






import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import MarianConfig, is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor


if is_flax_available():
  import os

  # The slow tests are often failing with OOM error on GPU
  # This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
  # but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
  __a :    int								=				"platform"

  import jax
  import jax.numpy as jnp

  from transformers import MarianTokenizer
  from transformers.models.marian.modeling_flax_marian import FlaxMarianModel, FlaxMarianMTModel, shift_tokens_right



def      _SCREAMING_SNAKE_CASE  (	__lowercase				:     Optional[int]     ,			__lowercase				:     Optional[int]     ,			__lowercase				:     List[str]=None     ,			__lowercase				:     Optional[Any]=None     ,			__lowercase				:     Optional[Any]=None     ,			__lowercase				:     Any=None     ,			__lowercase				:     int=None     ,			__lowercase				:     Optional[int]=None     ,			)  -> Optional[int]:





    """simple docstring"""

    if attention_mask is None:
        __A             =		np.where(input_ids != config.pad_token_id     ,			1     ,			0      )
    if decoder_attention_mask is None:
        __A             =		np.where(decoder_input_ids != config.pad_token_id     ,			1     ,			0      )
    if head_mask is None:
        __A             =		np.ones((config.encoder_layers, config.encoder_attention_heads)      )
    if decoder_head_mask is None:
        __A             =		np.ones((config.decoder_layers, config.decoder_attention_heads)      )
    if cross_attn_head_mask is None:
        __A             =		np.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": attention_mask,
    }


class  __lowercase :







     '''simple docstring'''




     def __init__(			self    :     Optional[Any]	,		UpperCamelCase_    :     List[str]	,		UpperCamelCase_    :     List[Any]=13	,		UpperCamelCase_    :     Optional[Any]=7	,		UpperCamelCase_    :     Union[str, Any]=True	,		UpperCamelCase_    :     str=False	,		UpperCamelCase_    :     Optional[int]=99	,		UpperCamelCase_    :     str=16	,		UpperCamelCase_    :     List[str]=2	,		UpperCamelCase_    :     Optional[Any]=4	,		UpperCamelCase_    :     Union[str, Any]=4	,		UpperCamelCase_    :     Optional[Any]="gelu"	,		UpperCamelCase_    :     str=0.1	,		UpperCamelCase_    :     str=0.1	,		UpperCamelCase_    :     int=32	,		UpperCamelCase_    :     int=2	,		UpperCamelCase_    :     Tuple=1	,		UpperCamelCase_    :     Union[str, Any]=0	,		UpperCamelCase_    :     Tuple=0.02	,		):


         """simple docstring"""



         __A             =		parent
         __A             =		batch_size
         __A             =		seq_length
         __A             =		is_training
         __A             =		use_labels
         __A             =		vocab_size
         __A             =		hidden_size
         __A             =		num_hidden_layers
         __A             =		num_attention_heads
         __A             =		intermediate_size
         __A             =		hidden_act
         __A             =		hidden_dropout_prob
         __A             =		attention_probs_dropout_prob
         __A             =		max_position_embeddings
         __A             =		eos_token_id
         __A             =		pad_token_id
         __A             =		bos_token_id
         __A             =		initializer_range




     def 						lowerCAmelCase_       (			self    :     Optional[int]			):


         """simple docstring"""



         __A             =		np.clip(ids_tensor([self.batch_size, self.seq_length - 1]	,		self.vocab_size			)	,		3	,		self.vocab_size			)
         __A             =		np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)	,		dtype=np.intaa			))	,		-1			)

         __A             =		shift_tokens_right(UpperCamelCase_	,		1	,		2			)

         __A             =		MarianConfig(
             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_id=self.eos_token_id	,		bos_token_id=self.bos_token_id	,		pad_token_id=self.pad_token_id	,		initializer_range=self.initializer_range	,		use_cache=UpperCamelCase_	,		)
         __A             =		prepare_marian_inputs_dict(UpperCamelCase_	,		UpperCamelCase_	,		UpperCamelCase_			)
         return config, inputs_dict




     def 						lowerCAmelCase_       (			self    :     Union[str, Any]			):


