
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)}""")





import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_deberta import DebertaTokenizer


if TYPE_CHECKING:
   from transformers.pipelines.conversational import Conversation


UpperCAmelCase__						:	str	     = logging.get_logger(__name__)

UpperCAmelCase__						:	int	     = {'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}

UpperCAmelCase__						:	int	     = {
    'vocab_file': {
        'microsoft/deberta-base': 'https://huggingface.co/microsoft/deberta-base/resolve/main/vocab.json',
        'microsoft/deberta-large': 'https://huggingface.co/microsoft/deberta-large/resolve/main/vocab.json',
        'microsoft/deberta-xlarge': 'https://huggingface.co/microsoft/deberta-xlarge/resolve/main/vocab.json',
        'microsoft/deberta-base-mnli': 'https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/vocab.json',
        'microsoft/deberta-large-mnli': 'https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/vocab.json',
        'microsoft/deberta-xlarge-mnli': (
            'https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/vocab.json'
        ),
    },
    'merges_file': {
        'microsoft/deberta-base': 'https://huggingface.co/microsoft/deberta-base/resolve/main/merges.txt',
        'microsoft/deberta-large': 'https://huggingface.co/microsoft/deberta-large/resolve/main/merges.txt',
        'microsoft/deberta-xlarge': 'https://huggingface.co/microsoft/deberta-xlarge/resolve/main/merges.txt',
        'microsoft/deberta-base-mnli': 'https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/merges.txt',
        'microsoft/deberta-large-mnli': 'https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/merges.txt',
        'microsoft/deberta-xlarge-mnli': (
            'https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/merges.txt'
        ),
    },
}

UpperCAmelCase__						:	List[str]	     = {
    'microsoft/deberta-base': 512,
    'microsoft/deberta-large': 512,
    'microsoft/deberta-xlarge': 512,
    'microsoft/deberta-base-mnli': 512,
    'microsoft/deberta-large-mnli': 512,
    'microsoft/deberta-xlarge-mnli': 512,
}

UpperCAmelCase__						:	Optional[Any]	     = {
    'microsoft/deberta-base': {'do_lower_case': False},
    'microsoft/deberta-large': {'do_lower_case': False},
}






class UpperCAmelCase  (		SCREAMING_SNAKE_CASE__  ):





   '''simple docstring'''
   __UpperCamelCase						:       Union[str, Any]												=       VOCAB_FILES_NAMES
   __UpperCamelCase						:       Union[str, Any]												=       PRETRAINED_VOCAB_FILES_MAP
   __UpperCamelCase						:       Union[str, Any]												=       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   __UpperCamelCase						:       str												=       ['''input_ids''', '''attention_mask''', '''token_type_ids''']
   __UpperCamelCase						:       Any												=       DebertaTokenizer


   def __init__(   self	:				Union[str, Any]     ,     lowerCAmelCase_	:				Optional[Any]=None     ,     lowerCAmelCase_	:				int=None     ,     lowerCAmelCase_	:				Dict=None     ,     lowerCAmelCase_	:				int="replace"     ,     lowerCAmelCase_	:				List[str]="[CLS]"     ,     lowerCAmelCase_	:				str="[SEP]"     ,     lowerCAmelCase_	:				int="[SEP]"     ,     lowerCAmelCase_	:				Any="[CLS]"     ,     lowerCAmelCase_	:				Dict="[UNK]"     ,     lowerCAmelCase_	:				Optional[int]="[PAD]"     ,     lowerCAmelCase_	:				List[str]="[MASK]"     ,     lowerCAmelCase_	:				Optional[Any]=False     ,     **lowerCAmelCase_	:				List[Any]     ,     ):

      """simple docstring"""
      super().__init__(
          lowerCAmelCase_     ,     lowerCAmelCase_     ,     tokenizer_file=lowerCAmelCase_     ,     errors=lowerCAmelCase_     ,     bos_token=lowerCAmelCase_     ,     eos_token=lowerCAmelCase_     ,     unk_token=lowerCAmelCase_     ,     sep_token=lowerCAmelCase_     ,     cls_token=lowerCAmelCase_     ,     pad_token=lowerCAmelCase_     ,     mask_token=lowerCAmelCase_     ,     add_prefix_space=lowerCAmelCase_     ,     **lowerCAmelCase_     ,     )
      _A:    List[str]						     =			kwargs.pop('''add_bos_token'''     ,     lowerCAmelCase_	)

