


"""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 	A_       (     _UpperCAmelCase   ):





  """simple docstring"""

  lowercase		:	torch.FloatTensor
  lowercase		:	Optional[torch.FloatTensor]   =						None




def        A_					(					UpperCAmelCase__       ,   UpperCAmelCase__=0.9_9_9       ,   UpperCAmelCase__="cosine"       ,   )     ->     Optional[Any]:
   if alpha_transform_type == "cosine":

      def alpha_bar_fn(UpperCAmelCase__				):
         return math.cos((t + 0.0_0_8) / 1.0_0_8 * math.pi / 2				) ** 2

   elif alpha_transform_type == "exp":

      def alpha_bar_fn(UpperCAmelCase__				):
         return math.exp(t * -1_2.0				)

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

   a   :	int						=    []
   for i in range(UpperCAmelCase__				):
      a   :	Tuple						=    i / num_diffusion_timesteps
      a   :	Union[str, Any]						=    (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 	A_       (     _UpperCAmelCase      ,  _UpperCAmelCase   ):





  """simple docstring"""

  lowercase		:	Dict   =						[e.name for e in KarrasDiffusionSchedulers]
  lowercase		:	Any   =						1
  @register_to_config
  def __init__(						self  ,					__UpperCAmelCase = 10_00  ,					__UpperCAmelCase = 0.0001  ,					__UpperCAmelCase = 0.02  ,					__UpperCAmelCase = "linear"  ,					__UpperCAmelCase = None  ,					__UpperCAmelCase = False  ,					__UpperCAmelCase = "epsilon"  ,					__UpperCAmelCase = "linspace"  ,					__UpperCAmelCase = 0  ,					)				->					Any:
     if trained_betas is not None:
        a   :	Tuple						=    torch.tensor(__UpperCAmelCase  ,					dtype=torch.floataa						)
     elif beta_schedule == "linear":
        a   :	Union[str, Any]						=    torch.linspace(__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					dtype=torch.floataa						)
     elif beta_schedule == "scaled_linear":
        # this schedule is very specific to the latent diffusion model.
        a   :	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
        a   :	Optional[int]						=    betas_for_alpha_bar(__UpperCAmelCase						)
     else:
        raise NotImplementedError(f'{beta_schedule} does is not implemented for {self.__class__}'						)

     a   :	Tuple						=    1.0 - self.betas
     a   :	List[str]						=    torch.cumprod(self.alphas  ,					dim=0						)

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

     # setable values
     a   :	int						=    None
     a   :	Optional[Any]						=    use_karras_sigmas
     self.set_timesteps(__UpperCAmelCase  ,					__UpperCAmelCase						)
     a   :	Any						=    []
     a   :	Union[str, Any]						=    False
  @property
  def 							lowercase_     (						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 							lowercase_     (						self  ,					__UpperCAmelCase  ,					__UpperCAmelCase						)				->					torch.FloatTensor:
     if isinstance(__UpperCAmelCase  ,					torch.Tensor						):
        a   :	int						=    timestep.to(self.timesteps.device						)
     a   :	Union[str, Any]						=    (self.timesteps == timestep).nonzero().item()
     a   :	List[str]						=    self.sigmas[step_index]
     a   :	Tuple						=    sample / ((sigma**2 + 1) ** 0.5)
     a   :	Dict						=    True
     return sample
  def 							lowercase_     (						self  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase						)				->					str:

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

     a   :	Union[str, Any]						=    integrate.quad(__UpperCAmelCase  ,					self.sigmas[t]  ,					self.sigmas[t + 1]  ,					epsrel=1E-4						)[0]

     return integrated_coeff
  def 							lowercase_     (						self  ,					__UpperCAmelCase  ,					__UpperCAmelCase = None						)				->					str:
     a   :	List[str]						=    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   :	Union[str, Any]						=    np.linspace(0  ,					self.config.num_train_timesteps - 1  ,					__UpperCAmelCase  ,					dtype=__UpperCAmelCase						)[
            ::-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   :	Dict						=    (np.arange(0  ,					__UpperCAmelCase						) * step_ratio).round()[::-1].copy().astype(__UpperCAmelCase						)
        timesteps += self.config.steps_offset
     elif self.config.timestep_spacing == "trailing":
        a   :	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   :	Tuple						=    (np.arange(self.config.num_train_timesteps  ,					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\'.'						)

