

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'''



  __lowerCamelCase			:      List[Any]					=   MBartConfig
  __lowerCamelCase			:      Dict					=   {}
  __lowerCamelCase			:      str					=   '''gelu'''





  def __init__(  self   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_=13   ,SCREAMING_SNAKE_CASE_=7   ,SCREAMING_SNAKE_CASE_=True   ,SCREAMING_SNAKE_CASE_=False   ,SCREAMING_SNAKE_CASE_=99   ,SCREAMING_SNAKE_CASE_=32   ,SCREAMING_SNAKE_CASE_=2   ,SCREAMING_SNAKE_CASE_=4   ,SCREAMING_SNAKE_CASE_=37   ,SCREAMING_SNAKE_CASE_=0.1   ,SCREAMING_SNAKE_CASE_=0.1   ,SCREAMING_SNAKE_CASE_=20   ,SCREAMING_SNAKE_CASE_=2   ,SCREAMING_SNAKE_CASE_=1   ,SCREAMING_SNAKE_CASE_=0   ,):

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





  def    snake_case_       (  self    ):

      '''simple docstring'''
      snake_case							:		str        =		ids_tensor([self.batch_size, self.seq_length - 1]   ,self.vocab_size    )
      snake_case							:		Optional[Any]        =		tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size    )   ,1    )
      snake_case							:		Optional[Any]        =		tf.concat([input_ids, eos_tensor]   ,axis=1    )

      snake_case							:		int        =		ids_tensor([self.batch_size, self.seq_length]   ,self.vocab_size    )

      snake_case							:		int        =		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   ,)
      snake_case							:		Tuple        =		prepare_mbart_inputs_dict(_UpperCAmelCase   ,_UpperCAmelCase   ,_UpperCAmelCase    )
      return config, inputs_dict





  def    snake_case_       (  self   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_    ):

      '''simple docstring'''
      snake_case							:		Dict        =		TFMBartModel(config=_UpperCAmelCase    ).get_decoder()
      snake_case							:		int        =		inputs_dict["""input_ids"""]

      snake_case							:		Tuple        =		input_ids[:1, :]
      snake_case							:		List[str]        =		inputs_dict["""attention_mask"""][:1, :]
      snake_case							:		Union[str, Any]        =		inputs_dict["""head_mask"""]
      snake_case							:		Tuple        =		1

      # first forward pass
      snake_case							:		Union[str, Any]        =		model(_UpperCAmelCase   ,attention_mask=_UpperCAmelCase   ,head_mask=_UpperCAmelCase   ,use_cache=_UpperCAmelCase    )

      snake_case      ,		snake_case							:		str        =		outputs.to_tuple()
      snake_case							:		Tuple        =		past_key_values[1]







def       lowercase					(	__A :					Optional[int] ,    __A :					Tuple ,    __A :					str ,    __A :					int=None ,    __A :					List[Any]=None ,    __A :					str=None ,    __A :					str=None ,    __A :					Optional[Any]=None ,    )				->			List[str]:





    '''simple docstring'''




    if attention_mask is None:
        snake_case							:		int        =		tf.cast(tf.math.not_equal(__lowercase ,    config.pad_token_id		) ,    tf.inta		)
    if decoder_attention_mask is None:
        snake_case							:		Tuple        =		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:
        snake_case							:		Any        =		tf.ones((config.encoder_layers, config.encoder_attention_heads)		)
    if decoder_head_mask is None:
        snake_case							:		Dict        =		tf.ones((config.decoder_layers, config.decoder_attention_heads)		)
    if cross_attn_head_mask is None:
        snake_case							:		Optional[int]        =		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'''



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





  def    snake_case_       (  self   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_    ):

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

      return False





  def    snake_case_       (  self    ):

      '''simple docstring'''
      snake_case							:		Tuple        =		TFMBartModelTester(self    )
      snake_case							:		str        =		ConfigTester(self   ,config_class=_UpperCAmelCase    )





  def    snake_case_       (  self    ):

      '''simple docstring'''
      self.config_tester.run_common_tests()





  def    snake_case_       (  self    ):

      '''simple docstring'''
      snake_case							:		Tuple        =		self.model_tester.prepare_config_and_inputs_for_common()
      self.model_tester.check_decoder_model_past_large_inputs(*_UpperCAmelCase    )







@require_sentencepiece
@require_tokenizers
@require_tf
class 						_A     (				unittest.TestCase				):





  '''simple docstring'''



