



'''simple docstring'''







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    lowerCamelCase_				(						lowerCAmelCase__	):






   '''simple docstring'''

   A			:  Tuple					=						42
   A			:  int					=						None





def     snake_case__			(lowerCAmelCase_       ,	lowerCAmelCase_=0.9_99       ,	lowerCAmelCase_="cosine"       ,	):
    if alpha_transform_type == "cosine":

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

    elif alpha_transform_type == "exp":

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

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

    UpperCamelCase__	     =      []
    for i in range(lowerCAmelCase_						):
        UpperCamelCase__	     =      i / num_diffusion_timesteps
        UpperCamelCase__	     =      (i + 1) / num_diffusion_timesteps
        betas.append(min(1 - alpha_bar_fn(lowerCAmelCase_						) / alpha_bar_fn(lowerCAmelCase_						)       ,	lowerCAmelCase_						)						)
    return torch.tensor(lowerCAmelCase_       ,	dtype=torch.floataa						)





class    lowerCamelCase_				(						lowerCAmelCase__       ,     lowerCAmelCase__	):






   '''simple docstring'''

   A			:  Dict					=						[e.name for e in KarrasDiffusionSchedulers]
   A			:  List[str]					=						1


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

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

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

       # setable values
       UpperCamelCase__	     =      None
       UpperCamelCase__	     =      use_karras_sigmas
       self.set_timesteps(lowerCAmelCase__  ,    lowerCAmelCase__			)
       UpperCamelCase__	     =      []
       UpperCamelCase__	     =      False


   @property
   def 						_UpperCamelCase							(	self			):
       # 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			):
       if isinstance(lowerCAmelCase__  ,    torch.Tensor			):
           UpperCamelCase__	     =      timestep.to(self.timesteps.device			)
       UpperCamelCase__	     =      (self.timesteps == timestep).nonzero().item()
       UpperCamelCase__	     =      self.sigmas[step_index]
       UpperCamelCase__	     =      sample / ((sigma**2 + 1) ** 0.5)
       UpperCamelCase__	     =      True
       return sample


   def 						_UpperCamelCase							(	self  ,    __UpperCamelCase  ,    __UpperCamelCase  ,    __UpperCamelCase			):

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

       UpperCamelCase__	     =      integrate.quad(lowerCAmelCase__  ,    self.sigmas[t]  ,    self.sigmas[t + 1]  ,    epsrel=1e-4			)[0]

       return integrated_coeff


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

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

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

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

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

       UpperCamelCase__	     =      []


   def 						_UpperCamelCase							(	self  ,    __UpperCamelCase  ,    __UpperCamelCase			):
       # get log sigma
       UpperCamelCase__	     =      np.log(lowerCAmelCase__			)

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

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

       UpperCamelCase__	     =      log_sigmas[low_idx]
       UpperCamelCase__	     =      log_sigmas[high_idx]

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

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


   def 						_UpperCamelCase							(	self  ,    __UpperCamelCase			):
       UpperCamelCase__	     =      in_sigmas[-1].item()
       UpperCamelCase__	     =      in_sigmas[0].item()

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


   def 						_UpperCamelCase							(	self  ,    __UpperCamelCase  ,    __UpperCamelCase  ,    __UpperCamelCase  ,    __UpperCamelCase = 4  ,    __UpperCamelCase = True  ,    ):
       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(lowerCAmelCase__  ,    torch.Tensor			):
           UpperCamelCase__	     =      timestep.to(self.timesteps.device			)
       UpperCamelCase__	     =      (self.timesteps == timestep).nonzero().item()
       UpperCamelCase__	     =      self.sigmas[step_index]

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

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

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

       if not return_dict:
           return (prev_sample,)

       return LMSDiscreteSchedulerOutput(prev_sample=lowerCAmelCase__  ,    pred_original_sample=lowerCAmelCase__			)


   def 						_UpperCamelCase							(	self  ,    __UpperCamelCase  ,    __UpperCamelCase  ,    __UpperCamelCase  ,    ):
       # Make sure sigmas and timesteps have the same device and dtype as original_samples
       UpperCamelCase__	     =      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
           UpperCamelCase__	     =      self.timesteps.to(original_samples.device  ,    dtype=torch.floataa			)
           UpperCamelCase__	     =      timesteps.to(original_samples.device  ,    dtype=torch.floataa			)
       else:
           UpperCamelCase__	     =      self.timesteps.to(original_samples.device			)
           UpperCamelCase__	     =      timesteps.to(original_samples.device			)

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

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

       UpperCamelCase__	     =      original_samples + noise * sigma
       return noisy_samples







   def __len__(	self			):
       return self.config.num_train_timesteps





"""simple docstring"""


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


__UpperCamelCase										=     logging.get_logger(__name__)

__UpperCamelCase										=     {
    '''uw-madison/nystromformer-512''': '''https://huggingface.co/uw-madison/nystromformer-512/resolve/main/config.json''',
    # See all Nystromformer models at https://huggingface.co/models?filter=nystromformer
}




class 					UpperCamelCase   (							lowerCAmelCase__  ):
       SCREAMING_SNAKE_CASE_						      = "nystromformer"







       def __init__(					self,				lowerCAmelCase__=3_0000,				lowerCAmelCase__=768,				lowerCAmelCase__=12,				lowerCAmelCase__=12,				lowerCAmelCase__=3072,				lowerCAmelCase__="gelu_new",				lowerCAmelCase__=0.1,				lowerCAmelCase__=0.1,				lowerCAmelCase__=510,				lowerCAmelCase__=2,				lowerCAmelCase__=64,				lowerCAmelCase__=64,				lowerCAmelCase__=65,				lowerCAmelCase__=False,				lowerCAmelCase__=0.02,				lowerCAmelCase__=1e-5,				lowerCAmelCase__=1,				lowerCAmelCase__=0,				lowerCAmelCase__=2,				**lowerCAmelCase__,				) -> Optional[Any]:
            snake_case_   			=   vocab_size
            snake_case_   			=   max_position_embeddings
            snake_case_   			=   hidden_size
            snake_case_   			=   num_hidden_layers
            snake_case_   			=   num_attention_heads
            snake_case_   			=   intermediate_size
            snake_case_   			=   hidden_act
            snake_case_   			=   hidden_dropout_prob
            snake_case_   			=   attention_probs_dropout_prob
            snake_case_   			=   initializer_range
            snake_case_   			=   type_vocab_size
            snake_case_   			=   segment_means_seq_len
            snake_case_   			=   num_landmarks
            snake_case_   			=   conv_kernel_size
            snake_case_   			=   inv_coeff_init_option
            snake_case_   			=   layer_norm_eps
            super().__init__(pad_token_id=lowerCAmelCase__,				bos_token_id=lowerCAmelCase__,				eos_token_id=lowerCAmelCase__,				**lowerCAmelCase__)

