code: 
  '''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.
  
  
  
  
  
  
  
style_context: 
  '''simple docstring'''
  
  
  
  import argparse
  import json
  
  import torch
  
  from diffusers import AutoencoderKL, DDPMPipeline, DDPMScheduler, UNetaDModel, VQModel
  
  
  
  
  
  def  _A		(							lowercase__		,		lowercase__=1	):
  						if n_shave_prefix_segments >= 0:
  												return ".".join(path.split("""."""	)[n_shave_prefix_segments:]	)
  						else:
  												return ".".join(path.split("""."""	)[:n_shave_prefix_segments]	)
  
  
  
  
  
  def  _A		(							lowercase__		,		lowercase__=0	):
  						lowercase__							 =       []
  						for old_item in old_list:
  												lowercase__							 =       old_item
  												lowercase__							 =       new_item.replace("""block."""		,		"""resnets."""	)
  												lowercase__							 =       new_item.replace("""conv_shorcut"""		,		"""conv1"""	)
  												lowercase__							 =       new_item.replace("""in_shortcut"""		,		"""conv_shortcut"""	)
  												lowercase__							 =       new_item.replace("""temb_proj"""		,		"""time_emb_proj"""	)
  
  												lowercase__							 =       shave_segments(lowercase__		,		n_shave_prefix_segments=lowercase__	)
  
  												mapping.append({"""old""": old_item, """new""": new_item}	)
  
  						return mapping
  
  
  
  
  
  def  _A		(							lowercase__		,		lowercase__=0		,		lowercase__=False	):
  						lowercase__							 =       []
  						for old_item in old_list:
  												lowercase__							 =       old_item
  
  												# In `model.mid`, the layer is called `attn`.
  												if not in_mid:
  																		lowercase__							 =       new_item.replace("""attn"""		,		"""attentions"""	)
  												lowercase__							 =       new_item.replace(""".k."""		,		""".key."""	)
  												lowercase__							 =       new_item.replace(""".v."""		,		""".value."""	)
  												lowercase__							 =       new_item.replace(""".q."""		,		""".query."""	)
  
  												lowercase__							 =       new_item.replace("""proj_out"""		,		"""proj_attn"""	)
  												lowercase__							 =       new_item.replace("""norm"""		,		"""group_norm"""	)
  
  												lowercase__							 =       shave_segments(lowercase__		,		n_shave_prefix_segments=lowercase__	)
  												mapping.append({"""old""": old_item, """new""": new_item}	)
  
  						return mapping
  
  
  
  
  
  def  _A		(							lowercase__		,		lowercase__		,		lowercase__		,		lowercase__=None		,		lowercase__=None		,		lowercase__=None	):
  						assert isinstance(lowercase__		,		lowercase__	), "Paths should be a list of dicts containing 'old' and 'new' keys."
  
  						if attention_paths_to_split is not None:
  												if config is None:
  																		raise ValueError("""Please specify the config if setting 'attention_paths_to_split' to 'True'."""	)
  
  												for path, path_map in attention_paths_to_split.items():
  																		lowercase__							 =       old_checkpoint[path]
  																		lowercase__							 =       old_tensor.shape[0] // 3
  
  																		lowercase__							 =       (-1, channels) if len(old_tensor.shape	) == 3 else (-1)
  
  																		lowercase__							 =       old_tensor.shape[0] // config.get("""num_head_channels"""		,		1	) // 3
  
  																		lowercase__							 =       old_tensor.reshape((num_heads, 3 * channels // num_heads) + old_tensor.shape[1:]	)
  																		lowercase__ ,						lowercase__ ,						lowercase__							 =       old_tensor.split(channels // num_heads		,		dim=1	)
  
  																		lowercase__							 =       query.reshape(lowercase__	).squeeze()
  																		lowercase__							 =       key.reshape(lowercase__	).squeeze()
  																		lowercase__							 =       value.reshape(lowercase__	).squeeze()
  
  						for path in paths:
  												lowercase__							 =       path["""new"""]
  
  												if attention_paths_to_split is not None and new_path in attention_paths_to_split:
  																		continue
  
  												lowercase__							 =       new_path.replace("""down."""		,		"""down_blocks."""	)
  												lowercase__							 =       new_path.replace("""up."""		,		"""up_blocks."""	)
  
  												if additional_replacements is not None:
  																		for replacement in additional_replacements:
  																								lowercase__							 =       new_path.replace(replacement["""old"""]		,		replacement["""new"""]	)
  
  												if "attentions" in new_path:
  																		lowercase__							 =       old_checkpoint[path["""old"""]].squeeze()
  												else:
  																		lowercase__							 =       old_checkpoint[path["""old"""]]
  
