






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      :







		'''simple docstring'''



		def __init__(							self				,		_UpperCamelCase = "cpu"				,		_UpperCamelCase = "openai/clip-vit-large-patch14"   )	->							None:
							UpperCAmelCase_    :      List[Any]            =						device
							UpperCAmelCase_    :      Any            =						CLIPTokenizerFast.from_pretrained(_UpperCamelCase   )
							UpperCAmelCase_    :      Optional[Any]            =						[0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73]
							UpperCAmelCase_    :      Union[str, Any]            =						[0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11]
							UpperCAmelCase_    :      Dict            =						torchvision.transforms.Normalize(self.image_mean				,		self.image_std   )
							UpperCAmelCase_    :      List[str]            =						torchvision.transforms.Resize(2_2_4   )
							UpperCAmelCase_    :      Tuple            =						torchvision.transforms.CenterCrop(2_2_4   )



		def 					__UpperCAmelCase			(							self				,		_UpperCamelCase   )	->							Tuple:
							UpperCAmelCase_    :      Optional[int]            =						self.resize(_UpperCamelCase   )
							UpperCAmelCase_    :      Optional[int]            =						self.center_crop(_UpperCamelCase   )
							UpperCAmelCase_    :      Dict            =						self.normalize(_UpperCamelCase   )
							return images



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





class   lowerCamelCase      (nn.Module			):







		'''simple docstring'''



		def __init__(							self				,		_UpperCamelCase=1_0				,		_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__()
							UpperCAmelCase_    :      Optional[Any]            =						None
							UpperCAmelCase_    :      Union[str, Any]            =						device if device else get_device()
							if vqgan:
												UpperCAmelCase_    :      List[str]            =						vqgan
							else:
												UpperCAmelCase_    :      Dict            =						load_vqgan(self.device				,		conf_path=_UpperCamelCase				,		ckpt_path=_UpperCamelCase   )
							self.vqgan.eval()
							if clip:
												UpperCAmelCase_    :      str            =						clip
							else:
												UpperCAmelCase_    :      Union[str, Any]            =						CLIPModel.from_pretrained('openai/clip-vit-base-patch32'   )
							self.clip.to(self.device   )
							UpperCAmelCase_    :      Tuple            =						ProcessorGradientFlow(device=self.device   )

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



		def 					__UpperCAmelCase			(							self				,		_UpperCamelCase=None				,		_UpperCamelCase=None				,		_UpperCamelCase=5				,		_UpperCamelCase=True   )	->							Optional[int]:
							UpperCAmelCase_    :      Optional[int]            =						[]
							if output_path is None:
												UpperCAmelCase_    :      List[Any]            =						'./animation.gif'
							if input_path is None:
												UpperCAmelCase_    :      Dict            =						self.save_path
							UpperCAmelCase_    :      Dict            =						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?)'   )
							UpperCAmelCase_    :      Dict            =						total_duration / len(_UpperCamelCase   )
							UpperCAmelCase_    :      Tuple            =						[frame_duration] * len(_UpperCamelCase   )
							if extend_frames:
												UpperCAmelCase_    :      int            =						1.5
												UpperCAmelCase_    :      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 					__UpperCAmelCase			(							self				,		_UpperCamelCase=None				,		_UpperCamelCase=None   )	->							int:
							if not (path or img):
												raise ValueError('Input either path or tensor'   )
							if img is not None:
												raise NotImplementedError
							UpperCAmelCase_    :      Optional[int]            =						preprocess(Image.open(_UpperCamelCase   )				,		target_image_size=2_5_6   ).to(self.device   )
							UpperCAmelCase_    :      Tuple            =						preprocess_vqgan(_UpperCamelCase   )
							UpperCAmelCase_       ,  *UpperCAmelCase_    :      List[Any]            =						self.vqgan.encode(_UpperCamelCase   )
							return z



		def 					__UpperCAmelCase			(							self				,		_UpperCamelCase   )	->							Tuple:
							UpperCAmelCase_    :      Optional[Any]            =						self.latent.detach().requires_grad_()
							UpperCAmelCase_    :      Optional[int]            =						base_latent + transform_vector
							if self.quantize:
												UpperCAmelCase_       ,  *UpperCAmelCase_    :      str            =						self.vqgan.quantize(_UpperCamelCase   )
							else:
												UpperCAmelCase_    :      Union[str, Any]            =						trans_latent
							return self.vqgan.decode(_UpperCamelCase   )



		def 					__UpperCAmelCase			(							self				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase=None   )	->							Optional[Any]:
							UpperCAmelCase_    :      Optional[int]            =						self.clip_preprocessor(text=_UpperCamelCase				,		images=_UpperCamelCase				,		return_tensors='pt'				,		padding=_UpperCamelCase   )
							UpperCAmelCase_    :      str            =						self.clip(**_UpperCamelCase   )
							UpperCAmelCase_    :      Union[str, Any]            =						clip_outputs.logits_per_image
							if weights is not None:
												UpperCAmelCase_    :      Union[str, Any]            =						similarity_logits * weights
							return similarity_logits.sum()



