






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 							lowercase    :






 '''simple docstring'''




 _SCREAMING_SNAKE_CASE							:			Optional[Any] =					MBartConfig
 _SCREAMING_SNAKE_CASE							:			Any =					{}
 _SCREAMING_SNAKE_CASE							:			Optional[int] =					"gelu"


 def __init__(	self							,  _A							,  _A=13							,  _A=7							,  _A=True							,  _A=False							,  _A=99							,  _A=32							,  _A=2							,  _A=4							,  _A=37							,  _A=0.1							,  _A=0.1							,  _A=20							,  _A=2							,  _A=1							,  _A=0							,  ) ->		int:

     """simple docstring"""



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


 def  snake_case__				(	self) ->		Any:

     """simple docstring"""



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

     _UpperCAmelCase				:      int			  =				ids_tensor([self.batch_size, self.seq_length]							,  self.vocab_size)

     _UpperCAmelCase				:      int			  =				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				:      Dict			  =				prepare_mbart_inputs_dict(_A							,  _A							,  _A)
     return config, inputs_dict


 def  snake_case__				(	self							,  _A							,  _A) ->		Any:

     """simple docstring"""



     _UpperCAmelCase				:      Tuple			  =				TFMBartModel(config=_A).get_decoder()
     _UpperCAmelCase				:      List[Any]			  =				inputs_dict['''input_ids''']

     _UpperCAmelCase				:      int			  =				input_ids[:1, :]
     _UpperCAmelCase				:      List[str]			  =				inputs_dict['''attention_mask'''][:1, :]
     _UpperCAmelCase				:      List[Any]			  =				inputs_dict['''head_mask''']
     _UpperCAmelCase				:      Optional[int]			  =				1

     # first forward pass
     _UpperCAmelCase				:      str			  =				model(_A							,  attention_mask=_A							,  head_mask=_A							,  use_cache=_A)

     _UpperCAmelCase				:      List[Any]			  =				outputs.to_tuple()
     _UpperCAmelCase				:      str			  =				past_key_values[1]



def      _lowerCamelCase	(						__A    :							Tuple		,		__A    :							str		,		__A    :							List[Any]		,		__A    :							Optional[int]=None		,		__A    :							List[Any]=None		,		__A    :							Any=None		,		__A    :							Tuple=None		,		__A    :							str=None		,		)      ->					Tuple:
    if attention_mask is None:
        _UpperCAmelCase				:      str			  =				tf.cast(tf.math.not_equal(__A		,		config.pad_token_id )		,		tf.inta )
    if decoder_attention_mask is None:
        _UpperCAmelCase				:      Optional[Any]			  =				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				:      List[str]			  =				tf.ones((config.encoder_layers, config.encoder_attention_heads) )
    if decoder_head_mask is None:
        _UpperCAmelCase				:      Optional[Any]			  =				tf.ones((config.decoder_layers, config.decoder_attention_heads) )
    if cross_attn_head_mask is None:
        _UpperCAmelCase				:      Dict			  =				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 							lowercase    (						__lowercase ,   __lowercase ,   unittest.TestCase			):






 '''simple docstring'''




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


 def  snake_case__				(	self							,  _A							,  _A							,  _A							,  _A							,  _A) ->		List[str]:

     """simple docstring"""



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

     return False


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

     """simple docstring"""



     _UpperCAmelCase				:      Optional[int]			  =				TFMBartModelTester(self)
     _UpperCAmelCase				:      Optional[int]			  =				ConfigTester(self							,  config_class=_A)


 def  snake_case__				(	self) ->		int:

     """simple docstring"""



     self.config_tester.run_common_tests()


 def  snake_case__				(	self) ->		Any:

     """simple docstring"""



     _UpperCAmelCase				:      int			  =				self.model_tester.prepare_config_and_inputs_for_common()
     self.model_tester.check_decoder_model_past_large_inputs(*_A)



@require_sentencepiece
@require_tokenizers
@require_tf
class 							lowercase    (						unittest.TestCase			):






 '''simple docstring'''




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


 @cached_property
 def  snake_case__				(	self) ->		Dict:

     """simple docstring"""



     return AutoTokenizer.from_pretrained(self.model_name)


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

     """simple docstring"""



     _UpperCAmelCase				:      Any			  =				TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name)
     return model


 def  snake_case__				(	self							,  **_A) ->		List[Any]:

     """simple docstring"""



     _UpperCAmelCase				:      Dict			  =				self.translate_src_text(**_A)
     self.assertListEqual(self.expected_text							,  _A)


 def  snake_case__				(	self							,  **_A) ->		List[Any]:

     """simple docstring"""



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


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

     """simple docstring"""



     self._assert_generated_batch_equal_expected()












import unittest

from knapsack import knapsack as k
class 							A_    (						unittest.TestCase			):






 '''simple docstring'''




 def  snake_case__				(	self) ->		Dict:

     """simple docstring"""



     _UpperCAmelCase				:      Optional[int]			  =				0
     _UpperCAmelCase				:      List[Any]			  =				[0]
     _UpperCAmelCase				:      Optional[Any]			  =				[0]
     _UpperCAmelCase				:      Optional[int]			  =				len(_A)
     self.assertEqual(k.knapsack(_A							,  _A							,  _A							,  _A)							,  0)

     _UpperCAmelCase				:      Optional[int]			  =				[60]
     _UpperCAmelCase				:      List[str]			  =				[10]
     _UpperCAmelCase				:      str			  =				len(_A)
     self.assertEqual(k.knapsack(_A							,  _A							,  _A							,  _A)							,  0)


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

     """simple docstring"""



     _UpperCAmelCase				:      Any			  =				3
     _UpperCAmelCase				:      int			  =				[1, 2, 3]
     _UpperCAmelCase				:      List[str]			  =				[3, 2, 1]
     _UpperCAmelCase				:      Union[str, Any]			  =				len(_A)
     self.assertEqual(k.knapsack(_A							,  _A							,  _A							,  _A)							,  5)


 def  snake_case__				(	self) ->		Tuple:

     """simple docstring"""



     _UpperCAmelCase				:      List[str]			  =				50
     _UpperCAmelCase				:      Tuple			  =				[60, 100, 120]
     _UpperCAmelCase				:      Optional[int]			  =				[10, 20, 30]
     _UpperCAmelCase				:      Optional[Any]			  =				len(_A)
     self.assertEqual(k.knapsack(_A							,  _A							,  _A							,  _A)							,  220)


if __name__ == "__main__":
  unittest.main()




