

from __future__ import annotations

import unittest

from transformers import DistilBertConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

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


if is_tf_available():
	import tensorflow as tf

	from transformers.models.distilbert.modeling_tf_distilbert import (
	    TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
	    TFDistilBertForMaskedLM,
	    TFDistilBertForMultipleChoice,
	    TFDistilBertForQuestionAnswering,
	    TFDistilBertForSequenceClassification,
	    TFDistilBertForTokenClassification,
	    TFDistilBertModel,
	)



class     _a						:


					'''simple docstring'''






					def __init__( self    ,       A__    ,       ):
						A__							:    List[str]        =		parent
						A__							:    List[str]        =		13
						A__							:    Union[str, Any]        =		7
						A__							:    int        =		True
						A__							:    str        =		True
						A__							:    Optional[Any]        =		False
						A__							:    List[str]        =		True
						A__							:    List[str]        =		99
						A__							:    List[str]        =		32
						A__							:    Union[str, Any]        =		2
						A__							:    Optional[int]        =		4
						A__							:    Dict        =		37
						A__							:    Union[str, Any]        =		"""gelu"""
						A__							:    Union[str, Any]        =		0.1
						A__							:    Union[str, Any]        =		0.1
						A__							:    Union[str, Any]        =		512
						A__							:    Optional[Any]        =		16
						A__							:    Tuple        =		2
						A__							:    Tuple        =		0.0_2
						A__							:    Any        =		3
						A__							:    Union[str, Any]        =		4
						A__							:    int        =		None






					def   __A			( self			):
						A__							:    List[str]        =		ids_tensor([self.batch_size, self.seq_length]    ,       self.vocab_size			)

						A__							:    Optional[int]        =		None
						if self.use_input_mask:
							A__							:    int        =		random_attention_mask([self.batch_size, self.seq_length]			)

						A__							:    List[Any]        =		None
						A__							:    Optional[int]        =		None
						A__							:    Any        =		None
						if self.use_labels:
							A__							:    str        =		ids_tensor([self.batch_size]    ,       self.type_sequence_label_size			)
							A__							:    List[Any]        =		ids_tensor([self.batch_size, self.seq_length]    ,       self.num_labels			)
							A__							:    List[Any]        =		ids_tensor([self.batch_size]    ,       self.num_choices			)

						A__							:    Optional[Any]        =		DistilBertConfig(
						    vocab_size=self.vocab_size    ,       dim=self.hidden_size    ,       n_layers=self.num_hidden_layers    ,       n_heads=self.num_attention_heads    ,       hidden_dim=self.intermediate_size    ,       hidden_act=self.hidden_act    ,       dropout=self.hidden_dropout_prob    ,       attention_dropout=self.attention_probs_dropout_prob    ,       max_position_embeddings=self.max_position_embeddings    ,       initializer_range=self.initializer_range    ,       )

						return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels






					def   __A			( self    ,       A__    ,       A__    ,       A__    ,       A__    ,       A__    ,       A__			):
						A__							:    Any        =		TFDistilBertModel(config=A__			)
						A__							:    int        =		{"""input_ids""": input_ids, """attention_mask""": input_mask}

						A__							:    List[str]        =		model(A__			)

						A__							:    Union[str, Any]        =		[input_ids, input_mask]

						A__							:    Dict        =		model(A__			)

						self.parent.assertEqual(result.last_hidden_state.shape    ,       (self.batch_size, self.seq_length, self.hidden_size)			)






					def   __A			( self    ,       A__    ,       A__    ,       A__    ,       A__    ,       A__    ,       A__			):
						A__							:    Optional[Any]        =		TFDistilBertForMaskedLM(config=A__			)
						A__							:    Optional[int]        =		{"""input_ids""": input_ids, """attention_mask""": input_mask}
						A__							:    Union[str, Any]        =		model(A__			)
						self.parent.assertEqual(result.logits.shape    ,       (self.batch_size, self.seq_length, self.vocab_size)			)






