






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  :







 def __init__(		self :       Tuple							,       SCREAMING_SNAKE_CASE :       Optional[int]							,       SCREAMING_SNAKE_CASE :       int=12							,       SCREAMING_SNAKE_CASE :       Tuple=7							,       SCREAMING_SNAKE_CASE :       List[str]=True							,       SCREAMING_SNAKE_CASE :       Union[str, Any]=True							,       SCREAMING_SNAKE_CASE :       Any=True							,       SCREAMING_SNAKE_CASE :       Optional[int]=99							,       SCREAMING_SNAKE_CASE :       Optional[Any]=32							,       SCREAMING_SNAKE_CASE :       Union[str, Any]=32							,       SCREAMING_SNAKE_CASE :       str=2							,       SCREAMING_SNAKE_CASE :       Union[str, Any]=4							,       SCREAMING_SNAKE_CASE :       str=37							,       SCREAMING_SNAKE_CASE :       Dict=0.1							,       SCREAMING_SNAKE_CASE :       Union[str, Any]=0.1							,       SCREAMING_SNAKE_CASE :       Optional[Any]=5_12							,       SCREAMING_SNAKE_CASE :       Tuple=0.0_2							,       SCREAMING_SNAKE_CASE :       List[str]=0							,       SCREAMING_SNAKE_CASE :       Tuple=None							,       ):


      '''simple docstring'''






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







 def  __lowercase (		self :       List[Any]						):


      '''simple docstring'''






      UpperCamelCase__      :       Tuple	    =  ids_tensor([self.batch_size, self.seq_length]							,       self.vocab_size						)

      UpperCamelCase__      :       int	    =  None
      if self.use_input_mask:
           UpperCamelCase__      :       Optional[Any]	    =  random_attention_mask([self.batch_size, self.seq_length]						)

      if input_mask is not None:
           UpperCamelCase__      :       Any	    =  input_mask.numpy()
           UpperCamelCase__    ,					UpperCamelCase__      :       List[str]	    =  input_mask.shape
           UpperCamelCase__      :       Any	    =  np.random.randint(1							,       seq_length - 1							,       size=(batch_size,)						)
           for batch_idx, start_index in enumerate(SCREAMING_SNAKE_CASE						):
                UpperCamelCase__      :       List[str]	    =  1
                UpperCamelCase__      :       Dict	    =  0

      UpperCamelCase__      :       Union[str, Any]	    =  self.get_config()

      return config, input_ids, tf.convert_to_tensor(SCREAMING_SNAKE_CASE						)







 def  __lowercase (		self :       Dict						):


      '''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  __lowercase (		self :       Any							,       SCREAMING_SNAKE_CASE :       Optional[Any]							,       SCREAMING_SNAKE_CASE :       Union[str, Any]							,       SCREAMING_SNAKE_CASE :       Tuple						):


      '''simple docstring'''






      UpperCamelCase__      :       List[Any]	    =  TFBlipTextModel(config=SCREAMING_SNAKE_CASE						)
      UpperCamelCase__      :       List[str]	    =  model(SCREAMING_SNAKE_CASE							,       attention_mask=SCREAMING_SNAKE_CASE							,       training=SCREAMING_SNAKE_CASE						)
      UpperCamelCase__      :       Optional[Any]	    =  model(SCREAMING_SNAKE_CASE							,       training=SCREAMING_SNAKE_CASE						)
      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  __lowercase (		self :       List[Any]						):


      '''simple docstring'''






      UpperCamelCase__      :       List[str]	    =  self.prepare_config_and_inputs()
      UpperCamelCase__    ,					UpperCamelCase__    ,					UpperCamelCase__      :       str	    =  config_and_inputs
      UpperCamelCase__      :       List[str]	    =  {"input_ids": input_ids, "attention_mask": input_mask}
      return config, inputs_dict







@require_tf
class       __a  (   A__	,       unittest.TestCase	):
 _lowerCAmelCase	:	Any				=			(TFBlipTextModel,) if is_tf_available() else ()
 _lowerCAmelCase	:	Optional[int]				=			False
 _lowerCAmelCase	:	int				=			False
 _lowerCAmelCase	:	List[Any]				=			False







 def  __lowercase (		self :       List[Any]						):


      '''simple docstring'''






      UpperCamelCase__      :       int	    =  BlipTextModelTester(self						)
      UpperCamelCase__      :       List[Any]	    =  ConfigTester(self							,       config_class=SCREAMING_SNAKE_CASE							,       hidden_size=37						)







 def  __lowercase (		self :       int						):


