
"""simple docstring"""

import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image

from diffusers import (
    DDIMScheduler,
    KandinskyVaaImgaImgPipeline,
    KandinskyVaaPriorPipeline,
    UNetaDConditionModel,
    VQModel,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()
class __lowerCAmelCase		(				__SCREAMING_SNAKE_CASE      ,  unittest.TestCase      ):
     lowercase						       =						KandinskyVaaImgaImgPipeline
     lowercase						       =						["image_embeds", "negative_image_embeds", "image"]
     lowercase						       =						[
         "image_embeds",
         "negative_image_embeds",
         "image",
     ]
     lowercase						       =						[
         "generator",
         "height",
         "width",
         "strength",
         "guidance_scale",
         "num_inference_steps",
         "return_dict",
         "guidance_scale",
         "num_images_per_prompt",
         "output_type",
         "return_dict",
     ]
     lowercase						       =						False





     @property
     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      return 32





     @property
     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      return 32





     @property
     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      return self.time_input_dim





     @property
     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      return self.time_input_dim * 4





     @property
     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      return 100





     @property
     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      torch.manual_seed(0				)

      __UpperCamelCase      =	{
          '''in_channels''': 4,
          # Out channels is double in channels because predicts mean and variance
          '''out_channels''': 8,
          '''addition_embed_type''': '''image''',
          '''down_block_types''': ('''ResnetDownsampleBlock2D''', '''SimpleCrossAttnDownBlock2D'''),
          '''up_block_types''': ('''SimpleCrossAttnUpBlock2D''', '''ResnetUpsampleBlock2D'''),
          '''mid_block_type''': '''UNetMidBlock2DSimpleCrossAttn''',
          '''block_out_channels''': (self.block_out_channels_a, self.block_out_channels_a * 2),
          '''layers_per_block''': 1,
          '''encoder_hid_dim''': self.text_embedder_hidden_size,
          '''encoder_hid_dim_type''': '''image_proj''',
          '''cross_attention_dim''': self.cross_attention_dim,
          '''attention_head_dim''': 4,
          '''resnet_time_scale_shift''': '''scale_shift''',
          '''class_embed_type''': None,
      }

      __UpperCamelCase      =	UNetaDConditionModel(**__UpperCAmelCase				)
      return model





     @property
     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      return {
          "block_out_channels": [32, 64],
          "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
          "in_channels": 3,
          "latent_channels": 4,
          "layers_per_block": 1,
          "norm_num_groups": 8,
          "norm_type": "spatial",
          "num_vq_embeddings": 12,
          "out_channels": 3,
          "up_block_types": [
              "AttnUpDecoderBlock2D",
              "UpDecoderBlock2D",
          ],
          "vq_embed_dim": 4,
      }





     @property
     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      torch.manual_seed(0				)
      __UpperCamelCase      =	VQModel(**self.dummy_movq_kwargs				)
      return model





     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      __UpperCamelCase      =	self.dummy_unet
      __UpperCamelCase      =	self.dummy_movq

      __UpperCamelCase      =	{
          '''num_train_timesteps''': 1000,
          '''beta_schedule''': '''linear''',
          '''beta_start''': 0.0_0_0_8_5,
          '''beta_end''': 0.0_1_2,
          '''clip_sample''': False,
          '''set_alpha_to_one''': False,
          '''steps_offset''': 0,
          '''prediction_type''': '''epsilon''',
          '''thresholding''': False,
      }

      __UpperCamelCase      =	DDIMScheduler(**__UpperCAmelCase				)

      __UpperCamelCase      =	{
          '''unet''': unet,
          '''scheduler''': scheduler,
          '''movq''': movq,
      }

      return components





     def 						UpperCAmelCase (     self     ,					__UpperCAmelCase     ,					__UpperCAmelCase=0				):

      '''simple docstring'''




