


import argparse
import os

import torch

from transformers import FlavaImageCodebook, FlavaImageCodebookConfig

def 						_UpperCAmelCase    (      SCREAMING_SNAKE_CASE__      : List[Any]      ,					SCREAMING_SNAKE_CASE__      : List[str]      ,					SCREAMING_SNAKE_CASE__      : Any      ,					SCREAMING_SNAKE_CASE__      : List[str]    ):
     __UpperCamelCase   							=s.rsplit(SCREAMING_SNAKE_CASE__      ,					SCREAMING_SNAKE_CASE__    )
     return new.join(SCREAMING_SNAKE_CASE__    )

def 						_UpperCAmelCase    (      SCREAMING_SNAKE_CASE__      : int    ):
     # 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 						_UpperCAmelCase    (      SCREAMING_SNAKE_CASE__      : Dict    ):
     __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(SCREAMING_SNAKE_CASE__      ,					'.w'      ,					'.weight'      ,					1    )
          if key.endswith('.b'    ):
               __UpperCamelCase   							=rreplace(SCREAMING_SNAKE_CASE__      ,					'.b'      ,					'.bias'      ,					1    )

          __UpperCamelCase   							=value.float()

     return upgrade

@torch.no_grad()
def 						_UpperCAmelCase    (      SCREAMING_SNAKE_CASE__      : Dict      ,					SCREAMING_SNAKE_CASE__      : List[str]      ,					SCREAMING_SNAKE_CASE__      : Optional[int]=None      ,					SCREAMING_SNAKE_CASE__      : Tuple=True    ):
     from dall_e import Encoder

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

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

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

     __UpperCamelCase   							=FlavaImageCodebook(SCREAMING_SNAKE_CASE__    ).eval()
     __UpperCamelCase   							=encoder.state_dict()

     __UpperCamelCase   							=upgrade_state_dict(SCREAMING_SNAKE_CASE__    )
     hf_model.load_state_dict(SCREAMING_SNAKE_CASE__    )
     __UpperCamelCase   							=hf_model.state_dict()
     __UpperCamelCase   							=count_parameters(SCREAMING_SNAKE_CASE__    )
     __UpperCamelCase   							=count_parameters(SCREAMING_SNAKE_CASE__    )

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

     if save_checkpoint:
          hf_model.save_pretrained(SCREAMING_SNAKE_CASE__    )
     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)









import logging
from pathlib import Path

import numpy as np
import pytorch_lightning as pl
import torch
from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
from pytorch_lightning.utilities import rank_zero_only
from utils_rag import save_json

def 						_UpperCAmelCase    (      SCREAMING_SNAKE_CASE__      : Tuple    ):
     __UpperCamelCase   							=filter(lambda SCREAMING_SNAKE_CASE__    : p.requires_grad      ,					model.parameters()    )
     __UpperCamelCase   							=sum([np.prod(p.size()    ) for p in model_parameters]    )
     return params


_A			=						logging.getLogger(__name__)

def 						_UpperCAmelCase    (      SCREAMING_SNAKE_CASE__      : Tuple      ,					SCREAMING_SNAKE_CASE__      : Optional[Any]    ):
     if metric == "rouge2":
          __UpperCamelCase   							='{val_avg_rouge2:.4f}-{step_count}'
     elif metric == "bleu":
          __UpperCamelCase   							='{val_avg_bleu:.4f}-{step_count}'
     elif metric == "em":
          __UpperCamelCase   							='{val_avg_em:.4f}-{step_count}'
     elif metric == "loss":
          __UpperCamelCase   							='{val_avg_loss:.4f}-{step_count}'
     else:
          raise NotImplementedError(
              F'seq2seq callbacks only support rouge2 and bleu, got {metric}, You can make your own by adding to this'
              ' function.'    )

     __UpperCamelCase   							=ModelCheckpoint(
         dirpath=SCREAMING_SNAKE_CASE__      ,					filename=SCREAMING_SNAKE_CASE__      ,					monitor=F'val_{metric}'      ,					mode='max'      ,					save_top_k=1      ,					every_n_epochs=1      ,					)

     return checkpoint_callback

def 						_UpperCAmelCase    (      SCREAMING_SNAKE_CASE__      : int      ,					SCREAMING_SNAKE_CASE__      : Union[str, Any]    ):
     return EarlyStopping(
         monitor=F'val_{metric}'      ,					mode='min' if 'loss' in metric else 'max'      ,					patience=SCREAMING_SNAKE_CASE__      ,					verbose=SCREAMING_SNAKE_CASE__      ,					)






class    UpperCAmelCase__	( pl.Callback		):



    """simple docstring"""






    def _a (      self ,       A_ ,       A_   )  ->	int:
         __UpperCamelCase   							={f'lr_group_{i}': param['lr'] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups   )}
         pl_module.logger.log_metrics(A_   )






    @rank_zero_only
    def _a (      self ,       A_ ,       A_ ,       A_ ,       A_=True   )  ->	None:
         logger.info(f'***** {type_path} results at step {trainer.global_step:05d} *****'   )
         __UpperCamelCase   							=trainer.callback_metrics
         trainer.logger.log_metrics({k: v for k, v in metrics.items() if k not in ['log', 'progress_bar', 'preds']}   )
         # Log results
         __UpperCamelCase   							=Path(pl_module.hparams.output_dir   )
         if type_path == "test":
              __UpperCamelCase   							=od / 'test_results.txt'
              __UpperCamelCase   							=od / 'test_generations.txt'
         else:
              # this never gets hit. I prefer not to save intermediate generations, and results are in metrics.json
              # If people want this it will be easy enough to add back.
              __UpperCamelCase   							=od / f'{type_path}_results/{trainer.global_step:05d}.txt'
              __UpperCamelCase   							=od / f'{type_path}_generations/{trainer.global_step:05d}.txt'
              results_file.parent.mkdir(exist_ok=A_   )
              generations_file.parent.mkdir(exist_ok=A_   )
         with open(A_ ,       'a+'   ) as writer:
              for key in sorted(A_   ):
                   if key in ["log", "progress_bar", "preds"]:
                        continue
                   __UpperCamelCase   							=metrics[key]
                   if isinstance(A_ ,       torch.Tensor   ):
                        __UpperCamelCase   							=val.item()
                   __UpperCamelCase   							=f'{key}: {val:.6f}\n'
                   writer.write(A_   )

         if not save_generations:
              return

         if "preds" in metrics:
              __UpperCamelCase   							='\n'.join(metrics['preds']   )
              generations_file.open('w+'   ).write(A_   )






    @rank_zero_only
    def _a (      self ,       A_ ,       A_   )  ->	Optional[int]:
         try:
              __UpperCamelCase   							=pl_module.model.model.num_parameters()
         except AttributeError:
              __UpperCamelCase   							=pl_module.model.num_parameters()

         __UpperCamelCase   							=count_trainable_parameters(A_   )
         # mp stands for million parameters
         trainer.logger.log_metrics({'n_params': npars, 'mp': npars / 1E6, 'grad_mp': n_trainable_pars / 1E6}   )






    @rank_zero_only
    def _a (      self ,       A_ ,       A_   )  ->	List[str]:
         save_json(pl_module.metrics ,       pl_module.metrics_save_path   )
         return self._write_logs(A_ ,       A_ ,       'test'   )






    @rank_zero_only
    def _a (      self ,       A_ ,       A_   )  ->	List[str]:
         save_json(pl_module.metrics ,       pl_module.metrics_save_path   )
         # Uncommenting this will save val generations
         # return self._write_logs(trainer, pl_module, "valid")





