





from typing import Optional

import torch
from PIL import Image
from tqdm.auto import tqdm
from transformers import CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, DDIMScheduler, DiffusionPipeline, UNetaDConditionModel
from diffusers.image_processor import VaeImageProcessor
from diffusers.utils import (
    deprecate,
)



class  _a  (					lowercase__				):






							"""simple docstring"""

							def __init__(   self					:  List[Any]				,						a					:  AutoencoderKL				,						a					:  CLIPTextModel				,						a					:  CLIPTokenizer				,						a					:  UNetaDConditionModel				,						a					:  DDIMScheduler				,						a					:  float = 0.93				,						a					:  bool = True				,						)						->Any:
													SCREAMING_SNAKE_CASE__  :					Dict =       mixing_coeff
													SCREAMING_SNAKE_CASE__  :					str =       leapfrog_steps

													super().__init__()
													self.register_modules(
													    vae=a				,						text_encoder=a				,						tokenizer=a				,						unet=a				,						scheduler=a				,						)

													SCREAMING_SNAKE_CASE__  :					Optional[int] =       2 ** (len(self.vae.config.block_out_channels     ) - 1)
													SCREAMING_SNAKE_CASE__  :					Optional[int] =       VaeImageProcessor(vae_scale_factor=self.vae_scale_factor     )

							def   A_	(   self					:  Dict				,						a					:  str				,						a					:  Optional[str] = None				,						a					:  bool = False     )						->Union[str, Any]:
													SCREAMING_SNAKE_CASE__  :					Union[str, Any] =       self.tokenizer(
													    a				,						padding="max_length"				,						max_length=self.tokenizer.model_max_length				,						truncation=a				,						return_tensors="pt"				,						)

													SCREAMING_SNAKE_CASE__  :					Optional[Any] =       self.text_encoder(text_inputs.input_ids.to(self.device     )     ).last_hidden_state

													SCREAMING_SNAKE_CASE__  :					Any =       prompt_embeds.to(dtype=self.text_encoder.dtype				,						device=self.device     )

													if do_classifier_free_guidance:
																			SCREAMING_SNAKE_CASE__  :					Any =       "" if negative_prompt is None else negative_prompt

																			SCREAMING_SNAKE_CASE__  :					Union[str, Any] =       self.tokenizer(
																			    a				,						padding="max_length"				,						max_length=self.tokenizer.model_max_length				,						truncation=a				,						return_tensors="pt"				,						)

																			SCREAMING_SNAKE_CASE__  :					str =       self.text_encoder(uncond_input.input_ids.to(self.device     )     ).last_hidden_state

																			SCREAMING_SNAKE_CASE__  :					Tuple =       torch.cat([negative_prompt_embeds, prompt_embeds]     )

													return prompt_embeds

							def   A_	(   self					:  Optional[Any]				,						a					:  torch.Tensor				,						a					:  torch.Tensor     )						->Tuple:
													SCREAMING_SNAKE_CASE__  :					Optional[int] =       self.mixing_coeff * x + (1 - self.mixing_coeff) * y
													SCREAMING_SNAKE_CASE__  :					List[Any] =       self.mixing_coeff * y + (1 - self.mixing_coeff) * x

													return [x, y]

							def   A_	(   self					:  Dict				,						a					:  torch.Tensor				,						a					:  torch.Tensor     )						->List[str]:
													SCREAMING_SNAKE_CASE__  :					List[str] =       (y - (1 - self.mixing_coeff) * x) / self.mixing_coeff
													SCREAMING_SNAKE_CASE__  :					Any =       (x - (1 - self.mixing_coeff) * y) / self.mixing_coeff

													return [x, y]

							def   A_	(   self					:  Tuple				,						a					:  torch.Tensor     )						->List[str]:
													# as self.alphas_cumprod is always in cpu
													SCREAMING_SNAKE_CASE__  :					Any =       int(a     )

