code: 
  
  
  """simple docstring"""
  import gc
  import os
  import tempfile
  import unittest
  
  import torch
  import torch.nn as nn
  import torch.nn.functional as F
  from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
  
  from diffusers import AutoencoderKL, DDIMScheduler, StableDiffusionPipeline, UNetaDConditionModel
  from diffusers.loaders import AttnProcsLayers, LoraLoaderMixin
  from diffusers.models.attention_processor import (
      Attention,
      AttnProcessor,
      AttnProcessor2_0,
      LoRAAttnProcessor,
      LoRAAttnProcessor2_0,
      LoRAXFormersAttnProcessor,
      XFormersAttnProcessor,
  )
  from diffusers.utils import TEXT_ENCODER_ATTN_MODULE, floats_tensor, torch_device
  
  def 							_snake_case			(      __snake_case       :			nn.Module      ):
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     _lowerCamelCase  :				Tuple      =		{}
     for name in unet.attn_processors.keys():
        _lowerCamelCase  :				Tuple      =		None if name.endswith("""attn1.processor"""      ) else unet.config.cross_attention_dim
        if name.startswith("""mid_block"""      ):
           _lowerCamelCase  :				Optional[int]      =		unet.config.block_out_channels[-1]
        elif name.startswith("""up_blocks"""      ):
           _lowerCamelCase  :				Optional[Any]      =		int(name[len("""up_blocks."""      )]      )
           _lowerCamelCase  :				List[Any]      =		list(reversed(unet.config.block_out_channels      )      )[block_id]
        elif name.startswith("""down_blocks"""      ):
           _lowerCamelCase  :				List[str]      =		int(name[len("""down_blocks."""      )]      )
           _lowerCamelCase  :				List[str]      =		unet.config.block_out_channels[block_id]
        _lowerCamelCase  :				Dict      =		(
            LoRAAttnProcessor2_0 if hasattr(__snake_case	,   """scaled_dot_product_attention"""      ) else LoRAAttnProcessor
        )
        _lowerCamelCase  :				Union[str, Any]      =		lora_attn_processor_class(
            hidden_size=__snake_case	,   cross_attention_dim=__snake_case      )
     _lowerCamelCase  :				Optional[Any]      =		AttnProcsLayers(__snake_case      )
     return lora_attn_procs, unet_lora_layers
  
  def 							_snake_case			(      __snake_case       :			nn.Module      ):
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     _lowerCamelCase  :				Dict      =		{}
     _lowerCamelCase  :				Union[str, Any]      =		(
         LoRAAttnProcessor2_0 if hasattr(__snake_case	,   """scaled_dot_product_attention"""      ) else LoRAAttnProcessor
     )
     for name, module in text_encoder.named_modules():
        if name.endswith(__snake_case      ):
           _lowerCamelCase  :				Tuple      =		lora_attn_processor_class(
               hidden_size=module.out_proj.out_features	,   cross_attention_dim=__snake_case      )
     return text_lora_attn_procs
  
  def 							_snake_case			(      __snake_case       :			nn.Module      ):
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     _lowerCamelCase  :				Tuple      =		create_text_encoder_lora_attn_procs(__snake_case      )
     _lowerCamelCase  :				Optional[Any]      =		AttnProcsLayers(__snake_case      )
     return text_encoder_lora_layers
  
  def 							_snake_case			(      __snake_case       :			List[Any]	,   __snake_case       :			List[Any]=False      ):
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     for _, attn_proc in text_lora_attn_procs.items():
        # set up.weights
        for layer_name, layer_module in attn_proc.named_modules():
           if layer_name.endswith("""_lora"""      ):
              _lowerCamelCase  :				Optional[Any]      =		(
                  torch.randn_like(layer_module.up.weight      )
                  if randn_weight
                  else torch.zeros_like(layer_module.up.weight      )
              )
              _lowerCamelCase  :				Tuple      =		torch.nn.Parameter(__snake_case      )
  
  
  
  
  
  class   lowercase__							(				unittest.TestCase							):
  
  
  
