

from dataclasses import dataclass
from typing import Optional, Tuple, Union

import torch
import torch.nn as nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .embeddings import GaussianFourierProjection, TimestepEmbedding, Timesteps
from .modeling_utils import ModelMixin
from .unet_ad_blocks import UNetMidBlockaD, get_down_block, get_up_block






@dataclass
class 			__magic_name__					(    lowerCAmelCase_   ):
			SCREAMING_SNAKE_CASE							     =		42






class 			__magic_name__					(    lowerCAmelCase_						,					lowerCAmelCase_   ):





			@register_to_config
			def __init__(	self      ,						__snake_case = None      ,						__snake_case = 3      ,						__snake_case = 3      ,						__snake_case = False      ,						__snake_case = "positional"      ,						__snake_case = 0      ,						__snake_case = True      ,						__snake_case = ("DownBlock2D", "AttnDownBlock2D", "AttnDownBlock2D", "AttnDownBlock2D")      ,						__snake_case = ("AttnUpBlock2D", "AttnUpBlock2D", "AttnUpBlock2D", "UpBlock2D")      ,						__snake_case = (224, 448, 672, 896)      ,						__snake_case = 2      ,						__snake_case = 1      ,						__snake_case = 1      ,						__snake_case = "conv"      ,						__snake_case = "conv"      ,						__snake_case = "silu"      ,						__snake_case = 8      ,						__snake_case = 32      ,						__snake_case = 1e-5      ,						__snake_case = "default"      ,						__snake_case = True      ,						__snake_case = None      ,						__snake_case = None      ,						)     ->				Any:




						'''simple docstring'''
						super().__init__()

						__a						   =sample_size
						__a						   =block_out_channels[0] * 4

						# Check inputs
						if len(__snake_case      ) != len(__snake_case      ):
									raise ValueError(
									    f'Must provide the same number of `down_block_types` as `up_block_types`. `down_block_types`: {down_block_types}. `up_block_types`: {up_block_types}.'      )

						if len(__snake_case      ) != len(__snake_case      ):
									raise ValueError(
									    f'Must provide the same number of `block_out_channels` as `down_block_types`. `block_out_channels`: {block_out_channels}. `down_block_types`: {down_block_types}.'      )

						# input
						__a						   =nn.Convad(__snake_case      ,						block_out_channels[0]      ,						kernel_size=3      ,						padding=(1, 1)      )

						# time
						if time_embedding_type == "fourier":
									__a						   =GaussianFourierProjection(embedding_size=block_out_channels[0]      ,						scale=16      )
									__a						   =2 * block_out_channels[0]
						elif time_embedding_type == "positional":
									__a						   =Timesteps(block_out_channels[0]      ,						__snake_case      ,						__snake_case      )
									__a						   =block_out_channels[0]

						__a						   =TimestepEmbedding(__snake_case      ,						__snake_case      )

						# class embedding
						if class_embed_type is None and num_class_embeds is not None:
									__a						   =nn.Embedding(__snake_case      ,						__snake_case      )
						elif class_embed_type == "timestep":
									__a						   =TimestepEmbedding(__snake_case      ,						__snake_case      )
						elif class_embed_type == "identity":
									__a						   =nn.Identity(__snake_case      ,						__snake_case      )
						else:
									__a						   =None

						__a						   =nn.ModuleList([]      )
						__a						   =None
						__a						   =nn.ModuleList([]      )

						# down
						__a						   =block_out_channels[0]
						for i, down_block_type in enumerate(__snake_case      ):
									__a						   =output_channel
									__a						   =block_out_channels[i]
									__a						   =i == len(__snake_case      ) - 1

									__a						   =get_down_block(
									    __snake_case      ,						num_layers=__snake_case      ,						in_channels=__snake_case      ,						out_channels=__snake_case      ,						temb_channels=__snake_case      ,						add_downsample=not is_final_block      ,						resnet_eps=__snake_case      ,						resnet_act_fn=__snake_case      ,						resnet_groups=__snake_case      ,						attention_head_dim=attention_head_dim if attention_head_dim is not None else output_channel      ,						downsample_padding=__snake_case      ,						resnet_time_scale_shift=__snake_case      ,						downsample_type=__snake_case      ,						)
									self.down_blocks.append(__snake_case      )

