






"""simple docstring"""


from dataclasses import dataclass
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, logging, randn_tensor
from .scheduling_utils import SchedulerMixin


lowercase_								=	logging.get_logger(__name__)  # pylint: disable=invalid-name


@dataclass
class    snake_case						(				_lowerCAmelCase		):


   '''simple docstring'''

   A_		:      torch.FloatTensor





class    snake_case						(				_lowerCAmelCase  ,      _lowerCAmelCase		):


   '''simple docstring'''

   A_		:      Optional[Any]							=					1


   @register_to_config
   def __init__(      self					:	Tuple,			_lowerCamelCase					:	int = 40,			_lowerCamelCase					:	float = 0.0_02,			_lowerCamelCase					:	float = 80.0,			_lowerCamelCase					:	float = 0.5,			_lowerCamelCase					:	float = 1.0,			_lowerCamelCase					:	float = 7.0,			_lowerCamelCase					:	bool = True,			):

     '''simple docstring'''
     # standard deviation of the initial noise distribution
     __A		     =					sigma_max

     __A		     =					np.linspace(0,			1,			_lowerCamelCase    )
     __A		     =					self._convert_to_karras(_lowerCamelCase    )
     __A		     =					self.sigma_to_t(_lowerCamelCase    )

     # setable values
     __A		     =					None
     __A		     =					torch.from_numpy(_lowerCamelCase    )
     __A		     =					torch.from_numpy(_lowerCamelCase    )
     __A		     =					False
     __A		     =					False


   def 							_SCREAMING_SNAKE_CASE							(      self					:	Any,			_lowerCamelCase					:	int,			_lowerCamelCase					:	Optional[Any]=None    ):

     '''simple docstring'''
     if schedule_timesteps is None:
       __A		     =					self.timesteps

     __A		     =					(schedule_timesteps == timestep).nonzero()
     return indices.item()


   def 							_SCREAMING_SNAKE_CASE							(      self					:	Optional[int],			_lowerCamelCase					:	torch.FloatTensor,			_lowerCamelCase					:	Union[float, torch.FloatTensor]    ):

     '''simple docstring'''
     # Get sigma corresponding to timestep
     if isinstance(_lowerCamelCase,			torch.Tensor    ):
       __A		     =					timestep.to(self.timesteps.device    )
     __A		     =					self.index_for_timestep(_lowerCamelCase    )
     __A		     =					self.sigmas[step_idx]

     __A		     =					sample / ((sigma**2 + self.config.sigma_data**2) ** 0.5)

     __A		     =					True
     return sample


   def 							_SCREAMING_SNAKE_CASE							(      self					:	Dict,			_lowerCamelCase					:	Union[float, np.ndarray]    ):

     '''simple docstring'''
     if not isinstance(_lowerCamelCase,			np.ndarray    ):
       __A		     =					np.array(_lowerCamelCase,			dtype=np.floataa    )

     __A		     =					10_00 * 0.25 * np.log(sigmas + 1e-44    )

     return timesteps


   def 							_SCREAMING_SNAKE_CASE							(      self					:	Dict,			_lowerCamelCase					:	Optional[int] = None,			_lowerCamelCase					:	Union[str, torch.device] = None,			_lowerCamelCase					:	Optional[List[int]] = None,			):

     '''simple docstring'''
     if num_inference_steps is None and timesteps is None:
       raise ValueError('''Exactly one of `num_inference_steps` or `timesteps` must be supplied.'''    )

     if num_inference_steps is not None and timesteps is not None:
       raise ValueError('''Can only pass one of `num_inference_steps` or `timesteps`.'''    )

     # Follow DDPMScheduler custom timesteps logic
     if timesteps is not None:
       for i in range(1,			len(_lowerCamelCase    )    ):
         if timesteps[i] >= timesteps[i - 1]:
           raise ValueError('''`timesteps` must be in descending order.'''    )

       if timesteps[0] >= self.config.num_train_timesteps:
         raise ValueError(
             f'`timesteps` must start before `self.config.train_timesteps`:'
             f' {self.config.num_train_timesteps}.'    )

