code: 
  
  
  """simple docstring"""
  
  
  
  
  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"""
  
  
  
  
     lowerCAmelCase_				:   Union[str, Any]     	=			MarianConfig
     lowerCAmelCase_				:   Dict     	=			{}
     lowerCAmelCase_				:   int     	=			'gelu'
  
  
  
  
     def __init__(		self							:Union[str, Any]				,  UpperCamelCase__							:Tuple				,  UpperCamelCase__							:str=13				,  UpperCamelCase__							:List[Any]=7				,  UpperCamelCase__							:Tuple=True				,  UpperCamelCase__							:Dict=False				,  UpperCamelCase__							:int=99				,  UpperCamelCase__							:str=32				,  UpperCamelCase__							:int=2				,  UpperCamelCase__							:Union[str, Any]=4				,  UpperCamelCase__							:Tuple=37				,  UpperCamelCase__							:Dict=0.1				,  UpperCamelCase__							:List[Any]=0.1				,  UpperCamelCase__							:List[Any]=20				,  UpperCamelCase__							:int=2				,  UpperCamelCase__							:Tuple=1				,  UpperCamelCase__							:Any=0				,  ):
            _a          =				parent
            _a          =				batch_size
            _a          =				seq_length
            _a          =				is_training
            _a          =				use_labels
            _a          =				vocab_size
            _a          =				hidden_size
            _a          =				num_hidden_layers
            _a          =				num_attention_heads
            _a          =				intermediate_size
  
            _a          =				hidden_dropout_prob
            _a          =				attention_probs_dropout_prob
            _a          =				max_position_embeddings
            _a          =				eos_token_id
            _a          =				pad_token_id
            _a          =				bos_token_id
  
  
  
  
     def SCREAMING_SNAKE_CASE_      (		self							:Optional[int]       ):
            _a          =				ids_tensor([self.batch_size, self.seq_length - 1]				,  self.vocab_size       )
            _a          =				tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size       )				,  1       )
            _a          =				tf.concat([input_ids, eos_tensor]				,  axis=1       )
  
            _a          =				ids_tensor([self.batch_size, self.seq_length]				,  self.vocab_size       )
  
            _a          =				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				,  )
            _a          =				prepare_marian_inputs_dict(UpperCamelCase__				,  UpperCamelCase__				,  UpperCamelCase__       )
            return config, inputs_dict
  
  
  
  
     def SCREAMING_SNAKE_CASE_      (		self							:str				,  UpperCamelCase__							:int				,  UpperCamelCase__							:Union[str, Any]       ):
            _a          =				TFMarianModel(config=UpperCamelCase__       ).get_decoder()
            _a          =				inputs_dict["input_ids"]
  
            _a          =				input_ids[:1, :]
            _a          =				inputs_dict["attention_mask"][:1, :]
            _a          =				inputs_dict["head_mask"]
            _a          =				1
  
            # first forward pass
            _a          =				model(UpperCamelCase__				,  attention_mask=UpperCamelCase__				,  head_mask=UpperCamelCase__				,  use_cache=UpperCamelCase__       )
  
            _a   , _a          =				outputs.to_tuple()
  
            # create hypothetical next token and extent to next_input_ids
            _a          =				ids_tensor((self.batch_size, 3)				,  config.vocab_size       )
            _a          =				tf.cast(ids_tensor((self.batch_size, 3)				,  2       )				,  tf.inta       )
  
            # append to next input_ids and
            _a          =				tf.concat([input_ids, next_tokens]				,  axis=-1       )
            _a          =				tf.concat([attention_mask, next_attn_mask]				,  axis=-1       )
  
            _a          =				model(UpperCamelCase__				,  attention_mask=UpperCamelCase__       )[0]
            _a          =				model(UpperCamelCase__				,  attention_mask=UpperCamelCase__				,  past_key_values=UpperCamelCase__       )[0]
  
            self.parent.assertEqual(next_tokens.shape[1]				,  output_from_past.shape[1]       )
  
            # select random slice
            _a          =				int(ids_tensor((1,)				,  output_from_past.shape[-1]       )       )
            _a          =				output_from_no_past[:, -3:, random_slice_idx]
            _a          =				output_from_past[:, :, random_slice_idx]
  
            # test that outputs are equal for slice
            tf.debugging.assert_near(UpperCamelCase__				,  UpperCamelCase__				,  rtol=1E-3       )
  
  
  
  
  
  
  def 						__a      (  a,		a,		a,		a=None,		a=None,		a=None,		a=None,		a=None,		):
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
  
         if attention_mask is None:
                _a          =				tf.cast(tf.math.not_equal(a,		config.pad_token_id					),		tf.inta					)
         if decoder_attention_mask is None:
                _a          =				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:
                _a          =				tf.ones((config.encoder_layers, config.encoder_attention_heads)					)
         if decoder_head_mask is None:
                _a          =				tf.ones((config.decoder_layers, config.decoder_attention_heads)					)
         if cross_attn_head_mask is None:
                _a          =				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		(					_SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       unittest.TestCase      ):
  