						# mid
						__a						   =UNetMidBlockaD(
						    in_channels=block_out_channels[-1]      ,						temb_channels=__snake_case      ,						resnet_eps=__snake_case      ,						resnet_act_fn=__snake_case      ,						output_scale_factor=__snake_case      ,						resnet_time_scale_shift=__snake_case      ,						attention_head_dim=attention_head_dim if attention_head_dim is not None else block_out_channels[-1]      ,						resnet_groups=__snake_case      ,						add_attention=__snake_case      ,						)

						# up
						__a						   =list(reversed(__snake_case      )      )
						__a						   =reversed_block_out_channels[0]
						for i, up_block_type in enumerate(__snake_case      ):
									__a						   =output_channel
									__a						   =reversed_block_out_channels[i]
									__a						   =reversed_block_out_channels[min(i + 1      ,						len(__snake_case      ) - 1      )]

									__a						   =i == len(__snake_case      ) - 1

									__a						   =get_up_block(
									    __snake_case      ,						num_layers=layers_per_block + 1      ,						in_channels=__snake_case      ,						out_channels=__snake_case      ,						prev_output_channel=__snake_case      ,						temb_channels=__snake_case      ,						add_upsample=not is_final_block      ,						resnet_eps=__snake_case      ,						resnet_act_fn=__snake_case      ,						resnet_groups=__snake_case      ,						attention_head_dim=attention_head_dim if attention_head_dim is not None else output_channel      ,						resnet_time_scale_shift=__snake_case      ,						upsample_type=__snake_case      ,						)
									self.up_blocks.append(__snake_case      )
									__a						   =output_channel

						# out
						__a						   =norm_num_groups if norm_num_groups is not None else min(block_out_channels[0] // 4      ,						32      )
						__a						   =nn.GroupNorm(num_channels=block_out_channels[0]      ,						num_groups=__snake_case      ,						eps=__snake_case      )
						__a						   =nn.SiLU()
						__a						   =nn.Convad(block_out_channels[0]      ,						__snake_case      ,						kernel_size=3      ,						padding=1      )





			def 	__magic_name__					(	self      ,						__snake_case      ,						__snake_case      ,						__snake_case = None      ,						__snake_case = True      ,						)     ->				Union[UNetaDOutput, Tuple]:




						'''simple docstring'''
						# 0. center input if necessary
						if self.config.center_input_sample:
									__a						   =2 * sample - 1.0

						# 1. time
						__a						   =timestep
						if not torch.is_tensor(__snake_case      ):
									__a						   =torch.tensor([timesteps]      ,						dtype=torch.long      ,						device=sample.device      )
						elif torch.is_tensor(__snake_case      ) and len(timesteps.shape      ) == 0:
									__a						   =timesteps[None].to(sample.device      )

						# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
						__a						   =timesteps * torch.ones(sample.shape[0]      ,						dtype=timesteps.dtype      ,						device=timesteps.device      )

						__a						   =self.time_proj(__snake_case      )

						# timesteps does not contain any weights and will always return f32 tensors
						# but time_embedding might actually be running in fp16. so we need to cast here.
						# there might be better ways to encapsulate this.
						__a						   =t_emb.to(dtype=self.dtype      )
						__a						   =self.time_embedding(__snake_case      )

						if self.class_embedding is not None:
									if class_labels is None:
												raise ValueError('class_labels should be provided when doing class conditioning'      )

									if self.config.class_embed_type == "timestep":
												__a						   =self.time_proj(__snake_case      )

