


"""simple docstring"""



import unittest

from transformers import MraConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_torch_available():
       import torch

       from transformers import (
           MraForMaskedLM,
           MraForMultipleChoice,
           MraForQuestionAnswering,
           MraForSequenceClassification,
           MraForTokenClassification,
           MraModel,
       )
       from transformers.models.mra.modeling_mra import MRA_PRETRAINED_MODEL_ARCHIVE_LIST






class 		__snake_case :





 """simple docstring"""


 def __init__(   self ,						__lowerCamelCase ,						__lowerCamelCase=2 ,						__lowerCamelCase=8 ,						__lowerCamelCase=True ,						__lowerCamelCase=True ,						__lowerCamelCase=True ,						__lowerCamelCase=True ,						__lowerCamelCase=99 ,						__lowerCamelCase=16 ,						__lowerCamelCase=5 ,						__lowerCamelCase=2 ,						__lowerCamelCase=36 ,						__lowerCamelCase="gelu" ,						__lowerCamelCase=0.0 ,						__lowerCamelCase=0.0 ,						__lowerCamelCase=512 ,						__lowerCamelCase=16 ,						__lowerCamelCase=2 ,						__lowerCamelCase=0.0_2 ,						__lowerCamelCase=3 ,						__lowerCamelCase=4 ,						__lowerCamelCase=None ,						):



     '''simple docstring'''




     __A   :						List[str]									=					parent
     __A   :						List[Any]									=					batch_size
     __A   :						Optional[int]									=					seq_length
     __A   :						Optional[Any]									=					is_training
     __A   :						Dict									=					use_input_mask
     __A   :						int									=					use_token_type_ids
     __A   :						str									=					use_labels
     __A   :						List[str]									=					vocab_size
     __A   :						Dict									=					hidden_size
     __A   :						Optional[Any]									=					num_hidden_layers
     __A   :						str									=					num_attention_heads
     __A   :						Optional[Any]									=					intermediate_size
     __A   :						Union[str, Any]									=					hidden_act
     __A   :						Any									=					hidden_dropout_prob
     __A   :						Any									=					attention_probs_dropout_prob
     __A   :						List[str]									=					max_position_embeddings
     __A   :						Union[str, Any]									=					type_vocab_size
     __A   :						Optional[int]									=					type_sequence_label_size
     __A   :						List[Any]									=					initializer_range
     __A   :						Tuple									=					num_labels
     __A   :						Union[str, Any]									=					num_choices
     __A   :						List[str]									=					scope

 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




     __A   :						Optional[int]									=					ids_tensor([self.batch_size, self.seq_length] ,						self.vocab_size  )

     __A   :						Dict									=					None
     if self.use_input_mask:
         __A   :						str									=					random_attention_mask([self.batch_size, self.seq_length]  )

     __A   :						Tuple									=					None
     if self.use_token_type_ids:
         __A   :						Optional[Any]									=					ids_tensor([self.batch_size, self.seq_length] ,						self.type_vocab_size  )

     __A   :						Union[str, Any]									=					None
     __A   :						Any									=					None
     __A   :						Optional[int]									=					None
     if self.use_labels:
         __A   :						List[Any]									=					ids_tensor([self.batch_size] ,						self.type_sequence_label_size  )
         __A   :						Tuple									=					ids_tensor([self.batch_size, self.seq_length] ,						self.num_labels  )
         __A   :						Optional[int]									=					ids_tensor([self.batch_size] ,						self.num_choices  )

     __A   :						Optional[Any]									=					self.get_config()

     return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels

 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




     return MraConfig(
         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 ,						type_vocab_size=self.type_vocab_size ,						is_decoder=__lowerCamelCase ,						initializer_range=self.initializer_range ,						)

 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




     __A   :						Optional[Any]									=					self.get_config()
     __A   :						List[str]									=					300
     return config

 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




     (
         __A	
     )   :						Union[str, Any]									=					self.prepare_config_and_inputs()

     __A   :						List[str]									=					True
     __A   :						str									=					floats_tensor([self.batch_size, self.seq_length, self.hidden_size]  )
     __A   :						Any									=					ids_tensor([self.batch_size, self.seq_length] ,						vocab_size=2  )

     return (
         config,
         input_ids,
         token_type_ids,
         input_mask,
         sequence_labels,
         token_labels,
         choice_labels,
         encoder_hidden_states,
         encoder_attention_mask,
     )

 def  UpperCamelCase__(   self ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase  ):



