

'''simple docstring'''



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

from transformers.testing_utils import TestCasePlus, get_gpu_count, slow


_lowercase    =		[
    os.path.join(os.path.dirname(__file__), dirname)
    for dirname in [
        """text-classification""",
        """language-modeling""",
        """summarization""",
        """token-classification""",
        """question-answering""",
    ]
]
sys.path.extend(SRC_DIRS)


if SRC_DIRS is not None:
						import run_clm_flax
						import run_flax_glue
						import run_flax_ner
						import run_mlm_flax
						import run_qa
						import run_summarization_flax
						import run_ta_mlm_flax


logging.basicConfig(level=logging.DEBUG)

_lowercase    =		logging.getLogger()


def A				():
						_lowerCAmelCase  						=			argparse.ArgumentParser()
						parser.add_argument("""-f"""		)
						_lowerCAmelCase  						=			parser.parse_args()
						return args.f


def A				(__lowerCamelCase				:int			,     __lowerCamelCase				:Optional[Any]="eval"		):
						_lowerCAmelCase  						=			os.path.join(__lowerCamelCase			,     f'{split}_results.json'		)
						if os.path.exists(__lowerCamelCase		):
												with open(__lowerCamelCase			,     """r"""		) as f:
																		return json.load(__lowerCamelCase		)
						raise ValueError(f'can\'t find {path}'		)


_lowercase    =		logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)





class   UpperCAmelCase_					(		_SCREAMING_SNAKE_CASE			):






						'''simple docstring'''






						def  _lowercase     ( self				):

												"""simple docstring"""



												_lowerCAmelCase  						=			self.get_auto_remove_tmp_dir()
												_lowerCAmelCase  						=			F'\n            run_glue.py\n            --model_name_or_path distilbert-base-uncased\n            --output_dir {tmp_dir}\n            --train_file ./tests/fixtures/tests_samples/MRPC/train.csv\n            --validation_file ./tests/fixtures/tests_samples/MRPC/dev.csv\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n            --learning_rate=1e-4\n            --eval_steps=2\n            --warmup_steps=2\n            --seed=42\n            --max_seq_length=128\n            '.split()

												with patch.object(_lowercase   ,       """argv"""   ,       _lowercase				):
																		run_flax_glue.main()
																		_lowerCAmelCase  						=			get_results(_lowercase				)
																		self.assertGreaterEqual(result["""eval_accuracy"""]   ,       0.75				)




						@slow
						def  _lowercase     ( self				):

												"""simple docstring"""



												_lowerCAmelCase  						=			self.get_auto_remove_tmp_dir()
												_lowerCAmelCase  						=			F'\n            run_clm_flax.py\n            --model_name_or_path distilgpt2\n            --train_file ./tests/fixtures/sample_text.txt\n            --validation_file ./tests/fixtures/sample_text.txt\n            --do_train\n            --do_eval\n            --block_size 128\n            --per_device_train_batch_size 4\n            --per_device_eval_batch_size 4\n            --num_train_epochs 2\n            --logging_steps 2 --eval_steps 2\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            '.split()

												with patch.object(_lowercase   ,       """argv"""   ,       _lowercase				):
																		run_clm_flax.main()
																		_lowerCAmelCase  						=			get_results(_lowercase				)
																		self.assertLess(result["""eval_perplexity"""]   ,       100				)




						@slow
						def  _lowercase     ( self				):

												"""simple docstring"""



												_lowerCAmelCase  						=			self.get_auto_remove_tmp_dir()
												_lowerCAmelCase  						=			F'\n            run_summarization.py\n            --model_name_or_path t5-small\n            --train_file tests/fixtures/tests_samples/xsum/sample.json\n            --validation_file tests/fixtures/tests_samples/xsum/sample.json\n            --test_file tests/fixtures/tests_samples/xsum/sample.json\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            --num_train_epochs=3\n            --warmup_steps=8\n            --do_train\n            --do_eval\n            --do_predict\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n            --predict_with_generate\n        '.split()

												with patch.object(_lowercase   ,       """argv"""   ,       _lowercase				):
																		run_summarization_flax.main()
																		_lowerCAmelCase  						=			get_results(_lowercase   ,       split="""test"""				)
																		self.assertGreaterEqual(result["""test_rouge1"""]   ,       10				)
																		self.assertGreaterEqual(result["""test_rouge2"""]   ,       2				)
																		self.assertGreaterEqual(result["""test_rougeL"""]   ,       7				)
																		self.assertGreaterEqual(result["""test_rougeLsum"""]   ,       7				)




						@slow
						def  _lowercase     ( self				):

												"""simple docstring"""



