code: 
  
  
  
  
  
  
  """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
  
  
  snake_case   :   str    		=		logging.get_logger(__name__)
  
  snake_case   :   str    		=		{
      """facebook/mbart-large-cc25""": """https://huggingface.co/facebook/mbart-large-cc25/resolve/main/config.json""",
      # See all MBART models at https://huggingface.co/models?filter=mbart
  }
  
  
  
  class 					UpperCamelCase__   (							a_):
  
  
  
  
  
  
  
      """simple docstring"""
      __UpperCAmelCase											=   """mbart"""
      __UpperCAmelCase											=   ["""past_key_values"""]
      __UpperCAmelCase											=   {"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}
  
  
  
  
  
  
  
      def __init__(		self   :  str							,						UpperCamelCase_   :  Optional[Any]=5_0_2_6_5							,						UpperCamelCase_   :  List[str]=1_0_2_4							,						UpperCamelCase_   :  Union[str, Any]=1_2							,						UpperCamelCase_   :  List[Any]=4_0_9_6							,						UpperCamelCase_   :  Dict=1_6							,						UpperCamelCase_   :  Dict=1_2							,						UpperCamelCase_   :  List[str]=4_0_9_6							,						UpperCamelCase_   :  List[str]=1_6							,						UpperCamelCase_   :  Tuple=0.0							,						UpperCamelCase_   :  Dict=0.0							,						UpperCamelCase_   :  Optional[Any]=True							,						UpperCamelCase_   :  Union[str, Any]=True							,						UpperCamelCase_   :  Optional[int]="gelu"							,						UpperCamelCase_   :  List[str]=1_0_2_4							,						UpperCamelCase_   :  List[Any]=0.1							,						UpperCamelCase_   :  Union[str, Any]=0.0							,						UpperCamelCase_   :  str=0.0							,						UpperCamelCase_   :  Union[str, Any]=0.02							,						UpperCamelCase_   :  int=0.0							,						UpperCamelCase_   :  Dict=False							,						UpperCamelCase_   :  Optional[int]=1							,						UpperCamelCase_   :  Optional[Any]=0							,						UpperCamelCase_   :  Dict=2							,						UpperCamelCase_   :  Tuple=2							,						**UpperCamelCase_   :  Any							,						):
  
  
  
  
  
             '''simple docstring'''
  
             __magic_name__										=						vocab_size
             __magic_name__										=						max_position_embeddings
             __magic_name__										=						d_model
             __magic_name__										=						encoder_ffn_dim
             __magic_name__										=						encoder_layers
             __magic_name__										=						encoder_attention_heads
             __magic_name__										=						decoder_ffn_dim
             __magic_name__										=						decoder_layers
             __magic_name__										=						decoder_attention_heads
             __magic_name__										=						dropout
             __magic_name__										=						attention_dropout
             __magic_name__										=						activation_dropout
             __magic_name__										=						activation_function
             __magic_name__										=						init_std
             __magic_name__										=						encoder_layerdrop
             __magic_name__										=						decoder_layerdrop
             __magic_name__										=						classifier_dropout
             __magic_name__										=						use_cache
             __magic_name__										=						encoder_layers
             __magic_name__										=						scale_embedding  # scale factor will be sqrt(d_model) if True
             super().__init__(
                 pad_token_id=UpperCamelCase_							,						bos_token_id=UpperCamelCase_							,						eos_token_id=UpperCamelCase_							,						is_encoder_decoder=UpperCamelCase_							,						forced_eos_token_id=UpperCamelCase_							,						**UpperCamelCase_							,						)
  
  
  
  class 					UpperCamelCase__   (							a_):
  
  
  
  
  
  
  
      """simple docstring"""
  
  
  
  
  
  
  
      @property
      def 							a__ (		self   :  Optional[int]   ):
  
  
  
  
  
