



'''simple docstring'''





import argparse

import requests
import torch
from PIL import Image
from torchvision.transforms import Compose, Normalize, Resize, ToTensor

from transformers import SwinaSRConfig, SwinaSRForImageSuperResolution, SwinaSRImageProcessor





def 						__snake_case(				_lowerCAmelCase		)		-> Any:
      snake_case__  :    Any    						=      SwinaSRConfig()

      if "Swin2SR_ClassicalSR_X4_64" in checkpoint_url:
            snake_case__  :    List[Any]    						=      4
      elif "Swin2SR_CompressedSR_X4_48" in checkpoint_url:
            snake_case__  :    Optional[int]    						=      4
            snake_case__  :    Any    						=      48
            snake_case__  :    List[Any]    						=      """pixelshuffle_aux"""
      elif "Swin2SR_Lightweight_X2_64" in checkpoint_url:
            snake_case__  :    Tuple    						=      [6, 6, 6, 6]
            snake_case__  :    Dict    						=      60
            snake_case__  :    str    						=      [6, 6, 6, 6]
            snake_case__  :    str    						=      """pixelshuffledirect"""
      elif "Swin2SR_RealworldSR_X4_64_BSRGAN_PSNR" in checkpoint_url:
            snake_case__  :    Dict    						=      4
            snake_case__  :    str    						=      """nearest+conv"""
      elif "Swin2SR_Jpeg_dynamic" in checkpoint_url:
            snake_case__  :    Any    						=      1
            snake_case__  :    Dict    						=      1
            snake_case__  :    Tuple    						=      126
            snake_case__  :    Dict    						=      7
            snake_case__  :    Tuple    						=      255.0
            snake_case__  :    Tuple    						=      """"""

      return config





def 						__snake_case(				_lowerCAmelCase     ,   _lowerCAmelCase		)		-> List[Any]:
      if "patch_embed.proj" in name and "layers" not in name:
            snake_case__  :    Any    						=      name.replace("""patch_embed.proj"""     ,   """embeddings.patch_embeddings.projection"""		)
      if "patch_embed.norm" in name:
            snake_case__  :    int    						=      name.replace("""patch_embed.norm"""     ,   """embeddings.patch_embeddings.layernorm"""		)
      if "layers" in name:
            snake_case__  :    List[str]    						=      name.replace("""layers"""     ,   """encoder.stages"""		)
      if "residual_group.blocks" in name:
            snake_case__  :    Optional[int]    						=      name.replace("""residual_group.blocks"""     ,   """layers"""		)
      if "attn.proj" in name:
            snake_case__  :    Optional[Any]    						=      name.replace("""attn.proj"""     ,   """attention.output.dense"""		)
      if "attn" in name:
            snake_case__  :    Optional[int]    						=      name.replace("""attn"""     ,   """attention.self"""		)
      if "norm1" in name:
            snake_case__  :    str    						=      name.replace("""norm1"""     ,   """layernorm_before"""		)
      if "norm2" in name:
            snake_case__  :    List[str]    						=      name.replace("""norm2"""     ,   """layernorm_after"""		)
      if "mlp.fc1" in name:
            snake_case__  :    int    						=      name.replace("""mlp.fc1"""     ,   """intermediate.dense"""		)
      if "mlp.fc2" in name:
            snake_case__  :    int    						=      name.replace("""mlp.fc2"""     ,   """output.dense"""		)
      if "q_bias" in name:
            snake_case__  :    Tuple    						=      name.replace("""q_bias"""     ,   """query.bias"""		)
      if "k_bias" in name:
            snake_case__  :    Dict    						=      name.replace("""k_bias"""     ,   """key.bias"""		)
      if "v_bias" in name:
            snake_case__  :    str    						=      name.replace("""v_bias"""     ,   """value.bias"""		)
      if "cpb_mlp" in name:
            snake_case__  :    Optional[int]    						=      name.replace("""cpb_mlp"""     ,   """continuous_position_bias_mlp"""		)
      if "patch_embed.proj" in name:
            snake_case__  :    List[str]    						=      name.replace("""patch_embed.proj"""     ,   """patch_embed.projection"""		)

      if name == "norm.weight":
            snake_case__  :    str    						=      """layernorm.weight"""
      if name == "norm.bias":
            snake_case__  :    Tuple    						=      """layernorm.bias"""

