
"""simple docstring"""






from __future__ import annotations

import unittest

from transformers import BlenderbotSmallConfig, BlenderbotSmallTokenizer, is_tf_available
from transformers.testing_utils import 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, TFBlenderbotSmallForConditionalGeneration, TFBlenderbotSmallModel

@require_tf
class  __SCREAMING_SNAKE_CASE			:


					'''simple docstring'''





					_a         =							BlenderbotSmallConfig
					_a         =							{}
					_a         =							'gelu'



					def __init__( self							:						Union[str, Any],						lowerCamelCase							:						List[str],						lowerCamelCase							:						Dict=13,						lowerCamelCase							:						Optional[Any]=7,						lowerCamelCase							:						Optional[int]=True,						lowerCamelCase							:						int=False,						lowerCamelCase							:						Union[str, Any]=99,						lowerCamelCase							:						str=32,						lowerCamelCase							:						List[Any]=2,						lowerCamelCase							:						Optional[int]=4,						lowerCamelCase							:						Union[str, Any]=37,						lowerCamelCase							:						str=0.1,						lowerCamelCase							:						Optional[int]=0.1,						lowerCamelCase							:						Optional[Any]=20,						lowerCamelCase							:						int=2,						lowerCamelCase							:						Any=1,						lowerCamelCase							:						Optional[Any]=0,						)->      List[str]:
								lowerCamelCase__	:	Any				=parent
								lowerCamelCase__	:	Dict				=batch_size
								lowerCamelCase__	:	Optional[int]				=seq_length
								lowerCamelCase__	:	Tuple				=is_training
								lowerCamelCase__	:	Dict				=use_labels
								lowerCamelCase__	:	List[Any]				=vocab_size
								lowerCamelCase__	:	str				=hidden_size
								lowerCamelCase__	:	str				=num_hidden_layers
								lowerCamelCase__	:	Union[str, Any]				=num_attention_heads
								lowerCamelCase__	:	Any				=intermediate_size

								lowerCamelCase__	:	Dict				=hidden_dropout_prob
								lowerCamelCase__	:	List[Any]				=attention_probs_dropout_prob
								lowerCamelCase__	:	str				=max_position_embeddings
								lowerCamelCase__	:	Optional[int]				=eos_token_id
								lowerCamelCase__	:	str				=pad_token_id
								lowerCamelCase__	:	Union[str, Any]				=bos_token_id



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

								lowerCamelCase__	:	Optional[int]				=ids_tensor([self.batch_size, self.seq_length],						self.vocab_size  )

								lowerCamelCase__	:	int				=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,						)
								lowerCamelCase__	:	Optional[int]				=prepare_blenderbot_small_inputs_dict(lowerCamelCase,						lowerCamelCase,						lowerCamelCase  )
								return config, inputs_dict




					def 	snake_case ( self							:						Any,						lowerCamelCase							:						str,						lowerCamelCase							:						Any  )->      Optional[Any]:
								lowerCamelCase__	:	Union[str, Any]				=TFBlenderbotSmallModel(config=lowerCamelCase  ).get_decoder()
								lowerCamelCase__	:	List[Any]				=inputs_dict['''input_ids''']

								lowerCamelCase__	:	Optional[int]				=input_ids[:1, :]
								lowerCamelCase__	:	str				=inputs_dict['''attention_mask'''][:1, :]
								lowerCamelCase__	:	Union[str, Any]				=inputs_dict['''head_mask''']
								lowerCamelCase__	:	Optional[Any]				=1

								# first forward pass
								lowerCamelCase__	:	Dict				=model(lowerCamelCase,						attention_mask=lowerCamelCase,						head_mask=lowerCamelCase,						use_cache=lowerCamelCase  )

								lowerCamelCase__	:	List[str]				=outputs.to_tuple()

