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
#
########################################################################


_SCREAMING_SNAKE_CASE:    int     						=							16
_SCREAMING_SNAKE_CASE:    List[Any]     						=							32






def  UpperCAmelCase__(    lowerCamelCase_				,						lowerCamelCase_	):



			"""simple docstring"""





			if args.with_tracking:
						_snake_case														=   Accelerator(
						    cpu=args.cpu				,						mixed_precision=args.mixed_precision				,						log_with="all"				,						project_dir=args.project_dir	)
			else:
						_snake_case														=   Accelerator(cpu=args.cpu				,						mixed_precision=args.mixed_precision	)

			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														=   os.path.split(lowerCamelCase_	)[-1].split("."	)[0]
						accelerator.init_trackers(lowerCamelCase_				,						lowerCamelCase_	)

			_snake_case														=   AutoTokenizer.from_pretrained("bert-base-cased"	)
			_snake_case														=   load_dataset("glue"				,						"mrpc"	)
			_snake_case														=   evaluate.load("glue"				,						"mrpc"	)

			def tokenize_function(lowerCamelCase_	):
						# max_length=None => use the model max length (it's actually the default)
						_snake_case														=   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():
						_snake_case														=   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
			_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(lowerCamelCase_	):
						# On TPU it's best to pad everything to the same length or training will be very slow.
						_snake_case														=   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":
									_snake_case														=   16
						elif accelerator.mixed_precision != "no":
									_snake_case														=   8
						else:
									_snake_case														=   None

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

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

			set_seed(lowerCamelCase_	)

			# Instantiate the model (we build the model here so that the seed also control new weights initialization)
			_snake_case														=   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).
			_snake_case														=   model.to(accelerator.device	)

			# Instantiate optimizer
			_snake_case														=   AdamW(params=model.parameters()				,						lr=lowerCamelCase_	)

			# Instantiate scheduler
			_snake_case														=   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.
			_snake_case							,			_snake_case							,			_snake_case							,			_snake_case							,			_snake_case														=   accelerator.prepare(
			    lowerCamelCase_				,						lowerCamelCase_				,						lowerCamelCase_				,						lowerCamelCase_				,						lowerCamelCase_	)

			# We need to keep track of how many total steps we have iterated over
			_snake_case														=   0
			# We also need to keep track of the stating epoch so files are named properly
			_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(lowerCamelCase_	)[0]

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

    # Now we train the model
			for epoch in range(lowerCamelCase_				,						lowerCamelCase_	):
						model.train()
						if args.with_tracking:
									_snake_case														=   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
									_snake_case														=   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
									_snake_case														=   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	)
									_snake_case														=   model(**lowerCamelCase_	)
									_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(lowerCamelCase_	)
									if step % gradient_accumulation_steps == 0:
												optimizer.step()
												lr_scheduler.step()
												optimizer.zero_grad()

									overall_step += 1

									if isinstance(lowerCamelCase_				,						lowerCamelCase_	):
												_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				,						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`.
									batch.to(accelerator.device	)
									with torch.no_grad():
												_snake_case														=   model(**lowerCamelCase_	)
									_snake_case														=   outputs.logits.argmax(dim=-1	)
									_snake_case							,			_snake_case														=   accelerator.gather_for_metrics((predictions, batch["labels"])	)
									metric.add_batch(
									    predictions=lowerCamelCase_				,						references=lowerCamelCase_				,						)

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

						if checkpointing_steps == "epoch":
									_snake_case														=   f'epoch_{epoch}'
									if args.output_dir is not None:
												_snake_case														=   os.path.join(args.output_dir				,						lowerCamelCase_	)
									accelerator.save_state(lowerCamelCase_	)

			if args.with_tracking:
						accelerator.end_training()






def  UpperCAmelCase__(    ):



			"""simple docstring"""





			_snake_case														=   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(
			    "--resume_from_checkpoint"				,						type=lowerCamelCase_				,						default=lowerCamelCase_				,						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=lowerCamelCase_				,						default="."				,						help="Optional save directory where all checkpoint folders will be stored. Default is the current working directory."				,						)
			parser.add_argument(
			    "--project_dir"				,						type=lowerCamelCase_				,						default="logs"				,						help="Location on where to store experiment tracking logs` and relevent project information"				,						)
			_snake_case														=   parser.parse_args()
			_snake_case														=   {"lr": 2E-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
			training_function(lowerCamelCase_				,						lowerCamelCase_	)


if __name__ == "__main__":
			main()


from __future__ import annotations

# Define the Gravitational Constant G and the function
_SCREAMING_SNAKE_CASE:    int     						=							6.6_743e-11  # unit of G : m^3 * kg^-1 * s^-2






def  UpperCAmelCase__(    lowerCamelCase_				,						lowerCamelCase_				,						lowerCamelCase_				,						lowerCamelCase_	):



			"""simple docstring"""





			_snake_case														=   mass_a * mass_a

			if (force, mass_a, mass_a, distance).count(0	) != 1:
						raise ValueError("One and only one argument must be 0"	)
			if force < 0:
						raise ValueError("Gravitational force can not be negative"	)
			if distance < 0:
						raise ValueError("Distance can not be negative"	)
			if mass_a < 0 or mass_a < 0:
						raise ValueError("Mass can not be negative"	)
			if force == 0:
						_snake_case														=   GRAVITATIONAL_CONSTANT * product_of_mass / (distance**2)
						return {"force": force}
			elif mass_a == 0:
						_snake_case														=   (force) * (distance**2) / (GRAVITATIONAL_CONSTANT * mass_a)
						return {"mass_1": mass_a}
			elif mass_a == 0:
						_snake_case														=   (force) * (distance**2) / (GRAVITATIONAL_CONSTANT * mass_a)
						return {"mass_2": mass_a}
			elif distance == 0:
						_snake_case														=   (GRAVITATIONAL_CONSTANT * product_of_mass / (force)) ** 0.5
						return {"distance": distance}
			raise ValueError("One and only one argument must be 0"	)


# Run doctest
if __name__ == "__main__":
			import doctest

			doctest.testmod()

