






'''simple docstring'''

from typing import TYPE_CHECKING

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


A:   Any						=					{
    "configuration_deta": ["DETA_PRETRAINED_CONFIG_ARCHIVE_MAP", "DetaConfig"],
}

try:
						if not is_vision_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						A:   Optional[Any]						=					["DetaImageProcessor"]

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						A:   Union[str, Any]						=					[
						    "DETA_PRETRAINED_MODEL_ARCHIVE_LIST",
						    "DetaForObjectDetection",
						    "DetaModel",
						    "DetaPreTrainedModel",
						]


if TYPE_CHECKING:
						from .configuration_deta import DETA_PRETRAINED_CONFIG_ARCHIVE_MAP, DetaConfig

						try:
												if not is_vision_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .image_processing_deta import DetaImageProcessor

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_deta import (
												    DETA_PRETRAINED_MODEL_ARCHIVE_LIST,
												    DetaForObjectDetection,
												    DetaModel,
												    DetaPreTrainedModel,
												)

else:
						import sys

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








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






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







	"""simple docstring"""



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






def 						_UpperCAmelCase     (	a		:						Any    ,      a		:						Dict=0.9_99    ,      a		:						Optional[int]="cosine"    ,      )      ->      Tuple:





						"""simple docstring"""
						if alpha_transform_type == "cosine":

												def alpha_bar_fn(a		:						Optional[int]				):
																		return math.cos((t + 0.0_08) / 1.0_08 * math.pi / 2				) ** 2

						elif alpha_transform_type == "exp":

												def alpha_bar_fn(a		:						Optional[Any]				):
																		return math.exp(t * -12.0				)

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

						lowercase_   : Optional[int]   	=      []
						for i in range(a				):
												lowercase_   : List[Any]   	=      i / num_diffusion_timesteps
												lowercase_   : Union[str, Any]   	=      (i + 1) / num_diffusion_timesteps
												betas.append(min(1 - alpha_bar_fn(a				) / alpha_bar_fn(a				)    ,      a				)				)
						return torch.tensor(a    ,      dtype=torch.floataa				)






class 				__magic_name__  (						UpperCAmelCase_,    UpperCAmelCase_					):







	"""simple docstring"""



	SCREAMING_SNAKE_CASE_       :   Union[str, Any]   =	[e.name for e in KarrasDiffusionSchedulers]
	SCREAMING_SNAKE_CASE_       :   Tuple   =	1
	@register_to_config
	def __init__(				self	,   _lowercase = 1000	,   _lowercase = 0.00_01	,   _lowercase = 0.02	,   _lowercase = "linear"	,   _lowercase = None	,   _lowercase = "epsilon"	,   _lowercase = "linspace"	,   _lowercase = 0	,   )    ->	List[str]:
							if trained_betas is not None:
													lowercase_   : Any   	=      torch.tensor(_lowercase	,   dtype=torch.floataa							)
							elif beta_schedule == "linear":
													lowercase_   : Optional[int]   	=      torch.linspace(_lowercase	,   _lowercase	,   _lowercase	,   dtype=torch.floataa							)
							elif beta_schedule == "scaled_linear":
													# this schedule is very specific to the latent diffusion model.
													lowercase_   : Dict   	=      (
													    torch.linspace(beta_start**0.5	,   beta_end**0.5	,   _lowercase	,   dtype=torch.floataa							) ** 2
													)
							elif beta_schedule == "squaredcos_cap_v2":
													# Glide cosine schedule
													lowercase_   : Any   	=      betas_for_alpha_bar(_lowercase							)
							else:
													raise NotImplementedError(f"{beta_schedule} does is not implemented for {self.__class__}"							)

							lowercase_   : Dict   	=      1.0 - self.betas
							lowercase_   : int   	=      torch.cumprod(self.alphas	,   dim=0							)

