




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


_lowerCAmelCase    : Any		= logging.getLogger(__name__)
@dataclass
class       _UpperCamelCase   :
      UpperCAmelCase_          =		field(
          metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""}		)
      UpperCAmelCase_          =		field(
          default=lowerCAmelCase						,							metadata={"""help""": """Pretrained config name or path if not the same as model_name"""}		)
      UpperCAmelCase_          =		field(
          default=lowerCAmelCase						,							metadata={"""help""": """Pretrained tokenizer name or path if not the same as model_name"""}		)
      UpperCAmelCase_          =		field(
          default=lowerCAmelCase						,							metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""}						,							)



@dataclass
class       _UpperCamelCase   :
      UpperCAmelCase_          =		field(
          metadata={"""help""": """The name of the task to train selected in the list: """ + """, """.join(hans_processors.keys()		)}		)
      UpperCAmelCase_          =		field(
          metadata={"""help""": """The input data dir. Should contain the .tsv files (or other data files) for the task."""}		)
      UpperCAmelCase_          =		field(
          default=1_28						,							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=lowerCAmelCase						,							metadata={"""help""": """Overwrite the cached training and evaluation sets"""}		)



def     lowerCAmelCase		(		_lowerCAmelCase				: List[InputFeatures]						):




 """simple docstring"""







 UpperCAmelCase__							=    default_data_collator(_lowerCAmelCase						)
 UpperCAmelCase__							=    batch.pop("pairID"    ,				_lowerCAmelCase						)
 return batch



def     lowerCAmelCase		(		):




 """simple docstring"""







 UpperCAmelCase__							=    HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)						)
 UpperCAmelCase__			,			UpperCAmelCase__			,			UpperCAmelCase__							=    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"    ,				_lowerCAmelCase						)

 # Set seed
 set_seed(training_args.seed						)

 try:
  UpperCAmelCase__							=    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.

 UpperCAmelCase__							=    AutoConfig.from_pretrained(
     model_args.config_name if model_args.config_name else model_args.model_name_or_path    ,				num_labels=_lowerCAmelCase    ,				finetuning_task=data_args.task_name    ,				cache_dir=model_args.cache_dir    ,				)
 UpperCAmelCase__							=    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    ,				)
 UpperCAmelCase__							=    AutoModelForSequenceClassification.from_pretrained(
     model_args.model_name_or_path    ,				from_tf=bool(".ckpt" in model_args.model_name_or_path						)    ,				config=_lowerCAmelCase    ,				cache_dir=model_args.cache_dir    ,				)

 # Get datasets
 UpperCAmelCase__							=    (
     HansDataset(
         data_dir=data_args.data_dir    ,				tokenizer=_lowerCAmelCase    ,				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
 )
 UpperCAmelCase__							=    (
     HansDataset(
         data_dir=data_args.data_dir    ,				tokenizer=_lowerCAmelCase    ,				task=data_args.task_name    ,				max_seq_length=data_args.max_seq_length    ,				overwrite_cache=data_args.overwrite_cache    ,				evaluate=_lowerCAmelCase    ,				)
     if training_args.do_eval
     else None
 )

 # Initialize our Trainer
 UpperCAmelCase__							=    Trainer(
     model=_lowerCAmelCase    ,				args=_lowerCAmelCase    ,				train_dataset=_lowerCAmelCase    ,				eval_dataset=_lowerCAmelCase    ,				data_collator=_lowerCAmelCase    ,				)

 # 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 ***"						)

  UpperCAmelCase__							=    trainer.predict(_lowerCAmelCase						)
  UpperCAmelCase__							=    output.predictions
  UpperCAmelCase__							=    np.argmax(_lowerCAmelCase    ,				axis=1						)

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

  trainer._log(output.metrics						)



def     lowerCAmelCase		(		_lowerCAmelCase				: List[Any]						):




 """simple docstring"""







 main()


if __name__ == "__main__":
  main()











from __future__ import annotations

from math import pi, pow



def     lowerCAmelCase		(		_lowerCAmelCase				: int | float						):