       __A		     =					np.array(_lowerCamelCase,			dtype=np.intaa    )
       __A		     =					True
     else:
       if num_inference_steps > self.config.num_train_timesteps:
         raise ValueError(
             f'`num_inference_steps`: {num_inference_steps} cannot be larger than `self.config.train_timesteps`:'
             f' {self.config.num_train_timesteps} as the unet model trained with this scheduler can only handle'
             f' maximal {self.config.num_train_timesteps} timesteps.'    )

       __A		     =					num_inference_steps

       __A		     =					self.config.num_train_timesteps // self.num_inference_steps
       __A		     =					(np.arange(0,			_lowerCamelCase    ) * step_ratio).round()[::-1].copy().astype(np.intaa    )
       __A		     =					False

     # Map timesteps to Karras sigmas directly for multistep sampling
     # See https://github.com/openai/consistency_models/blob/main/cm/karras_diffusion.py#L675
     __A		     =					self.config.num_train_timesteps
     __A		     =					timesteps[::-1].copy()
     __A		     =					ramp / (num_train_timesteps - 1)
     __A		     =					self._convert_to_karras(_lowerCamelCase    )
     __A		     =					self.sigma_to_t(_lowerCamelCase    )

     __A		     =					np.concatenate([sigmas, [self.sigma_min]]    ).astype(np.floataa    )
     __A		     =					torch.from_numpy(_lowerCamelCase    ).to(device=_lowerCamelCase    )

     if str(_lowerCamelCase    ).startswith('''mps'''    ):
       # mps does not support float64
       __A		     =					torch.from_numpy(_lowerCamelCase    ).to(_lowerCamelCase,			dtype=torch.floataa    )
     else:
       __A		     =					torch.from_numpy(_lowerCamelCase    ).to(device=_lowerCamelCase    )


   def 							_SCREAMING_SNAKE_CASE							(      self					:	Optional[Any],			_lowerCamelCase					:	Any    ):

     '''simple docstring'''
     __A		     =					self.config.sigma_min
     __A		     =					self.config.sigma_max

     __A		     =					self.config.rho
     __A		     =					sigma_min ** (1 / rho)
     __A		     =					sigma_max ** (1 / rho)
     __A		     =					(max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
     return sigmas


   def 							_SCREAMING_SNAKE_CASE							(      self					:	Optional[Any],			_lowerCamelCase					:	Optional[int]    ):

     '''simple docstring'''
     __A		     =					self.config.sigma_data

     __A		     =					sigma_data**2 / (sigma**2 + sigma_data**2)
     __A		     =					sigma * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
     return c_skip, c_out


   def 							_SCREAMING_SNAKE_CASE							(      self					:	List[str],			_lowerCamelCase					:	Dict    ):

     '''simple docstring'''
     __A		     =					self.config.sigma_min
     __A		     =					self.config.sigma_data

     __A		     =					sigma_data**2 / ((sigma - sigma_min) ** 2 + sigma_data**2)
     __A		     =					(sigma - sigma_min) * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
     return c_skip, c_out


   def 							_SCREAMING_SNAKE_CASE							(      self					:	Tuple,			_lowerCamelCase					:	torch.FloatTensor,			_lowerCamelCase					:	Union[float, torch.FloatTensor],			_lowerCamelCase					:	torch.FloatTensor,			_lowerCamelCase					:	Optional[torch.Generator] = None,			_lowerCamelCase					:	bool = True,			):

