"""simple docstring"""


import argparse

import huggingface_hub
import k_diffusion as K
import torch

from diffusers import UNetaDConditionModel


lowerCAmelCase_:       Union[str, Any]			     =       "pcuenq/k-upscaler"







def        __a (		A     ,					A     ,					*, A     ,					A   ):






     '''simple docstring'''



     lowercase__								=				{
         # 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 (		A     ,					*, A     ,					A   ):






     '''simple docstring'''



     lowercase__								=				checkpoint[f'''{attention_prefix}.qkv_proj.weight'''].chunk(3     ,					dim=0   )
     lowercase__								=				checkpoint[f'''{attention_prefix}.qkv_proj.bias'''].chunk(3     ,					dim=0   )
     lowercase__								=				{
         # 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 (		A     ,					*, A     ,					A     ,					A   ):






     '''simple docstring'''



     lowercase__								=				checkpoint[f'''{attention_prefix}.kv_proj.weight'''].chunk(2     ,					dim=0   )
     lowercase__								=				checkpoint[f'''{attention_prefix}.kv_proj.bias'''].chunk(2     ,					dim=0   )

     lowercase__								=				{
         # 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 (		A     ,					A     ,					A     ,					A   ):






     '''simple docstring'''



     lowercase__								=				'''inner_model.u_net.u_blocks''' if block_type == '''up''' else '''inner_model.u_net.d_blocks'''
     lowercase__								=				f'''{block_prefix}.{block_idx}'''

     lowercase__								=				{}

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

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

     if hasattr(__SCREAMING_SNAKE_CASE     ,					"attentions"   ):
          for attention_idx, attention in enumerate(block.attentions   ):
               lowercase__								=				f'''{block_type}_blocks.{block_idx}.attentions.{attention_idx}'''
               lowercase__								=				n * attention_idx + 1 if block_type == '''up''' else n * attention_idx + 2
               lowercase__								=				f'''{block_prefix}.{idx}'''
               lowercase__								=				f'''{block_prefix}.{idx }'''
               lowercase__								=				1 if not attention.add_self_attention else 2
               lowercase__								=				(
                   n * attention_idx + cross_attention_index
                   if block_type == '''up'''
                   else n * attention_idx + cross_attention_index + 1
               )
               lowercase__								=				f'''{block_prefix}.{idx }'''

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

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

     return diffusers_checkpoint







def        __a (		A     ,					A   ):






     '''simple docstring'''



     lowercase__								=				{}

     # 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(__SCREAMING_SNAKE_CASE     ,					__SCREAMING_SNAKE_CASE     ,					__SCREAMING_SNAKE_CASE     ,					"down"   )   )

     # up_blocks
     for up_block_idx, up_block in enumerate(model.up_blocks   ):
          diffusers_checkpoint.update(block_to_diffusers_checkpoint(__SCREAMING_SNAKE_CASE     ,					__SCREAMING_SNAKE_CASE     ,					__SCREAMING_SNAKE_CASE     ,					"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 (		A   ):






     '''simple docstring'''



     lowercase__								=				original_config['''input_channels'''] + original_config['''unet_cond_dim''']
     lowercase__								=				original_config['''input_channels'''] + (1 if original_config['''has_variance'''] else 0)

     lowercase__								=				original_config['''channels''']

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

     lowercase__								=				original_config['''mapping_cond_dim''']
     lowercase__								=				original_config['''cross_cond_dim''']

     lowercase__								=				[]
     lowercase__								=				[]
     for s, c in zip(original_config["self_attn_depths"]     ,					original_config["cross_attn_depths"]   ):
          if s:
               lowercase__								=				'''self'''
               lowercase__								=				'''cross''' if c else None
          elif c:
               lowercase__								=				'''cross'''
               lowercase__								=				None
          else:
               lowercase__								=				None
               lowercase__								=				None
          attna_types.append(__SCREAMING_SNAKE_CASE   )
          attna_types.append(__SCREAMING_SNAKE_CASE   )

     lowercase__								=				UNetaDConditionModel(
         in_channels=__SCREAMING_SNAKE_CASE     ,					out_channels=__SCREAMING_SNAKE_CASE     ,					down_block_types=("KDownBlock2D", "KCrossAttnDownBlock2D", "KCrossAttnDownBlock2D", "KCrossAttnDownBlock2D")     ,					mid_block_type=__SCREAMING_SNAKE_CASE     ,					up_block_types=("KCrossAttnUpBlock2D", "KCrossAttnUpBlock2D", "KCrossAttnUpBlock2D", "KUpBlock2D")     ,					block_out_channels=__SCREAMING_SNAKE_CASE     ,					layers_per_block=__SCREAMING_SNAKE_CASE     ,					act_fn="gelu"     ,					norm_num_groups=__SCREAMING_SNAKE_CASE     ,					cross_attention_dim=__SCREAMING_SNAKE_CASE     ,					attention_head_dim=64     ,					time_cond_proj_dim=__SCREAMING_SNAKE_CASE     ,					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 (		A   ):






