




import argparse

import huggingface_hub
import k_diffusion as K
import torch

from diffusers import UNetaDConditionModel


a__		    =    '''pcuenq/k-upscaler'''





def 							A__   (snake_case      :		List[str]		,  snake_case      :		Dict		,  *, snake_case      :		Union[str, Any]		,  snake_case      :		Dict							)    ->	int:
     __UpperCamelCase				:  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 							A__   (snake_case      :		List[str]		,  *, snake_case      :		str		,  snake_case      :		Optional[Any]							)    ->	str:
     __UpperCamelCase				:  str								=			checkpoint[F'''{attention_prefix}.qkv_proj.weight'''].chunk(3		,  dim=0							)
     __UpperCamelCase				:  List[Any]								=			checkpoint[F'''{attention_prefix}.qkv_proj.bias'''].chunk(3		,  dim=0							)
     __UpperCamelCase				:  List[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 							A__   (snake_case      :		int		,  *, snake_case      :		int		,  snake_case      :		Optional[int]		,  snake_case      :		List[str]							)    ->	int:
     __UpperCamelCase				:  Tuple								=			checkpoint[F'''{attention_prefix}.kv_proj.weight'''].chunk(2		,  dim=0							)
     __UpperCamelCase				:  List[Any]								=			checkpoint[F'''{attention_prefix}.kv_proj.bias'''].chunk(2		,  dim=0							)

     __UpperCamelCase				:  Optional[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 							A__   (snake_case      :		Optional[Any]		,  snake_case      :		Tuple		,  snake_case      :		Any		,  snake_case      :		int							)    ->	List[str]:
     __UpperCamelCase				:  Tuple								=			"""inner_model.u_net.u_blocks""" if block_type == """up""" else """inner_model.u_net.d_blocks"""
     __UpperCamelCase				:  Union[str, Any]								=			F'''{block_prefix}.{block_idx}'''

     __UpperCamelCase				:  int								=			{}

     if not hasattr(snake_case		,  """attentions"""							):
          __UpperCamelCase				:  Dict								=			1  # resnet only
     elif not block.attentions[0].add_self_attention:
          __UpperCamelCase				:  Optional[int]								=			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				:  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				:  Tuple								=			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				:  Dict								=			F'''{block_type}_blocks.{block_idx}.attentions.{attention_idx}'''
               __UpperCamelCase				:  int								=			n * attention_idx + 1 if block_type == """up""" else n * attention_idx + 2
               __UpperCamelCase				:  Any								=			F'''{block_prefix}.{idx}'''
               __UpperCamelCase				:  Optional[Any]								=			F'''{block_prefix}.{idx }'''
               __UpperCamelCase				:  Union[str, Any]								=			1 if not attention.add_self_attention else 2
               __UpperCamelCase				:  Optional[Any]								=			(
                   n * attention_idx + cross_attention_index
                   if block_type == """up"""
                   else n * attention_idx + cross_attention_index + 1
               )
               __UpperCamelCase				:  str								=			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      :		Any		,  snake_case      :		int							)    ->	Any:
     __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(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      :		Union[str, Any]							)    ->	Union[str, Any]:
     __UpperCamelCase				:  List[str]								=			original_config["""input_channels"""] + original_config["""unet_cond_dim"""]
     __UpperCamelCase				:  str								=			original_config["""input_channels"""] + (1 if original_config["""has_variance"""] else 0)

     __UpperCamelCase				:  List[Any]								=			original_config["""channels"""]

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

     __UpperCamelCase				:  str								=			original_config["""mapping_cond_dim"""]
     __UpperCamelCase				:  Optional[int]								=			original_config["""cross_cond_dim"""]

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

     __UpperCamelCase				:  Any								=			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      :		int							)    ->	Union[str, Any]:
     __UpperCamelCase				:  Dict								=			torch.device("""cuda""" if torch.cuda.is_available() else """cpu"""							)

     __UpperCamelCase				:  Tuple								=			huggingface_hub.hf_hub_download(snake_case		,  """config_laion_text_cond_latent_upscaler_2.json"""							)
     __UpperCamelCase				:  List[str]								=			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				:  List[str]								=			K.config.load_config(open(snake_case							)							)["""model"""]
     __UpperCamelCase				:  str								=			unet_model_from_original_config(snake_case							)

     __UpperCamelCase				:  List[Any]								=			torch.load(snake_case		,  map_location=snake_case							)["""model_ema"""]
     __UpperCamelCase				:  str								=			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__":
 a__		    =    argparse.ArgumentParser()

