code: 
  
  
  
  
  
  
  """simple docstring"""
  
  
  
  
  from typing import TYPE_CHECKING
  
  from ...utils import (
      OptionalDependencyNotAvailable,
      _LazyModule,
      is_flax_available,
      is_tf_available,
      is_tokenizers_available,
      is_torch_available,
  )
  
  
  __A  :					Optional[int]												=	{
      'configuration_mpnet': ['MPNET_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MPNetConfig'],
      'tokenization_mpnet': ['MPNetTokenizer'],
  }
  
  try:
  	if not is_tokenizers_available():
  		raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  	pass
  else:
  	__A  :					Any												=	['MPNetTokenizerFast']
  
  try:
  	if not is_torch_available():
  		raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  	pass
  else:
  	__A  :					Any												=	[
  	    'MPNET_PRETRAINED_MODEL_ARCHIVE_LIST',
  	    'MPNetForMaskedLM',
  	    'MPNetForMultipleChoice',
  	    'MPNetForQuestionAnswering',
  	    'MPNetForSequenceClassification',
  	    'MPNetForTokenClassification',
  	    'MPNetLayer',
  	    'MPNetModel',
  	    'MPNetPreTrainedModel',
  	]
  
  try:
  	if not is_tf_available():
  		raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  	pass
  else:
  	__A  :					List[Any]												=	[
  	    'TF_MPNET_PRETRAINED_MODEL_ARCHIVE_LIST',
  	    'TFMPNetEmbeddings',
  	    'TFMPNetForMaskedLM',
  	    'TFMPNetForMultipleChoice',
  	    'TFMPNetForQuestionAnswering',
  	    'TFMPNetForSequenceClassification',
  	    'TFMPNetForTokenClassification',
  	    'TFMPNetMainLayer',
  	    'TFMPNetModel',
  	    'TFMPNetPreTrainedModel',
  	]
  
  
  if TYPE_CHECKING:
  	from .configuration_mpnet import MPNET_PRETRAINED_CONFIG_ARCHIVE_MAP, MPNetConfig
  	from .tokenization_mpnet import MPNetTokenizer
  
  	try:
  		if not is_tokenizers_available():
  			raise OptionalDependencyNotAvailable()
  	except OptionalDependencyNotAvailable:
  		pass
  	else:
  		from .tokenization_mpnet_fast import MPNetTokenizerFast
  
  	try:
  		if not is_torch_available():
  			raise OptionalDependencyNotAvailable()
  	except OptionalDependencyNotAvailable:
  		pass
  	else:
  		from .modeling_mpnet import (
  		    MPNET_PRETRAINED_MODEL_ARCHIVE_LIST,
  		    MPNetForMaskedLM,
  		    MPNetForMultipleChoice,
  		    MPNetForQuestionAnswering,
  		    MPNetForSequenceClassification,
  		    MPNetForTokenClassification,
  		    MPNetLayer,
  		    MPNetModel,
  		    MPNetPreTrainedModel,
  		)
  
  	try:
  		if not is_tf_available():
  			raise OptionalDependencyNotAvailable()
  	except OptionalDependencyNotAvailable:
  		pass
  	else:
  		from .modeling_tf_mpnet import (
  		    TF_MPNET_PRETRAINED_MODEL_ARCHIVE_LIST,
  		    TFMPNetEmbeddings,
  		    TFMPNetForMaskedLM,
  		    TFMPNetForMultipleChoice,
  		    TFMPNetForQuestionAnswering,
  		    TFMPNetForSequenceClassification,
  		    TFMPNetForTokenClassification,
  		    TFMPNetMainLayer,
  		    TFMPNetModel,
  		    TFMPNetPreTrainedModel,
  		)
  
  else:
  	import sys
  
  	__A  :					Any												=	_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
  
  
  
  
  
  
style_context: 
  
  """simple docstring"""
  
