





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_							=				"""▁"""

UpperCAmelCase_							=				{"""vocab_file""": """sentencepiece.bpe.model""", """monolingual_vocab_file""": """dict.txt"""}

UpperCAmelCase_							=				{
    """vocab_file""": {
        """vinai/bartpho-syllable""": """https://huggingface.co/vinai/bartpho-syllable/resolve/main/sentencepiece.bpe.model""",
    },
    """monolingual_vocab_file""": {
        """vinai/bartpho-syllable""": """https://huggingface.co/vinai/bartpho-syllable/resolve/main/dict.txt""",
    },
}

UpperCAmelCase_							=				{"""vinai/bartpho-syllable""": 1_024}
class     UpperCamelCase__					(     lowerCamelCase__				):






		'''simple docstring'''






		__a       :       Union[str, Any]       =	VOCAB_FILES_NAMES
		__a       :       Any       =	PRETRAINED_VOCAB_FILES_MAP
		__a       :       int       =	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		__a       :       int       =	['input_ids', 'attention_mask']






		def __init__(							self,   snake_case__,   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__,   )       -> Optional[Any]:






									"""simple docstring"""
									lowercase_	:   int  				=     AddedToken(__lowerCAmelCase,   lstrip=__lowerCAmelCase,   rstrip=__lowerCAmelCase  ) if isinstance(__lowerCAmelCase,   __lowerCAmelCase  ) else mask_token

									lowercase_	:   List[Any]  				=     {} 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,   sp_model_kwargs=self.sp_model_kwargs,   **__lowerCAmelCase,   )

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

									# Load the reduced vocab

									# Keep order of special tokens for backward compatibility
									lowercase_	:   int  				=     {}
									lowercase_	:   Optional[int]  				=     0
									for token in [bos_token, pad_token, eos_token, unk_token, sep_token, cls_token]:
																if str(__lowerCAmelCase  ) not in self.fairseq_tokens_to_ids:
																							lowercase_	:   Optional[int]  				=     cnt
																							cnt += 1
									with open(__lowerCAmelCase,   """r""",   encoding="""utf-8"""  ) as f:
																for line in f.readlines():
																							lowercase_	:   List[Any]  				=     line.strip().split()[0]
																							lowercase_	:   Tuple  				=     len(self.fairseq_tokens_to_ids  )
									if str(__lowerCAmelCase  ) not in self.fairseq_tokens_to_ids:
																lowercase_	:   Tuple  				=     len(self.fairseq_tokens_to_ids  )

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






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






									"""simple docstring"""
									lowercase_	:   Optional[Any]  				=     self.__dict__.copy()
									lowercase_	:   Dict  				=     None
									lowercase_	:   Dict  				=     self.sp_model.serialized_model_proto()
									return state






		def __setstate__(							self,   snake_case__  )       -> Dict:






									"""simple docstring"""
									lowercase_	:   Optional[int]  				=     d

									# for backward compatibility
									if not hasattr(self,   """sp_model_kwargs"""  ):
																lowercase_	:   List[str]  				=     {}

									lowercase_	:   Union[str, Any]  				=     spm.SentencePieceProcessor(**self.sp_model_kwargs  )
									self.sp_model.LoadFromSerializedProto(self.sp_model_proto  )






		def 						snake_case__				(							self,   snake_case__,   snake_case__ = None  )       -> Any:






									"""simple docstring"""



									if token_ids_a is None:
																return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
									lowercase_	:   Optional[int]  				=     [self.cls_token_id]
									lowercase_	:   Any  				=     [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  )       -> Dict:






									"""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  )

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






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






									"""simple docstring"""
									lowercase_	:   Optional[Any]  				=     [self.sep_token_id]
									lowercase_	:   List[str]  				=     [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  )       -> Any:






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






		def 						snake_case__				(							self  )       -> Any:






									"""simple docstring"""
									lowercase_	:   int  				=     {self.convert_ids_to_tokens(__lowerCAmelCase  ): i for i in range(self.vocab_size  )}
									vocab.update(self.added_tokens_encoder  )
									return vocab






		def 						snake_case__				(							self,   snake_case__  )       -> Tuple:






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






		def 						snake_case__				(							self,   snake_case__  )       -> Any:






									"""simple docstring"""



									if token in self.fairseq_tokens_to_ids:
																return self.fairseq_tokens_to_ids[token]
									else:
																return self.unk_token_id






		def 						snake_case__				(							self,   snake_case__  )       -> str:






									"""simple docstring"""
									return self.fairseq_ids_to_tokens[index]






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






									"""simple docstring"""
									lowercase_	:   Any  				=     """""".join(__lowerCAmelCase  ).replace(__lowerCAmelCase,   """ """  ).strip()
									return out_string






