

import tempfile

import torch

from diffusers import (
    DEISMultistepScheduler,
    DPMSolverMultistepScheduler,
    DPMSolverSinglestepScheduler,
    UniPCMultistepScheduler,
)

from .test_schedulers import SchedulerCommonTest





class      snake_case_      (					_a				):


							"""simple docstring"""





							__UpperCAmelCase      =(DPMSolverMultistepScheduler,)
							__UpperCAmelCase      =(("""num_inference_steps""", 25),)




							def 							A__	(			self       ,    **_A				):
												__lowerCAmelCase       =				{
												    'num_train_timesteps': 1_0_0_0,
												    'beta_start': 0.0001,
												    'beta_end': 0.02,
												    'beta_schedule': 'linear',
												    'solver_order': 2,
												    'prediction_type': 'epsilon',
												    'thresholding': False,
												    'sample_max_value': 1.0,
												    'algorithm_type': 'dpmsolver++',
												    'solver_type': 'midpoint',
												    'lower_order_final': False,
												    'lambda_min_clipped': -float('inf'				),
												    'variance_type': None,
												}

												config.update(**_A				)
												return config




							def 							A__	(			self       ,    _A=0       ,    **_A				):
												__lowerCAmelCase       =				dict(self.forward_default_kwargs				)
												__lowerCAmelCase       =				kwargs.pop('num_inference_steps'       ,    _A				)
												__lowerCAmelCase       =				self.dummy_sample
												__lowerCAmelCase       =				0.1 * sample
												__lowerCAmelCase       =				[residual + 0.2, residual + 0.15, residual + 0.10]

												for scheduler_class in self.scheduler_classes:
																	__lowerCAmelCase       =				self.get_scheduler_config(**_A				)
																	__lowerCAmelCase       =				scheduler_class(**_A				)
																	scheduler.set_timesteps(_A				)
																	# copy over dummy past residuals
																	__lowerCAmelCase       =				dummy_past_residuals[: scheduler.config.solver_order]

																	with tempfile.TemporaryDirectory() as tmpdirname:
																						scheduler.save_config(_A				)
																						__lowerCAmelCase       =				scheduler_class.from_pretrained(_A				)
																						new_scheduler.set_timesteps(_A				)
																						# copy over dummy past residuals
																						__lowerCAmelCase       =				dummy_past_residuals[: new_scheduler.config.solver_order]

																	__lowerCAmelCase,						__lowerCAmelCase       =				sample, sample
																	for t in range(_A       ,    time_step + scheduler.config.solver_order + 1				):
																						__lowerCAmelCase       =				scheduler.step(_A       ,    _A       ,    _A       ,    **_A				).prev_sample
																						__lowerCAmelCase       =				new_scheduler.step(_A       ,    _A       ,    _A       ,    **_A				).prev_sample

																						assert torch.sum(torch.abs(output - new_output				)				) < 1e-5, "Scheduler outputs are not identical"




							def 							A__	(			self				):
												pass




							def 							A__	(			self       ,    _A=0       ,    **_A				):
												__lowerCAmelCase       =				dict(self.forward_default_kwargs				)
												__lowerCAmelCase       =				kwargs.pop('num_inference_steps'       ,    _A				)
												__lowerCAmelCase       =				self.dummy_sample
												__lowerCAmelCase       =				0.1 * sample
												__lowerCAmelCase       =				[residual + 0.2, residual + 0.15, residual + 0.10]

												for scheduler_class in self.scheduler_classes:
																	__lowerCAmelCase       =				self.get_scheduler_config()
																	__lowerCAmelCase       =				scheduler_class(**_A				)
																	scheduler.set_timesteps(_A				)

																	# copy over dummy past residuals (must be after setting timesteps)
																	__lowerCAmelCase       =				dummy_past_residuals[: scheduler.config.solver_order]

																	with tempfile.TemporaryDirectory() as tmpdirname:
																						scheduler.save_config(_A				)
																						__lowerCAmelCase       =				scheduler_class.from_pretrained(_A				)
																						# copy over dummy past residuals
																						new_scheduler.set_timesteps(_A				)

																						# copy over dummy past residual (must be after setting timesteps)
																						__lowerCAmelCase       =				dummy_past_residuals[: new_scheduler.config.solver_order]

