





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 					_snake_case						:






	def __init__(		self					,UpperCamelCase = "cpu"					,UpperCamelCase = "openai/clip-vit-large-patch14"       )						->    None:
					snake_case__      :str         =   device
					snake_case__      :Union[str, Any]         =   CLIPTokenizerFast.from_pretrained(UpperCamelCase       )
					snake_case__      :Any         =   [0.48145466, 0.4578275, 0.40821073]
					snake_case__      :Any         =   [0.26862954, 0.26130258, 0.27577711]
					snake_case__      :Optional[int]         =   torchvision.transforms.Normalize(self.image_mean					,self.image_std       )
					snake_case__      :str         =   torchvision.transforms.Resize(224       )
					snake_case__      :Optional[int]         =   torchvision.transforms.CenterCrop(224       )






	def    lowerCAmelCase_							(		self					,UpperCamelCase       )						->    Any:
					snake_case__      :str         =   self.resize(UpperCamelCase       )
					snake_case__      :List[str]         =   self.center_crop(UpperCamelCase       )
					snake_case__      :List[str]         =   self.normalize(UpperCamelCase       )
					return images






	def __call__(		self					,UpperCamelCase=None					,UpperCamelCase=None					,**UpperCamelCase       )						->    Union[str, Any]:
					snake_case__      :Optional[Any]         =   self.tokenizer(text=UpperCamelCase					,**UpperCamelCase       )
					snake_case__      :Tuple         =   self.preprocess_img(UpperCamelCase       )
					snake_case__      :Optional[Any]         =   {key: value.to(self.device       ) for (key, value) in encoding.items()}
					return encoding




class 					_snake_case						(			nn.Module ):






	def __init__(		self					,UpperCamelCase=10					,UpperCamelCase=0.01					,UpperCamelCase=None					,UpperCamelCase=None					,UpperCamelCase=None					,UpperCamelCase=None					,UpperCamelCase=None					,UpperCamelCase=None					,UpperCamelCase=False					,UpperCamelCase=True					,UpperCamelCase="image"					,UpperCamelCase=True					,UpperCamelCase=False					,UpperCamelCase=False					,UpperCamelCase=False					,)						->    None:
					super().__init__()
					snake_case__      :Optional[Any]         =   None
					snake_case__      :Any         =   device if device else get_device()
					if vqgan:
									snake_case__      :Tuple         =   vqgan
					else:
									snake_case__      :Optional[Any]         =   load_vqgan(self.device					,conf_path=UpperCamelCase					,ckpt_path=UpperCamelCase       )
					self.vqgan.eval()
					if clip:
									snake_case__      :List[str]         =   clip
					else:
									snake_case__      :Optional[Any]         =   CLIPModel.from_pretrained("openai/clip-vit-base-patch32"       )
					self.clip.to(self.device       )
					snake_case__      :Optional[int]         =   ProcessorGradientFlow(device=self.device       )

					snake_case__      :Union[str, Any]         =   iterations
					snake_case__      :str         =   lr
					snake_case__      :List[Any]         =   log
					snake_case__      :List[str]         =   make_grid
					snake_case__      :List[Any]         =   return_val
					snake_case__      :Union[str, Any]         =   quantize
					snake_case__      :List[Any]         =   self.vqgan.decoder.z_shape






