



"""simple docstring"""
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.


import warnings
from typing import List
from unittest.mock import Mock

import torch
from torch.utils.data import DataLoader, IterableDataset, TensorDataset

from accelerate.accelerator import Accelerator
from accelerate.utils.dataclasses import DistributedType


class    SCREAMING_SNAKE_CASE__ (							__UpperCAmelCase   ):

    def __init__(				self			:				int ,  lowerCAmelCase_			:				List[str]):







      """simple docstring"""







      lowercase_								= data




    def __iter__(				self			:				Optional[int]):







      """simple docstring"""







      for element in self.data:
        yield element







def 			_SCREAMING_SNAKE_CASE						(__lowerCAmelCase=True							) ->						List[Any]:







  '''simple docstring'''


  lowercase_								= Accelerator(even_batches=__lowerCAmelCase							)
  assert accelerator.num_processes == 2, "this script expects that two GPUs are available"
  return accelerator


def 			_SCREAMING_SNAKE_CASE						(__lowerCAmelCase ,  __lowerCAmelCase ,  __lowerCAmelCase ,  __lowerCAmelCase = False							) ->						Dict:







  '''simple docstring'''


  if iterable:
    lowercase_								= DummyIterableDataset(torch.as_tensor(range(__lowerCAmelCase							)							)							)
  else:
    lowercase_								= TensorDataset(torch.as_tensor(range(__lowerCAmelCase							)							)							)

  lowercase_								= DataLoader(__lowerCAmelCase ,  batch_size=__lowerCAmelCase							)
  lowercase_								= accelerator.prepare(__lowerCAmelCase							)

  return dl


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







  '''simple docstring'''


  lowercase_								= create_dataloader(accelerator=__lowerCAmelCase ,  dataset_size=__lowerCAmelCase ,  batch_size=__lowerCAmelCase							)

  lowercase_								= [len(batch[0]							) for batch in dl]

  if accelerator.process_index == 0:
    assert batch_sizes == process_0_expected_batch_sizes
  elif accelerator.process_index == 1:
    assert batch_sizes == process_1_expected_batch_sizes


def 			_SCREAMING_SNAKE_CASE						() ->						Tuple:







  '''simple docstring'''


  lowercase_								= create_accelerator()

  # without padding, we would expect a different number of batches
  verify_dataloader_batch_sizes(
      __lowerCAmelCase ,  dataset_size=3 ,  batch_size=1 ,  process_0_expected_batch_sizes=[1, 1] ,  process_1_expected_batch_sizes=[1, 1] ,  )

  # without padding, we would expect the same number of batches, but different sizes
  verify_dataloader_batch_sizes(
      __lowerCAmelCase ,  dataset_size=7 ,  batch_size=2 ,  process_0_expected_batch_sizes=[2, 2] ,  process_1_expected_batch_sizes=[2, 2] ,  )


def 			_SCREAMING_SNAKE_CASE						() ->						List[str]:







  '''simple docstring'''


  lowercase_								= create_accelerator(even_batches=__lowerCAmelCase							)

  verify_dataloader_batch_sizes(
      __lowerCAmelCase ,  dataset_size=3 ,  batch_size=1 ,  process_0_expected_batch_sizes=[1, 1] ,  process_1_expected_batch_sizes=[1] ,  )

  verify_dataloader_batch_sizes(
      __lowerCAmelCase ,  dataset_size=7 ,  batch_size=2 ,  process_0_expected_batch_sizes=[2, 2] ,  process_1_expected_batch_sizes=[2, 1] ,  )


def 			_SCREAMING_SNAKE_CASE						() ->						str:







  '''simple docstring'''


  lowercase_								= create_accelerator(even_batches=__lowerCAmelCase							)

  lowercase_								= torch.nn.Linear(1 ,  1							)
  lowercase_								= accelerator.prepare(__lowerCAmelCase							)

  lowercase_								= create_dataloader(__lowerCAmelCase ,  dataset_size=3 ,  batch_size=1							)

  lowercase_								= []
  with accelerator.join_uneven_inputs([ddp_model]							):
    for batch_idx, batch in enumerate(__lowerCAmelCase							):
      lowercase_								= ddp_model(batch[0].float()							)
      lowercase_								= output.sum()
      loss.backward()
      batch_idxs.append(__lowerCAmelCase							)

  accelerator.wait_for_everyone()

  if accelerator.process_index == 0:
    assert batch_idxs == [0, 1]
  elif accelerator.process_index == 1:
    assert batch_idxs == [0]


