





'''simple docstring'''

from typing import TYPE_CHECKING

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


lowercase__		:     Dict			=							{
    'configuration_rembert': ['REMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'RemBertConfig', 'RemBertOnnxConfig']
}

try:
   if not is_sentencepiece_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowercase__		:     Dict			=							['RemBertTokenizer']

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowercase__		:     List[str]			=							['RemBertTokenizerFast']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowercase__		:     Optional[Any]			=							[
       'REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
       'RemBertForCausalLM',
       'RemBertForMaskedLM',
       'RemBertForMultipleChoice',
       'RemBertForQuestionAnswering',
       'RemBertForSequenceClassification',
       'RemBertForTokenClassification',
       'RemBertLayer',
       'RemBertModel',
       'RemBertPreTrainedModel',
       'load_tf_weights_in_rembert',
   ]


try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowercase__		:     Union[str, Any]			=							[
       'TF_REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
       'TFRemBertForCausalLM',
       'TFRemBertForMaskedLM',
       'TFRemBertForMultipleChoice',
       'TFRemBertForQuestionAnswering',
       'TFRemBertForSequenceClassification',
       'TFRemBertForTokenClassification',
       'TFRemBertLayer',
       'TFRemBertModel',
       'TFRemBertPreTrainedModel',
   ]


if TYPE_CHECKING:
   from .configuration_rembert import REMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RemBertConfig, RemBertOnnxConfig

   try:
      if not is_sentencepiece_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_rembert import RemBertTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_rembert_fast import RemBertTokenizerFast

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_rembert import (
          REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
          RemBertForCausalLM,
          RemBertForMaskedLM,
          RemBertForMultipleChoice,
          RemBertForQuestionAnswering,
          RemBertForSequenceClassification,
          RemBertForTokenClassification,
          RemBertLayer,
          RemBertModel,
          RemBertPreTrainedModel,
          load_tf_weights_in_rembert,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_rembert import (
          TF_REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFRemBertForCausalLM,
          TFRemBertForMaskedLM,
          TFRemBertForMultipleChoice,
          TFRemBertForQuestionAnswering,
          TFRemBertForSequenceClassification,
          TFRemBertForTokenClassification,
          TFRemBertLayer,
          TFRemBertModel,
          TFRemBertPreTrainedModel,
      )


else:
   import sys

   lowercase__		:     Any			=							_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)








from collections.abc import Callable

import numpy as np






def  lowerCamelCase_	(      lowerCAmelCase__	:	Callable			,	lowerCAmelCase__	:	float			,	lowerCAmelCase__	:	float			,	lowerCAmelCase__	:	float			,	lowerCAmelCase__	:	float				)       ->							np.array:

							'''simple docstring'''






							A      						=    int(np.ceil((x_end - xa) / step_size				)				)
							A      						=    np.zeros((n + 1,)				)
							A      						=    ya
							A      						=    xa

							for k in range(lowerCAmelCase__				):
														A      						=    y[k] + step_size * ode_func(lowerCAmelCase__			,	y[k]				)
														A      						=    y[k] + (
														    (step_size / 2) * (ode_func(lowerCAmelCase__			,	y[k]				) + ode_func(x + step_size			,	lowerCAmelCase__				))
														)
														x += step_size

							return y


if __name__ == "__main__":
				import doctest

				doctest.testmod()