






"""simple docstring"""
import unittest

from transformers import BertGenerationTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_torch, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


lowerCAmelCase__    						=			'▁'

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


@require_sentencepiece
class _lowerCAmelCase						(    __UpperCAmelCase   ,  unittest.TestCase       ):
						SCREAMING_SNAKE_CASE_:					Any 					=				BertGenerationTokenizer
						SCREAMING_SNAKE_CASE_:					str 					=				False
						SCREAMING_SNAKE_CASE_:					Tuple 					=				True







						def 			A      (    self							)		->							Dict:
										super().setUp()

										_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    BertGenerationTokenizer(lowerCAmelCase_   ,				keep_accents=lowerCAmelCase_							)
										tokenizer.save_pretrained(self.tmpdirname							)







						def 			A      (    self							)		->							Optional[int]:
										_SCREAMING_SNAKE_CASE   :		Dict       		=    '<s>'
										_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    1

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







						def 			A      (    self							)		->							str:
										_SCREAMING_SNAKE_CASE   :		Any       		=    list(self.get_tokenizer().get_vocab().keys()							)

										self.assertEqual(vocab_keys[0]   ,				'<unk>'							)
										self.assertEqual(vocab_keys[1]   ,				'<s>'							)
										self.assertEqual(vocab_keys[-1]   ,				'<pad>'							)
										self.assertEqual(len(lowerCAmelCase_							)   ,				1_0_0_2							)







						def 			A      (    self							)		->							int:
										self.assertEqual(self.get_tokenizer().vocab_size   ,				1_0_0_0							)







						def 			A      (    self							)		->							str:
										_SCREAMING_SNAKE_CASE   :		List[Any]       		=    BertGenerationTokenizer(lowerCAmelCase_   ,				keep_accents=lowerCAmelCase_							)

										_SCREAMING_SNAKE_CASE   :		Dict       		=    tokenizer.tokenize('This is a test'							)
										self.assertListEqual(lowerCAmelCase_   ,				['▁This', '▁is', '▁a', '▁t', 'est']							)

										self.assertListEqual(
										    tokenizer.convert_tokens_to_ids(lowerCAmelCase_							)   ,				[2_8_5, 4_6, 1_0, 1_7_0, 3_8_2]   ,				)

										_SCREAMING_SNAKE_CASE   :		Tuple       		=    tokenizer.tokenize('I was born in 92000, and this is falsé.'							)
										self.assertListEqual(
										    lowerCAmelCase_   ,				[
										        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',
										        '.',
										    ]   ,				)
										_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    tokenizer.convert_tokens_to_ids(lowerCAmelCase_							)
										self.assertListEqual(
										    lowerCAmelCase_   ,				[8, 2_1, 8_4, 5_5, 2_4, 1_9, 7, 0, 6_0_2, 3_4_7, 3_4_7, 3_4_7, 3, 1_2, 6_6, 4_6, 7_2, 8_0, 6, 0, 4]   ,				)

										_SCREAMING_SNAKE_CASE   :		Tuple       		=    tokenizer.convert_ids_to_tokens(lowerCAmelCase_							)
										self.assertListEqual(
										    lowerCAmelCase_   ,				[
										        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>',
										        '.',
										    ]   ,				)







						@cached_property
						def 			A      (    self							)		->							List[Any]:
										return BertGenerationTokenizer.from_pretrained('google/bert_for_seq_generation_L-24_bbc_encoder'							)







						@slow
						def 			A      (    self							)		->							int:
										_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    'Hello World!'
										_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    [1_8_5_3_6, 2_2_6_0, 1_0_1]

										self.assertListEqual(lowerCAmelCase_   ,				self.big_tokenizer.encode(lowerCAmelCase_							)							)







