



"""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						:		Optional[Any]			= "true"


def 			_SCREAMING_SNAKE_CASE						(__lowerCAmelCase ,  __lowerCAmelCase=82 ,  __lowerCAmelCase=16							) ->						List[Any]:







  '''simple docstring'''


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


def 			_SCREAMING_SNAKE_CASE						(__lowerCAmelCase ,  __lowerCAmelCase=False							) ->						Union[str, Any]:







  '''simple docstring'''


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

  def tokenize_function(__lowerCAmelCase							):
    lowercase_								= tokenizer(examples["""sentence1"""] ,  examples["""sentence2"""] ,  truncation=__lowerCAmelCase ,  max_length=__lowerCAmelCase							)
    return outputs

  with accelerator.main_process_first():
    lowercase_								= dataset.map(
        __lowerCAmelCase ,  batched=__lowerCAmelCase ,  remove_columns=["""idx""", """sentence1""", """sentence2"""] ,  )

  lowercase_								= tokenized_datasets.rename_column("""label""" ,  """labels"""							)

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

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


def 			_SCREAMING_SNAKE_CASE						(__lowerCAmelCase ,  __lowerCAmelCase							) ->						str:







  '''simple docstring'''


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


def 			_SCREAMING_SNAKE_CASE						(__lowerCAmelCase ,  __lowerCAmelCase ,  __lowerCAmelCase							) ->						Tuple:







  '''simple docstring'''


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


def 			_SCREAMING_SNAKE_CASE						(__lowerCAmelCase ,  __lowerCAmelCase=82 ,  __lowerCAmelCase=False ,  __lowerCAmelCase=False ,  __lowerCAmelCase=16							) ->						Optional[int]:







  '''simple docstring'''


  lowercase_					,    lowercase_					,    lowercase_								= get_basic_setup(__lowerCAmelCase ,  __lowerCAmelCase ,  __lowerCAmelCase							)
  lowercase_					,    lowercase_								= generate_predictions(__lowerCAmelCase ,  __lowerCAmelCase ,  __lowerCAmelCase							)
  assert (
      len(__lowerCAmelCase							) == num_samples
  ), F'''Unexpected number of inputs:\n    Expected: {num_samples}\n    Actual: {len(__lowerCAmelCase							)}'''


def 			_SCREAMING_SNAKE_CASE						(__lowerCAmelCase = False ,  __lowerCAmelCase = False							) ->						Optional[int]:







  '''simple docstring'''


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







  '''simple docstring'''


  lowercase_								= Accelerator(split_batches=__lowerCAmelCase ,  dispatch_batches=__lowerCAmelCase							)
  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(__lowerCAmelCase ,  __lowerCAmelCase							)
        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=__lowerCAmelCase ,  dispatch_batches=__lowerCAmelCase							)
      if accelerator.is_local_main_process:
        print(F'''With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`, length=99'''							)
      test_torch_metrics(__lowerCAmelCase ,  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(__lowerCAmelCase ,  5_12							)
  accelerator.state._reset_state()


def 			_SCREAMING_SNAKE_CASE						(__lowerCAmelCase							) ->						Optional[Any]:







  '''simple docstring'''


  main()


if __name__ == "__main__":
       main()






"""simple docstring"""
from __future__ import annotations

import math
from collections import Counter
from string import ascii_lowercase


def 			_SCREAMING_SNAKE_CASE						(__lowerCAmelCase							) ->						None:







  '''simple docstring'''


  lowercase_					,    lowercase_								= analyze_text(__lowerCAmelCase							)
  lowercase_								= list(""" """ + ascii_lowercase							)
  # what is our total sum of probabilities.
  lowercase_								= sum(single_char_strings.values()							)

  # one length string
  lowercase_								= 0
  # for each alpha we go in our dict and if it is in it we calculate entropy
  for ch in my_alphas:
    if ch in single_char_strings:
      lowercase_								= single_char_strings[ch]
      lowercase_								= my_str / all_sum
      my_fir_sum += prob * math.loga(__lowerCAmelCase							)  # entropy formula.

    # print entropy
  print(F'''{round(-1 * my_fir_sum							):.1f}'''							)

  # two len string
  lowercase_								= sum(two_char_strings.values()							)
  lowercase_								= 0
  # for each alpha (two in size) calculate entropy.
  for cha in my_alphas:
    for cha in my_alphas:
      lowercase_								= cha + cha
      if sequence in two_char_strings:
        lowercase_								= two_char_strings[sequence]
        lowercase_								= int(__lowerCAmelCase							) / all_sum
        my_sec_sum += prob * math.loga(__lowerCAmelCase							)

    # print second entropy
  print(F'''{round(-1 * my_sec_sum							):.1f}'''							)

  # print the difference between them
  print(F'''{round((-1 * my_sec_sum) - (-1 * my_fir_sum)							):.1f}'''							)


def 			_SCREAMING_SNAKE_CASE						(__lowerCAmelCase							) ->						tuple[dict, dict]:







  '''simple docstring'''


  lowercase_								= Counter()  # type: ignore
  lowercase_								= Counter()  # type: ignore
  single_char_strings[text[-1]] += 1

  # first case when we have space at start.
  two_char_strings[" " + text[0]] += 1
  for i in range(0 ,  len(__lowerCAmelCase							) - 1							):
    single_char_strings[text[i]] += 1
    two_char_strings[text[i : i + 2]] += 1
  return single_char_strings, two_char_strings


def 			_SCREAMING_SNAKE_CASE						() ->						str:







  '''simple docstring'''


  import doctest

  doctest.testmod()
  # text = (
  #     "Had repulsive dashwoods suspicion sincerity but advantage now him. Remark "
  #     "easily garret nor nay. Civil those mrs enjoy shy fat merry. You greatest "
  #     "jointure saw horrible. He private he on be imagine suppose. Fertile "
  #     "beloved evident through no service elderly is. Blind there if every no so "
  #     "at. Own neglected you preferred way sincerity delivered his attempted. To "
  #     "of message cottage windows do besides against uncivil.  Delightful "
  #     "unreserved impossible few estimating men favourable see entreaties. She "
  #     "propriety immediate was improving. He or entrance humoured likewise "
  #     "moderate. Much nor game son say feel. Fat make met can must form into "
  #     "gate. Me we offending prevailed discovery. "
  # )

  # calculate_prob(text)


if __name__ == "__main__":
       main()

