


import argparse
import ast
import logging
import os
import sys

import pandas as pd
import torch
from tqdm import tqdm

from transformers import BartForConditionalGeneration, RagRetriever, RagSequenceForGeneration, RagTokenForGeneration
from transformers import logging as transformers_logging


sys.path.append(os.path.join(os.getcwd()))  # noqa: E402 # isort:skip
from utils_rag import exact_match_score, fa_score  # noqa: E402 # isort:skip


snake_case__			 =						logging.getLogger(__name__)
logging.basicConfig(level=logging.INFO)

transformers_logging.set_verbosity_info()


def 				lowerCamelCase__	(      a	:   Optional[int]				)    ->	Optional[Any]:





			"""simple docstring"""
			if "token" in model_name_or_path:
						return "rag_token"
			if "sequence" in model_name_or_path:
						return "rag_sequence"
			if "bart" in model_name_or_path:
						return "bart"
			return None


def 				lowerCamelCase__	(      a	:   List[Any]	,  a	:   str	,  a	:   List[str]				)    ->	Optional[int]:





			"""simple docstring"""
			return max(metric_fn(a	,  a				) for gt in ground_truths				)


def 				lowerCamelCase__	(      a	:   Optional[Any]	,  a	:   List[Any]	,  a	:   Optional[Any]				)    ->	Tuple:





			"""simple docstring"""
			a__ :List[Any]						  =    [line.strip() for line in open(a	,  "r"				).readlines()]
			a__ :List[str]						  =    []

			if args.gold_data_mode == "qa":
						a__ :Dict						  =    pd.read_csv(a	,  sep="\t"	,  header=a				)
						for answer_list in data[1]:
									a__ :Optional[int]						  =    ast.literal_eval(a				)
									answers.append(a				)
			else:
						a__ :Optional[int]						  =    [line.strip() for line in open(a	,  "r"				).readlines()]
						a__ :Optional[Any]						  =    [[reference] for reference in references]

			a__ :Any						  =    0
			for prediction, ground_truths in zip(a	,  a				):
						total += 1
						em += metric_max_over_ground_truths(a	,  a	,  a				)
						fa += metric_max_over_ground_truths(a	,  a	,  a				)

			a__ :str						  =    1_0_0.0 * em / total
			a__ :List[Any]						  =    1_0_0.0 * fa / total

			logger.info(F'''F1: {fa:.2f}'''				)
			logger.info(F'''EM: {em:.2f}'''				)


def 				lowerCamelCase__	(      a	:   str	,  a	:   Optional[int]	,  a	:   Tuple				)    ->	str:





			"""simple docstring"""
			a__ :str						  =    args.k
			a__ :Optional[int]						  =    [line.strip() for line in open(a	,  "r"				).readlines()]
			a__ :Any						  =    [line.strip() for line in open(a	,  "r"				).readlines()]

			a__ :str						  =    0
			for hypo, reference in zip(a	,  a				):
						a__ :str						  =    set(hypo.split("\t"				)[:k]				)
						a__ :Union[str, Any]						  =    set(reference.split("\t"				)				)
						total += 1
						em += len(hypo_provenance & ref_provenance				) / k

			a__ :Any						  =    1_0_0.0 * em / total
			logger.info(F'''Precision@{k}: {em: .2f}'''				)


def 				lowerCamelCase__	(      a	:   Tuple	,  a	:   Union[str, Any]	,  a	:   Dict				)    ->	Union[str, Any]:





			"""simple docstring"""
			def strip_title(a	:   List[Any]				):
						if title.startswith("\""				):
									a__ :str						  =    title[1:]
						if title.endswith("\""				):
									a__ :Tuple						  =    title[:-1]
						return title

			a__ :Any						  =    rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
			    a	,  return_tensors="pt"	,  padding=a	,  truncation=a	,  )["input_ids"].to(args.device				)

			a__ :Union[str, Any]						  =    rag_model.rag.question_encoder(a				)
			a__ :int						  =    question_enc_outputs[0]

			a__ :str						  =    rag_model.retriever(
			    a	,  question_enc_pool_output.cpu().detach().to(torch.floataa				).numpy()	,  prefix=rag_model.rag.generator.config.prefix	,  n_docs=rag_model.config.n_docs	,  return_tensors="pt"	,  )
			a__ :List[Any]						  =    rag_model.retriever.index.get_doc_dicts(result.doc_ids				)
			a__ :Any						  =    []
			for docs in all_docs:
						a__ :Dict						  =    [strip_title(a				) for title in docs["title"]]
						provenance_strings.append("\t".join(a				)				)
			return provenance_strings


def 				lowerCamelCase__	(      a	:   int	,  a	:   Dict	,  a	:   Any				)    ->	List[str]:





			"""simple docstring"""
			with torch.no_grad():
						a__ :int						  =    rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
						    a	,  return_tensors="pt"	,  padding=a	,  truncation=a				)

						a__ :Optional[int]						  =    inputs_dict.input_ids.to(args.device				)
						a__ :Union[str, Any]						  =    inputs_dict.attention_mask.to(args.device				)
						a__ :Dict						  =    rag_model.generate(  # rag_model overwrites generate
						    a	,  attention_mask=a	,  num_beams=args.num_beams	,  min_length=args.min_length	,  max_length=args.max_length	,  early_stopping=a	,  num_return_sequences=1	,  bad_words_ids=[[0, 0]]	,  )
						a__ :int						  =    rag_model.retriever.generator_tokenizer.batch_decode(a	,  skip_special_tokens=a				)

						if args.print_predictions:
									for q, a in zip(a	,  a				):
												logger.info("Q: {} - A: {}".format(a	,  a				)				)

						return answers


def 				lowerCamelCase__	(      )    ->	List[Any]:





			"""simple docstring"""
			a__ :int						  =    argparse.ArgumentParser()
			parser.add_argument(
			    "--model_type"	,  choices=["rag_sequence", "rag_token", "bart"]	,  type=a	,  help=(
			        "RAG model type: rag_sequence, rag_token or bart, if none specified, the type is inferred from the"
			        " model_name_or_path"
			    )	,  )
			parser.add_argument(
			    "--index_name"	,  default=a	,  choices=["exact", "compressed", "legacy"]	,  type=a	,  help="RAG model retriever type"	,  )
			parser.add_argument(
			    "--index_path"	,  default=a	,  type=a	,  help="Path to the retrieval index"	,  )
			parser.add_argument("--n_docs"	,  default=5	,  type=a	,  help="Number of retrieved docs"				)
			parser.add_argument(
			    "--model_name_or_path"	,  default=a	,  type=a	,  required=a	,  help="Path to pretrained checkpoints or model identifier from huggingface.co/models"	,  )
			parser.add_argument(
			    "--eval_mode"	,  choices=["e2e", "retrieval"]	,  default="e2e"	,  type=a	,  help=(
			        "Evaluation mode, e2e calculates exact match and F1 of the downstream task, retrieval calculates"
			        " precision@k."
			    )	,  )
			parser.add_argument("--k"	,  default=1	,  type=a	,  help="k for the precision@k calculation"				)
			parser.add_argument(
			    "--evaluation_set"	,  default=a	,  type=a	,  required=a	,  help="Path to a file containing evaluation samples"	,  )
			parser.add_argument(
			    "--gold_data_path"	,  default=a	,  type=a	,  required=a	,  help="Path to a tab-separated file with gold samples"	,  )
			parser.add_argument(
			    "--gold_data_mode"	,  default="qa"	,  type=a	,  choices=["qa", "ans"]	,  help=(
			        "Format of the gold data file"
			        "qa - a single line in the following format: question [tab] answer_list"
			        "ans - a single line of the gold file contains the expected answer string"
			    )	,  )
			parser.add_argument(
			    "--predictions_path"	,  type=a	,  default="predictions.txt"	,  help="Name of the predictions file, to be stored in the checkpoints directory"	,  )
			parser.add_argument(
			    "--eval_all_checkpoints"	,  action="store_true"	,  help="Evaluate all checkpoints starting with the same prefix as model_name ending and ending with step number"	,  )
			parser.add_argument(
			    "--eval_batch_size"	,  default=8	,  type=a	,  help="Batch size per GPU/CPU for evaluation."	,  )
			parser.add_argument(
			    "--recalculate"	,  help="Recalculate predictions even if the prediction file exists"	,  action="store_true"	,  )
			parser.add_argument(
			    "--num_beams"	,  default=4	,  type=a	,  help="Number of beams to be used when generating answers"	,  )
			parser.add_argument("--min_length"	,  default=1	,  type=a	,  help="Min length of the generated answers"				)
			parser.add_argument("--max_length"	,  default=50	,  type=a	,  help="Max length of the generated answers"				)

