




import math
import os
from copy import deepcopy

import datasets
import evaluate
import torch
import transformers
from datasets import load_dataset
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer

from accelerate import Accelerator
from accelerate.test_utils import RegressionDataset, RegressionModel
from accelerate.utils import is_tpu_available, set_seed


UpperCamelCase__         =      """true"""







def      _a				(						SCREAMING_SNAKE_CASE_							:    Optional[Any]				,				SCREAMING_SNAKE_CASE_							:    List[str]=82				,				SCREAMING_SNAKE_CASE_							:    str=16					):
					set_seed(42					)
					__lowerCAmelCase           =	RegressionModel()
					__lowerCAmelCase           =	deepcopy(SCREAMING_SNAKE_CASE_					)
					__lowerCAmelCase           =	RegressionDataset(length=SCREAMING_SNAKE_CASE_					)
					__lowerCAmelCase           =	DataLoader(SCREAMING_SNAKE_CASE_				,				batch_size=SCREAMING_SNAKE_CASE_					)
					model.to(accelerator.device					)
					__lowerCAmelCase       ,							__lowerCAmelCase           =	accelerator.prepare(SCREAMING_SNAKE_CASE_				,				SCREAMING_SNAKE_CASE_					)
					return model, ddp_model, dataloader







def      _a				(						SCREAMING_SNAKE_CASE_							:    Accelerator				,				SCREAMING_SNAKE_CASE_							:    Optional[int]=False					):
					__lowerCAmelCase           =	AutoTokenizer.from_pretrained("hf-internal-testing/mrpc-bert-base-cased"					)
					__lowerCAmelCase           =	load_dataset("glue"				,				"mrpc"				,				split="validation"					)

					def tokenize_function(SCREAMING_SNAKE_CASE_							:    List[Any]					):
										__lowerCAmelCase           =	tokenizer(examples["sentence1"]				,				examples["sentence2"]				,				truncation=SCREAMING_SNAKE_CASE_				,				max_length=SCREAMING_SNAKE_CASE_					)
										return outputs

					with accelerator.main_process_first():
										__lowerCAmelCase           =	dataset.map(
										    SCREAMING_SNAKE_CASE_				,				batched=SCREAMING_SNAKE_CASE_				,				remove_columns=["idx", "sentence1", "sentence2"]				,				)

					__lowerCAmelCase           =	tokenized_datasets.rename_column("label"				,				"labels"					)

					def collate_fn(SCREAMING_SNAKE_CASE_							:    Union[str, Any]					):
										if use_longest:
															return tokenizer.pad(SCREAMING_SNAKE_CASE_				,				padding="longest"				,				return_tensors="pt"					)
										return tokenizer.pad(SCREAMING_SNAKE_CASE_				,				padding="max_length"				,				max_length=1_28				,				return_tensors="pt"					)

					return DataLoader(SCREAMING_SNAKE_CASE_				,				shuffle=SCREAMING_SNAKE_CASE_				,				collate_fn=SCREAMING_SNAKE_CASE_				,				batch_size=16					)







def      _a				(						SCREAMING_SNAKE_CASE_							:    List[str]				,				SCREAMING_SNAKE_CASE_							:    int					):
					__lowerCAmelCase           =	Accelerator(dispatch_batches=SCREAMING_SNAKE_CASE_				,				split_batches=SCREAMING_SNAKE_CASE_					)
					__lowerCAmelCase           =	get_dataloader(SCREAMING_SNAKE_CASE_				,				not dispatch_batches					)
					__lowerCAmelCase           =	AutoModelForSequenceClassification.from_pretrained(
					    "hf-internal-testing/mrpc-bert-base-cased"				,				return_dict=SCREAMING_SNAKE_CASE_					)
					__lowerCAmelCase       ,							__lowerCAmelCase           =	accelerator.prepare(SCREAMING_SNAKE_CASE_				,				SCREAMING_SNAKE_CASE_					)
					return {"ddp": [ddp_model, ddp_dataloader, "cuda:0"], "no": [model, dataloader, accelerator.device]}, accelerator







def      _a				(						SCREAMING_SNAKE_CASE_							:    str				,				SCREAMING_SNAKE_CASE_							:    List[str]				,				SCREAMING_SNAKE_CASE_							:    Optional[int]					):
					__lowerCAmelCase           =	[]
					for batch in dataloader:
										__lowerCAmelCase       ,							__lowerCAmelCase           =	batch.values()
										with torch.no_grad():
															__lowerCAmelCase           =	model(SCREAMING_SNAKE_CASE_					)
															__lowerCAmelCase       ,							__lowerCAmelCase           =	accelerator.gather_for_metrics((logit, target)					)
															logits_and_targets.append((logit, target)					)
					__lowerCAmelCase       ,							__lowerCAmelCase           =	[], []
					for logit, targ in logits_and_targets:
										logits.append(SCREAMING_SNAKE_CASE_					)
										targs.append(SCREAMING_SNAKE_CASE_					)
					__lowerCAmelCase       ,							__lowerCAmelCase           =	torch.cat(SCREAMING_SNAKE_CASE_					), torch.cat(SCREAMING_SNAKE_CASE_					)
					return logits, targs







