

from dataclasses import dataclass
from typing import Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin







@dataclass
class        a__       (   UpperCAmelCase       ):







  """simple docstring"""




  UpperCAmelCase__      : torch.FloatTensor
  UpperCAmelCase__      : torch.FloatTensor
  UpperCAmelCase__      : Optional[torch.FloatTensor]			       =None







class        a__       (   UpperCAmelCase	,			UpperCAmelCase       ):







  """simple docstring"""




  UpperCAmelCase__      : Optional[Any]			       =2


  @register_to_config
  def __init__(			self    : Union[str, Any]				,				UpperCAmelCase__    : float = 0.02				,				UpperCAmelCase__    : float = 1_0_0				,				UpperCAmelCase__    : float = 1.0_07				,				UpperCAmelCase__    : float = 8_0				,				UpperCAmelCase__    : float = 0.05				,				UpperCAmelCase__    : float = 5_0				,				)					->Dict:
        """simple docstring"""







        SCREAMING_SNAKE_CASE :      int       =   sigma_max

        # setable values
        SCREAMING_SNAKE_CASE :      int       =   None
        SCREAMING_SNAKE_CASE :      np.IntTensor       =   None
        SCREAMING_SNAKE_CASE :      torch.FloatTensor       =   None  # sigma(t_i)


  def  _lowercase      (			self    : Union[str, Any]				,				UpperCAmelCase__    : torch.FloatTensor				,				UpperCAmelCase__    : Optional[int] = None		)					->torch.FloatTensor:
        """simple docstring"""







        return sample


  def  _lowercase      (			self    : Union[str, Any]				,				UpperCAmelCase__    : int				,				UpperCAmelCase__    : Union[str, torch.device] = None		)					->List[str]:
        """simple docstring"""







        SCREAMING_SNAKE_CASE :      Any       =   num_inference_steps
        SCREAMING_SNAKE_CASE :      Any       =   np.arange(0				,				self.num_inference_steps		)[::-1].copy()
        SCREAMING_SNAKE_CASE :      Any       =   torch.from_numpy(UpperCAmelCase__		).to(UpperCAmelCase__		)
        SCREAMING_SNAKE_CASE :      List[str]       =   [
            (
                self.config.sigma_max**2
                * (self.config.sigma_min**2 / self.config.sigma_max**2) ** (i / (num_inference_steps - 1))
            )
            for i in self.timesteps
        ]
        SCREAMING_SNAKE_CASE :      Dict       =   torch.tensor(UpperCAmelCase__				,				dtype=torch.floataa				,				device=UpperCAmelCase__		)


  def  _lowercase      (			self    : Optional[Any]				,				UpperCAmelCase__    : torch.FloatTensor				,				UpperCAmelCase__    : float				,				UpperCAmelCase__    : Optional[torch.Generator] = None		)					->Tuple[torch.FloatTensor, float]:
        """simple docstring"""







        if self.config.s_min <= sigma <= self.config.s_max:
              SCREAMING_SNAKE_CASE :      Union[str, Any]       =   min(self.config.s_churn / self.num_inference_steps				,				2**0.5 - 1		)
        else:
              SCREAMING_SNAKE_CASE :      Union[str, Any]       =   0

        # sample eps ~ N(0, S_noise^2 * I)
        SCREAMING_SNAKE_CASE :      Tuple       =   self.config.s_noise * randn_tensor(sample.shape				,				generator=UpperCAmelCase__		).to(sample.device		)
        SCREAMING_SNAKE_CASE :      str       =   sigma + gamma * sigma
        SCREAMING_SNAKE_CASE :      Any       =   sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)

        return sample_hat, sigma_hat


  def  _lowercase      (			self    : Optional[int]				,				UpperCAmelCase__    : torch.FloatTensor				,				UpperCAmelCase__    : float				,				UpperCAmelCase__    : float				,				UpperCAmelCase__    : torch.FloatTensor				,				UpperCAmelCase__    : bool = True				,				)					->Union[KarrasVeOutput, Tuple]:
        """simple docstring"""







