






"""simple docstring"""



import torch

from diffusers import HeunDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest




class        __lowercase(       _UpperCamelCase  ):
						lowerCAmelCase_		:							List[str]=       (HeunDiscreteScheduler,)
						lowerCAmelCase_		:							List[Any]=       10






						def 		lowerCAmelCase   (					self    ,						**_SCREAMING_SNAKE_CASE)			->  List[Any]:




											"""simple docstring"""





											lowerCamelCase__				={
											    'num_train_timesteps': 11_00,
											    'beta_start': 0.0001,
											    'beta_end': 0.02,
											    'beta_schedule': 'linear',
											}

											config.update(**_SCREAMING_SNAKE_CASE)
											return config






						def 		lowerCAmelCase   (					self)			->  str:




											"""simple docstring"""





											for timesteps in [10, 50, 1_00, 10_00]:
																self.check_over_configs(num_train_timesteps=_SCREAMING_SNAKE_CASE)






						def 		lowerCAmelCase   (					self)			->  List[str]:




											"""simple docstring"""





											for beta_start, beta_end in zip([0.00001, 0.0001, 0.001]    ,						[0.0002, 0.002, 0.02]):
																self.check_over_configs(beta_start=_SCREAMING_SNAKE_CASE    ,						beta_end=_SCREAMING_SNAKE_CASE)






						def 		lowerCAmelCase   (					self)			->  Tuple:




											"""simple docstring"""





											for schedule in ["linear", "scaled_linear", "exp"]:
																self.check_over_configs(beta_schedule=_SCREAMING_SNAKE_CASE)






						def 		lowerCAmelCase   (					self)			->  str:




											"""simple docstring"""





											for clip_sample_range in [1.0, 2.0, 3.0]:
																self.check_over_configs(clip_sample_range=_SCREAMING_SNAKE_CASE    ,						clip_sample=_SCREAMING_SNAKE_CASE)






						def 		lowerCAmelCase   (					self)			->  Optional[int]:




											"""simple docstring"""





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






						def 		lowerCAmelCase   (					self)			->  Union[str, Any]:




											"""simple docstring"""





											lowerCamelCase__				=self.scheduler_classes[0]
											lowerCamelCase__				=self.get_scheduler_config()
											lowerCamelCase__				=scheduler_class(**_SCREAMING_SNAKE_CASE)

											scheduler.set_timesteps(self.num_inference_steps)

											lowerCamelCase__				=self.dummy_model()
											lowerCamelCase__				=self.dummy_sample_deter * scheduler.init_noise_sigma
											lowerCamelCase__				=sample.to(_SCREAMING_SNAKE_CASE)

											for i, t in enumerate(scheduler.timesteps):
																lowerCamelCase__				=scheduler.scale_model_input(_SCREAMING_SNAKE_CASE    ,						_SCREAMING_SNAKE_CASE)

																lowerCamelCase__				=model(_SCREAMING_SNAKE_CASE    ,						_SCREAMING_SNAKE_CASE)

																lowerCamelCase__				=scheduler.step(_SCREAMING_SNAKE_CASE    ,						_SCREAMING_SNAKE_CASE    ,						_SCREAMING_SNAKE_CASE)
																lowerCamelCase__				=output.prev_sample

											lowerCamelCase__				=torch.sum(torch.abs(_SCREAMING_SNAKE_CASE))
											lowerCamelCase__				=torch.mean(torch.abs(_SCREAMING_SNAKE_CASE))

											if torch_device in ["cpu", "mps"]:
																assert abs(result_sum.item() - 0.1233) < 1E-2
																assert abs(result_mean.item() - 0.0002) < 1E-3
											else:
																# CUDA
																assert abs(result_sum.item() - 0.1233) < 1E-2
																assert abs(result_mean.item() - 0.0002) < 1E-3






						def 		lowerCAmelCase   (					self)			->  Any:




											"""simple docstring"""





											lowerCamelCase__				=self.scheduler_classes[0]
											lowerCamelCase__				=self.get_scheduler_config(prediction_type='v_prediction')
											lowerCamelCase__				=scheduler_class(**_SCREAMING_SNAKE_CASE)

											scheduler.set_timesteps(self.num_inference_steps)

											lowerCamelCase__				=self.dummy_model()
											lowerCamelCase__				=self.dummy_sample_deter * scheduler.init_noise_sigma
											lowerCamelCase__				=sample.to(_SCREAMING_SNAKE_CASE)

											for i, t in enumerate(scheduler.timesteps):
																lowerCamelCase__				=scheduler.scale_model_input(_SCREAMING_SNAKE_CASE    ,						_SCREAMING_SNAKE_CASE)

																lowerCamelCase__				=model(_SCREAMING_SNAKE_CASE    ,						_SCREAMING_SNAKE_CASE)

																lowerCamelCase__				=scheduler.step(_SCREAMING_SNAKE_CASE    ,						_SCREAMING_SNAKE_CASE    ,						_SCREAMING_SNAKE_CASE)
																lowerCamelCase__				=output.prev_sample

											lowerCamelCase__				=torch.sum(torch.abs(_SCREAMING_SNAKE_CASE))
											lowerCamelCase__				=torch.mean(torch.abs(_SCREAMING_SNAKE_CASE))

											if torch_device in ["cpu", "mps"]:
																assert abs(result_sum.item() - 4.6934E-07) < 1E-2
																assert abs(result_mean.item() - 6.1112E-10) < 1E-3
											else:
																# CUDA
																assert abs(result_sum.item() - 4.693428650170972E-07) < 1E-2
																assert abs(result_mean.item() - 0.0002) < 1E-3






						def 		lowerCAmelCase   (					self)			->  Union[str, Any]:




											"""simple docstring"""





											lowerCamelCase__				=self.scheduler_classes[0]
											lowerCamelCase__				=self.get_scheduler_config()
											lowerCamelCase__				=scheduler_class(**_SCREAMING_SNAKE_CASE)

											scheduler.set_timesteps(self.num_inference_steps    ,						device=_SCREAMING_SNAKE_CASE)

											lowerCamelCase__				=self.dummy_model()
											lowerCamelCase__				=self.dummy_sample_deter.to(_SCREAMING_SNAKE_CASE) * scheduler.init_noise_sigma

											for t in scheduler.timesteps:
																lowerCamelCase__				=scheduler.scale_model_input(_SCREAMING_SNAKE_CASE    ,						_SCREAMING_SNAKE_CASE)

																lowerCamelCase__				=model(_SCREAMING_SNAKE_CASE    ,						_SCREAMING_SNAKE_CASE)

																lowerCamelCase__				=scheduler.step(_SCREAMING_SNAKE_CASE    ,						_SCREAMING_SNAKE_CASE    ,						_SCREAMING_SNAKE_CASE)
																lowerCamelCase__				=output.prev_sample

											lowerCamelCase__				=torch.sum(torch.abs(_SCREAMING_SNAKE_CASE))
											lowerCamelCase__				=torch.mean(torch.abs(_SCREAMING_SNAKE_CASE))

											if str(_SCREAMING_SNAKE_CASE).startswith('cpu'):
																# The following sum varies between 148 and 156 on mps. Why?
																assert abs(result_sum.item() - 0.1233) < 1E-2
																assert abs(result_mean.item() - 0.0002) < 1E-3
											elif str(_SCREAMING_SNAKE_CASE).startswith('mps'):
																# Larger tolerance on mps
																assert abs(result_mean.item() - 0.0002) < 1E-2
											else:
																# CUDA
																assert abs(result_sum.item() - 0.1233) < 1E-2
																assert abs(result_mean.item() - 0.0002) < 1E-3






						def 		lowerCAmelCase   (					self)			->  int:




											"""simple docstring"""





											lowerCamelCase__				=self.scheduler_classes[0]
											lowerCamelCase__				=self.get_scheduler_config()
											lowerCamelCase__				=scheduler_class(**_SCREAMING_SNAKE_CASE    ,						use_karras_sigmas=_SCREAMING_SNAKE_CASE)

											scheduler.set_timesteps(self.num_inference_steps    ,						device=_SCREAMING_SNAKE_CASE)

											lowerCamelCase__				=self.dummy_model()
											lowerCamelCase__				=self.dummy_sample_deter.to(_SCREAMING_SNAKE_CASE) * scheduler.init_noise_sigma
											lowerCamelCase__				=sample.to(_SCREAMING_SNAKE_CASE)

											for t in scheduler.timesteps:
																lowerCamelCase__				=scheduler.scale_model_input(_SCREAMING_SNAKE_CASE    ,						_SCREAMING_SNAKE_CASE)

																lowerCamelCase__				=model(_SCREAMING_SNAKE_CASE    ,						_SCREAMING_SNAKE_CASE)

																lowerCamelCase__				=scheduler.step(_SCREAMING_SNAKE_CASE    ,						_SCREAMING_SNAKE_CASE    ,						_SCREAMING_SNAKE_CASE)
																lowerCamelCase__				=output.prev_sample

											lowerCamelCase__				=torch.sum(torch.abs(_SCREAMING_SNAKE_CASE))
											lowerCamelCase__				=torch.mean(torch.abs(_SCREAMING_SNAKE_CASE))

											assert abs(result_sum.item() - 0.00015) < 1E-2
											assert abs(result_mean.item() - 1.9869554535034695E-07) < 1E-2







"""simple docstring"""



import datasets

from .evaluate import (
    apply_no_ans_threshold,
    find_all_best_thresh,
    get_raw_scores,
    make_eval_dict,
    make_qid_to_has_ans,
    merge_eval,
)


_snake_case       :      Dict       =					"\\n@inproceedings{Rajpurkar2016SQuAD10,\n  title={SQuAD: 100, 000+ Questions for Machine Comprehension of Text},\n  author={Pranav Rajpurkar and Jian Zhang and Konstantin Lopyrev and Percy Liang},\n  booktitle={EMNLP},\n  year={2016}\n}\n"

_snake_case       :      List[str]       =					"\nThis metric wrap the official scoring script for version 2 of the Stanford Question\nAnswering Dataset (SQuAD).\n\nStanford Question Answering Dataset (SQuAD) is a reading comprehension dataset, consisting of questions posed by\ncrowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span,\nfrom the corresponding reading passage, or the question might be unanswerable.\n\nSQuAD2.0 combines the 100,000 questions in SQuAD1.1 with over 50,000 unanswerable questions\nwritten adversarially by crowdworkers to look similar to answerable ones.\nTo do well on SQuAD2.0, systems must not only answer questions when possible, but also\ndetermine when no answer is supported by the paragraph and abstain from answering.\n"

_snake_case       :      Dict       =					"\nComputes SQuAD v2 scores (F1 and EM).\nArgs:\n    predictions: List of triple for question-answers to score with the following elements:\n        - the question-answer 'id' field as given in the references (see below)\n        - the text of the answer\n        - the probability that the question has no answer\n    references: List of question-answers dictionaries with the following key-values:\n            - 'id': id of the question-answer pair (see above),\n            - 'answers': a list of Dict {'text': text of the answer as a string}\n    no_answer_threshold: float\n        Probability threshold to decide that a question has no answer.\nReturns:\n    'exact': Exact match (the normalized answer exactly match the gold answer)\n    'f1': The F-score of predicted tokens versus the gold answer\n    'total': Number of score considered\n    'HasAns_exact': Exact match (the normalized answer exactly match the gold answer)\n    'HasAns_f1': The F-score of predicted tokens versus the gold answer\n    'HasAns_total': Number of score considered\n    'NoAns_exact': Exact match (the normalized answer exactly match the gold answer)\n    'NoAns_f1': The F-score of predicted tokens versus the gold answer\n    'NoAns_total': Number of score considered\n    'best_exact': Best exact match (with varying threshold)\n    'best_exact_thresh': No-answer probability threshold associated to the best exact match\n    'best_f1': Best F1 (with varying threshold)\n    'best_f1_thresh': No-answer probability threshold associated to the best F1\nExamples:\n\n    >>> predictions = [{'prediction_text': '1976', 'id': '56e10a3be3433e1400422b22', 'no_answer_probability': 0.}]\n    >>> references = [{'answers': {'answer_start': [97], 'text': ['1976']}, 'id': '56e10a3be3433e1400422b22'}]\n    >>> squad_v2_metric = datasets.load_metric(\"squad_v2\")\n    >>> results = squad_v2_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {'exact': 100.0, 'f1': 100.0, 'total': 1, 'HasAns_exact': 100.0, 'HasAns_f1': 100.0, 'HasAns_total': 1, 'best_exact': 100.0, 'best_exact_thresh': 0.0, 'best_f1': 100.0, 'best_f1_thresh': 0.0}\n"




