

import argparse

import huggingface_hub
import k_diffusion as K
import torch

from diffusers import UNetaDConditionModel


__a						:Optional[Any]            = 'pcuenq/k-upscaler'

def 			__snake_case	(   __UpperCamelCase  :   Optional[Any]							,__UpperCamelCase  :   Tuple							,*, __UpperCamelCase  :   Dict							,__UpperCamelCase  :   List[Any]						):



      """simple docstring"""

      A_	     =							{
          # 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 			__snake_case	(   __UpperCamelCase  :   Any							,*, __UpperCamelCase  :   Union[str, Any]							,__UpperCamelCase  :   Dict						):



      """simple docstring"""

      A_     ,    A_     ,    A_	     =							checkpoint[f'''{attention_prefix}.qkv_proj.weight'''].chunk(3							,dim=0						)
      A_     ,    A_     ,    A_	     =							checkpoint[f'''{attention_prefix}.qkv_proj.bias'''].chunk(3							,dim=0						)
      A_	     =							{
          # 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 			__snake_case	(   __UpperCamelCase  :   Optional[Any]							,*, __UpperCamelCase  :   Optional[int]							,__UpperCamelCase  :   Tuple							,__UpperCamelCase  :   str						):



      """simple docstring"""

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

      A_	     =							{
          # 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 			__snake_case	(   __UpperCamelCase  :   Optional[int]							,__UpperCamelCase  :   int							,__UpperCamelCase  :   int							,__UpperCamelCase  :   Optional[Any]						):



      """simple docstring"""

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

      A_	     =							{}

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

            diffusers_checkpoint.update(
                resnet_to_diffusers_checkpoint(
                    __UpperCamelCase							,__UpperCamelCase							,diffusers_resnet_prefix=__UpperCamelCase							,resnet_prefix=__UpperCamelCase						)						)

      if hasattr(__UpperCamelCase							,"attentions"						):
            for attention_idx, attention in enumerate(block.attentions						):
                  A_	     =							f'''{block_type}_blocks.{block_idx}.attentions.{attention_idx}'''
                  A_	     =							n * attention_idx + 1 if block_type == "up" else n * attention_idx + 2
                  A_	     =							f'''{block_prefix}.{idx}'''
                  A_	     =							f'''{block_prefix}.{idx }'''
                  A_	     =							1 if not attention.add_self_attention else 2
                  A_	     =							(
                      n * attention_idx + cross_attention_index
                      if block_type == "up"
                      else n * attention_idx + cross_attention_index + 1
                  )
                  A_	     =							f'''{block_prefix}.{idx }'''

                  diffusers_checkpoint.update(
                      cross_attn_to_diffusers_checkpoint(
                          __UpperCamelCase							,diffusers_attention_prefix=__UpperCamelCase							,diffusers_attention_index=2							,attention_prefix=__UpperCamelCase							,)						)

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

      return diffusers_checkpoint

def 			__snake_case	(   __UpperCamelCase  :   Optional[int]							,__UpperCamelCase  :   List[str]						):



      """simple docstring"""

      A_	     =							{}

      # 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(__UpperCamelCase							,__UpperCamelCase							,__UpperCamelCase							,"down"						)						)

      # up_blocks
      for up_block_idx, up_block in enumerate(model.up_blocks						):
            diffusers_checkpoint.update(block_to_diffusers_checkpoint(__UpperCamelCase							,__UpperCamelCase							,__UpperCamelCase							,"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 			__snake_case	(   __UpperCamelCase  :   List[str]						):



      """simple docstring"""

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

      A_	     =							original_config["channels"]

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

      A_	     =							original_config["mapping_cond_dim"]
      A_	     =							original_config["cross_cond_dim"]

