






from __future__ import annotations

import unittest

from transformers import DebertaVaConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
 import tensorflow as tf

 from transformers import (
     TFDebertaVaForMaskedLM,
     TFDebertaVaForQuestionAnswering,
     TFDebertaVaForSequenceClassification,
     TFDebertaVaForTokenClassification,
     TFDebertaVaModel,
 )



class 				_UpperCamelCase      :


      def __init__(						self       ,    __UpperCamelCase       ,    __UpperCamelCase=1_3       ,    __UpperCamelCase=7       ,    __UpperCamelCase=True       ,    __UpperCamelCase=True       ,    __UpperCamelCase=True       ,    __UpperCamelCase=True       ,    __UpperCamelCase=9_9       ,    __UpperCamelCase=3_2       ,    __UpperCamelCase=2       ,    __UpperCamelCase=4       ,    __UpperCamelCase=3_7       ,    __UpperCamelCase="gelu"       ,    __UpperCamelCase=0.1       ,    __UpperCamelCase=0.1       ,    __UpperCamelCase=5_1_2       ,    __UpperCamelCase=1_6       ,    __UpperCamelCase=2       ,    __UpperCamelCase=0.0_2       ,    __UpperCamelCase=False       ,    __UpperCamelCase=True       ,    __UpperCamelCase="None"       ,    __UpperCamelCase=3       ,    __UpperCamelCase=4       ,    __UpperCamelCase=None       ,    )->						Tuple:
           __lowerCAmelCase             =       parent
           __lowerCAmelCase             =       batch_size
           __lowerCAmelCase             =       seq_length
           __lowerCAmelCase             =       is_training
           __lowerCAmelCase             =       use_input_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_labels
           __lowerCAmelCase             =       num_choices
           __lowerCAmelCase             =       relative_attention
           __lowerCAmelCase             =       position_biased_input
           __lowerCAmelCase             =       pos_att_type
           __lowerCAmelCase             =       scope


      def 			__UpperCAmelCase		(						self						)->						List[str]:
           __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]						)

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

           __lowerCAmelCase             =       None
           __lowerCAmelCase             =       None
           __lowerCAmelCase             =       None
           if self.use_labels:
                __lowerCAmelCase             =       ids_tensor([self.batch_size]       ,    self.type_sequence_label_size						)
                __lowerCAmelCase             =       ids_tensor([self.batch_size, self.seq_length]       ,    self.num_labels						)

           __lowerCAmelCase             =       DebertaVaConfig(
               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       ,    relative_attention=self.relative_attention       ,    position_biased_input=self.position_biased_input       ,    initializer_range=self.initializer_range       ,    return_dict=_UpperCamelCase       ,    )

           return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels


      def 			__UpperCAmelCase		(						self       ,    __UpperCamelCase       ,    __UpperCamelCase       ,    __UpperCamelCase       ,    __UpperCamelCase       ,    __UpperCamelCase       ,    __UpperCamelCase       ,    __UpperCamelCase						)->						Dict:
           __lowerCAmelCase             =       TFDebertaVaModel(config=_UpperCamelCase						)
           __lowerCAmelCase             =       {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}

           __lowerCAmelCase             =       [input_ids, input_mask]
           __lowerCAmelCase             =       model(_UpperCamelCase						)

           __lowerCAmelCase             =       model(_UpperCamelCase						)

           self.parent.assertEqual(result.last_hidden_state.shape       ,    (self.batch_size, self.seq_length, self.hidden_size)						)


      def 			__UpperCAmelCase		(						self       ,    __UpperCamelCase       ,    __UpperCamelCase       ,    __UpperCamelCase       ,    __UpperCamelCase       ,    __UpperCamelCase       ,    __UpperCamelCase       ,    __UpperCamelCase						)->						Any:
           __lowerCAmelCase             =       TFDebertaVaForMaskedLM(config=_UpperCamelCase						)
           __lowerCAmelCase             =       {
               "input_ids": input_ids,
               "attention_mask": input_mask,
               "token_type_ids": token_type_ids,
           }
           __lowerCAmelCase             =       model(_UpperCamelCase						)
           self.parent.assertEqual(result.logits.shape       ,    (self.batch_size, self.seq_length, self.vocab_size)						)


