





'''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


_SCREAMING_SNAKE_CASE									=				"true"


def 						__a(SCREAMING_SNAKE_CASE_	:       Optional[int]		,							SCREAMING_SNAKE_CASE_	:       str=82		,							SCREAMING_SNAKE_CASE_	:       Dict=16     ):



    '''simple docstring'''





    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_	:       int=False     ):



    '''simple docstring'''





    _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[str]     ):
        _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_	:       str     ):
        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=128		,							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_	:       Optional[Any]		,							SCREAMING_SNAKE_CASE_	:       List[str]     ):



    '''simple docstring'''





    _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_	:       int		,							SCREAMING_SNAKE_CASE_	:       Optional[int]		,							SCREAMING_SNAKE_CASE_	:       List[str]     ):



    '''simple docstring'''





    _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_	:       List[Any]=82		,							SCREAMING_SNAKE_CASE_	:       Optional[int]=False		,							SCREAMING_SNAKE_CASE_	:       List[Any]=False		,							SCREAMING_SNAKE_CASE_	:       Union[str, Any]=16     ):



    '''simple docstring'''





    _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     ):



    '''simple docstring'''





    _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():



    '''simple docstring'''





    _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_		,							512     )
    accelerator.state._reset_state()


def 						__a(SCREAMING_SNAKE_CASE_	:       Optional[Any]     ):



    '''simple docstring'''





    main()


if __name__ == "__main__":
       main()



'''simple docstring'''







import os


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_doctest_list.py
UpperCamelCase_      	=	'''.'''


if __name__ == "__main__":
		UpperCamelCase_      	=	os.path.join(REPO_PATH, '''utils/documentation_tests.txt''')
		UpperCamelCase_      	=	[]
		UpperCamelCase_      	=	[]
		with open(doctest_file_path) as fp:
				for line in fp:
						UpperCamelCase_      	=	line.strip()
						UpperCamelCase_      	=	os.path.join(REPO_PATH, line)
						if not (os.path.isfile(path) or os.path.isdir(path)):
								non_existent_paths.append(line)
						all_paths.append(path)
		if len(non_existent_paths) > 0:
				UpperCamelCase_      	=	'''\n'''.join(non_existent_paths)
				raise ValueError(f"`utils/documentation_tests.txt` contains non-existent paths:\n{non_existent_paths}")
		if all_paths != sorted(all_paths):
				raise ValueError('''Files in `utils/documentation_tests.txt` are not in alphabetical order.''')






