




"""simple docstring"""



import numpy as np
import qiskit







def     _SCREAMING_SNAKE_CASE  (				lowercase_     ,					lowercase_		)		->						qiskit.QuantumCircuit:
 A__			=							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:
  A__			=							np.random.randint(1     ,					2**num_qubits		)
  # Next, format 'b' as a binary string of length 'n', padded with zeros:
  A__			=							format(lowercase_     ,					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(lowercase_		):
   if bit == "1":
    oracle_qc.x(lowercase_		)
        # Do the controlled-NOT gates for each qubit, using the output qubit
        # as the target:
  for index in range(lowercase_		):
   oracle_qc.cx(lowercase_     ,					lowercase_		)
  # Next, place the final X-gates
  for index, bit in enumerate(lowercase_		):
   if bit == "1":
    oracle_qc.x(lowercase_		)

    # 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)
  A__			=							np.random.randint(2		)
  if output == 1:
   oracle_qc.x(lowercase_		)

 A__			=							oracle_qc.to_gate()
 A__			=							"Oracle"  # To show when we display the circuit
 return oracle_gate







def     _SCREAMING_SNAKE_CASE  (				lowercase_     ,					lowercase_		)		->						qiskit.QuantumCircuit:
 A__			=							qiskit.QuantumCircuit(num_qubits + 1     ,					lowercase_		)
 # Set up the output qubit:
 dj_circuit.x(lowercase_		)
 dj_circuit.h(lowercase_		)
 # And set up the input register:
 for qubit in range(lowercase_		):
  dj_circuit.h(lowercase_		)
 # Let's append the oracle gate to our circuit:
 dj_circuit.append(lowercase_     ,					range(num_qubits + 1		)		)
 # Finally, perform the H-gates again and measure:
 for qubit in range(lowercase_		):
  dj_circuit.h(lowercase_		)

 for i in range(lowercase_		):
  dj_circuit.measure(lowercase_     ,					lowercase_		)

 return dj_circuit







def     _SCREAMING_SNAKE_CASE  (				lowercase_     ,					lowercase_		)		->						qiskit.result.counts.Counts:
 A__			=							qiskit.Aer.get_backend("aer_simulator"		)

 A__			=							dj_oracle(lowercase_     ,					lowercase_		)
 A__			=							dj_algorithm(lowercase_     ,					lowercase_		)

 # Execute the circuit on the simulator
 A__			=							qiskit.execute(lowercase_     ,					lowercase_     ,					shots=10_00		)

 # Return the histogram data of the results of the experiment.
 return job.result().get_counts(lowercase_		)


if __name__ == "__main__":
      print(f'Deutsch Jozsa - Constant Oracle: {deutsch_jozsa("constant", 3)}')
      print(f'Deutsch Jozsa - Balanced Oracle: {deutsch_jozsa("balanced", 3)}')











"""simple docstring"""



from __future__ import annotations

import unittest

from transformers import MPNetConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
      import tensorflow as tf

      from transformers.models.mpnet.modeling_tf_mpnet import (
          TFMPNetForMaskedLM,
          TFMPNetForMultipleChoice,
          TFMPNetForQuestionAnswering,
          TFMPNetForSequenceClassification,
          TFMPNetForTokenClassification,
          TFMPNetModel,
      )




class       UpperCAmelCase_    :




    def __init__(       self		:			Dict      ,					snake_case_		:			List[str]      ,					snake_case_		:			List[Any]=13      ,					snake_case_		:			int=7      ,					snake_case_		:			List[Any]=True      ,					snake_case_		:			Tuple=True      ,					snake_case_		:			Tuple=False      ,					snake_case_		:			str=True      ,					snake_case_		:			str=99      ,					snake_case_		:			int=64      ,					snake_case_		:			Tuple=2      ,					snake_case_		:			Optional[int]=4      ,					snake_case_		:			List[Any]=64      ,					snake_case_		:			Any="gelu"      ,					snake_case_		:			List[Any]=0.1      ,					snake_case_		:			List[Any]=0.1      ,					snake_case_		:			Dict=512      ,					snake_case_		:			Dict=16      ,					snake_case_		:			Tuple=2      ,					snake_case_		:			Tuple=0.02      ,					snake_case_		:			Optional[Any]=3      ,					snake_case_		:			Dict=4      ,					snake_case_		:			List[str]=None      ,					) ->				Any:




     '''simple docstring'''

     A__			=							parent
     A__			=							batch_size
     A__			=							seq_length
     A__			=							is_training
     A__			=							use_input_mask
     A__			=							use_token_type_ids
     A__			=							use_labels
     A__			=							vocab_size
     A__			=							hidden_size
     A__			=							num_hidden_layers
     A__			=							num_attention_heads
     A__			=							intermediate_size
     A__			=							hidden_act
     A__			=							hidden_dropout_prob
     A__			=							attention_probs_dropout_prob
     A__			=							max_position_embeddings
     A__			=							type_vocab_size
     A__			=							type_sequence_label_size
     A__			=							initializer_range
     A__			=							num_labels
     A__			=							num_choices
     A__			=							scope




    def 				__magic_name__				(       self		:			int ) ->				Optional[int]:




     '''simple docstring'''

     A__			=							ids_tensor([self.batch_size, self.seq_length]      ,					self.vocab_size )

     A__			=							None
     if self.use_input_mask:
      A__			=							random_attention_mask([self.batch_size, self.seq_length] )

     A__			=							None
     A__			=							None
     A__			=							None
     if self.use_labels:
      A__			=							ids_tensor([self.batch_size]      ,					self.type_sequence_label_size )
      A__			=							ids_tensor([self.batch_size, self.seq_length]      ,					self.num_labels )
      A__			=							ids_tensor([self.batch_size]      ,					self.num_choices )

     A__			=							MPNetConfig(
         vocab_size=self.vocab_size      ,					hidden_size=self.hidden_size      ,					num_hidden_layers=self.num_hidden_layers      ,					num_attention_heads=self.num_attention_heads      ,					intermediate_size=self.intermediate_size      ,					hidden_act=self.hidden_act      ,					hidden_dropout_prob=self.hidden_dropout_prob      ,					attention_probs_dropout_prob=self.attention_probs_dropout_prob      ,					max_position_embeddings=self.max_position_embeddings      ,					initializer_range=self.initializer_range      ,					)
     return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels




    def 				__magic_name__				(       self		:			str      ,					snake_case_		:			int      ,					snake_case_		:			int      ,					snake_case_		:			Union[str, Any]      ,					snake_case_		:			Optional[Any]      ,					snake_case_		:			Any      ,					snake_case_		:			str ) ->				int:




     '''simple docstring'''

     A__			=							TFMPNetModel(config=snake_case_ )
     A__			=							{"input_ids": input_ids, "attention_mask": input_mask}
     A__			=							model(snake_case_ )
     A__			=							[input_ids, input_mask]
     A__			=							model(snake_case_ )
     self.parent.assertEqual(result.last_hidden_state.shape      ,					(self.batch_size, self.seq_length, self.hidden_size) )




    def 				__magic_name__				(       self		:			List[Any]      ,					snake_case_		:			Union[str, Any]      ,					snake_case_		:			List[str]      ,					snake_case_		:			Optional[int]      ,					snake_case_		:			Union[str, Any]      ,					snake_case_		:			str      ,					snake_case_		:			str ) ->				int:




     '''simple docstring'''

     A__			=							TFMPNetForMaskedLM(config=snake_case_ )
     A__			=							{"input_ids": input_ids, "attention_mask": input_mask}
     A__			=							model(snake_case_ )
     self.parent.assertEqual(result.logits.shape      ,					(self.batch_size, self.seq_length, self.vocab_size) )




    def 				__magic_name__				(       self		:			List[Any]      ,					snake_case_		:			Union[str, Any]      ,					snake_case_		:			Tuple      ,					snake_case_		:			Any      ,					snake_case_		:			str      ,					snake_case_		:			Union[str, Any]      ,					snake_case_		:			Dict ) ->				Tuple:




     '''simple docstring'''

     A__			=							TFMPNetForQuestionAnswering(config=snake_case_ )
     A__			=							{
         "input_ids": input_ids,
         "attention_mask": input_mask,
     }
     A__			=							model(snake_case_ )
     self.parent.assertEqual(result.start_logits.shape      ,					(self.batch_size, self.seq_length) )
     self.parent.assertEqual(result.end_logits.shape      ,					(self.batch_size, self.seq_length) )




    def 				__magic_name__				(       self		:			List[Any]      ,					snake_case_		:			Union[str, Any]      ,					snake_case_		:			List[str]      ,					snake_case_		:			Dict      ,					snake_case_		:			Optional[int]      ,					snake_case_		:			Optional[int]      ,					snake_case_		:			Optional[int] ) ->				Tuple:




     '''simple docstring'''

     A__			=							self.num_labels
     A__			=							TFMPNetForSequenceClassification(snake_case_ )
     A__			=							{"input_ids": input_ids, "attention_mask": input_mask}
     A__			=							model(snake_case_ )
     self.parent.assertEqual(result.logits.shape      ,					(self.batch_size, self.num_labels) )




    def 				__magic_name__				(       self		:			Dict      ,					snake_case_		:			Tuple      ,					snake_case_		:			int      ,					snake_case_		:			List[Any]      ,					snake_case_		:			Optional[int]      ,					snake_case_		:			str      ,					snake_case_		:			Dict ) ->				Optional[Any]:




     '''simple docstring'''

