






'''simple docstring'''







import json
from typing import Dict, List, Optional, Tuple, Union

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding, EncodedInput
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import PaddingStrategy, logging
from .tokenization_led import LEDTokenizer


_A     :      Union[str, Any]   					=logging.get_logger(__name__)


_A     :      str   					={'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', '''tokenizer_file''': '''tokenizer.json'''}

_A     :      Optional[int]   					={
    '''vocab_file''': {
        '''allenai/led-base-16384''': '''https://huggingface.co/allenai/led-base-16384/resolve/main/vocab.json''',
    },
    '''merges_file''': {
        '''allenai/led-base-16384''': '''https://huggingface.co/allenai/led-base-16384/resolve/main/merges.txt''',
    },
    '''tokenizer_file''': {
        '''allenai/led-base-16384''': '''https://huggingface.co/allenai/led-base-16384/resolve/main/tokenizer.json''',
    },
}

_A     :      List[str]   					={
    '''allenai/led-base-16384''': 16_384,
}

class _lowercase     (    lowerCamelCase_		):
							a    				=	VOCAB_FILES_NAMES
							a    				=	PRETRAINED_VOCAB_FILES_MAP
							a    				=	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							a    				=	LEDTokenizer
							a    				=	["""input_ids""", """attention_mask"""]




							def __init__(							self:    Any      ,							UpperCamelCase__:    Union[str, Any]=None      ,							UpperCamelCase__:    str=None      ,							UpperCamelCase__:    str=None      ,							UpperCamelCase__:    Any="replace"      ,							UpperCamelCase__:    List[Any]="<s>"      ,							UpperCamelCase__:    Dict="</s>"      ,							UpperCamelCase__:    Dict="</s>"      ,							UpperCamelCase__:    str="<s>"      ,							UpperCamelCase__:    Dict="<unk>"      ,							UpperCamelCase__:    Tuple="<pad>"      ,							UpperCamelCase__:    int="<mask>"      ,							UpperCamelCase__:    Any=False      ,							UpperCamelCase__:    Optional[Any]=True      ,							**UpperCamelCase__:    str      ,							):
														super().__init__(
														    lowerCAmelCase__      ,							lowerCAmelCase__      ,							tokenizer_file=lowerCAmelCase__      ,							errors=lowerCAmelCase__      ,							bos_token=lowerCAmelCase__      ,							eos_token=lowerCAmelCase__      ,							sep_token=lowerCAmelCase__      ,							cls_token=lowerCAmelCase__      ,							unk_token=lowerCAmelCase__      ,							pad_token=lowerCAmelCase__      ,							mask_token=lowerCAmelCase__      ,							add_prefix_space=lowerCAmelCase__      ,							trim_offsets=lowerCAmelCase__      ,							**lowerCAmelCase__      ,							)

														lowerCamelCase__ :     int	  =	json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()					)
														if pre_tok_state.get("""add_prefix_space"""      ,							lowerCAmelCase__					) != add_prefix_space:
																					lowerCamelCase__ :     int	  =	getattr(lowerCAmelCase__      ,							pre_tok_state.pop("""type"""					)					)
																					lowerCamelCase__ :     List[str]	  =	add_prefix_space
																					lowerCamelCase__ :     Dict	  =	pre_tok_class(**lowerCAmelCase__					)

														lowerCamelCase__ :     int	  =	add_prefix_space

														# the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
														lowerCamelCase__ :     Union[str, Any]	  =	"""post_processor"""
														lowerCamelCase__ :     Any	  =	getattr(self.backend_tokenizer      ,							lowerCAmelCase__      ,							lowerCAmelCase__					)
														if tokenizer_component_instance:
																					lowerCamelCase__ :     Optional[Any]	  =	json.loads(tokenizer_component_instance.__getstate__()					)

