code: 
  
  
  '''simple docstring'''
  
  
  
  import unittest
  
  import torch
  
  from diffusers import UNetaDModel
  from diffusers.utils import floats_tensor, slow, torch_device
  
  from .test_modeling_common import ModelTesterMixin, UNetTesterMixin
  
  
  
  
  
  class 							__UpperCamelCase       (	lowerCAmelCase_	,   lowerCAmelCase_	,   unittest.TestCase      ):
  	A_			    =					UNetaDModel
  	A_			    =					"sample"
  
  
  
  
  	@property
  	def    __UpperCAmelCase							(     self							):
  
  						'''simple docstring'''
  
  
  
  
  
  						__a    : List[str]      					=						4
  						__a    : str      					=						14
  						__a    : int      					=						16
  
  						__a    : List[str]      					=						floats_tensor((batch_size, num_features, seq_len)							).to(__a							)
  						__a    : Optional[Any]      					=						torch.tensor([10] * batch_size							).to(__a							)
  
  						return {"sample": noise, "timestep": time_step}
  
  
  
  
  	@property
  	def    __UpperCAmelCase							(     self							):
  
  						'''simple docstring'''
  
  
  
  
  
  						return (4, 14, 16)
  
  
  
  
  	@property
  	def    __UpperCAmelCase							(     self							):
  
  						'''simple docstring'''
  
  
  
  
  
  						return (4, 14, 16)
  
  
  
  
  	def    __UpperCAmelCase							(     self							):
  
  						'''simple docstring'''
  
  
  
  
  
  						pass
  
  
  
  
  	def    __UpperCAmelCase							(     self							):
  
  						'''simple docstring'''
  
  
  
  
  
  						pass
  
  
  
  
  	@unittest.skipIf(torch_device == 'mps'		,       'mish op not supported in MPS'							)
  	def    __UpperCAmelCase							(     self							):
  
  						'''simple docstring'''
  
  
  
  
  
  						super().test_determinism()
  
  
  
  
  	@unittest.skipIf(torch_device == 'mps'		,       'mish op not supported in MPS'							)
  	def    __UpperCAmelCase							(     self							):
  
  						'''simple docstring'''
  
  
  
  
  
  						super().test_outputs_equivalence()
  
  
  
  
  	@unittest.skipIf(torch_device == 'mps'		,       'mish op not supported in MPS'							)
  	def    __UpperCAmelCase							(     self							):
  
  						'''simple docstring'''
  
  
  
  
  
  						super().test_from_save_pretrained()
  
  
  
  
  	@unittest.skipIf(torch_device == 'mps'		,       'mish op not supported in MPS'							)
  	def    __UpperCAmelCase							(     self							):
  
  						'''simple docstring'''
  
  
  
  
  
  						super().test_from_save_pretrained_variant()
  
  
  
  
  	@unittest.skipIf(torch_device == 'mps'		,       'mish op not supported in MPS'							)
  	def    __UpperCAmelCase							(     self							):
  
  						'''simple docstring'''
  
  
  
  
  
  						super().test_model_from_pretrained()
  
  
  
  
  	@unittest.skipIf(torch_device == 'mps'		,       'mish op not supported in MPS'							)
  	def    __UpperCAmelCase							(     self							):
  
  						'''simple docstring'''
  
  
  
  
  
  						super().test_output()
  
  
  
  
  	def    __UpperCAmelCase							(     self							):
  
  						'''simple docstring'''
  
  
  
  
  
  						__a    : Union[str, Any]      					=						{
  						    'block_out_channels': (32, 64, 128, 256),
  						    'in_channels': 14,
  						    'out_channels': 14,
  						    'time_embedding_type': 'positional',
  						    'use_timestep_embedding': True,
  						    'flip_sin_to_cos': False,
  						    'freq_shift': 1.0,
  						    'out_block_type': 'OutConv1DBlock',
  						    'mid_block_type': 'MidResTemporalBlock1D',
  						    'down_block_types': ('DownResnetBlock1D', 'DownResnetBlock1D', 'DownResnetBlock1D', 'DownResnetBlock1D'),
  						    'up_block_types': ('UpResnetBlock1D', 'UpResnetBlock1D', 'UpResnetBlock1D'),
  						    'act_fn': 'mish',
  						}
  						__a    : str      					=						self.dummy_input
  						return init_dict, inputs_dict
  
  
  
  
  	@unittest.skipIf(torch_device == 'mps'		,       'mish op not supported in MPS'							)
  	def    __UpperCAmelCase							(     self							):
  
  						'''simple docstring'''
  
  
  
  
  
  						__a					,       __a    : List[str]      					=						UNetaDModel.from_pretrained(
  						    'bglick13/hopper-medium-v2-value-function-hor32'		,       output_loading_info=__a		,       subfolder='unet'							)
  						self.assertIsNotNone(__a							)
  						self.assertEqual(len(loading_info['missing_keys']							)		,       0							)
  
  						model.to(__a							)
  						__a    : Optional[int]      					=						model(**self.dummy_input							)
  
  						assert image is not None, "Make sure output is not None"
  
