


'''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_      (   A__ :       Any							,   A__ :       Union[str, Any]=82							,   A__ :       Dict=16 ):
  '''simple docstring'''





  set_seed(42 )
  lowerCAmelCase_      :				Dict      =       RegressionModel()
  lowerCAmelCase_      :				int      =       deepcopy(A__ )
  lowerCAmelCase_      :				List[Any]      =       RegressionDataset(length=A__ )
  lowerCAmelCase_      :				Any      =       DataLoader(A__							,   batch_size=A__ )
  model.to(accelerator.device )
  lowerCAmelCase_,							lowerCAmelCase_      :				Tuple      =       accelerator.prepare(A__							,   A__ )
  return model, ddp_model, dataloader







def     UpperCamelCase_      (   A__ :       Accelerator							,   A__ :       Optional[Any]=False ):
  '''simple docstring'''





  lowerCAmelCase_      :				Dict      =       AutoTokenizer.from_pretrained("""hf-internal-testing/mrpc-bert-base-cased""" )
  lowerCAmelCase_      :				List[Any]      =       load_dataset("""glue"""							,   """mrpc"""							,   split="""validation""" )

  def tokenize_function(A__ :       Any ):
    lowerCAmelCase_      :				Dict      =       tokenizer(examples["""sentence1"""]							,   examples["""sentence2"""]							,   truncation=A__							,   max_length=A__ )
    return outputs

  with accelerator.main_process_first():
    lowerCAmelCase_      :				Tuple      =       dataset.map(
        A__							,   batched=A__							,   remove_columns=["""idx""", """sentence1""", """sentence2"""]							,   )

  lowerCAmelCase_      :				Any      =       tokenized_datasets.rename_column("""label"""							,   """labels""" )

  def collate_fn(A__ :       Union[str, Any] ):
    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_      (   A__ :       Optional[Any]							,   A__ :       Optional[Any] ):
  '''simple docstring'''





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







def     UpperCamelCase_      (   A__ :       int							,   A__ :       Any							,   A__ :       Optional[Any] ):
  '''simple docstring'''





  lowerCAmelCase_      :				List[str]      =       []
  for batch in dataloader:
    lowerCAmelCase_,							lowerCAmelCase_      :				Optional[Any]      =       batch.values()
    with torch.no_grad():
      lowerCAmelCase_      :				int      =       model(A__ )
      lowerCAmelCase_,							lowerCAmelCase_      :				Any      =       accelerator.gather_for_metrics((logit, target) )
      logits_and_targets.append((logit, target) )
  lowerCAmelCase_,							lowerCAmelCase_      :				List[Any]      =       [], []
  for logit, targ in logits_and_targets:
    logits.append(A__ )
    targs.append(A__ )
  lowerCAmelCase_,							lowerCAmelCase_      :				List[Any]      =       torch.cat(A__ ), torch.cat(A__ )
  return logits, targs







def     UpperCamelCase_      (   A__ :       Accelerator							,   A__ :       Dict=82							,   A__ :       Union[str, Any]=False							,   A__ :       Dict=False							,   A__ :       List[str]=16 ):
  '''simple docstring'''





  lowerCAmelCase_,							lowerCAmelCase_,							lowerCAmelCase_      :				Any      =       get_basic_setup(A__							,   A__							,   A__ )
  lowerCAmelCase_,							lowerCAmelCase_      :				List[Any]      =       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_      (   A__ :       bool = False							,   A__ :       bool = False ):
  '''simple docstring'''





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

  # Then do distributed
  lowerCAmelCase_,							lowerCAmelCase_,							lowerCAmelCase_      :				str      =       setup["""ddp"""]
  model.eval()
  for batch in dataloader:
    with torch.inference_mode():
      lowerCAmelCase_      :				Optional[Any]      =       model(**A__ )
    lowerCAmelCase_      :				Any      =       outputs.logits.argmax(dim=-1 )
    lowerCAmelCase_      :				List[str]      =       batch["""labels"""]
    lowerCAmelCase_,							lowerCAmelCase_      :				Optional[int]      =       accelerator.gather_for_metrics((preds, references) )
    metric.add_batch(predictions=A__							,   references=A__ )
  lowerCAmelCase_      :				Optional[int]      =       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_      :				Dict      =       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_      :				Tuple      =       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_      :				Any      =       Accelerator()
  test_torch_metrics(A__							,   5_12 )
  accelerator.state._reset_state()







def     UpperCamelCase_      (   A__ :       Optional[int] ):
  '''simple docstring'''





  main()


if __name__ == "__main__":
  main()







'''simple docstring'''






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__A :  List[str]    							=				{
    "configuration_bigbird_pegasus": [
        "BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "BigBirdPegasusConfig",
        "BigBirdPegasusOnnxConfig",
    ],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __A :  Union[str, Any]    							=				[
      "BIGBIRD_PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST",
      "BigBirdPegasusForCausalLM",
      "BigBirdPegasusForConditionalGeneration",
      "BigBirdPegasusForQuestionAnswering",
      "BigBirdPegasusForSequenceClassification",
      "BigBirdPegasusModel",
      "BigBirdPegasusPreTrainedModel",
  ]


if TYPE_CHECKING:
  from .configuration_bigbird_pegasus import (
      BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP,
      BigBirdPegasusConfig,
      BigBirdPegasusOnnxConfig,
  )

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_bigbird_pegasus import (
        BIGBIRD_PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST,
        BigBirdPegasusForCausalLM,
        BigBirdPegasusForConditionalGeneration,
        BigBirdPegasusForQuestionAnswering,
        BigBirdPegasusForSequenceClassification,
        BigBirdPegasusModel,
        BigBirdPegasusPreTrainedModel,
    )


else:
  import sys

  __A :  List[Any]    							=				_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



