




"""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__           =	logging.get_logger(__name__)

lowerCamelCase__           =	{"""vocab_file""": """spiece.model"""}

lowerCamelCase__           =	{
    """vocab_file""": {
        """TsinghuaAI/CPM-Generate""": """https://huggingface.co/TsinghuaAI/CPM-Generate/resolve/main/spiece.model""",
    }
}




class 				A__ (			_lowerCamelCase):

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

			__lowerCAmelCase    :		Union[str, Any]		    =   {} if sp_model_kwargs is None else sp_model_kwargs

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

			__lowerCAmelCase    :		Optional[Any]		    =   3

			__lowerCAmelCase    :		Dict		    =   do_lower_case
			__lowerCAmelCase    :		Union[str, Any]		    =   remove_space
			__lowerCAmelCase    :		str		    =   keep_accents
			__lowerCAmelCase    :		Optional[Any]		    =   vocab_file

			__lowerCAmelCase    :		List[Any]		    =   spm.SentencePieceProcessor(**self.sp_model_kwargs				)
			self.sp_model.Load(_UpperCAmelCase				)

			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    :		str		    =   jieba
			__lowerCAmelCase    :		Union[str, Any]		    =   str.maketrans(' \n' ,	'\u2582\u2583'				)

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

		def 							__lowerCamelCase							(   self				):
			__lowerCAmelCase    :		List[Any]		    =   {self.convert_ids_to_tokens(_UpperCAmelCase				): i for i in range(self.vocab_size				)}
			vocab.update(self.added_tokens_encoder				)
			return vocab

		def __getstate__(   self				):
			__lowerCAmelCase    :		Dict		    =   self.__dict__.copy()
			__lowerCAmelCase    :		Optional[int]		    =   None
			return state

		def __setstate__(   self ,	_SCREAMING_SNAKE_CASE				):
			__lowerCAmelCase    :		str		    =   d

			# for backward compatibility
			if not hasattr(self ,	'sp_model_kwargs'				):
				__lowerCAmelCase    :		List[str]		    =   {}

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

		def 							__lowerCamelCase							(   self ,	_SCREAMING_SNAKE_CASE				):




			if self.remove_space:
				__lowerCAmelCase    :		Optional[Any]		    =   ' '.join(inputs.strip().split()				)
			else:
				__lowerCAmelCase    :		Dict		    =   inputs
			__lowerCAmelCase    :		Optional[Any]		    =   outputs.replace('``' ,	'\"'				).replace('\'\'' ,	'\"'				)

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

			return outputs

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

			return new_pieces

		def 							__lowerCamelCase							(   self ,	_SCREAMING_SNAKE_CASE				):
			return self.sp_model.PieceToId(_UpperCAmelCase				)

		def 							__lowerCamelCase							(   self ,	_SCREAMING_SNAKE_CASE				):
			return self.sp_model.IdToPiece(_UpperCAmelCase				)

		def 							__lowerCamelCase							(   self ,	_SCREAMING_SNAKE_CASE				):
			__lowerCAmelCase    :		str		    =   ''.join(_UpperCAmelCase				).replace(_UpperCAmelCase ,	' '				).strip()
			return out_string

		def 							__lowerCamelCase							(   self ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE = None				):
			__lowerCAmelCase    :		str		    =   [self.sep_token_id]
			__lowerCAmelCase    :		List[str]		    =   [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 							__lowerCamelCase							(   self ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE = None ,	_SCREAMING_SNAKE_CASE = False				):




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

		def 							__lowerCamelCase							(   self ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE = None				):
			__lowerCAmelCase    :		int		    =   [self.sep_token_id]
			__lowerCAmelCase    :		str		    =   [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 							__lowerCamelCase							(   self ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE = None				):




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

			if os.path.abspath(self.vocab_file				) != os.path.abspath(_UpperCAmelCase				) and os.path.isfile(self.vocab_file				):
				copyfile(self.vocab_file ,	_UpperCAmelCase				)
			elif not os.path.isfile(self.vocab_file				):
				with open(_UpperCAmelCase ,	'wb'				) as fi:
					__lowerCAmelCase    :		Dict		    =   self.sp_model.serialized_model_proto()
					fi.write(_UpperCAmelCase				)

			return (out_vocab_file,)







		def 							__lowerCamelCase							(   self ,	*_SCREAMING_SNAKE_CASE ,	**_SCREAMING_SNAKE_CASE				):
			__lowerCAmelCase    :		Optional[int]		    =   super()._decode(*_UpperCAmelCase ,	**_UpperCAmelCase				)
			__lowerCAmelCase    :		str		    =   text.replace(' ' ,	''				).replace('\u2582' ,	' '				).replace('\u2583' ,	'\n'				)
			return text

