
"""simple docstring"""

import unittest

import numpy as np
import torch

from diffusers import PNDMPipeline, PNDMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()
class __lowerCAmelCase		(				unittest.TestCase      ):





     @property
     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      torch.manual_seed(0				)
      __UpperCamelCase      =	UNetaDModel(
          block_out_channels=(32, 64)     ,					layers_per_block=2     ,					sample_size=32     ,					in_channels=3     ,					out_channels=3     ,					down_block_types=('DownBlock2D', 'AttnDownBlock2D')     ,					up_block_types=('AttnUpBlock2D', 'UpBlock2D')     ,					)
      return model







     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      __UpperCamelCase      =	self.dummy_uncond_unet
      __UpperCamelCase      =	PNDMScheduler()

      __UpperCamelCase      =	PNDMPipeline(unet=a_     ,					scheduler=a_				)
      pndm.to(a_				)
      pndm.set_progress_bar_config(disable=a_				)

      __UpperCamelCase      =	torch.manual_seed(0				)
      __UpperCamelCase      =	pndm(generator=a_     ,					num_inference_steps=20     ,					output_type='numpy'				).images

      __UpperCamelCase      =	torch.manual_seed(0				)
      __UpperCamelCase      =	pndm(generator=a_     ,					num_inference_steps=20     ,					output_type='numpy'     ,					return_dict=a_				)[0]

      __UpperCamelCase      =	image[0, -3:, -3:, -1]
      __UpperCamelCase      =	image_from_tuple[0, -3:, -3:, -1]

      assert image.shape == (1, 32, 32, 3)
      __UpperCamelCase      =	np.array([1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0]				)

      assert np.abs(image_slice.flatten() - expected_slice				).max() < 1E-2
      assert np.abs(image_from_tuple_slice.flatten() - expected_slice				).max() < 1E-2


@slow
@require_torch
class __lowerCAmelCase		(				unittest.TestCase      ):





     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      __UpperCamelCase      =	'''google/ddpm-cifar10-32'''

      __UpperCamelCase      =	UNetaDModel.from_pretrained(a_				)
      __UpperCamelCase      =	PNDMScheduler()

      __UpperCamelCase      =	PNDMPipeline(unet=a_     ,					scheduler=a_				)
      pndm.to(a_				)
      pndm.set_progress_bar_config(disable=a_				)
      __UpperCamelCase      =	torch.manual_seed(0				)
      __UpperCamelCase      =	pndm(generator=a_     ,					output_type='numpy'				).images

      __UpperCamelCase      =	image[0, -3:, -3:, -1]

      assert image.shape == (1, 32, 32, 3)
      __UpperCamelCase      =	np.array([0.1_5_6_4, 0.1_4_6_4_5, 0.1_4_0_6, 0.1_4_7_1_5, 0.1_2_4_2_5, 0.1_4_0_4_5, 0.1_3_1_1_5, 0.1_2_1_7_5, 0.1_2_5]				)

      assert np.abs(image_slice.flatten() - expected_slice				).max() < 1E-2





"""simple docstring"""

import argparse

import OmegaConf
import torch

from diffusers import DDIMScheduler, LDMPipeline, UNetLDMModel, VQModel






def 							A						(    snake_case				:Dict    , snake_case				:Optional[Any]    , snake_case				:List[Any]		)				->     Optional[Any]:
 __UpperCamelCase      =	OmegaConf.load(snake_case		)
 __UpperCamelCase      =	torch.load(snake_case    , map_location='cpu'		)['model']
 __UpperCamelCase      =	list(state_dict.keys()		)

 # extract state_dict for VQVAE
 __UpperCamelCase      =	{}
 __UpperCamelCase      =	'first_stage_model.'
 for key in keys:
  if key.startswith(snake_case		):
   __UpperCamelCase      =	state_dict[key]

    # extract state_dict for UNetLDM
 __UpperCamelCase      =	{}
 __UpperCamelCase      =	'model.diffusion_model.'
 for key in keys:
  if key.startswith(snake_case		):
   __UpperCamelCase      =	state_dict[key]

 __UpperCamelCase      =	config.model.params.first_stage_config.params
 __UpperCamelCase      =	config.model.params.unet_config.params

 __UpperCamelCase      =	VQModel(**snake_case		).eval()
 vqvae.load_state_dict(snake_case		)

 __UpperCamelCase      =	UNetLDMModel(**snake_case		).eval()
 unet.load_state_dict(snake_case		)

 __UpperCamelCase      =	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=snake_case    , )

 __UpperCamelCase      =	LDMPipeline(snake_case    , snake_case    , snake_case		)
 pipeline.save_pretrained(snake_case		)


if __name__ == "__main__":
      UpperCamelCase     :    Dict       =			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)
      UpperCamelCase     :    Optional[int]       =			parser.parse_args()

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



