

"""simple docstring"""




import argparse

import torch
from torch import nn

from transformers import MBartConfig, MBartForConditionalGeneration





def  __lowerCamelCase     (			UpperCamelCase__				):







							'''simple docstring'''






							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(UpperCamelCase__       ,							UpperCamelCase__				)





def  __lowerCamelCase     (			UpperCamelCase__				):







							'''simple docstring'''






							snake_case_    ,   snake_case_          =   emb.weight.shape
							snake_case_          =   nn.Linear(UpperCamelCase__       ,							UpperCamelCase__       ,							bias=UpperCamelCase__				)
							snake_case_          =   emb.weight.data
							return lin_layer





def  __lowerCamelCase     (			UpperCamelCase__       ,							UpperCamelCase__="facebook/mbart-large-en-ro"       ,							UpperCamelCase__=False       ,							UpperCamelCase__=False				):







							'''simple docstring'''






							snake_case_          =   torch.load(UpperCamelCase__       ,							map_location='cpu'				)['model']
							remove_ignore_keys_(UpperCamelCase__				)
							snake_case_          =   state_dict['encoder.embed_tokens.weight'].shape[0]

							snake_case_          =   MBartConfig.from_pretrained(UpperCamelCase__       ,							vocab_size=UpperCamelCase__				)
							if mbart_aa and finetuned:
														snake_case_          =   'relu'

							snake_case_          =   state_dict['decoder.embed_tokens.weight']
							snake_case_          =   MBartForConditionalGeneration(UpperCamelCase__				)
							model.model.load_state_dict(UpperCamelCase__				)

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

							return model


if __name__ == "__main__":
					_UpperCAmelCase		:       str						    =	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		:       Any						    =	parser.parse_args()
					_UpperCAmelCase		:       List[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)







from __future__ import annotations





def  __lowerCamelCase     (			UpperCamelCase__       ,							UpperCamelCase__				):







							'''simple docstring'''






							snake_case_          =   []
							create_all_state(1       ,							UpperCamelCase__       ,							UpperCamelCase__       ,							[]       ,							UpperCamelCase__				)
							return result





def  __lowerCamelCase     (			UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__       ,							):







							'''simple docstring'''






							if level == 0:
														total_list.append(current_list[:]				)
														return

							for i in range(UpperCamelCase__       ,							total_number - level + 2				):
														current_list.append(UpperCamelCase__				)
														create_all_state(i + 1       ,							UpperCamelCase__       ,							level - 1       ,							UpperCamelCase__       ,							UpperCamelCase__				)
														current_list.pop()





def  __lowerCamelCase     (			UpperCamelCase__				):







							'''simple docstring'''






							for i in total_list:
														print(*UpperCamelCase__				)


if __name__ == "__main__":
					_UpperCAmelCase		:       str						    =	4
					_UpperCAmelCase		:       Tuple						    =	2
					_UpperCAmelCase		:       Optional[int]						    =	generate_all_combinations(n, k)
					print_all_state(total_list)