@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION						,							_KWARGS_DESCRIPTION  )
class        __lowercase(       datasets.Metric  ):






						def 		lowerCAmelCase   (					self)			->  int:




											"""simple docstring"""





											return datasets.MetricInfo(
											    description=_DESCRIPTION    ,						citation=_CITATION    ,						inputs_description=_KWARGS_DESCRIPTION    ,						features=datasets.Features(
											        {
											            'predictions': {
											                'id': datasets.Value('string'),
											                'prediction_text': datasets.Value('string'),
											                'no_answer_probability': datasets.Value('float32'),
											            },
											            'references': {
											                'id': datasets.Value('string'),
											                'answers': datasets.features.Sequence(
											                    {'text': datasets.Value('string'), 'answer_start': datasets.Value('int32')}),
											            },
											        })    ,						codebase_urls=['https://rajpurkar.github.io/SQuAD-explorer/']    ,						reference_urls=['https://rajpurkar.github.io/SQuAD-explorer/']    ,						)






						def 		lowerCAmelCase   (					self    ,						_SCREAMING_SNAKE_CASE    ,						_SCREAMING_SNAKE_CASE    ,						_SCREAMING_SNAKE_CASE=1.0)			->  Optional[int]:




											"""simple docstring"""





											lowerCamelCase__				={p['id']: p['no_answer_probability'] for p in predictions}
											lowerCamelCase__				=[{'paragraphs': [{'qas': references}]}]
											lowerCamelCase__				={p['id']: p['prediction_text'] for p in predictions}

											lowerCamelCase__				=make_qid_to_has_ans(_SCREAMING_SNAKE_CASE)  # maps qid to True/False
											lowerCamelCase__				=[k for k, v in qid_to_has_ans.items() if v]
											lowerCamelCase__				=[k for k, v in qid_to_has_ans.items() if not v]

											lowerCamelCase__				,     lowerCamelCase__				=get_raw_scores(_SCREAMING_SNAKE_CASE    ,						_SCREAMING_SNAKE_CASE)
											lowerCamelCase__				=apply_no_ans_threshold(_SCREAMING_SNAKE_CASE    ,						_SCREAMING_SNAKE_CASE    ,						_SCREAMING_SNAKE_CASE    ,						_SCREAMING_SNAKE_CASE)
											lowerCamelCase__				=apply_no_ans_threshold(_SCREAMING_SNAKE_CASE    ,						_SCREAMING_SNAKE_CASE    ,						_SCREAMING_SNAKE_CASE    ,						_SCREAMING_SNAKE_CASE)
											lowerCamelCase__				=make_eval_dict(_SCREAMING_SNAKE_CASE    ,						_SCREAMING_SNAKE_CASE)

											if has_ans_qids:
																lowerCamelCase__				=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:
																lowerCamelCase__				=make_eval_dict(_SCREAMING_SNAKE_CASE    ,						_SCREAMING_SNAKE_CASE    ,						qid_list=_SCREAMING_SNAKE_CASE)
																merge_eval(_SCREAMING_SNAKE_CASE    ,						_SCREAMING_SNAKE_CASE    ,						'NoAns')
											find_all_best_thresh(_SCREAMING_SNAKE_CASE    ,						_SCREAMING_SNAKE_CASE    ,						_SCREAMING_SNAKE_CASE    ,						_SCREAMING_SNAKE_CASE    ,						_SCREAMING_SNAKE_CASE    ,						_SCREAMING_SNAKE_CASE)
											return dict(_SCREAMING_SNAKE_CASE)