																	__lowerCAmelCase       =				scheduler.step(_A       ,    _A       ,    _A       ,    **_A				).prev_sample
																	__lowerCAmelCase       =				new_scheduler.step(_A       ,    _A       ,    _A       ,    **_A				).prev_sample

																	assert torch.sum(torch.abs(output - new_output				)				) < 1e-5, "Scheduler outputs are not identical"




							def 							A__	(			self       ,    _A=None       ,    **_A				):
												if scheduler is None:
																	__lowerCAmelCase       =				self.scheduler_classes[0]
																	__lowerCAmelCase       =				self.get_scheduler_config(**_A				)
																	__lowerCAmelCase       =				scheduler_class(**_A				)

												__lowerCAmelCase       =				1_0
												__lowerCAmelCase       =				self.dummy_model()
												__lowerCAmelCase       =				self.dummy_sample_deter
												scheduler.set_timesteps(_A				)

												for i, t in enumerate(scheduler.timesteps				):
																	__lowerCAmelCase       =				model(_A       ,    _A				)
																	__lowerCAmelCase       =				scheduler.step(_A       ,    _A       ,    _A				).prev_sample

												return sample




							def 							A__	(			self				):
												__lowerCAmelCase       =				dict(self.forward_default_kwargs				)

												__lowerCAmelCase       =				kwargs.pop('num_inference_steps'       ,    _A				)

												for scheduler_class in self.scheduler_classes:
																	__lowerCAmelCase       =				self.get_scheduler_config()
																	__lowerCAmelCase       =				scheduler_class(**_A				)

																	__lowerCAmelCase       =				self.dummy_sample
																	__lowerCAmelCase       =				0.1 * sample

																	if num_inference_steps is not None and hasattr(_A       ,    'set_timesteps'				):
																						scheduler.set_timesteps(_A				)
																	elif num_inference_steps is not None and not hasattr(_A       ,    'set_timesteps'				):
																						__lowerCAmelCase       =				num_inference_steps

																	# copy over dummy past residuals (must be done after set_timesteps)
																	__lowerCAmelCase       =				[residual + 0.2, residual + 0.15, residual + 0.10]
																	__lowerCAmelCase       =				dummy_past_residuals[: scheduler.config.solver_order]

																	__lowerCAmelCase       =				scheduler.timesteps[5]
																	__lowerCAmelCase       =				scheduler.timesteps[6]

																	__lowerCAmelCase       =				scheduler.step(_A       ,    _A       ,    _A       ,    **_A				).prev_sample
																	__lowerCAmelCase       =				scheduler.step(_A       ,    _A       ,    _A       ,    **_A				).prev_sample

																	self.assertEqual(output_a.shape       ,    sample.shape				)
																	self.assertEqual(output_a.shape       ,    output_a.shape				)




							def 							A__	(			self				):
												for timesteps in [2_5, 5_0, 1_0_0, 9_9_9, 1_0_0_0]:
																	self.check_over_configs(num_train_timesteps=_A				)




							def 							A__	(			self				):
												self.check_over_configs(thresholding=_A				)
												for order in [1, 2, 3]:
																	for solver_type in ["midpoint", "heun"]:
																						for threshold in [0.5, 1.0, 2.0]:
																											for prediction_type in ["epsilon", "sample"]:
																																self.check_over_configs(
																																    thresholding=_A       ,    prediction_type=_A       ,    sample_max_value=_A       ,    algorithm_type='dpmsolver++'       ,    solver_order=_A       ,    solver_type=_A       ,    )




							def 							A__	(			self				):
												for prediction_type in ["epsilon", "v_prediction"]:
																	self.check_over_configs(prediction_type=_A				)




							def 							A__	(			self				):
												for algorithm_type in ["dpmsolver", "dpmsolver++", "sde-dpmsolver", "sde-dpmsolver++"]:
																	for solver_type in ["midpoint", "heun"]:
																						for order in [1, 2, 3]:
																											for prediction_type in ["epsilon", "sample"]:
																																if algorithm_type in ["sde-dpmsolver", "sde-dpmsolver++"]:
																																					if order == 3:
																																										continue
																																else:
																																					self.check_over_configs(
																																					    solver_order=_A       ,    solver_type=_A       ,    prediction_type=_A       ,    algorithm_type=_A       ,    )
																																__lowerCAmelCase       =				self.full_loop(
																																    solver_order=_A       ,    solver_type=_A       ,    prediction_type=_A       ,    algorithm_type=_A       ,    )
																																assert not torch.isnan(_A				).any(), "Samples have nan numbers"




