





'''simple docstring'''






from copy import deepcopy

import torch
import torch.nn.functional as F
from torch.optim import AdamW
from torch.optim.lr_scheduler import LambdaLR
from torch.utils.data import DataLoader

from accelerate.accelerator import Accelerator
from accelerate.state import GradientState
from accelerate.test_utils import RegressionDataset, RegressionModel
from accelerate.utils import DistributedType, is_torch_version, set_seed
def 							_lowerCamelCase(  UpperCamelCase__    : Dict    ,       UpperCamelCase__    : int    ,       UpperCamelCase__    : str    ,       UpperCamelCase__    : Tuple		)		->  Union[str, Any]:
    for param, grad_param in zip(model_a.parameters()    ,       model_b.parameters()		):
        if not param.requires_grad:
            continue
        if not did_step:
            # Grads should not be in sync
            assert (
                torch.allclose(param.grad    ,       grad_param.grad		) is False
            ), F'''Gradients in sync when they should not be at iteration {iteration}:\nmodel_a grad ({param.grad}) == model_b grad ({grad_param.grad})'''
        else:
            # Grads should be in sync
            assert (
                torch.allclose(param.grad    ,       grad_param.grad		) is True
            ), F'''Gradients not in sync when they should be at iteration {iteration}:\nmodel_a grad ({param.grad}) != model_b grad ({grad_param.grad})'''
def 							_lowerCamelCase(  UpperCamelCase__    : int    ,       UpperCamelCase__    : Dict    ,       UpperCamelCase__    : List[str]    ,       UpperCamelCase__    : Any    ,       UpperCamelCase__    : List[str]=True		)		->  Optional[int]:
    model.train()
    A       :       Dict							=						model(UpperCamelCase__		)
    A       :       str							=						F.mse_loss(UpperCamelCase__    ,       target.to(output.device		)		)
    if not do_backward:
        loss /= accelerator.gradient_accumulation_steps
        loss.backward()
    else:
        accelerator.backward(UpperCamelCase__		)
def 							_lowerCamelCase(  UpperCamelCase__    : Dict    ,       UpperCamelCase__    : Optional[int]=False		)		->  int:
    set_seed(42		)
    A       :       List[str]							=						RegressionModel()
    A       :       str							=						deepcopy(UpperCamelCase__		)
    A       :       Optional[int]							=						RegressionDataset(length=80		)
    A       :       Optional[int]							=						DataLoader(UpperCamelCase__    ,       batch_size=16		)
    model.to(accelerator.device		)
    if sched:
        A       :       Tuple							=						AdamW(params=model.parameters()    ,       lr=1e-3		)
        A       :       int							=						AdamW(params=ddp_model.parameters()    ,       lr=1e-3		)
        A       :       List[Any]							=						LambdaLR(UpperCamelCase__    ,       lr_lambda=lambda UpperCamelCase__		: epoch**0.6_5		)
        A       :       Tuple							=						LambdaLR(UpperCamelCase__    ,       lr_lambda=lambda UpperCamelCase__		: epoch**0.6_5		)
    # Make a copy of `model`
    if sched:
        A,	A,	A,	A       :       str							=						accelerator.prepare(UpperCamelCase__    ,       UpperCamelCase__    ,       UpperCamelCase__    ,       UpperCamelCase__		)
    else:
        A,	A       :       Tuple							=						accelerator.prepare(UpperCamelCase__    ,       UpperCamelCase__		)
    if sched:
        return (model, opt, sched, dataloader, ddp_model, ddp_opt, ddp_sched)
    return model, ddp_model, dataloader
def 							_lowerCamelCase(  UpperCamelCase__    : Tuple		)		->  int:
    # Test when on a single CPU or GPU that the context manager does nothing
    A,	A,	A       :       Union[str, Any]							=						get_training_setup(UpperCamelCase__		)
    # Use a single batch
    A,	A       :       Optional[int]							=						next(iter(UpperCamelCase__		)		).values()
    for iteration in range(3		):
        # Gather the distributed inputs and targs for the base model
        A,	A       :       List[Any]							=						accelerator.gather((ddp_input, ddp_target)		)
        A,	A       :       Optional[int]							=						input.to(accelerator.device		), target.to(accelerator.device		)
        # Perform our initial ground truth step in non "DDP"
        step_model(UpperCamelCase__    ,       UpperCamelCase__    ,       UpperCamelCase__    ,       UpperCamelCase__		)
        # Do "gradient accumulation" (noop)
        if iteration % 2 == 0:
            # Accumulate grads locally
            with accelerator.no_sync(UpperCamelCase__		):
                step_model(UpperCamelCase__    ,       UpperCamelCase__    ,       UpperCamelCase__    ,       UpperCamelCase__		)
        else:
            # Sync grads
            step_model(UpperCamelCase__    ,       UpperCamelCase__    ,       UpperCamelCase__    ,       UpperCamelCase__		)

