
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     UpperCamelCase    (      _a		)					->							Tuple:
     '''simple docstring'''





     lowercase_       :Dict     =       [
         '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     UpperCamelCase    (      _a		)					->							int:
     '''simple docstring'''





     lowercase_       :Optional[int]     =       emb.weight.shape
     lowercase_       :str     =       nn.Linear(_a       ,		_a       ,		bias=_a		)
     lowercase_       :int     =       emb.weight.data
     return lin_layer




def     UpperCamelCase    (      _a       ,		_a=None		)					->							List[str]:
     '''simple docstring'''





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




def     UpperCamelCase    (      _a       ,		_a       ,		_a       ,		_a       ,		_a = WEIGHTS_NAME		)					->							Dict:
     '''simple docstring'''





     lowercase_       :Any     =       []
     lowercase_       :Optional[int]     =       0
     os.makedirs(_a       ,		exist_ok=_a		)

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

    # Add the metadata
     lowercase_       :Tuple     =       {'total_size': total_size}
     lowercase_       :Dict     =       {'metadata': metadata, 'weight_map': weight_map}

     with open(os.path.join(_a       ,		_a		)       ,		'''w'''       ,		encoding='''utf-8'''		) as f:
          lowercase_       :int     =       json.dumps(_a       ,		indent=2       ,		sort_keys=_a		) + '\n'
          f.write(_a		)

     return metadata, index


if __name__ == "__main__":
   SCREAMING_SNAKE_CASE       :		Dict         =				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.",
   )
   SCREAMING_SNAKE_CASE       :		Any         =				parser.parse_args()
   SCREAMING_SNAKE_CASE     ,		SCREAMING_SNAKE_CASE       :		List[str]         =				shard_on_the_fly(
       args.nllb_moe_checkpoint_path,
       args.pytorch_dump_folder_path,
       128,
       args.dtype,
   )

   SCREAMING_SNAKE_CASE       :		List[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)
   SCREAMING_SNAKE_CASE       :		List[Any]         =				NllbMoeModel.from_pretrained(args.pytorch_dump_folder_path)
   print("Done")
   model.save_pretrained(args.pytorch_dump_folder_path)





from arguments import InitializationArguments

from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer, HfArgumentParser


# Configuration
SCREAMING_SNAKE_CASE       :		List[str]         =				HfArgumentParser(InitializationArguments)
SCREAMING_SNAKE_CASE       :		List[Any]         =				parser.parse_args()

# Load codeparrot tokenizer trained for Python code tokenization
SCREAMING_SNAKE_CASE       :		Union[str, Any]         =				AutoTokenizer.from_pretrained(args.tokenizer_name)

# Config: "scale_attn_by_layer_idx" and "reorder_and_upcast_attn" are Mistral stability tweaks
SCREAMING_SNAKE_CASE       :		Optional[Any]         =				{
    "vocab_size": len(tokenizer),
    "scale_attn_by_inverse_layer_idx": True,
    "reorder_and_upcast_attn": True,
}

# Load model config (GPT-2 large in this case)
SCREAMING_SNAKE_CASE       :		Any         =				AutoConfig.from_pretrained(args.config_name, **config_kwargs)

# Initialize new model with config
SCREAMING_SNAKE_CASE       :		Dict         =				AutoModelForCausalLM.from_config(config)

# Save model to the hub
model.save_pretrained(args.model_name, push_to_hub=args.push_to_hub)

