


'''simple docstring'''



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


A__		: Optional[int]												=  logging.get_logger(__name__)  # pylint: disable=invalid-name





@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->EulerAncestralDiscrete
class        UpperCAmelCase_  (_UpperCAmelCase  ):





    """simple docstring"""






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







def       UpperCAmelCase__ (     UpperCAmelCase_   :    Union[str, Any]				,   UpperCAmelCase_   :    int=0.999				,   UpperCAmelCase_   :    List[Any]="cosine"				,   )					->		Optional[Any]:
     if alpha_transform_type == "cosine":

          def alpha_bar_fn(UpperCAmelCase_   :    Optional[int]							):
               return math.cos((t + 0.008) / 1.008 * math.pi / 2							) ** 2

     elif alpha_transform_type == "exp":

          def alpha_bar_fn(UpperCAmelCase_   :    Union[str, Any]							):
               return math.exp(t * -12.0							)

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

     __lowerCamelCase       :     List[Any]        =		[]
     for i in range(UpperCAmelCase_							):
          __lowerCamelCase       :     Optional[int]        =		i / num_diffusion_timesteps
          __lowerCamelCase       :     str        =		(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_  (_UpperCAmelCase ,		_UpperCAmelCase  ):





    """simple docstring"""






    lowerCamelCase  : Tuple												=      [e.name for e in KarrasDiffusionSchedulers]
    lowerCamelCase  : Union[str, Any]												=      1







    @register_to_config
    def __init__(							self      ,					SCREAMING_SNAKE_CASE_ = 10_00      ,					SCREAMING_SNAKE_CASE_ = 0.0_0_0_1      ,					SCREAMING_SNAKE_CASE_ = 0.0_2      ,					SCREAMING_SNAKE_CASE_ = "linear"      ,					SCREAMING_SNAKE_CASE_ = None      ,					SCREAMING_SNAKE_CASE_ = "epsilon"      ,					SCREAMING_SNAKE_CASE_ = "linspace"      ,					SCREAMING_SNAKE_CASE_ = 0      ,					)    ->					List[str]:
         if trained_betas is not None:
              __lowerCamelCase       :     List[str]        =		torch.tensor(SCREAMING_SNAKE_CASE_      ,					dtype=torch.floataa     )
         elif beta_schedule == "linear":
              __lowerCamelCase       :     List[Any]        =		torch.linspace(SCREAMING_SNAKE_CASE_      ,					SCREAMING_SNAKE_CASE_      ,					SCREAMING_SNAKE_CASE_      ,					dtype=torch.floataa     )
         elif beta_schedule == "scaled_linear":
              # this schedule is very specific to the latent diffusion model.
              __lowerCamelCase       :     Dict        =		(
                  torch.linspace(beta_start**0.5      ,					beta_end**0.5      ,					SCREAMING_SNAKE_CASE_      ,					dtype=torch.floataa     ) ** 2
              )
         elif beta_schedule == "squaredcos_cap_v2":
              # Glide cosine schedule
              __lowerCamelCase       :     List[str]        =		betas_for_alpha_bar(SCREAMING_SNAKE_CASE_     )
         else:
              raise NotImplementedError(f'{beta_schedule} does is not implemented for {self.__class__}'     )

         __lowerCamelCase       :     Dict        =		1.0 - self.betas
         __lowerCamelCase       :     str        =		torch.cumprod(self.alphas      ,					dim=0     )

         __lowerCamelCase       :     Optional[Any]        =		np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5     )
         __lowerCamelCase       :     Tuple        =		np.concatenate([sigmas[::-1], [0.0]]     ).astype(np.floataa     )
         __lowerCamelCase       :     Union[str, Any]        =		torch.from_numpy(SCREAMING_SNAKE_CASE_     )

