


import math
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import SchedulerMixin, SchedulerOutput




class    UpperCAmelCase					(       __snake_case      , __snake_case    ):
   lowercase						= 1




   @register_to_config
   def __init__(						self							:		List[Any]       ,       __magic_name__							:		int = 1_0_0_0       ,       __magic_name__							:		Optional[Union[np.ndarray, List[float]]] = None						):







      """simple docstring"""







      self.set_timesteps(__magic_name__						)

      # standard deviation of the initial noise distribution
      UpperCamelCase         =					1.0

      # For now we only support F-PNDM, i.e. the runge-kutta method
      # For more information on the algorithm please take a look at the paper: https://arxiv.org/pdf/2202.09778.pdf
      # mainly at formula (9), (12), (13) and the Algorithm 2.
      UpperCamelCase         =					4

      # running values
      UpperCamelCase         =					[]




   def   lowerCamelCase_					(						self							:		List[str]       ,       __magic_name__							:		int       ,       __magic_name__							:		Union[str, torch.device] = None						):







      """simple docstring"""







      UpperCamelCase         =					num_inference_steps
      UpperCamelCase         =					torch.linspace(1       ,       0       ,       num_inference_steps + 1						)[:-1]
      UpperCamelCase         =					torch.cat([steps, torch.tensor([0.0]						)]						)

      if self.config.trained_betas is not None:
         UpperCamelCase         =					torch.tensor(self.config.trained_betas       ,       dtype=torch.floataa						)
      else:
         UpperCamelCase         =					torch.sin(steps * math.pi / 2						) ** 2

      UpperCamelCase         =					(1.0 - self.betas**2) ** 0.5

      UpperCamelCase         =					(torch.atana(self.betas       ,       self.alphas						) / math.pi * 2)[:-1]
      UpperCamelCase         =					timesteps.to(__magic_name__						)

      UpperCamelCase         =					[]




   def   lowerCamelCase_					(						self							:		Optional[int]       ,       __magic_name__							:		torch.FloatTensor       ,       __magic_name__							:		int       ,       __magic_name__							:		torch.FloatTensor       ,       __magic_name__							:		bool = True       ,       ):







      """simple docstring"""







      if self.num_inference_steps is None:
         raise ValueError(
             """Number of inference steps is 'None', you need to run 'set_timesteps' after creating the scheduler"""						)

      UpperCamelCase         =					(self.timesteps == timestep).nonzero().item()
      UpperCamelCase         =					timestep_index + 1

      UpperCamelCase         =					sample * self.betas[timestep_index] + model_output * self.alphas[timestep_index]
      self.ets.append(__magic_name__						)

      if len(self.ets						) == 1:
         UpperCamelCase         =					self.ets[-1]
      elif len(self.ets						) == 2:
         UpperCamelCase         =					(3 * self.ets[-1] - self.ets[-2]) / 2
      elif len(self.ets						) == 3:
         UpperCamelCase         =					(2_3 * self.ets[-1] - 1_6 * self.ets[-2] + 5 * self.ets[-3]) / 1_2
      else:
         UpperCamelCase         =					(1 / 2_4) * (5_5 * self.ets[-1] - 5_9 * self.ets[-2] + 3_7 * self.ets[-3] - 9 * self.ets[-4])

      UpperCamelCase         =					self._get_prev_sample(__magic_name__       ,       __magic_name__       ,       __magic_name__       ,       __magic_name__						)

      if not return_dict:
         return (prev_sample,)

      return SchedulerOutput(prev_sample=__magic_name__						)




   def   lowerCamelCase_					(						self							:		Dict       ,       __magic_name__							:		torch.FloatTensor       ,       *__magic_name__							:		Optional[int]       ,       **__magic_name__							:		Optional[Any]						):







      """simple docstring"""







      return sample




   def   lowerCamelCase_					(						self							:		Any       ,       __magic_name__							:		Union[str, Any]       ,       __magic_name__							:		str       ,       __magic_name__							:		List[str]       ,       __magic_name__							:		int						):







      """simple docstring"""