													SCREAMING_SNAKE_CASE__  :					Dict =       self.scheduler.alphas_cumprod[t] if t >= 0 else self.scheduler.final_alpha_cumprod

													return alpha_prod, 1 - alpha_prod

							def   A_	(   self					:  Tuple				,						a					:  torch.Tensor				,						a					:  torch.Tensor				,						a					:  torch.Tensor				,						a					:  torch.Tensor				,						)						->List[Any]:
													SCREAMING_SNAKE_CASE__  :					List[str] =       timestep - self.scheduler.config.num_train_timesteps / self.scheduler.num_inference_steps

													SCREAMING_SNAKE_CASE__,				SCREAMING_SNAKE_CASE__  :					Optional[int] =       self._get_alpha_and_beta(a     )
													SCREAMING_SNAKE_CASE__,				SCREAMING_SNAKE_CASE__  :					List[Any] =       self._get_alpha_and_beta(a     )

													SCREAMING_SNAKE_CASE__  :					Any =       (alpha_prod_t_prev / alpha_prod_t) ** 0.5
													SCREAMING_SNAKE_CASE__  :					Optional[int] =       -a_t * (beta_prod_t**0.5) + beta_prod_t_prev**0.5

													SCREAMING_SNAKE_CASE__  :					Optional[Any] =       (base - b_t * model_output) / a_t

													return model_input, next_model_input.to(base.dtype     )

							def   A_	(   self					:  List[Any]				,						a					:  torch.Tensor				,						a					:  torch.Tensor				,						a					:  torch.Tensor				,						a					:  torch.Tensor				,						)						->Union[str, Any]:
													SCREAMING_SNAKE_CASE__  :					Tuple =       timestep - self.scheduler.config.num_train_timesteps / self.scheduler.num_inference_steps

													SCREAMING_SNAKE_CASE__,				SCREAMING_SNAKE_CASE__  :					List[str] =       self._get_alpha_and_beta(a     )
													SCREAMING_SNAKE_CASE__,				SCREAMING_SNAKE_CASE__  :					int =       self._get_alpha_and_beta(a     )

													SCREAMING_SNAKE_CASE__  :					Tuple =       (alpha_prod_t_prev / alpha_prod_t) ** 0.5
													SCREAMING_SNAKE_CASE__  :					Any =       -a_t * (beta_prod_t**0.5) + beta_prod_t_prev**0.5
													SCREAMING_SNAKE_CASE__  :					Union[str, Any] =       a_t * base + b_t * model_output

													return model_input, next_model_input.to(base.dtype     )

							@torch.no_grad()
							def   A_	(   self					:  int				,						a					:  torch.Tensor     )						->int:
													SCREAMING_SNAKE_CASE__  :					Optional[Any] =       1 / self.vae.config.scaling_factor * latents
													SCREAMING_SNAKE_CASE__  :					List[str] =       self.vae.decode(a     ).sample
													SCREAMING_SNAKE_CASE__  :					Dict =       (image / 2 + 0.5).clamp(0				,						1     )
													return image

							@torch.no_grad()
							def   A_	(   self					:  Tuple				,						a					:  Image.Image				,						a					:  torch.Tensor				,						a					:  torch.Tensor				,						a					:  float				,						a					:  Optional[torch.Generator] = None				,						)						->Optional[int]:
													SCREAMING_SNAKE_CASE__  :					Optional[Any] =       guidance_scale > 1.0

													SCREAMING_SNAKE_CASE__  :					Tuple =       image.to(device=self.device				,						dtype=text_embeds.dtype     )
													SCREAMING_SNAKE_CASE__  :					List[Any] =       self.vae.encode(a     ).latent_dist.sample(a     )

													SCREAMING_SNAKE_CASE__  :					Union[str, Any] =       self.vae.config.scaling_factor * latent

													SCREAMING_SNAKE_CASE__  :					Tuple =       [latent.clone(), latent.clone()]

													for i, t in tqdm(enumerate(a     )				,						total=len(a     )     ):
																			SCREAMING_SNAKE_CASE__  :					Any =       self.noise_mixing_layer(x=coupled_latents[0]				,						y=coupled_latents[1]     )

																			# j - model_input index, k - base index
																			for j in range(2     ):
																									SCREAMING_SNAKE_CASE__  :					Tuple =       j ^ 1

