


from collections.abc import Sequence
def      _a					( SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE	):


		"""simple docstring"""




		return sum(c * (x**i) for i, c in enumerate(SCREAMING_SNAKE_CASE	)	)
def      _a					( SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE	):


		"""simple docstring"""




		lowercase__				= 0.0
		for coeff in reversed(SCREAMING_SNAKE_CASE	):
				lowercase__				= result * x + coeff
		return result


if __name__ == "__main__":
			lowerCAmelCase			=				(0.0, 0.0, 5.0, 9.3, 7.0)
			lowerCAmelCase			=				1_0.0
			print(evaluate_poly(poly, x))
			print(horner(poly, x))




'''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


snake_case									=    logging.get_logger(__name__)

snake_case									=    {
    """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    lowerCAmelCase       (     UpperCamelCase_  ):
 A_				:				List[str]	=			"""mobilenet_v2"""


 def __init__(    self  : Optional[int]			,							a__  : Dict=3			,							a__  : str=224			,							a__  : int=1.0			,							a__  : Dict=8			,							a__  : Dict=8			,							a__  : Dict=6			,							a__  : Optional[Any]=32			,							a__  : List[str]=True			,							a__  : int=True			,							a__  : List[str]="relu6"			,							a__  : List[str]=True			,							a__  : List[Any]=0.8			,							a__  : Tuple=0.02			,							a__  : Tuple=0.001			,							a__  : Optional[Any]=255			,							**a__  : str			,							):
  '''simple docstring'''

  super().__init__(**a__    )

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

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




class    lowerCAmelCase       (     UpperCamelCase_  ):
 A_				:				Optional[int]	=			version.parse("""1.11"""  )


 @property
 def    _A	(    self  : List[Any]    ):
  '''simple docstring'''

  return OrderedDict([("pixel_values", {0: "batch"})]    )


 @property
 def    _A	(    self  : Optional[int]    ):
  '''simple docstring'''

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


 @property
 def    _A	(    self  : List[str]    ):
  '''simple docstring'''

  return 1e-4





