


def 					UpperCamelCase   (      __magic_name__	:				str       ,       __magic_name__	:				int	)  ->    list:
		"""simple docstring"""




		lowercase__	     =						word.split()

		def justify(__magic_name__	:				list       ,       __magic_name__	:				int       ,       __magic_name__	:				int	) -> str:
				lowercase__	     =						max_width - width
				lowercase__	     =						len(__magic_name__	)
				if len(__magic_name__	) == 1:
						# if there is only word in line
						# just insert overall_spaces_count for the remainder of line
						return line[0] + " " * overall_spaces_count
				else:
						lowercase__	     =						words_count - 1
						# num_spaces_between_words_list[i] : tells you to insert
						# num_spaces_between_words_list[i] spaces
						# after word on line[i]
						lowercase__	     =						spaces_to_insert_between_words * [
						    overall_spaces_count // spaces_to_insert_between_words
						]
						lowercase__	     =						(
						    overall_spaces_count % spaces_to_insert_between_words
						)
						# distribute spaces via round robin to the left words
						for i in range(__magic_name__	):
								num_spaces_between_words_list[i] += 1
						lowercase__	     =						[]
						for i in range(__magic_name__	):
								# add the word
								aligned_words_list.append(line[i]	)
								# add the spaces to insert
								aligned_words_list.append(num_spaces_between_words_list[i] * """ """	)
						# just add the last word to the sentence
						aligned_words_list.append(line[-1]	)
						# join the aligned words list to form a justified line
						return "".join(__magic_name__	)

		lowercase__	     =						[]
		lowercase__	     =						[]
		lowercase__	     =						0
		for word in words:
				if width + len(__magic_name__	) + len(__magic_name__	) <= max_width:
						# keep adding words until we can fill out max_width
						# width = sum of length of all words (without overall_spaces_count)
						# len(word) = length of current word
						# len(line) = number of overall_spaces_count to insert between words
						line.append(__magic_name__	)
						width += len(__magic_name__	)
				else:
						# justify the line and add it to result
						answer.append(justify(__magic_name__       ,       __magic_name__       ,       __magic_name__	)	)
						# reset new line and new width
						lowercase__     ,      lowercase__	     =						[word], len(__magic_name__	)
		lowercase__	     =						max_width - width - len(__magic_name__	)
		answer.append(""" """.join(__magic_name__	) + (remaining_spaces + 1) * """ """	)
		return answer


if __name__ == "__main__":
	from doctest import testmod

	testmod()





import argparse

import torch
from torch import nn

from transformers import MBartConfig, MBartForConditionalGeneration






def 					UpperCamelCase   (      __magic_name__	:				Optional[Any]	)  ->    Tuple:
		"""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(__magic_name__       ,       __magic_name__	)






def 					UpperCamelCase   (      __magic_name__	:				int	)  ->    Union[str, Any]:
		"""simple docstring"""




		lowercase__     ,      lowercase__	     =						emb.weight.shape
		lowercase__	     =						nn.Linear(__magic_name__       ,       __magic_name__       ,       bias=__magic_name__	)
		lowercase__	     =						emb.weight.data
		return lin_layer






def 					UpperCamelCase   (      __magic_name__	:				str       ,       __magic_name__	:				str="facebook/mbart-large-en-ro"       ,       __magic_name__	:				Any=False       ,       __magic_name__	:				int=False	)  ->    Optional[int]:
		"""simple docstring"""




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

		lowercase__	     =						MBartConfig.from_pretrained(__magic_name__       ,       vocab_size=__magic_name__	)
		if mbart_aa and finetuned:
				lowercase__	     =						"""relu"""

		lowercase__	     =						state_dict["""decoder.embed_tokens.weight"""]
		lowercase__	     =						MBartForConditionalGeneration(__magic_name__	)
		model.model.load_state_dict(__magic_name__	)

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

		return model


if __name__ == "__main__":
	A			:						Optional[Any]        =  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			:						Any        =  parser.parse_args()
	A			:						str        =  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)

