



'''simple docstring'''







import collections

from .utils import ExplicitEnum, is_torch_available, logging


if is_torch_available():
							import torch


SCREAMING_SNAKE_CASE__     : Optional[int]				=   logging.get_logger(__name__)




class 				__lowerCAmelCase:






			def __init__(   self  :							List[str]      ,       SCREAMING_SNAKE_CASE  :							List[Any]      ,       SCREAMING_SNAKE_CASE  :							Union[str, Any]=21      ,       SCREAMING_SNAKE_CASE  :							str=[]      ,       SCREAMING_SNAKE_CASE  :							Dict=None	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:Optional[int]										=      model
					SCREAMING_SNAKE_CASE_						:List[Any]										=      trace_batch_nums
					SCREAMING_SNAKE_CASE_						:str										=      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
					SCREAMING_SNAKE_CASE_						:Any										=      collections.deque([]      ,       SCREAMING_SNAKE_CASE	)
					SCREAMING_SNAKE_CASE_						:List[str]										=      []
					SCREAMING_SNAKE_CASE_						:Optional[int]										=      0
					SCREAMING_SNAKE_CASE_						:Optional[Any]										=      0
					SCREAMING_SNAKE_CASE_						:List[Any]										=      False
					SCREAMING_SNAKE_CASE_						:Optional[Any]										=      '                 '

					self.analyse_model()

					self.register_forward_hook()






			def 				_lowercase	(   self  :							Tuple      ,       SCREAMING_SNAKE_CASE  :							Any=None	):




					"""simple docstring"""







					if frame is not None:
							self.expand_frame(SCREAMING_SNAKE_CASE	)
					self.frames.append('\n'.join(self.frame	)	)
					SCREAMING_SNAKE_CASE_						:List[str]										=      []  # start a new frame






			def 				_lowercase	(   self  :							Dict      ,       SCREAMING_SNAKE_CASE  :							Tuple	):




					"""simple docstring"""







					self.frame.append(SCREAMING_SNAKE_CASE	)






			def 				_lowercase	(   self  :							List[Any]	):




					"""simple docstring"""







					print('\n'.join(self.frames	)	)
					SCREAMING_SNAKE_CASE_						:Optional[int]										=      []






			def 				_lowercase	(   self  :							Dict	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:Tuple										=      []






			def 				_lowercase	(   self  :							Tuple	):




					"""simple docstring"""







					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'	)
					SCREAMING_SNAKE_CASE_						:Tuple										=      []






			def 				_lowercase	(   self  :							Dict	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:Tuple										=      {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 				_lowercase	(   self  :							Union[str, Any]      ,       SCREAMING_SNAKE_CASE  :							List[Any]      ,       SCREAMING_SNAKE_CASE  :							Optional[int]	):




					"""simple docstring"""







					if torch.is_tensor(SCREAMING_SNAKE_CASE	):
							self.expand_frame(get_abs_min_max(SCREAMING_SNAKE_CASE      ,       SCREAMING_SNAKE_CASE	)	)
							if detect_overflow(SCREAMING_SNAKE_CASE      ,       SCREAMING_SNAKE_CASE	):
									SCREAMING_SNAKE_CASE_						:Optional[int]										=      True
					elif var is None:
							self.expand_frame(f'{"None":>17} {ctx}'	)
					else:
							self.expand_frame(f'{"not a tensor":>17} {ctx}'	)






			def 				_lowercase	(   self  :							Tuple	):




					"""simple docstring"""







					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 				_lowercase	(   self  :							int	):




					"""simple docstring"""







					self.expand_frame(f'{self.prefix} *** Finished batch number={self.batch_number-1} ***\n\n'	)






			def 				_lowercase	(   self  :							Tuple      ,       SCREAMING_SNAKE_CASE  :							List[Any]      ,       SCREAMING_SNAKE_CASE  :							List[Any]      ,       SCREAMING_SNAKE_CASE  :							List[str]	):




					"""simple docstring"""







					self.expand_frame(f'{self.prefix} {self.module_names[module]} {module.__class__.__name__}'	)

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

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

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

					self.save_frame()






