






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


__A					=	{
    "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
}

__A					=	logging.get_logger(__name__)



class 				_A			(     UpperCamelCase				):
    """simple docstring"""

    lowerCamelCase	:							int				= 'maskformer'
    lowerCamelCase	:							Optional[Any]				= {'hidden_size': 'mask_feature_size'}
    lowerCamelCase	:							Union[str, Any]				= ['resnet', 'swin']
    lowerCamelCase	:							Union[str, Any]				= ['detr']



    def __init__(				self      :     List[str] ,     __SCREAMING_SNAKE_CASE      :     int = 256 ,     __SCREAMING_SNAKE_CASE      :     int = 256 ,     __SCREAMING_SNAKE_CASE      :     float = 0.1 ,     __SCREAMING_SNAKE_CASE      :     bool = False ,     __SCREAMING_SNAKE_CASE      :     Optional[Dict] = None ,     __SCREAMING_SNAKE_CASE      :     Optional[Dict] = None ,     __SCREAMING_SNAKE_CASE      :     float = 0.02 ,     __SCREAMING_SNAKE_CASE      :     float = 1.0 ,     __SCREAMING_SNAKE_CASE      :     float = 1.0 ,     __SCREAMING_SNAKE_CASE      :     float = 1.0 ,     __SCREAMING_SNAKE_CASE      :     float = 20.0 ,     __SCREAMING_SNAKE_CASE      :     Optional[bool] = None ,     **__SCREAMING_SNAKE_CASE      :     Optional[Any] ,     )      ->							Optional[int]:
           if backbone_config is None:
                  # fall back to https://huggingface.co/microsoft/swin-base-patch4-window12-384-in22k
                  __UpperCAmelCase     				=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(__SCREAMING_SNAKE_CASE ,     __SCREAMING_SNAKE_CASE      ):
                  __UpperCAmelCase     				=backbone_config.pop("""model_type"""      )
                  __UpperCAmelCase     				=CONFIG_MAPPING[backbone_model_type]
                  __UpperCAmelCase     				=config_class.from_dict(__SCREAMING_SNAKE_CASE      )

           # 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
                  __UpperCAmelCase     				=DetrConfig()
           else:
                  # verify that the decoder is supported
                  __UpperCAmelCase     				=(
                      decoder_config.pop("""model_type"""      ) if isinstance(__SCREAMING_SNAKE_CASE ,     __SCREAMING_SNAKE_CASE      ) 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(__SCREAMING_SNAKE_CASE ,     __SCREAMING_SNAKE_CASE      ):
                         __UpperCAmelCase     				=CONFIG_MAPPING[decoder_type]
                         __UpperCAmelCase     				=config_class.from_dict(__SCREAMING_SNAKE_CASE      )

           __UpperCAmelCase     				=backbone_config
           __UpperCAmelCase     				=decoder_config
           # main feature dimension for the model
           __UpperCAmelCase     				=fpn_feature_size
           __UpperCAmelCase     				=mask_feature_size
           # initializer
           __UpperCAmelCase     				=init_std
           __UpperCAmelCase     				=init_xavier_std
           # Hungarian matcher && loss
           __UpperCAmelCase     				=cross_entropy_weight
           __UpperCAmelCase     				=dice_weight
           __UpperCAmelCase     				=mask_weight
           __UpperCAmelCase     				=use_auxiliary_loss
           __UpperCAmelCase     				=no_object_weight
           __UpperCAmelCase     				=output_auxiliary_logits

           __UpperCAmelCase     				=self.decoder_config.encoder_attention_heads
           __UpperCAmelCase     				=self.decoder_config.num_hidden_layers
           super().__init__(**__SCREAMING_SNAKE_CASE      )



