import tempfile
import unittest

import numpy as np
import torch

from diffusers import ScoreSdeVeScheduler




class 					_snake_case						(						unittest.TestCase				):
					# TODO adapt with class SchedulerCommonTest (scheduler needs Numpy Integration)
					lowerCamelCase__:		str  =     (ScoreSdeVeScheduler,)
					lowerCamelCase__:		List[Any]  =     ()
					@property
					def    _lowerCamelCase (  self:				List[Any]     )    ->				Dict:
												__UpperCAmelCase						:			List[str]         =   4
												__UpperCAmelCase						:			List[str]         =   3
												__UpperCAmelCase						:			List[Any]         =   8
												__UpperCAmelCase						:			Dict         =   8

												__UpperCAmelCase						:			List[str]         =   torch.rand((batch_size, num_channels, height, width)     )

												return sample
					@property
					def    _lowerCamelCase (  self:				str     )    ->				Dict:
												__UpperCAmelCase						:			List[str]         =   4
												__UpperCAmelCase						:			Tuple         =   3
												__UpperCAmelCase						:			Union[str, Any]         =   8
												__UpperCAmelCase						:			Optional[int]         =   8

												__UpperCAmelCase						:			Tuple         =   batch_size * num_channels * height * width
												__UpperCAmelCase						:			int         =   torch.arange(__lowerCamelCase     )
												__UpperCAmelCase						:			Optional[Any]         =   sample.reshape(__lowerCamelCase	,  __lowerCamelCase	,  __lowerCamelCase	,  __lowerCamelCase     )
												__UpperCAmelCase						:			Any         =   sample / num_elems
												__UpperCAmelCase						:			Union[str, Any]         =   sample.permute(3	,  0	,  1	,  2     )

												return sample
					def    _lowerCamelCase (  self:				Union[str, Any]     )    ->				int:
												def model(__lowerCamelCase:				Dict	,  __lowerCamelCase:				Optional[Any]	,  *__lowerCamelCase:				Any     ):
																			return sample * t / (t + 1)

												return model
					def    _lowerCamelCase (  self:				Any	,  **__lowerCamelCase:				str     )    ->				List[Any]:
												__UpperCAmelCase						:			List[str]         =   {
												    "num_train_timesteps": 20_00,
												    "snr": 0.15,
												    "sigma_min": 0.01,
												    "sigma_max": 13_48,
												    "sampling_eps": 1e-5,
												}

												config.update(**__lowerCamelCase     )
												return config
					def    _lowerCamelCase (  self:				Union[str, Any]	,  __lowerCamelCase:				Any=0	,  **__lowerCamelCase:				List[Any]     )    ->				List[Any]:
												__UpperCAmelCase						:			str         =   dict(self.forward_default_kwargs     )

												for scheduler_class in self.scheduler_classes:
																			__UpperCAmelCase						:			List[Any]         =   self.dummy_sample
																			__UpperCAmelCase						:			int         =   0.1 * sample

																			__UpperCAmelCase						:			str         =   self.get_scheduler_config(**__lowerCamelCase     )
																			__UpperCAmelCase						:			List[Any]         =   scheduler_class(**__lowerCamelCase     )

																			with tempfile.TemporaryDirectory() as tmpdirname:
																										scheduler.save_config(__lowerCamelCase     )
																										__UpperCAmelCase						:			List[Any]         =   scheduler_class.from_pretrained(__lowerCamelCase     )

																			__UpperCAmelCase						:			Union[str, Any]         =   scheduler.step_pred(
																			    __lowerCamelCase	,  __lowerCamelCase	,  __lowerCamelCase	,  generator=torch.manual_seed(0     )	,  **__lowerCamelCase     ).prev_sample
																			__UpperCAmelCase						:			List[Any]         =   new_scheduler.step_pred(
																			    __lowerCamelCase	,  __lowerCamelCase	,  __lowerCamelCase	,  generator=torch.manual_seed(0     )	,  **__lowerCamelCase     ).prev_sample

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

																			__UpperCAmelCase						:			int         =   scheduler.step_correct(__lowerCamelCase	,  __lowerCamelCase	,  generator=torch.manual_seed(0     )	,  **__lowerCamelCase     ).prev_sample
																			__UpperCAmelCase						:			List[str]         =   new_scheduler.step_correct(
																			    __lowerCamelCase	,  __lowerCamelCase	,  generator=torch.manual_seed(0     )	,  **__lowerCamelCase     ).prev_sample

																			assert torch.sum(torch.abs(output - new_output     )     ) < 1e-5, "Scheduler correction are not identical"
					def    _lowerCamelCase (  self:				List[str]	,  __lowerCamelCase:				str=0	,  **__lowerCamelCase:				Any     )    ->				str:
												__UpperCAmelCase						:			Union[str, Any]         =   dict(self.forward_default_kwargs     )
												kwargs.update(__lowerCamelCase     )

												for scheduler_class in self.scheduler_classes:
																			__UpperCAmelCase						:			Dict         =   self.dummy_sample
																			__UpperCAmelCase						:			str         =   0.1 * sample

