




'''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									=get_tests_dir('fixtures/test_sentencepiece.model')


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

A									=5_00_03
A									=5_00_02


@require_sentencepiece
@require_tokenizers
class     _a						(			__a							,							unittest.TestCase				):
       __a       :  int            =      PLBartTokenizer
       __a       :  int            =      None
       __a       :  Union[str, Any]            =      False
       def 					A   (			self		:			int       ):



           '''simple docstring'''

           super().setUp()

           # We have a SentencePiece fixture for testing
           UpperCAmelCase     =	PLBartTokenizer(lowercase     ,       language_codes='''base'''     ,       keep_accents=lowercase       )
           tokenizer.save_pretrained(self.tmpdirname       )
       def 					A   (			self		:			str       ):



           '''simple docstring'''

           UpperCAmelCase     =	PLBartTokenizer(lowercase     ,       language_codes='''base'''     ,       keep_accents=lowercase       )

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

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

           UpperCAmelCase     =	tokenizer.tokenize('''I was born in 92000, and this is falsé.'''       )
           self.assertListEqual(
               lowercase     ,       [
                   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''',
                   '''.''',
               ]     ,       )
           UpperCAmelCase     =	tokenizer.convert_tokens_to_ids(lowercase       )
           self.assertListEqual(
               lowercase     ,       [
                   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]
               ]     ,       )

           UpperCAmelCase     =	tokenizer.convert_ids_to_tokens(lowercase       )
           self.assertListEqual(
               lowercase     ,       [
                   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>''',
                   '''.''',
               ]     ,       )

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

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

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







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



           '''simple docstring'''

           UpperCAmelCase     =	PLBartTokenizer(lowercase     ,       language_codes='''multi'''     ,       keep_accents=lowercase       )

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

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

           UpperCAmelCase     =	tokenizer.tokenize('''I was born in 92000, and this is falsé.'''       )
           self.assertListEqual(
               lowercase     ,       [
                   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''',
                   '''.''',
               ]     ,       )
           UpperCAmelCase     =	tokenizer.convert_tokens_to_ids(lowercase       )
           self.assertListEqual(
               lowercase     ,       [
                   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]
               ]     ,       )

           UpperCAmelCase     =	tokenizer.convert_ids_to_tokens(lowercase       )
           self.assertListEqual(
               lowercase     ,       [
                   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>''',
                   '''.''',
               ]     ,       )
           UpperCAmelCase     =	tokenizer.vocab_size
           UpperCAmelCase     =	[tokenizer.convert_ids_to_tokens(lowercase       ) for x in range(end - 7     ,       lowercase       )]

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






@require_torch
@require_sentencepiece
@require_tokenizers
class     _a						(			unittest.TestCase				):
       __a       :  List[str]            =      """uclanlp/plbart-python-en_XX"""
       __a       :  str            =      [
           """def maximum(a,b,c):NEW_LINE_INDENTreturn max([a,b,c])""",
           """def sum(a,b,c):NEW_LINE_INDENTreturn sum([a,b,c])""",
       ]
       __a       :  Union[str, Any]            =      [
           """Returns the maximum value of a b c.""",
           """Sums the values of a b c.""",
       ]
       __a       :  Optional[int]            =      [
           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 					A   (			cls		:			List[str]       ):



           '''simple docstring'''

           UpperCAmelCase     =	PLBartTokenizer.from_pretrained(
               cls.checkpoint_name     ,       language_codes='''base'''     ,       src_lang='''python'''     ,       tgt_lang='''en_XX'''       )
           UpperCAmelCase     =	1
           return cls
       def 					A   (			self		:			Optional[int]       ):



           '''simple docstring'''

           self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['''__java__''']     ,       50_001       )
           self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['''__python__''']     ,       50_002       )
           self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['''__en_XX__''']     ,       50_003       )
       def 					A   (			self		:			Union[str, Any]       ):



           '''simple docstring'''

           UpperCAmelCase     =	self.tokenizer.batch_encode_plus(self.src_text       ).input_ids[0]
           self.assertListEqual(self.expected_src_tokens     ,       lowercase       )
       def 					A   (			self		:			Any       ):