								# create hypothetical next token and extent to next_input_ids
								lowerCamelCase__	:	Union[str, Any]				=ids_tensor((self.batch_size, 3),						config.vocab_size  )
								lowerCamelCase__	:	Tuple				=tf.cast(ids_tensor((self.batch_size, 3),						2  ),						tf.inta  )

								# append to next input_ids and
								lowerCamelCase__	:	List[str]				=tf.concat([input_ids, next_tokens],						axis=-1  )
								lowerCamelCase__	:	str				=tf.concat([attention_mask, next_attn_mask],						axis=-1  )

								lowerCamelCase__	:	Optional[int]				=model(lowerCamelCase,						attention_mask=lowerCamelCase  )[0]
								lowerCamelCase__	:	Union[str, Any]				=model(lowerCamelCase,						attention_mask=lowerCamelCase,						past_key_values=lowerCamelCase  )[0]

								self.parent.assertEqual(next_tokens.shape[1],						output_from_past.shape[1]  )

								# select random slice
								lowerCamelCase__	:	Tuple				=int(ids_tensor((1,),						output_from_past.shape[-1]  )  )
								lowerCamelCase__	:	int				=output_from_no_past[:, -3:, random_slice_idx]
								lowerCamelCase__	:	List[str]				=output_from_past[:, :, random_slice_idx]

								# test that outputs are equal for slice
								tf.debugging.assert_near(lowerCamelCase,						lowerCamelCase,						rtol=1E-3  )






def   snake_case__     (	__lowerCamelCase       :					Tuple							,	__lowerCamelCase       :					Union[str, Any]							,	__lowerCamelCase       :					Union[str, Any]							,	__lowerCamelCase       :					Any=None							,	__lowerCamelCase       :					List[str]=None							,	__lowerCamelCase       :					Tuple=None							,	__lowerCamelCase       :					Dict=None							,	__lowerCamelCase       :					Optional[int]=None							,	):
			"""simple docstring"""
			if attention_mask is None:
						lowerCamelCase__	:	List[str]				=tf.cast(tf.math.not_equal(__lowerCamelCase							,	config.pad_token_id			)							,	tf.inta			)
			if decoder_attention_mask is None:
						lowerCamelCase__	:	str				=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:
						lowerCamelCase__	:	int				=tf.ones((config.encoder_layers, config.encoder_attention_heads)			)
			if decoder_head_mask is None:
						lowerCamelCase__	:	int				=tf.ones((config.decoder_layers, config.decoder_attention_heads)			)
			if cross_attn_head_mask is None:
						lowerCamelCase__	:	List[str]				=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  __SCREAMING_SNAKE_CASE			(   lowerCAmelCase_	,     lowerCAmelCase_	,     unittest.TestCase     ):


					'''simple docstring'''





					_a         =							(
					    (TFBlenderbotSmallForConditionalGeneration, TFBlenderbotSmallModel) if is_tf_available() else ()
					)
					_a         =							(TFBlenderbotSmallForConditionalGeneration,) if is_tf_available() else ()
					_a         =							(
					    {
					        'conversational': TFBlenderbotSmallForConditionalGeneration,
					        'feature-extraction': TFBlenderbotSmallModel,
					        'summarization': TFBlenderbotSmallForConditionalGeneration,
					        'text2text-generation': TFBlenderbotSmallForConditionalGeneration,
					        'translation': TFBlenderbotSmallForConditionalGeneration,
					    }
					    if is_tf_available()
					    else {}
					)
					_a         =							True
					_a         =							False
					_a         =							False



					def 	snake_case ( self							:						Any  )->      str:
								lowerCamelCase__	:	Tuple				=TFBlenderbotSmallModelTester(self  )
								lowerCamelCase__	:	Union[str, Any]				=ConfigTester(self,						config_class=lowerCamelCase  )



					def 	snake_case ( self							:						Any  )->      Optional[int]:
								self.config_tester.run_common_tests()




					def 	snake_case ( self							:						int  )->      str:
								lowerCamelCase__	:	List[Any]				=self.model_tester.prepare_config_and_inputs_for_common()
								self.model_tester.check_decoder_model_past_large_inputs(*lowerCamelCase  )





