





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

import numpy as np
import torch

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

@dataclass
class      A_	(a_      ):
	UpperCAmelCase__ =      42
	UpperCAmelCase__ =      42





def 						__SCREAMING_SNAKE_CASE  (    UpperCamelCase__      ,      UpperCamelCase__=0.999      ,      UpperCamelCase__="cosine"      ,      )  ->    List[str]:







			'''simple docstring'''

			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       		=    []
			for i in range(UpperCamelCase__			):
						UpperCAmelCase       		=    i / num_diffusion_timesteps
						UpperCAmelCase       		=    (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      A_	(a_					,							a_      ):
	UpperCAmelCase__ =      1
	@register_to_config
	def __init__(   self							, _A = 1_0_0_0							, _A = 0.00_01							, _A = 0.02							, _A = "linear"							, _A = 0.0							, _A = None							, _A = True							, ):






				'''simple docstring'''







				if trained_betas is not None:
							UpperCAmelCase       		=    torch.from_numpy(_A		)
				elif beta_schedule == "linear":
							UpperCAmelCase       		=    torch.linspace(_A							, _A							, _A							, dtype=torch.floataa		)
				elif beta_schedule == "scaled_linear":
							# this schedule is very specific to the latent diffusion model.
							UpperCAmelCase       		=    (
							    torch.linspace(beta_start**0.5							, beta_end**0.5							, _A							, dtype=torch.floataa		) ** 2
							)
				elif beta_schedule == "squaredcos_cap_v2":
							# Glide cosine schedule
							UpperCAmelCase       		=    betas_for_alpha_bar(_A		)
				elif beta_schedule == "sigmoid":
							# GeoDiff sigmoid schedule
							UpperCAmelCase       		=    torch.linspace(-6							, 6							, _A		)
							UpperCAmelCase       		=    torch.sigmoid(_A		) * (beta_end - beta_start) + beta_start
				else:
							raise NotImplementedError(F"""{beta_schedule} does is not implemented for {self.__class__}"""		)

				UpperCAmelCase       		=    1.0 - self.betas
				UpperCAmelCase       		=    torch.cumprod(self.alphas							, dim=0		)
				UpperCAmelCase       		=    torch.tensor(1.0		)

				UpperCAmelCase       		=    torch.tensor(1.0		)

				# standard deviation of the initial noise distribution
				UpperCAmelCase       		=    1.0

				# setable values
				UpperCAmelCase       		=    None
				UpperCAmelCase       		=    torch.from_numpy(np.arange(0							, _A		)[::-1].copy()		)

				UpperCAmelCase       		=    eta
	def 					_lowercase    (   self							, _A							, _A = None		):






				'''simple docstring'''







				return sample
	def 					_lowercase    (   self							, _A							, _A = 1_0							, _A = 1_0							, _A = None							, ):






				'''simple docstring'''







				UpperCAmelCase       		=    min(self.config.num_train_timesteps							, _A		)
				UpperCAmelCase       		=    num_inference_steps

				UpperCAmelCase       		=    []

				UpperCAmelCase       		=    {}
				for j in range(0							, num_inference_steps - jump_length							, _A		):
							UpperCAmelCase       		=    jump_n_sample - 1

				UpperCAmelCase       		=    num_inference_steps
				while t >= 1:
							UpperCAmelCase       		=    t - 1
							timesteps.append(_A		)

							if jumps.get(_A							, 0		) > 0:
										UpperCAmelCase       		=    jumps[t] - 1
										for _ in range(_A		):
													UpperCAmelCase       		=    t + 1
													timesteps.append(_A		)

				UpperCAmelCase       		=    np.array(_A		) * (self.config.num_train_timesteps // self.num_inference_steps)
				UpperCAmelCase       		=    torch.from_numpy(_A		).to(_A		)
	def 					_lowercase    (   self							, _A		):






				'''simple docstring'''







				UpperCAmelCase       		=    t - self.config.num_train_timesteps // self.num_inference_steps

