






'''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:      Optional[int]  				=				"sshleifer/mar_enro_6_3_student"






class 							__magic_name__     (	UpperCAmelCase_				):







	"""simple docstring"""



	def        lowerCamelCase__		(							self       )						->  Optional[int]:
							super().setUp()

							lowercase_      :   List[Any]        =			cached_path(
							    'https://cdn-datasets.huggingface.co/translation/wmt_en_ro-tr40k-va0.5k-te0.5k.tar.gz'  ,      extract_compressed_file=_lowercase  ,      )
							lowercase_      :   Optional[Any]        =			f"{data_cached}/wmt_en_ro-tr40k-va0.5k-te0.5k"
	@slow
	@require_torch_gpu
	def        lowerCamelCase__		(							self       )						->  Tuple:
							MarianMTModel.from_pretrained(_lowercase       )







	@slow
	@require_torch_gpu
	def        lowerCamelCase__		(							self       )						->  List[Any]:
							lowercase_      :   Union[str, Any]        =			{
							    '$MAX_LEN': 64,
							    '$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
							lowercase_      :   int        =			(self.test_file_dir / 'train_mbart_cc25_enro.sh').open().read().split('finetune.py'       )[1].strip()
							lowercase_      :   str        =			bash_script.replace('\\\n'  ,      ''       ).strip().replace('"$@"'  ,      ''       )
							for k, v in env_vars_to_replace.items():
													lowercase_      :   Tuple        =			bash_script.replace(_lowercase  ,      str(_lowercase       )       )
							lowercase_      :   Union[str, Any]        =			self.get_auto_remove_tmp_dir()

							# bash_script = bash_script.replace("--fp16 ", "")
							lowercase_      :   Any        =			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

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

							# Check metrics
							lowercase_      :   Dict        =			load_json(model.metrics_save_path       )
							lowercase_      :   Tuple        =			metrics['val'][0]
							lowercase_      :   Tuple        =			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}"]  ,      _lowercase       )

							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
							lowercase_      :   Optional[Any]        =			os.listdir(_lowercase       )
							lowercase_      :   Union[str, Any]        =			[x for x in contents if x.endswith('.ckpt'       )][0]
							lowercase_      :   Any        =			os.path.join(args.output_dir  ,      _lowercase       )
							lowercase_      :   List[Any]        =			torch.load(_lowercase  ,      map_location='cpu'       )
							lowercase_      :   Dict        =			'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:
													lowercase_      :   int        =			{os.path.basename(_lowercase       ) 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 							__magic_name__     (	UpperCAmelCase_				):







	"""simple docstring"""



	@timeout_decorator.timeout(600       )
	@slow
	@require_torch_gpu
	def        lowerCamelCase__		(							self       )						->  str:
							lowercase_      :   str        =			f"{self.test_file_dir_str}/test_data/wmt_en_ro"
							lowercase_      :   List[Any]        =			{
							    '--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
							lowercase_      :   List[Any]        =			(
							    (self.test_file_dir / 'distil_marian_no_teacher.sh').open().read().split('distillation.py'       )[1].strip()
							)
							lowercase_      :   Optional[int]        =			bash_script.replace('\\\n'  ,      ''       ).strip().replace('"$@"'  ,      ''       )
							lowercase_      :   int        =			bash_script.replace('--fp16 '  ,      ' '       )

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

													lowercase_      :   Optional[int]        =			distill_main(_lowercase       )

							# Check metrics
							lowercase_      :   Any        =			load_json(model.metrics_save_path       )
							lowercase_      :   Tuple        =			metrics['val'][0]
							lowercase_      :   Optional[Any]        =			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}"]  ,      _lowercase       )

							# check lightning ckpt can be loaded and has a reasonable statedict
							lowercase_      :   List[Any]        =			os.listdir(_lowercase       )
							lowercase_      :   List[str]        =			[x for x in contents if x.endswith('.ckpt'       )][0]
							lowercase_      :   int        =			os.path.join(args.output_dir  ,      _lowercase       )
							lowercase_      :   str        =			torch.load(_lowercase  ,      map_location='cpu'       )
							lowercase_      :   Any        =			'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:
													lowercase_      :   int        =			{os.path.basename(_lowercase       ) 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 	_UpperCAmelCase    (  a		:	str							)			->			str:





						"""simple docstring"""
						lowercase_      :   Dict        =			0

						# if input_string is "aba" than new_input_string become "a|b|a"
						lowercase_      :   Dict        =			''
						lowercase_      :   Any        =			''

						# append each character + "|" in new_string for range(0, length-1)
						for i in input_string[: len(a							) - 1]:
												new_input_string += i + "|"
						# append last character
						new_input_string += input_string[-1]

						# we will store the starting and ending of previous furthest ending palindromic
						# substring
						lowercase_  ,      lowercase_      :   Dict        =			0, 0

						# length[i] shows the length of palindromic substring with center i
						lowercase_      :   List[Any]        =			[1 for i in range(len(a							)							)]

						# for each character in new_string find corresponding palindromic string
						lowercase_      :   Dict        =			0
						for j in range(len(a							)							):
												lowercase_      :   Tuple        =			1 if j > r else min(length[l + r - j] // 2						,			r - j + 1							)
												while (
												    j - k >= 0
												    and j + k < len(a							)
												    and new_input_string[k + j] == new_input_string[j - k]
												):
																		k += 1

												lowercase_      :   int        =			2 * k - 1

												# does this string is ending after the previously explored end (that is r) ?
												# if yes the update the new r to the last index of this
												if j + k - 1 > r:
																		lowercase_      :   Tuple        =			j - k + 1  # noqa: E741
																		lowercase_      :   Tuple        =			j + k - 1

												# update max_length and start position
												if max_length < length[j]:
																		lowercase_      :   Tuple        =			length[j]
																		lowercase_      :   List[Any]        =			j

    # create that string
						lowercase_      :   str        =			new_input_string[start - max_length // 2 : start + max_length // 2 + 1]
						for i in s:
												if i != "|":
																		output_string += i

						return output_string


if __name__ == "__main__":
						import doctest

						doctest.testmod()
