




import torch

from diffusers import KDPMaAncestralDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest


class 				__UpperCamelCase      (			__UpperCAmelCase	):





				'''simple docstring'''



				__a				:			str					    =(KDPMaAncestralDiscreteScheduler,)
				__a				:			str					    =1_0



				def    __snake_case		(			self							,			**UpperCAmelCase_    ):
											lowerCAmelCase =		{
											    '''num_train_timesteps''': 11_00,
											    '''beta_start''': 0.0001,
											    '''beta_end''': 0.02,
											    '''beta_schedule''': '''linear''',
											}

											config.update(**UpperCAmelCase_    )
											return config



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



				def    __snake_case		(			self    ):
											for beta_start, beta_end in zip([0.00001, 0.0001, 0.001]							,			[0.0002, 0.002, 0.02]    ):
																		self.check_over_configs(beta_start=UpperCAmelCase_							,			beta_end=UpperCAmelCase_    )



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



				def    __snake_case		(			self    ):
											if torch_device == "mps":
																		return
											lowerCAmelCase =		self.scheduler_classes[0]
											lowerCAmelCase =		self.get_scheduler_config()
											lowerCAmelCase =		scheduler_class(**UpperCAmelCase_    )

											scheduler.set_timesteps(self.num_inference_steps    )

											lowerCAmelCase =		torch.manual_seed(0    )

											lowerCAmelCase =		self.dummy_model()
											lowerCAmelCase =		self.dummy_sample_deter * scheduler.init_noise_sigma
											lowerCAmelCase =		sample.to(UpperCAmelCase_    )

											for i, t in enumerate(scheduler.timesteps    ):
																		lowerCAmelCase =		scheduler.scale_model_input(UpperCAmelCase_							,			UpperCAmelCase_    )

																		lowerCAmelCase =		model(UpperCAmelCase_							,			UpperCAmelCase_    )

																		lowerCAmelCase =		scheduler.step(UpperCAmelCase_							,			UpperCAmelCase_							,			UpperCAmelCase_							,			generator=UpperCAmelCase_    )
																		lowerCAmelCase =		output.prev_sample

											lowerCAmelCase =		torch.sum(torch.abs(UpperCAmelCase_    )    )
											lowerCAmelCase =		torch.mean(torch.abs(UpperCAmelCase_    )    )

											assert abs(result_sum.item() - 13849.3877    ) < 1E-2
											assert abs(result_mean.item() - 18.0331    ) < 5E-3



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



				def    __snake_case		(			self    ):
											if torch_device == "mps":
																		return
											lowerCAmelCase =		self.scheduler_classes[0]
											lowerCAmelCase =		self.get_scheduler_config(prediction_type='''v_prediction'''    )
											lowerCAmelCase =		scheduler_class(**UpperCAmelCase_    )

											scheduler.set_timesteps(self.num_inference_steps    )

											lowerCAmelCase =		self.dummy_model()
											lowerCAmelCase =		self.dummy_sample_deter * scheduler.init_noise_sigma
											lowerCAmelCase =		sample.to(UpperCAmelCase_    )

											lowerCAmelCase =		torch.manual_seed(0    )

											for i, t in enumerate(scheduler.timesteps    ):
																		lowerCAmelCase =		scheduler.scale_model_input(UpperCAmelCase_							,			UpperCAmelCase_    )

																		lowerCAmelCase =		model(UpperCAmelCase_							,			UpperCAmelCase_    )

																		lowerCAmelCase =		scheduler.step(UpperCAmelCase_							,			UpperCAmelCase_							,			UpperCAmelCase_							,			generator=UpperCAmelCase_    )
																		lowerCAmelCase =		output.prev_sample

											lowerCAmelCase =		torch.sum(torch.abs(UpperCAmelCase_    )    )
											lowerCAmelCase =		torch.mean(torch.abs(UpperCAmelCase_    )    )

											assert abs(result_sum.item() - 328.9970    ) < 1E-2
											assert abs(result_mean.item() - 0.4284    ) < 1E-3






				def    __snake_case		(			self    ):
											if torch_device == "mps":
																		return
											lowerCAmelCase =		self.scheduler_classes[0]
											lowerCAmelCase =		self.get_scheduler_config()
											lowerCAmelCase =		scheduler_class(**UpperCAmelCase_    )

											scheduler.set_timesteps(self.num_inference_steps							,			device=UpperCAmelCase_    )
											lowerCAmelCase =		torch.manual_seed(0    )

											lowerCAmelCase =		self.dummy_model()
											lowerCAmelCase =		self.dummy_sample_deter.to(UpperCAmelCase_    ) * scheduler.init_noise_sigma