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

 main(args)












from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


a__		    =    logging.get_logger(__name__)

a__		    =    {
    '''apple/mobilevit-small''': '''https://huggingface.co/apple/mobilevit-small/resolve/main/config.json''',
    '''apple/mobilevit-x-small''': '''https://huggingface.co/apple/mobilevit-x-small/resolve/main/config.json''',
    '''apple/mobilevit-xx-small''': '''https://huggingface.co/apple/mobilevit-xx-small/resolve/main/config.json''',
    '''apple/deeplabv3-mobilevit-small''': (
        '''https://huggingface.co/apple/deeplabv3-mobilevit-small/resolve/main/config.json'''
    ),
    '''apple/deeplabv3-mobilevit-x-small''': (
        '''https://huggingface.co/apple/deeplabv3-mobilevit-x-small/resolve/main/config.json'''
    ),
    '''apple/deeplabv3-mobilevit-xx-small''': (
        '''https://huggingface.co/apple/deeplabv3-mobilevit-xx-small/resolve/main/config.json'''
    ),
    # See all MobileViT models at https://huggingface.co/models?filter=mobilevit
}


class       SCREAMING_SNAKE_CASE_							(					_UpperCamelCase      ):



	"""simple docstring"""


	__magic_name__      :					Any			  =		'mobilevit'






	def __init__(				self		:	int				,    lowerCAmelCase		:	Dict=3				,    lowerCAmelCase		:	Dict=256				,    lowerCAmelCase		:	Any=2				,    lowerCAmelCase		:	Any=[144, 192, 240]				,    lowerCAmelCase		:	int=[16, 32, 64, 96, 128, 160, 640]				,    lowerCAmelCase		:	List[Any]=4				,    lowerCAmelCase		:	Tuple=2.0				,    lowerCAmelCase		:	Tuple=4.0				,    lowerCAmelCase		:	List[str]="silu"				,    lowerCAmelCase		:	Tuple=3				,    lowerCAmelCase		:	Tuple=32				,    lowerCAmelCase		:	Any=0.1				,    lowerCAmelCase		:	List[str]=0.0				,    lowerCAmelCase		:	Union[str, Any]=0.1				,    lowerCAmelCase		:	List[str]=0.02				,    lowerCAmelCase		:	Optional[Any]=1E-5				,    lowerCAmelCase		:	Union[str, Any]=True				,    lowerCAmelCase		:	List[str]=256				,    lowerCAmelCase		:	List[Any]=[6, 12, 18]				,    lowerCAmelCase		:	Tuple=0.1				,    lowerCAmelCase		:	List[str]=255				,    **lowerCAmelCase		:	Union[str, Any]				,    )  ->					Optional[Any]:







						"""simple docstring"""







						super().__init__(**lowerCAmelCase  )

						__UpperCamelCase				:  Tuple								=			num_channels
						__UpperCamelCase				:  Union[str, Any]								=			image_size
						__UpperCamelCase				:  List[Any]								=			patch_size
						__UpperCamelCase				:  Optional[int]								=			hidden_sizes
						__UpperCamelCase				:  Union[str, Any]								=			neck_hidden_sizes
						__UpperCamelCase				:  List[str]								=			num_attention_heads
						__UpperCamelCase				:  Union[str, Any]								=			mlp_ratio
						__UpperCamelCase				:  Tuple								=			expand_ratio
						__UpperCamelCase				:  Optional[Any]								=			hidden_act
						__UpperCamelCase				:  int								=			conv_kernel_size
						__UpperCamelCase				:  Any								=			output_stride
						__UpperCamelCase				:  int								=			hidden_dropout_prob
						__UpperCamelCase				:  Union[str, Any]								=			attention_probs_dropout_prob
						__UpperCamelCase				:  Dict								=			classifier_dropout_prob
						__UpperCamelCase				:  Any								=			initializer_range
						__UpperCamelCase				:  List[str]								=			layer_norm_eps
						__UpperCamelCase				:  Union[str, Any]								=			qkv_bias

						# decode head attributes for semantic segmentation
						__UpperCamelCase				:  Any								=			aspp_out_channels
						__UpperCamelCase				:  Union[str, Any]								=			atrous_rates
						__UpperCamelCase				:  Union[str, Any]								=			aspp_dropout_prob
						__UpperCamelCase				:  List[str]								=			semantic_loss_ignore_index






class       SCREAMING_SNAKE_CASE_							(					_UpperCamelCase      ):



	"""simple docstring"""


	__magic_name__      :					Optional[Any]			  =		version.parse('1.11'      )






	@property
	def 				lowerCamelCase__    (				self		:	Any  )  ->					Mapping[str, Mapping[int, str]]:







						"""simple docstring"""







						return OrderedDict([("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""})]  )






	@property
	def 				lowerCamelCase__    (				self		:	List[Any]  )  ->					Mapping[str, Mapping[int, str]]:







						"""simple docstring"""







						if self.task == "image-classification":
											return OrderedDict([("""logits""", {0: """batch"""})]  )
						else:
											return OrderedDict([("""last_hidden_state""", {0: """batch"""}), ("""pooler_output""", {0: """batch"""})]  )






	@property
	def 				lowerCamelCase__    (				self		:	Optional[int]  )  ->					float:







						"""simple docstring"""







						return 1E-4






