




import math
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, logging, randn_tensor
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin


UpperCAmelCase_    =logging.get_logger(__name__)  # pylint: disable=invalid-name


@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->EulerAncestralDiscrete
class 				__UpperCamelCase      (			__UpperCAmelCase	):





				'''simple docstring'''



				__a				:			torch.FloatTensor
				__a				:			Optional[torch.FloatTensor]					    =None




def    UpperCAmelCase							(  _snake_case    ,			_snake_case=0.999    ,			_snake_case="cosine"    ,			):
							if alpha_transform_type == "cosine":

														def alpha_bar_fn(_snake_case				):
																					return math.cos((t + 0.008) / 1.008 * 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}"""				)

							lowerCAmelCase =		[]
							for i in range(_snake_case				):
														lowerCAmelCase =		i / num_diffusion_timesteps
														lowerCAmelCase =		(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 				__UpperCamelCase      (			__UpperCAmelCase  ,       __UpperCAmelCase	):





				'''simple docstring'''



				__a				:			Dict					    =[e.name for e in KarrasDiffusionSchedulers]
				__a				:			List[Any]					    =1



				@register_to_config
				def __init__(			self							,			UpperCAmelCase_ = 10_00							,			UpperCAmelCase_ = 0.0001							,			UpperCAmelCase_ = 0.02							,			UpperCAmelCase_ = "linear"							,			UpperCAmelCase_ = None							,			UpperCAmelCase_ = "epsilon"							,			UpperCAmelCase_ = "linspace"							,			UpperCAmelCase_ = 0							,			):
											if trained_betas is not None:
																		lowerCAmelCase =		torch.tensor(UpperCAmelCase_							,			dtype=torch.floataa    )
											elif beta_schedule == "linear":
																		lowerCAmelCase =		torch.linspace(UpperCAmelCase_							,			UpperCAmelCase_							,			UpperCAmelCase_							,			dtype=torch.floataa    )
											elif beta_schedule == "scaled_linear":
																		# this schedule is very specific to the latent diffusion model.
																		lowerCAmelCase =		(
																		    torch.linspace(beta_start**0.5							,			beta_end**0.5							,			UpperCAmelCase_							,			dtype=torch.floataa    ) ** 2
																		)
											elif beta_schedule == "squaredcos_cap_v2":
																		# Glide cosine schedule
																		lowerCAmelCase =		betas_for_alpha_bar(UpperCAmelCase_    )
											else:
																		raise NotImplementedError(F"""{beta_schedule} does is not implemented for {self.__class__}"""    )

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

											lowerCAmelCase =		np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5    )
											lowerCAmelCase =		np.concatenate([sigmas[::-1], [0.0]]    ).astype(np.floataa    )
											lowerCAmelCase =		torch.from_numpy(UpperCAmelCase_    )

											# setable values
											lowerCAmelCase =		None
											lowerCAmelCase =		np.linspace(0							,			num_train_timesteps - 1							,			UpperCAmelCase_							,			dtype=UpperCAmelCase_    )[::-1].copy()
											lowerCAmelCase =		torch.from_numpy(UpperCAmelCase_    )
											lowerCAmelCase =		False



				@property
				def    __snake_case		(			self    ):
											# 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    __snake_case		(			self							,			UpperCAmelCase_							,			UpperCAmelCase_    ):
											if isinstance(UpperCAmelCase_							,			torch.Tensor    ):
																		lowerCAmelCase =		timestep.to(self.timesteps.device    )
											lowerCAmelCase =		(self.timesteps == timestep).nonzero().item()
											lowerCAmelCase =		self.sigmas[step_index]
											lowerCAmelCase =		sample / ((sigma**2 + 1) ** 0.5)
											lowerCAmelCase =		True
											return sample



				def    __snake_case		(			self							,			UpperCAmelCase_							,			UpperCAmelCase_ = None    ):
											lowerCAmelCase =		num_inference_steps

											# "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
											if self.config.timestep_spacing == "linspace":
																		lowerCAmelCase =		np.linspace(0							,			self.config.num_train_timesteps - 1							,			UpperCAmelCase_							,			dtype=UpperCAmelCase_    )[
																		    ::-1
																		].copy()
											elif self.config.timestep_spacing == "leading":
																		lowerCAmelCase =		self.config.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
																		lowerCAmelCase =		(np.arange(0							,			UpperCAmelCase_    ) * step_ratio).round()[::-1].copy().astype(UpperCAmelCase_    )
																		timesteps += self.config.steps_offset
											elif self.config.timestep_spacing == "trailing":
																		lowerCAmelCase =		self.config.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
																		lowerCAmelCase =		(np.arange(self.config.num_train_timesteps							,			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'."""    )

											lowerCAmelCase =		np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5    )
											lowerCAmelCase =		np.interp(UpperCAmelCase_							,			np.arange(0							,			len(UpperCAmelCase_    )    )							,			UpperCAmelCase_    )
											lowerCAmelCase =		np.concatenate([sigmas, [0.0]]    ).astype(np.floataa    )
											lowerCAmelCase =		torch.from_numpy(UpperCAmelCase_    ).to(device=UpperCAmelCase_    )
											if str(UpperCAmelCase_    ).startswith('''mps'''    ):
																		# mps does not support float64
																		lowerCAmelCase =		torch.from_numpy(UpperCAmelCase_    ).to(UpperCAmelCase_							,			dtype=torch.floataa    )
											else:
																		lowerCAmelCase =		torch.from_numpy(UpperCAmelCase_    ).to(device=UpperCAmelCase_    )