					def   __A			( self    ,       A__    ,       A__    ,       A__    ,       A__    ,       A__    ,       A__			):
						A__							:    Tuple        =		TFDistilBertForQuestionAnswering(config=A__			)
						A__							:    Any        =		{
						    """input_ids""": input_ids,
						    """attention_mask""": input_mask,
						}
						A__							:    Any        =		model(A__			)
						self.parent.assertEqual(result.start_logits.shape    ,       (self.batch_size, self.seq_length)			)
						self.parent.assertEqual(result.end_logits.shape    ,       (self.batch_size, self.seq_length)			)






					def   __A			( self    ,       A__    ,       A__    ,       A__    ,       A__    ,       A__    ,       A__			):
						A__							:    List[Any]        =		self.num_labels
						A__							:    Optional[Any]        =		TFDistilBertForSequenceClassification(A__			)
						A__							:    List[Any]        =		{"""input_ids""": input_ids, """attention_mask""": input_mask}
						A__							:    Tuple        =		model(A__			)
						self.parent.assertEqual(result.logits.shape    ,       (self.batch_size, self.num_labels)			)






					def   __A			( self    ,       A__    ,       A__    ,       A__    ,       A__    ,       A__    ,       A__			):
						A__							:    str        =		self.num_choices
						A__							:    Dict        =		TFDistilBertForMultipleChoice(A__			)
						A__							:    Union[str, Any]        =		tf.tile(tf.expand_dims(A__    ,       1			)    ,       (1, self.num_choices, 1)			)
						A__							:    Union[str, Any]        =		tf.tile(tf.expand_dims(A__    ,       1			)    ,       (1, self.num_choices, 1)			)
						A__							:    Union[str, Any]        =		{
						    """input_ids""": multiple_choice_inputs_ids,
						    """attention_mask""": multiple_choice_input_mask,
						}
						A__							:    List[Any]        =		model(A__			)
						self.parent.assertEqual(result.logits.shape    ,       (self.batch_size, self.num_choices)			)






					def   __A			( self    ,       A__    ,       A__    ,       A__    ,       A__    ,       A__    ,       A__			):
						A__							:    Tuple        =		self.num_labels
						A__							:    Union[str, Any]        =		TFDistilBertForTokenClassification(A__			)
						A__							:    str        =		{"""input_ids""": input_ids, """attention_mask""": input_mask}
						A__							:    Dict        =		model(A__			)
						self.parent.assertEqual(result.logits.shape    ,       (self.batch_size, self.seq_length, self.num_labels)			)






					def   __A			( self			):
						A__							:    Union[str, Any]        =		self.prepare_config_and_inputs()
						((A__)   ,   (A__)   ,   (A__)   ,   (A__)   ,   (A__)   ,   (A__))							:    List[str]        =		config_and_inputs
						A__							:    Tuple        =		{"""input_ids""": input_ids, """attention_mask""": input_mask}
						return config, inputs_dict



@require_tf
class     _a						(__magic_name__		,   __magic_name__		,   unittest.TestCase				):


					'''simple docstring'''






					UpperCAmelCase__:   List[str]  							=       (
					    (
					        TFDistilBertModel,
					        TFDistilBertForMaskedLM,
					        TFDistilBertForQuestionAnswering,
					        TFDistilBertForSequenceClassification,
					        TFDistilBertForTokenClassification,
					        TFDistilBertForMultipleChoice,
					    )
					    if is_tf_available()
					    else None
					)
					UpperCAmelCase__:   int  							=       (
					    {
					        '''feature-extraction''': TFDistilBertModel,
					        '''fill-mask''': TFDistilBertForMaskedLM,
					        '''question-answering''': TFDistilBertForQuestionAnswering,
					        '''text-classification''': TFDistilBertForSequenceClassification,
					        '''token-classification''': TFDistilBertForTokenClassification,
					        '''zero-shot''': TFDistilBertForSequenceClassification,
					    }
					    if is_tf_available()
					    else {}
					)
					UpperCAmelCase__:   Optional[int]  							=       False
					UpperCAmelCase__:   Tuple  							=       False






