
'''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


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

@dataclass
class       _a (lowerCamelCase_):





       """simple docstring"""




       SCREAMING_SNAKE_CASE					=     42






class       _a (lowerCamelCase_     ,    lowerCamelCase_):





       """simple docstring"""




       SCREAMING_SNAKE_CASE					=     1






       @register_to_config
       def __init__(							self				,							A__ = 40				,							A__ = 0.002				,							A__ = 80.0				,							A__ = 0.5				,							A__ = 1.0				,							A__ = 7.0				,							A__ = True				,							)			->	Dict:
          _SCREAMING_SNAKE_CASE          = sigma_max

          _SCREAMING_SNAKE_CASE          = np.linspace(0				,							1				,							snake_case_					)
          _SCREAMING_SNAKE_CASE          = self._convert_to_karras(snake_case_					)
          _SCREAMING_SNAKE_CASE          = self.sigma_to_t(snake_case_					)

          # setable values
          _SCREAMING_SNAKE_CASE          = None
          _SCREAMING_SNAKE_CASE          = torch.from_numpy(snake_case_					)
          _SCREAMING_SNAKE_CASE          = torch.from_numpy(snake_case_					)
          _SCREAMING_SNAKE_CASE          = False
          _SCREAMING_SNAKE_CASE          = False






       def 				UpperCamelCase (							self				,							A__				,							A__=None					)			->	Tuple:

          if schedule_timesteps is None:
             _SCREAMING_SNAKE_CASE          = self.timesteps

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






       def 				UpperCamelCase (							self				,							A__				,							A__					)			->	Tuple:

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

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

          _SCREAMING_SNAKE_CASE          = True
          return sample






       def 				UpperCamelCase (							self				,							A__					)			->	str:

          if not isinstance(snake_case_				,							np.ndarray					):
             _SCREAMING_SNAKE_CASE          = np.array(snake_case_				,							dtype=np.floataa					)

          _SCREAMING_SNAKE_CASE          = 10_00 * 0.25 * np.log(sigmas + 1E-44					)

          return timesteps






       def 				UpperCamelCase (							self				,							A__ = None				,							A__ = None				,							A__ = None				,							)			->	Tuple:

          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(snake_case_					)					):
                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}."					)

             _SCREAMING_SNAKE_CASE          = np.array(snake_case_				,							dtype=np.intaa					)
             _SCREAMING_SNAKE_CASE          = 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."					)

             _SCREAMING_SNAKE_CASE          = num_inference_steps

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

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

          _SCREAMING_SNAKE_CASE          = np.concatenate([sigmas, [self.sigma_min]]					).astype(np.floataa					)
          _SCREAMING_SNAKE_CASE          = torch.from_numpy(snake_case_					).to(device=snake_case_					)

          if str(snake_case_					).startswith("""mps"""					):
             # mps does not support float64
             _SCREAMING_SNAKE_CASE          = torch.from_numpy(snake_case_					).to(snake_case_				,							dtype=torch.floataa					)
          else:
             _SCREAMING_SNAKE_CASE          = torch.from_numpy(snake_case_					).to(device=snake_case_					)






       def 				UpperCamelCase (							self				,							A__					)			->	Dict:
          _SCREAMING_SNAKE_CASE          = self.config.sigma_min
          _SCREAMING_SNAKE_CASE          = self.config.sigma_max

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






       def 				UpperCamelCase (							self				,							A__					)			->	Tuple:
          _SCREAMING_SNAKE_CASE          = self.config.sigma_data

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






       def 				UpperCamelCase (							self				,							A__					)			->	Dict:
          _SCREAMING_SNAKE_CASE          = self.config.sigma_min
          _SCREAMING_SNAKE_CASE          = self.config.sigma_data

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






       def 				UpperCamelCase (							self				,							A__				,							A__				,							A__				,							A__ = None				,							A__ = True				,							)			->	List[Any]:

          if (
              isinstance(snake_case_				,							snake_case_					)
              or isinstance(snake_case_				,							torch.IntTensor					)
              or isinstance(snake_case_				,							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(snake_case_				,							torch.Tensor					):
             _SCREAMING_SNAKE_CASE          = timestep.to(self.timesteps.device					)