																					# The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
																					if "sep" in state:
																												lowerCamelCase__ :     List[str]	  =	tuple(state["""sep"""]					)
																					if "cls" in state:
																												lowerCamelCase__ :     List[str]	  =	tuple(state["""cls"""]					)

																					lowerCamelCase__ :     Union[str, Any]	  =	False

																					if state.get("""add_prefix_space"""      ,							lowerCAmelCase__					) != add_prefix_space:
																												lowerCamelCase__ :     List[str]	  =	add_prefix_space
																												lowerCamelCase__ :     Optional[int]	  =	True

																					if state.get("""trim_offsets"""      ,							lowerCAmelCase__					) != trim_offsets:
																												lowerCamelCase__ :     Any	  =	trim_offsets
																												lowerCamelCase__ :     Any	  =	True

																					if changes_to_apply:
																												lowerCamelCase__ :     Union[str, Any]	  =	getattr(lowerCAmelCase__      ,							state.pop("""type"""					)					)
																												lowerCamelCase__ :     Optional[Any]	  =	component_class(**lowerCAmelCase__					)
																												setattr(self.backend_tokenizer      ,							lowerCAmelCase__      ,							lowerCAmelCase__					)




							@property
							# Copied from transformers.models.bart.tokenization_bart_fast.BartTokenizerFast.mask_token with BART->LED
							def 		lowerCamelCase_    (							self:    Optional[int]					):
														if self._mask_token is None:
																					if self.verbose:
																												logger.error("""Using mask_token, but it is not set yet."""					)
																					return None
														return str(self._mask_token					)




							@mask_token.setter
							def 		lowerCamelCase_    (							self:    List[Any]      ,							UpperCamelCase__:    str					):
														lowerCamelCase__ :     int	  =	AddedToken(lowerCAmelCase__      ,							lstrip=lowerCAmelCase__      ,							rstrip=lowerCAmelCase__					) if isinstance(lowerCAmelCase__      ,							lowerCAmelCase__					) else value
														lowerCamelCase__ :     Any	  =	value




							def 		lowerCamelCase_    (							self:    List[Any]      ,							*UpperCamelCase__:    str      ,							**UpperCamelCase__:    Union[str, Any]					):
														lowerCamelCase__ :     Tuple	  =	kwargs.get("""is_split_into_words"""      ,							lowerCAmelCase__					)

														if is_split_into_words and not self.add_prefix_space:
																					raise ValueError(
																					    F'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
																					    """to use it with pretokenized inputs."""					)

														return super()._batch_encode_plus(*lowerCAmelCase__      ,							**lowerCAmelCase__					)




							def 		lowerCamelCase_    (							self:    Dict      ,							*UpperCamelCase__:    str      ,							**UpperCamelCase__:    Dict					):
														lowerCamelCase__ :     Optional[Any]	  =	kwargs.get("""is_split_into_words"""      ,							lowerCAmelCase__					)

														if is_split_into_words and not self.add_prefix_space:
																					raise ValueError(
																					    F'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
																					    """to use it with pretokenized inputs."""					)

														return super()._encode_plus(*lowerCAmelCase__      ,							**lowerCAmelCase__					)




							def 		lowerCamelCase_    (							self:    str      ,							UpperCamelCase__:    Optional[Any]      ,							UpperCamelCase__:    Dict = None					):
														lowerCamelCase__ :     Tuple	  =	self._tokenizer.model.save(lowerCAmelCase__      ,							name=lowerCAmelCase__					)
														return tuple(lowerCAmelCase__					)




							def 		lowerCamelCase_    (							self:    str      ,							UpperCamelCase__:    Dict      ,							UpperCamelCase__:    Optional[Any]=None					):
														lowerCamelCase__ :     str	  =	[self.bos_token_id] + token_ids_a + [self.eos_token_id]
														if token_ids_a is None:
																					return output

														return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]




							def 		lowerCamelCase_    (							self:    Union[str, Any]      ,							UpperCamelCase__:    Tuple      ,							UpperCamelCase__:    Any = None					):
														lowerCamelCase__ :     Any	  =	[self.sep_token_id]
														lowerCamelCase__ :     int	  =	[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]




							def 		lowerCamelCase_    (							self:    Union[str, Any]      ,							UpperCamelCase__:    List[Any]      ,							UpperCamelCase__:    List[Any] = None      ,							UpperCamelCase__:    Dict = PaddingStrategy.DO_NOT_PAD      ,							UpperCamelCase__:    Tuple = None      ,							UpperCamelCase__:    Optional[Any] = None      ,							):
														lowerCamelCase__ :     str	  =	super()._pad(
														    encoded_inputs=lowerCAmelCase__      ,							max_length=lowerCAmelCase__      ,							padding_strategy=lowerCAmelCase__      ,							pad_to_multiple_of=lowerCAmelCase__      ,							return_attention_mask=lowerCAmelCase__      ,							)

