






"""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		:  List[Any]						=		'true'
def 						_SCREAMING_SNAKE_CASE (    __snake_case			:  Dict		,					__snake_case			:  Any=82		,					__snake_case			:  str=16    ):





  '''simple docstring'''





  set_seed(42    )
  lowercase							    =       RegressionModel()
  lowercase							    =       deepcopy(__snake_case    )
  lowercase							    =       RegressionDataset(length=__snake_case    )
  lowercase							    =       DataLoader(__snake_case		,					batch_size=__snake_case    )
  model.to(accelerator.device    )
  lowercase    ,     lowercase							    =       accelerator.prepare(__snake_case		,					__snake_case    )
  return model, ddp_model, dataloader
def 						_SCREAMING_SNAKE_CASE (    __snake_case			:  Accelerator		,					__snake_case			:  List[str]=False    ):





  '''simple docstring'''





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

  def tokenize_function(__snake_case			:  Optional[int]    ):
    lowercase							    =       tokenizer(examples['sentence1']		,					examples['sentence2']		,					truncation=__snake_case		,					max_length=__snake_case    )
    return outputs

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

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

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

  return DataLoader(__snake_case		,					shuffle=__snake_case		,					collate_fn=__snake_case		,					batch_size=16    )
def 						_SCREAMING_SNAKE_CASE (    __snake_case			:  List[Any]		,					__snake_case			:  Dict    ):





  '''simple docstring'''





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





  '''simple docstring'''





  lowercase							    =       []
  for batch in dataloader:
    lowercase    ,     lowercase							    =       batch.values()
    with torch.no_grad():
      lowercase							    =       model(__snake_case    )
      lowercase    ,     lowercase							    =       accelerator.gather_for_metrics((logit, target)    )
      logits_and_targets.append((logit, target)    )
  lowercase    ,     lowercase							    =       [], []
  for logit, targ in logits_and_targets:
    logits.append(__snake_case    )
    targs.append(__snake_case    )
  lowercase    ,     lowercase							    =       torch.cat(__snake_case    ), torch.cat(__snake_case    )
  return logits, targs
def 						_SCREAMING_SNAKE_CASE (    __snake_case			:  Accelerator		,					__snake_case			:  int=82		,					__snake_case			:  str=False		,					__snake_case			:  int=False		,					__snake_case			:  Optional[int]=16    ):





  '''simple docstring'''





  lowercase    ,     lowercase    ,     lowercase							    =       get_basic_setup(__snake_case		,					__snake_case		,					__snake_case    )
  lowercase    ,     lowercase							    =       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 						_SCREAMING_SNAKE_CASE (    __snake_case			:  bool = False		,					__snake_case			:  bool = False    ):





  '''simple docstring'''





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

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





  '''simple docstring'''





  lowercase							    =       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]:
      lowercase							    =       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**'    )
  lowercase							    =       Accelerator()
  test_torch_metrics(__snake_case		,					5_12    )
  accelerator.state._reset_state()
def 						_SCREAMING_SNAKE_CASE (    __snake_case			:  str    ):





  '''simple docstring'''





  main()


if __name__ == "__main__":
    main()










"""simple docstring"""



from __future__ import annotations

from collections.abc import Iterator
from typing import Generic, TypeVar

_UpperCamelCase		:  Any						=		TypeVar('T')

class        a     (   Generic[T]	):







    def __init__(			self							,		_lowerCamelCase       ):
      lowercase							    =       data
      lowercase							    =       None







    def __str__(			self       ):
      return F'{self.data}'


class        a     (   Generic[T]	):







    def __init__(			self       ):
      lowercase							    =       None







    def __iter__(			self       ):
      lowercase							    =       self.top
      while node:
        yield node.data
        lowercase							    =       node.next







    def __str__(			self       ):
      return "->".join([str(_lowerCamelCase       ) for item in self]       )







    def __len__(			self       ):
      return len(tuple(iter(self       )       )       )







    def 			UpperCamelCase_      (			self       ):
      return self.top is None







    def 			UpperCamelCase_      (			self							,		_lowerCamelCase       ):
      lowercase							    =       Node(_lowerCamelCase       )
      if not self.is_empty():
        lowercase							    =       self.top
      lowercase							    =       node







    def 			UpperCamelCase_      (			self       ):
      if self.is_empty():
        raise IndexError('pop from empty stack'       )
      assert isinstance(self.top							,		_lowerCamelCase       )
      lowercase							    =       self.top
      lowercase							    =       self.top.next
      return pop_node.data







    def 			UpperCamelCase_      (			self       ):
      if self.is_empty():
        raise IndexError('peek from empty stack'       )

      assert self.top is not None
      return self.top.data







    def 			UpperCamelCase_      (			self       ):
      lowercase							    =       None


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

    testmod()


