






"""simple docstring"""


import collections

from .utils import ExplicitEnum, is_torch_available, logging


if is_torch_available():
       import torch


lowercase_								=	logging.get_logger(__name__)


class    snake_case						:


   '''simple docstring'''

   def __init__(      self					:	Optional[int],			_lowerCamelCase					:	Tuple,			_lowerCamelCase					:	Dict=21,			_lowerCamelCase					:	Any=[],			_lowerCamelCase					:	Any=None    ):

     '''simple docstring'''
     __A		     =					model
     __A		     =					trace_batch_nums
     __A		     =					abort_after_batch_num

     # keep a LIFO buffer of frames to dump as soon as inf/nan is encountered to give context to the problem emergence
     __A		     =					collections.deque([],			_lowerCamelCase    )
     __A		     =					[]
     __A		     =					0
     __A		     =					0
     __A		     =					False
     __A		     =					'''                 '''

     self.analyse_model()

     self.register_forward_hook()


   def 							_SCREAMING_SNAKE_CASE							(      self					:	Union[str, Any],			_lowerCamelCase					:	Union[str, Any]=None    ):

     '''simple docstring'''
     if frame is not None:
       self.expand_frame(_lowerCamelCase    )
     self.frames.append('''\n'''.join(self.frame    )    )
     __A		     =					[]  # start a new frame


   def 							_SCREAMING_SNAKE_CASE							(      self					:	List[Any],			_lowerCamelCase					:	Union[str, Any]    ):

     '''simple docstring'''
     self.frame.append(_lowerCamelCase    )


   def 							_SCREAMING_SNAKE_CASE							(      self					:	List[Any]    ):

     '''simple docstring'''
     print('''\n'''.join(self.frames    )    )
     __A		     =					[]


   def 							_SCREAMING_SNAKE_CASE							(      self					:	int    ):

     '''simple docstring'''
     __A		     =					[]


   def 							_SCREAMING_SNAKE_CASE							(      self					:	Any    ):

     '''simple docstring'''
     print(f'\nDetected inf/nan during batch_number={self.batch_number}'    )
     print(f'Last {len(self.frames    )} forward frames:'    )
     print(f'{"abs min":8} {"abs max":8} metadata'    )
     print('''\n'''.join(self.frames    )    )
     print('''\n\n'''    )
     __A		     =					[]


   def 							_SCREAMING_SNAKE_CASE							(      self					:	Tuple    ):

     '''simple docstring'''
     # extract the fully qualified module names, to be able to report at run time. e.g.:
     # encoder.block.2.layer.0.SelfAttention.o
     #
     # for shared weights only the first shared module name will be registered
     __A		     =					{m: name for name, m in self.model.named_modules()}
     # self.longest_module_name = max(len(v) for v in self.module_names.values())


   def 							_SCREAMING_SNAKE_CASE							(      self					:	Optional[Any],			_lowerCamelCase					:	List[Any],			_lowerCamelCase					:	int    ):

     '''simple docstring'''
     if torch.is_tensor(_lowerCamelCase    ):
       self.expand_frame(get_abs_min_max(_lowerCamelCase,			_lowerCamelCase    )    )
       if detect_overflow(_lowerCamelCase,			_lowerCamelCase    ):
         __A		     =					True
     elif var is None:
       self.expand_frame(f'{"None":>17} {ctx}'    )
     else:
       self.expand_frame(f'{"not a tensor":>17} {ctx}'    )


   def 							_SCREAMING_SNAKE_CASE							(      self					:	str    ):

     '''simple docstring'''
     self.expand_frame(f'\n\n{self.prefix} *** Starting batch number={self.batch_number} ***'    )
     self.expand_frame(f'{"abs min":8} {"abs max":8} metadata'    )


   def 							_SCREAMING_SNAKE_CASE							(      self					:	Dict    ):

     '''simple docstring'''
     self.expand_frame(f'{self.prefix} *** Finished batch number={self.batch_number-1} ***\n\n'    )


   def 							_SCREAMING_SNAKE_CASE							(      self					:	List[str],			_lowerCamelCase					:	Union[str, Any],			_lowerCamelCase					:	Dict,			_lowerCamelCase					:	Optional[int]    ):