							def 							A__	(			self				):
												self.check_over_configs(lower_order_final=_A				)
												self.check_over_configs(lower_order_final=_A				)




							def 							A__	(			self				):
												self.check_over_configs(lambda_min_clipped=-float('inf'				)				)
												self.check_over_configs(lambda_min_clipped=-5.1				)




							def 							A__	(			self				):
												self.check_over_configs(variance_type=_A				)
												self.check_over_configs(variance_type='learned_range'				)




							def 							A__	(			self				):
												for num_inference_steps in [1, 2, 3, 5, 1_0, 5_0, 1_0_0, 9_9_9, 1_0_0_0]:
																	self.check_over_forward(num_inference_steps=_A       ,    time_step=0				)




							def 							A__	(			self				):
												__lowerCAmelCase       =				self.full_loop()
												__lowerCAmelCase       =				torch.mean(torch.abs(_A				)				)

												assert abs(result_mean.item() - 0.3301				) < 1e-3




							def 							A__	(			self				):
												__lowerCAmelCase       =				self.full_loop(thresholding=_A       ,    dynamic_thresholding_ratio=0.87       ,    sample_max_value=0.5				)
												__lowerCAmelCase       =				torch.mean(torch.abs(_A				)				)

												assert abs(result_mean.item() - 1.1364				) < 1e-3




							def 							A__	(			self				):
												__lowerCAmelCase       =				self.full_loop(prediction_type='v_prediction'				)
												__lowerCAmelCase       =				torch.mean(torch.abs(_A				)				)

												assert abs(result_mean.item() - 0.2251				) < 1e-3




							def 							A__	(			self				):
												__lowerCAmelCase       =				self.full_loop(prediction_type='v_prediction'       ,    use_karras_sigmas=_A				)
												__lowerCAmelCase       =				torch.mean(torch.abs(_A				)				)

												assert abs(result_mean.item() - 0.2096				) < 1e-3




							def 							A__	(			self				):
												# make sure that iterating over schedulers with same config names gives same results
												# for defaults
												__lowerCAmelCase       =				DPMSolverMultistepScheduler(**self.get_scheduler_config()				)
												__lowerCAmelCase       =				self.full_loop(scheduler=_A				)
												__lowerCAmelCase       =				torch.mean(torch.abs(_A				)				)

												assert abs(result_mean.item() - 0.3301				) < 1e-3

												__lowerCAmelCase       =				DPMSolverSinglestepScheduler.from_config(scheduler.config				)
												__lowerCAmelCase       =				UniPCMultistepScheduler.from_config(scheduler.config				)
												__lowerCAmelCase       =				DEISMultistepScheduler.from_config(scheduler.config				)
												__lowerCAmelCase       =				DPMSolverMultistepScheduler.from_config(scheduler.config				)

												__lowerCAmelCase       =				self.full_loop(scheduler=_A				)
												__lowerCAmelCase       =				torch.mean(torch.abs(_A				)				)

												assert abs(result_mean.item() - 0.3301				) < 1e-3




							def 							A__	(			self				):
												__lowerCAmelCase       =				self.scheduler_classes[0]
												__lowerCAmelCase       =				self.get_scheduler_config(thresholding=_A       ,    dynamic_thresholding_ratio=0				)
												__lowerCAmelCase       =				scheduler_class(**_A				)

												__lowerCAmelCase       =				1_0
												__lowerCAmelCase       =				self.dummy_model()
												__lowerCAmelCase       =				self.dummy_sample_deter.half()
												scheduler.set_timesteps(_A				)

												for i, t in enumerate(scheduler.timesteps				):
																	__lowerCAmelCase       =				model(_A       ,    _A				)
																	__lowerCAmelCase       =				scheduler.step(_A       ,    _A       ,    _A				).prev_sample

												assert sample.dtype == torch.floataa




							def 							A__	(			self       ,    **_A				):
												for scheduler_class in self.scheduler_classes:
																	__lowerCAmelCase       =				self.get_scheduler_config(**_A				)
																	__lowerCAmelCase       =				scheduler_class(**_A				)

																	scheduler.set_timesteps(scheduler.config.num_train_timesteps				)
																	assert len(scheduler.timesteps.unique()				) == scheduler.num_inference_steps









import jax
import jax.numpy as jnp
from bigbird_flax import FlaxBigBirdForNaturalQuestions
from datasets import load_from_disk

from transformers import BigBirdTokenizerFast


lowerCamelCase   					=  {0: '''null''', 1: '''short''', 2: '''long''', 3: '''yes''', 4: '''no'''}
lowerCamelCase   					=  set(''''''.join(['''‘''', '''’''', '''´''', '''`''', '''.''', ''',''', '''-''', '''"''']))


def 							__lowercase     (	UpperCAmelCase__		,       UpperCAmelCase__=0		,       UpperCAmelCase__=None   ):

