
def 				__SCREAMING_SNAKE_CASE  (						SCREAMING_SNAKE_CASE			: Union[str, Any]       ,				SCREAMING_SNAKE_CASE			: Any     )	->		Tuple:
				# "Simpson Rule"
				# int(f) = delta_x/2 * (b-a)/3*(f1 + 4f2 + 2f_3 + ... + fn)
				__lowercase								=   (boundary[1] - boundary[0]) / steps
				__lowercase								=   boundary[0]
				__lowercase								=   boundary[1]
				__lowercase								=   make_points(SCREAMING_SNAKE_CASE       ,				SCREAMING_SNAKE_CASE       ,				SCREAMING_SNAKE_CASE     )
				__lowercase								=   0.0
				y += (h / 3.0) * f(SCREAMING_SNAKE_CASE     )
				__lowercase								=   2
				for i in x_i:
								y += (h / 3) * (4 - 2 * (cnt % 2)) * f(SCREAMING_SNAKE_CASE     )
								cnt += 1
				y += (h / 3.0) * f(SCREAMING_SNAKE_CASE     )
				return y




def 				__SCREAMING_SNAKE_CASE  (						SCREAMING_SNAKE_CASE			: List[Any]       ,				SCREAMING_SNAKE_CASE			: Dict       ,				SCREAMING_SNAKE_CASE			: int     )	->		Any:
				__lowercase								=   a + h
				while x < (b - h):
								yield x
								__lowercase								=   x + h




def 				__SCREAMING_SNAKE_CASE  (						SCREAMING_SNAKE_CASE			: Any     )	->		List[Any]:  # enter your function here
				__lowercase								=   (x - 0) * (x - 0)
				return y




def 				__SCREAMING_SNAKE_CASE  (						)	->		List[str]:
				__lowercase								=   0.0  # Lower bound of integration
				__lowercase								=   1.0  # Upper bound of integration
				__lowercase								=   10.0  # define number of steps or resolution
				__lowercase								=   [a, b]  # define boundary of integration
				__lowercase								=   method_a(SCREAMING_SNAKE_CASE       ,				SCREAMING_SNAKE_CASE     )
				print(F"""y = {y}"""     )


if __name__ == "__main__":
							main()







from ctypes import c_float, sizeof
from enum import Enum
from typing import TYPE_CHECKING, Optional, Union


if TYPE_CHECKING:
							from .. import AutoFeatureExtractor, AutoProcessor, AutoTokenizer  # tests_ignore
class       A__  (		lowerCAmelCase__							):
							lowerCAmelCase__						:		Any            =				c_float





							@property
							def      a__					(					self    :  List[str]							)		->						int:



											"""simple docstring"""


											return sizeof(self.value							)






def 				__SCREAMING_SNAKE_CASE  (						SCREAMING_SNAKE_CASE			: int       ,				SCREAMING_SNAKE_CASE			: int       ,				SCREAMING_SNAKE_CASE			: int = 0     )	->		int:
				# < 0 is possible if using a dynamic axis
				if dimension <= 0:
								__lowercase								=   fixed_dimension

				dimension -= num_token_to_add
				return dimension




def 				__SCREAMING_SNAKE_CASE  (						SCREAMING_SNAKE_CASE			: int       ,				SCREAMING_SNAKE_CASE			: ParameterFormat     )	->		int:
				return num_parameters * dtype.size




def 				__SCREAMING_SNAKE_CASE  (						SCREAMING_SNAKE_CASE			: str     )	->		Optional[Union["AutoTokenizer", "AutoFeatureExtractor", "AutoProcessor"]]:
				from .. import AutoFeatureExtractor, AutoProcessor, AutoTokenizer  # tests_ignore

				try:
								return AutoProcessor.from_pretrained(SCREAMING_SNAKE_CASE     )
				except (ValueError, OSError, KeyError):
								__lowercase						,   __lowercase								=   None, None
								try:
												__lowercase								=   AutoTokenizer.from_pretrained(SCREAMING_SNAKE_CASE     )
								except (OSError, KeyError):
												pass
								try:
												__lowercase								=   AutoFeatureExtractor.from_pretrained(SCREAMING_SNAKE_CASE     )
								except (OSError, KeyError):
												pass

								if tokenizer is not None and feature_extractor is not None:
												raise ValueError(
												    F"""Couldn't auto-detect preprocessor for {model_name}. Found both a tokenizer and a feature extractor."""     )
								elif tokenizer is None and feature_extractor is None:
												return None
								elif tokenizer is not None:
												return tokenizer
								else:
												return feature_extractor





