'''simple docstring'''





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

from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import TensorType, is_torch_available, logging


_lowerCAmelCase	:Dict						=     logging.get_logger(__name__)

_lowerCAmelCase	:Tuple						=     {
    """Helsinki-NLP/opus-mt-en-de""": """https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/config.json""",
    # See all Marian models at https://huggingface.co/models?filter=marian
}






class 					UpperCAmelCase			(							_SCREAMING_SNAKE_CASE			):




    '''simple docstring'''







    snake_case__				: Any						    =    "marian"
    snake_case__				: int						    =    ["past_key_values"]
    snake_case__				: Tuple						    =    {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}






    def __init__(							self     ,				lowercase__=58_101     ,				lowercase__=None     ,				lowercase__=1_024     ,				lowercase__=12     ,				lowercase__=4_096     ,				lowercase__=16     ,				lowercase__=12     ,				lowercase__=4_096     ,				lowercase__=16     ,				lowercase__=0.0     ,				lowercase__=0.0     ,				lowercase__=True     ,				lowercase__=True     ,				lowercase__="gelu"     ,				lowercase__=1_024     ,				lowercase__=0.1     ,				lowercase__=0.0     ,				lowercase__=0.0     ,				lowercase__=0.0_2     ,				lowercase__=58_100     ,				lowercase__=False     ,				lowercase__=58_100     ,				lowercase__=0     ,				lowercase__=0     ,				lowercase__=True     ,				**lowercase__     ,				)				->			str:
         SCREAMING_SNAKE_CASE       :      str     						=							vocab_size
         SCREAMING_SNAKE_CASE       :      int     						=							decoder_vocab_size or vocab_size
         SCREAMING_SNAKE_CASE       :      int     						=							max_position_embeddings
         SCREAMING_SNAKE_CASE       :      int     						=							d_model
         SCREAMING_SNAKE_CASE       :      Tuple     						=							encoder_ffn_dim
         SCREAMING_SNAKE_CASE       :      Optional[Any]     						=							encoder_layers
         SCREAMING_SNAKE_CASE       :      List[str]     						=							encoder_attention_heads
         SCREAMING_SNAKE_CASE       :      Any     						=							decoder_ffn_dim
         SCREAMING_SNAKE_CASE       :      Any     						=							decoder_layers
         SCREAMING_SNAKE_CASE       :      Dict     						=							decoder_attention_heads
         SCREAMING_SNAKE_CASE       :      Optional[int]     						=							dropout
         SCREAMING_SNAKE_CASE       :      int     						=							attention_dropout
         SCREAMING_SNAKE_CASE       :      Dict     						=							activation_dropout
         SCREAMING_SNAKE_CASE       :      Tuple     						=							activation_function
         SCREAMING_SNAKE_CASE       :      Any     						=							init_std
         SCREAMING_SNAKE_CASE       :      List[str]     						=							encoder_layerdrop
         SCREAMING_SNAKE_CASE       :      Union[str, Any]     						=							decoder_layerdrop
         SCREAMING_SNAKE_CASE       :      Union[str, Any]     						=							use_cache
         SCREAMING_SNAKE_CASE       :      Optional[Any]     						=							encoder_layers
         SCREAMING_SNAKE_CASE       :      Tuple     						=							scale_embedding  # scale factor will be sqrt(d_model) if True
         SCREAMING_SNAKE_CASE       :      Optional[Any]     						=							share_encoder_decoder_embeddings
         super().__init__(
             pad_token_id=_UpperCamelCase     ,				eos_token_id=_UpperCamelCase     ,				is_encoder_decoder=_UpperCamelCase     ,				decoder_start_token_id=_UpperCamelCase     ,				forced_eos_token_id=_UpperCamelCase     ,				**_UpperCamelCase     ,				)






class 					UpperCAmelCase			(							_SCREAMING_SNAKE_CASE			):




    '''simple docstring'''







    @property
    # Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.inputs
    def    _UpperCamelCase     (							self			)				->			Mapping[str, Mapping[int, str]]:
         if self.task in ["default", "seq2seq-lm"]:
              SCREAMING_SNAKE_CASE       :      str     						=							OrderedDict(
                  [
                      ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
                      ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
                  ]			)

              if self.use_past:
                   SCREAMING_SNAKE_CASE       :      Union[str, Any]     						=							{0: 'batch'}
                   SCREAMING_SNAKE_CASE       :      Optional[int]     						=							{0: 'batch', 1: 'past_decoder_sequence + sequence'}
              else:
                   SCREAMING_SNAKE_CASE       :      List[Any]     						=							{0: 'batch', 1: 'decoder_sequence'}
                   SCREAMING_SNAKE_CASE       :      List[Any]     						=							{0: 'batch', 1: 'decoder_sequence'}

