





import math
from collections import defaultdict
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 KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput




def  lowerCAmelCase_ (							UpperCamelCase_					,		UpperCamelCase_=0.9_99					,		UpperCamelCase_="cosine"					,		)     ->		Any:
       if alpha_transform_type == "cosine":

              def alpha_bar_fn(UpperCamelCase_  ):
                     return math.cos((t + 0.0_08) / 1.0_08 * math.pi / 2  ) ** 2

       elif alpha_transform_type == "exp":

              def alpha_bar_fn(UpperCamelCase_  ):
                     return math.exp(t * -12.0  )

       else:
              raise ValueError(F'''Unsupported alpha_tranform_type: {alpha_transform_type}'''  )

       UpperCamelCase_          =  []
       for i in range(UpperCamelCase_  ):
              UpperCamelCase_          =  i / num_diffusion_timesteps
              UpperCamelCase_          =  (i + 1) / num_diffusion_timesteps
              betas.append(min(1 - alpha_bar_fn(UpperCamelCase_  ) / alpha_bar_fn(UpperCamelCase_  )					,		UpperCamelCase_  )  )
       return torch.tensor(UpperCamelCase_					,		dtype=torch.floataa  )







class 							_UpperCamelCase					(	lowerCAmelCase_  ,						lowerCAmelCase_     ):
       _UpperCamelCase				:					Tuple         =   [e.name for e in KarrasDiffusionSchedulers]
       _UpperCamelCase				:					Union[str, Any]         =   2

       @register_to_config
       def __init__(		self: Optional[int]					,   _SCREAMING_SNAKE_CASE: int = 1000					,   _SCREAMING_SNAKE_CASE: float = 0.0_00_85					,   _SCREAMING_SNAKE_CASE: float = 0.0_12					,   _SCREAMING_SNAKE_CASE: str = "linear"					,   _SCREAMING_SNAKE_CASE: Optional[Union[np.ndarray, List[float]]] = None					,   _SCREAMING_SNAKE_CASE: str = "epsilon"					,   _SCREAMING_SNAKE_CASE: str = "linspace"					,   _SCREAMING_SNAKE_CASE: int = 0					,   )       -> Union[str, Any]:




              """simple docstring"""


              if trained_betas is not None:
                     UpperCamelCase_          =  torch.tensor(_SCREAMING_SNAKE_CASE					,   dtype=torch.floataa		)
              elif beta_schedule == "linear":
                     UpperCamelCase_          =  torch.linspace(_SCREAMING_SNAKE_CASE					,   _SCREAMING_SNAKE_CASE					,   _SCREAMING_SNAKE_CASE					,   dtype=torch.floataa		)
              elif beta_schedule == "scaled_linear":
                     # this schedule is very specific to the latent diffusion model.
                     UpperCamelCase_          =  (
                         torch.linspace(beta_start**0.5					,   beta_end**0.5					,   _SCREAMING_SNAKE_CASE					,   dtype=torch.floataa		) ** 2
                     )
              elif beta_schedule == "squaredcos_cap_v2":
                     # Glide cosine schedule
                     UpperCamelCase_          =  betas_for_alpha_bar(_SCREAMING_SNAKE_CASE		)
              else:
                     raise NotImplementedError(f'''{beta_schedule} does is not implemented for {self.__class__}'''		)

              UpperCamelCase_          =  1.0 - self.betas
              UpperCamelCase_          =  torch.cumprod(self.alphas					,   dim=0		)

              #  set all values
              self.set_timesteps(_SCREAMING_SNAKE_CASE					,   _SCREAMING_SNAKE_CASE					,   _SCREAMING_SNAKE_CASE		)

       def lowercase (		self: Any					,   _SCREAMING_SNAKE_CASE: Dict					,   _SCREAMING_SNAKE_CASE: List[str]=None		)       -> List[Any]:




              """simple docstring"""


              if schedule_timesteps is None:
                     UpperCamelCase_          =  self.timesteps

              UpperCamelCase_          =  (schedule_timesteps == timestep).nonzero()

