






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







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



      """simple docstring"""
      UpperCamelCase_    : torch.FloatTensor
      UpperCamelCase_    : Optional[torch.FloatTensor]									=None







def   _A       (    SCREAMING_SNAKE_CASE__  :     Tuple					,  SCREAMING_SNAKE_CASE__  :     str=0.9_99					,  SCREAMING_SNAKE_CASE__  :     Dict="cosine"					,  ):

    if alpha_transform_type == "cosine":

        def alpha_bar_fn(SCREAMING_SNAKE_CASE__  :     List[Any]	):
            return math.cos((t + 0.0_08) / 1.0_08 * math.pi / 2	) ** 2

    elif alpha_transform_type == "exp":

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

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

    UpperCamelCase	:Dict     							=       []
    for i in range(lowercase_	):
        UpperCamelCase	:List[str]     							=       i / num_diffusion_timesteps
        UpperCamelCase	:Union[str, Any]     							=       (i + 1) / num_diffusion_timesteps
        betas.append(min(1 - alpha_bar_fn(lowercase_	) / alpha_bar_fn(lowercase_	)					,  lowercase_	)	)
    return torch.tensor(lowercase_					,  dtype=torch.floataa	)







class       UpperCAmelCase_						( lowercase__,     lowercase__			):



      """simple docstring"""
      UpperCamelCase_    : Dict									=[e.name for e in KarrasDiffusionSchedulers]
      UpperCamelCase_    : str									=1
      @register_to_config
      def __init__(    self   ,    SCREAMING_SNAKE_CASE_ = 1000   ,    SCREAMING_SNAKE_CASE_ = 0.0001   ,    SCREAMING_SNAKE_CASE_ = 0.02   ,    SCREAMING_SNAKE_CASE_ = "linear"   ,    SCREAMING_SNAKE_CASE_ = None   ,    SCREAMING_SNAKE_CASE_ = "epsilon"   ,    SCREAMING_SNAKE_CASE_ = "linear"   ,    SCREAMING_SNAKE_CASE_ = False   ,    SCREAMING_SNAKE_CASE_ = "linspace"   ,    SCREAMING_SNAKE_CASE_ = 0   ,    )    ->				Optional[Any]:
          if trained_betas is not None:
              UpperCamelCase	:Tuple     							=       torch.tensor(lowerCAmelCase__   ,    dtype=torch.floataa			)
          elif beta_schedule == "linear":
              UpperCamelCase	:Any     							=       torch.linspace(lowerCAmelCase__   ,    lowerCAmelCase__   ,    lowerCAmelCase__   ,    dtype=torch.floataa			)
          elif beta_schedule == "scaled_linear":
              # this schedule is very specific to the latent diffusion model.
              UpperCamelCase	:Optional[Any]     							=       (
                  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	:Tuple     							=       betas_for_alpha_bar(lowerCAmelCase__			)
          else:
              raise NotImplementedError(F'''{beta_schedule} does is not implemented for {self.__class__}'''			)

          UpperCamelCase	:Optional[Any]     							=       1.0 - self.betas
          UpperCamelCase	:Tuple     							=       torch.cumprod(self.alphas   ,    dim=0			)

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

          # setable values
          UpperCamelCase	:str     							=       None
          UpperCamelCase	:str     							=       np.linspace(0   ,    num_train_timesteps - 1   ,    lowerCAmelCase__   ,    dtype=lowerCAmelCase__			)[::-1].copy()
          UpperCamelCase	:Optional[int]     							=       torch.from_numpy(lowerCAmelCase__			)
          UpperCamelCase	:List[Any]     							=       False
          UpperCamelCase	:Any     							=       use_karras_sigmas
      @property
      def    UpperCAmelCase						(    self			)    ->				List[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   ,    SCREAMING_SNAKE_CASE_   ,    SCREAMING_SNAKE_CASE_			)    ->				torch.FloatTensor:
          if isinstance(lowerCAmelCase__   ,    torch.Tensor			):
              UpperCamelCase	:Optional[int]     							=       timestep.to(self.timesteps.device			)
          UpperCamelCase	:Optional[int]     							=       (self.timesteps == timestep).nonzero().item()
          UpperCamelCase	:List[Any]     							=       self.sigmas[step_index]

