





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


lowercase__						:      Optional[int]	=    'sshleifer/mar_enro_6_3_student'
class     __lowerCAmelCase					(   _snake_case			):






       """simple docstring"""





       def snake_case__						(							self       :		Optional[int]					)   ->					Optional[Any]:

            '''simple docstring'''




            super().setUp()

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





       @slow
       @require_torch_gpu
       def snake_case__						(							self       :		List[Any]					)   ->					Union[str, Any]:

            '''simple docstring'''




            MarianMTModel.from_pretrained(UpperCamelCase__					)







       @slow
       @require_torch_gpu
       def snake_case__						(							self       :		Dict					)   ->					Optional[int]:

            '''simple docstring'''




            _UpperCamelCase       =	{
                '''$MAX_LEN''': 64,
                '''$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
            _UpperCamelCase       =	(self.test_file_dir / '''train_mbart_cc25_enro.sh''').open().read().split('''finetune.py'''					)[1].strip()
            _UpperCamelCase       =	bash_script.replace('''\\\n'''	,			''''''					).strip().replace('''\"$@\"'''	,			''''''					)
            for k, v in env_vars_to_replace.items():
                 _UpperCamelCase       =	bash_script.replace(UpperCamelCase__	,			str(UpperCamelCase__					)					)
            _UpperCamelCase       =	self.get_auto_remove_tmp_dir()

            # bash_script = bash_script.replace("--fp16 ", "")
            _UpperCamelCase       =	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

            _UpperCamelCase       =	['''finetune.py'''] + bash_script.split() + args
            with patch.object(UpperCamelCase__	,			'''argv'''	,			UpperCamelCase__					):
                 _UpperCamelCase       =	argparse.ArgumentParser()
                 _UpperCamelCase       =	pl.Trainer.add_argparse_args(UpperCamelCase__					)
                 _UpperCamelCase       =	SummarizationModule.add_model_specific_args(UpperCamelCase__	,			os.getcwd()					)
                 _UpperCamelCase       =	parser.parse_args()
                 _UpperCamelCase       =	main(UpperCamelCase__					)

            # Check metrics
            _UpperCamelCase       =	load_json(model.metrics_save_path					)
            _UpperCamelCase       =	metrics['''val'''][0]
            _UpperCamelCase       =	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}"""]	,			UpperCamelCase__					)

            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
            _UpperCamelCase       =	os.listdir(UpperCamelCase__					)
            _UpperCamelCase       =	[x for x in contents if x.endswith('''.ckpt'''					)][0]
            _UpperCamelCase       =	os.path.join(args.output_dir	,			UpperCamelCase__					)
            _UpperCamelCase       =	torch.load(UpperCamelCase__	,			map_location='''cpu'''					)
            _UpperCamelCase       =	'''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:
                 _UpperCamelCase       =	{os.path.basename(UpperCamelCase__					) 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     __lowerCAmelCase					(   _snake_case			):






       """simple docstring"""





       @timeout_decorator.timeout(600					)
       @slow
       @require_torch_gpu
       def snake_case__						(							self       :		Union[str, Any]					)   ->					Optional[int]:

            '''simple docstring'''




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

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

                 _UpperCamelCase       =	distill_main(UpperCamelCase__					)

            # Check metrics
            _UpperCamelCase       =	load_json(model.metrics_save_path					)
            _UpperCamelCase       =	metrics['''val'''][0]
            _UpperCamelCase       =	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}"""]	,			UpperCamelCase__					)

            # check lightning ckpt can be loaded and has a reasonable statedict
            _UpperCamelCase       =	os.listdir(UpperCamelCase__					)
            _UpperCamelCase       =	[x for x in contents if x.endswith('''.ckpt'''					)][0]
            _UpperCamelCase       =	os.path.join(args.output_dir	,			UpperCamelCase__					)
            _UpperCamelCase       =	torch.load(UpperCamelCase__	,			map_location='''cpu'''					)
            _UpperCamelCase       =	'''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:
                 _UpperCamelCase       =	{os.path.basename(UpperCamelCase__					) 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










'''simple docstring'''

import unittest

from transformers import CamembertTokenizer, CamembertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import is_torch_available

from ...test_tokenization_common import TokenizerTesterMixin


lowercase__						:      Optional[Any]	=    get_tests_dir('fixtures/test_sentencepiece.model')
lowercase__						:      Any	=    get_tests_dir('fixtures/test_sentencepiece_bpe.model')

lowercase__						:      Tuple	=    'pt' if is_torch_available() else 'tf'
@require_sentencepiece
@require_tokenizers
class     __lowerCAmelCase					(   __magic_name__						,						unittest.TestCase			):






       """simple docstring"""
       _snake_case     :					Union[str, Any]						    =   CamembertTokenizer
       _snake_case     :					str						    =   CamembertTokenizerFast
       _snake_case     :					int						    =   True
       _snake_case     :					List[str]						    =   True





       def snake_case__						(							self       :		Dict					)   ->					Any:

            '''simple docstring'''




            super().setUp()

            # We have a SentencePiece fixture for testing
            _UpperCamelCase       =	CamembertTokenizer(lowerCAmelCase__					)
            tokenizer.save_pretrained(self.tmpdirname					)





       def snake_case__						(							self       :		Optional[int]					)   ->					List[Any]:

            '''simple docstring'''




