




import torch

from diffusers import EulerAncestralDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest






class    __lowerCAmelCase			(	UpperCAmelCase__			):
			snake_case_				:			Tuple								=					(EulerAncestralDiscreteScheduler,)
			snake_case_				:			Any								=					10







			def UpperCamelCase      (	self   :      List[str]   ,   **snake_case__   :      List[Any]				):




									"""simple docstring"""


									_UpperCAmelCase													=					{
									    "num_train_timesteps": 1_100,
									    "beta_start": 0.0_001,
									    "beta_end": 0.02,
									    "beta_schedule": "linear",
									}

									config.update(**snake_case__				)
									return config







			def UpperCamelCase      (	self   :      Any				):




									"""simple docstring"""


									for timesteps in [10, 50, 100, 1_000]:
															self.check_over_configs(num_train_timesteps=snake_case__				)







			def UpperCamelCase      (	self   :      Union[str, Any]				):




									"""simple docstring"""


									for beta_start, beta_end in zip([0.00_001, 0.0_001, 0.001]   ,   [0.0_002, 0.002, 0.02]				):
															self.check_over_configs(beta_start=snake_case__   ,   beta_end=snake_case__				)







			def UpperCamelCase      (	self   :      Optional[int]				):




									"""simple docstring"""


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







			def UpperCamelCase      (	self   :      Tuple				):




									"""simple docstring"""


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







			def UpperCamelCase      (	self   :      int				):




									"""simple docstring"""


									_UpperCAmelCase													=					self.scheduler_classes[0]
									_UpperCAmelCase													=					self.get_scheduler_config()
									_UpperCAmelCase													=					scheduler_class(**snake_case__				)

									scheduler.set_timesteps(self.num_inference_steps				)

									_UpperCAmelCase													=					torch.manual_seed(0				)

									_UpperCAmelCase													=					self.dummy_model()
									_UpperCAmelCase													=					self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
									_UpperCAmelCase													=					sample.to(snake_case__				)

									for i, t in enumerate(scheduler.timesteps				):
															_UpperCAmelCase													=					scheduler.scale_model_input(snake_case__   ,   snake_case__				)

															_UpperCAmelCase													=					model(snake_case__   ,   snake_case__				)

															_UpperCAmelCase													=					scheduler.step(snake_case__   ,   snake_case__   ,   snake_case__   ,   generator=snake_case__				)
															_UpperCAmelCase													=					output.prev_sample

									_UpperCAmelCase													=					torch.sum(torch.abs(snake_case__				)				)
									_UpperCAmelCase													=					torch.mean(torch.abs(snake_case__				)				)

									assert abs(result_sum.item() - 152.3_192				) < 1e-2
									assert abs(result_mean.item() - 0.1_983				) < 1e-3







			def UpperCamelCase      (	self   :      str				):




									"""simple docstring"""


									_UpperCAmelCase													=					self.scheduler_classes[0]
									_UpperCAmelCase													=					self.get_scheduler_config(prediction_type="v_prediction"				)
									_UpperCAmelCase													=					scheduler_class(**snake_case__				)

									scheduler.set_timesteps(self.num_inference_steps				)

									_UpperCAmelCase													=					torch.manual_seed(0				)

									_UpperCAmelCase													=					self.dummy_model()
									_UpperCAmelCase													=					self.dummy_sample_deter * scheduler.init_noise_sigma
									_UpperCAmelCase													=					sample.to(snake_case__				)

									for i, t in enumerate(scheduler.timesteps				):
															_UpperCAmelCase													=					scheduler.scale_model_input(snake_case__   ,   snake_case__				)

															_UpperCAmelCase													=					model(snake_case__   ,   snake_case__				)

															_UpperCAmelCase													=					scheduler.step(snake_case__   ,   snake_case__   ,   snake_case__   ,   generator=snake_case__				)
															_UpperCAmelCase													=					output.prev_sample

									_UpperCAmelCase													=					torch.sum(torch.abs(snake_case__				)				)
									_UpperCAmelCase													=					torch.mean(torch.abs(snake_case__				)				)

									assert abs(result_sum.item() - 108.4_439				) < 1e-2
									assert abs(result_mean.item() - 0.1_412				) < 1e-3







			def UpperCamelCase      (	self   :      Any				):




									"""simple docstring"""


									_UpperCAmelCase													=					self.scheduler_classes[0]
									_UpperCAmelCase													=					self.get_scheduler_config()
									_UpperCAmelCase													=					scheduler_class(**snake_case__				)

									scheduler.set_timesteps(self.num_inference_steps   ,   device=snake_case__				)
									_UpperCAmelCase													=					torch.manual_seed(0				)

									_UpperCAmelCase													=					self.dummy_model()
									_UpperCAmelCase													=					self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
									_UpperCAmelCase													=					sample.to(snake_case__				)

									for t in scheduler.timesteps:
															_UpperCAmelCase													=					scheduler.scale_model_input(snake_case__   ,   snake_case__				)

															_UpperCAmelCase													=					model(snake_case__   ,   snake_case__				)

															_UpperCAmelCase													=					scheduler.step(snake_case__   ,   snake_case__   ,   snake_case__   ,   generator=snake_case__				)
															_UpperCAmelCase													=					output.prev_sample

									_UpperCAmelCase													=					torch.sum(torch.abs(snake_case__				)				)
									_UpperCAmelCase													=					torch.mean(torch.abs(snake_case__				)				)

									assert abs(result_sum.item() - 152.3_192				) < 1e-2
									assert abs(result_mean.item() - 0.1_983				) < 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_     :    Union[str, Any]      =			datasets.utils.logging.get_logger(__name__)






@dataclass
class    __lowerCAmelCase			(	datasets.BuilderConfig			):
			snake_case_				:			Optional[datasets.Features]								=					None






class    __lowerCAmelCase			(	datasets.ArrowBasedBuilder			):
			snake_case_				:			str								=					ArrowConfig







			def UpperCamelCase      (	self   :      Optional[int]				):




									"""simple docstring"""


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







			def UpperCamelCase      (	self   :      str   ,   snake_case__   :      Optional[int]				):




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







			def UpperCamelCase      (	self   :      int   ,   snake_case__   :      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
															_UpperCAmelCase													=					table_cast(snake_case__   ,   self.info.features.arrow_schema				)
									return pa_table







			def UpperCamelCase      (	self   :      Optional[Any]   ,   snake_case__   :      int				):




									"""simple docstring"""


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




