import math
from collections import defaultdict
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput


def    a__       (						__UpperCamelCase   ,					__UpperCamelCase=0.999   ,					__UpperCamelCase="cosine"   ,					):
      if alpha_transform_type == "cosine":

            def alpha_bar_fn(__UpperCamelCase ):
                  return math.cos((t + 0.008) / 1.008 * math.pi / 2 ) ** 2

      elif alpha_transform_type == "exp":

            def alpha_bar_fn(__UpperCamelCase ):
                  return math.exp(t * -12.0 )

      else:
            raise ValueError(F'''Unsupported alpha_tranform_type: {alpha_transform_type}''' )

      SCREAMING_SNAKE_CASE_      =     []
      for i in range(__UpperCamelCase ):
            SCREAMING_SNAKE_CASE_      =     i / num_diffusion_timesteps
            SCREAMING_SNAKE_CASE_      =     (i + 1) / num_diffusion_timesteps
            betas.append(min(1 - alpha_bar_fn(__UpperCamelCase ) / alpha_bar_fn(__UpperCamelCase )   ,					__UpperCamelCase ) )
      return torch.tensor(__UpperCamelCase   ,					dtype=torch.floataa )





class      lowerCamelCase    (SCREAMING_SNAKE_CASE__     ,					SCREAMING_SNAKE_CASE__    ):
  """simple docstring"""







  lowerCamelCase__   =  [e.name for e in KarrasDiffusionSchedulers]
  lowerCamelCase__   =  2







  @register_to_config
  def __init__(							self       :				str     ,   __magic_name__       :				int = 1_000     ,   __magic_name__       :				float = 0.0_0085     ,   __magic_name__       :				float = 0.012     ,   __magic_name__       :				str = "linear"     ,   __magic_name__       :				Optional[Union[np.ndarray, List[float]]] = None     ,   __magic_name__       :				str = "epsilon"     ,   __magic_name__       :				str = "linspace"     ,   __magic_name__       :				int = 0     ,   )     ->		List[Any]:
        if trained_betas is not None:
              SCREAMING_SNAKE_CASE_      =     torch.tensor(__magic_name__     ,   dtype=torch.floataa      )
        elif beta_schedule == "linear":
              SCREAMING_SNAKE_CASE_      =     torch.linspace(__magic_name__     ,   __magic_name__     ,   __magic_name__     ,   dtype=torch.floataa      )
        elif beta_schedule == "scaled_linear":
              # this schedule is very specific to the latent diffusion model.
              SCREAMING_SNAKE_CASE_      =     (
                  torch.linspace(beta_start**0.5     ,   beta_end**0.5     ,   __magic_name__     ,   dtype=torch.floataa      ) ** 2
              )
        elif beta_schedule == "squaredcos_cap_v2":
              # Glide cosine schedule
              SCREAMING_SNAKE_CASE_      =     betas_for_alpha_bar(__magic_name__      )
        else:
              raise NotImplementedError(F'''{beta_schedule} does is not implemented for {self.__class__}'''      )

        SCREAMING_SNAKE_CASE_      =     1.0 - self.betas
        SCREAMING_SNAKE_CASE_      =     torch.cumprod(self.alphas     ,   dim=0      )

        #  set all values
        self.set_timesteps(__magic_name__     ,   __magic_name__     ,   __magic_name__      )







  def       __A   (							self       :				str     ,   __magic_name__       :				Any     ,   __magic_name__       :				Dict=None      )     ->		Any:
        if schedule_timesteps is None:
              SCREAMING_SNAKE_CASE_      =     self.timesteps

        SCREAMING_SNAKE_CASE_      =     (schedule_timesteps == timestep).nonzero()

