




'''simple docstring'''
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 SchedulerMixin


A							:				List[str]			     =	logging.get_logger(__name__)  # pylint: disable=invalid-name






@dataclass
class 	SCREAMING_SNAKE_CASE(    __A					):
       snake_case_			:					torch.FloatTensor






class 	SCREAMING_SNAKE_CASE(    __A       ,				__A					):
       snake_case_			:					List[str]   = 1

       @register_to_config
       def __init__(   self ,		lowerCamelCase__ = 40 ,		lowerCamelCase__ = 0.0_02 ,		lowerCamelCase__ = 80.0 ,		lowerCamelCase__ = 0.5 ,		lowerCamelCase__ = 1.0 ,		lowerCamelCase__ = 7.0 ,		lowerCamelCase__ = True ,		)   -> Tuple:

           """simple docstring"""



           __lowercase          =						sigma_max

           __lowercase          =						np.linspace(0 ,		1 ,		lowerCamelCase__					)
           __lowercase          =						self._convert_to_karras(lowerCamelCase__					)
           __lowercase          =						self.sigma_to_t(lowerCamelCase__					)

           # setable values
           __lowercase          =						None
           __lowercase          =						torch.from_numpy(lowerCamelCase__					)
           __lowercase          =						torch.from_numpy(lowerCamelCase__					)
           __lowercase          =						False
           __lowercase          =						False

       def    snake_case__      (   self ,		lowerCamelCase__ ,		lowerCamelCase__=None					)   -> Dict:

           """simple docstring"""



           if schedule_timesteps is None:
               __lowercase          =						self.timesteps

           __lowercase          =						(schedule_timesteps == timestep).nonzero()
           return indices.item()

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

           """simple docstring"""



           if isinstance(lowerCamelCase__ ,		torch.Tensor					):
               __lowercase          =						timestep.to(self.timesteps.device					)
           __lowercase          =						self.index_for_timestep(lowerCamelCase__					)
           __lowercase          =						self.sigmas[step_idx]

           __lowercase          =						sample / ((sigma**2 + self.config.sigma_data**2) ** 0.5)

           __lowercase          =						True
           return sample

       def    snake_case__      (   self ,		lowerCamelCase__					)   -> Tuple:

           """simple docstring"""



           if not isinstance(lowerCamelCase__ ,		np.ndarray					):
               __lowercase          =						np.array(lowerCamelCase__ ,		dtype=np.floataa					)

           __lowercase          =						1000 * 0.25 * np.log(sigmas + 1E-4_4					)

           return timesteps

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

           """simple docstring"""



           if num_inference_steps is None and timesteps is None:
               raise ValueError("""Exactly one of `num_inference_steps` or `timesteps` must be supplied."""					)

           if num_inference_steps is not None and timesteps is not None:
               raise ValueError("""Can only pass one of `num_inference_steps` or `timesteps`."""					)

           # Follow DDPMScheduler custom timesteps logic
           if timesteps is not None:
               for i in range(1 ,		len(lowerCamelCase__					)					):
                   if timesteps[i] >= timesteps[i - 1]:
                       raise ValueError("""`timesteps` must be in descending order."""					)

               if timesteps[0] >= self.config.num_train_timesteps:
                   raise ValueError(
                       F'`timesteps` must start before `self.config.train_timesteps`:'
                       F' {self.config.num_train_timesteps}.'					)

               __lowercase          =						np.array(lowerCamelCase__ ,		dtype=np.intaa					)
               __lowercase          =						True
           else:
               if num_inference_steps > self.config.num_train_timesteps:
                   raise ValueError(
                       F'`num_inference_steps`: {num_inference_steps} cannot be larger than `self.config.train_timesteps`:'
                       F' {self.config.num_train_timesteps} as the unet model trained with this scheduler can only handle'
                       F' maximal {self.config.num_train_timesteps} timesteps.'					)

