





from math import radians as angle_to_radians
from math import sin

# Acceleration Constant on Earth (unit m/s^2)
lowercase     :			Tuple     						=	9.8_0665


def      SCREAMING_SNAKE_CASE      (   lowerCAmelCase     ,							lowerCAmelCase       ):

       # Ensure valid instance
       if not isinstance(lowerCamelCase__     ,							(int, float)       ):
              raise TypeError('''Invalid velocity. Should be a positive number.'''       )

       if not isinstance(lowerCamelCase__     ,							(int, float)       ):
              raise TypeError('''Invalid angle. Range is 1-90 degrees.'''       )

       # Ensure valid angle
       if angle > 90 or angle < 1:
              raise ValueError('''Invalid angle. Range is 1-90 degrees.'''       )

       # Ensure valid velocity
       if init_velocity < 0:
              raise ValueError('''Invalid velocity. Should be a positive number.'''       )


def      SCREAMING_SNAKE_CASE      (   lowerCAmelCase     ,							lowerCAmelCase       ):
       check_args(lowerCamelCase__     ,							lowerCamelCase__       )
       _UpperCamelCase														=    angle_to_radians(2 * angle       )
       return round(init_velocity**2 * sin(lowerCamelCase__       ) / g     ,							2       )


def      SCREAMING_SNAKE_CASE      (   lowerCAmelCase     ,							lowerCAmelCase       ):
       check_args(lowerCamelCase__     ,							lowerCamelCase__       )
       _UpperCamelCase														=    angle_to_radians(lowerCamelCase__       )
       return round(init_velocity**2 * sin(lowerCamelCase__       ) ** 2 / (2 * g)     ,							2       )


def      SCREAMING_SNAKE_CASE      (   lowerCAmelCase     ,							lowerCAmelCase       ):
       check_args(lowerCamelCase__     ,							lowerCamelCase__       )
       _UpperCamelCase														=    angle_to_radians(lowerCamelCase__       )
       return round(2 * init_velocity * sin(lowerCamelCase__       ) / g     ,							2       )


def      SCREAMING_SNAKE_CASE      (   ):
       _UpperCamelCase							,		_UpperCamelCase														=    25, 20
       assert horizontal_distance(lowerCamelCase__     ,							lowerCamelCase__       ) == 4_0.9_7
       assert max_height(lowerCamelCase__     ,							lowerCamelCase__       ) == 3.7_3
       assert total_time(lowerCamelCase__     ,							lowerCamelCase__       ) == 1.7_4


if __name__ == "__main__":
       from doctest import testmod

       testmod()

       # Get input from user
       lowercase     :			Optional[Any]     						=	float(input("""Initial Velocity: """).strip())

       # Get input from user
       lowercase     :			Tuple     						=	float(input("""angle: """).strip())

       # Print results
       print()
       print("""Results: """)
       print(F'''Horizontal Distance: {horizontal_distance(init_vel, angle)!s} [m]''')
       print(F'''Maximum Height: {max_height(init_vel, angle)!s} [m]''')
       print(F'''Total Time: {total_time(init_vel, angle)!s} [s]''')











"""simple docstring"""







import argparse
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


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing the checkpointing capability,
# and builds off the `nlp_example.py` script.
#
# 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)
#
# To help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# 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     =      1_6
A     =      3_2

def 	lowerCAmelCase__      (  lowerCamelCase__					,   lowerCamelCase__ = 16      )			->				List[Any]:
							A    =       AutoTokenizer.from_pretrained('bert-base-cased'      )
							A    =       load_dataset('glue'					,   'mrpc'      )

							def tokenize_function(lowerCamelCase__      ):
														# max_length=None => use the model max length (it's actually the default)
														A    =       tokenizer(examples['sentence1']					,   examples['sentence2']					,   truncation=lowerCamelCase__					,   max_length=lowerCamelCase__      )
														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():
														A    =       datasets.map(
														    lowerCamelCase__					,   batched=lowerCamelCase__					,   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
							A    =       tokenized_datasets.rename_column('label'					,   'labels'      )

							def collate_fn(lowerCamelCase__      ):
														# On TPU it's best to pad everything to the same length or training will be very slow.
														A    =       128 if accelerator.distributed_type == DistributedType.TPU else None
														# When using mixed precision we want round multiples of 8/16
														if accelerator.mixed_precision == "fp8":
																					A    =       16
														elif accelerator.mixed_precision != "no":
																					A    =       8
														else:
																					A    =       None

														return tokenizer.pad(
														    lowerCamelCase__					,   padding='longest'					,   max_length=lowerCamelCase__					,   pad_to_multiple_of=lowerCamelCase__					,   return_tensors='pt'					,   )