		def 					__UpperCAmelCase			(							self				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase   )	->							Tuple:
							UpperCAmelCase_    :      Union[str, Any]            =						self._get_clip_similarity(pos_prompts['prompts']				,		_UpperCamelCase				,		weights=(1 / pos_prompts['weights'])   )
							if neg_prompts:
												UpperCAmelCase_    :      Any            =						self._get_clip_similarity(neg_prompts['prompts']				,		_UpperCamelCase				,		weights=neg_prompts['weights']   )
							else:
												UpperCAmelCase_    :      int            =						torch.tensor([1]				,		device=self.device   )
							UpperCAmelCase_    :      List[str]            =						-torch.log(_UpperCamelCase   ) + torch.log(_UpperCamelCase   )
							return loss



		def 					__UpperCAmelCase			(							self				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase   )	->							Union[str, Any]:
							UpperCAmelCase_    :      str            =						torch.randn_like(self.latent				,		requires_grad=_UpperCamelCase				,		device=self.device   )
							UpperCAmelCase_    :      Union[str, Any]            =						torch.optim.Adam([vector]				,		lr=self.lr   )

							for i in range(self.iterations   ):
												optim.zero_grad()
												UpperCAmelCase_    :      Dict            =						self._add_vector(_UpperCamelCase   )
												UpperCAmelCase_    :      List[Any]            =						loop_post_process(_UpperCamelCase   )
												UpperCAmelCase_    :      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 					__UpperCAmelCase			(							self				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase   )	->							Optional[Any]:
							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:
												UpperCAmelCase_    :      Dict            =						Image.open(_UpperCamelCase   )
												UpperCAmelCase_    :      Optional[Any]            =						image.resize((2_5_6, 2_5_6)   )
												wandb.log('Original Image'				,		wandb.Image(_UpperCamelCase   )   )



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



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

							UpperCAmelCase_    :      Optional[int]            =						self.vqgan.decode(self.latent   )[0]
							if show_intermediate:
												print('Original Image'   )
												show_pil(custom_to_pil(_UpperCamelCase   )   )

							UpperCAmelCase_    :      Union[str, 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"   )   )












from timeit import timeit

lowercase__				:     Union[str, Any] 		=						{
    "MALAYALAM": True,
    "String": False,
    "rotor": True,
    "level": True,
    "A": True,
    "BB": True,
    "ABC": False,
    "amanaplanacanalpanama": True,  # "a man a plan a canal panama"
}
# Ensure our test data is valid
assert all((key == key[::-1]) is value for key, value in test_data.items())


def 						lowerCamelCase__    (		_A							):







	'''simple docstring'''





	snake_case_		    =     0
	snake_case_		    =     len(_A							) - 1
	while start_i < end_i:
		if s[start_i] == s[end_i]:
			start_i += 1
			end_i -= 1
		else:
			return False
	return True


def 						lowerCamelCase__    (		_A							):







	'''simple docstring'''





	snake_case_		    =     len(_A							) // 2
	snake_case_		    =     len(_A							)

	# We need to traverse till half of the length of string
	# as we can get access of the i'th last element from
	# i'th index.
	# eg: [0,1,2,3,4,5] => 4th index can be accessed
	# with the help of 1st index (i==n-i-1)
	# where n is length of string
	return all(s[i] == s[n - i - 1] for i in range(_A							)							)


def 						lowerCamelCase__    (		_A							):







	'''simple docstring'''





	if len(_A							) <= 2:
		return True
	if s[0] == s[len(_A							) - 1]:
		return is_palindrome_recursive(s[1:-1]							)
	else:
		return False


def 						lowerCamelCase__    (		_A							):







	'''simple docstring'''





	return s == s[::-1]


def 						lowerCamelCase__    (		_A							):







	'''simple docstring'''





	snake_case_		    =     f"all({name}(key) is value for key, value in test_data.items())"
	snake_case_		    =     f"from __main__ import test_data, {name}"
	snake_case_		    =     500000
	snake_case_		    =     timeit(stmt=_A     ,	setup=_A     ,	number=_A							)
	print(f"{name:<35} finished {number:,} runs in {result:.5f} seconds"							)


if __name__ == "__main__":
		for key, value in test_data.items():
				assert is_palindrome(key) is is_palindrome_recursive(key)
				assert is_palindrome(key) is is_palindrome_slice(key)
				print(f'''{key:21} {value}''')
		print("a man a plan a canal panama")

		# finished 500,000 runs in 0.46793 seconds
		benchmark_function("is_palindrome_slice")
		# finished 500,000 runs in 0.85234 seconds
		benchmark_function("is_palindrome")
		# finished 500,000 runs in 1.32028 seconds
		benchmark_function("is_palindrome_recursive")
		# finished 500,000 runs in 2.08679 seconds
		benchmark_function("is_palindrome_traversal")
