






import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
  import torch

if is_vision_available():
  from PIL import Image

  from transformers import LevitImageProcessor
class 		SCREAMING_SNAKE_CASE__	(  unittest.TestCase     ):

  '''simple docstring'''







  def __init__(      self		:			List[Any]       ,				lowerCamelCase		:			List[Any]       ,				lowerCamelCase		:			Optional[Any]=7       ,				lowerCamelCase		:			int=3       ,				lowerCamelCase		:			int=18       ,				lowerCamelCase		:			Tuple=30       ,				lowerCamelCase		:			Dict=400       ,				lowerCamelCase		:			Optional[int]=True       ,				lowerCamelCase		:			int=None       ,				lowerCamelCase		:			List[str]=True       ,				lowerCamelCase		:			List[Any]=None       ,				lowerCamelCase		:			Any=True       ,				lowerCamelCase		:			int=[0.5, 0.5, 0.5]       ,				lowerCamelCase		:			List[Any]=[0.5, 0.5, 0.5]       ,				)						->	Optional[int]:


         """simple docstring"""
         _UpperCAmelCase							=    size if size is not None else {"""shortest_edge""": 18}
         _UpperCAmelCase							=    crop_size if crop_size is not None else {"""height""": 18, """width""": 18}
         _UpperCAmelCase							=    parent
         _UpperCAmelCase							=    batch_size
         _UpperCAmelCase							=    num_channels
         _UpperCAmelCase							=    image_size
         _UpperCAmelCase							=    min_resolution
         _UpperCAmelCase							=    max_resolution
         _UpperCAmelCase							=    do_resize
         _UpperCAmelCase							=    size
         _UpperCAmelCase							=    do_center_crop
         _UpperCAmelCase							=    crop_size
         _UpperCAmelCase							=    do_normalize
         _UpperCAmelCase							=    image_mean
         _UpperCAmelCase							=    image_std





  def  lowerCamelCase       (      self		:			Dict							)						->	List[str]:


         """simple docstring"""
         return {
             "image_mean": self.image_mean,
             "image_std": self.image_std,
             "do_normalize": self.do_normalize,
             "do_resize": self.do_resize,
             "do_center_crop": self.do_center_crop,
             "size": self.size,
             "crop_size": self.crop_size,
         }



@require_torch
@require_vision
class 		SCREAMING_SNAKE_CASE__	(  UpperCAmelCase					,      unittest.TestCase     ):

  '''simple docstring'''







  _lowerCamelCase    =  LevitImageProcessor if is_vision_available() else None


  def  lowerCamelCase       (      self		:			Dict							)						->	List[str]:


         """simple docstring"""
         _UpperCAmelCase							=    LevitImageProcessingTester(self							)


  @property
  def  lowerCamelCase       (      self		:			int							)						->	Dict:


         """simple docstring"""
         return self.image_processor_tester.prepare_image_processor_dict()


  def  lowerCamelCase       (      self		:			int							)						->	Tuple:


         """simple docstring"""
         _UpperCAmelCase							=    self.image_processing_class(**self.image_processor_dict							)
         self.assertTrue(hasattr(lowerCamelCase       ,				"""image_mean"""							)							)
         self.assertTrue(hasattr(lowerCamelCase       ,				"""image_std"""							)							)
         self.assertTrue(hasattr(lowerCamelCase       ,				"""do_normalize"""							)							)
         self.assertTrue(hasattr(lowerCamelCase       ,				"""do_resize"""							)							)
         self.assertTrue(hasattr(lowerCamelCase       ,				"""do_center_crop"""							)							)
         self.assertTrue(hasattr(lowerCamelCase       ,				"""size"""							)							)


  def  lowerCamelCase       (      self		:			Tuple							)						->	str:


         """simple docstring"""
         _UpperCAmelCase							=    self.image_processing_class.from_dict(self.image_processor_dict							)
         self.assertEqual(image_processor.size       ,				{"""shortest_edge""": 18}							)
         self.assertEqual(image_processor.crop_size       ,				{"""height""": 18, """width""": 18}							)

         _UpperCAmelCase							=    self.image_processing_class.from_dict(self.image_processor_dict       ,				size=42       ,				crop_size=84							)
         self.assertEqual(image_processor.size       ,				{"""shortest_edge""": 42}							)
         self.assertEqual(image_processor.crop_size       ,				{"""height""": 84, """width""": 84}							)


  def  lowerCamelCase       (      self		:			str							)						->	int:


         """simple docstring"""
         pass


  def  lowerCamelCase       (      self		:			List[Any]							)						->	Optional[int]:


         """simple docstring"""
         # Initialize image_processing
         _UpperCAmelCase							=    self.image_processing_class(**self.image_processor_dict							)
         # create random PIL images
         _UpperCAmelCase							=    prepare_image_inputs(self.image_processor_tester       ,				equal_resolution=lowerCamelCase							)
         for image in image_inputs:
                self.assertIsInstance(lowerCamelCase       ,				Image.Image							)

         # Test not batched input
         _UpperCAmelCase							=    image_processing(image_inputs[0]       ,				return_tensors="""pt"""							).pixel_values
         self.assertEqual(
             encoded_images.shape       ,				(
                 1,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.crop_size["""height"""],
                 self.image_processor_tester.crop_size["""width"""],
             )       ,				)

