


'''simple docstring'''
import argparse
import json
import os

import torch
from torch import nn

from transformers import NllbMoeConfig, NllbMoeModel
from transformers.modeling_utils import dtype_byte_size
from transformers.utils import WEIGHTS_INDEX_NAME, WEIGHTS_NAME


def 			a      (			__a   )  ->	Tuple:



					'''simple docstring'''






					UpperCamelCase__   :Tuple												= [
					    '''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(a__ ,					a__   )


def 			a      (			__a   )  ->	List[Any]:



					'''simple docstring'''






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


def 			a      (			__a ,					__a=None   )  ->	List[Any]:



					'''simple docstring'''






					UpperCamelCase__   :List[Any]												= {}
					for old_key in state_dict.keys():
										UpperCamelCase__   :List[Any]												= old_key
										if "moe_layer.experts." in key:
															if expert_idx is not None:
																				UpperCamelCase__   :Optional[int]												= key.replace('''moe_layer.experts.0''' ,					f'''ffn.experts.expert_{expert_idx}'''   )
															else:
																				UpperCamelCase__   :Any												= key.replace('''moe_layer.experts.''' ,					'''ffn.experts.expert_'''   )
										if "gate" in key:
															UpperCamelCase__   :int												= key.replace('''.moe_layer.gate.wg''' ,					'''.ffn.router.classifier'''   )
										if "fc2" and "experts" not in key:
															UpperCamelCase__   :Any												= key.replace('''.fc2.''' ,					'''.ffn.fc2.'''   )
										if "fc1" and "experts" not in key:
															UpperCamelCase__   :Any												= key.replace('''.fc1.''' ,					'''.ffn.fc1.'''   )
										if ".encoder_attn." in key:
															UpperCamelCase__   :List[str]												= key.replace('''.encoder_attn.''' ,					'''.cross_attention.'''   )
										if "encoder_attn_layer_norm" in key:
															UpperCamelCase__   :List[str]												= key.replace('''encoder_attn_layer_norm''' ,					'''cross_attention_layer_norm'''   )
										if "final_layer_norm" in key:
															UpperCamelCase__   :Any												= key.replace('''final_layer_norm''' ,					'''ff_layer_norm'''   )
										UpperCamelCase__   :Tuple												= state_dict[old_key]
					return new_dict


def 			a      (			__a ,					__a ,					__a ,					__a ,					__a = WEIGHTS_NAME   )  ->	Any:



					'''simple docstring'''






					UpperCamelCase__   :str												= []
					UpperCamelCase__   :Tuple												= 0
					os.makedirs(a__ ,					exist_ok=a__   )

					for expert in range(a__   ):
										UpperCamelCase__   :int												= switch_checkpoint_path + f'''-rank-{expert}.pt'''
										if os.path.isfile(a__   ):
															UpperCamelCase__   :List[str]												= torch.load(a__   )['''model''']
															remove_ignore_keys_(a__   )
															UpperCamelCase__   :List[str]												= rename_fairseq_keys(a__ ,					a__   )
															UpperCamelCase__   :Dict												= os.path.join(
															    a__ ,					weights_name.replace('''.bin''' ,					f'''-{len(a__   )+1:05d}-of-???.bin'''   )   )
															torch.save(a__ ,					a__   )
															sharded_state_dicts.append(expert_state.keys()   )
															total_size += sum([value.numel() for key, value in expert_state.items()]   ) * dtype_byte_size(
															    expert_state[list(a__   )[0]].dtype   )

    # Add the last block
					UpperCamelCase__   :int												= os.path.join(a__ ,					weights_name.replace('''.bin''' ,					f'''-{len(a__   )+1:05d}-of-???.bin'''   )   )
					UpperCamelCase__   :int												= torch.load(switch_checkpoint_path + '''-shared.pt'''   )['''model''']
					remove_ignore_keys_(a__   )
					UpperCamelCase__   :Union[str, Any]												= rename_fairseq_keys(a__ ,					a__   )
					UpperCamelCase__   :int												= shared_weights['''decoder.embed_tokens.weight''']
					sharded_state_dicts.append(shared_weights.keys()   )