             '''simple docstring'''
  
             if self.task in ["default", "seq2seq-lm"]:
                    __magic_name__										=						OrderedDict(
                        [
                            ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
                            ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
                        ]   )
  
                    if self.use_past:
                           __magic_name__										=						{0: 'batch'}
                           __magic_name__										=						{0: 'batch', 1: 'past_decoder_sequence + sequence'}
                    else:
                           __magic_name__										=						{0: 'batch', 1: 'decoder_sequence'}
                           __magic_name__										=						{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.
                    __magic_name__										=						OrderedDict(
                        [
                            ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
                            ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
                        ]   )
                    if self.use_past:
                           __magic_name__							,     __magic_name__										=						self.num_layers
                           for i in range(UpperCamelCase_   ):
                                  __magic_name__										=						{0: 'batch', 2: 'past_sequence + sequence'}
                                  __magic_name__										=						{0: 'batch', 2: 'past_sequence + sequence'}
             else:
                    __magic_name__										=						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
      def 							a__ (		self   :  Dict   ):
  
  
  
  
  
             '''simple docstring'''
  
             if self.task in ["default", "seq2seq-lm"]:
                    __magic_name__										=						super().outputs
             else:
                    __magic_name__										=						super(UpperCamelCase_							,						self   ).outputs
                    if self.use_past:
                           __magic_name__							,     __magic_name__										=						self.num_layers
                           for i in range(UpperCamelCase_   ):
                                  __magic_name__										=						{0: 'batch', 2: 'past_sequence + sequence'}
                                  __magic_name__										=						{0: 'batch', 2: 'past_sequence + sequence'}
             return common_outputs
  
  
  
  
  
  
  
      def 							a__ (		self   :  Optional[int]							,						UpperCamelCase_   :  PreTrainedTokenizer							,						UpperCamelCase_   :  int = -1							,						UpperCamelCase_   :  int = -1							,						UpperCamelCase_   :  bool = False							,						UpperCamelCase_   :  Optional[TensorType] = None							,						):
  
  
  
  
  
             '''simple docstring'''
  
             __magic_name__										=						self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
                 UpperCamelCase_							,						UpperCamelCase_							,						UpperCamelCase_							,						UpperCamelCase_							,						UpperCamelCase_   )
  
             # Generate decoder inputs
             __magic_name__										=						seq_length if not self.use_past else 1
             __magic_name__										=						self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
                 UpperCamelCase_							,						UpperCamelCase_							,						UpperCamelCase_							,						UpperCamelCase_							,						UpperCamelCase_   )
             __magic_name__										=						{f"""decoder_{name}""": tensor for name, tensor in decoder_inputs.items()}
             __magic_name__										=						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
                    __magic_name__							,     __magic_name__										=						common_inputs['input_ids'].shape
                    __magic_name__										=						common_inputs['decoder_input_ids'].shape[1]
                    __magic_name__							,     __magic_name__										=						self.num_attention_heads
                    __magic_name__										=						(
                        batch,
                        num_encoder_attention_heads,
                        encoder_seq_length,
                        self._config.hidden_size // num_encoder_attention_heads,
                    )
                    __magic_name__										=						decoder_seq_length + 3
                    __magic_name__										=						(
                        batch,
                        num_decoder_attention_heads,
                        decoder_past_length,
                        self._config.hidden_size // num_decoder_attention_heads,
                    )
  
                    __magic_name__										=						torch.cat(
                        [common_inputs['decoder_attention_mask'], torch.ones(UpperCamelCase_							,						UpperCamelCase_   )]							,						dim=1   )
  
                    __magic_name__										=						[]
                    # If the number of encoder and decoder layers are present in the model configuration, both are considered
                    __magic_name__							,     __magic_name__										=						self.num_layers
                    __magic_name__										=						min(UpperCamelCase_							,						UpperCamelCase_   )
                    __magic_name__										=						max(UpperCamelCase_							,						UpperCamelCase_   ) - min_num_layers
                    __magic_name__										=						'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.
                    __magic_name__										=						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 							a__ (		self   :  int							,						UpperCamelCase_   :  PreTrainedTokenizer							,						UpperCamelCase_   :  int = -1							,						UpperCamelCase_   :  int = -1							,						UpperCamelCase_   :  bool = False							,						UpperCamelCase_   :  Optional[TensorType] = None							,						):
  