					def   __A			( self			):
						A__							:    Dict        =		TFDistilBertModelTester(self			)
						A__							:    Any        =		ConfigTester(self    ,       config_class=A__    ,       dim=37			)






					def   __A			( self			):
						self.config_tester.run_common_tests()






					def   __A			( self			):
						A__							:    Union[str, Any]        =		self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_distilbert_model(*A__			)






					def   __A			( self			):
						A__							:    List[str]        =		self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_distilbert_for_masked_lm(*A__			)






					def   __A			( self			):
						A__							:    Dict        =		self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_distilbert_for_question_answering(*A__			)






					def   __A			( self			):
						A__							:    Union[str, Any]        =		self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_distilbert_for_sequence_classification(*A__			)






					def   __A			( self			):
						A__							:    Any        =		self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_distilbert_for_multiple_choice(*A__			)






					def   __A			( self			):
						A__							:    List[Any]        =		self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_distilbert_for_token_classification(*A__			)






					@slow
					def   __A			( self			):
						for model_name in list(TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]			):
							A__							:    str        =		TFDistilBertModel.from_pretrained(A__			)
							self.assertIsNotNone(A__			)



@require_tf
class     _a						(unittest.TestCase				):


					'''simple docstring'''






					@slow
					def   __A			( self			):
						A__							:    Any        =		TFDistilBertModel.from_pretrained("""distilbert-base-uncased"""			)
						A__							:    Union[str, Any]        =		tf.constant([[0, 1, 2, 3, 4, 5]]			)
						A__							:    Any        =		model(A__			)[0]

						A__							:    Any        =		[1, 6, 768]
						self.assertEqual(output.shape    ,       A__			)

						A__							:    Union[str, Any]        =		tf.constant(
						    [
						        [
						            [0.1_9_2_6_1_8_8_5, -0.1_3_7_3_2_9_5_5, 0.4_1_1_9_7_9_9],
						            [0.2_2_1_5_0_1_5_6, -0.0_7_4_2_2_6_6_1, 0.3_9_0_3_7_2_0_4],
						            [0.2_2_7_5_6_0_1_8, -0.0_8_9_6_4_1_4, 0.3_7_0_1_4_6_7],
						        ]
						    ]			)
						tf.debugging.assert_near(output[:, :3, :3]    ,       A__    ,       atol=1e-4			)






import tempfile
import unittest
from pathlib import Path
from shutil import copyfile

from transformers import MaMaaaTokenizer, is_torch_available
from transformers.testing_utils import (
    get_tests_dir,
    nested_simplify,
    require_sentencepiece,
    require_tokenizers,
    require_torch,
    slow,
)
from transformers.utils import is_sentencepiece_available


if is_sentencepiece_available():
	from transformers.models.mam_aaa.tokenization_mam_aaa import VOCAB_FILES_NAMES, save_json

from ...test_tokenization_common import TokenizerTesterMixin


if is_sentencepiece_available():
	A_       : Tuple         =			get_tests_dir('fixtures/test_sentencepiece.model')


if is_torch_available():
	from transformers.models.mam_aaa.modeling_mam_aaa import shift_tokens_right

A_       : Tuple         =			12_8022
A_       : Optional[Any]         =			12_8028



@require_sentencepiece
class     _a						(__magic_name__		,   unittest.TestCase				):


					'''simple docstring'''






					UpperCAmelCase__:   Tuple  							=       MaMaaaTokenizer
					UpperCAmelCase__:   List[Any]  							=       False
					UpperCAmelCase__:   Any  							=       False
					UpperCAmelCase__:   Optional[Any]  							=       True






					def   __A			( self			):
						super().setUp()