      UpperCamelCase         =					self.alphas[timestep_index]
      UpperCamelCase         =					self.betas[timestep_index]

      UpperCamelCase         =					self.alphas[prev_timestep_index]
      UpperCamelCase         =					self.betas[prev_timestep_index]

      UpperCamelCase         =					(sample - sigma * ets) / max(__magic_name__       ,       1e-8						)
      UpperCamelCase         =					next_alpha * pred + ets * next_sigma

      return prev_sample





   def __len__(						self							:		Tuple						):







      """simple docstring"""







      return self.config.num_train_timesteps








import functools
import gc
import inspect

import torch

from .imports import is_npu_available, is_xpu_available






def 							_lowercase    (		*SCREAMING_SNAKE_CASE_   :		List[Any]	):





			"""simple docstring"""




			if not isinstance(SCREAMING_SNAKE_CASE_       ,  SCREAMING_SNAKE_CASE_	):
						UpperCamelCase         =					list(SCREAMING_SNAKE_CASE_	)
			for i in range(len(SCREAMING_SNAKE_CASE_	)	):
						UpperCamelCase         =					None
			gc.collect()
			if is_xpu_available():
						torch.xpu.empty_cache()
			elif is_npu_available():
						torch.npu.empty_cache()
			else:
						torch.cuda.empty_cache()
			return objects






def 							_lowercase    (		SCREAMING_SNAKE_CASE_   :		Exception	):





			"""simple docstring"""




			UpperCamelCase         =					[
			    """CUDA out of memory.""",  # CUDA OOM
			    """cuDNN error: CUDNN_STATUS_NOT_SUPPORTED.""",  # CUDNN SNAFU
			    """DefaultCPUAllocator: can't allocate memory""",  # CPU OOM
			]
			if isinstance(SCREAMING_SNAKE_CASE_       ,  SCREAMING_SNAKE_CASE_	) and len(exception.args	) == 1:
						return any(err in exception.args[0] for err in _statements	)
			return False






def 							_lowercase    (		SCREAMING_SNAKE_CASE_   :		callable = None       ,  SCREAMING_SNAKE_CASE_   :		int = 128	):





			"""simple docstring"""




			if function is None:
						return functools.partial(SCREAMING_SNAKE_CASE_       ,  starting_batch_size=SCREAMING_SNAKE_CASE_	)

			UpperCamelCase         =					starting_batch_size

			def decorator(*SCREAMING_SNAKE_CASE_   :		Union[str, Any]       ,  **SCREAMING_SNAKE_CASE_   :		Tuple	):
						nonlocal batch_size
						gc.collect()
						if is_xpu_available():
									torch.xpu.empty_cache()
						elif is_npu_available():
									torch.npu.empty_cache()
						else:
									torch.cuda.empty_cache()
						UpperCamelCase         =					list(inspect.signature(SCREAMING_SNAKE_CASE_	).parameters.keys()	)
						# Guard against user error
						if len(SCREAMING_SNAKE_CASE_	) < (len(SCREAMING_SNAKE_CASE_	) + 1):
									UpperCamelCase         =					""", """.join([f'{arg}={value}' for arg, value in zip(params[1:]       ,  args[1:]	)]	)
									raise TypeError(
									    f'Batch size was passed into `{function.__name__}` as the first argument when called.'
									    f'Remove this as the decorator already does so: `{function.__name__}({arg_str})`'	)
						while True:
									if batch_size == 0:
												raise RuntimeError("""No executable batch size found, reached zero."""	)
									try:
												return function(SCREAMING_SNAKE_CASE_       ,  *SCREAMING_SNAKE_CASE_       ,  **SCREAMING_SNAKE_CASE_	)
									except Exception as e:
												if should_reduce_batch_size(SCREAMING_SNAKE_CASE_	):
															gc.collect()
															if is_xpu_available():
																		torch.xpu.empty_cache()
															elif is_npu_available():
																		torch.npu.empty_cache()
															else:
																		torch.cuda.empty_cache()
															batch_size //= 2
												else:
															raise

			return decorator




