
import argparse
import os
import sys
from unittest.mock import patch

import pytorch_lightning as pl
import timeout_decorator
import torch
from distillation import SummarizationDistiller, distill_main
from finetune import SummarizationModule, main

from transformers import MarianMTModel
from transformers.file_utils import cached_path
from transformers.testing_utils import TestCasePlus, require_torch_gpu, slow
from utils import load_json


UpperCamelCase_   	=	"sshleifer/mar_enro_6_3_student"

class       a		(   __UpperCAmelCase					):







							def       UpperCAmelCase__					(						self  :  Dict ):
								"""simple docstring"""







								super().setUp()

								__lowerCAmelCase         =							cached_path(
								    "https://cdn-datasets.huggingface.co/translation/wmt_en_ro-tr40k-va0.5k-te0.5k.tar.gz"      ,				extract_compressed_file=snake_case__      ,				)
								__lowerCAmelCase         =							F"{data_cached}/wmt_en_ro-tr40k-va0.5k-te0.5k"







							@slow
							@require_torch_gpu
							def       UpperCAmelCase__					(						self  :  Dict ):
								"""simple docstring"""







								MarianMTModel.from_pretrained(snake_case__ )







							@slow
							@require_torch_gpu
							def       UpperCAmelCase__					(						self  :  Optional[int] ):
								"""simple docstring"""







								__lowerCAmelCase         =							{
								    "$MAX_LEN": 64,
								    "$BS": 64,
								    "$GAS": 1,
								    "$ENRO_DIR": self.data_dir,
								    "facebook/mbart-large-cc25": MARIAN_MODEL,
								    # "val_check_interval=0.25": "val_check_interval=1.0",
								    "--learning_rate=3e-5": "--learning_rate 3e-4",
								    "--num_train_epochs 6": "--num_train_epochs 1",
								}

								# Clean up bash script
								__lowerCAmelCase         =							(self.test_file_dir / "train_mbart_cc25_enro.sh").open().read().split("finetune.py" )[1].strip()
								__lowerCAmelCase         =							bash_script.replace("\\\n"      ,				"" ).strip().replace("\"$@\""      ,				"" )
								for k, v in env_vars_to_replace.items():
									__lowerCAmelCase         =							bash_script.replace(snake_case__      ,				str(snake_case__ ) )
								__lowerCAmelCase         =							self.get_auto_remove_tmp_dir()

								# bash_script = bash_script.replace("--fp16 ", "")
								__lowerCAmelCase         =							F"\n            --output_dir {output_dir}\n            --tokenizer_name Helsinki-NLP/opus-mt-en-ro\n            --sortish_sampler\n            --do_predict\n            --gpus 1\n            --freeze_encoder\n            --n_train 40000\n            --n_val 500\n            --n_test 500\n            --fp16_opt_level O1\n            --num_sanity_val_steps 0\n            --eval_beams 2\n        ".split()
								# XXX: args.gpus > 1 : handle multi_gpu in the future

								__lowerCAmelCase         =							["finetune.py"] + bash_script.split() + args
								with patch.object(snake_case__      ,				"argv"      ,				snake_case__ ):
									__lowerCAmelCase         =							argparse.ArgumentParser()
									__lowerCAmelCase         =							pl.Trainer.add_argparse_args(snake_case__ )
									__lowerCAmelCase         =							SummarizationModule.add_model_specific_args(snake_case__      ,				os.getcwd() )
									__lowerCAmelCase         =							parser.parse_args()
									__lowerCAmelCase         =							main(snake_case__ )

								# Check metrics
								__lowerCAmelCase         =							load_json(model.metrics_save_path )
								__lowerCAmelCase         =							metrics["val"][0]
								__lowerCAmelCase         =							metrics["val"][-1]
								self.assertEqual(len(metrics["val"] )      ,				(args.max_epochs / args.val_check_interval) )
								assert isinstance(last_step_stats[F"val_avg_{model.val_metric}"]      ,				snake_case__ )

								self.assertGreater(last_step_stats["val_avg_gen_time"]      ,				0.0_1 )
								# model hanging on generate. Maybe bad config was saved. (XXX: old comment/assert?)
								self.assertLessEqual(last_step_stats["val_avg_gen_time"]      ,				1.0 )

								# test learning requirements:

								# 1. BLEU improves over the course of training by more than 2 pts
								self.assertGreater(last_step_stats["val_avg_bleu"] - first_step_stats["val_avg_bleu"]      ,				2 )

