





"""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 					a__    (    _SCREAMING_SNAKE_CASE     ,		_SCREAMING_SNAKE_CASE     ,		_SCREAMING_SNAKE_CASE=1_024     ,		_SCREAMING_SNAKE_CASE=1_024     ,		_SCREAMING_SNAKE_CASE=False     ,		**_SCREAMING_SNAKE_CASE			):




		"""simple docstring"""


		UpperCamelCase      =		AutoTokenizer.from_pretrained(__A			)
		UpperCamelCase      =		SeqaSeqDataset(__A     ,		__A     ,		__A     ,		__A     ,		type_path="train"     ,		**__A			)
		UpperCamelCase      =		tok.pad_token_id

		def get_lens(_SCREAMING_SNAKE_CASE			):
				UpperCamelCase      =		tqdm(
				    DataLoader(__A     ,		batch_size=512     ,		num_workers=8     ,		shuffle=__A     ,		collate_fn=ds.collate_fn			)     ,		desc=str(ds.len_file			)     ,		)
				UpperCamelCase      =		[]
				for batch in dl:
						UpperCamelCase      =		batch["input_ids"].ne(__A			).sum(1			).tolist()
						UpperCamelCase      =		batch["labels"].ne(__A			).sum(1			).tolist()
						if consider_target:
								for src, tgt in zip(__A     ,		__A			):
										max_lens.append(max(__A     ,		__A			)			)
						else:
								max_lens.extend(__A			)
				return max_lens

		UpperCamelCase      =		get_lens(__A			)
		UpperCamelCase      =		SeqaSeqDataset(__A     ,		__A     ,		__A     ,		__A     ,		type_path="val"     ,		**__A			)
		UpperCamelCase      =		get_lens(__A			)
		pickle_save(__A     ,		train_ds.len_file			)
		pickle_save(__A     ,		val_ds.len_file			)


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







import argparse

import torch
from torch import nn

from transformers import MBartConfig, MBartForConditionalGeneration




def    lowerCAmelCase_ (     __A							)     ->				Dict:







      '''simple docstring'''


      UpperCAmelCase__								=  [
          "encoder.version",
          "decoder.version",
          "model.encoder.version",
          "model.decoder.version",
          "_float_tensor",
          "decoder.output_projection.weight",
      ]
      for k in ignore_keys:
            state_dict.pop(__A,  __A							)




def    lowerCAmelCase_ (     __A							)     ->				Optional[int]:







      '''simple docstring'''


      UpperCAmelCase__		,      UpperCAmelCase__								=  emb.weight.shape
      UpperCAmelCase__								=  nn.Linear(__A,  __A,  bias=__A							)
      UpperCAmelCase__								=  emb.weight.data
      return lin_layer




def    lowerCAmelCase_ (     __A,  __A="facebook/mbart-large-en-ro",  __A=False,  __A=False							)     ->				Tuple:







      '''simple docstring'''


      UpperCAmelCase__								=  torch.load(__A,  map_location="cpu"							)["model"]
      remove_ignore_keys_(__A							)
      UpperCAmelCase__								=  state_dict["encoder.embed_tokens.weight"].shape[0]

      UpperCAmelCase__								=  MBartConfig.from_pretrained(__A,  vocab_size=__A							)
      if mbart_aa and finetuned:
            UpperCAmelCase__								=  "relu"

      UpperCAmelCase__								=  state_dict["decoder.embed_tokens.weight"]
      UpperCAmelCase__								=  MBartForConditionalGeneration(__A							)
      model.model.load_state_dict(__A							)

      if finetuned:
            UpperCAmelCase__								=  make_linear_from_emb(model.model.shared							)

      return model


if __name__ == "__main__":
      UpperCamelCase__											=					argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          'fairseq_path', type=str, help='bart.large, bart.large.cnn or a path to a model.pt on local filesystem.'
      )
      parser.add_argument('pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
      parser.add_argument(
          '--hf_config',
          default='facebook/mbart-large-cc25',
          type=str,
          help='Which huggingface architecture to use: mbart-large',
      )
      parser.add_argument('--mbart_50', action='store_true', help='whether the model is mMART-50 checkpoint')
      parser.add_argument('--finetuned', action='store_true', help='whether the model is a fine-tuned checkpoint')
      UpperCamelCase__											=					parser.parse_args()
      UpperCamelCase__											=					convert_fairseq_mbart_checkpoint_from_disk(
          args.fairseq_path, hf_config_path=args.hf_config, finetuned=args.finetuned, mbart_aa=args.mbart_aa
      )
      model.save_pretrained(args.pytorch_dump_folder_path)



