


"""simple docstring"""



import itertools
import json
import os
import unittest

from transformers import AddedToken, RobertaTokenizer, RobertaTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class 					__magic_name__		(			UpperCamelCase__			,						unittest.TestCase     ):
       __A			:      str        =   RobertaTokenizer
       __A			:      str        =   RobertaTokenizerFast
       __A			:      Any        =   True
       __A			:      Any        =   {"cls_token": "<s>"}






       def  __snake_case						(   self  :    List[str]				):

              '''simple docstring'''






              super().setUp()

              # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
              lowercase		:Dict       							=				[
                  '''l''',
                  '''o''',
                  '''w''',
                  '''e''',
                  '''r''',
                  '''s''',
                  '''t''',
                  '''i''',
                  '''d''',
                  '''n''',
                  '''\u0120''',
                  '''\u0120l''',
                  '''\u0120n''',
                  '''\u0120lo''',
                  '''\u0120low''',
                  '''er''',
                  '''\u0120lowest''',
                  '''\u0120newer''',
                  '''\u0120wider''',
                  '''<unk>''',
              ]
              lowercase		:Union[str, Any]       							=				dict(zip(__A  ,     range(len(__A				)				)				)				)
              lowercase		:str       							=				['''#version: 0.2''', '''\u0120 l''', '''\u0120l o''', '''\u0120lo w''', '''e r''', '''''']
              lowercase		:str       							=				{'''unk_token''': '''<unk>'''}

              lowercase		:Union[str, Any]       							=				os.path.join(self.tmpdirname  ,     VOCAB_FILES_NAMES['''vocab_file''']				)
              lowercase		:Tuple       							=				os.path.join(self.tmpdirname  ,     VOCAB_FILES_NAMES['''merges_file''']				)
              with open(self.vocab_file  ,     '''w'''  ,     encoding='''utf-8'''				) as fp:
                     fp.write(json.dumps(__A				) + '''\n'''				)
              with open(self.merges_file  ,     '''w'''  ,     encoding='''utf-8'''				) as fp:
                     fp.write('''\n'''.join(__A				)				)






       def  __snake_case						(   self  :    List[str]  ,     **snake_case__  :    List[Any]				):

              '''simple docstring'''






              kwargs.update(self.special_tokens_map				)
              return self.tokenizer_class.from_pretrained(self.tmpdirname  ,     **__A				)






       def  __snake_case						(   self  :    Union[str, Any]  ,     **snake_case__  :    int				):

              '''simple docstring'''






              kwargs.update(self.special_tokens_map				)
              return RobertaTokenizerFast.from_pretrained(self.tmpdirname  ,     **__A				)






       def  __snake_case						(   self  :    str  ,     snake_case__  :    Dict				):

              '''simple docstring'''






              lowercase		:str       							=				'''lower newer'''
              lowercase		:Optional[int]       							=				'''lower newer'''
              return input_text, output_text






       def  __snake_case						(   self  :    List[str]				):

              '''simple docstring'''






              lowercase		:str       							=				self.tokenizer_class(self.vocab_file  ,     self.merges_file  ,     **self.special_tokens_map				)
              lowercase		:Optional[int]       							=				'''lower newer'''
              lowercase		:int       							=				['''l''', '''o''', '''w''', '''er''', '''\u0120''', '''n''', '''e''', '''w''', '''er''']
              lowercase		:Any       							=				tokenizer.tokenize(__A				)  # , add_prefix_space=True)
              self.assertListEqual(__A  ,     __A				)

              lowercase		:Any       							=				tokens + [tokenizer.unk_token]
              lowercase		:Union[str, Any]       							=				[0, 1, 2, 1_5, 1_0, 9, 3, 2, 1_5, 1_9]
              self.assertListEqual(tokenizer.convert_tokens_to_ids(__A				)  ,     __A				)






       def  __snake_case						(   self  :    Optional[Any]				):

              '''simple docstring'''






              lowercase		:Union[str, Any]       							=				self.get_tokenizer()

              self.assertListEqual(tokenizer.encode('''Hello world!'''  ,     add_special_tokens=__A				)  ,     [0, 3_1_4_1_4, 2_3_2, 3_2_8, 2]				)
              self.assertListEqual(
                  tokenizer.encode('''Hello world! cécé herlolip 418'''  ,     add_special_tokens=__A				)  ,     [0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9, 4_6_0_7_8, 1_5_8_8, 2]  ,     )






