
"""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


UpperCamelCase__          =	'true'
def 					lowerCamelCase   (     _snake_case    ,_snake_case=82    ,_snake_case=16 ):
    set_seed(42 )
    UpperCAmelCase__	:      Optional[int]     							=					RegressionModel()
    UpperCAmelCase__	:      Tuple     							=					deepcopy(_snake_case )
    UpperCAmelCase__	:      List[str]     							=					RegressionDataset(length=_snake_case )
    UpperCAmelCase__	:      List[str]     							=					DataLoader(_snake_case    ,batch_size=_snake_case )
    model.to(accelerator.device )
    UpperCAmelCase__							,							UpperCAmelCase__	:      Dict     							=					accelerator.prepare(_snake_case    ,_snake_case )
    return model, ddp_model, dataloader
def 					lowerCamelCase   (     _snake_case    ,_snake_case=False ):
    UpperCAmelCase__	:      Any     							=					AutoTokenizer.from_pretrained('hf-internal-testing/mrpc-bert-base-cased' )
    UpperCAmelCase__	:      Tuple     							=					load_dataset('glue'    ,'mrpc'    ,split='validation' )

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

    with accelerator.main_process_first():
        UpperCAmelCase__	:      Union[str, Any]     							=					dataset.map(
            _snake_case    ,batched=_snake_case    ,remove_columns=['idx', 'sentence1', 'sentence2']    ,)

    UpperCAmelCase__	:      Optional[int]     							=					tokenized_datasets.rename_column('label'    ,'labels' )

    def collate_fn(_snake_case ):
        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   (     _snake_case    ,_snake_case ):
    UpperCAmelCase__	:      str     							=					Accelerator(dispatch_batches=_snake_case    ,split_batches=_snake_case )
    UpperCAmelCase__	:      Optional[int]     							=					get_dataloader(_snake_case    ,not dispatch_batches )
    UpperCAmelCase__	:      Union[str, Any]     							=					AutoModelForSequenceClassification.from_pretrained(
        'hf-internal-testing/mrpc-bert-base-cased'    ,return_dict=_snake_case )
    UpperCAmelCase__							,							UpperCAmelCase__	:      Optional[Any]     							=					accelerator.prepare(_snake_case    ,_snake_case )
    return {"ddp": [ddp_model, ddp_dataloader, "cuda:0"], "no": [model, dataloader, accelerator.device]}, accelerator
def 					lowerCamelCase   (     _snake_case    ,_snake_case    ,_snake_case ):
    UpperCAmelCase__	:      Dict     							=					[]
    for batch in dataloader:
        UpperCAmelCase__							,							UpperCAmelCase__	:      Any     							=					batch.values()
        with torch.no_grad():
            UpperCAmelCase__	:      Tuple     							=					model(_snake_case )
            UpperCAmelCase__							,							UpperCAmelCase__	:      int     							=					accelerator.gather_for_metrics((logit, target) )
            logits_and_targets.append((logit, target) )
    UpperCAmelCase__							,							UpperCAmelCase__	:      str     							=					[], []
    for logit, targ in logits_and_targets:
        logits.append(_snake_case )
        targs.append(_snake_case )
    UpperCAmelCase__							,							UpperCAmelCase__	:      Tuple     							=					torch.cat(_snake_case ), torch.cat(_snake_case )
    return logits, targs
def 					lowerCamelCase   (     _snake_case    ,_snake_case=82    ,_snake_case=False    ,_snake_case=False    ,_snake_case=16 ):
    UpperCAmelCase__							,							UpperCAmelCase__							,							UpperCAmelCase__	:      Optional[Any]     							=					get_basic_setup(_snake_case    ,_snake_case    ,_snake_case )
    UpperCAmelCase__							,							UpperCAmelCase__	:      Union[str, Any]     							=					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   (     _snake_case = False    ,_snake_case = False ):
    UpperCAmelCase__	:      List[Any]     							=					evaluate.load('glue'    ,'mrpc' )
    UpperCAmelCase__							,							UpperCAmelCase__	:      int     							=					get_mrpc_setup(_snake_case    ,_snake_case )
    # First do baseline
    UpperCAmelCase__							,							UpperCAmelCase__							,							UpperCAmelCase__	:      int     							=					setup['no']
    model.to(_snake_case )
    model.eval()
    for batch in dataloader:
        batch.to(_snake_case )
        with torch.inference_mode():
            UpperCAmelCase__	:      List[str]     							=					model(**_snake_case )
        UpperCAmelCase__	:      Optional[int]     							=					outputs.logits.argmax(dim=-1 )
        metric.add_batch(predictions=_snake_case    ,references=batch['labels'] )
    UpperCAmelCase__	:      List[Any]     							=					metric.compute()