      if "conv_first" in name:
            snake_case__  :    int    						=      name.replace("""conv_first"""     ,   """first_convolution"""		)

      if (
          "upsample" in name
          or "conv_before_upsample" in name
          or "conv_bicubic" in name
          or "conv_up" in name
          or "conv_hr" in name
          or "conv_last" in name
          or "aux" in name
      ):
            # heads
            if "conv_last" in name:
                  snake_case__  :    str    						=      name.replace("""conv_last"""     ,   """final_convolution"""		)
            if config.upsampler in ["pixelshuffle", "pixelshuffle_aux", "nearest+conv"]:
                  if "conv_before_upsample.0" in name:
                        snake_case__  :    List[Any]    						=      name.replace("""conv_before_upsample.0"""     ,   """conv_before_upsample"""		)
                  if "upsample.0" in name:
                        snake_case__  :    List[str]    						=      name.replace("""upsample.0"""     ,   """upsample.convolution_0"""		)
                  if "upsample.2" in name:
                        snake_case__  :    int    						=      name.replace("""upsample.2"""     ,   """upsample.convolution_1"""		)
                  snake_case__  :    Dict    						=      """upsample.""" + name
            elif config.upsampler == "pixelshuffledirect":
                  snake_case__  :    str    						=      name.replace("""upsample.0.weight"""     ,   """upsample.conv.weight"""		)
                  snake_case__  :    Optional[Any]    						=      name.replace("""upsample.0.bias"""     ,   """upsample.conv.bias"""		)
            else:
                  pass
      else:
            snake_case__  :    Any    						=      """swin2sr.""" + name

      return name





def 						__snake_case(				_lowerCAmelCase     ,   _lowerCAmelCase		)		-> Union[str, Any]:
      for key in orig_state_dict.copy().keys():
            snake_case__  :    Tuple    						=      orig_state_dict.pop(_lowerCAmelCase		)

            if "qkv" in key:
                  snake_case__  :    Optional[int]    						=      key.split("""."""		)
                  snake_case__  :    Tuple    						=      int(key_split[1]		)
                  snake_case__  :    List[Any]    						=      int(key_split[4]		)
                  snake_case__  :    List[Any]    						=      config.embed_dim

                  if "weight" in key:
                        snake_case__  :    Any    						=      val[:dim, :]
                        snake_case__  :    Dict    						=      val[dim : dim * 2, :]
                        snake_case__  :    Any    						=      val[-dim:, :]
                  else:
                        snake_case__  :    str    						=      val[:dim]
                        snake_case__  :    int    						=      val[dim : dim * 2]
                        snake_case__  :    List[Any]    						=      val[-dim:]
                  pass
            else:
                  snake_case__  :    Tuple    						=      val

      return orig_state_dict





def 						__snake_case(				_lowerCAmelCase     ,   _lowerCAmelCase     ,   _lowerCAmelCase		)		-> List[Any]:
      snake_case__  :    Tuple    						=      get_config(_lowerCAmelCase		)
      snake_case__  :    List[Any]    						=      SwinaSRForImageSuperResolution(_lowerCAmelCase		)
      model.eval()

      snake_case__  :    Union[str, Any]    						=      torch.hub.load_state_dict_from_url(_lowerCAmelCase     ,   map_location="""cpu"""		)
      snake_case__  :    Optional[int]    						=      convert_state_dict(_lowerCAmelCase     ,   _lowerCAmelCase		)
      snake_case__						,				snake_case__  :    Union[str, Any]    						=      model.load_state_dict(_lowerCAmelCase     ,   strict=_lowerCAmelCase		)

      if len(_lowerCAmelCase		) > 0:
            raise ValueError("""Missing keys when converting: {}""".format(_lowerCAmelCase		)		)
      for key in unexpected_keys:
            if not ("relative_position_index" in key or "relative_coords_table" in key or "self_mask" in key):
                  raise ValueError(f"Unexpected key {key} in state_dict"		)

