

from __future__ import annotations

from random import random
from typing import Generic, TypeVar

__UpperCAmelCase								=			TypeVar("""KT""")
__UpperCAmelCase								=			TypeVar("""VT""")



class SCREAMING_SNAKE_CASE     (  Generic[KT, VT]			):
  """simple docstring"""






  def __init__(      self			:   List[str]	,							lowerCAmelCase			:   KT | str = "root"	,							lowerCAmelCase			:   VT | None = None   )							->     Optional[Any]:






     """simple docstring"""







     __lowerCAmelCase  :      Optional[Any]    =						key
     __lowerCAmelCase  :      Union[str, Any]    =						value
     __lowerCAmelCase  :      list[Node[KT, VT]]    =						[]







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






     """simple docstring"""







     return f'''Node({self.key}: {self.value})'''







  @property
  def 	SCREAMING_SNAKE_CASE   (      self			:   int   )							->     int:






     """simple docstring"""







     return len(self.forward   )






class SCREAMING_SNAKE_CASE     (  Generic[KT, VT]			):
  """simple docstring"""






  def __init__(      self			:   Tuple	,							lowerCAmelCase			:   float = 0.5	,							lowerCAmelCase			:   int = 16   )							->     Optional[Any]:






     """simple docstring"""







     __lowerCAmelCase  :      Node[KT, VT]    =						Node[KT, VT]()
     __lowerCAmelCase  :      Optional[Any]    =						0
     __lowerCAmelCase  :      Dict    =						p
     __lowerCAmelCase  :      Optional[Any]    =						max_level







  def __str__(      self			:   str   )							->     str:






     """simple docstring"""







     __lowerCAmelCase  :      int    =						list(self   )

     if len(lowerCAmelCase   ) == 0:
        return f'''SkipList(level={self.level})'''

     __lowerCAmelCase  :      Union[str, Any]    =						max((len(str(lowerCAmelCase   )   ) for item in items)	,							default=4   )
     __lowerCAmelCase  :      int    =						max(lowerCAmelCase	,							4   ) + 4

     __lowerCAmelCase  :      int    =						self.head
     __lowerCAmelCase  :      Union[str, Any]    =						[]

     __lowerCAmelCase  :      Any    =						node.forward.copy()
     lines.append(f'''[{node.key}]'''.ljust(lowerCAmelCase	,							"""-"""   ) + """* """ * len(lowerCAmelCase   )   )
     lines.append(""" """ * label_size + """| """ * len(lowerCAmelCase   )   )

     while len(node.forward   ) != 0:
        __lowerCAmelCase  :      Dict    =						node.forward[0]

        lines.append(
            f'''[{node.key}]'''.ljust(lowerCAmelCase	,							"""-"""   )
            + """ """.join(str(n.key   ) if n.key == node.key else """|""" for n in forwards   )   )
        lines.append(""" """ * label_size + """| """ * len(lowerCAmelCase   )   )
        __lowerCAmelCase  :      Tuple    =						node.forward

     lines.append("""None""".ljust(lowerCAmelCase   ) + """* """ * len(lowerCAmelCase   )   )
     return f'''SkipList(level={self.level})\n''' + "\n".join(lowerCAmelCase   )







  def __iter__(      self			:   Optional[Any]   )							->     Optional[Any]:






     """simple docstring"""







     __lowerCAmelCase  :      Optional[Any]    =						self.head

     while len(node.forward   ) != 0:
        yield node.forward[0].key
        __lowerCAmelCase  :      List[str]    =						node.forward[0]







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






     """simple docstring"""







     __lowerCAmelCase  :      Any    =						1
     while random() < self.p and level < self.max_level:
        level += 1

     return level







  def 	SCREAMING_SNAKE_CASE   (      self			:   List[Any]	,							lowerCAmelCase			:   List[str]   )							->     tuple[Node[KT, VT] | None, list[Node[KT, VT]]]:






     """simple docstring"""







     __lowerCAmelCase  :      Optional[int]    =						[]

     __lowerCAmelCase  :      str    =						self.head

     for i in reversed(range(self.level   )   ):
        # i < node.level - When node level is lesser than `i` decrement `i`.
        # node.forward[i].key < key - Jumping to node with key value higher
        #                             or equal to searched key would result
        #                             in skipping searched key.
        while i < node.level and node.forward[i].key < key:
           __lowerCAmelCase  :      Dict    =						node.forward[i]
        # Each leftmost node (relative to searched node) will potentially have to
        # be updated.
        update_vector.append(lowerCAmelCase   )

     update_vector.reverse()  # Note that we were inserting values in reverse order.

