




import tempfile
import unittest

import numpy as np
import torch

from diffusers import ScoreSdeVeScheduler






class 	A	(						unittest.TestCase						):
     """simple docstring"""

     _snake_case:		List[str]   =(ScoreSdeVeScheduler,)
     _snake_case:		str   =()




     @property
     def        __snake_case      (			self     :      Optional[Any]	) ->				List[str]:
           lowerCamelCase__					     = 4
           lowerCamelCase__					     = 3
           lowerCamelCase__					     = 8
           lowerCamelCase__					     = 8

           lowerCamelCase__					     = torch.rand((batch_size, num_channels, height, width)	)

           return sample




     @property
     def        __snake_case      (			self     :      Optional[Any]	) ->				List[str]:
           lowerCamelCase__					     = 4
           lowerCamelCase__					     = 3
           lowerCamelCase__					     = 8
           lowerCamelCase__					     = 8

           lowerCamelCase__					     = batch_size * num_channels * height * width
           lowerCamelCase__					     = torch.arange(A__	)
           lowerCamelCase__					     = sample.reshape(A__		,     A__		,     A__		,     A__	)
           lowerCamelCase__					     = sample / num_elems
           lowerCamelCase__					     = sample.permute(3		,     0		,     1		,     2	)

           return sample




     def        __snake_case      (			self     :      Tuple	) ->				str:
           def model(A__     :      Union[str, Any]		,     A__     :      Optional[Any]		,     *A__     :      str	):
                 return sample * t / (t + 1)

           return model




     def        __snake_case      (			self     :      Dict		,     **A__     :      Dict	) ->				Union[str, Any]:
           lowerCamelCase__					     = {
               'num_train_timesteps': 2_0_0_0,
               'snr': 0.15,
               'sigma_min': 0.01,
               'sigma_max': 1_3_4_8,
               'sampling_eps': 1E-5,
           }

           config.update(**A__	)
           return config




     def        __snake_case      (			self     :      str		,     A__     :      Optional[int]=0		,     **A__     :      List[Any]	) ->				str:
           lowerCamelCase__					     = dict(self.forward_default_kwargs	)

           for scheduler_class in self.scheduler_classes:
                 lowerCamelCase__					     = self.dummy_sample
                 lowerCamelCase__					     = 0.1 * sample

                 lowerCamelCase__					     = self.get_scheduler_config(**A__	)
                 lowerCamelCase__					     = scheduler_class(**A__	)

                 with tempfile.TemporaryDirectory() as tmpdirname:
                       scheduler.save_config(A__	)
                       lowerCamelCase__					     = scheduler_class.from_pretrained(A__	)

                 lowerCamelCase__					     = scheduler.step_pred(
                     A__		,     A__		,     A__		,     generator=torch.manual_seed(0	)		,     **A__	).prev_sample
                 lowerCamelCase__					     = new_scheduler.step_pred(
                     A__		,     A__		,     A__		,     generator=torch.manual_seed(0	)		,     **A__	).prev_sample

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

                 lowerCamelCase__					     = scheduler.step_correct(A__		,     A__		,     generator=torch.manual_seed(0	)		,     **A__	).prev_sample
                 lowerCamelCase__					     = new_scheduler.step_correct(
                     A__		,     A__		,     generator=torch.manual_seed(0	)		,     **A__	).prev_sample

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




     def        __snake_case      (			self     :      Any		,     A__     :      Optional[int]=0		,     **A__     :      Dict	) ->				Any:
           lowerCamelCase__					     = dict(self.forward_default_kwargs	)
           kwargs.update(A__	)

           for scheduler_class in self.scheduler_classes:
                 lowerCamelCase__					     = self.dummy_sample
                 lowerCamelCase__					     = 0.1 * sample

                 lowerCamelCase__					     = self.get_scheduler_config()
                 lowerCamelCase__					     = scheduler_class(**A__	)

                 with tempfile.TemporaryDirectory() as tmpdirname:
                       scheduler.save_config(A__	)
                       lowerCamelCase__					     = scheduler_class.from_pretrained(A__	)

                 lowerCamelCase__					     = scheduler.step_pred(
                     A__		,     A__		,     A__		,     generator=torch.manual_seed(0	)		,     **A__	).prev_sample
                 lowerCamelCase__					     = new_scheduler.step_pred(
                     A__		,     A__		,     A__		,     generator=torch.manual_seed(0	)		,     **A__	).prev_sample

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

                 lowerCamelCase__					     = scheduler.step_correct(A__		,     A__		,     generator=torch.manual_seed(0	)		,     **A__	).prev_sample
                 lowerCamelCase__					     = new_scheduler.step_correct(
                     A__		,     A__		,     generator=torch.manual_seed(0	)		,     **A__	).prev_sample

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




     def        __snake_case      (			self     :      int	) ->				int:
           for timesteps in [1_0, 1_0_0, 1_0_0_0]:
                 self.check_over_configs(num_train_timesteps=A__	)




     def        __snake_case      (			self     :      str	) ->				Any:
           for sigma_min, sigma_max in zip([0.0001, 0.001, 0.01]		,     [1, 1_0_0, 1_0_0_0]	):
                 self.check_over_configs(sigma_min=A__		,     sigma_max=A__	)




     def        __snake_case      (			self     :      List[str]	) ->				Dict:
           for t in [0.1, 0.5, 0.75]:
                 self.check_over_forward(time_step=A__	)




     def        __snake_case      (			self     :      str	) ->				Any:
           lowerCamelCase__					     = dict(self.forward_default_kwargs	)