				UpperCAmelCase       		=    self.alphas_cumprod[t]
				UpperCAmelCase       		=    self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod
				UpperCAmelCase       		=    1 - alpha_prod_t
				UpperCAmelCase       		=    1 - alpha_prod_t_prev

				# For t > 0, compute predicted variance βt (see formula (6) and (7) from
				# https://arxiv.org/pdf/2006.11239.pdf) and sample from it to get
				# previous sample x_{t-1} ~ N(pred_prev_sample, variance) == add
				# variance to pred_sample
				# Is equivalent to formula (16) in https://arxiv.org/pdf/2010.02502.pdf
				# without eta.
				# variance = (1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * self.betas[t]
				UpperCAmelCase       		=    (beta_prod_t_prev / beta_prod_t) * (1 - alpha_prod_t / alpha_prod_t_prev)

				return variance
	def 					_lowercase    (   self							, _A							, _A							, _A							, _A							, _A							, _A = None							, _A = True							, ):






				'''simple docstring'''







				UpperCAmelCase       		=    timestep
				UpperCAmelCase       		=    timestep - self.config.num_train_timesteps // self.num_inference_steps

				# 1. compute alphas, betas
				UpperCAmelCase       		=    self.alphas_cumprod[t]
				UpperCAmelCase       		=    self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod
				UpperCAmelCase       		=    1 - alpha_prod_t

				# 2. compute predicted original sample from predicted noise also called
				# "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
				UpperCAmelCase       		=    (sample - beta_prod_t**0.5 * model_output) / alpha_prod_t**0.5

				# 3. Clip "predicted x_0"
				if self.config.clip_sample:
							UpperCAmelCase       		=    torch.clamp(_A							, -1							, 1		)

				# We choose to follow RePaint Algorithm 1 to get x_{t-1}, however we
				# substitute formula (7) in the algorithm coming from DDPM paper
				# (formula (4) Algorithm 2 - Sampling) with formula (12) from DDIM paper.
				# DDIM schedule gives the same results as DDPM with eta = 1.0
				# Noise is being reused in 7. and 8., but no impact on quality has
				# been observed.

				# 5. Add noise
				UpperCAmelCase       		=    model_output.device
				UpperCAmelCase       		=    randn_tensor(model_output.shape							, generator=_A							, device=_A							, dtype=model_output.dtype		)
				UpperCAmelCase       		=    self.eta * self._get_variance(_A		) ** 0.5

				UpperCAmelCase       		=    0
				if t > 0 and self.eta > 0:
							UpperCAmelCase       		=    std_dev_t * noise

				# 6. compute "direction pointing to x_t" of formula (12)
				# from https://arxiv.org/pdf/2010.02502.pdf
				UpperCAmelCase       		=    (1 - alpha_prod_t_prev - std_dev_t**2) ** 0.5 * model_output

				# 7. compute x_{t-1} of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
				UpperCAmelCase       		=    alpha_prod_t_prev**0.5 * pred_original_sample + pred_sample_direction + variance

				# 8. Algorithm 1 Line 5 https://arxiv.org/pdf/2201.09865.pdf
				UpperCAmelCase       		=    (alpha_prod_t_prev**0.5) * original_image + ((1 - alpha_prod_t_prev) ** 0.5) * noise

				# 9. Algorithm 1 Line 8 https://arxiv.org/pdf/2201.09865.pdf
				UpperCAmelCase       		=    mask * prev_known_part + (1.0 - mask) * prev_unknown_part

				if not return_dict:
							return (
							    pred_prev_sample,
							    pred_original_sample,
							)

				return RePaintSchedulerOutput(prev_sample=_A							, pred_original_sample=_A		)
	def 					_lowercase    (   self							, _A							, _A							, _A=None		):






				'''simple docstring'''