  
  
  
  
  def  _A		(							lowercase__		,		lowercase__	):
  						lowercase__							 =       {}
  
  						lowercase__							 =       checkpoint["""temb.dense.0.weight"""]
  						lowercase__							 =       checkpoint["""temb.dense.0.bias"""]
  						lowercase__							 =       checkpoint["""temb.dense.1.weight"""]
  						lowercase__							 =       checkpoint["""temb.dense.1.bias"""]
  
  						lowercase__							 =       checkpoint["""norm_out.weight"""]
  						lowercase__							 =       checkpoint["""norm_out.bias"""]
  
  						lowercase__							 =       checkpoint["""conv_in.weight"""]
  						lowercase__							 =       checkpoint["""conv_in.bias"""]
  						lowercase__							 =       checkpoint["""conv_out.weight"""]
  						lowercase__							 =       checkpoint["""conv_out.bias"""]
  
  						lowercase__							 =       len({""".""".join(layer.split("""."""	)[:2]	) for layer in checkpoint if """down""" in layer}	)
  						lowercase__							 =       {
  						    layer_id: [key for key in checkpoint if f'''down.{layer_id}''' in key] for layer_id in range(lowercase__	)
  						}
  
  						lowercase__							 =       len({""".""".join(layer.split("""."""	)[:2]	) for layer in checkpoint if """up""" in layer}	)
  						lowercase__							 =       {layer_id: [key for key in checkpoint if f'''up.{layer_id}''' in key] for layer_id in range(lowercase__	)}
  
  						for i in range(lowercase__	):
  												lowercase__							 =       (i - 1) // (config["""layers_per_block"""] + 1)
  
  												if any("""downsample""" in layer for layer in down_blocks[i]	):
  																		lowercase__							 =       checkpoint[
  																		    f'''down.{i}.downsample.op.weight'''
  																		]
  																		lowercase__							 =       checkpoint[f'''down.{i}.downsample.op.bias''']
  												#            new_checkpoint[f'down_blocks.{i}.downsamplers.0.op.weight'] = checkpoint[f'down.{i}.downsample.conv.weight']
  												#            new_checkpoint[f'down_blocks.{i}.downsamplers.0.op.bias'] = checkpoint[f'down.{i}.downsample.conv.bias']
  
  												if any("""block""" in layer for layer in down_blocks[i]	):
  																		lowercase__							 =       len(
  																		    {""".""".join(shave_segments(lowercase__		,		2	).split("""."""	)[:2]	) for layer in down_blocks[i] if """block""" in layer}	)
  																		lowercase__							 =       {
  																		    layer_id: [key for key in down_blocks[i] if f'''block.{layer_id}''' in key]
  																		    for layer_id in range(lowercase__	)
  																		}
  
  																		if num_blocks > 0:
  																								for j in range(config["""layers_per_block"""]	):
  																														lowercase__							 =       renew_resnet_paths(blocks[j]	)
  																														assign_to_checkpoint(lowercase__		,		lowercase__		,		lowercase__	)
  
  												if any("""attn""" in layer for layer in down_blocks[i]	):
  																		lowercase__							 =       len(
  																		    {""".""".join(shave_segments(lowercase__		,		2	).split("""."""	)[:2]	) for layer in down_blocks[i] if """attn""" in layer}	)
  																		lowercase__							 =       {
  																		    layer_id: [key for key in down_blocks[i] if f'''attn.{layer_id}''' in key]
  																		    for layer_id in range(lowercase__	)
  																		}
  
  																		if num_attn > 0:
  																								for j in range(config["""layers_per_block"""]	):
  																														lowercase__							 =       renew_attention_paths(attns[j]	)
  																														assign_to_checkpoint(lowercase__		,		lowercase__		,		lowercase__		,		config=lowercase__	)
  
  						lowercase__							 =       [key for key in checkpoint if """mid.block_1""" in key]
  						lowercase__							 =       [key for key in checkpoint if """mid.block_2""" in key]
  						lowercase__							 =       [key for key in checkpoint if """mid.attn_1""" in key]
  
