


'''simple docstring'''
import argparse

import huggingface_hub
import k_diffusion as K
import torch

from diffusers import UNetaDConditionModel


lowerCAmelCase__  =     'pcuenq/k-upscaler'



def 				lowerCAmelCase__	(       UpperCAmelCase    ,       UpperCAmelCase    ,       *, UpperCAmelCase    ,       UpperCAmelCase     ):






       """simple docstring"""





       snake_case__					:	List[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 				lowerCAmelCase__	(       UpperCAmelCase    ,       *, UpperCAmelCase    ,       UpperCAmelCase     ):






       """simple docstring"""





       snake_case__       ,   snake_case__       ,   snake_case__					:	Dict          =					checkpoint[f"""{attention_prefix}.qkv_proj.weight"""].chunk(3    ,       dim=0     )
       snake_case__       ,   snake_case__       ,   snake_case__					:	Any          =					checkpoint[f"""{attention_prefix}.qkv_proj.bias"""].chunk(3    ,       dim=0     )
       snake_case__					:	Union[str, 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 				lowerCAmelCase__	(       UpperCAmelCase    ,       *, UpperCAmelCase    ,       UpperCAmelCase    ,       UpperCAmelCase     ):






       """simple docstring"""





       snake_case__       ,   snake_case__					:	List[Any]          =					checkpoint[f"""{attention_prefix}.kv_proj.weight"""].chunk(2    ,       dim=0     )
       snake_case__       ,   snake_case__					:	Any          =					checkpoint[f"""{attention_prefix}.kv_proj.bias"""].chunk(2    ,       dim=0     )

       snake_case__					:	List[str]          =					{
           # 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 				lowerCAmelCase__	(       UpperCAmelCase    ,       UpperCAmelCase    ,       UpperCAmelCase    ,       UpperCAmelCase     ):






       """simple docstring"""





       snake_case__					:	int          =					"""inner_model.u_net.u_blocks""" if block_type == """up""" else """inner_model.u_net.d_blocks"""
       snake_case__					:	Optional[Any]          =					f"""{block_prefix}.{block_idx}"""

       snake_case__					:	int          =					{}

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

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

       if hasattr(lowercase_    ,       """attentions"""     ):
              for attention_idx, attention in enumerate(block.attentions     ):
                     snake_case__					:	Optional[Any]          =					f"""{block_type}_blocks.{block_idx}.attentions.{attention_idx}"""
                     snake_case__					:	Union[str, Any]          =					n * attention_idx + 1 if block_type == """up""" else n * attention_idx + 2
                     snake_case__					:	Union[str, Any]          =					f"""{block_prefix}.{idx}"""
                     snake_case__					:	List[str]          =					f"""{block_prefix}.{idx }"""
                     snake_case__					:	str          =					1 if not attention.add_self_attention else 2
                     snake_case__					:	str          =					(
                         n * attention_idx + cross_attention_index
                         if block_type == """up"""
                         else n * attention_idx + cross_attention_index + 1
                     )
                     snake_case__					:	Union[str, Any]          =					f"""{block_prefix}.{idx }"""

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

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

       return diffusers_checkpoint



def 				lowerCAmelCase__	(       UpperCAmelCase    ,       UpperCAmelCase     ):






       """simple docstring"""





       snake_case__					:	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(lowercase_    ,       lowercase_    ,       lowercase_    ,       """down"""     )     )

       # up_blocks
       for up_block_idx, up_block in enumerate(model.up_blocks     ):
              diffusers_checkpoint.update(block_to_diffusers_checkpoint(lowercase_    ,       lowercase_    ,       lowercase_    ,       """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 				lowerCAmelCase__	(       UpperCAmelCase     ):






       """simple docstring"""





       snake_case__					:	str          =					original_config["""input_channels"""] + original_config["""unet_cond_dim"""]
       snake_case__					:	Optional[int]          =					original_config["""input_channels"""] + (1 if original_config["""has_variance"""] else 0)

       snake_case__					:	List[str]          =					original_config["""channels"""]

