenizer.unk_token]
       __snake_case :   Tuple													=    [0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
       self.assertListEqual(tokenizer.convert_tokens_to_ids(__a  )      ,     __a  )
 def A_   (   self						:							List[Any]  )      ->   Dict:







       '''simple docstring'''
       __snake_case :   List[str]													=    self.get_tokenizer()
       __snake_case :   List[str]													=    tokenizer('Hello'      ,     'World'  )
       __snake_case :   int													=    [0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1]
       self.assertListEqual(tokd['token_type_ids']      ,     __a  )
 @slow
 def A_   (   self						:							Dict  )      ->   str:







       '''simple docstring'''
       __snake_case :   str													=    self.tokenizer_class.from_pretrained('microsoft/deberta-base'  )

       __snake_case :   str													=    tokenizer.encode('sequence builders'      ,     add_special_tokens=__a  )
       __snake_case :   Any													=    tokenizer.encode('multi-sequence build'      ,     add_special_tokens=__a  )

       __snake_case :   List[Any]													=    tokenizer.encode(
           'sequence builders'      ,     add_special_tokens=__a      ,     add_prefix_space=__a  )
       __snake_case :   Union[str, Any]													=    tokenizer.encode(
           'sequence builders'      ,     'multi-sequence build'      ,     add_special_tokens=__a      ,     add_prefix_space=__a  )

       __snake_case :   int													=    tokenizer.build_inputs_with_special_tokens(__a  )
       __snake_case :   Tuple													=    tokenizer.build_inputs_with_special_tokens(__a      ,     __a  )

       assert encoded_sentence == encoded_text_from_decode
       assert encoded_pair == encoded_pair_from_decode





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







       '''simple docstring'''
       __snake_case :   int													=    [self.tokenizer_class]
       if self.test_rust_tokenizer:
             tokenizer_classes.append(self.rust_tokenizer_class  )

       for tokenizer_class in tokenizer_classes:
             __snake_case :   List[Any]													=    tokenizer_class.from_pretrained('microsoft/deberta-base'  )

             __snake_case :   int													=    [
                 'ALBERT: A Lite BERT for Self-supervised Learning of Language Representations',
                 'ALBERT incorporates two parameter reduction techniques',
                 'The first one is a factorized embedding parameterization. By decomposing the large vocabulary'
                 ' embedding matrix into two small matrices, we separate the size of the hidden layers from the size of'
                 ' vocabulary embedding.',
             ]

             __snake_case :   Union[str, Any]													=    tokenizer(__a      ,     padding=__a  )
             __snake_case :   List[str]													=    [tokenizer.decode(__a      ,     skip_special_tokens=__a  ) for seq in encoding['input_ids']]

             # fmt: off
             __snake_case :   Optional[Any]													=    {
                 'input_ids': [
                     [1, 2118, 11126, 565, 35, 83, 25191, 163, 18854, 13, 12156, 12, 16101, 25376, 13807, 9, 22205, 27893, 1635, 2, 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, 2118, 11126, 565, 24536, 80, 43797, 4878, 7373, 2, 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, 133, 78, 65, 16, 10, 3724, 1538, 33183, 11303, 43797, 1938, 4, 870, 24165, 29105, 5, 739, 32644, 33183, 11303, 36173, 88, 80, 650, 7821, 45940, 6, 52, 2559, 5, 1836, 9, 5, 7397, 13171, 31, 5, 1836, 9, 32644, 33183, 11303, 4, 2]
                 ],
                 'token_type_ids': [
                     [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, 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, 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],
                     [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]
                 ]
             }
             # fmt: on

             __snake_case :   int													=    [
                 'ALBERT: A Lite BERT for Self-supervised Learning of Language Representations',
                 'ALBERT incorporates two parameter reduction techniques',
                 'The first one is a factorized embedding parameterization. By decomposing the large vocabulary'
                 ' embedding matrix into two small matrices, we separate the size of the hidden layers from the size of'
                 ' vocabulary embedding.',
             ]

             self.assertDictEqual(encoding.data      ,     __a  )

             for expected, decoded in zip(__a      ,     __a  ):
                   self.assertEqual(__a      ,     __a  )



