

"""simple docstring"""


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'''
     _SCREAMING_SNAKE_CASE						:Union[str, Any]       = MBartConfig
     _SCREAMING_SNAKE_CASE						:int       = {}
     _SCREAMING_SNAKE_CASE						: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"""


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

     def 					_a			(						self  )  ->      str:


           """simple docstring"""


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

           SCREAMING_SNAKE_CASE__   :      str      		=	ids_tensor([self.batch_size, self.seq_length]	,    self.vocab_size  )

           SCREAMING_SNAKE_CASE__   :      Any      		=	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	,    )
           SCREAMING_SNAKE_CASE__   :      int      		=	prepare_mbart_inputs_dict(_a	,    _a	,    _a  )
           return config, inputs_dict





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


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      Dict      		=	TFMBartModel(config=_a  ).get_decoder()
           SCREAMING_SNAKE_CASE__   :      Tuple      		=	inputs_dict["""input_ids"""]

           SCREAMING_SNAKE_CASE__   :      Any      		=	input_ids[:1, :]
           SCREAMING_SNAKE_CASE__   :      int      		=	inputs_dict["""attention_mask"""][:1, :]
           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	inputs_dict["""head_mask"""]
           SCREAMING_SNAKE_CASE__   :      Any      		=	1

           # first forward pass
           SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	model(_a	,    attention_mask=_a	,    head_mask=_a	,    use_cache=_a  )

           SCREAMING_SNAKE_CASE__		,		SCREAMING_SNAKE_CASE__   :      int      		=	outputs.to_tuple()
           SCREAMING_SNAKE_CASE__   :      str      		=	past_key_values[1]





def  _lowercase					(    __lowerCAmelCase  ,					__lowerCAmelCase  ,					__lowerCAmelCase  ,					__lowerCAmelCase=None  ,					__lowerCAmelCase=None  ,					__lowerCAmelCase=None  ,					__lowerCAmelCase=None  ,					__lowerCAmelCase=None  ,					)       ->				Any:
      if attention_mask is None:
            SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	tf.cast(tf.math.not_equal(__lowerCAmelCase  ,					config.pad_token_id							)  ,					tf.inta							)
      if decoder_attention_mask is None:
            SCREAMING_SNAKE_CASE__   :      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:
            SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	tf.ones((config.encoder_layers, config.encoder_attention_heads)							)
      if decoder_head_mask is None:
            SCREAMING_SNAKE_CASE__   :      Tuple      		=	tf.ones((config.decoder_layers, config.decoder_attention_heads)							)
      if cross_attn_head_mask is None:
            SCREAMING_SNAKE_CASE__   :      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      __a							(UpperCamelCase_   ,	UpperCamelCase_   ,	unittest.TestCase):






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

     def 					_a			(						self	,    _a	,    _a	,    _a	,    _a	,    _a  )  ->      Dict:


           """simple docstring"""


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

           return False

     def 					_a			(						self  )  ->      Tuple:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	TFMBartModelTester(self  )
           SCREAMING_SNAKE_CASE__   :      Dict      		=	ConfigTester(self	,    config_class=_a  )

     def 					_a			(						self  )  ->      str:


           """simple docstring"""


           self.config_tester.run_common_tests()





     def 					_a			(						self  )  ->      Any:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      str      		=	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      __a							(unittest.TestCase):






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

     @cached_property
     def 					_a			(						self  )  ->      str:


           """simple docstring"""


           return AutoTokenizer.from_pretrained(self.model_name  )

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


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      Dict      		=	TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name  )
           return model

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


           """simple docstring"""


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

     def 					_a			(						self	,    **_a  )  ->      Optional[Any]:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      List[str]      		=	self.tokenizer(self.src_text	,    **_a	,    return_tensors="""tf"""  )
           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	self.model.generate(
               model_inputs.input_ids	,    attention_mask=model_inputs.attention_mask	,    num_beams=2  )
           SCREAMING_SNAKE_CASE__   :      List[Any]      		=	self.tokenizer.batch_decode(_a	,    skip_special_tokens=_a  )
           return generated_words





     @slow
     def 					_a			(						self  )  ->      Union[str, Any]:


           """simple docstring"""


           self._assert_generated_batch_equal_expected()







"""simple docstring"""


class      __a							:






     '''simple docstring'''

     def __init__(						self	,    _a	,    _a	,    _a  )  ->      str:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      Any      		=	name
           SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	value
           SCREAMING_SNAKE_CASE__   :      List[Any]      		=	weight

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


           """simple docstring"""


           return f'''{self.__class__.__name__}({self.name}, {self.value}, {self.weight})'''

     def 					_a			(						self  )  ->      Dict:


           """simple docstring"""


           return self.value

     def 					_a			(						self  )  ->      int:


           """simple docstring"""


           return self.name

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


           """simple docstring"""


           return self.weight





     def 					_a			(						self  )  ->      Dict:


           """simple docstring"""


           return self.value / self.weight





def  _lowercase					(    __lowerCAmelCase  ,					__lowerCAmelCase  ,					__lowerCAmelCase							)       ->				Dict:
      SCREAMING_SNAKE_CASE__   :      Any      		=	[]
      for i in range(len(__lowerCAmelCase							)							):
            menu.append(Things(name[i]  ,					value[i]  ,					weight[i]							)							)
      return menu




def  _lowercase					(    __lowerCAmelCase  ,					__lowerCAmelCase  ,					__lowerCAmelCase							)       ->				Union[str, Any]:
      SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	sorted(__lowerCAmelCase  ,					key=__lowerCAmelCase  ,					reverse=__lowerCAmelCase							)
      SCREAMING_SNAKE_CASE__   :      str      		=	[]
      SCREAMING_SNAKE_CASE__		,		SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	0.0, 0.0
      for i in range(len(__lowerCAmelCase							)							):
            if (total_cost + items_copy[i].get_weight()) <= max_cost:
                  result.append(items_copy[i]							)
                  total_cost += items_copy[i].get_weight()
                  total_value += items_copy[i].get_value()
      return (result, total_value)




def  _lowercase					(    )       ->				List[str]:
      pass


if __name__ == "__main__":
  import doctest

  doctest.testmod()




