


'''simple docstring'''



import collections

from .utils import ExplicitEnum, is_torch_available, logging


if is_torch_available():
      import torch


A__	:      Any				=						logging.get_logger(__name__)





class      UpperCAmelCase_						:





    """simple docstring"""






    def __init__(							self    ,  SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_=21    ,  SCREAMING_SNAKE_CASE_=[]    ,  SCREAMING_SNAKE_CASE_=None			)       ->  List[Any]:
         __lowerCamelCase     :			Union[str, Any]              =	model
         __lowerCamelCase     :			Union[str, Any]              =	trace_batch_nums
         __lowerCamelCase     :			Optional[Any]              =	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     :			int              =	collections.deque([]    ,  _lowerCAmelCase			)
         __lowerCamelCase     :			str              =	[]
         __lowerCamelCase     :			Optional[int]              =	0
         __lowerCamelCase     :			int              =	0
         __lowerCamelCase     :			int              =	False
         __lowerCamelCase     :			List[str]              =	"""                 """

         self.analyse_model()

         self.register_forward_hook()







    def    lowercase_       (							self    ,  SCREAMING_SNAKE_CASE_=None			)       ->  List[str]:
         if frame is not None:
              self.expand_frame(_lowerCAmelCase			)
         self.frames.append('\n'.join(self.frame			)			)
         __lowerCamelCase     :			str              =	[]  # start a new frame







    def    lowercase_       (							self    ,  SCREAMING_SNAKE_CASE_			)       ->  Optional[Any]:
         self.frame.append(_lowerCAmelCase			)







    def    lowercase_       (							self			)       ->  str:
         print('\n'.join(self.frames			)			)
         __lowerCamelCase     :			List[Any]              =	[]







    def    lowercase_       (							self			)       ->  List[Any]:
         __lowerCamelCase     :			Optional[int]              =	[]







    def    lowercase_       (							self			)       ->  Optional[int]:
         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     :			Any              =	[]







    def    lowercase_       (							self			)       ->  Tuple:
         # 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     :			str              =	{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    ,  SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_			)       ->  Union[str, Any]:
         if torch.is_tensor(_lowerCAmelCase			):
              self.expand_frame(get_abs_min_max(_lowerCAmelCase    ,  _lowerCAmelCase			)			)
              if detect_overflow(_lowerCAmelCase    ,  _lowerCAmelCase			):
                   __lowerCamelCase     :			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:
         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			)       ->  Dict:
         self.expand_frame(f'{self.prefix} *** Finished batch number={self.batch_number-1} ***\n\n'			)







    def    lowercase_       (							self    ,  SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_			)       ->  Optional[Any]:
         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    lowercase_       (							self			)       ->  int:
         self.model.apply(self._register_forward_hook			)







    def    lowercase_       (							self    ,  SCREAMING_SNAKE_CASE_			)       ->  int:
         module.register_forward_hook(self.forward_hook			)







    def    lowercase_       (							self    ,  SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_			)       ->  List[str]:
         # - 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     :			Union[str, Any]              =	False

         __lowerCamelCase     :			List[str]              =	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     :			Dict              =	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     UpperCAmelCase__      (			UpperCAmelCase_					:       str   ,					UpperCAmelCase_					:       Union[str, Any]							)  ->	Optional[Any]:
     __lowerCamelCase     :			Dict              =	var.abs()
     return F'{abs_var.min():8.2e} {abs_var.max():8.2e} {ctx}'



def     UpperCAmelCase__      (			UpperCAmelCase_					:       List[Any]   ,					UpperCAmelCase_					:       Any							)  ->	int:
     __lowerCamelCase     :			str              =	False
     if torch.isnan(__lowerCamelCase							).any().item():
          __lowerCamelCase     :			List[Any]              =	True
          print(F'{ctx} has nans'							)
     if torch.isinf(__lowerCamelCase							).any().item():
          __lowerCamelCase     :			List[str]              =	True
          print(F'{ctx} has infs'							)

