





"""simple docstring"""



import unittest

from transformers import PegasusTokenizer, PegasusTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


a_			=      get_tests_dir("""fixtures/test_sentencepiece_no_bos.model""")






@require_sentencepiece
@require_tokenizers
class 		__snake_case (		SCREAMING_SNAKE_CASE__							,							unittest.TestCase					):





 """simple docstring"""


 _lowerCamelCase				=					PegasusTokenizer
 _lowerCamelCase				=					PegasusTokenizerFast
 _lowerCamelCase				=					True
 _lowerCamelCase				=					True

 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




     super().setUp()

     # We have a SentencePiece fixture for testing
     __A   :						Union[str, Any]									=					PegasusTokenizer(__lowerCamelCase  )
     tokenizer.save_pretrained(self.tmpdirname  )

 @cached_property
 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




     return PegasusTokenizer.from_pretrained('''google/pegasus-large'''  )

 def  UpperCamelCase__(   self ,						**__lowerCamelCase  ):



     '''simple docstring'''




     return PegasusTokenizer.from_pretrained(self.tmpdirname ,						**__lowerCamelCase  )

 def  UpperCamelCase__(   self ,						__lowerCamelCase  ):



     '''simple docstring'''




     return ("This is a test", "This is a test")

 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




     __A   :						List[Any]									=					'''</s>'''
     __A   :						Union[str, Any]									=					1

     self.assertEqual(self.get_tokenizer()._convert_token_to_id(__lowerCamelCase  ) ,						__lowerCamelCase  )
     self.assertEqual(self.get_tokenizer()._convert_id_to_token(__lowerCamelCase  ) ,						__lowerCamelCase  )

 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




     __A   :						Optional[Any]									=					list(self.get_tokenizer().get_vocab().keys()  )

     self.assertEqual(vocab_keys[0] ,						'''<pad>'''  )
     self.assertEqual(vocab_keys[1] ,						'''</s>'''  )
     self.assertEqual(vocab_keys[-1] ,						'''v'''  )
     self.assertEqual(len(__lowerCamelCase  ) ,						1103  )

 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




     self.assertEqual(self.get_tokenizer().vocab_size ,						1103  )

 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




     __A   :						Dict									=					self.rust_tokenizer_class.from_pretrained(self.tmpdirname  )
     __A   :						Tuple									=					self.tokenizer_class.from_pretrained(self.tmpdirname  )
     __A   :						Optional[int]									=					(
         '''Let\'s see which <unk> is the better <unk_token_11> one <mask_1> It seems like this <mask_2> was important'''
         ''' </s> <pad> <pad> <pad>'''
     )
     __A   :						List[Any]									=					rust_tokenizer([raw_input_str] ,						return_tensors=__lowerCamelCase ,						add_special_tokens=__lowerCamelCase  ).input_ids[0]
     __A   :						List[str]									=					py_tokenizer([raw_input_str] ,						return_tensors=__lowerCamelCase ,						add_special_tokens=__lowerCamelCase  ).input_ids[0]
     self.assertListEqual(__lowerCamelCase ,						__lowerCamelCase  )

 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




     __A   :						Optional[int]									=					self._large_tokenizer
     # <mask_1> masks whole sentence while <mask_2> masks single word
     __A   :						Any									=					'''<mask_1> To ensure a <mask_2> flow of bank resolutions.'''
     __A   :						Optional[int]									=					[2, 413, 615, 114, 3, 1971, 113, 1679, 1_0710, 107, 1]
     __A   :						str									=					tokenizer([raw_input_str] ,						return_tensors=__lowerCamelCase  ).input_ids[0]
     self.assertListEqual(__lowerCamelCase ,						__lowerCamelCase  )

 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




     __A   :						Union[str, Any]									=					self._large_tokenizer
     # The tracebacks for the following asserts are **better** without messages or self.assertEqual
     assert tokenizer.vocab_size == 9_6103
     assert tokenizer.pad_token_id == 0
     assert tokenizer.eos_token_id == 1
     assert tokenizer.offset == 103
     assert tokenizer.unk_token_id == tokenizer.offset + 2 == 105
     assert tokenizer.unk_token == "<unk>"
     assert tokenizer.model_max_length == 1024
     __A   :						Optional[int]									=					'''To ensure a smooth flow of bank resolutions.'''
     __A   :						str									=					[413, 615, 114, 2291, 1971, 113, 1679, 1_0710, 107, 1]
     __A   :						Dict									=					tokenizer([raw_input_str] ,						return_tensors=__lowerCamelCase  ).input_ids[0]
     self.assertListEqual(__lowerCamelCase ,						__lowerCamelCase  )
     assert tokenizer.convert_ids_to_tokens([0, 1, 2, 3]  ) == ["<pad>", "</s>", "<mask_1>", "<mask_2>"]

 @require_torch
 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




     __A   :						List[str]									=					['''This is going to be way too long.''' * 150, '''short example''']
     __A   :						Dict									=					['''not super long but more than 5 tokens''', '''tiny''']
     __A   :						List[str]									=					self._large_tokenizer(__lowerCamelCase ,						padding=__lowerCamelCase ,						truncation=__lowerCamelCase ,						return_tensors='''pt'''  )
     __A   :						int									=					self._large_tokenizer(
         text_target=__lowerCamelCase ,						max_length=5 ,						padding=__lowerCamelCase ,						truncation=__lowerCamelCase ,						return_tensors='''pt'''  )

     assert batch.input_ids.shape == (2, 1024)
     assert batch.attention_mask.shape == (2, 1024)
     assert targets["input_ids"].shape == (2, 5)
     assert len(__lowerCamelCase  ) == 2  # input_ids, attention_mask.







