'''simple docstring'''

from __future__ import annotations

import random

from matplotlib import animation
from matplotlib import pyplot as plt

# Frame rate of the animation
A_	   = 20

# Time between time steps in seconds
A_	   = INTERVAL / 1000




class 		SCREAMING_SNAKE_CASE_					:



			"""simple docstring"""






			def __init__(							self				,			snake_case_				,			snake_case_				,			snake_case_				,			snake_case_				,			snake_case_ = 1.0				,			snake_case_ = 1.0				,			snake_case_ = "blue"				,			):
									snake_case :       Union[str, Any]				=    position_x
									snake_case :       Optional[int]				=    position_y
									snake_case :       List[str]				=    velocity_x
									snake_case :       List[Any]				=    velocity_y
									snake_case :       Dict				=    mass
									snake_case :       Optional[Any]				=    size
									snake_case :       Any				=    color



			@property
			def   lowerCamelCase (							self					):
									return self.position_x, self.position_y



			@property
			def   lowerCamelCase (							self					):
									return self.velocity_x, self.velocity_y



			def   lowerCamelCase (							self				,			snake_case_				,			snake_case_				,			snake_case_					):
									self.velocity_x += force_x * delta_time
									self.velocity_y += force_y * delta_time






			def   lowerCamelCase (							self				,			snake_case_					):
									self.position_x += self.velocity_x * delta_time
									self.position_y += self.velocity_y * delta_time







class 		SCREAMING_SNAKE_CASE_					:



			"""simple docstring"""






			def __init__(							self				,			snake_case_				,			snake_case_ = 1.0				,			snake_case_ = 1.0				,			snake_case_ = 0.0				,			):
									snake_case :       Union[str, Any]				=    bodies
									snake_case :       Union[str, Any]				=    gravitation_constant
									snake_case :       str				=    time_factor
									snake_case :       str				=    softening_factor



			def __len__(							self					):
									return len(self.bodies					)






			def   lowerCamelCase (							self				,			snake_case_					):

									for bodya in self.bodies:
															snake_case :       Tuple				=    0.0
															snake_case :       int				=    0.0
															for bodya in self.bodies:
																					if bodya != bodya:
																											snake_case :       Any				=    bodya.position_x - bodya.position_x
																											snake_case :       Optional[int]				=    bodya.position_y - bodya.position_y

																											# Calculation of the distance using Pythagoras's theorem
																											# Extra factor due to the softening technique
																											snake_case :       List[str]				=    (dif_x**2 + dif_y**2 + self.softening_factor) ** (
																											    1 / 2
																											)

																											# Newton's law of universal gravitation.
																											force_x += (
																											    self.gravitation_constant * bodya.mass * dif_x / distance**3
																											)
																											force_y += (
																											    self.gravitation_constant * bodya.mass * dif_y / distance**3
																											)

            # Update the body's velocity once all the force components have been added
															bodya.update_velocity(_lowerCamelCase				,			_lowerCamelCase				,			delta_time * self.time_factor					)

									# Update the positions only after all the velocities have been updated
									for body in self.bodies:
															body.update_position(delta_time * self.time_factor					)







def        UpperCAmelCase__   (				_lowerCAmelCase    :			List[Any],      _lowerCAmelCase    :			Optional[Any],      _lowerCAmelCase    :			Union[str, Any])    ->		List[Any]:



						"""simple docstring"""

						body_system.update_system(_UpperCAmelCase)

						# Update the positions of the patches
						for patch, body in zip(_UpperCAmelCase,      body_system.bodies):
												snake_case :       str				=    (body.position_x, body.position_y)


def        UpperCAmelCase__   (				_lowerCAmelCase    :			Union[str, Any],      _lowerCAmelCase    :			str,      _lowerCAmelCase    :			Optional[int] = -1,      _lowerCAmelCase    :			Optional[Any] = 1,      _lowerCAmelCase    :			Union[str, Any] = -1,      _lowerCAmelCase    :			Optional[int] = 1,      )    ->		Tuple:



						"""simple docstring"""

						snake_case :       Optional[Any]				=    plt.figure()
						fig.canvas.set_window_title(_UpperCAmelCase)
						snake_case :       Union[str, Any]				=    plt.axes(
						    xlim=(x_start, x_end),      ylim=(y_start, y_end))  # Set section to be plotted
						plt.gca().set_aspect("equal")  # Fix aspect ratio

						# Each body is drawn as a patch by the plt-function
						snake_case :       List[str]				=    [
						    plt.Circle((body.position_x, body.position_y),      body.size,      fc=body.color)
						    for body in body_system.bodies
						]

						for patch in patches:
												ax.add_patch(_UpperCAmelCase)

						# Function called at each step of the animation
						def update(_lowerCAmelCase    :			Optional[Any]) -> list[plt.Circle]:
												update_step(_UpperCAmelCase,      _UpperCAmelCase,      _UpperCAmelCase)
												return patches

						snake_case :       str				=    animation.FuncAnimation(  # noqa: F841
						    _UpperCAmelCase,      _UpperCAmelCase,      interval=_UpperCAmelCase,      blit=_UpperCAmelCase)

						plt.show()


def        UpperCAmelCase__   (				)    ->		Optional[int]:



						"""simple docstring"""

						snake_case :       Optional[int]				=    0.970_0436
						snake_case :       Any				=    -0.2430_8753
						snake_case :       Tuple				=    0.4_6620_3685
						snake_case :       Any				=    0.4323_6573

						snake_case :       Dict				=    [
						    Body(_UpperCAmelCase,      _UpperCAmelCase,      _UpperCAmelCase,      _UpperCAmelCase,      size=0.2,      color="red"),
						    Body(-position_x,      -position_y,      _UpperCAmelCase,      _UpperCAmelCase,      size=0.2,      color="green"),
						    Body(0,      0,      -2 * velocity_x,      -2 * velocity_y,      size=0.2,      color="blue"),
						]
						return BodySystem(_UpperCAmelCase,      time_factor=3)


def        UpperCAmelCase__   (				)    ->		int:



						"""simple docstring"""

						snake_case :       int				=    7.3_476E22
						snake_case :       Optional[Any]				=    5.972E24
						snake_case :       List[Any]				=    1022
						snake_case :       Optional[int]				=    3_8439_9000
						snake_case :       Tuple				=    6.674E-11

						# Calculation of the respective velocities so that total impulse is zero,
						# i.e. the two bodies together don't move
						snake_case :       Any				=    earth_mass * velocity_dif / (earth_mass + moon_mass)
						snake_case :       List[Any]				=    moon_velocity - velocity_dif

						snake_case :       Optional[Any]				=    Body(-earth_moon_distance,      0,      0,      _UpperCAmelCase,      _UpperCAmelCase,      1000_0000,      "grey")
						snake_case :       List[Any]				=    Body(0,      0,      0,      _UpperCAmelCase,      _UpperCAmelCase,      5000_0000,      "blue")
						return BodySystem([earth, moon],      _UpperCAmelCase,      time_factor=100_0000)


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



						"""simple docstring"""

						snake_case :       Union[str, Any]				=    []
						for _ in range(10):
												snake_case :       str				=    random.uniform(-0.5,      0.5)
												snake_case :       Optional[Any]				=    random.uniform(-0.5,      0.5)

												# Bodies are created pairwise with opposite velocities so that the
												# total impulse remains zero
												bodies.append(
												    Body(
												        random.uniform(-0.5,      0.5),      random.uniform(-0.5,      0.5),      _UpperCAmelCase,      _UpperCAmelCase,      size=0.05,      ))
												bodies.append(
												    Body(
												        random.uniform(-0.5,      0.5),      random.uniform(-0.5,      0.5),      -velocity_x,      -velocity_y,      size=0.05,      ))
						return BodySystem(_UpperCAmelCase,      0.01,      10,      0.1)


if __name__ == "__main__":
						plot("Figure-8 solution to the 3-body-problem", example_a(), -2, 2, -2, 2)
						plot(
						    "Moon\'s orbit around the earth",
						    example_a(),
						    -4_3000_0000,
						    4_3000_0000,
						    -4_3000_0000,
						    4_3000_0000,
						)
						plot("Random system with many bodies", example_a(), -1.5, 1.5, -1.5, 1.5)











"""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
#
# 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)
#
# 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
#
########################################################################


_lowerCamelCase	:      Dict   =							16
_lowerCamelCase	:      Union[str, Any]   =							32

def 		lowercase_   (		_UpperCAmelCase	,   _UpperCAmelCase						):




	"""simple docstring"""






	if args.with_tracking:
		A_	:					Optional[int]						 =		Accelerator(
		    cpu=args.cpu	,   mixed_precision=args.mixed_precision	,   log_with='''all'''	,   project_dir=args.project_dir						)
	else:
		A_	:					str						 =		Accelerator(cpu=args.cpu	,   mixed_precision=args.mixed_precision						)

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

	# We need to initialize the trackers we use, and also store our configuration
	if args.with_tracking:
		A_	:					Dict						 =		os.path.split(_UpperCAmelCase						)[-1].split('''.'''						)[0]
		accelerator.init_trackers(_UpperCAmelCase	,   _UpperCAmelCase						)

	A_	:					Optional[Any]						 =		AutoTokenizer.from_pretrained('''bert-base-cased'''						)
	A_	:					Any						 =		load_dataset('''glue'''	,   '''mrpc'''						)
	A_	:					Optional[int]						 =		evaluate.load('''glue'''	,   '''mrpc'''						)

	def tokenize_function(_UpperCAmelCase						):
		# max_length=None => use the model max length (it's actually the default)
		A_	:					Any						 =		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():
		A_	:					List[Any]						 =		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
	A_	:					Any						 =		tokenized_datasets.rename_column('''label'''	,   '''labels'''						)

	# If the batch size is too big we use gradient accumulation
	A_	:					Optional[int]						 =		1
	if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
		A_	:					Union[str, Any]						 =		batch_size // MAX_GPU_BATCH_SIZE
		A_	:					List[str]						 =		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.
		A_	:					Union[str, Any]						 =		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_	:					Tuple						 =		16
		elif accelerator.mixed_precision != "no":
			A_	:					Dict						 =		8
		else:
			A_	:					Tuple						 =		None

