






from __future__ import annotations

from collections.abc import Callable







def 					SCREAMING_SNAKE_CASE    (			snake_case_ :							Callable[[int | float], int | float]					,  snake_case_ :							int | float					,  snake_case_ :							int | float					,  snake_case_ :							int = 100					,  ):
	snake_case__			:				Optional[Any]       =  x_start
	snake_case__			:				Union[str, Any]       =  fnc(snake_case_       )
	snake_case__			:				List[Any]       =  0.0
	for _ in range(snake_case_       ):
		# Approximates small segments of curve as linear and solve
		# for trapezoidal area
		snake_case__			:				Optional[Any]       =  (x_end - x_start) / steps + xa
		snake_case__			:				Dict       =  fnc(snake_case_       )
		area += abs(fxa + fxa       ) * (xa - xa) / 2
		# Increment step
		snake_case__			:				Optional[Any]       =  xa
		snake_case__			:				Optional[int]       =  fxa
	return area


if __name__ == "__main__":







	def 					SCREAMING_SNAKE_CASE    (			snake_case_ :							Any       ):
		return x**3 + x**2

	print("""f(x) = x^3 + x^2""")
	print("""The area between the curve, x = -5, x = 5 and the x axis is:""")
	__lowerCamelCase :      Optional[Any]										=				10
	while i <= 10_0000:
		print(f"with {i} steps: {trapezoidal_area(f, -5, 5, i)}")
		i *= 10











from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__lowerCamelCase :      Union[str, Any]										=				{
    """configuration_time_series_transformer""": [
        """TIME_SERIES_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """TimeSeriesTransformerConfig""",
    ],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__lowerCamelCase :      Tuple										=				[
	    """TIME_SERIES_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """TimeSeriesTransformerForPrediction""",
	    """TimeSeriesTransformerModel""",
	    """TimeSeriesTransformerPreTrainedModel""",
	]


if TYPE_CHECKING:
	from .configuration_time_series_transformer import (
	    TIME_SERIES_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
	    TimeSeriesTransformerConfig,
	)

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_time_series_transformer import (
		    TIME_SERIES_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TimeSeriesTransformerForPrediction,
		    TimeSeriesTransformerModel,
		    TimeSeriesTransformerPreTrainedModel,
		)

else:
	import sys

	__lowerCamelCase :      Tuple										=				_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



