




'''simple docstring'''

import gc
import random
import unittest

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

from diffusers import AutoencoderKL, PNDMScheduler, StableDiffusionInpaintPipeline, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_image, load_numpy, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow

from ..pipeline_params import TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()



class 		SCREAMING_SNAKE_CASE__  (			_UpperCamelCase				,     _UpperCamelCase				,     _UpperCamelCase				,     unittest.TestCase	):
			__SCREAMING_SNAKE_CASE    =			StableDiffusionInpaintPipeline
			__SCREAMING_SNAKE_CASE    =			TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
			__SCREAMING_SNAKE_CASE    =			TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
			__SCREAMING_SNAKE_CASE    =			frozenset(
			    []	)  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
			__SCREAMING_SNAKE_CASE    =			frozenset([]	)





			def 							A (      self						:    Tuple							):



						"""simple docstring"""




						torch.manual_seed(0							)
						__snake_case			=		UNetaDConditionModel(
						    block_out_channels=(32, 64)      ,		layers_per_block=2      ,		sample_size=32      ,		in_channels=9      ,		out_channels=4      ,		down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")      ,		up_block_types=("CrossAttnUpBlock2D", "UpBlock2D")      ,		cross_attention_dim=32      ,		attention_head_dim=(2, 4)      ,		use_linear_projection=a_      ,		)
						__snake_case			=		PNDMScheduler(skip_prk_steps=a_							)
						torch.manual_seed(0							)
						__snake_case			=		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      ,		sample_size=128      ,		)
						torch.manual_seed(0							)
						__snake_case			=		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=1_000      ,		hidden_act="gelu"      ,		projection_dim=512      ,		)
						__snake_case			=		CLIPTextModel(a_							)
						__snake_case			=		CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"							)

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





			def 							A (      self						:    Any      ,		a_						:    Optional[Any]      ,		a_						:    List[Any]=0							):



						"""simple docstring"""




						__snake_case			=		floats_tensor((1, 3, 32, 32)      ,		rng=random.Random(a_							)							).to(a_							)
						__snake_case			=		image.cpu().permute(0      ,		2      ,		3      ,		1							)[0]
						__snake_case			=		Image.fromarray(np.uinta(a_							)							).convert("RGB"							).resize((64, 64)							)
						__snake_case			=		Image.fromarray(np.uinta(image + 4							)							).convert("RGB"							).resize((64, 64)							)
						if str(a_							).startswith("mps"							):
									__snake_case			=		torch.manual_seed(a_							)
						else:
									__snake_case			=		torch.Generator(device=a_							).manual_seed(a_							)
						__snake_case			=		{
						    "prompt": "A painting of a squirrel eating a burger",
						    "image": init_image,
						    "mask_image": mask_image,
						    "generator": generator,
						    "num_inference_steps": 2,
						    "guidance_scale": 6.0,
						    "output_type": "numpy",
						}
						return inputs





			def 							A (      self						:    Optional[Any]							):



						"""simple docstring"""




						__snake_case			=		"cpu"  # ensure determinism for the device-dependent torch.Generator
						__snake_case			=		self.get_dummy_components()
						__snake_case			=		StableDiffusionInpaintPipeline(**a_							)
						__snake_case			=		sd_pipe.to(a_							)
						sd_pipe.set_progress_bar_config(disable=a_							)

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

						assert image.shape == (1, 64, 64, 3)
						__snake_case			=		np.array([0.4727, 0.5735, 0.3941, 0.5446, 0.5926, 0.4394, 0.5062, 0.4654, 0.4476]							)

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






			def 							A (      self						:    Dict							):



						"""simple docstring"""




						super().test_inference_batch_single_identical(expected_max_diff=3e-3							)







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





			def 							A (      self						:    Any							):



						"""simple docstring"""




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





			def 							A (      self						:    int							):



						"""simple docstring"""




						__snake_case			=		load_image(
						    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
						    "/sd2-inpaint/init_image.png"							)
						__snake_case			=		load_image(
						    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png"							)
						__snake_case			=		load_numpy(
						    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint"
						    "/yellow_cat_sitting_on_a_park_bench.npy"							)

