




import torch

from diffusers import LMSDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest
class 		UpperCAmelCase       (    lowerCAmelCase_      ):







						'''simple docstring'''





						lowerCAmelCase_							=       (LMSDiscreteScheduler,)
						lowerCAmelCase_							=       10






						def 	snake_case__						(						self							:					Any	,     **__lowercase							:					Any						):






							"""simple docstring"""






							snake_case_		   =					{
							    'num_train_timesteps': 11_00,
							    'beta_start': 0.0001,
							    'beta_end': 0.02,
							    'beta_schedule': 'linear',
							}

							config.update(**_snake_case						)
							return config






						def 	snake_case__						(						self							:					List[str]						):






							"""simple docstring"""






							for timesteps in [10, 50, 1_00, 10_00]:
								self.check_over_configs(num_train_timesteps=_snake_case						)






						def 	snake_case__						(						self							:					List[str]						):






							"""simple docstring"""






							for beta_start, beta_end in zip([0.0_0001, 0.0001, 0.001]	,     [0.0002, 0.002, 0.02]						):
								self.check_over_configs(beta_start=_snake_case	,     beta_end=_snake_case						)






						def 	snake_case__						(						self							:					List[Any]						):






							"""simple docstring"""






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






						def 	snake_case__						(						self							:					Any						):






							"""simple docstring"""






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






						def 	snake_case__						(						self							:					Dict						):






							"""simple docstring"""






							for t in [0, 5_00, 8_00]:
								self.check_over_forward(time_step=_snake_case						)






						def 	snake_case__						(						self							:					int						):






							"""simple docstring"""






							snake_case_		   =					self.scheduler_classes[0]
							snake_case_		   =					self.get_scheduler_config()
							snake_case_		   =					scheduler_class(**_snake_case						)

							scheduler.set_timesteps(self.num_inference_steps						)

							snake_case_		   =					self.dummy_model()
							snake_case_		   =					self.dummy_sample_deter * scheduler.init_noise_sigma

							for i, t in enumerate(scheduler.timesteps						):
								snake_case_		   =					scheduler.scale_model_input(_snake_case	,     _snake_case						)

								snake_case_		   =					model(_snake_case	,     _snake_case						)

								snake_case_		   =					scheduler.step(_snake_case	,     _snake_case	,     _snake_case						)
								snake_case_		   =					output.prev_sample

							snake_case_		   =					torch.sum(torch.abs(_snake_case						)						)
							snake_case_		   =					torch.mean(torch.abs(_snake_case						)						)

							assert abs(result_sum.item() - 10_06.3_88						) < 1E-2
							assert abs(result_mean.item() - 1.31						) < 1E-3






						def 	snake_case__						(						self							:					Optional[Any]						):






							"""simple docstring"""






							snake_case_		   =					self.scheduler_classes[0]
							snake_case_		   =					self.get_scheduler_config(prediction_type="v_prediction"						)
							snake_case_		   =					scheduler_class(**_snake_case						)

							scheduler.set_timesteps(self.num_inference_steps						)

							snake_case_		   =					self.dummy_model()
							snake_case_		   =					self.dummy_sample_deter * scheduler.init_noise_sigma

							for i, t in enumerate(scheduler.timesteps						):
								snake_case_		   =					scheduler.scale_model_input(_snake_case	,     _snake_case						)

								snake_case_		   =					model(_snake_case	,     _snake_case						)

								snake_case_		   =					scheduler.step(_snake_case	,     _snake_case	,     _snake_case						)
								snake_case_		   =					output.prev_sample

							snake_case_		   =					torch.sum(torch.abs(_snake_case						)						)
							snake_case_		   =					torch.mean(torch.abs(_snake_case						)						)

							assert abs(result_sum.item() - 0.0017						) < 1E-2
							assert abs(result_mean.item() - 2.2676E-06						) < 1E-3






						def 	snake_case__						(						self							:					Any						):






