



'''simple docstring'''

import copy

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


__lowerCAmelCase									=   logging.get_logger(__name__)

class 	__magic_name__						(    _UpperCamelCase   ):
							lowerCAmelCase       :    str   				=					'upernet'







							def __init__(							self					:       List[Any]	,_UpperCAmelCase					:       str=None	,_UpperCAmelCase					:       List[Any]=512	,_UpperCAmelCase					:       int=0.02	,_UpperCAmelCase					:       int=[1, 2, 3, 6]	,_UpperCAmelCase					:       int=True	,_UpperCAmelCase					:       Dict=0.4	,_UpperCAmelCase					:       Optional[int]=384	,_UpperCAmelCase					:       Optional[Any]=256	,_UpperCAmelCase					:       List[Any]=1	,_UpperCAmelCase					:       List[Any]=False	,_UpperCAmelCase					:       Tuple=255	,**_UpperCAmelCase					:       int	,):
									super().__init__(**_UpperCAmelCase		)

									if backbone_config is None:
											logger.info('`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.'		)
											_a     :      List[str]        =							CONFIG_MAPPING['resnet'](out_features=['stage1', 'stage2', 'stage3', 'stage4']		)
									elif isinstance(_UpperCAmelCase	,_UpperCAmelCase		):
											_a     :      List[Any]        =							backbone_config.get('model_type'		)
											_a     :      List[str]        =							CONFIG_MAPPING[backbone_model_type]
											_a     :      Optional[int]        =							config_class.from_dict(_UpperCAmelCase		)

									_a     :      Optional[int]        =							backbone_config
									_a     :      Union[str, Any]        =							hidden_size
									_a     :      str        =							initializer_range
									_a     :      Any        =							pool_scales
									_a     :      str        =							use_auxiliary_head
									_a     :      Tuple        =							auxiliary_loss_weight
									_a     :      Optional[Any]        =							auxiliary_in_channels
									_a     :      Union[str, Any]        =							auxiliary_channels
									_a     :      List[str]        =							auxiliary_num_convs
									_a     :      List[str]        =							auxiliary_concat_input
									_a     :      Any        =							loss_ignore_index







							def 							__lowercase			(							self					:       Optional[int]		):
									_a     :      int        =							copy.deepcopy(self.__dict__		)
									_a     :      List[Any]        =							self.backbone_config.to_dict()
									_a     :      Dict        =							self.__class__.model_type
									return output









'''simple docstring'''

import fire
from torch.utils.data import DataLoader
from tqdm import tqdm

from transformers import AutoTokenizer
from utils import SeqaSeqDataset, pickle_save



def      __lowerCamelCase     (      lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_=1024						,			lowerCAmelCase_=1024						,			lowerCAmelCase_=False						,			**lowerCAmelCase_  )			->      List[Any]:
		_a     :      str        =							AutoTokenizer.from_pretrained(lowerCAmelCase_  )
		_a     :      List[Any]        =							SeqaSeqDataset(lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			type_path='train'						,			**lowerCAmelCase_  )
		_a     :      List[str]        =							tok.pad_token_id

		def get_lens(lowerCAmelCase_  ):
				_a     :      Dict        =							tqdm(
				    DataLoader(lowerCAmelCase_						,			batch_size=512						,			num_workers=8						,			shuffle=lowerCAmelCase_						,			collate_fn=ds.collate_fn  )						,			desc=str(ds.len_file  )						,			)
				_a     :      Union[str, Any]        =							[]
				for batch in dl:
						_a     :      Optional[Any]        =							batch['input_ids'].ne(lowerCAmelCase_  ).sum(1  ).tolist()
						_a     :      Optional[Any]        =							batch['labels'].ne(lowerCAmelCase_  ).sum(1  ).tolist()
						if consider_target:
								for src, tgt in zip(lowerCAmelCase_						,			lowerCAmelCase_  ):
										max_lens.append(max(lowerCAmelCase_						,			lowerCAmelCase_  )  )
						else:
								max_lens.extend(lowerCAmelCase_  )
				return max_lens

		_a     :      str        =							get_lens(lowerCAmelCase_  )
		_a     :      Optional[int]        =							SeqaSeqDataset(lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			type_path='val'						,			**lowerCAmelCase_  )
		_a     :      Dict        =							get_lens(lowerCAmelCase_  )
		pickle_save(lowerCAmelCase_						,			train_ds.len_file  )
		pickle_save(lowerCAmelCase_						,			val_ds.len_file  )


if __name__ == "__main__":
							fire.Fire(save_len_file)




