






import argparse

import torch
from torch import nn

from transformers import MBartConfig, MBartForConditionalGeneration





def 				snake_case  (     lowerCamelCase					):




	'''simple docstring'''







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





def 				snake_case  (     lowerCamelCase					):




	'''simple docstring'''







	__lowercase       ,						__lowercase             =  emb.weight.shape
	__lowercase             =  nn.Linear(lowerCamelCase				,  lowerCamelCase				,  bias=lowerCamelCase					)
	__lowercase             =  emb.weight.data
	return lin_layer





def 				snake_case  (     lowerCamelCase				,  lowerCamelCase="facebook/mbart-large-en-ro"				,  lowerCamelCase=False				,  lowerCamelCase=False					):




	'''simple docstring'''







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

	__lowercase             =  MBartConfig.from_pretrained(lowerCamelCase				,  vocab_size=lowerCamelCase					)
	if mbart_aa and finetuned:
		__lowercase             =  """relu"""

	__lowercase             =  state_dict["""decoder.embed_tokens.weight"""]
	__lowercase             =  MBartForConditionalGeneration(lowerCamelCase					)
	model.model.load_state_dict(lowerCamelCase					)

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

	return model


if __name__ == "__main__":
						__UpperCamelCase      :      Dict     						=						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      :      List[str]     						=						parser.parse_args()
						__UpperCamelCase      :      Any     						=						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 __future__ import annotations





def 				snake_case  (     lowerCamelCase					):




	'''simple docstring'''







	if len(lowerCamelCase					) < 2:
		raise ValueError("""Monogons and Digons are not polygons in the Euclidean space"""					)
	if any(i <= 0 for i in nums					):
		raise ValueError("""All values must be greater than 0"""					)
	__lowercase             =  nums.copy()
	copy_nums.sort()
	return copy_nums[-1] < sum(copy_nums[:-1]					)


if __name__ == "__main__":
						import doctest

						doctest.testmod()

