






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 .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput




def      lowercase__      (     __snake_case    :							Optional[Any]     ,       __snake_case    :							List[str]=0.999     ,       __snake_case    :							str="cosine"     ,       ):


					'''simple docstring'''


					if alpha_transform_type == "cosine":

										def alpha_bar_fn(__snake_case    :							Union[str, Any]  ):
															return math.cos((t + 0.008) / 1.008 * math.pi / 2  ) ** 2

					elif alpha_transform_type == "exp":

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

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

					UpperCAmelCase_    :      Dict            =						[]
					for i in range(__snake_case  ):
										UpperCAmelCase_    :      Dict            =						i / num_diffusion_timesteps
										UpperCAmelCase_    :      List[str]            =						(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      (_snake_case ,  _snake_case			):







		'''simple docstring'''



		_snake_case    :    Union[str, Any]       						=  [e.name for e in KarrasDiffusionSchedulers]
		_snake_case    :    int       						=  2



		@register_to_config
		def __init__(							self				,		_UpperCamelCase = 1_0_0_0				,		_UpperCamelCase = 0.0_00_85				,		_UpperCamelCase = 0.0_12				,		_UpperCamelCase = "linear"				,		_UpperCamelCase = None				,		_UpperCamelCase = "epsilon"				,		_UpperCamelCase = "linspace"				,		_UpperCamelCase = 0				,		)	->							Optional[Any]:
							if trained_betas is not None:
												UpperCAmelCase_    :      Any            =						torch.tensor(_UpperCamelCase				,		dtype=torch.floataa   )
							elif beta_schedule == "linear":
												UpperCAmelCase_    :      Optional[int]            =						torch.linspace(_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase				,		dtype=torch.floataa   )
							elif beta_schedule == "scaled_linear":
												# this schedule is very specific to the latent diffusion model.
												UpperCAmelCase_    :      str            =						(
												    torch.linspace(beta_start**0.5				,		beta_end**0.5				,		_UpperCamelCase				,		dtype=torch.floataa   ) ** 2
												)
							elif beta_schedule == "squaredcos_cap_v2":
												# Glide cosine schedule
												UpperCAmelCase_    :      List[str]            =						betas_for_alpha_bar(_UpperCamelCase   )
							else:
												raise NotImplementedError(f"{beta_schedule} does is not implemented for {self.__class__}"   )

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

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



		def 					__UpperCAmelCase			(							self				,		_UpperCamelCase				,		_UpperCamelCase=None   )	->							List[Any]:
							if schedule_timesteps is None:
												UpperCAmelCase_    :      Optional[Any]            =						self.timesteps

							UpperCAmelCase_    :      Any            =						(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:
												UpperCAmelCase_    :      int            =						1 if len(_UpperCamelCase   ) > 1 else 0
							else:
												UpperCAmelCase_    :      Union[str, Any]            =						timestep.cpu().item() if torch.is_tensor(_UpperCamelCase   ) else timestep
												UpperCAmelCase_    :      Optional[int]            =						self._index_counter[timestep_int]

							return indices[pos].item()



		@property
		def 					__UpperCAmelCase			(							self   )	->							Optional[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				,		_UpperCamelCase				,		_UpperCamelCase				,		)	->							torch.FloatTensor:
							UpperCAmelCase_    :      List[Any]            =						self.index_for_timestep(_UpperCamelCase   )

							if self.state_in_first_order:
												UpperCAmelCase_    :      Dict            =						self.sigmas[step_index]
							else:
												UpperCAmelCase_    :      Union[str, Any]            =						self.sigmas_interpol[step_index]

							UpperCAmelCase_    :      Optional[int]            =						sample / ((sigma**2 + 1) ** 0.5)
							return sample



		def 					__UpperCAmelCase			(							self				,		_UpperCamelCase				,		_UpperCamelCase = None				,		_UpperCamelCase = None				,		)	->							List[str]:
							UpperCAmelCase_    :      Dict            =						num_inference_steps

