


"""simple docstring"""

import functools
import operator

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


UpperCAmelCase__					=logging.get_logger(__name__)

UpperCAmelCase__					={
    '''microsoft/unispeech-large-1500h-cv''': (
        '''https://huggingface.co/microsoft/unispeech-large-1500h-cv/resolve/main/config.json'''
    ),
    # See all UniSpeech models at https://huggingface.co/models?filter=unispeech
}





class  lowerCamelCase__    (		_a     ):
      a			:  int           =		"unispeech"
      def __init__(		self						:						List[str]   ,							A_						:						str=3_2   ,							A_						:						List[str]=7_6_8   ,							A_						:						Dict=1_2   ,							A_						:						Optional[int]=1_2   ,							A_						:						List[Any]=3_0_7_2   ,							A_						:						int="gelu"   ,							A_						:						Dict=0.1   ,							A_						:						Optional[Any]=0.1   ,							A_						:						Any=0.1   ,							A_						:						List[str]=0.0   ,							A_						:						List[str]=0.0   ,							A_						:						Optional[Any]=0.1   ,							A_						:						Any=0.1   ,							A_						:						List[str]=0.02   ,							A_						:						int=1e-5   ,							A_						:						List[Any]="group"   ,							A_						:						List[str]="gelu"   ,							A_						:						List[str]=(5_1_2, 5_1_2, 5_1_2, 5_1_2, 5_1_2, 5_1_2, 5_1_2)   ,							A_						:						Union[str, Any]=(5, 2, 2, 2, 2, 2, 2)   ,							A_						:						str=(1_0, 3, 3, 3, 3, 2, 2)   ,							A_						:						Optional[Any]=False   ,							A_						:						Optional[Any]=1_2_8   ,							A_						:						Tuple=1_6   ,							A_						:						Tuple=False   ,							A_						:						int=True   ,							A_						:						Union[str, Any]=0.05   ,							A_						:						Any=1_0   ,							A_						:						Optional[Any]=2   ,							A_						:						List[Any]=0.0   ,							A_						:						Union[str, Any]=1_0   ,							A_						:						Optional[Any]=0   ,							A_						:						str=3_2_0   ,							A_						:						int=2   ,							A_						:						str=0.1   ,							A_						:						str=1_0_0   ,							A_						:						Tuple=2_5_6   ,							A_						:						List[str]=2_5_6   ,							A_						:						str=0.1   ,							A_						:						Optional[int]="mean"   ,							A_						:						Optional[int]=False   ,							A_						:						Union[str, Any]=False   ,							A_						:						Dict=2_5_6   ,							A_						:						List[Any]=8_0   ,							A_						:						Optional[int]=0   ,							A_						:						List[Any]=1   ,							A_						:						str=2   ,							A_						:						int=0.5   ,							**A_						:						List[str]   ,							):






       '''simple docstring'''


       super().__init__(**A_   ,							pad_token_id=A_   ,							bos_token_id=A_   ,							eos_token_id=A_				)
       __lowercase						=						hidden_size
       __lowercase						=						feat_extract_norm
       __lowercase						=						feat_extract_activation
       __lowercase						=						list(A_				)
       __lowercase						=						list(A_				)
       __lowercase						=						list(A_				)
       __lowercase						=						conv_bias
       __lowercase						=						num_conv_pos_embeddings
       __lowercase						=						num_conv_pos_embedding_groups
       __lowercase						=						len(self.conv_dim				)
       __lowercase						=						num_hidden_layers
       __lowercase						=						intermediate_size
       __lowercase						=						hidden_act
       __lowercase						=						num_attention_heads
       __lowercase						=						hidden_dropout
       __lowercase						=						attention_dropout
       __lowercase						=						activation_dropout
       __lowercase						=						feat_proj_dropout
       __lowercase						=						final_dropout
       __lowercase						=						layerdrop
       __lowercase						=						layer_norm_eps
       __lowercase						=						initializer_range
       __lowercase						=						num_ctc_classes
       __lowercase						=						vocab_size
       __lowercase						=						do_stable_layer_norm
       __lowercase						=						use_weighted_layer_sum
       __lowercase						=						classifier_proj_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)`, but is `len(config.conv_dim) ="""
            F''' {len(self.conv_dim				)}`, `len(config.conv_stride) = {len(self.conv_stride				)}`,'''
            F''' `len(config.conv_kernel) = {len(self.conv_kernel				)}`.'''				)

       # fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
       __lowercase						=						apply_spec_augment
       __lowercase						=						mask_time_prob
       __lowercase						=						mask_time_length
       __lowercase						=						mask_time_min_masks
       __lowercase						=						mask_feature_prob
       __lowercase						=						mask_feature_length
       __lowercase						=						mask_feature_min_masks

       # parameters for pretraining with codevector quantized representations
       __lowercase						=						num_codevectors_per_group
       __lowercase						=						num_codevector_groups
       __lowercase						=						contrastive_logits_temperature
       __lowercase						=						feat_quantizer_dropout
       __lowercase						=						num_negatives
       __lowercase						=						codevector_dim
       __lowercase						=						proj_codevector_dim
       __lowercase						=						diversity_loss_weight

       # ctc loss
       __lowercase						=						ctc_loss_reduction
       __lowercase						=						ctc_zero_infinity

       # pretraining loss
       __lowercase						=						replace_prob






      @property
      def SCREAMING_SNAKE_CASE_						(		self						:						int				):






