




"""simple docstring"""







import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, BatchEncoding, PreTrainedTokenizer
from ...utils import logging


SCREAMING_SNAKE_CASE_	     =       logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_	     =       '▁'

SCREAMING_SNAKE_CASE_	     =       {'vocab_file': 'sentencepiece.bpe.model'}

SCREAMING_SNAKE_CASE_	     =       {
    'vocab_file': {
        'facebook/nllb-200-distilled-600M': (
            'https://huggingface.co/facebook/nllb-200-distilled-600M/blob/main/sentencepiece.bpe.model'
        ),
    }
}

SCREAMING_SNAKE_CASE_	     =       {
    'facebook/nllb-200-distilled-600M': 1024,
}

# fmt: off
SCREAMING_SNAKE_CASE_	     =       ['ace_Arab', 'ace_Latn', 'acm_Arab', 'acq_Arab', 'aeb_Arab', 'afr_Latn', 'ajp_Arab', 'aka_Latn', 'amh_Ethi', 'apc_Arab', 'arb_Arab', 'ars_Arab', 'ary_Arab', 'arz_Arab', 'asm_Beng', 'ast_Latn', 'awa_Deva', 'ayr_Latn', 'azb_Arab', 'azj_Latn', 'bak_Cyrl', 'bam_Latn', 'ban_Latn', 'bel_Cyrl', 'bem_Latn', 'ben_Beng', 'bho_Deva', 'bjn_Arab', 'bjn_Latn', 'bod_Tibt', 'bos_Latn', 'bug_Latn', 'bul_Cyrl', 'cat_Latn', 'ceb_Latn', 'ces_Latn', 'cjk_Latn', 'ckb_Arab', 'crh_Latn', 'cym_Latn', 'dan_Latn', 'deu_Latn', 'dik_Latn', 'dyu_Latn', 'dzo_Tibt', 'ell_Grek', 'eng_Latn', 'epo_Latn', 'est_Latn', 'eus_Latn', 'ewe_Latn', 'fao_Latn', 'pes_Arab', 'fij_Latn', 'fin_Latn', 'fon_Latn', 'fra_Latn', 'fur_Latn', 'fuv_Latn', 'gla_Latn', 'gle_Latn', 'glg_Latn', 'grn_Latn', 'guj_Gujr', 'hat_Latn', 'hau_Latn', 'heb_Hebr', 'hin_Deva', 'hne_Deva', 'hrv_Latn', 'hun_Latn', 'hye_Armn', 'ibo_Latn', 'ilo_Latn', 'ind_Latn', 'isl_Latn', 'ita_Latn', 'jav_Latn', 'jpn_Jpan', 'kab_Latn', 'kac_Latn', 'kam_Latn', 'kan_Knda', 'kas_Arab', 'kas_Deva', 'kat_Geor', 'knc_Arab', 'knc_Latn', 'kaz_Cyrl', 'kbp_Latn', 'kea_Latn', 'khm_Khmr', 'kik_Latn', 'kin_Latn', 'kir_Cyrl', 'kmb_Latn', 'kon_Latn', 'kor_Hang', 'kmr_Latn', 'lao_Laoo', 'lvs_Latn', 'lij_Latn', 'lim_Latn', 'lin_Latn', 'lit_Latn', 'lmo_Latn', 'ltg_Latn', 'ltz_Latn', 'lua_Latn', 'lug_Latn', 'luo_Latn', 'lus_Latn', 'mag_Deva', 'mai_Deva', 'mal_Mlym', 'mar_Deva', 'min_Latn', 'mkd_Cyrl', 'plt_Latn', 'mlt_Latn', 'mni_Beng', 'khk_Cyrl', 'mos_Latn', 'mri_Latn', 'zsm_Latn', 'mya_Mymr', 'nld_Latn', 'nno_Latn', 'nob_Latn', 'npi_Deva', 'nso_Latn', 'nus_Latn', 'nya_Latn', 'oci_Latn', 'gaz_Latn', 'ory_Orya', 'pag_Latn', 'pan_Guru', 'pap_Latn', 'pol_Latn', 'por_Latn', 'prs_Arab', 'pbt_Arab', 'quy_Latn', 'ron_Latn', 'run_Latn', 'rus_Cyrl', 'sag_Latn', 'san_Deva', 'sat_Beng', 'scn_Latn', 'shn_Mymr', 'sin_Sinh', 'slk_Latn', 'slv_Latn', 'smo_Latn', 'sna_Latn', 'snd_Arab', 'som_Latn', 'sot_Latn', 'spa_Latn', 'als_Latn', 'srd_Latn', 'srp_Cyrl', 'ssw_Latn', 'sun_Latn', 'swe_Latn', 'swh_Latn', 'szl_Latn', 'tam_Taml', 'tat_Cyrl', 'tel_Telu', 'tgk_Cyrl', 'tgl_Latn', 'tha_Thai', 'tir_Ethi', 'taq_Latn', 'taq_Tfng', 'tpi_Latn', 'tsn_Latn', 'tso_Latn', 'tuk_Latn', 'tum_Latn', 'tur_Latn', 'twi_Latn', 'tzm_Tfng', 'uig_Arab', 'ukr_Cyrl', 'umb_Latn', 'urd_Arab', 'uzn_Latn', 'vec_Latn', 'vie_Latn', 'war_Latn', 'wol_Latn', 'xho_Latn', 'ydd_Hebr', 'yor_Latn', 'yue_Hant', 'zho_Hans', 'zho_Hant', 'zul_Latn']

