
"""simple docstring"""






import inspect
import unittest

from transformers import MobileNetVaConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
		import torch

		from transformers import MobileNetVaForImageClassification, MobileNetVaModel
		from transformers.models.mobilenet_va.modeling_mobilenet_va import MOBILENET_V1_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
		from PIL import Image

		from transformers import MobileNetVaImageProcessor

class  __SCREAMING_SNAKE_CASE			(   lowerCAmelCase_     ):


					'''simple docstring'''





					def 	snake_case ( self							:						Union[str, Any]  )->      List[str]:
								lowerCamelCase__	:	Dict				=self.config_class(**self.inputs_dict  )
								self.parent.assertTrue(hasattr(lowerCamelCase,						'''tf_padding'''  )  )
								self.parent.assertTrue(hasattr(lowerCamelCase,						'''depth_multiplier'''  )  )





class  __SCREAMING_SNAKE_CASE			:


					'''simple docstring'''





					def __init__( self							:						Optional[Any],						lowerCamelCase							:						Optional[int],						lowerCamelCase							:						List[str]=13,						lowerCamelCase							:						str=3,						lowerCamelCase							:						List[str]=32,						lowerCamelCase							:						Any=0.25,						lowerCamelCase							:						Optional[int]=8,						lowerCamelCase							:						str=True,						lowerCamelCase							:						Tuple=1024,						lowerCamelCase							:						Optional[int]=32,						lowerCamelCase							:						Union[str, Any]="relu6",						lowerCamelCase							:						Any=0.1,						lowerCamelCase							:						Tuple=0.02,						lowerCamelCase							:						Union[str, Any]=True,						lowerCamelCase							:						Union[str, Any]=True,						lowerCamelCase							:						Optional[int]=10,						lowerCamelCase							:						int=None,						)->      Any:
								lowerCamelCase__	:	Any				=parent
								lowerCamelCase__	:	Optional[Any]				=batch_size
								lowerCamelCase__	:	Union[str, Any]				=num_channels
								lowerCamelCase__	:	str				=image_size
								lowerCamelCase__	:	Union[str, Any]				=depth_multiplier
								lowerCamelCase__	:	List[str]				=min_depth
								lowerCamelCase__	:	int				=tf_padding
								lowerCamelCase__	:	Optional[Any]				=int(last_hidden_size * depth_multiplier  )
								lowerCamelCase__	:	List[str]				=output_stride
								lowerCamelCase__	:	Optional[Any]				=hidden_act
								lowerCamelCase__	:	Tuple				=classifier_dropout_prob
								lowerCamelCase__	:	Any				=use_labels
								lowerCamelCase__	:	int				=is_training
								lowerCamelCase__	:	Union[str, Any]				=num_labels
								lowerCamelCase__	:	int				=initializer_range
								lowerCamelCase__	:	Optional[int]				=scope



					def 	snake_case ( self							:						List[str]  )->      Union[str, Any]:
								lowerCamelCase__	:	str				=floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]  )

								lowerCamelCase__	:	Any				=None
								lowerCamelCase__	:	Any				=None
								if self.use_labels:
											lowerCamelCase__	:	Dict				=ids_tensor([self.batch_size],						self.num_labels  )
											lowerCamelCase__	:	Union[str, Any]				=ids_tensor([self.batch_size, self.image_size, self.image_size],						self.num_labels  )

								lowerCamelCase__	:	Dict				=self.get_config()

								return config, pixel_values, labels, pixel_labels



					def 	snake_case ( self							:						int  )->      List[Any]:
								return MobileNetVaConfig(
								    num_channels=self.num_channels,						image_size=self.image_size,						depth_multiplier=self.depth_multiplier,						min_depth=self.min_depth,						tf_padding=self.tf_padding,						hidden_act=self.hidden_act,						classifier_dropout_prob=self.classifier_dropout_prob,						initializer_range=self.initializer_range,						)



					def 	snake_case ( self							:						Tuple,						lowerCamelCase							:						Optional[int],						lowerCamelCase							:						Tuple,						lowerCamelCase							:						List[Any],						lowerCamelCase							:						Optional[Any]  )->      Tuple:
								lowerCamelCase__	:	str				=MobileNetVaModel(config=lowerCamelCase  )
								model.to(lowerCamelCase  )
								model.eval()
								lowerCamelCase__	:	Optional[int]				=model(lowerCamelCase  )
								self.parent.assertEqual(
								    result.last_hidden_state.shape,						(
								        self.batch_size,
								        self.last_hidden_size,
								        self.image_size // self.output_stride,
								        self.image_size // self.output_stride,
								    ),						)



					def 	snake_case ( self							:						Tuple,						lowerCamelCase							:						Optional[Any],						lowerCamelCase							:						Tuple,						lowerCamelCase							:						Union[str, Any],						lowerCamelCase							:						int  )->      int:
								lowerCamelCase__	:	Optional[Any]				=self.num_labels
								lowerCamelCase__	:	Any				=MobileNetVaForImageClassification(lowerCamelCase  )
								model.to(lowerCamelCase  )
								model.eval()
								lowerCamelCase__	:	Optional[Any]				=model(lowerCamelCase,						labels=lowerCamelCase  )
								self.parent.assertEqual(result.logits.shape,						(self.batch_size, self.num_labels)  )




					def 	snake_case ( self							:						Union[str, Any]  )->      Optional[int]:
								lowerCamelCase__	:	str				=self.prepare_config_and_inputs()
								lowerCamelCase__	:	Tuple				=config_and_inputs
								lowerCamelCase__	:	Any				={'''pixel_values''': pixel_values}
								return config, inputs_dict