         # Test batched
         _UpperCAmelCase							=    image_processing(lowerCamelCase       ,				return_tensors="""pt"""							).pixel_values
         self.assertEqual(
             encoded_images.shape       ,				(
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.crop_size["""height"""],
                 self.image_processor_tester.crop_size["""width"""],
             )       ,				)


  def  lowerCamelCase       (      self		:			Optional[int]							)						->	Union[str, Any]:


         """simple docstring"""
         # Initialize image_processing
         _UpperCAmelCase							=    self.image_processing_class(**self.image_processor_dict							)
         # create random numpy tensors
         _UpperCAmelCase							=    prepare_image_inputs(self.image_processor_tester       ,				equal_resolution=lowerCamelCase       ,				numpify=lowerCamelCase							)
         for image in image_inputs:
                self.assertIsInstance(lowerCamelCase       ,				np.ndarray							)

         # Test not batched input
         _UpperCAmelCase							=    image_processing(image_inputs[0]       ,				return_tensors="""pt"""							).pixel_values
         self.assertEqual(
             encoded_images.shape       ,				(
                 1,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.crop_size["""height"""],
                 self.image_processor_tester.crop_size["""width"""],
             )       ,				)

         # Test batched
         _UpperCAmelCase							=    image_processing(lowerCamelCase       ,				return_tensors="""pt"""							).pixel_values
         self.assertEqual(
             encoded_images.shape       ,				(
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.crop_size["""height"""],
                 self.image_processor_tester.crop_size["""width"""],
             )       ,				)





  def  lowerCamelCase       (      self		:			Optional[int]							)						->	Any:


         """simple docstring"""
         # Initialize image_processing
         _UpperCAmelCase							=    self.image_processing_class(**self.image_processor_dict							)
         # create random PyTorch tensors
         _UpperCAmelCase							=    prepare_image_inputs(self.image_processor_tester       ,				equal_resolution=lowerCamelCase       ,				torchify=lowerCamelCase							)
         for image in image_inputs:
                self.assertIsInstance(lowerCamelCase       ,				torch.Tensor							)

         # Test not batched input
         _UpperCAmelCase							=    image_processing(image_inputs[0]       ,				return_tensors="""pt"""							).pixel_values
         self.assertEqual(
             encoded_images.shape       ,				(
                 1,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.crop_size["""height"""],
                 self.image_processor_tester.crop_size["""width"""],
             )       ,				)

         # Test batched
         _UpperCAmelCase							=    image_processing(lowerCamelCase       ,				return_tensors="""pt"""							).pixel_values
         self.assertEqual(
             encoded_images.shape       ,				(
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.crop_size["""height"""],
                 self.image_processor_tester.crop_size["""width"""],
             )       ,				)




'''simple docstring'''





import argparse

import OmegaConf
import torch

from diffusers import DDIMScheduler, LDMPipeline, UNetLDMModel, VQModel





def 		__snake_case(							_lowerCAmelCase	,  _lowerCAmelCase	,  _lowerCAmelCase						)						->					int:
      snake_case__    :       Tuple       		= OmegaConf.load(_lowerCAmelCase						)
      snake_case__    :       Tuple       		= torch.load(_lowerCAmelCase	,  map_location="""cpu"""						)["""model"""]
      snake_case__    :       int       		= list(state_dict.keys()						)

      # extract state_dict for VQVAE
      snake_case__    :       Dict       		= {}
      snake_case__    :       Tuple       		= """first_stage_model."""
      for key in keys:
            if key.startswith(_lowerCAmelCase						):
                  snake_case__    :       Tuple       		= state_dict[key]

    # extract state_dict for UNetLDM
      snake_case__    :       Optional[Any]       		= {}
      snake_case__    :       Optional[int]       		= """model.diffusion_model."""
      for key in keys:
            if key.startswith(_lowerCAmelCase						):
                  snake_case__    :       Tuple       		= state_dict[key]

      snake_case__    :       Dict       		= config.model.params.first_stage_config.params
      snake_case__    :       Optional[Any]       		= config.model.params.unet_config.params

      snake_case__    :       Tuple       		= VQModel(**_lowerCAmelCase						).eval()
      vqvae.load_state_dict(_lowerCAmelCase						)

      snake_case__    :       List[Any]       		= UNetLDMModel(**_lowerCAmelCase						).eval()
      unet.load_state_dict(_lowerCAmelCase						)

      snake_case__    :       List[Any]       		= DDIMScheduler(
          timesteps=config.model.params.timesteps	,  beta_schedule="""scaled_linear"""	,  beta_start=config.model.params.linear_start	,  beta_end=config.model.params.linear_end	,  clip_sample=_lowerCAmelCase	,  )

      snake_case__    :       List[str]       		= LDMPipeline(_lowerCAmelCase	,  _lowerCAmelCase	,  _lowerCAmelCase						)
      pipeline.save_pretrained(_lowerCAmelCase						)


if __name__ == "__main__":
       __a  			=						argparse.ArgumentParser()
       parser.add_argument("--checkpoint_path", type=str, required=True)
       parser.add_argument("--config_path", type=str, required=True)
       parser.add_argument("--output_path", type=str, required=True)
       __a  			=						parser.parse_args()

       convert_ldm_original(args.checkpoint_path, args.config_path, args.output_path)