     '''simple docstring'''




     __A   :						List[str]									=					MraModel(config=__lowerCamelCase  )
     model.to(__lowerCamelCase  )
     model.eval()
     __A   :						Dict									=					model(__lowerCamelCase ,						attention_mask=__lowerCamelCase ,						token_type_ids=__lowerCamelCase  )
     __A   :						Tuple									=					model(__lowerCamelCase ,						token_type_ids=__lowerCamelCase  )
     __A   :						List[str]									=					model(__lowerCamelCase  )
     self.parent.assertEqual(result.last_hidden_state.shape ,						(self.batch_size, self.seq_length, self.hidden_size)  )

 def  UpperCamelCase__(   self ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase ,						):



     '''simple docstring'''




     __A   :						Tuple									=					True
     __A   :						Any									=					MraModel(__lowerCamelCase  )
     model.to(__lowerCamelCase  )
     model.eval()
     __A   :						Tuple									=					model(
         __lowerCamelCase ,						attention_mask=__lowerCamelCase ,						token_type_ids=__lowerCamelCase ,						encoder_hidden_states=__lowerCamelCase ,						encoder_attention_mask=__lowerCamelCase ,						)
     __A   :						Union[str, Any]									=					model(
         __lowerCamelCase ,						attention_mask=__lowerCamelCase ,						token_type_ids=__lowerCamelCase ,						encoder_hidden_states=__lowerCamelCase ,						)
     __A   :						str									=					model(__lowerCamelCase ,						attention_mask=__lowerCamelCase ,						token_type_ids=__lowerCamelCase  )
     self.parent.assertEqual(result.last_hidden_state.shape ,						(self.batch_size, self.seq_length, self.hidden_size)  )

 def  UpperCamelCase__(   self ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase  ):



     '''simple docstring'''




     __A   :						List[Any]									=					MraForMaskedLM(config=__lowerCamelCase  )
     model.to(__lowerCamelCase  )
     model.eval()
     __A   :						Dict									=					model(__lowerCamelCase ,						attention_mask=__lowerCamelCase ,						token_type_ids=__lowerCamelCase ,						labels=__lowerCamelCase  )
     self.parent.assertEqual(result.logits.shape ,						(self.batch_size, self.seq_length, self.vocab_size)  )

 def  UpperCamelCase__(   self ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase  ):



     '''simple docstring'''




     __A   :						Optional[int]									=					MraForQuestionAnswering(config=__lowerCamelCase  )
     model.to(__lowerCamelCase  )
     model.eval()
     __A   :						Tuple									=					model(
         __lowerCamelCase ,						attention_mask=__lowerCamelCase ,						token_type_ids=__lowerCamelCase ,						start_positions=__lowerCamelCase ,						end_positions=__lowerCamelCase ,						)
     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  UpperCamelCase__(   self ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase  ):



     '''simple docstring'''




     __A   :						Dict									=					self.num_labels
     __A   :						List[str]									=					MraForSequenceClassification(__lowerCamelCase  )
     model.to(__lowerCamelCase  )
     model.eval()
     __A   :						List[Any]									=					model(__lowerCamelCase ,						attention_mask=__lowerCamelCase ,						token_type_ids=__lowerCamelCase ,						labels=__lowerCamelCase  )
     self.parent.assertEqual(result.logits.shape ,						(self.batch_size, self.num_labels)  )

 def  UpperCamelCase__(   self ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase  ):



     '''simple docstring'''




     __A   :						Optional[Any]									=					self.num_labels
     __A   :						List[Any]									=					MraForTokenClassification(config=__lowerCamelCase  )
     model.to(__lowerCamelCase  )
     model.eval()
     __A   :						Optional[int]									=					model(__lowerCamelCase ,						attention_mask=__lowerCamelCase ,						token_type_ids=__lowerCamelCase ,						labels=__lowerCamelCase  )
     self.parent.assertEqual(result.logits.shape ,						(self.batch_size, self.seq_length, self.num_labels)  )

 def  UpperCamelCase__(   self ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase  ):



     '''simple docstring'''




