





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

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


UpperCAmelCase_							=				logging.get_logger(__name__)

UpperCAmelCase_							=				{"""vocab_file""": """sentencepiece.bpe.model"""}

UpperCAmelCase_							=				{
    """vocab_file""": {
        """moussaKam/mbarthez""": """https://huggingface.co/moussaKam/mbarthez/resolve/main/sentencepiece.bpe.model""",
        """moussaKam/barthez""": """https://huggingface.co/moussaKam/barthez/resolve/main/sentencepiece.bpe.model""",
        """moussaKam/barthez-orangesum-title""": (
            """https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/sentencepiece.bpe.model"""
        ),
    },
}

UpperCAmelCase_							=				{
    """moussaKam/mbarthez""": 1_024,
    """moussaKam/barthez""": 1_024,
    """moussaKam/barthez-orangesum-title""": 1_024,
}

UpperCAmelCase_							=				"""▁"""
class     UpperCamelCase__					(     lowerCamelCase__				):






		'''simple docstring'''






		__a       :       Any       =	VOCAB_FILES_NAMES
		__a       :       Optional[int]       =	PRETRAINED_VOCAB_FILES_MAP
		__a       :       str       =	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		__a       :       Any       =	["""input_ids""", """attention_mask"""]






		def __init__(							self,   snake_case__,   snake_case__="<s>",   snake_case__="</s>",   snake_case__="</s>",   snake_case__="<s>",   snake_case__="<unk>",   snake_case__="<pad>",   snake_case__="<mask>",   snake_case__ = None,   **snake_case__,   )       -> None:






									"""simple docstring"""
									# Mask token behave like a normal word, i.e. include the space before it
									lowercase_	:   List[str]  				=     AddedToken(snake_case__,   lstrip=snake_case__,   rstrip=snake_case__  ) if isinstance(snake_case__,   snake_case__  ) else mask_token

									lowercase_	:   List[Any]  				=     {} if sp_model_kwargs is None else sp_model_kwargs

									super().__init__(
									    bos_token=snake_case__,   eos_token=snake_case__,   unk_token=snake_case__,   sep_token=snake_case__,   cls_token=snake_case__,   pad_token=snake_case__,   mask_token=snake_case__,   sp_model_kwargs=self.sp_model_kwargs,   **snake_case__,   )

									lowercase_	:   Optional[Any]  				=     vocab_file
									lowercase_	:   Optional[int]  				=     spm.SentencePieceProcessor(**self.sp_model_kwargs  )
									self.sp_model.Load(str(snake_case__  )  )

									lowercase_	:   List[Any]  				=     {"""<s>""": 0, """<pad>""": 1, """</s>""": 2, """<unk>""": 3}

									lowercase_	:   Tuple  				=     len(self.sp_model  ) - 1
									lowercase_	:   Tuple  				=     {v: k for k, v in self.fairseq_tokens_to_ids.items()}






		def 						snake_case__				(							self,   snake_case__,   snake_case__ = None  )       -> List[int]:






									"""simple docstring"""

									if token_ids_a is None:
																return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
									lowercase_	:   str  				=     [self.cls_token_id]
									lowercase_	:   Tuple  				=     [self.sep_token_id]
									return cls + token_ids_a + sep + sep + token_ids_a + sep






		def 						snake_case__				(							self,   snake_case__,   snake_case__ = None,   snake_case__ = False  )       -> List[int]:






									"""simple docstring"""
									if already_has_special_tokens:
																return super().get_special_tokens_mask(
																    token_ids_a=snake_case__,   token_ids_a=snake_case__,   already_has_special_tokens=snake_case__  )

									if token_ids_a is None:
																return [1] + ([0] * len(snake_case__  )) + [1]
									return [1] + ([0] * len(snake_case__  )) + [1, 1] + ([0] * len(snake_case__  )) + [1]






		def 						snake_case__				(							self,   snake_case__,   snake_case__ = None  )       -> List[int]:






									"""simple docstring"""
									lowercase_	:   List[str]  				=     [self.sep_token_id]
									lowercase_	:   Dict  				=     [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]






		@property
		def 						snake_case__				(							self  )       -> List[str]:






									"""simple docstring"""
									return len(self.sp_model  )






		def 						snake_case__				(							self  )       -> List[str]:






									"""simple docstring"""
									lowercase_	:   Union[str, Any]  				=     {self.convert_ids_to_tokens(snake_case__  ): i for i in range(self.vocab_size  )}
									vocab.update(self.added_tokens_encoder  )
									return vocab






		def 						snake_case__				(							self,   snake_case__  )       -> List[str]:






									"""simple docstring"""
									return self.sp_model.encode(snake_case__,   out_type=snake_case__  )






		def 						snake_case__				(							self,   snake_case__  )       -> int:






									"""simple docstring"""
									if token in self.fairseq_tokens_to_ids:
																return self.fairseq_tokens_to_ids[token]
									lowercase_	:   List[str]  				=     self.sp_model.PieceToId(snake_case__  )

									return spm_id if spm_id else self.unk_token_id






		def 						snake_case__				(							self,   snake_case__  )       -> Tuple:






									"""simple docstring"""
									if index in self.fairseq_ids_to_tokens:
																return self.fairseq_ids_to_tokens[index]
									return self.sp_model.IdToPiece(snake_case__  )






		def 						snake_case__				(							self,   snake_case__  )       -> Dict:






									"""simple docstring"""
									lowercase_	:   str  				=     []
									lowercase_	:   int  				=     """"""
									lowercase_	:   List[str]  				=     False
									for token in tokens:
																# make sure that special tokens are not decoded using sentencepiece model
																if token in self.all_special_tokens:
																							if not prev_is_special:
																														out_string += " "
																							out_string += self.sp_model.decode(snake_case__  ) + token
																							lowercase_	:   List[Any]  				=     True
																							lowercase_	:   int  				=     []
																else:
																							current_sub_tokens.append(snake_case__  )
																							lowercase_	:   List[str]  				=     False
									out_string += self.sp_model.decode(snake_case__  )
									return out_string.strip()






		def __getstate__(							self  )       -> Union[str, Any]:






									"""simple docstring"""
									lowercase_	:   Tuple  				=     self.__dict__.copy()
									lowercase_	:   Dict  				=     None
									return state






		def __setstate__(							self,   snake_case__  )       -> str:






									"""simple docstring"""
									lowercase_	:   Any  				=     d

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

									lowercase_	:   List[Any]  				=     spm.SentencePieceProcessor(**self.sp_model_kwargs  )
									self.sp_model.Load(self.vocab_file  )






		def 						snake_case__				(							self,   snake_case__,   snake_case__ = None  )       -> Tuple[str]:






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

									return (out_vocab_file,)






from typing import TYPE_CHECKING

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


UpperCAmelCase_							=				{
    """configuration_maskformer""": ["""MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MaskFormerConfig"""],
    """configuration_maskformer_swin""": ["""MaskFormerSwinConfig"""],
}

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


try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCAmelCase_							=				[
							    """MASKFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """MaskFormerForInstanceSegmentation""",
							    """MaskFormerModel""",
							    """MaskFormerPreTrainedModel""",
							]
							UpperCAmelCase_							=				[
							    """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

							UpperCAmelCase_							=				_LazyModule(__name__, globals()["""__file__"""], _import_structure)