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:
     __snake_case         =     None


__snake_case         =     logging.get_logger(__name__)

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

__snake_case         =     {
    '''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'''
        ),
    },
}

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

# fmt: off
__snake_case         =     ['''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 (  lowerCamelCase__ ):
 __lowerCamelCase     : Optional[Any] 	=   VOCAB_FILES_NAMES
 __lowerCamelCase     : Optional[Any] 	=   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 __lowerCamelCase     : Union[str, Any] 	=   PRETRAINED_VOCAB_FILES_MAP
 __lowerCamelCase     : List[str] 	=   ["""input_ids""", """attention_mask"""]
 __lowerCamelCase     : Tuple 	=   MBartaaTokenizer

 __lowerCamelCase     : List[int] 	=   []
 __lowerCamelCase     : List[int] 	=   []
 def __init__(	self		, snake_case__=None		, snake_case__=None		, snake_case__=None		, snake_case__=None		, snake_case__="</s>"		, snake_case__="</s>"		, snake_case__="<s>"		, snake_case__="<unk>"		, snake_case__="<pad>"		, snake_case__="<mask>"		, **snake_case__		, )						->				List[str]:
       '''simple docstring'''





       UpperCAmelCase     :			int     	=AddedToken(snake_case__		, lstrip=snake_case__		, rstrip=snake_case__    ) if isinstance(snake_case__		, snake_case__    ) else mask_token

       UpperCAmelCase     :			Dict     	=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     :			Union[str, Any]     	=vocab_file
       UpperCAmelCase     :			Tuple     	=False if not self.vocab_file else True

       UpperCAmelCase     :			List[Any]     	={
           lang_code: self.convert_tokens_to_ids(snake_case__    ) for lang_code in FAIRSEQ_LANGUAGE_CODES
       }

       UpperCAmelCase     :			Any     	=src_lang if src_lang is not None else '''en_XX'''
       UpperCAmelCase     :			Tuple     	=tgt_lang
       UpperCAmelCase     :			Optional[int]     	=self.lang_code_to_id[self._src_lang]
       self.set_src_lang_special_tokens(self._src_lang    )
 @property
 def 						UpperCAmelCase__							(	self    )						->				str:
       '''simple docstring'''





       return self._src_lang
 @src_lang.setter
 def 						UpperCAmelCase__							(	self		, snake_case__    )						->				None:
       '''simple docstring'''





       UpperCAmelCase     :			Optional[int]     	=new_src_lang
       self.set_src_lang_special_tokens(self._src_lang    )
 def 						UpperCAmelCase__							(	self		, snake_case__		, snake_case__ = None    )						->				List[int]:
       '''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		, snake_case__		, snake_case__ = "en_XX"		, snake_case__ = None		, snake_case__ = "ro_RO"		, **snake_case__		, )						->				BatchEncoding:
       '''simple docstring'''





       UpperCAmelCase     :			Tuple     	=src_lang
       UpperCAmelCase     :			Dict     	=tgt_lang
       return super().prepare_seqaseq_batch(snake_case__		, snake_case__		, **snake_case__    )
 def 						UpperCAmelCase__							(	self    )						->				str:
       '''simple docstring'''





       return self.set_src_lang_special_tokens(self.src_lang    )
 def 						UpperCAmelCase__							(	self    )						->				Dict:
       '''simple docstring'''





       return self.set_tgt_lang_special_tokens(self.tgt_lang    )
 def 						UpperCAmelCase__							(	self		, snake_case__    )						->				None:
       '''simple docstring'''





       UpperCAmelCase     :			List[str]     	=self.convert_tokens_to_ids(snake_case__    )
       UpperCAmelCase     :			Tuple     	=[self.cur_lang_code_id]
       UpperCAmelCase     :			List[str]     	=[self.eos_token_id]

       UpperCAmelCase     :			Union[str, Any]     	=self.convert_ids_to_tokens(self.prefix_tokens    )
       UpperCAmelCase     :			str     	=self.convert_ids_to_tokens(self.suffix_tokens    )

       UpperCAmelCase     :			List[str]     	=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		, snake_case__    )						->				None:
       '''simple docstring'''





       UpperCAmelCase     :			List[Any]     	=self.convert_tokens_to_ids(snake_case__    )
       UpperCAmelCase     :			Optional[Any]     	=[self.cur_lang_code_id]
       UpperCAmelCase     :			Optional[int]     	=[self.eos_token_id]

       UpperCAmelCase     :			int     	=self.convert_ids_to_tokens(self.prefix_tokens    )
       UpperCAmelCase     :			Any     	=self.convert_ids_to_tokens(self.suffix_tokens    )

       UpperCAmelCase     :			str     	=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		, snake_case__		, snake_case__		, snake_case__		, snake_case__		, **snake_case__    )						->				Optional[int]:
       '''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'''    )
       UpperCAmelCase     :			Dict     	=src_lang
       UpperCAmelCase     :			Optional[int]     	=self(snake_case__		, add_special_tokens=snake_case__		, return_tensors=snake_case__		, **snake_case__    )
       UpperCAmelCase     :			int     	=self.convert_tokens_to_ids(snake_case__    )
       UpperCAmelCase     :			Any     	=tgt_lang_id
       return inputs






 def 						UpperCAmelCase__							(	self		, snake_case__		, snake_case__ = None    )						->				Tuple[str]:
       '''simple docstring'''





       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     :			Any     	=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,)

from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


__snake_case         =     {'''configuration_gptj''': ['''GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GPTJConfig''', '''GPTJOnnxConfig''']}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __snake_case         =     [
         '''GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST''',
         '''GPTJForCausalLM''',
         '''GPTJForQuestionAnswering''',
         '''GPTJForSequenceClassification''',
         '''GPTJModel''',
         '''GPTJPreTrainedModel''',
     ]

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __snake_case         =     [
         '''TFGPTJForCausalLM''',
         '''TFGPTJForQuestionAnswering''',
         '''TFGPTJForSequenceClassification''',
         '''TFGPTJModel''',
         '''TFGPTJPreTrainedModel''',
     ]

try:
     if not is_flax_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __snake_case         =     [
         '''FlaxGPTJForCausalLM''',
         '''FlaxGPTJModel''',
         '''FlaxGPTJPreTrainedModel''',
     ]


if TYPE_CHECKING:
     from .configuration_gptj import GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTJConfig, GPTJOnnxConfig

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_gptj import (
              GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST,
              GPTJForCausalLM,
              GPTJForQuestionAnswering,
              GPTJForSequenceClassification,
              GPTJModel,
              GPTJPreTrainedModel,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_gptj import (
              TFGPTJForCausalLM,
              TFGPTJForQuestionAnswering,
              TFGPTJForSequenceClassification,
              TFGPTJModel,
              TFGPTJPreTrainedModel,
          )

     try:
          if not is_flax_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_flax_gptj import FlaxGPTJForCausalLM, FlaxGPTJModel, FlaxGPTJPreTrainedModel

else:
     import sys

     __snake_case         =     _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
