


'''simple docstring'''




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


_lowerCAmelCase  :		Optional[Any]		     =	logging.get_logger(__name__)

_lowerCAmelCase  :		Any		     =	{'vocab_file': 'sentencepiece.bpe.model'}

_lowerCAmelCase  :		List[str]		     =	{
    'vocab_file': {
        'moussaKam/mbarthez': 'https://huggingface.co/moussaKam/mbarthez/resolve/main/sentencepiece.bpe.model',
        'moussaKam/barthez': 'https://huggingface.co/moussaKam/barthez/resolve/main/sentencepiece.bpe.model',
        'moussaKam/barthez-orangesum-title': (
            'https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/sentencepiece.bpe.model'
        ),
    },
}

_lowerCAmelCase  :		Optional[int]		     =	{
    'moussaKam/mbarthez': 1024,
    'moussaKam/barthez': 1024,
    'moussaKam/barthez-orangesum-title': 1024,
}

_lowerCAmelCase  :		List[Any]		     =	'▁'



class  lowerCAmelCase				(				a							):
	_lowerCamelCase   :					List[Any]			      = VOCAB_FILES_NAMES
	_lowerCamelCase   :					str			      = PRETRAINED_VOCAB_FILES_MAP
	_lowerCamelCase   :					Dict			      = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	_lowerCamelCase   :					Union[str, Any]			      = ["""input_ids""", """attention_mask"""]



	def __init__(					self ,					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__ ,					):
		# Mask token behave like a normal word, i.e. include the space before it
		lowerCAmelCase					:    Optional[Any]      							= AddedToken(snake_case__ ,					lstrip=snake_case__ ,					rstrip=snake_case__    ) if isinstance(snake_case__ ,					snake_case__    ) else mask_token

		lowerCAmelCase					:    Tuple      							= {} if sp_model_kwargs is None else sp_model_kwargs

		super().__init__(
		    bos_token=snake_case__ ,					eos_token=snake_case__ ,					unk_token=snake_case__ ,					sep_token=snake_case__ ,					cls_token=snake_case__ ,					pad_token=snake_case__ ,					mask_token=snake_case__ ,					sp_model_kwargs=self.sp_model_kwargs ,					**snake_case__ ,					)

		lowerCAmelCase					:    Dict      							= vocab_file
		lowerCAmelCase					:    Tuple      							= spm.SentencePieceProcessor(**self.sp_model_kwargs    )
		self.sp_model.Load(str(snake_case__    )    )

		lowerCAmelCase					:    Optional[Any]      							= {'<s>': 0, '<pad>': 1, '</s>': 2, '<unk>': 3}

		lowerCAmelCase					:    List[str]      							= len(self.sp_model    ) - 1
		lowerCAmelCase					:    Tuple      							= {v: k for k, v in self.fairseq_tokens_to_ids.items()}



	def 				lowercase     (					self ,					snake_case__ ,					snake_case__ = None    ):

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



	def 				lowercase     (					self ,					snake_case__ ,					snake_case__ = None ,					snake_case__ = False    ):
		if already_has_special_tokens:
			return super().get_special_tokens_mask(
			    token_ids_a=snake_case__ ,					token_ids_a=snake_case__ ,					already_has_special_tokens=snake_case__    )

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



	def 				lowercase     (					self ,					snake_case__ ,					snake_case__ = None    ):
		lowerCAmelCase					:    Optional[int]      							= [self.sep_token_id]
		lowerCAmelCase					:    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 				lowercase     (					self    ):
		return len(self.sp_model    )



	def 				lowercase     (					self    ):
		lowerCAmelCase					:    int      							= {self.convert_ids_to_tokens(snake_case__    ): i for i in range(self.vocab_size    )}
		vocab.update(self.added_tokens_encoder    )
		return vocab



	def 				lowercase     (					self ,					snake_case__    ):
		return self.sp_model.encode(snake_case__ ,					out_type=snake_case__    )



	def 				lowercase     (					self ,					snake_case__    ):
		if token in self.fairseq_tokens_to_ids:
			return self.fairseq_tokens_to_ids[token]
		lowerCAmelCase					:    Dict      							= self.sp_model.PieceToId(snake_case__    )

