

from __future__ import annotations

import unittest

import numpy as np

from transformers import BlipTextConfig
from transformers.testing_utils import require_tf, slow
from transformers.utils import is_tf_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask


if is_tf_available():
     import tensorflow as tf

     from transformers import TFBlipTextModel
     from transformers.models.blip.modeling_tf_blip import TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST



class 						_A     :





  '''simple docstring'''



  def __init__(  self   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_=12   ,SCREAMING_SNAKE_CASE_=7   ,SCREAMING_SNAKE_CASE_=True   ,SCREAMING_SNAKE_CASE_=True   ,SCREAMING_SNAKE_CASE_=True   ,SCREAMING_SNAKE_CASE_=99   ,SCREAMING_SNAKE_CASE_=32   ,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_=512   ,SCREAMING_SNAKE_CASE_=0.02   ,SCREAMING_SNAKE_CASE_=0   ,SCREAMING_SNAKE_CASE_=None   ,):

      '''simple docstring'''
      snake_case							:		List[Any]        =		parent
      snake_case							:		int        =		batch_size
      snake_case							:		Union[str, Any]        =		seq_length
      snake_case							:		Any        =		is_training
      snake_case							:		int        =		use_input_mask
      snake_case							:		Tuple        =		use_labels
      snake_case							:		List[Any]        =		vocab_size
      snake_case							:		Optional[Any]        =		hidden_size
      snake_case							:		Union[str, Any]        =		projection_dim
      snake_case							:		int        =		num_hidden_layers
      snake_case							:		str        =		num_attention_heads
      snake_case							:		List[str]        =		intermediate_size
      snake_case							:		str        =		dropout
      snake_case							:		Tuple        =		attention_dropout
      snake_case							:		Tuple        =		max_position_embeddings
      snake_case							:		List[Any]        =		initializer_range
      snake_case							:		Optional[Any]        =		scope
      snake_case							:		Tuple        =		bos_token_id





  def    snake_case_       (  self    ):

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

      snake_case							:		List[str]        =		None
      if self.use_input_mask:
          snake_case							:		List[Any]        =		random_attention_mask([self.batch_size, self.seq_length]    )

      if input_mask is not None:
          snake_case							:		Optional[int]        =		input_mask.numpy()
          snake_case      ,		snake_case							:		Tuple        =		input_mask.shape
          snake_case							:		Optional[int]        =		np.random.randint(1   ,seq_length - 1   ,size=(batch_size,)    )
          for batch_idx, start_index in enumerate(_UpperCAmelCase    ):
              snake_case							:		Optional[Any]        =		1
              snake_case							:		Dict        =		0

      snake_case							:		Tuple        =		self.get_config()

      return config, input_ids, tf.convert_to_tensor(_UpperCAmelCase    )





  def    snake_case_       (  self    ):

      '''simple docstring'''
      return BlipTextConfig(
          vocab_size=self.vocab_size   ,hidden_size=self.hidden_size   ,projection_dim=self.projection_dim   ,num_hidden_layers=self.num_hidden_layers   ,num_attention_heads=self.num_attention_heads   ,intermediate_size=self.intermediate_size   ,dropout=self.dropout   ,attention_dropout=self.attention_dropout   ,max_position_embeddings=self.max_position_embeddings   ,initializer_range=self.initializer_range   ,bos_token_id=self.bos_token_id   ,)





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

      '''simple docstring'''
      snake_case							:		str        =		TFBlipTextModel(config=_UpperCAmelCase    )
      snake_case							:		Optional[int]        =		model(_UpperCAmelCase   ,attention_mask=_UpperCAmelCase   ,training=_UpperCAmelCase    )
      snake_case							:		List[Any]        =		model(_UpperCAmelCase   ,training=_UpperCAmelCase    )
      self.parent.assertEqual(result.last_hidden_state.shape   ,(self.batch_size, self.seq_length, self.hidden_size)    )
      self.parent.assertEqual(result.pooler_output.shape   ,(self.batch_size, self.hidden_size)    )





  def    snake_case_       (  self    ):

      '''simple docstring'''
      snake_case							:		List[str]        =		self.prepare_config_and_inputs()
      snake_case      ,		snake_case      ,		snake_case							:		Optional[int]        =		config_and_inputs
      snake_case							:		Union[str, Any]        =		{"""input_ids""": input_ids, """attention_mask""": input_mask}
      return config, inputs_dict







@require_tf
class 						_A     (				UpperCAmelCase__						,    unittest.TestCase				):





  '''simple docstring'''



  __lowerCamelCase			:      Any					=   (TFBlipTextModel,) if is_tf_available() else ()
  __lowerCamelCase			:      Tuple					=   False
  __lowerCamelCase			:      Optional[int]					=   False
  __lowerCamelCase			:      List[Any]					=   False





  def    snake_case_       (  self    ):

      '''simple docstring'''
      snake_case							:		List[Any]        =		BlipTextModelTester(self    )
      snake_case							:		Optional[int]        =		ConfigTester(self   ,config_class=_UpperCAmelCase   ,hidden_size=37    )





  def    snake_case_       (  self    ):

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





  def    snake_case_       (  self    ):

      '''simple docstring'''
      snake_case							:		Optional[Any]        =		self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*_UpperCAmelCase    )





  def    snake_case_       (  self    ):

      '''simple docstring'''
      pass





  def    snake_case_       (  self    ):

      '''simple docstring'''
      pass





  @unittest.skip(reason="""Blip does not use inputs_embeds"""    )
  def    snake_case_       (  self    ):

      '''simple docstring'''
      pass





  @unittest.skip(reason="""BlipTextModel has no base class and is not available in MODEL_MAPPING"""    )
  def    snake_case_       (  self    ):

      '''simple docstring'''
      pass





  @unittest.skip(reason="""BlipTextModel has no base class and is not available in MODEL_MAPPING"""    )
  def    snake_case_       (  self    ):

      '''simple docstring'''
      pass





  @slow
  def    snake_case_       (  self    ):

      '''simple docstring'''
      for model_name in TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
          snake_case							:		int        =		TFBlipTextModel.from_pretrained(_UpperCAmelCase    )
          self.assertIsNotNone(_UpperCAmelCase    )





  def    snake_case_       (  self   ,SCREAMING_SNAKE_CASE_=True    ):

      '''simple docstring'''
      super().test_pt_tf_model_equivalence(allow_missing_keys=_UpperCAmelCase    )











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





