




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

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


lowerCAmelCase_						=			'▁'

lowerCAmelCase_						=			{'vocab_file': 'spiece.model'}

lowerCAmelCase_						=			{
    'vocab_file': {'google/pegasus-xsum': 'https://huggingface.co/google/pegasus-xsum/resolve/main/spiece.model'}
}

lowerCAmelCase_						=			{
    'google/pegasus-xsum': 5_12,
}


lowerCAmelCase_						=			logging.get_logger(__name__)
class 						_A (  _lowerCamelCase				):
  _UpperCamelCase       :      int			=      VOCAB_FILES_NAMES

  _UpperCamelCase       :      Union[str, Any]			=      VOCAB_FILES_NAMES
  _UpperCamelCase       :      Optional[int]			=      PRETRAINED_VOCAB_FILES_MAP
  _UpperCamelCase       :      Dict			=      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  _UpperCamelCase       :      Any			=      ['''input_ids''', '''attention_mask''']






  def __init__(  self		:     str				,						_A		:     List[str]				,						_A		:     str="<pad>"				,						_A		:     int="</s>"				,						_A		:     Optional[Any]="<unk>"				,						_A		:     int="<mask_2>"				,						_A		:     Union[str, Any]="<mask_1>"				,						_A		:     List[str]=None				,						_A		:     Optional[int]=103				,						_A		:     Optional[Dict[str, Any]] = None				,						**_A		:     Optional[Any]				,						)		->   None:






       """simple docstring"""







       lowercase :       Union[str, Any]       =			offset
       if additional_special_tokens is not None:
            if not isinstance(_A				,						_A				):
                 raise TypeError(
                     f"""additional_special_tokens should be of type {type(_A				)}, but is"""
                     f""" {type(_A				)}"""				)

            lowercase :       List[Any]       =			(
                ([mask_token_sent] + additional_special_tokens)
                if mask_token_sent not in additional_special_tokens and mask_token_sent is not None
                else additional_special_tokens
            )
            # fill additional tokens with ..., <unk_token_102> in case not all additional tokens are already taken
            additional_special_tokens_extended += [
                f"""<unk_{i}>""" for i in range(len(_A				)				,						self.offset - 1				)
            ]

            if len(set(_A				)				) != len(_A				):
                 raise ValueError(
                     '''Please make sure that the provided additional_special_tokens do not contain an incorrectly'''
                     f""" shifted list of <unk_x> tokens. Found {additional_special_tokens_extended}."""				)
            lowercase :       Dict       =			additional_special_tokens_extended
       else:
            lowercase :       int       =			[mask_token_sent] if mask_token_sent is not None else []
            additional_special_tokens += [f"""<unk_{i}>""" for i in range(2				,						self.offset				)]

       lowercase :       Optional[int]       =			{} if sp_model_kwargs is None else sp_model_kwargs

       super().__init__(
           eos_token=_A				,						unk_token=_A				,						mask_token=_A				,						pad_token=_A				,						mask_token_sent=_A				,						offset=_A				,						additional_special_tokens=_A				,						sp_model_kwargs=self.sp_model_kwargs				,						**_A				,						)
       lowercase :       Optional[Any]       =			mask_token_sent
       lowercase :       str       =			vocab_file
       lowercase :       Tuple       =			spm.SentencePieceProcessor(**self.sp_model_kwargs				)
       self.sp_model.Load(_A				)

       # add special tokens to encoder dict
       lowercase :       Dict[int, str]       =			{
           0: self.pad_token,
           1: self.eos_token,
       }

       if self.mask_token_sent is not None:
            self.encoder.update(
                {
                    2: self.mask_token_sent,
                    3: self.mask_token,
                }				)

       if self.offset > 0:
            # entries 2-104 are only used for pretraining and called <mask_1>, <mask_2>, unk_2, ...unk_102
            # mask_token_sent is already added to list -> so start at 1
            self.encoder.update({i + 3: additional_special_tokens[i] for i in range(1				,						self.offset - 1				)}				)

       lowercase :       Dict[str, int]       =			{v: k for k, v in self.encoder.items()}






  @property
  def  __a      (  self		:     str				)		->   int:






       """simple docstring"""







       return len(self.sp_model				) + self.offset






  def  __a      (  self		:     List[str]				)		->   Dict[str, int]:






       """simple docstring"""







       lowercase :       Tuple       =			{self.convert_ids_to_tokens(_A				): i for i in range(self.vocab_size				)}
       vocab.update(self.added_tokens_encoder				)
       return vocab