              # The sigma index that is taken for the **very** first `step`
              # is always the second index (or the last index if there is only 1)
              # This way we can ensure we don't accidentally skip a sigma in
              # case we start in the middle of the denoising schedule (e.g. for image-to-image)
              if len(self._index_counter		) == 0:
                     UpperCamelCase_          =  1 if len(_SCREAMING_SNAKE_CASE		) > 1 else 0
              else:
                     UpperCamelCase_          =  timestep.cpu().item() if torch.is_tensor(_SCREAMING_SNAKE_CASE		) else timestep
                     UpperCamelCase_          =  self._index_counter[timestep_int]

              return indices[pos].item()

       @property
       def lowercase (		self: List[Any]		)       -> List[str]:




              """simple docstring"""


              if self.config.timestep_spacing in ["linspace", "trailing"]:
                     return self.sigmas.max()

              return (self.sigmas.max() ** 2 + 1) ** 0.5

       def lowercase (		self: List[Any]					,   _SCREAMING_SNAKE_CASE: torch.FloatTensor					,   _SCREAMING_SNAKE_CASE: Union[float, torch.FloatTensor]					,   )       -> torch.FloatTensor:




              """simple docstring"""


              UpperCamelCase_          =  self.index_for_timestep(_SCREAMING_SNAKE_CASE		)

              if self.state_in_first_order:
                     UpperCamelCase_          =  self.sigmas[step_index]
              else:
                     UpperCamelCase_          =  self.sigmas_interpol[step_index]

              UpperCamelCase_          =  sample / ((sigma**2 + 1) ** 0.5)
              return sample

       def lowercase (		self: str					,   _SCREAMING_SNAKE_CASE: int					,   _SCREAMING_SNAKE_CASE: Union[str, torch.device] = None					,   _SCREAMING_SNAKE_CASE: Optional[int] = None					,   )       -> Tuple:




              """simple docstring"""


              UpperCamelCase_          =  num_inference_steps

              UpperCamelCase_          =  num_train_timesteps or self.config.num_train_timesteps

              # "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
              if self.config.timestep_spacing == "linspace":
                     UpperCamelCase_          =  np.linspace(0					,   num_train_timesteps - 1					,   _SCREAMING_SNAKE_CASE					,   dtype=_SCREAMING_SNAKE_CASE		)[::-1].copy()
              elif self.config.timestep_spacing == "leading":
                     UpperCamelCase_          =  num_train_timesteps // self.num_inference_steps
                     # creates integer timesteps by multiplying by ratio
                     # casting to int to avoid issues when num_inference_step is power of 3
                     UpperCamelCase_          =  (np.arange(0					,   _SCREAMING_SNAKE_CASE		) * step_ratio).round()[::-1].copy().astype(_SCREAMING_SNAKE_CASE		)
                     timesteps += self.config.steps_offset
              elif self.config.timestep_spacing == "trailing":
                     UpperCamelCase_          =  num_train_timesteps / self.num_inference_steps
                     # creates integer timesteps by multiplying by ratio
                     # casting to int to avoid issues when num_inference_step is power of 3
                     UpperCamelCase_          =  (np.arange(_SCREAMING_SNAKE_CASE					,   0					,   -step_ratio		)).round().copy().astype(_SCREAMING_SNAKE_CASE		)
                     timesteps -= 1
              else:
                     raise ValueError(
                         f'''{self.config.timestep_spacing} is not supported. Please make sure to choose one of \'linspace\', \'leading\' or \'trailing\'.'''		)

              UpperCamelCase_          =  np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5		)
              UpperCamelCase_          =  torch.from_numpy(np.log(_SCREAMING_SNAKE_CASE		)		).to(_SCREAMING_SNAKE_CASE		)

              UpperCamelCase_          =  np.interp(_SCREAMING_SNAKE_CASE					,   np.arange(0					,   len(_SCREAMING_SNAKE_CASE		)		)					,   _SCREAMING_SNAKE_CASE		)
              UpperCamelCase_          =  np.concatenate([sigmas, [0.0]]		).astype(np.floataa		)
              UpperCamelCase_          =  torch.from_numpy(_SCREAMING_SNAKE_CASE		).to(device=_SCREAMING_SNAKE_CASE		)

              # interpolate sigmas
              UpperCamelCase_          =  sigmas.log().lerp(sigmas.roll(1		).log()					,   0.5		).exp()

