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 						snake_case			:
  			'''simple docstring'''
  
  			UpperCAmelCase       :       Union[str, Any]       = MarianConfig
  			UpperCAmelCase       :       Optional[Any]       = {}
  			UpperCAmelCase       :       Any       = """gelu"""
  
  			def __init__(	self     :     Tuple							,							lowerCAmelCase_     :     Optional[int]							,							lowerCAmelCase_     :     List[str]=13							,							lowerCAmelCase_     :     Optional[Any]=7							,							lowerCAmelCase_     :     List[str]=True							,							lowerCAmelCase_     :     int=False							,							lowerCAmelCase_     :     Any=99							,							lowerCAmelCase_     :     Optional[int]=32							,							lowerCAmelCase_     :     List[Any]=2							,							lowerCAmelCase_     :     Optional[int]=4							,							lowerCAmelCase_     :     Tuple=37							,							lowerCAmelCase_     :     Optional[Any]=0.1							,							lowerCAmelCase_     :     Optional[Any]=0.1							,							lowerCAmelCase_     :     Tuple=20							,							lowerCAmelCase_     :     List[str]=2							,							lowerCAmelCase_     :     Optional[Any]=1							,							lowerCAmelCase_     :     List[str]=0							,							)					->					Optional[int]:
  
  
  
  
  								"""simple docstring"""
  
  
  
  
  
  
  								SCREAMING_SNAKE_CASE_	      =       parent
  								SCREAMING_SNAKE_CASE_	      =       batch_size
  								SCREAMING_SNAKE_CASE_	      =       seq_length
  								SCREAMING_SNAKE_CASE_	      =       is_training
  								SCREAMING_SNAKE_CASE_	      =       use_labels
  								SCREAMING_SNAKE_CASE_	      =       vocab_size
  								SCREAMING_SNAKE_CASE_	      =       hidden_size
  								SCREAMING_SNAKE_CASE_	      =       num_hidden_layers
  								SCREAMING_SNAKE_CASE_	      =       num_attention_heads
  								SCREAMING_SNAKE_CASE_	      =       intermediate_size
  
  								SCREAMING_SNAKE_CASE_	      =       hidden_dropout_prob
  								SCREAMING_SNAKE_CASE_	      =       attention_probs_dropout_prob
  								SCREAMING_SNAKE_CASE_	      =       max_position_embeddings
  								SCREAMING_SNAKE_CASE_	      =       eos_token_id
  								SCREAMING_SNAKE_CASE_	      =       pad_token_id
  								SCREAMING_SNAKE_CASE_	      =       bos_token_id
  
  			def _lowercase (	self     :     Dict					)					->					Optional[Any]:
  
  
  
  
  								"""simple docstring"""
  
  
  
  
  
  
  								SCREAMING_SNAKE_CASE_	      =       ids_tensor([self.batch_size, self.seq_length - 1]							,							self.vocab_size					)
  								SCREAMING_SNAKE_CASE_	      =       tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size					)							,							1					)
  								SCREAMING_SNAKE_CASE_	      =       tf.concat([input_ids, eos_tensor]							,							axis=1					)
  
  								SCREAMING_SNAKE_CASE_	      =       ids_tensor([self.batch_size, self.seq_length]							,							self.vocab_size					)
  
  								SCREAMING_SNAKE_CASE_	      =       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_	      =       prepare_marian_inputs_dict(lowercase_							,							lowercase_							,							lowercase_					)
  								return config, inputs_dict
  
  
  
  
  			def _lowercase (	self     :     Union[str, Any]							,							lowerCAmelCase_     :     Union[str, Any]							,							lowerCAmelCase_     :     Optional[int]					)					->					List[Any]:
  
  
  
  
  								"""simple docstring"""
  
  
  
  
  
  
  								SCREAMING_SNAKE_CASE_	      =       TFMarianModel(config=lowercase_					).get_decoder()
  								SCREAMING_SNAKE_CASE_	      =       inputs_dict['''input_ids''']
  
  								SCREAMING_SNAKE_CASE_	      =       input_ids[:1, :]
  								SCREAMING_SNAKE_CASE_	      =       inputs_dict['''attention_mask'''][:1, :]
  								SCREAMING_SNAKE_CASE_	      =       inputs_dict['''head_mask''']
  								SCREAMING_SNAKE_CASE_	      =       1
  