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

      A_	     =							UNetaDConditionModel(
          in_channels=__UpperCamelCase							,out_channels=__UpperCamelCase							,down_block_types=("KDownBlock2D", "KCrossAttnDownBlock2D", "KCrossAttnDownBlock2D", "KCrossAttnDownBlock2D")							,mid_block_type=__UpperCamelCase							,up_block_types=("KCrossAttnUpBlock2D", "KCrossAttnUpBlock2D", "KCrossAttnUpBlock2D", "KUpBlock2D")							,block_out_channels=__UpperCamelCase							,layers_per_block=__UpperCamelCase							,act_fn="gelu"							,norm_num_groups=__UpperCamelCase							,cross_attention_dim=__UpperCamelCase							,attention_head_dim=64							,time_cond_proj_dim=__UpperCamelCase							,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 			__snake_case	(   __UpperCamelCase  :   Union[str, Any]						):



      """simple docstring"""

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

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

      A_	     =							torch.load(__UpperCamelCase							,map_location=__UpperCamelCase						)["model_ema"]
      A_	     =							unet_to_diffusers_checkpoint(__UpperCamelCase							,__UpperCamelCase						)

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


if __name__ == "__main__":
      __a						:Optional[Any]            = argparse.ArgumentParser()

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

      main(args)


import json
import os
import tempfile

import datasets
from datasets.arrow_writer import ArrowWriter
from datasets.features import ArrayaD
from utils import generate_examples, get_duration


__a						:Dict            = (30, 487)
__a						:int            = (36, 1024)
__a						:Tuple            = (100, 100)
__a						:Dict            = 100

__a						:List[str]            = datasets.Features(
    {'text': ArrayaD(SHAPE_TEST_1, dtype='float32'), 'image': ArrayaD(SHAPE_TEST_2, dtype='float32')}
)

__a      ,			__a						:str            = os.path.split(__file__)
__a						:Optional[Any]            = os.path.join(RESULTS_BASEPATH, 'results', RESULTS_FILENAME.replace('.py', '.json'))

@get_duration
def 			__snake_case	(   __UpperCamelCase  :   Optional[int]							,__UpperCamelCase  :   Union[str, Any]							,__UpperCamelCase  :   Dict						):



      """simple docstring"""

      with ArrowWriter(features=__UpperCamelCase							,path=os.path.join(__UpperCamelCase							,"beta.arrow"						)						) as writer:
            for key, record in dummy_data:
                  A_	     =							my_features.encode_example(__UpperCamelCase						)
                  writer.write(__UpperCamelCase						)
            A_     ,    A_	     =							writer.finalize()

@get_duration
def 			__snake_case	(   __UpperCamelCase  :   int							,__UpperCamelCase  :   Any						):



      """simple docstring"""

      A_	     =							datasets.Dataset.from_file(
          filename=os.path.join(__UpperCamelCase							,"beta.arrow"						)							,info=datasets.DatasetInfo(features=__UpperCamelCase						)						)
      for _ in dataset:
            pass

@get_duration
def 			__snake_case	(   __UpperCamelCase  :   Optional[Any]							,__UpperCamelCase  :   List[str]						):



      """simple docstring"""

      A_	     =							datasets.Dataset.from_file(
          filename=os.path.join(__UpperCamelCase							,"beta.arrow"						)							,info=datasets.DatasetInfo(features=__UpperCamelCase						)						)
      dataset.set_format("numpy"						)
      for _ in dataset:
            pass

@get_duration
def 			__snake_case	(   __UpperCamelCase  :   List[str]							,__UpperCamelCase  :   Tuple						):



      """simple docstring"""

      A_	     =							10
      A_	     =							datasets.Dataset.from_file(
          filename=os.path.join(__UpperCamelCase							,"beta.arrow"						)							,info=datasets.DatasetInfo(features=__UpperCamelCase						)						)
      for i in range(0							,len(__UpperCamelCase						)							,__UpperCamelCase						):
            A_	     =							dataset[i : i + batch_size]

@get_duration
def 			__snake_case	(   __UpperCamelCase  :   int							,__UpperCamelCase  :   List[Any]						):



      """simple docstring"""

