

import argparse

import huggingface_hub
import k_diffusion as K
import torch

from diffusers import UNetaDConditionModel


_lowerCamelCase							:  Optional[Any]     =				'pcuenq/k-upscaler'

def      a__	(						UpperCAmelCase				:     List[str]							,					UpperCAmelCase				:     List[str]							,					*, UpperCAmelCase				:     str							,					UpperCAmelCase				:     str     )    ->		Optional[int]:
  UpperCAmelCase			:				Any										=					{
      # norm1
      f'''{diffusers_resnet_prefix}.norm1.linear.weight''': checkpoint[f'''{resnet_prefix}.main.0.mapper.weight'''],
      f'''{diffusers_resnet_prefix}.norm1.linear.bias''': checkpoint[f'''{resnet_prefix}.main.0.mapper.bias'''],
      # conv1
      f'''{diffusers_resnet_prefix}.conv1.weight''': checkpoint[f'''{resnet_prefix}.main.2.weight'''],
      f'''{diffusers_resnet_prefix}.conv1.bias''': checkpoint[f'''{resnet_prefix}.main.2.bias'''],
      # norm2
      f'''{diffusers_resnet_prefix}.norm2.linear.weight''': checkpoint[f'''{resnet_prefix}.main.4.mapper.weight'''],
      f'''{diffusers_resnet_prefix}.norm2.linear.bias''': checkpoint[f'''{resnet_prefix}.main.4.mapper.bias'''],
      # conv2
      f'''{diffusers_resnet_prefix}.conv2.weight''': checkpoint[f'''{resnet_prefix}.main.6.weight'''],
      f'''{diffusers_resnet_prefix}.conv2.bias''': checkpoint[f'''{resnet_prefix}.main.6.bias'''],
  }

  if resnet.conv_shortcut is not None:
    rv.update(
        {
            f'''{diffusers_resnet_prefix}.conv_shortcut.weight''': checkpoint[f'''{resnet_prefix}.skip.weight'''],
        }     )

  return rv

def      a__	(						UpperCAmelCase				:     str							,					*, UpperCAmelCase				:     Dict							,					UpperCAmelCase				:     str     )    ->		str:
  UpperCAmelCase					,		UpperCAmelCase					,		UpperCAmelCase			:				int										=					checkpoint[f'''{attention_prefix}.qkv_proj.weight'''].chunk(3							,					dim=0     )
  UpperCAmelCase					,		UpperCAmelCase					,		UpperCAmelCase			:				Any										=					checkpoint[f'''{attention_prefix}.qkv_proj.bias'''].chunk(3							,					dim=0     )
  UpperCAmelCase			:				Tuple										=					{
      # norm
      f'''{diffusers_attention_prefix}.norm1.linear.weight''': checkpoint[f'''{attention_prefix}.norm_in.mapper.weight'''],
      f'''{diffusers_attention_prefix}.norm1.linear.bias''': checkpoint[f'''{attention_prefix}.norm_in.mapper.bias'''],
      # to_q
      f'''{diffusers_attention_prefix}.attn1.to_q.weight''': weight_q.squeeze(-1     ).squeeze(-1     ),
      f'''{diffusers_attention_prefix}.attn1.to_q.bias''': bias_q,
      # to_k
      f'''{diffusers_attention_prefix}.attn1.to_k.weight''': weight_k.squeeze(-1     ).squeeze(-1     ),
      f'''{diffusers_attention_prefix}.attn1.to_k.bias''': bias_k,
      # to_v
      f'''{diffusers_attention_prefix}.attn1.to_v.weight''': weight_v.squeeze(-1     ).squeeze(-1     ),
      f'''{diffusers_attention_prefix}.attn1.to_v.bias''': bias_v,
      # to_out
      f'''{diffusers_attention_prefix}.attn1.to_out.0.weight''': checkpoint[f'''{attention_prefix}.out_proj.weight''']
      .squeeze(-1     )
      .squeeze(-1     ),
      f'''{diffusers_attention_prefix}.attn1.to_out.0.bias''': checkpoint[f'''{attention_prefix}.out_proj.bias'''],
  }

  return rv

def      a__	(						UpperCAmelCase				:     List[str]							,					*, UpperCAmelCase				:     Optional[int]							,					UpperCAmelCase				:     Tuple							,					UpperCAmelCase				:     List[Any]     )    ->		Dict:
  UpperCAmelCase					,		UpperCAmelCase			:				int										=					checkpoint[f'''{attention_prefix}.kv_proj.weight'''].chunk(2							,					dim=0     )
  UpperCAmelCase					,		UpperCAmelCase			:				str										=					checkpoint[f'''{attention_prefix}.kv_proj.bias'''].chunk(2							,					dim=0     )

