





'''simple docstring'''







from .number_theory.prime_numbers import next_prime



class 							__SCREAMING_SNAKE_CASE			:
      '''simple docstring'''



      def __init__(							self    : List[str]   ,UpperCamelCase    : int   ,UpperCamelCase    : int | None = None   ,UpperCamelCase    : float | None = None   ,)		->  None:
           _lowercase							:      List[Any]  						=							size_table
           _lowercase							:      Optional[int]  						=							[None] * self.size_table
           _lowercase							:      Dict  						=							0.7_5 if lim_charge is None else lim_charge
           _lowercase							:      List[str]  						=							1 if charge_factor is None else charge_factor
           _lowercase							:      list  						=							[]
           _lowercase							:      dict  						=							{}

      def   _lowerCamelCase		(							self    : int  )		->  Dict:
           return self._keys

      def   _lowerCamelCase		(							self    : Dict  )		->  int:
           return sum(1 for slot in self.values if slot is not None  ) / (
               self.size_table * self.charge_factor
           )

      def   _lowerCamelCase		(							self    : Dict   ,UpperCamelCase    : int  )		->  List[Any]:
           return key % self.size_table

      def   _lowerCamelCase		(							self    : Optional[Any]   ,UpperCamelCase    : List[Any]  )		->  List[str]:
           print(F'''step {step_ord}'''  )
           print(list(range(len(self.values  )  )  )  )
           print(self.values  )

      def   _lowerCamelCase		(							self    : Any   ,UpperCamelCase    : Any  )		->  str:
           _lowercase							:      Dict  						=							1
           _lowercase							:      int  						=							values
           for value in values:
                self.insert_data(UpperCamelCase  )
                self._step_by_step(UpperCamelCase  )
                i += 1

      def   _lowerCamelCase		(							self    : int   ,UpperCamelCase    : int   ,UpperCamelCase    : Dict  )		->  int:
           _lowercase							:      str  						=							data
           _lowercase							:      Optional[int]  						=							data

      def   _lowerCamelCase		(							self    : List[Any]   ,UpperCamelCase    : str   ,UpperCamelCase    : List[Any]=None  )		->  Dict:
           _lowercase							:      Tuple  						=							self.hash_function(key + 1  )

           while self.values[new_key] is not None and self.values[new_key] != key:
                if self.values.count(UpperCamelCase  ) > 0:
                     _lowercase							:      Dict  						=							self.hash_function(new_key + 1  )
                else:
                     _lowercase							:      Dict  						=							None
                     break

           return new_key

      def   _lowerCamelCase		(							self    : Any  )		->  Tuple:
           _lowercase							:      int  						=							[value for value in self.values if value is not None]
           _lowercase							:      Any  						=							next_prime(self.size_table   ,factor=2  )
           self._keys.clear()
           _lowercase							:      List[str]  						=							[None] * self.size_table  # hell's pointers D: don't DRY ;/
           for value in survivor_values:
                self.insert_data(UpperCamelCase  )



      def   _lowerCamelCase		(							self    : Optional[Any]   ,UpperCamelCase    : Tuple  )		->  Dict:
           _lowercase							:      List[str]  						=							self.hash_function(UpperCamelCase  )

           if self.values[key] is None:
                self._set_value(UpperCamelCase   ,UpperCamelCase  )

           elif self.values[key] == data:
                pass

           else:
                _lowercase							:      List[str]  						=							self._collision_resolution(UpperCamelCase   ,UpperCamelCase  )
                if collision_resolution is not None:
                     self._set_value(UpperCamelCase   ,UpperCamelCase  )
                else:
                     self.rehashing()
                     self.insert_data(UpperCamelCase  )






'''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						    =							'''platform'''

    import jax
    import jax.numpy as jnp

    from transformers import FlaxOPTForCausalLM, FlaxOPTModel, GPTaTokenizer

def 		SCREAMING_SNAKE_CASE      (					lowerCAmelCase__     :	Optional[int]       ,  lowerCAmelCase__     :	Optional[int]       ,  lowerCAmelCase__     :	int=None       ,  lowerCAmelCase__     :	List[Any]=None)			->						Union[str, Any]:

     '''simple docstring'''




     if attention_mask is None:
          _lowercase							:      str  						=							np.where(input_ids != config.pad_token_id       ,  1       ,  0)
     return {
         "input_ids": input_ids,
         "attention_mask": attention_mask,
     }



@require_flax
class 							__SCREAMING_SNAKE_CASE			:
      '''simple docstring'''



      def __init__(							self    : List[str]   ,UpperCamelCase    : Dict   ,UpperCamelCase    : Union[str, Any]=13   ,UpperCamelCase    : Optional[Any]=7   ,UpperCamelCase    : Optional[int]=True   ,UpperCamelCase    : Any=False   ,UpperCamelCase    : int=99   ,UpperCamelCase    : Optional[int]=16   ,UpperCamelCase    : Optional[int]=2   ,UpperCamelCase    : int=4   ,UpperCamelCase    : Optional[int]=4   ,UpperCamelCase    : Dict="gelu"   ,UpperCamelCase    : Optional[Any]=0.1   ,UpperCamelCase    : Optional[int]=0.1   ,UpperCamelCase    : str=20   ,UpperCamelCase    : List[Any]=2   ,UpperCamelCase    : Union[str, Any]=1   ,UpperCamelCase    : List[Any]=0   ,UpperCamelCase    : Optional[Any]=16   ,UpperCamelCase    : List[Any]=16   ,UpperCamelCase    : Optional[Any]=0.0_2   ,)		->  Dict:
           _lowercase							:      List[Any]  						=							parent
           _lowercase							:      Union[str, Any]  						=							batch_size
           _lowercase							:      int  						=							seq_length
           _lowercase							:      Dict  						=							is_training
           _lowercase							:      Tuple  						=							use_labels
           _lowercase							:      List[str]  						=							vocab_size
           _lowercase							:      str  						=							hidden_size
           _lowercase							:      List[str]  						=							num_hidden_layers
           _lowercase							:      List[str]  						=							num_attention_heads
           _lowercase							:      Dict  						=							intermediate_size
           _lowercase							:      Tuple  						=							hidden_act
           _lowercase							:      List[str]  						=							hidden_dropout_prob
           _lowercase							:      Optional[Any]  						=							attention_probs_dropout_prob
           _lowercase							:      List[str]  						=							max_position_embeddings
           _lowercase							:      Union[str, Any]  						=							eos_token_id
           _lowercase							:      Tuple  						=							pad_token_id
           _lowercase							:      str  						=							bos_token_id
           _lowercase							:      List[str]  						=							embed_dim
           _lowercase							:      Tuple  						=							word_embed_proj_dim
           _lowercase							:      Tuple  						=							initializer_range
           _lowercase							:      Any  						=							False

      def   _lowerCamelCase		(							self    : str  )		->  Union[str, Any]:
           _lowercase							:      str  						=							np.clip(ids_tensor([self.batch_size, self.seq_length - 1]   ,self.vocab_size  )   ,3   ,self.vocab_size  )
           _lowercase							:      int  						=							np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)   ,dtype=np.intaa  ))   ,-1  )

           _lowercase							:      Optional[int]  						=							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=UpperCamelCase   ,word_embed_proj_dim=self.word_embed_proj_dim   ,initializer_range=self.initializer_range   ,use_cache=UpperCamelCase   ,)
           _lowercase							:      int  						=							prepare_opt_inputs_dict(UpperCamelCase   ,UpperCamelCase  )
           return config, inputs_dict

      def   _lowerCamelCase		(							self    : Tuple  )		->  List[str]:
           _lowercase						, _lowercase							:      Tuple  						=							self.prepare_config_and_inputs()
           return config, inputs_dict

      def   _lowerCamelCase		(							self    : Union[str, Any]   ,UpperCamelCase    : str   ,UpperCamelCase    : List[Any]   ,UpperCamelCase    : Optional[Any]  )		->  Optional[Any]:
           _lowercase							:      Dict  						=							20
           _lowercase							:      Any  						=							model_class_name(UpperCamelCase  )

           _lowercase							:      Dict  						=							inputs_dict['input_ids']
           _lowercase							:      Dict  						=							inputs_dict['attention_mask']

