


import argparse

import huggingface_hub
import k_diffusion as K
import torch

from diffusers import UNetaDConditionModel


_A									=		'pcuenq/k-upscaler'

def 		_UpperCAmelCase							(     SCREAMING_SNAKE_CASE__     :   Optional[int]     ,	SCREAMING_SNAKE_CASE__     :   Any     ,	*, SCREAMING_SNAKE_CASE__     :   Any     ,	SCREAMING_SNAKE_CASE__     :   Tuple							):
     __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 		_UpperCAmelCase							(     SCREAMING_SNAKE_CASE__     :   Dict     ,	*, SCREAMING_SNAKE_CASE__     :   Optional[int]     ,	SCREAMING_SNAKE_CASE__     :   int							):
     __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 		_UpperCAmelCase							(     SCREAMING_SNAKE_CASE__     :   Optional[Any]     ,	*, SCREAMING_SNAKE_CASE__     :   Union[str, Any]     ,	SCREAMING_SNAKE_CASE__     :   int     ,	SCREAMING_SNAKE_CASE__     :   int							):
     __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 		_UpperCAmelCase							(     SCREAMING_SNAKE_CASE__     :   List[Any]     ,	SCREAMING_SNAKE_CASE__     :   Any     ,	SCREAMING_SNAKE_CASE__     :   List[Any]     ,	SCREAMING_SNAKE_CASE__     :   Dict							):
     __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 		_UpperCAmelCase							(     SCREAMING_SNAKE_CASE__     :   Optional[Any]     ,	SCREAMING_SNAKE_CASE__     :   Any							):
     __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 		_UpperCAmelCase							(     SCREAMING_SNAKE_CASE__     :   Optional[Any]							):
     __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 		_UpperCAmelCase							(     SCREAMING_SNAKE_CASE__     :   Dict							):
     __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									=		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)









import json
import os
import tempfile

import datasets
from utils import generate_example_dataset, get_duration


_A									=		50_0000

_A			,	_A									=		os.path.split(__file__)
_A									=		os.path.join(RESULTS_BASEPATH, 'results', RESULTS_FILENAME.replace('.py', '.json'))

@get_duration
def 		_UpperCAmelCase							(     SCREAMING_SNAKE_CASE__     :   datasets.Dataset							):
     __UpperCamelCase       				=dataset.select(range(0     ,	len(SCREAMING_SNAKE_CASE__							)     ,	2							)							)

@get_duration
def 		_UpperCAmelCase							(     SCREAMING_SNAKE_CASE__     :   datasets.Dataset							):
     __UpperCamelCase       				=dataset.sort('numbers'							)

@get_duration
def 		_UpperCAmelCase							(     SCREAMING_SNAKE_CASE__     :   datasets.Dataset							):
     __UpperCamelCase       				=dataset.shuffle()

@get_duration
def 		_UpperCAmelCase							(     SCREAMING_SNAKE_CASE__     :   datasets.Dataset							):
     __UpperCamelCase       				=dataset.train_test_split(0.1							)

@get_duration
def 		_UpperCAmelCase							(     SCREAMING_SNAKE_CASE__     :   datasets.Dataset     ,	SCREAMING_SNAKE_CASE__     :   int=10							):
     for shard_id in range(SCREAMING_SNAKE_CASE__							):
          __UpperCamelCase       				=dataset.shard(SCREAMING_SNAKE_CASE__     ,	SCREAMING_SNAKE_CASE__							)

def 		_UpperCAmelCase							(     ):
     __UpperCamelCase       				={'num examples': SPEED_TEST_N_EXAMPLES}
     __UpperCamelCase       				=(select, sort, shuffle, train_test_split, shard)
     with tempfile.TemporaryDirectory() as tmp_dir:
          print('generating dataset'							)
          __UpperCamelCase       				=datasets.Features({'text': datasets.Value('string'							), 'numbers': datasets.Value('float32'							)}							)
          __UpperCamelCase       				=generate_example_dataset(
              os.path.join(SCREAMING_SNAKE_CASE__     ,	'dataset.arrow'							)     ,	SCREAMING_SNAKE_CASE__     ,	num_examples=SCREAMING_SNAKE_CASE__							)
          print('Functions'							)
          for func in functions:
               print(func.__name__							)
               __UpperCamelCase       				=func(SCREAMING_SNAKE_CASE__							)

     with open(SCREAMING_SNAKE_CASE__     ,	'wb'							) as f:
          f.write(json.dumps(SCREAMING_SNAKE_CASE__							).encode('utf-8'							)							)


if __name__ == "__main__":  # useful to run the profiler
   benchmark_indices_mapping()