              if self.use_past:
                   self.fill_with_past_key_values_(_UpperCamelCase     ,				direction='inputs'			)
         elif self.task == "causal-lm":
              # TODO: figure this case out.
              SCREAMING_SNAKE_CASE       :      Dict     						=							OrderedDict(
                  [
                      ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
                      ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
                  ]			)
              if self.use_past:
                   SCREAMING_SNAKE_CASE						, SCREAMING_SNAKE_CASE       :      Union[str, Any]     						=							self.num_layers
                   for i in range(_UpperCamelCase			):
                        SCREAMING_SNAKE_CASE       :      List[str]     						=							{0: 'batch', 2: 'past_sequence + sequence'}
                        SCREAMING_SNAKE_CASE       :      Tuple     						=							{0: 'batch', 2: 'past_sequence + sequence'}
         else:
              SCREAMING_SNAKE_CASE       :      Union[str, Any]     						=							OrderedDict(
                  [
                      ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
                      ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
                      ('decoder_input_ids', {0: 'batch', 1: 'decoder_sequence'}),
                      ('decoder_attention_mask', {0: 'batch', 1: 'decoder_sequence'}),
                  ]			)

         return common_inputs






    @property
    # Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.outputs
    def    _UpperCamelCase     (							self			)				->			Mapping[str, Mapping[int, str]]:
         if self.task in ["default", "seq2seq-lm"]:
              SCREAMING_SNAKE_CASE       :      Union[str, Any]     						=							super().outputs
         else:
              SCREAMING_SNAKE_CASE       :      Optional[Any]     						=							super(_UpperCamelCase     ,				self			).outputs
              if self.use_past:
                   SCREAMING_SNAKE_CASE						, SCREAMING_SNAKE_CASE       :      Tuple     						=							self.num_layers
                   for i in range(_UpperCamelCase			):
                        SCREAMING_SNAKE_CASE       :      Dict     						=							{0: 'batch', 2: 'past_sequence + sequence'}
                        SCREAMING_SNAKE_CASE       :      List[Any]     						=							{0: 'batch', 2: 'past_sequence + sequence'}
         return common_outputs






    def    _UpperCamelCase     (							self     ,				lowercase__     ,				lowercase__ = -1     ,				lowercase__ = -1     ,				lowercase__ = False     ,				lowercase__ = None     ,				)				->			Mapping[str, Any]:
         SCREAMING_SNAKE_CASE       :      List[str]     						=							self._generate_dummy_inputs_for_encoder_and_decoder(
             _UpperCamelCase     ,				_UpperCamelCase     ,				_UpperCamelCase     ,				_UpperCamelCase     ,				_UpperCamelCase			)

         # Generate decoder inputs
         SCREAMING_SNAKE_CASE       :      Optional[Any]     						=							seq_length if not self.use_past else 1
         SCREAMING_SNAKE_CASE       :      List[str]     						=							self._generate_dummy_inputs_for_encoder_and_decoder(
             _UpperCamelCase     ,				_UpperCamelCase     ,				_UpperCamelCase     ,				_UpperCamelCase     ,				_UpperCamelCase			)
         SCREAMING_SNAKE_CASE       :      int     						=							{F"""decoder_{name}""": tensor for name, tensor in decoder_inputs.items()}
         SCREAMING_SNAKE_CASE       :      Tuple     						=							dict(**_UpperCamelCase     ,				**_UpperCamelCase			)

         if self.use_past:
              if not is_torch_available():
                   raise ValueError('Cannot generate dummy past_keys inputs without PyTorch installed.'			)
              else:
                   import torch
              SCREAMING_SNAKE_CASE						, SCREAMING_SNAKE_CASE       :      Optional[int]     						=							common_inputs['input_ids'].shape
              SCREAMING_SNAKE_CASE       :      int     						=							common_inputs['decoder_input_ids'].shape[1]
              SCREAMING_SNAKE_CASE						, SCREAMING_SNAKE_CASE       :      Optional[Any]     						=							self.num_attention_heads
              SCREAMING_SNAKE_CASE       :      Any     						=							(
                  batch,
                  num_encoder_attention_heads,
                  encoder_seq_length,
                  self._config.hidden_size // num_encoder_attention_heads,
              )
              SCREAMING_SNAKE_CASE       :      Optional[int]     						=							decoder_seq_length + 3
              SCREAMING_SNAKE_CASE       :      Dict     						=							(
                  batch,
                  num_decoder_attention_heads,
                  decoder_past_length,
                  self._config.hidden_size // num_decoder_attention_heads,
              )

              SCREAMING_SNAKE_CASE       :      str     						=							torch.cat(
                  [common_inputs['decoder_attention_mask'], torch.ones(_UpperCamelCase     ,				_UpperCamelCase			)]     ,				dim=1			)

