





import torch

from diffusers import DDPMScheduler

from .test_schedulers import SchedulerCommonTest






class       __A					(					lowerCAmelCase				):

			'''simple docstring'''







			lowerCAmelCase_							  =		(DDPMScheduler,)





			def 	__lowerCamelCase       (					self ,						**__lowerCAmelCase							):



				'''simple docstring'''







				lowerCamelCase__              =	{
				    '''num_train_timesteps''': 1_0_0_0,
				    '''beta_start''': 0.0001,
				    '''beta_end''': 0.02,
				    '''beta_schedule''': '''linear''',
				    '''variance_type''': '''fixed_small''',
				    '''clip_sample''': True,
				}

				config.update(**__lowerCAmelCase							)
				return config





			def 	__lowerCamelCase       (					self							):



				'''simple docstring'''







				for timesteps in [1, 5, 1_0_0, 1_0_0_0]:
					self.check_over_configs(num_train_timesteps=__lowerCAmelCase							)





			def 	__lowerCamelCase       (					self							):



				'''simple docstring'''







				for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1] ,						[0.002, 0.02, 0.2, 2]							):
					self.check_over_configs(beta_start=__lowerCAmelCase ,						beta_end=__lowerCAmelCase							)





			def 	__lowerCamelCase       (					self							):



				'''simple docstring'''







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





			def 	__lowerCamelCase       (					self							):



				'''simple docstring'''







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





			def 	__lowerCamelCase       (					self							):



				'''simple docstring'''







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





			def 	__lowerCamelCase       (					self							):



				'''simple docstring'''







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





			def 	__lowerCamelCase       (					self							):



				'''simple docstring'''







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





			def 	__lowerCamelCase       (					self							):



				'''simple docstring'''







				for t in [0, 5_0_0, 9_9_9]:
					self.check_over_forward(time_step=__lowerCAmelCase							)





			def 	__lowerCamelCase       (					self							):



				'''simple docstring'''







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

				assert torch.sum(torch.abs(scheduler._get_variance(0							) - 0.0							)							) < 1E-5
				assert torch.sum(torch.abs(scheduler._get_variance(4_8_7							) - 0.0_0979							)							) < 1E-5
				assert torch.sum(torch.abs(scheduler._get_variance(9_9_9							) - 0.02							)							) < 1E-5





			def 	__lowerCamelCase       (					self							):



				'''simple docstring'''







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

				lowerCamelCase__              =	len(__lowerCAmelCase							)

				lowerCamelCase__              =	self.dummy_model()
				lowerCamelCase__              =	self.dummy_sample_deter
				lowerCamelCase__              =	torch.manual_seed(0							)

				for t in reversed(range(__lowerCAmelCase							)							):
					# 1. predict noise residual
					lowerCamelCase__              =	model(__lowerCAmelCase ,						__lowerCAmelCase							)

					# 2. predict previous mean of sample x_t-1
					lowerCamelCase__              =	scheduler.step(__lowerCAmelCase ,						__lowerCAmelCase ,						__lowerCAmelCase ,						generator=__lowerCAmelCase							).prev_sample

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

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

				assert abs(result_sum.item() - 258.9606							) < 1E-2
				assert abs(result_mean.item() - 0.3372							) < 1E-3





			def 	__lowerCamelCase       (					self							):



				'''simple docstring'''







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

				lowerCamelCase__              =	len(__lowerCAmelCase							)

				lowerCamelCase__              =	self.dummy_model()
				lowerCamelCase__              =	self.dummy_sample_deter
				lowerCamelCase__              =	torch.manual_seed(0							)

				for t in reversed(range(__lowerCAmelCase							)							):
					# 1. predict noise residual
					lowerCamelCase__              =	model(__lowerCAmelCase ,						__lowerCAmelCase							)

					# 2. predict previous mean of sample x_t-1
					lowerCamelCase__              =	scheduler.step(__lowerCAmelCase ,						__lowerCAmelCase ,						__lowerCAmelCase ,						generator=__lowerCAmelCase							).prev_sample

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

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

				assert abs(result_sum.item() - 202.0296							) < 1E-2
				assert abs(result_mean.item() - 0.2631							) < 1E-3





			def 	__lowerCamelCase       (					self							):



				'''simple docstring'''







