

"""simple docstring"""





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

import numpy as np
import torch
from scipy import integrate

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

@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->LMSDiscrete
class      __a (   snake_case__     ):

			"""simple docstring"""

			SCREAMING_SNAKE_CASE_      =    42
			SCREAMING_SNAKE_CASE_      =    None



def 							_lowerCAmelCase   (    UpperCAmelCase  :				List[Any]   ,				UpperCAmelCase  :				List[Any]=0.999   ,				UpperCAmelCase  :				List[str]="cosine"   ,				):


					'''simple docstring'''






					if alpha_transform_type == "cosine":

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

					elif alpha_transform_type == "exp":

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

					else:
										raise ValueError(F'''Unsupported alpha_tranform_type: {alpha_transform_type}'''					)

					UpperCamelCase__		:     List[str]								=[]
					for i in range(UpperCAmelCase					):
										UpperCamelCase__		:     List[Any]								=i / num_diffusion_timesteps
										UpperCamelCase__		:     List[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      __a (   snake_case__,     snake_case__     ):

			"""simple docstring"""

			SCREAMING_SNAKE_CASE_      =    [e.name for e in KarrasDiffusionSchedulers]
			SCREAMING_SNAKE_CASE_      =    1






			@register_to_config
			def __init__(				self  :       List[str]    ,	lowercase_  :       int = 1000    ,	lowercase_  :       float = 0.0_0_0_1    ,	lowercase_  :       float = 0.0_2    ,	lowercase_  :       str = "linear"    ,	lowercase_  :       Optional[Union[np.ndarray, List[float]]] = None    ,	lowercase_  :       Optional[bool] = False    ,	lowercase_  :       str = "epsilon"    ,	lowercase_  :       str = "linspace"    ,	lowercase_  :       int = 0    ,	):
								if trained_betas is not None:
													UpperCamelCase__		:     List[Any]								=torch.tensor(lowercase_    ,	dtype=torch.floataa    )
								elif beta_schedule == "linear":
													UpperCamelCase__		:     Union[str, Any]								=torch.linspace(lowercase_    ,	lowercase_    ,	lowercase_    ,	dtype=torch.floataa    )
								elif beta_schedule == "scaled_linear":
													# this schedule is very specific to the latent diffusion model.
													UpperCamelCase__		:     List[str]								=(
													    torch.linspace(beta_start**0.5    ,	beta_end**0.5    ,	lowercase_    ,	dtype=torch.floataa    ) ** 2
													)
								elif beta_schedule == "squaredcos_cap_v2":
													# Glide cosine schedule
													UpperCamelCase__		:     Union[str, Any]								=betas_for_alpha_bar(lowercase_    )
								else:
													raise NotImplementedError(f'''{beta_schedule} does is not implemented for {self.__class__}'''    )

								UpperCamelCase__		:     Tuple								=1.0 - self.betas
								UpperCamelCase__		:     Tuple								=torch.cumprod(self.alphas    ,	dim=0    )

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

								# setable values
								UpperCamelCase__		:     Any								=None
								UpperCamelCase__		:     List[str]								=use_karras_sigmas
								self.set_timesteps(lowercase_    ,	lowercase_    )
								UpperCamelCase__		:     Optional[Any]								=[]
								UpperCamelCase__		:     Tuple								=False






			@property
			def  _lowerCAmelCase      (				self  :       int    ):
								# 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  _lowerCAmelCase      (				self  :       List[str]    ,	lowercase_  :       torch.FloatTensor    ,	lowercase_  :       Union[float, torch.FloatTensor]    ):
								if isinstance(lowercase_    ,	torch.Tensor    ):
													UpperCamelCase__		:     int								=timestep.to(self.timesteps.device    )
								UpperCamelCase__		:     Tuple								=(self.timesteps == timestep).nonzero().item()
								UpperCamelCase__		:     Union[str, Any]								=self.sigmas[step_index]
								UpperCamelCase__		:     Dict								=sample / ((sigma**2 + 1) ** 0.5)
								UpperCamelCase__		:     Union[str, Any]								=True
								return sample






			def  _lowerCAmelCase      (				self  :       List[Any]    ,	lowercase_  :       Dict    ,	lowercase_  :       List[str]    ,	lowercase_  :       Optional[int]    ):

								def lms_derivative(lowercase_  :       Dict    ):
													UpperCamelCase__		:     int								=1.0
													for k in range(lowercase_    ):
																		if current_order == k:
																							continue
																		prod *= (tau - self.sigmas[t - k]) / (self.sigmas[t - current_order] - self.sigmas[t - k])
													return prod