  UpperCAmelCase			:				Any										=					{
      # norm2 (ada groupnorm)
      f'''{diffusers_attention_prefix}.norm{diffusers_attention_index}.linear.weight''': checkpoint[
          f'''{attention_prefix}.norm_dec.mapper.weight'''
      ],
      f'''{diffusers_attention_prefix}.norm{diffusers_attention_index}.linear.bias''': checkpoint[
          f'''{attention_prefix}.norm_dec.mapper.bias'''
      ],
      # layernorm on encoder_hidden_state
      f'''{diffusers_attention_prefix}.attn{diffusers_attention_index}.norm_cross.weight''': checkpoint[
          f'''{attention_prefix}.norm_enc.weight'''
      ],
      f'''{diffusers_attention_prefix}.attn{diffusers_attention_index}.norm_cross.bias''': checkpoint[
          f'''{attention_prefix}.norm_enc.bias'''
      ],
      # to_q
      f'''{diffusers_attention_prefix}.attn{diffusers_attention_index}.to_q.weight''': checkpoint[
          f'''{attention_prefix}.q_proj.weight'''
      ]
      .squeeze(-1     )
      .squeeze(-1     ),
      f'''{diffusers_attention_prefix}.attn{diffusers_attention_index}.to_q.bias''': checkpoint[
          f'''{attention_prefix}.q_proj.bias'''
      ],
      # to_k
      f'''{diffusers_attention_prefix}.attn{diffusers_attention_index}.to_k.weight''': weight_k.squeeze(-1     ).squeeze(-1     ),
      f'''{diffusers_attention_prefix}.attn{diffusers_attention_index}.to_k.bias''': bias_k,
      # to_v
      f'''{diffusers_attention_prefix}.attn{diffusers_attention_index}.to_v.weight''': weight_v.squeeze(-1     ).squeeze(-1     ),
      f'''{diffusers_attention_prefix}.attn{diffusers_attention_index}.to_v.bias''': bias_v,
      # to_out
      f'''{diffusers_attention_prefix}.attn{diffusers_attention_index}.to_out.0.weight''': checkpoint[
          f'''{attention_prefix}.out_proj.weight'''
      ]
      .squeeze(-1     )
      .squeeze(-1     ),
      f'''{diffusers_attention_prefix}.attn{diffusers_attention_index}.to_out.0.bias''': checkpoint[
          f'''{attention_prefix}.out_proj.bias'''
      ],
  }

  return rv

def      a__	(						UpperCAmelCase				:     Any							,					UpperCAmelCase				:     int							,					UpperCAmelCase				:     str							,					UpperCAmelCase				:     List[Any]     )    ->		List[str]:
  UpperCAmelCase			:				Union[str, Any]										=					'''inner_model.u_net.u_blocks''' if block_type == '''up''' else '''inner_model.u_net.d_blocks'''
  UpperCAmelCase			:				Dict										=					f'''{block_prefix}.{block_idx}'''

  UpperCAmelCase			:				int										=					{}

  if not hasattr(lowerCamelCase_							,					'''attentions'''     ):
    UpperCAmelCase			:				Optional[Any]										=					1  # resnet only
  elif not block.attentions[0].add_self_attention:
    UpperCAmelCase			:				Tuple										=					2  # resnet -> cross-attention
  else:
    UpperCAmelCase			:				Optional[int]										=					3  # resnet -> self-attention -> cross-attention)

  for resnet_idx, resnet in enumerate(block.resnets     ):
    # diffusers_resnet_prefix = f"{diffusers_up_block_prefix}.resnets.{resnet_idx}"
    UpperCAmelCase			:				Optional[int]										=					f'''{block_type}_blocks.{block_idx}.resnets.{resnet_idx}'''
    UpperCAmelCase			:				str										=					n * resnet_idx if block_type == '''up''' else n * resnet_idx + 1
    UpperCAmelCase			:				Dict										=					f'''{block_prefix}.{idx}''' if block_type == '''up''' else f'''{block_prefix}.{idx}'''

