





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 SchedulerMixin


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




@dataclass
class 	_UpperCAmelCase						( _lowercase       ):
					__A        = 42




class 	_UpperCAmelCase						( _lowercase			,							_lowercase       ):
					__A        = 1




					@register_to_config
					def __init__(    self     ,   __lowerCAmelCase = 40     ,   __lowerCAmelCase = 0.0_0_2     ,   __lowerCAmelCase = 8_0.0     ,   __lowerCAmelCase = 0.5     ,   __lowerCAmelCase = 1.0     ,   __lowerCAmelCase = 7.0     ,   __lowerCAmelCase = True     ,   )    ->	List[Any]:
							# standard deviation of the initial noise distribution
							UpperCamelCase__								=						sigma_max

							UpperCamelCase__								=						np.linspace(0     ,   1     ,   __lowerCAmelCase							)
							UpperCamelCase__								=						self._convert_to_karras(__lowerCAmelCase							)
							UpperCamelCase__								=						self.sigma_to_t(__lowerCAmelCase							)

							# setable values
							UpperCamelCase__								=						None
							UpperCamelCase__								=						torch.from_numpy(__lowerCAmelCase							)
							UpperCamelCase__								=						torch.from_numpy(__lowerCAmelCase							)
							UpperCamelCase__								=						False
							UpperCamelCase__								=						False




					def 						snake_case_		(    self     ,   __lowerCAmelCase     ,   __lowerCAmelCase=None							)    ->	List[Any]:
							if schedule_timesteps is None:
									UpperCamelCase__								=						self.timesteps

							UpperCamelCase__								=						(schedule_timesteps == timestep).nonzero()
							return indices.item()




					def 						snake_case_		(    self     ,   __lowerCAmelCase     ,   __lowerCAmelCase							)    ->	torch.FloatTensor:
							# Get sigma corresponding to timestep
							if isinstance(__lowerCAmelCase     ,   torch.Tensor							):
									UpperCamelCase__								=						timestep.to(self.timesteps.device							)
							UpperCamelCase__								=						self.index_for_timestep(__lowerCAmelCase							)
							UpperCamelCase__								=						self.sigmas[step_idx]

							UpperCamelCase__								=						sample / ((sigma**2 + self.config.sigma_data**2) ** 0.5)

							UpperCamelCase__								=						True
							return sample




					def 						snake_case_		(    self     ,   __lowerCAmelCase							)    ->	Tuple:
							if not isinstance(__lowerCAmelCase     ,   np.ndarray							):
									UpperCamelCase__								=						np.array(__lowerCAmelCase     ,   dtype=np.floataa							)

							UpperCamelCase__								=						1_000 * 0.2_5 * np.log(sigmas + 1E-4_4							)

							return timesteps




					def 						snake_case_		(    self     ,   __lowerCAmelCase = None     ,   __lowerCAmelCase = None     ,   __lowerCAmelCase = None     ,   )    ->	str:
							if num_inference_steps is None and timesteps is None:
									raise ValueError("""Exactly one of `num_inference_steps` or `timesteps` must be supplied."""							)

							if num_inference_steps is not None and timesteps is not None:
									raise ValueError("""Can only pass one of `num_inference_steps` or `timesteps`."""							)

							# Follow DDPMScheduler custom timesteps logic
							if timesteps is not None:
									for i in range(1     ,   len(__lowerCAmelCase							)							):
											if timesteps[i] >= timesteps[i - 1]:
													raise ValueError("""`timesteps` must be in descending order."""							)

									if timesteps[0] >= self.config.num_train_timesteps:
											raise ValueError(
											    F"""`timesteps` must start before `self.config.train_timesteps`:"""
											    F""" {self.config.num_train_timesteps}."""							)

									UpperCamelCase__								=						np.array(__lowerCAmelCase     ,   dtype=np.intaa							)
									UpperCamelCase__								=						True
							else:
									if num_inference_steps > self.config.num_train_timesteps:
											raise ValueError(
											    F"""`num_inference_steps`: {num_inference_steps} cannot be larger than `self.config.train_timesteps`:"""
											    F""" {self.config.num_train_timesteps} as the unet model trained with this scheduler can only handle"""
											    F""" maximal {self.config.num_train_timesteps} timesteps."""							)

