






import argparse
import os

import torch

from transformers import FlavaImageCodebook, FlavaImageCodebookConfig
def 						_a    (      a :Optional[Any]	,							a :int	,							a :List[str]	,							a :List[str] )							->			Tuple:
 a		  =      s.rsplit(a	,							a )
 return new.join(a )
def 						_a    (      a :Any )							->			List[Any]:
 # encoder.embeddings are double copied in original FLAVA
 return sum(param.float().sum() if '''encoder.embeddings''' not in key else 0 for key, param in state_dict.items() )
def 						_a    (      a :Any )							->			List[Any]:
 a		  =      {}

 a		  =      ['''group_1''', '''group_2''', '''group_3''', '''group_4''']
 for key, value in state_dict.items():
  for group_key in group_keys:
   if group_key in key:
    a		  =      key.replace(F"""{group_key}."""	,							F"""{group_key}.group.""" )

  if "res_path" in key:
   a		  =      key.replace('''res_path.'''	,							'''res_path.path.''' )

  if key.endswith('''.w''' ):
   a		  =      rreplace(a	,							'''.w'''	,							'''.weight'''	,							1 )
  if key.endswith('''.b''' ):
   a		  =      rreplace(a	,							'''.b'''	,							'''.bias'''	,							1 )

  a		  =      value.float()

 return upgrade
@torch.no_grad()
def 						_a    (      a :Union[str, Any]	,							a :Optional[Any]	,							a :Optional[int]=None	,							a :str=True )							->			Tuple:
 from dall_e import Encoder

 a		  =      Encoder()
 if os.path.exists(a ):
  a		  =      torch.load(a )
 else:
  a		  =      torch.hub.load_state_dict_from_url(a )

 if isinstance(a	,							a ):
  a		  =      ckpt.state_dict()
 encoder.load_state_dict(a )

 if config_path is not None:
  a		  =      FlavaImageCodebookConfig.from_pretrained(a )
 else:
  a		  =      FlavaImageCodebookConfig()

 a		  =      FlavaImageCodebook(a ).eval()
 a		  =      encoder.state_dict()

 a		  =      upgrade_state_dict(a )
 hf_model.load_state_dict(a )
 a		  =      hf_model.state_dict()
 a		  =      count_parameters(a )
 a		  =      count_parameters(a )

 assert torch.allclose(a	,							a	,							atol=1e-3 )

 if save_checkpoint:
  hf_model.save_pretrained(a )
 else:
  return hf_state_dict


if __name__ == "__main__":
     UpperCAmelCase__				   =		argparse.ArgumentParser()
     parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
     parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to flava checkpoint")
     parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
     UpperCAmelCase__				   =		parser.parse_args()

     convert_dalle_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)










from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCAmelCase__				   =		{
    "configuration_nllb_moe": [
        "NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "NllbMoeConfig",
    ]
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCAmelCase__				   =		[
         "NLLB_MOE_PRETRAINED_MODEL_ARCHIVE_LIST",
         "NllbMoeForConditionalGeneration",
         "NllbMoeModel",
         "NllbMoePreTrainedModel",
         "NllbMoeTop2Router",
         "NllbMoeSparseMLP",
     ]


if TYPE_CHECKING:
     from .configuration_nllb_moe import (
         NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP,
         NllbMoeConfig,
     )

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_nllb_moe import (
              NLLB_MOE_PRETRAINED_MODEL_ARCHIVE_LIST,
              NllbMoeForConditionalGeneration,
              NllbMoeModel,
              NllbMoePreTrainedModel,
              NllbMoeSparseMLP,
              NllbMoeTopaRouter,
          )


else:
     import sys

     UpperCAmelCase__				   =		_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


