

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


__snake_case							:			Any      		= logging.get_logger(__name__)  # pylint: disable=invalid-name



@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->EulerDiscrete
class 					A__(A__       ):
						"""simple docstring"""






						_A						:							Optional[int]									= 42
						_A						:							Union[str, Any]									= None







def 				_UpperCAmelCase							(	a__     ,  a__=0.999     ,  a__="cosine"     ,  ):

			'''simple docstring'''



			if alpha_transform_type == "cosine":

						def alpha_bar_fn(a__):
									return math.cos((t + 0.008) / 1.008 * 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}''')

			a_	:	str          =   []
			for i in range(a__):
						a_	:	str          =   i / num_diffusion_timesteps
						a_	:	List[str]          =   (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 					A__(A__,     A__       ):
						"""simple docstring"""






						_A						:							Any									= [e.name for e in KarrasDiffusionSchedulers]
						_A						:							Any									= 1





						@register_to_config
						def __init__(						self     ,			_lowercase = 1_000     ,			_lowercase = 0.0_0_0_1     ,			_lowercase = 0.0_2     ,			_lowercase = "linear"     ,			_lowercase = None     ,			_lowercase = "epsilon"     ,			_lowercase = "linear"     ,			_lowercase = False     ,			_lowercase = "linspace"     ,			_lowercase = 0     ,			)    ->   Dict:
									if trained_betas is not None:
												a_	:	List[Any]          =   torch.tensor(_lowerCAmelCase     ,			dtype=torch.floataa						)
									elif beta_schedule == "linear":
												a_	:	str          =   torch.linspace(_lowerCAmelCase     ,			_lowerCAmelCase     ,			_lowerCAmelCase     ,			dtype=torch.floataa						)
									elif beta_schedule == "scaled_linear":
												# this schedule is very specific to the latent diffusion model.
												a_	:	Tuple          =   (
												    torch.linspace(beta_start**0.5     ,			beta_end**0.5     ,			_lowerCAmelCase     ,			dtype=torch.floataa						) ** 2
												)
									elif beta_schedule == "squaredcos_cap_v2":
												# Glide cosine schedule
												a_	:	Any          =   betas_for_alpha_bar(_lowerCAmelCase						)
									else:
												raise NotImplementedError(F'''{beta_schedule} does is not implemented for {self.__class__}'''						)

									a_	:	int          =   1.0 - self.betas
									a_	:	List[Any]          =   torch.cumprod(self.alphas     ,			dim=0						)

									a_	:	Optional[Any]          =   np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5						)
									a_	:	List[Any]          =   np.concatenate([sigmas[::-1], [0.0]]						).astype(np.floataa						)
									a_	:	List[str]          =   torch.from_numpy(_lowerCAmelCase						)

									# setable values
									a_	:	Union[str, Any]          =   None
									a_	:	List[str]          =   np.linspace(0     ,			num_train_timesteps - 1     ,			_lowerCAmelCase     ,			dtype=_lowerCAmelCase						)[::-1].copy()
									a_	:	Optional[int]          =   torch.from_numpy(_lowerCAmelCase						)
									a_	:	Tuple          =   False
									a_	:	Optional[Any]          =   use_karras_sigmas





						@property
						def  UpperCamelCase__				(						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  UpperCamelCase__				(						self     ,			_lowercase     ,			_lowercase						)    ->   List[str]:
									if isinstance(_lowerCAmelCase     ,			torch.Tensor						):
												a_	:	Optional[Any]          =   timestep.to(self.timesteps.device						)
									a_	:	List[Any]          =   (self.timesteps == timestep).nonzero().item()
									a_	:	Tuple          =   self.sigmas[step_index]

									a_	:	Union[str, Any]          =   sample / ((sigma**2 + 1) ** 0.5)

									a_	:	List[Any]          =   True
									return sample





						def  UpperCamelCase__				(						self     ,			_lowercase     ,			_lowercase = None						)    ->   Dict:
									a_	:	Optional[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":
												a_	:	int          =   np.linspace(0     ,			self.config.num_train_timesteps - 1     ,			_lowerCAmelCase     ,			dtype=_lowerCAmelCase						)[
												    ::-1
												].copy()
									elif self.config.timestep_spacing == "leading":
												a_	:	str          =   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
												a_	:	Tuple          =   (np.arange(0     ,			_lowerCAmelCase						) * step_ratio).round()[::-1].copy().astype(_lowerCAmelCase						)
												timesteps += self.config.steps_offset
									elif self.config.timestep_spacing == "trailing":
												a_	:	List[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
												a_	:	Union[str, Any]          =   (np.arange(self.config.num_train_timesteps     ,			0     ,			-step_ratio						)).round().copy().astype(_lowerCAmelCase						)
												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_	:	Optional[Any]          =   np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5						)
									a_	:	int          =   np.log(_lowerCAmelCase						)

