code: 
  '''simple docstring'''
  
  
  
  import argparse
  
  import requests
  import torch
  from PIL import Image
  
  from transformers import (
      CLIPSegConfig,
      CLIPSegForImageSegmentation,
      CLIPSegProcessor,
      CLIPSegTextConfig,
      CLIPSegVisionConfig,
      CLIPTokenizer,
      ViTImageProcessor,
  )
  
  
  
  
  
  def  _A		(							lowercase__	):
  						lowercase__							 =       CLIPSegTextConfig()
  						lowercase__							 =       CLIPSegVisionConfig(patch_size=16	)
  
  						lowercase__							 =       True if """refined""" in model_name else False
  						lowercase__							 =       16 if """rd16""" in model_name else 64
  
  						lowercase__							 =       CLIPSegConfig.from_text_vision_configs(
  						    lowercase__		,		lowercase__		,		use_complex_transposed_convolution=lowercase__		,		reduce_dim=lowercase__		,		)
  						return config
  
  
  
  
  
  def  _A		(							lowercase__	):
  						# update prefixes
  						if "clip_model" in name:
  												lowercase__							 =       name.replace("""clip_model"""		,		"""clip"""	)
  						if "transformer" in name:
  												if "visual" in name:
  																		lowercase__							 =       name.replace("""visual.transformer"""		,		"""vision_model"""	)
  												else:
  																		lowercase__							 =       name.replace("""transformer"""		,		"""text_model"""	)
  						if "resblocks" in name:
  												lowercase__							 =       name.replace("""resblocks"""		,		"""encoder.layers"""	)
  						if "ln_1" in name:
  												lowercase__							 =       name.replace("""ln_1"""		,		"""layer_norm1"""	)
  						if "ln_2" in name:
  												lowercase__							 =       name.replace("""ln_2"""		,		"""layer_norm2"""	)
  						if "c_fc" in name:
  												lowercase__							 =       name.replace("""c_fc"""		,		"""fc1"""	)
  						if "c_proj" in name:
  												lowercase__							 =       name.replace("""c_proj"""		,		"""fc2"""	)
  						if "attn" in name and "self" not in name:
  												lowercase__							 =       name.replace("""attn"""		,		"""self_attn"""	)
  						# text encoder
  						if "token_embedding" in name:
  												lowercase__							 =       name.replace("""token_embedding"""		,		"""text_model.embeddings.token_embedding"""	)
  						if "positional_embedding" in name and "visual" not in name:
  												lowercase__							 =       name.replace("""positional_embedding"""		,		"""text_model.embeddings.position_embedding.weight"""	)
  						if "ln_final" in name:
  												lowercase__							 =       name.replace("""ln_final"""		,		"""text_model.final_layer_norm"""	)
  						# vision encoder
  						if "visual.class_embedding" in name:
  												lowercase__							 =       name.replace("""visual.class_embedding"""		,		"""vision_model.embeddings.class_embedding"""	)
  						if "visual.conv1" in name:
  												lowercase__							 =       name.replace("""visual.conv1"""		,		"""vision_model.embeddings.patch_embedding"""	)
  						if "visual.positional_embedding" in name:
  												lowercase__							 =       name.replace("""visual.positional_embedding"""		,		"""vision_model.embeddings.position_embedding.weight"""	)
  						if "visual.ln_pre" in name:
  												lowercase__							 =       name.replace("""visual.ln_pre"""		,		"""vision_model.pre_layrnorm"""	)
  						if "visual.ln_post" in name:
  												lowercase__							 =       name.replace("""visual.ln_post"""		,		"""vision_model.post_layernorm"""	)
  						# projection layers
  						if "visual.proj" in name:
  												lowercase__							 =       name.replace("""visual.proj"""		,		"""visual_projection.weight"""	)
  						if "text_projection" in name:
  												lowercase__							 =       name.replace("""text_projection"""		,		"""text_projection.weight"""	)
  						# decoder
  						if "trans_conv" in name:
  												lowercase__							 =       name.replace("""trans_conv"""		,		"""transposed_convolution"""	)
  						if "film_mul" in name or "film_add" in name or "reduce" in name or "transposed_convolution" in name:
  												lowercase__							 =       """decoder.""" + name
  						if "blocks" in name:
  												lowercase__							 =       name.replace("""blocks"""		,		"""decoder.layers"""	)
  						if "linear1" in name:
  												lowercase__							 =       name.replace("""linear1"""		,		"""mlp.fc1"""	)
  						if "linear2" in name:
  												lowercase__							 =       name.replace("""linear2"""		,		"""mlp.fc2"""	)
  						if "norm1" in name and "layer_" not in name:
  												lowercase__							 =       name.replace("""norm1"""		,		"""layer_norm1"""	)
  						if "norm2" in name and "layer_" not in name:
  												lowercase__							 =       name.replace("""norm2"""		,		"""layer_norm2"""	)
  
