


import argparse
import json
import os

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, DistributedType
from accelerate.utils.deepspeed import DummyOptim, DummyScheduler


UpperCAmelCase__       =	16
UpperCAmelCase__       =	32





def 							A     (					_UpperCAmelCase    :       Accelerator							,       _UpperCAmelCase    :       int = 16							,       _UpperCAmelCase    :       str = "bert-base-cased"   )    ->			Tuple:







			'''simple docstring'''
			_UpperCAmelCase			=       AutoTokenizer.from_pretrained(_UpperCAmelCase   )
			_UpperCAmelCase			=       load_dataset('glue'							,       'mrpc'   )

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

			# Apply the method we just defined to all the examples in all the splits of the dataset
			_UpperCAmelCase			=       datasets.map(
			    _UpperCAmelCase							,       batched=_UpperCAmelCase							,       remove_columns=['idx', 'sentence1', 'sentence2']							,       load_from_cache_file=_UpperCAmelCase   )

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

			def collate_fn(_UpperCAmelCase    :       List[Any]   ):
						# On TPU it's best to pad everything to the same length or training will be very slow.
						if accelerator.distributed_type == DistributedType.TPU:
									return tokenizer.pad(_UpperCAmelCase							,       padding='max_length'							,       max_length=128							,       return_tensors='pt'   )
						return tokenizer.pad(_UpperCAmelCase							,       padding='longest'							,       return_tensors='pt'   )

			# Instantiate dataloaders.
			_UpperCAmelCase			=       DataLoader(
			    tokenized_datasets['train']							,       shuffle=_UpperCAmelCase							,       collate_fn=_UpperCAmelCase							,       batch_size=_UpperCAmelCase   )
			_UpperCAmelCase			=       DataLoader(
			    tokenized_datasets['validation']							,       shuffle=_UpperCAmelCase							,       collate_fn=_UpperCAmelCase							,       batch_size=_UpperCAmelCase   )

			return train_dataloader, eval_dataloader





def 							A     (					_UpperCAmelCase    :       List[Any]							,       _UpperCAmelCase    :       int   )    ->			Dict:







			'''simple docstring'''
			_UpperCAmelCase			=       Accelerator()

			# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
			_UpperCAmelCase			=       config['lr']
			_UpperCAmelCase			=       int(config['num_epochs']   )
			_UpperCAmelCase			=       int(config['seed']   )
			_UpperCAmelCase			=       int(config['batch_size']   )
			_UpperCAmelCase			=       args.model_name_or_path

			set_seed(_UpperCAmelCase   )
			_UpperCAmelCase	,					_UpperCAmelCase			=       get_dataloaders(_UpperCAmelCase							,       _UpperCAmelCase							,       _UpperCAmelCase   )

			# Instantiate the model (we build the model here so that the seed also control new weights initialization)
			_UpperCAmelCase			=       AutoModelForSequenceClassification.from_pretrained(_UpperCAmelCase							,       return_dict=_UpperCAmelCase   )

			# Instantiate optimizer
			_UpperCAmelCase			=       (
			    AdamW
			    if accelerator.state.deepspeed_plugin is None
			    or 'optimizer' not in accelerator.state.deepspeed_plugin.deepspeed_config
			    else DummyOptim
			)
			_UpperCAmelCase			=       optimizer_cls(params=model.parameters()							,       lr=_UpperCAmelCase   )

			if accelerator.state.deepspeed_plugin is not None:
						_UpperCAmelCase			=       accelerator.state.deepspeed_plugin.deepspeed_config[
						    'gradient_accumulation_steps'
						]
			else:
						_UpperCAmelCase			=       1
			_UpperCAmelCase			=       (len(_UpperCAmelCase   ) * num_epochs) // gradient_accumulation_steps

			# Instantiate scheduler
			if (
			    accelerator.state.deepspeed_plugin is None
			    or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
			):
						_UpperCAmelCase			=       get_linear_schedule_with_warmup(
						    optimizer=_UpperCAmelCase							,       num_warmup_steps=0							,       num_training_steps=_UpperCAmelCase							,       )
			else:
						_UpperCAmelCase			=       DummyScheduler(_UpperCAmelCase							,       total_num_steps=_UpperCAmelCase							,       warmup_num_steps=0   )