								UpperCamelCase__		:     int								=integrate.quad(lowercase_    ,	self.sigmas[t]    ,	self.sigmas[t + 1]    ,	epsrel=1e-4    )[0]

								return integrated_coeff






			def  _lowerCAmelCase      (				self  :       int    ,	lowercase_  :       int    ,	lowercase_  :       Union[str, torch.device] = None    ):
								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__		:     Any								=np.linspace(0    ,	self.config.num_train_timesteps - 1    ,	lowercase_    ,	dtype=lowercase_    )[
													    ::-1
													].copy()
								elif self.config.timestep_spacing == "leading":
													UpperCamelCase__		:     Tuple								=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__		:     Dict								=(np.arange(0    ,	lowercase_    ) * step_ratio).round()[::-1].copy().astype(lowercase_    )
													timesteps += self.config.steps_offset
								elif self.config.timestep_spacing == "trailing":
													UpperCamelCase__		:     Dict								=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__		:     Dict								=(np.arange(self.config.num_train_timesteps    ,	0    ,	-step_ratio    )).round().copy().astype(lowercase_    )
													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__		:     Union[str, Any]								=np.log(lowercase_    )
								UpperCamelCase__		:     Tuple								=np.interp(lowercase_    ,	np.arange(0    ,	len(lowercase_    )    )    ,	lowercase_    )

								if self.use_karras_sigmas:
													UpperCamelCase__		:     Tuple								=self._convert_to_karras(in_sigmas=lowercase_    )
													UpperCamelCase__		:     Optional[Any]								=np.array([self._sigma_to_t(lowercase_    ,	lowercase_    ) for sigma in sigmas]    )

								UpperCamelCase__		:     List[str]								=np.concatenate([sigmas, [0.0]]    ).astype(np.floataa    )

								UpperCamelCase__		:     Tuple								=torch.from_numpy(lowercase_    ).to(device=lowercase_    )
								if str(lowercase_    ).startswith('''mps'''    ):
													# mps does not support float64
													UpperCamelCase__		:     Optional[int]								=torch.from_numpy(lowercase_    ).to(lowercase_    ,	dtype=torch.floataa    )
								else:
													UpperCamelCase__		:     Any								=torch.from_numpy(lowercase_    ).to(device=lowercase_    )

								UpperCamelCase__		:     Tuple								=[]






			def  _lowerCAmelCase      (				self  :       Dict    ,	lowercase_  :       Optional[int]    ,	lowercase_  :       List[str]    ):
								# get log sigma
								UpperCamelCase__		:     List[Any]								=np.log(lowercase_    )

								# get distribution
								UpperCamelCase__		:     Union[str, Any]								=log_sigma - log_sigmas[:, np.newaxis]

								# get sigmas range
								UpperCamelCase__		:     List[str]								=np.cumsum((dists >= 0)    ,	axis=0    ).argmax(axis=0    ).clip(max=log_sigmas.shape[0] - 2    )
								UpperCamelCase__		:     Tuple								=low_idx + 1

								UpperCamelCase__		:     List[str]								=log_sigmas[low_idx]
								UpperCamelCase__		:     Union[str, Any]								=log_sigmas[high_idx]

								# interpolate sigmas
								UpperCamelCase__		:     Optional[Any]								=(low - log_sigma) / (low - high)
								UpperCamelCase__		:     Optional[int]								=np.clip(lowercase_    ,	0    ,	1    )

								# transform interpolation to time range
								UpperCamelCase__		:     List[Any]								=(1 - w) * low_idx + w * high_idx
								UpperCamelCase__		:     Tuple								=t.reshape(sigma.shape    )
								return t






			def  _lowerCAmelCase      (				self  :       Optional[int]    ,	lowercase_  :       torch.FloatTensor    ):
								UpperCamelCase__		:     float								=in_sigmas[-1].item()
								UpperCamelCase__		:     float								=in_sigmas[0].item()

