

import argparse

import huggingface_hub
import k_diffusion as K
import torch

from diffusers import UNetaDConditionModel


SCREAMING_SNAKE_CASE						:Tuple      =			"""pcuenq/k-upscaler"""




def        lowerCAmelCase(	SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ,		*, SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )->		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        lowerCAmelCase(	SCREAMING_SNAKE_CASE_       ,		*, SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )->		List[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        lowerCAmelCase(	SCREAMING_SNAKE_CASE_       ,		*, SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )->		Union[str, 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        lowerCAmelCase(	SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )->		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(SCREAMING_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(
                SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ,		diffusers_resnet_prefix=SCREAMING_SNAKE_CASE_       ,		resnet_prefix=SCREAMING_SNAKE_CASE_       )       )

    if hasattr(SCREAMING_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(
                    SCREAMING_SNAKE_CASE_       ,		diffusers_attention_prefix=SCREAMING_SNAKE_CASE_       ,		diffusers_attention_index=2       ,		attention_prefix=SCREAMING_SNAKE_CASE_       ,		)       )

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

    return diffusers_checkpoint




def        lowerCAmelCase(	SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )->		Union[str, Any]:



    """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(SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ,		"down"       )       )

    # up_blocks
    for up_block_idx, up_block in enumerate(model.up_blocks       ):
        diffusers_checkpoint.update(block_to_diffusers_checkpoint(SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_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        lowerCAmelCase(	SCREAMING_SNAKE_CASE_       )->		Optional[int]:



    """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(SCREAMING_SNAKE_CASE_       )
        attna_types.append(SCREAMING_SNAKE_CASE_       )

    UpperCamelCase_          =		UNetaDConditionModel(
        in_channels=SCREAMING_SNAKE_CASE_       ,		out_channels=SCREAMING_SNAKE_CASE_       ,		down_block_types=("KDownBlock2D", "KCrossAttnDownBlock2D", "KCrossAttnDownBlock2D", "KCrossAttnDownBlock2D")       ,		mid_block_type=SCREAMING_SNAKE_CASE_       ,		up_block_types=("KCrossAttnUpBlock2D", "KCrossAttnUpBlock2D", "KCrossAttnUpBlock2D", "KUpBlock2D")       ,		block_out_channels=SCREAMING_SNAKE_CASE_       ,		layers_per_block=SCREAMING_SNAKE_CASE_       ,		act_fn="gelu"       ,		norm_num_groups=SCREAMING_SNAKE_CASE_       ,		cross_attention_dim=SCREAMING_SNAKE_CASE_       ,		attention_head_dim=6_4       ,		time_cond_proj_dim=SCREAMING_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        lowerCAmelCase(	SCREAMING_SNAKE_CASE_       )->		int:



    """simple docstring"""






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

    UpperCamelCase_          =		huggingface_hub.hf_hub_download(SCREAMING_SNAKE_CASE_       ,		"config_laion_text_cond_latent_upscaler_2.json"       )
    UpperCamelCase_          =		huggingface_hub.hf_hub_download(
        SCREAMING_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(SCREAMING_SNAKE_CASE_       )       )["model"]
    UpperCamelCase_          =		unet_model_from_original_config(SCREAMING_SNAKE_CASE_       )

    UpperCamelCase_          =		torch.load(SCREAMING_SNAKE_CASE_       ,		map_location=SCREAMING_SNAKE_CASE_       )["model_ema"]
    UpperCamelCase_          =		unet_to_diffusers_checkpoint(SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )

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


if __name__ == "__main__":
 SCREAMING_SNAKE_CASE						:str      =			argparse.ArgumentParser()

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

 main(args)



from functools import partial
from typing import Any, Callable, Dict, List, Type, TypeVar, Union, overload

import torch
import torch.nn as nn
import torch.types




def        lowerCAmelCase(	SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )->		torch.Tensor:



    """simple docstring"""






    if not inplace:
        UpperCamelCase_          =		ma + ma
    else:
        ma += ma

    return ma




def        lowerCAmelCase(	SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )->		torch.Tensor:



    """simple docstring"""






    UpperCamelCase_          =		-1 * len(SCREAMING_SNAKE_CASE_       )
    UpperCamelCase_          =		list(range(len(tensor.shape[:zero_index]       )       )       )
    return tensor.permute(first_inds + [zero_index + i for i in inds]       )




def        lowerCAmelCase(	SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )->		torch.Tensor:



    """simple docstring"""






    return t.reshape(t.shape[:-no_dims] + (-1,)       )




def        lowerCAmelCase(	SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_ = 1E-4       )->		torch.Tensor:



    """simple docstring"""






    UpperCamelCase_          =		mask.expand(*value.shape       )
    return torch.sum(mask * value       ,		dim=SCREAMING_SNAKE_CASE_       ) / (eps + torch.sum(SCREAMING_SNAKE_CASE_       ,		dim=SCREAMING_SNAKE_CASE_       ))




def        lowerCAmelCase(	SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_ = 2.3125       ,		SCREAMING_SNAKE_CASE_ = 21.6875       ,		SCREAMING_SNAKE_CASE_ = 6_4       )->		torch.Tensor:



    """simple docstring"""






    UpperCamelCase_          =		torch.linspace(SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ,		no_bins - 1       ,		device=pts.device       )
    UpperCamelCase_          =		torch.sqrt(torch.sum((pts.unsqueeze(-2       ) - pts.unsqueeze(-3       )) ** 2       ,		dim=-1       )       )
    return torch.bucketize(SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )




def        lowerCAmelCase(	SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )->		dict:



    """simple docstring"""






    UpperCamelCase_          =		dicts[0]
    UpperCamelCase_          =		{}
    for k, v in first.items():
        UpperCamelCase_          =		[d[k] for d in dicts]
        if isinstance(SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ):
            UpperCamelCase_          =		dict_multimap(SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )
        else:
            UpperCamelCase_          =		fn(SCREAMING_SNAKE_CASE_       )

    return new_dict




def        lowerCAmelCase(	SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )->		torch.Tensor:



    """simple docstring"""






    UpperCamelCase_          =		v_bins.view(((1,) * len(x.shape       )) + (len(SCREAMING_SNAKE_CASE_       ),)       )
    UpperCamelCase_          =		x[..., None] - reshaped_bins
    UpperCamelCase_          =		torch.argmin(torch.abs(SCREAMING_SNAKE_CASE_       )       ,		dim=-1       )
    return nn.functional.one_hot(SCREAMING_SNAKE_CASE_       ,		num_classes=len(SCREAMING_SNAKE_CASE_       )       ).float()




def        lowerCAmelCase(	SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_ = 0       ,		SCREAMING_SNAKE_CASE_ = 0       )->		torch.Tensor:



    """simple docstring"""






    UpperCamelCase_          =		[]
    for i, s in enumerate(data.shape[:no_batch_dims]       ):
        UpperCamelCase_          =		torch.arange(SCREAMING_SNAKE_CASE_       )
        UpperCamelCase_          =		r.view(*(*((1,) * i), -1, *((1,) * (len(inds.shape       ) - i - 1)))       )
        ranges.append(SCREAMING_SNAKE_CASE_       )

    UpperCamelCase_          =		[slice(SCREAMING_SNAKE_CASE_       ) for _ in range(len(data.shape       ) - no_batch_dims       )]
    UpperCamelCase_          =		inds
    ranges.extend(SCREAMING_SNAKE_CASE_       )
    # Matt note: Editing this to get around the behaviour of using a list as an array index changing
    # in recent Numpy versions
    return data[tuple(SCREAMING_SNAKE_CASE_       )]


SCREAMING_SNAKE_CASE						:Dict      =			TypeVar("""T""")




def        lowerCAmelCase(	SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )->		Dict[Any, Union[dict, list, tuple, Any]]:



    """simple docstring"""






    UpperCamelCase_          =		{}
    for k, v in dic.items():
        if isinstance(SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ):
            UpperCamelCase_          =		dict_map(SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )
        else:
            UpperCamelCase_          =		tree_map(SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )

    return new_dict




@overload
def        lowerCAmelCase(	SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )->		Any:



    """simple docstring"""






    ...




@overload
def        lowerCAmelCase(	SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )->		dict:



    """simple docstring"""






    ...




@overload
def        lowerCAmelCase(	SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )->		list:



    """simple docstring"""






    ...




@overload
def        lowerCAmelCase(	SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )->		tuple:



    """simple docstring"""






    ...




def        lowerCAmelCase(	SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )->		Optional[int]:



    """simple docstring"""






    if isinstance(SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ):
        return dict_map(SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )
    elif isinstance(SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ):
        return [tree_map(SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ) for x in tree]
    elif isinstance(SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ):
        return tuple(tree_map(SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ) for x in tree       )
    elif isinstance(SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ):
        return fn(SCREAMING_SNAKE_CASE_       )
    else:
        print(type(SCREAMING_SNAKE_CASE_       )       )
        raise ValueError("Not supported"       )
SCREAMING_SNAKE_CASE						:Dict      =			partial(tree_map, leaf_type=torch.Tensor)
