





'''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


A__    :       Tuple              ='''true'''
def        UpperCamelCase__				(		lowerCAmelCase							,     lowerCAmelCase=82							,     lowerCAmelCase=16  ):





      """simple docstring"""


      set_seed(42  )
      _lowerCAmelCase												=							RegressionModel()
      _lowerCAmelCase												=							deepcopy(lowerCAmelCase  )
      _lowerCAmelCase												=							RegressionDataset(length=lowerCAmelCase  )
      _lowerCAmelCase												=							DataLoader(lowerCAmelCase							,     batch_size=lowerCAmelCase  )
      model.to(accelerator.device  )
      _lowerCAmelCase					,							_lowerCAmelCase												=							accelerator.prepare(lowerCAmelCase							,     lowerCAmelCase  )
      return model, ddp_model, dataloader
def        UpperCamelCase__				(		lowerCAmelCase							,     lowerCAmelCase=False  ):





      """simple docstring"""


      _lowerCAmelCase												=							AutoTokenizer.from_pretrained("""hf-internal-testing/mrpc-bert-base-cased"""  )
      _lowerCAmelCase												=							load_dataset("""glue"""							,     """mrpc"""							,     split="""validation"""  )

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

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

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

      def collate_fn(lowerCAmelCase  ):
            if use_longest:
                  return tokenizer.pad(lowerCAmelCase							,     padding="""longest"""							,     return_tensors="""pt"""  )
            return tokenizer.pad(lowerCAmelCase							,     padding="""max_length"""							,     max_length=1_28							,     return_tensors="""pt"""  )

      return DataLoader(lowerCAmelCase							,     shuffle=lowerCAmelCase							,     collate_fn=lowerCAmelCase							,     batch_size=16  )
def        UpperCamelCase__				(		lowerCAmelCase							,     lowerCAmelCase  ):





      """simple docstring"""


      _lowerCAmelCase												=							Accelerator(dispatch_batches=lowerCAmelCase							,     split_batches=lowerCAmelCase  )
      _lowerCAmelCase												=							get_dataloader(lowerCAmelCase							,     not dispatch_batches  )
      _lowerCAmelCase												=							AutoModelForSequenceClassification.from_pretrained(
          """hf-internal-testing/mrpc-bert-base-cased"""							,     return_dict=lowerCAmelCase  )
      _lowerCAmelCase					,							_lowerCAmelCase												=							accelerator.prepare(lowerCAmelCase							,     lowerCAmelCase  )
      return {"ddp": [ddp_model, ddp_dataloader, "cuda:0"], "no": [model, dataloader, accelerator.device]}, accelerator
def        UpperCamelCase__				(		lowerCAmelCase							,     lowerCAmelCase							,     lowerCAmelCase  ):





      """simple docstring"""


      _lowerCAmelCase												=							[]
      for batch in dataloader:
            _lowerCAmelCase					,							_lowerCAmelCase												=							batch.values()
            with torch.no_grad():
                  _lowerCAmelCase												=							model(lowerCAmelCase  )
                  _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(lowerCAmelCase  )
            targs.append(lowerCAmelCase  )
      _lowerCAmelCase					,							_lowerCAmelCase												=							torch.cat(lowerCAmelCase  ), torch.cat(lowerCAmelCase  )
      return logits, targs
def        UpperCamelCase__				(		lowerCAmelCase							,     lowerCAmelCase=82							,     lowerCAmelCase=False							,     lowerCAmelCase=False							,     lowerCAmelCase=16  ):





      """simple docstring"""


      _lowerCAmelCase					,							_lowerCAmelCase					,							_lowerCAmelCase												=							get_basic_setup(lowerCAmelCase							,     lowerCAmelCase							,     lowerCAmelCase  )
      _lowerCAmelCase					,							_lowerCAmelCase												=							generate_predictions(lowerCAmelCase							,     lowerCAmelCase							,     lowerCAmelCase  )
      assert (
          len(lowerCAmelCase  ) == num_samples
      ), f"Unexpected number of inputs:\n    Expected: {num_samples}\n    Actual: {len(lowerCAmelCase  )}"
def        UpperCamelCase__				(		lowerCAmelCase = False							,     lowerCAmelCase = False  ):





      """simple docstring"""


      _lowerCAmelCase												=							evaluate.load("""glue"""							,     """mrpc"""  )
      _lowerCAmelCase					,							_lowerCAmelCase												=							get_mrpc_setup(lowerCAmelCase							,     lowerCAmelCase  )
      # First do baseline
      _lowerCAmelCase					,							_lowerCAmelCase					,							_lowerCAmelCase												=							setup["""no"""]
      model.to(lowerCAmelCase  )
      model.eval()
      for batch in dataloader:
            batch.to(lowerCAmelCase  )
            with torch.inference_mode():
                  _lowerCAmelCase												=							model(**lowerCAmelCase  )
            _lowerCAmelCase												=							outputs.logits.argmax(dim=-1  )
            metric.add_batch(predictions=lowerCAmelCase							,     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(**lowerCAmelCase  )
            _lowerCAmelCase												=							outputs.logits.argmax(dim=-1  )
            _lowerCAmelCase												=							batch["""labels"""]
            _lowerCAmelCase					,							_lowerCAmelCase												=							accelerator.gather_for_metrics((preds, references)  )
            metric.add_batch(predictions=lowerCAmelCase							,     references=lowerCAmelCase  )
      _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        UpperCamelCase__				(		):





      """simple docstring"""