  						return name
  
  
  
  
  
  def  _A		(							lowercase__		,		lowercase__	):
  						for key in orig_state_dict.copy().keys():
  												lowercase__							 =       orig_state_dict.pop(lowercase__	)
  
  												if key.startswith("""clip_model"""	) and "attn.in_proj" in key:
  																		lowercase__							 =       key.split("""."""	)
  																		if "visual" in key:
  																								lowercase__							 =       int(key_split[4]	)
  																								lowercase__							 =       config.vision_config.hidden_size
  																								lowercase__							 =       """vision_model"""
  																		else:
  																								lowercase__							 =       int(key_split[3]	)
  																								lowercase__							 =       config.text_config.hidden_size
  																								lowercase__							 =       """text_model"""
  
  																		if "weight" in key:
  																								lowercase__							 =       val[:dim, :]
  																								lowercase__							 =       val[
  																								    dim : dim * 2, :
  																								]
  																								lowercase__							 =       val[-dim:, :]
  																		else:
  																								lowercase__							 =       val[:dim]
  																								lowercase__							 =       val[dim : dim * 2]
  																								lowercase__							 =       val[-dim:]
  												elif "self_attn" in key and "out_proj" not in key:
  																		lowercase__							 =       key.split("""."""	)
  																		lowercase__							 =       int(key_split[1]	)
  																		lowercase__							 =       config.reduce_dim
  																		if "weight" in key:
  																								lowercase__							 =       val[:dim, :]
  																								lowercase__							 =       val[dim : dim * 2, :]
  																								lowercase__							 =       val[-dim:, :]
  																		else:
  																								lowercase__							 =       val[:dim]
  																								lowercase__							 =       val[dim : dim * 2]
  																								lowercase__							 =       val[-dim:]
  												else:
  																		lowercase__							 =       rename_key(lowercase__	)
  																		if "visual_projection" in new_name or "text_projection" in new_name:
  																								lowercase__							 =       val.T
  																		lowercase__							 =       val
  
  						return orig_state_dict
  
  
  
  
  
  def  _A		(							):
  						lowercase__							 =       """http://images.cocodataset.org/val2017/000000039769.jpg"""
  						lowercase__							 =       Image.open(requests.get(lowercase__		,		stream=lowercase__	).raw	)
  						return image
  
  
  
  
  
  def  _A		(							lowercase__		,		lowercase__		,		lowercase__		,		lowercase__	):
  						lowercase__							 =       get_clipseg_config(lowercase__	)
  						lowercase__							 =       CLIPSegForImageSegmentation(lowercase__	)
  						model.eval()
  
  						lowercase__							 =       torch.load(lowercase__		,		map_location="""cpu"""	)
  
  						# remove some keys
  						for key in state_dict.copy().keys():
  												if key.startswith("""model"""	):
  																		state_dict.pop(lowercase__		,		lowercase__	)
  