  
  
     def 		UpperCamelCase_     (       self)     ->     Union[str, Any]:
        torch.manual_seed(0)
        _lowerCamelCase  :				List[str]      =		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  :				Any      =		DDIMScheduler(
            beta_start=0.0_00_85			,      beta_end=0.0_12			,      beta_schedule="""scaled_linear"""			,      clip_sample=SCREAMING_SNAKE_CASE			,      set_alpha_to_one=SCREAMING_SNAKE_CASE			,      steps_offset=1			,      )
        torch.manual_seed(0)
        _lowerCamelCase  :				Optional[Any]      =		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			,      )
        _lowerCamelCase  :				str      =		CLIPTextConfig(
            bos_token_id=0			,      eos_token_id=2			,      hidden_size=32			,      intermediate_size=37			,      layer_norm_eps=1e-0_5			,      num_attention_heads=4			,      num_hidden_layers=5			,      pad_token_id=1			,      vocab_size=1000			,      )
        _lowerCamelCase  :				List[str]      =		CLIPTextModel(SCREAMING_SNAKE_CASE)
        _lowerCamelCase  :				List[Any]      =		CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip""")
  
        _lowerCamelCase      ,						_lowerCamelCase  :				List[str]      =		create_unet_lora_layers(SCREAMING_SNAKE_CASE)
        _lowerCamelCase  :				Optional[int]      =		create_text_encoder_lora_layers(SCREAMING_SNAKE_CASE)
  
        _lowerCamelCase  :				int      =		{
            """unet""": unet,
            """scheduler""": scheduler,
            """vae""": vae,
            """text_encoder""": text_encoder,
            """tokenizer""": tokenizer,
            """safety_checker""": None,
            """feature_extractor""": None,
        }
        _lowerCamelCase  :				Union[str, Any]      =		{
            """unet_lora_layers""": unet_lora_layers,
            """text_encoder_lora_layers""": text_encoder_lora_layers,
            """unet_lora_attn_procs""": unet_lora_attn_procs,
        }
        return pipeline_components, lora_components
  
  
  
  
  
     def 		UpperCamelCase_     (       self)     ->     Union[str, Any]:
        _lowerCamelCase  :				List[Any]      =		1
        _lowerCamelCase  :				Optional[Any]      =		10
        _lowerCamelCase  :				int      =		4
        _lowerCamelCase  :				Tuple      =		(32, 32)
  
        _lowerCamelCase  :				Optional[Any]      =		torch.manual_seed(0)
        _lowerCamelCase  :				Any      =		floats_tensor((batch_size, num_channels) + sizes)
        _lowerCamelCase  :				Dict      =		torch.randint(1			,      SCREAMING_SNAKE_CASE			,      size=(batch_size, sequence_length)			,      generator=SCREAMING_SNAKE_CASE)
  
        _lowerCamelCase  :				Dict      =		{
            """prompt""": """A painting of a squirrel eating a burger""",
            """generator""": generator,
            """num_inference_steps""": 2,
            """guidance_scale""": 6.0,
            """output_type""": """numpy""",
        }
  
        return noise, input_ids, pipeline_inputs
  
        # copied from: https://colab.research.google.com/gist/sayakpaul/df2ef6e1ae6d8c10a49d859883b10860/scratchpad.ipynb
  
  
  
  
  
     def 		UpperCamelCase_     (       self)     ->     List[str]:
        _lowerCamelCase  :				Optional[Any]      =		77
  
        _lowerCamelCase  :				Tuple      =		torch.randint(2			,      56			,      size=(1, max_seq_length)			,      generator=torch.manual_seed(0))
  
        _lowerCamelCase  :				Dict      =		{}
        _lowerCamelCase  :				str      =		inputs
        return prepared_inputs
  
  
  
