



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


__A   			="sshleifer/mar_enro_6_3_student"

class 		UpperCAmelCase__			(			__UpperCamelCase   ):




			'''simple docstring'''






			def  snake_case__     (   self							: Optional[int] ):
					'''simple docstring'''





					super().setUp()

					__UpperCAmelCase    :     List[Any]													=	cached_path(
					    '''https://cdn-datasets.huggingface.co/translation/wmt_en_ro-tr40k-va0.5k-te0.5k.tar.gz'''       ,       extract_compressed_file=a_       ,       )
					__UpperCAmelCase    :     List[str]													=	F'{data_cached}/wmt_en_ro-tr40k-va0.5k-te0.5k'




			@slow
			@require_torch_gpu
			def  snake_case__     (   self							: List[Any] ):
					'''simple docstring'''





					MarianMTModel.from_pretrained(a_ )




			@slow
			@require_torch_gpu
			def  snake_case__     (   self							: Optional[Any] ):
					'''simple docstring'''





					__UpperCAmelCase    :     int													=	{
					    '''$MAX_LEN''': 64,
					    '''$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
					__UpperCAmelCase    :     Any													=	(self.test_file_dir / '''train_mbart_cc25_enro.sh''').open().read().split('''finetune.py''' )[1].strip()
					__UpperCAmelCase    :     Optional[int]													=	bash_script.replace('''\\\n'''       ,       '''''' ).strip().replace('''"$@"'''       ,       '''''' )
					for k, v in env_vars_to_replace.items():
							__UpperCAmelCase    :     Any													=	bash_script.replace(a_       ,       str(a_ ) )
					__UpperCAmelCase    :     Dict													=	self.get_auto_remove_tmp_dir()

					# bash_script = bash_script.replace("--fp16 ", "")
					__UpperCAmelCase    :     int													=	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

					__UpperCAmelCase    :     List[str]													=	['''finetune.py'''] + bash_script.split() + args
					with patch.object(a_       ,       '''argv'''       ,       a_ ):
							__UpperCAmelCase    :     Optional[Any]													=	argparse.ArgumentParser()
							__UpperCAmelCase    :     Optional[int]													=	pl.Trainer.add_argparse_args(a_ )
							__UpperCAmelCase    :     Union[str, Any]													=	SummarizationModule.add_model_specific_args(a_       ,       os.getcwd() )
							__UpperCAmelCase    :     List[Any]													=	parser.parse_args()
							__UpperCAmelCase    :     List[Any]													=	main(a_ )

					# Check metrics
					__UpperCAmelCase    :     str													=	load_json(model.metrics_save_path )
					__UpperCAmelCase    :     Dict													=	metrics['''val'''][0]
					__UpperCAmelCase    :     List[str]													=	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}']       ,       a_ )

					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
					__UpperCAmelCase    :     Dict													=	os.listdir(a_ )
					__UpperCAmelCase    :     List[Any]													=	[x for x in contents if x.endswith('''.ckpt''' )][0]
					__UpperCAmelCase    :     List[Any]													=	os.path.join(args.output_dir       ,       a_ )
					__UpperCAmelCase    :     Union[str, Any]													=	torch.load(a_       ,       map_location='''cpu''' )
					__UpperCAmelCase    :     Tuple													=	'''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:
							__UpperCAmelCase    :     Any													=	{os.path.basename(a_ ) 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 		UpperCAmelCase__			(			__UpperCamelCase   ):




			'''simple docstring'''






			@timeout_decorator.timeout(6_00 )
			@slow
			@require_torch_gpu
			def  snake_case__     (   self							: Dict ):
					'''simple docstring'''





