

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        UpperCAmelCase_     (		_A   ,	_A=0.9_9_9   ,	_A="cosine"   ,	):





     '''simple docstring'''




     if alpha_transform_type == "cosine":

          def alpha_bar_fn(_A       ):
               return math.cos((t + 0.0_0_8) / 1.0_0_8 * math.pi / 2       ) ** 2

     elif alpha_transform_type == "exp":

          def alpha_bar_fn(_A       ):
               return math.exp(t * -1_2.0       )

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

     SCREAMING_SNAKE_CASE__					=					[]
     for i in range(_lowerCamelCase       ):
          SCREAMING_SNAKE_CASE__					=					i / num_diffusion_timesteps
          SCREAMING_SNAKE_CASE__					=					(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__	( a__		,  a__ ):






  """simple docstring"""


  a     =      [e.name for e in KarrasDiffusionSchedulers]
  a     =      2






  @register_to_config
  def __init__(							self		:	int			,     __lowerCamelCase		:	Any = 1000			,     __lowerCamelCase		:	Optional[int] = 0.00085			,     __lowerCamelCase		:	Optional[int] = 0.012			,     __lowerCamelCase		:	Tuple = "linear"			,     __lowerCamelCase		:	Tuple = None			,     __lowerCamelCase		:	List[Any] = "epsilon"			,     __lowerCamelCase		:	str = "linspace"			,     __lowerCamelCase		:	Optional[int] = 0			,     )	->							Tuple:

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

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

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






  def    lowercase_	(							self		:	int			,     __lowerCamelCase		:	Any			,     __lowerCamelCase		:	Union[str, Any]=None      )	->							Union[str, Any]:

       if schedule_timesteps is None:
            SCREAMING_SNAKE_CASE__					=					self.timesteps

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

       return indices[pos].item()






  @property
  def    lowercase_	(							self		:	Any      )	->							Union[str, Any]:

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

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






  def    lowercase_	(							self		:	Dict			,     __lowerCamelCase		:	str			,     __lowerCamelCase		:	List[Any]			,     )	->							Tuple:
       SCREAMING_SNAKE_CASE__					=					self.index_for_timestep(_A      )

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

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






  def    lowercase_	(							self		:	Any			,     __lowerCamelCase		:	Any			,     __lowerCamelCase		:	Union[str, Any] = None			,     __lowerCamelCase		:	Dict = None			,     )	->							Union[str, Any]:
       SCREAMING_SNAKE_CASE__					=					num_inference_steps

       SCREAMING_SNAKE_CASE__					=					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__					=					np.linspace(0			,     num_train_timesteps - 1			,     _A			,     dtype=_A      )[::-1].copy()
       elif self.config.timestep_spacing == "leading":
            SCREAMING_SNAKE_CASE__					=					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__					=					(np.arange(0			,     _A      ) * step_ratio).round()[::-1].copy().astype(_A      )
            timesteps += self.config.steps_offset
       elif self.config.timestep_spacing == "trailing":
            SCREAMING_SNAKE_CASE__					=					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__					=					(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\'.'''      )

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

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

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

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

       if str(_A      ).startswith('''mps'''      ):
            # mps does not support float64
            SCREAMING_SNAKE_CASE__					=					torch.from_numpy(_A      ).to(_A			,     dtype=torch.floataa      )
       else:
            SCREAMING_SNAKE_CASE__					=					torch.from_numpy(_A      ).to(_A      )

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

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

       SCREAMING_SNAKE_CASE__					=					None

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






  def    lowercase_	(							self		:	Optional[int]			,     __lowerCamelCase		:	Any      )	->							int:
       SCREAMING_SNAKE_CASE__					=					sigma.log()

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

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

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

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

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






  @property
  def    lowercase_	(							self		:	Dict      )	->							Optional[Any]:
       return self.sample is None






  def    lowercase_	(							self		:	int			,     __lowerCamelCase		:	List[str]			,     __lowerCamelCase		:	Union[str, Any]			,     __lowerCamelCase		:	Tuple			,     __lowerCamelCase		:	List[str] = True			,     )	->							Tuple:
       SCREAMING_SNAKE_CASE__					=					self.index_for_timestep(_A      )

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

       if self.state_in_first_order:
            SCREAMING_SNAKE_CASE__					=					self.sigmas[step_index]
            SCREAMING_SNAKE_CASE__					=					self.sigmas_interpol[step_index + 1]
            SCREAMING_SNAKE_CASE__					=					self.sigmas[step_index + 1]
       else:
            # 2nd order / KDPM2's method
            SCREAMING_SNAKE_CASE__					=					self.sigmas[step_index - 1]
            SCREAMING_SNAKE_CASE__					=					self.sigmas_interpol[step_index]
            SCREAMING_SNAKE_CASE__					=					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__					=					0
       SCREAMING_SNAKE_CASE__					=					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__					=					sigma_hat if self.state_in_first_order else sigma_interpol
            SCREAMING_SNAKE_CASE__					=					sample - sigma_input * model_output
       elif self.config.prediction_type == "v_prediction":
            SCREAMING_SNAKE_CASE__					=					sigma_hat if self.state_in_first_order else sigma_interpol
            SCREAMING_SNAKE_CASE__					=					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__					=					(sample - pred_original_sample) / sigma_hat
            # 3. delta timestep
            SCREAMING_SNAKE_CASE__					=					sigma_interpol - sigma_hat

