
"""simple docstring"""






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


_lowercase							=				logging.get_logger(__name__)

_lowercase							=				'''▁'''

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

_lowercase							=				{
    '''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'''
        ),
    }
}

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

# fmt: off
_lowercase							=				['''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 					__a     ( __a       ):



							'''simple docstring'''







							_lowerCamelCase					:			Dict          =   VOCAB_FILES_NAMES
							_lowerCamelCase					:			List[str]          =   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							_lowerCamelCase					:			Union[str, Any]          =   PRETRAINED_VOCAB_FILES_MAP
							_lowerCamelCase					:			int          =   ["""input_ids""", """attention_mask"""]

							_lowerCamelCase					:			List[int]          =   []
							_lowerCamelCase					:			List[int]          =   []
							def __init__(     self  ,    _lowerCamelCase  ,    _lowerCamelCase="<s>"  ,    _lowerCamelCase="</s>"  ,    _lowerCamelCase="</s>"  ,    _lowerCamelCase="<s>"  ,    _lowerCamelCase="<unk>"  ,    _lowerCamelCase="<pad>"  ,    _lowerCamelCase="<mask>"  ,    _lowerCamelCase=None  ,    _lowerCamelCase=None  ,    _lowerCamelCase=None  ,    _lowerCamelCase = None  ,    _lowerCamelCase=None  ,    **_lowerCamelCase  ,    )			->							List[Any]:




										'''simple docstring'''
										# Mask token behave like a normal word, i.e. include the space before it
										__lowercase       =       AddedToken(_lowerCamelCase  ,    lstrip=_lowerCamelCase  ,    rstrip=_lowerCamelCase       ) if isinstance(_lowerCamelCase  ,    _lowerCamelCase       ) else mask_token

										__lowercase       =       {} if sp_model_kwargs is None else sp_model_kwargs

										super().__init__(
										    bos_token=_lowerCamelCase  ,    eos_token=_lowerCamelCase  ,    unk_token=_lowerCamelCase  ,    sep_token=_lowerCamelCase  ,    cls_token=_lowerCamelCase  ,    pad_token=_lowerCamelCase  ,    mask_token=_lowerCamelCase  ,    tokenizer_file=_lowerCamelCase  ,    src_lang=_lowerCamelCase  ,    tgt_lang=_lowerCamelCase  ,    additional_special_tokens=_lowerCamelCase  ,    sp_model_kwargs=self.sp_model_kwargs  ,    **_lowerCamelCase  ,    )

										__lowercase       =       spm.SentencePieceProcessor(**self.sp_model_kwargs       )
										self.sp_model.Load(str(_lowerCamelCase       )       )
										__lowercase       =       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
										__lowercase       =       {"<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
										__lowercase       =       1

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

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

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

										__lowercase       =       src_lang if src_lang is not None else "en_XX"
										__lowercase       =       self.lang_code_to_id[self._src_lang]
										__lowercase       =       tgt_lang
										self.set_src_lang_special_tokens(self._src_lang       )
							def __getstate__(     self       )			->							Any:




										'''simple docstring'''
										__lowercase       =       self.__dict__.copy()
										__lowercase       =       None
										__lowercase       =       self.sp_model.serialized_model_proto()
										return state
							def __setstate__(     self  ,    _lowerCamelCase       )			->							Dict:




										'''simple docstring'''
										__lowercase       =       d

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

										__lowercase       =       spm.SentencePieceProcessor(**self.sp_model_kwargs       )
										self.sp_model.LoadFromSerializedProto(self.sp_model_proto       )
							@property
							def   SCREAMING_SNAKE_CASE						(     self       )			->							List[str]:




										'''simple docstring'''
										return len(self.sp_model       ) + len(self.lang_code_to_id       ) + self.fairseq_offset + 1  # Plus 1 for the mask token
							@property
							def   SCREAMING_SNAKE_CASE						(     self       )			->							str:




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




										'''simple docstring'''
										__lowercase       =       new_src_lang
										self.set_src_lang_special_tokens(self._src_lang       )
							def   SCREAMING_SNAKE_CASE						(     self  ,    _lowerCamelCase  ,    _lowerCamelCase = None  ,    _lowerCamelCase = False       )			->							List[int]:




										'''simple docstring'''

										if already_has_special_tokens:
													return super().get_special_tokens_mask(
													    token_ids_a=_lowerCamelCase  ,    token_ids_a=_lowerCamelCase  ,    already_has_special_tokens=_lowerCamelCase       )

										__lowercase       =       [1] * len(self.prefix_tokens       )
										__lowercase       =       [1] * len(self.suffix_tokens       )
										if token_ids_a is None:
													return prefix_ones + ([0] * len(_lowerCamelCase       )) + suffix_ones
										return prefix_ones + ([0] * len(_lowerCamelCase       )) + ([0] * len(_lowerCamelCase       )) + suffix_ones
							def   SCREAMING_SNAKE_CASE						(     self  ,    _lowerCamelCase  ,    _lowerCamelCase = 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   SCREAMING_SNAKE_CASE						(     self  ,    _lowerCamelCase  ,    _lowerCamelCase = None       )			->							List[int]:




										'''simple docstring'''
										__lowercase       =       [self.sep_token_id]
										__lowercase       =       [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   SCREAMING_SNAKE_CASE						(     self  ,    _lowerCamelCase  ,    _lowerCamelCase  ,    _lowerCamelCase  ,    _lowerCamelCase  ,    **_lowerCamelCase       )			->							Tuple:




										'''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"       )
										__lowercase       =       src_lang
										__lowercase       =       self(_lowerCamelCase  ,    add_special_tokens=_lowerCamelCase  ,    return_tensors=_lowerCamelCase  ,    **_lowerCamelCase       )
										__lowercase       =       self.convert_tokens_to_ids(_lowerCamelCase       )
										__lowercase       =       tgt_lang_id
										return inputs
							def   SCREAMING_SNAKE_CASE						(     self       )			->							List[str]:




										'''simple docstring'''
										__lowercase       =       {self.convert_ids_to_tokens(_lowerCamelCase       ): i for i in range(self.vocab_size       )}
										vocab.update(self.added_tokens_encoder       )
										return vocab
							def   SCREAMING_SNAKE_CASE						(     self  ,    _lowerCamelCase       )			->							List[str]:




										'''simple docstring'''
										return self.sp_model.encode(_lowerCamelCase  ,    out_type=_lowerCamelCase       )
							def   SCREAMING_SNAKE_CASE						(     self  ,    _lowerCamelCase       )			->							List[Any]:




										'''simple docstring'''
										if token in self.fairseq_tokens_to_ids:
													return self.fairseq_tokens_to_ids[token]
										__lowercase       =       self.sp_model.PieceToId(_lowerCamelCase       )

										# 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   SCREAMING_SNAKE_CASE						(     self  ,    _lowerCamelCase       )			->							int:




										'''simple docstring'''
										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   SCREAMING_SNAKE_CASE						(     self  ,    _lowerCamelCase       )			->							int:




										'''simple docstring'''
										__lowercase       =       "".join(_lowerCamelCase       ).replace(_lowerCamelCase  ,    " "       ).strip()
										return out_string
							def   SCREAMING_SNAKE_CASE						(     self  ,    _lowerCamelCase  ,    _lowerCamelCase = None       )			->							Tuple[str]:




										'''simple docstring'''
										if not os.path.isdir(_lowerCamelCase       ):
													logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''       )
													return
										__lowercase       =       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       ) and os.path.isfile(self.vocab_file       ):
													copyfile(self.vocab_file  ,    _lowerCamelCase       )
										elif not os.path.isfile(self.vocab_file       ):
													with open(_lowerCamelCase  ,    "wb"       ) as fi:
																__lowercase       =       self.sp_model.serialized_model_proto()
																fi.write(_lowerCamelCase       )

										return (out_vocab_file,)
							def   SCREAMING_SNAKE_CASE						(     self  ,    _lowerCamelCase  ,    _lowerCamelCase = "en_XX"  ,    _lowerCamelCase = None  ,    _lowerCamelCase = "ro_RO"  ,    **_lowerCamelCase  ,    )			->							BatchEncoding:




										'''simple docstring'''
										__lowercase       =       src_lang
										__lowercase       =       tgt_lang
										return super().prepare_seqaseq_batch(_lowerCamelCase  ,    _lowerCamelCase  ,    **_lowerCamelCase       )
							def   SCREAMING_SNAKE_CASE						(     self       )			->							str:




										'''simple docstring'''
										return self.set_src_lang_special_tokens(self.src_lang       )
							def   SCREAMING_SNAKE_CASE						(     self       )			->							Tuple:




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




										'''simple docstring'''
										__lowercase       =       self.lang_code_to_id[src_lang]
										__lowercase       =       []
										__lowercase       =       [self.eos_token_id, self.cur_lang_code]





							def   SCREAMING_SNAKE_CASE						(     self  ,    _lowerCamelCase       )			->							None:




										'''simple docstring'''
										__lowercase       =       self.lang_code_to_id[lang]
										__lowercase       =       []
										__lowercase       =       [self.eos_token_id, self.cur_lang_code]







"""simple docstring"""






from typing import TYPE_CHECKING

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


_lowercase							=				{
    '''configuration_mask2former''': [
        '''MASK2FORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''Mask2FormerConfig''',
    ],
}

try:
					if not is_vision_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					_lowercase							=				['''Mask2FormerImageProcessor''']

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					_lowercase							=				[
					    '''MASK2FORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''Mask2FormerForUniversalSegmentation''',
					    '''Mask2FormerModel''',
					    '''Mask2FormerPreTrainedModel''',
					]

if TYPE_CHECKING:
					from .configuration_maskaformer import MASK2FORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, MaskaFormerConfig

					try:
										if not is_vision_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .image_processing_maskaformer import MaskaFormerImageProcessor

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_maskaformer import (
										    MASK2FORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
										    MaskaFormerForUniversalSegmentation,
										    MaskaFormerModel,
										    MaskaFormerPreTrainedModel,
										)


else:
					import sys

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