				UpperCAmelCase       		=    self.config.num_train_timesteps // self.num_inference_steps

				for i in range(_A		):
							UpperCAmelCase       		=    self.betas[timestep + i]
							if sample.device.type == "mps":
										# randn does not work reproducibly on mps
										UpperCAmelCase       		=    randn_tensor(sample.shape							, dtype=sample.dtype							, generator=_A		)
										UpperCAmelCase       		=    noise.to(sample.device		)
							else:
										UpperCAmelCase       		=    randn_tensor(sample.shape							, generator=_A							, device=sample.device							, dtype=sample.dtype		)

							# 10. Algorithm 1 Line 10 https://arxiv.org/pdf/2201.09865.pdf
							UpperCAmelCase       		=    (1 - beta) ** 0.5 * sample + beta**0.5 * noise

				return sample
	def 					_lowercase    (   self							, _A							, _A							, _A							, ):






				'''simple docstring'''







				raise NotImplementedError('''Use `DDPMScheduler.add_noise()` to train for sampling with RePaint.'''		)







	def __len__(   self		):






				'''simple docstring'''







				return self.config.num_train_timesteps










import argparse
import json
import re
import string
import sys

import numpy as np


__A    :   Optional[Any]          =       0.5





def 						__SCREAMING_SNAKE_CASE  (    UpperCamelCase__      ,      UpperCamelCase__			)  ->    List[Any]:







			'''simple docstring'''

			UpperCAmelCase       		=    ['''.''', ''',''', ''';''', ''':''']

			for token in remove_tokens:
						UpperCAmelCase       		=    ground_truth.replace(UpperCamelCase__      ,      ''''''			)
						UpperCAmelCase       		=    prediction.replace(UpperCamelCase__      ,      ''''''			)

			UpperCAmelCase		,      UpperCAmelCase       		=    ground_truth.lower(), prediction.lower()
			UpperCAmelCase		,      UpperCAmelCase       		=    ground_truth.replace('''/'''      ,      ''' '''			), prediction.replace('''/'''      ,      ''' '''			)
			UpperCAmelCase		,      UpperCAmelCase       		=    set(ground_truth.split(''' '''			)			), set(prediction.split(''' '''			)			)

			UpperCAmelCase       		=    ground_truth.intersection(UpperCamelCase__			)
			UpperCAmelCase       		=    ground_truth.union(UpperCamelCase__			)
			UpperCAmelCase       		=    len(UpperCamelCase__			) / len(UpperCamelCase__			)
			return jaccard





def 						__SCREAMING_SNAKE_CASE  (    UpperCamelCase__			)  ->    Union[str, Any]:







			'''simple docstring'''

			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       		=    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__			)  ->    Optional[int]:







			'''simple docstring'''

			UpperCAmelCase		,      UpperCAmelCase		,      UpperCAmelCase       		=    0, 0, 0

			UpperCAmelCase       		=    '''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       		=    False
									for pred in predictions:
												if substr_ok:
															UpperCAmelCase       		=    get_jaccard(UpperCamelCase__      ,      UpperCamelCase__			) >= IOU_THRESH or ground_truth in pred
												else:
															UpperCAmelCase       		=    get_jaccard(UpperCamelCase__      ,      UpperCamelCase__			) >= IOU_THRESH
												if is_match:
															UpperCAmelCase       		=    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       		=    False
									for ground_truth in ground_truths:
												assert len(UpperCamelCase__			) > 0
												if substr_ok:
															UpperCAmelCase       		=    get_jaccard(UpperCamelCase__      ,      UpperCamelCase__			) >= IOU_THRESH or ground_truth in pred
												else:
															UpperCAmelCase       		=    get_jaccard(UpperCamelCase__      ,      UpperCamelCase__			) >= IOU_THRESH
												if is_match:
															UpperCAmelCase       		=    True

									if not match_found:
												fp += 1

			UpperCAmelCase       		=    tp / (tp + fp) if tp + fp > 0 else np.nan
			UpperCAmelCase       		=    tp / (tp + fn) if tp + fn > 0 else np.nan

			return precision, recall





def 						__SCREAMING_SNAKE_CASE  (    UpperCamelCase__			)  ->    List[str]:







			'''simple docstring'''

			UpperCAmelCase       		=    precisions[::-1]
			for i in range(1      ,      len(UpperCamelCase__			)			):
						UpperCAmelCase       		=    max(precision_best[i - 1]      ,      precision_best[i]			)
			UpperCAmelCase       		=    precision_best[::-1]
			return precisions





def 						__SCREAMING_SNAKE_CASE  (    UpperCamelCase__      ,      UpperCamelCase__			)  ->    str:







			'''simple docstring'''

			UpperCAmelCase       		=    process_precisions(UpperCamelCase__			)
			UpperCAmelCase       		=    np.trapz(UpperCamelCase__      ,      UpperCamelCase__			)
			if np.isnan(UpperCamelCase__			):
						return 0
			return aupr





def 						__SCREAMING_SNAKE_CASE  (    UpperCamelCase__      ,      UpperCamelCase__      ,      UpperCamelCase__			)  ->    Optional[int]:







			'''simple docstring'''

			UpperCAmelCase       		=    process_precisions(UpperCamelCase__			)
			UpperCAmelCase       		=    0
			for prec, recall in zip(UpperCamelCase__      ,      UpperCamelCase__			):
						if recall >= recall_thresh:
									UpperCAmelCase       		=    prec
									break
			return prec_at_recall





def 						__SCREAMING_SNAKE_CASE  (    UpperCamelCase__      ,      UpperCamelCase__			)  ->    str:







			'''simple docstring'''

			return normalize_answer(UpperCamelCase__			) == normalize_answer(UpperCamelCase__			)





def 						__SCREAMING_SNAKE_CASE  (    UpperCamelCase__      ,      UpperCamelCase__      ,      UpperCamelCase__			)  ->    str:







			'''simple docstring'''

			UpperCAmelCase       		=    0
			for pred in predictions:
						for ground_truth in ground_truths:
									UpperCAmelCase       		=    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__			)  ->    int:







			'''simple docstring'''

			UpperCAmelCase       		=    UpperCAmelCase       		=    UpperCAmelCase       		=    0
			UpperCAmelCase       		=    []
			UpperCAmelCase       		=    []
			for article in dataset:
						for paragraph in article["paragraphs"]:
									for qa in paragraph["qas"]:
												total += 1
												if qa["id"] not in predictions:
															UpperCAmelCase       		=    '''Unanswered question ''' + qa['''id'''] + ''' will receive score 0.'''
															print(UpperCamelCase__      ,      file=sys.stderr			)
															continue
												UpperCAmelCase       		=    [x['''text'''] for x in qa['''answers''']]
												UpperCAmelCase       		=    predictions[qa['''id''']]
												UpperCAmelCase		,      UpperCAmelCase       		=    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       		=    [x for _, x in sorted(zip(UpperCamelCase__      ,      UpperCamelCase__			)			)]
			recalls.sort()

			UpperCAmelCase       		=    100.0 * fa / total
			UpperCAmelCase       		=    100.0 * exact_match / total
			UpperCAmelCase       		=    get_aupr(UpperCamelCase__      ,      UpperCamelCase__			)

			UpperCAmelCase       		=    get_prec_at_recall(UpperCamelCase__      ,      UpperCamelCase__      ,      recall_thresh=0.9			)
			UpperCAmelCase       		=    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__":
				__A    :   Dict          =       argparse.ArgumentParser(description="Evaluation for CUAD")
				parser.add_argument("dataset_file", help="Dataset file")
				parser.add_argument("prediction_file", help="Prediction File")
				__A    :   Any          =       parser.parse_args()
				with open(args.dataset_file) as dataset_file:
								__A    :   Any          =       json.load(dataset_file)
								__A    :   Optional[int]          =       dataset_json["data"]
				with open(args.prediction_file) as prediction_file:
								__A    :   int          =       json.load(prediction_file)
				print(json.dumps(evaluate(dataset, predictions)))



