
from typing import List, Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_herbert import HerbertTokenizer


lowerCamelCase__	=							logging.get_logger(__name__)

lowerCamelCase__	=							{"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}

lowerCamelCase__	=							{
    "vocab_file": {
        "allegro/herbert-base-cased": "https://huggingface.co/allegro/herbert-base-cased/resolve/main/vocab.json"
    },
    "merges_file": {
        "allegro/herbert-base-cased": "https://huggingface.co/allegro/herbert-base-cased/resolve/main/merges.txt"
    },
}

lowerCamelCase__	=							{"allegro/herbert-base-cased": 514}
lowerCamelCase__	=							{}





class        lowerCAmelCase__      (   __snake_case	):
						UpperCamelCase_							: Dict	=		VOCAB_FILES_NAMES
						UpperCamelCase_							: Union[str, Any]	=		PRETRAINED_VOCAB_FILES_MAP
						UpperCamelCase_							: Optional[Any]	=		PRETRAINED_INIT_CONFIGURATION
						UpperCamelCase_							: Any	=		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						UpperCamelCase_							: Any	=		HerbertTokenizer

						def __init__(			self       ,							a=None       ,							a=None       ,							a=None       ,							a="<s>"       ,							a="<unk>"       ,							a="<pad>"       ,							a="<mask>"       ,							a="</s>"       ,							**a       ,							)					->      List[Any]:


								'''simple docstring'''

								super().__init__(
								    __UpperCamelCase       ,							__UpperCamelCase       ,							tokenizer_file=__UpperCamelCase       ,							cls_token=__UpperCamelCase       ,							unk_token=__UpperCamelCase       ,							pad_token=__UpperCamelCase       ,							mask_token=__UpperCamelCase       ,							sep_token=__UpperCamelCase       ,							**__UpperCamelCase       ,							)

						def  A_				(			self       ,							a       ,							a = None				)					->      Any:


								'''simple docstring'''

								_UpperCamelCase						    =	[self.cls_token_id]
								_UpperCamelCase						    =	[self.sep_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  A_				(			self       ,							a       ,							a = None       ,							a = False				)					->      Dict:


								'''simple docstring'''

								if already_has_special_tokens:
										return super().get_special_tokens_mask(
										    token_ids_a=__UpperCamelCase       ,							token_ids_a=__UpperCamelCase       ,							already_has_special_tokens=__UpperCamelCase				)

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

						def  A_				(			self       ,							a       ,							a = None				)					->      Optional[int]:


								'''simple docstring'''

								_UpperCamelCase						    =	[self.sep_token_id]
								_UpperCamelCase						    =	[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  A_				(			self       ,							a       ,							a = None				)					->      Dict:


								'''simple docstring'''

								_UpperCamelCase						    =	self._tokenizer.model.save(__UpperCamelCase       ,							name=__UpperCamelCase				)
								return tuple(__UpperCamelCase				)





import copy

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


lowerCamelCase__	=							logging.get_logger(__name__)

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







class        lowerCAmelCase__      (   __lowercase	):
						UpperCamelCase_							: int	=		"deta"
						UpperCamelCase_							: Union[str, Any]	=		{
						    "hidden_size": "d_model",
						    "num_attention_heads": "encoder_attention_heads",
						}

						def __init__(			self       ,							a=None       ,							a=9_00       ,							a=20_48       ,							a=6       ,							a=20_48       ,							a=8       ,							a=6       ,							a=10_24       ,							a=8       ,							a=0.0       ,							a=True       ,							a="relu"       ,							a=2_56       ,							a=0.1       ,							a=0.0       ,							a=0.0       ,							a=0.02       ,							a=1.0       ,							a=True       ,							a=False       ,							a="sine"       ,							a=5       ,							a=4       ,							a=4       ,							a=True       ,							a=3_00       ,							a=True       ,							a=True       ,							a=1       ,							a=5       ,							a=2       ,							a=1       ,							a=1       ,							a=5       ,							a=2       ,							a=0.1       ,							a=0.25       ,							**a       ,							)					->      Dict:


								'''simple docstring'''

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

								_UpperCamelCase						    =	backbone_config
								_UpperCamelCase						    =	num_queries
								_UpperCamelCase						    =	max_position_embeddings
								_UpperCamelCase						    =	d_model
								_UpperCamelCase						    =	encoder_ffn_dim
								_UpperCamelCase						    =	encoder_layers
								_UpperCamelCase						    =	encoder_attention_heads
								_UpperCamelCase						    =	decoder_ffn_dim
								_UpperCamelCase						    =	decoder_layers
								_UpperCamelCase						    =	decoder_attention_heads
								_UpperCamelCase						    =	dropout
								_UpperCamelCase						    =	attention_dropout
								_UpperCamelCase						    =	activation_dropout
								_UpperCamelCase						    =	activation_function
								_UpperCamelCase						    =	init_std
								_UpperCamelCase						    =	init_xavier_std
								_UpperCamelCase						    =	encoder_layerdrop
								_UpperCamelCase						    =	auxiliary_loss
								_UpperCamelCase						    =	position_embedding_type
								# deformable attributes
								_UpperCamelCase						    =	num_feature_levels
								_UpperCamelCase						    =	encoder_n_points
								_UpperCamelCase						    =	decoder_n_points
								_UpperCamelCase						    =	two_stage
								_UpperCamelCase						    =	two_stage_num_proposals
								_UpperCamelCase						    =	with_box_refine
								_UpperCamelCase						    =	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
								_UpperCamelCase						    =	class_cost
								_UpperCamelCase						    =	bbox_cost
								_UpperCamelCase						    =	giou_cost
								# Loss coefficients
								_UpperCamelCase						    =	mask_loss_coefficient
								_UpperCamelCase						    =	dice_loss_coefficient
								_UpperCamelCase						    =	bbox_loss_coefficient
								_UpperCamelCase						    =	giou_loss_coefficient
								_UpperCamelCase						    =	eos_coefficient
								_UpperCamelCase						    =	focal_alpha
								super().__init__(is_encoder_decoder=a       ,							**a				)

						@property
						def  A_				(			self				)					->      int:


								'''simple docstring'''

								return self.encoder_attention_heads

						@property
						def  A_				(			self				)					->      int:


								'''simple docstring'''

								return self.d_model







						def  A_				(			self				)					->      Tuple:


								'''simple docstring'''

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



