






"""simple docstring"""







import os
from shutil import copyfile
from typing import List, Optional, Tuple

from tokenizers import processors

from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
  from .tokenization_mbartaa import MBartaaTokenizer
else:
  _UpperCamelCase     			=  None


_UpperCamelCase     			=  logging.get_logger(__name__)

_UpperCamelCase     			=  {'''vocab_file''': '''sentencepiece.bpe.model''', '''tokenizer_file''': '''tokenizer.json'''}

_UpperCamelCase     			=  {
    '''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'''
        ),
    },
    '''tokenizer_file''': {
        '''facebook/mbart-large-50-one-to-many-mmt''': (
            '''https://huggingface.co/facebook/mbart-large-50-one-to-many-mmt/resolve/main/tokenizer.json'''
        ),
    },
}

_UpperCamelCase     			=  {
    '''facebook/mbart-large-50-one-to-many-mmt''': 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''', '''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 					lowerCamelCase__					(       a_ ):
    SCREAMING_SNAKE_CASE		=			VOCAB_FILES_NAMES
    SCREAMING_SNAKE_CASE		=			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    SCREAMING_SNAKE_CASE		=			PRETRAINED_VOCAB_FILES_MAP
    SCREAMING_SNAKE_CASE		=			['input_ids', 'attention_mask']
    SCREAMING_SNAKE_CASE		=			MBartaaTokenizer

    SCREAMING_SNAKE_CASE		=			[]
    SCREAMING_SNAKE_CASE		=			[]






    def __init__( self	,A=None	,A=None	,A=None	,A=None	,A="</s>"	,A="</s>"	,A="<s>"	,A="<unk>"	,A="<pad>"	,A="<mask>"	,**A	,):
          UpperCAmelCase							=       AddedToken(snake_case__	,lstrip=snake_case__	,rstrip=snake_case__					) if isinstance(snake_case__	,snake_case__					) else mask_token

          UpperCAmelCase							=       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__(
              snake_case__	,src_lang=snake_case__	,tgt_lang=snake_case__	,tokenizer_file=snake_case__	,eos_token=snake_case__	,sep_token=snake_case__	,cls_token=snake_case__	,unk_token=snake_case__	,pad_token=snake_case__	,mask_token=snake_case__	,**snake_case__	,)

          UpperCAmelCase							=       vocab_file
          UpperCAmelCase							=       False if not self.vocab_file else True

          UpperCAmelCase							=       {
              lang_code: self.convert_tokens_to_ids(snake_case__					) for lang_code in FAIRSEQ_LANGUAGE_CODES
          }

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






    @property
    def 				_UpperCamelCase							( self					):
          return self._src_lang






    @src_lang.setter
    def 				_UpperCamelCase							( self	,A					):
          UpperCAmelCase							=       new_src_lang
          self.set_src_lang_special_tokens(self._src_lang					)






    def 				_UpperCamelCase							( self	,A	,A = None					):





          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	,A	,A = "en_XX"	,A = None	,A = "ro_RO"	,**A	,):
          UpperCAmelCase							=       src_lang
          UpperCAmelCase							=       tgt_lang
          return super().prepare_seqaseq_batch(snake_case__	,snake_case__	,**snake_case__					)






    def 				_UpperCamelCase							( self					):
          return self.set_src_lang_special_tokens(self.src_lang					)






    def 				_UpperCamelCase							( self					):
          return self.set_tgt_lang_special_tokens(self.tgt_lang					)






    def 				_UpperCamelCase							( self	,A					):
          UpperCAmelCase							=       self.convert_tokens_to_ids(snake_case__					)
          UpperCAmelCase							=       [self.cur_lang_code_id]
          UpperCAmelCase							=       [self.eos_token_id]

          UpperCAmelCase							=       self.convert_ids_to_tokens(self.prefix_tokens					)
          UpperCAmelCase							=       self.convert_ids_to_tokens(self.suffix_tokens					)

          UpperCAmelCase							=       processors.TemplateProcessing(
              single=prefix_tokens_str + ["""$A"""] + suffix_tokens_str	,pair=prefix_tokens_str + ["""$A""", """$B"""] + suffix_tokens_str	,special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str	,self.prefix_tokens + self.suffix_tokens					)					)	,)






    def 				_UpperCamelCase							( self	,A					):
          UpperCAmelCase							=       self.convert_tokens_to_ids(snake_case__					)
          UpperCAmelCase							=       [self.cur_lang_code_id]
          UpperCAmelCase							=       [self.eos_token_id]

          UpperCAmelCase							=       self.convert_ids_to_tokens(self.prefix_tokens					)
          UpperCAmelCase							=       self.convert_ids_to_tokens(self.suffix_tokens					)