  __lowerCamelCase			:      Tuple					=   [
      ''' UN Chief Says There Is No Military Solution in Syria''',
  ]
  __lowerCamelCase			:      List[Any]					=   [
      '''Şeful ONU declară că nu există o soluţie militară în Siria''',
  ]
  __lowerCamelCase			:      Optional[Any]					=   '''facebook/mbart-large-en-ro'''





  @cached_property
  def    snake_case_       (  self    ):

      '''simple docstring'''
      return AutoTokenizer.from_pretrained(self.model_name    )





  @cached_property
  def    snake_case_       (  self    ):

      '''simple docstring'''
      snake_case							:		Optional[Any]        =		TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name    )
      return model





  def    snake_case_       (  self   ,**SCREAMING_SNAKE_CASE_    ):

      '''simple docstring'''
      snake_case							:		Any        =		self.translate_src_text(**_UpperCAmelCase    )
      self.assertListEqual(self.expected_text   ,_UpperCAmelCase    )





  def    snake_case_       (  self   ,**SCREAMING_SNAKE_CASE_    ):

      '''simple docstring'''
      snake_case							:		Optional[int]        =		self.tokenizer(self.src_text   ,**_UpperCAmelCase   ,return_tensors="""tf"""    )
      snake_case							:		Optional[int]        =		self.model.generate(
          model_inputs.input_ids   ,attention_mask=model_inputs.attention_mask   ,num_beams=2    )
      snake_case							:		Union[str, Any]        =		self.tokenizer.batch_decode(_UpperCAmelCase   ,skip_special_tokens=_UpperCAmelCase    )
      return generated_words





  @slow
  def    snake_case_       (  self    ):

      '''simple docstring'''
      self._assert_generated_batch_equal_expected()











from __future__ import annotations

from sys import maxsize
from typing import Generic, TypeVar

snake_case__   :						List[str]													=   TypeVar("""T""")






def      _snake_case     (__lowercase):
   return (position - 1) // 2






def      _snake_case     (__lowercase):
   return (2 * position) + 1






def      _snake_case     (__lowercase):
   return (2 * position) + 2


class     _a							(	Generic[T]     ):






  """simple docstring"""





  def __init__(  self			)      ->			None:
     UpperCamelCase_						=  []
     UpperCamelCase_						=  {}
     UpperCamelCase_						=  0




  def __len__(  self			)      ->			int:
     return self.elements




  def __repr__(  self			)      ->			str:
     return str(self.heap			)




  def  _UpperCAmelCase  (  self			)      ->			bool:
     # Check if the priority queue is empty
     return self.elements == 0




  def  _UpperCAmelCase  (  self		,		_UpperCAmelCase		,		_UpperCAmelCase			)      ->			None:
     # Add an element with given priority to the queue
     self.heap.append((elem, weight)			)
     UpperCamelCase_						=  self.elements
     self.elements += 1
     self._bubble_up(_UpperCAmelCase			)




  def  _UpperCAmelCase  (  self			)      ->			T:
     # Remove and return the element with lowest weight (highest priority)
     if self.elements > 1:
        self._swap_nodes(0		,		self.elements - 1			)
     UpperCamelCase_		,   UpperCamelCase_						=  self.heap.pop()
     del self.position_map[elem]
     self.elements -= 1
     if self.elements > 0:
        UpperCamelCase_		,   UpperCamelCase_						=  self.heap[0]
        self._bubble_down(_UpperCAmelCase			)
     return elem




  def  _UpperCAmelCase  (  self		,		_UpperCAmelCase		,		_UpperCAmelCase			)      ->			None:
     # Update the weight of the given key
     UpperCamelCase_						=  self.position_map[elem]
     UpperCamelCase_						=  (elem, weight)
     if position > 0:
        UpperCamelCase_						=  get_parent_position(_UpperCAmelCase			)
        UpperCamelCase_		,   UpperCamelCase_						=  self.heap[parent_position]
        if parent_weight > weight:
           self._bubble_up(_UpperCAmelCase			)
        else:
           self._bubble_down(_UpperCAmelCase			)
     else:
        self._bubble_down(_UpperCAmelCase			)




  def  _UpperCAmelCase  (  self		,		_UpperCAmelCase			)      ->			None:
     # Place a node at the proper position (upward movement) [to be used internally
     # only]
     UpperCamelCase_						=  self.position_map[elem]
     if curr_pos == 0:
        return None
     UpperCamelCase_						=  get_parent_position(_UpperCAmelCase			)
     UpperCamelCase_		,   UpperCamelCase_						=  self.heap[curr_pos]
     UpperCamelCase_		,   UpperCamelCase_						=  self.heap[parent_position]
     if parent_weight > weight:
        self._swap_nodes(_UpperCAmelCase		,		_UpperCAmelCase			)
        return self._bubble_up(_UpperCAmelCase			)
     return None




