
"""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). """				,lowercase__				,)
class A_	(lowercase__      ):




  '''simple docstring'''
  SCREAMING_SNAKE_CASE__			:						Union[str, Any]       =		RobertaConfig
  SCREAMING_SNAKE_CASE__			:						int       =		"""roberta"""







  def __init__(		self				,   lowercase_  ):

     """simple docstring"""
     super().__init__(lowercase_  )

     UpperCAmelCase_					:					int			     =    RobertaEmbeddings(lowercase_  )
     self.init_weights()




@add_start_docstrings(
    """RoBERTa Model (with early exiting - DeeRoBERTa) with a classifier on top,
    also takes care of multi-layer training. """				,lowercase__				,)
class A_	(lowercase__      ):




  '''simple docstring'''
  SCREAMING_SNAKE_CASE__			:						Optional[Any]       =		RobertaConfig
  SCREAMING_SNAKE_CASE__			:						Optional[int]       =		"""roberta"""







  def __init__(		self				,   lowercase_  ):

     """simple docstring"""
     super().__init__(lowercase_  )
     UpperCAmelCase_					:					Any			     =    config.num_labels
     UpperCAmelCase_					:					Optional[Any]			     =    config.num_hidden_layers

     UpperCAmelCase_					:					int			     =    DeeRobertaModel(lowercase_  )
     UpperCAmelCase_					:					Tuple			     =    nn.Dropout(config.hidden_dropout_prob  )
     UpperCAmelCase_					:					Dict			     =    nn.Linear(config.hidden_size				,   self.config.num_labels  )







  @add_start_docstrings_to_model_forward(lowercase_  )
  def        UpperCamelCase__					(		self				,   lowercase_=None				,   lowercase_=None				,   lowercase_=None				,   lowercase_=None				,   lowercase_=None				,   lowercase_=None				,   lowercase_=None				,   lowercase_=-1				,   lowercase_=False				,   ):

     """simple docstring"""
     UpperCAmelCase_					:					str			     =    self.num_layers
     try:
        UpperCAmelCase_					:					str			     =    self.roberta(
            lowercase_				,   attention_mask=lowercase_				,   token_type_ids=lowercase_				,   position_ids=lowercase_				,   head_mask=lowercase_				,   inputs_embeds=lowercase_				,   )

        UpperCAmelCase_					:					List[str]			     =    outputs[1]

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

     if not self.training:
        UpperCAmelCase_					:					Optional[int]			     =    entropy(lowercase_  )
        UpperCAmelCase_					:					List[str]			     =    []
        UpperCAmelCase_					:					Dict			     =    []
     if labels is not None:
        if self.num_labels == 1:
           #  We are doing regression
           UpperCAmelCase_					:					int			     =    MSELoss()
           UpperCAmelCase_					:					Union[str, Any]			     =    loss_fct(logits.view(-1  )				,   labels.view(-1  )  )
        else:
           UpperCAmelCase_					:					Dict			     =    CrossEntropyLoss()
           UpperCAmelCase_					:					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_					:					int			     =    highway_exit[0]
           if not self.training:
              highway_logits_all.append(lowercase_  )
              highway_entropy.append(highway_exit[2]  )
           if self.num_labels == 1:
              #  We are doing regression
              UpperCAmelCase_					:					str			     =    MSELoss()
              UpperCAmelCase_					:					Optional[Any]			     =    loss_fct(highway_logits.view(-1  )				,   labels.view(-1  )  )
           else:
              UpperCAmelCase_					:					List[Any]			     =    CrossEntropyLoss()
              UpperCAmelCase_					:					Union[str, Any]			     =    loss_fct(highway_logits.view(-1				,   self.num_labels  )				,   labels.view(-1  )  )
           highway_losses.append(lowercase_  )

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

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








from __future__ import annotations




def 					lowerCamelCase__       (						A__       :					list[int]  ,			A__       :					list[int]  ,			A__       :					list[int]  ,			A__       :					list[list[str]]  ,			A__       :					int  ,			):





       '''simple docstring'''



       __lowerCamelCase   =						len(A__	)

       # If row is equal to the size of the board it means there are a queen in each row in
       # the current board (possible_board)
       if row == n:
              # We convert the variable possible_board that looks like this: [1, 3, 0, 2] to
              # this: ['. Q . . ', '. . . Q ', 'Q . . . ', '. . Q . ']
              boards.append([""". """ * i + """Q """ + """. """ * (n - 1 - i) for i in possible_board]	)
              return

       # We iterate each column in the row to find all possible results in each row
       for col in range(A__	):
              # We apply that we learned previously. First we check that in the current board
              # (possible_board) there are not other same value because if there is it means
              # that there are a collision in vertical. Then we apply the two formulas we
              # learned before:
              #
              # 45º: y - x = b or 45: row - col = b
              # 135º: y + x = b or row + col = b.
              #
              # And we verify if the results of this two formulas not exist in their variables
              # respectively.  (diagonal_right_collisions, diagonal_left_collisions)
              #
              # If any or these are True it means there is a collision so we continue to the
              # next value in the for loop.
              if (
                  col in possible_board
                  or row - col in diagonal_right_collisions
                  or row + col in diagonal_left_collisions
              ):
                     continue

              # If it is False we call dfs function again and we update the inputs
              depth_first_search(
                  [*possible_board, col]  ,			[*diagonal_right_collisions, row - col]  ,			[*diagonal_left_collisions, row + col]  ,			A__  ,			A__  ,			)




def 					lowerCamelCase__       (						A__       :					int	):





       '''simple docstring'''



       __lowerCamelCase   =						[]
       depth_first_search([]  ,			[]  ,			[]  ,			A__  ,			A__	)

       # Print all the boards
       for board in boards:
              for column in board:
                     print(A__	)
              print(""""""	)

       print(len(A__	)  ,			"""solutions were found."""	)


if __name__ == "__main__":
       import doctest

       doctest.testmod()
       n_queens_solution(4)