						__snake_case			=		"stabilityai/stable-diffusion-2-inpainting"
						__snake_case			=		StableDiffusionInpaintPipeline.from_pretrained(a_      ,		safety_checker=a_							)
						pipe.to(a_							)
						pipe.set_progress_bar_config(disable=a_							)
						pipe.enable_attention_slicing()

						__snake_case			=		"Face of a yellow cat, high resolution, sitting on a park bench"

						__snake_case			=		torch.manual_seed(0							)
						__snake_case			=		pipe(
						    prompt=a_      ,		image=a_      ,		mask_image=a_      ,		generator=a_      ,		output_type="np"      ,		)
						__snake_case			=		output.images[0]

						assert image.shape == (512, 512, 3)
						assert np.abs(expected_image - image							).max() < 9e-3





			def 							A (      self						:    int							):



						"""simple docstring"""




						__snake_case			=		load_image(
						    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
						    "/sd2-inpaint/init_image.png"							)
						__snake_case			=		load_image(
						    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png"							)
						__snake_case			=		load_numpy(
						    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint"
						    "/yellow_cat_sitting_on_a_park_bench_fp16.npy"							)

						__snake_case			=		"stabilityai/stable-diffusion-2-inpainting"
						__snake_case			=		StableDiffusionInpaintPipeline.from_pretrained(
						    a_      ,		torch_dtype=torch.floataa      ,		safety_checker=a_      ,		)
						pipe.to(a_							)
						pipe.set_progress_bar_config(disable=a_							)
						pipe.enable_attention_slicing()

						__snake_case			=		"Face of a yellow cat, high resolution, sitting on a park bench"

						__snake_case			=		torch.manual_seed(0							)
						__snake_case			=		pipe(
						    prompt=a_      ,		image=a_      ,		mask_image=a_      ,		generator=a_      ,		output_type="np"      ,		)
						__snake_case			=		output.images[0]

						assert image.shape == (512, 512, 3)
						assert np.abs(expected_image - image							).max() < 5e-1






			def 							A (      self						:    List[Any]							):



						"""simple docstring"""




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

						__snake_case			=		load_image(
						    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
						    "/sd2-inpaint/init_image.png"							)
						__snake_case			=		load_image(
						    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png"							)

						__snake_case			=		"stabilityai/stable-diffusion-2-inpainting"
						__snake_case			=		PNDMScheduler.from_pretrained(a_      ,		subfolder="scheduler"							)
						__snake_case			=		StableDiffusionInpaintPipeline.from_pretrained(
						    a_      ,		safety_checker=a_      ,		scheduler=a_      ,		torch_dtype=torch.floataa      ,		)
						pipe.to(a_							)
						pipe.set_progress_bar_config(disable=a_							)
						pipe.enable_attention_slicing(1							)
						pipe.enable_sequential_cpu_offload()

						__snake_case			=		"Face of a yellow cat, high resolution, sitting on a park bench"

						__snake_case			=		torch.manual_seed(0							)
						__snake_case			=		pipe(
						    prompt=a_      ,		image=a_      ,		mask_image=a_      ,		generator=a_      ,		num_inference_steps=2      ,		output_type="np"      ,		)

						__snake_case			=		torch.cuda.max_memory_allocated()
						# make sure that less than 2.65 GB is allocated
						assert mem_bytes < 2.65 * 10**9













"""simple docstring"""



import argparse
import os

import torch

from diffusers import (
    CMStochasticIterativeScheduler,
    ConsistencyModelPipeline,
    UNetaDModel,
)


_lowerCAmelCase	:	Dict				=      {
    """sample_size""": 32,
    """in_channels""": 3,
    """out_channels""": 3,
    """layers_per_block""": 2,
    """num_class_embeds""": 1_000,
    """block_out_channels""": [32, 64],
    """attention_head_dim""": 8,
    """down_block_types""": [
        """ResnetDownsampleBlock2D""",
        """AttnDownBlock2D""",
    ],
    """up_block_types""": [
        """AttnUpBlock2D""",
        """ResnetUpsampleBlock2D""",
    ],
    """resnet_time_scale_shift""": """scale_shift""",
    """upsample_type""": """resnet""",
    """downsample_type""": """resnet""",
}