  
  
     def 		UpperCamelCase_     (       self			,      SCREAMING_SNAKE_CASE)     ->     Optional[Any]:
        _lowerCamelCase      ,						_lowerCamelCase  :				int      =		self.get_dummy_components()
        LoraLoaderMixin.save_lora_weights(
            save_directory=SCREAMING_SNAKE_CASE			,      unet_lora_layers=lora_components["""unet_lora_layers"""]			,      text_encoder_lora_layers=lora_components["""text_encoder_lora_layers"""]			,      )
        self.assertTrue(os.path.isfile(os.path.join(SCREAMING_SNAKE_CASE			,      """pytorch_lora_weights.bin""")))
  
  
  
  
  
     def 		UpperCamelCase_     (       self)     ->     Tuple:
        _lowerCamelCase      ,						_lowerCamelCase  :				int      =		self.get_dummy_components()
        _lowerCamelCase  :				List[Any]      =		StableDiffusionPipeline(**SCREAMING_SNAKE_CASE)
        _lowerCamelCase  :				List[str]      =		sd_pipe.to(SCREAMING_SNAKE_CASE)
        sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE)
  
        _lowerCamelCase      ,						_lowerCamelCase      ,						_lowerCamelCase  :				str      =		self.get_dummy_inputs()
  
        _lowerCamelCase  :				str      =		sd_pipe(**SCREAMING_SNAKE_CASE).images
        _lowerCamelCase  :				List[str]      =		original_images[0, -3:, -3:, -1]
  
        with tempfile.TemporaryDirectory() as tmpdirname:
           LoraLoaderMixin.save_lora_weights(
               save_directory=SCREAMING_SNAKE_CASE			,      unet_lora_layers=lora_components["""unet_lora_layers"""]			,      text_encoder_lora_layers=lora_components["""text_encoder_lora_layers"""]			,      )
           self.assertTrue(os.path.isfile(os.path.join(SCREAMING_SNAKE_CASE			,      """pytorch_lora_weights.bin""")))
           sd_pipe.load_lora_weights(SCREAMING_SNAKE_CASE)
  
        _lowerCamelCase  :				Union[str, Any]      =		sd_pipe(**SCREAMING_SNAKE_CASE).images
        _lowerCamelCase  :				List[Any]      =		lora_images[0, -3:, -3:, -1]
  
        # Outputs shouldn't match.
        self.assertFalse(torch.allclose(torch.from_numpy(SCREAMING_SNAKE_CASE)			,      torch.from_numpy(SCREAMING_SNAKE_CASE)))
  
  
  
  
  
     def 		UpperCamelCase_     (       self)     ->     List[str]:
        _lowerCamelCase      ,						_lowerCamelCase  :				str      =		self.get_dummy_components()
        _lowerCamelCase  :				Union[str, Any]      =		StableDiffusionPipeline(**SCREAMING_SNAKE_CASE)
        _lowerCamelCase  :				Any      =		sd_pipe.to(SCREAMING_SNAKE_CASE)
        sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE)
  
        _lowerCamelCase      ,						_lowerCamelCase      ,						_lowerCamelCase  :				List[str]      =		self.get_dummy_inputs()
  
        _lowerCamelCase  :				Tuple      =		sd_pipe(**SCREAMING_SNAKE_CASE).images
        _lowerCamelCase  :				Tuple      =		original_images[0, -3:, -3:, -1]
  
        with tempfile.TemporaryDirectory() as tmpdirname:
           LoraLoaderMixin.save_lora_weights(
               save_directory=SCREAMING_SNAKE_CASE			,      unet_lora_layers=lora_components["""unet_lora_layers"""]			,      text_encoder_lora_layers=lora_components["""text_encoder_lora_layers"""]			,      safe_serialization=SCREAMING_SNAKE_CASE			,      )
           self.assertTrue(os.path.isfile(os.path.join(SCREAMING_SNAKE_CASE			,      """pytorch_lora_weights.safetensors""")))
           sd_pipe.load_lora_weights(SCREAMING_SNAKE_CASE)
  
        _lowerCamelCase  :				List[str]      =		sd_pipe(**SCREAMING_SNAKE_CASE).images
        _lowerCamelCase  :				List[str]      =		lora_images[0, -3:, -3:, -1]
  