       @slow
       def  __snake_case						(   self  :    List[Any]				):

              '''simple docstring'''






              lowercase		:List[str]       							=				self.tokenizer_class.from_pretrained('''roberta-base'''				)

              lowercase		:Union[str, Any]       							=				tokenizer.encode('''sequence builders'''  ,     add_special_tokens=__A				)
              lowercase		:Union[str, Any]       							=				tokenizer.encode('''multi-sequence build'''  ,     add_special_tokens=__A				)

              lowercase		:Dict       							=				tokenizer.encode(
                  '''sequence builders'''  ,     add_special_tokens=__A  ,     add_prefix_space=__A				)
              lowercase		:int       							=				tokenizer.encode(
                  '''sequence builders'''  ,     '''multi-sequence build'''  ,     add_special_tokens=__A  ,     add_prefix_space=__A				)

              lowercase		:str       							=				tokenizer.build_inputs_with_special_tokens(__A				)
              lowercase		:int       							=				tokenizer.build_inputs_with_special_tokens(__A  ,     __A				)

              assert encoded_sentence == encoded_text_from_decode
              assert encoded_pair == encoded_pair_from_decode






       def  __snake_case						(   self  :    Any				):

              '''simple docstring'''






              lowercase		:Tuple       							=				self.get_tokenizer()

              lowercase		:Tuple       							=				'''Encode this sequence.'''
              lowercase		:str       							=				tokenizer.byte_encoder[''' '''.encode('''utf-8'''				)[0]]

              # Testing encoder arguments
              lowercase		:List[Any]       							=				tokenizer.encode(__A  ,     add_special_tokens=__A  ,     add_prefix_space=__A				)
              lowercase		:Any       							=				tokenizer.convert_ids_to_tokens(encoded[0]				)[0]
              self.assertNotEqual(__A  ,     __A				)

              lowercase		:Optional[Any]       							=				tokenizer.encode(__A  ,     add_special_tokens=__A  ,     add_prefix_space=__A				)
              lowercase		:List[Any]       							=				tokenizer.convert_ids_to_tokens(encoded[0]				)[0]
              self.assertEqual(__A  ,     __A				)

              tokenizer.add_special_tokens({'''bos_token''': '''<s>'''}				)
              lowercase		:List[Any]       							=				tokenizer.encode(__A  ,     add_special_tokens=__A				)
              lowercase		:List[Any]       							=				tokenizer.convert_ids_to_tokens(encoded[1]				)[0]
              self.assertNotEqual(__A  ,     __A				)

              # Testing spaces after special tokens
              lowercase		:Any       							=				'''<mask>'''
              tokenizer.add_special_tokens(
                  {'''mask_token''': AddedToken(__A  ,     lstrip=__A  ,     rstrip=__A				)}				)  # mask token has a left space
              lowercase		:int       							=				tokenizer.convert_tokens_to_ids(__A				)

              lowercase		:Any       							=				'''Encode <mask> sequence'''
              lowercase		:Dict       							=				'''Encode <mask>sequence'''

              lowercase		:int       							=				tokenizer.encode(__A				)
              lowercase		:Optional[int]       							=				encoded.index(__A				)
              lowercase		:int       							=				tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]				)[0]
              self.assertEqual(__A  ,     __A				)

              lowercase		:Tuple       							=				tokenizer.encode(__A				)
              lowercase		:List[Any]       							=				encoded.index(__A				)
              lowercase		:Any       							=				tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]				)[0]
              self.assertNotEqual(__A  ,     __A				)






       def  __snake_case						(   self  :    List[Any]				):

              '''simple docstring'''






              pass






       def  __snake_case						(   self  :    Any				):

