




import argparse
import json

import torch

from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel

def 					SCREAMING_SNAKE_CASE							(   __UpperCamelCase		,					__UpperCamelCase)			->     List[Any]:
     a      =      UNetaDModel(**lowerCamelCase_)
     a      =      checkpoint["all_modules.0.W"].data
     a      =      checkpoint["all_modules.0.W"].data
     a      =      checkpoint["all_modules.1.weight"].data
     a      =      checkpoint["all_modules.1.bias"].data

     a      =      checkpoint["all_modules.2.weight"].data
     a      =      checkpoint["all_modules.2.bias"].data

     a      =      checkpoint["all_modules.3.weight"].data
     a      =      checkpoint["all_modules.3.bias"].data

     a      =      checkpoint[list(checkpoint.keys())[-4]].data
     a      =      checkpoint[list(checkpoint.keys())[-3]].data
     a      =      checkpoint[list(checkpoint.keys())[-2]].data
     a      =      checkpoint[list(checkpoint.keys())[-1]].data

     a      =      4

     def set_attention_weights(__UpperCamelCase		,					__UpperCamelCase		,					__UpperCamelCase):
          a      =      old_checkpoint[f'''all_modules.{index}.NIN_0.W'''].data.T
          a      =      old_checkpoint[f'''all_modules.{index}.NIN_1.W'''].data.T
          a      =      old_checkpoint[f'''all_modules.{index}.NIN_2.W'''].data.T

          a      =      old_checkpoint[f'''all_modules.{index}.NIN_0.b'''].data
          a      =      old_checkpoint[f'''all_modules.{index}.NIN_1.b'''].data
          a      =      old_checkpoint[f'''all_modules.{index}.NIN_2.b'''].data

          a      =      old_checkpoint[f'''all_modules.{index}.NIN_3.W'''].data.T
          a      =      old_checkpoint[f'''all_modules.{index}.NIN_3.b'''].data

          a      =      old_checkpoint[f'''all_modules.{index}.GroupNorm_0.weight'''].data
          a      =      old_checkpoint[f'''all_modules.{index}.GroupNorm_0.bias'''].data

     def set_resnet_weights(__UpperCamelCase		,					__UpperCamelCase		,					__UpperCamelCase):
          a      =      old_checkpoint[f'''all_modules.{index}.Conv_0.weight'''].data
          a      =      old_checkpoint[f'''all_modules.{index}.Conv_0.bias'''].data
          a      =      old_checkpoint[f'''all_modules.{index}.GroupNorm_0.weight'''].data
          a      =      old_checkpoint[f'''all_modules.{index}.GroupNorm_0.bias'''].data

          a      =      old_checkpoint[f'''all_modules.{index}.Conv_1.weight'''].data
          a      =      old_checkpoint[f'''all_modules.{index}.Conv_1.bias'''].data
          a      =      old_checkpoint[f'''all_modules.{index}.GroupNorm_1.weight'''].data
          a      =      old_checkpoint[f'''all_modules.{index}.GroupNorm_1.bias'''].data

          a      =      old_checkpoint[f'''all_modules.{index}.Dense_0.weight'''].data
          a      =      old_checkpoint[f'''all_modules.{index}.Dense_0.bias'''].data

          if new_layer.in_channels != new_layer.out_channels or new_layer.up or new_layer.down:
               a      =      old_checkpoint[f'''all_modules.{index}.Conv_2.weight'''].data
               a      =      old_checkpoint[f'''all_modules.{index}.Conv_2.bias'''].data

     for i, block in enumerate(new_model_architecture.downsample_blocks):
          a      =      hasattr(lowerCamelCase_		,					"attentions")
          for j in range(len(block.resnets)):
               set_resnet_weights(block.resnets[j]		,					lowerCamelCase_		,					lowerCamelCase_)
               module_index += 1
               if has_attentions:
                    set_attention_weights(block.attentions[j]		,					lowerCamelCase_		,					lowerCamelCase_)
                    module_index += 1

          if hasattr(lowerCamelCase_		,					"downsamplers") and block.downsamplers is not None:
               set_resnet_weights(block.resnet_down		,					lowerCamelCase_		,					lowerCamelCase_)
               module_index += 1
               a      =      checkpoint[f'''all_modules.{module_index}.Conv_0.weight'''].data
               a      =      checkpoint[f'''all_modules.{module_index}.Conv_0.bias'''].data
               module_index += 1