    diffusers_checkpoint.update(
        resnet_to_diffusers_checkpoint(
            lowerCamelCase_							,					lowerCamelCase_							,					diffusers_resnet_prefix=lowerCamelCase_							,					resnet_prefix=lowerCamelCase_     )     )

  if hasattr(lowerCamelCase_							,					'''attentions'''     ):
    for attention_idx, attention in enumerate(block.attentions     ):
      UpperCAmelCase			:				List[str]										=					f'''{block_type}_blocks.{block_idx}.attentions.{attention_idx}'''
      UpperCAmelCase			:				str										=					n * attention_idx + 1 if block_type == '''up''' else n * attention_idx + 2
      UpperCAmelCase			:				Dict										=					f'''{block_prefix}.{idx}'''
      UpperCAmelCase			:				Any										=					f'''{block_prefix}.{idx }'''
      UpperCAmelCase			:				int										=					1 if not attention.add_self_attention else 2
      UpperCAmelCase			:				List[Any]										=					(
          n * attention_idx + cross_attention_index
          if block_type == '''up'''
          else n * attention_idx + cross_attention_index + 1
      )
      UpperCAmelCase			:				List[Any]										=					f'''{block_prefix}.{idx }'''

      diffusers_checkpoint.update(
          cross_attn_to_diffusers_checkpoint(
              lowerCamelCase_							,					diffusers_attention_prefix=lowerCamelCase_							,					diffusers_attention_index=2							,					attention_prefix=lowerCamelCase_							,					)     )

      if attention.add_self_attention is True:
        diffusers_checkpoint.update(
            self_attn_to_diffusers_checkpoint(
                lowerCamelCase_							,					diffusers_attention_prefix=lowerCamelCase_							,					attention_prefix=lowerCamelCase_							,					)     )

  return diffusers_checkpoint

def      a__	(						UpperCAmelCase				:     Dict							,					UpperCAmelCase				:     int     )    ->		str:
  UpperCAmelCase			:				List[str]										=					{}

  # pre-processing
  diffusers_checkpoint.update(
      {
          '''conv_in.weight''': checkpoint['''inner_model.proj_in.weight'''],
          '''conv_in.bias''': checkpoint['''inner_model.proj_in.bias'''],
      }     )

  # timestep and class embedding
  diffusers_checkpoint.update(
      {
          '''time_proj.weight''': checkpoint['''inner_model.timestep_embed.weight'''].squeeze(-1     ),
          '''time_embedding.linear_1.weight''': checkpoint['''inner_model.mapping.0.weight'''],
          '''time_embedding.linear_1.bias''': checkpoint['''inner_model.mapping.0.bias'''],
          '''time_embedding.linear_2.weight''': checkpoint['''inner_model.mapping.2.weight'''],
          '''time_embedding.linear_2.bias''': checkpoint['''inner_model.mapping.2.bias'''],
          '''time_embedding.cond_proj.weight''': checkpoint['''inner_model.mapping_cond.weight'''],
      }     )

  # down_blocks
  for down_block_idx, down_block in enumerate(model.down_blocks     ):
    diffusers_checkpoint.update(block_to_diffusers_checkpoint(lowerCamelCase_							,					lowerCamelCase_							,					lowerCamelCase_							,					'''down'''     )     )

  # up_blocks
  for up_block_idx, up_block in enumerate(model.up_blocks     ):
    diffusers_checkpoint.update(block_to_diffusers_checkpoint(lowerCamelCase_							,					lowerCamelCase_							,					lowerCamelCase_							,					'''up'''     )     )

  # post-processing
  diffusers_checkpoint.update(
      {
          '''conv_out.weight''': checkpoint['''inner_model.proj_out.weight'''],
          '''conv_out.bias''': checkpoint['''inner_model.proj_out.bias'''],
      }     )

  return diffusers_checkpoint

def      a__	(						UpperCAmelCase				:     str     )    ->		Tuple:
  UpperCAmelCase			:				List[Any]										=					original_config['''input_channels'''] + original_config['''unet_cond_dim''']
  UpperCAmelCase			:				Dict										=					original_config['''input_channels'''] + (1 if original_config['''has_variance'''] else 0)

  UpperCAmelCase			:				Optional[int]										=					original_config['''channels''']

  assert (
      len(set(original_config['''depths''']     )     ) == 1
  ), "UNet2DConditionModel currently do not support blocks with different number of layers"
  UpperCAmelCase			:				Union[str, Any]										=					original_config['''depths'''][0]

