






"""simple docstring"""







import tempfile
import unittest

from transformers import SPIECE_UNDERLINE, BatchEncoding, PLBartTokenizer, is_torch_available
from transformers.testing_utils import (
    get_tests_dir,
    nested_simplify,
    require_sentencepiece,
    require_tokenizers,
    require_torch,
)

from ...test_tokenization_common import TokenizerTesterMixin


__a       :	int             = get_tests_dir('fixtures/test_sentencepiece.model')


if is_torch_available():
	from transformers.models.plbart.modeling_plbart import shift_tokens_right

__a       :	Union[str, Any]             = 5_0003
__a       :	int             = 5_0002







@require_sentencepiece
@require_tokenizers
class        _SCREAMING_SNAKE_CASE						(  __snake_case       , unittest.TestCase							):





							"""simple docstring"""





							_SCREAMING_SNAKE_CASE    =PLBartTokenizer
							_SCREAMING_SNAKE_CASE    =None
							_SCREAMING_SNAKE_CASE    =False




							def  lowercase		(   self:     Tuple    ):

												'''simple docstring'''

												super().setUp()

												# We have a SentencePiece fixture for testing
												a__          =  PLBartTokenizer(__A				,		language_codes='''base'''				,		keep_accents=__A    )
												tokenizer.save_pretrained(self.tmpdirname    )




							def  lowercase		(   self:     Any    ):

												'''simple docstring'''

												a__          =  PLBartTokenizer(__A				,		language_codes='''base'''				,		keep_accents=__A    )

												a__          =  tokenizer.tokenize('''This is a test'''    )
												self.assertListEqual(__A				,		['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est''']    )

												self.assertListEqual(
												    tokenizer.convert_tokens_to_ids(__A    )				,		[value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]]				,		)

												a__          =  tokenizer.tokenize('''I was born in 92000, and this is falsé.'''    )
												self.assertListEqual(
												    __A				,		[
												        SPIECE_UNDERLINE + '''I''',
												        SPIECE_UNDERLINE + '''was''',
												        SPIECE_UNDERLINE + '''b''',
												        '''or''',
												        '''n''',
												        SPIECE_UNDERLINE + '''in''',
												        SPIECE_UNDERLINE + '''''',
												        '''9''',
												        '''2''',
												        '''0''',
												        '''0''',
												        '''0''',
												        ''',''',
												        SPIECE_UNDERLINE + '''and''',
												        SPIECE_UNDERLINE + '''this''',
												        SPIECE_UNDERLINE + '''is''',
												        SPIECE_UNDERLINE + '''f''',
												        '''al''',
												        '''s''',
												        '''é''',
												        '''.''',
												    ]				,		)
												a__          =  tokenizer.convert_tokens_to_ids(__A    )
												self.assertListEqual(
												    __A				,		[
												        value + tokenizer.fairseq_offset
												        for value in [8, 21, 84, 55, 24, 19, 7, 2, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 2, 4]
												    ]				,		)

												a__          =  tokenizer.convert_ids_to_tokens(__A    )
												self.assertListEqual(
												    __A				,		[
												        SPIECE_UNDERLINE + '''I''',
												        SPIECE_UNDERLINE + '''was''',
												        SPIECE_UNDERLINE + '''b''',
												        '''or''',
												        '''n''',
												        SPIECE_UNDERLINE + '''in''',
												        SPIECE_UNDERLINE + '''''',
												        '''<unk>''',
												        '''2''',
												        '''0''',
												        '''0''',
												        '''0''',
												        ''',''',
												        SPIECE_UNDERLINE + '''and''',
												        SPIECE_UNDERLINE + '''this''',
												        SPIECE_UNDERLINE + '''is''',
												        SPIECE_UNDERLINE + '''f''',
												        '''al''',
												        '''s''',
												        '''<unk>''',
												        '''.''',
												    ]				,		)

												a__          =  tokenizer.vocab_size
												a__          =  [tokenizer.convert_ids_to_tokens(__A    ) for x in range(end - 4				,		__A    )]

												self.assertListEqual(__A				,		['''__java__''', '''__python__''', '''__en_XX__''', '''<mask>''']    )

												a__          =  '''java.lang.Exception, python.lang.Exception, javascript, php, ruby, go'''
												a__          =  tokenizer(__A    ).input_ids
												self.assertEqual(
												    tokenizer.decode(__A				,		skip_special_tokens=__A				,		clean_up_tokenization_spaces=__A    )				,		__A				,		)





							def  lowercase		(   self:     str    ):

