



'''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       lowerCamelCase_					:
  """simple docstring"""




  a_							      =MarianConfig
  a_							      ={}
  a_							      ='''gelu'''




  def __init__(				self   :			Tuple    ,			_a   :			int    ,			_a   :			str=13    ,			_a   :			int=7    ,			_a   :			Union[str, Any]=True    ,			_a   :			Union[str, Any]=False    ,			_a   :			Dict=99    ,			_a   :			Union[str, Any]=32    ,			_a   :			List[str]=2    ,			_a   :			str=4    ,			_a   :			Optional[int]=37    ,			_a   :			Any=0.1    ,			_a   :			Optional[int]=0.1    ,			_a   :			List[Any]=20    ,			_a   :			Any=2    ,			_a   :			Union[str, Any]=1    ,			_a   :			Tuple=0    ,			)      ->      Dict:
    __lowerCamelCase : Union[str, Any]					 =				parent
    __lowerCamelCase : Dict					 =				batch_size
    __lowerCamelCase : Tuple					 =				seq_length
    __lowerCamelCase : Any					 =				is_training
    __lowerCamelCase : Optional[int]					 =				use_labels
    __lowerCamelCase : Optional[int]					 =				vocab_size
    __lowerCamelCase : Union[str, Any]					 =				hidden_size
    __lowerCamelCase : Optional[int]					 =				num_hidden_layers
    __lowerCamelCase : Tuple					 =				num_attention_heads
    __lowerCamelCase : str					 =				intermediate_size

    __lowerCamelCase : Dict					 =				hidden_dropout_prob
    __lowerCamelCase : Optional[Any]					 =				attention_probs_dropout_prob
    __lowerCamelCase : Tuple					 =				max_position_embeddings
    __lowerCamelCase : str					 =				eos_token_id
    __lowerCamelCase : Optional[Any]					 =				pad_token_id
    __lowerCamelCase : List[str]					 =				bos_token_id




  def 		_lowercase  (				self   :			Optional[Any]     )      ->      Any:
    __lowerCamelCase : List[Any]					 =				ids_tensor([self.batch_size, self.seq_length - 1]    ,			self.vocab_size     )
    __lowerCamelCase : List[Any]					 =				tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size     )    ,			1     )
    __lowerCamelCase : int					 =				tf.concat([input_ids, eos_tensor]    ,			axis=1     )

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

    __lowerCamelCase : 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    ,			)
    __lowerCamelCase : Optional[Any]					 =				prepare_marian_inputs_dict(_a    ,			_a    ,			_a     )
    return config, inputs_dict




  def 		_lowercase  (				self   :			int    ,			_a   :			Optional[int]    ,			_a   :			str     )      ->      Union[str, Any]:
    __lowerCamelCase : str					 =				TFMarianModel(config=_a     ).get_decoder()
    __lowerCamelCase : Any					 =				inputs_dict['input_ids']

    __lowerCamelCase : int					 =				input_ids[:1, :]
    __lowerCamelCase : List[str]					 =				inputs_dict['attention_mask'][:1, :]
    __lowerCamelCase : Tuple					 =				inputs_dict['head_mask']
    __lowerCamelCase : Dict					 =				1

    # first forward pass
    __lowerCamelCase : str					 =				model(_a    ,			attention_mask=_a    ,			head_mask=_a    ,			use_cache=_a     )

    __lowerCamelCase ,__lowerCamelCase : List[str]					 =				outputs.to_tuple()

    # create hypothetical next token and extent to next_input_ids
    __lowerCamelCase : List[str]					 =				ids_tensor((self.batch_size, 3)    ,			config.vocab_size     )
    __lowerCamelCase : Optional[Any]					 =				tf.cast(ids_tensor((self.batch_size, 3)    ,			2     )    ,			tf.inta     )

    # append to next input_ids and
    __lowerCamelCase : Any					 =				tf.concat([input_ids, next_tokens]    ,			axis=-1     )
    __lowerCamelCase : List[Any]					 =				tf.concat([attention_mask, next_attn_mask]    ,			axis=-1     )

