import itertools
import json
import linecache
import os
import pickle
import re
import socket
import string
from collections import Counter
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List

import git
import torch
from torch.utils.data import Dataset

from transformers import BartTokenizer, RagTokenizer, TaTokenizer


def    a__       (						__UpperCamelCase   ,					__UpperCamelCase   ,					__UpperCamelCase   ,					__UpperCamelCase   ,					__UpperCamelCase=True   ,					__UpperCamelCase="pt" ):
      SCREAMING_SNAKE_CASE_      =     {"add_prefix_space": True} if isinstance(__UpperCamelCase   ,					__UpperCamelCase ) and not line.startswith(" " ) else {}
      SCREAMING_SNAKE_CASE_      =     padding_side
      return tokenizer(
          [line]   ,					max_length=__UpperCamelCase   ,					padding="max_length" if pad_to_max_length else None   ,					truncation=__UpperCamelCase   ,					return_tensors=__UpperCamelCase   ,					add_special_tokens=__UpperCamelCase   ,					**__UpperCamelCase   ,					)


def    a__       (						__UpperCamelCase   ,					__UpperCamelCase   ,					__UpperCamelCase=None   ,					):
      SCREAMING_SNAKE_CASE_      =     input_ids.ne(__UpperCamelCase ).any(dim=0 )
      if attention_mask is None:
            return input_ids[:, keep_column_mask]
      else:
            return (input_ids[:, keep_column_mask], attention_mask[:, keep_column_mask])


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







  def __init__(							self       :				Tuple     ,   __magic_name__       :				List[str]     ,   __magic_name__       :				Optional[int]     ,   __magic_name__       :				int     ,   __magic_name__       :				int     ,   __magic_name__       :				Tuple="train"     ,   __magic_name__       :				int=None     ,   __magic_name__       :				str=None     ,   __magic_name__       :				Any=None     ,   __magic_name__       :				List[Any]=""     ,   )     ->		Union[str, Any]:
        super().__init__()
        SCREAMING_SNAKE_CASE_      =     Path(__magic_name__      ).joinpath(type_path + ".source"      )
        SCREAMING_SNAKE_CASE_      =     Path(__magic_name__      ).joinpath(type_path + ".target"      )
        SCREAMING_SNAKE_CASE_      =     self.get_char_lens(self.src_file      )
        SCREAMING_SNAKE_CASE_      =     max_source_length
        SCREAMING_SNAKE_CASE_      =     max_target_length
        assert min(self.src_lens      ) > 0, F'''found empty line in {self.src_file}'''
        SCREAMING_SNAKE_CASE_      =     tokenizer
        SCREAMING_SNAKE_CASE_      =     prefix
        if n_obs is not None:
              SCREAMING_SNAKE_CASE_      =     self.src_lens[:n_obs]
        SCREAMING_SNAKE_CASE_      =     src_lang
        SCREAMING_SNAKE_CASE_      =     tgt_lang







  def __len__(							self       :				List[str]      )     ->		Any:
        return len(self.src_lens      )







  def __getitem__(							self       :				Tuple     ,   __magic_name__       :				Tuple      )     ->		Dict[str, torch.Tensor]:
        SCREAMING_SNAKE_CASE_      =     index + 1  # linecache starts at 1
        SCREAMING_SNAKE_CASE_      =     self.prefix + linecache.getline(str(self.src_file      )     ,   __magic_name__      ).rstrip("\n"      )
        SCREAMING_SNAKE_CASE_      =     linecache.getline(str(self.tgt_file      )     ,   __magic_name__      ).rstrip("\n"      )
        assert source_line, F'''empty source line for index {index}'''
        assert tgt_line, F'''empty tgt line for index {index}'''

        # Need to add eos token manually for T5
        if isinstance(self.tokenizer     ,   __magic_name__      ):
              source_line += self.tokenizer.eos_token
              tgt_line += self.tokenizer.eos_token

