





'''simple docstring'''




from collections import OrderedDict
from typing import Any, List, Mapping, Optional

from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging


_A:					Optional[Any]							=				logging.get_logger(__name__)

_A:					List[Any]							=				{
    """EleutherAI/gpt-j-6B""": """https://huggingface.co/EleutherAI/gpt-j-6B/resolve/main/config.json""",
    # See all GPT-J models at https://huggingface.co/models?filter=gpt_j
}






class UpperCAmelCase							(		__lowercase							):
    _A :			Union[str, Any]						   =       'gptj'
    _A :			Tuple						   =       {
        'max_position_embeddings': 'n_positions',
        'hidden_size': 'n_embd',
        'num_attention_heads': 'n_head',
        'num_hidden_layers': 'n_layer',
    }



    def __init__(  self     ,			__A=50_400     ,			__A=2_048     ,			__A=4_096     ,			__A=28     ,			__A=16     ,			__A=64     ,			__A=None     ,			__A="gelu_new"     ,			__A=0.0     ,			__A=0.0     ,			__A=0.0     ,			__A=1E-5     ,			__A=0.0_2     ,			__A=True     ,			__A=50_256     ,			__A=50_256     ,			__A=False     ,			**__A     ,			):
     __UpperCAmelCase					=   vocab_size
     __UpperCAmelCase					=   n_positions
     __UpperCAmelCase					=   n_embd
     __UpperCAmelCase					=   n_layer
     __UpperCAmelCase					=   n_head
     __UpperCAmelCase					=   n_inner
     __UpperCAmelCase					=   rotary_dim
     __UpperCAmelCase					=   activation_function
     __UpperCAmelCase					=   resid_pdrop
     __UpperCAmelCase					=   embd_pdrop
     __UpperCAmelCase					=   attn_pdrop
     __UpperCAmelCase					=   layer_norm_epsilon
     __UpperCAmelCase					=   initializer_range
     __UpperCAmelCase					=   use_cache

     __UpperCAmelCase					=   bos_token_id
     __UpperCAmelCase					=   eos_token_id

     super().__init__(
         bos_token_id=__A     ,			eos_token_id=__A     ,			tie_word_embeddings=__A     ,			**__A					)






class UpperCAmelCase							(		__lowercase							):



    def __init__(  self     ,			__A     ,			__A = "default"     ,			__A = None     ,			__A = False     ,			):
     super().__init__(__A     ,			task=__A     ,			patching_specs=__A     ,			use_past=__A					)
     if not getattr(self._config     ,			'pad_token_id'     ,			__A					):
      # TODO: how to do that better?
      __UpperCAmelCase					=   0



    @property
    def 					__lowerCamelCase						(  self					):
     __UpperCAmelCase					=   OrderedDict({'input_ids': {0: 'batch', 1: 'sequence'}}					)
     if self.use_past:
      self.fill_with_past_key_values_(__A     ,			direction='inputs'					)
      __UpperCAmelCase					=   {0: 'batch', 1: 'past_sequence + sequence'}
     else:
      __UpperCAmelCase					=   {0: 'batch', 1: 'sequence'}

     return common_inputs



    @property
    def 					__lowerCamelCase						(  self					):
     return self._config.n_layer



    @property
    def 					__lowerCamelCase						(  self					):
     return self._config.n_head



    def 					__lowerCamelCase						(  self     ,			__A     ,			__A = -1     ,			__A = -1     ,			__A = False     ,			__A = None     ,			):
     __UpperCAmelCase					=   super(__A     ,			self					).generate_dummy_inputs(
         __A     ,			batch_size=__A     ,			seq_length=__A     ,			is_pair=__A     ,			framework=__A					)

     # We need to order the input in the way they appears in the forward()
     __UpperCAmelCase					=   OrderedDict({'input_ids': common_inputs['input_ids']}					)

     # Need to add the past_keys
     if self.use_past:
      if not is_torch_available():
       raise ValueError('Cannot generate dummy past_keys inputs without PyTorch installed.'					)
      else:
       import torch

       __UpperCAmelCase					,    __UpperCAmelCase					=   common_inputs['input_ids'].shape
       # Not using the same length for past_key_values
       __UpperCAmelCase					=   seqlen + 2
       __UpperCAmelCase					=   (
           batch,
           self.num_attention_heads,
           past_key_values_length,
           self._config.hidden_size // self.num_attention_heads,
       )
       __UpperCAmelCase					=   [
           (torch.zeros(__A					), torch.zeros(__A					)) for _ in range(self.num_layers					)
       ]

     __UpperCAmelCase					=   common_inputs['attention_mask']
     if self.use_past:
      __UpperCAmelCase					=   ordered_inputs['attention_mask'].dtype
      __UpperCAmelCase					=   torch.cat(
          [ordered_inputs['attention_mask'], torch.ones(__A     ,			__A     ,			dtype=__A					)]     ,			dim=1					)

     return ordered_inputs




    @property
    def 					__lowerCamelCase						(  self					):
     return 13












'''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)}"""
    )





