

import argparse

import requests
import torch
from PIL import Image

from transformers import ViTMAEConfig, ViTMAEForPreTraining, ViTMAEImageProcessor





def      A__					(			__A  :						Optional[int]			)					->Any:
      if "cls_token" in name:
            __A    =name.replace('''cls_token'''     ,				'''vit.embeddings.cls_token'''			)
      if "mask_token" in name:
            __A    =name.replace('''mask_token'''     ,				'''decoder.mask_token'''			)
      if "decoder_pos_embed" in name:
            __A    =name.replace('''decoder_pos_embed'''     ,				'''decoder.decoder_pos_embed'''			)
      if "pos_embed" in name and "decoder" not in name:
            __A    =name.replace('''pos_embed'''     ,				'''vit.embeddings.position_embeddings'''			)
      if "patch_embed.proj" in name:
            __A    =name.replace('''patch_embed.proj'''     ,				'''vit.embeddings.patch_embeddings.projection'''			)
      if "patch_embed.norm" in name:
            __A    =name.replace('''patch_embed.norm'''     ,				'''vit.embeddings.norm'''			)
      if "decoder_blocks" in name:
            __A    =name.replace('''decoder_blocks'''     ,				'''decoder.decoder_layers'''			)
      if "blocks" in name:
            __A    =name.replace('''blocks'''     ,				'''vit.encoder.layer'''			)
      if "attn.proj" in name:
            __A    =name.replace('''attn.proj'''     ,				'''attention.output.dense'''			)
      if "attn" in name:
            __A    =name.replace('''attn'''     ,				'''attention.self'''			)
      if "norm1" in name:
            __A    =name.replace('''norm1'''     ,				'''layernorm_before'''			)
      if "norm2" in name:
            __A    =name.replace('''norm2'''     ,				'''layernorm_after'''			)
      if "mlp.fc1" in name:
            __A    =name.replace('''mlp.fc1'''     ,				'''intermediate.dense'''			)
      if "mlp.fc2" in name:
            __A    =name.replace('''mlp.fc2'''     ,				'''output.dense'''			)
      if "decoder_embed" in name:
            __A    =name.replace('''decoder_embed'''     ,				'''decoder.decoder_embed'''			)
      if "decoder_norm" in name:
            __A    =name.replace('''decoder_norm'''     ,				'''decoder.decoder_norm'''			)
      if "decoder_pred" in name:
            __A    =name.replace('''decoder_pred'''     ,				'''decoder.decoder_pred'''			)
      if "norm.weight" in name and "decoder" not in name:
            __A    =name.replace('''norm.weight'''     ,				'''vit.layernorm.weight'''			)
      if "norm.bias" in name and "decoder" not in name:
            __A    =name.replace('''norm.bias'''     ,				'''vit.layernorm.bias'''			)

      return name





def      A__					(			__A  :						Dict     ,				__A  :						Optional[int]			)					->Optional[int]:
      for key in orig_state_dict.copy().keys():
            __A    =orig_state_dict.pop(__A			)

            if "qkv" in key:
                  __A    =key.split('''.'''			)
                  __A    =int(key_split[1]			)
                  if "decoder_blocks" in key:
                        __A    =config.decoder_hidden_size
                        __A    ='''decoder.decoder_layers.'''
                        if "weight" in key:
                              __A    =val[:dim, :]
                              __A    =val[dim : dim * 2, :]
                              __A    =val[-dim:, :]
                        elif "bias" in key:
                              __A    =val[:dim]
                              __A    =val[dim : dim * 2]
                              __A    =val[-dim:]
                  else:
                        __A    =config.hidden_size
                        __A    ='''vit.encoder.layer.'''
                        if "weight" in key:
                              __A    =val[:dim, :]
                              __A    =val[dim : dim * 2, :]
                              __A    =val[-dim:, :]
                        elif "bias" in key:
                              __A    =val[:dim]
                              __A    =val[dim : dim * 2]
                              __A    =val[-dim:]

            else:
                  __A    =val

      return orig_state_dict





def      A__					(			__A  :						Optional[int]     ,				__A  :						Any			)					->List[Any]:
      __A    =ViTMAEConfig()
      if "large" in checkpoint_url:
            __A    =10_24
            __A    =40_96
            __A    =24
            __A    =16
      elif "huge" in checkpoint_url:
            __A    =14
            __A    =12_80
            __A    =51_20
            __A    =32
            __A    =16

      __A    =ViTMAEForPreTraining(__A			)

      __A    =torch.hub.load_state_dict_from_url(__A     ,				map_location='''cpu'''			)['''model''']

      __A    =ViTMAEImageProcessor(size=config.image_size			)

      __A    =convert_state_dict(__A     ,				__A			)

      model.load_state_dict(__A			)
      model.eval()