     # if needed to monitor large elements can enable the following
     if 0:  # and detected:
          __lowerCamelCase     :			Dict              =	var[torch.ge(var.abs()   ,					1_00							)]
          if naaa.numel() > 0:
               print(F'{ctx}:  n100={naaa.numel()}'							)
          __lowerCamelCase     :			Tuple              =	var[torch.ge(var.abs()   ,					10_00							)]
          if naaaa.numel() > 0:
               print(F'{ctx}: n1000={naaaa.numel()}'							)
          __lowerCamelCase     :			Optional[int]              =	var[torch.ge(var.abs()   ,					1_00_00							)]
          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_						(__lowerCamelCase						):





    """simple docstring"""






    lowerCamelCase						:      Dict							  =    'underflow_overflow'
    lowerCamelCase						:      str							  =    'tpu_metrics_debug'



'''simple docstring'''



import torch

from diffusers import KDPMaAncestralDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest





class      UpperCAmelCase_						(_UpperCAmelCase						):





    """simple docstring"""






    lowerCamelCase						:      Optional[Any]							  =    (KDPMaAncestralDiscreteScheduler,)
    lowerCamelCase						:      Tuple							  =    1_0







    def    lowercase_       (							self    ,  **SCREAMING_SNAKE_CASE_			)       ->  Tuple:
         __lowerCamelCase     :			Union[str, Any]              =	{
             'num_train_timesteps': 11_00,
             'beta_start': 0.0_0_0_1,
             'beta_end': 0.0_2,
             'beta_schedule': 'linear',
         }

         config.update(**SCREAMING_SNAKE_CASE_			)
         return config







    def    lowercase_       (							self			)       ->  Union[str, Any]:
         for timesteps in [10, 50, 1_00, 10_00]:
              self.check_over_configs(num_train_timesteps=SCREAMING_SNAKE_CASE_			)







    def    lowercase_       (							self			)       ->  Dict:
         for beta_start, beta_end in zip([0.0_0_0_0_1, 0.0_0_0_1, 0.0_0_1]    ,  [0.0_0_0_2, 0.0_0_2, 0.0_2]			):
              self.check_over_configs(beta_start=SCREAMING_SNAKE_CASE_    ,  beta_end=SCREAMING_SNAKE_CASE_			)







    def    lowercase_       (							self			)       ->  List[str]:
         for schedule in ["linear", "scaled_linear"]:
              self.check_over_configs(beta_schedule=SCREAMING_SNAKE_CASE_			)







    def    lowercase_       (							self			)       ->  int:
         if torch_device == "mps":
              return
         __lowerCamelCase     :			Union[str, Any]              =	self.scheduler_classes[0]
         __lowerCamelCase     :			Optional[Any]              =	self.get_scheduler_config()
         __lowerCamelCase     :			Optional[int]              =	scheduler_class(**SCREAMING_SNAKE_CASE_			)

         scheduler.set_timesteps(self.num_inference_steps			)

         __lowerCamelCase     :			int              =	torch.manual_seed(0			)

         __lowerCamelCase     :			int              =	self.dummy_model()
         __lowerCamelCase     :			List[Any]              =	self.dummy_sample_deter * scheduler.init_noise_sigma
         __lowerCamelCase     :			Any              =	sample.to(SCREAMING_SNAKE_CASE_			)

         for i, t in enumerate(scheduler.timesteps			):
              __lowerCamelCase     :			Dict              =	scheduler.scale_model_input(SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_			)

              __lowerCamelCase     :			Dict              =	model(SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_			)

              __lowerCamelCase     :			Union[str, Any]              =	scheduler.step(SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_    ,  generator=SCREAMING_SNAKE_CASE_			)
              __lowerCamelCase     :			Any              =	output.prev_sample