         """simple docstring"""



         __A      ,		__A             =		self.prepare_config_and_inputs()
         return config, inputs_dict




     def 						lowerCAmelCase_       (			self    :     str	,		UpperCamelCase_    :     List[Any]	,		UpperCamelCase_    :     Dict	,		UpperCamelCase_    :     List[str]			):


         """simple docstring"""



         __A             =		20
         __A             =		model_class_name(UpperCamelCase_			)

         __A             =		model.encode(inputs_dict["""input_ids"""]			)

         __A      ,		__A             =		(
             inputs_dict["""decoder_input_ids"""],
             inputs_dict["""decoder_attention_mask"""],
         )

         __A             =		model.init_cache(decoder_input_ids.shape[0]	,		UpperCamelCase_	,		UpperCamelCase_			)
         __A             =		jnp.ones((decoder_input_ids.shape[0], max_decoder_length)	,		dtype="""i4"""			)

         __A             =		jnp.broadcast_to(
             jnp.arange(decoder_input_ids.shape[-1] - 1			)[None, :]	,		(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)	,		)
         __A             =		model.decode(
             decoder_input_ids[:, :-1]	,		UpperCamelCase_	,		decoder_attention_mask=UpperCamelCase_	,		past_key_values=UpperCamelCase_	,		decoder_position_ids=UpperCamelCase_	,		)

         __A             =		jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]	,		dtype="""i4"""			)
         __A             =		model.decode(
             decoder_input_ids[:, -1:]	,		UpperCamelCase_	,		decoder_attention_mask=UpperCamelCase_	,		past_key_values=outputs_cache.past_key_values	,		decoder_position_ids=UpperCamelCase_	,		)

         __A             =		model.decode(UpperCamelCase_	,		UpperCamelCase_			)

         __A             =		np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])			)			)
         self.parent.assertTrue(diff < 1e-3	,		msg=F"Max diff is {diff}"			)





     def 						lowerCAmelCase_       (			self    :     str	,		UpperCamelCase_    :     List[Any]	,		UpperCamelCase_    :     Tuple	,		UpperCamelCase_    :     int			):


         """simple docstring"""



         __A             =		20
         __A             =		model_class_name(UpperCamelCase_			)

         __A             =		model.encode(inputs_dict["""input_ids"""]			)

         __A      ,		__A             =		(
             inputs_dict["""decoder_input_ids"""],
             inputs_dict["""decoder_attention_mask"""],
         )

         __A             =		jnp.concatenate(
             [
                 decoder_attention_mask,
                 jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])			),
             ]	,		axis=-1	,		)

         __A             =		model.init_cache(decoder_input_ids.shape[0]	,		UpperCamelCase_	,		UpperCamelCase_			)
         __A             =		jnp.broadcast_to(
             jnp.arange(decoder_input_ids.shape[-1] - 1			)[None, :]	,		(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)	,		)

         __A             =		model.decode(
             decoder_input_ids[:, :-1]	,		UpperCamelCase_	,		decoder_attention_mask=UpperCamelCase_	,		past_key_values=UpperCamelCase_	,		decoder_position_ids=UpperCamelCase_	,		)
         __A             =		jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]	,		dtype="""i4"""			)
         __A             =		model.decode(
             decoder_input_ids[:, -1:]	,		UpperCamelCase_	,		past_key_values=outputs_cache.past_key_values	,		decoder_attention_mask=UpperCamelCase_	,		decoder_position_ids=UpperCamelCase_	,		)

         __A             =		model.decode(UpperCamelCase_	,		UpperCamelCase_	,		decoder_attention_mask=UpperCamelCase_			)

         __A             =		np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])			)			)
         self.parent.assertTrue(diff < 1e-3	,		msg=F"Max diff is {diff}"			)


@require_flax
class  __lowercase (		lowercase_     , unittest.TestCase     , lowercase_					):