           '''simple docstring'''

           self.assertIn(lowercase     ,       self.tokenizer.all_special_ids       )
           UpperCAmelCase     =	[EN_CODE, 9_037, 33_442, 57, 752, 153, 14, 56, 18, 9, 2]
           UpperCAmelCase     =	self.tokenizer.decode(lowercase     ,       skip_special_tokens=lowercase       )
           UpperCAmelCase     =	self.tokenizer.decode(generated_ids[1:]     ,       skip_special_tokens=lowercase       )
           self.assertEqual(lowercase     ,       lowercase       )
           self.assertNotIn(self.tokenizer.eos_token     ,       lowercase       )
       def 					A   (			self		:			int       ):



           '''simple docstring'''

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



           '''simple docstring'''

           self.assertListEqual(self.tokenizer.convert_tokens_to_ids(['''<mask>''', '''__java__''']       )     ,       [50_004, 50_001]       )
       def 					A   (			self		:			Tuple       ):



           '''simple docstring'''

           UpperCAmelCase     =	tempfile.mkdtemp()
           UpperCAmelCase     =	self.tokenizer.fairseq_tokens_to_ids
           self.tokenizer.save_pretrained(lowercase       )
           UpperCAmelCase     =	PLBartTokenizer.from_pretrained(lowercase       )
           self.assertDictEqual(new_tok.fairseq_tokens_to_ids     ,       lowercase       )
       @require_torch
       def 					A   (			self		:			List[Any]       ):



           '''simple docstring'''

           UpperCAmelCase     =	self.tokenizer(self.src_text     ,       text_target=self.tgt_text     ,       padding=lowercase     ,       return_tensors='''pt'''       )
           UpperCAmelCase     =	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]     ,       lowercase       )
           self.assertEqual(batch.decoder_input_ids[1][-1]     ,       2       )
           self.assertEqual(batch.labels[1][-2:].tolist()     ,       [2, EN_CODE]       )
       @require_torch
       def 					A   (			self		:			List[str]       ):



           '''simple docstring'''

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

           self.assertIsInstance(lowercase     ,       lowercase       )

           self.assertEqual((2, 26)     ,       batch.input_ids.shape       )
           self.assertEqual((2, 26)     ,       batch.attention_mask.shape       )
           UpperCAmelCase     =	batch.input_ids.tolist()[0]
           self.assertListEqual(self.expected_src_tokens     ,       lowercase       )
           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 					A   (			self		:			List[Any]       ):



           '''simple docstring'''

           UpperCAmelCase     =	self.tokenizer(self.src_text     ,       padding=lowercase     ,       truncation=lowercase     ,       max_length=3     ,       return_tensors='''pt'''       )
           UpperCAmelCase     =	self.tokenizer(
               text_target=self.tgt_text     ,       padding=lowercase     ,       truncation=lowercase     ,       max_length=10     ,       return_tensors='''pt'''       )
           UpperCAmelCase     =	targets['''input_ids''']
           UpperCAmelCase     =	shift_tokens_right(lowercase     ,       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 					A   (			self		:			Dict       ):



           '''simple docstring'''

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

           self.assertEqual(
               nested_simplify(lowercase       )     ,       {
                   # A, test, EOS, en_XX
                   '''input_ids''': [[150, 242, 2, 50_003]],
                   '''attention_mask''': [[1, 1, 1, 1]],
                   # java
                   '''forced_bos_token_id''': 50_001,
               }     ,       )