    # verify values
      snake_case__  :    Optional[Any]    						=      """https://github.com/mv-lab/swin2sr/blob/main/testsets/real-inputs/shanghai.jpg?raw=true"""
      snake_case__  :    List[Any]    						=      Image.open(requests.get(_lowerCAmelCase     ,   stream=_lowerCAmelCase		).raw		).convert("""RGB"""		)
      snake_case__  :    Tuple    						=      SwinaSRImageProcessor()
      # pixel_values = processor(image, return_tensors="pt").pixel_values

      snake_case__  :    List[Any]    						=      126 if """Jpeg""" in checkpoint_url else 256
      snake_case__  :    Tuple    						=      Compose(
          [
              Resize((image_size, image_size)		),
              ToTensor(),
              Normalize(mean=[0.485, 0.456, 0.406]     ,   std=[0.229, 0.224, 0.225]		),
          ]		)
      snake_case__  :    Union[str, Any]    						=      transforms(_lowerCAmelCase		).unsqueeze(0		)

      if config.num_channels == 1:
            snake_case__  :    List[Any]    						=      pixel_values[:, 0, :, :].unsqueeze(1		)

      snake_case__  :    Optional[int]    						=      model(_lowerCAmelCase		)

      # assert values
      if "Swin2SR_ClassicalSR_X2_64" in checkpoint_url:
            snake_case__  :    Optional[Any]    						=      torch.Size([1, 3, 512, 512]		)
            snake_case__  :    Any    						=      torch.tensor(
                [[-0.7087, -0.7138, -0.6721], [-0.8340, -0.8095, -0.7298], [-0.9149, -0.8414, -0.7940]]		)
      elif "Swin2SR_ClassicalSR_X4_64" in checkpoint_url:
            snake_case__  :    List[str]    						=      torch.Size([1, 3, 1_024, 1_024]		)
            snake_case__  :    Any    						=      torch.tensor(
                [[-0.7775, -0.8105, -0.8933], [-0.7764, -0.8356, -0.9225], [-0.7976, -0.8686, -0.9579]]		)
      elif "Swin2SR_CompressedSR_X4_48" in checkpoint_url:
            # TODO values didn't match exactly here
            snake_case__  :    List[Any]    						=      torch.Size([1, 3, 1_024, 1_024]		)
            snake_case__  :    Tuple    						=      torch.tensor(
                [[-0.8035, -0.7504, -0.7491], [-0.8538, -0.8124, -0.7782], [-0.8804, -0.8651, -0.8493]]		)
      elif "Swin2SR_Lightweight_X2_64" in checkpoint_url:
            snake_case__  :    Any    						=      torch.Size([1, 3, 512, 512]		)
            snake_case__  :    Dict    						=      torch.tensor(
                [[-0.7669, -0.8662, -0.8767], [-0.8810, -0.9962, -0.9820], [-0.9340, -1.0322, -1.1149]]		)
      elif "Swin2SR_RealworldSR_X4_64_BSRGAN_PSNR" in checkpoint_url:
            snake_case__  :    Union[str, Any]    						=      torch.Size([1, 3, 1_024, 1_024]		)
            snake_case__  :    Optional[Any]    						=      torch.tensor(
                [[-0.5238, -0.5557, -0.6321], [-0.6016, -0.5903, -0.6391], [-0.6244, -0.6334, -0.6889]]		)

      assert (
          outputs.reconstruction.shape == expected_shape
      ), f"Shape of reconstruction should be {expected_shape}, but is {outputs.reconstruction.shape}"
      assert torch.allclose(outputs.reconstruction[0, 0, :3, :3]     ,   _lowerCAmelCase     ,   atol=1e-3		)
      print("""Looks ok!"""		)

      snake_case__  :    Optional[Any]    						=      {
          """https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_ClassicalSR_X2_64.pth""": (
              """swin2SR-classical-sr-x2-64"""
          ),
          """https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_ClassicalSR_X4_64.pth""": (
              """swin2SR-classical-sr-x4-64"""
          ),
          """https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_CompressedSR_X4_48.pth""": (
              """swin2SR-compressed-sr-x4-48"""
          ),
          """https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_Lightweight_X2_64.pth""": (
              """swin2SR-lightweight-x2-64"""
          ),
          """https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_RealworldSR_X4_64_BSRGAN_PSNR.pth""": (
              """swin2SR-realworld-sr-x4-64-bsrgan-psnr"""
          ),
      }
      snake_case__  :    Union[str, Any]    						=      url_to_name[checkpoint_url]

