





'''simple docstring'''

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


lowercase_      							=					"""true"""





def lowerCamelCase		(	__lowerCamelCase					:       Any   ,			__lowerCamelCase					:       Tuple=82   ,			__lowerCamelCase					:       Tuple=16       )		->int:
    set_seed(42       )
    _SCREAMING_SNAKE_CASE   							=     RegressionModel()
    _SCREAMING_SNAKE_CASE   							=     deepcopy(_snake_case       )
    _SCREAMING_SNAKE_CASE   							=     RegressionDataset(length=_snake_case       )
    _SCREAMING_SNAKE_CASE   							=     DataLoader(_snake_case   ,			batch_size=_snake_case       )
    model.to(accelerator.device       )
    _SCREAMING_SNAKE_CASE   							=     accelerator.prepare(_snake_case   ,			_snake_case       )
    return model, ddp_model, dataloader





def lowerCamelCase		(	__lowerCamelCase					:       List[Any]   ,			__lowerCamelCase					:       List[Any]=False       )		->str:
    _SCREAMING_SNAKE_CASE   							=     AutoTokenizer.from_pretrained("""hf-internal-testing/mrpc-bert-base-cased"""       )
    _SCREAMING_SNAKE_CASE   							=     load_dataset("""glue"""   ,			"""mrpc"""   ,			split="""validation"""       )

    def tokenize_function(__lowerCamelCase					:       Union[str, Any]       ):
        _SCREAMING_SNAKE_CASE   							=     tokenizer(examples["""sentence1"""]   ,			examples["""sentence2"""]   ,			truncation=_snake_case   ,			max_length=_snake_case       )
        return outputs

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

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

    def collate_fn(__lowerCamelCase					:       Tuple       ):
        if use_longest:
            return tokenizer.pad(_snake_case   ,			padding="""longest"""   ,			return_tensors="""pt"""       )
        return tokenizer.pad(_snake_case   ,			padding="""max_length"""   ,			max_length=128   ,			return_tensors="""pt"""       )

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





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





def lowerCamelCase		(	__lowerCamelCase					:       Any   ,			__lowerCamelCase					:       Optional[Any]   ,			__lowerCamelCase					:       Optional[Any]       )		->Tuple:
    _SCREAMING_SNAKE_CASE   							=     []
    for batch in dataloader:
        _SCREAMING_SNAKE_CASE   							=     batch.values()
        with torch.no_grad():
            _SCREAMING_SNAKE_CASE   							=     model(_snake_case       )
            _SCREAMING_SNAKE_CASE   							=     accelerator.gather_for_metrics((logit, target)       )
            logits_and_targets.append((logit, target)       )
    _SCREAMING_SNAKE_CASE   							=     [], []
    for logit, targ in logits_and_targets:
        logits.append(_snake_case       )
        targs.append(_snake_case       )
    _SCREAMING_SNAKE_CASE   							=     torch.cat(_snake_case       ), torch.cat(_snake_case       )
    return logits, targs





def lowerCamelCase		(	__lowerCamelCase					:       Dict   ,			__lowerCamelCase					:       Dict=82   ,			__lowerCamelCase					:       Tuple=False   ,			__lowerCamelCase					:       List[str]=False   ,			__lowerCamelCase					:       Union[str, Any]=16       )		->Optional[int]:
    _SCREAMING_SNAKE_CASE   							=     get_basic_setup(_snake_case   ,			_snake_case   ,			_snake_case       )
    _SCREAMING_SNAKE_CASE   							=     generate_predictions(_snake_case   ,			_snake_case   ,			_snake_case       )
    assert (
        len(_snake_case       ) == num_samples
    ), F'Unexpected number of inputs:\n    Expected: {num_samples}\n    Actual: {len(_snake_case       )}'





def lowerCamelCase		(	__lowerCamelCase					:       List[str] = False   ,			__lowerCamelCase					:       int = False       )		->Union[str, Any]:
    _SCREAMING_SNAKE_CASE   							=     evaluate.load("""glue"""   ,			"""mrpc"""       )
    _SCREAMING_SNAKE_CASE   							=     get_mrpc_setup(_snake_case   ,			_snake_case       )
    # First do baseline
    _SCREAMING_SNAKE_CASE   							=     setup['no']
    model.to(_snake_case       )
    model.eval()
    for batch in dataloader:
        batch.to(_snake_case       )
        with torch.inference_mode():
            _SCREAMING_SNAKE_CASE   							=     model(**_snake_case       )
        _SCREAMING_SNAKE_CASE   							=     outputs.logits.argmax(dim=-1       )
        metric.add_batch(predictions=_snake_case   ,			references=batch["""labels"""]       )
    _SCREAMING_SNAKE_CASE   							=     metric.compute()

    # Then do distributed
    _SCREAMING_SNAKE_CASE   							=     setup['ddp']
    model.eval()
    for batch in dataloader:
        with torch.inference_mode():
            _SCREAMING_SNAKE_CASE   							=     model(**_snake_case       )
        _SCREAMING_SNAKE_CASE   							=     outputs.logits.argmax(dim=-1       )
        _SCREAMING_SNAKE_CASE   							=     batch['labels']
        _SCREAMING_SNAKE_CASE   							=     accelerator.gather_for_metrics((preds, references)       )
        metric.add_batch(predictions=_snake_case   ,			references=_snake_case       )
    _SCREAMING_SNAKE_CASE   							=     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 lowerCamelCase		(	)		->Optional[int]:
    _SCREAMING_SNAKE_CASE   							=     Accelerator(split_batches=_snake_case   ,			dispatch_batches=_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(_snake_case   ,			_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   							=     Accelerator(split_batches=_snake_case   ,			dispatch_batches=_snake_case       )
            if accelerator.is_local_main_process:
                print(F'With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`, length=99'       )
            test_torch_metrics(_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   							=     Accelerator()
    test_torch_metrics(_snake_case   ,			512       )
    accelerator.state._reset_state()





def lowerCamelCase		(	__lowerCamelCase					:       Tuple       )		->Optional[Any]:
    # For xla_spawn (TPUs)
    main()


if __name__ == "__main__":
      main()





"""simple docstring"""



def 					lowerCamelCase   (     _snake_case    ,_snake_case ):
    return int((input_a, input_a).count(0 ) == 0 )
def 					lowerCamelCase   (     ):
    assert and_gate(0    ,0 ) == 0
    assert and_gate(0    ,1 ) == 0
    assert and_gate(1    ,0 ) == 0
    assert and_gate(1    ,1 ) == 1


if __name__ == "__main__":
    test_and_gate()
    print(and_gate(1, 0))
    print(and_gate(0, 0))
    print(and_gate(0, 1))
    print(and_gate(1, 1))

