



import argparse

import huggingface_hub
import k_diffusion as K
import torch

from diffusers import UNetaDConditionModel


A__			   = """pcuenq/k-upscaler"""


def 	_UpperCAmelCase       (   snake_case			,		snake_case			,		*, snake_case			,		snake_case    ):





     """simple docstring"""

     _lowerCAmelCase      			=					{
         # 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 	_UpperCAmelCase       (   snake_case			,		*, snake_case			,		snake_case    ):





     """simple docstring"""

     _lowerCAmelCase			,			_lowerCAmelCase			,			_lowerCAmelCase      			=					checkpoint[F'{attention_prefix}.qkv_proj.weight'].chunk(3			,		dim=0    )
     _lowerCAmelCase			,			_lowerCAmelCase			,			_lowerCAmelCase      			=					checkpoint[F'{attention_prefix}.qkv_proj.bias'].chunk(3			,		dim=0    )
     _lowerCAmelCase      			=					{
         # 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 	_UpperCAmelCase       (   snake_case			,		*, snake_case			,		snake_case			,		snake_case    ):





     """simple docstring"""

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

     _lowerCAmelCase      			=					{
         # 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 	_UpperCAmelCase       (   snake_case			,		snake_case			,		snake_case			,		snake_case    ):





     """simple docstring"""

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

     _lowerCAmelCase      			=					{}

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





     """simple docstring"""

     _lowerCAmelCase      			=					{}

     # 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 	_UpperCAmelCase       (   snake_case    ):





     """simple docstring"""

     _lowerCAmelCase      			=					original_config["""input_channels"""] + original_config["""unet_cond_dim"""]
     _lowerCAmelCase      			=					original_config["""input_channels"""] + (1 if original_config["""has_variance"""] else 0)

     _lowerCAmelCase      			=					original_config["""channels"""]

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

     _lowerCAmelCase      			=					original_config["""mapping_cond_dim"""]
     _lowerCAmelCase      			=					original_config["""cross_cond_dim"""]

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

     _lowerCAmelCase      			=					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 	_UpperCAmelCase       (   snake_case    ):





     """simple docstring"""

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

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

     _lowerCAmelCase      			=					torch.load(snake_case			,		map_location=snake_case    )["""model_ema"""]
     _lowerCAmelCase      			=					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 typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


A__			   = {"""configuration_hubert""": ["""HUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """HubertConfig"""]}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	A__			   = [
	    """HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """HubertForCTC""",
	    """HubertForSequenceClassification""",
	    """HubertModel""",
	    """HubertPreTrainedModel""",
	]


try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	A__			   = [
	    """TF_HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """TFHubertForCTC""",
	    """TFHubertModel""",
	    """TFHubertPreTrainedModel""",
	]

if TYPE_CHECKING:
	from .configuration_hubert import HUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, HubertConfig

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_hubert import (
		    HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    HubertForCTC,
		    HubertForSequenceClassification,
		    HubertModel,
		    HubertPreTrainedModel,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_hubert import (
		    TF_HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFHubertForCTC,
		    TFHubertModel,
		    TFHubertPreTrainedModel,
		)


else:
	import sys

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