     '''simple docstring'''



     lowercase__								=				torch.device("cuda" if torch.cuda.is_available() else "cpu"   )

     lowercase__								=				huggingface_hub.hf_hub_download(__SCREAMING_SNAKE_CASE     ,					"config_laion_text_cond_latent_upscaler_2.json"   )
     lowercase__								=				huggingface_hub.hf_hub_download(
         __SCREAMING_SNAKE_CASE     ,					"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"   )
     lowercase__								=				K.config.load_config(open(__SCREAMING_SNAKE_CASE   )   )['''model''']
     lowercase__								=				unet_model_from_original_config(__SCREAMING_SNAKE_CASE   )

     lowercase__								=				torch.load(__SCREAMING_SNAKE_CASE     ,					map_location=__SCREAMING_SNAKE_CASE   )['''model_ema''']
     lowercase__								=				unet_to_diffusers_checkpoint(__SCREAMING_SNAKE_CASE     ,					__SCREAMING_SNAKE_CASE   )

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


if __name__ == "__main__":
 lowerCAmelCase_:       Optional[int]			     =       argparse.ArgumentParser()

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

 main(args)






'''simple docstring'''







import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
							import torch

if is_vision_available():
							from PIL import Image

							from transformers import DeiTImageProcessor






class 					lowerCAmelCase__  (    unittest.TestCase	):






					def __init__(  self							,				__SCREAMING_SNAKE_CASE							,				__SCREAMING_SNAKE_CASE=7							,				__SCREAMING_SNAKE_CASE=3							,				__SCREAMING_SNAKE_CASE=18							,				__SCREAMING_SNAKE_CASE=30							,				__SCREAMING_SNAKE_CASE=4_00							,				__SCREAMING_SNAKE_CASE=True							,				__SCREAMING_SNAKE_CASE=None							,				__SCREAMING_SNAKE_CASE=True							,				__SCREAMING_SNAKE_CASE=None							,				__SCREAMING_SNAKE_CASE=True							,				__SCREAMING_SNAKE_CASE=[0.5, 0.5, 0.5]							,				__SCREAMING_SNAKE_CASE=[0.5, 0.5, 0.5]							,				):


											"""simple docstring"""





											lowercase_      :	Union[str, Any]						=			size if size is not None else {'''height''': 20, '''width''': 20}
											lowercase_      :	List[Any]						=			crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}

											lowercase_      :	List[str]						=			parent
											lowercase_      :	Optional[Any]						=			batch_size
											lowercase_      :	int						=			num_channels
											lowercase_      :	List[str]						=			image_size
											lowercase_      :	Optional[Any]						=			min_resolution
											lowercase_      :	Tuple						=			max_resolution
											lowercase_      :	Union[str, Any]						=			do_resize
											lowercase_      :	List[str]						=			size
											lowercase_      :	Tuple						=			do_center_crop
											lowercase_      :	Dict						=			crop_size
											lowercase_      :	int						=			do_normalize
											lowercase_      :	Optional[int]						=			image_mean
											lowercase_      :	List[str]						=			image_std






					def       _snake_case       (  self						):


											"""simple docstring"""





											return {
											    "do_resize": self.do_resize,
											    "size": self.size,
											    "do_center_crop": self.do_center_crop,
											    "crop_size": self.crop_size,
											    "do_normalize": self.do_normalize,
											    "image_mean": self.image_mean,
											    "image_std": self.image_std,
											}







@require_torch
@require_vision
class 					lowerCAmelCase__  (    lowerCamelCase_       ,		unittest.TestCase	):
					lowerCAmelCase_	  =	DeiTImageProcessor if is_vision_available() else None
					lowerCAmelCase_	  =	True






					def       _snake_case       (  self						):


											"""simple docstring"""





											lowercase_      :	List[str]						=			DeiTImageProcessingTester(self						)






					@property
					def       _snake_case       (  self						):


											"""simple docstring"""





											return self.image_processor_tester.prepare_image_processor_dict()






					def       _snake_case       (  self						):


											"""simple docstring"""





											lowercase_      :	str						=			self.image_processing_class(**self.image_processor_dict						)
											self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE							,				'''do_resize'''						)						)
											self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE							,				'''size'''						)						)
											self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE							,				'''do_center_crop'''						)						)
											self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE							,				'''center_crop'''						)						)
											self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE							,				'''do_normalize'''						)						)
											self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE							,				'''image_mean'''						)						)
											self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE							,				'''image_std'''						)						)






					def       _snake_case       (  self						):


