



import torch

from diffusers import DDIMScheduler

from .test_schedulers import SchedulerCommonTest






class 				SCREAMING_SNAKE_CASE    (							lowerCamelCase__   ):
							__lowerCamelCase		:		Optional[Any]      =(DDIMScheduler,)
							__lowerCamelCase		:		List[str]      =(('eta', 0.0), ('num_inference_steps', 50))





							def   UpperCamelCase_   (				self						:						Dict ,			**__lowercase						:						str		):

											'''simple docstring'''
											__a					       =						{
											    "num_train_timesteps": 1000,
											    "beta_start": 0.0001,
											    "beta_end": 0.02,
											    "beta_schedule": "linear",
											    "clip_sample": True,
											}

											config.update(**__lowercase		)
											return config





							def   UpperCamelCase_   (				self						:						int ,			**__lowercase						:						Any		):

											'''simple docstring'''
											__a					       =						self.scheduler_classes[0]
											__a					       =						self.get_scheduler_config(**__lowercase		)
											__a					       =						scheduler_class(**__lowercase		)

											__a					       =						10, 0.0

											__a					       =						self.dummy_model()
											__a					       =						self.dummy_sample_deter

											scheduler.set_timesteps(__lowercase		)

											for t in scheduler.timesteps:
															__a					       =						model(__lowercase ,			__lowercase		)
															__a					       =						scheduler.step(__lowercase ,			__lowercase ,			__lowercase ,			__lowercase		).prev_sample

											return sample





							def   UpperCamelCase_   (				self						:						Optional[Any]		):

											'''simple docstring'''






											for timesteps in [100, 500, 1000]:
															self.check_over_configs(num_train_timesteps=__lowercase		)





							def   UpperCamelCase_   (				self						:						Dict		):

											'''simple docstring'''






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

											__a					       =						self.scheduler_classes[0]
											__a					       =						self.get_scheduler_config(steps_offset=1		)
											__a					       =						scheduler_class(**__lowercase		)
											scheduler.set_timesteps(5		)
											assert torch.equal(scheduler.timesteps ,			torch.LongTensor([801, 601, 401, 201, 1]		)		)





							def   UpperCamelCase_   (				self						:						Dict		):

											'''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=__lowercase ,			beta_end=__lowercase		)





							def   UpperCamelCase_   (				self						:						Dict		):

											'''simple docstring'''






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





							def   UpperCamelCase_   (				self						:						Optional[int]		):

											'''simple docstring'''






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





							def   UpperCamelCase_   (				self						:						List[Any]		):

											'''simple docstring'''






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





							def   UpperCamelCase_   (				self						:						Optional[Any]		):

											'''simple docstring'''






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





							def   UpperCamelCase_   (				self						:						Dict		):

											'''simple docstring'''






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





							def   UpperCamelCase_   (				self						:						str		):

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





							def   UpperCamelCase_   (				self						:						Optional[Any]		):

											'''simple docstring'''






											for t in [1, 10, 49]:
															self.check_over_forward(time_step=__lowercase		)





							def   UpperCamelCase_   (				self						:						Any		):

											'''simple docstring'''






											for t, num_inference_steps in zip([1, 10, 50] ,			[10, 50, 500]		):
															self.check_over_forward(time_step=__lowercase ,			num_inference_steps=__lowercase		)





							def   UpperCamelCase_   (				self						:						str		):

											'''simple docstring'''






											for t, eta in zip([1, 10, 49] ,			[0.0, 0.5, 1.0]		):
															self.check_over_forward(time_step=__lowercase ,			eta=__lowercase		)





							def   UpperCamelCase_   (				self						:						Optional[Any]		):

											'''simple docstring'''
											__a					       =						self.scheduler_classes[0]
											__a					       =						self.get_scheduler_config()
											__a					       =						scheduler_class(**__lowercase		)

											assert torch.sum(torch.abs(scheduler._get_variance(0 ,			0		) - 0.0		)		) < 1E-5
											assert torch.sum(torch.abs(scheduler._get_variance(420 ,			400		) - 0.14771		)		) < 1E-5
											assert torch.sum(torch.abs(scheduler._get_variance(980 ,			960		) - 0.32460		)		) < 1E-5
											assert torch.sum(torch.abs(scheduler._get_variance(0 ,			0		) - 0.0		)		) < 1E-5
											assert torch.sum(torch.abs(scheduler._get_variance(487 ,			486		) - 0.00979		)		) < 1E-5
											assert torch.sum(torch.abs(scheduler._get_variance(999 ,			998		) - 0.02		)		) < 1E-5





							def   UpperCamelCase_   (				self						:						Optional[Any]		):

											'''simple docstring'''
											__a					       =						self.full_loop()

											__a					       =						torch.sum(torch.abs(__lowercase		)		)
											__a					       =						torch.mean(torch.abs(__lowercase		)		)

											assert abs(result_sum.item() - 172.0067		) < 1E-2
											assert abs(result_mean.item() - 0.223967		) < 1E-3





							def   UpperCamelCase_   (				self						:						List[Any]		):

											'''simple docstring'''
											__a					       =						self.full_loop(prediction_type="""v_prediction"""		)

											__a					       =						torch.sum(torch.abs(__lowercase		)		)
											__a					       =						torch.mean(torch.abs(__lowercase		)		)

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





							def   UpperCamelCase_   (				self						:						Dict		):