              '''simple docstring'''






              for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
                     with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""				):
                            lowercase		:Tuple       							=				self.rust_tokenizer_class.from_pretrained(__A  ,     **__A				)
                            lowercase		:Optional[int]       							=				self.tokenizer_class.from_pretrained(__A  ,     **__A				)
                            lowercase		:Tuple       							=				'''A, <mask> AllenNLP sentence.'''
                            lowercase		:Optional[int]       							=				tokenizer_r.encode_plus(__A  ,     add_special_tokens=__A  ,     return_token_type_ids=__A				)
                            lowercase		:Dict       							=				tokenizer_p.encode_plus(__A  ,     add_special_tokens=__A  ,     return_token_type_ids=__A				)

                            # token_type_ids should put 0 everywhere
                            self.assertEqual(sum(tokens_r['''token_type_ids''']				)  ,     sum(tokens_p['''token_type_ids''']				)				)

                            # attention_mask should put 1 everywhere, so sum over length should be 1
                            self.assertEqual(
                                sum(tokens_r['''attention_mask''']				) / len(tokens_r['''attention_mask''']				)  ,     sum(tokens_p['''attention_mask''']				) / len(tokens_p['''attention_mask''']				)  ,     )

                            lowercase		:Optional[int]       							=				tokenizer_r.convert_ids_to_tokens(tokens_r['''input_ids''']				)
                            lowercase		:int       							=				tokenizer_p.convert_ids_to_tokens(tokens_p['''input_ids''']				)

                            # Rust correctly handles the space before the mask while python doesnt
                            self.assertSequenceEqual(tokens_p['''input_ids''']  ,     [0, 2_5_0, 6, 5_0_2_6_4, 3_8_2_3, 4_8_7, 2_1_9_9_2, 3_6_4_5, 4, 2]				)
                            self.assertSequenceEqual(tokens_r['''input_ids''']  ,     [0, 2_5_0, 6, 5_0_2_6_4, 3_8_2_3, 4_8_7, 2_1_9_9_2, 3_6_4_5, 4, 2]				)

                            self.assertSequenceEqual(
                                __A  ,     ['''<s>''', '''A''', ''',''', '''<mask>''', '''ĠAllen''', '''N''', '''LP''', '''Ġsentence''', '''.''', '''</s>''']				)
                            self.assertSequenceEqual(
                                __A  ,     ['''<s>''', '''A''', ''',''', '''<mask>''', '''ĠAllen''', '''N''', '''LP''', '''Ġsentence''', '''.''', '''</s>''']				)






       def  __snake_case						(   self  :    List[str]				):

              '''simple docstring'''






              for trim_offsets, add_prefix_space in itertools.product([True, False]  ,     repeat=2				):
                     lowercase		:str       							=				self.rust_tokenizer_class.from_pretrained(
                         self.tmpdirname  ,     use_fast=__A  ,     add_prefix_space=__A  ,     trim_offsets=__A				)

                     lowercase		:Dict       							=				json.loads(tokenizer_r.backend_tokenizer.pre_tokenizer.__getstate__()				)
                     lowercase		:str       							=				json.loads(tokenizer_r.backend_tokenizer.post_processor.__getstate__()				)

                     self.assertEqual(pre_tokenizer_state['''add_prefix_space''']  ,     __A				)

                     self.assertEqual(post_processor_state['''add_prefix_space''']  ,     __A				)
                     self.assertEqual(post_processor_state['''trim_offsets''']  ,     __A				)







       def  __snake_case						(   self  :    Optional[int]				):

              '''simple docstring'''






              for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
                     with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""				):
                            lowercase		:Union[str, Any]       							=				'''hello'''  # `hello` is a token in the vocabulary of `pretrained_name`
                            lowercase		:List[Any]       							=				f"""{text_of_1_token} {text_of_1_token}"""

                            lowercase		:Optional[int]       							=				self.rust_tokenizer_class.from_pretrained(
                                __A  ,     use_fast=__A  ,     add_prefix_space=__A  ,     trim_offsets=__A				)
                            lowercase		:Any       							=				tokenizer_r(__A  ,     return_offsets_mapping=__A  ,     add_special_tokens=__A				)
                            self.assertEqual(encoding.offset_mapping[0]  ,     (0, len(__A				))				)
                            self.assertEqual(
                                encoding.offset_mapping[1]  ,     (len(__A				) + 1, len(__A				) + 1 + len(__A				))  ,     )

