





'''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__    :       Dict              ='''true'''
def        UpperCamelCase__				(		lowerCAmelCase							,     lowerCAmelCase=82							,     lowerCAmelCase=16  ):





      """simple docstring"""


      set_seed(42  )
      _lowerCAmelCase												=							RegressionModel()
      _lowerCAmelCase												=							deepcopy(_a  )
      _lowerCAmelCase												=							RegressionDataset(length=_a  )
      _lowerCAmelCase												=							DataLoader(_a							,     batch_size=_a  )
      model.to(accelerator.device  )
      _lowerCAmelCase												=							accelerator.prepare(_a							,     _a  )
      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=_a							,     max_length=_a  )
            return outputs

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

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

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

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





      """simple docstring"""


      _lowerCAmelCase												=							Accelerator(dispatch_batches=_a							,     split_batches=_a  )
      _lowerCAmelCase												=							get_dataloader(_a							,     not dispatch_batches  )
      _lowerCAmelCase												=							AutoModelForSequenceClassification.from_pretrained(
          """hf-internal-testing/mrpc-bert-base-cased"""							,     return_dict=_a  )
      _lowerCAmelCase												=							accelerator.prepare(_a							,     _a  )
      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												=							batch.values()
            with torch.no_grad():
                  _lowerCAmelCase												=							model(_a  )
                  _lowerCAmelCase												=							accelerator.gather_for_metrics((logit, target)  )
                  logits_and_targets.append((logit, target)  )
      _lowerCAmelCase												=							[], []
      for logit, targ in logits_and_targets:
            logits.append(_a  )
            targs.append(_a  )
      _lowerCAmelCase												=							torch.cat(_a  ), torch.cat(_a  )
      return logits, targs
def        UpperCamelCase__				(		lowerCAmelCase							,     lowerCAmelCase=82							,     lowerCAmelCase=False							,     lowerCAmelCase=False							,     lowerCAmelCase=16  ):





      """simple docstring"""


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





      """simple docstring"""


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

      # Then do distributed
      _lowerCAmelCase												=							setup["""ddp"""]
      model.eval()
      for batch in dataloader:
            with torch.inference_mode():
                  _lowerCAmelCase												=							model(**_a  )
            _lowerCAmelCase												=							outputs.logits.argmax(dim=-1  )
            _lowerCAmelCase												=							batch["""labels"""]
            _lowerCAmelCase												=							accelerator.gather_for_metrics((preds, references)  )
            metric.add_batch(predictions=_a							,     references=_a  )
      _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=_a							,     dispatch_batches=_a  )
      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(_a							,     _a  )
                        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=_a							,     dispatch_batches=_a  )
                  if accelerator.is_local_main_process:
                        print(f"With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`, length=99"  )
                  test_torch_metrics(_a							,     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(_a							,     5_12  )
      accelerator.state._reset_state()
def        UpperCamelCase__				(		lowerCAmelCase  ):





      """simple docstring"""


      main()


if __name__ == "__main__":
      main()













'''simple docstring'''






import unittest

from transformers import MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING, is_vision_available
from transformers.pipelines import pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_vision_available():
      from PIL import Image
else:




      class 			UpperCAmelCase					:




            @staticmethod
            def     lowercase__   (      *__snake_case      :			Optional[Any]      ,      **__snake_case      :			Any  )    ->				Tuple:
                  pass




@is_pipeline_test
@require_torch
@require_vision
class 			UpperCAmelCase					(      unittest.TestCase       ):
      _lowercase:      Union[str, Any]											=       MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING




      def     lowercase__   (      self      :			List[str]      ,      __snake_case      :			Optional[Any]      ,      __snake_case      :			str      ,      __snake_case      :			List[str]  )    ->				int:
            _lowerCAmelCase												=							pipeline("""visual-question-answering"""      ,      model="""hf-internal-testing/tiny-vilt-random-vqa"""  )
            _lowerCAmelCase												=							[
                {
                    """image""": Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""  ),
                    """question""": """How many cats are there?""",
                },
                {
                    """image""": """./tests/fixtures/tests_samples/COCO/000000039769.png""",
                    """question""": """How many cats are there?""",
                },
            ]
            return vqa_pipeline, examples




      def     lowercase__   (      self      :			Any      ,      __snake_case      :			List[Any]      ,      __snake_case      :			List[Any]  )    ->				Union[str, Any]:
            _lowerCAmelCase												=							vqa_pipeline(__snake_case      ,      top_k=1  )
            self.assertEqual(
                __snake_case      ,      [
                    [{"""score""": ANY(__snake_case  ), """answer""": ANY(__snake_case  )}],
                    [{"""score""": ANY(__snake_case  ), """answer""": ANY(__snake_case  )}],
                ]      ,      )




      @require_torch
      def     lowercase__   (      self      :			str  )    ->				int:
            _lowerCAmelCase												=							pipeline("""visual-question-answering"""      ,      model="""hf-internal-testing/tiny-vilt-random-vqa"""  )
            _lowerCAmelCase												=							"""./tests/fixtures/tests_samples/COCO/000000039769.png"""
            _lowerCAmelCase												=							"""How many cats are there?"""

            _lowerCAmelCase												=							vqa_pipeline(image=__snake_case      ,      question="""How many cats are there?"""      ,      top_k=2  )
            self.assertEqual(
                __snake_case      ,      [{"""score""": ANY(__snake_case  ), """answer""": ANY(__snake_case  )}, {"""score""": ANY(__snake_case  ), """answer""": ANY(__snake_case  )}]  )

            _lowerCAmelCase												=							vqa_pipeline({"""image""": image, """question""": question}      ,      top_k=2  )
            self.assertEqual(
                __snake_case      ,      [{"""score""": ANY(__snake_case  ), """answer""": ANY(__snake_case  )}, {"""score""": ANY(__snake_case  ), """answer""": ANY(__snake_case  )}]  )




      @slow
      @require_torch
      def     lowercase__   (      self      :			List[Any]  )    ->				List[str]:
            _lowerCAmelCase												=							pipeline("""visual-question-answering"""      ,      model="""dandelin/vilt-b32-finetuned-vqa"""  )
            _lowerCAmelCase												=							"""./tests/fixtures/tests_samples/COCO/000000039769.png"""
            _lowerCAmelCase												=							"""How many cats are there?"""

            _lowerCAmelCase												=							vqa_pipeline(image=__snake_case      ,      question=__snake_case      ,      top_k=2  )
            self.assertEqual(
                nested_simplify(__snake_case      ,      decimals=4  )      ,      [{"""score""": 0.87_99, """answer""": """2"""}, {"""score""": 0.2_96, """answer""": """1"""}]  )

            _lowerCAmelCase												=							vqa_pipeline({"""image""": image, """question""": question}      ,      top_k=2  )
            self.assertEqual(
                nested_simplify(__snake_case      ,      decimals=4  )      ,      [{"""score""": 0.87_99, """answer""": """2"""}, {"""score""": 0.2_96, """answer""": """1"""}]  )

            _lowerCAmelCase												=							vqa_pipeline(
                [{"""image""": image, """question""": question}, {"""image""": image, """question""": question}]      ,      top_k=2  )
            self.assertEqual(
                nested_simplify(__snake_case      ,      decimals=4  )      ,      [[{"""score""": 0.87_99, """answer""": """2"""}, {"""score""": 0.2_96, """answer""": """1"""}]] * 2      ,      )




      @require_tf
      @unittest.skip("""Visual question answering not implemented in TF"""  )
      def     lowercase__   (      self      :			List[str]  )    ->				Union[str, Any]:
            pass






