






"""simple docstring"""







import logging
import os
from dataclasses import dataclass
from typing import List, Optional, Union

import tqdm
from filelock import FileLock

from transformers import (
    BartTokenizer,
    BartTokenizerFast,
    DataProcessor,
    PreTrainedTokenizer,
    RobertaTokenizer,
    RobertaTokenizerFast,
    XLMRobertaTokenizer,
    is_tf_available,
    is_torch_available,
)


_lowerCAmelCase            =	logging.getLogger(__name__)


@dataclass(frozen=__snake_case     )
class      UpperCamelCase		:
						_SCREAMING_SNAKE_CASE  : str
						_SCREAMING_SNAKE_CASE  : str
						_SCREAMING_SNAKE_CASE  : Optional[str]												=		None
						_SCREAMING_SNAKE_CASE  : Optional[str]												=		None
						_SCREAMING_SNAKE_CASE  : Optional[str]												=		None


@dataclass(frozen=__snake_case     )
class      UpperCamelCase		:
						_SCREAMING_SNAKE_CASE  : List[int]
						_SCREAMING_SNAKE_CASE  : Optional[List[int]]												=		None
						_SCREAMING_SNAKE_CASE  : Optional[List[int]]												=		None
						_SCREAMING_SNAKE_CASE  : Optional[Union[int, float]]												=		None
						_SCREAMING_SNAKE_CASE  : Optional[int]												=		None


if is_torch_available():
				import torch
				from torch.utils.data import Dataset


				class      UpperCamelCase		(__snake_case     ):
										_SCREAMING_SNAKE_CASE  : List[InputFeatures]






										def __init__(		self  :Optional[Any]			, __magic_name__  :str			, __magic_name__  :PreTrainedTokenizer			, __magic_name__  :str			, __magic_name__  :Optional[int] = None			, __magic_name__  :Any=False			, __magic_name__  :bool = False			, )      ->str:
														lowercase    :      Dict       = hans_processors[task]()

														lowercase    :      List[Any]       = os.path.join(
														    __magic_name__			, """cached_{}_{}_{}_{}""".format(
														        """dev""" if evaluate else """train"""			, tokenizer.__class__.__name__			, str(__magic_name__				)			, __magic_name__			, )			, )
														lowercase    :      Any       = processor.get_labels()
														if tokenizer.__class__ in (
														    RobertaTokenizer,
														    RobertaTokenizerFast,
														    XLMRobertaTokenizer,
														    BartTokenizer,
														    BartTokenizerFast,
														):
																		# HACK(label indices are swapped in RoBERTa pretrained model)
																		lowercase      ,      lowercase    :      str       = label_list[2], label_list[1]
														lowercase    :      Union[str, Any]       = label_list

														# Make sure only the first process in distributed training processes the dataset,
														# and the others will use the cache.
														lowercase    :      Optional[int]       = cached_features_file + """.lock"""
														with FileLock(__magic_name__				):
																		if os.path.exists(__magic_name__				) and not overwrite_cache:
																						logger.info(f"""Loading features from cached file {cached_features_file}"""				)
																						lowercase    :      Any       = torch.load(__magic_name__				)
																		else:
																						logger.info(f"""Creating features from dataset file at {data_dir}"""				)

																						lowercase    :      Tuple       = (
																						    processor.get_dev_examples(__magic_name__				) if evaluate else processor.get_train_examples(__magic_name__				)
																						)

																						logger.info("""Training examples: %s"""			, len(__magic_name__				)				)
																						lowercase    :      Optional[int]       = hans_convert_examples_to_features(__magic_name__			, __magic_name__			, __magic_name__			, __magic_name__				)
																						logger.info("""Saving features into cached file %s"""			, __magic_name__				)
																						torch.save(self.features			, __magic_name__				)






										def __len__(		self  :str				)      ->str:
														return len(self.features				)






										def __getitem__(		self  :List[str]			, __magic_name__  :Union[str, Any]				)      ->InputFeatures:
														return self.features[i]






										def      __snake_case      (		self  :Tuple				)      ->Any:
														return self.label_list


if is_tf_available():
				import tensorflow as tf


				class      UpperCamelCase		:
										_SCREAMING_SNAKE_CASE  : List[InputFeatures]






