





'''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[Any]       =				logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE     :Dict       =				{
    '''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_	(  lowerCAmelCase_			):
 _lowerCamelCase  : List[Any]           =							"""mobilenet_v2"""




 def __init__(					self       :    str	,			snake_case_       :    List[str]=3	,			snake_case_       :    Any=2_2_4	,			snake_case_       :    Union[str, Any]=1.0	,			snake_case_       :    int=8	,			snake_case_       :    List[str]=8	,			snake_case_       :    Dict=6	,			snake_case_       :    Union[str, Any]=3_2	,			snake_case_       :    Optional[int]=True	,			snake_case_       :    Optional[Any]=True	,			snake_case_       :    Optional[Any]="relu6"	,			snake_case_       :    int=True	,			snake_case_       :    Any=0.8	,			snake_case_       :    List[str]=0.0_2	,			snake_case_       :    Optional[int]=0.0_0_1	,			snake_case_       :    Dict=2_5_5	,			**snake_case_       :    Dict	,			):
      super().__init__(**snake_case_    )

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

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







class   A_	(  lowerCAmelCase_			):
 _lowerCamelCase  : Optional[int]           =							version.parse("""1.11"""			)




 @property
 def      lowercase	(					self       :    Optional[int]    ):
      return OrderedDict([("pixel_values", {0: "batch"})]    )




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




 @property
 def      lowercase	(					self       :    List[Any]    ):
      return 1e-4







from __future__ import annotations


def   __SCREAMING_SNAKE_CASE					(SCREAMING_SNAKE_CASE__       ):
						snake_case_   =	len(SCREAMING_SNAKE_CASE__       )
						# We need to create solution object to save path.
						snake_case_   =	[[0 for _ in range(SCREAMING_SNAKE_CASE__       )] for _ in range(SCREAMING_SNAKE_CASE__       )]
						snake_case_   =	run_maze(SCREAMING_SNAKE_CASE__  ,		0  ,		0  ,		SCREAMING_SNAKE_CASE__       )
						if solved:
												print('''\n'''.join(str(SCREAMING_SNAKE_CASE__       ) for row in solutions       )       )
						else:
												print('''No solution exists!'''       )
						return solved


def   __SCREAMING_SNAKE_CASE					(SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__       ):
						snake_case_   =	len(SCREAMING_SNAKE_CASE__       )
						# Final check point.
						if i == j == (size - 1):
												snake_case_   =	1
												return True

						snake_case_   =	(not i < 0) and (not j < 0)  # Check lower bounds
						snake_case_   =	(i < size) and (j < size)  # Check upper bounds

						if lower_flag and upper_flag:
												# check for already visited and block points.
												snake_case_   =	(not solutions[i][j]) and (not maze[i][j])
												if block_flag:
																		# check visited
																		snake_case_   =	1

																		# check for directions
																		if (
																		    run_maze(SCREAMING_SNAKE_CASE__  ,		i + 1  ,		SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__       )
																		    or run_maze(SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__  ,		j + 1  ,		SCREAMING_SNAKE_CASE__       )
																		    or run_maze(SCREAMING_SNAKE_CASE__  ,		i - 1  ,		SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__       )
																		    or run_maze(SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__  ,		j - 1  ,		SCREAMING_SNAKE_CASE__       )
																		):
																								return True

																		snake_case_   =	0
																		return False
						return False


if __name__ == "__main__":
					import doctest

					doctest.testmod()