





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


_snake_case    :       int		       =    "sshleifer/mar_enro_6_3_student"



class 							_UpperCAmelCase       (       UpperCAmelCase__       ):

  """simple docstring"""


  def 							lowercase						(							self					:						Tuple	)				->		List[str]:
     super().setUp()

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






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






  @slow
  @require_torch_gpu
  def 							lowercase						(							self					:						Optional[Any]	)				->		Tuple:
     __lowerCAmelCase				      =      {
         '$MAX_LEN': 6_4,
         '$BS': 6_4,
         '$GAS': 1,
         '$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(__lowercase   ,    str(__lowercase	)	)
     __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(__lowercase   ,    'argv'   ,    __lowercase	):
        __lowerCAmelCase				      =      argparse.ArgumentParser()
        __lowerCAmelCase				      =      pl.Trainer.add_argparse_args(__lowercase	)
        __lowerCAmelCase				      =      SummarizationModule.add_model_specific_args(__lowercase   ,    os.getcwd()	)
        __lowerCAmelCase				      =      parser.parse_args()
        __lowerCAmelCase				      =      main(__lowercase	)

     # 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}"""]   ,    __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']   ,    1_7	)

     # 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(__lowercase	)
     __lowerCAmelCase				      =      [x for x in contents if x.endswith('.ckpt'	)][0]
     __lowerCAmelCase				      =      os.path.join(args.output_dir   ,    __lowercase	)
     __lowerCAmelCase				      =      torch.load(__lowercase   ,    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(__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 							_UpperCAmelCase       (       UpperCAmelCase__       ):

  """simple docstring"""


  @timeout_decorator.timeout(6_0_0	)
  @slow
  @require_torch_gpu
  def 							lowercase						(							self					:						Optional[Any]	)				->		List[str]:
     __lowerCAmelCase				      =      f"""{self.test_file_dir_str}/test_data/wmt_en_ro"""
     __lowerCAmelCase				      =      {
         '--fp16_opt_level=O1': '',
         '$MAX_LEN': 1_2_8,
         '$BS': 1_6,
         '$GAS': 1,
         '$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(__lowercase   ,    str(__lowercase	)	)
     __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(__lowercase   ,    'argv'   ,    __lowercase	):
        __lowerCAmelCase				      =      argparse.ArgumentParser()
        __lowerCAmelCase				      =      pl.Trainer.add_argparse_args(__lowercase	)
        __lowerCAmelCase				      =      SummarizationDistiller.add_model_specific_args(__lowercase   ,    os.getcwd()	)
        __lowerCAmelCase				      =      parser.parse_args()
        # assert args.gpus == gpus THIS BREAKS for multi_gpu

        __lowerCAmelCase				      =      distill_main(__lowercase	)

     # 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"] >= 0.01

     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
     __lowerCAmelCase				      =      os.listdir(__lowercase	)
     __lowerCAmelCase				      =      [x for x in contents if x.endswith('.ckpt'	)][0]
     __lowerCAmelCase				      =      os.path.join(args.output_dir   ,    __lowercase	)
     __lowerCAmelCase				      =      torch.load(__lowercase   ,    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(__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







import json
import os
import subprocess
import unittest
from ast import literal_eval

import pytest
from parameterized import parameterized, parameterized_class

from . import is_sagemaker_available


if is_sagemaker_available():
		from sagemaker import Session, TrainingJobAnalytics
		from sagemaker.huggingface import HuggingFace
@pytest.mark.skipif(
    literal_eval(os.getenv('''TEST_SAGEMAKER'''			,  '''False'''     )     ) is not True			,  reason='''Skipping test because should only be run when releasing minor transformers version'''			,  )
@pytest.mark.usefixtures('''sm_env'''     )
@parameterized_class(
    [
        {
            '''framework''': '''pytorch''',
            '''script''': '''run_glue_model_parallelism.py''',
            '''model_name_or_path''': '''roberta-large''',
            '''instance_type''': '''ml.p3dn.24xlarge''',
            '''results''': {'''train_runtime''': 1600, '''eval_accuracy''': 0.3, '''eval_loss''': 1.2},
        },
        {
            '''framework''': '''pytorch''',
            '''script''': '''run_glue.py''',
            '''model_name_or_path''': '''roberta-large''',
            '''instance_type''': '''ml.p3dn.24xlarge''',
            '''results''': {'''train_runtime''': 1600, '''eval_accuracy''': 0.3, '''eval_loss''': 1.2},
        },
    ]     )
class 						UpperCAmelCase  (							unittest.TestCase     ):