  UpperCAmelCase			:				Tuple										=					original_config['''mapping_cond_dim''']
  UpperCAmelCase			:				Any										=					original_config['''cross_cond_dim''']

  UpperCAmelCase			:				Dict										=					[]
  UpperCAmelCase			:				Optional[Any]										=					[]
  for s, c in zip(original_config['''self_attn_depths''']							,					original_config['''cross_attn_depths''']     ):
    if s:
      UpperCAmelCase			:				Dict										=					'''self'''
      UpperCAmelCase			:				Any										=					'''cross''' if c else None
    elif c:
      UpperCAmelCase			:				str										=					'''cross'''
      UpperCAmelCase			:				Optional[Any]										=					None
    else:
      UpperCAmelCase			:				Tuple										=					None
      UpperCAmelCase			:				Optional[int]										=					None
    attna_types.append(lowerCamelCase_     )
    attna_types.append(lowerCamelCase_     )

  UpperCAmelCase			:				List[Any]										=					UNetaDConditionModel(
      in_channels=lowerCamelCase_							,					out_channels=lowerCamelCase_							,					down_block_types=('''KDownBlock2D''', '''KCrossAttnDownBlock2D''', '''KCrossAttnDownBlock2D''', '''KCrossAttnDownBlock2D''')							,					mid_block_type=lowerCamelCase_							,					up_block_types=('''KCrossAttnUpBlock2D''', '''KCrossAttnUpBlock2D''', '''KCrossAttnUpBlock2D''', '''KUpBlock2D''')							,					block_out_channels=lowerCamelCase_							,					layers_per_block=lowerCamelCase_							,					act_fn='''gelu'''							,					norm_num_groups=lowerCamelCase_							,					cross_attention_dim=lowerCamelCase_							,					attention_head_dim=64							,					time_cond_proj_dim=lowerCamelCase_							,					resnet_time_scale_shift='''scale_shift'''							,					time_embedding_type='''fourier'''							,					timestep_post_act='''gelu'''							,					conv_in_kernel=1							,					conv_out_kernel=1							,					)

  return unet

def      a__	(						UpperCAmelCase				:     Optional[int]     )    ->		Tuple:
  UpperCAmelCase			:				List[str]										=					torch.device('''cuda''' if torch.cuda.is_available() else '''cpu'''     )

  UpperCAmelCase			:				List[Any]										=					huggingface_hub.hf_hub_download(lowerCamelCase_							,					'''config_laion_text_cond_latent_upscaler_2.json'''     )
  UpperCAmelCase			:				List[Any]										=					huggingface_hub.hf_hub_download(
      lowerCamelCase_							,					'''laion_text_cond_latent_upscaler_2_1_00470000_slim.pth'''     )
  print(f'''loading original model configuration from {orig_config_path}'''     )
  print(f'''loading original model checkpoint from {orig_weights_path}'''     )

  print('''converting to diffusers unet'''     )
  UpperCAmelCase			:				int										=					K.config.load_config(open(lowerCamelCase_     )     )['''model''']
  UpperCAmelCase			:				List[str]										=					unet_model_from_original_config(lowerCamelCase_     )

  UpperCAmelCase			:				Optional[int]										=					torch.load(lowerCamelCase_							,					map_location=lowerCamelCase_     )['''model_ema''']
  UpperCAmelCase			:				Union[str, Any]										=					unet_to_diffusers_checkpoint(lowerCamelCase_							,					lowerCamelCase_     )

  model.load_state_dict(lowerCamelCase_							,					strict=lowerCamelCase_     )
  model.save_pretrained(args.dump_path     )
  print(f'''saving converted unet model in {args.dump_path}'''     )


if __name__ == "__main__":
     _lowerCamelCase							:  List[Any]     =				argparse.ArgumentParser()

     parser.add_argument("--dump_path", default=None, type=str, required=True, help="Path to the output model.")
     _lowerCamelCase							:  List[str]     =				parser.parse_args()

     main(args)













"""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 							_SCREAMING_SNAKE_CASE    (	__snake_case							,			__snake_case							,			unittest.TestCase				):





							"""simple docstring"""





							_SCREAMING_SNAKE_CASE     =UNetaDModel
							_SCREAMING_SNAKE_CASE     ='sample'




							@property
							def 			lowercase		( self:  Any     ):