  
  
  
  	@unittest.skipIf(torch_device == 'mps'		,       'mish op not supported in MPS'							)
  	def    __UpperCAmelCase							(     self							):
  
  						'''simple docstring'''
  
  
  
  
  
  						__a    : List[Any]      					=						UNetaDModel.from_pretrained('bglick13/hopper-medium-v2-value-function-hor32'		,       subfolder='unet'							)
  						torch.manual_seed(0							)
  						if torch.cuda.is_available():
  											torch.cuda.manual_seed_all(0							)
  
  						__a    : str      					=						model.config.in_channels
  						__a    : int      					=						16
  						__a    : List[str]      					=						torch.randn((1, seq_len, num_features)							).permute(
  						    0		,       2		,       1							)  # match original, we can update values and remove
  						__a    : List[Any]      					=						torch.full((num_features,)		,       0							)
  
  						with torch.no_grad():
  											__a    : Tuple      					=						model(__a		,       __a							).sample.permute(0		,       2		,       1							)
  
  						__a    : int      					=						output[0, -3:, -3:].flatten()
  						# fmt: off
  						__a    : List[str]      					=						torch.tensor([-2.137172, 1.1426016, 0.3688687, -0.766922, 0.7303146, 0.11038864, -0.4760633, 0.13270172, 0.02591348]							)
  						# fmt: on
  						self.assertTrue(torch.allclose(__a		,       __a		,       rtol=1E-3							)							)
  
  
  
  
  	def    __UpperCAmelCase							(     self							):
  
  						'''simple docstring'''
  
  
  
  
  
  						pass
  
  
  
  
  
  	@slow
  	def    __UpperCAmelCase							(     self							):
  
  						'''simple docstring'''
  
  
  
  
  
  						__a    : Tuple      					=						'harmonai/maestro-150k'
  						__a    : Any      					=						UNetaDModel.from_pretrained(__a		,       subfolder='unet'							)
  						model.to(__a							)
  
  						__a    : Optional[Any]      					=						6_5536
  						__a    : str      					=						torch.sin(torch.arange(__a							)[None, None, :].repeat(1		,       2		,       1							)							).to(__a							)
  						__a    : int      					=						torch.tensor([1]							).to(__a							)
  
  						with torch.no_grad():
  											__a    : Optional[int]      					=						model(__a		,       __a							).sample
  
  						__a    : Union[str, Any]      					=						output.abs().sum()
  						__a    : Dict      					=						output.abs().max()
  
  						assert (output_sum - 224.0896).abs() < 0.5
  						assert (output_max - 0.0607).abs() < 4E-4
  
  
  
  
  
  class 							__UpperCamelCase       (	lowerCAmelCase_	,   lowerCAmelCase_	,   unittest.TestCase      ):
  	A_			    =					UNetaDModel
  	A_			    =					"sample"
  
  
  
  
  	@property
  	def    __UpperCAmelCase							(     self							):
  
  						'''simple docstring'''
  
  
  
  
  
  						__a    : str      					=						4
  						__a    : str      					=						14
  						__a    : Optional[int]      					=						16
  
  						__a    : str      					=						floats_tensor((batch_size, num_features, seq_len)							).to(__a							)
  						__a    : Optional[Any]      					=						torch.tensor([10] * batch_size							).to(__a							)
  
  						return {"sample": noise, "timestep": time_step}
  
  
  
  
  	@property
  	def    __UpperCAmelCase							(     self							):
  
  						'''simple docstring'''
  
  
  
  
  
  						return (4, 14, 16)
  
  
  
  
  	@property
  	def    __UpperCAmelCase							(     self							):
  
  						'''simple docstring'''
  
  
  
  
  
  						return (4, 14, 1)
  
  
  
  
  	@unittest.skipIf(torch_device == 'mps'		,       'mish op not supported in MPS'							)
  	def    __UpperCAmelCase							(     self							):
  
  						'''simple docstring'''
  
  
  
  
  
  						super().test_determinism()
  
  
  
  
  	@unittest.skipIf(torch_device == 'mps'		,       'mish op not supported in MPS'							)
  	def    __UpperCAmelCase							(     self							):
  
  						'''simple docstring'''
  
  
  
  
  
  						super().test_outputs_equivalence()
  
  
  
  
  	@unittest.skipIf(torch_device == 'mps'		,       'mish op not supported in MPS'							)
  	def    __UpperCAmelCase							(     self							):
  
  						'''simple docstring'''
  
  
  
  
  
  						super().test_from_save_pretrained()
  
  
  
  
  	@unittest.skipIf(torch_device == 'mps'		,       'mish op not supported in MPS'							)
  	def    __UpperCAmelCase							(     self							):
  
  						'''simple docstring'''
  
  
  
  
  
  						super().test_from_save_pretrained_variant()
  
  
  
  
  	@unittest.skipIf(torch_device == 'mps'		,       'mish op not supported in MPS'							)
  	def    __UpperCAmelCase							(     self							):
  
