






"""simple docstring"""


import argparse
import gc
import os
import threading

import evaluate
import psutil
import torch
from datasets import load_dataset
from torch.distributed.fsdp.fully_sharded_data_parallel import FullOptimStateDictConfig, FullStateDictConfig
from torch.utils.data import DataLoader
from transformers import (
    AutoModelForSequenceClassification,
    AutoTokenizer,
    get_linear_schedule_with_warmup,
    set_seed,
)

from accelerate import Accelerator, DistributedType, FullyShardedDataParallelPlugin


########################################################################
# This is a fully working simple example to use Accelerate
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
#   - single CPU or single GPU
#   - multi GPUS (using PyTorch distributed mode)
#   - (multi) TPUs
#   - fp16 (mixed-precision) or fp32 (normal precision)
#   - FSDP
#
# This example also demonstrates the checkpointing and sharding capabilities
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################


a__   :       List[Any]   						=			1_6
a__   :       Dict   						=			3_2





def 							a__     (					_UpperCAmelCase		)      ->					Tuple:

 '''simple docstring'''






 return int(x / 2**2_0		)



class        lowercase_			:

       def __enter__(     self       ):






        '''simple docstring'''

        gc.collect()
        torch.cuda.empty_cache()
        torch.cuda.reset_max_memory_allocated()  # reset the peak gauge to zero
        snake_case__      							=			torch.cuda.memory_allocated()
        snake_case__      							=			psutil.Process()

        snake_case__      							=			self.cpu_mem_used()
        snake_case__      							=			True
        snake_case__      							=			threading.Thread(target=self.peak_monitor_func       )
        snake_case__      							=			True
        peak_monitor_thread.start()
        return self

       def 						_SCREAMING_SNAKE_CASE(     self       ):






        '''simple docstring'''

        return self.process.memory_info().rss

       def 						_SCREAMING_SNAKE_CASE(     self       ):






        '''simple docstring'''

        snake_case__      							=			-1

        while True:
         snake_case__      							=			max(self.cpu_mem_used()				,   self.cpu_peak       )

         # can't sleep or will not catch the peak right (this comment is here on purpose)
         # time.sleep(0.001) # 1msec

         if not self.peak_monitoring:
          break

       def __exit__(     self				,   *__SCREAMING_SNAKE_CASE       ):






        '''simple docstring'''

        snake_case__      							=			False

        gc.collect()
        torch.cuda.empty_cache()
        snake_case__      							=			torch.cuda.memory_allocated()
        snake_case__      							=			torch.cuda.max_memory_allocated()
        snake_case__      							=			bamb(self.end - self.begin       )
        snake_case__      							=			bamb(self.peak - self.begin       )

        snake_case__      							=			self.cpu_mem_used()
        snake_case__      							=			bamb(self.cpu_end - self.cpu_begin       )
        snake_case__      							=			bamb(self.cpu_peak - self.cpu_begin       )
        # print(f"delta used/peak {self.used:4d}/{self.peaked:4d}")


# For testing only
if os.environ.get("""TESTING_MOCKED_DATALOADERS""", None) == "1":
   from accelerate.test_utils.training import mocked_dataloaders

   a__   :       int   						=			mocked_dataloaders  # noqa: F811





def 							a__     (					_UpperCAmelCase		,       _UpperCAmelCase		)      ->					Union[str, Any]:

 '''simple docstring'''






 if os.environ.get("TESTING_MOCKED_DATALOADERS"		,       _UpperCAmelCase		) == "1":
  snake_case__      							=			2

 # New Code #
 # Pass the advanced FSDP settings not part of the accelerate config by creating fsdp_plugin
 snake_case__      							=			FullyShardedDataParallelPlugin(
     state_dict_config=FullStateDictConfig(offload_to_cpu=_UpperCAmelCase		,       ranka_only=_UpperCAmelCase		)		,       optim_state_dict_config=FullOptimStateDictConfig(offload_to_cpu=_UpperCAmelCase		,       ranka_only=_UpperCAmelCase		)		,       )