	def    lowerCAmelCase_							(		self					,UpperCamelCase=None					,UpperCamelCase=None					,UpperCamelCase=5					,UpperCamelCase=True       )						->    Union[str, Any]:
					snake_case__      :Union[str, Any]         =   []
					if output_path is None:
									snake_case__      :Dict         =   "./animation.gif"
					if input_path is None:
									snake_case__      :Optional[Any]         =   self.save_path
					snake_case__      :Union[str, Any]         =   sorted(glob(input_path + "/*"       )       )
					if not len(UpperCamelCase       ):
									raise ValueError(
									    "No images found in save path, aborting (did you pass save_intermediate=True to the generate"
									    " function?)"       )
					if len(UpperCamelCase       ) == 1:
									print("Only one image found in save path, (did you pass save_intermediate=True to the generate function?)"       )
					snake_case__      :List[str]         =   total_duration / len(UpperCamelCase       )
					snake_case__      :str         =   [frame_duration] * len(UpperCamelCase       )
					if extend_frames:
									snake_case__      :Dict         =   1.5
									snake_case__      :List[str]         =   3
					for file_name in paths:
									if file_name.endswith(".png"       ):
													images.append(imageio.imread(UpperCamelCase       )       )
					imageio.mimsave(UpperCamelCase					,UpperCamelCase					,duration=UpperCamelCase       )
					print(f'gif saved to {output_path}'       )






	def    lowerCAmelCase_							(		self					,UpperCamelCase=None					,UpperCamelCase=None       )						->    str:
					if not (path or img):
									raise ValueError("Input either path or tensor"       )
					if img is not None:
									raise NotImplementedError
					snake_case__      :Dict         =   preprocess(Image.open(UpperCamelCase       )					,target_image_size=256       ).to(self.device       )
					snake_case__      :Tuple         =   preprocess_vqgan(UpperCamelCase       )
					snake_case__      :int         =   self.vqgan.encode(UpperCamelCase       )
					return z






	def    lowerCAmelCase_							(		self					,UpperCamelCase       )						->    List[Any]:
					snake_case__      :Tuple         =   self.latent.detach().requires_grad_()
					snake_case__      :Dict         =   base_latent + transform_vector
					if self.quantize:
									snake_case__      :str         =   self.vqgan.quantize(UpperCamelCase       )
					else:
									snake_case__      :int         =   trans_latent
					return self.vqgan.decode(UpperCamelCase       )






	def    lowerCAmelCase_							(		self					,UpperCamelCase					,UpperCamelCase					,UpperCamelCase=None       )						->    Union[str, Any]:
					snake_case__      :Tuple         =   self.clip_preprocessor(text=UpperCamelCase					,images=UpperCamelCase					,return_tensors="pt"					,padding=UpperCamelCase       )
					snake_case__      :Tuple         =   self.clip(**UpperCamelCase       )
					snake_case__      :Optional[Any]         =   clip_outputs.logits_per_image
					if weights is not None:
									snake_case__      :str         =   similarity_logits * weights
					return similarity_logits.sum()






	def    lowerCAmelCase_							(		self					,UpperCamelCase					,UpperCamelCase					,UpperCamelCase       )						->    int:
					snake_case__      :List[str]         =   self._get_clip_similarity(pos_prompts["prompts"]					,UpperCamelCase					,weights=(1 / pos_prompts["weights"])       )
					if neg_prompts:
									snake_case__      :Tuple         =   self._get_clip_similarity(neg_prompts["prompts"]					,UpperCamelCase					,weights=neg_prompts["weights"]       )
					else:
									snake_case__      :str         =   torch.tensor([1]					,device=self.device       )
					snake_case__      :Optional[int]         =   -torch.log(UpperCamelCase       ) + torch.log(UpperCamelCase       )
					return loss






	def    lowerCAmelCase_							(		self					,UpperCamelCase					,UpperCamelCase					,UpperCamelCase       )						->    Any:
					snake_case__      :int         =   torch.randn_like(self.latent					,requires_grad=UpperCamelCase					,device=self.device       )
					snake_case__      :Any         =   torch.optim.Adam([vector]					,lr=self.lr       )