        # Since `no_sync` is a noop, `ddp_model` and `model` grads should always be in sync
        check_model_parameters(UpperCamelCase__    ,       UpperCamelCase__    ,       UpperCamelCase__    ,       UpperCamelCase__		)
        for param, ddp_param in zip(model.parameters()    ,       ddp_model.parameters()		):
            if not param.requires_grad:
                continue
            assert torch.allclose(
                param.grad    ,       ddp_param.grad		), F'''Gradients not in sync when they should be:\nModel grad ({param.grad}) != DDP grad ({ddp_param.grad})'''

        # Shuffle ddp_input on each iteration
        torch.manual_seed(1_337 + iteration		)
        A       :       Optional[Any]							=						ddp_input[torch.randperm(len(UpperCamelCase__		)		)]
def 							_lowerCamelCase(  UpperCamelCase__    : Optional[int]		)		->  Any:
    # Test on distributed setup that context manager behaves properly
    A,	A,	A       :       List[Any]							=						get_training_setup(UpperCamelCase__		)
    # Use a single batch
    A,	A       :       Optional[int]							=						next(iter(UpperCamelCase__		)		).values()
    for iteration in range(3		):
        # Gather the distributed inputs and targs for the base model
        A,	A       :       List[str]							=						accelerator.gather((ddp_input, ddp_target)		)
        A,	A       :       Tuple							=						input.to(accelerator.device		), target.to(accelerator.device		)
        # Perform our initial ground truth step in non "DDP"
        step_model(UpperCamelCase__    ,       UpperCamelCase__    ,       UpperCamelCase__    ,       UpperCamelCase__		)
        # Do "gradient accumulation" (noop)
        if iteration % 2 == 0:
            # Accumulate grads locally
            with accelerator.no_sync(UpperCamelCase__		):
                step_model(UpperCamelCase__    ,       UpperCamelCase__    ,       UpperCamelCase__    ,       UpperCamelCase__		)
        else:
            # Sync grads
            step_model(UpperCamelCase__    ,       UpperCamelCase__    ,       UpperCamelCase__    ,       UpperCamelCase__		)

        # DDP model and model should only be in sync when not (iteration % 2 == 0)
        for param, ddp_param in zip(model.parameters()    ,       ddp_model.parameters()		):
            if not param.requires_grad:
                continue
            if iteration % 2 == 0:
                # Grads should not be in sync
                assert (
                    torch.allclose(param.grad    ,       ddp_param.grad		) is False
                ), F'''Gradients in sync when they should not be:\nModel grad ({param.grad}) == DDP grad ({ddp_param.grad})'''
            else:
                # Grads should be in sync
                assert (
                    torch.allclose(param.grad    ,       ddp_param.grad		) is True
                ), F'''Gradients not in sync when they should be:\nModel grad ({param.grad}) != DDP grad ({ddp_param.grad})'''

