






"""simple docstring"""


import math
from collections import defaultdict
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput




def       snake_case  (A_				:Tuple     ,      A_				:str=0.999     ,      A_				:List[str]="cosine"     ,      ):







  '''simple docstring'''

  if alpha_transform_type == "cosine":

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

  elif alpha_transform_type == "exp":

    def alpha_bar_fn(A_				:List[Any]				):
      return math.exp(t * -12.0				)

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

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






class 						snake_case  (  UpperCAmelCase       ,			UpperCAmelCase	):
    __magic_name__   					=					[e.name for e in KarrasDiffusionSchedulers]
    __magic_name__   					=					2





    @register_to_config
    def __init__(							self     : List[str]    ,		A     : int = 1_0_0_0    ,		A     : float = 0.0_00_85    ,		A     : float = 0.0_12    ,		A     : str = "linear"    ,		A     : Optional[Union[np.ndarray, List[float]]] = None    ,		A     : str = "epsilon"    ,		A     : Optional[bool] = False    ,		A     : Optional[bool] = False    ,		A     : float = 1.0    ,		A     : str = "linspace"    ,		A     : int = 0    ,		):


      '''simple docstring'''




      if trained_betas is not None:
        a					:      List[str]    =       torch.tensor(A    ,		dtype=torch.floataa      )
      elif beta_schedule == "linear":
        a					:      str    =       torch.linspace(A    ,		A    ,		A    ,		dtype=torch.floataa      )
      elif beta_schedule == "scaled_linear":
        # this schedule is very specific to the latent diffusion model.
        a					:      Optional[int]    =       (
            torch.linspace(beta_start**0.5    ,		beta_end**0.5    ,		A    ,		dtype=torch.floataa      ) ** 2
        )
      elif beta_schedule == "squaredcos_cap_v2":
        # Glide cosine schedule
        a					:      Tuple    =       betas_for_alpha_bar(A    ,		alpha_transform_type='cosine'      )
      elif beta_schedule == "exp":
        a					:      Union[str, Any]    =       betas_for_alpha_bar(A    ,		alpha_transform_type='exp'      )
      else:
        raise NotImplementedError(F'''{beta_schedule} does is not implemented for {self.__class__}'''      )

      a					:      int    =       1.0 - self.betas
      a					:      Optional[int]    =       torch.cumprod(self.alphas    ,		dim=0      )

      #  set all values
      self.set_timesteps(A    ,		A    ,		A      )
      a					:      Dict    =       use_karras_sigmas





    def     lowerCamelCase__				(							self     : List[str]    ,		A     : Optional[Any]    ,		A     : Tuple=None      ):


      '''simple docstring'''




      if schedule_timesteps is None:
        a					:      Tuple    =       self.timesteps

      a					:      Optional[int]    =       (schedule_timesteps == timestep).nonzero()

      # The sigma index that is taken for the **very** first `step`
      # is always the second index (or the last index if there is only 1)
      # This way we can ensure we don't accidentally skip a sigma in
      # case we start in the middle of the denoising schedule (e.g. for image-to-image)
      if len(self._index_counter      ) == 0:
        a					:      Tuple    =       1 if len(A      ) > 1 else 0
      else:
        a					:      Any    =       timestep.cpu().item() if torch.is_tensor(A      ) else timestep
        a					:      Optional[int]    =       self._index_counter[timestep_int]

      return indices[pos].item()





    @property
    def     lowerCamelCase__				(							self     : Dict      ):


      '''simple docstring'''




      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]    ,		A     : torch.FloatTensor    ,		A     : Union[float, torch.FloatTensor]    ,		):


      '''simple docstring'''




      a					:      int    =       self.index_for_timestep(A      )

      a					:      List[Any]    =       self.sigmas[step_index]
      a					:      Optional[Any]    =       sample / ((sigma**2 + 1) ** 0.5)
      return sample





    def     lowerCamelCase__				(							self     : Optional[Any]    ,		A     : int    ,		A     : Union[str, torch.device] = None    ,		A     : Optional[int] = None    ,		):


