






"""simple docstring"""



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


__A		:	Optional[int]      =		logging.get_logger(__name__)  # pylint: disable=invalid-name





@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->EulerDiscrete
class  _UpperCAmelCase	(			A__							):
					SCREAMING_SNAKE_CASE_		:    Dict							   =       42
					SCREAMING_SNAKE_CASE_		:    int							   =       None



def 						lowercase   (    __snake_case    :			Dict		, __snake_case    :			str=0.999		, __snake_case    :			Dict="cosine"		, ):
		if alpha_transform_type == "cosine":

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

		elif alpha_transform_type == "exp":

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

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

		lowercase_			:			Optional[Any]								=       []
		for i in range(_lowerCAmelCase     ):
				lowercase_			:			Optional[int]								=       i / num_diffusion_timesteps
				lowercase_			:			Union[str, Any]								=       (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__							):
					SCREAMING_SNAKE_CASE_		:    Dict							   =       [e.name for e in KarrasDiffusionSchedulers]
					SCREAMING_SNAKE_CASE_		:    Any							   =       1




					@register_to_config
					def __init__(    self					:						Tuple   ,						A					:						Optional[Any] = 10_00   ,						A					:						List[Any] = 0.0001   ,						A					:						List[str] = 0.02   ,						A					:						Union[str, Any] = "linear"   ,						A					:						Any = None   ,						A					:						Tuple = "epsilon"   ,						A					:						Union[str, Any] = "linear"   ,						A					:						Tuple = False   ,						A					:						List[Any] = "linspace"   ,						A					:						Dict = 0   ,						)  ->  Any:
							if trained_betas is not None:
									lowercase_			:			List[Any]								=       torch.tensor(snake_case_   ,						dtype=torch.floataa       )
							elif beta_schedule == "linear":
									lowercase_			:			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.
									lowercase_			:			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
									lowercase_			:			List[str]								=       betas_for_alpha_bar(snake_case_       )
							else:
									raise NotImplementedError(F'''{beta_schedule} does is not implemented for {self.__class__}'''       )

							lowercase_			:			Tuple								=       1.0 - self.betas
							lowercase_			:			Union[str, Any]								=       torch.cumprod(self.alphas   ,						dim=0       )

							lowercase_			:			List[str]								=       np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5       )
							lowercase_			:			Tuple								=       np.concatenate([sigmas[::-1], [0.0]]       ).astype(np.floataa       )
							lowercase_			:			Dict								=       torch.from_numpy(snake_case_       )

							# setable values
							lowercase_			:			Optional[Any]								=       None
							lowercase_			:			Union[str, Any]								=       np.linspace(0   ,						num_train_timesteps - 1   ,						snake_case_   ,						dtype=snake_case_       )[::-1].copy()
							lowercase_			:			Union[str, Any]								=       torch.from_numpy(snake_case_       )
							lowercase_			:			int								=       False
							lowercase_			:			List[Any]								=       use_karras_sigmas




					@property
					def   A  (    self					:						int       )  ->  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   A  (    self					:						Any   ,						A					:						Optional[int]   ,						A					:						Any       )  ->  torch.FloatTensor:
							if isinstance(snake_case_   ,						torch.Tensor       ):
									lowercase_			:			int								=       timestep.to(self.timesteps.device       )
							lowercase_			:			List[str]								=       (self.timesteps == timestep).nonzero().item()
							lowercase_			:			List[str]								=       self.sigmas[step_index]

							lowercase_			:			Tuple								=       sample / ((sigma**2 + 1) ** 0.5)