  						'''simple docstring'''
  
  
  
  
  
  						super().test_model_from_pretrained()
  
  
  
  
  	@unittest.skipIf(torch_device == 'mps'		,       'mish op not supported in MPS'							)
  	def    __UpperCAmelCase							(     self							):
  
  						'''simple docstring'''
  
  
  
  
  
  						__a					,       __a    : List[Any]      					=						self.prepare_init_args_and_inputs_for_common()
  						__a    : int      					=						self.model_class(**__a							)
  						model.to(__a							)
  						model.eval()
  
  						with torch.no_grad():
  											__a    : Optional[Any]      					=						model(**__a							)
  
  											if isinstance(__a		,       __a							):
  																__a    : Any      					=						output.sample
  
  						self.assertIsNotNone(__a							)
  						__a    : Tuple      					=						torch.Size((inputs_dict['sample'].shape[0], 1)							)
  						self.assertEqual(output.shape		,       __a		,       'Input and output shapes do not match'							)
  
  
  
  
  	def    __UpperCAmelCase							(     self							):
  
  						'''simple docstring'''
  
  
  
  
  
  						pass
  
  
  
  
  	def    __UpperCAmelCase							(     self							):
  
  						'''simple docstring'''
  
  
  
  
  
  						pass
  
  
  
  
  	def    __UpperCAmelCase							(     self							):
  
  						'''simple docstring'''
  
  
  
  
  
  						__a    : int      					=						{
  						    'in_channels': 14,
  						    'out_channels': 14,
  						    'down_block_types': ['DownResnetBlock1D', 'DownResnetBlock1D', 'DownResnetBlock1D', 'DownResnetBlock1D'],
  						    'up_block_types': [],
  						    'out_block_type': 'ValueFunction',
  						    'mid_block_type': 'ValueFunctionMidBlock1D',
  						    'block_out_channels': [32, 64, 128, 256],
  						    'layers_per_block': 1,
  						    'downsample_each_block': True,
  						    'use_timestep_embedding': True,
  						    'freq_shift': 1.0,
  						    'flip_sin_to_cos': False,
  						    'time_embedding_type': 'positional',
  						    'act_fn': 'mish',
  						}
  						__a    : Union[str, Any]      					=						self.dummy_input
  						return init_dict, inputs_dict
  
  
  
  
  	@unittest.skipIf(torch_device == 'mps'		,       'mish op not supported in MPS'							)
  	def    __UpperCAmelCase							(     self							):
  
  						'''simple docstring'''
  
  
  
  
  
  						__a					,       __a    : List[str]      					=						UNetaDModel.from_pretrained(
  						    'bglick13/hopper-medium-v2-value-function-hor32'		,       output_loading_info=__a		,       subfolder='value_function'							)
  						self.assertIsNotNone(__a							)
  						self.assertEqual(len(vf_loading_info['missing_keys']							)		,       0							)
  
  						value_function.to(__a							)
  						__a    : Optional[Any]      					=						value_function(**self.dummy_input							)
  
  						assert image is not None, "Make sure output is not None"
  
  
  
  
  	@unittest.skipIf(torch_device == 'mps'		,       'mish op not supported in MPS'							)
  	def    __UpperCAmelCase							(     self							):
  
  						'''simple docstring'''
  
  
  
  
  
  						__a					,       __a    : Dict      					=						UNetaDModel.from_pretrained(
  						    'bglick13/hopper-medium-v2-value-function-hor32'		,       output_loading_info=__a		,       subfolder='value_function'							)
  						torch.manual_seed(0							)
  						if torch.cuda.is_available():
  											torch.cuda.manual_seed_all(0							)
  
  						__a    : str      					=						value_function.config.in_channels
  						__a    : Dict      					=						14
  						__a    : List[str]      					=						torch.randn((1, seq_len, num_features)							).permute(
  						    0		,       2		,       1							)  # match original, we can update values and remove
  						__a    : List[Any]      					=						torch.full((num_features,)		,       0							)
  
  						with torch.no_grad():
  											__a    : Any      					=						value_function(__a		,       __a							).sample
  
  						# fmt: off
  						__a    : str      					=						torch.tensor([165.25] * seq_len							)
  						# fmt: on
  						self.assertTrue(torch.allclose(__a		,       __a		,       rtol=1E-3							)							)
  
  
  
  
  
  	def    __UpperCAmelCase							(     self							):
  
  						'''simple docstring'''
  
  
  
  
  
  						pass
  
  
  
  
style_context: 
  
  
  '''simple docstring'''
  
  
  
  import argparse
  
  import torch
  
  from diffusers import UNetaDConditionModel
  
  
  
  
  
  
  def 						lowerCamelCase							(_SCREAMING_SNAKE_CASE   :     List[str]		,   _SCREAMING_SNAKE_CASE   :     Dict		,   _SCREAMING_SNAKE_CASE   :     Any		,   _SCREAMING_SNAKE_CASE   :     Tuple=None		,   _SCREAMING_SNAKE_CASE   :     Any=None		,   _SCREAMING_SNAKE_CASE   :     List[Any]=None     ):
  					assert isinstance(_SCREAMING_SNAKE_CASE		,   _SCREAMING_SNAKE_CASE     ), "Paths should be a list of dicts containing 'old' and 'new' keys."
  
