





import argparse

import requests
import torch
from PIL import Image

from transformers import ViTMAEConfig, ViTMAEForPreTraining, ViTMAEImageProcessor



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

 return name



def 		A_				(     _lowerCAmelCase							,   _lowerCAmelCase     )						-> Union[str, Any]:
 for key in orig_state_dict.copy().keys():
  UpperCamelCase	:					int       	=   orig_state_dict.pop(_lowerCAmelCase     )

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

  else:
   UpperCamelCase	:					List[str]       	=   val

 return orig_state_dict



def 		A_				(     _lowerCAmelCase							,   _lowerCAmelCase     )						-> Optional[int]:
 UpperCamelCase	:					Union[str, Any]       	=   ViTMAEConfig()
 if "large" in checkpoint_url:
  UpperCamelCase	:					Optional[Any]       	=   1024
  UpperCamelCase	:					Union[str, Any]       	=   4096
  UpperCamelCase	:					List[Any]       	=   24
  UpperCamelCase	:					Optional[Any]       	=   16
 elif "huge" in checkpoint_url:
  UpperCamelCase	:					List[str]       	=   14
  UpperCamelCase	:					Dict       	=   1280
  UpperCamelCase	:					str       	=   5120
  UpperCamelCase	:					Any       	=   32
  UpperCamelCase	:					Any       	=   16

 UpperCamelCase	:					Optional[Any]       	=   ViTMAEForPreTraining(_lowerCAmelCase     )

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

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

 UpperCamelCase	:					Any       	=   convert_state_dict(_lowerCAmelCase							,   _lowerCAmelCase     )

 model.load_state_dict(_lowerCAmelCase     )
 model.eval()

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

 UpperCamelCase	:					List[str]       	=   Image.open(requests.get(_lowerCAmelCase							,   stream=_lowerCAmelCase     ).raw     )
 UpperCamelCase	:					List[Any]       	=   ViTMAEImageProcessor(size=config.image_size     )
 UpperCamelCase	:					Union[str, Any]       	=   image_processor(images=_lowerCAmelCase							,   return_tensors="pt"     )

 # forward pass
 torch.manual_seed(2     )
 UpperCamelCase	:					str       	=   model(**_lowerCAmelCase     )
 UpperCamelCase	:					Union[str, Any]       	=   outputs.logits

 if "large" in checkpoint_url:
  UpperCamelCase	:					Optional[int]       	=   torch.tensor(
      [[-0.7_309, -0.7_128, -1.0_169], [-1.0_161, -0.9_058, -1.1_878], [-1.0_478, -0.9_411, -1.1_911]]     )
 elif "huge" in checkpoint_url:
  UpperCamelCase	:					Optional[Any]       	=   torch.tensor(
      [[-1.1_599, -0.9_199, -1.2_221], [-1.1_952, -0.9_269, -1.2_307], [-1.2_143, -0.9_337, -1.2_262]]     )
 else:
  UpperCamelCase	:					int       	=   torch.tensor(
      [[-0.9_192, -0.8_481, -1.1_259], [-1.1_349, -1.0_034, -1.2_599], [-1.1_757, -1.0_429, -1.2_726]]     )

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

 print(F"""Saving model to {pytorch_dump_folder_path}"""     )
 model.save_pretrained(_lowerCAmelCase     )

 print(F"""Saving image processor to {pytorch_dump_folder_path}"""     )
 image_processor.save_pretrained(_lowerCAmelCase     )


if __name__ == "__main__":
    __lowerCamelCase       :     Tuple   =     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       :     Optional[int]   =     parser.parse_args()
    convert_vit_mae_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)







'''simple docstring'''

from __future__ import annotations






class    UpperCAmelCase	:
 def __init__(				self      :Optional[int]						,       lowercase_      :int							)->							None:
     A__				=			order

     # a_{0} ... a_{k}
     A__				=			[1.0] + [0.0] * order
     # b_{0} ... b_{k}
     A__				=			[1.0] + [0.0] * order

     # x[n-1] ... x[n-k]
     A__				=			[0.0] * self.order
     # y[n-1] ... y[n-k]
     A__				=			[0.0] * self.order
 def      UpperCAmelCase_							(				self      :List[str]						,       lowercase_      :list[float]						,       lowercase_      :list[float]							)->							None:
     if len(lowercase_							) < self.order:
         A__				=			[1.0, *a_coeffs]

     if len(lowercase_							) != self.order + 1:
         A__				=			(
             F"Expected a_coeffs to have {self.order + 1} elements "
             F"for {self.order}-order filter, got {len(lowercase_							)}"
         )
         raise ValueError(lowercase_							)

     if len(lowercase_							) != self.order + 1:
         A__				=			(
             F"Expected b_coeffs to have {self.order + 1} elements "
             F"for {self.order}-order filter, got {len(lowercase_							)}"
         )
         raise ValueError(lowercase_							)

     A__				=			a_coeffs
     A__				=			b_coeffs



 def      UpperCAmelCase_							(				self      :Optional[int]						,       lowercase_      :float							)->							float:
     A__				=			0.0

     # Start at index 1 and do index 0 at the end.
     for i in range(1						,       self.order + 1							):
         result += (
             self.b_coeffs[i] * self.input_history[i - 1]
             - self.a_coeffs[i] * self.output_history[i - 1]
         )

     A__				=			(result + self.b_coeffs[0] * sample) / self.a_coeffs[0]

     A__				=			self.input_history[:-1]
     A__				=			self.output_history[:-1]

     A__				=			sample
     A__				=			result

     return result