     set_resnet_weights(new_model_architecture.mid_block.resnets[0]		,					lowerCamelCase_		,					lowerCamelCase_)
     module_index += 1
     set_attention_weights(new_model_architecture.mid_block.attentions[0]		,					lowerCamelCase_		,					lowerCamelCase_)
     module_index += 1
     set_resnet_weights(new_model_architecture.mid_block.resnets[1]		,					lowerCamelCase_		,					lowerCamelCase_)
     module_index += 1

     for i, block in enumerate(new_model_architecture.up_blocks):
          a      =      hasattr(lowerCamelCase_		,					"attentions")
          for j in range(len(block.resnets)):
               set_resnet_weights(block.resnets[j]		,					lowerCamelCase_		,					lowerCamelCase_)
               module_index += 1
          if has_attentions:
               set_attention_weights(
                   block.attentions[0]		,					lowerCamelCase_		,					lowerCamelCase_)  # why can there only be a single attention layer for up?
               module_index += 1

          if hasattr(lowerCamelCase_		,					"resnet_up") and block.resnet_up is not None:
               a      =      checkpoint[f'''all_modules.{module_index}.weight'''].data
               a      =      checkpoint[f'''all_modules.{module_index}.bias'''].data
               module_index += 1
               a      =      checkpoint[f'''all_modules.{module_index}.weight'''].data
               a      =      checkpoint[f'''all_modules.{module_index}.bias'''].data
               module_index += 1
               set_resnet_weights(block.resnet_up		,					lowerCamelCase_		,					lowerCamelCase_)
               module_index += 1

     a      =      checkpoint[f'''all_modules.{module_index}.weight'''].data
     a      =      checkpoint[f'''all_modules.{module_index}.bias'''].data
     module_index += 1
     a      =      checkpoint[f'''all_modules.{module_index}.weight'''].data
     a      =      checkpoint[f'''all_modules.{module_index}.bias'''].data

     return new_model_architecture.state_dict()


if __name__ == "__main__":
   lowercase__      :			Any           =					argparse.ArgumentParser()

   parser.add_argument(
       "--checkpoint_path",
       default="/Users/arthurzucker/Work/diffusers/ArthurZ/diffusion_pytorch_model.bin",
       type=str,
       required=False,
       help="Path to the checkpoint to convert.",
   )

   parser.add_argument(
       "--config_file",
       default="/Users/arthurzucker/Work/diffusers/ArthurZ/config.json",
       type=str,
       required=False,
       help="The config json file corresponding to the architecture.",
   )

   parser.add_argument(
       "--dump_path",
       default="/Users/arthurzucker/Work/diffusers/ArthurZ/diffusion_model_new.pt",
       type=str,
       required=False,
       help="Path to the output model.",
   )

   lowercase__      :			Tuple           =					parser.parse_args()

   lowercase__      :			int           =					torch.load(args.checkpoint_path, map_location="cpu")

   with open(args.config_file) as f:
      lowercase__      :			Optional[Any]           =					json.loads(f.read())

   lowercase__      :			Dict           =					convert_ncsnpp_checkpoint(
       checkpoint,
       config,
   )

   if "sde" in config:
      del config["sde"]

   lowercase__      :			Tuple           =					UNetaDModel(**config)
   model.load_state_dict(converted_checkpoint)

   try:
      lowercase__      :			List[Any]           =					ScoreSdeVeScheduler.from_config("/".join(args.checkpoint_path.split("/")[:-1]))

      lowercase__      :			Dict           =					ScoreSdeVePipeline(unet=model, scheduler=scheduler)
      pipe.save_pretrained(args.dump_path)
   except:  # noqa: E722
      model.save_pretrained(args.dump_path)








'''simple docstring'''



import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    EulerAncestralDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionModelEditingPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()






@skip_mps
class 							__lowercase				(					lowerCAmelCase__       ,					lowerCAmelCase__       ,					lowerCAmelCase__       ,					unittest.TestCase  ):







	'''simple docstring'''

	a			: str      =				StableDiffusionModelEditingPipeline
	a			: int      =				TEXT_TO_IMAGE_PARAMS
	a			: List[Any]      =				TEXT_TO_IMAGE_BATCH_PARAMS
	a			: Tuple      =				TEXT_TO_IMAGE_IMAGE_PARAMS
	a			: Any      =				TEXT_TO_IMAGE_IMAGE_PARAMS


	def   _UpperCAmelCase   (self       )							->							Dict:







				'''simple docstring'''





				torch.manual_seed(0       )
				__lowercase   =							UNetaDConditionModel(
				    block_out_channels=(32, 64)   ,layers_per_block=2   ,sample_size=32   ,in_channels=4   ,out_channels=4   ,down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')   ,up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')   ,cross_attention_dim=32   ,)
				__lowercase   =							DDIMScheduler()
				torch.manual_seed(0       )
				__lowercase   =							AutoencoderKL(
				    block_out_channels=[32, 64]   ,in_channels=3   ,out_channels=3   ,down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']   ,up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']   ,latent_channels=4   ,)
				torch.manual_seed(0       )
				__lowercase   =							CLIPTextConfig(
				    bos_token_id=0   ,eos_token_id=2   ,hidden_size=32   ,intermediate_size=37   ,layer_norm_eps=1E-0_5   ,num_attention_heads=4   ,num_hidden_layers=5   ,pad_token_id=1   ,vocab_size=1000   ,)
				__lowercase   =							CLIPTextModel(_lowerCamelCase       )
				__lowercase   =							CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''       )

				__lowercase   =							{
				    '''unet''': unet,
				    '''scheduler''': scheduler,
				    '''vae''': vae,
				    '''text_encoder''': text_encoder,
				    '''tokenizer''': tokenizer,
				    '''safety_checker''': None,
				    '''feature_extractor''': None,
				}
				return components


	def   _UpperCAmelCase   (self   ,_lowerCamelCase   ,_lowerCamelCase=0       )							->							Any:







				'''simple docstring'''





				__lowercase   =							torch.manual_seed(_lowerCamelCase       )
				__lowercase   =							{
				    '''prompt''': '''A field of roses''',
				    '''generator''': generator,
				    # Setting height and width to None to prevent OOMs on CPU.
				    '''height''': None,
				    '''width''': None,
				    '''num_inference_steps''': 2,
				    '''guidance_scale''': 6.0,
				    '''output_type''': '''numpy''',
				}
				return inputs


	def   _UpperCAmelCase   (self       )							->							Tuple:







				'''simple docstring'''





				__lowercase   =							'''cpu'''  # ensure determinism for the device-dependent torch.Generator
				__lowercase   =							self.get_dummy_components()
				__lowercase   =							StableDiffusionModelEditingPipeline(**_lowerCamelCase       )
				__lowercase   =							sd_pipe.to(_lowerCamelCase       )
				sd_pipe.set_progress_bar_config(disable=_lowerCamelCase       )

				__lowercase   =							self.get_dummy_inputs(_lowerCamelCase       )
				__lowercase   =							sd_pipe(**_lowerCamelCase       ).images
				__lowercase   =							image[0, -3:, -3:, -1]
				assert image.shape == (1, 64, 64, 3)

				__lowercase   =							np.array([0.4_7_5_5, 0.5_1_3_2, 0.4_9_7_6, 0.3_9_0_4, 0.3_5_5_4, 0.4_7_6_5, 0.5_1_3_9, 0.5_1_5_8, 0.4_8_8_9]       )

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


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







				'''simple docstring'''





				__lowercase   =							'''cpu'''  # ensure determinism for the device-dependent torch.Generator
				__lowercase   =							self.get_dummy_components()
				__lowercase   =							StableDiffusionModelEditingPipeline(**_lowerCamelCase       )
				__lowercase   =							sd_pipe.to(_lowerCamelCase       )
				sd_pipe.set_progress_bar_config(disable=_lowerCamelCase       )

				__lowercase   =							self.get_dummy_inputs(_lowerCamelCase       )
				__lowercase   =							'''french fries'''
				__lowercase   =							sd_pipe(**_lowerCamelCase   ,negative_prompt=_lowerCamelCase       )
				__lowercase   =							output.images
				__lowercase   =							image[0, -3:, -3:, -1]

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

				__lowercase   =							np.array([0.4_9_9_2, 0.5_1_0_1, 0.5_0_0_4, 0.3_9_4_9, 0.3_6_0_4, 0.4_7_3_5, 0.5_2_1_6, 0.5_2_0_4, 0.4_9_1_3]       )

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


	def   _UpperCAmelCase   (self       )							->							Tuple:







				'''simple docstring'''





				__lowercase   =							'''cpu'''  # ensure determinism for the device-dependent torch.Generator
				__lowercase   =							self.get_dummy_components()
				__lowercase   =							EulerAncestralDiscreteScheduler(
				    beta_start=0.0_0_0_8_5   ,beta_end=0.0_1_2   ,beta_schedule='''scaled_linear'''       )
				__lowercase   =							StableDiffusionModelEditingPipeline(**_lowerCamelCase       )
				__lowercase   =							sd_pipe.to(_lowerCamelCase       )
				sd_pipe.set_progress_bar_config(disable=_lowerCamelCase       )

				__lowercase   =							self.get_dummy_inputs(_lowerCamelCase       )
				__lowercase   =							sd_pipe(**_lowerCamelCase       ).images
				__lowercase   =							image[0, -3:, -3:, -1]