        # Outputs shouldn't match.
        self.assertFalse(torch.allclose(torch.from_numpy(SCREAMING_SNAKE_CASE)			,      torch.from_numpy(SCREAMING_SNAKE_CASE)))
  
  
  
  
  
     def 		UpperCamelCase_     (       self)     ->     List[str]:
        _lowerCamelCase      ,						_lowerCamelCase  :				List[str]      =		self.get_dummy_components()
        _lowerCamelCase  :				Any      =		lora_components["""unet_lora_attn_procs"""]
        _lowerCamelCase  :				Optional[Any]      =		StableDiffusionPipeline(**SCREAMING_SNAKE_CASE)
        _lowerCamelCase  :				Union[str, Any]      =		sd_pipe.to(SCREAMING_SNAKE_CASE)
        sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE)
  
        _lowerCamelCase      ,						_lowerCamelCase      ,						_lowerCamelCase  :				Union[str, Any]      =		self.get_dummy_inputs()
  
        _lowerCamelCase  :				Optional[int]      =		sd_pipe(**SCREAMING_SNAKE_CASE).images
        _lowerCamelCase  :				Tuple      =		original_images[0, -3:, -3:, -1]
  
        with tempfile.TemporaryDirectory() as tmpdirname:
           _lowerCamelCase  :				Dict      =		sd_pipe.unet
           unet.set_attn_processor(SCREAMING_SNAKE_CASE)
           unet.save_attn_procs(SCREAMING_SNAKE_CASE)
           self.assertTrue(os.path.isfile(os.path.join(SCREAMING_SNAKE_CASE			,      """pytorch_lora_weights.bin""")))
           sd_pipe.load_lora_weights(SCREAMING_SNAKE_CASE)
  
        _lowerCamelCase  :				Optional[int]      =		sd_pipe(**SCREAMING_SNAKE_CASE).images
        _lowerCamelCase  :				Union[str, Any]      =		lora_images[0, -3:, -3:, -1]
  
        # Outputs shouldn't match.
        self.assertFalse(torch.allclose(torch.from_numpy(SCREAMING_SNAKE_CASE)			,      torch.from_numpy(SCREAMING_SNAKE_CASE)))
  
  
  
  
  
     def 		UpperCamelCase_     (       self)     ->     str:
        _lowerCamelCase      ,						_lowerCamelCase  :				Union[str, Any]      =		self.get_dummy_components()
        _lowerCamelCase  :				List[str]      =		StableDiffusionPipeline(**SCREAMING_SNAKE_CASE)
  
        _lowerCamelCase  :				Dict      =		self.get_dummy_tokens()
  
        # inference without lora
        _lowerCamelCase  :				Dict      =		pipe.text_encoder(**SCREAMING_SNAKE_CASE)[0]
        assert outputs_without_lora.shape == (1, 77, 32)
  
        # create lora_attn_procs with zeroed out up.weights
        _lowerCamelCase  :				Optional[Any]      =		create_text_encoder_lora_attn_procs(pipe.text_encoder)
        set_lora_up_weights(SCREAMING_SNAKE_CASE			,      randn_weight=SCREAMING_SNAKE_CASE)
  
        # monkey patch
        pipe._modify_text_encoder(SCREAMING_SNAKE_CASE)
  
        # verify that it's okay to release the text_attn_procs which holds the LoRAAttnProcessor.
        del text_attn_procs
        gc.collect()
  
        # inference with lora
        _lowerCamelCase  :				List[str]      =		pipe.text_encoder(**SCREAMING_SNAKE_CASE)[0]
        assert outputs_with_lora.shape == (1, 77, 32)
  
        assert torch.allclose(
            SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE), "lora_up_weight are all zero, so the lora outputs should be the same to without lora outputs"
  
        # create lora_attn_procs with randn up.weights
        _lowerCamelCase  :				Any      =		create_text_encoder_lora_attn_procs(pipe.text_encoder)
        set_lora_up_weights(SCREAMING_SNAKE_CASE			,      randn_weight=SCREAMING_SNAKE_CASE)
  