      '''simple docstring'''




      a					:      Union[str, Any]    =       num_inference_steps

      a					:      Dict    =       num_train_timesteps or self.config.num_train_timesteps

      # "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
      if self.config.timestep_spacing == "linspace":
        a					:      Dict    =       np.linspace(0    ,		num_train_timesteps - 1    ,		A    ,		dtype=A      )[::-1].copy()
      elif self.config.timestep_spacing == "leading":
        a					:      Optional[Any]    =       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
        a					:      str    =       (np.arange(0    ,		A      ) * step_ratio).round()[::-1].copy().astype(A      )
        timesteps += self.config.steps_offset
      elif self.config.timestep_spacing == "trailing":
        a					:      List[str]    =       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
        a					:      Dict    =       (np.arange(A    ,		0    ,		-step_ratio      )).round().copy().astype(A      )
        timesteps -= 1
      else:
        raise ValueError(
            F'''{self.config.timestep_spacing} is not supported. Please make sure to choose one of \'linspace\', \'leading\' or \'trailing\'.'''      )

      a					:      int    =       np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5      )
      a					:      int    =       np.log(A      )
      a					:      Dict    =       np.interp(A    ,		np.arange(0    ,		len(A      )      )    ,		A      )

      if self.config.use_karras_sigmas:
        a					:      List[Any]    =       self._convert_to_karras(in_sigmas=A    ,		num_inference_steps=self.num_inference_steps      )
        a					:      Any    =       np.array([self._sigma_to_t(A    ,		A      ) for sigma in sigmas]      )

      a					:      Any    =       np.concatenate([sigmas, [0.0]]      ).astype(np.floataa      )
      a					:      Tuple    =       torch.from_numpy(A      ).to(device=A      )
      a					:      Dict    =       torch.cat([sigmas[:1], sigmas[1:-1].repeat_interleave(2      ), sigmas[-1:]]      )

      a					:      Union[str, Any]    =       torch.from_numpy(A      )
      a					:      Optional[int]    =       torch.cat([timesteps[:1], timesteps[1:].repeat_interleave(2      )]      )

      if str(A      ).startswith('mps'      ):
        # mps does not support float64
        a					:      Optional[Any]    =       timesteps.to(A    ,		dtype=torch.floataa      )
      else:
        a					:      Tuple    =       timesteps.to(device=A      )

      # empty dt and derivative
      a					:      Any    =       None
      a					:      Any    =       None

      # for exp beta schedules, such as the one for `pipeline_shap_e.py`
      # we need an index counter
      a					:      Optional[int]    =       defaultdict(A      )





    def     lowerCamelCase__				(							self     : str    ,		A     : Optional[Any]    ,		A     : Tuple      ):


      '''simple docstring'''




      a					:      Union[str, Any]    =       np.log(A      )

      # get distribution
      a					:      Any    =       log_sigma - log_sigmas[:, np.newaxis]

      # get sigmas range
      a					:      Optional[int]    =       np.cumsum((dists >= 0)    ,		axis=0      ).argmax(axis=0      ).clip(max=log_sigmas.shape[0] - 2      )
      a					:      Any    =       low_idx + 1

      a					:      Optional[Any]    =       log_sigmas[low_idx]
      a					:      Optional[Any]    =       log_sigmas[high_idx]

      # interpolate sigmas
      a					:      str    =       (low - log_sigma) / (low - high)
      a					:      Union[str, Any]    =       np.clip(A    ,		0    ,		1      )

      # transform interpolation to time range
      a					:      int    =       (1 - w) * low_idx + w * high_idx
      a					:      Dict    =       t.reshape(sigma.shape      )
      return t





    def     lowerCamelCase__				(							self     : int    ,		A     : torch.FloatTensor    ,		A     : Optional[int]      ):


