





import argparse

import huggingface_hub
import k_diffusion as K
import torch

from diffusers import UNetaDConditionModel


UpperCAmelCase						:       Union[str, Any]        =					'''pcuenq/k-upscaler'''







def 			_SCREAMING_SNAKE_CASE    (       a ,						a ,						*, a ,						a  )				->				Dict:
     __A     :						Tuple          =    {
         # 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 			_SCREAMING_SNAKE_CASE    (       a ,						*, a ,						a  )				->				Optional[int]:
     __A     :						Tuple          =    checkpoint[F"""{attention_prefix}.qkv_proj.weight"""].chunk(3 ,						dim=0  )
     __A     :						Dict          =    checkpoint[F"""{attention_prefix}.qkv_proj.bias"""].chunk(3 ,						dim=0  )
     __A     :						Any          =    {
         # 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 			_SCREAMING_SNAKE_CASE    (       a ,						*, a ,						a ,						a  )				->				List[Any]:
     __A     :						Dict          =    checkpoint[F"""{attention_prefix}.kv_proj.weight"""].chunk(2 ,						dim=0  )
     __A     :						str          =    checkpoint[F"""{attention_prefix}.kv_proj.bias"""].chunk(2 ,						dim=0  )

     __A     :						Optional[Any]          =    {
         # 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 			_SCREAMING_SNAKE_CASE    (       a ,						a ,						a ,						a  )				->				Dict:
     __A     :						List[str]          =    'inner_model.u_net.u_blocks' if block_type == 'up' else 'inner_model.u_net.d_blocks'
     __A     :						int          =    F"""{block_prefix}.{block_idx}"""

     __A     :						Optional[int]          =    {}

     if not hasattr(a ,						'attentions'  ):
          __A     :						Tuple          =    1  # resnet only
     elif not block.attentions[0].add_self_attention:
          __A     :						Any          =    2  # resnet -> cross-attention
     else:
          __A     :						Optional[Any]          =    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     :						List[str]          =    F"""{block_type}_blocks.{block_idx}.resnets.{resnet_idx}"""
          __A     :						Optional[Any]          =    n * resnet_idx if block_type == 'up' else n * resnet_idx + 1
          __A     :						Any          =    F"""{block_prefix}.{idx}""" if block_type == 'up' else F"""{block_prefix}.{idx}"""

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

     if hasattr(a ,						'attentions'  ):
          for attention_idx, attention in enumerate(block.attentions  ):
               __A     :						str          =    F"""{block_type}_blocks.{block_idx}.attentions.{attention_idx}"""
               __A     :						Tuple          =    n * attention_idx + 1 if block_type == 'up' else n * attention_idx + 2
               __A     :						Dict          =    F"""{block_prefix}.{idx}"""
               __A     :						Tuple          =    F"""{block_prefix}.{idx }"""
               __A     :						Union[str, Any]          =    1 if not attention.add_self_attention else 2
               __A     :						str          =    (
                   n * attention_idx + cross_attention_index
                   if block_type == 'up'
                   else n * attention_idx + cross_attention_index + 1
               )
               __A     :						Tuple          =    F"""{block_prefix}.{idx }"""

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

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

     return diffusers_checkpoint







def 			_SCREAMING_SNAKE_CASE    (       a ,						a  )				->				List[str]:
     __A     :						Optional[Any]          =    {}

     # 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(a ,						a ,						a ,						'down'  )  )

     # up_blocks
     for up_block_idx, up_block in enumerate(model.up_blocks  ):
          diffusers_checkpoint.update(block_to_diffusers_checkpoint(a ,						a ,						a ,						'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 			_SCREAMING_SNAKE_CASE    (       a  )				->				Any:
     __A     :						str          =    original_config['input_channels'] + original_config['unet_cond_dim']
     __A     :						Tuple          =    original_config['input_channels'] + (1 if original_config['has_variance'] else 0)

     __A     :						str          =    original_config['channels']

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

     __A     :						str          =    original_config['mapping_cond_dim']
     __A     :						Union[str, Any]          =    original_config['cross_cond_dim']

     __A     :						Optional[int]          =    []
     __A     :						Union[str, Any]          =    []
     for s, c in zip(original_config['self_attn_depths'] ,						original_config['cross_attn_depths']  ):
          if s:
               __A     :						Dict          =    'self'
               __A     :						Optional[Any]          =    'cross' if c else None
          elif c:
               __A     :						Tuple          =    'cross'
               __A     :						List[str]          =    None
          else:
               __A     :						Any          =    None
               __A     :						Union[str, Any]          =    None
          attna_types.append(a  )
          attna_types.append(a  )

