






"""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_camembert import CamembertTokenizer
else:
			lowerCamelCase_					=      None


lowerCamelCase_					=      logging.get_logger(__name__)

lowerCamelCase_					=      {'''vocab_file''': '''sentencepiece.bpe.model''', '''tokenizer_file''': '''tokenizer.json'''}

lowerCamelCase_					=      {
    '''vocab_file''': {
        '''camembert-base''': '''https://huggingface.co/camembert-base/resolve/main/sentencepiece.bpe.model''',
    },
    '''tokenizer_file''': {
        '''camembert-base''': '''https://huggingface.co/camembert-base/resolve/main/tokenizer.json''',
    },
}

lowerCamelCase_					=      {
    '''camembert-base''': 512,
}

lowerCamelCase_					=      '''▁'''







class 			UpperCamelCase_       (__A     ):
	__magic_name__	      =				VOCAB_FILES_NAMES
	__magic_name__	      =				PRETRAINED_VOCAB_FILES_MAP
	__magic_name__	      =				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	__magic_name__	      =				['''input_ids''', '''attention_mask''']
	__magic_name__	      =				CamembertTokenizer





	def __init__(      self					: int	,	lowerCAmelCase_					: List[str]=None	,	lowerCAmelCase_					: int=None	,	lowerCAmelCase_					: Dict="<s>"	,	lowerCAmelCase_					: Tuple="</s>"	,	lowerCAmelCase_					: int="</s>"	,	lowerCAmelCase_					: Any="<s>"	,	lowerCAmelCase_					: Optional[int]="<unk>"	,	lowerCAmelCase_					: Optional[Any]="<pad>"	,	lowerCAmelCase_					: Optional[Any]="<mask>"	,	lowerCAmelCase_					: Optional[int]=["<s>NOTUSED", "</s>NOTUSED"]	,	**lowerCAmelCase_					: Optional[Any]	,	)						->			Any:
				# Mask token behave like a normal word, i.e. include the space before it
				UpperCAmelCase_     : List[Any]			     =					AddedToken(lowerCAmelCase_	,	lstrip=lowerCAmelCase_	,	rstrip=lowerCAmelCase_						) if isinstance(lowerCAmelCase_	,	lowerCAmelCase_						) else mask_token

				super().__init__(
				    lowerCAmelCase_	,	tokenizer_file=lowerCAmelCase_	,	bos_token=lowerCAmelCase_	,	eos_token=lowerCAmelCase_	,	sep_token=lowerCAmelCase_	,	cls_token=lowerCAmelCase_	,	unk_token=lowerCAmelCase_	,	pad_token=lowerCAmelCase_	,	mask_token=lowerCAmelCase_	,	additional_special_tokens=lowerCAmelCase_	,	**lowerCAmelCase_	,	)

				UpperCAmelCase_     : Dict			     =					vocab_file
				UpperCAmelCase_     : str			     =					False if not self.vocab_file else True





	def      _SCREAMING_SNAKE_CASE      (      self					: List[str]	,	lowerCAmelCase_					: List[int]	,	lowerCAmelCase_					: Optional[List[int]] = None						)						->			List[int]:

				if token_ids_a is None:
							return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
				UpperCAmelCase_     : Union[str, Any]			     =					[self.cls_token_id]
				UpperCAmelCase_     : List[str]			     =					[self.sep_token_id]
				return cls + token_ids_a + sep + sep + token_ids_a + sep





	def      _SCREAMING_SNAKE_CASE      (      self					: List[Any]	,	lowerCAmelCase_					: List[int]	,	lowerCAmelCase_					: Optional[List[int]] = None						)						->			List[int]:
				UpperCAmelCase_     : str			     =					[self.sep_token_id]
				UpperCAmelCase_     : Optional[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      _SCREAMING_SNAKE_CASE      (      self					: List[Any]	,	lowerCAmelCase_					: str	,	lowerCAmelCase_					: Optional[str] = None						)						->			Tuple[str]:
				if not self.can_save_slow_tokenizer:
							raise ValueError(
							    "Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
							    "tokenizer."						)

				if not os.path.isdir(lowerCAmelCase_						):
							logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""						)
							return
				UpperCAmelCase_     : str			     =					os.path.join(
				    lowerCAmelCase_	,	(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]						)