      # rename some keys
  						lowercase__							 =       convert_state_dict(lowercase__		,		lowercase__	)
  						lowercase__ ,						lowercase__							 =       model.load_state_dict(lowercase__		,		strict=lowercase__	)
  
  						if missing_keys != ["clip.text_model.embeddings.position_ids", "clip.vision_model.embeddings.position_ids"]:
  												raise ValueError("""Missing keys that are not expected: {}""".format(lowercase__	)	)
  						if unexpected_keys != ["decoder.reduce.weight", "decoder.reduce.bias"]:
  												raise ValueError(f'''Unexpected keys: {unexpected_keys}'''	)
  
  						lowercase__							 =       ViTImageProcessor(size=352	)
  						lowercase__							 =       CLIPTokenizer.from_pretrained("""openai/clip-vit-base-patch32"""	)
  						lowercase__							 =       CLIPSegProcessor(image_processor=lowercase__		,		tokenizer=lowercase__	)
  
  						lowercase__							 =       prepare_img()
  						lowercase__							 =       ["""a glass""", """something to fill""", """wood""", """a jar"""]
  
  						lowercase__							 =       processor(text=lowercase__		,		images=[image] * len(lowercase__	)		,		padding="""max_length"""		,		return_tensors="""pt"""	)
  
  						with torch.no_grad():
  												lowercase__							 =       model(**lowercase__	)
  
  						# verify values
  						lowercase__							 =       torch.tensor([0.1_1_1_0, -0.1_8_8_2, 0.1_6_4_5]	)
  						lowercase__							 =       torch.tensor([0.2_6_9_2, -0.7_1_9_7, -0.1_3_2_8]	)
  						if model_name == "clipseg-rd64-refined":
  												lowercase__							 =       torch.tensor(
  												    [[-10.0407, -9.9_4_3_1, -10.2646], [-9.9_7_5_1, -9.7_0_6_4, -9.9_5_8_6], [-9.6_8_9_1, -9.5_6_4_5, -9.9_6_1_8]]	)
  						elif model_name == "clipseg-rd64":
  												lowercase__							 =       torch.tensor(
  												    [[-7.2_8_7_7, -7.2_7_1_1, -7.2_4_6_3], [-7.2_6_5_2, -7.2_7_8_0, -7.2_5_2_0], [-7.2_2_3_9, -7.2_2_0_4, -7.2_0_0_1]]	)
  						elif model_name == "clipseg-rd16":
  												lowercase__							 =       torch.tensor(
  												    [[-6.3_9_5_5, -6.4_0_5_5, -6.4_1_5_1], [-6.3_9_1_1, -6.4_0_3_3, -6.4_1_0_0], [-6.3_4_7_4, -6.3_7_0_2, -6.3_7_6_2]]	)
  						else:
  												raise ValueError(f'''Model name {model_name} not supported.'''	)
  
  						assert torch.allclose(outputs.logits[0, :3, :3]		,		lowercase__		,		atol=1e-3	)
  						assert torch.allclose(outputs.conditional_embeddings[0, :3]		,		lowercase__		,		atol=1e-3	)
  						assert torch.allclose(outputs.pooled_output[0, :3]		,		lowercase__		,		atol=1e-3	)
  						print("""Looks ok!"""	)
  
  						if pytorch_dump_folder_path is not None:
  												print(f'''Saving model and processor to {pytorch_dump_folder_path}'''	)
  												model.save_pretrained(lowercase__	)
  												processor.save_pretrained(lowercase__	)
  
  						if push_to_hub:
  												print(f'''Pushing model and processor for {model_name} to the hub'''	)
  												model.push_to_hub(f'''CIDAS/{model_name}'''	)
  												processor.push_to_hub(f'''CIDAS/{model_name}'''	)
  
