



from __future__ import annotations

UpperCAmelCase_		 =					[
    [-1, 0],  # left
    [0, -1],  # down
    [1, 0],  # right
    [0, 1],  # up
]

def 	SCREAMING_SNAKE_CASE_							(	_snake_case	:list[list[int]]	,   _snake_case	:list[int]	,   _snake_case	:list[int]	,   _snake_case	:int	,   _snake_case	:list[list[int]]	,   )  ->     tuple[list[list[int]], list[list[int]]]:
   _A      =       [
       [0 for col in range(len(grid[0]				)				)] for row in range(len(_snake_case				)				)
   ]  # the reference grid
   _A      =       1
   _A      =       [
       [0 for col in range(len(grid[0]				)				)] for row in range(len(_snake_case				)				)
   ]  # the action grid

   _A      =       init[0]
   _A      =       init[1]
   _A      =       0
   _A      =       g + heuristic[x][y]  # cost from starting cell to destination cell
   _A      =       [[f, g, x, y]]

   _A      =       False  # flag that is set when search is complete
   _A      =       False  # flag set if we can't find expand

   while not found and not resign:
      if len(_snake_case				) == 0:
         raise ValueError('''Algorithm is unable to find solution'''				)
      else:  # to choose the least costliest action so as to move closer to the goal
         cell.sort()
         cell.reverse()
         _A      =       cell.pop()
         _A      =       next_cell[2]
         _A      =       next_cell[3]
         _A      =       next_cell[1]

         if x == goal[0] and y == goal[1]:
            _A      =       True
         else:
            for i in range(len(_snake_case				)				):  # to try out different valid actions
               _A      =       x + DIRECTIONS[i][0]
               _A      =       y + DIRECTIONS[i][1]
               if xa >= 0 and xa < len(_snake_case				) and ya >= 0 and ya < len(grid[0]				):
                  if closed[xa][ya] == 0 and grid[xa][ya] == 0:
                     _A      =       g + cost
                     _A      =       ga + heuristic[xa][ya]
                     cell.append([fa, ga, xa, ya]				)
                     _A      =       1
                     _A      =       i
   _A      =       []
   _A      =       goal[0]
   _A      =       goal[1]
   invpath.append([x, y]				)  # we get the reverse path from here
   while x != init[0] or y != init[1]:
      _A      =       x - DIRECTIONS[action[x][y]][0]
      _A      =       y - DIRECTIONS[action[x][y]][1]
      _A      =       xa
      _A      =       ya
      invpath.append([x, y]				)

   _A      =       []
   for i in range(len(_snake_case				)				):
      path.append(invpath[len(_snake_case				) - 1 - i]				)
   return path, action


if __name__ == "__main__":
   UpperCAmelCase_		 =					[
       [0, 1, 0, 0, 0, 0],
       [0, 1, 0, 0, 0, 0],  # 0 are free path whereas 1's are obstacles
       [0, 1, 0, 0, 0, 0],
       [0, 1, 0, 0, 1, 0],
       [0, 0, 0, 0, 1, 0],
   ]

   UpperCAmelCase_		 =					[0, 0]
   # all coordinates are given in format [y,x]
   UpperCAmelCase_		 =					[len(grid) - 1, len(grid[0]) - 1]
   UpperCAmelCase_		 =					1

   # the cost map which pushes the path closer to the goal
   UpperCAmelCase_		 =					[[0 for row in range(len(grid[0]))] for col in range(len(grid))]
   for i in range(len(grid)):
      for j in range(len(grid[0])):
         UpperCAmelCase_		 =					abs(i - goal[0]) + abs(j - goal[1])
         if grid[i][j] == 1:
            # added extra penalty in the heuristic map
            UpperCAmelCase_		 =					9_9

   UpperCAmelCase_ ,UpperCAmelCase_		 =					search(grid, init, goal, cost, heuristic)

   print("""ACTION MAP""")
   for i in range(len(action)):
      print(action[i])

   for i in range(len(path)):
      print(path[i])











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 							snake_case_       :

