
"""simple docstring"""




import argparse
import json
import os

import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType
from accelerate.utils.deepspeed import DummyOptim, DummyScheduler


UpperCAmelCase__   :					Union[str, Any]												=				1_6
UpperCAmelCase__   :					Optional[Any]												=				3_2

def lowercase_						(     _snake_case     ,_snake_case = 16     ,_snake_case = "bert-base-cased"		):
							SCREAMING_SNAKE_CASE__		: Dict     =     AutoTokenizer.from_pretrained(_snake_case		)
							SCREAMING_SNAKE_CASE__		: Optional[int]     =     load_dataset("""glue"""     ,"""mrpc"""		)

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

							# Apply the method we just defined to all the examples in all the splits of the dataset
							SCREAMING_SNAKE_CASE__		: str     =     datasets.map(
							    _snake_case     ,batched=_snake_case     ,remove_columns=["""idx""", """sentence1""", """sentence2"""]     ,load_from_cache_file=_snake_case		)

							# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
							# transformers library
							SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     tokenized_datasets.rename_column("""label"""     ,"""labels"""		)

							def collate_fn(_snake_case		):
														# On TPU it's best to pad everything to the same length or training will be very slow.
														if accelerator.distributed_type == DistributedType.TPU:
																					return tokenizer.pad(_snake_case     ,padding="""max_length"""     ,max_length=128     ,return_tensors="""pt"""		)
														return tokenizer.pad(_snake_case     ,padding="""longest"""     ,return_tensors="""pt"""		)

							# Instantiate dataloaders.
							SCREAMING_SNAKE_CASE__		: Optional[Any]     =     DataLoader(
							    tokenized_datasets["""train"""]     ,shuffle=_snake_case     ,collate_fn=_snake_case     ,batch_size=_snake_case		)
							SCREAMING_SNAKE_CASE__		: Any     =     DataLoader(
							    tokenized_datasets["""validation"""]     ,shuffle=_snake_case     ,collate_fn=_snake_case     ,batch_size=_snake_case		)

							return train_dataloader, eval_dataloader

def lowercase_						(     _snake_case     ,_snake_case		):
							# Initialize accelerator
							SCREAMING_SNAKE_CASE__		: List[str]     =     Accelerator()

							# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
							SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     config["""lr"""]
							SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     int(config["""num_epochs"""]		)
							SCREAMING_SNAKE_CASE__		: Tuple     =     int(config["""seed"""]		)
							SCREAMING_SNAKE_CASE__		: int     =     int(config["""batch_size"""]		)
							SCREAMING_SNAKE_CASE__		: List[Any]     =     args.model_name_or_path

							set_seed(_snake_case		)
							SCREAMING_SNAKE_CASE__   ,   SCREAMING_SNAKE_CASE__		: List[Any]     =     get_dataloaders(_snake_case     ,_snake_case     ,_snake_case		)

							# Instantiate the model (we build the model here so that the seed also control new weights initialization)
							SCREAMING_SNAKE_CASE__		: Optional[Any]     =     AutoModelForSequenceClassification.from_pretrained(_snake_case     ,return_dict=_snake_case		)

							# Instantiate optimizer
							SCREAMING_SNAKE_CASE__		: Optional[Any]     =     (
							    AdamW
							    if accelerator.state.deepspeed_plugin is None
							    or """optimizer""" not in accelerator.state.deepspeed_plugin.deepspeed_config
							    else DummyOptim
							)
							SCREAMING_SNAKE_CASE__		: Optional[int]     =     optimizer_cls(params=model.parameters()     ,lr=_snake_case		)

							if accelerator.state.deepspeed_plugin is not None:
														SCREAMING_SNAKE_CASE__		: Tuple     =     accelerator.state.deepspeed_plugin.deepspeed_config[
														    """gradient_accumulation_steps"""
														]
							else:
														SCREAMING_SNAKE_CASE__		: Dict     =     1
							SCREAMING_SNAKE_CASE__		: List[str]     =     (len(_snake_case		) * num_epochs) // gradient_accumulation_steps

							# Instantiate scheduler
							if (
							    accelerator.state.deepspeed_plugin is None
							    or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
							):
														SCREAMING_SNAKE_CASE__		: Any     =     get_linear_schedule_with_warmup(
														    optimizer=_snake_case     ,num_warmup_steps=0     ,num_training_steps=_snake_case     ,)
							else:
														SCREAMING_SNAKE_CASE__		: Tuple     =     DummyScheduler(_snake_case     ,total_num_steps=_snake_case     ,warmup_num_steps=0		)

							# Prepare everything
							# 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.
							SCREAMING_SNAKE_CASE__   ,   SCREAMING_SNAKE_CASE__   ,   SCREAMING_SNAKE_CASE__   ,   SCREAMING_SNAKE_CASE__   ,   SCREAMING_SNAKE_CASE__		: Dict     =     accelerator.prepare(
							    _snake_case     ,_snake_case     ,_snake_case     ,_snake_case     ,_snake_case		)

							# We need to keep track of how many total steps we have iterated over
							SCREAMING_SNAKE_CASE__		: str     =     0
							# We also need to keep track of the stating epoch so files are named properly
							SCREAMING_SNAKE_CASE__		: Tuple     =     0

