



# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch

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

import torch

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



@dataclass
class 		_lowercase		(       UpperCAmelCase__	):
   _UpperCAmelCase     	=			42
   _UpperCAmelCase     	=			42



class 		_lowercase		(       UpperCAmelCase__,   UpperCAmelCase__	):
   _UpperCAmelCase     	=			1

   @register_to_config
   def __init__(      self			:       List[Any]							,   __lowerCAmelCase			:       int = 2000							,   __lowerCAmelCase			:       float = 0.1_5							,   __lowerCAmelCase			:       float = 0.0_1							,   __lowerCAmelCase			:       float = 1_3_4_8.0							,   __lowerCAmelCase			:       float = 1E-5							,   __lowerCAmelCase			:       int = 1							,   )       ->				Optional[Any]:


    """simple docstring"""






    a						=		sigma_max

    # setable values
    a						=		None

    self.set_sigmas(__lowerCAmelCase							,   __lowerCAmelCase							,   __lowerCAmelCase							,   __lowerCAmelCase							)

   def 		A							(      self			:       int							,   __lowerCAmelCase			:       torch.FloatTensor							,   __lowerCAmelCase			:       Optional[int] = None							)       ->				torch.FloatTensor:


    """simple docstring"""






    return sample

   def 		A							(      self			:       Tuple							,   __lowerCAmelCase			:       int							,   __lowerCAmelCase			:       float = None							,   __lowerCAmelCase			:       Union[str, torch.device] = None							)       ->				Optional[Any]:


    """simple docstring"""






    a						=		sampling_eps if sampling_eps is not None else self.config.sampling_eps

    a						=		torch.linspace(1							,   __lowerCAmelCase							,   __lowerCAmelCase							,   device=__lowerCAmelCase							)

   def 		A							(      self			:       Optional[Any]							,   __lowerCAmelCase			:       int							,   __lowerCAmelCase			:       float = None							,   __lowerCAmelCase			:       float = None							,   __lowerCAmelCase			:       float = None							)       ->				Union[str, Any]:


    """simple docstring"""






    a						=		sigma_min if sigma_min is not None else self.config.sigma_min
    a						=		sigma_max if sigma_max is not None else self.config.sigma_max
    a						=		sampling_eps if sampling_eps is not None else self.config.sampling_eps
    if self.timesteps is None:
     self.set_timesteps(__lowerCAmelCase							,   __lowerCAmelCase							)

    a						=		sigma_min * (sigma_max / sigma_min) ** (self.timesteps / sampling_eps)
    a						=		torch.exp(torch.linspace(math.log(__lowerCAmelCase							)							,   math.log(__lowerCAmelCase							)							,   __lowerCAmelCase							)							)
    a						=		torch.tensor([sigma_min * (sigma_max / sigma_min) ** t for t in self.timesteps]							)

   def 		A							(      self			:       List[str]							,   __lowerCAmelCase			:       Optional[int]							,   __lowerCAmelCase			:       Optional[Any]							)       ->				str:


    """simple docstring"""






    return torch.where(
        timesteps == 0							,   torch.zeros_like(t.to(timesteps.device							)							)							,   self.discrete_sigmas[timesteps - 1].to(timesteps.device							)							,   )

   def 		A							(      self			:       List[str]							,   __lowerCAmelCase			:       torch.FloatTensor							,   __lowerCAmelCase			:       int							,   __lowerCAmelCase			:       torch.FloatTensor							,   __lowerCAmelCase			:       Optional[torch.Generator] = None							,   __lowerCAmelCase			:       bool = True							,   )       ->				Union[SdeVeOutput, Tuple]:


    """simple docstring"""






    if self.timesteps is None:
     raise ValueError(
         "`self.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"							)

    a						=		timestep * torch.ones(
        sample.shape[0]							,   device=sample.device							)  # torch.repeat_interleave(timestep, sample.shape[0])
    a						=		(timestep * (len(self.timesteps							) - 1)).long()

    # mps requires indices to be in the same device, so we use cpu as is the default with cuda
    a						=		timesteps.to(self.discrete_sigmas.device							)

    a						=		self.discrete_sigmas[timesteps].to(sample.device							)
    a						=		self.get_adjacent_sigma(__lowerCAmelCase							,   __lowerCAmelCase							).to(sample.device							)
    a						=		torch.zeros_like(__lowerCAmelCase							)
    a						=		(sigma**2 - adjacent_sigma**2) ** 0.5

    # equation 6 in the paper: the model_output modeled by the network is grad_x log pt(x)
    # also equation 47 shows the analog from SDE models to ancestral sampling methods
    a						=		diffusion.flatten()
    while len(diffusion.shape							) < len(sample.shape							):
     a						=		diffusion.unsqueeze(-1							)
    a						=		drift - diffusion**2 * model_output