        # monkey patch
        pipe._modify_text_encoder(SCREAMING_SNAKE_CASE)
  
        # verify that it's okay to release the text_attn_procs which holds the LoRAAttnProcessor.
        del text_attn_procs
        gc.collect()
  
        # inference with lora
        _lowerCamelCase  :				Dict      =		pipe.text_encoder(**SCREAMING_SNAKE_CASE)[0]
        assert outputs_with_lora.shape == (1, 77, 32)
  
        assert not torch.allclose(
            SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE), "lora_up_weight are not zero, so the lora outputs should be different to without lora outputs"
  
  
  
  
  
     def 		UpperCamelCase_     (       self)     ->     Optional[int]:
        _lowerCamelCase      ,						_lowerCamelCase  :				Union[str, Any]      =		self.get_dummy_components()
        _lowerCamelCase  :				Dict      =		StableDiffusionPipeline(**SCREAMING_SNAKE_CASE)
  
        _lowerCamelCase  :				Optional[Any]      =		self.get_dummy_tokens()
  
        # inference without lora
        _lowerCamelCase  :				Dict      =		pipe.text_encoder(**SCREAMING_SNAKE_CASE)[0]
        assert outputs_without_lora.shape == (1, 77, 32)
  
        # create lora_attn_procs with randn up.weights
        _lowerCamelCase  :				Any      =		create_text_encoder_lora_attn_procs(pipe.text_encoder)
        set_lora_up_weights(SCREAMING_SNAKE_CASE			,      randn_weight=SCREAMING_SNAKE_CASE)
  
        # monkey patch
        pipe._modify_text_encoder(SCREAMING_SNAKE_CASE)
  
        # verify that it's okay to release the text_attn_procs which holds the LoRAAttnProcessor.
        del text_attn_procs
        gc.collect()
  
        # inference with lora
        _lowerCamelCase  :				List[str]      =		pipe.text_encoder(**SCREAMING_SNAKE_CASE)[0]
        assert outputs_with_lora.shape == (1, 77, 32)
  
        assert not torch.allclose(
            SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE), "lora outputs should be different to without lora outputs"
  
        # remove monkey patch
        pipe._remove_text_encoder_monkey_patch()
  
        # inference with removed lora
        _lowerCamelCase  :				Any      =		pipe.text_encoder(**SCREAMING_SNAKE_CASE)[0]
        assert outputs_without_lora_removed.shape == (1, 77, 32)
  
        assert torch.allclose(
            SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE), "remove lora monkey patch should restore the original outputs"
  
  
  
  
  
     def 		UpperCamelCase_     (       self)     ->     str:
        _lowerCamelCase      ,						_lowerCamelCase  :				List[Any]      =		self.get_dummy_components()
        _lowerCamelCase  :				Union[str, Any]      =		StableDiffusionPipeline(**SCREAMING_SNAKE_CASE)
        _lowerCamelCase  :				List[Any]      =		sd_pipe.to(SCREAMING_SNAKE_CASE)
        sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE)
  
        _lowerCamelCase      ,						_lowerCamelCase      ,						_lowerCamelCase  :				List[str]      =		self.get_dummy_inputs()
  
        with tempfile.TemporaryDirectory() as tmpdirname:
           LoraLoaderMixin.save_lora_weights(
               save_directory=SCREAMING_SNAKE_CASE			,      unet_lora_layers=lora_components["""unet_lora_layers"""]			,      text_encoder_lora_layers=lora_components["""text_encoder_lora_layers"""]			,      )
           self.assertTrue(os.path.isfile(os.path.join(SCREAMING_SNAKE_CASE			,      """pytorch_lora_weights.bin""")))
           sd_pipe.load_lora_weights(SCREAMING_SNAKE_CASE)
  
        _lowerCamelCase  :				List[Any]      =		sd_pipe(**SCREAMING_SNAKE_CASE).images
        _lowerCamelCase  :				List[str]      =		lora_images[0, -3:, -3:, -1]
  
