




'''simple docstring'''


def 		snake_case_   (UpperCamelCase	:  int = 100_0000				):





							'''simple docstring'''


							_a     					=    limit + 1
							_a     					=    [0] * limit
							for first_term in range(1			,						UpperCamelCase				):
														for n in range(UpperCamelCase			,						UpperCamelCase			,						UpperCamelCase				):
																					_a     					=    first_term + n / first_term
																					if common_difference % 4:  # d must be divisble by 4
																												continue
																					else:
																												common_difference /= 4
																												if (
																												    first_term > common_difference
																												    and first_term < 4 * common_difference
																												):  # since x,y,z are positive integers
																																			frequency[n] += 1  # so z>0 and a>d ,also 4d<a

							_a     					=    sum(1 for x in frequency[1:limit] if x == 10				)

							return count


if __name__ == "__main__":
				print(F'''{solution() = }''')







'''simple docstring'''


from typing import TYPE_CHECKING

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


_snake_case				:				List[str]       							=		{'configuration_xlnet': ['XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP', 'XLNetConfig']}

try:
				if not is_sentencepiece_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_snake_case				:				List[Any]       							=		['XLNetTokenizer']

try:
				if not is_tokenizers_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_snake_case				:				int       							=		['XLNetTokenizerFast']

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_snake_case				:				Optional[int]       							=		[
				    'XLNET_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'XLNetForMultipleChoice',
				    'XLNetForQuestionAnswering',
				    'XLNetForQuestionAnsweringSimple',
				    'XLNetForSequenceClassification',
				    'XLNetForTokenClassification',
				    'XLNetLMHeadModel',
				    'XLNetModel',
				    'XLNetPreTrainedModel',
				    'load_tf_weights_in_xlnet',
				]

try:
				if not is_tf_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_snake_case				:				str       							=		[
				    'TF_XLNET_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'TFXLNetForMultipleChoice',
				    'TFXLNetForQuestionAnsweringSimple',
				    'TFXLNetForSequenceClassification',
				    'TFXLNetForTokenClassification',
				    'TFXLNetLMHeadModel',
				    'TFXLNetMainLayer',
				    'TFXLNetModel',
				    'TFXLNetPreTrainedModel',
				]


if TYPE_CHECKING:
				from .configuration_xlnet import XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP, XLNetConfig

				try:
								if not is_sentencepiece_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_xlnet import XLNetTokenizer

				try:
								if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_xlnet_fast import XLNetTokenizerFast

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_xlnet import (
								    XLNET_PRETRAINED_MODEL_ARCHIVE_LIST,
								    XLNetForMultipleChoice,
								    XLNetForQuestionAnswering,
								    XLNetForQuestionAnsweringSimple,
								    XLNetForSequenceClassification,
								    XLNetForTokenClassification,
								    XLNetLMHeadModel,
								    XLNetModel,
								    XLNetPreTrainedModel,
								    load_tf_weights_in_xlnet,
								)

				try:
								if not is_tf_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_tf_xlnet import (
								    TF_XLNET_PRETRAINED_MODEL_ARCHIVE_LIST,
								    TFXLNetForMultipleChoice,
								    TFXLNetForQuestionAnsweringSimple,
								    TFXLNetForSequenceClassification,
								    TFXLNetForTokenClassification,
								    TFXLNetLMHeadModel,
								    TFXLNetMainLayer,
								    TFXLNetModel,
								    TFXLNetPreTrainedModel,
								)

else:
				import sys

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

