


"""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 __lowercase     (      lowerCamelCase_  :      Dict  ):
    SCREAMING_SNAKE_CASE__     							=				[
        "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 __lowercase     (      lowerCamelCase_  :      List[Any]  ):
    SCREAMING_SNAKE_CASE__							,      SCREAMING_SNAKE_CASE__     							=				emb.weight.shape
    SCREAMING_SNAKE_CASE__     							=				nn.Linear(lowercase__						,					lowercase__						,					bias=lowercase__  )
    SCREAMING_SNAKE_CASE__     							=				emb.weight.data
    return lin_layer


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


def __lowercase     (      lowerCamelCase_  :      List[str]						,					lowerCamelCase_  :      Dict						,					lowerCamelCase_  :      int						,					lowerCamelCase_  :      Optional[int]						,					lowerCamelCase_  :      Dict = WEIGHTS_NAME  ):
    SCREAMING_SNAKE_CASE__     							=				[]
    SCREAMING_SNAKE_CASE__     							=				0
    os.makedirs(lowercase__						,					exist_ok=lowercase__  )

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

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

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

    return metadata, index


if __name__ == "__main__":
 _lowerCamelCase     =  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     =  parser.parse_args()
 _lowerCamelCase     ,     _lowerCamelCase     =  shard_on_the_fly(
     args.nllb_moe_checkpoint_path,
     args.pytorch_dump_folder_path,
     128,
     args.dtype,
 )

 _lowerCamelCase     =  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     =  NllbMoeModel.from_pretrained(args.pytorch_dump_folder_path)
 print('Done')
 model.save_pretrained(args.pytorch_dump_folder_path)




"""simple docstring"""






def __lowercase     (      lowerCamelCase_  :      List[Any]  ):
				SCREAMING_SNAKE_CASE__     							=				[]
				SCREAMING_SNAKE_CASE__     							=				set({"(", "[", "{"}  )
				SCREAMING_SNAKE_CASE__     							=				set({")", "]", "}"}  )
				SCREAMING_SNAKE_CASE__     							=				{"{": "}", "[": "]", "(": ")"}

				for i in range(len(lowerCamelCase_  )  ):
								if s[i] in open_brackets:
												stack.append(s[i]  )

								elif s[i] in closed_brackets and (
								    len(lowerCamelCase_  ) == 0 or (len(lowerCamelCase_  ) > 0 and open_to_closed[stack.pop()] != s[i])
								):
												return False

				return len(lowerCamelCase_  ) == 0


def __lowercase     (      ):
				SCREAMING_SNAKE_CASE__     							=				input("Enter sequence of brackets: "  )
				if is_balanced(lowerCamelCase_  ):
								print(lowerCamelCase_						,					"is balanced"  )
				else:
								print(lowerCamelCase_						,					"is not balanced"  )


if __name__ == "__main__":
	main()