@require_tokenizers
@require_tf
class  __SCREAMING_SNAKE_CASE			(   unittest.TestCase     ):


					'''simple docstring'''





					_a         =							[
					    'Social anxiety\nWow, I am never shy. Do you have anxiety?\nYes. I end up sweating and blushing and feel like '
					    '  i\'m going to throw up.\nand why is that?'
					]
					_a         =							'facebook/blenderbot_small-90M'



					@cached_property
					def 	snake_case ( self							:						Any  )->      List[Any]:
								# use "old" tokenizer here because of bug when downloading new tokenizer
								return BlenderbotSmallTokenizer.from_pretrained('''facebook/blenderbot-90M'''  )



					@cached_property
					def 	snake_case ( self							:						int  )->      List[Any]:
								lowerCamelCase__	:	str				=TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name  )
								return model




					@slow
					def 	snake_case ( self							:						Tuple  )->      int:
								lowerCamelCase__	:	Dict				=self.tokenizer(self.src_text,						return_tensors='''tf'''  )
								lowerCamelCase__	:	Any				=self.model.generate(
								    model_inputs.input_ids,						attention_mask=model_inputs.attention_mask,						num_beams=2,						use_cache=lowerCamelCase,						)
								lowerCamelCase__	:	Any				=self.tokenizer.batch_decode(generated_ids.numpy(),						skip_special_tokens=lowerCamelCase  )[0]
								assert generated_words in (
								    "i don't know. i just feel like i'm going to throw up. it's not fun.",
								    "i'm not sure. i just feel like i've been feeling like i have to be in a certain place",
								    "i'm not sure. i just feel like i've been in a bad situation.",
								)









"""simple docstring"""






import shutil
import tempfile
import unittest

from transformers import SPIECE_UNDERLINE, BatchEncoding, MBartTokenizer, MBartTokenizerFast, 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


_lowercase				:				List[str]   		=		get_tests_dir("fixtures/test_sentencepiece.model")


if is_torch_available():
		from transformers.models.mbart.modeling_mbart import shift_tokens_right

_lowercase				:				List[str]   		=		2_5_0_0_0_4
_lowercase				:				Optional[Any]   		=		2_5_0_0_2_0

@require_sentencepiece
@require_tokenizers
class  __SCREAMING_SNAKE_CASE			(   lowerCAmelCase_	,     unittest.TestCase     ):


					'''simple docstring'''





					_a         =							MBartTokenizer
					_a         =							MBartTokenizerFast
					_a         =							True
					_a         =							True



					def 	snake_case ( self							:						Tuple  )->      Union[str, Any]:
								super().setUp()

								# We have a SentencePiece fixture for testing
								lowerCamelCase__	:	Union[str, Any]				=MBartTokenizer(lowerCamelCase,						keep_accents=lowerCamelCase  )
								tokenizer.save_pretrained(self.tmpdirname  )



					def 	snake_case ( self							:						Dict  )->      Union[str, Any]:
								lowerCamelCase__	:	Any				=MBartTokenizer(lowerCamelCase,						keep_accents=lowerCamelCase  )

								lowerCamelCase__	:	List[Any]				=tokenizer.tokenize('''This is a test'''  )
								self.assertListEqual(lowerCamelCase,						['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est''']  )