'''simple docstring'''







import unittest

import numpy as np

from transformers import RobertaPreLayerNormConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
   import jax.numpy as jnp

   from transformers.models.roberta_prelayernorm.modeling_flax_roberta_prelayernorm import (
       FlaxRobertaPreLayerNormForCausalLM,
       FlaxRobertaPreLayerNormForMaskedLM,
       FlaxRobertaPreLayerNormForMultipleChoice,
       FlaxRobertaPreLayerNormForQuestionAnswering,
       FlaxRobertaPreLayerNormForSequenceClassification,
       FlaxRobertaPreLayerNormForTokenClassification,
       FlaxRobertaPreLayerNormModel,
   )


class     _a						(			unittest.TestCase				):
       def __init__(			self		:			List[Any]     ,       lowercase		:			Dict     ,       lowercase		:			List[str]=13     ,       lowercase		:			str=7     ,       lowercase		:			List[str]=True     ,       lowercase		:			List[str]=True     ,       lowercase		:			Optional[Any]=True     ,       lowercase		:			Optional[Any]=True     ,       lowercase		:			Any=99     ,       lowercase		:			Any=32     ,       lowercase		:			Any=5     ,       lowercase		:			Tuple=4     ,       lowercase		:			List[Any]=37     ,       lowercase		:			List[Any]="gelu"     ,       lowercase		:			int=0.1     ,       lowercase		:			Any=0.1     ,       lowercase		:			Optional[int]=512     ,       lowercase		:			List[str]=16     ,       lowercase		:			Union[str, Any]=2     ,       lowercase		:			int=0.02     ,       lowercase		:			int=4     ,       ):



           '''simple docstring'''

           UpperCAmelCase     =	parent
           UpperCAmelCase     =	batch_size
           UpperCAmelCase     =	seq_length
           UpperCAmelCase     =	is_training
           UpperCAmelCase     =	use_attention_mask
           UpperCAmelCase     =	use_token_type_ids
           UpperCAmelCase     =	use_labels
           UpperCAmelCase     =	vocab_size
           UpperCAmelCase     =	hidden_size
           UpperCAmelCase     =	num_hidden_layers
           UpperCAmelCase     =	num_attention_heads
           UpperCAmelCase     =	intermediate_size
           UpperCAmelCase     =	hidden_act
           UpperCAmelCase     =	hidden_dropout_prob
           UpperCAmelCase     =	attention_probs_dropout_prob
           UpperCAmelCase     =	max_position_embeddings
           UpperCAmelCase     =	type_vocab_size
           UpperCAmelCase     =	type_sequence_label_size
           UpperCAmelCase     =	initializer_range
           UpperCAmelCase     =	num_choices
       def 					A   (			self		:			Any       ):



           '''simple docstring'''

           UpperCAmelCase     =	ids_tensor([self.batch_size, self.seq_length]     ,       self.vocab_size       )

           UpperCAmelCase     =	None
           if self.use_attention_mask:
               UpperCAmelCase     =	random_attention_mask([self.batch_size, self.seq_length]       )

           UpperCAmelCase     =	None
           if self.use_token_type_ids:
               UpperCAmelCase     =	ids_tensor([self.batch_size, self.seq_length]     ,       self.type_vocab_size       )

           UpperCAmelCase     =	RobertaPreLayerNormConfig(
               vocab_size=self.vocab_size     ,       hidden_size=self.hidden_size     ,       num_hidden_layers=self.num_hidden_layers     ,       num_attention_heads=self.num_attention_heads     ,       intermediate_size=self.intermediate_size     ,       hidden_act=self.hidden_act     ,       hidden_dropout_prob=self.hidden_dropout_prob     ,       attention_probs_dropout_prob=self.attention_probs_dropout_prob     ,       max_position_embeddings=self.max_position_embeddings     ,       type_vocab_size=self.type_vocab_size     ,       is_decoder=lowercase     ,       initializer_range=self.initializer_range     ,       )

           return config, input_ids, token_type_ids, attention_mask
       def 					A   (			self		:			List[Any]       ):



           '''simple docstring'''

           UpperCAmelCase     =	self.prepare_config_and_inputs()
           UpperCAmelCase ,  UpperCAmelCase ,  UpperCAmelCase ,  UpperCAmelCase     =	config_and_inputs
           UpperCAmelCase     =	{'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': attention_mask}
           return config, inputs_dict







       def 					A   (			self		:			Optional[Any]       ):



           '''simple docstring'''

