
"""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()


snake_case					  =						logging.getLogger(__name__)




@dataclass
class 	UpperCamelCase      :

				"""simple docstring"""



				UpperCAmelCase_							: str		    =							field(
				    metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"} )
				UpperCAmelCase_							: Optional[str]		    =							field(
				    default=__magic_name__    , metadata={"help": "Pretrained config name or path if not the same as model_name"} )
				UpperCAmelCase_							: Optional[str]		    =							field(
				    default="NER"    , metadata={"help": "Task type to fine tune in training (e.g. NER, POS, etc)"} )
				UpperCAmelCase_							: Optional[str]		    =							field(
				    default=__magic_name__    , metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"} )
				UpperCAmelCase_							: bool		    =							field(default=__magic_name__    , 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.
				UpperCAmelCase_							: Optional[str]		    =							field(
				    default=__magic_name__    , metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"}    , )






@dataclass
class 	UpperCamelCase      :

				"""simple docstring"""



				UpperCAmelCase_							: str		    =							field(
				    metadata={"help": "The input data dir. Should contain the .txt files for a CoNLL-2003-formatted task."} )
				UpperCAmelCase_							: Optional[str]		    =							field(
				    metadata={"help": "Path to a file containing all labels. If not specified, CoNLL-2003 labels are used."} )
				UpperCAmelCase_							: int		    =							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."
				        )
				    }    , )
				UpperCAmelCase_							: bool		    =							field(
				    default=__magic_name__    , metadata={"help": "Overwrite the cached training and evaluation sets"} )






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.
					SCREAMING_SNAKE_CASE           =      HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)     )
					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.'     )

					SCREAMING_SNAKE_CASE           =      import_module('tasks'     )

					try:
										SCREAMING_SNAKE_CASE           =      getattr(SCREAMING_SNAKE_CASE_,	model_args.task_type     )
										SCREAMING_SNAKE_CASE           =      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',	SCREAMING_SNAKE_CASE_     )

					# Prepare Token Classification task
					SCREAMING_SNAKE_CASE           =      token_classification_task.get_labels(data_args.labels     )
					SCREAMING_SNAKE_CASE           =      dict(enumerate(SCREAMING_SNAKE_CASE_     )     )
					SCREAMING_SNAKE_CASE           =      len(SCREAMING_SNAKE_CASE_     )

					# 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=SCREAMING_SNAKE_CASE_,	idalabel=SCREAMING_SNAKE_CASE_,	labelaid={label: i for i, label in enumerate(SCREAMING_SNAKE_CASE_     )},	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,	use_fast=model_args.use_fast,	)

					with training_args.strategy.scope():
										SCREAMING_SNAKE_CASE           =      TFAutoModelForTokenClassification.from_pretrained(
										    model_args.model_name_or_path,	from_pt=bool('.bin' in model_args.model_name_or_path     ),	config=SCREAMING_SNAKE_CASE_,	cache_dir=model_args.cache_dir,	)

					# Get datasets
					SCREAMING_SNAKE_CASE           =      (
					    TFTokenClassificationDataset(
					        token_classification_task=SCREAMING_SNAKE_CASE_,	data_dir=data_args.data_dir,	tokenizer=SCREAMING_SNAKE_CASE_,	labels=SCREAMING_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
					)
					SCREAMING_SNAKE_CASE           =      (
					    TFTokenClassificationDataset(
					        token_classification_task=SCREAMING_SNAKE_CASE_,	data_dir=data_args.data_dir,	tokenizer=SCREAMING_SNAKE_CASE_,	labels=SCREAMING_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(SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_     ) -> Tuple[List[int], List[int]]:
										SCREAMING_SNAKE_CASE           =      np.argmax(SCREAMING_SNAKE_CASE_,	axis=2     )
										SCREAMING_SNAKE_CASE      ,       SCREAMING_SNAKE_CASE           =      preds.shape
										SCREAMING_SNAKE_CASE           =      [[] for _ in range(SCREAMING_SNAKE_CASE_     )]
										SCREAMING_SNAKE_CASE           =      [[] for _ in range(SCREAMING_SNAKE_CASE_     )]

										for i in range(SCREAMING_SNAKE_CASE_     ):
															for j in range(SCREAMING_SNAKE_CASE_     ):
																				if label_ids[i, j] != -1_0_0:
																									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(SCREAMING_SNAKE_CASE_     ) -> Dict:
										SCREAMING_SNAKE_CASE      ,       SCREAMING_SNAKE_CASE           =      align_predictions(p.predictions,	p.label_ids     )