        # Shuffle ddp_input on each iteration
        torch.manual_seed(1_337 + iteration		)
        A       :       int							=						ddp_input[torch.randperm(len(UpperCamelCase__		)		)]
def 							_lowerCamelCase(  UpperCamelCase__    : Union[str, Any]=False    ,       UpperCamelCase__    : List[Any]=False		)		->  str:
    A       :       Optional[Any]							=						Accelerator(
        split_batches=UpperCamelCase__    ,       dispatch_batches=UpperCamelCase__    ,       gradient_accumulation_steps=2		)
    # Test that context manager behaves properly
    A,	A,	A       :       Optional[Any]							=						get_training_setup(UpperCamelCase__		)
    for iteration, batch in enumerate(UpperCamelCase__		):
        A,	A       :       List[str]							=						batch.values()
        # Gather the distributed inputs and targs for the base model
        A,	A       :       List[Any]							=						accelerator.gather((ddp_input, ddp_target)		)
        A,	A       :       Tuple							=						input.to(accelerator.device		), target.to(accelerator.device		)
        # Perform our initial ground truth step in non "DDP"
        step_model(UpperCamelCase__    ,       UpperCamelCase__    ,       UpperCamelCase__    ,       UpperCamelCase__    ,       UpperCamelCase__		)
        # Do "gradient accumulation" (noop)
        with accelerator.accumulate(UpperCamelCase__		):
            step_model(UpperCamelCase__    ,       UpperCamelCase__    ,       UpperCamelCase__    ,       UpperCamelCase__		)

        # DDP model and model should only be in sync when not (iteration % 2 == 0)
        for param, ddp_param in zip(model.parameters()    ,       ddp_model.parameters()		):
            if not param.requires_grad:
                continue
            if ((iteration + 1) % 2 == 0) or (iteration == len(UpperCamelCase__		) - 1):
                # Grads should be in sync
                assert (
                    torch.allclose(param.grad    ,       ddp_param.grad		) is True
                ), F'''Gradients not in sync when they should be at iteration {iteration}:\nModel grad ({param.grad}) != DDP grad ({ddp_param.grad})'''
            else:
                # Grads should not be in sync
                assert (
                    torch.allclose(param.grad    ,       ddp_param.grad		) is False
                ), F'''Gradients in sync when they should not be at iteration {iteration}:\nModel grad ({param.grad}) == DDP grad ({ddp_param.grad})'''

        # Shuffle ddp_input on each iteration
        torch.manual_seed(1_337 + iteration		)
        A       :       Optional[int]							=						ddp_input[torch.randperm(len(UpperCamelCase__		)		)]
    GradientState._reset_state()
def 							_lowerCamelCase(  UpperCamelCase__    : List[str]=False    ,       UpperCamelCase__    : List[str]=False		)		->  Dict:
    A       :       Tuple							=						Accelerator(
        split_batches=UpperCamelCase__    ,       dispatch_batches=UpperCamelCase__    ,       gradient_accumulation_steps=2		)
    # Test that context manager behaves properly
    A,	A,	A,	A,	A,	A,	A       :       Any							=						get_training_setup(UpperCamelCase__    ,       UpperCamelCase__		)
    for iteration, batch in enumerate(UpperCamelCase__		):
        A,	A       :       Dict							=						batch.values()
        # Gather the distributed inputs and targs for the base model
        A,	A       :       Dict							=						accelerator.gather((ddp_input, ddp_target)		)
        A,	A       :       Dict							=						input.to(accelerator.device		), target.to(accelerator.device		)
        # Perform our initial ground truth step in non "DDP"
        model.train()
        ddp_model.train()
        step_model(UpperCamelCase__    ,       UpperCamelCase__    ,       UpperCamelCase__    ,       UpperCamelCase__    ,       UpperCamelCase__		)
        opt.step()

        if ((iteration + 1) % 2 == 0) or ((iteration + 1) == len(UpperCamelCase__		)):
            if split_batches:
                sched.step()
            else:
                for _ in range(accelerator.num_processes		):
                    sched.step()
        opt.zero_grad()
        # Perform gradient accumulation under wrapper
        with accelerator.accumulate(UpperCamelCase__		):
            step_model(UpperCamelCase__    ,       UpperCamelCase__    ,       UpperCamelCase__    ,       UpperCamelCase__		)
            ddp_opt.step()
            ddp_sched.step()
            ddp_opt.zero_grad()

