





'''simple docstring'''






import math

import numpy as np
import qiskit
from qiskit import Aer, ClassicalRegister, QuantumCircuit, QuantumRegister, execute


def 					_a   (__SCREAMING_SNAKE_CASE = 3   ):


  """simple docstring"""

  if isinstance(SCREAMING_SNAKE_CASE_  ,      SCREAMING_SNAKE_CASE_   ):
    raise TypeError('''number of qubits must be a integer.'''   )
  if number_of_qubits <= 0:
    raise ValueError('''number of qubits must be > 0.'''   )
  if math.floor(SCREAMING_SNAKE_CASE_   ) != number_of_qubits:
    raise ValueError('''number of qubits must be exact integer.'''   )
  if number_of_qubits > 10:
    raise ValueError('''number of qubits too large to simulate(>10).'''   )

  _UpperCamelCase       			=QuantumRegister(SCREAMING_SNAKE_CASE_  ,      '''qr'''   )
  _UpperCamelCase       			=ClassicalRegister(SCREAMING_SNAKE_CASE_  ,      '''cr'''   )

  _UpperCamelCase       			=QuantumCircuit(SCREAMING_SNAKE_CASE_  ,      SCREAMING_SNAKE_CASE_   )

  _UpperCamelCase       			=number_of_qubits

  for i in range(SCREAMING_SNAKE_CASE_   ):
    quantum_circuit.h(number_of_qubits - i - 1   )
    counter -= 1
    for j in range(SCREAMING_SNAKE_CASE_   ):
      quantum_circuit.cp(np.pi / 2 ** (counter - j)  ,      SCREAMING_SNAKE_CASE_  ,      SCREAMING_SNAKE_CASE_   )

  for k in range(number_of_qubits // 2   ):
    quantum_circuit.swap(SCREAMING_SNAKE_CASE_  ,      number_of_qubits - k - 1   )

  # measure all the qubits
  quantum_circuit.measure(SCREAMING_SNAKE_CASE_  ,      SCREAMING_SNAKE_CASE_   )
  # simulate with 10000 shots
  _UpperCamelCase       			=Aer.get_backend('''qasm_simulator'''   )
  _UpperCamelCase       			=execute(SCREAMING_SNAKE_CASE_  ,      SCREAMING_SNAKE_CASE_  ,      shots=1_0000   )

  return job.result().get_counts(SCREAMING_SNAKE_CASE_   )


if __name__ == "__main__":
      print(
          F"""Total count for quantum fourier transform state is: \
    {quantum_fourier_transform(3)}"""
      )











'''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 		UpperCAmelCase   (				lowercase_):


  """simple docstring"""







  lowerCAmelCase_					=				RobertaConfig
  lowerCAmelCase_					=				"""roberta"""


  def __init__(				self    :					Dict							,	UpperCamelCase__    :					List[str]  )     ->						List[Any]:
    super().__init__(UpperCamelCase__  )

    _UpperCamelCase       			=RobertaEmbeddings(UpperCamelCase__  )
    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 		UpperCAmelCase   (				lowercase_):


  """simple docstring"""







  lowerCAmelCase_					=				RobertaConfig
  lowerCAmelCase_					=				"""roberta"""


  def __init__(				self    :					Tuple							,	UpperCamelCase__    :					Any  )     ->						Dict:
    super().__init__(UpperCamelCase__  )
    _UpperCamelCase       			=config.num_labels
    _UpperCamelCase       			=config.num_hidden_layers

    _UpperCamelCase       			=DeeRobertaModel(UpperCamelCase__  )
    _UpperCamelCase       			=nn.Dropout(config.hidden_dropout_prob  )
    _UpperCamelCase       			=nn.Linear(config.hidden_size							,	self.config.num_labels  )




  @add_start_docstrings_to_model_forward(UpperCamelCase__  )
  def   UpperCamelCase__      (				self    :					List[Any]							,	UpperCamelCase__    :					Optional[int]=None							,	UpperCamelCase__    :					Union[str, Any]=None							,	UpperCamelCase__    :					List[str]=None							,	UpperCamelCase__    :					Optional[Any]=None							,	UpperCamelCase__    :					int=None							,	UpperCamelCase__    :					Union[str, Any]=None							,	UpperCamelCase__    :					Tuple=None							,	UpperCamelCase__    :					Union[str, Any]=-1							,	UpperCamelCase__    :					Dict=False							,	)     ->						Union[str, Any]:
    _UpperCamelCase       			=self.num_layers
    try:
      _UpperCamelCase       			=self.roberta(
          UpperCamelCase__							,	attention_mask=UpperCamelCase__							,	token_type_ids=UpperCamelCase__							,	position_ids=UpperCamelCase__							,	head_mask=UpperCamelCase__							,	inputs_embeds=UpperCamelCase__							,	)

      _UpperCamelCase       			=outputs[1]

      _UpperCamelCase       			=self.dropout(UpperCamelCase__  )
      _UpperCamelCase       			=self.classifier(UpperCamelCase__  )
      _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(UpperCamelCase__  )
      _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(UpperCamelCase__  )
          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(UpperCamelCase__  )

      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






