






"""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


UpperCAmelCase_     :				Tuple         =				"""sshleifer/mar_enro_6_3_student"""




class 							lowerCAmelCase__       (    UpperCAmelCase__				):






						'''simple docstring'''

						def _SCREAMING_SNAKE_CASE				(		self  :						Optional[Any]):




										'''simple docstring'''


										super().setUp()

										SCREAMING_SNAKE_CASE_   :						Optional[int]							=	cached_path(
										    '''https://cdn-datasets.huggingface.co/translation/wmt_en_ro-tr40k-va0.5k-te0.5k.tar.gz'''		,			extract_compressed_file=lowercase_		,			)
										SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	F'{data_cached}/wmt_en_ro-tr40k-va0.5k-te0.5k'
						@slow
						@require_torch_gpu
						def _SCREAMING_SNAKE_CASE				(		self  :						Any):




										'''simple docstring'''


										MarianMTModel.from_pretrained(lowercase_)



						@slow
						@require_torch_gpu
						def _SCREAMING_SNAKE_CASE				(		self  :						Tuple):




										'''simple docstring'''


										SCREAMING_SNAKE_CASE_   :						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
										SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	(self.test_file_dir / '''train_mbart_cc25_enro.sh''').open().read().split('''finetune.py''')[1].strip()
										SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	bash_script.replace('''\\\n'''		,			'''''').strip().replace('''"$@"'''		,			'''''')
										for k, v in env_vars_to_replace.items():
														SCREAMING_SNAKE_CASE_   :						str							=	bash_script.replace(lowercase_		,			str(lowercase_))
										SCREAMING_SNAKE_CASE_   :						Dict							=	self.get_auto_remove_tmp_dir()

										# bash_script = bash_script.replace("--fp16 ", "")
										SCREAMING_SNAKE_CASE_   :						Tuple							=	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

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

										# Check metrics
										SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	load_json(model.metrics_save_path)
										SCREAMING_SNAKE_CASE_   :						str							=	metrics['''val'''][0]
										SCREAMING_SNAKE_CASE_   :						Dict							=	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
										SCREAMING_SNAKE_CASE_   :						Tuple							=	os.listdir(lowercase_)
										SCREAMING_SNAKE_CASE_   :						List[Any]							=	[x for x in contents if x.endswith('''.ckpt''')][0]
										SCREAMING_SNAKE_CASE_   :						Tuple							=	os.path.join(args.output_dir		,			lowercase_)
										SCREAMING_SNAKE_CASE_   :						Tuple							=	torch.load(lowercase_		,			map_location='''cpu''')
										SCREAMING_SNAKE_CASE_   :						str							=	'''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:
														SCREAMING_SNAKE_CASE_   :						Dict							=	{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 							lowerCAmelCase__       (    UpperCAmelCase__				):






						'''simple docstring'''

						@timeout_decorator.timeout(600)
						@slow
						@require_torch_gpu
						def _SCREAMING_SNAKE_CASE				(		self  :						Union[str, Any]):




										'''simple docstring'''


										SCREAMING_SNAKE_CASE_   :						Tuple							=	F'{self.test_file_dir_str}/test_data/wmt_en_ro'
										SCREAMING_SNAKE_CASE_   :						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
										SCREAMING_SNAKE_CASE_   :						str							=	(
										    (self.test_file_dir / '''distil_marian_no_teacher.sh''').open().read().split('''distillation.py''')[1].strip()
										)
										SCREAMING_SNAKE_CASE_   :						str							=	bash_script.replace('''\\\n'''		,			'''''').strip().replace('''"$@"'''		,			'''''')
										SCREAMING_SNAKE_CASE_   :						List[Any]							=	bash_script.replace('''--fp16 '''		,			''' ''')

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

														SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	distill_main(lowercase_)

										# Check metrics
										SCREAMING_SNAKE_CASE_   :						Dict							=	load_json(model.metrics_save_path)
										SCREAMING_SNAKE_CASE_   :						Tuple							=	metrics['''val'''][0]
										SCREAMING_SNAKE_CASE_   :						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
										SCREAMING_SNAKE_CASE_   :						int							=	os.listdir(lowercase_)
										SCREAMING_SNAKE_CASE_   :						int							=	[x for x in contents if x.endswith('''.ckpt''')][0]
										SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	os.path.join(args.output_dir		,			lowercase_)
										SCREAMING_SNAKE_CASE_   :						List[Any]							=	torch.load(lowercase_		,			map_location='''cpu''')
										SCREAMING_SNAKE_CASE_   :						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:
														SCREAMING_SNAKE_CASE_   :						Dict							=	{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"""






from __future__ import annotations



def       _A    (__a					,       __a					,       __a							)				-> int | float:
    """simple docstring"""






    if len(__a							) == 0:
        raise ValueError('''find_max() arg is an empty sequence'''							)
    if (
        left >= len(__a							)
        or left < -len(__a							)
        or right >= len(__a							)
        or right < -len(__a							)
    ):
        raise IndexError('''list index out of range'''							)
    if left == right:
        return nums[left]
    SCREAMING_SNAKE_CASE_   :						str							=	(left + right) >> 1  # the middle
    SCREAMING_SNAKE_CASE_   :						int							=	find_max(__a					,       __a					,       __a							)  # find max in range[left, mid]
    SCREAMING_SNAKE_CASE_   :						str							=	find_max(__a					,       mid + 1					,       __a							)  # find max in range[mid + 1, right]

    return left_max if left_max >= right_max else right_max


if __name__ == "__main__":
  import doctest

  doctest.testmod(verbose=True)