        # Learning rates should be the same
        assert (
            opt.param_groups[0]["lr"] == ddp_opt.param_groups[0]["lr"]
        ), F'''Learning rates found in each optimizer did not align\nopt: {opt.param_groups[0]['lr']}\nDDP opt: {ddp_opt.param_groups[0]['lr']}\n'''
        A       :       Tuple							=						(((iteration + 1) % 2) == 0) or ((iteration + 1) == len(UpperCamelCase__		))
        if accelerator.num_processes > 1:
            check_model_parameters(UpperCamelCase__    ,       UpperCamelCase__    ,       UpperCamelCase__    ,       UpperCamelCase__		)
        # Shuffle ddp_input on each iteration
        torch.manual_seed(1_337 + iteration		)
    GradientState._reset_state()
def 							_lowerCamelCase(  )		->  Union[str, Any]:
    A       :       str							=						Accelerator()

    A       :       Tuple							=						RegressionDataset(length=80		)
    A       :       str							=						DataLoader(UpperCamelCase__    ,       batch_size=16		)
    A       :       List[str]							=						RegressionDataset(length=96		)
    A       :       Any							=						DataLoader(UpperCamelCase__    ,       batch_size=16		)
    A,	A       :       List[Any]							=						accelerator.prepare(UpperCamelCase__    ,       UpperCamelCase__		)
    assert accelerator.gradient_state.active_dataloader is None
    for iteration, _ in enumerate(UpperCamelCase__		):
        assert id(accelerator.gradient_state.active_dataloader		) == id(UpperCamelCase__		)
        if iteration < len(UpperCamelCase__		) - 1:
            assert not accelerator.gradient_state.end_of_dataloader
            if iteration == 1:
                for batch_num, _ in enumerate(UpperCamelCase__		):
                    assert id(accelerator.gradient_state.active_dataloader		) == id(UpperCamelCase__		)
                    if batch_num < len(UpperCamelCase__		) - 1:
                        assert not accelerator.gradient_state.end_of_dataloader
                    else:
                        assert accelerator.gradient_state.end_of_dataloader
        else:
            assert accelerator.gradient_state.end_of_dataloader
    assert accelerator.gradient_state.active_dataloader is None
def 							_lowerCamelCase(  )		->  Union[str, Any]:
    A       :       List[str]							=						Accelerator()
    A       :       int							=						accelerator.state
    if state.local_process_index == 0:
        print('''**Test `accumulate` gradient accumulation with dataloader break**'''		)
    test_dataloader_break()
    if state.distributed_type == DistributedType.NO:
        if state.local_process_index == 0:
            print('''**Test NOOP `no_sync` context manager**'''		)
        test_noop_sync(UpperCamelCase__		)
    if state.distributed_type in (DistributedType.MULTI_GPU, DistributedType.MULTI_CPU):
        if state.local_process_index == 0:
            print('''**Test Distributed `no_sync` context manager**'''		)
        test_distributed_sync(UpperCamelCase__		)
    if state.distributed_type == DistributedType.MULTI_GPU:
        for split_batch in [True, False]:
            for dispatch_batches in [True, False]:
                if state.local_process_index == 0:
                    print(
                        '''**Test `accumulate` gradient accumulation, '''    ,       F'''`split_batches={split_batch}` and `dispatch_batches={dispatch_batches}`**'''    ,       )
                test_gradient_accumulation(UpperCamelCase__    ,       UpperCamelCase__		)

    # Currently will break on torch 2.0 +, need to investigate why
    if is_torch_version('''<'''    ,       '''2.0'''		) or state.distributed_type == DistributedType.NO:
        if state.local_process_index == 0:
            print(
                '''**Test `accumulate` gradient accumulation with optimizer and scheduler, '''    ,       '''`split_batches=False`, `dispatch_batches=False`**'''    ,       )
        test_gradient_accumulation_with_opt_and_scheduler()
        if state.distributed_type == DistributedType.MULTI_GPU:
            for split_batch in [True, False]:
                for dispatch_batches in [True, False]:
                    if not split_batch and not dispatch_batches:
                        continue
                    if state.local_process_index == 0:
                        print(
                            '''**Test `accumulate` gradient accumulation with optimizer and scheduler, '''    ,       F'''`split_batches={split_batch}` and `dispatch_batches={dispatch_batches}`**'''    ,       )
                    test_gradient_accumulation_with_opt_and_scheduler(UpperCamelCase__    ,       UpperCamelCase__		)
def 							_lowerCamelCase(  UpperCamelCase__    : Optional[Any]		)		->  List[Any]:
    # For xla_spawn (TPUs)
    main()


if __name__ == "__main__":
     main()