     '''simple docstring'''
     SCREAMING_SNAKE_CASE				  =			True
     SCREAMING_SNAKE_CASE				  =			(FlaxMarianModel, FlaxMarianMTModel) if is_flax_available() else ()
     SCREAMING_SNAKE_CASE				  =			(FlaxMarianMTModel,) if is_flax_available() else ()




     def 						lowerCAmelCase_       (			self    :     Dict			):


         """simple docstring"""



         __A             =		FlaxMarianModelTester(self			)




     def 						lowerCAmelCase_       (			self    :     Any			):


         """simple docstring"""



         __A      ,		__A             =		self.model_tester.prepare_config_and_inputs()
         for model_class in self.all_model_classes:
             self.model_tester.check_use_cache_forward(UpperCamelCase_	,		UpperCamelCase_	,		UpperCamelCase_			)




     def 						lowerCAmelCase_       (			self    :     int			):


         """simple docstring"""



         __A      ,		__A             =		self.model_tester.prepare_config_and_inputs()
         for model_class in self.all_model_classes:
             self.model_tester.check_use_cache_forward_with_attn_mask(UpperCamelCase_	,		UpperCamelCase_	,		UpperCamelCase_			)




     def 						lowerCAmelCase_       (			self    :     Tuple			):


         """simple docstring"""



         __A      ,		__A             =		self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
             with self.subTest(model_class.__name__			):
                 __A             =		self._prepare_for_class(UpperCamelCase_	,		UpperCamelCase_			)
                 __A             =		model_class(UpperCamelCase_			)

                 @jax.jit
                 def encode_jitted(UpperCamelCase_    :     Tuple	,		UpperCamelCase_    :     Dict=None	,		**UpperCamelCase_    :     List[Any]			):
                     return model.encode(input_ids=UpperCamelCase_	,		attention_mask=UpperCamelCase_			)

                 with self.subTest("""JIT Enabled"""			):
                     __A             =		encode_jitted(**UpperCamelCase_			).to_tuple()

                 with self.subTest("""JIT Disabled"""			):
                     with jax.disable_jit():
                         __A             =		encode_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 						lowerCAmelCase_       (			self    :     Any			):


         """simple docstring"""



         __A      ,		__A             =		self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
             with self.subTest(model_class.__name__			):
                 __A             =		model_class(UpperCamelCase_			)
                 __A             =		model.encode(inputs_dict["""input_ids"""]	,		inputs_dict["""attention_mask"""]			)

                 __A             =		{
                     """decoder_input_ids""": inputs_dict["""decoder_input_ids"""],
                     """decoder_attention_mask""": inputs_dict["""decoder_attention_mask"""],
                     """encoder_outputs""": encoder_outputs,
                 }

                 @jax.jit
                 def decode_jitted(UpperCamelCase_    :     Any	,		UpperCamelCase_    :     str	,		UpperCamelCase_    :     Optional[int]			):
                     return model.decode(
                         decoder_input_ids=UpperCamelCase_	,		decoder_attention_mask=UpperCamelCase_	,		encoder_outputs=UpperCamelCase_	,		)

                 with self.subTest("""JIT Enabled"""			):
                     __A             =		decode_jitted(**UpperCamelCase_			).to_tuple()

                 with self.subTest("""JIT Disabled"""			):
                     with jax.disable_jit():
                         __A             =		decode_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			)





     @slow
     def 						lowerCAmelCase_       (			self    :     List[str]			):


         """simple docstring"""



         for model_class_name in self.all_model_classes:
             __A             =		model_class_name.from_pretrained("""Helsinki-NLP/opus-mt-en-de"""			)
             # FlaxMarianForSequenceClassification expects eos token in input_ids
             __A             =		np.ones((1, 1)			) * model.config.eos_token_id
             __A             =		model(UpperCamelCase_			)
             self.assertIsNotNone(UpperCamelCase_			)