      __UpperCamelCase      =	floats_tensor((1, self.text_embedder_hidden_size)     ,					rng=random.Random(__UpperCAmelCase				)				).to(__UpperCAmelCase				)
      __UpperCamelCase      =	floats_tensor((1, self.text_embedder_hidden_size)     ,					rng=random.Random(seed + 1				)				).to(
          __UpperCAmelCase				)
      # create init_image
      __UpperCamelCase      =	floats_tensor((1, 3, 64, 64)     ,					rng=random.Random(__UpperCAmelCase				)				).to(__UpperCAmelCase				)
      __UpperCamelCase      =	image.cpu().permute(0     ,					2     ,					3     ,					1				)[0]
      __UpperCamelCase      =	Image.fromarray(np.uinta(__UpperCAmelCase				)				).convert('RGB'				).resize((256, 256)				)

      if str(__UpperCAmelCase				).startswith('mps'				):
       __UpperCamelCase      =	torch.manual_seed(__UpperCAmelCase				)
      else:
       __UpperCamelCase      =	torch.Generator(device=__UpperCAmelCase				).manual_seed(__UpperCAmelCase				)
      __UpperCamelCase      =	{
          '''image''': init_image,
          '''image_embeds''': image_embeds,
          '''negative_image_embeds''': negative_image_embeds,
          '''generator''': generator,
          '''height''': 64,
          '''width''': 64,
          '''num_inference_steps''': 10,
          '''guidance_scale''': 7.0,
          '''strength''': 0.2,
          '''output_type''': '''np''',
      }
      return inputs







     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      __UpperCamelCase      =	'''cpu'''

      __UpperCamelCase      =	self.get_dummy_components()

      __UpperCamelCase      =	self.pipeline_class(**__UpperCAmelCase				)
      __UpperCamelCase      =	pipe.to(__UpperCAmelCase				)

      pipe.set_progress_bar_config(disable=__UpperCAmelCase				)

      __UpperCamelCase      =	pipe(**self.get_dummy_inputs(__UpperCAmelCase				)				)
      __UpperCamelCase      =	output.images

      __UpperCamelCase      =	pipe(
          **self.get_dummy_inputs(__UpperCAmelCase				)     ,					return_dict=__UpperCAmelCase     ,					)[0]

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

      assert image.shape == (1, 64, 64, 3)

      __UpperCamelCase      =	np.array(
          [0.6_1_9_9_7_7_8, 0.6_3_9_8_4_4_0_6, 0.4_6_1_4_5_7_8_5, 0.6_2_9_4_4_9_8_4, 0.5_6_2_2_2_1_5, 0.4_7_3_0_6_1_3_2, 0.4_7_4_4_1_4_5_6, 0.4_6_0_7_6_0_6, 0.4_8_7_1_9_2_6_3]				)
      assert (
          np.abs(image_slice.flatten() - expected_slice				).max() < 1E-2
      ), F' expected_slice {expected_slice}, but got {image_slice.flatten()}'
      assert (
          np.abs(image_from_tuple_slice.flatten() - expected_slice				).max() < 1E-2
      ), F' expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}'


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





     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      super().tearDown()
      gc.collect()
      torch.cuda.empty_cache()







     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      __UpperCamelCase      =	load_numpy(
          'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
          '/kandinskyv22/kandinskyv22_img2img_frog.npy'				)

      __UpperCamelCase      =	load_image(
          'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main' '/kandinsky/cat.png'				)
      __UpperCamelCase      =	'''A red cartoon frog, 4k'''

      __UpperCamelCase      =	KandinskyVaaPriorPipeline.from_pretrained(
          'kandinsky-community/kandinsky-2-2-prior'     ,					torch_dtype=torch.floataa				)
      pipe_prior.to(__UpperCAmelCase				)

      __UpperCamelCase      =	KandinskyVaaImgaImgPipeline.from_pretrained(
          'kandinsky-community/kandinsky-2-2-decoder'     ,					torch_dtype=torch.floataa				)
      __UpperCamelCase      =	pipeline.to(__UpperCAmelCase				)

      pipeline.set_progress_bar_config(disable=__UpperCAmelCase				)

      __UpperCamelCase      =	torch.Generator(device='cpu'				).manual_seed(0				)
      __UpperCamelCase      =	pipe_prior(
          __UpperCAmelCase     ,					generator=__UpperCAmelCase     ,					num_inference_steps=5     ,					negative_prompt=''     ,					).to_tuple()

      __UpperCamelCase      =	pipeline(
          image=__UpperCAmelCase     ,					image_embeds=__UpperCAmelCase     ,					negative_image_embeds=__UpperCAmelCase     ,					generator=__UpperCAmelCase     ,					num_inference_steps=100     ,					height=768     ,					width=768     ,					strength=0.2     ,					output_type='np'     ,					)

      __UpperCamelCase      =	output.images[0]

      assert image.shape == (768, 768, 3)

      assert_mean_pixel_difference(__UpperCAmelCase     ,					__UpperCAmelCase				)





"""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)



