

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						:     Tuple					,      __A						:     str = "cpu"					,      __A						:     str = "openai/clip-vit-large-patch14"							):
		__A :			List[str]		    =			device
		__A :			List[Any]		    =			CLIPTokenizerFast.from_pretrained(__A							)
		__A :			Union[str, Any]		    =			[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]
		__A :			Dict		    =			[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]
		__A :			Optional[Any]		    =			torchvision.transforms.Normalize(self.image_mean					,      self.image_std							)
		__A :			Dict		    =			torchvision.transforms.Resize(224							)
		__A :			Optional[int]		    =			torchvision.transforms.CenterCrop(224							)




	def        lowerCAmelCase_       (     self						:     Optional[Any]					,      __A						:     str							):
		__A :			str		    =			self.resize(__A							)
		__A :			Any		    =			self.center_crop(__A							)
		__A :			Any		    =			self.normalize(__A							)
		return images




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






class      lowerCamelCase_      (	nn.Module	):




	def __init__(     self						:     Any					,      __A						:     List[str]=10					,      __A						:     Dict=0.0_1					,      __A						:     List[Any]=None					,      __A						:     Dict=None					,      __A						:     Any=None					,      __A						:     List[str]=None					,      __A						:     Optional[Any]=None					,      __A						:     Optional[Any]=None					,      __A						:     Optional[int]=False					,      __A						:     Optional[Any]=True					,      __A						:     List[Any]="image"					,      __A						:     str=True					,      __A						:     Optional[Any]=False					,      __A						:     Optional[int]=False					,      __A						:     Any=False					,      ):
		super().__init__()
		__A :			List[str]		    =			None
		__A :			Tuple		    =			device if device else get_device()
		if vqgan:
			__A :			Optional[Any]		    =			vqgan
		else:
			__A :			Optional[Any]		    =			load_vqgan(self.device					,      conf_path=__A					,      ckpt_path=__A							)
		self.vqgan.eval()
		if clip:
			__A :			List[Any]		    =			clip
		else:
			__A :			List[str]		    =			CLIPModel.from_pretrained("""openai/clip-vit-base-patch32"""							)
		self.clip.to(self.device							)
		__A :			Optional[int]		    =			ProcessorGradientFlow(device=self.device							)

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




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




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




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




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




	def        lowerCAmelCase_       (     self						:     Optional[Any]					,      __A						:     Dict					,      __A						:     Tuple					,      __A						:     Dict							):
		__A :			Union[str, Any]		    =			torch.randn_like(self.latent					,      requires_grad=__A					,      device=self.device							)
		__A :			Tuple		    =			torch.optim.Adam([vector]					,      lr=self.lr							)

		for i in range(self.iterations							):
			optim.zero_grad()
			__A :			Optional[Any]		    =			self._add_vector(__A							)
			__A :			List[str]		    =			loop_post_process(__A							)
			__A :			str		    =			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						:     List[str]					,      __A						:     Dict					,      __A						:     Any					,      __A						:     Tuple							):
		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:
			__A :			str		    =			Image.open(__A							)
			__A :			Dict		    =			image.resize((256, 256)							)
			wandb.log("""Original Image"""					,      wandb.Image(__A							)							)




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




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

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

		__A :			List[str]		    =			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 collections.abc import Sequence
def 							__SCREAMING_SNAKE_CASE     (   a__    :   Sequence[float]			,a__    :   float       )					->				float:
	return sum(c * (x**i) for i, c in enumerate(a__       )       )
def 							__SCREAMING_SNAKE_CASE     (   a__    :   Sequence[float]			,a__    :   float       )					->				float:
	__A :			Any		    =			0.0
	for coeff in reversed(a__       ):
		__A :			List[str]		    =			result * x + coeff
	return result


if __name__ == "__main__":
			UpperCAmelCase_							:					List[str]		 =   (0.0, 0.0, 5.0, 9.3, 7.0)
			UpperCAmelCase_							:					str		 =   10.0
			print(evaluate_poly(poly, x))
			print(horner(poly, x))





