



import argparse
import os
from pathlib import Path

import torch
from bark.generation import _load_model as _bark_load_model
from huggingface_hub import hf_hub_download

from transformers import EncodecConfig, EncodecModel, set_seed
from transformers.models.bark.configuration_bark import (
    BarkCoarseConfig,
    BarkConfig,
    BarkFineConfig,
    BarkSemanticConfig,
)
from transformers.models.bark.generation_configuration_bark import (
    BarkCoarseGenerationConfig,
    BarkFineGenerationConfig,
    BarkGenerationConfig,
    BarkSemanticGenerationConfig,
)
from transformers.models.bark.modeling_bark import BarkCoarseModel, BarkFineModel, BarkModel, BarkSemanticModel
from transformers.utils import logging


logging.set_verbosity_info()
__UpperCAmelCase          =       logging.get_logger(__name__)

set_seed(770)


__UpperCAmelCase          =       {
    '''c_attn''': '''att_proj''',
    '''c_proj''': '''out_proj''',
    '''c_fc''': '''in_proj''',
    '''transformer.''': '''''',
    '''h.''': '''layers.''',
    '''ln_1''': '''layernorm_1''',
    '''ln_2''': '''layernorm_2''',
    '''ln_f''': '''layernorm_final''',
    '''wpe''': '''position_embeds_layer''',
    '''wte''': '''input_embeds_layer''',
}


__UpperCAmelCase          =       {
    '''text_small''': {
        '''repo_id''': '''suno/bark''',
        '''file_name''': '''text.pt''',
    },
    '''coarse_small''': {
        '''repo_id''': '''suno/bark''',
        '''file_name''': '''coarse.pt''',
    },
    '''fine_small''': {
        '''repo_id''': '''suno/bark''',
        '''file_name''': '''fine.pt''',
    },
    '''text''': {
        '''repo_id''': '''suno/bark''',
        '''file_name''': '''text_2.pt''',
    },
    '''coarse''': {
        '''repo_id''': '''suno/bark''',
        '''file_name''': '''coarse_2.pt''',
    },
    '''fine''': {
        '''repo_id''': '''suno/bark''',
        '''file_name''': '''fine_2.pt''',
    },
}

__UpperCAmelCase          =       os.path.dirname(os.path.abspath(__file__))
__UpperCAmelCase          =       os.path.join(os.path.expanduser('''~'''), '''.cache''')
__UpperCAmelCase          =       os.path.join(os.getenv('''XDG_CACHE_HOME''', default_cache_dir), '''suno''', '''bark_v0''')
def 		UpperCamelCase	(   snake_case__       :    Optional[int]       ,     snake_case__       :    Optional[int]=False	)	->     Optional[Any]:
   UpperCamelCase    :  int							=	model_type
   if use_small:
      key += "_small"
   return os.path.join(snake_case__       ,     REMOTE_MODEL_PATHS[key]['file_name']	)
def 		UpperCamelCase	(   snake_case__       :    int       ,     snake_case__       :    Optional[Any]	)	->     Optional[int]:
   os.makedirs(snake_case__       ,     exist_ok=snake_case__	)
   hf_hub_download(repo_id=snake_case__       ,     filename=snake_case__       ,     local_dir=snake_case__	)
def 		UpperCamelCase	(   snake_case__       :    Optional[Any]       ,     snake_case__       :    Optional[int]       ,     snake_case__       :    str=False       ,     snake_case__       :    int="text"	)	->     Optional[int]:
   if model_type == "text":
      UpperCamelCase    :  Tuple							=	BarkSemanticModel
      UpperCamelCase    :  List[str]							=	BarkSemanticConfig
      UpperCamelCase    :  List[str]							=	BarkSemanticGenerationConfig
   elif model_type == "coarse":
      UpperCamelCase    :  int							=	BarkCoarseModel
      UpperCamelCase    :  Optional[int]							=	BarkCoarseConfig
      UpperCamelCase    :  Optional[int]							=	BarkCoarseGenerationConfig
   elif model_type == "fine":
      UpperCamelCase    :  Any							=	BarkFineModel
      UpperCamelCase    :  List[str]							=	BarkFineConfig
      UpperCamelCase    :  str							=	BarkFineGenerationConfig
   else:
      raise NotImplementedError()
   UpperCamelCase    :  Any							=	F"""{model_type}_small""" if use_small else model_type
   UpperCamelCase    :  Optional[Any]							=	REMOTE_MODEL_PATHS[model_key]
   if not os.path.exists(snake_case__	):
      logger.info(F"""{model_type} model not found, downloading into `{CACHE_DIR}`."""	)
      _download(model_info['repo_id']       ,     model_info['file_name']	)
   UpperCamelCase    :  Dict							=	torch.load(snake_case__       ,     map_location=snake_case__	)
   # this is a hack
   UpperCamelCase    :  Union[str, Any]							=	checkpoint['model_args']
   if "input_vocab_size" not in model_args:
      UpperCamelCase    :  int							=	model_args['vocab_size']
      UpperCamelCase    :  Dict							=	model_args['vocab_size']
      del model_args["vocab_size"]

