



'''simple docstring'''
import tempfile
import unittest

import numpy as np
import torch

from diffusers import ScoreSdeVeScheduler


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


	A__    :    List[str]						=				(ScoreSdeVeScheduler,)
	A__    :    Any						=				()


	@property
	def      a__ (		self			) ->							str:
				A:      Any														=   4
				A:      Union[str, Any]														=   3
				A:      Union[str, Any]														=   8
				A:      Optional[Any]														=   8

				A:      Optional[int]														=   torch.rand((batch_size, num_channels, height, width)			)

				return sample


	@property
	def      a__ (		self			) ->							Optional[Any]:
				A:      str														=   4
				A:      Optional[int]														=   3
				A:      Dict														=   8
				A:      Optional[Any]														=   8

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

				return sample


	def      a__ (		self			) ->							Optional[int]:
				def model(A					,   A					,   *A			):
							return sample * t / (t + 1)

				return model


	def      a__ (		self					,   **A			) ->							Dict:
				A:      Dict														=   {
				    """num_train_timesteps""": 20_00,
				    """snr""": 0.15,
				    """sigma_min""": 0.01,
				    """sigma_max""": 13_48,
				    """sampling_eps""": 1e-5,
				}

				config.update(**A			)
				return config


	def      a__ (		self					,   A=0					,   **A			) ->							int:
				A:      str														=   dict(self.forward_default_kwargs			)

				for scheduler_class in self.scheduler_classes:
							A:      List[Any]														=   self.dummy_sample
							A:      Union[str, Any]														=   0.1 * sample

							A:      str														=   self.get_scheduler_config(**A			)
							A:      List[str]														=   scheduler_class(**A			)

							with tempfile.TemporaryDirectory() as tmpdirname:
										scheduler.save_config(A			)
										A:      int														=   scheduler_class.from_pretrained(A			)

							A:      int														=   scheduler.step_pred(
							    A					,   A					,   A					,   generator=torch.manual_seed(0			)					,   **A			).prev_sample
							A:      Optional[Any]														=   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"

							A:      Tuple														=   scheduler.step_correct(A					,   A					,   generator=torch.manual_seed(0			)					,   **A			).prev_sample
							A:      Dict														=   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      a__ (		self					,   A=0					,   **A			) ->							Optional[int]:
				A:      Union[str, Any]														=   dict(self.forward_default_kwargs			)
				kwargs.update(A			)

				for scheduler_class in self.scheduler_classes:
							A:      Tuple														=   self.dummy_sample
							A:      Optional[Any]														=   0.1 * sample

							A:      Optional[Any]														=   self.get_scheduler_config()
							A:      Union[str, Any]														=   scheduler_class(**A			)

							with tempfile.TemporaryDirectory() as tmpdirname:
										scheduler.save_config(A			)
										A:      Dict														=   scheduler_class.from_pretrained(A			)

							A:      Tuple														=   scheduler.step_pred(
							    A					,   A					,   A					,   generator=torch.manual_seed(0			)					,   **A			).prev_sample
							A:      List[Any]														=   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"

							A:      int														=   scheduler.step_correct(A					,   A					,   generator=torch.manual_seed(0			)					,   **A			).prev_sample
							A:      Optional[Any]														=   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      a__ (		self			) ->							Dict:
				for timesteps in [10, 1_00, 10_00]:
							self.check_over_configs(num_train_timesteps=A			)


	def      a__ (		self			) ->							Any:
				for sigma_min, sigma_max in zip([0.0001, 0.001, 0.01]					,   [1, 1_00, 10_00]			):
							self.check_over_configs(sigma_min=A					,   sigma_max=A			)


	def      a__ (		self			) ->							int:
				for t in [0.1, 0.5, 0.75]:
							self.check_over_forward(time_step=A			)


	def      a__ (		self			) ->							Optional[int]:
				A:      Union[str, Any]														=   dict(self.forward_default_kwargs			)

				A:      Optional[int]														=   self.scheduler_classes[0]
				A:      Union[str, Any]														=   self.get_scheduler_config()
				A:      Union[str, Any]														=   scheduler_class(**A			)

