
import argparse

import huggingface_hub
import k_diffusion as K
import torch

from diffusers import UNetaDConditionModel


lowercase__				:				List[str]						=      "pcuenq/k-upscaler"




def 						A_			(				snake_case				: str			,   snake_case				: Dict			,   *, snake_case				: Any			,   snake_case				: List[str]			)   ->     Optional[Any]:



						'''simple docstring'''

						__UpperCamelCase  	=      {
						    # 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_			(				snake_case				: List[Any]			,   *, snake_case				: int			,   snake_case				: int			)   ->     str:



						'''simple docstring'''

						__UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase  	=      checkpoint[f"{attention_prefix}.qkv_proj.weight"].chunk(3			,   dim=0			)
						__UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase  	=      checkpoint[f"{attention_prefix}.qkv_proj.bias"].chunk(3			,   dim=0			)
						__UpperCamelCase  	=      {
						    # 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_			(				snake_case				: Optional[Any]			,   *, snake_case				: List[str]			,   snake_case				: int			,   snake_case				: Optional[int]			)   ->     List[Any]:



						'''simple docstring'''

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

						__UpperCamelCase  	=      {
						    # 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_			(				snake_case				: List[str]			,   snake_case				: str			,   snake_case				: int			,   snake_case				: List[Any]			)   ->     List[Any]:



						'''simple docstring'''

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

						__UpperCamelCase  	=      {}

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

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

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

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

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

						return diffusers_checkpoint




def 						A_			(				snake_case				: int			,   snake_case				: Any			)   ->     str:



						'''simple docstring'''

						__UpperCamelCase  	=      {}

						# 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(snake_case			,   snake_case			,   snake_case			,   '''down'''			)			)

						# up_blocks
						for up_block_idx, up_block in enumerate(model.up_blocks			):
												diffusers_checkpoint.update(block_to_diffusers_checkpoint(snake_case			,   snake_case			,   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_			(				snake_case				: Dict			)   ->     Union[str, Any]:



						'''simple docstring'''

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

						__UpperCamelCase  	=      original_config['''channels''']

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

						__UpperCamelCase  	=      original_config['''mapping_cond_dim''']
						__UpperCamelCase  	=      original_config['''cross_cond_dim''']

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

						__UpperCamelCase  	=      UNetaDConditionModel(
						    in_channels=snake_case			,   out_channels=snake_case			,   down_block_types=('''KDownBlock2D''', '''KCrossAttnDownBlock2D''', '''KCrossAttnDownBlock2D''', '''KCrossAttnDownBlock2D''')			,   mid_block_type=snake_case			,   up_block_types=('''KCrossAttnUpBlock2D''', '''KCrossAttnUpBlock2D''', '''KCrossAttnUpBlock2D''', '''KUpBlock2D''')			,   block_out_channels=snake_case			,   layers_per_block=snake_case			,   act_fn='''gelu'''			,   norm_num_groups=snake_case			,   cross_attention_dim=snake_case			,   attention_head_dim=64			,   time_cond_proj_dim=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_			(				snake_case				: Dict			)   ->     List[str]:



						'''simple docstring'''

						__UpperCamelCase  	=      torch.device('''cuda''' if torch.cuda.is_available() else '''cpu'''			)

						__UpperCamelCase  	=      huggingface_hub.hf_hub_download(snake_case			,   '''config_laion_text_cond_latent_upscaler_2.json'''			)
						__UpperCamelCase  	=      huggingface_hub.hf_hub_download(
						    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'''			)
						__UpperCamelCase  	=      K.config.load_config(open(snake_case			)			)['''model''']
						__UpperCamelCase  	=      unet_model_from_original_config(snake_case			)

						__UpperCamelCase  	=      torch.load(snake_case			,   map_location=snake_case			)['''model_ema''']
						__UpperCamelCase  	=      unet_to_diffusers_checkpoint(snake_case			,   snake_case			)

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


if __name__ == "__main__":
				lowercase__				:				Optional[Any]						=      argparse.ArgumentParser()

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

				main(args)








"""simple docstring"""






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
    is_vision_available,
)


lowercase  		=			{
    """configuration_mobilevitv2""": [
        """MOBILEVITV2_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """MobileViTV2Config""",
        """MobileViTV2OnnxConfig""",
    ],
}


try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowercase  		=			[
					    """MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """MobileViTV2ForImageClassification""",
					    """MobileViTV2ForSemanticSegmentation""",
					    """MobileViTV2Model""",
					    """MobileViTV2PreTrainedModel""",
					]

if TYPE_CHECKING:
					from .configuration_mobilevitva import (
					    MOBILEVITV2_PRETRAINED_CONFIG_ARCHIVE_MAP,
					    MobileViTVaConfig,
					    MobileViTVaOnnxConfig,
					)

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_mobilevitva import (
										    MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST,
										    MobileViTVaForImageClassification,
										    MobileViTVaForSemanticSegmentation,
										    MobileViTVaModel,
										    MobileViTVaPreTrainedModel,
										)

else:
					import sys

					lowercase  		=			_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)





