

from __future__ import annotations

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





def      snake_case_				(__A					:						list[list[int]] ,       __A					:						list[int] ,       __A					:						list[int] ,       __A					:						int ,       __A					:						list[list[int]] ,       )      ->     tuple[list[list[int]], list[list[int]]]:
   __lowerCAmelCase  :      Any    =						[
       [0 for col in range(len(grid[0]					)					)] for row in range(len(__A					)					)
   ]  # the reference grid
   __lowerCAmelCase  :      Dict    =						1
   __lowerCAmelCase  :      List[str]    =						[
       [0 for col in range(len(grid[0]					)					)] for row in range(len(__A					)					)
   ]  # the action grid

   __lowerCAmelCase  :      Optional[int]    =						init[0]
   __lowerCAmelCase  :      int    =						init[1]
   __lowerCAmelCase  :      str    =						0
   __lowerCAmelCase  :      Optional[Any]    =						g + heuristic[x][y]  # cost from starting cell to destination cell
   __lowerCAmelCase  :      Optional[int]    =						[[f, g, x, y]]

   __lowerCAmelCase  :      Optional[Any]    =						False  # flag that is set when search is complete
   __lowerCAmelCase  :      str    =						False  # flag set if we can't find expand

   while not found and not resign:
      if len(__A					) == 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()
         __lowerCAmelCase  :      List[str]    =						cell.pop()
         __lowerCAmelCase  :      Optional[Any]    =						next_cell[2]
         __lowerCAmelCase  :      List[str]    =						next_cell[3]
         __lowerCAmelCase  :      Dict    =						next_cell[1]

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

   __lowerCAmelCase  :      Any    =						[]
   for i in range(len(__A					)					):
      path.append(invpath[len(__A					) - 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								=			99

   __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])





import unittest

import numpy as np

from transformers import BertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
   from transformers.models.bert.modeling_flax_bert import (
       FlaxBertForMaskedLM,
       FlaxBertForMultipleChoice,
       FlaxBertForNextSentencePrediction,
       FlaxBertForPreTraining,
       FlaxBertForQuestionAnswering,
       FlaxBertForSequenceClassification,
       FlaxBertForTokenClassification,
       FlaxBertModel,
   )



class SCREAMING_SNAKE_CASE     (  unittest.TestCase			):
  """simple docstring"""






  def __init__(      self			:   Tuple	,							lowerCAmelCase			:   Tuple	,							lowerCAmelCase			:   str=13	,							lowerCAmelCase			:   Optional[Any]=7	,							lowerCAmelCase			:   Optional[Any]=True	,							lowerCAmelCase			:   Any=True	,							lowerCAmelCase			:   str=True	,							lowerCAmelCase			:   Optional[Any]=True	,							lowerCAmelCase			:   Dict=99	,							lowerCAmelCase			:   Any=32	,							lowerCAmelCase			:   int=5	,							lowerCAmelCase			:   Tuple=4	,							lowerCAmelCase			:   Optional[Any]=37	,							lowerCAmelCase			:   str="gelu"	,							lowerCAmelCase			:   str=0.1	,							lowerCAmelCase			:   str=0.1	,							lowerCAmelCase			:   Any=5_12	,							lowerCAmelCase			:   Optional[Any]=16	,							lowerCAmelCase			:   Dict=2	,							lowerCAmelCase			:   Tuple=0.02	,							lowerCAmelCase			:   Optional[int]=4	,							)							->     List[Any]:






     """simple docstring"""







