'''simple docstring'''







import logging

import torch
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss

from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.bert.modeling_bert import (
    BERT_INPUTS_DOCSTRING,
    BERT_START_DOCSTRING,
    BertEncoder,
    BertModel,
    BertPreTrainedModel,
)


UpperCamelCase_      	=	logging.getLogger(__name__)




class _SCREAMING_SNAKE_CASE(							_SCREAMING_SNAKE_CASE					):




						def      __lowerCamelCase       (    self :       List[Any]							,							UpperCamelCase_ :       int							,							UpperCamelCase_ :       Dict							,							UpperCamelCase_ :       List[Any]=None							,							UpperCamelCase_ :       Dict=None      )     ->				Any:
													SCREAMING_SNAKE_CASE__     :Tuple						    = self.layer[current_layer](UpperCamelCase_							,							UpperCamelCase_							,							head_mask[current_layer]      )

													SCREAMING_SNAKE_CASE__     :int						    = layer_outputs[0]

													return hidden_states




@add_start_docstrings(
    'The bare Bert Model transformer with PABEE outputting raw hidden-states without any specific head on top.'						,						_SCREAMING_SNAKE_CASE						,						)
class _SCREAMING_SNAKE_CASE(							_SCREAMING_SNAKE_CASE					):




						def __init__(    self :       List[str]							,							UpperCamelCase_ :       List[Any]      )     ->				Union[str, Any]:
													super().__init__(UpperCamelCase_      )

													SCREAMING_SNAKE_CASE__     :Dict						    = BertEncoderWithPabee(UpperCamelCase_      )

													self.init_weights()
													SCREAMING_SNAKE_CASE__     :Optional[Any]						    = 0
													SCREAMING_SNAKE_CASE__     :List[str]						    = 0
													SCREAMING_SNAKE_CASE__     :List[Any]						    = 0

													SCREAMING_SNAKE_CASE__     :List[Any]						    = 0




						def      __lowerCamelCase       (    self :       str							,							UpperCamelCase_ :       Union[str, Any]      )     ->				int:
													SCREAMING_SNAKE_CASE__     :Optional[Any]						    = threshold




						def      __lowerCamelCase       (    self :       Optional[Any]							,							UpperCamelCase_ :       Tuple      )     ->				int:
													SCREAMING_SNAKE_CASE__     :Tuple						    = patience




						def      __lowerCamelCase       (    self :       List[str]      )     ->				Optional[int]:
													SCREAMING_SNAKE_CASE__     :Optional[Any]						    = 0
													SCREAMING_SNAKE_CASE__     :List[Any]						    = 0




						def      __lowerCamelCase       (    self :       Tuple      )     ->				List[str]:
													SCREAMING_SNAKE_CASE__     :List[str]						    = self.inference_layers_num / self.inference_instances_num
													SCREAMING_SNAKE_CASE__     :Optional[Any]						    = (
													    f'''*** Patience = {self.patience} Avg. Inference Layers = {avg_inf_layers:.2f} Speed Up ='''
													    f''' {1 - avg_inf_layers / self.config.num_hidden_layers:.2f} ***'''
													)
													print(UpperCamelCase_      )




						@add_start_docstrings_to_model_forward(UpperCamelCase_      )
						def      __lowerCamelCase       (    self :       int							,							UpperCamelCase_ :       Dict=None							,							UpperCamelCase_ :       Optional[Any]=None							,							UpperCamelCase_ :       Union[str, Any]=None							,							UpperCamelCase_ :       str=None							,							UpperCamelCase_ :       Tuple=None							,							UpperCamelCase_ :       Any=None							,							UpperCamelCase_ :       Dict=None							,							UpperCamelCase_ :       Optional[int]=None							,							UpperCamelCase_ :       Optional[int]=None							,							UpperCamelCase_ :       int=None							,							UpperCamelCase_ :       int=False							,							)     ->				Any:

													if input_ids is not None and inputs_embeds is not None:
																				raise ValueError('You cannot specify both input_ids and inputs_embeds at the same time'      )
													elif input_ids is not None:
																				SCREAMING_SNAKE_CASE__     :str						    = input_ids.size()
													elif inputs_embeds is not None:
																				SCREAMING_SNAKE_CASE__     :Optional[int]						    = inputs_embeds.size()[:-1]
													else:
																				raise ValueError('You have to specify either input_ids or inputs_embeds'      )

													SCREAMING_SNAKE_CASE__     :Dict						    = input_ids.device if input_ids is not None else inputs_embeds.device

													if attention_mask is None:
																				SCREAMING_SNAKE_CASE__     :Dict						    = torch.ones(UpperCamelCase_							,							device=UpperCamelCase_      )
													if token_type_ids is None:
																				SCREAMING_SNAKE_CASE__     :Dict						    = torch.zeros(UpperCamelCase_							,							dtype=torch.long							,							device=UpperCamelCase_      )

