






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


@dataclass
class  __lowercase (		lowercase_					):







     '''simple docstring'''
     SCREAMING_SNAKE_CASE				  =			42


class  __lowercase (		lowercase_     , lowercase_					):







     '''simple docstring'''
     SCREAMING_SNAKE_CASE				  =			1




     @register_to_config
     def __init__(			self    :     Optional[int]	,		UpperCamelCase_    :     int = 40	,		UpperCamelCase_    :     float = 0.002	,		UpperCamelCase_    :     float = 80.0	,		UpperCamelCase_    :     float = 0.5	,		UpperCamelCase_    :     float = 1.0	,		UpperCamelCase_    :     float = 7.0	,		UpperCamelCase_    :     bool = True	,		):


         """simple docstring"""



         __A             =		sigma_max

         __A             =		np.linspace(0	,		1	,		UpperCamelCase_			)
         __A             =		self._convert_to_karras(UpperCamelCase_			)
         __A             =		self.sigma_to_t(UpperCamelCase_			)

         # setable values
         __A             =		None
         __A             =		torch.from_numpy(UpperCamelCase_			)
         __A             =		torch.from_numpy(UpperCamelCase_			)
         __A             =		False
         __A             =		False




     def 						lowerCAmelCase_       (			self    :     Tuple	,		UpperCamelCase_    :     List[str]	,		UpperCamelCase_    :     Dict=None			):


         """simple docstring"""



         if schedule_timesteps is None:
             __A             =		self.timesteps

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




     def 						lowerCAmelCase_       (			self    :     Optional[int]	,		UpperCamelCase_    :     torch.FloatTensor	,		UpperCamelCase_    :     Union[float, torch.FloatTensor]			):


         """simple docstring"""



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

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

         __A             =		True
         return sample




     def 						lowerCAmelCase_       (			self    :     Union[str, Any]	,		UpperCamelCase_    :     Union[float, np.ndarray]			):


         """simple docstring"""



         if not isinstance(UpperCamelCase_	,		np.ndarray			):
             __A             =		np.array(UpperCamelCase_	,		dtype=np.floataa			)

         __A             =		1_000 * 0.25 * np.log(sigmas + 1e-44			)

         return timesteps




     def 						lowerCAmelCase_       (			self    :     List[Any]	,		UpperCamelCase_    :     Optional[int] = None	,		UpperCamelCase_    :     Union[str, torch.device] = None	,		UpperCamelCase_    :     Optional[List[int]] = None	,		):


         """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(UpperCamelCase_			)			):
                 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(UpperCamelCase_	,		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	,		UpperCamelCase_			) * 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(UpperCamelCase_			)
         __A             =		self.sigma_to_t(UpperCamelCase_			)

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

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




     def 						lowerCAmelCase_       (			self    :     Tuple	,		UpperCamelCase_    :     Union[str, Any]			):


         """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 						lowerCAmelCase_       (			self    :     Union[str, Any]	,		UpperCamelCase_    :     Optional[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 						lowerCAmelCase_       (			self    :     Tuple	,		UpperCamelCase_    :     Union[str, Any]			):


         """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 						lowerCAmelCase_       (			self    :     Dict	,		UpperCamelCase_    :     torch.FloatTensor	,		UpperCamelCase_    :     Union[float, torch.FloatTensor]	,		UpperCamelCase_    :     torch.FloatTensor	,		UpperCamelCase_    :     Optional[torch.Generator] = None	,		UpperCamelCase_    :     bool = True	,		):


         """simple docstring"""



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

         __A             =		self.config.sigma_min
         __A             =		self.config.sigma_max

         __A             =		self.index_for_timestep(UpperCamelCase_			)

         # 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(UpperCamelCase_			)

         # 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=UpperCamelCase_			)
         else:
             __A             =		torch.zeros_like(UpperCamelCase_			)
         __A             =		noise * self.config.s_noise

         __A             =		sigma_next.clamp(min=UpperCamelCase_	,		max=UpperCamelCase_			)

         # 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=UpperCamelCase_			)




     def 						lowerCAmelCase_       (			self    :     List[str]	,		UpperCamelCase_    :     torch.FloatTensor	,		UpperCamelCase_    :     torch.FloatTensor	,		UpperCamelCase_    :     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(UpperCamelCase_			):
             # 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    :     List[str]			):


         """simple docstring"""



         return self.config.num_train_timesteps












import copy
from typing import Dict

from ...generation.configuration_utils import GenerationConfig
from ...utils import logging


__a :    str								=				logging.get_logger(__name__)


class  __lowercase (		lowercase_					):







     '''simple docstring'''
     SCREAMING_SNAKE_CASE				  =			"semantic"




     def __init__(			self    :     Optional[int]	,		UpperCamelCase_    :     List[Any]=10_000	,		UpperCamelCase_    :     int=True	,		UpperCamelCase_    :     Dict=768	,		UpperCamelCase_    :     str=False	,		UpperCamelCase_    :     Dict=False	,		UpperCamelCase_    :     List[Any]=False	,		UpperCamelCase_    :     Any=False	,		UpperCamelCase_    :     Any=0.7	,		UpperCamelCase_    :     List[Any]=True	,		UpperCamelCase_    :     Dict=10_048	,		UpperCamelCase_    :     Tuple=129_595	,		UpperCamelCase_    :     List[str]=129_599	,		UpperCamelCase_    :     Optional[int]=10_000	,		UpperCamelCase_    :     Dict=256	,		UpperCamelCase_    :     int=49.9	,		**UpperCamelCase_    :     Any	,		):