@require_torch
class  __SCREAMING_SNAKE_CASE			(   lowerCAmelCase_	,     lowerCAmelCase_	,     unittest.TestCase     ):


					'''simple docstring'''





					_a         =							(MobileNetVaModel, MobileNetVaForImageClassification) if is_torch_available() else ()
					_a         =							(
					    {'feature-extraction': MobileNetVaModel, 'image-classification': MobileNetVaForImageClassification}
					    if is_torch_available()
					    else {}
					)

					_a         =							False
					_a         =							False
					_a         =							False
					_a         =							False



					def 	snake_case ( self							:						Any  )->      Union[str, Any]:
								lowerCamelCase__	:	str				=MobileNetVaModelTester(self  )
								lowerCamelCase__	:	int				=MobileNetVaConfigTester(self,						config_class=lowerCamelCase,						has_text_modality=lowerCamelCase  )



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



					@unittest.skip(reason='''MobileNetV1 does not use inputs_embeds'''  )
					def 	snake_case ( self							:						Dict  )->      Union[str, Any]:
								pass



					@unittest.skip(reason='''MobileNetV1 does not support input and output embeddings'''  )
					def 	snake_case ( self							:						Dict  )->      str:
								pass



					@unittest.skip(reason='''MobileNetV1 does not output attentions'''  )
					def 	snake_case ( self							:						List[str]  )->      Tuple:
								pass



					def 	snake_case ( self							:						Dict  )->      Optional[Any]:
								lowerCamelCase__	:	int				=self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
											lowerCamelCase__	:	Optional[int]				=model_class(lowerCamelCase  )
											lowerCamelCase__	:	int				=inspect.signature(model.forward  )
											# signature.parameters is an OrderedDict => so arg_names order is deterministic
											lowerCamelCase__	:	Optional[int]				=[*signature.parameters.keys()]

											lowerCamelCase__	:	Union[str, Any]				=['''pixel_values''']
											self.assertListEqual(arg_names[:1],						lowerCamelCase  )



					def 	snake_case ( self							:						Optional[Any]  )->      List[str]:
								lowerCamelCase__	:	Any				=self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*lowerCamelCase  )



					def 	snake_case ( self							:						int  )->      List[Any]:
								def check_hidden_states_output(lowerCamelCase							:						Dict,						lowerCamelCase							:						Union[str, Any],						lowerCamelCase							:						List[str]  ):
											lowerCamelCase__	:	int				=model_class(lowerCamelCase  )
											model.to(lowerCamelCase  )
											model.eval()

											with torch.no_grad():
														lowerCamelCase__	:	Optional[Any]				=model(**self._prepare_for_class(lowerCamelCase,						lowerCamelCase  )  )

											lowerCamelCase__	:	List[Any]				=outputs.hidden_states

											lowerCamelCase__	:	Any				=26
											self.assertEqual(len(lowerCamelCase  ),						lowerCamelCase  )

								lowerCamelCase__	:	List[Any]				=self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
											lowerCamelCase__	:	int				=True
											check_hidden_states_output(lowerCamelCase,						lowerCamelCase,						lowerCamelCase  )

											# check that output_hidden_states also work using config
											del inputs_dict["output_hidden_states"]
											lowerCamelCase__	:	List[str]				=True

											check_hidden_states_output(lowerCamelCase,						lowerCamelCase,						lowerCamelCase  )



					def 	snake_case ( self							:						Any  )->      List[Any]:
								lowerCamelCase__	:	List[str]				=self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_image_classification(*lowerCamelCase  )




					@slow
					def 	snake_case ( self							:						Optional[Any]  )->      List[Any]:
								for model_name in MOBILENET_V1_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
											lowerCamelCase__	:	Optional[int]				=MobileNetVaModel.from_pretrained(lowerCamelCase  )
											self.assertIsNotNone(lowerCamelCase  )






def   snake_case__     (	):
			"""simple docstring"""
			lowerCamelCase__	:	Optional[Any]				=Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''			)
			return image

@require_torch
@require_vision
class  __SCREAMING_SNAKE_CASE			(   unittest.TestCase     ):


					'''simple docstring'''





					@cached_property
					def 	snake_case ( self							:						List[Any]  )->      List[Any]:
								return (
								    MobileNetVaImageProcessor.from_pretrained('''google/mobilenet_v1_1.0_224'''  ) if is_vision_available() else None
								)




					@slow
					def 	snake_case ( self							:						Optional[int]  )->      Any:
								lowerCamelCase__	:	Dict				=MobileNetVaForImageClassification.from_pretrained('''google/mobilenet_v1_1.0_224'''  ).to(lowerCamelCase  )

								lowerCamelCase__	:	Any				=self.default_image_processor
								lowerCamelCase__	:	Tuple				=prepare_img()
								lowerCamelCase__	:	List[str]				=image_processor(images=lowerCamelCase,						return_tensors='''pt'''  ).to(lowerCamelCase  )

								# forward pass
								with torch.no_grad():
											lowerCamelCase__	:	str				=model(**lowerCamelCase  )

								# verify the logits
								lowerCamelCase__	:	str				=torch.Size((1, 1001)  )
								self.assertEqual(outputs.logits.shape,						lowerCamelCase  )

								lowerCamelCase__	:	Dict				=torch.tensor([-4.1_739, -1.1_233, 3.1_205]  ).to(lowerCamelCase  )

								self.assertTrue(torch.allclose(outputs.logits[0, :3],						lowerCamelCase,						atol=1E-4  )  )









"""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,
								    },						)