          UpperCAmelCase							=       processors.TemplateProcessing(
              single=prefix_tokens_str + ["""$A"""] + suffix_tokens_str	,pair=prefix_tokens_str + ["""$A""", """$B"""] + suffix_tokens_str	,special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str	,self.prefix_tokens + self.suffix_tokens					)					)	,)






    def 				_UpperCamelCase							( self	,A	,A	,A	,A	,**A					):





          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							=       src_lang
          UpperCAmelCase							=       self(snake_case__	,add_special_tokens=snake_case__	,return_tensors=snake_case__	,**snake_case__					)
          UpperCAmelCase							=       self.convert_tokens_to_ids(snake_case__					)
          UpperCAmelCase							=       tgt_lang_id
          return inputs






    def 				_UpperCamelCase							( self	,A	,A = None					):





          if not self.can_save_slow_tokenizer:
                raise ValueError(
                    """Your fast tokenizer does not have the necessary information to save the vocabulary for a slow """
                    """tokenizer."""					)

          if not os.path.isdir(snake_case__					):
                logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''					)
                return
          UpperCAmelCase							=       os.path.join(
              snake_case__	,(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]					)

          if os.path.abspath(self.vocab_file					) != os.path.abspath(snake_case__					):
                copyfile(self.vocab_file	,snake_case__					)

          return (out_vocab_file,)











"""simple docstring"""







from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCAmelCase_		:			str				   =					logging.get_logger(__name__)

lowerCAmelCase_		:			Optional[int]				   =					{
    '''google/reformer-crime-and-punishment''': (
        '''https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/config.json'''
    ),
    '''google/reformer-enwik8''': '''https://huggingface.co/google/reformer-enwik8/resolve/main/config.json''',
}





class      UpperCamelCase_       (				a_     ):
  _A				:    int =		'reformer'
  _A				:    Any =		['past_buckets_states']
  _A				:    Optional[Any] =		{}



  def __init__(					self       ,						snake_case__=64       ,						snake_case__=["local", "lsh", "local", "lsh", "local", "lsh"]       ,						snake_case__=1.0       ,						snake_case__=True       ,						snake_case__=[64, 64]       ,						snake_case__=[64, 1_92]       ,						snake_case__=0       ,						snake_case__=2       ,						snake_case__=5_12       ,						snake_case__=None       ,						snake_case__="relu"       ,						snake_case__=0.05       ,						snake_case__=2_56       ,						snake_case__=0.02       ,						snake_case__=False       ,						snake_case__=1e-12       ,						snake_case__=1       ,						snake_case__=0       ,						snake_case__=0.05       ,						snake_case__=64       ,						snake_case__=64       ,						snake_case__=0.0       ,						snake_case__=1       ,						snake_case__=0       ,						snake_case__=40_96       ,						snake_case__=12       ,						snake_case__=None       ,						snake_case__=1       ,						snake_case__=0       ,						snake_case__=3_20       ,						snake_case__=False       ,						snake_case__=True       ,						snake_case__=None       ,						**snake_case__       ,						)	->	List[Any]:



         """simple docstring"""





         UpperCAmelCase					=	hash_seed
         UpperCAmelCase					=	vocab_size
         UpperCAmelCase					=	attention_head_size
         UpperCAmelCase					=	hidden_size
         UpperCAmelCase					=	num_attention_heads
         UpperCAmelCase					=	num_hashes
         UpperCAmelCase					=	len(snake_case__ )
         UpperCAmelCase					=	tuple(snake_case__ ) if isinstance(snake_case__       ,						snake_case__ ) else num_buckets
         UpperCAmelCase					=	lsh_attn_chunk_length
         UpperCAmelCase					=	local_attn_chunk_length
         UpperCAmelCase					=	lsh_num_chunks_after
         UpperCAmelCase					=	lsh_num_chunks_before
         UpperCAmelCase					=	local_num_chunks_after
         UpperCAmelCase					=	local_num_chunks_before
         UpperCAmelCase					=	hidden_act
         UpperCAmelCase					=	feed_forward_size
         UpperCAmelCase					=	hidden_dropout_prob
         UpperCAmelCase					=	lsh_attention_probs_dropout_prob
         UpperCAmelCase					=	local_attention_probs_dropout_prob
         UpperCAmelCase					=	max_position_embeddings
         UpperCAmelCase					=	initializer_range
         UpperCAmelCase					=	layer_norm_eps
         UpperCAmelCase					=	axial_pos_embds
         UpperCAmelCase					=	tuple(snake_case__ )
         UpperCAmelCase					=	tuple(snake_case__ )
         UpperCAmelCase					=	axial_norm_std
         UpperCAmelCase					=	chunk_size_lm_head
         UpperCAmelCase					=	attn_layers
         UpperCAmelCase					=	use_cache
         UpperCAmelCase					=	classifier_dropout
         super().__init__(
             pad_token_id=snake_case__       ,						eos_token_id=snake_case__       ,						is_decoder=snake_case__       ,						tie_word_embeddings=snake_case__       ,						**snake_case__       ,						)