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






									"""simple docstring"""



									if not os.path.isdir(__lowerCAmelCase  ):
																logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""  )
																return
									lowercase_	:   Dict  				=     os.path.join(
									    __lowerCAmelCase,   (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]  )
									lowercase_	:   Optional[int]  				=     os.path.join(
									    __lowerCAmelCase,   (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""monolingual_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_	:   List[Any]  				=     self.sp_model.serialized_model_proto()
																							fi.write(__lowerCAmelCase  )

									if os.path.abspath(self.monolingual_vocab_file  ) != os.path.abspath(
									    __lowerCAmelCase  ) and os.path.isfile(self.monolingual_vocab_file  ):
																copyfile(self.monolingual_vocab_file,   __lowerCAmelCase  )
									elif not os.path.isfile(self.monolingual_vocab_file  ):
																with open(__lowerCAmelCase,   """w""",   encoding="""utf-8"""  ) as fp:
																							for token in self.fairseq_tokens_to_ids:
																														if token not in self.all_special_tokens:
																																					fp.write(f"""{str(__lowerCAmelCase  )} \n"""  )

									return out_vocab_file, out_monolingual_vocab_file
"""simple docstring"""






import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


__lowerCAmelCase       :			Tuple   	=logging.get_logger(__name__)

__lowerCAmelCase       :			Dict   	={
    """ut/deta""": """https://huggingface.co/ut/deta/resolve/main/config.json""",
}





class 					_A		(			lowerCAmelCase      ):
      snake_case__     :  Optional[int]				    =      'deta'
      snake_case__     :  Dict				    =      {
          'hidden_size': 'd_model',
          'num_attention_heads': 'encoder_attention_heads',
      }

      def __init__(		self						,			__lowerCAmelCase=None						,			__lowerCAmelCase=900						,			__lowerCAmelCase=2048						,			__lowerCAmelCase=6						,			__lowerCAmelCase=2048						,			__lowerCAmelCase=8						,			__lowerCAmelCase=6						,			__lowerCAmelCase=1024						,			__lowerCAmelCase=8						,			__lowerCAmelCase=0.0						,			__lowerCAmelCase=True						,			__lowerCAmelCase="relu"						,			__lowerCAmelCase=256						,			__lowerCAmelCase=0.1						,			__lowerCAmelCase=0.0						,			__lowerCAmelCase=0.0						,			__lowerCAmelCase=0.0_2						,			__lowerCAmelCase=1.0						,			__lowerCAmelCase=True						,			__lowerCAmelCase=False						,			__lowerCAmelCase="sine"						,			__lowerCAmelCase=5						,			__lowerCAmelCase=4						,			__lowerCAmelCase=4						,			__lowerCAmelCase=True						,			__lowerCAmelCase=300						,			__lowerCAmelCase=True						,			__lowerCAmelCase=True						,			__lowerCAmelCase=1						,			__lowerCAmelCase=5						,			__lowerCAmelCase=2						,			__lowerCAmelCase=1						,			__lowerCAmelCase=1						,			__lowerCAmelCase=5						,			__lowerCAmelCase=2						,			__lowerCAmelCase=0.1						,			__lowerCAmelCase=0.2_5						,			**__lowerCAmelCase						,			):







            """simple docstring"""



            if backbone_config is None:
                  logger.info("""`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone."""		)
                  lowercase								=      CONFIG_MAPPING["""resnet"""](out_features=["""stage2""", """stage3""", """stage4"""]		)
            else:
                  if isinstance(__lowerCAmelCase						,			__lowerCAmelCase		):
                        lowercase								=      backbone_config.pop("""model_type"""		)
                        lowercase								=      CONFIG_MAPPING[backbone_model_type]
                        lowercase								=      config_class.from_dict(__lowerCAmelCase		)

            lowercase								=      backbone_config
            lowercase								=      num_queries
            lowercase								=      max_position_embeddings
            lowercase								=      d_model
            lowercase								=      encoder_ffn_dim
            lowercase								=      encoder_layers
            lowercase								=      encoder_attention_heads
            lowercase								=      decoder_ffn_dim
            lowercase								=      decoder_layers
            lowercase								=      decoder_attention_heads
            lowercase								=      dropout
            lowercase								=      attention_dropout
            lowercase								=      activation_dropout
            lowercase								=      activation_function
            lowercase								=      init_std
            lowercase								=      init_xavier_std
            lowercase								=      encoder_layerdrop
            lowercase								=      auxiliary_loss
            lowercase								=      position_embedding_type
            # deformable attributes
            lowercase								=      num_feature_levels
            lowercase								=      encoder_n_points
            lowercase								=      decoder_n_points
            lowercase								=      two_stage
            lowercase								=      two_stage_num_proposals
            lowercase								=      with_box_refine
            lowercase								=      assign_first_stage
            if two_stage is True and with_box_refine is False:
                  raise ValueError("""If two_stage is True, with_box_refine must be True."""		)
            # Hungarian matcher
            lowercase								=      class_cost
            lowercase								=      bbox_cost
            lowercase								=      giou_cost
            # Loss coefficients
            lowercase								=      mask_loss_coefficient
            lowercase								=      dice_loss_coefficient
            lowercase								=      bbox_loss_coefficient
            lowercase								=      giou_loss_coefficient
            lowercase								=      eos_coefficient
            lowercase								=      focal_alpha
            super().__init__(is_encoder_decoder=__lowerCAmelCase						,			**__lowerCAmelCase		)

      @property
      def 							A__			(		self		):







            """simple docstring"""



            return self.encoder_attention_heads

      @property
      def 							A__			(		self		):







            """simple docstring"""



            return self.d_model







      def 							A__			(		self		):







            """simple docstring"""



            lowercase								=      copy.deepcopy(self.__dict__		)
            lowercase								=      self.backbone_config.to_dict()
            lowercase								=      self.__class__.model_type
            return output