								UpperCamelCase__		:     Optional[int]								=7.0  # 7.0 is the value used in the paper
								UpperCamelCase__		:     Any								=np.linspace(0    ,	1    ,	self.num_inference_steps    )
								UpperCamelCase__		:     int								=sigma_min ** (1 / rho)
								UpperCamelCase__		:     List[Any]								=sigma_max ** (1 / rho)
								UpperCamelCase__		:     int								=(max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
								return sigmas






			def  _lowerCAmelCase      (				self  :       Optional[int]    ,	lowercase_  :       torch.FloatTensor    ,	lowercase_  :       Union[float, torch.FloatTensor]    ,	lowercase_  :       torch.FloatTensor    ,	lowercase_  :       int = 4    ,	lowercase_  :       bool = True    ,	):
								if not self.is_scale_input_called:
													warnings.warn(
													    '''The `scale_model_input` function should be called before `step` to ensure correct denoising. '''
													    '''See `StableDiffusionPipeline` for a usage example.'''    )

								if isinstance(lowercase_    ,	torch.Tensor    ):
													UpperCamelCase__		:     Dict								=timestep.to(self.timesteps.device    )
								UpperCamelCase__		:     Union[str, Any]								=(self.timesteps == timestep).nonzero().item()
								UpperCamelCase__		:     List[Any]								=self.sigmas[step_index]

								# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
								if self.config.prediction_type == "epsilon":
													UpperCamelCase__		:     List[Any]								=sample - sigma * model_output
								elif self.config.prediction_type == "v_prediction":
													# * c_out + input * c_skip
													UpperCamelCase__		:     Dict								=model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
								elif self.config.prediction_type == "sample":
													UpperCamelCase__		:     Optional[Any]								=model_output
								else:
													raise ValueError(
													    f'''prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`'''    )

								# 2. Convert to an ODE derivative
								UpperCamelCase__		:     Tuple								=(sample - pred_original_sample) / sigma
								self.derivatives.append(lowercase_    )
								if len(self.derivatives    ) > order:
													self.derivatives.pop(0    )

								# 3. Compute linear multistep coefficients
								UpperCamelCase__		:     List[Any]								=min(step_index + 1    ,	lowercase_    )
								UpperCamelCase__		:     Union[str, Any]								=[self.get_lms_coefficient(lowercase_    ,	lowercase_    ,	lowercase_    ) for curr_order in range(lowercase_    )]

								# 4. Compute previous sample based on the derivatives path
								UpperCamelCase__		:     Union[str, Any]								=sample + sum(
								    coeff * derivative for coeff, derivative in zip(lowercase_    ,	reversed(self.derivatives    )    )    )

								if not return_dict:
													return (prev_sample,)

								return LMSDiscreteSchedulerOutput(prev_sample=lowercase_    ,	pred_original_sample=lowercase_    )






			def  _lowerCAmelCase      (				self  :       str    ,	lowercase_  :       torch.FloatTensor    ,	lowercase_  :       torch.FloatTensor    ,	lowercase_  :       torch.FloatTensor    ,	):
								# Make sure sigmas and timesteps have the same device and dtype as original_samples
								UpperCamelCase__		:     int								=self.sigmas.to(device=original_samples.device    ,	dtype=original_samples.dtype    )
								if original_samples.device.type == "mps" and torch.is_floating_point(lowercase_    ):
													# mps does not support float64
													UpperCamelCase__		:     List[str]								=self.timesteps.to(original_samples.device    ,	dtype=torch.floataa    )
													UpperCamelCase__		:     Optional[int]								=timesteps.to(original_samples.device    ,	dtype=torch.floataa    )
								else:
													UpperCamelCase__		:     Tuple								=self.timesteps.to(original_samples.device    )
													UpperCamelCase__		:     Union[str, Any]								=timesteps.to(original_samples.device    )

								UpperCamelCase__		:     Dict								=[(schedule_timesteps == t).nonzero().item() for t in timesteps]

								UpperCamelCase__		:     Optional[int]								=sigmas[step_indices].flatten()
								while len(sigma.shape    ) < len(original_samples.shape    ):
													UpperCamelCase__		:     Any								=sigma.unsqueeze(-1    )

								UpperCamelCase__		:     Tuple								=original_samples + noise * sigma
								return noisy_samples






			def __len__(				self  :       Dict    ):
								return self.config.num_train_timesteps





"""simple docstring"""





import argparse
import json
import re
import string
import sys

import numpy as np


_SCREAMING_SNAKE_CASE							:	str										=			0.5



def 							_lowerCAmelCase   (    UpperCAmelCase  :				str   ,				UpperCAmelCase  :				Optional[int]					):


					'''simple docstring'''