												'''simple docstring'''

												a__          =  PLBartTokenizer(__A				,		language_codes='''multi'''				,		keep_accents=__A    )

												a__          =  tokenizer.tokenize('''This is a test'''    )
												self.assertListEqual(__A				,		['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est''']    )

												self.assertListEqual(
												    tokenizer.convert_tokens_to_ids(__A    )				,		[value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]]				,		)

												a__          =  tokenizer.tokenize('''I was born in 92000, and this is falsé.'''    )
												self.assertListEqual(
												    __A				,		[
												        SPIECE_UNDERLINE + '''I''',
												        SPIECE_UNDERLINE + '''was''',
												        SPIECE_UNDERLINE + '''b''',
												        '''or''',
												        '''n''',
												        SPIECE_UNDERLINE + '''in''',
												        SPIECE_UNDERLINE + '''''',
												        '''9''',
												        '''2''',
												        '''0''',
												        '''0''',
												        '''0''',
												        ''',''',
												        SPIECE_UNDERLINE + '''and''',
												        SPIECE_UNDERLINE + '''this''',
												        SPIECE_UNDERLINE + '''is''',
												        SPIECE_UNDERLINE + '''f''',
												        '''al''',
												        '''s''',
												        '''é''',
												        '''.''',
												    ]				,		)
												a__          =  tokenizer.convert_tokens_to_ids(__A    )
												self.assertListEqual(
												    __A				,		[
												        value + tokenizer.fairseq_offset
												        for value in [8, 21, 84, 55, 24, 19, 7, 2, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 2, 4]
												    ]				,		)

												a__          =  tokenizer.convert_ids_to_tokens(__A    )
												self.assertListEqual(
												    __A				,		[
												        SPIECE_UNDERLINE + '''I''',
												        SPIECE_UNDERLINE + '''was''',
												        SPIECE_UNDERLINE + '''b''',
												        '''or''',
												        '''n''',
												        SPIECE_UNDERLINE + '''in''',
												        SPIECE_UNDERLINE + '''''',
												        '''<unk>''',
												        '''2''',
												        '''0''',
												        '''0''',
												        '''0''',
												        ''',''',
												        SPIECE_UNDERLINE + '''and''',
												        SPIECE_UNDERLINE + '''this''',
												        SPIECE_UNDERLINE + '''is''',
												        SPIECE_UNDERLINE + '''f''',
												        '''al''',
												        '''s''',
												        '''<unk>''',
												        '''.''',
												    ]				,		)
												a__          =  tokenizer.vocab_size
												a__          =  [tokenizer.convert_ids_to_tokens(__A    ) for x in range(end - 7				,		__A    )]

												self.assertListEqual(
												    __A				,		['''__java__''', '''__python__''', '''__en_XX__''', '''__javascript__''', '''__php__''', '''__ruby__''', '''__go__''']    )
												a__          =  '''java.lang.Exception, python.lang.Exception, javascript, php, ruby, go'''
												a__          =  tokenizer(__A    ).input_ids
												self.assertEqual(
												    tokenizer.decode(__A				,		skip_special_tokens=__A				,		clean_up_tokenization_spaces=__A    )				,		__A				,		)







@require_torch
@require_sentencepiece
@require_tokenizers
class        _SCREAMING_SNAKE_CASE						(  unittest.TestCase							):





							"""simple docstring"""





							_SCREAMING_SNAKE_CASE    ='uclanlp/plbart-python-en_XX'
							_SCREAMING_SNAKE_CASE    =[
							    'def maximum(a,b,c):NEW_LINE_INDENTreturn max([a,b,c])',
							    'def sum(a,b,c):NEW_LINE_INDENTreturn sum([a,b,c])',
							]
							_SCREAMING_SNAKE_CASE    =[
							    'Returns the maximum value of a b c.',
							    'Sums the values of a b c.',
							]
							_SCREAMING_SNAKE_CASE    =[
							    134,
							    5_452,
							    33_460,
							    33_441,
							    33_463,
							    33_465,
							    33_463,
							    33_449,
							    988,
							    20,
							    33_456,
							    19,
							    33_456,
							    771,
							    39,
							    4_258,
							    889,
							    3_318,
							    33_441,
							    33_463,
							    33_465,
							    33_463,
							    33_449,
							    2_471,
							    2,
							    PYTHON_CODE,
							]




							@classmethod
							def  lowercase		(   cls:     str    ):

												'''simple docstring'''

												a__          =  PLBartTokenizer.from_pretrained(
												    cls.checkpoint_name				,		language_codes='''base'''				,		src_lang='''python'''				,		tgt_lang='''en_XX'''    )
												a__          =  1
												return cls




							def  lowercase		(   self:     Union[str, Any]    ):

												'''simple docstring'''

												self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['''__java__''']				,		50001    )
												self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['''__python__''']				,		50002    )
												self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['''__en_XX__''']				,		50003    )




							def  lowercase		(   self:     Any    ):

												'''simple docstring'''

												a__          =  self.tokenizer.batch_encode_plus(self.src_text    ).input_ids[0]
												self.assertListEqual(self.expected_src_tokens				,		__A    )




							def  lowercase		(   self:     Tuple    ):

