
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


lowercase  : List[Any]						   =							{"""configuration_plbart""": ["""PLBART_PRETRAINED_CONFIG_ARCHIVE_MAP""", """PLBartConfig"""]}

try:
  if not is_sentencepiece_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowercase  : str						   =							["""PLBartTokenizer"""]

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowercase  : Dict						   =							[
      """PLBART_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """PLBartForCausalLM""",
      """PLBartForConditionalGeneration""",
      """PLBartForSequenceClassification""",
      """PLBartModel""",
      """PLBartPreTrainedModel""",
  ]


if TYPE_CHECKING:
  from .configuration_plbart import PLBART_PRETRAINED_CONFIG_ARCHIVE_MAP, PLBartConfig

  try:
    if not is_sentencepiece_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_plbart import PLBartTokenizer

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_plbart import (
        PLBART_PRETRAINED_MODEL_ARCHIVE_LIST,
        PLBartForCausalLM,
        PLBartForConditionalGeneration,
        PLBartForSequenceClassification,
        PLBartModel,
        PLBartPreTrainedModel,
    )


else:
  import sys

  lowercase  : Any						   =							_LazyModule(__name__, globals()["""__file__"""], _import_structure)


from collections.abc import Callable

import numpy as np







def 						_snake_case(  SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__    )			->   np.array:
       lowercase							:					Optional[int]	     =				int(np.ceil((x_end - xa) / step_size    )    )
       lowercase							:					List[Any]	     =				np.zeros((n + 1,)    )
       lowercase							:					Optional[int]	     =				ya
       lowercase							:					Optional[int]	     =				xa

       for k in range(SCREAMING_SNAKE_CASE__    ):
              lowercase							:					str	     =				y[k] + step_size * ode_func(SCREAMING_SNAKE_CASE__ ,     y[k]    )
              lowercase							:					Union[str, Any]	     =				y[k] + (
                  (step_size / 2) * (ode_func(SCREAMING_SNAKE_CASE__ ,     y[k]    ) + ode_func(x + step_size ,     SCREAMING_SNAKE_CASE__    ))
              )
              x += step_size

       return y


if __name__ == "__main__":
  import doctest

  doctest.testmod()
