



'''simple docstring'''

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

import numpy as np


snake_case_			=     re.compile(R"""\b(a|an|the)\b""", re.UNICODE)

snake_case_			=     None






def       __lowercase  ():
				SCREAMING_SNAKE_CASE  :						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=_SCREAMING_SNAKE_CASE    ,	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=_SCREAMING_SNAKE_CASE    ,	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       __lowercase  (_SCREAMING_SNAKE_CASE      :Optional[int]       ):
				SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= {}
				for article in dataset:
								for p in article["paragraphs"]:
												for qa in p["qas"]:
																SCREAMING_SNAKE_CASE  :						List[str]      					= bool(qa['''answers''']['''text''']       )
				return qid_to_has_ans






def       __lowercase  (_SCREAMING_SNAKE_CASE      :str       ):

				def remove_articles(_SCREAMING_SNAKE_CASE      :Optional[Any]       ):
								return ARTICLES_REGEX.sub(''' '''    ,	_SCREAMING_SNAKE_CASE       )

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

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

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

				return white_space_fix(remove_articles(remove_punc(lower(_SCREAMING_SNAKE_CASE       )       )       )       )






def       __lowercase  (_SCREAMING_SNAKE_CASE      :List[Any]       ):
				if not s:
								return []
				return normalize_answer(_SCREAMING_SNAKE_CASE       ).split()






def       __lowercase  (_SCREAMING_SNAKE_CASE      :Optional[Any]    ,	_SCREAMING_SNAKE_CASE      :Union[str, Any]       ):
				return int(normalize_answer(_SCREAMING_SNAKE_CASE       ) == normalize_answer(_SCREAMING_SNAKE_CASE       )       )






def       __lowercase  (_SCREAMING_SNAKE_CASE      :Union[str, Any]    ,	_SCREAMING_SNAKE_CASE      :Optional[Any]       ):
				SCREAMING_SNAKE_CASE  :						Optional[int]      					= get_tokens(_SCREAMING_SNAKE_CASE       )
				SCREAMING_SNAKE_CASE  :						List[Any]      					= get_tokens(_SCREAMING_SNAKE_CASE       )
				SCREAMING_SNAKE_CASE  :						List[Any]      					= collections.Counter(_SCREAMING_SNAKE_CASE       ) & collections.Counter(_SCREAMING_SNAKE_CASE       )
				SCREAMING_SNAKE_CASE  :						Optional[int]      					= sum(common.values()       )
				if len(_SCREAMING_SNAKE_CASE       ) == 0 or len(_SCREAMING_SNAKE_CASE       ) == 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
				SCREAMING_SNAKE_CASE  :						Tuple      					= 1.0 * num_same / len(_SCREAMING_SNAKE_CASE       )
				SCREAMING_SNAKE_CASE  :						Dict      					= 1.0 * num_same / len(_SCREAMING_SNAKE_CASE       )
				SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= (2 * precision * recall) / (precision + recall)
				return fa






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






def       __lowercase  (_SCREAMING_SNAKE_CASE      :Dict    ,	_SCREAMING_SNAKE_CASE      :Dict    ,	_SCREAMING_SNAKE_CASE      :Any    ,	_SCREAMING_SNAKE_CASE      :Optional[int]       ):
				SCREAMING_SNAKE_CASE  :						Tuple      					= {}
				for qid, s in scores.items():
								SCREAMING_SNAKE_CASE  :						int      					= na_probs[qid] > na_prob_thresh
								if pred_na:
												SCREAMING_SNAKE_CASE  :						Dict      					= float(not qid_to_has_ans[qid]       )
								else:
												SCREAMING_SNAKE_CASE  :						Optional[int]      					= s
				return new_scores






def       __lowercase  (_SCREAMING_SNAKE_CASE      :List[Any]    ,	_SCREAMING_SNAKE_CASE      :str    ,	_SCREAMING_SNAKE_CASE      :int=None       ):
				if not qid_list:
								SCREAMING_SNAKE_CASE  :						Optional[int]      					= len(_SCREAMING_SNAKE_CASE       )
								return collections.OrderedDict(
								    [
								        ('''exact''', 100.0 * sum(exact_scores.values()       ) / total),
								        ('''f1''', 100.0 * sum(fa_scores.values()       ) / total),
								        ('''total''', total),
								    ]       )
				else:
								SCREAMING_SNAKE_CASE  :						Any      					= len(_SCREAMING_SNAKE_CASE       )
								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       __lowercase  (_SCREAMING_SNAKE_CASE      :Dict    ,	_SCREAMING_SNAKE_CASE      :Dict    ,	_SCREAMING_SNAKE_CASE      :Optional[int]       ):
				for k in new_eval:
								SCREAMING_SNAKE_CASE  :						Tuple      					= new_eval[k]






def       __lowercase  (_SCREAMING_SNAKE_CASE      :Any    ,	_SCREAMING_SNAKE_CASE      :List[str]    ,	_SCREAMING_SNAKE_CASE      :List[str]    ,	_SCREAMING_SNAKE_CASE      :Dict       ):
				plt.step(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	color='''b'''    ,	alpha=0.2    ,	where='''post'''       )
				plt.fill_between(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	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(_SCREAMING_SNAKE_CASE       )
				plt.savefig(_SCREAMING_SNAKE_CASE       )
				plt.clf()