        # Pad source and target to the right
        SCREAMING_SNAKE_CASE_      =     (
            self.tokenizer.question_encoder if isinstance(self.tokenizer     ,   __magic_name__      ) else self.tokenizer
        )
        SCREAMING_SNAKE_CASE_      =     self.tokenizer.generator if isinstance(self.tokenizer     ,   __magic_name__      ) else self.tokenizer

        SCREAMING_SNAKE_CASE_      =     encode_line(__magic_name__     ,   __magic_name__     ,   self.max_source_length     ,   "right"      )
        SCREAMING_SNAKE_CASE_      =     encode_line(__magic_name__     ,   __magic_name__     ,   self.max_target_length     ,   "right"      )

        SCREAMING_SNAKE_CASE_      =     source_inputs["input_ids"].squeeze()
        SCREAMING_SNAKE_CASE_      =     target_inputs["input_ids"].squeeze()
        SCREAMING_SNAKE_CASE_      =     source_inputs["attention_mask"].squeeze()
        return {
            "input_ids": source_ids,
            "attention_mask": src_mask,
            "decoder_input_ids": target_ids,
        }







  @staticmethod
  def       __A   (							__magic_name__       :				Union[str, Any]      )     ->		Optional[int]:
        return [len(__magic_name__      ) for x in Path(__magic_name__      ).open().readlines()]







  def       __A   (							self       :				int     ,   __magic_name__       :				Any      )     ->		Dict[str, torch.Tensor]:
        SCREAMING_SNAKE_CASE_      =     torch.stack([x["input_ids"] for x in batch]      )
        SCREAMING_SNAKE_CASE_      =     torch.stack([x["attention_mask"] for x in batch]      )
        SCREAMING_SNAKE_CASE_      =     torch.stack([x["decoder_input_ids"] for x in batch]      )
        SCREAMING_SNAKE_CASE_      =     (
            self.tokenizer.generator.pad_token_id
            if isinstance(self.tokenizer     ,   __magic_name__      )
            else self.tokenizer.pad_token_id
        )
        SCREAMING_SNAKE_CASE_      =     (
            self.tokenizer.question_encoder.pad_token_id
            if isinstance(self.tokenizer     ,   __magic_name__      )
            else self.tokenizer.pad_token_id
        )
        SCREAMING_SNAKE_CASE_      =     trim_batch(__magic_name__     ,   __magic_name__      )
        SCREAMING_SNAKE_CASE_   ,       SCREAMING_SNAKE_CASE_      =     trim_batch(__magic_name__     ,   __magic_name__     ,   attention_mask=__magic_name__      )
        SCREAMING_SNAKE_CASE_      =     {
            "input_ids": source_ids,
            "attention_mask": source_mask,
            "decoder_input_ids": y,
        }
        return batch



A   :     List[str]       =				getLogger(__name__)


def    a__       (						__UpperCamelCase ):
      return list(itertools.chain.from_iterable(__UpperCamelCase ) )


def    a__       (						__UpperCamelCase ):
      SCREAMING_SNAKE_CASE_      =     get_git_info()
      save_json(__UpperCamelCase   ,					os.path.join(__UpperCamelCase   ,					"git_log.json" ) )


def    a__       (						__UpperCamelCase   ,					__UpperCamelCase   ,					__UpperCamelCase=4   ,					**__UpperCamelCase ):
      with open(__UpperCamelCase   ,					"w" ) as f:
            json.dump(__UpperCamelCase   ,					__UpperCamelCase   ,					indent=__UpperCamelCase   ,					**__UpperCamelCase )


def    a__       (						__UpperCamelCase ):
      with open(__UpperCamelCase ) as f:
            return json.load(__UpperCamelCase )


def    a__       (						):
      SCREAMING_SNAKE_CASE_      =     git.Repo(search_parent_directories=__UpperCamelCase )
      SCREAMING_SNAKE_CASE_      =     {
          "repo_id": str(__UpperCamelCase ),
          "repo_sha": str(repo.head.object.hexsha ),
          "repo_branch": str(repo.active_branch ),
          "hostname": str(socket.gethostname() ),
      }
      return repo_infos


