




"""simple docstring"""



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


SCREAMING_SNAKE_CASE				=       logging.get_logger(__name__)

SCREAMING_SNAKE_CASE				=       {}




class 	UpperCAmelCase_  (						A_							):
    lowercase__											=      '''llama'''
    lowercase__											=      ['''past_key_values''']




    def __init__(    self						:       Any	, snake_case_						:       str=32_000	, snake_case_						:       Union[str, Any]=4_096	, snake_case_						:       Tuple=11_008	, snake_case_						:       Any=32	, snake_case_						:       Any=32	, snake_case_						:       List[str]=None	, snake_case_						:       List[str]="silu"	, snake_case_						:       Any=2_048	, snake_case_						:       Tuple=0.02	, snake_case_						:       Optional[int]=1e-6	, snake_case_						:       int=True	, snake_case_						:       List[Any]=0	, snake_case_						:       str=1	, snake_case_						:       Any=2	, snake_case_						:       List[Any]=1	, snake_case_						:       Union[str, Any]=False	, snake_case_						:       Union[str, Any]=None	, **snake_case_						:       int	, )      ->							Union[str, Any]:




     '''simple docstring'''

     A__       =				vocab_size
     A__       =				max_position_embeddings
     A__       =				hidden_size
     A__       =				intermediate_size
     A__       =				num_hidden_layers
     A__       =				num_attention_heads

     # for backward compatibility
     if num_key_value_heads is None:
      A__       =				num_attention_heads

     A__       =				num_key_value_heads
     A__       =				hidden_act
     A__       =				initializer_range
     A__       =				rms_norm_eps
     A__       =				pretraining_tp
     A__       =				use_cache
     A__       =				rope_scaling
     self._rope_scaling_validation()

     super().__init__(
         pad_token_id=SCREAMING_SNAKE_CASE__	, bos_token_id=SCREAMING_SNAKE_CASE__	, eos_token_id=SCREAMING_SNAKE_CASE__	, tie_word_embeddings=SCREAMING_SNAKE_CASE__	, **SCREAMING_SNAKE_CASE__	, )




    def 							__magic_name__							(    self						:       Union[str, Any]      )      ->							List[Any]:




     '''simple docstring'''

     if self.rope_scaling is None:
      return

     if not isinstance(self.rope_scaling	, SCREAMING_SNAKE_CASE__      ) or len(self.rope_scaling      ) != 2:
      raise ValueError(
          "`rope_scaling` must be a dictionary with with two fields, `name` and `factor`, "
          F"""got {self.rope_scaling}"""      )
     A__       =				self.rope_scaling.get("type"	, SCREAMING_SNAKE_CASE__      )
     A__       =				self.rope_scaling.get("factor"	, SCREAMING_SNAKE_CASE__      )
     if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
      raise ValueError(
          F"""`rope_scaling`\'s name field must be one of [\'linear\', \'dynamic\'], got {rope_scaling_type}"""      )
     if rope_scaling_factor is None or not isinstance(SCREAMING_SNAKE_CASE__	, SCREAMING_SNAKE_CASE__      ) or rope_scaling_factor <= 1.0:
      raise ValueError(F"""`rope_scaling`\'s factor field must be an float > 1, got {rope_scaling_factor}"""      )












"""simple docstring"""
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 					_SCREAMING_SNAKE_CASE(   A  ):
       SCREAMING_SNAKE_CASE_   :      torch.FloatTensor
       SCREAMING_SNAKE_CASE_   :      torch.FloatTensor
       SCREAMING_SNAKE_CASE_   :      Optional[torch.FloatTensor]							=  None






class 					_SCREAMING_SNAKE_CASE(   A    ,	A  ):
       SCREAMING_SNAKE_CASE_   :      Any							=  2




       @register_to_config
       def __init__(						self		,SCREAMING_SNAKE_CASE__ = 0.0_2		,SCREAMING_SNAKE_CASE__ = 1_00		,SCREAMING_SNAKE_CASE__ = 1.0_0_7		,SCREAMING_SNAKE_CASE__ = 80		,SCREAMING_SNAKE_CASE__ = 0.0_5		,SCREAMING_SNAKE_CASE__ = 50		,)			->      Union[str, Any]:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE   :Any						=  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     _UpperCamelCase     (						self		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__ = None							)			->      torch.FloatTensor:





             """simple docstring"""






             return sample




       def     _UpperCamelCase     (						self		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__ = None							)			->      List[Any]:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE   :Optional[Any]						=  num_inference_steps
             __SCREAMING_SNAKE_CASE   :int						=  np.arange(0		,self.num_inference_steps							)[::-1].copy()
             __SCREAMING_SNAKE_CASE   :Optional[int]						=  torch.from_numpy(SCREAMING_SNAKE_CASE__							).to(SCREAMING_SNAKE_CASE__							)
             __SCREAMING_SNAKE_CASE   :Any						=  [
                 (
                     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   :List[str]						=  torch.tensor(SCREAMING_SNAKE_CASE__		,dtype=torch.floataa		,device=SCREAMING_SNAKE_CASE__							)




       def     _UpperCamelCase     (						self		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__ = None							)			->      Tuple[torch.FloatTensor, float]:





             """simple docstring"""






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

             # sample eps ~ N(0, S_noise^2 * I)
             __SCREAMING_SNAKE_CASE   :Optional[int]						=  self.config.s_noise * randn_tensor(sample.shape		,generator=SCREAMING_SNAKE_CASE__							).to(sample.device							)
             __SCREAMING_SNAKE_CASE   :List[str]						=  sigma + gamma * sigma
             __SCREAMING_SNAKE_CASE   :str						=  sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)

             return sample_hat, sigma_hat




       def     _UpperCamelCase     (						self		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__ = True		,)			->      Union[KarrasVeOutput, Tuple]:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE   :str						=  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=SCREAMING_SNAKE_CASE__		,derivative=SCREAMING_SNAKE_CASE__		,pred_original_sample=SCREAMING_SNAKE_CASE__							)




       def     _UpperCamelCase     (						self		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__ = True		,)			->      Union[KarrasVeOutput, Tuple]:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE   :Any						=  sample_prev + sigma_prev * model_output
             __SCREAMING_SNAKE_CASE   :List[Any]						=  (sample_prev - pred_original_sample) / sigma_prev
             __SCREAMING_SNAKE_CASE   :Dict						=  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=SCREAMING_SNAKE_CASE__		,derivative=SCREAMING_SNAKE_CASE__		,pred_original_sample=SCREAMING_SNAKE_CASE__							)




       def     _UpperCamelCase     (						self		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__							)			->      Union[str, Any]:





             """simple docstring"""






             raise NotImplementedError()