         # setable values
         __lowerCamelCase       :     Optional[int]        =		None
         __lowerCamelCase       :     Tuple        =		np.linspace(0      ,					num_train_timesteps - 1      ,					SCREAMING_SNAKE_CASE_      ,					dtype=SCREAMING_SNAKE_CASE_     )[::-1].copy()
         __lowerCamelCase       :     int        =		torch.from_numpy(SCREAMING_SNAKE_CASE_     )
         __lowerCamelCase       :     Any        =		False







    @property
    def 				lowercase_    (							self     )    ->					Dict:
         # 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      ,					SCREAMING_SNAKE_CASE_      ,					SCREAMING_SNAKE_CASE_     )    ->					torch.FloatTensor:
         if isinstance(SCREAMING_SNAKE_CASE_      ,					torch.Tensor     ):
              __lowerCamelCase       :     int        =		timestep.to(self.timesteps.device     )
         __lowerCamelCase       :     List[str]        =		(self.timesteps == timestep).nonzero().item()
         __lowerCamelCase       :     Tuple        =		self.sigmas[step_index]
         __lowerCamelCase       :     Dict        =		sample / ((sigma**2 + 1) ** 0.5)
         __lowerCamelCase       :     Optional[int]        =		True
         return sample







    def 				lowercase_    (							self      ,					SCREAMING_SNAKE_CASE_      ,					SCREAMING_SNAKE_CASE_ = None     )    ->					List[Any]:
         __lowerCamelCase       :     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":
              __lowerCamelCase       :     str        =		np.linspace(0      ,					self.config.num_train_timesteps - 1      ,					SCREAMING_SNAKE_CASE_      ,					dtype=SCREAMING_SNAKE_CASE_     )[
                  ::-1
              ].copy()
         elif self.config.timestep_spacing == "leading":
              __lowerCamelCase       :     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
              __lowerCamelCase       :     Dict        =		(np.arange(0      ,					SCREAMING_SNAKE_CASE_     ) * step_ratio).round()[::-1].copy().astype(SCREAMING_SNAKE_CASE_     )
              timesteps += self.config.steps_offset
         elif self.config.timestep_spacing == "trailing":
              __lowerCamelCase       :     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
              __lowerCamelCase       :     Tuple        =		(np.arange(self.config.num_train_timesteps      ,					0      ,					-step_ratio     )).round().copy().astype(SCREAMING_SNAKE_CASE_     )
              timesteps -= 1
         else:
              raise ValueError(
                  f'{self.config.timestep_spacing} is not supported. Please make sure to choose one of \'linspace\', \'leading\' or \'trailing\'.'     )

         __lowerCamelCase       :     Union[str, Any]        =		np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5     )
         __lowerCamelCase       :     Dict        =		np.interp(SCREAMING_SNAKE_CASE_      ,					np.arange(0      ,					len(SCREAMING_SNAKE_CASE_     )     )      ,					SCREAMING_SNAKE_CASE_     )
         __lowerCamelCase       :     str        =		np.concatenate([sigmas, [0.0]]     ).astype(np.floataa     )
         __lowerCamelCase       :     Optional[int]        =		torch.from_numpy(SCREAMING_SNAKE_CASE_     ).to(device=SCREAMING_SNAKE_CASE_     )
         if str(SCREAMING_SNAKE_CASE_     ).startswith('mps'     ):
              # mps does not support float64
              __lowerCamelCase       :     Tuple        =		torch.from_numpy(SCREAMING_SNAKE_CASE_     ).to(SCREAMING_SNAKE_CASE_      ,					dtype=torch.floataa     )
         else:
              __lowerCamelCase       :     List[str]        =		torch.from_numpy(SCREAMING_SNAKE_CASE_     ).to(device=SCREAMING_SNAKE_CASE_     )







    def 				lowercase_    (							self      ,					SCREAMING_SNAKE_CASE_      ,					SCREAMING_SNAKE_CASE_      ,					SCREAMING_SNAKE_CASE_      ,					SCREAMING_SNAKE_CASE_ = None      ,					SCREAMING_SNAKE_CASE_ = True      ,					)    ->					Union[EulerAncestralDiscreteSchedulerOutput, Tuple]:

         if (
             isinstance(SCREAMING_SNAKE_CASE_      ,					SCREAMING_SNAKE_CASE_     )
             or isinstance(SCREAMING_SNAKE_CASE_      ,					torch.IntTensor     )
             or isinstance(SCREAMING_SNAKE_CASE_      ,					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(SCREAMING_SNAKE_CASE_      ,					torch.Tensor     ):
              __lowerCamelCase       :     int        =		timestep.to(self.timesteps.device     )