        # The sigma index that is taken for the **very** first `step`
        # is always the second index (or the last index if there is only 1)
        # This way we can ensure we don't accidentally skip a sigma in
        # case we start in the middle of the denoising schedule (e.g. for image-to-image)
        if len(self._index_counter      ) == 0:
              SCREAMING_SNAKE_CASE_      =     1 if len(__magic_name__      ) > 1 else 0
        else:
              SCREAMING_SNAKE_CASE_      =     timestep.cpu().item() if torch.is_tensor(__magic_name__      ) else timestep
              SCREAMING_SNAKE_CASE_      =     self._index_counter[timestep_int]

        return indices[pos].item()







  @property
  def       __A   (							self       :				Any      )     ->		str:
        # standard deviation of the initial noise distribution
        if self.config.timestep_spacing in ["linspace", "trailing"]:
              return self.sigmas.max()

        return (self.sigmas.max() ** 2 + 1) ** 0.5







  def       __A   (							self       :				Union[str, Any]     ,   __magic_name__       :				torch.FloatTensor     ,   __magic_name__       :				Union[float, torch.FloatTensor]     ,   )     ->		torch.FloatTensor:
        SCREAMING_SNAKE_CASE_      =     self.index_for_timestep(__magic_name__      )

        if self.state_in_first_order:
              SCREAMING_SNAKE_CASE_      =     self.sigmas[step_index]
        else:
              SCREAMING_SNAKE_CASE_      =     self.sigmas_interpol[step_index]

        SCREAMING_SNAKE_CASE_      =     sample / ((sigma**2 + 1) ** 0.5)
        return sample







  def       __A   (							self       :				Dict     ,   __magic_name__       :				int     ,   __magic_name__       :				Union[str, torch.device] = None     ,   __magic_name__       :				Optional[int] = None     ,   )     ->		int:
        SCREAMING_SNAKE_CASE_      =     num_inference_steps

        SCREAMING_SNAKE_CASE_      =     num_train_timesteps or self.config.num_train_timesteps

        # "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
        if self.config.timestep_spacing == "linspace":
              SCREAMING_SNAKE_CASE_      =     np.linspace(0     ,   num_train_timesteps - 1     ,   __magic_name__     ,   dtype=__magic_name__      )[::-1].copy()
        elif self.config.timestep_spacing == "leading":
              SCREAMING_SNAKE_CASE_      =     num_train_timesteps // self.num_inference_steps
              # creates integer timesteps by multiplying by ratio
              # casting to int to avoid issues when num_inference_step is power of 3
              SCREAMING_SNAKE_CASE_      =     (np.arange(0     ,   __magic_name__      ) * step_ratio).round()[::-1].copy().astype(__magic_name__      )
              timesteps += self.config.steps_offset
        elif self.config.timestep_spacing == "trailing":
              SCREAMING_SNAKE_CASE_      =     num_train_timesteps / self.num_inference_steps
              # creates integer timesteps by multiplying by ratio
              # casting to int to avoid issues when num_inference_step is power of 3
              SCREAMING_SNAKE_CASE_      =     (np.arange(__magic_name__     ,   0     ,   -step_ratio      )).round().copy().astype(__magic_name__      )
              timesteps -= 1
        else:
              raise ValueError(
                  F'''{self.config.timestep_spacing} is not supported. Please make sure to choose one of \'linspace\', \'leading\' or \'trailing\'.'''      )

        SCREAMING_SNAKE_CASE_      =     np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5      )
        SCREAMING_SNAKE_CASE_      =     torch.from_numpy(np.log(__magic_name__      )      ).to(__magic_name__      )

        SCREAMING_SNAKE_CASE_      =     np.interp(__magic_name__     ,   np.arange(0     ,   len(__magic_name__      )      )     ,   __magic_name__      )
        SCREAMING_SNAKE_CASE_      =     np.concatenate([sigmas, [0.0]]      ).astype(np.floataa      )
        SCREAMING_SNAKE_CASE_      =     torch.from_numpy(__magic_name__      ).to(device=__magic_name__      )

        # interpolate sigmas
        SCREAMING_SNAKE_CASE_      =     sigmas.log().lerp(sigmas.roll(1      ).log()     ,   0.5      ).exp()

