



"""simple docstring"""






from __future__ import annotations

from math import pi, pow


def 							A__ (     UpperCamelCase__    ):


  '''simple docstring'''


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


def 							A__ (     UpperCamelCase__       ,							UpperCamelCase__    ):


  '''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(_snake_case       ,							2    ) * (3 * radius - height)


def 							A__ (     UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__    ):


  '''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(_snake_case       ,							_snake_case    )    )

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

  return vol_spherical_cap(_snake_case       ,							_snake_case    ) + vol_spherical_cap(_snake_case       ,							_snake_case    )


def 							A__ (     UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__    ):


  '''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(_snake_case       ,							_snake_case    )    )

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


def 							A__ (     UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__    ):


  '''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 							A__ (     UpperCamelCase__       ,							UpperCamelCase__    ):


  '''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 							A__ (     UpperCamelCase__       ,							UpperCamelCase__    ):


  '''simple docstring'''


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


def 							A__ (     UpperCamelCase__       ,							UpperCamelCase__    ):


  '''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 							A__ (     UpperCamelCase__       ,							UpperCamelCase__    ):


  '''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 							A__ (     UpperCamelCase__    ):


  '''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(_snake_case       ,							3    )


def 							A__ (     UpperCamelCase__    ):


  '''simple docstring'''


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


def 							A__ (     UpperCamelCase__       ,							UpperCamelCase__    ):


  '''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(_snake_case       ,							2    ) * height * pi


def 							A__ (     UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__    ):


  '''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(_snake_case       ,							2    ) - pow(_snake_case       ,							2    )) * height


def 							A__ (     UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__    ):


  '''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(_snake_case       ,							2    ) + pow(_snake_case       ,							2    ) + radius_a * radius_a)
  )


def 							A__ (     UpperCamelCase__       ,							UpperCamelCase__    ):


  '''simple docstring'''


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


def 							A__ (     ):


  '''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()




"""simple docstring"""
import logging
import os
from dataclasses import dataclass, field
from importlib import import_module
from typing import Dict, List, Optional, Tuple

import numpy as np
from seqeval.metrics import classification_report, fa_score, precision_score, recall_score
from utils_ner import Split, TFTokenClassificationDataset, TokenClassificationTask

from transformers import (
    AutoConfig,
    AutoTokenizer,
    EvalPrediction,
    HfArgumentParser,
    TFAutoModelForTokenClassification,
    TFTrainer,
    TFTrainingArguments,
)
from transformers.utils import logging as hf_logging


hf_logging.set_verbosity_info()
hf_logging.enable_default_handler()
hf_logging.enable_explicit_format()


SCREAMING_SNAKE_CASE					:       Optional[Any]							   =							logging.getLogger(__name__)



@dataclass
class      _UpperCAmelCase					:






		'''simple docstring'''

		lowerCamelCase__           =field(
		    metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'}							)
		lowerCamelCase__           =field(
		    default=__snake_case,							metadata={'help': 'Pretrained config name or path if not the same as model_name'}							)
		lowerCamelCase__           =field(
		    default='NER',							metadata={'help': 'Task type to fine tune in training (e.g. NER, POS, etc)'}							)
		lowerCamelCase__           =field(
		    default=__snake_case,							metadata={'help': 'Pretrained tokenizer name or path if not the same as model_name'}							)
		lowerCamelCase__           =field(default=__snake_case,							metadata={'help': 'Set this flag to use fast tokenization.'}							)
		# If you want to tweak more attributes on your tokenizer, you should do it in a distinct script,
		# or just modify its tokenizer_config.json.
		lowerCamelCase__           =field(
		    default=__snake_case,							metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'},							)



@dataclass
class      _UpperCAmelCase					:






		'''simple docstring'''

		lowerCamelCase__           =field(
		    metadata={'help': 'The input data dir. Should contain the .txt files for a CoNLL-2003-formatted task.'}							)
		lowerCamelCase__           =field(
		    metadata={'help': 'Path to a file containing all labels. If not specified, CoNLL-2003 labels are used.'}							)
		lowerCamelCase__           =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.'
		        )
		    },							)
		lowerCamelCase__           =field(
		    default=__snake_case,							metadata={'help': 'Overwrite the cached training and evaluation sets'}							)






def 						lowercase				(	)	->Dict:
	"""simple docstring"""





	__snake_case      :		str         =							HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)	)
	__snake_case      ,				__snake_case      ,				__snake_case      :		Dict         =							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.'''	)

	__snake_case      :		List[Any]         =							import_module('''tasks'''	)

	try:
		__snake_case      :		Optional[int]         =							getattr(_snake_case	,  model_args.task_type	)
		__snake_case      :		TokenClassificationTask         =							token_classification_task_clazz()
	except AttributeError:
		raise ValueError(
		    f"""Task {model_args.task_type} needs to be defined as a TokenClassificationTask subclass in {module}. """
		    f"""Available tasks classes are: {TokenClassificationTask.__subclasses__()}"""	)

	# Setup logging
	logging.basicConfig(
	    format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s'''	,  datefmt='''%m/%d/%Y %H:%M:%S'''	,  level=logging.INFO	,  )
	logger.info(
	    '''n_replicas: %s, distributed training: %s, 16-bits training: %s'''	,  training_args.n_replicas	,  bool(training_args.n_replicas > 1	)	,  training_args.fpaa	,  )
	logger.info('''Training/evaluation parameters %s'''	,  _snake_case	)

	# Prepare Token Classification task
	__snake_case      :		Union[str, Any]         =							token_classification_task.get_labels(data_args.labels	)
	__snake_case      :		Dict[int, str]         =							dict(enumerate(_snake_case	)	)
	__snake_case      :		List[Any]         =							len(_snake_case	)

	# Load pretrained model and tokenizer
	#
	# Distributed training:
	# The .from_pretrained methods guarantee that only one local process can concurrently
	# download model & vocab.

	__snake_case      :		Any         =							AutoConfig.from_pretrained(
	    model_args.config_name if model_args.config_name else model_args.model_name_or_path	,  num_labels=_snake_case	,  idalabel=_snake_case	,  labelaid={label: i for i, label in enumerate(_snake_case	)}	,  cache_dir=model_args.cache_dir	,  )
	__snake_case      :		Tuple         =							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	,  use_fast=model_args.use_fast	,  )

	with training_args.strategy.scope():
		__snake_case      :		Optional[int]         =							TFAutoModelForTokenClassification.from_pretrained(
		    model_args.model_name_or_path	,  from_pt=bool('''.bin''' in model_args.model_name_or_path	)	,  config=_snake_case	,  cache_dir=model_args.cache_dir	,  )

	# Get datasets
	__snake_case      :		Any         =							(
	    TFTokenClassificationDataset(
	        token_classification_task=_snake_case	,  data_dir=data_args.data_dir	,  tokenizer=_snake_case	,  labels=_snake_case	,  model_type=config.model_type	,  max_seq_length=data_args.max_seq_length	,  overwrite_cache=data_args.overwrite_cache	,  mode=Split.train	,  )
	    if training_args.do_train
	    else None
	)
	__snake_case      :		List[Any]         =							(
	    TFTokenClassificationDataset(
	        token_classification_task=_snake_case	,  data_dir=data_args.data_dir	,  tokenizer=_snake_case	,  labels=_snake_case	,  model_type=config.model_type	,  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 align_predictions(_snake_case		: np.ndarray	,  _snake_case		: np.ndarray	) -> Tuple[List[int], List[int]]:
		__snake_case      :		List[Any]         =							np.argmax(_snake_case	,  axis=2	)
		__snake_case      ,				__snake_case      :		Dict         =							preds.shape
		__snake_case      :		Union[str, Any]         =							[[] for _ in range(_snake_case	)]
		__snake_case      :		str         =							[[] for _ in range(_snake_case	)]

