'''simple docstring'''




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 ..utils import randn_tensor
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput

def 			UpperCamelCase_							(      _UpperCAmelCase						:   Union[str, Any]    ,			_UpperCAmelCase						:   Any=0.9_9_9    ,			_UpperCAmelCase						:   List[str]="cosine"    ,			)					->					Tuple:


       """simple docstring"""







       if alpha_transform_type == "cosine":

              def alpha_bar_fn(_UpperCAmelCase						:   Any					):
                     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(_UpperCAmelCase						:   str					):
                     return math.exp(t * -1_2.0					)

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

       _UpperCAmelCase  :						Dict							   =					[]
       for i in range(_UpperCAmelCase					):
              _UpperCAmelCase  :						Dict							   =					i / num_diffusion_timesteps
              _UpperCAmelCase  :						List[Any]							   =					(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 			lowerCamelCase_			(snake_case__							,				snake_case__					):
      '''simple docstring'''

      __UpperCamelCase:						Any      			=		[e.name for e in KarrasDiffusionSchedulers]
      __UpperCamelCase:						List[str]      			=		2




      @register_to_config
      def __init__( self		:       Optional[Any]		,      A		:       int = 1000		,      A		:       float = 0.00_085		,      A		:       float = 0.012		,      A		:       str = "linear"		,      A		:       Optional[Union[np.ndarray, List[float]]] = None		,      A		:       str = "epsilon"		,      A		:       str = "linspace"		,      A		:       int = 0		,      ):
             if trained_betas is not None:
                    _UpperCAmelCase  :						Dict							   =					torch.tensor(A		,      dtype=torch.floataa )
             elif beta_schedule == "linear":
                    _UpperCAmelCase  :						Any							   =					torch.linspace(A		,      A		,      A		,      dtype=torch.floataa )
             elif beta_schedule == "scaled_linear":
                    # this schedule is very specific to the latent diffusion model.
                    _UpperCAmelCase  :						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
                    _UpperCAmelCase  :						Dict							   =					betas_for_alpha_bar(A )
             else:
                    raise NotImplementedError(F"""{beta_schedule} does is not implemented for {self.__class__}""" )

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

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




      def   _A			( self		:       Optional[int]		,      A		:       Any		,      A		:       List[str]=None ):
             if schedule_timesteps is None:
                    _UpperCAmelCase  :						Tuple							   =					self.timesteps

             _UpperCAmelCase  :						List[Any]							   =					(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  :						Tuple							   =					1 if len(A ) > 1 else 0
             else:
                    _UpperCAmelCase  :						int							   =					timestep.cpu().item() if torch.is_tensor(A ) else timestep
                    _UpperCAmelCase  :						List[str]							   =					self._index_counter[timestep_int]

             return indices[pos].item()




      @property
      def   _A			( self		:       str ):
             # 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		:       List[str]		,      A		:       torch.FloatTensor		,      A		:       Union[float, torch.FloatTensor]		,      ):
             _UpperCAmelCase  :						Tuple							   =					self.index_for_timestep(A )

             if self.state_in_first_order:
                    _UpperCAmelCase  :						Dict							   =					self.sigmas[step_index]
             else:
                    _UpperCAmelCase  :						Dict							   =					self.sigmas_interpol[step_index - 1]

             _UpperCAmelCase  :						Union[str, Any]							   =					sample / ((sigma**2 + 1) ** 0.5)
             return sample




      def   _A			( self		:       List[Any]		,      A		:       int		,      A		:       Union[str, torch.device] = None		,      A		:       Optional[int] = None		,      ):
             _UpperCAmelCase  :						List[Any]							   =					num_inference_steps

             _UpperCAmelCase  :						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":
                    _UpperCAmelCase  :						Union[str, Any]							   =					np.linspace(0		,      num_train_timesteps - 1		,      A		,      dtype=A )[::-1].copy()
             elif self.config.timestep_spacing == "leading":
                    _UpperCAmelCase  :						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
                    _UpperCAmelCase  :						Optional[int]							   =					(np.arange(0		,      A ) * step_ratio).round()[::-1].copy().astype(A )
                    timesteps += self.config.steps_offset
             elif self.config.timestep_spacing == "trailing":
                    _UpperCAmelCase  :						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
                    _UpperCAmelCase  :						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'.""" )

             _UpperCAmelCase  :						List[str]							   =					np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5 )
             _UpperCAmelCase  :						List[Any]							   =					torch.from_numpy(np.log(A ) ).to(A )

             _UpperCAmelCase  :						str							   =					np.interp(A		,      np.arange(0		,      len(A ) )		,      A )
             _UpperCAmelCase  :						Dict							   =					np.concatenate([sigmas, [0.0]] ).astype(np.floataa )
             _UpperCAmelCase  :						Optional[Any]							   =					torch.from_numpy(A ).to(device=A )

