

'''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 experiment tracking 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
#
########################################################################

__snake_case			:     int										=      16
__snake_case			:     str										=      32
def 						__lowerCamelCase	(       __snake_case					:		Accelerator,		__snake_case					:		int = 16	)	->							int:






						"""simple docstring"""





						A__					:       List[str]     =AutoTokenizer.from_pretrained("""bert-base-cased"""	)
						A__					:       Dict     =load_dataset("""glue""",		"""mrpc"""	)

						def tokenize_function(__snake_case					:		str	):
												# max_length=None => use the model max length (it's actually the default)
												A__					:       Optional[Any]     =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
						# starting with the main process first:
						with accelerator.main_process_first():
												A__					:       List[str]     =datasets.map(
												    __snake_case,		batched=__snake_case,		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__					:       List[Any]     =tokenized_datasets.rename_column("""label""",		"""labels"""	)

						def collate_fn(__snake_case					:		str	):
												# On TPU it's best to pad everything to the same length or training will be very slow.
												A__					:       Optional[int]     =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__					:       Optional[int]     =16
												elif accelerator.mixed_precision != "no":
																		A__					:       str     =8
												else:
																		A__					:       Any     =None

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

						# Instantiate dataloaders.
						A__					:       List[Any]     =DataLoader(
						    tokenized_datasets["""train"""],		shuffle=__snake_case,		collate_fn=__snake_case,		batch_size=__snake_case	)
						A__					:       Union[str, Any]     =DataLoader(
						    tokenized_datasets["""validation"""],		shuffle=__snake_case,		collate_fn=__snake_case,		batch_size=__snake_case	)

						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

		__snake_case			:     Union[str, Any]										=      mocked_dataloaders  # noqa: F811
def 						__lowerCamelCase	(       __snake_case					:		Tuple,		__snake_case					:		Union[str, Any]	)	->							Union[str, Any]:






						"""simple docstring"""





						if os.environ.get("""TESTING_MOCKED_DATALOADERS""",		__snake_case	) == "1":
												A__					:       Any     =2
						# Initialize Accelerator

						# New Code #
						# We pass in "all" to `log_with` to grab all available trackers in the environment
						# Note: If using a custom `Tracker` class, should be passed in here such as:
						# >>> log_with = ["all", MyCustomTrackerClassInstance()]
						if args.with_tracking:
												A__					:       List[Any]     =Accelerator(
												    cpu=args.cpu,		mixed_precision=args.mixed_precision,		log_with="""all""",		project_dir=args.project_dir	)
						else:
												A__					:       List[str]     =Accelerator(cpu=args.cpu,		mixed_precision=args.mixed_precision	)
						# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
						A__					:       Tuple     =config["""lr"""]
						A__					:       int     =int(config["""num_epochs"""]	)
						A__					:       List[Any]     =int(config["""seed"""]	)
						A__					:       Optional[Any]     =int(config["""batch_size"""]	)
						set_seed(__snake_case	)

						A__   ,						A__					:       Dict     =get_dataloaders(__snake_case,		__snake_case	)
						A__					:       Any     =evaluate.load("""glue""",		"""mrpc"""	)

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

						# Instantiate the model (we build the model here so that the seed also control new weights initialization)
						A__					:       Optional[Any]     =AutoModelForSequenceClassification.from_pretrained("""bert-base-cased""",		return_dict=__snake_case	)

						# 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__					:       Optional[int]     =model.to(accelerator.device	)

						# Instantiate optimizer
						A__					:       Union[str, Any]     =AdamW(params=model.parameters(),		lr=__snake_case	)

						# Instantiate scheduler
						A__					:       Tuple     =get_linear_schedule_with_warmup(
						    optimizer=__snake_case,		num_warmup_steps=100,		num_training_steps=(len(__snake_case	) * 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[Any]     =accelerator.prepare(
						    __snake_case,		__snake_case,		__snake_case,		__snake_case,		__snake_case	)

						# New Code #
						# We need to initialize the trackers we use. Overall configurations can also be stored
						if args.with_tracking:
												A__					:       Any     =os.path.split(__snake_case	)[-1].split("""."""	)[0]
												accelerator.init_trackers(__snake_case,		__snake_case	)

						# Now we train the model
						for epoch in range(__snake_case	):
												model.train()
												# New Code #
												# For our tracking example, we will log the total loss of each epoch
												if args.with_tracking:
																		A__					:       int     =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	)
																		A__					:       Optional[Any]     =model(**__snake_case	)
																		A__					:       List[Any]     =outputs.loss
																		# New Code #
																		if args.with_tracking:
																								total_loss += loss.detach().float()
																		A__					:       Optional[int]     =loss / gradient_accumulation_steps
																		accelerator.backward(__snake_case	)
																		if step % gradient_accumulation_steps == 0:
																								optimizer.step()
																								lr_scheduler.step()
																								optimizer.zero_grad()

												model.eval()
												for step, batch in enumerate(__snake_case	):
																		# 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__					:       List[Any]     =model(**__snake_case	)
																		A__					:       List[Any]     =outputs.logits.argmax(dim=-1	)
																		A__   ,						A__					:       Any     =accelerator.gather_for_metrics((predictions, batch["""labels"""])	)
																		metric.add_batch(
																		    predictions=__snake_case,		references=__snake_case,		)

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

												# New Code #
												# To actually log, we call `Accelerator.log`
												# The values passed can be of `str`, `int`, `float` or `dict` of `str` to `float`/`int`
												if args.with_tracking:
																		accelerator.log(
																		    {
																		        """accuracy""": eval_metric["""accuracy"""],
																		        """f1""": eval_metric["""f1"""],
																		        """train_loss""": total_loss.item() / len(__snake_case	),
																		        """epoch""": epoch,
																		    },		step=__snake_case,		)

    # New Code #
    # When a run is finished, you should call `accelerator.end_training()`
    # to close all of the open trackers
						if args.with_tracking:
												accelerator.end_training()
def 						__lowerCamelCase	(       )	->							List[str]:






						"""simple docstring"""





						A__					:       List[str]     =argparse.ArgumentParser(description="""Simple example of training script."""	)
						parser.add_argument(
						    """--mixed_precision""",		type=__snake_case,		default=__snake_case,		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(
						    """--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(
						    """--project_dir""",		type=__snake_case,		default="""logs""",		help="""Location on where to store experiment tracking logs` and relevent project information""",		)
						A__					:       Tuple     =parser.parse_args()
						A__					:       Optional[Any]     ={"""lr""": 2E-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
						training_function(__snake_case,		__snake_case	)


if __name__ == "__main__":
		main()




'''simple docstring'''



import math

__snake_case			:     List[Any]										=      10
__snake_case			:     Dict										=      7
__snake_case			:     str										=      BALLS_PER_COLOUR * NUM_COLOURS
def 						__lowerCamelCase	(       __snake_case					:		int = 20	)	->							str:






						"""simple docstring"""





						A__					:       Union[str, Any]     =math.comb(__snake_case,		__snake_case	)
						A__					:       str     =math.comb(NUM_BALLS - BALLS_PER_COLOUR,		__snake_case	)

						A__					:       Optional[int]     =NUM_COLOURS * (1 - missing_colour / total)

						return f"{result:.9f}"


if __name__ == "__main__":
		print(solution(20))