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

           lowerCamelCase__					     = 3

           lowerCamelCase__					     = self.dummy_model()
           lowerCamelCase__					     = self.dummy_sample_deter

           scheduler.set_sigmas(A__	)
           scheduler.set_timesteps(A__	)
           lowerCamelCase__					     = torch.manual_seed(0	)

           for i, t in enumerate(scheduler.timesteps	):
                 lowerCamelCase__					     = scheduler.sigmas[i]

                 for _ in range(scheduler.config.correct_steps	):
                       with torch.no_grad():
                             lowerCamelCase__					     = model(A__		,     A__	)
                       lowerCamelCase__					     = scheduler.step_correct(A__		,     A__		,     generator=A__		,     **A__	).prev_sample

                 with torch.no_grad():
                       lowerCamelCase__					     = model(A__		,     A__	)

                 lowerCamelCase__					     = scheduler.step_pred(A__		,     A__		,     A__		,     generator=A__		,     **A__	)
                 lowerCamelCase__     ,						lowerCamelCase__					     = output.prev_sample, output.prev_sample_mean

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

           assert np.isclose(result_sum.item()		,     14372758528.0	)
           assert np.isclose(result_mean.item()		,     18714530.0	)




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

                 lowerCamelCase__					     = scheduler.step_pred(A__		,     0		,     A__		,     generator=torch.manual_seed(0	)		,     **A__	).prev_sample
                 lowerCamelCase__					     = scheduler.step_pred(A__		,     1		,     A__		,     generator=torch.manual_seed(0	)		,     **A__	).prev_sample

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





import json
import os
from dataclasses import dataclass

import numpy as np
import pyarrow as pa

import datasets
from utils import get_duration


UpperCamelCase__              =						1_0_0_0_0_0_0_0_0_0_0_0
UpperCamelCase__              =						1_0_0_0_0

UpperCamelCase__       , UpperCamelCase__              =						os.path.split(__file__)
UpperCamelCase__              =						os.path.join(RESULTS_BASEPATH, 'results', RESULTS_FILENAME.replace('.py', '.json'))






def 					snake_case					(							A						,   A			)							->							datasets.Dataset:




	'''simple docstring'''


	lowerCamelCase_							:		int							   =							pa.Table.from_pydict({'col': [0] * chunk_size}			)
	lowerCamelCase_							:		List[str]							   =							pa.concat_tables([table] * (num_examples // chunk_size)			)
	return datasets.Dataset(A						,   fingerprint='table_100B'			)


@dataclass
class 				__SCREAMING_SNAKE_CASE							:
					_lowerCamelCase		  = 42
					_lowerCamelCase		  = 42
					_lowerCamelCase		  = 42
					_lowerCamelCase		  = 42

					def 	lowercase_							(				self					):







						'''simple docstring'''




						lowerCamelCase_							:		Optional[Any]							   =							np.random.default_rng(self.seed					)
						lowerCamelCase_							:		Any							   =							rng.integers(low=self.low ,       high=self.high ,       size=self.size					).tolist()

					def __iter__(				self					):







						'''simple docstring'''




						return iter(self._sampled_values					)





					def __len__(				self					):







						'''simple docstring'''




						return self.size






@get_duration
def 					snake_case					(							A			)							->							Optional[Any]:




	'''simple docstring'''


	lowerCamelCase_							:		Union[str, Any]							   =							dataset[0]






@get_duration
def 					snake_case					(							A			)							->							Any:




	'''simple docstring'''


	lowerCamelCase_							:		List[Any]							   =							dataset[-1]






@get_duration
def 					snake_case					(							A			)							->							Optional[Any]:




	'''simple docstring'''


	lowerCamelCase_							:		Tuple							   =							dataset[range(len(A			) // 2						,   len(A			) // 2 + 1024			)]






@get_duration
def 					snake_case					(							A			)							->							str:




	'''simple docstring'''


	lowerCamelCase_							:		List[str]							   =							dataset[RandIter(0						,   len(A			)						,   1024						,   seed=42			)]






def 					snake_case					(							)							->							List[Any]:




	'''simple docstring'''


	lowerCamelCase_							:		Tuple							   =							{'num examples': SPEED_TEST_N_EXAMPLES}
	lowerCamelCase_							:		Optional[int]							   =							(get_first_row, get_last_row, get_batch_of_aaaa_rows, get_batch_of_aaaa_random_rows)
	print('generating dataset'			)
	lowerCamelCase_							:		List[Any]							   =							generate_100B_dataset(num_examples=A						,   chunk_size=A			)
	print('Functions'			)
	for func in functions:
		print(func.__name__			)
		lowerCamelCase_							:		str							   =							func(A			)

	with open(A						,   'wb'			) as f:
		f.write(json.dumps(A			).encode('utf-8'			)			)


if __name__ == "__main__":  # useful to run the profiler
							benchmark_table_100B()
