


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	: Optional[Any]  				='sshleifer/mar_enro_6_3_student'
class        _lowercase      (			A__       ):






				'''simple docstring'''



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

										__SCREAMING_SNAKE_CASE						:     Tuple					=		cached_path(
										    '''https://cdn-datasets.huggingface.co/translation/wmt_en_ro-tr40k-va0.5k-te0.5k.tar.gz'''			,			extract_compressed_file=lowerCAmelCase__			,			)
										__SCREAMING_SNAKE_CASE						:     Optional[int]					=		f'''{data_cached}/wmt_en_ro-tr40k-va0.5k-te0.5k'''


				@slow
				@require_torch_gpu
				def 		__magic_name__(       self			:List[Any]	)		->			List[str]:
										MarianMTModel.from_pretrained(lowerCAmelCase__	)



				@slow
				@require_torch_gpu
				def 		__magic_name__(       self			:Dict	)		->			Optional[Any]:
										__SCREAMING_SNAKE_CASE						:     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
										__SCREAMING_SNAKE_CASE						:     List[Any]					=		(self.test_file_dir / '''train_mbart_cc25_enro.sh''').open().read().split('''finetune.py'''	)[1].strip()
										__SCREAMING_SNAKE_CASE						:     Any					=		bash_script.replace('''\\\n'''			,			''''''	).strip().replace('''"$@"'''			,			''''''	)
										for k, v in env_vars_to_replace.items():
																__SCREAMING_SNAKE_CASE						:     List[Any]					=		bash_script.replace(lowerCAmelCase__			,			str(lowerCAmelCase__	)	)
										__SCREAMING_SNAKE_CASE						:     Any					=		self.get_auto_remove_tmp_dir()

										# bash_script = bash_script.replace("--fp16 ", "")
										__SCREAMING_SNAKE_CASE						:     Dict					=		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

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

										# Check metrics
										__SCREAMING_SNAKE_CASE						:     List[str]					=		load_json(model.metrics_save_path	)
										__SCREAMING_SNAKE_CASE						:     int					=		metrics['''val'''][0]
										__SCREAMING_SNAKE_CASE						:     Dict					=		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}''']			,			lowerCAmelCase__	)

										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
										__SCREAMING_SNAKE_CASE						:     Dict					=		os.listdir(lowerCAmelCase__	)
										__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		[x for x in contents if x.endswith('''.ckpt'''	)][0]
										__SCREAMING_SNAKE_CASE						:     int					=		os.path.join(args.output_dir			,			lowerCAmelCase__	)
										__SCREAMING_SNAKE_CASE						:     int					=		torch.load(lowerCAmelCase__			,			map_location='''cpu'''	)
										__SCREAMING_SNAKE_CASE						:     Union[str, 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:
																__SCREAMING_SNAKE_CASE						:     Dict					=		{os.path.basename(lowerCAmelCase__	) 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        _lowercase      (			A__       ):






				'''simple docstring'''



				@timeout_decorator.timeout(600	)
				@slow
				@require_torch_gpu
				def 		__magic_name__(       self			:List[Any]	)		->			int:
										__SCREAMING_SNAKE_CASE						:     str					=		f'''{self.test_file_dir_str}/test_data/wmt_en_ro'''
										__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		{
										    '''--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
										__SCREAMING_SNAKE_CASE						:     int					=		(
										    (self.test_file_dir / '''distil_marian_no_teacher.sh''').open().read().split('''distillation.py'''	)[1].strip()
										)
										__SCREAMING_SNAKE_CASE						:     Optional[int]					=		bash_script.replace('''\\\n'''			,			''''''	).strip().replace('''"$@"'''			,			''''''	)
										__SCREAMING_SNAKE_CASE						:     Tuple					=		bash_script.replace('''--fp16 '''			,			''' '''	)

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

																__SCREAMING_SNAKE_CASE						:     Any					=		distill_main(lowerCAmelCase__	)

										# Check metrics
										__SCREAMING_SNAKE_CASE						:     List[Any]					=		load_json(model.metrics_save_path	)
										__SCREAMING_SNAKE_CASE						:     List[str]					=		metrics['''val'''][0]
										__SCREAMING_SNAKE_CASE						:     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}''']			,			lowerCAmelCase__	)

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

from transformers.models.transfo_xl.tokenization_transfo_xl import VOCAB_FILES_NAMES, TransfoXLTokenizer

from ...test_tokenization_common import TokenizerTesterMixin
class        _lowercase      (			A__						,			unittest.TestCase       ):






				'''simple docstring'''



				SCREAMING_SNAKE_CASE__				:			Optional[int]						=		TransfoXLTokenizer
				SCREAMING_SNAKE_CASE__				:			int						=		False
				SCREAMING_SNAKE_CASE__				:			Optional[Any]						=		False


				def 		__magic_name__(       self			:str	)		->			Dict:
										super().setUp()