									if self.config.interpolation_type == "linear":
												a_	:	List[str]          =   np.interp(_lowerCAmelCase     ,			np.arange(0     ,			len(_lowerCAmelCase						)						)     ,			_lowerCAmelCase						)
									elif self.config.interpolation_type == "log_linear":
												a_	:	Any          =   torch.linspace(np.log(sigmas[-1]						)     ,			np.log(sigmas[0]						)     ,			num_inference_steps + 1						).exp()
									else:
												raise ValueError(
												    F'''{self.config.interpolation_type} is not implemented. Please specify interpolation_type to either'''
												    """ 'linear' or 'log_linear'"""						)

									if self.use_karras_sigmas:
												a_	:	List[Any]          =   self._convert_to_karras(in_sigmas=_lowerCAmelCase     ,			num_inference_steps=self.num_inference_steps						)
												a_	:	str          =   np.array([self._sigma_to_t(_lowerCAmelCase     ,			_lowerCAmelCase						) for sigma in sigmas]						)

									a_	:	Optional[int]          =   np.concatenate([sigmas, [0.0]]						).astype(np.floataa						)
									a_	:	Union[str, Any]          =   torch.from_numpy(_lowerCAmelCase						).to(device=_lowerCAmelCase						)
									if str(_lowerCAmelCase						).startswith("""mps"""						):
												# mps does not support float64
												a_	:	Dict          =   torch.from_numpy(_lowerCAmelCase						).to(_lowerCAmelCase     ,			dtype=torch.floataa						)
									else:
												a_	:	Union[str, Any]          =   torch.from_numpy(_lowerCAmelCase						).to(device=_lowerCAmelCase						)





						def  UpperCamelCase__				(						self     ,			_lowercase     ,			_lowercase						)    ->   Any:
									# get log sigma
									a_	:	int          =   np.log(_lowerCAmelCase						)

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

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

									a_	:	Optional[int]          =   log_sigmas[low_idx]
									a_	:	Optional[int]          =   log_sigmas[high_idx]

									# interpolate sigmas
									a_	:	Optional[int]          =   (low - log_sigma) / (low - high)
									a_	:	Dict          =   np.clip(_lowerCAmelCase     ,			0     ,			1						)

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





						def  UpperCamelCase__				(						self     ,			_lowercase     ,			_lowercase						)    ->   Tuple:
									a_	:	int          =   in_sigmas[-1].item()
									a_	:	Optional[Any]          =   in_sigmas[0].item()

									a_	:	Union[str, Any]          =   7.0  # 7.0 is the value used in the paper
									a_	:	List[str]          =   np.linspace(0     ,			1     ,			_lowerCAmelCase						)
									a_	:	Any          =   sigma_min ** (1 / rho)
									a_	:	List[str]          =   sigma_max ** (1 / rho)
									a_	:	List[Any]          =   (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
									return sigmas





						def  UpperCamelCase__				(						self     ,			_lowercase     ,			_lowercase     ,			_lowercase     ,			_lowercase = 0.0     ,			_lowercase = 0.0     ,			_lowercase = float("""inf"""						)     ,			_lowercase = 1.0     ,			_lowercase = None     ,			_lowercase = True     ,			)    ->   List[Any]:

									if (
									    isinstance(_lowerCAmelCase     ,			_lowerCAmelCase						)
									    or isinstance(_lowerCAmelCase     ,			torch.IntTensor						)
									    or isinstance(_lowerCAmelCase     ,			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(_lowerCAmelCase     ,			torch.Tensor						):
												a_	:	List[Any]          =   timestep.to(self.timesteps.device						)

									a_	:	Union[str, Any]          =   (self.timesteps == timestep).nonzero().item()
									a_	:	Optional[int]          =   self.sigmas[step_index]

									a_	:	Dict          =   min(s_churn / (len(self.sigmas						) - 1)     ,			2**0.5 - 1						) if s_tmin <= sigma <= s_tmax else 0.0

									a_	:	List[str]          =   randn_tensor(
									    model_output.shape     ,			dtype=model_output.dtype     ,			device=model_output.device     ,			generator=_lowerCAmelCase						)

									a_	:	Union[str, Any]          =   noise * s_noise
									a_	:	Dict          =   sigma * (gamma + 1)

									if gamma > 0:
												a_	:	Optional[int]          =   sample + eps * (sigma_hat**2 - sigma**2) ** 0.5

									# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
									# NOTE: "original_sample" should not be an expected prediction_type but is left in for
									# backwards compatibility
									if self.config.prediction_type == "original_sample" or self.config.prediction_type == "sample":
												a_	:	Union[str, Any]          =   model_output
									elif self.config.prediction_type == "epsilon":
												a_	:	Union[str, Any]          =   sample - sigma_hat * model_output
									elif self.config.prediction_type == "v_prediction":
												# * c_out + input * c_skip
												a_	:	Any          =   model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
									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
									a_	:	Tuple          =   (sample - pred_original_sample) / sigma_hat

									a_	:	Union[str, Any]          =   self.sigmas[step_index + 1] - sigma_hat

									a_	:	Optional[Any]          =   sample + derivative * dt

									if not return_dict:
												return (prev_sample,)

									return EulerDiscreteSchedulerOutput(prev_sample=_lowerCAmelCase     ,			pred_original_sample=_lowerCAmelCase						)





						def  UpperCamelCase__				(						self     ,			_lowercase     ,			_lowercase     ,			_lowercase     ,			)    ->   Tuple:
									# Make sure sigmas and timesteps have the same device and dtype as original_samples
									a_	:	Dict          =   self.sigmas.to(device=original_samples.device     ,			dtype=original_samples.dtype						)
									if original_samples.device.type == "mps" and torch.is_floating_point(_lowerCAmelCase						):
												# mps does not support float64
												a_	:	List[Any]          =   self.timesteps.to(original_samples.device     ,			dtype=torch.floataa						)
												a_	:	str          =   timesteps.to(original_samples.device     ,			dtype=torch.floataa						)
									else:
												a_	:	Union[str, Any]          =   self.timesteps.to(original_samples.device						)
												a_	:	str          =   timesteps.to(original_samples.device						)

									a_	:	List[Any]          =   [(schedule_timesteps == t).nonzero().item() for t in timesteps]

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

									a_	:	Dict          =   original_samples + noise * sigma
									return noisy_samples





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





import copy
from typing import Dict

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import CONFIG_MAPPING
from ..bit import BitConfig


__snake_case							:			int      		= logging.get_logger(__name__)

__snake_case							:			Dict      		= {
    """google/vit-hybrid-base-bit-384""": """https://huggingface.co/vit-hybrid-base-bit-384/resolve/main/config.json""",
    # See all ViT hybrid models at https://huggingface.co/models?filter=vit
}



class 					A__(a_       ):
						"""simple docstring"""






						_A						:							Dict									= '''vit-hybrid'''





						def __init__(						self     ,			_lowercase=None     ,			_lowercase=768     ,			_lowercase=12     ,			_lowercase=12     ,			_lowercase=3_072     ,			_lowercase="gelu"     ,			_lowercase=0.0     ,			_lowercase=0.0     ,			_lowercase=0.0_2     ,			_lowercase=1e-12     ,			_lowercase=224     ,			_lowercase=1     ,			_lowercase=3     ,			_lowercase=[1, 1_024, 24, 24]     ,			_lowercase=True     ,			**_lowercase     ,			)    ->   Tuple:
									super().__init__(**_lowercase						)

									if backbone_config is None:
												logger.info("""`backbone_config` is `None`. Initializing the config with a `BiT` backbone."""						)
												a_	:	Union[str, Any]          =   {
												    """global_padding""": """same""",
												    """layer_type""": """bottleneck""",
												    """depths""": [3, 4, 9],
												    """out_features""": ["""stage3"""],
												    """embedding_dynamic_padding""": True,
												}

									if isinstance(_lowercase     ,			_lowercase						):
												if "model_type" in backbone_config:
															a_	:	Any          =   CONFIG_MAPPING[backbone_config["""model_type"""]]
												else:
															logger.info(
															    """`model_type` is not found in `backbone_config`. Use `Bit` as the backbone configuration class."""						)
															a_	:	Optional[int]          =   BitConfig
												a_	:	Dict          =   backbone_config_class(**_lowercase						)

									a_	:	List[str]          =   backbone_featmap_shape
									a_	:	int          =   backbone_config
									a_	:	Union[str, Any]          =   hidden_size
									a_	:	Optional[int]          =   num_hidden_layers
									a_	:	Dict          =   num_attention_heads
									a_	:	Optional[int]          =   intermediate_size
									a_	:	Dict          =   hidden_act
									a_	:	Optional[Any]          =   hidden_dropout_prob
									a_	:	List[str]          =   attention_probs_dropout_prob
									a_	:	Tuple          =   initializer_range
									a_	:	Any          =   layer_norm_eps
									a_	:	List[Any]          =   image_size
									a_	:	Optional[Any]          =   patch_size
									a_	:	Optional[Any]          =   num_channels
									a_	:	str          =   qkv_bias





						def  UpperCamelCase__				(						self						)    ->   Dict[str, any]:
									a_	:	List[str]          =   copy.deepcopy(self.__dict__						)
									a_	:	Optional[int]          =   self.backbone_config.to_dict()
									a_	:	List[str]          =   self.__class__.model_type
									return output