     __A   :						Dict									=					self.num_choices
     __A   :						List[Any]									=					MraForMultipleChoice(config=__lowerCamelCase  )
     model.to(__lowerCamelCase  )
     model.eval()
     __A   :						Tuple									=					input_ids.unsqueeze(1  ).expand(-1 ,						self.num_choices ,						-1  ).contiguous()
     __A   :						Any									=					token_type_ids.unsqueeze(1  ).expand(-1 ,						self.num_choices ,						-1  ).contiguous()
     __A   :						Union[str, Any]									=					input_mask.unsqueeze(1  ).expand(-1 ,						self.num_choices ,						-1  ).contiguous()
     __A   :						List[str]									=					model(
         __lowerCamelCase ,						attention_mask=__lowerCamelCase ,						token_type_ids=__lowerCamelCase ,						labels=__lowerCamelCase ,						)
     self.parent.assertEqual(result.logits.shape ,						(self.batch_size, self.num_choices)  )







 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




     __A   :						Optional[int]									=					self.prepare_config_and_inputs()
     (
         __A	
     )   :						Tuple									=					config_and_inputs
     __A   :						Optional[int]									=					{'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
     return config, inputs_dict






@require_torch
class 		__snake_case (		SCREAMING_SNAKE_CASE__							,							unittest.TestCase					):





 """simple docstring"""


 _lowerCamelCase				=					(
     (
         MraModel,
         MraForMaskedLM,
         MraForMultipleChoice,
         MraForQuestionAnswering,
         MraForSequenceClassification,
         MraForTokenClassification,
     )
     if is_torch_available()
     else ()
 )
 _lowerCamelCase				=					False
 _lowerCamelCase				=					False
 _lowerCamelCase				=					False
 _lowerCamelCase				=					False

 _lowerCamelCase				=					()

 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




     __A   :						int									=					MraModelTester(self  )
     __A   :						str									=					ConfigTester(self ,						config_class=__lowerCamelCase ,						hidden_size=37  )

 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




     self.config_tester.run_common_tests()

 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




     __A   :						List[Any]									=					self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_model(*__lowerCamelCase  )

 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




     __A   :						int									=					self.model_tester.prepare_config_and_inputs()
     for type in ["absolute", "relative_key", "relative_key_query"]:
         __A   :						Union[str, Any]									=					type
         self.model_tester.create_and_check_model(*__lowerCamelCase  )

 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




     __A   :						List[Any]									=					self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_masked_lm(*__lowerCamelCase  )

 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




     __A   :						Any									=					self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_multiple_choice(*__lowerCamelCase  )

 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




     __A   :						Optional[int]									=					self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_question_answering(*__lowerCamelCase  )

 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




     __A   :						Union[str, Any]									=					self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_sequence_classification(*__lowerCamelCase  )

 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




     __A   :						int									=					self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_token_classification(*__lowerCamelCase  )

 @slow
 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




     for model_name in MRA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
         __A   :						str									=					MraModel.from_pretrained(__lowerCamelCase  )
         self.assertIsNotNone(__lowerCamelCase  )







 @unittest.skip(reason='''MRA does not output attentions'''  )
 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




     return






@require_torch
class 		__snake_case (		unittest.TestCase					):





 """simple docstring"""


 @slow
 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




     __A   :						Optional[Any]									=					MraModel.from_pretrained('''uw-madison/mra-base-512-4'''  )
     __A   :						Optional[Any]									=					torch.arange(256  ).unsqueeze(0  )

     with torch.no_grad():
         __A   :						List[str]									=					model(__lowerCamelCase  )[0]

     __A   :						Union[str, Any]									=					torch.Size((1, 256, 768)  )
     self.assertEqual(output.shape ,						__lowerCamelCase  )

     __A   :						Tuple									=					torch.tensor(
         [[[-0.0_1_4_0, 0.0_8_3_0, -0.0_3_8_1], [0.1_5_4_6, 0.1_4_0_2, 0.0_2_2_0], [0.1_1_6_2, 0.0_8_5_1, 0.0_1_6_5]]]  )

     self.assertTrue(torch.allclose(output[:, :3, :3] ,						__lowerCamelCase ,						atol=1e-4  )  )

 @slow
 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




     __A   :						Optional[Any]									=					MraForMaskedLM.from_pretrained('''uw-madison/mra-base-512-4'''  )
     __A   :						Dict									=					torch.arange(256  ).unsqueeze(0  )

     with torch.no_grad():
         __A   :						List[str]									=					model(__lowerCamelCase  )[0]

