



"""simple docstring"""






import argparse
import os

import torch

from diffusers import (
    CMStochasticIterativeScheduler,
    ConsistencyModelPipeline,
    UNetaDModel,
)


lowerCAmelCase__        =				{
    '''sample_size''': 32,
    '''in_channels''': 3,
    '''out_channels''': 3,
    '''layers_per_block''': 2,
    '''num_class_embeds''': 1000,
    '''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__        =				{
    '''sample_size''': 64,
    '''in_channels''': 3,
    '''out_channels''': 3,
    '''layers_per_block''': 3,
    '''num_class_embeds''': 1000,
    '''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__        =				{
    '''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__        =				{
    '''num_train_timesteps''': 40,
    '''sigma_min''': 0.0_0_2,
    '''sigma_max''': 8_0.0,
}

lowerCAmelCase__        =				{
    '''num_train_timesteps''': 201,
    '''sigma_min''': 0.0_0_2,
    '''sigma_max''': 8_0.0,
}

lowerCAmelCase__        =				{
    '''num_train_timesteps''': 151,
    '''sigma_min''': 0.0_0_2,
    '''sigma_max''': 8_0.0,
}






def        snake_case_  (      A_					:		Optional[Any]						):


       '''simple docstring'''





       if isinstance(A_,				A_						):
              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        snake_case_  (      A_					:		int,				A_					:		Tuple,				A_					:		Any,				A_					:		Tuple,				A_					:		Dict=False						):


       '''simple docstring'''





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

       if has_skip:
              _lowerCamelCase						:				Dict    			= checkpoint[F'''{old_prefix}.skip_connection.weight''']
              _lowerCamelCase						:				int    			= checkpoint[F'''{old_prefix}.skip_connection.bias''']

       return new_checkpoint






def        snake_case_  (      A_					:		str,				A_					:		int,				A_					:		str,				A_					:		List[str],				A_					:		Union[str, Any]=None						):


       '''simple docstring'''





       _lowerCamelCase			,    _lowerCamelCase			,    _lowerCamelCase						:				str    			= checkpoint[F'''{old_prefix}.qkv.weight'''].chunk(3,				dim=0						)
       _lowerCamelCase			,    _lowerCamelCase			,    _lowerCamelCase						:				str    			= checkpoint[F'''{old_prefix}.qkv.bias'''].chunk(3,				dim=0						)

       _lowerCamelCase						:				Union[str, Any]    			= checkpoint[F'''{old_prefix}.norm.weight''']
       _lowerCamelCase						:				Optional[int]    			= checkpoint[F'''{old_prefix}.norm.bias''']

       _lowerCamelCase						:				Union[str, Any]    			= weight_q.squeeze(-1						).squeeze(-1						)
       _lowerCamelCase						:				List[Any]    			= bias_q.squeeze(-1						).squeeze(-1						)
       _lowerCamelCase						:				List[Any]    			= weight_k.squeeze(-1						).squeeze(-1						)
       _lowerCamelCase						:				Any    			= bias_k.squeeze(-1						).squeeze(-1						)
       _lowerCamelCase						:				Union[str, Any]    			= weight_v.squeeze(-1						).squeeze(-1						)
       _lowerCamelCase						:				List[Any]    			= bias_v.squeeze(-1						).squeeze(-1						)

       _lowerCamelCase						:				List[Any]    			= (
           checkpoint[F'''{old_prefix}.proj_out.weight'''].squeeze(-1						).squeeze(-1						)
       )
       _lowerCamelCase						:				Tuple    			= checkpoint[F'''{old_prefix}.proj_out.bias'''].squeeze(-1						).squeeze(-1						)

       return new_checkpoint






def        snake_case_  (      A_					:		str,				A_					:		Tuple						):


       '''simple docstring'''





       _lowerCamelCase						:				Union[str, Any]    			= torch.load(A_,				map_location='''cpu'''						)
       _lowerCamelCase						:				Dict    			= {}

       _lowerCamelCase						:				List[str]    			= checkpoint['''time_embed.0.weight''']
       _lowerCamelCase						:				Union[str, Any]    			= checkpoint['''time_embed.0.bias''']
       _lowerCamelCase						:				List[str]    			= checkpoint['''time_embed.2.weight''']
       _lowerCamelCase						:				Union[str, Any]    			= checkpoint['''time_embed.2.bias''']

       if unet_config["num_class_embeds"] is not None:
              _lowerCamelCase						:				List[str]    			= checkpoint['''label_emb.weight''']

       _lowerCamelCase						:				Any    			= checkpoint['''input_blocks.0.0.weight''']
       _lowerCamelCase						:				Union[str, Any]    			= checkpoint['''input_blocks.0.0.bias''']

