
'''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 						snake_case__	(      UpperCamelCase			,UpperCamelCase=0.999			,UpperCamelCase="cosine"			,) ->      List[Any]:
		if alpha_transform_type == "cosine":

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

		elif alpha_transform_type == "exp":

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

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

		_UpperCamelCase       : Tuple    =      []
		for i in range(UpperCamelCase	):
				_UpperCamelCase       : str    =      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 			UpperCAmelCase				(					a_  ,		a_ ):





		"""simple docstring"""





		A__  :   Any          =    [e.name for e in KarrasDiffusionSchedulers]
		A__  :   Optional[int]          =    2
		@register_to_config
		def __init__(    self  ,					_snake_case = 1000  ,					_snake_case = 0.00_085  ,					_snake_case = 0.012  ,					_snake_case = "linear"  ,					_snake_case = None  ,					_snake_case = "epsilon"  ,					_snake_case = "linspace"  ,					_snake_case = 0  ,					)				->    List[Any]:
				if trained_betas is not None:
						_UpperCamelCase       : Optional[Any]    =      torch.tensor(_snake_case  ,					dtype=torch.floataa     )
				elif beta_schedule == "linear":
						_UpperCamelCase       : Tuple    =      torch.linspace(_snake_case  ,					_snake_case  ,					_snake_case  ,					dtype=torch.floataa     )
				elif beta_schedule == "scaled_linear":
						# this schedule is very specific to the latent diffusion model.
						_UpperCamelCase       : Optional[Any]    =      (
						    torch.linspace(beta_start**0.5  ,					beta_end**0.5  ,					_snake_case  ,					dtype=torch.floataa     ) ** 2
						)
				elif beta_schedule == "squaredcos_cap_v2":
						# Glide cosine schedule
						_UpperCamelCase       : Tuple    =      betas_for_alpha_bar(_snake_case     )
				else:
						raise NotImplementedError(F'''{beta_schedule} does is not implemented for {self.__class__}'''     )

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

				#  set all values
				self.set_timesteps(_snake_case  ,					_snake_case  ,					_snake_case     )
		def _lowercase  (    self  ,					_snake_case  ,					_snake_case=None     )				->    Any:
				if schedule_timesteps is None:
						_UpperCamelCase       : int    =      self.timesteps

				_UpperCamelCase       : int    =      (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       : Any    =      1 if len(_snake_case     ) > 1 else 0
				else:
						_UpperCamelCase       : Union[str, Any]    =      timestep.cpu().item() if torch.is_tensor(_snake_case     ) else timestep
						_UpperCamelCase       : List[str]    =      self._index_counter[timestep_int]

				return indices[pos].item()
		@property
		def _lowercase  (    self     )				->    Union[str, 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 _lowercase  (    self  ,					_snake_case  ,					_snake_case  ,					)				->    torch.FloatTensor:
				_UpperCamelCase       : Optional[int]    =      self.index_for_timestep(_snake_case     )

				if self.state_in_first_order:
						_UpperCamelCase       : Optional[Any]    =      self.sigmas[step_index]
				else:
						_UpperCamelCase       : Dict    =      self.sigmas_interpol[step_index - 1]

				_UpperCamelCase       : Any    =      sample / ((sigma**2 + 1) ** 0.5)
				return sample
		def _lowercase  (    self  ,					_snake_case  ,					_snake_case = None  ,					_snake_case = None  ,					)				->    Dict:
				_UpperCamelCase       : List[str]    =      num_inference_steps

				_UpperCamelCase       : List[str]    =      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       : Tuple    =      np.linspace(0  ,					num_train_timesteps - 1  ,					_snake_case  ,					dtype=_snake_case     )[::-1].copy()
				elif self.config.timestep_spacing == "leading":
						_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(0  ,					_snake_case     ) * step_ratio).round()[::-1].copy().astype(_snake_case     )
						timesteps += self.config.steps_offset
				elif self.config.timestep_spacing == "trailing":
						_UpperCamelCase       : List[str]    =      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[Any]    =      (np.arange(_snake_case  ,					0  ,					-step_ratio     )).round().copy().astype(_snake_case     )
						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       : Dict    =      torch.from_numpy(np.log(_snake_case     )     ).to(_snake_case     )

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

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

				# compute interpolated sigmas
				_UpperCamelCase       : str    =      sigmas.log().lerp(sigmas_down.log()  ,					0.5     ).exp()
				_UpperCamelCase       : Any    =      0.0

				# set sigmas
				_UpperCamelCase       : List[str]    =      torch.cat([sigmas[:1], sigmas[1:].repeat_interleave(2     ), sigmas[-1:]]     )
				_UpperCamelCase       : Dict    =      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       : int    =      torch.cat([sigmas_down[:1], sigmas_down[1:].repeat_interleave(2     ), sigmas_down[-1:]]     )

				if str(_snake_case     ).startswith('''mps'''     ):
						# mps does not support float64
						_UpperCamelCase       : List[Any]    =      torch.from_numpy(_snake_case     ).to(_snake_case  ,					dtype=torch.floataa     )
				else:
						_UpperCamelCase       : Optional[int]    =      torch.from_numpy(_snake_case     ).to(_snake_case     )