					"""simple docstring"""






					return [" ".join(x.split(' '   )[begin:end]   ) for x in answers if len(x.split(' '   )   ) > 1]


def 							__lowercase     (	UpperCAmelCase__		,       UpperCAmelCase__=False   ):

					"""simple docstring"""






					if make_sub_answers:
										# if answers are longer than one word, make sure a predictions is correct if it coresponds to the complete 1: or :-1 sub word
										# *e.g.* if the correct answer contains a prefix such as "the", or "a"
										__lowerCAmelCase       =				(
										    given_answers + get_sub_answers(UpperCAmelCase__		,       begin=1   ) + get_sub_answers(UpperCAmelCase__		,       end=-1   )
										)
					__lowerCAmelCase       =				[]
					for answer in given_answers:
										__lowerCAmelCase       =				answer.replace('_'		,       ' '   ).lower()
										__lowerCAmelCase       =				''.join(c if c not in PUNCTUATION_SET_TO_EXCLUDE else ' ' for c in alias   )
										answers.append(' '.join(alias.split()   ).strip()   )
					return set(UpperCAmelCase__   )


def 							__lowercase     (	UpperCAmelCase__		,       UpperCAmelCase__		,       UpperCAmelCase__=1		,       UpperCAmelCase__=100   ):

					"""simple docstring"""






					__lowerCAmelCase,						__lowerCAmelCase       =				jax.lax.top_k(UpperCAmelCase__		,       UpperCAmelCase__   )
					__lowerCAmelCase,						__lowerCAmelCase       =				jax.lax.top_k(UpperCAmelCase__		,       UpperCAmelCase__   )

					__lowerCAmelCase       =				best_end_idx[:, None] - best_start_idx[None, :]
					__lowerCAmelCase       =				jnp.logical_or(widths < 0		,       widths > max_size   )
					__lowerCAmelCase       =				(best_end_scores[:, None] + best_start_scores[None, :]) - (1E8 * mask)
					__lowerCAmelCase       =				jnp.argmax(UpperCAmelCase__   ).item()

					return best_start_idx[best_score % top_k], best_end_idx[best_score // top_k]


def 							__lowercase     (	UpperCAmelCase__   ):

					"""simple docstring"""






					__lowerCAmelCase       =				sample['question']['text']
					__lowerCAmelCase       =				sample['document']['tokens']['token']
					__lowerCAmelCase       =				sample['document']['tokens']['is_html']
					__lowerCAmelCase       =				sample['annotations']['long_answer']
					__lowerCAmelCase       =				sample['annotations']['short_answers']

					__lowerCAmelCase       =				' '.join([context[i] for i in range(len(UpperCAmelCase__   )   ) if not is_html[i]]   )

					# 0 - No ; 1 - Yes
					for answer in sample["annotations"]["yes_no_answer"]:
										if answer == 0 or answer == 1:
															return {
															    "question": question,
															    "context": context_string,
															    "short": [],
															    "long": [],
															    "category": "no" if answer == 0 else "yes",
															}

					__lowerCAmelCase       =				[]
					for s in short_answers:
										short_targets.extend(s['text']   )
					__lowerCAmelCase       =				list(set(UpperCAmelCase__   )   )

					__lowerCAmelCase       =				[]
					for s in long_answers:
										if s["start_token"] == -1:
															continue
										__lowerCAmelCase       =				context[s['start_token'] : s['end_token']]
										__lowerCAmelCase       =				is_html[s['start_token'] : s['end_token']]
										__lowerCAmelCase       =				' '.join([answer[i] for i in range(len(UpperCAmelCase__   )   ) if not html[i]]   )
										if new_answer not in long_targets:
															long_targets.append(UpperCAmelCase__   )

