


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"""







	A             =					[
	    """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"""







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





def 	_lowerCAmelCase					( UpperCamelCase__:  Optional[int]				,       UpperCamelCase__:  Tuple=None    )			-> str:





	"""simple docstring"""







	A             =					{}
	for old_key in state_dict.keys():
		A             =					old_key
		if "moe_layer.experts." in key:
			if expert_idx is not None:
				A             =					key.replace("""moe_layer.experts.0"""				,       f'ffn.experts.expert_{expert_idx}'    )
			else:
				A             =					key.replace("""moe_layer.experts."""				,       """ffn.experts.expert_"""    )
		if "gate" in key:
			A             =					key.replace(""".moe_layer.gate.wg"""				,       """.ffn.router.classifier"""    )
		if "fc2" and "experts" not in key:
			A             =					key.replace(""".fc2."""				,       """.ffn.fc2."""    )
		if "fc1" and "experts" not in key:
			A             =					key.replace(""".fc1."""				,       """.ffn.fc1."""    )
		if ".encoder_attn." in key:
			A             =					key.replace(""".encoder_attn."""				,       """.cross_attention."""    )
		if "encoder_attn_layer_norm" in key:
			A             =					key.replace("""encoder_attn_layer_norm"""				,       """cross_attention_layer_norm"""    )
		if "final_layer_norm" in key:
			A             =					key.replace("""final_layer_norm"""				,       """ff_layer_norm"""    )
		A             =					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"""







	A             =					[]
	A             =					0
	os.makedirs(UpperCamelCase__				,       exist_ok=UpperCamelCase__    )

	for expert in range(UpperCamelCase__    ):
		A             =					switch_checkpoint_path + f'-rank-{expert}.pt'
		if os.path.isfile(UpperCamelCase__    ):
			A             =					torch.load(UpperCamelCase__    )["""model"""]
			remove_ignore_keys_(UpperCamelCase__    )
			A             =					rename_fairseq_keys(UpperCamelCase__				,       UpperCamelCase__    )
			A             =					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
	A             =					os.path.join(UpperCamelCase__				,       weights_name.replace(""".bin"""				,       f'-{len(UpperCamelCase__    )+1:05d}-of-???.bin'    )    )
	A             =					torch.load(switch_checkpoint_path + """-shared.pt"""    )["""model"""]
	remove_ignore_keys_(UpperCamelCase__    )
	A             =					rename_fairseq_keys(UpperCamelCase__				,       UpperCamelCase__    )
	A             =					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:
		A             =					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
	A             =					{}
	for idx, shard in enumerate(UpperCamelCase__    ):
		A             =					weights_name.replace(""".bin"""				,       f'-{idx+1:05d}-of-{len(UpperCamelCase__    ):05d}.bin'    )
		A             =					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:
			A             =					shard_file

    # Add the metadata
	A             =					{"""total_size""": total_size}
	A             =					{"""metadata""": metadata, """weight_map""": weight_map}

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

	return metadata, index


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

				_lowercase							:   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)
				_lowercase							:   Any						=						NllbMoeModel.from_pretrained(args.pytorch_dump_folder_path)
				print("Done")
				model.save_pretrained(args.pytorch_dump_folder_path)







_lowercase							:   Dict						=						{
    "Pillow": "Pillow",
    "accelerate": "accelerate>=0.11.0",
    "compel": "compel==0.1.8",
    "black": "black~=23.1",
    "datasets": "datasets",
    "filelock": "filelock",
    "flax": "flax>=0.4.1",
    "hf-doc-builder": "hf-doc-builder>=0.3.0",
    "huggingface-hub": "huggingface-hub>=0.13.2",
    "requests-mock": "requests-mock==1.10.0",
    "importlib_metadata": "importlib_metadata",
    "invisible-watermark": "invisible-watermark",
    "isort": "isort>=5.5.4",
    "jax": "jax>=0.2.8,!=0.3.2",
    "jaxlib": "jaxlib>=0.1.65",
    "Jinja2": "Jinja2",
    "k-diffusion": "k-diffusion>=0.0.12",
    "torchsde": "torchsde",
    "note_seq": "note_seq",
    "librosa": "librosa",
    "numpy": "numpy",
    "omegaconf": "omegaconf",
    "parameterized": "parameterized",
    "protobuf": "protobuf>=3.20.3,<4",
    "pytest": "pytest",
    "pytest-timeout": "pytest-timeout",
    "pytest-xdist": "pytest-xdist",
    "ruff": "ruff>=0.0.241",
    "safetensors": "safetensors",
    "sentencepiece": "sentencepiece>=0.1.91,!=0.1.92",
    "scipy": "scipy",
    "onnx": "onnx",
    "regex": "regex!=2019.12.17",
    "requests": "requests",
    "tensorboard": "tensorboard",
    "torch": "torch>=1.4",
    "torchvision": "torchvision",
    "transformers": "transformers>=4.25.1",
    "urllib3": "urllib3<=2.0.0",
}



