"""simple docstring"""


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

import numpy as np
import torch
from utils_hans import HansDataset, InputFeatures, hans_processors, hans_tasks_num_labels

import transformers
from transformers import (
    AutoConfig,
    AutoModelForSequenceClassification,
    AutoTokenizer,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    default_data_collator,
    set_seed,
)
from transformers.trainer_utils import is_main_process


__UpperCamelCase       :    str         =		logging.getLogger(__name__)
@dataclass
class   UpperCAmelCase_	:
    snake_case_											=      field(
        metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""}   )
    snake_case_											=      field(
        default=lowercase__,    metadata={"""help""": """Pretrained config name or path if not the same as model_name"""}   )
    snake_case_											=      field(
        default=lowercase__,    metadata={"""help""": """Pretrained tokenizer name or path if not the same as model_name"""}   )
    snake_case_											=      field(
        default=lowercase__,    metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""},    )
@dataclass
class   UpperCAmelCase_	:
    snake_case_											=      field(
        metadata={"""help""": """The name of the task to train selected in the list: """ + """, """.join(hans_processors.keys()   )}   )
    snake_case_											=      field(
        metadata={"""help""": """The input data dir. Should contain the .tsv files (or other data files) for the task."""}   )
    snake_case_											=      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."""
            )
        },    )
    snake_case_											=      field(
        default=lowercase__,    metadata={"""help""": """Overwrite the cached training and evaluation sets"""}   )


def   __UpperCAmelCase     (	_snake_case		:       List[InputFeatures]       ):
   _lowercase							     =       default_data_collator(_snake_case       )
   _lowercase							     =       batch.pop("pairID",				_snake_case       )
   return batch


def   __UpperCAmelCase     (	):
   # 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.

   _lowercase							     =       HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)       )
   _lowercase     ,  _lowercase     ,  _lowercase							     =       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:
      _lowercase							     =       hans_tasks_num_labels[data_args.task_name]
   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.

   _lowercase							     =       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,				)
   _lowercase							     =       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,				)
   _lowercase							     =       AutoModelForSequenceClassification.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
   _lowercase							     =       (
       HansDataset(
           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,				)
       if training_args.do_train
       else None
   )
   _lowercase							     =       (
       HansDataset(
           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,				evaluate=_snake_case,				)
       if training_args.do_eval
       else None
   )

   # Initialize our Trainer
   _lowercase							     =       Trainer(
       model=_snake_case,				args=_snake_case,				train_dataset=_snake_case,				eval_dataset=_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
   if training_args.do_eval:
      logger.info("*** Evaluate ***"       )

      _lowercase							     =       trainer.predict(_snake_case       )
      _lowercase							     =       output.predictions
      _lowercase							     =       np.argmax(_snake_case,				axis=1       )

      _lowercase							     =       [ex.pairID for ex in eval_dataset]
      _lowercase							     =       os.path.join(training_args.output_dir,				"hans_predictions.txt"       )
      _lowercase							     =       eval_dataset.get_labels()
      if trainer.is_world_master():
         with open(_snake_case,				"w"       ) as writer:
            writer.write("pairID,gold_label\n"       )
            for pid, pred in zip(_snake_case,				_snake_case       ):
               writer.write("ex" + str(_snake_case       ) + "," + label_list[int(_snake_case       )] + "\n"       )

      trainer._log(output.metrics       )


def   __UpperCAmelCase     (	_snake_case		:       Union[str, Any]       ):
   # For xla_spawn (TPUs)
   main()


if __name__ == "__main__":
  main()
"""simple docstring"""


from .number_theory.prime_numbers import next_prime
class   UpperCAmelCase_	:
    def __init__(	self			:       str   ,						_lowercase			:       int   ,						_lowercase			:       int | None = None   ,						_lowercase			:       float | None = None   ,						)						->    None:
       _lowercase							     =       size_table
       _lowercase							     =       [None] * self.size_table
       _lowercase							     =       0.75 if lim_charge is None else lim_charge
       _lowercase							     =       1 if charge_factor is None else charge_factor
       _lowercase							     =       []
       _lowercase							     =       {}
    def 		_lowerCamelCase      (	self			:       Any		)						->    Optional[Any]:
       return self._keys
    def 		_lowerCamelCase      (	self			:       Dict		)						->    Dict:
       return sum(1 for slot in self.values if slot is not None		) / (
           self.size_table * self.charge_factor
       )
    def 		_lowerCamelCase      (	self			:       Union[str, Any]   ,						_lowercase			:       List[str]		)						->    str:
       return key % self.size_table
    def 		_lowerCamelCase      (	self			:       Any   ,						_lowercase			:       Union[str, Any]		)						->    Optional[Any]:
       print(f"""step {step_ord}"""		)
       print(list(range(len(self.values		)		)		)		)
       print(self.values		)
    def 		_lowerCamelCase      (	self			:       str   ,						_lowercase			:       List[Any]		)						->    List[Any]:
       _lowercase							     =       1
       _lowercase							     =       values
       for value in values:
          self.insert_data(_lowercase		)
          self._step_by_step(_lowercase		)
          i += 1
    def 		_lowerCamelCase      (	self			:       Union[str, Any]   ,						_lowercase			:       Optional[int]   ,						_lowercase			:       Union[str, Any]		)						->    List[Any]:
       _lowercase							     =       data
       _lowercase							     =       data
    def 		_lowerCamelCase      (	self			:       int   ,						_lowercase			:       Optional[Any]   ,						_lowercase			:       Tuple=None		)						->    Any:
       _lowercase							     =       self.hash_function(key + 1		)

       while self.values[new_key] is not None and self.values[new_key] != key:
          if self.values.count(_lowercase		) > 0:
             _lowercase							     =       self.hash_function(new_key + 1		)
          else:
             _lowercase							     =       None
             break

       return new_key
    def 		_lowerCamelCase      (	self			:       str		)						->    Any:
       _lowercase							     =       [value for value in self.values if value is not None]
       _lowercase							     =       next_prime(self.size_table   ,						factor=2		)
       self._keys.clear()
       _lowercase							     =       [None] * self.size_table  # hell's pointers D: don't DRY ;/
       for value in survivor_values:
          self.insert_data(_lowercase		)




    def 		_lowerCamelCase      (	self			:       Union[str, Any]   ,						_lowercase			:       Tuple		)						->    Optional[int]:
       _lowercase							     =       self.hash_function(_lowercase		)

       if self.values[key] is None:
          self._set_value(_lowercase   ,						_lowercase		)

       elif self.values[key] == data:
          pass

       else:
          _lowercase							     =       self._collision_resolution(_lowercase   ,						_lowercase		)
          if collision_resolution is not None:
             self._set_value(_lowercase   ,						_lowercase		)
          else:
             self.rehashing()
             self.insert_data(_lowercase		)