              UpperCamelCase_          =  torch.cat([sigmas[:1], sigmas[1:].repeat_interleave(2		), sigmas[-1:]]		)
              UpperCamelCase_          =  torch.cat(
                  [sigmas_interpol[:1], sigmas_interpol[1:].repeat_interleave(2		), sigmas_interpol[-1:]]		)

              if str(_SCREAMING_SNAKE_CASE		).startswith("mps"		):
                     # mps does not support float64
                     UpperCamelCase_          =  torch.from_numpy(_SCREAMING_SNAKE_CASE		).to(_SCREAMING_SNAKE_CASE					,   dtype=torch.floataa		)
              else:
                     UpperCamelCase_          =  torch.from_numpy(_SCREAMING_SNAKE_CASE		).to(_SCREAMING_SNAKE_CASE		)

              # interpolate timesteps
              UpperCamelCase_          =  self.sigma_to_t(_SCREAMING_SNAKE_CASE		).to(_SCREAMING_SNAKE_CASE					,   dtype=timesteps.dtype		)
              UpperCamelCase_          =  torch.stack((timesteps_interpol[1:-1, None], timesteps[1:, None])					,   dim=-1		).flatten()

              UpperCamelCase_          =  torch.cat([timesteps[:1], interleaved_timesteps]		)

              UpperCamelCase_          =  None

              # for exp beta schedules, such as the one for `pipeline_shap_e.py`
              # we need an index counter
              UpperCamelCase_          =  defaultdict(_SCREAMING_SNAKE_CASE		)

       def lowercase (		self: str					,   _SCREAMING_SNAKE_CASE: List[str]		)       -> str:




              """simple docstring"""


              UpperCamelCase_          =  sigma.log()

              # get distribution
              UpperCamelCase_          =  log_sigma - self.log_sigmas[:, None]

              # get sigmas range
              UpperCamelCase_          =  dists.ge(0		).cumsum(dim=0		).argmax(dim=0		).clamp(max=self.log_sigmas.shape[0] - 2		)
              UpperCamelCase_          =  low_idx + 1

              UpperCamelCase_          =  self.log_sigmas[low_idx]
              UpperCamelCase_          =  self.log_sigmas[high_idx]

              # interpolate sigmas
              UpperCamelCase_          =  (low - log_sigma) / (low - high)
              UpperCamelCase_          =  w.clamp(0					,   1		)

              # transform interpolation to time range
              UpperCamelCase_          =  (1 - w) * low_idx + w * high_idx
              UpperCamelCase_          =  t.view(sigma.shape		)
              return t

       @property
       def lowercase (		self: List[str]		)       -> Any:




              """simple docstring"""


              return self.sample is None

       def lowercase (		self: Union[str, Any]					,   _SCREAMING_SNAKE_CASE: Union[torch.FloatTensor, np.ndarray]					,   _SCREAMING_SNAKE_CASE: Union[float, torch.FloatTensor]					,   _SCREAMING_SNAKE_CASE: Union[torch.FloatTensor, np.ndarray]					,   _SCREAMING_SNAKE_CASE: bool = True					,   )       -> Union[SchedulerOutput, Tuple]:




              """simple docstring"""


              UpperCamelCase_          =  self.index_for_timestep(_SCREAMING_SNAKE_CASE		)

              # advance index counter by 1
              UpperCamelCase_          =  timestep.cpu().item() if torch.is_tensor(_SCREAMING_SNAKE_CASE		) else timestep
              self._index_counter[timestep_int] += 1

              if self.state_in_first_order:
                     UpperCamelCase_          =  self.sigmas[step_index]
                     UpperCamelCase_          =  self.sigmas_interpol[step_index + 1]
                     UpperCamelCase_          =  self.sigmas[step_index + 1]
              else:
                     # 2nd order / KDPM2's method
                     UpperCamelCase_          =  self.sigmas[step_index - 1]
                     UpperCamelCase_          =  self.sigmas_interpol[step_index]
                     UpperCamelCase_          =  self.sigmas[step_index]

              # currently only gamma=0 is supported. This usually works best anyways.
              # We can support gamma in the future but then need to scale the timestep before
              # passing it to the model which requires a change in API
              UpperCamelCase_          =  0
              UpperCamelCase_          =  sigma * (gamma + 1)  # Note: sigma_hat == sigma for now

              # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
              if self.config.prediction_type == "epsilon":
                     UpperCamelCase_          =  sigma_hat if self.state_in_first_order else sigma_interpol
                     UpperCamelCase_          =  sample - sigma_input * model_output
              elif self.config.prediction_type == "v_prediction":
                     UpperCamelCase_          =  sigma_hat if self.state_in_first_order else sigma_interpol
                     UpperCamelCase_          =  model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
                         sample / (sigma_input**2 + 1)
                     )
              elif self.config.prediction_type == "sample":
                     raise NotImplementedError("prediction_type not implemented yet: sample"		)
              else:
                     raise ValueError(
                         f'''prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`'''		)

              if self.state_in_first_order:
                     # 2. Convert to an ODE derivative for 1st order
                     UpperCamelCase_          =  (sample - pred_original_sample) / sigma_hat
                     # 3. delta timestep
                     UpperCamelCase_          =  sigma_interpol - sigma_hat

                     # store for 2nd order step
                     UpperCamelCase_          =  sample
              else:
                     # DPM-Solver-2
                     # 2. Convert to an ODE derivative for 2nd order
                     UpperCamelCase_          =  (sample - pred_original_sample) / sigma_interpol

                     # 3. delta timestep
                     UpperCamelCase_          =  sigma_next - sigma_hat

                     UpperCamelCase_          =  self.sample
                     UpperCamelCase_          =  None

              UpperCamelCase_          =  sample + derivative * dt

              if not return_dict:
                     return (prev_sample,)

              return SchedulerOutput(prev_sample=_SCREAMING_SNAKE_CASE		)

       def lowercase (		self: Optional[Any]					,   _SCREAMING_SNAKE_CASE: torch.FloatTensor					,   _SCREAMING_SNAKE_CASE: torch.FloatTensor					,   _SCREAMING_SNAKE_CASE: torch.FloatTensor					,   )       -> torch.FloatTensor:




              """simple docstring"""


              UpperCamelCase_          =  self.sigmas.to(device=original_samples.device					,   dtype=original_samples.dtype		)
              if original_samples.device.type == "mps" and torch.is_floating_point(_SCREAMING_SNAKE_CASE		):
                     # mps does not support float64
                     UpperCamelCase_          =  self.timesteps.to(original_samples.device					,   dtype=torch.floataa		)
                     UpperCamelCase_          =  timesteps.to(original_samples.device					,   dtype=torch.floataa		)
              else:
                     UpperCamelCase_          =  self.timesteps.to(original_samples.device		)
                     UpperCamelCase_          =  timesteps.to(original_samples.device		)

              UpperCamelCase_          =  [self.index_for_timestep(_SCREAMING_SNAKE_CASE					,   _SCREAMING_SNAKE_CASE		) for t in timesteps]

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

              UpperCamelCase_          =  original_samples + noise * sigma
              return noisy_samples



       def __len__(		self: Optional[int]		)       -> Dict:




              """simple docstring"""


              return self.config.num_train_timesteps








import unittest

import torch
from torch import nn

from accelerate.test_utils import require_cuda
from accelerate.utils.memory import find_executable_batch_size, release_memory




def  lowerCAmelCase_ (							)     ->		Dict:
       raise RuntimeError("CUDA out of memory."  )



class 							_UpperCamelCase					(	nn.Module     ):

       def __init__(		self: int		)       -> List[Any]:




              """simple docstring"""


              super().__init__()
              UpperCamelCase_          =  nn.Linear(3					,   4		)
              UpperCamelCase_          =  nn.BatchNormad(4		)
              UpperCamelCase_          =  nn.Linear(4					,   5		)



       def lowercase (		self: Any					,   _SCREAMING_SNAKE_CASE: Optional[int]		)       -> Optional[Any]:




              """simple docstring"""


              return self.lineara(self.batchnorm(self.lineara(_SCREAMING_SNAKE_CASE		)		)		)



class 							_UpperCamelCase					(	unittest.TestCase     ):

       def lowercase (		self: List[Any]		)       -> Any:




              """simple docstring"""


              UpperCamelCase_          =  []

