code: 
  
  
  
  
  
  
  from __future__ import annotations
  
  import unittest
  import warnings
  
  from transformers import AutoTokenizer, MarianConfig, MarianTokenizer, TranslationPipeline, 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, TFMarianModel, TFMarianMTModel
  
  
  
  
  
  @require_tf
  class     __a						:
   _lowerCAmelCase : Any 				=				MarianConfig
   _lowerCAmelCase : Optional[Any] 				=				{}
   _lowerCAmelCase : int 				=				'gelu'
  
  
  
  
  
  
  
   def __init__(	self      :     Optional[Any]							,     SCREAMING_SNAKE_CASE      :     Tuple							,     SCREAMING_SNAKE_CASE      :     List[Any]=13							,     SCREAMING_SNAKE_CASE      :     int=7							,     SCREAMING_SNAKE_CASE      :     Tuple=True							,     SCREAMING_SNAKE_CASE      :     List[str]=False							,     SCREAMING_SNAKE_CASE      :     Tuple=99							,     SCREAMING_SNAKE_CASE      :     int=32							,     SCREAMING_SNAKE_CASE      :     Union[str, Any]=2							,     SCREAMING_SNAKE_CASE      :     Optional[int]=4							,     SCREAMING_SNAKE_CASE      :     Dict=37							,     SCREAMING_SNAKE_CASE      :     Optional[int]=0.1							,     SCREAMING_SNAKE_CASE      :     Dict=0.1							,     SCREAMING_SNAKE_CASE      :     Optional[int]=20							,     SCREAMING_SNAKE_CASE      :     List[str]=2							,     SCREAMING_SNAKE_CASE      :     Dict=1							,     SCREAMING_SNAKE_CASE      :     int=0							,     ):
  
  
        '''simple docstring'''
  
  
  
  
  
  
        UpperCamelCase__    :     int        =						parent
        UpperCamelCase__    :     int        =						batch_size
        UpperCamelCase__    :     str        =						seq_length
        UpperCamelCase__    :     Tuple        =						is_training
        UpperCamelCase__    :     List[Any]        =						use_labels
        UpperCamelCase__    :     Tuple        =						vocab_size
        UpperCamelCase__    :     int        =						hidden_size
        UpperCamelCase__    :     List[str]        =						num_hidden_layers
        UpperCamelCase__    :     Tuple        =						num_attention_heads
        UpperCamelCase__    :     Any        =						intermediate_size
  
        UpperCamelCase__    :     List[str]        =						hidden_dropout_prob
        UpperCamelCase__    :     Optional[int]        =						attention_probs_dropout_prob
        UpperCamelCase__    :     int        =						max_position_embeddings
        UpperCamelCase__    :     List[str]        =						eos_token_id
        UpperCamelCase__    :     Optional[int]        =						pad_token_id
        UpperCamelCase__    :     Any        =						bos_token_id
  
  
  
  
  
  
  
   def       __lowercase      (	self      :     Dict		):
  
  
        '''simple docstring'''
  
  
  
  
  
  
        UpperCamelCase__    :     Optional[int]        =						ids_tensor([self.batch_size, self.seq_length - 1]							,     self.vocab_size		)
        UpperCamelCase__    :     List[Any]        =						tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size		)							,     1		)
        UpperCamelCase__    :     Any        =						tf.concat([input_ids, eos_tensor]							,     axis=1		)
  
        UpperCamelCase__    :     Dict        =						ids_tensor([self.batch_size, self.seq_length]							,     self.vocab_size		)
  
        UpperCamelCase__    :     List[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							,     )
        UpperCamelCase__    :     Optional[Any]        =						prepare_marian_inputs_dict(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE		)
        return config, inputs_dict
  
  
  
  
  
  
  
   def       __lowercase      (	self      :     List[Any]							,     SCREAMING_SNAKE_CASE      :     List[Any]							,     SCREAMING_SNAKE_CASE      :     Dict		):
  
