




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      _SCREAMING_SNAKE_CASE     (				lowercase      :			str	):


      '''simple docstring'''



      lowerCamelCase_							=       [
          "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      _SCREAMING_SNAKE_CASE     (				lowercase      :			List[str]	):


      '''simple docstring'''



      lowerCamelCase_							=       emb.weight.shape
      lowerCamelCase_							=       nn.Linear(lowerCAmelCase__      ,				lowerCAmelCase__      ,				bias=lowerCAmelCase__	)
      lowerCamelCase_							=       emb.weight.data
      return lin_layer

def      _SCREAMING_SNAKE_CASE     (				lowercase      :			Optional[int]      ,				lowercase      :			Union[str, Any]=None	):


      '''simple docstring'''



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

def      _SCREAMING_SNAKE_CASE     (				lowercase      :			Dict      ,				lowercase      :			int      ,				lowercase      :			int      ,				lowercase      :			List[Any]      ,				lowercase      :			str = WEIGHTS_NAME	):


      '''simple docstring'''



      lowerCamelCase_							=       []
      lowerCamelCase_							=       0
      os.makedirs(lowerCAmelCase__      ,				exist_ok=lowerCAmelCase__	)

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

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

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

      return metadata, index


if __name__ == "__main__":
 lowerCamelCase							:						Tuple					       =  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.",
 )
 lowerCamelCase							:						Union[str, Any]					       =  parser.parse_args()
 lowerCamelCase       ,       lowerCamelCase							:						Dict					       =  shard_on_the_fly(
     args.nllb_moe_checkpoint_path,
     args.pytorch_dump_folder_path,
     128,
     args.dtype,
 )

 lowerCamelCase							:						Tuple					       =  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)
 lowerCamelCase							:						Dict					       =  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__			)