






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

import numpy as np
import torch
from scipy import integrate

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




@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->LMSDiscrete
class 			UpperCamelCase_				(      snake_case_    ):



	'''simple docstring'''




	lowerCAmelCase													=			42
	lowerCAmelCase													=			None






def 						__UpperCAmelCase      (					a_							,					a_=0.9_99							,					a_="cosine"							,					):
							if alpha_transform_type == "cosine":

														def alpha_bar_fn(a_):
																					return math.cos((t + 0.0_08) / 1.0_08 * math.pi / 2) ** 2

							elif alpha_transform_type == "exp":

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

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

							snake_case_           =							[]
							for i in range(a_):
														snake_case_           =							i / num_diffusion_timesteps
														snake_case_           =							(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 			UpperCamelCase_				(      snake_case_	, snake_case_    ):



	'''simple docstring'''




	lowerCAmelCase													=			[e.name for e in KarrasDiffusionSchedulers]
	lowerCAmelCase													=			1




	@register_to_config
	def __init__(	self	,      a = 10_00	,      a = 0.0_001	,      a = 0.02	,      a = "linear"	,      a = None	,      a = False	,      a = "epsilon"	,      a = "linspace"	,      a = 0	,      )	->   Union[str, Any]:
								if trained_betas is not None:
															snake_case_           =							torch.tensor(a	,      dtype=torch.floataa )
								elif beta_schedule == "linear":
															snake_case_           =							torch.linspace(a	,      a	,      a	,      dtype=torch.floataa )
								elif beta_schedule == "scaled_linear":
															# this schedule is very specific to the latent diffusion model.
															snake_case_           =							(
															    torch.linspace(beta_start**0.5	,      beta_end**0.5	,      a	,      dtype=torch.floataa ) ** 2
															)
								elif beta_schedule == "squaredcos_cap_v2":
															# Glide cosine schedule
															snake_case_           =							betas_for_alpha_bar(a )
								else:
															raise NotImplementedError(F'''{beta_schedule} does is not implemented for {self.__class__}''' )

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

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

								# setable values
								snake_case_           =							None
								snake_case_           =							use_karras_sigmas
								self.set_timesteps(a	,      a )
								snake_case_           =							[]
								snake_case_           =							False




	@property
	def 							_UpperCamelCase      (	self )	->   List[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 							_UpperCamelCase      (	self	,      a	,      a )	->   torch.FloatTensor:
								if isinstance(a	,      torch.Tensor ):
															snake_case_           =							timestep.to(self.timesteps.device )
								snake_case_           =							(self.timesteps == timestep).nonzero().item()
								snake_case_           =							self.sigmas[step_index]
								snake_case_           =							sample / ((sigma**2 + 1) ** 0.5)
								snake_case_           =							True
								return sample




	def 							_UpperCamelCase      (	self	,      a	,      a	,      a )	->   Dict:

								def lms_derivative(a ):
															snake_case_           =							1.0
															for k in range(a ):
																						if current_order == k:
																													continue
																						prod *= (tau - self.sigmas[t - k]) / (self.sigmas[t - current_order] - self.sigmas[t - k])
															return prod

								snake_case_           =							integrate.quad(a	,      self.sigmas[t]	,      self.sigmas[t + 1]	,      epsrel=1E-4 )[0]

								return integrated_coeff




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

								snake_case_           =							np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5 )
								snake_case_           =							np.log(a )
								snake_case_           =							np.interp(a	,      np.arange(0	,      len(a ) )	,      a )

								if self.use_karras_sigmas:
															snake_case_           =							self._convert_to_karras(in_sigmas=a )
															snake_case_           =							np.array([self._sigma_to_t(a	,      a ) for sigma in sigmas] )

								snake_case_           =							np.concatenate([sigmas, [0.0]] ).astype(np.floataa )

								snake_case_           =							torch.from_numpy(a ).to(device=a )
								if str(a ).startswith('mps' ):
															# mps does not support float64
															snake_case_           =							torch.from_numpy(a ).to(a	,      dtype=torch.floataa )
								else:
															snake_case_           =							torch.from_numpy(a ).to(device=a )

								snake_case_           =							[]




	def 							_UpperCamelCase      (	self	,      a	,      a )	->   List[str]:
								# get log sigma
								snake_case_           =							np.log(a )