												'''simple docstring'''

												a__ 							=	4
												a__ 							=	14
												a__ 							=	16

												a__ 							=	floats_tensor((batch_size, num_features, seq_len)     ).to(__A     )
												a__ 							=	torch.tensor([10] * batch_size     ).to(__A     )

												return {"sample": noise, "timestep": time_step}




							@property
							def 			lowercase		( self:  List[Any]     ):

												'''simple docstring'''

												return (4, 14, 16)




							@property
							def 			lowercase		( self:  List[Any]     ):

												'''simple docstring'''

												return (4, 14, 16)




							def 			lowercase		( self:  Optional[Any]     ):

												'''simple docstring'''

												pass




							def 			lowercase		( self:  Any     ):

												'''simple docstring'''

												pass




							@unittest.skipIf(torch_device == '''mps'''					,		'''mish op not supported in MPS'''     )
							def 			lowercase		( self:  Optional[int]     ):

												'''simple docstring'''

												super().test_determinism()




							@unittest.skipIf(torch_device == '''mps'''					,		'''mish op not supported in MPS'''     )
							def 			lowercase		( self:  Optional[Any]     ):

												'''simple docstring'''

												super().test_outputs_equivalence()




							@unittest.skipIf(torch_device == '''mps'''					,		'''mish op not supported in MPS'''     )
							def 			lowercase		( self:  Tuple     ):

												'''simple docstring'''

												super().test_from_save_pretrained()




							@unittest.skipIf(torch_device == '''mps'''					,		'''mish op not supported in MPS'''     )
							def 			lowercase		( self:  int     ):

												'''simple docstring'''

												super().test_from_save_pretrained_variant()




							@unittest.skipIf(torch_device == '''mps'''					,		'''mish op not supported in MPS'''     )
							def 			lowercase		( self:  int     ):

												'''simple docstring'''

												super().test_model_from_pretrained()




							@unittest.skipIf(torch_device == '''mps'''					,		'''mish op not supported in MPS'''     )
							def 			lowercase		( self:  Tuple     ):

												'''simple docstring'''

												super().test_output()




							def 			lowercase		( self:  Optional[Any]     ):

												'''simple docstring'''

												a__ 							=	{
												    '''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__ 							=	self.dummy_input
												return init_dict, inputs_dict




							@unittest.skipIf(torch_device == '''mps'''					,		'''mish op not supported in MPS'''     )
							def 			lowercase		( self:  str     ):

												'''simple docstring'''

												a__							,a__ 							=	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__ 							=	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 			lowercase		( self:  str     ):

												'''simple docstring'''

												a__ 							=	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__ 							=	model.config.in_channels
												a__ 							=	16
												a__ 							=	torch.randn((1, seq_len, num_features)     ).permute(
												    0					,		2					,		1     )  # match original, we can update values and remove
												a__ 							=	torch.full((num_features,)					,		0     )

												with torch.no_grad():
																	a__ 							=	model(__A					,		__A     ).sample.permute(0					,		2					,		1     )

												a__ 							=	output[0, -3:, -3:].flatten()
												# fmt: off
												a__ 							=	torch.tensor([-2.1_3_7_1_7_2, 1.1_4_2_6_0_1_6, 0.3_6_8_8_6_8_7, -0.7_6_6_9_2_2, 0.7_3_0_3_1_4_6, 0.1_1_0_3_8_8_6_4, -0.4_7_6_0_6_3_3, 0.1_3_2_7_0_1_7_2, 0.0_2_5_9_1_3_4_8]     )
												# fmt: on
												self.assertTrue(torch.allclose(__A					,		__A					,		rtol=1e-3     )     )




							def 			lowercase		( self:  int     ):

												'''simple docstring'''

												pass





							@slow
							def 			lowercase		( self:  Any     ):

												'''simple docstring'''

												a__ 							=	'''harmonai/maestro-150k'''
												a__ 							=	UNetaDModel.from_pretrained(__A					,		subfolder='''unet'''     )
												model.to(__A     )

												a__ 							=	65536
												a__ 							=	torch.sin(torch.arange(__A     )[None, None, :].repeat(1					,		2					,		1     )     ).to(__A     )
												a__ 							=	torch.tensor([1]     ).to(__A     )

												with torch.no_grad():
																	a__ 							=	model(__A					,		__A     ).sample

												a__ 							=	output.abs().sum()
												a__ 							=	output.abs().max()

												assert (output_sum - 2_2_4.0_8_9_6).abs() < 0.5
												assert (output_max - 0.0_6_0_7).abs() < 4e-4







class 							_SCREAMING_SNAKE_CASE    (	__snake_case							,			__snake_case							,			unittest.TestCase				):