				A:      Tuple														=   3

				A:      Tuple														=   self.dummy_model()
				A:      List[Any]														=   self.dummy_sample_deter

				scheduler.set_sigmas(A			)
				scheduler.set_timesteps(A			)
				A:      Tuple														=   torch.manual_seed(0			)

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

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

							with torch.no_grad():
										A:      Optional[Any]														=   model(A					,   A			)

							A:      List[str]														=   scheduler.step_pred(A					,   A					,   A					,   generator=A					,   **A			)
							A							,		A:      int														=   output.prev_sample, output.prev_sample_mean

				A:      Tuple														=   torch.sum(torch.abs(A			)			)
				A:      int														=   torch.mean(torch.abs(A			)			)

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





	def      a__ (		self			) ->							Tuple:
				A:      Dict														=   dict(self.forward_default_kwargs			)

				A:      Tuple														=   kwargs.pop("""num_inference_steps"""					,   A			)

				for scheduler_class in self.scheduler_classes:
							A:      List[Any]														=   self.get_scheduler_config()
							A:      List[Any]														=   scheduler_class(**A			)

							A:      Tuple														=   self.dummy_sample
							A:      int														=   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"""			):
										A:      str														=   num_inference_steps

							A:      int														=   scheduler.step_pred(A					,   0					,   A					,   generator=torch.manual_seed(0			)					,   **A			).prev_sample
							A:      Optional[Any]														=   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			)










'''simple docstring'''
import itertools
from dataclasses import dataclass
from typing import Optional

import pyarrow as pa

import datasets
from datasets.table import table_cast


__SCREAMING_SNAKE_CASE :      List[Any]             =datasets.utils.logging.get_logger(__name__)


@dataclass
class   SCREAMING_SNAKE_CASE__				(   datasets.BuilderConfig					):
	"""simple docstring"""


	A__    :    Optional[datasets.Features]						=				None







class   SCREAMING_SNAKE_CASE__				(   datasets.ArrowBasedBuilder					):
	"""simple docstring"""


	A__    :    Optional[int]						=				ArrowConfig


	def      a__ (		self			) ->							Union[str, Any]:
				return datasets.DatasetInfo(features=self.config.features			)


	def      a__ (		self					,   A			) ->							Any:
				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}'			)
				A:      Tuple														=   dl_manager.download_and_extract(self.config.data_files			)
				if isinstance(A					,   (str, list, tuple)			):
							A:      Union[str, Any]														=   data_files
							if isinstance(A					,   A			):
										A:      Tuple														=   [files]
							# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
							A:      str														=   [dl_manager.iter_files(A			) for file in files]
							return [datasets.SplitGenerator(name=datasets.Split.TRAIN					,   gen_kwargs={"""files""": files}			)]
				A:      Union[str, Any]														=   []
				for split_name, files in data_files.items():
							if isinstance(A					,   A			):
										A:      List[str]														=   [files]
							# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
							A:      List[Any]														=   [dl_manager.iter_files(A			) 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(A			):
													with open(A					,   """rb"""			) as f:
																A:      Dict														=   datasets.Features.from_arrow_schema(pa.ipc.open_stream(A			).schema			)
													break
							splits.append(datasets.SplitGenerator(name=A					,   gen_kwargs={"""files""": files}			)			)
				return splits


	def      a__ (		self					,   A			) ->							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
							A:      Dict														=   table_cast(A					,   self.info.features.arrow_schema			)
				return pa_table





	def      a__ (		self					,   A			) ->							Dict:
				for file_idx, file in enumerate(itertools.chain.from_iterable(A			)			):
							with open(A					,   """rb"""			) as f:
										try:
													for batch_idx, record_batch in enumerate(pa.ipc.open_stream(A			)			):
																A:      Union[str, Any]														=   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(A			)
										except ValueError as e:
													logger.error(f'Failed to read file \'{file}\' with error {type(A			)}: {e}'			)
													raise





