
"""simple docstring"""




from typing import TYPE_CHECKING

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


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

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    a		:					str    	=					[
        'Constraint',
        'ConstraintListState',
        'DisjunctiveConstraint',
        'PhrasalConstraint',
    ]
    a		:					str    	=					[
        'BeamHypotheses',
        'BeamScorer',
        'BeamSearchScorer',
        'ConstrainedBeamSearchScorer',
    ]
    a		:					List[str]    	=					[
        'EpsilonLogitsWarper',
        'EtaLogitsWarper',
        'ForcedBOSTokenLogitsProcessor',
        'ForcedEOSTokenLogitsProcessor',
        'HammingDiversityLogitsProcessor',
        'InfNanRemoveLogitsProcessor',
        'LogitsProcessor',
        'LogitsProcessorList',
        'LogitsWarper',
        'MinLengthLogitsProcessor',
        'MinNewTokensLengthLogitsProcessor',
        'NoBadWordsLogitsProcessor',
        'NoRepeatNGramLogitsProcessor',
        'PrefixConstrainedLogitsProcessor',
        'RepetitionPenaltyLogitsProcessor',
        'SequenceBiasLogitsProcessor',
        'EncoderRepetitionPenaltyLogitsProcessor',
        'TemperatureLogitsWarper',
        'TopKLogitsWarper',
        'TopPLogitsWarper',
        'TypicalLogitsWarper',
        'EncoderNoRepeatNGramLogitsProcessor',
        'ExponentialDecayLengthPenalty',
        'LogitNormalization',
    ]
    a		:					Tuple    	=					[
        'MaxNewTokensCriteria',
        'MaxLengthCriteria',
        'MaxTimeCriteria',
        'StoppingCriteria',
        'StoppingCriteriaList',
        'validate_stopping_criteria',
    ]
    a		:					int    	=					[
        '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:
    a		:					Any    	=					[
        'TFForcedBOSTokenLogitsProcessor',
        'TFForcedEOSTokenLogitsProcessor',
        'TFLogitsProcessor',
        'TFLogitsProcessorList',
        'TFLogitsWarper',
        'TFMinLengthLogitsProcessor',
        'TFNoBadWordsLogitsProcessor',
        'TFNoRepeatNGramLogitsProcessor',
        'TFRepetitionPenaltyLogitsProcessor',
        'TFTemperatureLogitsWarper',
        'TFTopKLogitsWarper',
        'TFTopPLogitsWarper',
        'TFForceTokensLogitsProcessor',
        'TFSuppressTokensAtBeginLogitsProcessor',
        'TFSuppressTokensLogitsProcessor',
    ]
    a		:					List[Any]    	=					[
        '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:
    a		:					Dict    	=					[
        'FlaxForcedBOSTokenLogitsProcessor',
        'FlaxForcedEOSTokenLogitsProcessor',
        'FlaxLogitsProcessor',
        'FlaxLogitsProcessorList',
        'FlaxLogitsWarper',
        'FlaxMinLengthLogitsProcessor',
        'FlaxTemperatureLogitsWarper',
        'FlaxTopKLogitsWarper',
        'FlaxTopPLogitsWarper',
    ]
    a		:					Dict    	=					[
        '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

    a		:					Dict    	=					_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




"""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[Any]    	=					logging.get_logger(__name__)  # pylint: disable=invalid-name







@dataclass
class 						lowercase(_lowercase					):
     __snake_case:  torch.FloatTensor







class 						lowercase(_lowercase						,	_lowercase					):
     __snake_case:  List[Any]  				=    1



     @register_to_config
     def __init__(       self    ,							__SCREAMING_SNAKE_CASE = 4_0    ,							__SCREAMING_SNAKE_CASE = 0.0_02    ,							__SCREAMING_SNAKE_CASE = 80.0    ,							__SCREAMING_SNAKE_CASE = 0.5    ,							__SCREAMING_SNAKE_CASE = 1.0    ,							__SCREAMING_SNAKE_CASE = 7.0    ,							__SCREAMING_SNAKE_CASE = True    ,							)		->    Dict:







         """simple docstring"""



         a__    = sigma_max

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

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



     def  lowercase__     (       self    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE=None      )		->    Union[str, Any]:







         """simple docstring"""



         if schedule_timesteps is None:
             a__    = self.timesteps

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



     def  lowercase__     (       self    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE      )		->    torch.FloatTensor:







         """simple docstring"""



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

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

         a__    = True
         return sample



     def  lowercase__     (       self    ,							__SCREAMING_SNAKE_CASE      )		->    int:







         """simple docstring"""



         if not isinstance(__SCREAMING_SNAKE_CASE    ,							np.ndarray      ):
             a__    = np.array(__SCREAMING_SNAKE_CASE    ,							dtype=np.floataa      )

         a__    = 1_0_0_0 * 0.25 * np.log(sigmas + 1e-44      )

         return timesteps



     def  lowercase__     (       self    ,							__SCREAMING_SNAKE_CASE = None    ,							__SCREAMING_SNAKE_CASE = None    ,							__SCREAMING_SNAKE_CASE = None    ,							)		->    Optional[int]:







         """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(__SCREAMING_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}.'      )

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

             a__    = num_inference_steps

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

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

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

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



     def  lowercase__     (       self    ,							__SCREAMING_SNAKE_CASE      )		->    Optional[int]:







         """simple docstring"""



         a__    = self.config.sigma_min
         a__    = self.config.sigma_max

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



     def  lowercase__     (       self    ,							__SCREAMING_SNAKE_CASE      )		->    Union[str, Any]:







         """simple docstring"""



         a__    = self.config.sigma_data

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



     def  lowercase__     (       self    ,							__SCREAMING_SNAKE_CASE      )		->    List[str]:







         """simple docstring"""



         a__    = self.config.sigma_min
         a__    = self.config.sigma_data

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



     def  lowercase__     (       self    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE = None    ,							__SCREAMING_SNAKE_CASE = True    ,							)		->    Union[CMStochasticIterativeSchedulerOutput, Tuple]:







         """simple docstring"""



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

         a__    = self.config.sigma_min
         a__    = self.config.sigma_max

         a__    = self.index_for_timestep(__SCREAMING_SNAKE_CASE      )

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

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

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

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

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

         if not return_dict:
             return (prev_sample,)

         return CMStochasticIterativeSchedulerOutput(prev_sample=__SCREAMING_SNAKE_CASE      )



     def  lowercase__     (       self    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							)		->    torch.FloatTensor:







         """simple docstring"""



         a__    = self.sigmas.to(device=original_samples.device    ,							dtype=original_samples.dtype      )
         if original_samples.device.type == "mps" and torch.is_floating_point(__SCREAMING_SNAKE_CASE      ):
             # mps does not support float64
             a__    = self.timesteps.to(original_samples.device    ,							dtype=torch.floataa      )
             a__    = timesteps.to(original_samples.device    ,							dtype=torch.floataa      )
         else:
             a__    = self.timesteps.to(original_samples.device      )
             a__    = timesteps.to(original_samples.device      )

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

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

         a__    = original_samples + noise * sigma
         return noisy_samples




     def __len__(       self      )		->    str:







         """simple docstring"""



         return self.config.num_train_timesteps


