



'''simple docstring'''

from __future__ import annotations

import unittest

from transformers import 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 numpy
	import tensorflow as tf

	from transformers import (
	    TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
	    TF_DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
	    TF_DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
	    BertConfig,
	    DPRConfig,
	    TFDPRContextEncoder,
	    TFDPRQuestionEncoder,
	    TFDPRReader,
	)






class 						a_		:


							def __init__(				self   :						Dict      ,						a_   :						Optional[Any]      ,						a_   :						List[Any]=1_3      ,						a_   :						Union[str, Any]=7      ,						a_   :						str=True      ,						a_   :						Optional[Any]=True      ,						a_   :						Any=True      ,						a_   :						Union[str, Any]=True      ,						a_   :						Tuple=9_9      ,						a_   :						Tuple=3_2      ,						a_   :						Optional[Any]=2      ,						a_   :						int=4      ,						a_   :						Tuple=3_7      ,						a_   :						Union[str, Any]="gelu"      ,						a_   :						Optional[int]=0.1      ,						a_   :						int=0.1      ,						a_   :						Dict=5_1_2      ,						a_   :						List[Any]=1_6      ,						a_   :						Dict=2      ,						a_   :						List[Any]=0.0_2      ,						a_   :						List[Any]=3      ,						a_   :						Union[str, Any]=4      ,						a_   :						Any=None      ,						a_   :						int=0      ,						)			->					int:
										snake_case:    str      							=parent
										snake_case:    Optional[Any]      							=batch_size
										snake_case:    List[Any]      							=seq_length
										snake_case:    Any      							=is_training
										snake_case:    Optional[int]      							=use_input_mask
										snake_case:    Optional[Any]      							=use_token_type_ids
										snake_case:    int      							=use_labels
										snake_case:    str      							=vocab_size
										snake_case:    Dict      							=hidden_size
										snake_case:    Dict      							=num_hidden_layers
										snake_case:    List[str]      							=num_attention_heads
										snake_case:    Dict      							=intermediate_size
										snake_case:    Tuple      							=hidden_act
										snake_case:    List[str]      							=hidden_dropout_prob
										snake_case:    Any      							=attention_probs_dropout_prob
										snake_case:    Optional[int]      							=max_position_embeddings
										snake_case:    List[str]      							=type_vocab_size
										snake_case:    List[Any]      							=type_sequence_label_size
										snake_case:    Optional[int]      							=initializer_range
										snake_case:    Optional[int]      							=num_labels
										snake_case:    Optional[int]      							=num_choices
										snake_case:    Optional[Any]      							=scope
										snake_case:    Tuple      							=projection_dim


							def    UpperCamelCase     (				self   :						Union[str, Any]			)			->					int:
										snake_case:    str      							=ids_tensor([self.batch_size, self.seq_length]      ,						self.vocab_size			)

										snake_case:    Dict      							=None
										if self.use_input_mask:
													# follow test_modeling_tf_ctrl.py
													snake_case:    List[str]      							=random_attention_mask([self.batch_size, self.seq_length]			)

										snake_case:    Dict      							=None
										if self.use_token_type_ids:
													snake_case:    Optional[int]      							=ids_tensor([self.batch_size, self.seq_length]      ,						self.type_vocab_size			)

										snake_case:    List[str]      							=None
										snake_case:    Optional[int]      							=None
										snake_case:    Any      							=None
										if self.use_labels:
													snake_case:    Optional[int]      							=ids_tensor([self.batch_size]      ,						self.type_sequence_label_size			)
													snake_case:    Optional[Any]      							=ids_tensor([self.batch_size, self.seq_length]      ,						self.num_labels			)
													snake_case:    Optional[Any]      							=ids_tensor([self.batch_size]      ,						self.num_choices			)

										snake_case:    Optional[int]      							=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=a_      ,						initializer_range=self.initializer_range      ,						)
										snake_case:    str      							=DPRConfig(projection_dim=self.projection_dim      ,						**config.to_dict()			)

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


							def    UpperCamelCase     (				self   :						Union[str, Any]      ,						a_   :						int      ,						a_   :						int      ,						a_   :						List[Any]      ,						a_   :						Dict      ,						a_   :						List[Any]      ,						a_   :						List[str]      ,						a_   :						Any			)			->					List[str]:
										snake_case:    Optional[int]      							=TFDPRContextEncoder(config=a_			)
										snake_case:    List[str]      							=model(a_      ,						attention_mask=a_      ,						token_type_ids=a_			)
										snake_case:    Union[str, Any]      							=model(a_      ,						token_type_ids=a_			)
										snake_case:    Union[str, Any]      							=model(a_			)
										self.parent.assertEqual(result.pooler_output.shape      ,						(self.batch_size, self.projection_dim or self.hidden_size)			)