														# Load from model defaults
														if return_attention_mask is None:
																					lowerCamelCase__ :     Union[str, Any]	  =	"""attention_mask""" in self.model_input_names

														if return_attention_mask and "global_attention_mask" in encoded_inputs:
																					lowerCamelCase__ :     List[str]	  =	encoded_inputs[self.model_input_names[0]]
																					# `global_attention_mask` need to have the same length as other (sequential) inputs.
																					lowerCamelCase__ :     Any	  =	len(encoded_inputs["""global_attention_mask"""]					) != len(lowerCAmelCase__					)

																					if needs_to_be_padded:
																												lowerCamelCase__ :     List[Any]	  =	len(lowerCAmelCase__					) - len(encoded_inputs["""global_attention_mask"""]					)

																												if self.padding_side == "right":
																																			# Use `-1` since `0` in `global_attention_mask` means `local attention` instead of `not to attend`
																																			lowerCamelCase__ :     Dict	  =	(
																																			    encoded_inputs["""global_attention_mask"""] + [-1] * difference
																																			)
																												elif self.padding_side == "left":
																																			lowerCamelCase__ :     Optional[Any]	  =	[-1] * difference + encoded_inputs[
																																			    """global_attention_mask"""
																																			]
																												else:
																																			raise ValueError("""Invalid padding strategy:""" + str(self.padding_side					)					)

														return encoded_inputs











'''simple docstring'''







import copy

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


_A     :      Union[str, Any]   					=logging.get_logger(__name__)

_A     :      List[Any]   					={
    '''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 _lowercase     (    _lowercase		):
							a    				=	"""deformable_detr"""
							a    				=	{
							    """hidden_size""": """d_model""",
							    """num_attention_heads""": """encoder_attention_heads""",
							}