     A__			=							self.num_choices
     A__			=							TFMPNetForMultipleChoice(snake_case_ )
     A__			=							tf.tile(tf.expand_dims(snake_case_      ,					1 )      ,					(1, self.num_choices, 1) )
     A__			=							tf.tile(tf.expand_dims(snake_case_      ,					1 )      ,					(1, self.num_choices, 1) )
     A__			=							{
         "input_ids": multiple_choice_inputs_ids,
         "attention_mask": multiple_choice_input_mask,
     }
     A__			=							model(snake_case_ )
     self.parent.assertEqual(result.logits.shape      ,					(self.batch_size, self.num_choices) )




    def 				__magic_name__				(       self		:			List[str]      ,					snake_case_		:			str      ,					snake_case_		:			Tuple      ,					snake_case_		:			Union[str, Any]      ,					snake_case_		:			List[str]      ,					snake_case_		:			Optional[Any]      ,					snake_case_		:			List[Any] ) ->				int:




     '''simple docstring'''

     A__			=							self.num_labels
     A__			=							TFMPNetForTokenClassification(snake_case_ )
     A__			=							{"input_ids": input_ids, "attention_mask": input_mask}
     A__			=							model(snake_case_ )
     self.parent.assertEqual(result.logits.shape      ,					(self.batch_size, self.seq_length, self.num_labels) )




    def 				__magic_name__				(       self		:			Tuple ) ->				Dict:




     '''simple docstring'''

     A__			=							self.prepare_config_and_inputs()
     ((A__), (A__), (A__), (A__), (A__), (A__))			=							config_and_inputs
     A__			=							{"input_ids": input_ids, "attention_mask": input_mask}
     return config, inputs_dict






@require_tf
class       UpperCAmelCase_    (		A_,  A_,  unittest.TestCase				):
    lowercase__		   = (
        (
            TFMPNetForMaskedLM,
            TFMPNetForMultipleChoice,
            TFMPNetForQuestionAnswering,
            TFMPNetForSequenceClassification,
            TFMPNetForTokenClassification,
            TFMPNetModel,
        )
        if is_tf_available()
        else ()
    )
    lowercase__		   = (
        {
            '''feature-extraction''': TFMPNetModel,
            '''fill-mask''': TFMPNetForMaskedLM,
            '''question-answering''': TFMPNetForQuestionAnswering,
            '''text-classification''': TFMPNetForSequenceClassification,
            '''token-classification''': TFMPNetForTokenClassification,
            '''zero-shot''': TFMPNetForSequenceClassification,
        }
        if is_tf_available()
        else {}
    )
    lowercase__		   = False
    lowercase__		   = False




    def 				__magic_name__				(       self		:			Any ) ->				List[str]:




     '''simple docstring'''

     A__			=							TFMPNetModelTester(self )
     A__			=							ConfigTester(self      ,					config_class=snake_case_      ,					hidden_size=37 )




    def 				__magic_name__				(       self		:			Union[str, Any] ) ->				Tuple:




     '''simple docstring'''

     self.config_tester.run_common_tests()




    def 				__magic_name__				(       self		:			Union[str, Any] ) ->				Dict:




     '''simple docstring'''

     A__			=							self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_mpnet_model(*snake_case_ )




    def 				__magic_name__				(       self		:			Optional[int] ) ->				List[Any]:




     '''simple docstring'''

     A__			=							self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_mpnet_for_masked_lm(*snake_case_ )




    def 				__magic_name__				(       self		:			Dict ) ->				Optional[Any]:




     '''simple docstring'''

     A__			=							self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_mpnet_for_question_answering(*snake_case_ )




    def 				__magic_name__				(       self		:			int ) ->				List[Any]:




     '''simple docstring'''

     A__			=							self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_mpnet_for_sequence_classification(*snake_case_ )




    def 				__magic_name__				(       self		:			Optional[Any] ) ->				int:




     '''simple docstring'''

     A__			=							self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_mpnet_for_multiple_choice(*snake_case_ )




    def 				__magic_name__				(       self		:			Tuple ) ->				Any:




     '''simple docstring'''

     A__			=							self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_mpnet_for_token_classification(*snake_case_ )




    @slow
    def 				__magic_name__				(       self		:			Dict ) ->				Tuple:




     '''simple docstring'''

     for model_name in ["microsoft/mpnet-base"]:
      A__			=							TFMPNetModel.from_pretrained(snake_case_ )
      self.assertIsNotNone(snake_case_ )






@require_tf
class       UpperCAmelCase_    (		unittest.TestCase				):




    @slow
    def 				__magic_name__				(       self		:			str ) ->				List[str]:




     '''simple docstring'''

     A__			=							TFMPNetModel.from_pretrained("microsoft/mpnet-base" )
     A__			=							tf.constant([[0, 1, 2, 3, 4, 5]] )
     A__			=							model(snake_case_ )[0]

     A__			=							[1, 6, 768]
     self.assertEqual(output.shape      ,					snake_case_ )

     A__			=							tf.constant(
         [
             [
                 [-0.106_7172, 0.0821_6473, 0.002_4543],
                 [-0.0346_5879, 0.835_4118, -0.0325_2288],
                 [-0.0656_9476, -0.1242_4111, -0.049_4436],
             ]
         ] )
     tf.debugging.assert_near(output[:, :3, :3]      ,					snake_case_      ,					atol=1e-4 )