           _lowercase							:      Dict  						=							model.init_cache(input_ids.shape[0]   ,UpperCamelCase  )
           _lowercase							:      List[str]  						=							jnp.ones((input_ids.shape[0], max_length)   ,dtype='i4'  )

           _lowercase							:      Dict  						=							jnp.broadcast_to(
               jnp.arange(input_ids.shape[-1] - 1  )[None, :]   ,(input_ids.shape[0], input_ids.shape[-1] - 1)   ,)
           _lowercase							:      List[Any]  						=							model(
               input_ids[:, :-1]   ,attention_mask=UpperCamelCase   ,past_key_values=UpperCamelCase   ,position_ids=UpperCamelCase   ,)

           _lowercase							:      str  						=							jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]   ,dtype='i4'  )
           _lowercase							:      Optional[int]  						=							model(
               input_ids[:, -1:]   ,attention_mask=UpperCamelCase   ,past_key_values=outputs_cache.past_key_values   ,position_ids=UpperCamelCase   ,)

           _lowercase							:      Dict  						=							model(UpperCamelCase  )

           _lowercase							:      Dict  						=							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    : List[Any]   ,UpperCamelCase    : List[Any]   ,UpperCamelCase    : List[Any]   ,UpperCamelCase    : str  )		->  Optional[Any]:
           _lowercase							:      Optional[int]  						=							20
           _lowercase							:      Dict  						=							model_class_name(UpperCamelCase  )

           _lowercase						, _lowercase							:      Tuple  						=							(
               inputs_dict['input_ids'],
               inputs_dict['attention_mask'],
           )

           _lowercase							:      int  						=							jnp.concatenate(
               [
                   attention_mask,
                   jnp.zeros((attention_mask.shape[0], max_length - attention_mask.shape[1])  ),
               ]   ,axis=-1   ,)

           _lowercase							:      Dict  						=							model.init_cache(input_ids.shape[0]   ,UpperCamelCase  )
           _lowercase							:      Union[str, Any]  						=							jnp.broadcast_to(
               jnp.arange(input_ids.shape[-1] - 1  )[None, :]   ,(input_ids.shape[0], input_ids.shape[-1] - 1)   ,)

           _lowercase							:      List[Any]  						=							model(
               input_ids[:, :-1]   ,attention_mask=UpperCamelCase   ,past_key_values=UpperCamelCase   ,position_ids=UpperCamelCase   ,)
           _lowercase							:      Any  						=							jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]   ,dtype='i4'  )
           _lowercase							:      List[Any]  						=							model(
               input_ids[:, -1:]   ,past_key_values=outputs_cache.past_key_values   ,attention_mask=UpperCamelCase   ,position_ids=UpperCamelCase   ,)

           _lowercase							:      Tuple  						=							model(UpperCamelCase   ,attention_mask=UpperCamelCase  )

           _lowercase							:      List[Any]  						=							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 							__SCREAMING_SNAKE_CASE			(       lowerCAmelCase__							,   unittest.TestCase							,   lowerCAmelCase__      ):
      '''simple docstring'''



      lowerCAmelCase__       :      List[str] 		=		(FlaxOPTModel, FlaxOPTForCausalLM) if is_flax_available() else ()
      lowerCAmelCase__       :      List[Any] 		=		() if is_flax_available() else ()

      def   _lowerCamelCase		(							self    : Optional[int]  )		->  str:
           _lowercase							:      Dict  						=							FlaxOPTModelTester(self  )

      def   _lowerCamelCase		(							self    : int  )		->  List[str]:
           _lowercase						, _lowercase							:      List[Any]  						=							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    : Any  )		->  Dict:
           _lowercase						, _lowercase							:      Union[str, Any]  						=							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  )



      @slow
      def   _lowerCamelCase		(							self    : Optional[int]  )		->  int:
           for model_class_name in self.all_model_classes:
                _lowercase							:      Optional[int]  						=							model_class_name.from_pretrained('facebook/opt-125m'  )
                _lowercase							:      Any  						=							np.ones((1, 1)  ) * model.config.eos_token_id
                _lowercase							:      str  						=							model(UpperCamelCase  )
                self.assertIsNotNone(UpperCamelCase  )



@require_sentencepiece
@require_flax
class 							__SCREAMING_SNAKE_CASE			(       unittest.TestCase      ):
      '''simple docstring'''