       _lowerCamelCase						:				Tuple    			= unet_config['''down_block_types''']
       _lowerCamelCase						:				Tuple    			= unet_config['''layers_per_block''']
       _lowerCamelCase						:				List[Any]    			= unet_config['''attention_head_dim''']
       _lowerCamelCase						:				str    			= unet_config['''block_out_channels''']
       _lowerCamelCase						:				Dict    			= 1
       _lowerCamelCase						:				str    			= channels_list[0]

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

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

              if i != len(A_						) - 1:
                     _lowerCamelCase						:				List[Any]    			= F'''down_blocks.{i}.downsamplers.0'''
                     _lowerCamelCase						:				List[str]    			= F'''input_blocks.{current_layer}.0'''
                     _lowerCamelCase						:				str    			= convert_resnet(A_,				A_,				A_,				A_						)
                     current_layer += 1

              _lowerCamelCase						:				Tuple    			= current_channels

       # hardcoded the mid-block for now
       _lowerCamelCase						:				Tuple    			= '''mid_block.resnets.0'''
       _lowerCamelCase						:				int    			= '''middle_block.0'''
       _lowerCamelCase						:				List[str]    			= convert_resnet(A_,				A_,				A_,				A_						)
       _lowerCamelCase						:				List[str]    			= '''mid_block.attentions.0'''
       _lowerCamelCase						:				Any    			= '''middle_block.1'''
       _lowerCamelCase						:				List[Any]    			= convert_attention(A_,				A_,				A_,				A_,				A_						)
       _lowerCamelCase						:				Tuple    			= '''mid_block.resnets.1'''
       _lowerCamelCase						:				Tuple    			= '''middle_block.2'''
       _lowerCamelCase						:				Dict    			= convert_resnet(A_,				A_,				A_,				A_						)

       _lowerCamelCase						:				Optional[int]    			= 0
       _lowerCamelCase						:				str    			= unet_config['''up_block_types''']

       for i, layer_type in enumerate(A_						):
              if layer_type == "ResnetUpsampleBlock2D":
                     for j in range(layers_per_block + 1						):
                            _lowerCamelCase						:				List[str]    			= F'''up_blocks.{i}.resnets.{j}'''
                            _lowerCamelCase						:				Dict    			= F'''output_blocks.{current_layer}.0'''
                            _lowerCamelCase						:				Union[str, Any]    			= convert_resnet(A_,				A_,				A_,				A_,				has_skip=A_						)
                            current_layer += 1

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

                     if i != len(A_						) - 1:
                            _lowerCamelCase						:				Optional[int]    			= F'''up_blocks.{i}.upsamplers.0'''
                            _lowerCamelCase						:				Tuple    			= F'''output_blocks.{current_layer-1}.2'''
                            _lowerCamelCase						:				Any    			= convert_resnet(A_,				A_,				A_,				A_						)

       _lowerCamelCase						:				Optional[int]    			= checkpoint['''out.0.weight''']
       _lowerCamelCase						:				int    			= checkpoint['''out.0.bias''']
       _lowerCamelCase						:				Union[str, Any]    			= checkpoint['''out.2.weight''']
       _lowerCamelCase						:				Optional[int]    			= checkpoint['''out.2.bias''']

       return new_checkpoint


if __name__ == "__main__":
   lowerCAmelCase__        =				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__        =				parser.parse_args()
   lowerCAmelCase__        =				strabool(args.class_cond)

   lowerCAmelCase__        =				os.path.basename(args.unet_path)
   print(F"""Checkpoint: {ckpt_name}""")

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

   if not args.class_cond:
      lowerCAmelCase__        =				None

   lowerCAmelCase__        =				con_pt_to_diffuser(args.unet_path, unet_config)

   lowerCAmelCase__        =				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__        =				CD_SCHEDULER_CONFIG
   elif "ct" in ckpt_name and "imagenet64" in ckpt_name:
      lowerCAmelCase__        =				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__        =				CT_LSUN_256_SCHEDULER_CONFIG
   else:
      raise ValueError(F"""Checkpoint type {ckpt_name} is not currently supported.""")

   lowerCAmelCase__        =				CMStochasticIterativeScheduler(**scheduler_config)

   lowerCAmelCase__        =				ConsistencyModelPipeline(unet=image_unet, scheduler=cm_scheduler)
   consistency_model.save_pretrained(args.dump_path)








import gc
import random
import unittest

import numpy as np
import torch
from transformers import CLIPImageProcessor, CLIPVisionConfig, CLIPVisionModel

from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEImgaImgPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.utils import floats_tensor, load_image, load_numpy, slow
from diffusers.utils.testing_utils import require_torch_gpu, torch_device