									UpperCamelCase__								=						num_inference_steps

									UpperCamelCase__								=						self.config.num_train_timesteps // self.num_inference_steps
									UpperCamelCase__								=						(np.arange(0     ,   __lowerCAmelCase							) * step_ratio).round()[::-1].copy().astype(np.intaa							)
									UpperCamelCase__								=						False

							# Map timesteps to Karras sigmas directly for multistep sampling
							# See https://github.com/openai/consistency_models/blob/main/cm/karras_diffusion.py#L675
							UpperCamelCase__								=						self.config.num_train_timesteps
							UpperCamelCase__								=						timesteps[::-1].copy()
							UpperCamelCase__								=						ramp / (num_train_timesteps - 1)
							UpperCamelCase__								=						self._convert_to_karras(__lowerCAmelCase							)
							UpperCamelCase__								=						self.sigma_to_t(__lowerCAmelCase							)

							UpperCamelCase__								=						np.concatenate([sigmas, [self.sigma_min]]							).astype(np.floataa							)
							UpperCamelCase__								=						torch.from_numpy(__lowerCAmelCase							).to(device=__lowerCAmelCase							)

							if str(__lowerCAmelCase							).startswith("""mps"""							):
									# mps does not support float64
									UpperCamelCase__								=						torch.from_numpy(__lowerCAmelCase							).to(__lowerCAmelCase     ,   dtype=torch.floataa							)
							else:
									UpperCamelCase__								=						torch.from_numpy(__lowerCAmelCase							).to(device=__lowerCAmelCase							)




					def 						snake_case_		(    self     ,   __lowerCAmelCase							)    ->	Any:
							UpperCamelCase__								=						self.config.sigma_min
							UpperCamelCase__								=						self.config.sigma_max

							UpperCamelCase__								=						self.config.rho
							UpperCamelCase__								=						sigma_min ** (1 / rho)
							UpperCamelCase__								=						sigma_max ** (1 / rho)
							UpperCamelCase__								=						(max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
							return sigmas




					def 						snake_case_		(    self     ,   __lowerCAmelCase							)    ->	str:
							UpperCamelCase__								=						self.config.sigma_data

							UpperCamelCase__								=						sigma_data**2 / (sigma**2 + sigma_data**2)
							UpperCamelCase__								=						sigma * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
							return c_skip, c_out




					def 						snake_case_		(    self     ,   __lowerCAmelCase							)    ->	Tuple:
							UpperCamelCase__								=						self.config.sigma_min
							UpperCamelCase__								=						self.config.sigma_data

							UpperCamelCase__								=						sigma_data**2 / ((sigma - sigma_min) ** 2 + sigma_data**2)
							UpperCamelCase__								=						(sigma - sigma_min) * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
							return c_skip, c_out




					def 						snake_case_		(    self     ,   __lowerCAmelCase     ,   __lowerCAmelCase     ,   __lowerCAmelCase     ,   __lowerCAmelCase = None     ,   __lowerCAmelCase = True     ,   )    ->	Union[CMStochasticIterativeSchedulerOutput, Tuple]:

							if (
							    isinstance(__lowerCAmelCase     ,   __lowerCAmelCase							)
							    or isinstance(__lowerCAmelCase     ,   torch.IntTensor							)
							    or isinstance(__lowerCAmelCase     ,   torch.LongTensor							)
							):
									raise ValueError(
									    (
									        """Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to"""
									        F""" `{self.__class__}.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(__lowerCAmelCase     ,   torch.Tensor							):
									UpperCamelCase__								=						timestep.to(self.timesteps.device							)

							UpperCamelCase__								=						self.config.sigma_min
							UpperCamelCase__								=						self.config.sigma_max

							UpperCamelCase__								=						self.index_for_timestep(__lowerCAmelCase							)