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






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






def       __lowercase  (_SCREAMING_SNAKE_CASE      :Tuple    ,	_SCREAMING_SNAKE_CASE      :Tuple    ,	_SCREAMING_SNAKE_CASE      :Tuple    ,	_SCREAMING_SNAKE_CASE      :Dict       ):
				if not qid_list:
								return
				SCREAMING_SNAKE_CASE  :						Dict      					= [na_probs[k] for k in qid_list]
				SCREAMING_SNAKE_CASE  :						List[Any]      					= np.ones_like(_SCREAMING_SNAKE_CASE       ) / float(len(_SCREAMING_SNAKE_CASE       )       )
				plt.hist(_SCREAMING_SNAKE_CASE    ,	weights=_SCREAMING_SNAKE_CASE    ,	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(_SCREAMING_SNAKE_CASE    ,	F'''na_prob_hist_{name}.png'''       )       )
				plt.clf()






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






def       __lowercase  (_SCREAMING_SNAKE_CASE      :Optional[Any]    ,	_SCREAMING_SNAKE_CASE      :List[Any]    ,	_SCREAMING_SNAKE_CASE      :Optional[int]    ,	_SCREAMING_SNAKE_CASE      :Tuple    ,	_SCREAMING_SNAKE_CASE      :Tuple    ,	_SCREAMING_SNAKE_CASE      :List[Any]       ):
				SCREAMING_SNAKE_CASE  :						List[Any]      					= find_best_thresh(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE       )
				SCREAMING_SNAKE_CASE  :						Any      					= find_best_thresh(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE       )
				SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= best_exact
				SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= exact_thresh
				SCREAMING_SNAKE_CASE  :						str      					= best_fa
				SCREAMING_SNAKE_CASE  :						Any      					= fa_thresh






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


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

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









'''simple docstring'''

from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import ScoreSdeVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


class 	a__     (				_lowercase	):
	__magic_name__		:    UNetaDModel
	__magic_name__		:    ScoreSdeVeScheduler


	def __init__(self : List[str],					__UpperCAmelCase : UNetaDModel,					__UpperCAmelCase : ScoreSdeVeScheduler      )     ->			Tuple:

					"""simple docstring"""

					super().__init__()
					self.register_modules(unet=__UpperCAmelCase,					scheduler=__UpperCAmelCase      )






	@torch.no_grad()
	def __call__(self : int,					__UpperCAmelCase : int = 1,					__UpperCAmelCase : int = 2000,					__UpperCAmelCase : Optional[Union[torch.Generator, List[torch.Generator]]] = None,					__UpperCAmelCase : Optional[str] = "pil",					__UpperCAmelCase : bool = True,					**__UpperCAmelCase : List[str],					)     ->			Union[ImagePipelineOutput, Tuple]:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= self.unet.config.sample_size
					SCREAMING_SNAKE_CASE  :						Dict      					= (batch_size, 3, img_size, img_size)

					SCREAMING_SNAKE_CASE  :						List[Any]      					= self.unet

					SCREAMING_SNAKE_CASE  :						str      					= randn_tensor(__UpperCAmelCase,					generator=__UpperCAmelCase      ) * self.scheduler.init_noise_sigma
					SCREAMING_SNAKE_CASE  :						Any      					= sample.to(self.device      )

					self.scheduler.set_timesteps(__UpperCAmelCase      )
					self.scheduler.set_sigmas(__UpperCAmelCase      )

					for i, t in enumerate(self.progress_bar(self.scheduler.timesteps      )      ):
									SCREAMING_SNAKE_CASE  :						int      					= self.scheduler.sigmas[i] * torch.ones(shape[0],					device=self.device      )

									# correction step
									for _ in range(self.scheduler.config.correct_steps      ):
													SCREAMING_SNAKE_CASE  :						Any      					= self.unet(__UpperCAmelCase,					__UpperCAmelCase      ).sample
													SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= self.scheduler.step_correct(__UpperCAmelCase,					__UpperCAmelCase,					generator=__UpperCAmelCase      ).prev_sample

									# prediction step
									SCREAMING_SNAKE_CASE  :						str      					= model(__UpperCAmelCase,					__UpperCAmelCase      ).sample
									SCREAMING_SNAKE_CASE  :						Dict      					= self.scheduler.step_pred(__UpperCAmelCase,					__UpperCAmelCase,					__UpperCAmelCase,					generator=__UpperCAmelCase      )

									SCREAMING_SNAKE_CASE					,					SCREAMING_SNAKE_CASE  :						Optional[int]      					= output.prev_sample, output.prev_sample_mean

					SCREAMING_SNAKE_CASE  :						Dict      					= sample_mean.clamp(0,					1      )
					SCREAMING_SNAKE_CASE  :						str      					= sample.cpu().permute(0,					2,					3,					1      ).numpy()
					if output_type == "pil":
									SCREAMING_SNAKE_CASE  :						int      					= self.numpy_to_pil(__UpperCAmelCase      )

					if not return_dict:
									return (sample,)

					return ImagePipelineOutput(images=__UpperCAmelCase      )