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




					"""simple docstring"""







					self.model.apply(self._register_forward_hook	)






			def 				_lowercase	(   self  :							Optional[int]      ,       SCREAMING_SNAKE_CASE  :							List[Any]	):




					"""simple docstring"""







					module.register_forward_hook(self.forward_hook	)






			def 				_lowercase	(   self  :							str      ,       SCREAMING_SNAKE_CASE  :							Any      ,       SCREAMING_SNAKE_CASE  :							List[str]      ,       SCREAMING_SNAKE_CASE  :							Optional[int]	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:List[Any]										=      False

					SCREAMING_SNAKE_CASE_						:Tuple										=      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
							SCREAMING_SNAKE_CASE_						:List[str]										=      True

					self.create_frame(SCREAMING_SNAKE_CASE      ,       SCREAMING_SNAKE_CASE      ,       SCREAMING_SNAKE_CASE	)

					# 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  SCREAMING_SNAKE_CASE__ (				SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE     ):
		SCREAMING_SNAKE_CASE_						:Optional[int]										=      var.abs()
		return F'{abs_var.min():8.2e} {abs_var.max():8.2e} {ctx}'







def  SCREAMING_SNAKE_CASE__ (				SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE     ):
		SCREAMING_SNAKE_CASE_						:Any										=      False
		if torch.isnan(SCREAMING_SNAKE_CASE     ).any().item():
				SCREAMING_SNAKE_CASE_						:Tuple										=      True
				print(F'{ctx} has nans'     )
		if torch.isinf(SCREAMING_SNAKE_CASE     ).any().item():
				SCREAMING_SNAKE_CASE_						:List[str]										=      True
				print(F'{ctx} has infs'     )

		# if needed to monitor large elements can enable the following
		if 0:  # and detected:
				SCREAMING_SNAKE_CASE_						:Dict										=      var[torch.ge(var.abs() ,		100     )]
				if naaa.numel() > 0:
						print(F'{ctx}:  n100={naaa.numel()}'     )
				SCREAMING_SNAKE_CASE_						:Dict										=      var[torch.ge(var.abs() ,		1000     )]
				if naaaa.numel() > 0:
						print(F'{ctx}: n1000={naaaa.numel()}'     )
				SCREAMING_SNAKE_CASE_						:Tuple										=      var[torch.ge(var.abs() ,		1_0000     )]
				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 				__lowerCAmelCase(							lowerCAmelCase__      ):
			__snake_case  :		List[Any]		=      'underflow_overflow'
			__snake_case  :		Optional[int]		=      'tpu_metrics_debug'


import tempfile
import unittest

import numpy as np
import torch

from diffusers import ScoreSdeVeScheduler

class 		lowercase_							(							unittest.TestCase					):
 # TODO adapt with class SchedulerCommonTest (scheduler needs Numpy Integration)
 A__    :					int    =     (ScoreSdeVeScheduler,)
 A__    :					Union[str, Any]    =     ()

 @property
 def lowerCamelCase_					(   self  ):




        """simple docstring"""


        UpperCamelCase_											=							4
        UpperCamelCase_											=							3
        UpperCamelCase_											=							8
        UpperCamelCase_											=							8

        UpperCamelCase_											=							torch.rand((batch_size, num_channels, height, width)  )

        return sample

 @property
 def lowerCamelCase_					(   self  ):




        """simple docstring"""


        UpperCamelCase_											=							4
        UpperCamelCase_											=							3
        UpperCamelCase_											=							8
        UpperCamelCase_											=							8

        UpperCamelCase_											=							batch_size * num_channels * height * width
        UpperCamelCase_											=							torch.arange(__UpperCamelCase  )
        UpperCamelCase_											=							sample.reshape(__UpperCamelCase  ,						__UpperCamelCase  ,						__UpperCamelCase  ,						__UpperCamelCase  )
        UpperCamelCase_											=							sample / num_elems
        UpperCamelCase_											=							sample.permute(3  ,						0  ,						1  ,						2  )

        return sample

 def lowerCamelCase_					(   self  ):




