






"""simple docstring"""



import argparse
import os
import re

import numpy as np
import PIL
import torch
from timm import create_model
from torch.optim.lr_scheduler import OneCycleLR
from torch.utils.data import DataLoader, Dataset
from torchvision.transforms import Compose, RandomResizedCrop, Resize, ToTensor

from accelerate import Accelerator


def 						lowerCamelCase_				(  __A  :		Optional[Any]			):
					lowerCamelCase__				=fname.split(os.path.sep			)[-1]
					return re.search(r'^(.*)_\d+\.jpg$'			,	__A			).groups()[0]




class        __lowercase(       a__  ):






						def __init__(					self    ,						_SCREAMING_SNAKE_CASE    ,						_SCREAMING_SNAKE_CASE=None    ,						_SCREAMING_SNAKE_CASE=None)			->  List[str]:




											"""simple docstring"""





											lowerCamelCase__				=file_names
											lowerCamelCase__				=image_transform
											lowerCamelCase__				=label_to_id






						def __len__(					self)			->  Dict:




											"""simple docstring"""





											return len(self.file_names)






						def __getitem__(					self    ,						_SCREAMING_SNAKE_CASE)			->  List[str]:




											"""simple docstring"""





											lowerCamelCase__				=self.file_names[idx]
											lowerCamelCase__				=PIL.Image.open(__A)
											lowerCamelCase__				=raw_image.convert('RGB')
											if self.image_transform is not None:
																lowerCamelCase__				=self.image_transform(__A)
											lowerCamelCase__				=extract_label(__A)
											if self.label_to_id is not None:
																lowerCamelCase__				=self.label_to_id[label]
											return {"image": image, "label": label}



def 						lowerCamelCase_				(  __A  :		List[Any]			,	__A  :		str			):
					lowerCamelCase__				=Accelerator(cpu=args.cpu			,	mixed_precision=args.mixed_precision			)

					# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
					lowerCamelCase__				=config["lr"]
					lowerCamelCase__				=int(config['num_epochs']			)
					lowerCamelCase__				=int(config['seed']			)
					lowerCamelCase__				=int(config['batch_size']			)
					lowerCamelCase__				=config["image_size"]
					if not isinstance(__A			,	(list, tuple)			):
										lowerCamelCase__				=(image_size, image_size)

					# Grab all the image filenames
					lowerCamelCase__				=[os.path.join(args.data_dir			,	__A			) for fname in os.listdir(args.data_dir			) if fname.endswith('.jpg'			)]

					# Build the label correspondences
					lowerCamelCase__				=[extract_label(__A			) for fname in file_names]
					lowerCamelCase__				=list(set(__A			)			)
					id_to_label.sort()
					lowerCamelCase__				={lbl: i for i, lbl in enumerate(__A			)}

					# Set the seed before splitting the data.
					np.random.seed(__A			)
					torch.manual_seed(__A			)
					torch.cuda.manual_seed_all(__A			)

					# Split our filenames between train and validation
					lowerCamelCase__				=np.random.permutation(len(__A			)			)
					lowerCamelCase__				=int(0.8 * len(__A			)			)
					lowerCamelCase__				=random_perm[:cut]
					lowerCamelCase__				=random_perm[cut:]

					# For training we use a simple RandomResizedCrop
					lowerCamelCase__				=Compose([RandomResizedCrop(__A			,	scale=(0.5, 1.0)			), ToTensor()]			)
					lowerCamelCase__				=PetsDataset(
					    [file_names[i] for i in train_split]			,	image_transform=__A			,	label_to_id=__A			)

					# For evaluation, we use a deterministic Resize
					lowerCamelCase__				=Compose([Resize(__A			), ToTensor()]			)
					lowerCamelCase__				=PetsDataset([file_names[i] for i in eval_split]			,	image_transform=__A			,	label_to_id=__A			)

					# Instantiate dataloaders.
					lowerCamelCase__				=DataLoader(__A			,	shuffle=__A			,	batch_size=__A			,	num_workers=4			)
					lowerCamelCase__				=DataLoader(__A			,	shuffle=__A			,	batch_size=__A			,	num_workers=4			)

					# Instantiate the model (we build the model here so that the seed also control new weights initialization)
					lowerCamelCase__				=create_model('resnet50d'			,	pretrained=__A			,	num_classes=len(__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).
					lowerCamelCase__				=model.to(accelerator.device			)

					# Freezing the base model
					for param in model.parameters():
										lowerCamelCase__				=False
					for param in model.get_classifier().parameters():
										lowerCamelCase__				=True

					# We normalize the batches of images to be a bit faster.
					lowerCamelCase__				=torch.tensor(model.default_cfg['mean']			)[None, :, None, None].to(accelerator.device			)
					lowerCamelCase__				=torch.tensor(model.default_cfg['std']			)[None, :, None, None].to(accelerator.device			)

					# Instantiate optimizer
					lowerCamelCase__				=torch.optim.Adam(params=model.parameters()			,	lr=lr / 25			)

					# Instantiate learning rate scheduler
					lowerCamelCase__				=OneCycleLR(optimizer=__A			,	max_lr=__A			,	epochs=__A			,	steps_per_epoch=len(__A			)			)