  
  if __name__ == "__main__":
  				__A          =    argparse.ArgumentParser()
  				# Required parameters
  				parser.add_argument(
  				    "--model_name",
  				    default="clipseg-rd64",
  				    type=str,
  				    choices=["clipseg-rd16", "clipseg-rd64", "clipseg-rd64-refined"],
  				    help=(
  				        "Name of the model. Supported models are: clipseg-rd64, clipseg-rd16 and clipseg-rd64-refined (rd meaning"
  				        " reduce dimension)"
  				    ),
  				)
  				parser.add_argument(
  				    "--checkpoint_path",
  				    default="/Users/nielsrogge/Documents/CLIPSeg/clip_plus_rd64-uni.pth",
  				    type=str,
  				    help=(
  				        "Path to the original checkpoint. Note that the script assumes that the checkpoint includes both CLIP and"
  				        " the decoder weights."
  				    ),
  				)
  				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 or not to push the converted model to the 🤗 hub."
  				)
  
  				__A          =    parser.parse_args()
  				convert_clipseg_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)
  
  
  
  
  
  
  
style_context: 
  '''simple docstring'''
  
  
  
  import os
  import tempfile
  import unittest
  
  import numpy as np
  import torch
  
  from diffusers.models import ModelMixin, UNetaDConditionModel
  from diffusers.models.attention_processor import AttnProcessor, LoRAAttnProcessor
  from diffusers.utils import (
      floats_tensor,
      logging,
      skip_mps,
      torch_device,
  )
  from diffusers.utils.import_utils import is_xformers_available
  from diffusers.utils.testing_utils import enable_full_determinism
  
  from .test_modeling_common import ModelTesterMixin, UNetTesterMixin
  
  
  enable_full_determinism()
  
  __A          =    logging.get_logger(__name__)
  
  
  
  
  
  def  _A		(							lowercase__		,		lowercase__ = True	):
  						lowercase__							 =       {}
  						for name in model.attn_processors.keys():
  												lowercase__							 =       name.endswith("""attn2.processor"""	) and not (
  												    name.startswith("""transformer_in"""	) or """temp_attentions""" in name.split("""."""	)
  												)
  												lowercase__							 =       model.config.cross_attention_dim if has_cross_attention else None
  												if name.startswith("""mid_block"""	):
  																		lowercase__							 =       model.config.block_out_channels[-1]
  												elif name.startswith("""up_blocks"""	):
  																		lowercase__							 =       int(name[len("""up_blocks."""	)]	)
  																		lowercase__							 =       list(reversed(model.config.block_out_channels	)	)[block_id]
  												elif name.startswith("""down_blocks"""	):
  																		lowercase__							 =       int(name[len("""down_blocks."""	)]	)
  																		lowercase__							 =       model.config.block_out_channels[block_id]
  												elif name.startswith("""transformer_in"""	):
  																		# Note that the `8 * ...` comes from: https://github.com/huggingface/diffusers/blob/7139f0e874f10b2463caa8cbd585762a309d12d6/src/diffusers/models/unet_3d_condition.py#L148
  																		lowercase__							 =       8 * model.config.attention_head_dim
  
  												lowercase__							 =       LoRAAttnProcessor(hidden_size=lowercase__		,		cross_attention_dim=lowercase__	)
  												lowercase__							 =       lora_attn_procs[name].to(model.device	)
  
  												if mock_weights:
  																		# add 1 to weights to mock trained weights
  																		with torch.no_grad():
  																								lora_attn_procs[name].to_q_lora.up.weight += 1
  																								lora_attn_procs[name].to_k_lora.up.weight += 1
  																								lora_attn_procs[name].to_v_lora.up.weight += 1
  																								lora_attn_procs[name].to_out_lora.up.weight += 1
  
  						return lora_attn_procs
  
  
  
  
  
  
  
  @skip_mps
  class 			A							(       __UpperCAmelCase		,						__UpperCAmelCase		,						unittest.TestCase				):
  			lowerCamelCase							:							List[Any]       		=  UNetaDConditionModel
  			lowerCamelCase							:							int       		=  """sample"""
  
  
  