						"""simple docstring"""



						def __init__(self:					Optional[Any] ,					__UpperCAmelCase:					List[str] ,					__UpperCAmelCase:					int=12 ,					__UpperCAmelCase:					Dict=7 ,					__UpperCAmelCase:					str=True ,					__UpperCAmelCase:					Tuple=True ,					__UpperCAmelCase:					Tuple=True ,					__UpperCAmelCase:					Optional[int]=99 ,					__UpperCAmelCase:					str=32 ,					__UpperCAmelCase:					Any=32 ,					__UpperCAmelCase:					int=2 ,					__UpperCAmelCase:					List[str]=4 ,					__UpperCAmelCase:					Tuple=37 ,					__UpperCAmelCase:					Optional[int]=0.1 ,					__UpperCAmelCase:					List[Any]=0.1 ,					__UpperCAmelCase:					Dict=512 ,					__UpperCAmelCase:					Union[str, Any]=0.02 ,					__UpperCAmelCase:					List[Any]=0 ,					__UpperCAmelCase:					str=None ,					)    ->     Tuple:




											'''simple docstring'''



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



						def        UpperCAmelCase__					(self:					str   )    ->     Union[str, Any]:




											'''simple docstring'''



											__a					:						Optional[Any]      =					ids_tensor([self.batch_size, self.seq_length] ,					self.vocab_size   )

											__a					:						Any      =					None
											if self.use_input_mask:
																__a					:						Tuple      =					random_attention_mask([self.batch_size, self.seq_length]   )

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

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

											return config, input_ids, tf.convert_to_tensor(__UpperCAmelCase   )



						def        UpperCAmelCase__					(self:					List[str]   )    ->     Optional[int]:




											'''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        UpperCAmelCase__					(self:					List[str] ,					__UpperCAmelCase:					Any ,					__UpperCAmelCase:					Dict ,					__UpperCAmelCase:					str   )    ->     List[str]:




											'''simple docstring'''



											__a					:						int      =					TFBlipTextModel(config=__UpperCAmelCase   )
											__a					:						List[str]      =					model(__UpperCAmelCase ,					attention_mask=__UpperCAmelCase ,					training=__UpperCAmelCase   )
											__a					:						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        UpperCAmelCase__					(self:					List[str]   )    ->     str:




											'''simple docstring'''



											__a					:						Tuple      =					self.prepare_config_and_inputs()
											__a      ,			__a      ,			__a					:						Dict      =					config_and_inputs
											__a					:						Union[str, Any]      =					{"input_ids": input_ids, "attention_mask": input_mask}
											return config, inputs_dict




@require_tf
class 							snake_case_       (    __UpperCamelCase		,							unittest.TestCase      ):

						"""simple docstring"""



						snake_case__        =      (TFBlipTextModel,) if is_tf_available() else ()
						snake_case__        =      False
						snake_case__        =      False
						snake_case__        =      False



						def        UpperCAmelCase__					(self:					List[str]   )    ->     Optional[int]:




											'''simple docstring'''



											__a					:						str      =					BlipTextModelTester(self   )
											__a					:						str      =					ConfigTester(self ,					config_class=__UpperCAmelCase ,					hidden_size=37   )



						def        UpperCAmelCase__					(self:					str   )    ->     str:




											'''simple docstring'''



											self.config_tester.run_common_tests()



						def        UpperCAmelCase__					(self:					Any   )    ->     Union[str, Any]:




											'''simple docstring'''



											__a					:						int      =					self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_model(*__UpperCAmelCase   )



						def        UpperCAmelCase__					(self:					Tuple   )    ->     Dict:




											'''simple docstring'''



											pass



						def        UpperCAmelCase__					(self:					Union[str, Any]   )    ->     Optional[int]:




											'''simple docstring'''



											pass



						@unittest.skip(reason="Blip does not use inputs_embeds"   )
						def        UpperCAmelCase__					(self:					Any   )    ->     Dict:




											'''simple docstring'''



											pass



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




											'''simple docstring'''



											pass



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




											'''simple docstring'''



											pass



						@slow
						def        UpperCAmelCase__					(self:					Optional[Any]   )    ->     Tuple:




											'''simple docstring'''



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



						def        UpperCAmelCase__					(self:					Dict ,					__UpperCAmelCase:					Optional[Any]=True   )    ->     Any:




											'''simple docstring'''



											super().test_pt_tf_model_equivalence(allow_missing_keys=__UpperCAmelCase   )