																									if self.leapfrog_steps:
																															if i % 2 == 0:
																																					SCREAMING_SNAKE_CASE__,				SCREAMING_SNAKE_CASE__  :					Optional[int] =       j, k

																									SCREAMING_SNAKE_CASE__  :					List[Any] =       coupled_latents[j]
																									SCREAMING_SNAKE_CASE__  :					Optional[Any] =       coupled_latents[k]

																									SCREAMING_SNAKE_CASE__  :					List[str] =       torch.cat([model_input] * 2     ) if do_classifier_free_guidance else model_input

																									SCREAMING_SNAKE_CASE__  :					List[str] =       self.unet(a				,						a				,						encoder_hidden_states=a     ).sample

																									if do_classifier_free_guidance:
																															SCREAMING_SNAKE_CASE__,				SCREAMING_SNAKE_CASE__  :					Optional[Any] =       noise_pred.chunk(2     )
																															SCREAMING_SNAKE_CASE__  :					str =       noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

																									SCREAMING_SNAKE_CASE__,				SCREAMING_SNAKE_CASE__  :					List[str] =       self.noise_step(
																									    base=a				,						model_input=a				,						model_output=a				,						timestep=a				,						)

																									SCREAMING_SNAKE_CASE__  :					List[Any] =       model_input

													return coupled_latents






							@torch.no_grad()
							def __call__(   self					:  Union[str, Any]				,						a					:  str				,						a					:  str				,						a					:  Image.Image				,						a					:  float = 3.0				,						a					:  int = 50				,						a					:  float = 0.8				,						a					:  Optional[str] = None				,						a					:  Optional[torch.Generator] = None				,						a					:  Optional[str] = "pil"				,						)						->Optional[int]:
													SCREAMING_SNAKE_CASE__  :					List[Any] =       guidance_scale > 1.0

													SCREAMING_SNAKE_CASE__  :					Optional[Any] =       self.image_processor.preprocess(a     )

													SCREAMING_SNAKE_CASE__  :					Any =       self._encode_prompt(a				,						a				,						a     )
													SCREAMING_SNAKE_CASE__  :					Union[str, Any] =       self._encode_prompt(a				,						a				,						a     )

													self.scheduler.set_timesteps(a				,						self.device     )

													SCREAMING_SNAKE_CASE__  :					int =       num_inference_steps - int(num_inference_steps * strength     )
													SCREAMING_SNAKE_CASE__  :					Optional[int] =       self.scheduler.timesteps[t_limit:]
													SCREAMING_SNAKE_CASE__  :					Any =       fwd_timesteps.flip(0     )

													SCREAMING_SNAKE_CASE__  :					Dict =       self.prepare_latents(a				,						a				,						a				,						a				,						a     )

													for i, t in tqdm(enumerate(a     )				,						total=len(a     )     ):
																			# j - model_input index, k - base index
																			for k in range(2     ):
																									SCREAMING_SNAKE_CASE__  :					Optional[int] =       k ^ 1

																									if self.leapfrog_steps:
																															if i % 2 == 1:
																																					SCREAMING_SNAKE_CASE__,				SCREAMING_SNAKE_CASE__  :					Dict =       j, k

																									SCREAMING_SNAKE_CASE__  :					Union[str, Any] =       coupled_latents[j]
																									SCREAMING_SNAKE_CASE__  :					int =       coupled_latents[k]

																									SCREAMING_SNAKE_CASE__  :					Any =       torch.cat([model_input] * 2     ) if do_classifier_free_guidance else model_input

																									SCREAMING_SNAKE_CASE__  :					Tuple =       self.unet(a				,						a				,						encoder_hidden_states=a     ).sample

																									if do_classifier_free_guidance:
																															SCREAMING_SNAKE_CASE__,				SCREAMING_SNAKE_CASE__  :					Tuple =       noise_pred.chunk(2     )
																															SCREAMING_SNAKE_CASE__  :					List[Any] =       noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

																									SCREAMING_SNAKE_CASE__,				SCREAMING_SNAKE_CASE__  :					str =       self.denoise_step(
																									    base=a				,						model_input=a				,						model_output=a				,						timestep=a				,						)