      A_	     =							10
      A_	     =							datasets.Dataset.from_file(
          filename=os.path.join(__UpperCamelCase							,"beta.arrow"						)							,info=datasets.DatasetInfo(features=__UpperCamelCase						)						)
      dataset.set_format("numpy"						)
      for i in range(0							,len(__UpperCamelCase						)							,__UpperCamelCase						):
            A_	     =							dataset[i : i + batch_size]

@get_duration
def 			__snake_case	(   __UpperCamelCase  :   int							,__UpperCamelCase  :   Tuple						):



      """simple docstring"""

      A_	     =							datasets.Dataset.from_file(
          filename=os.path.join(__UpperCamelCase							,"beta.arrow"						)							,info=datasets.DatasetInfo(features=__UpperCamelCase						)						)
      for col in feats:
            A_	     =							dataset[col]

@get_duration
def 			__snake_case	(   __UpperCamelCase  :   Optional[int]							,__UpperCamelCase  :   Optional[Any]						):



      """simple docstring"""

      A_	     =							datasets.Dataset.from_file(
          filename=os.path.join(__UpperCamelCase							,"beta.arrow"						)							,info=datasets.DatasetInfo(features=__UpperCamelCase						)						)
      dataset.set_format("numpy"						)
      for col in feats:
            A_	     =							dataset[col]

def 			__snake_case	(   ):



      """simple docstring"""

      A_	     =							{}
      A_	     =							(
          read_unformated,
          read_formatted_as_numpy,
          read_batch_unformated,
          read_batch_formatted_as_numpy,
          read_col_unformated,
          read_col_formatted_as_numpy,
      )
      with tempfile.TemporaryDirectory() as tmp_dir:
            A_	     =							datasets.Features({"image": ArrayaD(__UpperCamelCase							,dtype="float32"						)}						)
            A_	     =							generate_examples(features=__UpperCamelCase							,num_examples=__UpperCamelCase						)
            A_	     =							write(__UpperCamelCase							,__UpperCamelCase							,__UpperCamelCase						)
            for read_func in read_functions:
                  A_	     =							read_func(__UpperCamelCase							,__UpperCamelCase						)

      with tempfile.TemporaryDirectory() as tmp_dir:
            # don't use fixed length for fair comparison
            # feats = datasets.Features(
            #     {"image": datasets.Sequence(datasets.Sequence(datasets.Value("float32"), SPEED_TEST_SHAPE[1]), SPEED_TEST_SHAPE[0])}
            # )
            A_	     =							datasets.Features({"image": datasets.Sequence(datasets.Sequence(datasets.Value("float32"						)						)						)}						)
            A_	     =							generate_examples(
                features=__UpperCamelCase							,num_examples=__UpperCamelCase							,seq_shapes={"image": SPEED_TEST_SHAPE}						)
            A_	     =							write(__UpperCamelCase							,__UpperCamelCase							,__UpperCamelCase						)
            for read_func in read_functions:
                  A_	     =							read_func(__UpperCamelCase							,__UpperCamelCase						)

      with tempfile.TemporaryDirectory() as tmp_dir:
            # don't use fixed length for fair comparison
            # feats = datasets.Features(
            #     {"image": datasets.Sequence(datasets.Value("float32"), SPEED_TEST_SHAPE[0] * SPEED_TEST_SHAPE[1])}
            # )
            A_	     =							datasets.Features({"image": datasets.Sequence(datasets.Value("float32"						)						)}						)
            A_	     =							generate_examples(
                features=__UpperCamelCase							,num_examples=__UpperCamelCase							,seq_shapes={"image": [SPEED_TEST_SHAPE[0] * SPEED_TEST_SHAPE[1]]}							,)
            A_	     =							write(__UpperCamelCase							,__UpperCamelCase							,__UpperCamelCase						)
            for read_func in read_functions:
                  A_	     =							read_func(__UpperCamelCase							,__UpperCamelCase						)

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


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