





"""simple docstring"""


import copy
import os
from typing import Dict, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...utils import add_start_docstrings, logging
from ..auto import AutoConfig


a__       :  Tuple       		=					logging.get_logger(__name__)


a__       :  str       		=					{
    """suno/bark-small""": """https://huggingface.co/suno/bark-small/resolve/main/config.json""",
    """suno/bark""": """https://huggingface.co/suno/bark/resolve/main/config.json""",
}

a__       :  Tuple       		=					"""
    This is the configuration class to store the configuration of a [`{model}`]. It is used to instantiate the model
    according to the specified arguments, defining the model architecture. Instantiating a configuration with the
    defaults will yield a similar configuration to that of the Bark [suno/bark](https://huggingface.co/suno/bark)
    architecture.

    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
    documentation from [`PretrainedConfig`] for more information.

    Args:
        block_size (`int`, *optional*, defaults to 1024):
            The maximum sequence length that this model might ever be used with. Typically set this to something large
            just in case (e.g., 512 or 1024 or 2048).
        input_vocab_size (`int`, *optional*, defaults to 10_048):
            Vocabulary size of a Bark sub-model. Defines the number of different tokens that can be represented by the
            `inputs_ids` passed when calling [`{model}`]. Defaults to 10_048 but should be carefully thought with
            regards to the chosen sub-model.
        output_vocab_size (`int`, *optional*, defaults to 10_048):
            Output vocabulary size of a Bark sub-model. Defines the number of different tokens that can be represented
            by the: `output_ids` when passing forward a [`{model}`]. Defaults to 10_048 but should be carefully thought
            with regards to the chosen sub-model.
        num_layers (`int`, *optional*, defaults to 12):
            Number of hidden layers in the given sub-model.
        num_heads (`int`, *optional*, defaults to 12):
            Number of attention heads for each attention layer in the Transformer architecture.
        hidden_size (`int`, *optional*, defaults to 768):
            Dimensionality of the \"intermediate\" (often named feed-forward) layer in the architecture.
        dropout (`float`, *optional*, defaults to 0.0):
            The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
        bias (`bool`, *optional*, defaults to `True`):
            Whether or not to use bias in the linear layers and layer norm layers.
        initializer_range (`float`, *optional*, defaults to 0.02):
            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
        use_cache (`bool`, *optional*, defaults to `True`):
            Whether or not the model should return the last key/values attentions (not used by all models).
"""



class   lowercase_					(  __lowerCAmelCase				):
       snake_case__					:				Any									=  """bark_module"""
       snake_case__					:				Union[str, Any]									=  ["""past_key_values"""]

       snake_case__					:				str									=  {
           """num_attention_heads""": """num_heads""",
           """num_hidden_layers""": """num_layers""",
           """vocab_size""": """input_vocab_size""",
           """window_size""": """block_size""",
       }

       def __init__(      self ,       __SCREAMING_SNAKE_CASE=1024 ,       __SCREAMING_SNAKE_CASE=1_0048 ,       __SCREAMING_SNAKE_CASE=1_0048 ,       __SCREAMING_SNAKE_CASE=12 ,       __SCREAMING_SNAKE_CASE=12 ,       __SCREAMING_SNAKE_CASE=768 ,       __SCREAMING_SNAKE_CASE=0.0 ,       __SCREAMING_SNAKE_CASE=True ,       __SCREAMING_SNAKE_CASE=0.02 ,       __SCREAMING_SNAKE_CASE=True ,       **__SCREAMING_SNAKE_CASE ,       ):






        '''simple docstring'''

        snake_case__     				=					block_size
        snake_case__     				=					input_vocab_size
        snake_case__     				=					output_vocab_size
        snake_case__     				=					num_layers
        snake_case__     				=					num_heads
        snake_case__     				=					hidden_size
        snake_case__     				=					dropout
        snake_case__     				=					bias
        snake_case__     				=					use_cache
        snake_case__     				=					initializer_range

        super().__init__(**__SCREAMING_SNAKE_CASE  )

       @classmethod
       def 		_SCREAMING_SNAKE_CASE(      cls ,       __SCREAMING_SNAKE_CASE ,       __SCREAMING_SNAKE_CASE = None ,       __SCREAMING_SNAKE_CASE = False ,       __SCREAMING_SNAKE_CASE = False ,       __SCREAMING_SNAKE_CASE = None ,       __SCREAMING_SNAKE_CASE = "main" ,       **__SCREAMING_SNAKE_CASE ,       ):






