


'''simple docstring'''




import os
import unicodedata
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 SPIECE_UNDERLINE, logging


_lowerCAmelCase  :		str		     =	logging.get_logger(__name__)

_lowerCAmelCase  :		str		     =	{'vocab_file': 'spiece.model'}

_lowerCAmelCase  :		int		     =	{
    'vocab_file': {
        'TsinghuaAI/CPM-Generate': 'https://huggingface.co/TsinghuaAI/CPM-Generate/resolve/main/spiece.model',
    }
}



class  lowerCAmelCase				(				a							):



	def __init__(					self ,					snake_case__ ,					snake_case__=False ,					snake_case__=True ,					snake_case__=False ,					snake_case__="<s>" ,					snake_case__="</s>" ,					snake_case__="<unk>" ,					snake_case__="<sep>" ,					snake_case__="<pad>" ,					snake_case__="<cls>" ,					snake_case__="<mask>" ,					snake_case__=["<eop>", "<eod>"] ,					snake_case__ = None ,					**snake_case__ ,					):
		lowerCAmelCase					:    int      							= AddedToken(snake_case__ ,					lstrip=snake_case__ ,					rstrip=snake_case__    ) if isinstance(snake_case__ ,					snake_case__    ) else mask_token

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

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

		lowerCAmelCase					:    List[str]      							= 3

		lowerCAmelCase					:    Tuple      							= do_lower_case
		lowerCAmelCase					:    Any      							= remove_space
		lowerCAmelCase					:    Tuple      							= keep_accents
		lowerCAmelCase					:    Dict      							= vocab_file

		lowerCAmelCase					:    Any      							= spm.SentencePieceProcessor(**self.sp_model_kwargs    )
		self.sp_model.Load(snake_case__    )

		try:
			import jieba
		except ModuleNotFoundError as error:
			raise error.__class__(
			    'You need to install jieba to use CpmTokenizer or CpmTokenizerFast. '
			    'See https://pypi.org/project/jieba/ for installation.'    )
		lowerCAmelCase					:    Optional[Any]      							= jieba
		lowerCAmelCase					:    List[str]      							= str.maketrans(' \n' ,					'\u2582\u2583'    )



	@property
	# Copied from transformers.models.xlnet.tokenization_xlnet.XLNetTokenizer.vocab_size
	def 				lowercase     (					self    ):
		return len(self.sp_model    )



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



	def __getstate__(					self    ):
		lowerCAmelCase					:    Tuple      							= self.__dict__.copy()
		lowerCAmelCase					:    int      							= None
		return state



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

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

		lowerCAmelCase					:    Optional[Any]      							= spm.SentencePieceProcessor(**self.sp_model_kwargs    )
		self.sp_model.Load(self.vocab_file    )



	def 				lowercase     (					self ,					snake_case__    ):
		if self.remove_space:
			lowerCAmelCase					:    Optional[Any]      							= ' '.join(inputs.strip().split()    )
		else:
			lowerCAmelCase					:    Any      							= inputs
		lowerCAmelCase					:    Optional[int]      							= outputs.replace('``' ,					'"'    ).replace('\'\'' ,					'"'    )

		if not self.keep_accents:
			lowerCAmelCase					:    List[Any]      							= unicodedata.normalize('NFKD' ,					snake_case__    )
			lowerCAmelCase					:    List[str]      							= ''.join([c for c in outputs if not unicodedata.combining(snake_case__    )]    )
		if self.do_lower_case:
			lowerCAmelCase					:    Any      							= outputs.lower()

		return outputs



	def 				lowercase     (					self ,					snake_case__    ):
		lowerCAmelCase					:    str      							= self.preprocess_text(snake_case__    )
		lowerCAmelCase					:    Optional[int]      							= self.sp_model.encode(snake_case__ ,					out_type=snake_case__    )
		lowerCAmelCase					:    Any      							= []
		for piece in pieces:
			if len(snake_case__    ) > 1 and piece[-1] == str(','    ) and piece[-2].isdigit():
				lowerCAmelCase					:    Optional[Any]      							= self.sp_model.EncodeAsPieces(piece[:-1].replace(snake_case__ ,					''    )    )
				if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
					if len(cur_pieces[0]    ) == 1:
						lowerCAmelCase					:    int      							= cur_pieces[1:]
					else:
						lowerCAmelCase					:    Optional[int]      							= cur_pieces[0][1:]
				cur_pieces.append(piece[-1]    )
				new_pieces.extend(snake_case__    )
			else:
				new_pieces.append(snake_case__    )

		return new_pieces



	def 				lowercase     (					self ,					snake_case__    ):
		return self.sp_model.PieceToId(snake_case__    )



	def 				lowercase     (					self ,					snake_case__    ):
		return self.sp_model.IdToPiece(snake_case__    )



	def 				lowercase     (					self ,					snake_case__    ):
		lowerCAmelCase					:    Union[str, Any]      							= ''.join(snake_case__    ).replace(snake_case__ ,					' '    ).strip()
		return out_string



	def 				lowercase     (					self ,					snake_case__ ,					snake_case__ = None    ):
		lowerCAmelCase					:    List[Any]      							= [self.sep_token_id]
		lowerCAmelCase					:    Dict      							= [self.cls_token_id]
		if token_ids_a is None:
			return token_ids_a + sep + cls
		return token_ids_a + sep + token_ids_a + sep + cls



	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 not None:
			return ([0] * len(snake_case__    )) + [1] + ([0] * len(snake_case__    )) + [1, 1]
		return ([0] * len(snake_case__    )) + [1, 1]



	def 				lowercase     (					self ,					snake_case__ ,					snake_case__ = None    ):
		lowerCAmelCase					:    str      							= [self.sep_token_id]
		lowerCAmelCase					:    int      							= [2]

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



	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					:    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,)



	def 				lowercase     (					self ,					*snake_case__ ,					**snake_case__    ):
		lowerCAmelCase					:    str      							= super()._decode(*snake_case__ ,					**snake_case__    )
		lowerCAmelCase					:    Optional[int]      							= text.replace(' ' ,					''    ).replace('\u2582' ,					' '    ).replace('\u2583' ,					'\n'    )
		return text




'''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