										def __init__(		self  :Any			, __magic_name__  :str			, __magic_name__  :PreTrainedTokenizer			, __magic_name__  :str			, __magic_name__  :Optional[int] = 128			, __magic_name__  :Any=False			, __magic_name__  :bool = False			, )      ->Optional[Any]:
														lowercase    :      List[str]       = hans_processors[task]()
														lowercase    :      Any       = processor.get_labels()
														if tokenizer.__class__ in (
														    RobertaTokenizer,
														    RobertaTokenizerFast,
														    XLMRobertaTokenizer,
														    BartTokenizer,
														    BartTokenizerFast,
														):
																		# HACK(label indices are swapped in RoBERTa pretrained model)
																		lowercase      ,      lowercase    :      Tuple       = label_list[2], label_list[1]
														lowercase    :      Tuple       = label_list

														lowercase    :      List[str]       = processor.get_dev_examples(__magic_name__				) if evaluate else processor.get_train_examples(__magic_name__				)
														lowercase    :      Union[str, Any]       = hans_convert_examples_to_features(__magic_name__			, __magic_name__			, __magic_name__			, __magic_name__				)

														def gen():
																		for ex_index, ex in tqdm.tqdm(enumerate(self.features				)			, desc="""convert examples to features"""				):
																						if ex_index % 10_000 == 0:
																										logger.info("""Writing example %d of %d""" % (ex_index, len(__magic_name__				))				)

																						yield (
																						    {
																						        "example_id": 0,
																						        "input_ids": ex.input_ids,
																						        "attention_mask": ex.attention_mask,
																						        "token_type_ids": ex.token_type_ids,
																						    },
																						    ex.label,
																						)

														lowercase    :      str       = tf.data.Dataset.from_generator(
														    __magic_name__			, (
														        {
														            """example_id""": tf.intaa,
														            """input_ids""": tf.intaa,
														            """attention_mask""": tf.intaa,
														            """token_type_ids""": tf.intaa,
														        },
														        tf.intaa,
														    )			, (
														        {
														            """example_id""": tf.TensorShape([]				),
														            """input_ids""": tf.TensorShape([None, None]				),
														            """attention_mask""": tf.TensorShape([None, None]				),
														            """token_type_ids""": tf.TensorShape([None, None]				),
														        },
														        tf.TensorShape([]				),
														    )			, )






										def      __snake_case      (		self  :Union[str, Any]				)      ->int:
														return self.dataset






										def __len__(		self  :Optional[int]				)      ->int:
														return len(self.features				)






										def __getitem__(		self  :List[Any]			, __magic_name__  :List[Any]				)      ->InputFeatures:
														return self.features[i]






										def      __snake_case      (		self  :List[str]				)      ->List[str]:
														return self.label_list


class      UpperCamelCase		(__snake_case     ):






						def      __snake_case      (		self  :int			, __magic_name__  :Tuple				)      ->List[str]:
										return self._create_examples(self._read_tsv(os.path.join(__magic_name__			, """heuristics_train_set.txt"""				)				)			, """train"""				)






						def      __snake_case      (		self  :str			, __magic_name__  :Tuple				)      ->int:
										return self._create_examples(self._read_tsv(os.path.join(__magic_name__			, """heuristics_evaluation_set.txt"""				)				)			, """dev"""				)






						def      __snake_case      (		self  :Optional[int]				)      ->Union[str, Any]:
										return ["contradiction", "entailment", "neutral"]






						def      __snake_case      (		self  :Optional[int]			, __magic_name__  :Optional[int]			, __magic_name__  :Dict				)      ->Optional[Any]:
										lowercase    :      int       = []
										for i, line in enumerate(__magic_name__				):
														if i == 0:
																		continue
														lowercase    :      Dict       = """%s-%s""" % (set_type, line[0])
														lowercase    :      List[str]       = line[5]
														lowercase    :      Optional[Any]       = line[6]
														lowercase    :      List[Any]       = line[7][2:] if line[7].startswith("""ex"""				) else line[7]
														lowercase    :      List[str]       = line[0]
														examples.append(InputExample(guid=__magic_name__			, text_a=__magic_name__			, text_b=__magic_name__			, label=__magic_name__			, pairID=__magic_name__				)				)
										return examples


def      UpperCamelCase       ( _A  ,				_A  ,				_A  ,				_A  ,				)     ->       List[str]:
				lowercase    :      Dict       = {label: i for i, label in enumerate(_A )}

				lowercase    :      str       = []
				for ex_index, example in tqdm.tqdm(enumerate(_A )  ,				desc="""convert examples to features""" ):
								if ex_index % 10_000 == 0:
												logger.info("""Writing example %d""" % (ex_index) )