						@slow
						def 			A      (    self							)		->							Optional[int]:
										_SCREAMING_SNAKE_CASE   :		int       		=    (
										    'This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) " [ ] ! : - . Also we will'
										    ' add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth'
										)
										_SCREAMING_SNAKE_CASE   :		int       		=    [
										    8_7_1,
										    4_1_9,
										    3_5_8,
										    9_4_6,
										    9_9_1,
										    2_5_2_1,
										    4_5_2,
										    3_5_8,
										    1_3_5_7,
										    3_8_7,
										    7_7_5_1,
										    3_5_3_6,
										    1_1_2,
										    9_8_5,
										    4_5_6,
										    1_2_6,
										    8_6_5,
										    9_3_8,
										    5_4_0_0,
										    5_7_3_4,
										    4_5_8,
										    1_3_6_8,
										    4_6_7,
										    7_8_6,
										    2_4_6_2,
										    5_2_4_6,
										    1_1_5_9,
										    6_3_3,
										    8_6_5,
										    4_5_1_9,
										    4_5_7,
										    5_8_2,
										    8_5_2,
										    2_5_5_7,
										    4_2_7,
										    9_1_6,
										    5_0_8,
										    4_0_5,
										    3_4_3_2_4,
										    4_9_7,
										    3_9_1,
										    4_0_8,
										    1_1_3_4_2,
										    1_2_4_4,
										    3_8_5,
										    1_0_0,
										    9_3_8,
										    9_8_5,
										    4_5_6,
										    5_7_4,
										    3_6_2,
										    1_2_5_9_7,
										    3_2_0_0,
										    3_1_2_9,
										    1_1_7_2,
										]

										self.assertListEqual(lowerCAmelCase_   ,				self.big_tokenizer.encode(lowerCAmelCase_							)							)







						@require_torch
						@slow
						def 			A      (    self							)		->							int:
										import torch

										from transformers import BertGenerationConfig, BertGenerationEncoder

										# Build sequence
										_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    list(self.big_tokenizer.get_vocab().keys()							)[:1_0]
										_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    ' '.join(lowerCAmelCase_							)
										_SCREAMING_SNAKE_CASE   :		str       		=    self.big_tokenizer.encode_plus(lowerCAmelCase_   ,				return_tensors='pt'   ,				return_token_type_ids=lowerCAmelCase_							)
										_SCREAMING_SNAKE_CASE   :		str       		=    self.big_tokenizer.batch_encode_plus(
										    [sequence + ' ' + sequence]   ,				return_tensors='pt'   ,				return_token_type_ids=lowerCAmelCase_							)

										_SCREAMING_SNAKE_CASE   :		List[Any]       		=    BertGenerationConfig()
										_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    BertGenerationEncoder(lowerCAmelCase_							)

										assert model.get_input_embeddings().weight.shape[0] >= self.big_tokenizer.vocab_size

										with torch.no_grad():
														model(**lowerCAmelCase_							)
														model(**lowerCAmelCase_							)







						@slow
						def 			A      (    self							)		->							List[Any]:
										# fmt: off
										_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    {'input_ids': [[3_9_2_8_6, 4_5_8, 3_6_3_3_5, 2_0_0_1, 4_5_6, 1_3_0_7_3, 1_3_2_6_6, 4_5_5, 1_1_3, 7_7_4_6, 1_7_4_1, 1_1_1_5_7, 3_9_1, 1_3_0_7_3, 1_3_2_6_6, 4_5_5, 1_1_3, 3_9_6_7, 3_5_4_1_2, 1_1_3, 4_9_3_6, 1_0_9, 3_8_7_0, 2_3_7_7, 1_1_3, 3_0_0_8_4, 4_5_7_2_0, 4_5_8, 1_3_4, 1_7_4_9_6, 1_1_2, 5_0_3, 1_1_6_7_2, 1_1_3, 1_1_8, 1_1_2, 5_6_6_5, 1_3_3_4_7, 3_8_6_8_7, 1_1_2, 1_4_9_6, 3_1_3_8_9, 1_1_2, 3_2_6_8, 4_7_2_6_4, 1_3_4, 9_6_2, 1_1_2, 1_6_3_7_7, 8_0_3_5, 2_3_1_3_0, 4_3_0, 1_2_1_6_9, 1_5_5_1_8, 2_8_5_9_2, 4_5_8, 1_4_6, 4_1_6_9_7, 1_0_9, 3_9_1, 1_2_1_6_9, 1_5_5_1_8, 1_6_6_8_9, 4_5_8, 1_4_6, 4_1_3_5_8, 1_0_9, 4_5_2, 7_2_6, 4_0_3_4, 1_1_1, 7_6_3, 3_5_4_1_2, 5_0_8_2, 3_8_8, 1_9_0_3, 1_1_1, 9_0_5_1, 3_9_1, 2_8_7_0, 4_8_9_1_8, 1_9_0_0, 1_1_2_3, 5_5_0, 9_9_8, 1_1_2, 9_5_8_6, 1_5_9_8_5, 4_5_5, 3_9_1, 4_1_0, 2_2_9_5_5, 3_7_6_3_6, 1_1_4], [4_4_8, 1_7_4_9_6, 4_1_9, 3_6_6_3, 3_8_5, 7_6_3, 1_1_3, 2_7_5_3_3, 2_8_7_0, 3_2_8_3, 1_3_0_4_3, 1_6_3_9, 2_4_7_1_3, 5_2_3, 6_5_6, 2_4_0_1_3, 1_8_5_5_0, 2_5_2_1, 5_1_7, 2_7_0_1_4, 2_1_2_4_4, 4_2_0, 1_2_1_2, 1_4_6_5, 3_9_1, 9_2_7, 4_8_3_3, 3_8_8, 5_7_8, 1_1_7_8_6, 1_1_4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [4_8_4, 2_1_6_9, 7_6_8_7, 2_1_9_3_2, 1_8_1_4_6, 7_2_6, 3_6_3, 1_7_0_3_2, 3_3_9_1, 1_1_4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], '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, 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, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
										# fmt: on