             # compute up and down sigmas
             _UpperCAmelCase  :						List[str]							   =					sigmas.roll(-1 )
             _UpperCAmelCase  :						List[str]							   =					0.0
             _UpperCAmelCase  :						Tuple							   =					(sigmas_next**2 * (sigmas**2 - sigmas_next**2) / sigmas**2) ** 0.5
             _UpperCAmelCase  :						Dict							   =					(sigmas_next**2 - sigmas_up**2) ** 0.5
             _UpperCAmelCase  :						Union[str, Any]							   =					0.0

             # compute interpolated sigmas
             _UpperCAmelCase  :						Dict							   =					sigmas.log().lerp(sigmas_down.log()		,      0.5 ).exp()
             _UpperCAmelCase  :						Dict							   =					0.0

             # set sigmas
             _UpperCAmelCase  :						Optional[int]							   =					torch.cat([sigmas[:1], sigmas[1:].repeat_interleave(2 ), sigmas[-1:]] )
             _UpperCAmelCase  :						Tuple							   =					torch.cat(
                 [sigmas_interpol[:1], sigmas_interpol[1:].repeat_interleave(2 ), sigmas_interpol[-1:]] )
             _UpperCAmelCase  :						Dict							   =					torch.cat([sigmas_up[:1], sigmas_up[1:].repeat_interleave(2 ), sigmas_up[-1:]] )
             _UpperCAmelCase  :						Optional[Any]							   =					torch.cat([sigmas_down[:1], sigmas_down[1:].repeat_interleave(2 ), sigmas_down[-1:]] )

             if str(A ).startswith("mps" ):
                    # mps does not support float64
                    _UpperCAmelCase  :						List[Any]							   =					torch.from_numpy(A ).to(A		,      dtype=torch.floataa )
             else:
                    _UpperCAmelCase  :						int							   =					torch.from_numpy(A ).to(A )

             _UpperCAmelCase  :						Any							   =					self.sigma_to_t(A ).to(A		,      dtype=timesteps.dtype )
             _UpperCAmelCase  :						Dict							   =					torch.stack((timesteps_interpol[:-2, None], timesteps[1:, None])		,      dim=-1 ).flatten()

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

             _UpperCAmelCase  :						Tuple							   =					None

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




      def   _A			( self		:       List[str]		,      A		:       List[str] ):
             # get log sigma
             _UpperCAmelCase  :						int							   =					sigma.log()

             # get distribution
             _UpperCAmelCase  :						List[Any]							   =					log_sigma - self.log_sigmas[:, None]

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

             _UpperCAmelCase  :						Optional[int]							   =					self.log_sigmas[low_idx]
             _UpperCAmelCase  :						List[Any]							   =					self.log_sigmas[high_idx]

             # interpolate sigmas
             _UpperCAmelCase  :						Any							   =					(low - log_sigma) / (low - high)
             _UpperCAmelCase  :						Optional[int]							   =					w.clamp(0		,      1 )

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




      @property
      def   _A			( self		:       Optional[int] ):
             return self.sample is None




      def   _A			( self		:       str		,      A		:       Union[torch.FloatTensor, np.ndarray]		,      A		:       Union[float, torch.FloatTensor]		,      A		:       Union[torch.FloatTensor, np.ndarray]		,      A		:       Optional[torch.Generator] = None		,      A		:       bool = True		,      ):
             _UpperCAmelCase  :						Dict							   =					self.index_for_timestep(A )

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

             if self.state_in_first_order:
                    _UpperCAmelCase  :						Union[str, Any]							   =					self.sigmas[step_index]
                    _UpperCAmelCase  :						str							   =					self.sigmas_interpol[step_index]
                    _UpperCAmelCase  :						Any							   =					self.sigmas_up[step_index]
                    _UpperCAmelCase  :						Union[str, Any]							   =					self.sigmas_down[step_index - 1]
             else:
                    # 2nd order / KPDM2's method
                    _UpperCAmelCase  :						List[Any]							   =					self.sigmas[step_index - 1]
                    _UpperCAmelCase  :						List[str]							   =					self.sigmas_interpol[step_index - 1]
                    _UpperCAmelCase  :						List[Any]							   =					self.sigmas_up[step_index - 1]
                    _UpperCAmelCase  :						Optional[int]							   =					self.sigmas_down[step_index - 1]

             # 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  :						List[Any]							   =					0
             _UpperCAmelCase  :						List[str]							   =					sigma * (gamma + 1)  # Note: sigma_hat == sigma for now

