






# 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

__UpperCamelCase					:		Optional[Any]	  =						"""facebook/wmt19-en-de"""
__UpperCamelCase					:		int	  =						FSMTTokenizer.from_pretrained(mname)
# get the correct vocab sizes, etc. from the master model
__UpperCamelCase					:		Optional[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,
    )
)

__UpperCamelCase					:		List[Any]	  =						FSMTForConditionalGeneration(config)
print(f'''num of params {tiny_model.num_parameters()}''')

# Test
__UpperCamelCase					:		Optional[Any]	  =						tokenizer(["""Making tiny model"""], return_tensors="""pt""")
__UpperCamelCase					:		int	  =						tiny_model(**batch)

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

# Save
__UpperCamelCase					:		Optional[int]	  =						"""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












import qiskit
def      a_ (      _A = 2  )			->			qiskit.result.counts.Counts:







				"""simple docstring"""
				snake_case__									=     qubits

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

				# Creating a Quantum Circuit acting on the q register
				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
				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)}''')