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







  '''simple docstring'''


  with warnings.catch_warnings(record=__lowerCAmelCase							) as w:
    with accelerator.join_uneven_inputs([Mock()]							):
      pass

    assert issubclass(w[-1].category ,  __lowerCAmelCase							)
    assert "only supported for multi-GPU" in str(w[-1].message							)


def 			_SCREAMING_SNAKE_CASE						() ->						int:







  '''simple docstring'''


  lowercase_								= True
  lowercase_								= False
  lowercase_								= create_accelerator(even_batches=__lowerCAmelCase							)
  lowercase_								= torch.nn.Linear(1 ,  1							)
  lowercase_								= accelerator.prepare(__lowerCAmelCase							)
  lowercase_								= create_dataloader(__lowerCAmelCase ,  dataset_size=3 ,  batch_size=1							)
  lowercase_								= create_dataloader(__lowerCAmelCase ,  dataset_size=3 ,  batch_size=1							)

  with accelerator.join_uneven_inputs([ddp_model] ,  even_batches=__lowerCAmelCase							):
    lowercase_								= train_dl.batch_sampler.even_batches
    lowercase_								= valid_dl.batch_sampler.even_batches

  assert train_dl_overridden_value == overridden_even_batches
  assert valid_dl_overridden_value == overridden_even_batches
  assert train_dl.batch_sampler.even_batches == default_even_batches
  assert valid_dl.batch_sampler.even_batches == default_even_batches


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







  '''simple docstring'''


  lowercase_								= True
  lowercase_								= False
  lowercase_								= create_accelerator(even_batches=__lowerCAmelCase							)
  lowercase_								= torch.nn.Linear(1 ,  1							)
  lowercase_								= accelerator.prepare(__lowerCAmelCase							)
  create_dataloader(__lowerCAmelCase ,  dataset_size=3 ,  batch_size=1 ,  iterable=__lowerCAmelCase							)
  lowercase_								= create_dataloader(__lowerCAmelCase ,  dataset_size=3 ,  batch_size=1							)

  with warnings.catch_warnings():
    warnings.filterwarnings("""ignore"""							)
    try:
      with accelerator.join_uneven_inputs([ddp_model] ,  even_batches=__lowerCAmelCase							):
        lowercase_								= batch_dl.batch_sampler.even_batches
    except AttributeError:
      # ensure attribute error is not raised when processing iterable dl
      raise AssertionError

  assert batch_dl_overridden_value == overridden_even_batches
  assert batch_dl.batch_sampler.even_batches == default_even_batches


def 			_SCREAMING_SNAKE_CASE						() ->						int:







  '''simple docstring'''


  lowercase_								= create_accelerator()
  lowercase_								= torch.nn.Linear(1 ,  1							)
  lowercase_								= accelerator.prepare(__lowerCAmelCase							)
  create_dataloader(__lowerCAmelCase ,  dataset_size=3 ,  batch_size=1 ,  iterable=__lowerCAmelCase							)

  with warnings.catch_warnings(record=__lowerCAmelCase							) as w:
    with accelerator.join_uneven_inputs([ddp_model] ,  even_batches=__lowerCAmelCase							):
      pass

    assert issubclass(w[-1].category ,  __lowerCAmelCase							)
    assert "only supported for map-style datasets" in str(w[-1].message							)


def 			_SCREAMING_SNAKE_CASE						() ->						Tuple:







  '''simple docstring'''


  lowercase_								= create_accelerator()

  accelerator.print("""Test that even_batches variable ensures uniform batches across processes"""							)
  test_default_ensures_even_batch_sizes()

  accelerator.print("""Run tests with even_batches disabled"""							)
  test_can_disable_even_batches()

  accelerator.print("""Test joining uneven inputs"""							)
  test_can_join_uneven_inputs()

  accelerator.print("""Test overriding even_batches when joining uneven inputs"""							)
  test_join_can_override_even_batches()

  accelerator.print("""Test overriding even_batches for mixed dataloader types"""							)
  test_join_can_override_for_mixed_type_dataloaders()

  accelerator.print("""Test overriding even_batches raises a warning for iterable dataloaders"""							)
  test_join_raises_warning_for_iterable_when_overriding_even_batches()

  accelerator.print("""Test join with non DDP distributed raises warning"""							)
  lowercase_								= accelerator.state.distributed_type
  lowercase_								= DistributedType.FSDP
  test_join_raises_warning_for_non_ddp_distributed(__lowerCAmelCase							)
  lowercase_								= original_state


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()

