

"""simple docstring"""



from math import pi, sqrt, tan





def  _snake_case     (    UpperCAmelCase_  :					float			):



     if side_length < 0:
          raise ValueError("""surface_area_cube() only accepts non-negative values"""			)
     return 6 * side_length**2





def  _snake_case     (    UpperCAmelCase_  :					float					,   UpperCAmelCase_  :					float					,   UpperCAmelCase_  :					float			):



     if length < 0 or breadth < 0 or height < 0:
          raise ValueError("""surface_area_cuboid() only accepts non-negative values"""			)
     return 2 * ((length * breadth) + (breadth * height) + (length * height))





def  _snake_case     (    UpperCAmelCase_  :					float			):



     if radius < 0:
          raise ValueError("""surface_area_sphere() only accepts non-negative values"""			)
     return 4 * pi * radius**2





def  _snake_case     (    UpperCAmelCase_  :					float			):



     if radius < 0:
          raise ValueError("""surface_area_hemisphere() only accepts non-negative values"""			)
     return 3 * pi * radius**2





def  _snake_case     (    UpperCAmelCase_  :					float					,   UpperCAmelCase_  :					float			):



     if radius < 0 or height < 0:
          raise ValueError("""surface_area_cone() only accepts non-negative values"""			)
     return pi * radius * (radius + (height**2 + radius**2) ** 0.5)





def  _snake_case     (    UpperCAmelCase_  :					float					,   UpperCAmelCase_  :					float					,   UpperCAmelCase_  :					float			):



     if radius_a < 0 or radius_a < 0 or height < 0:
          raise ValueError(
              """surface_area_conical_frustum() only accepts non-negative values"""			)
     A__										=  (height**2 + (radius_a - radius_a) ** 2) ** 0.5
     return pi * ((slant_height * (radius_a + radius_a)) + radius_a**2 + radius_a**2)





def  _snake_case     (    UpperCAmelCase_  :					float					,   UpperCAmelCase_  :					float			):



     if radius < 0 or height < 0:
          raise ValueError("""surface_area_cylinder() only accepts non-negative values"""			)
     return 2 * pi * radius * (height + radius)





def  _snake_case     (    UpperCAmelCase_  :					float					,   UpperCAmelCase_  :					float			):



     if torus_radius < 0 or tube_radius < 0:
          raise ValueError("""surface_area_torus() only accepts non-negative values"""			)
     if torus_radius < tube_radius:
          raise ValueError(
              """surface_area_torus() does not support spindle or self intersecting tori"""			)
     return 4 * pow(_SCREAMING_SNAKE_CASE					,   2			) * torus_radius * tube_radius





def  _snake_case     (    UpperCAmelCase_  :					float					,   UpperCAmelCase_  :					float			):



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





def  _snake_case     (    UpperCAmelCase_  :					float			):



     if side_length < 0:
          raise ValueError("""area_square() only accepts non-negative values"""			)
     return side_length**2





def  _snake_case     (    UpperCAmelCase_  :					float					,   UpperCAmelCase_  :					float			):



     if base < 0 or height < 0:
          raise ValueError("""area_triangle() only accepts non-negative values"""			)
     return (base * height) / 2





def  _snake_case     (    UpperCAmelCase_  :					float					,   UpperCAmelCase_  :					float					,   UpperCAmelCase_  :					float			):



     if sidea < 0 or sidea < 0 or sidea < 0:
          raise ValueError("""area_triangle_three_sides() only accepts non-negative values"""			)
     elif sidea + sidea < sidea or sidea + sidea < sidea or sidea + sidea < sidea:
          raise ValueError("""Given three sides do not form a triangle"""			)
     A__										=  (sidea + sidea + sidea) / 2
     A__										=  sqrt(
         semi_perimeter
         * (semi_perimeter - sidea)
         * (semi_perimeter - sidea)
         * (semi_perimeter - sidea)			)
     return area





def  _snake_case     (    UpperCAmelCase_  :					float					,   UpperCAmelCase_  :					float			):



     if base < 0 or height < 0:
          raise ValueError("""area_parallelogram() only accepts non-negative values"""			)
     return base * height





def  _snake_case     (    UpperCAmelCase_  :					float					,   UpperCAmelCase_  :					float					,   UpperCAmelCase_  :					float			):



     if basea < 0 or basea < 0 or height < 0:
          raise ValueError("""area_trapezium() only accepts non-negative values"""			)
     return 1 / 2 * (basea + basea) * height





def  _snake_case     (    UpperCAmelCase_  :					float			):



