
from typing import Optional, Union

from ...file_utils import TensorType
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, TruncationStrategy






class UpperCAmelCase  (		SCREAMING_SNAKE_CASE__  ):





   '''simple docstring'''
   __UpperCamelCase						:       List[Any]												=       '''MarkupLMFeatureExtractor'''
   __UpperCamelCase						:       Optional[int]												=       ('''MarkupLMTokenizer''', '''MarkupLMTokenizerFast''')
   __UpperCamelCase						:       List[Any]												=       True


   def __call__(   self	:				List[str]     ,     lowerCAmelCase_	:				List[str]=None     ,     lowerCAmelCase_	:				str=None     ,     lowerCAmelCase_	:				int=None     ,     lowerCAmelCase_	:				str=None     ,     lowerCAmelCase_	:				List[Any]=None     ,     lowerCAmelCase_	:				bool = True     ,     lowerCAmelCase_	:				Union[bool, str, PaddingStrategy] = False     ,     lowerCAmelCase_	:				Union[bool, str, TruncationStrategy] = None     ,     lowerCAmelCase_	:				Optional[int] = None     ,     lowerCAmelCase_	:				int = 0     ,     lowerCAmelCase_	:				Optional[int] = None     ,     lowerCAmelCase_	:				Optional[bool] = None     ,     lowerCAmelCase_	:				Optional[bool] = None     ,     lowerCAmelCase_	:				bool = False     ,     lowerCAmelCase_	:				bool = False     ,     lowerCAmelCase_	:				bool = False     ,     lowerCAmelCase_	:				bool = False     ,     lowerCAmelCase_	:				bool = True     ,     lowerCAmelCase_	:				Optional[Union[str, TensorType]] = None     ,     **lowerCAmelCase_	:				Dict     ,     ):

      """simple docstring"""
      # first, create nodes and xpaths
      if self.parse_html:
         if html_strings is None:
            raise ValueError('''Make sure to pass HTML strings in case `parse_html` is set to `True`'''	)

         if nodes is not None or xpaths is not None or node_labels is not None:
            raise ValueError(
                '''Please don\'t pass nodes, xpaths nor node labels in case `parse_html` is set to `True`'''	)

         _A:    Tuple						     =			self.feature_extractor(lowerCAmelCase_	)
         _A:    Union[str, Any]						     =			features['''nodes''']
         _A:    Union[str, Any]						     =			features['''xpaths''']
      else:
         if html_strings is not None:
            raise ValueError('''You have passed HTML strings but `parse_html` is set to `False`.'''	)
         if nodes is None or xpaths is None:
            raise ValueError('''Make sure to pass nodes and xpaths in case `parse_html` is set to `False`'''	)

        # # second, apply the tokenizer
      if questions is not None and self.parse_html:
         if isinstance(lowerCAmelCase_     ,     lowerCAmelCase_	):
            _A:    Optional[Any]						     =			[questions]  # add batch dimension (as the feature extractor always adds a batch dimension)

      _A:    str						     =			self.tokenizer(
          text=questions if questions is not None else nodes     ,     text_pair=nodes if questions is not None else None     ,     xpaths=lowerCAmelCase_     ,     node_labels=lowerCAmelCase_     ,     add_special_tokens=lowerCAmelCase_     ,     padding=lowerCAmelCase_     ,     truncation=lowerCAmelCase_     ,     max_length=lowerCAmelCase_     ,     stride=lowerCAmelCase_     ,     pad_to_multiple_of=lowerCAmelCase_     ,     return_token_type_ids=lowerCAmelCase_     ,     return_attention_mask=lowerCAmelCase_     ,     return_overflowing_tokens=lowerCAmelCase_     ,     return_special_tokens_mask=lowerCAmelCase_     ,     return_offsets_mapping=lowerCAmelCase_     ,     return_length=lowerCAmelCase_     ,     verbose=lowerCAmelCase_     ,     return_tensors=lowerCAmelCase_     ,     **lowerCAmelCase_     ,     )

      return encoded_inputs


   def 					__magic_name__							(   self	:				List[Any]     ,     *lowerCAmelCase_	:				List[str]     ,     **lowerCAmelCase_	:				List[Any]	):

