"""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 how to properly calculate the metrics on the
# validation dataset when in a distributed system, 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
#
########################################################################


lowerCAmelCase_     =	16
lowerCAmelCase_     =	32



def      lowerCamelCase_(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE = 16  )->    List[str]:
							_SCREAMING_SNAKE_CASE			  =    AutoTokenizer.from_pretrained("""bert-base-cased"""  )
							_SCREAMING_SNAKE_CASE			  =    load_dataset("""glue"""     ,	"""mrpc"""  )

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

							def collate_fn(__SCREAMING_SNAKE_CASE  ):
														# On TPU it's best to pad everything to the same length or training will be very slow.
														_SCREAMING_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":
																					_SCREAMING_SNAKE_CASE			  =    16
														elif accelerator.mixed_precision != "no":
																					_SCREAMING_SNAKE_CASE			  =    8
														else:
																					_SCREAMING_SNAKE_CASE			  =    None

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

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

							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

			lowerCAmelCase_     =	mocked_dataloaders  # noqa: F811



def      lowerCamelCase_(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  )->    Dict:






							if os.environ.get("""TESTING_MOCKED_DATALOADERS"""     ,	a__  ) == "1":
														_SCREAMING_SNAKE_CASE			  =    2
							# Initialize accelerator
							_SCREAMING_SNAKE_CASE			  =    Accelerator(cpu=args.cpu     ,	mixed_precision=args.mixed_precision  )
							# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
							_SCREAMING_SNAKE_CASE			  =    config["""lr"""]
							_SCREAMING_SNAKE_CASE			  =    int(config["""num_epochs"""]  )
							_SCREAMING_SNAKE_CASE			  =    int(config["""seed"""]  )
							_SCREAMING_SNAKE_CASE			  =    int(config["""batch_size"""]  )

							_SCREAMING_SNAKE_CASE			  =    evaluate.load("""glue"""     ,	"""mrpc"""  )

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

							set_seed(a__  )
							_SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE			  =    get_dataloaders(a__     ,	a__  )
							# Instantiate the model (we build the model here so that the seed also control new weights initialization)
							_SCREAMING_SNAKE_CASE			  =    AutoModelForSequenceClassification.from_pretrained("""bert-base-cased"""     ,	return_dict=a__  )

							# 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).
							_SCREAMING_SNAKE_CASE			  =    model.to(accelerator.device  )

							# Instantiate optimizer
							_SCREAMING_SNAKE_CASE			  =    AdamW(params=model.parameters()     ,	lr=a__  )

							# Instantiate scheduler
							_SCREAMING_SNAKE_CASE			  =    get_linear_schedule_with_warmup(
							    optimizer=a__     ,	num_warmup_steps=100     ,	num_training_steps=(len(a__  ) * 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.
							_SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE			  =    accelerator.prepare(
							    a__     ,	a__     ,	a__     ,	a__     ,	a__  )

							# Now we train the model
							for epoch in range(a__  ):
														model.train()
														for step, batch in enumerate(a__  ):
																					# We could avoid this line since we set the accelerator with `device_placement=True`.
																					batch.to(accelerator.device  )
																					_SCREAMING_SNAKE_CASE			  =    model(**a__  )
																					_SCREAMING_SNAKE_CASE			  =    outputs.loss
																					_SCREAMING_SNAKE_CASE			  =    loss / gradient_accumulation_steps
																					accelerator.backward(a__  )
																					if step % gradient_accumulation_steps == 0:
																												optimizer.step()
																												lr_scheduler.step()
																												optimizer.zero_grad()

														model.eval()
														_SCREAMING_SNAKE_CASE			  =    0
														for step, batch in enumerate(a__  ):
																					# 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			  =    model(**a__  )
																					_SCREAMING_SNAKE_CASE			  =    outputs.logits.argmax(dim=-1  )
																					_SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE			  =    accelerator.gather((predictions, batch["""labels"""])  )
																					# New Code #
																					# First we check if it's a distributed system
																					if accelerator.use_distributed:
																												# Then see if we're on the last batch of our eval dataloader
																												if step == len(a__  ) - 1:
																																			# Last batch needs to be truncated on distributed systems as it contains additional samples
																																			_SCREAMING_SNAKE_CASE			  =    predictions[: len(eval_dataloader.dataset  ) - samples_seen]
																																			_SCREAMING_SNAKE_CASE			  =    references[: len(eval_dataloader.dataset  ) - samples_seen]
																												else:
																																			# Otherwise we add the number of samples seen
																																			samples_seen += references.shape[0]
            # All of this can be avoided if you use `Accelerator.gather_for_metrics` instead of `Accelerator.gather`:
            # accelerator.gather_for_metrics((predictions, batch["labels"]))
																					metric.add_batch(
																					    predictions=a__     ,	references=a__     ,	)

														_SCREAMING_SNAKE_CASE			  =    metric.compute()
														# Use accelerator.print to print only on the main process.
														accelerator.print(F"""epoch {epoch}:"""     ,	a__  )



def      lowerCamelCase_()->    List[str]:
							_SCREAMING_SNAKE_CASE			  =    argparse.ArgumentParser(description="""Simple example of training script."""  )
							parser.add_argument(
							    """--mixed_precision"""     ,	type=a__     ,	default=a__     ,	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."""  )
							_SCREAMING_SNAKE_CASE			  =    parser.parse_args()
							_SCREAMING_SNAKE_CASE			  =    {"""lr""": 2e-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
							training_function(a__     ,	a__  )


if __name__ == "__main__":
			main()



"""simple docstring"""





from __future__ import annotations



def      lowerCamelCase_(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	)->    tuple:
							if (electron_conc, hole_conc, intrinsic_conc).count(0  ) != 1:
														raise ValueError("""You cannot supply more or less than 2 values"""  )
							elif electron_conc < 0:
														raise ValueError("""Electron concentration cannot be negative in a semiconductor"""  )
							elif hole_conc < 0:
														raise ValueError("""Hole concentration cannot be negative in a semiconductor"""  )
							elif intrinsic_conc < 0:
														raise ValueError(
														    """Intrinsic concentration cannot be negative in a semiconductor"""  )
							elif electron_conc == 0:
														return (
														    "electron_conc",
														    intrinsic_conc**2 / hole_conc,
														)
							elif hole_conc == 0:
														return (
														    "hole_conc",
														    intrinsic_conc**2 / electron_conc,
														)
							elif intrinsic_conc == 0:
														return (
														    "intrinsic_conc",
														    (electron_conc * hole_conc) ** 0.5,
														)
							else:
														return (-1, -1)


if __name__ == "__main__":
			import doctest

			doctest.testmod()