        '''simple docstring'''

        snake_case__     				=					cache_dir
        snake_case__     				=					force_download
        snake_case__     				=					local_files_only
        snake_case__     				=					revision

        cls._set_token_in_kwargs(__SCREAMING_SNAKE_CASE ,       __SCREAMING_SNAKE_CASE  )

        snake_case__				, snake_case__     				=					cls.get_config_dict(__SCREAMING_SNAKE_CASE ,       **__SCREAMING_SNAKE_CASE  )

        # get the config dict if we are loading from Bark
        if config_dict.get("model_type"  ) == "bark":
         snake_case__     				=					config_dict[F"{cls.model_type}_config"]

        if "model_type" in config_dict and hasattr(cls ,       "model_type"  ) and config_dict["model_type"] != cls.model_type:
         logger.warning(
             F"You are using a model of type {config_dict['model_type']} to instantiate a model of type "
             F"{cls.model_type}. This is not supported for all configurations of models and can yield errors."  )

        return cls.from_dict(__SCREAMING_SNAKE_CASE ,       **__SCREAMING_SNAKE_CASE  )



@add_start_docstrings(
    BARK_SUBMODELCONFIG_START_DOCSTRING.format(config="""BarkSemanticConfig"""					,model="""BarkSemanticModel"""				)					,"""
    Example:

    ```python
    >>> from transformers import BarkSemanticConfig, BarkSemanticModel

    >>> # Initializing a Bark sub-module style configuration
    >>> configuration = BarkSemanticConfig()

    >>> # Initializing a model (with random weights) from the suno/bark style configuration
    >>> model = BarkSemanticModel(configuration)

    >>> # Accessing the model configuration
    >>> configuration = model.config
    ```"""					,)
class   lowercase_					(  __lowerCAmelCase				):
       snake_case__					:				List[Any]									=  """semantic"""



@add_start_docstrings(
    BARK_SUBMODELCONFIG_START_DOCSTRING.format(config="""BarkCoarseConfig"""					,model="""BarkCoarseModel"""				)					,"""
    Example:

    ```python
    >>> from transformers import BarkCoarseConfig, BarkCoarseModel

    >>> # Initializing a Bark sub-module style configuration
    >>> configuration = BarkCoarseConfig()

    >>> # Initializing a model (with random weights) from the suno/bark style configuration
    >>> model = BarkCoarseModel(configuration)

    >>> # Accessing the model configuration
    >>> configuration = model.config
    ```"""					,)
class   lowercase_					(  __lowerCAmelCase				):
       snake_case__					:				Any									=  """coarse_acoustics"""



@add_start_docstrings(
    BARK_SUBMODELCONFIG_START_DOCSTRING.format(config="""BarkFineConfig"""					,model="""BarkFineModel"""				)					,"""
        n_codes_total (`int`, *optional*, defaults to 8):
            The total number of audio codebooks predicted. Used in the fine acoustics sub-model.
        n_codes_given (`int`, *optional*, defaults to 1):
            The number of audio codebooks predicted in the coarse acoustics sub-model. Used in the acoustics
            sub-models.
    Example:

    ```python
    >>> from transformers import BarkFineConfig, BarkFineModel

    >>> # Initializing a Bark sub-module style configuration
    >>> configuration = BarkFineConfig()

    >>> # Initializing a model (with random weights) from the suno/bark style configuration
    >>> model = BarkFineModel(configuration)

    >>> # Accessing the model configuration
    >>> configuration = model.config
    ```"""					,)
class   lowercase_					(  __lowerCAmelCase				):
       snake_case__					:				int									=  """fine_acoustics"""

       def __init__(      self ,       __SCREAMING_SNAKE_CASE=True ,       __SCREAMING_SNAKE_CASE=8 ,       __SCREAMING_SNAKE_CASE=1 ,       **__SCREAMING_SNAKE_CASE  ):






        '''simple docstring'''

        snake_case__     				=					n_codes_total
        snake_case__     				=					n_codes_given

        super().__init__(tie_word_embeddings=__SCREAMING_SNAKE_CASE ,       **__SCREAMING_SNAKE_CASE  )



class   lowercase_					(  __lowerCAmelCase				):
       snake_case__					:				Union[str, Any]									=  """bark"""
       snake_case__					:				Union[str, Any]									=  True

       def __init__(      self ,       __SCREAMING_SNAKE_CASE = None ,       __SCREAMING_SNAKE_CASE = None ,       __SCREAMING_SNAKE_CASE = None ,       __SCREAMING_SNAKE_CASE = None ,       __SCREAMING_SNAKE_CASE=0.02 ,       **__SCREAMING_SNAKE_CASE ,       ):