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

			# We need to keep track of how many total steps we have iterated over
			_UpperCAmelCase			=       0
			# We also need to keep track of the stating epoch so files are named properly
			_UpperCAmelCase			=       0

			# Now we train the model
			_UpperCAmelCase			=       evaluate.load('glue'							,       'mrpc'   )
			_UpperCAmelCase			=       0
			_UpperCAmelCase			=       {}
			for epoch in range(_UpperCAmelCase							,       _UpperCAmelCase   ):
						model.train()
						for step, batch in enumerate(_UpperCAmelCase   ):
									_UpperCAmelCase			=       model(**_UpperCAmelCase   )
									_UpperCAmelCase			=       outputs.loss
									_UpperCAmelCase			=       loss / gradient_accumulation_steps
									accelerator.backward(_UpperCAmelCase   )
									if step % gradient_accumulation_steps == 0:
												optimizer.step()
												lr_scheduler.step()
												optimizer.zero_grad()

									overall_step += 1

						model.eval()
						_UpperCAmelCase			=       0
						for step, batch in enumerate(_UpperCAmelCase   ):
									# We could avoid this line since we set the accelerator with `device_placement=True`.
									batch.to(accelerator.device   )
									with torch.no_grad():
												_UpperCAmelCase			=       model(**_UpperCAmelCase   )
									_UpperCAmelCase			=       outputs.logits.argmax(dim=-1   )
									# It is slightly faster to call this once, than multiple times
									_UpperCAmelCase	,					_UpperCAmelCase			=       accelerator.gather(
									    (predictions, batch['labels'])   )  # If we are in a multiprocess environment, the last batch has duplicates
									if accelerator.use_distributed:
												if step == len(_UpperCAmelCase   ) - 1:
															_UpperCAmelCase			=       predictions[: len(eval_dataloader.dataset   ) - samples_seen]
															_UpperCAmelCase			=       references[: len(eval_dataloader.dataset   ) - samples_seen]
												else:
															samples_seen += references.shape[0]
									metric.add_batch(
									    predictions=_UpperCAmelCase							,       references=_UpperCAmelCase							,       )

						_UpperCAmelCase			=       metric.compute()
						# Use accelerator.print to print only on the main process.
						accelerator.print(F"epoch {epoch}:"							,       _UpperCAmelCase   )
						_UpperCAmelCase			=       eval_metric['accuracy']

						if best_performance < eval_metric["accuracy"]:
									_UpperCAmelCase			=       eval_metric['accuracy']

			if args.performance_lower_bound is not None:
						assert (
						    args.performance_lower_bound <= best_performance
						), F"Best performance metric {best_performance} is lower than the lower bound {args.performance_lower_bound}"

			accelerator.wait_for_everyone()
			if accelerator.is_main_process:
						with open(os.path.join(args.output_dir							,       'all_results.json'   )							,       'w'   ) as f:
									json.dump(_UpperCAmelCase							,       _UpperCAmelCase   )





def 							A     (					)    ->			List[str]:







			'''simple docstring'''
			_UpperCAmelCase			=       argparse.ArgumentParser(description='Simple example of training script tracking peak GPU memory usage.'   )
			parser.add_argument(
			    '--model_name_or_path'							,       type=_UpperCAmelCase							,       default='bert-base-cased'							,       help='Path to pretrained model or model identifier from huggingface.co/models.'							,       required=_UpperCAmelCase							,       )
			parser.add_argument(
			    '--output_dir'							,       type=_UpperCAmelCase							,       default='.'							,       help='Optional save directory where all checkpoint folders will be stored. Default is the current working directory.'							,       )
			parser.add_argument(
			    '--performance_lower_bound'							,       type=_UpperCAmelCase							,       default=_UpperCAmelCase							,       help='Optional lower bound for the performance metric. If set, the training will throw error when the performance metric drops below this value.'							,       )
			parser.add_argument(
			    '--num_epochs'							,       type=_UpperCAmelCase							,       default=3							,       help='Number of train epochs.'							,       )
			_UpperCAmelCase			=       parser.parse_args()
			_UpperCAmelCase			=       {'lr': 2E-5, 'num_epochs': args.num_epochs, 'seed': 42, 'batch_size': 16}
			training_function(_UpperCAmelCase							,       _UpperCAmelCase   )


