


'''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 				UpperCAmelCase			(			__lowerCAmelCase						):
 UpperCAmelCase				=					42
 UpperCAmelCase				=					42
 UpperCAmelCase				=					None




class 				UpperCAmelCase			(			__lowerCAmelCase						, __lowerCAmelCase						):
 UpperCAmelCase				=					2


 @register_to_config
 def __init__(      self							:	List[Any]			,       __lowerCamelCase							:	Optional[int] = 0.02			,       __lowerCamelCase							:	Tuple = 1_0_0			,       __lowerCamelCase							:	List[Any] = 1.0_07			,       __lowerCamelCase							:	Tuple = 8_0			,       __lowerCamelCase							:	Union[str, Any] = 0.05			,       __lowerCamelCase							:	Tuple = 5_0			,       ):
    # standard deviation of the initial noise distribution
    UpperCAmelCase__	:List[str]      =      sigma_max

    # setable values
    UpperCAmelCase__	:Dict      =      None
    UpperCAmelCase__	:List[str]      =      None
    UpperCAmelCase__	:Tuple      =      None  # sigma(t_i)


 def        __SCREAMING_SNAKE_CASE					(      self							:	Any			,       __lowerCamelCase							:	Optional[int]			,       __lowerCamelCase							:	Tuple = None ):
    return sample


 def        __SCREAMING_SNAKE_CASE					(      self							:	int			,       __lowerCamelCase							:	Union[str, Any]			,       __lowerCamelCase							:	Union[str, Any] = None ):
    UpperCAmelCase__	:Optional[int]      =      num_inference_steps
    UpperCAmelCase__	:Optional[Any]      =      np.arange(0			,       self.num_inference_steps )[::-1].copy()
    UpperCAmelCase__	:List[Any]      =      torch.from_numpy(_SCREAMING_SNAKE_CASE ).to(_SCREAMING_SNAKE_CASE )
    UpperCAmelCase__	:Union[str, 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
    ]
    UpperCAmelCase__	:int      =      torch.tensor(_SCREAMING_SNAKE_CASE			,       dtype=torch.floataa			,       device=_SCREAMING_SNAKE_CASE )


 def        __SCREAMING_SNAKE_CASE					(      self							:	Optional[int]			,       __lowerCamelCase							:	List[str]			,       __lowerCamelCase							:	int			,       __lowerCamelCase							:	Tuple = None ):
    if self.config.s_min <= sigma <= self.config.s_max:
       UpperCAmelCase__	:Dict      =      min(self.config.s_churn / self.num_inference_steps			,       2**0.5 - 1 )
    else:
       UpperCAmelCase__	:Union[str, Any]      =      0

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

    return sample_hat, sigma_hat


 def        __SCREAMING_SNAKE_CASE					(      self							:	Optional[Any]			,       __lowerCamelCase							:	Union[str, Any]			,       __lowerCamelCase							:	Union[str, Any]			,       __lowerCamelCase							:	Union[str, Any]			,       __lowerCamelCase							:	Optional[Any]			,       __lowerCamelCase							:	str = True			,       ):
    UpperCAmelCase__	:Any      =      sample_hat + sigma_hat * model_output
    UpperCAmelCase__	:List[str]      =      (sample_hat - pred_original_sample) / sigma_hat
    UpperCAmelCase__	:Optional[int]      =      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        __SCREAMING_SNAKE_CASE					(      self							:	List[str]			,       __lowerCamelCase							:	List[Any]			,       __lowerCamelCase							:	Tuple			,       __lowerCamelCase							:	Optional[int]			,       __lowerCamelCase							:	Dict			,       __lowerCamelCase							:	Optional[Any]			,       __lowerCamelCase							:	Any			,       __lowerCamelCase							:	Optional[Any] = True			,       ):
    UpperCAmelCase__	:List[str]      =      sample_prev + sigma_prev * model_output
    UpperCAmelCase__	:Any      =      (sample_prev - pred_original_sample) / sigma_prev
    UpperCAmelCase__	:Union[str, 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=_SCREAMING_SNAKE_CASE			,       derivative=_SCREAMING_SNAKE_CASE			,       pred_original_sample=_SCREAMING_SNAKE_CASE )





 def        __SCREAMING_SNAKE_CASE					(      self							:	Union[str, Any]			,       __lowerCamelCase							:	Union[str, Any]			,       __lowerCamelCase							:	str			,       __lowerCamelCase							:	Optional[Any] ):
    raise NotImplementedError()