  						# Mid new 2
  						lowercase__							 =       renew_resnet_paths(lowercase__	)
  						assign_to_checkpoint(
  						    lowercase__		,		lowercase__		,		lowercase__		,		additional_replacements=[{"""old""": """mid.""", """new""": """mid_new_2."""}, {"""old""": """block_1""", """new""": """resnets.0"""}]		,		)
  
  						lowercase__							 =       renew_resnet_paths(lowercase__	)
  						assign_to_checkpoint(
  						    lowercase__		,		lowercase__		,		lowercase__		,		additional_replacements=[{"""old""": """mid.""", """new""": """mid_new_2."""}, {"""old""": """block_2""", """new""": """resnets.1"""}]		,		)
  
  						lowercase__							 =       renew_attention_paths(lowercase__		,		in_mid=lowercase__	)
  						assign_to_checkpoint(
  						    lowercase__		,		lowercase__		,		lowercase__		,		additional_replacements=[{"""old""": """mid.""", """new""": """mid_new_2."""}, {"""old""": """attn_1""", """new""": """attentions.0"""}]		,		)
  
  						for i in range(lowercase__	):
  												lowercase__							 =       num_up_blocks - 1 - i
  
  												if any("""upsample""" in layer for layer in up_blocks[i]	):
  																		lowercase__							 =       checkpoint[
  																		    f'''up.{i}.upsample.conv.weight'''
  																		]
  																		lowercase__							 =       checkpoint[f'''up.{i}.upsample.conv.bias''']
  
  												if any("""block""" in layer for layer in up_blocks[i]	):
  																		lowercase__							 =       len(
  																		    {""".""".join(shave_segments(lowercase__		,		2	).split("""."""	)[:2]	) for layer in up_blocks[i] if """block""" in layer}	)
  																		lowercase__							 =       {
  																		    layer_id: [key for key in up_blocks[i] if f'''block.{layer_id}''' in key] for layer_id in range(lowercase__	)
  																		}
  
  																		if num_blocks > 0:
  																								for j in range(config["""layers_per_block"""] + 1	):
  																														lowercase__							 =       {"""old""": f'''up_blocks.{i}''', """new""": f'''up_blocks.{block_id}'''}
  																														lowercase__							 =       renew_resnet_paths(blocks[j]	)
  																														assign_to_checkpoint(lowercase__		,		lowercase__		,		lowercase__		,		additional_replacements=[replace_indices]	)
  
  												if any("""attn""" in layer for layer in up_blocks[i]	):
  																		lowercase__							 =       len(
  																		    {""".""".join(shave_segments(lowercase__		,		2	).split("""."""	)[:2]	) for layer in up_blocks[i] if """attn""" in layer}	)
  																		lowercase__							 =       {
  																		    layer_id: [key for key in up_blocks[i] if f'''attn.{layer_id}''' in key] for layer_id in range(lowercase__	)
  																		}
  
  																		if num_attn > 0:
  																								for j in range(config["""layers_per_block"""] + 1	):
  																														lowercase__							 =       {"""old""": f'''up_blocks.{i}''', """new""": f'''up_blocks.{block_id}'''}
  																														lowercase__							 =       renew_attention_paths(attns[j]	)
  																														assign_to_checkpoint(lowercase__		,		lowercase__		,		lowercase__		,		additional_replacements=[replace_indices]	)
  
  						lowercase__							 =       {k.replace("""mid_new_2"""		,		"""mid_block"""	): v for k, v in new_checkpoint.items()}
  						return new_checkpoint
  
  
  
  
  
  def  _A		(							lowercase__		,		lowercase__	):
  						lowercase__							 =       {}
  
  						lowercase__							 =       checkpoint["""encoder.norm_out.weight"""]
  						lowercase__							 =       checkpoint["""encoder.norm_out.bias"""]
  
  						lowercase__							 =       checkpoint["""encoder.conv_in.weight"""]
  						lowercase__							 =       checkpoint["""encoder.conv_in.bias"""]
  						lowercase__							 =       checkpoint["""encoder.conv_out.weight"""]
  						lowercase__							 =       checkpoint["""encoder.conv_out.bias"""]
  
  						lowercase__							 =       checkpoint["""decoder.norm_out.weight"""]
  						lowercase__							 =       checkpoint["""decoder.norm_out.bias"""]
  
  						lowercase__							 =       checkpoint["""decoder.conv_in.weight"""]
  						lowercase__							 =       checkpoint["""decoder.conv_in.bias"""]
  						lowercase__							 =       checkpoint["""decoder.conv_out.weight"""]
  						lowercase__							 =       checkpoint["""decoder.conv_out.bias"""]
  
