





import argparse

import requests
import torch
from PIL import Image

from transformers import ViTMAEConfig, ViTMAEForPreTraining, ViTMAEImageProcessor
def 					_A	(		SCREAMING_SNAKE_CASE					:       str						):





  """simple docstring"""



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

  return name
def 					_A	(		SCREAMING_SNAKE_CASE					:       Any	,	SCREAMING_SNAKE_CASE					:       Dict						):





  """simple docstring"""



  for key in orig_state_dict.copy().keys():
    a__     : List[str]  =orig_state_dict.pop(SCREAMING_SNAKE_CASE						)

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

    else:
      a__     : int  =val

  return orig_state_dict
def 					_A	(		SCREAMING_SNAKE_CASE					:       Optional[Any]	,	SCREAMING_SNAKE_CASE					:       str						):





  """simple docstring"""



  a__     : Optional[Any]  =ViTMAEConfig()
  if "large" in checkpoint_url:
    a__     : int  =1_024
    a__     : Optional[Any]  =4_096
    a__     : Union[str, Any]  =24
    a__     : Union[str, Any]  =16
  elif "huge" in checkpoint_url:
    a__     : int  =14
    a__     : Tuple  =1_280
    a__     : Tuple  =5_120
    a__     : int  =32
    a__     : Optional[int]  =16

  a__     : int  =ViTMAEForPreTraining(SCREAMING_SNAKE_CASE						)

  a__     : Optional[int]  =torch.hub.load_state_dict_from_url(SCREAMING_SNAKE_CASE	,	map_location="cpu"						)["model"]

  a__     : Union[str, Any]  =ViTMAEImageProcessor(size=config.image_size						)

  a__     : Tuple  =convert_state_dict(SCREAMING_SNAKE_CASE	,	SCREAMING_SNAKE_CASE						)

  model.load_state_dict(SCREAMING_SNAKE_CASE						)
  model.eval()

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

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

  # forward pass
  torch.manual_seed(2						)
  a__     : Tuple  =model(**SCREAMING_SNAKE_CASE						)
  a__     : Optional[int]  =outputs.logits

  if "large" in checkpoint_url:
    a__     : Union[str, Any]  =torch.tensor(
        [[-0.7_3_0_9, -0.7_1_2_8, -1.0_1_6_9], [-1.0_1_6_1, -0.9_0_5_8, -1.1_8_7_8], [-1.0_4_7_8, -0.9_4_1_1, -1.1_9_1_1]]						)
  elif "huge" in checkpoint_url:
    a__     : List[str]  =torch.tensor(
        [[-1.1_5_9_9, -0.9_1_9_9, -1.2_2_2_1], [-1.1_9_5_2, -0.9_2_6_9, -1.2_3_0_7], [-1.2_1_4_3, -0.9_3_3_7, -1.2_2_6_2]]						)
  else:
    a__     : Optional[int]  =torch.tensor(
        [[-0.9_1_9_2, -0.8_4_8_1, -1.1_2_5_9], [-1.1_3_4_9, -1.0_0_3_4, -1.2_5_9_9], [-1.1_7_5_7, -1.0_4_2_9, -1.2_7_2_6]]						)

  # 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__":
     UpperCAmelCase     :     List[str]							=	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     :     List[str]							=	parser.parse_args()
     convert_vit_mae_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)













import tempfile

import torch

from diffusers import PNDMScheduler

from .test_schedulers import SchedulerCommonTest







class     __lowerCAmelCase	(   UpperCamelCase__):
      _lowercase    : Any            =      (PNDMScheduler,)
      _lowercase    : str            =      (("""num_inference_steps""", 50),)







      def        _lowercase  (   self		,				**lowerCAmelCase__							)       ->						int:



        '''simple docstring'''




        a__     : Dict  ={
            "num_train_timesteps": 1_0_0_0,
            "beta_start": 0.00_01,
            "beta_end": 0.02,
            "beta_schedule": "linear",
        }

        config.update(**lowerCAmelCase__							)
        return config







      def        _lowercase  (   self		,				lowerCAmelCase__=0		,				**lowerCAmelCase__							)       ->						List[Any]:



        '''simple docstring'''




        a__     : Optional[int]  =dict(self.forward_default_kwargs							)
        a__     : Tuple  =kwargs.pop("num_inference_steps"		,				lowerCAmelCase__							)
        a__     : List[str]  =self.dummy_sample
        a__     : List[str]  =0.1 * sample
        a__     : str  =[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

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

          with tempfile.TemporaryDirectory() as tmpdirname:
            scheduler.save_config(lowerCAmelCase__							)
            a__     : List[Any]  =scheduler_class.from_pretrained(lowerCAmelCase__							)
            new_scheduler.set_timesteps(lowerCAmelCase__							)
            # copy over dummy past residuals
            a__     : str  =dummy_past_residuals[:]

          a__     : Any  =scheduler.step_prk(lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__		,				**lowerCAmelCase__							).prev_sample
          a__     : Dict  =new_scheduler.step_prk(lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__		,				**lowerCAmelCase__							).prev_sample