  
  
  
  import unittest
  
  import numpy as np
  import timeout_decorator  # noqa
  
  from transformers import OPTConfig, is_flax_available
  from transformers.testing_utils import require_flax, require_sentencepiece, slow
  
  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  :					Optional[int]												=	'platform'
  
  	import jax
  	import jax.numpy as jnp
  
  	from transformers import FlaxOPTForCausalLM, FlaxOPTModel, GPTaTokenizer
  def      __SCREAMING_SNAKE_CASE		(		lowercase__    ,							lowercase__    ,							lowercase__=None    ,							lowercase__=None   ):
  					"""simple docstring"""
  					if attention_mask is None:
  										A    =      np.where(input_ids != config.pad_token_id    ,							1    ,							0   )
  					return {
  					    "input_ids": input_ids,
  					    "attention_mask": attention_mask,
  					}
  
  
  
  
  
  @require_flax
  class     __UpperCamelCase				:
  
  
  		def __init__(self				:	str  ,  __SCREAMING_SNAKE_CASE				:	List[str]  ,  __SCREAMING_SNAKE_CASE				:	int=1_3  ,  __SCREAMING_SNAKE_CASE				:	List[str]=7  ,  __SCREAMING_SNAKE_CASE				:	Optional[Any]=True  ,  __SCREAMING_SNAKE_CASE				:	Dict=False  ,  __SCREAMING_SNAKE_CASE				:	Union[str, Any]=9_9  ,  __SCREAMING_SNAKE_CASE				:	Optional[Any]=1_6  ,  __SCREAMING_SNAKE_CASE				:	List[str]=2  ,  __SCREAMING_SNAKE_CASE				:	Optional[int]=4  ,  __SCREAMING_SNAKE_CASE				:	Any=4  ,  __SCREAMING_SNAKE_CASE				:	str="gelu"  ,  __SCREAMING_SNAKE_CASE				:	List[str]=0.1  ,  __SCREAMING_SNAKE_CASE				:	Any=0.1  ,  __SCREAMING_SNAKE_CASE				:	Dict=2_0  ,  __SCREAMING_SNAKE_CASE				:	Any=2  ,  __SCREAMING_SNAKE_CASE				:	Optional[Any]=1  ,  __SCREAMING_SNAKE_CASE				:	Any=0  ,  __SCREAMING_SNAKE_CASE				:	str=1_6  ,  __SCREAMING_SNAKE_CASE				:	Optional[Any]=1_6  ,  __SCREAMING_SNAKE_CASE				:	Union[str, Any]=0.0_2  ,  ):
  							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    =      embed_dim
  							A    =      word_embed_proj_dim
  							A    =      initializer_range
  							A    =      False
  
  
  		def       SCREAMING_SNAKE_CASE__     (self				:	Any):
  							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    =      OPTConfig(
  							    vocab_size=self.vocab_size  ,  hidden_size=self.hidden_size  ,  num_hidden_layers=self.num_hidden_layers  ,  num_attention_heads=self.num_attention_heads  ,  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  ,  embed_dim=self.embed_dim  ,  is_encoder_decoder=__SCREAMING_SNAKE_CASE  ,  word_embed_proj_dim=self.word_embed_proj_dim  ,  initializer_range=self.initializer_range  ,  use_cache=__SCREAMING_SNAKE_CASE  ,  )
  							A    =      prepare_opt_inputs_dict(__SCREAMING_SNAKE_CASE  ,  __SCREAMING_SNAKE_CASE)
  							return config, inputs_dict
  
  
  		def       SCREAMING_SNAKE_CASE__     (self				:	Union[str, Any]):
  							A    ,					A    =      self.prepare_config_and_inputs()
  							return config, inputs_dict
  
  
  		def       SCREAMING_SNAKE_CASE__     (self				:	Union[str, Any]  ,  __SCREAMING_SNAKE_CASE				:	str  ,  __SCREAMING_SNAKE_CASE				:	int  ,  __SCREAMING_SNAKE_CASE				:	Tuple):
  							A    =      2_0
  							A    =      model_class_name(__SCREAMING_SNAKE_CASE)
  