_lowerCAmelCase	:	str				=      {
    """sample_size""": 64,
    """in_channels""": 3,
    """out_channels""": 3,
    """layers_per_block""": 3,
    """num_class_embeds""": 1_000,
    """block_out_channels""": [192, 192 * 2, 192 * 3, 192 * 4],
    """attention_head_dim""": 64,
    """down_block_types""": [
        """ResnetDownsampleBlock2D""",
        """AttnDownBlock2D""",
        """AttnDownBlock2D""",
        """AttnDownBlock2D""",
    ],
    """up_block_types""": [
        """AttnUpBlock2D""",
        """AttnUpBlock2D""",
        """AttnUpBlock2D""",
        """ResnetUpsampleBlock2D""",
    ],
    """resnet_time_scale_shift""": """scale_shift""",
    """upsample_type""": """resnet""",
    """downsample_type""": """resnet""",
}

_lowerCAmelCase	:	Dict				=      {
    """sample_size""": 256,
    """in_channels""": 3,
    """out_channels""": 3,
    """layers_per_block""": 2,
    """num_class_embeds""": None,
    """block_out_channels""": [256, 256, 256 * 2, 256 * 2, 256 * 4, 256 * 4],
    """attention_head_dim""": 64,
    """down_block_types""": [
        """ResnetDownsampleBlock2D""",
        """ResnetDownsampleBlock2D""",
        """ResnetDownsampleBlock2D""",
        """AttnDownBlock2D""",
        """AttnDownBlock2D""",
        """AttnDownBlock2D""",
    ],
    """up_block_types""": [
        """AttnUpBlock2D""",
        """AttnUpBlock2D""",
        """AttnUpBlock2D""",
        """ResnetUpsampleBlock2D""",
        """ResnetUpsampleBlock2D""",
        """ResnetUpsampleBlock2D""",
    ],
    """resnet_time_scale_shift""": """default""",
    """upsample_type""": """resnet""",
    """downsample_type""": """resnet""",
}

_lowerCAmelCase	:	int				=      {
    """num_train_timesteps""": 40,
    """sigma_min""": 0.0_02,
    """sigma_max""": 80.0,
}

_lowerCAmelCase	:	List[Any]				=      {
    """num_train_timesteps""": 201,
    """sigma_min""": 0.0_02,
    """sigma_max""": 80.0,
}

_lowerCAmelCase	:	List[Any]				=      {
    """num_train_timesteps""": 151,
    """sigma_min""": 0.0_02,
    """sigma_max""": 80.0,
}


def 	SCREAMING_SNAKE_CASE__       (  snake_case       : str				)->		str:






     '''simple docstring'''



     if isinstance(snake_case			,					snake_case				):
          return v
     if v.lower() in ("yes", "true", "t", "y", "1"):
          return True
     elif v.lower() in ("no", "false", "f", "n", "0"):
          return False
     else:
          raise argparse.ArgumentTypeError("boolean value expected"				)


def 	SCREAMING_SNAKE_CASE__       (  snake_case       : Any			,					snake_case       : Optional[Any]			,					snake_case       : List[str]			,					snake_case       : Any			,					snake_case       : str=False				)->		Tuple:






     '''simple docstring'''



     UpperCAmelCase__						:    Dict							=							checkpoint[f'{old_prefix}.in_layers.0.weight']
     UpperCAmelCase__						:    List[Any]							=							checkpoint[f'{old_prefix}.in_layers.0.bias']
     UpperCAmelCase__						:    Dict							=							checkpoint[f'{old_prefix}.in_layers.2.weight']
     UpperCAmelCase__						:    List[Any]							=							checkpoint[f'{old_prefix}.in_layers.2.bias']
     UpperCAmelCase__						:    Optional[int]							=							checkpoint[f'{old_prefix}.emb_layers.1.weight']
     UpperCAmelCase__						:    Dict							=							checkpoint[f'{old_prefix}.emb_layers.1.bias']
     UpperCAmelCase__						:    Any							=							checkpoint[f'{old_prefix}.out_layers.0.weight']
     UpperCAmelCase__						:    List[Any]							=							checkpoint[f'{old_prefix}.out_layers.0.bias']
     UpperCAmelCase__						:    int							=							checkpoint[f'{old_prefix}.out_layers.3.weight']
     UpperCAmelCase__						:    int							=							checkpoint[f'{old_prefix}.out_layers.3.bias']

     if has_skip:
          UpperCAmelCase__						:    str							=							checkpoint[f'{old_prefix}.skip_connection.weight']
          UpperCAmelCase__						:    Any							=							checkpoint[f'{old_prefix}.skip_connection.bias']

     return new_checkpoint


def 	SCREAMING_SNAKE_CASE__       (  snake_case       : List[Any]			,					snake_case       : Union[str, Any]			,					snake_case       : Any			,					snake_case       : Tuple			,					snake_case       : Dict=None				)->		Tuple:






     '''simple docstring'''



     UpperCAmelCase__							,				UpperCAmelCase__							,				UpperCAmelCase__						:    Optional[Any]							=							checkpoint[f'{old_prefix}.qkv.weight'].chunk(3			,					dim=0				)
     UpperCAmelCase__							,				UpperCAmelCase__							,				UpperCAmelCase__						:    str							=							checkpoint[f'{old_prefix}.qkv.bias'].chunk(3			,					dim=0				)

     UpperCAmelCase__						:    Dict							=							checkpoint[f'{old_prefix}.norm.weight']
     UpperCAmelCase__						:    Tuple							=							checkpoint[f'{old_prefix}.norm.bias']

     UpperCAmelCase__						:    Tuple							=							weight_q.squeeze(-1				).squeeze(-1				)
     UpperCAmelCase__						:    int							=							bias_q.squeeze(-1				).squeeze(-1				)
     UpperCAmelCase__						:    Optional[Any]							=							weight_k.squeeze(-1				).squeeze(-1				)
     UpperCAmelCase__						:    Optional[int]							=							bias_k.squeeze(-1				).squeeze(-1				)
     UpperCAmelCase__						:    Tuple							=							weight_v.squeeze(-1				).squeeze(-1				)
     UpperCAmelCase__						:    Optional[Any]							=							bias_v.squeeze(-1				).squeeze(-1				)

     UpperCAmelCase__						:    Union[str, Any]							=							(
         checkpoint[f'{old_prefix}.proj_out.weight'].squeeze(-1				).squeeze(-1				)
     )
     UpperCAmelCase__						:    List[str]							=							checkpoint[f'{old_prefix}.proj_out.bias'].squeeze(-1				).squeeze(-1				)

     return new_checkpoint


def 	SCREAMING_SNAKE_CASE__       (  snake_case       : str			,					snake_case       : Union[str, Any]				)->		List[str]:






     '''simple docstring'''



     UpperCAmelCase__						:    Any							=							torch.load(snake_case			,					map_location="cpu"				)
     UpperCAmelCase__						:    Any							=							{}

     UpperCAmelCase__						:    Any							=							checkpoint["time_embed.0.weight"]
     UpperCAmelCase__						:    Optional[int]							=							checkpoint["time_embed.0.bias"]
     UpperCAmelCase__						:    Any							=							checkpoint["time_embed.2.weight"]
     UpperCAmelCase__						:    Optional[Any]							=							checkpoint["time_embed.2.bias"]

     if unet_config["num_class_embeds"] is not None:
          UpperCAmelCase__						:    List[Any]							=							checkpoint["label_emb.weight"]

     UpperCAmelCase__						:    Dict							=							checkpoint["input_blocks.0.0.weight"]
     UpperCAmelCase__						:    List[Any]							=							checkpoint["input_blocks.0.0.bias"]

     UpperCAmelCase__						:    Any							=							unet_config["down_block_types"]
     UpperCAmelCase__						:    Tuple							=							unet_config["layers_per_block"]
     UpperCAmelCase__						:    str							=							unet_config["attention_head_dim"]
     UpperCAmelCase__						:    str							=							unet_config["block_out_channels"]
     UpperCAmelCase__						:    Optional[int]							=							1
     UpperCAmelCase__						:    Tuple							=							channels_list[0]

