



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					(   lowerCAmelCase_       : List[str]    )				->			Optional[Any]:






						'''simple docstring'''



						UpperCAmelCase_=       [
						    """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(lowerCAmelCase_      ,lowerCAmelCase_    )



def 	__a					(   lowerCAmelCase_       : int    )				->			int:






						'''simple docstring'''



						UpperCAmelCase_,       UpperCAmelCase_=       emb.weight.shape
						UpperCAmelCase_=       nn.Linear(lowerCAmelCase_      ,lowerCAmelCase_      ,bias=lowerCAmelCase_    )
						UpperCAmelCase_=       emb.weight.data
						return lin_layer



def 	__a					(   lowerCAmelCase_       : Optional[int]      ,lowerCAmelCase_       : Union[str, Any]=None    )				->			Optional[int]:






						'''simple docstring'''



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



def 	__a					(   lowerCAmelCase_       : Optional[Any]      ,lowerCAmelCase_       : str      ,lowerCAmelCase_       : Union[str, Any]      ,lowerCAmelCase_       : List[str]      ,lowerCAmelCase_       : str = WEIGHTS_NAME    )				->			Dict:






						'''simple docstring'''



						UpperCAmelCase_=       []
						UpperCAmelCase_=       0
						os.makedirs(lowerCAmelCase_      ,exist_ok=lowerCAmelCase_    )

						for expert in range(lowerCAmelCase_    ):
												UpperCAmelCase_=       switch_checkpoint_path + F"""-rank-{expert}.pt"""
												if os.path.isfile(lowerCAmelCase_    ):
																		UpperCAmelCase_=       torch.load(lowerCAmelCase_    )["""model"""]
																		remove_ignore_keys_(lowerCAmelCase_    )
																		UpperCAmelCase_=       rename_fairseq_keys(lowerCAmelCase_      ,lowerCAmelCase_    )
																		UpperCAmelCase_=       os.path.join(
																		    lowerCAmelCase_      ,weights_name.replace(""".bin"""      ,F"""-{len(lowerCAmelCase_    )+1:05d}-of-???.bin"""    )    )
																		torch.save(lowerCAmelCase_      ,lowerCAmelCase_    )
																		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(lowerCAmelCase_    )[0]].dtype    )

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

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

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

						return metadata, index


if __name__ == "__main__":
	__A       =						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.''',
	)
	__A       =						parser.parse_args()
	__A   ,      __A       =						shard_on_the_fly(
	    args.nllb_moe_checkpoint_path,
	    args.pytorch_dump_folder_path,
	    128,
	    args.dtype,
	)

	__A       =						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)
	__A       =						NllbMoeModel.from_pretrained(args.pytorch_dump_folder_path)
	print('''Done''')
	model.save_pretrained(args.pytorch_dump_folder_path)











import pytest

import datasets


# Import fixture modules as plugins
__A       =						['''tests.fixtures.files''', '''tests.fixtures.hub''', '''tests.fixtures.fsspec''']



def 	__a					(   lowerCAmelCase_       : Optional[Any]      ,lowerCAmelCase_       : Any    )				->			Tuple:






						'''simple docstring'''



						for item in items:
												if any(marker in item.keywords for marker in ["""integration""", """unit"""]    ):
																		continue
												item.add_marker(pytest.mark.unit    )



def 	__a					(   lowerCAmelCase_       : Tuple    )				->			Optional[Any]:






						'''simple docstring'''



						config.addinivalue_line("""markers"""      ,"""torchaudio_latest: mark test to run with torchaudio>=0.12"""    )



@pytest.fixture(autouse=lowerCAmelCase_    )
def 	__a					(   lowerCAmelCase_       : List[str]      ,lowerCAmelCase_       : List[str]    )				->			int:






						'''simple docstring'''



						UpperCAmelCase_=       tmp_path_factory.getbasetemp() / """cache"""
						UpperCAmelCase_=       test_hf_cache_home / """datasets"""
						UpperCAmelCase_=       test_hf_cache_home / """metrics"""
						UpperCAmelCase_=       test_hf_cache_home / """modules"""
						monkeypatch.setattr("""datasets.config.HF_DATASETS_CACHE"""      ,str(lowerCAmelCase_    )    )
						monkeypatch.setattr("""datasets.config.HF_METRICS_CACHE"""      ,str(lowerCAmelCase_    )    )
						monkeypatch.setattr("""datasets.config.HF_MODULES_CACHE"""      ,str(lowerCAmelCase_    )    )
						UpperCAmelCase_=       test_hf_datasets_cache / """downloads"""
						monkeypatch.setattr("""datasets.config.DOWNLOADED_DATASETS_PATH"""      ,str(lowerCAmelCase_    )    )
						UpperCAmelCase_=       test_hf_datasets_cache / """downloads""" / """extracted"""
						monkeypatch.setattr("""datasets.config.EXTRACTED_DATASETS_PATH"""      ,str(lowerCAmelCase_    )    )



@pytest.fixture(autouse=lowerCAmelCase_      ,scope="""session"""    )
def 	__a					(   )				->			Optional[int]:






						'''simple docstring'''



						datasets.disable_progress_bar()



@pytest.fixture(autouse=lowerCAmelCase_    )
def 	__a					(   lowerCAmelCase_       : int    )				->			str:






						'''simple docstring'''



						monkeypatch.setattr("""datasets.config.HF_UPDATE_DOWNLOAD_COUNTS"""      ,lowerCAmelCase_    )



@pytest.fixture
def 	__a					(   lowerCAmelCase_       : List[str]    )				->			Union[str, Any]:






						'''simple docstring'''



						monkeypatch.setattr("""sqlalchemy.util.deprecations.SILENCE_UBER_WARNING"""      ,lowerCAmelCase_    )