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

				lowerCamelCase__              =	[1_0_0, 8_7, 5_0, 1, 0]

				scheduler.set_timesteps(timesteps=__lowerCAmelCase							)

				lowerCamelCase__              =	scheduler.timesteps

				for i, timestep in enumerate(__lowerCAmelCase							):
					if i == len(__lowerCAmelCase							) - 1:
						lowerCamelCase__              =	-1
					else:
						lowerCamelCase__              =	timesteps[i + 1]

					lowerCamelCase__              =	scheduler.previous_timestep(__lowerCAmelCase							)
					lowerCamelCase__              =	prev_t.item()

					self.assertEqual(__lowerCAmelCase ,						__lowerCAmelCase							)





			def 	__lowerCamelCase       (					self							):



				'''simple docstring'''







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

				lowerCamelCase__              =	[1_0_0, 8_7, 5_0, 5_1, 0]

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





			def 	__lowerCamelCase       (					self							):



				'''simple docstring'''







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

				lowerCamelCase__              =	[1_0_0, 8_7, 5_0, 1, 0]
				lowerCamelCase__              =	len(__lowerCAmelCase							)

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





			def 	__lowerCamelCase       (					self							):



				'''simple docstring'''







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

				lowerCamelCase__              =	[scheduler.config.num_train_timesteps]

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










import os
from collections import namedtuple

import pytest

from datasets import ClassLabel, Features, Sequence, Value
from datasets.commands.test import TestCommand
from datasets.info import DatasetInfo, DatasetInfosDict


_a						=   namedtuple(
    "_TestCommandArgs",
    [
        "dataset",
        "name",
        "cache_dir",
        "data_dir",
        "all_configs",
        "save_infos",
        "ignore_verifications",
        "force_redownload",
        "clear_cache",
    ],
    defaults=[None, None, None, False, False, False, False, False],
)





def lowerCAmelCase__(__snake_case ,__snake_case							)       ->   List[str]:







	'''simple docstring'''







	return (abs(source - target							) / target) < 0.0_1





@pytest.mark.integration
def lowerCAmelCase__(__snake_case							)       ->   Tuple:







	'''simple docstring'''







	lowerCamelCase__              =	_TestCommandArgs(dataset=__snake_case ,all_configs=__snake_case ,save_infos=__snake_case							)
	lowerCamelCase__              =	TestCommand(*__snake_case							)
	test_command.run()
	lowerCamelCase__              =	os.path.join(__snake_case ,'''README.md'''							)
	assert os.path.exists(__snake_case							)
	lowerCamelCase__              =	DatasetInfosDict.from_directory(__snake_case							)
	lowerCamelCase__              =	DatasetInfosDict(
	    {
	        '''default''': DatasetInfo(
	            features=Features(
	                {
	                    '''tokens''': Sequence(Value('''string'''							)							),
	                    '''ner_tags''': Sequence(
	                        ClassLabel(names=['''O''', '''B-PER''', '''I-PER''', '''B-ORG''', '''I-ORG''', '''B-LOC''', '''I-LOC''']							)							),
	                    '''langs''': Sequence(Value('''string'''							)							),
	                    '''spans''': Sequence(Value('''string'''							)							),
	                }							) ,splits=[
	                {
	                    '''name''': '''train''',
	                    '''num_bytes''': 2351563,
	                    '''num_examples''': 10000,
	                },
	                {
	                    '''name''': '''validation''',
	                    '''num_bytes''': 238418,
	                    '''num_examples''': 1000,
	                },
	            ] ,download_size=3940680 ,dataset_size=2589981 ,)
	    }							)
	assert dataset_infos.keys() == expected_dataset_infos.keys()
	for key in DatasetInfo._INCLUDED_INFO_IN_YAML:
		lowerCamelCase__       ,			lowerCamelCase__              =	getattr(dataset_infos['''default'''] ,__snake_case							), getattr(expected_dataset_infos['''default'''] ,__snake_case							)
		if key == "num_bytes":
			assert is_apercent_close(__snake_case ,__snake_case							)
		elif key == "splits":
			assert list(__snake_case							) == list(__snake_case							)
			for split in result:
				assert result[split].name == expected[split].name
				assert result[split].num_examples == expected[split].num_examples
				assert is_apercent_close(result[split].num_bytes ,expected[split].num_bytes							)
		else:
			result == expected