                            lowercase		:Union[str, Any]       							=				self.rust_tokenizer_class.from_pretrained(
                                __A  ,     use_fast=__A  ,     add_prefix_space=__A  ,     trim_offsets=__A				)
                            lowercase		:str       							=				tokenizer_r(__A  ,     return_offsets_mapping=__A  ,     add_special_tokens=__A				)
                            self.assertEqual(encoding.offset_mapping[0]  ,     (0, len(__A				))				)
                            self.assertEqual(
                                encoding.offset_mapping[1]  ,     (len(__A				) + 1, len(__A				) + 1 + len(__A				))  ,     )

                            lowercase		:List[Any]       							=				self.rust_tokenizer_class.from_pretrained(
                                __A  ,     use_fast=__A  ,     add_prefix_space=__A  ,     trim_offsets=__A				)
                            lowercase		:Any       							=				tokenizer_r(__A  ,     return_offsets_mapping=__A  ,     add_special_tokens=__A				)
                            self.assertEqual(encoding.offset_mapping[0]  ,     (0, len(__A				))				)
                            self.assertEqual(
                                encoding.offset_mapping[1]  ,     (len(__A				), len(__A				) + 1 + len(__A				))  ,     )

                            lowercase		:str       							=				self.rust_tokenizer_class.from_pretrained(
                                __A  ,     use_fast=__A  ,     add_prefix_space=__A  ,     trim_offsets=__A				)
                            lowercase		:Any       							=				tokenizer_r(__A  ,     return_offsets_mapping=__A  ,     add_special_tokens=__A				)
                            self.assertEqual(encoding.offset_mapping[0]  ,     (0, len(__A				))				)
                            self.assertEqual(
                                encoding.offset_mapping[1]  ,     (len(__A				), len(__A				) + 1 + len(__A				))  ,     )

                            lowercase		:Union[str, Any]       							=				f""" {text}"""

                            # tokenizer_r = self.rust_tokenizer_class.from_pretrained(
                            #     pretrained_name, use_fast=True, add_prefix_space=True, trim_offsets=True
                            # )
                            # encoding = tokenizer_r(text, return_offsets_mapping=True, add_special_tokens=False)
                            # self.assertEqual(encoding.offset_mapping[0], (1, 1 + len(text_of_1_token)))
                            # self.assertEqual(
                            #     encoding.offset_mapping[1],
                            #     (1 + len(text_of_1_token) + 1, 1 + len(text_of_1_token) + 1 + len(text_of_1_token)),
                            # )

                            lowercase		:List[Any]       							=				self.rust_tokenizer_class.from_pretrained(
                                __A  ,     use_fast=__A  ,     add_prefix_space=__A  ,     trim_offsets=__A				)
                            lowercase		:str       							=				tokenizer_r(__A  ,     return_offsets_mapping=__A  ,     add_special_tokens=__A				)
                            self.assertEqual(encoding.offset_mapping[0]  ,     (1, 1 + len(__A				))				)
                            self.assertEqual(
                                encoding.offset_mapping[1]  ,     (1 + len(__A				) + 1, 1 + len(__A				) + 1 + len(__A				))  ,     )

                            lowercase		:str       							=				self.rust_tokenizer_class.from_pretrained(
                                __A  ,     use_fast=__A  ,     add_prefix_space=__A  ,     trim_offsets=__A				)
                            lowercase		:int       							=				tokenizer_r(__A  ,     return_offsets_mapping=__A  ,     add_special_tokens=__A				)
                            self.assertEqual(encoding.offset_mapping[0]  ,     (0, 1 + len(__A				))				)
                            self.assertEqual(
                                encoding.offset_mapping[1]  ,     (1 + len(__A				), 1 + len(__A				) + 1 + len(__A				))  ,     )

                            lowercase		:Dict       							=				self.rust_tokenizer_class.from_pretrained(
                                __A  ,     use_fast=__A  ,     add_prefix_space=__A  ,     trim_offsets=__A				)
                            lowercase		:List[Any]       							=				tokenizer_r(__A  ,     return_offsets_mapping=__A  ,     add_special_tokens=__A				)
                            self.assertEqual(encoding.offset_mapping[0]  ,     (0, 1 + len(__A				))				)
                            self.assertEqual(
                                encoding.offset_mapping[1]  ,     (1 + len(__A				), 1 + len(__A				) + 1 + len(__A				))  ,     )





