




'''simple docstring'''


import tempfile
import unittest
from pathlib import Path
from shutil import copyfile

from transformers import MaMaaaTokenizer, is_torch_available
from transformers.testing_utils import (
    get_tests_dir,
    nested_simplify,
    require_sentencepiece,
    require_tokenizers,
    require_torch,
    slow,
)
from transformers.utils import is_sentencepiece_available


if is_sentencepiece_available():
 from transformers.models.mam_aaa.tokenization_mam_aaa import VOCAB_FILES_NAMES, save_json

from ...test_tokenization_common import TokenizerTesterMixin


if is_sentencepiece_available():
 lowerCAmelCase_      :  Any				     =  get_tests_dir('fixtures/test_sentencepiece.model')


if is_torch_available():
 from transformers.models.mam_aaa.modeling_mam_aaa import shift_tokens_right

lowerCAmelCase_      :  List[Any]				     =  12_8022
lowerCAmelCase_      :  str				     =  12_8028







@require_sentencepiece
class      SCREAMING_SNAKE_CASE    (						snake_case_							,      unittest.TestCase				):
      __magic_name__  :       str   							=					MaMaaaTokenizer
      __magic_name__  :       Union[str, Any]   							=					False
      __magic_name__  :       List[Any]   							=					False
      __magic_name__  :       Optional[Any]   							=					True






      def 							lowercase_						( self   :  Optional[int]       ):


           '''simple docstring'''

           super().setUp()

           a_     :							List[Any]     						=  ["""</s>""", """<unk>""", """▁This""", """▁is""", """▁a""", """▁t""", """est""", """\u0120""", """<pad>"""]
           a_     :							Union[str, Any]     						=  dict(zip(lowercase__						,						range(len(lowercase__       )       )       )       )
           a_     :							int     						=  Path(self.tmpdirname       )
           save_json(lowercase__						,						save_dir / VOCAB_FILES_NAMES["""vocab_file"""]       )
           if not (save_dir / VOCAB_FILES_NAMES["spm_file"]).exists():
                copyfile(lowercase__						,						save_dir / VOCAB_FILES_NAMES["""spm_file"""]       )

           a_     :							Tuple     						=  MaMaaaTokenizer.from_pretrained(self.tmpdirname       )
           tokenizer.save_pretrained(self.tmpdirname       )






      def 							lowercase_						( self   :  Tuple						,						**lowercase__   :  Optional[int]       ):


           '''simple docstring'''

           return MaMaaaTokenizer.from_pretrained(self.tmpdirname						,						**lowercase__       )






      def 							lowercase_						( self   :  Dict						,						lowercase__   :  List[str]       ):


           '''simple docstring'''

           return (
               "This is a test",
               "This is a test",
           )






      def 							lowercase_						( self   :  str       ):


           '''simple docstring'''

           a_     :							Dict     						=  """</s>"""
           a_     :							Optional[Any]     						=  0

           self.assertEqual(self.get_tokenizer()._convert_token_to_id(lowercase__       )						,						lowercase__       )
           self.assertEqual(self.get_tokenizer()._convert_id_to_token(lowercase__       )						,						lowercase__       )






      def 							lowercase_						( self   :  Any       ):


           '''simple docstring'''

           a_     :							Dict     						=  self.get_tokenizer()
           a_     :							List[Any]     						=  list(tokenizer.get_vocab().keys()       )

           self.assertEqual(vocab_keys[0]						,						"""</s>"""       )
           self.assertEqual(vocab_keys[1]						,						"""<unk>"""       )
           self.assertEqual(vocab_keys[-1]						,						"""<s>"""       )
           self.assertEqual(len(lowercase__       )						,						tokenizer.vocab_size + len(tokenizer.get_added_vocab()       )       )






      @unittest.skip("""Skip this test while all models are still to be uploaded."""       )
      def 							lowercase_						( self   :  int       ):


           '''simple docstring'''

           pass






      def 							lowercase_						( self   :  Optional[Any]       ):


