

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 (     lowercase__						):
      __lowerCAmelCase					:						List[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(lowercase__   ,	lowercase__						)







def 		_lowercase (     lowercase__						):
      __lowerCAmelCase,							__lowerCAmelCase					:						Dict					=					emb.weight.shape
      __lowerCAmelCase					:						int					=					nn.Linear(lowercase__   ,	lowercase__   ,	bias=lowercase__						)
      __lowerCAmelCase					:						Tuple					=					emb.weight.data
      return lin_layer







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







def 		_lowercase (     lowercase__   ,	lowercase__   ,	lowercase__   ,	lowercase__   ,	lowercase__ = WEIGHTS_NAME						):
      __lowerCAmelCase					:						Tuple					=					[]
      __lowerCAmelCase					:						Optional[Any]					=					0
      os.makedirs(lowercase__   ,	exist_ok=lowercase__						)

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

    # Add the metadata
      __lowerCAmelCase					:						List[Any]					=					{'''total_size''': total_size}
      __lowerCAmelCase					:						Union[str, Any]					=					{'''metadata''': metadata, '''weight_map''': weight_map}

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

      return metadata, index


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

 _UpperCamelCase 			=		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)
 _UpperCamelCase 			=		NllbMoeModel.from_pretrained(args.pytorch_dump_folder_path)
 print("Done")
 model.save_pretrained(args.pytorch_dump_folder_path)





def 		_lowercase (     lowercase__ = 2_0_0						):
      __lowerCAmelCase					:						Union[str, Any]					=					[1, 2, 5, 1_0, 2_0, 5_0, 1_0_0, 2_0_0]
      __lowerCAmelCase					:						Dict					=					[0] * (pence + 1)
      __lowerCAmelCase					:						Optional[int]					=					1  # base case: 1 way to make 0 pence

      for coin in coins:
            for i in range(lowercase__   ,	pence + 1   ,	1						):
                  number_of_ways[i] += number_of_ways[i - coin]
      return number_of_ways[pence]


if __name__ == "__main__":
 assert solution(200) == 7_3682