           UpperCAmelCase     =	self.prepare_config_and_inputs()
           UpperCAmelCase ,  UpperCAmelCase ,  UpperCAmelCase ,  UpperCAmelCase     =	config_and_inputs

           UpperCAmelCase     =	True
           UpperCAmelCase     =	floats_tensor([self.batch_size, self.seq_length, self.hidden_size]       )
           UpperCAmelCase     =	ids_tensor([self.batch_size, self.seq_length]     ,       vocab_size=2       )

           return (
               config,
               input_ids,
               token_type_ids,
               encoder_hidden_states,
               encoder_attention_mask,
           )






@require_flax
# Copied from tests.models.roberta.test_modelling_flax_roberta.FlaxRobertaPreLayerNormModelTest with ROBERTA->ROBERTA_PRELAYERNORM,Roberta->RobertaPreLayerNorm,roberta-base->andreasmadsen/efficient_mlm_m0.40
class     _a						(			__a							,							unittest.TestCase				):
       __a       :  Any            =      True

       __a       :  str            =      (
           (
               FlaxRobertaPreLayerNormModel,
               FlaxRobertaPreLayerNormForCausalLM,
               FlaxRobertaPreLayerNormForMaskedLM,
               FlaxRobertaPreLayerNormForSequenceClassification,
               FlaxRobertaPreLayerNormForTokenClassification,
               FlaxRobertaPreLayerNormForMultipleChoice,
               FlaxRobertaPreLayerNormForQuestionAnswering,
           )
           if is_flax_available()
           else ()
       )
       def 					A   (			self		:			Any       ):



           '''simple docstring'''

           UpperCAmelCase     =	FlaxRobertaPreLayerNormModelTester(self       )







       @slow
       def 					A   (			self		:			Optional[int]       ):



           '''simple docstring'''

           for model_class_name in self.all_model_classes:
               UpperCAmelCase     =	model_class_name.from_pretrained('''andreasmadsen/efficient_mlm_m0.40'''     ,       from_pt=lowercase       )
               UpperCAmelCase     =	model(np.ones((1, 1)       )       )
               self.assertIsNotNone(lowercase       )






@require_flax
class     _a						(			unittest.TestCase				):
       @slow
       def 					A   (			self		:			Union[str, Any]       ):



           '''simple docstring'''

           UpperCAmelCase     =	FlaxRobertaPreLayerNormForMaskedLM.from_pretrained('''andreasmadsen/efficient_mlm_m0.40'''     ,       from_pt=lowercase       )

           UpperCAmelCase     =	np.array([[0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]]     ,       dtype=jnp.intaa       )
           UpperCAmelCase     =	model(lowercase       )[0]
           UpperCAmelCase     =	[1, 11, 50_265]
           self.assertEqual(list(output.shape       )     ,       lowercase       )
           # compare the actual values for a slice.
           UpperCAmelCase     =	np.array(
               [[[40.4880, 18.0199, -5.2367], [-1.8877, -4.0885, 10.7085], [-2.2613, -5.6110, 7.2665]]]     ,       dtype=np.floataa       )
           self.assertTrue(np.allclose(output[:, :3, :3]     ,       lowercase     ,       atol=1E-4       )       )







       @slow
       def 					A   (			self		:			Optional[Any]       ):



           '''simple docstring'''

           UpperCAmelCase     =	FlaxRobertaPreLayerNormModel.from_pretrained('''andreasmadsen/efficient_mlm_m0.40'''     ,       from_pt=lowercase       )

           UpperCAmelCase     =	np.array([[0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]]     ,       dtype=jnp.intaa       )
           UpperCAmelCase     =	model(lowercase       )[0]
           # compare the actual values for a slice.
           UpperCAmelCase     =	np.array(
               [[[0.0208, -0.0356, 0.0237], [-0.1569, -0.0411, -0.2626], [0.1879, 0.0125, -0.0089]]]     ,       dtype=np.floataa       )
           self.assertTrue(np.allclose(output[:, :3, :3]     ,       lowercase     ,       atol=1E-4       )       )




