






"""simple docstring"""



import argparse

import requests
import torch
from PIL import Image

from transformers import ViTMAEConfig, ViTMAEForPreTraining, ViTMAEImageProcessor
def 						_SCREAMING_SNAKE_CASE (    __snake_case			:  str    ):





  '''simple docstring'''





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

  return name
def 						_SCREAMING_SNAKE_CASE (    __snake_case			:  List[Any]		,					__snake_case			:  Tuple    ):





  '''simple docstring'''





  for key in orig_state_dict.copy().keys():
    lowercase							    =       orig_state_dict.pop(__snake_case    )

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

    else:
      lowercase							    =       val

  return orig_state_dict
def 						_SCREAMING_SNAKE_CASE (    __snake_case			:  Union[str, Any]		,					__snake_case			:  Tuple    ):





  '''simple docstring'''





  lowercase							    =       ViTMAEConfig()
  if "large" in checkpoint_url:
    lowercase							    =       10_24
    lowercase							    =       40_96
    lowercase							    =       24
    lowercase							    =       16
  elif "huge" in checkpoint_url:
    lowercase							    =       14
    lowercase							    =       12_80
    lowercase							    =       51_20
    lowercase							    =       32
    lowercase							    =       16

  lowercase							    =       ViTMAEForPreTraining(__snake_case    )

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

  lowercase							    =       ViTMAEImageProcessor(size=config.image_size    )

  lowercase							    =       convert_state_dict(__snake_case		,					__snake_case    )

  model.load_state_dict(__snake_case    )
  model.eval()

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

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

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

  if "large" in checkpoint_url:
    lowercase							    =       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:
    lowercase							    =       torch.tensor(
        [[-1.1599, -0.9199, -1.2221], [-1.1952, -0.9269, -1.2307], [-1.2143, -0.9337, -1.2262]]    )
  else:
    lowercase							    =       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]		,					__snake_case		,					atol=1e-4    )

  print(f'Saving model to {pytorch_dump_folder_path}'    )
  model.save_pretrained(__snake_case    )

  print(f'Saving image processor to {pytorch_dump_folder_path}'    )
  image_processor.save_pretrained(__snake_case    )


if __name__ == "__main__":
    _UpperCamelCase		:  Any						=		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		:  Any						=		parser.parse_args()
    convert_vit_mae_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)










"""simple docstring"""



from dataclasses import dataclass
from typing import Tuple

import numpy as np
import torch

@dataclass
class        a     :
    UpperCAmelCase_ :     torch.Tensor  # [batch_size x 3]
    UpperCAmelCase_ :     torch.Tensor  # [batch_size x 3]
    UpperCAmelCase_ :     torch.Tensor  # [batch_size x 3]
    UpperCAmelCase_ :     torch.Tensor  # [batch_size x 3]
    UpperCAmelCase_ :     int
    UpperCAmelCase_ :     int
    UpperCAmelCase_ :     float
    UpperCAmelCase_ :     float
    UpperCAmelCase_ :     Tuple[int]







    def 			UpperCamelCase_      (			self       ):
      assert self.x.shape[0] == self.y.shape[0] == self.z.shape[0] == self.origin.shape[0]
      assert self.x.shape[1] == self.y.shape[1] == self.z.shape[1] == self.origin.shape[1] == 3
      assert len(self.x.shape       ) == len(self.y.shape       ) == len(self.z.shape       ) == len(self.origin.shape       ) == 2







    def 			UpperCamelCase_      (			self       ):
      return torch.from_numpy(np.array([self.width, self.height]							,		dtype=np.floataa       )       )







    def 			UpperCamelCase_      (			self       ):
      return torch.from_numpy(np.array([self.x_fov, self.y_fov]							,		dtype=np.floataa       )       )







    def 			UpperCamelCase_      (			self       ):
      lowercase							    =       torch.arange(self.height * self.width       )
      lowercase							    =       torch.stack(
          [
              pixel_indices % self.width,
              torch.div(_lowerCamelCase							,		self.width							,		rounding_mode='trunc'       ),
          ]							,		axis=1							,		)
      return coords







