




import unittest

import numpy as np

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

from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
       import jax.numpy as jnp

       from transformers.models.distilbert.modeling_flax_distilbert import (
           FlaxDistilBertForMaskedLM,
           FlaxDistilBertForMultipleChoice,
           FlaxDistilBertForQuestionAnswering,
           FlaxDistilBertForSequenceClassification,
           FlaxDistilBertForTokenClassification,
           FlaxDistilBertModel,
       )





class     lowerCamelCase__(					unittest.TestCase):
      def __init__(				self: Tuple    ,       UpperCamelCase_: Optional[Any]    ,       UpperCamelCase_: List[str]=13    ,       UpperCamelCase_: Any=7    ,       UpperCamelCase_: str=True    ,       UpperCamelCase_: Any=True    ,       UpperCamelCase_: Optional[Any]=True    ,       UpperCamelCase_: int=True    ,       UpperCamelCase_: Optional[Any]=99    ,       UpperCamelCase_: Optional[int]=32    ,       UpperCamelCase_: Optional[Any]=5    ,       UpperCamelCase_: List[Any]=4    ,       UpperCamelCase_: Optional[int]=37    ,       UpperCamelCase_: List[str]="gelu"    ,       UpperCamelCase_: Tuple=0.1    ,       UpperCamelCase_: Dict=0.1    ,       UpperCamelCase_: List[Any]=5_12    ,       UpperCamelCase_: Union[str, Any]=16    ,       UpperCamelCase_: Optional[Any]=2    ,       UpperCamelCase_: Optional[Any]=0.02    ,       UpperCamelCase_: Dict=4    ,       ):
             __lowerCamelCase   =						parent
             __lowerCamelCase   =						batch_size
             __lowerCamelCase   =						seq_length
             __lowerCamelCase   =						is_training
             __lowerCamelCase   =						use_attention_mask
             __lowerCamelCase   =						use_token_type_ids
             __lowerCamelCase   =						use_labels
             __lowerCamelCase   =						vocab_size
             __lowerCamelCase   =						hidden_size
             __lowerCamelCase   =						num_hidden_layers
             __lowerCamelCase   =						num_attention_heads
             __lowerCamelCase   =						intermediate_size
             __lowerCamelCase   =						hidden_act
             __lowerCamelCase   =						hidden_dropout_prob
             __lowerCamelCase   =						attention_probs_dropout_prob
             __lowerCamelCase   =						max_position_embeddings
             __lowerCamelCase   =						type_vocab_size
             __lowerCamelCase   =						type_sequence_label_size
             __lowerCamelCase   =						initializer_range
             __lowerCamelCase   =						num_choices
      def 			lowerCAmelCase__						(				self: int   ):
             __lowerCamelCase   =						ids_tensor([self.batch_size, self.seq_length]    ,       self.vocab_size   )

             __lowerCamelCase   =						None
             if self.use_attention_mask:
                    __lowerCamelCase   =						random_attention_mask([self.batch_size, self.seq_length]   )

             __lowerCamelCase   =						DistilBertConfig(
                 vocab_size=self.vocab_size    ,       dim=self.hidden_size    ,       n_layers=self.num_hidden_layers    ,       n_heads=self.num_attention_heads    ,       hidden_dim=self.intermediate_size    ,       hidden_act=self.hidden_act    ,       dropout=self.hidden_dropout_prob    ,       attention_dropout=self.attention_probs_dropout_prob    ,       max_position_embeddings=self.max_position_embeddings    ,       initializer_range=self.initializer_range    ,       tie_weights_=UpperCamelCase_    ,       )

             return config, input_ids, attention_mask




      def 			lowerCAmelCase__						(				self: int   ):
             __lowerCamelCase   =						self.prepare_config_and_inputs()
             __lowerCamelCase,   __lowerCamelCase,   __lowerCamelCase   =						config_and_inputs
             __lowerCamelCase   =						{"""input_ids""": input_ids, """attention_mask""": attention_mask}
             return config, inputs_dict





