






"""simple docstring"""




import argparse

import requests
import torch
from PIL import Image

from transformers import SwinConfig, SwinForMaskedImageModeling, ViTImageProcessor

def 			lowerCAmelCase__     (     UpperCamelCase__				):



  '''simple docstring'''
  _a  :       Union[str, Any]             =	SwinConfig(image_size=1_9_2				)

  if "base" in model_name:
    _a  :       List[str]             =	6
    _a  :       str             =	1_2_8
    _a  :       Optional[Any]             =	(2, 2, 1_8, 2)
    _a  :       Union[str, Any]             =	(4, 8, 1_6, 3_2)
  elif "large" in model_name:
    _a  :       Optional[Any]             =	1_2
    _a  :       Optional[Any]             =	1_9_2
    _a  :       int             =	(2, 2, 1_8, 2)
    _a  :       Union[str, Any]             =	(6, 1_2, 2_4, 4_8)
  else:
    raise ValueError("""Model not supported, only supports base and large variants"""				)

  _a  :       str             =	window_size
  _a  :       List[str]             =	embed_dim
  _a  :       Tuple             =	depths
  _a  :       int             =	num_heads

  return config

def 			lowerCAmelCase__     (     UpperCamelCase__				):



  '''simple docstring'''
  if "encoder.mask_token" in name:
    _a  :       List[str]             =	name.replace("""encoder.mask_token"""     ,   """embeddings.mask_token"""				)
  if "encoder.patch_embed.proj" in name:
    _a  :       Tuple             =	name.replace("""encoder.patch_embed.proj"""     ,   """embeddings.patch_embeddings.projection"""				)
  if "encoder.patch_embed.norm" in name:
    _a  :       Any             =	name.replace("""encoder.patch_embed.norm"""     ,   """embeddings.norm"""				)
  if "attn.proj" in name:
    _a  :       str             =	name.replace("""attn.proj"""     ,   """attention.output.dense"""				)
  if "attn" in name:
    _a  :       Dict             =	name.replace("""attn"""     ,   """attention.self"""				)
  if "norm1" in name:
    _a  :       Union[str, Any]             =	name.replace("""norm1"""     ,   """layernorm_before"""				)
  if "norm2" in name:
    _a  :       str             =	name.replace("""norm2"""     ,   """layernorm_after"""				)
  if "mlp.fc1" in name:
    _a  :       List[str]             =	name.replace("""mlp.fc1"""     ,   """intermediate.dense"""				)
  if "mlp.fc2" in name:
    _a  :       Optional[int]             =	name.replace("""mlp.fc2"""     ,   """output.dense"""				)

  if name == "encoder.norm.weight":
    _a  :       Tuple             =	"""layernorm.weight"""
  if name == "encoder.norm.bias":
    _a  :       str             =	"""layernorm.bias"""

  if "decoder" in name:
    pass
  else:
    _a  :       List[Any]             =	"""swin.""" + name

  return name

def 			lowerCAmelCase__     (     UpperCamelCase__     ,   UpperCamelCase__				):



  '''simple docstring'''
  for key in orig_state_dict.copy().keys():
    _a  :       Optional[int]             =	orig_state_dict.pop(UpperCamelCase__				)

    if "attn_mask" in key:
      pass
    elif "qkv" in key:
      _a  :       List[Any]             =	key.split("""."""				)
      _a  :       Dict             =	int(key_split[2]				)
      _a  :       str             =	int(key_split[4]				)
      _a  :       Optional[int]             =	model.swin.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size

      if "weight" in key:
        _a  :       Dict             =	val[:dim, :]
        _a  :       Tuple             =	val[
            dim : dim * 2, :
        ]
        _a  :       List[Any]             =	val[-dim:, :]
      else:
        _a  :       Tuple             =	val[
            :dim
        ]
        _a  :       Union[str, Any]             =	val[
            dim : dim * 2
        ]
        _a  :       Union[str, Any]             =	val[
            -dim:
        ]
    else:
      _a  :       int             =	val

  return orig_state_dict

def 			lowerCAmelCase__     (     UpperCamelCase__     ,   UpperCamelCase__     ,   UpperCamelCase__     ,   UpperCamelCase__				):