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






	def    lowerCAmelCase_							(		self					,UpperCamelCase					,UpperCamelCase					,UpperCamelCase       )						->    List[str]:
					wandb.init(reinit=UpperCamelCase					,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:
									snake_case__      :List[str]         =   Image.open(UpperCamelCase       )
									snake_case__      :int         =   image.resize((256, 256)       )
									wandb.log("Original Image"					,wandb.Image(UpperCamelCase       )       )






	def    lowerCAmelCase_							(		self					,UpperCamelCase       )						->    int:
					if not prompts:
									return []
					snake_case__      :int         =   []
					snake_case__      :Any         =   []
					if isinstance(UpperCamelCase					,UpperCamelCase       ):
									snake_case__      :int         =   [prompt.strip() for prompt in prompts.split("|"       )]
					for prompt in prompts:
									if isinstance(UpperCamelCase					,(tuple, list)       ):
													snake_case__      :List[Any]         =   prompt[0]
													snake_case__      :List[Any]         =   float(prompt[1]       )
									elif ":" in prompt:
													snake_case__      :str         =   prompt.split(":"       )
													snake_case__      :int         =   float(UpperCamelCase       )
									else:
													snake_case__      :int         =   prompt
													snake_case__      :Tuple         =   1.0
									processed_prompts.append(UpperCamelCase       )
									weights.append(UpperCamelCase       )
					return {
					    "prompts": processed_prompts,
					    "weights": torch.tensor(UpperCamelCase					,device=self.device       ),
					}






	def    lowerCAmelCase_							(		self					,UpperCamelCase					,UpperCamelCase=None					,UpperCamelCase=None					,UpperCamelCase=True					,UpperCamelCase=False					,UpperCamelCase=True					,UpperCamelCase=True					,UpperCamelCase=None					,)						->    Union[str, Any]:
					if image_path:
									snake_case__      :Tuple         =   self._get_latent(UpperCamelCase       )
					else:
									snake_case__      :str         =   torch.randn(self.latent_dim					,device=self.device       )
					if self.log:
									self._init_logging(UpperCamelCase					,UpperCamelCase					,UpperCamelCase       )

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

					snake_case__      :List[Any]         =   self.vqgan.decode(self.latent       )[0]
					if show_intermediate:
									print("Original Image"       )
									show_pil(custom_to_pil(UpperCamelCase       )       )

					snake_case__      :int         =   loop_post_process(UpperCamelCase       )
					for iter, transformed_img in enumerate(self._optimize_CLIP(UpperCamelCase					,UpperCamelCase					,UpperCamelCase       )       ):
									if show_intermediate:
													show_pil(UpperCamelCase       )
									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(UpperCamelCase       )}       )
					if show_final:
									show_pil(UpperCamelCase       )
					if save_final:
									transformed_img.save(os.path.join(self.save_path					,f'iter_{iter:03d}_final.png'       )       )







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							:   Optional[Any]							=  1_6
__UpperCAmelCase							:   Optional[int]							=  3_2





def 					lowercase_ (    __snake_case     :							Accelerator	,	__snake_case     :							int = 16	,	__snake_case     :							str = "bert-base-cased"      )     -> Optional[Any]:





				'''simple docstring'''


				snake_case__      :int         =   AutoTokenizer.from_pretrained(__snake_case      )
				snake_case__      :Optional[int]         =   load_dataset("glue"	,	"mrpc"      )

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

				# Apply the method we just defined to all the examples in all the splits of the dataset
				snake_case__      :List[Any]         =   datasets.map(
				    __snake_case	,	batched=__snake_case	,	remove_columns=["idx", "sentence1", "sentence2"]	,	load_from_cache_file=__snake_case      )

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

				def collate_fn(__snake_case     :							Dict      ):
								# 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(__snake_case	,	padding="max_length"	,	max_length=1_28	,	return_tensors="pt"      )
								return tokenizer.pad(__snake_case	,	padding="longest"	,	return_tensors="pt"      )

				# Instantiate dataloaders.
				snake_case__      :Any         =   DataLoader(
				    tokenized_datasets["train"]	,	shuffle=__snake_case	,	collate_fn=__snake_case	,	batch_size=__snake_case      )
				snake_case__      :Tuple         =   DataLoader(
				    tokenized_datasets["validation"]	,	shuffle=__snake_case	,	collate_fn=__snake_case	,	batch_size=__snake_case      )

				return train_dataloader, eval_dataloader





def 					lowercase_ (    __snake_case     :							List[Any]	,	__snake_case     :							Union[str, Any]	,	__snake_case     :							int	,	__snake_case     :							Optional[int]      )     -> Tuple:





				'''simple docstring'''


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

				snake_case__      :int         =   metric.compute()
				return eval_metric["accuracy"]





def 					lowercase_ (    __snake_case     :							Union[str, Any]	,	__snake_case     :							Optional[Any]      )     -> Any:





				'''simple docstring'''


				snake_case__      :Any         =   Accelerator()

				# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
				snake_case__      :Union[str, Any]         =   config["lr"]
				snake_case__      :List[str]         =   int(config["num_epochs"]      )
				snake_case__      :Optional[Any]         =   int(config["seed"]      )
				snake_case__      :List[Any]         =   int(config["batch_size"]      )
				snake_case__      :List[Any]         =   args.model_name_or_path

				set_seed(__snake_case      )
				snake_case__    ,						snake_case__      :List[Any]         =   get_dataloaders(__snake_case	,	__snake_case	,	__snake_case      )

				# Instantiate the model (we build the model here so that the seed also control new weights initialization)
				snake_case__      :List[Any]         =   AutoModelForSequenceClassification.from_pretrained(__snake_case	,	return_dict=__snake_case      )

				# Instantiate optimizer
				snake_case__      :int         =   (
				    AdamW
				    if accelerator.state.deepspeed_plugin is None
				    or "optimizer" not in accelerator.state.deepspeed_plugin.deepspeed_config
				    else DummyOptim
				)
				snake_case__      :Tuple         =   optimizer_cls(params=model.parameters()	,	lr=__snake_case      )

				if accelerator.state.deepspeed_plugin is not None:
								snake_case__      :List[str]         =   accelerator.state.deepspeed_plugin.deepspeed_config[
								    "gradient_accumulation_steps"
								]
				else:
								snake_case__      :Any         =   1
				snake_case__      :List[Any]         =   (len(__snake_case      ) * num_epochs) // gradient_accumulation_steps

				# Instantiate scheduler
				if (
				    accelerator.state.deepspeed_plugin is None
				    or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
				):
								snake_case__      :Optional[Any]         =   get_linear_schedule_with_warmup(
								    optimizer=__snake_case	,	num_warmup_steps=0	,	num_training_steps=__snake_case	,	)
				else:
								snake_case__      :Any         =   DummyScheduler(__snake_case	,	total_num_steps=__snake_case	,	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.
				snake_case__    ,						snake_case__    ,						snake_case__    ,						snake_case__    ,						snake_case__      :int         =   accelerator.prepare(
				    __snake_case	,	__snake_case	,	__snake_case	,	__snake_case	,	__snake_case      )

				# We need to keep track of how many total steps we have iterated over
				snake_case__      :Dict         =   0
				# We also need to keep track of the stating epoch so files are named properly
				snake_case__      :Union[str, Any]         =   0
				snake_case__      :List[str]         =   evaluate.load("glue"	,	"mrpc"      )
				snake_case__      :Optional[Any]         =   num_epochs

				if args.partial_train_epoch is not None:
								snake_case__      :List[Any]         =   args.partial_train_epoch