     # len(node.forward) != 0 - If current node doesn't contain any further
     #                          references then searched key is not present.
     # node.forward[0].key == key - Next node key should be equal to search key
     #                              if key is present.
     if len(node.forward   ) != 0 and node.forward[0].key == key:
        return node.forward[0], update_vector
     else:
        return None, update_vector







  def 	SCREAMING_SNAKE_CASE   (      self			:   Union[str, Any]	,							lowerCAmelCase			:   KT   )							->     Dict:






     """simple docstring"""







     __lowerCAmelCase   ,__lowerCAmelCase  :      str    =						self._locate_node(lowerCAmelCase   )

     if node is not None:
        for i, update_node in enumerate(lowerCAmelCase   ):
           # Remove or replace all references to removed node.
           if update_node.level > i and update_node.forward[i].key == key:
              if node.level > i:
                 __lowerCAmelCase  :      List[str]    =						node.forward[i]
              else:
                 __lowerCAmelCase  :      List[str]    =						update_node.forward[:i]







  def 	SCREAMING_SNAKE_CASE   (      self			:   int	,							lowerCAmelCase			:   KT	,							lowerCAmelCase			:   VT   )							->     Union[str, Any]:






     """simple docstring"""







     __lowerCAmelCase   ,__lowerCAmelCase  :      Any    =						self._locate_node(lowerCAmelCase   )
     if node is not None:
        __lowerCAmelCase  :      List[Any]    =						value
     else:
        __lowerCAmelCase  :      Dict    =						self.random_level()

        if level > self.level:
           # After level increase we have to add additional nodes to head.
           for _ in range(self.level - 1	,							lowerCAmelCase   ):
              update_vector.append(self.head   )
           __lowerCAmelCase  :      int    =						level

        __lowerCAmelCase  :      int    =						Node(lowerCAmelCase	,							lowerCAmelCase   )

        for i, update_node in enumerate(update_vector[:level]   ):
           # Change references to pass through new node.
           if update_node.level > i:
              new_node.forward.append(update_node.forward[i]   )

           if update_node.level < i + 1:
              update_node.forward.append(lowerCAmelCase   )
           else:
              __lowerCAmelCase  :      Union[str, Any]    =						new_node







  def 	SCREAMING_SNAKE_CASE   (      self			:   Union[str, Any]	,							lowerCAmelCase			:   VT   )							->     VT | None:






     """simple docstring"""







     __lowerCAmelCase   ,__lowerCAmelCase  :      Tuple    =						self._locate_node(lowerCAmelCase   )

     if node is not None:
        return node.value

     return None






def      snake_case_				()      ->     Optional[Any]:
   __lowerCAmelCase  :      Tuple    =						SkipList()
   skip_list.insert("""Key1""" ,       3					)
   skip_list.insert("""Key2""" ,       1_2					)
   skip_list.insert("""Key3""" ,       4_1					)
   skip_list.insert("""Key4""" ,       -1_9					)

   __lowerCAmelCase  :      Union[str, Any]    =						skip_list.head
   __lowerCAmelCase  :      List[str]    =						{}
   while node.level != 0:
      __lowerCAmelCase  :      Optional[int]    =						node.forward[0]
      __lowerCAmelCase  :      Optional[Any]    =						node.value

   assert len(__A					) == 4
   assert all_values["Key1"] == 3
   assert all_values["Key2"] == 1_2
   assert all_values["Key3"] == 4_1
   assert all_values["Key4"] == -1_9





def      snake_case_				()      ->     Tuple:
   __lowerCAmelCase  :      Dict    =						SkipList()
   skip_list.insert("""Key1""" ,       1_0					)
   skip_list.insert("""Key1""" ,       1_2					)

   skip_list.insert("""Key5""" ,       7					)
   skip_list.insert("""Key7""" ,       1_0					)
   skip_list.insert("""Key10""" ,       5					)

   skip_list.insert("""Key7""" ,       7					)
   skip_list.insert("""Key5""" ,       5					)
   skip_list.insert("""Key10""" ,       1_0					)