 """simple docstring"""







 if side_length < 0:
  raise ValueError("vol_cube() only accepts non-negative values"						)
 return pow(_lowerCAmelCase    ,				3						)



def     lowerCAmelCase		(		_lowerCAmelCase				: float    ,				_lowerCAmelCase				: float						):




 """simple docstring"""







 if height < 0 or radius < 0:
  raise ValueError("vol_spherical_cap() only accepts non-negative values"						)
 # Volume is 1/3 pi * height squared * (3 * radius - height)
 return 1 / 3 * pi * pow(_lowerCAmelCase    ,				2						) * (3 * radius - height)



def     lowerCAmelCase		(		_lowerCAmelCase				: float    ,				_lowerCAmelCase				: float    ,				_lowerCAmelCase				: float						):




 """simple docstring"""







 if radius_a < 0 or radius_a < 0 or centers_distance < 0:
  raise ValueError("vol_spheres_intersect() only accepts non-negative values"						)
 if centers_distance == 0:
  return vol_sphere(min(_lowerCAmelCase    ,				_lowerCAmelCase						)						)

 UpperCAmelCase__							=    (
     (radius_a - radius_a + centers_distance)
     * (radius_a + radius_a - centers_distance)
     / (2 * centers_distance)
 )
 UpperCAmelCase__							=    (
     (radius_a - radius_a + centers_distance)
     * (radius_a + radius_a - centers_distance)
     / (2 * centers_distance)
 )

 return vol_spherical_cap(_lowerCAmelCase    ,				_lowerCAmelCase						) + vol_spherical_cap(_lowerCAmelCase    ,				_lowerCAmelCase						)



def     lowerCAmelCase		(		_lowerCAmelCase				: float    ,				_lowerCAmelCase				: float    ,				_lowerCAmelCase				: float						):




 """simple docstring"""







 if radius_a <= 0 or radius_a <= 0 or centers_distance < 0:
  raise ValueError(
      "vol_spheres_union() only accepts non-negative values, non-zero radius"						)

 if centers_distance == 0:
  return vol_sphere(max(_lowerCAmelCase    ,				_lowerCAmelCase						)						)

 return (
     vol_sphere(_lowerCAmelCase						)
     + vol_sphere(_lowerCAmelCase						)
     - vol_spheres_intersect(_lowerCAmelCase    ,				_lowerCAmelCase    ,				_lowerCAmelCase						)
 )



def     lowerCAmelCase		(		_lowerCAmelCase				: float    ,				_lowerCAmelCase				: float    ,				_lowerCAmelCase				: float						):




 """simple docstring"""







 if width < 0 or height < 0 or length < 0:
  raise ValueError("vol_cuboid() only accepts non-negative values"						)
 return float(width * height * length						)



def     lowerCAmelCase		(		_lowerCAmelCase				: float    ,				_lowerCAmelCase				: float						):




 """simple docstring"""







 if height < 0 or area_of_base < 0:
  raise ValueError("vol_cone() only accepts non-negative values"						)
 return area_of_base * height / 3.0



def     lowerCAmelCase		(		_lowerCAmelCase				: float    ,				_lowerCAmelCase				: float						):




 """simple docstring"""







 if height < 0 or radius < 0:
  raise ValueError("vol_right_circ_cone() only accepts non-negative values"						)
 return pi * pow(_lowerCAmelCase    ,				2						) * height / 3.0



def     lowerCAmelCase		(		_lowerCAmelCase				: float    ,				_lowerCAmelCase				: float						):




 """simple docstring"""







 if height < 0 or area_of_base < 0:
  raise ValueError("vol_prism() only accepts non-negative values"						)
 return float(area_of_base * height						)



def     lowerCAmelCase		(		_lowerCAmelCase				: float    ,				_lowerCAmelCase				: float						):




 """simple docstring"""







 if height < 0 or area_of_base < 0:
  raise ValueError("vol_pyramid() only accepts non-negative values"						)
 return area_of_base * height / 3.0



def     lowerCAmelCase		(		_lowerCAmelCase				: float						):




 """simple docstring"""







 if radius < 0:
  raise ValueError("vol_sphere() only accepts non-negative values"						)
 # Volume is 4/3 * pi * radius cubed
 return 4 / 3 * pi * pow(_lowerCAmelCase    ,				3						)



def     lowerCAmelCase		(		_lowerCAmelCase				: float						):