     for i, layer_type in enumerate(snake_case				):
          UpperCAmelCase__						:    List[str]							=							channels_list[i]
          UpperCAmelCase__						:    Optional[int]							=							current_channels != prev_channels
          if layer_type == "ResnetDownsampleBlock2D":
               for j in range(snake_case				):
                    UpperCAmelCase__						:    int							=							f'down_blocks.{i}.resnets.{j}'
                    UpperCAmelCase__						:    Union[str, Any]							=							f'input_blocks.{current_layer}.0'
                    UpperCAmelCase__						:    List[str]							=							True if j == 0 and downsample_block_has_skip else False
                    UpperCAmelCase__						:    Optional[Any]							=							convert_resnet(snake_case			,					snake_case			,					snake_case			,					snake_case			,					has_skip=snake_case				)
                    current_layer += 1

          elif layer_type == "AttnDownBlock2D":
               for j in range(snake_case				):
                    UpperCAmelCase__						:    List[str]							=							f'down_blocks.{i}.resnets.{j}'
                    UpperCAmelCase__						:    Optional[Any]							=							f'input_blocks.{current_layer}.0'
                    UpperCAmelCase__						:    Optional[int]							=							True if j == 0 and downsample_block_has_skip else False
                    UpperCAmelCase__						:    Any							=							convert_resnet(snake_case			,					snake_case			,					snake_case			,					snake_case			,					has_skip=snake_case				)
                    UpperCAmelCase__						:    Dict							=							f'down_blocks.{i}.attentions.{j}'
                    UpperCAmelCase__						:    List[Any]							=							f'input_blocks.{current_layer}.1'
                    UpperCAmelCase__						:    Optional[Any]							=							convert_attention(
                        snake_case			,					snake_case			,					snake_case			,					snake_case			,					snake_case				)
                    current_layer += 1

          if i != len(snake_case				) - 1:
               UpperCAmelCase__						:    Dict							=							f'down_blocks.{i}.downsamplers.0'
               UpperCAmelCase__						:    Optional[int]							=							f'input_blocks.{current_layer}.0'
               UpperCAmelCase__						:    Tuple							=							convert_resnet(snake_case			,					snake_case			,					snake_case			,					snake_case				)
               current_layer += 1

          UpperCAmelCase__						:    Dict							=							current_channels

     # hardcoded the mid-block for now
     UpperCAmelCase__						:    int							=							"mid_block.resnets.0"
     UpperCAmelCase__						:    Optional[int]							=							"middle_block.0"
     UpperCAmelCase__						:    Any							=							convert_resnet(snake_case			,					snake_case			,					snake_case			,					snake_case				)
     UpperCAmelCase__						:    Optional[int]							=							"mid_block.attentions.0"
     UpperCAmelCase__						:    Tuple							=							"middle_block.1"
     UpperCAmelCase__						:    Any							=							convert_attention(snake_case			,					snake_case			,					snake_case			,					snake_case			,					snake_case				)
     UpperCAmelCase__						:    Optional[int]							=							"mid_block.resnets.1"
     UpperCAmelCase__						:    Union[str, Any]							=							"middle_block.2"
     UpperCAmelCase__						:    Optional[int]							=							convert_resnet(snake_case			,					snake_case			,					snake_case			,					snake_case				)

     UpperCAmelCase__						:    Optional[Any]							=							0
     UpperCAmelCase__						:    int							=							unet_config["up_block_types"]

     for i, layer_type in enumerate(snake_case				):
          if layer_type == "ResnetUpsampleBlock2D":
               for j in range(layers_per_block + 1				):
                    UpperCAmelCase__						:    Optional[Any]							=							f'up_blocks.{i}.resnets.{j}'
                    UpperCAmelCase__						:    List[str]							=							f'output_blocks.{current_layer}.0'
                    UpperCAmelCase__						:    List[str]							=							convert_resnet(snake_case			,					snake_case			,					snake_case			,					snake_case			,					has_skip=snake_case				)
                    current_layer += 1