  
        '''simple docstring'''
  
  
  
  
  
  
        UpperCamelCase__    :     Optional[Any]        =						TFMarianModel(config=SCREAMING_SNAKE_CASE		).get_decoder()
        UpperCamelCase__    :     Any        =						inputs_dict['input_ids']
  
        UpperCamelCase__    :     Tuple        =						input_ids[:1, :]
        UpperCamelCase__    :     Any        =						inputs_dict['attention_mask'][:1, :]
        UpperCamelCase__    :     Optional[Any]        =						inputs_dict['head_mask']
        UpperCamelCase__    :     List[Any]        =						1
  
        # first forward pass
        UpperCamelCase__    :     Optional[int]        =						model(SCREAMING_SNAKE_CASE							,     attention_mask=SCREAMING_SNAKE_CASE							,     head_mask=SCREAMING_SNAKE_CASE							,     use_cache=SCREAMING_SNAKE_CASE		)
  
        UpperCamelCase__    :     int        =						outputs.to_tuple()
  
        # create hypothetical next token and extent to next_input_ids
        UpperCamelCase__    :     List[Any]        =						ids_tensor((self.batch_size, 3)							,     config.vocab_size		)
        UpperCamelCase__    :     Any        =						tf.cast(ids_tensor((self.batch_size, 3)							,     2		)							,     tf.inta		)
  
        # append to next input_ids and
        UpperCamelCase__    :     str        =						tf.concat([input_ids, next_tokens]							,     axis=-1		)
        UpperCamelCase__    :     Union[str, Any]        =						tf.concat([attention_mask, next_attn_mask]							,     axis=-1		)
  
        UpperCamelCase__    :     Optional[Any]        =						model(SCREAMING_SNAKE_CASE							,     attention_mask=SCREAMING_SNAKE_CASE		)[0]
        UpperCamelCase__    :     Union[str, Any]        =						model(SCREAMING_SNAKE_CASE							,     attention_mask=SCREAMING_SNAKE_CASE							,     past_key_values=SCREAMING_SNAKE_CASE		)[0]
  
        self.parent.assertEqual(next_tokens.shape[1]							,     output_from_past.shape[1]		)
  
        # select random slice
        UpperCamelCase__    :     List[str]        =						int(ids_tensor((1,)							,     output_from_past.shape[-1]		)		)
        UpperCamelCase__    :     Any        =						output_from_no_past[:, -3:, random_slice_idx]
        UpperCamelCase__    :     Any        =						output_from_past[:, :, random_slice_idx]
  
        # test that outputs are equal for slice
        tf.debugging.assert_near(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE							,     rtol=1e-3		)
  
  
  
  
  
  
  
  def 	SCREAMING_SNAKE_CASE							(      __lowerCAmelCase      ,					__lowerCAmelCase      ,					__lowerCAmelCase      ,					__lowerCAmelCase=None      ,					__lowerCAmelCase=None      ,					__lowerCAmelCase=None      ,					__lowerCAmelCase=None      ,					__lowerCAmelCase=None      ,					)				->			List[Any]:
       if attention_mask is None:
            UpperCamelCase__    :     Any        =						tf.cast(tf.math.not_equal(lowerCAmelCase__      ,					config.pad_token_id    )      ,					tf.inta    )
       if decoder_attention_mask is None:
            UpperCamelCase__    :     Union[str, 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__    :     Optional[int]        =						tf.ones((config.encoder_layers, config.encoder_attention_heads)    )
       if decoder_head_mask is None:
            UpperCamelCase__    :     List[Any]        =						tf.ones((config.decoder_layers, config.decoder_attention_heads)    )
       if cross_attn_head_mask is None:
            UpperCamelCase__    :     List[str]        =						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						(					snake_case__ ,     snake_case__ ,     unittest.TestCase ):
   _lowerCAmelCase : Tuple 				=				(TFMarianMTModel, TFMarianModel) if is_tf_available() else ()
   _lowerCAmelCase : Optional[int] 				=				(TFMarianMTModel,) if is_tf_available() else ()
   _lowerCAmelCase : List[str] 				=				(
       {
           'conversational': TFMarianMTModel,
           'feature-extraction': TFMarianModel,
           'summarization': TFMarianMTModel,
           'text2text-generation': TFMarianMTModel,
           'translation': TFMarianMTModel,
       }
       if is_tf_available()
       else {}
   )
   _lowerCAmelCase : str 				=				True
   _lowerCAmelCase : Any 				=				False
   _lowerCAmelCase : Optional[Any] 				=				False
  