    # Then do distributed
    UpperCAmelCase__							,							UpperCAmelCase__							,							UpperCAmelCase__	:      Optional[int]     							=					setup['ddp']
    model.eval()
    for batch in dataloader:
        with torch.inference_mode():
            UpperCAmelCase__	:      str     							=					model(**_snake_case )
        UpperCAmelCase__	:      int     							=					outputs.logits.argmax(dim=-1 )
        UpperCAmelCase__	:      Optional[Any]     							=					batch['labels']
        UpperCAmelCase__							,							UpperCAmelCase__	:      Dict     							=					accelerator.gather_for_metrics((preds, references) )
        metric.add_batch(predictions=_snake_case    ,references=_snake_case )
    UpperCAmelCase__	:      Any     							=					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   (     ):
    UpperCAmelCase__	:      Optional[Any]     							=					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]:
            UpperCAmelCase__	:      Dict     							=					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**' )
    UpperCAmelCase__	:      Any     							=					Accelerator()
    test_torch_metrics(_snake_case    ,512 )
    accelerator.state._reset_state()
def 					lowerCamelCase   (     _snake_case ):
    # For xla_spawn (TPUs)
    main()


if __name__ == "__main__":
    main()



"""simple docstring"""



UpperCamelCase__          =	{
    'meter': 'm',
    'kilometer': 'km',
    'megametre': 'Mm',
    'gigametre': 'Gm',
    'terametre': 'Tm',
    'petametre': 'Pm',
    'exametre': 'Em',
    'zettametre': 'Zm',
    'yottametre': 'Ym',
}
# Exponent of the factor(meter)
UpperCamelCase__          =	{
    'm': 0,
    'km': 3,
    'Mm': 6,
    'Gm': 9,
    'Tm': 12,
    'Pm': 15,
    'Em': 18,
    'Zm': 21,
    'Ym': 24,
}
def 					lowerCamelCase   (     _snake_case    ,_snake_case    ,_snake_case ):
    UpperCAmelCase__	:      int     							=					from_type.lower().strip('s' )
    UpperCAmelCase__	:      List[Any]     							=					to_type.lower().strip('s' )

    UpperCAmelCase__	:      Optional[int]     							=					UNIT_SYMBOL.get(_snake_case    ,_snake_case )
    UpperCAmelCase__	:      Optional[Any]     							=					UNIT_SYMBOL.get(_snake_case    ,_snake_case )

    if from_sanitized not in METRIC_CONVERSION:
        UpperCAmelCase__	:      Optional[int]     							=					(
            F'''Invalid \'from_type\' value: {from_type!r}.\n'''
            F'''Conversion abbreviations are: {', '.join(_snake_case )}'''
        )
        raise ValueError(_snake_case )
    if to_sanitized not in METRIC_CONVERSION:
        UpperCAmelCase__	:      int     							=					(
            F'''Invalid \'to_type\' value: {to_type!r}.\n'''
            F'''Conversion abbreviations are: {', '.join(_snake_case )}'''
        )
        raise ValueError(_snake_case )
    UpperCAmelCase__	:      str     							=					METRIC_CONVERSION[from_sanitized]
    UpperCAmelCase__	:      Optional[int]     							=					METRIC_CONVERSION[to_sanitized]
    UpperCAmelCase__	:      int     							=					1

    if from_exponent > to_exponent:
        UpperCAmelCase__	:      List[str]     							=					from_exponent - to_exponent
    else:
        UpperCAmelCase__	:      int     							=					-(to_exponent - from_exponent)

    return value * pow(10    ,_snake_case )


if __name__ == "__main__":
    from doctest import testmod

    testmod()

