


import argparse

import requests
import torch
from PIL import Image

from transformers import ViTMAEConfig, ViTMAEForPreTraining, ViTMAEImageProcessor


def      a	(							SCREAMING_SNAKE_CASE_      :		Optional[Any]       ):






       """simple docstring"""





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

       return name


def      a	(							SCREAMING_SNAKE_CASE_      :		Union[str, Any]							,			SCREAMING_SNAKE_CASE_      :		Optional[Any]       ):






       """simple docstring"""





       for key in orig_state_dict.copy().keys():
              UpperCamelCase    :	Tuple   	=		orig_state_dict.pop(SCREAMING_SNAKE_CASE_       )

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

              else:
                     UpperCamelCase    :	str   	=		val

       return orig_state_dict


def      a	(							SCREAMING_SNAKE_CASE_      :		List[Any]							,			SCREAMING_SNAKE_CASE_      :		Optional[Any]       ):






       """simple docstring"""





       UpperCamelCase    :	Union[str, Any]   	=		ViTMAEConfig()
       if "large" in checkpoint_url:
              UpperCamelCase    :	Tuple   	=		1_0_2_4
              UpperCamelCase    :	Tuple   	=		4_0_9_6
              UpperCamelCase    :	Any   	=		2_4
              UpperCamelCase    :	Union[str, Any]   	=		1_6
       elif "huge" in checkpoint_url:
              UpperCamelCase    :	List[str]   	=		1_4
              UpperCamelCase    :	Union[str, Any]   	=		1_2_8_0
              UpperCamelCase    :	Tuple   	=		5_1_2_0
              UpperCamelCase    :	int   	=		3_2
              UpperCamelCase    :	str   	=		1_6

       UpperCamelCase    :	Tuple   	=		ViTMAEForPreTraining(SCREAMING_SNAKE_CASE_       )

       UpperCamelCase    :	Dict   	=		torch.hub.load_state_dict_from_url(SCREAMING_SNAKE_CASE_							,			map_location='''cpu'''       )['''model''']

       UpperCamelCase    :	Tuple   	=		ViTMAEImageProcessor(size=config.image_size       )

       UpperCamelCase    :	List[str]   	=		convert_state_dict(SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_       )

       model.load_state_dict(SCREAMING_SNAKE_CASE_       )
       model.eval()

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

       UpperCamelCase    :	Optional[Any]   	=		Image.open(requests.get(SCREAMING_SNAKE_CASE_							,			stream=SCREAMING_SNAKE_CASE_       ).raw       )
       UpperCamelCase    :	Optional[int]   	=		ViTMAEImageProcessor(size=config.image_size       )
       UpperCamelCase    :	Tuple   	=		image_processor(images=SCREAMING_SNAKE_CASE_							,			return_tensors='''pt'''       )

       # forward pass
       torch.manual_seed(2       )
       UpperCamelCase    :	List[str]   	=		model(**SCREAMING_SNAKE_CASE_       )
       UpperCamelCase    :	Optional[Any]   	=		outputs.logits

       if "large" in checkpoint_url:
              UpperCamelCase    :	List[str]   	=		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:
              UpperCamelCase    :	Optional[Any]   	=		torch.tensor(
                  [[-1.1599, -0.9199, -1.2221], [-1.1952, -0.9269, -1.2307], [-1.2143, -0.9337, -1.2262]]       )
       else:
              UpperCamelCase    :	List[str]   	=		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]							,			SCREAMING_SNAKE_CASE_							,			atol=1E-4       )

       print(F"""Saving model 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 __name__ == "__main__":
  __UpperCAmelCase : 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."
  )

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





from .imports import is_tqdm_available


if is_tqdm_available():
  from tqdm.auto import tqdm as _tqdm

from ..state import PartialState


def      a	(							SCREAMING_SNAKE_CASE_      :		bool = True							,			*SCREAMING_SNAKE_CASE_      :		List[str]							,			**SCREAMING_SNAKE_CASE_      :		Tuple       ):






       """simple docstring"""





       if not is_tqdm_available():
              raise ImportError('''Accelerate\'s `tqdm` module requires `tqdm` to be installed. Please run `pip install tqdm`.'''       )
       UpperCamelCase    :	int   	=		False
       if main_process_only:
              UpperCamelCase    :	int   	=		PartialState().local_process_index == 0
       return _tqdm(*SCREAMING_SNAKE_CASE_							,			**SCREAMING_SNAKE_CASE_							,			disable=SCREAMING_SNAKE_CASE_       )

