


"""simple docstring"""






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

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


__snake_case												=							logging.get_logger(__name__)

__snake_case												=							{
    "microsoft/layoutlm-base-uncased": (
        "https://huggingface.co/microsoft/layoutlm-base-uncased/resolve/main/config.json"
    ),
    "microsoft/layoutlm-large-uncased": (
        "https://huggingface.co/microsoft/layoutlm-large-uncased/resolve/main/config.json"
    ),
}


class      __lowerCamelCase	(       _a					):

     '''simple docstring'''
     A_							:						Dict     =		'layoutlm'


     def __init__(   self     ,					__UpperCAmelCase=30522     ,					__UpperCAmelCase=768     ,					__UpperCAmelCase=12     ,					__UpperCAmelCase=12     ,					__UpperCAmelCase=3072     ,					__UpperCAmelCase="gelu"     ,					__UpperCAmelCase=0.1     ,					__UpperCAmelCase=0.1     ,					__UpperCAmelCase=512     ,					__UpperCAmelCase=2     ,					__UpperCAmelCase=0.02     ,					__UpperCAmelCase=1e-1_2     ,					__UpperCAmelCase=0     ,					__UpperCAmelCase="absolute"     ,					__UpperCAmelCase=True     ,					__UpperCAmelCase=1024     ,					**__UpperCAmelCase     ,					)							->				str:
          super().__init__(pad_token_id=_UpperCAmelCase     ,					**_UpperCAmelCase				)
          _a     						=					vocab_size
          _a     						=					hidden_size
          _a     						=					num_hidden_layers
          _a     						=					num_attention_heads
          _a     						=					hidden_act
          _a     						=					intermediate_size
          _a     						=					hidden_dropout_prob
          _a     						=					attention_probs_dropout_prob
          _a     						=					max_position_embeddings
          _a     						=					type_vocab_size
          _a     						=					initializer_range
          _a     						=					layer_norm_eps
          _a     						=					position_embedding_type
          _a     						=					use_cache
          _a     						=					max_ad_position_embeddings







class      __lowerCamelCase	(       _a					):

     '''simple docstring'''


     def __init__(   self     ,					__UpperCAmelCase     ,					__UpperCAmelCase = "default"     ,					__UpperCAmelCase = None     ,					)							->				Any:
          super().__init__(_UpperCAmelCase     ,					task=_UpperCAmelCase     ,					patching_specs=_UpperCAmelCase				)
          _a     						=					config.max_ad_position_embeddings - 1


     @property
     def 				_UpperCAmelCase   (   self				)							->				Dict:
          return OrderedDict(
              [
                  ('''input_ids''', {0: '''batch''', 1: '''sequence'''}),
                  ('''bbox''', {0: '''batch''', 1: '''sequence'''}),
                  ('''attention_mask''', {0: '''batch''', 1: '''sequence'''}),
                  ('''token_type_ids''', {0: '''batch''', 1: '''sequence'''}),
              ]				)





     def 				_UpperCAmelCase   (   self     ,					__UpperCAmelCase     ,					__UpperCAmelCase = -1     ,					__UpperCAmelCase = -1     ,					__UpperCAmelCase = False     ,					__UpperCAmelCase = None     ,					)							->				Optional[int]:
          _a     						=					super().generate_dummy_inputs(
              _UpperCAmelCase     ,					batch_size=_UpperCAmelCase     ,					seq_length=_UpperCAmelCase     ,					is_pair=_UpperCAmelCase     ,					framework=_UpperCAmelCase				)

          # Generate a dummy bbox
          _a     						=					[48, 84, 73, 128]

          if not framework == TensorType.PYTORCH:
               raise NotImplementedError('''Exporting LayoutLM to ONNX is currently only supported for PyTorch.'''				)

          if not is_torch_available():
               raise ValueError('''Cannot generate dummy inputs without PyTorch installed.'''				)
          import torch

          _a						,   _a     						=					input_dict['''input_ids'''].shape
          _a     						=					torch.tensor([*[box] * seq_length]				).tile(_UpperCAmelCase     ,					1     ,					1				)
          return input_dict
"""simple docstring"""


import qiskit
from qiskit.providers import Backend







def        __a (		A     ,					A   ):






     '''simple docstring'''



     lowercase__			,    lowercase__								=				bin(A   )[2:], bin(A   )[2:]  # Remove leading '0b'

     # Ensure that both strings are of the same length
     if len(A   ) > len(A   ):
          lowercase__								=				y.zfill(len(A   )   )
     else:
          lowercase__								=				x.zfill(len(A   )   )

     # We need (3 * number of bits in the larger number)+1 qBits
     # The second parameter is the number of classical registers, to measure the result
     lowercase__								=				qiskit.QuantumCircuit((len(A   ) * 3) + 1     ,					len(A   ) + 1   )

     # We are essentially "not-ing" the bits that are 1
     # Reversed because it's easier to perform ops on more significant bits
     for i in range(len(A   )   ):
          if x[::-1][i] == "1":
               circuit.x(A   )
     for j in range(len(A   )   ):
          if y[::-1][j] == "1":
               circuit.x(len(A   ) + j   )

     return circuit, x, y







def        __a (		A     ,					A     ,					A     ,					A     ,					A     ,					):






     '''simple docstring'''



     circuit.ccx(A     ,					A     ,					A   )
     circuit.cx(A     ,					A   )
     circuit.ccx(A     ,					A     ,					A   )
     circuit.cx(A     ,					A   )
     circuit.cx(A     ,					A   )







def        __a (		A     ,					A     ,					A = qiskit.Aer.get_backend("aer_simulator"   )     ,					):






     '''simple docstring'''



     if vala < 0 or vala < 0:
          raise ValueError("Both Integers must be positive!"   )

     # Store the Integers
     lowercase__			,    lowercase__			,    lowercase__								=				store_two_classics(A     ,					A   )
     for i in range(len(A   )   ):
          full_adder(A     ,					A     ,					len(A   ) + i     ,					len(A   ) + len(A   ) + i     ,					len(A   ) + len(A   ) + i + 1   )
          circuit.barrier()  # Optional, just for aesthetics

     # Measure the resultant qBits
     for i in range(len(A   ) + 1   ):
          circuit.measure([(len(A   ) * 2) + i]     ,					[i]   )

     lowercase__								=				qiskit.execute(A     ,					A     ,					shots=1   ).result()

     # The result is in binary. Convert it back to int
     return int(list(res.get_counts()   )[0]     ,					2   )


if __name__ == "__main__":
 import doctest

 doctest.testmod()