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

								lowerCamelCase__	:	str				=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''',
								        '''.''',
								    ],						)
								lowerCamelCase__	:	Union[str, Any]				=tokenizer.convert_tokens_to_ids(lowerCamelCase  )
								self.assertListEqual(
								    lowerCamelCase,						[
								        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]
								        #                                       ^ unk: 2 + 1 = 3                  unk: 2 + 1 = 3 ^
								    ],						)

								lowerCamelCase__	:	str				=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>''',
								        '''.''',
								    ],						)




					def 	snake_case ( self							:						Tuple  )->      List[Any]:
								if not self.test_slow_tokenizer:
											# as we don't have a slow version, we can't compare the outputs between slow and fast versions
											return

								lowerCamelCase__	:	int				=(self.rust_tokenizer_class, '''hf-internal-testing/tiny-random-mbart''', {})
								for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
											with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''  ):
														lowerCamelCase__	:	Union[str, Any]				=self.rust_tokenizer_class.from_pretrained(lowerCamelCase,						**lowerCamelCase  )
														lowerCamelCase__	:	str				=self.tokenizer_class.from_pretrained(lowerCamelCase,						**lowerCamelCase  )

														lowerCamelCase__	:	List[str]				=tempfile.mkdtemp()

														lowerCamelCase__	:	Union[str, Any]				=tokenizer_r.save_pretrained(lowerCamelCase  )
														lowerCamelCase__	:	Optional[int]				=tokenizer_p.save_pretrained(lowerCamelCase  )

														# Checks it save with the same files + the tokenizer.json file for the fast one
														self.assertTrue(any('''tokenizer.json''' in f for f in tokenizer_r_files  )  )
														lowerCamelCase__	:	List[str]				=tuple(f for f in tokenizer_r_files if '''tokenizer.json''' not in f  )
														self.assertSequenceEqual(lowerCamelCase,						lowerCamelCase  )

														# Checks everything loads correctly in the same way
														lowerCamelCase__	:	Any				=tokenizer_r.from_pretrained(lowerCamelCase  )
														lowerCamelCase__	:	Dict				=tokenizer_p.from_pretrained(lowerCamelCase  )

														# Check special tokens are set accordingly on Rust and Python
														for key in tokenizer_pp.special_tokens_map:
																	self.assertTrue(hasattr(lowerCamelCase,						lowerCamelCase  )  )
																	# self.assertEqual(getattr(tokenizer_rp, key), getattr(tokenizer_pp, key))
																	# self.assertEqual(getattr(tokenizer_rp, key + "_id"), getattr(tokenizer_pp, key + "_id"))

														shutil.rmtree(lowerCamelCase  )

														# Save tokenizer rust, legacy_format=True
														lowerCamelCase__	:	Dict				=tempfile.mkdtemp()

														lowerCamelCase__	:	List[str]				=tokenizer_r.save_pretrained(lowerCamelCase,						legacy_format=lowerCamelCase  )
														lowerCamelCase__	:	Tuple				=tokenizer_p.save_pretrained(lowerCamelCase  )

														# Checks it save with the same files
														self.assertSequenceEqual(lowerCamelCase,						lowerCamelCase  )

														# Checks everything loads correctly in the same way
														lowerCamelCase__	:	Optional[int]				=tokenizer_r.from_pretrained(lowerCamelCase  )
														lowerCamelCase__	:	Any				=tokenizer_p.from_pretrained(lowerCamelCase  )

														# Check special tokens are set accordingly on Rust and Python
														for key in tokenizer_pp.special_tokens_map:
																	self.assertTrue(hasattr(lowerCamelCase,						lowerCamelCase  )  )

														shutil.rmtree(lowerCamelCase  )

														# Save tokenizer rust, legacy_format=False
														lowerCamelCase__	:	Optional[int]				=tempfile.mkdtemp()

														lowerCamelCase__	:	int				=tokenizer_r.save_pretrained(lowerCamelCase,						legacy_format=lowerCamelCase  )
														lowerCamelCase__	:	Dict				=tokenizer_p.save_pretrained(lowerCamelCase  )

														# Checks it saved the tokenizer.json file
														self.assertTrue(any('''tokenizer.json''' in f for f in tokenizer_r_files  )  )