         __lowerCamelCase       :     Tuple        =		(self.timesteps == timestep).nonzero().item()
         __lowerCamelCase       :     Union[str, Any]        =		self.sigmas[step_index]

         # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
         if self.config.prediction_type == "epsilon":
              __lowerCamelCase       :     int        =		sample - sigma * model_output
         elif self.config.prediction_type == "v_prediction":
              # * c_out + input * c_skip
              __lowerCamelCase       :     Dict        =		model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**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`'     )

         __lowerCamelCase       :     str        =		self.sigmas[step_index]
         __lowerCamelCase       :     int        =		self.sigmas[step_index + 1]
         __lowerCamelCase       :     List[str]        =		(sigma_to**2 * (sigma_from**2 - sigma_to**2) / sigma_from**2) ** 0.5
         __lowerCamelCase       :     Optional[Any]        =		(sigma_to**2 - sigma_up**2) ** 0.5

         # 2. Convert to an ODE derivative
         __lowerCamelCase       :     Optional[Any]        =		(sample - pred_original_sample) / sigma

         __lowerCamelCase       :     int        =		sigma_down - sigma

         __lowerCamelCase       :     List[Any]        =		sample + derivative * dt

         __lowerCamelCase       :     List[str]        =		model_output.device
         __lowerCamelCase       :     int        =		randn_tensor(model_output.shape      ,					dtype=model_output.dtype      ,					device=SCREAMING_SNAKE_CASE_      ,					generator=SCREAMING_SNAKE_CASE_     )

         __lowerCamelCase       :     Optional[int]        =		prev_sample + noise * sigma_up

         if not return_dict:
              return (prev_sample,)

         return EulerAncestralDiscreteSchedulerOutput(
             prev_sample=SCREAMING_SNAKE_CASE_      ,					pred_original_sample=SCREAMING_SNAKE_CASE_     )







    def 				lowercase_    (							self      ,					SCREAMING_SNAKE_CASE_      ,					SCREAMING_SNAKE_CASE_      ,					SCREAMING_SNAKE_CASE_      ,					)    ->					torch.FloatTensor:
         # Make sure sigmas and timesteps have the same device and dtype as original_samples
         __lowerCamelCase       :     str        =		self.sigmas.to(device=original_samples.device      ,					dtype=original_samples.dtype     )
         if original_samples.device.type == "mps" and torch.is_floating_point(SCREAMING_SNAKE_CASE_     ):
              # mps does not support float64
              __lowerCamelCase       :     int        =		self.timesteps.to(original_samples.device      ,					dtype=torch.floataa     )
              __lowerCamelCase       :     str        =		timesteps.to(original_samples.device      ,					dtype=torch.floataa     )
         else:
              __lowerCamelCase       :     Tuple        =		self.timesteps.to(original_samples.device     )
              __lowerCamelCase       :     List[Any]        =		timesteps.to(original_samples.device     )

         __lowerCamelCase       :     int        =		[(schedule_timesteps == t).nonzero().item() for t in timesteps]

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

         __lowerCamelCase       :     str        =		original_samples + noise * sigma
         return noisy_samples







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










'''simple docstring'''



import torch

from transformers import AutoTokenizer





class        UpperCAmelCase_  (_UpperCAmelCase  ):





