"""simple docstring"""


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    a__    (   UpperCAmelCase__	):
   snake_case_							=      42
   snake_case_							=      42






class    a__    (   UpperCAmelCase__					,					UpperCAmelCase__	):
   snake_case_							=      1


   @register_to_config
   def __init__(     self,				_UpperCAmelCase = 2000,				_UpperCAmelCase = 0.15,				_UpperCAmelCase = 0.01,				_UpperCAmelCase = 1348.0,				_UpperCAmelCase = 1E-5,				_UpperCAmelCase = 1,				):
        '''simple docstring'''



        lowercase__				 =							sigma_max

        # setable values
        lowercase__				 =							None

        self.set_sigmas(_UpperCAmelCase,				_UpperCAmelCase,				_UpperCAmelCase,				_UpperCAmelCase  )


   def 	snake_case__	(     self,				_UpperCAmelCase,				_UpperCAmelCase = None  ):
        '''simple docstring'''



        return sample


   def 	snake_case__	(     self,				_UpperCAmelCase,				_UpperCAmelCase = None,				_UpperCAmelCase = None  ):
        '''simple docstring'''



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

        lowercase__				 =							torch.linspace(1,				_UpperCAmelCase,				_UpperCAmelCase,				device=_UpperCAmelCase  )


   def 	snake_case__	(     self,				_UpperCAmelCase,				_UpperCAmelCase = None,				_UpperCAmelCase = None,				_UpperCAmelCase = None  ):
        '''simple docstring'''



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

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


   def 	snake_case__	(     self,				_UpperCAmelCase,				_UpperCAmelCase  ):
        '''simple docstring'''



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


   def 	snake_case__	(     self,				_UpperCAmelCase,				_UpperCAmelCase,				_UpperCAmelCase,				_UpperCAmelCase = None,				_UpperCAmelCase = True,				):
        '''simple docstring'''



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

        lowercase__				 =							timestep * torch.ones(
            sample.shape[0],				device=sample.device  )  # torch.repeat_interleave(timestep, sample.shape[0])
        lowercase__				 =							(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
        lowercase__				 =							timesteps.to(self.discrete_sigmas.device  )

        lowercase__				 =							self.discrete_sigmas[timesteps].to(sample.device  )
        lowercase__				 =							self.get_adjacent_sigma(_UpperCAmelCase,				_UpperCAmelCase  ).to(sample.device  )
        lowercase__				 =							torch.zeros_like(_UpperCAmelCase  )
        lowercase__				 =							(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
        lowercase__				 =							diffusion.flatten()
        while len(diffusion.shape  ) < len(sample.shape  ):
             lowercase__				 =							diffusion.unsqueeze(-1  )
        lowercase__				 =							drift - diffusion**2 * model_output

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


   def 	snake_case__	(     self,				_UpperCAmelCase,				_UpperCAmelCase,				_UpperCAmelCase = None,				_UpperCAmelCase = True,				):
        '''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
        lowercase__				 =							randn_tensor(sample.shape,				layout=sample.layout,				generator=_UpperCAmelCase  ).to(sample.device  )

        # compute step size from the model_output, the noise, and the snr
        lowercase__				 =							torch.norm(model_output.reshape(model_output.shape[0],				-1  ),				dim=-1  ).mean()
        lowercase__				 =							torch.norm(noise.reshape(noise.shape[0],				-1  ),				dim=-1  ).mean()
        lowercase__				 =							(self.config.snr * noise_norm / grad_norm) ** 2 * 2
        lowercase__				 =							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
        lowercase__				 =							step_size.flatten()
        while len(step_size.shape  ) < len(sample.shape  ):
             lowercase__				 =							step_size.unsqueeze(-1  )
        lowercase__				 =							sample + step_size * model_output
        lowercase__				 =							prev_sample_mean + ((step_size * 2) ** 0.5) * noise

        if not return_dict:
             return (prev_sample,)

        return SchedulerOutput(prev_sample=_UpperCAmelCase  )


   def 	snake_case__	(     self,				_UpperCAmelCase,				_UpperCAmelCase,				_UpperCAmelCase,				):
        '''simple docstring'''



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


   def __len__(     self  ):
        '''simple docstring'''



        return self.config.num_train_timesteps





"""simple docstring"""


import os
import sys

lowerCAmelCase_:					Any     =					os.path.join(os.path.dirname(__file__), "src")
sys.path.append(SRC_DIR)


from transformers import (
    AutoConfig,
    AutoModel,
    AutoModelForCausalLM,
    AutoModelForMaskedLM,
    AutoModelForQuestionAnswering,
    AutoModelForSequenceClassification,
    AutoTokenizer,
    add_start_docstrings,
)


lowerCAmelCase_:					Union[str, Any]     =					[
    "torch",
    "numpy",
    "tokenizers",
    "filelock",
    "requests",
    "tqdm",
    "regex",
    "sentencepiece",
    "sacremoses",
    "importlib_metadata",
    "huggingface_hub",
]







@add_start_docstrings(AutoConfig.__doc__      )
def 					__a   (		*A    ,				**A      ):






     '''simple docstring'''



     return AutoConfig.from_pretrained(*A    ,				**A      )







@add_start_docstrings(AutoTokenizer.__doc__      )
def 					__a   (		*A    ,				**A      ):






     '''simple docstring'''



     return AutoTokenizer.from_pretrained(*A    ,				**A      )







@add_start_docstrings(AutoModel.__doc__      )
def 					__a   (		*A    ,				**A      ):






     '''simple docstring'''



     return AutoModel.from_pretrained(*A    ,				**A      )







@add_start_docstrings(AutoModelForCausalLM.__doc__      )
def 					__a   (		*A    ,				**A      ):






     '''simple docstring'''



     return AutoModelForCausalLM.from_pretrained(*A    ,				**A      )







@add_start_docstrings(AutoModelForMaskedLM.__doc__      )
def 					__a   (		*A    ,				**A      ):






     '''simple docstring'''



     return AutoModelForMaskedLM.from_pretrained(*A    ,				**A      )







@add_start_docstrings(AutoModelForSequenceClassification.__doc__      )
def 					__a   (		*A    ,				**A      ):






     '''simple docstring'''



     return AutoModelForSequenceClassification.from_pretrained(*A    ,				**A      )







@add_start_docstrings(AutoModelForQuestionAnswering.__doc__      )
def 					__a   (		*A    ,				**A      ):






     '''simple docstring'''



     return AutoModelForQuestionAnswering.from_pretrained(*A    ,				**A      )