        '''simple docstring'''

        if semantic_config is None:
         snake_case__     				=					{}
         logger.info("semantic_config is None. initializing the semantic model with default values."  )

        if coarse_acoustics_config is None:
         snake_case__     				=					{}
         logger.info("coarse_acoustics_config is None. initializing the coarse model with default values."  )

        if fine_acoustics_config is None:
         snake_case__     				=					{}
         logger.info("fine_acoustics_config is None. initializing the fine model with default values."  )

        if codec_config is None:
         snake_case__     				=					{}
         logger.info("codec_config is None. initializing the codec model with default values."  )

        snake_case__     				=					BarkSemanticConfig(**__SCREAMING_SNAKE_CASE  )
        snake_case__     				=					BarkCoarseConfig(**__SCREAMING_SNAKE_CASE  )
        snake_case__     				=					BarkFineConfig(**__SCREAMING_SNAKE_CASE  )
        snake_case__     				=					AutoConfig.for_model(**__SCREAMING_SNAKE_CASE  )

        snake_case__     				=					initializer_range

        super().__init__(**__SCREAMING_SNAKE_CASE  )

       @classmethod
       def 		_SCREAMING_SNAKE_CASE(      cls ,       __SCREAMING_SNAKE_CASE ,       __SCREAMING_SNAKE_CASE ,       __SCREAMING_SNAKE_CASE ,       __SCREAMING_SNAKE_CASE ,       **__SCREAMING_SNAKE_CASE ,       ):






        '''simple docstring'''

        return cls(
            semantic_config=semantic_config.to_dict() ,       coarse_acoustics_config=coarse_acoustics_config.to_dict() ,       fine_acoustics_config=fine_acoustics_config.to_dict() ,       codec_config=codec_config.to_dict() ,       **__SCREAMING_SNAKE_CASE ,       )

       def 		_SCREAMING_SNAKE_CASE(      self  ):






        '''simple docstring'''

        snake_case__     				=					copy.deepcopy(self.__dict__  )

        snake_case__     				=					self.semantic_config.to_dict()
        snake_case__     				=					self.coarse_acoustics_config.to_dict()
        snake_case__     				=					self.fine_acoustics_config.to_dict()
        snake_case__     				=					self.codec_config.to_dict()

        snake_case__     				=					self.__class__.model_type
        return output








"""simple docstring"""


import math
from collections import defaultdict
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import randn_tensor
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput





def 				a__      (	_UpperCAmelCase						,  _UpperCAmelCase=0.999						,  _UpperCAmelCase="cosine"						,  )     ->	str:

 '''simple docstring'''






 if alpha_transform_type == "cosine":

  def alpha_bar_fn(_UpperCAmelCase						):
   return math.cos((t + 0.008) / 1.008 * math.pi / 2						) ** 2

 elif alpha_transform_type == "exp":

  def alpha_bar_fn(_UpperCAmelCase						):
   return math.exp(t * -12.0						)

 else:
  raise ValueError(f"Unsupported alpha_tranform_type: {alpha_transform_type}"						)

 snake_case__     				=					[]
 for i in range(_UpperCAmelCase						):
  snake_case__     				=					i / num_diffusion_timesteps
  snake_case__     				=					(i + 1) / num_diffusion_timesteps
  betas.append(min(1 - alpha_bar_fn(_UpperCAmelCase						) / alpha_bar_fn(_UpperCAmelCase						)						,  _UpperCAmelCase						)						)
 return torch.tensor(_UpperCAmelCase						,  dtype=torch.floataa						)



class   lowercase_					(  __lowerCAmelCase					,__lowerCAmelCase				):
       snake_case__					:				Tuple									=  [e.name for e in KarrasDiffusionSchedulers]
       snake_case__					:				Any									=  2

       @register_to_config
       def __init__(      self ,       __SCREAMING_SNAKE_CASE = 1000 ,       __SCREAMING_SNAKE_CASE = 0.00085 ,       __SCREAMING_SNAKE_CASE = 0.012 ,       __SCREAMING_SNAKE_CASE = "linear" ,       __SCREAMING_SNAKE_CASE = None ,       __SCREAMING_SNAKE_CASE = "epsilon" ,       __SCREAMING_SNAKE_CASE = "linspace" ,       __SCREAMING_SNAKE_CASE = 0 ,       ):