  			@property
  			def A__ (					self       )			->		Optional[Any]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       4
  									lowercase__							 =       4
  									lowercase__							 =       4
  									lowercase__							 =       (32, 32)
  
  									lowercase__							 =       floats_tensor((batch_size, num_channels, num_frames) + sizes       ).to(lowerCamelCase__       )
  									lowercase__							 =       torch.tensor([10]       ).to(lowerCamelCase__       )
  									lowercase__							 =       floats_tensor((batch_size, 4, 32)       ).to(lowerCamelCase__       )
  
  									return {"sample": noise, "timestep": time_step, "encoder_hidden_states": encoder_hidden_states}
  
  
  
  			@property
  			def A__ (					self       )			->		Any:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									return (4, 4, 32, 32)
  
  
  
  			@property
  			def A__ (					self       )			->		Union[str, Any]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									return (4, 4, 32, 32)
  
  
  
  			def A__ (					self       )			->		Optional[int]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       {
  									    """block_out_channels""": (32, 64),
  									    """down_block_types""": (
  									        """CrossAttnDownBlock3D""",
  									        """DownBlock3D""",
  									    ),
  									    """up_block_types""": ("""UpBlock3D""", """CrossAttnUpBlock3D"""),
  									    """cross_attention_dim""": 32,
  									    """attention_head_dim""": 8,
  									    """out_channels""": 4,
  									    """in_channels""": 4,
  									    """layers_per_block""": 1,
  									    """sample_size""": 32,
  									}
  									lowercase__							 =       self.dummy_input
  									return init_dict, inputs_dict
  
  
  
  			@unittest.skipIf(
  			    torch_device != """cuda""" or not is_xformers_available()       ,     reason="""XFormers attention is only available with CUDA and `xformers` installed"""       ,     )
  			def A__ (					self       )			->		str:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__ ,						lowercase__							 =       self.prepare_init_args_and_inputs_for_common()
  									lowercase__							 =       self.model_class(**lowerCamelCase__       )
  
  									model.enable_xformers_memory_efficient_attention()
  
  									assert (
  									    model.mid_block.attentions[0].transformer_blocks[0].attna.processor.__class__.__name__
  									    == "XFormersAttnProcessor"
  									), "xformers is not enabled"
  
  
  
  			def A__ (					self       )			->		Dict:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__ ,						lowercase__							 =       self.prepare_init_args_and_inputs_for_common()
  
  									lowercase__							 =       32
  
  									lowercase__							 =       self.model_class(**lowerCamelCase__       )
  									model.to(lowerCamelCase__       )
  									model.eval()
  
  									with torch.no_grad():
  															lowercase__							 =       model(**lowerCamelCase__       )
  
  															if isinstance(lowerCamelCase__       ,     lowerCamelCase__       ):
  																					lowercase__							 =       output.sample
  
  									self.assertIsNotNone(lowerCamelCase__       )
  									lowercase__							 =       inputs_dict["""sample"""].shape
  									self.assertEqual(output.shape       ,     lowerCamelCase__       ,     """Input and output shapes do not match"""       )
  
  
  
  			def A__ (					self       )			->		List[Any]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__ ,						lowercase__							 =       self.prepare_init_args_and_inputs_for_common()
  									lowercase__							 =       self.model_class(**lowerCamelCase__       )
  									model.to(lowerCamelCase__       )
  									model.eval()
  
  									with torch.no_grad():
  															# Warmup pass when using mps (see #372)
  															if torch_device == "mps" and isinstance(lowerCamelCase__       ,     lowerCamelCase__       ):
  																					model(**self.dummy_input       )
  
  															lowercase__							 =       model(**lowerCamelCase__       )
  															if isinstance(lowerCamelCase__       ,     lowerCamelCase__       ):
  																					lowercase__							 =       first.sample
  
  															lowercase__							 =       model(**lowerCamelCase__       )
  															if isinstance(lowerCamelCase__       ,     lowerCamelCase__       ):
  																					lowercase__							 =       second.sample
  
  									lowercase__							 =       first.cpu().numpy()
  									lowercase__							 =       second.cpu().numpy()
  									lowercase__							 =       out_a[~np.isnan(lowerCamelCase__       )]
  									lowercase__							 =       out_a[~np.isnan(lowerCamelCase__       )]
  									lowercase__							 =       np.amax(np.abs(out_a - out_a       )       )
  									self.assertLessEqual(lowerCamelCase__       ,     1e-5       )
  
