





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__   : int         =       'true'


def  A							(			snake_case__   :						Union[str, Any] ,				snake_case__   :						List[str]=82 ,				snake_case__   :						str=16				)					->				Optional[int]:
 '''simple docstring'''
 set_seed(42				)
 __snake_case         =			RegressionModel()
 __snake_case         =			deepcopy(UpperCamelCase__				)
 __snake_case         =			RegressionDataset(length=UpperCamelCase__				)
 __snake_case         =			DataLoader(UpperCamelCase__ ,				batch_size=UpperCamelCase__				)
 model.to(accelerator.device				)
 __snake_case         =			accelerator.prepare(UpperCamelCase__ ,				UpperCamelCase__				)
 return model, ddp_model, dataloader


def  A							(			snake_case__   :						Accelerator ,				snake_case__   :						Optional[Any]=False				)					->				int:
 '''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(snake_case__   :						List[str]				):
  __snake_case         =			tokenizer(examples['sentence1'] ,				examples['sentence2'] ,				truncation=UpperCamelCase__ ,				max_length=UpperCamelCase__				)
  return outputs

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

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

 def collate_fn(snake_case__   :						Dict				):
  if use_longest:
   return tokenizer.pad(UpperCamelCase__ ,				padding='longest' ,				return_tensors='pt'				)
  return tokenizer.pad(UpperCamelCase__ ,				padding='max_length' ,				max_length=128 ,				return_tensors='pt'				)

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


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


def  A							(			snake_case__   :						List[Any] ,				snake_case__   :						Tuple ,				snake_case__   :						int				)					->				Optional[int]:
 '''simple docstring'''
 __snake_case         =			[]
 for batch in dataloader:
  __snake_case         =			batch.values()
  with torch.no_grad():
   __snake_case         =			model(UpperCamelCase__				)
   __snake_case         =			accelerator.gather_for_metrics((logit, target)				)
   logits_and_targets.append((logit, target)				)
 __snake_case         =			[], []
 for logit, targ in logits_and_targets:
  logits.append(UpperCamelCase__				)
  targs.append(UpperCamelCase__				)
 __snake_case         =			torch.cat(UpperCamelCase__				), torch.cat(UpperCamelCase__				)
 return logits, targs


def  A							(			snake_case__   :						Accelerator ,				snake_case__   :						Union[str, Any]=82 ,				snake_case__   :						Any=False ,				snake_case__   :						Tuple=False ,				snake_case__   :						str=16				)					->				int:
 '''simple docstring'''
 __snake_case         =			get_basic_setup(UpperCamelCase__ ,				UpperCamelCase__ ,				UpperCamelCase__				)
 __snake_case         =			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							(			snake_case__   :						bool = False ,				snake_case__   :						bool = False				)					->				Optional[int]:
 '''simple docstring'''
 __snake_case         =			evaluate.load('glue' ,				'mrpc'				)
 __snake_case         =			get_mrpc_setup(UpperCamelCase__ ,				UpperCamelCase__				)
 # First do baseline
 __snake_case         =			setup["""no"""]
 model.to(UpperCamelCase__				)
 model.eval()
 for batch in dataloader:
  batch.to(UpperCamelCase__				)
  with torch.inference_mode():
   __snake_case         =			model(**UpperCamelCase__				)
  __snake_case         =			outputs.logits.argmax(dim=-1				)
  metric.add_batch(predictions=UpperCamelCase__ ,				references=batch['labels']				)
 __snake_case         =			metric.compute()

 # Then do distributed
 __snake_case         =			setup["""ddp"""]
 model.eval()
 for batch in dataloader:
  with torch.inference_mode():
   __snake_case         =			model(**UpperCamelCase__				)
  __snake_case         =			outputs.logits.argmax(dim=-1				)
  __snake_case         =			batch["""labels"""]
  __snake_case         =			accelerator.gather_for_metrics((preds, references)				)
  metric.add_batch(predictions=UpperCamelCase__ ,				references=UpperCamelCase__				)
 __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  A							(			)					->				Dict:
 '''simple docstring'''
 __snake_case         =			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]:
   __snake_case         =			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**'				)
 __snake_case         =			Accelerator()
 test_torch_metrics(UpperCamelCase__ ,				512				)
 accelerator.state._reset_state()


def  A							(			snake_case__   :						Any				)					->				Any:
 '''simple docstring'''
 main()


if __name__ == "__main__":
  main()







'''simple docstring'''


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available


lowerCAmelCase_      :  Optional[Any]				     =  {}

try:
 if not is_sentencepiece_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCAmelCase_      :  Any				     =  ['GPTSw3Tokenizer']


if TYPE_CHECKING:
 try:
  if not is_sentencepiece_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_gpt_swa import GPTSwaTokenizer

else:
 import sys

 lowerCAmelCase_      :  int				     =  _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