'''simple docstring'''






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   _lowercase						:


   def __init__( self    ,   _UpperCAmelCase = "cpu"    ,   _UpperCAmelCase = "openai/clip-vit-large-patch14"					):
       A       :       List[Any]							=						device
       A       :       Optional[int]							=						CLIPTokenizerFast.from_pretrained(_UpperCAmelCase					)
       A       :       Dict							=						[0.48145466, 0.4578275, 0.40821073]
       A       :       List[str]							=						[0.26862954, 0.26130258, 0.27577711]
       A       :       List[Any]							=						torchvision.transforms.Normalize(self.image_mean    ,   self.image_std					)
       A       :       Dict							=						torchvision.transforms.Resize(224					)
       A       :       List[str]							=						torchvision.transforms.CenterCrop(224					)


   def 					snake_case				( self    ,   _UpperCAmelCase					):
       A       :       Any							=						self.resize(_UpperCAmelCase					)
       A       :       Optional[int]							=						self.center_crop(_UpperCAmelCase					)
       A       :       List[Any]							=						self.normalize(_UpperCAmelCase					)
       return images


   def __call__( self    ,   _UpperCAmelCase=None    ,   _UpperCAmelCase=None    ,   **_UpperCAmelCase					):
       A       :       Any							=						self.tokenizer(text=_UpperCAmelCase    ,   **_UpperCAmelCase					)
       A       :       List[str]							=						self.preprocess_img(_UpperCAmelCase					)
       A       :       Any							=						{key: value.to(self.device					) for (key, value) in encoding.items()}
       return encoding







class   _lowercase						(							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    ,   ):
       super().__init__()
       A       :       Any							=						None
       A       :       Dict							=						device if device else get_device()
       if vqgan:
           A       :       Union[str, Any]							=						vqgan
       else:
           A       :       List[str]							=						load_vqgan(self.device    ,   conf_path=_UpperCAmelCase    ,   ckpt_path=_UpperCAmelCase					)
       self.vqgan.eval()
       if clip:
           A       :       str							=						clip
       else:
           A       :       Tuple							=						CLIPModel.from_pretrained('''openai/clip-vit-base-patch32'''					)
       self.clip.to(self.device					)
       A       :       Dict							=						ProcessorGradientFlow(device=self.device					)

       A       :       Optional[Any]							=						iterations
       A       :       str							=						lr
       A       :       Dict							=						log
       A       :       Optional[Any]							=						make_grid
       A       :       Union[str, Any]							=						return_val
       A       :       Tuple							=						quantize
       A       :       Optional[Any]							=						self.vqgan.decoder.z_shape


   def 					snake_case				( self    ,   _UpperCAmelCase=None    ,   _UpperCAmelCase=None    ,   _UpperCAmelCase=5    ,   _UpperCAmelCase=True					):
       A       :       Any							=						[]
       if output_path is None:
           A       :       List[str]							=						'''./animation.gif'''
       if input_path is None:
           A       :       Union[str, Any]							=						self.save_path
       A       :       Optional[int]							=						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?)'''					)
       A       :       Optional[int]							=						total_duration / len(_UpperCAmelCase					)
       A       :       str							=						[frame_duration] * len(_UpperCAmelCase					)
       if extend_frames:
           A       :       Any							=						1.5
           A       :       Optional[int]							=						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 					snake_case				( self    ,   _UpperCAmelCase=None    ,   _UpperCAmelCase=None					):
       if not (path or img):
           raise ValueError('''Input either path or tensor'''					)
       if img is not None:
           raise NotImplementedError
       A       :       Tuple							=						preprocess(Image.open(_UpperCAmelCase					)    ,   target_image_size=256					).to(self.device					)
       A       :       List[str]							=						preprocess_vqgan(_UpperCAmelCase					)
       A,	*A       :       Dict							=						self.vqgan.encode(_UpperCAmelCase					)
       return z


