import argparse

import requests
import torch
from PIL import Image

from transformers import ViTMAEConfig, ViTMAEForPreTraining, ViTMAEImageProcessor







def  __UpperCamelCase    (						A							):
     if "cls_token" in name:
          UpperCamelCase__										=      name.replace('''cls_token''' ,							'''vit.embeddings.cls_token'''							)
     if "mask_token" in name:
          UpperCamelCase__										=      name.replace('''mask_token''' ,							'''decoder.mask_token'''							)
     if "decoder_pos_embed" in name:
          UpperCamelCase__										=      name.replace('''decoder_pos_embed''' ,							'''decoder.decoder_pos_embed'''							)
     if "pos_embed" in name and "decoder" not in name:
          UpperCamelCase__										=      name.replace('''pos_embed''' ,							'''vit.embeddings.position_embeddings'''							)
     if "patch_embed.proj" in name:
          UpperCamelCase__										=      name.replace('''patch_embed.proj''' ,							'''vit.embeddings.patch_embeddings.projection'''							)
     if "patch_embed.norm" in name:
          UpperCamelCase__										=      name.replace('''patch_embed.norm''' ,							'''vit.embeddings.norm'''							)
     if "decoder_blocks" in name:
          UpperCamelCase__										=      name.replace('''decoder_blocks''' ,							'''decoder.decoder_layers'''							)
     if "blocks" in name:
          UpperCamelCase__										=      name.replace('''blocks''' ,							'''vit.encoder.layer'''							)
     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 "decoder_embed" in name:
          UpperCamelCase__										=      name.replace('''decoder_embed''' ,							'''decoder.decoder_embed'''							)
     if "decoder_norm" in name:
          UpperCamelCase__										=      name.replace('''decoder_norm''' ,							'''decoder.decoder_norm'''							)
     if "decoder_pred" in name:
          UpperCamelCase__										=      name.replace('''decoder_pred''' ,							'''decoder.decoder_pred'''							)
     if "norm.weight" in name and "decoder" not in name:
          UpperCamelCase__										=      name.replace('''norm.weight''' ,							'''vit.layernorm.weight'''							)
     if "norm.bias" in name and "decoder" not in name:
          UpperCamelCase__										=      name.replace('''norm.bias''' ,							'''vit.layernorm.bias'''							)

     return name







def  __UpperCamelCase    (						A ,							A							):
     for key in orig_state_dict.copy().keys():
          UpperCamelCase__										=      orig_state_dict.pop(_lowercase							)

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

          else:
               UpperCamelCase__										=      val

     return orig_state_dict







def  __UpperCamelCase    (						A ,							A							):
     UpperCamelCase__										=      ViTMAEConfig()
     if "large" in checkpoint_url:
          UpperCamelCase__										=      1024
          UpperCamelCase__										=      4096
          UpperCamelCase__										=      24
          UpperCamelCase__										=      16
     elif "huge" in checkpoint_url:
          UpperCamelCase__										=      14
          UpperCamelCase__										=      1280
          UpperCamelCase__										=      5120
          UpperCamelCase__										=      32
          UpperCamelCase__										=      16

     UpperCamelCase__										=      ViTMAEForPreTraining(_lowercase							)

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

     UpperCamelCase__										=      ViTMAEImageProcessor(size=config.image_size							)

     UpperCamelCase__										=      convert_state_dict(_lowercase ,							_lowercase							)

     model.load_state_dict(_lowercase							)
     model.eval()

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

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

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

     if "large" in checkpoint_url:
          UpperCamelCase__										=      torch.tensor(
              [[-0.73_09, -0.71_28, -1.01_69], [-1.01_61, -0.90_58, -1.18_78], [-1.04_78, -0.94_11, -1.19_11]]							)
     elif "huge" in checkpoint_url:
          UpperCamelCase__										=      torch.tensor(
              [[-1.15_99, -0.91_99, -1.22_21], [-1.19_52, -0.92_69, -1.23_07], [-1.21_43, -0.93_37, -1.22_62]]							)
     else:
          UpperCamelCase__										=      torch.tensor(
              [[-0.91_92, -0.84_81, -1.12_59], [-1.13_49, -1.00_34, -1.25_99], [-1.17_57, -1.04_29, -1.27_26]]							)

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

     print(f"Saving model to {pytorch_dump_folder_path}"							)
     model.save_pretrained(_lowercase							)

     print(f"Saving image processor to {pytorch_dump_folder_path}"							)
     image_processor.save_pretrained(_lowercase							)


if __name__ == "__main__":
     __magic_name__								=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.'''
     )

     __magic_name__								=parser.parse_args()
     convert_vit_mae_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)




from datetime import datetime

import matplotlib.pyplot as plt
import torch







def  __UpperCamelCase    (						A							):
     for param in module.parameters():
          UpperCamelCase__										=      False







def  __UpperCamelCase    (						):
     UpperCamelCase__										=      '''cuda''' if torch.cuda.is_available() else '''cpu'''
     if torch.backends.mps.is_available() and torch.backends.mps.is_built():
          UpperCamelCase__										=      '''mps'''
     if device == "mps":
          print(
              '''WARNING: MPS currently doesn\'t seem to work, and messes up backpropagation without any visible torch'''
              ''' errors. I recommend using CUDA on a colab notebook or CPU instead if you\'re facing inexplicable issues'''
              ''' with generations.'''							)
     return device







def  __UpperCamelCase    (						A							):
     UpperCamelCase__										=      plt.imshow(A							)
     fig.axes.get_xaxis().set_visible(A							)
     fig.axes.get_yaxis().set_visible(A							)
     plt.show()







def  __UpperCamelCase    (						):
     UpperCamelCase__										=      datetime.now()
     UpperCamelCase__										=      current_time.strftime('''%H:%M:%S'''							)
     return timestamp



