


"""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  a__      (    SCREAMING_SNAKE_CASE   :   Optional[int]    ):


  '''simple docstring'''







  lowerCAmelCase					:					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(SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE    )
def  a__      (    SCREAMING_SNAKE_CASE   :   Tuple    ):


  '''simple docstring'''







  lowerCAmelCase						,  lowerCAmelCase					:					int								=							emb.weight.shape
  lowerCAmelCase					:					List[str]								=							nn.Linear(SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE    ,				bias=SCREAMING_SNAKE_CASE    )
  lowerCAmelCase					:					List[str]								=							emb.weight.data
  return lin_layer
def  a__      (    SCREAMING_SNAKE_CASE   :   List[Any]    ,				SCREAMING_SNAKE_CASE   :   str=None    ):


  '''simple docstring'''







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


  '''simple docstring'''







  lowerCAmelCase					:					Optional[Any]								=							[]
  lowerCAmelCase					:					Tuple								=							0
  os.makedirs(SCREAMING_SNAKE_CASE    ,				exist_ok=SCREAMING_SNAKE_CASE    )

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

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

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

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

  return metadata, index


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

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









"""simple docstring"""







import pyarrow.parquet as pq
import pytest

from datasets import Audio, Dataset, DatasetDict, Features, NamedSplit, Sequence, Value, config
from datasets.features.image import Image
from datasets.io.parquet import ParquetDatasetReader, ParquetDatasetWriter, get_writer_batch_size

from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases
def  a__      (    SCREAMING_SNAKE_CASE   :   str    ,				SCREAMING_SNAKE_CASE   :   Any    ):


  '''simple docstring'''







  assert isinstance(SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE    )
  assert dataset.num_rows == 4
  assert dataset.num_columns == 3
  assert dataset.column_names == ["col_1", "col_2", "col_3"]
  for feature, expected_dtype in expected_features.items():
    assert dataset.features[feature].dtype == expected_dtype
@pytest.mark.parametrize("keep_in_memory"    ,				[False, True]    )
def  a__      (    SCREAMING_SNAKE_CASE   :   Any    ,				SCREAMING_SNAKE_CASE   :   List[str]    ,				SCREAMING_SNAKE_CASE   :   int    ):


  '''simple docstring'''







  lowerCAmelCase					:					List[Any]								=							tmp_path / "cache"
  lowerCAmelCase					:					Optional[int]								=							{"col_1": "string", "col_2": "int64", "col_3": "float64"}
  with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
    lowerCAmelCase					:					str								=							ParquetDatasetReader(SCREAMING_SNAKE_CASE    ,				cache_dir=SCREAMING_SNAKE_CASE    ,				keep_in_memory=SCREAMING_SNAKE_CASE    ).read()
  _check_parquet_dataset(SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE    )
@pytest.mark.parametrize(
    "features"    ,				[
        None,
        {"col_1": "string", "col_2": "int64", "col_3": "float64"},
        {"col_1": "string", "col_2": "string", "col_3": "string"},
        {"col_1": "int32", "col_2": "int32", "col_3": "int32"},
        {"col_1": "float32", "col_2": "float32", "col_3": "float32"},
    ]    ,				)
def  a__      (    SCREAMING_SNAKE_CASE   :   Tuple    ,				SCREAMING_SNAKE_CASE   :   Optional[Any]    ,				SCREAMING_SNAKE_CASE   :   Tuple    ):


  '''simple docstring'''







  lowerCAmelCase					:					Optional[int]								=							tmp_path / "cache"
  lowerCAmelCase					:					Optional[Any]								=							{"col_1": "string", "col_2": "int64", "col_3": "float64"}
  lowerCAmelCase					:					Tuple								=							features.copy() if features else default_expected_features
  lowerCAmelCase					:					List[str]								=							(
      Features({feature: Value(SCREAMING_SNAKE_CASE    ) for feature, dtype in features.items()}    ) if features is not None else None
  )
  lowerCAmelCase					:					List[str]								=							ParquetDatasetReader(SCREAMING_SNAKE_CASE    ,				features=SCREAMING_SNAKE_CASE    ,				cache_dir=SCREAMING_SNAKE_CASE    ).read()
  _check_parquet_dataset(SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE    )
@pytest.mark.parametrize("split"    ,				[None, NamedSplit("train"    ), "train", "test"]    )
def  a__      (    SCREAMING_SNAKE_CASE   :   Union[str, Any]    ,				SCREAMING_SNAKE_CASE   :   Union[str, Any]    ,				SCREAMING_SNAKE_CASE   :   Optional[int]    ):


