





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 :    Tuple  							= '''true'''




def _a (  SCREAMING_SNAKE_CASE__      :  Any			,				SCREAMING_SNAKE_CASE__      :  int=82			,				SCREAMING_SNAKE_CASE__      :  Optional[Any]=16       )	->    Any:

							'''simple docstring'''






							set_seed(42       )
							SCREAMING_SNAKE_CASE__       :      List[Any]     =						RegressionModel()
							SCREAMING_SNAKE_CASE__       :      List[Any]     =						deepcopy(SCREAMING_SNAKE_CASE__       )
							SCREAMING_SNAKE_CASE__       :      List[str]     =						RegressionDataset(length=SCREAMING_SNAKE_CASE__       )
							SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						DataLoader(SCREAMING_SNAKE_CASE__			,				batch_size=SCREAMING_SNAKE_CASE__       )
							model.to(accelerator.device       )
							SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__       :      Any     =						accelerator.prepare(SCREAMING_SNAKE_CASE__			,				SCREAMING_SNAKE_CASE__       )
							return model, ddp_model, dataloader




def _a (  SCREAMING_SNAKE_CASE__      :  Accelerator			,				SCREAMING_SNAKE_CASE__      :  Union[str, Any]=False       )	->    int:

							'''simple docstring'''






							SCREAMING_SNAKE_CASE__       :      Tuple     =						AutoTokenizer.from_pretrained("hf-internal-testing/mrpc-bert-base-cased"       )
							SCREAMING_SNAKE_CASE__       :      Optional[int]     =						load_dataset("glue"			,				"mrpc"			,				split="validation"       )

							def tokenize_function(SCREAMING_SNAKE_CASE__      :  Dict       ):
														SCREAMING_SNAKE_CASE__       :      List[str]     =						tokenizer(examples["sentence1"]			,				examples["sentence2"]			,				truncation=SCREAMING_SNAKE_CASE__			,				max_length=SCREAMING_SNAKE_CASE__       )
														return outputs

							with accelerator.main_process_first():
														SCREAMING_SNAKE_CASE__       :      List[str]     =						dataset.map(
														    SCREAMING_SNAKE_CASE__			,				batched=SCREAMING_SNAKE_CASE__			,				remove_columns=["idx", "sentence1", "sentence2"]			,				)

							SCREAMING_SNAKE_CASE__       :      int     =						tokenized_datasets.rename_column("label"			,				"labels"       )

							def collate_fn(SCREAMING_SNAKE_CASE__      :  Tuple       ):
														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[Any]			,				SCREAMING_SNAKE_CASE__      :  Tuple       )	->    List[str]:

							'''simple docstring'''






							SCREAMING_SNAKE_CASE__       :      Tuple     =						Accelerator(dispatch_batches=SCREAMING_SNAKE_CASE__			,				split_batches=SCREAMING_SNAKE_CASE__       )
							SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						get_dataloader(SCREAMING_SNAKE_CASE__			,				not dispatch_batches       )
							SCREAMING_SNAKE_CASE__       :      int     =						AutoModelForSequenceClassification.from_pretrained(
							    "hf-internal-testing/mrpc-bert-base-cased"			,				return_dict=SCREAMING_SNAKE_CASE__       )
							SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__       :      Optional[int]     =						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[Any]       )	->    Tuple:

							'''simple docstring'''






							SCREAMING_SNAKE_CASE__       :      Any     =						[]
							for batch in dataloader:
														SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__       :      Any     =						batch.values()
														with torch.no_grad():
																					SCREAMING_SNAKE_CASE__       :      int     =						model(SCREAMING_SNAKE_CASE__       )
																					SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						accelerator.gather_for_metrics((logit, target)       )
																					logits_and_targets.append((logit, target)       )
							SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						[], []
							for logit, targ in logits_and_targets:
														logits.append(SCREAMING_SNAKE_CASE__       )
														targs.append(SCREAMING_SNAKE_CASE__       )
							SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__       :      int     =						torch.cat(SCREAMING_SNAKE_CASE__       ), torch.cat(SCREAMING_SNAKE_CASE__       )
							return logits, targs




def _a (  SCREAMING_SNAKE_CASE__      :  Accelerator			,				SCREAMING_SNAKE_CASE__      :  Optional[Any]=82			,				SCREAMING_SNAKE_CASE__      :  int=False			,				SCREAMING_SNAKE_CASE__      :  List[str]=False			,				SCREAMING_SNAKE_CASE__      :  Union[str, Any]=16       )	->    Tuple:

							'''simple docstring'''






							SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						get_basic_setup(SCREAMING_SNAKE_CASE__			,				SCREAMING_SNAKE_CASE__			,				SCREAMING_SNAKE_CASE__       )
							SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__       :      Tuple     =						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       )	->    str:

							'''simple docstring'''






							SCREAMING_SNAKE_CASE__       :      str     =						evaluate.load("glue"			,				"mrpc"       )
							SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__       :      Dict     =						get_mrpc_setup(SCREAMING_SNAKE_CASE__			,				SCREAMING_SNAKE_CASE__       )
							# First do baseline
							SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__       :      int     =						setup["no"]
							model.to(SCREAMING_SNAKE_CASE__       )
							model.eval()
							for batch in dataloader:
														batch.to(SCREAMING_SNAKE_CASE__       )
														with torch.inference_mode():
																					SCREAMING_SNAKE_CASE__       :      Tuple     =						model(**SCREAMING_SNAKE_CASE__       )
														SCREAMING_SNAKE_CASE__       :      Optional[int]     =						outputs.logits.argmax(dim=-1       )
														metric.add_batch(predictions=SCREAMING_SNAKE_CASE__			,				references=batch["labels"]       )
							SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						metric.compute()

							# Then do distributed
							SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						setup["ddp"]
							model.eval()
							for batch in dataloader:
														with torch.inference_mode():
																					SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						model(**SCREAMING_SNAKE_CASE__       )
														SCREAMING_SNAKE_CASE__       :      Dict     =						outputs.logits.argmax(dim=-1       )
														SCREAMING_SNAKE_CASE__       :      List[Any]     =						batch["labels"]
														SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__       :      int     =						accelerator.gather_for_metrics((preds, references)       )
														metric.add_batch(predictions=SCREAMING_SNAKE_CASE__			,				references=SCREAMING_SNAKE_CASE__       )
							SCREAMING_SNAKE_CASE__       :      Tuple     =						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 (  )	->    Tuple:

							'''simple docstring'''






							SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						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]:
																					SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						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**"       )
							SCREAMING_SNAKE_CASE__       :      str     =						Accelerator()
							test_torch_metrics(SCREAMING_SNAKE_CASE__			,				5_12       )
							accelerator.state._reset_state()




def _a (  SCREAMING_SNAKE_CASE__      :  Optional[Any]       )	->    Any:

							'''simple docstring'''






							main()


if __name__ == "__main__":
		main()













import webbrowser
from sys import argv
from urllib.parse import parse_qs, quote

import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent

if __name__ == "__main__":
		_lowerCamelCase :    Union[str, Any]  							= '''%20'''.join(argv[1:]) if len(argv) > 1 else quote(str(input('''Search: ''')))

		print('''Googling.....''')

		_lowerCamelCase :    Any  							= f"https://www.google.com/search?q={query}&num=100"

		_lowerCamelCase :    Optional[Any]  							= requests.get(
		    url,
		    headers={'''User-Agent''': str(UserAgent().random)},
		)

		try:
				_lowerCamelCase :    Union[str, Any]  							= (
				    BeautifulSoup(res.text, '''html.parser''')
				    .find('''div''', attrs={'''class''': '''yuRUbf'''})
				    .find('''a''')
				    .get('''href''')
				)

		except AttributeError:
				_lowerCamelCase :    Optional[Any]  							= parse_qs(
				    BeautifulSoup(res.text, '''html.parser''')
				    .find('''div''', attrs={'''class''': '''kCrYT'''})
				    .find('''a''')
				    .get('''href''')
				)['''url'''][0]

		webbrowser.open(link)