        '''simple docstring'''

        if trained_betas is not None:
         snake_case__     				=					torch.tensor(__SCREAMING_SNAKE_CASE ,       dtype=torch.floataa  )
        elif beta_schedule == "linear":
         snake_case__     				=					torch.linspace(__SCREAMING_SNAKE_CASE ,       __SCREAMING_SNAKE_CASE ,       __SCREAMING_SNAKE_CASE ,       dtype=torch.floataa  )
        elif beta_schedule == "scaled_linear":
         # this schedule is very specific to the latent diffusion model.
         snake_case__     				=					(
             torch.linspace(beta_start**0.5 ,       beta_end**0.5 ,       __SCREAMING_SNAKE_CASE ,       dtype=torch.floataa  ) ** 2
         )
        elif beta_schedule == "squaredcos_cap_v2":
         # Glide cosine schedule
         snake_case__     				=					betas_for_alpha_bar(__SCREAMING_SNAKE_CASE  )
        else:
         raise NotImplementedError(F"{beta_schedule} does is not implemented for {self.__class__}"  )

        snake_case__     				=					1.0 - self.betas
        snake_case__     				=					torch.cumprod(self.alphas ,       dim=0  )

        #  set all values
        self.set_timesteps(__SCREAMING_SNAKE_CASE ,       __SCREAMING_SNAKE_CASE ,       __SCREAMING_SNAKE_CASE  )

       def 		_SCREAMING_SNAKE_CASE(      self ,       __SCREAMING_SNAKE_CASE ,       __SCREAMING_SNAKE_CASE=None  ):






        '''simple docstring'''

        if schedule_timesteps is None:
         snake_case__     				=					self.timesteps

        snake_case__     				=					(schedule_timesteps == timestep).nonzero()

        # The sigma index that is taken for the **very** first `step`
        # is always the second index (or the last index if there is only 1)
        # This way we can ensure we don't accidentally skip a sigma in
        # case we start in the middle of the denoising schedule (e.g. for image-to-image)
        if len(self._index_counter  ) == 0:
         snake_case__     				=					1 if len(__SCREAMING_SNAKE_CASE  ) > 1 else 0
        else:
         snake_case__     				=					timestep.cpu().item() if torch.is_tensor(__SCREAMING_SNAKE_CASE  ) else timestep
         snake_case__     				=					self._index_counter[timestep_int]

        return indices[pos].item()

       @property
       def 		_SCREAMING_SNAKE_CASE(      self  ):






        '''simple docstring'''

        if self.config.timestep_spacing in ["linspace", "trailing"]:
         return self.sigmas.max()

        return (self.sigmas.max() ** 2 + 1) ** 0.5

       def 		_SCREAMING_SNAKE_CASE(      self ,       __SCREAMING_SNAKE_CASE ,       __SCREAMING_SNAKE_CASE ,       ):






        '''simple docstring'''

        snake_case__     				=					self.index_for_timestep(__SCREAMING_SNAKE_CASE  )

        if self.state_in_first_order:
         snake_case__     				=					self.sigmas[step_index]
        else:
         snake_case__     				=					self.sigmas_interpol[step_index - 1]

        snake_case__     				=					sample / ((sigma**2 + 1) ** 0.5)
        return sample

       def 		_SCREAMING_SNAKE_CASE(      self ,       __SCREAMING_SNAKE_CASE ,       __SCREAMING_SNAKE_CASE = None ,       __SCREAMING_SNAKE_CASE = None ,       ):






        '''simple docstring'''

        snake_case__     				=					num_inference_steps

        snake_case__     				=					num_train_timesteps or self.config.num_train_timesteps