							# Now we train the model
							SCREAMING_SNAKE_CASE__		: Optional[int]     =     evaluate.load("""glue"""     ,"""mrpc"""		)
							SCREAMING_SNAKE_CASE__		: Any     =     0
							SCREAMING_SNAKE_CASE__		: str     =     {}
							for epoch in range(_snake_case     ,_snake_case		):
														model.train()
														for step, batch in enumerate(_snake_case		):
																					SCREAMING_SNAKE_CASE__		: Tuple     =     model(**_snake_case		)
																					SCREAMING_SNAKE_CASE__		: List[Any]     =     outputs.loss
																					SCREAMING_SNAKE_CASE__		: int     =     loss / gradient_accumulation_steps
																					accelerator.backward(_snake_case		)
																					if step % gradient_accumulation_steps == 0:
																												optimizer.step()
																												lr_scheduler.step()
																												optimizer.zero_grad()

																					overall_step += 1

														model.eval()
														SCREAMING_SNAKE_CASE__		: Optional[Any]     =     0
														for step, batch in enumerate(_snake_case		):
																					# We could avoid this line since we set the accelerator with `device_placement=True`.
																					batch.to(accelerator.device		)
																					with torch.no_grad():
																												SCREAMING_SNAKE_CASE__		: int     =     model(**_snake_case		)
																					SCREAMING_SNAKE_CASE__		: Dict     =     outputs.logits.argmax(dim=-1		)
																					# It is slightly faster to call this once, than multiple times
																					SCREAMING_SNAKE_CASE__   ,   SCREAMING_SNAKE_CASE__		: Optional[Any]     =     accelerator.gather(
																					    (predictions, batch["""labels"""])		)  # If we are in a multiprocess environment, the last batch has duplicates
																					if accelerator.use_distributed:
																												if step == len(_snake_case		) - 1:
																																			SCREAMING_SNAKE_CASE__		: Optional[int]     =     predictions[: len(eval_dataloader.dataset		) - samples_seen]
																																			SCREAMING_SNAKE_CASE__		: str     =     references[: len(eval_dataloader.dataset		) - samples_seen]
																												else:
																																			samples_seen += references.shape[0]
																					metric.add_batch(
																					    predictions=_snake_case     ,references=_snake_case     ,)

														SCREAMING_SNAKE_CASE__		: Any     =     metric.compute()
														# Use accelerator.print to print only on the main process.
														accelerator.print(f'''epoch {epoch}:'''     ,_snake_case		)
														SCREAMING_SNAKE_CASE__		: Optional[Any]     =     eval_metric["""accuracy"""]

														if best_performance < eval_metric["accuracy"]:
																					SCREAMING_SNAKE_CASE__		: str     =     eval_metric["""accuracy"""]

							if args.performance_lower_bound is not None:
														assert (
														    args.performance_lower_bound <= best_performance
														), f'''Best performance metric {best_performance} is lower than the lower bound {args.performance_lower_bound}'''

							accelerator.wait_for_everyone()
							if accelerator.is_main_process:
														with open(os.path.join(args.output_dir     ,"""all_results.json"""		)     ,"""w"""		) as f:
																					json.dump(_snake_case     ,_snake_case		)

def lowercase_						(     ):
							SCREAMING_SNAKE_CASE__		: Dict     =     argparse.ArgumentParser(description="""Simple example of training script tracking peak GPU memory usage."""		)
							parser.add_argument(
							    """--model_name_or_path"""     ,type=_snake_case     ,default="""bert-base-cased"""     ,help="""Path to pretrained model or model identifier from huggingface.co/models."""     ,required=_snake_case     ,)
							parser.add_argument(
							    """--output_dir"""     ,type=_snake_case     ,default="""."""     ,help="""Optional save directory where all checkpoint folders will be stored. Default is the current working directory."""     ,)
							parser.add_argument(
							    """--performance_lower_bound"""     ,type=_snake_case     ,default=_snake_case     ,help="""Optional lower bound for the performance metric. If set, the training will throw error when the performance metric drops below this value."""     ,)
							parser.add_argument(
							    """--num_epochs"""     ,type=_snake_case     ,default=3     ,help="""Number of train epochs."""     ,)
							SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     parser.parse_args()
							SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     {"""lr""": 2E-5, """num_epochs""": args.num_epochs, """seed""": 42, """batch_size""": 16}
							training_function(_snake_case     ,_snake_case		)


if __name__ == "__main__":
					main()






"""simple docstring"""




from math import pi, sqrt, tan

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

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

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

def lowercase_						(     _snake_case     ,_snake_case		):
							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 lowercase_						(     _snake_case     ,_snake_case     ,_snake_case		):
							if radius_a < 0 or radius_a < 0 or height < 0:
														raise ValueError(
														    """surface_area_conical_frustum() only accepts non-negative values"""		)
							SCREAMING_SNAKE_CASE__		: int     =     (height**2 + (radius_a - radius_a) ** 2) ** 0.5
							return pi * ((slant_height * (radius_a + radius_a)) + radius_a**2 + radius_a**2)

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

def lowercase_						(     _snake_case     ,_snake_case		):
							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(_snake_case     ,2		) * torus_radius * tube_radius

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

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

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

def lowercase_						(     _snake_case     ,_snake_case     ,_snake_case		):
							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"""		)
							SCREAMING_SNAKE_CASE__		: List[str]     =     (sidea + sidea + sidea) / 2
							SCREAMING_SNAKE_CASE__		: List[Any]     =     sqrt(
							    semi_perimeter
							    * (semi_perimeter - sidea)
							    * (semi_perimeter - sidea)
							    * (semi_perimeter - sidea)		)
							return area

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

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

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

def lowercase_						(     _snake_case     ,_snake_case		):
							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 lowercase_						(     _snake_case     ,_snake_case		):
							if not isinstance(_snake_case     ,_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) = }""")




