




"""simple docstring"""







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       SCREAMING_SNAKE_CASE__					:


      def __init__(							self				:			Union[str, Any]						,						lowerCAmelCase				:			List[str]						,						lowerCAmelCase				:			Any=12						,						lowerCAmelCase				:			Union[str, Any]=7						,						lowerCAmelCase				:			str=True						,						lowerCAmelCase				:			Dict=True						,						lowerCAmelCase				:			Optional[int]=True						,						lowerCAmelCase				:			Any=99						,						lowerCAmelCase				:			List[str]=32						,						lowerCAmelCase				:			List[str]=32						,						lowerCAmelCase				:			int=2						,						lowerCAmelCase				:			List[str]=4						,						lowerCAmelCase				:			Optional[Any]=37						,						lowerCAmelCase				:			List[str]=0.1						,						lowerCAmelCase				:			Any=0.1						,						lowerCAmelCase				:			Optional[int]=512						,						lowerCAmelCase				:			Dict=0.02						,						lowerCAmelCase				:			List[Any]=0						,						lowerCAmelCase				:			Dict=None						,						):
          lowerCAmelCase  =				parent
          lowerCAmelCase  =				batch_size
          lowerCAmelCase  =				seq_length
          lowerCAmelCase  =				is_training
          lowerCAmelCase  =				use_input_mask
          lowerCAmelCase  =				use_labels
          lowerCAmelCase  =				vocab_size
          lowerCAmelCase  =				hidden_size
          lowerCAmelCase  =				projection_dim
          lowerCAmelCase  =				num_hidden_layers
          lowerCAmelCase  =				num_attention_heads
          lowerCAmelCase  =				intermediate_size
          lowerCAmelCase  =				dropout
          lowerCAmelCase  =				attention_dropout
          lowerCAmelCase  =				max_position_embeddings
          lowerCAmelCase  =				initializer_range
          lowerCAmelCase  =				scope
          lowerCAmelCase  =				bos_token_id


      def   __lowercase (							self				:			Union[str, Any]					):
          lowerCAmelCase  =				ids_tensor([self.batch_size, self.seq_length]						,						self.vocab_size					)

          lowerCAmelCase  =				None
          if self.use_input_mask:
              lowerCAmelCase  =				random_attention_mask([self.batch_size, self.seq_length]					)

          if input_mask is not None:
              lowerCAmelCase  =				input_mask.numpy()
              lowerCAmelCase ,						lowerCAmelCase  =				input_mask.shape
              lowerCAmelCase  =				np.random.randint(1						,						seq_length - 1						,						size=(batch_size,)					)
              for batch_idx, start_index in enumerate(_lowercase					):
                  lowerCAmelCase  =				1
                  lowerCAmelCase  =				0

          lowerCAmelCase  =				self.get_config()

          return config, input_ids, tf.convert_to_tensor(_lowercase					)


      def   __lowercase (							self				:			str					):
          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				:			int						,						lowerCAmelCase				:			List[str]						,						lowerCAmelCase				:			List[Any]						,						lowerCAmelCase				:			Dict					):
          lowerCAmelCase  =				TFBlipTextModel(config=_lowercase					)
          lowerCAmelCase  =				model(_lowercase						,						attention_mask=_lowercase						,						training=_lowercase					)
          lowerCAmelCase  =				model(_lowercase						,						training=_lowercase					)
          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				:			Union[str, Any]					):
          lowerCAmelCase  =				self.prepare_config_and_inputs()
          lowerCAmelCase ,						lowerCAmelCase ,						lowerCAmelCase  =				config_and_inputs
          lowerCAmelCase  =				{"""input_ids""": input_ids, """attention_mask""": input_mask}
          return config, inputs_dict






