

'''simple docstring'''

import argparse
import os

import torch

from transformers import FlavaConfig, FlavaForPreTraining
from transformers.models.flava.convert_dalle_to_flava_codebook import convert_dalle_checkpoint

def  __UpperCamelCase							(		lowercase__  :     Optional[int]       ):






     '''simple docstring'''





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

def  __UpperCamelCase							(		lowercase__  :     Optional[Any],    lowercase__  :     List[str]       ):






     '''simple docstring'''





     __lowercase							={}

     for key, value in state_dict.items():
          if "text_encoder.embeddings" in key or "image_encoder.embeddings" in key:
               continue

          __lowercase							=key.replace('heads.cmd.mim_head.cls.predictions',    'mmm_image_head'       )
          __lowercase							=key.replace('heads.cmd.mlm_head.cls.predictions',    'mmm_text_head'       )
          __lowercase							=key.replace('heads.cmd.itm_head.cls',    'itm_head'       )
          __lowercase							=key.replace('heads.cmd.itm_head.pooler',    'itm_head.pooler'       )
          __lowercase							=key.replace('heads.cmd.clip_head.logit_scale',    'flava.logit_scale'       )
          __lowercase							=key.replace('heads.fairseq_mlm.cls.predictions',    'mlm_head'       )
          __lowercase							=key.replace('heads.imagenet.mim_head.cls.predictions',    'mim_head'       )
          __lowercase							=key.replace('mm_text_projection',    'flava.text_to_mm_projection'       )
          __lowercase							=key.replace('mm_image_projection',    'flava.image_to_mm_projection'       )
          __lowercase							=key.replace('image_encoder.module',    'flava.image_model'       )
          __lowercase							=key.replace('text_encoder.module',    'flava.text_model'       )
          __lowercase							=key.replace('mm_encoder.module.encoder.cls_token',    'flava.multimodal_model.cls_token'       )
          __lowercase							=key.replace('mm_encoder.module',    'flava.multimodal_model'       )
          __lowercase							=key.replace('text_projection',    'flava.text_projection'       )
          __lowercase							=key.replace('image_projection',    'flava.image_projection'       )

          __lowercase							=value.float()

     for key, value in codebook_state_dict.items():
          __lowercase							=value

     return upgrade

@torch.no_grad()
def  __UpperCamelCase							(		lowercase__  :     Optional[int],    lowercase__  :     Union[str, Any],    lowercase__  :     List[str],    lowercase__  :     Union[str, Any]=None       ):






     '''simple docstring'''





     if config_path is not None:
          __lowercase							=FlavaConfig.from_pretrained(lowercase__       )
     else:
          __lowercase							=FlavaConfig()

     __lowercase							=FlavaForPreTraining(lowercase__       ).eval()

     __lowercase							=convert_dalle_checkpoint(lowercase__,    lowercase__,    save_checkpoint=lowercase__       )

     if os.path.exists(lowercase__       ):
          __lowercase							=torch.load(lowercase__,    map_location='cpu'       )
     else:
          __lowercase							=torch.hub.load_state_dict_from_url(lowercase__,    map_location='cpu'       )

     __lowercase							=upgrade_state_dict(lowercase__,    lowercase__       )
     hf_model.load_state_dict(lowercase__       )
     __lowercase							=hf_model.state_dict()
     __lowercase							=count_parameters(lowercase__       )
     __lowercase							=count_parameters(lowercase__       ) + count_parameters(lowercase__       )

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

     hf_model.save_pretrained(lowercase__       )


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('''--codebook_path''', default=None, type=str, help='''Path to flava codebook 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_flava_checkpoint(args.checkpoint_path, args.codebook_path, args.pytorch_dump_folder_path, args.config_path)






'''simple docstring'''

# Lint as: python3
import itertools
import os
import re


lowerCamelCase							:   Any  						=	re.compile(R'([A-Z]+)([A-Z][a-z])')
lowerCamelCase							:   str  						=	re.compile(R'([a-z\d])([A-Z])')

lowerCamelCase							:   Optional[int]  						=	re.compile(R'(?<!_)_(?!_)')
lowerCamelCase							:   List[Any]  						=	re.compile(R'(_{2,})')

lowerCamelCase							:   str  						=	R'^\w+(\.\w+)*$'

lowerCamelCase							:   Dict  						=	R'<>:/\|?*'

def _SCREAMING_SNAKE_CASE      (A       )       ->       Any:






       """simple docstring"""







       lowercase__      			= _uppercase_uppercase_re.sub(R'''\1_\2'''  , A       )
       lowercase__      			= _lowercase_uppercase_re.sub(R'''\1_\2'''  , A       )
       return name.lower()

def _SCREAMING_SNAKE_CASE      (A       )       ->       Tuple:






       """simple docstring"""







       lowercase__      			= _single_underscore_re.split(A       )
       lowercase__      			= [_multiple_underscores_re.split(A       ) for n in name]
       return "".join(n.capitalize() for n in itertools.chain.from_iterable(A       ) if n != ''''''       )

def _SCREAMING_SNAKE_CASE      (A       )       ->       Tuple:






       """simple docstring"""







       if os.path.basename(A       ) != name:
              raise ValueError(f"Should be a dataset name, not a path: {name}"       )
       return camelcase_to_snakecase(A       )

def _SCREAMING_SNAKE_CASE      (A  , A       )       ->       Optional[Any]:






       """simple docstring"""







       if os.path.basename(A       ) != name:
              raise ValueError(f"Should be a dataset name, not a path: {name}"       )
       if not re.match(_split_re  , A       ):
              raise ValueError(f"Split name should match '{_split_re}'' but got '{split}'."       )
       return f"{filename_prefix_for_name(A       )}-{split}"

def _SCREAMING_SNAKE_CASE      (A  , A  , A  , A=None       )       ->       List[str]:






       """simple docstring"""







       lowercase__      			= filename_prefix_for_split(A  , A       )
       if filetype_suffix:
              prefix += f".{filetype_suffix}"
       lowercase__      			= os.path.join(A  , A       )
       return f"{filepath}*"

def _SCREAMING_SNAKE_CASE      (A  , A  , A  , A=None  , A=None       )       ->       Optional[Any]:






       """simple docstring"""







       lowercase__      			= filename_prefix_for_split(A  , A       )
       lowercase__      			= os.path.join(A  , A       )

       if shard_lengths:
              lowercase__      			= len(A       )
              lowercase__      			= [f"{prefix}-{shard_id:05d}-of-{num_shards:05d}" for shard_id in range(A       )]
              if filetype_suffix:
                     lowercase__      			= [filename + f".{filetype_suffix}" for filename in filenames]
              return filenames
       else:
              lowercase__      			= prefix
              if filetype_suffix:
                     filename += f".{filetype_suffix}"
              return [filename]