							# Instantiate dataloaders.
							A    =       DataLoader(
							    tokenized_datasets['train']					,   shuffle=lowerCamelCase__					,   collate_fn=lowerCamelCase__					,   batch_size=lowerCamelCase__      )
							A    =       DataLoader(
							    tokenized_datasets['validation']					,   shuffle=lowerCamelCase__					,   collate_fn=lowerCamelCase__					,   batch_size=lowerCamelCase__      )

							return train_dataloader, eval_dataloader


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

							A     =      mocked_dataloaders  # noqa: F811

def 	lowerCAmelCase__      (  lowerCamelCase__					,   lowerCamelCase__      )			->				List[str]:
							# For testing only
							if os.environ.get('TESTING_MOCKED_DATALOADERS'					,   lowerCamelCase__      ) == "1":
														A    =       2
							# Initialize accelerator
							A    =       Accelerator(cpu=args.cpu					,   mixed_precision=args.mixed_precision      )
							# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
							A    =       config['lr']
							A    =       int(config['num_epochs']      )
							A    =       int(config['seed']      )
							A    =       int(config['batch_size']      )

							# New Code #
							# Parse out whether we are saving every epoch or after a certain number of batches
							if hasattr(args.checkpointing_steps					,   'isdigit'      ):
														if args.checkpointing_steps == "epoch":
																					A    =       args.checkpointing_steps
														elif args.checkpointing_steps.isdigit():
																					A    =       int(args.checkpointing_steps      )
														else:
																					raise ValueError(
																					    f"""Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed."""      )
							else:
														A    =       None

							set_seed(lowerCamelCase__      )

							A   ,	A    =       get_dataloaders(lowerCamelCase__					,   lowerCamelCase__      )
							A    =       evaluate.load('glue'					,   'mrpc'      )

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

							# Instantiate the model (we build the model here so that the seed also control new weights initialization)
							A    =       AutoModelForSequenceClassification.from_pretrained('bert-base-cased'					,   return_dict=lowerCamelCase__      )

							# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
							# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
							# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
							A    =       model.to(accelerator.device      )

							# Instantiate optimizer
							A    =       AdamW(params=model.parameters()					,   lr=lowerCamelCase__      )

							# Instantiate scheduler
							A    =       get_linear_schedule_with_warmup(
							    optimizer=lowerCamelCase__					,   num_warmup_steps=100					,   num_training_steps=(len(lowerCamelCase__      ) * num_epochs) // gradient_accumulation_steps					,   )

							# 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.
							A   ,	A   ,	A   ,	A   ,	A    =       accelerator.prepare(
							    lowerCamelCase__					,   lowerCamelCase__					,   lowerCamelCase__					,   lowerCamelCase__					,   lowerCamelCase__      )

							# New Code #
							# We need to keep track of how many total steps we have iterated over
							A    =       0
							# We also need to keep track of the stating epoch so files are named properly
							A    =       0

							# We need to load the checkpoint back in before training here with `load_state`
							# The total number of epochs is adjusted based on where the state is being loaded from,
							# as we assume continuation of the same training script
							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      )
																					A    =       os.path.basename(args.resume_from_checkpoint      )
														else:
																					# Get the most recent checkpoint
																					A    =       [f.name for f in os.scandir(os.getcwd()      ) if f.is_dir()]
																					dirs.sort(key=os.path.getctime      )
																					A    =       dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
														# Extract `epoch_{i}` or `step_{i}`
														A    =       os.path.splitext(lowerCamelCase__      )[0]

														if "epoch" in training_difference:
																					A    =       int(training_difference.replace('epoch_'					,   ''      )      ) + 1
																					A    =       None
														else:
																					A    =       int(training_difference.replace('step_'					,   ''      )      )
																					A    =       resume_step // len(lowerCamelCase__      )
																					resume_step -= starting_epoch * len(lowerCamelCase__      )