    __lowerCamelCase : Union[str, Any]					 =				model(_a    ,			attention_mask=_a     )[0]
    __lowerCamelCase : List[Any]					 =				model(_a    ,			attention_mask=_a    ,			past_key_values=_a     )[0]

    self.parent.assertEqual(next_tokens.shape[1]    ,			output_from_past.shape[1]     )

    # select random slice
    __lowerCamelCase : Union[str, Any]					 =				int(ids_tensor((1,)    ,			output_from_past.shape[-1]     )     )
    __lowerCamelCase : Dict					 =				output_from_no_past[:, -3:, random_slice_idx]
    __lowerCamelCase : int					 =				output_from_past[:, :, random_slice_idx]

    # test that outputs are equal for slice
    tf.debugging.assert_near(_a    ,			_a    ,			rtol=1e-3     )




def  a_  (							_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase=None ,_lowerCAmelCase=None ,_lowerCAmelCase=None ,_lowerCAmelCase=None ,_lowerCAmelCase=None ,)   ->   Any:

  '''simple docstring'''





  if attention_mask is None:
    __lowerCamelCase : int					 =				tf.cast(tf.math.not_equal(lowercase__ ,config.pad_token_id ) ,tf.inta )
  if decoder_attention_mask is None:
    __lowerCamelCase : Dict					 =				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:
    __lowerCamelCase : Dict					 =				tf.ones((config.encoder_layers, config.encoder_attention_heads) )
  if decoder_head_mask is None:
    __lowerCamelCase : str					 =				tf.ones((config.decoder_layers, config.decoder_attention_heads) )
  if cross_attn_head_mask is None:
    __lowerCamelCase : 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       lowerCamelCase_					(       A_     ,     A_     ,     unittest.TestCase  ):
  """simple docstring"""




  a_							      =(TFMarianMTModel, TFMarianModel) if is_tf_available() else ()
  a_							      =(TFMarianMTModel,) if is_tf_available() else ()
  a_							      =(
      {
          '''conversational''': TFMarianMTModel,
          '''feature-extraction''': TFMarianModel,
          '''summarization''': TFMarianMTModel,
          '''text2text-generation''': TFMarianMTModel,
          '''translation''': TFMarianMTModel,
      }
      if is_tf_available()
      else {}
  )
  a_							      =True
  a_							      =False
  a_							      =False




  def 		_lowercase  (				self   :			str     )      ->      Optional[Any]:
    __lowerCamelCase : List[Any]					 =				TFMarianModelTester(self     )
    __lowerCamelCase : int					 =				ConfigTester(self    ,			config_class=_a     )




  def 		_lowercase  (				self   :			Union[str, Any]     )      ->      Union[str, Any]:
    self.config_tester.run_common_tests()




  def 		_lowercase  (				self   :			Optional[Any]     )      ->      int:
    __lowerCamelCase : str					 =				self.model_tester.prepare_config_and_inputs_for_common()
    self.model_tester.check_decoder_model_past_large_inputs(*_a     )






@require_tf
class       lowerCamelCase_					(       unittest.TestCase  ):
  """simple docstring"""




  a_							      =1000  # show more chars for failing integration tests




  @classmethod
  def 		_lowercase  (				cls   :			str     )      ->      List[str]:
    __lowerCamelCase : Dict					 =				f'Helsinki-NLP/opus-mt-{cls.src}-{cls.tgt}'
    return cls




  @cached_property
  def 		_lowercase  (				self   :			str     )      ->      int:
    return AutoTokenizer.from_pretrained(self.model_name     )




  @property
  def 		_lowercase  (				self   :			Dict     )      ->      str:
    return self.tokenizer.eos_token_id