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

					# Initialize our Trainer
					SCREAMING_SNAKE_CASE           =      TFTrainer(
					    model=SCREAMING_SNAKE_CASE_,	args=SCREAMING_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=SCREAMING_SNAKE_CASE_,	)

					# Training
					if training_args.do_train:
										trainer.train()
										trainer.save_model()
										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'     )

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

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

															results.update(SCREAMING_SNAKE_CASE_     )

    # Predict
					if training_args.do_predict:
										SCREAMING_SNAKE_CASE           =      TFTokenClassificationDataset(
										    token_classification_task=SCREAMING_SNAKE_CASE_,	data_dir=data_args.data_dir,	tokenizer=SCREAMING_SNAKE_CASE_,	labels=SCREAMING_SNAKE_CASE_,	model_type=config.model_type,	max_seq_length=data_args.max_seq_length,	overwrite_cache=data_args.overwrite_cache,	mode=Split.test,	)

										SCREAMING_SNAKE_CASE      ,       SCREAMING_SNAKE_CASE      ,       SCREAMING_SNAKE_CASE           =      trainer.predict(test_dataset.get_dataset()     )
										SCREAMING_SNAKE_CASE      ,       SCREAMING_SNAKE_CASE           =      align_predictions(SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_     )
										SCREAMING_SNAKE_CASE           =      classification_report(SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_     )

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

										SCREAMING_SNAKE_CASE           =      os.path.join(training_args.output_dir,	'test_results.txt'     )

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

										# Save predictions
										SCREAMING_SNAKE_CASE           =      os.path.join(training_args.output_dir,	'test_predictions.txt'     )

										with open(SCREAMING_SNAKE_CASE_,	'w'     ) as writer:
															with open(os.path.join(data_args.data_dir,	'test.txt'     ),	'r'     ) as f:
																				SCREAMING_SNAKE_CASE           =      0

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

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

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

					return results


if __name__ == "__main__":
	main()


"""simple docstring"""




from __future__ import annotations

from math import pi, pow

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

def     UpperCamelCase_    (						SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_     ):
					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(SCREAMING_SNAKE_CASE_,	2     ) * (3 * radius - height)

def     UpperCamelCase_    (						SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_     ):
					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(SCREAMING_SNAKE_CASE_,	SCREAMING_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(SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_     ) + vol_spherical_cap(SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_     )

def     UpperCamelCase_    (						SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_     ):

					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(SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_     )     )

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

def     UpperCamelCase_    (						SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_     ):
					if width < 0 or height < 0 or length < 0:
										raise ValueError('vol_cuboid() only accepts non-negative values'     )
					return float(width * height * length     )

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

def     UpperCamelCase_    (						SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_     ):
					if height < 0 or area_of_base < 0:
										raise ValueError('vol_prism() only accepts non-negative values'     )
					return float(area_of_base * height     )

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

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

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

def     UpperCamelCase_    (						SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_     ):
					# Volume - (outer_radius squared - inner_radius squared) * pi * height
					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(SCREAMING_SNAKE_CASE_,	2     ) - pow(SCREAMING_SNAKE_CASE_,	2     )) * height

def     UpperCamelCase_    (						SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_     ):
					# Volume is 1/3 * pi * height *
					#           (radius_1 squared + radius_2 squared + radius_1 * radius_2)
					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(SCREAMING_SNAKE_CASE_,	2     ) + pow(SCREAMING_SNAKE_CASE_,	2     ) + radius_a * radius_a)
					)

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

def     UpperCamelCase_    (						):
					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()