  def  _UpperCAmelCase  (  self		,		_UpperCAmelCase			)      ->			None:
     # Place a node at the proper position (downward movement) [to be used
     # internally only]
     UpperCamelCase_						=  self.position_map[elem]
     UpperCamelCase_		,   UpperCamelCase_						=  self.heap[curr_pos]
     UpperCamelCase_						=  get_child_left_position(_UpperCAmelCase			)
     UpperCamelCase_						=  get_child_right_position(_UpperCAmelCase			)
     if child_left_position < self.elements and child_right_position < self.elements:
        UpperCamelCase_		,   UpperCamelCase_						=  self.heap[child_left_position]
        UpperCamelCase_		,   UpperCamelCase_						=  self.heap[child_right_position]
        if child_right_weight < child_left_weight and child_right_weight < weight:
           self._swap_nodes(_UpperCAmelCase		,		_UpperCAmelCase			)
           return self._bubble_down(_UpperCAmelCase			)
     if child_left_position < self.elements:
        UpperCamelCase_		,   UpperCamelCase_						=  self.heap[child_left_position]
        if child_left_weight < weight:
           self._swap_nodes(_UpperCAmelCase		,		_UpperCAmelCase			)
           return self._bubble_down(_UpperCAmelCase			)
     else:
        return None
     if child_right_position < self.elements:
        UpperCamelCase_		,   UpperCamelCase_						=  self.heap[child_right_position]
        if child_right_weight < weight:
           self._swap_nodes(_UpperCAmelCase		,		_UpperCAmelCase			)
           return self._bubble_down(_UpperCAmelCase			)
     return None




  def  _UpperCAmelCase  (  self		,		_UpperCAmelCase		,		_UpperCAmelCase			)      ->			None:
     # Swap the nodes at the given positions
     UpperCamelCase_						=  self.heap[nodea_pos][0]
     UpperCamelCase_						=  self.heap[nodea_pos][0]
     UpperCamelCase_		,   UpperCamelCase_						=  (
         self.heap[nodea_pos],
         self.heap[nodea_pos],
     )
     UpperCamelCase_						=  nodea_pos
     UpperCamelCase_						=  nodea_pos




class     _a							(	Generic[T]     ):






  """simple docstring"""





  def __init__(  self			)      ->			None:
     UpperCamelCase_						=  {}
     UpperCamelCase_						=  0




  def __repr__(  self			)      ->			str:
     return str(self.connections			)




  def __len__(  self			)      ->			int:
     return self.nodes




  def  _UpperCAmelCase  (  self		,		_UpperCAmelCase			)      ->			None:
     # Add a node in the graph if it is not in the graph
     if node not in self.connections:
        UpperCamelCase_						=  {}
        self.nodes += 1




  def  _UpperCAmelCase  (  self		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase			)      ->			None:
     # Add an edge between 2 nodes in the graph
     self.add_node(_UpperCAmelCase			)
     self.add_node(_UpperCAmelCase			)
     UpperCamelCase_						=  weight
     UpperCamelCase_						=  weight






def      _snake_case     (__lowercase     ,   ):
   UpperCamelCase_						=  {node: maxsize for node in graph.connections}
   UpperCamelCase_						=  {node: None for node in graph.connections}

   UpperCamelCase_						=  MinPriorityQueue()
   for node, weight in dist.items():
      priority_queue.push(__lowercase     ,   __lowercase)

   if priority_queue.is_empty():
      return dist, parent

   # initialization
   UpperCamelCase_						=  priority_queue.extract_min()
   UpperCamelCase_						=  0
   for neighbour in graph.connections[node]:
      if dist[neighbour] > dist[node] + graph.connections[node][neighbour]:
         UpperCamelCase_						=  dist[node] + graph.connections[node][neighbour]
         priority_queue.update_key(__lowercase     ,   dist[neighbour])
         UpperCamelCase_						=  node

    # running prim's algorithm
   while not priority_queue.is_empty():
      UpperCamelCase_						=  priority_queue.extract_min()
      for neighbour in graph.connections[node]:
         if dist[neighbour] > dist[node] + graph.connections[node][neighbour]:
            UpperCamelCase_						=  dist[node] + graph.connections[node][neighbour]
            priority_queue.update_key(__lowercase     ,   dist[neighbour])
            UpperCamelCase_						=  node
   return dist, parent