@require_flax
class     lowerCamelCase__(					__lowerCamelCase ,     unittest.TestCase):
      UpperCAmelCase__      :	int    		=   (
          (
              FlaxDistilBertModel,
              FlaxDistilBertForMaskedLM,
              FlaxDistilBertForMultipleChoice,
              FlaxDistilBertForQuestionAnswering,
              FlaxDistilBertForSequenceClassification,
              FlaxDistilBertForTokenClassification,
              FlaxDistilBertForQuestionAnswering,
          )
          if is_flax_available()
          else ()
      )
      def 			lowerCAmelCase__						(				self: List[str]   ):
             __lowerCamelCase   =						FlaxDistilBertModelTester(self   )




      @slow
      def 			lowerCAmelCase__						(				self: Any   ):
             for model_class_name in self.all_model_classes:
                    __lowerCamelCase   =						model_class_name.from_pretrained("""distilbert-base-uncased"""   )
                    __lowerCamelCase   =						model(np.ones((1, 1)   )   )
                    self.assertIsNotNone(UpperCamelCase_   )





@require_flax
class     lowerCamelCase__(					unittest.TestCase):
      @slow
      def 			lowerCAmelCase__						(				self: List[Any]   ):
             __lowerCamelCase   =						FlaxDistilBertModel.from_pretrained("""distilbert-base-uncased"""   )
             __lowerCamelCase   =						np.array([[0, 3_45, 2_32, 3_28, 7_40, 1_40, 16_95, 69, 60_78, 15_88, 2]]   )
             __lowerCamelCase   =						np.array([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]   )
             __lowerCamelCase   =						model(UpperCamelCase_    ,       attention_mask=UpperCamelCase_   )[0]
             __lowerCamelCase   =						(1, 11, 7_68)
             self.assertEqual(output.shape    ,       UpperCamelCase_   )
             __lowerCamelCase   =						np.array([[[-0.1639, 0.3299, 0.1648], [-0.1746, 0.3289, 0.1710], [-0.1884, 0.3357, 0.1810]]]   )

             self.assertTrue(jnp.allclose(output[:, 1:4, 1:4]    ,       UpperCamelCase_    ,       atol=1E-4   )   )







from __future__ import annotations

lowercase__				:     str 		=						[
    [-1, 0],  # left
    [0, -1],  # down
    [1, 0],  # right
    [0, 1],  # up
]


def 						lowerCamelCase__    (		_A     ,	_A     ,	_A     ,	_A     ,	_A     ,	):







	'''simple docstring'''





	snake_case_		    =     [
	    [0 for col in range(len(grid[0]							)							)] for row in range(len(_A							)							)
	]  # the reference grid
	snake_case_		    =     1
	snake_case_		    =     [
	    [0 for col in range(len(grid[0]							)							)] for row in range(len(_A							)							)
	]  # the action grid

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

	snake_case_		    =     False  # flag that is set when search is complete
	snake_case_		    =     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()
			snake_case_		    =     cell.pop()
			snake_case_		    =     next_cell[2]
			snake_case_		    =     next_cell[3]
			snake_case_		    =     next_cell[1]

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

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


if __name__ == "__main__":
		lowercase__				:     Union[str, Any] 		=						[
		    [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],
		]

		lowercase__				:     Optional[Any] 		=						[0, 0]
		# all coordinates are given in format [y,x]
		lowercase__				:     Tuple 		=						[len(grid) - 1, len(grid[0]) - 1]
		lowercase__				:     Dict 		=						1

		# the cost map which pushes the path closer to the goal
		lowercase__				:     Any 		=						[[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])):
						lowercase__				:     Union[str, Any] 		=						abs(i - goal[0]) + abs(j - goal[1])
						if grid[i][j] == 1:
								# added extra penalty in the heuristic map
								lowercase__				:     int 		=						99

		lowercase__		,       lowercase__				:     Tuple 		=						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])
