
# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch

import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union

import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin, SchedulerOutput

@dataclass
class __snake_case		(      lowerCAmelCase     ):
    _a		:						torch.FloatTensor
    _a		:						torch.FloatTensor






class __snake_case		(      lowerCAmelCase					,	lowerCAmelCase     ):
    _a		:						Optional[int]=				1
    @register_to_config
    def __init__(  self		,snake_case = 2000		,snake_case = 0.15		,snake_case = 0.01		,snake_case = 1_348.0		,snake_case = 1e-5		,snake_case = 1		,):



           '''simple docstring'''







           lowercase							:     int       	=      sigma_max

           # setable values
           lowercase							:     Optional[int]       	=      None

           self.set_sigmas(snake_case		,snake_case		,snake_case		,snake_case      )
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case		,snake_case = None      ):



           '''simple docstring'''







           return sample
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case		,snake_case = None		,snake_case = None      ):



           '''simple docstring'''







           lowercase							:     int       	=      sampling_eps if sampling_eps is not None else self.config.sampling_eps

           lowercase							:     Any       	=      torch.linspace(1		,snake_case		,snake_case		,device=snake_case      )
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case		,snake_case = None		,snake_case = None		,snake_case = None      ):



           '''simple docstring'''







           lowercase							:     Optional[int]       	=      sigma_min if sigma_min is not None else self.config.sigma_min
           lowercase							:     Dict       	=      sigma_max if sigma_max is not None else self.config.sigma_max
           lowercase							:     Dict       	=      sampling_eps if sampling_eps is not None else self.config.sampling_eps
           if self.timesteps is None:
                  self.set_timesteps(snake_case		,snake_case      )

           lowercase							:     str       	=      sigma_min * (sigma_max / sigma_min) ** (self.timesteps / sampling_eps)
           lowercase							:     List[str]       	=      torch.exp(torch.linspace(math.log(snake_case      )		,math.log(snake_case      )		,snake_case      )      )
           lowercase							:     List[Any]       	=      torch.tensor([sigma_min * (sigma_max / sigma_min) ** t for t in self.timesteps]      )
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case		,snake_case      ):



           '''simple docstring'''







           return torch.where(
               timesteps == 0		,torch.zeros_like(t.to(timesteps.device      )      )		,self.discrete_sigmas[timesteps - 1].to(timesteps.device      )		,)
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case		,snake_case		,snake_case		,snake_case = None		,snake_case = True		,):



           '''simple docstring'''







           if self.timesteps is None:
                  raise ValueError(
                      """`self.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"""      )

           lowercase							:     str       	=      timestep * torch.ones(
               sample.shape[0]		,device=sample.device      )  # torch.repeat_interleave(timestep, sample.shape[0])
           lowercase							:     Tuple       	=      (timestep * (len(self.timesteps      ) - 1)).long()

           # mps requires indices to be in the same device, so we use cpu as is the default with cuda
           lowercase							:     Union[str, Any]       	=      timesteps.to(self.discrete_sigmas.device      )

           lowercase							:     Optional[int]       	=      self.discrete_sigmas[timesteps].to(sample.device      )
           lowercase							:     Dict       	=      self.get_adjacent_sigma(snake_case		,snake_case      ).to(sample.device      )
           lowercase							:     List[str]       	=      torch.zeros_like(snake_case      )
           lowercase							:     List[Any]       	=      (sigma**2 - adjacent_sigma**2) ** 0.5

           # equation 6 in the paper: the model_output modeled by the network is grad_x log pt(x)
           # also equation 47 shows the analog from SDE models to ancestral sampling methods
           lowercase							:     Dict       	=      diffusion.flatten()
           while len(diffusion.shape      ) < len(sample.shape      ):
                  lowercase							:     List[Any]       	=      diffusion.unsqueeze(-1      )
           lowercase							:     int       	=      drift - diffusion**2 * model_output

           #  equation 6: sample noise for the diffusion term of
           lowercase							:     Any       	=      randn_tensor(
               sample.shape		,layout=sample.layout		,generator=snake_case		,device=sample.device		,dtype=sample.dtype      )
           lowercase							:     List[Any]       	=      sample - drift  # subtract because `dt` is a small negative timestep
           # TODO is the variable diffusion the correct scaling term for the noise?
           lowercase							:     int       	=      prev_sample_mean + diffusion * noise  # add impact of diffusion field g

           if not return_dict:
                  return (prev_sample, prev_sample_mean)

           return SdeVeOutput(prev_sample=snake_case		,prev_sample_mean=snake_case      )
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case		,snake_case		,snake_case = None		,snake_case = True		,):



           '''simple docstring'''







           if self.timesteps is None:
                  raise ValueError(
                      """`self.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"""      )

