



import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_tf


if is_tf_available():
  import tensorflow as tf
  from tensorflow.python.eager import context
  from tensorflow.python.framework import ops

  from transformers import GradientAccumulator, create_optimizer



@require_tf
class  __lowerCAmelCase     (	unittest.TestCase   ):




 def        lowerCamelCase      (						self	,				__UpperCAmelCase	,				__UpperCAmelCase	,				__UpperCAmelCase				):



    '''simple docstring'''
    self.assertEqual(len(__UpperCAmelCase				)	,				len(__UpperCAmelCase				)				)
    for a, b in zip(__UpperCAmelCase	,				__UpperCAmelCase				):
       self.assertAlmostEqual(__UpperCAmelCase	,				__UpperCAmelCase	,				delta=__UpperCAmelCase				)




 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    __lowerCamelCase													=    GradientAccumulator()
    accumulator([tf.constant([1.0, 2.0]				)]				)
    accumulator([tf.constant([-2.0, 1.0]				)]				)
    accumulator([tf.constant([-1.0, 2.0]				)]				)
    with self.assertRaises(__UpperCAmelCase				):
       accumulator([tf.constant([1.0, 1.0]				), tf.constant([2.0, 2.0]				)]				)
    self.assertEqual(accumulator.step	,				3				)
    self.assertEqual(len(accumulator.gradients				)	,				1				)
    self.assertListAlmostEqual(accumulator.gradients[0].numpy().tolist()	,				[-2.0, 5.0]	,				tol=1E-2				)
    accumulator.reset()
    self.assertEqual(accumulator.step	,				0				)
    self.assertListAlmostEqual(accumulator.gradients[0].numpy().tolist()	,				[0.0, 0.0]	,				tol=1E-2				)




 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    __lowerCamelCase													=    None
    ops.enable_eager_execution_internal()
    __lowerCamelCase													=    tf.config.list_physical_devices('''CPU'''				)
    if len(__UpperCAmelCase				) == 1:
       tf.config.set_logical_device_configuration(
           physical_devices[0]	,				[tf.config.LogicalDeviceConfiguration(), tf.config.LogicalDeviceConfiguration()]				)
    __lowerCamelCase													=    tf.config.list_logical_devices(device_type='''CPU'''				)
    __lowerCamelCase													=    tf.distribute.MirroredStrategy(devices=devices[:2]				)

    with strategy.scope():
       __lowerCamelCase													=    GradientAccumulator()
       __lowerCamelCase													=    tf.Variable([4.0, 3.0]				)
       __lowerCamelCase							,__lowerCamelCase													=    create_optimizer(5E-5	,				10	,				5				)
       __lowerCamelCase													=    tf.Variable([0.0, 0.0]	,				trainable=__UpperCAmelCase				)

    def accumulate_on_replica(__UpperCAmelCase				):
       accumulator([gradient]				)

    def apply_on_replica():
       optimizer.apply_gradients(list(zip(accumulator.gradients	,				[variable]				)				)				)

    @tf.function
    def accumulate(__UpperCAmelCase	,				__UpperCAmelCase				):
       with strategy.scope():
          __lowerCamelCase													=    strategy.experimental_local_results(__UpperCAmelCase				)
          local_variables[0].assign(__UpperCAmelCase				)
          local_variables[1].assign(__UpperCAmelCase				)
          strategy.run(__UpperCAmelCase	,				args=(gradient_placeholder,)				)

    @tf.function
    def apply_grad():
       with strategy.scope():
          strategy.run(__UpperCAmelCase				)

    def _check_local_values(__UpperCAmelCase	,				__UpperCAmelCase				):
       __lowerCamelCase													=    strategy.experimental_local_results(accumulator._gradients[0]				)
       self.assertListAlmostEqual(values[0].value()	,				__UpperCAmelCase	,				tol=1E-2				)
       self.assertListAlmostEqual(values[1].value()	,				__UpperCAmelCase	,				tol=1E-2				)

    accumulate([1.0, 2.0]	,				[-1.0, 1.0]				)
    accumulate([3.0, -1.0]	,				[-1.0, -1.0]				)
    accumulate([-2.0, 2.0]	,				[3.0, -2.0]				)
    self.assertEqual(accumulator.step	,				3				)
    _check_local_values([2.0, 3.0]	,				[1.0, -2.0]				)
    apply_grad()
    self.assertListAlmostEqual(variable.value()	,				[4.0, 3.0]	,				tol=1E-2				)
    accumulator.reset()
    self.assertEqual(accumulator.step	,				0				)
    _check_local_values([0.0, 0.0]	,				[0.0, 0.0]				)











