




"""simple docstring"""




import os
from typing import List, Union

import tensorflow as tf
from tensorflow_text import BertTokenizer as BertTokenizerLayer
from tensorflow_text import FastBertTokenizer, ShrinkLongestTrimmer, case_fold_utfa, combine_segments, pad_model_inputs

from .tokenization_bert import BertTokenizer




class 		UpperCamelCase__     (tf.keras.layers.Layer					):

    '''simple docstring'''



    def __init__(   self					,  UpperCamelCase__					,  UpperCamelCase__					,  UpperCamelCase__ = None					,  UpperCamelCase__ = None					,  UpperCamelCase__ = None					,  UpperCamelCase__ = "longest"					,  UpperCamelCase__ = True					,  UpperCamelCase__ = 512					,  UpperCamelCase__ = None					,  UpperCamelCase__ = True					,  UpperCamelCase__ = True					,  UpperCamelCase__ = True					,  **UpperCamelCase__					,  )					->				Union[str, Any]:
           super().__init__()
           if use_fast_bert_tokenizer:
                  lowerCamelCase       :				Tuple      			= FastBertTokenizer(
                      UpperCamelCase__					,  token_out_type=tf.intaa					,  lower_case_nfd_strip_accents=UpperCamelCase__					,  **UpperCamelCase__					)
           else:
                  lowerCamelCase       :				Any      			= tf.lookup.StaticVocabularyTable(
                      tf.lookup.KeyValueTensorInitializer(
                          keys=UpperCamelCase__					,  key_dtype=tf.string					,  values=tf.range(tf.size(UpperCamelCase__					,  out_type=tf.intaa					)					,  dtype=tf.intaa					)					,  value_dtype=tf.intaa					,  )					,  num_oov_buckets=1					,  )
                  lowerCamelCase       :				Union[str, Any]      			= BertTokenizerLayer(
                      UpperCamelCase__					,  token_out_type=tf.intaa					,  lower_case=UpperCamelCase__					,  **UpperCamelCase__					)

           lowerCamelCase       :				int      			= vocab_list
           lowerCamelCase       :				Optional[Any]      			= do_lower_case
           lowerCamelCase       :				List[Any]      			= cls_token_id or vocab_list.index("[CLS]"					)
           lowerCamelCase       :				Dict      			= sep_token_id or vocab_list.index("[SEP]"					)
           lowerCamelCase       :				Tuple      			= pad_token_id or vocab_list.index("[PAD]"					)
           lowerCamelCase       :				Any      			= ShrinkLongestTrimmer(max_length - 3					,  axis=1					)  # Allow room for special tokens
           lowerCamelCase       :				Optional[int]      			= max_length
           lowerCamelCase       :				List[Any]      			= padding
           lowerCamelCase       :				Optional[int]      			= truncation
           lowerCamelCase       :				Optional[int]      			= pad_to_multiple_of
           lowerCamelCase       :				Union[str, Any]      			= return_token_type_ids
           lowerCamelCase       :				Any      			= return_attention_mask







    @classmethod
    def    _lowercase					(   cls					,  UpperCamelCase__					,  **UpperCamelCase__					)					->				Dict:  # noqa: F821
           lowerCamelCase       :				List[Any]      			= kwargs.pop("do_lower_case"					,  UpperCamelCase__					)
           lowerCamelCase       :				Tuple      			= tokenizer.do_lower_case if do_lower_case is None else do_lower_case
           lowerCamelCase       :				int      			= kwargs.pop("cls_token_id"					,  UpperCamelCase__					)
           lowerCamelCase       :				int      			= tokenizer.cls_token_id if cls_token_id is None else cls_token_id
           lowerCamelCase       :				Optional[int]      			= kwargs.pop("sep_token_id"					,  UpperCamelCase__					)
           lowerCamelCase       :				int      			= tokenizer.sep_token_id if sep_token_id is None else sep_token_id
           lowerCamelCase       :				Optional[int]      			= kwargs.pop("pad_token_id"					,  UpperCamelCase__					)
           lowerCamelCase       :				Union[str, Any]      			= tokenizer.pad_token_id if pad_token_id is None else pad_token_id

           lowerCamelCase       :				List[Any]      			= tokenizer.get_vocab()
           lowerCamelCase       :				Dict      			= sorted([(wordpiece, idx) for wordpiece, idx in vocab.items()]					,  key=lambda UpperCamelCase__					: x[1]					)
           lowerCamelCase       :				Optional[Any]      			= [entry[0] for entry in vocab]
           return cls(
               vocab_list=UpperCamelCase__					,  do_lower_case=UpperCamelCase__					,  cls_token_id=UpperCamelCase__					,  sep_token_id=UpperCamelCase__					,  pad_token_id=UpperCamelCase__					,  **UpperCamelCase__					,  )