							"""simple docstring"""






							snake_case_		   =					self.scheduler_classes[0]
							snake_case_		   =					self.get_scheduler_config()
							snake_case_		   =					scheduler_class(**_snake_case						)

							scheduler.set_timesteps(self.num_inference_steps	,     device=_snake_case						)

							snake_case_		   =					self.dummy_model()
							snake_case_		   =					self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
							snake_case_		   =					sample.to(_snake_case						)

							for i, t in enumerate(scheduler.timesteps						):
								snake_case_		   =					scheduler.scale_model_input(_snake_case	,     _snake_case						)

								snake_case_		   =					model(_snake_case	,     _snake_case						)

								snake_case_		   =					scheduler.step(_snake_case	,     _snake_case	,     _snake_case						)
								snake_case_		   =					output.prev_sample

							snake_case_		   =					torch.sum(torch.abs(_snake_case						)						)
							snake_case_		   =					torch.mean(torch.abs(_snake_case						)						)

							assert abs(result_sum.item() - 10_06.3_88						) < 1E-2
							assert abs(result_mean.item() - 1.31						) < 1E-3






						def 	snake_case__						(						self							:					int						):






							"""simple docstring"""






							snake_case_		   =					self.scheduler_classes[0]
							snake_case_		   =					self.get_scheduler_config()
							snake_case_		   =					scheduler_class(**_snake_case	,     use_karras_sigmas=_snake_case						)

							scheduler.set_timesteps(self.num_inference_steps	,     device=_snake_case						)

							snake_case_		   =					self.dummy_model()
							snake_case_		   =					self.dummy_sample_deter.to(_snake_case						) * scheduler.init_noise_sigma
							snake_case_		   =					sample.to(_snake_case						)

							for t in scheduler.timesteps:
								snake_case_		   =					scheduler.scale_model_input(_snake_case	,     _snake_case						)

								snake_case_		   =					model(_snake_case	,     _snake_case						)

								snake_case_		   =					scheduler.step(_snake_case	,     _snake_case	,     _snake_case						)
								snake_case_		   =					output.prev_sample

							snake_case_		   =					torch.sum(torch.abs(_snake_case						)						)
							snake_case_		   =					torch.mean(torch.abs(_snake_case						)						)

							assert abs(result_sum.item() - 38_12.99_27						) < 2E-2
							assert abs(result_mean.item() - 4.9648						) < 1E-3






import itertools
from dataclasses import dataclass
from typing import Optional

import pyarrow as pa

import datasets
from datasets.table import table_cast


lowercase__    :					str					=						datasets.utils.logging.get_logger(__name__)
@dataclass
class 		UpperCAmelCase       (    datasets.BuilderConfig      ):







						'''simple docstring'''





						lowerCAmelCase_							=       None







class 		UpperCAmelCase       (    datasets.ArrowBasedBuilder      ):







						'''simple docstring'''





						lowerCAmelCase_							=       ArrowConfig






						def 	snake_case__						(						self							:					Dict						):






							"""simple docstring"""






							return datasets.DatasetInfo(features=self.config.features						)






						def 	snake_case__						(						self							:					Tuple	,     __lowercase							:					Union[str, Any]						):






							"""simple docstring"""






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






						def 	snake_case__						(						self							:					str	,     __lowercase							:					pa.Table						):






							"""simple docstring"""






							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
								snake_case_		   =					table_cast(__lowercase	,     self.info.features.arrow_schema						)
							return pa_table






						def 	snake_case__						(						self							:					Tuple	,     __lowercase							:					Optional[Any]						):






							"""simple docstring"""






							for file_idx, file in enumerate(itertools.chain.from_iterable(__lowercase						)						):
								with open(__lowercase	,     "rb"						) as f:
									try:
										for batch_idx, record_batch in enumerate(pa.ipc.open_stream(__lowercase						)						):
											snake_case_		   =					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(__lowercase						)
									except ValueError as e:
										logger.error(f"Failed to read file '{file}' with error {type(__lowercase						)}: {e}"						)
										raise