    """simple docstring"""






    def __init__(							self      ,					SCREAMING_SNAKE_CASE_     )    ->					int:
         __lowerCamelCase       :     Dict        =		AutoTokenizer.from_pretrained(SCREAMING_SNAKE_CASE_     )







    def 				lowercase_    (							self      ,					SCREAMING_SNAKE_CASE_     )    ->					int:

         if isinstance(SCREAMING_SNAKE_CASE_      ,					SCREAMING_SNAKE_CASE_     ) and all(isinstance(_x      ,					SCREAMING_SNAKE_CASE_     ) for _x in x     ):
              __lowerCamelCase       :     Union[str, Any]        =		None
              for l in x:
                   __lowerCamelCase       :     Tuple        =		self.tokenizer(
                       SCREAMING_SNAKE_CASE_      ,					padding='max_length'      ,					max_length=3_84      ,					truncation=SCREAMING_SNAKE_CASE_      ,					return_tensors='pt'      ,					)
                   __lowerCamelCase       :     str        =		torch.tensor([len(SCREAMING_SNAKE_CASE_     )]     )
                   if d is not None:
                        for k in d.keys():
                             __lowerCamelCase       :     List[Any]        =		torch.cat((d[k], t[k])      ,					0     )
                   else:
                        __lowerCamelCase       :     str        =		t

              __lowerCamelCase       :     List[Any]        =		torch.tensor(self.tokenizer.convert_tokens_to_ids('[E]'     )     )
              __lowerCamelCase       :     Tuple        =		torch.tensor(self.tokenizer.convert_tokens_to_ids('[/E]'     )     )

         elif isinstance(SCREAMING_SNAKE_CASE_      ,					SCREAMING_SNAKE_CASE_     ) and all(isinstance(_x      ,					SCREAMING_SNAKE_CASE_     ) for _x in x     ):
              __lowerCamelCase       :     Optional[Any]        =		self.tokenizer(
                  SCREAMING_SNAKE_CASE_      ,					padding='max_length'      ,					max_length=3_84      ,					truncation=SCREAMING_SNAKE_CASE_      ,					return_tensors='pt'      ,					)

         else:
              raise Exception(
                  'Type of parameter x was not recognized! Only `list of strings` for query or `list of lists of'
                  ' strings` for supports are supported.'     )

         return d







    def 				lowercase_    (							self      ,					SCREAMING_SNAKE_CASE_      ,					SCREAMING_SNAKE_CASE_      ,					SCREAMING_SNAKE_CASE_      ,					SCREAMING_SNAKE_CASE_      ,					SCREAMING_SNAKE_CASE_=0.7_0     )    ->					str:
         __lowerCamelCase       :     str        =		[]
         for idx in range(len(W_query['input_ids']     )     ):
              __lowerCamelCase       :     Optional[Any]        =		range(p_start.shape[1]     )

              __lowerCamelCase       :     List[Any]        =		[]
              for start_id in start_indexes:
                   for end_id in end_indexes:
                        if start_id < end_id:
                             output.append(
                                 (
                                     start_id,
                                     end_id,
                                     p_start[idx][start_id].item(),
                                     p_end[idx][end_id].item(),
                                 )     )

              output.sort(key=lambda SCREAMING_SNAKE_CASE_     : (tup[2] * tup[3])      ,					reverse=SCREAMING_SNAKE_CASE_     )
              __lowerCamelCase       :     str        =		[]
              for k in range(len(SCREAMING_SNAKE_CASE_     )     ):
                   if output[k][2] * output[k][3] >= thresh:
                        __lowerCamelCase    ,	__lowerCamelCase       :     Union[str, Any]        =		output[k][0], output[k][1]
                        __lowerCamelCase       :     List[str]        =		self.tokenizer.decode(W_query['input_ids'][idx][c_start_pos:c_end_pos]     )
                        temp.append((decoded, output[k][2] * output[k][3])     )

              final_outputs.append(SCREAMING_SNAKE_CASE_     )

         return final_outputs