     '''simple docstring'''
     self.expand_frame(f'{self.prefix} {self.module_names[module]} {module.__class__.__name__}'    )

     # params
     for name, p in module.named_parameters(recurse=_lowerCamelCase    ):
       self.analyse_variable(_lowerCamelCase,			_lowerCamelCase    )

     # inputs
     if isinstance(_lowerCamelCase,			_lowerCamelCase    ):
       for i, x in enumerate(_lowerCamelCase    ):
         self.analyse_variable(_lowerCamelCase,			f'input[{i}]'    )
     else:
       self.analyse_variable(_lowerCamelCase,			'''input'''    )

     # outputs
     if isinstance(_lowerCamelCase,			_lowerCamelCase    ):
       for i, x in enumerate(_lowerCamelCase    ):
         # possibly a tuple of tuples
         if isinstance(_lowerCamelCase,			_lowerCamelCase    ):
           for j, y in enumerate(_lowerCamelCase    ):
             self.analyse_variable(_lowerCamelCase,			f'output[{i}][{j}]'    )
         else:
           self.analyse_variable(_lowerCamelCase,			f'output[{i}]'    )
     else:
       self.analyse_variable(_lowerCamelCase,			'''output'''    )

     self.save_frame()


   def 							_SCREAMING_SNAKE_CASE							(      self					:	Dict    ):

     '''simple docstring'''
     self.model.apply(self._register_forward_hook    )


   def 							_SCREAMING_SNAKE_CASE							(      self					:	Dict,			_lowerCamelCase					:	Tuple    ):

     '''simple docstring'''
     module.register_forward_hook(self.forward_hook    )






   def 							_SCREAMING_SNAKE_CASE							(      self					:	int,			_lowerCamelCase					:	str,			_lowerCamelCase					:	str,			_lowerCamelCase					:	Dict    ):

     '''simple docstring'''
     # - input is a tuple of packed inputs (could be non-Tensors)
     # - output could be a Tensor or a tuple of Tensors and non-Tensors

     __A		     =					False

     __A		     =					True if self.batch_number in self.trace_batch_nums else False
     if trace_mode:
       self.reset_saved_frames()

     if self.total_calls == 0:
       self.batch_start_frame()
     self.total_calls += 1

     # count batch numbers - the very first forward hook of the batch will be called when the
     # batch completes - i.e. it gets called very last - we know this batch has finished
     if module == self.model:
       self.batch_number += 1
       __A		     =					True

     self.create_frame(_lowerCamelCase,			_lowerCamelCase,			_lowerCamelCase    )

     # if last_frame_of_batch:
     #     self.batch_end_frame()

     if trace_mode:
       self.trace_frames()

     if last_frame_of_batch:
       self.batch_start_frame()

     if self.detected_overflow and not trace_mode:
       self.dump_saved_frames()

       # now we can abort, as it's pointless to continue running
       raise ValueError(
           '''DebugUnderflowOverflow: inf/nan detected, aborting as there is no point running further. '''
           '''Please scroll up above this traceback to see the activation values prior to this event.'''    )

     # abort after certain batch if requested to do so
     if self.abort_after_batch_num is not None and self.batch_number > self.abort_after_batch_num:
       raise ValueError(
           f'DebugUnderflowOverflow: aborting after {self.batch_number} batches due to'
           f' `abort_after_batch_num={self.abort_after_batch_num}` arg'    )





def    lowerCAmelCase							(       __UpperCamelCase						,			__UpperCamelCase					):




  """simple docstring"""







  __A		     =					var.abs()
  return f'{abs_var.min():8.2e} {abs_var.max():8.2e} {ctx}'



def    lowerCAmelCase							(       __UpperCamelCase						,			__UpperCamelCase					):




  """simple docstring"""







  __A		     =					False
  if torch.isnan(__UpperCamelCase					).any().item():
    __A		     =					True
    print(f'{ctx} has nans'					)
  if torch.isinf(__UpperCamelCase					).any().item():
    __A		     =					True
    print(f'{ctx} has infs'					)

