"""simple docstring"""


import logging

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,
    BertEncoder,
    BertModel,
    BertPreTrainedModel,
)


lowerCAmelCase_:					Union[str, Any]     =					logging.getLogger(__name__)






class    a__    (   _A	):


   def 	snake_case__	(     self,				_UpperCAmelCase,				_UpperCAmelCase,				_UpperCAmelCase=None,				_UpperCAmelCase=None  ):
        '''simple docstring'''



        lowercase__				 =							self.layer[current_layer](UpperCamelCase__,				UpperCamelCase__,				head_mask[current_layer]  )

        lowercase__				 =							layer_outputs[0]

        return hidden_states






@add_start_docstrings(
    "The bare Bert Model transformer with PABEE outputting raw hidden-states without any specific head on top."					,					_A					,					)
class    a__    (   _A	):


   def __init__(     self,				_UpperCAmelCase  ):
        '''simple docstring'''



        super().__init__(UpperCamelCase__  )

        lowercase__				 =							BertEncoderWithPabee(UpperCamelCase__  )

        self.init_weights()
        lowercase__				 =							0
        lowercase__				 =							0
        lowercase__				 =							0

        lowercase__				 =							0


   def 	snake_case__	(     self,				_UpperCAmelCase  ):
        '''simple docstring'''



        lowercase__				 =							threshold


   def 	snake_case__	(     self,				_UpperCAmelCase  ):
        '''simple docstring'''



        lowercase__				 =							patience


   def 	snake_case__	(     self  ):
        '''simple docstring'''



        lowercase__				 =							0
        lowercase__				 =							0


   def 	snake_case__	(     self  ):
        '''simple docstring'''



        lowercase__				 =							self.inference_layers_num / self.inference_instances_num
        lowercase__				 =							(
            F'''*** Patience = {self.patience} Avg. Inference Layers = {avg_inf_layers:.2f} Speed Up ='''
            F''' {1 - avg_inf_layers / self.config.num_hidden_layers:.2f} ***'''
        )
        print(UpperCamelCase__  )


   @add_start_docstrings_to_model_forward(UpperCamelCase__  )
   def 	snake_case__	(     self,				_UpperCAmelCase=None,				_UpperCAmelCase=None,				_UpperCAmelCase=None,				_UpperCAmelCase=None,				_UpperCAmelCase=None,				_UpperCAmelCase=None,				_UpperCAmelCase=None,				_UpperCAmelCase=None,				_UpperCAmelCase=None,				_UpperCAmelCase=None,				_UpperCAmelCase=False,				):
        '''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:
             lowercase__				 =							input_ids.size()
        elif inputs_embeds is not None:
             lowercase__				 =							inputs_embeds.size()[:-1]
        else:
             raise ValueError("You have to specify either input_ids or inputs_embeds"  )

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

        if attention_mask is None:
             lowercase__				 =							torch.ones(UpperCamelCase__,				device=UpperCamelCase__  )
        if token_type_ids is None:
             lowercase__				 =							torch.zeros(UpperCamelCase__,				dtype=torch.long,				device=UpperCamelCase__  )

        # 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.
        lowercase__				 =							self.get_extended_attention_mask(UpperCamelCase__,				UpperCamelCase__,				UpperCamelCase__  )

        # 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 self.config.is_decoder and encoder_hidden_states is not None:
             lowercase__ ,						lowercase__ ,						lowercase__				 =							encoder_hidden_states.size()
             lowercase__				 =							(encoder_batch_size, encoder_sequence_length)
             if encoder_attention_mask is None:
                  lowercase__				 =							torch.ones(UpperCamelCase__,				device=UpperCamelCase__  )
             lowercase__				 =							self.invert_attention_mask(UpperCamelCase__  )
        else:
             lowercase__				 =							None

        # 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]
        lowercase__				 =							self.get_head_mask(UpperCamelCase__,				self.config.num_hidden_layers  )

        lowercase__				 =							self.embeddings(
            input_ids=UpperCamelCase__,				position_ids=UpperCamelCase__,				token_type_ids=UpperCamelCase__,				inputs_embeds=UpperCamelCase__  )
        lowercase__				 =							embedding_output

        if self.training:
             lowercase__				 =							[]
             for i in range(self.config.num_hidden_layers  ):
                  lowercase__				 =							self.encoder.adaptive_forward(
                      UpperCamelCase__,				current_layer=UpperCamelCase__,				attention_mask=UpperCamelCase__,				head_mask=UpperCamelCase__  )

                  lowercase__				 =							self.pooler(UpperCamelCase__  )
                  lowercase__				 =							output_layers[i](output_dropout(UpperCamelCase__  )  )
                  res.append(UpperCamelCase__  )
        elif self.patience == 0:  # Use all layers for inference
             lowercase__				 =							self.encoder(
                 UpperCamelCase__,				attention_mask=UpperCamelCase__,				head_mask=UpperCamelCase__,				encoder_hidden_states=UpperCamelCase__,				encoder_attention_mask=UpperCamelCase__,				)
             lowercase__				 =							self.pooler(encoder_outputs[0]  )
             lowercase__				 =							[output_layers[self.config.num_hidden_layers - 1](UpperCamelCase__  )]
        else:
             lowercase__				 =							0
             lowercase__				 =							None
             lowercase__				 =							0
             for i in range(self.config.num_hidden_layers  ):
                  calculated_layer_num += 1
                  lowercase__				 =							self.encoder.adaptive_forward(
                      UpperCamelCase__,				current_layer=UpperCamelCase__,				attention_mask=UpperCamelCase__,				head_mask=UpperCamelCase__  )