        _lowerCamelCase  :				Optional[Any]      =		sd_pipe(**SCREAMING_SNAKE_CASE			,      cross_attention_kwargs={"""scale""": 0.5}).images
        _lowerCamelCase  :				List[str]      =		lora_images_with_scale[0, -3:, -3:, -1]
  
        # Outputs shouldn't match.
        self.assertFalse(
            torch.allclose(torch.from_numpy(SCREAMING_SNAKE_CASE)			,      torch.from_numpy(SCREAMING_SNAKE_CASE)))
  
  
  
  
  
     def 		UpperCamelCase_     (       self)     ->     Optional[int]:
        with tempfile.TemporaryDirectory() as tmpdirname:
           self.create_lora_weight_file(SCREAMING_SNAKE_CASE)
  
           _lowerCamelCase      ,						_lowerCamelCase  :				Union[str, Any]      =		self.get_dummy_components()
           _lowerCamelCase  :				Tuple      =		StableDiffusionPipeline(**SCREAMING_SNAKE_CASE)
           _lowerCamelCase  :				Union[str, Any]      =		sd_pipe.to(SCREAMING_SNAKE_CASE)
           sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE)
  
           # check if vanilla attention processors are used
           for _, module in sd_pipe.unet.named_modules():
              if isinstance(SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE):
                 self.assertIsInstance(module.processor			,      (AttnProcessor, AttnProcessor2_0))
  
              # load LoRA weight file
           sd_pipe.load_lora_weights(SCREAMING_SNAKE_CASE)
  
           # check if lora attention processors are used
           for _, module in sd_pipe.unet.named_modules():
              if isinstance(SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE):
                 _lowerCamelCase  :				int      =		(
                     LoRAAttnProcessor2_0 if hasattr(SCREAMING_SNAKE_CASE			,      """scaled_dot_product_attention""") else LoRAAttnProcessor
                 )
                 self.assertIsInstance(module.processor			,      SCREAMING_SNAKE_CASE)
  
  
  
  
  
     @unittest.skipIf(torch_device != """cuda"""			,      """This test is supposed to run on GPU""")
     def 		UpperCamelCase_     (       self)     ->     Optional[Any]:
        with tempfile.TemporaryDirectory() as tmpdirname:
           self.create_lora_weight_file(SCREAMING_SNAKE_CASE)
  
           _lowerCamelCase      ,						_lowerCamelCase  :				List[Any]      =		self.get_dummy_components()
           _lowerCamelCase  :				List[str]      =		StableDiffusionPipeline(**SCREAMING_SNAKE_CASE)
           _lowerCamelCase  :				str      =		sd_pipe.to(SCREAMING_SNAKE_CASE)
           sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE)
  
           # enable XFormers
           sd_pipe.enable_xformers_memory_efficient_attention()
  
           # check if xFormers attention processors are used
           for _, module in sd_pipe.unet.named_modules():
              if isinstance(SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE):
                 self.assertIsInstance(module.processor			,      SCREAMING_SNAKE_CASE)
  
              # load LoRA weight file
           sd_pipe.load_lora_weights(SCREAMING_SNAKE_CASE)
  
           # check if lora attention processors are used
           for _, module in sd_pipe.unet.named_modules():
              if isinstance(SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE):
                 self.assertIsInstance(module.processor			,      SCREAMING_SNAKE_CASE)
  
  
  
  
  
     @unittest.skipIf(torch_device != """cuda"""			,      """This test is supposed to run on GPU""")
     def 		UpperCamelCase_     (       self)     ->     Optional[int]:
        _lowerCamelCase      ,						_lowerCamelCase  :				Any      =		self.get_dummy_components()
        _lowerCamelCase  :				List[Any]      =		StableDiffusionPipeline(**SCREAMING_SNAKE_CASE)
        _lowerCamelCase  :				int      =		sd_pipe.to(SCREAMING_SNAKE_CASE)
        sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE)
  
        _lowerCamelCase      ,						_lowerCamelCase      ,						_lowerCamelCase  :				List[Any]      =		self.get_dummy_inputs()
  
