





'''simple docstring'''




import json
import os
import unittest

from transformers import BatchEncoding, MvpTokenizer, MvpTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin, filter_roberta_detectors






@require_tokenizers
class 				__snake_case     (					__magic_name__       ,       unittest.TestCase							):
	__lowerCAmelCase       						=  MvpTokenizer
	__lowerCAmelCase       						=  MvpTokenizerFast
	__lowerCAmelCase       						=  True
	__lowerCAmelCase       						=  filter_roberta_detectors





	def   _snake_case   (		self		)      ->		Optional[Any]:
		super().setUp()
		snake_case__	       =   [
		    'l',
		    'o',
		    'w',
		    'e',
		    'r',
		    's',
		    't',
		    'i',
		    'd',
		    'n',
		    '\u0120',
		    '\u0120l',
		    '\u0120n',
		    '\u0120lo',
		    '\u0120low',
		    'er',
		    '\u0120lowest',
		    '\u0120newer',
		    '\u0120wider',
		    '<unk>',
		]
		snake_case__	       =   dict(zip(UpperCamelCase_      ,       range(len(UpperCamelCase_		)		)		)		)
		snake_case__	       =   ['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', '']
		snake_case__	       =   {'unk_token': '<unk>'}

		snake_case__	       =   os.path.join(self.tmpdirname      ,       VOCAB_FILES_NAMES['vocab_file']		)
		snake_case__	       =   os.path.join(self.tmpdirname      ,       VOCAB_FILES_NAMES['merges_file']		)
		with open(self.vocab_file      ,       'w'      ,       encoding='utf-8'		) as fp:
			fp.write(json.dumps(UpperCamelCase_		) + '\n'		)
		with open(self.merges_file      ,       'w'      ,       encoding='utf-8'		) as fp:
			fp.write('\n'.join(UpperCamelCase_		)		)





	def   _snake_case   (		self      ,       **UpperCamelCase_		)      ->		List[Any]:
		kwargs.update(self.special_tokens_map		)
		return self.tokenizer_class.from_pretrained(self.tmpdirname      ,       **UpperCamelCase_		)





	def   _snake_case   (		self      ,       **UpperCamelCase_		)      ->		Union[str, Any]:
		kwargs.update(self.special_tokens_map		)
		return self.rust_tokenizer_class.from_pretrained(self.tmpdirname      ,       **UpperCamelCase_		)





	def   _snake_case   (		self      ,       UpperCamelCase_		)      ->		Union[str, Any]:
		return "lower newer", "lower newer"





	@cached_property
	def   _snake_case   (		self		)      ->		List[str]:
		return MvpTokenizer.from_pretrained('RUCAIBox/mvp'		)





	@cached_property
	def   _snake_case   (		self		)      ->		int:
		return MvpTokenizerFast.from_pretrained('RUCAIBox/mvp'		)





	@require_torch
	def   _snake_case   (		self		)      ->		Any:
		snake_case__	       =   ['A long paragraph for summarization.', 'Another paragraph for summarization.']
		snake_case__	       =   [0, 250, 251, 1_7818, 13, 3_9186, 1938, 4, 2]

		for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
			snake_case__	       =   tokenizer(UpperCamelCase_      ,       max_length=len(UpperCamelCase_		)      ,       padding=UpperCamelCase_      ,       return_tensors='pt'		)
			self.assertIsInstance(UpperCamelCase_      ,       UpperCamelCase_		)

			self.assertEqual((2, 9)      ,       batch.input_ids.shape		)
			self.assertEqual((2, 9)      ,       batch.attention_mask.shape		)
			snake_case__	       =   batch.input_ids.tolist()[0]
			self.assertListEqual(UpperCamelCase_      ,       UpperCamelCase_		)
			# Test that special tokens are reset





	@require_torch
	def   _snake_case   (		self		)      ->		int:
		snake_case__	       =   ['A long paragraph for summarization.', 'Another paragraph for summarization.']
		for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
			snake_case__	       =   tokenizer(UpperCamelCase_      ,       padding=UpperCamelCase_      ,       return_tensors='pt'		)
			# check if input_ids are returned and no labels
			self.assertIn('input_ids'      ,       UpperCamelCase_		)
			self.assertIn('attention_mask'      ,       UpperCamelCase_		)
			self.assertNotIn('labels'      ,       UpperCamelCase_		)
			self.assertNotIn('decoder_attention_mask'      ,       UpperCamelCase_		)





	@require_torch
	def   _snake_case   (		self		)      ->		List[Any]:
		snake_case__	       =   [
		    'Summary of the text.',
		    'Another summary.',
		]
		for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
			snake_case__	       =   tokenizer(text_target=UpperCamelCase_      ,       max_length=32      ,       padding='max_length'      ,       return_tensors='pt'		)
			self.assertEqual(32      ,       targets['input_ids'].shape[1]		)





	@require_torch
	def   _snake_case   (		self		)      ->		Tuple:
		for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
			snake_case__	       =   tokenizer(
			    ['I am a small frog' * 1024, 'I am a small frog']      ,       padding=UpperCamelCase_      ,       truncation=UpperCamelCase_      ,       return_tensors='pt'		)
			self.assertIsInstance(UpperCamelCase_      ,       UpperCamelCase_		)
			self.assertEqual(batch.input_ids.shape      ,       (2, 1024)		)