     __A     :						str          =    UNetaDConditionModel(
         in_channels=a ,						out_channels=a ,						down_block_types=('KDownBlock2D', 'KCrossAttnDownBlock2D', 'KCrossAttnDownBlock2D', 'KCrossAttnDownBlock2D') ,						mid_block_type=a ,						up_block_types=('KCrossAttnUpBlock2D', 'KCrossAttnUpBlock2D', 'KCrossAttnUpBlock2D', 'KUpBlock2D') ,						block_out_channels=a ,						layers_per_block=a ,						act_fn='gelu' ,						norm_num_groups=a ,						cross_attention_dim=a ,						attention_head_dim=64 ,						time_cond_proj_dim=a ,						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 			_SCREAMING_SNAKE_CASE    (       a  )				->				List[str]:
     __A     :						Dict          =    torch.device('cuda' if torch.cuda.is_available() else 'cpu'  )

     __A     :						List[Any]          =    huggingface_hub.hf_hub_download(a ,						'config_laion_text_cond_latent_upscaler_2.json'  )
     __A     :						Union[str, Any]          =    huggingface_hub.hf_hub_download(
         a ,						'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     :						Optional[Any]          =    K.config.load_config(open(a  )  )['model']
     __A     :						Tuple          =    unet_model_from_original_config(a  )

     __A     :						Dict          =    torch.load(a ,						map_location=a  )['model_ema']
     __A     :						int          =    unet_to_diffusers_checkpoint(a ,						a  )

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


if __name__ == "__main__":
    UpperCAmelCase						:       Optional[int]        =					argparse.ArgumentParser()

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

    main(args)











import argparse
import os

# New Code #
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator
from accelerate.utils import find_executable_batch_size


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to combine both the gradient accumulation
# and automatic batch size finder utilities of Accelerate to perfrom
# automatic gradient accumulation
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
#   - single CPU or single GPU
#   - multi GPUS (using PyTorch distributed mode)
#   - (multi) TPUs
#   - fp16 (mixed-precision) or fp32 (normal precision)
#
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################

UpperCAmelCase						:       List[Any]        =					32







def 			_SCREAMING_SNAKE_CASE    (       a ,						a = 16  )				->				List[Any]:
					__A     :						List[str]          =    AutoTokenizer.from_pretrained('bert-base-cased'  )
					__A     :						int          =    load_dataset('glue' ,						'mrpc'  )

					def tokenize_function(a  ):
										# max_length=None => use the model max length (it's actually the default)
										__A     :						str          =    tokenizer(examples['sentence1'] ,						examples['sentence2'] ,						truncation=a ,						max_length=a  )
										return outputs

					# Apply the method we just defined to all the examples in all the splits of the dataset
					# starting with the main process first:
					with accelerator.main_process_first():
										__A     :						Optional[int]          =    datasets.map(
										    a ,						batched=a ,						remove_columns=['idx', 'sentence1', 'sentence2'] ,						)

					# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
					# transformers library
					__A     :						Tuple          =    tokenized_datasets.rename_column('label' ,						'labels'  )

					def collate_fn(a  ):
										# When using mixed precision we want round multiples of 8/16
										if accelerator.mixed_precision == "fp8":
															__A     :						Union[str, Any]          =    16
										elif accelerator.mixed_precision != "no":
															__A     :						Any          =    8
										else:
															__A     :						Union[str, Any]          =    None

										return tokenizer.pad(
										    a ,						padding='longest' ,						pad_to_multiple_of=a ,						return_tensors='pt' ,						)

					# Instantiate dataloaders.
					__A     :						Any          =    DataLoader(
					    tokenized_datasets['train'] ,						shuffle=a ,						collate_fn=a ,						batch_size=a  )
					__A     :						List[Any]          =    DataLoader(
					    tokenized_datasets['validation'] ,						shuffle=a ,						collate_fn=a ,						batch_size=a  )

					return train_dataloader, eval_dataloader


# For testing only
if os.environ.get('''TESTING_MOCKED_DATALOADERS''', None) == "1":
				from accelerate.test_utils.training import mocked_dataloaders

				UpperCAmelCase						:       Tuple        =					mocked_dataloaders  # noqa: F811







def 			_SCREAMING_SNAKE_CASE    (       a ,						a  )				->				Optional[Any]:
					# For testing only
					if os.environ.get('TESTING_MOCKED_DATALOADERS' ,						a  ) == "1":
										__A     :						Dict          =    2
					# Initialize accelerator
					__A     :						int          =    Accelerator(cpu=args.cpu ,						mixed_precision=args.mixed_precision  )
					# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
					__A     :						Dict          =    config['lr']
					__A     :						Optional[Any]          =    int(config['num_epochs']  )
					__A     :						int          =    int(config['seed']  )
					__A     :						int          =    int(config['batch_size']  )