 # Initialize accelerator
 if args.with_tracking:
  snake_case__      							=			Accelerator(
      cpu=args.cpu		,       mixed_precision=args.mixed_precision		,       log_with="wandb"		,       project_dir=args.logging_dir		,       fsdp_plugin=_UpperCAmelCase		,       )
 else:
  snake_case__      							=			Accelerator(fsdp_plugin=_UpperCAmelCase		)
 accelerator.print(accelerator.distributed_type		)

 if hasattr(args.checkpointing_steps		,       "isdigit"		):
  if args.checkpointing_steps == "epoch":
   snake_case__      							=			args.checkpointing_steps
  elif args.checkpointing_steps.isdigit():
   snake_case__      							=			int(args.checkpointing_steps		)
  else:
   raise ValueError(
       f"Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed."		)
 else:
  snake_case__      							=			None
 # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
 snake_case__      							=			config["lr"]
 snake_case__      							=			int(config["num_epochs"]		)
 snake_case__      							=			int(config["seed"]		)
 snake_case__      							=			int(config["batch_size"]		)

 # We need to initialize the trackers we use, and also store our configuration
 if args.with_tracking:
  snake_case__      							=			vars(_UpperCAmelCase		)
  accelerator.init_trackers("fsdp_glue_no_trainer"		,       _UpperCAmelCase		)

 snake_case__      							=			AutoTokenizer.from_pretrained(args.model_name_or_path		)
 snake_case__      							=			load_dataset("glue"		,       "mrpc"		)
 snake_case__      							=			evaluate.load("glue"		,       "mrpc"		)

 def tokenize_function(_UpperCAmelCase		):
  # max_length=None => use the model max length (it's actually the default)
  snake_case__      							=			tokenizer(examples["sentence1"]		,       examples["sentence2"]		,       truncation=_UpperCAmelCase		,       max_length=_UpperCAmelCase		)
  return outputs

 # Apply the method we just defined to all the examples in all the splits of the dataset
 # starting with the main process first:
 with accelerator.main_process_first():
  snake_case__      							=			datasets.map(
      _UpperCAmelCase		,       batched=_UpperCAmelCase		,       remove_columns=["idx", "sentence1", "sentence2"]		,       )

 # We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
 # transformers library
 snake_case__      							=			tokenized_datasets.rename_column("label"		,       "labels"		)

 # If the batch size is too big we use gradient accumulation
 snake_case__      							=			1
 if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
  snake_case__      							=			batch_size // MAX_GPU_BATCH_SIZE
  snake_case__      							=			MAX_GPU_BATCH_SIZE

 def collate_fn(_UpperCAmelCase		):
  # On TPU it's best to pad everything to the same length or training will be very slow.
  snake_case__      							=			1_2_8 if accelerator.distributed_type == DistributedType.TPU else None
  # When using mixed precision we want round multiples of 8/16
  if accelerator.mixed_precision == "fp8":
   snake_case__      							=			1_6
  elif accelerator.mixed_precision != "no":
   snake_case__      							=			8
  else:
   snake_case__      							=			None

  return tokenizer.pad(
      _UpperCAmelCase		,       padding="longest"		,       max_length=_UpperCAmelCase		,       pad_to_multiple_of=_UpperCAmelCase		,       return_tensors="pt"		,       )

 # Instantiate dataloaders.
 snake_case__      							=			DataLoader(
     tokenized_datasets["train"]		,       shuffle=_UpperCAmelCase		,       collate_fn=_UpperCAmelCase		,       batch_size=_UpperCAmelCase		)
 snake_case__      							=			DataLoader(
     tokenized_datasets["validation"]		,       shuffle=_UpperCAmelCase		,       collate_fn=_UpperCAmelCase		,       batch_size=_UpperCAmelCase		)

 set_seed(_UpperCAmelCase		)

 # Instantiate the model (we build the model here so that the seed also control new weights initialization)
 snake_case__      							=			AutoModelForSequenceClassification.from_pretrained(
     args.model_name_or_path		,       return_dict=_UpperCAmelCase		,       low_cpu_mem_usage=_UpperCAmelCase		)