													# We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
													# ourselves in which case we just need to make it broadcastable to all heads.
													SCREAMING_SNAKE_CASE__     :torch.Tensor						    = self.get_extended_attention_mask(UpperCamelCase_							,							UpperCamelCase_							,							UpperCamelCase_      )

													# If a 2D ou 3D attention mask is provided for the cross-attention
													# we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length]
													if self.config.is_decoder and encoder_hidden_states is not None:
																				SCREAMING_SNAKE_CASE__    ,      SCREAMING_SNAKE_CASE__    ,      SCREAMING_SNAKE_CASE__     :Optional[int]						    = encoder_hidden_states.size()
																				SCREAMING_SNAKE_CASE__     :Optional[Any]						    = (encoder_batch_size, encoder_sequence_length)
																				if encoder_attention_mask is None:
																											SCREAMING_SNAKE_CASE__     :Optional[int]						    = torch.ones(UpperCamelCase_							,							device=UpperCamelCase_      )
																				SCREAMING_SNAKE_CASE__     :Optional[Any]						    = self.invert_attention_mask(UpperCamelCase_      )
													else:
																				SCREAMING_SNAKE_CASE__     :str						    = None

													# Prepare head mask if needed
													# 1.0 in head_mask indicate we keep the head
													# attention_probs has shape bsz x n_heads x N x N
													# input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
													# and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
													SCREAMING_SNAKE_CASE__     :Optional[int]						    = self.get_head_mask(UpperCamelCase_							,							self.config.num_hidden_layers      )

													SCREAMING_SNAKE_CASE__     :List[str]						    = self.embeddings(
													    input_ids=UpperCamelCase_							,							position_ids=UpperCamelCase_							,							token_type_ids=UpperCamelCase_							,							inputs_embeds=UpperCamelCase_      )
													SCREAMING_SNAKE_CASE__     :str						    = embedding_output

													if self.training:
																				SCREAMING_SNAKE_CASE__     :str						    = []
																				for i in range(self.config.num_hidden_layers      ):
																											SCREAMING_SNAKE_CASE__     :Dict						    = self.encoder.adaptive_forward(
																											    UpperCamelCase_							,							current_layer=UpperCamelCase_							,							attention_mask=UpperCamelCase_							,							head_mask=UpperCamelCase_      )

																											SCREAMING_SNAKE_CASE__     :Optional[int]						    = self.pooler(UpperCamelCase_      )
																											SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = output_layers[i](output_dropout(UpperCamelCase_      )      )
																											res.append(UpperCamelCase_      )
													elif self.patience == 0:  # Use all layers for inference
																				SCREAMING_SNAKE_CASE__     :Optional[Any]						    = self.encoder(
																				    UpperCamelCase_							,							attention_mask=UpperCamelCase_							,							head_mask=UpperCamelCase_							,							encoder_hidden_states=UpperCamelCase_							,							encoder_attention_mask=UpperCamelCase_							,							)
																				SCREAMING_SNAKE_CASE__     :Dict						    = self.pooler(encoder_outputs[0]      )
																				SCREAMING_SNAKE_CASE__     :Any						    = [output_layers[self.config.num_hidden_layers - 1](UpperCamelCase_      )]
													else:
																				SCREAMING_SNAKE_CASE__     :Any						    = 0
																				SCREAMING_SNAKE_CASE__     :Optional[int]						    = None
																				SCREAMING_SNAKE_CASE__     :Optional[int]						    = 0
																				for i in range(self.config.num_hidden_layers      ):
																											calculated_layer_num += 1
																											SCREAMING_SNAKE_CASE__     :str						    = self.encoder.adaptive_forward(
																											    UpperCamelCase_							,							current_layer=UpperCamelCase_							,							attention_mask=UpperCamelCase_							,							head_mask=UpperCamelCase_      )

																											SCREAMING_SNAKE_CASE__     :Optional[int]						    = self.pooler(UpperCamelCase_      )
																											SCREAMING_SNAKE_CASE__     :Any						    = output_layers[i](UpperCamelCase_      )
																											if regression:
																																		SCREAMING_SNAKE_CASE__     :List[Any]						    = logits.detach()
																																		if patient_result is not None:
																																									SCREAMING_SNAKE_CASE__     :Optional[Any]						    = patient_result.detach()
																																		if (patient_result is not None) and torch.abs(patient_result - labels      ) < self.regression_threshold:
																																									patient_counter += 1
																																		else:
																																									SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = 0
																											else:
																																		SCREAMING_SNAKE_CASE__     :Optional[int]						    = logits.detach().argmax(dim=1      )
																																		if patient_result is not None:
																																									SCREAMING_SNAKE_CASE__     :Dict						    = patient_result.detach().argmax(dim=1      )
																																		if (patient_result is not None) and torch.all(labels.eq(UpperCamelCase_      )      ):
																																									patient_counter += 1
																																		else:
																																									SCREAMING_SNAKE_CASE__     :Dict						    = 0

