



'''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 lowerCAmelCase  (__A):



     """simple docstring"""



     _a      =  [
         '''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 lowerCAmelCase  (__A):



     """simple docstring"""



     _a  ,    _a      =  emb.weight.shape
     _a      =  nn.Linear(__A  ,	__A  ,	bias=__A)
     _a      =  emb.weight.data
     return lin_layer




def lowerCAmelCase  (__A  ,	__A=None):



     """simple docstring"""



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




def lowerCAmelCase  (__A  ,	__A  ,	__A  ,	__A  ,	__A = WEIGHTS_NAME):



     """simple docstring"""



     _a      =  []
     _a      =  0
     os.makedirs(__A  ,	exist_ok=__A)

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

    # Add the metadata
     _a      =  {'''total_size''': total_size}
     _a      =  {'''metadata''': metadata, '''weight_map''': weight_map}

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

     return metadata, index


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

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








'''simple docstring'''





from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowercase_		       =  logging.get_logger(__name__)

lowercase_		       =  {
    "vinvino02/glpn-kitti": "https://huggingface.co/vinvino02/glpn-kitti/resolve/main/config.json",
    # See all GLPN models at https://huggingface.co/models?filter=glpn
}






class __A			(  A						):



 '''simple docstring'''







 __lowerCamelCase    :       str          =       'glpn'



 def __init__(self ,   A=3 ,   A=4 ,   A=[2, 2, 2, 2] ,   A=[8, 4, 2, 1] ,   A=[32, 64, 160, 256] ,   A=[7, 3, 3, 3] ,   A=[4, 2, 2, 2] ,   A=[1, 2, 5, 8] ,   A=[4, 4, 4, 4] ,   A="gelu" ,   A=0.0 ,   A=0.0 ,   A=0.02 ,   A=0.1 ,   A=1E-6 ,   A=64 ,   A=10 ,   A=-1 ,   **A ,   )    ->     Any:


      """simple docstring"""



      super().__init__(**A       )

      _a      =  num_channels
      _a      =  num_encoder_blocks
      _a      =  depths
      _a      =  sr_ratios
      _a      =  hidden_sizes
      _a      =  patch_sizes
      _a      =  strides
      _a      =  mlp_ratios
      _a      =  num_attention_heads
      _a      =  hidden_act
      _a      =  hidden_dropout_prob
      _a      =  attention_probs_dropout_prob
      _a      =  initializer_range
      _a      =  drop_path_rate
      _a      =  layer_norm_eps
      _a      =  decoder_hidden_size
      _a      =  max_depth
      _a      =  head_in_index