  								# first forward pass
  								SCREAMING_SNAKE_CASE_	      =       model(lowercase_							,							attention_mask=lowercase_							,							head_mask=lowercase_							,							use_cache=lowercase_					)
  
  								SCREAMING_SNAKE_CASE_	      =       outputs.to_tuple()
  
  								# create hypothetical next token and extent to next_input_ids
  								SCREAMING_SNAKE_CASE_	      =       ids_tensor((self.batch_size, 3)							,							config.vocab_size					)
  								SCREAMING_SNAKE_CASE_	      =       tf.cast(ids_tensor((self.batch_size, 3)							,							2					)							,							tf.inta					)
  
  								# append to next input_ids and
  								SCREAMING_SNAKE_CASE_	      =       tf.concat([input_ids, next_tokens]							,							axis=-1					)
  								SCREAMING_SNAKE_CASE_	      =       tf.concat([attention_mask, next_attn_mask]							,							axis=-1					)
  
  								SCREAMING_SNAKE_CASE_	      =       model(lowercase_							,							attention_mask=lowercase_					)[0]
  								SCREAMING_SNAKE_CASE_	      =       model(lowercase_							,							attention_mask=lowercase_							,							past_key_values=lowercase_					)[0]
  
  								self.parent.assertEqual(next_tokens.shape[1]							,							output_from_past.shape[1]					)
  
  								# select random slice
  								SCREAMING_SNAKE_CASE_	      =       int(ids_tensor((1,)							,							output_from_past.shape[-1]					)					)
  								SCREAMING_SNAKE_CASE_	      =       output_from_no_past[:, -3:, random_slice_idx]
  								SCREAMING_SNAKE_CASE_	      =       output_from_past[:, :, random_slice_idx]
  
  								# test that outputs are equal for slice
  								tf.debugging.assert_near(lowercase_							,							lowercase_							,							rtol=1e-3					)
  
  
  
  
  
  
  
  def 							UpperCAmelCase				(     UpperCAmelCase				,UpperCAmelCase				,UpperCAmelCase				,UpperCAmelCase=None				,UpperCAmelCase=None				,UpperCAmelCase=None				,UpperCAmelCase=None				,UpperCAmelCase=None				,)->		Optional[int]:
  
  
  
  
  
  
  
  					'''simple docstring'''
  
  
  
  					if attention_mask is None:
  										SCREAMING_SNAKE_CASE_	      =       tf.cast(tf.math.not_equal(lowercase_				,config.pad_token_id      )				,tf.inta      )
  					if decoder_attention_mask is None:
  										SCREAMING_SNAKE_CASE_	      =       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_	      =       tf.ones((config.encoder_layers, config.encoder_attention_heads)      )
  					if decoder_head_mask is None:
  										SCREAMING_SNAKE_CASE_	      =       tf.ones((config.decoder_layers, config.decoder_attention_heads)      )
  					if cross_attn_head_mask is None:
  										SCREAMING_SNAKE_CASE_	      =       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 						snake_case			(       __UpperCAmelCase	, __UpperCAmelCase	, unittest.TestCase	):
  			'''simple docstring'''
  
  			UpperCAmelCase       :       Optional[Any]       = (TFMarianMTModel, TFMarianModel) if is_tf_available() else ()
  			UpperCAmelCase       :       Optional[Any]       = (TFMarianMTModel,) if is_tf_available() else ()
  			UpperCAmelCase       :       str       = (
  			    {
  			        """conversational""": TFMarianMTModel,
  			        """feature-extraction""": TFMarianModel,
  			        """summarization""": TFMarianMTModel,
  			        """text2text-generation""": TFMarianMTModel,
  			        """translation""": TFMarianMTModel,
  			    }
  			    if is_tf_available()
  			    else {}
  			)
  			UpperCAmelCase       :       List[str]       = True
  			UpperCAmelCase       :       str       = False
  			UpperCAmelCase       :       Union[str, Any]       = False
  
  			def _lowercase (	self     :     int					)					->					List[str]:
  
  
  
  
  								"""simple docstring"""
  
  
  
  
  