      '''simple docstring'''






      self.config_tester.run_common_tests()







 def  __lowercase (		self :       List[Any]						):


      '''simple docstring'''






      UpperCamelCase__      :       str	    =  self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE						)







 def  __lowercase (		self :       Optional[int]						):


      '''simple docstring'''






      pass







 def  __lowercase (		self :       Dict						):


      '''simple docstring'''






      pass







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


      '''simple docstring'''






      pass







 @unittest.skip(reason="BlipTextModel has no base class and is not available in MODEL_MAPPING"						)
 def  __lowercase (		self :       List[str]						):


      '''simple docstring'''






      pass







 @unittest.skip(reason="BlipTextModel has no base class and is not available in MODEL_MAPPING"						)
 def  __lowercase (		self :       Optional[int]						):


      '''simple docstring'''






      pass







 @slow
 def  __lowercase (		self :       List[Any]						):


      '''simple docstring'''






      for model_name in TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
           UpperCamelCase__      :       Optional[Any]	    =  TFBlipTextModel.from_pretrained(SCREAMING_SNAKE_CASE						)
           self.assertIsNotNone(SCREAMING_SNAKE_CASE						)







 def  __lowercase (		self :       Dict							,       SCREAMING_SNAKE_CASE :       Tuple=True						):


      '''simple docstring'''






      super().test_pt_tf_model_equivalence(allow_missing_keys=SCREAMING_SNAKE_CASE						)







lowerCamelCase     :							Optional[int]    ={
    '''A''': ['''B''', '''C''', '''E'''],
    '''B''': ['''A''', '''D''', '''E'''],
    '''C''': ['''A''', '''F''', '''G'''],
    '''D''': ['''B'''],
    '''E''': ['''A''', '''B''', '''D'''],
    '''F''': ['''C'''],
    '''G''': ['''C'''],
}







def 		SCREAMING_SNAKE_CASE							( __lowerCAmelCase ,   __lowerCAmelCase ,   __lowerCAmelCase      )			->     list[str]:
     UpperCamelCase__      :       Optional[Any]	    =  set()
     # keep track of all the paths to be checked
     UpperCamelCase__      :       Optional[Any]	    =  [[start]]

     # return path if start is goal
     if start == goal:
          return [start]

     # keeps looping until all possible paths have been checked
     while queue:
          # pop the first path from the queue
          UpperCamelCase__      :       int	    =  queue.pop(0      )
          # get the last node from the path
          UpperCamelCase__      :       Dict	    =  path[-1]
          if node not in explored:
               UpperCamelCase__      :       Tuple	    =  graph[node]
               # go through all neighbour nodes, construct a new path and
               # push it into the queue
               for neighbour in neighbours:
                    UpperCamelCase__      :       List[str]	    =  list(__lowerCAmelCase      )
                    new_path.append(__lowerCAmelCase      )
                    queue.append(__lowerCAmelCase      )
                    # return path if neighbour is goal
                    if neighbour == goal:
                         return new_path

            # mark node as explored
               explored.add(__lowerCAmelCase      )

    # in case there's no path between the 2 nodes
     return []







def 		SCREAMING_SNAKE_CASE							( __lowerCAmelCase ,   __lowerCAmelCase ,   __lowerCAmelCase      )			->     int:
     if not graph or start not in graph or target not in graph:
          return -1
     if start == target:
          return 0
     UpperCamelCase__      :       Tuple	    =  [start]
     UpperCamelCase__      :       Optional[int]	    =  set(__lowerCAmelCase      )
     # Keep tab on distances from `start` node.
     UpperCamelCase__      :       str	    =  {start: 0, target: -1}
     while queue:
          UpperCamelCase__      :       Any	    =  queue.pop(0      )
          if node == target:
               UpperCamelCase__      :       Union[str, Any]	    =  (
                   dist[node] if dist[target] == -1 else min(dist[target] ,   dist[node]      )
               )
          for adjacent in graph[node]:
               if adjacent not in visited:
                    visited.add(__lowerCAmelCase      )
                    queue.append(__lowerCAmelCase      )
                    UpperCamelCase__      :       List[Any]	    =  dist[node] + 1
     return dist[target]


if __name__ == "__main__":
 print(bfs_shortest_path(demo_graph, '''G''', '''D'''))  # returns ['G', 'C', 'A', 'B', 'D']
 print(bfs_shortest_path_distance(demo_graph, '''G''', '''D'''))  # returns 4