												_lowerCAmelCase  						=			self.get_auto_remove_tmp_dir()
												_lowerCAmelCase  						=			F'\n            run_mlm.py\n            --model_name_or_path distilroberta-base\n            --train_file ./tests/fixtures/sample_text.txt\n            --validation_file ./tests/fixtures/sample_text.txt\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            --max_seq_length 128\n            --per_device_train_batch_size 4\n            --per_device_eval_batch_size 4\n            --logging_steps 2 --eval_steps 2\n            --do_train\n            --do_eval\n            --num_train_epochs=1\n        '.split()

												with patch.object(_lowercase   ,       """argv"""   ,       _lowercase				):
																		run_mlm_flax.main()
																		_lowerCAmelCase  						=			get_results(_lowercase				)
																		self.assertLess(result["""eval_perplexity"""]   ,       42				)




						@slow
						def  _lowercase     ( self				):

												"""simple docstring"""



												_lowerCAmelCase  						=			self.get_auto_remove_tmp_dir()
												_lowerCAmelCase  						=			F'\n            run_t5_mlm_flax.py\n            --model_name_or_path t5-small\n            --train_file ./tests/fixtures/sample_text.txt\n            --validation_file ./tests/fixtures/sample_text.txt\n            --do_train\n            --do_eval\n            --max_seq_length 128\n            --per_device_train_batch_size 4\n            --per_device_eval_batch_size 4\n            --num_train_epochs 2\n            --logging_steps 2 --eval_steps 2\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            '.split()

												with patch.object(_lowercase   ,       """argv"""   ,       _lowercase				):
																		run_ta_mlm_flax.main()
																		_lowerCAmelCase  						=			get_results(_lowercase				)
																		self.assertGreaterEqual(result["""eval_accuracy"""]   ,       0.42				)




						@slow
						def  _lowercase     ( self				):

												"""simple docstring"""



												_lowerCAmelCase  						=			7 if get_gpu_count() > 1 else 2

												_lowerCAmelCase  						=			self.get_auto_remove_tmp_dir()
												_lowerCAmelCase  						=			F'\n            run_flax_ner.py\n            --model_name_or_path bert-base-uncased\n            --train_file tests/fixtures/tests_samples/conll/sample.json\n            --validation_file tests/fixtures/tests_samples/conll/sample.json\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            --do_train\n            --do_eval\n            --warmup_steps=2\n            --learning_rate=2e-4\n            --logging_steps 2 --eval_steps 2\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=2\n            --num_train_epochs={epochs}\n            --seed 7\n        '.split()

												with patch.object(_lowercase   ,       """argv"""   ,       _lowercase				):
																		run_flax_ner.main()
																		_lowerCAmelCase  						=			get_results(_lowercase				)
																		self.assertGreaterEqual(result["""eval_accuracy"""]   ,       0.75				)
																		self.assertGreaterEqual(result["""eval_f1"""]   ,       0.3				)




						@slow
						def  _lowercase     ( self				):

												"""simple docstring"""



												_lowerCAmelCase  						=			self.get_auto_remove_tmp_dir()
												_lowerCAmelCase  						=			F'\n            run_qa.py\n            --model_name_or_path bert-base-uncased\n            --version_2_with_negative\n            --train_file tests/fixtures/tests_samples/SQUAD/sample.json\n            --validation_file tests/fixtures/tests_samples/SQUAD/sample.json\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            --num_train_epochs=3\n            --warmup_steps=2\n            --do_train\n            --do_eval\n            --logging_steps 2 --eval_steps 2\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n        '.split()

												with patch.object(_lowercase   ,       """argv"""   ,       _lowercase				):
																		run_qa.main()
																		_lowerCAmelCase  						=			get_results(_lowercase				)
																		self.assertGreaterEqual(result["""eval_f1"""]   ,       30				)
																		self.assertGreaterEqual(result["""eval_exact"""]   ,       30				)


'''simple docstring'''



import numpy as np
import qiskit


def A				(__lowerCamelCase				:int = 8			,     __lowerCamelCase				:int | None = None		):
						_lowerCAmelCase  						=			np.random.default_rng(seed=__lowerCamelCase		)

						# 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=__lowerCamelCase		)
						# The set of states Alice will prepare.
						_lowerCAmelCase  						=			rng.integers(2			,     size=__lowerCamelCase		)
						# Measurement basis for Bob's qubits.
						_lowerCAmelCase  						=			rng.integers(2			,     size=__lowerCamelCase		)

						# Quantum Circuit to simulate BB84
						_lowerCAmelCase  						=			qiskit.QuantumCircuit(__lowerCamelCase			,     name="""BB84"""		)

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

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

						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(__lowerCamelCase			,     __lowerCamelCase			,     shots=1			,     seed_simulator=__lowerCamelCase		)
						# Returns the result of measurement.
						_lowerCAmelCase  						=			job.result().get_counts(__lowerCamelCase		).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(
						            __lowerCamelCase			,     __lowerCamelCase			,     __lowerCamelCase		)
						        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(__lowerCamelCase		) >= key_len else gen_key.ljust(__lowerCamelCase			,     """0"""		)
						return key


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

						testmod()