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

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

								snake_case_           =							log_sigmas[low_idx]
								snake_case_           =							log_sigmas[high_idx]

								# interpolate sigmas
								snake_case_           =							(low - log_sigma) / (low - high)
								snake_case_           =							np.clip(a	,      0	,      1 )

								# transform interpolation to time range
								snake_case_           =							(1 - w) * low_idx + w * high_idx
								snake_case_           =							t.reshape(sigma.shape )
								return t




	def 							_UpperCamelCase      (	self	,      a )	->   torch.FloatTensor:
								snake_case_           =							in_sigmas[-1].item()
								snake_case_           =							in_sigmas[0].item()

								snake_case_           =							7.0  # 7.0 is the value used in the paper
								snake_case_           =							np.linspace(0	,      1	,      self.num_inference_steps )
								snake_case_           =							sigma_min ** (1 / rho)
								snake_case_           =							sigma_max ** (1 / rho)
								snake_case_           =							(max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
								return sigmas




	def 							_UpperCamelCase      (	self	,      a	,      a	,      a	,      a = 4	,      a = True	,      )	->   Union[LMSDiscreteSchedulerOutput, Tuple]:
								if not self.is_scale_input_called:
															warnings.warn(
															    '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 ):
															snake_case_           =							timestep.to(self.timesteps.device )
								snake_case_           =							(self.timesteps == timestep).nonzero().item()
								snake_case_           =							self.sigmas[step_index]

								# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
								if self.config.prediction_type == "epsilon":
															snake_case_           =							sample - sigma * model_output
								elif self.config.prediction_type == "v_prediction":
															# * c_out + input * c_skip
															snake_case_           =							model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
								elif self.config.prediction_type == "sample":
															snake_case_           =							model_output
								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
								snake_case_           =							(sample - pred_original_sample) / sigma
								self.derivatives.append(a )
								if len(self.derivatives ) > order:
															self.derivatives.pop(0 )

								# 3. Compute linear multistep coefficients
								snake_case_           =							min(step_index + 1	,      a )
								snake_case_           =							[self.get_lms_coefficient(a	,      a	,      a ) for curr_order in range(a )]

								# 4. Compute previous sample based on the derivatives path
								snake_case_           =							sample + sum(
								    coeff * derivative for coeff, derivative in zip(a	,      reversed(self.derivatives ) ) )

								if not return_dict:
															return (prev_sample,)

								return LMSDiscreteSchedulerOutput(prev_sample=a	,      pred_original_sample=a )




	def 							_UpperCamelCase      (	self	,      a	,      a	,      a	,      )	->   torch.FloatTensor:
								# Make sure sigmas and timesteps have the same device and dtype as original_samples
								snake_case_           =							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
															snake_case_           =							self.timesteps.to(original_samples.device	,      dtype=torch.floataa )
															snake_case_           =							timesteps.to(original_samples.device	,      dtype=torch.floataa )
								else:
															snake_case_           =							self.timesteps.to(original_samples.device )
															snake_case_           =							timesteps.to(original_samples.device )

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

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

								snake_case_           =							original_samples + noise * sigma
								return noisy_samples






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













from typing import TYPE_CHECKING

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


lowercase  							= {
    "configuration_deformable_detr": ["DEFORMABLE_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP", "DeformableDetrConfig"],
}

try:
	if not is_vision_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowercase  							= ["DeformableDetrFeatureExtractor"]
	lowercase  							= ["DeformableDetrImageProcessor"]

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowercase  							= [
	    "DEFORMABLE_DETR_PRETRAINED_MODEL_ARCHIVE_LIST",
	    "DeformableDetrForObjectDetection",
	    "DeformableDetrModel",
	    "DeformableDetrPreTrainedModel",
	]


if TYPE_CHECKING:
	from .configuration_deformable_detr import DEFORMABLE_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP, DeformableDetrConfig

	try:
		if not is_vision_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .feature_extraction_deformable_detr import DeformableDetrFeatureExtractor
		from .image_processing_deformable_detr import DeformableDetrImageProcessor

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_deformable_detr import (
		    DEFORMABLE_DETR_PRETRAINED_MODEL_ARCHIVE_LIST,
		    DeformableDetrForObjectDetection,
		    DeformableDetrModel,
		    DeformableDetrPreTrainedModel,
		)

else:
	import sys

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





