


'''simple docstring'''


import collections

from .utils import ExplicitEnum, is_torch_available, logging


if is_torch_available():
      import torch


_UpperCamelCase          =     logging.get_logger(__name__)







class        __magic_name__  :

     """simple docstring"""






     def __init__(      self ,    lowerCamelCase ,    lowerCamelCase=21 ,    lowerCamelCase=[] ,    lowerCamelCase=None		):


          '''simple docstring'''





          __A  :					Dict						     =       model
          __A  :					Dict						     =       trace_batch_nums
          __A  :					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
          __A  :					Any						     =       collections.deque([] ,    lowerCamelCase		)
          __A  :					Optional[int]						     =       []
          __A  :					List[Any]						     =       0
          __A  :					List[Any]						     =       0
          __A  :					str						     =       False
          __A  :					str						     =       "                 "

          self.analyse_model()

          self.register_forward_hook()


     def lowerCAmelCase__     (      self ,    lowerCamelCase=None		):


          '''simple docstring'''





          if frame is not None:
               self.expand_frame(lowerCamelCase		)
          self.frames.append("\n".join(self.frame		)		)
          __A  :					int						     =       []  # start a new frame


     def lowerCAmelCase__     (      self ,    lowerCamelCase		):


          '''simple docstring'''





          self.frame.append(lowerCamelCase		)


     def lowerCAmelCase__     (      self		):


          '''simple docstring'''





          print("\n".join(self.frames		)		)
          __A  :					Union[str, Any]						     =       []


     def lowerCAmelCase__     (      self		):


          '''simple docstring'''





          __A  :					Dict						     =       []


     def lowerCAmelCase__     (      self		):


          '''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"		)
          __A  :					Union[str, Any]						     =       []


     def lowerCAmelCase__     (      self		):


          '''simple docstring'''





          __A  :					List[Any]						     =       {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 lowerCAmelCase__     (      self ,    lowerCamelCase ,    lowerCamelCase		):


          '''simple docstring'''





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


     def lowerCAmelCase__     (      self		):


          '''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 lowerCAmelCase__     (      self		):


          '''simple docstring'''





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


     def lowerCAmelCase__     (      self ,    lowerCamelCase ,    lowerCamelCase ,    lowerCamelCase		):


          '''simple docstring'''





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

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

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

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

          self.save_frame()


     def lowerCAmelCase__     (      self		):


          '''simple docstring'''





          self.model.apply(self._register_forward_hook		)


     def lowerCAmelCase__     (      self ,    lowerCamelCase		):


          '''simple docstring'''





          module.register_forward_hook(self.forward_hook		)


     def lowerCAmelCase__     (      self ,    lowerCamelCase ,    lowerCamelCase ,    lowerCamelCase		):


          '''simple docstring'''





          __A  :					int						     =       False

          __A  :					int						     =       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
               __A  :					List[str]						     =       True

          self.create_frame(lowerCamelCase ,    lowerCamelCase ,    lowerCamelCase		)

          # 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 		_lowercase   (SCREAMING_SNAKE_CASE					,				SCREAMING_SNAKE_CASE       ):




     '''simple docstring'''





     __A  :					Union[str, Any]						     =       var.abs()
     return f"{abs_var.min():8.2e} {abs_var.max():8.2e} {ctx}"


def 		_lowercase   (SCREAMING_SNAKE_CASE					,				SCREAMING_SNAKE_CASE       ):




     '''simple docstring'''





     __A  :					List[Any]						     =       False
     if torch.isnan(SCREAMING_SNAKE_CASE       ).any().item():
          __A  :					Any						     =       True
          print(f"{ctx} has nans"       )
     if torch.isinf(SCREAMING_SNAKE_CASE       ).any().item():
          __A  :					Union[str, Any]						     =       True
          print(f"{ctx} has infs"       )

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

     """simple docstring"""






     lowerCamelCase__						=							"underflow_overflow"
     lowerCamelCase__						=							"tpu_metrics_debug"









'''simple docstring'''


import tempfile
import unittest

import numpy as np
import torch

from diffusers import ScoreSdeVeScheduler







class        __magic_name__  (					unittest.TestCase   ):

     """simple docstring"""






     lowerCamelCase__						=							(ScoreSdeVeScheduler,)
     lowerCamelCase__						=							()