"""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


UpperCAmelCase      		=			'''sshleifer/mar_enro_6_3_student'''
class 					__magic_name__		(			__UpperCAmelCase     ):






       def  __snake_case						(   self  :    int				):

              '''simple docstring'''






              super().setUp()

              lowercase		:Union[str, Any]       							=				cached_path(
                  '''https://cdn-datasets.huggingface.co/translation/wmt_en_ro-tr40k-va0.5k-te0.5k.tar.gz'''  ,     extract_compressed_file=snake_case__  ,     )
              lowercase		:int       							=				f"""{data_cached}/wmt_en_ro-tr40k-va0.5k-te0.5k"""






       @slow
       @require_torch_gpu
       def  __snake_case						(   self  :    Any				):

              '''simple docstring'''






              MarianMTModel.from_pretrained(snake_case__				)







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

              '''simple docstring'''






              lowercase		:str       							=				{
                  '''$MAX_LEN''': 6_4,
                  '''$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
              lowercase		:str       							=				(self.test_file_dir / '''train_mbart_cc25_enro.sh''').open().read().split('''finetune.py'''				)[1].strip()
              lowercase		:Any       							=				bash_script.replace('''\\\n'''  ,     ''''''				).strip().replace('''"$@"'''  ,     ''''''				)
              for k, v in env_vars_to_replace.items():
                     lowercase		:Dict       							=				bash_script.replace(snake_case__  ,     str(snake_case__				)				)
              lowercase		:Any       							=				self.get_auto_remove_tmp_dir()

              # bash_script = bash_script.replace("--fp16 ", "")
              lowercase		:Optional[Any]       							=				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

              lowercase		:str       							=				['''finetune.py'''] + bash_script.split() + args
              with patch.object(snake_case__  ,     '''argv'''  ,     snake_case__				):
                     lowercase		:Optional[int]       							=				argparse.ArgumentParser()
                     lowercase		:List[Any]       							=				pl.Trainer.add_argparse_args(snake_case__				)
                     lowercase		:Union[str, Any]       							=				SummarizationModule.add_model_specific_args(snake_case__  ,     os.getcwd()				)
                     lowercase		:int       							=				parser.parse_args()
                     lowercase		:int       							=				main(snake_case__				)

              # Check metrics
              lowercase		:int       							=				load_json(model.metrics_save_path				)
              lowercase		:Tuple       							=				metrics['''val'''][0]
              lowercase		:Any       							=				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}"""]  ,     snake_case__				)

              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
              lowercase		:Any       							=				os.listdir(snake_case__				)
              lowercase		:int       							=				[x for x in contents if x.endswith('''.ckpt'''				)][0]
              lowercase		:Any       							=				os.path.join(args.output_dir  ,     snake_case__				)
              lowercase		:int       							=				torch.load(snake_case__  ,     map_location='''cpu'''				)
              lowercase		:Any       							=				'''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:
                     lowercase		:Optional[Any]       							=				{os.path.basename(snake_case__				) 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 					__magic_name__		(			__UpperCAmelCase     ):






       @timeout_decorator.timeout(6_0_0				)
       @slow
       @require_torch_gpu
       def  __snake_case						(   self  :    Optional[int]				):

              '''simple docstring'''






              lowercase		:Union[str, Any]       							=				f"""{self.test_file_dir_str}/test_data/wmt_en_ro"""
              lowercase		:List[Any]       							=				{
                  '''--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
              lowercase		:Dict       							=				(
                  (self.test_file_dir / '''distil_marian_no_teacher.sh''').open().read().split('''distillation.py'''				)[1].strip()
              )
              lowercase		:Union[str, Any]       							=				bash_script.replace('''\\\n'''  ,     ''''''				).strip().replace('''"$@"'''  ,     ''''''				)
              lowercase		:Dict       							=				bash_script.replace('''--fp16 '''  ,     ''' '''				)

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

                     lowercase		:Tuple       							=				distill_main(snake_case__				)

              # Check metrics
              lowercase		:Tuple       							=				load_json(model.metrics_save_path				)
              lowercase		:int       							=				metrics['''val'''][0]
              lowercase		:List[str]       							=				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}"""]  ,     snake_case__				)

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