  					# Splits the attention layers into three variables.
  					if attention_paths_to_split is not None:
  										for path, path_map in attention_paths_to_split.items():
  															__a    : Union[str, Any]      					=						old_checkpoint[path]
  															__a    : List[str]      					=						old_tensor.shape[0] // 3
  
  															__a    : Dict      					=						(-1, channels) if len(old_tensor.shape     ) == 3 else (-1)
  
  															__a    : List[str]      					=						old_tensor.shape[0] // config['num_head_channels'] // 3
  
  															__a    : str      					=						old_tensor.reshape((num_heads, 3 * channels // num_heads) + old_tensor.shape[1:]     )
  															__a					,       __a					,       __a    : Dict      					=						old_tensor.split(channels // num_heads		,   dim=1     )
  
  															__a    : Optional[int]      					=						query.reshape(_SCREAMING_SNAKE_CASE     )
  															__a    : Tuple      					=						key.reshape(_SCREAMING_SNAKE_CASE     )
  															__a    : List[str]      					=						value.reshape(_SCREAMING_SNAKE_CASE     )
  
  					for path in paths:
  										__a    : Tuple      					=						path['new']
  
  										# These have already been assigned
  										if attention_paths_to_split is not None and new_path in attention_paths_to_split:
  															continue
  
  										if additional_replacements is not None:
  															for replacement in additional_replacements:
  																				__a    : int      					=						new_path.replace(replacement['old']		,   replacement['new']     )
  
          # proj_attn.weight has to be converted from conv 1D to linear
  										__a    : Any      					=						old_checkpoint[path['old']]
  										__a    : Tuple      					=						['proj_attn.weight']
  										__a    : Tuple      					=						['proj_out.weight', 'proj_in.weight']
  										if any(k in new_path for k in names     ):
  															__a    : Tuple      					=						weight[:, :, 0]
  										elif any(k in new_path for k in names_a     ) and len(weight.shape     ) > 2 and ".attentions." not in new_path:
  															__a    : int      					=						weight[:, :, 0]
  										else:
  															__a    : Union[str, Any]      					=						weight
  
  
  
  
  
  
  def 						lowerCamelCase							(_SCREAMING_SNAKE_CASE   :     Optional[int]		,   _SCREAMING_SNAKE_CASE   :     str=0     ):
  					__a    : Tuple      					=						[]
  					for old_item in old_list:
  										__a    : Dict      					=						old_item
  
  										#         new_item = new_item.replace('norm.weight', 'group_norm.weight')
  										#         new_item = new_item.replace('norm.bias', 'group_norm.bias')
  
  										#         new_item = new_item.replace('proj_out.weight', 'proj_attn.weight')
  										#         new_item = new_item.replace('proj_out.bias', 'proj_attn.bias')
  
  										#         new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments)
  
  										mapping.append({'old': old_item, 'new': new_item}     )
  
  					return mapping
  
  
  
  
  
  
  def 						lowerCamelCase							(_SCREAMING_SNAKE_CASE   :     Optional[Any]		,   _SCREAMING_SNAKE_CASE   :     Optional[int]=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 						lowerCamelCase							(_SCREAMING_SNAKE_CASE   :     List[str]		,   _SCREAMING_SNAKE_CASE   :     str=0     ):
  					__a    : Optional[Any]      					=						[]
  					for old_item in old_list:
  										mapping.append({'old': old_item, 'new': old_item}     )
  
  					return mapping
  
  
  
  
  
  
  def 						lowerCamelCase							(_SCREAMING_SNAKE_CASE   :     Any		,   _SCREAMING_SNAKE_CASE   :     Union[str, Any]=0     ):
  					__a    : Tuple      					=						[]
  					for old_item in old_list:
  										__a    : Optional[int]      					=						old_item.replace('in_layers.0'		,   'norm1'     )
  										__a    : Optional[Any]      					=						new_item.replace('in_layers.2'		,   'conv1'     )
  
  										__a    : str      					=						new_item.replace('out_layers.0'		,   'norm2'     )
  										__a    : str      					=						new_item.replace('out_layers.3'		,   'conv2'     )
  
  										__a    : List[Any]      					=						new_item.replace('emb_layers.1'		,   'time_emb_proj'     )
  										__a    : List[Any]      					=						new_item.replace('skip_connection'		,   'conv_shortcut'     )
  
  										__a    : int      					=						shave_segments(_SCREAMING_SNAKE_CASE		,   n_shave_prefix_segments=_SCREAMING_SNAKE_CASE     )
  
  										if "temopral_conv" not in old_item:
  															mapping.append({'old': old_item, 'new': new_item}     )
  
  					return mapping
  
  
  
