

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_		:
							"""simple docstring"""







							def __init__(			self       ,							_a = "cpu"       ,							_a = "openai/clip-vit-large-patch14"       )    ->    str:
												_a	:    Dict         =		device
												_a	:    Any         =		CLIPTokenizerFast.from_pretrained(lowerCAmelCase__       )
												_a	:    str         =		[0.4814_5466, 0.457_8275, 0.4082_1073]
												_a	:    Tuple         =		[0.2686_2954, 0.2613_0258, 0.2757_7711]
												_a	:    Optional[int]         =		torchvision.transforms.Normalize(self.image_mean       ,							self.image_std       )
												_a	:    List[Any]         =		torchvision.transforms.Resize(2_2_4       )
												_a	:    Optional[int]         =		torchvision.transforms.CenterCrop(2_2_4       )


							def 				__lowercase				(			self       ,							_a       )    ->    Optional[int]:
												_a	:    Optional[int]         =		self.resize(lowerCAmelCase__       )
												_a	:    List[str]         =		self.center_crop(lowerCAmelCase__       )
												_a	:    Any         =		self.normalize(lowerCAmelCase__       )
												return images




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



class 				UpperCAmelCase_		(			nn.Module   ):
							"""simple docstring"""







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

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


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


							def 				__lowercase				(			self       ,							_a=None       ,							_a=None       )    ->    List[Any]:
												if not (path or img):
																	raise ValueError('''Input either path or tensor'''       )
												if img is not None:
																	raise NotImplementedError
												_a	:    Any         =		preprocess(Image.open(lowerCAmelCase__       )       ,							target_image_size=2_5_6       ).to(self.device       )
												_a	:    List[str]         =		preprocess_vqgan(lowerCAmelCase__       )
												_a      ,					*_a	:    str         =		self.vqgan.encode(lowerCAmelCase__       )
												return z


							def 				__lowercase				(			self       ,							_a       )    ->    Tuple:
												_a	:    List[Any]         =		self.latent.detach().requires_grad_()
												_a	:    Optional[Any]         =		base_latent + transform_vector
												if self.quantize:
																	_a      ,					*_a	:    Tuple         =		self.vqgan.quantize(lowerCAmelCase__       )
												else:
																	_a	:    str         =		trans_latent
												return self.vqgan.decode(lowerCAmelCase__       )


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


							def 				__lowercase				(			self       ,							_a       ,							_a       ,							_a       )    ->    Optional[Any]:
												_a	:    str         =		self._get_clip_similarity(pos_prompts['''prompts''']       ,							lowerCAmelCase__       ,							weights=(1 / pos_prompts['''weights'''])       )
												if neg_prompts:
																	_a	:    Tuple         =		self._get_clip_similarity(neg_prompts['''prompts''']       ,							lowerCAmelCase__       ,							weights=neg_prompts['''weights''']       )
												else:
																	_a	:    List[str]         =		torch.tensor([1]       ,							device=self.device       )
												_a	:    Optional[Any]         =		-torch.log(lowerCAmelCase__       ) + torch.log(lowerCAmelCase__       )
												return loss


							def 				__lowercase				(			self       ,							_a       ,							_a       ,							_a       )    ->    Optional[int]:
												_a	:    str         =		torch.randn_like(self.latent       ,							requires_grad=lowerCAmelCase__       ,							device=self.device       )
												_a	:    Union[str, Any]         =		torch.optim.Adam([vector]       ,							lr=self.lr       )

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


							def 				__lowercase				(			self       ,							_a       ,							_a       ,							_a       )    ->    Dict:
												wandb.init(reinit=lowerCAmelCase__       ,							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	:    List[str]         =		Image.open(lowerCAmelCase__       )
																	_a	:    Union[str, Any]         =		image.resize((2_5_6, 2_5_6)       )
																	wandb.log('''Original Image'''       ,							wandb.Image(lowerCAmelCase__       )       )


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




							def 				__lowercase				(			self       ,							_a       ,							_a=None       ,							_a=None       ,							_a=True       ,							_a=False       ,							_a=True       ,							_a=True       ,							_a=None       ,							)    ->    str:
												if image_path:
																	_a	:    Any         =		self._get_latent(lowerCAmelCase__       )
												else:
																	_a	:    Dict         =		torch.randn(self.latent_dim       ,							device=self.device       )
												if self.log:
																	self._init_logging(lowerCAmelCase__       ,							lowerCAmelCase__       ,							lowerCAmelCase__       )

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

												_a	:    List[str]         =		self.vqgan.decode(self.latent       )[0]
												if show_intermediate:
																	print('''Original Image'''       )
																	show_pil(custom_to_pil(lowerCAmelCase__       )       )

												_a	:    Optional[int]         =		loop_post_process(lowerCAmelCase__       )
												for iter, transformed_img in enumerate(self._optimize_CLIP(lowerCAmelCase__       ,							lowerCAmelCase__       ,							lowerCAmelCase__       )       ):
																	if show_intermediate:
																						show_pil(lowerCAmelCase__       )
																	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(lowerCAmelCase__       )}       )
												if show_final:
																	show_pil(lowerCAmelCase__       )
												if save_final:
																	transformed_img.save(os.path.join(self.save_path       ,							F"""iter_{iter:03d}_final.png"""       )       )





"""simple docstring"""





from typing import List, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...utils import logging


__magic_name__       =			logging.get_logger(__name__)

__magic_name__       =			{
    "huggingface/informer-tourism-monthly": (
        "https://huggingface.co/huggingface/informer-tourism-monthly/resolve/main/config.json"
    ),
    # See all Informer models at https://huggingface.co/models?filter=informer
}
class 						SCREAMING_SNAKE_CASE_ (						__a ):