      _lowerCAmelCase												=							Accelerator(split_batches=lowerCAmelCase							,     dispatch_batches=lowerCAmelCase  )
      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(lowerCAmelCase							,     lowerCAmelCase  )
                        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=lowerCAmelCase							,     dispatch_batches=lowerCAmelCase  )
                  if accelerator.is_local_main_process:
                        print(f"With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`, length=99"  )
                  test_torch_metrics(lowerCAmelCase							,     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(lowerCAmelCase							,     5_12  )
      accelerator.state._reset_state()
def        UpperCamelCase__				(		lowerCAmelCase  ):





      """simple docstring"""


      main()


if __name__ == "__main__":
      main()













'''simple docstring'''






import json
import os
import tempfile

import datasets
from utils import generate_example_dataset, get_duration


A__    :       str              =5_00_00
A__    :       Optional[int]              =50_00

A__       ,						A__    :       Optional[int]              =os.path.split(__file__)
A__    :       Tuple              =os.path.join(RESULTS_BASEPATH, '''results''', RESULTS_FILENAME.replace('''.py''', '''.json'''))
@get_duration
def        UpperCamelCase__				(		lowerCAmelCase							,     lowerCAmelCase  ):





      """simple docstring"""


      for i in range(lowerCAmelCase  ):
            _lowerCAmelCase												=							dataset[i]
@get_duration
def        UpperCamelCase__				(		lowerCAmelCase							,     lowerCAmelCase							,     lowerCAmelCase  ):





      """simple docstring"""


      for i in range(0							,     len(lowerCAmelCase  )							,     lowerCAmelCase  ):
            _lowerCAmelCase												=							dataset[i : i + batch_size]
@get_duration
def        UpperCamelCase__				(		lowerCAmelCase							,     lowerCAmelCase							,     lowerCAmelCase  ):





      """simple docstring"""


      with dataset.formatted_as(type=lowerCAmelCase  ):
            for i in range(lowerCAmelCase  ):
                  _lowerCAmelCase												=							dataset[i]
@get_duration
def        UpperCamelCase__				(		lowerCAmelCase							,     lowerCAmelCase							,     lowerCAmelCase							,     lowerCAmelCase  ):





      """simple docstring"""


      with dataset.formatted_as(type=lowerCAmelCase  ):
            for i in range(0							,     lowerCAmelCase							,     lowerCAmelCase  ):
                  _lowerCAmelCase												=							dataset[i : i + batch_size]
def        UpperCamelCase__				(		):





      """simple docstring"""


      _lowerCAmelCase												=							{"""num examples""": SPEED_TEST_N_EXAMPLES}
      _lowerCAmelCase												=							[
          (read, {"""length""": SMALL_TEST}),
          (read, {"""length""": SPEED_TEST_N_EXAMPLES}),
          (read_batch, {"""length""": SPEED_TEST_N_EXAMPLES, """batch_size""": 10}),
          (read_batch, {"""length""": SPEED_TEST_N_EXAMPLES, """batch_size""": 1_00}),
          (read_batch, {"""length""": SPEED_TEST_N_EXAMPLES, """batch_size""": 10_00}),
          (read_formatted, {"""type""": """numpy""", """length""": SMALL_TEST}),
          (read_formatted, {"""type""": """pandas""", """length""": SMALL_TEST}),
          (read_formatted, {"""type""": """torch""", """length""": SMALL_TEST}),
          (read_formatted, {"""type""": """tensorflow""", """length""": SMALL_TEST}),
          (read_formatted_batch, {"""type""": """numpy""", """length""": SMALL_TEST, """batch_size""": 10}),
          (read_formatted_batch, {"""type""": """numpy""", """length""": SMALL_TEST, """batch_size""": 10_00}),
      ]

      _lowerCAmelCase												=							[
          (read, {"""length""": SMALL_TEST}),
          (read, {"""length""": SPEED_TEST_N_EXAMPLES}),
          (read_batch, {"""length""": SPEED_TEST_N_EXAMPLES, """batch_size""": 10}),
          (read_batch, {"""length""": SPEED_TEST_N_EXAMPLES, """batch_size""": 1_00}),
          (read_batch, {"""length""": SPEED_TEST_N_EXAMPLES, """batch_size""": 10_00}),
          (read_formatted, {"""type""": """numpy""", """length""": SMALL_TEST}),
          (read_formatted_batch, {"""type""": """numpy""", """length""": SMALL_TEST, """batch_size""": 10}),
          (read_formatted_batch, {"""type""": """numpy""", """length""": SMALL_TEST, """batch_size""": 10_00}),
      ]
      with tempfile.TemporaryDirectory() as tmp_dir:
            print("""generating dataset"""  )
            _lowerCAmelCase												=							datasets.Features(
                {"""list""": datasets.Sequence(datasets.Value("""float32"""  )  ), """numbers""": datasets.Value("""float32"""  )}  )
            _lowerCAmelCase												=							generate_example_dataset(
                os.path.join(lowerCAmelCase							,     """dataset.arrow"""  )							,     lowerCAmelCase							,     num_examples=lowerCAmelCase							,     seq_shapes={"""list""": (1_00,)}							,     )
            print("""first set of iterations"""  )
            for func, kwargs in functions:
                  print(func.__name__							,     str(lowerCAmelCase  )  )
                  _lowerCAmelCase												=							func(lowerCAmelCase							,     **lowerCAmelCase  )

            print("""shuffling dataset"""  )
            _lowerCAmelCase												=							dataset.shuffle()
            print("""Second set of iterations (after shuffling"""  )
            for func, kwargs in functions_shuffled:
                  print("""shuffled """							,     func.__name__							,     str(lowerCAmelCase  )  )
                  _lowerCAmelCase												=							func(
                      lowerCAmelCase							,     **lowerCAmelCase  )

      with open(lowerCAmelCase							,     """wb"""  ) as f:
            f.write(json.dumps(lowerCAmelCase  ).encode("""utf-8"""  )  )


if __name__ == "__main__":  # useful to run the profiler
      benchmark_iterating()