 """simple docstring"""







 if radius < 0:
  raise ValueError("vol_hemisphere() only accepts non-negative values"						)
 # Volume is radius cubed * pi * 2/3
 return pow(_lowerCAmelCase    ,				3						) * pi * 2 / 3



def     lowerCAmelCase		(		_lowerCAmelCase				: float    ,				_lowerCAmelCase				: float						):




 """simple docstring"""







 if height < 0 or radius < 0:
  raise ValueError("vol_circular_cylinder() only accepts non-negative values"						)
 # Volume is radius squared * height * pi
 return pow(_lowerCAmelCase    ,				2						) * height * pi



def     lowerCAmelCase		(		_lowerCAmelCase				: float    ,				_lowerCAmelCase				: float    ,				_lowerCAmelCase				: float						):




 """simple docstring"""







 if inner_radius < 0 or outer_radius < 0 or height < 0:
  raise ValueError(
      "vol_hollow_circular_cylinder() only accepts non-negative values"						)
 if outer_radius <= inner_radius:
  raise ValueError("outer_radius must be greater than inner_radius"						)
 return pi * (pow(_lowerCAmelCase    ,				2						) - pow(_lowerCAmelCase    ,				2						)) * height



def     lowerCAmelCase		(		_lowerCAmelCase				: float    ,				_lowerCAmelCase				: float    ,				_lowerCAmelCase				: float						):




 """simple docstring"""







 if radius_a < 0 or radius_a < 0 or height < 0:
  raise ValueError("vol_conical_frustum() only accepts non-negative values"						)
 return (
     1
     / 3
     * pi
     * height
     * (pow(_lowerCAmelCase    ,				2						) + pow(_lowerCAmelCase    ,				2						) + radius_a * radius_a)
 )



def     lowerCAmelCase		(		_lowerCAmelCase				: float    ,				_lowerCAmelCase				: float						):




 """simple docstring"""







 if torus_radius < 0 or tube_radius < 0:
  raise ValueError("vol_torus() only accepts non-negative values"						)
 return 2 * pow(_lowerCAmelCase    ,				2						) * torus_radius * pow(_lowerCAmelCase    ,				2						)



def     lowerCAmelCase		(		):




 """simple docstring"""







 print("Volumes:"						)
 print(F'''Cube: {vol_cube(2						) = }'''						)  # = 8
 print(F'''Cuboid: {vol_cuboid(2    ,				2    ,				2						) = }'''						)  # = 8
 print(F'''Cone: {vol_cone(2    ,				2						) = }'''						)  # ~= 1.33
 print(F'''Right Circular Cone: {vol_right_circ_cone(2    ,				2						) = }'''						)  # ~= 8.38
 print(F'''Prism: {vol_prism(2    ,				2						) = }'''						)  # = 4
 print(F'''Pyramid: {vol_pyramid(2    ,				2						) = }'''						)  # ~= 1.33
 print(F'''Sphere: {vol_sphere(2						) = }'''						)  # ~= 33.5
 print(F'''Hemisphere: {vol_hemisphere(2						) = }'''						)  # ~= 16.75
 print(F'''Circular Cylinder: {vol_circular_cylinder(2    ,				2						) = }'''						)  # ~= 25.1
 print(F'''Torus: {vol_torus(2    ,				2						) = }'''						)  # ~= 157.9
 print(F'''Conical Frustum: {vol_conical_frustum(2    ,				2    ,				4						) = }'''						)  # ~= 58.6
 print(F'''Spherical cap: {vol_spherical_cap(1    ,				2						) = }'''						)  # ~= 5.24
 print(F'''Spheres intersetion: {vol_spheres_intersect(2    ,				2    ,				1						) = }'''						)  # ~= 21.21
 print(F'''Spheres union: {vol_spheres_union(2    ,				2    ,				1						) = }'''						)  # ~= 45.81
 print(
     F'''Hollow Circular Cylinder: {vol_hollow_circular_cylinder(1    ,				2    ,				3						) = }'''						)  # ~= 28.3


if __name__ == "__main__":
  main()