           # For small batch sizes, the paper "suggest replacing norm(z) with sqrt(d), where d is the dim. of z"
           # sample noise for correction
           lowercase							:     Tuple       	=      randn_tensor(sample.shape		,layout=sample.layout		,generator=snake_case      ).to(sample.device      )

           # compute step size from the model_output, the noise, and the snr
           lowercase							:     Any       	=      torch.norm(model_output.reshape(model_output.shape[0]		,-1      )		,dim=-1      ).mean()
           lowercase							:     Optional[int]       	=      torch.norm(noise.reshape(noise.shape[0]		,-1      )		,dim=-1      ).mean()
           lowercase							:     Optional[int]       	=      (self.config.snr * noise_norm / grad_norm) ** 2 * 2
           lowercase							:     str       	=      step_size * torch.ones(sample.shape[0]      ).to(sample.device      )
           # self.repeat_scalar(step_size, sample.shape[0])

           # compute corrected sample: model_output term and noise term
           lowercase							:     Union[str, Any]       	=      step_size.flatten()
           while len(step_size.shape      ) < len(sample.shape      ):
                  lowercase							:     Optional[Any]       	=      step_size.unsqueeze(-1      )
           lowercase							:     Tuple       	=      sample + step_size * model_output
           lowercase							:     Union[str, Any]       	=      prev_sample_mean + ((step_size * 2) ** 0.5) * noise

           if not return_dict:
                  return (prev_sample,)

           return SchedulerOutput(prev_sample=snake_case      )
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case		,snake_case		,snake_case		,):



           '''simple docstring'''







           lowercase							:     List[Any]       	=      timesteps.to(original_samples.device      )
           lowercase							:     Optional[Any]       	=      self.discrete_sigmas.to(original_samples.device      )[timesteps]
           lowercase							:     List[Any]       	=      (
               noise * sigmas[:, None, None, None]
               if noise is not None
               else torch.randn_like(snake_case      ) * sigmas[:, None, None, None]
           )
           lowercase							:     Tuple       	=      noise + original_samples
           return noisy_samples
    def __len__(  self      ):



           '''simple docstring'''







           return self.config.num_train_timesteps


import argparse

import requests
import torch
from PIL import Image

from transformers import ViTMAEConfig, ViTMAEForPreTraining, ViTMAEImageProcessor







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

       return name







def 						_snake_case(  SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				) ->    List[Any]:
       for key in orig_state_dict.copy().keys():
              lowercase							:     List[Any]       	=      orig_state_dict.pop(SCREAMING_SNAKE_CASE__				)

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

              else:
                     lowercase							:     Union[str, Any]       	=      val

       return orig_state_dict







def 						_snake_case(  SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				) ->    int:
       lowercase							:     int       	=      ViTMAEConfig()
       if "large" in checkpoint_url:
              lowercase							:     Dict       	=      1_024
              lowercase							:     str       	=      4_096
              lowercase							:     Optional[Any]       	=      24
              lowercase							:     Optional[Any]       	=      16
       elif "huge" in checkpoint_url:
              lowercase							:     int       	=      14
              lowercase							:     List[Any]       	=      1_280
              lowercase							:     int       	=      5_120
              lowercase							:     List[Any]       	=      32
              lowercase							:     Any       	=      16

       lowercase							:     List[str]       	=      ViTMAEForPreTraining(SCREAMING_SNAKE_CASE__				)

       lowercase							:     Optional[Any]       	=      torch.hub.load_state_dict_from_url(SCREAMING_SNAKE_CASE__					,	map_location="""cpu"""				)["""model"""]

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

       lowercase							:     Optional[int]       	=      convert_state_dict(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				)

       model.load_state_dict(SCREAMING_SNAKE_CASE__				)
       model.eval()

       lowercase							:     Union[str, Any]       	=      """https://user-images.githubusercontent.com/11435359/147738734-196fd92f-9260-48d5-ba7e-bf103d29364d.jpg"""

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

       # forward pass
       torch.manual_seed(2				)
       lowercase							:     int       	=      model(**SCREAMING_SNAKE_CASE__				)
       lowercase							:     str       	=      outputs.logits

       if "large" in checkpoint_url:
              lowercase							:     List[Any]       	=      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							:     Tuple       	=      torch.tensor(
                  [[-1.1599, -0.9199, -1.2221], [-1.1952, -0.9269, -1.2307], [-1.2143, -0.9337, -1.2262]]				)
       else:
              lowercase							:     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__":
  lowercase  :					Union[str, 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."""
  )

  lowercase  :					List[Any]						    =							parser.parse_args()
  convert_vit_mae_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)
