



'''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 			__magic_name__		(					_UpperCamelCase     ):
							lowerCAmelCase :					torch.FloatTensor
							lowerCAmelCase :					Optional[torch.FloatTensor]            =      None



def   __lowerCamelCase  (   lowerCAmelCase_		,    lowerCAmelCase_=0.999		,    lowerCAmelCase_="cosine"		,    )    ->   str:
		if alpha_transform_type == "cosine":

				def alpha_bar_fn(lowerCAmelCase_						):
						return math.cos((t + 0.008) / 1.008 * 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}"""						)

		_a  :   List[Any]				 =							[]
		for i in range(lowerCAmelCase_						):
				_a  :   List[Any]				 =							i / num_diffusion_timesteps
				_a  :   Union[str, Any]				 =							(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 			__magic_name__		(					_UpperCamelCase    ,		_UpperCamelCase     ):
							lowerCAmelCase :					List[str]            =      [e.name for e in KarrasDiffusionSchedulers]
							lowerCAmelCase :					str            =      1







							@register_to_config
							def __init__(							self      : Optional[int]			,_UpperCAmelCase      : int = 1000			,_UpperCAmelCase      : float = 0.00_01			,_UpperCAmelCase      : float = 0.02			,_UpperCAmelCase      : str = "linear"			,_UpperCAmelCase      : Optional[Union[np.ndarray, List[float]]] = None			,_UpperCAmelCase      : Optional[bool] = False			,_UpperCAmelCase      : str = "epsilon"			,_UpperCAmelCase      : str = "linspace"			,_UpperCAmelCase      : int = 0			,):
									if trained_betas is not None:
											_a  :   List[Any]				 =							torch.tensor(_UpperCAmelCase			,dtype=torch.floataa	)
									elif beta_schedule == "linear":
											_a  :   Optional[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  :   Union[str, Any]				 =							(
											    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  :   List[str]				 =							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  :   Optional[int]				 =							np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5	)
									_a  :   Union[str, Any]				 =							np.concatenate([sigmas[::-1], [0.0]]	).astype(np.floataa	)
									_a  :   Tuple				 =							torch.from_numpy(_UpperCAmelCase	)

									# setable values
									_a  :   int				 =							None
									_a  :   Optional[int]				 =							use_karras_sigmas
									self.set_timesteps(_UpperCAmelCase			,_UpperCAmelCase	)
									_a  :   Any				 =							[]
									_a  :   List[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      : List[Any]			,_UpperCAmelCase      : torch.FloatTensor			,_UpperCAmelCase      : Union[float, torch.FloatTensor]	):
									if isinstance(_UpperCAmelCase			,torch.Tensor	):
											_a  :   List[Any]				 =							timestep.to(self.timesteps.device	)
									_a  :   List[str]				 =							(self.timesteps == timestep).nonzero().item()
									_a  :   Any				 =							self.sigmas[step_index]
									_a  :   Dict				 =							sample / ((sigma**2 + 1) ** 0.5)
									_a  :   int				 =							True
									return sample







							def 							__lowercase    (							self      : List[Any]			,_UpperCAmelCase      : Union[str, Any]			,_UpperCAmelCase      : Optional[Any]			,_UpperCAmelCase      : Any	):

									def lms_derivative(_UpperCAmelCase      : Optional[int]	):
											_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  :   str				 =							integrate.quad(_UpperCAmelCase			,self.sigmas[t]			,self.sigmas[t + 1]			,epsrel=1E-4	)[0]

									return integrated_coeff







							def 							__lowercase    (							self      : Tuple			,_UpperCAmelCase      : int			,_UpperCAmelCase      : Union[str, torch.device] = None	):
									_a  :   List[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  :   Union[str, Any]				 =							np.linspace(0			,self.config.num_train_timesteps - 1			,_UpperCAmelCase			,dtype=_UpperCAmelCase	)[
											    ::-1
											].copy()
									elif self.config.timestep_spacing == "leading":
											_a  :   Optional[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  :   List[str]				 =							(np.arange(0			,_UpperCAmelCase	) * step_ratio).round()[::-1].copy().astype(_UpperCAmelCase	)
											timesteps += self.config.steps_offset
									elif self.config.timestep_spacing == "trailing":
											_a  :   Optional[int]				 =							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  :   Any				 =							(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  :   str				 =							np.log(_UpperCAmelCase	)
									_a  :   int				 =							np.interp(_UpperCAmelCase			,np.arange(0			,len(_UpperCAmelCase	)	)			,_UpperCAmelCase	)

									if self.use_karras_sigmas:
											_a  :   Tuple				 =							self._convert_to_karras(in_sigmas=_UpperCAmelCase	)
											_a  :   List[str]				 =							np.array([self._sigma_to_t(_UpperCAmelCase			,_UpperCAmelCase	) for sigma in sigmas]	)

									_a  :   Union[str, 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  :   Tuple				 =							torch.from_numpy(_UpperCAmelCase	).to(device=_UpperCAmelCase	)