											for t in scheduler.timesteps:
																		lowerCAmelCase =		scheduler.scale_model_input(UpperCAmelCase_							,			UpperCAmelCase_    )

																		lowerCAmelCase =		model(UpperCAmelCase_							,			UpperCAmelCase_    )

																		lowerCAmelCase =		scheduler.step(UpperCAmelCase_							,			UpperCAmelCase_							,			UpperCAmelCase_							,			generator=UpperCAmelCase_    )
																		lowerCAmelCase =		output.prev_sample

											lowerCAmelCase =		torch.sum(torch.abs(UpperCAmelCase_    )    )
											lowerCAmelCase =		torch.mean(torch.abs(UpperCAmelCase_    )    )

											assert abs(result_sum.item() - 13849.3818    ) < 1E-1
											assert abs(result_mean.item() - 18.0331    ) < 1E-3









import collections

from .utils import ExplicitEnum, is_torch_available, logging


if is_torch_available():
		import torch


UpperCAmelCase_    =logging.get_logger(__name__)


class 				__UpperCamelCase      :





				'''simple docstring'''



				def __init__(			self							,			UpperCAmelCase_							,			UpperCAmelCase_=21							,			UpperCAmelCase_=[]							,			UpperCAmelCase_=None    ):
											lowerCAmelCase =		model
											lowerCAmelCase =		trace_batch_nums
											lowerCAmelCase =		abort_after_batch_num

											# keep a LIFO buffer of frames to dump as soon as inf/nan is encountered to give context to the problem emergence
											lowerCAmelCase =		collections.deque([]							,			UpperCAmelCase_    )
											lowerCAmelCase =		[]
											lowerCAmelCase =		0
											lowerCAmelCase =		0
											lowerCAmelCase =		False
											lowerCAmelCase =		'''                 '''

											self.analyse_model()

											self.register_forward_hook()



				def    __snake_case		(			self							,			UpperCAmelCase_=None    ):
											if frame is not None:
																		self.expand_frame(UpperCAmelCase_    )
											self.frames.append('''\n'''.join(self.frame    )    )
											lowerCAmelCase =		[]  # start a new frame



				def    __snake_case		(			self							,			UpperCAmelCase_    ):
											self.frame.append(UpperCAmelCase_    )



				def    __snake_case		(			self    ):
											print('''\n'''.join(self.frames    )    )
											lowerCAmelCase =		[]



				def    __snake_case		(			self    ):
											lowerCAmelCase =		[]



				def    __snake_case		(			self    ):
											print(F"""\nDetected inf/nan during batch_number={self.batch_number}"""    )
											print(F"""Last {len(self.frames    )} forward frames:"""    )
											print(F"""{"abs min":8} {"abs max":8} metadata"""    )
											print('''\n'''.join(self.frames    )    )
											print('''\n\n'''    )
											lowerCAmelCase =		[]



				def    __snake_case		(			self    ):
											# extract the fully qualified module names, to be able to report at run time. e.g.:
											# encoder.block.2.layer.0.SelfAttention.o
											#
											# for shared weights only the first shared module name will be registered
											lowerCAmelCase =		{m: name for name, m in self.model.named_modules()}
											# self.longest_module_name = max(len(v) for v in self.module_names.values())



				def    __snake_case		(			self							,			UpperCAmelCase_							,			UpperCAmelCase_    ):
											if torch.is_tensor(UpperCAmelCase_    ):
																		self.expand_frame(get_abs_min_max(UpperCAmelCase_							,			UpperCAmelCase_    )    )
																		if detect_overflow(UpperCAmelCase_							,			UpperCAmelCase_    ):
																									lowerCAmelCase =		True
											elif var is None:
																		self.expand_frame(F"""{"None":>17} {ctx}"""    )
											else:
																		self.expand_frame(F"""{"not a tensor":>17} {ctx}"""    )



				def    __snake_case		(			self    ):
											self.expand_frame(F"""\n\n{self.prefix} *** Starting batch number={self.batch_number} ***"""    )
											self.expand_frame(F"""{"abs min":8} {"abs max":8} metadata"""    )



				def    __snake_case		(			self    ):
											self.expand_frame(F"""{self.prefix} *** Finished batch number={self.batch_number-1} ***\n\n"""    )



				def    __snake_case		(			self							,			UpperCAmelCase_							,			UpperCAmelCase_							,			UpperCAmelCase_    ):
											self.expand_frame(F"""{self.prefix} {self.module_names[module]} {module.__class__.__name__}"""    )

											# params
											for name, p in module.named_parameters(recurse=UpperCAmelCase_    ):
																		self.analyse_variable(UpperCAmelCase_							,			UpperCAmelCase_    )