											"""simple docstring"""





											lowercase_      :	List[str]						=			self.image_processing_class.from_dict(self.image_processor_dict						)
											self.assertEqual(image_processor.size							,				{'''height''': 20, '''width''': 20}						)
											self.assertEqual(image_processor.crop_size							,				{'''height''': 18, '''width''': 18}						)

											lowercase_      :	List[str]						=			self.image_processing_class.from_dict(self.image_processor_dict							,				size=42							,				crop_size=84						)
											self.assertEqual(image_processor.size							,				{'''height''': 42, '''width''': 42}						)
											self.assertEqual(image_processor.crop_size							,				{'''height''': 84, '''width''': 84}						)






					def       _snake_case       (  self						):


											"""simple docstring"""





											pass






					def       _snake_case       (  self						):


											"""simple docstring"""





											lowercase_      :	Tuple						=			self.image_processing_class(**self.image_processor_dict						)
											# create random PIL images
											lowercase_      :	Optional[int]						=			prepare_image_inputs(self.image_processor_tester							,				equal_resolution=__SCREAMING_SNAKE_CASE						)
											for image in image_inputs:
																	self.assertIsInstance(__SCREAMING_SNAKE_CASE							,				Image.Image						)

											# Test not batched input
											lowercase_      :	List[Any]						=			image_processing(image_inputs[0]							,				return_tensors='''pt'''						).pixel_values
											self.assertEqual(
											    encoded_images.shape							,				(
											        1,
											        self.image_processor_tester.num_channels,
											        self.image_processor_tester.crop_size['''height'''],
											        self.image_processor_tester.crop_size['''width'''],
											    )							,				)

											# Test batched
											lowercase_      :	List[str]						=			image_processing(__SCREAMING_SNAKE_CASE							,				return_tensors='''pt'''						).pixel_values
											self.assertEqual(
											    encoded_images.shape							,				(
											        self.image_processor_tester.batch_size,
											        self.image_processor_tester.num_channels,
											        self.image_processor_tester.crop_size['''height'''],
											        self.image_processor_tester.crop_size['''width'''],
											    )							,				)






					def       _snake_case       (  self						):


											"""simple docstring"""





											lowercase_      :	List[Any]						=			self.image_processing_class(**self.image_processor_dict						)
											# create random numpy tensors
											lowercase_      :	Tuple						=			prepare_image_inputs(self.image_processor_tester							,				equal_resolution=__SCREAMING_SNAKE_CASE							,				numpify=__SCREAMING_SNAKE_CASE						)
											for image in image_inputs:
																	self.assertIsInstance(__SCREAMING_SNAKE_CASE							,				np.ndarray						)

											# Test not batched input
											lowercase_      :	Dict						=			image_processing(image_inputs[0]							,				return_tensors='''pt'''						).pixel_values
											self.assertEqual(
											    encoded_images.shape							,				(
											        1,
											        self.image_processor_tester.num_channels,
											        self.image_processor_tester.crop_size['''height'''],
											        self.image_processor_tester.crop_size['''width'''],
											    )							,				)

											# Test batched
											lowercase_      :	Union[str, Any]						=			image_processing(__SCREAMING_SNAKE_CASE							,				return_tensors='''pt'''						).pixel_values
											self.assertEqual(
											    encoded_images.shape							,				(
											        self.image_processor_tester.batch_size,
											        self.image_processor_tester.num_channels,
											        self.image_processor_tester.crop_size['''height'''],
											        self.image_processor_tester.crop_size['''width'''],
											    )							,				)






					def       _snake_case       (  self						):


											"""simple docstring"""





											lowercase_      :	List[str]						=			self.image_processing_class(**self.image_processor_dict						)
											# create random PyTorch tensors
											lowercase_      :	List[Any]						=			prepare_image_inputs(self.image_processor_tester							,				equal_resolution=__SCREAMING_SNAKE_CASE							,				torchify=__SCREAMING_SNAKE_CASE						)
											for image in image_inputs:
																	self.assertIsInstance(__SCREAMING_SNAKE_CASE							,				torch.Tensor						)

											# Test not batched input
											lowercase_      :	List[str]						=			image_processing(image_inputs[0]							,				return_tensors='''pt'''						).pixel_values
											self.assertEqual(
											    encoded_images.shape							,				(
											        1,
											        self.image_processor_tester.num_channels,
											        self.image_processor_tester.crop_size['''height'''],
											        self.image_processor_tester.crop_size['''width'''],
											    )							,				)

											# Test batched
											lowercase_      :	Optional[int]						=			image_processing(__SCREAMING_SNAKE_CASE							,				return_tensors='''pt'''						).pixel_values
											self.assertEqual(
											    encoded_images.shape							,				(
											        self.image_processor_tester.batch_size,
											        self.image_processor_tester.num_channels,
											        self.image_processor_tester.crop_size['''height'''],
											        self.image_processor_tester.crop_size['''width'''],
											    )							,				)