							# sigma_next corresponds to next_t in original implementation
							UpperCamelCase__								=						self.sigmas[step_index]
							if step_index + 1 < self.config.num_train_timesteps:
									UpperCamelCase__								=						self.sigmas[step_index + 1]
							else:
									# Set sigma_next to sigma_min
									UpperCamelCase__								=						self.sigmas[-1]

							# Get scalings for boundary conditions
							UpperCamelCase__	,      UpperCamelCase__								=						self.get_scalings_for_boundary_condition(__lowerCAmelCase							)

							# 1. Denoise model output using boundary conditions
							UpperCamelCase__								=						c_out * model_output + c_skip * sample
							if self.config.clip_denoised:
									UpperCamelCase__								=						denoised.clamp(-1     ,   1							)

							# 2. Sample z ~ N(0, s_noise^2 * I)
							# Noise is not used for onestep sampling.
							if len(self.timesteps							) > 1:
									UpperCamelCase__								=						randn_tensor(
									    model_output.shape     ,   dtype=model_output.dtype     ,   device=model_output.device     ,   generator=__lowerCAmelCase							)
							else:
									UpperCamelCase__								=						torch.zeros_like(__lowerCAmelCase							)
							UpperCamelCase__								=						noise * self.config.s_noise

							UpperCamelCase__								=						sigma_next.clamp(min=__lowerCAmelCase     ,   max=__lowerCAmelCase							)

							# 3. Return noisy sample
							# tau = sigma_hat, eps = sigma_min
							UpperCamelCase__								=						denoised + z * (sigma_hat**2 - sigma_min**2) ** 0.5

							if not return_dict:
									return (prev_sample,)

							return CMStochasticIterativeSchedulerOutput(prev_sample=__lowerCAmelCase							)




					def 						snake_case_		(    self     ,   __lowerCAmelCase     ,   __lowerCAmelCase     ,   __lowerCAmelCase     ,   )    ->	torch.FloatTensor:
							# Make sure sigmas and timesteps have the same device and dtype as original_samples
							UpperCamelCase__								=						self.sigmas.to(device=original_samples.device     ,   dtype=original_samples.dtype							)
							if original_samples.device.type == "mps" and torch.is_floating_point(__lowerCAmelCase							):
									# mps does not support float64
									UpperCamelCase__								=						self.timesteps.to(original_samples.device     ,   dtype=torch.floataa							)
									UpperCamelCase__								=						timesteps.to(original_samples.device     ,   dtype=torch.floataa							)
							else:
									UpperCamelCase__								=						self.timesteps.to(original_samples.device							)
									UpperCamelCase__								=						timesteps.to(original_samples.device							)

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

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

							UpperCamelCase__								=						original_samples + noise * sigma
							return noisy_samples




					def __len__(    self							)    ->	Union[str, Any]:
							return self.config.num_train_timesteps












from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__lowerCAmelCase 	=      {"configuration_visual_bert": ["VISUAL_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "VisualBertConfig"]}

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__lowerCAmelCase 	=      [
				    "VISUAL_BERT_PRETRAINED_MODEL_ARCHIVE_LIST",
				    "VisualBertForMultipleChoice",
				    "VisualBertForPreTraining",
				    "VisualBertForQuestionAnswering",
				    "VisualBertForRegionToPhraseAlignment",
				    "VisualBertForVisualReasoning",
				    "VisualBertLayer",
				    "VisualBertModel",
				    "VisualBertPreTrainedModel",
				]


if TYPE_CHECKING:
				from .configuration_visual_bert import VISUAL_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, VisualBertConfig

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_visual_bert import (
								    VISUAL_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
								    VisualBertForMultipleChoice,
								    VisualBertForPreTraining,
								    VisualBertForQuestionAnswering,
								    VisualBertForRegionToPhraseAlignment,
								    VisualBertForVisualReasoning,
								    VisualBertLayer,
								    VisualBertModel,
								    VisualBertPreTrainedModel,
								)


else:
				import sys

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





