






import os
import tempfile
from functools import partial
from unittest import TestCase
from unittest.mock import patch

import datasets
import datasets.config

from .utils import require_beam


class       __SCREAMING_SNAKE_CASE   (				datasets.BeamBasedBuilder):






						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												return datasets.DatasetInfo(
												    features=datasets.Features({'content': datasets.Value('string'				)}				)       ,  supervised_keys=_UpperCamelCase       ,  )






						def  UpperCamelCase__ (		self       ,  _UpperCamelCase       ,  _UpperCamelCase				):






												"""simple docstring"""
												return [datasets.SplitGenerator(name=datasets.Split.TRAIN       ,  gen_kwargs={'examples': get_test_dummy_examples()}				)]







						def  UpperCamelCase__ (		self       ,  _UpperCamelCase       ,  _UpperCamelCase				):






												"""simple docstring"""
												import apache_beam as beam

												return pipeline | "Load Examples" >> beam.Create(_UpperCamelCase				)




class       __SCREAMING_SNAKE_CASE   (				datasets.BeamBasedBuilder):






						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												return datasets.DatasetInfo(
												    features=datasets.Features({'a': datasets.Sequence({'b': datasets.Value('string'				)}				)}				)       ,  supervised_keys=_UpperCamelCase       ,  )






						def  UpperCamelCase__ (		self       ,  _UpperCamelCase       ,  _UpperCamelCase				):






												"""simple docstring"""
												return [
												    datasets.SplitGenerator(name=datasets.Split.TRAIN       ,  gen_kwargs={'examples': get_test_nested_examples()}				)
												]







						def  UpperCamelCase__ (		self       ,  _UpperCamelCase       ,  _UpperCamelCase				):






												"""simple docstring"""
												import apache_beam as beam

												return pipeline | "Load Examples" >> beam.Create(_UpperCamelCase				)







def 		_UpperCamelCase				(      )					->				List[Any]:

						"""simple docstring"""



						return [(i, {"content": content}) for i, content in enumerate(['foo', 'bar', 'foobar']						)]







def 		_UpperCamelCase				(      )					->				Optional[int]:

						"""simple docstring"""



						return [(i, {"a": {"b": [content]}}) for i, content in enumerate(['foo', 'bar', 'foobar']						)]


class       __SCREAMING_SNAKE_CASE   (				SCREAMING_SNAKE_CASE__):






						@require_beam
						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												lowerCAmelCase__        =  len(get_test_dummy_examples()				)
												with tempfile.TemporaryDirectory() as tmp_cache_dir:
																		lowerCAmelCase__        =  DummyBeamDataset(cache_dir=_UpperCamelCase       ,  beam_runner='DirectRunner'				)
																		builder.download_and_prepare()
																		self.assertTrue(
																		    os.path.exists(
																		        os.path.join(_UpperCamelCase       ,  builder.name       ,  'default'       ,  '0.0.0'       ,  F"{builder.name}-train.arrow"				)				)				)
																		self.assertDictEqual(builder.info.features       ,  datasets.Features({'content': datasets.Value('string'				)}				)				)
																		lowerCAmelCase__        =  builder.as_dataset()
																		self.assertEqual(dset['train'].num_rows       ,  _UpperCamelCase				)
																		self.assertEqual(dset['train'].info.splits['train'].num_examples       ,  _UpperCamelCase				)
																		self.assertDictEqual(dset['train'][0]       ,  get_test_dummy_examples()[0][1]				)
																		self.assertDictEqual(
																		    dset['train'][expected_num_examples - 1]       ,  get_test_dummy_examples()[expected_num_examples - 1][1]				)
																		self.assertTrue(
																		    os.path.exists(os.path.join(_UpperCamelCase       ,  builder.name       ,  'default'       ,  '0.0.0'       ,  'dataset_info.json'				)				)				)
																		del dset






						@require_beam
						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												import apache_beam as beam

												lowerCAmelCase__        =  beam.io.parquetio.WriteToParquet

												lowerCAmelCase__        =  len(get_test_dummy_examples()				)
												with tempfile.TemporaryDirectory() as tmp_cache_dir:
																		lowerCAmelCase__        =  DummyBeamDataset(cache_dir=_UpperCamelCase       ,  beam_runner='DirectRunner'				)
																		with patch('apache_beam.io.parquetio.WriteToParquet'				) as write_parquet_mock:
																								lowerCAmelCase__        =  partial(_UpperCamelCase       ,  num_shards=2				)
																								builder.download_and_prepare()
																		self.assertTrue(
																		    os.path.exists(
																		        os.path.join(
																		            _UpperCamelCase       ,  builder.name       ,  'default'       ,  '0.0.0'       ,  F"{builder.name}-train-00000-of-00002.arrow"				)				)				)
																		self.assertTrue(
																		    os.path.exists(
																		        os.path.join(
																		            _UpperCamelCase       ,  builder.name       ,  'default'       ,  '0.0.0'       ,  F"{builder.name}-train-00000-of-00002.arrow"				)				)				)
																		self.assertDictEqual(builder.info.features       ,  datasets.Features({'content': datasets.Value('string'				)}				)				)
																		lowerCAmelCase__        =  builder.as_dataset()
																		self.assertEqual(dset['train'].num_rows       ,  _UpperCamelCase				)
																		self.assertEqual(dset['train'].info.splits['train'].num_examples       ,  _UpperCamelCase				)
																		# Order is not preserved when sharding, so we just check that all the elements are there
																		self.assertListEqual(sorted(dset['train']['content']				)       ,  sorted(['foo', 'bar', 'foobar']				)				)
																		self.assertTrue(
																		    os.path.exists(os.path.join(_UpperCamelCase       ,  builder.name       ,  'default'       ,  '0.0.0'       ,  'dataset_info.json'				)				)				)
																		del dset