  
  
  
  
             '''simple docstring'''
  
             __magic_name__										=						self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
                 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
                    __magic_name__							,     __magic_name__										=						common_inputs['input_ids'].shape
                    # Not using the same length for past_key_values
                    __magic_name__										=						seqlen + 2
                    __magic_name__							,     __magic_name__										=						self.num_layers
                    __magic_name__							,     __magic_name__										=						self.num_attention_heads
                    __magic_name__										=						(
                        batch,
                        num_encoder_attention_heads,
                        past_key_values_length,
                        self._config.hidden_size // num_encoder_attention_heads,
                    )
  
                    __magic_name__										=						common_inputs['attention_mask'].dtype
                    __magic_name__										=						torch.cat(
                        [common_inputs['attention_mask'], torch.ones(UpperCamelCase_							,						UpperCamelCase_							,						dtype=UpperCamelCase_   )]							,						dim=1   )
                    __magic_name__										=						[
                        (torch.zeros(UpperCamelCase_   ), torch.zeros(UpperCamelCase_   )) for _ in range(UpperCamelCase_   )
                    ]
             return common_inputs
  
  
  
  
  
  
  
      def 							a__ (		self   :  List[str]							,						UpperCamelCase_   :  PreTrainedTokenizer							,						UpperCamelCase_   :  int = -1							,						UpperCamelCase_   :  int = -1							,						UpperCamelCase_   :  bool = False							,						UpperCamelCase_   :  Optional[TensorType] = None							,						):
  
  
  
  
  
             '''simple docstring'''
  
             __magic_name__										=						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
             __magic_name__										=						tokenizer.num_special_tokens_to_add(UpperCamelCase_   )
             __magic_name__										=						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
             __magic_name__										=						[' '.join([tokenizer.unk_token]   ) * seq_length] * batch_size
             __magic_name__										=						dict(tokenizer(UpperCamelCase_							,						return_tensors=UpperCamelCase_   )   )
             return common_inputs
  
  
  
  
  
  
  
      def 							a__ (		self   :  Dict							,						UpperCamelCase_   :  PreTrainedTokenizer							,						UpperCamelCase_   :  int = -1							,						UpperCamelCase_   :  int = -1							,						UpperCamelCase_   :  bool = False							,						UpperCamelCase_   :  Optional[TensorType] = None							,						):
  
  
  
  
  
             '''simple docstring'''
  
             if self.task in ["default", "seq2seq-lm"]:
                    __magic_name__										=						self._generate_dummy_inputs_for_default_and_seqaseq_lm(
                        UpperCamelCase_							,						batch_size=UpperCamelCase_							,						seq_length=UpperCamelCase_							,						is_pair=UpperCamelCase_							,						framework=UpperCamelCase_   )
  
             elif self.task == "causal-lm":
                    __magic_name__										=						self._generate_dummy_inputs_for_causal_lm(
                        UpperCamelCase_							,						batch_size=UpperCamelCase_							,						seq_length=UpperCamelCase_							,						is_pair=UpperCamelCase_							,						framework=UpperCamelCase_   )
             else:
                    __magic_name__										=						self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
                        UpperCamelCase_							,						batch_size=UpperCamelCase_							,						seq_length=UpperCamelCase_							,						is_pair=UpperCamelCase_							,						framework=UpperCamelCase_   )
  
             return common_inputs
  
  
  
  
  
  
  
      def 							a__ (		self   :  Optional[int]							,						UpperCamelCase_   :  int							,						UpperCamelCase_   :  Dict							,						UpperCamelCase_   :  Any							,						UpperCamelCase_   :  Optional[int]   ):
  
  
  
  
  
             '''simple docstring'''
  
             if self.task in ["default", "seq2seq-lm"]:
                    __magic_name__										=						super()._flatten_past_key_values_(UpperCamelCase_							,						UpperCamelCase_							,						UpperCamelCase_							,						UpperCamelCase_   )
             else:
                    __magic_name__										=						super(UpperCamelCase_							,						self   )._flatten_past_key_values_(
                        UpperCamelCase_							,						UpperCamelCase_							,						UpperCamelCase_							,						UpperCamelCase_   )
style_context: 
  