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

       snake_case__					:	Optional[int]          =					original_config["""mapping_cond_dim"""]
       snake_case__					:	List[Any]          =					original_config["""cross_cond_dim"""]

       snake_case__					:	Any          =					[]
       snake_case__					:	Optional[Any]          =					[]
       for s, c in zip(original_config["""self_attn_depths"""]    ,       original_config["""cross_attn_depths"""]     ):
              if s:
                     snake_case__					:	int          =					"""self"""
                     snake_case__					:	Dict          =					"""cross""" if c else None
              elif c:
                     snake_case__					:	Any          =					"""cross"""
                     snake_case__					:	Any          =					None
              else:
                     snake_case__					:	Optional[int]          =					None
                     snake_case__					:	List[Any]          =					None
              attna_types.append(lowercase_     )
              attna_types.append(lowercase_     )

       snake_case__					:	Union[str, Any]          =					UNetaDConditionModel(
           in_channels=lowercase_    ,       out_channels=lowercase_    ,       down_block_types=("""KDownBlock2D""", """KCrossAttnDownBlock2D""", """KCrossAttnDownBlock2D""", """KCrossAttnDownBlock2D""")    ,       mid_block_type=lowercase_    ,       up_block_types=("""KCrossAttnUpBlock2D""", """KCrossAttnUpBlock2D""", """KCrossAttnUpBlock2D""", """KUpBlock2D""")    ,       block_out_channels=lowercase_    ,       layers_per_block=lowercase_    ,       act_fn="""gelu"""    ,       norm_num_groups=lowercase_    ,       cross_attention_dim=lowercase_    ,       attention_head_dim=64    ,       time_cond_proj_dim=lowercase_    ,       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 				lowerCAmelCase__	(       UpperCAmelCase     ):






       """simple docstring"""





       snake_case__					:	int          =					torch.device("""cuda""" if torch.cuda.is_available() else """cpu"""     )

       snake_case__					:	int          =					huggingface_hub.hf_hub_download(lowercase_    ,       """config_laion_text_cond_latent_upscaler_2.json"""     )
       snake_case__					:	Dict          =					huggingface_hub.hf_hub_download(
           lowercase_    ,       """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__					:	Dict          =					K.config.load_config(open(lowercase_     )     )["""model"""]
       snake_case__					:	Dict          =					unet_model_from_original_config(lowercase_     )

       snake_case__					:	List[str]          =					torch.load(lowercase_    ,       map_location=lowercase_     )["""model_ema"""]
       snake_case__					:	Dict          =					unet_to_diffusers_checkpoint(lowercase_    ,       lowercase_     )

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


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

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

     main(args)










'''simple docstring'''







import argparse
import os
import re


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_dummies.py
_lowerCamelCase				:		str				      =    'src/transformers'

# Matches is_xxx_available()
_lowerCamelCase				:		str				      =    re.compile(R'is\_([a-z_]*)_available()')
# Matches from xxx import bla
_lowerCamelCase				:		Union[str, Any]				      =    re.compile(R'\s+from\s+\S*\s+import\s+([^\(\s].*)\n')
_lowerCamelCase				:		Dict				      =    re.compile(R'^\s+if\s+not\s+\(?is\_[a-z_]*\_available\(\)')


_lowerCamelCase				:		int				      =    '\n{0} = None\n'

_lowerCamelCase				:		List[Any]				      =    '\nclass {0}(metaclass=DummyObject):\n    _backends = {1}\n\n    def __init__(self, *args, **kwargs):\n        requires_backends(self, {1})\n'


_lowerCamelCase				:		Any				      =    '\ndef {0}(*args, **kwargs):\n    requires_backends({0}, {1})\n'






def 						_lowercase						(      lowercase_    ):



		'''simple docstring'''







		if _re_test_backend.search(lowercase_    ) is None:
				return None
		_snake_case      			=			[b[0] for b in _re_backend.findall(lowercase_    )]
		backends.sort()
		return "_and_".join(lowercase_    )






def 						_lowercase						(      ):



		'''simple docstring'''







		with open(os.path.join(lowercase_		,				"__init__.py"    )		,				"r"		,				encoding="utf-8"		,				newline="\n"    ) as f:
				_snake_case      			=			f.readlines()

		# Get to the point we do the actual imports for type checking
		_snake_case      			=			0
		while not lines[line_index].startswith("if TYPE_CHECKING"    ):
				line_index += 1