					# 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.
					lowerCamelCase__				=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			):
															# We could avoid this line since we set the accelerator with `device_placement=True`.
															lowerCamelCase__				={k: v.to(accelerator.device			) for k, v in batch.items()}
															lowerCamelCase__				=(batch["image"] - mean) / std
															lowerCamelCase__				=model(__A			)
															lowerCamelCase__				=torch.nn.functional.cross_entropy(__A			,	batch['label']			)
															accelerator.backward(__A			)
															optimizer.step()
															lr_scheduler.step()
															optimizer.zero_grad()

										model.eval()
										lowerCamelCase__				=0
										lowerCamelCase__				=0
										for _, batch in enumerate(__A			):
															# We could avoid this line since we set the accelerator with `device_placement=True`.
															lowerCamelCase__				={k: v.to(accelerator.device			) for k, v in batch.items()}
															lowerCamelCase__				=(batch["image"] - mean) / std
															with torch.no_grad():
																				lowerCamelCase__				=model(__A			)
															lowerCamelCase__				=outputs.argmax(dim=-1			)
															lowerCamelCase__				=accelerator.gather_for_metrics((predictions, batch['label'])			)
															lowerCamelCase__				=predictions == references
															num_elems += accurate_preds.shape[0]
															accurate += accurate_preds.long().sum()

										lowerCamelCase__				=accurate.item() / num_elems
										# Use accelerator.print to print only on the main process.
										accelerator.print(f"epoch {epoch}: {100 * eval_metric:.2f}"			)


def 						lowerCamelCase_				(  ):
					lowerCamelCase__				=argparse.ArgumentParser(description='Simple example of training script.'			)
					parser.add_argument('--data_dir'			,	required=__A			,	help='The data folder on disk.'			)
					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(
					    '--checkpointing_steps'			,	type=__A			,	default=__A			,	help='Whether the various states should be saved at the end of every n steps, or \'epoch\' for each epoch.'			,	)
					parser.add_argument('--cpu'			,	action='store_true'			,	help='If passed, will train on the CPU.'			)
					lowerCamelCase__				=parser.parse_args()
					lowerCamelCase__				={"lr": 3E-2, "num_epochs": 3, "seed": 42, "batch_size": 64, "image_size": 224}
					training_function(__A			,	__A			)


if __name__ == "__main__":
						main()







"""simple docstring"""



import argparse

import huggingface_hub
import k_diffusion as K
import torch

from diffusers import UNetaDConditionModel


_snake_case       :      Optional[Any]       =					"pcuenq/k-upscaler"




def 						lowerCamelCase_				(  __A  :		List[Any]			,	__A  :		Optional[int]			,	*, __A  :		int			,	__A  :		Union[str, Any]			):
					lowerCamelCase__				={
					    # 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_				(  __A  :		Dict			,	*, __A  :		Union[str, Any]			,	__A  :		Union[str, Any]			):
					lowerCamelCase__				,     lowerCamelCase__				,     lowerCamelCase__				=checkpoint[f"{attention_prefix}.qkv_proj.weight"].chunk(3			,	dim=0			)
					lowerCamelCase__				,     lowerCamelCase__				,     lowerCamelCase__				=checkpoint[f"{attention_prefix}.qkv_proj.bias"].chunk(3			,	dim=0			)
					lowerCamelCase__				={
					    # 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_				(  __A  :		List[str]			,	*, __A  :		Optional[Any]			,	__A  :		List[str]			,	__A  :		Optional[int]			):
					lowerCamelCase__				,     lowerCamelCase__				=checkpoint[f"{attention_prefix}.kv_proj.weight"].chunk(2			,	dim=0			)
					lowerCamelCase__				,     lowerCamelCase__				=checkpoint[f"{attention_prefix}.kv_proj.bias"].chunk(2			,	dim=0			)

					lowerCamelCase__				={
					    # 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_				(  __A  :		Tuple			,	__A  :		Union[str, Any]			,	__A  :		List[str]			,	__A  :		List[str]			):
					lowerCamelCase__				='inner_model.u_net.u_blocks' if block_type == 'up' else 'inner_model.u_net.d_blocks'
					lowerCamelCase__				=f"{block_prefix}.{block_idx}"

					lowerCamelCase__				={}

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

					# 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 						lowerCamelCase_				(  __A  :		Optional[int]			):
					lowerCamelCase__				=original_config['input_channels'] + original_config['unet_cond_dim']
					lowerCamelCase__				=original_config['input_channels'] + (1 if original_config['has_variance'] else 0)

					lowerCamelCase__				=original_config['channels']

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

					lowerCamelCase__				=original_config['mapping_cond_dim']
					lowerCamelCase__				=original_config['cross_cond_dim']

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

					lowerCamelCase__				=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 						lowerCamelCase_				(  __A  :		int			):
					lowerCamelCase__				=torch.device('cuda' if torch.cuda.is_available() else 'cpu'			)

					lowerCamelCase__				=huggingface_hub.hf_hub_download(__A			,	'config_laion_text_cond_latent_upscaler_2.json'			)
					lowerCamelCase__				=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'			)
					lowerCamelCase__				=K.config.load_config(open(__A			)			)['model']
					lowerCamelCase__				=unet_model_from_original_config(__A			)

					lowerCamelCase__				=torch.load(__A			,	map_location=__A			)['model_ema']
					lowerCamelCase__				=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__":
						_snake_case       :      Optional[int]       =					argparse.ArgumentParser()

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

						main(args)