






import argparse

import requests
import torch
from PIL import Image

from transformers import SwinConfig, SwinForMaskedImageModeling, ViTImageProcessor






def      _snake_case     (__lowercase):
   UpperCamelCase_						=  SwinConfig(image_size=192)

   if "base" in model_name:
      UpperCamelCase_						=  6
      UpperCamelCase_						=  128
      UpperCamelCase_						=  (2, 2, 18, 2)
      UpperCamelCase_						=  (4, 8, 16, 32)
   elif "large" in model_name:
      UpperCamelCase_						=  12
      UpperCamelCase_						=  192
      UpperCamelCase_						=  (2, 2, 18, 2)
      UpperCamelCase_						=  (6, 12, 24, 48)
   else:
      raise ValueError('Model not supported, only supports base and large variants')

   UpperCamelCase_						=  window_size
   UpperCamelCase_						=  embed_dim
   UpperCamelCase_						=  depths
   UpperCamelCase_						=  num_heads

   return config






def      _snake_case     (__lowercase):
   if "encoder.mask_token" in name:
      UpperCamelCase_						=  name.replace('encoder.mask_token'     ,   'embeddings.mask_token')
   if "encoder.patch_embed.proj" in name:
      UpperCamelCase_						=  name.replace('encoder.patch_embed.proj'     ,   'embeddings.patch_embeddings.projection')
   if "encoder.patch_embed.norm" in name:
      UpperCamelCase_						=  name.replace('encoder.patch_embed.norm'     ,   'embeddings.norm')
   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 name == "encoder.norm.weight":
      UpperCamelCase_						=  'layernorm.weight'
   if name == "encoder.norm.bias":
      UpperCamelCase_						=  'layernorm.bias'

   if "decoder" in name:
      pass
   else:
      UpperCamelCase_						=  'swin.' + name

   return name






def      _snake_case     (__lowercase     ,   __lowercase):
   for key in orig_state_dict.copy().keys():
      UpperCamelCase_						=  orig_state_dict.pop(__lowercase)

      if "attn_mask" in key:
         pass
      elif "qkv" in key:
         UpperCamelCase_						=  key.split('.')
         UpperCamelCase_						=  int(key_split[2])
         UpperCamelCase_						=  int(key_split[4])
         UpperCamelCase_						=  model.swin.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size

         if "weight" in key:
            UpperCamelCase_						=  val[:dim, :]
            UpperCamelCase_						=  val[
                dim : dim * 2, :
            ]
            UpperCamelCase_						=  val[-dim:, :]
         else:
            UpperCamelCase_						=  val[
                :dim
            ]
            UpperCamelCase_						=  val[
                dim : dim * 2
            ]
            UpperCamelCase_						=  val[
                -dim:
            ]
      else:
         UpperCamelCase_						=  val

   return orig_state_dict






def      _snake_case     (__lowercase     ,   __lowercase     ,   __lowercase     ,   __lowercase):
   UpperCamelCase_						=  torch.load(__lowercase     ,   map_location='cpu')['model']

   UpperCamelCase_						=  get_swin_config(__lowercase)
   UpperCamelCase_						=  SwinForMaskedImageModeling(__lowercase)
   model.eval()

   UpperCamelCase_						=  convert_state_dict(__lowercase     ,   __lowercase)
   model.load_state_dict(__lowercase)

   UpperCamelCase_						=  'http://images.cocodataset.org/val2017/000000039769.jpg'

   UpperCamelCase_						=  ViTImageProcessor(size={'height': 192, 'width': 192})
   UpperCamelCase_						=  Image.open(requests.get(__lowercase     ,   stream=__lowercase).raw)
   UpperCamelCase_						=  image_processor(images=__lowercase     ,   return_tensors='pt')

   with torch.no_grad():
      UpperCamelCase_						=  model(**__lowercase).logits

   print(outputs.keys())
   print('Looks ok!')

