

'''simple docstring'''




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

import numpy as np


_a					:			Any							=    re.compile(R'\b(a|an|the)\b', re.UNICODE)

_a					:			Optional[int]							=    None


def    lowerCamelCase__		(    ):
  UpperCAmelCase    				=						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=lowercase_							, 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=lowercase_							, 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    lowerCamelCase__		(    SCREAMING_SNAKE_CASE   : Optional[int]    ):
  UpperCAmelCase    				=						{}
  for article in dataset:
    for p in article["paragraphs"]:
      for qa in p["qas"]:
        UpperCAmelCase    				=						bool(qa['answers']['text']    )
  return qid_to_has_ans


def    lowerCamelCase__		(    SCREAMING_SNAKE_CASE   : Optional[Any]    ):






  def remove_articles(SCREAMING_SNAKE_CASE   : List[str]    ):
    return ARTICLES_REGEX.sub(' '							, lowercase_    )

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

  def remove_punc(SCREAMING_SNAKE_CASE   : Tuple    ):
    UpperCAmelCase    				=						set(string.punctuation    )
    return "".join(ch for ch in text if ch not in exclude    )

  def lower(SCREAMING_SNAKE_CASE   : Optional[int]    ):
    return text.lower()

  return white_space_fix(remove_articles(remove_punc(lower(lowercase_    )    )    )    )


def    lowerCamelCase__		(    SCREAMING_SNAKE_CASE   : Union[str, Any]    ):






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


def    lowerCamelCase__		(    SCREAMING_SNAKE_CASE   : str							, SCREAMING_SNAKE_CASE   : List[Any]    ):
  return int(normalize_answer(lowercase_    ) == normalize_answer(lowercase_    )    )


def    lowerCamelCase__		(    SCREAMING_SNAKE_CASE   : List[Any]							, SCREAMING_SNAKE_CASE   : str    ):
  UpperCAmelCase    				=						get_tokens(lowercase_    )
  UpperCAmelCase    				=						get_tokens(lowercase_    )
  UpperCAmelCase    				=						collections.Counter(lowercase_    ) & collections.Counter(lowercase_    )
  UpperCAmelCase    				=						sum(common.values()    )
  if len(lowercase_    ) == 0 or len(lowercase_    ) == 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
  UpperCAmelCase    				=						1.0 * num_same / len(lowercase_    )
  UpperCAmelCase    				=						1.0 * num_same / len(lowercase_    )
  UpperCAmelCase    				=						(2 * precision * recall) / (precision + recall)
  return fa


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


def    lowerCamelCase__		(    SCREAMING_SNAKE_CASE   : Optional[Any]							, SCREAMING_SNAKE_CASE   : str							, SCREAMING_SNAKE_CASE   : List[Any]							, SCREAMING_SNAKE_CASE   : Optional[Any]    ):
  UpperCAmelCase    				=						{}
  for qid, s in scores.items():
    UpperCAmelCase    				=						na_probs[qid] > na_prob_thresh
    if pred_na:
      UpperCAmelCase    				=						float(not qid_to_has_ans[qid]    )
    else:
      UpperCAmelCase    				=						s
  return new_scores


def    lowerCamelCase__		(    SCREAMING_SNAKE_CASE   : Any							, SCREAMING_SNAKE_CASE   : Optional[Any]							, SCREAMING_SNAKE_CASE   : int=None    ):






  if not qid_list:
    UpperCAmelCase    				=						len(lowercase_    )
    return collections.OrderedDict(
        [
            ('exact', 100.0 * sum(exact_scores.values()    ) / total),
            ('f1', 100.0 * sum(fa_scores.values()    ) / total),
            ('total', total),
        ]    )
  else:
    UpperCAmelCase    				=						len(lowercase_    )
    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    lowerCamelCase__		(    SCREAMING_SNAKE_CASE   : str							, SCREAMING_SNAKE_CASE   : List[str]							, SCREAMING_SNAKE_CASE   : Optional[int]    ):






  for k in new_eval:
    UpperCAmelCase    				=						new_eval[k]


def    lowerCamelCase__		(    SCREAMING_SNAKE_CASE   : Dict							, SCREAMING_SNAKE_CASE   : List[str]							, SCREAMING_SNAKE_CASE   : Optional[int]							, SCREAMING_SNAKE_CASE   : str    ):
  plt.step(lowercase_							, lowercase_							, color='b'							, alpha=0.2							, where='post'    )
  plt.fill_between(lowercase_							, lowercase_							, 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(lowercase_    )
  plt.savefig(lowercase_    )
  plt.clf()