"""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 				A__ (			_lowerCamelCase):
		A_		: List[str]            =			'detr'
		A_		: Union[str, Any]            =			['past_key_values']
		A_		: Any            =			{
		    'hidden_size': 'd_model',
		    'num_attention_heads': 'encoder_attention_heads',
		}

		def __init__(   self ,	_SCREAMING_SNAKE_CASE=True ,	_SCREAMING_SNAKE_CASE=None ,	_SCREAMING_SNAKE_CASE=3 ,	_SCREAMING_SNAKE_CASE=1_00 ,	_SCREAMING_SNAKE_CASE=6 ,	_SCREAMING_SNAKE_CASE=20_48 ,	_SCREAMING_SNAKE_CASE=8 ,	_SCREAMING_SNAKE_CASE=6 ,	_SCREAMING_SNAKE_CASE=20_48 ,	_SCREAMING_SNAKE_CASE=8 ,	_SCREAMING_SNAKE_CASE=0.0 ,	_SCREAMING_SNAKE_CASE=0.0 ,	_SCREAMING_SNAKE_CASE=True ,	_SCREAMING_SNAKE_CASE="relu" ,	_SCREAMING_SNAKE_CASE=2_56 ,	_SCREAMING_SNAKE_CASE=0.1 ,	_SCREAMING_SNAKE_CASE=0.0 ,	_SCREAMING_SNAKE_CASE=0.0 ,	_SCREAMING_SNAKE_CASE=0.02 ,	_SCREAMING_SNAKE_CASE=1.0 ,	_SCREAMING_SNAKE_CASE=False ,	_SCREAMING_SNAKE_CASE="sine" ,	_SCREAMING_SNAKE_CASE="resnet50" ,	_SCREAMING_SNAKE_CASE=True ,	_SCREAMING_SNAKE_CASE=False ,	_SCREAMING_SNAKE_CASE=1 ,	_SCREAMING_SNAKE_CASE=5 ,	_SCREAMING_SNAKE_CASE=2 ,	_SCREAMING_SNAKE_CASE=1 ,	_SCREAMING_SNAKE_CASE=1 ,	_SCREAMING_SNAKE_CASE=5 ,	_SCREAMING_SNAKE_CASE=2 ,	_SCREAMING_SNAKE_CASE=0.1 ,	**_SCREAMING_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    :		str		    =   CONFIG_MAPPING['resnet'](out_features=['stage4']				)
				elif isinstance(_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				):
					__lowerCAmelCase    :		str		    =   backbone_config.get('model_type'				)
					__lowerCAmelCase    :		str		    =   CONFIG_MAPPING[backbone_model_type]
					__lowerCAmelCase    :		str		    =   config_class.from_dict(_SCREAMING_SNAKE_CASE				)
				# set timm attributes to None
				__lowerCAmelCase    ,     __lowerCAmelCase    ,     __lowerCAmelCase    :		Union[str, Any]		    =   None, None, None

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

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

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

		@classmethod
		def 							__lowerCamelCase							(   cls ,	_SCREAMING_SNAKE_CASE ,	**_SCREAMING_SNAKE_CASE				):
			return cls(backbone_config=_SCREAMING_SNAKE_CASE ,	**_SCREAMING_SNAKE_CASE				)







		def 							__lowerCamelCase							(   self				):
			__lowerCAmelCase    :		List[str]		    =   copy.deepcopy(self.__dict__				)
			if output["backbone_config"] is not None:
				__lowerCAmelCase    :		Dict		    =   self.backbone_config.to_dict()
			__lowerCAmelCase    :		List[str]		    =   self.__class__.model_type
			return output




class 				A__ (			_lowerCamelCase):
		A_		: List[str]            =			version.parse('1.11')

		@property
		def 							__lowerCamelCase							(   self				):
			return OrderedDict(
			    [
			        ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
			        ('pixel_mask', {0: 'batch'}),
			    ]				)

		@property
		def 							__lowerCamelCase							(   self				):
			return 1E-5







		@property
		def 							__lowerCamelCase							(   self				):
			return 12