

'''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


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

       def tokenize_function(UpperCAmelCase__					):
              A_						       =   tokenizer(examples["""sentence1"""],							examples["""sentence2"""],							truncation=UpperCAmelCase__,							max_length=UpperCAmelCase__					)
              return outputs

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

       A_						       =   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=1_28,							return_tensors="""pt"""					)

       return DataLoader(UpperCAmelCase__,							shuffle=UpperCAmelCase__,							collate_fn=UpperCAmelCase__,							batch_size=16					)
def    UpperCAmelCase__     (   UpperCAmelCase__,							UpperCAmelCase__					)      ->      Any:
       A_						       =   Accelerator(dispatch_batches=UpperCAmelCase__,							split_batches=UpperCAmelCase__					)
       A_						       =   get_dataloader(UpperCAmelCase__,							not dispatch_batches					)
       A_						       =   AutoModelForSequenceClassification.from_pretrained(
           """hf-internal-testing/mrpc-bert-base-cased""",							return_dict=UpperCAmelCase__					)
       A_       ,      A_						       =   accelerator.prepare(UpperCAmelCase__,							UpperCAmelCase__					)
       return {"ddp": [ddp_model, ddp_dataloader, "cuda:0"], "no": [model, dataloader, accelerator.device]}, accelerator
def    UpperCAmelCase__     (   UpperCAmelCase__,							UpperCAmelCase__,							UpperCAmelCase__					)      ->      List[str]:
       A_						       =   []
       for batch in dataloader:
              A_       ,      A_						       =   batch.values()
              with torch.no_grad():
                     A_						       =   model(UpperCAmelCase__					)
                     A_       ,      A_						       =   accelerator.gather_for_metrics((logit, target)					)
                     logits_and_targets.append((logit, target)					)
       A_       ,      A_						       =   [], []
       for logit, targ in logits_and_targets:
              logits.append(UpperCAmelCase__					)
              targs.append(UpperCAmelCase__					)
       A_       ,      A_						       =   torch.cat(UpperCAmelCase__					), torch.cat(UpperCAmelCase__					)
       return logits, targs
def    UpperCAmelCase__     (   UpperCAmelCase__,							UpperCAmelCase__=82,							UpperCAmelCase__=False,							UpperCAmelCase__=False,							UpperCAmelCase__=16					)      ->      List[Any]:
       A_       ,      A_       ,      A_						       =   get_basic_setup(UpperCAmelCase__,							UpperCAmelCase__,							UpperCAmelCase__					)
       A_       ,      A_						       =   generate_predictions(UpperCAmelCase__,							UpperCAmelCase__,							UpperCAmelCase__					)
       assert (
           len(UpperCAmelCase__					) == num_samples
       ), F'''Unexpected number of inputs:\n    Expected: {num_samples}\n    Actual: {len(UpperCAmelCase__					)}'''
def    UpperCAmelCase__     (   UpperCAmelCase__ = False,							UpperCAmelCase__ = False					)      ->      Tuple:
       A_						       =   evaluate.load("""glue""",							"""mrpc"""					)
       A_       ,      A_						       =   get_mrpc_setup(UpperCAmelCase__,							UpperCAmelCase__					)
       # First do baseline
       A_       ,      A_       ,      A_						       =   setup["""no"""]
       model.to(UpperCAmelCase__					)
       model.eval()
       for batch in dataloader:
              batch.to(UpperCAmelCase__					)
              with torch.inference_mode():
                     A_						       =   model(**UpperCAmelCase__					)
              A_						       =   outputs.logits.argmax(dim=-1					)
              metric.add_batch(predictions=UpperCAmelCase__,							references=batch["""labels"""]					)
       A_						       =   metric.compute()

       # Then do distributed
       A_       ,      A_       ,      A_						       =   setup["""ddp"""]
       model.eval()
       for batch in dataloader:
              with torch.inference_mode():
                     A_						       =   model(**UpperCAmelCase__					)
              A_						       =   outputs.logits.argmax(dim=-1					)
              A_						       =   batch["""labels"""]
              A_       ,      A_						       =   accelerator.gather_for_metrics((preds, references)					)
              metric.add_batch(predictions=UpperCAmelCase__,							references=UpperCAmelCase__					)
       A_						       =   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    UpperCAmelCase__     (   )      ->      Union[str, Any]:
       A_						       =   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]:
                     A_						       =   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**"""					)
       A_						       =   Accelerator()
       test_torch_metrics(UpperCAmelCase__,							5_12					)
       accelerator.state._reset_state()
def    UpperCAmelCase__     (   UpperCAmelCase__					)      ->      Union[str, Any]:
       # For xla_spawn (TPUs)
       main()


if __name__ == "__main__":
      main()



'''simple docstring'''







import pytest

import datasets.config
from datasets.utils.info_utils import is_small_dataset
@pytest.mark.parametrize("""dataset_size""",							[None, 4_00 * 2**20, 6_00 * 2**20]					)
@pytest.mark.parametrize("""input_in_memory_max_size""",							["""default""", 0, 1_00 * 2**20, 9_00 * 2**20]					)
def    UpperCAmelCase__     (   UpperCAmelCase__,							UpperCAmelCase__,							UpperCAmelCase__					)      ->      Dict:
       if input_in_memory_max_size != "default":
              monkeypatch.setattr(datasets.config,							"""IN_MEMORY_MAX_SIZE""",							UpperCAmelCase__					)
       A_						       =   datasets.config.IN_MEMORY_MAX_SIZE
       if input_in_memory_max_size == "default":
              assert in_memory_max_size == 0
       else:
              assert in_memory_max_size == input_in_memory_max_size
       if dataset_size and in_memory_max_size:
              A_						       =   dataset_size < in_memory_max_size
       else:
              A_						       =   False
       A_						       =   is_small_dataset(UpperCAmelCase__					)
       assert result == expected