      _A:    Optional[int]						     =			json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()	)
      if pre_tok_state.get('''add_prefix_space'''     ,     lowerCAmelCase_	) != add_prefix_space:
         _A:    Dict						     =			getattr(lowerCAmelCase_     ,     pre_tok_state.pop('''type'''	)	)
         _A:    List[Any]						     =			add_prefix_space
         _A:    Optional[int]						     =			pre_tok_class(**lowerCAmelCase_	)

      _A:    Optional[Any]						     =			add_prefix_space


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

      """simple docstring"""
      if self._mask_token is None:
         if self.verbose:
            logger.error('''Using mask_token, but it is not set yet.'''	)
         return None
      return str(self._mask_token	)


   @mask_token.setter
   def 					__magic_name__							(   self	:				str     ,     lowerCAmelCase_	:				str	):

      """simple docstring"""
      _A:    Dict						     =			AddedToken(lowerCAmelCase_     ,     lstrip=lowerCAmelCase_     ,     rstrip=lowerCAmelCase_	) if isinstance(lowerCAmelCase_     ,     lowerCAmelCase_	) else value
      _A:    str						     =			value


   def 					__magic_name__							(   self	:				Optional[Any]     ,     lowerCAmelCase_	:				List[int]     ,     lowerCAmelCase_	:				Optional[List[int]] = None	):

      """simple docstring"""
      if token_ids_a is None:
         return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
      _A:    str						     =			[self.cls_token_id]
      _A:    Any						     =			[self.sep_token_id]
      return cls + token_ids_a + sep + token_ids_a + sep


   def 					__magic_name__							(   self	:				Optional[int]     ,     lowerCAmelCase_	:				List[int]     ,     lowerCAmelCase_	:				Optional[List[int]] = None	):

      """simple docstring"""
      _A:    Dict						     =			[self.sep_token_id]
      _A:    Any						     =			[self.cls_token_id]

      if token_ids_a is None:
         return len(cls + token_ids_a + sep	) * [0]
      return len(cls + token_ids_a + sep	) * [0] + len(token_ids_a + sep	) * [1]


   def 					__magic_name__							(   self	:				Tuple     ,     *lowerCAmelCase_	:				Dict     ,     **lowerCAmelCase_	:				Tuple	):

      """simple docstring"""
      _A:    Any						     =			kwargs.get('''is_split_into_words'''     ,     lowerCAmelCase_	)
      assert self.add_prefix_space or not is_split_into_words, (
          F"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
          "to use it with pretokenized inputs."
      )

      return super()._batch_encode_plus(*lowerCAmelCase_     ,     **lowerCAmelCase_	)


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

      """simple docstring"""
      _A:    Optional[Any]						     =			kwargs.get('''is_split_into_words'''     ,     lowerCAmelCase_	)

      assert self.add_prefix_space or not is_split_into_words, (
          F"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
          "to use it with pretokenized inputs."
      )

      return super()._encode_plus(*lowerCAmelCase_     ,     **lowerCAmelCase_	)


   def 					__magic_name__							(   self	:				Optional[int]     ,     lowerCAmelCase_	:				str     ,     lowerCAmelCase_	:				Optional[str] = None	):

      """simple docstring"""
      _A:    Union[str, Any]						     =			self._tokenizer.model.save(lowerCAmelCase_     ,     name=lowerCAmelCase_	)
      return tuple(lowerCAmelCase_	)


   def 					__magic_name__							(   self	:				Union[str, Any]     ,     lowerCAmelCase_	:				"Conversation"	):

      """simple docstring"""
      _A:    List[Any]						     =			[]
      for is_user, text in conversation.iter_texts():
         input_ids.extend(self.encode(lowerCAmelCase_     ,     add_special_tokens=lowerCAmelCase_	) + [self.eos_token_id]	)

      if len(lowerCAmelCase_	) > self.model_max_length:
         _A:    Union[str, Any]						     =			input_ids[-self.model_max_length :]
      return input_ids



