




import argparse
import json
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
from accelerate.utils.deepspeed import DummyOptim, DummyScheduler


UpperCAmelCase_						=16
UpperCAmelCase_						=32



def       UpperCAmelCase       (     _snake_case						, _snake_case = 16						, _snake_case = "bert-base-cased"							):
							lowerCAmelCase   =		AutoTokenizer.from_pretrained(snake_case_							)
							lowerCAmelCase   =		load_dataset('''glue'''						, '''mrpc'''							)

							def tokenize_function(_snake_case							):
														# max_length=None => use the model max length (it's actually the default)
														lowerCAmelCase   =		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
							lowerCAmelCase   =		datasets.map(
							    snake_case_						, batched=snake_case_						, remove_columns=['''idx''', '''sentence1''', '''sentence2''']						, load_from_cache_file=snake_case_							)

							# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
							# transformers library
							lowerCAmelCase   =		tokenized_datasets.rename_column('''label'''						, '''labels'''							)

							def collate_fn(_snake_case							):
														# On TPU it's best to pad everything to the same length or training will be very slow.
														if accelerator.distributed_type == DistributedType.TPU:
																					return tokenizer.pad(snake_case_						, padding='''max_length'''						, max_length=128						, return_tensors='''pt'''							)
														return tokenizer.pad(snake_case_						, padding='''longest'''						, return_tensors='''pt'''							)

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

							return train_dataloader, eval_dataloader



def       UpperCAmelCase       (     _snake_case						, _snake_case							):
							# Initialize accelerator
							lowerCAmelCase   =		Accelerator()

							# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
							lowerCAmelCase   =		config["lr"]
							lowerCAmelCase   =		int(config['''num_epochs''']							)
							lowerCAmelCase   =		int(config['''seed''']							)
							lowerCAmelCase   =		int(config['''batch_size''']							)
							lowerCAmelCase   =		args.model_name_or_path

							set_seed(snake_case_							)
							lowerCAmelCase   =		get_dataloaders(snake_case_						, snake_case_						, snake_case_							)

							# Instantiate the model (we build the model here so that the seed also control new weights initialization)
							lowerCAmelCase   =		AutoModelForSequenceClassification.from_pretrained(snake_case_						, return_dict=snake_case_							)

							# Instantiate optimizer
							lowerCAmelCase   =		(
							    AdamW
							    if accelerator.state.deepspeed_plugin is None
							    or "optimizer" not in accelerator.state.deepspeed_plugin.deepspeed_config
							    else DummyOptim
							)
							lowerCAmelCase   =		optimizer_cls(params=model.parameters()						, lr=snake_case_							)

							if accelerator.state.deepspeed_plugin is not None:
														lowerCAmelCase   =		accelerator.state.deepspeed_plugin.deepspeed_config[
														    "gradient_accumulation_steps"
														]
							else:
														lowerCAmelCase   =		1
							lowerCAmelCase   =		(len(snake_case_							) * num_epochs) // gradient_accumulation_steps

							# Instantiate scheduler
							if (
							    accelerator.state.deepspeed_plugin is None
							    or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
							):
														lowerCAmelCase   =		get_linear_schedule_with_warmup(
														    optimizer=snake_case_						, num_warmup_steps=0						, num_training_steps=snake_case_						, )
							else:
														lowerCAmelCase   =		DummyScheduler(snake_case_						, total_num_steps=snake_case_						, warmup_num_steps=0							)

							# 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.
							lowerCAmelCase   =		accelerator.prepare(
							    snake_case_						, snake_case_						, snake_case_						, snake_case_						, snake_case_							)

							# We need to keep track of how many total steps we have iterated over
							lowerCAmelCase   =		0
							# We also need to keep track of the stating epoch so files are named properly
							lowerCAmelCase   =		0

							# Now we train the model
							lowerCAmelCase   =		evaluate.load('''glue'''						, '''mrpc'''							)
							lowerCAmelCase   =		0
							lowerCAmelCase   =		{}
							for epoch in range(snake_case_						, snake_case_							):
														model.train()
														for step, batch in enumerate(snake_case_							):
																					lowerCAmelCase   =		model(**snake_case_							)
																					lowerCAmelCase   =		outputs.loss
																					lowerCAmelCase   =		loss / gradient_accumulation_steps
																					accelerator.backward(snake_case_							)
																					if step % gradient_accumulation_steps == 0:
																												optimizer.step()
																												lr_scheduler.step()
																												optimizer.zero_grad()