               __lowercase          =						num_inference_steps

               __lowercase          =						self.config.num_train_timesteps // self.num_inference_steps
               __lowercase          =						(np.arange(0 ,		lowerCamelCase__					) * step_ratio).round()[::-1].copy().astype(np.intaa					)
               __lowercase          =						False

           # Map timesteps to Karras sigmas directly for multistep sampling
           # See https://github.com/openai/consistency_models/blob/main/cm/karras_diffusion.py#L675
           __lowercase          =						self.config.num_train_timesteps
           __lowercase          =						timesteps[::-1].copy()
           __lowercase          =						ramp / (num_train_timesteps - 1)
           __lowercase          =						self._convert_to_karras(lowerCamelCase__					)
           __lowercase          =						self.sigma_to_t(lowerCamelCase__					)

           __lowercase          =						np.concatenate([sigmas, [self.sigma_min]]					).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__					)   -> List[Any]:

           """simple docstring"""



           __lowercase          =						self.config.sigma_min
           __lowercase          =						self.config.sigma_max

           __lowercase          =						self.config.rho
           __lowercase          =						sigma_min ** (1 / rho)
           __lowercase          =						sigma_max ** (1 / rho)
           __lowercase          =						(max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
           return sigmas

       def    snake_case__      (   self ,		lowerCamelCase__					)   -> Dict:

           """simple docstring"""



           __lowercase          =						self.config.sigma_data

           __lowercase          =						sigma_data**2 / (sigma**2 + sigma_data**2)
           __lowercase          =						sigma * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
           return c_skip, c_out

       def    snake_case__      (   self ,		lowerCamelCase__					)   -> Tuple:

           """simple docstring"""



           __lowercase          =						self.config.sigma_min
           __lowercase          =						self.config.sigma_data

           __lowercase          =						sigma_data**2 / ((sigma - sigma_min) ** 2 + sigma_data**2)
           __lowercase          =						(sigma - sigma_min) * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
           return c_skip, c_out

       def    snake_case__      (   self ,		lowerCamelCase__ ,		lowerCamelCase__ ,		lowerCamelCase__ ,		lowerCamelCase__ = None ,		lowerCamelCase__ = True ,		)   -> Union[CMStochasticIterativeSchedulerOutput, 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"""
                       F' `{self.__class__}.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.config.sigma_min
           __lowercase          =						self.config.sigma_max

           __lowercase          =						self.index_for_timestep(lowerCamelCase__					)

           # sigma_next corresponds to next_t in original implementation
           __lowercase          =						self.sigmas[step_index]
           if step_index + 1 < self.config.num_train_timesteps:
               __lowercase          =						self.sigmas[step_index + 1]
           else:
               # Set sigma_next to sigma_min
               __lowercase          =						self.sigmas[-1]

           # Get scalings for boundary conditions
           __lowercase    ,__lowercase          =						self.get_scalings_for_boundary_condition(lowerCamelCase__					)

           # 1. Denoise model output using boundary conditions
           __lowercase          =						c_out * model_output + c_skip * sample
           if self.config.clip_denoised:
               __lowercase          =						denoised.clamp(-1 ,		1					)

           # 2. Sample z ~ N(0, s_noise^2 * I)
           # Noise is not used for onestep sampling.
           if len(self.timesteps					) > 1:
               __lowercase          =						randn_tensor(
                   model_output.shape ,		dtype=model_output.dtype ,		device=model_output.device ,		generator=lowerCamelCase__					)
           else:
               __lowercase          =						torch.zeros_like(lowerCamelCase__					)
           __lowercase          =						noise * self.config.s_noise

           __lowercase          =						sigma_next.clamp(min=lowerCamelCase__ ,		max=lowerCamelCase__					)

           # 3. Return noisy sample
           # tau = sigma_hat, eps = sigma_min
           __lowercase          =						denoised + z * (sigma_hat**2 - sigma_min**2) ** 0.5

           if not return_dict:
               return (prev_sample,)

           return CMStochasticIterativeSchedulerOutput(prev_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					)   -> Union[str, 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__)