  
  
  
  
  
  
     """simple docstring"""
  
  
  
  
     lowerCAmelCase_				:   Any     	=			(TFMarianMTModel, TFMarianModel) if is_tf_available() else ()
     lowerCAmelCase_				:   Tuple     	=			(TFMarianMTModel,) if is_tf_available() else ()
     lowerCAmelCase_				:   Tuple     	=			(
         {
             'conversational': TFMarianMTModel,
             'feature-extraction': TFMarianModel,
             'summarization': TFMarianMTModel,
             'text2text-generation': TFMarianMTModel,
             'translation': TFMarianMTModel,
         }
         if is_tf_available()
         else {}
     )
     lowerCAmelCase_				:   int     	=			True
     lowerCAmelCase_				:   List[str]     	=			False
     lowerCAmelCase_				:   Union[str, Any]     	=			False
  
  
  
  
     def SCREAMING_SNAKE_CASE_      (		self							:int       ):
            _a          =				TFMarianModelTester(self       )
            _a          =				ConfigTester(self				,  config_class=UpperCamelCase__       )
  
  
  
  
     def SCREAMING_SNAKE_CASE_      (		self							:Optional[int]       ):
            self.config_tester.run_common_tests()
  
  
  
  
     def SCREAMING_SNAKE_CASE_      (		self							:Tuple       ):
            _a          =				self.model_tester.prepare_config_and_inputs_for_common()
            self.model_tester.check_decoder_model_past_large_inputs(*UpperCamelCase__       )
  
  
  
  
  
  
  @require_tf
  class __snake_case		(					unittest.TestCase      ):
  
  
  
  
  
  
  
     """simple docstring"""
  
  
  
  
     lowerCAmelCase_				:   List[Any]     	=			1_000  # show more chars for failing integration tests
  
  
  
  
     @classmethod
     def SCREAMING_SNAKE_CASE_      (		cls							:str       ):
            _a          =				f'Helsinki-NLP/opus-mt-{cls.src}-{cls.tgt}'
            return cls
  
  
  
  
     @cached_property
     def SCREAMING_SNAKE_CASE_      (		self							:Union[str, Any]       ):
            return AutoTokenizer.from_pretrained(self.model_name       )
  
  
  
  
     @property
     def SCREAMING_SNAKE_CASE_      (		self							:List[str]       ):
            return self.tokenizer.eos_token_id
  
  
  
  
     @cached_property
     def SCREAMING_SNAKE_CASE_      (		self							:Any       ):
            warnings.simplefilter("error"       )
            _a          =				TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name       )
            assert isinstance(UpperCamelCase__				,  UpperCamelCase__       )
            _a          =				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 SCREAMING_SNAKE_CASE_      (		self							:Optional[Any]				,  **UpperCamelCase__							:List[str]       ):
            _a          =				self.translate_src_text(**UpperCamelCase__       )
            self.assertListEqual(self.expected_text				,  UpperCamelCase__       )
  
  
  
  
     def SCREAMING_SNAKE_CASE_      (		self							:Any				,  **UpperCamelCase__							:List[str]       ):
            _a          =				self.tokenizer(self.src_text				,  **UpperCamelCase__				,  padding=UpperCamelCase__				,  return_tensors="tf"       )
            _a          =				self.model.generate(
                model_inputs.input_ids				,  attention_mask=model_inputs.attention_mask				,  num_beams=2				,  max_length=128       )
            _a          =				self.tokenizer.batch_decode(generated_ids.numpy()				,  skip_special_tokens=UpperCamelCase__       )
            return generated_words
  
  
  
  
  
  
  @require_sentencepiece
  @require_tokenizers
  @require_tf
  class __snake_case		(					_SCREAMING_SNAKE_CASE      ):
  
  
  
  
  
  
  
     """simple docstring"""
  
  
  
  
     lowerCAmelCase_				:   str     	=			'mt'
     lowerCAmelCase_				:   List[str]     	=			'en'
     lowerCAmelCase_				:   List[str]     	=			['Billi messu b\'mod ġentili, Ġesù fejjaq raġel li kien milqut bil - marda kerha tal - ġdiem.']
     lowerCAmelCase_				:   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 SCREAMING_SNAKE_CASE_      (		self							:List[str]       ):
            self._assert_generated_batch_equal_expected()
  
  
  
  
  
  
  @require_sentencepiece
  @require_tokenizers
  @require_tf
  class __snake_case		(					_SCREAMING_SNAKE_CASE      ):
  
  
  
  
  
  
  
     """simple docstring"""
  
  
  
  
     lowerCAmelCase_				:   str     	=			'en'
     lowerCAmelCase_				:   Optional[Any]     	=			'zh'
     lowerCAmelCase_				:   List[str]     	=			['My name is Wolfgang and I live in Berlin']
     lowerCAmelCase_				:   str     	=			['我叫沃尔夫冈 我住在柏林']
  
  
  
  
     @unittest.skip("Skipping until #12647 is resolved."       )
     @slow
     def SCREAMING_SNAKE_CASE_      (		self							:List[str]       ):
            self._assert_generated_batch_equal_expected()
  
