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

@dataclass
class 		__magic_name__				( lowercase_					):


  """simple docstring"""





  _UpperCamelCase						 = 42







class 		__magic_name__				( lowercase_						,lowercase_					):


  """simple docstring"""





  _UpperCamelCase						 = 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	,		):
    # standard deviation of the initial noise distribution
    _lowerCamelCase      =						sigma_max

    _lowerCamelCase      =						np.linspace(0	,		1	,		a__		)
    _lowerCamelCase      =						self._convert_to_karras(a__		)
    _lowerCamelCase      =						self.sigma_to_t(a__		)

    # setable values
    _lowerCamelCase      =						None
    _lowerCamelCase      =						torch.from_numpy(a__		)
    _lowerCamelCase      =						torch.from_numpy(a__		)
    _lowerCamelCase      =						False
    _lowerCamelCase      =						False


  def     _UpperCAmelCase  (					self	,		a__	,		a__=None		):
    if schedule_timesteps is None:
      _lowerCamelCase      =						self.timesteps

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


  def     _UpperCAmelCase  (					self	,		a__	,		a__		):
    # Get sigma corresponding to timestep
    if isinstance(a__	,		torch.Tensor		):
      _lowerCamelCase      =						timestep.to(self.timesteps.device		)
    _lowerCamelCase      =						self.index_for_timestep(a__		)
    _lowerCamelCase      =						self.sigmas[step_idx]

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

    _lowerCamelCase      =						True
    return sample


  def     _UpperCAmelCase  (					self	,		a__		):
    if not isinstance(a__	,		np.ndarray		):
      _lowerCamelCase      =						np.array(a__	,		dtype=np.floataa		)

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

    return timesteps


  def     _UpperCAmelCase  (					self	,		a__ = None	,		a__ = None	,		a__ = None	,		):
    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(a__		)		):
        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}.'''		)

      _lowerCamelCase      =						np.array(a__	,		dtype=np.intaa		)
      _lowerCamelCase      =						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.'''		)

      _lowerCamelCase      =						num_inference_steps

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

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

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

    if str(a__		).startswith('''mps'''		):
      # mps does not support float64
      _lowerCamelCase      =						torch.from_numpy(a__		).to(a__	,		dtype=torch.floataa		)
    else:
      _lowerCamelCase      =						torch.from_numpy(a__		).to(device=a__		)


  def     _UpperCAmelCase  (					self	,		a__		):
    _lowerCamelCase      =						self.config.sigma_min
    _lowerCamelCase      =						self.config.sigma_max

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


  def     _UpperCAmelCase  (					self	,		a__		):
    _lowerCamelCase      =						self.config.sigma_data

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


  def     _UpperCAmelCase  (					self	,		a__		):
    _lowerCamelCase      =						self.config.sigma_min
    _lowerCamelCase      =						self.config.sigma_data

    _lowerCamelCase      =						sigma_data**2 / ((sigma - sigma_min) ** 2 + sigma_data**2)
    _lowerCamelCase      =						(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	,		):

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

    _lowerCamelCase      =						self.config.sigma_min
    _lowerCamelCase      =						self.config.sigma_max

    _lowerCamelCase      =						self.index_for_timestep(a__		)

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

    # Get scalings for boundary conditions
    _lowerCamelCase    ,    _lowerCamelCase      =						self.get_scalings_for_boundary_condition(a__		)

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

    _lowerCamelCase      =						sigma_next.clamp(min=a__	,		max=a__		)

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

    if not return_dict:
      return (prev_sample,)

    return CMStochasticIterativeSchedulerOutput(prev_sample=a__		)


  def     _UpperCAmelCase  (					self	,		a__	,		a__	,		a__	,		):
    # Make sure sigmas and timesteps have the same device and dtype as original_samples
    _lowerCamelCase      =						self.sigmas.to(device=original_samples.device	,		dtype=original_samples.dtype		)
    if original_samples.device.type == "mps" and torch.is_floating_point(a__		):
      # mps does not support float64
      _lowerCamelCase      =						self.timesteps.to(original_samples.device	,		dtype=torch.floataa		)
      _lowerCamelCase      =						timesteps.to(original_samples.device	,		dtype=torch.floataa		)
    else:
      _lowerCamelCase      =						self.timesteps.to(original_samples.device		)
      _lowerCamelCase      =						timesteps.to(original_samples.device		)

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

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

    _lowerCamelCase      =						original_samples + noise * sigma
    return noisy_samples


  def __len__(					self		):
    return self.config.num_train_timesteps






import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin

class 		__magic_name__				( lowercase_					):


  """simple docstring"""





  _UpperCamelCase						 = ["image_processor", "tokenizer"]
  _UpperCamelCase						 = "AutoImageProcessor"
  _UpperCamelCase						 = "AutoTokenizer"


  def __init__(					self	,		a__=None	,		a__=None	,		**a__		):
    _lowerCamelCase      =						None
    if "feature_extractor" in kwargs:
      warnings.warn(
          '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
          ''' instead.'''	,		a__	,		)
      _lowerCamelCase      =						kwargs.pop('''feature_extractor'''		)

    _lowerCamelCase      =						image_processor if image_processor is not None else feature_extractor
    if image_processor is None:
      raise ValueError('''You need to specify an `image_processor`.'''		)
    if tokenizer is None:
      raise ValueError('''You need to specify a `tokenizer`.'''		)

    super().__init__(a__	,		a__		)
    _lowerCamelCase      =						self.image_processor
    _lowerCamelCase      =						False


  def __call__(					self	,		*a__	,		**a__		):
    # For backward compatibility
    if self._in_target_context_manager:
      return self.current_processor(*a__	,		**a__		)

    _lowerCamelCase      =						kwargs.pop('''images'''	,		a__		)
    _lowerCamelCase      =						kwargs.pop('''text'''	,		a__		)
    if len(a__		) > 0:
      _lowerCamelCase      =						args[0]
      _lowerCamelCase      =						args[1:]

    if images is None and text is None:
      raise ValueError('''You need to specify either an `images` or `text` input to process.'''		)

    if images is not None:
      _lowerCamelCase      =						self.image_processor(a__	,		*a__	,		**a__		)
    if text is not None:
      _lowerCamelCase      =						self.tokenizer(a__	,		**a__		)

    if text is None:
      return inputs
    elif images is None:
      return encodings
    else:
      _lowerCamelCase      =						encodings['''input_ids''']
      return inputs


  def     _UpperCAmelCase  (					self	,		*a__	,		**a__		):
    return self.tokenizer.batch_decode(*a__	,		**a__		)


  def     _UpperCAmelCase  (					self	,		*a__	,		**a__		):
    return self.tokenizer.decode(*a__	,		**a__		)


  @contextmanager
  def     _UpperCAmelCase  (					self		):
    warnings.warn(
        '''`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your '''
        '''labels by using the argument `text` of the regular `__call__` method (either in the same call as '''
        '''your images inputs, or in a separate call.'''		)
    _lowerCamelCase      =						True
    _lowerCamelCase      =						self.tokenizer
    yield
    _lowerCamelCase      =						self.image_processor
    _lowerCamelCase      =						False


  @property
  def     _UpperCAmelCase  (					self		):
    warnings.warn(
        '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'''	,		a__	,		)
    return self.image_processor_class


  @property
  def     _UpperCAmelCase  (					self		):
    warnings.warn(
        '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'''	,		a__	,		)
    return self.image_processor