      '''simple docstring'''




      a					:      float    =       in_sigmas[-1].item()
      a					:      float    =       in_sigmas[0].item()

      a					:      Any    =       7.0  # 7.0 is the value used in the paper
      a					:      List[str]    =       np.linspace(0    ,		1    ,		A      )
      a					:      Tuple    =       sigma_min ** (1 / rho)
      a					:      Tuple    =       sigma_max ** (1 / rho)
      a					:      str    =       (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
      return sigmas





    @property
    def     lowerCamelCase__				(							self     : Optional[Any]      ):


      '''simple docstring'''




      return self.dt is None





    def     lowerCamelCase__				(							self     : List[Any]    ,		A     : Union[torch.FloatTensor, np.ndarray]    ,		A     : Union[float, torch.FloatTensor]    ,		A     : Union[torch.FloatTensor, np.ndarray]    ,		A     : bool = True    ,		):


      '''simple docstring'''




      a					:      Union[str, Any]    =       self.index_for_timestep(A      )

      # advance index counter by 1
      a					:      Dict    =       timestep.cpu().item() if torch.is_tensor(A      ) else timestep
      self._index_counter[timestep_int] += 1

      if self.state_in_first_order:
        a					:      Optional[int]    =       self.sigmas[step_index]
        a					:      Union[str, Any]    =       self.sigmas[step_index + 1]
      else:
        # 2nd order / Heun's method
        a					:      Optional[Any]    =       self.sigmas[step_index - 1]
        a					:      Tuple    =       self.sigmas[step_index]

      # currently only gamma=0 is supported. This usually works best anyways.
      # We can support gamma in the future but then need to scale the timestep before
      # passing it to the model which requires a change in API
      a					:      str    =       0
      a					:      Optional[int]    =       sigma * (gamma + 1)  # Note: sigma_hat == sigma for now

      # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
      if self.config.prediction_type == "epsilon":
        a					:      Any    =       sigma_hat if self.state_in_first_order else sigma_next
        a					:      Optional[Any]    =       sample - sigma_input * model_output
      elif self.config.prediction_type == "v_prediction":
        a					:      Union[str, Any]    =       sigma_hat if self.state_in_first_order else sigma_next
        a					:      str    =       model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
            sample / (sigma_input**2 + 1)
        )
      elif self.config.prediction_type == "sample":
        a					:      List[str]    =       model_output
      else:
        raise ValueError(
            F'''prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`'''      )

      if self.config.clip_sample:
        a					:      List[Any]    =       pred_original_sample.clamp(
            -self.config.clip_sample_range    ,		self.config.clip_sample_range      )

      if self.state_in_first_order:
        # 2. Convert to an ODE derivative for 1st order
        a					:      Union[str, Any]    =       (sample - pred_original_sample) / sigma_hat
        # 3. delta timestep
        a					:      Optional[Any]    =       sigma_next - sigma_hat

        # store for 2nd order step
        a					:      Any    =       derivative
        a					:      Dict    =       dt
        a					:      Optional[int]    =       sample
      else:
        # 2. 2nd order / Heun's method
        a					:      Union[str, Any]    =       (sample - pred_original_sample) / sigma_next
        a					:      Dict    =       (self.prev_derivative + derivative) / 2

        # 3. take prev timestep & sample
        a					:      Tuple    =       self.dt
        a					:      List[str]    =       self.sample

        # free dt and derivative
        # Note, this puts the scheduler in "first order mode"
        a					:      Any    =       None
        a					:      List[Any]    =       None
        a					:      int    =       None

      a					:      Union[str, Any]    =       sample + derivative * dt

      if not return_dict:
        return (prev_sample,)

      return SchedulerOutput(prev_sample=A      )





    def     lowerCamelCase__				(							self     : List[str]    ,		A     : torch.FloatTensor    ,		A     : torch.FloatTensor    ,		A     : torch.FloatTensor    ,		):