		for i in range(_snake_case	):
			for j in range(_snake_case	):
				if label_ids[i, j] != -100:
					out_label_list[i].append(label_map[label_ids[i][j]]	)
					preds_list[i].append(label_map[preds[i][j]]	)

		return preds_list, out_label_list

	def compute_metrics(_snake_case		: EvalPrediction	) -> Dict:
		__snake_case      ,				__snake_case      :		Optional[int]         =							align_predictions(p.predictions	,  p.label_ids	)

		return {
		    "precision": precision_score(_snake_case	,  _snake_case	),
		    "recall": recall_score(_snake_case	,  _snake_case	),
		    "f1": fa_score(_snake_case	,  _snake_case	),
		}

	# Initialize our Trainer
	__snake_case      :		Optional[Any]         =							TFTrainer(
	    model=_snake_case	,  args=_snake_case	,  train_dataset=train_dataset.get_dataset() if train_dataset else None	,  eval_dataset=eval_dataset.get_dataset() if eval_dataset else None	,  compute_metrics=_snake_case	,  )

	# Training
	if training_args.do_train:
		trainer.train()
		trainer.save_model()
		tokenizer.save_pretrained(training_args.output_dir	)

	# Evaluation
	__snake_case      :		Union[str, Any]         =							{}
	if training_args.do_eval:
		logger.info('''*** Evaluate ***'''	)

		__snake_case      :		int         =							trainer.evaluate()
		__snake_case      :		List[Any]         =							os.path.join(training_args.output_dir	,  '''eval_results.txt'''	)

		with open(_snake_case	,  '''w'''	) as writer:
			logger.info('''***** Eval results *****'''	)

			for key, value in result.items():
				logger.info('''  %s = %s'''	,  _snake_case	,  _snake_case	)
				writer.write('''%s = %s\n''' % (key, value)	)

			results.update(_snake_case	)

    # Predict
	if training_args.do_predict:
		__snake_case      :		Optional[Any]         =							TFTokenClassificationDataset(
		    token_classification_task=_snake_case	,  data_dir=data_args.data_dir	,  tokenizer=_snake_case	,  labels=_snake_case	,  model_type=config.model_type	,  max_seq_length=data_args.max_seq_length	,  overwrite_cache=data_args.overwrite_cache	,  mode=Split.test	,  )

		__snake_case      ,				__snake_case      ,				__snake_case      :		str         =							trainer.predict(test_dataset.get_dataset()	)
		__snake_case      ,				__snake_case      :		Optional[int]         =							align_predictions(_snake_case	,  _snake_case	)
		__snake_case      :		Dict         =							classification_report(_snake_case	,  _snake_case	)

		logger.info('''\n%s'''	,  _snake_case	)

		__snake_case      :		List[str]         =							os.path.join(training_args.output_dir	,  '''test_results.txt'''	)

		with open(_snake_case	,  '''w'''	) as writer:
			writer.write('''%s\n''' % report	)

		# Save predictions
		__snake_case      :		List[str]         =							os.path.join(training_args.output_dir	,  '''test_predictions.txt'''	)

		with open(_snake_case	,  '''w'''	) as writer:
			with open(os.path.join(data_args.data_dir	,  '''test.txt'''	)	,  '''r'''	) as f:
				__snake_case      :		Union[str, Any]         =							0

				for line in f:
					if line.startswith('''-DOCSTART-'''	) or line == "" or line == "\n":
						writer.write(_snake_case	)

						if not preds_list[example_id]:
							example_id += 1
					elif preds_list[example_id]:
						__snake_case      :		List[str]         =							line.split()[0] + ''' ''' + preds_list[example_id].pop(0	) + '''\n'''

						writer.write(_snake_case	)
					else:
						logger.warning('''Maximum sequence length exceeded: No prediction for \'%s\'.'''	,  line.split()[0]	)

	return results


if __name__ == "__main__":
			main()





