




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_			(  snake_case_,snake_case_=0.9_99,snake_case_="cosine",):
      if alpha_transform_type == "cosine":

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

      elif alpha_transform_type == "exp":

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

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

      _A							:  Tuple    =		[]
      for i in range(snake_case_  ):
            _A							:  str    =		i / num_diffusion_timesteps
            _A							:  List[Any]    =		(i + 1) / num_diffusion_timesteps
            betas.append(min(1 - alpha_bar_fn(snake_case_  ) / alpha_bar_fn(snake_case_  ),snake_case_  )  )
      return torch.tensor(snake_case_,dtype=torch.floataa  )







class 		lowercase							(     UpperCamelCase__,UpperCamelCase__	):
     _a											=		[e.name for e in KarrasDiffusionSchedulers]
     _a											=		2

     @register_to_config
     def __init__(						self	,  _a = 1000	,  _a = 0.00085	,  _a = 0.012	,  _a = "linear"	,  _a = None	,  _a = "epsilon"	,  _a = False	,  _a = False	,  _a = 1.0	,  _a = "linspace"	,  _a = 0	,  )		->							int:
           if trained_betas is not None:
                 _A							:  Optional[int]    =		torch.tensor(_a	,  dtype=torch.floataa       )
           elif beta_schedule == "linear":
                 _A							:  List[str]    =		torch.linspace(_a	,  _a	,  _a	,  dtype=torch.floataa       )
           elif beta_schedule == "scaled_linear":
                 # this schedule is very specific to the latent diffusion model.
                 _A							:  List[str]    =		(
                     torch.linspace(beta_start**0.5	,  beta_end**0.5	,  _a	,  dtype=torch.floataa       ) ** 2
                 )
           elif beta_schedule == "squaredcos_cap_v2":
                 # Glide cosine schedule
                 _A							:  int    =		betas_for_alpha_bar(_a	,  alpha_transform_type="""cosine"""       )
           elif beta_schedule == "exp":
                 _A							:  List[str]    =		betas_for_alpha_bar(_a	,  alpha_transform_type="""exp"""       )
           else:
                 raise NotImplementedError(F'''{beta_schedule} does is not implemented for {self.__class__}'''       )

           _A							:  Tuple    =		1.0 - self.betas
           _A							:  Dict    =		torch.cumprod(self.alphas	,  dim=0       )

           #  set all values
           self.set_timesteps(_a	,  _a	,  _a       )
           _A							:  Any    =		use_karras_sigmas

     def       a__			(						self	,  _a	,  _a=None       )		->							str:
           if schedule_timesteps is None:
                 _A							:  Optional[Any]    =		self.timesteps

           _A							:  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:
                 _A							:  List[Any]    =		1 if len(_a       ) > 1 else 0
           else:
                 _A							:  Any    =		timestep.cpu().item() if torch.is_tensor(_a       ) else timestep
                 _A							:  Any    =		self._index_counter[timestep_int]

           return indices[pos].item()

     @property
     def       a__			(						self       )		->							Any:
           # standard deviation of the initial noise distribution
           if self.config.timestep_spacing in ["linspace", "trailing"]:
                 return self.sigmas.max()

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

     def       a__			(						self	,  _a	,  _a	,  )		->							torch.FloatTensor:
           _A							:  Dict    =		self.index_for_timestep(_a       )

           _A							:  Optional[int]    =		self.sigmas[step_index]
           _A							:  Any    =		sample / ((sigma**2 + 1) ** 0.5)
           return sample

     def       a__			(						self	,  _a	,  _a = None	,  _a = None	,  )		->							Union[str, Any]:
           _A							:  str    =		num_inference_steps

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

           _A							:  str    =		np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5       )
           _A							:  Optional[int]    =		np.log(_a       )
           _A							:  List[str]    =		np.interp(_a	,  np.arange(0	,  len(_a       )       )	,  _a       )

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

           _A							:  Union[str, Any]    =		np.concatenate([sigmas, [0.0]]       ).astype(np.floataa       )
           _A							:  str    =		torch.from_numpy(_a       ).to(device=_a       )
           _A							:  int    =		torch.cat([sigmas[:1], sigmas[1:-1].repeat_interleave(2       ), sigmas[-1:]]       )

           _A							:  Dict    =		torch.from_numpy(_a       )
           _A							:  str    =		torch.cat([timesteps[:1], timesteps[1:].repeat_interleave(2       )]       )

           if str(_a       ).startswith("""mps"""       ):
                 # mps does not support float64
                 _A							:  Tuple    =		timesteps.to(_a	,  dtype=torch.floataa       )
           else:
                 _A							:  Optional[int]    =		timesteps.to(device=_a       )

           # empty dt and derivative
           _A							:  Union[str, Any]    =		None
           _A							:  Optional[Any]    =		None

           # for exp beta schedules, such as the one for `pipeline_shap_e.py`
           # we need an index counter
           _A							:  Dict    =		defaultdict(_a       )

     def       a__			(						self	,  _a	,  _a       )		->							List[Any]:
           # get log sigma
           _A							:  str    =		np.log(_a       )

           # get distribution
           _A							:  List[str]    =		log_sigma - log_sigmas[:, np.newaxis]

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

           _A							:  Dict    =		log_sigmas[low_idx]
           _A							:  List[str]    =		log_sigmas[high_idx]

           # interpolate sigmas
           _A							:  Tuple    =		(low - log_sigma) / (low - high)
           _A							:  Optional[int]    =		np.clip(_a	,  0	,  1       )

           # transform interpolation to time range
           _A							:  List[Any]    =		(1 - w) * low_idx + w * high_idx
           _A							:  str    =		t.reshape(sigma.shape       )
           return t

     def       a__			(						self	,  _a	,  _a       )		->							torch.FloatTensor:
           _A							:  float    =		in_sigmas[-1].item()
           _A							:  float    =		in_sigmas[0].item()