   # convert Bark model arguments to HF Bark model arguments
   UpperCamelCase    :  int							=	model_args.pop('n_head'	)
   UpperCamelCase    :  Tuple							=	model_args.pop('n_embd'	)
   UpperCamelCase    :  Dict							=	model_args.pop('n_layer'	)

   UpperCamelCase    :  List[Any]							=	ConfigClass(**checkpoint['model_args']	)
   UpperCamelCase    :  Tuple							=	ModelClass(config=snake_case__	)
   UpperCamelCase    :  Dict							=	GenerationConfigClass()

   UpperCamelCase    :  Tuple							=	model_generation_config
   UpperCamelCase    :  str							=	checkpoint['model']
   # fixup checkpoint
   UpperCamelCase    :  Union[str, Any]							=	'_orig_mod.'
   for k, v in list(state_dict.items()	):
      if k.startswith(snake_case__	):
         # replace part of the key with corresponding layer name in HF implementation
         UpperCamelCase    :  Union[str, Any]							=	k[len(snake_case__	) :]
         for old_layer_name in new_layer_name_dict:
            UpperCamelCase    :  str							=	new_k.replace(snake_case__       ,     new_layer_name_dict[old_layer_name]	)

         UpperCamelCase    :  Dict							=	state_dict.pop(snake_case__	)

   UpperCamelCase    :  List[str]							=	set(state_dict.keys()	) - set(model.state_dict().keys()	)
   UpperCamelCase    :  Union[str, Any]							=	{k for k in extra_keys if not k.endswith('.attn.bias'	)}
   UpperCamelCase    :  Optional[int]							=	set(model.state_dict().keys()	) - set(state_dict.keys()	)
   UpperCamelCase    :  List[Any]							=	{k for k in missing_keys if not k.endswith('.attn.bias'	)}
   if len(snake_case__	) != 0:
      raise ValueError(F"""extra keys found: {extra_keys}"""	)
   if len(snake_case__	) != 0:
      raise ValueError(F"""missing keys: {missing_keys}"""	)
   model.load_state_dict(snake_case__       ,     strict=snake_case__	)
   UpperCamelCase    :  str							=	model.num_parameters(exclude_embeddings=snake_case__	)
   UpperCamelCase    :  str							=	checkpoint['best_val_loss'].item()
   logger.info(F"""model loaded: {round(n_params/1E6       ,     1	)}M params, {round(snake_case__       ,     3	)} loss"""	)
   model.eval()
   model.to(snake_case__	)
   del checkpoint, state_dict

   return model
def 		UpperCamelCase	(   snake_case__       :    int       ,     snake_case__       :    int=False       ,     snake_case__       :    Union[str, Any]="text"	)	->     int:
   if model_type not in ("text", "coarse", "fine"):
      raise NotImplementedError()

   UpperCamelCase    :  Optional[Any]							=	'cpu'  # do conversion on cpu

   UpperCamelCase    :  int							=	_get_ckpt_path(snake_case__       ,     use_small=snake_case__	)
   UpperCamelCase    :  Tuple							=	_load_model(snake_case__       ,     snake_case__       ,     model_type=snake_case__       ,     use_small=snake_case__	)

   # load bark initial model
   UpperCamelCase    :  Dict							=	_bark_load_model(snake_case__       ,     'cpu'       ,     model_type=snake_case__       ,     use_small=snake_case__	)

   if model_type == "text":
      UpperCamelCase    :  Optional[int]							=	bark_model['model']

   if model.num_parameters(exclude_embeddings=snake_case__	) != bark_model.get_num_params():
      raise ValueError('initial and new models don\'t have the same number of parameters'	)

