
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,
    BertEmbeddings,
    BertLayer,
    BertPooler,
    BertPreTrainedModel,
)






def 		_UpperCAmelCase   (	UpperCamelCase:     List[str]  ):






 """simple docstring"""


 __lowerCAmelCase         =							torch.exp(UpperCamelCase  )
 __lowerCAmelCase         =							torch.sum(UpperCamelCase			,							dim=1  )  # sum of exp(x_i)
 __lowerCAmelCase         =							torch.sum(x * exp_x			,							dim=1  )  # sum of x_i * exp(x_i)
 return torch.log(UpperCamelCase  ) - B / A

class       a		(   nn.Module					):







       def __init__(						self  :  Tuple      ,				snake_case__  :  str ):
        """simple docstring"""







        super().__init__()
        __lowerCAmelCase         =							config.output_attentions
        __lowerCAmelCase         =							config.output_hidden_states
        __lowerCAmelCase         =							nn.ModuleList([BertLayer(snake_case__ ) for _ in range(config.num_hidden_layers )] )
        __lowerCAmelCase         =							nn.ModuleList([BertHighway(snake_case__ ) for _ in range(config.num_hidden_layers )] )

        __lowerCAmelCase         =							[-1 for _ in range(config.num_hidden_layers )]







       def       UpperCAmelCase__					(						self  :  List[Any]      ,				snake_case__  :  List[str] ):
        """simple docstring"""







        if (type(snake_case__ ) is float) or (type(snake_case__ ) is int):
         for i in range(len(self.early_exit_entropy ) ):
          __lowerCAmelCase         =							x
        else:
         __lowerCAmelCase         =							x







       def       UpperCAmelCase__					(						self  :  List[str]      ,				snake_case__  :  Tuple ):
        """simple docstring"""







        __lowerCAmelCase         =							pooler.state_dict()
        for highway in self.highway:
         for name, param in highway.pooler.state_dict().items():
          param.copy_(loaded_model[name] )







       def       UpperCAmelCase__					(						self  :  Union[str, Any]      ,				snake_case__  :  Union[str, Any]      ,				snake_case__  :  Dict=None      ,				snake_case__  :  Union[str, Any]=None      ,				snake_case__  :  str=None      ,				snake_case__  :  Optional[Any]=None      ,				):
        """simple docstring"""







        __lowerCAmelCase         =							()
        __lowerCAmelCase         =							()
        __lowerCAmelCase         =							()
        for i, layer_module in enumerate(self.layer ):
         if self.output_hidden_states:
          __lowerCAmelCase         =							all_hidden_states + (hidden_states,)

         __lowerCAmelCase         =							layer_module(
             snake_case__      ,				snake_case__      ,				head_mask[i]      ,				snake_case__      ,				snake_case__ )
         __lowerCAmelCase         =							layer_outputs[0]

         if self.output_attentions:
          __lowerCAmelCase         =							all_attentions + (layer_outputs[1],)

         __lowerCAmelCase         =							(hidden_states,)
         if self.output_hidden_states:
          __lowerCAmelCase         =							current_outputs + (all_hidden_states,)
         if self.output_attentions:
          __lowerCAmelCase         =							current_outputs + (all_attentions,)

         __lowerCAmelCase         =							self.highway[i](snake_case__ )
         # logits, pooled_output

         if not self.training:
          __lowerCAmelCase         =							highway_exit[0]
          __lowerCAmelCase         =							entropy(snake_case__ )
          __lowerCAmelCase         =							highway_exit + (highway_entropy,)  # logits, hidden_states(?), entropy
          __lowerCAmelCase         =							all_highway_exits + (highway_exit,)

          if highway_entropy < self.early_exit_entropy[i]:
           __lowerCAmelCase         =							(highway_logits,) + current_outputs[1:] + (all_highway_exits,)
           raise HighwayException(snake_case__      ,				i + 1 )
         else:
          __lowerCAmelCase         =							all_highway_exits + (highway_exit,)

        # Add last layer
        if self.output_hidden_states:
         __lowerCAmelCase         =							all_hidden_states + (hidden_states,)

        __lowerCAmelCase         =							(hidden_states,)
        if self.output_hidden_states:
         __lowerCAmelCase         =							outputs + (all_hidden_states,)
        if self.output_attentions:
         __lowerCAmelCase         =							outputs + (all_attentions,)

        __lowerCAmelCase         =							outputs + (all_highway_exits,)
        return outputs  # last-layer hidden state, (all hidden states), (all attentions), all highway exits