def    lowerCamelCase__		(    SCREAMING_SNAKE_CASE   : Tuple							, SCREAMING_SNAKE_CASE   : List[Any]							, SCREAMING_SNAKE_CASE   : Tuple							, SCREAMING_SNAKE_CASE   : Optional[int]							, SCREAMING_SNAKE_CASE   : Optional[int]=None							, SCREAMING_SNAKE_CASE   : Tuple=None    ):
  UpperCAmelCase    				=						sorted(lowercase_							, key=lambda SCREAMING_SNAKE_CASE    : na_probs[k]    )
  UpperCAmelCase    				=						0.0
  UpperCAmelCase    				=						1.0
  UpperCAmelCase    				=						0.0
  UpperCAmelCase    				=						[1.0]
  UpperCAmelCase    				=						[0.0]
  UpperCAmelCase    				=						0.0
  for i, qid in enumerate(lowercase_    ):
    if qid_to_has_ans[qid]:
      true_pos += scores[qid]
    UpperCAmelCase    				=						true_pos / float(i + 1    )
    UpperCAmelCase    				=						true_pos / float(lowercase_    )
    if i == len(lowercase_    ) - 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(lowercase_    )
      recalls.append(lowercase_    )
  if out_image:
    plot_pr_curve(lowercase_							, lowercase_							, lowercase_							, lowercase_    )
  return {"ap": 100.0 * avg_prec}


def    lowerCamelCase__		(    SCREAMING_SNAKE_CASE   : Optional[int]							, SCREAMING_SNAKE_CASE   : Dict							, SCREAMING_SNAKE_CASE   : List[str]							, SCREAMING_SNAKE_CASE   : List[Any]							, SCREAMING_SNAKE_CASE   : Optional[Any]							, SCREAMING_SNAKE_CASE   : Dict    ):






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


def    lowerCamelCase__		(    SCREAMING_SNAKE_CASE   : Optional[Any]							, SCREAMING_SNAKE_CASE   : int							, SCREAMING_SNAKE_CASE   : str							, SCREAMING_SNAKE_CASE   : Dict    ):






  if not qid_list:
    return
  UpperCAmelCase    				=						[na_probs[k] for k in qid_list]
  UpperCAmelCase    				=						np.ones_like(lowercase_    ) / float(len(lowercase_    )    )
  plt.hist(lowercase_							, weights=lowercase_							, bins=20							, 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(lowercase_							, f'''na_prob_hist_{name}.png'''    )    )
  plt.clf()


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


def    lowerCamelCase__		(    SCREAMING_SNAKE_CASE   : Dict							, SCREAMING_SNAKE_CASE   : str							, SCREAMING_SNAKE_CASE   : List[Any]							, SCREAMING_SNAKE_CASE   : str							, SCREAMING_SNAKE_CASE   : Optional[Any]							, SCREAMING_SNAKE_CASE   : Dict    ):
  UpperCAmelCase    				=						find_best_thresh(lowercase_							, lowercase_							, lowercase_							, lowercase_    )
  UpperCAmelCase    				=						find_best_thresh(lowercase_							, lowercase_							, lowercase_							, lowercase_    )
  UpperCAmelCase    				=						best_exact
  UpperCAmelCase    				=						exact_thresh
  UpperCAmelCase    				=						best_fa
  UpperCAmelCase    				=						fa_thresh


def    lowerCamelCase__		(    ):






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


if __name__ == "__main__":
  _a					:			List[str]							=    parse_args()
  if OPTS.out_image_dir:
    import matplotlib

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




import torch

from diffusers import DDPMScheduler

from .test_schedulers import SchedulerCommonTest



class    _snake_case (						UpperCAmelCase_				):
 __lowerCAmelCase	:  int	   =      (DDPMScheduler,)


 def   lowercase__  (       self       ,	**SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   lowercase__	: Tuple     		=   {
       """num_train_timesteps""": 10_00,
       """beta_start""": 0.0_0_0_1,
       """beta_end""": 0.0_2,
       """beta_schedule""": """linear""",
       """variance_type""": """fixed_small""",
       """clip_sample""": True,
   }

   config.update(**SCREAMING_SNAKE_CASE_)
   return config


 def   lowercase__  (       self):


