






import argparse

import torch

from transformers import YosoConfig, YosoForMaskedLM

def        __lowerCamelCase			(		snake_case__		)							->  List[str]:






						"""simple docstring"""





						if "model" in orig_key:
												_SCREAMING_SNAKE_CASE	      =  orig_key.replace("""model."""       ,""""""		)
						if "norm1" in orig_key:
												_SCREAMING_SNAKE_CASE	      =  orig_key.replace("""norm1"""       ,"""attention.output.LayerNorm"""		)
						if "norm2" in orig_key:
												_SCREAMING_SNAKE_CASE	      =  orig_key.replace("""norm2"""       ,"""output.LayerNorm"""		)
						if "norm" in orig_key:
												_SCREAMING_SNAKE_CASE	      =  orig_key.replace("""norm"""       ,"""LayerNorm"""		)
						if "transformer" in orig_key:
												_SCREAMING_SNAKE_CASE	      =  orig_key.split("""."""		)[0].split("""_"""		)[-1]
												_SCREAMING_SNAKE_CASE	      =  orig_key.replace(F'transformer_{layer_num}'       ,F'encoder.layer.{layer_num}'		)
						if "mha.attn" in orig_key:
												_SCREAMING_SNAKE_CASE	      =  orig_key.replace("""mha.attn"""       ,"""attention.self"""		)
						if "mha" in orig_key:
												_SCREAMING_SNAKE_CASE	      =  orig_key.replace("""mha"""       ,"""attention"""		)
						if "W_q" in orig_key:
												_SCREAMING_SNAKE_CASE	      =  orig_key.replace("""W_q"""       ,"""self.query"""		)
						if "W_k" in orig_key:
												_SCREAMING_SNAKE_CASE	      =  orig_key.replace("""W_k"""       ,"""self.key"""		)
						if "W_v" in orig_key:
												_SCREAMING_SNAKE_CASE	      =  orig_key.replace("""W_v"""       ,"""self.value"""		)
						if "ff1" in orig_key:
												_SCREAMING_SNAKE_CASE	      =  orig_key.replace("""ff1"""       ,"""intermediate.dense"""		)
						if "ff2" in orig_key:
												_SCREAMING_SNAKE_CASE	      =  orig_key.replace("""ff2"""       ,"""output.dense"""		)
						if "ff" in orig_key:
												_SCREAMING_SNAKE_CASE	      =  orig_key.replace("""ff"""       ,"""output.dense"""		)
						if "mlm_class" in orig_key:
												_SCREAMING_SNAKE_CASE	      =  orig_key.replace("""mlm.mlm_class"""       ,"""cls.predictions.decoder"""		)
						if "mlm" in orig_key:
												_SCREAMING_SNAKE_CASE	      =  orig_key.replace("""mlm"""       ,"""cls.predictions.transform"""		)
						if "cls" not in orig_key:
												_SCREAMING_SNAKE_CASE	      =  """yoso.""" + orig_key

						return orig_key

def        __lowerCamelCase			(		snake_case__       ,snake_case__		)							->  Any:






						"""simple docstring"""





						for key in orig_state_dict.copy().keys():
												_SCREAMING_SNAKE_CASE	      =  orig_state_dict.pop(lowerCAmelCase_		)

												if ("pooler" in key) or ("sen_class" in key):
																		continue
												else:
																		_SCREAMING_SNAKE_CASE	      =  val

						_SCREAMING_SNAKE_CASE	      =  orig_state_dict["""cls.predictions.decoder.bias"""]
						_SCREAMING_SNAKE_CASE	      =  torch.arange(lowerCAmelCase_		).expand((1, -1)		) + 2

						return orig_state_dict

def        __lowerCamelCase			(		snake_case__       ,snake_case__       ,snake_case__		)							->  int:






						"""simple docstring"""





						_SCREAMING_SNAKE_CASE	      =  torch.load(lowerCAmelCase_       ,map_location="""cpu"""		)["""model_state_dict"""]
						_SCREAMING_SNAKE_CASE	      =  YosoConfig.from_json_file(lowerCAmelCase_		)
						_SCREAMING_SNAKE_CASE	      =  YosoForMaskedLM(lowerCAmelCase_		)

						_SCREAMING_SNAKE_CASE	      =  convert_checkpoint_helper(config.max_position_embeddings       ,lowerCAmelCase_		)

						print(model.load_state_dict(lowerCAmelCase_		)		)
						model.eval()
						model.save_pretrained(lowerCAmelCase_		)

						print(F'Checkpoint successfuly converted. Model saved at {pytorch_dump_path}'		)


if __name__ == "__main__":
		UpperCamelCase 				=	argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '''--pytorch_model_path''', default=None, type=str, required=True, help='''Path to YOSO pytorch checkpoint.'''
		)
		parser.add_argument(
		    '''--config_file''',
		    default=None,
		    type=str,
		    required=True,
		    help='''The json file for YOSO model config.''',
		)
		parser.add_argument(
		    '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
		)
		UpperCamelCase 				=	parser.parse_args()
		convert_yoso_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)








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

from transformers import AutoTokenizer
from utils import SeqaSeqDataset, pickle_save

def   a_    ( lowerCAmelCase_      :			List[str],	lowerCAmelCase_      :			Dict,	lowerCAmelCase_      :			Tuple=1024,	lowerCAmelCase_      :			Optional[Any]=1024,	lowerCAmelCase_      :			Tuple=False,	**lowerCAmelCase_      :			Union[str, Any]	):
   __lowerCAmelCase								=	AutoTokenizer.from_pretrained(lowerCAmelCase_	)
   __lowerCAmelCase								=	SeqaSeqDataset(lowerCAmelCase_,	lowerCAmelCase_,	lowerCAmelCase_,	lowerCAmelCase_,	type_path='train',	**lowerCAmelCase_	)
   __lowerCAmelCase								=	tok.pad_token_id

   def get_lens(lowerCAmelCase_      :			Optional[Any]	):
      __lowerCAmelCase								=	tqdm(
          DataLoader(lowerCAmelCase_,	batch_size=512,	num_workers=8,	shuffle=lowerCAmelCase_,	collate_fn=ds.collate_fn	),	desc=str(ds.len_file	),	)
      __lowerCAmelCase								=	[]
      for batch in dl:
         __lowerCAmelCase								=	batch['input_ids'].ne(lowerCAmelCase_	).sum(1	).tolist()
         __lowerCAmelCase								=	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

   __lowerCAmelCase								=	get_lens(lowerCAmelCase_	)
   __lowerCAmelCase								=	SeqaSeqDataset(lowerCAmelCase_,	lowerCAmelCase_,	lowerCAmelCase_,	lowerCAmelCase_,	type_path='val',	**lowerCAmelCase_	)
   __lowerCAmelCase								=	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)