   __lowerCAmelCase  :      Dict    =						skip_list.head
   __lowerCAmelCase  :      List[Any]    =						{}
   while node.level != 0:
      __lowerCAmelCase  :      str    =						node.forward[0]
      __lowerCAmelCase  :      Any    =						node.value

   if len(__A					) != 4:
      print()
   assert len(__A					) == 4
   assert all_values["Key1"] == 1_2
   assert all_values["Key7"] == 7
   assert all_values["Key5"] == 5
   assert all_values["Key10"] == 1_0





def      snake_case_				()      ->     Dict:
   __lowerCAmelCase  :      List[str]    =						SkipList()
   assert skip_list.find("""Some key"""					) is None





def      snake_case_				()      ->     str:
   __lowerCAmelCase  :      int    =						SkipList()

   skip_list.insert("""Key2""" ,       2_0					)
   assert skip_list.find("""Key2"""					) == 2_0

   skip_list.insert("""Some Key""" ,       1_0					)
   skip_list.insert("""Key2""" ,       8					)
   skip_list.insert("""V""" ,       1_3					)

   assert skip_list.find("""Y"""					) is None
   assert skip_list.find("""Key2"""					) == 8
   assert skip_list.find("""Some Key"""					) == 1_0
   assert skip_list.find("""V"""					) == 1_3





def      snake_case_				()      ->     int:
   __lowerCAmelCase  :      int    =						SkipList()
   skip_list.delete("""Some key"""					)

   assert len(skip_list.head.forward					) == 0





def      snake_case_				()      ->     int:
   __lowerCAmelCase  :      int    =						SkipList()

   skip_list.insert("""Key1""" ,       1_2					)
   skip_list.insert("""V""" ,       1_3					)
   skip_list.insert("""X""" ,       1_4					)
   skip_list.insert("""Key2""" ,       1_5					)

   skip_list.delete("""V"""					)
   skip_list.delete("""Key2"""					)

   assert skip_list.find("""V"""					) is None
   assert skip_list.find("""Key2"""					) is None





def      snake_case_				()      ->     str:
   __lowerCAmelCase  :      List[Any]    =						SkipList()

   skip_list.insert("""Key1""" ,       1_2					)
   skip_list.insert("""V""" ,       1_3					)
   skip_list.insert("""X""" ,       1_4					)
   skip_list.insert("""Key2""" ,       1_5					)

   skip_list.delete("""V"""					)
   assert skip_list.find("""V"""					) is None
   assert skip_list.find("""X"""					) == 1_4
   assert skip_list.find("""Key1"""					) == 1_2
   assert skip_list.find("""Key2"""					) == 1_5

   skip_list.delete("""X"""					)
   assert skip_list.find("""V"""					) is None
   assert skip_list.find("""X"""					) is None
   assert skip_list.find("""Key1"""					) == 1_2
   assert skip_list.find("""Key2"""					) == 1_5

   skip_list.delete("""Key1"""					)
   assert skip_list.find("""V"""					) is None
   assert skip_list.find("""X"""					) is None
   assert skip_list.find("""Key1"""					) is None
   assert skip_list.find("""Key2"""					) == 1_5

   skip_list.delete("""Key2"""					)
   assert skip_list.find("""V"""					) is None
   assert skip_list.find("""X"""					) is None
   assert skip_list.find("""Key1"""					) is None
   assert skip_list.find("""Key2"""					) is None





def      snake_case_				()      ->     str:
   __lowerCAmelCase  :      Dict    =						SkipList()

   skip_list.insert("""Key1""" ,       1_2					)
   skip_list.insert("""V""" ,       1_3					)
   skip_list.insert("""X""" ,       1_4_2					)
   skip_list.insert("""Key2""" ,       1_5					)

   skip_list.delete("""X"""					)

   def traverse_keys(__A					:						List[Any]					):
      yield node.key
      for forward_node in node.forward:
         yield from traverse_keys(__A					)

   assert len(set(traverse_keys(skip_list.head					)					)					) == 4





def      snake_case_				()      ->     Any:
   def is_sorted(__A					:						Any					):
      return all(next_item >= item for item, next_item in zip(__A ,       lst[1:]					)					)

