


"""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     :   Optional[int]			    =			"sshleifer/mar_enro_6_3_student"
class   _UpperCamelCase  (   lowerCAmelCase__		):
							'''simple docstring'''







							def 			snake_case				(     self	):
										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 			snake_case				(     self	):
										MarianMTModel.from_pretrained(__a	)



							@slow
							@require_torch_gpu
							def 			snake_case				(     self	):
										__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"\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

										__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"]    ,   0.0_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
										__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   _UpperCamelCase  (   lowerCAmelCase__		):
							'''simple docstring'''







							@timeout_decorator.timeout(6_00	)
							@slow
							@require_torch_gpu
							def 			snake_case				(     self	):
										__lowerCAmelCase				    =			f"{self.test_file_dir_str}/test_data/wmt_en_ro"
										__lowerCAmelCase				    =			{
										    "--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
										__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"] >= 0.0_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}"]    ,   __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








"""simple docstring"""


from diffusers.utils.testing_utils import require_onnxruntime
@require_onnxruntime
class   _UpperCamelCase  :
							'''simple docstring'''







							pass