  							A    =      inputs_dict["input_ids"]
  							A    =      inputs_dict["attention_mask"]
  
  							A    =      model.init_cache(input_ids.shape[0]  ,  __SCREAMING_SNAKE_CASE)
  							A    =      jnp.ones((input_ids.shape[0], max_length)  ,  dtype="i4")
  
  							A    =      jnp.broadcast_to(
  							    jnp.arange(input_ids.shape[-1] - 1)[None, :]  ,  (input_ids.shape[0], input_ids.shape[-1] - 1)  ,  )
  							A    =      model(
  							    input_ids[:, :-1]  ,  attention_mask=__SCREAMING_SNAKE_CASE  ,  past_key_values=__SCREAMING_SNAKE_CASE  ,  position_ids=__SCREAMING_SNAKE_CASE  ,  )
  
  							A    =      jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]  ,  dtype="i4")
  							A    =      model(
  							    input_ids[:, -1:]  ,  attention_mask=__SCREAMING_SNAKE_CASE  ,  past_key_values=outputs_cache.past_key_values  ,  position_ids=__SCREAMING_SNAKE_CASE  ,  )
  
  							A    =      model(__SCREAMING_SNAKE_CASE)
  
  							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       SCREAMING_SNAKE_CASE__     (self				:	List[Any]  ,  __SCREAMING_SNAKE_CASE				:	Union[str, Any]  ,  __SCREAMING_SNAKE_CASE				:	List[Any]  ,  __SCREAMING_SNAKE_CASE				:	Dict):
  							A    =      2_0
  							A    =      model_class_name(__SCREAMING_SNAKE_CASE)
  
  							A    ,					A    =      (
  							    inputs_dict["input_ids"],
  							    inputs_dict["attention_mask"],
  							)
  
  							A    =      jnp.concatenate(
  							    [
  							        attention_mask,
  							        jnp.zeros((attention_mask.shape[0], max_length - attention_mask.shape[1])),
  							    ]  ,  axis=-1  ,  )
  
  							A    =      model.init_cache(input_ids.shape[0]  ,  __SCREAMING_SNAKE_CASE)
  							A    =      jnp.broadcast_to(
  							    jnp.arange(input_ids.shape[-1] - 1)[None, :]  ,  (input_ids.shape[0], input_ids.shape[-1] - 1)  ,  )
  
  							A    =      model(
  							    input_ids[:, :-1]  ,  attention_mask=__SCREAMING_SNAKE_CASE  ,  past_key_values=__SCREAMING_SNAKE_CASE  ,  position_ids=__SCREAMING_SNAKE_CASE  ,  )
  							A    =      jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]  ,  dtype="i4")
  							A    =      model(
  							    input_ids[:, -1:]  ,  past_key_values=outputs_cache.past_key_values  ,  attention_mask=__SCREAMING_SNAKE_CASE  ,  position_ids=__SCREAMING_SNAKE_CASE  ,  )
  
  							A    =      model(__SCREAMING_SNAKE_CASE  ,  attention_mask=__SCREAMING_SNAKE_CASE)
  
