



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







							def      snake_case__   (     self,    __a):

														'''simple docstring'''

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







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

														'''simple docstring'''

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

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







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

														'''simple docstring'''

														_lowerCAmelCase  :							Optional[int]				     =					[]
														if output_path is None:
																					_lowerCAmelCase  :							Optional[int]				     =					"""./animation.gif"""
														if input_path is None:
																					_lowerCAmelCase  :							List[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  :							List[Any]				     =					total_duration / len(__a)
														_lowerCAmelCase  :							int				     =					[frame_duration] * len(__a)
														if extend_frames:
																					_lowerCAmelCase  :							Tuple				     =					1.5
																					_lowerCAmelCase  :							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  :							Tuple				     =					preprocess(Image.open(__a),    target_image_size=256).to(self.device)
														_lowerCAmelCase  :							Dict				     =					preprocess_vqgan(__a)
														_lowerCAmelCase  :							List[str]				     =					self.vqgan.encode(__a)
														return z







							def      snake_case__   (     self,    __a):

														'''simple docstring'''

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







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

														'''simple docstring'''

														_lowerCAmelCase  :							Optional[int]				     =					self.clip_preprocessor(text=__a,    images=__a,    return_tensors="pt",    padding=__a)
														_lowerCAmelCase  :							Optional[int]				     =					self.clip(**__a)
														_lowerCAmelCase  :							Optional[int]				     =					clip_outputs.logits_per_image
														if weights is not None:
																					_lowerCAmelCase  :							List[str]				     =					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  :							Optional[Any]				     =					self._get_clip_similarity(neg_prompts["prompts"],    __a,    weights=neg_prompts["weights"])
														else:
																					_lowerCAmelCase  :							int				     =					torch.tensor([1],    device=self.device)
														_lowerCAmelCase  :							Dict				     =					-torch.log(__a) + torch.log(__a)
														return loss







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

														'''simple docstring'''

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

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







							def      snake_case__   (     self,    __a):

														'''simple docstring'''

														if not prompts:
																					return []
														_lowerCAmelCase  :							Dict				     =					[]
														_lowerCAmelCase  :							str				     =					[]
														if isinstance(__a,    __a):
																					_lowerCAmelCase  :							int				     =					[prompt.strip() for prompt in prompts.split("|")]
														for prompt in prompts:
																					if isinstance(__a,    (tuple, list)):
																												_lowerCAmelCase  :							str				     =					prompt[0]
																												_lowerCAmelCase  :							Optional[Any]				     =					float(prompt[1])
																					elif ":" in prompt:
																												_lowerCAmelCase  :							str				     =					prompt.split(":")
																												_lowerCAmelCase  :							int				     =					float(__a)
																					else:
																												_lowerCAmelCase  :							Union[str, Any]				     =					prompt
																												_lowerCAmelCase  :							Optional[int]				     =					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  :							str				     =					self._get_latent(__a)
														else:
																					_lowerCAmelCase  :							List[str]				     =					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  :							int				     =					self.process_prompts(__a)
														if save_final and save_path is None:
																					_lowerCAmelCase  :							str				     =					os.path.join("./outputs/",    "_".join(pos_prompts["prompts"]))
														if not os.path.exists(__a):
																					os.makedirs(__a)
														else:
																					_lowerCAmelCase  :							Dict				     =					save_path + """_""" + get_timestamp()
																					os.makedirs(__a)
														_lowerCAmelCase  :							Tuple				     =					save_path

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

														_lowerCAmelCase  :							Optional[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"))





import datasets

from .nmt_bleu import compute_bleu  # From: https://github.com/tensorflow/nmt/blob/master/nmt/scripts/bleu.py


_snake_case	=						"\\n@INPROCEEDINGS{Papineni02bleu:a,\n    author = {Kishore Papineni and Salim Roukos and Todd Ward and Wei-jing Zhu},\n    title = {BLEU: a Method for Automatic Evaluation of Machine Translation},\n    booktitle = {},\n    year = {2002},\n    pages = {311--318}\n}\n@inproceedings{lin-och-2004-orange,\n    title = \"{ORANGE}: a Method for Evaluating Automatic Evaluation Metrics for Machine Translation\",\n    author = \"Lin, Chin-Yew  and\n      Och, Franz Josef\",\n    booktitle = \"{COLING} 2004: Proceedings of the 20th International Conference on Computational Linguistics\",\n    month = \"aug 23{--}aug 27\",\n    year = \"2004\",\n    address = \"Geneva, Switzerland\",\n    publisher = \"COLING\",\n    url = \"https://www.aclweb.org/anthology/C04-1072\",\n    pages = \"501--507\",\n}\n"