      '''simple docstring'''




      a					:      List[str]    =       self.sigmas.to(device=original_samples.device    ,		dtype=original_samples.dtype      )
      if original_samples.device.type == "mps" and torch.is_floating_point(A      ):
        # mps does not support float64
        a					:      List[str]    =       self.timesteps.to(original_samples.device    ,		dtype=torch.floataa      )
        a					:      List[str]    =       timesteps.to(original_samples.device    ,		dtype=torch.floataa      )
      else:
        a					:      List[str]    =       self.timesteps.to(original_samples.device      )
        a					:      str    =       timesteps.to(original_samples.device      )

      a					:      Optional[int]    =       [self.index_for_timestep(A    ,		A      ) for t in timesteps]

      a					:      Dict    =       sigmas[step_indices].flatten()
      while len(sigma.shape      ) < len(original_samples.shape      ):
        a					:      List[Any]    =       sigma.unsqueeze(-1      )

      a					:      str    =       original_samples + noise * sigma
      return noisy_samples





    def __len__(							self     : Optional[int]      ):


      '''simple docstring'''




      return self.config.num_train_timesteps












"""simple docstring"""


import argparse
import collections
import json
import os
import re
import string
import sys

import numpy as np


_UpperCamelCase    :   Optional[Any]   			=						re.compile(r'\b(a|an|the)\b', re.UNICODE)

_UpperCamelCase    :   str   			=						None




def       snake_case  ():







  '''simple docstring'''

  a					:      Any    =       argparse.ArgumentParser('Official evaluation script for SQuAD version 2.0.'				)
  parser.add_argument('data_file'     ,      metavar='data.json'     ,      help='Input data JSON file.'				)
  parser.add_argument('pred_file'     ,      metavar='pred.json'     ,      help='Model predictions.'				)
  parser.add_argument(
      '--out-file'     ,      '-o'     ,      metavar='eval.json'     ,      help='Write accuracy metrics to file (default is stdout).'				)
  parser.add_argument(
      '--na-prob-file'     ,      '-n'     ,      metavar='na_prob.json'     ,      help='Model estimates of probability of no answer.'				)
  parser.add_argument(
      '--na-prob-thresh'     ,      '-t'     ,      type=A_     ,      default=1.0     ,      help='Predict "" if no-answer probability exceeds this (default = 1.0).'     ,      )
  parser.add_argument(
      '--out-image-dir'     ,      '-p'     ,      metavar='out_images'     ,      default=A_     ,      help='Save precision-recall curves to directory.'				)
  parser.add_argument('--verbose'     ,      '-v'     ,      action='store_true'				)
  if len(sys.argv				) == 1:
    parser.print_help()
    sys.exit(1				)
  return parser.parse_args()




def       snake_case  (A_				:Optional[int]				):







  '''simple docstring'''

  a					:      str    =       {}
  for article in dataset:
    for p in article["paragraphs"]:
      for qa in p["qas"]:
        a					:      Optional[int]    =       bool(qa['answers']['text']				)
  return qid_to_has_ans




def       snake_case  (A_				:List[Any]				):







  '''simple docstring'''

  def remove_articles(A_				:str				):
    return ARTICLES_REGEX.sub(' '     ,      A_				)

  def white_space_fix(A_				:str				):
    return " ".join(text.split()				)

  def remove_punc(A_				:Dict				):
    a					:      int    =       set(string.punctuation				)
    return "".join(ch for ch in text if ch not in exclude				)

  def lower(A_				:Union[str, Any]				):
    return text.lower()

  return white_space_fix(remove_articles(remove_punc(lower(A_				)				)				)				)




def       snake_case  (A_				:str				):







  '''simple docstring'''

  if not s:
    return []
  return normalize_answer(A_				).split()




def       snake_case  (A_				:int     ,      A_				:Union[str, Any]				):







  '''simple docstring'''

  return int(normalize_answer(A_				) == normalize_answer(A_				)				)




def       snake_case  (A_				:Optional[int]     ,      A_				:str				):







