





'''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						(   lowerCamelCase_     ):


		"""simple docstring"""



		SCREAMING_SNAKE_CASE      :							str						=       [
		    "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(__a       ,       __a     )



def 						__A						(   lowerCamelCase_     ):


		"""simple docstring"""



		SCREAMING_SNAKE_CASE      :							Tuple						=       emb.weight.shape
		SCREAMING_SNAKE_CASE      :							Tuple						=       nn.Linear(__a       ,       __a       ,       bias=__a     )
		SCREAMING_SNAKE_CASE      :							int						=       emb.weight.data
		return lin_layer



def 						__A						(   lowerCamelCase_       ,       lowerCamelCase_=None     ):


		"""simple docstring"""



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



def 						__A						(   lowerCamelCase_       ,       lowerCamelCase_       ,       lowerCamelCase_       ,       lowerCamelCase_       ,       lowerCamelCase_ = WEIGHTS_NAME     ):


		"""simple docstring"""



		SCREAMING_SNAKE_CASE      :							List[str]						=       []
		SCREAMING_SNAKE_CASE      :							Any						=       0
		os.makedirs(__a       ,       exist_ok=__a     )

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

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

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

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

		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)










from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__A									=				{"configuration_ibert": ["IBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "IBertConfig", "IBertOnnxConfig"]}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __A									=				[
         "IBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
         "IBertForMaskedLM",
         "IBertForMultipleChoice",
         "IBertForQuestionAnswering",
         "IBertForSequenceClassification",
         "IBertForTokenClassification",
         "IBertModel",
         "IBertPreTrainedModel",
     ]

if TYPE_CHECKING:
     from .configuration_ibert import IBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, IBertConfig, IBertOnnxConfig

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_ibert import (
              IBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
              IBertForMaskedLM,
              IBertForMultipleChoice,
              IBertForQuestionAnswering,
              IBertForSequenceClassification,
              IBertForTokenClassification,
              IBertModel,
              IBertPreTrainedModel,
          )

else:
     import sys

     __A									=				_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

