


import argparse
import json

import torch

from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel







def   lowerCamelCase(      a__		,a__):
				_SCREAMING_SNAKE_CASE	     =UNetaDModel(**a__)
				_SCREAMING_SNAKE_CASE	     =checkpoint['''all_modules.0.W'''].data
				_SCREAMING_SNAKE_CASE	     =checkpoint['''all_modules.0.W'''].data
				_SCREAMING_SNAKE_CASE	     =checkpoint['''all_modules.1.weight'''].data
				_SCREAMING_SNAKE_CASE	     =checkpoint['''all_modules.1.bias'''].data

				_SCREAMING_SNAKE_CASE	     =checkpoint['''all_modules.2.weight'''].data
				_SCREAMING_SNAKE_CASE	     =checkpoint['''all_modules.2.bias'''].data

				_SCREAMING_SNAKE_CASE	     =checkpoint['''all_modules.3.weight'''].data
				_SCREAMING_SNAKE_CASE	     =checkpoint['''all_modules.3.bias'''].data

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

				_SCREAMING_SNAKE_CASE	     =4

				def set_attention_weights(a__		,a__		,a__):
								_SCREAMING_SNAKE_CASE	     =old_checkpoint[f"all_modules.{index}.NIN_0.W"].data.T
								_SCREAMING_SNAKE_CASE	     =old_checkpoint[f"all_modules.{index}.NIN_1.W"].data.T
								_SCREAMING_SNAKE_CASE	     =old_checkpoint[f"all_modules.{index}.NIN_2.W"].data.T

								_SCREAMING_SNAKE_CASE	     =old_checkpoint[f"all_modules.{index}.NIN_0.b"].data
								_SCREAMING_SNAKE_CASE	     =old_checkpoint[f"all_modules.{index}.NIN_1.b"].data
								_SCREAMING_SNAKE_CASE	     =old_checkpoint[f"all_modules.{index}.NIN_2.b"].data

								_SCREAMING_SNAKE_CASE	     =old_checkpoint[f"all_modules.{index}.NIN_3.W"].data.T
								_SCREAMING_SNAKE_CASE	     =old_checkpoint[f"all_modules.{index}.NIN_3.b"].data

								_SCREAMING_SNAKE_CASE	     =old_checkpoint[f"all_modules.{index}.GroupNorm_0.weight"].data
								_SCREAMING_SNAKE_CASE	     =old_checkpoint[f"all_modules.{index}.GroupNorm_0.bias"].data

				def set_resnet_weights(a__		,a__		,a__):
								_SCREAMING_SNAKE_CASE	     =old_checkpoint[f"all_modules.{index}.Conv_0.weight"].data
								_SCREAMING_SNAKE_CASE	     =old_checkpoint[f"all_modules.{index}.Conv_0.bias"].data
								_SCREAMING_SNAKE_CASE	     =old_checkpoint[f"all_modules.{index}.GroupNorm_0.weight"].data
								_SCREAMING_SNAKE_CASE	     =old_checkpoint[f"all_modules.{index}.GroupNorm_0.bias"].data

								_SCREAMING_SNAKE_CASE	     =old_checkpoint[f"all_modules.{index}.Conv_1.weight"].data
								_SCREAMING_SNAKE_CASE	     =old_checkpoint[f"all_modules.{index}.Conv_1.bias"].data
								_SCREAMING_SNAKE_CASE	     =old_checkpoint[f"all_modules.{index}.GroupNorm_1.weight"].data
								_SCREAMING_SNAKE_CASE	     =old_checkpoint[f"all_modules.{index}.GroupNorm_1.bias"].data

								_SCREAMING_SNAKE_CASE	     =old_checkpoint[f"all_modules.{index}.Dense_0.weight"].data
								_SCREAMING_SNAKE_CASE	     =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:
												_SCREAMING_SNAKE_CASE	     =old_checkpoint[f"all_modules.{index}.Conv_2.weight"].data
												_SCREAMING_SNAKE_CASE	     =old_checkpoint[f"all_modules.{index}.Conv_2.bias"].data

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

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

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

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

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

				_SCREAMING_SNAKE_CASE	     =checkpoint[f"all_modules.{module_index}.weight"].data
				_SCREAMING_SNAKE_CASE	     =checkpoint[f"all_modules.{module_index}.bias"].data
				module_index += 1
				_SCREAMING_SNAKE_CASE	     =checkpoint[f"all_modules.{module_index}.weight"].data
				_SCREAMING_SNAKE_CASE	     =checkpoint[f"all_modules.{module_index}.bias"].data

				return new_model_architecture.state_dict()


if __name__ == "__main__":
							snake_case_  :	Tuple							  =							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.''',
							)

							snake_case_  :	List[str]							  =							parser.parse_args()

							snake_case_  :	List[Any]							  =							torch.load(args.checkpoint_path, map_location='''cpu''')

							with open(args.config_file) as f:
														snake_case_  :	List[str]							  =							json.loads(f.read())

							snake_case_  :	Optional[int]							  =							convert_ncsnpp_checkpoint(
							    checkpoint,
							    config,
							)

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

							snake_case_  :	Union[str, Any]							  =							UNetaDModel(**config)
							model.load_state_dict(converted_checkpoint)

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

														snake_case_  :	int							  =							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 __snake_case(      _lowerCAmelCase		,							_lowerCAmelCase		,							_lowerCAmelCase		,							unittest.TestCase					):


						'''simple docstring'''



