






'''simple docstring'''
from __future__ import annotations





def 	_a(					UpperCamelCase__			:  int,		UpperCamelCase__			:  int							):




      '''simple docstring'''






      if partitions <= 0:
            raise ValueError('''partitions must be a positive number!'''							)
      if partitions > number_of_bytes:
            raise ValueError('''partitions can not > number_of_bytes!'''							)
      SCREAMING_SNAKE_CASE__       :			Optional[int]													=number_of_bytes // partitions
      SCREAMING_SNAKE_CASE__       :			str													=[]
      for i in range(UpperCamelCase__							):
            SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=i * bytes_per_partition + 1
            SCREAMING_SNAKE_CASE__       :			Dict													=(
                number_of_bytes if i == partitions - 1 else (i + 1) * bytes_per_partition
            )
            allocation_list.append(f"{start_bytes}-{end_bytes}"							)
      return allocation_list


if __name__ == "__main__":
  import doctest

  doctest.testmod()







'''simple docstring'''
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


a_					=	{
    'configuration_megatron_bert': ['MEGATRON_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MegatronBertConfig'],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  a_					=	[
      'MEGATRON_BERT_PRETRAINED_MODEL_ARCHIVE_LIST',
      'MegatronBertForCausalLM',
      'MegatronBertForMaskedLM',
      'MegatronBertForMultipleChoice',
      'MegatronBertForNextSentencePrediction',
      'MegatronBertForPreTraining',
      'MegatronBertForQuestionAnswering',
      'MegatronBertForSequenceClassification',
      'MegatronBertForTokenClassification',
      'MegatronBertModel',
      'MegatronBertPreTrainedModel',
  ]

if TYPE_CHECKING:
  from .configuration_megatron_bert import MEGATRON_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, MegatronBertConfig

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_megatron_bert import (
        MEGATRON_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
        MegatronBertForCausalLM,
        MegatronBertForMaskedLM,
        MegatronBertForMultipleChoice,
        MegatronBertForNextSentencePrediction,
        MegatronBertForPreTraining,
        MegatronBertForQuestionAnswering,
        MegatronBertForSequenceClassification,
        MegatronBertForTokenClassification,
        MegatronBertModel,
        MegatronBertPreTrainedModel,
    )

else:
  import sys

  a_					=	_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)