														# Checks everything loads correctly in the same way
														lowerCamelCase__	:	Dict				=tokenizer_r.from_pretrained(lowerCamelCase  )
														lowerCamelCase__	:	int				=tokenizer_p.from_pretrained(lowerCamelCase  )

														# Check special tokens are set accordingly on Rust and Python
														for key in tokenizer_pp.special_tokens_map:
																	self.assertTrue(hasattr(lowerCamelCase,						lowerCamelCase  )  )

														shutil.rmtree(lowerCamelCase  )





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


					'''simple docstring'''





					_a         =							'facebook/mbart-large-en-ro'
					_a         =							[
					    ' UN Chief Says There Is No Military Solution in Syria',
					    ' Secretary-General Ban Ki-moon says his response to Russia\'s stepped up military support for Syria is that "there is no military solution" to the nearly five-year conflict and more weapons will only worsen the violence and misery for millions of people.',
					]
					_a         =							[
					    'Şeful ONU declară că nu există o soluţie militară în Siria',
					    'Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar al Rusiei'
					    ' pentru Siria este că "nu există o soluţie militară" la conflictul de aproape cinci ani şi că noi arme nu vor'
					    ' face decât să înrăutăţească violenţele şi mizeria pentru milioane de oameni.',
					]
					_a         =							[8_2_7_4, 1_2_7_8_7_3, 2_5_9_1_6, 7, 8_6_2_2, 2_0_7_1, 4_3_8, 6_7_4_8_5, 5_3, 1_8_7_8_9_5, 2_3, 5_1_7_1_2, 2, EN_CODE]



					@classmethod
					def 	snake_case ( cls							:						List[Any]  )->      Optional[int]:
								lowerCamelCase__	:	MBartTokenizer				=MBartTokenizer.from_pretrained(
								    cls.checkpoint_name,						src_lang='''en_XX''',						tgt_lang='''ro_RO'''  )
								lowerCamelCase__	:	Optional[int]				=1
								return cls



					def 	snake_case ( self							:						Optional[Any]  )->      List[str]:
								self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['''ar_AR'''],						25_0001  )
								self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['''en_EN'''],						25_0004  )
								self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['''ro_RO'''],						25_0020  )



					def 	snake_case ( self							:						Optional[int]  )->      List[Any]:
								lowerCamelCase__	:	Union[str, Any]				=self.tokenizer.batch_encode_plus(self.src_text  ).input_ids[0]
								self.assertListEqual(self.expected_src_tokens,						lowerCamelCase  )



					def 	snake_case ( self							:						Optional[Any]  )->      str:
								self.assertIn(lowerCamelCase,						self.tokenizer.all_special_ids  )
								lowerCamelCase__	:	Optional[int]				=[RO_CODE, 884, 9019, 96, 9, 916, 8_6792, 36, 1_8743, 1_5596, 5, 2]
								lowerCamelCase__	:	Any				=self.tokenizer.decode(lowerCamelCase,						skip_special_tokens=lowerCamelCase  )
								lowerCamelCase__	:	str				=self.tokenizer.decode(generated_ids[1:],						skip_special_tokens=lowerCamelCase  )
								self.assertEqual(lowerCamelCase,						lowerCamelCase  )
								self.assertNotIn(self.tokenizer.eos_token,						lowerCamelCase  )



					def 	snake_case ( self							:						Tuple  )->      int:
								lowerCamelCase__	:	Optional[int]				=['''this is gunna be a long sentence ''' * 20]
								assert isinstance(src_text[0],						lowerCamelCase  )
								lowerCamelCase__	:	Dict				=10
								lowerCamelCase__	:	Optional[int]				=self.tokenizer(lowerCamelCase,						max_length=lowerCamelCase,						truncation=lowerCamelCase  ).input_ids[0]
								self.assertEqual(ids[-2],						2  )
								self.assertEqual(ids[-1],						lowerCamelCase  )
								self.assertEqual(len(lowerCamelCase  ),						lowerCamelCase  )



