
"""simple docstring"""




import copy
from typing import Dict, Optional

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


snake_case    =		logging.get_logger(__name__)

snake_case    =		{
    'shi-labs/oneformer_ade20k_swin_tiny': (
        'https://huggingface.co/shi-labs/oneformer_ade20k_swin_tiny/blob/main/config.json'
    ),
    # See all OneFormer models at https://huggingface.co/models?filter=oneformer
}




class      UpperCamelCase	(       __magic_name__      ):

				"""simple docstring"""



				UpperCAmelCase_				:      Any						  =				"oneformer"
				UpperCAmelCase_				:      int						  =				{"hidden_size": "hidden_dim"}


				def __init__(				self		,	lowercase__ = None		,	lowercase__ = 255		,	lowercase__ = 150		,	lowercase__ = 0.1		,	lowercase__ = 2.0		,	lowercase__ = 5.0		,	lowercase__ = 5.0		,	lowercase__ = 0.5		,	lowercase__ = 0.07		,	lowercase__ = 12544		,	lowercase__ = 3.0		,	lowercase__ = 0.75		,	lowercase__ = 0.02		,	lowercase__ = 1.0		,	lowercase__ = 1E-05		,	lowercase__ = False		,	lowercase__ = True		,	lowercase__ = True		,	lowercase__ = [4, 8, 16, 32]		,	lowercase__ = 77		,	lowercase__ = 256		,	lowercase__ = 77		,	lowercase__ = 6		,	lowercase__ = 49408		,	lowercase__ = 2		,	lowercase__ = 16		,	lowercase__ = 256		,	lowercase__ = 256		,	lowercase__ = 256		,	lowercase__ = 1024		,	lowercase__ = "GN"		,	lowercase__ = 6		,	lowercase__ = 10		,	lowercase__ = True		,	lowercase__ = 8		,	lowercase__ = 0.1		,	lowercase__ = 2048		,	lowercase__ = False		,	lowercase__ = False		,	lowercase__ = 2		,	lowercase__ = 4		,	**lowercase__		,	)    ->    List[str]:
									"""simple docstring"""







									if backbone_config is None:
														logger.info('`backbone_config` is unset. Initializing the config with the default `Swin` backbone.'			)
														SCREAMING_SNAKE_CASE				=	CONFIG_MAPPING['swin'](
														    image_size=224		,	in_channels=3		,	patch_size=4		,	embed_dim=96		,	depths=[2, 2, 6, 2]		,	num_heads=[3, 6, 12, 24]		,	window_size=7		,	drop_path_rate=0.3		,	use_absolute_embeddings=lowercase__		,	out_features=['stage1', 'stage2', 'stage3', 'stage4']		,	)
									elif isinstance(lowercase__		,	lowercase__			):
														SCREAMING_SNAKE_CASE				=	backbone_config.get('model_type'			)
														SCREAMING_SNAKE_CASE				=	CONFIG_MAPPING[backbone_model_type]
														SCREAMING_SNAKE_CASE				=	config_class.from_dict(lowercase__			)

									SCREAMING_SNAKE_CASE				=	backbone_config

									SCREAMING_SNAKE_CASE				=	ignore_value
									SCREAMING_SNAKE_CASE				=	num_queries
									SCREAMING_SNAKE_CASE				=	no_object_weight
									SCREAMING_SNAKE_CASE				=	class_weight
									SCREAMING_SNAKE_CASE				=	mask_weight
									SCREAMING_SNAKE_CASE				=	dice_weight
									SCREAMING_SNAKE_CASE				=	contrastive_weight
									SCREAMING_SNAKE_CASE				=	contrastive_temperature
									SCREAMING_SNAKE_CASE				=	train_num_points
									SCREAMING_SNAKE_CASE				=	oversample_ratio
									SCREAMING_SNAKE_CASE				=	importance_sample_ratio
									SCREAMING_SNAKE_CASE				=	init_std
									SCREAMING_SNAKE_CASE				=	init_xavier_std
									SCREAMING_SNAKE_CASE				=	layer_norm_eps
									SCREAMING_SNAKE_CASE				=	is_training
									SCREAMING_SNAKE_CASE				=	use_auxiliary_loss
									SCREAMING_SNAKE_CASE				=	output_auxiliary_logits
									SCREAMING_SNAKE_CASE				=	strides
									SCREAMING_SNAKE_CASE				=	task_seq_len
									SCREAMING_SNAKE_CASE				=	text_encoder_width
									SCREAMING_SNAKE_CASE				=	text_encoder_context_length
									SCREAMING_SNAKE_CASE				=	text_encoder_num_layers
									SCREAMING_SNAKE_CASE				=	text_encoder_vocab_size
									SCREAMING_SNAKE_CASE				=	text_encoder_proj_layers
									SCREAMING_SNAKE_CASE				=	text_encoder_n_ctx
									SCREAMING_SNAKE_CASE				=	conv_dim
									SCREAMING_SNAKE_CASE				=	mask_dim
									SCREAMING_SNAKE_CASE				=	hidden_dim
									SCREAMING_SNAKE_CASE				=	encoder_feedforward_dim
									SCREAMING_SNAKE_CASE				=	norm
									SCREAMING_SNAKE_CASE				=	encoder_layers
									SCREAMING_SNAKE_CASE				=	decoder_layers
									SCREAMING_SNAKE_CASE				=	use_task_norm
									SCREAMING_SNAKE_CASE				=	num_attention_heads
									SCREAMING_SNAKE_CASE				=	dropout
									SCREAMING_SNAKE_CASE				=	dim_feedforward
									SCREAMING_SNAKE_CASE				=	pre_norm
									SCREAMING_SNAKE_CASE				=	enforce_input_proj
									SCREAMING_SNAKE_CASE				=	query_dec_layers
									SCREAMING_SNAKE_CASE				=	common_stride
									SCREAMING_SNAKE_CASE				=	decoder_layers