     a   :	Union[str, Any]						=    np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5						)
     a   :	Tuple						=    np.log(__UpperCAmelCase						)
     a   :	Union[str, Any]						=    np.interp(__UpperCAmelCase  ,					np.arange(0  ,					len(__UpperCAmelCase						)						)  ,					__UpperCAmelCase						)

     if self.use_karras_sigmas:
        a   :	Union[str, Any]						=    self._convert_to_karras(in_sigmas=__UpperCAmelCase						)
        a   :	Tuple						=    np.array([self._sigma_to_t(__UpperCAmelCase  ,					__UpperCAmelCase						) for sigma in sigmas]						)

     a   :	List[Any]						=    np.concatenate([sigmas, [0.0]]						).astype(np.floataa						)

     a   :	Dict						=    torch.from_numpy(__UpperCAmelCase						).to(device=__UpperCAmelCase						)
     if str(__UpperCAmelCase						).startswith('mps'						):
        # mps does not support float64
        a   :	Dict						=    torch.from_numpy(__UpperCAmelCase						).to(__UpperCAmelCase  ,					dtype=torch.floataa						)
     else:
        a   :	Dict						=    torch.from_numpy(__UpperCAmelCase						).to(device=__UpperCAmelCase						)

     a   :	List[Any]						=    []
  def 							lowercase_     (						self  ,					__UpperCAmelCase  ,					__UpperCAmelCase						)				->					int:
     # get log sigma
     a   :	str						=    np.log(__UpperCAmelCase						)

     # get distribution
     a   :	List[str]						=    log_sigma - log_sigmas[:, np.newaxis]

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

     a   :	Tuple						=    log_sigmas[low_idx]
     a   :	int						=    log_sigmas[high_idx]

     # interpolate sigmas
     a   :	Optional[int]						=    (low - log_sigma) / (low - high)
     a   :	Union[str, Any]						=    np.clip(__UpperCAmelCase  ,					0  ,					1						)

     # transform interpolation to time range
     a   :	Optional[int]						=    (1 - w) * low_idx + w * high_idx
     a   :	Any						=    t.reshape(sigma.shape						)
     return t
  def 							lowercase_     (						self  ,					__UpperCAmelCase						)				->					torch.FloatTensor:
     a   :	float						=    in_sigmas[-1].item()
     a   :	float						=    in_sigmas[0].item()

     a   :	Tuple						=    7.0  # 7.0 is the value used in the paper
     a   :	Union[str, Any]						=    np.linspace(0  ,					1  ,					self.num_inference_steps						)
     a   :	Optional[Any]						=    sigma_min ** (1 / rho)
     a   :	Optional[int]						=    sigma_max ** (1 / rho)
     a   :	Tuple						=    (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
     return sigmas
  def 							lowercase_     (						self  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase = 4  ,					__UpperCAmelCase = True  ,					)				->					Union[LMSDiscreteSchedulerOutput, Tuple]:
     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(__UpperCAmelCase  ,					torch.Tensor						):
        a   :	List[Any]						=    timestep.to(self.timesteps.device						)
     a   :	List[str]						=    (self.timesteps == timestep).nonzero().item()
     a   :	Optional[Any]						=    self.sigmas[step_index]

     # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
     if self.config.prediction_type == "epsilon":
        a   :	Union[str, Any]						=    sample - sigma * model_output
     elif self.config.prediction_type == "v_prediction":
        # * c_out + input * c_skip
        a   :	Optional[Any]						=    model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
     elif self.config.prediction_type == "sample":
        a   :	List[Any]						=    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
     a   :	Union[str, Any]						=    (sample - pred_original_sample) / sigma
     self.derivatives.append(__UpperCAmelCase						)
     if len(self.derivatives						) > order:
        self.derivatives.pop(0						)