@require_flax
@require_sentencepiece
@require_tokenizers
class  __lowercase (		unittest.TestCase					):







     '''simple docstring'''
     SCREAMING_SNAKE_CASE				  =			None
     SCREAMING_SNAKE_CASE				  =			None




     @classmethod
     def 						lowerCAmelCase_       (			cls    :     List[str]			):


         """simple docstring"""



         __A             =		F"Helsinki-NLP/opus-mt-{cls.src}-{cls.tgt}"
         return cls




     @cached_property
     def 						lowerCAmelCase_       (			self    :     str			):


         """simple docstring"""



         return MarianTokenizer.from_pretrained(self.model_name			)




     @property
     def 						lowerCAmelCase_       (			self    :     Optional[int]			):


         """simple docstring"""



         return self.tokenizer.eos_token_id




     @cached_property
     def 						lowerCAmelCase_       (			self    :     Union[str, Any]			):


         """simple docstring"""



         __A             =		FlaxMarianMTModel.from_pretrained(self.model_name			)
         self.assertEqual(model.config.decoder_start_token_id	,		model.config.pad_token_id			)
         return model




     def 						lowerCAmelCase_       (			self    :     str	,		**UpperCamelCase_    :     Any			):


         """simple docstring"""



         __A             =		self.translate_src_text(**UpperCamelCase_			)
         self.assertListEqual(self.expected_text	,		UpperCamelCase_			)





     def 						lowerCAmelCase_       (			self    :     Any	,		**UpperCamelCase_    :     str			):


         """simple docstring"""



         __A             =		self.tokenizer(self.src_text	,		padding=UpperCamelCase_	,		return_tensors="""np"""	,		**UpperCamelCase_			)
         __A             =		self.model.generate(
             model_inputs.input_ids	,		attention_mask=model_inputs.attention_mask	,		num_beams=2	,		max_length=128	,		).sequences
         __A             =		self.tokenizer.batch_decode(UpperCamelCase_	,		skip_special_tokens=UpperCamelCase_			)
         return generated_words


@require_flax
@require_sentencepiece
@require_tokenizers
class  __lowercase (		lowercase_					):







     '''simple docstring'''
     SCREAMING_SNAKE_CASE				  =			"en"
     SCREAMING_SNAKE_CASE				  =			"fr"
     SCREAMING_SNAKE_CASE				  =			[
         "I am a small frog.",
         "Now I can forget the 100 words of german that I know.",
     ]
     SCREAMING_SNAKE_CASE				  =			[
         "Je suis une petite grenouille.",
         "Maintenant, je peux oublier les 100 mots d'allemand que je connais.",
     ]




     @slow
     def 						lowerCAmelCase_       (			self    :     List[Any]			):


         """simple docstring"""



         self._assert_generated_batch_equal_expected()


@require_flax
@require_sentencepiece
@require_tokenizers
class  __lowercase (		lowercase_					):







     '''simple docstring'''
     SCREAMING_SNAKE_CASE				  =			"fr"
     SCREAMING_SNAKE_CASE				  =			"en"
     SCREAMING_SNAKE_CASE				  =			[
         "Donnez moi le micro.",
         "Tom et Mary étaient assis à une table.",  # Accents
     ]
     SCREAMING_SNAKE_CASE				  =			[
         "Give me the microphone.",
         "Tom and Mary were sitting at a table.",
     ]




     @slow
     def 						lowerCAmelCase_       (			self    :     Any			):


         """simple docstring"""



         self._assert_generated_batch_equal_expected()


@require_flax
@require_sentencepiece
@require_tokenizers
class  __lowercase (		lowercase_					):







     '''simple docstring'''
     SCREAMING_SNAKE_CASE				  =			"mt"
     SCREAMING_SNAKE_CASE				  =			"en"
     SCREAMING_SNAKE_CASE				  =			["Billi messu b'mod ġentili, Ġesù fejjaq raġel li kien milqut bil - marda kerha tal - ġdiem."]
     SCREAMING_SNAKE_CASE				  =			["Touching gently, Jesus healed a man who was affected by the sad disease of leprosy."]




