


import pickle
import shutil
import tempfile
import unittest

from transformers import SPIECE_UNDERLINE, XLMRobertaTokenizer, XLMRobertaTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


_lowercase       :      int	=	get_tests_dir("fixtures/test_sentencepiece.model")






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

    """simple docstring"""







    lowerCAmelCase 		= XLMRobertaTokenizer
    lowerCAmelCase 		= XLMRobertaTokenizerFast
    lowerCAmelCase 		= True
    lowerCAmelCase 		= True

    def      _UpperCAmelCase					(						self   )	->				Tuple:
     super().setUp()

     # We have a SentencePiece fixture for testing
     A								=				XLMRobertaTokenizer(a__				,   keep_accents=a__   )
     tokenizer.save_pretrained(self.tmpdirname   )

    def      _UpperCAmelCase					(						self   )	->				Tuple:
     A								=				"""<pad>"""
     A								=				1

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

    def      _UpperCAmelCase					(						self   )	->				Any:
     A								=				list(self.get_tokenizer().get_vocab().keys()   )

     self.assertEqual(vocab_keys[0]				,   """<s>"""   )
     self.assertEqual(vocab_keys[1]				,   """<pad>"""   )
     self.assertEqual(vocab_keys[-1]				,   """<mask>"""   )
     self.assertEqual(len(a__   )				,   1002   )

    def      _UpperCAmelCase					(						self   )	->				Tuple:
     self.assertEqual(self.get_tokenizer().vocab_size				,   1002   )

    def      _UpperCAmelCase					(						self   )	->				str:
     A								=				XLMRobertaTokenizer(a__				,   keep_accents=a__   )

     A								=				tokenizer.tokenize("""This is a test"""   )
     self.assertListEqual(a__				,   ["""▁This""", """▁is""", """▁a""", """▁t""", """est"""]   )

     self.assertListEqual(
         tokenizer.convert_tokens_to_ids(a__   )				,   [value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]]				,   )

     A								=				tokenizer.tokenize("""I was born in 92000, and this is falsé."""   )
     self.assertListEqual(
         a__				,   [
             SPIECE_UNDERLINE + """I""",
             SPIECE_UNDERLINE + """was""",
             SPIECE_UNDERLINE + """b""",
             """or""",
             """n""",
             SPIECE_UNDERLINE + """in""",
             SPIECE_UNDERLINE + """""",
             """9""",
             """2""",
             """0""",
             """0""",
             """0""",
             """,""",
             SPIECE_UNDERLINE + """and""",
             SPIECE_UNDERLINE + """this""",
             SPIECE_UNDERLINE + """is""",
             SPIECE_UNDERLINE + """f""",
             """al""",
             """s""",
             """é""",
             """.""",
         ]				,   )
     A								=				tokenizer.convert_tokens_to_ids(a__   )
     self.assertListEqual(
         a__				,   [
             value + tokenizer.fairseq_offset
             for value in [8, 21, 84, 55, 24, 19, 7, 2, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 2, 4]
             #                                       ^ unk: 2 + 1 = 3                  unk: 2 + 1 = 3 ^
         ]				,   )

     A								=				tokenizer.convert_ids_to_tokens(a__   )
     self.assertListEqual(
         a__				,   [
             SPIECE_UNDERLINE + """I""",
             SPIECE_UNDERLINE + """was""",
             SPIECE_UNDERLINE + """b""",
             """or""",
             """n""",
             SPIECE_UNDERLINE + """in""",
             SPIECE_UNDERLINE + """""",
             """<unk>""",
             """2""",
             """0""",
             """0""",
             """0""",
             """,""",
             SPIECE_UNDERLINE + """and""",
             SPIECE_UNDERLINE + """this""",
             SPIECE_UNDERLINE + """is""",
             SPIECE_UNDERLINE + """f""",
             """al""",
             """s""",
             """<unk>""",
             """.""",
         ]				,   )

    def      _UpperCAmelCase					(						self   )	->				Any:
     if not self.test_slow_tokenizer:
      # as we don't have a slow version, we can't compare the outputs between slow and fast versions
      return

     A								=				(self.rust_tokenizer_class, """hf-internal-testing/tiny-xlm-roberta""", {})
     for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
      with self.subTest(f'{tokenizer.__class__.__name__} ({pretrained_name})'   ):
       A								=				self.rust_tokenizer_class.from_pretrained(a__				,   **a__   )
       A								=				self.tokenizer_class.from_pretrained(a__				,   **a__   )

       A								=				tempfile.mkdtemp()

