




"""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_mbart import MBartTokenizer
else:
       a							      =     None


a							      =     logging.get_logger(__name__)


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

a							      =     {
    '''vocab_file''': {
        '''facebook/mbart-large-en-ro''': (
            '''https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/sentencepiece.bpe.model'''
        ),
        '''facebook/mbart-large-cc25''': (
            '''https://huggingface.co/facebook/mbart-large-cc25/resolve/main/sentencepiece.bpe.model'''
        ),
    },
    '''tokenizer_file''': {
        '''facebook/mbart-large-en-ro''': '''https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/tokenizer.json''',
        '''facebook/mbart-large-cc25''': '''https://huggingface.co/facebook/mbart-large-cc25/resolve/main/tokenizer.json''',
    },
}

a							      =     {
    '''facebook/mbart-large-en-ro''': 1_024,
    '''facebook/mbart-large-cc25''': 1_024,
}

# fmt: off
a							      =     ['''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''']





class 							lowercase_			(	__lowerCAmelCase				):



 '''simple docstring'''
 UpperCAmelCase       :	int     =      VOCAB_FILES_NAMES
 UpperCAmelCase       :	Any     =      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 UpperCAmelCase       :	Union[str, Any]     =      PRETRAINED_VOCAB_FILES_MAP
 UpperCAmelCase       :	List[str]     =      ['''input_ids''', '''attention_mask''']
 UpperCAmelCase       :	Tuple     =      MBartTokenizer

 UpperCAmelCase       :	List[int]     =      []
 UpperCAmelCase       :	List[int]     =      []

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

       super().__init__(
           vocab_file=_UpperCAmelCase       ,				tokenizer_file=_UpperCAmelCase       ,				bos_token=_UpperCAmelCase       ,				eos_token=_UpperCAmelCase       ,				sep_token=_UpperCAmelCase       ,				cls_token=_UpperCAmelCase       ,				unk_token=_UpperCAmelCase       ,				pad_token=_UpperCAmelCase       ,				mask_token=_UpperCAmelCase       ,				src_lang=_UpperCAmelCase       ,				tgt_lang=_UpperCAmelCase       ,				additional_special_tokens=_UpperCAmelCase       ,				**_UpperCAmelCase       ,				)

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

       _A     =  FAIRSEQ_LANGUAGE_CODES.copy()

       if additional_special_tokens is not None:
             # Only add those special tokens if they are not already there.
             _additional_special_tokens.extend(
                 [t for t in additional_special_tokens if t not in _additional_special_tokens]						)

       self.add_special_tokens({'additional_special_tokens': _additional_special_tokens}						)
       _A     =  {
           lang_code: self.convert_tokens_to_ids(_UpperCAmelCase						) for lang_code in FAIRSEQ_LANGUAGE_CODES
       }

       _A     =  src_lang if src_lang is not None else 'en_XX'
       _A     =  self.convert_tokens_to_ids(self._src_lang						)
       _A     =  tgt_lang
       self.set_src_lang_special_tokens(self._src_lang						)

 @property
 def      lowerCAmelCase_				(					self			:			int						):
       return self._src_lang

 @src_lang.setter
 def      lowerCAmelCase_				(					self			:			int       ,				_UpperCAmelCase			:			str						):
       _A     =  new_src_lang
       self.set_src_lang_special_tokens(self._src_lang						)

 def      lowerCAmelCase_				(					self			:			List[Any]       ,				_UpperCAmelCase			:			List[int]       ,				_UpperCAmelCase			:			Optional[List[int]] = 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      lowerCAmelCase_				(					self			:			Optional[Any]       ,				_UpperCAmelCase			:			List[int]       ,				_UpperCAmelCase			:			Optional[List[int]] = None						):
       _A     =  [self.sep_token_id]
       _A     =  [self.cls_token_id]

       if token_ids_a is None:
             return len(cls + token_ids_a + sep						) * [0]
       return len(cls + token_ids_a + sep + sep + token_ids_a + sep						) * [0]

