





import argparse
import os

import torch

from transformers import FlavaImageCodebook, FlavaImageCodebookConfig




def  lowerCAmelCase_ (							UpperCamelCase_					,		UpperCamelCase_					,		UpperCamelCase_					,		UpperCamelCase_  )     ->		Tuple:
       UpperCamelCase_          =  s.rsplit(UpperCamelCase_					,		UpperCamelCase_  )
       return new.join(UpperCamelCase_  )




def  lowerCAmelCase_ (							UpperCamelCase_  )     ->		Union[str, 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  lowerCAmelCase_ (							UpperCamelCase_  )     ->		str:
       UpperCamelCase_          =  {}

       UpperCamelCase_          =  ["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_          =  key.replace(F'''{group_key}.'''					,		F'''{group_key}.group.'''  )

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

              if key.endswith(".w"  ):
                     UpperCamelCase_          =  rreplace(UpperCamelCase_					,		".w"					,		".weight"					,		1  )
              if key.endswith(".b"  ):
                     UpperCamelCase_          =  rreplace(UpperCamelCase_					,		".b"					,		".bias"					,		1  )

              UpperCamelCase_          =  value.float()

       return upgrade




@torch.no_grad()
def  lowerCAmelCase_ (							UpperCamelCase_					,		UpperCamelCase_					,		UpperCamelCase_=None					,		UpperCamelCase_=True  )     ->		str:
       from dall_e import Encoder

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

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

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

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

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

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

       if save_checkpoint:
              hf_model.save_pretrained(UpperCamelCase_  )
       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)








import argparse
import os
import re


_UpperCAmelCase        =				'src/transformers/models/auto'


# re pattern that matches mapping introductions:
#    SUPER_MODEL_MAPPING_NAMES = OrderedDict or SUPER_MODEL_MAPPING = OrderedDict
_UpperCAmelCase        =				re.compile(r'[A-Z_]+_MAPPING(\s+|_[A-Z_]+\s+)=\s+OrderedDict')
# re pattern that matches identifiers in mappings
_UpperCAmelCase        =				re.compile(r'\s*\(\s*"(\S[^"]+)"')




def  lowerCAmelCase_ (							UpperCamelCase_					,		UpperCamelCase_ = False  )     ->		Union[str, Any]:
       with open(UpperCamelCase_					,		"r"					,		encoding="utf-8"  ) as f:
              UpperCamelCase_          =  f.read()

       UpperCamelCase_          =  content.split("\n"  )
       UpperCamelCase_          =  []
       UpperCamelCase_          =  0
       while line_idx < len(UpperCamelCase_  ):
              if _re_intro_mapping.search(lines[line_idx]  ) is not None:
                     UpperCamelCase_          =  len(re.search(r"^(\s*)\S"					,		lines[line_idx]  ).groups()[0]  ) + 8
                     # Start of a new mapping!
                     while not lines[line_idx].startswith(" " * indent + "("  ):
                            new_lines.append(lines[line_idx]  )
                            line_idx += 1

                     UpperCamelCase_          =  []
                     while lines[line_idx].strip() != "]":
                            # Blocks either fit in one line or not
                            if lines[line_idx].strip() == "(":
                                   UpperCamelCase_          =  line_idx
                                   while not lines[line_idx].startswith(" " * indent + ")"  ):
                                          line_idx += 1
                                   blocks.append("\n".join(lines[start_idx : line_idx + 1]  )  )
                            else:
                                   blocks.append(lines[line_idx]  )
                            line_idx += 1

                     # Sort blocks by their identifiers
                     UpperCamelCase_          =  sorted(UpperCamelCase_					,		key=lambda UpperCamelCase_  : _re_identifier.search(UpperCamelCase_  ).groups()[0]  )
                     new_lines += blocks
              else:
                     new_lines.append(lines[line_idx]  )
                     line_idx += 1

       if overwrite:
              with open(UpperCamelCase_					,		"w"					,		encoding="utf-8"  ) as f:
                     f.write("\n".join(UpperCamelCase_  )  )
       elif "\n".join(UpperCamelCase_  ) != content:
              return True




def  lowerCAmelCase_ (							UpperCamelCase_ = False  )     ->		List[Any]:
       UpperCamelCase_          =  [os.path.join(UpperCamelCase_					,		UpperCamelCase_  ) for f in os.listdir(UpperCamelCase_  ) if f.endswith(".py"  )]
       UpperCamelCase_          =  [sort_auto_mapping(UpperCamelCase_					,		overwrite=UpperCamelCase_  ) for fname in fnames]

       if not overwrite and any(UpperCamelCase_  ):
              UpperCamelCase_          =  [f for f, d in zip(UpperCamelCase_					,		UpperCamelCase_  ) if d]
              raise ValueError(
                  F'''The following files have auto mappings that need sorting: {", ".join(UpperCamelCase_  )}. Run `make style` to fix'''
                  " this."  )


if __name__ == "__main__":
     _UpperCAmelCase        =				argparse.ArgumentParser()
     parser.add_argument('--check_only', action='store_true', help='Whether to only check or fix style.')
     _UpperCAmelCase        =				parser.parse_args()

     sort_all_auto_mappings(not args.check_only)

