

'''simple docstring'''


import argparse
import json
import re
import string
import sys

import numpy as np


lowerCAmelCase__      				=			0.5







def     SCREAMING_SNAKE_CASE(	UpperCamelCase ,UpperCamelCase	)     ->     Any:
	UpperCAmelCase_		:   Union[str, Any]													=       ['.', ',', ';', ':']

	for token in remove_tokens:
		UpperCAmelCase_		:   Dict													=       ground_truth.replace(UpperCamelCase ,''	)
		UpperCAmelCase_		:   Tuple													=       prediction.replace(UpperCamelCase ,''	)

	UpperCAmelCase_						,     UpperCAmelCase_		:   List[Any]													=       ground_truth.lower(), prediction.lower()
	UpperCAmelCase_						,     UpperCAmelCase_		:   List[str]													=       ground_truth.replace('/' ,' '	), prediction.replace('/' ,' '	)
	UpperCAmelCase_						,     UpperCAmelCase_		:   int													=       set(ground_truth.split(' '	)	), set(prediction.split(' '	)	)

	UpperCAmelCase_		:   List[str]													=       ground_truth.intersection(UpperCamelCase	)
	UpperCAmelCase_		:   Tuple													=       ground_truth.union(UpperCamelCase	)
	UpperCAmelCase_		:   Optional[Any]													=       len(UpperCamelCase	) / len(UpperCamelCase	)
	return jaccard







def     SCREAMING_SNAKE_CASE(	UpperCamelCase	)     ->     Dict:

	def remove_articles(UpperCamelCase	):
		return re.sub(r'\b(a|an|the)\b' ,' ' ,UpperCamelCase	)

	def white_space_fix(UpperCamelCase	):
		return " ".join(text.split()	)

	def remove_punc(UpperCamelCase	):
		UpperCAmelCase_		:   Any													=       set(string.punctuation	)
		return "".join(ch for ch in text if ch not in exclude	)

	def lower(UpperCamelCase	):
		return text.lower()

	return white_space_fix(remove_articles(remove_punc(lower(UpperCamelCase	)	)	)	)







def     SCREAMING_SNAKE_CASE(	UpperCamelCase ,UpperCamelCase ,UpperCamelCase	)     ->     Tuple:
	UpperCAmelCase_						,     UpperCAmelCase_						,     UpperCAmelCase_		:   int													=       0, 0, 0

	UpperCAmelCase_		:   Dict													=       'Parties' in qa_id

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

			if not match_found:
				fp += 1

	UpperCAmelCase_		:   Any													=       tp / (tp + fp) if tp + fp > 0 else np.nan
	UpperCAmelCase_		:   str													=       tp / (tp + fn) if tp + fn > 0 else np.nan

	return precision, recall







def     SCREAMING_SNAKE_CASE(	UpperCamelCase	)     ->     Optional[Any]:
	UpperCAmelCase_		:   Any													=       precisions[::-1]
	for i in range(1 ,len(UpperCamelCase	)	):
		UpperCAmelCase_		:   List[str]													=       max(precision_best[i - 1] ,precision_best[i]	)
	UpperCAmelCase_		:   Any													=       precision_best[::-1]
	return precisions







def     SCREAMING_SNAKE_CASE(	UpperCamelCase ,UpperCamelCase	)     ->     Optional[Any]:
	UpperCAmelCase_		:   Optional[int]													=       process_precisions(UpperCamelCase	)
	UpperCAmelCase_		:   List[Any]													=       np.trapz(UpperCamelCase ,UpperCamelCase	)
	if np.isnan(UpperCamelCase	):
		return 0
	return aupr







def     SCREAMING_SNAKE_CASE(	UpperCamelCase ,UpperCamelCase ,UpperCamelCase	)     ->     Dict:
	UpperCAmelCase_		:   str													=       process_precisions(UpperCamelCase	)
	UpperCAmelCase_		:   Optional[Any]													=       0
	for prec, recall in zip(UpperCamelCase ,UpperCamelCase	):
		if recall >= recall_thresh:
			UpperCAmelCase_		:   List[str]													=       prec
			break
	return prec_at_recall







def     SCREAMING_SNAKE_CASE(	UpperCamelCase ,UpperCamelCase	)     ->     str:
	return normalize_answer(UpperCamelCase	) == normalize_answer(UpperCamelCase	)







def     SCREAMING_SNAKE_CASE(	UpperCamelCase ,UpperCamelCase ,UpperCamelCase	)     ->     int:
	UpperCAmelCase_		:   Union[str, Any]													=       0
	for pred in predictions:
		for ground_truth in ground_truths:
			UpperCAmelCase_		:   Optional[Any]													=       metric_fn(UpperCamelCase ,UpperCamelCase	)
			if score == 1:  # break the loop when one prediction matches the ground truth
				break
		if score == 1:
			break
	return score