   __lowerCAmelCase  :      Dict    =						SkipList()
   for i in range(1_0					):
      skip_list.insert(__A ,       __A					)
   assert is_sorted(list(__A					)					)
   skip_list.delete(5					)
   skip_list.delete(8					)
   skip_list.delete(2					)
   assert is_sorted(list(__A					)					)
   skip_list.insert(-1_2 ,       -1_2					)
   skip_list.insert(7_7 ,       7_7					)
   assert is_sorted(list(__A					)					)





def      snake_case_				()      ->     Optional[Any]:
   for _ in range(1_0_0					):
      # Repeat test 100 times due to the probabilistic nature of skip list
      # random values == random bugs
      test_insert()
      test_insert_overrides_existing_value()

      test_searching_empty_list_returns_none()
      test_search()

      test_deleting_item_from_empty_list_do_nothing()
      test_deleted_items_are_not_founded_by_find_method()
      test_delete_removes_only_given_key()
      test_delete_doesnt_leave_dead_nodes()

      test_iter_always_yields_sorted_values()





def      snake_case_				()      ->     List[Any]:
   __lowerCAmelCase  :      Dict    =						SkipList()
   skip_list.insert(2 ,       """2"""					)
   skip_list.insert(4 ,       """4"""					)
   skip_list.insert(6 ,       """4"""					)
   skip_list.insert(4 ,       """5"""					)
   skip_list.insert(8 ,       """4"""					)
   skip_list.insert(9 ,       """4"""					)

   skip_list.delete(4					)

   print(__A					)


if __name__ == "__main__":
   import doctest

   doctest.testmod()
   main()





from __future__ import annotations

import unittest

from transformers import AutoTokenizer, MBartConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
   import tensorflow as tf

   from transformers import TFAutoModelForSeqaSeqLM, TFMBartForConditionalGeneration, TFMBartModel



@require_tf
class SCREAMING_SNAKE_CASE     :
  """simple docstring"""






  lowerCamelCase   :      Union[str, Any]										=MBartConfig
  lowerCamelCase   :      Optional[Any]										={}
  lowerCamelCase   :      Dict										="gelu"







  def __init__(      self			:   str	,							lowerCAmelCase			:   Any	,							lowerCAmelCase			:   List[Any]=13	,							lowerCAmelCase			:   List[str]=7	,							lowerCAmelCase			:   List[str]=True	,							lowerCAmelCase			:   Optional[Any]=False	,							lowerCAmelCase			:   Union[str, Any]=99	,							lowerCAmelCase			:   List[str]=32	,							lowerCAmelCase			:   List[Any]=2	,							lowerCAmelCase			:   Tuple=4	,							lowerCAmelCase			:   Any=37	,							lowerCAmelCase			:   Optional[int]=0.1	,							lowerCAmelCase			:   List[Any]=0.1	,							lowerCAmelCase			:   Dict=20	,							lowerCAmelCase			:   Any=2	,							lowerCAmelCase			:   Union[str, Any]=1	,							lowerCAmelCase			:   str=0	,							)							->     Any:






     """simple docstring"""







     __lowerCAmelCase  :      str    =						parent
     __lowerCAmelCase  :      int    =						batch_size
     __lowerCAmelCase  :      int    =						seq_length
     __lowerCAmelCase  :      Tuple    =						is_training
     __lowerCAmelCase  :      Optional[int]    =						use_labels
     __lowerCAmelCase  :      str    =						vocab_size
     __lowerCAmelCase  :      List[str]    =						hidden_size
     __lowerCAmelCase  :      Dict    =						num_hidden_layers
     __lowerCAmelCase  :      int    =						num_attention_heads
     __lowerCAmelCase  :      Any    =						intermediate_size
     __lowerCAmelCase  :      Dict    =						hidden_dropout_prob
     __lowerCAmelCase  :      List[Any]    =						attention_probs_dropout_prob
     __lowerCAmelCase  :      Tuple    =						max_position_embeddings
     __lowerCAmelCase  :      Union[str, Any]    =						eos_token_id
     __lowerCAmelCase  :      Optional[Any]    =						pad_token_id
     __lowerCAmelCase  :      int    =						bos_token_id







  def 	SCREAMING_SNAKE_CASE   (      self			:   List[Any]   )							->     List[Any]:






     """simple docstring"""







     __lowerCAmelCase  :      List[Any]    =						ids_tensor([self.batch_size, self.seq_length - 1]	,							self.vocab_size   )
     __lowerCAmelCase  :      Optional[Any]    =						tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size   )	,							1   )
     __lowerCAmelCase  :      int    =						tf.concat([input_ids, eos_tensor]	,							axis=1   )

     __lowerCAmelCase  :      Any    =						ids_tensor([self.batch_size, self.seq_length]	,							self.vocab_size   )

     __lowerCAmelCase  :      Dict    =						self.config_cls(
         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_ids=[2]	,							bos_token_id=self.bos_token_id	,							pad_token_id=self.pad_token_id	,							decoder_start_token_id=self.pad_token_id	,							**self.config_updates	,							)
     __lowerCAmelCase  :      Tuple    =						prepare_mbart_inputs_dict(lowerCAmelCase	,							lowerCAmelCase	,							lowerCAmelCase   )
     return config, inputs_dict







  def 	SCREAMING_SNAKE_CASE   (      self			:   List[str]	,							lowerCAmelCase			:   Any	,							lowerCAmelCase			:   str   )							->     Optional[Any]:






     """simple docstring"""







     __lowerCAmelCase  :      List[str]    =						TFMBartModel(config=lowerCAmelCase   ).get_decoder()
     __lowerCAmelCase  :      Tuple    =						inputs_dict["""input_ids"""]

     __lowerCAmelCase  :      Optional[Any]    =						input_ids[:1, :]
     __lowerCAmelCase  :      Union[str, Any]    =						inputs_dict["""attention_mask"""][:1, :]
     __lowerCAmelCase  :      Tuple    =						inputs_dict["""head_mask"""]
     __lowerCAmelCase  :      Any    =						1

     # first forward pass
     __lowerCAmelCase  :      List[Any]    =						model(lowerCAmelCase	,							attention_mask=lowerCAmelCase	,							head_mask=lowerCAmelCase	,							use_cache=lowerCAmelCase   )

     __lowerCAmelCase   ,__lowerCAmelCase  :      List[str]    =						outputs.to_tuple()
     __lowerCAmelCase  :      Union[str, Any]    =						past_key_values[1]






def      snake_case_				(__A					:						str ,       __A					:						Union[str, Any] ,       __A					:						Tuple ,       __A					:						Tuple=None ,       __A					:						Optional[Any]=None ,       __A					:						Optional[Any]=None ,       __A					:						Optional[int]=None ,       __A					:						Optional[Any]=None ,       )      ->     int:
   if attention_mask is None:
      __lowerCAmelCase  :      Dict    =						tf.cast(tf.math.not_equal(__A ,       config.pad_token_id					) ,       tf.inta					)
   if decoder_attention_mask is None:
      __lowerCAmelCase  :      str    =						tf.concat(
          [
              tf.ones(decoder_input_ids[:, :1].shape ,       dtype=tf.inta					),
              tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:] ,       config.pad_token_id					) ,       tf.inta					),
          ] ,       axis=-1 ,       )
   if head_mask is None:
      __lowerCAmelCase  :      Tuple    =						tf.ones((config.encoder_layers, config.encoder_attention_heads)					)
   if decoder_head_mask is None:
      __lowerCAmelCase  :      List[Any]    =						tf.ones((config.decoder_layers, config.decoder_attention_heads)					)
   if cross_attn_head_mask is None:
      __lowerCAmelCase  :      Any    =						tf.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,
       "head_mask": head_mask,
       "decoder_head_mask": decoder_head_mask,
       "cross_attn_head_mask": cross_attn_head_mask,
   }