@require_tf
class       SCREAMING_SNAKE_CASE__					(					UpperCAmelCase_						,				unittest.TestCase					):
      _a    						=						(TFBlipTextModel,) if is_tf_available() else ()
      _a    						=						False
      _a    						=						False
      _a    						=						False


      def   __lowercase (							self				:			Any					):
          lowerCAmelCase  =				BlipTextModelTester(self					)
          lowerCAmelCase  =				ConfigTester(self						,						config_class=_lowercase						,						hidden_size=37					)


      def   __lowercase (							self				:			Dict					):
          self.config_tester.run_common_tests()


      def   __lowercase (							self				:			Dict					):
          lowerCAmelCase  =				self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*_lowercase					)


      def   __lowercase (							self				:			Optional[Any]					):
          pass


      def   __lowercase (							self				:			List[Any]					):
          pass


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


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


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


      @slow
      def   __lowercase (							self				:			List[Any]					):
          for model_name in TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              lowerCAmelCase  =				TFBlipTextModel.from_pretrained(_lowercase					)
              self.assertIsNotNone(_lowercase					)




      def   __lowercase (							self				:			List[str]						,						lowerCAmelCase				:			Tuple=True					):
          super().test_pt_tf_model_equivalence(allow_missing_keys=_lowercase					)











"""simple docstring"""







from queue import PriorityQueue
from typing import Any

import numpy as np





def 							lowercase					(snake_case__   : dict      ,       snake_case__   : str      ,       snake_case__   : set      ,       snake_case__   : set      ,       snake_case__   : dict      ,       snake_case__   : dict      ,       snake_case__   : PriorityQueue      ,       snake_case__   : dict      ,       snake_case__   : float | int      ,       )     ->  float | int:


				'''simple docstring'''






				for nxt, d in graph[v]:
								if nxt in visited_forward:
												continue
								lowerCAmelCase  =				cst_fwd.get(snake_case__      ,       np.inf )
								lowerCAmelCase  =				cst_fwd[v] + d
								if new_cost_f < old_cost_f:
												queue.put((new_cost_f, nxt) )
												lowerCAmelCase  =				new_cost_f
												lowerCAmelCase  =				v
								if nxt in visited_backward:
												if cst_fwd[v] + d + cst_bwd[nxt] < shortest_distance:
																lowerCAmelCase  =				cst_fwd[v] + d + cst_bwd[nxt]
				return shortest_distance





def 							lowercase					(snake_case__   : str      ,       snake_case__   : str      ,       snake_case__   : dict      ,       snake_case__   : dict )     ->  int:


				'''simple docstring'''






				lowerCAmelCase  =				-1

				lowerCAmelCase  =				set()
				lowerCAmelCase  =				set()
				lowerCAmelCase  =				{source: 0}
				lowerCAmelCase  =				{destination: 0}
				lowerCAmelCase  =				{source: None}
				lowerCAmelCase  =				{destination: None}
				lowerCAmelCase  =				PriorityQueue()
				lowerCAmelCase  =				PriorityQueue()

				lowerCAmelCase  =				np.inf

				queue_forward.put((0, source) )
				queue_backward.put((0, destination) )

				if source == destination:
								return 0

				while not queue_forward.empty() and not queue_backward.empty():
								lowerCAmelCase ,						lowerCAmelCase  =				queue_forward.get()
								visited_forward.add(snake_case__ )

								lowerCAmelCase ,						lowerCAmelCase  =				queue_backward.get()
								visited_backward.add(snake_case__ )

								lowerCAmelCase  =				pass_and_relaxation(
								    snake_case__      ,       snake_case__      ,       snake_case__      ,       snake_case__      ,       snake_case__      ,       snake_case__      ,       snake_case__      ,       snake_case__      ,       snake_case__      ,       )

								lowerCAmelCase  =				pass_and_relaxation(
								    snake_case__      ,       snake_case__      ,       snake_case__      ,       snake_case__      ,       snake_case__      ,       snake_case__      ,       snake_case__      ,       snake_case__      ,       snake_case__      ,       )

								if cst_fwd[v_fwd] + cst_bwd[v_bwd] >= shortest_distance:
												break

				if shortest_distance != np.inf:
								lowerCAmelCase  =				shortest_distance
				return shortest_path_distance


a      =							{
    'B': [['C', 1]],
    'C': [['D', 1]],
    'D': [['F', 1]],
    'E': [['B', 1], ['G', 2]],
    'F': [],
    'G': [['F', 1]],
}
a      =							{
    'B': [['E', 1]],
    'C': [['B', 1]],
    'D': [['C', 1]],
    'F': [['D', 1], ['G', 1]],
    'E': [[None, np.inf]],
    'G': [['E', 2]],
}

if __name__ == "__main__":
		import doctest

		doctest.testmod()