										self.tokenizer_integration_test_util(
										    expected_encoding=lowerCAmelCase_   ,				model_name='google/bert_for_seq_generation_L-24_bbc_encoder'   ,				revision='c817d1fd1be2ffa69431227a1fe320544943d4db'   ,				)









"""simple docstring"""
from __future__ import annotations

import unittest

from transformers import AutoTokenizer, MBartConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
			import tensorflow as tf

			from transformers import TFAutoModelForSeqaSeqLM, TFMBartForConditionalGeneration, TFMBartModel


@require_tf
class _lowerCAmelCase						:
						SCREAMING_SNAKE_CASE_:					Union[str, Any] 					=				MBartConfig
						SCREAMING_SNAKE_CASE_:					Optional[Any] 					=				{}
						SCREAMING_SNAKE_CASE_:					Tuple 					=				'gelu'







						def __init__(    self   ,				lowerCAmelCase_   ,				lowerCAmelCase_=1_3   ,				lowerCAmelCase_=7   ,				lowerCAmelCase_=True   ,				lowerCAmelCase_=False   ,				lowerCAmelCase_=9_9   ,				lowerCAmelCase_=3_2   ,				lowerCAmelCase_=2   ,				lowerCAmelCase_=4   ,				lowerCAmelCase_=3_7   ,				lowerCAmelCase_=0.1   ,				lowerCAmelCase_=0.1   ,				lowerCAmelCase_=2_0   ,				lowerCAmelCase_=2   ,				lowerCAmelCase_=1   ,				lowerCAmelCase_=0   ,				)		->							int:
										_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    parent
										_SCREAMING_SNAKE_CASE   :		str       		=    batch_size
										_SCREAMING_SNAKE_CASE   :		str       		=    seq_length
										_SCREAMING_SNAKE_CASE   :		Dict       		=    is_training
										_SCREAMING_SNAKE_CASE   :		int       		=    use_labels
										_SCREAMING_SNAKE_CASE   :		str       		=    vocab_size
										_SCREAMING_SNAKE_CASE   :		List[Any]       		=    hidden_size
										_SCREAMING_SNAKE_CASE   :		Tuple       		=    num_hidden_layers
										_SCREAMING_SNAKE_CASE   :		Tuple       		=    num_attention_heads
										_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    intermediate_size
										_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    hidden_dropout_prob
										_SCREAMING_SNAKE_CASE   :		int       		=    attention_probs_dropout_prob
										_SCREAMING_SNAKE_CASE   :		List[str]       		=    max_position_embeddings
										_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    eos_token_id
										_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    pad_token_id
										_SCREAMING_SNAKE_CASE   :		Tuple       		=    bos_token_id







						def 			A      (    self							)		->							Tuple:
										_SCREAMING_SNAKE_CASE   :		List[str]       		=    ids_tensor([self.batch_size, self.seq_length - 1]   ,				self.vocab_size							)
										_SCREAMING_SNAKE_CASE   :		List[Any]       		=    tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size							)   ,				1							)
										_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    tf.concat([input_ids, eos_tensor]   ,				axis=1							)