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference





class     lowerCamelCase__(					__lowerCamelCase ,     unittest.TestCase):
      UpperCAmelCase__      :	Tuple    		=   ShapEImgaImgPipeline
      UpperCAmelCase__      :	Optional[Any]    		=   ['image']
      UpperCAmelCase__      :	int    		=   ['image']
      UpperCAmelCase__      :	Any    		=   [
          'num_images_per_prompt',
          'num_inference_steps',
          'generator',
          'latents',
          'guidance_scale',
          'frame_size',
          'output_type',
          'return_dict',
      ]
      UpperCAmelCase__      :	int    		=   False
      @property
      def 			lowerCAmelCase__						(				self: int   ):
             return 32
      @property
      def 			lowerCAmelCase__						(				self: List[str]   ):
             return 32
      @property
      def 			lowerCAmelCase__						(				self: Any   ):
             return self.time_input_dim * 4
      @property
      def 			lowerCAmelCase__						(				self: Dict   ):
             return 8
      @property
      def 			lowerCAmelCase__						(				self: int   ):
             torch.manual_seed(0   )
             __lowerCamelCase   =						CLIPVisionConfig(
                 hidden_size=self.text_embedder_hidden_size    ,       image_size=64    ,       projection_dim=self.text_embedder_hidden_size    ,       intermediate_size=37    ,       num_attention_heads=4    ,       num_channels=3    ,       num_hidden_layers=5    ,       patch_size=1    ,       )

             __lowerCamelCase   =						CLIPVisionModel(UpperCamelCase_   )
             return model
      @property
      def 			lowerCAmelCase__						(				self: Dict   ):
             __lowerCamelCase   =						CLIPImageProcessor(
                 crop_size=2_24    ,       do_center_crop=UpperCamelCase_    ,       do_normalize=UpperCamelCase_    ,       do_resize=UpperCamelCase_    ,       image_mean=[0.4814_5466, 0.457_8275, 0.4082_1073]    ,       image_std=[0.2686_2954, 0.2613_0258, 0.2757_7711]    ,       resample=3    ,       size=2_24    ,       )

             return image_processor
      @property
      def 			lowerCAmelCase__						(				self: Tuple   ):
             torch.manual_seed(0   )

             __lowerCamelCase   =						{
                 """num_attention_heads""": 2,
                 """attention_head_dim""": 16,
                 """embedding_dim""": self.time_input_dim,
                 """num_embeddings""": 32,
                 """embedding_proj_dim""": self.text_embedder_hidden_size,
                 """time_embed_dim""": self.time_embed_dim,
                 """num_layers""": 1,
                 """clip_embed_dim""": self.time_input_dim * 2,
                 """additional_embeddings""": 0,
                 """time_embed_act_fn""": """gelu""",
                 """norm_in_type""": """layer""",
                 """embedding_proj_norm_type""": """layer""",
                 """encoder_hid_proj_type""": None,
                 """added_emb_type""": None,
             }

             __lowerCamelCase   =						PriorTransformer(**UpperCamelCase_   )
             return model
      @property
      def 			lowerCAmelCase__						(				self: List[Any]   ):
             torch.manual_seed(0   )

             __lowerCamelCase   =						{
                 """param_shapes""": (
                     (self.renderer_dim, 93),
                     (self.renderer_dim, 8),
                     (self.renderer_dim, 8),
                     (self.renderer_dim, 8),
                 ),
                 """d_latent""": self.time_input_dim,
                 """d_hidden""": self.renderer_dim,
                 """n_output""": 12,
                 """background""": (
                     0.1,
                     0.1,
                     0.1,
                 ),
             }
             __lowerCamelCase   =						ShapERenderer(**UpperCamelCase_   )
             return model
      def 			lowerCAmelCase__						(				self: List[str]   ):
             __lowerCamelCase   =						self.dummy_prior
             __lowerCamelCase   =						self.dummy_image_encoder
             __lowerCamelCase   =						self.dummy_image_processor
             __lowerCamelCase   =						self.dummy_renderer

