from typing import Optional, Tuple, Union

import torch
from einops import rearrange, reduce

from diffusers import DDIMScheduler, DDPMScheduler, DiffusionPipeline, ImagePipelineOutput, UNetaDConditionModel
from diffusers.schedulers.scheduling_ddim import DDIMSchedulerOutput
from diffusers.schedulers.scheduling_ddpm import DDPMSchedulerOutput


A   :     List[Any]       =				8


def    a__       (						__UpperCamelCase   ,					__UpperCamelCase=BITS ):
      SCREAMING_SNAKE_CASE_      =     x.device

      SCREAMING_SNAKE_CASE_      =     (x * 2_5_5).int().clamp(0   ,					2_5_5 )

      SCREAMING_SNAKE_CASE_      =     2 ** torch.arange(bits - 1   ,					-1   ,					-1   ,					device=__UpperCamelCase )
      SCREAMING_SNAKE_CASE_      =     rearrange(__UpperCamelCase   ,					"d -> d 1 1" )
      SCREAMING_SNAKE_CASE_      =     rearrange(__UpperCamelCase   ,					"b c h w -> b c 1 h w" )

      SCREAMING_SNAKE_CASE_      =     ((x & mask) != 0).float()
      SCREAMING_SNAKE_CASE_      =     rearrange(__UpperCamelCase   ,					"b c d h w -> b (c d) h w" )
      SCREAMING_SNAKE_CASE_      =     bits * 2 - 1
      return bits


def    a__       (						__UpperCamelCase   ,					__UpperCamelCase=BITS ):
      SCREAMING_SNAKE_CASE_      =     x.device

      SCREAMING_SNAKE_CASE_      =     (x > 0).int()
      SCREAMING_SNAKE_CASE_      =     2 ** torch.arange(bits - 1   ,					-1   ,					-1   ,					device=__UpperCamelCase   ,					dtype=torch.intaa )

      SCREAMING_SNAKE_CASE_      =     rearrange(__UpperCamelCase   ,					"d -> d 1 1" )
      SCREAMING_SNAKE_CASE_      =     rearrange(__UpperCamelCase   ,					"b (c d) h w -> b c d h w"   ,					d=8 )
      SCREAMING_SNAKE_CASE_      =     reduce(x * mask   ,					"b c d h w -> b c h w"   ,					"sum" )
      return (dec / 2_5_5).clamp(0.0   ,					1.0 )


def    a__       (						self   ,					__UpperCamelCase   ,					__UpperCamelCase   ,					__UpperCamelCase   ,					__UpperCamelCase = 0.0   ,					__UpperCamelCase = True   ,					__UpperCamelCase=None   ,					__UpperCamelCase = True   ,					):
      if self.num_inference_steps is None:
            raise ValueError(
                "Number of inference steps is 'None', you need to run 'set_timesteps' after creating the scheduler" )

      # See formulas (12) and (16) of DDIM paper https://arxiv.org/pdf/2010.02502.pdf
      # Ideally, read DDIM paper in-detail understanding

      # Notation (<variable name> -> <name in paper>
      # - pred_noise_t -> e_theta(x_t, t)
      # - pred_original_sample -> f_theta(x_t, t) or x_0
      # - std_dev_t -> sigma_t
      # - eta -> η
      # - pred_sample_direction -> "direction pointing to x_t"
      # - pred_prev_sample -> "x_t-1"

      # 1. get previous step value (=t-1)
      SCREAMING_SNAKE_CASE_      =     timestep - self.config.num_train_timesteps // self.num_inference_steps

      # 2. compute alphas, betas
      SCREAMING_SNAKE_CASE_      =     self.alphas_cumprod[timestep]
      SCREAMING_SNAKE_CASE_      =     self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod

      SCREAMING_SNAKE_CASE_      =     1 - alpha_prod_t

      # 3. compute predicted original sample from predicted noise also called
      # "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
      SCREAMING_SNAKE_CASE_      =     (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5

      # 4. Clip "predicted x_0"
      SCREAMING_SNAKE_CASE_      =     self.bit_scale
      if self.config.clip_sample:
            SCREAMING_SNAKE_CASE_      =     torch.clamp(__UpperCamelCase   ,					-scale   ,					__UpperCamelCase )

      # 5. compute variance: "sigma_t(η)" -> see formula (16)
      # σ_t = sqrt((1 − α_t−1)/(1 − α_t)) * sqrt(1 − α_t/α_t−1)
      SCREAMING_SNAKE_CASE_      =     self._get_variance(__UpperCamelCase   ,					__UpperCamelCase )
      SCREAMING_SNAKE_CASE_      =     eta * variance ** 0.5

      if use_clipped_model_output:
            # the model_output is always re-derived from the clipped x_0 in Glide
            SCREAMING_SNAKE_CASE_      =     (sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5

      # 6. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
      SCREAMING_SNAKE_CASE_      =     (1 - alpha_prod_t_prev - std_dev_t**2) ** 0.5 * model_output

      # 7. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
      SCREAMING_SNAKE_CASE_      =     alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction

      if eta > 0:
            # randn_like does not support generator https://github.com/pytorch/pytorch/issues/27072
            SCREAMING_SNAKE_CASE_      =     model_output.device if torch.is_tensor(__UpperCamelCase ) else "cpu"
            SCREAMING_SNAKE_CASE_      =     torch.randn(model_output.shape   ,					dtype=model_output.dtype   ,					generator=__UpperCamelCase ).to(__UpperCamelCase )
            SCREAMING_SNAKE_CASE_      =     self._get_variance(__UpperCamelCase   ,					__UpperCamelCase ) ** 0.5 * eta * noise