		return spm_id if spm_id else self.unk_token_id



	def 				lowercase     (					self ,					snake_case__    ):
		if index in self.fairseq_ids_to_tokens:
			return self.fairseq_ids_to_tokens[index]
		return self.sp_model.IdToPiece(snake_case__    )



	def 				lowercase     (					self ,					snake_case__    ):
		lowerCAmelCase					:    Optional[Any]      							= []
		lowerCAmelCase					:    Optional[int]      							= ''
		lowerCAmelCase					:    str      							= False
		for token in tokens:
			# make sure that special tokens are not decoded using sentencepiece model
			if token in self.all_special_tokens:
				if not prev_is_special:
					out_string += " "
				out_string += self.sp_model.decode(snake_case__    ) + token
				lowerCAmelCase					:    Optional[Any]      							= True
				lowerCAmelCase					:    List[Any]      							= []
			else:
				current_sub_tokens.append(snake_case__    )
				lowerCAmelCase					:    str      							= False
		out_string += self.sp_model.decode(snake_case__    )
		return out_string.strip()



	def __getstate__(					self    ):
		lowerCAmelCase					:    Union[str, Any]      							= self.__dict__.copy()
		lowerCAmelCase					:    Union[str, Any]      							= None
		return state



	def __setstate__(					self ,					snake_case__    ):
		lowerCAmelCase					:    Any      							= d

		# for backward compatibility
		if not hasattr(self ,					'sp_model_kwargs'    ):
			lowerCAmelCase					:    Tuple      							= {}

		lowerCAmelCase					:    Tuple      							= spm.SentencePieceProcessor(**self.sp_model_kwargs    )
		self.sp_model.Load(self.vocab_file    )



	def 				lowercase     (					self ,					snake_case__ ,					snake_case__ = None    ):
		if not os.path.isdir(snake_case__    ):
			logger.error(f"Vocabulary path ({save_directory}) should be a directory"    )
			return
		lowerCAmelCase					:    List[str]      							= os.path.join(
		    snake_case__ ,					(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']    )

		if os.path.abspath(self.vocab_file    ) != os.path.abspath(snake_case__    ) and os.path.isfile(self.vocab_file    ):
			copyfile(self.vocab_file ,					snake_case__    )
		elif not os.path.isfile(self.vocab_file    ):
			with open(snake_case__ ,					'wb'    ) as fi:
				lowerCAmelCase					:    Union[str, Any]      							= self.sp_model.serialized_model_proto()
				fi.write(snake_case__    )

		return (out_vocab_file,)




'''simple docstring'''




import copy

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


_lowerCAmelCase  :		Optional[Any]		     =	logging.get_logger(__name__)

_lowerCAmelCase  :		Dict		     =	{
    '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  lowerCAmelCase				(				a							):
	_lowerCamelCase   :					Any			      = """deformable_detr"""
	_lowerCamelCase   :					List[str]			      = {
	    """hidden_size""": """d_model""",
	    """num_attention_heads""": """encoder_attention_heads""",
	}