  # if needed to monitor large elements can enable the following
  if 0:  # and detected:
    __A		     =					var[torch.ge(var.abs()						,			1_0_0					)]
    if naaa.numel() > 0:
      print(f'{ctx}:  n100={naaa.numel()}'					)
    __A		     =					var[torch.ge(var.abs()						,			1_0_0_0					)]
    if naaaa.numel() > 0:
      print(f'{ctx}: n1000={naaaa.numel()}'					)
    __A		     =					var[torch.ge(var.abs()						,			1_0_0_0_0					)]
    if naaaaa.numel() > 0:
      print(f'{ctx}: n10000={naaaaa.numel()}'					)

  if 0:
    print(f'min={var.min():9.2e} max={var.max():9.2e}'					)

  if 0:
    print(f'min={var.min():9.2e} max={var.max():9.2e} var={var.var():9.2e} mean={var.mean():9.2e} ({ctx})'					)

  return detected





class    snake_case						(				_lowerCAmelCase		):


   '''simple docstring'''

   A_		:      int							=					"underflow_overflow"
   A_		:      Optional[Any]							=					"tpu_metrics_debug"







'''simple docstring'''

import argparse

import huggingface_hub
import k_diffusion as K
import torch

from diffusers import UNetaDConditionModel


lowercase__            =     "pcuenq/k-upscaler"




def  UpperCamelCase(	UpperCAmelCase_    ,			UpperCAmelCase_    ,			*, UpperCAmelCase_    ,			UpperCAmelCase_      ):
 UpperCAmelCase			:      Tuple		   =					{
     # 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  UpperCamelCase(	UpperCAmelCase_    ,			*, UpperCAmelCase_    ,			UpperCAmelCase_      ):
 UpperCAmelCase   ,					UpperCAmelCase   ,					UpperCAmelCase			:      List[str]		   =					checkpoint[F"""{attention_prefix}.qkv_proj.weight"""].chunk(3    ,			dim=0      )
 UpperCAmelCase   ,					UpperCAmelCase   ,					UpperCAmelCase			:      List[Any]		   =					checkpoint[F"""{attention_prefix}.qkv_proj.bias"""].chunk(3    ,			dim=0      )
 UpperCAmelCase			:      Optional[Any]		   =					{
     # 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  UpperCamelCase(	UpperCAmelCase_    ,			*, UpperCAmelCase_    ,			UpperCAmelCase_    ,			UpperCAmelCase_      ):
 UpperCAmelCase   ,					UpperCAmelCase			:      Union[str, Any]		   =					checkpoint[F"""{attention_prefix}.kv_proj.weight"""].chunk(2    ,			dim=0      )
 UpperCAmelCase   ,					UpperCAmelCase			:      Any		   =					checkpoint[F"""{attention_prefix}.kv_proj.bias"""].chunk(2    ,			dim=0      )

 UpperCAmelCase			:      Optional[int]		   =					{
     # 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  UpperCamelCase(	UpperCAmelCase_    ,			UpperCAmelCase_    ,			UpperCAmelCase_    ,			UpperCAmelCase_      ):
 UpperCAmelCase			:      str		   =					'inner_model.u_net.u_blocks' if block_type == 'up' else 'inner_model.u_net.d_blocks'
 UpperCAmelCase			:      Tuple		   =					F"""{block_prefix}.{block_idx}"""

 UpperCAmelCase			:      Union[str, Any]		   =					{}

 if not hasattr(UpperCAmelCase_    ,			'attentions'      ):
  UpperCAmelCase			:      List[Any]		   =					1  # resnet only
 elif not block.attentions[0].add_self_attention:
  UpperCAmelCase			:      str		   =					2  # resnet -> cross-attention
 else:
  UpperCAmelCase			:      List[str]		   =					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[Any]		   =					F"""{block_type}_blocks.{block_idx}.resnets.{resnet_idx}"""
  UpperCAmelCase			:      List[Any]		   =					n * resnet_idx if block_type == 'up' else n * resnet_idx + 1
  UpperCAmelCase			:      Optional[Any]		   =					F"""{block_prefix}.{idx}""" if block_type == 'up' else F"""{block_prefix}.{idx}"""

