






import math
from collections import defaultdict
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import randn_tensor
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput





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													=			2




	@register_to_config
	def __init__(	self	,      a = 10_00	,      a = 0.00_085	,      a = 0.012	,      a = "linear"	,      a = None	,      a = "epsilon"	,      a = "linspace"	,      a = 0	,      )	->   Dict:
								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 )

								#  set all values
								self.set_timesteps(a	,      a	,      a )




	def 							_UpperCamelCase      (	self	,      a	,      a=None )	->   Any:
								if schedule_timesteps is None:
															snake_case_           =							self.timesteps

								snake_case_           =							(schedule_timesteps == timestep).nonzero()

								# The sigma index that is taken for the **very** first `step`
								# is always the second index (or the last index if there is only 1)
								# This way we can ensure we don't accidentally skip a sigma in
								# case we start in the middle of the denoising schedule (e.g. for image-to-image)
								if len(self._index_counter ) == 0:
															snake_case_           =							1 if len(a ) > 1 else 0
								else:
															snake_case_           =							timestep.cpu().item() if torch.is_tensor(a ) else timestep
															snake_case_           =							self._index_counter[timestep_int]

								return indices[pos].item()




	@property
	def 							_UpperCamelCase      (	self )	->   int:
								# 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:
								snake_case_           =							self.index_for_timestep(a )

								if self.state_in_first_order:
															snake_case_           =							self.sigmas[step_index]
								else:
															snake_case_           =							self.sigmas_interpol[step_index - 1]

								snake_case_           =							sample / ((sigma**2 + 1) ** 0.5)
								return sample




	def 							_UpperCamelCase      (	self	,      a	,      a = None	,      a = None	,      )	->   List[str]:
								snake_case_           =							num_inference_steps

								snake_case_           =							num_train_timesteps or self.config.num_train_timesteps

								# "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	,      num_train_timesteps - 1	,      a	,      dtype=a )[::-1].copy()
								elif self.config.timestep_spacing == "leading":
															snake_case_           =							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_           =							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(a	,      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_           =							torch.from_numpy(np.log(a ) ).to(a )

								snake_case_           =							np.interp(a	,      np.arange(0	,      len(a ) )	,      a )
								snake_case_           =							np.concatenate([sigmas, [0.0]] ).astype(np.floataa )
								snake_case_           =							torch.from_numpy(a ).to(device=a )

								# compute up and down sigmas
								snake_case_           =							sigmas.roll(-1 )
								snake_case_           =							0.0
								snake_case_           =							(sigmas_next**2 * (sigmas**2 - sigmas_next**2) / sigmas**2) ** 0.5
								snake_case_           =							(sigmas_next**2 - sigmas_up**2) ** 0.5
								snake_case_           =							0.0

								# compute interpolated sigmas
								snake_case_           =							sigmas.log().lerp(sigmas_down.log()	,      0.5 ).exp()
								snake_case_           =							0.0

								# set sigmas
								snake_case_           =							torch.cat([sigmas[:1], sigmas[1:].repeat_interleave(2 ), sigmas[-1:]] )
								snake_case_           =							torch.cat(
								    [sigmas_interpol[:1], sigmas_interpol[1:].repeat_interleave(2 ), sigmas_interpol[-1:]] )
								snake_case_           =							torch.cat([sigmas_up[:1], sigmas_up[1:].repeat_interleave(2 ), sigmas_up[-1:]] )
								snake_case_           =							torch.cat([sigmas_down[:1], sigmas_down[1:].repeat_interleave(2 ), sigmas_down[-1:]] )

								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(a )

								snake_case_           =							self.sigma_to_t(a ).to(a	,      dtype=timesteps.dtype )
								snake_case_           =							torch.stack((timesteps_interpol[:-2, None], timesteps[1:, None])	,      dim=-1 ).flatten()

								snake_case_           =							torch.cat([timesteps[:1], interleaved_timesteps] )

								snake_case_           =							None

								# for exp beta schedules, such as the one for `pipeline_shap_e.py`
								# we need an index counter
								snake_case_           =							defaultdict(a )




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

								# get distribution
								snake_case_           =							log_sigma - self.log_sigmas[:, None]

								# get sigmas range
								snake_case_           =							dists.ge(0 ).cumsum(dim=0 ).argmax(dim=0 ).clamp(max=self.log_sigmas.shape[0] - 2 )
								snake_case_           =							low_idx + 1

								snake_case_           =							self.log_sigmas[low_idx]
								snake_case_           =							self.log_sigmas[high_idx]

								# interpolate sigmas
								snake_case_           =							(low - log_sigma) / (low - high)
								snake_case_           =							w.clamp(0	,      1 )

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




	@property
	def 							_UpperCamelCase      (	self )	->   Any:
								return self.sample is None




	def 							_UpperCamelCase      (	self	,      a	,      a	,      a	,      a = None	,      a = True	,      )	->   Union[SchedulerOutput, Tuple]:
								snake_case_           =							self.index_for_timestep(a )

								# advance index counter by 1
								snake_case_           =							timestep.cpu().item() if torch.is_tensor(a ) else timestep
								self._index_counter[timestep_int] += 1

								if self.state_in_first_order:
															snake_case_           =							self.sigmas[step_index]
															snake_case_           =							self.sigmas_interpol[step_index]
															snake_case_           =							self.sigmas_up[step_index]
															snake_case_           =							self.sigmas_down[step_index - 1]
								else:
															# 2nd order / KPDM2's method
															snake_case_           =							self.sigmas[step_index - 1]
															snake_case_           =							self.sigmas_interpol[step_index - 1]
															snake_case_           =							self.sigmas_up[step_index - 1]
															snake_case_           =							self.sigmas_down[step_index - 1]

								# currently only gamma=0 is supported. This usually works best anyways.
								# We can support gamma in the future but then need to scale the timestep before
								# passing it to the model which requires a change in API
								snake_case_           =							0
								snake_case_           =							sigma * (gamma + 1)  # Note: sigma_hat == sigma for now

								snake_case_           =							model_output.device
								snake_case_           =							randn_tensor(model_output.shape	,      dtype=model_output.dtype	,      device=a	,      generator=a )

								# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
								if self.config.prediction_type == "epsilon":
															snake_case_           =							sigma_hat if self.state_in_first_order else sigma_interpol
															snake_case_           =							sample - sigma_input * model_output
								elif self.config.prediction_type == "v_prediction":
															snake_case_           =							sigma_hat if self.state_in_first_order else sigma_interpol
															snake_case_           =							model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
															    sample / (sigma_input**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`''' )

								if self.state_in_first_order:
															# 2. Convert to an ODE derivative for 1st order
															snake_case_           =							(sample - pred_original_sample) / sigma_hat
															# 3. delta timestep
															snake_case_           =							sigma_interpol - sigma_hat

															# store for 2nd order step
															snake_case_           =							sample
															snake_case_           =							dt
															snake_case_           =							sample + derivative * dt
								else:
															# DPM-Solver-2
															# 2. Convert to an ODE derivative for 2nd order
															snake_case_           =							(sample - pred_original_sample) / sigma_interpol
															# 3. delta timestep
															snake_case_           =							sigma_down - sigma_hat

															snake_case_           =							self.sample
															snake_case_           =							None

															snake_case_           =							sample + derivative * dt
															snake_case_           =							prev_sample + noise * sigma_up

								if not return_dict:
															return (prev_sample,)

								return SchedulerOutput(prev_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_           =							[self.index_for_timestep(a	,      a ) 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 )	->   List[str]:
								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__)