     @slow
     def 						lowerCAmelCase_       (			self    :     List[str]			):


         """simple docstring"""



         self._assert_generated_batch_equal_expected()


@require_flax
@require_sentencepiece
@require_tokenizers
class  __lowercase (		lowercase_					):







     '''simple docstring'''
     SCREAMING_SNAKE_CASE				  =			"en"
     SCREAMING_SNAKE_CASE				  =			"de"
     SCREAMING_SNAKE_CASE				  =			[
         "I am a small frog.",
         "Now I can forget the 100 words of german that I know.",
         "Tom asked his teacher for advice.",
         "That's how I would do it.",
         "Tom really admired Mary's courage.",
         "Turn around and close your eyes.",
     ]
     SCREAMING_SNAKE_CASE				  =			[
         "Ich bin ein kleiner Frosch.",
         "Jetzt kann ich die 100 Wörter des Deutschen vergessen, die ich kenne.",
         "Tom bat seinen Lehrer um Rat.",
         "So würde ich das machen.",
         "Tom bewunderte Marias Mut wirklich.",
         "Drehen Sie sich um und schließen Sie die Augen.",
     ]




     @slow
     def 						lowerCAmelCase_       (			self    :     Tuple			):


         """simple docstring"""



         self._assert_generated_batch_equal_expected()


@require_flax
@require_sentencepiece
@require_tokenizers
class  __lowercase (		lowercase_					):







     '''simple docstring'''
     SCREAMING_SNAKE_CASE				  =			"en"
     SCREAMING_SNAKE_CASE				  =			"zh"
     SCREAMING_SNAKE_CASE				  =			["My name is Wolfgang and I live in Berlin"]
     SCREAMING_SNAKE_CASE				  =			["我叫沃尔夫冈 我住在柏林"]




     @slow
     def 						lowerCAmelCase_       (			self    :     Any			):


         """simple docstring"""



         self._assert_generated_batch_equal_expected()


@require_flax
@require_sentencepiece
@require_tokenizers
class  __lowercase (		lowercase_					):







     '''simple docstring'''
     SCREAMING_SNAKE_CASE				  =			"ru"
     SCREAMING_SNAKE_CASE				  =			"fr"
     SCREAMING_SNAKE_CASE				  =			["Он показал мне рукопись своей новой пьесы."]
     SCREAMING_SNAKE_CASE				  =			["Il m'a montré le manuscrit de sa nouvelle pièce."]




     @slow
     def 						lowerCAmelCase_       (			self    :     str			):


         """simple docstring"""



         self._assert_generated_batch_equal_expected()


@require_flax
@require_sentencepiece
@require_tokenizers
class  __lowercase (		lowercase_					):







     '''simple docstring'''
     SCREAMING_SNAKE_CASE				  =			"en"
     SCREAMING_SNAKE_CASE				  =			"ROMANCE"
     SCREAMING_SNAKE_CASE				  =			[
         ">>fr<< Don't spend so much time watching TV.",
         ">>pt<< Your message has been sent.",
         ">>es<< He's two years older than me.",
     ]
     SCREAMING_SNAKE_CASE				  =			[
         "Ne passez pas autant de temps à regarder la télé.",
         "A sua mensagem foi enviada.",
         "Es dos años más viejo que yo.",
     ]




     @slow
     def 						lowerCAmelCase_       (			self    :     List[Any]			):


         """simple docstring"""



         self._assert_generated_batch_equal_expected()












import unittest

from transformers import ErnieMTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


__a :    Optional[Any]								=				get_tests_dir("fixtures/spiece.model")


@require_sentencepiece
@require_tokenizers
class  __lowercase (		lowercase_     , unittest.TestCase					):