     '''simple docstring'''

     if (
         isinstance(_lowerCamelCase,			_lowerCamelCase    )
         or isinstance(_lowerCamelCase,			torch.IntTensor    )
         or isinstance(_lowerCamelCase,			torch.LongTensor    )
     ):
       raise ValueError(
           (
               '''Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to'''
               f' `{self.__class__}.step()` is not supported. Make sure to pass'
               ''' one of the `scheduler.timesteps` as a timestep.'''
           ),			)

     if not self.is_scale_input_called:
       logger.warning(
           '''The `scale_model_input` function should be called before `step` to ensure correct denoising. '''
           '''See `StableDiffusionPipeline` for a usage example.'''    )

     if isinstance(_lowerCamelCase,			torch.Tensor    ):
       __A		     =					timestep.to(self.timesteps.device    )

     __A		     =					self.config.sigma_min
     __A		     =					self.config.sigma_max

     __A		     =					self.index_for_timestep(_lowerCamelCase    )

     # sigma_next corresponds to next_t in original implementation
     __A		     =					self.sigmas[step_index]
     if step_index + 1 < self.config.num_train_timesteps:
       __A		     =					self.sigmas[step_index + 1]
     else:
       # Set sigma_next to sigma_min
       __A		     =					self.sigmas[-1]

     # Get scalings for boundary conditions
     __A     ,       __A		     =					self.get_scalings_for_boundary_condition(_lowerCamelCase    )

     # 1. Denoise model output using boundary conditions
     __A		     =					c_out * model_output + c_skip * sample
     if self.config.clip_denoised:
       __A		     =					denoised.clamp(-1,			1    )

     # 2. Sample z ~ N(0, s_noise^2 * I)
     # Noise is not used for onestep sampling.
     if len(self.timesteps    ) > 1:
       __A		     =					randn_tensor(
           model_output.shape,			dtype=model_output.dtype,			device=model_output.device,			generator=_lowerCamelCase    )
     else:
       __A		     =					torch.zeros_like(_lowerCamelCase    )
     __A		     =					noise * self.config.s_noise

     __A		     =					sigma_next.clamp(min=_lowerCamelCase,			max=_lowerCamelCase    )

     # 3. Return noisy sample
     # tau = sigma_hat, eps = sigma_min
     __A		     =					denoised + z * (sigma_hat**2 - sigma_min**2) ** 0.5

     if not return_dict:
       return (prev_sample,)

     return CMStochasticIterativeSchedulerOutput(prev_sample=_lowerCamelCase    )


   def 							_SCREAMING_SNAKE_CASE							(      self					:	Any,			_lowerCamelCase					:	torch.FloatTensor,			_lowerCamelCase					:	torch.FloatTensor,			_lowerCamelCase					:	torch.FloatTensor,			):

     '''simple docstring'''
     # Make sure sigmas and timesteps have the same device and dtype as original_samples
     __A		     =					self.sigmas.to(device=original_samples.device,			dtype=original_samples.dtype    )
     if original_samples.device.type == "mps" and torch.is_floating_point(_lowerCamelCase    ):
       # mps does not support float64
       __A		     =					self.timesteps.to(original_samples.device,			dtype=torch.floataa    )
       __A		     =					timesteps.to(original_samples.device,			dtype=torch.floataa    )
     else:
       __A		     =					self.timesteps.to(original_samples.device    )
       __A		     =					timesteps.to(original_samples.device    )

     __A		     =					[(schedule_timesteps == t).nonzero().item() for t in timesteps]

     __A		     =					sigmas[step_indices].flatten()
     while len(sigma.shape    ) < len(original_samples.shape    ):
       __A		     =					sigma.unsqueeze(-1    )

     __A		     =					original_samples + noise * sigma
     return noisy_samples






   def __len__(      self					:	Any    ):