  '''simple docstring'''
  _a  :       List[Any]             =	torch.load(UpperCamelCase__     ,   map_location="""cpu"""				)["""model"""]

  _a  :       List[Any]             =	get_swin_config(UpperCamelCase__				)
  _a  :       Any             =	SwinForMaskedImageModeling(UpperCamelCase__				)
  model.eval()

  _a  :       int             =	convert_state_dict(UpperCamelCase__     ,   UpperCamelCase__				)
  model.load_state_dict(UpperCamelCase__				)

  _a  :       int             =	"""http://images.cocodataset.org/val2017/000000039769.jpg"""

  _a  :       List[Any]             =	ViTImageProcessor(size={"""height""": 1_9_2, """width""": 1_9_2}				)
  _a  :       Optional[int]             =	Image.open(requests.get(UpperCamelCase__     ,   stream=UpperCamelCase__				).raw				)
  _a  :       Union[str, Any]             =	image_processor(images=UpperCamelCase__     ,   return_tensors="""pt"""				)

  with torch.no_grad():
    _a  :       Any             =	model(**UpperCamelCase__				).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(UpperCamelCase__				)

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

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









"""simple docstring"""




import tempfile

import torch

from diffusers import IPNDMScheduler

from .test_schedulers import SchedulerCommonTest






class        UpperCamelCase			(	snake_case_      ):
 UpperCamelCase					:       int			=					(IPNDMScheduler,)
 UpperCamelCase					:       int			=					(('''num_inference_steps''', 50),)


 def _lowercase	(     self		:						Union[str, Any]							,						**UpperCAmelCase__		:						Tuple							)					->       int:
   _a  :       Optional[int]             =	{"""num_train_timesteps""": 1000}
   config.update(**UpperCAmelCase__							)
   return config


 def _lowercase	(     self		:						Dict							,						UpperCAmelCase__		:						Any=0							,						**UpperCAmelCase__		:						Optional[Any]							)					->       Union[str, Any]:
   _a  :       Optional[int]             =	dict(self.forward_default_kwargs							)
   _a  :       Dict             =	kwargs.pop("""num_inference_steps"""							,						UpperCAmelCase__							)
   _a  :       Optional[Any]             =	self.dummy_sample
   _a  :       Union[str, Any]             =	0.1 * sample
   _a  :       Union[str, Any]             =	[residual + 0.2, residual + 0.1_5, residual + 0.1, residual + 0.0_5]

   for scheduler_class in self.scheduler_classes:
     _a  :       Optional[int]             =	self.get_scheduler_config(**UpperCAmelCase__							)
     _a  :       Union[str, Any]             =	scheduler_class(**UpperCAmelCase__							)
     scheduler.set_timesteps(UpperCAmelCase__							)
     # copy over dummy past residuals
     _a  :       Any             =	dummy_past_residuals[:]

     if time_step is None:
       _a  :       str             =	scheduler.timesteps[len(scheduler.timesteps							) // 2]

     with tempfile.TemporaryDirectory() as tmpdirname:
       scheduler.save_config(UpperCAmelCase__							)
       _a  :       Union[str, Any]             =	scheduler_class.from_pretrained(UpperCAmelCase__							)
       new_scheduler.set_timesteps(UpperCAmelCase__							)
       # copy over dummy past residuals
       _a  :       Optional[Any]             =	dummy_past_residuals[:]

     _a  :       List[Any]             =	scheduler.step(UpperCAmelCase__							,						UpperCAmelCase__							,						UpperCAmelCase__							,						**UpperCAmelCase__							).prev_sample
     _a  :       str             =	new_scheduler.step(UpperCAmelCase__							,						UpperCAmelCase__							,						UpperCAmelCase__							,						**UpperCAmelCase__							).prev_sample

     assert torch.sum(torch.abs(output - new_output							)							) < 1E-5, "Scheduler outputs are not identical"