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






       """simple docstring"""







       lowercase :       Dict       =			self.__dict__.copy()
       lowercase :       Any       =			None
       return state






  def __setstate__(  self		:     Dict				,						_A		:     Any				)		->   Optional[Any]:






       """simple docstring"""







       lowercase :       Dict       =			d

       # for backward compatibility
       if not hasattr(self				,						'''sp_model_kwargs'''				):
            lowercase :       Optional[int]       =			{}

       lowercase :       Tuple       =			spm.SentencePieceProcessor(**self.sp_model_kwargs				)
       self.sp_model.Load(self.vocab_file				)






  def  __a      (  self		:     Tuple				,						_A		:     str				)		->   List[str]:






       """simple docstring"""







       return self.sp_model.encode(_A				,						out_type=_A				)






  def  __a      (  self		:     Any				,						_A		:     str				)		->   int:






       """simple docstring"""







       if token in self.decoder:
            return self.decoder[token]
       elif token in self.added_tokens_decoder:
            return self.added_tokens_decoder[token]
       lowercase :       Optional[Any]       =			self.sp_model.piece_to_id(_A				)
       return sp_id + self.offset






  def  __a      (  self		:     int				,						_A		:     int				)		->   str:






       """simple docstring"""







       if index in self.encoder:
            return self.encoder[index]
       elif index in self.added_tokens_encoder:
            return self.added_tokens_encoder[index]
       else:
            lowercase :       Union[str, Any]       =			self.sp_model.IdToPiece(index - self.offset				)
       return token






  def  __a      (  self		:     List[str]				,						_A		:     Dict				)		->   Optional[int]:






       """simple docstring"""







       lowercase :       Optional[Any]       =			[]
       lowercase :       Any       =			''''''
       for token in tokens:
            # make sure that special tokens are not decoded using sentencepiece model
            if token in self.all_special_tokens:
                 out_string += self.sp_model.decode(_A				) + token
                 lowercase :       Dict       =			[]
            else:
                 current_sub_tokens.append(_A				)
       out_string += self.sp_model.decode(_A				)
       return out_string.strip()






  def  __a      (  self		:     Optional[Any]				,						_A		:     Any=False				)		->   List[Any]:






       """simple docstring"""







       return 1






  def  __a      (  self		:     Tuple				,						_A		:     List[str]				)		->   Optional[int]:






       """simple docstring"""







       lowercase :       int       =			set(self.all_special_ids				)  # call it once instead of inside list comp
       all_special_ids.remove(self.unk_token_id				)  # <unk> is only sometimes special

       return [1 if x in all_special_ids else 0 for x in seq]






  def  __a      (  self		:     List[str]				,						_A		:     List				,						_A		:     Optional[List] = None				,						_A		:     bool = False				)		->   List[int]:






       """simple docstring"""







       if already_has_special_tokens:
            return self._special_token_mask(_A				)
       elif token_ids_a is None:
            return self._special_token_mask(_A				) + [1]
       else:
            return self._special_token_mask(token_ids_a + token_ids_a				) + [1]






  def  __a      (  self		:     List[str]				,						_A		:     List[str]				,						_A		:     int=None				)		->   List[int]:






       """simple docstring"""







       if token_ids_a is None:
            return token_ids_a + [self.eos_token_id]
       # We don't expect to process pairs, but leave the pair logic for API consistency
       return token_ids_a + token_ids_a + [self.eos_token_id]






  def  __a      (  self		:     Optional[int]				,						_A		:     str				,						_A		:     Optional[str] = None				)		->   Tuple[str]:






       """simple docstring"""







       if not os.path.isdir(_A				):
            logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""				)
            return
       lowercase :       Any       =			os.path.join(
           _A				,						(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']				)

       if os.path.abspath(self.vocab_file				) != os.path.abspath(_A				) and os.path.isfile(self.vocab_file				):
            copyfile(self.vocab_file				,						_A				)
       elif not os.path.isfile(self.vocab_file				):
            with open(_A				,						'''wb'''				) as fi:
                 lowercase :       str       =			self.sp_model.serialized_model_proto()
                 fi.write(_A				)

       return (out_vocab_file,)





import copy
from typing import Dict, Optional

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
from ..detr import DetrConfig
from ..swin import SwinConfig


lowerCAmelCase_						=			{
    'facebook/maskformer-swin-base-ade': (
        'https://huggingface.co/facebook/maskformer-swin-base-ade/blob/main/config.json'
    )
    # See all MaskFormer models at https://huggingface.co/models?filter=maskformer
}