        SCREAMING_SNAKE_CASE_      =     torch.cat([sigmas[:1], sigmas[1:].repeat_interleave(2      ), sigmas[-1:]]      )
        SCREAMING_SNAKE_CASE_      =     torch.cat(
            [sigmas_interpol[:1], sigmas_interpol[1:].repeat_interleave(2      ), sigmas_interpol[-1:]]      )

        if str(__magic_name__      ).startswith("mps"      ):
              # mps does not support float64
              SCREAMING_SNAKE_CASE_      =     torch.from_numpy(__magic_name__      ).to(__magic_name__     ,   dtype=torch.floataa      )
        else:
              SCREAMING_SNAKE_CASE_      =     torch.from_numpy(__magic_name__      ).to(__magic_name__      )

        # interpolate timesteps
        SCREAMING_SNAKE_CASE_      =     self.sigma_to_t(__magic_name__      ).to(__magic_name__     ,   dtype=timesteps.dtype      )
        SCREAMING_SNAKE_CASE_      =     torch.stack((timesteps_interpol[1:-1, None], timesteps[1:, None])     ,   dim=-1      ).flatten()

        SCREAMING_SNAKE_CASE_      =     torch.cat([timesteps[:1], interleaved_timesteps]      )

        SCREAMING_SNAKE_CASE_      =     None

        # for exp beta schedules, such as the one for `pipeline_shap_e.py`
        # we need an index counter
        SCREAMING_SNAKE_CASE_      =     defaultdict(__magic_name__      )







  def       __A   (							self       :				Optional[int]     ,   __magic_name__       :				int      )     ->		Union[str, Any]:
        # get log sigma
        SCREAMING_SNAKE_CASE_      =     sigma.log()

        # get distribution
        SCREAMING_SNAKE_CASE_      =     log_sigma - self.log_sigmas[:, None]

        # get sigmas range
        SCREAMING_SNAKE_CASE_      =     dists.ge(0      ).cumsum(dim=0      ).argmax(dim=0      ).clamp(max=self.log_sigmas.shape[0] - 2      )
        SCREAMING_SNAKE_CASE_      =     low_idx + 1

        SCREAMING_SNAKE_CASE_      =     self.log_sigmas[low_idx]
        SCREAMING_SNAKE_CASE_      =     self.log_sigmas[high_idx]

        # interpolate sigmas
        SCREAMING_SNAKE_CASE_      =     (low - log_sigma) / (low - high)
        SCREAMING_SNAKE_CASE_      =     w.clamp(0     ,   1      )

        # transform interpolation to time range
        SCREAMING_SNAKE_CASE_      =     (1 - w) * low_idx + w * high_idx
        SCREAMING_SNAKE_CASE_      =     t.view(sigma.shape      )
        return t







  @property
  def       __A   (							self       :				List[Any]      )     ->		Optional[int]:
        return self.sample is None







  def       __A   (							self       :				Dict     ,   __magic_name__       :				Union[torch.FloatTensor, np.ndarray]     ,   __magic_name__       :				Union[float, torch.FloatTensor]     ,   __magic_name__       :				Union[torch.FloatTensor, np.ndarray]     ,   __magic_name__       :				bool = True     ,   )     ->		Union[SchedulerOutput, Tuple]:
        SCREAMING_SNAKE_CASE_      =     self.index_for_timestep(__magic_name__      )

        # advance index counter by 1
        SCREAMING_SNAKE_CASE_      =     timestep.cpu().item() if torch.is_tensor(__magic_name__      ) else timestep
        self._index_counter[timestep_int] += 1

        if self.state_in_first_order:
              SCREAMING_SNAKE_CASE_      =     self.sigmas[step_index]
              SCREAMING_SNAKE_CASE_      =     self.sigmas_interpol[step_index + 1]
              SCREAMING_SNAKE_CASE_      =     self.sigmas[step_index + 1]
        else:
              # 2nd order / KDPM2's method
              SCREAMING_SNAKE_CASE_      =     self.sigmas[step_index - 1]
              SCREAMING_SNAKE_CASE_      =     self.sigmas_interpol[step_index]
              SCREAMING_SNAKE_CASE_      =     self.sigmas[step_index]