						@require_beam
						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												with tempfile.TemporaryDirectory() as tmp_cache_dir:
																		lowerCAmelCase__        =  DummyBeamDataset(cache_dir=_UpperCamelCase				)
																		self.assertRaises(datasets.builder.MissingBeamOptions       ,  builder.download_and_prepare				)







						@require_beam
						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												lowerCAmelCase__        =  len(get_test_nested_examples()				)
												with tempfile.TemporaryDirectory() as tmp_cache_dir:
																		lowerCAmelCase__        =  NestedBeamDataset(cache_dir=_UpperCamelCase       ,  beam_runner='DirectRunner'				)
																		builder.download_and_prepare()
																		self.assertTrue(
																		    os.path.exists(
																		        os.path.join(_UpperCamelCase       ,  builder.name       ,  'default'       ,  '0.0.0'       ,  F"{builder.name}-train.arrow"				)				)				)
																		self.assertDictEqual(
																		    builder.info.features       ,  datasets.Features({'a': datasets.Sequence({'b': datasets.Value('string'				)}				)}				)				)
																		lowerCAmelCase__        =  builder.as_dataset()
																		self.assertEqual(dset['train'].num_rows       ,  _UpperCamelCase				)
																		self.assertEqual(dset['train'].info.splits['train'].num_examples       ,  _UpperCamelCase				)
																		self.assertDictEqual(dset['train'][0]       ,  get_test_nested_examples()[0][1]				)
																		self.assertDictEqual(
																		    dset['train'][expected_num_examples - 1]       ,  get_test_nested_examples()[expected_num_examples - 1][1]				)
																		self.assertTrue(
																		    os.path.exists(os.path.join(_UpperCamelCase       ,  builder.name       ,  'default'       ,  '0.0.0'       ,  'dataset_info.json'				)				)				)
																		del dset













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


__snake_case      :      Tuple             =       """sshleifer/mar_enro_6_3_student"""


class       __SCREAMING_SNAKE_CASE   (				__lowercase):






						def  UpperCamelCase__ (		self				):






												"""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=_UpperCamelCase       ,  )
												lowerCAmelCase__        =  F"{data_cached}/wmt_en_ro-tr40k-va0.5k-te0.5k"






						@slow
						@require_torch_gpu
						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												MarianMTModel.from_pretrained(_UpperCamelCase				)







						@slow
						@require_torch_gpu
						def  UpperCamelCase__ (		self				):






												"""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(_UpperCamelCase       ,  str(_UpperCamelCase				)				)
												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(_UpperCamelCase       ,  'argv'       ,  _UpperCamelCase				):
																		lowerCAmelCase__        =  argparse.ArgumentParser()
																		lowerCAmelCase__        =  pl.Trainer.add_argparse_args(_UpperCamelCase				)
																		lowerCAmelCase__        =  SummarizationModule.add_model_specific_args(_UpperCamelCase       ,  os.getcwd()				)
																		lowerCAmelCase__        =  parser.parse_args()
																		lowerCAmelCase__        =  main(_UpperCamelCase				)

												# 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}"]       ,  _UpperCamelCase				)

												self.assertGreater(last_step_stats['val_avg_gen_time']       ,  0.01				)
												# 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(_UpperCamelCase				)
												lowerCAmelCase__        =  [x for x in contents if x.endswith('.ckpt'				)][0]
												lowerCAmelCase__        =  os.path.join(args.output_dir       ,  _UpperCamelCase				)
												lowerCAmelCase__        =  torch.load(_UpperCamelCase       ,  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(_UpperCamelCase				) 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       __SCREAMING_SNAKE_CASE   (				__lowercase):






						@timeout_decorator.timeout(6_00				)
						@slow
						@require_torch_gpu
						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												lowerCAmelCase__        =  F"{self.test_file_dir_str}/test_data/wmt_en_ro"
												lowerCAmelCase__        =  {
												    '--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
												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(_UpperCamelCase       ,  str(_UpperCamelCase				)				)
												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(_UpperCamelCase       ,  'argv'       ,  _UpperCamelCase				):
																		lowerCAmelCase__        =  argparse.ArgumentParser()
																		lowerCAmelCase__        =  pl.Trainer.add_argparse_args(_UpperCamelCase				)
																		lowerCAmelCase__        =  SummarizationDistiller.add_model_specific_args(_UpperCamelCase       ,  os.getcwd()				)
																		lowerCAmelCase__        =  parser.parse_args()
																		# assert args.gpus == gpus THIS BREAKS for multi_gpu

																		lowerCAmelCase__        =  distill_main(_UpperCamelCase				)

												# 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"] >= 0.01

												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}"]       ,  _UpperCamelCase				)

												# check lightning ckpt can be loaded and has a reasonable statedict
												lowerCAmelCase__        =  os.listdir(_UpperCamelCase				)
												lowerCAmelCase__        =  [x for x in contents if x.endswith('.ckpt'				)][0]
												lowerCAmelCase__        =  os.path.join(args.output_dir       ,  _UpperCamelCase				)
												lowerCAmelCase__        =  torch.load(_UpperCamelCase       ,  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(_UpperCamelCase				) 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