     if radius < 0:
          raise ValueError("""area_circle() only accepts non-negative values"""			)
     return pi * radius**2





def  _snake_case     (    UpperCAmelCase_  :					float					,   UpperCAmelCase_  :					float			):



     if radius_x < 0 or radius_y < 0:
          raise ValueError("""area_ellipse() only accepts non-negative values"""			)
     return pi * radius_x * radius_y





def  _snake_case     (    UpperCAmelCase_  :					float					,   UpperCAmelCase_  :					float			):



     if diagonal_a < 0 or diagonal_a < 0:
          raise ValueError("""area_rhombus() only accepts non-negative values"""			)
     return 1 / 2 * diagonal_a * diagonal_a





def  _snake_case     (    UpperCAmelCase_  :					int					,   UpperCAmelCase_  :					float			):



     if not isinstance(_SCREAMING_SNAKE_CASE					,   _SCREAMING_SNAKE_CASE			) or sides < 3:
          raise ValueError(
              """area_reg_polygon() only accepts integers greater than or \
equal to three as number of sides"""			)
     elif length < 0:
          raise ValueError(
              """area_reg_polygon() only accepts non-negative values as \
length of a side"""			)
     return (sides * length**2) / (4 * tan(pi / sides			))
     return (sides * length**2) / (4 * tan(pi / sides			))


if __name__ == "__main__":
 import doctest

 doctest.testmod(verbose=True)  # verbose so we can see methods missing tests

 print('[DEMO] Areas of various geometric shapes: \n')
 print(f"""Rectangle: {area_rectangle(1_0, 2_0) = }""")
 print(f"""Square: {area_square(1_0) = }""")
 print(f"""Triangle: {area_triangle(1_0, 1_0) = }""")
 print(f"""Triangle: {area_triangle_three_sides(5, 1_2, 1_3) = }""")
 print(f"""Parallelogram: {area_parallelogram(1_0, 2_0) = }""")
 print(f"""Rhombus: {area_rhombus(1_0, 2_0) = }""")
 print(f"""Trapezium: {area_trapezium(1_0, 2_0, 3_0) = }""")
 print(f"""Circle: {area_circle(2_0) = }""")
 print(f"""Ellipse: {area_ellipse(1_0, 2_0) = }""")
 print('\nSurface Areas of various geometric shapes: \n')
 print(f"""Cube: {surface_area_cube(2_0) = }""")
 print(f"""Cuboid: {surface_area_cuboid(1_0, 2_0, 3_0) = }""")
 print(f"""Sphere: {surface_area_sphere(2_0) = }""")
 print(f"""Hemisphere: {surface_area_hemisphere(2_0) = }""")
 print(f"""Cone: {surface_area_cone(1_0, 2_0) = }""")
 print(f"""Conical Frustum: {surface_area_conical_frustum(1_0, 2_0, 3_0) = }""")
 print(f"""Cylinder: {surface_area_cylinder(1_0, 2_0) = }""")
 print(f"""Torus: {surface_area_torus(2_0, 1_0) = }""")
 print(f"""Equilateral Triangle: {area_reg_polygon(3, 1_0) = }""")
 print(f"""Square: {area_reg_polygon(4, 1_0) = }""")
 print(f"""Reqular Pentagon: {area_reg_polygon(5, 1_0) = }""")







"""simple docstring"""



import argparse
from copy import deepcopy

import numpy as np
from datasets import ClassLabel, DatasetDict, load_dataset
from evaluate import load

from transformers import (
    AutoModelForSequenceClassification,
    AutoTokenizer,
    DataCollatorWithPadding,
    Trainer,
    TrainerCallback,
    TrainingArguments,
    set_seed,
)





def  _snake_case     (    ):
     A__										=  argparse.ArgumentParser()
     parser.add_argument("""--model_ckpt"""					,   type=UpperCAmelCase_					,   default="""microsoft/unixcoder-base-nine"""			)
     parser.add_argument("""--num_epochs"""					,   type=UpperCAmelCase_					,   default=5			)
     parser.add_argument("""--batch_size"""					,   type=UpperCAmelCase_					,   default=6			)
     parser.add_argument("""--gradient_accumulation_steps"""					,   type=UpperCAmelCase_					,   default=1			)
     parser.add_argument("""--freeze"""					,   type=UpperCAmelCase_					,   default=UpperCAmelCase_			)
     parser.add_argument("""--learning_rate"""					,   type=UpperCAmelCase_					,   default=5e-4			)
     parser.add_argument("""--seed"""					,   type=UpperCAmelCase_					,   default=0			)
     parser.add_argument("""--lr_scheduler_type"""					,   type=UpperCAmelCase_					,   default="""cosine"""			)
     parser.add_argument("""--num_warmup_steps"""					,   type=UpperCAmelCase_					,   default=10			)
     parser.add_argument("""--weight_decay"""					,   type=UpperCAmelCase_					,   default=0.01			)
     parser.add_argument("""--output_dir"""					,   type=UpperCAmelCase_					,   default="""./results"""			)
     return parser.parse_args()


