




'''simple docstring'''







import argparse
import os

import torch

from transformers import FlavaImageCodebook, FlavaImageCodebookConfig



def        lowerCamelCase__				(      __lowercase     ,   __lowercase     ,   __lowercase     ,   __lowercase				):
     snake_case			:      Optional[int]        =			s.rsplit(__lowercase     ,   __lowercase				)
     return new.join(__lowercase				)



def        lowerCamelCase__				(      __lowercase				):
     # 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__				(      __lowercase				):
     snake_case			:      Union[str, Any]        =			{}

     snake_case			:      List[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:
                    snake_case			:      Any        =			key.replace(F'''{group_key}.'''     ,   F'''{group_key}.group.'''				)

          if "res_path" in key:
               snake_case			:      List[Any]        =			key.replace("""res_path."""     ,   """res_path.path."""				)

          if key.endswith(""".w"""				):
               snake_case			:      int        =			rreplace(__lowercase     ,   """.w"""     ,   """.weight"""     ,   1				)
          if key.endswith(""".b"""				):
               snake_case			:      Optional[Any]        =			rreplace(__lowercase     ,   """.b"""     ,   """.bias"""     ,   1				)

          snake_case			:      int        =			value.float()

     return upgrade



@torch.no_grad()
def        lowerCamelCase__				(      __lowercase     ,   __lowercase     ,   __lowercase=None     ,   __lowercase=True				):
     from dall_e import Encoder

     snake_case			:      List[Any]        =			Encoder()
     if os.path.exists(__lowercase				):
          snake_case			:      List[str]        =			torch.load(__lowercase				)
     else:
          snake_case			:      List[str]        =			torch.hub.load_state_dict_from_url(__lowercase				)

     if isinstance(__lowercase     ,   __lowercase				):
          snake_case			:      List[str]        =			ckpt.state_dict()
     encoder.load_state_dict(__lowercase				)

     if config_path is not None:
          snake_case			:      Any        =			FlavaImageCodebookConfig.from_pretrained(__lowercase				)
     else:
          snake_case			:      Any        =			FlavaImageCodebookConfig()

     snake_case			:      List[Any]        =			FlavaImageCodebook(__lowercase				).eval()
     snake_case			:      int        =			encoder.state_dict()

     snake_case			:      List[Any]        =			upgrade_state_dict(__lowercase				)
     hf_model.load_state_dict(__lowercase				)
     snake_case			:      Optional[int]        =			hf_model.state_dict()
     snake_case			:      Optional[int]        =			count_parameters(__lowercase				)
     snake_case			:      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__":
      lowercase      :   Optional[int]      =       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""")
      lowercase      :   Tuple      =       parser.parse_args()

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









'''simple docstring'''







# We ignore warnings about stepping the scheduler since we step it ourselves during gradient accumulation

import warnings

from .state import AcceleratorState, GradientState


warnings.filterwarnings("""ignore""", category=UserWarning, module="""torch.optim.lr_scheduler""")







class 					_a :





     '''simple docstring'''



     def __init__(				self							,__a							,__a							,__a = True							,__a = False							)      ->				Dict:
          snake_case			:      List[Any]        =			scheduler
          snake_case			:      List[Any]        =			optimizers if isinstance(__a							,(list, tuple)							) else [optimizers]
          snake_case			:      List[str]        =			split_batches
          snake_case			:      List[str]        =			step_with_optimizer
          snake_case			:      int        =			GradientState()







     def 			snake_case_      (				self							,*__a							,**__a							)      ->				Optional[Any]:
          if not self.step_with_optimizer:
               # No link between scheduler and optimizer -> just step
               self.scheduler.step(*__a							,**__a							)
               return

          # Otherwise, first make sure the optimizer was stepped.
          if not self.gradient_state.sync_gradients:
               if self.gradient_state.adjust_scheduler:
                    self.scheduler._step_count += 1
               return

          for opt in self.optimizers:
               if opt.step_was_skipped:
                    return
          if self.split_batches:
               # Split batches -> the training dataloader batch size is not changed so one step per training step
               self.scheduler.step(*__a							,**__a							)
          else:
               # Otherwise the training dataloader batch size was multiplied by `num_processes`, so we need to do
               # num_processes steps per training step
               snake_case			:      List[str]        =			AcceleratorState().num_processes
               for _ in range(__a							):
                    # Special case when using OneCycle and `drop_last` was not used
                    if hasattr(self.scheduler							,"""total_steps"""							):
                         if self.scheduler._step_count <= self.scheduler.total_steps:
                              self.scheduler.step(*__a							,**__a							)
                    else:
                         self.scheduler.step(*__a							,**__a							)







     def 			snake_case_      (				self							)      ->				Dict:
          return self.scheduler.get_last_lr()







     def 			snake_case_      (				self							)      ->				int:
          return self.scheduler.state_dict()







     def 			snake_case_      (				self							,__a							)      ->				Any:
          self.scheduler.load_state_dict(__a							)







     def 			snake_case_      (				self							)      ->				Any:
          return self.scheduler.get_lr()







     def 			snake_case_      (				self							,*__a							,**__a							)      ->				Optional[Any]:
          return self.scheduler.print_lr(*__a							,**__a							)