    @classmethod
    def _a							(				cls      :     Optional[Any] ,     __SCREAMING_SNAKE_CASE      :     PretrainedConfig ,     __SCREAMING_SNAKE_CASE      :     PretrainedConfig ,     **__SCREAMING_SNAKE_CASE      :     List[str]      )      ->							int:
           return cls(
               backbone_config=__SCREAMING_SNAKE_CASE ,     decoder_config=__SCREAMING_SNAKE_CASE ,     **__SCREAMING_SNAKE_CASE ,     )



    def _a							(				self      :     Union[str, Any]      )      ->							Dict[str, any]:
           __UpperCAmelCase     				=copy.deepcopy(self.__dict__      )
           __UpperCAmelCase     				=self.backbone_config.to_dict()
           __UpperCAmelCase     				=self.decoder_config.to_dict()
           __UpperCAmelCase     				=self.__class__.model_type
           return output









"""simple docstring"""







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


a_							=    logging.get_logger(__name__)


a_							=    """▁"""

a_							=    {"""vocab_file""": """spiece.model"""}

a_							=    {
    """vocab_file""": {
        """google/reformer-crime-and-punishment""": (
            """https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/spiece.model"""
        )
    }
}

a_							=    {
    """google/reformer-crime-and-punishment""": 52_42_88,
}







class 					A_(SCREAMING_SNAKE_CASE_      ):







			"""simple docstring"""



			a_       :			Optional[int]												=			VOCAB_FILES_NAMES
			a_       :			int												=			PRETRAINED_VOCAB_FILES_MAP
			a_       :			str												=			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			a_       :			Optional[Any]												=			["""input_ids""", """attention_mask"""]
			def __init__(	self				,   A				,   A="</s>"				,   A="<unk>"				,   A=[]				,   A = None				,   **A				,   ):
						_lowerCamelCase					:					Any	       =    {} if sp_model_kwargs is None else sp_model_kwargs

						super().__init__(
						    eos_token=A				,   unk_token=A				,   additional_special_tokens=A				,   sp_model_kwargs=self.sp_model_kwargs				,   **A				,   )

						_lowerCamelCase					:					Dict	       =    vocab_file
						_lowerCamelCase					:					List[Any]	       =    spm.SentencePieceProcessor(**self.sp_model_kwargs							)
						self.sp_model.Load(A							)
			@property
			def    _lowerCAmelCase		(	self							):
						return self.sp_model.get_piece_size()
			def    _lowerCAmelCase		(	self							):
						_lowerCamelCase					:					int	       =    {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							):
						_lowerCamelCase					:					Optional[int]	       =    self.__dict__.copy()
						_lowerCamelCase					:					Union[str, Any]	       =    None
						return state
			def __setstate__(	self				,   A							):
						_lowerCamelCase					:					int	       =    d

						# for backward compatibility
						if not hasattr(self				,   'sp_model_kwargs'							):
									_lowerCamelCase					:					Union[str, Any]	       =    {}

						_lowerCamelCase					:					Dict	       =    spm.SentencePieceProcessor(**self.sp_model_kwargs							)
						self.sp_model.Load(self.vocab_file							)
			def    _lowerCAmelCase		(	self				,   A							):
						return self.sp_model.encode(A				,   out_type=A							)
			def    _lowerCAmelCase		(	self				,   A							):
						return self.sp_model.piece_to_id(A							)
			def    _lowerCAmelCase		(	self				,   A							):
						if index < self.sp_model.get_piece_size():
									_lowerCamelCase					:					Dict	       =    self.sp_model.IdToPiece(A							)
						return token
			def    _lowerCAmelCase		(	self				,   A							):
						_lowerCamelCase					:					Tuple	       =    []
						_lowerCamelCase					:					Optional[int]	       =    ''
						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
												_lowerCamelCase					:					Dict	       =    []
									else:
												current_sub_tokens.append(A							)
						out_string += self.sp_model.decode(A							)
						return out_string.strip()

			def    _lowerCAmelCase		(	self				,   A				,   A = None							):
						if not os.path.isdir(A							):
									logger.error(F"Vocabulary path ({save_directory}) should be a directory"							)
									return
						_lowerCamelCase					:					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:
												_lowerCamelCase					:					List[str]	       =    self.sp_model.serialized_model_proto()
												fi.write(A							)

						return (out_vocab_file,)