							lowercase_			:			Dict								=       True
							return sample




					def   A  (    self					:						List[str]   ,						A					:						Dict   ,						A					:						Tuple = None       )  ->  Tuple:
							lowercase_			:			Any								=       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":
									lowercase_			:			Tuple								=       np.linspace(0   ,						self.config.num_train_timesteps - 1   ,						snake_case_   ,						dtype=snake_case_       )[
									    ::-1
									].copy()
							elif self.config.timestep_spacing == "leading":
									lowercase_			:			List[Any]								=       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
									lowercase_			:			Optional[int]								=       (np.arange(0   ,						snake_case_       ) * step_ratio).round()[::-1].copy().astype(snake_case_       )
									timesteps += self.config.steps_offset
							elif self.config.timestep_spacing == "trailing":
									lowercase_			:			List[str]								=       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
									lowercase_			:			Any								=       (np.arange(self.config.num_train_timesteps   ,						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\'.'''       )

							lowercase_			:			Optional[int]								=       np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5       )
							lowercase_			:			List[str]								=       np.log(snake_case_       )

							if self.config.interpolation_type == "linear":
									lowercase_			:			Union[str, Any]								=       np.interp(snake_case_   ,						np.arange(0   ,						len(snake_case_       )       )   ,						snake_case_       )
							elif self.config.interpolation_type == "log_linear":
									lowercase_			:			int								=       torch.linspace(np.log(sigmas[-1]       )   ,						np.log(sigmas[0]       )   ,						num_inference_steps + 1       ).exp()
							else:
									raise ValueError(
									    F'''{self.config.interpolation_type} is not implemented. Please specify interpolation_type to either'''
									    ''' \'linear\' or \'log_linear\''''       )

							if self.use_karras_sigmas:
									lowercase_			:			Union[str, Any]								=       self._convert_to_karras(in_sigmas=snake_case_   ,						num_inference_steps=self.num_inference_steps       )
									lowercase_			:			Optional[int]								=       np.array([self._sigma_to_t(snake_case_   ,						snake_case_       ) for sigma in sigmas]       )

							lowercase_			:			Any								=       np.concatenate([sigmas, [0.0]]       ).astype(np.floataa       )
							lowercase_			:			Dict								=       torch.from_numpy(snake_case_       ).to(device=snake_case_       )
							if str(snake_case_       ).startswith('''mps'''       ):
									# mps does not support float64
									lowercase_			:			Optional[int]								=       torch.from_numpy(snake_case_       ).to(snake_case_   ,						dtype=torch.floataa       )
							else:
									lowercase_			:			Union[str, Any]								=       torch.from_numpy(snake_case_       ).to(device=snake_case_       )




					def   A  (    self					:						List[str]   ,						A					:						Any   ,						A					:						Optional[Any]       )  ->  Union[str, Any]:
							# get log sigma
							lowercase_			:			Optional[Any]								=       np.log(snake_case_       )

							# get distribution
							lowercase_			:			Tuple								=       log_sigma - log_sigmas[:, np.newaxis]

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

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

							# interpolate sigmas
							lowercase_			:			int								=       (low - log_sigma) / (low - high)
							lowercase_			:			List[str]								=       np.clip(snake_case_   ,						0   ,						1       )

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




					def   A  (    self					:						Tuple   ,						A					:						List[str]   ,						A					:						Dict       )  ->  torch.FloatTensor:
							lowercase_			:			List[str]								=       in_sigmas[-1].item()
							lowercase_			:			List[Any]								=       in_sigmas[0].item()

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




					def   A  (    self					:						List[str]   ,						A					:						int   ,						A					:						Tuple   ,						A					:						str   ,						A					:						int = 0.0   ,						A					:						Dict = 0.0   ,						A					:						List[Any] = float('''inf'''       )   ,						A					:						List[Any] = 1.0   ,						A					:						Dict = None   ,						A					:						List[str] = True   ,						)  ->  Union[EulerDiscreteSchedulerOutput, Tuple]:

							if (
							    isinstance(snake_case_   ,						snake_case_       )
							    or isinstance(snake_case_   ,						torch.IntTensor       )
							    or isinstance(snake_case_   ,						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(snake_case_   ,						torch.Tensor       ):
									lowercase_			:			int								=       timestep.to(self.timesteps.device       )