								# 2. BLEU finishes above 17
								self.assertGreater(last_step_stats["val_avg_bleu"]      ,				17 )

								# 3. test BLEU and val BLEU within ~1.1 pt.
								self.assertLess(abs(metrics["val"][-1]["val_avg_bleu"] - metrics["test"][-1]["test_avg_bleu"] )      ,				1.1 )

								# check lightning ckpt can be loaded and has a reasonable statedict
								__lowerCAmelCase         =							os.listdir(snake_case__ )
								__lowerCAmelCase         =							[x for x in contents if x.endswith(".ckpt" )][0]
								__lowerCAmelCase         =							os.path.join(args.output_dir      ,				snake_case__ )
								__lowerCAmelCase         =							torch.load(snake_case__      ,				map_location="cpu" )
								__lowerCAmelCase         =							"model.model.decoder.layers.0.encoder_attn_layer_norm.weight"
								assert expected_key in ckpt["state_dict"]
								assert ckpt["state_dict"]["model.model.decoder.layers.0.encoder_attn_layer_norm.weight"].dtype == torch.floataa

								# TODO: turn on args.do_predict when PL bug fixed.
								if args.do_predict:
									__lowerCAmelCase         =							{os.path.basename(snake_case__ ) for p in contents}
									assert "test_generations.txt" in contents
									assert "test_results.txt" in contents
									# assert len(metrics["val"]) ==  desired_n_evals
									assert len(metrics["test"] ) == 1

class       a		(   __UpperCAmelCase					):







							@timeout_decorator.timeout(600 )
							@slow
							@require_torch_gpu
							def       UpperCAmelCase__					(						self  :  List[str] ):
								"""simple docstring"""







								__lowerCAmelCase         =							F"{self.test_file_dir_str}/test_data/wmt_en_ro"
								__lowerCAmelCase         =							{
								    "--fp16_opt_level=O1": "",
								    "$MAX_LEN": 128,
								    "$BS": 16,
								    "$GAS": 1,
								    "$ENRO_DIR": data_dir,
								    "$m": "sshleifer/student_marian_en_ro_6_1",
								    "val_check_interval=0.25": "val_check_interval=1.0",
								}

								# Clean up bash script
								__lowerCAmelCase         =							(
								    (self.test_file_dir / "distil_marian_no_teacher.sh").open().read().split("distillation.py" )[1].strip()
								)
								__lowerCAmelCase         =							bash_script.replace("\\\n"      ,				"" ).strip().replace("\"$@\""      ,				"" )
								__lowerCAmelCase         =							bash_script.replace("--fp16 "      ,				" " )

								for k, v in env_vars_to_replace.items():
									__lowerCAmelCase         =							bash_script.replace(snake_case__      ,				str(snake_case__ ) )
								__lowerCAmelCase         =							self.get_auto_remove_tmp_dir()
								__lowerCAmelCase         =							bash_script.replace("--fp16"      ,				"" )
								__lowerCAmelCase         =							6
								__lowerCAmelCase         =							(
								    ["distillation.py"]
								    + bash_script.split()
								    + [
								        F"--output_dir={output_dir}",
								        "--gpus=1",
								        "--learning_rate=1e-3",
								        F"--num_train_epochs={epochs}",
								        "--warmup_steps=10",
								        "--val_check_interval=1.0",
								        "--do_predict",
								    ]
								)
								with patch.object(snake_case__      ,				"argv"      ,				snake_case__ ):
									__lowerCAmelCase         =							argparse.ArgumentParser()
									__lowerCAmelCase         =							pl.Trainer.add_argparse_args(snake_case__ )
									__lowerCAmelCase         =							SummarizationDistiller.add_model_specific_args(snake_case__      ,				os.getcwd() )
									__lowerCAmelCase         =							parser.parse_args()
									# assert args.gpus == gpus THIS BREAKS for multi_gpu

									__lowerCAmelCase         =							distill_main(snake_case__ )

								# Check metrics
								__lowerCAmelCase         =							load_json(model.metrics_save_path )
								__lowerCAmelCase         =							metrics["val"][0]
								__lowerCAmelCase         =							metrics["val"][-1]
								assert len(metrics["val"] ) >= (args.max_epochs / args.val_check_interval)  # +1 accounts for val_sanity_check

								assert last_step_stats["val_avg_gen_time"] >= 0.0_1

								assert first_step_stats["val_avg_bleu"] < last_step_stats["val_avg_bleu"]  # model learned nothing
								assert 1.0 >= last_step_stats["val_avg_gen_time"]  # model hanging on generate. Maybe bad config was saved.
								assert isinstance(last_step_stats[F"val_avg_{model.val_metric}"]      ,				snake_case__ )

