




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






@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->EulerAncestralDiscrete
class 	SCREAMING_SNAKE_CASE(    __A					):
       snake_case_			:					torch.FloatTensor
       snake_case_			:					Optional[torch.FloatTensor]   = None

def     snake_case_  (       a__		:     Optional[int]   ,a__		:     List[str]=0.9_9_9   ,a__		:     Any="cosine"   ,):



    """simple docstring"""




    if alpha_transform_type == "cosine":

        def alpha_bar_fn(a__		:     List[Any]      ):
            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(a__		:     List[str]      ):
            return math.exp(t * -1_2.0      )

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

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






class 	SCREAMING_SNAKE_CASE(    __A       ,				__A					):
       snake_case_			:					Tuple   = [e.name for e in KarrasDiffusionSchedulers]
       snake_case_			:					int   = 1

       @register_to_config
       def __init__(   self ,		lowerCamelCase__ = 1000 ,		lowerCamelCase__ = 0.00_01 ,		lowerCamelCase__ = 0.02 ,		lowerCamelCase__ = "linear" ,		lowerCamelCase__ = None ,		lowerCamelCase__ = "epsilon" ,		lowerCamelCase__ = "linspace" ,		lowerCamelCase__ = 0 ,		)   -> List[Any]:

           """simple docstring"""



           if trained_betas is not None:
               __lowercase          =						torch.tensor(lowerCamelCase__ ,		dtype=torch.floataa					)
           elif beta_schedule == "linear":
               __lowercase          =						torch.linspace(lowerCamelCase__ ,		lowerCamelCase__ ,		lowerCamelCase__ ,		dtype=torch.floataa					)
           elif beta_schedule == "scaled_linear":
               # this schedule is very specific to the latent diffusion model.
               __lowercase          =						(
                   torch.linspace(beta_start**0.5 ,		beta_end**0.5 ,		lowerCamelCase__ ,		dtype=torch.floataa					) ** 2
               )
           elif beta_schedule == "squaredcos_cap_v2":
               # Glide cosine schedule
               __lowercase          =						betas_for_alpha_bar(lowerCamelCase__					)
           else:
               raise NotImplementedError(F'{beta_schedule} does is not implemented for {self.__class__}'					)

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

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

           # setable values
           __lowercase          =						None
           __lowercase          =						np.linspace(0 ,		num_train_timesteps - 1 ,		lowerCamelCase__ ,		dtype=lowerCamelCase__					)[::-1].copy()
           __lowercase          =						torch.from_numpy(lowerCamelCase__					)
           __lowercase          =						False

       @property
       def    snake_case__      (   self					)   -> Tuple:

           """simple docstring"""



           if self.config.timestep_spacing in ["linspace", "trailing"]:
               return self.sigmas.max()

           return (self.sigmas.max() ** 2 + 1) ** 0.5

       def    snake_case__      (   self ,		lowerCamelCase__ ,		lowerCamelCase__					)   -> torch.FloatTensor:

           """simple docstring"""



           if isinstance(lowerCamelCase__ ,		torch.Tensor					):
               __lowercase          =						timestep.to(self.timesteps.device					)
           __lowercase          =						(self.timesteps == timestep).nonzero().item()
           __lowercase          =						self.sigmas[step_index]
           __lowercase          =						sample / ((sigma**2 + 1) ** 0.5)
           __lowercase          =						True
           return sample

       def    snake_case__      (   self ,		lowerCamelCase__ ,		lowerCamelCase__ = None					)   -> Union[str, Any]:

           """simple docstring"""



           __lowercase          =						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":
               __lowercase          =						np.linspace(0 ,		self.config.num_train_timesteps - 1 ,		lowerCamelCase__ ,		dtype=lowerCamelCase__					)[
                   ::-1
               ].copy()
           elif self.config.timestep_spacing == "leading":
               __lowercase          =						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
               __lowercase          =						(np.arange(0 ,		lowerCamelCase__					) * step_ratio).round()[::-1].copy().astype(lowerCamelCase__					)
               timesteps += self.config.steps_offset
           elif self.config.timestep_spacing == "trailing":
               __lowercase          =						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
               __lowercase          =						(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\'.'					)

           __lowercase          =						np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5					)
           __lowercase          =						np.interp(lowerCamelCase__ ,		np.arange(0 ,		len(lowerCamelCase__					)					) ,		lowerCamelCase__					)
           __lowercase          =						np.concatenate([sigmas, [0.0]]					).astype(np.floataa					)
           __lowercase          =						torch.from_numpy(lowerCamelCase__					).to(device=lowerCamelCase__					)
           if str(lowerCamelCase__					).startswith("""mps"""					):
               # mps does not support float64
               __lowercase          =						torch.from_numpy(lowerCamelCase__					).to(lowerCamelCase__ ,		dtype=torch.floataa					)
           else:
               __lowercase          =						torch.from_numpy(lowerCamelCase__					).to(device=lowerCamelCase__					)

