




'''simple docstring'''





import argparse
import os




class 				_snake_case	:
			lowerCAmelCase_  :   List[Any]		 =					"block_{0}.dat"




			def __init__(      self       ,							a__							)       ->       List[str]:

										'''simple docstring'''




										snake_case_			      =   filename
										snake_case_			      =   []




			def      lowerCAmelCase__       (      self       ,							a__       ,							a__							)       ->       Optional[int]:

										'''simple docstring'''




										snake_case_			      =   self.BLOCK_FILENAME_FORMAT.format(a__							)
										with open(a__       ,							"w"							) as file:
																	file.write(a__							)
										self.block_filenames.append(a__							)




			def      lowerCAmelCase__       (      self							)       ->       Any:

										'''simple docstring'''




										return self.block_filenames




			def      lowerCAmelCase__       (      self       ,							a__       ,							a__=None							)       ->       int:

										'''simple docstring'''




										snake_case_			      =   0
										with open(self.filename							) as file:
																	while True:
																								snake_case_			      =   file.readlines(a__							)

																								if lines == []:
																															break

																								if sort_key is None:
																															lines.sort()
																								else:
																															lines.sort(key=a__							)

																								self.write_block("".join(a__							)       ,							a__							)
																								i += 1




			def      lowerCAmelCase__       (      self							)       ->       List[str]:

										'''simple docstring'''




										map(os.remove       ,							self.block_filenames							)




class 				_snake_case	:




			def      lowerCAmelCase__       (      self       ,							a__							)       ->       int:

										'''simple docstring'''




										snake_case_			      =   -1
										snake_case_			      =   None

										for i in range(len(a__							)							):
																	if min_str is None or choices[i] < min_str:
																								snake_case_			      =   i

										return min_index




class 				_snake_case	:




			def __init__(      self       ,							a__							)       ->       int:

										'''simple docstring'''




										snake_case_			      =   files
										snake_case_			      =   set()
										snake_case_			      =   len(a__							)
										snake_case_			      =   {i: None for i in range(self.num_buffers							)}




			def      lowerCAmelCase__       (      self							)       ->       List[str]:

										'''simple docstring'''




										return {
										    i: self.buffers[i] for i in range(self.num_buffers							) if i not in self.empty
										}




			def      lowerCAmelCase__       (      self							)       ->       Dict:

										'''simple docstring'''




										for i in range(self.num_buffers							):
																	if self.buffers[i] is None and i not in self.empty:
																								snake_case_			      =   self.files[i].readline()

																								if self.buffers[i] == "":
																															self.empty.add(a__							)
																															self.files[i].close()

										if len(self.empty							) == self.num_buffers:
																	return False

										return True




			def      lowerCAmelCase__       (      self       ,							a__							)       ->       Optional[int]:

										'''simple docstring'''




										snake_case_			      =   self.buffers[index]
										snake_case_			      =   None

										return value




class 				_snake_case	:




			def __init__(      self       ,							a__							)       ->       Any:

										'''simple docstring'''




										snake_case_			      =   merge_strategy




			def      lowerCAmelCase__       (      self       ,							a__       ,							a__       ,							a__							)       ->       int:

										'''simple docstring'''




										snake_case_			      =   FilesArray(self.get_file_handles(a__       ,							a__							)							)
										with open(a__       ,							"w"       ,							a__							) as outfile:
																	while buffers.refresh():
																								snake_case_			      =   self.merge_strategy.select(buffers.get_dict()							)
																								outfile.write(buffers.unshift(a__							)							)




			def      lowerCAmelCase__       (      self       ,							a__       ,							a__							)       ->       Optional[int]:

										'''simple docstring'''




										snake_case_			      =   {}

										for i in range(len(a__							)							):
																	snake_case_			      =   open(filenames[i]       ,							"r"       ,							a__							)  # noqa: UP015

										return files




class 				_snake_case	:




			def __init__(      self       ,							a__							)       ->       List[Any]:

										'''simple docstring'''




										snake_case_			      =   block_size




			def      lowerCAmelCase__       (      self       ,							a__       ,							a__=None							)       ->       str:

										'''simple docstring'''




										snake_case_			      =   self.get_number_blocks(a__       ,							self.block_size							)
										snake_case_			      =   FileSplitter(a__							)
										splitter.split(self.block_size       ,							a__							)

										snake_case_			      =   FileMerger(NWayMerge()							)
										snake_case_			      =   self.block_size / (num_blocks + 1)
										merger.merge(splitter.get_block_filenames()       ,							filename + ".out"       ,							a__							)

										splitter.cleanup()




			def      lowerCAmelCase__       (      self       ,							a__       ,							a__							)       ->       List[Any]:

										'''simple docstring'''




										return (os.stat(a__							).st_size / block_size) + 1


def   UpperCamelCase_(			snake_case							: List[Any]  ):

							'''simple docstring'''






							if string[-1].lower() == "k":
														return int(string[:-1]  ) * 1_0_2_4
							elif string[-1].lower() == "m":
														return int(string[:-1]  ) * 1_0_2_4 * 1_0_2_4
							elif string[-1].lower() == "g":
														return int(string[:-1]  ) * 1_0_2_4 * 1_0_2_4 * 1_0_2_4
							else:
														return int(snake_case  )


def   UpperCamelCase_(			):

							'''simple docstring'''






							snake_case_			      =   argparse.ArgumentParser()
							parser.add_argument(
							    "-m"							,       "--mem"							,       help="amount of memory to use for sorting"							,       default="100M"  )
							parser.add_argument(
							    "filename"							,       metavar="<filename>"							,       nargs=1							,       help="name of file to sort"  )
							snake_case_			      =   parser.parse_args()

							snake_case_			      =   ExternalSort(parse_memory(args.mem  )  )
							sorter.sort(args.filename[0]  )


if __name__ == "__main__":
		main()










'''simple docstring'''





import tempfile

import torch

from diffusers import (
    DEISMultistepScheduler,
    DPMSolverMultistepScheduler,
    DPMSolverSinglestepScheduler,
    UniPCMultistepScheduler,
)

from .test_schedulers import SchedulerCommonTest




class 				_snake_case	(			lowercase_							):
			lowerCAmelCase_  :   Optional[int]		 =					(DPMSolverMultistepScheduler,)
			lowerCAmelCase_  :   int		 =					(("num_inference_steps", 25),)




			def      lowerCAmelCase__       (      self       ,							**a__							)       ->       Optional[int]:

										'''simple docstring'''




										snake_case_			      =   {
										    "num_train_timesteps": 1_000,
										    "beta_start": 0.0_0_0_1,
										    "beta_end": 0.0_2,
										    "beta_schedule": "linear",
										    "solver_order": 2,
										    "prediction_type": "epsilon",
										    "thresholding": False,
										    "sample_max_value": 1.0,
										    "algorithm_type": "dpmsolver++",
										    "solver_type": "midpoint",
										    "lower_order_final": False,
										    "lambda_min_clipped": -float("inf"							),
										    "variance_type": None,
										}

										config.update(**a__							)
										return config




			def      lowerCAmelCase__       (      self       ,							a__=0       ,							**a__							)       ->       Dict:

										'''simple docstring'''




										snake_case_			      =   dict(self.forward_default_kwargs							)
										snake_case_			      =   kwargs.pop("num_inference_steps"       ,							a__							)
										snake_case_			      =   self.dummy_sample
										snake_case_			      =   0.1 * sample
										snake_case_			      =   [residual + 0.2, residual + 0.1_5, residual + 0.1_0]

										for scheduler_class in self.scheduler_classes:
																	snake_case_			      =   self.get_scheduler_config(**a__							)
																	snake_case_			      =   scheduler_class(**a__							)
																	scheduler.set_timesteps(a__							)
																	# copy over dummy past residuals
																	snake_case_			      =   dummy_past_residuals[: scheduler.config.solver_order]

																	with tempfile.TemporaryDirectory() as tmpdirname:
																								scheduler.save_config(a__							)
																								snake_case_			      =   scheduler_class.from_pretrained(a__							)
																								new_scheduler.set_timesteps(a__							)
																								# copy over dummy past residuals
																								snake_case_			      =   dummy_past_residuals[: new_scheduler.config.solver_order]

																	snake_case_      ,   snake_case_			      =   sample, sample
																	for t in range(a__       ,							time_step + scheduler.config.solver_order + 1							):
																								snake_case_			      =   scheduler.step(a__       ,							a__       ,							a__       ,							**a__							).prev_sample
																								snake_case_			      =   new_scheduler.step(a__       ,							a__       ,							a__       ,							**a__							).prev_sample

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




			def      lowerCAmelCase__       (      self							)       ->       Dict:

										'''simple docstring'''




										pass




			def      lowerCAmelCase__       (      self       ,							a__=0       ,							**a__							)       ->       Optional[int]:

										'''simple docstring'''




										snake_case_			      =   dict(self.forward_default_kwargs							)
										snake_case_			      =   kwargs.pop("num_inference_steps"       ,							a__							)
										snake_case_			      =   self.dummy_sample
										snake_case_			      =   0.1 * sample
										snake_case_			      =   [residual + 0.2, residual + 0.1_5, residual + 0.1_0]

										for scheduler_class in self.scheduler_classes:
																	snake_case_			      =   self.get_scheduler_config()
																	snake_case_			      =   scheduler_class(**a__							)
																	scheduler.set_timesteps(a__							)