  
  
  
  
  
  
   def       __lowercase      (	self      :     Optional[Any]		):
  
  
        '''simple docstring'''
  
  
  
  
  
  
        UpperCamelCase__    :     str        =						TFMarianModelTester(self		)
        UpperCamelCase__    :     Tuple        =						ConfigTester(self							,     config_class=SCREAMING_SNAKE_CASE		)
  
  
  
  
  
  
  
   def       __lowercase      (	self      :     Tuple		):
  
  
        '''simple docstring'''
  
  
  
  
  
  
        self.config_tester.run_common_tests()
  
  
  
  
  
  
  
   def       __lowercase      (	self      :     Any		):
  
  
        '''simple docstring'''
  
  
  
  
  
  
        UpperCamelCase__    :     int        =						self.model_tester.prepare_config_and_inputs_for_common()
        self.model_tester.check_decoder_model_past_large_inputs(*SCREAMING_SNAKE_CASE		)
  
  
  
  
  
  
  
  @require_tf
  class     __a						(					unittest.TestCase ):
   _lowerCAmelCase : List[str] 				=				1_0_0_0  # show more chars for failing integration tests
  
  
  
  
  
  
  
   @classmethod
   def       __lowercase      (	cls      :     Dict		):
  
  
        '''simple docstring'''
  
  
  
  
  
  
        UpperCamelCase__    :     Optional[Any]        =						F'Helsinki-NLP/opus-mt-{cls.src}-{cls.tgt}'
        return cls
  
  
  
  
  
  
  
   @cached_property
   def       __lowercase      (	self      :     Union[str, Any]		):
  
  
        '''simple docstring'''
  
  
  
  
  
  
        return AutoTokenizer.from_pretrained(self.model_name		)
  
  
  
  
  
  
  
   @property
   def       __lowercase      (	self      :     Optional[Any]		):
  
  
        '''simple docstring'''
  
  
  
  
  
  
        return self.tokenizer.eos_token_id
  
  
  
  
  
  
  
   @cached_property
   def       __lowercase      (	self      :     Any		):
  
  
        '''simple docstring'''
  
  
  
  
  
  
        warnings.simplefilter("error"		)
        UpperCamelCase__    :     TFMarianMTModel        =						TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name		)
        assert isinstance(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE		)
        UpperCamelCase__    :     List[Any]        =						model.config
        self.assertListEqual(c.bad_words_ids							,     [[c.pad_token_id]]		)
        self.assertEqual(c.max_length							,     5_12		)
        self.assertEqual(c.decoder_start_token_id							,     c.pad_token_id		)
        return model
  
  
  
  
  
  
  
   def       __lowercase      (	self      :     int							,     **SCREAMING_SNAKE_CASE      :     int		):
  
  
        '''simple docstring'''
  
  
  
  
  
  
        UpperCamelCase__    :     List[str]        =						self.translate_src_text(**SCREAMING_SNAKE_CASE		)
        self.assertListEqual(self.expected_text							,     SCREAMING_SNAKE_CASE		)
  
  
  
  
  
  
  
   def       __lowercase      (	self      :     Optional[int]							,     **SCREAMING_SNAKE_CASE      :     int		):
  
  
        '''simple docstring'''
  
  
  
  
  
