




"""simple docstring"""







import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import MBartConfig, 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
		SCREAMING_SNAKE_CASE_      =					'platform'

		import jax
		import jax.numpy as jnp

		from transformers import AutoTokenizer
		from transformers.models.mbart.modeling_flax_mbart import (
		    FlaxMBartForConditionalGeneration,
		    FlaxMBartForQuestionAnswering,
		    FlaxMBartForSequenceClassification,
		    FlaxMBartModel,
		    shift_tokens_right,
		)





def     __snake_case   (							_lowercase ,_lowercase ,_lowercase=None ,_lowercase=None ,_lowercase=None ,_lowercase=None ,_lowercase=None ,_lowercase=None ,):


				"""simple docstring"""







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

class 							snake_case_ (				unittest.TestCase ):







			"""simple docstring"""




			def __init__(		self					,				lowerCamelCase_					,				lowerCamelCase_=1_3					,				lowerCamelCase_=7					,				lowerCamelCase_=True					,				lowerCamelCase_=False					,				lowerCamelCase_=9_9					,				lowerCamelCase_=1_6					,				lowerCamelCase_=2					,				lowerCamelCase_=4					,				lowerCamelCase_=4					,				lowerCamelCase_="gelu"					,				lowerCamelCase_=0.1					,				lowerCamelCase_=0.1					,				lowerCamelCase_=3_2					,				lowerCamelCase_=2					,				lowerCamelCase_=1					,				lowerCamelCase_=0					,				lowerCamelCase_=2					,				lowerCamelCase_=0.02					,				)							->      Dict:
							UpperCamelCase										=  parent
							UpperCamelCase										=  batch_size
							UpperCamelCase										=  seq_length
							UpperCamelCase										=  is_training
							UpperCamelCase										=  use_labels
							UpperCamelCase										=  vocab_size
							UpperCamelCase										=  hidden_size
							UpperCamelCase										=  num_hidden_layers
							UpperCamelCase										=  num_attention_heads
							UpperCamelCase										=  intermediate_size
							UpperCamelCase										=  hidden_act
							UpperCamelCase										=  hidden_dropout_prob
							UpperCamelCase										=  attention_probs_dropout_prob
							UpperCamelCase										=  max_position_embeddings
							UpperCamelCase										=  eos_token_id
							UpperCamelCase										=  pad_token_id
							UpperCamelCase										=  bos_token_id
							UpperCamelCase										=  decoder_start_token_id
							UpperCamelCase										=  initializer_range







			def      UpperCAmelCase__ (		self)							->      Tuple:
							UpperCamelCase										=  np.clip(ids_tensor([self.batch_size, self.seq_length - 1]					,				self.vocab_size)					,				3					,				self.vocab_size)
							UpperCamelCase										=  np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)					,				dtype=np.intaa))					,				-1)

							UpperCamelCase										=  shift_tokens_right(lowerCamelCase_					,				1)

							UpperCamelCase										=  MBartConfig(
							    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					,				decoder_start_token_id=self.decoder_start_token_id					,				initializer_range=self.initializer_range					,				use_cache=lowerCamelCase_					,				)
							UpperCamelCase										=  prepare_mbart_inputs_dict(lowerCamelCase_					,				lowerCamelCase_					,				lowerCamelCase_)
							return config, inputs_dict







			def      UpperCAmelCase__ (		self)							->      Any:
							UpperCamelCase					,  UpperCamelCase										=  self.prepare_config_and_inputs()
							return config, inputs_dict







			def      UpperCAmelCase__ (		self					,				lowerCamelCase_					,				lowerCamelCase_					,				lowerCamelCase_)							->      Optional[Any]:
							UpperCamelCase										=  2_0
							UpperCamelCase										=  model_class_name(lowerCamelCase_)

							UpperCamelCase										=  model.encode(inputs_dict['''input_ids'''])

							UpperCamelCase					,  UpperCamelCase										=  (
							    inputs_dict['''decoder_input_ids'''],
							    inputs_dict['''decoder_attention_mask'''],
							)

