



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 		UpperCAmelCase    (    _lowerCAmelCase					):
   SCREAMING_SNAKE_CASE__					      =  42






class 		UpperCAmelCase    (    _lowerCAmelCase							,_lowerCAmelCase					):
   SCREAMING_SNAKE_CASE__					      =  1





   @register_to_config
   def __init__(						self							,  _lowerCAmelCase = 40							,  _lowerCAmelCase = 0.002							,  _lowerCAmelCase = 80.0							,  _lowerCAmelCase = 0.5							,  _lowerCAmelCase = 1.0							,  _lowerCAmelCase = 7.0							,  _lowerCAmelCase = True							,  ):
         _lowerCAmelCase			      =	sigma_max

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

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





   def 						__lowerCAmelCase				(						self							,  _lowerCAmelCase							,  _lowerCAmelCase=None      ):
         if schedule_timesteps is None:
               _lowerCAmelCase			      =	self.timesteps

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





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

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

         _lowerCAmelCase			      =	True
         return sample





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

         _lowerCAmelCase			      =	1_000 * 0.25 * np.log(sigmas + 1E-44      )

         return timesteps





   def 						__lowerCAmelCase				(						self							,  _lowerCAmelCase = None							,  _lowerCAmelCase = None							,  _lowerCAmelCase = 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(_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}.'''      )

               _lowerCAmelCase			      =	np.array(_lowerCamelCase							,  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							,  _lowerCamelCase      ) * 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(_lowerCamelCase      )
         _lowerCAmelCase			      =	self.sigma_to_t(_lowerCamelCase      )

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

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





   def 						__lowerCAmelCase				(						self							,  _lowerCAmelCase      ):
         _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 						__lowerCAmelCase				(						self							,  _lowerCAmelCase      ):
         _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 						__lowerCAmelCase				(						self							,  _lowerCAmelCase      ):
         _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 						__lowerCAmelCase				(						self							,  _lowerCAmelCase							,  _lowerCAmelCase							,  _lowerCAmelCase							,  _lowerCAmelCase = None							,  _lowerCAmelCase = True							,  ):

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

         _lowerCAmelCase			      =	self.config.sigma_min
         _lowerCAmelCase			      =	self.config.sigma_max

         _lowerCAmelCase			      =	self.index_for_timestep(_lowerCamelCase      )

         # 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(_lowerCamelCase      )

         # 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=_lowerCamelCase      )
         else:
               _lowerCAmelCase			      =	torch.zeros_like(_lowerCamelCase      )
         _lowerCAmelCase			      =	noise * self.config.s_noise

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

         # 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=_lowerCamelCase      )





   def 						__lowerCAmelCase				(						self							,  _lowerCAmelCase							,  _lowerCAmelCase							,  _lowerCAmelCase							,  ):
         _lowerCAmelCase			      =	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
               _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







"""simple docstring"""


from typing import TYPE_CHECKING

from ..utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_tf_available, is_torch_available