              @find_executable_batch_size(starting_batch_size=128		)
              def mock_training_loop_function(_SCREAMING_SNAKE_CASE: str		):
                     nonlocal batch_sizes
                     batch_sizes.append(_SCREAMING_SNAKE_CASE		)
                     if batch_size != 8:
                            raise_fake_out_of_memory()

              mock_training_loop_function()
              self.assertListEqual(_SCREAMING_SNAKE_CASE					,   [128, 64, 32, 16, 8]		)

       def lowercase (		self: Optional[int]		)       -> List[Any]:




              """simple docstring"""


              UpperCamelCase_          =  []

              @find_executable_batch_size(starting_batch_size=128		)
              def mock_training_loop_function(_SCREAMING_SNAKE_CASE: List[str]					,   _SCREAMING_SNAKE_CASE: Optional[Any]		):
                     nonlocal batch_sizes
                     batch_sizes.append(_SCREAMING_SNAKE_CASE		)
                     if batch_size != 8:
                            raise_fake_out_of_memory()
                     return batch_size, arga

              UpperCamelCase_       , UpperCamelCase_          =  mock_training_loop_function("hello"		)
              self.assertListEqual(_SCREAMING_SNAKE_CASE					,   [128, 64, 32, 16, 8]		)
              self.assertListEqual([bs, arga]					,   [8, "hello"]		)

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




              """simple docstring"""


              @find_executable_batch_size(starting_batch_size=0		)
              def mock_training_loop_function(_SCREAMING_SNAKE_CASE: Union[str, Any]		):
                     pass

              with self.assertRaises(_SCREAMING_SNAKE_CASE		) as cm:
                     mock_training_loop_function()
                     self.assertIn("No executable batch size found, reached zero."					,   cm.exception.args[0]		)

       def lowercase (		self: Dict		)       -> Optional[int]:




              """simple docstring"""


              @find_executable_batch_size(starting_batch_size=16		)
              def mock_training_loop_function(_SCREAMING_SNAKE_CASE: Optional[int]		):
                     if batch_size > 0:
                            raise_fake_out_of_memory()
                     pass

              with self.assertRaises(_SCREAMING_SNAKE_CASE		) as cm:
                     mock_training_loop_function()
                     self.assertIn("No executable batch size found, reached zero."					,   cm.exception.args[0]		)

       def lowercase (		self: Optional[int]		)       -> Dict:




              """simple docstring"""


              @find_executable_batch_size(starting_batch_size=128		)
              def mock_training_loop_function(_SCREAMING_SNAKE_CASE: Any					,   _SCREAMING_SNAKE_CASE: int					,   _SCREAMING_SNAKE_CASE: Tuple		):
                     if batch_size != 8:
                            raise raise_fake_out_of_memory()

              with self.assertRaises(_SCREAMING_SNAKE_CASE		) as cm:
                     mock_training_loop_function(128					,   "hello"					,   "world"		)
                     self.assertIn("Batch size was passed into `f`"					,   cm.exception.args[0]		)
                     self.assertIn("`f(arg1='hello', arg2='world')"					,   cm.exception.args[0]		)

       def lowercase (		self: List[str]		)       -> Optional[Any]:




              """simple docstring"""


              @find_executable_batch_size(starting_batch_size=16		)
              def mock_training_loop_function(_SCREAMING_SNAKE_CASE: List[Any]		):
                     raise ValueError("Oops, we had an error!"		)

              with self.assertRaises(_SCREAMING_SNAKE_CASE		) as cm:
                     mock_training_loop_function()
                     self.assertIn("Oops, we had an error!"					,   cm.exception.args[0]		)



       @require_cuda
       def lowercase (		self: Optional[int]		)       -> Union[str, Any]:




              """simple docstring"""


              UpperCamelCase_          =  torch.cuda.memory_allocated()
              UpperCamelCase_          =  ModelForTest()
              model.cuda()
              self.assertGreater(torch.cuda.memory_allocated()					,   _SCREAMING_SNAKE_CASE		)
              UpperCamelCase_          =  release_memory(_SCREAMING_SNAKE_CASE		)
              self.assertEqual(torch.cuda.memory_allocated()					,   _SCREAMING_SNAKE_CASE		)

