




import argparse
import os

import torch

from transformers import FlavaImageCodebook, FlavaImageCodebookConfig







def lowerCamelCase__  (     _lowercase					,		_lowercase					,		_lowercase					,		_lowercase ):

     '''simple docstring'''


     UpperCAmelCase_     :				List[Any]  =     s.rsplit(_lowercase					,		_lowercase )
     return new.join(_lowercase )







def lowerCamelCase__  (     _lowercase ):

     '''simple docstring'''


     return sum(param.float().sum() if '''encoder.embeddings''' not in key else 0 for key, param in state_dict.items() )







def lowerCamelCase__  (     _lowercase ):

     '''simple docstring'''


     UpperCAmelCase_     :				Optional[Any]  =     {}

     UpperCAmelCase_     :				Any  =     ['''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:
                    UpperCAmelCase_     :				List[str]  =     key.replace(f'''{group_key}.'''					,		f'''{group_key}.group.''' )

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

          if key.endswith('''.w''' ):
               UpperCAmelCase_     :				Tuple  =     rreplace(_lowercase					,		'''.w'''					,		'''.weight'''					,		1 )
          if key.endswith('''.b''' ):
               UpperCAmelCase_     :				int  =     rreplace(_lowercase					,		'''.b'''					,		'''.bias'''					,		1 )

          UpperCAmelCase_     :				str  =     value.float()

     return upgrade







@torch.no_grad()
def lowerCamelCase__  (     _lowercase					,		_lowercase					,		_lowercase=None					,		_lowercase=True ):

     '''simple docstring'''


     from dall_e import Encoder

     UpperCAmelCase_     :				Dict  =     Encoder()
     if os.path.exists(_lowercase ):
          UpperCAmelCase_     :				List[str]  =     torch.load(_lowercase )
     else:
          UpperCAmelCase_     :				Dict  =     torch.hub.load_state_dict_from_url(_lowercase )

     if isinstance(_lowercase					,		_lowercase ):
          UpperCAmelCase_     :				Optional[int]  =     ckpt.state_dict()
     encoder.load_state_dict(_lowercase )

     if config_path is not None:
          UpperCAmelCase_     :				Dict  =     FlavaImageCodebookConfig.from_pretrained(_lowercase )
     else:
          UpperCAmelCase_     :				str  =     FlavaImageCodebookConfig()

     UpperCAmelCase_     :				Optional[Any]  =     FlavaImageCodebook(_lowercase ).eval()
     UpperCAmelCase_     :				Optional[int]  =     encoder.state_dict()

     UpperCAmelCase_     :				Tuple  =     upgrade_state_dict(_lowercase )
     hf_model.load_state_dict(_lowercase )
     UpperCAmelCase_     :				List[str]  =     hf_model.state_dict()
     UpperCAmelCase_     :				List[Any]  =     count_parameters(_lowercase )
     UpperCAmelCase_     :				List[str]  =     count_parameters(_lowercase )

     assert torch.allclose(_lowercase					,		_lowercase					,		atol=1E-3 )

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


if __name__ == "__main__":
     __a							     =					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')
     __a							     =					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_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


__a							     =					{}

try:
     if not is_sentencepiece_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __a							     =					['NllbTokenizer']

try:
     if not is_tokenizers_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __a							     =					['NllbTokenizerFast']


if TYPE_CHECKING:
     try:
          if not is_sentencepiece_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_nllb import NllbTokenizer

     try:
          if not is_tokenizers_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_nllb_fast import NllbTokenizerFast

else:
     import sys

     __a							     =					_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)