     '''simple docstring'''
     return self.config.num_train_timesteps






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 ,     _A ,     _A=21 ,     _A=[] ,     _A=None	)				->				List[Any]:
          SCREAMING_SNAKE_CASE_	=      model
          SCREAMING_SNAKE_CASE_	=      trace_batch_nums
          SCREAMING_SNAKE_CASE_	=      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_	=      collections.deque([] ,     _A	)
          SCREAMING_SNAKE_CASE_	=      []
          SCREAMING_SNAKE_CASE_	=      0
          SCREAMING_SNAKE_CASE_	=      0
          SCREAMING_SNAKE_CASE_	=      False
          SCREAMING_SNAKE_CASE_	=      '''                 '''

          self.analyse_model()

          self.register_forward_hook()



       def     _UpperCamelCase	(							self ,     _A=None	)				->				Any:
          if frame is not None:
             self.expand_frame(_A	)
          self.frames.append('''\n'''.join(self.frame	)	)
          SCREAMING_SNAKE_CASE_	=      []  # start a new frame



       def     _UpperCamelCase	(							self ,     _A	)				->				List[Any]:
          self.frame.append(_A	)



       def     _UpperCamelCase	(							self	)				->				Optional[Any]:
          print('''\n'''.join(self.frames	)	)
          SCREAMING_SNAKE_CASE_	=      []



       def     _UpperCamelCase	(							self	)				->				int:
          SCREAMING_SNAKE_CASE_	=      []



       def     _UpperCamelCase	(							self	)				->				List[str]:
          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_	=      []



       def     _UpperCamelCase	(							self	)				->				Optional[int]:
          # 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
          SCREAMING_SNAKE_CASE_	=      {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     _UpperCamelCase	(							self ,     _A ,     _A	)				->				Union[str, Any]:
          if torch.is_tensor(_A	):
             self.expand_frame(get_abs_min_max(_A ,     _A	)	)
             if detect_overflow(_A ,     _A	):
                SCREAMING_SNAKE_CASE_	=      True
          elif var is None:
             self.expand_frame(F'''{"None":>17} {ctx}'''	)
          else:
             self.expand_frame(F'''{"not a tensor":>17} {ctx}'''	)



       def     _UpperCamelCase	(							self	)				->				List[str]:
          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     _UpperCamelCase	(							self	)				->				List[Any]:
          self.expand_frame(F'''{self.prefix} *** Finished batch number={self.batch_number-1} ***\n\n'''	)



       def     _UpperCamelCase	(							self ,     _A ,     _A ,     _A	)				->				Union[str, Any]:
          self.expand_frame(F'''{self.prefix} {self.module_names[module]} {module.__class__.__name__}'''	)

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

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

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

          self.save_frame()



       def     _UpperCamelCase	(							self	)				->				List[Any]:
          self.model.apply(self._register_forward_hook	)



       def     _UpperCamelCase	(							self ,     _A	)				->				Tuple:
          module.register_forward_hook(self.forward_hook	)




       def     _UpperCamelCase	(							self ,     _A ,     _A ,     _A	)				->				Any:
          # - input is a tuple of packed inputs (could be non-Tensors)
          # - output could be a Tensor or a tuple of Tensors and non-Tensors

          SCREAMING_SNAKE_CASE_	=      False

          SCREAMING_SNAKE_CASE_	=      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_	=      True

          self.create_frame(_A ,     _A ,     _A	)

          # 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  A__ (       __lowerCamelCase,  __lowerCamelCase						):
   SCREAMING_SNAKE_CASE_	=      var.abs()
   return F'''{abs_var.min():8.2e} {abs_var.max():8.2e} {ctx}'''


def  A__ (       __lowerCamelCase,  __lowerCamelCase						):
   SCREAMING_SNAKE_CASE_	=      False
   if torch.isnan(__lowerCamelCase						).any().item():
      SCREAMING_SNAKE_CASE_	=      True
      print(F'''{ctx} has nans'''						)
   if torch.isinf(__lowerCamelCase						).any().item():
      SCREAMING_SNAKE_CASE_	=      True
      print(F'''{ctx} has infs'''						)

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






       """simple docstring"""


       UpperCAmelCase_				       ="underflow_overflow"
       UpperCAmelCase_				       ="tpu_metrics_debug"