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_			=					"""true"""





def 		a__			(		_UpperCamelCase			: Union[str, Any] ,_UpperCamelCase			: List[str]=82 ,_UpperCamelCase			: Optional[Any]=16							):
   set_seed(42							)
   __lowerCamelCase													=    RegressionModel()
   __lowerCamelCase													=    deepcopy(_UpperCamelCase							)
   __lowerCamelCase													=    RegressionDataset(length=_UpperCamelCase							)
   __lowerCamelCase													=    DataLoader(_UpperCamelCase ,batch_size=_UpperCamelCase							)
   model.to(accelerator.device							)
   __lowerCamelCase							,__lowerCamelCase													=    accelerator.prepare(_UpperCamelCase ,_UpperCamelCase							)
   return model, ddp_model, dataloader





def 		a__			(		_UpperCamelCase			: Accelerator ,_UpperCamelCase			: str=False							):
   __lowerCamelCase													=    AutoTokenizer.from_pretrained('''hf-internal-testing/mrpc-bert-base-cased'''							)
   __lowerCamelCase													=    load_dataset('''glue''' ,'''mrpc''' ,split='''validation'''							)

   def tokenize_function(_UpperCamelCase			: int							):
      __lowerCamelCase													=    tokenizer(examples['''sentence1'''] ,examples['''sentence2'''] ,truncation=_UpperCamelCase ,max_length=_UpperCamelCase							)
      return outputs

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

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

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

   return DataLoader(_UpperCamelCase ,shuffle=_UpperCamelCase ,collate_fn=_UpperCamelCase ,batch_size=16							)





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





def 		a__			(		_UpperCamelCase			: Dict ,_UpperCamelCase			: Optional[Any] ,_UpperCamelCase			: Union[str, Any]							):
   __lowerCamelCase													=    []
   for batch in dataloader:
      __lowerCamelCase							,__lowerCamelCase													=    batch.values()
      with torch.no_grad():
         __lowerCamelCase													=    model(_UpperCamelCase							)
         __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(_UpperCamelCase							)
      targs.append(_UpperCamelCase							)
   __lowerCamelCase							,__lowerCamelCase													=    torch.cat(_UpperCamelCase							), torch.cat(_UpperCamelCase							)
   return logits, targs





def 		a__			(		_UpperCamelCase			: Accelerator ,_UpperCamelCase			: List[Any]=82 ,_UpperCamelCase			: str=False ,_UpperCamelCase			: List[str]=False ,_UpperCamelCase			: Optional[int]=16							):
   __lowerCamelCase							,__lowerCamelCase							,__lowerCamelCase													=    get_basic_setup(_UpperCamelCase ,_UpperCamelCase ,_UpperCamelCase							)
   __lowerCamelCase							,__lowerCamelCase													=    generate_predictions(_UpperCamelCase ,_UpperCamelCase ,_UpperCamelCase							)
   assert (
       len(_UpperCamelCase							) == num_samples
   ), F"""Unexpected number of inputs:\n    Expected: {num_samples}\n    Actual: {len(_UpperCamelCase							)}"""





def 		a__			(		_UpperCamelCase			: bool = False ,_UpperCamelCase			: bool = False							):
   __lowerCamelCase													=    evaluate.load('''glue''' ,'''mrpc'''							)
   __lowerCamelCase							,__lowerCamelCase													=    get_mrpc_setup(_UpperCamelCase ,_UpperCamelCase							)
   # First do baseline
   __lowerCamelCase							,__lowerCamelCase							,__lowerCamelCase													=    setup['''no''']
   model.to(_UpperCamelCase							)
   model.eval()
   for batch in dataloader:
      batch.to(_UpperCamelCase							)
      with torch.inference_mode():
         __lowerCamelCase													=    model(**_UpperCamelCase							)
      __lowerCamelCase													=    outputs.logits.argmax(dim=-1							)
      metric.add_batch(predictions=_UpperCamelCase ,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(**_UpperCamelCase							)
      __lowerCamelCase													=    outputs.logits.argmax(dim=-1							)
      __lowerCamelCase													=    batch['''labels''']
      __lowerCamelCase							,__lowerCamelCase													=    accelerator.gather_for_metrics((preds, references)							)
      metric.add_batch(predictions=_UpperCamelCase ,references=_UpperCamelCase							)
   __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 		a__			(		):
   __lowerCamelCase													=    Accelerator(split_batches=_UpperCamelCase ,dispatch_batches=_UpperCamelCase							)
   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(_UpperCamelCase ,_UpperCamelCase							)
            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=_UpperCamelCase ,dispatch_batches=_UpperCamelCase							)
         if accelerator.is_local_main_process:
            print(F"""With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`, length=99"""							)
         test_torch_metrics(_UpperCamelCase ,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(_UpperCamelCase ,5_12							)
   accelerator.state._reset_state()





def 		a__			(		_UpperCamelCase			: Optional[int]							):
   # For xla_spawn (TPUs)
   main()


if __name__ == "__main__":
  main()