   '''simple docstring'''




   for timesteps in [1, 5, 1_00, 10_00]:
     self.check_over_configs(num_train_timesteps=SCREAMING_SNAKE_CASE_)


 def   lowercase__  (       self):


   '''simple docstring'''




   for beta_start, beta_end in zip([0.0_0_0_1, 0.0_0_1, 0.0_1, 0.1]       ,	[0.0_0_2, 0.0_2, 0.2, 2]):
     self.check_over_configs(beta_start=SCREAMING_SNAKE_CASE_       ,	beta_end=SCREAMING_SNAKE_CASE_)


 def   lowercase__  (       self):


   '''simple docstring'''




   for schedule in ["linear", "squaredcos_cap_v2"]:
     self.check_over_configs(beta_schedule=SCREAMING_SNAKE_CASE_)


 def   lowercase__  (       self):


   '''simple docstring'''




   for variance in ["fixed_small", "fixed_large", "other"]:
     self.check_over_configs(variance_type=SCREAMING_SNAKE_CASE_)


 def   lowercase__  (       self):


   '''simple docstring'''




   for clip_sample in [True, False]:
     self.check_over_configs(clip_sample=SCREAMING_SNAKE_CASE_)


 def   lowercase__  (       self):


   '''simple docstring'''




   self.check_over_configs(thresholding=SCREAMING_SNAKE_CASE_)
   for threshold in [0.5, 1.0, 2.0]:
     for prediction_type in ["epsilon", "sample", "v_prediction"]:
       self.check_over_configs(
           thresholding=SCREAMING_SNAKE_CASE_       ,	prediction_type=SCREAMING_SNAKE_CASE_       ,	sample_max_value=SCREAMING_SNAKE_CASE_       ,	)


 def   lowercase__  (       self):


   '''simple docstring'''




   for prediction_type in ["epsilon", "sample", "v_prediction"]:
     self.check_over_configs(prediction_type=SCREAMING_SNAKE_CASE_)


 def   lowercase__  (       self):


   '''simple docstring'''




   for t in [0, 5_00, 9_99]:
     self.check_over_forward(time_step=SCREAMING_SNAKE_CASE_)


 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: Union[str, Any]     		=   self.scheduler_classes[0]
   lowercase__	: Union[str, Any]     		=   self.get_scheduler_config()
   lowercase__	: List[Any]     		=   scheduler_class(**SCREAMING_SNAKE_CASE_)

   assert torch.sum(torch.abs(scheduler._get_variance(0) - 0.0)) < 1E-5
   assert torch.sum(torch.abs(scheduler._get_variance(4_87) - 0.0_0_9_7_9)) < 1E-5
   assert torch.sum(torch.abs(scheduler._get_variance(9_99) - 0.0_2)) < 1E-5


 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: Dict     		=   self.scheduler_classes[0]
   lowercase__	: str     		=   self.get_scheduler_config()
   lowercase__	: Tuple     		=   scheduler_class(**SCREAMING_SNAKE_CASE_)

   lowercase__	: int     		=   len(SCREAMING_SNAKE_CASE_)

   lowercase__	: Any     		=   self.dummy_model()
   lowercase__	: List[Any]     		=   self.dummy_sample_deter
   lowercase__	: str     		=   torch.manual_seed(0)

   for t in reversed(range(SCREAMING_SNAKE_CASE_)):
     # 1. predict noise residual
     lowercase__	: Dict     		=   model(SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_)

     # 2. predict previous mean of sample x_t-1
     lowercase__	: List[str]     		=   scheduler.step(SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	generator=SCREAMING_SNAKE_CASE_).prev_sample

     # if t > 0:
     #     noise = self.dummy_sample_deter
     #     variance = scheduler.get_variance(t) ** (0.5) * noise
     #
     # sample = pred_prev_sample + variance
     lowercase__	: str     		=   pred_prev_sample

   lowercase__	: Optional[int]     		=   torch.sum(torch.abs(SCREAMING_SNAKE_CASE_))
   lowercase__	: Optional[Any]     		=   torch.mean(torch.abs(SCREAMING_SNAKE_CASE_))

   assert abs(result_sum.item() - 2_5_8.9_6_0_6) < 1E-2
   assert abs(result_mean.item() - 0.3_3_7_2) < 1E-3