      if pytorch_dump_folder_path is not None:
            print(f"Saving model {model_name} to {pytorch_dump_folder_path}"		)
            model.save_pretrained(_lowerCAmelCase		)
            print(f"Saving image processor to {pytorch_dump_folder_path}"		)
            processor.save_pretrained(_lowerCAmelCase		)

      if push_to_hub:
            model.push_to_hub(f"caidas/{model_name}"		)
            processor.push_to_hub(f"caidas/{model_name}"		)


if __name__ == "__main__":
       __a          =		argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           "--checkpoint_url",
           default="https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_ClassicalSR_X2_64.pth",
           type=str,
           help="URL of the original Swin2SR checkpoint you'd like to convert.",
       )
       parser.add_argument(
           "--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
       )
       parser.add_argument("--push_to_hub", action="store_true", help="Whether to push the converted model to the hub.")

       __a          =		parser.parse_args()
       convert_swinasr_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)



import gc
import unittest

import numpy as np
import torch
from torch.backends.cuda import sdp_kernel

from diffusers import (
    CMStochasticIterativeScheduler,
    ConsistencyModelPipeline,
    UNetaDModel,
)
from diffusers.utils import randn_tensor, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_a, require_torch_gpu

from ..pipeline_params import UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS, UNCONDITIONAL_IMAGE_GENERATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()







class   __lowercase      (_UpperCAmelCase		,   unittest.TestCase ):
 _UpperCamelCase			  =  ConsistencyModelPipeline
 _UpperCamelCase			  =  UNCONDITIONAL_IMAGE_GENERATION_PARAMS
 _UpperCamelCase			  =  UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS

 # Override required_optional_params to remove num_images_per_prompt
 _UpperCamelCase			  =  frozenset(
     [
         """num_inference_steps""",
         """generator""",
         """latents""",
         """output_type""",
         """return_dict""",
         """callback""",
         """callback_steps""",
     ] )
 @property
 def 	UpperCamelCase__  (							self					)					->List[str]:



       '''simple docstring'''





       __lowerCAmelCase					:						List[Any]					=					UNetaDModel.from_pretrained(
           '''diffusers/consistency-models-test'''      ,							subfolder='''test_unet'''      ,							)
       return unet
 @property
 def 	UpperCamelCase__  (							self					)					->Optional[int]:



       '''simple docstring'''





       __lowerCAmelCase					:						List[str]					=					UNetaDModel.from_pretrained(
           '''diffusers/consistency-models-test'''      ,							subfolder='''test_unet_class_cond'''      ,							)
       return unet
 def 	UpperCamelCase__  (							self      ,							A_=False					)					->Dict:



       '''simple docstring'''





       if class_cond:
             __lowerCAmelCase					:						List[str]					=					self.dummy_cond_unet
       else:
             __lowerCAmelCase					:						Optional[Any]					=					self.dummy_uncond_unet

       # Default to CM multistep sampler
       __lowerCAmelCase					:						List[str]					=					CMStochasticIterativeScheduler(
           num_train_timesteps=40      ,							sigma_min=0.002      ,							sigma_max=80.0      ,							)

       __lowerCAmelCase					:						Dict					=					{
           '''unet''': unet,
           '''scheduler''': scheduler,
       }

       return components
 def 	UpperCamelCase__  (							self      ,							A_      ,							A_=0					)					->Tuple:



       '''simple docstring'''





       if str(A_					).startswith('''mps'''					):
             __lowerCAmelCase					:						str					=					torch.manual_seed(A_					)
       else:
             __lowerCAmelCase					:						Dict					=					torch.Generator(device=A_					).manual_seed(A_					)

       __lowerCAmelCase					:						Tuple					=					{
           '''batch_size''': 1,
           '''num_inference_steps''': None,
           '''timesteps''': [22, 0],
           '''generator''': generator,
           '''output_type''': '''np''',
       }

       return inputs
 def 	UpperCamelCase__  (							self					)					->Tuple:



       '''simple docstring'''





