

'''simple docstring'''

from __future__ import annotations

a   :			Optional[int]         = [
    [-1, 0],  # left
    [0, -1],  # down
    [1, 0],  # right
    [0, 1],  # up
]





def       lowercase					(     __magic_name__ ,    __magic_name__ ,    __magic_name__ ,    __magic_name__ ,    __magic_name__ ,    ):



					'''simple docstring'''







					UpperCAmelCase		:							List[Any]   					=   [
					    [0 for col in range(len(grid[0]							)							)] for row in range(len(SCREAMING_SNAKE_CASE_							)							)
					]  # the reference grid
					UpperCAmelCase		:							Dict   					=   1
					UpperCAmelCase		:							Dict   					=   [
					    [0 for col in range(len(grid[0]							)							)] for row in range(len(SCREAMING_SNAKE_CASE_							)							)
					]  # the action grid

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

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

					while not found and not resign:
										if len(SCREAMING_SNAKE_CASE_							) == 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()
															UpperCAmelCase		:							int   					=   cell.pop()
															UpperCAmelCase		:							List[Any]   					=   next_cell[2]
															UpperCAmelCase		:							List[Any]   					=   next_cell[3]
															UpperCAmelCase		:							Optional[int]   					=   next_cell[1]

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

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


if __name__ == "__main__":
				a   :			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],
				]

				a   :			int         = [0, 0]
				# all coordinates are given in format [y,x]
				a   :			str         = [len(grid) - 1, len(grid[0]) - 1]
				a   :			Optional[Any]         = 1

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

				a       ,						a   :			int         = 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])


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      A__							(     __magic_name__						,   unittest.TestCase	):
				lowercase			    =  MvpTokenizer
				lowercase			    =  MvpTokenizerFast
				lowercase			    =  True
				lowercase			    =  filter_roberta_detectors






				def 							_lowerCamelCase		(						self					:      int   ):






							'''simple docstring'''







							super().setUp()
							lowerCAmelCase__      :      Dict         =		[
							    'l',
							    'o',
							    'w',
							    'e',
							    'r',
							    's',
							    't',
							    'i',
							    'd',
							    'n',
							    '\u0120',
							    '\u0120l',
							    '\u0120n',
							    '\u0120lo',
							    '\u0120low',
							    'er',
							    '\u0120lowest',
							    '\u0120newer',
							    '\u0120wider',
							    '<unk>',
							]
							lowerCAmelCase__      :      Any         =		dict(zip(a    ,     range(len(a   )   )   )   )
							lowerCAmelCase__      :      Union[str, Any]         =		['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', '']
							lowerCAmelCase__      :      Any         =		{'unk_token': '<unk>'}

							lowerCAmelCase__      :      int         =		os.path.join(self.tmpdirname    ,     VOCAB_FILES_NAMES['vocab_file']   )
							lowerCAmelCase__      :      Optional[int]         =		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(a   ) + '\n'   )
							with open(self.merges_file    ,     'w'    ,     encoding='utf-8'   ) as fp:
										fp.write('\n'.join(a   )   )






				def 							_lowerCamelCase		(						self					:      str    ,     **a					:      Tuple   ):






							'''simple docstring'''







							kwargs.update(self.special_tokens_map   )
							return self.tokenizer_class.from_pretrained(self.tmpdirname    ,     **a   )






				def 							_lowerCamelCase		(						self					:      Dict    ,     **a					:      Optional[int]   ):






							'''simple docstring'''







							kwargs.update(self.special_tokens_map   )
							return self.rust_tokenizer_class.from_pretrained(self.tmpdirname    ,     **a   )






				def 							_lowerCamelCase		(						self					:      Tuple    ,     a					:      Dict   ):






							'''simple docstring'''







							return "lower newer", "lower newer"






				@cached_property
				def 							_lowerCamelCase		(						self					:      List[str]   ):






							'''simple docstring'''







							return MvpTokenizer.from_pretrained('RUCAIBox/mvp'   )






				@cached_property
				def 							_lowerCamelCase		(						self					:      Dict   ):






							'''simple docstring'''







							return MvpTokenizerFast.from_pretrained('RUCAIBox/mvp'   )






				@require_torch
				def 							_lowerCamelCase		(						self					:      Optional[int]   ):






