



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   UpperCAmelCase_    :







							def __init__(   self,			__a = "cpu",			__a = "openai/clip-vit-large-patch14"):

														'''simple docstring'''

														_lowerCAmelCase     :							Optional[int]    =    device
														_lowerCAmelCase     :							Optional[int]    =    CLIPTokenizerFast.from_pretrained(__a)
														_lowerCAmelCase     :							Any    =    [0.48_145_466, 0.4_578_275, 0.40_821_073]
														_lowerCAmelCase     :							Union[str, Any]    =    [0.26_862_954, 0.26_130_258, 0.27_577_711]
														_lowerCAmelCase     :							Tuple    =    torchvision.transforms.Normalize(self.image_mean,			self.image_std)
														_lowerCAmelCase     :							Optional[int]    =    torchvision.transforms.Resize(224)
														_lowerCAmelCase     :							Dict    =    torchvision.transforms.CenterCrop(224)







							def    snake_case__      (   self,			__a):

														'''simple docstring'''

														_lowerCAmelCase     :							Optional[Any]    =    self.resize(__a)
														_lowerCAmelCase     :							List[str]    =    self.center_crop(__a)
														_lowerCAmelCase     :							Optional[Any]    =    self.normalize(__a)
														return images







							def __call__(   self,			__a=None,			__a=None,			**__a):

														'''simple docstring'''

														_lowerCAmelCase     :							str    =    self.tokenizer(text=__a,			**__a)
														_lowerCAmelCase     :							List[str]    =    self.preprocess_img(__a)
														_lowerCAmelCase     :							Tuple    =    {key: value.to(self.device) for (key, value) in encoding.items()}
														return encoding



class   UpperCAmelCase_    (       nn.Module):







							def __init__(   self,			__a=10,			__a=0.01,			__a=None,			__a=None,			__a=None,			__a=None,			__a=None,			__a=None,			__a=False,			__a=True,			__a="image",			__a=True,			__a=False,			__a=False,			__a=False,			):

														'''simple docstring'''

														super().__init__()
														_lowerCAmelCase     :							List[str]    =    None
														_lowerCAmelCase     :							List[str]    =    device if device else get_device()
														if vqgan:
																					_lowerCAmelCase     :							Union[str, Any]    =    vqgan
														else:
																					_lowerCAmelCase     :							Optional[Any]    =    load_vqgan(self.device,			conf_path=__a,			ckpt_path=__a)
														self.vqgan.eval()
														if clip:
																					_lowerCAmelCase     :							str    =    clip
														else:
																					_lowerCAmelCase     :							int    =    CLIPModel.from_pretrained("openai/clip-vit-base-patch32")
														self.clip.to(self.device)
														_lowerCAmelCase     :							Optional[int]    =    ProcessorGradientFlow(device=self.device)

														_lowerCAmelCase     :							Any    =    iterations
														_lowerCAmelCase     :							List[Any]    =    lr
														_lowerCAmelCase     :							Tuple    =    log
														_lowerCAmelCase     :							List[str]    =    make_grid
														_lowerCAmelCase     :							int    =    return_val
														_lowerCAmelCase     :							Dict    =    quantize
														_lowerCAmelCase     :							Any    =    self.vqgan.decoder.z_shape







							def    snake_case__      (   self,			__a=None,			__a=None,			__a=5,			__a=True):

														'''simple docstring'''

														_lowerCAmelCase     :							Union[str, Any]    =    []
														if output_path is None:
																					_lowerCAmelCase     :							List[Any]    =    "./animation.gif"
														if input_path is None:
																					_lowerCAmelCase     :							str    =    self.save_path
														_lowerCAmelCase     :							str    =    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?)")
														_lowerCAmelCase     :							Optional[int]    =    total_duration / len(__a)
														_lowerCAmelCase     :							Union[str, Any]    =    [frame_duration] * len(__a)
														if extend_frames:
																					_lowerCAmelCase     :							Any    =    1.5
																					_lowerCAmelCase     :							List[str]    =    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    snake_case__      (   self,			__a=None,			__a=None):

														'''simple docstring'''

														if not (path or img):
																					raise ValueError("Input either path or tensor")
														if img is not None:
																					raise NotImplementedError
														_lowerCAmelCase     :							Dict    =    preprocess(Image.open(__a),			target_image_size=256).to(self.device)
														_lowerCAmelCase     :							Dict    =    preprocess_vqgan(__a)
														_lowerCAmelCase   ,    *_lowerCAmelCase     :							str    =    self.vqgan.encode(__a)
														return z







							def    snake_case__      (   self,			__a):