  
  
  			def A__ (					self       )			->		Any:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__ ,						lowercase__							 =       self.prepare_init_args_and_inputs_for_common()
  
  									lowercase__							 =       8
  
  									lowercase__							 =       self.model_class(**lowerCamelCase__       )
  									model.to(lowerCamelCase__       )
  									model.eval()
  
  									model.set_attention_slice("""auto"""       )
  									with torch.no_grad():
  															lowercase__							 =       model(**lowerCamelCase__       )
  									assert output is not None
  
  									model.set_attention_slice("""max"""       )
  									with torch.no_grad():
  															lowercase__							 =       model(**lowerCamelCase__       )
  									assert output is not None
  
  									model.set_attention_slice(2       )
  									with torch.no_grad():
  															lowercase__							 =       model(**lowerCamelCase__       )
  									assert output is not None
  
  
  
  			def A__ (					self       )			->		Union[str, Any]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__ ,						lowercase__							 =       self.prepare_init_args_and_inputs_for_common()
  
  									lowercase__							 =       8
  
  									lowercase__							 =       self.model_class(**lowerCamelCase__       )
  									model.to(lowerCamelCase__       )
  
  									with torch.no_grad():
  															lowercase__							 =       model(**lowerCamelCase__       ).sample
  
  									lowercase__							 =       create_lora_layers(lowerCamelCase__       )
  
  									# make sure we can set a list of attention processors
  									model.set_attn_processor(lowerCamelCase__       )
  									model.to(lowerCamelCase__       )
  
  									# test that attn processors can be set to itself
  									model.set_attn_processor(model.attn_processors       )
  
  									with torch.no_grad():
  															lowercase__							 =       model(**lowerCamelCase__       ,     cross_attention_kwargs={"""scale""": 0.0}       ).sample
  															lowercase__							 =       model(**lowerCamelCase__       ,     cross_attention_kwargs={"""scale""": 0.5}       ).sample
  															lowercase__							 =       model(**lowerCamelCase__       ,     cross_attention_kwargs={"""scale""": 0.5}       ).sample
  
  									assert (samplea - samplea).abs().max() < 3e-3
  									assert (samplea - samplea).abs().max() < 3e-3
  
  									# sample 2 and sample 3 should be different
  									assert (samplea - samplea).abs().max() > 3e-3
  
  
  
  			def A__ (					self       )			->		Optional[int]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__ ,						lowercase__							 =       self.prepare_init_args_and_inputs_for_common()
  
  									lowercase__							 =       8
  
  									torch.manual_seed(0       )
  									lowercase__							 =       self.model_class(**lowerCamelCase__       )
  									model.to(lowerCamelCase__       )
  
  									with torch.no_grad():
  															lowercase__							 =       model(**lowerCamelCase__       ).sample
  
  									lowercase__							 =       create_lora_layers(lowerCamelCase__       )
  									model.set_attn_processor(lowerCamelCase__       )
  
  									with torch.no_grad():
  															lowercase__							 =       model(**lowerCamelCase__       ,     cross_attention_kwargs={"""scale""": 0.5}       ).sample
  
  									with tempfile.TemporaryDirectory() as tmpdirname:
  															model.save_attn_procs(lowerCamelCase__       )
  															self.assertTrue(os.path.isfile(os.path.join(lowerCamelCase__       ,     """pytorch_lora_weights.bin"""       )       )       )
  															torch.manual_seed(0       )
  															lowercase__							 =       self.model_class(**lowerCamelCase__       )
  															new_model.to(lowerCamelCase__       )
  															new_model.load_attn_procs(lowerCamelCase__       )
  