							'''simple docstring'''







							lowerCAmelCase__      :      List[Any]         =		['A long paragraph for summarization.', 'Another paragraph for summarization.']
							lowerCAmelCase__      :      List[Any]         =		[0, 250, 251, 17_818, 13, 39_186, 1_938, 4, 2]

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

										self.assertEqual((2, 9)    ,     batch.input_ids.shape   )
										self.assertEqual((2, 9)    ,     batch.attention_mask.shape   )
										lowerCAmelCase__      :      List[Any]         =		batch.input_ids.tolist()[0]
										self.assertListEqual(a    ,     a   )
										# Test that special tokens are reset






				@require_torch
				def 							_lowerCamelCase		(						self					:      str   ):






							'''simple docstring'''







							lowerCAmelCase__      :      List[Any]         =		['A long paragraph for summarization.', 'Another paragraph for summarization.']
							for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
										lowerCAmelCase__      :      Any         =		tokenizer(a    ,     padding=a    ,     return_tensors='pt'   )
										# check if input_ids are returned and no labels
										self.assertIn('input_ids'    ,     a   )
										self.assertIn('attention_mask'    ,     a   )
										self.assertNotIn('labels'    ,     a   )
										self.assertNotIn('decoder_attention_mask'    ,     a   )






				@require_torch
				def 							_lowerCamelCase		(						self					:      Optional[int]   ):






							'''simple docstring'''







							lowerCAmelCase__      :      Union[str, Any]         =		[
							    'Summary of the text.',
							    'Another summary.',
							]
							for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
										lowerCAmelCase__      :      Tuple         =		tokenizer(text_target=a    ,     max_length=32    ,     padding='max_length'    ,     return_tensors='pt'   )
										self.assertEqual(32    ,     targets['input_ids'].shape[1]   )






				@require_torch
				def 							_lowerCamelCase		(						self					:      Optional[int]   ):






							'''simple docstring'''







							for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
										lowerCAmelCase__      :      str         =		tokenizer(
										    ['I am a small frog' * 1_024, 'I am a small frog']    ,     padding=a    ,     truncation=a    ,     return_tensors='pt'   )
										self.assertIsInstance(a    ,     a   )
										self.assertEqual(batch.input_ids.shape    ,     (2, 1_024)   )






				@require_torch
				def 							_lowerCamelCase		(						self					:      Any   ):






							'''simple docstring'''







							lowerCAmelCase__      :      Union[str, Any]         =		['A long paragraph for summarization.']
							lowerCAmelCase__      :      Any         =		[
							    'Summary of the text.',
							]
							for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
										lowerCAmelCase__      :      List[Any]         =		tokenizer(a    ,     text_target=a    ,     return_tensors='pt'   )
										lowerCAmelCase__      :      Optional[int]         =		inputs['input_ids']
										lowerCAmelCase__      :      str         =		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 							_lowerCamelCase		(						self					:      Any   ):






							'''simple docstring'''







							pass






				def 							_lowerCamelCase		(						self					:      Tuple   ):






							'''simple docstring'''







							for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
										with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})'''   ):
													lowerCAmelCase__      :      str         =		self.rust_tokenizer_class.from_pretrained(a    ,     **a   )
													lowerCAmelCase__      :      List[str]         =		self.tokenizer_class.from_pretrained(a    ,     **a   )
													lowerCAmelCase__      :      Optional[int]         =		'A, <mask> AllenNLP sentence.'
													lowerCAmelCase__      :      int         =		tokenizer_r.encode_plus(a    ,     add_special_tokens=a    ,     return_token_type_ids=a   )
													lowerCAmelCase__      :      Optional[int]         =		tokenizer_p.encode_plus(a    ,     add_special_tokens=a    ,     return_token_type_ids=a   )

													# 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']   )    ,     )

													lowerCAmelCase__      :      List[Any]         =		tokenizer_r.convert_ids_to_tokens(tokens_r['input_ids']   )
													lowerCAmelCase__      :      Dict         =		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, 50_264, 3_823, 487, 21_992, 3_645, 4, 2]   )
													self.assertSequenceEqual(tokens_r['input_ids']    ,     [0, 250, 6, 50_264, 3_823, 487, 21_992, 3_645, 4, 2]   )

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