  							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     __UpperCamelCase				(     _A  ,	unittest.TestCase  ,	_A		):
  		SCREAMING_SNAKE_CASE              =					(FlaxOPTModel, FlaxOPTForCausalLM) if is_flax_available() else ()
  		SCREAMING_SNAKE_CASE              =					() if is_flax_available() else ()
  
  
  		def       SCREAMING_SNAKE_CASE__     (self				:	Union[str, Any]):
  							A    =      FlaxOPTModelTester(self)
  
  
  		def       SCREAMING_SNAKE_CASE__     (self				:	List[str]):
  							A    ,					A    =      self.model_tester.prepare_config_and_inputs()
  							for model_class in self.all_model_classes:
  												self.model_tester.check_use_cache_forward(__SCREAMING_SNAKE_CASE  ,  __SCREAMING_SNAKE_CASE  ,  __SCREAMING_SNAKE_CASE)
  
  
  		def       SCREAMING_SNAKE_CASE__     (self				:	Optional[Any]):
  							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(__SCREAMING_SNAKE_CASE  ,  __SCREAMING_SNAKE_CASE  ,  __SCREAMING_SNAKE_CASE)
  
  
  		@slow
  		def       SCREAMING_SNAKE_CASE__     (self				:	Any):
  							for model_class_name in self.all_model_classes:
  												A    =      model_class_name.from_pretrained("facebook/opt-125m")
  												A    =      np.ones((1, 1)) * model.config.eos_token_id
  												A    =      model(__SCREAMING_SNAKE_CASE)
  												self.assertIsNotNone(__SCREAMING_SNAKE_CASE)
  
  
  
  
  
  @require_sentencepiece
  @require_flax
  class     __UpperCamelCase				(     unittest.TestCase		):
  
  
  		@slow
  		def       SCREAMING_SNAKE_CASE__     (self				:	Optional[Any]):
  							A    =      FlaxOPTModel.from_pretrained("facebook/opt-350m")
  							A    =      jnp.array([[0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9, 4_6_0_7_8, 1_5_8_8, 2]])
  							A    =      model(input_ids=__SCREAMING_SNAKE_CASE).last_hidden_state
  							A    =      (1, 1_1, 5_1_2)
  							self.assertEqual(output.shape  ,  __SCREAMING_SNAKE_CASE)
  							A    =      jnp.array(
  							    [[-0.2_8_6_7, -1.9_2_5_6, -0.3_0_6_2], [-1.2_7_1_1, -0.1_3_3_7, -0.1_8_9_7], [0.4_1_0_9, 0.1_1_8_7, -1.3_1_4_2]])
  							self.assertTrue(jnp.allclose(output[:, :3, :3]  ,  __SCREAMING_SNAKE_CASE  ,  atol=4E-2))
  
  
  
  
  
  @require_flax
  @slow
  class     __UpperCamelCase				(     unittest.TestCase		):
  
  
  		def       SCREAMING_SNAKE_CASE__     (self				:	Dict):
  							super().setUp()
  							A    =      "facebook/opt-350m"
  
  
  		def       SCREAMING_SNAKE_CASE__     (self				:	List[Any]):
  							A    =      FlaxOPTForCausalLM.from_pretrained(self.path_model)
  							A    =      GPTaTokenizer.from_pretrained(self.path_model)
  
  							A    =      [
  							    "Today is a beautiful day and I want to",
  							    "In the city of",
  							    "Paris is the capital of France and",
  							    "Computers and mobile phones have taken",
  							]
  							# verify that prompt without BOS token is identical to Metaseq -> add_special_tokens=False
  							A    =      tokenizer(__SCREAMING_SNAKE_CASE  ,  return_tensors="jax"  ,  padding=__SCREAMING_SNAKE_CASE  ,  add_special_tokens=__SCREAMING_SNAKE_CASE)
  							A    =      model(inputs.input_ids  ,  attention_mask=inputs.attention_mask)[0].mean(axis=-1)
  							A    =      jnp.array(
  							    [
  							        [1.3_8_5_1, -1_3.8_9_2_3, -1_0.5_2_2_9, -1_0.7_5_3_3, -0.2_3_0_9, -1_0.2_3_8_4, -0.5_3_6_5, -9.0_9_4_7, -5.1_6_7_0],
  							        [-4.7_0_7_3, -1_0.6_2_7_6, -3.9_4_1_5, -2_1.5_2_4_2, -0.2_8_2_2, -0.2_8_2_2, -0.2_8_2_2, -0.2_8_2_2, -0.2_8_2_2],
  							        [0.6_2_4_7, -3.4_2_2_9, -8.9_1_7_9, -1.4_2_9_7, -1_4.1_6_5_0, 1.4_1_4_6, -9.0_2_1_8, -0.2_7_0_3, -0.2_7_0_3],
  							        [6.4_7_8_3, -1.9_9_1_3, -1_0.7_9_2_6, -2.3_3_3_6, 1.5_0_9_2, -0.9_9_7_4, -6.8_2_1_3, 1.3_4_7_7, 1.3_4_7_7],
  							    ])
  							self.assertTrue(jnp.allclose(__SCREAMING_SNAKE_CASE  ,  __SCREAMING_SNAKE_CASE  ,  atol=4E-2))
  