           '''simple docstring'''

           a_     :							List[str]     						=  self.get_tokenizer()

           a_     :							List[str]     						=  tokenizer.tokenize("""This is a test"""       )
           self.assertListEqual(lowercase__						,						["""▁This""", """▁is""", """▁a""", """▁t""", """est"""]       )

           self.assertListEqual(
               tokenizer.convert_tokens_to_ids(lowercase__       )						,						[2, 3, 4, 5, 6]						,						)

           a_     :							Optional[int]     						=  tokenizer.convert_ids_to_tokens([2, 3, 4, 5, 6]       )
           self.assertListEqual(lowercase__						,						["""▁This""", """▁is""", """▁a""", """▁t""", """est"""]       )

           a_     :							int     						=  tokenizer.convert_tokens_to_string(lowercase__       )
           self.assertEqual(lowercase__						,						"""This is a test"""       )






      @slow
      def 							lowercase_						( self   :  List[str]       ):


           '''simple docstring'''

           a_     :							List[Any]     						=  {"""input_ids""": [[12_8022, 11_0108, 397, 11, 3_8272, 2247, 12_4811, 285, 1_8105, 1586, 207, 7, 3_9534, 4428, 397, 1019, 1_8105, 1586, 207, 7, 4_1337, 1_6786, 241, 7, 2_0214, 17, 12_5690, 1_0398, 7, 4_4378, 5_8069, 6_8342, 7798, 7343, 11, 299, 3_3310, 4, 158, 3_7350, 9_4077, 4569, 299, 3_3310, 90, 4, 5_2840, 290, 4, 3_1270, 112, 299, 682, 4, 5_2840, 3_9953, 1_4079, 193, 5_2519, 9_0894, 1_7894, 12_0697, 11, 4_0445, 551, 17, 1019, 5_2519, 9_0894, 1_7756, 963, 11, 4_0445, 480, 17, 9792, 1120, 5173, 1393, 6240, 1_6786, 241, 12_0996, 28, 1245, 1393, 11_8240, 1_1123, 1019, 9_3612, 2691, 1_0618, 9_8058, 12_0409, 1928, 279, 4, 4_0683, 367, 178, 207, 1019, 103, 10_3121, 506, 6_5296, 5, 2], [12_8022, 2_1217, 367, 117, 12_5450, 128, 719, 7, 7308, 40, 9_3612, 1_2669, 1116, 1_6704, 71, 1_7785, 3699, 1_5592, 35, 144, 9584, 241, 1_1943, 713, 950, 799, 2247, 8_8427, 150, 149, 11_8813, 12_0706, 1019, 10_6906, 8_1518, 28, 1224, 2_2799, 397, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [12_8022, 1658, 12_3311, 5155, 5578, 4722, 279, 1_4947, 2366, 1120, 1197, 14, 1348, 9232, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], """attention_mask""": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
           # fmt: on

           self.tokenizer_integration_test_util(
               expected_encoding=lowercase__						,						model_name="""facebook/m2m100_418M"""						,						revision="""c168bae485c864188cf9aa0e4108b0b6934dc91e"""						,						)







@require_torch
@require_sentencepiece
@require_tokenizers
class      SCREAMING_SNAKE_CASE    (						unittest.TestCase				):
      __magic_name__  :       str   							=					'''facebook/m2m100_418M'''
      __magic_name__  :       int   							=					[
          '''In my opinion, there are two levels of response from the French government.''',
          '''NSA Affair Emphasizes Complete Lack of Debate on Intelligence''',
      ]
      __magic_name__  :       str   							=					[
          '''Selon moi, il y a deux niveaux de réponse de la part du gouvernement français.''',
          '''L\'affaire NSA souligne l\'absence totale de débat sur le renseignement''',
      ]

      # fmt: off
      __magic_name__  :       str   							=					[EN_CODE, 593, 1949, 11_5781, 4, 7_1586, 4234, 6_0633, 12_6233, 432, 12_3808, 1_5592, 1197, 11_7132, 12_0618, 5, 2]






      @classmethod
      def 							lowercase_						( cls   :  Union[str, Any]       ):


           '''simple docstring'''

           a_     :							MaMaaaTokenizer     						=  MaMaaaTokenizer.from_pretrained(
               cls.checkpoint_name						,						src_lang="""en"""						,						tgt_lang="""fr"""       )
           a_     :							int     						=  1
           return cls






      def 							lowercase_						( self   :  Any       ):


           '''simple docstring'''

           self.assertEqual(self.tokenizer.get_lang_id("""ar"""       )						,						12_8006       )
           self.assertEqual(self.tokenizer.get_lang_id("""en"""       )						,						12_8022       )
           self.assertEqual(self.tokenizer.get_lang_id("""ro"""       )						,						12_8076       )
           self.assertEqual(self.tokenizer.get_lang_id("""mr"""       )						,						12_8063       )






      def 							lowercase_						( self   :  Optional[Any]       ):