  '''simple docstring'''

  a					:      int    =       get_tokens(A_				)
  a					:      Tuple    =       get_tokens(A_				)
  a					:      List[Any]    =       collections.Counter(A_				) & collections.Counter(A_				)
  a					:      Dict    =       sum(common.values()				)
  if len(A_				) == 0 or len(A_				) == 0:
    # If either is no-answer, then F1 is 1 if they agree, 0 otherwise
    return int(gold_toks == pred_toks				)
  if num_same == 0:
    return 0
  a					:      List[Any]    =       1.0 * num_same / len(A_				)
  a					:      Optional[Any]    =       1.0 * num_same / len(A_				)
  a					:      Union[str, Any]    =       (2 * precision * recall) / (precision + recall)
  return fa




def       snake_case  (A_				:Any     ,      A_				:Dict				):







  '''simple docstring'''

  a					:      Union[str, Any]    =       {}
  a					:      List[Any]    =       {}
  for article in dataset:
    for p in article["paragraphs"]:
      for qa in p["qas"]:
        a					:      str    =       qa['id']
        a					:      Dict    =       [t for t in qa['answers']['text'] if normalize_answer(A_				)]
        if not gold_answers:
          # For unanswerable questions, only correct answer is empty string
          a					:      Dict    =       ['']
        if qid not in preds:
          print(f'''Missing prediction for {qid}'''				)
          continue
        a					:      Optional[Any]    =       preds[qid]
        # Take max over all gold answers
        a					:      str    =       max(compute_exact(A_     ,      A_				) for a in gold_answers				)
        a					:      List[Any]    =       max(compute_fa(A_     ,      A_				) for a in gold_answers				)
  return exact_scores, fa_scores




def       snake_case  (A_				:Union[str, Any]     ,      A_				:List[Any]     ,      A_				:List[Any]     ,      A_				:Dict				):







  '''simple docstring'''

  a					:      List[str]    =       {}
  for qid, s in scores.items():
    a					:      Union[str, Any]    =       na_probs[qid] > na_prob_thresh
    if pred_na:
      a					:      int    =       float(not qid_to_has_ans[qid]				)
    else:
      a					:      Union[str, Any]    =       s
  return new_scores




def       snake_case  (A_				:Tuple     ,      A_				:int     ,      A_				:Tuple=None				):







  '''simple docstring'''

  if not qid_list:
    a					:      Optional[int]    =       len(A_				)
    return collections.OrderedDict(
        [
            ('exact', 100.0 * sum(exact_scores.values()				) / total),
            ('f1', 100.0 * sum(fa_scores.values()				) / total),
            ('total', total),
        ]				)
  else:
    a					:      List[Any]    =       len(A_				)
    return collections.OrderedDict(
        [
            ('exact', 100.0 * sum(exact_scores[k] for k in qid_list				) / total),
            ('f1', 100.0 * sum(fa_scores[k] for k in qid_list				) / total),
            ('total', total),
        ]				)




def       snake_case  (A_				:str     ,      A_				:Dict     ,      A_				:List[Any]				):







  '''simple docstring'''

  for k in new_eval:
    a					:      Union[str, Any]    =       new_eval[k]




def       snake_case  (A_				:Optional[Any]     ,      A_				:Any     ,      A_				:Dict     ,      A_				:Optional[int]				):







  '''simple docstring'''

  plt.step(A_     ,      A_     ,      color='b'     ,      alpha=0.2     ,      where='post'				)
  plt.fill_between(A_     ,      A_     ,      step='post'     ,      alpha=0.2     ,      color='b'				)
  plt.xlabel('Recall'				)
  plt.ylabel('Precision'				)
  plt.xlim([0.0, 1.05]				)
  plt.ylim([0.0, 1.05]				)
  plt.title(A_				)
  plt.savefig(A_				)
  plt.clf()




def       snake_case  (A_				:List[str]     ,      A_				:str     ,      A_				:Any     ,      A_				:Any     ,      A_				:List[Any]=None     ,      A_				:Union[str, Any]=None				):