  '''simple docstring'''







  lowerCAmelCase					:					List[Any]								=							tmp_path / "cache"
  lowerCAmelCase					:					int								=							{"col_1": "string", "col_2": "int64", "col_3": "float64"}
  lowerCAmelCase					:					Tuple								=							ParquetDatasetReader(SCREAMING_SNAKE_CASE    ,				cache_dir=SCREAMING_SNAKE_CASE    ,				split=SCREAMING_SNAKE_CASE    ).read()
  _check_parquet_dataset(SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE    )
  assert dataset.split == split if split else "train"
@pytest.mark.parametrize("path_type"    ,				[str, list]    )
def  a__      (    SCREAMING_SNAKE_CASE   :   List[Any]    ,				SCREAMING_SNAKE_CASE   :   Dict    ,				SCREAMING_SNAKE_CASE   :   List[str]    ):


  '''simple docstring'''







  if issubclass(SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE    ):
    lowerCAmelCase					:					Optional[Any]								=							parquet_path
  elif issubclass(SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE    ):
    lowerCAmelCase					:					Dict								=							[parquet_path]
  lowerCAmelCase					:					Optional[Any]								=							tmp_path / "cache"
  lowerCAmelCase					:					Any								=							{"col_1": "string", "col_2": "int64", "col_3": "float64"}
  lowerCAmelCase					:					Optional[Any]								=							ParquetDatasetReader(SCREAMING_SNAKE_CASE    ,				cache_dir=SCREAMING_SNAKE_CASE    ).read()
  _check_parquet_dataset(SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE    )
def  a__      (    SCREAMING_SNAKE_CASE   :   Dict    ,				SCREAMING_SNAKE_CASE   :   List[str]    ,				SCREAMING_SNAKE_CASE   :   Tuple=("train",)    ):


  '''simple docstring'''







  assert isinstance(SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE    )
  for split in splits:
    lowerCAmelCase					:					List[Any]								=							dataset_dict[split]
    assert dataset.num_rows == 4
    assert dataset.num_columns == 3
    assert dataset.column_names == ["col_1", "col_2", "col_3"]
    for feature, expected_dtype in expected_features.items():
      assert dataset.features[feature].dtype == expected_dtype
@pytest.mark.parametrize("keep_in_memory"    ,				[False, True]    )
def  a__      (    SCREAMING_SNAKE_CASE   :   Optional[int]    ,				SCREAMING_SNAKE_CASE   :   Tuple    ,				SCREAMING_SNAKE_CASE   :   Tuple    ):


  '''simple docstring'''







  lowerCAmelCase					:					Tuple								=							tmp_path / "cache"
  lowerCAmelCase					:					List[Any]								=							{"col_1": "string", "col_2": "int64", "col_3": "float64"}
  with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
    lowerCAmelCase					:					Optional[Any]								=							ParquetDatasetReader(
        {"train": parquet_path}    ,				cache_dir=SCREAMING_SNAKE_CASE    ,				keep_in_memory=SCREAMING_SNAKE_CASE    ).read()
  _check_parquet_datasetdict(SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE    )
@pytest.mark.parametrize(
    "features"    ,				[
        None,
        {"col_1": "string", "col_2": "int64", "col_3": "float64"},
        {"col_1": "string", "col_2": "string", "col_3": "string"},
        {"col_1": "int32", "col_2": "int32", "col_3": "int32"},
        {"col_1": "float32", "col_2": "float32", "col_3": "float32"},
    ]    ,				)
def  a__      (    SCREAMING_SNAKE_CASE   :   Optional[int]    ,				SCREAMING_SNAKE_CASE   :   Dict    ,				SCREAMING_SNAKE_CASE   :   Optional[int]    ):