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

	# Instantiate dataloaders.
	A_	:					List[Any]						 =		DataLoader(
	    tokenized_datasets['''train''']	,   shuffle=_UpperCAmelCase	,   collate_fn=_UpperCAmelCase	,   batch_size=_UpperCAmelCase						)
	A_	:					Optional[int]						 =		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)
	A_	:					Optional[int]						 =		AutoModelForSequenceClassification.from_pretrained('''bert-base-cased'''	,   return_dict=_UpperCAmelCase						)

	# 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_	:					str						 =		model.to(accelerator.device						)

	# Instantiate optimizer
	A_	:					str						 =		AdamW(params=model.parameters()	,   lr=_UpperCAmelCase						)

	# Instantiate scheduler
	A_	:					List[str]						 =		get_linear_schedule_with_warmup(
	    optimizer=_UpperCAmelCase	,   num_warmup_steps=100	,   num_training_steps=(len(_UpperCAmelCase						) * 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_	:					Optional[int]						 =		accelerator.prepare(
	    _UpperCAmelCase	,   _UpperCAmelCase	,   _UpperCAmelCase	,   _UpperCAmelCase	,   _UpperCAmelCase						)

	# We need to keep track of how many total steps we have iterated over
	A_	:					Tuple						 =		0
	# We also need to keep track of the stating epoch so files are named properly
	A_	:					Union[str, Any]						 =		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						)
			A_	:					Union[str, Any]						 =		os.path.basename(args.resume_from_checkpoint						)
		else:
			# Get the most recent checkpoint
			A_	:					Union[str, Any]						 =		[f.name for f in os.scandir(os.getcwd()						) if f.is_dir()]
			dirs.sort(key=os.path.getctime						)
			A_	:					Optional[int]						 =		dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
		# Extract `epoch_{i}` or `step_{i}`
		A_	:					Any						 =		os.path.splitext(_UpperCAmelCase						)[0]

		if "epoch" in training_difference:
			A_	:					Any						 =		int(training_difference.replace('''epoch_'''	,   ''''''						)						) + 1
			A_	:					str						 =		None
		else:
			A_	:					str						 =		int(training_difference.replace('''step_'''	,   ''''''						)						)
			A_	:					List[str]						 =		resume_step // len(_UpperCAmelCase						)
			resume_step -= starting_epoch * len(_UpperCAmelCase						)

    # Now we train the model
	for epoch in range(_UpperCAmelCase	,   _UpperCAmelCase						):
		model.train()
		if args.with_tracking:
			A_	:					Optional[Any]						 =		0
		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_	:					int						 =		accelerator.skip_first_batches(_UpperCAmelCase	,   _UpperCAmelCase						)
			overall_step += resume_step
		else:
			# After the first iteration though, we need to go back to the original dataloader
			A_	:					Dict						 =		train_dataloader
		for step, batch in enumerate(_UpperCAmelCase						):
			# We could avoid this line since we set the accelerator with `device_placement=True`.
			batch.to(accelerator.device						)
			A_	:					Any						 =		model(**_UpperCAmelCase						)
			A_	:					Any						 =		outputs.loss
			A_	:					str						 =		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()

			overall_step += 1

			if isinstance(_UpperCAmelCase	,   _UpperCAmelCase						):
				A_	:					List[Any]						 =		f"""step_{overall_step}"""
				if overall_step % checkpointing_steps == 0:
					if args.output_dir is not None:
						A_	:					Tuple						 =		os.path.join(args.output_dir	,   _UpperCAmelCase						)
					accelerator.save_state(_UpperCAmelCase						)

		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():
				A_	:					Dict						 =		model(**_UpperCAmelCase						)
			A_	:					List[str]						 =		outputs.logits.argmax(dim=-1						)
			A_ ,			A_	:					Optional[int]						 =		accelerator.gather_for_metrics((predictions, batch['''labels'''])						)
			metric.add_batch(
			    predictions=_UpperCAmelCase	,   references=_UpperCAmelCase	,   )

		A_	:					Union[str, Any]						 =		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						),
			        '''epoch''': epoch,
			    }	,   step=_UpperCAmelCase	,   )

		if checkpointing_steps == "epoch":
			A_	:					str						 =		f"""epoch_{epoch}"""
			if args.output_dir is not None:
				A_	:					int						 =		os.path.join(args.output_dir	,   _UpperCAmelCase						)
			accelerator.save_state(_UpperCAmelCase						)

	if args.with_tracking:
		accelerator.end_training()

def 		lowercase_   (		):




	"""simple docstring"""






	A_	:					List[Any]						 =		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(
	    '''--project_dir'''	,   type=_UpperCAmelCase	,   default='''logs'''	,   help='''Location on where to store experiment tracking logs` and relevent project information'''	,   )
	A_	:					Any						 =		parser.parse_args()
	A_	:					List[Any]						 =		{'''lr''': 2E-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
	training_function(_UpperCAmelCase	,   _UpperCAmelCase						)


if __name__ == "__main__":
		main()