           '''simple docstring'''

           a_     :							Dict     						=  self.tokenizer.get_vocab()
           self.assertEqual(len(lowercase__       )						,						self.tokenizer.vocab_size       )
           self.assertEqual(vocab["""<unk>"""]						,						3       )
           self.assertIn(self.tokenizer.get_lang_token("""en"""       )						,						lowercase__       )






      def 							lowercase_						( self   :  Any       ):


           '''simple docstring'''

           a_     :							Optional[Any]     						=  """en"""
           a_     :							Dict     						=  self.tokenizer.batch_encode_plus(self.src_text       ).input_ids[0]
           self.assertListEqual(self.expected_src_tokens						,						lowercase__       )






      def 							lowercase_						( self   :  List[Any]       ):


           '''simple docstring'''

           self.assertIn(lowercase__						,						self.tokenizer.all_special_ids       )
           # fmt: off
           a_     :							List[Any]     						=  [FR_CODE, 5364, 82, 8642, 4, 294, 47, 8, 1_4028, 136, 3286, 9706, 6, 9_0797, 6, 14_4012, 162, 8_8128, 3_0061, 5, 2]
           # fmt: on
           a_     :							List[str]     						=  self.tokenizer.decode(lowercase__						,						skip_special_tokens=lowercase__       )
           a_     :							List[str]     						=  self.tokenizer.decode(generated_ids[1:]						,						skip_special_tokens=lowercase__       )
           self.assertEqual(lowercase__						,						lowercase__       )
           self.assertNotIn(self.tokenizer.eos_token						,						lowercase__       )






      def 							lowercase_						( self   :  Dict       ):


           '''simple docstring'''

           a_     :							List[Any]     						=  tempfile.mkdtemp()
           a_     :							Any     						=  self.tokenizer.lang_token_to_id
           self.tokenizer.save_pretrained(lowercase__       )
           a_     :							str     						=  MaMaaaTokenizer.from_pretrained(lowercase__       )
           self.assertDictEqual(new_tok.lang_token_to_id						,						lowercase__       )






      @require_torch
      def 							lowercase_						( self   :  Optional[int]       ):


           '''simple docstring'''

           a_     :							Optional[int]     						=  """en"""
           a_     :							Optional[Any]     						=  """fr"""

           a_     :							int     						=  self.tokenizer(self.src_text						,						text_target=self.tgt_text						,						padding=lowercase__						,						return_tensors="""pt"""       )

           a_     :							Any     						=  shift_tokens_right(
               batch["""labels"""]						,						self.tokenizer.pad_token_id						,						self.tokenizer.eos_token_id       )

           for k in batch:
                a_     :							Union[str, Any]     						=  batch[k].tolist()
           # batch = {k: v.tolist() for k,v in batch.items()}
           # fairseq batch: https://gist.github.com/sshleifer/cba08bc2109361a74ac3760a7e30e4f4
           # batch.decoder_inputs_ids[0][0] ==
           assert batch.input_ids[1][0] == EN_CODE
           assert batch.input_ids[1][-1] == 2
           assert batch.labels[1][0] == FR_CODE
           assert batch.labels[1][-1] == 2
           assert batch.decoder_input_ids[1][:2] == [2, FR_CODE]






      @require_torch
      def 							lowercase_						( self   :  str       ):


           '''simple docstring'''

           a_     :							int     						=  """mr"""
           self.assertListEqual(self.tokenizer.prefix_tokens						,						[self.tokenizer.get_lang_id("""mr"""       )]       )
           self.assertListEqual(self.tokenizer.suffix_tokens						,						[self.tokenizer.eos_token_id]       )

           a_     :							Tuple     						=  """zh"""
           self.assertListEqual(self.tokenizer.prefix_tokens						,						[self.tokenizer.get_lang_id("""zh"""       )]       )
           self.assertListEqual(self.tokenizer.suffix_tokens						,						[self.tokenizer.eos_token_id]       )






      @require_torch
      def 							lowercase_						( self   :  List[str]       ):