lowercase_								=	{
    'configuration_utils': ['GenerationConfig'],
    'streamers': ['TextIteratorStreamer', 'TextStreamer'],
}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowercase_								=	[
           'Constraint',
           'ConstraintListState',
           'DisjunctiveConstraint',
           'PhrasalConstraint',
       ]
       lowercase_								=	[
           'BeamHypotheses',
           'BeamScorer',
           'BeamSearchScorer',
           'ConstrainedBeamSearchScorer',
       ]
       lowercase_								=	[
           'EpsilonLogitsWarper',
           'EtaLogitsWarper',
           'ForcedBOSTokenLogitsProcessor',
           'ForcedEOSTokenLogitsProcessor',
           'HammingDiversityLogitsProcessor',
           'InfNanRemoveLogitsProcessor',
           'LogitsProcessor',
           'LogitsProcessorList',
           'LogitsWarper',
           'MinLengthLogitsProcessor',
           'MinNewTokensLengthLogitsProcessor',
           'NoBadWordsLogitsProcessor',
           'NoRepeatNGramLogitsProcessor',
           'PrefixConstrainedLogitsProcessor',
           'RepetitionPenaltyLogitsProcessor',
           'SequenceBiasLogitsProcessor',
           'EncoderRepetitionPenaltyLogitsProcessor',
           'TemperatureLogitsWarper',
           'TopKLogitsWarper',
           'TopPLogitsWarper',
           'TypicalLogitsWarper',
           'EncoderNoRepeatNGramLogitsProcessor',
           'ExponentialDecayLengthPenalty',
           'LogitNormalization',
       ]
       lowercase_								=	[
           'MaxNewTokensCriteria',
           'MaxLengthCriteria',
           'MaxTimeCriteria',
           'StoppingCriteria',
           'StoppingCriteriaList',
           'validate_stopping_criteria',
       ]
       lowercase_								=	[
           'GenerationMixin',
           'top_k_top_p_filtering',
           'GreedySearchEncoderDecoderOutput',
           'GreedySearchDecoderOnlyOutput',
           'SampleEncoderDecoderOutput',
           'SampleDecoderOnlyOutput',
           'BeamSearchEncoderDecoderOutput',
           'BeamSearchDecoderOnlyOutput',
           'BeamSampleEncoderDecoderOutput',
           'BeamSampleDecoderOnlyOutput',
           'ContrastiveSearchEncoderDecoderOutput',
           'ContrastiveSearchDecoderOnlyOutput',
       ]

try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowercase_								=	[
           'TFForcedBOSTokenLogitsProcessor',
           'TFForcedEOSTokenLogitsProcessor',
           'TFLogitsProcessor',
           'TFLogitsProcessorList',
           'TFLogitsWarper',
           'TFMinLengthLogitsProcessor',
           'TFNoBadWordsLogitsProcessor',
           'TFNoRepeatNGramLogitsProcessor',
           'TFRepetitionPenaltyLogitsProcessor',
           'TFTemperatureLogitsWarper',
           'TFTopKLogitsWarper',
           'TFTopPLogitsWarper',
           'TFForceTokensLogitsProcessor',
           'TFSuppressTokensAtBeginLogitsProcessor',
           'TFSuppressTokensLogitsProcessor',
       ]
       lowercase_								=	[
           'TFGenerationMixin',
           'tf_top_k_top_p_filtering',
           'TFGreedySearchDecoderOnlyOutput',
           'TFGreedySearchEncoderDecoderOutput',
           'TFSampleEncoderDecoderOutput',
           'TFSampleDecoderOnlyOutput',
           'TFBeamSearchEncoderDecoderOutput',
           'TFBeamSearchDecoderOnlyOutput',
           'TFBeamSampleEncoderDecoderOutput',
           'TFBeamSampleDecoderOnlyOutput',
           'TFContrastiveSearchEncoderDecoderOutput',
           'TFContrastiveSearchDecoderOnlyOutput',
       ]

try:
       if not is_flax_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowercase_								=	[
           'FlaxForcedBOSTokenLogitsProcessor',
           'FlaxForcedEOSTokenLogitsProcessor',
           'FlaxLogitsProcessor',
           'FlaxLogitsProcessorList',
           'FlaxLogitsWarper',
           'FlaxMinLengthLogitsProcessor',
           'FlaxTemperatureLogitsWarper',
           'FlaxTopKLogitsWarper',
           'FlaxTopPLogitsWarper',
       ]
       lowercase_								=	[
           'FlaxGenerationMixin',
           'FlaxGreedySearchOutput',
           'FlaxSampleOutput',
           'FlaxBeamSearchOutput',
       ]

if TYPE_CHECKING:
       from .configuration_utils import GenerationConfig
       from .streamers import TextIteratorStreamer, TextStreamer