      """simple docstring"""
      return self.tokenizer.batch_decode(*lowerCAmelCase_     ,     **lowerCAmelCase_	)


   def 					__magic_name__							(   self	:				Optional[Any]     ,     *lowerCAmelCase_	:				List[str]     ,     **lowerCAmelCase_	:				Tuple	):

      """simple docstring"""
      return self.tokenizer.decode(*lowerCAmelCase_     ,     **lowerCAmelCase_	)


   @property
   def 					__magic_name__							(   self	:				Optional[int]	):

      """simple docstring"""
      _A:    int						     =			self.tokenizer.model_input_names
      return tokenizer_input_names





import numpy as np
import qiskit






def     lowerCamelCase__				(   a						,						a ) ->  qiskit.QuantumCircuit:
   _A:    List[Any]						     =			qiskit.QuantumCircuit(num_qubits + 1 )

   # First, let's deal with the case in which oracle is balanced
   if case == "balanced":
      # First generate a random number that tells us which CNOTs to
      # wrap in X-gates:
      _A:    Any						     =			np.random.randint(1						,						2**num_qubits )
      # Next, format 'b' as a binary string of length 'n', padded with zeros:
      _A:    int						     =			format(a						,						f"""0{num_qubits}b""" )
      # Next, we place the first X-gates. Each digit in our binary string
      # corresponds to a qubit, if the digit is 0, we do nothing, if it's 1
      # we apply an X-gate to that qubit:
      for index, bit in enumerate(a ):
         if bit == "1":
            oracle_qc.x(a )
        # Do the controlled-NOT gates for each qubit, using the output qubit
        # as the target:
      for index in range(a ):
         oracle_qc.cx(a						,						a )
      # Next, place the final X-gates
      for index, bit in enumerate(a ):
         if bit == "1":
            oracle_qc.x(a )

    # Case in which oracle is constant
   if case == "constant":
      # First decide what the fixed output of the oracle will be
      # (either always 0 or always 1)
      _A:    int						     =			np.random.randint(2 )
      if output == 1:
         oracle_qc.x(a )

   _A:    int						     =			oracle_qc.to_gate()
   _A:    str						     =			'''Oracle'''  # To show when we display the circuit
   return oracle_gate






def     lowerCamelCase__				(   a						,						a ) ->  qiskit.QuantumCircuit:
   _A:    Optional[int]						     =			qiskit.QuantumCircuit(num_qubits + 1						,						a )
   # Set up the output qubit:
   dj_circuit.x(a )
   dj_circuit.h(a )
   # And set up the input register:
   for qubit in range(a ):
      dj_circuit.h(a )
   # Let's append the oracle gate to our circuit:
   dj_circuit.append(a						,						range(num_qubits + 1 ) )
   # Finally, perform the H-gates again and measure:
   for qubit in range(a ):
      dj_circuit.h(a )

   for i in range(a ):
      dj_circuit.measure(a						,						a )

   return dj_circuit






def     lowerCamelCase__				(   a						,						a ) ->  qiskit.result.counts.Counts:
   _A:    int						     =			qiskit.Aer.get_backend('''aer_simulator''' )

   _A:    Optional[Any]						     =			dj_oracle(a						,						a )
   _A:    int						     =			dj_algorithm(a						,						a )

   # Execute the circuit on the simulator
   _A:    int						     =			qiskit.execute(a						,						a						,						shots=10_00 )

   # Return the histogram data of the results of the experiment.
   return job.result().get_counts(a )


if __name__ == "__main__":
   print(F"""Deutsch Jozsa - Constant Oracle: {deutsch_jozsa("constant", 3)}""")
   print(F"""Deutsch Jozsa - Balanced Oracle: {deutsch_jozsa("balanced", 3)}""")