        """simple docstring"""


        def model(__UpperCamelCase  ,						__UpperCamelCase  ,						*__UpperCamelCase  ):
               return sample * t / (t + 1)

        return model

 def lowerCamelCase_					(   self  ,						**__UpperCamelCase  ):




        """simple docstring"""


        UpperCamelCase_											=							{
            """num_train_timesteps""": 2_0_0_0,
            """snr""": 0.15,
            """sigma_min""": 0.01,
            """sigma_max""": 1_3_4_8,
            """sampling_eps""": 1e-5,
        }

        config.update(**__UpperCamelCase  )
        return config

 def lowerCamelCase_					(   self  ,						__UpperCamelCase=0  ,						**__UpperCamelCase  ):




        """simple docstring"""


        UpperCamelCase_											=							dict(self.forward_default_kwargs  )

        for scheduler_class in self.scheduler_classes:
               UpperCamelCase_											=							self.dummy_sample
               UpperCamelCase_											=							0.1 * sample

               UpperCamelCase_											=							self.get_scheduler_config(**__UpperCamelCase  )
               UpperCamelCase_											=							scheduler_class(**__UpperCamelCase  )

               with tempfile.TemporaryDirectory() as tmpdirname:
                      scheduler.save_config(__UpperCamelCase  )
                      UpperCamelCase_											=							scheduler_class.from_pretrained(__UpperCamelCase  )

               UpperCamelCase_											=							scheduler.step_pred(
                   __UpperCamelCase  ,						__UpperCamelCase  ,						__UpperCamelCase  ,						generator=torch.manual_seed(0  )  ,						**__UpperCamelCase  ).prev_sample
               UpperCamelCase_											=							new_scheduler.step_pred(
                   __UpperCamelCase  ,						__UpperCamelCase  ,						__UpperCamelCase  ,						generator=torch.manual_seed(0  )  ,						**__UpperCamelCase  ).prev_sample

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

               UpperCamelCase_											=							scheduler.step_correct(__UpperCamelCase  ,						__UpperCamelCase  ,						generator=torch.manual_seed(0  )  ,						**__UpperCamelCase  ).prev_sample
               UpperCamelCase_											=							new_scheduler.step_correct(
                   __UpperCamelCase  ,						__UpperCamelCase  ,						generator=torch.manual_seed(0  )  ,						**__UpperCamelCase  ).prev_sample

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

 def lowerCamelCase_					(   self  ,						__UpperCamelCase=0  ,						**__UpperCamelCase  ):




        """simple docstring"""


        UpperCamelCase_											=							dict(self.forward_default_kwargs  )
        kwargs.update(__UpperCamelCase  )

        for scheduler_class in self.scheduler_classes:
               UpperCamelCase_											=							self.dummy_sample
               UpperCamelCase_											=							0.1 * sample

               UpperCamelCase_											=							self.get_scheduler_config()
               UpperCamelCase_											=							scheduler_class(**__UpperCamelCase  )

               with tempfile.TemporaryDirectory() as tmpdirname:
                      scheduler.save_config(__UpperCamelCase  )
                      UpperCamelCase_											=							scheduler_class.from_pretrained(__UpperCamelCase  )

               UpperCamelCase_											=							scheduler.step_pred(
                   __UpperCamelCase  ,						__UpperCamelCase  ,						__UpperCamelCase  ,						generator=torch.manual_seed(0  )  ,						**__UpperCamelCase  ).prev_sample
               UpperCamelCase_											=							new_scheduler.step_pred(
                   __UpperCamelCase  ,						__UpperCamelCase  ,						__UpperCamelCase  ,						generator=torch.manual_seed(0  )  ,						**__UpperCamelCase  ).prev_sample

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

               UpperCamelCase_											=							scheduler.step_correct(__UpperCamelCase  ,						__UpperCamelCase  ,						generator=torch.manual_seed(0  )  ,						**__UpperCamelCase  ).prev_sample
               UpperCamelCase_											=							new_scheduler.step_correct(
                   __UpperCamelCase  ,						__UpperCamelCase  ,						generator=torch.manual_seed(0  )  ,						**__UpperCamelCase  ).prev_sample

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

 def lowerCamelCase_					(   self  ):