                  lowercase__				 =							self.pooler(UpperCamelCase__  )
                  lowercase__				 =							output_layers[i](UpperCamelCase__  )
                  if regression:
                       lowercase__				 =							logits.detach()
                       if patient_result is not None:
                            lowercase__				 =							patient_result.detach()
                       if (patient_result is not None) and torch.abs(patient_result - labels  ) < self.regression_threshold:
                            patient_counter += 1
                       else:
                            lowercase__				 =							0
                  else:
                       lowercase__				 =							logits.detach().argmax(dim=1  )
                       if patient_result is not None:
                            lowercase__				 =							patient_result.detach().argmax(dim=1  )
                       if (patient_result is not None) and torch.all(labels.eq(UpperCamelCase__  )  ):
                            patient_counter += 1
                       else:
                            lowercase__				 =							0

                  lowercase__				 =							logits
                  if patient_counter == self.patience:
                       break
             lowercase__				 =							[patient_result]
             self.inference_layers_num += calculated_layer_num
             self.inference_instances_num += 1

        return res






@add_start_docstrings(
    "Bert Model transformer with PABEE and a sequence classification/regression head on top (a linear layer on top of\n    the pooled output) e.g. for GLUE tasks. "					,					_A					,					)
class    a__    (   _A	):


   def __init__(     self,				_UpperCAmelCase  ):
        '''simple docstring'''



        super().__init__(UpperCamelCase__  )
        lowercase__				 =							config.num_labels

        lowercase__				 =							BertModelWithPabee(UpperCamelCase__  )
        lowercase__				 =							nn.Dropout(config.hidden_dropout_prob  )
        lowercase__				 =							nn.ModuleList(
            [nn.Linear(config.hidden_size,				self.config.num_labels  ) for _ in range(config.num_hidden_layers  )]  )

        self.init_weights()


   @add_start_docstrings_to_model_forward(UpperCamelCase__  )
   def 	snake_case__	(     self,				_UpperCAmelCase=None,				_UpperCAmelCase=None,				_UpperCAmelCase=None,				_UpperCAmelCase=None,				_UpperCAmelCase=None,				_UpperCAmelCase=None,				_UpperCAmelCase=None,				):
        '''simple docstring'''



        lowercase__				 =							self.bert(
            input_ids=UpperCamelCase__,				attention_mask=UpperCamelCase__,				token_type_ids=UpperCamelCase__,				position_ids=UpperCamelCase__,				head_mask=UpperCamelCase__,				inputs_embeds=UpperCamelCase__,				output_dropout=self.dropout,				output_layers=self.classifiers,				regression=self.num_labels == 1,				)

        lowercase__				 =							(logits[-1],)

        if labels is not None:
             lowercase__				 =							None
             lowercase__				 =							0
             for ix, logits_item in enumerate(UpperCamelCase__  ):
                  if self.num_labels == 1:
                       #  We are doing regression
                       lowercase__				 =							MSELoss()
                       lowercase__				 =							loss_fct(logits_item.view(-1  ),				labels.view(-1  )  )
                  else:
                       lowercase__				 =							CrossEntropyLoss()
                       lowercase__				 =							loss_fct(logits_item.view(-1,				self.num_labels  ),				labels.view(-1  )  )
                  if total_loss is None:
                       lowercase__				 =							loss
                  else:
                       total_loss += loss * (ix + 1)
                  total_weights += ix + 1
             lowercase__				 =							(total_loss / total_weights,) + outputs

        return outputs





"""simple docstring"""


from sympy import diff, lambdify, symbols
from sympy.functions import *  # noqa: F403







def 					__a   (		A    ,				A    ,				A = "x"    ,				A = 10**-10    ,				A = 1    ,				):






     '''simple docstring'''



     lowercase__				 =							symbols(A      )
     lowercase__				 =							lambdify(A    ,				A      )
     lowercase__				 =							lambdify(A    ,				diff(A    ,				A      )      )

     lowercase__				 =							starting_point

     while True:
          if diff_function(A      ) != 0:
               lowercase__				 =							prev_guess - multiplicity * func(A      ) / diff_function(
                   A      )
          else:
               raise ZeroDivisionError("Could not find root"      ) from None

          # Precision is checked by comparing the difference of consecutive guesses
          if abs(next_guess - prev_guess      ) < precision:
               return next_guess

          lowercase__				 =							next_guess


# Let's Execute
if __name__ == "__main__":
 # Find root of trigonometric function
 # Find value of pi
 print(F'The root of sin(x) = 0 is {newton_raphson("sin(x)", 2)}')

 # Find root of polynomial
 # Find fourth Root of 5
 print(F'The root of x**4 - 5 = 0 is {newton_raphson("x**4 -5", 0.4 +5j)}')

 # Find value of e
 print(
     "The root of log(y) - 1 = 0 is ",
     F'{newton_raphson("log(y) - 1", 2, variable="y")}',
 )

 # Exponential Roots
 print(
     "The root of exp(x) - 1 = 0 is",
     F'{newton_raphson("exp(x) - 1", 1_0, precision=0.005)}',
 )

 # Find root of cos(x)
 print(F'The root of cos(x) = 0 is {newton_raphson("cos(x)", 0)}')