lowerCAmelCase_						=			logging.get_logger(__name__)
class 						_A (  _lowerCamelCase				):
  _UpperCamelCase       :      int			=      '''maskformer'''
  _UpperCamelCase       :      Union[str, Any]			=      {'''hidden_size''': '''mask_feature_size'''}
  _UpperCamelCase       :      Dict			=      ['''resnet''', '''swin''']
  _UpperCamelCase       :      Optional[int]			=      ['''detr''']






  def __init__(  self		:     Any				,						_A		:     int = 256				,						_A		:     int = 256				,						_A		:     float = 0.1				,						_A		:     bool = False				,						_A		:     Optional[Dict] = None				,						_A		:     Optional[Dict] = None				,						_A		:     float = 0.02				,						_A		:     float = 1.0				,						_A		:     float = 1.0				,						_A		:     float = 1.0				,						_A		:     float = 20.0				,						_A		:     Optional[bool] = None				,						**_A		:     str				,						)		->   str:






       """simple docstring"""







       if backbone_config is None:
            # fall back to https://huggingface.co/microsoft/swin-base-patch4-window12-384-in22k
            lowercase :       List[str]       =			SwinConfig(
                image_size=384				,						in_channels=3				,						patch_size=4				,						embed_dim=128				,						depths=[2, 2, 18, 2]				,						num_heads=[4, 8, 16, 32]				,						window_size=12				,						drop_path_rate=0.3				,						out_features=['''stage1''', '''stage2''', '''stage3''', '''stage4''']				,						)

       if isinstance(_A				,						_A				):
            lowercase :       Optional[int]       =			backbone_config.pop('''model_type'''				)
            lowercase :       List[str]       =			CONFIG_MAPPING[backbone_model_type]
            lowercase :       Union[str, Any]       =			config_class.from_dict(_A				)

       # verify that the backbone is supported
       if backbone_config.model_type not in self.backbones_supported:
            logger.warning_once(
                f"""Backbone {backbone_config.model_type} is not a supported model and may not be compatible with MaskFormer. """
                f"""Supported model types: {','.join(self.backbones_supported				)}"""				)

       if decoder_config is None:
            # fall back to https://huggingface.co/facebook/detr-resnet-50
            lowercase :       Any       =			DetrConfig()
       else:
            # verify that the decoder is supported
            lowercase :       Union[str, Any]       =			(
                decoder_config.pop('''model_type'''				) if isinstance(_A				,						_A				) else decoder_config.model_type
            )
            if decoder_type not in self.decoders_supported:
                 raise ValueError(
                     f"""Transformer Decoder {decoder_type} not supported, please use one of"""
                     f""" {','.join(self.decoders_supported				)}"""				)
            if isinstance(_A				,						_A				):
                 lowercase :       str       =			CONFIG_MAPPING[decoder_type]
                 lowercase :       Dict       =			config_class.from_dict(_A				)

       lowercase :       Tuple       =			backbone_config
       lowercase :       List[Any]       =			decoder_config
       # main feature dimension for the model
       lowercase :       Optional[int]       =			fpn_feature_size
       lowercase :       List[Any]       =			mask_feature_size
       # initializer
       lowercase :       Union[str, Any]       =			init_std
       lowercase :       Tuple       =			init_xavier_std
       # Hungarian matcher && loss
       lowercase :       List[str]       =			cross_entropy_weight
       lowercase :       int       =			dice_weight
       lowercase :       List[Any]       =			mask_weight
       lowercase :       Tuple       =			use_auxiliary_loss
       lowercase :       Tuple       =			no_object_weight
       lowercase :       int       =			output_auxiliary_logits

       lowercase :       List[Any]       =			self.decoder_config.encoder_attention_heads
       lowercase :       List[Any]       =			self.decoder_config.num_hidden_layers
       super().__init__(**_A				)






  @classmethod
  def  __a      (  cls		:     Optional[int]				,						_A		:     PretrainedConfig				,						_A		:     PretrainedConfig				,						**_A		:     str				)		->   Any:






       """simple docstring"""







       return cls(
           backbone_config=_A				,						decoder_config=_A				,						**_A				,						)






  def  __a      (  self		:     Optional[int]				)		->   Dict[str, any]:






       """simple docstring"""







       lowercase :       str       =			copy.deepcopy(self.__dict__				)
       lowercase :       Optional[Any]       =			self.backbone_config.to_dict()
       lowercase :       List[Any]       =			self.decoder_config.to_dict()
       lowercase :       Optional[int]       =			self.__class__.model_type
       return output