'''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     __magic_name__						:







			def __init__(	self				:  Union[str, Any]				,			lowercase_				:  str = "cpu"				,			lowercase_				:  str = "openai/clip-vit-large-patch14"    ):
										lowercase_					:			int										=       device
										lowercase_					:			str										=       CLIPTokenizerFast.from_pretrained(lowercase_    )
										lowercase_					:			str										=       [0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73]
										lowercase_					:			int										=       [0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11]
										lowercase_					:			Tuple										=       torchvision.transforms.Normalize(self.image_mean				,			self.image_std    )
										lowercase_					:			Optional[int]										=       torchvision.transforms.Resize(224    )
										lowercase_					:			str										=       torchvision.transforms.CenterCrop(224    )







			def 	SCREAMING_SNAKE_CASE_     (	self				:  Dict				,			lowercase_				:  Union[str, Any]    ):
										lowercase_					:			Optional[Any]										=       self.resize(lowercase_    )
										lowercase_					:			Optional[Any]										=       self.center_crop(lowercase_    )
										lowercase_					:			List[Any]										=       self.normalize(lowercase_    )
										return images







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





class     __magic_name__						(    nn.Module):







			def __init__(	self				:  Optional[Any]				,			lowercase_				:  int=10				,			lowercase_				:  Union[str, Any]=0.01				,			lowercase_				:  int=None				,			lowercase_				:  Tuple=None				,			lowercase_				:  Optional[Any]=None				,			lowercase_				:  Dict=None				,			lowercase_				:  Tuple=None				,			lowercase_				:  Any=None				,			lowercase_				:  List[Any]=False				,			lowercase_				:  List[str]=True				,			lowercase_				:  Dict="image"				,			lowercase_				:  Dict=True				,			lowercase_				:  Any=False				,			lowercase_				:  Any=False				,			lowercase_				:  Union[str, Any]=False				,			):
										super().__init__()
										lowercase_					:			str										=       None
										lowercase_					:			List[str]										=       device if device else get_device()
										if vqgan:
																	lowercase_					:			List[Any]										=       vqgan
										else:
																	lowercase_					:			Dict										=       load_vqgan(self.device				,			conf_path=lowercase_				,			ckpt_path=lowercase_    )
										self.vqgan.eval()
										if clip:
																	lowercase_					:			Union[str, Any]										=       clip
										else:
																	lowercase_					:			Dict										=       CLIPModel.from_pretrained("""openai/clip-vit-base-patch32"""    )
										self.clip.to(self.device    )
										lowercase_					:			Tuple										=       ProcessorGradientFlow(device=self.device    )

										lowercase_					:			Optional[int]										=       iterations
										lowercase_					:			Optional[int]										=       lr
										lowercase_					:			Tuple										=       log
										lowercase_					:			int										=       make_grid
										lowercase_					:			Dict										=       return_val
										lowercase_					:			str										=       quantize
										lowercase_					:			Optional[int]										=       self.vqgan.decoder.z_shape







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







			def 	SCREAMING_SNAKE_CASE_     (	self				:  int				,			lowercase_				:  Tuple=None				,			lowercase_				:  Optional[Any]=None    ):
										if not (path or img):
																	raise ValueError("""Input either path or tensor"""    )
										if img is not None:
																	raise NotImplementedError
										lowercase_					:			int										=       preprocess(Image.open(lowercase_    )				,			target_image_size=256    ).to(self.device    )
										lowercase_					:			Any										=       preprocess_vqgan(lowercase_    )
										lowercase_						,						*lowercase_					:			List[Any]										=       self.vqgan.encode(lowercase_    )
										return z







			def 	SCREAMING_SNAKE_CASE_     (	self				:  str				,			lowercase_				:  Any    ):
										lowercase_					:			int										=       self.latent.detach().requires_grad_()
										lowercase_					:			int										=       base_latent + transform_vector
										if self.quantize:
																	lowercase_						,						*lowercase_					:			Optional[Any]										=       self.vqgan.quantize(lowercase_    )
										else:
																	lowercase_					:			str										=       trans_latent
										return self.vqgan.decode(lowercase_    )







