


"""simple docstring"""


from ...configuration_utils import PretrainedConfig
from ...utils import logging


A_      =    logging.get_logger(__name__)

A_      =    {
    '''microsoft/trocr-base-handwritten''': (
        '''https://huggingface.co/microsoft/trocr-base-handwritten/resolve/main/config.json'''
    ),
    # See all TrOCR models at https://huggingface.co/models?filter=trocr
}






class  lowercase(					__a			):







 '''simple docstring'''





 lowercase__			 = "trocr"
 lowercase__			 = ["past_key_values"]
 lowercase__			 = {
     "num_attention_heads": "decoder_attention_heads",
     "hidden_size": "d_model",
     "num_hidden_layers": "decoder_layers",
 }


 def __init__(    self:		List[str],				a_:		List[str]=50_265,				a_:		Optional[Any]=1_024,				a_:		Any=12,				a_:		List[Any]=16,				a_:		List[str]=4_096,				a_:		Optional[int]="gelu",				a_:		Optional[int]=512,				a_:		Optional[int]=0.1,				a_:		List[str]=0.0,				a_:		Any=0.0,				a_:		str=2,				a_:		List[str]=0.02,				a_:		str=0.0,				a_:		Tuple=True,				a_:		List[Any]=False,				a_:		str=True,				a_:		str=True,				a_:		List[Any]=1,				a_:		int=0,				a_:		Any=2,				**a_:		Optional[int],				):




   '''simple docstring'''



   _snake_case							:		str								=  vocab_size
   _snake_case							:		Dict								=  d_model
   _snake_case							:		Tuple								=  decoder_layers
   _snake_case							:		Dict								=  decoder_attention_heads
   _snake_case							:		int								=  decoder_ffn_dim
   _snake_case							:		List[Any]								=  activation_function
   _snake_case							:		str								=  max_position_embeddings
   _snake_case							:		str								=  dropout
   _snake_case							:		List[Any]								=  attention_dropout
   _snake_case							:		Dict								=  activation_dropout
   _snake_case							:		Tuple								=  init_std
   _snake_case							:		int								=  decoder_layerdrop
   _snake_case							:		Optional[int]								=  use_cache
   _snake_case							:		List[Any]								=  scale_embedding
   _snake_case							:		Optional[Any]								=  use_learned_position_embeddings
   _snake_case							:		str								=  layernorm_embedding

   super().__init__(
       pad_token_id=a_,				bos_token_id=a_,				eos_token_id=a_,				decoder_start_token_id=a_,				**a_,				)








"""simple docstring"""


import math

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


def  UpperCAmelCase__	(snake_case__							: int = 3   ):
  """simple docstring"""

  if isinstance(snake_case__		,       snake_case__   ):
    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(snake_case__   ) != 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							:		Optional[int]								=  QuantumRegister(snake_case__		,       """qr"""   )
  _snake_case							:		List[Any]								=  ClassicalRegister(snake_case__		,       """cr"""   )

  _snake_case							:		Optional[int]								=  QuantumCircuit(snake_case__		,       snake_case__   )

  _snake_case							:		Dict								=  number_of_qubits

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

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

  # measure all the qubits
  quantum_circuit.measure(snake_case__		,       snake_case__   )
  # simulate with 10000 shots
  _snake_case							:		Optional[int]								=  Aer.get_backend("""qasm_simulator"""   )
  _snake_case							:		Optional[int]								=  execute(snake_case__		,       snake_case__		,       shots=1_00_00   )

  return job.result().get_counts(snake_case__   )


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


