
'''simple docstring'''

import torch

from diffusers import DDIMScheduler

from .test_schedulers import SchedulerCommonTest







class   UpperCamelCase__   (       lowercase__       ):


		"""simple docstring"""

		SCREAMING_SNAKE_CASE__  :     Optional[Any]       =							(DDIMScheduler,)
		SCREAMING_SNAKE_CASE__  :     List[str]       =							(('eta', 0.0), ('num_inference_steps', 50))







		def 				A_	(			self   ,      **snake_case      ):


							'''simple docstring'''

							UpperCAmelCase		:							List[Any]        =       {
							    "num_train_timesteps": 1_0_0_0,
							    "beta_start": 0.0001,
							    "beta_end": 0.02,
							    "beta_schedule": "linear",
							    "clip_sample": True,
							}

							config.update(**snake_case      )
							return config







		def 				A_	(			self   ,      **snake_case      ):


							'''simple docstring'''

							UpperCAmelCase		:							Dict        =       self.scheduler_classes[0]
							UpperCAmelCase		:							List[str]        =       self.get_scheduler_config(**snake_case      )
							UpperCAmelCase		:							Optional[Any]        =       scheduler_class(**snake_case      )

							UpperCAmelCase		:							Dict        =       1_0, 0.0

							UpperCAmelCase		:							List[Any]        =       self.dummy_model()
							UpperCAmelCase		:							Optional[int]        =       self.dummy_sample_deter

							scheduler.set_timesteps(snake_case      )

							for t in scheduler.timesteps:
												UpperCAmelCase		:							Union[str, Any]        =       model(snake_case   ,      snake_case      )
												UpperCAmelCase		:							Tuple        =       scheduler.step(snake_case   ,      snake_case   ,      snake_case   ,      snake_case      ).prev_sample

							return sample







		def 				A_	(			self      ):


							'''simple docstring'''

							for timesteps in [1_0_0, 5_0_0, 1_0_0_0]:
												self.check_over_configs(num_train_timesteps=snake_case      )







		def 				A_	(			self      ):


							'''simple docstring'''

							for steps_offset in [0, 1]:
												self.check_over_configs(steps_offset=snake_case      )

							UpperCAmelCase		:							Union[str, Any]        =       self.scheduler_classes[0]
							UpperCAmelCase		:							Optional[int]        =       self.get_scheduler_config(steps_offset=1      )
							UpperCAmelCase		:							Optional[int]        =       scheduler_class(**snake_case      )
							scheduler.set_timesteps(5      )
							assert torch.equal(scheduler.timesteps   ,      torch.LongTensor([8_0_1, 6_0_1, 4_0_1, 2_0_1, 1]      )      )







		def 				A_	(			self      ):


							'''simple docstring'''

							for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1]   ,      [0.002, 0.02, 0.2, 2]      ):
												self.check_over_configs(beta_start=snake_case   ,      beta_end=snake_case      )







		def 				A_	(			self      ):


							'''simple docstring'''

							for schedule in ["linear", "squaredcos_cap_v2"]:
												self.check_over_configs(beta_schedule=snake_case      )







		def 				A_	(			self      ):


							'''simple docstring'''

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







		def 				A_	(			self      ):


							'''simple docstring'''

							for clip_sample in [True, False]:
												self.check_over_configs(clip_sample=snake_case      )







		def 				A_	(			self      ):


							'''simple docstring'''

							for timestep_spacing in ["trailing", "leading"]:
												self.check_over_configs(timestep_spacing=snake_case      )







		def 				A_	(			self      ):


							'''simple docstring'''

							for rescale_betas_zero_snr in [True, False]:
												self.check_over_configs(rescale_betas_zero_snr=snake_case      )







		def 				A_	(			self      ):


							'''simple docstring'''

							self.check_over_configs(thresholding=snake_case      )
							for threshold in [0.5, 1.0, 2.0]:
												for prediction_type in ["epsilon", "v_prediction"]:
																	self.check_over_configs(
																	    thresholding=snake_case   ,      prediction_type=snake_case   ,      sample_max_value=snake_case   ,      )







		def 				A_	(			self      ):


							'''simple docstring'''

							for t in [1, 1_0, 4_9]:
												self.check_over_forward(time_step=snake_case      )







		def 				A_	(			self      ):


							'''simple docstring'''

							for t, num_inference_steps in zip([1, 1_0, 5_0]   ,      [1_0, 5_0, 5_0_0]      ):
												self.check_over_forward(time_step=snake_case   ,      num_inference_steps=snake_case      )







		def 				A_	(			self      ):


