

'''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


lowerCAmelCase__											=	'''true'''


def 		_A     (    A__     ,				A__=82     ,				A__=16      ):






 """simple docstring"""



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


def 		_A     (    A__     ,				A__=False      ):






 """simple docstring"""



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

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

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

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

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

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


def 		_A     (    A__     ,				A__      ):






 """simple docstring"""



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


def 		_A     (    A__     ,				A__     ,				A__      ):






 """simple docstring"""



 __lowercase							  = []
 for batch in dataloader:
  __lowercase  ,	__lowercase							  = batch.values()
  with torch.no_grad():
   __lowercase							  = model(A__      )
   __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(A__      )
  targs.append(A__      )
 __lowercase  ,	__lowercase							  = torch.cat(A__      ), torch.cat(A__      )
 return logits, targs


def 		_A     (    A__     ,				A__=82     ,				A__=False     ,				A__=False     ,				A__=16      ):






 """simple docstring"""



 __lowercase  ,	__lowercase  ,	__lowercase							  = get_basic_setup(A__     ,				A__     ,				A__      )
 __lowercase  ,	__lowercase							  = generate_predictions(A__     ,				A__     ,				A__      )
 assert (
     len(A__      ) == num_samples
 ), F"Unexpected number of inputs:\n    Expected: {num_samples}\n    Actual: {len(A__      )}"


def 		_A     (    A__ = False     ,				A__ = False      ):






 """simple docstring"""



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






 """simple docstring"""



 __lowercase							  = 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]:
   __lowercase							  = 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**'''      )
 __lowercase							  = Accelerator()
 test_torch_metrics(A__     ,				512      )
 accelerator.state._reset_state()


def 		_A     (    A__      ):






 """simple docstring"""



 main()


if __name__ == "__main__":
 main()




'''simple docstring'''






import os

lowerCAmelCase__											=	{'''I''': 1, '''V''': 5, '''X''': 10, '''L''': 50, '''C''': 100, '''D''': 500, '''M''': 1000}


def 		_A     (    A__      ):






 """simple docstring"""



 __lowercase							  = 0

 __lowercase							  = 0
 while index < len(A__      ) - 1:
  __lowercase							  = SYMBOLS[numerals[index]]
  __lowercase							  = SYMBOLS[numerals[index + 1]]
  if current_value < next_value:
   total_value -= current_value
  else:
   total_value += current_value
  index += 1
 total_value += SYMBOLS[numerals[index]]

 return total_value


def 		_A     (    A__      ):






 """simple docstring"""



 __lowercase							  = ''''''

 __lowercase							  = num // 1000
 numerals += m_count * "M"
 num %= 1000

 __lowercase							  = num // 100
 if c_count == 9:
  numerals += "CM"
  c_count -= 9
 elif c_count == 4:
  numerals += "CD"
  c_count -= 4
 if c_count >= 5:
  numerals += "D"
  c_count -= 5
 numerals += c_count * "C"
 num %= 100

 __lowercase							  = num // 10
 if x_count == 9:
  numerals += "XC"
  x_count -= 9
 elif x_count == 4:
  numerals += "XL"
  x_count -= 4
 if x_count >= 5:
  numerals += "L"
  x_count -= 5
 numerals += x_count * "X"
 num %= 10

 if num == 9:
  numerals += "IX"
  num -= 9
 elif num == 4:
  numerals += "IV"
  num -= 4
 if num >= 5:
  numerals += "V"
  num -= 5
 numerals += num * "I"

 return numerals


def 		_A     (    A__ = "/p089_roman.txt"      ):






 """simple docstring"""



 __lowercase							  = 0

 with open(os.path.dirname(A__      ) + roman_numerals_filename      ) as filea:
  __lowercase							  = filea.readlines()

 for line in lines:
  __lowercase							  = line.strip()
  __lowercase							  = parse_roman_numerals(A__      )
  __lowercase							  = generate_roman_numerals(A__      )
  savings += len(A__      ) - len(A__      )

 return savings


if __name__ == "__main__":
 print(f'{solution() = }')