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

				__lowercase   =							np.array([0.4_7_4_7, 0.5_3_7_2, 0.4_7_7_9, 0.4_9_8_2, 0.5_5_4_3, 0.4_8_1_6, 0.5_2_3_8, 0.4_9_0_4, 0.5_0_2_7]       )

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


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







				'''simple docstring'''





				__lowercase   =							'''cpu'''  # ensure determinism for the device-dependent torch.Generator
				__lowercase   =							self.get_dummy_components()
				__lowercase   =							PNDMScheduler()
				__lowercase   =							StableDiffusionModelEditingPipeline(**_lowerCamelCase       )
				__lowercase   =							sd_pipe.to(_lowerCamelCase       )
				sd_pipe.set_progress_bar_config(disable=_lowerCamelCase       )

				__lowercase   =							self.get_dummy_inputs(_lowerCamelCase       )
				# the pipeline does not expect pndm so test if it raises error.
				with self.assertRaises(_lowerCamelCase       ):
							__lowercase   =							sd_pipe(**_lowerCamelCase       ).images


	def   _UpperCAmelCase   (self       )							->							int:







				'''simple docstring'''





				super().test_inference_batch_single_identical(expected_max_diff=5E-3       )


	def   _UpperCAmelCase   (self       )							->							str:







				'''simple docstring'''





				super().test_attention_slicing_forward_pass(expected_max_diff=5E-3       )






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







	'''simple docstring'''

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







				'''simple docstring'''





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


	def   _UpperCAmelCase   (self   ,_lowerCamelCase=0       )							->							Any:







				'''simple docstring'''





				__lowercase   =							torch.manual_seed(_lowerCamelCase       )
				__lowercase   =							{
				    '''prompt''': '''A field of roses''',
				    '''generator''': generator,
				    '''num_inference_steps''': 3,
				    '''guidance_scale''': 7.5,
				    '''output_type''': '''numpy''',
				}
				return inputs


	def   _UpperCAmelCase   (self       )							->							str:







				'''simple docstring'''





				__lowercase   =							'''CompVis/stable-diffusion-v1-4'''
				__lowercase   =							StableDiffusionModelEditingPipeline.from_pretrained(_lowerCamelCase   ,safety_checker=_lowerCamelCase       )
				pipe.to(_lowerCamelCase       )
				pipe.set_progress_bar_config(disable=_lowerCamelCase       )
				pipe.enable_attention_slicing()

				__lowercase   =							self.get_inputs()
				__lowercase   =							pipe(**_lowerCamelCase       ).images
				__lowercase   =							image[0, -3:, -3:, -1].flatten()

				assert image.shape == (1, 512, 512, 3)

				__lowercase   =							np.array(
				    [0.6_7_4_9_4_9_6, 0.6_3_8_6_4_5_3, 0.5_1_4_4_3_2_6_7, 0.6_6_0_9_4_9_0_5, 0.6_1_9_2_1_2_1_5, 0.5_4_9_1_3_3_2, 0.5_7_4_4_4_1_7, 0.5_8_0_7_5_1_0_6, 0.5_1_7_4_6_5_8]       )

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

				# make sure image changes after editing
				pipe.edit_model('''A pack of roses'''   ,'''A pack of blue roses'''       )

				__lowercase   =							pipe(**_lowerCamelCase       ).images
				__lowercase   =							image[0, -3:, -3:, -1].flatten()

				assert image.shape == (1, 512, 512, 3)

				assert np.abs(expected_slice - image_slice       ).max() > 1E-1


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







				'''simple docstring'''





				torch.cuda.empty_cache()
				torch.cuda.reset_max_memory_allocated()
				torch.cuda.reset_peak_memory_stats()

				__lowercase   =							'''CompVis/stable-diffusion-v1-4'''
				__lowercase   =							DDIMScheduler.from_pretrained(_lowerCamelCase   ,subfolder='''scheduler'''       )
				__lowercase   =							StableDiffusionModelEditingPipeline.from_pretrained(
				    _lowerCamelCase   ,scheduler=_lowerCamelCase   ,safety_checker=_lowerCamelCase       )
				__lowercase   =							pipe.to(_lowerCamelCase       )
				pipe.set_progress_bar_config(disable=_lowerCamelCase       )
				pipe.enable_attention_slicing(1       )
				pipe.enable_sequential_cpu_offload()

				__lowercase   =							self.get_inputs()
				__lowercase   =							pipe(**_lowerCamelCase       )

				__lowercase   =							torch.cuda.max_memory_allocated()
				# make sure that less than 4.4 GB is allocated
				assert mem_bytes < 4.4 * 10**9