def    a__       (						__UpperCamelCase   ,					__UpperCamelCase ):
      return list(map(__UpperCamelCase   ,					__UpperCamelCase ) )


def    a__       (						__UpperCamelCase   ,					__UpperCamelCase ):
      with open(__UpperCamelCase   ,					"wb" ) as f:
            return pickle.dump(__UpperCamelCase   ,					__UpperCamelCase )


def    a__       (						__UpperCamelCase ):

      def remove_articles(__UpperCamelCase ):
            return re.sub(r"\b(a|an|the)\b"   ,					" "   ,					__UpperCamelCase )

      def white_space_fix(__UpperCamelCase ):
            return " ".join(text.split() )

      def remove_punc(__UpperCamelCase ):
            SCREAMING_SNAKE_CASE_      =     set(string.punctuation )
            return "".join(ch for ch in text if ch not in exclude )

      def lower(__UpperCamelCase ):
            return text.lower()

      return white_space_fix(remove_articles(remove_punc(lower(__UpperCamelCase ) ) ) )


def    a__       (						__UpperCamelCase   ,					__UpperCamelCase ):
      SCREAMING_SNAKE_CASE_      =     normalize_answer(__UpperCamelCase ).split()
      SCREAMING_SNAKE_CASE_      =     normalize_answer(__UpperCamelCase ).split()
      SCREAMING_SNAKE_CASE_      =     Counter(__UpperCamelCase ) & Counter(__UpperCamelCase )
      SCREAMING_SNAKE_CASE_      =     sum(common.values() )
      if num_same == 0:
            return 0
      SCREAMING_SNAKE_CASE_      =     1.0 * num_same / len(__UpperCamelCase )
      SCREAMING_SNAKE_CASE_      =     1.0 * num_same / len(__UpperCamelCase )
      SCREAMING_SNAKE_CASE_      =     (2 * precision * recall) / (precision + recall)
      return fa


def    a__       (						__UpperCamelCase   ,					__UpperCamelCase ):
      return normalize_answer(__UpperCamelCase ) == normalize_answer(__UpperCamelCase )


def    a__       (						__UpperCamelCase   ,					__UpperCamelCase ):
      assert len(__UpperCamelCase ) == len(__UpperCamelCase )
      SCREAMING_SNAKE_CASE_      =     0
      for hypo, pred in zip(__UpperCamelCase   ,					__UpperCamelCase ):
            em += exact_match_score(__UpperCamelCase   ,					__UpperCamelCase )
      if len(__UpperCamelCase ) > 0:
            em /= len(__UpperCamelCase )
      return {"em": em}


def    a__       (						__UpperCamelCase ):
      return model_prefix.startswith("rag" )





def    a__       (						__UpperCamelCase   ,					__UpperCamelCase   ,					__UpperCamelCase ):
      SCREAMING_SNAKE_CASE_      =     {p: p for p in extra_params}
      # T5 models don't have `dropout` param, they have `dropout_rate` instead
      SCREAMING_SNAKE_CASE_      =     "dropout_rate"
      for p in extra_params:
            if getattr(__UpperCamelCase   ,					__UpperCamelCase   ,					__UpperCamelCase ):
                  if not hasattr(__UpperCamelCase   ,					__UpperCamelCase ) and not hasattr(__UpperCamelCase   ,					equivalent_param[p] ):
                        logger.info("config doesn't have a `{}` attribute".format(__UpperCamelCase ) )
                        delattr(__UpperCamelCase   ,					__UpperCamelCase )
                        continue
                  SCREAMING_SNAKE_CASE_      =     p if hasattr(__UpperCamelCase   ,					__UpperCamelCase ) else equivalent_param[p]
                  setattr(__UpperCamelCase   ,					__UpperCamelCase   ,					getattr(__UpperCamelCase   ,					__UpperCamelCase ) )
                  delattr(__UpperCamelCase   ,					__UpperCamelCase )
      return hparams, config



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()


