'''simple docstring'''


from typing import TYPE_CHECKING

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


snake_case__	:     str      =    {
    '''configuration_deberta_v2''': ['''DEBERTA_V2_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''DebertaV2Config''', '''DebertaV2OnnxConfig'''],
    '''tokenization_deberta_v2''': ['''DebertaV2Tokenizer'''],
}

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 snake_case__	:     Dict      =    ['''DebertaV2TokenizerFast''']

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 snake_case__	:     List[str]      =    [
     '''TF_DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''TFDebertaV2ForMaskedLM''',
     '''TFDebertaV2ForQuestionAnswering''',
     '''TFDebertaV2ForSequenceClassification''',
     '''TFDebertaV2ForTokenClassification''',
     '''TFDebertaV2Model''',
     '''TFDebertaV2PreTrainedModel''',
 ]

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 snake_case__	:     Any      =    [
     '''DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''DebertaV2ForMaskedLM''',
     '''DebertaV2ForMultipleChoice''',
     '''DebertaV2ForQuestionAnswering''',
     '''DebertaV2ForSequenceClassification''',
     '''DebertaV2ForTokenClassification''',
     '''DebertaV2Model''',
     '''DebertaV2PreTrainedModel''',
 ]


if TYPE_CHECKING:
 from .configuration_deberta_va import (
     DEBERTA_V2_PRETRAINED_CONFIG_ARCHIVE_MAP,
     DebertaVaConfig,
     DebertaVaOnnxConfig,
 )
 from .tokenization_deberta_va import DebertaVaTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_deberta_va_fast import DebertaVaTokenizerFast

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_deberta_va import (
      TF_DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFDebertaVaForMaskedLM,
      TFDebertaVaForQuestionAnswering,
      TFDebertaVaForSequenceClassification,
      TFDebertaVaForTokenClassification,
      TFDebertaVaModel,
      TFDebertaVaPreTrainedModel,
  )

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_deberta_va import (
      DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST,
      DebertaVaForMaskedLM,
      DebertaVaForMultipleChoice,
      DebertaVaForQuestionAnswering,
      DebertaVaForSequenceClassification,
      DebertaVaForTokenClassification,
      DebertaVaModel,
      DebertaVaPreTrainedModel,
  )

else:
 import sys

 snake_case__	:     int      =    _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)










"""simple docstring"""
SCREAMING_SNAKE_CASE					:       List[Any]							   =							1000






def 						lowercase				(	_snake_case		: float	)	->float:
	"""simple docstring"""





	return x * x






def 						lowercase				(	_snake_case		: Union[str, Any]	,  _snake_case		: float	,  _snake_case		: float	,  _snake_case		: int = 4	)	->float:
	"""simple docstring"""





	assert callable(
	    _snake_case	), f"""the function(object) passed should be callable your input : {function}"""
	assert isinstance(_snake_case	,  (float, int)	), f"""a should be float or integer your input : {a}"""
	assert isinstance(function(_snake_case	)	,  (float, int)	), (
	    "the function should return integer or float return type of your function, "
	    f"""{type(_snake_case	)}"""
	)
	assert isinstance(_snake_case	,  (float, int)	), f"""b should be float or integer your input : {b}"""
	assert (
	    isinstance(_snake_case	,  _snake_case	) and precision > 0
	), f"""precision should be positive integer your input : {precision}"""

	# just applying the formula of simpson for approximate integration written in
	# mentioned article in first comment of this file and above this function

	__snake_case      :		Tuple         =							(b - a) / N_STEPS
	__snake_case      :		Union[str, Any]         =							function(_snake_case	) + function(_snake_case	)

	for i in range(1	,  _snake_case	):
		__snake_case      :		List[str]         =							a + h * i
		result += function(_snake_case	) * (4 if i % 2 else 2)

	result *= h / 3
	return round(_snake_case	,  _snake_case	)


if __name__ == "__main__":
			import doctest

			doctest.testmod()