																			__UpperCAmelCase						:			int         =   self.get_scheduler_config()
																			__UpperCAmelCase						:			List[str]         =   scheduler_class(**__lowerCamelCase     )

																			with tempfile.TemporaryDirectory() as tmpdirname:
																										scheduler.save_config(__lowerCamelCase     )
																										__UpperCAmelCase						:			Tuple         =   scheduler_class.from_pretrained(__lowerCamelCase     )

																			__UpperCAmelCase						:			Optional[Any]         =   scheduler.step_pred(
																			    __lowerCamelCase	,  __lowerCamelCase	,  __lowerCamelCase	,  generator=torch.manual_seed(0     )	,  **__lowerCamelCase     ).prev_sample
																			__UpperCAmelCase						:			List[str]         =   new_scheduler.step_pred(
																			    __lowerCamelCase	,  __lowerCamelCase	,  __lowerCamelCase	,  generator=torch.manual_seed(0     )	,  **__lowerCamelCase     ).prev_sample

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

																			__UpperCAmelCase						:			str         =   scheduler.step_correct(__lowerCamelCase	,  __lowerCamelCase	,  generator=torch.manual_seed(0     )	,  **__lowerCamelCase     ).prev_sample
																			__UpperCAmelCase						:			Any         =   new_scheduler.step_correct(
																			    __lowerCamelCase	,  __lowerCamelCase	,  generator=torch.manual_seed(0     )	,  **__lowerCamelCase     ).prev_sample

																			assert torch.sum(torch.abs(output - new_output     )     ) < 1e-5, "Scheduler correction are not identical"
					def    _lowerCamelCase (  self:				Optional[Any]     )    ->				Dict:
												for timesteps in [10, 1_00, 10_00]:
																			self.check_over_configs(num_train_timesteps=__lowerCamelCase     )
					def    _lowerCamelCase (  self:				List[Any]     )    ->				Optional[Any]:
												for sigma_min, sigma_max in zip([0.00_01, 0.0_01, 0.01]	,  [1, 1_00, 10_00]     ):
																			self.check_over_configs(sigma_min=__lowerCamelCase	,  sigma_max=__lowerCamelCase     )
					def    _lowerCamelCase (  self:				List[Any]     )    ->				Tuple:
												for t in [0.1, 0.5, 0.75]:
																			self.check_over_forward(time_step=__lowerCamelCase     )
					def    _lowerCamelCase (  self:				Dict     )    ->				Union[str, Any]:
												__UpperCAmelCase						:			Optional[int]         =   dict(self.forward_default_kwargs     )

												__UpperCAmelCase						:			int         =   self.scheduler_classes[0]
												__UpperCAmelCase						:			Optional[Any]         =   self.get_scheduler_config()
												__UpperCAmelCase						:			Optional[int]         =   scheduler_class(**__lowerCamelCase     )

												__UpperCAmelCase						:			int         =   3

												__UpperCAmelCase						:			Dict         =   self.dummy_model()
												__UpperCAmelCase						:			List[str]         =   self.dummy_sample_deter

												scheduler.set_sigmas(__lowerCamelCase     )
												scheduler.set_timesteps(__lowerCamelCase     )
												__UpperCAmelCase						:			List[str]         =   torch.manual_seed(0     )

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

																			for _ in range(scheduler.config.correct_steps     ):
																										with torch.no_grad():
																																	__UpperCAmelCase						:			str         =   model(__lowerCamelCase	,  __lowerCamelCase     )
																										__UpperCAmelCase						:			List[Any]         =   scheduler.step_correct(__lowerCamelCase	,  __lowerCamelCase	,  generator=__lowerCamelCase	,  **__lowerCamelCase     ).prev_sample

																			with torch.no_grad():
																										__UpperCAmelCase						:			Any         =   model(__lowerCamelCase	,  __lowerCamelCase     )

																			__UpperCAmelCase						:			Union[str, Any]         =   scheduler.step_pred(__lowerCamelCase	,  __lowerCamelCase	,  __lowerCamelCase	,  generator=__lowerCamelCase	,  **__lowerCamelCase     )
																			__UpperCAmelCase    ,	__UpperCAmelCase						:			Dict         =   output.prev_sample, output.prev_sample_mean

												__UpperCAmelCase						:			int         =   torch.sum(torch.abs(__lowerCamelCase     )     )
												__UpperCAmelCase						:			List[str]         =   torch.mean(torch.abs(__lowerCamelCase     )     )

												assert np.isclose(result_sum.item()	,  1_43_72_75_85_28.0     )
												assert np.isclose(result_mean.item()	,  18_71_45_30.0     )





					def    _lowerCamelCase (  self:				int     )    ->				Optional[int]:
												__UpperCAmelCase						:			List[str]         =   dict(self.forward_default_kwargs     )

												__UpperCAmelCase						:			Optional[Any]         =   kwargs.pop("num_inference_steps"	,  __lowerCamelCase     )

												for scheduler_class in self.scheduler_classes:
																			__UpperCAmelCase						:			List[str]         =   self.get_scheduler_config()
																			__UpperCAmelCase						:			int         =   scheduler_class(**__lowerCamelCase     )