        # currently only gamma=0 is supported. This usually works best anyways.
        # We can support gamma in the future but then need to scale the timestep before
        # passing it to the model which requires a change in API
        SCREAMING_SNAKE_CASE_      =     0
        SCREAMING_SNAKE_CASE_      =     sigma * (gamma + 1)  # Note: sigma_hat == sigma for now

        # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
        if self.config.prediction_type == "epsilon":
              SCREAMING_SNAKE_CASE_      =     sigma_hat if self.state_in_first_order else sigma_interpol
              SCREAMING_SNAKE_CASE_      =     sample - sigma_input * model_output
        elif self.config.prediction_type == "v_prediction":
              SCREAMING_SNAKE_CASE_      =     sigma_hat if self.state_in_first_order else sigma_interpol
              SCREAMING_SNAKE_CASE_      =     model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
                  sample / (sigma_input**2 + 1)
              )
        elif self.config.prediction_type == "sample":
              raise NotImplementedError("prediction_type not implemented yet: sample"      )
        else:
              raise ValueError(
                  F'''prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`'''      )

        if self.state_in_first_order:
              # 2. Convert to an ODE derivative for 1st order
              SCREAMING_SNAKE_CASE_      =     (sample - pred_original_sample) / sigma_hat
              # 3. delta timestep
              SCREAMING_SNAKE_CASE_      =     sigma_interpol - sigma_hat

              # store for 2nd order step
              SCREAMING_SNAKE_CASE_      =     sample
        else:
              # DPM-Solver-2
              # 2. Convert to an ODE derivative for 2nd order
              SCREAMING_SNAKE_CASE_      =     (sample - pred_original_sample) / sigma_interpol

              # 3. delta timestep
              SCREAMING_SNAKE_CASE_      =     sigma_next - sigma_hat

              SCREAMING_SNAKE_CASE_      =     self.sample
              SCREAMING_SNAKE_CASE_      =     None

        SCREAMING_SNAKE_CASE_      =     sample + derivative * dt

        if not return_dict:
              return (prev_sample,)

        return SchedulerOutput(prev_sample=__magic_name__      )







  def       __A   (							self       :				Any     ,   __magic_name__       :				torch.FloatTensor     ,   __magic_name__       :				torch.FloatTensor     ,   __magic_name__       :				torch.FloatTensor     ,   )     ->		torch.FloatTensor:
        # Make sure sigmas and timesteps have the same device and dtype as original_samples
        SCREAMING_SNAKE_CASE_      =     self.sigmas.to(device=original_samples.device     ,   dtype=original_samples.dtype      )
        if original_samples.device.type == "mps" and torch.is_floating_point(__magic_name__      ):
              # mps does not support float64
              SCREAMING_SNAKE_CASE_      =     self.timesteps.to(original_samples.device     ,   dtype=torch.floataa      )
              SCREAMING_SNAKE_CASE_      =     timesteps.to(original_samples.device     ,   dtype=torch.floataa      )
        else:
              SCREAMING_SNAKE_CASE_      =     self.timesteps.to(original_samples.device      )
              SCREAMING_SNAKE_CASE_      =     timesteps.to(original_samples.device      )

        SCREAMING_SNAKE_CASE_      =     [self.index_for_timestep(__magic_name__     ,   __magic_name__      ) for t in timesteps]

        SCREAMING_SNAKE_CASE_      =     sigmas[step_indices].flatten()
        while len(sigma.shape      ) < len(original_samples.shape      ):
              SCREAMING_SNAKE_CASE_      =     sigma.unsqueeze(-1      )

        SCREAMING_SNAKE_CASE_      =     original_samples + noise * sigma
        return noisy_samples







  def __len__(							self       :				int      )     ->		str:
        return self.config.num_train_timesteps