  @cached_property
  def 		_lowercase  (				self   :			List[Any]     )      ->      Any:
    warnings.simplefilter('error'     )
    __lowerCamelCase : Any					 =				TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name     )
    assert isinstance(_a    ,			_a     )
    __lowerCamelCase : Union[str, Any]					 =				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   :			int    ,			**_a   :			Optional[Any]     )      ->      List[str]:
    __lowerCamelCase : Any					 =				self.translate_src_text(**_a     )
    self.assertListEqual(self.expected_text    ,			_a     )




  def 		_lowercase  (				self   :			Optional[int]    ,			**_a   :			List[Any]     )      ->      str:
    __lowerCamelCase : Dict					 =				self.tokenizer(self.src_text    ,			**_a    ,			padding=_a    ,			return_tensors='tf'     )
    __lowerCamelCase : str					 =				self.model.generate(
        model_inputs.input_ids    ,			attention_mask=model_inputs.attention_mask    ,			num_beams=2    ,			max_length=128     )
    __lowerCamelCase : List[str]					 =				self.tokenizer.batch_decode(generated_ids.numpy()    ,			skip_special_tokens=_a     )
    return generated_words






@require_sentencepiece
@require_tokenizers
@require_tf
class       lowerCamelCase_					(       A_  ):
  """simple docstring"""




  a_							      ='''mt'''
  a_							      ='''en'''
  a_							      =['''Billi messu b\'mod ġentili, Ġesù fejjaq raġel li kien milqut bil - marda kerha tal - ġdiem.''']
  a_							      =['''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   :			Dict     )      ->      Union[str, Any]:
    self._assert_generated_batch_equal_expected()






@require_sentencepiece
@require_tokenizers
@require_tf
class       lowerCamelCase_					(       A_  ):
  """simple docstring"""




  a_							      ='''en'''
  a_							      ='''zh'''
  a_							      =['''My name is Wolfgang and I live in Berlin''']
  a_							      =['''我叫沃尔夫冈 我住在柏林''']




  @unittest.skip('Skipping until #12647 is resolved.'     )
  @slow
  def 		_lowercase  (				self   :			List[str]     )      ->      Union[str, Any]:
    self._assert_generated_batch_equal_expected()






@require_sentencepiece
@require_tokenizers
@require_tf
class       lowerCamelCase_					(       A_  ):
  """simple docstring"""




  a_							      ='''en'''
  a_							      ='''ROMANCE'''
  a_							      =[
      '''>>fr<< Don\'t spend so much time watching TV.''',
      '''>>pt<< Your message has been sent.''',
      '''>>es<< He\'s two years older than me.''',
  ]
  a_							      =[
      '''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   :			Union[str, Any]     )      ->      Union[str, Any]:
    self._assert_generated_batch_equal_expected()




  @unittest.skip('Skipping until #12647 is resolved.'     )
  @slow
  def 		_lowercase  (				self   :			str     )      ->      int:
    __lowerCamelCase : Optional[Any]					 =				TranslationPipeline(self.model    ,			self.tokenizer    ,			framework='tf'     )
    __lowerCamelCase : Any					 =				pipeline(self.src_text     )
    self.assertEqual(self.expected_text    ,			[x['translation_text'] for x in output]     )











'''simple docstring'''





from __future__ import annotations

from abc import abstractmethod
from collections.abc import Iterable
from typing import Generic, Protocol, TypeVar






class       lowerCamelCase_					(       SCREAMING_SNAKE_CASE_  ):
  """simple docstring"""




  @abstractmethod
  def __lt__(				self   :			T    ,			_a   :			T     )      ->      bool:
    pass




  @abstractmethod
  def __gt__(				self   :			T    ,			_a   :			T     )      ->      bool:
    pass




  @abstractmethod
  def __eq__(				self   :			T    ,			_a   :			object     )      ->      bool:
    pass




_UpperCamelCase							       =		TypeVar('T', bound=Comparable)






class       lowerCamelCase_					(       Generic[T]  ):
  """simple docstring"""




  def __init__(				self   :			List[str]     )      ->      None:
    __lowerCamelCase : list[T]					 =				[]
    __lowerCamelCase : int					 =				0




  def __repr__(				self   :			int     )      ->      str:
    return str(self.h     )




  def 		_lowercase  (				self   :			str    ,			_a   :			int     )      ->      int | None:
    if child_idx > 0:
      return (child_idx - 1) // 2
    return None




