

import numpy as np

class   _a					:




      """simple docstring"""

      def __init__(   self							:				str	):
            A_						  =				(0, 0)
            A_						  =				None
            A_						  =				0
            A_						  =				0
            A_						  =				0





      def __eq__(   self							:				str						,			UpperCAmelCase							:				str	):
            return self.position == cell.position







      def 		__A	(   self							:				Union[str, Any]	):
            print(self.position	)




class   _a					:




      """simple docstring"""

      def __init__(   self							:				Tuple						,			UpperCAmelCase							:				Union[str, Any]=(5, 5)	):
            A_						  =				np.zeros(UpperCAmelCase	)
            A_						  =				world_size[0]
            A_						  =				world_size[1]





      def 		__A	(   self							:				Dict	):
            print(self.w	)







      def 		__A	(   self							:				List[Any]						,			UpperCAmelCase							:				List[str]	):
            A_						  =				[
                (-1, -1),
                (-1, 0),
                (-1, 1),
                (0, -1),
                (0, 1),
                (1, -1),
                (1, 0),
                (1, 1),
            ]
            A_						  =				cell.position[0]
            A_						  =				cell.position[1]
            A_						  =				[]
            for n in neughbour_cord:
                  A_						  =				current_x + n[0]
                  A_						  =				current_y + n[1]
                  if 0 <= x < self.world_x_limit and 0 <= y < self.world_y_limit:
                        A_						  =				Cell()
                        A_						  =				(x, y)
                        A_						  =				cell
                        neighbours.append(UpperCAmelCase	)
            return neighbours




def       __snake_case						( __UpperCamelCase	: Any				,__UpperCamelCase	: str				,__UpperCamelCase	: List[Any]    ):



      """simple docstring"""

      A_						  =				[]
      A_						  =				[]
      _open.append(__UpperCamelCase    )

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

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


if __name__ == "__main__":
      __a    :List[Any]      =			Gridworld()
      # Start position and goal
      __a    :str      =			Cell()
      __a    :Union[str, Any]      =			(0, 0)
      __a    :int      =			Cell()
      __a    :int      =			(4, 4)
      print(F"path from {start.position} to {goal.position}")
      __a    :List[str]      =			astar(world, start, goal)
      #   Just for visual reasons.
      for i in s:
            __a    :Tuple      =			1
      print(world.w)
'''simple docstring'''

from __future__ import absolute_import, division, print_function, unicode_literals

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

from transformers import RobertaConfig
from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.roberta.modeling_roberta import (
    ROBERTA_INPUTS_DOCSTRING,
    ROBERTA_START_DOCSTRING,
    RobertaEmbeddings,
)

from .modeling_highway_bert import BertPreTrainedModel, DeeBertModel, HighwayException, entropy


@add_start_docstrings(
    '''The RoBERTa Model transformer with early exiting (DeeRoBERTa). '''		,lowerCAmelCase		,)
class 					_lowercase						(   lowerCAmelCase ):
      '''simple docstring'''


      UpperCAmelCase_   :   int   				= RobertaConfig
      UpperCAmelCase_   :   Tuple   				= '''roberta'''




      def __init__(					self       ,lowerCamelCase_				)      ->      List[Any]:



         '''simple docstring'''



         super().__init__(lowerCamelCase_				)

         UpperCAmelCase__				:					Optional[Any] 					=	RobertaEmbeddings(lowerCamelCase_				)
         self.init_weights()




@add_start_docstrings(
    '''RoBERTa Model (with early exiting - DeeRoBERTa) with a classifier on top,
    also takes care of multi-layer training. '''		,lowerCAmelCase		,)
class 					_lowercase						(   lowerCAmelCase ):
      '''simple docstring'''


      UpperCAmelCase_   :   Tuple   				= RobertaConfig
      UpperCAmelCase_   :   Optional[Any]   				= '''roberta'''




      def __init__(					self       ,lowerCamelCase_				)      ->      str:



         '''simple docstring'''



         super().__init__(lowerCamelCase_				)
         UpperCAmelCase__				:					Any 					=	config.num_labels
         UpperCAmelCase__				:					Union[str, Any] 					=	config.num_hidden_layers

         UpperCAmelCase__				:					str 					=	DeeRobertaModel(lowerCamelCase_				)
         UpperCAmelCase__				:					Optional[int] 					=	nn.Dropout(config.hidden_dropout_prob				)
         UpperCAmelCase__				:					Union[str, Any] 					=	nn.Linear(config.hidden_size       ,self.config.num_labels				)




      @add_start_docstrings_to_model_forward(lowerCamelCase_				)
      def 		lowerCAmelCase__       (					self       ,lowerCamelCase_=None       ,lowerCamelCase_=None       ,lowerCamelCase_=None       ,lowerCamelCase_=None       ,lowerCamelCase_=None       ,lowerCamelCase_=None       ,lowerCamelCase_=None       ,lowerCamelCase_=-1       ,lowerCamelCase_=False       ,)      ->      Optional[Any]:



         '''simple docstring'''



         UpperCAmelCase__				:					Tuple 					=	self.num_layers
         try:
            UpperCAmelCase__				:					Union[str, Any] 					=	self.roberta(
                lowerCamelCase_       ,attention_mask=lowerCamelCase_       ,token_type_ids=lowerCamelCase_       ,position_ids=lowerCamelCase_       ,head_mask=lowerCamelCase_       ,inputs_embeds=lowerCamelCase_       ,)

            UpperCAmelCase__				:					List[Any] 					=	outputs[1]

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

         if not self.training:
            UpperCAmelCase__				:					Dict 					=	entropy(lowerCamelCase_				)
            UpperCAmelCase__				:					List[Any] 					=	[]
            UpperCAmelCase__				:					str 					=	[]
         if labels is not None:
            if self.num_labels == 1:
               #  We are doing regression
               UpperCAmelCase__				:					str 					=	MSELoss()
               UpperCAmelCase__				:					Union[str, Any] 					=	loss_fct(logits.view(-1				)       ,labels.view(-1				)				)
            else:
               UpperCAmelCase__				:					Any 					=	CrossEntropyLoss()
               UpperCAmelCase__				:					Optional[Any] 					=	loss_fct(logits.view(-1       ,self.num_labels				)       ,labels.view(-1				)				)

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

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

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




