



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       _snake_case				(     lowerCamelCase_ ):






   '''simple docstring'''






   A__      : List[Any]       =						VOCAB_FILES_NAMES
   A__      : int       =						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   A__      : str       =						PRETRAINED_VOCAB_FILES_MAP
   A__      : Optional[Any]       =						["input_ids", "attention_mask"]
   A__      : List[str]       =						MBartaaTokenizer

   A__      : Any       =						[]
   A__      : Dict       =						[]


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

       UpperCAmelCase_     :       Tuple  		=					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__(
           lowerCAmelCase_  ,src_lang=lowerCAmelCase_  ,tgt_lang=lowerCAmelCase_  ,tokenizer_file=lowerCAmelCase_  ,eos_token=lowerCAmelCase_  ,sep_token=lowerCAmelCase_  ,cls_token=lowerCAmelCase_  ,unk_token=lowerCAmelCase_  ,pad_token=lowerCAmelCase_  ,mask_token=lowerCAmelCase_  ,**lowerCAmelCase_  ,)

       UpperCAmelCase_     :       str  		=					vocab_file
       UpperCAmelCase_     :       Tuple  		=					False if not self.vocab_file else True

       UpperCAmelCase_     :       List[str]  		=					{
           lang_code: self.convert_tokens_to_ids(lowerCAmelCase_							) for lang_code in FAIRSEQ_LANGUAGE_CODES
       }

       UpperCAmelCase_     :       List[Any]  		=					src_lang if src_lang is not None else """en_XX"""
       UpperCAmelCase_     :       str  		=					tgt_lang
       UpperCAmelCase_     :       List[Any]  		=					self.lang_code_to_id[self._src_lang]
       self.set_src_lang_special_tokens(self._src_lang							)


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


   @src_lang.setter
   def       A__    (    self:     Dict  ,lowerCamelCase_:     Union[str, Any]							)	->      List[str]:
       UpperCAmelCase_     :       Any  		=					new_src_lang
       self.set_src_lang_special_tokens(self._src_lang							)


   def       A__    (    self:     Optional[int]  ,lowerCamelCase_:     Tuple  ,lowerCamelCase_:     int = None							)	->      str:
       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       A__    (    self:     List[Any]  ,lowerCamelCase_:     Dict  ,lowerCamelCase_:     Dict = "en_XX"  ,lowerCamelCase_:     Union[str, Any] = None  ,lowerCamelCase_:     Tuple = "ro_RO"  ,**lowerCamelCase_:     Union[str, Any]  ,)	->      Any:
       UpperCAmelCase_     :       str  		=					src_lang
       UpperCAmelCase_     :       Optional[int]  		=					tgt_lang
       return super().prepare_seqaseq_batch(lowerCAmelCase_  ,lowerCAmelCase_  ,**lowerCAmelCase_							)


   def       A__    (    self:     str							)	->      Optional[Any]:
       return self.set_src_lang_special_tokens(self.src_lang							)


   def       A__    (    self:     Dict							)	->      Dict:
       return self.set_tgt_lang_special_tokens(self.tgt_lang							)


   def       A__    (    self:     Any  ,lowerCamelCase_:     Optional[Any]							)	->      List[Any]:
       UpperCAmelCase_     :       Optional[Any]  		=					self.convert_tokens_to_ids(lowerCAmelCase_							)
       UpperCAmelCase_     :       str  		=					[self.cur_lang_code_id]
       UpperCAmelCase_     :       Union[str, Any]  		=					[self.eos_token_id]

       UpperCAmelCase_     :       Any  		=					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       A__    (    self:     Optional[Any]  ,lowerCamelCase_:     Optional[Any]							)	->      Optional[Any]:
       UpperCAmelCase_     :       Any  		=					self.convert_tokens_to_ids(lowerCAmelCase_							)
       UpperCAmelCase_     :       Tuple  		=					[self.cur_lang_code_id]
       UpperCAmelCase_     :       int  		=					[self.eos_token_id]

       UpperCAmelCase_     :       List[Any]  		=					self.convert_ids_to_tokens(self.prefix_tokens							)
       UpperCAmelCase_     :       List[Any]  		=					self.convert_ids_to_tokens(self.suffix_tokens							)

       UpperCAmelCase_     :       Optional[int]  		=					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       A__    (    self:     Optional[int]  ,lowerCamelCase_:     Union[str, Any]  ,lowerCamelCase_:     Tuple  ,lowerCamelCase_:     Union[str, Any]  ,lowerCamelCase_:     Optional[int]  ,**lowerCamelCase_:     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"""							)
       UpperCAmelCase_     :       str  		=					src_lang
       UpperCAmelCase_     :       List[str]  		=					self(lowerCAmelCase_  ,add_special_tokens=lowerCAmelCase_  ,return_tensors=lowerCAmelCase_  ,**lowerCAmelCase_							)
       UpperCAmelCase_     :       Tuple  		=					self.convert_tokens_to_ids(lowerCAmelCase_							)
       UpperCAmelCase_     :       str  		=					tgt_lang_id
       return inputs






   def       A__    (    self:     str  ,lowerCamelCase_:     Optional[int]  ,lowerCamelCase_:     Optional[int] = None							)	->      Any:
       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(lowerCAmelCase_							):
           logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''							)
           return
       UpperCAmelCase_     :       Dict  		=					os.path.join(
           lowerCAmelCase_  ,(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]							)

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

       return (out_vocab_file,)

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


lowerCAmelCase__  	=      {
    "configuration_imagegpt": ["IMAGEGPT_PRETRAINED_CONFIG_ARCHIVE_MAP", "ImageGPTConfig", "ImageGPTOnnxConfig"]
}

try:
						if not is_vision_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCAmelCase__  	=      ["ImageGPTFeatureExtractor"]
						lowerCAmelCase__  	=      ["ImageGPTImageProcessor"]

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCAmelCase__  	=      [
						    "IMAGEGPT_PRETRAINED_MODEL_ARCHIVE_LIST",
						    "ImageGPTForCausalImageModeling",
						    "ImageGPTForImageClassification",
						    "ImageGPTModel",
						    "ImageGPTPreTrainedModel",
						    "load_tf_weights_in_imagegpt",
						]


if TYPE_CHECKING:
						from .configuration_imagegpt import IMAGEGPT_PRETRAINED_CONFIG_ARCHIVE_MAP, ImageGPTConfig, ImageGPTOnnxConfig

						try:
												if not is_vision_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .feature_extraction_imagegpt import ImageGPTFeatureExtractor
												from .image_processing_imagegpt import ImageGPTImageProcessor

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_imagegpt import (
												    IMAGEGPT_PRETRAINED_MODEL_ARCHIVE_LIST,
												    ImageGPTForCausalImageModeling,
												    ImageGPTForImageClassification,
												    ImageGPTModel,
												    ImageGPTPreTrainedModel,
												    load_tf_weights_in_imagegpt,
												)

else:
						import sys

						lowerCAmelCase__  	=      _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)