     # 3. Compute linear multistep coefficients
     a   :	Optional[Any]						=    min(step_index + 1  ,					__UpperCAmelCase						)
     a   :	Dict						=    [self.get_lms_coefficient(__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase						) for curr_order in range(__UpperCAmelCase						)]

     # 4. Compute previous sample based on the derivatives path
     a   :	Optional[Any]						=    sample + sum(
         coeff * derivative for coeff, derivative in zip(__UpperCAmelCase  ,					reversed(self.derivatives						)						)						)

     if not return_dict:
        return (prev_sample,)

     return LMSDiscreteSchedulerOutput(prev_sample=__UpperCAmelCase  ,					pred_original_sample=__UpperCAmelCase						)
  def 							lowercase_     (						self  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					)				->					torch.FloatTensor:
     # 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(__UpperCAmelCase						):
        # mps does not support float64
        a   :	Tuple						=    self.timesteps.to(original_samples.device  ,					dtype=torch.floataa						)
        a   :	Any						=    timesteps.to(original_samples.device  ,					dtype=torch.floataa						)
     else:
        a   :	int						=    self.timesteps.to(original_samples.device						)
        a   :	Tuple						=    timesteps.to(original_samples.device						)

     a   :	Dict						=    [(schedule_timesteps == t).nonzero().item() for t in timesteps]

     a   :	Union[str, Any]						=    sigmas[step_indices].flatten()
     while len(sigma.shape						) < len(original_samples.shape						):
        a   :	List[Any]						=    sigma.unsqueeze(-1						)

     a   :	Any						=    original_samples + noise * sigma
     return noisy_samples






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









"""simple docstring"""






import unittest

from transformers import BlenderbotTokenizer, BlenderbotTokenizerFast
from transformers.utils import cached_property


class 	A_       (     unittest.TestCase   ):





  """simple docstring"""

  @cached_property
  def 							lowercase_     (						self						)				->					Optional[Any]:
     return BlenderbotTokenizer.from_pretrained('facebook/blenderbot-3B'						)
  @cached_property
  def 							lowercase_     (						self						)				->					Optional[int]:
     return BlenderbotTokenizerFast.from_pretrained('facebook/blenderbot-3B'						)
  def 							lowercase_     (						self						)				->					Union[str, Any]:
     a   :	List[Any]						=    self.tokenizer_ab
     a   :	int						=    ' I am a small frog.'
     a   :	Any						=    tok([src_text]  ,					padding=__UpperCAmelCase  ,					truncation=__UpperCAmelCase						)['input_ids']
     a   :	List[str]						=    tok.batch_decode(__UpperCAmelCase  ,					skip_special_tokens=__UpperCAmelCase  ,					clean_up_tokenization_spaces=__UpperCAmelCase						)[0]
     assert src_text == decoded
  def 							lowercase_     (						self						)				->					Optional[Any]:
     a   :	Optional[int]						=    self.rust_tokenizer_ab
     a   :	List[Any]						=    ' I am a small frog.'
     a   :	Union[str, Any]						=    tok([src_text]  ,					padding=__UpperCAmelCase  ,					truncation=__UpperCAmelCase						)['input_ids']
     a   :	Optional[Any]						=    tok.batch_decode(__UpperCAmelCase  ,					skip_special_tokens=__UpperCAmelCase  ,					clean_up_tokenization_spaces=__UpperCAmelCase						)[0]
     assert src_text == decoded
  def 							lowercase_     (						self						)				->					Tuple:
     assert self.tokenizer_ab.add_prefix_space
     assert self.tokenizer_ab([' Sam', 'Sam']						).input_ids == [[55_02, 2], [55_02, 2]]






  def 							lowercase_     (						self						)				->					Tuple:
     assert self.rust_tokenizer_ab.add_prefix_space
     assert self.rust_tokenizer_ab([' Sam', 'Sam']						).input_ids == [[55_02, 2], [55_02, 2]]