							UpperCAmelCase_    :      str            =						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":
												UpperCAmelCase_    :      List[Any]            =						np.linspace(0				,		num_train_timesteps - 1				,		_UpperCamelCase				,		dtype=_UpperCamelCase   )[::-1].copy()
							elif self.config.timestep_spacing == "leading":
												UpperCAmelCase_    :      str            =						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
												UpperCAmelCase_    :      List[Any]            =						(np.arange(0				,		_UpperCamelCase   ) * step_ratio).round()[::-1].copy().astype(_UpperCamelCase   )
												timesteps += self.config.steps_offset
							elif self.config.timestep_spacing == "trailing":
												UpperCAmelCase_    :      Optional[Any]            =						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
												UpperCAmelCase_    :      List[str]            =						(np.arange(_UpperCamelCase				,		0				,		-step_ratio   )).round().copy().astype(_UpperCamelCase   )
												timesteps -= 1
							else:
												raise ValueError(
												    f"{self.config.timestep_spacing} is not supported. Please make sure to choose one of 'linspace', 'leading' or 'trailing'."   )

							UpperCAmelCase_    :      Union[str, Any]            =						np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5   )
							UpperCAmelCase_    :      List[str]            =						torch.from_numpy(np.log(_UpperCamelCase   )   ).to(_UpperCamelCase   )

							UpperCAmelCase_    :      List[Any]            =						np.interp(_UpperCamelCase				,		np.arange(0				,		len(_UpperCamelCase   )   )				,		_UpperCamelCase   )
							UpperCAmelCase_    :      List[str]            =						np.concatenate([sigmas, [0.0]]   ).astype(np.floataa   )
							UpperCAmelCase_    :      str            =						torch.from_numpy(_UpperCamelCase   ).to(device=_UpperCamelCase   )

							# interpolate sigmas
							UpperCAmelCase_    :      Optional[int]            =						sigmas.log().lerp(sigmas.roll(1   ).log()				,		0.5   ).exp()

							UpperCAmelCase_    :      Optional[Any]            =						torch.cat([sigmas[:1], sigmas[1:].repeat_interleave(2   ), sigmas[-1:]]   )
							UpperCAmelCase_    :      Optional[int]            =						torch.cat(
							    [sigmas_interpol[:1], sigmas_interpol[1:].repeat_interleave(2   ), sigmas_interpol[-1:]]   )

							if str(_UpperCamelCase   ).startswith('mps'   ):
												# mps does not support float64
												UpperCAmelCase_    :      Optional[Any]            =						torch.from_numpy(_UpperCamelCase   ).to(_UpperCamelCase				,		dtype=torch.floataa   )
							else:
												UpperCAmelCase_    :      Any            =						torch.from_numpy(_UpperCamelCase   ).to(_UpperCamelCase   )

							# interpolate timesteps
							UpperCAmelCase_    :      Optional[int]            =						self.sigma_to_t(_UpperCamelCase   ).to(_UpperCamelCase				,		dtype=timesteps.dtype   )
							UpperCAmelCase_    :      Optional[Any]            =						torch.stack((timesteps_interpol[1:-1, None], timesteps[1:, None])				,		dim=-1   ).flatten()

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

							UpperCAmelCase_    :      Dict            =						None

							# for exp beta schedules, such as the one for `pipeline_shap_e.py`
							# we need an index counter
							UpperCAmelCase_    :      Dict            =						defaultdict(_UpperCamelCase   )



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

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

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

							UpperCAmelCase_    :      List[str]            =						self.log_sigmas[low_idx]
							UpperCAmelCase_    :      int            =						self.log_sigmas[high_idx]

							# interpolate sigmas
							UpperCAmelCase_    :      Optional[Any]            =						(low - log_sigma) / (low - high)
							UpperCAmelCase_    :      Any            =						w.clamp(0				,		1   )

