





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       :   str					= """sshleifer/mar_enro_6_3_student"""


class     A     (_UpperCamelCase       ):
			'''simple docstring'''




			def   a_			(     self  :       Dict  )      ->      Optional[Any]:







					"""simple docstring"""






					super().setUp()

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






			@slow
			@require_torch_gpu
			def   a_			(     self  :       str  )      ->      Union[str, Any]:







					"""simple docstring"""






					MarianMTModel.from_pretrained(lowerCamelCase_  )







			@slow
			@require_torch_gpu
			def   a_			(     self  :       Optional[Any]  )      ->      List[Any]:







					"""simple docstring"""






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

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

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

					# Check metrics
					A__     =  load_json(model.metrics_save_path  )
					A__     =  metrics['val'][0]
					A__     =  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"""]				,      0.0_1  )
					# 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
					A__     =  os.listdir(lowerCamelCase_  )
					A__     =  [x for x in contents if x.endswith(""".ckpt"""  )][0]
					A__     =  os.path.join(args.output_dir				,      lowerCamelCase_  )
					A__     =  torch.load(lowerCamelCase_				,      map_location="""cpu"""  )
					A__     =  '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:
							A__     =  {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     (_UpperCamelCase       ):
			'''simple docstring'''




			@timeout_decorator.timeout(6_00  )
			@slow
			@require_torch_gpu
			def   a_			(     self  :       int  )      ->      List[str]:







					"""simple docstring"""






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

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

							A__     =  distill_main(lowerCamelCase_  )

					# Check metrics
					A__     =  load_json(model.metrics_save_path  )
					A__     =  metrics['val'][0]
					A__     =  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"] >= 0.0_1

					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
					A__     =  os.listdir(lowerCamelCase_  )
					A__     =  [x for x in contents if x.endswith(""".ckpt"""  )][0]
					A__     =  os.path.join(args.output_dir				,      lowerCamelCase_  )
					A__     =  torch.load(lowerCamelCase_				,      map_location="""cpu"""  )
					A__     =  '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:
							A__     =  {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










import numpy as np
from cva import destroyAllWindows, imread, imshow, waitKey


class       __lowerCAmelCase					:


	'''simple docstring'''





	def __init__(						self:						List[str],					lowerCamelCase_:						Optional[Any],					lowerCamelCase_:						int,					lowerCamelCase_:						int			):
			if dst_width < 0 or dst_height < 0:
					raise ValueError('Destination width/height should be > 0'			)

			lowercase__					:      str      =		img
			lowercase__					:      Tuple      =		img.shape[1]
			lowercase__					:      str      =		img.shape[0]
			lowercase__					:      Tuple      =		dst_width
			lowercase__					:      Any      =		dst_height

			lowercase__					:      Tuple      =		self.src_w / self.dst_w
			lowercase__					:      Union[str, Any]      =		self.src_h / self.dst_h

			lowercase__					:      List[Any]      =		(
			    np.ones((self.dst_h, self.dst_w, 3),					np.uinta			) * 255
			)

	def    snake_case__(						self:						Dict			):
			for i in range(self.dst_h			):
					for j in range(self.dst_w			):
							lowercase__					:      Tuple      =		self.img[self.get_y(lowerCamelCase_			)][self.get_x(lowerCamelCase_			)]

	def    snake_case__(						self:						List[str],					lowerCamelCase_:						int			):
			return int(self.ratio_x * x			)





	def    snake_case__(						self:						int,					lowerCamelCase_:						int			):
			return int(self.ratio_y * y			)


if __name__ == "__main__":
			__UpperCamelCase,	__UpperCamelCase:	List[Any] = 8_0_0, 6_0_0
			__UpperCamelCase:	Optional[Any] = imread("""image_data/lena.jpg""", 1)
			__UpperCamelCase:	Union[str, Any] = NearestNeighbour(im, dst_w, dst_h)
			n.process()

			imshow(
			    F'Image resized from: {im.shape[1]}x{im.shape[0]} to {dst_w}x{dst_h}', n.output
			)
			waitKey(0)
			destroyAllWindows()


