






"""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_				(			__UpperCAmelCase				)						->    Dict:
      lowerCAmelCase__      :			int   =	[
          """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 	lowercase_				(			__UpperCAmelCase				)						->    Dict:
      lowerCAmelCase__      :			Optional[Any]   =	emb.weight.shape
      lowerCAmelCase__      :			Optional[int]   =	nn.Linear(__UpperCAmelCase    ,		__UpperCAmelCase    ,		bias=__UpperCAmelCase				)
      lowerCAmelCase__      :			Dict   =	emb.weight.data
      return lin_layer



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



def 	lowercase_				(			__UpperCAmelCase    ,		__UpperCAmelCase    ,		__UpperCAmelCase    ,		__UpperCAmelCase    ,		__UpperCAmelCase = WEIGHTS_NAME				)						->    Tuple:
      lowerCAmelCase__      :			Dict   =	[]
      lowerCAmelCase__      :			Union[str, Any]   =	0
      os.makedirs(__UpperCAmelCase    ,		exist_ok=__UpperCAmelCase				)

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

    # Add the metadata
      lowerCAmelCase__      :			List[str]   =	{"""total_size""": total_size}
      lowerCAmelCase__      :			Tuple   =	{"""metadata""": metadata, """weight_map""": weight_map}

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

      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,
      1_2_8,
      args.dtype,
  )

  _A													=      NllbMoeConfig.from_pretrained(
      """facebook/nllb-200-3.3B""", encoder_sparse_step=4, decoder_sparse_step=4, num_experts=1_2_8
  )
  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)











"""simple docstring"""


# This is the module that test_patching.py uses to test patch_submodule()

import os  # noqa: this is just for tests
import os as renamed_os  # noqa: this is just for tests
from os import path  # noqa: this is just for tests
from os import path as renamed_path  # noqa: this is just for tests
from os.path import join  # noqa: this is just for tests
from os.path import join as renamed_join  # noqa: this is just for tests


_A													=      open  # noqa: we just need to have a builtin inside this module to test it properly