	@require_torch
	def   _snake_case   (		self		)      ->		str:
		snake_case__	       =   ['A long paragraph for summarization.']
		snake_case__	       =   [
		    'Summary of the text.',
		]
		for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
			snake_case__	       =   tokenizer(UpperCamelCase_      ,       text_target=UpperCamelCase_      ,       return_tensors='pt'		)
			snake_case__	       =   inputs['input_ids']
			snake_case__	       =   inputs['labels']
			self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item()		)
			self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item()		)
			self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item()		)
			self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item()		)





	def   _snake_case   (		self		)      ->		int:
		pass





	def   _snake_case   (		self		)      ->		Any:
		for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
			with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''		):
				snake_case__	       =   self.rust_tokenizer_class.from_pretrained(UpperCamelCase_      ,       **UpperCamelCase_		)
				snake_case__	       =   self.tokenizer_class.from_pretrained(UpperCamelCase_      ,       **UpperCamelCase_		)
				snake_case__	       =   'A, <mask> AllenNLP sentence.'
				snake_case__	       =   tokenizer_r.encode_plus(UpperCamelCase_      ,       add_special_tokens=UpperCamelCase_      ,       return_token_type_ids=UpperCamelCase_		)
				snake_case__	       =   tokenizer_p.encode_plus(UpperCamelCase_      ,       add_special_tokens=UpperCamelCase_      ,       return_token_type_ids=UpperCamelCase_		)

				# token_type_ids should put 0 everywhere
				self.assertEqual(sum(tokens_r['token_type_ids']		)      ,       sum(tokens_p['token_type_ids']		)		)

				# attention_mask should put 1 everywhere, so sum over length should be 1
				self.assertEqual(
				    sum(tokens_r['attention_mask']		) / len(tokens_r['attention_mask']		)      ,       sum(tokens_p['attention_mask']		) / len(tokens_p['attention_mask']		)      ,       )

				snake_case__	       =   tokenizer_r.convert_ids_to_tokens(tokens_r['input_ids']		)
				snake_case__	       =   tokenizer_p.convert_ids_to_tokens(tokens_p['input_ids']		)

				# Rust correctly handles the space before the mask while python doesnt
				self.assertSequenceEqual(tokens_p['input_ids']      ,       [0, 250, 6, 5_0264, 3823, 487, 2_1992, 3645, 4, 2]		)
				self.assertSequenceEqual(tokens_r['input_ids']      ,       [0, 250, 6, 5_0264, 3823, 487, 2_1992, 3645, 4, 2]		)

				self.assertSequenceEqual(
				    UpperCamelCase_      ,       ['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>']		)
				self.assertSequenceEqual(
				    UpperCamelCase_      ,       ['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>']		)










'''simple docstring'''




from __future__ import annotations

a__					:			Optional[int]          =     list[tuple[int, int]]

a__					:			List[Any]          =     [
    [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__					:			Optional[int]          =     ([-1, 0], [0, -1], [1, 0], [0, 1])  # up, left, down, right






class 				__snake_case     :





	def __init__(		self      ,       UpperCamelCase_      ,       UpperCamelCase_      ,       UpperCamelCase_      ,       UpperCamelCase_      ,       UpperCamelCase_      ,       UpperCamelCase_      ,       )      ->		Any:
		snake_case__	       =   pos_x
		snake_case__	       =   pos_y
		snake_case__	       =   (pos_y, pos_x)
		snake_case__	       =   goal_x
		snake_case__	       =   goal_y
		snake_case__	       =   g_cost
		snake_case__	       =   parent
		snake_case__	       =   self.calculate_heuristic()





	def   _snake_case   (		self		)      ->		float:
		snake_case__	       =   abs(self.pos_x - self.goal_x		)
		snake_case__	       =   abs(self.pos_y - self.goal_y		)
		return dx + dy





	def __lt__(		self      ,       UpperCamelCase_		)      ->		bool:
		return self.f_cost < other.f_cost






class 				__snake_case     :





	def __init__(		self      ,       UpperCamelCase_      ,       UpperCamelCase_		)      ->		Any:
		snake_case__	       =   Node(start[1]      ,       start[0]      ,       goal[1]      ,       goal[0]      ,       0      ,       UpperCamelCase_		)
		snake_case__	       =   Node(goal[1]      ,       goal[0]      ,       goal[1]      ,       goal[0]      ,       9_9999      ,       UpperCamelCase_		)

		snake_case__	       =   [self.start]
		snake_case__	       =   []

		snake_case__	       =   False





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

			if current_node.pos == self.target.pos:
				snake_case__	       =   True
				return self.retrace_path(UpperCamelCase_		)

			self.closed_nodes.append(UpperCamelCase_		)
			snake_case__	       =   self.get_successors(UpperCamelCase_		)

			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(UpperCamelCase_		)
				else:
					# retrieve the best current path
					snake_case__	       =   self.open_nodes.pop(self.open_nodes.index(UpperCamelCase_		)		)

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

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





	def   _snake_case   (		self      ,       UpperCamelCase_		)      ->		list[Node]:
		snake_case__	       =   []
		for action in delta:
			snake_case__	       =   parent.pos_x + action[1]
			snake_case__	       =   parent.pos_y + action[0]

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

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

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





	def   _snake_case   (		self      ,       UpperCamelCase_		)      ->		Path:
		snake_case__	       =   node
		snake_case__	       =   []
		while current_node is not None:
			path.append((current_node.pos_y, current_node.pos_x)		)
			snake_case__	       =   current_node.parent
		path.reverse()
		return path


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

			print('''------''')

			a__					:			Optional[int]          =     GreedyBestFirst(init, goal)
			a__					:			Optional[int]          =     greedy_bf.search()
			if path:
						for pos_x, pos_y in path:
									a__					:			Tuple          =     2

						for elem in grid:
									print(elem)