							UpperCamelCase										=  model.init_cache(decoder_input_ids.shape[0]					,				lowerCamelCase_					,				lowerCamelCase_)
							UpperCamelCase										=  jnp.ones((decoder_input_ids.shape[0], max_decoder_length)					,				dtype='''i4''')

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

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

							UpperCamelCase										=  model.decode(lowerCamelCase_					,				lowerCamelCase_)

							UpperCamelCase										=  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      UpperCAmelCase__ (		self					,				lowerCamelCase_					,				lowerCamelCase_					,				lowerCamelCase_)							->      Optional[Any]:
							UpperCamelCase										=  2_0
							UpperCamelCase										=  model_class_name(lowerCamelCase_)

							UpperCamelCase										=  model.encode(inputs_dict['''input_ids'''])

							UpperCamelCase					,  UpperCamelCase										=  (
							    inputs_dict['''decoder_input_ids'''],
							    inputs_dict['''decoder_attention_mask'''],
							)

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

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

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

							UpperCamelCase										=  model.decode(lowerCamelCase_					,				lowerCamelCase_					,				decoder_attention_mask=lowerCamelCase_)

							UpperCamelCase										=  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 							snake_case_ (				unittest.TestCase ):







			"""simple docstring"""




			A_										=					99







			def      UpperCAmelCase__ (		self)							->      List[Any]:
							UpperCamelCase										=  np.array(
							    [
							        [7_1, 8_2, 1_8, 3_3, 4_6, 9_1, 2],
							        [6_8, 3_4, 2_6, 5_8, 3_0, 8_2, 2],
							        [5, 9_7, 1_7, 3_9, 9_4, 4_0, 2],
							        [7_6, 8_3, 9_4, 2_5, 7_0, 7_8, 2],
							        [8_7, 5_9, 4_1, 3_5, 4_8, 6_6, 2],
							        [5_5, 1_3, 1_6, 5_8, 5, 2, 1],  # note padding
							        [6_4, 2_7, 3_1, 5_1, 1_2, 7_5, 2],
							        [5_2, 6_4, 8_6, 1_7, 8_3, 3_9, 2],
							        [4_8, 6_1, 9, 2_4, 7_1, 8_2, 2],
							        [2_6, 1, 6_0, 4_8, 2_2, 1_3, 2],
							        [2_1, 5, 6_2, 2_8, 1_4, 7_6, 2],
							        [4_5, 9_8, 3_7, 8_6, 5_9, 4_8, 2],
							        [7_0, 7_0, 5_0, 9, 2_8, 0, 2],
							    ]					,				dtype=np.intaa					,				)

							UpperCamelCase										=  input_ids.shape[0]
							UpperCamelCase										=  MBartConfig(
							    vocab_size=self.vocab_size					,				d_model=2_4					,				encoder_layers=2					,				decoder_layers=2					,				encoder_attention_heads=2					,				decoder_attention_heads=2					,				encoder_ffn_dim=3_2					,				decoder_ffn_dim=3_2					,				max_position_embeddings=4_8					,				eos_token_id=2					,				pad_token_id=1					,				bos_token_id=0					,				)
							return config, input_ids, batch_size







			def      UpperCAmelCase__ (		self)							->      Tuple:
							UpperCamelCase					,  UpperCamelCase					,  UpperCamelCase										=  self._get_config_and_data()
							UpperCamelCase										=  FlaxMBartForSequenceClassification(lowerCamelCase_)
							UpperCamelCase										=  model(input_ids=lowerCamelCase_					,				decoder_input_ids=lowerCamelCase_)
							UpperCamelCase										=  (batch_size, config.num_labels)
							self.assertEqual(outputs['''logits'''].shape					,				lowerCamelCase_)







			def      UpperCAmelCase__ (		self)							->      Optional[Any]:
							UpperCamelCase					,  UpperCamelCase					,  UpperCamelCase										=  self._get_config_and_data()
							UpperCamelCase										=  FlaxMBartForQuestionAnswering(lowerCamelCase_)
							UpperCamelCase										=  model(input_ids=lowerCamelCase_)

