


"""simple docstring"""


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 	_a   (   SCREAMING_SNAKE_CASE_			):
    def __init__(			self				:       Dict				,       SCREAMING_SNAKE_CASE__				:       str					):
          lowerCamelCase__							     =		data


    def __iter__(			self				:       Dict					):
          for element in self.data:
                yield element





def 	snake_case    (					_a:  Optional[Any]=True				)->		Optional[int]:
      '''simple docstring'''


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



def 	snake_case    (					_a:  List[str]		, _a:  Optional[Any]		, _a:  Tuple		, _a:  List[Any] = False				)->		List[str]:
      '''simple docstring'''


      if iterable:
            lowerCamelCase__							     =		DummyIterableDataset(torch.as_tensor(range(_lowerCAmelCase				)				)				)
      else:
            lowerCamelCase__							     =		TensorDataset(torch.as_tensor(range(_lowerCAmelCase				)				)				)

      lowerCamelCase__							     =		DataLoader(_lowerCAmelCase		, batch_size=_lowerCAmelCase				)
      lowerCamelCase__							     =		accelerator.prepare(_lowerCAmelCase				)

      return dl



def 	snake_case    (					_a:  Union[str, Any]		, _a:  Optional[int]		, _a:  Any		, _a:  Dict		, _a:  List[str]		, )->		str:
      '''simple docstring'''


      lowerCamelCase__							     =		create_dataloader(accelerator=_lowerCAmelCase		, dataset_size=_lowerCAmelCase		, batch_size=_lowerCAmelCase				)

      lowerCamelCase__							     =		[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 	snake_case    (					)->		Optional[int]:
      '''simple docstring'''


      lowerCamelCase__							     =		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 	snake_case    (					)->		Any:
      '''simple docstring'''


      lowerCamelCase__							     =		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 	snake_case    (					)->		Optional[Any]:
      '''simple docstring'''


      lowerCamelCase__							     =		create_accelerator(even_batches=_lowerCAmelCase				)

      lowerCamelCase__							     =		torch.nn.Linear(1		, 1				)
      lowerCamelCase__							     =		accelerator.prepare(_lowerCAmelCase				)

      lowerCamelCase__							     =		create_dataloader(_lowerCAmelCase		, dataset_size=3		, batch_size=1				)

      lowerCamelCase__							     =		[]
      with accelerator.join_uneven_inputs([ddp_model]				):
            for batch_idx, batch in enumerate(_lowerCAmelCase				):
                  lowerCamelCase__							     =		ddp_model(batch[0].float()				)
                  lowerCamelCase__							     =		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 	snake_case    (					_a:  str				)->		Optional[int]:
      '''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 	snake_case    (					)->		Any:
      '''simple docstring'''


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

      with accelerator.join_uneven_inputs([ddp_model]		, even_batches=_lowerCAmelCase				):
            lowerCamelCase__							     =		train_dl.batch_sampler.even_batches
            lowerCamelCase__							     =		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 	snake_case    (					)->		Optional[int]:
      '''simple docstring'''


      lowerCamelCase__							     =		True
      lowerCamelCase__							     =		False
      lowerCamelCase__							     =		create_accelerator(even_batches=_lowerCAmelCase				)
      lowerCamelCase__							     =		torch.nn.Linear(1		, 1				)
      lowerCamelCase__							     =		accelerator.prepare(_lowerCAmelCase				)
      create_dataloader(_lowerCAmelCase		, dataset_size=3		, batch_size=1		, iterable=_lowerCAmelCase				)
      lowerCamelCase__							     =		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				):
                        lowerCamelCase__							     =		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 	snake_case    (					)->		List[str]:
      '''simple docstring'''


      lowerCamelCase__							     =		create_accelerator()
      lowerCamelCase__							     =		torch.nn.Linear(1		, 1				)
      lowerCamelCase__							     =		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 	snake_case    (					)->		Union[str, Any]:
      '''simple docstring'''


      lowerCamelCase__							     =		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'				)
      lowerCamelCase__							     =		accelerator.state.distributed_type
      lowerCamelCase__							     =		DistributedType.FSDP
      test_join_raises_warning_for_non_ddp_distributed(_lowerCAmelCase				)
      lowerCamelCase__							     =		original_state


if __name__ == "__main__":
   main()








'''simple docstring'''





import json
import os
from typing import Optional, Tuple

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


_UpperCamelCase											=		logging.get_logger(__name__)

_UpperCamelCase											=		{'vocab_file': 'vocab.json'}

_UpperCamelCase											=		{
    'vocab_file': {
        'mgp-str': 'https://huggingface.co/alibaba-damo/mgp-str-base/blob/main/vocab.json',
    }
}

_UpperCamelCase											=		{'mgp-str': 27}






class    lowerCamelCase_       (							SCREAMING_SNAKE_CASE_	):
  """simple docstring"""




  a_				   =VOCAB_FILES_NAMES
  a_				   =PRETRAINED_VOCAB_FILES_MAP
  a_				   =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES




  def __init__(					self :      Optional[Any]     ,      _a :      Optional[Any]     ,      _a :      Any="[GO]"     ,      _a :      Optional[Any]="[GO]"     ,      _a :      Optional[int]="[s]"     ,      _a :      Optional[int]="[GO]"     ,      **_a :      Union[str, Any]  )   ->   Optional[int]:
    super().__init__(
        unk_token=_a     ,      bos_token=_a     ,      eos_token=_a     ,      pad_token=_a     ,      **_a     ,      )

    with open(_a     ,      encoding='utf-8'  ) as vocab_handle:
      __lowerCamelCase			:			List[str]       			=					json.load(_a  )
    __lowerCamelCase			:			Tuple       			=					{v: k for k, v in self.vocab.items()}




  @property
  def 		_lowercase	(					self :      Union[str, Any]  )   ->   List[str]:
    return len(self.vocab  )




  def 		_lowercase	(					self :      Union[str, Any]  )   ->   Tuple:
    return dict(self.vocab     ,      **self.added_tokens_encoder  )




  def 		_lowercase	(					self :      Dict     ,      _a :      Tuple  )   ->   int:
    __lowerCamelCase			:			Optional[Any]       			=					[]
    for s in text:
      char_tokens.extend(_a  )
    return char_tokens




  def 		_lowercase	(					self :      Optional[Any]     ,      _a :      Any  )   ->   Dict:
    return self.vocab.get(_a     ,      self.vocab.get(self.unk_token  )  )




  def 		_lowercase	(					self :      List[Any]     ,      _a :      Dict  )   ->   Optional[int]:
    return self.decoder.get(_a  )




  def 		_lowercase	(					self :      Union[str, Any]     ,      _a :      str     ,      _a :      Optional[str] = None  )   ->   Tuple[str]:
    if not os.path.isdir(_a  ):
      logger.error('Vocabulary path ({}) should be a directory'.format(_a  )  )
      return
    __lowerCamelCase			:			List[Any]       			=					os.path.join(
        _a     ,      (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']  )

    with open(_a     ,      'w'     ,      encoding='utf-8'  ) as f:
      f.write(json.dumps(self.vocab     ,      indent=2     ,      sort_keys=_a     ,      ensure_ascii=_a  ) + '\n'  )

    return (vocab_file,)