					__A     :						Tuple          =    evaluate.load('glue' ,						'mrpc'  )

					# New Code #
					# We use the `find_executable_batch_size` decorator, passing in the desired observed batch size
					# to train on. If a CUDA OOM error occurs, it will retry this loop cutting the batch size in
					# half each time. From this, we can calculate the number of gradient accumulation steps needed
					# and modify the Accelerator object as a result
					@find_executable_batch_size(starting_batch_size=int(a  )  )
					def inner_training_loop(a  ):
										# Since we need to modify the outside accelerator object, we need to bring it
										# to the local scope
										nonlocal accelerator

										# We can calculate the number of gradient accumulation steps based on the current
										# batch size vs the starting batch size
										__A     :						Optional[Any]          =    observed_batch_size // batch_size

										# And then set it in the Accelerator directly:
										__A     :						List[str]          =    num_gradient_accumulation_steps

										# Next we need to free all of the stored model references in the Accelerator each time
										accelerator.free_memory()

										# And set the seed so our results are reproducable each reset
										set_seed(a  )

										# Instantiate the model (we build the model here so that the seed also control new weights initialization)
										__A     :						List[Any]          =    AutoModelForSequenceClassification.from_pretrained('bert-base-cased' ,						return_dict=a  )

										# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
										# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
										# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
										__A     :						Dict          =    model.to(accelerator.device  )

										# Instantiate optimizer
										__A     :						Optional[Any]          =    AdamW(params=model.parameters() ,						lr=a  )
										__A   ,    __A     :						Dict          =    get_dataloaders(a ,						a  )

										# Instantiate scheduler
										__A     :						List[Any]          =    get_linear_schedule_with_warmup(
										    optimizer=a ,						num_warmup_steps=1_00 ,						num_training_steps=(len(a  ) * num_epochs) ,						)

										# Prepare everything
										# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
										# prepare method.
										__A   ,    __A   ,    __A   ,    __A   ,    __A     :						Tuple          =    accelerator.prepare(
										    a ,						a ,						a ,						a ,						a  )

										# Now we train the model
										for epoch in range(a  ):
															model.train()
															for step, batch in enumerate(a  ):
																				# And perform gradient accumulation
																				with accelerator.accumulate(a  ):
																									# We could avoid this line since we set the accelerator with `device_placement=True`.
																									batch.to(accelerator.device  )
																									__A     :						List[str]          =    model(**a  )
																									__A     :						List[str]          =    outputs.loss
																									accelerator.backward(a  )
																									optimizer.step()
																									lr_scheduler.step()
																									optimizer.zero_grad()

															model.eval()
															for step, batch in enumerate(a  ):
																				# We could avoid this line since we set the accelerator with `device_placement=True`.
																				batch.to(accelerator.device  )
																				with torch.no_grad():
																									__A     :						List[Any]          =    model(**a  )
																				__A     :						List[Any]          =    outputs.logits.argmax(dim=-1  )
																				__A   ,    __A     :						Tuple          =    accelerator.gather_for_metrics((predictions, batch['labels'])  )
																				metric.add_batch(
																				    predictions=a ,						references=a ,						)

															__A     :						Optional[int]          =    metric.compute()
															# Use accelerator.print to print only on the main process.
															accelerator.print(F"""epoch {epoch}:""" ,						a  )

    # New Code #
    # And call it at the end with no arguments
    # Note: You could also refactor this outside of your training loop function
					inner_training_loop()







def 			_SCREAMING_SNAKE_CASE    (       )				->				Union[str, Any]:
					__A     :						List[Any]          =    argparse.ArgumentParser(description='Simple example of training script.'  )
					parser.add_argument(
					    '--mixed_precision' ,						type=a ,						default=a ,						choices=['no', 'fp16', 'bf16', 'fp8'] ,						help='Whether to use mixed precision. Choose'
					    'between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10.'
					    'and an Nvidia Ampere GPU.' ,						)
					parser.add_argument('--cpu' ,						action='store_true' ,						help='If passed, will train on the CPU.'  )
					__A     :						int          =    parser.parse_args()
					# New Code #
					# We modify the starting batch size to be an observed batch size of 256, to guarentee an initial CUDA OOM
					__A     :						Optional[int]          =    {'lr': 2e-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 2_56}
					training_function(a ,						a  )


if __name__ == "__main__":
				main()