   # check if same output as the bark model
   UpperCamelCase    :  List[Any]							=	5
   UpperCamelCase    :  List[Any]							=	10

   if model_type in ["text", "coarse"]:
      UpperCamelCase    :  Any							=	torch.randint(256       ,     (batch_size, sequence_length)       ,     dtype=torch.int	)
      UpperCamelCase    :  Union[str, Any]							=	bark_model(snake_case__	)[0]

      UpperCamelCase    :  List[Any]							=	model(snake_case__	)

      # take last logits
      UpperCamelCase    :  List[Any]							=	output_new_model_total.logits[:, [-1], :]

   else:
      UpperCamelCase    :  Dict							=	3
      UpperCamelCase    :  List[str]							=	8
      UpperCamelCase    :  Optional[Any]							=	torch.randint(256       ,     (batch_size, sequence_length, n_codes_total)       ,     dtype=torch.int	)

      UpperCamelCase    :  str							=	model(snake_case__       ,     snake_case__	)
      UpperCamelCase    :  Dict							=	bark_model(snake_case__       ,     snake_case__	)

      UpperCamelCase    :  Optional[int]							=	output_new_model_total.logits

   # output difference should come from the difference of self-attention implementation design
   if output_new_model.shape != output_old_model.shape:
      raise ValueError('initial and new outputs don\'t have the same shape'	)
   if (output_new_model - output_old_model).abs().max().item() > 1E-3:
      raise ValueError('initial and new outputs are not equal'	)

   Path(snake_case__	).mkdir(exist_ok=snake_case__	)
   model.save_pretrained(snake_case__	)
def 		UpperCamelCase	(   snake_case__       :    Dict       ,     snake_case__       :    Any       ,     snake_case__       :    str       ,     snake_case__       :    List[str]       ,     snake_case__       :    Union[str, Any]       ,     snake_case__       :    Optional[Any]       ,     )	->     List[Any]:
   UpperCamelCase    :  List[str]							=	os.path.join(snake_case__       ,     snake_case__	)

   UpperCamelCase    :  Any							=	BarkSemanticConfig.from_pretrained(os.path.join(snake_case__       ,     'config.json'	)	)
   UpperCamelCase    :  int							=	BarkCoarseConfig.from_pretrained(os.path.join(snake_case__       ,     'config.json'	)	)
   UpperCamelCase    :  Any							=	BarkFineConfig.from_pretrained(os.path.join(snake_case__       ,     'config.json'	)	)
   UpperCamelCase    :  Any							=	EncodecConfig.from_pretrained('facebook/encodec_24khz'	)

   UpperCamelCase    :  Optional[Any]							=	BarkSemanticModel.from_pretrained(snake_case__	)
   UpperCamelCase    :  Optional[Any]							=	BarkCoarseModel.from_pretrained(snake_case__	)
   UpperCamelCase    :  int							=	BarkFineModel.from_pretrained(snake_case__	)
   UpperCamelCase    :  Dict							=	EncodecModel.from_pretrained('facebook/encodec_24khz'	)

   UpperCamelCase    :  List[Any]							=	BarkConfig.from_sub_model_configs(
       snake_case__       ,     snake_case__       ,     snake_case__       ,     snake_case__	)

   UpperCamelCase    :  Optional[Any]							=	BarkGenerationConfig.from_sub_model_configs(
       semantic.generation_config       ,     coarseAcoustic.generation_config       ,     fineAcoustic.generation_config	)

   UpperCamelCase    :  Optional[int]							=	BarkModel(snake_case__	)

   UpperCamelCase    :  Dict							=	semantic
   UpperCamelCase    :  int							=	coarseAcoustic
   UpperCamelCase    :  Optional[int]							=	fineAcoustic
   UpperCamelCase    :  List[Any]							=	codec

   UpperCamelCase    :  Optional[Any]							=	bark_generation_config

   Path(snake_case__	).mkdir(exist_ok=snake_case__	)
   bark.save_pretrained(snake_case__       ,     repo_id=snake_case__       ,     push_to_hub=snake_case__	)


if __name__ == "__main__":
    __UpperCAmelCase          =       argparse.ArgumentParser()
    # Required parameters

    parser.add_argument('''model_type''', type=str, help='''text, coarse or fine.''')
    parser.add_argument('''pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
    parser.add_argument('''--is_small''', action='''store_true''', help='''convert the small version instead of the large.''')