																									SCREAMING_SNAKE_CASE__  :					Optional[int] =       model_input

																			SCREAMING_SNAKE_CASE__  :					List[str] =       self.denoise_mixing_layer(x=coupled_latents[0]				,						y=coupled_latents[1]     )

													# either one is fine
													SCREAMING_SNAKE_CASE__  :					Any =       coupled_latents[0]

													if output_type not in ["latent", "pt", "np", "pil"]:
																			SCREAMING_SNAKE_CASE__  :					Dict =       (
																			    f"""the output_type {output_type} is outdated. Please make sure to set it to one of these instead: """
																			    "`pil`, `np`, `pt`, `latent`"
																			)
																			deprecate("Unsupported output_type"				,						"1.0.0"				,						a				,						standard_warn=a     )
																			SCREAMING_SNAKE_CASE__  :					Optional[int] =       "np"

													if output_type == "latent":
																			SCREAMING_SNAKE_CASE__  :					Optional[int] =       final_latent
													else:
																			SCREAMING_SNAKE_CASE__  :					Any =       self.decode_latents(a     )
																			SCREAMING_SNAKE_CASE__  :					List[Any] =       self.image_processor.postprocess(a				,						output_type=a     )

													return image






import argparse
import json
import re
import string
import sys

import numpy as np


__lowercase							:Optional[Any]							=	0.5







def    UpperCAmelCase				(  _lowerCamelCase   :	int      , _lowerCamelCase   :	Tuple  ):





						'''simple docstring'''


						SCREAMING_SNAKE_CASE__  :					List[Any] =       [".", ",", ";", ":"]

						for token in remove_tokens:
												SCREAMING_SNAKE_CASE__  :					List[Any] =       ground_truth.replace(_lowerCamelCase      , ""  )
												SCREAMING_SNAKE_CASE__  :					List[Any] =       prediction.replace(_lowerCamelCase      , ""  )

						SCREAMING_SNAKE_CASE__,				SCREAMING_SNAKE_CASE__  :					List[Any] =       ground_truth.lower(), prediction.lower()
						SCREAMING_SNAKE_CASE__,				SCREAMING_SNAKE_CASE__  :					Dict =       ground_truth.replace("/"      , " "  ), prediction.replace("/"      , " "  )
						SCREAMING_SNAKE_CASE__,				SCREAMING_SNAKE_CASE__  :					str =       set(ground_truth.split(" "  )  ), set(prediction.split(" "  )  )

						SCREAMING_SNAKE_CASE__  :					Optional[int] =       ground_truth.intersection(_lowerCamelCase  )
						SCREAMING_SNAKE_CASE__  :					Union[str, Any] =       ground_truth.union(_lowerCamelCase  )
						SCREAMING_SNAKE_CASE__  :					Dict =       len(_lowerCamelCase  ) / len(_lowerCamelCase  )
						return jaccard







def    UpperCAmelCase				(  _lowerCamelCase   :	List[Any]  ):





						'''simple docstring'''


						def remove_articles(_lowerCamelCase   :	Any  ):
												return re.sub(r"\b(a|an|the)\b"      , " "      , _lowerCamelCase  )

						def white_space_fix(_lowerCamelCase   :	Any  ):
												return " ".join(text.split()  )

						def remove_punc(_lowerCamelCase   :	List[Any]  ):
												SCREAMING_SNAKE_CASE__  :					Tuple =       set(string.punctuation  )
												return "".join(ch for ch in text if ch not in exclude  )

						def lower(_lowerCamelCase   :	str  ):
												return text.lower()

						return white_space_fix(remove_articles(remove_punc(lower(_lowerCamelCase  )  )  )  )







def    UpperCAmelCase				(  _lowerCamelCase   :	Optional[Any]      , _lowerCamelCase   :	Optional[Any]      , _lowerCamelCase   :	Tuple  ):





						'''simple docstring'''