				_UpperCamelCase       : str    =      self.sigma_to_t(_snake_case     ).to(_snake_case  ,					dtype=timesteps.dtype     )
				_UpperCamelCase       : List[Any]    =      torch.stack((timesteps_interpol[:-2, None], timesteps[1:, None])  ,					dim=-1     ).flatten()

				_UpperCamelCase       : Dict    =      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       : str    =      defaultdict(_snake_case     )
		def _lowercase  (    self  ,					_snake_case     )				->    List[Any]:
				# get log sigma
				_UpperCamelCase       : List[Any]    =      sigma.log()

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

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

				_UpperCamelCase       : List[str]    =      self.log_sigmas[low_idx]
				_UpperCamelCase       : List[Any]    =      self.log_sigmas[high_idx]

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

				# transform interpolation to time range
				_UpperCamelCase       : List[Any]    =      (1 - w) * low_idx + w * high_idx
				_UpperCamelCase       : Optional[Any]    =      t.view(sigma.shape     )
				return t
		@property
		def _lowercase  (    self     )				->    Any:
				return self.sample is None
		def _lowercase  (    self  ,					_snake_case  ,					_snake_case  ,					_snake_case  ,					_snake_case = None  ,					_snake_case = True  ,					)				->    Union[SchedulerOutput, Tuple]:
				_UpperCamelCase       : str    =      self.index_for_timestep(_snake_case     )

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

				if self.state_in_first_order:
						_UpperCamelCase       : List[Any]    =      self.sigmas[step_index]
						_UpperCamelCase       : Union[str, Any]    =      self.sigmas_interpol[step_index]
						_UpperCamelCase       : Dict    =      self.sigmas_up[step_index]
						_UpperCamelCase       : Union[str, Any]    =      self.sigmas_down[step_index - 1]
				else:
						# 2nd order / KPDM2's method
						_UpperCamelCase       : Optional[Any]    =      self.sigmas[step_index - 1]
						_UpperCamelCase       : List[str]    =      self.sigmas_interpol[step_index - 1]
						_UpperCamelCase       : Any    =      self.sigmas_up[step_index - 1]
						_UpperCamelCase       : Any    =      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       : Optional[Any]    =      0
				_UpperCamelCase       : Optional[Any]    =      sigma * (gamma + 1)  # Note: sigma_hat == sigma for now

				_UpperCamelCase       : Optional[Any]    =      model_output.device
				_UpperCamelCase       : Tuple    =      randn_tensor(model_output.shape  ,					dtype=model_output.dtype  ,					device=_snake_case  ,					generator=_snake_case     )

				# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
				if self.config.prediction_type == "epsilon":
						_UpperCamelCase       : int    =      sigma_hat if self.state_in_first_order else sigma_interpol
						_UpperCamelCase       : List[str]    =      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       : Any    =      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       : str    =      (sample - pred_original_sample) / sigma_hat
						# 3. delta timestep
						_UpperCamelCase       : Dict    =      sigma_interpol - sigma_hat

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

						_UpperCamelCase       : Union[str, Any]    =      self.sample
						_UpperCamelCase       : Tuple    =      None

						_UpperCamelCase       : int    =      sample + derivative * dt
						_UpperCamelCase       : int    =      prev_sample + noise * sigma_up

				if not return_dict:
						return (prev_sample,)

				return SchedulerOutput(prev_sample=_snake_case     )
		def _lowercase  (    self  ,					_snake_case  ,					_snake_case  ,					_snake_case  ,					)				->    torch.FloatTensor:
				# Make sure sigmas and timesteps have the same device and dtype as original_samples
				_UpperCamelCase       : List[Any]    =      self.sigmas.to(device=original_samples.device  ,					dtype=original_samples.dtype     )
				if original_samples.device.type == "mps" and torch.is_floating_point(_snake_case     ):
						# mps does not support float64
						_UpperCamelCase       : Any    =      self.timesteps.to(original_samples.device  ,					dtype=torch.floataa     )
						_UpperCamelCase       : Optional[Any]    =      timesteps.to(original_samples.device  ,					dtype=torch.floataa     )
				else:
						_UpperCamelCase       : int    =      self.timesteps.to(original_samples.device     )
						_UpperCamelCase       : List[Any]    =      timesteps.to(original_samples.device     )

				_UpperCamelCase       : Optional[int]    =      [self.index_for_timestep(_snake_case  ,					_snake_case     ) for t in timesteps]

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

				_UpperCamelCase       : Dict    =      original_samples + noise * sigma
				return noisy_samples




		def __len__(    self     )				->    str:
				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,
)


_UpperCAmelCase   :      int 						=  {
    """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:
	_UpperCAmelCase   :      int 						=  [
	    """GPTSAN_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """GPTSanJapaneseForConditionalGeneration""",
	    """GPTSanJapaneseModel""",
	    """GPTSanJapanesePreTrainedModel""",
	]
	_UpperCAmelCase   :      str 						=  [
	    """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

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