          UpperCamelCase	:Any     							=       sample / ((sigma**2 + 1) ** 0.5)

          UpperCamelCase	:int     							=       True
          return sample
      def    UpperCAmelCase						(    self   ,    SCREAMING_SNAKE_CASE_   ,    SCREAMING_SNAKE_CASE_ = None			)    ->				List[str]:
          UpperCamelCase	:Union[str, 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":
              UpperCamelCase	:Optional[int]     							=       np.linspace(0   ,    self.config.num_train_timesteps - 1   ,    lowerCAmelCase__   ,    dtype=lowerCAmelCase__			)[
                  ::-1
              ].copy()
          elif self.config.timestep_spacing == "leading":
              UpperCamelCase	:List[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
              UpperCamelCase	:Union[str, Any]     							=       (np.arange(0   ,    lowerCAmelCase__			) * step_ratio).round()[::-1].copy().astype(lowerCAmelCase__			)
              timesteps += self.config.steps_offset
          elif self.config.timestep_spacing == "trailing":
              UpperCamelCase	:List[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
              UpperCamelCase	:Optional[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\'.'''			)

          UpperCamelCase	:str     							=       np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5			)
          UpperCamelCase	:Optional[int]     							=       np.log(lowerCAmelCase__			)

          if self.config.interpolation_type == "linear":
              UpperCamelCase	:List[Any]     							=       np.interp(lowerCAmelCase__   ,    np.arange(0   ,    len(lowerCAmelCase__			)			)   ,    lowerCAmelCase__			)
          elif self.config.interpolation_type == "log_linear":
              UpperCamelCase	:Union[str, 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:
              UpperCamelCase	:Any     							=       self._convert_to_karras(in_sigmas=lowerCAmelCase__   ,    num_inference_steps=self.num_inference_steps			)
              UpperCamelCase	:Tuple     							=       np.array([self._sigma_to_t(lowerCAmelCase__   ,    lowerCAmelCase__			) for sigma in sigmas]			)

          UpperCamelCase	:Optional[Any]     							=       np.concatenate([sigmas, [0.0]]			).astype(np.floataa			)
          UpperCamelCase	:List[Any]     							=       torch.from_numpy(lowerCAmelCase__			).to(device=lowerCAmelCase__			)
          if str(lowerCAmelCase__			).startswith('''mps'''			):
              # mps does not support float64
              UpperCamelCase	:Optional[Any]     							=       torch.from_numpy(lowerCAmelCase__			).to(lowerCAmelCase__   ,    dtype=torch.floataa			)
          else:
              UpperCamelCase	:List[str]     							=       torch.from_numpy(lowerCAmelCase__			).to(device=lowerCAmelCase__			)
      def    UpperCAmelCase						(    self   ,    SCREAMING_SNAKE_CASE_   ,    SCREAMING_SNAKE_CASE_			)    ->				Optional[Any]:
          UpperCamelCase	:Dict     							=       np.log(lowerCAmelCase__			)

          # get distribution
          UpperCamelCase	:Optional[int]     							=       log_sigma - log_sigmas[:, np.newaxis]

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

          UpperCamelCase	:Dict     							=       log_sigmas[low_idx]
          UpperCamelCase	:Union[str, Any]     							=       log_sigmas[high_idx]

          # interpolate sigmas
          UpperCamelCase	:Optional[Any]     							=       (low - log_sigma) / (low - high)
          UpperCamelCase	:str     							=       np.clip(lowerCAmelCase__   ,    0   ,    1			)

          # transform interpolation to time range
          UpperCamelCase	:List[Any]     							=       (1 - w) * low_idx + w * high_idx
          UpperCamelCase	:Any     							=       t.reshape(sigma.shape			)
          return t
      def    UpperCAmelCase						(    self   ,    SCREAMING_SNAKE_CASE_   ,    SCREAMING_SNAKE_CASE_			)    ->				torch.FloatTensor:
          UpperCamelCase	:Tuple     							=       in_sigmas[-1].item()
          UpperCamelCase	:List[str]     							=       in_sigmas[0].item()