     _a  :       Optional[int]             =	scheduler.step(UpperCAmelCase__							,						UpperCAmelCase__							,						UpperCAmelCase__							,						**UpperCAmelCase__							).prev_sample
     _a  :       Tuple             =	new_scheduler.step(UpperCAmelCase__							,						UpperCAmelCase__							,						UpperCAmelCase__							,						**UpperCAmelCase__							).prev_sample

     assert torch.sum(torch.abs(output - new_output							)							) < 1E-5, "Scheduler outputs are not identical"


 def _lowercase	(     self		:						Tuple							)					->       List[str]:
   pass


 def _lowercase	(     self		:						Optional[int]							,						UpperCAmelCase__		:						List[str]=0							,						**UpperCAmelCase__		:						Optional[Any]							)					->       List[Any]:
   _a  :       Optional[Any]             =	dict(self.forward_default_kwargs							)
   _a  :       Optional[Any]             =	kwargs.pop("""num_inference_steps"""							,						UpperCAmelCase__							)
   _a  :       Optional[Any]             =	self.dummy_sample
   _a  :       List[Any]             =	0.1 * sample
   _a  :       Optional[Any]             =	[residual + 0.2, residual + 0.1_5, residual + 0.1, residual + 0.0_5]

   for scheduler_class in self.scheduler_classes:
     _a  :       Union[str, Any]             =	self.get_scheduler_config()
     _a  :       Optional[Any]             =	scheduler_class(**UpperCAmelCase__							)
     scheduler.set_timesteps(UpperCAmelCase__							)

     # copy over dummy past residuals (must be after setting timesteps)
     _a  :       Any             =	dummy_past_residuals[:]

     if time_step is None:
       _a  :       List[Any]             =	scheduler.timesteps[len(scheduler.timesteps							) // 2]

     with tempfile.TemporaryDirectory() as tmpdirname:
       scheduler.save_config(UpperCAmelCase__							)
       _a  :       Any             =	scheduler_class.from_pretrained(UpperCAmelCase__							)
       # copy over dummy past residuals
       new_scheduler.set_timesteps(UpperCAmelCase__							)

       # copy over dummy past residual (must be after setting timesteps)
       _a  :       Optional[Any]             =	dummy_past_residuals[:]

     _a  :       List[str]             =	scheduler.step(UpperCAmelCase__							,						UpperCAmelCase__							,						UpperCAmelCase__							,						**UpperCAmelCase__							).prev_sample
     _a  :       Tuple             =	new_scheduler.step(UpperCAmelCase__							,						UpperCAmelCase__							,						UpperCAmelCase__							,						**UpperCAmelCase__							).prev_sample

     assert torch.sum(torch.abs(output - new_output							)							) < 1E-5, "Scheduler outputs are not identical"

     _a  :       Union[str, Any]             =	scheduler.step(UpperCAmelCase__							,						UpperCAmelCase__							,						UpperCAmelCase__							,						**UpperCAmelCase__							).prev_sample
     _a  :       int             =	new_scheduler.step(UpperCAmelCase__							,						UpperCAmelCase__							,						UpperCAmelCase__							,						**UpperCAmelCase__							).prev_sample

     assert torch.sum(torch.abs(output - new_output							)							) < 1E-5, "Scheduler outputs are not identical"


 def _lowercase	(     self		:						str							,						**UpperCAmelCase__		:						Any							)					->       List[str]:
   _a  :       Optional[int]             =	self.scheduler_classes[0]
   _a  :       Optional[Any]             =	self.get_scheduler_config(**UpperCAmelCase__							)
   _a  :       Union[str, Any]             =	scheduler_class(**UpperCAmelCase__							)

