



'''simple docstring'''




from typing import TYPE_CHECKING

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


UpperCamelCase__	:     Union[str, Any]    =			{
    '''configuration_led''': ['''LED_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''LEDConfig'''],
    '''tokenization_led''': ['''LEDTokenizer'''],
}

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

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						UpperCamelCase__	:     Dict    =			[
						    '''LED_PRETRAINED_MODEL_ARCHIVE_LIST''',
						    '''LEDForConditionalGeneration''',
						    '''LEDForQuestionAnswering''',
						    '''LEDForSequenceClassification''',
						    '''LEDModel''',
						    '''LEDPreTrainedModel''',
						]


try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						UpperCamelCase__	:     List[Any]    =			['''TFLEDForConditionalGeneration''', '''TFLEDModel''', '''TFLEDPreTrainedModel''']


if TYPE_CHECKING:
						from .configuration_led import LED_PRETRAINED_CONFIG_ARCHIVE_MAP, LEDConfig
						from .tokenization_led import LEDTokenizer

						try:
												if not is_tokenizers_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_led_fast import LEDTokenizerFast

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_led import (
												    LED_PRETRAINED_MODEL_ARCHIVE_LIST,
												    LEDForConditionalGeneration,
												    LEDForQuestionAnswering,
												    LEDForSequenceClassification,
												    LEDModel,
												    LEDPreTrainedModel,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_led import TFLEDForConditionalGeneration, TFLEDModel, TFLEDPreTrainedModel

else:
						import sys

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




'''simple docstring'''




UpperCamelCase__	:     List[Any]    =			10_00


def     lowerCAmelCase_       (     _lowerCamelCase:       float ):
		return x * x


def     lowerCAmelCase_       (     _lowerCamelCase:       Any	,				_lowerCamelCase:       float	,				_lowerCamelCase:       float	,				_lowerCamelCase:       int = 4 ):
		assert callable(
		    _lowerCamelCase ), F"the function(object) passed should be callable your input : {function}"
		assert isinstance(_lowerCamelCase	,				(float, int) ), F"a should be float or integer your input : {a}"
		assert isinstance(function(_lowerCamelCase )	,				(float, int) ), (
		    "the function should return integer or float return type of your function, "
		    F"{type(_lowerCamelCase )}"
		)
		assert isinstance(_lowerCamelCase	,				(float, int) ), F"b should be float or integer your input : {b}"
		assert (
		    isinstance(_lowerCamelCase	,				_lowerCamelCase ) 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

		__SCREAMING_SNAKE_CASE	:		List[Any]   	=		(b - a) / N_STEPS
		__SCREAMING_SNAKE_CASE	:		List[Any]   	=		function(_lowerCamelCase ) + function(_lowerCamelCase )

		for i in range(1	,				_lowerCamelCase ):
				__SCREAMING_SNAKE_CASE	:		Union[str, Any]   	=		a + h * i
				result += function(_lowerCamelCase ) * (4 if i % 2 else 2)

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


if __name__ == "__main__":
						import doctest

						doctest.testmod()