    @classmethod
    def    _lowercase					(   cls					,  UpperCamelCase__					,  *UpperCamelCase__					,  **UpperCamelCase__					)					->				List[str]:






           try:
                  lowerCamelCase       :				Optional[int]      			= BertTokenizer.from_pretrained(UpperCamelCase__					,  *UpperCamelCase__					,  **UpperCamelCase__					)
           except:  # noqa: E722
                  from .tokenization_bert_fast import BertTokenizerFast

                  lowerCamelCase       :				List[Any]      			= BertTokenizerFast.from_pretrained(UpperCamelCase__					,  *UpperCamelCase__					,  **UpperCamelCase__					)
           return cls.from_tokenizer(UpperCamelCase__					,  **UpperCamelCase__					)







    def    _lowercase					(   self					,  UpperCamelCase__					)					->				int:






           if self.do_lower_case:
                  lowerCamelCase       :				str      			= case_fold_utfa(UpperCamelCase__					)
           lowerCamelCase       :				Optional[int]      			= self.tf_tokenizer.tokenize(UpperCamelCase__					)
           return tokens.merge_dims(1					,  -1					)







    def    _lowercase					(   self					,  UpperCamelCase__					,  UpperCamelCase__=None					,  UpperCamelCase__=None					,  UpperCamelCase__=None					,  UpperCamelCase__=None					,  UpperCamelCase__=None					,  UpperCamelCase__=None					,  UpperCamelCase__=None					,  )					->				Optional[int]:






           if padding is None:
                  lowerCamelCase       :				int      			= self.padding
           if padding not in ("longest", "max_length"):
                  raise ValueError("Padding must be either 'longest' or 'max_length'!"					)
           if max_length is not None and text_pair is not None:
                  # Because we have to instantiate a Trimmer to do it properly
                  raise ValueError("max_length cannot be overridden at call time when truncating paired texts!"					)
           if max_length is None:
                  lowerCamelCase       :				Any      			= self.max_length
           if truncation is None:
                  lowerCamelCase       :				Optional[int]      			= self.truncation
           if pad_to_multiple_of is None:
                  lowerCamelCase       :				List[str]      			= self.pad_to_multiple_of
           if return_token_type_ids is None:
                  lowerCamelCase       :				Dict      			= self.return_token_type_ids
           if return_attention_mask is None:
                  lowerCamelCase       :				Optional[Any]      			= self.return_attention_mask
           if not isinstance(UpperCamelCase__					,  tf.Tensor					):
                  lowerCamelCase       :				Optional[Any]      			= tf.convert_to_tensor(UpperCamelCase__					)
           if text_pair is not None and not isinstance(UpperCamelCase__					,  tf.Tensor					):
                  lowerCamelCase       :				Optional[Any]      			= tf.convert_to_tensor(UpperCamelCase__					)
           if text_pair is not None:
                  if text.shape.rank > 1:
                         raise ValueError("text argument should not be multidimensional when a text pair is supplied!"					)
                  if text_pair.shape.rank > 1:
                         raise ValueError("text_pair should not be multidimensional!"					)
           if text.shape.rank == 2:
                  lowerCamelCase			,				lowerCamelCase       :				Tuple      			= text[:, 0], text[:, 1]
           lowerCamelCase       :				Union[str, Any]      			= self.unpaired_tokenize(UpperCamelCase__					)
           if text_pair is None:  # Unpaired text
                  if truncation:
                         lowerCamelCase       :				Tuple      			= text[:, : max_length - 2]  # Allow room for special tokens
                  lowerCamelCase			,				lowerCamelCase       :				Optional[Any]      			= combine_segments(
                      (text,)					,  start_of_sequence_id=self.cls_token_id					,  end_of_segment_id=self.sep_token_id					)
           else:  # Paired text
                  lowerCamelCase       :				int      			= self.unpaired_tokenize(UpperCamelCase__					)
                  if truncation:
                         lowerCamelCase			,				lowerCamelCase       :				List[str]      			= self.paired_trimmer.trim([text, text_pair]					)
                  lowerCamelCase			,				lowerCamelCase       :				Tuple      			= combine_segments(
                      (text, text_pair)					,  start_of_sequence_id=self.cls_token_id					,  end_of_segment_id=self.sep_token_id					)
           if padding == "longest":
                  lowerCamelCase       :				Any      			= input_ids.bounding_shape(axis=1					)
                  if pad_to_multiple_of is not None:
                         # No ceiling division in tensorflow, so we negate floordiv instead
                         lowerCamelCase       :				List[str]      			= pad_to_multiple_of * (-tf.math.floordiv(-pad_length					,  UpperCamelCase__					))
           else:
                  lowerCamelCase       :				List[str]      			= max_length

