

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 					__lowercase      (     _A						,    _A=0.999						,    _A="cosine"						,    )     ->						Dict:


						if alpha_transform_type == "cosine":

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

						elif alpha_transform_type == "exp":

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

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

						SCREAMING_SNAKE_CASE :      Any       =   []
						for i in range(lowerCamelCase__			):
												SCREAMING_SNAKE_CASE :      Tuple       =   i / num_diffusion_timesteps
												SCREAMING_SNAKE_CASE :      Dict       =   (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        a__       (   UpperCAmelCase	,			UpperCAmelCase       ):







		"""simple docstring"""




		UpperCAmelCase__      : Optional[int]			       =[e.name for e in KarrasDiffusionSchedulers]
		UpperCAmelCase__      : Dict			       =2


		@register_to_config
		def __init__(			self    : List[Any]				,				UpperCAmelCase__    : int = 1_0_0_0				,				UpperCAmelCase__    : List[Any] = 0.0_00_85				,				UpperCAmelCase__    : Optional[int] = 0.0_12				,				UpperCAmelCase__    : str = "linear"				,				UpperCAmelCase__    : Optional[int] = None				,				UpperCAmelCase__    : List[Any] = "epsilon"				,				UpperCAmelCase__    : Any = "linspace"				,				UpperCAmelCase__    : Any = 0				,				)					->Any:
								"""simple docstring"""







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

								SCREAMING_SNAKE_CASE :      Optional[Any]       =   1.0 - self.betas
								SCREAMING_SNAKE_CASE :      Optional[Any]       =   torch.cumprod(self.alphas				,				dim=0		)

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


		def  _lowercase      (			self    : int				,				UpperCAmelCase__    : Tuple				,				UpperCAmelCase__    : Dict=None		)					->Union[str, Any]:
								"""simple docstring"""







								if schedule_timesteps is None:
														SCREAMING_SNAKE_CASE :      int       =   self.timesteps

								SCREAMING_SNAKE_CASE :      str       =   (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:
														SCREAMING_SNAKE_CASE :      Union[str, Any]       =   1 if len(UpperCamelCase_		) > 1 else 0
								else:
														SCREAMING_SNAKE_CASE :      Union[str, Any]       =   timestep.cpu().item() if torch.is_tensor(UpperCamelCase_		) else timestep
														SCREAMING_SNAKE_CASE :      Dict       =   self._index_counter[timestep_int]

								return indices[pos].item()


		@property
		def  _lowercase      (			self    : Optional[int]		)					->Union[str, Any]:
								"""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]				,				UpperCAmelCase__    : Dict				,				UpperCAmelCase__    : List[Any]				,				)					->List[str]:
								"""simple docstring"""







								SCREAMING_SNAKE_CASE :      Tuple       =   self.index_for_timestep(UpperCamelCase_		)

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

								SCREAMING_SNAKE_CASE :      Any       =   sample / ((sigma**2 + 1) ** 0.5)
								return sample


		def  _lowercase      (			self    : int				,				UpperCAmelCase__    : Dict				,				UpperCAmelCase__    : Dict = None				,				UpperCAmelCase__    : Tuple = None				,				)					->str:
								"""simple docstring"""







								SCREAMING_SNAKE_CASE :      Union[str, Any]       =   num_inference_steps

								SCREAMING_SNAKE_CASE :      Any       =   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":
														SCREAMING_SNAKE_CASE :      int       =   np.linspace(0				,				num_train_timesteps - 1				,				UpperCamelCase_				,				dtype=UpperCamelCase_		)[::-1].copy()
								elif self.config.timestep_spacing == "leading":
														SCREAMING_SNAKE_CASE :      Union[str, Any]       =   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
														SCREAMING_SNAKE_CASE :      Tuple       =   (np.arange(0				,				UpperCamelCase_		) * step_ratio).round()[::-1].copy().astype(UpperCamelCase_		)
														timesteps += self.config.steps_offset
								elif self.config.timestep_spacing == "trailing":
														SCREAMING_SNAKE_CASE :      List[Any]       =   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
														SCREAMING_SNAKE_CASE :      Union[str, Any]       =   (np.arange(UpperCamelCase_				,				0				,				-step_ratio		)).round().copy().astype(UpperCamelCase_		)
														timesteps -= 1
								else:
														raise ValueError(
														    f"{self.config.timestep_spacing} is not supported. Please make sure to choose one of 'linspace', 'leading' or 'trailing'."		)

								SCREAMING_SNAKE_CASE :      Union[str, Any]       =   np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5		)
								SCREAMING_SNAKE_CASE :      str       =   torch.from_numpy(np.log(UpperCamelCase_		)		).to(UpperCamelCase_		)