					__UpperCAmelCase    :     Any													=	F'{self.test_file_dir_str}/test_data/wmt_en_ro'
					__UpperCAmelCase    :     List[Any]													=	{
					    '''--fp16_opt_level=O1''': '''''',
					    '''$MAX_LEN''': 1_28,
					    '''$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
					__UpperCAmelCase    :     Any													=	(
					    (self.test_file_dir / '''distil_marian_no_teacher.sh''').open().read().split('''distillation.py''' )[1].strip()
					)
					__UpperCAmelCase    :     Any													=	bash_script.replace('''\\\n'''       ,       '''''' ).strip().replace('''"$@"'''       ,       '''''' )
					__UpperCAmelCase    :     Optional[int]													=	bash_script.replace('''--fp16 '''       ,       ''' ''' )

					for k, v in env_vars_to_replace.items():
							__UpperCAmelCase    :     List[Any]													=	bash_script.replace(a_       ,       str(a_ ) )
					__UpperCAmelCase    :     int													=	self.get_auto_remove_tmp_dir()
					__UpperCAmelCase    :     Tuple													=	bash_script.replace('''--fp16'''       ,       '''''' )
					__UpperCAmelCase    :     Dict													=	6
					__UpperCAmelCase    :     Optional[Any]													=	(
					    ['''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(a_       ,       '''argv'''       ,       a_ ):
							__UpperCAmelCase    :     Optional[Any]													=	argparse.ArgumentParser()
							__UpperCAmelCase    :     Union[str, Any]													=	pl.Trainer.add_argparse_args(a_ )
							__UpperCAmelCase    :     Optional[int]													=	SummarizationDistiller.add_model_specific_args(a_       ,       os.getcwd() )
							__UpperCAmelCase    :     List[Any]													=	parser.parse_args()
							# assert args.gpus == gpus THIS BREAKS for multi_gpu

							__UpperCAmelCase    :     str													=	distill_main(a_ )

					# Check metrics
					__UpperCAmelCase    :     str													=	load_json(model.metrics_save_path )
					__UpperCAmelCase    :     int													=	metrics['''val'''][0]
					__UpperCAmelCase    :     Tuple													=	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}']       ,       a_ )

					# check lightning ckpt can be loaded and has a reasonable statedict
					__UpperCAmelCase    :     Any													=	os.listdir(a_ )
					__UpperCAmelCase    :     Optional[Any]													=	[x for x in contents if x.endswith('''.ckpt''' )][0]
					__UpperCAmelCase    :     Tuple													=	os.path.join(args.output_dir       ,       a_ )
					__UpperCAmelCase    :     Optional[int]													=	torch.load(a_       ,       map_location='''cpu''' )
					__UpperCAmelCase    :     int													=	'''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:
							__UpperCAmelCase    :     List[Any]													=	{os.path.basename(a_ ) 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 qiskit

def        a						(      _UpperCAmelCase							:					int ,   _UpperCAmelCase							:					int						):







		'''simple docstring'''





		__UpperCAmelCase    :     List[str]													=	qiskit.Aer.get_backend('''aer_simulator'''						)

		__UpperCAmelCase    :     Optional[Any]													=	qiskit.QuantumCircuit(4 ,   2						)
		# encode inputs in qubits 0 and 1
		if bita == 1:
				qc_ha.x(0						)
		if bita == 1:
				qc_ha.x(1						)
		qc_ha.barrier()

		# use cnots to write XOR of the inputs on qubit2
		qc_ha.cx(0 ,   2						)
		qc_ha.cx(1 ,   2						)

		# use ccx / toffoli gate to write AND of the inputs on qubit3
		qc_ha.ccx(0 ,   1 ,   3						)
		qc_ha.barrier()

		# extract outputs
		qc_ha.measure(2 ,   0						)  # extract XOR value
		qc_ha.measure(3 ,   1						)  # extract AND value

		# Execute the circuit on the qasm simulator
		__UpperCAmelCase    :     str													=	qiskit.execute(_UpperCAmelCase ,   _UpperCAmelCase ,   shots=10_00						)

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


if __name__ == "__main__":
				__A   			=half_adder(1, 1)
				print(f'''Half Adder Output Qubit Counts: {counts}''')