  
  
  
  
  
  @require_sentencepiece
  @require_tokenizers
  @require_tf
  class __snake_case		(					_SCREAMING_SNAKE_CASE      ):
  
  
  
  
  
  
  
     """simple docstring"""
  
  
  
  
     lowerCAmelCase_				:   Optional[Any]     	=			'en'
     lowerCAmelCase_				:   str     	=			'ROMANCE'
     lowerCAmelCase_				:   List[Any]     	=			[
         '>>fr<< Don\'t spend so much time watching TV.',
         '>>pt<< Your message has been sent.',
         '>>es<< He\'s two years older than me.',
     ]
     lowerCAmelCase_				:   Tuple     	=			[
         '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 SCREAMING_SNAKE_CASE_      (		self							:Optional[Any]       ):
            self._assert_generated_batch_equal_expected()
  
  
  
  
     @unittest.skip("Skipping until #12647 is resolved."       )
     @slow
     def SCREAMING_SNAKE_CASE_      (		self							:Tuple       ):
            _a          =				TranslationPipeline(self.model				,  self.tokenizer				,  framework="tf"       )
            _a          =				pipeline(self.src_text       )
            self.assertEqual(self.expected_text				,  [x["translation_text"] for x in output]       )
  
style_context: 
  
  
  """simple docstring"""
  
  
  
  
  from __future__ import annotations
  
  __SCREAMING_SNAKE_CASE					=						[2, 1, 4, 5, 6, 0, 8, 9, 1, 2, 0, 6, 4, 2, 0, 6, 5, 3, 2, 7]
  
  
  
  
  
  class __snake_case		:
  
  
  
  
  
  
  
     """simple docstring"""
  
  
  
  
     def __init__(		self							:Optional[Any]				,  UpperCamelCase__							:int       ):
            _a          =				-1
            _a          =				-1
            _a          =				[-1] * length
            _a          =				None
            _a          =				None
  
  
  
  
     def __repr__(		self							:Optional[Any]       ):
            return f'Node(min_value={self.minn} max_value={self.maxx})'
  
  
  
  
  
  
  def 						__a      (  a					):
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
  
         _a          =				Node(len(a					)					)
         _a   , _a          =				min(a					), max(a					)
         # Leaf node case where the node contains only one unique value
         if root.minn == root.maxx:
                return root
         _a          =				(root.minn + root.maxx) // 2
  
         _a          =				[]
         _a          =				[]
  
         for index, num in enumerate(a					):
                if num <= pivot:
                       left_arr.append(a					)
                else:
                       right_arr.append(a					)
                _a          =				len(a					)
         _a          =				build_tree(a					)
         _a          =				build_tree(a					)
         return root
  
  
  
  def 						__a      (  a,		a,		a					):
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
  
         if index < 0 or node is None:
                return 0
         # Leaf node cases
         if node.minn == node.maxx:
                return index + 1 if node.minn == num else 0
         _a          =				(node.minn + node.maxx) // 2
         if num <= pivot:
                # go the left subtree and map index to the left subtree
                return rank_till_index(node.left,		a,		node.map_left[index] - 1					)
         else:
                # go to the right subtree and map index to the right subtree
                return rank_till_index(node.right,		a,		index - node.map_left[index]					)
  
  
  
  def 						__a      (  a,		a,		a,		a					):
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
  
         if start > end:
                return 0
         _a          =				rank_till_index(a,		a,		a					)
         _a          =				rank_till_index(a,		a,		start - 1					)
         return rank_till_end - rank_before_start
  
  
  
  def 						__a      (  a,		a,		a,		a					):
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
  
         if index > (end - start) or start > end or node is None:
                return -1
         # Leaf node case
         if node.minn == node.maxx:
                return node.minn
         # Number of elements in the left subtree in interval [start, end]
         _a          =				node.map_left[end] - (
             node.map_left[start - 1] if start else 0
         )
         if num_elements_in_left_tree > index:
                return quantile(
                    node.left,		a,		(node.map_left[start - 1] if start else 0),		node.map_left[end] - 1,		)
         else:
                return quantile(
                    node.right,		index - num_elements_in_left_tree,		start - (node.map_left[start - 1] if start else 0),		end - node.map_left[end],		)
  
  
  
  def 						__a      (  a,		a,		a,		a,		a					):
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
  
         if (
             start > end
             or node is None
             or start_num > end_num
             or node.minn > end_num
             or node.maxx < start_num
         ):
                return 0
         if start_num <= node.minn and node.maxx <= end_num:
                return end - start + 1
         _a          =				range_counting(
             node.left,		(node.map_left[start - 1] if start else 0),		node.map_left[end] - 1,		a,		a,		)
         _a          =				range_counting(
             node.right,		start - (node.map_left[start - 1] if start else 0),		end - node.map_left[end],		a,		a,		)
         return left + right
  
  
  if __name__ == "__main__":
     import doctest
  
     doctest.testmod()
  