    __UpperCAmelCase          =       parser.parse_args()

    load_model(args.pytorch_dump_folder_path, model_type=args.model_type, use_small=args.is_small)








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 .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput
def 		UpperCamelCase	(   snake_case__       :    Dict       ,     snake_case__       :    Any=0.999       ,     snake_case__       :    List[Any]="cosine"       ,     )	->     Optional[int]:
   if alpha_transform_type == "cosine":

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

   elif alpha_transform_type == "exp":

      def alpha_bar_fn(snake_case__       :    Optional[Any]	):
         return math.exp(t * -12.0	)

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

   UpperCamelCase    :  List[Any]							=	[]
   for i in range(snake_case__	):
      UpperCamelCase    :  Optional[Any]							=	i / num_diffusion_timesteps
      UpperCamelCase    :  Union[str, Any]							=	(i + 1) / num_diffusion_timesteps
      betas.append(min(1 - alpha_bar_fn(snake_case__	) / alpha_bar_fn(snake_case__	)       ,     snake_case__	)	)
   return torch.tensor(snake_case__       ,     dtype=torch.floataa	)



class 				lowerCAmelCase_		(  a__       ,    a__							):
       UpperCAmelCase__				:					List[Any]        =  [e.name for e in KarrasDiffusionSchedulers]
       UpperCAmelCase__				:					List[str]        =  2
       @register_to_config
       def __init__(			self,			SCREAMING_SNAKE_CASE_ = 1000,			SCREAMING_SNAKE_CASE_ = 0.0_00_85,			SCREAMING_SNAKE_CASE_ = 0.0_12,			SCREAMING_SNAKE_CASE_ = "linear",			SCREAMING_SNAKE_CASE_ = None,			SCREAMING_SNAKE_CASE_ = "epsilon",			SCREAMING_SNAKE_CASE_ = "linspace",			SCREAMING_SNAKE_CASE_ = 0,			)     ->		List[str]:
          if trained_betas is not None:
             UpperCamelCase    :  Union[str, Any]							=	torch.tensor(SCREAMING_SNAKE_CASE_,			dtype=torch.floataa       )
          elif beta_schedule == "linear":
             UpperCamelCase    :  List[Any]							=	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.
             UpperCamelCase    :  int							=	(
                 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
             UpperCamelCase    :  Optional[int]							=	betas_for_alpha_bar(SCREAMING_SNAKE_CASE_       )
          else:
             raise NotImplementedError(F"""{beta_schedule} does is not implemented for {self.__class__}"""       )

          UpperCamelCase    :  Optional[int]							=	1.0 - self.betas
          UpperCamelCase    :  int							=	torch.cumprod(self.alphas,			dim=0       )

          #  set all values
          self.set_timesteps(SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_       )
       def 					snake_case_       (			self,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_=None       )     ->		str:
          if schedule_timesteps is None:
             UpperCamelCase    :  Union[str, Any]							=	self.timesteps

          UpperCamelCase    :  Dict							=	(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:
             UpperCamelCase    :  Optional[int]							=	1 if len(SCREAMING_SNAKE_CASE_       ) > 1 else 0
          else:
             UpperCamelCase    :  str							=	timestep.cpu().item() if torch.is_tensor(SCREAMING_SNAKE_CASE_       ) else timestep
             UpperCamelCase    :  int							=	self._index_counter[timestep_int]

          return indices[pos].item()
       @property
       def 					snake_case_       (			self       )     ->		List[Any]:
          # standard deviation of the initial noise distribution
          if self.config.timestep_spacing in ["linspace", "trailing"]:
             return self.sigmas.max()

          return (self.sigmas.max() ** 2 + 1) ** 0.5
       def 					snake_case_       (			self,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_,			)     ->		torch.FloatTensor:
          UpperCamelCase    :  Optional[int]							=	self.index_for_timestep(SCREAMING_SNAKE_CASE_       )

          if self.state_in_first_order:
             UpperCamelCase    :  Dict							=	self.sigmas[step_index]
          else:
             UpperCamelCase    :  int							=	self.sigmas_interpol[step_index]