      __A    ='''https://user-images.githubusercontent.com/11435359/147738734-196fd92f-9260-48d5-ba7e-bf103d29364d.jpg'''

      __A    =Image.open(requests.get(__A     ,				stream=__A			).raw			)
      __A    =ViTMAEImageProcessor(size=config.image_size			)
      __A    =image_processor(images=__A     ,				return_tensors='''pt'''			)

      # forward pass
      torch.manual_seed(2			)
      __A    =model(**__A			)
      __A    =outputs.logits

      if "large" in checkpoint_url:
            __A    =torch.tensor(
                [[-0.7309, -0.7128, -1.0169], [-1.0161, -0.9058, -1.1878], [-1.0478, -0.9411, -1.1911]]			)
      elif "huge" in checkpoint_url:
            __A    =torch.tensor(
                [[-1.1599, -0.9199, -1.2221], [-1.1952, -0.9269, -1.2307], [-1.2143, -0.9337, -1.2262]]			)
      else:
            __A    =torch.tensor(
                [[-0.9192, -0.8481, -1.1259], [-1.1349, -1.0034, -1.2599], [-1.1757, -1.0429, -1.2726]]			)

      # verify logits
      assert torch.allclose(logits[0, :3, :3]     ,				__A     ,				atol=1e-4			)

      print(F'''Saving model to {pytorch_dump_folder_path}'''			)
      model.save_pretrained(__A			)

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


if __name__ == "__main__":
   _lowerCamelCase      :					Optional[int]					    =      argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       '''--checkpoint_url''',
       default='''https://dl.fbaipublicfiles.com/mae/visualize/mae_visualize_vit_base.pth''',
       type=str,
       help='''URL of the checkpoint you\'d like to convert.''',
   )
   parser.add_argument(
       '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.'''
   )

   _lowerCamelCase      :					Dict					    =      parser.parse_args()
   convert_vit_mae_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)





import gc
import threading
import time

import psutil
import torch





class        lowerCAmelCase__  :



       '''simple docstring'''







       def __init__(							self     ):







             '''simple docstring'''



             __A    =psutil.Process()
             __A    =False

       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             __A    =-1

             while True:
                   __A    =max(self.process.memory_info().rss ,   self.cpu_memory_peak     )

                   # can't sleep or will not catch the peak right (this comment is here on purpose)
                   if not self.peak_monitoring:
                         break

       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             __A    =True
             __A    =threading.Thread(target=self.peak_monitor     )
             __A    =True
             self.thread.start()


       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             __A    =False
             self.thread.join()
             return self.cpu_memory_peak

_lowerCamelCase      :					Union[str, Any]					    =      PeakCPUMemory()





def      A__					(			)					->Dict:
      # Time
      __A    ={'''time''': time.time()}

      gc.collect()
      torch.cuda.empty_cache()

      # CPU mem
      __A    =psutil.Process().memory_info().rss
      cpu_peak_tracker.start()

      # GPU mem
      for i in range(torch.cuda.device_count()			):
            __A    =torch.cuda.memory_allocated(__A			)
      torch.cuda.reset_peak_memory_stats()

      return measures





def      A__					(			__A  :						Any			)					->Any:
      # Time
      __A    ={'''time''': time.time() - start_measures['''time''']}

      gc.collect()
      torch.cuda.empty_cache()

      # CPU mem
      __A    =(psutil.Process().memory_info().rss - start_measures['''cpu''']) / 2**20
      __A    =(cpu_peak_tracker.stop() - start_measures['''cpu''']) / 2**20

      # GPU mem
      for i in range(torch.cuda.device_count()			):
            __A    =(torch.cuda.memory_allocated(__A			) - start_measures[str(__A			)]) / 2**20
            __A    =(torch.cuda.max_memory_allocated(__A			) - start_measures[str(__A			)]) / 2**20

      return measures





def      A__					(			__A  :						Union[str, Any]     ,				__A  :						Any			)					->Union[str, Any]:
      print(F'''{description}:'''			)
      print(F'''- Time: {measures['time']:.2f}s'''			)
      for i in range(torch.cuda.device_count()			):
            print(F'''- GPU {i} allocated: {measures[str(__A			)]:.2f}MiB'''			)
            __A    =measures[F'''{i}-peak''']
            print(F'''- GPU {i} peak: {peak:.2f}MiB'''			)
      print(F'''- CPU RAM allocated: {measures['cpu']:.2f}MiB'''			)
      print(F'''- CPU RAM peak: {measures['cpu-peak']:.2f}MiB'''			)