@require_tf
class SCREAMING_SNAKE_CASE     (  a_       ,							a_       ,							unittest.TestCase			):
  """simple docstring"""






  lowerCamelCase   :      Optional[int]										=(TFMBartForConditionalGeneration, TFMBartModel) if is_tf_available() else ()
  lowerCamelCase   :      List[str]										=(TFMBartForConditionalGeneration,) if is_tf_available() else ()
  lowerCamelCase   :      Union[str, Any]										=(
      {
          "conversational": TFMBartForConditionalGeneration,
          "feature-extraction": TFMBartModel,
          "summarization": TFMBartForConditionalGeneration,
          "text2text-generation": TFMBartForConditionalGeneration,
          "translation": TFMBartForConditionalGeneration,
      }
      if is_tf_available()
      else {}
  )
  lowerCamelCase   :      str										=True
  lowerCamelCase   :      Tuple										=False
  lowerCamelCase   :      Dict										=False







  def 	SCREAMING_SNAKE_CASE   (      self			:   str	,							lowerCAmelCase			:   Optional[Any]	,							lowerCAmelCase			:   Union[str, Any]	,							lowerCAmelCase			:   List[str]	,							lowerCAmelCase			:   Any	,							lowerCAmelCase			:   Optional[Any]   )							->     Optional[int]:






     """simple docstring"""







     if pipeline_test_casse_name != "FeatureExtractionPipelineTests":
        # Exception encountered when calling layer '...'
        return True

     return False







  def 	SCREAMING_SNAKE_CASE   (      self			:   Tuple   )							->     Optional[int]:






     """simple docstring"""







     __lowerCAmelCase  :      Union[str, Any]    =						TFMBartModelTester(self   )
     __lowerCAmelCase  :      Union[str, Any]    =						ConfigTester(self	,							config_class=lowerCAmelCase   )







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






     """simple docstring"""







     self.config_tester.run_common_tests()







  def 	SCREAMING_SNAKE_CASE   (      self			:   Tuple   )							->     List[str]:






     """simple docstring"""







     __lowerCAmelCase  :      List[Any]    =						self.model_tester.prepare_config_and_inputs_for_common()
     self.model_tester.check_decoder_model_past_large_inputs(*lowerCAmelCase   )






@require_sentencepiece
@require_tokenizers
@require_tf
class SCREAMING_SNAKE_CASE     (  unittest.TestCase			):
  """simple docstring"""






  lowerCamelCase   :      Union[str, Any]										=[
      " UN Chief Says There Is No Military Solution in Syria",
  ]
  lowerCamelCase   :      Tuple										=[
      "Şeful ONU declară că nu există o soluţie militară în Siria",
  ]
  lowerCamelCase   :      List[Any]										="facebook/mbart-large-en-ro"







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






     """simple docstring"""







     return AutoTokenizer.from_pretrained(self.model_name   )







  @cached_property
  def 	SCREAMING_SNAKE_CASE   (      self			:   List[str]   )							->     Union[str, Any]:






     """simple docstring"""







     __lowerCAmelCase  :      int    =						TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name   )
     return model







  def 	SCREAMING_SNAKE_CASE   (      self			:   Optional[Any]	,							**lowerCAmelCase			:   Dict   )							->     Union[str, Any]:






     """simple docstring"""







     __lowerCAmelCase  :      Union[str, Any]    =						self.translate_src_text(**lowerCAmelCase   )
     self.assertListEqual(self.expected_text	,							lowerCAmelCase   )







  def 	SCREAMING_SNAKE_CASE   (      self			:   Union[str, Any]	,							**lowerCAmelCase			:   int   )							->     str:






     """simple docstring"""







     __lowerCAmelCase  :      Dict    =						self.tokenizer(self.src_text	,							**lowerCAmelCase	,							return_tensors="""tf"""   )
     __lowerCAmelCase  :      Union[str, Any]    =						self.model.generate(
         model_inputs.input_ids	,							attention_mask=model_inputs.attention_mask	,							num_beams=2   )
     __lowerCAmelCase  :      List[str]    =						self.tokenizer.batch_decode(lowerCAmelCase	,							skip_special_tokens=lowerCAmelCase   )
     return generated_words







  @slow
  def 	SCREAMING_SNAKE_CASE   (      self			:   Optional[int]   )							->     Any:






     """simple docstring"""







     self._assert_generated_batch_equal_expected()


