
"""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). "     ,			SCREAMING_SNAKE_CASE_     ,			)
class      _UpperCAmelCase							(		SCREAMING_SNAKE_CASE_							):
						__SCREAMING_SNAKE_CASE   :							Optional[Any]							      =	RobertaConfig
						__SCREAMING_SNAKE_CASE   :							Union[str, Any]							      =	"roberta"




						def __init__(							self   ,       lowercase_							)						->					List[str]:
										super().__init__(lowercase_							)

										UpperCAmelCase									=					RobertaEmbeddings(lowercase_							)
										self.init_weights()

@add_start_docstrings(
    "RoBERTa Model (with early exiting - DeeRoBERTa) with a classifier on top,\n    also takes care of multi-layer training. "     ,			SCREAMING_SNAKE_CASE_     ,			)
class      _UpperCAmelCase							(		SCREAMING_SNAKE_CASE_							):
						__SCREAMING_SNAKE_CASE   :							Optional[Any]							      =	RobertaConfig
						__SCREAMING_SNAKE_CASE   :							List[Any]							      =	"roberta"




						def __init__(							self   ,       lowercase_							)						->					List[Any]:
										super().__init__(lowercase_							)
										UpperCAmelCase									=					config.num_labels
										UpperCAmelCase									=					config.num_hidden_layers

										UpperCAmelCase									=					DeeRobertaModel(lowercase_							)
										UpperCAmelCase									=					nn.Dropout(config.hidden_dropout_prob							)
										UpperCAmelCase									=					nn.Linear(config.hidden_size   ,       self.config.num_labels							)




						@add_start_docstrings_to_model_forward(lowercase_							)
						def     a_      (							self   ,       lowercase_=None   ,       lowercase_=None   ,       lowercase_=None   ,       lowercase_=None   ,       lowercase_=None   ,       lowercase_=None   ,       lowercase_=None   ,       lowercase_=-1   ,       lowercase_=False   ,       )						->					Dict:
										UpperCAmelCase									=					self.num_layers
										try:
														UpperCAmelCase									=					self.roberta(
														    lowercase_   ,       attention_mask=lowercase_   ,       token_type_ids=lowercase_   ,       position_ids=lowercase_   ,       head_mask=lowercase_   ,       inputs_embeds=lowercase_   ,       )

														UpperCAmelCase									=					outputs[1]

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

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

														# work with highway exits
														UpperCAmelCase									=					[]
														for highway_exit in outputs[-1]:
																		UpperCAmelCase									=					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									=					MSELoss()
																						UpperCAmelCase									=					loss_fct(highway_logits.view(-1							)   ,       labels.view(-1							)							)
																		else:
																						UpperCAmelCase									=					CrossEntropyLoss()
																						UpperCAmelCase									=					loss_fct(highway_logits.view(-1   ,       self.num_labels							)   ,       labels.view(-1							)							)
																		highway_losses.append(lowercase_							)

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

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





"""simple docstring"""

def     lowercase__						(					lowerCAmelCase				:					int ,				lowerCAmelCase				:					list ) ->      int:






    """simple docstring"""




    _enforce_args(lowerCAmelCase ,				lowerCAmelCase )
    if n == 0:
        return 0
    UpperCAmelCase									=					float('-inf' )
    for i in range(1 ,				n + 1 ):
        UpperCAmelCase									=					max(
            lowerCAmelCase ,				prices[i - 1] + naive_cut_rod_recursive(n - i ,				lowerCAmelCase ) )

    return max_revue




def     lowercase__						(					lowerCAmelCase				:					int ,				lowerCAmelCase				:					list ) ->      Dict:






    """simple docstring"""




    _enforce_args(lowerCAmelCase ,				lowerCAmelCase )
    UpperCAmelCase									=					[float('-inf' ) for _ in range(n + 1 )]
    return _top_down_cut_rod_recursive(lowerCAmelCase ,				lowerCAmelCase ,				lowerCAmelCase )




def     lowercase__						(					lowerCAmelCase				:					int ,				lowerCAmelCase				:					list ,				lowerCAmelCase				:					list ) ->      int:






    """simple docstring"""




    if max_rev[n] >= 0:
        return max_rev[n]
    elif n == 0:
        return 0
    else:
        UpperCAmelCase									=					float('-inf' )
        for i in range(1 ,				n + 1 ):
            UpperCAmelCase									=					max(
                lowerCAmelCase ,				prices[i - 1] + _top_down_cut_rod_recursive(n - i ,				lowerCAmelCase ,				lowerCAmelCase ) ,				)

        UpperCAmelCase									=					max_revenue

    return max_rev[n]




def     lowercase__						(					lowerCAmelCase				:					int ,				lowerCAmelCase				:					list ) ->      Optional[int]:






    """simple docstring"""




    _enforce_args(lowerCAmelCase ,				lowerCAmelCase )

    # length(max_rev) = n + 1, to accommodate for the revenue obtainable from a rod of
    # length 0.
    UpperCAmelCase									=					[float('-inf' ) for _ in range(n + 1 )]
    UpperCAmelCase									=					0

    for i in range(1 ,				n + 1 ):
        UpperCAmelCase									=					max_rev[i]
        for j in range(1 ,				i + 1 ):
            UpperCAmelCase									=					max(lowerCAmelCase ,				prices[j - 1] + max_rev[i - j] )

        UpperCAmelCase									=					max_revenue_i

    return max_rev[n]




def     lowercase__						(					lowerCAmelCase				:					int ,				lowerCAmelCase				:					list ) ->      Union[str, Any]:






    """simple docstring"""




    if n < 0:
        UpperCAmelCase									=					F"n must be greater than or equal to 0. Got n = {n}"
        raise ValueError(lowerCAmelCase )

    if n > len(lowerCAmelCase ):
        UpperCAmelCase									=					(
            'Each integral piece of rod must have a corresponding price. '
            F"Got n = {n} but length of prices = {len(lowerCAmelCase )}"
        )
        raise ValueError(lowerCAmelCase )




def     lowercase__						(					) ->      Optional[int]:






    """simple docstring"""




    UpperCAmelCase									=					[6, 10, 12, 15, 20, 23]
    UpperCAmelCase									=					len(lowerCAmelCase )

    # the best revenue comes from cutting the rod into 6 pieces, each
    # of length 1 resulting in a revenue of 6 * 6 = 36.
    UpperCAmelCase									=					36

    UpperCAmelCase									=					top_down_cut_rod(lowerCAmelCase ,				lowerCAmelCase )
    UpperCAmelCase									=					bottom_up_cut_rod(lowerCAmelCase ,				lowerCAmelCase )
    UpperCAmelCase									=					naive_cut_rod_recursive(lowerCAmelCase ,				lowerCAmelCase )

    assert expected_max_revenue == max_rev_top_down
    assert max_rev_top_down == max_rev_bottom_up
    assert max_rev_bottom_up == max_rev_naive


if __name__ == "__main__":
   main()



