



import argparse
import logging

import numpy as np
import torch

from transformers import AutoModelForCausalLM, AutoTokenizer


logging.basicConfig(
    format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
    datefmt="%m/%d/%Y %H:%M:%S",
    level=logging.INFO,
)
UpperCamelCase_      			=  logging.getLogger(__name__)







def 							A     (						__UpperCAmelCase      )			->			Any:



						'''simple docstring'''






						np.random.seed(args.seed      )
						torch.manual_seed(args.seed      )
						if args.n_gpu > 0:
												torch.cuda.manual_seed_all(args.seed      )







def 							A     (						)			->			Dict:



						'''simple docstring'''






						UpperCAmelCase_					=	argparse.ArgumentParser()
						parser.add_argument(
						    '''--model_name_or_path'''				,   default=__UpperCAmelCase				,   type=__UpperCAmelCase				,   required=__UpperCAmelCase				,   )
						parser.add_argument('''--prompt'''				,   type=__UpperCAmelCase				,   default=''''''      )
						parser.add_argument('''--length'''				,   type=__UpperCAmelCase				,   default=20      )
						parser.add_argument('''--stop_token'''				,   type=__UpperCAmelCase				,   default=__UpperCAmelCase				,   help='''Token at which text generation is stopped'''      )
						parser.add_argument(
						    '''--temperature'''				,   type=__UpperCAmelCase				,   default=1.0				,   help='''temperature of 1.0 has no effect, lower tend toward greedy sampling'''				,   )
						parser.add_argument(
						    '''--repetition_penalty'''				,   type=__UpperCAmelCase				,   default=1.0				,   help='''primarily useful for CTRL model; in that case, use 1.2'''      )
						parser.add_argument('''--k'''				,   type=__UpperCAmelCase				,   default=0      )
						parser.add_argument('''--penalty_alpha'''				,   type=__UpperCAmelCase				,   default=0.0      )
						parser.add_argument('''--p'''				,   type=__UpperCAmelCase				,   default=0.9      )

						parser.add_argument('''--prefix'''				,   type=__UpperCAmelCase				,   default=''''''				,   help='''Text added prior to input.'''      )
						parser.add_argument('''--padding_text'''				,   type=__UpperCAmelCase				,   default=''''''				,   help='''Deprecated, the use of `--prefix` is preferred.'''      )
						parser.add_argument('''--xlm_language'''				,   type=__UpperCAmelCase				,   default=''''''				,   help='''Optional language when used with the XLM model.'''      )

						parser.add_argument('''--seed'''				,   type=__UpperCAmelCase				,   default=42				,   help='''random seed for initialization'''      )
						parser.add_argument('''--no_cuda'''				,   action='''store_true'''				,   help='''Avoid using CUDA when available'''      )
						parser.add_argument(
						    '''--fp16'''				,   action='''store_true'''				,   help='''Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit'''				,   )
						UpperCAmelCase_					=	parser.parse_args()

						UpperCAmelCase_					=	torch.device('''cuda''' if torch.cuda.is_available() and not args.no_cuda else '''cpu'''      )
						UpperCAmelCase_					=	0 if args.no_cuda else torch.cuda.device_count()

						logger.warning(f"device: {args.device}, n_gpu: {args.n_gpu}, 16-bits training: {args.fpaa}"      )

						set_seed(__UpperCAmelCase      )

						# Initialize the model and tokenizer
						UpperCAmelCase_					=	AutoTokenizer.from_pretrained(args.model_name_or_path      )
						UpperCAmelCase_					=	AutoModelForCausalLM.from_pretrained(args.model_name_or_path      )

						# tokenizer = GPT2Tokenizer.from_pretrained(args.model_name_or_path)
						# model = OPTForCausalLM.from_pretrained(args.model_name_or_path)
						model.to(args.device      )

						if args.fpaa:
												model.half()

						logger.info(__UpperCAmelCase      )
						UpperCAmelCase_					=	args.prompt if args.prompt else input('''Model prompt >>> '''      )

						UpperCAmelCase_					=	tokenizer(__UpperCAmelCase				,   return_tensors='''pt'''				,   add_special_tokens=__UpperCAmelCase      )
						UpperCAmelCase_					=	{key: value.to(args.device      ) for key, value in inputs.items()}

						UpperCAmelCase_					=	model.generate(
						    **__UpperCAmelCase				,   max_length=args.length + len(inputs['''input_ids'''][0]      )				,   penalty_alpha=args.penalty_alpha				,   top_k=args.k				,   )

						UpperCAmelCase_					=	[]
						for generated_sequence_idx, generated_sequence in enumerate(__UpperCAmelCase      ):
												print(f"=== GENERATED SEQUENCE {generated_sequence_idx + 1} ==="      )
												UpperCAmelCase_					=	generated_sequence.tolist()