          UpperCamelCase	:Dict     							=       7.0  # 7.0 is the value used in the paper
          UpperCamelCase	:Optional[int]     							=       np.linspace(0   ,    1   ,    lowerCAmelCase__			)
          UpperCamelCase	:Union[str, Any]     							=       sigma_min ** (1 / rho)
          UpperCamelCase	:Any     							=       sigma_max ** (1 / rho)
          UpperCamelCase	:Optional[Any]     							=       (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
          return sigmas
      def    UpperCAmelCase						(    self   ,    SCREAMING_SNAKE_CASE_   ,    SCREAMING_SNAKE_CASE_   ,    SCREAMING_SNAKE_CASE_   ,    SCREAMING_SNAKE_CASE_ = 0.0   ,    SCREAMING_SNAKE_CASE_ = 0.0   ,    SCREAMING_SNAKE_CASE_ = float('''inf'''			)   ,    SCREAMING_SNAKE_CASE_ = 1.0   ,    SCREAMING_SNAKE_CASE_ = None   ,    SCREAMING_SNAKE_CASE_ = True   ,    )    ->				Union[EulerDiscreteSchedulerOutput, Tuple]:

          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			):
              UpperCamelCase	:Optional[int]     							=       timestep.to(self.timesteps.device			)

          UpperCamelCase	:List[Any]     							=       (self.timesteps == timestep).nonzero().item()
          UpperCamelCase	:int     							=       self.sigmas[step_index]

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

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

          UpperCamelCase	:Tuple     							=       noise * s_noise
          UpperCamelCase	:Optional[Any]     							=       sigma * (gamma + 1)

          if gamma > 0:
              UpperCamelCase	:List[Any]     							=       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":
              UpperCamelCase	:List[str]     							=       model_output
          elif self.config.prediction_type == "epsilon":
              UpperCamelCase	:str     							=       sample - sigma_hat * model_output
          elif self.config.prediction_type == "v_prediction":
              # * c_out + input * c_skip
              UpperCamelCase	:Optional[int]     							=       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
          UpperCamelCase	:List[str]     							=       (sample - pred_original_sample) / sigma_hat

          UpperCamelCase	:Tuple     							=       self.sigmas[step_index + 1] - sigma_hat

          UpperCamelCase	:str     							=       sample + derivative * dt

          if not return_dict:
              return (prev_sample,)

          return EulerDiscreteSchedulerOutput(prev_sample=lowerCAmelCase__   ,    pred_original_sample=lowerCAmelCase__			)
      def    UpperCAmelCase						(    self   ,    SCREAMING_SNAKE_CASE_   ,    SCREAMING_SNAKE_CASE_   ,    SCREAMING_SNAKE_CASE_   ,    )    ->				torch.FloatTensor:
          UpperCamelCase	:Optional[Any]     							=       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	:List[Any]     							=       self.timesteps.to(original_samples.device   ,    dtype=torch.floataa			)
              UpperCamelCase	:Dict     							=       timesteps.to(original_samples.device   ,    dtype=torch.floataa			)
          else:
              UpperCamelCase	:Optional[int]     							=       self.timesteps.to(original_samples.device			)
              UpperCamelCase	:List[str]     							=       timesteps.to(original_samples.device			)

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

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

          UpperCamelCase	:str     							=       original_samples + noise * sigma
          return noisy_samples




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













from transformers import (
    DPRContextEncoderTokenizer,
    DPRContextEncoderTokenizerFast,
    DPRQuestionEncoderTokenizer,
    DPRQuestionEncoderTokenizerFast,
    DPRReaderOutput,
    DPRReaderTokenizer,
    DPRReaderTokenizerFast,
)
from transformers.testing_utils import require_tokenizers, slow
from transformers.tokenization_utils_base import BatchEncoding

from ..bert.test_tokenization_bert import BertTokenizationTest







@require_tokenizers
class       UpperCAmelCase_						( lowercase			):



      """simple docstring"""
      UpperCamelCase_    : Optional[int]									=DPRContextEncoderTokenizer
      UpperCamelCase_    : Any									=DPRContextEncoderTokenizerFast
      UpperCamelCase_    : Optional[int]									=True