							self.assertEqual(outputs['''start_logits'''].shape					,				input_ids.shape)
							self.assertEqual(outputs['''end_logits'''].shape					,				input_ids.shape)







			def      UpperCAmelCase__ (		self)							->      Union[str, Any]:
							UpperCamelCase					,  UpperCamelCase					,  UpperCamelCase										=  self._get_config_and_data()
							UpperCamelCase										=  FlaxMBartForConditionalGeneration(lowerCamelCase_)
							UpperCamelCase										=  lm_model(input_ids=lowerCamelCase_)
							UpperCamelCase										=  (batch_size, input_ids.shape[1], config.vocab_size)
							self.assertEqual(outputs['''logits'''].shape					,				lowerCamelCase_)







			def      UpperCAmelCase__ (		self)							->      int:
							UpperCamelCase										=  MBartConfig(
							    vocab_size=self.vocab_size					,				d_model=1_4					,				encoder_layers=2					,				decoder_layers=2					,				encoder_attention_heads=2					,				decoder_attention_heads=2					,				encoder_ffn_dim=8					,				decoder_ffn_dim=8					,				max_position_embeddings=4_8					,				)
							UpperCamelCase										=  FlaxMBartForConditionalGeneration(lowerCamelCase_)
							UpperCamelCase										=  np.array([[7_1, 8_2, 1_8, 3_3, 4_6, 9_1, 2], [6_8, 3_4, 2_6, 5_8, 3_0, 2, 1]]					,				dtype=np.intaa)
							UpperCamelCase										=  np.array([[8_2, 7_1, 8_2, 1_8, 2], [5_8, 6_8, 2, 1, 1]]					,				dtype=np.intaa)
							UpperCamelCase										=  lm_model(input_ids=lowerCamelCase_					,				decoder_input_ids=lowerCamelCase_)
							UpperCamelCase										=  (*summary.shape, config.vocab_size)
							self.assertEqual(outputs['''logits'''].shape					,				lowerCamelCase_)







			def      UpperCAmelCase__ (		self)							->      Optional[Any]:
							UpperCamelCase										=  np.array([[7_1, 8_2, 1_8, 3_3, 2, 1, 1], [6_8, 3_4, 2_6, 5_8, 3_0, 8_2, 2]]					,				dtype=np.intaa)
							UpperCamelCase										=  shift_tokens_right(lowerCamelCase_					,				1)
							UpperCamelCase										=  np.equal(lowerCamelCase_					,				1).astype(np.floataa).sum()
							UpperCamelCase										=  np.equal(lowerCamelCase_					,				1).astype(np.floataa).sum()
							self.assertEqual(shifted.shape					,				input_ids.shape)
							self.assertEqual(lowerCamelCase_					,				n_pad_before - 1)
							self.assertTrue(np.equal(shifted[:, 0]					,				2).all())

@require_flax
class 							snake_case_ (				lowerCamelCase_  ,   unittest.TestCase  ,   lowerCamelCase_ ):







			"""simple docstring"""




			A_										=					True
			A_										=					(
			    (
			        FlaxMBartModel,
			        FlaxMBartForConditionalGeneration,
			        FlaxMBartForSequenceClassification,
			        FlaxMBartForQuestionAnswering,
			    )
			    if is_flax_available()
			    else ()
			)
			A_										=					(FlaxMBartForConditionalGeneration,) if is_flax_available() else ()







			def      UpperCAmelCase__ (		self)							->      Any:
							UpperCamelCase										=  FlaxMBartModelTester(self)







			def      UpperCAmelCase__ (		self)							->      Union[str, Any]:
							UpperCamelCase					,  UpperCamelCase										=  self.model_tester.prepare_config_and_inputs()
							for model_class in self.all_model_classes:
											self.model_tester.check_use_cache_forward(lowerCamelCase_					,				lowerCamelCase_					,				lowerCamelCase_)







			def      UpperCAmelCase__ (		self)							->      Dict:
							UpperCamelCase					,  UpperCamelCase										=  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(lowerCamelCase_					,				lowerCamelCase_					,				lowerCamelCase_)







			def      UpperCAmelCase__ (		self)							->      Optional[int]:
							UpperCamelCase					,  UpperCamelCase										=  self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
											with self.subTest(model_class.__name__):
															UpperCamelCase										=  self._prepare_for_class(lowerCamelCase_					,				lowerCamelCase_)
															UpperCamelCase										=  model_class(lowerCamelCase_)

