'''simple docstring'''





def     __A				(  UpperCAmelCase  ,UpperCAmelCase       )  ->      Dict:




						'''simple docstring'''
						_UpperCamelCase      :      Optional[Any]       =    len(__lowercase       )
						_UpperCamelCase      :      Tuple       =    [[False] * (required_sum + 1) for _ in range(arr_len + 1       )]

						# for each arr value, a sum of zero(0) can be formed by not taking any element
						# hence True/1
						for i in range(arr_len + 1       ):
												_UpperCamelCase      :      int       =    True

						# sum is not zero and set is empty then false
						for i in range(1  ,required_sum + 1       ):
												_UpperCamelCase      :      Tuple       =    False

						for i in range(1  ,arr_len + 1       ):
												for j in range(1  ,required_sum + 1       ):
																		if arr[i - 1] > j:
																								_UpperCamelCase      :      Optional[int]       =    subset[i - 1][j]
																		if arr[i - 1] <= j:
																								_UpperCamelCase      :      List[str]       =    subset[i - 1][j] or subset[i - 1][j - arr[i - 1]]

						return subset[arr_len][required_sum]


if __name__ == "__main__":
			import doctest

			doctest.testmod()










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     _a							:






  """simple docstring"""





  A_						       = MBartConfig
  A_						       = {}
  A_						       = """gelu"""




  def __init__(  self		,		_UpperCAmelCase		,		_UpperCAmelCase=13		,		_UpperCAmelCase=7		,		_UpperCAmelCase=True		,		_UpperCAmelCase=False		,		_UpperCAmelCase=99		,		_UpperCAmelCase=32		,		_UpperCAmelCase=2		,		_UpperCAmelCase=4		,		_UpperCAmelCase=37		,		_UpperCAmelCase=0.1		,		_UpperCAmelCase=0.1		,		_UpperCAmelCase=20		,		_UpperCAmelCase=2		,		_UpperCAmelCase=1		,		_UpperCAmelCase=0		,		)      ->			Union[str, Any]:
     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_dropout_prob
     UpperCamelCase_						=  attention_probs_dropout_prob
     UpperCamelCase_						=  max_position_embeddings
     UpperCamelCase_						=  eos_token_id
     UpperCamelCase_						=  pad_token_id
     UpperCamelCase_						=  bos_token_id




  def  _UpperCAmelCase  (  self			)      ->			List[Any]:
     UpperCamelCase_						=  ids_tensor([self.batch_size, self.seq_length - 1]		,		self.vocab_size			)
     UpperCamelCase_						=  tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size			)		,		1			)
     UpperCamelCase_						=  tf.concat([input_ids, eos_tensor]		,		axis=1			)

     UpperCamelCase_						=  ids_tensor([self.batch_size, self.seq_length]		,		self.vocab_size			)

     UpperCamelCase_						=  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		,		)
     UpperCamelCase_						=  prepare_mbart_inputs_dict(_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase			)
     return config, inputs_dict




  def  _UpperCAmelCase  (  self		,		_UpperCAmelCase		,		_UpperCAmelCase			)      ->			List[str]:
     UpperCamelCase_						=  TFMBartModel(config=_UpperCAmelCase			).get_decoder()
     UpperCamelCase_						=  inputs_dict['input_ids']

     UpperCamelCase_						=  input_ids[:1, :]
     UpperCamelCase_						=  inputs_dict['attention_mask'][:1, :]
     UpperCamelCase_						=  inputs_dict['head_mask']
     UpperCamelCase_						=  1

     # first forward pass
     UpperCamelCase_						=  model(_UpperCAmelCase		,		attention_mask=_UpperCAmelCase		,		head_mask=_UpperCAmelCase		,		use_cache=_UpperCAmelCase			)

     UpperCamelCase_		,   UpperCamelCase_						=  outputs.to_tuple()
     UpperCamelCase_						=  past_key_values[1]






def      _snake_case     (__lowercase     ,   __lowercase     ,   __lowercase     ,   __lowercase=None     ,   __lowercase=None     ,   __lowercase=None     ,   __lowercase=None     ,   __lowercase=None     ,   ):
   if attention_mask is None:
      UpperCamelCase_						=  tf.cast(tf.math.not_equal(__lowercase     ,   config.pad_token_id)     ,   tf.inta)
   if decoder_attention_mask is None:
      UpperCamelCase_						=  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:
      UpperCamelCase_						=  tf.ones((config.encoder_layers, config.encoder_attention_heads))
   if decoder_head_mask is None:
      UpperCamelCase_						=  tf.ones((config.decoder_layers, config.decoder_attention_heads))
   if cross_attn_head_mask is None:
      UpperCamelCase_						=  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     _a							(	UpperCAmelCase__    ,							UpperCAmelCase__    ,							unittest.TestCase     ):






  """simple docstring"""





  A_						       = (TFMBartForConditionalGeneration, TFMBartModel) if is_tf_available() else ()
  A_						       = (TFMBartForConditionalGeneration,) if is_tf_available() else ()
  A_						       = (
      {
          """conversational""": TFMBartForConditionalGeneration,
          """feature-extraction""": TFMBartModel,
          """summarization""": TFMBartForConditionalGeneration,
          """text2text-generation""": TFMBartForConditionalGeneration,
          """translation""": TFMBartForConditionalGeneration,
      }
      if is_tf_available()
      else {}
  )
  A_						       = True
  A_						       = False
  A_						       = False




  def  _UpperCAmelCase  (  self		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase			)      ->			Tuple:
     if pipeline_test_casse_name != "FeatureExtractionPipelineTests":
        # Exception encountered when calling layer '...'
        return True

     return False




  def  _UpperCAmelCase  (  self			)      ->			Optional[int]:
     UpperCamelCase_						=  TFMBartModelTester(self			)
     UpperCamelCase_						=  ConfigTester(self		,		config_class=_UpperCAmelCase			)




  def  _UpperCAmelCase  (  self			)      ->			Optional[int]:
     self.config_tester.run_common_tests()




  def  _UpperCAmelCase  (  self			)      ->			List[Any]:
     UpperCamelCase_						=  self.model_tester.prepare_config_and_inputs_for_common()
     self.model_tester.check_decoder_model_past_large_inputs(*_UpperCAmelCase			)




@require_sentencepiece
@require_tokenizers
@require_tf
class     _a							(	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			)      ->			Any:
     return AutoTokenizer.from_pretrained(self.model_name			)




  @cached_property
  def  _UpperCAmelCase  (  self			)      ->			List[str]:
     UpperCamelCase_						=  TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name			)
     return model




  def  _UpperCAmelCase  (  self		,		**_UpperCAmelCase			)      ->			int:
     UpperCamelCase_						=  self.translate_src_text(**_UpperCAmelCase			)
     self.assertListEqual(self.expected_text		,		_UpperCAmelCase			)




  def  _UpperCAmelCase  (  self		,		**_UpperCAmelCase			)      ->			List[str]:
     UpperCamelCase_						=  self.tokenizer(self.src_text		,		**_UpperCAmelCase		,		return_tensors='tf'			)
     UpperCamelCase_						=  self.model.generate(
         model_inputs.input_ids		,		attention_mask=model_inputs.attention_mask		,		num_beams=2			)
     UpperCamelCase_						=  self.tokenizer.batch_decode(_UpperCAmelCase		,		skip_special_tokens=_UpperCAmelCase			)
     return generated_words




  @slow
  def  _UpperCAmelCase  (  self			)      ->			List[Any]:
     self._assert_generated_batch_equal_expected()