@add_start_docstrings(
    'The Bert Model transformer with early exiting (DeeBERT). '      ,	__UpperCAmelCase      ,	)
class       a		(   __UpperCAmelCase					):







       def __init__(						self  :  int      ,				snake_case__  :  Optional[Any] ):
        """simple docstring"""







        super().__init__(snake_case__ )
        __lowerCAmelCase         =							config

        __lowerCAmelCase         =							BertEmbeddings(snake_case__ )
        __lowerCAmelCase         =							DeeBertEncoder(snake_case__ )
        __lowerCAmelCase         =							BertPooler(snake_case__ )

        self.init_weights()







       def       UpperCAmelCase__					(						self  :  Dict ):
        """simple docstring"""







        self.encoder.init_highway_pooler(self.pooler )







       def       UpperCAmelCase__					(						self  :  Any ):
        """simple docstring"""







        return self.embeddings.word_embeddings







       def       UpperCAmelCase__					(						self  :  Union[str, Any]      ,				snake_case__  :  Optional[Any] ):
        """simple docstring"""







        __lowerCAmelCase         =							value







       def       UpperCAmelCase__					(						self  :  Any      ,				snake_case__  :  Optional[int] ):
        """simple docstring"""







        for layer, heads in heads_to_prune.items():
         self.encoder.layer[layer].attention.prune_heads(snake_case__ )







       @add_start_docstrings_to_model_forward(snake_case__ )
       def       UpperCAmelCase__					(						self  :  str      ,				snake_case__  :  int=None      ,				snake_case__  :  Tuple=None      ,				snake_case__  :  Union[str, Any]=None      ,				snake_case__  :  int=None      ,				snake_case__  :  Optional[Any]=None      ,				snake_case__  :  List[str]=None      ,				snake_case__  :  str=None      ,				snake_case__  :  Union[str, Any]=None      ,				):
        """simple docstring"""







        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:
         __lowerCAmelCase         =							input_ids.size()
        elif inputs_embeds is not None:
         __lowerCAmelCase         =							inputs_embeds.size()[:-1]
        else:
         raise ValueError("You have to specify either input_ids or inputs_embeds" )

        __lowerCAmelCase         =							input_ids.device if input_ids is not None else inputs_embeds.device

        if attention_mask is None:
         __lowerCAmelCase         =							torch.ones(snake_case__      ,				device=snake_case__ )
        if encoder_attention_mask is None:
         __lowerCAmelCase         =							torch.ones(snake_case__      ,				device=snake_case__ )
        if token_type_ids is None:
         __lowerCAmelCase         =							torch.zeros(snake_case__      ,				dtype=torch.long      ,				device=snake_case__ )

        # 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.
        __lowerCAmelCase         =							self.get_extended_attention_mask(snake_case__      ,				snake_case__      ,				snake_case__ )

        # 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 encoder_attention_mask.dim() == 3:
         __lowerCAmelCase         =							encoder_attention_mask[:, None, :, :]
        if encoder_attention_mask.dim() == 2:
         __lowerCAmelCase         =							encoder_attention_mask[:, None, None, :]

        __lowerCAmelCase         =							encoder_extended_attention_mask.to(
            dtype=next(self.parameters() ).dtype )  # fp16 compatibility
        __lowerCAmelCase         =							(1.0 - encoder_extended_attention_mask) * -10_000.0

        # 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]
        __lowerCAmelCase         =							self.get_head_mask(snake_case__      ,				self.config.num_hidden_layers )

        __lowerCAmelCase         =							self.embeddings(
            input_ids=snake_case__      ,				position_ids=snake_case__      ,				token_type_ids=snake_case__      ,				inputs_embeds=snake_case__ )
        __lowerCAmelCase         =							self.encoder(
            snake_case__      ,				attention_mask=snake_case__      ,				head_mask=snake_case__      ,				encoder_hidden_states=snake_case__      ,				encoder_attention_mask=snake_case__      ,				)
        __lowerCAmelCase         =							encoder_outputs[0]
        __lowerCAmelCase         =							self.pooler(snake_case__ )

        __lowerCAmelCase         =							(
            sequence_output,
            pooled_output,
        ) + encoder_outputs[
            1:
        ]  # add hidden_states and attentions if they are here
        return outputs  # sequence_output, pooled_output, (hidden_states), (attentions), highway exits

class       a		(   __UpperCAmelCase					):







       def __init__(						self  :  Dict      ,				snake_case__  :  int      ,				snake_case__  :  List[Any] ):
        """simple docstring"""