								SCREAMING_SNAKE_CASE :      List[Any]       =   np.interp(UpperCamelCase_				,				np.arange(0				,				len(UpperCamelCase_		)		)				,				UpperCamelCase_		)
								SCREAMING_SNAKE_CASE :      Any       =   np.concatenate([sigmas, [0.0]]		).astype(np.floataa		)
								SCREAMING_SNAKE_CASE :      int       =   torch.from_numpy(UpperCamelCase_		).to(device=UpperCamelCase_		)

								# interpolate sigmas
								SCREAMING_SNAKE_CASE :      Tuple       =   sigmas.log().lerp(sigmas.roll(1		).log()				,				0.5		).exp()

								SCREAMING_SNAKE_CASE :      Tuple       =   torch.cat([sigmas[:1], sigmas[1:].repeat_interleave(2		), sigmas[-1:]]		)
								SCREAMING_SNAKE_CASE :      Union[str, Any]       =   torch.cat(
								    [sigmas_interpol[:1], sigmas_interpol[1:].repeat_interleave(2		), sigmas_interpol[-1:]]		)

								if str(UpperCamelCase_		).startswith("""mps"""		):
														# mps does not support float64
														SCREAMING_SNAKE_CASE :      Union[str, Any]       =   torch.from_numpy(UpperCamelCase_		).to(UpperCamelCase_				,				dtype=torch.floataa		)
								else:
														SCREAMING_SNAKE_CASE :      Tuple       =   torch.from_numpy(UpperCamelCase_		).to(UpperCamelCase_		)

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

								SCREAMING_SNAKE_CASE :      List[str]       =   torch.cat([timesteps[:1], interleaved_timesteps]		)

								SCREAMING_SNAKE_CASE :      str       =   None

								# for exp beta schedules, such as the one for `pipeline_shap_e.py`
								# we need an index counter
								SCREAMING_SNAKE_CASE :      List[str]       =   defaultdict(UpperCamelCase_		)


		def  _lowercase      (			self    : str				,				UpperCAmelCase__    : Tuple		)					->Any:
								"""simple docstring"""







								SCREAMING_SNAKE_CASE :      Union[str, Any]       =   sigma.log()

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

								# get sigmas range
								SCREAMING_SNAKE_CASE :      Optional[int]       =   dists.ge(0		).cumsum(dim=0		).argmax(dim=0		).clamp(max=self.log_sigmas.shape[0] - 2		)
								SCREAMING_SNAKE_CASE :      List[str]       =   low_idx + 1

								SCREAMING_SNAKE_CASE :      Union[str, Any]       =   self.log_sigmas[low_idx]
								SCREAMING_SNAKE_CASE :      List[Any]       =   self.log_sigmas[high_idx]

								# interpolate sigmas
								SCREAMING_SNAKE_CASE :      Tuple       =   (low - log_sigma) / (low - high)
								SCREAMING_SNAKE_CASE :      int       =   w.clamp(0				,				1		)

								# transform interpolation to time range
								SCREAMING_SNAKE_CASE :      Optional[Any]       =   (1 - w) * low_idx + w * high_idx
								SCREAMING_SNAKE_CASE :      Dict       =   t.view(sigma.shape		)
								return t


		@property
		def  _lowercase      (			self    : int		)					->int:
								"""simple docstring"""







								return self.sample is None


		def  _lowercase      (			self    : Optional[int]				,				UpperCAmelCase__    : Optional[Any]				,				UpperCAmelCase__    : Optional[Any]				,				UpperCAmelCase__    : Dict				,				UpperCAmelCase__    : Optional[int] = True				,				)					->Tuple:
								"""simple docstring"""







								SCREAMING_SNAKE_CASE :      Optional[int]       =   self.index_for_timestep(UpperCamelCase_		)

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

								if self.state_in_first_order:
														SCREAMING_SNAKE_CASE :      int       =   self.sigmas[step_index]
														SCREAMING_SNAKE_CASE :      str       =   self.sigmas_interpol[step_index + 1]
														SCREAMING_SNAKE_CASE :      Optional[int]       =   self.sigmas[step_index + 1]
								else:
														# 2nd order / KDPM2's method
														SCREAMING_SNAKE_CASE :      List[str]       =   self.sigmas[step_index - 1]
														SCREAMING_SNAKE_CASE :      int       =   self.sigmas_interpol[step_index]
														SCREAMING_SNAKE_CASE :      int       =   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
								SCREAMING_SNAKE_CASE :      Tuple       =   0
								SCREAMING_SNAKE_CASE :      Optional[int]       =   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":
														SCREAMING_SNAKE_CASE :      Optional[int]       =   sigma_hat if self.state_in_first_order else sigma_interpol
														SCREAMING_SNAKE_CASE :      Union[str, Any]       =   sample - sigma_input * model_output
								elif self.config.prediction_type == "v_prediction":
														SCREAMING_SNAKE_CASE :      Tuple       =   sigma_hat if self.state_in_first_order else sigma_interpol
														SCREAMING_SNAKE_CASE :      int       =   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
														SCREAMING_SNAKE_CASE :      Optional[int]       =   (sample - pred_original_sample) / sigma_hat
														# 3. delta timestep
														SCREAMING_SNAKE_CASE :      List[Any]       =   sigma_interpol - sigma_hat