         __lowerCamelCase     :			Tuple              =	torch.sum(torch.abs(SCREAMING_SNAKE_CASE_			)			)
         __lowerCamelCase     :			Optional[int]              =	torch.mean(torch.abs(SCREAMING_SNAKE_CASE_			)			)

         assert abs(result_sum.item() - 1_3_8_4_9.3_8_7_7			) < 1E-2
         assert abs(result_mean.item() - 1_8.0_3_3_1			) < 5E-3







    def    lowercase_       (							self			)       ->  List[str]:
         for prediction_type in ["epsilon", "v_prediction"]:
              self.check_over_configs(prediction_type=SCREAMING_SNAKE_CASE_			)







    def    lowercase_       (							self			)       ->  List[str]:
         if torch_device == "mps":
              return
         __lowerCamelCase     :			Union[str, Any]              =	self.scheduler_classes[0]
         __lowerCamelCase     :			Optional[Any]              =	self.get_scheduler_config(prediction_type='v_prediction'			)
         __lowerCamelCase     :			str              =	scheduler_class(**SCREAMING_SNAKE_CASE_			)

         scheduler.set_timesteps(self.num_inference_steps			)

         __lowerCamelCase     :			int              =	self.dummy_model()
         __lowerCamelCase     :			int              =	self.dummy_sample_deter * scheduler.init_noise_sigma
         __lowerCamelCase     :			Dict              =	sample.to(SCREAMING_SNAKE_CASE_			)

         __lowerCamelCase     :			int              =	torch.manual_seed(0			)

         for i, t in enumerate(scheduler.timesteps			):
              __lowerCamelCase     :			Any              =	scheduler.scale_model_input(SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_			)

              __lowerCamelCase     :			List[str]              =	model(SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_			)

              __lowerCamelCase     :			str              =	scheduler.step(SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_    ,  generator=SCREAMING_SNAKE_CASE_			)
              __lowerCamelCase     :			Tuple              =	output.prev_sample

         __lowerCamelCase     :			int              =	torch.sum(torch.abs(SCREAMING_SNAKE_CASE_			)			)
         __lowerCamelCase     :			int              =	torch.mean(torch.abs(SCREAMING_SNAKE_CASE_			)			)

         assert abs(result_sum.item() - 3_2_8.9_9_7_0			) < 1E-2
         assert abs(result_mean.item() - 0.4_2_8_4			) < 1E-3







    def    lowercase_       (							self			)       ->  Dict:
         if torch_device == "mps":
              return
         __lowerCamelCase     :			Optional[int]              =	self.scheduler_classes[0]
         __lowerCamelCase     :			str              =	self.get_scheduler_config()
         __lowerCamelCase     :			Union[str, Any]              =	scheduler_class(**SCREAMING_SNAKE_CASE_			)

         scheduler.set_timesteps(self.num_inference_steps    ,  device=SCREAMING_SNAKE_CASE_			)
         __lowerCamelCase     :			List[Any]              =	torch.manual_seed(0			)

         __lowerCamelCase     :			Optional[Any]              =	self.dummy_model()
         __lowerCamelCase     :			Dict              =	self.dummy_sample_deter.to(SCREAMING_SNAKE_CASE_			) * scheduler.init_noise_sigma

         for t in scheduler.timesteps:
              __lowerCamelCase     :			Any              =	scheduler.scale_model_input(SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_			)

              __lowerCamelCase     :			Optional[Any]              =	model(SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_			)

              __lowerCamelCase     :			Dict              =	scheduler.step(SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_    ,  generator=SCREAMING_SNAKE_CASE_			)
              __lowerCamelCase     :			Union[str, Any]              =	output.prev_sample

         __lowerCamelCase     :			int              =	torch.sum(torch.abs(SCREAMING_SNAKE_CASE_			)			)
         __lowerCamelCase     :			Dict              =	torch.mean(torch.abs(SCREAMING_SNAKE_CASE_			)			)

         assert abs(result_sum.item() - 1_3_8_4_9.3_8_1_8			) < 1E-1
         assert abs(result_mean.item() - 1_8.0_3_3_1			) < 1E-3