if __name__ == "__main__":
							main()





import os
from glob import glob

import imageio
import torch
import torchvision
import wandb
from img_processing import custom_to_pil, loop_post_process, preprocess, preprocess_vqgan
from loaders import load_vqgan
from PIL import Image
from torch import nn

from transformers import CLIPModel, CLIPTokenizerFast
from utils import get_device, get_timestamp, show_pil







class       __lowerCAmelCase   :



			def __init__(						self       :  Any			,		A       :  str = "cpu"			,		A       :  str = "openai/clip-vit-large-patch14")       ->   None:



						"""simple docstring"""






						_UpperCAmelCase			=       device
						_UpperCAmelCase			=       CLIPTokenizerFast.from_pretrained(A)
						_UpperCAmelCase			=       [0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3]
						_UpperCAmelCase			=       [0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1]
						_UpperCAmelCase			=       torchvision.transforms.Normalize(self.image_mean			,		self.image_std)
						_UpperCAmelCase			=       torchvision.transforms.Resize(2_24)
						_UpperCAmelCase			=       torchvision.transforms.CenterCrop(2_24)



			def    _lowerCamelCase	(						self       :  str			,		A       :  Any)       ->   str:



						"""simple docstring"""






						_UpperCAmelCase			=       self.resize(A)
						_UpperCAmelCase			=       self.center_crop(A)
						_UpperCAmelCase			=       self.normalize(A)
						return images



			def __call__(						self       :  Any			,		A       :  Dict=None			,		A       :  Dict=None			,		**A       :  List[Any])       ->   Dict:



						"""simple docstring"""






						_UpperCAmelCase			=       self.tokenizer(text=A			,		**A)
						_UpperCAmelCase			=       self.preprocess_img(A)
						_UpperCAmelCase			=       {key: value.to(self.device) for (key, value) in encoding.items()}
						return encoding







class       __lowerCAmelCase   (      nn.Module						):



			def __init__(						self       :  List[Any]			,		A       :  Any=10			,		A       :  List[Any]=0.0_1			,		A       :  Optional[int]=None			,		A       :  int=None			,		A       :  Dict=None			,		A       :  Tuple=None			,		A       :  str=None			,		A       :  Dict=None			,		A       :  Union[str, Any]=False			,		A       :  Any=True			,		A       :  Any="image"			,		A       :  Tuple=True			,		A       :  List[Any]=False			,		A       :  int=False			,		A       :  int=False			,		)       ->   None:



						"""simple docstring"""






						super().__init__()
						_UpperCAmelCase			=       None
						_UpperCAmelCase			=       device if device else get_device()
						if vqgan:
									_UpperCAmelCase			=       vqgan
						else:
									_UpperCAmelCase			=       load_vqgan(self.device			,		conf_path=A			,		ckpt_path=A)
						self.vqgan.eval()
						if clip:
									_UpperCAmelCase			=       clip
						else:
									_UpperCAmelCase			=       CLIPModel.from_pretrained('openai/clip-vit-base-patch32')
						self.clip.to(self.device)
						_UpperCAmelCase			=       ProcessorGradientFlow(device=self.device)

						_UpperCAmelCase			=       iterations
						_UpperCAmelCase			=       lr
						_UpperCAmelCase			=       log
						_UpperCAmelCase			=       make_grid
						_UpperCAmelCase			=       return_val
						_UpperCAmelCase			=       quantize
						_UpperCAmelCase			=       self.vqgan.decoder.z_shape



			def    _lowerCamelCase	(						self       :  Optional[int]			,		A       :  int=None			,		A       :  Union[str, Any]=None			,		A       :  Dict=5			,		A       :  Optional[Any]=True)       ->   Optional[Any]:



						"""simple docstring"""






						_UpperCAmelCase			=       []
						if output_path is None:
									_UpperCAmelCase			=       './animation.gif'
						if input_path is None:
									_UpperCAmelCase			=       self.save_path
						_UpperCAmelCase			=       sorted(glob(input_path + '/*'))
						if not len(A):
									raise ValueError(
									    'No images found in save path, aborting (did you pass save_intermediate=True to the generate'
									    ' function?)')
						if len(A) == 1:
									print('Only one image found in save path, (did you pass save_intermediate=True to the generate function?)')
						_UpperCAmelCase			=       total_duration / len(A)
						_UpperCAmelCase			=       [frame_duration] * len(A)
						if extend_frames:
									_UpperCAmelCase			=       1.5
									_UpperCAmelCase			=       3
						for file_name in paths:
									if file_name.endswith('.png'):
												images.append(imageio.imread(A))
						imageio.mimsave(A			,		A			,		duration=A)
						print(F"gif saved to {output_path}")



			def    _lowerCamelCase	(						self       :  List[str]			,		A       :  Optional[Any]=None			,		A       :  Optional[int]=None)       ->   int:



						"""simple docstring"""






						if not (path or img):
									raise ValueError('Input either path or tensor')
						if img is not None:
									raise NotImplementedError
						_UpperCAmelCase			=       preprocess(Image.open(A)			,		target_image_size=2_56).to(self.device)
						_UpperCAmelCase			=       preprocess_vqgan(A)
						_UpperCAmelCase	,					*_UpperCAmelCase			=       self.vqgan.encode(A)
						return z



			def    _lowerCamelCase	(						self       :  List[str]			,		A       :  int)       ->   Dict:



						"""simple docstring"""






						_UpperCAmelCase			=       self.latent.detach().requires_grad_()
						_UpperCAmelCase			=       base_latent + transform_vector
						if self.quantize:
									_UpperCAmelCase	,					*_UpperCAmelCase			=       self.vqgan.quantize(A)
						else:
									_UpperCAmelCase			=       trans_latent
						return self.vqgan.decode(A)



			def    _lowerCamelCase	(						self       :  Any			,		A       :  Dict			,		A       :  Dict			,		A       :  Optional[Any]=None)       ->   Any:



						"""simple docstring"""






						_UpperCAmelCase			=       self.clip_preprocessor(text=A			,		images=A			,		return_tensors='pt'			,		padding=A)
						_UpperCAmelCase			=       self.clip(**A)
						_UpperCAmelCase			=       clip_outputs.logits_per_image
						if weights is not None:
									_UpperCAmelCase			=       similarity_logits * weights
						return similarity_logits.sum()



			def    _lowerCamelCase	(						self       :  Optional[int]			,		A       :  Dict			,		A       :  int			,		A       :  Tuple)       ->   str:



						"""simple docstring"""






						_UpperCAmelCase			=       self._get_clip_similarity(pos_prompts['prompts']			,		A			,		weights=(1 / pos_prompts['weights']))
						if neg_prompts:
									_UpperCAmelCase			=       self._get_clip_similarity(neg_prompts['prompts']			,		A			,		weights=neg_prompts['weights'])
						else:
									_UpperCAmelCase			=       torch.tensor([1]			,		device=self.device)
						_UpperCAmelCase			=       -torch.log(A) + torch.log(A)
						return loss



			def    _lowerCamelCase	(						self       :  Tuple			,		A       :  Optional[int]			,		A       :  List[Any]			,		A       :  Optional[int])       ->   Union[str, Any]:



						"""simple docstring"""