   def 					snake_case				( self    ,   _UpperCAmelCase					):
       A       :       int							=						self.latent.detach().requires_grad_()
       A       :       List[str]							=						base_latent + transform_vector
       if self.quantize:
           A,	*A       :       List[str]							=						self.vqgan.quantize(_UpperCAmelCase					)
       else:
           A       :       Any							=						trans_latent
       return self.vqgan.decode(_UpperCAmelCase					)


   def 					snake_case				( self    ,   _UpperCAmelCase    ,   _UpperCAmelCase    ,   _UpperCAmelCase=None					):
       A       :       List[str]							=						self.clip_preprocessor(text=_UpperCAmelCase    ,   images=_UpperCAmelCase    ,   return_tensors='''pt'''    ,   padding=_UpperCAmelCase					)
       A       :       int							=						self.clip(**_UpperCAmelCase					)
       A       :       Dict							=						clip_outputs.logits_per_image
       if weights is not None:
           A       :       Optional[Any]							=						similarity_logits * weights
       return similarity_logits.sum()


   def 					snake_case				( self    ,   _UpperCAmelCase    ,   _UpperCAmelCase    ,   _UpperCAmelCase					):
       A       :       List[Any]							=						self._get_clip_similarity(pos_prompts['''prompts''']    ,   _UpperCAmelCase    ,   weights=(1 / pos_prompts['''weights'''])					)
       if neg_prompts:
           A       :       str							=						self._get_clip_similarity(neg_prompts['''prompts''']    ,   _UpperCAmelCase    ,   weights=neg_prompts['''weights''']					)
       else:
           A       :       Union[str, Any]							=						torch.tensor([1]    ,   device=self.device					)
       A       :       Optional[int]							=						-torch.log(_UpperCAmelCase					) + torch.log(_UpperCAmelCase					)
       return loss


   def 					snake_case				( self    ,   _UpperCAmelCase    ,   _UpperCAmelCase    ,   _UpperCAmelCase					):
       A       :       Optional[int]							=						torch.randn_like(self.latent    ,   requires_grad=_UpperCAmelCase    ,   device=self.device					)
       A       :       Optional[int]							=						torch.optim.Adam([vector]    ,   lr=self.lr					)

       for i in range(self.iterations					):
           optim.zero_grad()
           A       :       List[Any]							=						self._add_vector(_UpperCAmelCase					)
           A       :       str							=						loop_post_process(_UpperCAmelCase					)
           A       :       Optional[int]							=						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 					snake_case				( self    ,   _UpperCAmelCase    ,   _UpperCAmelCase    ,   _UpperCAmelCase					):
       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:
           A       :       Optional[Any]							=						Image.open(_UpperCAmelCase					)
           A       :       Dict							=						image.resize((256, 256)					)
           wandb.log('''Original Image'''    ,   wandb.Image(_UpperCAmelCase					)					)


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


   def 					snake_case				( self    ,   _UpperCAmelCase    ,   _UpperCAmelCase=None    ,   _UpperCAmelCase=None    ,   _UpperCAmelCase=True    ,   _UpperCAmelCase=False    ,   _UpperCAmelCase=True    ,   _UpperCAmelCase=True    ,   _UpperCAmelCase=None    ,   ):
       if image_path:
           A       :       List[str]							=						self._get_latent(_UpperCAmelCase					)
       else:
           A       :       List[Any]							=						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."
       A       :       Optional[int]							=						self.process_prompts(_UpperCAmelCase					)
       A       :       str							=						self.process_prompts(_UpperCAmelCase					)
       if save_final and save_path is None:
           A       :       List[Any]							=						os.path.join('''./outputs/'''    ,   '''_'''.join(pos_prompts['''prompts''']					)					)
       if not os.path.exists(_UpperCAmelCase					):
           os.makedirs(_UpperCAmelCase					)
       else:
           A       :       Union[str, Any]							=						save_path + '''_''' + get_timestamp()
           os.makedirs(_UpperCAmelCase					)
       A       :       Optional[int]							=						save_path

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

       A       :       List[Any]							=						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'''					)					)