       '''simple docstring'''


       return functools.reduce(operator.mul   ,							self.conv_stride   ,							1				)




'''simple docstring'''
from __future__ import annotations

__SCREAMING_SNAKE_CASE					:				Optional[int]									=		list[tuple[int, int]]

__SCREAMING_SNAKE_CASE					:				Union[str, Any]									=		[
    [0, 0, 0, 0, 0, 0, 0],
    [0, 1, 0, 0, 0, 0, 0],  # 0 are free path whereas 1's are obstacles
    [0, 0, 0, 0, 0, 0, 0],
    [0, 0, 1, 0, 0, 0, 0],
    [1, 0, 1, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 1, 0, 0],
]

__SCREAMING_SNAKE_CASE					:				Optional[int]									=		([-1, 0], [0, -1], [1, 0], [0, 1])  # up, left, down, right






class 					__lowerCAmelCase   :

						"""simple docstring"""






						def __init__(  self							:      List[str] ,  lowerCAmelCase							:      int ,  lowerCAmelCase							:      int ,  lowerCAmelCase							:      int ,  lowerCAmelCase							:      int ,  lowerCAmelCase							:      float ,  lowerCAmelCase							:      Node | None ,  ):
													A_			    =		pos_x
													A_			    =		pos_y
													A_			    =		(pos_y, pos_x)
													A_			    =		goal_x
													A_			    =		goal_y
													A_			    =		g_cost
													A_			    =		parent
													A_			    =		self.calculate_heuristic()






						def 				_UpperCAmelCase       (  self							:      Tuple	):
													A_			    =		abs(self.pos_x - self.goal_x	)
													A_			    =		abs(self.pos_y - self.goal_y	)
													return dx + dy






						def __lt__(  self							:      Tuple ,  lowerCAmelCase							:      Any	):
													return self.f_cost < other.f_cost






class 					__lowerCAmelCase   :

						"""simple docstring"""






						def __init__(  self							:      Union[str, Any] ,  lowerCAmelCase							:      tuple[int, int] ,  lowerCAmelCase							:      tuple[int, int]	):
													A_			    =		Node(start[1] ,  start[0] ,  goal[1] ,  goal[0] ,  0 ,  lowerCAmelCase	)
													A_			    =		Node(goal[1] ,  goal[0] ,  goal[1] ,  goal[0] ,  9_99_99 ,  lowerCAmelCase	)

													A_			    =		[self.start]
													A_			    =		[]

													A_			    =		False






						def 				_UpperCAmelCase       (  self							:      str	):
													while self.open_nodes:
																				# Open Nodes are sorted using __lt__
																				self.open_nodes.sort()
																				A_			    =		self.open_nodes.pop(0	)

																				if current_node.pos == self.target.pos:
																											A_			    =		True
																											return self.retrace_path(lowerCAmelCase	)

																				self.closed_nodes.append(lowerCAmelCase	)
																				A_			    =		self.get_successors(lowerCAmelCase	)

																				for child_node in successors:
																											if child_node in self.closed_nodes:
																																		continue

																											if child_node not in self.open_nodes:
																																		self.open_nodes.append(lowerCAmelCase	)
																											else:
																																		# retrieve the best current path
																																		A_			    =		self.open_nodes.pop(self.open_nodes.index(lowerCAmelCase	)	)

																																		if child_node.g_cost < better_node.g_cost:
																																									self.open_nodes.append(lowerCAmelCase	)
																																		else:
																																									self.open_nodes.append(lowerCAmelCase	)

													if not self.reached:
																				return [self.start.pos]
													return None






						def 				_UpperCAmelCase       (  self							:      List[Any] ,  lowerCAmelCase							:      Node	):
													A_			    =		[]
													for action in delta:
																				A_			    =		parent.pos_x + action[1]
																				A_			    =		parent.pos_y + action[0]

																				if not (0 <= pos_x <= len(grid[0]	) - 1 and 0 <= pos_y <= len(lowerCAmelCase	) - 1):
																											continue

																				if grid[pos_y][pos_x] != 0:
																											continue

																				successors.append(
																				    Node(
																				        lowerCAmelCase ,  lowerCAmelCase ,  self.target.pos_y ,  self.target.pos_x ,  parent.g_cost + 1 ,  lowerCAmelCase ,  )	)
													return successors






						def 				_UpperCAmelCase       (  self							:      Tuple ,  lowerCAmelCase							:      Node | None	):
													A_			    =		node
													A_			    =		[]
													while current_node is not None:
																				path.append((current_node.pos_y, current_node.pos_x)	)
																				A_			    =		current_node.parent
													path.reverse()
													return path


if __name__ == "__main__":
							__SCREAMING_SNAKE_CASE					:				List[Any]									=		(0, 0)
							__SCREAMING_SNAKE_CASE					:				Dict									=		(len(grid) - 1, len(grid[0]) - 1)
							for elem in grid:
														print(elem)

							print('''------''')

							__SCREAMING_SNAKE_CASE					:				int									=		GreedyBestFirst(init, goal)
							__SCREAMING_SNAKE_CASE					:				Optional[Any]									=		greedy_bf.search()
							if path:
														for pos_x, pos_y in path:
																					__SCREAMING_SNAKE_CASE					:				List[Any]									=		2

														for elem in grid:
																					print(elem)





