


import argparse

import requests
import torch
from PIL import Image

from transformers import SwinConfig, SwinForMaskedImageModeling, ViTImageProcessor

def 						_UpperCAmelCase    (      SCREAMING_SNAKE_CASE__      : Union[str, Any]    ):
     __UpperCamelCase   							=SwinConfig(image_size=1_92    )

     if "base" in model_name:
          __UpperCamelCase   							=6
          __UpperCamelCase   							=1_28
          __UpperCamelCase   							=(2, 2, 18, 2)
          __UpperCamelCase   							=(4, 8, 16, 32)
     elif "large" in model_name:
          __UpperCamelCase   							=12
          __UpperCamelCase   							=1_92
          __UpperCamelCase   							=(2, 2, 18, 2)
          __UpperCamelCase   							=(6, 12, 24, 48)
     else:
          raise ValueError('Model not supported, only supports base and large variants'    )

     __UpperCamelCase   							=window_size
     __UpperCamelCase   							=embed_dim
     __UpperCamelCase   							=depths
     __UpperCamelCase   							=num_heads

     return config

def 						_UpperCAmelCase    (      SCREAMING_SNAKE_CASE__      : Tuple    ):
     if "encoder.mask_token" in name:
          __UpperCamelCase   							=name.replace('encoder.mask_token'      ,					'embeddings.mask_token'    )
     if "encoder.patch_embed.proj" in name:
          __UpperCamelCase   							=name.replace('encoder.patch_embed.proj'      ,					'embeddings.patch_embeddings.projection'    )
     if "encoder.patch_embed.norm" in name:
          __UpperCamelCase   							=name.replace('encoder.patch_embed.norm'      ,					'embeddings.norm'    )
     if "attn.proj" in name:
          __UpperCamelCase   							=name.replace('attn.proj'      ,					'attention.output.dense'    )
     if "attn" in name:
          __UpperCamelCase   							=name.replace('attn'      ,					'attention.self'    )
     if "norm1" in name:
          __UpperCamelCase   							=name.replace('norm1'      ,					'layernorm_before'    )
     if "norm2" in name:
          __UpperCamelCase   							=name.replace('norm2'      ,					'layernorm_after'    )
     if "mlp.fc1" in name:
          __UpperCamelCase   							=name.replace('mlp.fc1'      ,					'intermediate.dense'    )
     if "mlp.fc2" in name:
          __UpperCamelCase   							=name.replace('mlp.fc2'      ,					'output.dense'    )

     if name == "encoder.norm.weight":
          __UpperCamelCase   							='layernorm.weight'
     if name == "encoder.norm.bias":
          __UpperCamelCase   							='layernorm.bias'

     if "decoder" in name:
          pass
     else:
          __UpperCamelCase   							='swin.' + name

     return name

def 						_UpperCAmelCase    (      SCREAMING_SNAKE_CASE__      : int      ,					SCREAMING_SNAKE_CASE__      : Tuple    ):
     for key in orig_state_dict.copy().keys():
          __UpperCamelCase   							=orig_state_dict.pop(SCREAMING_SNAKE_CASE__    )

          if "attn_mask" in key:
               pass
          elif "qkv" in key:
               __UpperCamelCase   							=key.split('.'    )
               __UpperCamelCase   							=int(key_split[2]    )
               __UpperCamelCase   							=int(key_split[4]    )
               __UpperCamelCase   							=model.swin.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size

               if "weight" in key:
                    __UpperCamelCase   							=val[:dim, :]
                    __UpperCamelCase   							=val[
                        dim : dim * 2, :
                    ]
                    __UpperCamelCase   							=val[-dim:, :]
               else:
                    __UpperCamelCase   							=val[
                        :dim
                    ]
                    __UpperCamelCase   							=val[
                        dim : dim * 2
                    ]
                    __UpperCamelCase   							=val[
                        -dim:
                    ]
          else:
               __UpperCamelCase   							=val

     return orig_state_dict

def 						_UpperCAmelCase    (      SCREAMING_SNAKE_CASE__      : Optional[int]      ,					SCREAMING_SNAKE_CASE__      : Union[str, Any]      ,					SCREAMING_SNAKE_CASE__      : List[str]      ,					SCREAMING_SNAKE_CASE__      : List[str]    ):
     __UpperCamelCase   							=torch.load(SCREAMING_SNAKE_CASE__      ,					map_location='cpu'    )['model']

     __UpperCamelCase   							=get_swin_config(SCREAMING_SNAKE_CASE__    )
     __UpperCamelCase   							=SwinForMaskedImageModeling(SCREAMING_SNAKE_CASE__    )
     model.eval()

     __UpperCamelCase   							=convert_state_dict(SCREAMING_SNAKE_CASE__      ,					SCREAMING_SNAKE_CASE__    )
     model.load_state_dict(SCREAMING_SNAKE_CASE__    )

     __UpperCamelCase   							='http://images.cocodataset.org/val2017/000000039769.jpg'

     __UpperCamelCase   							=ViTImageProcessor(size={'height': 1_92, 'width': 1_92}    )
     __UpperCamelCase   							=Image.open(requests.get(SCREAMING_SNAKE_CASE__      ,					stream=SCREAMING_SNAKE_CASE__    ).raw    )
     __UpperCamelCase   							=image_processor(images=SCREAMING_SNAKE_CASE__      ,					return_tensors='pt'    )

     with torch.no_grad():
          __UpperCamelCase   							=model(**SCREAMING_SNAKE_CASE__    ).logits

     print(outputs.keys()    )
     print('Looks ok!'    )

     if pytorch_dump_folder_path is not None:
          print(F'Saving model {model_name} to {pytorch_dump_folder_path}'    )
          model.save_pretrained(SCREAMING_SNAKE_CASE__    )

          print(F'Saving image processor to {pytorch_dump_folder_path}'    )
          image_processor.save_pretrained(SCREAMING_SNAKE_CASE__    )

     if push_to_hub:
          print(F'Pushing model and image processor for {model_name} to hub'    )
          model.push_to_hub(F'microsoft/{model_name}'    )
          image_processor.push_to_hub(F'microsoft/{model_name}'    )


if __name__ == "__main__":
   _A			=						argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       '--model_name',
       default='swin-base-simmim-window6-192',
       type=str,
       choices=['swin-base-simmim-window6-192', 'swin-large-simmim-window12-192'],
       help='Name of the Swin SimMIM model you\'d like to convert.',
   )
   parser.add_argument(
       '--checkpoint_path',
       default='/Users/nielsrogge/Documents/SwinSimMIM/simmim_pretrain__swin_base__img192_window6__100ep.pth',
       type=str,
       help='Path to the original PyTorch checkpoint (.pth file).',
   )
   parser.add_argument(
       '--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
   )
   parser.add_argument(
       '--push_to_hub', action='store_true', help='Whether or not to push the converted model to the 🤗 hub.'
   )

   _A			=						parser.parse_args()
   convert_swin_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)









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")