																											SCREAMING_SNAKE_CASE__     :List[str]						    = logits
																											if patient_counter == self.patience:
																																		break
																				SCREAMING_SNAKE_CASE__     :List[Any]						    = [patient_result]
																				self.inference_layers_num += calculated_layer_num
																				self.inference_instances_num += 1

													return res




@add_start_docstrings(
    'Bert Model transformer with PABEE and a sequence classification/regression head on top (a linear layer on top of\n    the pooled output) e.g. for GLUE tasks. '						,						_SCREAMING_SNAKE_CASE						,						)
class _SCREAMING_SNAKE_CASE(							_SCREAMING_SNAKE_CASE					):




						def __init__(    self :       List[str]							,							UpperCamelCase_ :       int      )     ->				Optional[Any]:
													super().__init__(UpperCamelCase_      )
													SCREAMING_SNAKE_CASE__     :Dict						    = config.num_labels

													SCREAMING_SNAKE_CASE__     :Optional[int]						    = BertModelWithPabee(UpperCamelCase_      )
													SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = nn.Dropout(config.hidden_dropout_prob      )
													SCREAMING_SNAKE_CASE__     :str						    = nn.ModuleList(
													    [nn.Linear(config.hidden_size							,							self.config.num_labels      ) for _ in range(config.num_hidden_layers      )]      )

													self.init_weights()




						@add_start_docstrings_to_model_forward(UpperCamelCase_      )
						def      __lowerCamelCase       (    self :       Any							,							UpperCamelCase_ :       str=None							,							UpperCamelCase_ :       Optional[Any]=None							,							UpperCamelCase_ :       Union[str, Any]=None							,							UpperCamelCase_ :       Union[str, Any]=None							,							UpperCamelCase_ :       Dict=None							,							UpperCamelCase_ :       Optional[int]=None							,							UpperCamelCase_ :       Optional[Any]=None							,							)     ->				Tuple:
													SCREAMING_SNAKE_CASE__     :Any						    = self.bert(
													    input_ids=UpperCamelCase_							,							attention_mask=UpperCamelCase_							,							token_type_ids=UpperCamelCase_							,							position_ids=UpperCamelCase_							,							head_mask=UpperCamelCase_							,							inputs_embeds=UpperCamelCase_							,							output_dropout=self.dropout							,							output_layers=self.classifiers							,							regression=self.num_labels == 1							,							)

													SCREAMING_SNAKE_CASE__     :Optional[int]						    = (logits[-1],)

													if labels is not None:
																				SCREAMING_SNAKE_CASE__     :Tuple						    = None
																				SCREAMING_SNAKE_CASE__     :Dict						    = 0
																				for ix, logits_item in enumerate(UpperCamelCase_      ):
																											if self.num_labels == 1:
																																		#  We are doing regression
																																		SCREAMING_SNAKE_CASE__     :List[Any]						    = MSELoss()
																																		SCREAMING_SNAKE_CASE__     :Any						    = loss_fct(logits_item.view(-1      )							,							labels.view(-1      )      )
																											else:
																																		SCREAMING_SNAKE_CASE__     :List[str]						    = CrossEntropyLoss()
																																		SCREAMING_SNAKE_CASE__     :int						    = loss_fct(logits_item.view(-1							,							self.num_labels      )							,							labels.view(-1      )      )
																											if total_loss is None:
																																		SCREAMING_SNAKE_CASE__     :List[str]						    = loss
																											else:
																																		total_loss += loss * (ix + 1)
																											total_weights += ix + 1
																				SCREAMING_SNAKE_CASE__     :Any						    = (total_loss / total_weights,) + outputs

													return outputs







'''simple docstring'''







from __future__ import annotations

UpperCamelCase_      	=	[
    [-1, 0],  # left
    [0, -1],  # down
    [1, 0],  # right
    [0, 1],  # up
]


def    lowerCamelCase       (			UpperCAmelCase__				:  list[list[int]]				,						UpperCAmelCase__				:  list[int]				,						UpperCAmelCase__				:  list[int]				,						UpperCAmelCase__				:  int				,						UpperCAmelCase__				:  list[list[int]]				,						)			->	tuple[list[list[int]], list[list[int]]]:


							'''simple docstring'''