        """simple docstring"""


        for timesteps in [1_0, 1_0_0, 1_0_0_0]:
               self.check_over_configs(num_train_timesteps=__UpperCamelCase  )

 def lowerCamelCase_					(   self  ):




        """simple docstring"""


        for sigma_min, sigma_max in zip([0.0_001, 0.001, 0.01]  ,						[1, 1_0_0, 1_0_0_0]  ):
               self.check_over_configs(sigma_min=__UpperCamelCase  ,						sigma_max=__UpperCamelCase  )

 def lowerCamelCase_					(   self  ):




        """simple docstring"""


        for t in [0.1, 0.5, 0.75]:
               self.check_over_forward(time_step=__UpperCamelCase  )

 def lowerCamelCase_					(   self  ):




        """simple docstring"""


        UpperCamelCase_											=							dict(self.forward_default_kwargs  )

        UpperCamelCase_											=							self.scheduler_classes[0]
        UpperCamelCase_											=							self.get_scheduler_config()
        UpperCamelCase_											=							scheduler_class(**__UpperCamelCase  )

        UpperCamelCase_											=							3

        UpperCamelCase_											=							self.dummy_model()
        UpperCamelCase_											=							self.dummy_sample_deter

        scheduler.set_sigmas(__UpperCamelCase  )
        scheduler.set_timesteps(__UpperCamelCase  )
        UpperCamelCase_											=							torch.manual_seed(0  )

        for i, t in enumerate(scheduler.timesteps  ):
               UpperCamelCase_											=							scheduler.sigmas[i]

               for _ in range(scheduler.config.correct_steps  ):
                      with torch.no_grad():
                             UpperCamelCase_											=							model(__UpperCamelCase  ,						__UpperCamelCase  )
                      UpperCamelCase_											=							scheduler.step_correct(__UpperCamelCase  ,						__UpperCamelCase  ,						generator=__UpperCamelCase  ,						**__UpperCamelCase  ).prev_sample

               with torch.no_grad():
                      UpperCamelCase_											=							model(__UpperCamelCase  ,						__UpperCamelCase  )

               UpperCamelCase_											=							scheduler.step_pred(__UpperCamelCase  ,						__UpperCamelCase  ,						__UpperCamelCase  ,						generator=__UpperCamelCase  ,						**__UpperCamelCase  )
               UpperCamelCase_							,					UpperCamelCase_											=							output.prev_sample, output.prev_sample_mean

        UpperCamelCase_											=							torch.sum(torch.abs(__UpperCamelCase  )  )
        UpperCamelCase_											=							torch.mean(torch.abs(__UpperCamelCase  )  )

        assert np.isclose(result_sum.item()  ,						14_372_758_528.0  )
        assert np.isclose(result_mean.item()  ,						18_714_530.0  )

 def lowerCamelCase_					(   self  ):




        """simple docstring"""


        UpperCamelCase_											=							dict(self.forward_default_kwargs  )

        UpperCamelCase_											=							kwargs.pop("""num_inference_steps"""  ,						__UpperCamelCase  )

        for scheduler_class in self.scheduler_classes:
               UpperCamelCase_											=							self.get_scheduler_config()
               UpperCamelCase_											=							scheduler_class(**__UpperCamelCase  )

               UpperCamelCase_											=							self.dummy_sample
               UpperCamelCase_											=							0.1 * sample

               if num_inference_steps is not None and hasattr(__UpperCamelCase  ,						"""set_timesteps"""  ):
                      scheduler.set_timesteps(__UpperCamelCase  )
               elif num_inference_steps is not None and not hasattr(__UpperCamelCase  ,						"""set_timesteps"""  ):
                      UpperCamelCase_											=							num_inference_steps

               UpperCamelCase_											=							scheduler.step_pred(__UpperCamelCase  ,						0  ,						__UpperCamelCase  ,						generator=torch.manual_seed(0  )  ,						**__UpperCamelCase  ).prev_sample
               UpperCamelCase_											=							scheduler.step_pred(__UpperCamelCase  ,						1  ,						__UpperCamelCase  ,						generator=torch.manual_seed(0  )  ,						**__UpperCamelCase  ).prev_sample

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