							# transform interpolation to time range
							UpperCAmelCase_    :      Union[str, Any]            =						(1 - w) * low_idx + w * high_idx
							UpperCAmelCase_    :      Optional[Any]            =						t.view(sigma.shape   )
							return t



		@property
		def 					__UpperCAmelCase			(							self   )	->							int:
							return self.sample is None



		def 					__UpperCAmelCase			(							self				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase = True				,		)	->							Union[SchedulerOutput, Tuple]:
							UpperCAmelCase_    :      List[Any]            =						self.index_for_timestep(_UpperCamelCase   )

							# advance index counter by 1
							UpperCAmelCase_    :      Union[str, Any]            =						timestep.cpu().item() if torch.is_tensor(_UpperCamelCase   ) else timestep
							self._index_counter[timestep_int] += 1

							if self.state_in_first_order:
												UpperCAmelCase_    :      List[str]            =						self.sigmas[step_index]
												UpperCAmelCase_    :      List[str]            =						self.sigmas_interpol[step_index + 1]
												UpperCAmelCase_    :      Optional[Any]            =						self.sigmas[step_index + 1]
							else:
												# 2nd order / KDPM2's method
												UpperCAmelCase_    :      Union[str, Any]            =						self.sigmas[step_index - 1]
												UpperCAmelCase_    :      Any            =						self.sigmas_interpol[step_index]
												UpperCAmelCase_    :      List[Any]            =						self.sigmas[step_index]

							# 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
							UpperCAmelCase_    :      str            =						0
							UpperCAmelCase_    :      int            =						sigma * (gamma + 1)  # Note: sigma_hat == sigma for now

							# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
							if self.config.prediction_type == "epsilon":
												UpperCAmelCase_    :      Dict            =						sigma_hat if self.state_in_first_order else sigma_interpol
												UpperCAmelCase_    :      Optional[int]            =						sample - sigma_input * model_output
							elif self.config.prediction_type == "v_prediction":
												UpperCAmelCase_    :      Any            =						sigma_hat if self.state_in_first_order else sigma_interpol
												UpperCAmelCase_    :      Optional[int]            =						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
												UpperCAmelCase_    :      List[str]            =						(sample - pred_original_sample) / sigma_hat
												# 3. delta timestep
												UpperCAmelCase_    :      Dict            =						sigma_interpol - sigma_hat

												# store for 2nd order step
												UpperCAmelCase_    :      Any            =						sample
							else:
												# DPM-Solver-2
												# 2. Convert to an ODE derivative for 2nd order
												UpperCAmelCase_    :      Union[str, Any]            =						(sample - pred_original_sample) / sigma_interpol

												# 3. delta timestep
												UpperCAmelCase_    :      Any            =						sigma_next - sigma_hat

												UpperCAmelCase_    :      Dict            =						self.sample
												UpperCAmelCase_    :      str            =						None

							UpperCAmelCase_    :      List[str]            =						sample + derivative * dt

							if not return_dict:
												return (prev_sample,)

							return SchedulerOutput(prev_sample=_UpperCamelCase   )



		def 					__UpperCAmelCase			(							self				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase				,		)	->							torch.FloatTensor:
							# Make sure sigmas and timesteps have the same device and dtype as original_samples
							UpperCAmelCase_    :      Tuple            =						self.sigmas.to(device=original_samples.device				,		dtype=original_samples.dtype   )
							if original_samples.device.type == "mps" and torch.is_floating_point(_UpperCamelCase   ):
												# mps does not support float64
												UpperCAmelCase_    :      Optional[Any]            =						self.timesteps.to(original_samples.device				,		dtype=torch.floataa   )
												UpperCAmelCase_    :      Union[str, Any]            =						timesteps.to(original_samples.device				,		dtype=torch.floataa   )
							else:
												UpperCAmelCase_    :      Any            =						self.timesteps.to(original_samples.device   )
												UpperCAmelCase_    :      Optional[Any]            =						timesteps.to(original_samples.device   )