											'''simple docstring'''
											__a					       =						self.full_loop(set_alpha_to_one=__lowercase ,			beta_start=0.01		)
											__a					       =						torch.sum(torch.abs(__lowercase		)		)
											__a					       =						torch.mean(torch.abs(__lowercase		)		)

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







							def   UpperCamelCase_   (				self						:						Tuple		):

											'''simple docstring'''
											__a					       =						self.full_loop(set_alpha_to_one=__lowercase ,			beta_start=0.01		)
											__a					       =						torch.sum(torch.abs(__lowercase		)		)
											__a					       =						torch.mean(torch.abs(__lowercase		)		)

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





#
# Sort large text files in a minimum amount of memory
#
import argparse
import os






class 				SCREAMING_SNAKE_CASE     :
							__lowerCamelCase		:		Tuple      ='block_{0}.dat'





							def __init__(				self						:						int ,			__lowercase						:						Dict		):

											'''simple docstring'''
											__a					       =						filename
											__a					       =						[]





							def   UpperCamelCase_   (				self						:						int ,			__lowercase						:						Dict ,			__lowercase						:						Optional[Any]		):

											'''simple docstring'''
											__a					       =						self.BLOCK_FILENAME_FORMAT.format(__lowercase		)
											with open(__lowercase ,			"""w"""		) as file:
															file.write(__lowercase		)
											self.block_filenames.append(__lowercase		)





							def   UpperCamelCase_   (				self						:						List[str]		):

											'''simple docstring'''
											return self.block_filenames





							def   UpperCamelCase_   (				self						:						Any ,			__lowercase						:						str ,			__lowercase						:						Tuple=None		):

											'''simple docstring'''
											__a					       =						0
											with open(self.filename		) as file:
															while True:
																			__a					       =						file.readlines(__lowercase		)

																			if lines == []:
																							break

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

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







							def   UpperCamelCase_   (				self						:						List[Any]		):

											'''simple docstring'''
											map(os.remove ,			self.block_filenames		)






class 				SCREAMING_SNAKE_CASE     :





							def   UpperCamelCase_   (				self						:						Dict ,			__lowercase						:						Optional[Any]		):

											'''simple docstring'''
											__a					       =						-1
											__a					       =						None

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

											return min_index






class 				SCREAMING_SNAKE_CASE     :





							def __init__(				self						:						Optional[int] ,			__lowercase						:						Optional[Any]		):

											'''simple docstring'''
											__a					       =						files
											__a					       =						set()
											__a					       =						len(__lowercase		)
											__a					       =						{i: None for i in range(self.num_buffers		)}





							def   UpperCamelCase_   (				self						:						int		):

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





							def   UpperCamelCase_   (				self						:						List[str]		):

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

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

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

											return True







							def   UpperCamelCase_   (				self						:						List[str] ,			__lowercase						:						List[Any]		):

											'''simple docstring'''
											__a					       =						self.buffers[index]
											__a					       =						None

											return value






class 				SCREAMING_SNAKE_CASE     :





							def __init__(				self						:						Optional[int] ,			__lowercase						:						Any		):

											'''simple docstring'''
											__a					       =						merge_strategy





							def   UpperCamelCase_   (				self						:						str ,			__lowercase						:						Union[str, Any] ,			__lowercase						:						List[str] ,			__lowercase						:						Dict		):

											'''simple docstring'''
											__a					       =						FilesArray(self.get_file_handles(__lowercase ,			__lowercase		)		)
											with open(__lowercase ,			"""w""" ,			__lowercase		) as outfile:
															while buffers.refresh():
																			__a					       =						self.merge_strategy.select(buffers.get_dict()		)
																			outfile.write(buffers.unshift(__lowercase		)		)







							def   UpperCamelCase_   (				self						:						List[Any] ,			__lowercase						:						str ,			__lowercase						:						Dict		):

											'''simple docstring'''
											__a					       =						{}

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

											return files






class 				SCREAMING_SNAKE_CASE     :





							def __init__(				self						:						str ,			__lowercase						:						Tuple		):

											'''simple docstring'''
											__a					       =						block_size





							def   UpperCamelCase_   (				self						:						Tuple ,			__lowercase						:						str ,			__lowercase						:						Union[str, Any]=None		):

											'''simple docstring'''
											__a					       =						self.get_number_blocks(__lowercase ,			self.block_size		)
											__a					       =						FileSplitter(__lowercase		)
											splitter.split(self.block_size ,			__lowercase		)

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

											splitter.cleanup()







							def   UpperCamelCase_   (				self						:						str ,			__lowercase						:						Optional[Any] ,			__lowercase						:						Dict		):

											'''simple docstring'''
											return (os.stat(__lowercase		).st_size / block_size) + 1



def 					lowerCAmelCase__  (							_SCREAMING_SNAKE_CASE    :     Tuple      ):
				"""simple docstring"""







				if string[-1].lower() == "k":
								return int(string[:-1]      ) * 1024
				elif string[-1].lower() == "m":
								return int(string[:-1]      ) * 1024 * 1024
				elif string[-1].lower() == "g":
								return int(string[:-1]      ) * 1024 * 1024 * 1024
				else:
								return int(_SCREAMING_SNAKE_CASE      )

def 					lowerCAmelCase__  (							):
				"""simple docstring"""







				__a					       =						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"""      )
				__a					       =						parser.parse_args()

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


if __name__ == "__main__":
						main()
