


"""simple docstring"""





# 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 						snake_case_(				a__	):
     __UpperCamelCase     =    42
     __UpperCamelCase     =    42



class 						snake_case_(				a__							,    a__	):
     __UpperCamelCase     =    1






     @register_to_config
     def __init__(		self     :    str    , UpperCamelCase_     :    int = 2_0_0_0    , UpperCamelCase_     :    float = 0.15    , UpperCamelCase_     :    float = 0.01    , UpperCamelCase_     :    float = 1_348.0    , UpperCamelCase_     :    float = 1E-5    , UpperCamelCase_     :    int = 1    , ):
           # standard deviation of the initial noise distribution
           lowerCAmelCase    :		Union[str, Any]				=      sigma_max

           # setable values
           lowerCAmelCase    :		Dict				=      None

           self.set_sigmas(UpperCamelCase_    , UpperCamelCase_    , UpperCamelCase_    , UpperCamelCase_ )






     def    lowerCamelCase__   (		self     :    Tuple    , UpperCamelCase_     :    torch.FloatTensor    , UpperCamelCase_     :    Optional[int] = None ):
           return sample






     def    lowerCamelCase__   (		self     :    Optional[int]    , UpperCamelCase_     :    int    , UpperCamelCase_     :    float = None    , UpperCamelCase_     :    Union[str, torch.device] = None ):
           lowerCAmelCase    :		Optional[Any]				=      sampling_eps if sampling_eps is not None else self.config.sampling_eps

           lowerCAmelCase    :		Dict				=      torch.linspace(1    , UpperCamelCase_    , UpperCamelCase_    , device=UpperCamelCase_ )






     def    lowerCamelCase__   (		self     :    str    , UpperCamelCase_     :    int    , UpperCamelCase_     :    float = None    , UpperCamelCase_     :    float = None    , UpperCamelCase_     :    float = None ):
           lowerCAmelCase    :		Optional[Any]				=      sigma_min if sigma_min is not None else self.config.sigma_min
           lowerCAmelCase    :		Union[str, Any]				=      sigma_max if sigma_max is not None else self.config.sigma_max
           lowerCAmelCase    :		int				=      sampling_eps if sampling_eps is not None else self.config.sampling_eps
           if self.timesteps is None:
                 self.set_timesteps(UpperCamelCase_    , UpperCamelCase_ )

           lowerCAmelCase    :		Optional[int]				=      sigma_min * (sigma_max / sigma_min) ** (self.timesteps / sampling_eps)
           lowerCAmelCase    :		Any				=      torch.exp(torch.linspace(math.log(UpperCamelCase_ )    , math.log(UpperCamelCase_ )    , UpperCamelCase_ ) )
           lowerCAmelCase    :		Dict				=      torch.tensor([sigma_min * (sigma_max / sigma_min) ** t for t in self.timesteps] )






     def    lowerCamelCase__   (		self     :    Union[str, Any]    , UpperCamelCase_     :    Union[str, Any]    , UpperCamelCase_     :    List[Any] ):
           return torch.where(
               timesteps == 0    , torch.zeros_like(t.to(timesteps.device ) )    , self.discrete_sigmas[timesteps - 1].to(timesteps.device )    , )






     def    lowerCamelCase__   (		self     :    List[Any]    , UpperCamelCase_     :    torch.FloatTensor    , UpperCamelCase_     :    int    , UpperCamelCase_     :    torch.FloatTensor    , UpperCamelCase_     :    Optional[torch.Generator] = None    , UpperCamelCase_     :    bool = True    , ):
           if self.timesteps is None:
                 raise ValueError(
                     '''`self.timesteps` is not set, you need to run \'set_timesteps\' after creating the scheduler''' )

           lowerCAmelCase    :		Optional[int]				=      timestep * torch.ones(
               sample.shape[0]    , device=sample.device )  # torch.repeat_interleave(timestep, sample.shape[0])
           lowerCAmelCase    :		Any				=      (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
           lowerCAmelCase    :		int				=      timesteps.to(self.discrete_sigmas.device )

           lowerCAmelCase    :		Union[str, Any]				=      self.discrete_sigmas[timesteps].to(sample.device )
           lowerCAmelCase    :		Any				=      self.get_adjacent_sigma(UpperCamelCase_    , UpperCamelCase_ ).to(sample.device )
           lowerCAmelCase    :		Union[str, Any]				=      torch.zeros_like(UpperCamelCase_ )
           lowerCAmelCase    :		str				=      (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
           lowerCAmelCase    :		List[str]				=      diffusion.flatten()
           while len(diffusion.shape ) < len(sample.shape ):
                 lowerCAmelCase    :		List[Any]				=      diffusion.unsqueeze(-1 )
           lowerCAmelCase    :		int				=      drift - diffusion**2 * model_output