							UpperCAmelCase_    :      Any            =						[self.index_for_timestep(_UpperCamelCase				,		_UpperCamelCase   ) for t in timesteps]

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

							UpperCAmelCase_    :      int            =						original_samples + noise * sigma
							return noisy_samples



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














from typing import List

from ...configuration_utils import PretrainedConfig
from ...utils import logging


__UpperCAmelCase								=						logging.get_logger(__name__)

__UpperCAmelCase								=						{
    'snap-research/efficientformer-l1-300': (
        'https://huggingface.co/snap-research/efficientformer-l1-300/resolve/main/config.json'
    ),
}



class   lowerCamelCase      (_snake_case			):







		'''simple docstring'''



		_snake_case    :    Optional[int]       						=  '''efficientformer'''



		def __init__(							self				,		_UpperCamelCase = [3, 2, 6, 4]				,		_UpperCamelCase = [4_8, 9_6, 2_2_4, 4_4_8]				,		_UpperCamelCase = [True, True, True, True]				,		_UpperCamelCase = 4_4_8				,		_UpperCamelCase = 3_2				,		_UpperCamelCase = 4				,		_UpperCamelCase = 7				,		_UpperCamelCase = 5				,		_UpperCamelCase = 8				,		_UpperCamelCase = 4				,		_UpperCamelCase = 0.0				,		_UpperCamelCase = 1_6				,		_UpperCamelCase = 3				,		_UpperCamelCase = 3				,		_UpperCamelCase = 3				,		_UpperCamelCase = 2				,		_UpperCamelCase = 1				,		_UpperCamelCase = 0.0				,		_UpperCamelCase = 1				,		_UpperCamelCase = True				,		_UpperCamelCase = True				,		_UpperCamelCase = 1E-5				,		_UpperCamelCase = "gelu"				,		_UpperCamelCase = 0.02				,		_UpperCamelCase = 1E-12				,		_UpperCamelCase = 2_2_4				,		_UpperCamelCase = 1E-05				,		**_UpperCamelCase				,		)	->							None:
							super().__init__(**_UpperCamelCase   )

							UpperCAmelCase_    :      int            =						hidden_act
							UpperCAmelCase_    :      Union[str, Any]            =						hidden_dropout_prob
							UpperCAmelCase_    :      Tuple            =						hidden_sizes
							UpperCAmelCase_    :      Union[str, Any]            =						num_hidden_layers
							UpperCAmelCase_    :      List[str]            =						num_attention_heads
							UpperCAmelCase_    :      List[Any]            =						initializer_range
							UpperCAmelCase_    :      int            =						layer_norm_eps
							UpperCAmelCase_    :      List[str]            =						patch_size
							UpperCAmelCase_    :      Union[str, Any]            =						num_channels
							UpperCAmelCase_    :      Optional[Any]            =						depths
							UpperCAmelCase_    :      List[Any]            =						mlp_expansion_ratio
							UpperCAmelCase_    :      List[str]            =						downsamples
							UpperCAmelCase_    :      List[Any]            =						dim
							UpperCAmelCase_    :      Tuple            =						key_dim
							UpperCAmelCase_    :      Optional[int]            =						attention_ratio
							UpperCAmelCase_    :      str            =						resolution
							UpperCAmelCase_    :      Dict            =						pool_size
							UpperCAmelCase_    :      Union[str, Any]            =						downsample_patch_size
							UpperCAmelCase_    :      List[str]            =						downsample_stride
							UpperCAmelCase_    :      List[str]            =						downsample_pad
							UpperCAmelCase_    :      Any            =						drop_path_rate
							UpperCAmelCase_    :      Dict            =						num_metaad_blocks
							UpperCAmelCase_    :      Dict            =						distillation
							UpperCAmelCase_    :      int            =						use_layer_scale
							UpperCAmelCase_    :      Any            =						layer_scale_init_value
							UpperCAmelCase_    :      Any            =						image_size
							UpperCAmelCase_    :      Dict            =						batch_norm_eps