	def __init__(					self ,					snake_case__=True ,					snake_case__=None ,					snake_case__=3 ,					snake_case__=300 ,					snake_case__=1024 ,					snake_case__=6 ,					snake_case__=1024 ,					snake_case__=8 ,					snake_case__=6 ,					snake_case__=1024 ,					snake_case__=8 ,					snake_case__=0.0 ,					snake_case__=True ,					snake_case__="relu" ,					snake_case__=256 ,					snake_case__=0.1 ,					snake_case__=0.0 ,					snake_case__=0.0 ,					snake_case__=0.0_2 ,					snake_case__=1.0 ,					snake_case__=True ,					snake_case__=False ,					snake_case__="sine" ,					snake_case__="resnet50" ,					snake_case__=True ,					snake_case__=False ,					snake_case__=4 ,					snake_case__=4 ,					snake_case__=4 ,					snake_case__=False ,					snake_case__=300 ,					snake_case__=False ,					snake_case__=1 ,					snake_case__=5 ,					snake_case__=2 ,					snake_case__=1 ,					snake_case__=1 ,					snake_case__=5 ,					snake_case__=2 ,					snake_case__=0.1 ,					snake_case__=0.2_5 ,					snake_case__=False ,					**snake_case__ ,					):
		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					:    Optional[int]      							= CONFIG_MAPPING['resnet'](out_features=['stage4']    )
			elif isinstance(snake_case__ ,					snake_case__    ):
				lowerCAmelCase					:    List[str]      							= backbone_config.get('model_type'    )
				lowerCAmelCase					:    str      							= CONFIG_MAPPING[backbone_model_type]
				lowerCAmelCase					:    Optional[Any]      							= config_class.from_dict(snake_case__    )
		lowerCAmelCase					:    Union[str, Any]      							= use_timm_backbone
		lowerCAmelCase					:    List[Any]      							= backbone_config
		lowerCAmelCase					:    Any      							= num_channels
		lowerCAmelCase					:    Tuple      							= num_queries
		lowerCAmelCase					:    Dict      							= max_position_embeddings
		lowerCAmelCase					:    int      							= d_model
		lowerCAmelCase					:    List[str]      							= encoder_ffn_dim
		lowerCAmelCase					:    List[str]      							= encoder_layers
		lowerCAmelCase					:    int      							= encoder_attention_heads
		lowerCAmelCase					:    str      							= decoder_ffn_dim
		lowerCAmelCase					:    str      							= decoder_layers
		lowerCAmelCase					:    Dict      							= decoder_attention_heads
		lowerCAmelCase					:    str      							= dropout
		lowerCAmelCase					:    List[str]      							= attention_dropout
		lowerCAmelCase					:    Union[str, Any]      							= activation_dropout
		lowerCAmelCase					:    str      							= activation_function
		lowerCAmelCase					:    Any      							= init_std
		lowerCAmelCase					:    Any      							= init_xavier_std
		lowerCAmelCase					:    Dict      							= encoder_layerdrop
		lowerCAmelCase					:    int      							= auxiliary_loss
		lowerCAmelCase					:    Optional[Any]      							= position_embedding_type
		lowerCAmelCase					:    List[str]      							= backbone
		lowerCAmelCase					:    int      							= use_pretrained_backbone
		lowerCAmelCase					:    int      							= dilation
		# deformable attributes
		lowerCAmelCase					:    List[str]      							= num_feature_levels
		lowerCAmelCase					:    List[str]      							= encoder_n_points
		lowerCAmelCase					:    Union[str, Any]      							= decoder_n_points
		lowerCAmelCase					:    Tuple      							= two_stage
		lowerCAmelCase					:    Dict      							= two_stage_num_proposals
		lowerCAmelCase					:    Union[str, 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					:    Union[str, Any]      							= class_cost
		lowerCAmelCase					:    Dict      							= bbox_cost
		lowerCAmelCase					:    List[Any]      							= giou_cost
		# Loss coefficients
		lowerCAmelCase					:    Dict      							= mask_loss_coefficient
		lowerCAmelCase					:    Any      							= dice_loss_coefficient
		lowerCAmelCase					:    str      							= bbox_loss_coefficient
		lowerCAmelCase					:    Tuple      							= giou_loss_coefficient
		lowerCAmelCase					:    List[str]      							= eos_coefficient
		lowerCAmelCase					:    Any      							= focal_alpha
		lowerCAmelCase					:    Dict      							= disable_custom_kernels
		super().__init__(is_encoder_decoder=snake_case__ ,					**snake_case__    )



	@property
	def 				lowercase     (					self    ):
		return self.encoder_attention_heads



	@property
	def 				lowercase     (					self    ):
		return self.d_model



	def 				lowercase     (					self    ):
		lowerCAmelCase					:    Union[str, Any]      							= copy.deepcopy(self.__dict__    )
		if self.backbone_config is not None:
			lowerCAmelCase					:    List[Any]      							= self.backbone_config.to_dict()
		lowerCAmelCase					:    str      							= self.__class__.model_type
		return output