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

            # 3. delta timestep
            SCREAMING_SNAKE_CASE__					=					sigma_next - sigma_hat

            SCREAMING_SNAKE_CASE__					=					self.sample
            SCREAMING_SNAKE_CASE__					=					None

       SCREAMING_SNAKE_CASE__					=					sample + derivative * dt

       if not return_dict:
            return (prev_sample,)

       return SchedulerOutput(prev_sample=_A      )






  def    lowercase_	(							self		:	Optional[int]			,     __lowerCamelCase		:	Optional[int]			,     __lowerCamelCase		:	Optional[Any]			,     __lowerCamelCase		:	Union[str, Any]			,     )	->							Any:
       SCREAMING_SNAKE_CASE__					=					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
            SCREAMING_SNAKE_CASE__					=					self.timesteps.to(original_samples.device			,     dtype=torch.floataa      )
            SCREAMING_SNAKE_CASE__					=					timesteps.to(original_samples.device			,     dtype=torch.floataa      )
       else:
            SCREAMING_SNAKE_CASE__					=					self.timesteps.to(original_samples.device      )
            SCREAMING_SNAKE_CASE__					=					timesteps.to(original_samples.device      )

       SCREAMING_SNAKE_CASE__					=					[self.index_for_timestep(_A			,     _A      ) for t in timesteps]

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

       SCREAMING_SNAKE_CASE__					=					original_samples + noise * sigma
       return noisy_samples







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





from typing import TYPE_CHECKING

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


_SCREAMING_SNAKE_CASE  :						List[Any]         =    {
    '''configuration_roberta''': ['''ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''RobertaConfig''', '''RobertaOnnxConfig'''],
    '''tokenization_roberta''': ['''RobertaTokenizer'''],
}

try:
       if not is_tokenizers_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       _SCREAMING_SNAKE_CASE  :						Optional[int]         =    ['''RobertaTokenizerFast''']

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       _SCREAMING_SNAKE_CASE  :						Tuple         =    [
           '''ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST''',
           '''RobertaForCausalLM''',
           '''RobertaForMaskedLM''',
           '''RobertaForMultipleChoice''',
           '''RobertaForQuestionAnswering''',
           '''RobertaForSequenceClassification''',
           '''RobertaForTokenClassification''',
           '''RobertaModel''',
           '''RobertaPreTrainedModel''',
       ]

try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       _SCREAMING_SNAKE_CASE  :						List[Any]         =    [
           '''TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST''',
           '''TFRobertaForCausalLM''',
           '''TFRobertaForMaskedLM''',
           '''TFRobertaForMultipleChoice''',
           '''TFRobertaForQuestionAnswering''',
           '''TFRobertaForSequenceClassification''',
           '''TFRobertaForTokenClassification''',
           '''TFRobertaMainLayer''',
           '''TFRobertaModel''',
           '''TFRobertaPreTrainedModel''',
       ]

try:
       if not is_flax_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       _SCREAMING_SNAKE_CASE  :						Optional[Any]         =    [
           '''FlaxRobertaForCausalLM''',
           '''FlaxRobertaForMaskedLM''',
           '''FlaxRobertaForMultipleChoice''',
           '''FlaxRobertaForQuestionAnswering''',
           '''FlaxRobertaForSequenceClassification''',
           '''FlaxRobertaForTokenClassification''',
           '''FlaxRobertaModel''',
           '''FlaxRobertaPreTrainedModel''',
       ]


if TYPE_CHECKING:
       from .configuration_roberta import ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, RobertaConfig, RobertaOnnxConfig
       from .tokenization_roberta import RobertaTokenizer

       try:
              if not is_tokenizers_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_roberta_fast import RobertaTokenizerFast

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_roberta import (
                  ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
                  RobertaForCausalLM,
                  RobertaForMaskedLM,
                  RobertaForMultipleChoice,
                  RobertaForQuestionAnswering,
                  RobertaForSequenceClassification,
                  RobertaForTokenClassification,
                  RobertaModel,
                  RobertaPreTrainedModel,
              )

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_roberta import (
                  TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
                  TFRobertaForCausalLM,
                  TFRobertaForMaskedLM,
                  TFRobertaForMultipleChoice,
                  TFRobertaForQuestionAnswering,
                  TFRobertaForSequenceClassification,
                  TFRobertaForTokenClassification,
                  TFRobertaMainLayer,
                  TFRobertaModel,
                  TFRobertaPreTrainedModel,
              )

       try:
              if not is_flax_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_flax_roberta import (
                  FlaxRobertaForCausalLM,
                  FlaxRobertaForMaskedLM,
                  FlaxRobertaForMultipleChoice,
                  FlaxRobertaForQuestionAnswering,
                  FlaxRobertaForSequenceClassification,
                  FlaxRobertaForTokenClassification,
                  FlaxRobertaModel,
                  FlaxRobertaPreTrainedModel,
              )

else:
       import sys

       _SCREAMING_SNAKE_CASE  :						str         =    _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)