       __lowerCAmelCase					:						Union[str, Any]					=					'''cpu'''  # ensure determinism for the device-dependent torch.Generator
       __lowerCAmelCase					:						Tuple					=					self.get_dummy_components()
       __lowerCAmelCase					:						Union[str, Any]					=					ConsistencyModelPipeline(**A_					)
       __lowerCAmelCase					:						List[str]					=					pipe.to(A_					)
       pipe.set_progress_bar_config(disable=A_					)

       __lowerCAmelCase					:						Any					=					self.get_dummy_inputs(A_					)
       __lowerCAmelCase					:						int					=					pipe(**A_					).images
       assert image.shape == (1, 32, 32, 3)

       __lowerCAmelCase					:						Tuple					=					image[0, -3:, -3:, -1]
       __lowerCAmelCase					:						str					=					np.array([0.3_572, 0.6_273, 0.4_031, 0.3_961, 0.4_321, 0.5_730, 0.5_266, 0.4_780, 0.5_004]					)

       assert np.abs(image_slice.flatten() - expected_slice					).max() < 1e-3
 def 	UpperCamelCase__  (							self					)					->Tuple:



       '''simple docstring'''





       __lowerCAmelCase					:						List[Any]					=					'''cpu'''  # ensure determinism for the device-dependent torch.Generator
       __lowerCAmelCase					:						str					=					self.get_dummy_components(class_cond=A_					)
       __lowerCAmelCase					:						List[str]					=					ConsistencyModelPipeline(**A_					)
       __lowerCAmelCase					:						List[Any]					=					pipe.to(A_					)
       pipe.set_progress_bar_config(disable=A_					)

       __lowerCAmelCase					:						Optional[Any]					=					self.get_dummy_inputs(A_					)
       __lowerCAmelCase					:						Optional[int]					=					0
       __lowerCAmelCase					:						int					=					pipe(**A_					).images
       assert image.shape == (1, 32, 32, 3)

       __lowerCAmelCase					:						Optional[int]					=					image[0, -3:, -3:, -1]
       __lowerCAmelCase					:						List[str]					=					np.array([0.3_572, 0.6_273, 0.4_031, 0.3_961, 0.4_321, 0.5_730, 0.5_266, 0.4_780, 0.5_004]					)

       assert np.abs(image_slice.flatten() - expected_slice					).max() < 1e-3
 def 	UpperCamelCase__  (							self					)					->Optional[Any]:



       '''simple docstring'''





       __lowerCAmelCase					:						Any					=					'''cpu'''  # ensure determinism for the device-dependent torch.Generator
       __lowerCAmelCase					:						Union[str, Any]					=					self.get_dummy_components()
       __lowerCAmelCase					:						List[Any]					=					ConsistencyModelPipeline(**A_					)
       __lowerCAmelCase					:						int					=					pipe.to(A_					)
       pipe.set_progress_bar_config(disable=A_					)

       __lowerCAmelCase					:						Tuple					=					self.get_dummy_inputs(A_					)
       __lowerCAmelCase					:						Any					=					1
       __lowerCAmelCase					:						List[Any]					=					None
       __lowerCAmelCase					:						Dict					=					pipe(**A_					).images
       assert image.shape == (1, 32, 32, 3)

       __lowerCAmelCase					:						Any					=					image[0, -3:, -3:, -1]
       __lowerCAmelCase					:						List[Any]					=					np.array([0.5_004, 0.5_004, 0.4_994, 0.5_008, 0.4_976, 0.5_018, 0.4_990, 0.4_982, 0.4_987]					)

       assert np.abs(image_slice.flatten() - expected_slice					).max() < 1e-3





 def 	UpperCamelCase__  (							self					)					->Optional[Any]:



       '''simple docstring'''





       __lowerCAmelCase					:						List[Any]					=					'''cpu'''  # ensure determinism for the device-dependent torch.Generator
       __lowerCAmelCase					:						Optional[Any]					=					self.get_dummy_components(class_cond=A_					)
       __lowerCAmelCase					:						List[str]					=					ConsistencyModelPipeline(**A_					)
       __lowerCAmelCase					:						Union[str, Any]					=					pipe.to(A_					)
       pipe.set_progress_bar_config(disable=A_					)