						UpperCAmelCase							:    Optional[Any]           =		StableDiffusionModelEditingPipeline
						UpperCAmelCase							:    List[str]           =		TEXT_TO_IMAGE_PARAMS
						UpperCAmelCase							:    Optional[Any]           =		TEXT_TO_IMAGE_BATCH_PARAMS
						UpperCAmelCase							:    Optional[int]           =		TEXT_TO_IMAGE_IMAGE_PARAMS
						UpperCAmelCase							:    Optional[int]           =		TEXT_TO_IMAGE_IMAGE_PARAMS


						def 				__snake_case    (					self      )	->	Optional[Any]:
							torch.manual_seed(0      )
							lowerCAmelCase    =	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	,       )
							lowerCAmelCase    =	DDIMScheduler()
							torch.manual_seed(0      )
							lowerCAmelCase    =	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      )
							lowerCAmelCase    =	CLIPTextConfig(
							    bos_token_id=0	,       eos_token_id=2	,       hidden_size=32	,       intermediate_size=37	,       layer_norm_eps=1e-05	,       num_attention_heads=4	,       num_hidden_layers=5	,       pad_token_id=1	,       vocab_size=1000	,       )
							lowerCAmelCase    =	CLIPTextModel(A_      )
							lowerCAmelCase    =	CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""      )

							lowerCAmelCase    =	{
							    """unet""": unet,
							    """scheduler""": scheduler,
							    """vae""": vae,
							    """text_encoder""": text_encoder,
							    """tokenizer""": tokenizer,
							    """safety_checker""": None,
							    """feature_extractor""": None,
							}
							return components


						def 				__snake_case    (					self	,       A_	,       A_=0      )	->	Optional[int]:
							lowerCAmelCase    =	torch.manual_seed(A_      )
							lowerCAmelCase    =	{
							    """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 				__snake_case    (					self      )	->	List[str]:
							lowerCAmelCase    =	"""cpu"""  # ensure determinism for the device-dependent torch.Generator
							lowerCAmelCase    =	self.get_dummy_components()
							lowerCAmelCase    =	StableDiffusionModelEditingPipeline(**A_      )
							lowerCAmelCase    =	sd_pipe.to(A_      )
							sd_pipe.set_progress_bar_config(disable=A_      )

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

							lowerCAmelCase    =	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 				__snake_case    (					self      )	->	str:
							lowerCAmelCase    =	"""cpu"""  # ensure determinism for the device-dependent torch.Generator
							lowerCAmelCase    =	self.get_dummy_components()
							lowerCAmelCase    =	StableDiffusionModelEditingPipeline(**A_      )
							lowerCAmelCase    =	sd_pipe.to(A_      )
							sd_pipe.set_progress_bar_config(disable=A_      )

							lowerCAmelCase    =	self.get_dummy_inputs(A_      )
							lowerCAmelCase    =	"""french fries"""
							lowerCAmelCase    =	sd_pipe(**A_	,       negative_prompt=A_      )
							lowerCAmelCase    =	output.images
							lowerCAmelCase    =	image[0, -3:, -3:, -1]

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

							lowerCAmelCase    =	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 				__snake_case    (					self      )	->	Optional[int]:
							lowerCAmelCase    =	"""cpu"""  # ensure determinism for the device-dependent torch.Generator
							lowerCAmelCase    =	self.get_dummy_components()
							lowerCAmelCase    =	EulerAncestralDiscreteScheduler(
							    beta_start=0.0_0_0_8_5	,       beta_end=0.0_1_2	,       beta_schedule="""scaled_linear"""      )
							lowerCAmelCase    =	StableDiffusionModelEditingPipeline(**A_      )
							lowerCAmelCase    =	sd_pipe.to(A_      )
							sd_pipe.set_progress_bar_config(disable=A_      )

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

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

							lowerCAmelCase    =	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 				__snake_case    (					self      )	->	Optional[Any]:
							lowerCAmelCase    =	"""cpu"""  # ensure determinism for the device-dependent torch.Generator
							lowerCAmelCase    =	self.get_dummy_components()
							lowerCAmelCase    =	PNDMScheduler()
							lowerCAmelCase    =	StableDiffusionModelEditingPipeline(**A_      )
							lowerCAmelCase    =	sd_pipe.to(A_      )
							sd_pipe.set_progress_bar_config(disable=A_      )

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


						def 				__snake_case    (					self      )	->	str:
							super().test_inference_batch_single_identical(expected_max_diff=5e-3      )




						def 				__snake_case    (					self      )	->	Any:
							super().test_attention_slicing_forward_pass(expected_max_diff=5e-3      )







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


						'''simple docstring'''



						def 				__snake_case    (					self      )	->	int:
							super().tearDown()
							gc.collect()
							torch.cuda.empty_cache()


						def 				__snake_case    (					self	,       A_=0      )	->	Dict:
							lowerCAmelCase    =	torch.manual_seed(A_      )
							lowerCAmelCase    =	{
							    """prompt""": """A field of roses""",
							    """generator""": generator,
							    """num_inference_steps""": 3,
							    """guidance_scale""": 7.5,
							    """output_type""": """numpy""",
							}
							return inputs


						def 				__snake_case    (					self      )	->	int:
							lowerCAmelCase    =	"""CompVis/stable-diffusion-v1-4"""
							lowerCAmelCase    =	StableDiffusionModelEditingPipeline.from_pretrained(A_	,       safety_checker=A_      )
							pipe.to(A_      )
							pipe.set_progress_bar_config(disable=A_      )
							pipe.enable_attention_slicing()

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

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

							lowerCAmelCase    =	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"""      )

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

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

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




						def 				__snake_case    (					self      )	->	str:
							torch.cuda.empty_cache()
							torch.cuda.reset_max_memory_allocated()
							torch.cuda.reset_peak_memory_stats()

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

							lowerCAmelCase    =	self.get_inputs()
							lowerCAmelCase    =	pipe(**A_      )

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