def      _a				(						SCREAMING_SNAKE_CASE_							:    Accelerator				,				SCREAMING_SNAKE_CASE_							:    Union[str, Any]=82				,				SCREAMING_SNAKE_CASE_							:    Union[str, Any]=False				,				SCREAMING_SNAKE_CASE_							:    Any=False				,				SCREAMING_SNAKE_CASE_							:    Optional[int]=16					):
					__lowerCAmelCase       ,							__lowerCAmelCase       ,							__lowerCAmelCase           =	get_basic_setup(SCREAMING_SNAKE_CASE_				,				SCREAMING_SNAKE_CASE_				,				SCREAMING_SNAKE_CASE_					)
					__lowerCAmelCase       ,							__lowerCAmelCase           =	generate_predictions(SCREAMING_SNAKE_CASE_				,				SCREAMING_SNAKE_CASE_				,				SCREAMING_SNAKE_CASE_					)
					assert (
					    len(SCREAMING_SNAKE_CASE_					) == num_samples
					), F"""Unexpected number of inputs:\n    Expected: {num_samples}\n    Actual: {len(SCREAMING_SNAKE_CASE_					)}"""







def      _a				(						SCREAMING_SNAKE_CASE_							:    bool = False				,				SCREAMING_SNAKE_CASE_							:    bool = False					):
					__lowerCAmelCase           =	evaluate.load("glue"				,				"mrpc"					)
					__lowerCAmelCase       ,							__lowerCAmelCase           =	get_mrpc_setup(SCREAMING_SNAKE_CASE_				,				SCREAMING_SNAKE_CASE_					)
					# First do baseline
					__lowerCAmelCase       ,							__lowerCAmelCase       ,							__lowerCAmelCase           =	setup["no"]
					model.to(SCREAMING_SNAKE_CASE_					)
					model.eval()
					for batch in dataloader:
										batch.to(SCREAMING_SNAKE_CASE_					)
										with torch.inference_mode():
															__lowerCAmelCase           =	model(**SCREAMING_SNAKE_CASE_					)
										__lowerCAmelCase           =	outputs.logits.argmax(dim=-1					)
										metric.add_batch(predictions=SCREAMING_SNAKE_CASE_				,				references=batch["labels"]					)
					__lowerCAmelCase           =	metric.compute()

					# Then do distributed
					__lowerCAmelCase       ,							__lowerCAmelCase       ,							__lowerCAmelCase           =	setup["ddp"]
					model.eval()
					for batch in dataloader:
										with torch.inference_mode():
															__lowerCAmelCase           =	model(**SCREAMING_SNAKE_CASE_					)
										__lowerCAmelCase           =	outputs.logits.argmax(dim=-1					)
										__lowerCAmelCase           =	batch["labels"]
										__lowerCAmelCase       ,							__lowerCAmelCase           =	accelerator.gather_for_metrics((preds, references)					)
										metric.add_batch(predictions=SCREAMING_SNAKE_CASE_				,				references=SCREAMING_SNAKE_CASE_					)
					__lowerCAmelCase           =	metric.compute()

					for key in "accuracy f1".split():
										assert math.isclose(
										    baseline[key]				,				distributed[key]					), F"""Baseline and Distributed are not the same for key {key}:\n\tBaseline: {baseline[key]}\n\tDistributed: {distributed[key]}\n"""







def      _a				(						):
					__lowerCAmelCase           =	Accelerator(split_batches=SCREAMING_SNAKE_CASE_				,				dispatch_batches=SCREAMING_SNAKE_CASE_					)
					if accelerator.is_local_main_process:
										datasets.utils.logging.set_verbosity_warning()
										transformers.utils.logging.set_verbosity_warning()
					else:
										datasets.utils.logging.set_verbosity_error()
										transformers.utils.logging.set_verbosity_error()
					# These are a bit slower so they should only be ran on the GPU or TPU
					if torch.cuda.is_available() or is_tpu_available():
										if accelerator.is_local_main_process:
															print("**Testing gather_for_metrics**"					)
										for split_batches in [True, False]:
															for dispatch_batches in [True, False]:
																				if accelerator.is_local_main_process:
																									print(F"""With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`"""					)
																				test_mrpc(SCREAMING_SNAKE_CASE_				,				SCREAMING_SNAKE_CASE_					)
																				accelerator.state._reset_state()
					if accelerator.is_local_main_process:
										print("**Test torch metrics**"					)
					for split_batches in [True, False]:
										for dispatch_batches in [True, False]:
															__lowerCAmelCase           =	Accelerator(split_batches=SCREAMING_SNAKE_CASE_				,				dispatch_batches=SCREAMING_SNAKE_CASE_					)
															if accelerator.is_local_main_process:
																				print(F"""With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`, length=99"""					)
															test_torch_metrics(SCREAMING_SNAKE_CASE_				,				99					)
															accelerator.state._reset_state()
					if accelerator.is_local_main_process:
										print("**Test last batch is not dropped when perfectly divisible**"					)
					__lowerCAmelCase           =	Accelerator()
					test_torch_metrics(SCREAMING_SNAKE_CASE_				,				5_12					)
					accelerator.state._reset_state()







def      _a				(						SCREAMING_SNAKE_CASE_							:    List[str]					):
					# For xla_spawn (TPUs)
					main()


if __name__ == "__main__":
					main()










def      _a				(						SCREAMING_SNAKE_CASE_							:    List[Any]					):
					__lowerCAmelCase       ,							__lowerCAmelCase           =	[], []
					while len(SCREAMING_SNAKE_CASE_					) > 1:
										__lowerCAmelCase       ,							__lowerCAmelCase           =	min(SCREAMING_SNAKE_CASE_					), max(SCREAMING_SNAKE_CASE_					)
										start.append(SCREAMING_SNAKE_CASE_					)
										end.append(SCREAMING_SNAKE_CASE_					)
										collection.remove(SCREAMING_SNAKE_CASE_					)
										collection.remove(SCREAMING_SNAKE_CASE_					)
					end.reverse()
					return start + collection + end


if __name__ == "__main__":
					UpperCamelCase__         =      input("""Enter numbers separated by a comma:\n""").strip()
					UpperCamelCase__         =      [int(item) for item in user_input.split(""",""")]
					print(*merge_sort(unsorted), sep=""",""")