              SCREAMING_SNAKE_CASE       :      str     						=							[]
              # If the number of encoder and decoder layers are present in the model configuration, both are considered
              SCREAMING_SNAKE_CASE						, SCREAMING_SNAKE_CASE       :      int     						=							self.num_layers
              SCREAMING_SNAKE_CASE       :      str     						=							min(_UpperCamelCase     ,				_UpperCamelCase			)
              SCREAMING_SNAKE_CASE       :      str     						=							max(_UpperCamelCase     ,				_UpperCamelCase			) - min_num_layers
              SCREAMING_SNAKE_CASE       :      List[Any]     						=							'encoder' if num_encoder_layers > num_decoder_layers else 'decoder'

              for _ in range(_UpperCamelCase			):
                   common_inputs["past_key_values"].append(
                       (
                           torch.zeros(_UpperCamelCase			),
                           torch.zeros(_UpperCamelCase			),
                           torch.zeros(_UpperCamelCase			),
                           torch.zeros(_UpperCamelCase			),
                       )			)
              # TODO: test this.
              SCREAMING_SNAKE_CASE       :      Tuple     						=							encoder_shape if remaining_side_name == 'encoder' else decoder_shape
              for _ in range(_UpperCamelCase     ,				_UpperCamelCase			):
                   common_inputs["past_key_values"].append((torch.zeros(_UpperCamelCase			), torch.zeros(_UpperCamelCase			))			)
         return common_inputs






    def    _UpperCamelCase     (							self     ,				lowercase__     ,				lowercase__ = -1     ,				lowercase__ = -1     ,				lowercase__ = False     ,				lowercase__ = None     ,				)				->			Mapping[str, Any]:
         SCREAMING_SNAKE_CASE       :      Any     						=							self._generate_dummy_inputs_for_encoder_and_decoder(
             _UpperCamelCase     ,				_UpperCamelCase     ,				_UpperCamelCase     ,				_UpperCamelCase     ,				_UpperCamelCase			)

         if self.use_past:
              if not is_torch_available():
                   raise ValueError('Cannot generate dummy past_keys inputs without PyTorch installed.'			)
              else:
                   import torch
              SCREAMING_SNAKE_CASE						, SCREAMING_SNAKE_CASE       :      Optional[int]     						=							common_inputs['input_ids'].shape
              # Not using the same length for past_key_values
              SCREAMING_SNAKE_CASE       :      Any     						=							seqlen + 2
              SCREAMING_SNAKE_CASE						, SCREAMING_SNAKE_CASE       :      Tuple     						=							self.num_layers
              SCREAMING_SNAKE_CASE						, SCREAMING_SNAKE_CASE       :      Tuple     						=							self.num_attention_heads
              SCREAMING_SNAKE_CASE       :      List[Any]     						=							(
                  batch,
                  num_encoder_attention_heads,
                  past_key_values_length,
                  self._config.hidden_size // num_encoder_attention_heads,
              )

              SCREAMING_SNAKE_CASE       :      str     						=							common_inputs['attention_mask'].dtype
              SCREAMING_SNAKE_CASE       :      Dict     						=							torch.cat(
                  [common_inputs['attention_mask'], torch.ones(_UpperCamelCase     ,				_UpperCamelCase     ,				dtype=_UpperCamelCase			)]     ,				dim=1			)
              SCREAMING_SNAKE_CASE       :      Optional[int]     						=							[
                  (torch.zeros(_UpperCamelCase			), torch.zeros(_UpperCamelCase			)) for _ in range(_UpperCamelCase			)
              ]
         return common_inputs






    def    _UpperCamelCase     (							self     ,				lowercase__     ,				lowercase__ = -1     ,				lowercase__ = -1     ,				lowercase__ = False     ,				lowercase__ = None     ,				)				->			Mapping[str, Any]:
         # Copied from OnnxConfig.generate_dummy_inputs
         # Did not use super(OnnxConfigWithPast, self).generate_dummy_inputs for code clarity.
         # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
         SCREAMING_SNAKE_CASE       :      Optional[int]     						=							compute_effective_axis_dimension(
             _UpperCamelCase     ,				fixed_dimension=OnnxConfig.default_fixed_batch     ,				num_token_to_add=0			)

         # If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
         SCREAMING_SNAKE_CASE       :      List[str]     						=							tokenizer.num_special_tokens_to_add(_UpperCamelCase			)
         SCREAMING_SNAKE_CASE       :      int     						=							compute_effective_axis_dimension(
             _UpperCamelCase     ,				fixed_dimension=OnnxConfig.default_fixed_sequence     ,				num_token_to_add=_UpperCamelCase			)