         """simple docstring"""



         super().__init__(
             temperature=UpperCamelCase_	,		do_sample=UpperCamelCase_	,		eos_token_id=UpperCamelCase_	,		renormalize_logits=UpperCamelCase_	,		max_new_tokens=UpperCamelCase_	,		output_scores=UpperCamelCase_	,		return_dict_in_generate=UpperCamelCase_	,		output_hidden_states=UpperCamelCase_	,		output_attentions=UpperCamelCase_	,		**UpperCamelCase_	,		)

         __A             =		text_encoding_offset
         __A             =		text_pad_token
         __A             =		eos_token_id
         __A             =		semantic_infer_token
         __A             =		semantic_vocab_size
         __A             =		max_input_semantic_length
         __A             =		semantic_rate_hz


class  __lowercase (		lowercase_					):







     '''simple docstring'''
     SCREAMING_SNAKE_CASE				  =			"coarse_acoustics"




     def __init__(			self    :     int	,		UpperCamelCase_    :     List[Any]=True	,		UpperCamelCase_    :     int=False	,		UpperCamelCase_    :     Dict=False	,		UpperCamelCase_    :     Dict=False	,		UpperCamelCase_    :     Union[str, Any]=False	,		UpperCamelCase_    :     Any=0.7	,		UpperCamelCase_    :     Optional[Any]=True	,		UpperCamelCase_    :     str=12_048	,		UpperCamelCase_    :     int=75	,		UpperCamelCase_    :     Dict=2	,		UpperCamelCase_    :     List[str]=12_050	,		UpperCamelCase_    :     str=256	,		UpperCamelCase_    :     int = 630	,		UpperCamelCase_    :     int = 60	,		**UpperCamelCase_    :     Union[str, Any]	,		):


         """simple docstring"""



         super().__init__(
             temperature=UpperCamelCase_	,		do_sample=UpperCamelCase_	,		renormalize_logits=UpperCamelCase_	,		output_scores=UpperCamelCase_	,		return_dict_in_generate=UpperCamelCase_	,		output_hidden_states=UpperCamelCase_	,		output_attentions=UpperCamelCase_	,		**UpperCamelCase_	,		)

         __A             =		coarse_semantic_pad_token
         __A             =		coarse_rate_hz
         __A             =		n_coarse_codebooks
         __A             =		coarse_infer_token
         __A             =		max_coarse_input_length
         __A             =		max_coarse_history
         __A             =		sliding_window_len


class  __lowercase (		lowercase_					):







     '''simple docstring'''
     SCREAMING_SNAKE_CASE				  =			"fine_acoustics"




     def __init__(			self    :     Any	,		UpperCamelCase_    :     Dict=0.5	,		UpperCamelCase_    :     List[Any]=512	,		UpperCamelCase_    :     int=1_024	,		UpperCamelCase_    :     Union[str, Any]=8	,		**UpperCamelCase_    :     int	,		):


         """simple docstring"""



         super().__init__(temperature=UpperCamelCase_			)

         __A             =		max_fine_history_length
         __A             =		max_fine_input_length
         __A             =		n_fine_codebooks


class  __lowercase (		lowercase_					):







     '''simple docstring'''
     SCREAMING_SNAKE_CASE				  =			"bark"
     SCREAMING_SNAKE_CASE				  =			True




     def __init__(			self    :     Dict	,		UpperCamelCase_    :     Dict = None	,		UpperCamelCase_    :     Dict = None	,		UpperCamelCase_    :     Dict = None	,		UpperCamelCase_    :     List[str]=24_000	,		UpperCamelCase_    :     Union[str, Any]=1_024	,		**UpperCamelCase_    :     List[str]	,		):


         """simple docstring"""



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

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

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

         __A             =		BarkSemanticGenerationConfig(**UpperCamelCase_			)
         __A             =		BarkCoarseGenerationConfig(**UpperCamelCase_			)
         __A             =		BarkFineGenerationConfig(**UpperCamelCase_			)

         __A             =		sample_rate
         __A             =		codebook_size




     @classmethod
     def 						lowerCAmelCase_       (			cls    :     int	,		UpperCamelCase_    :     BarkSemanticGenerationConfig	,		UpperCamelCase_    :     BarkCoarseGenerationConfig	,		UpperCamelCase_    :     BarkFineGenerationConfig	,		**UpperCamelCase_    :     Tuple	,		):


         """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()	,		**UpperCamelCase_	,		)





     def 						lowerCAmelCase_       (			self    :     List[str]			):


         """simple docstring"""



         __A             =		copy.deepcopy(self.__dict__			)

         __A             =		self.semantic_config.to_dict()
         __A             =		self.coarse_acoustics_config.to_dict()
         __A             =		self.fine_acoustics_config.to_dict()

         __A             =		self.__class__.model_type
         return output