           lowerCamelCase			,				lowerCamelCase       :				Optional[int]      			= pad_model_inputs(UpperCamelCase__					,  max_seq_length=UpperCamelCase__					,  pad_value=self.pad_token_id					)
           lowerCamelCase       :				int      			= {"input_ids": input_ids}
           if return_attention_mask:
                  lowerCamelCase       :				List[Any]      			= attention_mask
           if return_token_type_ids:
                  lowerCamelCase			,				lowerCamelCase       :				Union[str, Any]      			= pad_model_inputs(
                      UpperCamelCase__					,  max_seq_length=UpperCamelCase__					,  pad_value=self.pad_token_id					)
                  lowerCamelCase       :				Optional[int]      			= token_type_ids
           return output







    def    _lowercase					(   self					)					->				List[Any]:
           return {
               "vocab_list": self.vocab_list,
               "do_lower_case": self.do_lower_case,
               "cls_token_id": self.cls_token_id,
               "sep_token_id": self.sep_token_id,
               "pad_token_id": self.pad_token_id,
           }












import numpy as np
import qiskit

def 					A				(				_SCREAMING_SNAKE_CASE			,_SCREAMING_SNAKE_CASE   )     ->			qiskit.QuantumCircuit:
       lowerCamelCase       :				Optional[int]      			= 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:
              lowerCamelCase       :				Optional[int]      			= np.random.randint(1			,2**num_qubits   )
              # Next, format 'b' as a binary string of length 'n', padded with zeros:
              lowerCamelCase       :				str      			= format(_SCREAMING_SNAKE_CASE			,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(_SCREAMING_SNAKE_CASE   ):
                     if bit == "1":
                            oracle_qc.x(_SCREAMING_SNAKE_CASE   )
        # Do the controlled-NOT gates for each qubit, using the output qubit
        # as the target:
              for index in range(_SCREAMING_SNAKE_CASE   ):
                     oracle_qc.cx(_SCREAMING_SNAKE_CASE			,_SCREAMING_SNAKE_CASE   )
              # Next, place the final X-gates
              for index, bit in enumerate(_SCREAMING_SNAKE_CASE   ):
                     if bit == "1":
                            oracle_qc.x(_SCREAMING_SNAKE_CASE   )

    # 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)
              lowerCamelCase       :				str      			= np.random.randint(2   )
              if output == 1:
                     oracle_qc.x(_SCREAMING_SNAKE_CASE   )

       lowerCamelCase       :				int      			= oracle_qc.to_gate()
       lowerCamelCase       :				Tuple      			= "Oracle"  # To show when we display the circuit
       return oracle_gate

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

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

       return dj_circuit

def 					A				(				_SCREAMING_SNAKE_CASE			,_SCREAMING_SNAKE_CASE   )     ->			qiskit.result.counts.Counts:
       lowerCamelCase       :				Tuple      			= qiskit.Aer.get_backend("aer_simulator"   )

       lowerCamelCase       :				Tuple      			= dj_oracle(_SCREAMING_SNAKE_CASE			,_SCREAMING_SNAKE_CASE   )
       lowerCamelCase       :				List[Any]      			= dj_algorithm(_SCREAMING_SNAKE_CASE			,_SCREAMING_SNAKE_CASE   )

       # Execute the circuit on the simulator
       lowerCamelCase       :				List[Any]      			= qiskit.execute(_SCREAMING_SNAKE_CASE			,_SCREAMING_SNAKE_CASE			,shots=1000   )

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


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