        # enable XFormers
        sd_pipe.enable_xformers_memory_efficient_attention()
  
        _lowerCamelCase  :				List[str]      =		sd_pipe(**SCREAMING_SNAKE_CASE).images
        _lowerCamelCase  :				int      =		original_images[0, -3:, -3:, -1]
  
        with tempfile.TemporaryDirectory() as tmpdirname:
           LoraLoaderMixin.save_lora_weights(
               save_directory=SCREAMING_SNAKE_CASE			,      unet_lora_layers=lora_components["""unet_lora_layers"""]			,      text_encoder_lora_layers=lora_components["""text_encoder_lora_layers"""]			,      )
           self.assertTrue(os.path.isfile(os.path.join(SCREAMING_SNAKE_CASE			,      """pytorch_lora_weights.bin""")))
           sd_pipe.load_lora_weights(SCREAMING_SNAKE_CASE)
  
        _lowerCamelCase  :				Union[str, Any]      =		sd_pipe(**SCREAMING_SNAKE_CASE).images
        _lowerCamelCase  :				int      =		lora_images[0, -3:, -3:, -1]
  
        # Outputs shouldn't match.
        self.assertFalse(torch.allclose(torch.from_numpy(SCREAMING_SNAKE_CASE)			,      torch.from_numpy(SCREAMING_SNAKE_CASE)))
  
  
style_context: 
  
  
  """simple docstring"""
  import torch
  import trajectory.utils as utils
  
  from transformers import TrajectoryTransformerModel
  
  
  
  
  class   lowercase__							(				utils.Parser							):
     __UpperCAmelCase       					=				"halfcheetah-medium-expert-v2"
     __UpperCAmelCase       					=				"config.offline"
  
  
  
  
  
  
  
  def 							_snake_case			(      __snake_case       :			Any	,   __snake_case       :			Optional[Any]	,   __snake_case       :			Optional[int]	,   __snake_case       :			List[str]	,   __snake_case       :			Union[str, Any]      ):
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     _lowerCamelCase      ,						_lowerCamelCase  :				int      =		utils.load_model(__snake_case	,   __snake_case	,   __snake_case	,   epoch=__snake_case	,   device=__snake_case      )
     _lowerCamelCase  :				List[str]      =		TrajectoryTransformerModel(gpt.config      )
  
     trajectory_transformer.tok_emb.load_state_dict(gpt.tok_emb.state_dict()      )
     _lowerCamelCase  :				str      =		gpt.pos_emb
     trajectory_transformer.drop.load_state_dict(gpt.drop.state_dict()      )
     trajectory_transformer.ln_f.load_state_dict(gpt.ln_f.state_dict()      )
     trajectory_transformer.head.load_state_dict(gpt.head.state_dict()      )
  
     for i, block in enumerate(gpt.blocks      ):
        trajectory_transformer.blocks[i].lna.load_state_dict(gpt.blocks[i].lna.state_dict()      )
        trajectory_transformer.blocks[i].lna.load_state_dict(gpt.blocks[i].lna.state_dict()      )
        trajectory_transformer.blocks[i].attn.load_state_dict(gpt.blocks[i].attn.state_dict()      )
  
        trajectory_transformer.blocks[i].la.load_state_dict(gpt.blocks[i].mlp[0].state_dict()      )
        trajectory_transformer.blocks[i].act.load_state_dict(gpt.blocks[i].mlp[1].state_dict()      )
        trajectory_transformer.blocks[i].la.load_state_dict(gpt.blocks[i].mlp[2].state_dict()      )
        trajectory_transformer.blocks[i].drop.load_state_dict(gpt.blocks[i].mlp[3].state_dict()      )
  
     torch.save(trajectory_transformer.state_dict()	,   """pytorch_model.bin"""      )
  
  
  if __name__ == "__main__":
   UpperCAmelCase   =	Parser().parse_args("""plan""")
   convert_trajectory_transformer_original_pytorch_checkpoint_to_pytorch(
       args.logbase, args.dataset, args.gpt_loadpath, args.gpt_epoch, args.device
   )
  
  