        __lowerCAmelCase         =							message
        __lowerCAmelCase         =							exit_layer  # start from 1!

class       a		(   nn.Module					):







       def __init__(						self  :  Any      ,				snake_case__  :  Tuple ):
        """simple docstring"""







        super().__init__()
        __lowerCAmelCase         =							BertPooler(snake_case__ )
        __lowerCAmelCase         =							nn.Dropout(config.hidden_dropout_prob )
        __lowerCAmelCase         =							nn.Linear(config.hidden_size      ,				config.num_labels )







       def       UpperCAmelCase__					(						self  :  int      ,				snake_case__  :  List[Any] ):
        """simple docstring"""







        __lowerCAmelCase         =							encoder_outputs[0]
        __lowerCAmelCase         =							self.pooler(snake_case__ )
        # "return" pooler_output

        # BertModel
        __lowerCAmelCase         =							(pooler_input, pooler_output) + encoder_outputs[1:]
        # "return" bmodel_output

        # Dropout and classification
        __lowerCAmelCase         =							bmodel_output[1]

        __lowerCAmelCase         =							self.dropout(snake_case__ )
        __lowerCAmelCase         =							self.classifier(snake_case__ )

        return logits, pooled_output

@add_start_docstrings(
    'Bert Model (with early exiting - DeeBERT) with a classifier on top,\n    also takes care of multi-layer training. '      ,	__UpperCAmelCase      ,	)
class       a		(   __UpperCAmelCase					):







       def __init__(						self  :  Any      ,				snake_case__  :  Union[str, Any] ):
        """simple docstring"""







        super().__init__(snake_case__ )
        __lowerCAmelCase         =							config.num_labels
        __lowerCAmelCase         =							config.num_hidden_layers

        __lowerCAmelCase         =							DeeBertModel(snake_case__ )
        __lowerCAmelCase         =							nn.Dropout(config.hidden_dropout_prob )
        __lowerCAmelCase         =							nn.Linear(config.hidden_size      ,				self.config.num_labels )

        self.init_weights()







       @add_start_docstrings_to_model_forward(snake_case__ )
       def       UpperCAmelCase__					(						self  :  Union[str, Any]      ,				snake_case__  :  str=None      ,				snake_case__  :  int=None      ,				snake_case__  :  Union[str, Any]=None      ,				snake_case__  :  Any=None      ,				snake_case__  :  List[str]=None      ,				snake_case__  :  int=None      ,				snake_case__  :  Union[str, Any]=None      ,				snake_case__  :  Dict=-1      ,				snake_case__  :  Tuple=False      ,				):
        """simple docstring"""







        __lowerCAmelCase         =							self.num_layers
        try:
         __lowerCAmelCase         =							self.bert(
             snake_case__      ,				attention_mask=snake_case__      ,				token_type_ids=snake_case__      ,				position_ids=snake_case__      ,				head_mask=snake_case__      ,				inputs_embeds=snake_case__      ,				)
         # sequence_output, pooled_output, (hidden_states), (attentions), highway exits

         __lowerCAmelCase         =							outputs[1]

         __lowerCAmelCase         =							self.dropout(snake_case__ )
         __lowerCAmelCase         =							self.classifier(snake_case__ )
         __lowerCAmelCase         =							(logits,) + outputs[2:]  # add hidden states and attention if they are here
        except HighwayException as e:
         __lowerCAmelCase         =							e.message
         __lowerCAmelCase         =							e.exit_layer
         __lowerCAmelCase         =							outputs[0]

        if not self.training:
         __lowerCAmelCase         =							entropy(snake_case__ )
         __lowerCAmelCase         =							[]
         __lowerCAmelCase         =							[]
        if labels is not None:
         if self.num_labels == 1:
          #  We are doing regression
          __lowerCAmelCase         =							MSELoss()
          __lowerCAmelCase         =							loss_fct(logits.view(-1 )      ,				labels.view(-1 ) )
         else:
          __lowerCAmelCase         =							CrossEntropyLoss()
          __lowerCAmelCase         =							loss_fct(logits.view(-1      ,				self.num_labels )      ,				labels.view(-1 ) )