 # New Code #
 # For FSDP feature, it is highly recommended and efficient to prepare the model before creating optimizer
 snake_case__      							=			accelerator.prepare(_UpperCAmelCase		)
 accelerator.print(_UpperCAmelCase		)

 # Instantiate optimizer
 # New Code #
 # For FSDP feature, at present it doesn't support multiple parameter groups,
 # so we need to create a single parameter group for the whole model
 snake_case__      							=			torch.optim.AdamW(params=model.parameters()		,       lr=_UpperCAmelCase		,       weight_decay=2e-4		)

 # Instantiate scheduler
 snake_case__      							=			get_linear_schedule_with_warmup(
     optimizer=_UpperCAmelCase		,       num_warmup_steps=1_0		,       num_training_steps=(len(_UpperCAmelCase		) * num_epochs) // gradient_accumulation_steps		,       )

 # New Code #
 # For FSDP feature, prepare everything except the model as we have already prepared the model
 # before creating the optimizer
 # There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
 # prepare method.
 snake_case__							,				snake_case__							,				snake_case__							,				snake_case__      							=			accelerator.prepare(
     _UpperCAmelCase		,       _UpperCAmelCase		,       _UpperCAmelCase		,       _UpperCAmelCase		)

 snake_case__      							=			0

 # Potentially load in the weights and states from a previous save
 if args.resume_from_checkpoint:
  if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "":
   accelerator.print(f"Resumed from checkpoint: {args.resume_from_checkpoint}"		)
   accelerator.load_state(args.resume_from_checkpoint		)
   snake_case__      							=			os.path.basename(args.resume_from_checkpoint		)
  else:
   # Get the most recent checkpoint
   snake_case__      							=			[f.name for f in os.scandir(os.getcwd()		) if f.is_dir()]
   dirs.sort(key=os.path.getctime		)
   snake_case__      							=			dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
  # Extract `epoch_{i}` or `step_{i}`
  snake_case__      							=			os.path.splitext(_UpperCAmelCase		)[0]

  if "epoch" in training_difference:
   num_epochs -= int(training_difference.replace("epoch_"		,       ""		)		)
   snake_case__      							=			None
  else:
   snake_case__      							=			int(training_difference.replace("step_"		,       ""		)		)
   num_epochs -= resume_step // len(_UpperCAmelCase		)
   # If resuming by step, we also need to know exactly how far into the DataLoader we went
   snake_case__      							=			(num_epochs * len(_UpperCAmelCase		)) - resume_step

    # Now we train the model
 for epoch in range(_UpperCAmelCase		):
  # New Code #
  # context manager to track the peak memory usage during the training epoch
  with TorchTracemalloc() as tracemalloc:
   model.train()
   if args.with_tracking:
    snake_case__      							=			0
   for step, batch in enumerate(_UpperCAmelCase		):
    # We need to skip steps until we reach the resumed step
    if args.resume_from_checkpoint and epoch == 0:
     if resume_step is not None and step < resume_step:
      pass
                # We could avoid this line since we set the accelerator with `device_placement=True`.
    batch.to(accelerator.device		)
    snake_case__      							=			model(**_UpperCAmelCase		)
    snake_case__      							=			outputs.loss
    snake_case__      							=			loss / gradient_accumulation_steps
    # We keep track of the loss at each epoch
    if args.with_tracking:
     total_loss += loss.detach().float()
    accelerator.backward(_UpperCAmelCase		)
    if step % gradient_accumulation_steps == 0:
     optimizer.step()
     lr_scheduler.step()
     optimizer.zero_grad()
     # accelerator.print(lr_scheduler.get_lr())