 def      lowerCAmelCase_				(					self			:			List[Any]       ,				_UpperCAmelCase			:			List[str]       ,				_UpperCAmelCase			:			str       ,				_UpperCAmelCase			:			Optional[str]       ,				_UpperCAmelCase			:			Optional[str]       ,				**_UpperCAmelCase			:			Dict						):
       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     =  src_lang
       _A     =  self(_UpperCAmelCase       ,				add_special_tokens=_UpperCAmelCase       ,				return_tensors=_UpperCAmelCase       ,				**_UpperCAmelCase						)
       _A     =  self.convert_tokens_to_ids(_UpperCAmelCase						)
       _A     =  tgt_lang_id
       return inputs

 def      lowerCAmelCase_				(					self			:			Dict       ,				_UpperCAmelCase			:			List[str]       ,				_UpperCAmelCase			:			str = "en_XX"       ,				_UpperCAmelCase			:			Optional[List[str]] = None       ,				_UpperCAmelCase			:			str = "ro_RO"       ,				**_UpperCAmelCase			:			Any       ,				):
       _A     =  src_lang
       _A     =  tgt_lang
       return super().prepare_seqaseq_batch(_UpperCAmelCase       ,				_UpperCAmelCase       ,				**_UpperCAmelCase						)

 def      lowerCAmelCase_				(					self			:			List[Any]						):
       return self.set_src_lang_special_tokens(self.src_lang						)

 def      lowerCAmelCase_				(					self			:			List[Any]						):
       return self.set_tgt_lang_special_tokens(self.tgt_lang						)

 def      lowerCAmelCase_				(					self			:			List[str]       ,				_UpperCAmelCase			:			Union[str, Any]						):
       _A     =  self.convert_tokens_to_ids(_UpperCAmelCase						)
       _A     =  []
       _A     =  [self.eos_token_id, self.cur_lang_code]

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

       _A     =  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      lowerCAmelCase_				(					self			:			Union[str, Any]       ,				_UpperCAmelCase			:			str						):
       _A     =  self.convert_tokens_to_ids(_UpperCAmelCase						)
       _A     =  []
       _A     =  [self.eos_token_id, self.cur_lang_code]

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

       _A     =  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      lowerCAmelCase_				(					self			:			str       ,				_UpperCAmelCase			:			str       ,				_UpperCAmelCase			:			Optional[str] = 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(_UpperCAmelCase						):
             logger.error(F'''Vocabulary path ({save_directory}) should be a directory.'''						)
             return
       _A     =  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						):
             copyfile(self.vocab_file       ,				_UpperCAmelCase						)

       return (out_vocab_file,)





'''simple docstring'''





from typing import TYPE_CHECKING

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


__lowerCamelCase												=						{
    '''configuration_maskformer''': ['''MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MaskFormerConfig'''],
    '''configuration_maskformer_swin''': ['''MaskFormerSwinConfig'''],
}

try:
    if not is_vision_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __lowerCamelCase												=						['''MaskFormerFeatureExtractor''']
    __lowerCamelCase												=						['''MaskFormerImageProcessor''']


try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __lowerCamelCase												=						[
        '''MASKFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
        '''MaskFormerForInstanceSegmentation''',
        '''MaskFormerModel''',
        '''MaskFormerPreTrainedModel''',
    ]
    __lowerCamelCase												=						[
        '''MaskFormerSwinBackbone''',
        '''MaskFormerSwinModel''',
        '''MaskFormerSwinPreTrainedModel''',
    ]

if TYPE_CHECKING:
    from .configuration_maskformer import MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, MaskFormerConfig
    from .configuration_maskformer_swin import MaskFormerSwinConfig

    try:
        if not is_vision_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .feature_extraction_maskformer import MaskFormerFeatureExtractor
        from .image_processing_maskformer import MaskFormerImageProcessor
    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_maskformer import (
            MASKFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
            MaskFormerForInstanceSegmentation,
            MaskFormerModel,
            MaskFormerPreTrainedModel,
        )
        from .modeling_maskformer_swin import (
            MaskFormerSwinBackbone,
            MaskFormerSwinModel,
            MaskFormerSwinPreTrainedModel,
        )


else:
    import sys

    __lowerCamelCase												=						_LazyModule(__name__, globals()['''__file__'''], _import_structure)