					# If we only have the shared weights (dummy model/experts saved on the same file)
					if len(a__   ) == 1:
										UpperCamelCase__   :List[str]												= os.path.join(a__ ,					a__   )
										torch.save(a__ ,					a__   )
										return {weights_name: sharded_state_dicts[0]}, None
					else:
										torch.save(a__ ,					a__   )
					# Otherwise, let's build the index
					UpperCamelCase__   :str												= {}
					for idx, shard in enumerate(a__   ):
										UpperCamelCase__   :str												= weights_name.replace('''.bin''' ,					f'''-{idx+1:05d}-of-{len(a__   ):05d}.bin'''   )
										UpperCamelCase__   :List[Any]												= os.path.join(a__ ,					weights_name.replace('''.bin''' ,					f'''-{idx+1:05d}-of-???.bin'''   )   )
										os.rename(a__ ,					os.path.join(a__ ,					a__   )   )
										for key in shard:
															UpperCamelCase__   :int												= shard_file

    # Add the metadata
					UpperCamelCase__   :Optional[int]												= {'''total_size''': total_size}
					UpperCamelCase__   :List[Any]												= {'''metadata''': metadata, '''weight_map''': weight_map}

					with open(os.path.join(a__ ,					a__   ) ,					'''w''' ,					encoding='''utf-8'''   ) as f:
										UpperCamelCase__   :Tuple												= json.dumps(a__ ,					indent=2 ,					sort_keys=a__   ) + '''\n'''
										f.write(a__   )

					return metadata, index


if __name__ == "__main__":
		__snake_case							      =     argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '''--nllb_moe_checkpoint_path''',
		    default='''/home/arthur_huggingface_co/fairseq/weights/checkpoints/model_moe_54b/checkpoint_2_300000''',
		    type=str,
		    required=False,
		    help='''Path to a directory containing a folder per layer. Follows the original Google format.''',
		)
		parser.add_argument('''--dtype''', default='''float32''', type=str, required=False, help='''dtype of the saved model''')
		parser.add_argument(
		    '''--pytorch_dump_folder_path''',
		    default='''/home/arthur_huggingface_co/fairseq/weights/checkpoints/hf-converted-moe-54b''',
		    type=str,
		    required=False,
		    help='''Path to the output pytorch model.''',
		)
		__snake_case							      =     parser.parse_args()
		__snake_case							      =     shard_on_the_fly(
		    args.nllb_moe_checkpoint_path,
		    args.pytorch_dump_folder_path,
		    128,
		    args.dtype,
		)

		__snake_case							      =     NllbMoeConfig.from_pretrained(
		    '''facebook/nllb-200-3.3B''', encoder_sparse_step=4, decoder_sparse_step=4, num_experts=128
		)
		config.save_pretrained(args.pytorch_dump_folder_path)
		__snake_case							      =     NllbMoeModel.from_pretrained(args.pytorch_dump_folder_path)
		print('''Done''')
		model.save_pretrained(args.pytorch_dump_folder_path)

def     __lowerCAmelCase			(			a__  )       ->  str:
				__a										=    []
				__a										=    set({'''(''', '''[''', '''{'''}  )
				__a										=    set({''')''', ''']''', '''}'''}  )
				__a										=    {'''{''': '''}''', '''[''': ''']''', '''(''': ''')'''}

				for i in range(len(a__  )  ):
								if s[i] in open_brackets:
												stack.append(s[i]  )

								elif s[i] in closed_brackets and (
								    len(a__  ) == 0 or (len(a__  ) > 0 and open_to_closed[stack.pop()] != s[i])
								):
												return False

				return len(a__  ) == 0


def     __lowerCAmelCase			(			)       ->  Dict:
				__a										=    input('''Enter sequence of brackets: '''  )
				if is_balanced(a__  ):
								print(a__						,    '''is balanced'''  )
				else:
								print(a__						,    '''is not balanced'''  )


if __name__ == "__main__":
					main()