							lowercase_			:			List[str]								=       (self.timesteps == timestep).nonzero().item()
							lowercase_			:			Optional[int]								=       self.sigmas[step_index]

							lowercase_			:			Dict								=       min(s_churn / (len(self.sigmas       ) - 1)   ,						2**0.5 - 1       ) if s_tmin <= sigma <= s_tmax else 0.0

							lowercase_			:			List[Any]								=       randn_tensor(
							    model_output.shape   ,						dtype=model_output.dtype   ,						device=model_output.device   ,						generator=snake_case_       )

							lowercase_			:			Tuple								=       noise * s_noise
							lowercase_			:			Any								=       sigma * (gamma + 1)

							if gamma > 0:
									lowercase_			:			Any								=       sample + eps * (sigma_hat**2 - sigma**2) ** 0.5

							# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
							# NOTE: "original_sample" should not be an expected prediction_type but is left in for
							# backwards compatibility
							if self.config.prediction_type == "original_sample" or self.config.prediction_type == "sample":
									lowercase_			:			Dict								=       model_output
							elif self.config.prediction_type == "epsilon":
									lowercase_			:			Any								=       sample - sigma_hat * model_output
							elif self.config.prediction_type == "v_prediction":
									# * c_out + input * c_skip
									lowercase_			:			List[str]								=       model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
							else:
									raise ValueError(
									    F'''prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`'''       )

							# 2. Convert to an ODE derivative
							lowercase_			:			str								=       (sample - pred_original_sample) / sigma_hat

							lowercase_			:			Dict								=       self.sigmas[step_index + 1] - sigma_hat

							lowercase_			:			Any								=       sample + derivative * dt

							if not return_dict:
									return (prev_sample,)

							return EulerDiscreteSchedulerOutput(prev_sample=snake_case_   ,						pred_original_sample=snake_case_       )




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

							lowercase_			:			Any								=       [(schedule_timesteps == t).nonzero().item() for t in timesteps]

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

							lowercase_			:			List[Any]								=       original_samples + noise * sigma
							return noisy_samples






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











"""simple docstring"""



from typing import TYPE_CHECKING

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


__A		:	Any      =		{
    '''configuration_markuplm''': ['''MARKUPLM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MarkupLMConfig'''],
    '''feature_extraction_markuplm''': ['''MarkupLMFeatureExtractor'''],
    '''processing_markuplm''': ['''MarkupLMProcessor'''],
    '''tokenization_markuplm''': ['''MarkupLMTokenizer'''],
}

try:
						if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__A		:	List[str]      =		['''MarkupLMTokenizerFast''']

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__A		:	Optional[int]      =		[
						    '''MARKUPLM_PRETRAINED_MODEL_ARCHIVE_LIST''',
						    '''MarkupLMForQuestionAnswering''',
						    '''MarkupLMForSequenceClassification''',
						    '''MarkupLMForTokenClassification''',
						    '''MarkupLMModel''',
						    '''MarkupLMPreTrainedModel''',
						]


if TYPE_CHECKING:
						from .configuration_markuplm import MARKUPLM_PRETRAINED_CONFIG_ARCHIVE_MAP, MarkupLMConfig
						from .feature_extraction_markuplm import MarkupLMFeatureExtractor
						from .processing_markuplm import MarkupLMProcessor
						from .tokenization_markuplm import MarkupLMTokenizer

						try:
												if not is_tokenizers_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_markuplm_fast import MarkupLMTokenizerFast

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_markuplm import (
												    MARKUPLM_PRETRAINED_MODEL_ARCHIVE_LIST,
												    MarkupLMForQuestionAnswering,
												    MarkupLMForSequenceClassification,
												    MarkupLMForTokenClassification,
												    MarkupLMModel,
												    MarkupLMPreTrainedModel,
												)


else:
						import sys

						__A		:	Optional[Any]      =		_LazyModule(__name__, globals()['''__file__'''], _import_structure)