      @slow
      def   _lowerCamelCase		(							self    : int  )		->  Optional[int]:
           _lowercase							:      Optional[int]  						=							FlaxOPTModel.from_pretrained('facebook/opt-350m'  )
           _lowercase							:      int  						=							jnp.array([[0, 3_1414, 232, 328, 740, 1140, 1_2695, 69, 4_6078, 1588, 2]]  )
           _lowercase							:      Tuple  						=							model(input_ids=UpperCamelCase  ).last_hidden_state
           _lowercase							:      Union[str, Any]  						=							(1, 11, 512)
           self.assertEqual(output.shape   ,UpperCamelCase  )
           _lowercase							:      Union[str, Any]  						=							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]   ,UpperCamelCase   ,atol=4e-2  )  )



@require_flax
@slow
class 							__SCREAMING_SNAKE_CASE			(       unittest.TestCase      ):
      '''simple docstring'''



      def   _lowerCamelCase		(							self    : Optional[Any]  )		->  Tuple:
           super().setUp()
           _lowercase							:      Optional[int]  						=							'facebook/opt-350m'



      def   _lowerCamelCase		(							self    : Optional[int]  )		->  Union[str, Any]:
           _lowercase							:      Tuple  						=							FlaxOPTForCausalLM.from_pretrained(self.path_model  )
           _lowercase							:      str  						=							GPTaTokenizer.from_pretrained(self.path_model  )

           _lowercase							:      Any  						=							[
               '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
           _lowercase							:      str  						=							tokenizer(UpperCamelCase   ,return_tensors='jax'   ,padding=UpperCamelCase   ,add_special_tokens=UpperCamelCase  )
           _lowercase							:      Optional[int]  						=							model(inputs.input_ids   ,attention_mask=inputs.attention_mask  )[0].mean(axis=-1  )
           _lowercase							:      int  						=							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(UpperCamelCase   ,UpperCamelCase   ,atol=4e-2  )  )

           _lowercase							:      Any  						=							jax.jit(UpperCamelCase  )
           _lowercase							:      Union[str, Any]  						=							model(inputs.input_ids   ,attention_mask=inputs.attention_mask  )[0].mean(axis=-1  )
           self.assertTrue(jnp.allclose(UpperCamelCase   ,UpperCamelCase   ,atol=4e-2  )  )



@require_flax
@slow
class 							__SCREAMING_SNAKE_CASE			(       unittest.TestCase      ):
      '''simple docstring'''



      @property
      def   _lowerCamelCase		(							self    : Optional[int]  )		->  Optional[int]:
           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   _lowerCamelCase		(							self    : int  )		->  Optional[int]:
           _lowercase							:      Dict  						=							'facebook/opt-125m'

           _lowercase							:      Optional[int]  						=							[
               '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',
           ]

           _lowercase							:      int  						=							[]

           _lowercase							:      Tuple  						=							FlaxOPTForCausalLM.from_pretrained(UpperCamelCase  )
           _lowercase							:      Union[str, Any]  						=							GPTaTokenizer.from_pretrained(UpperCamelCase  )

           for prompt in self.prompts:
                _lowercase							:      List[str]  						=							tokenizer(UpperCamelCase   ,return_tensors='jax'  ).input_ids

                _lowercase							:      Optional[Any]  						=							model.generate(UpperCamelCase   ,max_length=10  )
                _lowercase							:      Union[str, Any]  						=							generated_ids[0]

                _lowercase							:      Any  						=							tokenizer.batch_decode(UpperCamelCase   ,skip_special_tokens=UpperCamelCase  )
                predicted_outputs += generated_string

           self.assertListEqual(UpperCamelCase   ,UpperCamelCase  )

      def   _lowerCamelCase		(							self    : List[str]  )		->  Dict:
           _lowercase							:      int  						=							'facebook/opt-350m'

           _lowercase							:      Tuple  						=							[
               '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',
           ]

           _lowercase							:      Union[str, Any]  						=							[]
           _lowercase							:      List[Any]  						=							FlaxOPTForCausalLM.from_pretrained(UpperCamelCase  )
           _lowercase							:      Optional[int]  						=							GPTaTokenizer.from_pretrained(UpperCamelCase  )

           for prompt in self.prompts:
                _lowercase							:      Optional[int]  						=							tokenizer(UpperCamelCase   ,return_tensors='jax'  ).input_ids