  
  
  
  
  
  """simple docstring"""
  import numpy as np
  import qiskit
  
  
  def        A	(							__snake_case:   str   ,    __snake_case:   int     )						->		qiskit.QuantumCircuit:
  
         """simple docstring"""
  
  
  
  
  
         __magic_name__										=						qiskit.QuantumCircuit(num_qubits + 1     )
  
         # First, let's deal with the case in which oracle is balanced
         if case == "balanced":
                # First generate a random number that tells us which CNOTs to
                # wrap in X-gates:
                __magic_name__										=						np.random.randint(1   ,    2**num_qubits     )
                # Next, format 'b' as a binary string of length 'n', padded with zeros:
                __magic_name__										=						format(__snake_case   ,    F"""0{num_qubits}b"""     )
                # Next, we place the first X-gates. Each digit in our binary string
                # corresponds to a qubit, if the digit is 0, we do nothing, if it's 1
                # we apply an X-gate to that qubit:
                for index, bit in enumerate(__snake_case     ):
                       if bit == "1":
                              oracle_qc.x(__snake_case     )
          # Do the controlled-NOT gates for each qubit, using the output qubit
          # as the target:
                for index in range(__snake_case     ):
                       oracle_qc.cx(__snake_case   ,    __snake_case     )
                # Next, place the final X-gates
                for index, bit in enumerate(__snake_case     ):
                       if bit == "1":
                              oracle_qc.x(__snake_case     )
  
      # Case in which oracle is constant
         if case == "constant":
                # First decide what the fixed output of the oracle will be
                # (either always 0 or always 1)
                __magic_name__										=						np.random.randint(2     )
                if output == 1:
                       oracle_qc.x(__snake_case     )
  
         __magic_name__										=						oracle_qc.to_gate()
         __magic_name__										=						'Oracle'  # To show when we display the circuit
         return oracle_gate
  
  
  def        A	(							__snake_case:   qiskit.QuantumCircuit   ,    __snake_case:   int     )						->		qiskit.QuantumCircuit:
  
         """simple docstring"""
  
  
  
  
  
         __magic_name__										=						qiskit.QuantumCircuit(num_qubits + 1   ,    __snake_case     )
         # Set up the output qubit:
         dj_circuit.x(__snake_case     )
         dj_circuit.h(__snake_case     )
         # And set up the input register:
         for qubit in range(__snake_case     ):
                dj_circuit.h(__snake_case     )
         # Let's append the oracle gate to our circuit:
         dj_circuit.append(__snake_case   ,    range(num_qubits + 1     )     )
         # Finally, perform the H-gates again and measure:
         for qubit in range(__snake_case     ):
                dj_circuit.h(__snake_case     )
  
         for i in range(__snake_case     ):
                dj_circuit.measure(__snake_case   ,    __snake_case     )
  
         return dj_circuit
  
  
  def        A	(							__snake_case:   str   ,    __snake_case:   int     )						->		qiskit.result.counts.Counts:
  
         """simple docstring"""
  
  
  
  
  
         __magic_name__										=						qiskit.Aer.get_backend('aer_simulator'     )
  
         __magic_name__										=						dj_oracle(__snake_case   ,    __snake_case     )
         __magic_name__										=						dj_algorithm(__snake_case   ,    __snake_case     )
  
         # Execute the circuit on the simulator
         __magic_name__										=						qiskit.execute(__snake_case   ,    __snake_case   ,    shots=1_0_0_0     )
  
         # Return the histogram data of the results of the experiment.
         return job.result().get_counts(__snake_case     )
  
  
  if __name__ == "__main__":
   print(f"""Deutsch Jozsa - Constant Oracle: {deutsch_jozsa('constant', 3)}""")
   print(f"""Deutsch Jozsa - Balanced Oracle: {deutsch_jozsa('balanced', 3)}""")