						A__							:    Union[str, Any]        =		["""</s>""", """<unk>""", """▁This""", """▁is""", """▁a""", """▁t""", """est""", """\u0120""", """<pad>"""]
						A__							:    Optional[Any]        =		dict(zip(A__    ,       range(len(A__			)			)			)			)
						A__							:    Optional[int]        =		Path(self.tmpdirname			)
						save_json(A__    ,       save_dir / VOCAB_FILES_NAMES["""vocab_file"""]			)
						if not (save_dir / VOCAB_FILES_NAMES["spm_file"]).exists():
							copyfile(A__    ,       save_dir / VOCAB_FILES_NAMES["""spm_file"""]			)

						A__							:    Tuple        =		MaMaaaTokenizer.from_pretrained(self.tmpdirname			)
						tokenizer.save_pretrained(self.tmpdirname			)






					def   __A			( self    ,       **A__			):
						return MaMaaaTokenizer.from_pretrained(self.tmpdirname    ,       **A__			)






					def   __A			( self    ,       A__			):
						return (
						    "This is a test",
						    "This is a test",
						)






					def   __A			( self			):
						A__							:    Any        =		"""</s>"""
						A__							:    str        =		0

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






					def   __A			( self			):
						A__							:    str        =		self.get_tokenizer()
						A__							:    Dict        =		list(tokenizer.get_vocab().keys()			)

						self.assertEqual(vocab_keys[0]    ,       """</s>"""			)
						self.assertEqual(vocab_keys[1]    ,       """<unk>"""			)
						self.assertEqual(vocab_keys[-1]    ,       """<s>"""			)
						self.assertEqual(len(A__			)    ,       tokenizer.vocab_size + len(tokenizer.get_added_vocab()			)			)






					@unittest.skip("""Skip this test while all models are still to be uploaded."""			)
					def   __A			( self			):
						pass






					def   __A			( self			):
						A__							:    Optional[int]        =		self.get_tokenizer()

						A__							:    int        =		tokenizer.tokenize("""This is a test"""			)
						self.assertListEqual(A__    ,       ["""▁This""", """▁is""", """▁a""", """▁t""", """est"""]			)

						self.assertListEqual(
						    tokenizer.convert_tokens_to_ids(A__			)    ,       [2, 3, 4, 5, 6]    ,       )

						A__							:    Dict        =		tokenizer.convert_ids_to_tokens([2, 3, 4, 5, 6]			)
						self.assertListEqual(A__    ,       ["""▁This""", """▁is""", """▁a""", """▁t""", """est"""]			)

						A__							:    Any        =		tokenizer.convert_tokens_to_string(A__			)
						self.assertEqual(A__    ,       """This is a test"""			)






					@slow
					def   __A			( self			):
						# fmt: off
						A__							:    int        =		{"""input_ids""": [[12_8022, 11_0108, 397, 11, 3_8272, 2247, 12_4811, 285, 1_8105, 1586, 207, 7, 3_9534, 4428, 397, 1019, 1_8105, 1586, 207, 7, 4_1337, 1_6786, 241, 7, 2_0214, 17, 12_5690, 1_0398, 7, 4_4378, 5_8069, 6_8342, 7798, 7343, 11, 299, 3_3310, 4, 158, 3_7350, 9_4077, 4569, 299, 3_3310, 90, 4, 5_2840, 290, 4, 3_1270, 112, 299, 682, 4, 5_2840, 3_9953, 1_4079, 193, 5_2519, 9_0894, 1_7894, 12_0697, 11, 4_0445, 551, 17, 1019, 5_2519, 9_0894, 1_7756, 963, 11, 4_0445, 480, 17, 9792, 1120, 5173, 1393, 6240, 1_6786, 241, 12_0996, 28, 1245, 1393, 11_8240, 1_1123, 1019, 9_3612, 2691, 1_0618, 9_8058, 12_0409, 1928, 279, 4, 4_0683, 367, 178, 207, 1019, 103, 10_3121, 506, 6_5296, 5, 2], [12_8022, 2_1217, 367, 117, 12_5450, 128, 719, 7, 7308, 40, 9_3612, 1_2669, 1116, 1_6704, 71, 1_7785, 3699, 1_5592, 35, 144, 9584, 241, 1_1943, 713, 950, 799, 2247, 8_8427, 150, 149, 11_8813, 12_0706, 1019, 10_6906, 8_1518, 28, 1224, 2_2799, 397, 5, 2, 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], [12_8022, 1658, 12_3311, 5155, 5578, 4722, 279, 1_4947, 2366, 1120, 1197, 14, 1348, 9232, 5, 2, 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]], """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, 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, 0, 0, 0, 0], [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, 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=A__    ,       model_name="""facebook/m2m100_418M"""    ,       revision="""c168bae485c864188cf9aa0e4108b0b6934dc91e"""    ,       )