  						lowercase__							 =       len({""".""".join(layer.split("""."""	)[:3]	) for layer in checkpoint if """down""" in layer}	)
  						lowercase__							 =       {
  						    layer_id: [key for key in checkpoint if f'''down.{layer_id}''' in key] for layer_id in range(lowercase__	)
  						}
  
  						lowercase__							 =       len({""".""".join(layer.split("""."""	)[:3]	) for layer in checkpoint if """up""" in layer}	)
  						lowercase__							 =       {layer_id: [key for key in checkpoint if f'''up.{layer_id}''' in key] for layer_id in range(lowercase__	)}
  
  						for i in range(lowercase__	):
  												lowercase__							 =       (i - 1) // (config["""layers_per_block"""] + 1)
  
  												if any("""downsample""" in layer for layer in down_blocks[i]	):
  																		lowercase__							 =       checkpoint[
  																		    f'''encoder.down.{i}.downsample.conv.weight'''
  																		]
  																		lowercase__							 =       checkpoint[
  																		    f'''encoder.down.{i}.downsample.conv.bias'''
  																		]
  
  												if any("""block""" in layer for layer in down_blocks[i]	):
  																		lowercase__							 =       len(
  																		    {""".""".join(shave_segments(lowercase__		,		3	).split("""."""	)[:3]	) for layer in down_blocks[i] if """block""" in layer}	)
  																		lowercase__							 =       {
  																		    layer_id: [key for key in down_blocks[i] if f'''block.{layer_id}''' in key]
  																		    for layer_id in range(lowercase__	)
  																		}
  
  																		if num_blocks > 0:
  																								for j in range(config["""layers_per_block"""]	):
  																														lowercase__							 =       renew_resnet_paths(blocks[j]	)
  																														assign_to_checkpoint(lowercase__		,		lowercase__		,		lowercase__	)
  
  												if any("""attn""" in layer for layer in down_blocks[i]	):
  																		lowercase__							 =       len(
  																		    {""".""".join(shave_segments(lowercase__		,		3	).split("""."""	)[:3]	) for layer in down_blocks[i] if """attn""" in layer}	)
  																		lowercase__							 =       {
  																		    layer_id: [key for key in down_blocks[i] if f'''attn.{layer_id}''' in key]
  																		    for layer_id in range(lowercase__	)
  																		}
  
  																		if num_attn > 0:
  																								for j in range(config["""layers_per_block"""]	):
  																														lowercase__							 =       renew_attention_paths(attns[j]	)
  																														assign_to_checkpoint(lowercase__		,		lowercase__		,		lowercase__		,		config=lowercase__	)
  
  						lowercase__							 =       [key for key in checkpoint if """mid.block_1""" in key]
  						lowercase__							 =       [key for key in checkpoint if """mid.block_2""" in key]
  						lowercase__							 =       [key for key in checkpoint if """mid.attn_1""" in key]
  
  						# Mid new 2
  						lowercase__							 =       renew_resnet_paths(lowercase__	)
  						assign_to_checkpoint(
  						    lowercase__		,		lowercase__		,		lowercase__		,		additional_replacements=[{"""old""": """mid.""", """new""": """mid_new_2."""}, {"""old""": """block_1""", """new""": """resnets.0"""}]		,		)
  
  						lowercase__							 =       renew_resnet_paths(lowercase__	)
  						assign_to_checkpoint(
  						    lowercase__		,		lowercase__		,		lowercase__		,		additional_replacements=[{"""old""": """mid.""", """new""": """mid_new_2."""}, {"""old""": """block_2""", """new""": """resnets.1"""}]		,		)
  
  						lowercase__							 =       renew_attention_paths(lowercase__		,		in_mid=lowercase__	)
  						assign_to_checkpoint(
  						    lowercase__		,		lowercase__		,		lowercase__		,		additional_replacements=[{"""old""": """mid.""", """new""": """mid_new_2."""}, {"""old""": """attn_1""", """new""": """attentions.0"""}]		,		)
  
  						for i in range(lowercase__	):
  												lowercase__							 =       num_up_blocks - 1 - i
  
  												if any("""upsample""" in layer for layer in up_blocks[i]	):
  																		lowercase__							 =       checkpoint[
  																		    f'''decoder.up.{i}.upsample.conv.weight'''
  																		]
  																		lowercase__							 =       checkpoint[
  																		    f'''decoder.up.{i}.upsample.conv.bias'''
  																		]
  
