





"""simple docstring"""




import argparse

import huggingface_hub
import k_diffusion as K
import torch

from diffusers import UNetaDConditionModel


UpperCamelCase_	 ="""pcuenq/k-upscaler"""





def 			a_							(				_lowercase			,		_lowercase			,		*, _lowercase			,		_lowercase  ):
						_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 			a_							(				_lowercase			,		*, _lowercase			,		_lowercase  ):
						_UpperCamelCase  ,				_UpperCamelCase  ,				_UpperCamelCase     :   str      =       checkpoint[F"""{attention_prefix}.qkv_proj.weight"""].chunk(3			,		dim=0  )
						_UpperCamelCase  ,				_UpperCamelCase  ,				_UpperCamelCase     :   str      =       checkpoint[F"""{attention_prefix}.qkv_proj.bias"""].chunk(3			,		dim=0  )
						_UpperCamelCase     :   List[str]      =       {
						    # 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_							(				_lowercase			,		*, _lowercase			,		_lowercase			,		_lowercase  ):
						_UpperCamelCase  ,				_UpperCamelCase     :   Optional[Any]      =       checkpoint[F"""{attention_prefix}.kv_proj.weight"""].chunk(2			,		dim=0  )
						_UpperCamelCase  ,				_UpperCamelCase     :   Tuple      =       checkpoint[F"""{attention_prefix}.kv_proj.bias"""].chunk(2			,		dim=0  )

						_UpperCamelCase     :   Tuple      =       {
						    # 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_							(				_lowercase			,		_lowercase			,		_lowercase			,		_lowercase  ):
						_UpperCamelCase     :   Optional[int]      =       '''inner_model.u_net.u_blocks''' if block_type == '''up''' else '''inner_model.u_net.d_blocks'''
						_UpperCamelCase     :   Any      =       F"""{block_prefix}.{block_idx}"""

						_UpperCamelCase     :   Union[str, Any]      =       {}

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

						# 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 			a_							(				_lowercase  ):
						_UpperCamelCase     :   int      =       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     :   int      =       original_config['''depths'''][0]

						_UpperCamelCase     :   Optional[int]      =       original_config['''mapping_cond_dim''']
						_UpperCamelCase     :   Optional[int]      =       original_config['''cross_cond_dim''']

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

						_UpperCamelCase     :   Dict      =       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 			a_							(				_lowercase  ):
						_UpperCamelCase     :   Tuple      =       torch.device('''cuda''' if torch.cuda.is_available() else '''cpu'''  )

						_UpperCamelCase     :   str      =       huggingface_hub.hf_hub_download(_lowercase			,		'''config_laion_text_cond_latent_upscaler_2.json'''  )
						_UpperCamelCase     :   List[Any]      =       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'''  )
						_UpperCamelCase     :   Any      =       K.config.load_config(open(_lowercase  )  )['''model''']
						_UpperCamelCase     :   Tuple      =       unet_model_from_original_config(_lowercase  )

						_UpperCamelCase     :   List[Any]      =       torch.load(_lowercase			,		map_location=_lowercase  )['''model_ema''']
						_UpperCamelCase     :   Optional[int]      =       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__":
						UpperCamelCase_	 =argparse.ArgumentParser()

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

						main(args)








from collections.abc import Callable

__SCREAMING_SNAKE_CASE     =	Callable[[float], float]  # type alias for a real -> real function




def 						UpperCAmelCase (    _lowerCamelCase			,     _lowerCamelCase			,     _lowerCamelCase			,     ):
			A				:       int														=    float(_lowerCamelCase     )
			while True:
						try:
									A				:       Optional[int]														=    prev_guess - function(_lowerCamelCase     ) / derivative(_lowerCamelCase     )
						except ZeroDivisionError:
									raise ZeroDivisionError("Could not find root"     ) from None
						if abs(prev_guess - next_guess     ) < 10**-5:
									return next_guess
						A				:       Any														=    next_guess




def 						UpperCAmelCase (    _lowerCamelCase     ):
			return (x**3) - (2 * x) - 5




def 						UpperCAmelCase (    _lowerCamelCase     ):
			return 3 * (x**2) - 2


if __name__ == "__main__":
							print(newton(f, fa, 3))