






import os
import time
from dataclasses import dataclass, field
from enum import Enum
from typing import Dict, List, Optional, Union

import torch
from filelock import FileLock
from torch.utils.data import Dataset

from ...models.auto.modeling_auto import MODEL_FOR_QUESTION_ANSWERING_MAPPING
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
from ..processors.squad import SquadFeatures, SquadVaProcessor, SquadVaProcessor, squad_convert_examples_to_features


__UpperCamelCase      :      Dict     						=						logging.get_logger(__name__)

__UpperCamelCase      :      Any     						=						list(MODEL_FOR_QUESTION_ANSWERING_MAPPING.keys())
__UpperCamelCase      :      Any     						=						tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)


@dataclass
class        __UpperCamelCase    :
	__snake_case	:str   =       field(
	    default=_lowerCAmelCase   ,			metadata={'help': 'Model type selected in the list: ' + ', '.join(_lowerCAmelCase   )}   )
	__snake_case	:str   =       field(
	    default=_lowerCAmelCase   ,			metadata={'help': 'The input data dir. Should contain the .json files for the SQuAD task.'}   )
	__snake_case	:int   =       field(
	    default=1_2_8   ,			metadata={
	        'help': (
	            'The maximum total input sequence length after tokenization. Sequences longer '
	            'than this will be truncated, sequences shorter will be padded.'
	        )
	    }   ,			)
	__snake_case	:int   =       field(
	    default=1_2_8   ,			metadata={'help': 'When splitting up a long document into chunks, how much stride to take between chunks.'}   ,			)
	__snake_case	:int   =       field(
	    default=6_4   ,			metadata={
	        'help': (
	            'The maximum number of tokens for the question. Questions longer than this will '
	            'be truncated to this length.'
	        )
	    }   ,			)
	__snake_case	:int   =       field(
	    default=3_0   ,			metadata={
	        'help': (
	            'The maximum length of an answer that can be generated. This is needed because the start '
	            'and end predictions are not conditioned on one another.'
	        )
	    }   ,			)
	__snake_case	:bool   =       field(
	    default=_lowerCAmelCase   ,			metadata={'help': 'Overwrite the cached training and evaluation sets'}   )
	__snake_case	:bool   =       field(
	    default=_lowerCAmelCase   ,			metadata={'help': 'If true, the SQuAD examples contain some that do not have an answer.'}   )
	__snake_case	:float   =       field(
	    default=0.0   ,			metadata={'help': 'If null_score - best_non_null is greater than the threshold predict null.'}   )
	__snake_case	:int   =       field(
	    default=2_0   ,			metadata={'help': 'If null_score - best_non_null is greater than the threshold predict null.'}   )
	__snake_case	:int   =       field(
	    default=0   ,			metadata={
	        'help': (
	            'language id of input for language-specific xlm models (see'
	            ' tokenization_xlm.PRETRAINED_INIT_CONFIGURATION)'
	        )
	    }   ,			)
	__snake_case	:int   =       field(default=1   ,			metadata={'help': 'multiple threads for converting example to features'}   )


class        __UpperCamelCase    (     _lowerCAmelCase   ):
	__snake_case	:Tuple   =       'train'
	__snake_case	:Union[str, Any]   =       'dev'


class        __UpperCamelCase    (     _lowerCAmelCase   ):
	__snake_case	:SquadDataTrainingArguments
	__snake_case	:List[SquadFeatures]
	__snake_case	:Split
	__snake_case	:bool







	def __init__(  self      :							Union[str, Any]       ,				_lowerCAmelCase      :							SquadDataTrainingArguments       ,				_lowerCAmelCase      :							PreTrainedTokenizer       ,				_lowerCAmelCase      :							Optional[int] = None       ,				_lowerCAmelCase      :							Union[str, Split] = Split.train       ,				_lowerCAmelCase      :							Optional[bool] = False       ,				_lowerCAmelCase      :							Optional[str] = None       ,				_lowerCAmelCase      :							Optional[str] = "pt"       ,				)		-> int:
		"""simple docstring"""







		__lowercase             =  args
		__lowercase             =  is_language_sensitive
		__lowercase             =  SquadVaProcessor() if args.version_2_with_negative else SquadVaProcessor()
		if isinstance(_lowerCAmelCase       ,				_lowerCAmelCase			):
			try:
				__lowercase             =  Split[mode]
			except KeyError:
				raise KeyError("""mode is not a valid split name"""			)
		__lowercase             =  mode
		# Load data features from cache or dataset file
		__lowercase             =  """v2""" if args.version_2_with_negative else """v1"""
		__lowercase             =  os.path.join(
		    cache_dir if cache_dir is not None else args.data_dir       ,				F'cached_{mode.value}_{tokenizer.__class__.__name__}_{args.max_seq_length}_{version_tag}'       ,				)