       __lowerCAmelCase					:						Any					=					self.get_dummy_inputs(A_					)
       __lowerCAmelCase					:						List[str]					=					1
       __lowerCAmelCase					:						Dict					=					None
       __lowerCAmelCase					:						Tuple					=					0
       __lowerCAmelCase					:						Dict					=					pipe(**A_					).images
       assert image.shape == (1, 32, 32, 3)

       __lowerCAmelCase					:						List[Any]					=					image[0, -3:, -3:, -1]
       __lowerCAmelCase					:						Any					=					np.array([0.5_004, 0.5_004, 0.4_994, 0.5_008, 0.4_976, 0.5_018, 0.4_990, 0.4_982, 0.4_987]					)

       assert np.abs(image_slice.flatten() - expected_slice					).max() < 1e-3







@slow
@require_torch_gpu
class   __lowercase      (unittest.TestCase ):
 def 	UpperCamelCase__  (							self					)					->int:



       '''simple docstring'''





       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()
 def 	UpperCamelCase__  (							self      ,							A_=0      ,							A_=False      ,							A_="cpu"      ,							A_=torch.floataa      ,							A_=(1, 3, 64, 64)					)					->str:



       '''simple docstring'''





       __lowerCAmelCase					:						Dict					=					torch.manual_seed(A_					)

       __lowerCAmelCase					:						Tuple					=					{
           '''num_inference_steps''': None,
           '''timesteps''': [22, 0],
           '''class_labels''': 0,
           '''generator''': generator,
           '''output_type''': '''np''',
       }

       if get_fixed_latents:
             __lowerCAmelCase					:						List[str]					=					self.get_fixed_latents(seed=A_      ,							device=A_      ,							dtype=A_      ,							shape=A_					)
             __lowerCAmelCase					:						Union[str, Any]					=					latents

       return inputs
 def 	UpperCamelCase__  (							self      ,							A_=0      ,							A_="cpu"      ,							A_=torch.floataa      ,							A_=(1, 3, 64, 64)					)					->Optional[int]:



       '''simple docstring'''





       if type(A_					) == str:
             __lowerCAmelCase					:						int					=					torch.device(A_					)
       __lowerCAmelCase					:						Optional[Any]					=					torch.Generator(device=A_					).manual_seed(A_					)
       __lowerCAmelCase					:						Union[str, Any]					=					randn_tensor(A_      ,							generator=A_      ,							device=A_      ,							dtype=A_					)
       return latents
 def 	UpperCamelCase__  (							self					)					->Optional[Any]:



       '''simple docstring'''





       __lowerCAmelCase					:						int					=					UNetaDModel.from_pretrained('''diffusers/consistency_models'''      ,							subfolder='''diffusers_cd_imagenet64_l2'''					)
       __lowerCAmelCase					:						List[Any]					=					CMStochasticIterativeScheduler(
           num_train_timesteps=40      ,							sigma_min=0.002      ,							sigma_max=80.0      ,							)
       __lowerCAmelCase					:						List[str]					=					ConsistencyModelPipeline(unet=A_      ,							scheduler=A_					)
       pipe.to(torch_device=A_					)
       pipe.set_progress_bar_config(disable=A_					)

       __lowerCAmelCase					:						str					=					self.get_inputs()
       __lowerCAmelCase					:						Any					=					pipe(**A_					).images
       assert image.shape == (1, 64, 64, 3)

       __lowerCAmelCase					:						Dict					=					image[0, -3:, -3:, -1]

       __lowerCAmelCase					:						Optional[int]					=					np.array([0.0_888, 0.0_881, 0.0_666, 0.0_479, 0.0_292, 0.0_195, 0.0_201, 0.0_163, 0.0_254]					)

       assert np.abs(image_slice.flatten() - expected_slice					).max() < 2e-2
 def 	UpperCamelCase__  (							self					)					->Any:



       '''simple docstring'''





       __lowerCAmelCase					:						int					=					UNetaDModel.from_pretrained('''diffusers/consistency_models'''      ,							subfolder='''diffusers_cd_imagenet64_l2'''					)
       __lowerCAmelCase					:						Optional[Any]					=					CMStochasticIterativeScheduler(
           num_train_timesteps=40      ,							sigma_min=0.002      ,							sigma_max=80.0      ,							)
       __lowerCAmelCase					:						List[str]					=					ConsistencyModelPipeline(unet=A_      ,							scheduler=A_					)
       pipe.to(torch_device=A_					)
       pipe.set_progress_bar_config(disable=A_					)

