


"""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 	lowerCamelCase			(	_UpperCamelCase						: Optional[int]				)						->			Optional[int]:

							'''simple docstring'''



							__UpperCAmelCase  :		Union[str, Any] 				=   [
							    """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(UpperCamelCase__			,     UpperCamelCase__				)
def 	lowerCamelCase			(	_UpperCamelCase						: Tuple				)						->			List[str]:

							'''simple docstring'''



							__UpperCAmelCase				,__UpperCAmelCase  :		Dict 				=   emb.weight.shape
							__UpperCAmelCase  :		int 				=   nn.Linear(UpperCamelCase__			,     UpperCamelCase__			,     bias=UpperCamelCase__				)
							__UpperCAmelCase  :		str 				=   emb.weight.data
							return lin_layer
def 	lowerCamelCase			(	_UpperCamelCase						: Optional[int]			,     _UpperCamelCase						: Tuple=None				)						->			str:

							'''simple docstring'''



							__UpperCAmelCase  :		Any 				=   {}
							for old_key in state_dict.keys():
														__UpperCAmelCase  :		Dict 				=   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  :		Dict 				=   key.replace("""moe_layer.experts."""			,     """ffn.experts.expert_"""				)
														if "gate" in key:
																					__UpperCAmelCase  :		Union[str, Any] 				=   key.replace(""".moe_layer.gate.wg"""			,     """.ffn.router.classifier"""				)
														if "fc2" and "experts" not in key:
																					__UpperCAmelCase  :		Union[str, Any] 				=   key.replace(""".fc2."""			,     """.ffn.fc2."""				)
														if "fc1" and "experts" not in key:
																					__UpperCAmelCase  :		str 				=   key.replace(""".fc1."""			,     """.ffn.fc1."""				)
														if ".encoder_attn." in key:
																					__UpperCAmelCase  :		int 				=   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  :		List[Any] 				=   key.replace("""final_layer_norm"""			,     """ff_layer_norm"""				)
														__UpperCAmelCase  :		Any 				=   state_dict[old_key]
							return new_dict
def 	lowerCamelCase			(	_UpperCamelCase						: List[Any]			,     _UpperCamelCase						: Any			,     _UpperCamelCase						: str			,     _UpperCamelCase						: List[Any]			,     _UpperCamelCase						: str = WEIGHTS_NAME				)						->			Dict:

							'''simple docstring'''



							__UpperCAmelCase  :		Optional[int] 				=   []
							__UpperCAmelCase  :		Optional[Any] 				=   0
							os.makedirs(UpperCamelCase__			,     exist_ok=UpperCamelCase__				)

							for expert in range(UpperCamelCase__				):
														__UpperCAmelCase  :		int 				=   switch_checkpoint_path + f'''-rank-{expert}.pt'''
														if os.path.isfile(UpperCamelCase__				):
																					__UpperCAmelCase  :		str 				=   torch.load(UpperCamelCase__				)["""model"""]
																					remove_ignore_keys_(UpperCamelCase__				)
																					__UpperCAmelCase  :		Optional[Any] 				=   rename_fairseq_keys(UpperCamelCase__			,     UpperCamelCase__				)
																					__UpperCAmelCase  :		Dict 				=   os.path.join(
																					    UpperCamelCase__			,     weights_name.replace(""".bin"""			,     f'''-{len(UpperCamelCase__				)+1:05d}-of-???.bin'''				)				)
																					torch.save(UpperCamelCase__			,     UpperCamelCase__				)
																					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(UpperCamelCase__				)[0]].dtype				)

    # Add the last block
							__UpperCAmelCase  :		Optional[Any] 				=   os.path.join(UpperCamelCase__			,     weights_name.replace(""".bin"""			,     f'''-{len(UpperCamelCase__				)+1:05d}-of-???.bin'''				)				)
							__UpperCAmelCase  :		Tuple 				=   torch.load(switch_checkpoint_path + """-shared.pt"""				)["""model"""]
							remove_ignore_keys_(UpperCamelCase__				)
							__UpperCAmelCase  :		Any 				=   rename_fairseq_keys(UpperCamelCase__			,     UpperCamelCase__				)
							__UpperCAmelCase  :		str 				=   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(UpperCamelCase__				) == 1:
														__UpperCAmelCase  :		Tuple 				=   os.path.join(UpperCamelCase__			,     UpperCamelCase__				)
														torch.save(UpperCamelCase__			,     UpperCamelCase__				)
														return {weights_name: sharded_state_dicts[0]}, None
							else:
														torch.save(UpperCamelCase__			,     UpperCamelCase__				)
							# Otherwise, let's build the index
							__UpperCAmelCase  :		Dict 				=   {}
							for idx, shard in enumerate(UpperCamelCase__				):
														__UpperCAmelCase  :		Optional[int] 				=   weights_name.replace(""".bin"""			,     f'''-{idx+1:05d}-of-{len(UpperCamelCase__				):05d}.bin'''				)
														__UpperCAmelCase  :		Dict 				=   os.path.join(UpperCamelCase__			,     weights_name.replace(""".bin"""			,     f'''-{idx+1:05d}-of-???.bin'''				)				)
														os.rename(UpperCamelCase__			,     os.path.join(UpperCamelCase__			,     UpperCamelCase__				)				)
														for key in shard:
																					__UpperCAmelCase  :		int 				=   shard_file

    # Add the metadata
							__UpperCAmelCase  :		int 				=   {"""total_size""": total_size}
							__UpperCAmelCase  :		Optional[Any] 				=   {"""metadata""": metadata, """weight_map""": weight_map}

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

							return metadata, index


if __name__ == "__main__":
							UpperCAmelCase       :	Any  		=						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.',
							)
							UpperCAmelCase       :	List[Any]  		=						parser.parse_args()
							UpperCAmelCase       :	Any  		=						shard_on_the_fly(
							    args.nllb_moe_checkpoint_path,
							    args.pytorch_dump_folder_path,
							    128,
							    args.dtype,
							)

							UpperCAmelCase       :	str  		=						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)
							UpperCAmelCase       :	Any  		=						NllbMoeModel.from_pretrained(args.pytorch_dump_folder_path)
							print('Done')
							model.save_pretrained(args.pytorch_dump_folder_path)




from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments





def 	_lowerCAmelCase					( )			-> str:





	"""simple docstring"""







	A             =					HfArgumentParser(UpperCamelCase__    )
	A             =					parser.parse_args_into_dataclasses()[0]
	A             =					TensorFlowBenchmark(args=UpperCamelCase__    )
	try:
		A             =					parser.parse_args_into_dataclasses()[0]
	except ValueError as e:
		A             =					"""Arg --no_{0} is no longer used, please use --no-{0} instead."""
		A             =					""" """.join(str(UpperCamelCase__    ).split(""" """    )[:-1]    )
		A             =					""""""
		A             =					eval(str(UpperCamelCase__    ).split(""" """    )[-1]    )
		A             =					[]
		for arg in depreciated_args:
			# arg[2:] removes '--'
			if arg[2:] in TensorFlowBenchmark.deprecated_args:
				# arg[5:] removes '--no_'
				full_error_msg += arg_error_msg.format(arg[5:]    )
			else:
				wrong_args.append(UpperCamelCase__    )
		if len(UpperCamelCase__    ) > 0:
			A             =					full_error_msg + begin_error_msg + str(UpperCamelCase__    )
		raise ValueError(UpperCamelCase__    )
	benchmark.run()


if __name__ == "__main__":
				main()