    @property
    def 			UpperCamelCase_      (			self       ):
      lowercase    ,     *lowercase							    =       self.shape
      lowercase							    =       int(np.prod(_lowerCamelCase       )       )

      lowercase							    =       self.get_image_coords()
      lowercase							    =       torch.broadcast_to(coords.unsqueeze(0       )							,		[batch_size * inner_batch_size, *coords.shape]       )
      lowercase							    =       self.get_camera_rays(_lowerCamelCase       )

      lowercase							    =       rays.view(_lowerCamelCase							,		inner_batch_size * self.height * self.width							,		2							,		3       )

      return rays







    def 			UpperCamelCase_      (			self							,		_lowerCamelCase       ):
      lowercase    ,     *lowercase    ,     lowercase							    =       coords.shape
      assert n_coords == 2
      assert batch_size == self.origin.shape[0]

      lowercase							    =       coords.view(_lowerCamelCase							,		-1							,		2       )

      lowercase							    =       self.resolution()
      lowercase							    =       self.fov()

      lowercase							    =       (flat.float() / (res - 1)) * 2 - 1
      lowercase							    =       fracs * torch.tan(fov / 2       )

      lowercase							    =       fracs.view(_lowerCamelCase							,		-1							,		2       )
      lowercase							    =       (
          self.z.view(_lowerCamelCase							,		1							,		3       )
          + self.x.view(_lowerCamelCase							,		1							,		3       ) * fracs[:, :, :1]
          + self.y.view(_lowerCamelCase							,		1							,		3       ) * fracs[:, :, 1:]
      )
      lowercase							    =       directions / directions.norm(dim=-1							,		keepdim=_lowerCamelCase       )
      lowercase							    =       torch.stack(
          [
              torch.broadcast_to(self.origin.view(_lowerCamelCase							,		1							,		3       )							,		[batch_size, directions.shape[1], 3]       ),
              directions,
          ]							,		dim=2							,		)
      return rays.view(_lowerCamelCase							,		*_lowerCamelCase							,		2							,		3       )







    def 			UpperCamelCase_      (			self							,		_lowerCamelCase							,		_lowerCamelCase       ):
      assert width * self.height == height * self.width, "The aspect ratio should not change."
      return DifferentiableProjectiveCamera(
          origin=self.origin							,		x=self.x							,		y=self.y							,		z=self.z							,		width=_lowerCamelCase							,		height=_lowerCamelCase							,		x_fov=self.x_fov							,		y_fov=self.y_fov							,		)


def 						_SCREAMING_SNAKE_CASE (    __snake_case			:  int    ):





  '''simple docstring'''





  lowercase							    =       []
  lowercase							    =       []
  lowercase							    =       []
  lowercase							    =       []
  for theta in np.linspace(0		,					2 * np.pi		,					num=20    ):
    lowercase							    =       np.array([np.sin(__snake_case    ), np.cos(__snake_case    ), -0.5]    )
    z /= np.sqrt(np.sum(z**2    )    )
    lowercase							    =       -z * 4
    lowercase							    =       np.array([np.cos(__snake_case    ), -np.sin(__snake_case    ), 0.0]    )
    lowercase							    =       np.cross(__snake_case		,					__snake_case    )
    origins.append(__snake_case    )
    xs.append(__snake_case    )
    ys.append(__snake_case    )
    zs.append(__snake_case    )
  return DifferentiableProjectiveCamera(
      origin=torch.from_numpy(np.stack(__snake_case		,					axis=0    )    ).float()		,					x=torch.from_numpy(np.stack(__snake_case		,					axis=0    )    ).float()		,					y=torch.from_numpy(np.stack(__snake_case		,					axis=0    )    ).float()		,					z=torch.from_numpy(np.stack(__snake_case		,					axis=0    )    ).float()		,					width=__snake_case		,					height=__snake_case		,					x_fov=0.7		,					y_fov=0.7		,					shape=(1, len(__snake_case    ))		,					)


