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 			__magic_name__	(		__a     ):





   """simple docstring"""




   lowerCAmelCase   :     List[Any]	      = RobertaConfig
   lowerCAmelCase   :     Optional[Any]	      = '''roberta'''



   def __init__(						self       :	Tuple		,	_lowercase       :	Union[str, Any]				):



    """simple docstring"""



    super().__init__(_lowercase				)

    _UpperCamelCase:  Optional[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. '''   ,						__a   ,						)
class 			__magic_name__	(		__a     ):





   """simple docstring"""




   lowerCAmelCase   :     Dict	      = RobertaConfig
   lowerCAmelCase   :     Union[str, Any]	      = '''roberta'''



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



    """simple docstring"""



    super().__init__(_lowercase				)
    _UpperCamelCase:  Union[str, Any]    						=			config.num_labels
    _UpperCamelCase:  Tuple    						=			config.num_hidden_layers

    _UpperCamelCase:  int    						=			DeeRobertaModel(_lowercase				)
    _UpperCamelCase:  str    						=			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(_lowercase				)
   def 	lowerCAmelCase    (						self       :	List[str]		,	_lowercase       :	Optional[Any]=None		,	_lowercase       :	Dict=None		,	_lowercase       :	str=None		,	_lowercase       :	Optional[Any]=None		,	_lowercase       :	Dict=None		,	_lowercase       :	Tuple=None		,	_lowercase       :	Optional[Any]=None		,	_lowercase       :	List[str]=-1		,	_lowercase       :	Any=False		,	):



    """simple docstring"""



    _UpperCamelCase:  Optional[Any]    						=			self.num_layers
    try:
     _UpperCamelCase:  Optional[int]    						=			self.roberta(
         _lowercase		,	attention_mask=_lowercase		,	token_type_ids=_lowercase		,	position_ids=_lowercase		,	head_mask=_lowercase		,	inputs_embeds=_lowercase		,	)

     _UpperCamelCase:  str    						=			outputs[1]

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

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

     # work with highway exits
     _UpperCamelCase:  Dict    						=			[]
     for highway_exit in outputs[-1]:
      _UpperCamelCase:  Optional[Any]    						=			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:  List[str]    						=			MSELoss()
       _UpperCamelCase:  List[str]    						=			loss_fct(highway_logits.view(-1				)		,	labels.view(-1				)				)
      else:
       _UpperCamelCase:  Union[str, Any]    						=			CrossEntropyLoss()
       _UpperCamelCase:  Optional[Any]    						=			loss_fct(highway_logits.view(-1		,	self.num_labels				)		,	labels.view(-1				)				)
      highway_losses.append(_lowercase				)

     if train_highway:
      _UpperCamelCase:  int    						=			(sum(highway_losses[:-1]				),) + outputs
      # exclude the final highway, of course
     else:
      _UpperCamelCase:  Tuple    						=			(loss,) + outputs
    if not self.training:
     _UpperCamelCase:  List[Any]    						=			outputs + ((original_entropy, highway_entropy), exit_layer)
     if output_layer >= 0:
      _UpperCamelCase:  Tuple    						=			(
          (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 sympy import diff, lambdify, symbols
from sympy.functions import *  # noqa: F403




def    lowerCAmelCase_  (		lowercase:				str	,       lowercase:				complex	,       lowercase:				str = "x"	,       lowercase:				float = 10**-10	,       lowercase:				int = 1	,       )     ->					complex:







 '''simple docstring'''
 _UpperCamelCase:  Any    						=			symbols(lowercase		)
 _UpperCamelCase:  str    						=			lambdify(lowercase	,       lowercase		)
 _UpperCamelCase:  str    						=			lambdify(lowercase	,       diff(lowercase	,       lowercase		)		)

 _UpperCamelCase:  Optional[int]    						=			starting_point

 while True:
  if diff_function(lowercase		) != 0:
   _UpperCamelCase:  int    						=			prev_guess - multiplicity * func(lowercase		) / diff_function(
       lowercase		)
  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

  _UpperCamelCase:  Any    						=			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=0.0_0_5)}""",
  )

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