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

				precisions.append(UpperCamelCase	)
				recalls.append(UpperCamelCase	)

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

				exact_match += metric_max_over_ground_truths(UpperCamelCase ,UpperCamelCase ,UpperCamelCase	)

	UpperCAmelCase_		:   Dict													=       [x for _, x in sorted(zip(UpperCamelCase ,UpperCamelCase	)	)]
	recalls.sort()

	UpperCAmelCase_		:   List[Any]													=       100.0 * fa / total
	UpperCAmelCase_		:   str													=       100.0 * exact_match / total
	UpperCAmelCase_		:   Dict													=       get_aupr(UpperCamelCase ,UpperCamelCase	)

	UpperCAmelCase_		:   List[str]													=       get_prec_at_recall(UpperCamelCase ,UpperCamelCase ,recall_thresh=0.9	)
	UpperCAmelCase_		:   Any													=       get_prec_at_recall(UpperCamelCase ,UpperCamelCase ,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__":
						lowerCAmelCase__      				=			argparse.ArgumentParser(description="Evaluation for CUAD")
						parser.add_argument("dataset_file", help="Dataset file")
						parser.add_argument("prediction_file", help="Prediction File")
						lowerCAmelCase__      				=			parser.parse_args()
						with open(args.dataset_file) as dataset_file:
												lowerCAmelCase__      				=			json.load(dataset_file)
												lowerCAmelCase__      				=			dataset_json["data"]
						with open(args.prediction_file) as prediction_file:
												lowerCAmelCase__      				=			json.load(prediction_file)
						print(json.dumps(evaluate(dataset, predictions)))









'''simple docstring'''


import math
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, logging, randn_tensor
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin


lowerCAmelCase__      				=			logging.get_logger(__name__)  # pylint: disable=invalid-name



@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->EulerAncestralDiscrete
class        lowercase    (	a_			):
							_lowerCamelCase		:   torch.FloatTensor
							_lowerCamelCase		:   Optional[torch.FloatTensor]= None







def     SCREAMING_SNAKE_CASE(	UpperCamelCase ,UpperCamelCase=0.999 ,UpperCamelCase="cosine" ,)     ->     List[str]:
	if alpha_transform_type == "cosine":

		def alpha_bar_fn(UpperCamelCase	):
			return math.cos((t + 0.008) / 1.008 * math.pi / 2	) ** 2

	elif alpha_transform_type == "exp":

		def alpha_bar_fn(UpperCamelCase	):
			return math.exp(t * -12.0	)

	else:
		raise ValueError(f"""Unsupported alpha_tranform_type: {alpha_transform_type}"""	)

	UpperCAmelCase_		:   Optional[int]													=       []
	for i in range(UpperCamelCase	):
		UpperCAmelCase_		:   Optional[Any]													=       i / num_diffusion_timesteps
		UpperCAmelCase_		:   Any													=       (i + 1) / num_diffusion_timesteps
		betas.append(min(1 - alpha_bar_fn(UpperCamelCase	) / alpha_bar_fn(UpperCamelCase	) ,UpperCamelCase	)	)
	return torch.tensor(UpperCamelCase ,dtype=torch.floataa	)







class        lowercase    (	a_,       a_			):
							_lowerCamelCase		:   List[str]= [e.name for e in KarrasDiffusionSchedulers]
							_lowerCamelCase		:   Optional[int]= 1


							@register_to_config
							def __init__(      self     ,      _snake_case = 1000     ,      _snake_case = 0.0_001     ,      _snake_case = 0.02     ,      _snake_case = "linear"     ,      _snake_case = None     ,      _snake_case = "epsilon"     ,      _snake_case = "linspace"     ,      _snake_case = 0     ,      )				->					List[str]:
								if trained_betas is not None:
									UpperCAmelCase_		:   List[str]													=       torch.tensor(_snake_case     ,      dtype=torch.floataa)
								elif beta_schedule == "linear":
									UpperCAmelCase_		:   List[Any]													=       torch.linspace(_snake_case     ,      _snake_case     ,      _snake_case     ,      dtype=torch.floataa)
								elif beta_schedule == "scaled_linear":
									# this schedule is very specific to the latent diffusion model.
									UpperCAmelCase_		:   List[Any]													=       (
									    torch.linspace(beta_start**0.5     ,      beta_end**0.5     ,      _snake_case     ,      dtype=torch.floataa) ** 2
									)
								elif beta_schedule == "squaredcos_cap_v2":
									# Glide cosine schedule
									UpperCAmelCase_		:   int													=       betas_for_alpha_bar(_snake_case)
								else:
									raise NotImplementedError(F"""{beta_schedule} does is not implemented for {self.__class__}""")