     '''simple docstring'''
     SCREAMING_SNAKE_CASE				  =			ErnieMTokenizer
     SCREAMING_SNAKE_CASE				  =			False
     SCREAMING_SNAKE_CASE				  =			True
     SCREAMING_SNAKE_CASE				  =			False
     SCREAMING_SNAKE_CASE				  =			False




     def 						lowerCAmelCase_       (			self    :     Optional[Any]			):


         """simple docstring"""



         super().setUp()

         # We have a SentencePiece fixture for testing
         __A             =		ErnieMTokenizer(UpperCamelCase_	,		unk_token="""<unk>"""	,		pad_token="""<pad>"""			)
         tokenizer.save_pretrained(self.tmpdirname			)




     def 						lowerCAmelCase_       (			self    :     Optional[Any]	,		UpperCamelCase_    :     Tuple			):


         """simple docstring"""



         __A             =		"""this is a test"""
         __A             =		"""this is a test"""
         return input_text, output_text




     def 						lowerCAmelCase_       (			self    :     Optional[int]			):


         """simple docstring"""



         __A             =		"""<pad>"""
         __A             =		0

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




     def 						lowerCAmelCase_       (			self    :     Tuple			):


         """simple docstring"""



         __A             =		list(self.get_tokenizer().get_vocab().keys()			)

         self.assertEqual(vocab_keys[0]	,		"""<pad>"""			)
         self.assertEqual(vocab_keys[1]	,		"""<unk>"""			)
         self.assertEqual(vocab_keys[-1]	,		"""▁eloquent"""			)
         self.assertEqual(len(UpperCamelCase_			)	,		30_000			)




     def 						lowerCAmelCase_       (			self    :     Tuple			):


         """simple docstring"""



         self.assertEqual(self.get_tokenizer().vocab_size	,		30_000			)




     def 						lowerCAmelCase_       (			self    :     Union[str, Any]			):


         """simple docstring"""



         if not self.test_rust_tokenizer:
             return

         __A             =		self.get_tokenizer()
         __A             =		self.get_rust_tokenizer()

         __A             =		"""I was born in 92000, and this is falsé."""

         __A             =		tokenizer.tokenize(UpperCamelCase_			)
         __A             =		rust_tokenizer.tokenize(UpperCamelCase_			)
         self.assertListEqual(UpperCamelCase_	,		UpperCamelCase_			)

         __A             =		tokenizer.encode(UpperCamelCase_	,		add_special_tokens=UpperCamelCase_			)
         __A             =		rust_tokenizer.encode(UpperCamelCase_	,		add_special_tokens=UpperCamelCase_			)
         self.assertListEqual(UpperCamelCase_	,		UpperCamelCase_			)

         __A             =		self.get_rust_tokenizer()
         __A             =		tokenizer.encode(UpperCamelCase_			)
         __A             =		rust_tokenizer.encode(UpperCamelCase_			)
         self.assertListEqual(UpperCamelCase_	,		UpperCamelCase_			)




     def 						lowerCAmelCase_       (			self    :     Union[str, Any]			):


         """simple docstring"""



         __A             =		ErnieMTokenizer(UpperCamelCase_	,		do_lower_case=UpperCamelCase_	,		unk_token="""<unk>"""	,		pad_token="""<pad>"""			)

         __A             =		tokenizer.tokenize("""This is a test"""			)
         self.assertListEqual(UpperCamelCase_	,		["""▁this""", """▁is""", """▁a""", """▁test"""]			)

         self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCamelCase_			)	,		[48, 25, 21, 1_289]			)

         __A             =		tokenizer.tokenize("""I was born in 92000, and this is falsé."""			)
         # ErnieMTokenizer(paddlenlp implementation) outputs '9' instead of '_9' so to mimic that '_9' is changed to '9'
         self.assertListEqual(
             UpperCamelCase_	,		["""▁i""", """▁was""", """▁born""", """▁in""", """9""", """2000""", """,""", """▁and""", """▁this""", """▁is""", """▁fal""", """s""", """é""", """."""]			)
         __A             =		tokenizer.convert_tokens_to_ids(UpperCamelCase_			)
         self.assertListEqual(UpperCamelCase_	,		[31, 23, 386, 19, 518, 3_050, 15, 17, 48, 25, 8_256, 18, 1, 9]			)