_snake_case	=						"\\nBLEU (bilingual evaluation understudy) is an algorithm for evaluating the quality of text which has been machine-translated from one natural language to another.\nQuality is considered to be the correspondence between a machine's output and that of a human: \"the closer a machine translation is to a professional human translation,\nthe better it is\" – this is the central idea behind BLEU. BLEU was one of the first metrics to claim a high correlation with human judgements of quality, and\nremains one of the most popular automated and inexpensive metrics.\n\nScores are calculated for individual translated segments—generally sentences—by comparing them with a set of good quality reference translations.\nThose scores are then averaged over the whole corpus to reach an estimate of the translation's overall quality. Intelligibility or grammatical correctness\nare not taken into account[citation needed].\n\nBLEU's output is always a number between 0 and 1. This value indicates how similar the candidate text is to the reference texts, with values closer to 1\nrepresenting more similar texts. Few human translations will attain a score of 1, since this would indicate that the candidate is identical to one of the\nreference translations. For this reason, it is not necessary to attain a score of 1. Because there are more opportunities to match, adding additional\nreference translations will increase the BLEU score.\n"

_snake_case	=						"\nComputes BLEU score of translated segments against one or more references.\nArgs:\n    predictions: list of translations to score.\n        Each translation should be tokenized into a list of tokens.\n    references: list of lists of references for each translation.\n        Each reference should be tokenized into a list of tokens.\n    max_order: Maximum n-gram order to use when computing BLEU score.\n    smooth: Whether or not to apply Lin et al. 2004 smoothing.\nReturns:\n    'bleu': bleu score,\n    'precisions': geometric mean of n-gram precisions,\n    'brevity_penalty': brevity penalty,\n    'length_ratio': ratio of lengths,\n    'translation_length': translation_length,\n    'reference_length': reference_length\nExamples:\n\n    >>> predictions = [\n    ...     [\"hello\", \"there\", \"general\", \"kenobi\"],                             # tokenized prediction of the first sample\n    ...     [\"foo\", \"bar\", \"foobar\"]                                             # tokenized prediction of the second sample\n    ... ]\n    >>> references = [\n    ...     [[\"hello\", \"there\", \"general\", \"kenobi\"], [\"hello\", \"there\", \"!\"]],  # tokenized references for the first sample (2 references)\n    ...     [[\"foo\", \"bar\", \"foobar\"]]                                           # tokenized references for the second sample (1 reference)\n    ... ]\n    >>> bleu = datasets.load_metric(\"bleu\")\n    >>> results = bleu.compute(predictions=predictions, references=references)\n    >>> print(results[\"bleu\"])\n    1.0\n"



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







							def      snake_case__   (     self):

														'''simple docstring'''

														return datasets.MetricInfo(
														    description=_DESCRIPTION,    citation=_CITATION,    inputs_description=_KWARGS_DESCRIPTION,    features=datasets.Features(
														        {
														            "predictions": datasets.Sequence(datasets.Value("string",    id="token"),    id="sequence"),
														            "references": datasets.Sequence(
														                datasets.Sequence(datasets.Value("string",    id="token"),    id="sequence"),    id="references"),
														        }),    codebase_urls=["https://github.com/tensorflow/nmt/blob/master/nmt/scripts/bleu.py"],    reference_urls=[
														        "https://en.wikipedia.org/wiki/BLEU",
														        "https://towardsdatascience.com/evaluating-text-output-in-nlp-bleu-at-your-own-risk-e8609665a213",
														    ],    )







							def      snake_case__   (     self,    __a,    __a,    __a=4,    __a=False):

														'''simple docstring'''

														_lowerCAmelCase  :							List[str]				     =					compute_bleu(
														    reference_corpus=__a,    translation_corpus=__a,    max_order=__a,    smooth=__a)
														((_lowerCAmelCase)     ,					(_lowerCAmelCase)     ,					(_lowerCAmelCase)     ,					(_lowerCAmelCase)     ,					(_lowerCAmelCase)     ,					(_lowerCAmelCase))  :							Dict				     =					score
														return {
														    "bleu": bleu,
														    "precisions": precisions,
														    "brevity_penalty": bp,
														    "length_ratio": ratio,
														    "translation_length": translation_length,
														    "reference_length": reference_length,
														}