            _UpperCamelCase       =	'''<pad>'''
            _UpperCamelCase       =	1

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





       def snake_case__						(							self       :		Dict					)   ->					List[Any]:

            '''simple docstring'''




            _UpperCamelCase       =	list(self.get_tokenizer().get_vocab().keys()					)

            self.assertEqual(vocab_keys[0]	,			'''<s>NOTUSED'''					)
            self.assertEqual(vocab_keys[1]	,			'''<pad>'''					)
            self.assertEqual(vocab_keys[-1]	,			'''<mask>'''					)
            self.assertEqual(len(lowerCAmelCase__					)	,			1004					)





       def snake_case__						(							self       :		Optional[Any]					)   ->					List[str]:

            '''simple docstring'''




            self.assertEqual(self.get_tokenizer().vocab_size	,			1005					)





       def snake_case__						(							self       :		int					)   ->					Tuple:

            '''simple docstring'''




            _UpperCamelCase       =	CamembertTokenizer(lowerCAmelCase__					)
            tokenizer.save_pretrained(self.tmpdirname					)
            _UpperCamelCase       =	CamembertTokenizerFast.from_pretrained(self.tmpdirname					)

            _UpperCamelCase       =	'''I was born in 92000, and this is falsé.'''

            _UpperCamelCase       =	tokenizer.encode(lowerCAmelCase__					)
            _UpperCamelCase       =	rust_tokenizer.encode(lowerCAmelCase__					)
            self.assertListEqual(lowerCAmelCase__	,			lowerCAmelCase__					)

            _UpperCamelCase       =	tokenizer.encode(lowerCAmelCase__	,			add_special_tokens=lowerCAmelCase__					)
            _UpperCamelCase       =	rust_tokenizer.encode(lowerCAmelCase__	,			add_special_tokens=lowerCAmelCase__					)
            self.assertListEqual(lowerCAmelCase__	,			lowerCAmelCase__					)

            # <unk> tokens are not the same for `rust` than for `slow`.
            # Because spm gives back raw token instead of `unk` in EncodeAsPieces
            # tokens = tokenizer.tokenize(sequence)
            _UpperCamelCase       =	tokenizer.convert_ids_to_tokens(lowerCAmelCase__					)
            _UpperCamelCase       =	rust_tokenizer.tokenize(lowerCAmelCase__					)
            self.assertListEqual(lowerCAmelCase__	,			lowerCAmelCase__					)





       def snake_case__						(							self       :		Optional[int]					)   ->					List[Any]:

            '''simple docstring'''




            if not self.test_rust_tokenizer:
                 return

            _UpperCamelCase       =	self.get_tokenizer()
            _UpperCamelCase       =	self.get_rust_tokenizer()

            _UpperCamelCase       =	'''I was born in 92000, and this is falsé.'''

            _UpperCamelCase       =	tokenizer.tokenize(lowerCAmelCase__					)
            _UpperCamelCase       =	rust_tokenizer.tokenize(lowerCAmelCase__					)
            self.assertListEqual(lowerCAmelCase__	,			lowerCAmelCase__					)

            _UpperCamelCase       =	tokenizer.encode(lowerCAmelCase__	,			add_special_tokens=lowerCAmelCase__					)
            _UpperCamelCase       =	rust_tokenizer.encode(lowerCAmelCase__	,			add_special_tokens=lowerCAmelCase__					)
            self.assertListEqual(lowerCAmelCase__	,			lowerCAmelCase__					)

            _UpperCamelCase       =	self.get_rust_tokenizer()
            _UpperCamelCase       =	tokenizer.encode(lowerCAmelCase__					)
            _UpperCamelCase       =	rust_tokenizer.encode(lowerCAmelCase__					)
            self.assertListEqual(lowerCAmelCase__	,			lowerCAmelCase__					)







       @slow
       def snake_case__						(							self       :		Any					)   ->					Optional[Any]:

            '''simple docstring'''




            _UpperCamelCase       =	{'''input_ids''': [[5, 54, 7196, 297, 30, 23, 776, 18, 11, 3215, 3705, 8252, 22, 3164, 1181, 2116, 29, 16, 813, 25, 791, 3314, 20, 3446, 38, 27575, 120, 6, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [5, 468, 17, 11, 9088, 20, 1517, 8, 22804, 18818, 10, 38, 629, 607, 607, 142, 19, 7196, 867, 56, 10326, 24, 2267, 20, 416, 5072, 15612, 233, 734, 7, 2399, 27, 16, 3015, 1649, 7, 24, 20, 4338, 2399, 27, 13, 3400, 14, 13, 6189, 8, 930, 9, 6]], '''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, 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, 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]]}  # noqa: E501
            # fmt: on

            # camembert is a french model. So we also use french texts.
            _UpperCamelCase       =	[
                '''Le transformeur est un modèle d\'apprentissage profond introduit en 2017, '''
                '''utilisé principalement dans le domaine du traitement automatique des langues (TAL).''',
                '''À l\'instar des réseaux de neurones récurrents (RNN), les transformeurs sont conçus '''
                '''pour gérer des données séquentielles, telles que le langage naturel, pour des tâches '''
                '''telles que la traduction et la synthèse de texte.''',
            ]

            self.tokenizer_integration_test_util(
                expected_encoding=lowerCAmelCase__	,			model_name='''camembert-base'''	,			revision='''3a0641d9a1aeb7e848a74299e7e4c4bca216b4cf'''	,			sequences=lowerCAmelCase__	,			)