  def 		_lowercase  (				self   :			Tuple    ,			_a   :			int     )      ->      int | None:
    __lowerCamelCase : Optional[Any]					 =				2 * parent_idx + 1
    if left_child_index < self.heap_size:
      return left_child_index
    return None




  def 		_lowercase  (				self   :			Tuple    ,			_a   :			int     )      ->      int | None:
    __lowerCamelCase : Any					 =				2 * parent_idx + 2
    if right_child_index < self.heap_size:
      return right_child_index
    return None




  def 		_lowercase  (				self   :			int    ,			_a   :			int     )      ->      None:
    if index < self.heap_size:
      __lowerCamelCase : int					 =				index
      __lowerCamelCase : Dict					 =				self.left_child_idx(_a     )
      __lowerCamelCase : str					 =				self.right_child_idx(_a     )
      # check which child is larger than its parent
      if left_child is not None and self.h[left_child] > self.h[violation]:
        __lowerCamelCase : Optional[int]					 =				left_child
      if right_child is not None and self.h[right_child] > self.h[violation]:
        __lowerCamelCase : Union[str, Any]					 =				right_child
      # if violation indeed exists
      if violation != index:
        # swap to fix the violation
        __lowerCamelCase ,__lowerCamelCase : int					 =				self.h[index], self.h[violation]
        # fix the subsequent violation recursively if any
        self.max_heapify(_a     )




  def 		_lowercase  (				self   :			int    ,			_a   :			Iterable[T]     )      ->      None:
    __lowerCamelCase : Dict					 =				list(_a     )
    __lowerCamelCase : Any					 =				len(self.h     )
    if self.heap_size > 1:
      # max_heapify from right to left but exclude leaves (last level)
      for i in range(self.heap_size // 2 - 1    ,			-1    ,			-1     ):
        self.max_heapify(_a     )




  def 		_lowercase  (				self   :			int     )      ->      T:
    if self.heap_size >= 2:
      __lowerCamelCase : Any					 =				self.h[0]
      __lowerCamelCase : Dict					 =				self.h.pop(-1     )
      self.heap_size -= 1
      self.max_heapify(0     )
      return me
    elif self.heap_size == 1:
      self.heap_size -= 1
      return self.h.pop(-1     )
    else:
      raise Exception('Empty heap'     )




  def 		_lowercase  (				self   :			Optional[Any]    ,			_a   :			T     )      ->      None:
    self.h.append(_a     )
    __lowerCamelCase : List[Any]					 =				(self.heap_size - 1) // 2
    self.heap_size += 1
    while idx >= 0:
      self.max_heapify(_a     )
      __lowerCamelCase : Union[str, Any]					 =				(idx - 1) // 2




  def 		_lowercase  (				self   :			Tuple     )      ->      None:
    __lowerCamelCase : Optional[Any]					 =				self.heap_size
    for j in range(size - 1    ,			0    ,			-1     ):
      __lowerCamelCase ,__lowerCamelCase : List[str]					 =				self.h[j], self.h[0]
      self.heap_size -= 1
      self.max_heapify(0     )
    __lowerCamelCase : Tuple					 =				size


if __name__ == "__main__":
   import doctest

   # run doc test
   doctest.testmod()

   # demo
   for unsorted in [
       [0],
       [2],
       [3, 5],
       [5, 3],
       [5, 5],
       [0, 0, 0, 0],
       [1, 1, 1, 1],
       [2, 2, 3, 5],
       [0, 2, 2, 3, 5],
       [2, 5, 3, 0, 2, 3, 0, 3],
       [6, 1, 2, 7, 9, 3, 4, 5, 10, 8],
       [103, 9, 1, 7, 11, 15, 25, 201, 209, 107, 5],
       [-45, -2, -5],
   ]:
      print(f'''unsorted array: {unsorted}''')

      _UpperCamelCase							       =		Heap()
      heap.build_max_heap(unsorted)
      print(f'''after build heap: {heap}''')

      print(f'''max value: {heap.extract_max()}''')
      print(f'''after max value removed: {heap}''')

      heap.insert(100)
      print(f'''after new value 100 inserted: {heap}''')

      heap.heap_sort()
      print(f'''heap-sorted array: {heap}\n''')