      def 			__UpperCAmelCase		(						self       ,    __UpperCamelCase       ,    __UpperCamelCase       ,    __UpperCamelCase       ,    __UpperCamelCase       ,    __UpperCamelCase       ,    __UpperCamelCase       ,    __UpperCamelCase						)->						str:
           __lowerCAmelCase             =       self.num_labels
           __lowerCAmelCase             =       TFDebertaVaForSequenceClassification(config=_UpperCamelCase						)
           __lowerCAmelCase             =       {
               "input_ids": input_ids,
               "attention_mask": input_mask,
               "token_type_ids": token_type_ids,
           }

           __lowerCAmelCase             =       model(_UpperCamelCase						)
           self.parent.assertEqual(result.logits.shape       ,    (self.batch_size, self.num_labels)						)


      def 			__UpperCAmelCase		(						self       ,    __UpperCamelCase       ,    __UpperCamelCase       ,    __UpperCamelCase       ,    __UpperCamelCase       ,    __UpperCamelCase       ,    __UpperCamelCase       ,    __UpperCamelCase						)->						int:
           __lowerCAmelCase             =       self.num_labels
           __lowerCAmelCase             =       TFDebertaVaForTokenClassification(config=_UpperCamelCase						)
           __lowerCAmelCase             =       {
               "input_ids": input_ids,
               "attention_mask": input_mask,
               "token_type_ids": token_type_ids,
           }
           __lowerCAmelCase             =       model(_UpperCamelCase						)
           self.parent.assertEqual(result.logits.shape       ,    (self.batch_size, self.seq_length, self.num_labels)						)


      def 			__UpperCAmelCase		(						self       ,    __UpperCamelCase       ,    __UpperCamelCase       ,    __UpperCamelCase       ,    __UpperCamelCase       ,    __UpperCamelCase       ,    __UpperCamelCase       ,    __UpperCamelCase						)->						Dict:
           __lowerCAmelCase             =       TFDebertaVaForQuestionAnswering(config=_UpperCamelCase						)
           __lowerCAmelCase             =       {
               "input_ids": input_ids,
               "attention_mask": input_mask,
               "token_type_ids": token_type_ids,
           }

           __lowerCAmelCase             =       model(_UpperCamelCase						)
           self.parent.assertEqual(result.start_logits.shape       ,    (self.batch_size, self.seq_length)						)
           self.parent.assertEqual(result.end_logits.shape       ,    (self.batch_size, self.seq_length)						)


      def 			__UpperCAmelCase		(						self						)->						Tuple:
           __lowerCAmelCase             =       self.prepare_config_and_inputs()
           (
               (
               __lowerCAmelCase    
           )      ,			(
               __lowerCAmelCase    
           )      ,			(
               __lowerCAmelCase    
           )      ,			(
               __lowerCAmelCase    
           )      ,			(
               __lowerCAmelCase    
           )      ,			(
               __lowerCAmelCase    
           )      ,			(
               __lowerCAmelCase    
           )      ,			    
           )             =       config_and_inputs
           __lowerCAmelCase             =       {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
           return config, inputs_dict