 @slow
 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




     __A   :						int									=					{'''input_ids''': [[3_8979, 143, 1_8485, 606, 130, 2_6669, 8_7686, 121, 5_4189, 1129, 111, 2_6669, 8_7686, 121, 9114, 1_4787, 121, 1_3249, 158, 592, 956, 121, 1_4621, 3_1576, 143, 6_2613, 108, 9688, 930, 4_3430, 1_1562, 6_2613, 304, 108, 1_1443, 897, 108, 9314, 1_7415, 6_3399, 108, 1_1443, 7614, 1_8316, 118, 4284, 7148, 1_2430, 143, 1400, 2_5703, 158, 111, 4284, 7148, 1_1772, 143, 2_1297, 1064, 158, 122, 204, 3506, 1754, 1133, 1_4787, 1581, 115, 3_3224, 4482, 111, 1355, 110, 2_9173, 317, 5_0833, 108, 2_0147, 9_4665, 111, 7_7198, 107, 1], [110, 6_2613, 117, 638, 112, 1133, 121, 2_0098, 1355, 7_9050, 1_3872, 135, 1596, 5_3541, 1352, 141, 1_3039, 5542, 124, 302, 518, 111, 268, 2956, 115, 149, 4427, 107, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [139, 1235, 2799, 1_8289, 1_7780, 204, 109, 9474, 1296, 107, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], '''attention_mask''': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
     # fmt: on

     self.tokenizer_integration_test_util(
         expected_encoding=__lowerCamelCase ,						model_name='''google/bigbird-pegasus-large-arxiv''' ,						revision='''ba85d0851d708441f91440d509690f1ab6353415''' ,						)






@require_sentencepiece
@require_tokenizers
class 		__snake_case (		SCREAMING_SNAKE_CASE__							,							unittest.TestCase					):





 """simple docstring"""


 _lowerCamelCase				=					PegasusTokenizer
 _lowerCamelCase				=					PegasusTokenizerFast
 _lowerCamelCase				=					True
 _lowerCamelCase				=					True

 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




     super().setUp()

     # We have a SentencePiece fixture for testing
     __A   :						List[str]									=					PegasusTokenizer(__lowerCamelCase ,						offset=0 ,						mask_token_sent=__lowerCamelCase ,						mask_token='''[MASK]'''  )
     tokenizer.save_pretrained(self.tmpdirname  )

 @cached_property
 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




     return PegasusTokenizer.from_pretrained('''google/bigbird-pegasus-large-arxiv'''  )

 def  UpperCamelCase__(   self ,						**__lowerCamelCase  ):



     '''simple docstring'''




     return PegasusTokenizer.from_pretrained(self.tmpdirname ,						**__lowerCamelCase  )

 def  UpperCamelCase__(   self ,						__lowerCamelCase  ):



     '''simple docstring'''




     return ("This is a test", "This is a test")

 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




     __A   :						List[Any]									=					self.rust_tokenizer_class.from_pretrained(self.tmpdirname  )
     __A   :						int									=					self.tokenizer_class.from_pretrained(self.tmpdirname  )
     __A   :						Optional[Any]									=					(
         '''Let\'s see which <unk> is the better <unk_token> one [MASK] It seems like this [MASK] was important </s>'''
         ''' <pad> <pad> <pad>'''
     )
     __A   :						List[Any]									=					rust_tokenizer([raw_input_str] ,						return_tensors=__lowerCamelCase ,						add_special_tokens=__lowerCamelCase  ).input_ids[0]
     __A   :						int									=					py_tokenizer([raw_input_str] ,						return_tensors=__lowerCamelCase ,						add_special_tokens=__lowerCamelCase  ).input_ids[0]
     self.assertListEqual(__lowerCamelCase ,						__lowerCamelCase  )

 @require_torch
 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




     __A   :						Optional[Any]									=					['''This is going to be way too long.''' * 1000, '''short example''']
     __A   :						List[Any]									=					['''not super long but more than 5 tokens''', '''tiny''']
     __A   :						List[str]									=					self._large_tokenizer(__lowerCamelCase ,						padding=__lowerCamelCase ,						truncation=__lowerCamelCase ,						return_tensors='''pt'''  )
     __A   :						Dict									=					self._large_tokenizer(
         text_target=__lowerCamelCase ,						max_length=5 ,						padding=__lowerCamelCase ,						truncation=__lowerCamelCase ,						return_tensors='''pt'''  )

     assert batch.input_ids.shape == (2, 4096)
     assert batch.attention_mask.shape == (2, 4096)
     assert targets["input_ids"].shape == (2, 5)
     assert len(__lowerCamelCase  ) == 2  # input_ids, attention_mask.







 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




     __A   :						str									=					(
         '''This is an example string that is used to test the original TF implementation against the HF'''
         ''' implementation'''
     )

     __A   :						Optional[Any]									=					self._large_tokenizer(__lowerCamelCase  ).input_ids

     self.assertListEqual(
         __lowerCamelCase ,						[182, 117, 142, 587, 4211, 120, 117, 263, 112, 804, 109, 856, 2_5016, 3137, 464, 109, 2_6955, 3137, 1] ,						)











"""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()