					def 	snake_case ( self							:						int  )->      Any:
								self.assertListEqual(self.tokenizer.convert_tokens_to_ids(['''<mask>''', '''ar_AR''']  ),						[25_0026, 25_0001]  )



					def 	snake_case ( self							:						Tuple  )->      Optional[Any]:
								lowerCamelCase__	:	int				=tempfile.mkdtemp()
								lowerCamelCase__	:	Optional[int]				=self.tokenizer.fairseq_tokens_to_ids
								self.tokenizer.save_pretrained(lowerCamelCase  )
								lowerCamelCase__	:	Optional[Any]				=MBartTokenizer.from_pretrained(lowerCamelCase  )
								self.assertDictEqual(new_tok.fairseq_tokens_to_ids,						lowerCamelCase  )



					@require_torch
					def 	snake_case ( self							:						Optional[Any]  )->      Tuple:
								lowerCamelCase__	:	Optional[Any]				=self.tokenizer(self.src_text,						text_target=self.tgt_text,						padding=lowerCamelCase,						return_tensors='''pt'''  )
								lowerCamelCase__	:	Dict				=shift_tokens_right(batch['''labels'''],						self.tokenizer.pad_token_id  )

								# fairseq batch: https://gist.github.com/sshleifer/cba08bc2109361a74ac3760a7e30e4f4
								assert batch.input_ids[1][-2:].tolist() == [2, EN_CODE]
								assert batch.decoder_input_ids[1][0].tolist() == RO_CODE
								assert batch.decoder_input_ids[1][-1] == 2
								assert batch.labels[1][-2:].tolist() == [2, RO_CODE]



					@require_torch
					def 	snake_case ( self							:						Optional[Any]  )->      Any:
								lowerCamelCase__	:	str				=self.tokenizer(
								    self.src_text,						text_target=self.tgt_text,						padding=lowerCamelCase,						truncation=lowerCamelCase,						max_length=len(self.expected_src_tokens  ),						return_tensors='''pt''',						)

								lowerCamelCase__	:	List[Any]				=shift_tokens_right(batch['''labels'''],						self.tokenizer.pad_token_id  )

								self.assertIsInstance(lowerCamelCase,						lowerCamelCase  )

								self.assertEqual((2, 14),						batch.input_ids.shape  )
								self.assertEqual((2, 14),						batch.attention_mask.shape  )
								lowerCamelCase__	:	Any				=batch.input_ids.tolist()[0]
								self.assertListEqual(self.expected_src_tokens,						lowerCamelCase  )
								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, EN_CODE]  )



					def 	snake_case ( self							:						List[Any]  )->      Dict:
								lowerCamelCase__	:	Any				=self.tokenizer(self.src_text,						padding=lowerCamelCase,						truncation=lowerCamelCase,						max_length=3,						return_tensors='''pt'''  )
								lowerCamelCase__	:	Tuple				=self.tokenizer(
								    text_target=self.tgt_text,						padding=lowerCamelCase,						truncation=lowerCamelCase,						max_length=10,						return_tensors='''pt'''  )
								lowerCamelCase__	:	Union[str, Any]				=targets['''input_ids''']
								lowerCamelCase__	:	List[Any]				=shift_tokens_right(lowerCamelCase,						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 	snake_case ( self							:						Optional[int]  )->      List[Any]:
								lowerCamelCase__	:	str				=self.tokenizer._build_translation_inputs(
								    '''A test''',						return_tensors='''pt''',						src_lang='''en_XX''',						tgt_lang='''ar_AR'''  )

								self.assertEqual(
								    nested_simplify(lowerCamelCase  ),						{
								        # A, test, EOS, en_XX
								        '''input_ids''': [[62, 3034, 2, 25_0004]],
								        '''attention_mask''': [[1, 1, 1, 1]],
								        # ar_AR
								        '''forced_bos_token_id''': 25_0001,
								    },						)