						SCREAMING_SNAKE_CASE__,				SCREAMING_SNAKE_CASE__,				SCREAMING_SNAKE_CASE__  :					Optional[int] =       0, 0, 0

						SCREAMING_SNAKE_CASE__  :					Optional[int] =       "Parties" in qa_id

						# first check if ground truth is empty
						if len(_lowerCamelCase  ) == 0:
												if len(_lowerCamelCase  ) > 0:
																		fp += len(_lowerCamelCase  )  # false positive for each one
						else:
												for ground_truth in ground_truths:
																		assert len(_lowerCamelCase  ) > 0
																		# check if there is a match
																		SCREAMING_SNAKE_CASE__  :					Tuple =       False
																		for pred in predictions:
																								if substr_ok:
																														SCREAMING_SNAKE_CASE__  :					Optional[Any] =       get_jaccard(_lowerCamelCase      , _lowerCamelCase  ) >= IOU_THRESH or ground_truth in pred
																								else:
																														SCREAMING_SNAKE_CASE__  :					Tuple =       get_jaccard(_lowerCamelCase      , _lowerCamelCase  ) >= IOU_THRESH
																								if is_match:
																														SCREAMING_SNAKE_CASE__  :					str =       True

																		if match_found:
																								tp += 1
																		else:
																								fn += 1

        # now also get any fps by looping through preds
												for pred in predictions:
																		# Check if there's a match. if so, don't count (don't want to double count based on the above)
																		# but if there's no match, then this is a false positive.
																		# (Note: we get the true positives in the above loop instead of this loop so that we don't double count
																		# multiple predictions that are matched with the same answer.)
																		SCREAMING_SNAKE_CASE__  :					Tuple =       False
																		for ground_truth in ground_truths:
																								assert len(_lowerCamelCase  ) > 0
																								if substr_ok:
																														SCREAMING_SNAKE_CASE__  :					Tuple =       get_jaccard(_lowerCamelCase      , _lowerCamelCase  ) >= IOU_THRESH or ground_truth in pred
																								else:
																														SCREAMING_SNAKE_CASE__  :					Dict =       get_jaccard(_lowerCamelCase      , _lowerCamelCase  ) >= IOU_THRESH
																								if is_match:
																														SCREAMING_SNAKE_CASE__  :					List[Any] =       True

																		if not match_found:
																								fp += 1

						SCREAMING_SNAKE_CASE__  :					Union[str, Any] =       tp / (tp + fp) if tp + fp > 0 else np.nan
						SCREAMING_SNAKE_CASE__  :					Dict =       tp / (tp + fn) if tp + fn > 0 else np.nan

						return precision, recall







def    UpperCAmelCase				(  _lowerCamelCase   :	Optional[int]  ):





						'''simple docstring'''


						SCREAMING_SNAKE_CASE__  :					int =       precisions[::-1]
						for i in range(1      , len(_lowerCamelCase  )  ):
												SCREAMING_SNAKE_CASE__  :					Union[str, Any] =       max(precision_best[i - 1]      , precision_best[i]  )
						SCREAMING_SNAKE_CASE__  :					Optional[Any] =       precision_best[::-1]
						return precisions







def    UpperCAmelCase				(  _lowerCamelCase   :	Dict      , _lowerCamelCase   :	str  ):





						'''simple docstring'''


						SCREAMING_SNAKE_CASE__  :					Tuple =       process_precisions(_lowerCamelCase  )
						SCREAMING_SNAKE_CASE__  :					List[Any] =       np.trapz(_lowerCamelCase      , _lowerCamelCase  )
						if np.isnan(_lowerCamelCase  ):
												return 0
						return aupr







def    UpperCAmelCase				(  _lowerCamelCase   :	Optional[int]      , _lowerCamelCase   :	Any      , _lowerCamelCase   :	List[str]  ):





						'''simple docstring'''


						SCREAMING_SNAKE_CASE__  :					Any =       process_precisions(_lowerCamelCase  )
						SCREAMING_SNAKE_CASE__  :					List[str] =       0
						for prec, recall in zip(_lowerCamelCase      , _lowerCamelCase  ):
												if recall >= recall_thresh:
																		SCREAMING_SNAKE_CASE__  :					Optional[int] =       prec
																		break
						return prec_at_recall







def    UpperCAmelCase				(  _lowerCamelCase   :	Optional[int]      , _lowerCamelCase   :	Optional[Any]  ):