'''simple docstring'''





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


UpperCamelCase_		:     List[Any]          =		logging.get_logger(__name__)

UpperCamelCase_		:     Tuple          =		{
    '''RWKV/rwkv-4-169m-pile''': '''https://huggingface.co/RWKV/rwkv-4-169m-pile/resolve/main/config.json''',
    '''RWKV/rwkv-4-430m-pile''': '''https://huggingface.co/RWKV/rwkv-4-430m-pile/resolve/main/config.json''',
    '''RWKV/rwkv-4-1b5-pile''': '''https://huggingface.co/RWKV/rwkv-4-1b5-pile/resolve/main/config.json''',
    '''RWKV/rwkv-4-3b-pile''': '''https://huggingface.co/RWKV/rwkv-4-3b-pile/resolve/main/config.json''',
    '''RWKV/rwkv-4-7b-pile''': '''https://huggingface.co/RWKV/rwkv-4-7b-pile/resolve/main/config.json''',
    '''RWKV/rwkv-4-14b-pile''': '''https://huggingface.co/RWKV/rwkv-4-14b-pile/resolve/main/config.json''',
    '''RWKV/rwkv-raven-1b5''': '''https://huggingface.co/RWKV/rwkv-raven-1b5/resolve/main/config.json''',
    '''RWKV/rwkv-raven-3b''': '''https://huggingface.co/RWKV/rwkv-raven-3b/resolve/main/config.json''',
    '''RWKV/rwkv-raven-7b''': '''https://huggingface.co/RWKV/rwkv-raven-7b/resolve/main/config.json''',
    '''RWKV/rwkv-raven-14b''': '''https://huggingface.co/RWKV/rwkv-raven-14b/resolve/main/config.json''',
}

class   _a							(		__lowerCAmelCase				):
     SCREAMING_SNAKE_CASE_				:       List[str]       							=		"""rwkv"""
     SCREAMING_SNAKE_CASE_				:       Any       							=		{"""max_position_embeddings""": """context_length"""}






     def __init__(					self   ,_SCREAMING_SNAKE_CASE=50_277   ,_SCREAMING_SNAKE_CASE=1_024   ,_SCREAMING_SNAKE_CASE=4_096   ,_SCREAMING_SNAKE_CASE=32   ,_SCREAMING_SNAKE_CASE=None   ,_SCREAMING_SNAKE_CASE=None   ,_SCREAMING_SNAKE_CASE=1e-5   ,_SCREAMING_SNAKE_CASE=0   ,_SCREAMING_SNAKE_CASE=0   ,_SCREAMING_SNAKE_CASE=6   ,_SCREAMING_SNAKE_CASE=False   ,_SCREAMING_SNAKE_CASE=True   ,**_SCREAMING_SNAKE_CASE   ,)				->     List[str]:
          _snake_case				= vocab_size
          _snake_case				= context_length
          _snake_case				= hidden_size
          _snake_case				= num_hidden_layers
          _snake_case				= attention_hidden_size if attention_hidden_size is not None else hidden_size
          _snake_case				= intermediate_size if intermediate_size is not None else 4 * hidden_size
          _snake_case				= layer_norm_epsilon
          _snake_case				= rescale_every
          _snake_case				= use_cache

          _snake_case				= bos_token_id
          _snake_case				= eos_token_id

          super().__init__(
              tie_word_embeddings=_SCREAMING_SNAKE_CASE   ,bos_token_id=_SCREAMING_SNAKE_CASE   ,eos_token_id=_SCREAMING_SNAKE_CASE   ,**_SCREAMING_SNAKE_CASE       )