SCREAMING_SNAKE_CASE_					:	Union[str, Any]							  =						load('accuracy')





def  _snake_case     (    UpperCAmelCase_  :					Tuple			):
     A__			,				A__										=  eval_pred
     A__										=  np.argmax(UpperCAmelCase_					,   axis=1			)
     return metric.compute(predictions=UpperCAmelCase_					,   references=UpperCAmelCase_			)



class 					a						(       _lowerCamelCase				):



    """simple docstring"""



    def __init__(     self: Optional[int]    ,						UpperCamelCase: Optional[Any]       ):






         """simple docstring"""


         super().__init__()
         A__										=  trainer





    def 				UpperCamelCase	(     self: int    ,						UpperCamelCase: Tuple    ,						UpperCamelCase: Optional[int]    ,						UpperCamelCase: Dict    ,						**UpperCamelCase: int       ):






         """simple docstring"""


         if control.should_evaluate:
              A__										=  deepcopy(UpperCamelCase       )
              self._trainer.evaluate(eval_dataset=self._trainer.train_dataset    ,						metric_key_prefix="""train"""       )
              return control_copy





def  _snake_case     (    ):
     A__										=  get_args()
     set_seed(args.seed			)

     A__										=  load_dataset("""codeparrot/codecomplex"""					,   split="""train"""			)
     A__										=  dataset.train_test_split(test_size=0.2			)
     A__										=  train_test["""test"""].train_test_split(test_size=0.5			)
     A__										=  DatasetDict(
         {
             """train""": train_test["""train"""],
             """test""": test_validation["""train"""],
             """valid""": test_validation["""test"""],
         }			)

     print("""Loading tokenizer and model"""			)
     A__										=  AutoTokenizer.from_pretrained(args.model_ckpt			)
     A__										=  tokenizer.eos_token
     A__										=  AutoModelForSequenceClassification.from_pretrained(args.model_ckpt					,   num_labels=7			)
     A__										=  model.config.eos_token_id

     if args.freeze:
          for param in model.roberta.parameters():
               A__										=  False

     A__										=  ClassLabel(num_classes=7					,   names=list(set(train_test_validation["""train"""]["""complexity"""]			)			)			)

     def tokenize(UpperCAmelCase_  :					int			):
          A__										=  tokenizer(example["""src"""]					,   truncation=UpperCAmelCase_					,   max_length=1024			)
          A__										=  labels.straint(example["""complexity"""]			)
          return {
              "input_ids": inputs["input_ids"],
              "attention_mask": inputs["attention_mask"],
              "label": label,
          }

     A__										=  train_test_validation.map(
         UpperCAmelCase_					,   batched=UpperCAmelCase_					,   remove_columns=train_test_validation["""train"""].column_names					,   )
     A__										=  DataCollatorWithPadding(tokenizer=UpperCAmelCase_			)

     A__										=  TrainingArguments(
         output_dir=args.output_dir					,   learning_rate=args.learning_rate					,   lr_scheduler_type=args.lr_scheduler_type					,   evaluation_strategy="""epoch"""					,   save_strategy="""epoch"""					,   logging_strategy="""epoch"""					,   per_device_train_batch_size=args.batch_size					,   per_device_eval_batch_size=args.batch_size					,   num_train_epochs=args.num_epochs					,   gradient_accumulation_steps=args.gradient_accumulation_steps					,   weight_decay=0.01					,   metric_for_best_model="""accuracy"""					,   run_name="""complexity-java"""					,   report_to="""wandb"""					,   )

     A__										=  Trainer(
         model=UpperCAmelCase_					,   args=UpperCAmelCase_					,   train_dataset=tokenized_datasets["""train"""]					,   eval_dataset=tokenized_datasets["""valid"""]					,   tokenizer=UpperCAmelCase_					,   data_collator=UpperCAmelCase_					,   compute_metrics=UpperCAmelCase_					,   )

     print("""Training..."""			)
     trainer.add_callback(CustomCallback(UpperCAmelCase_			)			)
     trainer.train()


if __name__ == "__main__":
 main()






