





'''simple docstring'''




import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_electra import ElectraTokenizer


_A:					int							=				{"""vocab_file""": """vocab.txt""", """tokenizer_file""": """tokenizer.json"""}

_A:					Optional[Any]							=				{
    """vocab_file""": {
        """google/electra-small-generator""": (
            """https://huggingface.co/google/electra-small-generator/resolve/main/vocab.txt"""
        ),
        """google/electra-base-generator""": """https://huggingface.co/google/electra-base-generator/resolve/main/vocab.txt""",
        """google/electra-large-generator""": (
            """https://huggingface.co/google/electra-large-generator/resolve/main/vocab.txt"""
        ),
        """google/electra-small-discriminator""": (
            """https://huggingface.co/google/electra-small-discriminator/resolve/main/vocab.txt"""
        ),
        """google/electra-base-discriminator""": (
            """https://huggingface.co/google/electra-base-discriminator/resolve/main/vocab.txt"""
        ),
        """google/electra-large-discriminator""": (
            """https://huggingface.co/google/electra-large-discriminator/resolve/main/vocab.txt"""
        ),
    },
    """tokenizer_file""": {
        """google/electra-small-generator""": (
            """https://huggingface.co/google/electra-small-generator/resolve/main/tokenizer.json"""
        ),
        """google/electra-base-generator""": (
            """https://huggingface.co/google/electra-base-generator/resolve/main/tokenizer.json"""
        ),
        """google/electra-large-generator""": (
            """https://huggingface.co/google/electra-large-generator/resolve/main/tokenizer.json"""
        ),
        """google/electra-small-discriminator""": (
            """https://huggingface.co/google/electra-small-discriminator/resolve/main/tokenizer.json"""
        ),
        """google/electra-base-discriminator""": (
            """https://huggingface.co/google/electra-base-discriminator/resolve/main/tokenizer.json"""
        ),
        """google/electra-large-discriminator""": (
            """https://huggingface.co/google/electra-large-discriminator/resolve/main/tokenizer.json"""
        ),
    },
}

_A:					List[str]							=				{
    """google/electra-small-generator""": 512,
    """google/electra-base-generator""": 512,
    """google/electra-large-generator""": 512,
    """google/electra-small-discriminator""": 512,
    """google/electra-base-discriminator""": 512,
    """google/electra-large-discriminator""": 512,
}

_A:					List[Any]							=				{
    """google/electra-small-generator""": {"""do_lower_case""": True},
    """google/electra-base-generator""": {"""do_lower_case""": True},
    """google/electra-large-generator""": {"""do_lower_case""": True},
    """google/electra-small-discriminator""": {"""do_lower_case""": True},
    """google/electra-base-discriminator""": {"""do_lower_case""": True},
    """google/electra-large-discriminator""": {"""do_lower_case""": True},
}






class UpperCAmelCase							(		UpperCAmelCase_							):
    _A :			Optional[int]						   =       VOCAB_FILES_NAMES
    _A :			Union[str, Any]						   =       PRETRAINED_VOCAB_FILES_MAP
    _A :			int						   =       PRETRAINED_INIT_CONFIGURATION
    _A :			Any						   =       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    _A :			Any						   =       ElectraTokenizer



    def __init__(  self     ,			__A=None     ,			__A=None     ,			__A=True     ,			__A="[UNK]"     ,			__A="[SEP]"     ,			__A="[PAD]"     ,			__A="[CLS]"     ,			__A="[MASK]"     ,			__A=True     ,			__A=None     ,			**__A     ,			):
     super().__init__(
         __A     ,			tokenizer_file=__A     ,			do_lower_case=__A     ,			unk_token=__A     ,			sep_token=__A     ,			pad_token=__A     ,			cls_token=__A     ,			mask_token=__A     ,			tokenize_chinese_chars=__A     ,			strip_accents=__A     ,			**__A     ,			)

     __UpperCAmelCase					=   json.loads(self.backend_tokenizer.normalizer.__getstate__()					)
     if (
         normalizer_state.get('lowercase'     ,			__A					) != do_lower_case
         or normalizer_state.get('strip_accents'     ,			__A					) != strip_accents
         or normalizer_state.get('handle_chinese_chars'     ,			__A					) != tokenize_chinese_chars
     ):
      __UpperCAmelCase					=   getattr(__A     ,			normalizer_state.pop('type'					)					)
      __UpperCAmelCase					=   do_lower_case
      __UpperCAmelCase					=   strip_accents
      __UpperCAmelCase					=   tokenize_chinese_chars
      __UpperCAmelCase					=   normalizer_class(**__A					)

     __UpperCAmelCase					=   do_lower_case



    def 					__lowerCamelCase						(  self     ,			__A     ,			__A=None					):
     __UpperCAmelCase					=   [self.cls_token_id] + token_ids_a + [self.sep_token_id]

     if token_ids_a:
      output += token_ids_a + [self.sep_token_id]

     return output



    def 					__lowerCamelCase						(  self     ,			__A     ,			__A = None					):
     __UpperCAmelCase					=   [self.sep_token_id]
     __UpperCAmelCase					=   [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 					__lowerCamelCase						(  self     ,			__A     ,			__A = None					):
     __UpperCAmelCase					=   self._tokenizer.model.save(__A     ,			name=__A					)
     return tuple(__A					)












'''simple docstring'''




import math

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







def _lowerCAmelCase     (      _lowerCAmelCase = 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).'    )

 __UpperCAmelCase					=   QuantumRegister(_lowerCAmelCase       ,    'qr'    )
 __UpperCAmelCase					=   ClassicalRegister(_lowerCAmelCase       ,    'cr'    )

 __UpperCAmelCase					=   QuantumCircuit(_lowerCAmelCase       ,    _lowerCAmelCase    )

 __UpperCAmelCase					=   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
 __UpperCAmelCase					=   Aer.get_backend('qasm_simulator'    )
 __UpperCAmelCase					=   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)}"""
    )