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

				return (out_vocab_file,)











"""simple docstring"""
import copy
from collections import OrderedDict
from typing import Dict, Mapping

from packaging import version

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


lowerCamelCase_					=      logging.get_logger(__name__)

lowerCamelCase_					=      {
    '''facebook/detr-resnet-50''': '''https://huggingface.co/facebook/detr-resnet-50/resolve/main/config.json''',
    # See all DETR models at https://huggingface.co/models?filter=detr
}







class 			UpperCamelCase_       (__A     ):
	__magic_name__	      =				'''detr'''
	__magic_name__	      =				['''past_key_values''']
	__magic_name__	      =				{
	    '''hidden_size''': '''d_model''',
	    '''num_attention_heads''': '''encoder_attention_heads''',
	}





	def __init__(      self					: Dict	,	lowerCAmelCase_					: str=True	,	lowerCAmelCase_					: Union[str, Any]=None	,	lowerCAmelCase_					: Dict=3	,	lowerCAmelCase_					: List[Any]=100	,	lowerCAmelCase_					: int=6	,	lowerCAmelCase_					: Union[str, Any]=2_048	,	lowerCAmelCase_					: int=8	,	lowerCAmelCase_					: int=6	,	lowerCAmelCase_					: Union[str, Any]=2_048	,	lowerCAmelCase_					: Any=8	,	lowerCAmelCase_					: Dict=0.0	,	lowerCAmelCase_					: Union[str, Any]=0.0	,	lowerCAmelCase_					: Dict=True	,	lowerCAmelCase_					: List[Any]="relu"	,	lowerCAmelCase_					: List[Any]=256	,	lowerCAmelCase_					: Tuple=0.1	,	lowerCAmelCase_					: Optional[Any]=0.0	,	lowerCAmelCase_					: List[Any]=0.0	,	lowerCAmelCase_					: List[Any]=0.0_2	,	lowerCAmelCase_					: Optional[int]=1.0	,	lowerCAmelCase_					: str=False	,	lowerCAmelCase_					: Tuple="sine"	,	lowerCAmelCase_					: str="resnet50"	,	lowerCAmelCase_					: List[Any]=True	,	lowerCAmelCase_					: int=False	,	lowerCAmelCase_					: Optional[Any]=1	,	lowerCAmelCase_					: int=5	,	lowerCAmelCase_					: Optional[Any]=2	,	lowerCAmelCase_					: List[Any]=1	,	lowerCAmelCase_					: List[Any]=1	,	lowerCAmelCase_					: Dict=5	,	lowerCAmelCase_					: Optional[Any]=2	,	lowerCAmelCase_					: Union[str, Any]=0.1	,	**lowerCAmelCase_					: Dict	,	)						->			int:
				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."						)
										UpperCAmelCase_     : Tuple			     =					CONFIG_MAPPING["resnet"](out_features=["stage4"]						)
							elif isinstance(lowerCAmelCase_	,	lowerCAmelCase_						):
										UpperCAmelCase_     : Dict			     =					backbone_config.get("model_type"						)
										UpperCAmelCase_     : Dict			     =					CONFIG_MAPPING[backbone_model_type]
										UpperCAmelCase_     : Tuple			     =					config_class.from_dict(lowerCAmelCase_						)
							# set timm attributes to None
							UpperCAmelCase_     ,	UpperCAmelCase_     ,	UpperCAmelCase_     : Optional[Any]			     =					None, None, None

