
"""simple docstring"""



import os
from typing import Dict, List, Union

import tensorflow as tf
from keras_nlp.tokenizers import BytePairTokenizer
from tensorflow_text import pad_model_inputs

from .tokenization_gpta import GPTaTokenizer

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







     def __init__(							self			:			Union[str, Any]    ,      _lowerCamelCase			:			Dict[str, int]    ,      _lowerCamelCase			:			List[str]    ,      _lowerCamelCase			:			int = None    ,      _lowerCamelCase			:			int = None			):
       super().__init__()
       _snake_case					     =       pad_token_id
       _snake_case					     =       max_length
       _snake_case					     =       vocab
       _snake_case					     =       merges
       _snake_case					     =       BytePairTokenizer(_lowerCamelCase    ,      _lowerCamelCase    ,      sequence_length=_lowerCamelCase			)







     @classmethod
     def      lowercase	(							cls			:			Tuple    ,      _lowerCamelCase			:			GPTaTokenizer    ,      *_lowerCamelCase			:			str    ,      **_lowerCamelCase			:			List[str]			):
       _snake_case					     =       [''' '''.join(_lowerCamelCase			) for m in tokenizer.bpe_ranks.keys()]
       _snake_case					     =       tokenizer.get_vocab()
       return cls(_lowerCamelCase    ,      _lowerCamelCase    ,      *_lowerCamelCase    ,      **_lowerCamelCase			)







     @classmethod
     def      lowercase	(							cls			:			str    ,      _lowerCamelCase			:			Union[str, os.PathLike]    ,      *_lowerCamelCase			:			Optional[int]    ,      **_lowerCamelCase			:			List[Any]			):
       _snake_case					     =       GPTaTokenizer.from_pretrained(_lowerCamelCase    ,      *_lowerCamelCase    ,      **_lowerCamelCase			)
       return cls.from_tokenizer(_lowerCamelCase    ,      *_lowerCamelCase    ,      **_lowerCamelCase			)







     @classmethod
     def      lowercase	(							cls			:			Union[str, Any]    ,      _lowerCamelCase			:			Union[str, Any]			):
       return cls(**_lowerCamelCase			)







     def      lowercase	(							self			:			List[Any]			):
       return {
           "vocab": self.vocab,
           "merges": self.merges,
           "max_length": self.max_length,
           "pad_token_id": self.pad_token_id,
       }







     def      lowercase	(							self			:			Optional[int]    ,      _lowerCamelCase			:			Any    ,      _lowerCamelCase			:			int = None			):
       _snake_case					     =       self.tf_tokenizer(_lowerCamelCase			)
       _snake_case					     =       tf.ones_like(_lowerCamelCase			)

       if self.pad_token_id is not None:
         # pad the tokens up to max length
         _snake_case					     =       max_length if max_length is not None else self.max_length

         if max_length is not None:
           _snake_case     ,					_snake_case					     =       pad_model_inputs(
               _lowerCamelCase    ,      max_seq_length=_lowerCamelCase    ,      pad_value=self.pad_token_id			)

       return {"attention_mask": attention_mask, "input_ids": input_ids}





"""simple docstring"""



import math

import numpy as np
import qiskit
from qiskit import Aer, ClassicalRegister, QuantumCircuit, QuantumRegister, execute
def 					_UpperCAmelCase      ( __lowerCamelCase   :       int = 3		)     ->					qiskit.result.counts.Counts:
  if isinstance(__lowerCamelCase ,					__lowerCamelCase		):
    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(__lowerCamelCase		) != 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).'''		)

  _snake_case					     =       QuantumRegister(__lowerCamelCase ,					'''qr'''		)
  _snake_case					     =       ClassicalRegister(__lowerCamelCase ,					'''cr'''		)

  _snake_case					     =       QuantumCircuit(__lowerCamelCase ,					__lowerCamelCase		)

  _snake_case					     =       number_of_qubits

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

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

  # measure all the qubits
  quantum_circuit.measure(__lowerCamelCase ,					__lowerCamelCase		)
  # simulate with 10000 shots
  _snake_case					     =       Aer.get_backend('''qasm_simulator'''		)
  _snake_case					     =       execute(__lowerCamelCase ,					__lowerCamelCase ,					shots=1_00_00		)

  return job.result().get_counts(__lowerCamelCase		)


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