        # "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
        if self.config.timestep_spacing == "linspace":
         snake_case__     				=					np.linspace(0 ,       num_train_timesteps - 1 ,       __SCREAMING_SNAKE_CASE ,       dtype=__SCREAMING_SNAKE_CASE  )[::-1].copy()
        elif self.config.timestep_spacing == "leading":
         snake_case__     				=					num_train_timesteps // self.num_inference_steps
         # creates integer timesteps by multiplying by ratio
         # casting to int to avoid issues when num_inference_step is power of 3
         snake_case__     				=					(np.arange(0 ,       __SCREAMING_SNAKE_CASE  ) * step_ratio).round()[::-1].copy().astype(__SCREAMING_SNAKE_CASE  )
         timesteps += self.config.steps_offset
        elif self.config.timestep_spacing == "trailing":
         snake_case__     				=					num_train_timesteps / self.num_inference_steps
         # creates integer timesteps by multiplying by ratio
         # casting to int to avoid issues when num_inference_step is power of 3
         snake_case__     				=					(np.arange(__SCREAMING_SNAKE_CASE ,       0 ,       -step_ratio  )).round().copy().astype(__SCREAMING_SNAKE_CASE  )
         timesteps -= 1
        else:
         raise ValueError(
             F"{self.config.timestep_spacing} is not supported. Please make sure to choose one of 'linspace', 'leading' or 'trailing'."  )

        snake_case__     				=					np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5  )
        snake_case__     				=					torch.from_numpy(np.log(__SCREAMING_SNAKE_CASE  )  ).to(__SCREAMING_SNAKE_CASE  )

        snake_case__     				=					np.interp(__SCREAMING_SNAKE_CASE ,       np.arange(0 ,       len(__SCREAMING_SNAKE_CASE  )  ) ,       __SCREAMING_SNAKE_CASE  )
        snake_case__     				=					np.concatenate([sigmas, [0.0]]  ).astype(np.floataa  )
        snake_case__     				=					torch.from_numpy(__SCREAMING_SNAKE_CASE  ).to(device=__SCREAMING_SNAKE_CASE  )

        # compute up and down sigmas
        snake_case__     				=					sigmas.roll(-1  )
        snake_case__     				=					0.0
        snake_case__     				=					(sigmas_next**2 * (sigmas**2 - sigmas_next**2) / sigmas**2) ** 0.5
        snake_case__     				=					(sigmas_next**2 - sigmas_up**2) ** 0.5
        snake_case__     				=					0.0

        # compute interpolated sigmas
        snake_case__     				=					sigmas.log().lerp(sigmas_down.log() ,       0.5  ).exp()
        snake_case__     				=					0.0

        # set sigmas
        snake_case__     				=					torch.cat([sigmas[:1], sigmas[1:].repeat_interleave(2  ), sigmas[-1:]]  )
        snake_case__     				=					torch.cat(
            [sigmas_interpol[:1], sigmas_interpol[1:].repeat_interleave(2  ), sigmas_interpol[-1:]]  )
        snake_case__     				=					torch.cat([sigmas_up[:1], sigmas_up[1:].repeat_interleave(2  ), sigmas_up[-1:]]  )
        snake_case__     				=					torch.cat([sigmas_down[:1], sigmas_down[1:].repeat_interleave(2  ), sigmas_down[-1:]]  )

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

        snake_case__     				=					self.sigma_to_t(__SCREAMING_SNAKE_CASE  ).to(__SCREAMING_SNAKE_CASE ,       dtype=timesteps.dtype  )
        snake_case__     				=					torch.stack((timesteps_interpol[:-2, None], timesteps[1:, None]) ,       dim=-1  ).flatten()

        snake_case__     				=					torch.cat([timesteps[:1], interleaved_timesteps]  )

        snake_case__     				=					None

        # for exp beta schedules, such as the one for `pipeline_shap_e.py`
        # we need an index counter
        snake_case__     				=					defaultdict(__SCREAMING_SNAKE_CASE  )

       def 		_SCREAMING_SNAKE_CASE(      self ,       __SCREAMING_SNAKE_CASE  ):






        '''simple docstring'''

        snake_case__     				=					sigma.log()

        # get distribution
        snake_case__     				=					log_sigma - self.log_sigmas[:, None]

        # get sigmas range
        snake_case__     				=					dists.ge(0  ).cumsum(dim=0  ).argmax(dim=0  ).clamp(max=self.log_sigmas.shape[0] - 2  )
        snake_case__     				=					low_idx + 1

        snake_case__     				=					self.log_sigmas[low_idx]
        snake_case__     				=					self.log_sigmas[high_idx]

        # interpolate sigmas
        snake_case__     				=					(low - log_sigma) / (low - high)
        snake_case__     				=					w.clamp(0 ,       1  )

        # transform interpolation to time range
        snake_case__     				=					(1 - w) * low_idx + w * high_idx
        snake_case__     				=					t.view(sigma.shape  )
        return t

       @property
       def 		_SCREAMING_SNAKE_CASE(      self  ):