       A								=				tokenizer_r.save_pretrained(a__   )
       A								=				tokenizer_p.save_pretrained(a__   )

       # Checks it save with the same files + the tokenizer.json file for the fast one
       self.assertTrue(any("""tokenizer.json""" in f for f in tokenizer_r_files   )   )
       A								=				tuple(f for f in tokenizer_r_files if """tokenizer.json""" not in f   )
       self.assertSequenceEqual(a__				,   a__   )

       # Checks everything loads correctly in the same way
       A								=				tokenizer_r.from_pretrained(a__   )
       A								=				tokenizer_p.from_pretrained(a__   )

       # Check special tokens are set accordingly on Rust and Python
       for key in tokenizer_pp.special_tokens_map:
        self.assertTrue(hasattr(a__				,   a__   )   )
        # self.assertEqual(getattr(tokenizer_rp, key), getattr(tokenizer_pp, key))
        # self.assertEqual(getattr(tokenizer_rp, key + "_id"), getattr(tokenizer_pp, key + "_id"))

       shutil.rmtree(a__   )

       # Save tokenizer rust, legacy_format=True
       A								=				tempfile.mkdtemp()

       A								=				tokenizer_r.save_pretrained(a__				,   legacy_format=a__   )
       A								=				tokenizer_p.save_pretrained(a__   )

       # Checks it save with the same files
       self.assertSequenceEqual(a__				,   a__   )

       # Checks everything loads correctly in the same way
       A								=				tokenizer_r.from_pretrained(a__   )
       A								=				tokenizer_p.from_pretrained(a__   )

       # Check special tokens are set accordingly on Rust and Python
       for key in tokenizer_pp.special_tokens_map:
        self.assertTrue(hasattr(a__				,   a__   )   )

       shutil.rmtree(a__   )

       # Save tokenizer rust, legacy_format=False
       A								=				tempfile.mkdtemp()

       A								=				tokenizer_r.save_pretrained(a__				,   legacy_format=a__   )
       A								=				tokenizer_p.save_pretrained(a__   )

       # Checks it saved the tokenizer.json file
       self.assertTrue(any("""tokenizer.json""" in f for f in tokenizer_r_files   )   )

       # Checks everything loads correctly in the same way
       A								=				tokenizer_r.from_pretrained(a__   )
       A								=				tokenizer_p.from_pretrained(a__   )

       # Check special tokens are set accordingly on Rust and Python
       for key in tokenizer_pp.special_tokens_map:
        self.assertTrue(hasattr(a__				,   a__   )   )

       shutil.rmtree(a__   )

    @cached_property
    def      _UpperCAmelCase					(						self   )	->				Optional[int]:
     return XLMRobertaTokenizer.from_pretrained("""xlm-roberta-base"""   )

    def      _UpperCAmelCase					(						self   )	->				int:
     with tempfile.NamedTemporaryFile() as f:
      shutil.copyfile(a__				,   f.name   )
      A								=				XLMRobertaTokenizer(f.name				,   keep_accents=a__   )
      A								=				pickle.dumps(a__   )
     pickle.loads(a__   )

    def      _UpperCAmelCase					(						self   )	->				Tuple:
     if not self.test_rust_tokenizer:
      return

     A								=				self.get_tokenizer()
     A								=				self.get_rust_tokenizer()

     A								=				"""I was born in 92000, and this is falsé."""

     A								=				tokenizer.tokenize(a__   )
     A								=				rust_tokenizer.tokenize(a__   )
     self.assertListEqual(a__				,   a__   )

     A								=				tokenizer.encode(a__				,   add_special_tokens=a__   )
     A								=				rust_tokenizer.encode(a__				,   add_special_tokens=a__   )
     self.assertListEqual(a__				,   a__   )

     A								=				self.get_rust_tokenizer()
     A								=				tokenizer.encode(a__   )
     A								=				rust_tokenizer.encode(a__   )
     self.assertListEqual(a__				,   a__   )

    @slow
    def      _UpperCAmelCase					(						self   )	->				Any:
     A								=				"""Hello World!"""
     A								=				[0, 3_5378, 6661, 38, 2]
     # xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')  # xlmr.large has same tokenizer
     # xlmr.eval()
     # xlmr.encode(symbols)

     self.assertListEqual(a__				,   self.big_tokenizer.encode(a__   )   )