					__lowerCAmelCase       =				'long_short' if len(short_targets + long_targets   ) > 0 else 'null'

					return {
					    "question": question,
					    "context": context_string,
					    "short": short_targets,
					    "long": long_targets,
					    "category": category,
					}


def 							__lowercase     (	):

					"""simple docstring"""






					__lowerCAmelCase       =				load_from_disk('natural-questions-validation'   )
					__lowerCAmelCase       =				dataset.map(UpperCAmelCase__   ).remove_columns(['annotations', 'document', 'id']   )
					print(UpperCAmelCase__   )

					__lowerCAmelCase       =				dataset.filter(lambda UpperCAmelCase__   : (len(x['question']   ) + len(x['context']   )) < 4 * 4_096   )
					__lowerCAmelCase       =				short_validation_dataset.filter(lambda UpperCAmelCase__   : x["category"] != "null"   )
					short_validation_dataset

					__lowerCAmelCase       =				'vasudevgupta/flax-bigbird-natural-questions'
					__lowerCAmelCase       =				FlaxBigBirdForNaturalQuestions.from_pretrained(UpperCAmelCase__   )
					__lowerCAmelCase       =				BigBirdTokenizerFast.from_pretrained(UpperCAmelCase__   )

					@jax.jit
					def forward(*UpperCAmelCase__		,       **UpperCAmelCase__   ):
										__lowerCAmelCase,						__lowerCAmelCase,						__lowerCAmelCase       =				model(*UpperCAmelCase__		,       **UpperCAmelCase__   )
										return start_logits, end_logits, jnp.argmax(UpperCAmelCase__		,       axis=-1   )

					def evaluate(UpperCAmelCase__   ):
										# encode question and context so that they are separated by a tokenizer.sep_token and cut at max_length
										__lowerCAmelCase       =				tokenizer(
										    example['question']		,       example['context']		,       return_tensors='np'		,       max_length=4_096		,       padding='max_length'		,       truncation=UpperCAmelCase__		,       )

										__lowerCAmelCase,						__lowerCAmelCase,						__lowerCAmelCase       =				forward(**UpperCAmelCase__   )

										__lowerCAmelCase       =				CATEGORY_MAPPING[category.item()]

										__lowerCAmelCase       =				example['long'] + example['short']
										if example["category"] in ["yes", "no", "null"]:
															__lowerCAmelCase       =				[example['category']]
										__lowerCAmelCase       =				example['category'] != 'null'
										# Now target can be: "yes", "no", "null", "list of long & short answers"

										if predicted_category in ["yes", "no", "null"]:
															__lowerCAmelCase       =				[predicted_category]
															__lowerCAmelCase       =				example['output'] == example['targets']
															__lowerCAmelCase       =				predicted_category != 'null'
															return example

										__lowerCAmelCase       =				38 if predicted_category == 'short' else 1_024
										__lowerCAmelCase,						__lowerCAmelCase       =				get_best_valid_start_end_idx(
										    start_scores[0]		,       end_scores[0]		,       top_k=8		,       max_size=UpperCAmelCase__   )

										__lowerCAmelCase       =				inputs['input_ids'][0].tolist()
										__lowerCAmelCase       =				[tokenizer.decode(input_ids[start_score : end_score + 1]   )]

										__lowerCAmelCase       =				expand_to_aliases(example['targets']		,       make_sub_answers=UpperCAmelCase__   )
										__lowerCAmelCase       =				expand_to_aliases(example['output']   )

										# some preprocessing to both prediction and answer
										__lowerCAmelCase       =				{''.join(a.split()   ) for a in answers}
										__lowerCAmelCase       =				{''.join(p.split()   ) for p in predictions}
										__lowerCAmelCase       =				{s for s in predictions if s not in ['``', '\'\'', '`', '\'']}

										# if there is a common element, it's a exact match
										__lowerCAmelCase       =				len(list(answers & predictions   )   ) > 0
										__lowerCAmelCase       =				predicted_category != 'null' and len(UpperCAmelCase__   ) > 0

										return example

					__lowerCAmelCase       =				short_validation_dataset.map(UpperCAmelCase__   )

					__lowerCAmelCase       =				len(UpperCAmelCase__   )
					__lowerCAmelCase       =				len(short_validation_dataset.filter(lambda UpperCAmelCase__   : x["match"] == 1   )   )
					print('EM score:'		,       (matched / total) * 100		,       '%'   )


if __name__ == "__main__":
		main()