															@jax.jit
															def encode_jitted(lowerCamelCase_					,				lowerCamelCase_=None					,				**lowerCamelCase_):
																			return model.encode(input_ids=lowerCamelCase_					,				attention_mask=lowerCamelCase_)

															with self.subTest('''JIT Enabled'''):
																			UpperCamelCase										=  encode_jitted(**lowerCamelCase_).to_tuple()

															with self.subTest('''JIT Disabled'''):
																			with jax.disable_jit():
																							UpperCamelCase										=  encode_jitted(**lowerCamelCase_).to_tuple()

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







			def      UpperCAmelCase__ (		self)							->      List[str]:
							UpperCamelCase					,  UpperCamelCase										=  self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
											with self.subTest(model_class.__name__):
															UpperCamelCase										=  model_class(lowerCamelCase_)
															UpperCamelCase										=  model.encode(inputs_dict['''input_ids''']					,				inputs_dict['''attention_mask'''])

															UpperCamelCase										=  {
															    '''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(lowerCamelCase_					,				lowerCamelCase_					,				lowerCamelCase_):
																			return model.decode(
																			    decoder_input_ids=lowerCamelCase_					,				decoder_attention_mask=lowerCamelCase_					,				encoder_outputs=lowerCamelCase_					,				)

															with self.subTest('''JIT Enabled'''):
																			UpperCamelCase										=  decode_jitted(**lowerCamelCase_).to_tuple()

															with self.subTest('''JIT Disabled'''):
																			with jax.disable_jit():
																							UpperCamelCase										=  decode_jitted(**lowerCamelCase_).to_tuple()

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







			@slow
			def      UpperCAmelCase__ (		self)							->      Any:
							for model_class_name in self.all_model_classes:
											UpperCamelCase										=  model_class_name.from_pretrained('''facebook/mbart-large-cc25'''					,				from_pt=lowerCamelCase_)
											# FlaxMBartForSequenceClassification expects eos token in input_ids
											UpperCamelCase										=  np.ones((1, 1)) * model.config.eos_token_id
											UpperCamelCase										=  model(lowerCamelCase_)
											self.assertIsNotNone(lowerCamelCase_)

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







			"""simple docstring"""




			A_										=					[
			    ''' UN Chief Says There Is No Military Solution in Syria''',
			]
			A_										=					[
			    '''Şeful ONU declară că nu există o soluţie militară în Siria''',
			]
			A_										=					'''facebook/mbart-large-en-ro'''







			@cached_property
			def      UpperCAmelCase__ (		self)							->      List[str]:
							return AutoTokenizer.from_pretrained(self.model_name)







			@cached_property
			def      UpperCAmelCase__ (		self)							->      Union[str, Any]:
							UpperCamelCase										=  FlaxMBartForConditionalGeneration.from_pretrained(self.model_name					,				from_pt=lowerCamelCase_)
							return model







			def      UpperCAmelCase__ (		self					,				**lowerCamelCase_)							->      int:
							UpperCamelCase										=  self.translate_src_text(**lowerCamelCase_)
							self.assertListEqual(self.expected_text					,				lowerCamelCase_)







			def      UpperCAmelCase__ (		self					,				**lowerCamelCase_)							->      Optional[int]:
							UpperCamelCase										=  self.tokenizer(self.src_text					,				**lowerCamelCase_					,				return_tensors='''np''')
							UpperCamelCase										=  self.model.generate(
							    model_inputs.input_ids					,				attention_mask=model_inputs.attention_mask					,				decoder_start_token_id=self.tokenizer.lang_code_to_id['''ro_RO''']					,				early_stopping=lowerCamelCase_					,				num_beams=2					,				).sequences
							UpperCamelCase										=  self.tokenizer.batch_decode(lowerCamelCase_					,				skip_special_tokens=lowerCamelCase_)
							return generated_words