							"""simple docstring"""





							_SCREAMING_SNAKE_CASE     =UNetaDModel
							_SCREAMING_SNAKE_CASE     ='sample'




							@property
							def 			lowercase		( self:  List[str]     ):

												'''simple docstring'''

												a__ 							=	4
												a__ 							=	14
												a__ 							=	16

												a__ 							=	floats_tensor((batch_size, num_features, seq_len)     ).to(__A     )
												a__ 							=	torch.tensor([10] * batch_size     ).to(__A     )

												return {"sample": noise, "timestep": time_step}




							@property
							def 			lowercase		( self:  Dict     ):

												'''simple docstring'''

												return (4, 14, 16)




							@property
							def 			lowercase		( self:  int     ):

												'''simple docstring'''

												return (4, 14, 1)




							@unittest.skipIf(torch_device == '''mps'''					,		'''mish op not supported in MPS'''     )
							def 			lowercase		( self:  int     ):

												'''simple docstring'''

												super().test_determinism()




							@unittest.skipIf(torch_device == '''mps'''					,		'''mish op not supported in MPS'''     )
							def 			lowercase		( self:  str     ):

												'''simple docstring'''

												super().test_outputs_equivalence()




							@unittest.skipIf(torch_device == '''mps'''					,		'''mish op not supported in MPS'''     )
							def 			lowercase		( self:  Optional[Any]     ):

												'''simple docstring'''

												super().test_from_save_pretrained()




							@unittest.skipIf(torch_device == '''mps'''					,		'''mish op not supported in MPS'''     )
							def 			lowercase		( self:  List[str]     ):

												'''simple docstring'''

												super().test_from_save_pretrained_variant()




							@unittest.skipIf(torch_device == '''mps'''					,		'''mish op not supported in MPS'''     )
							def 			lowercase		( self:  Optional[int]     ):

												'''simple docstring'''

												super().test_model_from_pretrained()




							@unittest.skipIf(torch_device == '''mps'''					,		'''mish op not supported in MPS'''     )
							def 			lowercase		( self:  int     ):

												'''simple docstring'''

												a__							,a__ 							=	self.prepare_init_args_and_inputs_for_common()
												a__ 							=	self.model_class(**__A     )
												model.to(__A     )
												model.eval()

												with torch.no_grad():
																	a__ 							=	model(**__A     )

																	if isinstance(__A					,		__A     ):
																						a__ 							=	output.sample

												self.assertIsNotNone(__A     )
												a__ 							=	torch.Size((inputs_dict['''sample'''].shape[0], 1)     )
												self.assertEqual(output.shape					,		__A					,		'''Input and output shapes do not match'''     )




							def 			lowercase		( self:  Dict     ):

												'''simple docstring'''

												pass




							def 			lowercase		( self:  Dict     ):

												'''simple docstring'''

												pass




							def 			lowercase		( self:  Any     ):

												'''simple docstring'''

												a__ 							=	{
												    '''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__ 							=	self.dummy_input
												return init_dict, inputs_dict




							@unittest.skipIf(torch_device == '''mps'''					,		'''mish op not supported in MPS'''     )
							def 			lowercase		( self:  List[Any]     ):

												'''simple docstring'''

												a__							,a__ 							=	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__ 							=	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 			lowercase		( self:  Union[str, Any]     ):

												'''simple docstring'''

												a__							,a__ 							=	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__ 							=	value_function.config.in_channels
												a__ 							=	14
												a__ 							=	torch.randn((1, seq_len, num_features)     ).permute(
												    0					,		2					,		1     )  # match original, we can update values and remove
												a__ 							=	torch.full((num_features,)					,		0     )

												with torch.no_grad():
																	a__ 							=	value_function(__A					,		__A     ).sample

												# fmt: off
												a__ 							=	torch.tensor([1_6_5.2_5] * seq_len     )
												# fmt: on
												self.assertTrue(torch.allclose(__A					,		__A					,		rtol=1e-3     )     )





							def 			lowercase		( self:  List[str]     ):

												'''simple docstring'''

												pass