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


					'''simple docstring'''






					UpperCAmelCase__:   Optional[int]  							=       '''facebook/m2m100_418M'''
					UpperCAmelCase__:   Any  							=       [
					    '''In my opinion, there are two levels of response from the French government.''',
					    '''NSA Affair Emphasizes Complete Lack of Debate on Intelligence''',
					]
					UpperCAmelCase__:   Any  							=       [
					    '''Selon moi, il y a deux niveaux de réponse de la part du gouvernement français.''',
					    '''L\'affaire NSA souligne l\'absence totale de débat sur le renseignement''',
					]

					# fmt: off
					UpperCAmelCase__:   List[str]  							=       [EN_CODE, 5_93, 19_49, 11_57_81, 4, 7_15_86, 42_34, 6_06_33, 12_62_33, 4_32, 12_38_08, 1_55_92, 11_97, 11_71_32, 12_06_18, 5, 2]






					@classmethod
					def   __A			( cls			):
						A__							:    MaMaaaTokenizer        =		MaMaaaTokenizer.from_pretrained(
						    cls.checkpoint_name    ,       src_lang="""en"""    ,       tgt_lang="""fr"""			)
						A__							:    int        =		1
						return cls






					def   __A			( self			):
						self.assertEqual(self.tokenizer.get_lang_id("""ar"""			)    ,       12_8006			)
						self.assertEqual(self.tokenizer.get_lang_id("""en"""			)    ,       12_8022			)
						self.assertEqual(self.tokenizer.get_lang_id("""ro"""			)    ,       12_8076			)
						self.assertEqual(self.tokenizer.get_lang_id("""mr"""			)    ,       12_8063			)






					def   __A			( self			):
						A__							:    Optional[Any]        =		self.tokenizer.get_vocab()
						self.assertEqual(len(A__			)    ,       self.tokenizer.vocab_size			)
						self.assertEqual(vocab["""<unk>"""]    ,       3			)
						self.assertIn(self.tokenizer.get_lang_token("""en"""			)    ,       A__			)






					def   __A			( self			):
						A__							:    List[Any]        =		"""en"""
						A__							:    str        =		self.tokenizer.batch_encode_plus(self.src_text			).input_ids[0]
						self.assertListEqual(self.expected_src_tokens    ,       A__			)






					def   __A			( self			):
						self.assertIn(A__    ,       self.tokenizer.all_special_ids			)
						# fmt: off
						A__							:    Dict        =		[FR_CODE, 5364, 82, 8642, 4, 294, 47, 8, 1_4028, 136, 3286, 9706, 6, 9_0797, 6, 14_4012, 162, 8_8128, 3_0061, 5, 2]
						# fmt: on
						A__							:    Dict        =		self.tokenizer.decode(A__    ,       skip_special_tokens=A__			)
						A__							:    List[str]        =		self.tokenizer.decode(generated_ids[1:]    ,       skip_special_tokens=A__			)
						self.assertEqual(A__    ,       A__			)
						self.assertNotIn(self.tokenizer.eos_token    ,       A__			)






					def   __A			( self			):
						A__							:    str        =		tempfile.mkdtemp()
						A__							:    Dict        =		self.tokenizer.lang_token_to_id
						self.tokenizer.save_pretrained(A__			)
						A__							:    List[Any]        =		MaMaaaTokenizer.from_pretrained(A__			)
						self.assertDictEqual(new_tok.lang_token_to_id    ,       A__			)






