

def 						UpperCamelCase (lowercase_:			Dict   ,				lowercase_:			str )					->				Dict:
	A__							:   int					       =		(boundary[1] - boundary[0]) / steps
	A__							:   List[str]					       =		boundary[0]
	A__							:   int					       =		boundary[1]
	A__							:   Any					       =		make_points(__UpperCamelCase   ,				__UpperCamelCase   ,				__UpperCamelCase )
	A__							:   int					       =		0.0
	y += (h / 2.0) * f(__UpperCamelCase )
	for i in x_i:
		# print(i)
		y += h * f(__UpperCamelCase )
	y += (h / 2.0) * f(__UpperCamelCase )
	return y

def 						UpperCamelCase (lowercase_:			Dict   ,				lowercase_:			Tuple   ,				lowercase_:			int )					->				str:
	A__							:   Any					       =		a + h
	while x < (b - h):
		yield x
		A__							:   str					       =		x + h

def 						UpperCamelCase (lowercase_:			Optional[int] )					->				str:  # enter your function here
	A__							:   Union[str, Any]					       =		(x - 0) * (x - 0)
	return y

def 						UpperCamelCase ()					->				str:
	A__							:   Any					       =		0.0  # Lower bound of integration
	A__							:   List[Any]					       =		1.0  # Upper bound of integration
	A__							:   Any					       =		10.0  # define number of steps or resolution
	A__							:   List[Any]					       =		[a, b]  # define boundary of integration
	A__							:   str					       =		method_a(__UpperCamelCase   ,				__UpperCamelCase )
	print(f"""y = {y}""" )


if __name__ == "__main__":
	main()






from typing import TYPE_CHECKING

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


A_	:			Optional[int]  	=      {'configuration_fnet': ['FNET_PRETRAINED_CONFIG_ARCHIVE_MAP', 'FNetConfig']}

try:
	if not is_sentencepiece_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	A_	:			Optional[Any]  	=      ['FNetTokenizer']

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	A_	:			Tuple  	=      ['FNetTokenizerFast']

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	A_	:			List[Any]  	=      [
	    'FNET_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'FNetForMaskedLM',
	    'FNetForMultipleChoice',
	    'FNetForNextSentencePrediction',
	    'FNetForPreTraining',
	    'FNetForQuestionAnswering',
	    'FNetForSequenceClassification',
	    'FNetForTokenClassification',
	    'FNetLayer',
	    'FNetModel',
	    'FNetPreTrainedModel',
	]


if TYPE_CHECKING:
	from .configuration_fnet import FNET_PRETRAINED_CONFIG_ARCHIVE_MAP, FNetConfig

	try:
		if not is_sentencepiece_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_fnet import FNetTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_fnet_fast import FNetTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_fnet import (
		    FNET_PRETRAINED_MODEL_ARCHIVE_LIST,
		    FNetForMaskedLM,
		    FNetForMultipleChoice,
		    FNetForNextSentencePrediction,
		    FNetForPreTraining,
		    FNetForQuestionAnswering,
		    FNetForSequenceClassification,
		    FNetForTokenClassification,
		    FNetLayer,
		    FNetModel,
		    FNetPreTrainedModel,
		)


else:
	import sys

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