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

          a__     : Optional[int]  =scheduler.step_plms(lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__		,				**lowerCAmelCase__							).prev_sample
          a__     : Union[str, Any]  =new_scheduler.step_plms(lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__		,				**lowerCAmelCase__							).prev_sample

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







      def        _lowercase  (   self							)       ->						int:



        '''simple docstring'''




        pass







      def        _lowercase  (   self		,				lowerCAmelCase__=0		,				**lowerCAmelCase__							)       ->						Optional[Any]:



        '''simple docstring'''




        a__     : Optional[int]  =dict(self.forward_default_kwargs							)
        a__     : List[str]  =kwargs.pop("num_inference_steps"		,				lowerCAmelCase__							)
        a__     : List[str]  =self.dummy_sample
        a__     : int  =0.1 * sample
        a__     : Tuple  =[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

        for scheduler_class in self.scheduler_classes:
          a__     : Dict  =self.get_scheduler_config()
          a__     : List[str]  =scheduler_class(**lowerCAmelCase__							)
          scheduler.set_timesteps(lowerCAmelCase__							)

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

          with tempfile.TemporaryDirectory() as tmpdirname:
            scheduler.save_config(lowerCAmelCase__							)
            a__     : Dict  =scheduler_class.from_pretrained(lowerCAmelCase__							)
            # copy over dummy past residuals
            new_scheduler.set_timesteps(lowerCAmelCase__							)

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

          a__     : Optional[Any]  =scheduler.step_prk(lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__		,				**lowerCAmelCase__							).prev_sample
          a__     : List[Any]  =new_scheduler.step_prk(lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__		,				**lowerCAmelCase__							).prev_sample

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

          a__     : List[str]  =scheduler.step_plms(lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__		,				**lowerCAmelCase__							).prev_sample
          a__     : Any  =new_scheduler.step_plms(lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__		,				**lowerCAmelCase__							).prev_sample

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







      def        _lowercase  (   self		,				**lowerCAmelCase__							)       ->						int:



        '''simple docstring'''




        a__     : Union[str, Any]  =self.scheduler_classes[0]
        a__     : Optional[Any]  =self.get_scheduler_config(**lowerCAmelCase__							)
        a__     : Any  =scheduler_class(**lowerCAmelCase__							)

        a__     : int  =1_0
        a__     : Union[str, Any]  =self.dummy_model()
        a__     : Optional[int]  =self.dummy_sample_deter
        scheduler.set_timesteps(lowerCAmelCase__							)

        for i, t in enumerate(scheduler.prk_timesteps							):
          a__     : List[Any]  =model(lowerCAmelCase__		,				lowerCAmelCase__							)
          a__     : Optional[Any]  =scheduler.step_prk(lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__							).prev_sample

        for i, t in enumerate(scheduler.plms_timesteps							):
          a__     : int  =model(lowerCAmelCase__		,				lowerCAmelCase__							)
          a__     : int  =scheduler.step_plms(lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__							).prev_sample

        return sample







      def        _lowercase  (   self							)       ->						Union[str, Any]:



        '''simple docstring'''




        a__     : str  =dict(self.forward_default_kwargs							)

        a__     : Tuple  =kwargs.pop("num_inference_steps"		,				lowerCAmelCase__							)

        for scheduler_class in self.scheduler_classes:
          a__     : Union[str, Any]  =self.get_scheduler_config()
          a__     : List[str]  =scheduler_class(**lowerCAmelCase__							)

          a__     : List[Any]  =self.dummy_sample
          a__     : Dict  =0.1 * sample

          if num_inference_steps is not None and hasattr(lowerCAmelCase__		,				"set_timesteps"							):
            scheduler.set_timesteps(lowerCAmelCase__							)
          elif num_inference_steps is not None and not hasattr(lowerCAmelCase__		,				"set_timesteps"							):
            a__     : int  =num_inference_steps

          # copy over dummy past residuals (must be done after set_timesteps)
          a__     : Tuple  =[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
          a__     : str  =dummy_past_residuals[:]

          a__     : List[Any]  =scheduler.step_prk(lowerCAmelCase__		,				0		,				lowerCAmelCase__		,				**lowerCAmelCase__							).prev_sample
          a__     : int  =scheduler.step_prk(lowerCAmelCase__		,				1		,				lowerCAmelCase__		,				**lowerCAmelCase__							).prev_sample

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

          a__     : List[str]  =scheduler.step_plms(lowerCAmelCase__		,				0		,				lowerCAmelCase__		,				**lowerCAmelCase__							).prev_sample
          a__     : Dict  =scheduler.step_plms(lowerCAmelCase__		,				1		,				lowerCAmelCase__		,				**lowerCAmelCase__							).prev_sample

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







      def        _lowercase  (   self							)       ->						Tuple:



        '''simple docstring'''




        for timesteps in [1_0_0, 1_0_0_0]:
          self.check_over_configs(num_train_timesteps=lowerCAmelCase__							)







      def        _lowercase  (   self							)       ->						Optional[Any]:



        '''simple docstring'''




        for steps_offset in [0, 1]:
          self.check_over_configs(steps_offset=lowerCAmelCase__							)

        a__     : Optional[Any]  =self.scheduler_classes[0]
        a__     : Tuple  =self.get_scheduler_config(steps_offset=1							)
        a__     : Optional[Any]  =scheduler_class(**lowerCAmelCase__							)
        scheduler.set_timesteps(1_0							)
        assert torch.equal(
            scheduler.timesteps		,				torch.LongTensor(
                [9_0_1, 8_5_1, 8_5_1, 8_0_1, 8_0_1, 7_5_1, 7_5_1, 7_0_1, 7_0_1, 6_5_1, 6_5_1, 6_0_1, 6_0_1, 5_0_1, 4_0_1, 3_0_1, 2_0_1, 1_0_1, 1]							)		,				)







      def        _lowercase  (   self							)       ->						Tuple:



        '''simple docstring'''




        for beta_start, beta_end in zip([0.00_01, 0.0_01]		,				[0.0_02, 0.02]							):
          self.check_over_configs(beta_start=lowerCAmelCase__		,				beta_end=lowerCAmelCase__							)







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



        '''simple docstring'''




        for schedule in ["linear", "squaredcos_cap_v2"]:
          self.check_over_configs(beta_schedule=lowerCAmelCase__							)







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



        '''simple docstring'''




        for prediction_type in ["epsilon", "v_prediction"]:
          self.check_over_configs(prediction_type=lowerCAmelCase__							)







      def        _lowercase  (   self							)       ->						Dict:



        '''simple docstring'''




        for t in [1, 5, 1_0]:
          self.check_over_forward(time_step=lowerCAmelCase__							)







      def        _lowercase  (   self							)       ->						List[Any]:



        '''simple docstring'''




        for t, num_inference_steps in zip([1, 5, 1_0]		,				[1_0, 5_0, 1_0_0]							):
          self.check_over_forward(num_inference_steps=lowerCAmelCase__							)







      def        _lowercase  (   self							)       ->						str:



        '''simple docstring'''




        a__     : Dict  =2_7

        for scheduler_class in self.scheduler_classes:
          a__     : Tuple  =self.dummy_sample
          a__     : Dict  =0.1 * sample

          a__     : Dict  =self.get_scheduler_config()
          a__     : int  =scheduler_class(**lowerCAmelCase__							)

          scheduler.set_timesteps(lowerCAmelCase__							)

          # before power of 3 fix, would error on first step, so we only need to do two
          for i, t in enumerate(scheduler.prk_timesteps[:2]							):
            a__     : Any  =scheduler.step_prk(lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__							).prev_sample







      def        _lowercase  (   self							)       ->						Optional[Any]:



        '''simple docstring'''




        with self.assertRaises(lowerCAmelCase__							):
          a__     : List[Any]  =self.scheduler_classes[0]
          a__     : Dict  =self.get_scheduler_config()
          a__     : Tuple  =scheduler_class(**lowerCAmelCase__							)

          scheduler.step_plms(self.dummy_sample		,				1		,				self.dummy_sample							).prev_sample







      def        _lowercase  (   self							)       ->						Optional[Any]:



        '''simple docstring'''




        a__     : List[Any]  =self.full_loop()
        a__     : str  =torch.sum(torch.abs(lowerCAmelCase__							)							)
        a__     : Optional[Any]  =torch.mean(torch.abs(lowerCAmelCase__							)							)

        assert abs(result_sum.item() - 1_98.13_18							) < 1E-2
        assert abs(result_mean.item() - 0.25_80							) < 1E-3







      def        _lowercase  (   self							)       ->						str:



        '''simple docstring'''




        a__     : str  =self.full_loop(prediction_type="v_prediction"							)
        a__     : int  =torch.sum(torch.abs(lowerCAmelCase__							)							)
        a__     : Optional[int]  =torch.mean(torch.abs(lowerCAmelCase__							)							)

        assert abs(result_sum.item() - 67.39_86							) < 1E-2
        assert abs(result_mean.item() - 0.08_78							) < 1E-3







      def        _lowercase  (   self							)       ->						Optional[int]:



        '''simple docstring'''




        a__     : Tuple  =self.full_loop(set_alpha_to_one=lowerCAmelCase__		,				beta_start=0.01							)
        a__     : str  =torch.sum(torch.abs(lowerCAmelCase__							)							)
        a__     : Dict  =torch.mean(torch.abs(lowerCAmelCase__							)							)

        assert abs(result_sum.item() - 2_30.03_99							) < 1E-2
        assert abs(result_mean.item() - 0.29_95							) < 1E-3







      def        _lowercase  (   self							)       ->						Union[str, Any]:



        '''simple docstring'''




        a__     : Dict  =self.full_loop(set_alpha_to_one=lowerCAmelCase__		,				beta_start=0.01							)
        a__     : Union[str, Any]  =torch.sum(torch.abs(lowerCAmelCase__							)							)
        a__     : Union[str, Any]  =torch.mean(torch.abs(lowerCAmelCase__							)							)

        assert abs(result_sum.item() - 1_86.94_82							) < 1E-2
        assert abs(result_mean.item() - 0.24_34							) < 1E-3






