





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_	(      lowerCAmelCase:	Union[str, Any]				)->		Dict:
		_snake_case   :				str														=	[
		    "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(__snake_case    ,							__snake_case				)


def     lowerCamelCase_	(      lowerCAmelCase:	Optional[int]				)->		str:
		_snake_case   :				Optional[int]														=	emb.weight.shape
		_snake_case   :				Any														=	nn.Linear(__snake_case    ,							__snake_case    ,							bias=__snake_case				)
		_snake_case   :				Dict														=	emb.weight.data
		return lin_layer


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


def     lowerCamelCase_	(      lowerCAmelCase:	Optional[int]    ,							lowerCAmelCase:	Optional[Any]    ,							lowerCAmelCase:	Tuple    ,							lowerCAmelCase:	List[Any]    ,							lowerCAmelCase:	str = WEIGHTS_NAME				)->		List[Any]:
		_snake_case   :				Union[str, Any]														=	[]
		_snake_case   :				Union[str, Any]														=	0
		os.makedirs(__snake_case    ,							exist_ok=__snake_case				)

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

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

    # Add the metadata
		_snake_case   :				Tuple														=	{"total_size": total_size}
		_snake_case   :				Optional[Any]														=	{"metadata": metadata, "weight_map": weight_map}

		with open(os.path.join(__snake_case    ,							__snake_case				)    ,							'w'    ,							encoding='utf-8'				) as f:
				_snake_case   :				List[Any]														=	json.dumps(__snake_case    ,							indent=2    ,							sort_keys=__snake_case				) + "\n"
				f.write(__snake_case				)

		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_						   =    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)











# Function to print upper half of diamond (pyramid)


def     lowerCamelCase_	(      lowerCAmelCase:	Optional[Any]				)->		List[str]:
  for i in range(0    ,							lowerCAmelCase				):
    for _ in range(0    ,							n - i - 1				):  # printing spaces
      print(' '    ,							end=''				)
    for _ in range(0    ,							i + 1				):  # printing stars
      print('* '    ,							end=''				)
    print()


def     lowerCamelCase_	(      lowerCAmelCase:	Optional[int]				)->		List[Any]:
  for i in range(lowerCAmelCase    ,							0    ,							-1				):
    for _ in range(lowerCAmelCase    ,							0    ,							-1				):  # printing stars
      print('* '    ,							end=''				)
    print()
    for _ in range(n - i + 1    ,							0    ,							-1				):  # printing spaces
      print(' '    ,							end=''				)


def     lowerCamelCase_	(      lowerCAmelCase:	Tuple				)->		int:
  if n <= 0:
    print('       ...       ....        nothing printing :('				)
    return
  floyd(lowerCAmelCase				)  # upper half
  reverse_floyd(lowerCAmelCase				)  # lower half


if __name__ == "__main__":
      print(r"""| /\ | |- |  |-  |--| |\  /| |-""")
      print(r"""|/  \| |- |_ |_  |__| | \/ | |_""")
      lowerCAmelCase_						   =    1
      while K:
            lowerCAmelCase_						   =    int(input("""enter the number and , and see the magic : """))
            print()
            pretty_print(user_number)
            lowerCAmelCase_						   =    int(input("""press 0 to exit... and 1 to continue..."""))

      print("""Good Bye...""")