			@slow
			def      UpperCAmelCase__ (		self)							->      int:
							self._assert_generated_batch_equal_expected()





"""simple docstring"""







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


SCREAMING_SNAKE_CASE_      =					get_tests_dir('fixtures/spiece.model')

@require_sentencepiece
@require_tokenizers
class 							snake_case_ (				lowerCamelCase_  ,   unittest.TestCase ):







   """simple docstring"""




   A_										=					ErnieMTokenizer
   A_										=					False
   A_										=					True
   A_										=					False
   A_										=					False







   def      UpperCAmelCase__ (		self)							->      List[str]:
       super().setUp()

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







   def      UpperCAmelCase__ (		self					,				lowerCamelCase_)							->      Optional[Any]:
       UpperCamelCase										=  '''this is a test'''
       UpperCamelCase										=  '''this is a test'''
       return input_text, output_text







   def      UpperCAmelCase__ (		self)							->      Optional[Any]:
       UpperCamelCase										=  '''<pad>'''
       UpperCamelCase										=  0

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







   def      UpperCAmelCase__ (		self)							->      Optional[Any]:
       UpperCamelCase										=  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(lowerCamelCase_)					,				3_0_0_0_0)







   def      UpperCAmelCase__ (		self)							->      int:
       self.assertEqual(self.get_tokenizer().vocab_size					,				3_0_0_0_0)







   def      UpperCAmelCase__ (		self)							->      List[Any]:
       if not self.test_rust_tokenizer:
           return

       UpperCamelCase										=  self.get_tokenizer()
       UpperCamelCase										=  self.get_rust_tokenizer()

       UpperCamelCase										=  '''I was born in 92000, and this is falsé.'''

       UpperCamelCase										=  tokenizer.tokenize(lowerCamelCase_)
       UpperCamelCase										=  rust_tokenizer.tokenize(lowerCamelCase_)
       self.assertListEqual(lowerCamelCase_					,				lowerCamelCase_)

       UpperCamelCase										=  tokenizer.encode(lowerCamelCase_					,				add_special_tokens=lowerCamelCase_)
       UpperCamelCase										=  rust_tokenizer.encode(lowerCamelCase_					,				add_special_tokens=lowerCamelCase_)
       self.assertListEqual(lowerCamelCase_					,				lowerCamelCase_)

       UpperCamelCase										=  self.get_rust_tokenizer()
       UpperCamelCase										=  tokenizer.encode(lowerCamelCase_)
       UpperCamelCase										=  rust_tokenizer.encode(lowerCamelCase_)
       self.assertListEqual(lowerCamelCase_					,				lowerCamelCase_)







   def      UpperCAmelCase__ (		self)							->      List[Any]:
       UpperCamelCase										=  ErnieMTokenizer(lowerCamelCase_					,				do_lower_case=lowerCamelCase_					,				unk_token='''<unk>'''					,				pad_token='''<pad>''')

       UpperCamelCase										=  tokenizer.tokenize('''This is a test''')
       self.assertListEqual(lowerCamelCase_					,				['''▁this''', '''▁is''', '''▁a''', '''▁test'''])

       self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCamelCase_)					,				[4_8, 2_5, 2_1, 1_2_8_9])

       UpperCamelCase										=  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(
           lowerCamelCase_					,				['''▁i''', '''▁was''', '''▁born''', '''▁in''', '''9''', '''2000''', ''',''', '''▁and''', '''▁this''', '''▁is''', '''▁fal''', '''s''', '''é''', '''.'''])
       UpperCamelCase										=  tokenizer.convert_tokens_to_ids(lowerCamelCase_)
       self.assertListEqual(lowerCamelCase_					,				[3_1, 2_3, 3_8_6, 1_9, 5_1_8, 3_0_5_0, 1_5, 1_7, 4_8, 2_5, 8_2_5_6, 1_8, 1, 9])