  												if any("""block""" in layer for layer in up_blocks[i]	):
  																		lowercase__							 =       len(
  																		    {""".""".join(shave_segments(lowercase__		,		3	).split("""."""	)[:3]	) for layer in up_blocks[i] if """block""" in layer}	)
  																		lowercase__							 =       {
  																		    layer_id: [key for key in up_blocks[i] if f'''block.{layer_id}''' in key] for layer_id in range(lowercase__	)
  																		}
  
  																		if num_blocks > 0:
  																								for j in range(config["""layers_per_block"""] + 1	):
  																														lowercase__							 =       {"""old""": f'''up_blocks.{i}''', """new""": f'''up_blocks.{block_id}'''}
  																														lowercase__							 =       renew_resnet_paths(blocks[j]	)
  																														assign_to_checkpoint(lowercase__		,		lowercase__		,		lowercase__		,		additional_replacements=[replace_indices]	)
  
  												if any("""attn""" in layer for layer in up_blocks[i]	):
  																		lowercase__							 =       len(
  																		    {""".""".join(shave_segments(lowercase__		,		3	).split("""."""	)[:3]	) for layer in up_blocks[i] if """attn""" in layer}	)
  																		lowercase__							 =       {
  																		    layer_id: [key for key in up_blocks[i] if f'''attn.{layer_id}''' in key] for layer_id in range(lowercase__	)
  																		}
  
  																		if num_attn > 0:
  																								for j in range(config["""layers_per_block"""] + 1	):
  																														lowercase__							 =       {"""old""": f'''up_blocks.{i}''', """new""": f'''up_blocks.{block_id}'''}
  																														lowercase__							 =       renew_attention_paths(attns[j]	)
  																														assign_to_checkpoint(lowercase__		,		lowercase__		,		lowercase__		,		additional_replacements=[replace_indices]	)
  
  						lowercase__							 =       {k.replace("""mid_new_2"""		,		"""mid_block"""	): v for k, v in new_checkpoint.items()}
  						lowercase__							 =       checkpoint["""quant_conv.weight"""]
  						lowercase__							 =       checkpoint["""quant_conv.bias"""]
  						if "quantize.embedding.weight" in checkpoint:
  												lowercase__							 =       checkpoint["""quantize.embedding.weight"""]
  						lowercase__							 =       checkpoint["""post_quant_conv.weight"""]
  						lowercase__							 =       checkpoint["""post_quant_conv.bias"""]
  
  						return new_checkpoint
  
  
  if __name__ == "__main__":
  				__A          =    argparse.ArgumentParser()
  
  				parser.add_argument(
  				    "--checkpoint_path", default=None, type=str, required=True, help="Path to the checkpoint to convert."
  				)
  
  				parser.add_argument(
  				    "--config_file",
  				    default=None,
  				    type=str,
  				    required=True,
  				    help="The config json file corresponding to the architecture.",
  				)
  
  				parser.add_argument("--dump_path", default=None, type=str, required=True, help="Path to the output model.")
  
  				__A          =    parser.parse_args()
  				__A          =    torch.load(args.checkpoint_path)
  
  				with open(args.config_file) as f:
  								__A          =    json.loads(f.read())
  
  				# unet case
  				__A          =    {key.split(".")[0] for key in checkpoint.keys()}
  				if "encoder" in key_prefix_set and "decoder" in key_prefix_set:
  								__A          =    convert_vq_autoenc_checkpoint(checkpoint, config)
  				else:
  								__A          =    convert_ddpm_checkpoint(checkpoint, config)
  
  				if "ddpm" in config:
  								del config["ddpm"]
  
  				if config["_class_name"] == "VQModel":
  								__A          =    VQModel(**config)
  								model.load_state_dict(converted_checkpoint)
  								model.save_pretrained(args.dump_path)
  				elif config["_class_name"] == "AutoencoderKL":
  								__A          =    AutoencoderKL(**config)
  								model.load_state_dict(converted_checkpoint)
  								model.save_pretrained(args.dump_path)
  				else:
  								__A          =    UNetaDModel(**config)
  								model.load_state_dict(converted_checkpoint)
  
  								__A          =    DDPMScheduler.from_config("/".join(args.checkpoint_path.split("/")[:-1]))
  
  								__A          =    DDPMPipeline(unet=model, scheduler=scheduler)
  								pipe.save_pretrained(args.dump_path)
  
  
  
  
  
  
  