     @property
     def lowerCAmelCase__     (      self		):


          '''simple docstring'''





          __A  :					int						     =       4
          __A  :					Union[str, Any]						     =       3
          __A  :					Optional[int]						     =       8
          __A  :					Dict						     =       8

          __A  :					Tuple						     =       torch.rand((batch_size, num_channels, height, width)		)

          return sample


     @property
     def lowerCAmelCase__     (      self		):


          '''simple docstring'''





          __A  :					Tuple						     =       4
          __A  :					Dict						     =       3
          __A  :					str						     =       8
          __A  :					List[Any]						     =       8

          __A  :					Dict						     =       batch_size * num_channels * height * width
          __A  :					List[str]						     =       torch.arange(lowerCamelCase		)
          __A  :					Union[str, Any]						     =       sample.reshape(lowerCamelCase ,    lowerCamelCase ,    lowerCamelCase ,    lowerCamelCase		)
          __A  :					List[Any]						     =       sample / num_elems
          __A  :					Dict						     =       sample.permute(3 ,    0 ,    1 ,    2		)

          return sample


     def lowerCAmelCase__     (      self		):


          '''simple docstring'''





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

          return model


     def lowerCAmelCase__     (      self ,    **lowerCamelCase		):


          '''simple docstring'''





          __A  :					List[str]						     =       {
              "num_train_timesteps": 2000,
              "snr": 0.15,
              "sigma_min": 0.01,
              "sigma_max": 1348,
              "sampling_eps": 1E-5,
          }

          config.update(**lowerCamelCase		)
          return config


     def lowerCAmelCase__     (      self ,    lowerCamelCase=0 ,    **lowerCamelCase		):


          '''simple docstring'''





          __A  :					List[str]						     =       dict(self.forward_default_kwargs		)

          for scheduler_class in self.scheduler_classes:
               __A  :					List[str]						     =       self.dummy_sample
               __A  :					List[Any]						     =       0.1 * sample

               __A  :					Any						     =       self.get_scheduler_config(**lowerCamelCase		)
               __A  :					Tuple						     =       scheduler_class(**lowerCamelCase		)

               with tempfile.TemporaryDirectory() as tmpdirname:
                    scheduler.save_config(lowerCamelCase		)
                    __A  :					List[str]						     =       scheduler_class.from_pretrained(lowerCamelCase		)

               __A  :					List[str]						     =       scheduler.step_pred(
                   lowerCamelCase ,    lowerCamelCase ,    lowerCamelCase ,    generator=torch.manual_seed(0		) ,    **lowerCamelCase		).prev_sample
               __A  :					List[str]						     =       new_scheduler.step_pred(
                   lowerCamelCase ,    lowerCamelCase ,    lowerCamelCase ,    generator=torch.manual_seed(0		) ,    **lowerCamelCase		).prev_sample

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

               __A  :					Dict						     =       scheduler.step_correct(lowerCamelCase ,    lowerCamelCase ,    generator=torch.manual_seed(0		) ,    **lowerCamelCase		).prev_sample
               __A  :					List[Any]						     =       new_scheduler.step_correct(
                   lowerCamelCase ,    lowerCamelCase ,    generator=torch.manual_seed(0		) ,    **lowerCamelCase		).prev_sample

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


     def lowerCAmelCase__     (      self ,    lowerCamelCase=0 ,    **lowerCamelCase		):


          '''simple docstring'''





          __A  :					Any						     =       dict(self.forward_default_kwargs		)
          kwargs.update(lowerCamelCase		)

          for scheduler_class in self.scheduler_classes:
               __A  :					Optional[Any]						     =       self.dummy_sample
               __A  :					List[str]						     =       0.1 * sample

               __A  :					str						     =       self.get_scheduler_config()
               __A  :					Optional[Any]						     =       scheduler_class(**lowerCamelCase		)

               with tempfile.TemporaryDirectory() as tmpdirname:
                    scheduler.save_config(lowerCamelCase		)
                    __A  :					Dict						     =       scheduler_class.from_pretrained(lowerCamelCase		)

               __A  :					List[str]						     =       scheduler.step_pred(
                   lowerCamelCase ,    lowerCamelCase ,    lowerCamelCase ,    generator=torch.manual_seed(0		) ,    **lowerCamelCase		).prev_sample
               __A  :					str						     =       new_scheduler.step_pred(
                   lowerCamelCase ,    lowerCamelCase ,    lowerCamelCase ,    generator=torch.manual_seed(0		) ,    **lowerCamelCase		).prev_sample