   if pytorch_dump_folder_path is not None:
      print(f"""Saving model {model_name} 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 push_to_hub:
      print(f"""Pushing model and image processor for {model_name} to hub""")
      model.push_to_hub(f"""microsoft/{model_name}""")
      image_processor.push_to_hub(f"""microsoft/{model_name}""")


if __name__ == "__main__":
 snake_case__   :						List[Any]													=   argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     """--model_name""",
     default="""swin-base-simmim-window6-192""",
     type=str,
     choices=["""swin-base-simmim-window6-192""", """swin-large-simmim-window12-192"""],
     help="""Name of the Swin SimMIM model you'd like to convert.""",
 )
 parser.add_argument(
     """--checkpoint_path""",
     default="""/Users/nielsrogge/Documents/SwinSimMIM/simmim_pretrain__swin_base__img192_window6__100ep.pth""",
     type=str,
     help="""Path to the original PyTorch checkpoint (.pth file).""",
 )
 parser.add_argument(
     """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model directory."""
 )
 parser.add_argument(
     """--push_to_hub""", action="""store_true""", help="""Whether or not to push the converted model to the 🤗 hub."""
 )

 snake_case__   :						Tuple													=   parser.parse_args()
 convert_swin_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)













# 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     _a							(	UpperCAmelCase__     ):






  """simple docstring"""





  A_						       = 42
  A_						       = 42




class     _a							(	UpperCAmelCase__    ,							UpperCAmelCase__     ):






  """simple docstring"""





  A_						       = 1




  @register_to_config
  def __init__(  self		,		_UpperCAmelCase = 2000		,		_UpperCAmelCase = 0.1_5		,		_UpperCAmelCase = 0.0_1		,		_UpperCAmelCase = 1_3_4_8.0		,		_UpperCAmelCase = 1e-5		,		_UpperCAmelCase = 1		,		)      ->			Tuple:
     # standard deviation of the initial noise distribution
     UpperCamelCase_						=  sigma_max

     # setable values
     UpperCamelCase_						=  None

     self.set_sigmas(_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase			)




  def  _UpperCAmelCase  (  self		,		_UpperCAmelCase		,		_UpperCAmelCase = None			)      ->			torch.FloatTensor:
     return sample




  def  _UpperCAmelCase  (  self		,		_UpperCAmelCase		,		_UpperCAmelCase = None		,		_UpperCAmelCase = None			)      ->			str:
     UpperCamelCase_						=  sampling_eps if sampling_eps is not None else self.config.sampling_eps

     UpperCamelCase_						=  torch.linspace(1		,		_UpperCAmelCase		,		_UpperCAmelCase		,		device=_UpperCAmelCase			)




  def  _UpperCAmelCase  (  self		,		_UpperCAmelCase		,		_UpperCAmelCase = None		,		_UpperCAmelCase = None		,		_UpperCAmelCase = None			)      ->			Any:
     UpperCamelCase_						=  sigma_min if sigma_min is not None else self.config.sigma_min
     UpperCamelCase_						=  sigma_max if sigma_max is not None else self.config.sigma_max
     UpperCamelCase_						=  sampling_eps if sampling_eps is not None else self.config.sampling_eps
     if self.timesteps is None:
        self.set_timesteps(_UpperCAmelCase		,		_UpperCAmelCase			)

     UpperCamelCase_						=  sigma_min * (sigma_max / sigma_min) ** (self.timesteps / sampling_eps)
     UpperCamelCase_						=  torch.exp(torch.linspace(math.log(_UpperCAmelCase			)		,		math.log(_UpperCAmelCase			)		,		_UpperCAmelCase			)			)
     UpperCamelCase_						=  torch.tensor([sigma_min * (sigma_max / sigma_min) ** t for t in self.timesteps]			)




  def  _UpperCAmelCase  (  self		,		_UpperCAmelCase		,		_UpperCAmelCase			)      ->			List[Any]:
     return torch.where(
         timesteps == 0		,		torch.zeros_like(t.to(timesteps.device			)			)		,		self.discrete_sigmas[timesteps - 1].to(timesteps.device			)		,		)




  def  _UpperCAmelCase  (  self		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase = None		,		_UpperCAmelCase = True		,		)      ->			Union[SdeVeOutput, Tuple]:
     if self.timesteps is None:
        raise ValueError(
            '`self.timesteps` is not set, you need to run \'set_timesteps\' after creating the scheduler'			)