  
  								SCREAMING_SNAKE_CASE_	      =       TFMarianModelTester(self					)
  								SCREAMING_SNAKE_CASE_	      =       ConfigTester(self							,							config_class=lowercase_					)
  
  			def _lowercase (	self     :     Optional[int]					)					->					Any:
  
  
  
  
  								"""simple docstring"""
  
  
  
  
  
  
  								self.config_tester.run_common_tests()
  
  
  
  
  			def _lowercase (	self     :     List[Any]					)					->					Any:
  
  
  
  
  								"""simple docstring"""
  
  
  
  
  
  
  								SCREAMING_SNAKE_CASE_	      =       self.model_tester.prepare_config_and_inputs_for_common()
  								self.model_tester.check_decoder_model_past_large_inputs(*lowercase_					)
  
  @require_tf
  class 						snake_case			(       unittest.TestCase	):
  			'''simple docstring'''
  
  			UpperCAmelCase       :       str       = 1000  # show more chars for failing integration tests
  
  			@classmethod
  			def _lowercase (	cls     :     Any					)					->					None:
  
  
  
  
  								"""simple docstring"""
  
  
  
  
  
  
  								SCREAMING_SNAKE_CASE_	      =       F'''Helsinki-NLP/opus-mt-{cls.src}-{cls.tgt}'''
  								return cls
  
  			@cached_property
  			def _lowercase (	self     :     List[Any]					)					->					MarianTokenizer:
  
  
  
  
  								"""simple docstring"""
  
  
  
  
  
  
  								return AutoTokenizer.from_pretrained(self.model_name					)
  
  			@property
  			def _lowercase (	self     :     Dict					)					->					int:
  
  
  
  
  								"""simple docstring"""
  
  
  
  
  
  
  								return self.tokenizer.eos_token_id
  
  			@cached_property
  			def _lowercase (	self     :     int					)					->					List[str]:
  
  
  
  
  								"""simple docstring"""
  
  
  
  
  
  
  								warnings.simplefilter('''error'''					)
  								SCREAMING_SNAKE_CASE_	      =       TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name					)
  								assert isinstance(lowercase_							,							lowercase_					)
  								SCREAMING_SNAKE_CASE_	      =       model.config
  								self.assertListEqual(c.bad_words_ids							,							[[c.pad_token_id]]					)
  								self.assertEqual(c.max_length							,							512					)
  								self.assertEqual(c.decoder_start_token_id							,							c.pad_token_id					)
  								return model
  
  			def _lowercase (	self     :     Optional[Any]							,							**lowerCAmelCase_     :     Optional[Any]					)					->					List[Any]:
  
  
  
  
  								"""simple docstring"""
  
  
  
  
  
  
  								SCREAMING_SNAKE_CASE_	      =       self.translate_src_text(**lowercase_					)
  								self.assertListEqual(self.expected_text							,							lowercase_					)
  
  
  
  
  			def _lowercase (	self     :     List[Any]							,							**lowerCAmelCase_     :     Optional[Any]					)					->					int:
  
  
  
  
  								"""simple docstring"""
  
  
  
  
  
  
  								SCREAMING_SNAKE_CASE_	      =       self.tokenizer(self.src_text							,							**lowercase_							,							padding=lowercase_							,							return_tensors='''tf'''					)
  								SCREAMING_SNAKE_CASE_	      =       self.model.generate(
  								    model_inputs.input_ids							,							attention_mask=model_inputs.attention_mask							,							num_beams=2							,							max_length=128					)
  								SCREAMING_SNAKE_CASE_	      =       self.tokenizer.batch_decode(generated_ids.numpy()							,							skip_special_tokens=lowercase_					)
  								return generated_words
  
  @require_sentencepiece
  @require_tokenizers
  @require_tf
  class 						snake_case			(       __UpperCAmelCase	):
  			'''simple docstring'''
  
  			UpperCAmelCase       :       Dict       = """mt"""
  			UpperCAmelCase       :       Optional[Any]       = """en"""
  			UpperCAmelCase       :       int       = ["""Billi messu b'mod ġentili, Ġesù fejjaq raġel li kien milqut bil - marda kerha tal - ġdiem."""]
  			UpperCAmelCase       :       List[str]       = ["""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     :     str					)					->					str:
  
  
  
  
  								"""simple docstring"""
  
  
  
  
  
  
  								self._assert_generated_batch_equal_expected()
  