     __A   :						Optional[int]									=					5_0265

     __A   :						int									=					torch.Size((1, 256, vocab_size)  )
     self.assertEqual(output.shape ,						__lowerCamelCase  )

     __A   :						Union[str, Any]									=					torch.tensor(
         [[[9.2_5_9_5, -3.6_0_3_8, 11.8819], [9.3_8_6_9, -3.2_6_9_3, 11.0956], [11.8524, -3.4_9_3_8, 13.1210]]]  )

     self.assertTrue(torch.allclose(output[:, :3, :3] ,						__lowerCamelCase ,						atol=1e-4  )  )







 @slow
 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




     __A   :						Dict									=					MraForMaskedLM.from_pretrained('''uw-madison/mra-base-4096-8-d3'''  )
     __A   :						List[str]									=					torch.arange(4096  ).unsqueeze(0  )

     with torch.no_grad():
         __A   :						Dict									=					model(__lowerCamelCase  )[0]

     __A   :						Optional[int]									=					5_0265

     __A   :						Optional[Any]									=					torch.Size((1, 4096, vocab_size)  )
     self.assertEqual(output.shape ,						__lowerCamelCase  )

     __A   :						Optional[Any]									=					torch.tensor(
         [[[5.4_7_8_9, -2.3_5_6_4, 7.5_0_6_4], [7.9_0_6_7, -1.3_3_6_9, 9.9_6_6_8], [9.0_7_1_2, -1.8_1_0_6, 7.0_3_8_0]]]  )

     self.assertTrue(torch.allclose(output[:, :3, :3] ,						__lowerCamelCase ,						atol=1e-4  )  )











"""simple docstring"""



import numpy as np
import qiskit







def 		__lowercase					(       snake_case_  :  int = 8    ,snake_case_  :  int | None = None						)       ->str:



    '''simple docstring'''







    __A   :						str									=					np.random.default_rng(seed=snake_case_						)

    # Roughly 25% of the qubits will contribute to the key.
    # So we take more than we need.
    __A   :						str									=					6 * key_len
    # Measurement basis for Alice's qubits.
    __A   :						Any									=					rng.integers(2    ,size=snake_case_						)
    # The set of states Alice will prepare.
    __A   :						Any									=					rng.integers(2    ,size=snake_case_						)
    # Measurement basis for Bob's qubits.
    __A   :						str									=					rng.integers(2    ,size=snake_case_						)

    # Quantum Circuit to simulate BB84
    __A   :						Dict									=					qiskit.QuantumCircuit(snake_case_    ,name='''BB84'''						)

    # Alice prepares her qubits according to rules above.
    for index, _ in enumerate(snake_case_						):
        if alice_state[index] == 1:
            bbaa_circ.x(snake_case_						)
        if alice_basis[index] == 1:
            bbaa_circ.h(snake_case_						)
    bbaa_circ.barrier()

    # Bob measures the received qubits according to rules above.
    for index, _ in enumerate(snake_case_						):
        if bob_basis[index] == 1:
            bbaa_circ.h(snake_case_						)

    bbaa_circ.barrier()
    bbaa_circ.measure_all()

    # Simulate the quantum circuit.
    __A   :						List[str]									=					qiskit.Aer.get_backend('''aer_simulator'''						)
    # We only need to run one shot because the key is unique.
    # Multiple shots will produce the same key.
    __A   :						List[str]									=					qiskit.execute(snake_case_    ,snake_case_    ,shots=1    ,seed_simulator=snake_case_						)
    # Returns the result of measurement.
    __A   :						Union[str, Any]									=					job.result().get_counts(snake_case_						).most_frequent()

    # Extracting the generated key from the simulation results.
    # Only keep measurement results where Alice and Bob chose the same basis.
    __A   :						int									=					''''''.join(
        [
            result_bit
            for alice_basis_bit, bob_basis_bit, result_bit in zip(
                snake_case_    ,snake_case_    ,snake_case_						)
            if alice_basis_bit == bob_basis_bit
        ]						)

    # Get final key. Pad with 0 if too short, otherwise truncate.
    __A   :						Union[str, Any]									=					gen_key[:key_len] if len(snake_case_						) >= key_len else gen_key.ljust(snake_case_    ,'''0'''						)
    return key


if __name__ == "__main__":
       print(f'''The generated key is : {bbaa(8, seed=0)}''')
       from doctest import testmod

       testmod()




