





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							:			Union[str, Any]        =							"""true"""


def 			SCREAMING_SNAKE_CASE (			_UpperCAmelCase		,      _UpperCAmelCase=82		,      _UpperCAmelCase=16					)     ->	Union[str, Any]:
    set_seed(42					)
    lowerCamelCase__       : Dict   					=						RegressionModel()
    lowerCamelCase__       : List[str]   					=						deepcopy(_UpperCAmelCase					)
    lowerCamelCase__       : List[str]   					=						RegressionDataset(length=_UpperCAmelCase					)
    lowerCamelCase__       : Union[str, Any]   					=						DataLoader(_UpperCAmelCase		,      batch_size=_UpperCAmelCase					)
    model.to(accelerator.device					)
    lowerCamelCase__					,				lowerCamelCase__       : Optional[Any]   					=						accelerator.prepare(_UpperCAmelCase		,      _UpperCAmelCase					)
    return model, ddp_model, dataloader


def 			SCREAMING_SNAKE_CASE (			_UpperCAmelCase		,      _UpperCAmelCase=False					)     ->	str:
    lowerCamelCase__       : str   					=						AutoTokenizer.from_pretrained('hf-internal-testing/mrpc-bert-base-cased'					)
    lowerCamelCase__       : Dict   					=						load_dataset('glue'		,      'mrpc'		,      split='validation'					)

    def tokenize_function(_UpperCAmelCase					):
        lowerCamelCase__       : Dict   					=						tokenizer(examples['sentence1']		,      examples['sentence2']		,      truncation=_UpperCAmelCase		,      max_length=_UpperCAmelCase					)
        return outputs

    with accelerator.main_process_first():
        lowerCamelCase__       : List[Any]   					=						dataset.map(
            _UpperCAmelCase		,      batched=_UpperCAmelCase		,      remove_columns=['idx', 'sentence1', 'sentence2']		,      )

    lowerCamelCase__       : Union[str, Any]   					=						tokenized_datasets.rename_column('label'		,      'labels'					)

    def collate_fn(_UpperCAmelCase					):
        if use_longest:
            return tokenizer.pad(_UpperCAmelCase		,      padding='longest'		,      return_tensors='pt'					)
        return tokenizer.pad(_UpperCAmelCase		,      padding='max_length'		,      max_length=128		,      return_tensors='pt'					)

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


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


def 			SCREAMING_SNAKE_CASE (			_UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase					)     ->	int:
    lowerCamelCase__       : Dict   					=						[]
    for batch in dataloader:
        lowerCamelCase__					,				lowerCamelCase__       : Any   					=						batch.values()
        with torch.no_grad():
            lowerCamelCase__       : Union[str, Any]   					=						model(_UpperCAmelCase					)
            lowerCamelCase__					,				lowerCamelCase__       : str   					=						accelerator.gather_for_metrics((logit, target)					)
            logits_and_targets.append((logit, target)					)
    lowerCamelCase__					,				lowerCamelCase__       : Optional[Any]   					=						[], []
    for logit, targ in logits_and_targets:
        logits.append(_UpperCAmelCase					)
        targs.append(_UpperCAmelCase					)
    lowerCamelCase__					,				lowerCamelCase__       : List[str]   					=						torch.cat(_UpperCAmelCase					), torch.cat(_UpperCAmelCase					)
    return logits, targs


def 			SCREAMING_SNAKE_CASE (			_UpperCAmelCase		,      _UpperCAmelCase=82		,      _UpperCAmelCase=False		,      _UpperCAmelCase=False		,      _UpperCAmelCase=16					)     ->	Any:
    lowerCamelCase__					,				lowerCamelCase__					,				lowerCamelCase__       : Optional[Any]   					=						get_basic_setup(_UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase					)
    lowerCamelCase__					,				lowerCamelCase__       : str   					=						generate_predictions(_UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase					)
    assert (
        len(_UpperCAmelCase					) == num_samples
    ), F"""Unexpected number of inputs:\n    Expected: {num_samples}\n    Actual: {len(_UpperCAmelCase					)}"""