  
  
  
  def 						lowerCamelCase							(_SCREAMING_SNAKE_CASE   :     str		,   _SCREAMING_SNAKE_CASE   :     Union[str, Any]		,   _SCREAMING_SNAKE_CASE   :     Any=None		,   _SCREAMING_SNAKE_CASE   :     Optional[Any]=False     ):
  					__a    : Dict      					=						{}
  					__a    : Tuple      					=						list(checkpoint.keys()     )
  
  					__a    : Optional[int]      					=						'model.diffusion_model.'
  
  					# at least a 100 parameters have to start with `model_ema` in order for the checkpoint to be EMA
  					if sum(k.startswith('model_ema'     ) for k in keys     ) > 100 and extract_ema:
  										print(F"""Checkpoint {path} has both EMA and non-EMA weights."""     )
  										print(
  										    'In this conversion only the EMA weights are extracted. If you want to instead extract the non-EMA'
  										    ' weights (useful to continue fine-tuning), please make sure to remove the `--extract_ema` flag.'     )
  										for key in keys:
  															if key.startswith('model.diffusion_model'     ):
  																				__a    : int      					=						'model_ema.' + ''.join(key.split('.'     )[1:]     )
  																				__a    : Optional[int]      					=						checkpoint.pop(_SCREAMING_SNAKE_CASE     )
  					else:
  										if sum(k.startswith('model_ema'     ) for k in keys     ) > 100:
  															print(
  															    'In this conversion only the non-EMA weights are extracted. If you want to instead extract the EMA'
  															    ' weights (usually better for inference), please make sure to add the `--extract_ema` flag.'     )
  
  										for key in keys:
  															__a    : Optional[Any]      					=						checkpoint.pop(_SCREAMING_SNAKE_CASE     )
  
  					__a    : Union[str, Any]      					=						{}
  
  					__a    : Optional[Any]      					=						unet_state_dict['time_embed.0.weight']
  					__a    : Union[str, Any]      					=						unet_state_dict['time_embed.0.bias']
  					__a    : Any      					=						unet_state_dict['time_embed.2.weight']
  					__a    : Dict      					=						unet_state_dict['time_embed.2.bias']
  
  					if config["class_embed_type"] is None:
  										# No parameters to port
  										...
  					elif config["class_embed_type"] == "timestep" or config["class_embed_type"] == "projection":
  										__a    : List[Any]      					=						unet_state_dict['label_emb.0.0.weight']
  										__a    : Union[str, Any]      					=						unet_state_dict['label_emb.0.0.bias']
  										__a    : Optional[Any]      					=						unet_state_dict['label_emb.0.2.weight']
  										__a    : Union[str, Any]      					=						unet_state_dict['label_emb.0.2.bias']
  					else:
  										raise NotImplementedError(F"""Not implemented `class_embed_type`: {config["class_embed_type"]}"""     )
  
  					__a    : str      					=						unet_state_dict['input_blocks.0.0.weight']
  					__a    : Tuple      					=						unet_state_dict['input_blocks.0.0.bias']
  
  					__a    : Optional[int]      					=						[v for v in unet_state_dict if v.startswith('input_blocks.0.1'     )]
  					__a    : List[str]      					=						renew_attention_paths(_SCREAMING_SNAKE_CASE     )
  					__a    : List[str]      					=						{'old': 'input_blocks.0.1', 'new': 'transformer_in'}
  					assign_to_checkpoint(_SCREAMING_SNAKE_CASE		,   _SCREAMING_SNAKE_CASE		,   _SCREAMING_SNAKE_CASE		,   additional_replacements=[meta_path]		,   config=_SCREAMING_SNAKE_CASE     )
  
  					__a    : Any      					=						unet_state_dict['out.0.weight']
  					__a    : Union[str, Any]      					=						unet_state_dict['out.0.bias']
  					__a    : Optional[int]      					=						unet_state_dict['out.2.weight']
  					__a    : Union[str, Any]      					=						unet_state_dict['out.2.bias']
  
  					# Retrieves the keys for the input blocks only
  					__a    : Optional[Any]      					=						len({'.'.join(layer.split('.'     )[:2]     ) for layer in unet_state_dict if 'input_blocks' in layer}     )
  					__a    : Dict      					=						{
  					    layer_id: [key for key in unet_state_dict if F"""input_blocks.{layer_id}""" in key]
  					    for layer_id in range(_SCREAMING_SNAKE_CASE     )
  					}
  
  					# Retrieves the keys for the middle blocks only
  					__a    : Dict      					=						len({'.'.join(layer.split('.'     )[:2]     ) for layer in unet_state_dict if 'middle_block' in layer}     )
  					__a    : Optional[Any]      					=						{
  					    layer_id: [key for key in unet_state_dict if F"""middle_block.{layer_id}""" in key]
  					    for layer_id in range(_SCREAMING_SNAKE_CASE     )
  					}
  