class 				snake_case_			(					snake_case_			):







   """simple docstring"""




   A_				      =       VOCAB_FILES_NAMES
   A_				      =       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   A_				      =       PRETRAINED_VOCAB_FILES_MAP
   A_				      =       ['''input_ids''', '''attention_mask''']

   A_				      =       []
   A_				      =       []







   def __init__(		self       ,					lowerCamelCase_       ,					lowerCamelCase_="<s>"       ,					lowerCamelCase_="</s>"       ,					lowerCamelCase_="</s>"       ,					lowerCamelCase_="<s>"       ,					lowerCamelCase_="<unk>"       ,					lowerCamelCase_="<pad>"       ,					lowerCamelCase_="<mask>"       ,					lowerCamelCase_=None       ,					lowerCamelCase_=None       ,					lowerCamelCase_=None       ,					lowerCamelCase_ = None       ,					lowerCamelCase_=None       ,					lowerCamelCase_=False       ,					**lowerCamelCase_       ,					)				->   str:
       UpperCamelCase              =	AddedToken(lowercase__       ,					lstrip=lowercase__       ,					rstrip=lowercase__) if isinstance(lowercase__       ,					lowercase__) else mask_token

       UpperCamelCase              =	{} if sp_model_kwargs is None else sp_model_kwargs
       UpperCamelCase              =	legacy_behaviour

       super().__init__(
           bos_token=lowercase__       ,					eos_token=lowercase__       ,					unk_token=lowercase__       ,					sep_token=lowercase__       ,					cls_token=lowercase__       ,					pad_token=lowercase__       ,					mask_token=lowercase__       ,					tokenizer_file=lowercase__       ,					src_lang=lowercase__       ,					tgt_lang=lowercase__       ,					additional_special_tokens=lowercase__       ,					sp_model_kwargs=self.sp_model_kwargs       ,					legacy_behaviour=lowercase__       ,					**lowercase__       ,					)

       UpperCamelCase              =	spm.SentencePieceProcessor(**self.sp_model_kwargs)
       self.sp_model.Load(str(lowercase__))
       UpperCamelCase              =	vocab_file
       # Original fairseq vocab and spm vocab must be "aligned":
       # Vocab    |    0    |    1    |   2    |    3    |  4   |  5   |  6   |   7  |   8  |  9
       # -------- | ------- | ------- | ------ | ------- | ---- | ---- | ---- | ---- | ---- | ----
       # fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | 'an' | '▁n' | '▁m' | '▁t' | '▁k' | '▁a'
       # spm      | '<unk>' | '<s>'   | '</s>' | 'an'    | '▁n' | '▁m' | '▁t' | '▁k' | '▁a' | '▁s'

       # Mimic fairseq token-to-id alignment for the first 4 token
       UpperCamelCase              =	{"""<s>""": 0, """<pad>""": 1, """</s>""": 2, """<unk>""": 3}

