



'''simple docstring'''
from math import factorial, pi






def A__       (	UpperCAmelCase_    ,    UpperCAmelCase_ = 3_0				):

						if not isinstance(UpperCAmelCase_    ,    (int, float)				):
												raise ValueError('maclaurin_sin() requires either an int or float for theta'				)

						if not isinstance(UpperCAmelCase_    ,    UpperCAmelCase_				) or accuracy <= 0:
												raise ValueError('maclaurin_sin() requires a positive int for accuracy'				)

						_UpperCamelCase   :						int												=  float(UpperCAmelCase_				)
						_UpperCamelCase   :						int												=  theta // (2 * pi)
						theta -= 2 * div * pi
						return sum(
						    (-1) ** r * theta ** (2 * r + 1) / factorial(2 * r + 1				) for r in range(UpperCAmelCase_				)				)






def A__       (	UpperCAmelCase_    ,    UpperCAmelCase_ = 3_0				):

						if not isinstance(UpperCAmelCase_    ,    (int, float)				):
												raise ValueError('maclaurin_cos() requires either an int or float for theta'				)

						if not isinstance(UpperCAmelCase_    ,    UpperCAmelCase_				) or accuracy <= 0:
												raise ValueError('maclaurin_cos() requires a positive int for accuracy'				)

						_UpperCamelCase   :						Union[str, Any]												=  float(UpperCAmelCase_				)
						_UpperCamelCase   :						List[Any]												=  theta // (2 * pi)
						theta -= 2 * div * pi
						return sum((-1) ** r * theta ** (2 * r) / factorial(2 * r				) for r in range(UpperCAmelCase_				)				)


if __name__ == "__main__":
					import doctest

					doctest.testmod()

					print(maclaurin_sin(10))
					print(maclaurin_sin(-10))
					print(maclaurin_sin(10, 15))
					print(maclaurin_sin(-10, 15))

					print(maclaurin_cos(5))
					print(maclaurin_cos(-5))
					print(maclaurin_cos(10, 15))
					print(maclaurin_cos(-10, 15))







'''simple docstring'''
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


snake_case_					:      Union[str, Any]    			=    {
    'configuration_transfo_xl': ['TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP', 'TransfoXLConfig'],
    'tokenization_transfo_xl': ['TransfoXLCorpus', 'TransfoXLTokenizer'],
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					snake_case_					:      str    			=    [
					    'TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'AdaptiveEmbedding',
					    'TransfoXLForSequenceClassification',
					    'TransfoXLLMHeadModel',
					    'TransfoXLModel',
					    'TransfoXLPreTrainedModel',
					    'load_tf_weights_in_transfo_xl',
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					snake_case_					:      Any    			=    [
					    'TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'TFAdaptiveEmbedding',
					    'TFTransfoXLForSequenceClassification',
					    'TFTransfoXLLMHeadModel',
					    'TFTransfoXLMainLayer',
					    'TFTransfoXLModel',
					    'TFTransfoXLPreTrainedModel',
					]


if TYPE_CHECKING:
					from .configuration_transfo_xl import TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP, TransfoXLConfig
					from .tokenization_transfo_xl import TransfoXLCorpus, TransfoXLTokenizer

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_transfo_xl import (
										    TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
										    AdaptiveEmbedding,
										    TransfoXLForSequenceClassification,
										    TransfoXLLMHeadModel,
										    TransfoXLModel,
										    TransfoXLPreTrainedModel,
										    load_tf_weights_in_transfo_xl,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_transfo_xl import (
										    TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFAdaptiveEmbedding,
										    TFTransfoXLForSequenceClassification,
										    TFTransfoXLLMHeadModel,
										    TFTransfoXLMainLayer,
										    TFTransfoXLModel,
										    TFTransfoXLPreTrainedModel,
										)

else:
					import sys

					snake_case_					:      Dict    			=    _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)