     __lowerCAmelCase  :      int    =						parent
     __lowerCAmelCase  :      Dict    =						batch_size
     __lowerCAmelCase  :      Dict    =						seq_length
     __lowerCAmelCase  :      Union[str, Any]    =						is_training
     __lowerCAmelCase  :      List[Any]    =						use_attention_mask
     __lowerCAmelCase  :      List[Any]    =						use_token_type_ids
     __lowerCAmelCase  :      Optional[int]    =						use_labels
     __lowerCAmelCase  :      str    =						vocab_size
     __lowerCAmelCase  :      Any    =						hidden_size
     __lowerCAmelCase  :      Optional[int]    =						num_hidden_layers
     __lowerCAmelCase  :      Optional[int]    =						num_attention_heads
     __lowerCAmelCase  :      Dict    =						intermediate_size
     __lowerCAmelCase  :      Tuple    =						hidden_act
     __lowerCAmelCase  :      Dict    =						hidden_dropout_prob
     __lowerCAmelCase  :      Any    =						attention_probs_dropout_prob
     __lowerCAmelCase  :      Union[str, Any]    =						max_position_embeddings
     __lowerCAmelCase  :      int    =						type_vocab_size
     __lowerCAmelCase  :      Tuple    =						type_sequence_label_size
     __lowerCAmelCase  :      int    =						initializer_range
     __lowerCAmelCase  :      Optional[int]    =						num_choices







  def 	SCREAMING_SNAKE_CASE   (      self			:   Optional[int]   )							->     Dict:






     """simple docstring"""







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

     __lowerCAmelCase  :      Dict    =						None
     if self.use_attention_mask:
        __lowerCAmelCase  :      Optional[int]    =						random_attention_mask([self.batch_size, self.seq_length]   )

     __lowerCAmelCase  :      Union[str, Any]    =						None
     if self.use_token_type_ids:
        __lowerCAmelCase  :      str    =						ids_tensor([self.batch_size, self.seq_length]	,							self.type_vocab_size   )

     __lowerCAmelCase  :      List[str]    =						BertConfig(
         vocab_size=self.vocab_size	,							hidden_size=self.hidden_size	,							num_hidden_layers=self.num_hidden_layers	,							num_attention_heads=self.num_attention_heads	,							intermediate_size=self.intermediate_size	,							hidden_act=self.hidden_act	,							hidden_dropout_prob=self.hidden_dropout_prob	,							attention_probs_dropout_prob=self.attention_probs_dropout_prob	,							max_position_embeddings=self.max_position_embeddings	,							type_vocab_size=self.type_vocab_size	,							is_decoder=lowerCAmelCase	,							initializer_range=self.initializer_range	,							)

     return config, input_ids, token_type_ids, attention_mask







  def 	SCREAMING_SNAKE_CASE   (      self			:   List[Any]   )							->     Optional[Any]:






     """simple docstring"""







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







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






     """simple docstring"""







     __lowerCAmelCase  :      List[str]    =						self.prepare_config_and_inputs()
     __lowerCAmelCase   ,__lowerCAmelCase   ,__lowerCAmelCase   ,__lowerCAmelCase  :      Dict    =						config_and_inputs

     __lowerCAmelCase  :      Any    =						True
     __lowerCAmelCase  :      str    =						floats_tensor([self.batch_size, self.seq_length, self.hidden_size]   )
     __lowerCAmelCase  :      int    =						ids_tensor([self.batch_size, self.seq_length]	,							vocab_size=2   )

     return (
         config,
         input_ids,
         attention_mask,
         encoder_hidden_states,
         encoder_attention_mask,
     )






@require_flax
class SCREAMING_SNAKE_CASE     (  a_       ,							unittest.TestCase			):
  """simple docstring"""






  lowerCamelCase   :      int										=True

  lowerCamelCase   :      Any										=(
      (
          FlaxBertModel,
          FlaxBertForPreTraining,
          FlaxBertForMaskedLM,
          FlaxBertForMultipleChoice,
          FlaxBertForQuestionAnswering,
          FlaxBertForNextSentencePrediction,
          FlaxBertForSequenceClassification,
          FlaxBertForTokenClassification,
          FlaxBertForQuestionAnswering,
      )
      if is_flax_available()
      else ()
  )







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






     """simple docstring"""







     __lowerCAmelCase  :      Union[str, Any]    =						FlaxBertModelTester(self   )







  @slow
  def 	SCREAMING_SNAKE_CASE   (      self			:   Dict   )							->     Dict:






     """simple docstring"""







     __lowerCAmelCase  :      int    =						FlaxBertModel.from_pretrained("""bert-base-cased"""   )
     __lowerCAmelCase  :      str    =						model(np.ones((1, 1)   )   )
     self.assertIsNotNone(lowerCAmelCase   )