									__a						   =self.class_embedding(__snake_case      ).to(dtype=self.dtype      )
									__a						   =emb + class_emb

						# 2. pre-process
						__a						   =sample
						__a						   =self.conv_in(__snake_case      )

						# 3. down
						__a						   =(sample,)
						for downsample_block in self.down_blocks:
									if hasattr(__snake_case      ,						'skip_conv'      ):
												__a   ,					__a   ,					__a						   =downsample_block(
												    hidden_states=__snake_case      ,						temb=__snake_case      ,						skip_sample=__snake_case      )
									else:
												__a   ,					__a						   =downsample_block(hidden_states=__snake_case      ,						temb=__snake_case      )

									down_block_res_samples += res_samples

						# 4. mid
						__a						   =self.mid_block(__snake_case      ,						__snake_case      )

						# 5. up
						__a						   =None
						for upsample_block in self.up_blocks:
									__a						   =down_block_res_samples[-len(upsample_block.resnets      ) :]
									__a						   =down_block_res_samples[: -len(upsample_block.resnets      )]

									if hasattr(__snake_case      ,						'skip_conv'      ):
												__a   ,					__a						   =upsample_block(__snake_case      ,						__snake_case      ,						__snake_case      ,						__snake_case      )
									else:
												__a						   =upsample_block(__snake_case      ,						__snake_case      ,						__snake_case      )

        # 6. post-process
						__a						   =self.conv_norm_out(__snake_case      )
						__a						   =self.conv_act(__snake_case      )
						__a						   =self.conv_out(__snake_case      )

						if skip_sample is not None:
									sample += skip_sample

						if self.config.time_embedding_type == "fourier":
									__a						   =timesteps.reshape((sample.shape[0], *([1] * len(sample.shape[1:]      )))      )
									__a						   =sample / timesteps

						if not return_dict:
									return (sample,)

						return UNetaDOutput(sample=__snake_case      )









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  UpperCamelCase_(							_snake_case     :						Union[str, Any]			):




			"""simple docstring"""






			__a						   =fname.split(os.path.sep			)[-1]
			return re.search(r'^(.*)_\d+\.jpg$' ,  _snake_case			).groups()[0]






class 			__magic_name__					(    lowerCAmelCase_   ):





			def __init__(	self      ,						__snake_case      ,						__snake_case=None      ,						__snake_case=None      )     ->				Tuple:




						'''simple docstring'''
						__a						   =file_names
						__a						   =image_transform
						__a						   =label_to_id





			def __len__(	self      )     ->				int:




						'''simple docstring'''
						return len(self.file_names      )





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




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






def  UpperCamelCase_(							_snake_case     :						Union[str, Any] ,  _snake_case     :						Optional[int]			):




			"""simple docstring"""






			__a						   =Accelerator(cpu=args.cpu ,  mixed_precision=args.mixed_precision			)

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

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

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

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

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

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

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

			# Instantiate dataloaders.
			__a						   =DataLoader(_snake_case ,  shuffle=_snake_case ,  batch_size=_snake_case ,  num_workers=4			)
			__a						   =DataLoader(_snake_case ,  shuffle=_snake_case ,  batch_size=_snake_case ,  num_workers=4			)

			# Instantiate the model (we build the model here so that the seed also control new weights initialization)
			__a						   =create_model('resnet50d' ,  pretrained=_snake_case ,  num_classes=len(_snake_case			)			)

			# 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						   =model.to(accelerator.device			)

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

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

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

			# Instantiate learning rate scheduler
			__a						   =OneCycleLR(optimizer=_snake_case ,  max_lr=_snake_case ,  epochs=_snake_case ,  steps_per_epoch=len(_snake_case			)			)

			# 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						   =accelerator.prepare(
			    _snake_case ,  _snake_case ,  _snake_case ,  _snake_case ,  _snake_case			)

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

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

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






def  UpperCamelCase_(							):




			"""simple docstring"""






			__a						   =argparse.ArgumentParser(description='Simple example of training script.'			)
			parser.add_argument('--data_dir' ,  required=_snake_case ,  help='The data folder on disk.'			)
			parser.add_argument(
			    '--mixed_precision' ,  type=_snake_case ,  default=_snake_case ,  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=_snake_case ,  default=_snake_case ,  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.'			)
			__a						   =parser.parse_args()
			__a						   ={'lr': 3e-2, 'num_epochs': 3, 'seed': 42, 'batch_size': 64, 'image_size': 224}
			training_function(_snake_case ,  _snake_case			)


if __name__ == "__main__":
						main()