 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: List[Any]     		=   self.scheduler_classes[0]
   lowercase__	: Tuple     		=   self.get_scheduler_config(prediction_type="""v_prediction""")
   lowercase__	: Dict     		=   scheduler_class(**SCREAMING_SNAKE_CASE_)

   lowercase__	: Dict     		=   len(SCREAMING_SNAKE_CASE_)

   lowercase__	: Tuple     		=   self.dummy_model()
   lowercase__	: Union[str, Any]     		=   self.dummy_sample_deter
   lowercase__	: int     		=   torch.manual_seed(0)

   for t in reversed(range(SCREAMING_SNAKE_CASE_)):
     # 1. predict noise residual
     lowercase__	: List[Any]     		=   model(SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_)

     # 2. predict previous mean of sample x_t-1
     lowercase__	: int     		=   scheduler.step(SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	generator=SCREAMING_SNAKE_CASE_).prev_sample

     # if t > 0:
     #     noise = self.dummy_sample_deter
     #     variance = scheduler.get_variance(t) ** (0.5) * noise
     #
     # sample = pred_prev_sample + variance
     lowercase__	: Tuple     		=   pred_prev_sample

   lowercase__	: Union[str, Any]     		=   torch.sum(torch.abs(SCREAMING_SNAKE_CASE_))
   lowercase__	: int     		=   torch.mean(torch.abs(SCREAMING_SNAKE_CASE_))

   assert abs(result_sum.item() - 2_0_2.0_2_9_6) < 1E-2
   assert abs(result_mean.item() - 0.2_6_3_1) < 1E-3


 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: str     		=   self.scheduler_classes[0]
   lowercase__	: int     		=   self.get_scheduler_config()
   lowercase__	: str     		=   scheduler_class(**SCREAMING_SNAKE_CASE_)

   lowercase__	: List[str]     		=   [1_00, 87, 50, 1, 0]

   scheduler.set_timesteps(timesteps=SCREAMING_SNAKE_CASE_)

   lowercase__	: List[Any]     		=   scheduler.timesteps

   for i, timestep in enumerate(SCREAMING_SNAKE_CASE_):
     if i == len(SCREAMING_SNAKE_CASE_) - 1:
       lowercase__	: Optional[int]     		=   -1
     else:
       lowercase__	: Tuple     		=   timesteps[i + 1]

     lowercase__	: Any     		=   scheduler.previous_timestep(SCREAMING_SNAKE_CASE_)
     lowercase__	: int     		=   prev_t.item()

     self.assertEqual(SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_)


 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: Optional[int]     		=   self.scheduler_classes[0]
   lowercase__	: List[Any]     		=   self.get_scheduler_config()
   lowercase__	: int     		=   scheduler_class(**SCREAMING_SNAKE_CASE_)

   lowercase__	: List[Any]     		=   [1_00, 87, 50, 51, 0]

   with self.assertRaises(SCREAMING_SNAKE_CASE_       ,	msg="""`custom_timesteps` must be in descending order."""):
     scheduler.set_timesteps(timesteps=SCREAMING_SNAKE_CASE_)


 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: Union[str, Any]     		=   self.scheduler_classes[0]
   lowercase__	: List[Any]     		=   self.get_scheduler_config()
   lowercase__	: int     		=   scheduler_class(**SCREAMING_SNAKE_CASE_)

   lowercase__	: int     		=   [1_00, 87, 50, 1, 0]
   lowercase__	: Union[str, Any]     		=   len(SCREAMING_SNAKE_CASE_)

   with self.assertRaises(SCREAMING_SNAKE_CASE_       ,	msg="""Can only pass one of `num_inference_steps` or `custom_timesteps`."""):
     scheduler.set_timesteps(num_inference_steps=SCREAMING_SNAKE_CASE_       ,	timesteps=SCREAMING_SNAKE_CASE_)



 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: Optional[int]     		=   self.scheduler_classes[0]
   lowercase__	: int     		=   self.get_scheduler_config()
   lowercase__	: Dict     		=   scheduler_class(**SCREAMING_SNAKE_CASE_)

   lowercase__	: str     		=   [scheduler.config.num_train_timesteps]

   with self.assertRaises(
       SCREAMING_SNAKE_CASE_       ,	msg="""`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}"""       ,	):
     scheduler.set_timesteps(timesteps=SCREAMING_SNAKE_CASE_)