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


A   :     Any       =				logging.getLogger(__name__)


def    a__       (						__UpperCamelCase   ,					__UpperCamelCase ):
      return (preds == labels).mean()


@dataclass
class      lowerCamelCase    :
  """simple docstring"""







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



@dataclass
class      lowerCamelCase    :
  """simple docstring"""







  lowerCamelCase__   =  field(metadata={'''help''': '''The name of the task to train on: ''' + ''', '''.join(processors.keys()    )}    )
  lowerCamelCase__   =  field(metadata={'''help''': '''Should contain the data files for the task.'''}    )
  lowerCamelCase__   =  field(
      default=1_2_8     ,					metadata={
          '''help''': (
              '''The maximum total input sequence length after tokenization. Sequences longer '''
              '''than this will be truncated, sequences shorter will be padded.'''
          )
      }     ,					)
  lowerCamelCase__   =  field(
      default=SCREAMING_SNAKE_CASE__     ,					metadata={'''help''': '''Overwrite the cached training and evaluation sets'''}    )



def    a__       (						):
      # 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.

      SCREAMING_SNAKE_CASE_      =     HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
      SCREAMING_SNAKE_CASE_   ,       SCREAMING_SNAKE_CASE_   ,       SCREAMING_SNAKE_CASE_      =     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"   ,					__UpperCamelCase )

      # Set seed
      set_seed(training_args.seed )

      try:
            SCREAMING_SNAKE_CASE_      =     processors[data_args.task_name]()
            SCREAMING_SNAKE_CASE_      =     processor.get_labels()
            SCREAMING_SNAKE_CASE_      =     len(__UpperCamelCase )
      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.

      SCREAMING_SNAKE_CASE_      =     AutoConfig.from_pretrained(
          model_args.config_name if model_args.config_name else model_args.model_name_or_path   ,					num_labels=__UpperCamelCase   ,					finetuning_task=data_args.task_name   ,					cache_dir=model_args.cache_dir   ,					)
      SCREAMING_SNAKE_CASE_      =     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   ,					)
      SCREAMING_SNAKE_CASE_      =     AutoModelForMultipleChoice.from_pretrained(
          model_args.model_name_or_path   ,					from_tf=bool(".ckpt" in model_args.model_name_or_path )   ,					config=__UpperCamelCase   ,					cache_dir=model_args.cache_dir   ,					)

      # Get datasets
      SCREAMING_SNAKE_CASE_      =     (
          MultipleChoiceDataset(
              data_dir=data_args.data_dir   ,					tokenizer=__UpperCamelCase   ,					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
      )
      SCREAMING_SNAKE_CASE_      =     (
          MultipleChoiceDataset(
              data_dir=data_args.data_dir   ,					tokenizer=__UpperCamelCase   ,					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(__UpperCamelCase ) -> Dict:
            SCREAMING_SNAKE_CASE_      =     np.argmax(p.predictions   ,					axis=1 )
            return {"acc": simple_accuracy(__UpperCamelCase   ,					p.label_ids )}

      # Data collator
      SCREAMING_SNAKE_CASE_      =     DataCollatorWithPadding(__UpperCamelCase   ,					pad_to_multiple_of=8 ) if training_args.fpaa else None

      # Initialize our Trainer
      SCREAMING_SNAKE_CASE_      =     Trainer(
          model=__UpperCamelCase   ,					args=__UpperCamelCase   ,					train_dataset=__UpperCamelCase   ,					eval_dataset=__UpperCamelCase   ,					compute_metrics=__UpperCamelCase   ,					data_collator=__UpperCamelCase   ,					)

      # 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
      SCREAMING_SNAKE_CASE_      =     {}
      if training_args.do_eval:
            logger.info("*** Evaluate ***" )

            SCREAMING_SNAKE_CASE_      =     trainer.evaluate()

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

                        results.update(__UpperCamelCase )

      return results


def    a__       (						__UpperCamelCase ):
      # For xla_spawn (TPUs)
      main()


if __name__ == "__main__":
      main()