								UpperCAmelCase_		:   List[Any]													=       1.0 - self.betas
								UpperCAmelCase_		:   Union[str, Any]													=       torch.cumprod(self.alphas     ,      dim=0)

								UpperCAmelCase_		:   Union[str, Any]													=       np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5)
								UpperCAmelCase_		:   int													=       np.concatenate([sigmas[::-1], [0.0]]).astype(np.floataa)
								UpperCAmelCase_		:   int													=       torch.from_numpy(_snake_case)

								# setable values
								UpperCAmelCase_		:   List[str]													=       None
								UpperCAmelCase_		:   Union[str, Any]													=       np.linspace(0     ,      num_train_timesteps - 1     ,      _snake_case     ,      dtype=_snake_case)[::-1].copy()
								UpperCAmelCase_		:   Optional[int]													=       torch.from_numpy(_snake_case)
								UpperCAmelCase_		:   Optional[int]													=       False


							@property
							def     _snake_case				(      self)				->					Optional[Any]:
								# standard deviation of the initial noise distribution
								if self.config.timestep_spacing in ["linspace", "trailing"]:
									return self.sigmas.max()

								return (self.sigmas.max() ** 2 + 1) ** 0.5


							def     _snake_case				(      self     ,      _snake_case     ,      _snake_case)				->					torch.FloatTensor:
								if isinstance(_snake_case     ,      torch.Tensor):
									UpperCAmelCase_		:   List[Any]													=       timestep.to(self.timesteps.device)
								UpperCAmelCase_		:   int													=       (self.timesteps == timestep).nonzero().item()
								UpperCAmelCase_		:   Union[str, Any]													=       self.sigmas[step_index]
								UpperCAmelCase_		:   Union[str, Any]													=       sample / ((sigma**2 + 1) ** 0.5)
								UpperCAmelCase_		:   Optional[Any]													=       True
								return sample


							def     _snake_case				(      self     ,      _snake_case     ,      _snake_case = None)				->					Optional[Any]:
								UpperCAmelCase_		:   Optional[int]													=       num_inference_steps

								# "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
								if self.config.timestep_spacing == "linspace":
									UpperCAmelCase_		:   List[Any]													=       np.linspace(0     ,      self.config.num_train_timesteps - 1     ,      _snake_case     ,      dtype=_snake_case)[
									    ::-1
									].copy()
								elif self.config.timestep_spacing == "leading":
									UpperCAmelCase_		:   Any													=       self.config.num_train_timesteps // self.num_inference_steps
									# creates integer timesteps by multiplying by ratio
									# casting to int to avoid issues when num_inference_step is power of 3
									UpperCAmelCase_		:   List[Any]													=       (np.arange(0     ,      _snake_case) * step_ratio).round()[::-1].copy().astype(_snake_case)
									timesteps += self.config.steps_offset
								elif self.config.timestep_spacing == "trailing":
									UpperCAmelCase_		:   List[Any]													=       self.config.num_train_timesteps / self.num_inference_steps
									# creates integer timesteps by multiplying by ratio
									# casting to int to avoid issues when num_inference_step is power of 3
									UpperCAmelCase_		:   Any													=       (np.arange(self.config.num_train_timesteps     ,      0     ,      -step_ratio)).round().copy().astype(_snake_case)
									timesteps -= 1
								else:
									raise ValueError(
									    F"""{self.config.timestep_spacing} is not supported. Please make sure to choose one of 'linspace', 'leading' or 'trailing'.""")

								UpperCAmelCase_		:   Any													=       np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5)
								UpperCAmelCase_		:   Dict													=       np.interp(_snake_case     ,      np.arange(0     ,      len(_snake_case))     ,      _snake_case)
								UpperCAmelCase_		:   Optional[int]													=       np.concatenate([sigmas, [0.0]]).astype(np.floataa)
								UpperCAmelCase_		:   int													=       torch.from_numpy(_snake_case).to(device=_snake_case)
								if str(_snake_case).startswith('mps'):
									# mps does not support float64
									UpperCAmelCase_		:   Optional[int]													=       torch.from_numpy(_snake_case).to(_snake_case     ,      dtype=torch.floataa)
								else:
									UpperCAmelCase_		:   List[Any]													=       torch.from_numpy(_snake_case).to(device=_snake_case)


