
'''simple docstring'''






from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


UpperCAmelCase_							: str =							logging.get_logger(__name__)

UpperCAmelCase_							: List[str] =							{
    'google/mobilenet_v2_1.4_224': 'https://huggingface.co/google/mobilenet_v2_1.4_224/resolve/main/config.json',
    'google/mobilenet_v2_1.0_224': 'https://huggingface.co/google/mobilenet_v2_1.0_224/resolve/main/config.json',
    'google/mobilenet_v2_0.75_160': 'https://huggingface.co/google/mobilenet_v2_0.75_160/resolve/main/config.json',
    'google/mobilenet_v2_0.35_96': 'https://huggingface.co/google/mobilenet_v2_0.35_96/resolve/main/config.json',
    # See all MobileNetV2 models at https://huggingface.co/models?filter=mobilenet_v2
}

class 			UpperCAmelCase__    (							A			):
    lowerCAmelCase_					     =		'mobilenet_v2'



    def __init__(						self   :							str,__A   :							Dict=3,__A   :							int=2_2_4,__A   :							List[Any]=1.0,__A   :							Any=8,__A   :							List[Any]=8,__A   :							Dict=6,__A   :							Tuple=3_2,__A   :							List[Any]=True,__A   :							Union[str, Any]=True,__A   :							int="relu6",__A   :							Tuple=True,__A   :							Optional[Any]=0.8,__A   :							List[Any]=0.02,__A   :							List[Any]=0.001,__A   :							str=2_5_5,**__A   :							Dict,):
          super().__init__(**__A  )

          if depth_multiplier <= 0:
                raise ValueError("depth_multiplier must be greater than zero."  )

          _lowerCamelCase					: List[str]		    = num_channels
          _lowerCamelCase					: Tuple		    = image_size
          _lowerCamelCase					: Optional[Any]		    = depth_multiplier
          _lowerCamelCase					: Optional[Any]		    = depth_divisible_by
          _lowerCamelCase					: Any		    = min_depth
          _lowerCamelCase					: Dict		    = expand_ratio
          _lowerCamelCase					: Tuple		    = output_stride
          _lowerCamelCase					: Tuple		    = first_layer_is_expansion
          _lowerCamelCase					: Tuple		    = finegrained_output
          _lowerCamelCase					: str		    = hidden_act
          _lowerCamelCase					: List[Any]		    = tf_padding
          _lowerCamelCase					: Dict		    = classifier_dropout_prob
          _lowerCamelCase					: Any		    = initializer_range
          _lowerCamelCase					: str		    = layer_norm_eps
          _lowerCamelCase					: Any		    = semantic_loss_ignore_index


class 			UpperCAmelCase__    (							A			):
    lowerCAmelCase_					     =		version.parse('1.11'			)



    @property
    def 	lowerCamelCase_	(						self   :							Dict  ):
          return OrderedDict([("pixel_values", {0: "batch"})]  )



    @property
    def 	lowerCamelCase_	(						self   :							Dict  ):
          if self.task == "image-classification":
                return OrderedDict([("logits", {0: "batch"})]  )
          else:
                return OrderedDict([("last_hidden_state", {0: "batch"}), ("pooler_output", {0: "batch"})]  )



    @property
    def 	lowerCamelCase_	(						self   :							Dict  ):
          return 1e-4


from math import factorial, pi

def 			lowerCamelCase__			(       _a				,      _a = 30):

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

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

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

def 			lowerCamelCase__			(       _a				,      _a = 30):

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

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

	SCREAMING_SNAKE_CASE :   str					  =							float(_a)
	SCREAMING_SNAKE_CASE :   Any					  =							theta // (2 * pi)
	theta -= 2 * div * pi
	return sum((-1) ** r * theta ** (2 * r) / factorial(2 * r) for r in range(_a))


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))