						_UpperCAmelCase			=       torch.randn_like(self.latent			,		requires_grad=A			,		device=self.device)
						_UpperCAmelCase			=       torch.optim.Adam([vector]			,		lr=self.lr)

						for i in range(self.iterations):
									optim.zero_grad()
									_UpperCAmelCase			=       self._add_vector(A)
									_UpperCAmelCase			=       loop_post_process(A)
									_UpperCAmelCase			=       self._get_CLIP_loss(A			,		A			,		A)
									print('CLIP loss'			,		A)
									if self.log:
												wandb.log({'CLIP Loss': clip_loss})
									clip_loss.backward(retain_graph=A)
									optim.step()
									if self.return_val == "image":
												yield custom_to_pil(transformed_img[0])
									else:
												yield vector



			def    _lowerCamelCase	(						self       :  Dict			,		A       :  Any			,		A       :  Optional[int]			,		A       :  str)       ->   Any:



						"""simple docstring"""






						wandb.init(reinit=A			,		project='face-editor')
						wandb.config.update({'Positive Prompts': positive_prompts})
						wandb.config.update({'Negative Prompts': negative_prompts})
						wandb.config.update({'lr': self.lr, 'iterations': self.iterations})
						if image_path:
									_UpperCAmelCase			=       Image.open(A)
									_UpperCAmelCase			=       image.resize((2_56, 2_56))
									wandb.log('Original Image'			,		wandb.Image(A))



			def    _lowerCamelCase	(						self       :  Dict			,		A       :  int)       ->   Dict:



						"""simple docstring"""






						if not prompts:
									return []
						_UpperCAmelCase			=       []
						_UpperCAmelCase			=       []
						if isinstance(A			,		A):
									_UpperCAmelCase			=       [prompt.strip() for prompt in prompts.split('|')]
						for prompt in prompts:
									if isinstance(A			,		(tuple, list)):
												_UpperCAmelCase			=       prompt[0]
												_UpperCAmelCase			=       float(prompt[1])
									elif ":" in prompt:
												_UpperCAmelCase	,					_UpperCAmelCase			=       prompt.split(':')
												_UpperCAmelCase			=       float(A)
									else:
												_UpperCAmelCase			=       prompt
												_UpperCAmelCase			=       1.0
									processed_prompts.append(A)
									weights.append(A)
						return {
						    "prompts": processed_prompts,
						    "weights": torch.tensor(A			,		device=self.device),
						}



			def    _lowerCamelCase	(						self       :  Optional[int]			,		A       :  Union[str, Any]			,		A       :  Union[str, Any]=None			,		A       :  int=None			,		A       :  Optional[Any]=True			,		A       :  Dict=False			,		A       :  Union[str, Any]=True			,		A       :  Any=True			,		A       :  Any=None			,		)       ->   Dict:



						"""simple docstring"""






						if image_path:
									_UpperCAmelCase			=       self._get_latent(A)
						else:
									_UpperCAmelCase			=       torch.randn(self.latent_dim			,		device=self.device)
						if self.log:
									self._init_logging(A			,		A			,		A)

						assert pos_prompts, "You must provide at least one positive prompt."
						_UpperCAmelCase			=       self.process_prompts(A)
						_UpperCAmelCase			=       self.process_prompts(A)
						if save_final and save_path is None:
									_UpperCAmelCase			=       os.path.join('./outputs/'			,		'_'.join(pos_prompts['prompts']))
						if not os.path.exists(A):
									os.makedirs(A)
						else:
									_UpperCAmelCase			=       save_path + '_' + get_timestamp()
									os.makedirs(A)
						_UpperCAmelCase			=       save_path

						_UpperCAmelCase			=       self.vqgan.decode(self.latent)[0]
						if show_intermediate:
									print('Original Image')
									show_pil(custom_to_pil(A))

						_UpperCAmelCase			=       loop_post_process(A)
						for iter, transformed_img in enumerate(self._optimize_CLIP(A			,		A			,		A)):
									if show_intermediate:
												show_pil(A)
									if save_intermediate:
												transformed_img.save(os.path.join(self.save_path			,		F"iter_{iter:03d}.png"))
									if self.log:
												wandb.log({'Image': wandb.Image(A)})
						if show_final:
									show_pil(A)
						if save_final:
									transformed_img.save(os.path.join(self.save_path			,		F"iter_{iter:03d}_final.png"))