  
        UpperCamelCase__    :     str        =						self.tokenizer(self.src_text							,     **SCREAMING_SNAKE_CASE							,     padding=SCREAMING_SNAKE_CASE							,     return_tensors="tf"		)
        UpperCamelCase__    :     Dict        =						self.model.generate(
            model_inputs.input_ids							,     attention_mask=model_inputs.attention_mask							,     num_beams=2							,     max_length=1_28		)
        UpperCamelCase__    :     Dict        =						self.tokenizer.batch_decode(generated_ids.numpy()							,     skip_special_tokens=SCREAMING_SNAKE_CASE		)
        return generated_words
  
  
  
  
  
  
  
  @require_sentencepiece
  @require_tokenizers
  @require_tf
  class     __a						(					snake_case__ ):
   _lowerCAmelCase : Dict 				=				'mt'
   _lowerCAmelCase : Optional[Any] 				=				'en'
   _lowerCAmelCase : int 				=				['Billi messu b\'mod ġentili, Ġesù fejjaq raġel li kien milqut bil - marda kerha tal - ġdiem.']
   _lowerCAmelCase : Optional[Any] 				=				['Touching gently, Jesus healed a man who was affected by the sad disease of leprosy.']
  
  
  
  
  
  
  
   @unittest.skip("Skipping until #12647 is resolved."		)
   @slow
   def       __lowercase      (	self      :     Optional[Any]		):
  
  
        '''simple docstring'''
  
  
  
  
  
  
        self._assert_generated_batch_equal_expected()
  
  
  
  
  
  
  
  @require_sentencepiece
  @require_tokenizers
  @require_tf
  class     __a						(					snake_case__ ):
   _lowerCAmelCase : Optional[Any] 				=				'en'
   _lowerCAmelCase : Optional[Any] 				=				'zh'
   _lowerCAmelCase : Union[str, Any] 				=				['My name is Wolfgang and I live in Berlin']
   _lowerCAmelCase : Dict 				=				['我叫沃尔夫冈 我住在柏林']
  
  
  
  
  
  
  
   @unittest.skip("Skipping until #12647 is resolved."		)
   @slow
   def       __lowercase      (	self      :     Tuple		):
  
  
        '''simple docstring'''
  
  
  
  
  
  
        self._assert_generated_batch_equal_expected()
  
  
  
  
  
  
  
  @require_sentencepiece
  @require_tokenizers
  @require_tf
  class     __a						(					snake_case__ ):
   _lowerCAmelCase : str 				=				'en'
   _lowerCAmelCase : Optional[int] 				=				'ROMANCE'
   _lowerCAmelCase : Dict 				=				[
       '>>fr<< Don\'t spend so much time watching TV.',
       '>>pt<< Your message has been sent.',
       '>>es<< He\'s two years older than me.',
   ]
   _lowerCAmelCase : List[str] 				=				[
       'Ne passez pas autant de temps à regarder la télé.',
       'A sua mensagem foi enviada.',
       'Es dos años más viejo que yo.',
   ]
  
  
  
  
  
  
  
   @unittest.skip("Skipping until #12647 is resolved."		)
   @slow
   def       __lowercase      (	self      :     List[Any]		):
  
  
        '''simple docstring'''
  
  
  
  
  
  
        self._assert_generated_batch_equal_expected()
  
  
  
  
  
  
  
   @unittest.skip("Skipping until #12647 is resolved."		)
   @slow
   def       __lowercase      (	self      :     Any		):
  
  
        '''simple docstring'''
  
  
  
  
  
  
        UpperCamelCase__    :     Union[str, Any]        =						TranslationPipeline(self.model							,     self.tokenizer							,     framework="tf"		)
        UpperCamelCase__    :     List[Any]        =						pipeline(self.src_text		)
        self.assertEqual(self.expected_text							,     [x["translation_text"] for x in output]		)
style_context: 
  
  
  