																			__UpperCAmelCase						:			List[Any]         =   self.dummy_sample
																			__UpperCAmelCase						:			Union[str, Any]         =   0.1 * sample

																			if num_inference_steps is not None and hasattr(__lowerCamelCase	,  "set_timesteps"     ):
																										scheduler.set_timesteps(__lowerCamelCase     )
																			elif num_inference_steps is not None and not hasattr(__lowerCamelCase	,  "set_timesteps"     ):
																										__UpperCAmelCase						:			List[Any]         =   num_inference_steps

																			__UpperCAmelCase						:			Optional[int]         =   scheduler.step_pred(__lowerCamelCase	,  0	,  __lowerCamelCase	,  generator=torch.manual_seed(0     )	,  **__lowerCamelCase     ).prev_sample
																			__UpperCAmelCase						:			Dict         =   scheduler.step_pred(__lowerCamelCase	,  1	,  __lowerCamelCase	,  generator=torch.manual_seed(0     )	,  **__lowerCamelCase     ).prev_sample

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





import itertools
from dataclasses import dataclass
from typing import Optional

import pyarrow as pa

import datasets
from datasets.table import table_cast


_snake_case			=						datasets.utils.logging.get_logger(__name__)




@dataclass
class 					_snake_case						(						datasets.BuilderConfig				):
					lowerCamelCase__:		Optional[datasets.Features]  =     None




class 					_snake_case						(						datasets.ArrowBasedBuilder				):
					lowerCamelCase__:		Union[str, Any]  =     ArrowConfig
					def    _lowerCamelCase (  self:				Optional[int]     )    ->				Optional[int]:
												return datasets.DatasetInfo(features=self.config.features     )
					def    _lowerCamelCase (  self:				List[Any]	,  __lowerCamelCase:				Tuple     )    ->				str:
												if not self.config.data_files:
																			raise ValueError(f'''At least one data file must be specified, but got data_files={self.config.data_files}'''     )
												__UpperCAmelCase						:			Dict         =   dl_manager.download_and_extract(self.config.data_files     )
												if isinstance(__lowerCamelCase	,  (str, list, tuple)     ):
																			__UpperCAmelCase						:			str         =   data_files
																			if isinstance(__lowerCamelCase	,  __lowerCamelCase     ):
																										__UpperCAmelCase						:			List[str]         =   [files]
																			# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
																			__UpperCAmelCase						:			List[Any]         =   [dl_manager.iter_files(__lowerCamelCase     ) for file in files]
																			return [datasets.SplitGenerator(name=datasets.Split.TRAIN	,  gen_kwargs={"files": files}     )]
												__UpperCAmelCase						:			Union[str, Any]         =   []
												for split_name, files in data_files.items():
																			if isinstance(__lowerCamelCase	,  __lowerCamelCase     ):
																										__UpperCAmelCase						:			Tuple         =   [files]
																			# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
																			__UpperCAmelCase						:			List[Any]         =   [dl_manager.iter_files(__lowerCamelCase     ) for file in files]
																			# Infer features is they are stoed in the arrow schema
																			if self.info.features is None:
																										for file in itertools.chain.from_iterable(__lowerCamelCase     ):
																																	with open(__lowerCamelCase	,  "rb"     ) as f:
																																								__UpperCAmelCase						:			Union[str, Any]         =   datasets.Features.from_arrow_schema(pa.ipc.open_stream(__lowerCamelCase     ).schema     )
																																	break
																			splits.append(datasets.SplitGenerator(name=__lowerCamelCase	,  gen_kwargs={"files": files}     )     )
												return splits
					def    _lowerCamelCase (  self:				str	,  __lowerCamelCase:				pa.Table     )    ->				pa.Table:
												if self.info.features is not None:
																			# more expensive cast to support nested features with keys in a different order
																			# allows str <-> int/float or str to Audio for example
																			__UpperCAmelCase						:			List[str]         =   table_cast(__lowerCamelCase	,  self.info.features.arrow_schema     )
												return pa_table





					def    _lowerCamelCase (  self:				Any	,  __lowerCamelCase:				Any     )    ->				Any:
												for file_idx, file in enumerate(itertools.chain.from_iterable(__lowerCamelCase     )     ):
																			with open(__lowerCamelCase	,  "rb"     ) as f:
																										try:
																																	for batch_idx, record_batch in enumerate(pa.ipc.open_stream(__lowerCamelCase     )     ):
																																								__UpperCAmelCase						:			Optional[int]         =   pa.Table.from_batches([record_batch]     )
																																								# Uncomment for debugging (will print the Arrow table size and elements)
																																								# logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
																																								# logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
																																								yield f'''{file_idx}_{batch_idx}''', self._cast_table(__lowerCamelCase     )
																										except ValueError as e:
																																	logger.error(f'''Failed to read file \'{file}\' with error {type(__lowerCamelCase     )}: {e}'''     )
																																	raise




