





"""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      lowercase__ (					lowerCAmelCase__   :			str			)    ->			Any:



			'''simple docstring'''
			a__  :	Dict		 =			[
			    "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      lowercase__ (					lowerCAmelCase__   :			List[str]			)    ->			Optional[Any]:



			'''simple docstring'''
			a__ ,	a__  :	List[Any]		 =			emb.weight.shape
			a__  :	Dict		 =			nn.Linear(lowerCAmelCase__							,			lowerCAmelCase__							,			bias=lowerCAmelCase__			)
			a__  :	Tuple		 =			emb.weight.data
			return lin_layer






def      lowercase__ (					lowerCAmelCase__   :			Optional[int]							,			lowerCAmelCase__   :			Union[str, Any]=None			)    ->			List[str]:



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






def      lowercase__ (					lowerCAmelCase__   :			Dict							,			lowerCAmelCase__   :			int							,			lowerCAmelCase__   :			int							,			lowerCAmelCase__   :			List[Any]							,			lowerCAmelCase__   :			str = WEIGHTS_NAME			)    ->			List[str]:



			'''simple docstring'''
			a__  :	Tuple		 =			[]
			a__  :	List[str]		 =			0
			os.makedirs(lowerCAmelCase__							,			exist_ok=lowerCAmelCase__			)

			for expert in range(lowerCAmelCase__			):
						a__  :	List[Any]		 =			switch_checkpoint_path + F"-rank-{expert}.pt"
						if os.path.isfile(lowerCAmelCase__			):
									a__  :	List[str]		 =			torch.load(lowerCAmelCase__			)["model"]
									remove_ignore_keys_(lowerCAmelCase__			)
									a__  :	int		 =			rename_fairseq_keys(lowerCAmelCase__							,			lowerCAmelCase__			)
									a__  :	Tuple		 =			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
			a__  :	Dict		 =			os.path.join(lowerCAmelCase__							,			weights_name.replace(".bin"							,			F"-{len(lowerCAmelCase__			)+1:05d}-of-???.bin"			)			)
			a__  :	Union[str, Any]		 =			torch.load(switch_checkpoint_path + "-shared.pt"			)["model"]
			remove_ignore_keys_(lowerCAmelCase__			)
			a__  :	int		 =			rename_fairseq_keys(lowerCAmelCase__							,			lowerCAmelCase__			)
			a__  :	Optional[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(lowerCAmelCase__			) == 1:
						a__  :	List[str]		 =			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
			a__  :	List[Any]		 =			{}
			for idx, shard in enumerate(lowerCAmelCase__			):
						a__  :	Optional[Any]		 =			weights_name.replace(".bin"							,			F"-{idx+1:05d}-of-{len(lowerCAmelCase__			):05d}.bin"			)
						a__  :	List[Any]		 =			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:
									a__  :	Optional[Any]		 =			shard_file

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

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

			return metadata, index


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

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






"""simple docstring"""






import pytest

import datasets


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






def      lowercase__ (					lowerCAmelCase__   :			Optional[Any]							,			lowerCAmelCase__   :			str			)    ->			str:



			'''simple docstring'''
			# Mark tests as "unit" by default if not marked as "integration" (or already marked as "unit")
			for item in items:
						if any(marker in item.keywords for marker in ["integration", "unit"]			):
									continue
						item.add_marker(pytest.mark.unit			)






def      lowercase__ (					lowerCAmelCase__   :			List[Any]			)    ->			Any:



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






@pytest.fixture(autouse=lowerCAmelCase__			)
def      lowercase__ (					lowerCAmelCase__   :			Optional[Any]							,			lowerCAmelCase__   :			Dict			)    ->			List[str]:



			'''simple docstring'''
			# test_hf_cache_home = tmp_path_factory.mktemp("cache")  # TODO: why a cache dir per test function does not work?
			a__  :	Dict		 =			tmp_path_factory.getbasetemp() / "cache"
			a__  :	int		 =			test_hf_cache_home / "datasets"
			a__  :	Tuple		 =			test_hf_cache_home / "metrics"
			a__  :	Any		 =			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__			)			)
			a__  :	Optional[int]		 =			test_hf_datasets_cache / "downloads"
			monkeypatch.setattr("datasets.config.DOWNLOADED_DATASETS_PATH"							,			str(lowerCAmelCase__			)			)
			a__  :	Tuple		 =			test_hf_datasets_cache / "downloads" / "extracted"
			monkeypatch.setattr("datasets.config.EXTRACTED_DATASETS_PATH"							,			str(lowerCAmelCase__			)			)






@pytest.fixture(autouse=lowerCAmelCase__							,			scope="session"			)
def      lowercase__ (					)    ->			Union[str, Any]:



			'''simple docstring'''
			datasets.disable_progress_bar()






@pytest.fixture(autouse=lowerCAmelCase__			)
def      lowercase__ (					lowerCAmelCase__   :			Union[str, Any]			)    ->			Optional[Any]:



			'''simple docstring'''
			# don't take tests into account when counting downloads
			monkeypatch.setattr("datasets.config.HF_UPDATE_DOWNLOAD_COUNTS"							,			lowerCAmelCase__			)






@pytest.fixture
def      lowercase__ (					lowerCAmelCase__   :			Optional[Any]			)    ->			str:



			'''simple docstring'''
			# Required to suppress RemovedIn20Warning when feature(s) are not compatible with SQLAlchemy 2.0
			# To be removed once SQLAlchemy 2.0 supported
			monkeypatch.setattr("sqlalchemy.util.deprecations.SILENCE_UBER_WARNING"							,			lowerCAmelCase__			)