				def    __snake_case		(			self							,			UpperCAmelCase_							,			UpperCAmelCase_							,			UpperCAmelCase_							,			UpperCAmelCase_ = None							,			UpperCAmelCase_ = True							,			):

											if (
											    isinstance(UpperCAmelCase_							,			UpperCAmelCase_    )
											    or isinstance(UpperCAmelCase_							,			torch.IntTensor    )
											    or isinstance(UpperCAmelCase_							,			torch.LongTensor    )
											):
																		raise ValueError(
																		    (
																		        '''Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to'''
																		        ''' `EulerDiscreteScheduler.step()` is not supported. Make sure to pass'''
																		        ''' one of the `scheduler.timesteps` as a timestep.'''
																		    )							,			)

											if not self.is_scale_input_called:
																		logger.warning(
																		    '''The `scale_model_input` function should be called before `step` to ensure correct denoising. '''
																		    '''See `StableDiffusionPipeline` for a usage example.'''    )

											if isinstance(UpperCAmelCase_							,			torch.Tensor    ):
																		lowerCAmelCase =		timestep.to(self.timesteps.device    )

											lowerCAmelCase =		(self.timesteps == timestep).nonzero().item()
											lowerCAmelCase =		self.sigmas[step_index]

											# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
											if self.config.prediction_type == "epsilon":
																		lowerCAmelCase =		sample - sigma * model_output
											elif self.config.prediction_type == "v_prediction":
																		# * c_out + input * c_skip
																		lowerCAmelCase =		model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**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`"""    )

											lowerCAmelCase =		self.sigmas[step_index]
											lowerCAmelCase =		self.sigmas[step_index + 1]
											lowerCAmelCase =		(sigma_to**2 * (sigma_from**2 - sigma_to**2) / sigma_from**2) ** 0.5
											lowerCAmelCase =		(sigma_to**2 - sigma_up**2) ** 0.5

											# 2. Convert to an ODE derivative
											lowerCAmelCase =		(sample - pred_original_sample) / sigma

											lowerCAmelCase =		sigma_down - sigma

											lowerCAmelCase =		sample + derivative * dt

											lowerCAmelCase =		model_output.device
											lowerCAmelCase =		randn_tensor(model_output.shape							,			dtype=model_output.dtype							,			device=UpperCAmelCase_							,			generator=UpperCAmelCase_    )

											lowerCAmelCase =		prev_sample + noise * sigma_up

											if not return_dict:
																		return (prev_sample,)

											return EulerAncestralDiscreteSchedulerOutput(
											    prev_sample=UpperCAmelCase_							,			pred_original_sample=UpperCAmelCase_    )



				def    __snake_case		(			self							,			UpperCAmelCase_							,			UpperCAmelCase_							,			UpperCAmelCase_							,			):
											# Make sure sigmas and timesteps have the same device and dtype as original_samples
											lowerCAmelCase =		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
																		lowerCAmelCase =		self.timesteps.to(original_samples.device							,			dtype=torch.floataa    )
																		lowerCAmelCase =		timesteps.to(original_samples.device							,			dtype=torch.floataa    )
											else:
																		lowerCAmelCase =		self.timesteps.to(original_samples.device    )
																		lowerCAmelCase =		timesteps.to(original_samples.device    )

											lowerCAmelCase =		[(schedule_timesteps == t).nonzero().item() for t in timesteps]

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

											lowerCAmelCase =		original_samples + noise * sigma
											return noisy_samples






				def __len__(			self    ):
											return self.config.num_train_timesteps









from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


UpperCAmelCase_    ={
    """configuration_splinter""": ["""SPLINTER_PRETRAINED_CONFIG_ARCHIVE_MAP""", """SplinterConfig"""],
    """tokenization_splinter""": ["""SplinterTokenizer"""],
}

try:
		if not is_tokenizers_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		UpperCAmelCase_    =["""SplinterTokenizerFast"""]

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		UpperCAmelCase_    =[
		    """SPLINTER_PRETRAINED_MODEL_ARCHIVE_LIST""",
		    """SplinterForQuestionAnswering""",
		    """SplinterForPreTraining""",
		    """SplinterLayer""",
		    """SplinterModel""",
		    """SplinterPreTrainedModel""",
		]


if TYPE_CHECKING:
		from .configuration_splinter import SPLINTER_PRETRAINED_CONFIG_ARCHIVE_MAP, SplinterConfig
		from .tokenization_splinter import SplinterTokenizer

		try:
				if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_splinter_fast import SplinterTokenizerFast

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_splinter import (
				    SPLINTER_PRETRAINED_MODEL_ARCHIVE_LIST,
				    SplinterForPreTraining,
				    SplinterForQuestionAnswering,
				    SplinterLayer,
				    SplinterModel,
				    SplinterPreTrainedModel,
				)


else:
		import sys

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