         __A             =		tokenizer.convert_ids_to_tokens(UpperCamelCase_			)
         self.assertListEqual(
             UpperCamelCase_	,		["""▁i""", """▁was""", """▁born""", """▁in""", """9""", """2000""", """,""", """▁and""", """▁this""", """▁is""", """▁fal""", """s""", """<unk>""", """."""]	,		)




     def 						lowerCAmelCase_       (			self    :     List[str]			):


         """simple docstring"""



         __A             =		ErnieMTokenizer(UpperCamelCase_	,		unk_token="""<unk>"""	,		pad_token="""<pad>"""			)

         __A             =		tokenizer.encode("""sequence builders"""			)
         __A             =		tokenizer.encode("""multi-sequence build"""			)

         __A             =		tokenizer.build_inputs_with_special_tokens(UpperCamelCase_			)
         __A             =		tokenizer.build_inputs_with_special_tokens(UpperCamelCase_	,		UpperCamelCase_			)

         assert encoded_sentence == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id]
         assert encoded_pair == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + [
             tokenizer.sep_token_id
         ] + text_a + [tokenizer.sep_token_id]





     @slow
     def 						lowerCAmelCase_       (			self    :     List[str]			):


         """simple docstring"""



         __A             =		{"""input_ids""": [[0, 11_062, 82_772, 7, 15, 82_772, 538, 51_529, 237, 17_198, 1_290, 206, 9, 215_175, 1_314, 136, 17_198, 1_290, 206, 9, 56_359, 42, 122_009, 9, 16_466, 16, 87_344, 4_537, 9, 4_717, 78_381, 6, 159_958, 7, 15, 24_480, 618, 4, 527, 22_693, 9, 304, 4, 2_777, 24_480, 9_874, 4, 43_523, 594, 4, 803, 18_392, 33_189, 18, 4, 43_523, 24_447, 5, 5, 5, 16, 100, 24_955, 83_658, 9_626, 144_057, 15, 839, 22_335, 16, 136, 24_955, 83_658, 83_479, 15, 39_102, 724, 16, 678, 645, 6_460, 1_328, 4_589, 42, 122_009, 115_774, 23, 3_559, 1_328, 46_876, 7, 136, 53_894, 1_940, 42_227, 41_159, 17_721, 823, 425, 4, 27_512, 98_722, 206, 136, 5_531, 4_970, 919, 17_336, 5, 2], [0, 20_080, 618, 83, 82_775, 47, 479, 9, 1_517, 73, 53_894, 333, 80_581, 110_117, 18_811, 5_256, 1_295, 51, 152_526, 297, 7_986, 390, 124_416, 538, 35_431, 214, 98, 15_044, 25_737, 136, 7_108, 43_701, 23, 756, 135_355, 7, 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], [0, 581, 63_773, 119_455, 6, 147_797, 88_203, 7, 645, 70, 21, 3_285, 10_269, 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, 1, 1, 1]]}
         # fmt: on

         self.tokenizer_integration_test_util(
             expected_encoding=UpperCamelCase_	,		model_name="""susnato/ernie-m-base_pytorch"""	,		sequences=[
                 """Transformers (formerly known as pytorch-transformers and pytorch-pretrained-bert) provides """
                 """general-purpose architectures (BERT, GPT-2, RoBERTa, XLM, DistilBert, XLNet...) for Natural """
                 """Language Understanding (NLU) and Natural Language Generation (NLG) with over32+ pretrained """
                 """models in100+ languages and deep interoperability between Jax, PyTorch and TensorFlow.""",
                 """BERT is designed to pre-train deep bidirectional representations from unlabeled text by jointly """
                 """conditioning on both left and right context in all layers.""",
                 """The quick brown fox jumps over the lazy dog.""",
             ]	,		)