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

    # Then do distributed
    lowerCamelCase__					,				lowerCamelCase__					,				lowerCamelCase__       : Optional[int]   					=						setup['ddp']
    model.eval()
    for batch in dataloader:
        with torch.inference_mode():
            lowerCamelCase__       : Union[str, Any]   					=						model(**_UpperCAmelCase					)
        lowerCamelCase__       : int   					=						outputs.logits.argmax(dim=-1					)
        lowerCamelCase__       : Dict   					=						batch['labels']
        lowerCamelCase__					,				lowerCamelCase__       : int   					=						accelerator.gather_for_metrics((preds, references)					)
        metric.add_batch(predictions=_UpperCAmelCase		,      references=_UpperCAmelCase					)
    lowerCamelCase__       : 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 			SCREAMING_SNAKE_CASE (			)     ->	Dict:
    lowerCamelCase__       : Optional[int]   					=						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]:
            lowerCamelCase__       : List[str]   					=						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**'					)
    lowerCamelCase__       : List[Any]   					=						Accelerator()
    test_torch_metrics(_UpperCAmelCase		,      512					)
    accelerator.state._reset_state()


def 			SCREAMING_SNAKE_CASE (			_UpperCAmelCase					)     ->	Any:
    # For xla_spawn (TPUs)
    main()


if __name__ == "__main__":
    main()












import pytest


_UpperCAmelCase							:			Optional[int]        =							"""__dummy_dataset1__"""

_UpperCAmelCase							:			Dict        =							"""
import json
import os

import datasets


REPO_URL = \"https://huggingface.co/datasets/albertvillanova/tests-raw-jsonl/resolve/main/\"
URLS = {\"train\": REPO_URL + \"wikiann-bn-train.jsonl\", \"validation\": REPO_URL + \"wikiann-bn-validation.jsonl\"}


class __DummyDataset1__(datasets.GeneratorBasedBuilder):

    def _info(self):
        features = datasets.Features(
            {
                \"tokens\": datasets.Sequence(datasets.Value(\"string\")),
                \"ner_tags\": datasets.Sequence(
                    datasets.features.ClassLabel(
                        names=[
                            \"O\",
                            \"B-PER\",
                            \"I-PER\",
                            \"B-ORG\",
                            \"I-ORG\",
                            \"B-LOC\",
                            \"I-LOC\",
                        ]
                    )
                ),
                \"langs\": datasets.Sequence(datasets.Value(\"string\")),
                \"spans\": datasets.Sequence(datasets.Value(\"string\")),
            }
        )
        return datasets.DatasetInfo(features=features)

    def _split_generators(self, dl_manager):
        dl_path = dl_manager.download(URLS)
        return [
            datasets.SplitGenerator(datasets.Split.TRAIN, gen_kwargs={\"filepath\": dl_path[\"train\"]}),
            datasets.SplitGenerator(datasets.Split.VALIDATION, gen_kwargs={\"filepath\": dl_path[\"validation\"]}),
        ]

    def _generate_examples(self, filepath):
        with open(filepath, \"r\", encoding=\"utf-8\") as f:
            for i, line in enumerate(f):
                yield i, json.loads(line)
"""


@pytest.fixture
def 			SCREAMING_SNAKE_CASE (			)     ->	Dict:
    return DATASET_LOADING_SCRIPT_NAME


@pytest.fixture
def 			SCREAMING_SNAKE_CASE (			)     ->	List[Any]:
    return DATASET_LOADING_SCRIPT_CODE


@pytest.fixture
def 			SCREAMING_SNAKE_CASE (			_UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase					)     ->	List[Any]:
    lowerCamelCase__       : int   					=						dataset_loading_script_name
    lowerCamelCase__       : Any   					=						tmp_path / 'datasets' / script_name
    script_dir.mkdir(parents=_UpperCAmelCase					)
    lowerCamelCase__       : str   					=						script_dir / F"""{script_name}.py"""
    with open(_UpperCAmelCase		,      'w'					) as f:
        f.write(_UpperCAmelCase					)
    return str(_UpperCAmelCase					)