														'''simple docstring'''

														_lowerCAmelCase     :							Optional[Any]    =    self.latent.detach().requires_grad_()
														_lowerCAmelCase     :							Dict    =    base_latent + transform_vector
														if self.quantize:
																					_lowerCAmelCase   ,    *_lowerCAmelCase     :							List[Any]    =    self.vqgan.quantize(__a)
														else:
																					_lowerCAmelCase     :							Any    =    trans_latent
														return self.vqgan.decode(__a)







							def    snake_case__      (   self,			__a,			__a,			__a=None):

														'''simple docstring'''

														_lowerCAmelCase     :							int    =    self.clip_preprocessor(text=__a,			images=__a,			return_tensors="pt",			padding=__a)
														_lowerCAmelCase     :							Optional[int]    =    self.clip(**__a)
														_lowerCAmelCase     :							Any    =    clip_outputs.logits_per_image
														if weights is not None:
																					_lowerCAmelCase     :							Tuple    =    similarity_logits * weights
														return similarity_logits.sum()







							def    snake_case__      (   self,			__a,			__a,			__a):

														'''simple docstring'''

														_lowerCAmelCase     :							List[Any]    =    self._get_clip_similarity(pos_prompts["prompts"],			__a,			weights=(1 / pos_prompts["weights"]))
														if neg_prompts:
																					_lowerCAmelCase     :							List[Any]    =    self._get_clip_similarity(neg_prompts["prompts"],			__a,			weights=neg_prompts["weights"])
														else:
																					_lowerCAmelCase     :							Union[str, Any]    =    torch.tensor([1],			device=self.device)
														_lowerCAmelCase     :							List[str]    =    -torch.log(__a) + torch.log(__a)
														return loss







							def    snake_case__      (   self,			__a,			__a,			__a):

														'''simple docstring'''

														_lowerCAmelCase     :							Optional[Any]    =    torch.randn_like(self.latent,			requires_grad=__a,			device=self.device)
														_lowerCAmelCase     :							Optional[int]    =    torch.optim.Adam([vector],			lr=self.lr)

														for i in range(self.iterations):
																					optim.zero_grad()
																					_lowerCAmelCase     :							Any    =    self._add_vector(__a)
																					_lowerCAmelCase     :							Optional[Any]    =    loop_post_process(__a)
																					_lowerCAmelCase     :							Optional[Any]    =    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    snake_case__      (   self,			__a,			__a,			__a):

														'''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:
																					_lowerCAmelCase     :							str    =    Image.open(__a)
																					_lowerCAmelCase     :							int    =    image.resize((256, 256))
																					wandb.log("Original Image",			wandb.Image(__a))







							def    snake_case__      (   self,			__a):

														'''simple docstring'''

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







							def    snake_case__      (   self,			__a,			__a=None,			__a=None,			__a=True,			__a=False,			__a=True,			__a=True,			__a=None,			):

														'''simple docstring'''

														if image_path:
																					_lowerCAmelCase     :							List[Any]    =    self._get_latent(__a)
														else:
																					_lowerCAmelCase     :							Any    =    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."
														_lowerCAmelCase     :							int    =    self.process_prompts(__a)
														_lowerCAmelCase     :							List[str]    =    self.process_prompts(__a)
														if save_final and save_path is None:
																					_lowerCAmelCase     :							int    =    os.path.join("./outputs/",			"_".join(pos_prompts["prompts"]))
														if not os.path.exists(__a):
																					os.makedirs(__a)
														else:
																					_lowerCAmelCase     :							Tuple    =    save_path + "_" + get_timestamp()
																					os.makedirs(__a)
														_lowerCAmelCase     :							Tuple    =    save_path

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

														_lowerCAmelCase     :							int    =    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"))








from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__UpperCAmelCase								=						{
    'configuration_time_series_transformer': [
        'TIME_SERIES_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'TimeSeriesTransformerConfig',
    ],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__UpperCAmelCase								=						[
		    'TIME_SERIES_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'TimeSeriesTransformerForPrediction',
		    'TimeSeriesTransformerModel',
		    'TimeSeriesTransformerPreTrainedModel',
		]


if TYPE_CHECKING:
		from .configuration_time_series_transformer import (
		    TIME_SERIES_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    TimeSeriesTransformerConfig,
		)

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_time_series_transformer import (
				    TIME_SERIES_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TimeSeriesTransformerForPrediction,
				    TimeSeriesTransformerModel,
				    TimeSeriesTransformerPreTrainedModel,
				)

else:
		import sys

		__UpperCAmelCase								=						_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)






