


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 	_lowerCAmelCase					( UpperCamelCase__:  Dict				,       UpperCamelCase__:  Any=0.9_99				,       UpperCamelCase__:  Any="cosine"				,       )			-> Tuple:





	"""simple docstring"""







	if alpha_transform_type == "cosine":

		def alpha_bar_fn(UpperCamelCase__:  List[str]    ):
			return math.cos((t + 0.0_08) / 1.0_08 * math.pi / 2    ) ** 2

	elif alpha_transform_type == "exp":

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

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

	A             =					[]
	for i in range(UpperCamelCase__    ):
		A             =					i / num_diffusion_timesteps
		A             =					(i + 1) / num_diffusion_timesteps
		betas.append(min(1 - alpha_bar_fn(UpperCamelCase__    ) / alpha_bar_fn(UpperCamelCase__    )				,       UpperCamelCase__    )    )
	return torch.tensor(UpperCamelCase__				,       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__ = 1000					, a__ = 0.0_00_85					, a__ = 0.0_12					, a__ = "linear"					, a__ = None					, a__ = "epsilon"					, a__ = "linspace"					, a__ = 0					, )						->					List[str]:
					if trained_betas is not None:
						A             =					torch.tensor(a__					, dtype=torch.floataa )
					elif beta_schedule == "linear":
						A             =					torch.linspace(a__					, a__					, a__					, dtype=torch.floataa )
					elif beta_schedule == "scaled_linear":
						# this schedule is very specific to the latent diffusion model.
						A             =					(
						    torch.linspace(beta_start**0.5					, beta_end**0.5					, a__					, dtype=torch.floataa ) ** 2
						)
					elif beta_schedule == "squaredcos_cap_v2":
						# Glide cosine schedule
						A             =					betas_for_alpha_bar(a__ )
					else:
						raise NotImplementedError(f'{beta_schedule} does is not implemented for {self.__class__}' )

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

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

				def   _UpperCAmelCase       (    self					, a__					, a__=None )						->					List[Any]:
					if schedule_timesteps is None:
						A             =					self.timesteps

					A             =					(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:
						A             =					1 if len(a__ ) > 1 else 0
					else:
						A             =					timestep.cpu().item() if torch.is_tensor(a__ ) else timestep
						A             =					self._index_counter[timestep_int]

					return indices[pos].item()

				@property
				def   _UpperCAmelCase       (    self )						->					Tuple:
					# 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:
					A             =					self.index_for_timestep(a__ )

					if self.state_in_first_order:
						A             =					self.sigmas[step_index]
					else:
						A             =					self.sigmas_interpol[step_index]

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

				def   _UpperCAmelCase       (    self					, a__					, a__ = None					, a__ = None					, )						->					int:
					A             =					num_inference_steps

					A             =					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":
						A             =					np.linspace(0					, num_train_timesteps - 1					, a__					, dtype=a__ )[::-1].copy()
					elif self.config.timestep_spacing == "leading":
						A             =					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
						A             =					(np.arange(0					, a__ ) * step_ratio).round()[::-1].copy().astype(a__ )
						timesteps += self.config.steps_offset
					elif self.config.timestep_spacing == "trailing":
						A             =					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
						A             =					(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\'.' )

					A             =					np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5 )
					A             =					torch.from_numpy(np.log(a__ ) ).to(a__ )

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

					# interpolate sigmas
					A             =					sigmas.log().lerp(sigmas.roll(1 ).log()					, 0.5 ).exp()

					A             =					torch.cat([sigmas[:1], sigmas[1:].repeat_interleave(2 ), sigmas[-1:]] )
					A             =					torch.cat(
					    [sigmas_interpol[:1], sigmas_interpol[1:].repeat_interleave(2 ), sigmas_interpol[-1:]] )

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

					# interpolate timesteps
					A             =					self.sigma_to_t(a__ ).to(a__					, dtype=timesteps.dtype )
					A             =					torch.stack((timesteps_interpol[1:-1, None], timesteps[1:, None])					, dim=-1 ).flatten()