        '''simple docstring'''

        return self.sample is None

       def 		_SCREAMING_SNAKE_CASE(      self ,       __SCREAMING_SNAKE_CASE ,       __SCREAMING_SNAKE_CASE ,       __SCREAMING_SNAKE_CASE ,       __SCREAMING_SNAKE_CASE = None ,       __SCREAMING_SNAKE_CASE = True ,       ):






        '''simple docstring'''

        snake_case__     				=					self.index_for_timestep(__SCREAMING_SNAKE_CASE  )

        # advance index counter by 1
        snake_case__     				=					timestep.cpu().item() if torch.is_tensor(__SCREAMING_SNAKE_CASE  ) else timestep
        self._index_counter[timestep_int] += 1

        if self.state_in_first_order:
         snake_case__     				=					self.sigmas[step_index]
         snake_case__     				=					self.sigmas_interpol[step_index]
         snake_case__     				=					self.sigmas_up[step_index]
         snake_case__     				=					self.sigmas_down[step_index - 1]
        else:
         # 2nd order / KPDM2's method
         snake_case__     				=					self.sigmas[step_index - 1]
         snake_case__     				=					self.sigmas_interpol[step_index - 1]
         snake_case__     				=					self.sigmas_up[step_index - 1]
         snake_case__     				=					self.sigmas_down[step_index - 1]

        # currently only gamma=0 is supported. This usually works best anyways.
        # We can support gamma in the future but then need to scale the timestep before
        # passing it to the model which requires a change in API
        snake_case__     				=					0
        snake_case__     				=					sigma * (gamma + 1)  # Note: sigma_hat == sigma for now

        snake_case__     				=					model_output.device
        snake_case__     				=					randn_tensor(model_output.shape ,       dtype=model_output.dtype ,       device=__SCREAMING_SNAKE_CASE ,       generator=__SCREAMING_SNAKE_CASE  )

        # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
        if self.config.prediction_type == "epsilon":
         snake_case__     				=					sigma_hat if self.state_in_first_order else sigma_interpol
         snake_case__     				=					sample - sigma_input * model_output
        elif self.config.prediction_type == "v_prediction":
         snake_case__     				=					sigma_hat if self.state_in_first_order else sigma_interpol
         snake_case__     				=					model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
             sample / (sigma_input**2 + 1)
         )
        elif self.config.prediction_type == "sample":
         raise NotImplementedError("prediction_type not implemented yet: sample"  )
        else:
         raise ValueError(
             F"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`"  )

        if self.state_in_first_order:
         # 2. Convert to an ODE derivative for 1st order
         snake_case__     				=					(sample - pred_original_sample) / sigma_hat
         # 3. delta timestep
         snake_case__     				=					sigma_interpol - sigma_hat

         # store for 2nd order step
         snake_case__     				=					sample
         snake_case__     				=					dt
         snake_case__     				=					sample + derivative * dt
        else:
         # DPM-Solver-2
         # 2. Convert to an ODE derivative for 2nd order
         snake_case__     				=					(sample - pred_original_sample) / sigma_interpol
         # 3. delta timestep
         snake_case__     				=					sigma_down - sigma_hat

         snake_case__     				=					self.sample
         snake_case__     				=					None

         snake_case__     				=					sample + derivative * dt
         snake_case__     				=					prev_sample + noise * sigma_up

        if not return_dict:
         return (prev_sample,)

        return SchedulerOutput(prev_sample=__SCREAMING_SNAKE_CASE  )

       def 		_SCREAMING_SNAKE_CASE(      self ,       __SCREAMING_SNAKE_CASE ,       __SCREAMING_SNAKE_CASE ,       __SCREAMING_SNAKE_CASE ,       ):






        '''simple docstring'''

        snake_case__     				=					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
         snake_case__     				=					self.timesteps.to(original_samples.device ,       dtype=torch.floataa  )
         snake_case__     				=					timesteps.to(original_samples.device ,       dtype=torch.floataa  )
        else:
         snake_case__     				=					self.timesteps.to(original_samples.device  )
         snake_case__     				=					timesteps.to(original_samples.device  )

        snake_case__     				=					[self.index_for_timestep(__SCREAMING_SNAKE_CASE ,       __SCREAMING_SNAKE_CASE  ) for t in timesteps]

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

        snake_case__     				=					original_samples + noise * sigma
        return noisy_samples

       def __len__(      self  ):






        '''simple docstring'''

        return self.config.num_train_timesteps