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

               __A  :					List[str]						     =       scheduler.step_correct(lowerCamelCase ,    lowerCamelCase ,    generator=torch.manual_seed(0		) ,    **lowerCamelCase		).prev_sample
               __A  :					Optional[int]						     =       new_scheduler.step_correct(
                   lowerCamelCase ,    lowerCamelCase ,    generator=torch.manual_seed(0		) ,    **lowerCamelCase		).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 [10, 100, 1000]:
               self.check_over_configs(num_train_timesteps=lowerCamelCase		)


     def lowerCAmelCase__     (      self		):


          '''simple docstring'''





          for sigma_min, sigma_max in zip([0.0001, 0.001, 0.01] ,    [1, 100, 1000]		):
               self.check_over_configs(sigma_min=lowerCamelCase ,    sigma_max=lowerCamelCase		)


     def lowerCAmelCase__     (      self		):


          '''simple docstring'''





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


     def lowerCAmelCase__     (      self		):


          '''simple docstring'''





          __A  :					int						     =       dict(self.forward_default_kwargs		)

          __A  :					Dict						     =       self.scheduler_classes[0]
          __A  :					str						     =       self.get_scheduler_config()
          __A  :					List[Any]						     =       scheduler_class(**lowerCamelCase		)

          __A  :					Union[str, Any]						     =       3

          __A  :					Union[str, Any]						     =       self.dummy_model()
          __A  :					Union[str, Any]						     =       self.dummy_sample_deter

          scheduler.set_sigmas(lowerCamelCase		)
          scheduler.set_timesteps(lowerCamelCase		)
          __A  :					Optional[int]						     =       torch.manual_seed(0		)

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

               for _ in range(scheduler.config.correct_steps		):
                    with torch.no_grad():
                         __A  :					Tuple						     =       model(lowerCamelCase ,    lowerCamelCase		)
                    __A  :					Optional[int]						     =       scheduler.step_correct(lowerCamelCase ,    lowerCamelCase ,    generator=lowerCamelCase ,    **lowerCamelCase		).prev_sample

               with torch.no_grad():
                    __A  :					Tuple						     =       model(lowerCamelCase ,    lowerCamelCase		)

               __A  :					str						     =       scheduler.step_pred(lowerCamelCase ,    lowerCamelCase ,    lowerCamelCase ,    generator=lowerCamelCase ,    **lowerCamelCase		)
               __A     ,__A  :					int						     =       output.prev_sample, output.prev_sample_mean

          __A  :					Union[str, Any]						     =       torch.sum(torch.abs(lowerCamelCase		)		)
          __A  :					str						     =       torch.mean(torch.abs(lowerCamelCase		)		)

          assert np.isclose(result_sum.item() ,    143_7275_8528.0		)
          assert np.isclose(result_mean.item() ,    1871_4530.0		)


     def lowerCAmelCase__     (      self		):


          '''simple docstring'''





          __A  :					int						     =       dict(self.forward_default_kwargs		)

          __A  :					List[Any]						     =       kwargs.pop("num_inference_steps" ,    lowerCamelCase		)

          for scheduler_class in self.scheduler_classes:
               __A  :					int						     =       self.get_scheduler_config()
               __A  :					Any						     =       scheduler_class(**lowerCamelCase		)

               __A  :					List[str]						     =       self.dummy_sample
               __A  :					List[str]						     =       0.1 * sample

               if num_inference_steps is not None and hasattr(lowerCamelCase ,    "set_timesteps"		):
                    scheduler.set_timesteps(lowerCamelCase		)
               elif num_inference_steps is not None and not hasattr(lowerCamelCase ,    "set_timesteps"		):
                    __A  :					Any						     =       num_inference_steps

               __A  :					str						     =       scheduler.step_pred(lowerCamelCase ,    0 ,    lowerCamelCase ,    generator=torch.manual_seed(0		) ,    **lowerCamelCase		).prev_sample
               __A  :					Optional[int]						     =       scheduler.step_pred(lowerCamelCase ,    1 ,    lowerCamelCase ,    generator=torch.manual_seed(0		) ,    **lowerCamelCase		).prev_sample

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