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .beam_constraints import Constraint, ConstraintListState, DisjunctiveConstraint, PhrasalConstraint
              from .beam_search import BeamHypotheses, BeamScorer, BeamSearchScorer, ConstrainedBeamSearchScorer
              from .logits_process import (
                  EncoderNoRepeatNGramLogitsProcessor,
                  EncoderRepetitionPenaltyLogitsProcessor,
                  EpsilonLogitsWarper,
                  EtaLogitsWarper,
                  ExponentialDecayLengthPenalty,
                  ForcedBOSTokenLogitsProcessor,
                  ForcedEOSTokenLogitsProcessor,
                  HammingDiversityLogitsProcessor,
                  InfNanRemoveLogitsProcessor,
                  LogitNormalization,
                  LogitsProcessor,
                  LogitsProcessorList,
                  LogitsWarper,
                  MinLengthLogitsProcessor,
                  MinNewTokensLengthLogitsProcessor,
                  NoBadWordsLogitsProcessor,
                  NoRepeatNGramLogitsProcessor,
                  PrefixConstrainedLogitsProcessor,
                  RepetitionPenaltyLogitsProcessor,
                  SequenceBiasLogitsProcessor,
                  TemperatureLogitsWarper,
                  TopKLogitsWarper,
                  TopPLogitsWarper,
                  TypicalLogitsWarper,
              )
              from .stopping_criteria import (
                  MaxLengthCriteria,
                  MaxNewTokensCriteria,
                  MaxTimeCriteria,
                  StoppingCriteria,
                  StoppingCriteriaList,
                  validate_stopping_criteria,
              )
              from .utils import (
                  BeamSampleDecoderOnlyOutput,
                  BeamSampleEncoderDecoderOutput,
                  BeamSearchDecoderOnlyOutput,
                  BeamSearchEncoderDecoderOutput,
                  ContrastiveSearchDecoderOnlyOutput,
                  ContrastiveSearchEncoderDecoderOutput,
                  GenerationMixin,
                  GreedySearchDecoderOnlyOutput,
                  GreedySearchEncoderDecoderOutput,
                  SampleDecoderOnlyOutput,
                  SampleEncoderDecoderOutput,
                  top_k_top_p_filtering,
              )

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tf_logits_process import (
                  TFForcedBOSTokenLogitsProcessor,
                  TFForcedEOSTokenLogitsProcessor,
                  TFForceTokensLogitsProcessor,
                  TFLogitsProcessor,
                  TFLogitsProcessorList,
                  TFLogitsWarper,
                  TFMinLengthLogitsProcessor,
                  TFNoBadWordsLogitsProcessor,
                  TFNoRepeatNGramLogitsProcessor,
                  TFRepetitionPenaltyLogitsProcessor,
                  TFSuppressTokensAtBeginLogitsProcessor,
                  TFSuppressTokensLogitsProcessor,
                  TFTemperatureLogitsWarper,
                  TFTopKLogitsWarper,
                  TFTopPLogitsWarper,
              )
              from .tf_utils import (
                  TFBeamSampleDecoderOnlyOutput,
                  TFBeamSampleEncoderDecoderOutput,
                  TFBeamSearchDecoderOnlyOutput,
                  TFBeamSearchEncoderDecoderOutput,
                  TFContrastiveSearchDecoderOnlyOutput,
                  TFContrastiveSearchEncoderDecoderOutput,
                  TFGenerationMixin,
                  TFGreedySearchDecoderOnlyOutput,
                  TFGreedySearchEncoderDecoderOutput,
                  TFSampleDecoderOnlyOutput,
                  TFSampleEncoderDecoderOutput,
                  tf_top_k_top_p_filtering,
              )

       try:
              if not is_flax_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .flax_logits_process import (
                  FlaxForcedBOSTokenLogitsProcessor,
                  FlaxForcedEOSTokenLogitsProcessor,
                  FlaxLogitsProcessor,
                  FlaxLogitsProcessorList,
                  FlaxLogitsWarper,
                  FlaxMinLengthLogitsProcessor,
                  FlaxTemperatureLogitsWarper,
                  FlaxTopKLogitsWarper,
                  FlaxTopPLogitsWarper,
              )
              from .flax_utils import FlaxBeamSearchOutput, FlaxGenerationMixin, FlaxGreedySearchOutput, FlaxSampleOutput
else:
       import sys

       lowercase_								=	_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