					@require_torch
					def   __A			( self			):
						A__							:    List[str]        =		"""en"""
						A__							:    List[str]        =		"""fr"""

						A__							:    int        =		self.tokenizer(self.src_text    ,       text_target=self.tgt_text    ,       padding=A__    ,       return_tensors="""pt"""			)

						A__							:    int        =		shift_tokens_right(
						    batch["""labels"""]    ,       self.tokenizer.pad_token_id    ,       self.tokenizer.eos_token_id			)

						for k in batch:
							A__							:    Any        =		batch[k].tolist()
						# batch = {k: v.tolist() for k,v in batch.items()}
						# fairseq batch: https://gist.github.com/sshleifer/cba08bc2109361a74ac3760a7e30e4f4
						# batch.decoder_inputs_ids[0][0] ==
						assert batch.input_ids[1][0] == EN_CODE
						assert batch.input_ids[1][-1] == 2
						assert batch.labels[1][0] == FR_CODE
						assert batch.labels[1][-1] == 2
						assert batch.decoder_input_ids[1][:2] == [2, FR_CODE]






					@require_torch
					def   __A			( self			):
						A__							:    List[str]        =		"""mr"""
						self.assertListEqual(self.tokenizer.prefix_tokens    ,       [self.tokenizer.get_lang_id("""mr"""			)]			)
						self.assertListEqual(self.tokenizer.suffix_tokens    ,       [self.tokenizer.eos_token_id]			)

						A__							:    Any        =		"""zh"""
						self.assertListEqual(self.tokenizer.prefix_tokens    ,       [self.tokenizer.get_lang_id("""zh"""			)]			)
						self.assertListEqual(self.tokenizer.suffix_tokens    ,       [self.tokenizer.eos_token_id]			)






					@require_torch
					def   __A			( self			):
						A__							:    Optional[int]        =		"""mr"""
						self.tokenizer._switch_to_target_mode()
						self.assertListEqual(self.tokenizer.prefix_tokens    ,       [self.tokenizer.get_lang_id("""mr"""			)]			)
						self.assertListEqual(self.tokenizer.suffix_tokens    ,       [self.tokenizer.eos_token_id]			)
						self.tokenizer._switch_to_input_mode()
						self.assertListEqual(self.tokenizer.prefix_tokens    ,       [self.tokenizer.get_lang_id(self.tokenizer.src_lang			)]			)

						A__							:    Any        =		"""zh"""
						self.tokenizer._switch_to_target_mode()
						self.assertListEqual(self.tokenizer.prefix_tokens    ,       [self.tokenizer.get_lang_id("""zh"""			)]			)
						self.assertListEqual(self.tokenizer.suffix_tokens    ,       [self.tokenizer.eos_token_id]			)
						self.tokenizer._switch_to_input_mode()
						self.assertListEqual(self.tokenizer.prefix_tokens    ,       [self.tokenizer.get_lang_id(self.tokenizer.src_lang			)]			)






					@require_torch
					def   __A			( self			):
						A__							:    Any        =		self.tokenizer._build_translation_inputs("""A test"""    ,       return_tensors="""pt"""    ,       src_lang="""en"""    ,       tgt_lang="""ar"""			)

						self.assertEqual(
						    nested_simplify(A__			)    ,       {
						        # en_XX, A, test, EOS
						        """input_ids""": [[12_8022, 58, 4183, 2]],
						        """attention_mask""": [[1, 1, 1, 1]],
						        # ar_AR
						        """forced_bos_token_id""": 12_8006,
						    }    ,       )