								# check lightning ckpt can be loaded and has a reasonable statedict
								__lowerCAmelCase         =							os.listdir(snake_case__ )
								__lowerCAmelCase         =							[x for x in contents if x.endswith(".ckpt" )][0]
								__lowerCAmelCase         =							os.path.join(args.output_dir      ,				snake_case__ )
								__lowerCAmelCase         =							torch.load(snake_case__      ,				map_location="cpu" )
								__lowerCAmelCase         =							"model.model.decoder.layers.0.encoder_attn_layer_norm.weight"
								assert expected_key in ckpt["state_dict"]
								assert ckpt["state_dict"]["model.model.decoder.layers.0.encoder_attn_layer_norm.weight"].dtype == torch.floataa

								# TODO: turn on args.do_predict when PL bug fixed.
								if args.do_predict:
									__lowerCAmelCase         =							{os.path.basename(snake_case__ ) for p in contents}
									assert "test_generations.txt" in contents
									assert "test_results.txt" in contents
									# assert len(metrics["val"]) ==  desired_n_evals
									assert len(metrics["test"] ) == 1








import numpy as np
import qiskit






def 		_UpperCAmelCase   (	UpperCamelCase:     int = 8			,							UpperCamelCase:     int | None = None  ):






	"""simple docstring"""


	__lowerCAmelCase         =							np.random.default_rng(seed=UpperCamelCase  )

	# Roughly 25% of the qubits will contribute to the key.
	# So we take more than we need.
	__lowerCAmelCase         =							6 * key_len
	# Measurement basis for Alice's qubits.
	__lowerCAmelCase         =							rng.integers(2			,							size=UpperCamelCase  )
	# The set of states Alice will prepare.
	__lowerCAmelCase         =							rng.integers(2			,							size=UpperCamelCase  )
	# Measurement basis for Bob's qubits.
	__lowerCAmelCase         =							rng.integers(2			,							size=UpperCamelCase  )

	# Quantum Circuit to simulate BB84
	__lowerCAmelCase         =							qiskit.QuantumCircuit(UpperCamelCase			,							name="BB84"  )

	# Alice prepares her qubits according to rules above.
	for index, _ in enumerate(UpperCamelCase  ):
		if alice_state[index] == 1:
			bbaa_circ.x(UpperCamelCase  )
		if alice_basis[index] == 1:
			bbaa_circ.h(UpperCamelCase  )
	bbaa_circ.barrier()

	# Bob measures the received qubits according to rules above.
	for index, _ in enumerate(UpperCamelCase  ):
		if bob_basis[index] == 1:
			bbaa_circ.h(UpperCamelCase  )

	bbaa_circ.barrier()
	bbaa_circ.measure_all()

	# Simulate the quantum circuit.
	__lowerCAmelCase         =							qiskit.Aer.get_backend("aer_simulator"  )
	# We only need to run one shot because the key is unique.
	# Multiple shots will produce the same key.
	__lowerCAmelCase         =							qiskit.execute(UpperCamelCase			,							UpperCamelCase			,							shots=1			,							seed_simulator=UpperCamelCase  )
	# Returns the result of measurement.
	__lowerCAmelCase         =							job.result().get_counts(UpperCamelCase  ).most_frequent()

	# Extracting the generated key from the simulation results.
	# Only keep measurement results where Alice and Bob chose the same basis.
	__lowerCAmelCase         =							"".join(
	    [
	        result_bit
	        for alice_basis_bit, bob_basis_bit, result_bit in zip(
	            UpperCamelCase			,							UpperCamelCase			,							UpperCamelCase  )
	        if alice_basis_bit == bob_basis_bit
	    ]  )

	# Get final key. Pad with 0 if too short, otherwise truncate.
	__lowerCAmelCase         =							gen_key[:key_len] if len(UpperCamelCase  ) >= key_len else gen_key.ljust(UpperCamelCase			,							"0"  )
	return key


if __name__ == "__main__":
					print(f'''The generated key is : {bbaa(8, seed=0)}''')
					from doctest import testmod

					testmod()






