






'''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 	__magic_name__( _A					):
						'''simple docstring'''





						UpperCamelCase__         = [
						    """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 	__magic_name__( _A					):
						'''simple docstring'''





						UpperCamelCase__    ,   UpperCamelCase__         = emb.weight.shape
						UpperCamelCase__         = nn.Linear(_A						,      _A						,      bias=_A					)
						UpperCamelCase__         = emb.weight.data
						return lin_layer







def 	__magic_name__( _A						,      _A=None					):
						'''simple docstring'''





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







def 	__magic_name__( _A						,      _A						,      _A						,      _A						,      _A = WEIGHTS_NAME					):
						'''simple docstring'''





						UpperCamelCase__         = []
						UpperCamelCase__         = 0
						os.makedirs(_A						,      exist_ok=_A					)

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

    # Add the metadata
						UpperCamelCase__         = {"""total_size""": total_size}
						UpperCamelCase__         = {"""metadata""": metadata, """weight_map""": weight_map}

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

						return metadata, index


if __name__ == "__main__":
					lowerCamelCase_				: int    	=							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_				: str    	=							parser.parse_args()
					lowerCamelCase_	,     lowerCamelCase_				: Union[str, Any]    	=							shard_on_the_fly(
					    args.nllb_moe_checkpoint_path,
					    args.pytorch_dump_folder_path,
					    128,
					    args.dtype,
					)

					lowerCamelCase_				: Optional[Any]    	=							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_				: List[Any]    	=							NllbMoeModel.from_pretrained(args.pytorch_dump_folder_path)
					print('''Done''')
					model.save_pretrained(args.pytorch_dump_folder_path)








'''simple docstring'''


import math







def 	__magic_name__( _A					):
						'''simple docstring'''





						assert isinstance(_A						,      _A					) and (
						    number >= 0
						), "'number' must been an int and positive"

						if 1 < number < 4:
												# 2 and 3 are primes
												return True
						elif number < 2 or not number % 2:
												# Negatives, 0, 1 and all even numbers are not primes
												return False

						UpperCamelCase__         = range(3						,      int(math.sqrt(_A					) + 1					)						,      2					)
						return not any(not number % i for i in odd_numbers					)







def 	__magic_name__( _A						,      _A=1						,      **_A					):
						'''simple docstring'''





						UpperCamelCase__         = factor * value
						UpperCamelCase__         = value

						while not is_prime(_A					):
												value += 1 if not ("desc" in kwargs and kwargs["desc"] is True) else -1

						if value == first_value_val:
												return next_prime(value + 1						,      **_A					)
						return value