		"""simple docstring"""





		__lowercase							:  Dict						   =		'''informer'''
		__lowercase							:  Union[str, Any]						   =		{
		    '''hidden_size''': '''d_model''',
		    '''num_attention_heads''': '''encoder_attention_heads''',
		    '''num_hidden_layers''': '''encoder_layers''',
		}




		def __init__(     self							,  lowerCAmelCase__ = None							,  lowerCAmelCase__ = None							,  lowerCAmelCase__ = "student_t"							,  lowerCAmelCase__ = "nll"							,  lowerCAmelCase__ = 1							,  lowerCAmelCase__ = None							,  lowerCAmelCase__ = "mean"							,  lowerCAmelCase__ = 0							,  lowerCAmelCase__ = 0							,  lowerCAmelCase__ = 0							,  lowerCAmelCase__ = 0							,  lowerCAmelCase__ = None							,  lowerCAmelCase__ = None							,  lowerCAmelCase__ = 6_4							,  lowerCAmelCase__ = 3_2							,  lowerCAmelCase__ = 3_2							,  lowerCAmelCase__ = 2							,  lowerCAmelCase__ = 2							,  lowerCAmelCase__ = 2							,  lowerCAmelCase__ = 2							,  lowerCAmelCase__ = True							,  lowerCAmelCase__ = "gelu"							,  lowerCAmelCase__ = 0.05							,  lowerCAmelCase__ = 0.1							,  lowerCAmelCase__ = 0.1							,  lowerCAmelCase__ = 0.1							,  lowerCAmelCase__ = 0.1							,  lowerCAmelCase__ = 1_0_0							,  lowerCAmelCase__ = 0.02							,  lowerCAmelCase__=True							,  lowerCAmelCase__ = "prob"							,  lowerCAmelCase__ = 5							,  lowerCAmelCase__ = True							,  **lowerCAmelCase__							,  ):
				# time series specific configuration
				__SCREAMING_SNAKE_CASE            = prediction_length
				__SCREAMING_SNAKE_CASE            = context_length or prediction_length
				__SCREAMING_SNAKE_CASE            = distribution_output
				__SCREAMING_SNAKE_CASE            = loss
				__SCREAMING_SNAKE_CASE            = input_size
				__SCREAMING_SNAKE_CASE            = num_time_features
				__SCREAMING_SNAKE_CASE            = lags_sequence if lags_sequence is not None else [1, 2, 3, 4, 5, 6, 7]
				__SCREAMING_SNAKE_CASE            = scaling
				__SCREAMING_SNAKE_CASE            = num_dynamic_real_features
				__SCREAMING_SNAKE_CASE            = num_static_real_features
				__SCREAMING_SNAKE_CASE            = num_static_categorical_features

				# set cardinality
				if cardinality and num_static_categorical_features > 0:
						if len(lowerCAmelCase__) != num_static_categorical_features:
								raise ValueError(
								    """The cardinality should be a list of the same length as `num_static_categorical_features`""")
						__SCREAMING_SNAKE_CASE            = cardinality
				else:
						__SCREAMING_SNAKE_CASE            = [0]

				# set embedding_dimension
				if embedding_dimension and num_static_categorical_features > 0:
						if len(lowerCAmelCase__) != num_static_categorical_features:
								raise ValueError(
								    """The embedding dimension should be a list of the same length as `num_static_categorical_features`""")
						__SCREAMING_SNAKE_CASE            = embedding_dimension
				else:
						__SCREAMING_SNAKE_CASE            = [min(5_0							,  (cat + 1) // 2) for cat in self.cardinality]

				__SCREAMING_SNAKE_CASE            = num_parallel_samples

				# Transformer architecture configuration
				__SCREAMING_SNAKE_CASE            = input_size * len(self.lags_sequence) + self._number_of_features
				__SCREAMING_SNAKE_CASE            = d_model
				__SCREAMING_SNAKE_CASE            = encoder_attention_heads
				__SCREAMING_SNAKE_CASE            = decoder_attention_heads
				__SCREAMING_SNAKE_CASE            = encoder_ffn_dim
				__SCREAMING_SNAKE_CASE            = decoder_ffn_dim
				__SCREAMING_SNAKE_CASE            = encoder_layers
				__SCREAMING_SNAKE_CASE            = decoder_layers

				__SCREAMING_SNAKE_CASE            = dropout
				__SCREAMING_SNAKE_CASE            = attention_dropout
				__SCREAMING_SNAKE_CASE            = activation_dropout
				__SCREAMING_SNAKE_CASE            = encoder_layerdrop
				__SCREAMING_SNAKE_CASE            = decoder_layerdrop

				__SCREAMING_SNAKE_CASE            = activation_function
				__SCREAMING_SNAKE_CASE            = init_std

				__SCREAMING_SNAKE_CASE            = use_cache

				# Informer
				__SCREAMING_SNAKE_CASE            = attention_type
				__SCREAMING_SNAKE_CASE            = sampling_factor
				__SCREAMING_SNAKE_CASE            = distil

				super().__init__(is_encoder_decoder=lowerCAmelCase__							,  **lowerCAmelCase__)




		@property
		def 	snake_case_     (     self):
				return (
				    sum(self.embedding_dimension)
				    + self.num_dynamic_real_features
				    + self.num_time_features
				    + self.num_static_real_features
				    + self.input_size * 2  # the log1p(abs(loc)) and log(scale) features
				)