          _SCREAMING_SNAKE_CASE          = self.config.sigma_min
          _SCREAMING_SNAKE_CASE          = self.config.sigma_max

          _SCREAMING_SNAKE_CASE          = self.index_for_timestep(snake_case_					)

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

          # Get scalings for boundary conditions
          _SCREAMING_SNAKE_CASE          = self.get_scalings_for_boundary_condition(snake_case_					)

          # 1. Denoise model output using boundary conditions
          _SCREAMING_SNAKE_CASE          = c_out * model_output + c_skip * sample
          if self.config.clip_denoised:
             _SCREAMING_SNAKE_CASE          = 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:
             _SCREAMING_SNAKE_CASE          = randn_tensor(
                 model_output.shape				,							dtype=model_output.dtype				,							device=model_output.device				,							generator=snake_case_					)
          else:
             _SCREAMING_SNAKE_CASE          = torch.zeros_like(snake_case_					)
          _SCREAMING_SNAKE_CASE          = noise * self.config.s_noise

          _SCREAMING_SNAKE_CASE          = sigma_next.clamp(min=snake_case_				,							max=snake_case_					)

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

          if not return_dict:
             return (prev_sample,)

          return CMStochasticIterativeSchedulerOutput(prev_sample=snake_case_					)






       def 				UpperCamelCase (							self				,							A__				,							A__				,							A__				,							)			->	Any:
          _SCREAMING_SNAKE_CASE          = self.sigmas.to(device=original_samples.device				,							dtype=original_samples.dtype					)
          if original_samples.device.type == "mps" and torch.is_floating_point(snake_case_					):
             # mps does not support float64
             _SCREAMING_SNAKE_CASE          = self.timesteps.to(original_samples.device				,							dtype=torch.floataa					)
             _SCREAMING_SNAKE_CASE          = timesteps.to(original_samples.device				,							dtype=torch.floataa					)
          else:
             _SCREAMING_SNAKE_CASE          = self.timesteps.to(original_samples.device					)
             _SCREAMING_SNAKE_CASE          = timesteps.to(original_samples.device					)

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

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

          _SCREAMING_SNAKE_CASE          = original_samples + noise * sigma
          return noisy_samples






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







'''simple docstring'''


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


snake_case__     :			int      			=  {
    '''configuration_markuplm''': ['''MARKUPLM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MarkupLMConfig'''],
    '''feature_extraction_markuplm''': ['''MarkupLMFeatureExtractor'''],
    '''processing_markuplm''': ['''MarkupLMProcessor'''],
    '''tokenization_markuplm''': ['''MarkupLMTokenizer'''],
}

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 snake_case__     :			List[str]      			=  ['''MarkupLMTokenizerFast''']

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 snake_case__     :			Dict      			=  [
     '''MARKUPLM_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''MarkupLMForQuestionAnswering''',
     '''MarkupLMForSequenceClassification''',
     '''MarkupLMForTokenClassification''',
     '''MarkupLMModel''',
     '''MarkupLMPreTrainedModel''',
 ]


if TYPE_CHECKING:
 from .configuration_markuplm import MARKUPLM_PRETRAINED_CONFIG_ARCHIVE_MAP, MarkupLMConfig
 from .feature_extraction_markuplm import MarkupLMFeatureExtractor
 from .processing_markuplm import MarkupLMProcessor
 from .tokenization_markuplm import MarkupLMTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_markuplm_fast import MarkupLMTokenizerFast

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_markuplm import (
      MARKUPLM_PRETRAINED_MODEL_ARCHIVE_LIST,
      MarkupLMForQuestionAnswering,
      MarkupLMForSequenceClassification,
      MarkupLMForTokenClassification,
      MarkupLMModel,
      MarkupLMPreTrainedModel,
  )


else:
 import sys

 snake_case__     :			Optional[Any]      			=  _LazyModule(__name__, globals()['''__file__'''], _import_structure)