  '''simple docstring'''

  a					:      Optional[int]    =       sorted(A_     ,      key=lambda A_				: na_probs[k]				)
  a					:      Tuple    =       0.0
  a					:      Tuple    =       1.0
  a					:      Any    =       0.0
  a					:      int    =       [1.0]
  a					:      int    =       [0.0]
  a					:      str    =       0.0
  for i, qid in enumerate(A_				):
    if qid_to_has_ans[qid]:
      true_pos += scores[qid]
    a					:      Tuple    =       true_pos / float(i + 1				)
    a					:      Any    =       true_pos / float(A_				)
    if i == len(A_				) - 1 or na_probs[qid] != na_probs[qid_list[i + 1]]:
      # i.e., if we can put a threshold after this point
      avg_prec += cur_p * (cur_r - recalls[-1])
      precisions.append(A_				)
      recalls.append(A_				)
  if out_image:
    plot_pr_curve(A_     ,      A_     ,      A_     ,      A_				)
  return {"ap": 100.0 * avg_prec}




def       snake_case  (A_				:Optional[int]     ,      A_				:Any     ,      A_				:List[Any]     ,      A_				:int     ,      A_				:int     ,      A_				:List[str]				):







  '''simple docstring'''

  if out_image_dir and not os.path.exists(A_				):
    os.makedirs(A_				)
  a					:      List[str]    =       sum(1 for v in qid_to_has_ans.values() if v				)
  if num_true_pos == 0:
    return
  a					:      List[str]    =       make_precision_recall_eval(
      A_     ,      A_     ,      A_     ,      A_     ,      out_image=os.path.join(A_     ,      'pr_exact.png'				)     ,      title='Precision-Recall curve for Exact Match score'     ,      )
  a					:      Optional[Any]    =       make_precision_recall_eval(
      A_     ,      A_     ,      A_     ,      A_     ,      out_image=os.path.join(A_     ,      'pr_f1.png'				)     ,      title='Precision-Recall curve for F1 score'     ,      )
  a					:      Any    =       {k: float(A_				) for k, v in qid_to_has_ans.items()}
  a					:      Optional[int]    =       make_precision_recall_eval(
      A_     ,      A_     ,      A_     ,      A_     ,      out_image=os.path.join(A_     ,      'pr_oracle.png'				)     ,      title='Oracle Precision-Recall curve (binary task of HasAns vs. NoAns)'     ,      )
  merge_eval(A_     ,      A_     ,      'pr_exact'				)
  merge_eval(A_     ,      A_     ,      'pr_f1'				)
  merge_eval(A_     ,      A_     ,      'pr_oracle'				)




def       snake_case  (A_				:List[str]     ,      A_				:List[str]     ,      A_				:List[Any]     ,      A_				:str				):







  '''simple docstring'''

  if not qid_list:
    return
  a					:      List[Any]    =       [na_probs[k] for k in qid_list]
  a					:      List[str]    =       np.ones_like(A_				) / float(len(A_				)				)
  plt.hist(A_     ,      weights=A_     ,      bins=2_0     ,      range=(0.0, 1.0)				)
  plt.xlabel('Model probability of no-answer'				)
  plt.ylabel('Proportion of dataset'				)
  plt.title(f'''Histogram of no-answer probability: {name}'''				)
  plt.savefig(os.path.join(A_     ,      f'''na_prob_hist_{name}.png'''				)				)
  plt.clf()




def       snake_case  (A_				:Tuple     ,      A_				:Tuple     ,      A_				:List[str]     ,      A_				:List[str]				):