									super().__init__(**lowercase__			)




				def 							A					(				self			)    ->    Dict[str, any]:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE				=	copy.deepcopy(self.__dict__			)
									SCREAMING_SNAKE_CASE				=	self.backbone_config.to_dict()
									SCREAMING_SNAKE_CASE				=	self.__class__.model_type
									return output


"""simple docstring"""




import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils_fast import AddedToken, PreTrainedTokenizerFast
from ...utils import logging


snake_case    =		logging.get_logger(__name__)

snake_case    =		{'vocab_file': 'spiece.model', 'tokenizer_file': 'tokenizer.json'}

snake_case    =		{
    'vocab_file': {
        'TsinghuaAI/CPM-Generate': 'https://huggingface.co/TsinghuaAI/CPM-Generate/resolve/main/spiece.model',
    },
    'tokenizer_file': {
        'TsinghuaAI/CPM-Generate': 'https://huggingface.co/TsinghuaAI/CPM-Generate/resolve/main/tokenizer.json',
    },
}




class      UpperCamelCase	(       __magic_name__      ):

				"""simple docstring"""



				def __init__(				self		,	lowercase__=None		,	lowercase__=None		,	lowercase__=False		,	lowercase__=True		,	lowercase__=False		,	lowercase__="<s>"		,	lowercase__="</s>"		,	lowercase__="<unk>"		,	lowercase__="<sep>"		,	lowercase__="<pad>"		,	lowercase__="<cls>"		,	lowercase__="<mask>"		,	lowercase__=["<eop>", "<eod>"]		,	**lowercase__		,	)    ->    int:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE				=	AddedToken(lowercase__		,	lstrip=lowercase__		,	rstrip=lowercase__			) if isinstance(lowercase__		,	lowercase__			) else mask_token

									super().__init__(
									    vocab_file=lowercase__		,	tokenizer_file=lowercase__		,	do_lower_case=lowercase__		,	remove_space=lowercase__		,	keep_accents=lowercase__		,	bos_token=lowercase__		,	eos_token=lowercase__		,	unk_token=lowercase__		,	sep_token=lowercase__		,	pad_token=lowercase__		,	cls_token=lowercase__		,	mask_token=lowercase__		,	additional_special_tokens=lowercase__		,	**lowercase__		,	)

									SCREAMING_SNAKE_CASE				=	3
									SCREAMING_SNAKE_CASE				=	do_lower_case
									SCREAMING_SNAKE_CASE				=	remove_space
									SCREAMING_SNAKE_CASE				=	keep_accents
									SCREAMING_SNAKE_CASE				=	vocab_file
									SCREAMING_SNAKE_CASE				=	False if not self.vocab_file else True

									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.'			)
									SCREAMING_SNAKE_CASE				=	jieba
									SCREAMING_SNAKE_CASE				=	str.maketrans(' \n'		,	'\u2582\u2583'			)


				def 							A					(				self		,	lowercase__		,	lowercase__ = None			)    ->    List[int]:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE				=	[self.sep_token_id]
									SCREAMING_SNAKE_CASE				=	[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 							A					(				self		,	lowercase__		,	lowercase__ = None			)    ->    List[int]:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE				=	[self.sep_token_id]
									SCREAMING_SNAKE_CASE				=	[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 							A					(				self		,	lowercase__		,	lowercase__ = None			)    ->    Tuple[str]:
									"""simple docstring"""







									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(lowercase__			):
														logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''			)
														return
									SCREAMING_SNAKE_CASE				=	os.path.join(
									    lowercase__		,	(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']			)

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

									return (out_vocab_file,)


				def 							A					(				self		,	lowercase__		,	*lowercase__		,	**lowercase__			)    ->    Any:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE				=	[
									    ' '.join([x.translate(self.translator			) for x in self.jieba.cut(lowercase__		,	cut_all=lowercase__			)]			)
									    for text in batch_text_or_text_pairs
									]
									return super()._batch_encode_plus(lowercase__		,	*lowercase__		,	**lowercase__			)




				def 							A					(				self		,	*lowercase__		,	**lowercase__			)    ->    Optional[int]:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE				=	super()._decode(*lowercase__		,	**lowercase__			)
									SCREAMING_SNAKE_CASE				=	text.replace(' '		,	''			).replace('\u2582'		,	' '			).replace('\u2583'		,	'\n'			)
									return text
