






"""simple docstring"""





import argparse

import huggingface_hub
import k_diffusion as K
import torch

from diffusers import UNetaDConditionModel


A__       :			Optional[int]=						"""pcuenq/k-upscaler"""







def 	lowerCAmelCase_(			SCREAMING_SNAKE_CASE			,						SCREAMING_SNAKE_CASE			,						*, SCREAMING_SNAKE_CASE			,						SCREAMING_SNAKE_CASE       )       ->						str:
    """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       )       ->						Union[str, Any]:
    """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       )       ->						List[str]:
    """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       )       ->						str:
    """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=64			,						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       )       ->						Dict:
    """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__":
  A__       :			Any=						argparse.ArgumentParser()

  parser.add_argument("""--dump_path""", default=None, type=str, required=True, help="""Path to the output model.""")
  A__       :			List[Any]=						parser.parse_args()

  main(args)









import numpy as np
def 				UpperCAmelCase__		(					_SCREAMING_SNAKE_CASE :						Tuple					)->Union[str, Any]:
      _lowerCAmelCase      , _lowerCAmelCase			      =	a.shape
      _lowerCAmelCase			      =	min(_SCREAMING_SNAKE_CASE  ,					_SCREAMING_SNAKE_CASE					)
      _lowerCAmelCase			      =	np.eye(_SCREAMING_SNAKE_CASE					)
      _lowerCAmelCase			      =	a.copy()

      for k in range(t - 1					):
            # select a column of modified matrix A':
            _lowerCAmelCase			      =	r[k:, [k]]
            # construct first basis vector
            _lowerCAmelCase			      =	np.zeros_like(_SCREAMING_SNAKE_CASE					)
            _lowerCAmelCase			      =	1.0
            # determine scaling factor
            _lowerCAmelCase			      =	np.linalg.norm(_SCREAMING_SNAKE_CASE					)
            # construct vector v for Householder reflection
            _lowerCAmelCase			      =	x + np.sign(x[0]					) * alpha * ea
            v /= np.linalg.norm(_SCREAMING_SNAKE_CASE					)

            # construct the Householder matrix
            _lowerCAmelCase			      =	np.eye(m - k					) - 2.0 * v @ v.T
            # pad with ones and zeros as necessary
            _lowerCAmelCase			      =	np.block([[np.eye(_SCREAMING_SNAKE_CASE					), np.zeros((k, m - k)					)], [np.zeros((m - k, k)					), q_k]]					)

            _lowerCAmelCase			      =	q @ q_k.T
            _lowerCAmelCase			      =	q_k @ r

      return q, r


if __name__ == "__main__":
    import doctest

    doctest.testmod()