           _A							:  List[str]    =		7.0  # 7.0 is the value used in the paper
           _A							:  Optional[int]    =		np.linspace(0	,  1	,  _a       )
           _A							:  Optional[Any]    =		sigma_min ** (1 / rho)
           _A							:  Optional[Any]    =		sigma_max ** (1 / rho)
           _A							:  Union[str, Any]    =		(max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
           return sigmas

     @property
     def       a__			(						self       )		->							Dict:
           return self.dt is None

     def       a__			(						self	,  _a	,  _a	,  _a	,  _a = True	,  )		->							Union[SchedulerOutput, Tuple]:
           _A							:  str    =		self.index_for_timestep(_a       )

           # advance index counter by 1
           _A							:  Optional[int]    =		timestep.cpu().item() if torch.is_tensor(_a       ) else timestep
           self._index_counter[timestep_int] += 1

           if self.state_in_first_order:
                 _A							:  Optional[Any]    =		self.sigmas[step_index]
                 _A							:  Any    =		self.sigmas[step_index + 1]
           else:
                 # 2nd order / Heun's method
                 _A							:  Optional[int]    =		self.sigmas[step_index - 1]
                 _A							:  List[str]    =		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
           _A							:  Union[str, Any]    =		0
           _A							:  Tuple    =		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":
                 _A							:  List[str]    =		sigma_hat if self.state_in_first_order else sigma_next
                 _A							:  Optional[Any]    =		sample - sigma_input * model_output
           elif self.config.prediction_type == "v_prediction":
                 _A							:  Union[str, Any]    =		sigma_hat if self.state_in_first_order else sigma_next
                 _A							:  Tuple    =		model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
                     sample / (sigma_input**2 + 1)
                 )
           elif self.config.prediction_type == "sample":
                 _A							:  Optional[Any]    =		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:
                 _A							:  Tuple    =		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
                 _A							:  Any    =		(sample - pred_original_sample) / sigma_hat
                 # 3. delta timestep
                 _A							:  Tuple    =		sigma_next - sigma_hat

                 # store for 2nd order step
                 _A							:  List[Any]    =		derivative
                 _A							:  Dict    =		dt
                 _A							:  Dict    =		sample
           else:
                 # 2. 2nd order / Heun's method
                 _A							:  Dict    =		(sample - pred_original_sample) / sigma_next
                 _A							:  Dict    =		(self.prev_derivative + derivative) / 2

                 # 3. take prev timestep & sample
                 _A							:  Any    =		self.dt
                 _A							:  List[str]    =		self.sample

                 # free dt and derivative
                 # Note, this puts the scheduler in "first order mode"
                 _A							:  List[str]    =		None
                 _A							:  List[Any]    =		None
                 _A							:  List[Any]    =		None

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

           if not return_dict:
                 return (prev_sample,)

           return SchedulerOutput(prev_sample=_a       )

     def       a__			(						self	,  _a	,  _a	,  _a	,  )		->							torch.FloatTensor:
           # Make sure sigmas and timesteps have the same device and dtype as original_samples
           _A							:  List[Any]    =		self.sigmas.to(device=original_samples.device	,  dtype=original_samples.dtype       )
           if original_samples.device.type == "mps" and torch.is_floating_point(_a       ):
                 # mps does not support float64
                 _A							:  List[Any]    =		self.timesteps.to(original_samples.device	,  dtype=torch.floataa       )
                 _A							:  Tuple    =		timesteps.to(original_samples.device	,  dtype=torch.floataa       )
           else:
                 _A							:  str    =		self.timesteps.to(original_samples.device       )
                 _A							:  List[str]    =		timesteps.to(original_samples.device       )

           _A							:  Union[str, Any]    =		[self.index_for_timestep(_a	,  _a       ) for t in timesteps]

           _A							:  Union[str, Any]    =		sigmas[step_indices].flatten()
           while len(sigma.shape       ) < len(original_samples.shape       ):
                 _A							:  Optional[int]    =		sigma.unsqueeze(-1       )

           _A							:  Dict    =		original_samples + noise * sigma
           return noisy_samples



     def __len__(						self       )		->							List[str]:
           return self.config.num_train_timesteps







from typing import TYPE_CHECKING

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


_snake_case            = {
    "configuration_efficientformer": [
        "EFFICIENTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "EfficientFormerConfig",
    ]
}

try:
  if not is_vision_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _snake_case            = ["EfficientFormerImageProcessor"]

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _snake_case            = [
      "EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
      "EfficientFormerForImageClassification",
      "EfficientFormerForImageClassificationWithTeacher",
      "EfficientFormerModel",
      "EfficientFormerPreTrainedModel",
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _snake_case            = [
      "TF_EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
      "TFEfficientFormerForImageClassification",
      "TFEfficientFormerForImageClassificationWithTeacher",
      "TFEfficientFormerModel",
      "TFEfficientFormerPreTrainedModel",
  ]

if TYPE_CHECKING:
  from .configuration_efficientformer import EFFICIENTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, EfficientFormerConfig

  try:
    if not is_vision_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .image_processing_efficientformer import EfficientFormerImageProcessor

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_efficientformer import (
        EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
        EfficientFormerForImageClassification,
        EfficientFormerForImageClassificationWithTeacher,
        EfficientFormerModel,
        EfficientFormerPreTrainedModel,
    )
  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_efficientformer import (
        TF_EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFEfficientFormerForImageClassification,
        TFEfficientFormerForImageClassificationWithTeacher,
        TFEfficientFormerModel,
        TFEfficientFormerPreTrainedModel,
    )

else:
  import sys

  _snake_case            = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