     UpperCamelCase_						=  timestep * torch.ones(
         sample.shape[0]		,		device=sample.device			)  # torch.repeat_interleave(timestep, sample.shape[0])
     UpperCamelCase_						=  (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
     UpperCamelCase_						=  timesteps.to(self.discrete_sigmas.device			)

     UpperCamelCase_						=  self.discrete_sigmas[timesteps].to(sample.device			)
     UpperCamelCase_						=  self.get_adjacent_sigma(_UpperCAmelCase		,		_UpperCAmelCase			).to(sample.device			)
     UpperCamelCase_						=  torch.zeros_like(_UpperCAmelCase			)
     UpperCamelCase_						=  (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
     UpperCamelCase_						=  diffusion.flatten()
     while len(diffusion.shape			) < len(sample.shape			):
        UpperCamelCase_						=  diffusion.unsqueeze(-1			)
     UpperCamelCase_						=  drift - diffusion**2 * model_output

     #  equation 6: sample noise for the diffusion term of
     UpperCamelCase_						=  randn_tensor(
         sample.shape		,		layout=sample.layout		,		generator=_UpperCAmelCase		,		device=sample.device		,		dtype=sample.dtype			)
     UpperCamelCase_						=  sample - drift  # subtract because `dt` is a small negative timestep
     # TODO is the variable diffusion the correct scaling term for the noise?
     UpperCamelCase_						=  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=_UpperCAmelCase		,		prev_sample_mean=_UpperCAmelCase			)




  def  _UpperCAmelCase  (  self		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase = None		,		_UpperCAmelCase = True		,		)      ->			Union[SchedulerOutput, Tuple]:
     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
     UpperCamelCase_						=  randn_tensor(sample.shape		,		layout=sample.layout		,		generator=_UpperCAmelCase			).to(sample.device			)

     # compute step size from the model_output, the noise, and the snr
     UpperCamelCase_						=  torch.norm(model_output.reshape(model_output.shape[0]		,		-1			)		,		dim=-1			).mean()
     UpperCamelCase_						=  torch.norm(noise.reshape(noise.shape[0]		,		-1			)		,		dim=-1			).mean()
     UpperCamelCase_						=  (self.config.snr * noise_norm / grad_norm) ** 2 * 2
     UpperCamelCase_						=  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
     UpperCamelCase_						=  step_size.flatten()
     while len(step_size.shape			) < len(sample.shape			):
        UpperCamelCase_						=  step_size.unsqueeze(-1			)
     UpperCamelCase_						=  sample + step_size * model_output
     UpperCamelCase_						=  prev_sample_mean + ((step_size * 2) ** 0.5) * noise

     if not return_dict:
        return (prev_sample,)

     return SchedulerOutput(prev_sample=_UpperCAmelCase			)




  def  _UpperCAmelCase  (  self		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase		,		)      ->			torch.FloatTensor:
     # Make sure sigmas and timesteps have the same device and dtype as original_samples
     UpperCamelCase_						=  timesteps.to(original_samples.device			)
     UpperCamelCase_						=  self.discrete_sigmas.to(original_samples.device			)[timesteps]
     UpperCamelCase_						=  (
         noise * sigmas[:, None, None, None]
         if noise is not None
         else torch.randn_like(_UpperCAmelCase			) * sigmas[:, None, None, None]
     )
     UpperCamelCase_						=  noise + original_samples
     return noisy_samples




  def __len__(  self			)      ->			Optional[int]:
     return self.config.num_train_timesteps