    #  equation 6: sample noise for the diffusion term of
    a						=		randn_tensor(
        sample.shape							,   layout=sample.layout							,   generator=__lowerCAmelCase							,   device=sample.device							,   dtype=sample.dtype							)
    a						=		sample - drift  # subtract because `dt` is a small negative timestep
    # TODO is the variable diffusion the correct scaling term for the noise?
    a						=		prev_sample_mean + diffusion * noise  # add impact of diffusion field g

    if not return_dict:
     return (prev_sample, prev_sample_mean)

    return SdeVeOutput(prev_sample=__lowerCAmelCase							,   prev_sample_mean=__lowerCAmelCase							)

   def 		A							(      self			:       Optional[Any]							,   __lowerCAmelCase			:       torch.FloatTensor							,   __lowerCAmelCase			:       torch.FloatTensor							,   __lowerCAmelCase			:       Optional[torch.Generator] = None							,   __lowerCAmelCase			:       bool = True							,   )       ->				Union[SchedulerOutput, Tuple]:


    """simple docstring"""






    if self.timesteps is None:
     raise ValueError(
         "`self.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"							)

    # For small batch sizes, the paper "suggest replacing norm(z) with sqrt(d), where d is the dim. of z"
    # sample noise for correction
    a						=		randn_tensor(sample.shape							,   layout=sample.layout							,   generator=__lowerCAmelCase							).to(sample.device							)

    # compute step size from the model_output, the noise, and the snr
    a						=		torch.norm(model_output.reshape(model_output.shape[0]							,   -1							)							,   dim=-1							).mean()
    a						=		torch.norm(noise.reshape(noise.shape[0]							,   -1							)							,   dim=-1							).mean()
    a						=		(self.config.snr * noise_norm / grad_norm) ** 2 * 2
    a						=		step_size * torch.ones(sample.shape[0]							).to(sample.device							)
    # self.repeat_scalar(step_size, sample.shape[0])

    # compute corrected sample: model_output term and noise term
    a						=		step_size.flatten()
    while len(step_size.shape							) < len(sample.shape							):
     a						=		step_size.unsqueeze(-1							)
    a						=		sample + step_size * model_output
    a						=		prev_sample_mean + ((step_size * 2) ** 0.5) * noise

    if not return_dict:
     return (prev_sample,)

    return SchedulerOutput(prev_sample=__lowerCAmelCase							)

   def 		A							(      self			:       List[str]							,   __lowerCAmelCase			:       torch.FloatTensor							,   __lowerCAmelCase			:       torch.FloatTensor							,   __lowerCAmelCase			:       torch.FloatTensor							,   )       ->				torch.FloatTensor:


    """simple docstring"""






    a						=		timesteps.to(original_samples.device							)
    a						=		self.discrete_sigmas.to(original_samples.device							)[timesteps]
    a						=		(
        noise * sigmas[:, None, None, None]
        if noise is not None
        else torch.randn_like(__lowerCAmelCase							) * sigmas[:, None, None, None]
    )
    a						=		noise + original_samples
    return noisy_samples




   def __len__(      self			:       List[Any]							)       ->				Optional[int]:


    """simple docstring"""






    return self.config.num_train_timesteps






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


A_		:     str        =     logging.get_logger(__name__)

A_		:     List[Any]        =     {
    '''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 		_lowercase		(       UpperCAmelCase__	):
   _UpperCAmelCase     	=			'''rwkv'''
   _UpperCAmelCase     	=			{'''max_position_embeddings''': '''context_length'''}

   def __init__(      self			:       List[str]							,   __lowerCAmelCase			:       Union[str, Any]=5_0277							,   __lowerCAmelCase			:       str=1024							,   __lowerCAmelCase			:       Union[str, Any]=4096							,   __lowerCAmelCase			:       Optional[int]=32							,   __lowerCAmelCase			:       Optional[int]=None							,   __lowerCAmelCase			:       Optional[Any]=None							,   __lowerCAmelCase			:       List[Any]=1E-5							,   __lowerCAmelCase			:       Union[str, Any]=0							,   __lowerCAmelCase			:       Optional[Any]=0							,   __lowerCAmelCase			:       Dict=6							,   __lowerCAmelCase			:       int=False							,   __lowerCAmelCase			:       Tuple=True							,   **__lowerCAmelCase			:       List[str]							,   )       ->				List[Any]:


    """simple docstring"""






    a						=		vocab_size
    a						=		context_length
    a						=		hidden_size
    a						=		num_hidden_layers
    a						=		attention_hidden_size if attention_hidden_size is not None else hidden_size
    a						=		intermediate_size if intermediate_size is not None else 4 * hidden_size
    a						=		layer_norm_epsilon
    a						=		rescale_every
    a						=		use_cache

    a						=		bos_token_id
    a						=		eos_token_id

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

