




'''simple docstring'''







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     :   Dict									='sshleifer/mar_enro_6_3_student'


class     _a						(			UpperCAmelCase_				):
       def 					A   (			self		:			Dict       ):



           '''simple docstring'''

           super().setUp()

           UpperCAmelCase     =	cached_path(
               '''https://cdn-datasets.huggingface.co/translation/wmt_en_ro-tr40k-va0.5k-te0.5k.tar.gz'''     ,       extract_compressed_file=_snake_case     ,       )
           UpperCAmelCase     =	f"{data_cached}/wmt_en_ro-tr40k-va0.5k-te0.5k"
       @slow
       @require_torch_gpu
       def 					A   (			self		:			Dict       ):



           '''simple docstring'''

           MarianMTModel.from_pretrained(_snake_case       )







       @slow
       @require_torch_gpu
       def 					A   (			self		:			str       ):



           '''simple docstring'''

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

           # bash_script = bash_script.replace("--fp16 ", "")
           UpperCAmelCase     =	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     =	['''finetune.py'''] + bash_script.split() + args
           with patch.object(_snake_case     ,       '''argv'''     ,       _snake_case       ):
               UpperCAmelCase     =	argparse.ArgumentParser()
               UpperCAmelCase     =	pl.Trainer.add_argparse_args(_snake_case       )
               UpperCAmelCase     =	SummarizationModule.add_model_specific_args(_snake_case     ,       os.getcwd()       )
               UpperCAmelCase     =	parser.parse_args()
               UpperCAmelCase     =	main(_snake_case       )

           # Check metrics
           UpperCAmelCase     =	load_json(model.metrics_save_path       )
           UpperCAmelCase     =	metrics['''val'''][0]
           UpperCAmelCase     =	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''']     ,       0.01       )
           # 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
           UpperCAmelCase     =	os.listdir(_snake_case       )
           UpperCAmelCase     =	[x for x in contents if x.endswith('''.ckpt'''       )][0]
           UpperCAmelCase     =	os.path.join(args.output_dir     ,       _snake_case       )
           UpperCAmelCase     =	torch.load(_snake_case     ,       map_location='''cpu'''       )
           UpperCAmelCase     =	'''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     =	{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 					A   (			self		:			List[Any]       ):



           '''simple docstring'''

           UpperCAmelCase     =	f"{self.test_file_dir_str}/test_data/wmt_en_ro"
           UpperCAmelCase     =	{
               '''--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
           UpperCAmelCase     =	(
               (self.test_file_dir / '''distil_marian_no_teacher.sh''').open().read().split('''distillation.py'''       )[1].strip()
           )
           UpperCAmelCase     =	bash_script.replace('''\\\n'''     ,       ''''''       ).strip().replace('''"$@"'''     ,       ''''''       )
           UpperCAmelCase     =	bash_script.replace('''--fp16 '''     ,       ''' '''       )

           for k, v in env_vars_to_replace.items():
               UpperCAmelCase     =	bash_script.replace(_snake_case     ,       str(_snake_case       )       )
           UpperCAmelCase     =	self.get_auto_remove_tmp_dir()
           UpperCAmelCase     =	bash_script.replace('''--fp16'''     ,       ''''''       )
           UpperCAmelCase     =	6
           UpperCAmelCase     =	(
               ['''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       ):
               UpperCAmelCase     =	argparse.ArgumentParser()
               UpperCAmelCase     =	pl.Trainer.add_argparse_args(_snake_case       )
               UpperCAmelCase     =	SummarizationDistiller.add_model_specific_args(_snake_case     ,       os.getcwd()       )
               UpperCAmelCase     =	parser.parse_args()
               # assert args.gpus == gpus THIS BREAKS for multi_gpu

               UpperCAmelCase     =	distill_main(_snake_case       )

           # Check metrics
           UpperCAmelCase     =	load_json(model.metrics_save_path       )
           UpperCAmelCase     =	metrics['''val'''][0]
           UpperCAmelCase     =	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}"]     ,       _snake_case       )

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










'''simple docstring'''







from .configuration_bert_masked import MaskedBertConfig
from .modeling_bert_masked import (
    MaskedBertForMultipleChoice,
    MaskedBertForQuestionAnswering,
    MaskedBertForSequenceClassification,
    MaskedBertForTokenClassification,
    MaskedBertModel,
)
from .modules import *




