




'''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


_SCREAMING_SNAKE_CASE				:  Optional[int] 						=					logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE				:  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 					_snake_case						(      SCREAMING_SNAKE_CASE__				):
			lowerCAmelCase_	: List[Any]					=       "mobilenet_v2"




			def __init__(   self							,				a__=3							,				a__=224							,				a__=1.0							,				a__=8							,				a__=8							,				a__=6							,				a__=32							,				a__=True							,				a__=True							,				a__="relu6"							,				a__=True							,				a__=0.8							,				a__=0.0_2							,				a__=0.0_0_1							,				a__=255							,				**a__							,				)							->							Dict:

										'''simple docstring'''




										super().__init__(**a__				)

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

										snake_case_         = num_channels
										snake_case_         = image_size
										snake_case_         = depth_multiplier
										snake_case_         = depth_divisible_by
										snake_case_         = min_depth
										snake_case_         = expand_ratio
										snake_case_         = output_stride
										snake_case_         = first_layer_is_expansion
										snake_case_         = finegrained_output
										snake_case_         = hidden_act
										snake_case_         = tf_padding
										snake_case_         = classifier_dropout_prob
										snake_case_         = initializer_range
										snake_case_         = layer_norm_eps
										snake_case_         = semantic_loss_ignore_index




class 					_snake_case						(      SCREAMING_SNAKE_CASE__				):
			lowerCAmelCase_	: str					=       version.parse("1.11"				)




			@property
			def   lowerCAmelCase__							(   self				)							->							Mapping[str, Mapping[int, str]]:

										'''simple docstring'''




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




			@property
			def   lowerCAmelCase__							(   self				)							->							Mapping[str, Mapping[int, str]]:

										'''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   lowerCAmelCase__							(   self				)							->							float:

										'''simple docstring'''




										return 1e-4










def lowercase    (  SCREAMING_SNAKE_CASE )			->							str:


			'''simple docstring'''







			SCREAMING_SNAKE_CASE_								=						len(SCREAMING_SNAKE_CASE )
			while cur > 1:
						# Find the maximum number in arr
						SCREAMING_SNAKE_CASE_								=						arr.index(max(arr[0:cur] ) )
						# Reverse from 0 to mi
						SCREAMING_SNAKE_CASE_								=						arr[mi::-1] + arr[mi + 1 : len(SCREAMING_SNAKE_CASE )]
						# Reverse whole list
						SCREAMING_SNAKE_CASE_								=						arr[cur - 1 :: -1] + arr[cur : len(SCREAMING_SNAKE_CASE )]
						cur -= 1
			return arr


if __name__ == "__main__":
				SCREAMING_SNAKE_CASE__     :     List[str]		    =       input("Enter numbers separated by a comma:\n").strip()
				SCREAMING_SNAKE_CASE__     :     Optional[int]		    =       [int(item) for item in user_input.split(",")]
				print(pancake_sort(unsorted))