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







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







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

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







			def 	SCREAMING_SNAKE_CASE_     (	self				:  int				,			lowercase_				:  List[Any]				,			lowercase_				:  Tuple				,			lowercase_				:  Optional[int]    ):
										wandb.init(reinit=lowercase_				,			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:
																	lowercase_					:			Dict										=       Image.open(lowercase_    )
																	lowercase_					:			Dict										=       image.resize((256, 256)    )
																	wandb.log("""Original Image"""				,			wandb.Image(lowercase_    )    )







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







			def 	SCREAMING_SNAKE_CASE_     (	self				:  Union[str, Any]				,			lowercase_				:  Union[str, Any]				,			lowercase_				:  int=None				,			lowercase_				:  List[Any]=None				,			lowercase_				:  Dict=True				,			lowercase_				:  Dict=False				,			lowercase_				:  str=True				,			lowercase_				:  List[str]=True				,			lowercase_				:  Any=None				,			):
										if image_path:
																	lowercase_					:			List[str]										=       self._get_latent(lowercase_    )
										else:
																	lowercase_					:			str										=       torch.randn(self.latent_dim				,			device=self.device    )
										if self.log:
																	self._init_logging(lowercase_				,			lowercase_				,			lowercase_    )

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

										lowercase_					:			Union[str, Any]										=       self.vqgan.decode(self.latent    )[0]
										if show_intermediate:
																	print("""Original Image"""    )
																	show_pil(custom_to_pil(lowercase_    )    )

										lowercase_					:			int										=       loop_post_process(lowercase_    )
										for iter, transformed_img in enumerate(self._optimize_CLIP(lowercase_				,			lowercase_				,			lowercase_    )    ):
																	if show_intermediate:
																								show_pil(lowercase_    )
																	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(lowercase_    )}    )
										if show_final:
																	show_pil(lowercase_    )
										if save_final:
																	transformed_img.save(os.path.join(self.save_path				,			f'''iter_{iter:03d}_final.png'''    )    )






'''simple docstring'''




import faiss  # noqa: F401 # Here to have a nice missing dependency error message early on
import numpy  # noqa: F401 # Here to have a nice missing dependency error message early on
import requests  # noqa: F401 # Here to have a nice missing dependency error message early on
import sklearn  # noqa: F401 # Here to have a nice missing dependency error message early on
import tqdm  # noqa: F401 # Here to have a nice missing dependency error message early on
from mauve import compute_mauve  # From: mauve-text

import datasets


_lowercase						: Optional[int]						=						"\\n@inproceedings{pillutla-etal:mauve:neurips2021,\n  title={MAUVE: Measuring the Gap Between Neural Text and Human Text using Divergence Frontiers},\n  author={Pillutla, Krishna and Swayamdipta, Swabha and Zellers, Rowan and Thickstun, John and Welleck, Sean and Choi, Yejin and Harchaoui, Zaid},\n  booktitle = {NeurIPS},\n  year      = {2021}\n}\n\n"

_lowercase						: List[Any]						=						"\\nMAUVE is a library built on PyTorch and HuggingFace Transformers to measure the gap between neural text and human text with the eponymous MAUVE measure.\n\nMAUVE summarizes both Type I and Type II errors measured softly using Kullback–Leibler (KL) divergences.\n\nFor details, see the MAUVE paper: https://arxiv.org/abs/2102.01454 (Neurips, 2021).\n\nThis metrics is a wrapper around the official implementation of MAUVE:\nhttps://github.com/krishnap25/mauve\n"

