
"""simple docstring"""


import argparse
from typing import List

import evaluate
import numpy as np
import torch
from datasets import DatasetDict, load_dataset

# New Code #
# We'll be using StratifiedKFold for this example
from sklearn.model_selection import StratifiedKFold
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 perform Cross Validation,
# 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
#
########################################################################


_lowercase							   =				16
_lowercase							   =				32






def 						_snake_case				(		snake_case__   :     Accelerator	,      snake_case__   :     DatasetDict	,      snake_case__   :     List[int]	,      snake_case__   :     List[int]	,      snake_case__   :     int = 16     ):
	A						   =   AutoTokenizer.from_pretrained('bert-base-cased'     )
	A						   =   DatasetDict(
	    {
	        'train': dataset['train'].select(snake_case__     ),
	        'validation': dataset['train'].select(snake_case__     ),
	        'test': dataset['validation'],
	    }     )

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

	def collate_fn(snake_case__   :     Optional[Any]     ):
		# 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(
		    snake_case__	,      padding='longest'	,      max_length=snake_case__	,      pad_to_multiple_of=snake_case__	,      return_tensors='pt'	,      )

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

	A						   =   DataLoader(
	    tokenized_datasets['test']	,      shuffle=snake_case__	,      collate_fn=snake_case__	,      batch_size=snake_case__     )

	return train_dataloader, eval_dataloader, test_dataloader






def 						_snake_case				(		snake_case__   :     str	,      snake_case__   :     List[str]     ):
	# New Code #
	A						   =   []
	# Download the dataset
	A						   =   load_dataset('glue'	,      'mrpc'     )
	# Create our splits
	A						   =   StratifiedKFold(n_splits=int(args.num_folds     )     )
	# 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']     )

	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

	set_seed(snake_case__     )

	# New Code #
	# Create our folds:
	A						   =   kfold.split(np.zeros(datasets['train'].num_rows     )	,      datasets['train']['label']     )
	A						   =   []
	# Iterate over them
	for i, (train_idxs, valid_idxs) in enumerate(snake_case__     ):
		A   ,      A   ,      A						   =   get_fold_dataloaders(
		    snake_case__	,      snake_case__	,      snake_case__	,      snake_case__	,      )
		# 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=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						   =   model.to(accelerator.device     )

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

		# Instantiate scheduler
		A						   =   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						   =   accelerator.prepare(
		    snake_case__	,      snake_case__	,      snake_case__	,      snake_case__	,      snake_case__     )

		# Now we train the model
		for epoch in range(snake_case__     ):
			model.train()
			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						   =   model(**snake_case__     )
				A						   =   outputs.loss
				A						   =   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`.
				batch.to(accelerator.device     )
				with torch.no_grad():
					A						   =   model(**snake_case__     )
				A						   =   outputs.logits.argmax(dim=-1     )
				A   ,      A						   =   accelerator.gather_for_metrics((predictions, batch['labels'])     )
				metric.add_batch(
				    predictions=snake_case__	,      references=snake_case__	,      )

			A						   =   metric.compute()
			# Use accelerator.print to print only on the main process.
			accelerator.print(F'epoch {epoch}:'	,      snake_case__     )

		# New Code #
		# We also run predictions on the test set at the very end
		A						   =   []
		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     )
			with torch.no_grad():
				A						   =   model(**snake_case__     )
			A						   =   outputs.logits
			A   ,      A						   =   accelerator.gather_for_metrics((predictions, batch['labels'])     )
			fold_predictions.append(predictions.cpu()     )
			if i == 0:
				# We need all of the test predictions
				test_references.append(references.cpu()     )
        # Use accelerator.print to print only on the main process.
		test_predictions.append(torch.cat(snake_case__	,      dim=0     )     )
		# We now need to release all our memory and get rid of the current model, optimizer, etc
		accelerator.free_memory()
	# New Code #
	# Finally we check the accuracy of our folded results:
	A						   =   torch.cat(snake_case__	,      dim=0     )
	A						   =   torch.stack(snake_case__	,      dim=0     ).sum(dim=0     ).div(int(args.num_folds     )     ).argmax(dim=-1     )
	A						   =   metric.compute(predictions=snake_case__	,      references=snake_case__     )
	accelerator.print('Average test metrics from all folds:'	,      snake_case__     )






def 						_snake_case				(		):
	A						   =   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.'     )
	# New Code #
	parser.add_argument('--num_folds'	,      type=snake_case__	,      default=3	,      help='The number of splits to perform across the dataset'     )
	A						   =   parser.parse_args()
	A						   =   {'lr': 2e-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
	training_function(snake_case__	,      snake_case__     )


if __name__ == "__main__":
		main()

"""simple docstring"""


from math import pi, sqrt






def       _snake_case				(		snake_case__   :     float     ):
	if num <= 0:
		raise ValueError('math domain error'     )
	if num > 171.5:
		raise OverflowError('math range error'     )
	elif num - int(snake_case__     ) not in (0, 0.5):
		raise NotImplementedError('num must be an integer or a half-integer'     )
	elif num == 0.5:
		return sqrt(snake_case__     )
	else:
		return 1.0 if num == 1 else (num - 1) * gamma(num - 1     )






def       _snake_case				(		):
	assert gamma(0.5     ) == sqrt(snake_case__     )
	assert gamma(1     ) == 1.0
	assert gamma(2     ) == 1.0


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

		testmod()
		_lowercase							   =				1.0
		while num:
				_lowercase							   =				float(input('''Gamma of: '''))
				print(F"""gamma({num}) = {gamma(num)}""")
				print('''\nEnter 0 to exit...''')