

import qiskit





def 	__a     (      A__			:  int = 2       ):
    SCREAMING_SNAKE_CASE										=   qubits

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

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

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

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

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

    # 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
    SCREAMING_SNAKE_CASE										=   qiskit.execute(A__			,  A__			,  shots=1000       )

    return job.result().get_counts(A__       )


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


import tempfile
import unittest

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from transformers.testing_utils import (
    is_torch_available,
    require_optimum,
    require_torch,
    slow,
)


if is_torch_available():
    import torch





@require_torch
@require_optimum
@slow
class 			_SCREAMING_SNAKE_CASE (   unittest.TestCase		):







   '''simple docstring'''




   def      _snake_case		(       self					:			Any			):
       SCREAMING_SNAKE_CASE										=   "hf-internal-testing/tiny-random-t5"
       SCREAMING_SNAKE_CASE										=   AutoTokenizer.from_pretrained(__lowerCamelCase			)
       SCREAMING_SNAKE_CASE										=   AutoModelForSeqaSeqLM.from_pretrained(__lowerCamelCase			)

       SCREAMING_SNAKE_CASE										=   tokenizer("This is me"     , return_tensors="pt"			)

       SCREAMING_SNAKE_CASE										=   model.to_bettertransformer()

       self.assertTrue(any("BetterTransformer" in mod.__class__.__name__ for _, mod in model.named_modules()			)			)

       SCREAMING_SNAKE_CASE										=   model.generate(**__lowerCamelCase			)

       SCREAMING_SNAKE_CASE										=   model.reverse_bettertransformer()

       self.assertFalse(any("BetterTransformer" in mod.__class__.__name__ for _, mod in model.named_modules()			)			)

       with tempfile.TemporaryDirectory() as tmpdirname:
           model.save_pretrained(__lowerCamelCase			)

           SCREAMING_SNAKE_CASE										=   AutoModelForSeqaSeqLM.from_pretrained(__lowerCamelCase			)

           self.assertFalse(
               any("BetterTransformer" in mod.__class__.__name__ for _, mod in model_reloaded.named_modules()			)			)

           SCREAMING_SNAKE_CASE										=   model_reloaded.generate(**__lowerCamelCase			)
           self.assertTrue(torch.allclose(__lowerCamelCase     , __lowerCamelCase			)			)





   def      _snake_case		(       self					:			int			):
       SCREAMING_SNAKE_CASE										=   "hf-internal-testing/tiny-random-t5"
       SCREAMING_SNAKE_CASE										=   AutoModelForSeqaSeqLM.from_pretrained(__lowerCamelCase			)

       SCREAMING_SNAKE_CASE										=   model.to_bettertransformer()

       with tempfile.TemporaryDirectory() as tmpdirname:
           with self.assertRaises(__lowerCamelCase			):
               model.save_pretrained(__lowerCamelCase			)

           SCREAMING_SNAKE_CASE										=   model.reverse_bettertransformer()
           model.save_pretrained(__lowerCamelCase			)