       # The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
       UpperCamelCase              =	1

       UpperCamelCase              =	len(self.sp_model)
       UpperCamelCase              =	{
           code: self.sp_model_size + i + self.fairseq_offset for i, code in enumerate(lowercase__)
       }
       UpperCamelCase              =	{v: k for k, v in self.lang_code_to_id.items()}
       UpperCamelCase              =	len(self.sp_model) + len(self.lang_code_to_id) + self.fairseq_offset

       self.fairseq_tokens_to_ids.update(self.lang_code_to_id)
       UpperCamelCase              =	{v: k for k, v in self.fairseq_tokens_to_ids.items()}
       UpperCamelCase              =	list(self.lang_code_to_id.keys())

       if additional_special_tokens is not None:
           # Only add those special tokens if they are not already there.
           self._additional_special_tokens.extend(
               [t for t in additional_special_tokens if t not in self._additional_special_tokens])

       UpperCamelCase              =	src_lang if src_lang is not None else """eng_Latn"""
       UpperCamelCase              =	self.lang_code_to_id[self._src_lang]
       UpperCamelCase              =	tgt_lang
       self.set_src_lang_special_tokens(self._src_lang)







   def __getstate__(		self)				->   str:
       UpperCamelCase              =	self.__dict__.copy()
       UpperCamelCase              =	None
       UpperCamelCase              =	self.sp_model.serialized_model_proto()
       return state







   def __setstate__(		self       ,					lowerCamelCase_)				->   List[str]:
       UpperCamelCase              =	d

       # for backward compatibility
       if not hasattr(self       ,					'''sp_model_kwargs'''):
           UpperCamelCase              =	{}

       UpperCamelCase              =	spm.SentencePieceProcessor(**self.sp_model_kwargs)
       self.sp_model.LoadFromSerializedProto(self.sp_model_proto)







   @property
   def   UpperCAmelCase__			(		self)				->   Tuple:
       return len(self.sp_model) + len(self.lang_code_to_id) + self.fairseq_offset + 1  # Plus 1 for the mask token







   @property
   def   UpperCAmelCase__			(		self)				->   Union[str, Any]:
       return self._src_lang







   @src_lang.setter
   def   UpperCAmelCase__			(		self       ,					lowerCamelCase_)				->   Union[str, Any]:
       UpperCamelCase              =	new_src_lang
       self.set_src_lang_special_tokens(self._src_lang)







   def   UpperCAmelCase__			(		self       ,					lowerCamelCase_       ,					lowerCamelCase_ = None       ,					lowerCamelCase_ = False)				->   Any:

       if already_has_special_tokens:
           return super().get_special_tokens_mask(
               token_ids_a=lowercase__       ,					token_ids_a=lowercase__       ,					already_has_special_tokens=lowercase__)

       UpperCamelCase              =	[1] * len(self.prefix_tokens)
       UpperCamelCase              =	[1] * len(self.suffix_tokens)
       if token_ids_a is None:
           return prefix_ones + ([0] * len(lowercase__)) + suffix_ones
       return prefix_ones + ([0] * len(lowercase__)) + ([0] * len(lowercase__)) + suffix_ones







   def   UpperCAmelCase__			(		self       ,					lowerCamelCase_       ,					lowerCamelCase_ = None)				->   Tuple:

       if token_ids_a is None:
           return self.prefix_tokens + token_ids_a + self.suffix_tokens
       # We don't expect to process pairs, but leave the pair logic for API consistency
       return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens







   def   UpperCAmelCase__			(		self       ,					lowerCamelCase_       ,					lowerCamelCase_ = None)				->   Tuple:
       UpperCamelCase              =	[self.sep_token_id]
       UpperCamelCase              =	[self.cls_token_id]

       if token_ids_a is None:
           return len(cls + token_ids_a + sep) * [0]
       return len(cls + token_ids_a + sep + sep + token_ids_a + sep) * [0]







   def   UpperCAmelCase__			(		self       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					**lowerCamelCase_)				->   Any:

       if src_lang is None or tgt_lang is None:
           raise ValueError('''Translation requires a `src_lang` and a `tgt_lang` for this model''')
       UpperCamelCase              =	src_lang
       UpperCamelCase              =	self(lowercase__       ,					add_special_tokens=lowercase__       ,					return_tensors=lowercase__       ,					**lowercase__)
       UpperCamelCase              =	self.convert_tokens_to_ids(lowercase__)
       UpperCamelCase              =	tgt_lang_id
       return inputs







   def   UpperCAmelCase__			(		self)				->   Optional[int]:
       UpperCamelCase              =	{self.convert_ids_to_tokens(lowercase__): i for i in range(self.vocab_size)}
       vocab.update(self.added_tokens_encoder)
       return vocab







   def   UpperCAmelCase__			(		self       ,					lowerCamelCase_)				->   Optional[Any]:
       return self.sp_model.encode(lowercase__       ,					out_type=lowercase__)







   def   UpperCAmelCase__			(		self       ,					lowerCamelCase_)				->   int:

       if token in self.fairseq_tokens_to_ids:
           return self.fairseq_tokens_to_ids[token]
       UpperCamelCase              =	self.sp_model.PieceToId(lowercase__)

       # Need to return unknown token if the SP model returned 0
       return spm_id + self.fairseq_offset if spm_id else self.unk_token_id







   def   UpperCAmelCase__			(		self       ,					lowerCamelCase_)				->   Optional[int]:

       if index in self.fairseq_ids_to_tokens:
           return self.fairseq_ids_to_tokens[index]
       return self.sp_model.IdToPiece(index - self.fairseq_offset)







   def   UpperCAmelCase__			(		self       ,					lowerCamelCase_)				->   List[str]:
       UpperCamelCase              =	"""""".join(lowercase__).replace(lowercase__       ,					''' ''').strip()
       return out_string







   def   UpperCAmelCase__			(		self       ,					lowerCamelCase_       ,					lowerCamelCase_ = None)				->   Optional[int]:

       if not os.path.isdir(lowercase__):
           logger.error(F'Vocabulary path ({save_directory}) should be a directory')
           return
       UpperCamelCase              =	os.path.join(
           lowercase__       ,					(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''])

       if os.path.abspath(self.vocab_file) != os.path.abspath(lowercase__) and os.path.isfile(self.vocab_file):
           copyfile(self.vocab_file       ,					lowercase__)
       elif not os.path.isfile(self.vocab_file):
           with open(lowercase__       ,					'''wb''') as fi:
               UpperCamelCase              =	self.sp_model.serialized_model_proto()
               fi.write(lowercase__)

       return (out_vocab_file,)







   def   UpperCAmelCase__			(		self       ,					lowerCamelCase_       ,					lowerCamelCase_ = "eng_Latn"       ,					lowerCamelCase_ = None       ,					lowerCamelCase_ = "fra_Latn"       ,					**lowerCamelCase_       ,					)				->   int:
       UpperCamelCase              =	src_lang
       UpperCamelCase              =	tgt_lang
       return super().prepare_seqaseq_batch(lowercase__       ,					lowercase__       ,					**lowercase__)







   def   UpperCAmelCase__			(		self)				->   Union[str, Any]:
       return self.set_src_lang_special_tokens(self.src_lang)







   def   UpperCAmelCase__			(		self)				->   str:
       return self.set_tgt_lang_special_tokens(self.tgt_lang)







   def   UpperCAmelCase__			(		self       ,					lowerCamelCase_)				->   Dict:
       UpperCamelCase              =	self.lang_code_to_id[src_lang]
       if self.legacy_behaviour:
           UpperCamelCase              =	[]
           UpperCamelCase              =	[self.eos_token_id, self.cur_lang_code]
       else:
           UpperCamelCase              =	[self.cur_lang_code]
           UpperCamelCase              =	[self.eos_token_id]







   def   UpperCAmelCase__			(		self       ,					lowerCamelCase_)				->   int:
       UpperCamelCase              =	self.lang_code_to_id[lang]
       if self.legacy_behaviour:
           UpperCamelCase              =	[]
           UpperCamelCase              =	[self.eos_token_id, self.cur_lang_code]
       else:
           UpperCamelCase              =	[self.cur_lang_code]
           UpperCamelCase              =	[self.eos_token_id]





'''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