       __lowerCAmelCase					:						List[Any]					=					self.get_inputs()
       __lowerCAmelCase					:						Tuple					=					1
       __lowerCAmelCase					:						Optional[Any]					=					None
       __lowerCAmelCase					:						str					=					pipe(**A_					).images
       assert image.shape == (1, 64, 64, 3)

       __lowerCAmelCase					:						Optional[int]					=					image[0, -3:, -3:, -1]

       __lowerCAmelCase					:						List[Any]					=					np.array([0.0_340, 0.0_152, 0.0_063, 0.0_267, 0.0_221, 0.0_107, 0.0_416, 0.0_186, 0.0_217]					)

       assert np.abs(image_slice.flatten() - expected_slice					).max() < 2e-2
 @require_torch_a
 def 	UpperCamelCase__  (							self					)					->Optional[Any]:



       '''simple docstring'''





       __lowerCAmelCase					:						Tuple					=					UNetaDModel.from_pretrained('''diffusers/consistency_models'''      ,							subfolder='''diffusers_cd_imagenet64_l2'''					)
       __lowerCAmelCase					:						List[str]					=					CMStochasticIterativeScheduler(
           num_train_timesteps=40      ,							sigma_min=0.002      ,							sigma_max=80.0      ,							)
       __lowerCAmelCase					:						Union[str, Any]					=					ConsistencyModelPipeline(unet=A_      ,							scheduler=A_					)
       pipe.to(torch_device=A_      ,							torch_dtype=torch.floataa					)
       pipe.set_progress_bar_config(disable=A_					)

       __lowerCAmelCase					:						Any					=					self.get_inputs(get_fixed_latents=A_      ,							device=A_					)
       # Ensure usage of flash attention in torch 2.0
       with sdp_kernel(enable_flash=A_      ,							enable_math=A_      ,							enable_mem_efficient=A_					):
             __lowerCAmelCase					:						Dict					=					pipe(**A_					).images
       assert image.shape == (1, 64, 64, 3)

       __lowerCAmelCase					:						Dict					=					image[0, -3:, -3:, -1]

       __lowerCAmelCase					:						Optional[int]					=					np.array([0.1_875, 0.1_428, 0.1_289, 0.2_151, 0.2_092, 0.1_477, 0.1_877, 0.1_641, 0.1_353]					)

       assert np.abs(image_slice.flatten() - expected_slice					).max() < 1e-3





 @require_torch_a
 def 	UpperCamelCase__  (							self					)					->List[str]:



       '''simple docstring'''





       __lowerCAmelCase					:						Optional[Any]					=					UNetaDModel.from_pretrained('''diffusers/consistency_models'''      ,							subfolder='''diffusers_cd_imagenet64_l2'''					)
       __lowerCAmelCase					:						List[Any]					=					CMStochasticIterativeScheduler(
           num_train_timesteps=40      ,							sigma_min=0.002      ,							sigma_max=80.0      ,							)
       __lowerCAmelCase					:						Union[str, Any]					=					ConsistencyModelPipeline(unet=A_      ,							scheduler=A_					)
       pipe.to(torch_device=A_      ,							torch_dtype=torch.floataa					)
       pipe.set_progress_bar_config(disable=A_					)

       __lowerCAmelCase					:						Union[str, Any]					=					self.get_inputs(get_fixed_latents=A_      ,							device=A_					)
       __lowerCAmelCase					:						Any					=					1
       __lowerCAmelCase					:						int					=					None
       # Ensure usage of flash attention in torch 2.0
       with sdp_kernel(enable_flash=A_      ,							enable_math=A_      ,							enable_mem_efficient=A_					):
             __lowerCAmelCase					:						int					=					pipe(**A_					).images
       assert image.shape == (1, 64, 64, 3)

       __lowerCAmelCase					:						str					=					image[0, -3:, -3:, -1]

       __lowerCAmelCase					:						Any					=					np.array([0.1_663, 0.1_948, 0.2_275, 0.1_680, 0.1_204, 0.1_245, 0.1_858, 0.1_338, 0.2_095]					)

       assert np.abs(image_slice.flatten() - expected_slice					).max() < 1e-3