					UpperCamelCase__		:     Optional[Any]								=['''.''', ''',''', ''';''', ''':''']

					for token in remove_tokens:
										UpperCamelCase__		:     Optional[int]								=ground_truth.replace(UpperCAmelCase   ,				''''''					)
										UpperCamelCase__		:     List[Any]								=prediction.replace(UpperCAmelCase   ,				''''''					)

					UpperCamelCase__	,      UpperCamelCase__		:     List[Any]								=ground_truth.lower(), prediction.lower()
					UpperCamelCase__	,      UpperCamelCase__		:     Dict								=ground_truth.replace('''/'''   ,				''' '''					), prediction.replace('''/'''   ,				''' '''					)
					UpperCamelCase__	,      UpperCamelCase__		:     Any								=set(ground_truth.split(''' '''					)					), set(prediction.split(''' '''					)					)

					UpperCamelCase__		:     Optional[Any]								=ground_truth.intersection(UpperCAmelCase					)
					UpperCamelCase__		:     Optional[Any]								=ground_truth.union(UpperCAmelCase					)
					UpperCamelCase__		:     str								=len(UpperCAmelCase					) / len(UpperCAmelCase					)
					return jaccard



def 							_lowerCAmelCase   (    UpperCAmelCase  :				Union[str, Any]					):


					'''simple docstring'''






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

					def white_space_fix(UpperCAmelCase  :				Union[str, Any]					):
										return " ".join(text.split()					)

					def remove_punc(UpperCAmelCase  :				Optional[int]					):
										UpperCamelCase__		:     Dict								=set(string.punctuation					)
										return "".join(ch for ch in text if ch not in exclude					)

					def lower(UpperCAmelCase  :				Optional[Any]					):
										return text.lower()

					return white_space_fix(remove_articles(remove_punc(lower(UpperCAmelCase					)					)					)					)



def 							_lowerCAmelCase   (    UpperCAmelCase  :				int   ,				UpperCAmelCase  :				str   ,				UpperCAmelCase  :				Any					):


					'''simple docstring'''






					UpperCamelCase__	,      UpperCamelCase__	,      UpperCamelCase__		:     Optional[Any]								=0, 0, 0

					UpperCamelCase__		:     int								='''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__		:     Union[str, Any]								=False
															for pred in predictions:
																				if substr_ok:
																									UpperCamelCase__		:     List[str]								=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__		:     Dict								=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__		:     Any								=False
															for ground_truth in ground_truths:
																				assert len(UpperCAmelCase					) > 0
																				if substr_ok:
																									UpperCamelCase__		:     Union[str, Any]								=get_jaccard(UpperCAmelCase   ,				UpperCAmelCase					) >= IOU_THRESH or ground_truth in pred
																				else:
																									UpperCamelCase__		:     Dict								=get_jaccard(UpperCAmelCase   ,				UpperCAmelCase					) >= IOU_THRESH
																				if is_match:
																									UpperCamelCase__		:     str								=True

															if not match_found:
																				fp += 1

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

					return precision, recall



def 							_lowerCAmelCase   (    UpperCAmelCase  :				List[Any]					):


					'''simple docstring'''






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



def 							_lowerCAmelCase   (    UpperCAmelCase  :				List[str]   ,				UpperCAmelCase  :				Any					):


					'''simple docstring'''






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



def 							_lowerCAmelCase   (    UpperCAmelCase  :				Dict   ,				UpperCAmelCase  :				int   ,				UpperCAmelCase  :				int					):


					'''simple docstring'''






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



def 							_lowerCAmelCase   (    UpperCAmelCase  :				Any   ,				UpperCAmelCase  :				Dict					):


					'''simple docstring'''






					return normalize_answer(UpperCAmelCase					) == normalize_answer(UpperCAmelCase					)



def 							_lowerCAmelCase   (    UpperCAmelCase  :				int   ,				UpperCAmelCase  :				Dict   ,				UpperCAmelCase  :				int					):


					'''simple docstring'''






					UpperCamelCase__		:     List[str]								=0
					for pred in predictions:
										for ground_truth in ground_truths:
															UpperCamelCase__		:     str								=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 							_lowerCAmelCase   (    UpperCAmelCase  :				Optional[int]   ,				UpperCAmelCase  :				str					):


					'''simple docstring'''






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

					UpperCamelCase__		:     Optional[int]								=100.0 * fa / total
					UpperCamelCase__		:     List[Any]								=100.0 * exact_match / total
					UpperCamelCase__		:     Dict								=get_aupr(UpperCAmelCase   ,				UpperCAmelCase					)

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