										_SCREAMING_SNAKE_CASE   :		Any       		=    ids_tensor([self.batch_size, self.seq_length]   ,				self.vocab_size							)

										_SCREAMING_SNAKE_CASE   :		Tuple       		=    self.config_cls(
										    vocab_size=self.vocab_size   ,				d_model=self.hidden_size   ,				encoder_layers=self.num_hidden_layers   ,				decoder_layers=self.num_hidden_layers   ,				encoder_attention_heads=self.num_attention_heads   ,				decoder_attention_heads=self.num_attention_heads   ,				encoder_ffn_dim=self.intermediate_size   ,				decoder_ffn_dim=self.intermediate_size   ,				dropout=self.hidden_dropout_prob   ,				attention_dropout=self.attention_probs_dropout_prob   ,				max_position_embeddings=self.max_position_embeddings   ,				eos_token_ids=[2]   ,				bos_token_id=self.bos_token_id   ,				pad_token_id=self.pad_token_id   ,				decoder_start_token_id=self.pad_token_id   ,				**self.config_updates   ,				)
										_SCREAMING_SNAKE_CASE   :		Any       		=    prepare_mbart_inputs_dict(lowerCAmelCase_   ,				lowerCAmelCase_   ,				lowerCAmelCase_							)
										return config, inputs_dict







						def 			A      (    self   ,				lowerCAmelCase_   ,				lowerCAmelCase_							)		->							int:
										_SCREAMING_SNAKE_CASE   :		int       		=    TFMBartModel(config=lowerCAmelCase_							).get_decoder()
										_SCREAMING_SNAKE_CASE   :		int       		=    inputs_dict['input_ids']

										_SCREAMING_SNAKE_CASE   :		Tuple       		=    input_ids[:1, :]
										_SCREAMING_SNAKE_CASE   :		Any       		=    inputs_dict['attention_mask'][:1, :]
										_SCREAMING_SNAKE_CASE   :		List[str]       		=    inputs_dict['head_mask']
										_SCREAMING_SNAKE_CASE   :		List[Any]       		=    1

										# first forward pass
										_SCREAMING_SNAKE_CASE   :		Tuple       		=    model(lowerCAmelCase_   ,				attention_mask=lowerCAmelCase_   ,				head_mask=lowerCAmelCase_   ,				use_cache=lowerCAmelCase_							)

										_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE   :		str       		=    outputs.to_tuple()
										_SCREAMING_SNAKE_CASE   :		str       		=    past_key_values[1]







def 				lowercase__		(					lowerCamelCase,						lowerCamelCase,						lowerCamelCase,						lowerCamelCase=None,						lowerCamelCase=None,						lowerCamelCase=None,						lowerCamelCase=None,						lowerCamelCase=None,						):
				if attention_mask is None:
								_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    tf.cast(tf.math.not_equal(lowerCamelCase,						config.pad_token_id       ),						tf.inta       )
				if decoder_attention_mask is None:
								_SCREAMING_SNAKE_CASE   :		Dict       		=    tf.concat(
								    [
								        tf.ones(decoder_input_ids[:, :1].shape,						dtype=tf.inta       ),
								        tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:],						config.pad_token_id       ),						tf.inta       ),
								    ],						axis=-1,						)
				if head_mask is None:
								_SCREAMING_SNAKE_CASE   :		Dict       		=    tf.ones((config.encoder_layers, config.encoder_attention_heads)       )
				if decoder_head_mask is None:
								_SCREAMING_SNAKE_CASE   :		int       		=    tf.ones((config.decoder_layers, config.decoder_attention_heads)       )
				if cross_attn_head_mask is None:
								_SCREAMING_SNAKE_CASE   :		int       		=    tf.ones((config.decoder_layers, config.decoder_attention_heads)       )
				return {
				    "input_ids": input_ids,
				    "decoder_input_ids": decoder_input_ids,
				    "attention_mask": attention_mask,
				    "decoder_attention_mask": decoder_attention_mask,
				    "head_mask": head_mask,
				    "decoder_head_mask": decoder_head_mask,
				    "cross_attn_head_mask": cross_attn_head_mask,
				}