											# inputs
											if isinstance(UpperCAmelCase_							,			UpperCAmelCase_    ):
																		for i, x in enumerate(UpperCAmelCase_    ):
																									self.analyse_variable(UpperCAmelCase_							,			F"""input[{i}]"""    )
											else:
																		self.analyse_variable(UpperCAmelCase_							,			'''input'''    )

											# outputs
											if isinstance(UpperCAmelCase_							,			UpperCAmelCase_    ):
																		for i, x in enumerate(UpperCAmelCase_    ):
																									# possibly a tuple of tuples
																									if isinstance(UpperCAmelCase_							,			UpperCAmelCase_    ):
																																for j, y in enumerate(UpperCAmelCase_    ):
																																							self.analyse_variable(UpperCAmelCase_							,			F"""output[{i}][{j}]"""    )
																									else:
																																self.analyse_variable(UpperCAmelCase_							,			F"""output[{i}]"""    )
											else:
																		self.analyse_variable(UpperCAmelCase_							,			'''output'''    )

											self.save_frame()



				def    __snake_case		(			self    ):
											self.model.apply(self._register_forward_hook    )



				def    __snake_case		(			self							,			UpperCAmelCase_    ):
											module.register_forward_hook(self.forward_hook    )






				def    __snake_case		(			self							,			UpperCAmelCase_							,			UpperCAmelCase_							,			UpperCAmelCase_    ):
											# - input is a tuple of packed inputs (could be non-Tensors)
											# - output could be a Tensor or a tuple of Tensors and non-Tensors

											lowerCAmelCase =		False

											lowerCAmelCase =		True if self.batch_number in self.trace_batch_nums else False
											if trace_mode:
																		self.reset_saved_frames()

											if self.total_calls == 0:
																		self.batch_start_frame()
											self.total_calls += 1

											# count batch numbers - the very first forward hook of the batch will be called when the
											# batch completes - i.e. it gets called very last - we know this batch has finished
											if module == self.model:
																		self.batch_number += 1
																		lowerCAmelCase =		True

											self.create_frame(UpperCAmelCase_							,			UpperCAmelCase_							,			UpperCAmelCase_    )

											# if last_frame_of_batch:
											#     self.batch_end_frame()

											if trace_mode:
																		self.trace_frames()

											if last_frame_of_batch:
																		self.batch_start_frame()

											if self.detected_overflow and not trace_mode:
																		self.dump_saved_frames()

																		# now we can abort, as it's pointless to continue running
																		raise ValueError(
																		    '''DebugUnderflowOverflow: inf/nan detected, aborting as there is no point running further. '''
																		    '''Please scroll up above this traceback to see the activation values prior to this event.'''    )

											# abort after certain batch if requested to do so
											if self.abort_after_batch_num is not None and self.batch_number > self.abort_after_batch_num:
																		raise ValueError(
																		    F"""DebugUnderflowOverflow: aborting after {self.batch_number} batches due to"""
																		    F""" `abort_after_batch_num={self.abort_after_batch_num}` arg"""    )




def    UpperCAmelCase							(  _snake_case    ,			_snake_case				):
							lowerCAmelCase =		var.abs()
							return F"""{abs_var.min():8.2e} {abs_var.max():8.2e} {ctx}"""



def    UpperCAmelCase							(  _snake_case    ,			_snake_case				):
							lowerCAmelCase =		False
							if torch.isnan(_snake_case				).any().item():
														lowerCAmelCase =		True
														print(F"""{ctx} has nans"""				)
							if torch.isinf(_snake_case				).any().item():
														lowerCAmelCase =		True
														print(F"""{ctx} has infs"""				)

							# if needed to monitor large elements can enable the following
							if 0:  # and detected:
														lowerCAmelCase =		var[torch.ge(var.abs()    ,			100				)]
														if naaa.numel() > 0:
																					print(F"""{ctx}:  n100={naaa.numel()}"""				)
														lowerCAmelCase =		var[torch.ge(var.abs()    ,			1000				)]
														if naaaa.numel() > 0:
																					print(F"""{ctx}: n1000={naaaa.numel()}"""				)
														lowerCAmelCase =		var[torch.ge(var.abs()    ,			10000				)]
														if naaaaa.numel() > 0:
																					print(F"""{ctx}: n10000={naaaaa.numel()}"""				)

							if 0:
														print(F"""min={var.min():9.2e} max={var.max():9.2e}"""				)

							if 0:
														print(F"""min={var.min():9.2e} max={var.max():9.2e} var={var.var():9.2e} mean={var.mean():9.2e} ({ctx})"""				)

							return detected


class 				__UpperCamelCase      (			__UpperCAmelCase	):





				'''simple docstring'''



				__a				:			Tuple					    ="""underflow_overflow"""
				__a				:			Dict					    ="""tpu_metrics_debug"""