          UpperCamelCase    :  Any							=	sample / ((sigma**2 + 1) ** 0.5)
          return sample
       def 					snake_case_       (			self,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_ = None,			SCREAMING_SNAKE_CASE_ = None,			)     ->		Optional[int]:
          UpperCamelCase    :  Dict							=	num_inference_steps

          UpperCamelCase    :  List[Any]							=	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":
             UpperCamelCase    :  int							=	np.linspace(0,			num_train_timesteps - 1,			SCREAMING_SNAKE_CASE_,			dtype=SCREAMING_SNAKE_CASE_       )[::-1].copy()
          elif self.config.timestep_spacing == "leading":
             UpperCamelCase    :  List[Any]							=	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
             UpperCamelCase    :  Optional[Any]							=	(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":
             UpperCamelCase    :  Optional[int]							=	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
             UpperCamelCase    :  Any							=	(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'."""       )

          UpperCamelCase    :  Tuple							=	np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5       )
          UpperCamelCase    :  Optional[int]							=	torch.from_numpy(np.log(SCREAMING_SNAKE_CASE_       )       ).to(SCREAMING_SNAKE_CASE_       )

          UpperCamelCase    :  Optional[int]							=	np.interp(SCREAMING_SNAKE_CASE_,			np.arange(0,			len(SCREAMING_SNAKE_CASE_       )       ),			SCREAMING_SNAKE_CASE_       )
          UpperCamelCase    :  Dict							=	np.concatenate([sigmas, [0.0]]       ).astype(np.floataa       )
          UpperCamelCase    :  Optional[int]							=	torch.from_numpy(SCREAMING_SNAKE_CASE_       ).to(device=SCREAMING_SNAKE_CASE_       )

          # interpolate sigmas
          UpperCamelCase    :  Union[str, Any]							=	sigmas.log().lerp(sigmas.roll(1       ).log(),			0.5       ).exp()

          UpperCamelCase    :  Any							=	torch.cat([sigmas[:1], sigmas[1:].repeat_interleave(2       ), sigmas[-1:]]       )
          UpperCamelCase    :  Optional[Any]							=	torch.cat(
              [sigmas_interpol[:1], sigmas_interpol[1:].repeat_interleave(2       ), sigmas_interpol[-1:]]       )

          if str(SCREAMING_SNAKE_CASE_       ).startswith('mps'       ):
             # mps does not support float64
             UpperCamelCase    :  Any							=	torch.from_numpy(SCREAMING_SNAKE_CASE_       ).to(SCREAMING_SNAKE_CASE_,			dtype=torch.floataa       )
          else:
             UpperCamelCase    :  Dict							=	torch.from_numpy(SCREAMING_SNAKE_CASE_       ).to(SCREAMING_SNAKE_CASE_       )

          # interpolate timesteps
          UpperCamelCase    :  int							=	self.sigma_to_t(SCREAMING_SNAKE_CASE_       ).to(SCREAMING_SNAKE_CASE_,			dtype=timesteps.dtype       )
          UpperCamelCase    :  List[str]							=	torch.stack((timesteps_interpol[1:-1, None], timesteps[1:, None]),			dim=-1       ).flatten()

          UpperCamelCase    :  Optional[Any]							=	torch.cat([timesteps[:1], interleaved_timesteps]       )

          UpperCamelCase    :  Optional[Any]							=	None

          # for exp beta schedules, such as the one for `pipeline_shap_e.py`
          # we need an index counter
          UpperCamelCase    :  Dict							=	defaultdict(SCREAMING_SNAKE_CASE_       )
       def 					snake_case_       (			self,			SCREAMING_SNAKE_CASE_       )     ->		Union[str, Any]:
          # get log sigma
          UpperCamelCase    :  List[Any]							=	sigma.log()

          # get distribution
          UpperCamelCase    :  Optional[int]							=	log_sigma - self.log_sigmas[:, None]

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

          UpperCamelCase    :  List[str]							=	self.log_sigmas[low_idx]
          UpperCamelCase    :  Optional[Any]							=	self.log_sigmas[high_idx]