												'''simple docstring'''

												self.assertIn(__A				,		self.tokenizer.all_special_ids    )
												a__          =  [EN_CODE, 9037, 33442, 57, 752, 153, 14, 56, 18, 9, 2]
												a__          =  self.tokenizer.decode(__A				,		skip_special_tokens=__A    )
												a__          =  self.tokenizer.decode(generated_ids[1:]				,		skip_special_tokens=__A    )
												self.assertEqual(__A				,		__A    )
												self.assertNotIn(self.tokenizer.eos_token				,		__A    )




							def  lowercase		(   self:     Union[str, Any]    ):

												'''simple docstring'''

												a__          =  ['''def sum(a,b,c):NEW_LINE_INDENTreturn sum([a,b,c])''' * 20]
												self.assertIsInstance(src_text[0]				,		__A    )
												a__          =  10
												a__          =  self.tokenizer(__A				,		max_length=__A				,		truncation=__A    ).input_ids[0]
												self.assertEqual(ids[-2]				,		2    )
												self.assertEqual(ids[-1]				,		__A    )
												self.assertEqual(len(__A    )				,		__A    )




							def  lowercase		(   self:     List[Any]    ):

												'''simple docstring'''

												self.assertListEqual(self.tokenizer.convert_tokens_to_ids(['''<mask>''', '''__java__''']    )				,		[50004, 50001]    )




							def  lowercase		(   self:     Dict    ):

												'''simple docstring'''

												a__          =  tempfile.mkdtemp()
												a__          =  self.tokenizer.fairseq_tokens_to_ids
												self.tokenizer.save_pretrained(__A    )
												a__          =  PLBartTokenizer.from_pretrained(__A    )
												self.assertDictEqual(new_tok.fairseq_tokens_to_ids				,		__A    )




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

												'''simple docstring'''

												a__          =  self.tokenizer(self.src_text				,		text_target=self.tgt_text				,		padding=__A				,		return_tensors='''pt'''    )
												a__          =  shift_tokens_right(batch['''labels''']				,		self.tokenizer.pad_token_id    )

												# fairseq batch: https://gist.github.com/sshleifer/cba08bc2109361a74ac3760a7e30e4f4
												self.assertEqual(batch.input_ids[1][-2:].tolist()				,		[2, PYTHON_CODE]    )
												self.assertEqual(batch.decoder_input_ids[1][0]				,		__A    )
												self.assertEqual(batch.decoder_input_ids[1][-1]				,		2    )
												self.assertEqual(batch.labels[1][-2:].tolist()				,		[2, EN_CODE]    )




							@require_torch
							def  lowercase		(   self:     Any    ):

												'''simple docstring'''

												a__          =  self.tokenizer(
												    self.src_text				,		text_target=self.tgt_text				,		padding=__A				,		truncation=__A				,		max_length=len(self.expected_src_tokens    )				,		return_tensors='''pt'''				,		)
												a__          =  shift_tokens_right(batch['''labels''']				,		self.tokenizer.pad_token_id    )

												self.assertIsInstance(__A				,		__A    )

												self.assertEqual((2, 26)				,		batch.input_ids.shape    )
												self.assertEqual((2, 26)				,		batch.attention_mask.shape    )
												a__          =  batch.input_ids.tolist()[0]
												self.assertListEqual(self.expected_src_tokens				,		__A    )
												self.assertEqual(2				,		batch.decoder_input_ids[0, -1]    )  # EOS
												# Test that special tokens are reset
												self.assertEqual(self.tokenizer.prefix_tokens				,		[]    )
												self.assertEqual(self.tokenizer.suffix_tokens				,		[self.tokenizer.eos_token_id, PYTHON_CODE]    )




							def  lowercase		(   self:     Any    ):

												'''simple docstring'''

												a__          =  self.tokenizer(self.src_text				,		padding=__A				,		truncation=__A				,		max_length=3				,		return_tensors='''pt'''    )
												a__          =  self.tokenizer(
												    text_target=self.tgt_text				,		padding=__A				,		truncation=__A				,		max_length=10				,		return_tensors='''pt'''    )
												a__          =  targets['''input_ids''']
												a__          =  shift_tokens_right(__A				,		self.tokenizer.pad_token_id    )

												self.assertEqual(batch.input_ids.shape[1]				,		3    )
												self.assertEqual(batch.decoder_input_ids.shape[1]				,		10    )





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

												'''simple docstring'''

												a__          =  self.tokenizer._build_translation_inputs(
												    '''A test'''				,		return_tensors='''pt'''				,		src_lang='''en_XX'''				,		tgt_lang='''java'''    )

												self.assertEqual(
												    nested_simplify(__A    )				,		{
												        # A, test, EOS, en_XX
												        '''input_ids''': [[150, 242, 2, 50003]],
												        '''attention_mask''': [[1, 1, 1, 1]],
												        # java
												        '''forced_bos_token_id''': 50001,
												    }				,		)