							SCREAMING_SNAKE_CASE__     :str						    = [
							    [0 for col in range(len(grid[0]		)		)] for row in range(len(UpperCAmelCase__		)		)
							]  # the reference grid
							SCREAMING_SNAKE_CASE__     :Any						    = 1
							SCREAMING_SNAKE_CASE__     :Dict						    = [
							    [0 for col in range(len(grid[0]		)		)] for row in range(len(UpperCAmelCase__		)		)
							]  # the action grid

							SCREAMING_SNAKE_CASE__     :int						    = init[0]
							SCREAMING_SNAKE_CASE__     :Optional[Any]						    = init[1]
							SCREAMING_SNAKE_CASE__     :List[str]						    = 0
							SCREAMING_SNAKE_CASE__     :List[Any]						    = g + heuristic[x][y]  # cost from starting cell to destination cell
							SCREAMING_SNAKE_CASE__     :List[Any]						    = [[f, g, x, y]]

							SCREAMING_SNAKE_CASE__     :Any						    = False  # flag that is set when search is complete
							SCREAMING_SNAKE_CASE__     :str						    = False  # flag set if we can't find expand

							while not found and not resign:
														if len(UpperCAmelCase__		) == 0:
																					raise ValueError('Algorithm is unable to find solution'		)
														else:  # to choose the least costliest action so as to move closer to the goal
																					cell.sort()
																					cell.reverse()
																					SCREAMING_SNAKE_CASE__     :List[Any]						    = cell.pop()
																					SCREAMING_SNAKE_CASE__     :Optional[int]						    = next_cell[2]
																					SCREAMING_SNAKE_CASE__     :Any						    = next_cell[3]
																					SCREAMING_SNAKE_CASE__     :Dict						    = next_cell[1]

																					if x == goal[0] and y == goal[1]:
																												SCREAMING_SNAKE_CASE__     :Tuple						    = True
																					else:
																												for i in range(len(UpperCAmelCase__		)		):  # to try out different valid actions
																																			SCREAMING_SNAKE_CASE__     :Optional[int]						    = x + DIRECTIONS[i][0]
																																			SCREAMING_SNAKE_CASE__     :int						    = y + DIRECTIONS[i][1]
																																			if xa >= 0 and xa < len(UpperCAmelCase__		) and ya >= 0 and ya < len(grid[0]		):
																																										if closed[xa][ya] == 0 and grid[xa][ya] == 0:
																																																	SCREAMING_SNAKE_CASE__     :str						    = g + cost
																																																	SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = ga + heuristic[xa][ya]
																																																	cell.append([fa, ga, xa, ya]		)
																																																	SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = 1
																																																	SCREAMING_SNAKE_CASE__     :Any						    = i
							SCREAMING_SNAKE_CASE__     :int						    = []
							SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = goal[0]
							SCREAMING_SNAKE_CASE__     :Optional[int]						    = goal[1]
							invpath.append([x, y]		)  # we get the reverse path from here
							while x != init[0] or y != init[1]:
														SCREAMING_SNAKE_CASE__     :Optional[Any]						    = x - DIRECTIONS[action[x][y]][0]
														SCREAMING_SNAKE_CASE__     :Optional[Any]						    = y - DIRECTIONS[action[x][y]][1]
														SCREAMING_SNAKE_CASE__     :Optional[int]						    = xa
														SCREAMING_SNAKE_CASE__     :int						    = ya
														invpath.append([x, y]		)

							SCREAMING_SNAKE_CASE__     :int						    = []
							for i in range(len(UpperCAmelCase__		)		):
														path.append(invpath[len(UpperCAmelCase__		) - 1 - i]		)
							return path, action


if __name__ == "__main__":
		UpperCamelCase_      	=	[
		    [0, 1, 0, 0, 0, 0],
		    [0, 1, 0, 0, 0, 0],  # 0 are free path whereas 1's are obstacles
		    [0, 1, 0, 0, 0, 0],
		    [0, 1, 0, 0, 1, 0],
		    [0, 0, 0, 0, 1, 0],
		]

		UpperCamelCase_      	=	[0, 0]
		# all coordinates are given in format [y,x]
		UpperCamelCase_      	=	[len(grid) - 1, len(grid[0]) - 1]
		UpperCamelCase_      	=	1

		# the cost map which pushes the path closer to the goal
		UpperCamelCase_      	=	[[0 for row in range(len(grid[0]))] for col in range(len(grid))]
		for i in range(len(grid)):
				for j in range(len(grid[0])):
						UpperCamelCase_      	=	abs(i - goal[0]) + abs(j - goal[1])
						if grid[i][j] == 1:
								# added extra penalty in the heuristic map
								UpperCamelCase_      	=	99

		UpperCamelCase_	,			UpperCamelCase_      	=	search(grid, init, goal, cost, heuristic)

		print('''ACTION MAP''')
		for i in range(len(action)):
				print(action[i])

		for i in range(len(path)):
				print(path[i])