           '''simple docstring'''

           a_     :							Optional[int]     						=  """mr"""
           self.tokenizer._switch_to_target_mode()
           self.assertListEqual(self.tokenizer.prefix_tokens						,						[self.tokenizer.get_lang_id("""mr"""       )]       )
           self.assertListEqual(self.tokenizer.suffix_tokens						,						[self.tokenizer.eos_token_id]       )
           self.tokenizer._switch_to_input_mode()
           self.assertListEqual(self.tokenizer.prefix_tokens						,						[self.tokenizer.get_lang_id(self.tokenizer.src_lang       )]       )

           a_     :							str     						=  """zh"""
           self.tokenizer._switch_to_target_mode()
           self.assertListEqual(self.tokenizer.prefix_tokens						,						[self.tokenizer.get_lang_id("""zh"""       )]       )
           self.assertListEqual(self.tokenizer.suffix_tokens						,						[self.tokenizer.eos_token_id]       )
           self.tokenizer._switch_to_input_mode()
           self.assertListEqual(self.tokenizer.prefix_tokens						,						[self.tokenizer.get_lang_id(self.tokenizer.src_lang       )]       )






      @require_torch
      def 							lowercase_						( self   :  Optional[int]       ):


           '''simple docstring'''

           a_     :							Tuple     						=  self.tokenizer._build_translation_inputs("""A test"""						,						return_tensors="""pt"""						,						src_lang="""en"""						,						tgt_lang="""ar"""       )

           self.assertEqual(
               nested_simplify(lowercase__       )						,						{
                   # en_XX, A, test, EOS
                   """input_ids""": [[12_8022, 58, 4183, 2]],
                   """attention_mask""": [[1, 1, 1, 1]],
                   # ar_AR
                   """forced_bos_token_id""": 12_8006,
               }						,						)






'''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


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







class      SCREAMING_SNAKE_CASE    (						snake_case_				):






      def 							lowercase_						( self   :  int       ):


           '''simple docstring'''

           super().setUp()

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






      @slow
      @require_torch_gpu
      def 							lowercase_						( self   :  Union[str, Any]       ):


           '''simple docstring'''

           MarianMTModel.from_pretrained(lowercase__       )






      @slow
      @require_torch_gpu
      def 							lowercase_						( self   :  Tuple       ):


           '''simple docstring'''

           a_     :							Union[str, Any]     						=  {
               """$MAX_LEN""": 64,
               """$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_     :							Optional[int]     						=  (self.test_file_dir / """train_mbart_cc25_enro.sh""").open().read().split("""finetune.py"""       )[1].strip()
           a_     :							List[str]     						=  bash_script.replace("""\\\n"""						,						""""""       ).strip().replace("""\"$@\""""						,						""""""       )
           for k, v in env_vars_to_replace.items():
                a_     :							Optional[Any]     						=  bash_script.replace(lowercase__						,						str(lowercase__       )       )
           a_     :							Union[str, Any]     						=  self.get_auto_remove_tmp_dir()

           # bash_script = bash_script.replace("--fp16 ", "")
           a_     :							List[str]     						=  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_     :							Dict     						=  ["""finetune.py"""] + bash_script.split() + args
           with patch.object(lowercase__						,						"""argv"""						,						lowercase__       ):
                a_     :							List[Any]     						=  argparse.ArgumentParser()
                a_     :							int     						=  pl.Trainer.add_argparse_args(lowercase__       )
                a_     :							Dict     						=  SummarizationModule.add_model_specific_args(lowercase__						,						os.getcwd()       )
                a_     :							List[str]     						=  parser.parse_args()
                a_     :							List[Any]     						=  main(lowercase__       )

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






      @timeout_decorator.timeout(600       )
      @slow
      @require_torch_gpu
      def 							lowercase_						( self   :  Optional[int]       ):


           '''simple docstring'''

           a_     :							int     						=  F"{self.test_file_dir_str}/test_data/wmt_en_ro"
           a_     :							Dict     						=  {
               """--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_     :							List[str]     						=  (
               (self.test_file_dir / """distil_marian_no_teacher.sh""").open().read().split("""distillation.py"""       )[1].strip()
           )
           a_     :							List[str]     						=  bash_script.replace("""\\\n"""						,						""""""       ).strip().replace("""\"$@\""""						,						""""""       )
           a_     :							Tuple     						=  bash_script.replace("""--fp16 """						,						""" """       )

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

                a_     :							Union[str, Any]     						=  distill_main(lowercase__       )

           # Check metrics
           a_     :							Union[str, Any]     						=  load_json(model.metrics_save_path       )
           a_     :							List[str]     						=  metrics["""val"""][0]
           a_     :							Optional[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}"]						,						lowercase__       )

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