   _a  :       int             =	10
   _a  :       List[Any]             =	self.dummy_model()
   _a  :       str             =	self.dummy_sample_deter
   scheduler.set_timesteps(UpperCAmelCase__							)

   for i, t in enumerate(scheduler.timesteps							):
     _a  :       str             =	model(UpperCAmelCase__							,						UpperCAmelCase__							)
     _a  :       List[Any]             =	scheduler.step(UpperCAmelCase__							,						UpperCAmelCase__							,						UpperCAmelCase__							).prev_sample

   for i, t in enumerate(scheduler.timesteps							):
     _a  :       Union[str, Any]             =	model(UpperCAmelCase__							,						UpperCAmelCase__							)
     _a  :       Any             =	scheduler.step(UpperCAmelCase__							,						UpperCAmelCase__							,						UpperCAmelCase__							).prev_sample

   return sample


 def _lowercase	(     self		:						int							)					->       str:
   _a  :       Dict             =	dict(self.forward_default_kwargs							)

   _a  :       int             =	kwargs.pop("""num_inference_steps"""							,						UpperCAmelCase__							)

   for scheduler_class in self.scheduler_classes:
     _a  :       Optional[int]             =	self.get_scheduler_config()
     _a  :       Tuple             =	scheduler_class(**UpperCAmelCase__							)

     _a  :       Tuple             =	self.dummy_sample
     _a  :       Optional[Any]             =	0.1 * sample

     if num_inference_steps is not None and hasattr(UpperCAmelCase__							,						"""set_timesteps"""							):
       scheduler.set_timesteps(UpperCAmelCase__							)
     elif num_inference_steps is not None and not hasattr(UpperCAmelCase__							,						"""set_timesteps"""							):
       _a  :       List[str]             =	num_inference_steps

     # copy over dummy past residuals (must be done after set_timesteps)
     _a  :       Union[str, Any]             =	[residual + 0.2, residual + 0.1_5, residual + 0.1, residual + 0.0_5]
     _a  :       Optional[Any]             =	dummy_past_residuals[:]

     _a  :       Optional[Any]             =	scheduler.timesteps[5]
     _a  :       str             =	scheduler.timesteps[6]

     _a  :       Optional[int]             =	scheduler.step(UpperCAmelCase__							,						UpperCAmelCase__							,						UpperCAmelCase__							,						**UpperCAmelCase__							).prev_sample
     _a  :       Union[str, Any]             =	scheduler.step(UpperCAmelCase__							,						UpperCAmelCase__							,						UpperCAmelCase__							,						**UpperCAmelCase__							).prev_sample

     self.assertEqual(output_a.shape							,						sample.shape							)
     self.assertEqual(output_a.shape							,						output_a.shape							)

     _a  :       Tuple             =	scheduler.step(UpperCAmelCase__							,						UpperCAmelCase__							,						UpperCAmelCase__							,						**UpperCAmelCase__							).prev_sample
     _a  :       List[str]             =	scheduler.step(UpperCAmelCase__							,						UpperCAmelCase__							,						UpperCAmelCase__							,						**UpperCAmelCase__							).prev_sample

     self.assertEqual(output_a.shape							,						sample.shape							)
     self.assertEqual(output_a.shape							,						output_a.shape							)


 def _lowercase	(     self		:						List[str]							)					->       List[str]:
   for timesteps in [100, 1000]:
     self.check_over_configs(num_train_timesteps=UpperCAmelCase__							,						time_step=UpperCAmelCase__							)


 def _lowercase	(     self		:						List[str]							)					->       List[str]:
   for t, num_inference_steps in zip([1, 5, 10]							,						[10, 50, 100]							):
     self.check_over_forward(num_inference_steps=UpperCAmelCase__							,						time_step=UpperCAmelCase__							)







 def _lowercase	(     self		:						int							)					->       List[Any]:
   _a  :       str             =	self.full_loop()
   _a  :       List[Any]             =	torch.mean(torch.abs(UpperCAmelCase__							)							)

   assert abs(result_mean.item() - 2540529							) < 10