    # Now we train the model
							for epoch in range(lowerCamelCase__					,   lowerCamelCase__      ):
														model.train()
														# New Code #
														if args.resume_from_checkpoint and epoch == starting_epoch and resume_step is not None:
																					# We need to skip steps until we reach the resumed step
																					A    =       accelerator.skip_first_batches(lowerCamelCase__					,   lowerCamelCase__      )
																					overall_step += resume_step
														else:
																					# After the first iteration though, we need to go back to the original dataloader
																					A    =       train_dataloader
														for step, batch in enumerate(lowerCamelCase__      ):
																					# We could avoid this line since we set the accelerator with `device_placement=True`.
																					batch.to(accelerator.device      )
																					A    =       model(**lowerCamelCase__      )
																					A    =       outputs.loss
																					A    =       loss / gradient_accumulation_steps
																					accelerator.backward(lowerCamelCase__      )
																					if step % gradient_accumulation_steps == 0:
																												optimizer.step()
																												lr_scheduler.step()
																												optimizer.zero_grad()
																					# New Code #
																					overall_step += 1

																					# New Code #
																					# We save the model, optimizer, lr_scheduler, and seed states by calling `save_state`
																					# These are saved to folders named `step_{overall_step}`
																					# Will contain files: "pytorch_model.bin", "optimizer.bin", "scheduler.bin", and "random_states.pkl"
																					# If mixed precision was used, will also save a "scalar.bin" file
																					if isinstance(lowerCamelCase__					,   lowerCamelCase__      ):
																												A    =       f"""step_{overall_step}"""
																												if overall_step % checkpointing_steps == 0:
																																			if args.output_dir is not None:
																																										A    =       os.path.join(args.output_dir					,   lowerCamelCase__      )
																																			accelerator.save_state(lowerCamelCase__      )

														model.eval()
														for step, batch in enumerate(lowerCamelCase__      ):
																					# We could avoid this line since we set the accelerator with `device_placement=True` (the default).
																					batch.to(accelerator.device      )
																					with torch.no_grad():
																												A    =       model(**lowerCamelCase__      )
																					A    =       outputs.logits.argmax(dim=-1      )
																					A   ,	A    =       accelerator.gather_for_metrics((predictions, batch['labels'])      )
																					metric.add_batch(
																					    predictions=lowerCamelCase__					,   references=lowerCamelCase__					,   )

														A    =       metric.compute()
														# Use accelerator.print to print only on the main process.
														accelerator.print(f"""epoch {epoch}:"""					,   lowerCamelCase__      )

														# New Code #
														# We save the model, optimizer, lr_scheduler, and seed states by calling `save_state`
														# These are saved to folders named `epoch_{epoch}`
														# Will contain files: "pytorch_model.bin", "optimizer.bin", "scheduler.bin", and "random_states.pkl"
														# If mixed precision was used, will also save a "scalar.bin" file
														if checkpointing_steps == "epoch":
																					A    =       f"""epoch_{epoch}"""
																					if args.output_dir is not None:
																												A    =       os.path.join(args.output_dir					,   lowerCamelCase__      )
																					accelerator.save_state(lowerCamelCase__      )

def 	lowerCAmelCase__      (  )			->				Optional[int]:
							A    =       argparse.ArgumentParser(description='Simple example of training script.'      )
							parser.add_argument(
							    '--mixed_precision'					,   type=lowerCamelCase__					,   default=lowerCamelCase__					,   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=lowerCamelCase__					,   default=lowerCamelCase__					,   help='Whether the various states should be saved at the end of every n steps, or \'epoch\' for each epoch.'					,   )
							parser.add_argument(
							    '--output_dir'					,   type=lowerCamelCase__					,   default='.'					,   help='Optional save directory where all checkpoint folders will be stored. Default is the current working directory.'					,   )
							parser.add_argument(
							    '--resume_from_checkpoint'					,   type=lowerCamelCase__					,   default=lowerCamelCase__					,   help='If the training should continue from a checkpoint folder.'					,   )
							A    =       parser.parse_args()
							A    =       {'lr': 2E-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
							training_function(lowerCamelCase__					,   lowerCamelCase__      )


if __name__ == "__main__":
							main()