               if i != len(snake_case				) - 1:
                    UpperCAmelCase__						:    Dict							=							f'up_blocks.{i}.upsamplers.0'
                    UpperCAmelCase__						:    Optional[Any]							=							f'output_blocks.{current_layer-1}.1'
                    UpperCAmelCase__						:    Optional[Any]							=							convert_resnet(snake_case			,					snake_case			,					snake_case			,					snake_case				)
          elif layer_type == "AttnUpBlock2D":
               for j in range(layers_per_block + 1				):
                    UpperCAmelCase__						:    Optional[Any]							=							f'up_blocks.{i}.resnets.{j}'
                    UpperCAmelCase__						:    Tuple							=							f'output_blocks.{current_layer}.0'
                    UpperCAmelCase__						:    Union[str, Any]							=							convert_resnet(snake_case			,					snake_case			,					snake_case			,					snake_case			,					has_skip=snake_case				)
                    UpperCAmelCase__						:    Optional[int]							=							f'up_blocks.{i}.attentions.{j}'
                    UpperCAmelCase__						:    List[Any]							=							f'output_blocks.{current_layer}.1'
                    UpperCAmelCase__						:    Any							=							convert_attention(
                        snake_case			,					snake_case			,					snake_case			,					snake_case			,					snake_case				)
                    current_layer += 1

               if i != len(snake_case				) - 1:
                    UpperCAmelCase__						:    Dict							=							f'up_blocks.{i}.upsamplers.0'
                    UpperCAmelCase__						:    Any							=							f'output_blocks.{current_layer-1}.2'
                    UpperCAmelCase__						:    int							=							convert_resnet(snake_case			,					snake_case			,					snake_case			,					snake_case				)

     UpperCAmelCase__						:    Union[str, Any]							=							checkpoint["out.0.weight"]
     UpperCAmelCase__						:    Any							=							checkpoint["out.0.bias"]
     UpperCAmelCase__						:    int							=							checkpoint["out.2.weight"]
     UpperCAmelCase__						:    str							=							checkpoint["out.2.bias"]

     return new_checkpoint


if __name__ == "__main__":
 _lowerCAmelCase	:	str				=      argparse.ArgumentParser()

 parser.add_argument("""--unet_path""", default=None, type=str, required=True, help="""Path to the unet.pt to convert.""")
 parser.add_argument(
     """--dump_path""", default=None, type=str, required=True, help="""Path to output the converted UNet model."""
 )
 parser.add_argument("""--class_cond""", default=True, type=str, help="""Whether the model is class-conditional.""")

 _lowerCAmelCase	:	int				=      parser.parse_args()
 _lowerCAmelCase	:	Optional[int]				=      strabool(args.class_cond)

 _lowerCAmelCase	:	Optional[Any]				=      os.path.basename(args.unet_path)
 print(F"""Checkpoint: {ckpt_name}""")

 # Get U-Net config
 if "imagenet64" in ckpt_name:
  _lowerCAmelCase	:	Dict				=      IMAGENET_64_UNET_CONFIG
 elif "256" in ckpt_name and (("bedroom" in ckpt_name) or ("cat" in ckpt_name)):
  _lowerCAmelCase	:	Dict				=      LSUN_256_UNET_CONFIG
 elif "test" in ckpt_name:
  _lowerCAmelCase	:	str				=      TEST_UNET_CONFIG
 else:
  raise ValueError(F"""Checkpoint type {ckpt_name} is not currently supported.""")

 if not args.class_cond:
  _lowerCAmelCase	:	int				=      None

 _lowerCAmelCase	:	Optional[int]				=      con_pt_to_diffuser(args.unet_path, unet_config)

 _lowerCAmelCase	:	Optional[Any]				=      UNetaDModel(**unet_config)
 image_unet.load_state_dict(converted_unet_ckpt)

 # Get scheduler config
 if "cd" in ckpt_name or "test" in ckpt_name:
  _lowerCAmelCase	:	Optional[int]				=      CD_SCHEDULER_CONFIG
 elif "ct" in ckpt_name and "imagenet64" in ckpt_name:
  _lowerCAmelCase	:	str				=      CT_IMAGENET_64_SCHEDULER_CONFIG
 elif "ct" in ckpt_name and "256" in ckpt_name and (("bedroom" in ckpt_name) or ("cat" in ckpt_name)):
  _lowerCAmelCase	:	Any				=      CT_LSUN_256_SCHEDULER_CONFIG
 else:
  raise ValueError(F"""Checkpoint type {ckpt_name} is not currently supported.""")

 _lowerCAmelCase	:	Dict				=      CMStochasticIterativeScheduler(**scheduler_config)

 _lowerCAmelCase	:	Tuple				=      ConsistencyModelPipeline(unet=image_unet, scheduler=cm_scheduler)
 consistency_model.save_pretrained(args.dump_path)