							def     _snake_case				(      self     ,      _snake_case     ,      _snake_case     ,      _snake_case     ,      _snake_case = None     ,      _snake_case = True     ,      )				->					Union[EulerAncestralDiscreteSchedulerOutput, Tuple]:

								if (
								    isinstance(_snake_case     ,      _snake_case)
								    or isinstance(_snake_case     ,      torch.IntTensor)
								    or isinstance(_snake_case     ,      torch.LongTensor)
								):
									raise ValueError(
									    (
									        'Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to'
									        ' `EulerDiscreteScheduler.step()` is not supported. Make sure to pass'
									        ' one of the `scheduler.timesteps` as a timestep.'
									    )     ,      )

								if not self.is_scale_input_called:
									logger.warning(
									    'The `scale_model_input` function should be called before `step` to ensure correct denoising. '
									    'See `StableDiffusionPipeline` for a usage example.')

								if isinstance(_snake_case     ,      torch.Tensor):
									UpperCAmelCase_		:   Optional[int]													=       timestep.to(self.timesteps.device)

								UpperCAmelCase_		:   List[Any]													=       (self.timesteps == timestep).nonzero().item()
								UpperCAmelCase_		:   int													=       self.sigmas[step_index]

								# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
								if self.config.prediction_type == "epsilon":
									UpperCAmelCase_		:   Tuple													=       sample - sigma * model_output
								elif self.config.prediction_type == "v_prediction":
									# * c_out + input * c_skip
									UpperCAmelCase_		:   str													=       model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
								elif self.config.prediction_type == "sample":
									raise NotImplementedError('prediction_type not implemented yet: sample')
								else:
									raise ValueError(
									    F"""prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`""")

								UpperCAmelCase_		:   Dict													=       self.sigmas[step_index]
								UpperCAmelCase_		:   Union[str, Any]													=       self.sigmas[step_index + 1]
								UpperCAmelCase_		:   str													=       (sigma_to**2 * (sigma_from**2 - sigma_to**2) / sigma_from**2) ** 0.5
								UpperCAmelCase_		:   Any													=       (sigma_to**2 - sigma_up**2) ** 0.5

								# 2. Convert to an ODE derivative
								UpperCAmelCase_		:   Dict													=       (sample - pred_original_sample) / sigma

								UpperCAmelCase_		:   Tuple													=       sigma_down - sigma

								UpperCAmelCase_		:   List[str]													=       sample + derivative * dt

								UpperCAmelCase_		:   Any													=       model_output.device
								UpperCAmelCase_		:   List[Any]													=       randn_tensor(model_output.shape     ,      dtype=model_output.dtype     ,      device=_snake_case     ,      generator=_snake_case)

								UpperCAmelCase_		:   Union[str, Any]													=       prev_sample + noise * sigma_up

								if not return_dict:
									return (prev_sample,)

								return EulerAncestralDiscreteSchedulerOutput(
								    prev_sample=_snake_case     ,      pred_original_sample=_snake_case)


							def     _snake_case				(      self     ,      _snake_case     ,      _snake_case     ,      _snake_case     ,      )				->					torch.FloatTensor:
								# Make sure sigmas and timesteps have the same device and dtype as original_samples
								UpperCAmelCase_		:   List[str]													=       self.sigmas.to(device=original_samples.device     ,      dtype=original_samples.dtype)
								if original_samples.device.type == "mps" and torch.is_floating_point(_snake_case):
									# mps does not support float64
									UpperCAmelCase_		:   str													=       self.timesteps.to(original_samples.device     ,      dtype=torch.floataa)
									UpperCAmelCase_		:   List[Any]													=       timesteps.to(original_samples.device     ,      dtype=torch.floataa)
								else:
									UpperCAmelCase_		:   List[Any]													=       self.timesteps.to(original_samples.device)
									UpperCAmelCase_		:   str													=       timesteps.to(original_samples.device)

								UpperCAmelCase_		:   Optional[Any]													=       [(schedule_timesteps == t).nonzero().item() for t in timesteps]

								UpperCAmelCase_		:   Any													=       sigmas[step_indices].flatten()
								while len(sigma.shape) < len(original_samples.shape):
									UpperCAmelCase_		:   Union[str, Any]													=       sigma.unsqueeze(-1)

								UpperCAmelCase_		:   int													=       original_samples + noise * sigma
								return noisy_samples





							def __len__(      self)				->					Optional[Any]:
								return self.config.num_train_timesteps