									_a  :   Optional[int]				 =							[]







							def 							__lowercase    (							self      : Optional[int]			,_UpperCAmelCase      : Optional[int]			,_UpperCAmelCase      : Union[str, Any]	):
									# get log sigma
									_a  :   Dict				 =							np.log(_UpperCAmelCase	)

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

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

									_a  :   Optional[int]				 =							log_sigmas[low_idx]
									_a  :   List[str]				 =							log_sigmas[high_idx]

									# interpolate sigmas
									_a  :   List[str]				 =							(low - log_sigma) / (low - high)
									_a  :   Optional[int]				 =							np.clip(_UpperCAmelCase			,0			,1	)

									# transform interpolation to time range
									_a  :   Optional[Any]				 =							(1 - w) * low_idx + w * high_idx
									_a  :   Dict				 =							t.reshape(sigma.shape	)
									return t







							def 							__lowercase    (							self      : List[Any]			,_UpperCAmelCase      : torch.FloatTensor	):
									_a  :   float				 =							in_sigmas[-1].item()
									_a  :   float				 =							in_sigmas[0].item()

									_a  :   Dict				 =							7.0  # 7.0 is the value used in the paper
									_a  :   Optional[int]				 =							np.linspace(0			,1			,self.num_inference_steps	)
									_a  :   Optional[Any]				 =							sigma_min ** (1 / rho)
									_a  :   Any				 =							sigma_max ** (1 / rho)
									_a  :   Optional[int]				 =							(max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
									return sigmas







							def 							__lowercase    (							self      : List[Any]			,_UpperCAmelCase      : torch.FloatTensor			,_UpperCAmelCase      : Union[float, torch.FloatTensor]			,_UpperCAmelCase      : torch.FloatTensor			,_UpperCAmelCase      : int = 4			,_UpperCAmelCase      : bool = 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(_UpperCAmelCase			,torch.Tensor	):
											_a  :   Any				 =							timestep.to(self.timesteps.device	)
									_a  :   str				 =							(self.timesteps == timestep).nonzero().item()
									_a  :   Any				 =							self.sigmas[step_index]

									# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
									if self.config.prediction_type == "epsilon":
											_a  :   str				 =							sample - sigma * model_output
									elif self.config.prediction_type == "v_prediction":
											# * c_out + input * c_skip
											_a  :   List[str]				 =							model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
									elif self.config.prediction_type == "sample":
											_a  :   Union[str, 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[int]				 =							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  :   List[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      : Union[str, Any]			,_UpperCAmelCase      : torch.FloatTensor			,_UpperCAmelCase      : torch.FloatTensor			,_UpperCAmelCase      : torch.FloatTensor			,):
									# Make sure sigmas and timesteps have the same device and dtype as original_samples
									_a  :   Optional[int]				 =							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  :   int				 =							self.timesteps.to(original_samples.device			,dtype=torch.floataa	)
											_a  :   Optional[Any]				 =							timesteps.to(original_samples.device			,dtype=torch.floataa	)
									else:
											_a  :   Union[str, Any]				 =							self.timesteps.to(original_samples.device	)
											_a  :   List[Any]				 =							timesteps.to(original_samples.device	)

									_a  :   Dict				 =							[(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  :   Any				 =							sigma.unsqueeze(-1	)

									_a  :   Any				 =							original_samples + noise * sigma
									return noisy_samples







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









'''simple docstring'''

from transformers import SqueezeBertTokenizer, SqueezeBertTokenizerFast
from transformers.testing_utils import require_tokenizers, slow

from ..bert.test_tokenization_bert import BertTokenizationTest

@require_tokenizers
class 			__magic_name__		(					_UpperCamelCase     ):
							lowerCAmelCase :					int            =      SqueezeBertTokenizer
							lowerCAmelCase :					int            =      SqueezeBertTokenizerFast
							lowerCAmelCase :					List[Any]            =      True







							def 							__lowercase    (							self      : List[str]			,**_UpperCAmelCase      : Tuple	):
									return SqueezeBertTokenizerFast.from_pretrained(self.tmpdirname			,**_UpperCAmelCase	)







							@slow
							def 							__lowercase    (							self      : Tuple	):
									_a  :   Optional[int]				 =							SqueezeBertTokenizer.from_pretrained('squeezebert/squeezebert-mnli-headless'	)

									_a  :   int				 =							tokenizer.encode('sequence builders'			,add_special_tokens=_UpperCAmelCase	)
									_a  :   List[Any]				 =							tokenizer.encode('multi-sequence build'			,add_special_tokens=_UpperCAmelCase	)

									_a  :   Union[str, Any]				 =							tokenizer.build_inputs_with_special_tokens(_UpperCAmelCase	)
									_a  :   Dict				 =							tokenizer.build_inputs_with_special_tokens(_UpperCAmelCase			,_UpperCAmelCase	)

									assert encoded_sentence == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id]
									assert encoded_pair == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + text_a + [
									    tokenizer.sep_token_id
									]




