

'''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 							_lowerCAmelCase (      __magic_name__	:    Any	)							->     str:
						lowercase   :       Optional[Any]								=[
						    '''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(__magic_name__      ,   __magic_name__	)






def 							_lowerCAmelCase (      __magic_name__	:    List[str]	)							->     str:
						lowercase		,  lowercase   :       int								=emb.weight.shape
						lowercase   :       str								=nn.Linear(__magic_name__      ,   __magic_name__      ,   bias=__magic_name__	)
						lowercase   :       List[Any]								=emb.weight.data
						return lin_layer






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






def 							_lowerCAmelCase (      __magic_name__	:    Tuple      ,   __magic_name__	:    List[str]      ,   __magic_name__	:    Optional[int]      ,   __magic_name__	:    Optional[Any]      ,   __magic_name__	:    str = WEIGHTS_NAME	)							->     str:
						lowercase   :       Tuple								=[]
						lowercase   :       Union[str, Any]								=0
						os.makedirs(__magic_name__      ,   exist_ok=__magic_name__	)

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

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

    # Add the metadata
						lowercase   :       Optional[int]								={'''total_size''': total_size}
						lowercase   :       str								={'''metadata''': metadata, '''weight_map''': weight_map}

						with open(os.path.join(__magic_name__      ,   __magic_name__	)      ,   '''w'''      ,   encoding='''utf-8'''	) as f:
												lowercase   :       Union[str, Any]								=json.dumps(__magic_name__      ,   indent=2      ,   sort_keys=__magic_name__	) + '''\n'''
												f.write(__magic_name__	)

						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'''





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


UpperCamelCase_							=					{"""configuration_plbart""": ["""PLBART_PRETRAINED_CONFIG_ARCHIVE_MAP""", """PLBartConfig"""]}

try:
							if not is_sentencepiece_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCamelCase_							=					["""PLBartTokenizer"""]

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCamelCase_							=					[
							    """PLBART_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """PLBartForCausalLM""",
							    """PLBartForConditionalGeneration""",
							    """PLBartForSequenceClassification""",
							    """PLBartModel""",
							    """PLBartPreTrainedModel""",
							]


if TYPE_CHECKING:
							from .configuration_plbart import PLBART_PRETRAINED_CONFIG_ARCHIVE_MAP, PLBartConfig

							try:
														if not is_sentencepiece_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_plbart import PLBartTokenizer

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_plbart import (
														    PLBART_PRETRAINED_MODEL_ARCHIVE_LIST,
														    PLBartForCausalLM,
														    PLBartForConditionalGeneration,
														    PLBartForSequenceClassification,
														    PLBartModel,
														    PLBartPreTrainedModel,
														)


else:
							import sys

							UpperCamelCase_							=					_LazyModule(__name__, globals()["""__file__"""], _import_structure)