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

 if hasattr(UpperCAmelCase_    ,			'attentions'      ):
  for attention_idx, attention in enumerate(block.attentions      ):
   UpperCAmelCase			:      str		   =					F"""{block_type}_blocks.{block_idx}.attentions.{attention_idx}"""
   UpperCAmelCase			:      Optional[int]		   =					n * attention_idx + 1 if block_type == 'up' else n * attention_idx + 2
   UpperCAmelCase			:      int		   =					F"""{block_prefix}.{idx}"""
   UpperCAmelCase			:      Optional[int]		   =					F"""{block_prefix}.{idx }"""
   UpperCAmelCase			:      int		   =					1 if not attention.add_self_attention else 2
   UpperCAmelCase			:      Optional[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(
           UpperCAmelCase_    ,			diffusers_attention_prefix=UpperCAmelCase_    ,			diffusers_attention_index=2    ,			attention_prefix=UpperCAmelCase_    ,			)      )

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

 return diffusers_checkpoint




def  UpperCamelCase(	UpperCAmelCase_    ,			UpperCAmelCase_      ):
 UpperCAmelCase			:      Tuple		   =					{}

 # 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(UpperCAmelCase_    ,			UpperCAmelCase_    ,			UpperCAmelCase_    ,			'down'      )      )

 # up_blocks
 for up_block_idx, up_block in enumerate(model.up_blocks      ):
  diffusers_checkpoint.update(block_to_diffusers_checkpoint(UpperCAmelCase_    ,			UpperCAmelCase_    ,			UpperCAmelCase_    ,			'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  UpperCamelCase(	UpperCAmelCase_      ):
 UpperCAmelCase			:      List[Any]		   =					original_config['input_channels'] + original_config['unet_cond_dim']
 UpperCAmelCase			:      Any		   =					original_config['input_channels'] + (1 if original_config['has_variance'] else 0)

 UpperCAmelCase			:      Tuple		   =					original_config['channels']

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

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

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

 UpperCAmelCase			:      List[Any]		   =					UNetaDConditionModel(
     in_channels=UpperCAmelCase_    ,			out_channels=UpperCAmelCase_    ,			down_block_types=('KDownBlock2D', 'KCrossAttnDownBlock2D', 'KCrossAttnDownBlock2D', 'KCrossAttnDownBlock2D')    ,			mid_block_type=UpperCAmelCase_    ,			up_block_types=('KCrossAttnUpBlock2D', 'KCrossAttnUpBlock2D', 'KCrossAttnUpBlock2D', 'KUpBlock2D')    ,			block_out_channels=UpperCAmelCase_    ,			layers_per_block=UpperCAmelCase_    ,			act_fn='gelu'    ,			norm_num_groups=UpperCAmelCase_    ,			cross_attention_dim=UpperCAmelCase_    ,			attention_head_dim=64    ,			time_cond_proj_dim=UpperCAmelCase_    ,			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  UpperCamelCase(	UpperCAmelCase_      ):
 UpperCAmelCase			:      Optional[Any]		   =					torch.device('cuda' if torch.cuda.is_available() else 'cpu'      )

 UpperCAmelCase			:      str		   =					huggingface_hub.hf_hub_download(UpperCAmelCase_    ,			'config_laion_text_cond_latent_upscaler_2.json'      )
 UpperCAmelCase			:      Optional[int]		   =					huggingface_hub.hf_hub_download(
     UpperCAmelCase_    ,			'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			:      List[Any]		   =					K.config.load_config(open(UpperCAmelCase_      )      )['model']
 UpperCAmelCase			:      int		   =					unet_model_from_original_config(UpperCAmelCase_      )

 UpperCAmelCase			:      List[Any]		   =					torch.load(UpperCAmelCase_    ,			map_location=UpperCAmelCase_      )['model_ema']
 UpperCAmelCase			:      Optional[Any]		   =					unet_to_diffusers_checkpoint(UpperCAmelCase_    ,			UpperCAmelCase_      )

 model.load_state_dict(UpperCAmelCase_    ,			strict=UpperCAmelCase_      )
 model.save_pretrained(args.dump_path      )
 print(F"""saving converted unet model in {args.dump_path}"""      )


if __name__ == "__main__":
 lowercase__            =     argparse.ArgumentParser()

 parser.add_argument("--dump_path", default=None, type=str, required=True, help="Path to the output model.")
 lowercase__            =     parser.parse_args()

 main(args)