  '''simple docstring'''







  lowerCAmelCase					:					Any								=							tmp_path / "cache"
  lowerCAmelCase					:					Union[str, Any]								=							{"col_1": "string", "col_2": "int64", "col_3": "float64"}
  lowerCAmelCase					:					List[Any]								=							features.copy() if features else default_expected_features
  lowerCAmelCase					:					List[Any]								=							(
      Features({feature: Value(SCREAMING_SNAKE_CASE    ) for feature, dtype in features.items()}    ) if features is not None else None
  )
  lowerCAmelCase					:					Optional[int]								=							ParquetDatasetReader({"train": parquet_path}    ,				features=SCREAMING_SNAKE_CASE    ,				cache_dir=SCREAMING_SNAKE_CASE    ).read()
  _check_parquet_datasetdict(SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE    )
@pytest.mark.parametrize("split"    ,				[None, NamedSplit("train"    ), "train", "test"]    )
def  a__      (    SCREAMING_SNAKE_CASE   :   str    ,				SCREAMING_SNAKE_CASE   :   Dict    ,				SCREAMING_SNAKE_CASE   :   List[str]    ):


  '''simple docstring'''







  if split:
    lowerCAmelCase					:					List[str]								=							{split: parquet_path}
  else:
    lowerCAmelCase					:					List[str]								=							"train"
    lowerCAmelCase					:					str								=							{"train": parquet_path, "test": parquet_path}
  lowerCAmelCase					:					Optional[int]								=							tmp_path / "cache"
  lowerCAmelCase					:					List[Any]								=							{"col_1": "string", "col_2": "int64", "col_3": "float64"}
  lowerCAmelCase					:					List[str]								=							ParquetDatasetReader(SCREAMING_SNAKE_CASE    ,				cache_dir=SCREAMING_SNAKE_CASE    ).read()
  _check_parquet_datasetdict(SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE    ,				splits=list(path.keys()    )    )
  assert all(dataset[split].split == split for split in path.keys()    )
def  a__      (    SCREAMING_SNAKE_CASE   :   Optional[Any]    ,				SCREAMING_SNAKE_CASE   :   Any    ):


  '''simple docstring'''







  lowerCAmelCase					:					Optional[int]								=							ParquetDatasetWriter(SCREAMING_SNAKE_CASE    ,				tmp_path / "foo.parquet"    )
  assert writer.write() > 0
  lowerCAmelCase					:					List[str]								=							pq.ParquetFile(tmp_path / "foo.parquet"    )
  lowerCAmelCase					:					Union[str, Any]								=							pf.read()
  assert dataset.data.table == output_table
def  a__      (    SCREAMING_SNAKE_CASE   :   List[Any]    ,				SCREAMING_SNAKE_CASE   :   List[str]    ):


  '''simple docstring'''







  lowerCAmelCase					:					str								=							str(shared_datadir / "test_image_rgb.jpg"    )
  lowerCAmelCase					:					List[str]								=							{"image": [image_path]}
  lowerCAmelCase					:					str								=							Features({"image": Image()}    )
  lowerCAmelCase					:					Optional[Any]								=							Dataset.from_dict(SCREAMING_SNAKE_CASE    ,				features=SCREAMING_SNAKE_CASE    )
  lowerCAmelCase					:					List[Any]								=							ParquetDatasetWriter(SCREAMING_SNAKE_CASE    ,				tmp_path / "foo.parquet"    )
  assert writer.write() > 0

  lowerCAmelCase					:					Any								=							Dataset.from_parquet(str(tmp_path / "foo.parquet"    )    )
  assert dataset.features == reloaded_dataset.features

  lowerCAmelCase					:					Optional[int]								=							ParquetDatasetReader(str(tmp_path / "foo.parquet"    )    ,				streaming=SCREAMING_SNAKE_CASE    ).read()
  assert dataset.features == reloaded_iterable_dataset.features





@pytest.mark.parametrize(
    "feature, expected"    ,				[
        (Features({"foo": Value("int32"    )}    ), None),
        (Features({"image": Image(), "foo": Value("int32"    )}    ), config.PARQUET_ROW_GROUP_SIZE_FOR_IMAGE_DATASETS),
        (Features({"nested": Sequence(Audio()    )}    ), config.PARQUET_ROW_GROUP_SIZE_FOR_AUDIO_DATASETS),
    ]    ,				)
def  a__      (    SCREAMING_SNAKE_CASE   :   str    ,				SCREAMING_SNAKE_CASE   :   List[str]    ):


  '''simple docstring'''







  assert get_writer_batch_size(SCREAMING_SNAKE_CASE    ) == expected