        SCREAMING_SNAKE_CASE :      Optional[Any]       =   sample_hat + sigma_hat * model_output
        SCREAMING_SNAKE_CASE :      Tuple       =   (sample_hat - pred_original_sample) / sigma_hat
        SCREAMING_SNAKE_CASE :      List[Any]       =   sample_hat + (sigma_prev - sigma_hat) * derivative

        if not return_dict:
              return (sample_prev, derivative)

        return KarrasVeOutput(
            prev_sample=UpperCAmelCase__				,				derivative=UpperCAmelCase__				,				pred_original_sample=UpperCAmelCase__		)


  def  _lowercase      (			self    : int				,				UpperCAmelCase__    : torch.FloatTensor				,				UpperCAmelCase__    : float				,				UpperCAmelCase__    : float				,				UpperCAmelCase__    : torch.FloatTensor				,				UpperCAmelCase__    : torch.FloatTensor				,				UpperCAmelCase__    : torch.FloatTensor				,				UpperCAmelCase__    : bool = True				,				)					->Union[KarrasVeOutput, Tuple]:
        """simple docstring"""







        SCREAMING_SNAKE_CASE :      Optional[Any]       =   sample_prev + sigma_prev * model_output
        SCREAMING_SNAKE_CASE :      str       =   (sample_prev - pred_original_sample) / sigma_prev
        SCREAMING_SNAKE_CASE :      Optional[Any]       =   sample_hat + (sigma_prev - sigma_hat) * (0.5 * derivative + 0.5 * derivative_corr)

        if not return_dict:
              return (sample_prev, derivative)

        return KarrasVeOutput(
            prev_sample=UpperCAmelCase__				,				derivative=UpperCAmelCase__				,				pred_original_sample=UpperCAmelCase__		)




  def  _lowercase      (			self    : Union[str, Any]				,				UpperCAmelCase__    : Dict				,				UpperCAmelCase__    : List[Any]				,				UpperCAmelCase__    : Tuple		)					->int:
        """simple docstring"""







        raise NotImplementedError()







from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCAmelCase__ :     Dict				    =    logging.get_logger(__name__)

UpperCAmelCase__ :     int				    =    {
    """abeja/gpt-neox-japanese-2.7b""": """https://huggingface.co/abeja/gpt-neox-japanese-2.7b/resolve/main/config.json""",
}







class        a__       (   UpperCAmelCase       ):







  """simple docstring"""




  UpperCAmelCase__      : Any			       ="""gpt_neox_japanese"""


  def __init__(			self    : Any				,				UpperCAmelCase__    : Any=3_2_0_0_0				,				UpperCAmelCase__    : Dict=2_5_6_0				,				UpperCAmelCase__    : List[str]=3_2				,				UpperCAmelCase__    : Optional[int]=3_2				,				UpperCAmelCase__    : List[str]=4				,				UpperCAmelCase__    : List[str]="gelu"				,				UpperCAmelCase__    : Optional[int]=1.00				,				UpperCAmelCase__    : List[Any]=1_0_0_0_0				,				UpperCAmelCase__    : Tuple=2_0_4_8				,				UpperCAmelCase__    : Optional[int]=0.02				,				UpperCAmelCase__    : Dict=1e-5				,				UpperCAmelCase__    : Dict=True				,				UpperCAmelCase__    : Tuple=3_1_9_9_6				,				UpperCAmelCase__    : Tuple=3_1_9_9_9				,				UpperCAmelCase__    : Dict=0.1				,				UpperCAmelCase__    : Union[str, Any]=0.0				,				**UpperCAmelCase__    : Optional[Any]				,				)					->Optional[Any]:
        """simple docstring"""







        super().__init__(bos_token_id=UpperCAmelCase__				,				eos_token_id=UpperCAmelCase__				,				**UpperCAmelCase__		)
        SCREAMING_SNAKE_CASE :      List[str]       =   vocab_size
        SCREAMING_SNAKE_CASE :      List[str]       =   max_position_embeddings
        SCREAMING_SNAKE_CASE :      Tuple       =   hidden_size
        SCREAMING_SNAKE_CASE :      Union[str, Any]       =   num_hidden_layers
        SCREAMING_SNAKE_CASE :      Optional[int]       =   num_attention_heads
        SCREAMING_SNAKE_CASE :      Any       =   intermediate_multiple_size
        SCREAMING_SNAKE_CASE :      Tuple       =   hidden_act
        SCREAMING_SNAKE_CASE :      Any       =   rotary_pct
        SCREAMING_SNAKE_CASE :      Tuple       =   rotary_emb_base
        SCREAMING_SNAKE_CASE :      Union[str, Any]       =   initializer_range
        SCREAMING_SNAKE_CASE :      Optional[Any]       =   layer_norm_eps
        SCREAMING_SNAKE_CASE :      str       =   use_cache
        SCREAMING_SNAKE_CASE :      Any       =   attention_dropout
        SCREAMING_SNAKE_CASE :      str       =   hidden_dropout