										__SCREAMING_SNAKE_CASE						:     List[str]					=		[
										    '''<unk>''',
										    '''[CLS]''',
										    '''[SEP]''',
										    '''want''',
										    '''unwanted''',
										    '''wa''',
										    '''un''',
										    '''running''',
										    ''',''',
										    '''low''',
										    '''l''',
										]
										__SCREAMING_SNAKE_CASE						:     Any					=		os.path.join(self.tmpdirname			,			VOCAB_FILES_NAMES['''vocab_file''']	)
										with open(self.vocab_file			,			'''w'''			,			encoding='''utf-8'''	) as vocab_writer:
																vocab_writer.write(''''''.join([x + '''\n''' for x in vocab_tokens]	)	)


				def 		__magic_name__(       self			:Any			,			**lowerCAmelCase__			:int	)		->			str:
										__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		True
										return TransfoXLTokenizer.from_pretrained(self.tmpdirname			,			**lowerCAmelCase__	)


				def 		__magic_name__(       self			:Union[str, Any]			,			lowerCAmelCase__			:List[Any]	)		->			Tuple:
										__SCREAMING_SNAKE_CASE						:     Dict					=		'''<unk> UNwanted , running'''
										__SCREAMING_SNAKE_CASE						:     List[str]					=		'''<unk> unwanted, running'''
										return input_text, output_text


				def 		__magic_name__(       self			:Any	)		->			Union[str, Any]:
										__SCREAMING_SNAKE_CASE						:     int					=		TransfoXLTokenizer(vocab_file=self.vocab_file			,			lower_case=lowerCAmelCase__	)

										__SCREAMING_SNAKE_CASE						:     int					=		tokenizer.tokenize('''<unk> UNwanted , running'''	)
										self.assertListEqual(lowerCAmelCase__			,			['''<unk>''', '''unwanted''', ''',''', '''running''']	)

										self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCAmelCase__	)			,			[0, 4, 8, 7]	)


				def 		__magic_name__(       self			:Tuple	)		->			Union[str, Any]:
										__SCREAMING_SNAKE_CASE						:     Optional[int]					=		TransfoXLTokenizer(lower_case=lowerCAmelCase__	)

										self.assertListEqual(
										    tokenizer.tokenize(''' \tHeLLo ! how  \n Are yoU ?  '''	)			,			['''hello''', '''!''', '''how''', '''are''', '''you''', '''?''']	)


				def 		__magic_name__(       self			:Tuple	)		->			List[Any]:
										__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		TransfoXLTokenizer(lower_case=lowerCAmelCase__	)

										self.assertListEqual(
										    tokenizer.tokenize(''' \tHeLLo ! how  \n Are yoU ?  '''	)			,			['''HeLLo''', '''!''', '''how''', '''Are''', '''yoU''', '''?''']	)


				def 		__magic_name__(       self			:Dict	)		->			List[Any]:
										__SCREAMING_SNAKE_CASE						:     List[str]					=		TransfoXLTokenizer(lower_case=lowerCAmelCase__	)
										__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		'''Hello (bracket) and side-scrolled [and] Henry\'s $5,000 with 3.34 m. What\'s up!?'''
										__SCREAMING_SNAKE_CASE						:     Optional[int]					=		[
										    '''Hello''',
										    '''(''',
										    '''bracket''',
										    ''')''',
										    '''and''',
										    '''side''',
										    '''@-@''',
										    '''scrolled''',
										    '''[''',
										    '''and''',
										    ''']''',
										    '''Henry''',
										    '''\'s''',
										    '''$''',
										    '''5''',
										    '''@,@''',
										    '''000''',
										    '''with''',
										    '''3''',
										    '''@.@''',
										    '''34''',
										    '''m''',
										    '''.''',
										    '''What''',
										    '''\'s''',
										    '''up''',
										    '''!''',
										    '''?''',
										]

										self.assertListEqual(tokenizer.tokenize(lowerCAmelCase__	)			,			lowerCAmelCase__	)

										self.assertEqual(tokenizer.convert_tokens_to_string(lowerCAmelCase__	)			,			lowerCAmelCase__	)



				def 		__magic_name__(       self			:str	)		->			int:
										__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		self.get_tokenizer()
										__SCREAMING_SNAKE_CASE						:     Any					=		len(lowerCAmelCase__	)

										tokenizer.add_tokens(['''new1''', '''new2''']	)
										tokenizer.move_added_token('''new1'''			,			1	)

										# Check that moved token is not copied (duplicate)
										self.assertEqual(len(lowerCAmelCase__	)			,			original_len + 2	)
										# Check that token is moved to specified id
										self.assertEqual(tokenizer.encode('''new1'''	)			,			[1]	)
										self.assertEqual(tokenizer.decode([1]	)			,			'''new1'''	)
