
"""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


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







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



						'''simple docstring'''


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






def  _snake_case			(						snake_case__				:       Any		, snake_case__				:       Optional[Any]=0.999		, snake_case__				:       Any="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__				:       Any       ):
			return math.exp(t * -12.0       )

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

	A  				=				[]
	for i in range(snake_case__       ):
		A  				=				i / num_diffusion_timesteps
		A  				=				(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 			lowerCAmelCase_			( _lowercase						,   _lowercase      ):



						'''simple docstring'''


						_lowerCamelCase:      Optional[Any] =      [e.name for e in KarrasDiffusionSchedulers]
						_lowerCamelCase:      Any =      1


						@register_to_config
						def __init__(	self       : Any					,A_       : int = 1000					,A_       : float = 0.00_01					,A_       : float = 0.02					,A_       : str = "linear"					,A_       : Optional[Union[np.ndarray, List[float]]] = None					,A_       : str = "epsilon"					,A_       : str = "linspace"					,A_       : int = 0					,)       ->				int:
							if trained_betas is not None:
								A  				=				torch.tensor(A_					,dtype=torch.floataa )
							elif beta_schedule == "linear":
								A  				=				torch.linspace(A_					,A_					,A_					,dtype=torch.floataa )
							elif beta_schedule == "scaled_linear":
								# this schedule is very specific to the latent diffusion model.
								A  				=				(
								    torch.linspace(beta_start**0.5					,beta_end**0.5					,A_					,dtype=torch.floataa ) ** 2
								)
							elif beta_schedule == "squaredcos_cap_v2":
								# Glide cosine schedule
								A  				=				betas_for_alpha_bar(A_ )
							else:
								raise NotImplementedError(F'{beta_schedule} does is not implemented for {self.__class__}' )

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

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

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


						@property
						def   _SCREAMING_SNAKE_CASE (	self       : Optional[Any] )       ->				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   _SCREAMING_SNAKE_CASE (	self       : int					,A_       : torch.FloatTensor					,A_       : Union[float, torch.FloatTensor] )       ->				torch.FloatTensor:
							if isinstance(A_					,torch.Tensor ):
								A  				=				timestep.to(self.timesteps.device )
							A  				=				(self.timesteps == timestep).nonzero().item()
							A  				=				self.sigmas[step_index]
							A  				=				sample / ((sigma**2 + 1) ** 0.5)
							A  				=				True
							return sample


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

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


						def   _SCREAMING_SNAKE_CASE (	self       : int					,A_       : torch.FloatTensor					,A_       : Union[float, torch.FloatTensor]					,A_       : torch.FloatTensor					,A_       : Optional[torch.Generator] = None					,A_       : bool = True					,)       ->				Union[EulerAncestralDiscreteSchedulerOutput, Tuple]:

							if (
							    isinstance(A_					,A_ )
							    or isinstance(A_					,torch.IntTensor )
							    or isinstance(A_					,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(A_					,torch.Tensor ):
								A  				=				timestep.to(self.timesteps.device )

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

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

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

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

							A  				=				sigma_down - sigma

							A  				=				sample + derivative * dt

							A  				=				model_output.device
							A  				=				randn_tensor(model_output.shape					,dtype=model_output.dtype					,device=A_					,generator=A_ )

							A  				=				prev_sample + noise * sigma_up

							if not return_dict:
								return (prev_sample,)

							return EulerAncestralDiscreteSchedulerOutput(
							    prev_sample=A_					,pred_original_sample=A_ )


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

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

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

							A  				=				original_samples + noise * sigma
							return noisy_samples




						def __len__(	self       : Optional[Any] )       ->				Tuple:
							return self.config.num_train_timesteps

"""simple docstring"""


from typing import TYPE_CHECKING

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


_lowercase											=			{'''configuration_vilt''': ['''VILT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ViltConfig''']}

try:
		if not is_vision_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_lowercase											=			['''ViltFeatureExtractor''']
		_lowercase											=			['''ViltImageProcessor''']
		_lowercase											=			['''ViltProcessor''']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_lowercase											=			[
		    '''VILT_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''ViltForImageAndTextRetrieval''',
		    '''ViltForImagesAndTextClassification''',
		    '''ViltForTokenClassification''',
		    '''ViltForMaskedLM''',
		    '''ViltForQuestionAnswering''',
		    '''ViltLayer''',
		    '''ViltModel''',
		    '''ViltPreTrainedModel''',
		]


if TYPE_CHECKING:
		from .configuration_vilt import VILT_PRETRAINED_CONFIG_ARCHIVE_MAP, ViltConfig

		try:
				if not is_vision_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .feature_extraction_vilt import ViltFeatureExtractor
				from .image_processing_vilt import ViltImageProcessor
				from .processing_vilt import ViltProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_vilt import (
				    VILT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    ViltForImageAndTextRetrieval,
				    ViltForImagesAndTextClassification,
				    ViltForMaskedLM,
				    ViltForQuestionAnswering,
				    ViltForTokenClassification,
				    ViltLayer,
				    ViltModel,
				    ViltPreTrainedModel,
				)


else:
		import sys

		_lowercase											=			_LazyModule(__name__, globals()['''__file__'''], _import_structure)