          # interpolate sigmas
          UpperCamelCase    :  int							=	(low - log_sigma) / (low - high)
          UpperCamelCase    :  Tuple							=	w.clamp(0,			1       )

          # transform interpolation to time range
          UpperCamelCase    :  List[str]							=	(1 - w) * low_idx + w * high_idx
          UpperCamelCase    :  Dict							=	t.view(sigma.shape       )
          return t
       @property
       def 					snake_case_       (			self       )     ->		Optional[int]:
          return self.sample is None
       def 					snake_case_       (			self,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_ = True,			)     ->		Union[SchedulerOutput, Tuple]:
          UpperCamelCase    :  str							=	self.index_for_timestep(SCREAMING_SNAKE_CASE_       )

          # advance index counter by 1
          UpperCamelCase    :  Optional[int]							=	timestep.cpu().item() if torch.is_tensor(SCREAMING_SNAKE_CASE_       ) else timestep
          self._index_counter[timestep_int] += 1

          if self.state_in_first_order:
             UpperCamelCase    :  Tuple							=	self.sigmas[step_index]
             UpperCamelCase    :  Dict							=	self.sigmas_interpol[step_index + 1]
             UpperCamelCase    :  Optional[int]							=	self.sigmas[step_index + 1]
          else:
             # 2nd order / KDPM2's method
             UpperCamelCase    :  str							=	self.sigmas[step_index - 1]
             UpperCamelCase    :  Dict							=	self.sigmas_interpol[step_index]
             UpperCamelCase    :  Any							=	self.sigmas[step_index]

          # 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
          UpperCamelCase    :  Dict							=	0
          UpperCamelCase    :  Any							=	sigma * (gamma + 1)  # Note: sigma_hat == sigma for now

          # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
          if self.config.prediction_type == "epsilon":
             UpperCamelCase    :  Any							=	sigma_hat if self.state_in_first_order else sigma_interpol
             UpperCamelCase    :  List[Any]							=	sample - sigma_input * model_output
          elif self.config.prediction_type == "v_prediction":
             UpperCamelCase    :  Union[str, Any]							=	sigma_hat if self.state_in_first_order else sigma_interpol
             UpperCamelCase    :  Optional[int]							=	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
             UpperCamelCase    :  int							=	(sample - pred_original_sample) / sigma_hat
             # 3. delta timestep
             UpperCamelCase    :  Any							=	sigma_interpol - sigma_hat

             # store for 2nd order step
             UpperCamelCase    :  Tuple							=	sample
          else:
             # DPM-Solver-2
             # 2. Convert to an ODE derivative for 2nd order
             UpperCamelCase    :  Union[str, Any]							=	(sample - pred_original_sample) / sigma_interpol

             # 3. delta timestep
             UpperCamelCase    :  Dict							=	sigma_next - sigma_hat

             UpperCamelCase    :  Any							=	self.sample
             UpperCamelCase    :  str							=	None

          UpperCamelCase    :  Any							=	sample + derivative * dt

          if not return_dict:
             return (prev_sample,)

          return SchedulerOutput(prev_sample=SCREAMING_SNAKE_CASE_       )
       def 					snake_case_       (			self,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_,			)     ->		torch.FloatTensor:
          # Make sure sigmas and timesteps have the same device and dtype as original_samples
          UpperCamelCase    :  Optional[Any]							=	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
             UpperCamelCase    :  List[str]							=	self.timesteps.to(original_samples.device,			dtype=torch.floataa       )
             UpperCamelCase    :  str							=	timesteps.to(original_samples.device,			dtype=torch.floataa       )
          else:
             UpperCamelCase    :  Dict							=	self.timesteps.to(original_samples.device       )
             UpperCamelCase    :  int							=	timesteps.to(original_samples.device       )

          UpperCamelCase    :  str							=	[self.index_for_timestep(SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_       ) for t in timesteps]

          UpperCamelCase    :  List[Any]							=	sigmas[step_indices].flatten()
          while len(sigma.shape       ) < len(original_samples.shape       ):
             UpperCamelCase    :  int							=	sigma.unsqueeze(-1       )

          UpperCamelCase    :  Any							=	original_samples + noise * sigma
          return noisy_samples






       def __len__(			self       )     ->		Optional[int]:
          return self.config.num_train_timesteps



