





"""simple docstring"""




import argparse

import torch
from torch import nn

from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration






def       _SCREAMING_SNAKE_CASE		(	_lowercase						:    List[Any]					) ->Dict:






	'''simple docstring'''






	a      :		List[Any]	=				[
	    "encoder.version",
	    "decoder.version",
	    "model.encoder.version",
	    "model.decoder.version",
	    "decoder.output_projection.weight",
	    "_float_tensor",
	    "encoder.embed_positions._float_tensor",
	    "decoder.embed_positions._float_tensor",
	]
	for k in ignore_keys:
		state_dict.pop(_lowercase    ,       _lowercase					)






def       _SCREAMING_SNAKE_CASE		(	_lowercase						:    Any					) ->str:






	'''simple docstring'''






	a,       a      :		Union[str, Any]	=				emb.weight.shape
	a      :		Tuple	=				nn.Linear(_lowercase    ,       _lowercase    ,       bias=_lowercase					)
	a      :		List[Any]	=				emb.weight.data
	return lin_layer






def       _SCREAMING_SNAKE_CASE		(	_lowercase						:    Dict					) ->Optional[Any]:






	'''simple docstring'''






	a      :		str	=				torch.load(_lowercase    ,       map_location="cpu"					)
	a      :		List[str]	=				mam_aaa["args"] or mam_aaa["cfg"]["model"]
	a      :		Optional[int]	=				mam_aaa["model"]
	remove_ignore_keys_(_lowercase					)
	a      :		Optional[int]	=				state_dict["encoder.embed_tokens.weight"].shape[0]

	a      :		Optional[Any]	=				MaMaaaConfig(
	    vocab_size=_lowercase    ,       max_position_embeddings=1024    ,       encoder_layers=args.encoder_layers    ,       decoder_layers=args.decoder_layers    ,       encoder_attention_heads=args.encoder_attention_heads    ,       decoder_attention_heads=args.decoder_attention_heads    ,       encoder_ffn_dim=args.encoder_ffn_embed_dim    ,       decoder_ffn_dim=args.decoder_ffn_embed_dim    ,       d_model=args.encoder_embed_dim    ,       encoder_layerdrop=args.encoder_layerdrop    ,       decoder_layerdrop=args.decoder_layerdrop    ,       dropout=args.dropout    ,       attention_dropout=args.attention_dropout    ,       activation_dropout=args.activation_dropout    ,       activation_function="relu"    ,       )

	a      :		Optional[Any]	=				state_dict["decoder.embed_tokens.weight"]
	a      :		Optional[Any]	=				MaMaaaForConditionalGeneration(_lowercase					)
	model.model.load_state_dict(_lowercase    ,       strict=_lowercase					)
	a      :		List[str]	=				make_linear_from_emb(model.model.shared					)

	return model


if __name__ == "__main__":
		a						: List[str]     	=			argparse.ArgumentParser()
		# Required parameters
		parser.add_argument('''fairseq_path''', type=str, help='''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.''')
		a						: Dict     	=			parser.parse_args()
		a						: Any     	=			convert_fairseq_mamaaa_checkpoint_from_disk(args.fairseq_pathß)
		model.save_pretrained(args.pytorch_dump_folder_path)






'''simple docstring'''
import re

from filelock import FileLock


try:
  import nltk

  UpperCamelCase__    					=      True
except (ImportError, ModuleNotFoundError):
  UpperCamelCase__    					=      False

if NLTK_AVAILABLE:
  with FileLock('''.lock''') as lock:
    nltk.download('''punkt''', quiet=True)





def 					a__       (					lowerCAmelCase__						)    ->	str:
    re.sub('''<n>'''  ,					''''''  ,					lowerCAmelCase__						)  # remove pegasus newline char
    assert NLTK_AVAILABLE, "nltk must be installed to separate newlines between sentences. (pip install nltk)"
    return "\n".join(nltk.sent_tokenize(lowerCAmelCase__						)						)
