




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). '						,				_a						,				)
class 		SCREAMING_SNAKE_CASE__    (	_a  ):
      A_						:    Tuple							=   RobertaConfig
      A_						:    Optional[int]							=   'roberta'


      def __init__(self	:				Any    ,      a__	:				List[str]				):





         """simple docstring"""



         super().__init__(snake_case_				)

         __snake_case								=       RobertaEmbeddings(snake_case_				)
         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. '						,				_a						,				)
class 		SCREAMING_SNAKE_CASE__    (	_a  ):
      A_						:    Tuple							=   RobertaConfig
      A_						:    int							=   'roberta'


      def __init__(self	:				int    ,      a__	:				Union[str, Any]				):





         """simple docstring"""



         super().__init__(snake_case_				)
         __snake_case								=       config.num_labels
         __snake_case								=       config.num_hidden_layers

         __snake_case								=       DeeRobertaModel(snake_case_				)
         __snake_case								=       nn.Dropout(config.hidden_dropout_prob				)
         __snake_case								=       nn.Linear(config.hidden_size    ,      self.config.num_labels				)


      @add_start_docstrings_to_model_forward(snake_case_				)
      def        a			(self	:				List[Any]    ,      a__	:				List[Any]=None    ,      a__	:				Tuple=None    ,      a__	:				Dict=None    ,      a__	:				int=None    ,      a__	:				Dict=None    ,      a__	:				List[str]=None    ,      a__	:				Optional[Any]=None    ,      a__	:				Optional[Any]=-1    ,      a__	:				Optional[Any]=False    ,      ):





         """simple docstring"""



         __snake_case								=       self.num_layers
         try:
            __snake_case								=       self.roberta(
                snake_case_    ,      attention_mask=snake_case_    ,      token_type_ids=snake_case_    ,      position_ids=snake_case_    ,      head_mask=snake_case_    ,      inputs_embeds=snake_case_    ,      )

            __snake_case								=       outputs[1]

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

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

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

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

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








import math






def lowerCamelCase__   (							snake_case_     :  int	)				->    list[int]:
   __snake_case								=       []
   __snake_case								=       2
   __snake_case								=       int(math.sqrt(snake_case_	)	)  # Size of every segment
   __snake_case								=       [True] * (end + 1)
   __snake_case								=       []

   while start <= end:
      if temp[start] is True:
         in_prime.append(snake_case_	)
         for i in range(start * start	,				end + 1	,				snake_case_	):
            __snake_case								=       False
      start += 1
   prime += in_prime

   __snake_case								=       end + 1
   __snake_case								=       min(2 * end	,				snake_case_	)

   while low <= n:
      __snake_case								=       [True] * (high - low + 1)
      for each in in_prime:
         __snake_case								=       math.floor(low / each	) * each
         if t < low:
            t += each

         for j in range(snake_case_	,				high + 1	,				snake_case_	):
            __snake_case								=       False

      for j in range(len(snake_case_	)	):
         if temp[j] is True:
            prime.append(j + low	)

      __snake_case								=       high + 1
      __snake_case								=       min(high + end	,				snake_case_	)

   return prime







print(sieve(10**6))