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

           """simple docstring"""



           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					):
               __lowercase          =						timestep.to(self.timesteps.device					)

           __lowercase          =						(self.timesteps == timestep).nonzero().item()
           __lowercase          =						self.sigmas[step_index]

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

           __lowercase          =						self.sigmas[step_index]
           __lowercase          =						self.sigmas[step_index + 1]
           __lowercase          =						(sigma_to**2 * (sigma_from**2 - sigma_to**2) / sigma_from**2) ** 0.5
           __lowercase          =						(sigma_to**2 - sigma_up**2) ** 0.5

           # 2. Convert to an ODE derivative
           __lowercase          =						(sample - pred_original_sample) / sigma

           __lowercase          =						sigma_down - sigma

           __lowercase          =						sample + derivative * dt

           __lowercase          =						model_output.device
           __lowercase          =						randn_tensor(model_output.shape ,		dtype=model_output.dtype ,		device=lowerCamelCase__ ,		generator=lowerCamelCase__					)

           __lowercase          =						prev_sample + noise * sigma_up

           if not return_dict:
               return (prev_sample,)

           return EulerAncestralDiscreteSchedulerOutput(
               prev_sample=lowerCamelCase__ ,		pred_original_sample=lowerCamelCase__					)

       def    snake_case__      (   self ,		lowerCamelCase__ ,		lowerCamelCase__ ,		lowerCamelCase__ ,		)   -> torch.FloatTensor:

           """simple docstring"""



           __lowercase          =						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
               __lowercase          =						self.timesteps.to(original_samples.device ,		dtype=torch.floataa					)
               __lowercase          =						timesteps.to(original_samples.device ,		dtype=torch.floataa					)
           else:
               __lowercase          =						self.timesteps.to(original_samples.device					)
               __lowercase          =						timesteps.to(original_samples.device					)

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

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

           __lowercase          =						original_samples + noise * sigma
           return noisy_samples




       def __len__(   self					)   -> Optional[Any]:

           """simple docstring"""



           return self.config.num_train_timesteps










'''simple docstring'''
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


A							:				Tuple			     =	{
    """configuration_dpr""": ["""DPR_PRETRAINED_CONFIG_ARCHIVE_MAP""", """DPRConfig"""],
    """tokenization_dpr""": [
        """DPRContextEncoderTokenizer""",
        """DPRQuestionEncoderTokenizer""",
        """DPRReaderOutput""",
        """DPRReaderTokenizer""",
    ],
}


try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  A							:				str			     =	[
      """DPRContextEncoderTokenizerFast""",
      """DPRQuestionEncoderTokenizerFast""",
      """DPRReaderTokenizerFast""",
  ]

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  A							:				Tuple			     =	[
      """DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """DPRContextEncoder""",
      """DPRPretrainedContextEncoder""",
      """DPRPreTrainedModel""",
      """DPRPretrainedQuestionEncoder""",
      """DPRPretrainedReader""",
      """DPRQuestionEncoder""",
      """DPRReader""",
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  A							:				Tuple			     =	[
      """TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """TF_DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """TF_DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """TFDPRContextEncoder""",
      """TFDPRPretrainedContextEncoder""",
      """TFDPRPretrainedQuestionEncoder""",
      """TFDPRPretrainedReader""",
      """TFDPRQuestionEncoder""",
      """TFDPRReader""",
  ]


if TYPE_CHECKING:
  from .configuration_dpr import DPR_PRETRAINED_CONFIG_ARCHIVE_MAP, DPRConfig
  from .tokenization_dpr import (
      DPRContextEncoderTokenizer,
      DPRQuestionEncoderTokenizer,
      DPRReaderOutput,
      DPRReaderTokenizer,
  )

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_dpr_fast import (
        DPRContextEncoderTokenizerFast,
        DPRQuestionEncoderTokenizerFast,
        DPRReaderTokenizerFast,
    )

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_dpr import (
        DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
        DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
        DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
        DPRContextEncoder,
        DPRPretrainedContextEncoder,
        DPRPreTrainedModel,
        DPRPretrainedQuestionEncoder,
        DPRPretrainedReader,
        DPRQuestionEncoder,
        DPRReader,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_dpr import (
        TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
        TF_DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
        TF_DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFDPRContextEncoder,
        TFDPRPretrainedContextEncoder,
        TFDPRPretrainedQuestionEncoder,
        TFDPRPretrainedReader,
        TFDPRQuestionEncoder,
        TFDPRReader,
    )

else:
  import sys

  A							:				int			     =	_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