@require_tf
class 				_UpperCamelCase      (__a				,		__a				,		unittest.TestCase      ):
      snake_case_	=				(
          (
              TFDebertaVaModel,
              TFDebertaVaForMaskedLM,
              TFDebertaVaForQuestionAnswering,
              TFDebertaVaForSequenceClassification,
              TFDebertaVaForTokenClassification,
          )
          if is_tf_available()
          else ()
      )
      snake_case_	=				(
          {
              """feature-extraction""": TFDebertaVaModel,
              """fill-mask""": TFDebertaVaForMaskedLM,
              """question-answering""": TFDebertaVaForQuestionAnswering,
              """text-classification""": TFDebertaVaForSequenceClassification,
              """token-classification""": TFDebertaVaForTokenClassification,
              """zero-shot""": TFDebertaVaForSequenceClassification,
          }
          if is_tf_available()
          else {}
      )

      snake_case_	=				False
      snake_case_	=				False


      def 			__UpperCAmelCase		(						self						)->						Optional[int]:
           __lowerCAmelCase             =       TFDebertaVaModelTester(self						)
           __lowerCAmelCase             =       ConfigTester(self       ,    config_class=_UpperCamelCase       ,    hidden_size=3_7						)


      def 			__UpperCAmelCase		(						self						)->						str:
           self.config_tester.run_common_tests()


      def 			__UpperCAmelCase		(						self						)->						int:
           __lowerCAmelCase             =       self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*_UpperCamelCase						)


      def 			__UpperCAmelCase		(						self						)->						List[Any]:
           __lowerCAmelCase             =       self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_masked_lm(*_UpperCamelCase						)


      def 			__UpperCAmelCase		(						self						)->						List[str]:
           __lowerCAmelCase             =       self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_question_answering(*_UpperCamelCase						)


      def 			__UpperCAmelCase		(						self						)->						List[Any]:
           __lowerCAmelCase             =       self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_sequence_classification(*_UpperCamelCase						)


      def 			__UpperCAmelCase		(						self						)->						int:
           __lowerCAmelCase             =       self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_token_classification(*_UpperCamelCase						)


      @slow
      def 			__UpperCAmelCase		(						self						)->						Union[str, Any]:
           __lowerCAmelCase             =       TFDebertaVaModel.from_pretrained("kamalkraj/deberta-v2-xlarge"						)
           self.assertIsNotNone(_UpperCamelCase						)



@require_tf
class 				_UpperCamelCase      (unittest.TestCase      ):


      @unittest.skip(reason="Model not available yet"						)
      def 			__UpperCAmelCase		(						self						)->						List[str]:
           pass


      @slow
      def 			__UpperCAmelCase		(						self						)->						Dict:
           __lowerCAmelCase             =       TFDebertaVaModel.from_pretrained("kamalkraj/deberta-v2-xlarge"						)
           __lowerCAmelCase             =       tf.constant([[0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9, 4_6_0_7_8, 1_5_8_8, 2]]						)
           __lowerCAmelCase             =       tf.constant([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]						)
           __lowerCAmelCase             =       model(_UpperCamelCase       ,    attention_mask=_UpperCamelCase						)[0]

           __lowerCAmelCase             =       tf.constant(
               [[[0.2_3_5_6, 0.1_9_4_8, 0.0_3_6_9], [-0.1_0_6_3, 0.3_5_8_6, -0.5_1_5_2], [-0.6_3_9_9, -0.0_2_5_9, -0.2_5_2_5]]]						)
           tf.debugging.assert_near(output[:, 1:4, 1:4]       ,    _UpperCamelCase       ,    atol=1e-4						)








from __future__ import annotations

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






def   lowerCamelCase__							(							__lowerCAmelCase    :    list[list[int]]       ,				__lowerCAmelCase    :    list[int]       ,				__lowerCAmelCase    :    list[int]       ,				__lowerCAmelCase    :    int       ,				__lowerCAmelCase    :    list[list[int]]       ,				):




			"""simple docstring"""


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

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

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

			while not found and not resign:
						if len(__lowerCAmelCase							) == 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_         =      cell.pop()
									lowerCAmelCase_         =      next_cell[2]
									lowerCAmelCase_         =      next_cell[3]
									lowerCAmelCase_         =      next_cell[1]

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

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


if __name__ == "__main__":
			_A  =	[
			    [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],
			]

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

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

			_A, _A  =	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])