								lowercase    :      Optional[int]       = tokenizer(
								    example.text_a  ,				example.text_b  ,				add_special_tokens=_A  ,				max_length=_A  ,				padding="""max_length"""  ,				truncation=_A  ,				return_overflowing_tokens=_A  ,				)

								lowercase    :      Union[str, Any]       = label_map[example.label] if example.label in label_map else 0

								lowercase    :      List[Any]       = int(example.pairID )

								features.append(InputFeatures(**_A  ,				label=_A  ,				pairID=_A ) )

				for i, example in enumerate(examples[:5] ):
								logger.info("""*** Example ***""" )
								logger.info(F"""guid: {example}""" )
								logger.info(F"""features: {features[i]}""" )

				return features


_lowerCAmelCase            =	{
    'hans': 3,
}
_lowerCAmelCase            =	{
    'hans': HansProcessor,
}








"""simple docstring"""







import numpy as np


class      UpperCamelCase		:






						def __init__(		self  :List[Any]				)      ->Optional[Any]:
										lowercase    :      Dict       = (0, 0)
										lowercase    :      str       = None
										lowercase    :      Any       = 0
										lowercase    :      List[Any]       = 0
										lowercase    :      Union[str, Any]       = 0






						def __eq__(		self  :List[Any]			, __magic_name__  :Optional[Any]				)      ->Tuple:
										return self.position == cell.position






						def      __snake_case      (		self  :Any				)      ->List[Any]:
										print(self.position				)


class      UpperCamelCase		:






						def __init__(		self  :Any			, __magic_name__  :int=(5, 5)				)      ->Dict:
										lowercase    :      Tuple       = np.zeros(__magic_name__				)
										lowercase    :      List[str]       = world_size[0]
										lowercase    :      str       = world_size[1]






						def      __snake_case      (		self  :str				)      ->int:
										print(self.w				)






						def      __snake_case      (		self  :Any			, __magic_name__  :Any				)      ->Optional[int]:
										lowercase    :      Optional[Any]       = [
										    (-1, -1),
										    (-1, 0),
										    (-1, 1),
										    (0, -1),
										    (0, 1),
										    (1, -1),
										    (1, 0),
										    (1, 1),
										]
										lowercase    :      List[str]       = cell.position[0]
										lowercase    :      List[str]       = cell.position[1]
										lowercase    :      Optional[int]       = []
										for n in neughbour_cord:
														lowercase    :      List[Any]       = current_x + n[0]
														lowercase    :      List[Any]       = current_y + n[1]
														if 0 <= x < self.world_x_limit and 0 <= y < self.world_y_limit:
																		lowercase    :      Tuple       = Cell()
																		lowercase    :      Union[str, Any]       = (x, y)
																		lowercase    :      Dict       = cell
																		neighbours.append(__magic_name__				)
										return neighbours


def      UpperCamelCase       ( _A  ,				_A  ,				_A )     ->       Tuple:
				lowercase    :      Dict       = []
				lowercase    :      List[str]       = []
				_open.append(_A )

				while _open:
								lowercase    :      List[str]       = np.argmin([n.f for n in _open] )
								lowercase    :      int       = _open[min_f]
								_closed.append(_open.pop(_A ) )
								if current == goal:
												break
								for n in world.get_neigbours(_A ):
												for c in _closed:
																if c == n:
																				continue
												lowercase    :      List[Any]       = current.g + 1
												lowercase      ,      lowercase    :      Any       = n.position
												lowercase      ,      lowercase    :      List[str]       = goal.position
												lowercase    :      Any       = (ya - ya) ** 2 + (xa - xa) ** 2
												lowercase    :      Union[str, Any]       = n.h + n.g

												for c in _open:
																if c == n and c.f < n.f:
																				continue
												_open.append(_A )
				lowercase    :      List[str]       = []
				while current.parent is not None:
								path.append(current.position )
								lowercase    :      int       = current.parent
				path.append(current.position )
				return path[::-1]


if __name__ == "__main__":
				_lowerCAmelCase            =	Gridworld()
				# Start position and goal
				_lowerCAmelCase            =	Cell()
				_lowerCAmelCase            =	(0, 0)
				_lowerCAmelCase            =	Cell()
				_lowerCAmelCase            =	(4, 4)
				print(F'path from {start.position} to {goal.position}')
				_lowerCAmelCase            =	astar(world, start, goal)
				#   Just for visual reasons.
				for i in s:
								_lowerCAmelCase            =	1
				print(world.w)
