




'''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
#
########################################################################


a  :				int									=  16
a  :				Optional[Any]									=  32


def 		__magic_name__    (	__UpperCAmelCase,      __UpperCAmelCase = 16	)		->					str:

					'''simple docstring'''

					snake_case_									=				AutoTokenizer.from_pretrained('''bert-base-cased'''	)
					snake_case_									=				load_dataset('''glue''',      '''mrpc'''	)

					def tokenize_function(__UpperCAmelCase	):
										# max_length=None => use the model max length (it's actually the default)
										snake_case_									=				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():
										snake_case_									=				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
					snake_case_									=				tokenized_datasets.rename_column('''label''',      '''labels'''	)

					def collate_fn(__UpperCAmelCase	):
										# 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(
										    __UpperCAmelCase,      padding='''longest''',      max_length=__UpperCAmelCase,      pad_to_multiple_of=__UpperCAmelCase,      return_tensors='''pt''',      )

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

					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

							a  :				Dict									=  mocked_dataloaders  # noqa: F811


def 		__magic_name__    (	__UpperCAmelCase,      __UpperCAmelCase	)		->					str:

					'''simple docstring'''

					if os.environ.get('''TESTING_MOCKED_DATALOADERS''',      __UpperCAmelCase	) == "1":
										snake_case_									=				2
					# Initialize accelerator
					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
					snake_case_									=				config['''lr''']
					snake_case_									=				int(config['''num_epochs''']	)
					snake_case_									=				int(config['''seed''']	)
					snake_case_									=				int(config['''batch_size''']	)

					snake_case_									=				evaluate.load('''glue''',      '''mrpc'''	)

					# 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

					set_seed(__UpperCAmelCase	)
					snake_case_			,snake_case_									=				get_dataloaders(__UpperCAmelCase,      __UpperCAmelCase	)
					# 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=__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).
					snake_case_									=				model.to(accelerator.device	)

					# Instantiate optimizer
					snake_case_									=				AdamW(params=model.parameters(),      lr=__UpperCAmelCase	)

					# Instantiate scheduler
					snake_case_									=				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.
					snake_case_			,snake_case_			,snake_case_			,snake_case_			,snake_case_									=				accelerator.prepare(
					    __UpperCAmelCase,      __UpperCAmelCase,      __UpperCAmelCase,      __UpperCAmelCase,      __UpperCAmelCase	)

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

										model.eval()
										snake_case_									=				0
										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():
																				snake_case_									=				model(**__UpperCAmelCase	)
															snake_case_									=				outputs.logits.argmax(dim=-1	)
															snake_case_			,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(__UpperCAmelCase	) - 1:
																									# Last batch needs to be truncated on distributed systems as it contains additional samples
																									snake_case_									=				predictions[: len(eval_dataloader.dataset	) - samples_seen]
																									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=__UpperCAmelCase,      references=__UpperCAmelCase,      )

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


def 		__magic_name__    (	)		->					Tuple:

					'''simple docstring'''

					snake_case_									=				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.'''	)
					snake_case_									=				parser.parse_args()
					snake_case_									=				{'''lr''': 2e-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
					training_function(__UpperCAmelCase,      __UpperCAmelCase	)


if __name__ == "__main__":
							main()







'''simple docstring'''

from __future__ import annotations

import math


def 		__magic_name__    (	__UpperCAmelCase,      __UpperCAmelCase,      __UpperCAmelCase,      __UpperCAmelCase,      __UpperCAmelCase	)		->					int:

					'''simple docstring'''

					if depth < 0:
										raise ValueError('''Depth cannot be less than 0'''	)

					if len(__UpperCAmelCase	) == 0:
										raise ValueError('''Scores cannot be empty'''	)

					if depth == height:
										return scores[node_index]

					if is_max:
										return max(
										    minimax(depth + 1,      node_index * 2,      __UpperCAmelCase,      __UpperCAmelCase,      __UpperCAmelCase	),      minimax(depth + 1,      node_index * 2 + 1,      __UpperCAmelCase,      __UpperCAmelCase,      __UpperCAmelCase	),      )

					return min(
					    minimax(depth + 1,      node_index * 2,      __UpperCAmelCase,      __UpperCAmelCase,      __UpperCAmelCase	),      minimax(depth + 1,      node_index * 2 + 1,      __UpperCAmelCase,      __UpperCAmelCase,      __UpperCAmelCase	),      )


def 		__magic_name__    (	)		->					None:

					'''simple docstring'''

					snake_case_									=				[90, 23, 6, 33, 21, 65, 123, 3_4423]
					snake_case_									=				math.log(len(__UpperCAmelCase	),      2	)
					print('''Optimal value : ''',      end=''''''	)
					print(minimax(0,      0,      __UpperCAmelCase,      __UpperCAmelCase,      __UpperCAmelCase	)	)


if __name__ == "__main__":
							import doctest

							doctest.testmod()
							main()

