
'''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					:							List[str]   =      "sshleifer/mar_enro_6_3_student"


class 		__UpperCAmelCase   (      _lowerCamelCase		):


							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								super().setUp()

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


							@slow
							@require_torch_gpu
							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								MarianMTModel.from_pretrained(lowerCAmelCase_						)




							@slow
							@require_torch_gpu
							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






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

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

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

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


							@timeout_decorator.timeout(6_00						)
							@slow
							@require_torch_gpu
							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






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

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

									_snake_case     		=	distill_main(lowerCAmelCase_						)

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







import glob
import os
import random
from string import ascii_lowercase, digits

import cva
lowercase					:							int   =      ""
lowercase					:							Union[str, Any]   =      ""
lowercase					:							Tuple   =      ""
lowercase					:							Optional[int]   =      1  # (0 is vertical, 1 is horizontal)







def        SCREAMING_SNAKE_CASE__			(     ) ->			None:
	_snake_case		,				_snake_case     		=	get_dataset(__A    ,       __A			)
	print('Processing...'			)
	_snake_case		,				_snake_case		,				_snake_case     		=	update_image_and_anno(__A    ,       __A    ,       __A			)

	for index, image in enumerate(__A			):
		# Get random string code: '7b7ad245cdff75241935e4dd860f3bad'
		_snake_case     		=	random_chars(32			)
		_snake_case     		=	paths[index].split(os.sep			)[-1].rsplit('.'    ,       1			)[0]
		_snake_case     		=	F'{OUTPUT_DIR}/{file_name}_FLIP_{letter_code}'
		cva.imwrite(F'/{file_root}.jpg'    ,       __A    ,       [cva.IMWRITE_JPEG_QUALITY, 85]			)
		print(F'Success {index+1}/{len(__A			)} with {file_name}'			)
		_snake_case     		=	[]
		for anno in new_annos[index]:
			_snake_case     		=	F'{anno[0]} {anno[1]} {anno[2]} {anno[3]} {anno[4]}'
			annos_list.append(__A			)
		with open(F'/{file_root}.txt'    ,       'w'			) as outfile:
			outfile.write('\n'.join(line for line in annos_list			)			)







def        SCREAMING_SNAKE_CASE__			(     __A    ,       __A			) ->			tuple[list, list]:
	_snake_case     		=	[]
	_snake_case     		=	[]
	for label_file in glob.glob(os.path.join(__A    ,       '*.txt'			)			):
		_snake_case     		=	label_file.split(os.sep			)[-1].rsplit('.'    ,       1			)[0]
		with open(__A			) as in_file:
			_snake_case     		=	in_file.readlines()
		_snake_case     		=	os.path.join(__A    ,       F'{label_name}.jpg'			)

		_snake_case     		=	[]
		for obj_list in obj_lists:
			_snake_case     		=	obj_list.rstrip('\n'			).split(' '			)
			boxes.append(
			    [
			        int(obj[0]			),
			        float(obj[1]			),
			        float(obj[2]			),
			        float(obj[3]			),
			        float(obj[4]			),
			    ]			)
		if not boxes:
			continue
		img_paths.append(__A			)
		labels.append(__A			)
	return img_paths, labels







def        SCREAMING_SNAKE_CASE__			(     __A    ,       __A    ,       __A = 1			) ->			tuple[list, list, list]:
	_snake_case     		=	[]
	_snake_case     		=	[]
	_snake_case     		=	[]
	for idx in range(len(__A			)			):
		_snake_case     		=	[]
		_snake_case     		=	img_list[idx]
		path_list.append(__A			)
		_snake_case     		=	anno_list[idx]
		_snake_case     		=	cva.imread(__A			)
		if flip_type == 1:
			_snake_case     		=	cva.flip(__A    ,       __A			)
			for bbox in img_annos:
				_snake_case     		=	1 - bbox[1]
				new_annos.append([bbox[0], x_center_new, bbox[2], bbox[3], bbox[4]]			)
		elif flip_type == 0:
			_snake_case     		=	cva.flip(__A    ,       __A			)
			for bbox in img_annos:
				_snake_case     		=	1 - bbox[2]
				new_annos.append([bbox[0], bbox[1], y_center_new, bbox[3], bbox[4]]			)
		new_annos_lists.append(__A			)
		new_imgs_list.append(__A			)
	return new_imgs_list, new_annos_lists, path_list







def        SCREAMING_SNAKE_CASE__			(     __A = 32			) ->			str:
	assert number_char > 1, "The number of character should greater than 1"
	_snake_case     		=	ascii_lowercase + digits
	return "".join(random.choice(__A			) for _ in range(__A			)			)


if __name__ == "__main__":
					main()
					print("DONE ✅")