				UpperCAmelCase_     : str			     =					use_timm_backbone
				UpperCAmelCase_     : Optional[Any]			     =					backbone_config
				UpperCAmelCase_     : Tuple			     =					num_channels
				UpperCAmelCase_     : Dict			     =					num_queries
				UpperCAmelCase_     : str			     =					d_model
				UpperCAmelCase_     : Any			     =					encoder_ffn_dim
				UpperCAmelCase_     : Union[str, Any]			     =					encoder_layers
				UpperCAmelCase_     : Optional[int]			     =					encoder_attention_heads
				UpperCAmelCase_     : List[str]			     =					decoder_ffn_dim
				UpperCAmelCase_     : Tuple			     =					decoder_layers
				UpperCAmelCase_     : Optional[int]			     =					decoder_attention_heads
				UpperCAmelCase_     : List[Any]			     =					dropout
				UpperCAmelCase_     : Union[str, Any]			     =					attention_dropout
				UpperCAmelCase_     : int			     =					activation_dropout
				UpperCAmelCase_     : List[str]			     =					activation_function
				UpperCAmelCase_     : Optional[int]			     =					init_std
				UpperCAmelCase_     : Union[str, Any]			     =					init_xavier_std
				UpperCAmelCase_     : List[str]			     =					encoder_layerdrop
				UpperCAmelCase_     : Tuple			     =					decoder_layerdrop
				UpperCAmelCase_     : str			     =					encoder_layers
				UpperCAmelCase_     : Any			     =					auxiliary_loss
				UpperCAmelCase_     : Optional[int]			     =					position_embedding_type
				UpperCAmelCase_     : List[str]			     =					backbone
				UpperCAmelCase_     : int			     =					use_pretrained_backbone
				UpperCAmelCase_     : Any			     =					dilation
				# Hungarian matcher
				UpperCAmelCase_     : str			     =					class_cost
				UpperCAmelCase_     : Any			     =					bbox_cost
				UpperCAmelCase_     : int			     =					giou_cost
				# Loss coefficients
				UpperCAmelCase_     : List[str]			     =					mask_loss_coefficient
				UpperCAmelCase_     : Dict			     =					dice_loss_coefficient
				UpperCAmelCase_     : Any			     =					bbox_loss_coefficient
				UpperCAmelCase_     : Union[str, Any]			     =					giou_loss_coefficient
				UpperCAmelCase_     : int			     =					eos_coefficient
				super().__init__(is_encoder_decoder=lowerCAmelCase_	,	**lowerCAmelCase_						)





	@property
	def      _SCREAMING_SNAKE_CASE      (      self					: List[Any]						)						->			int:
				return self.encoder_attention_heads





	@property
	def      _SCREAMING_SNAKE_CASE      (      self					: int						)						->			int:
				return self.d_model





	@classmethod
	def      _SCREAMING_SNAKE_CASE      (      cls					: Union[str, Any]	,	lowerCAmelCase_					: PretrainedConfig	,	**lowerCAmelCase_					: Tuple						)						->			List[Any]:
				return cls(backbone_config=lowerCAmelCase_	,	**lowerCAmelCase_						)





	def      _SCREAMING_SNAKE_CASE      (      self					: Dict						)						->			Dict[str, any]:
				UpperCAmelCase_     : Tuple			     =					copy.deepcopy(self.__dict__						)
				if output["backbone_config"] is not None:
							UpperCAmelCase_     : Union[str, Any]			     =					self.backbone_config.to_dict()
				UpperCAmelCase_     : Any			     =					self.__class__.model_type
				return output







class 			UpperCamelCase_       (__A     ):
	__magic_name__	      =				version.parse('''1.11'''     )





	@property
	def      _SCREAMING_SNAKE_CASE      (      self					: Dict						)						->			Mapping[str, Mapping[int, str]]:
				return OrderedDict(
				    [
				        ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
				        ("pixel_mask", {0: "batch"}),
				    ]						)





	@property
	def      _SCREAMING_SNAKE_CASE      (      self					: Union[str, Any]						)						->			float:
				return 1e-5





	@property
	def      _SCREAMING_SNAKE_CASE      (      self					: Union[str, Any]						)						->			int:
				return 12