  									with torch.no_grad():
  															lowercase__							 =       new_model(**lowerCamelCase__       ,     cross_attention_kwargs={"""scale""": 0.5}       ).sample
  
  									assert (sample - new_sample).abs().max() < 1e-3
  
  									# LoRA and no LoRA should NOT be the same
  									assert (sample - old_sample).abs().max() > 1e-4
  
  
  
  			def A__ (					self       )			->		Any:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__ ,						lowercase__							 =       self.prepare_init_args_and_inputs_for_common()
  
  									lowercase__							 =       8
  
  									torch.manual_seed(0       )
  									lowercase__							 =       self.model_class(**lowerCamelCase__       )
  									model.to(lowerCamelCase__       )
  
  									with torch.no_grad():
  															lowercase__							 =       model(**lowerCamelCase__       ).sample
  
  									lowercase__							 =       create_lora_layers(lowerCamelCase__       )
  									model.set_attn_processor(lowerCamelCase__       )
  
  									with torch.no_grad():
  															lowercase__							 =       model(**lowerCamelCase__       ,     cross_attention_kwargs={"""scale""": 0.5}       ).sample
  
  									with tempfile.TemporaryDirectory() as tmpdirname:
  															model.save_attn_procs(lowerCamelCase__       ,     safe_serialization=lowerCamelCase__       )
  															self.assertTrue(os.path.isfile(os.path.join(lowerCamelCase__       ,     """pytorch_lora_weights.safetensors"""       )       )       )
  															torch.manual_seed(0       )
  															lowercase__							 =       self.model_class(**lowerCamelCase__       )
  															new_model.to(lowerCamelCase__       )
  															new_model.load_attn_procs(lowerCamelCase__       )
  
  									with torch.no_grad():
  															lowercase__							 =       new_model(**lowerCamelCase__       ,     cross_attention_kwargs={"""scale""": 0.5}       ).sample
  
  									assert (sample - new_sample).abs().max() < 3e-3
  
  									# LoRA and no LoRA should NOT be the same
  									assert (sample - old_sample).abs().max() > 1e-4
  
  
  
  			def A__ (					self       )			->		Dict:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__ ,						lowercase__							 =       self.prepare_init_args_and_inputs_for_common()
  
  									lowercase__							 =       8
  
  									torch.manual_seed(0       )
  									lowercase__							 =       self.model_class(**lowerCamelCase__       )
  									model.to(lowerCamelCase__       )
  
  									lowercase__							 =       create_lora_layers(lowerCamelCase__       ,     mock_weights=lowerCamelCase__       )
  									model.set_attn_processor(lowerCamelCase__       )
  									# Saving as torch, properly reloads with directly filename
  									with tempfile.TemporaryDirectory() as tmpdirname:
  															model.save_attn_procs(lowerCamelCase__       )
  															self.assertTrue(os.path.isfile(os.path.join(lowerCamelCase__       ,     """pytorch_lora_weights.bin"""       )       )       )
  															torch.manual_seed(0       )
  															lowercase__							 =       self.model_class(**lowerCamelCase__       )
  															new_model.to(lowerCamelCase__       )
  															new_model.load_attn_procs(lowerCamelCase__       ,     weight_name="""pytorch_lora_weights.bin"""       )
  