				if args.resume_from_checkpoint:
								accelerator.load_state(args.resume_from_checkpoint      )
								snake_case__      :Union[str, Any]         =   args.resume_from_checkpoint.split("epoch_"      )[1]
								snake_case__      :Dict         =   ""
								for char in epoch_string:
												if char.isdigit():
																state_epoch_num += char
												else:
																break
								snake_case__      :str         =   int(__snake_case      ) + 1
								snake_case__      :List[Any]         =   evaluation_loop(__snake_case	,	__snake_case	,	__snake_case	,	__snake_case      )
								accelerator.print("resumed checkpoint performance:"	,	__snake_case      )
								accelerator.print("resumed checkpoint's scheduler's lr:"	,	lr_scheduler.get_lr()[0]      )
								accelerator.print("resumed optimizers's lr:"	,	optimizer.param_groups[0]["lr"]      )
								with open(os.path.join(args.output_dir	,	F'state_{starting_epoch-1}.json'      )	,	"r"      ) as f:
												snake_case__      :Tuple         =   json.load(__snake_case      )
												assert resumed_state["accuracy"] == accuracy, "Accuracy mismatch, loading from checkpoint failed"
												assert (
												    resumed_state["lr"] == lr_scheduler.get_lr()[0]
												), "Scheduler learning rate mismatch, loading from checkpoint failed"
												assert (
												    resumed_state["optimizer_lr"] == optimizer.param_groups[0]["lr"]
												), "Optimizer learning rate mismatch, loading from checkpoint failed"
												assert resumed_state["epoch"] == starting_epoch - 1, "Epoch mismatch, loading from checkpoint failed"
												return

    # Now we train the model
				snake_case__      :Optional[int]         =   {}
				for epoch in range(__snake_case	,	__snake_case      ):
								model.train()
								for step, batch in enumerate(__snake_case      ):
												snake_case__      :str         =   model(**__snake_case      )
												snake_case__      :List[str]         =   outputs.loss
												snake_case__      :List[Any]         =   loss / gradient_accumulation_steps
												accelerator.backward(__snake_case      )
												if step % gradient_accumulation_steps == 0:
																optimizer.step()
																lr_scheduler.step()
																optimizer.zero_grad()

												overall_step += 1
								snake_case__      :int         =   F'epoch_{epoch}'
								snake_case__      :str         =   os.path.join(args.output_dir	,	__snake_case      )
								accelerator.save_state(__snake_case      )
								snake_case__      :Union[str, Any]         =   evaluation_loop(__snake_case	,	__snake_case	,	__snake_case	,	__snake_case      )
								snake_case__      :List[str]         =   accuracy
								snake_case__      :List[str]         =   lr_scheduler.get_lr()[0]
								snake_case__      :List[Any]         =   optimizer.param_groups[0]["lr"]
								snake_case__      :Dict         =   epoch
								snake_case__      :List[Any]         =   overall_step
								accelerator.print(F'epoch {epoch}:'	,	__snake_case      )

								accelerator.wait_for_everyone()
								if accelerator.is_main_process:
												with open(os.path.join(args.output_dir	,	F'state_{epoch}.json'      )	,	"w"      ) as f:
																json.dump(__snake_case	,	__snake_case      )





def 					lowercase_ (    )     -> Any:





				'''simple docstring'''


				snake_case__      :List[Any]         =   argparse.ArgumentParser(description="Simple example of training script tracking peak GPU memory usage."      )
				parser.add_argument(
				    "--model_name_or_path"	,	type=__snake_case	,	default="bert-base-cased"	,	help="Path to pretrained model or model identifier from huggingface.co/models."	,	required=__snake_case	,	)
				parser.add_argument(
				    "--output_dir"	,	type=__snake_case	,	default="."	,	help="Optional save directory where all checkpoint folders will be stored. Default is the current working directory."	,	)
				parser.add_argument(
				    "--resume_from_checkpoint"	,	type=__snake_case	,	default=__snake_case	,	help="If the training should continue from a checkpoint folder."	,	)
				parser.add_argument(
				    "--partial_train_epoch"	,	type=__snake_case	,	default=__snake_case	,	help="If passed, the training will stop after this number of epochs."	,	)
				parser.add_argument(
				    "--num_epochs"	,	type=__snake_case	,	default=2	,	help="Number of train epochs."	,	)
				snake_case__      :Any         =   parser.parse_args()
				snake_case__      :int         =   {"lr": 2e-5, "num_epochs": args.num_epochs, "seed": 42, "batch_size": 16}

				training_function(__snake_case	,	__snake_case      )


if __name__ == "__main__":
	main()