






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


lowerCAmelCase_           = 'true'







def      snake_case		(							UpperCAmelCase	:       int,						UpperCAmelCase	:       Dict=82,						UpperCAmelCase	:       Union[str, Any]=16   ):
				set_seed(42   )
				A	 =      RegressionModel()
				A	 =      deepcopy(UpperCAmelCase   )
				A	 =      RegressionDataset(length=UpperCAmelCase   )
				A	 =      DataLoader(UpperCAmelCase,						batch_size=UpperCAmelCase   )
				model.to(accelerator.device   )
				A ,		A	 =      accelerator.prepare(UpperCAmelCase,						UpperCAmelCase   )
				return model, ddp_model, dataloader







def      snake_case		(							UpperCAmelCase	:       Accelerator,						UpperCAmelCase	:       List[str]=False   ):
				A	 =      AutoTokenizer.from_pretrained('hf-internal-testing/mrpc-bert-base-cased'   )
				A	 =      load_dataset('glue',						'mrpc',						split='validation'   )

				def tokenize_function(UpperCAmelCase	:       Tuple   ):
								A	 =      tokenizer(examples['sentence1'],						examples['sentence2'],						truncation=UpperCAmelCase,						max_length=UpperCAmelCase   )
								return outputs

				with accelerator.main_process_first():
								A	 =      dataset.map(
								    UpperCAmelCase,						batched=UpperCAmelCase,						remove_columns=['idx', 'sentence1', 'sentence2'],						)

				A	 =      tokenized_datasets.rename_column('label',						'labels'   )

				def collate_fn(UpperCAmelCase	:       Optional[int]   ):
								if use_longest:
												return tokenizer.pad(UpperCAmelCase,						padding='longest',						return_tensors='pt'   )
								return tokenizer.pad(UpperCAmelCase,						padding='max_length',						max_length=1_28,						return_tensors='pt'   )

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







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







def      snake_case		(							UpperCAmelCase	:       Tuple,						UpperCAmelCase	:       str,						UpperCAmelCase	:       List[str]   ):
				A	 =      []
				for batch in dataloader:
								A ,		A	 =      batch.values()
								with torch.no_grad():
												A	 =      model(UpperCAmelCase   )
												A ,		A	 =      accelerator.gather_for_metrics((logit, target)   )
												logits_and_targets.append((logit, target)   )
				A ,		A	 =      [], []
				for logit, targ in logits_and_targets:
								logits.append(UpperCAmelCase   )
								targs.append(UpperCAmelCase   )
				A ,		A	 =      torch.cat(UpperCAmelCase   ), torch.cat(UpperCAmelCase   )
				return logits, targs







def      snake_case		(							UpperCAmelCase	:       Accelerator,						UpperCAmelCase	:       List[str]=82,						UpperCAmelCase	:       Tuple=False,						UpperCAmelCase	:       Union[str, Any]=False,						UpperCAmelCase	:       Optional[int]=16   ):
				A ,		A ,		A	 =      get_basic_setup(UpperCAmelCase,						UpperCAmelCase,						UpperCAmelCase   )
				A ,		A	 =      generate_predictions(UpperCAmelCase,						UpperCAmelCase,						UpperCAmelCase   )
				assert (
				    len(UpperCAmelCase   ) == num_samples
				), f'Unexpected number of inputs:\n    Expected: {num_samples}\n    Actual: {len(UpperCAmelCase   )}'







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

				# Then do distributed
				A ,		A ,		A	 =      setup['ddp']
				model.eval()
				for batch in dataloader:
								with torch.inference_mode():
												A	 =      model(**UpperCAmelCase   )
								A	 =      outputs.logits.argmax(dim=-1   )
								A	 =      batch['labels']
								A ,		A	 =      accelerator.gather_for_metrics((preds, references)   )
								metric.add_batch(predictions=UpperCAmelCase,						references=UpperCAmelCase   )
				A	 =      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      snake_case		(							):
				A	 =      Accelerator(split_batches=UpperCAmelCase,						dispatch_batches=UpperCAmelCase   )
				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(UpperCAmelCase,						UpperCAmelCase   )
																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]:
												A	 =      Accelerator(split_batches=UpperCAmelCase,						dispatch_batches=UpperCAmelCase   )
												if accelerator.is_local_main_process:
																print(f'With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`, length=99'   )
												test_torch_metrics(UpperCAmelCase,						99   )
												accelerator.state._reset_state()
				if accelerator.is_local_main_process:
								print('**Test last batch is not dropped when perfectly divisible**'   )
				A	 =      Accelerator()
				test_torch_metrics(UpperCAmelCase,						5_12   )
				accelerator.state._reset_state()







def      snake_case		(							UpperCAmelCase	:       int   ):
				# For xla_spawn (TPUs)
				main()


if __name__ == "__main__":
					main()













import math







def      snake_case		(							UpperCAmelCase	:       list,						UpperCAmelCase	:       int   ):
				A	 =      len(UpperCAmelCase   )
				A	 =      int(math.floor(math.sqrt(UpperCAmelCase   )   )   )
				A	 =      0
				while arr[min(UpperCAmelCase,						UpperCAmelCase   ) - 1] < x:
								A	 =      step
								step += int(math.floor(math.sqrt(UpperCAmelCase   )   )   )
								if prev >= n:
												return -1

				while arr[prev] < x:
								A	 =      prev + 1
								if prev == min(UpperCAmelCase,						UpperCAmelCase   ):
												return -1
				if arr[prev] == x:
								return prev
				return -1


if __name__ == "__main__":
					lowerCAmelCase_           = input('Enter numbers separated by a comma:\n').strip()
					lowerCAmelCase_           = [int(item) for item in user_input.split(',')]
					lowerCAmelCase_           = int(input('Enter the number to be searched:\n'))
					lowerCAmelCase_           = jump_search(arr, x)
					if res == -1:
										print('Number not found!')
					else:
										print(f'''Number {x} is at index {res}''')





