
import inspect
import unittest

from transformers import RegNetConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from transformers.utils import cached_property, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor


if is_flax_available():
    import jax
    import jax.numpy as jnp

    from transformers.models.regnet.modeling_flax_regnet import FlaxRegNetForImageClassification, FlaxRegNetModel

if is_vision_available():
    from PIL import Image

    from transformers import AutoImageProcessor




class        UpperCAmelCase_			( unittest.TestCase     ):


  '''simple docstring'''







  def __init__( self			: Optional[Any]			,							UpperCamelCase__			: Tuple			,							UpperCamelCase__			: Optional[Any]=3			,							UpperCamelCase__			: Any=32			,							UpperCamelCase__			: Dict=3			,							UpperCamelCase__			: Union[str, Any]=10			,							UpperCamelCase__			: List[str]=[10, 20, 30, 40]			,							UpperCamelCase__			: Optional[int]=[1, 1, 2, 1]			,							UpperCamelCase__			: Optional[Any]=True			,							UpperCamelCase__			: Dict=True			,							UpperCamelCase__			: str="relu"			,							UpperCamelCase__			: Dict=3			,							UpperCamelCase__			: Tuple=None			,							)			->	List[str]:







    """simple docstring"""






    __magic_name__				     =       parent
    __magic_name__				     =       batch_size
    __magic_name__				     =       image_size
    __magic_name__				     =       num_channels
    __magic_name__				     =       embeddings_size
    __magic_name__				     =       hidden_sizes
    __magic_name__				     =       depths
    __magic_name__				     =       is_training
    __magic_name__				     =       use_labels
    __magic_name__				     =       hidden_act
    __magic_name__				     =       num_labels
    __magic_name__				     =       scope
    __magic_name__				     =       len(UpperCamelCase__    )
  def 							_lowercase							( self			: Optional[Any]    )			->	Tuple:







    """simple docstring"""






    __magic_name__				     =       floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]    )

    __magic_name__				     =       self.get_config()

    return config, pixel_values
  def 							_lowercase							( self			: List[Any]    )			->	Optional[Any]:







    """simple docstring"""






    return RegNetConfig(
        num_channels=self.num_channels			,							embeddings_size=self.embeddings_size			,							hidden_sizes=self.hidden_sizes			,							depths=self.depths			,							hidden_act=self.hidden_act			,							num_labels=self.num_labels			,							image_size=self.image_size			,							)
  def 							_lowercase							( self			: Union[str, Any]			,							UpperCamelCase__			: Any			,							UpperCamelCase__			: Dict    )			->	List[str]:







    """simple docstring"""






    __magic_name__				     =       FlaxRegNetModel(config=UpperCamelCase__    )
    __magic_name__				     =       model(UpperCamelCase__    )

    # Output shape (b, c, h, w)
    self.parent.assertEqual(
        result.last_hidden_state.shape			,							(self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)			,							)
  def 							_lowercase							( self			: Dict			,							UpperCamelCase__			: str			,							UpperCamelCase__			: Union[str, Any]    )			->	Union[str, Any]:







    """simple docstring"""






    __magic_name__				     =       self.num_labels
    __magic_name__				     =       FlaxRegNetForImageClassification(config=UpperCamelCase__    )
    __magic_name__				     =       model(UpperCamelCase__    )
    self.parent.assertEqual(result.logits.shape			,							(self.batch_size, self.num_labels)    )





  def 							_lowercase							( self			: Tuple    )			->	str:







    """simple docstring"""






    __magic_name__				     =       self.prepare_config_and_inputs()
    __magic_name__     ,    __magic_name__				     =       config_and_inputs
    __magic_name__				     =       {"""pixel_values""": pixel_values}
    return config, inputs_dict







@require_flax
class        UpperCAmelCase_			( _A  ,       unittest.TestCase     ):


  '''simple docstring'''







  a__           =		(FlaxRegNetModel, FlaxRegNetForImageClassification) if is_flax_available() else ()

  a__           =		False
  a__           =		False
  a__           =		False
  def 							_lowercase							( self			: int    )			->	None:







    """simple docstring"""






    __magic_name__				     =       FlaxRegNetModelTester(self    )
    __magic_name__				     =       ConfigTester(self			,							config_class=UpperCamelCase__			,							has_text_modality=UpperCamelCase__    )
  def 							_lowercase							( self			: Union[str, Any]    )			->	int:







    """simple docstring"""






    self.create_and_test_config_common_properties()
    self.config_tester.create_and_test_config_to_json_string()
    self.config_tester.create_and_test_config_to_json_file()
    self.config_tester.create_and_test_config_from_and_save_pretrained()
    self.config_tester.create_and_test_config_with_num_labels()
    self.config_tester.check_config_can_be_init_without_params()
    self.config_tester.check_config_arguments_init()
  def 							_lowercase							( self			: Optional[Any]    )			->	Union[str, Any]:







    """simple docstring"""






    return
  def 							_lowercase							( self			: List[str]    )			->	Any:







    """simple docstring"""