_lowercase						: List[Any]						=						"\nCalculates MAUVE scores between two lists of generated text and reference text.\nArgs:\n    predictions: list of generated text to score. Each predictions\n        should be a string with tokens separated by spaces.\n    references: list of reference for each prediction. Each\n        reference should be a string with tokens separated by spaces.\nOptional Args:\n    num_buckets: the size of the histogram to quantize P and Q. Options: 'auto' (default) or an integer\n    pca_max_data: the number data points to use for PCA dimensionality reduction prior to clustering. If -1, use all the data. Default -1\n    kmeans_explained_var: amount of variance of the data to keep in dimensionality reduction by PCA. Default 0.9\n    kmeans_num_redo: number of times to redo k-means clustering (the best objective is kept). Default 5\n    kmeans_max_iter: maximum number of k-means iterations. Default 500\n    featurize_model_name: name of the model from which features are obtained. Default 'gpt2-large' Use one of ['gpt2', 'gpt2-medium', 'gpt2-large', 'gpt2-xl'].\n    device_id: Device for featurization. Supply a GPU id (e.g. 0 or 3) to use GPU. If no GPU with this id is found, use CPU\n    max_text_length: maximum number of tokens to consider. Default 1024\n    divergence_curve_discretization_size: Number of points to consider on the divergence curve. Default 25\n    mauve_scaling_factor: \"c\" from the paper. Default 5.\n    verbose: If True (default), print running time updates\n    seed: random seed to initialize k-means cluster assignments.\nReturns:\n    mauve: MAUVE score, a number between 0 and 1. Larger values indicate that P and Q are closer,\n    frontier_integral: Frontier Integral, a number between 0 and 1. Smaller values indicate that P and Q are closer,\n    divergence_curve: a numpy.ndarray of shape (m, 2); plot it with matplotlib to view the divergence curve,\n    p_hist: a discrete distribution, which is a quantized version of the text distribution p_text,\n    q_hist: same as above, but with q_text.\nExamples:\n\n    >>> # faiss segfaults in doctest for some reason, so the .compute call is not tested with doctest\n    >>> import datasets\n    >>> mauve = datasets.load_metric('mauve')\n    >>> predictions = [\"hello there\", \"general kenobi\"]\n    >>> references = [\"hello there\", \"general kenobi\"]\n    >>> out = mauve.compute(predictions=predictions, references=references) # doctest: +SKIP\n    >>> print(out.mauve) # doctest: +SKIP\n    1.0\n"





@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION,     _KWARGS_DESCRIPTION)
class     __magic_name__						(    datasets.Metric):







			def 	SCREAMING_SNAKE_CASE_     (	self				:  List[Any]    ):
										return datasets.MetricInfo(
										    description=_DESCRIPTION				,			citation=_CITATION				,			homepage="""https://github.com/krishnap25/mauve"""				,			inputs_description=_KWARGS_DESCRIPTION				,			features=datasets.Features(
										        {
										            """predictions""": datasets.Value("""string"""				,			id="""sequence"""    ),
										            """references""": datasets.Value("""string"""				,			id="""sequence"""    ),
										        }    )				,			codebase_urls=["""https://github.com/krishnap25/mauve"""]				,			reference_urls=[
										        """https://arxiv.org/abs/2102.01454""",
										        """https://github.com/krishnap25/mauve""",
										    ]				,			)







			def 	SCREAMING_SNAKE_CASE_     (	self				:  Optional[int]				,			lowercase_				:  Union[str, Any]				,			lowercase_				:  Any				,			lowercase_				:  Any=None				,			lowercase_				:  str=None				,			lowercase_				:  Dict=None				,			lowercase_				:  Any=None				,			lowercase_				:  int="auto"				,			lowercase_				:  Tuple=-1				,			lowercase_				:  str=0.9				,			lowercase_				:  Union[str, Any]=5				,			lowercase_				:  List[str]=500				,			lowercase_				:  Union[str, Any]="gpt2-large"				,			lowercase_				:  List[Any]=-1				,			lowercase_				:  str=1024				,			lowercase_				:  List[str]=25				,			lowercase_				:  str=5				,			lowercase_				:  List[Any]=True				,			lowercase_				:  Tuple=25				,			):
										lowercase_					:			List[str]										=       compute_mauve(
										    p_text=lowercase_				,			q_text=lowercase_				,			p_features=lowercase_				,			q_features=lowercase_				,			p_tokens=lowercase_				,			q_tokens=lowercase_				,			num_buckets=lowercase_				,			pca_max_data=lowercase_				,			kmeans_explained_var=lowercase_				,			kmeans_num_redo=lowercase_				,			kmeans_max_iter=lowercase_				,			featurize_model_name=lowercase_				,			device_id=lowercase_				,			max_text_length=lowercase_				,			divergence_curve_discretization_size=lowercase_				,			mauve_scaling_factor=lowercase_				,			verbose=lowercase_				,			seed=lowercase_				,			)
										return out





