


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					(SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE=82      ,      SCREAMING_SNAKE_CASE=16      )->						Tuple:




      """simple docstring"""

      set_seed(42      )
      snake_case_ =   RegressionModel()
      snake_case_ =   deepcopy(SCREAMING_SNAKE_CASE      )
      snake_case_ =   RegressionDataset(length=SCREAMING_SNAKE_CASE      )
      snake_case_ =   DataLoader(SCREAMING_SNAKE_CASE      ,      batch_size=SCREAMING_SNAKE_CASE      )
      model.to(accelerator.device      )
      snake_case_ ,					snake_case_ =   accelerator.prepare(SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      )
      return model, ddp_model, dataloader






def  __lowerCAmelCase					(SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE=False      )->						Any:




      """simple docstring"""

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

      def tokenize_function(SCREAMING_SNAKE_CASE      ):
            snake_case_ =   tokenizer(examples['''sentence1''']      ,      examples['''sentence2''']      ,      truncation=SCREAMING_SNAKE_CASE      ,      max_length=SCREAMING_SNAKE_CASE      )
            return outputs

      with accelerator.main_process_first():
            snake_case_ =   dataset.map(
                SCREAMING_SNAKE_CASE      ,      batched=SCREAMING_SNAKE_CASE      ,      remove_columns=['''idx''', '''sentence1''', '''sentence2''']      ,      )

      snake_case_ =   tokenized_datasets.rename_column('''label'''      ,      '''labels'''      )

      def collate_fn(SCREAMING_SNAKE_CASE      ):
            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  __lowerCAmelCase					(SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      )->						Optional[Any]:




      """simple docstring"""

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






def  __lowerCAmelCase					(SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      )->						List[Any]:




      """simple docstring"""

      snake_case_ =   []
      for batch in dataloader:
            snake_case_ ,					snake_case_ =   batch.values()
            with torch.no_grad():
                  snake_case_ =   model(SCREAMING_SNAKE_CASE      )
                  snake_case_ ,					snake_case_ =   accelerator.gather_for_metrics((logit, target)      )
                  logits_and_targets.append((logit, target)      )
      snake_case_ ,					snake_case_ =   [], []
      for logit, targ in logits_and_targets:
            logits.append(SCREAMING_SNAKE_CASE      )
            targs.append(SCREAMING_SNAKE_CASE      )
      snake_case_ ,					snake_case_ =   torch.cat(SCREAMING_SNAKE_CASE      ), torch.cat(SCREAMING_SNAKE_CASE      )
      return logits, targs






def  __lowerCAmelCase					(SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE=82      ,      SCREAMING_SNAKE_CASE=False      ,      SCREAMING_SNAKE_CASE=False      ,      SCREAMING_SNAKE_CASE=16      )->						Dict:




      """simple docstring"""

      snake_case_ ,					snake_case_ ,					snake_case_ =   get_basic_setup(SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      )
      snake_case_ ,					snake_case_ =   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  __lowerCAmelCase					(SCREAMING_SNAKE_CASE = False      ,      SCREAMING_SNAKE_CASE = False      )->						Optional[int]:




      """simple docstring"""

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

      # Then do distributed
      snake_case_ ,					snake_case_ ,					snake_case_ =   setup['''ddp''']
      model.eval()
      for batch in dataloader:
            with torch.inference_mode():
                  snake_case_ =   model(**SCREAMING_SNAKE_CASE      )
            snake_case_ =   outputs.logits.argmax(dim=-1      )
            snake_case_ =   batch['''labels''']
            snake_case_ ,					snake_case_ =   accelerator.gather_for_metrics((preds, references)      )
            metric.add_batch(predictions=SCREAMING_SNAKE_CASE      ,      references=SCREAMING_SNAKE_CASE      )
      snake_case_ =   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					()->						Union[str, Any]:




      """simple docstring"""

      snake_case_ =   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]:
                  snake_case_ =   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**'''      )
      snake_case_ =   Accelerator()
      test_torch_metrics(SCREAMING_SNAKE_CASE      ,      512      )
      accelerator.state._reset_state()






def  __lowerCAmelCase					(SCREAMING_SNAKE_CASE      )->						Dict:




      """simple docstring"""

      main()


if __name__ == "__main__":
   main()



def  __lowerCAmelCase					(SCREAMING_SNAKE_CASE = 3      ,      SCREAMING_SNAKE_CASE = 7      ,      SCREAMING_SNAKE_CASE = 100_0000      )->						int:




      """simple docstring"""

      snake_case_ =   0
      snake_case_ =   1

      for current_denominator in range(1      ,      limit + 1      ):
            snake_case_ =   current_denominator * numerator // denominator
            if current_denominator % denominator == 0:
                  current_numerator -= 1
            if current_numerator * max_denominator > current_denominator * max_numerator:
                  snake_case_ =   current_numerator
                  snake_case_ =   current_denominator
      return max_numerator


if __name__ == "__main__":
   print(solution(numerator=3, denominator=7, limit=100_0000))