






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 				snake_case  (     lowerCamelCase				,  lowerCamelCase=0.999				,  lowerCamelCase="cosine"				,  ):




	'''simple docstring'''







	if alpha_transform_type == "cosine":

		def alpha_bar_fn(lowerCamelCase					):
			return math.cos((t + 0.008) / 1.008 * math.pi / 2					) ** 2

	elif alpha_transform_type == "exp":

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

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

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




class        __UpperCamelCase    (     _lowerCAmelCase   ,			_lowerCAmelCase   ):
	__snake_case	:Tuple   =       [e.name for e in KarrasDiffusionSchedulers]
	__snake_case	:Optional[int]   =       2







	@register_to_config
	def __init__(  self      :							List[str]       ,				_lowerCAmelCase      :							int = 1000       ,				_lowerCAmelCase      :							float = 0.00_085       ,				_lowerCAmelCase      :							float = 0.012       ,				_lowerCAmelCase      :							str = "linear"       ,				_lowerCAmelCase      :							Optional[Union[np.ndarray, List[float]]] = None       ,				_lowerCAmelCase      :							str = "epsilon"       ,				_lowerCAmelCase      :							Optional[bool] = False       ,				_lowerCAmelCase      :							Optional[bool] = False       ,				_lowerCAmelCase      :							float = 1.0       ,				_lowerCAmelCase      :							str = "linspace"       ,				_lowerCAmelCase      :							int = 0       ,				)		-> List[Any]:
		"""simple docstring"""







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

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

		#  set all values
		self.set_timesteps(_lowerCAmelCase       ,				_lowerCAmelCase       ,				_lowerCAmelCase			)
		__lowercase             =  use_karras_sigmas







	def  _a   (  self      :							List[Any]       ,				_lowerCAmelCase      :							List[str]       ,				_lowerCAmelCase      :							Optional[Any]=None			)		-> Optional[int]:
		"""simple docstring"""







		if schedule_timesteps is None:
			__lowercase             =  self.timesteps

		__lowercase             =  (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:
			__lowercase             =  1 if len(_lowerCAmelCase			) > 1 else 0
		else:
			__lowercase             =  timestep.cpu().item() if torch.is_tensor(_lowerCAmelCase			) else timestep
			__lowercase             =  self._index_counter[timestep_int]

		return indices[pos].item()







	@property
	def  _a   (  self      :							Union[str, Any]			)		-> Any:
		"""simple docstring"""







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

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







	def  _a   (  self      :							int       ,				_lowerCAmelCase      :							torch.FloatTensor       ,				_lowerCAmelCase      :							Union[float, torch.FloatTensor]       ,				)		-> torch.FloatTensor:
		"""simple docstring"""







		__lowercase             =  self.index_for_timestep(_lowerCAmelCase			)

		__lowercase             =  self.sigmas[step_index]
		__lowercase             =  sample / ((sigma**2 + 1) ** 0.5)
		return sample







	def  _a   (  self      :							Tuple       ,				_lowerCAmelCase      :							int       ,				_lowerCAmelCase      :							Union[str, torch.device] = None       ,				_lowerCAmelCase      :							Optional[int] = None       ,				)		-> Union[str, Any]:
		"""simple docstring"""







		__lowercase             =  num_inference_steps

		__lowercase             =  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":
			__lowercase             =  np.linspace(0       ,				num_train_timesteps - 1       ,				_lowerCAmelCase       ,				dtype=_lowerCAmelCase			)[::-1].copy()
		elif self.config.timestep_spacing == "leading":
			__lowercase             =  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
			__lowercase             =  (np.arange(0       ,				_lowerCAmelCase			) * step_ratio).round()[::-1].copy().astype(_lowerCAmelCase			)
			timesteps += self.config.steps_offset
		elif self.config.timestep_spacing == "trailing":
			__lowercase             =  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
			__lowercase             =  (np.arange(_lowerCAmelCase       ,				0       ,				-step_ratio			)).round().copy().astype(_lowerCAmelCase			)
			timesteps -= 1
		else:
			raise ValueError(
			    F'{self.config.timestep_spacing} is not supported. Please make sure to choose one of \'linspace\', \'leading\' or \'trailing\'.'			)