           #  equation 6: sample noise for the diffusion term of
           lowerCAmelCase    :		str				=      randn_tensor(
               sample.shape    , layout=sample.layout    , generator=UpperCamelCase_    , device=sample.device    , dtype=sample.dtype )
           lowerCAmelCase    :		Optional[Any]				=      sample - drift  # subtract because `dt` is a small negative timestep
           # TODO is the variable diffusion the correct scaling term for the noise?
           lowerCAmelCase    :		Union[str, Any]				=      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    lowerCamelCase__   (		self     :    Any    , UpperCamelCase_     :    torch.FloatTensor    , UpperCamelCase_     :    torch.FloatTensor    , UpperCamelCase_     :    Optional[torch.Generator] = None    , UpperCamelCase_     :    bool = True    , ):
           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
           lowerCAmelCase    :		Union[str, Any]				=      randn_tensor(sample.shape    , layout=sample.layout    , generator=UpperCamelCase_ ).to(sample.device )

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

           if not return_dict:
                 return (prev_sample,)

           return SchedulerOutput(prev_sample=UpperCamelCase_ )






     def    lowerCamelCase__   (		self     :    str    , UpperCamelCase_     :    torch.FloatTensor    , UpperCamelCase_     :    torch.FloatTensor    , UpperCamelCase_     :    torch.FloatTensor    , ):
           # Make sure sigmas and timesteps have the same device and dtype as original_samples
           lowerCAmelCase    :		List[Any]				=      timesteps.to(original_samples.device )
           lowerCAmelCase    :		Optional[int]				=      self.discrete_sigmas.to(original_samples.device )[timesteps]
           lowerCAmelCase    :		str				=      (
               noise * sigmas[:, None, None, None]
               if noise is not None
               else torch.randn_like(UpperCamelCase_ ) * sigmas[:, None, None, None]
           )
           lowerCAmelCase    :		int				=      noise + original_samples
           return noisy_samples






     def __len__(		self     :    Tuple ):
           return self.config.num_train_timesteps






"""simple docstring"""





import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional

import numpy as np
from utils_multiple_choice import MultipleChoiceDataset, Split, processors

import transformers
from transformers import (
    AutoConfig,
    AutoModelForMultipleChoice,
    AutoTokenizer,
    DataCollatorWithPadding,
    EvalPrediction,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
)
from transformers.trainer_utils import is_main_process


snake_case__						:   Dict			 = logging.getLogger(__name__)


def    _snake_case  (  _snake_case :	Any       ,      _snake_case :	Any	):
      return (preds == labels).mean()



@dataclass
class 						snake_case_:
     __UpperCamelCase     =    field(
         metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''}	)
     __UpperCamelCase     =    field(
         default=a__							,    metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''}	)
     __UpperCamelCase     =    field(
         default=a__							,    metadata={'''help''': '''Pretrained tokenizer name or path if not the same as model_name'''}	)
     __UpperCamelCase     =    field(
         default=a__							,    metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''}							,    )



@dataclass
class 						snake_case_:
     __UpperCamelCase     =    field(metadata={'''help''': '''The name of the task to train on: ''' + ''', '''.join(processors.keys()	)}	)
     __UpperCamelCase     =    field(metadata={'''help''': '''Should contain the data files for the task.'''}	)
     __UpperCamelCase     =    field(
         default=128							,    metadata={
             '''help''': (
                 '''The maximum total input sequence length after tokenization. Sequences longer '''
                 '''than this will be truncated, sequences shorter will be padded.'''
             )
         }							,    )
     __UpperCamelCase     =    field(
         default=a__							,    metadata={'''help''': '''Overwrite the cached training and evaluation sets'''}	)



def    _snake_case  (  ):
      # See all possible arguments in src/transformers/training_args.py
      # or by passing the --help flag to this script.
      # We now keep distinct sets of args, for a cleaner separation of concerns.

      lowerCAmelCase    :		str				=      HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)	)
      lowerCAmelCase,					lowerCAmelCase,					lowerCAmelCase    :		Optional[int]				=      parser.parse_args_into_dataclasses()

      if (
          os.path.exists(training_args.output_dir	)
          and os.listdir(training_args.output_dir	)
          and training_args.do_train
          and not training_args.overwrite_output_dir
      ):
            raise ValueError(
                f'''Output directory ({training_args.output_dir}) already exists and is not empty. Use'''
                ''' --overwrite_output_dir to overcome.'''	)