							'''simple docstring'''

							for t, eta in zip([1, 1_0, 4_9]   ,      [0.0, 0.5, 1.0]      ):
												self.check_over_forward(time_step=snake_case   ,      eta=snake_case      )







		def 				A_	(			self      ):


							'''simple docstring'''

							UpperCAmelCase		:							Tuple        =       self.scheduler_classes[0]
							UpperCAmelCase		:							Optional[Any]        =       self.get_scheduler_config()
							UpperCAmelCase		:							Optional[int]        =       scheduler_class(**snake_case      )

							assert torch.sum(torch.abs(scheduler._get_variance(0   ,      0      ) - 0.0      )      ) < 1e-5
							assert torch.sum(torch.abs(scheduler._get_variance(4_2_0   ,      4_0_0      ) - 0.1_4771      )      ) < 1e-5
							assert torch.sum(torch.abs(scheduler._get_variance(9_8_0   ,      9_6_0      ) - 0.3_2460      )      ) < 1e-5
							assert torch.sum(torch.abs(scheduler._get_variance(0   ,      0      ) - 0.0      )      ) < 1e-5
							assert torch.sum(torch.abs(scheduler._get_variance(4_8_7   ,      4_8_6      ) - 0.0_0979      )      ) < 1e-5
							assert torch.sum(torch.abs(scheduler._get_variance(9_9_9   ,      9_9_8      ) - 0.02      )      ) < 1e-5







		def 				A_	(			self      ):


							'''simple docstring'''

							UpperCAmelCase		:							List[str]        =       self.full_loop()

							UpperCAmelCase		:							List[str]        =       torch.sum(torch.abs(snake_case      )      )
							UpperCAmelCase		:							Dict        =       torch.mean(torch.abs(snake_case      )      )

							assert abs(result_sum.item() - 172.0067      ) < 1e-2
							assert abs(result_mean.item() - 0.22_3967      ) < 1e-3







		def 				A_	(			self      ):


							'''simple docstring'''

							UpperCAmelCase		:							str        =       self.full_loop(prediction_type="v_prediction"      )

							UpperCAmelCase		:							List[Any]        =       torch.sum(torch.abs(snake_case      )      )
							UpperCAmelCase		:							Tuple        =       torch.mean(torch.abs(snake_case      )      )

							assert abs(result_sum.item() - 52.5302      ) < 1e-2
							assert abs(result_mean.item() - 0.0684      ) < 1e-3







		def 				A_	(			self      ):


							'''simple docstring'''

							UpperCAmelCase		:							int        =       self.full_loop(set_alpha_to_one=snake_case   ,      beta_start=0.01      )
							UpperCAmelCase		:							Tuple        =       torch.sum(torch.abs(snake_case      )      )
							UpperCAmelCase		:							int        =       torch.mean(torch.abs(snake_case      )      )

							assert abs(result_sum.item() - 149.8295      ) < 1e-2
							assert abs(result_mean.item() - 0.1951      ) < 1e-3







		def 				A_	(			self      ):


							'''simple docstring'''

							UpperCAmelCase		:							Union[str, Any]        =       self.full_loop(set_alpha_to_one=snake_case   ,      beta_start=0.01      )
							UpperCAmelCase		:							int        =       torch.sum(torch.abs(snake_case      )      )
							UpperCAmelCase		:							Tuple        =       torch.mean(torch.abs(snake_case      )      )

							assert abs(result_sum.item() - 149.0784      ) < 1e-2
							assert abs(result_mean.item() - 0.1941      ) < 1e-3



'''simple docstring'''

import multiprocessing
import os
from typing import BinaryIO, Optional, Union

from .. import Dataset, Features, NamedSplit, config
from ..formatting import query_table
from ..packaged_modules.csv.csv import Csv
from ..utils import logging
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader







class   UpperCamelCase__   (       lowercase__       ):


		"""simple docstring"""

		def __init__(			self   ,      snake_case   ,      snake_case = None   ,      snake_case = None   ,      snake_case = None   ,      snake_case = False   ,      snake_case = False   ,      snake_case = None   ,      **snake_case   ,      ):


							'''simple docstring'''

							super().__init__(
							    snake_case   ,      split=snake_case   ,      features=snake_case   ,      cache_dir=snake_case   ,      keep_in_memory=snake_case   ,      streaming=snake_case   ,      num_proc=snake_case   ,      **snake_case   ,      )
							UpperCAmelCase		:							str        =       path_or_paths if isinstance(snake_case   ,      snake_case      ) else {self.split: path_or_paths}
							UpperCAmelCase		:							int        =       Csv(
							    cache_dir=snake_case   ,      data_files=snake_case   ,      features=snake_case   ,      **snake_case   ,      )







		def 				A_	(			self      ):