		__lowercase             =  np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5			)
		__lowercase             =  np.log(_lowerCAmelCase			)
		__lowercase             =  np.interp(_lowerCAmelCase       ,				np.arange(0       ,				len(_lowerCAmelCase			)			)       ,				_lowerCAmelCase			)

		if self.config.use_karras_sigmas:
			__lowercase             =  self._convert_to_karras(in_sigmas=_lowerCAmelCase       ,				num_inference_steps=self.num_inference_steps			)
			__lowercase             =  np.array([self._sigma_to_t(_lowerCAmelCase       ,				_lowerCAmelCase			) for sigma in sigmas]			)

		__lowercase             =  np.concatenate([sigmas, [0.0]]			).astype(np.floataa			)
		__lowercase             =  torch.from_numpy(_lowerCAmelCase			).to(device=_lowerCAmelCase			)
		__lowercase             =  torch.cat([sigmas[:1], sigmas[1:-1].repeat_interleave(2			), sigmas[-1:]]			)

		__lowercase             =  torch.from_numpy(_lowerCAmelCase			)
		__lowercase             =  torch.cat([timesteps[:1], timesteps[1:].repeat_interleave(2			)]			)

		if str(_lowerCAmelCase			).startswith("""mps"""			):
			# mps does not support float64
			__lowercase             =  timesteps.to(_lowerCAmelCase       ,				dtype=torch.floataa			)
		else:
			__lowercase             =  timesteps.to(device=_lowerCAmelCase			)

		# empty dt and derivative
		__lowercase             =  None
		__lowercase             =  None

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







	def  _a   (  self      :							Optional[Any]       ,				_lowerCAmelCase      :							Optional[int]       ,				_lowerCAmelCase      :							Optional[int]			)		-> int:
		"""simple docstring"""







		__lowercase             =  np.log(_lowerCAmelCase			)

		# get distribution
		__lowercase             =  log_sigma - log_sigmas[:, np.newaxis]

		# get sigmas range
		__lowercase             =  np.cumsum((dists >= 0)       ,				axis=0			).argmax(axis=0			).clip(max=log_sigmas.shape[0] - 2			)
		__lowercase             =  low_idx + 1

		__lowercase             =  log_sigmas[low_idx]
		__lowercase             =  log_sigmas[high_idx]

		# interpolate sigmas
		__lowercase             =  (low - log_sigma) / (low - high)
		__lowercase             =  np.clip(_lowerCAmelCase       ,				0       ,				1			)

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







	def  _a   (  self      :							Tuple       ,				_lowerCAmelCase      :							torch.FloatTensor       ,				_lowerCAmelCase      :							int			)		-> torch.FloatTensor:
		"""simple docstring"""







		__lowercase             =  in_sigmas[-1].item()
		__lowercase             =  in_sigmas[0].item()

		__lowercase             =  7.0  # 7.0 is the value used in the paper
		__lowercase             =  np.linspace(0       ,				1       ,				_lowerCAmelCase			)
		__lowercase             =  sigma_min ** (1 / rho)
		__lowercase             =  sigma_max ** (1 / rho)
		__lowercase             =  (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
		return sigmas







	@property
	def  _a   (  self      :							Any			)		-> Optional[int]:
		"""simple docstring"""







		return self.dt is None







	def  _a   (  self      :							Union[str, Any]       ,				_lowerCAmelCase      :							Union[torch.FloatTensor, np.ndarray]       ,				_lowerCAmelCase      :							Union[float, torch.FloatTensor]       ,				_lowerCAmelCase      :							Union[torch.FloatTensor, np.ndarray]       ,				_lowerCAmelCase      :							bool = True       ,				)		-> Union[SchedulerOutput, Tuple]:
		"""simple docstring"""







