


"""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_ (	lowercase__ :Dict     ):
			__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(lowercase__,			lowercase__     )





def       a_ (	lowercase__ :Tuple     ):
			__lowerCamelCase     ,__lowerCamelCase          =      emb.weight.shape
			__lowerCamelCase          =      nn.Linear(lowercase__,			lowercase__,			bias=lowercase__     )
			__lowerCamelCase          =      emb.weight.data
			return lin_layer





def       a_ (	lowercase__ :List[str],			lowercase__ :Optional[int]=None     ):
			__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       a_ (	lowercase__ :int,			lowercase__ :int,			lowercase__ :Tuple,			lowercase__ :Union[str, Any],			lowercase__ :str = WEIGHTS_NAME     ):
			__lowerCamelCase          =      []
			__lowerCamelCase          =      0
			os.makedirs(lowercase__,			exist_ok=lowercase__     )

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

    # Add the last block
			__lowerCamelCase          =      os.path.join(lowercase__,			weights_name.replace(""".bin""",			f'-{len(lowercase__     )+1:05d}-of-???.bin'     )     )
			__lowerCamelCase          =      torch.load(switch_checkpoint_path + """-shared.pt"""     )["""model"""]
			remove_ignore_keys_(lowercase__     )
			__lowerCamelCase          =      rename_fairseq_keys(lowercase__,			lowercase__     )
			__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(lowercase__     ) == 1:
						__lowerCamelCase          =      os.path.join(lowercase__,			lowercase__     )
						torch.save(lowercase__,			lowercase__     )
						return {weights_name: sharded_state_dicts[0]}, None
			else:
						torch.save(lowercase__,			lowercase__     )
			# Otherwise, let's build the index
			__lowerCamelCase          =      {}
			for idx, shard in enumerate(lowercase__     ):
						__lowerCamelCase          =      weights_name.replace(""".bin""",			f'-{idx+1:05d}-of-{len(lowercase__     ):05d}.bin'     )
						__lowerCamelCase          =      os.path.join(lowercase__,			weights_name.replace(""".bin""",			f'-{idx+1:05d}-of-???.bin'     )     )
						os.rename(lowercase__,			os.path.join(lowercase__,			lowercase__     )     )
						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(lowercase__,			lowercase__     ),			"""w""",			encoding="""utf-8"""     ) as f:
						__lowerCamelCase          =      json.dumps(lowercase__,			indent=2,			sort_keys=lowercase__     ) + """\n"""
						f.write(lowercase__     )

			return metadata, index


if __name__ == "__main__":
					__magic_name__						: Optional[Any]    =				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.',
					)
					__magic_name__						: List[str]    =				parser.parse_args()
					__magic_name__  , __magic_name__						: Dict    =				shard_on_the_fly(
					    args.nllb_moe_checkpoint_path,
					    args.pytorch_dump_folder_path,
					    1_2_8,
					    args.dtype,
					)

					__magic_name__						: Tuple    =				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)
					__magic_name__						: List[Any]    =				NllbMoeModel.from_pretrained(args.pytorch_dump_folder_path)
					print('Done')
					model.save_pretrained(args.pytorch_dump_folder_path)








"""simple docstring"""




from __future__ import annotations





def       a_ (	lowercase__ :list[float]     ):
			if len(lowercase__     ) < 2:
						raise ValueError("""Monogons and Digons are not polygons in the Euclidean space"""     )
			if any(i <= 0 for i in nums     ):
						raise ValueError("""All values must be greater than 0"""     )
			__lowerCamelCase          =      nums.copy()
			copy_nums.sort()
			return copy_nums[-1] < sum(copy_nums[:-1]     )


if __name__ == "__main__":
					import doctest

					doctest.testmod()