  '''simple docstring'''

  a					:      Any    =       sum(1 for k in qid_to_has_ans if not qid_to_has_ans[k]				)
  a					:      List[str]    =       num_no_ans
  a					:      List[str]    =       cur_score
  a					:      str    =       0.0
  a					:      Union[str, Any]    =       sorted(A_     ,      key=lambda A_				: na_probs[k]				)
  for i, qid in enumerate(A_				):
    if qid not in scores:
      continue
    if qid_to_has_ans[qid]:
      a					:      Optional[int]    =       scores[qid]
    else:
      if preds[qid]:
        a					:      Dict    =       -1
      else:
        a					:      Optional[Any]    =       0
    cur_score += diff
    if cur_score > best_score:
      a					:      List[Any]    =       cur_score
      a					:      str    =       na_probs[qid]
  return 100.0 * best_score / len(A_				), best_thresh




def       snake_case  (A_				:List[Any]     ,      A_				:List[Any]     ,      A_				:str     ,      A_				:int     ,      A_				:Optional[Any]     ,      A_				:Union[str, Any]				):







  '''simple docstring'''

  a,					a					:      Any    =       find_best_thresh(A_     ,      A_     ,      A_     ,      A_				)
  a,					a					:      List[Any]    =       find_best_thresh(A_     ,      A_     ,      A_     ,      A_				)
  a					:      Union[str, Any]    =       best_exact
  a					:      List[Any]    =       exact_thresh
  a					:      List[str]    =       best_fa
  a					:      Any    =       fa_thresh




def       snake_case  ():







  '''simple docstring'''

  with open(OPTS.data_file				) as f:
    a					:      List[str]    =       json.load(A_				)
    a					:      Tuple    =       dataset_json['data']
  with open(OPTS.pred_file				) as f:
    a					:      List[Any]    =       json.load(A_				)
  if OPTS.na_prob_file:
    with open(OPTS.na_prob_file				) as f:
      a					:      int    =       json.load(A_				)
  else:
    a					:      List[Any]    =       {k: 0.0 for k in preds}
  a					:      List[str]    =       make_qid_to_has_ans(A_				)  # maps qid to True/False
  a					:      Union[str, Any]    =       [k for k, v in qid_to_has_ans.items() if v]
  a					:      Dict    =       [k for k, v in qid_to_has_ans.items() if not v]
  a,					a					:      List[Any]    =       get_raw_scores(A_     ,      A_				)
  a					:      Any    =       apply_no_ans_threshold(A_     ,      A_     ,      A_     ,      OPTS.na_prob_thresh				)
  a					:      Any    =       apply_no_ans_threshold(A_     ,      A_     ,      A_     ,      OPTS.na_prob_thresh				)
  a					:      Union[str, Any]    =       make_eval_dict(A_     ,      A_				)
  if has_ans_qids:
    a					:      Dict    =       make_eval_dict(A_     ,      A_     ,      qid_list=A_				)
    merge_eval(A_     ,      A_     ,      'HasAns'				)
  if no_ans_qids:
    a					:      Tuple    =       make_eval_dict(A_     ,      A_     ,      qid_list=A_				)
    merge_eval(A_     ,      A_     ,      'NoAns'				)
  if OPTS.na_prob_file:
    find_all_best_thresh(A_     ,      A_     ,      A_     ,      A_     ,      A_     ,      A_				)
  if OPTS.na_prob_file and OPTS.out_image_dir:
    run_precision_recall_analysis(A_     ,      A_     ,      A_     ,      A_     ,      A_     ,      OPTS.out_image_dir				)
    histogram_na_prob(A_     ,      A_     ,      OPTS.out_image_dir     ,      'hasAns'				)
    histogram_na_prob(A_     ,      A_     ,      OPTS.out_image_dir     ,      'noAns'				)
  if OPTS.out_file:
    with open(OPTS.out_file     ,      'w'				) as f:
      json.dump(A_     ,      A_				)
  else:
    print(json.dumps(A_     ,      indent=2				)				)


if __name__ == "__main__":
   _UpperCamelCase    :   Tuple   			=						parse_args()
   if OPTS.out_image_dir:
      import matplotlib

      matplotlib.use('Agg')
      import matplotlib.pyplot as plt
   main()




