





import functools
import operator

from ...configuration_utils import PretrainedConfig
from ...utils import logging


_A   :       Union[str, Any]    					=    logging.get_logger(__name__)

_A   :       Dict    					=    {
    'asapp/sew-tiny-100k': 'https://huggingface.co/asapp/sew-tiny-100k/resolve/main/config.json',
    # See all SEW models at https://huggingface.co/models?filter=sew
}


class   __SCREAMING_SNAKE_CASE     (					__snake_case   ):
       _UpperCAmelCase :    List[str]					       =       """sew"""







       def __init__( self	:      str						,   A	:      List[Any]=3_2						,   A	:      Optional[int]=7_6_8						,   A	:      Optional[int]=1_2						,   A	:      str=1_2						,   A	:      Optional[int]=3_0_7_2						,   A	:      Optional[int]=2						,   A	:      Union[str, Any]="gelu"						,   A	:      Any=0.1						,   A	:      int=0.1						,   A	:      List[str]=0.1						,   A	:      Optional[Any]=0.0						,   A	:      Optional[Any]=0.1						,   A	:      Tuple=0.1						,   A	:      Optional[int]=0.02						,   A	:      Optional[int]=1e-5						,   A	:      Tuple="group"						,   A	:      List[Any]="gelu"						,   A	:      Any=(6_4, 1_2_8, 1_2_8, 1_2_8, 1_2_8, 2_5_6, 2_5_6, 2_5_6, 2_5_6, 5_1_2, 5_1_2, 5_1_2, 5_1_2)						,   A	:      str=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1)						,   A	:      List[str]=(1_0, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1)						,   A	:      List[str]=False						,   A	:      Dict=1_2_8						,   A	:      Optional[int]=1_6						,   A	:      Tuple=True						,   A	:      int=0.05						,   A	:      List[str]=1_0						,   A	:      List[Any]=2						,   A	:      int=0.0						,   A	:      int=1_0						,   A	:      Any=0						,   A	:      Dict="mean"						,   A	:      List[str]=False						,   A	:      List[str]=False						,   A	:      List[Any]=2_5_6						,   A	:      Optional[Any]=0						,   A	:      str=1						,   A	:      Tuple=2						,   **A	:      Union[str, Any]						,   )  ->Tuple:
          super().__init__(**A						,   pad_token_id=A						,   bos_token_id=A						,   eos_token_id=A     )
          lowerCamelCase__  :		Optional[Any]     =				hidden_size
          lowerCamelCase__  :		Optional[Any]     =				feat_extract_norm
          lowerCamelCase__  :		str     =				feat_extract_activation
          lowerCamelCase__  :		int     =				list(A     )
          lowerCamelCase__  :		Optional[int]     =				list(A     )
          lowerCamelCase__  :		Optional[Any]     =				list(A     )
          lowerCamelCase__  :		Optional[Any]     =				conv_bias
          lowerCamelCase__  :		Dict     =				num_conv_pos_embeddings
          lowerCamelCase__  :		List[str]     =				num_conv_pos_embedding_groups
          lowerCamelCase__  :		str     =				len(self.conv_dim     )
          lowerCamelCase__  :		List[str]     =				num_hidden_layers
          lowerCamelCase__  :		int     =				intermediate_size
          lowerCamelCase__  :		str     =				squeeze_factor
          lowerCamelCase__  :		Union[str, Any]     =				hidden_act
          lowerCamelCase__  :		List[Any]     =				num_attention_heads
          lowerCamelCase__  :		Optional[Any]     =				hidden_dropout
          lowerCamelCase__  :		Any     =				attention_dropout
          lowerCamelCase__  :		Optional[Any]     =				activation_dropout
          lowerCamelCase__  :		Tuple     =				feat_proj_dropout
          lowerCamelCase__  :		List[str]     =				final_dropout
          lowerCamelCase__  :		int     =				layerdrop
          lowerCamelCase__  :		Any     =				layer_norm_eps
          lowerCamelCase__  :		str     =				initializer_range
          lowerCamelCase__  :		str     =				vocab_size

          if (
              (len(self.conv_stride     ) != self.num_feat_extract_layers)
              or (len(self.conv_kernel     ) != self.num_feat_extract_layers)
              or (len(self.conv_dim     ) != self.num_feat_extract_layers)
          ):
             raise ValueError(
                 '''Configuration for convolutional layers is incorrect.'''
                 '''It is required that `len(config.conv_dim)` == `len(config.conv_stride)` == `len(config.conv_kernel)`,'''
                 F"but is `len(config.conv_dim) = {len(self.conv_dim     )}`, `len(config.conv_stride)"
                 F"= {len(self.conv_stride     )}`, `len(config.conv_kernel) = {len(self.conv_kernel     )}`."     )

          # fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
          lowerCamelCase__  :		int     =				apply_spec_augment
          lowerCamelCase__  :		Dict     =				mask_time_prob
          lowerCamelCase__  :		Union[str, Any]     =				mask_time_length
          lowerCamelCase__  :		List[str]     =				mask_time_min_masks
          lowerCamelCase__  :		List[str]     =				mask_feature_prob
          lowerCamelCase__  :		Union[str, Any]     =				mask_feature_length
          lowerCamelCase__  :		Dict     =				mask_feature_min_masks

          # ctc loss
          lowerCamelCase__  :		Optional[int]     =				ctc_loss_reduction
          lowerCamelCase__  :		Union[str, Any]     =				ctc_zero_infinity

          # sequence classification
          lowerCamelCase__  :		Optional[Any]     =				use_weighted_layer_sum
          lowerCamelCase__  :		Optional[int]     =				classifier_proj_size







       @property
       def 		__lowerCamelCase       ( self	:      Optional[Any]     )  ->Union[str, Any]:
          return functools.reduce(operator.mul						,   self.conv_stride						,   1     )











"""simple docstring"""







import numpy as np


class      UpperCamelCase		:






						def __init__(		self  :List[Any]				)      ->Optional[Any]:
										lowercase    :      Dict       = (0, 0)
										lowercase    :      str       = None
										lowercase    :      Any       = 0
										lowercase    :      List[Any]       = 0
										lowercase    :      Union[str, Any]       = 0






						def __eq__(		self  :List[Any]			, __magic_name__  :Optional[Any]				)      ->Tuple:
										return self.position == cell.position






						def      __snake_case      (		self  :Any				)      ->List[Any]:
										print(self.position				)


class      UpperCamelCase		:






						def __init__(		self  :Any			, __magic_name__  :int=(5, 5)				)      ->Dict:
										lowercase    :      Tuple       = np.zeros(__magic_name__				)
										lowercase    :      List[str]       = world_size[0]
										lowercase    :      str       = world_size[1]






						def      __snake_case      (		self  :str				)      ->int:
										print(self.w				)






						def      __snake_case      (		self  :Any			, __magic_name__  :Any				)      ->Optional[int]:
										lowercase    :      Optional[Any]       = [
										    (-1, -1),
										    (-1, 0),
										    (-1, 1),
										    (0, -1),
										    (0, 1),
										    (1, -1),
										    (1, 0),
										    (1, 1),
										]
										lowercase    :      List[str]       = cell.position[0]
										lowercase    :      List[str]       = cell.position[1]
										lowercase    :      Optional[int]       = []
										for n in neughbour_cord:
														lowercase    :      List[Any]       = current_x + n[0]
														lowercase    :      List[Any]       = current_y + n[1]
														if 0 <= x < self.world_x_limit and 0 <= y < self.world_y_limit:
																		lowercase    :      Tuple       = Cell()
																		lowercase    :      Union[str, Any]       = (x, y)
																		lowercase    :      Dict       = cell
																		neighbours.append(__magic_name__				)
										return neighbours


def      UpperCamelCase       ( _A  ,				_A  ,				_A )     ->       Tuple:
				lowercase    :      Dict       = []
				lowercase    :      List[str]       = []
				_open.append(_A )

				while _open:
								lowercase    :      List[str]       = np.argmin([n.f for n in _open] )
								lowercase    :      int       = _open[min_f]
								_closed.append(_open.pop(_A ) )
								if current == goal:
												break
								for n in world.get_neigbours(_A ):
												for c in _closed:
																if c == n:
																				continue
												lowercase    :      List[Any]       = current.g + 1
												lowercase      ,      lowercase    :      Any       = n.position
												lowercase      ,      lowercase    :      List[str]       = goal.position
												lowercase    :      Any       = (ya - ya) ** 2 + (xa - xa) ** 2
												lowercase    :      Union[str, Any]       = n.h + n.g

												for c in _open:
																if c == n and c.f < n.f:
																				continue
												_open.append(_A )
				lowercase    :      List[str]       = []
				while current.parent is not None:
								path.append(current.position )
								lowercase    :      int       = current.parent
				path.append(current.position )
				return path[::-1]


if __name__ == "__main__":
				_lowerCAmelCase            =	Gridworld()
				# Start position and goal
				_lowerCAmelCase            =	Cell()
				_lowerCAmelCase            =	(0, 0)
				_lowerCAmelCase            =	Cell()
				_lowerCAmelCase            =	(4, 4)
				print(F'path from {start.position} to {goal.position}')
				_lowerCAmelCase            =	astar(world, start, goal)
				#   Just for visual reasons.
				for i in s:
								_lowerCAmelCase            =	1
				print(world.w)