																					overall_step += 1

														model.eval()
														lowerCAmelCase   =		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							)
																					with torch.no_grad():
																												lowerCAmelCase   =		model(**snake_case_							)
																					lowerCAmelCase   =		outputs.logits.argmax(dim=-1							)
																					# It is slightly faster to call this once, than multiple times
																					lowerCAmelCase   =		accelerator.gather(
																					    (predictions, batch['''labels'''])							)  # If we are in a multiprocess environment, the last batch has duplicates
																					if accelerator.use_distributed:
																												if step == len(snake_case_							) - 1:
																																			lowerCAmelCase   =		predictions[: len(eval_dataloader.dataset							) - samples_seen]
																																			lowerCAmelCase   =		references[: len(eval_dataloader.dataset							) - samples_seen]
																												else:
																																			samples_seen += references.shape[0]
																					metric.add_batch(
																					    predictions=snake_case_						, references=snake_case_						, )

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

														if best_performance < eval_metric["accuracy"]:
																					lowerCAmelCase   =		eval_metric["accuracy"]

							if args.performance_lower_bound is not None:
														assert (
														    args.performance_lower_bound <= best_performance
														), F"""Best performance metric {best_performance} is lower than the lower bound {args.performance_lower_bound}"""

							accelerator.wait_for_everyone()
							if accelerator.is_main_process:
														with open(os.path.join(args.output_dir						, '''all_results.json'''							)						, '''w'''							) as f:
																					json.dump(snake_case_						, snake_case_							)



def       UpperCAmelCase       (     ):
							lowerCAmelCase   =		argparse.ArgumentParser(description='''Simple example of training script tracking peak GPU memory usage.'''							)
							parser.add_argument(
							    '''--model_name_or_path'''						, type=snake_case_						, default='''bert-base-cased'''						, help='''Path to pretrained model or model identifier from huggingface.co/models.'''						, required=snake_case_						, )
							parser.add_argument(
							    '''--output_dir'''						, type=snake_case_						, default='''.'''						, help='''Optional save directory where all checkpoint folders will be stored. Default is the current working directory.'''						, )
							parser.add_argument(
							    '''--performance_lower_bound'''						, type=snake_case_						, default=snake_case_						, help='''Optional lower bound for the performance metric. If set, the training will throw error when the performance metric drops below this value.'''						, )
							parser.add_argument(
							    '''--num_epochs'''						, type=snake_case_						, default=3						, help='''Number of train epochs.'''						, )
							lowerCAmelCase   =		parser.parse_args()
							lowerCAmelCase   =		{"lr": 2E-5, "num_epochs": args.num_epochs, "seed": 42, "batch_size": 16}
							training_function(snake_case_						, snake_case_							)


if __name__ == "__main__":
		main()









def       UpperCAmelCase       (     _snake_case						, _snake_case						, _snake_case							):

							def count_of_possible_combinations(_snake_case							) -> int:
														if target < 0:
																					return 0
														if target == 0:
																					return 1
														return sum(count_of_possible_combinations(target - item							) for item in array							)

							return count_of_possible_combinations(_snake_case							)



def       UpperCAmelCase       (     _snake_case						, _snake_case						, _snake_case							):

							def count_of_possible_combinations_with_dp_array(
							    _snake_case						, _snake_case							) -> int:
														if target < 0:
																					return 0
														if target == 0:
																					return 1
														if dp_array[target] != -1:
																					return dp_array[target]
														lowerCAmelCase   =		sum(
														    count_of_possible_combinations_with_dp_array(target - item						, _snake_case							)
														    for item in array							)
														lowerCAmelCase   =		answer
														return answer

							lowerCAmelCase   =		[-1] * (target + 1)
							return count_of_possible_combinations_with_dp_array(_snake_case						, _snake_case							)



def       UpperCAmelCase       (     _snake_case						, _snake_case						, _snake_case							):
							lowerCAmelCase   =		[0] * (target + 1)
							lowerCAmelCase   =		1

							for i in range(1						, target + 1							):
														for j in range(_snake_case							):
																					if i - array[j] >= 0:
																												dp_array[i] += dp_array[i - array[j]]

							return dp_array[target]


if __name__ == "__main__":
		import doctest

		doctest.testmod()
		UpperCAmelCase_						=3
		UpperCAmelCase_						=5
		UpperCAmelCase_						=[1, 2, 5]
		print(combination_sum_iv(n, array, target))