            SCREAMING_SNAKE_CASE_      =     prev_sample + variance

      if not return_dict:
            return (prev_sample,)

      return DDIMSchedulerOutput(prev_sample=__UpperCamelCase   ,					pred_original_sample=__UpperCamelCase )


def    a__       (						self   ,					__UpperCamelCase   ,					__UpperCamelCase   ,					__UpperCamelCase   ,					__UpperCamelCase="epsilon"   ,					__UpperCamelCase=None   ,					__UpperCamelCase = True   ,					):
      SCREAMING_SNAKE_CASE_      =     timestep

      if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type in ["learned", "learned_range"]:
            SCREAMING_SNAKE_CASE_   ,       SCREAMING_SNAKE_CASE_      =     torch.split(__UpperCamelCase   ,					sample.shape[1]   ,					dim=1 )
      else:
            SCREAMING_SNAKE_CASE_      =     None

      # 1. compute alphas, betas
      SCREAMING_SNAKE_CASE_      =     self.alphas_cumprod[t]
      SCREAMING_SNAKE_CASE_      =     self.alphas_cumprod[t - 1] if t > 0 else self.one
      SCREAMING_SNAKE_CASE_      =     1 - alpha_prod_t
      SCREAMING_SNAKE_CASE_      =     1 - alpha_prod_t_prev

      # 2. compute predicted original sample from predicted noise also called
      # "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
      if prediction_type == "epsilon":
            SCREAMING_SNAKE_CASE_      =     (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
      elif prediction_type == "sample":
            SCREAMING_SNAKE_CASE_      =     model_output
      else:
            raise ValueError(F'''Unsupported prediction_type {prediction_type}.''' )

      # 3. Clip "predicted x_0"
      SCREAMING_SNAKE_CASE_      =     self.bit_scale
      if self.config.clip_sample:
            SCREAMING_SNAKE_CASE_      =     torch.clamp(__UpperCamelCase   ,					-scale   ,					__UpperCamelCase )

      # 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
      # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
      SCREAMING_SNAKE_CASE_      =     (alpha_prod_t_prev ** 0.5 * self.betas[t]) / beta_prod_t
      SCREAMING_SNAKE_CASE_      =     self.alphas[t] ** 0.5 * beta_prod_t_prev / beta_prod_t

      # 5. Compute predicted previous sample µ_t
      # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
      SCREAMING_SNAKE_CASE_      =     pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

      # 6. Add noise
      SCREAMING_SNAKE_CASE_      =     0
      if t > 0:
            SCREAMING_SNAKE_CASE_      =     torch.randn(
                model_output.size()   ,					dtype=model_output.dtype   ,					layout=model_output.layout   ,					generator=__UpperCamelCase ).to(model_output.device )
            SCREAMING_SNAKE_CASE_      =     (self._get_variance(__UpperCamelCase   ,					predicted_variance=__UpperCamelCase ) ** 0.5) * noise

      SCREAMING_SNAKE_CASE_      =     pred_prev_sample + variance

      if not return_dict:
            return (pred_prev_sample,)

      return DDPMSchedulerOutput(prev_sample=__UpperCamelCase   ,					pred_original_sample=__UpperCamelCase )





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







  def __init__(							self       :				Union[str, Any]     ,   __magic_name__       :				UNetaDConditionModel     ,   __magic_name__       :				Union[DDIMScheduler, DDPMScheduler]     ,   __magic_name__       :				Optional[float] = 1.0     ,   )     ->		Union[str, Any]:
        super().__init__()
        SCREAMING_SNAKE_CASE_      =     bit_scale
        SCREAMING_SNAKE_CASE_      =     (
            ddim_bit_scheduler_step if isinstance(__magic_name__     ,   __magic_name__      ) else ddpm_bit_scheduler_step
        )

        self.register_modules(unet=__magic_name__     ,   scheduler=__magic_name__      )







  @torch.no_grad()
  def __call__(							self       :				List[Any]     ,   __magic_name__       :				Optional[int] = 256     ,   __magic_name__       :				Optional[int] = 256     ,   __magic_name__       :				Optional[int] = 50     ,   __magic_name__       :				Optional[torch.Generator] = None     ,   __magic_name__       :				Optional[int] = 1     ,   __magic_name__       :				Optional[str] = "pil"     ,   __magic_name__       :				bool = True     ,   **__magic_name__       :				int     ,   )     ->		Union[Tuple, ImagePipelineOutput]:
        SCREAMING_SNAKE_CASE_      =     torch.randn(
            (batch_size, self.unet.config.in_channels, height, width)     ,   generator=__magic_name__     ,   )
        SCREAMING_SNAKE_CASE_      =     decimal_to_bits(__magic_name__      ) * self.bit_scale
        SCREAMING_SNAKE_CASE_      =     latents.to(self.device      )

        self.scheduler.set_timesteps(__magic_name__      )

        for t in self.progress_bar(self.scheduler.timesteps      ):
              # predict the noise residual
              SCREAMING_SNAKE_CASE_      =     self.unet(__magic_name__     ,   __magic_name__      ).sample

              # compute the previous noisy sample x_t -> x_t-1
              SCREAMING_SNAKE_CASE_      =     self.scheduler.step(__magic_name__     ,   __magic_name__     ,   __magic_name__      ).prev_sample

        SCREAMING_SNAKE_CASE_      =     bits_to_decimal(__magic_name__      )

        if output_type == "pil":
              SCREAMING_SNAKE_CASE_      =     self.numpy_to_pil(__magic_name__      )

        if not return_dict:
              return (image,)

        return ImagePipelineOutput(images=__magic_name__      )



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()