  @require_sentencepiece
  @require_tokenizers
  @require_tf
  class 						snake_case			(       __UpperCAmelCase	):
  			'''simple docstring'''
  
  			UpperCAmelCase       :       List[str]       = """en"""
  			UpperCAmelCase       :       Union[str, Any]       = """zh"""
  			UpperCAmelCase       :       int       = ["""My name is Wolfgang and I live in Berlin"""]
  			UpperCAmelCase       :       Any       = ["""我叫沃尔夫冈 我住在柏林"""]
  
  			@unittest.skip('''Skipping until #12647 is resolved.'''					)
  			@slow
  			def _lowercase (	self     :     Union[str, Any]					)					->					Any:
  
  
  
  
  								"""simple docstring"""
  
  
  
  
  
  
  								self._assert_generated_batch_equal_expected()
  
  @require_sentencepiece
  @require_tokenizers
  @require_tf
  class 						snake_case			(       __UpperCAmelCase	):
  			'''simple docstring'''
  
  			UpperCAmelCase       :       int       = """en"""
  			UpperCAmelCase       :       Optional[Any]       = """ROMANCE"""
  			UpperCAmelCase       :       Optional[int]       = [
  			    """>>fr<< Don't spend so much time watching TV.""",
  			    """>>pt<< Your message has been sent.""",
  			    """>>es<< He's two years older than me.""",
  			]
  			UpperCAmelCase       :       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     :     Optional[Any]					)					->					Union[str, Any]:
  
  
  
  
  								"""simple docstring"""
  
  
  
  
  
  
  								self._assert_generated_batch_equal_expected()
  
  
  
  
  			@unittest.skip('''Skipping until #12647 is resolved.'''					)
  			@slow
  			def _lowercase (	self     :     str					)					->					Tuple:
  
  
  
  
  								"""simple docstring"""
  
  
  
  
  
  
  								SCREAMING_SNAKE_CASE_	      =       TranslationPipeline(self.model							,							self.tokenizer							,							framework='''tf'''					)
  								SCREAMING_SNAKE_CASE_	      =       pipeline(self.src_text					)
  								self.assertEqual(self.expected_text							,							[x['''translation_text'''] for x in output]					)
  
  
  
style_context: 
  
  
  from __future__ import annotations
  
  class 						snake_case			:
  			'''simple docstring'''
  
  			def __init__(	self     :     int							,							lowerCAmelCase_     :     int					)					->					None:
  
  
  
  
  								"""simple docstring"""
  
  
  
  
  
  
  								SCREAMING_SNAKE_CASE_	      =       value
  								SCREAMING_SNAKE_CASE_	      =       None
  								SCREAMING_SNAKE_CASE_	      =       None
  
  class 						snake_case			:
  			'''simple docstring'''
  
  			UpperCAmelCase       :       int
  
  			def __init__(	self     :     Union[str, Any]					)					->					None:
  
  
  
  
  								"""simple docstring"""
  
  
  
  
  
  
  								SCREAMING_SNAKE_CASE_	      =       0
  
  			def _lowercase (	self     :     Optional[int]							,							lowerCAmelCase_     :     Node | None							,							lowerCAmelCase_     :     int					)					->					None:
  
  
  
  
  								"""simple docstring"""
  
  
  
  
  
  
  								if node is None:
  													return
  
  								if path_sum == self.target:
  													self.paths += 1
  
  								if node.left:
  													self.depth_first_search(node.left							,							path_sum + node.left.value					)
  								if node.right:
  													self.depth_first_search(node.right							,							path_sum + node.right.value					)
  
  
  
  
  			def _lowercase (	self     :     str							,							lowerCAmelCase_     :     Node | None							,							lowerCAmelCase_     :     int | None = None					)					->					int:
  
  
  
  
  								"""simple docstring"""
  
  
  
  
  
  
  								if node is None:
  													return 0
  								if target is not None:
  													SCREAMING_SNAKE_CASE_	      =       target
  
  								self.depth_first_search(lowerCAmelCase_							,							node.value					)
  								self.path_sum(node.left					)
  								self.path_sum(node.right					)
  
  								return self.paths
  
  
  if __name__ == "__main__":
  		import doctest
  
  		doctest.testmod()
  
  
  