  					# Retrieves the keys for the output blocks only
  					__a    : Optional[Any]      					=						len({'.'.join(layer.split('.'     )[:2]     ) for layer in unet_state_dict if 'output_blocks' in layer}     )
  					__a    : Dict      					=						{
  					    layer_id: [key for key in unet_state_dict if F"""output_blocks.{layer_id}""" in key]
  					    for layer_id in range(_SCREAMING_SNAKE_CASE     )
  					}
  
  					for i in range(1		,   _SCREAMING_SNAKE_CASE     ):
  										__a    : List[str]      					=						(i - 1) // (config['layers_per_block'] + 1)
  										__a    : Tuple      					=						(i - 1) % (config['layers_per_block'] + 1)
  
  										__a    : Optional[Any]      					=						[
  										    key for key in input_blocks[i] if F"""input_blocks.{i}.0""" in key and F"""input_blocks.{i}.0.op""" not in key
  										]
  										__a    : List[str]      					=						[key for key in input_blocks[i] if F"""input_blocks.{i}.1""" in key]
  										__a    : Tuple      					=						[key for key in input_blocks[i] if F"""input_blocks.{i}.2""" in key]
  
  										if F"""input_blocks.{i}.op.weight""" in unet_state_dict:
  															__a    : Optional[Any]      					=						unet_state_dict.pop(
  															    F"""input_blocks.{i}.op.weight"""     )
  															__a    : Optional[int]      					=						unet_state_dict.pop(
  															    F"""input_blocks.{i}.op.bias"""     )
  
  										__a    : str      					=						renew_resnet_paths(_SCREAMING_SNAKE_CASE     )
  										__a    : Tuple      					=						{'old': F"""input_blocks.{i}.0""", 'new': F"""down_blocks.{block_id}.resnets.{layer_in_block_id}"""}
  										assign_to_checkpoint(
  										    _SCREAMING_SNAKE_CASE		,   _SCREAMING_SNAKE_CASE		,   _SCREAMING_SNAKE_CASE		,   additional_replacements=[meta_path]		,   config=_SCREAMING_SNAKE_CASE     )
  
  										__a    : Optional[int]      					=						[key for key in resnets if 'temopral_conv' in key]
  										__a    : Tuple      					=						renew_temp_conv_paths(_SCREAMING_SNAKE_CASE     )
  										__a    : List[Any]      					=						{
  										    'old': F"""input_blocks.{i}.0.temopral_conv""",
  										    'new': F"""down_blocks.{block_id}.temp_convs.{layer_in_block_id}""",
  										}
  										assign_to_checkpoint(
  										    _SCREAMING_SNAKE_CASE		,   _SCREAMING_SNAKE_CASE		,   _SCREAMING_SNAKE_CASE		,   additional_replacements=[meta_path]		,   config=_SCREAMING_SNAKE_CASE     )
  
  										if len(_SCREAMING_SNAKE_CASE     ):
  															__a    : List[Any]      					=						renew_attention_paths(_SCREAMING_SNAKE_CASE     )
  															__a    : Optional[Any]      					=						{'old': F"""input_blocks.{i}.1""", 'new': F"""down_blocks.{block_id}.attentions.{layer_in_block_id}"""}
  															assign_to_checkpoint(
  															    _SCREAMING_SNAKE_CASE		,   _SCREAMING_SNAKE_CASE		,   _SCREAMING_SNAKE_CASE		,   additional_replacements=[meta_path]		,   config=_SCREAMING_SNAKE_CASE     )
  
  										if len(_SCREAMING_SNAKE_CASE     ):
  															__a    : List[str]      					=						renew_attention_paths(_SCREAMING_SNAKE_CASE     )
  															__a    : str      					=						{
  															    'old': F"""input_blocks.{i}.2""",
  															    'new': F"""down_blocks.{block_id}.temp_attentions.{layer_in_block_id}""",
  															}
  															assign_to_checkpoint(
  															    _SCREAMING_SNAKE_CASE		,   _SCREAMING_SNAKE_CASE		,   _SCREAMING_SNAKE_CASE		,   additional_replacements=[meta_path]		,   config=_SCREAMING_SNAKE_CASE     )
  
  					__a    : Union[str, Any]      					=						middle_blocks[0]
  					__a    : str      					=						[key for key in resnet_a if 'temopral_conv' in key]
  					__a    : Dict      					=						middle_blocks[1]
  					__a    : str      					=						middle_blocks[2]
  					__a    : List[str]      					=						middle_blocks[3]
  					__a    : Any      					=						[key for key in resnet_a if 'temopral_conv' in key]
  
  					__a    : Optional[int]      					=						renew_resnet_paths(_SCREAMING_SNAKE_CASE     )
  					__a    : List[Any]      					=						{'old': 'middle_block.0', 'new': 'mid_block.resnets.0'}
  					assign_to_checkpoint(
  					    _SCREAMING_SNAKE_CASE		,   _SCREAMING_SNAKE_CASE		,   _SCREAMING_SNAKE_CASE		,   config=_SCREAMING_SNAKE_CASE		,   additional_replacements=[meta_path]     )
  
