



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()


A_		:  int										=  logging.getLogger(__name__)



@dataclass
class 		_lowercase		:
   _UpperCAmelCase  						=      field(
       metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''}						)
   _UpperCAmelCase  						=      field(
       default=UpperCAmelCase__, metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''}						)
   _UpperCAmelCase  						=      field(
       default='''NER''', metadata={'''help''': '''Task type to fine tune in training (e.g. NER, POS, etc)'''}						)
   _UpperCAmelCase  						=      field(
       default=UpperCAmelCase__, metadata={'''help''': '''Pretrained tokenizer name or path if not the same as model_name'''}						)
   _UpperCAmelCase  						=      field(default=UpperCAmelCase__, 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  						=      field(
       default=UpperCAmelCase__, metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''}, )



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


def 						UpperCAmelCase__   (   ):






 '''simple docstring'''

 a							=		HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)       )
 a		,	a		,	a							=		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."       )

 a							=		import_module("tasks"       )

 try:
  a							=		getattr(UpperCAmelCase__			,       model_args.task_type       )
  a							=		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"			,       UpperCAmelCase__       )

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

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

 a							=		AutoConfig.from_pretrained(
     model_args.config_name if model_args.config_name else model_args.model_name_or_path			,       num_labels=UpperCAmelCase__			,       idalabel=UpperCAmelCase__			,       labelaid={label: i for i, label in enumerate(UpperCAmelCase__       )}			,       cache_dir=model_args.cache_dir			,       )
 a							=		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():
  a							=		TFAutoModelForTokenClassification.from_pretrained(
      model_args.model_name_or_path			,       from_pt=bool(".bin" in model_args.model_name_or_path       )			,       config=UpperCAmelCase__			,       cache_dir=model_args.cache_dir			,       )

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

  for i in range(UpperCAmelCase__       ):
   for j in range(UpperCAmelCase__       ):
    if label_ids[i, j] != -1_00:
     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(UpperCAmelCase__			:EvalPrediction       ) -> Dict:
  a		,	a							=		align_predictions(p.predictions			,       p.label_ids       )

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

 # Initialize our Trainer
 a							=		TFTrainer(
     model=UpperCAmelCase__			,       args=UpperCAmelCase__			,       train_dataset=train_dataset.get_dataset() if train_dataset else None			,       eval_dataset=eval_dataset.get_dataset() if eval_dataset else None			,       compute_metrics=UpperCAmelCase__			,       )

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

 # Evaluation
 a							=		{}
 if training_args.do_eval:
  logger.info("*** Evaluate ***"       )

  a							=		trainer.evaluate()
  a							=		os.path.join(training_args.output_dir			,       "eval_results.txt"       )

  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__       )

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

  a		,	a		,	a							=		trainer.predict(test_dataset.get_dataset()       )
  a		,	a							=		align_predictions(UpperCAmelCase__			,       UpperCAmelCase__       )
  a							=		classification_report(UpperCAmelCase__			,       UpperCAmelCase__       )

  logger.info("\n%s"			,       UpperCAmelCase__       )

  a							=		os.path.join(training_args.output_dir			,       "test_results.txt"       )

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

  # Save predictions
  a							=		os.path.join(training_args.output_dir			,       "test_predictions.txt"       )

  with open(UpperCAmelCase__			,       "w"       ) as writer:
   with open(os.path.join(data_args.data_dir			,       "test.txt"       )			,       "r"       ) as f:
    a							=		0

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

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

      writer.write(UpperCAmelCase__       )
     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       snake_case__		(   _SCREAMING_SNAKE_CASE			)    ->float:
 if side_length < 0:
  raise ValueError("""vol_cube() only accepts non-negative values"""			)
 return pow(_SCREAMING_SNAKE_CASE       ,	3			)




def       snake_case__		(   _SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE			)    ->float:
 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       snake_case__		(   _SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE			)    ->float:
 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			)			)

 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(_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE			) + vol_spherical_cap(_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE			)




def       snake_case__		(   _SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE			)    ->float:

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




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




def       snake_case__		(   _SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE			)    ->float:
 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       snake_case__		(   _SCREAMING_SNAKE_CASE			)    ->float:
 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       snake_case__		(   _SCREAMING_SNAKE_CASE			)    ->float:
 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       snake_case__		(   _SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE			)    ->float:
 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       snake_case__		(   _SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE			)    ->float:
 # 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       snake_case__		(   _SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE			)    ->float:
 # 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       snake_case__		(   _SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE			)    ->float:
 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       snake_case__		(   )    ->str:
 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()



