



import unittest

import numpy as np

from transformers import RoFormerConfig, 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.roformer.modeling_flax_roformer import (
				    FlaxRoFormerForMaskedLM,
				    FlaxRoFormerForMultipleChoice,
				    FlaxRoFormerForQuestionAnswering,
				    FlaxRoFormerForSequenceClassification,
				    FlaxRoFormerForTokenClassification,
				    FlaxRoFormerModel,
				)

class 				snake_case__(unittest.TestCase      ):

					"""simple docstring"""






					def __init__(							self :     str				,       SCREAMING_SNAKE_CASE :     Union[str, Any]				,       SCREAMING_SNAKE_CASE :     Optional[Any]=13				,       SCREAMING_SNAKE_CASE :     Optional[int]=7				,       SCREAMING_SNAKE_CASE :     Union[str, Any]=True				,       SCREAMING_SNAKE_CASE :     Dict=True				,       SCREAMING_SNAKE_CASE :     List[str]=True				,       SCREAMING_SNAKE_CASE :     int=True				,       SCREAMING_SNAKE_CASE :     Dict=99				,       SCREAMING_SNAKE_CASE :     int=32				,       SCREAMING_SNAKE_CASE :     int=5				,       SCREAMING_SNAKE_CASE :     Optional[int]=4				,       SCREAMING_SNAKE_CASE :     Any=37				,       SCREAMING_SNAKE_CASE :     Dict="gelu"				,       SCREAMING_SNAKE_CASE :     List[str]=0.1				,       SCREAMING_SNAKE_CASE :     List[str]=0.1				,       SCREAMING_SNAKE_CASE :     Optional[Any]=512				,       SCREAMING_SNAKE_CASE :     str=16				,       SCREAMING_SNAKE_CASE :     str=2				,       SCREAMING_SNAKE_CASE :     int=0.02				,       SCREAMING_SNAKE_CASE :     Union[str, Any]=4				,       ):
									lowercase__					:   Optional[Any]													=					parent
									lowercase__					:   List[Any]													=					batch_size
									lowercase__					:   List[Any]													=					seq_length
									lowercase__					:   List[str]													=					is_training
									lowercase__					:   int													=					use_attention_mask
									lowercase__					:   Optional[int]													=					use_token_type_ids
									lowercase__					:   Dict													=					use_labels
									lowercase__					:   Tuple													=					vocab_size
									lowercase__					:   Union[str, Any]													=					hidden_size
									lowercase__					:   Dict													=					num_hidden_layers
									lowercase__					:   List[str]													=					num_attention_heads
									lowercase__					:   str													=					intermediate_size
									lowercase__					:   List[str]													=					hidden_act
									lowercase__					:   str													=					hidden_dropout_prob
									lowercase__					:   Optional[Any]													=					attention_probs_dropout_prob
									lowercase__					:   Union[str, Any]													=					max_position_embeddings
									lowercase__					:   int													=					type_vocab_size
									lowercase__					:   List[str]													=					type_sequence_label_size
									lowercase__					:   List[Any]													=					initializer_range
									lowercase__					:   List[Any]													=					num_choices







					def 			snake_case					(							self :     Optional[Any]					):
									lowercase__					:   int													=					ids_tensor([self.batch_size, self.seq_length]				,       self.vocab_size					)

									lowercase__					:   List[str]													=					None
									if self.use_attention_mask:
													lowercase__					:   List[str]													=					random_attention_mask([self.batch_size, self.seq_length]					)

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

									lowercase__					:   Optional[int]													=					RoFormerConfig(
									    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=SCREAMING_SNAKE_CASE				,       initializer_range=self.initializer_range				,       )

									return config, input_ids, token_type_ids, attention_mask







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






@require_flax
class 				snake_case__(_UpperCamelCase    ,      unittest.TestCase      ):

					"""simple docstring"""






					lowercase_								=		True

					lowercase_								=		(
					    (
					        FlaxRoFormerModel,
					        FlaxRoFormerForMaskedLM,
					        FlaxRoFormerForSequenceClassification,
					        FlaxRoFormerForTokenClassification,
					        FlaxRoFormerForMultipleChoice,
					        FlaxRoFormerForQuestionAnswering,
					    )
					    if is_flax_available()
					    else ()
					)







					def 			snake_case					(							self :     str					):
									lowercase__					:   int													=					FlaxRoFormerModelTester(self					)







					@slow
					def 			snake_case					(							self :     Tuple					):
									for model_class_name in self.all_model_classes:
													lowercase__					:   Tuple													=					model_class_name.from_pretrained("junnyu/roformer_chinese_small"				,       from_pt=SCREAMING_SNAKE_CASE					)
													lowercase__					:   str													=					model(np.ones((1, 1)					)					)
													self.assertIsNotNone(SCREAMING_SNAKE_CASE					)






@require_flax
class 				snake_case__(unittest.TestCase      ):

					"""simple docstring"""






					@slow
					def 			snake_case					(							self :     Any					):
									lowercase__					:   List[Any]													=					FlaxRoFormerForMaskedLM.from_pretrained("junnyu/roformer_chinese_base"					)
									lowercase__					:   Tuple													=					jnp.array([[0, 1, 2, 3, 4, 5]]					)
									lowercase__					:   Tuple													=					model(SCREAMING_SNAKE_CASE					)[0]

									lowercase__					:   Optional[int]													=					50_000

									lowercase__					:   int													=					(1, 6, vocab_size)
									self.assertEqual(output.shape				,       SCREAMING_SNAKE_CASE					)

									lowercase__					:   int													=					jnp.array(
									    [[[-0.1_205, -1.0_265, 0.2_922], [-1.5_134, 0.1_974, 0.1_519], [-5.0_135, -3.9_003, -0.8_404]]]					)

									self.assertTrue(jnp.allclose(output[:, :3, :3]				,       SCREAMING_SNAKE_CASE				,       atol=1E-4					)					)









from __future__ import annotations

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


def  __lowerCamelCase (       lowerCamelCase__					,			lowerCamelCase__					,			lowerCamelCase__					,			lowerCamelCase__					,			lowerCamelCase__					,			):

				"""simple docstring"""


				lowercase__					:   Optional[int]													=					[
				    [0 for col in range(len(grid[0]						)						)] for row in range(len(lowerCamelCase__						)						)
				]  # the reference grid
				lowercase__					:   List[Any]													=					1
				lowercase__					:   Tuple													=					[
				    [0 for col in range(len(grid[0]						)						)] for row in range(len(lowerCamelCase__						)						)
				]  # the action grid

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

				lowercase__					:   Union[str, Any]													=					False  # flag that is set when search is complete
				lowercase__					:   Any													=					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()
												lowercase__					:   Tuple													=					cell.pop()
												lowercase__					:   Optional[Any]													=					next_cell[2]
												lowercase__					:   int													=					next_cell[3]
												lowercase__					:   Union[str, Any]													=					next_cell[1]

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

				lowercase__					:   List[str]													=					[]
				for i in range(len(lowerCamelCase__						)						):
								path.append(invpath[len(lowerCamelCase__						) - 1 - i]						)
				return path, action


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

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

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

				lowerCAmelCase__  ,						lowerCAmelCase__					  =  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])