  					__a    : Optional[int]      					=						renew_temp_conv_paths(_SCREAMING_SNAKE_CASE     )
  					__a    : List[Any]      					=						{'old': 'middle_block.0.temopral_conv', 'new': 'mid_block.temp_convs.0'}
  					assign_to_checkpoint(
  					    _SCREAMING_SNAKE_CASE		,   _SCREAMING_SNAKE_CASE		,   _SCREAMING_SNAKE_CASE		,   config=_SCREAMING_SNAKE_CASE		,   additional_replacements=[meta_path]     )
  
  					__a    : Optional[int]      					=						renew_resnet_paths(_SCREAMING_SNAKE_CASE     )
  					__a    : Dict      					=						{'old': 'middle_block.3', 'new': 'mid_block.resnets.1'}
  					assign_to_checkpoint(
  					    _SCREAMING_SNAKE_CASE		,   _SCREAMING_SNAKE_CASE		,   _SCREAMING_SNAKE_CASE		,   config=_SCREAMING_SNAKE_CASE		,   additional_replacements=[meta_path]     )
  
  					__a    : Dict      					=						renew_temp_conv_paths(_SCREAMING_SNAKE_CASE     )
  					__a    : Optional[int]      					=						{'old': 'middle_block.3.temopral_conv', 'new': 'mid_block.temp_convs.1'}
  					assign_to_checkpoint(
  					    _SCREAMING_SNAKE_CASE		,   _SCREAMING_SNAKE_CASE		,   _SCREAMING_SNAKE_CASE		,   config=_SCREAMING_SNAKE_CASE		,   additional_replacements=[meta_path]     )
  
  					__a    : List[Any]      					=						renew_attention_paths(_SCREAMING_SNAKE_CASE     )
  					__a    : int      					=						{'old': 'middle_block.1', 'new': 'mid_block.attentions.0'}
  					assign_to_checkpoint(
  					    _SCREAMING_SNAKE_CASE		,   _SCREAMING_SNAKE_CASE		,   _SCREAMING_SNAKE_CASE		,   additional_replacements=[meta_path]		,   config=_SCREAMING_SNAKE_CASE     )
  
  					__a    : str      					=						renew_attention_paths(_SCREAMING_SNAKE_CASE     )
  					__a    : Optional[Any]      					=						{'old': 'middle_block.2', 'new': 'mid_block.temp_attentions.0'}
  					assign_to_checkpoint(
  					    _SCREAMING_SNAKE_CASE		,   _SCREAMING_SNAKE_CASE		,   _SCREAMING_SNAKE_CASE		,   additional_replacements=[meta_path]		,   config=_SCREAMING_SNAKE_CASE     )
  
  					for i in range(_SCREAMING_SNAKE_CASE     ):
  										__a    : List[str]      					=						i // (config['layers_per_block'] + 1)
  										__a    : Optional[int]      					=						i % (config['layers_per_block'] + 1)
  										__a    : Any      					=						[shave_segments(_SCREAMING_SNAKE_CASE		,   2     ) for name in output_blocks[i]]
  										__a    : Optional[Any]      					=						{}
  
  										for layer in output_block_layers:
  															__a					,       __a    : Optional[int]      					=						layer.split('.'     )[0], shave_segments(_SCREAMING_SNAKE_CASE		,   1     )
  															if layer_id in output_block_list:
  																				output_block_list[layer_id].append(_SCREAMING_SNAKE_CASE     )
  															else:
  																				__a    : int      					=						[layer_name]
  
  										if len(_SCREAMING_SNAKE_CASE     ) > 1:
  															__a    : Any      					=						[key for key in output_blocks[i] if F"""output_blocks.{i}.0""" in key]
  															__a    : List[Any]      					=						[key for key in output_blocks[i] if F"""output_blocks.{i}.1""" in key]
  															__a    : Optional[Any]      					=						[key for key in output_blocks[i] if F"""output_blocks.{i}.2""" in key]
  
  															__a    : Optional[int]      					=						renew_resnet_paths(_SCREAMING_SNAKE_CASE     )
  															__a    : List[Any]      					=						renew_resnet_paths(_SCREAMING_SNAKE_CASE     )
  
  															__a    : Dict      					=						{'old': F"""output_blocks.{i}.0""", 'new': F"""up_blocks.{block_id}.resnets.{layer_in_block_id}"""}
  															assign_to_checkpoint(
  															    _SCREAMING_SNAKE_CASE		,   _SCREAMING_SNAKE_CASE		,   _SCREAMING_SNAKE_CASE		,   additional_replacements=[meta_path]		,   config=_SCREAMING_SNAKE_CASE     )
  
  															__a    : Dict      					=						[key for key in resnets if 'temopral_conv' in key]
  															__a    : Tuple      					=						renew_temp_conv_paths(_SCREAMING_SNAKE_CASE     )
  															__a    : List[str]      					=						{
  															    'old': F"""output_blocks.{i}.0.temopral_conv""",
  															    'new': F"""up_blocks.{block_id}.temp_convs.{layer_in_block_id}""",
  															}
  															assign_to_checkpoint(
  															    _SCREAMING_SNAKE_CASE		,   _SCREAMING_SNAKE_CASE		,   _SCREAMING_SNAKE_CASE		,   additional_replacements=[meta_path]		,   config=_SCREAMING_SNAKE_CASE     )
  
