'''simple docstring'''

from __future__ import annotations

from collections import namedtuple
from dataclasses import dataclass
@dataclass
class   SCREAMING_SNAKE_CASE			:







		"""simple docstring"""
		lowercase   :      int
		lowercase   :      TreeNode | None       =							None
		lowercase   :      TreeNode | None       =							None

lowercase_     		= namedtuple('''CoinsDistribResult''', '''moves excess''')



def 							UpperCamelCase__			(			a__      ):


						'''simple docstring'''






						if root is None:
												return 0

						# Validation
						def count_nodes(a__      ) -> int:
												if node is None:
																		return 0

												return count_nodes(node.left      ) + count_nodes(node.right      ) + 1

						def count_coins(a__      ) -> int:
												if node is None:
																		return 0

												return count_coins(node.left      ) + count_coins(node.right      ) + node.data

						if count_nodes(a__      ) != count_coins(a__      ):
												raise ValueError('The nodes number should be same as the number of coins'      )

						# Main calculation
						def get_distrib(a__      ) -> CoinsDistribResult:

												if node is None:
																		return CoinsDistribResult(0			,      1      )

												_lowerCAmelCase						,				_lowerCAmelCase										=get_distrib(node.left      )
												_lowerCAmelCase						,				_lowerCAmelCase										=get_distrib(node.right      )

												_lowerCAmelCase										=1 - left_distrib_excess
												_lowerCAmelCase										=1 - right_distrib_excess

												_lowerCAmelCase										=(
												    left_distrib_moves
												    + right_distrib_moves
												    + abs(a__      )
												    + abs(a__      )
												)
												_lowerCAmelCase										=node.data - coins_to_left - coins_to_right

												return CoinsDistribResult(a__			,      a__      )

						return get_distrib(a__      )[0]


if __name__ == "__main__":
							import doctest

							doctest.testmod()






'''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


lowercase_     		= '''sshleifer/mar_enro_6_3_student'''
class   SCREAMING_SNAKE_CASE			(						__lowercase):







		"""simple docstring"""






		def 		UpperCamelCase__       (   self				) ->     Optional[Any]:
								super().setUp()

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






		@slow
		@require_torch_gpu
		def 		UpperCamelCase__       (   self				) ->     Union[str, Any]:
								MarianMTModel.from_pretrained(__A				)






		@slow
		@require_torch_gpu
		def 		UpperCamelCase__       (   self				) ->     Union[str, Any]:
								_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(__A		,						str(__A				)				)
								_lowerCAmelCase										=self.get_auto_remove_tmp_dir()

								# bash_script = bash_script.replace("--fp16 ", "")
								_lowerCAmelCase										=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

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

								# 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}''']		,						__A				)

								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(__A				)
								_lowerCAmelCase										=[x for x in contents if x.endswith('.ckpt'				)][0]
								_lowerCAmelCase										=os.path.join(args.output_dir		,						__A				)
								_lowerCAmelCase										=torch.load(__A		,						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(__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   SCREAMING_SNAKE_CASE			(						__lowercase):







		"""simple docstring"""






		@timeout_decorator.timeout(600				)
		@slow
		@require_torch_gpu
		def 		UpperCamelCase__       (   self				) ->     Tuple:
								_lowerCAmelCase										=F'''{self.test_file_dir_str}/test_data/wmt_en_ro'''
								_lowerCAmelCase										={
								    '--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
								_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(__A		,						str(__A				)				)
								_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(__A		,						'argv'		,						__A				):
														_lowerCAmelCase										=argparse.ArgumentParser()
														_lowerCAmelCase										=pl.Trainer.add_argparse_args(__A				)
														_lowerCAmelCase										=SummarizationDistiller.add_model_specific_args(__A		,						os.getcwd()				)
														_lowerCAmelCase										=parser.parse_args()
														# assert args.gpus == gpus THIS BREAKS for multi_gpu

														_lowerCAmelCase										=distill_main(__A				)

								# 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}''']		,						__A				)

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





