


'''simple docstring'''
import argparse

import huggingface_hub
import k_diffusion as K
import torch

from diffusers import UNetaDConditionModel


__SCREAMING_SNAKE_CASE				:							str		       =		'''pcuenq/k-upscaler'''







def 			a_							(  UpperCamelCase_	,     UpperCamelCase_	,     *, UpperCamelCase_	,     UpperCamelCase_   ):
       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 			a_							(  UpperCamelCase_	,     *, UpperCamelCase_	,     UpperCamelCase_   ):
       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 			a_							(  UpperCamelCase_	,     *, UpperCamelCase_	,     UpperCamelCase_	,     UpperCamelCase_   ):
       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 			a_							(  UpperCamelCase_	,     UpperCamelCase_	,     UpperCamelCase_	,     UpperCamelCase_   ):
       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 			a_							(  UpperCamelCase_	,     UpperCamelCase_   ):
       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 			a_							(  UpperCamelCase_   ):
       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=6_4	,     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 			a_							(  UpperCamelCase_   ):
       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__":
       __SCREAMING_SNAKE_CASE				:							int		       =		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)









'''simple docstring'''
import argparse
import logging
import sys
from unittest.mock import patch

import run_glue_with_pabee

from transformers.testing_utils import TestCasePlus


logging.basicConfig(level=logging.DEBUG)

__SCREAMING_SNAKE_CASE				:							str		       =		logging.getLogger()







def 			a_							(  ):
							A_ 						=		argparse.ArgumentParser()
							parser.add_argument("-f"   )
							A_ 						=		parser.parse_args()
							return args.f







class 				__lowerCAmelCase      (      lowercase	):

						"""simple docstring"""






						def 							_UpperCAmelCase					(     self       :			List[Any]  ):
													A_ 						=		logging.StreamHandler(sys.stdout  )
													logger.addHandler(lowerCAmelCase  )

													A_ 						=		self.get_auto_remove_tmp_dir()
													A_ 						=		F"\n            run_glue_with_pabee.py\n            --model_type albert\n            --model_name_or_path albert-base-v2\n            --data_dir ./tests/fixtures/tests_samples/MRPC/\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            --task_name mrpc\n            --do_train\n            --do_eval\n            --per_gpu_train_batch_size=2\n            --per_gpu_eval_batch_size=1\n            --learning_rate=2e-5\n            --max_steps=50\n            --warmup_steps=2\n            --seed=42\n            --max_seq_length=128\n            ".split()

													with patch.object(lowerCAmelCase   ,     "argv"   ,     lowerCAmelCase  ):
																				A_ 						=		run_glue_with_pabee.main()
																				for value in result.values():
																											self.assertGreaterEqual(lowerCAmelCase   ,     0.7_5  )