  							A    =      jax.jit(__SCREAMING_SNAKE_CASE)
  							A    =      model(inputs.input_ids  ,  attention_mask=inputs.attention_mask)[0].mean(axis=-1)
  							self.assertTrue(jnp.allclose(__SCREAMING_SNAKE_CASE  ,  __SCREAMING_SNAKE_CASE  ,  atol=4E-2))
  
  
  
  
  
  @require_flax
  @slow
  class     __UpperCamelCase				(     unittest.TestCase		):
  
  
  		@property
  		def       SCREAMING_SNAKE_CASE__     (self				:	Dict):
  							return [
  							    "Today is a beautiful day and I want",
  							    "In the city of",
  							    "Paris is the capital of France and",
  							    "Computers and mobile phones have taken",
  							]
  
  
  		def       SCREAMING_SNAKE_CASE__     (self				:	List[Any]):
  							A    =      "facebook/opt-125m"
  
  							A    =      [
  							    "Today is a beautiful day and I want to",
  							    "In the city of New York, the city",
  							    "Paris is the capital of France and the capital",
  							    "Computers and mobile phones have taken over the",
  							]
  
  							A    =      []
  
  							A    =      FlaxOPTForCausalLM.from_pretrained(__SCREAMING_SNAKE_CASE)
  							A    =      GPTaTokenizer.from_pretrained(__SCREAMING_SNAKE_CASE)
  
  							for prompt in self.prompts:
  												A    =      tokenizer(__SCREAMING_SNAKE_CASE  ,  return_tensors="jax").input_ids
  
  												A    =      model.generate(__SCREAMING_SNAKE_CASE  ,  max_length=1_0)
  												A    =      generated_ids[0]
  
  												A    =      tokenizer.batch_decode(__SCREAMING_SNAKE_CASE  ,  skip_special_tokens=__SCREAMING_SNAKE_CASE)
  												predicted_outputs += generated_string
  
  							self.assertListEqual(__SCREAMING_SNAKE_CASE  ,  __SCREAMING_SNAKE_CASE)
  
  
  		def       SCREAMING_SNAKE_CASE__     (self				:	Tuple):
  							A    =      "facebook/opt-350m"
  
  							A    =      [
  							    "Today is a beautiful day and I want to",
  							    "In the city of San Francisco, the city",
  							    "Paris is the capital of France and the capital",
  							    "Computers and mobile phones have taken over the",
  							]
  
  							A    =      []
  							A    =      FlaxOPTForCausalLM.from_pretrained(__SCREAMING_SNAKE_CASE)
  							A    =      GPTaTokenizer.from_pretrained(__SCREAMING_SNAKE_CASE)
  
  							for prompt in self.prompts:
  												A    =      tokenizer(__SCREAMING_SNAKE_CASE  ,  return_tensors="jax").input_ids
  
  												A    =      model.generate(__SCREAMING_SNAKE_CASE  ,  max_length=1_0)
  												A    =      generated_ids[0]
  
