






'''simple docstring'''





import argparse

import torch
from torch import nn

from transformers import MBartConfig, MBartForConditionalGeneration

def _UpperCAmelCase   (	a						:	List[str] ):
							snake_case__	  =       [
							    """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 _UpperCAmelCase   (	a						:	Optional[Any] ):
							snake_case__  , snake_case__	  =       emb.weight.shape
							snake_case__	  =       nn.Linear(a  ,					a  ,					bias=a )
							snake_case__	  =       emb.weight.data
							return lin_layer

def _UpperCAmelCase   (	a						:	List[Any]  ,					a						:	List[Any]="facebook/mbart-large-en-ro"  ,					a						:	int=False  ,					a						:	Tuple=False ):
							snake_case__	  =       torch.load(a  ,					map_location="""cpu""" )["""model"""]
							remove_ignore_keys_(a )
							snake_case__	  =       state_dict["""encoder.embed_tokens.weight"""].shape[0]

							snake_case__	  =       MBartConfig.from_pretrained(a  ,					vocab_size=a )
							if mbart_aa and finetuned:
														snake_case__	  =       """relu"""

							snake_case__	  =       state_dict["""decoder.embed_tokens.weight"""]
							snake_case__	  =       MBartForConditionalGeneration(a )
							model.model.load_state_dict(a )

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

							return model


if __name__ == "__main__":
	a__       =     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""")
	a__       =     parser.parse_args()
	a__       =     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)








from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


a__       =     {
    """configuration_lilt""": ["""LILT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """LiltConfig"""],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	a__       =     [
	    """LILT_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """LiltForQuestionAnswering""",
	    """LiltForSequenceClassification""",
	    """LiltForTokenClassification""",
	    """LiltModel""",
	    """LiltPreTrainedModel""",
	]

if TYPE_CHECKING:
	from .configuration_lilt import LILT_PRETRAINED_CONFIG_ARCHIVE_MAP, LiltConfig

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_lilt import (
		    LILT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    LiltForQuestionAnswering,
		    LiltForSequenceClassification,
		    LiltForTokenClassification,
		    LiltModel,
		    LiltPreTrainedModel,
		)

else:
	import sys

	a__       =     _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
