
"""simple docstring"""




import qiskit


def     __magic_name__      ( __snake_case							:			int	,					__snake_case							:			int    )					->							qiskit.result.counts.Counts:
       lowercase       :							Optional[Any]									=				qiskit.Aer.get_backend("aer_simulator"    )

       # Create a Quantum Circuit acting on the q register
       lowercase       :							Union[str, Any]									=				qiskit.QuantumCircuit(__snake_case	,					__snake_case    )

       # Apply X (NOT) Gate to Qubits 0 & 1
       circuit.x(0    )
       circuit.x(1    )

       # Map the quantum measurement to the classical bits
       circuit.measure([0, 1]	,					[0, 1]    )

       # Execute the circuit on the qasm simulator
       lowercase       :							Any									=				qiskit.execute(__snake_case	,					__snake_case	,					shots=1000    )

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


if __name__ == "__main__":
   _A     :			int  						=			single_qubit_measure(2, 2)
   print(F"Total count for various states are: {counts}")




"""simple docstring"""




# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny model through reduction of a normal pre-trained model, but keeping the
# full vocab, merges file, and thus also resulting in a larger model due to a large vocab size.
# This gives ~3MB in total for all files.
#
# If you want a 50 times smaller than this see `fsmt-make-super-tiny-model.py`, which is slightly more complicated
#
#
# It will be used then as "stas/tiny-wmt19-en-de"

# Build
from transformers import FSMTTokenizer, FSMTConfig, FSMTForConditionalGeneration

_A     :			Dict  						=			"""facebook/wmt19-en-de"""
_A     :			str  						=			FSMTTokenizer.from_pretrained(mname)
# get the correct vocab sizes, etc. from the master model
_A     :			List[Any]  						=			FSMTConfig.from_pretrained(mname)
config.update(
    dict(
        d_model=4,
        encoder_layers=1,
        decoder_layers=1,
        encoder_ffn_dim=4,
        decoder_ffn_dim=4,
        encoder_attention_heads=1,
        decoder_attention_heads=1,
    )
)

_A     :			int  						=			FSMTForConditionalGeneration(config)
print(F"num of params {tiny_model.num_parameters()}")

# Test
_A     :			Dict  						=			tokenizer(["""Making tiny model"""], return_tensors="""pt""")
_A     :			List[Any]  						=			tiny_model(**batch)

print("""test output:""", len(outputs.logits[0]))

# Save
_A     :			List[Any]  						=			"""tiny-wmt19-en-de"""
tiny_model.half()  # makes it smaller
tiny_model.save_pretrained(mname_tiny)
tokenizer.save_pretrained(mname_tiny)

print(F"Generated {mname_tiny}")

# Upload
# transformers-cli upload tiny-wmt19-en-de