    overall_step += 1

    if isinstance(_UpperCAmelCase		,       _UpperCAmelCase		):
     snake_case__      							=			f"step_{overall_step}"
     if overall_step % checkpointing_steps == 0:
      if args.output_dir is not None:
       snake_case__      							=			os.path.join(args.output_dir		,       _UpperCAmelCase		)
      accelerator.save_state(_UpperCAmelCase		)
        # New Code #
        # Printing the GPU memory usage details such as allocated memory, peak memory, and total memory usage
  accelerator.print("Memory before entering the train : {}".format(bamb(tracemalloc.begin		)		)		)
  accelerator.print("Memory consumed at the end of the train (end-begin): {}".format(tracemalloc.used		)		)
  accelerator.print("Peak Memory consumed during the train (max-begin): {}".format(tracemalloc.peaked		)		)
  accelerator.print(
      "Total Peak Memory consumed during the train (max): {}".format(
          tracemalloc.peaked + bamb(tracemalloc.begin		)		)		)
  # Logging the peak memory usage of the GPU to the tracker
  if args.with_tracking:
   accelerator.log(
       {
           "train_total_peak_memory": tracemalloc.peaked + bamb(tracemalloc.begin		),
       }		,       step=_UpperCAmelCase		,       )

  # New Code #
  # context manager to track the peak memory usage during the evaluation
  with TorchTracemalloc() as tracemalloc:
   model.eval()
   for step, batch in enumerate(_UpperCAmelCase		):
    # We could avoid this line since we set the accelerator with `device_placement=True`.
    batch.to(accelerator.device		)
    with torch.no_grad():
     snake_case__      							=			model(**_UpperCAmelCase		)
    snake_case__      							=			outputs.logits.argmax(dim=-1		)
    snake_case__							,				snake_case__      							=			accelerator.gather_for_metrics((predictions, batch["labels"])		)
    metric.add_batch(
        predictions=_UpperCAmelCase		,       references=_UpperCAmelCase		,       )

   snake_case__      							=			metric.compute()
   # Use accelerator.print to print only on the main process.
   accelerator.print(f"epoch {epoch}:"		,       _UpperCAmelCase		)
   if args.with_tracking:
    accelerator.log(
        {
            "accuracy": eval_metric["accuracy"],
            "f1": eval_metric["f1"],
            "train_loss": total_loss.item() / len(_UpperCAmelCase		),
        }		,       step=_UpperCAmelCase		,       )

   if checkpointing_steps == "epoch":
    snake_case__      							=			f"epoch_{epoch}"
    if args.output_dir is not None:
     snake_case__      							=			os.path.join(args.output_dir		,       _UpperCAmelCase		)
    accelerator.save_state(_UpperCAmelCase		)
        # New Code #
        # Printing the GPU memory usage details such as allocated memory, peak memory, and total memory usage
  accelerator.print("Memory before entering the eval : {}".format(bamb(tracemalloc.begin		)		)		)
  accelerator.print("Memory consumed at the end of the eval (end-begin): {}".format(tracemalloc.used		)		)
  accelerator.print("Peak Memory consumed during the eval (max-begin): {}".format(tracemalloc.peaked		)		)
  accelerator.print(
      "Total Peak Memory consumed during the eval (max): {}".format(tracemalloc.peaked + bamb(tracemalloc.begin		)		)		)
  # Logging the peak memory usage of the GPU to the tracker
  if args.with_tracking:
   accelerator.log(
       {
           "eval_total_peak_memory": tracemalloc.peaked + bamb(tracemalloc.begin		),
       }		,       step=_UpperCAmelCase		,       )

 if args.with_tracking:
  accelerator.end_training()





def 							a__     (					)      ->					Dict:

 '''simple docstring'''






 snake_case__      							=			argparse.ArgumentParser(description="Simple example of training script."		)
 parser.add_argument(
     "--mixed_precision"		,       type=_UpperCAmelCase		,       default=_UpperCAmelCase		,       choices=["no", "fp16", "bf16", "fp8"]		,       help="Whether to use mixed precision. Choose"
     "between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."
     "and an Nvidia Ampere GPU."		,       )
 parser.add_argument("--cpu"		,       action="store_true"		,       help="If passed, will train on the CPU."		)
 parser.add_argument(
     "--checkpointing_steps"		,       type=_UpperCAmelCase		,       default=_UpperCAmelCase		,       help="Whether the various states should be saved at the end of every n steps, or 'epoch' for each epoch."		,       )
 parser.add_argument(
     "--resume_from_checkpoint"		,       type=_UpperCAmelCase		,       default=_UpperCAmelCase		,       help="If the training should continue from a checkpoint folder."		,       )
 parser.add_argument(
     "--with_tracking"		,       action="store_true"		,       help="Whether to load in all available experiment trackers from the environment and use them for logging."		,       )
 parser.add_argument(
     "--output_dir"		,       type=_UpperCAmelCase		,       default="."		,       help="Optional save directory where all checkpoint folders will be stored. Default is the current working directory."		,       )
 parser.add_argument(
     "--logging_dir"		,       type=_UpperCAmelCase		,       default="logs"		,       help="Location on where to store experiment tracking logs`"		,       )
 parser.add_argument(
     "--model_name_or_path"		,       type=_UpperCAmelCase		,       help="Path to pretrained model or model identifier from huggingface.co/models."		,       required=_UpperCAmelCase		,       )
 snake_case__      							=			parser.parse_args()
 snake_case__      							=			{"lr": 2e-5, "num_epochs": 3, "seed": 4_2, "batch_size": 1_6}
 training_function(_UpperCAmelCase		,       _UpperCAmelCase		)


if __name__ == "__main__":
   main()








"""simple docstring"""


from __future__ import annotations

from math import pi, pow





def 							a__     (					_UpperCAmelCase		)      ->					float:

 '''simple docstring'''






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





def 							a__     (					_UpperCAmelCase		,       _UpperCAmelCase		)      ->					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(_UpperCAmelCase		,       2		) * (3 * radius - height)





def 							a__     (					_UpperCAmelCase		,       _UpperCAmelCase		,       _UpperCAmelCase		)      ->					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(_UpperCAmelCase		,       _UpperCAmelCase		)		)

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

 return vol_spherical_cap(_UpperCAmelCase		,       _UpperCAmelCase		) + vol_spherical_cap(_UpperCAmelCase		,       _UpperCAmelCase		)





def 							a__     (					_UpperCAmelCase		,       _UpperCAmelCase		,       _UpperCAmelCase		)      ->					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(_UpperCAmelCase		,       _UpperCAmelCase		)		)

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





def 							a__     (					_UpperCAmelCase		,       _UpperCAmelCase		,       _UpperCAmelCase		)      ->					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 							a__     (					_UpperCAmelCase		,       _UpperCAmelCase		)      ->					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 							a__     (					_UpperCAmelCase		,       _UpperCAmelCase		)      ->					float:

 '''simple docstring'''






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





def 							a__     (					_UpperCAmelCase		,       _UpperCAmelCase		)      ->					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 							a__     (					_UpperCAmelCase		,       _UpperCAmelCase		)      ->					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 							a__     (					_UpperCAmelCase		)      ->					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(_UpperCAmelCase		,       3		)





def 							a__     (					_UpperCAmelCase		)      ->					float:

 '''simple docstring'''






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





def 							a__     (					_UpperCAmelCase		,       _UpperCAmelCase		)      ->					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(_UpperCAmelCase		,       2		) * height * pi





def 							a__     (					_UpperCAmelCase		,       _UpperCAmelCase		,       _UpperCAmelCase		)      ->					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(_UpperCAmelCase		,       2		) - pow(_UpperCAmelCase		,       2		)) * height





def 							a__     (					_UpperCAmelCase		,       _UpperCAmelCase		,       _UpperCAmelCase		)      ->					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(_UpperCAmelCase		,       2		) + pow(_UpperCAmelCase		,       2		) + radius_a * radius_a)
 )





def 							a__     (					_UpperCAmelCase		,       _UpperCAmelCase		)      ->					float:

 '''simple docstring'''






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





def 							a__     (					)      ->					Union[str, Any]:

 '''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()