						'''simple docstring'''





						def 			snake_case__		(		self				:	int		):






							"""simple docstring"""






							if self.framework == "pytorch":
								subprocess.run(
								    f"cp ./examples/pytorch/text-classification/run_glue.py {self.env.test_path}/run_glue.py".split()			,      encoding="utf-8"			,      check=__lowercase			,      )
							assert hasattr(self			,      "env"		)






						def 			snake_case__		(		self				:	Any			,      __lowercase				:	Dict		):






							"""simple docstring"""






							snake_case_						       =	{
							    "enabled": True,
							    "processes_per_host": 8,
							}
							snake_case_						       =	{
							    "enabled": True,
							    "parameters": {
							        "microbatches": 4,
							        "placement_strategy": "spread",
							        "pipeline": "interleaved",
							        "optimize": "speed",
							        "partitions": 4,
							        "ddp": True,
							    },
							}

							snake_case_						       =	{"smdistributed": {"modelparallel": smp_options}, "mpi": mpi_options}

							snake_case_						       =	"trainer" if self.script == "run_glue.py" else "smtrainer"
							# creates estimator
							return HuggingFace(
							    entry_point=self.script			,      source_dir=self.env.test_path			,      role=self.env.role			,      image_uri=self.env.image_uri			,      base_job_name=f"{self.env.base_job_name}-{instance_count}-smp-{name_extension}"			,      instance_count=__lowercase			,      instance_type=self.instance_type			,      debugger_hook_config=__lowercase			,      hyperparameters={
							        **self.env.hyperparameters,
							        "model_name_or_path": self.model_name_or_path,
							        "max_steps": 5_00,
							    }			,      metric_definitions=self.env.metric_definitions			,      distribution=__lowercase			,      py_version="py36"			,      )






						def 			snake_case__		(		self				:	Tuple			,      __lowercase				:	Dict		):






							"""simple docstring"""






							TrainingJobAnalytics(__lowercase		).export_csv(f"{self.env.test_path}/{job_name}_metrics.csv"		)






						@parameterized.expand([(1,)]		)
						def 			snake_case__		(		self				:	Any			,      __lowercase				:	int		):






							"""simple docstring"""






							snake_case_						       =	self.create_estimator(__lowercase		)

							# run training
							estimator.fit()

							# result dataframe
							snake_case_						       =	TrainingJobAnalytics(estimator.latest_training_job.name		).dataframe()

							# extract kpis
							snake_case_						       =	list(result_metrics_df[result_metrics_df.metric_name == "eval_accuracy"]["value"]		)
							snake_case_						       =	list(result_metrics_df[result_metrics_df.metric_name == "eval_loss"]["value"]		)
							# get train time from SageMaker job, this includes starting, preprocessing, stopping
							snake_case_						       =	(
							    Session().describe_training_job(estimator.latest_training_job.name		).get("TrainingTimeInSeconds"			,      99_99_99		)
							)

							# assert kpis
							assert train_runtime <= self.results["train_runtime"]
							assert all(t >= self.results["eval_accuracy"] for t in eval_accuracy		)
							assert all(t <= self.results["eval_loss"] for t in eval_loss		)

							# dump tests result into json file to share in PR
							with open(f"{estimator.latest_training_job.name}.json"			,      "w"		) as outfile:
								json.dump({"train_time": train_runtime, "eval_accuracy": eval_accuracy, "eval_loss": eval_loss}			,      __lowercase		)