  
  
  
  def 	SCREAMING_SNAKE_CASE							(      __lowerCAmelCase    )				->			str:
       if len(__lowerCAmelCase    ) == 0 or not all(len(__lowerCAmelCase    ) == 2 for pair in coordinates    ):
            raise ValueError("The program cannot work out a fitting polynomial."    )
  
       if len({tuple(__lowerCAmelCase    ) for pair in coordinates}    ) != len(__lowerCAmelCase    ):
            raise ValueError("The program cannot work out a fitting polynomial."    )
  
       UpperCamelCase__    :     List[str]        =						{x for x, _ in coordinates}
       if len(__lowerCAmelCase    ) == 1:
            return f'x={coordinates[0][0]}'
  
       if len(__lowerCAmelCase    ) != len(__lowerCAmelCase    ):
            raise ValueError("The program cannot work out a fitting polynomial."    )
  
       UpperCamelCase__    :     Any        =						len(__lowerCAmelCase    )
  
       UpperCamelCase__    :     str        =						0
       UpperCamelCase__    :     list[list[float]]        =						[]
       # put the x and x to the power values in a matrix
       while count_of_line < x:
            UpperCamelCase__    :     int        =						0
            UpperCamelCase__    :     List[Any]        =						coordinates[count_of_line][0]
            UpperCamelCase__    :     list[float]        =						[]
            while count_in_line < x:
                 count_line.append(a ** (x - (count_in_line + 1))    )
                 count_in_line += 1
            matrix.append(__lowerCAmelCase    )
            count_of_line += 1
  
       UpperCamelCase__    :     List[Any]        =						0
       # put the y values into a vector
       UpperCamelCase__    :     list[float]        =						[]
       while count_of_line < x:
            vector.append(coordinates[count_of_line][1]    )
            count_of_line += 1
  
       UpperCamelCase__    :     Tuple        =						0
  
       while count < x:
            UpperCamelCase__    :     Dict        =						0
            while zahlen < x:
                 if count == zahlen:
                      zahlen += 1
                 if zahlen == x:
                      break
                 UpperCamelCase__    :     Optional[Any]        =						matrix[zahlen][count] / matrix[count][count]
                 for counting_columns, item in enumerate(matrix[count]    ):
                      # manipulating all the values in the matrix
                      matrix[zahlen][counting_columns] -= item * bruch
                 # manipulating the values in the vector
                 vector[zahlen] -= vector[count] * bruch
                 zahlen += 1
            count += 1
  
       UpperCamelCase__    :     Tuple        =						0
       # make solutions
       UpperCamelCase__    :     list[str]        =						[]
       while count < x:
            solution.append(str(vector[count] / matrix[count][count]    )    )
            count += 1
  
       UpperCamelCase__    :     int        =						0
       UpperCamelCase__    :     List[str]        =						"f(x)="
  
       while count < x:
            UpperCamelCase__    :     list[str]        =						solution[count].split("E"    )
            if len(__lowerCAmelCase    ) > 1:
                 UpperCamelCase__    :     Union[str, Any]        =						f'{remove_e[0]}*10^{remove_e[1]}'
            solved += f'x^{x - (count + 1)}*{solution[count]}'
            if count + 1 != x:
                 solved += "+"
            count += 1
  
       return solved
  
  
  if __name__ == "__main__":
   print(points_to_polynomial([]))
   print(points_to_polynomial([[]]))
   print(points_to_polynomial([[1, 0], [2, 0], [3, 0]]))
   print(points_to_polynomial([[1, 1], [2, 1], [3, 1]]))
   print(points_to_polynomial([[1, 3], [2, 3], [3, 3]]))
   print(points_to_polynomial([[1, 1], [2, 2], [3, 3]]))
   print(points_to_polynomial([[1, 1], [2, 4], [3, 9]]))
   print(points_to_polynomial([[1, 3], [2, 6], [3, 11]]))
   print(points_to_polynomial([[1, -3], [2, -6], [3, -11]]))
   print(points_to_polynomial([[1, 5], [2, 2], [3, 9]]))