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

					A             =					None

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

				def   _UpperCAmelCase       (    self					, a__ )						->					int:
					# get log sigma
					A             =					sigma.log()

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

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

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

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

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

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

				def   _UpperCAmelCase       (    self					, a__					, a__					, a__					, a__ = True					, )						->					Union[SchedulerOutput, Tuple]:
					A             =					self.index_for_timestep(a__ )

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

					if self.state_in_first_order:
						A             =					self.sigmas[step_index]
						A             =					self.sigmas_interpol[step_index + 1]
						A             =					self.sigmas[step_index + 1]
					else:
						# 2nd order / KDPM2's method
						A             =					self.sigmas[step_index - 1]
						A             =					self.sigmas_interpol[step_index]
						A             =					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
					A             =					0
					A             =					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":
						A             =					sigma_hat if self.state_in_first_order else sigma_interpol
						A             =					sample - sigma_input * model_output
					elif self.config.prediction_type == "v_prediction":
						A             =					sigma_hat if self.state_in_first_order else sigma_interpol
						A             =					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
						A             =					(sample - pred_original_sample) / sigma_hat
						# 3. delta timestep
						A             =					sigma_interpol - sigma_hat

						# store for 2nd order step
						A             =					sample
					else:
						# DPM-Solver-2
						# 2. Convert to an ODE derivative for 2nd order
						A             =					(sample - pred_original_sample) / sigma_interpol

						# 3. delta timestep
						A             =					sigma_next - sigma_hat

						A             =					self.sample
						A             =					None

					A             =					sample + derivative * dt

					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
					A             =					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
						A             =					self.timesteps.to(original_samples.device					, dtype=torch.floataa )
						A             =					timesteps.to(original_samples.device					, dtype=torch.floataa )
					else:
						A             =					self.timesteps.to(original_samples.device )
						A             =					timesteps.to(original_samples.device )

					A             =					[self.index_for_timestep(a__					, a__ ) for t in timesteps]

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

					A             =					original_samples + noise * sigma
					return noisy_samples

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







from typing import List

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


_lowercase							:   Any						=						logging.get_logger(__name__)

_lowercase							:   Union[str, Any]						=						{
    "snap-research/efficientformer-l1-300": (
        "https://huggingface.co/snap-research/efficientformer-l1-300/resolve/main/config.json"
    ),
}






class    _UpperCamelCase     (       __snake_case					):

				"""simple docstring"""







				lowerCAmelCase					=			'efficientformer'

				def __init__(    self					, a__ = [3, 2, 6, 4]					, a__ = [48, 96, 224, 448]					, a__ = [True, True, True, True]					, a__ = 448					, a__ = 32					, a__ = 4					, a__ = 7					, a__ = 5					, a__ = 8					, a__ = 4					, a__ = 0.0					, a__ = 16					, a__ = 3					, a__ = 3					, a__ = 3					, a__ = 2					, a__ = 1					, a__ = 0.0					, a__ = 1					, a__ = True					, a__ = True					, a__ = 1e-5					, a__ = "gelu"					, a__ = 0.02					, a__ = 1e-12					, a__ = 224					, a__ = 1e-05					, **a__					, )						->					None:
					super().__init__(**a__ )

					A             =					hidden_act
					A             =					hidden_dropout_prob
					A             =					hidden_sizes
					A             =					num_hidden_layers
					A             =					num_attention_heads
					A             =					initializer_range
					A             =					layer_norm_eps
					A             =					patch_size
					A             =					num_channels
					A             =					depths
					A             =					mlp_expansion_ratio
					A             =					downsamples
					A             =					dim
					A             =					key_dim
					A             =					attention_ratio
					A             =					resolution
					A             =					pool_size
					A             =					downsample_patch_size
					A             =					downsample_stride
					A             =					downsample_pad
					A             =					drop_path_rate
					A             =					num_metaad_blocks
					A             =					distillation
					A             =					use_layer_scale
					A             =					layer_scale_init_value
					A             =					image_size
					A             =					batch_norm_eps