      # Setup logging
      logging.basicConfig(
          format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s'''       ,      datefmt='''%m/%d/%Y %H:%M:%S'''       ,      level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN       ,      )
      logger.warning(
          '''Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s'''       ,      training_args.local_rank       ,      training_args.device       ,      training_args.n_gpu       ,      bool(training_args.local_rank != -1	)       ,      training_args.fpaa       ,      )
      # Set the verbosity to info of the Transformers logger (on main process only):
      if is_main_process(training_args.local_rank	):
            transformers.utils.logging.set_verbosity_info()
            transformers.utils.logging.enable_default_handler()
            transformers.utils.logging.enable_explicit_format()
      logger.info('''Training/evaluation parameters %s'''       ,      _snake_case	)

      # Set seed
      set_seed(training_args.seed	)

      try:
            lowerCAmelCase    :		Tuple				=      processors[data_args.task_name]()
            lowerCAmelCase    :		Any				=      processor.get_labels()
            lowerCAmelCase    :		Union[str, Any]				=      len(_snake_case	)
      except KeyError:
            raise ValueError('''Task not found: %s''' % (data_args.task_name)	)

      # Load pretrained model and tokenizer
      #
      # Distributed training:
      # The .from_pretrained methods guarantee that only one local process can concurrently
      # download model & vocab.

      lowerCAmelCase    :		List[Any]				=      AutoConfig.from_pretrained(
          model_args.config_name if model_args.config_name else model_args.model_name_or_path       ,      num_labels=_snake_case       ,      finetuning_task=data_args.task_name       ,      cache_dir=model_args.cache_dir       ,      )
      lowerCAmelCase    :		Optional[Any]				=      AutoTokenizer.from_pretrained(
          model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path       ,      cache_dir=model_args.cache_dir       ,      )
      lowerCAmelCase    :		List[str]				=      AutoModelForMultipleChoice.from_pretrained(
          model_args.model_name_or_path       ,      from_tf=bool('''.ckpt''' in model_args.model_name_or_path	)       ,      config=_snake_case       ,      cache_dir=model_args.cache_dir       ,      )

      # Get datasets
      lowerCAmelCase    :		Dict				=      (
          MultipleChoiceDataset(
              data_dir=data_args.data_dir       ,      tokenizer=_snake_case       ,      task=data_args.task_name       ,      max_seq_length=data_args.max_seq_length       ,      overwrite_cache=data_args.overwrite_cache       ,      mode=Split.train       ,      )
          if training_args.do_train
          else None
      )
      lowerCAmelCase    :		Any				=      (
          MultipleChoiceDataset(
              data_dir=data_args.data_dir       ,      tokenizer=_snake_case       ,      task=data_args.task_name       ,      max_seq_length=data_args.max_seq_length       ,      overwrite_cache=data_args.overwrite_cache       ,      mode=Split.dev       ,      )
          if training_args.do_eval
          else None
      )

      def compute_metrics(_snake_case :	EvalPrediction	) -> Dict:
            lowerCAmelCase    :		int				=      np.argmax(p.predictions       ,      axis=1	)
            return {"acc": simple_accuracy(_snake_case       ,      p.label_ids	)}

      # Data collator
      lowerCAmelCase    :		List[Any]				=      DataCollatorWithPadding(_snake_case       ,      pad_to_multiple_of=8	) if training_args.fpaa else None

      # Initialize our Trainer
      lowerCAmelCase    :		Union[str, Any]				=      Trainer(
          model=_snake_case       ,      args=_snake_case       ,      train_dataset=_snake_case       ,      eval_dataset=_snake_case       ,      compute_metrics=_snake_case       ,      data_collator=_snake_case       ,      )

      # Training
      if training_args.do_train:
            trainer.train(
                model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path	) else None	)
            trainer.save_model()
            # For convenience, we also re-save the tokenizer to the same directory,
            # so that you can share your model easily on huggingface.co/models =)
            if trainer.is_world_master():
                  tokenizer.save_pretrained(training_args.output_dir	)

    # Evaluation
      lowerCAmelCase    :		int				=      {}
      if training_args.do_eval:
            logger.info('''*** Evaluate ***'''	)

            lowerCAmelCase    :		Any				=      trainer.evaluate()

            lowerCAmelCase    :		int				=      os.path.join(training_args.output_dir       ,      '''eval_results.txt'''	)
            if trainer.is_world_master():
                  with open(_snake_case       ,      '''w'''	) as writer:
                        logger.info('''***** Eval results *****'''	)
                        for key, value in result.items():
                              logger.info('''  %s = %s'''       ,      _snake_case       ,      _snake_case	)
                              writer.write('''%s = %s\n''' % (key, value)	)

                        results.update(_snake_case	)

      return results


def    _snake_case  (  _snake_case :	List[str]	):
      # For xla_spawn (TPUs)
      main()


if __name__ == "__main__":
 main()