												# Decode text
												UpperCAmelCase_					=	tokenizer.decode(__UpperCAmelCase				,   clean_up_tokenization_spaces=__UpperCAmelCase				,   add_special_tokens=__UpperCAmelCase      )

												# Remove all text after the stop token
												UpperCAmelCase_					=	text[: text.find(args.stop_token      ) if args.stop_token else None]

												# Add the prompt at the beginning of the sequence. Remove the excess text that was used for pre-processing
												UpperCAmelCase_					=	(
												    prompt_text + text[len(tokenizer.decode(inputs['''input_ids'''][0]				,   clean_up_tokenization_spaces=__UpperCAmelCase      )      ) :]
												)

												generated_sequences.append(__UpperCAmelCase      )
												print(__UpperCAmelCase      )

						return generated_sequences


if __name__ == "__main__":
							main()










import numpy as np
import qiskit







def 							A     (						__UpperCAmelCase				,   __UpperCAmelCase      )			->			qiskit.QuantumCircuit:



						'''simple docstring'''






						UpperCAmelCase_					=	qiskit.QuantumCircuit(num_qubits + 1      )

						# First, let's deal with the case in which oracle is balanced
						if case == "balanced":
												# First generate a random number that tells us which CNOTs to
												# wrap in X-gates:
												UpperCAmelCase_					=	np.random.randint(1				,   2**num_qubits      )
												# Next, format 'b' as a binary string of length 'n', padded with zeros:
												UpperCAmelCase_					=	format(__UpperCAmelCase				,   f"0{num_qubits}b"      )
												# Next, we place the first X-gates. Each digit in our binary string
												# corresponds to a qubit, if the digit is 0, we do nothing, if it's 1
												# we apply an X-gate to that qubit:
												for index, bit in enumerate(__UpperCAmelCase      ):
																		if bit == "1":
																								oracle_qc.x(__UpperCAmelCase      )
        # Do the controlled-NOT gates for each qubit, using the output qubit
        # as the target:
												for index in range(__UpperCAmelCase      ):
																		oracle_qc.cx(__UpperCAmelCase				,   __UpperCAmelCase      )
												# Next, place the final X-gates
												for index, bit in enumerate(__UpperCAmelCase      ):
																		if bit == "1":
																								oracle_qc.x(__UpperCAmelCase      )

    # Case in which oracle is constant
						if case == "constant":
												# First decide what the fixed output of the oracle will be
												# (either always 0 or always 1)
												UpperCAmelCase_					=	np.random.randint(2      )
												if output == 1:
																		oracle_qc.x(__UpperCAmelCase      )

						UpperCAmelCase_					=	oracle_qc.to_gate()
						UpperCAmelCase_					=	'''Oracle'''  # To show when we display the circuit
						return oracle_gate







def 							A     (						__UpperCAmelCase				,   __UpperCAmelCase      )			->			qiskit.QuantumCircuit:



						'''simple docstring'''






						UpperCAmelCase_					=	qiskit.QuantumCircuit(num_qubits + 1				,   __UpperCAmelCase      )
						# Set up the output qubit:
						dj_circuit.x(__UpperCAmelCase      )
						dj_circuit.h(__UpperCAmelCase      )
						# And set up the input register:
						for qubit in range(__UpperCAmelCase      ):
												dj_circuit.h(__UpperCAmelCase      )
						# Let's append the oracle gate to our circuit:
						dj_circuit.append(__UpperCAmelCase				,   range(num_qubits + 1      )      )
						# Finally, perform the H-gates again and measure:
						for qubit in range(__UpperCAmelCase      ):
												dj_circuit.h(__UpperCAmelCase      )

						for i in range(__UpperCAmelCase      ):
												dj_circuit.measure(__UpperCAmelCase				,   __UpperCAmelCase      )

						return dj_circuit







def 							A     (						__UpperCAmelCase				,   __UpperCAmelCase      )			->			qiskit.result.counts.Counts:



						'''simple docstring'''






						UpperCAmelCase_					=	qiskit.Aer.get_backend('''aer_simulator'''      )

						UpperCAmelCase_					=	dj_oracle(__UpperCAmelCase				,   __UpperCAmelCase      )
						UpperCAmelCase_					=	dj_algorithm(__UpperCAmelCase				,   __UpperCAmelCase      )

						# Execute the circuit on the simulator
						UpperCAmelCase_					=	qiskit.execute(__UpperCAmelCase				,   __UpperCAmelCase				,   shots=1000      )

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


if __name__ == "__main__":
							print(f"Deutsch Jozsa - Constant Oracle: {deutsch_jozsa('constant', 3)}")
							print(f"Deutsch Jozsa - Balanced Oracle: {deutsch_jozsa('balanced', 3)}")