    @slow
    def      _UpperCAmelCase					(						self   )	->				Tuple:
     A								=				(
         """This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) \" [ ] ! : - . Also we will"""
         """ add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth"""
     )
     A								=				[
         0,
         3293,
         83,
         10,
         4552,
         4989,
         7986,
         678,
         10,
         5915,
         111,
         17_9459,
         12_4850,
         4,
         6044,
         237,
         12,
         6,
         5,
         6,
         4,
         6780,
         705,
         15,
         1388,
         44,
         378,
         1_0114,
         711,
         152,
         20,
         6,
         5,
         2_2376,
         642,
         1221,
         1_5190,
         3_4153,
         450,
         5608,
         959,
         1119,
         5_7702,
         136,
         186,
         47,
         1098,
         2_9367,
         47,
         # 4426, # What fairseq tokenizes from "<unk>": "_<"
         # 3678, # What fairseq tokenizes from "<unk>": "unk"
         # 2740, # What fairseq tokenizes from "<unk>": ">"
         3,  # What we tokenize from "<unk>": "<unk>"
         6,  # Residue from the tokenization: an extra sentencepiece underline
         4,
         6044,
         237,
         6284,
         5_0901,
         528,
         31,
         90,
         34,
         927,
         2,
     ]
     # xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')  # xlmr.large has same tokenizer
     # xlmr.eval()
     # xlmr.encode(symbols)

     self.assertListEqual(a__				,   self.big_tokenizer.encode(a__   )   )

    @slow
    def      _UpperCAmelCase					(						self   )	->				Union[str, Any]:
     # fmt: off
     A								=				{"""input_ids""": [[0, 1_1062, 8_2772, 7, 15, 8_2772, 538, 5_1529, 237, 1_7198, 1290, 206, 9, 21_5175, 1314, 136, 1_7198, 1290, 206, 9, 5_6359, 42, 12_2009, 9, 1_6466, 16, 8_7344, 4537, 9, 4717, 7_8381, 6, 15_9958, 7, 15, 2_4480, 618, 4, 527, 2_2693, 5428, 4, 2777, 2_4480, 9874, 4, 4_3523, 594, 4, 803, 1_8392, 3_3189, 18, 4, 4_3523, 2_4447, 1_2399, 100, 2_4955, 8_3658, 9626, 14_4057, 15, 839, 2_2335, 16, 136, 2_4955, 8_3658, 8_3479, 15, 3_9102, 724, 16, 678, 645, 2789, 1328, 4589, 42, 12_2009, 11_5774, 23, 805, 1328, 4_6876, 7, 136, 5_3894, 1940, 4_2227, 4_1159, 1_7721, 823, 425, 4, 2_7512, 9_8722, 206, 136, 5531, 4970, 919, 1_7336, 5, 2], [0, 2_0080, 618, 83, 8_2775, 47, 479, 9, 1517, 73, 5_3894, 333, 8_0581, 11_0117, 1_8811, 5256, 1295, 51, 15_2526, 297, 7986, 390, 12_4416, 538, 3_5431, 214, 98, 1_5044, 2_5737, 136, 7108, 4_3701, 23, 756, 13_5355, 7, 5, 2, 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, 581, 6_3773, 11_9455, 6, 14_7797, 8_8203, 7, 645, 70, 21, 3285, 1_0269, 5, 2, 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]], """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, 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, 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, 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, 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=a__				,   model_name="""xlm-roberta-base"""				,   revision="""d9d8a8ea5eb94b1c6654ae9249df7793cd2933d3"""				,   )







import qiskit





def   _lowerCAmelCase				(   UpperCamelCase__:     int = 2						) ->   qiskit.result.counts.Counts:





	"""simple docstring"""







	A								=				qubits

	# Using Aer's simulator
	A								=				qiskit.Aer.get_backend("""aer_simulator"""						)

	# Creating a Quantum Circuit acting on the q register
	A								=				qiskit.QuantumCircuit(UpperCamelCase__							,					UpperCamelCase__						)

	# Adding a H gate on qubit 0 (now q0 in superposition)
	circuit.h(0						)

	for i in range(1							,					UpperCamelCase__						):
		# Adding CX (CNOT) gate
		circuit.cx(i - 1							,					UpperCamelCase__						)

	# Mapping the quantum measurement to the classical bits
	circuit.measure(list(range(UpperCamelCase__						)						)							,					list(range(UpperCamelCase__						)						)						)

	# Now measuring any one qubit would affect other qubits to collapse
	# their super position and have same state as the measured one.

	# Executing the circuit on the simulator
	A								=				qiskit.execute(UpperCamelCase__							,					UpperCamelCase__							,					shots=10_00						)

	return job.result().get_counts(UpperCamelCase__						)


if __name__ == "__main__":
				print(f'''Total count for various states are: {quantum_entanglement(3)}''')