@require_tokenizers
class       UpperCAmelCase_						( lowercase			):



      """simple docstring"""
      UpperCamelCase_    : Tuple									=DPRQuestionEncoderTokenizer
      UpperCamelCase_    : Union[str, Any]									=DPRQuestionEncoderTokenizerFast
      UpperCamelCase_    : Any									=True







@require_tokenizers
class       UpperCAmelCase_						( lowercase			):



      """simple docstring"""
      UpperCamelCase_    : Tuple									=DPRReaderTokenizer
      UpperCamelCase_    : List[Any]									=DPRReaderTokenizerFast
      UpperCamelCase_    : Union[str, Any]									=True
      @slow
      def    UpperCAmelCase						(    self			)    ->				Dict:
          UpperCamelCase	:Optional[int]     							=       self.tokenizer_class.from_pretrained('''bert-base-uncased'''			)

          UpperCamelCase	:Optional[Any]     							=       tokenizer.encode('''question sequence'''   ,    add_special_tokens=SCREAMING_SNAKE_CASE_			)
          UpperCamelCase	:Optional[Any]     							=       tokenizer.encode('''title sequence'''   ,    add_special_tokens=SCREAMING_SNAKE_CASE_			)
          UpperCamelCase	:List[str]     							=       tokenizer.encode('''text sequence ''' * 4   ,    add_special_tokens=SCREAMING_SNAKE_CASE_			)
          UpperCamelCase	:Optional[int]     							=       [[101] + text_a + [102] + text_a + [102] + text_a]
          UpperCamelCase	:Tuple     							=       BatchEncoding({'''input_ids''': input_ids}			)

          UpperCamelCase	:Dict     							=       [[0] * len(input_ids[0]			)]
          UpperCamelCase	:List[Any]     							=       [[0] * len(input_ids[0]			)]
          UpperCamelCase	:Tuple     							=       [0]
          UpperCamelCase	:Union[str, Any]     							=       DPRReaderOutput(SCREAMING_SNAKE_CASE_   ,    SCREAMING_SNAKE_CASE_   ,    SCREAMING_SNAKE_CASE_			)

          UpperCamelCase							,       UpperCamelCase	:Union[str, Any]     							=       8, 9
          UpperCamelCase	:Any     							=       10
          UpperCamelCase	:Optional[Any]     							=       10
          UpperCamelCase	:Optional[int]     							=       tokenizer.decode_best_spans(SCREAMING_SNAKE_CASE_   ,    SCREAMING_SNAKE_CASE_			)
          self.assertEqual(predicted_spans[0].start_index   ,    SCREAMING_SNAKE_CASE_			)
          self.assertEqual(predicted_spans[0].end_index   ,    SCREAMING_SNAKE_CASE_			)
          self.assertEqual(predicted_spans[0].doc_id   ,    0			)




      @slow
      def    UpperCAmelCase						(    self			)    ->				Any:
          UpperCamelCase	:List[str]     							=       self.tokenizer_class.from_pretrained('''bert-base-uncased'''			)

          UpperCamelCase	:Any     							=       tokenizer.encode('''question sequence'''   ,    add_special_tokens=SCREAMING_SNAKE_CASE_			)
          UpperCamelCase	:str     							=       tokenizer.encode('''title sequence'''   ,    add_special_tokens=SCREAMING_SNAKE_CASE_			)
          UpperCamelCase	:Union[str, Any]     							=       tokenizer.encode('''text sequence'''   ,    add_special_tokens=SCREAMING_SNAKE_CASE_			)
          UpperCamelCase	:List[Any]     							=       [101] + text_a + [102] + text_a + [102] + text_a
          UpperCamelCase	:Any     							=       tokenizer(questions=['''question sequence''']   ,    titles=['''title sequence''']   ,    texts=['''text sequence''']			)
          self.assertIn('''input_ids'''   ,    SCREAMING_SNAKE_CASE_			)
          self.assertIn('''attention_mask'''   ,    SCREAMING_SNAKE_CASE_			)
          self.assertListEqual(encoded_input['''input_ids'''][0]   ,    SCREAMING_SNAKE_CASE_			)





