



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_    					=	"""sshleifer/mar_enro_6_3_student"""







class 				A_      (					_a    ):



						"""simple docstring"""






						def      UpperCAmelCase__					( self			:Union[str, Any] )    ->      List[Any]:
												super().setUp()

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






						@slow
						@require_torch_gpu
						def      UpperCAmelCase__					( self			:str )    ->      Tuple:
												MarianMTModel.from_pretrained(__lowerCAmelCase )






						@slow
						@require_torch_gpu
						def      UpperCAmelCase__					( self			:List[Any] )    ->      int:
												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(__lowerCAmelCase       ,  str(__lowerCAmelCase ) )
												UpperCAmelCase					=			self.get_auto_remove_tmp_dir()

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

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

												# 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}"""]       ,  __lowerCAmelCase )

												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(__lowerCAmelCase )
												UpperCAmelCase					=			[x for x in contents if x.endswith('.ckpt' )][0]
												UpperCAmelCase					=			os.path.join(args.output_dir       ,  __lowerCAmelCase )
												UpperCAmelCase					=			torch.load(__lowerCAmelCase       ,  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(__lowerCAmelCase ) 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_      (					_a    ):



						"""simple docstring"""






						@timeout_decorator.timeout(6_00 )
						@slow
						@require_torch_gpu
						def      UpperCAmelCase__					( self			:Tuple )    ->      Tuple:
												UpperCAmelCase					=			f"""{self.test_file_dir_str}/test_data/wmt_en_ro"""
												UpperCAmelCase					=			{
												    '--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					=			(
												    (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(__lowerCAmelCase       ,  str(__lowerCAmelCase ) )
												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(__lowerCAmelCase       ,  'argv'       ,  __lowerCAmelCase ):
																		UpperCAmelCase					=			argparse.ArgumentParser()
																		UpperCAmelCase					=			pl.Trainer.add_argparse_args(__lowerCAmelCase )
																		UpperCAmelCase					=			SummarizationDistiller.add_model_specific_args(__lowerCAmelCase       ,  os.getcwd() )
																		UpperCAmelCase					=			parser.parse_args()
																		# assert args.gpus == gpus THIS BREAKS for multi_gpu

																		UpperCAmelCase					=			distill_main(__lowerCAmelCase )

												# 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}"""]       ,  __lowerCAmelCase )

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


def 			_lowerCAmelCase				(       ):
						for n in range(1		,     1000000						):
												yield n * (n + 1) // 2

def 			_lowerCAmelCase				(       lowercase_						):
						UpperCAmelCase					=			1
						UpperCAmelCase					=			2
						while i * i <= n:
												UpperCAmelCase					=			0
												while n % i == 0:
																		n //= i
																		multiplicity += 1
												divisors_count *= multiplicity + 1
												i += 1
						if n > 1:
												divisors_count *= 2
						return divisors_count

def 			_lowerCAmelCase				(       ):
						return next(i for i in triangle_number_generator() if count_divisors(lowercase_						) > 500						)


if __name__ == "__main__":
				print(solution())