							'''simple docstring'''

							if self.streaming:
												UpperCAmelCase		:							Optional[Any]        =       self.builder.as_streaming_dataset(split=self.split      )
							# Build regular (map-style) dataset
							else:
												UpperCAmelCase		:							Union[str, Any]        =       None
												UpperCAmelCase		:							str        =       None
												UpperCAmelCase		:							Union[str, Any]        =       None
												UpperCAmelCase		:							int        =       None

												self.builder.download_and_prepare(
												    download_config=snake_case   ,      download_mode=snake_case   ,      verification_mode=snake_case   ,      base_path=snake_case   ,      num_proc=self.num_proc   ,      )
												UpperCAmelCase		:							Tuple        =       self.builder.as_dataset(
												    split=self.split   ,      verification_mode=snake_case   ,      in_memory=self.keep_in_memory      )
							return dataset







class   UpperCamelCase__   :


		"""simple docstring"""

		def __init__(			self   ,      snake_case   ,      snake_case   ,      snake_case = None   ,      snake_case = None   ,      **snake_case   ,      ):


							'''simple docstring'''

							if num_proc is not None and num_proc <= 0:
												raise ValueError(f"num_proc {num_proc} must be an integer > 0."      )

							UpperCAmelCase		:							Tuple        =       dataset
							UpperCAmelCase		:							Dict        =       path_or_buf
							UpperCAmelCase		:							List[Any]        =       batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
							UpperCAmelCase		:							str        =       num_proc
							UpperCAmelCase		:							List[Any]        =       "utf-8"
							UpperCAmelCase		:							Tuple        =       to_csv_kwargs







		def 				A_	(			self      ):


							'''simple docstring'''

							UpperCAmelCase		:							List[str]        =       self.to_csv_kwargs.pop("path_or_buf"   ,      snake_case      )
							UpperCAmelCase		:							Any        =       self.to_csv_kwargs.pop("header"   ,      snake_case      )
							UpperCAmelCase		:							Dict        =       self.to_csv_kwargs.pop("index"   ,      snake_case      )

							if isinstance(self.path_or_buf   ,      (str, bytes, os.PathLike)      ):
												with open(self.path_or_buf   ,      "wb+"      ) as buffer:
																	UpperCAmelCase		:							Tuple        =       self._write(file_obj=snake_case   ,      header=snake_case   ,      index=snake_case   ,      **self.to_csv_kwargs      )
							else:
												UpperCAmelCase		:							Any        =       self._write(file_obj=self.path_or_buf   ,      header=snake_case   ,      index=snake_case   ,      **self.to_csv_kwargs      )
							return written







		def 				A_	(			self   ,      snake_case      ):


							'''simple docstring'''

							UpperCAmelCase ,			UpperCAmelCase ,			UpperCAmelCase ,			UpperCAmelCase		:							Optional[int]        =       args

							UpperCAmelCase		:							List[str]        =       query_table(
							    table=self.dataset.data   ,      key=slice(snake_case   ,      offset + self.batch_size      )   ,      indices=self.dataset._indices   ,      )
							UpperCAmelCase		:							int        =       batch.to_pandas().to_csv(
							    path_or_buf=snake_case   ,      header=header if (offset == 0) else False   ,      index=snake_case   ,      **snake_case      )
							return csv_str.encode(self.encoding      )







		def 				A_	(			self   ,      snake_case   ,      snake_case   ,      snake_case   ,      **snake_case      ):


							'''simple docstring'''

							UpperCAmelCase		:							List[str]        =       0

							if self.num_proc is None or self.num_proc == 1:
												for offset in logging.tqdm(
												    range(0   ,      len(self.dataset      )   ,      self.batch_size      )   ,      unit="ba"   ,      disable=not logging.is_progress_bar_enabled()   ,      desc="Creating CSV from Arrow format"   ,      ):
																	UpperCAmelCase		:							Any        =       self._batch_csv((offset, header, index, to_csv_kwargs)      )
																	written += file_obj.write(snake_case      )

							else:
												UpperCAmelCase ,			UpperCAmelCase		:							Union[str, Any]        =       len(self.dataset      ), self.batch_size
												with multiprocessing.Pool(self.num_proc      ) as pool:
																	for csv_str in logging.tqdm(
																	    pool.imap(
																	        self._batch_csv   ,      [(offset, header, index, to_csv_kwargs) for offset in range(0   ,      snake_case   ,      snake_case      )]   ,      )   ,      total=(num_rows // batch_size) + 1 if num_rows % batch_size else num_rows // batch_size   ,      unit="ba"   ,      disable=not logging.is_progress_bar_enabled()   ,      desc="Creating CSV from Arrow format"   ,      ):
																						written += file_obj.write(snake_case      )

							return written