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

							# setable values
							lowercase_   : Union[str, Any]   	=      None
							lowercase_   : Optional[Any]   	=      np.linspace(0	,   num_train_timesteps - 1	,   _lowercase	,   dtype=_lowercase							)[::-1].copy()
							lowercase_   : Dict   	=      torch.from_numpy(_lowercase							)
							lowercase_   : int   	=      False
	@property
	def 							lowerCamelCase__		(				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 							lowerCamelCase__		(				self	,   _lowercase	,   _lowercase							)    ->	torch.FloatTensor:
							if isinstance(_lowercase	,   torch.Tensor							):
													lowercase_   : List[str]   	=      timestep.to(self.timesteps.device							)
							lowercase_   : List[str]   	=      (self.timesteps == timestep).nonzero().item()
							lowercase_   : Any   	=      self.sigmas[step_index]
							lowercase_   : Tuple   	=      sample / ((sigma**2 + 1) ** 0.5)
							lowercase_   : List[str]   	=      True
							return sample
	def 							lowerCamelCase__		(				self	,   _lowercase	,   _lowercase = 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	,   _lowercase	,   dtype=_lowercase							)[
													    ::-1
													].copy()
							elif self.config.timestep_spacing == "leading":
													lowercase_   : int   	=      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	,   _lowercase							) * step_ratio).round()[::-1].copy().astype(_lowercase							)
													timesteps += self.config.steps_offset
							elif self.config.timestep_spacing == "trailing":
													lowercase_   : Optional[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_   : Dict   	=      (np.arange(self.config.num_train_timesteps	,   0	,   -step_ratio							)).round().copy().astype(_lowercase							)
													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_   : str   	=      np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5							)
							lowercase_   : Dict   	=      np.interp(_lowercase	,   np.arange(0	,   len(_lowercase							)							)	,   _lowercase							)
							lowercase_   : Any   	=      np.concatenate([sigmas, [0.0]]							).astype(np.floataa							)
							lowercase_   : Dict   	=      torch.from_numpy(_lowercase							).to(device=_lowercase							)
							if str(_lowercase							).startswith('mps'							):
													# mps does not support float64
													lowercase_   : Union[str, Any]   	=      torch.from_numpy(_lowercase							).to(_lowercase	,   dtype=torch.floataa							)
							else:
													lowercase_   : Optional[int]   	=      torch.from_numpy(_lowercase							).to(device=_lowercase							)
	def 							lowerCamelCase__		(				self	,   _lowercase	,   _lowercase	,   _lowercase	,   _lowercase = None	,   _lowercase = True	,   )    ->	Union[EulerAncestralDiscreteSchedulerOutput, Tuple]:

							if (
							    isinstance(_lowercase	,   _lowercase							)
							    or isinstance(_lowercase	,   torch.IntTensor							)
							    or isinstance(_lowercase	,   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(_lowercase	,   torch.Tensor							):
													lowercase_   : Optional[int]   	=      timestep.to(self.timesteps.device							)

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

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

							lowercase_   : Tuple   	=      self.sigmas[step_index]
							lowercase_   : str   	=      self.sigmas[step_index + 1]
							lowercase_   : Dict   	=      (sigma_to**2 * (sigma_from**2 - sigma_to**2) / sigma_from**2) ** 0.5
							lowercase_   : Optional[Any]   	=      (sigma_to**2 - sigma_up**2) ** 0.5

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

							lowercase_   : Tuple   	=      sigma_down - sigma

							lowercase_   : str   	=      sample + derivative * dt

							lowercase_   : Optional[int]   	=      model_output.device
							lowercase_   : Union[str, Any]   	=      randn_tensor(model_output.shape	,   dtype=model_output.dtype	,   device=_lowercase	,   generator=_lowercase							)

							lowercase_   : int   	=      prev_sample + noise * sigma_up

							if not return_dict:
													return (prev_sample,)

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

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

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

							lowercase_   : str   	=      original_samples + noise * sigma
							return noisy_samples







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