




"""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					:      Tuple    ,    lowerCAmelCase					:      Dict    ,    lowerCAmelCase					:      Dict=12    ,    lowerCAmelCase					:      Tuple=7    ,    lowerCAmelCase					:      str=True    ,    lowerCAmelCase					:      List[str]=True    ,    lowerCAmelCase					:      Tuple=True    ,    lowerCAmelCase					:      List[Any]=99    ,    lowerCAmelCase					:      Tuple=32    ,    lowerCAmelCase					:      Any=32    ,    lowerCAmelCase					:      str=2    ,    lowerCAmelCase					:      Union[str, Any]=4    ,    lowerCAmelCase					:      Tuple=37    ,    lowerCAmelCase					:      str=0.1    ,    lowerCAmelCase					:      Any=0.1    ,    lowerCAmelCase					:      Any=512    ,    lowerCAmelCase					:      Optional[Any]=0.02    ,    lowerCAmelCase					:      Union[str, Any]=0    ,    lowerCAmelCase					:      Union[str, Any]=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					:      Optional[int]       ):
										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(__UpperCAmelCase       ):
																		lowerCAmelCase						=					1
																		lowerCAmelCase						=					0

										lowerCAmelCase						=					self.get_config()

										return config, input_ids, tf.convert_to_tensor(__UpperCAmelCase       )


						def 	__lowercase			(				self					:      Tuple       ):
										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					:      Tuple    ,    lowerCAmelCase					:      Dict    ,    lowerCAmelCase					:      List[str]    ,    lowerCAmelCase					:      str       ):
										lowerCAmelCase						=					TFBlipTextModel(config=__UpperCAmelCase       )
										lowerCAmelCase						=					model(__UpperCAmelCase    ,    attention_mask=__UpperCAmelCase    ,    training=__UpperCAmelCase       )
										lowerCAmelCase						=					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 	__lowercase			(				self					:      str       ):
										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__       (       _a    ,					unittest.TestCase       ):
						_a         =    (TFBlipTextModel,) if is_tf_available() else ()
						_a         =    False
						_a         =    False
						_a         =    False


						def 	__lowercase			(				self					:      Union[str, Any]       ):
										lowerCAmelCase						=					BlipTextModelTester(self       )
										lowerCAmelCase						=					ConfigTester(self    ,    config_class=__UpperCAmelCase    ,    hidden_size=37       )


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


						def 	__lowercase			(				self					:      Any       ):
										lowerCAmelCase						=					self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*__UpperCAmelCase       )


						def 	__lowercase			(				self					:      Dict       ):
										pass


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


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


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


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


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


										for model_name in TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
														lowerCAmelCase						=					TFBlipTextModel.from_pretrained(__UpperCAmelCase       )
														self.assertIsNotNone(__UpperCAmelCase       )




						def 	__lowercase			(				self					:      List[Any]    ,    lowerCAmelCase					:      Any=True       ):
										super().test_pt_tf_model_equivalence(allow_missing_keys=__UpperCAmelCase       )













from __future__ import annotations

UpperCAmelCase__							  =						list[tuple[int, int]]

UpperCAmelCase__							  =						[
    [0, 0, 0, 0, 0, 0, 0],
    [0, 1, 0, 0, 0, 0, 0],  # 0 are free path whereas 1's are obstacles
    [0, 0, 0, 0, 0, 0, 0],
    [0, 0, 1, 0, 0, 0, 0],
    [1, 0, 1, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 1, 0, 0],
]

UpperCAmelCase__							  =						([-1, 0], [0, -1], [1, 0], [0, 1])  # up, left, down, right






class 						lowercase_   :






      '''simple docstring'''




      def __init__( self :		Dict					,   __UpperCAmelCase :		int					,   __UpperCAmelCase :		int					,   __UpperCAmelCase :		int					,   __UpperCAmelCase :		int					,   __UpperCAmelCase :		float					,   __UpperCAmelCase :		Node | None					,   )       ->int:




       """simple docstring"""


       a						    = pos_x
       a						    = pos_y
       a						    = (pos_y, pos_x)
       a						    = goal_x
       a						    = goal_y
       a						    = g_cost
       a						    = parent
       a						    = self.calculate_heuristic()







      def  __lowerCAmelCase   ( self :		Any    )       ->float:




       """simple docstring"""


       a						    = abs(self.pos_x - self.goal_x    )
       a						    = abs(self.pos_y - self.goal_y    )
       return dx + dy







      def __lt__( self :		Any					,   __UpperCAmelCase :		Tuple    )       ->bool:




       """simple docstring"""


       return self.f_cost < other.f_cost






class 						lowercase_   :






      '''simple docstring'''




      def __init__( self :		Optional[Any]					,   __UpperCAmelCase :		tuple[int, int]					,   __UpperCAmelCase :		tuple[int, int]    )       ->Dict:




       """simple docstring"""


       a						    = Node(start[1]					,   start[0]					,   goal[1]					,   goal[0]					,   0					,   __UpperCAmelCase    )
       a						    = Node(goal[1]					,   goal[0]					,   goal[1]					,   goal[0]					,   99_999					,   __UpperCAmelCase    )

       a						    = [self.start]
       a						    = []

       a						    = False







      def  __lowerCAmelCase   ( self :		str    )       ->Path | None:




       """simple docstring"""


       while self.open_nodes:
        # Open Nodes are sorted using __lt__
        self.open_nodes.sort()
        a						    = self.open_nodes.pop(0    )

        if current_node.pos == self.target.pos:
         a						    = True
         return self.retrace_path(__UpperCAmelCase    )

        self.closed_nodes.append(__UpperCAmelCase    )
        a						    = self.get_successors(__UpperCAmelCase    )

        for child_node in successors:
         if child_node in self.closed_nodes:
          continue

         if child_node not in self.open_nodes:
          self.open_nodes.append(__UpperCAmelCase    )
         else:
          # retrieve the best current path
          a						    = self.open_nodes.pop(self.open_nodes.index(__UpperCAmelCase    )    )

          if child_node.g_cost < better_node.g_cost:
           self.open_nodes.append(__UpperCAmelCase    )
          else:
           self.open_nodes.append(__UpperCAmelCase    )

       if not self.reached:
        return [self.start.pos]
       return None







      def  __lowerCAmelCase   ( self :		Dict					,   __UpperCAmelCase :		Node    )       ->list[Node]:




       """simple docstring"""


       a						    = []
       for action in delta:
        a						    = parent.pos_x + action[1]
        a						    = parent.pos_y + action[0]

        if not (0 <= pos_x <= len(grid[0]    ) - 1 and 0 <= pos_y <= len(__UpperCAmelCase    ) - 1):
         continue

        if grid[pos_y][pos_x] != 0:
         continue

        successors.append(
            Node(
                __UpperCAmelCase					,   __UpperCAmelCase					,   self.target.pos_y					,   self.target.pos_x					,   parent.g_cost + 1					,   __UpperCAmelCase					,   )    )
       return successors







      def  __lowerCAmelCase   ( self :		Optional[Any]					,   __UpperCAmelCase :		Node | None    )       ->Path:




       """simple docstring"""


       a						    = node
       a						    = []
       while current_node is not None:
        path.append((current_node.pos_y, current_node.pos_x)    )
        a						    = current_node.parent
       path.reverse()
       return path


if __name__ == "__main__":
     UpperCAmelCase__							  =						(0, 0)
     UpperCAmelCase__							  =						(len(grid) - 1, len(grid[0]) - 1)
     for elem in grid:
          print(elem)

     print("------")

     UpperCAmelCase__							  =						GreedyBestFirst(init, goal)
     UpperCAmelCase__							  =						greedy_bf.search()
     if path:
          for pos_x, pos_y in path:
               UpperCAmelCase__							  =						2

          for elem in grid:
               print(elem)