                _lowercase							:      Tuple  						=							model.generate(UpperCamelCase   ,max_length=10  )
                _lowercase							:      Any  						=							generated_ids[0]

                _lowercase							:      Tuple  						=							tokenizer.batch_decode(UpperCamelCase   ,skip_special_tokens=UpperCamelCase  )
                predicted_outputs += generated_string

           self.assertListEqual(UpperCamelCase   ,UpperCamelCase  )

      def   _lowerCamelCase		(							self    : Tuple  )		->  Union[str, Any]:
           _lowercase							:      Union[str, Any]  						=							'facebook/opt-125m'
           _lowercase							:      List[Any]  						=							[
               'Today is a beautiful day and I want to thank',
               'In the city of Rome Canaver Canaver Canaver Canaver',
           ]
           _lowercase							:      List[str]  						=							FlaxOPTForCausalLM.from_pretrained(UpperCamelCase  )
           _lowercase							:      Optional[int]  						=							GPTaTokenizer.from_pretrained(UpperCamelCase  )
           _lowercase							:      Optional[Any]  						=							tokenizer(
               [
                   'Today is a beautiful day and I want to',
                   'In the city of',
               ]   ,return_tensors='jax'   ,padding=UpperCamelCase   ,)

           _lowercase							:      str  						=							jax.jit(model.generate  )

           _lowercase							:      List[str]  						=							jit_generate(inputs['input_ids']   ,attention_mask=inputs['attention_mask']  ).sequences

           _lowercase							:      Union[str, Any]  						=							tokenizer.batch_decode(UpperCamelCase   ,skip_special_tokens=UpperCamelCase  )

           self.assertIsNotNone(UpperCamelCase   ,UpperCamelCase  )



      def   _lowerCamelCase		(							self    : Dict  )		->  List[str]:
           _lowercase							:      str  						=							'facebook/opt-350m'

           _lowercase							:      List[str]  						=							GPTaTokenizer.from_pretrained(UpperCamelCase  )
           _lowercase							:      List[str]  						=							FlaxOPTForCausalLM.from_pretrained(UpperCamelCase  )

           _lowercase							:      int  						=							'left'

           # use different length sentences to test batching
           _lowercase							:      Tuple  						=							[
               'Hello, my dog is a little',
               'Today, I',
           ]

           _lowercase							:      int  						=							tokenizer(UpperCamelCase   ,return_tensors='jax'   ,padding=UpperCamelCase  )
           _lowercase							:      Union[str, Any]  						=							inputs['input_ids']

           _lowercase							:      str  						=							model.generate(input_ids=UpperCamelCase   ,attention_mask=inputs['attention_mask']   ,trace=UpperCamelCase  )

           _lowercase							:      Tuple  						=							tokenizer(sentences[0]   ,return_tensors='jax'  ).input_ids
           _lowercase							:      Optional[Any]  						=							model.generate(input_ids=UpperCamelCase  )

           _lowercase							:      Optional[int]  						=							inputs_non_padded.shape[-1] - inputs['attention_mask'][-1].sum()
           _lowercase							:      Optional[int]  						=							tokenizer(sentences[1]   ,return_tensors='jax'  ).input_ids
           _lowercase							:      List[str]  						=							model.generate(input_ids=UpperCamelCase   ,max_length=model.config.max_length - num_paddings  )

           _lowercase							:      Dict  						=							tokenizer.batch_decode(outputs[0]   ,skip_special_tokens=UpperCamelCase  )
           _lowercase							:      Optional[Any]  						=							tokenizer.decode(output_non_padded[0][0]   ,skip_special_tokens=UpperCamelCase  )
           _lowercase							:      Optional[Any]  						=							tokenizer.decode(output_padded[0][0]   ,skip_special_tokens=UpperCamelCase  )

           _lowercase							:      Union[str, Any]  						=							[
               '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(UpperCamelCase   ,UpperCamelCase  )
           self.assertListEqual(UpperCamelCase   ,[non_padded_sentence, padded_sentence]  )