							def    UpperCamelCase     (				self   :						List[Any]      ,						a_   :						Dict      ,						a_   :						Any      ,						a_   :						Dict      ,						a_   :						Any      ,						a_   :						Dict      ,						a_   :						Union[str, Any]      ,						a_   :						str			)			->					List[str]:
										snake_case:    Union[str, Any]      							=TFDPRQuestionEncoder(config=a_			)
										snake_case:    int      							=model(a_      ,						attention_mask=a_      ,						token_type_ids=a_			)
										snake_case:    Dict      							=model(a_      ,						token_type_ids=a_			)
										snake_case:    Dict      							=model(a_			)
										self.parent.assertEqual(result.pooler_output.shape      ,						(self.batch_size, self.projection_dim or self.hidden_size)			)


							def    UpperCamelCase     (				self   :						Dict      ,						a_   :						Any      ,						a_   :						Tuple      ,						a_   :						Any      ,						a_   :						List[Any]      ,						a_   :						Optional[Any]      ,						a_   :						List[Any]      ,						a_   :						Tuple			)			->					Optional[Any]:
										snake_case:    int      							=TFDPRReader(config=a_			)
										snake_case:    Dict      							=model(a_      ,						attention_mask=a_			)

										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)			)
										self.parent.assertEqual(result.relevance_logits.shape      ,						(self.batch_size,)			)




							def    UpperCamelCase     (				self   :						Any			)			->					Dict:
										snake_case:    str      							=self.prepare_config_and_inputs()
										(
										    (
										    snake_case
										)							,  (
										    snake_case
										)							,  (
										    snake_case
										)							,  (
										    snake_case
										)							,  (
										    snake_case
										)							,  (
										    snake_case
										)							,  (
										    snake_case
										)							,  
										):    str      							=config_and_inputs
										snake_case:    Dict      							={'input_ids': input_ids}
										return config, inputs_dict






@require_tf
class 						a_		(     snake_case    ,   snake_case    ,   unittest.TestCase    ):
							UpperCAmelCase			:		List[str]        =				(
							    (
							        TFDPRContextEncoder,
							        TFDPRQuestionEncoder,
							        TFDPRReader,
							    )
							    if is_tf_available()
							    else ()
							)
							UpperCAmelCase			:		List[Any]        =				{"""feature-extraction""": TFDPRQuestionEncoder} if is_tf_available() else {}

							UpperCAmelCase			:		Optional[int]        =				False
							UpperCAmelCase			:		Dict        =				False
							UpperCAmelCase			:		Optional[Any]        =				False
							UpperCAmelCase			:		str        =				False
							UpperCAmelCase			:		int        =				False


							def    UpperCamelCase     (				self   :						Tuple			)			->					Tuple:
										snake_case:    List[str]      							=TFDPRModelTester(self			)
										snake_case:    Tuple      							=ConfigTester(self      ,						config_class=a_      ,						hidden_size=3_7			)


							def    UpperCamelCase     (				self   :						Any			)			->					str:
										self.config_tester.run_common_tests()


							def    UpperCamelCase     (				self   :						str			)			->					List[str]:
										snake_case:    Dict      							=self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_dpr_context_encoder(*a_			)


							def    UpperCamelCase     (				self   :						Union[str, Any]			)			->					Union[str, Any]:
										snake_case:    Optional[Any]      							=self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_dpr_question_encoder(*a_			)


							def    UpperCamelCase     (				self   :						Optional[Any]			)			->					List[str]:
										snake_case:    Dict      							=self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_dpr_reader(*a_			)




							@slow
							def    UpperCamelCase     (				self   :						Union[str, Any]			)			->					Optional[int]:
										for model_name in TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
													snake_case:    Dict      							=TFDPRContextEncoder.from_pretrained(a_			)
													self.assertIsNotNone(a_			)

										for model_name in TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
													snake_case:    str      							=TFDPRContextEncoder.from_pretrained(a_			)
													self.assertIsNotNone(a_			)

										for model_name in TF_DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
													snake_case:    List[str]      							=TFDPRQuestionEncoder.from_pretrained(a_			)
													self.assertIsNotNone(a_			)

										for model_name in TF_DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
													snake_case:    Optional[Any]      							=TFDPRReader.from_pretrained(a_			)
													self.assertIsNotNone(a_			)






@require_tf
class 						a_		(     unittest.TestCase    ):


							@slow
							def    UpperCamelCase     (				self   :						Dict			)			->					Union[str, Any]:
										snake_case:    Dict      							=TFDPRQuestionEncoder.from_pretrained('facebook/dpr-question_encoder-single-nq-base'			)

										snake_case:    List[Any]      							=tf.constant(
										    [[1_0_1, 7_5_9_2, 1_0_1_0, 2_0_0_3, 2_0_2_6, 3_8_9_9, 1_0_1_4_0, 1_0_2_9, 1_0_2]]			)  # [CLS] hello, is my dog cute? [SEP]
										snake_case:    int      							=model(a_			)[0]  # embedding shape = (1, 768)
										# compare the actual values for a slice.
										snake_case:    Union[str, Any]      							=tf.constant(
										    [
										        [
										            0.0_3_2_3_6_2_5_3,
										            0.1_2_7_5_3_3_3_5,
										            0.1_6_8_1_8_5_0_9,
										            0.0_0_2_7_9_7_8_6,
										            0.3_8_9_6_9_3_3,
										            0.2_4_2_6_4_9_4_5,
										            0.2_1_7_8_9_7_1,
										            -0.0_2_3_3_5_2_2_7,
										            -0.0_8_4_8_1_9_5_9,
										            -0.1_4_3_2_4_1_1_7,
										        ]
										    ]			)
										self.assertTrue(numpy.allclose(output[:, :1_0].numpy()      ,						expected_slice.numpy()      ,						atol=1E-4			)			)