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







   def      UpperCAmelCase__ (		self)							->      Dict:
       UpperCamelCase										=  ErnieMTokenizer(lowerCamelCase_					,				unk_token='''<unk>'''					,				pad_token='''<pad>''')

       UpperCamelCase										=  tokenizer.encode('''sequence builders''')
       UpperCamelCase										=  tokenizer.encode('''multi-sequence build''')

       UpperCamelCase										=  tokenizer.build_inputs_with_special_tokens(lowerCamelCase_)
       UpperCamelCase										=  tokenizer.build_inputs_with_special_tokens(lowerCamelCase_					,				lowerCamelCase_)

       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      UpperCAmelCase__ (		self)							->      List[str]:
       # fmt: off
       UpperCamelCase										=  {'''input_ids''': [[0, 1_1_0_6_2, 8_2_7_7_2, 7, 1_5, 8_2_7_7_2, 5_3_8, 5_1_5_2_9, 2_3_7, 1_7_1_9_8, 1_2_9_0, 2_0_6, 9, 2_1_5_1_7_5, 1_3_1_4, 1_3_6, 1_7_1_9_8, 1_2_9_0, 2_0_6, 9, 5_6_3_5_9, 4_2, 1_2_2_0_0_9, 9, 1_6_4_6_6, 1_6, 8_7_3_4_4, 4_5_3_7, 9, 4_7_1_7, 7_8_3_8_1, 6, 1_5_9_9_5_8, 7, 1_5, 2_4_4_8_0, 6_1_8, 4, 5_2_7, 2_2_6_9_3, 9, 3_0_4, 4, 2_7_7_7, 2_4_4_8_0, 9_8_7_4, 4, 4_3_5_2_3, 5_9_4, 4, 8_0_3, 1_8_3_9_2, 3_3_1_8_9, 1_8, 4, 4_3_5_2_3, 2_4_4_4_7, 5, 5, 5, 1_6, 1_0_0, 2_4_9_5_5, 8_3_6_5_8, 9_6_2_6, 1_4_4_0_5_7, 1_5, 8_3_9, 2_2_3_3_5, 1_6, 1_3_6, 2_4_9_5_5, 8_3_6_5_8, 8_3_4_7_9, 1_5, 3_9_1_0_2, 7_2_4, 1_6, 6_7_8, 6_4_5, 6_4_6_0, 1_3_2_8, 4_5_8_9, 4_2, 1_2_2_0_0_9, 1_1_5_7_7_4, 2_3, 3_5_5_9, 1_3_2_8, 4_6_8_7_6, 7, 1_3_6, 5_3_8_9_4, 1_9_4_0, 4_2_2_2_7, 4_1_1_5_9, 1_7_7_2_1, 8_2_3, 4_2_5, 4, 2_7_5_1_2, 9_8_7_2_2, 2_0_6, 1_3_6, 5_5_3_1, 4_9_7_0, 9_1_9, 1_7_3_3_6, 5, 2], [0, 2_0_0_8_0, 6_1_8, 8_3, 8_2_7_7_5, 4_7, 4_7_9, 9, 1_5_1_7, 7_3, 5_3_8_9_4, 3_3_3, 8_0_5_8_1, 1_1_0_1_1_7, 1_8_8_1_1, 5_2_5_6, 1_2_9_5, 5_1, 1_5_2_5_2_6, 2_9_7, 7_9_8_6, 3_9_0, 1_2_4_4_1_6, 5_3_8, 3_5_4_3_1, 2_1_4, 9_8, 1_5_0_4_4, 2_5_7_3_7, 1_3_6, 7_1_0_8, 4_3_7_0_1, 2_3, 7_5_6, 1_3_5_3_5_5, 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, 5_8_1, 6_3_7_7_3, 1_1_9_4_5_5, 6, 1_4_7_7_9_7, 8_8_2_0_3, 7, 6_4_5, 7_0, 2_1, 3_2_8_5, 1_0_2_6_9, 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=lowerCamelCase_					,				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.''',
           ]					,				)