  												A    =      tokenizer.batch_decode(__SCREAMING_SNAKE_CASE  ,  skip_special_tokens=__SCREAMING_SNAKE_CASE)
  												predicted_outputs += generated_string
  
  							self.assertListEqual(__SCREAMING_SNAKE_CASE  ,  __SCREAMING_SNAKE_CASE)
  
  
  		def       SCREAMING_SNAKE_CASE__     (self				:	List[str]):
  							A    =      "facebook/opt-125m"
  							A    =      [
  							    "Today is a beautiful day and I want to thank",
  							    "In the city of Rome Canaver Canaver Canaver Canaver",
  							]
  							A    =      FlaxOPTForCausalLM.from_pretrained(__SCREAMING_SNAKE_CASE)
  							A    =      GPTaTokenizer.from_pretrained(__SCREAMING_SNAKE_CASE)
  							A    =      tokenizer(
  							    [
  							        "Today is a beautiful day and I want to",
  							        "In the city of",
  							    ]  ,  return_tensors="jax"  ,  padding=__SCREAMING_SNAKE_CASE  ,  )
  
  							A    =      jax.jit(model.generate)
  
  							A    =      jit_generate(inputs["input_ids"]  ,  attention_mask=inputs["attention_mask"]).sequences
  
  							A    =      tokenizer.batch_decode(__SCREAMING_SNAKE_CASE  ,  skip_special_tokens=__SCREAMING_SNAKE_CASE)
  
  							self.assertIsNotNone(__SCREAMING_SNAKE_CASE  ,  __SCREAMING_SNAKE_CASE)
  
  
  		def       SCREAMING_SNAKE_CASE__     (self				:	Dict):
  							A    =      "facebook/opt-350m"
  
  							A    =      GPTaTokenizer.from_pretrained(__SCREAMING_SNAKE_CASE)
  							A    =      FlaxOPTForCausalLM.from_pretrained(__SCREAMING_SNAKE_CASE)
  
  							A    =      "left"
  
  							# use different length sentences to test batching
  							A    =      [
  							    "Hello, my dog is a little",
  							    "Today, I",
  							]
  
  							A    =      tokenizer(__SCREAMING_SNAKE_CASE  ,  return_tensors="jax"  ,  padding=__SCREAMING_SNAKE_CASE)
  							A    =      inputs["input_ids"]
  
  							A    =      model.generate(input_ids=__SCREAMING_SNAKE_CASE  ,  attention_mask=inputs["attention_mask"]  ,  trace=__SCREAMING_SNAKE_CASE)
  
  							A    =      tokenizer(sentences[0]  ,  return_tensors="jax").input_ids
  							A    =      model.generate(input_ids=__SCREAMING_SNAKE_CASE)
  
  							A    =      inputs_non_padded.shape[-1] - inputs["attention_mask"][-1].sum()
  							A    =      tokenizer(sentences[1]  ,  return_tensors="jax").input_ids
  							A    =      model.generate(input_ids=__SCREAMING_SNAKE_CASE  ,  max_length=model.config.max_length - num_paddings)
  
  							A    =      tokenizer.batch_decode(outputs[0]  ,  skip_special_tokens=__SCREAMING_SNAKE_CASE)
  							A    =      tokenizer.decode(output_non_padded[0][0]  ,  skip_special_tokens=__SCREAMING_SNAKE_CASE)
  							A    =      tokenizer.decode(output_padded[0][0]  ,  skip_special_tokens=__SCREAMING_SNAKE_CASE)
  
  							A    =      [
  							    "Hello, my dog is a little bit of a dork.\nI'm a little bit",
  							    "Today, I was in the middle of a conversation with a friend about the",
  							]
  							self.assertListEqual(__SCREAMING_SNAKE_CASE  ,  __SCREAMING_SNAKE_CASE)
  							self.assertListEqual(__SCREAMING_SNAKE_CASE  ,  [non_padded_sentence, padded_sentence])
  
  
  
  
  
  