

"""simple docstring"""


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

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
			from .tokenization_rembert import RemBertTokenizer
else:
			__A						   =			None

__A						   =			logging.get_logger(__name__)
__A						   =			{"vocab_file": "sentencepiece.model", "tokenizer_file": "tokenizer.json"}

__A						   =			{
    "vocab_file": {
        "google/rembert": "https://huggingface.co/google/rembert/resolve/main/sentencepiece.model",
    },
    "tokenizer_file": {
        "google/rembert": "https://huggingface.co/google/rembert/resolve/main/tokenizer.json",
    },
}

__A						   =			{
    "google/rembert": 256,
}

__A						   =			"▁"





class     snake_case	(				__snake_case  ):
				SCREAMING_SNAKE_CASE_   :      Tuple         =  VOCAB_FILES_NAMES
				SCREAMING_SNAKE_CASE_   :      Any         =  PRETRAINED_VOCAB_FILES_MAP
				SCREAMING_SNAKE_CASE_   :      Tuple         =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				SCREAMING_SNAKE_CASE_   :      Dict         =  RemBertTokenizer

				def __init__(	self							:				Tuple     ,  UpperCamelCase__							:				Dict=None     ,  UpperCamelCase__							:				List[Any]=None     ,  UpperCamelCase__							:				str=True     ,  UpperCamelCase__							:				Optional[int]=True     ,  UpperCamelCase__							:				Optional[Any]=False     ,  UpperCamelCase__							:				int="[CLS]"     ,  UpperCamelCase__							:				Optional[Any]="[SEP]"     ,  UpperCamelCase__							:				List[str]="<unk>"     ,  UpperCamelCase__							:				Dict="[SEP]"     ,  UpperCamelCase__							:				int="<pad>"     ,  UpperCamelCase__							:				Any="[CLS]"     ,  UpperCamelCase__							:				str="[MASK]"     ,  **UpperCamelCase__							:				Optional[Any]     ,  )->   List[Any]:



						'''simple docstring'''


						__lowerCAmelCase:       int          =			AddedToken(UpperCamelCase__     ,  lstrip=UpperCamelCase__     ,  rstrip=UpperCamelCase__) if isinstance(UpperCamelCase__     ,  UpperCamelCase__) else mask_token

						super().__init__(
						    UpperCamelCase__     ,  tokenizer_file=UpperCamelCase__     ,  do_lower_case=UpperCamelCase__     ,  remove_space=UpperCamelCase__     ,  keep_accents=UpperCamelCase__     ,  bos_token=UpperCamelCase__     ,  eos_token=UpperCamelCase__     ,  unk_token=UpperCamelCase__     ,  sep_token=UpperCamelCase__     ,  pad_token=UpperCamelCase__     ,  cls_token=UpperCamelCase__     ,  mask_token=UpperCamelCase__     ,  **UpperCamelCase__     ,  )

						__lowerCAmelCase:       Optional[int]          =			do_lower_case
						__lowerCAmelCase:       int          =			remove_space
						__lowerCAmelCase:       int          =			keep_accents
						__lowerCAmelCase:       str          =			vocab_file
						__lowerCAmelCase:       Tuple          =			False if not self.vocab_file else True

				def 							lowercase_     (	self							:				Any     ,  UpperCamelCase__							:				List[int]     ,  UpperCamelCase__							:				Optional[List[int]] = None)->   List[int]:



						'''simple docstring'''


						__lowerCAmelCase:       Optional[int]          =			[self.sep_token_id]
						__lowerCAmelCase:       Any          =			[self.cls_token_id]
						if token_ids_a is None:
								return cls + token_ids_a + sep
						return cls + token_ids_a + sep + token_ids_a + sep

				def 							lowercase_     (	self							:				Optional[Any]     ,  UpperCamelCase__							:				List[int]     ,  UpperCamelCase__							:				Optional[List[int]] = None     ,  UpperCamelCase__							:				bool = False)->   List[int]:



						'''simple docstring'''


						if already_has_special_tokens:
								if token_ids_a is not None:
										raise ValueError(
										    "You should not supply a second sequence if the provided sequence of "
										    "ids is already formatted with special tokens for the model.")
								return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]

						if token_ids_a is not None:
								return [1] + ([0] * len(UpperCamelCase__)) + [1] + ([0] * len(UpperCamelCase__)) + [1]
						return [1] + ([0] * len(UpperCamelCase__)) + [1]

				def 							lowercase_     (	self							:				Tuple     ,  UpperCamelCase__							:				List[int]     ,  UpperCamelCase__							:				Optional[List[int]] = None)->   List[int]:



						'''simple docstring'''


						__lowerCAmelCase:       Optional[int]          =			[self.sep_token_id]
						__lowerCAmelCase:       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) * [0] + len(token_ids_a + sep) * [1]




				def 							lowercase_     (	self							:				Union[str, Any]     ,  UpperCamelCase__							:				str     ,  UpperCamelCase__							:				Optional[str] = None)->   Tuple[str]:



						'''simple docstring'''


						if not os.path.isdir(UpperCamelCase__):
								logger.error("Vocabulary path ({}) should be a directory".format(UpperCamelCase__))
								return
						__lowerCAmelCase:       Optional[Any]          =			os.path.join(
						    UpperCamelCase__     ,  (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"])

						if os.path.abspath(self.vocab_file) != os.path.abspath(UpperCamelCase__):
								copyfile(self.vocab_file     ,  UpperCamelCase__)

						return (out_vocab_file,)





"""simple docstring"""


import copy

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


__A						   =			logging.get_logger(__name__)

__A						   =			{
    "SenseTime/deformable-detr": "https://huggingface.co/sensetime/deformable-detr/resolve/main/config.json",
    # See all Deformable DETR models at https://huggingface.co/models?filter=deformable-detr
}





class     snake_case	(				__snake_case  ):
				SCREAMING_SNAKE_CASE_   :      str         =  """deformable_detr"""
				SCREAMING_SNAKE_CASE_   :      int         =  {
				    """hidden_size""": """d_model""",
				    """num_attention_heads""": """encoder_attention_heads""",
				}

				def __init__(	self							:				int     ,  UpperCamelCase__							:				int=True     ,  UpperCamelCase__							:				str=None     ,  UpperCamelCase__							:				int=3     ,  UpperCamelCase__							:				Dict=3_0_0     ,  UpperCamelCase__							:				Optional[int]=1_0_2_4     ,  UpperCamelCase__							:				int=6     ,  UpperCamelCase__							:				List[Any]=1_0_2_4     ,  UpperCamelCase__							:				List[Any]=8     ,  UpperCamelCase__							:				str=6     ,  UpperCamelCase__							:				str=1_0_2_4     ,  UpperCamelCase__							:				Union[str, Any]=8     ,  UpperCamelCase__							:				Dict=0.0     ,  UpperCamelCase__							:				str=True     ,  UpperCamelCase__							:				List[Any]="relu"     ,  UpperCamelCase__							:				Tuple=2_5_6     ,  UpperCamelCase__							:				Tuple=0.1     ,  UpperCamelCase__							:				Optional[int]=0.0     ,  UpperCamelCase__							:				Dict=0.0     ,  UpperCamelCase__							:				Optional[Any]=0.02     ,  UpperCamelCase__							:				Dict=1.0     ,  UpperCamelCase__							:				Dict=True     ,  UpperCamelCase__							:				Optional[Any]=False     ,  UpperCamelCase__							:				List[str]="sine"     ,  UpperCamelCase__							:				Any="resnet50"     ,  UpperCamelCase__							:				List[Any]=True     ,  UpperCamelCase__							:				Any=False     ,  UpperCamelCase__							:				Optional[int]=4     ,  UpperCamelCase__							:				List[str]=4     ,  UpperCamelCase__							:				Tuple=4     ,  UpperCamelCase__							:				List[str]=False     ,  UpperCamelCase__							:				Optional[int]=3_0_0     ,  UpperCamelCase__							:				int=False     ,  UpperCamelCase__							:				List[Any]=1     ,  UpperCamelCase__							:				Optional[Any]=5     ,  UpperCamelCase__							:				List[Any]=2     ,  UpperCamelCase__							:				str=1     ,  UpperCamelCase__							:				int=1     ,  UpperCamelCase__							:				Dict=5     ,  UpperCamelCase__							:				Tuple=2     ,  UpperCamelCase__							:				Optional[Any]=0.1     ,  UpperCamelCase__							:				Union[str, Any]=0.25     ,  UpperCamelCase__							:				List[Any]=False     ,  **UpperCamelCase__							:				Dict     ,  )->   Optional[int]:



						'''simple docstring'''


						if backbone_config is not None and use_timm_backbone:
								raise ValueError("You can't specify both `backbone_config` and `use_timm_backbone`.")

						if not use_timm_backbone:
								if backbone_config is None:
										logger.info("`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.")
										__lowerCAmelCase:       List[Any]          =			CONFIG_MAPPING["resnet"](out_features=["stage4"])
								elif isinstance(UpperCamelCase__     ,  UpperCamelCase__):
										__lowerCAmelCase:       int          =			backbone_config.get("model_type")
										__lowerCAmelCase:       List[str]          =			CONFIG_MAPPING[backbone_model_type]
										__lowerCAmelCase:       Any          =			config_class.from_dict(UpperCamelCase__)
						__lowerCAmelCase:       int          =			use_timm_backbone
						__lowerCAmelCase:       Any          =			backbone_config
						__lowerCAmelCase:       Tuple          =			num_channels
						__lowerCAmelCase:       str          =			num_queries
						__lowerCAmelCase:       List[str]          =			max_position_embeddings
						__lowerCAmelCase:       List[Any]          =			d_model
						__lowerCAmelCase:       Union[str, Any]          =			encoder_ffn_dim
						__lowerCAmelCase:       Tuple          =			encoder_layers
						__lowerCAmelCase:       List[str]          =			encoder_attention_heads
						__lowerCAmelCase:       Any          =			decoder_ffn_dim
						__lowerCAmelCase:       Union[str, Any]          =			decoder_layers
						__lowerCAmelCase:       List[Any]          =			decoder_attention_heads
						__lowerCAmelCase:       List[Any]          =			dropout
						__lowerCAmelCase:       Optional[Any]          =			attention_dropout
						__lowerCAmelCase:       Union[str, Any]          =			activation_dropout
						__lowerCAmelCase:       Union[str, Any]          =			activation_function
						__lowerCAmelCase:       Dict          =			init_std
						__lowerCAmelCase:       int          =			init_xavier_std
						__lowerCAmelCase:       str          =			encoder_layerdrop
						__lowerCAmelCase:       Union[str, Any]          =			auxiliary_loss
						__lowerCAmelCase:       List[Any]          =			position_embedding_type
						__lowerCAmelCase:       str          =			backbone
						__lowerCAmelCase:       Tuple          =			use_pretrained_backbone
						__lowerCAmelCase:       int          =			dilation
						# deformable attributes
						__lowerCAmelCase:       Union[str, Any]          =			num_feature_levels
						__lowerCAmelCase:       Optional[Any]          =			encoder_n_points
						__lowerCAmelCase:       Dict          =			decoder_n_points
						__lowerCAmelCase:       Optional[Any]          =			two_stage
						__lowerCAmelCase:       Tuple          =			two_stage_num_proposals
						__lowerCAmelCase:       int          =			with_box_refine
						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
						__lowerCAmelCase:       str          =			class_cost
						__lowerCAmelCase:       List[str]          =			bbox_cost
						__lowerCAmelCase:       List[str]          =			giou_cost
						# Loss coefficients
						__lowerCAmelCase:       Tuple          =			mask_loss_coefficient
						__lowerCAmelCase:       int          =			dice_loss_coefficient
						__lowerCAmelCase:       Any          =			bbox_loss_coefficient
						__lowerCAmelCase:       str          =			giou_loss_coefficient
						__lowerCAmelCase:       int          =			eos_coefficient
						__lowerCAmelCase:       Tuple          =			focal_alpha
						__lowerCAmelCase:       Optional[Any]          =			disable_custom_kernels
						super().__init__(is_encoder_decoder=UpperCamelCase__     ,  **UpperCamelCase__)

				@property
				def 							lowercase_     (	self							:				List[Any])->   int:



						'''simple docstring'''


						return self.encoder_attention_heads

				@property
				def 							lowercase_     (	self							:				Optional[Any])->   int:



						'''simple docstring'''


						return self.d_model




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



						'''simple docstring'''


						__lowerCAmelCase:       Tuple          =			copy.deepcopy(self.__dict__)
						if self.backbone_config is not None:
								__lowerCAmelCase:       str          =			self.backbone_config.to_dict()
						__lowerCAmelCase:       Tuple          =			self.__class__.model_type
						return output


