





import argparse
import os

import torch

from transformers import FlavaImageCodebook, FlavaImageCodebookConfig



def 	_a						(   UpperCAmelCase      ,  UpperCAmelCase      ,  UpperCAmelCase      ,  UpperCAmelCase					)    ->    Union[str, Any]:







   """simple docstring"""
   lowerCamelCase__       :						List[str]        =							s.rsplit(UpperCAmelCase      ,  UpperCAmelCase					)
   return new.join(UpperCAmelCase					)



def 	_a						(   UpperCAmelCase					)    ->    Dict:







   """simple docstring"""
   # 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						(   UpperCAmelCase					)    ->    List[str]:







   """simple docstring"""
   lowerCamelCase__       :						str        =							{}

   lowerCamelCase__       :						Tuple        =							['''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:
            lowerCamelCase__       :						List[str]        =							key.replace(f"{group_key}."      ,  f"{group_key}.group."					)

      if "res_path" in key:
         lowerCamelCase__       :						List[str]        =							key.replace('''res_path.'''      ,  '''res_path.path.'''					)

      if key.endswith('''.w'''					):
         lowerCamelCase__       :						Any        =							rreplace(UpperCAmelCase      ,  '''.w'''      ,  '''.weight'''      ,  1					)
      if key.endswith('''.b'''					):
         lowerCamelCase__       :						List[Any]        =							rreplace(UpperCAmelCase      ,  '''.b'''      ,  '''.bias'''      ,  1					)

      lowerCamelCase__       :						List[Any]        =							value.float()

   return upgrade



@torch.no_grad()
def 	_a						(   UpperCAmelCase      ,  UpperCAmelCase      ,  UpperCAmelCase=None      ,  UpperCAmelCase=True					)    ->    Optional[int]:







   """simple docstring"""
   from dall_e import Encoder

   lowerCamelCase__       :						Optional[int]        =							Encoder()
   if os.path.exists(UpperCAmelCase					):
      lowerCamelCase__       :						str        =							torch.load(UpperCAmelCase					)
   else:
      lowerCamelCase__       :						int        =							torch.hub.load_state_dict_from_url(UpperCAmelCase					)

   if isinstance(UpperCAmelCase      ,  UpperCAmelCase					):
      lowerCamelCase__       :						Optional[Any]        =							ckpt.state_dict()
   encoder.load_state_dict(UpperCAmelCase					)

   if config_path is not None:
      lowerCamelCase__       :						Union[str, Any]        =							FlavaImageCodebookConfig.from_pretrained(UpperCAmelCase					)
   else:
      lowerCamelCase__       :						int        =							FlavaImageCodebookConfig()

   lowerCamelCase__       :						Tuple        =							FlavaImageCodebook(UpperCAmelCase					).eval()
   lowerCamelCase__       :						Dict        =							encoder.state_dict()

   lowerCamelCase__       :						Any        =							upgrade_state_dict(UpperCAmelCase					)
   hf_model.load_state_dict(UpperCAmelCase					)
   lowerCamelCase__       :						int        =							hf_model.state_dict()
   lowerCamelCase__       :						List[str]        =							count_parameters(UpperCAmelCase					)
   lowerCamelCase__       :						List[str]        =							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__":
       _A :   Union[str, Any]  			=  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 :   List[str]  			=  parser.parse_args()

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










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


_A :   Any  			=  logging.get_logger(__name__)

_A :   int  			=  {
    'facebook/timesformer': 'https://huggingface.co/facebook/timesformer/resolve/main/config.json',
}


class        __SCREAMING_SNAKE_CASE      (			lowerCAmelCase_ ):
       _UpperCAmelCase				:  Dict			   =   "timesformer"







       def __init__(				self					:     List[str]		, A					:     int=2_2_4		, A					:     Optional[Any]=1_6		, A					:     str=3		, A					:     str=8		, A					:     Any=7_6_8		, A					:     Dict=1_2		, A					:     Optional[Any]=1_2		, A					:     Any=3_0_7_2		, A					:     str="gelu"		, A					:     Optional[int]=0.0		, A					:     Union[str, Any]=0.0		, A					:     List[Any]=0.02		, A					:     int=1e-6		, A					:     Tuple=True		, A					:     Any="divided_space_time"		, A					:     Optional[Any]=0		, **A					:     Tuple		, )       ->str:
          super().__init__(**A      )

          lowerCamelCase__       :						Optional[Any]        =							image_size
          lowerCamelCase__       :						int        =							patch_size
          lowerCamelCase__       :						Any        =							num_channels
          lowerCamelCase__       :						Optional[int]        =							num_frames

          lowerCamelCase__       :						Optional[Any]        =							hidden_size
          lowerCamelCase__       :						Optional[int]        =							num_hidden_layers
          lowerCamelCase__       :						Optional[int]        =							num_attention_heads
          lowerCamelCase__       :						Dict        =							intermediate_size
          lowerCamelCase__       :						Optional[Any]        =							hidden_act
          lowerCamelCase__       :						Union[str, Any]        =							hidden_dropout_prob
          lowerCamelCase__       :						Dict        =							attention_probs_dropout_prob
          lowerCamelCase__       :						List[Any]        =							initializer_range
          lowerCamelCase__       :						str        =							layer_norm_eps
          lowerCamelCase__       :						Union[str, Any]        =							qkv_bias

          lowerCamelCase__       :						str        =							attention_type
          lowerCamelCase__       :						List[str]        =							drop_path_rate