'''simple docstring'''

from __future__ import annotations

a							   =				list[tuple[int, int]]

a							   =				[
    [0, 0, 0, 0, 0, 0, 0],
    [0, 1, 0, 0, 0, 0, 0],  # 0 are free path whereas 1's are obstacles
    [0, 0, 0, 0, 0, 0, 0],
    [0, 0, 1, 0, 0, 0, 0],
    [1, 0, 1, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 1, 0, 0],
]

a							   =				([-1, 0], [0, -1], [1, 0], [0, 1])  # up, left, down, right






class 						a_		:


							def __init__(				self   :						Tuple      ,						a_   :						int      ,						a_   :						int      ,						a_   :						int      ,						a_   :						int      ,						a_   :						float      ,						a_   :						Node | None      ,						)			->					Optional[Any]:
										snake_case:    str      							=pos_x
										snake_case:    str      							=pos_y
										snake_case:    Optional[Any]      							=(pos_y, pos_x)
										snake_case:    Union[str, Any]      							=goal_x
										snake_case:    Tuple      							=goal_y
										snake_case:    Tuple      							=g_cost
										snake_case:    Tuple      							=parent
										snake_case:    Optional[Any]      							=self.calculate_heuristic()


							def    UpperCamelCase     (				self   :						Union[str, Any]			)			->					float:
										snake_case:    Optional[Any]      							=abs(self.pos_x - self.goal_x			)
										snake_case:    Optional[int]      							=abs(self.pos_y - self.goal_y			)
										return dx + dy




							def __lt__(				self   :						Union[str, Any]      ,						a_   :						Tuple			)			->					bool:
										return self.f_cost < other.f_cost






class 						a_		:


							def __init__(				self   :						Any      ,						a_   :						tuple[int, int]      ,						a_   :						tuple[int, int]			)			->					Optional[Any]:
										snake_case:    Any      							=Node(start[1]      ,						start[0]      ,						goal[1]      ,						goal[0]      ,						0      ,						a_			)
										snake_case:    str      							=Node(goal[1]      ,						goal[0]      ,						goal[1]      ,						goal[0]      ,						9_9_9_9_9      ,						a_			)

										snake_case:    int      							=[self.start]
										snake_case:    list[Node]      							=[]

										snake_case:    Tuple      							=False


							def    UpperCamelCase     (				self   :						str			)			->					Path | None:
										while self.open_nodes:
													# Open Nodes are sorted using __lt__
													self.open_nodes.sort()
													snake_case:    Tuple      							=self.open_nodes.pop(0			)

													if current_node.pos == self.target.pos:
																snake_case:    Tuple      							=True
																return self.retrace_path(a_			)

													self.closed_nodes.append(a_			)
													snake_case:    Optional[Any]      							=self.get_successors(a_			)

													for child_node in successors:
																if child_node in self.closed_nodes:
																			continue

																if child_node not in self.open_nodes:
																			self.open_nodes.append(a_			)
																else:
																			# retrieve the best current path
																			snake_case:    List[Any]      							=self.open_nodes.pop(self.open_nodes.index(a_			)			)

																			if child_node.g_cost < better_node.g_cost:
																						self.open_nodes.append(a_			)
																			else:
																						self.open_nodes.append(a_			)

										if not self.reached:
													return [self.start.pos]
										return None


							def    UpperCamelCase     (				self   :						str      ,						a_   :						Node			)			->					list[Node]:
										snake_case:    Any      							=[]
										for action in delta:
													snake_case:    Any      							=parent.pos_x + action[1]
													snake_case:    str      							=parent.pos_y + action[0]

													if not (0 <= pos_x <= len(grid[0]			) - 1 and 0 <= pos_y <= len(a_			) - 1):
																continue

													if grid[pos_y][pos_x] != 0:
																continue

													successors.append(
													    Node(
													        a_      ,						a_      ,						self.target.pos_y      ,						self.target.pos_x      ,						parent.g_cost + 1      ,						a_      ,						)			)
										return successors




							def    UpperCamelCase     (				self   :						Dict      ,						a_   :						Node | None			)			->					Path:
										snake_case:    Dict      							=node
										snake_case:    Optional[int]      							=[]
										while current_node is not None:
													path.append((current_node.pos_y, current_node.pos_x)			)
													snake_case:    List[str]      							=current_node.parent
										path.reverse()
										return path


if __name__ == "__main__":
	a							   =				(0, 0)
	a							   =				(len(grid) - 1, len(grid[0]) - 1)
	for elem in grid:
		print(elem)

	print('------')

	a							   =				GreedyBestFirst(init, goal)
	a							   =				greedy_bf.search()
	if path:
		for pos_x, pos_y in path:
			a							   =				2

		for elem in grid:
			print(elem)