         # work with highway exits
         __lowerCAmelCase         =							[]
         for highway_exit in outputs[-1]:
          __lowerCAmelCase         =							highway_exit[0]
          if not self.training:
           highway_logits_all.append(snake_case__ )
           highway_entropy.append(highway_exit[2] )
          if self.num_labels == 1:
           #  We are doing regression
           __lowerCAmelCase         =							MSELoss()
           __lowerCAmelCase         =							loss_fct(highway_logits.view(-1 )      ,				labels.view(-1 ) )
          else:
           __lowerCAmelCase         =							CrossEntropyLoss()
           __lowerCAmelCase         =							loss_fct(highway_logits.view(-1      ,				self.num_labels )      ,				labels.view(-1 ) )
          highway_losses.append(snake_case__ )

         if train_highway:
          __lowerCAmelCase         =							(sum(highway_losses[:-1] ),) + outputs
          # exclude the final highway, of course
         else:
          __lowerCAmelCase         =							(loss,) + outputs
        if not self.training:
         __lowerCAmelCase         =							outputs + ((original_entropy, highway_entropy), exit_layer)
         if output_layer >= 0:
          __lowerCAmelCase         =							(
              (outputs[0],) + (highway_logits_all[output_layer],) + outputs[2:]
          )  # use the highway of the last layer

        return outputs  # (loss), logits, (hidden_states), (attentions), (highway_exits)








from __future__ import annotations

UpperCamelCase_   	=	list[tuple[int, int]]

UpperCamelCase_   	=	[
    [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],
]

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

class       a		:







							def __init__(						self  :  Optional[Any]      ,				snake_case__  :  int      ,				snake_case__  :  int      ,				snake_case__  :  int      ,				snake_case__  :  int      ,				snake_case__  :  float      ,				snake_case__  :  Node | None      ,				):
								"""simple docstring"""







								__lowerCAmelCase         =							pos_x
								__lowerCAmelCase         =							pos_y
								__lowerCAmelCase         =							(pos_y, pos_x)
								__lowerCAmelCase         =							goal_x
								__lowerCAmelCase         =							goal_y
								__lowerCAmelCase         =							g_cost
								__lowerCAmelCase         =							parent
								__lowerCAmelCase         =							self.calculate_heuristic()







							def       UpperCAmelCase__					(						self  :  List[str] ):
								"""simple docstring"""







								__lowerCAmelCase         =							abs(self.pos_x - self.goal_x )
								__lowerCAmelCase         =							abs(self.pos_y - self.goal_y )
								return dx + dy







							def __lt__(						self  :  Optional[int]      ,				snake_case__  :  int ):
								"""simple docstring"""







								return self.f_cost < other.f_cost

class       a		:







							def __init__(						self  :  Dict      ,				snake_case__  :  tuple[int, int]      ,				snake_case__  :  tuple[int, int] ):
								"""simple docstring"""







								__lowerCAmelCase         =							Node(start[1]      ,				start[0]      ,				goal[1]      ,				goal[0]      ,				0      ,				snake_case__ )
								__lowerCAmelCase         =							Node(goal[1]      ,				goal[0]      ,				goal[1]      ,				goal[0]      ,				99_999      ,				snake_case__ )

								__lowerCAmelCase         =							[self.start]
								__lowerCAmelCase         =							[]

								__lowerCAmelCase         =							False







							def       UpperCAmelCase__					(						self  :  Dict ):
								"""simple docstring"""







								while self.open_nodes:
									# Open Nodes are sorted using __lt__
									self.open_nodes.sort()
									__lowerCAmelCase         =							self.open_nodes.pop(0 )

									if current_node.pos == self.target.pos:
										__lowerCAmelCase         =							True
										return self.retrace_path(snake_case__ )

									self.closed_nodes.append(snake_case__ )
									__lowerCAmelCase         =							self.get_successors(snake_case__ )

									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(snake_case__ )
										else:
											# retrieve the best current path
											__lowerCAmelCase         =							self.open_nodes.pop(self.open_nodes.index(snake_case__ ) )

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

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







							def       UpperCAmelCase__					(						self  :  int      ,				snake_case__  :  Node ):
								"""simple docstring"""







								__lowerCAmelCase         =							[]
								for action in delta:
									__lowerCAmelCase         =							parent.pos_x + action[1]
									__lowerCAmelCase         =							parent.pos_y + action[0]

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

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

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







							def       UpperCAmelCase__					(						self  :  List[str]      ,				snake_case__  :  Node | None ):
								"""simple docstring"""







								__lowerCAmelCase         =							node
								__lowerCAmelCase         =							[]
								while current_node is not None:
									path.append((current_node.pos_y, current_node.pos_x) )
									__lowerCAmelCase         =							current_node.parent
								path.reverse()
								return path


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

					print("------")

					UpperCamelCase_   	=	GreedyBestFirst(init, goal)
					UpperCamelCase_   	=	greedy_bf.search()
					if path:
										for pos_x, pos_y in path:
															UpperCamelCase_   	=	2

										for elem in grid:
															print(elem)