						'''simple docstring'''


						return normalize_answer(_lowerCamelCase  ) == normalize_answer(_lowerCamelCase  )







def    UpperCAmelCase				(  _lowerCamelCase   :	Optional[int]      , _lowerCamelCase   :	str      , _lowerCamelCase   :	List[Any]  ):





						'''simple docstring'''


						SCREAMING_SNAKE_CASE__  :					Optional[Any] =       0
						for pred in predictions:
												for ground_truth in ground_truths:
																		SCREAMING_SNAKE_CASE__  :					Optional[Any] =       metric_fn(_lowerCamelCase      , _lowerCamelCase  )
																		if score == 1:  # break the loop when one prediction matches the ground truth
																								break
												if score == 1:
																		break
						return score







def    UpperCAmelCase				(  _lowerCamelCase   :	Optional[int]      , _lowerCamelCase   :	int  ):





						'''simple docstring'''


						SCREAMING_SNAKE_CASE__  :					Any =       0
						SCREAMING_SNAKE_CASE__  :					List[str] =       []
						SCREAMING_SNAKE_CASE__  :					Union[str, Any] =       []
						for article in dataset:
												for paragraph in article["paragraphs"]:
																		for qa in paragraph["qas"]:
																								total += 1
																								if qa["id"] not in predictions:
																														SCREAMING_SNAKE_CASE__  :					Union[str, Any] =       "Unanswered question " + qa["id"] + " will receive score 0."
																														print(_lowerCamelCase      , file=sys.stderr  )
																														continue
																								SCREAMING_SNAKE_CASE__  :					Any =       [x["text"] for x in qa["answers"]]
																								SCREAMING_SNAKE_CASE__  :					int =       predictions[qa["id"]]
																								SCREAMING_SNAKE_CASE__,				SCREAMING_SNAKE_CASE__  :					Tuple =       compute_precision_recall(_lowerCamelCase      , _lowerCamelCase      , qa["id"]  )

																								precisions.append(_lowerCamelCase  )
																								recalls.append(_lowerCamelCase  )

																								if precision == 0 and recall == 0:
																														fa += 0
																								else:
																														fa += 2 * (precision * recall) / (precision + recall)

																								exact_match += metric_max_over_ground_truths(_lowerCamelCase      , _lowerCamelCase      , _lowerCamelCase  )

						SCREAMING_SNAKE_CASE__  :					Optional[int] =       [x for _, x in sorted(zip(_lowerCamelCase      , _lowerCamelCase  )  )]
						recalls.sort()

						SCREAMING_SNAKE_CASE__  :					Union[str, Any] =       1_0_0.0 * fa / total
						SCREAMING_SNAKE_CASE__  :					Dict =       1_0_0.0 * exact_match / total
						SCREAMING_SNAKE_CASE__  :					str =       get_aupr(_lowerCamelCase      , _lowerCamelCase  )

						SCREAMING_SNAKE_CASE__  :					Optional[int] =       get_prec_at_recall(_lowerCamelCase      , _lowerCamelCase      , recall_thresh=0.9  )
						SCREAMING_SNAKE_CASE__  :					Optional[Any] =       get_prec_at_recall(_lowerCamelCase      , _lowerCamelCase      , recall_thresh=0.8  )

						return {
						    "exact_match": exact_match,
						    "f1": fa,
						    "aupr": aupr,
						    "prec_at_80_recall": prec_at_aa_recall,
						    "prec_at_90_recall": prec_at_aa_recall,
						}


if __name__ == "__main__":
				__lowercase							:Tuple							=	argparse.ArgumentParser(description="Evaluation for CUAD")
				parser.add_argument("dataset_file", help="Dataset file")
				parser.add_argument("prediction_file", help="Prediction File")
				__lowercase							:Tuple							=	parser.parse_args()
				with open(args.dataset_file) as dataset_file:
								__lowercase							:Union[str, Any]							=	json.load(dataset_file)
								__lowercase							:Union[str, Any]							=	dataset_json["data"]
				with open(args.prediction_file) as prediction_file:
								__lowercase							:Optional[Any]							=	json.load(prediction_file)
				print(json.dumps(evaluate(dataset, predictions)))