																	# copy over dummy past residuals (must be after setting timesteps)
																	snake_case_			      =   dummy_past_residuals[: scheduler.config.solver_order]

																	with tempfile.TemporaryDirectory() as tmpdirname:
																								scheduler.save_config(a__							)
																								snake_case_			      =   scheduler_class.from_pretrained(a__							)
																								# copy over dummy past residuals
																								new_scheduler.set_timesteps(a__							)

																								# copy over dummy past residual (must be after setting timesteps)
																								snake_case_			      =   dummy_past_residuals[: new_scheduler.config.solver_order]

																	snake_case_			      =   scheduler.step(a__       ,							a__       ,							a__       ,							**a__							).prev_sample
																	snake_case_			      =   new_scheduler.step(a__       ,							a__       ,							a__       ,							**a__							).prev_sample

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




			def      lowerCAmelCase__       (      self       ,							a__=None       ,							**a__							)       ->       Tuple:

										'''simple docstring'''




										if scheduler is None:
																	snake_case_			      =   self.scheduler_classes[0]
																	snake_case_			      =   self.get_scheduler_config(**a__							)
																	snake_case_			      =   scheduler_class(**a__							)

										snake_case_			      =   10
										snake_case_			      =   self.dummy_model()
										snake_case_			      =   self.dummy_sample_deter
										scheduler.set_timesteps(a__							)

										for i, t in enumerate(scheduler.timesteps							):
																	snake_case_			      =   model(a__       ,							a__							)
																	snake_case_			      =   scheduler.step(a__       ,							a__       ,							a__							).prev_sample

										return sample




			def      lowerCAmelCase__       (      self							)       ->       Dict:

										'''simple docstring'''




										snake_case_			      =   dict(self.forward_default_kwargs							)

										snake_case_			      =   kwargs.pop("num_inference_steps"       ,							a__							)

										for scheduler_class in self.scheduler_classes:
																	snake_case_			      =   self.get_scheduler_config()
																	snake_case_			      =   scheduler_class(**a__							)

																	snake_case_			      =   self.dummy_sample
																	snake_case_			      =   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"							):
																								snake_case_			      =   num_inference_steps

																	# copy over dummy past residuals (must be done after set_timesteps)
																	snake_case_			      =   [residual + 0.2, residual + 0.1_5, residual + 0.1_0]
																	snake_case_			      =   dummy_past_residuals[: scheduler.config.solver_order]

																	snake_case_			      =   scheduler.timesteps[5]
																	snake_case_			      =   scheduler.timesteps[6]

																	snake_case_			      =   scheduler.step(a__       ,							a__       ,							a__       ,							**a__							).prev_sample
																	snake_case_			      =   scheduler.step(a__       ,							a__       ,							a__       ,							**a__							).prev_sample

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




			def      lowerCAmelCase__       (      self							)       ->       List[str]:

										'''simple docstring'''




										for timesteps in [25, 50, 100, 999, 1_000]:
																	self.check_over_configs(num_train_timesteps=a__							)




			def      lowerCAmelCase__       (      self							)       ->       str:

										'''simple docstring'''




										self.check_over_configs(thresholding=a__							)
										for order in [1, 2, 3]:
																	for solver_type in ["midpoint", "heun"]:
																								for threshold in [0.5, 1.0, 2.0]:
																															for prediction_type in ["epsilon", "sample"]:
																																						self.check_over_configs(
																																						    thresholding=a__       ,							prediction_type=a__       ,							sample_max_value=a__       ,							algorithm_type="dpmsolver++"       ,							solver_order=a__       ,							solver_type=a__       ,							)




			def      lowerCAmelCase__       (      self							)       ->       int:

										'''simple docstring'''




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




			def      lowerCAmelCase__       (      self							)       ->       Optional[int]:

										'''simple docstring'''




										for algorithm_type in ["dpmsolver", "dpmsolver++", "sde-dpmsolver", "sde-dpmsolver++"]:
																	for solver_type in ["midpoint", "heun"]:
																								for order in [1, 2, 3]:
																															for prediction_type in ["epsilon", "sample"]:
																																						if algorithm_type in ["sde-dpmsolver", "sde-dpmsolver++"]:
																																													if order == 3:
																																																				continue
																																						else:
																																													self.check_over_configs(
																																													    solver_order=a__       ,							solver_type=a__       ,							prediction_type=a__       ,							algorithm_type=a__       ,							)
																																						snake_case_			      =   self.full_loop(
																																						    solver_order=a__       ,							solver_type=a__       ,							prediction_type=a__       ,							algorithm_type=a__       ,							)
																																						assert not torch.isnan(a__							).any(), "Samples have nan numbers"




			def      lowerCAmelCase__       (      self							)       ->       Union[str, Any]:

										'''simple docstring'''




										self.check_over_configs(lower_order_final=a__							)
										self.check_over_configs(lower_order_final=a__							)




			def      lowerCAmelCase__       (      self							)       ->       Optional[Any]:

										'''simple docstring'''




										self.check_over_configs(lambda_min_clipped=-float("inf"							)							)
										self.check_over_configs(lambda_min_clipped=-5.1							)




			def      lowerCAmelCase__       (      self							)       ->       Any:

										'''simple docstring'''




										self.check_over_configs(variance_type=a__							)
										self.check_over_configs(variance_type="learned_range"							)




			def      lowerCAmelCase__       (      self							)       ->       Union[str, Any]:

										'''simple docstring'''




										for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1_000]:
																	self.check_over_forward(num_inference_steps=a__       ,							time_step=0							)




			def      lowerCAmelCase__       (      self							)       ->       List[str]:

										'''simple docstring'''




										snake_case_			      =   self.full_loop()
										snake_case_			      =   torch.mean(torch.abs(a__							)							)

										assert abs(result_mean.item() - 0.3_3_0_1							) < 1e-3




			def      lowerCAmelCase__       (      self							)       ->       Union[str, Any]:

										'''simple docstring'''




										snake_case_			      =   self.full_loop(thresholding=a__       ,							dynamic_thresholding_ratio=0.8_7       ,							sample_max_value=0.5							)
										snake_case_			      =   torch.mean(torch.abs(a__							)							)

										assert abs(result_mean.item() - 1.1_3_6_4							) < 1e-3




			def      lowerCAmelCase__       (      self							)       ->       List[str]:

										'''simple docstring'''




										snake_case_			      =   self.full_loop(prediction_type="v_prediction"							)
										snake_case_			      =   torch.mean(torch.abs(a__							)							)

										assert abs(result_mean.item() - 0.2_2_5_1							) < 1e-3




			def      lowerCAmelCase__       (      self							)       ->       List[str]:

										'''simple docstring'''




										snake_case_			      =   self.full_loop(prediction_type="v_prediction"       ,							use_karras_sigmas=a__							)
										snake_case_			      =   torch.mean(torch.abs(a__							)							)

										assert abs(result_mean.item() - 0.2_0_9_6							) < 1e-3




			def      lowerCAmelCase__       (      self							)       ->       Union[str, Any]:

										'''simple docstring'''




										snake_case_			      =   DPMSolverMultistepScheduler(**self.get_scheduler_config()							)
										snake_case_			      =   self.full_loop(scheduler=a__							)
										snake_case_			      =   torch.mean(torch.abs(a__							)							)

										assert abs(result_mean.item() - 0.3_3_0_1							) < 1e-3

										snake_case_			      =   DPMSolverSinglestepScheduler.from_config(scheduler.config							)
										snake_case_			      =   UniPCMultistepScheduler.from_config(scheduler.config							)
										snake_case_			      =   DEISMultistepScheduler.from_config(scheduler.config							)
										snake_case_			      =   DPMSolverMultistepScheduler.from_config(scheduler.config							)

										snake_case_			      =   self.full_loop(scheduler=a__							)
										snake_case_			      =   torch.mean(torch.abs(a__							)							)

										assert abs(result_mean.item() - 0.3_3_0_1							) < 1e-3




			def      lowerCAmelCase__       (      self							)       ->       int:

										'''simple docstring'''




										snake_case_			      =   self.scheduler_classes[0]
										snake_case_			      =   self.get_scheduler_config(thresholding=a__       ,							dynamic_thresholding_ratio=0							)
										snake_case_			      =   scheduler_class(**a__							)

										snake_case_			      =   10
										snake_case_			      =   self.dummy_model()
										snake_case_			      =   self.dummy_sample_deter.half()
										scheduler.set_timesteps(a__							)

										for i, t in enumerate(scheduler.timesteps							):
																	snake_case_			      =   model(a__       ,							a__							)
																	snake_case_			      =   scheduler.step(a__       ,							a__       ,							a__							).prev_sample

										assert sample.dtype == torch.floataa




			def      lowerCAmelCase__       (      self       ,							**a__							)       ->       Dict:

										'''simple docstring'''




										for scheduler_class in self.scheduler_classes:
																	snake_case_			      =   self.get_scheduler_config(**a__							)
																	snake_case_			      =   scheduler_class(**a__							)

																	scheduler.set_timesteps(scheduler.config.num_train_timesteps							)
																	assert len(scheduler.timesteps.unique()							) == scheduler.num_inference_steps




