





"""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						: Tuple     	=			'''sshleifer/mar_enro_6_3_student'''


class        __UpperCamelCase				(						a__						):






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

								a      :		Any	=				cached_path(
								    "https://cdn-datasets.huggingface.co/translation/wmt_en_ro-tr40k-va0.5k-te0.5k.tar.gz"      ,					extract_compressed_file=lowerCAmelCase__      ,					)
								a      :		List[Any]	=				f"""{data_cached}/wmt_en_ro-tr40k-va0.5k-te0.5k"""






							@slow
							@require_torch_gpu
							def     __a   (   self      )	-> List[Any]:
								MarianMTModel.from_pretrained(lowerCAmelCase__      )






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

								# bash_script = bash_script.replace("--fp16 ", "")
								a      :		List[str]	=				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

								a      :		Dict	=				["finetune.py"] + bash_script.split() + args
								with patch.object(lowerCAmelCase__      ,					"argv"      ,					lowerCAmelCase__      ):
									a      :		List[Any]	=				argparse.ArgumentParser()
									a      :		Dict	=				pl.Trainer.add_argparse_args(lowerCAmelCase__      )
									a      :		List[str]	=				SummarizationModule.add_model_specific_args(lowerCAmelCase__      ,					os.getcwd()      )
									a      :		str	=				parser.parse_args()
									a      :		Union[str, Any]	=				main(lowerCAmelCase__      )

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






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

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

									a      :		Optional[int]	=				distill_main(lowerCAmelCase__      )

								# Check metrics
								a      :		Tuple	=				load_json(model.metrics_save_path      )
								a      :		Union[str, Any]	=				metrics["val"][0]
								a      :		List[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}"""]      ,					lowerCAmelCase__      )

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




def       _SCREAMING_SNAKE_CASE		(	_lowercase						:    list					) ->int:






	'''simple docstring'''






	if not grid or not grid[0]:
		raise TypeError("The grid does not contain the appropriate information"					)

	for cell_n in range(1    ,       len(grid[0]					)					):
		grid[0][cell_n] += grid[0][cell_n - 1]
	a      :		Union[str, Any]	=				grid[0]

	for row_n in range(1    ,       len(_lowercase					)					):
		a      :		Optional[Any]	=				grid[row_n]
		a      :		Tuple	=				fill_row(_lowercase    ,       _lowercase					)
		a      :		List[Any]	=				grid[row_n]

	return grid[-1][-1]






def       _SCREAMING_SNAKE_CASE		(	_lowercase						:    list    ,       _lowercase						:    list					) ->list:






	'''simple docstring'''






	current_row[0] += row_above[0]
	for cell_n in range(1    ,       len(_lowercase					)					):
		current_row[cell_n] += min(current_row[cell_n - 1]    ,       row_above[cell_n]					)

	return current_row


if __name__ == "__main__":
		import doctest

		doctest.testmod()