  															__a    : List[str]      					=						{k: sorted(_SCREAMING_SNAKE_CASE     ) for k, v in output_block_list.items()}
  															if ["conv.bias", "conv.weight"] in output_block_list.values():
  																				__a    : Tuple      					=						list(output_block_list.values()     ).index(['conv.bias', 'conv.weight']     )
  																				__a    : str      					=						unet_state_dict[
  																				    F"""output_blocks.{i}.{index}.conv.weight"""
  																				]
  																				__a    : Tuple      					=						unet_state_dict[
  																				    F"""output_blocks.{i}.{index}.conv.bias"""
  																				]
  
  																				# Clear attentions as they have been attributed above.
  																				if len(_SCREAMING_SNAKE_CASE     ) == 2:
  																									__a    : Optional[Any]      					=						[]
  
  															if len(_SCREAMING_SNAKE_CASE     ):
  																				__a    : Tuple      					=						renew_attention_paths(_SCREAMING_SNAKE_CASE     )
  																				__a    : int      					=						{
  																				    'old': F"""output_blocks.{i}.1""",
  																				    'new': F"""up_blocks.{block_id}.attentions.{layer_in_block_id}""",
  																				}
  																				assign_to_checkpoint(
  																				    _SCREAMING_SNAKE_CASE		,   _SCREAMING_SNAKE_CASE		,   _SCREAMING_SNAKE_CASE		,   additional_replacements=[meta_path]		,   config=_SCREAMING_SNAKE_CASE     )
  
  															if len(_SCREAMING_SNAKE_CASE     ):
  																				__a    : Optional[Any]      					=						renew_attention_paths(_SCREAMING_SNAKE_CASE     )
  																				__a    : Optional[Any]      					=						{
  																				    'old': F"""output_blocks.{i}.2""",
  																				    'new': F"""up_blocks.{block_id}.temp_attentions.{layer_in_block_id}""",
  																				}
  																				assign_to_checkpoint(
  																				    _SCREAMING_SNAKE_CASE		,   _SCREAMING_SNAKE_CASE		,   _SCREAMING_SNAKE_CASE		,   additional_replacements=[meta_path]		,   config=_SCREAMING_SNAKE_CASE     )
  										else:
  															__a    : int      					=						renew_resnet_paths(_SCREAMING_SNAKE_CASE		,   n_shave_prefix_segments=1     )
  															for path in resnet_0_paths:
  																				__a    : str      					=						'.'.join(['output_blocks', str(_SCREAMING_SNAKE_CASE     ), path['old']]     )
  																				__a    : Dict      					=						'.'.join(['up_blocks', str(_SCREAMING_SNAKE_CASE     ), 'resnets', str(_SCREAMING_SNAKE_CASE     ), path['new']]     )
  																				__a    : Optional[int]      					=						unet_state_dict[old_path]
  
  															__a    : Union[str, Any]      					=						[l for l in output_block_layers if 'temopral_conv' in l]
  															for path in temopral_conv_paths:
  																				__a    : Any      					=						path.split('temopral_conv.'     )[-1]
  																				__a    : int      					=						'.'.join(['output_blocks', str(_SCREAMING_SNAKE_CASE     ), str(_SCREAMING_SNAKE_CASE     ), 'temopral_conv', pruned_path]     )
  																				__a    : str      					=						'.'.join(['up_blocks', str(_SCREAMING_SNAKE_CASE     ), 'temp_convs', str(_SCREAMING_SNAKE_CASE     ), pruned_path]     )
  																				__a    : Optional[Any]      					=						unet_state_dict[old_path]
  
  					return new_checkpoint
  
  
  if __name__ == "__main__":
  	__lowercase			:		Optional[int] =			argparse.ArgumentParser()
  
  	parser.add_argument(
  	    '--checkpoint_path', default=None, type=str, required=True, help='Path to the checkpoint to convert.'
  	)
  	parser.add_argument('--dump_path', default=None, type=str, required=True, help='Path to the output model.')
  	__lowercase			:		str =			parser.parse_args()
  
  	__lowercase			:		Dict =			torch.load(args.checkpoint_path, map_location='cpu')
  	__lowercase			:		Union[str, Any] =			UNetaDConditionModel()
  
  	__lowercase			:		Optional[Any] =			convert_ldm_unet_checkpoint(unet_checkpoint, unet.config)
  
  	__lowercase			:		Optional[int] =			set(unet.state_dict().keys()) - set(converted_ckpt.keys())
  	__lowercase			:		List[str] =			set(converted_ckpt.keys()) - set(unet.state_dict().keys())
  
  	assert len(diff_a) == len(diff_a) == 0, "Converted weights don't match"
  
  	# load state_dict
  	unet.load_state_dict(converted_ckpt)
  
  	unet.save_pretrained(args.dump_path)
  
  	# -- finish converting the unet --
  
  
  
  