							def __init__(							self:    Optional[int]      ,							UpperCamelCase__:    Tuple=True      ,							UpperCamelCase__:    Tuple=None      ,							UpperCamelCase__:    List[str]=3      ,							UpperCamelCase__:    Any=300      ,							UpperCamelCase__:    Optional[int]=1_024      ,							UpperCamelCase__:    int=6      ,							UpperCamelCase__:    str=1_024      ,							UpperCamelCase__:    Optional[Any]=8      ,							UpperCamelCase__:    Optional[Any]=6      ,							UpperCamelCase__:    Union[str, Any]=1_024      ,							UpperCamelCase__:    Tuple=8      ,							UpperCamelCase__:    str=0.0      ,							UpperCamelCase__:    Tuple=True      ,							UpperCamelCase__:    Any="relu"      ,							UpperCamelCase__:    Any=256      ,							UpperCamelCase__:    List[Any]=0.1      ,							UpperCamelCase__:    Optional[int]=0.0      ,							UpperCamelCase__:    Any=0.0      ,							UpperCamelCase__:    List[str]=0.02      ,							UpperCamelCase__:    str=1.0      ,							UpperCamelCase__:    Any=True      ,							UpperCamelCase__:    List[Any]=False      ,							UpperCamelCase__:    str="sine"      ,							UpperCamelCase__:    Optional[Any]="resnet50"      ,							UpperCamelCase__:    Optional[int]=True      ,							UpperCamelCase__:    Optional[int]=False      ,							UpperCamelCase__:    List[Any]=4      ,							UpperCamelCase__:    Any=4      ,							UpperCamelCase__:    int=4      ,							UpperCamelCase__:    int=False      ,							UpperCamelCase__:    Optional[Any]=300      ,							UpperCamelCase__:    str=False      ,							UpperCamelCase__:    int=1      ,							UpperCamelCase__:    Tuple=5      ,							UpperCamelCase__:    List[Any]=2      ,							UpperCamelCase__:    Optional[int]=1      ,							UpperCamelCase__:    int=1      ,							UpperCamelCase__:    Tuple=5      ,							UpperCamelCase__:    str=2      ,							UpperCamelCase__:    str=0.1      ,							UpperCamelCase__:    List[str]=0.25      ,							UpperCamelCase__:    Any=False      ,							**UpperCamelCase__:    Optional[Any]      ,							):
														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__ :     Union[str, Any]	  =	CONFIG_MAPPING["""resnet"""](out_features=["""stage4"""]					)
																					elif isinstance(UpperCamelCase__      ,							UpperCamelCase__					):
																												lowerCamelCase__ :     Tuple	  =	backbone_config.get("""model_type"""					)
																												lowerCamelCase__ :     Dict	  =	CONFIG_MAPPING[backbone_model_type]
																												lowerCamelCase__ :     Optional[Any]	  =	config_class.from_dict(UpperCamelCase__					)
														lowerCamelCase__ :     Tuple	  =	use_timm_backbone
														lowerCamelCase__ :     Tuple	  =	backbone_config
														lowerCamelCase__ :     Union[str, Any]	  =	num_channels
														lowerCamelCase__ :     str	  =	num_queries
														lowerCamelCase__ :     int	  =	max_position_embeddings
														lowerCamelCase__ :     str	  =	d_model
														lowerCamelCase__ :     Dict	  =	encoder_ffn_dim
														lowerCamelCase__ :     Union[str, Any]	  =	encoder_layers
														lowerCamelCase__ :     int	  =	encoder_attention_heads
														lowerCamelCase__ :     str	  =	decoder_ffn_dim
														lowerCamelCase__ :     Optional[Any]	  =	decoder_layers
														lowerCamelCase__ :     Optional[int]	  =	decoder_attention_heads
														lowerCamelCase__ :     int	  =	dropout
														lowerCamelCase__ :     Optional[Any]	  =	attention_dropout
														lowerCamelCase__ :     List[Any]	  =	activation_dropout
														lowerCamelCase__ :     List[Any]	  =	activation_function
														lowerCamelCase__ :     int	  =	init_std
														lowerCamelCase__ :     Dict	  =	init_xavier_std
														lowerCamelCase__ :     Tuple	  =	encoder_layerdrop
														lowerCamelCase__ :     str	  =	auxiliary_loss
														lowerCamelCase__ :     int	  =	position_embedding_type
														lowerCamelCase__ :     Tuple	  =	backbone
														lowerCamelCase__ :     Tuple	  =	use_pretrained_backbone
														lowerCamelCase__ :     Optional[int]	  =	dilation
														# deformable attributes
														lowerCamelCase__ :     Optional[int]	  =	num_feature_levels
														lowerCamelCase__ :     Tuple	  =	encoder_n_points
														lowerCamelCase__ :     Tuple	  =	decoder_n_points
														lowerCamelCase__ :     Dict	  =	two_stage
														lowerCamelCase__ :     int	  =	two_stage_num_proposals
														lowerCamelCase__ :     Any	  =	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__ :     Optional[int]	  =	class_cost
														lowerCamelCase__ :     List[str]	  =	bbox_cost
														lowerCamelCase__ :     Any	  =	giou_cost
														# Loss coefficients
														lowerCamelCase__ :     Union[str, Any]	  =	mask_loss_coefficient
														lowerCamelCase__ :     Tuple	  =	dice_loss_coefficient
														lowerCamelCase__ :     int	  =	bbox_loss_coefficient
														lowerCamelCase__ :     Optional[Any]	  =	giou_loss_coefficient
														lowerCamelCase__ :     Dict	  =	eos_coefficient
														lowerCamelCase__ :     Any	  =	focal_alpha
														lowerCamelCase__ :     Optional[Any]	  =	disable_custom_kernels
														super().__init__(is_encoder_decoder=UpperCamelCase__      ,							**UpperCamelCase__					)




							@property
							def 		lowerCamelCase_    (							self:    Dict					):
														return self.encoder_attention_heads




							@property
							def 		lowerCamelCase_    (							self:    Any					):
														return self.d_model




							def 		lowerCamelCase_    (							self:    List[str]					):
														lowerCamelCase__ :     Optional[int]	  =	copy.deepcopy(self.__dict__					)
														if self.backbone_config is not None:
																					lowerCamelCase__ :     int	  =	self.backbone_config.to_dict()
														lowerCamelCase__ :     int	  =	self.__class__.model_type
														return output