"""simple docstring"""







import json
import logging
import os
import sys
from pathlib import Path

import finetune_rag

from transformers.file_utils import is_apex_available
from transformers.testing_utils import (
    TestCasePlus,
    execute_subprocess_async,
    require_ray,
    require_torch_gpu,
    require_torch_multi_gpu,
)


logging.basicConfig(level=logging.DEBUG)
__a       :	Optional[int]             = logging.getLogger()

__a       :	Any             = logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)







class        _SCREAMING_SNAKE_CASE						(  __snake_case							):





							"""simple docstring"""





							def  lowercase		(   self:     List[Any]				,		__A:     List[Any]    ):

												'''simple docstring'''

												os.makedirs(__A				,		exist_ok=__A    )
												a__          =  {'''source''': '''What is love ?''', '''target''': '''life'''}
												a__          =  {'''train''': 12, '''val''': 2, '''test''': 2}
												for split in ["train", "test", "val"]:
																	for field in ["source", "target"]:
																						a__          =  '''\n'''.join([contents[field]] * n_lines[split]    )
																						with open(os.path.join(__A				,		F'{split}.{field}'    )				,		'''w'''    ) as f:
																											f.write(__A    )




							def  lowercase		(   self:     Optional[int]				,		__A:     int				,		__A:     str = "pytorch"    ):

												'''simple docstring'''

												a__          =  self.get_auto_remove_tmp_dir()
												a__          =  os.path.join(__A				,		'''output'''    )
												a__          =  os.path.join(__A				,		'''data'''    )
												self._create_dummy_data(data_dir=__A    )

												a__          =  F'\n                --data_dir {data_dir} \\n                --output_dir {output_dir} \\n                --model_name_or_path facebook/rag-sequence-base \\n                --model_type rag_sequence \\n                --do_train \\n                --do_predict \\n                --n_val -1 \\n                --val_check_interval 1.0 \\n                --train_batch_size 2 \\n                --eval_batch_size 1 \\n                --max_source_length 25 \\n                --max_target_length 25 \\n                --val_max_target_length 25 \\n                --test_max_target_length 25 \\n                --label_smoothing 0.1 \\n                --dropout 0.1 \\n                --attention_dropout 0.1 \\n                --weight_decay 0.001 \\n                --adam_epsilon 1e-08 \\n                --max_grad_norm 0.1 \\n                --lr_scheduler polynomial \\n                --learning_rate 3e-04 \\n                --num_train_epochs 1 \\n                --warmup_steps 4 \\n                --gradient_accumulation_steps 1 \\n                --distributed-port 8787 \\n                --use_dummy_dataset 1 \\n                --distributed_retriever {distributed_retriever} \\n            '.split()

												if gpus > 0:
																	testargs.append(F'--gpus={gpus}'    )
																	if is_apex_available():
																						testargs.append('''--fp16'''    )
												else:
																	testargs.append('''--gpus=0'''    )
																	testargs.append('''--distributed_backend=ddp_cpu'''    )
																	testargs.append('''--num_processes=2'''    )

												a__          =  [sys.executable, str(Path(finetune_rag.__file__    ).resolve()    )] + testargs
												execute_subprocess_async(__A				,		env=self.get_env()    )

												a__          =  os.path.join(__A				,		'''metrics.json'''    )
												with open(__A    ) as f:
																	a__          =  json.load(__A    )
												return result




							@require_torch_gpu
							def  lowercase		(   self:     Optional[int]    ):

												'''simple docstring'''

												a__          =  self._run_finetune(gpus=1    )
												self.assertGreaterEqual(result['''test'''][0]['''test_avg_em''']				,		0.2    )




							@require_torch_multi_gpu
							def  lowercase		(   self:     str    ):

												'''simple docstring'''

												a__          =  self._run_finetune(gpus=2    )
												self.assertGreaterEqual(result['''test'''][0]['''test_avg_em''']				,		0.2    )




							@require_torch_gpu
							@require_ray
							def  lowercase		(   self:     Dict    ):

												'''simple docstring'''

												a__          =  self._run_finetune(gpus=1				,		distributed_retriever='''ray'''    )
												self.assertGreaterEqual(result['''test'''][0]['''test_avg_em''']				,		0.2    )





							@require_torch_multi_gpu
							@require_ray
							def  lowercase		(   self:     str    ):

												'''simple docstring'''

												a__          =  self._run_finetune(gpus=1				,		distributed_retriever='''ray'''    )
												self.assertGreaterEqual(result['''test'''][0]['''test_avg_em''']				,		0.2    )