			parser.add_argument(
			    "--print_predictions"	,  action="store_true"	,  help="If True, prints predictions while evaluating."	,  )
			parser.add_argument(
			    "--print_docs"	,  action="store_true"	,  help="If True, prints docs retried while generating."	,  )
			a__ :str						  =    parser.parse_args()
			a__ :Dict						  =    torch.device("cuda" if torch.cuda.is_available() else "cpu"				)
			return args


def 				lowerCamelCase__	(      a	:   Any				)    ->	Optional[int]:





			"""simple docstring"""
			a__ :Dict						  =    {}
			if args.model_type is None:
						a__ :Tuple						  =    infer_model_type(args.model_name_or_path				)
						assert args.model_type is not None
			if args.model_type.startswith("rag"				):
						a__ :str						  =    RagTokenForGeneration if args.model_type == "rag_token" else RagSequenceForGeneration
						a__ :int						  =    args.n_docs
						if args.index_name is not None:
									a__ :Any						  =    args.index_name
						if args.index_path is not None:
									a__ :str						  =    args.index_path
			else:
						a__ :List[str]						  =    BartForConditionalGeneration

			a__ :int						  =    (
			    [f.path for f in os.scandir(args.model_name_or_path				) if f.is_dir()]
			    if args.eval_all_checkpoints
			    else [args.model_name_or_path]
			)

			logger.info("Evaluate the following checkpoints: %s"	,  a				)

			a__ :Any						  =    get_scores if args.eval_mode == "e2e" else get_precision_at_k
			a__ :Any						  =    evaluate_batch_eae if args.eval_mode == "e2e" else evaluate_batch_retrieval

			for checkpoint in checkpoints:
						if os.path.exists(args.predictions_path				) and (not args.recalculate):
									logger.info("Calculating metrics based on an existing predictions file: {}".format(args.predictions_path				)				)
									score_fn(a	,  args.predictions_path	,  args.gold_data_path				)
									continue

						logger.info("***** Running evaluation for {} *****".format(a				)				)
						logger.info("  Batch size = %d"	,  args.eval_batch_size				)
						logger.info("  Predictions will be stored under {}".format(args.predictions_path				)				)

						if args.model_type.startswith("rag"				):
									a__ :Optional[int]						  =    RagRetriever.from_pretrained(a	,  **a				)
									a__ :int						  =    model_class.from_pretrained(a	,  retriever=a	,  **a				)
									model.retriever.init_retrieval()
						else:
									a__ :Union[str, Any]						  =    model_class.from_pretrained(a	,  **a				)
						model.to(args.device				)

						with open(args.evaluation_set	,  "r"				) as eval_file, open(args.predictions_path	,  "w"				) as preds_file:
									a__ :str						  =    []
									for line in tqdm(a				):
												questions.append(line.strip()				)
												if len(a				) == args.eval_batch_size:
															a__ :List[Any]						  =    evaluate_batch_fn(a	,  a	,  a				)
															preds_file.write("\n".join(a				) + "\n"				)
															preds_file.flush()
															a__ :int						  =    []
									if len(a				) > 0:
												a__ :List[Any]						  =    evaluate_batch_fn(a	,  a	,  a				)
												preds_file.write("\n".join(a				)				)
												preds_file.flush()

									score_fn(a	,  args.predictions_path	,  args.gold_data_path				)


if __name__ == "__main__":
		snake_case__			 =						get_args()
		main(args)






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						:   str   ,     __A						:   str = "cpu"   ,     __A						:   str = "openai/clip-vit-large-patch14"						)      ->None:






									"""simple docstring"""






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




						def  _snake_case					(	self						:   int   ,     __A						:   int						)      ->List[Any]:






									"""simple docstring"""






									a__ :List[str]						  =    self.resize(__A						)
									a__ :Union[str, Any]						  =    self.center_crop(__A						)
									a__ :List[str]						  =    self.normalize(__A						)
									return images






						def __call__(	self						:   Union[str, Any]   ,     __A						:   Dict=None   ,     __A						:   List[Any]=None   ,     **__A						:   Union[str, Any]						)      ->Tuple:






									"""simple docstring"""






									a__ :Optional[int]						  =    self.tokenizer(text=__A   ,     **__A						)
									a__ :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						:   List[str]   ,     __A						:   List[str]=10   ,     __A						:   List[Any]=0.01   ,     __A						:   Optional[int]=None   ,     __A						:   List[str]=None   ,     __A						:   Dict=None   ,     __A						:   List[str]=None   ,     __A						:   Tuple=None   ,     __A						:   str=None   ,     __A						:   Any=False   ,     __A						:   Union[str, Any]=True   ,     __A						:   Tuple="image"   ,     __A						:   Tuple=True   ,     __A						:   int=False   ,     __A						:   Tuple=False   ,     __A						:   Union[str, Any]=False   ,     )      ->None:






									"""simple docstring"""






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

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




						def  _snake_case					(	self						:   str   ,     __A						:   str=None   ,     __A						:   Optional[int]=None   ,     __A						:   int=5   ,     __A						:   Any=True						)      ->Optional[int]:






									"""simple docstring"""






									a__ :Optional[int]						  =    []
									if output_path is None:
												a__ :Any						  =    "./animation.gif"
									if input_path is None:
												a__ :Dict						  =    self.save_path
									a__ :Dict						  =    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__ :str						  =    total_duration / len(__A						)
									a__ :str						  =    [frame_duration] * len(__A						)
									if extend_frames:
												a__ :Optional[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  _snake_case					(	self						:   str   ,     __A						:   Tuple=None   ,     __A						:   Optional[int]=None						)      ->Optional[int]:






									"""simple docstring"""






									if not (path or img):
												raise ValueError("Input either path or tensor"						)
									if img is not None:
												raise NotImplementedError
									a__ :List[Any]						  =    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  _snake_case					(	self						:   List[str]   ,     __A						:   Any						)      ->Optional[int]:






									"""simple docstring"""






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




						def  _snake_case					(	self						:   List[str]   ,     __A						:   Optional[Any]   ,     __A						:   Optional[Any]   ,     __A						:   List[Any]=None						)      ->str:






									"""simple docstring"""






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




						def  _snake_case					(	self						:   List[str]   ,     __A						:   List[Any]   ,     __A						:   Optional[int]   ,     __A						:   Optional[int]						)      ->int:






									"""simple docstring"""






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




						def  _snake_case					(	self						:   int   ,     __A						:   Dict   ,     __A						:   Union[str, Any]   ,     __A						:   List[str]						)      ->List[str]:






									"""simple docstring"""






									a__ :List[Any]						  =    torch.randn_like(self.latent   ,     requires_grad=__A   ,     device=self.device						)
									a__ :Optional[Any]						  =    torch.optim.Adam([vector]   ,     lr=self.lr						)

									for i in range(self.iterations						):
												optim.zero_grad()
												a__ :Tuple						  =    self._add_vector(__A						)
												a__ :Dict						  =    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  _snake_case					(	self						:   Union[str, Any]   ,     __A						:   Optional[Any]   ,     __A						:   Union[str, Any]   ,     __A						:   Optional[Any]						)      ->Optional[int]:






									"""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:
												a__ :List[str]						  =    Image.open(__A						)
												a__ :Any						  =    image.resize((256, 256)						)
												wandb.log("Original Image"   ,     wandb.Image(__A						)						)




						def  _snake_case					(	self						:   Union[str, Any]   ,     __A						:   Union[str, Any]						)      ->int:






									"""simple docstring"""






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






						def  _snake_case					(	self						:   Optional[Any]   ,     __A						:   Any   ,     __A						:   int=None   ,     __A						:   int=None   ,     __A						:   str=True   ,     __A						:   List[Any]=False   ,     __A						:   Optional[int]=True   ,     __A						:   Any=True   ,     __A						:   Optional[Any]=None   ,     )      ->Optional[Any]:






									"""simple docstring"""






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

									a__ :Optional[int]						  =    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'''						)						)