             __lowerCamelCase   =						HeunDiscreteScheduler(
                 beta_schedule="""exp"""    ,       num_train_timesteps=10_24    ,       prediction_type="""sample"""    ,       use_karras_sigmas=UpperCamelCase_    ,       clip_sample=UpperCamelCase_    ,       clip_sample_range=1.0    ,       )
             __lowerCamelCase   =						{
                 """prior""": prior,
                 """image_encoder""": image_encoder,
                 """image_processor""": image_processor,
                 """renderer""": renderer,
                 """scheduler""": scheduler,
             }

             return components
      def 			lowerCAmelCase__						(				self: int    ,       UpperCamelCase_: List[Any]    ,       UpperCamelCase_: Dict=0   ):
             __lowerCamelCase   =						floats_tensor((1, 3, 64, 64)    ,       rng=random.Random(UpperCamelCase_   )   ).to(UpperCamelCase_   )

             if str(UpperCamelCase_   ).startswith("""mps"""   ):
                    __lowerCamelCase   =						torch.manual_seed(UpperCamelCase_   )
             else:
                    __lowerCamelCase   =						torch.Generator(device=UpperCamelCase_   ).manual_seed(UpperCamelCase_   )
             __lowerCamelCase   =						{
                 """image""": input_image,
                 """generator""": generator,
                 """num_inference_steps""": 1,
                 """frame_size""": 32,
                 """output_type""": """np""",
             }
             return inputs
      def 			lowerCAmelCase__						(				self: Tuple   ):
             __lowerCamelCase   =						"""cpu"""

             __lowerCamelCase   =						self.get_dummy_components()

             __lowerCamelCase   =						self.pipeline_class(**UpperCamelCase_   )
             __lowerCamelCase   =						pipe.to(UpperCamelCase_   )

             pipe.set_progress_bar_config(disable=UpperCamelCase_   )

             __lowerCamelCase   =						pipe(**self.get_dummy_inputs(UpperCamelCase_   )   )
             __lowerCamelCase   =						output.images[0]
             __lowerCamelCase   =						image[0, -3:, -3:, -1]

             assert image.shape == (20, 32, 32, 3)

             __lowerCamelCase   =						np.array(
                 [
                     0.0003_9216,
                     0.0003_9216,
                     0.0003_9216,
                     0.0003_9216,
                     0.0003_9216,
                     0.0003_9216,
                     0.0003_9216,
                     0.0003_9216,
                     0.0003_9216,
                 ]   )

             assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1E-2
      def 			lowerCAmelCase__						(				self: List[str]   ):
             # NOTE: Larger batch sizes cause this test to timeout, only test on smaller batches
             self._test_inference_batch_consistent(batch_sizes=[1, 2]   )
      def 			lowerCAmelCase__						(				self: Any   ):
             __lowerCamelCase   =						torch_device == """cpu"""
             __lowerCamelCase   =						True
             self._test_inference_batch_single_identical(
                 batch_size=2    ,       test_max_difference=UpperCamelCase_    ,       relax_max_difference=UpperCamelCase_    ,       )




      def 			lowerCAmelCase__						(				self: Any   ):
             __lowerCamelCase   =						self.get_dummy_components()
             __lowerCamelCase   =						self.pipeline_class(**UpperCamelCase_   )
             __lowerCamelCase   =						pipe.to(UpperCamelCase_   )
             pipe.set_progress_bar_config(disable=UpperCamelCase_   )

             __lowerCamelCase   =						1
             __lowerCamelCase   =						2

             __lowerCamelCase   =						self.get_dummy_inputs(UpperCamelCase_   )

             for key in inputs.keys():
                    if key in self.batch_params:
                           __lowerCamelCase   =						batch_size * [inputs[key]]

             __lowerCamelCase   =						pipe(**UpperCamelCase_    ,       num_images_per_prompt=UpperCamelCase_   )[0]

             assert images.shape[0] == batch_size * num_images_per_prompt





@slow
@require_torch_gpu
class     lowerCamelCase__(					unittest.TestCase):
      def 			lowerCAmelCase__						(				self: Union[str, Any]   ):
             # clean up the VRAM after each test
             super().tearDown()
             gc.collect()
             torch.cuda.empty_cache()




      def 			lowerCAmelCase__						(				self: Any   ):
             __lowerCamelCase   =						load_image(
                 """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main""" """/shap_e/corgi.png"""   )
             __lowerCamelCase   =						load_numpy(
                 """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
                 """/shap_e/test_shap_e_img2img_out.npy"""   )
             __lowerCamelCase   =						ShapEImgaImgPipeline.from_pretrained("""openai/shap-e-img2img"""   )
             __lowerCamelCase   =						pipe.to(UpperCamelCase_   )
             pipe.set_progress_bar_config(disable=UpperCamelCase_   )

             __lowerCamelCase   =						torch.Generator(device=UpperCamelCase_   ).manual_seed(0   )

             __lowerCamelCase   =						pipe(
                 UpperCamelCase_    ,       generator=UpperCamelCase_    ,       guidance_scale=3.0    ,       num_inference_steps=64    ,       frame_size=64    ,       output_type="""np"""    ,       ).images[0]

             assert images.shape == (20, 64, 64, 3)

             assert_mean_pixel_difference(UpperCamelCase_    ,       UpperCamelCase_   )