  
  
  			def A__ (					self       )			->		List[Any]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__ ,						lowercase__							 =       self.prepare_init_args_and_inputs_for_common()
  
  									lowercase__							 =       8
  
  									torch.manual_seed(0       )
  									lowercase__							 =       self.model_class(**lowerCamelCase__       )
  									model.to(lowerCamelCase__       )
  
  									lowercase__							 =       create_lora_layers(lowerCamelCase__       ,     mock_weights=lowerCamelCase__       )
  									model.set_attn_processor(lowerCamelCase__       )
  									# Saving as torch, properly reloads with directly filename
  									with tempfile.TemporaryDirectory() as tmpdirname:
  															model.save_attn_procs(lowerCamelCase__       )
  															self.assertTrue(os.path.isfile(os.path.join(lowerCamelCase__       ,     """pytorch_lora_weights.bin"""       )       )       )
  															torch.manual_seed(0       )
  															lowercase__							 =       self.model_class(**lowerCamelCase__       )
  															new_model.to(lowerCamelCase__       )
  															with self.assertRaises(lowerCamelCase__       ) as e:
  																					new_model.load_attn_procs(lowerCamelCase__       ,     use_safetensors=lowerCamelCase__       )
  															self.assertIn("""Error no file named pytorch_lora_weights.safetensors"""       ,     str(e.exception       )       )
  
  
  
  			def A__ (					self       )			->		Any:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__ ,						lowercase__							 =       self.prepare_init_args_and_inputs_for_common()
  
  									lowercase__							 =       8
  
  									torch.manual_seed(0       )
  									lowercase__							 =       self.model_class(**lowerCamelCase__       )
  									model.to(lowerCamelCase__       )
  
  									with torch.no_grad():
  															lowercase__							 =       model(**lowerCamelCase__       ).sample
  
  									lowercase__							 =       create_lora_layers(lowerCamelCase__       )
  									model.set_attn_processor(lowerCamelCase__       )
  
  									with torch.no_grad():
  															lowercase__							 =       model(**lowerCamelCase__       ,     cross_attention_kwargs={"""scale""": 0.0}       ).sample
  
  									model.set_attn_processor(AttnProcessor()       )
  
  									with torch.no_grad():
  															lowercase__							 =       model(**lowerCamelCase__       ).sample
  
  									assert (sample - new_sample).abs().max() < 1e-4
  									assert (sample - old_sample).abs().max() < 3e-3
  
  
  
  			@unittest.skipIf(
  			    torch_device != """cuda""" or not is_xformers_available()       ,     reason="""XFormers attention is only available with CUDA and `xformers` installed"""       ,     )
  			def A__ (					self       )			->		Optional[int]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__ ,						lowercase__							 =       self.prepare_init_args_and_inputs_for_common()
  
  									lowercase__							 =       4
  
  									torch.manual_seed(0       )
  									lowercase__							 =       self.model_class(**lowerCamelCase__       )
  									model.to(lowerCamelCase__       )
  									lowercase__							 =       create_lora_layers(lowerCamelCase__       )
  									model.set_attn_processor(lowerCamelCase__       )
  
  									# default
  									with torch.no_grad():
  															lowercase__							 =       model(**lowerCamelCase__       ).sample
  
  															model.enable_xformers_memory_efficient_attention()
  															lowercase__							 =       model(**lowerCamelCase__       ).sample
  
  															model.disable_xformers_memory_efficient_attention()
  															lowercase__							 =       model(**lowerCamelCase__       ).sample
  
  									assert (sample - on_sample).abs().max() < 1e-4
  									assert (sample - off_sample).abs().max() < 1e-4
  
  
  
  			def A__ (					self       )			->		List[Any]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__ ,						lowercase__							 =       self.prepare_init_args_and_inputs_for_common()
  									lowercase__							 =       32
  
  									lowercase__							 =       self.model_class(**lowerCamelCase__       )
  									model.to(lowerCamelCase__       )
  									model.eval()
  
  									with torch.no_grad():
  															lowercase__							 =       model(**lowerCamelCase__       )[0]
  
  									model.enable_forward_chunking()
  									with torch.no_grad():
  															lowercase__							 =       model(**lowerCamelCase__       )[0]
  
  									self.assertEqual(output.shape       ,     output_a.shape       ,     """Shape doesn't match"""       )
  									assert np.abs(output.cpu() - output_a.cpu()       ).max() < 1e-2
  
  
  # (todo: sayakpaul) implement SLOW tests.
  
  
  
  
  
  
  