    __magic_name__				     =       self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_model(*UpperCamelCase__    )
  def 							_lowercase							( self			: Any    )			->	Optional[int]:







    """simple docstring"""






    __magic_name__				     =       self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_image_classification(*UpperCamelCase__    )
  @unittest.skip(reason="""RegNet does not use inputs_embeds"""    )
  def 							_lowercase							( self			: Union[str, Any]    )			->	int:







    """simple docstring"""






    pass
  @unittest.skip(reason="""RegNet does not support input and output embeddings"""    )
  def 							_lowercase							( self			: Dict    )			->	Dict:







    """simple docstring"""






    pass
  def 							_lowercase							( self			: List[Any]    )			->	List[Any]:







    """simple docstring"""






    __magic_name__     ,    __magic_name__				     =       self.model_tester.prepare_config_and_inputs_for_common()

    for model_class in self.all_model_classes:
      __magic_name__				     =       model_class(UpperCamelCase__    )
      __magic_name__				     =       inspect.signature(model.__call__    )
      # signature.parameters is an OrderedDict => so arg_names order is deterministic
      __magic_name__				     =       [*signature.parameters.keys()]

      __magic_name__				     =       ["""pixel_values"""]
      self.assertListEqual(arg_names[:1]			,							UpperCamelCase__    )
  def 							_lowercase							( self			: List[Any]    )			->	Any:







    """simple docstring"""






    def check_hidden_states_output(UpperCamelCase__			: List[Any]			,							UpperCamelCase__			: Tuple			,							UpperCamelCase__			: Union[str, Any]    ):
      __magic_name__				     =       model_class(UpperCamelCase__    )

      __magic_name__				     =       model(**self._prepare_for_class(UpperCamelCase__			,							UpperCamelCase__    )    )

      __magic_name__				     =       outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

      __magic_name__				     =       self.model_tester.num_stages
      self.assertEqual(len(UpperCamelCase__    )			,							expected_num_stages + 1    )

    __magic_name__     ,    __magic_name__				     =       self.model_tester.prepare_config_and_inputs_for_common()

    for model_class in self.all_model_classes:
      __magic_name__				     =       True
      check_hidden_states_output(UpperCamelCase__			,							UpperCamelCase__			,							UpperCamelCase__    )

      # check that output_hidden_states also work using config
      del inputs_dict["output_hidden_states"]
      __magic_name__				     =       True

      check_hidden_states_output(UpperCamelCase__			,							UpperCamelCase__			,							UpperCamelCase__    )





  def 							_lowercase							( self			: Union[str, Any]    )			->	int:







    """simple docstring"""






    __magic_name__     ,    __magic_name__				     =       self.model_tester.prepare_config_and_inputs_for_common()

    for model_class in self.all_model_classes:
      with self.subTest(model_class.__name__    ):
        __magic_name__				     =       self._prepare_for_class(UpperCamelCase__			,							UpperCamelCase__    )
        __magic_name__				     =       model_class(UpperCamelCase__    )

        @jax.jit
        def model_jitted(UpperCamelCase__			: Optional[Any]			,							**UpperCamelCase__			: Any    ):
          return model(pixel_values=UpperCamelCase__			,							**UpperCamelCase__    )

        with self.subTest("""JIT Enabled"""    ):
          __magic_name__				     =       model_jitted(**UpperCamelCase__    ).to_tuple()

        with self.subTest("""JIT Disabled"""    ):
          with jax.disable_jit():
            __magic_name__				     =       model_jitted(**UpperCamelCase__    ).to_tuple()

        self.assertEqual(len(UpperCamelCase__    )			,							len(UpperCamelCase__    )    )
        for jitted_output, output in zip(UpperCamelCase__			,							UpperCamelCase__    ):
          self.assertEqual(jitted_output.shape			,							output.shape    )







def a__    (       ):






  '''simple docstring'''





  __magic_name__				     =       Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""					)
  return image





@require_flax
class        UpperCAmelCase_			( unittest.TestCase     ):


  '''simple docstring'''







  @cached_property
  def 							_lowercase							( self			: List[str]    )			->	Optional[Any]:







    """simple docstring"""






    return AutoImageProcessor.from_pretrained("""facebook/regnet-y-040"""    ) if is_vision_available() else None





  @slow
  def 							_lowercase							( self			: Optional[Any]    )			->	Tuple:







    """simple docstring"""






    __magic_name__				     =       FlaxRegNetForImageClassification.from_pretrained("""facebook/regnet-y-040"""    )

    __magic_name__				     =       self.default_image_processor
    __magic_name__				     =       prepare_img()
    __magic_name__				     =       image_processor(images=UpperCamelCase__			,							return_tensors="""np"""    )

    __magic_name__				     =       model(**UpperCamelCase__    )

    # verify the logits
    __magic_name__				     =       (1, 1000)
    self.assertEqual(outputs.logits.shape			,							UpperCamelCase__    )

    __magic_name__				     =       jnp.array([-0.4180, -1.5051, -3.4836]    )

    self.assertTrue(jnp.allclose(outputs.logits[0, :3]			,							UpperCamelCase__			,							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,
						    }    ,       )