		__lowercase             =  self.index_for_timestep(_lowerCAmelCase			)

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

		if self.state_in_first_order:
			__lowercase             =  self.sigmas[step_index]
			__lowercase             =  self.sigmas[step_index + 1]
		else:
			# 2nd order / Heun's method
			__lowercase             =  self.sigmas[step_index - 1]
			__lowercase             =  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
		__lowercase             =  0
		__lowercase             =  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":
			__lowercase             =  sigma_hat if self.state_in_first_order else sigma_next
			__lowercase             =  sample - sigma_input * model_output
		elif self.config.prediction_type == "v_prediction":
			__lowercase             =  sigma_hat if self.state_in_first_order else sigma_next
			__lowercase             =  model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
			    sample / (sigma_input**2 + 1)
			)
		elif self.config.prediction_type == "sample":
			__lowercase             =  model_output
		else:
			raise ValueError(
			    F'prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`'			)

		if self.config.clip_sample:
			__lowercase             =  pred_original_sample.clamp(
			    -self.config.clip_sample_range       ,				self.config.clip_sample_range			)

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

			# store for 2nd order step
			__lowercase             =  derivative
			__lowercase             =  dt
			__lowercase             =  sample
		else:
			# 2. 2nd order / Heun's method
			__lowercase             =  (sample - pred_original_sample) / sigma_next
			__lowercase             =  (self.prev_derivative + derivative) / 2

			# 3. take prev timestep & sample
			__lowercase             =  self.dt
			__lowercase             =  self.sample

			# free dt and derivative
			# Note, this puts the scheduler in "first order mode"
			__lowercase             =  None
			__lowercase             =  None
			__lowercase             =  None

		__lowercase             =  sample + derivative * dt

		if not return_dict:
			return (prev_sample,)

		return SchedulerOutput(prev_sample=_lowerCAmelCase			)







	def  _a   (  self      :							Tuple       ,				_lowerCAmelCase      :							torch.FloatTensor       ,				_lowerCAmelCase      :							torch.FloatTensor       ,				_lowerCAmelCase      :							torch.FloatTensor       ,				)		-> torch.FloatTensor:
		"""simple docstring"""







		__lowercase             =  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
			__lowercase             =  self.timesteps.to(original_samples.device       ,				dtype=torch.floataa			)
			__lowercase             =  timesteps.to(original_samples.device       ,				dtype=torch.floataa			)
		else:
			__lowercase             =  self.timesteps.to(original_samples.device			)
			__lowercase             =  timesteps.to(original_samples.device			)

		__lowercase             =  [self.index_for_timestep(_lowerCAmelCase       ,				_lowerCAmelCase			) for t in timesteps]

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

		__lowercase             =  original_samples + noise * sigma
		return noisy_samples







	def __len__(  self      :							Tuple			)		-> Any:
		"""simple docstring"""







		return self.config.num_train_timesteps









from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


__UpperCamelCase      :      Tuple     						=						{
    """configuration_swiftformer""": [
        """SWIFTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """SwiftFormerConfig""",
        """SwiftFormerOnnxConfig""",
    ]
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__UpperCamelCase      :      Optional[Any]     						=						[
						    """SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
						    """SwiftFormerForImageClassification""",
						    """SwiftFormerModel""",
						    """SwiftFormerPreTrainedModel""",
						]

if TYPE_CHECKING:
						from .configuration_swiftformer import (
						    SWIFTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
						    SwiftFormerConfig,
						    SwiftFormerOnnxConfig,
						)

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_swiftformer import (
												    SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
												    SwiftFormerForImageClassification,
												    SwiftFormerModel,
												    SwiftFormerPreTrainedModel,
												)

else:
						import sys

						__UpperCamelCase      :      List[Any]     						=						_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)

