




class       a_       :





	def __init__(							self      )  ->					Tuple:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						''
				SCREAMING_SNAKE_CASE_								=						''
				SCREAMING_SNAKE_CASE_								=						[]





	def 						A_(							self      ,     SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      )  ->					int:




				"""simple docstring"""



				if m == -1:
							return n + 1
				elif n == -1:
							return m + 1
				elif self.dp[m][n] > -1:
							return self.dp[m][n]
				else:
							if self.worda[m] == self.worda[n]:
										SCREAMING_SNAKE_CASE_								=						self.__min_dist_top_down_dp(m - 1      ,     n - 1      )
							else:
										SCREAMING_SNAKE_CASE_								=						self.__min_dist_top_down_dp(SCREAMING_SNAKE_CASE      ,     n - 1      )
										SCREAMING_SNAKE_CASE_								=						self.__min_dist_top_down_dp(m - 1      ,     SCREAMING_SNAKE_CASE      )
										SCREAMING_SNAKE_CASE_								=						self.__min_dist_top_down_dp(m - 1      ,     n - 1      )
										SCREAMING_SNAKE_CASE_								=						1 + min(SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      )

							return self.dp[m][n]





	def 						A_(							self      ,     SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      )  ->					int:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						worda
				SCREAMING_SNAKE_CASE_								=						worda
				SCREAMING_SNAKE_CASE_								=						[[-1 for _ in range(len(SCREAMING_SNAKE_CASE      )      )] for _ in range(len(SCREAMING_SNAKE_CASE      )      )]

				return self.__min_dist_top_down_dp(len(SCREAMING_SNAKE_CASE      ) - 1      ,     len(SCREAMING_SNAKE_CASE      ) - 1      )





	def 						A_(							self      ,     SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      )  ->					int:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						worda
				SCREAMING_SNAKE_CASE_								=						worda
				SCREAMING_SNAKE_CASE_								=						len(SCREAMING_SNAKE_CASE      )
				SCREAMING_SNAKE_CASE_								=						len(SCREAMING_SNAKE_CASE      )
				SCREAMING_SNAKE_CASE_								=						[[0 for _ in range(n + 1      )] for _ in range(m + 1      )]

				for i in range(m + 1      ):
							for j in range(n + 1      ):
										if i == 0:  # first string is empty
													SCREAMING_SNAKE_CASE_								=						j
										elif j == 0:  # second string is empty
													SCREAMING_SNAKE_CASE_								=						i
										elif worda[i - 1] == worda[j - 1]:  # last characters are equal
													SCREAMING_SNAKE_CASE_								=						self.dp[i - 1][j - 1]
										else:
													SCREAMING_SNAKE_CASE_								=						self.dp[i][j - 1]
													SCREAMING_SNAKE_CASE_								=						self.dp[i - 1][j]
													SCREAMING_SNAKE_CASE_								=						self.dp[i - 1][j - 1]
													SCREAMING_SNAKE_CASE_								=						1 + min(SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      )
				return self.dp[m][n]


if __name__ == "__main__":
				SCREAMING_SNAKE_CASE__     :     List[Any]		    =       EditDistance()

				print("****************** Testing Edit Distance DP Algorithm ******************")
				print()

				SCREAMING_SNAKE_CASE__     :     Any		    =       input("Enter the first string: ").strip()
				SCREAMING_SNAKE_CASE__     :     Dict		    =       input("Enter the second string: ").strip()

				print()
				print(f"""The minimum edit distance is: {solver.min_dist_top_down(Sa, Sa)}""")
				print(f"""The minimum edit distance is: {solver.min_dist_bottom_up(Sa, Sa)}""")
				print()
				print("*************** End of Testing Edit Distance DP Algorithm ***************")






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       a_       (     SCREAMING_SNAKE_CASE__     ):
	A    							= '''mobilenet_v2'''





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




				"""simple docstring"""



				super().__init__(**SCREAMING_SNAKE_CASE      )

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

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




class       a_       (     SCREAMING_SNAKE_CASE__     ):
	A    							= version.parse('''1.11'''     )





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




				"""simple docstring"""



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





	@property
	def 						A_(							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 						A_(							self      )  ->					float:




				"""simple docstring"""



				return 1e-4