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

														# 3. delta timestep
														SCREAMING_SNAKE_CASE :      Any       =   sigma_next - sigma_hat

														SCREAMING_SNAKE_CASE :      Dict       =   self.sample
														SCREAMING_SNAKE_CASE :      str       =   None

								SCREAMING_SNAKE_CASE :      List[Any]       =   sample + derivative * dt

								if not return_dict:
														return (prev_sample,)

								return SchedulerOutput(prev_sample=UpperCamelCase_		)


		def  _lowercase      (			self    : Union[str, Any]				,				UpperCAmelCase__    : Union[str, Any]				,				UpperCAmelCase__    : Dict				,				UpperCAmelCase__    : Any				,				)					->List[str]:
								"""simple docstring"""







								SCREAMING_SNAKE_CASE :      Any       =   self.sigmas.to(device=original_samples.device				,				dtype=original_samples.dtype		)
								if original_samples.device.type == "mps" and torch.is_floating_point(UpperCamelCase_		):
														# mps does not support float64
														SCREAMING_SNAKE_CASE :      int       =   self.timesteps.to(original_samples.device				,				dtype=torch.floataa		)
														SCREAMING_SNAKE_CASE :      List[Any]       =   timesteps.to(original_samples.device				,				dtype=torch.floataa		)
								else:
														SCREAMING_SNAKE_CASE :      Dict       =   self.timesteps.to(original_samples.device		)
														SCREAMING_SNAKE_CASE :      Optional[Any]       =   timesteps.to(original_samples.device		)

								SCREAMING_SNAKE_CASE :      Tuple       =   [self.index_for_timestep(UpperCamelCase_				,				UpperCamelCase_		) for t in timesteps]

								SCREAMING_SNAKE_CASE :      List[str]       =   sigmas[step_indices].flatten()
								while len(sigma.shape		) < len(original_samples.shape		):
														SCREAMING_SNAKE_CASE :      Optional[int]       =   sigma.unsqueeze(-1		)

								SCREAMING_SNAKE_CASE :      Union[str, Any]       =   original_samples + noise * sigma
								return noisy_samples




		def __len__(			self    : str		)					->Optional[int]:
								"""simple docstring"""







								return self.config.num_train_timesteps







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


UpperCAmelCase__ :     List[Any]				    =    {"""configuration_vit""": ["""VIT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """ViTConfig""", """ViTOnnxConfig"""]}

try:
  if not is_vision_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase__ :     Union[str, Any]				    =    ["""ViTFeatureExtractor"""]
  UpperCAmelCase__ :     List[Any]				    =    ["""ViTImageProcessor"""]

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase__ :     List[Any]				    =    [
      """VIT_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """ViTForImageClassification""",
      """ViTForMaskedImageModeling""",
      """ViTModel""",
      """ViTPreTrainedModel""",
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase__ :     Tuple				    =    [
      """TFViTForImageClassification""",
      """TFViTModel""",
      """TFViTPreTrainedModel""",
  ]

try:
  if not is_flax_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase__ :     str				    =    [
      """FlaxViTForImageClassification""",
      """FlaxViTModel""",
      """FlaxViTPreTrainedModel""",
  ]

if TYPE_CHECKING:
  from .configuration_vit import VIT_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTConfig, ViTOnnxConfig

  try:
    if not is_vision_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .feature_extraction_vit import ViTFeatureExtractor
    from .image_processing_vit import ViTImageProcessor

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_vit import (
        VIT_PRETRAINED_MODEL_ARCHIVE_LIST,
        ViTForImageClassification,
        ViTForMaskedImageModeling,
        ViTModel,
        ViTPreTrainedModel,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_vit import TFViTForImageClassification, TFViTModel, TFViTPreTrainedModel

  try:
    if not is_flax_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_flax_vit import FlaxViTForImageClassification, FlaxViTModel, FlaxViTPreTrainedModel


else:
  import sys

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