@require_tf
class _lowerCAmelCase						(    __UpperCAmelCase   ,  __UpperCAmelCase   ,  unittest.TestCase       ):
						SCREAMING_SNAKE_CASE_:					List[str] 					=				(TFMBartForConditionalGeneration, TFMBartModel) if is_tf_available() else ()
						SCREAMING_SNAKE_CASE_:					Dict 					=				(TFMBartForConditionalGeneration,) if is_tf_available() else ()
						SCREAMING_SNAKE_CASE_:					Tuple 					=				(
						    {
						        'conversational': TFMBartForConditionalGeneration,
						        'feature-extraction': TFMBartModel,
						        'summarization': TFMBartForConditionalGeneration,
						        'text2text-generation': TFMBartForConditionalGeneration,
						        'translation': TFMBartForConditionalGeneration,
						    }
						    if is_tf_available()
						    else {}
						)
						SCREAMING_SNAKE_CASE_:					Any 					=				True
						SCREAMING_SNAKE_CASE_:					int 					=				False
						SCREAMING_SNAKE_CASE_:					Optional[int] 					=				False







						def 			A      (    self   ,				lowerCAmelCase_   ,				lowerCAmelCase_   ,				lowerCAmelCase_   ,				lowerCAmelCase_   ,				lowerCAmelCase_							)		->							List[Any]:
										if pipeline_test_casse_name != "FeatureExtractionPipelineTests":
														# Exception encountered when calling layer '...'
														return True

										return False







						def 			A      (    self							)		->							Optional[int]:
										_SCREAMING_SNAKE_CASE   :		List[Any]       		=    TFMBartModelTester(self							)
										_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    ConfigTester(self   ,				config_class=lowerCAmelCase_							)







						def 			A      (    self							)		->							List[Any]:
										self.config_tester.run_common_tests()







						def 			A      (    self							)		->							Dict:
										_SCREAMING_SNAKE_CASE   :		Dict       		=    self.model_tester.prepare_config_and_inputs_for_common()
										self.model_tester.check_decoder_model_past_large_inputs(*lowerCAmelCase_							)







@require_sentencepiece
@require_tokenizers
@require_tf
class _lowerCAmelCase						(    unittest.TestCase       ):
						SCREAMING_SNAKE_CASE_:					str 					=				[
						    ' UN Chief Says There Is No Military Solution in Syria',
						]
						SCREAMING_SNAKE_CASE_:					Union[str, Any] 					=				[
						    'Şeful ONU declară că nu există o soluţie militară în Siria',
						]
						SCREAMING_SNAKE_CASE_:					Dict 					=				'facebook/mbart-large-en-ro'







						@cached_property
						def 			A      (    self							)		->							Union[str, Any]:
										return AutoTokenizer.from_pretrained(self.model_name							)







						@cached_property
						def 			A      (    self							)		->							Optional[int]:
										_SCREAMING_SNAKE_CASE   :		List[str]       		=    TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name							)
										return model







						def 			A      (    self   ,				**lowerCAmelCase_							)		->							Optional[int]:
										_SCREAMING_SNAKE_CASE   :		Tuple       		=    self.translate_src_text(**lowerCAmelCase_							)
										self.assertListEqual(self.expected_text   ,				lowerCAmelCase_							)







						def 			A      (    self   ,				**lowerCAmelCase_							)		->							Tuple:
										_SCREAMING_SNAKE_CASE   :		Tuple       		=    self.tokenizer(self.src_text   ,				**lowerCAmelCase_   ,				return_tensors='tf'							)
										_SCREAMING_SNAKE_CASE   :		int       		=    self.model.generate(
										    model_inputs.input_ids   ,				attention_mask=model_inputs.attention_mask   ,				num_beams=2							)
										_SCREAMING_SNAKE_CASE   :		int       		=    self.tokenizer.batch_decode(lowerCAmelCase_   ,				skip_special_tokens=lowerCAmelCase_							)
										return generated_words







						@slow
						def 			A      (    self							)		->							Any:
										self._assert_generated_batch_equal_expected()