             _UpperCAmelCase  :						Any							   =					model_output.device
             _UpperCAmelCase  :						Tuple							   =					randn_tensor(model_output.shape		,      dtype=model_output.dtype		,      device=A		,      generator=A )

             # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
             if self.config.prediction_type == "epsilon":
                    _UpperCAmelCase  :						Dict							   =					sigma_hat if self.state_in_first_order else sigma_interpol
                    _UpperCAmelCase  :						Optional[int]							   =					sample - sigma_input * model_output
             elif self.config.prediction_type == "v_prediction":
                    _UpperCAmelCase  :						List[Any]							   =					sigma_hat if self.state_in_first_order else sigma_interpol
                    _UpperCAmelCase  :						Dict							   =					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  :						int							   =					(sample - pred_original_sample) / sigma_hat
                    # 3. delta timestep
                    _UpperCAmelCase  :						Tuple							   =					sigma_interpol - sigma_hat

                    # store for 2nd order step
                    _UpperCAmelCase  :						Tuple							   =					sample
                    _UpperCAmelCase  :						Optional[Any]							   =					dt
                    _UpperCAmelCase  :						str							   =					sample + derivative * dt
             else:
                    # DPM-Solver-2
                    # 2. Convert to an ODE derivative for 2nd order
                    _UpperCAmelCase  :						str							   =					(sample - pred_original_sample) / sigma_interpol
                    # 3. delta timestep
                    _UpperCAmelCase  :						str							   =					sigma_down - sigma_hat

                    _UpperCAmelCase  :						Any							   =					self.sample
                    _UpperCAmelCase  :						Optional[Any]							   =					None

                    _UpperCAmelCase  :						Union[str, Any]							   =					sample + derivative * dt
                    _UpperCAmelCase  :						int							   =					prev_sample + noise * sigma_up

             if not return_dict:
                    return (prev_sample,)

             return SchedulerOutput(prev_sample=A )




      def   _A			( self		:       Union[str, Any]		,      A		:       torch.FloatTensor		,      A		:       torch.FloatTensor		,      A		:       torch.FloatTensor		,      ):
             # Make sure sigmas and timesteps have the same device and dtype as original_samples
             _UpperCAmelCase  :						List[str]							   =					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
                    _UpperCAmelCase  :						Tuple							   =					self.timesteps.to(original_samples.device		,      dtype=torch.floataa )
                    _UpperCAmelCase  :						Optional[Any]							   =					timesteps.to(original_samples.device		,      dtype=torch.floataa )
             else:
                    _UpperCAmelCase  :						str							   =					self.timesteps.to(original_samples.device )
                    _UpperCAmelCase  :						Optional[int]							   =					timesteps.to(original_samples.device )

             _UpperCAmelCase  :						Tuple							   =					[self.index_for_timestep(A		,      A ) for t in timesteps]

             _UpperCAmelCase  :						Optional[Any]							   =					sigmas[step_indices].flatten()
             while len(sigma.shape ) < len(original_samples.shape ):
                    _UpperCAmelCase  :						Dict							   =					sigma.unsqueeze(-1 )

             _UpperCAmelCase  :						Optional[int]							   =					original_samples + noise * sigma
             return noisy_samples





      def __len__( self		:       Optional[int] ):
             return self.config.num_train_timesteps


'''simple docstring'''




from typing import TYPE_CHECKING

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


__SCREAMING_SNAKE_CASE :  Union[str, Any]  =						{
    """configuration_gptsan_japanese""": ["""GPTSAN_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GPTSanJapaneseConfig"""],
    """tokenization_gptsan_japanese""": ["""GPTSanJapaneseTokenizer"""],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __SCREAMING_SNAKE_CASE :  Optional[int]  =						[
      """GPTSAN_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """GPTSanJapaneseForConditionalGeneration""",
      """GPTSanJapaneseModel""",
      """GPTSanJapanesePreTrainedModel""",
  ]
  __SCREAMING_SNAKE_CASE :  Union[str, Any]  =						[
      """GPTSanJapaneseTokenizer""",
  ]


if TYPE_CHECKING:
  from .configuration_gptsan_japanese import GPTSAN_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTSanJapaneseConfig
  from .tokenization_gptsan_japanese import GPTSanJapaneseTokenizer

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_gptsan_japanese import (
        GPTSAN_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST,
        GPTSanJapaneseForConditionalGeneration,
        GPTSanJapaneseModel,
        GPTSanJapanesePreTrainedModel,
    )
    from .tokenization_gptsan_japanese import GPTSanJapaneseTokenizer


else:
  import sys

  __SCREAMING_SNAKE_CASE :  Union[str, Any]  =						_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)

