


import argparse

import huggingface_hub
import k_diffusion as K
import torch

from diffusers import UNetaDConditionModel


a					:				Union[str, Any]       =      'pcuenq/k-upscaler'






def  _UpperCAmelCase						( __lowerCamelCase			,  __lowerCamelCase			,  *, __lowerCamelCase			,  __lowerCamelCase							)		->List[str]:


 '''simple docstring'''
 snake_case :			Optional[int]							=   {
     # 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						( __lowerCamelCase			,  *, __lowerCamelCase			,  __lowerCamelCase							)		->int:


 '''simple docstring'''
 snake_case :			Any							=   checkpoint[f'''{attention_prefix}.qkv_proj.weight'''].chunk(3			,  dim=0							)
 snake_case :			Tuple							=   checkpoint[f'''{attention_prefix}.qkv_proj.bias'''].chunk(3			,  dim=0							)
 snake_case :			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						( __lowerCamelCase			,  *, __lowerCamelCase			,  __lowerCamelCase			,  __lowerCamelCase							)		->str:


 '''simple docstring'''
 snake_case :			Any							=   checkpoint[f'''{attention_prefix}.kv_proj.weight'''].chunk(2			,  dim=0							)
 snake_case :			List[Any]							=   checkpoint[f'''{attention_prefix}.kv_proj.bias'''].chunk(2			,  dim=0							)

 snake_case :			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  _UpperCAmelCase						( __lowerCamelCase			,  __lowerCamelCase			,  __lowerCamelCase			,  __lowerCamelCase							)		->str:


 '''simple docstring'''
 snake_case :			Tuple							=   """inner_model.u_net.u_blocks""" if block_type == """up""" else """inner_model.u_net.d_blocks"""
 snake_case :			Optional[int]							=   f'''{block_prefix}.{block_idx}'''

 snake_case :			List[Any]							=   {}

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


 '''simple docstring'''
 snake_case :			int							=   {}

 # 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						( __lowerCamelCase							)		->int:


 '''simple docstring'''
 snake_case :			List[str]							=   original_config["""input_channels"""] + original_config["""unet_cond_dim"""]
 snake_case :			Tuple							=   original_config["""input_channels"""] + (1 if original_config["""has_variance"""] else 0)

 snake_case :			Optional[int]							=   original_config["""channels"""]

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

 snake_case :			Tuple							=   original_config["""mapping_cond_dim"""]
 snake_case :			Optional[Any]							=   original_config["""cross_cond_dim"""]

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

 snake_case :			List[str]							=   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						( __lowerCamelCase							)		->Optional[Any]:


 '''simple docstring'''
 snake_case :			Dict							=   torch.device('''cuda''' if torch.cuda.is_available() else '''cpu'''							)

 snake_case :			int							=   huggingface_hub.hf_hub_download(UpperCAmelCase__			,  '''config_laion_text_cond_latent_upscaler_2.json'''							)
 snake_case :			Union[str, Any]							=   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'''							)
 snake_case :			Optional[int]							=   K.config.load_config(open(UpperCAmelCase__							)							)["""model"""]
 snake_case :			Any							=   unet_model_from_original_config(UpperCAmelCase__							)

 snake_case :			Optional[int]							=   torch.load(UpperCAmelCase__			,  map_location=UpperCAmelCase__							)["""model_ema"""]
 snake_case :			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__":
  a					:				Optional[int]       =      argparse.ArgumentParser()

  parser.add_argument('--dump_path', default=None, type=str, required=True, help='Path to the output model.')
  a					:				Union[str, Any]       =      parser.parse_args()

  main(args)














"""simple docstring"""



import unittest

from transformers import load_tool

from .test_tools_common import ToolTesterMixin


A_     =				'''
Hugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf originally as a company that developed a chatbot app targeted at teenagers.[2] After open-sourcing the model behind the chatbot, the company pivoted to focus on being a platform for machine learning.

In March 2021, Hugging Face raised $40 million in a Series B funding round.[3]

On April 28, 2021, the company launched the BigScience Research Workshop in collaboration with several other research groups to release an open large language model.[4] In 2022, the workshop concluded with the announcement of BLOOM, a multilingual large language model with 176 billion parameters.[5]
'''






class 				__SCREAMING_SNAKE_CASE    (					unittest.TestCase							,		UpperCamelCase		):


							def     _UpperCamelCase							(      self   :    int				):
													'''simple docstring'''





													A__    :	Any			=	load_tool("""summarization"""				)
													self.tool.setup()
													A__    :	List[Any]			=	load_tool("""summarization"""      ,     remote=snake_case				)


							def     _UpperCamelCase							(      self   :    Any				):
													'''simple docstring'''





													A__    :	List[Any]			=	self.tool(snake_case				)
													self.assertEqual(
													    snake_case      ,     """Hugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf. In March 2021, Hugging Face raised $40 million in a Series B funding round. On April 28, 2021, the company launched the BigScience Research Workshop in collaboration with several other research groups to release an open large language model. In 2022, the workshop concluded with the announcement of BLOOM."""      ,     )


							def     _UpperCamelCase							(      self   :    Optional[int]				):
													'''simple docstring'''





													A__    :	List[str]			=	self.remote_tool(snake_case				)
													self.assertEqual(
													    snake_case      ,     """Hugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf. In March 2021, Hugging Face raised $40 million in a Series B funding round. On April 28, 2021, the company launched the BigScience Research Workshop in collaboration with several other research groups to release an open large language model. In 2022, the workshop concluded with the announcement of BLOOM."""      ,     )


							def     _UpperCamelCase							(      self   :    Dict				):
													'''simple docstring'''





													A__    :	List[Any]			=	self.tool(text=snake_case				)
													self.assertEqual(
													    snake_case      ,     """Hugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf. In March 2021, Hugging Face raised $40 million in a Series B funding round. On April 28, 2021, the company launched the BigScience Research Workshop in collaboration with several other research groups to release an open large language model. In 2022, the workshop concluded with the announcement of BLOOM."""      ,     )


							def     _UpperCamelCase							(      self   :    Union[str, Any]				):
													'''simple docstring'''





													A__    :	Optional[int]			=	self.remote_tool(text=snake_case				)
													self.assertEqual(
													    snake_case      ,     """Hugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf. In March 2021, Hugging Face raised $40 million in a Series B funding round. On April 28, 2021, the company launched the BigScience Research Workshop in collaboration with several other research groups to release an open large language model. In 2022, the workshop concluded with the announcement of BLOOM."""      ,     )