		_snake_case      			=			{}
		# Go through the end of the file
		while line_index < len(lowercase_    ):
				# If the line is an if is_backend_available, we grab all objects associated.
				_snake_case      			=			find_backend(lines[line_index]    )
				if backend is not None:
						while not lines[line_index].startswith("    else:"    ):
								line_index += 1
						line_index += 1

						_snake_case      			=			[]
						# Until we unindent, add backend objects to the list
						while len(lines[line_index]    ) <= 1 or lines[line_index].startswith(" " * 8    ):
								_snake_case      			=			lines[line_index]
								_snake_case      			=			_re_single_line_import.search(lowercase_    )
								if single_line_import_search is not None:
										objects.extend(single_line_import_search.groups()[0].split(", "    )    )
								elif line.startswith(" " * 12    ):
										objects.append(line[12:-2]    )
								line_index += 1

						_snake_case      			=			objects
				else:
						line_index += 1

		return backend_specific_objects






def 						_lowercase						(      lowercase_		,				lowercase_    ):



		'''simple docstring'''







		if name.isupper():
				return DUMMY_CONSTANT.format(lowercase_    )
		elif name.islower():
				return DUMMY_FUNCTION.format(lowercase_		,				lowercase_    )
		else:
				return DUMMY_CLASS.format(lowercase_		,				lowercase_    )






def 						_lowercase						(      lowercase_=None    ):



		'''simple docstring'''







		if backend_specific_objects is None:
				_snake_case      			=			read_init()
		# For special correspondence backend to module name as used in the function requires_modulename
		_snake_case      			=			{}

		for backend, objects in backend_specific_objects.items():
				_snake_case      			=			"[" + ", ".join(f'''"{b}"''' for b in backend.split("_and_"    )    ) + "]"
				_snake_case      			=			"# This file is autogenerated by the command `make fix-copies`, do not edit.\n"
				dummy_file += "from ..utils import DummyObject, requires_backends\n\n"
				dummy_file += "\n".join([create_dummy_object(lowercase_		,				lowercase_    ) for o in objects]    )
				_snake_case      			=			dummy_file

		return dummy_files






def 						_lowercase						(      lowercase_=False    ):



		'''simple docstring'''







		_snake_case      			=			create_dummy_files()
		# For special correspondence backend to shortcut as used in utils/dummy_xxx_objects.py
		_snake_case      			=			{"torch": "pt"}

		# Locate actual dummy modules and read their content.
		_snake_case      			=			os.path.join(lowercase_		,				"utils"    )
		_snake_case      			=			{
		    backend: os.path.join(lowercase_		,				f'''dummy_{short_names.get(lowercase_		,				lowercase_    )}_objects.py'''    )
		    for backend in dummy_files.keys()
		}

		_snake_case      			=			{}
		for backend, file_path in dummy_file_paths.items():
				if os.path.isfile(lowercase_    ):
						with open(lowercase_		,				"r"		,				encoding="utf-8"		,				newline="\n"    ) as f:
								_snake_case      			=			f.read()
				else:
						_snake_case      			=			""

		for backend in dummy_files.keys():
				if dummy_files[backend] != actual_dummies[backend]:
						if overwrite:
								print(
								    f'''Updating transformers.utils.dummy_{short_names.get(lowercase_		,				lowercase_    )}_objects.py as the main '''
								    "__init__ has new objects."    )
								with open(dummy_file_paths[backend]		,				"w"		,				encoding="utf-8"		,				newline="\n"    ) as f:
										f.write(dummy_files[backend]    )
						else:
								raise ValueError(
								    "The main __init__ has objects that are not present in "
								    f'''transformers.utils.dummy_{short_names.get(lowercase_		,				lowercase_    )}_objects.py. Run `make fix-copies` '''
								    "to fix this."    )


if __name__ == "__main__":
						_lowerCamelCase				:		int				      =    argparse.ArgumentParser()
						parser.add_argument('--fix_and_overwrite', action='store_true', help='Whether to fix inconsistencies.')
						_lowerCamelCase				:		List[Any]				      =    parser.parse_args()

						check_dummies(args.fix_and_overwrite)