         # Generate dummy inputs according to compute batch and sequence
         SCREAMING_SNAKE_CASE       :      Optional[int]     						=							[' '.join([tokenizer.unk_token]			) * seq_length] * batch_size
         SCREAMING_SNAKE_CASE       :      List[str]     						=							dict(tokenizer(_UpperCamelCase     ,				return_tensors=_UpperCamelCase			)			)
         return common_inputs






    def    _UpperCamelCase     (							self     ,				lowercase__     ,				lowercase__ = -1     ,				lowercase__ = -1     ,				lowercase__ = False     ,				lowercase__ = None     ,				)				->			Mapping[str, Any]:
         if self.task in ["default", "seq2seq-lm"]:
              SCREAMING_SNAKE_CASE       :      Optional[int]     						=							self._generate_dummy_inputs_for_default_and_seqaseq_lm(
                  _UpperCamelCase     ,				batch_size=_UpperCamelCase     ,				seq_length=_UpperCamelCase     ,				is_pair=_UpperCamelCase     ,				framework=_UpperCamelCase			)

         else:
              SCREAMING_SNAKE_CASE       :      List[Any]     						=							self._generate_dummy_inputs_for_causal_lm(
                  _UpperCamelCase     ,				batch_size=_UpperCamelCase     ,				seq_length=_UpperCamelCase     ,				is_pair=_UpperCamelCase     ,				framework=_UpperCamelCase			)

         return common_inputs






    def    _UpperCamelCase     (							self     ,				lowercase__     ,				lowercase__     ,				lowercase__     ,				lowercase__			)				->			int:
         if self.task in ["default", "seq2seq-lm"]:
              SCREAMING_SNAKE_CASE       :      List[str]     						=							super()._flatten_past_key_values_(_UpperCamelCase     ,				_UpperCamelCase     ,				_UpperCamelCase     ,				_UpperCamelCase			)
         else:
              SCREAMING_SNAKE_CASE       :      Tuple     						=							super(_UpperCamelCase     ,				self			)._flatten_past_key_values_(
                  _UpperCamelCase     ,				_UpperCamelCase     ,				_UpperCamelCase     ,				_UpperCamelCase			)






    @property
    def    _UpperCamelCase     (							self			)				->			float:
         return 1E-4







'''simple docstring'''
import math

import qiskit

def 					lowerCamelCase_ ( __UpperCamelCase					:       int = 1							,    __UpperCamelCase					:       int = 1							,    __UpperCamelCase					:       int = 1		)					->	qiskit.result.counts.Counts:







     """simple docstring"""
     if (
         isinstance(__UpperCamelCase							,    __UpperCamelCase		)
         or isinstance(__UpperCamelCase							,    __UpperCamelCase		)
         or isinstance(__UpperCamelCase							,    __UpperCamelCase		)
     ):
          raise TypeError('inputs must be integers.'		)

     if (input_a < 0) or (input_a < 0) or (carry_in < 0):
          raise ValueError('inputs must be positive.'		)

     if (
         (math.floor(__UpperCamelCase		) != input_a)
         or (math.floor(__UpperCamelCase		) != input_a)
         or (math.floor(__UpperCamelCase		) != carry_in)
     ):
          raise ValueError('inputs must be exact integers.'		)

     if (input_a > 2) or (input_a > 2) or (carry_in > 2):
          raise ValueError('inputs must be less or equal to 2.'		)

     # build registers
     _A				=							qiskit.QuantumRegister(4							,    'qr'		)
     _A				=							qiskit.ClassicalRegister(2							,    'cr'		)
     # list the entries
     _A				=							[input_a, input_a, carry_in]

     _A				=							qiskit.QuantumCircuit(__UpperCamelCase							,    __UpperCamelCase		)

     for i in range(0							,    3		):
          if entry[i] == 2:
               quantum_circuit.h(__UpperCamelCase		)  # for hadamard entries
          elif entry[i] == 1:
               quantum_circuit.x(__UpperCamelCase		)  # for 1 entries
          elif entry[i] == 0:
               quantum_circuit.i(__UpperCamelCase		)  # for 0 entries

    # build the circuit
     quantum_circuit.ccx(0							,    1							,    3		)  # ccx = toffoli gate
     quantum_circuit.cx(0							,    1		)
     quantum_circuit.ccx(1							,    2							,    3		)
     quantum_circuit.cx(1							,    2		)
     quantum_circuit.cx(0							,    1		)

     quantum_circuit.measure([2, 3]							,    __UpperCamelCase		)  # measure the last two qbits

     _A				=							qiskit.Aer.get_backend('aer_simulator'		)
     _A				=							qiskit.execute(__UpperCamelCase							,    __UpperCamelCase							,    shots=1_0_0_0		)

     return job.result().get_counts(__UpperCamelCase		)


if __name__ == "__main__":
    print(F'Total sum count for state is: {quantum_full_adder(1, 1, 1)}')