		# Make sure only the first process in distributed training processes the dataset,
		# and the others will use the cache.
		__lowercase             =  cached_features_file + """.lock"""
		with FileLock(_lowerCAmelCase			):
			if os.path.exists(_lowerCAmelCase			) and not args.overwrite_cache:
				__lowercase             =  time.time()
				__lowercase             =  torch.load(_lowerCAmelCase			)

				# Legacy cache files have only features, while new cache files
				# will have dataset and examples also.
				__lowercase             =  self.old_features["""features"""]
				__lowercase             =  self.old_features.get("""dataset"""       ,				_lowerCAmelCase			)
				__lowercase             =  self.old_features.get("""examples"""       ,				_lowerCAmelCase			)
				logger.info(
				    F'Loading features from cached file {cached_features_file} [took %.3f s]'       ,				time.time() - start			)

				if self.dataset is None or self.examples is None:
					logger.warning(
					    F'Deleting cached file {cached_features_file} will allow dataset and examples to be cached in'
					    """ future run"""			)
			else:
				if mode == Split.dev:
					__lowercase             =  self.processor.get_dev_examples(args.data_dir			)
				else:
					__lowercase             =  self.processor.get_train_examples(args.data_dir			)

				__lowercase       ,						__lowercase             =  squad_convert_examples_to_features(
				    examples=self.examples       ,				tokenizer=_lowerCAmelCase       ,				max_seq_length=args.max_seq_length       ,				doc_stride=args.doc_stride       ,				max_query_length=args.max_query_length       ,				is_training=mode == Split.train       ,				threads=args.threads       ,				return_dataset=_lowerCAmelCase       ,				)

				__lowercase             =  time.time()
				torch.save(
				    {"""features""": self.features, """dataset""": self.dataset, """examples""": self.examples}       ,				_lowerCAmelCase       ,				)
				# ^ This seems to take a lot of time so I want to investigate why and how we can improve.
				logger.info(
				    F'Saving features into cached file {cached_features_file} [took {time.time() - start:.3f} s]'			)







	def __len__(  self      :							str			)		-> str:
		"""simple docstring"""







		return len(self.features			)







	def __getitem__(  self      :							Any       ,				_lowerCAmelCase      :							Union[str, Any]			)		-> Dict[str, torch.Tensor]:
		"""simple docstring"""







		__lowercase             =  self.features[i]

		__lowercase             =  torch.tensor(feature.input_ids       ,				dtype=torch.long			)
		__lowercase             =  torch.tensor(feature.attention_mask       ,				dtype=torch.long			)
		__lowercase             =  torch.tensor(feature.token_type_ids       ,				dtype=torch.long			)
		__lowercase             =  torch.tensor(feature.cls_index       ,				dtype=torch.long			)
		__lowercase             =  torch.tensor(feature.p_mask       ,				dtype=torch.float			)
		__lowercase             =  torch.tensor(feature.is_impossible       ,				dtype=torch.float			)

		__lowercase             =  {
		    """input_ids""": input_ids,
		    """attention_mask""": attention_mask,
		    """token_type_ids""": token_type_ids,
		}

		if self.args.model_type in ["xlm", "roberta", "distilbert", "camembert"]:
			del inputs["token_type_ids"]

		if self.args.model_type in ["xlnet", "xlm"]:
			inputs.update({"""cls_index""": cls_index, """p_mask""": p_mask}			)
			if self.args.version_2_with_negative:
				inputs.update({"""is_impossible""": is_impossible}			)
			if self.is_language_sensitive:
				inputs.update({"""langs""": (torch.ones(input_ids.shape       ,				dtype=torch.intaa			) * self.args.lang_id)}			)

		if self.mode == Split.train:
			__lowercase             =  torch.tensor(feature.start_position       ,				dtype=torch.long			)
			__lowercase             =  torch.tensor(feature.end_position       ,				dtype=torch.long			)
			inputs.update({"""start_positions""": start_positions, """end_positions""": end_positions}			)

		return inputs









import heapq
import sys

import numpy as np

__UpperCamelCase      :      List[str]     						=						tuple[int, int]


class        __UpperCamelCase    :







	def __init__(  self      :							Optional[int]			)		-> Dict:
		"""simple docstring"""







		__lowercase             =  []
		__lowercase             =  set()







	def  _a   (  self      :							int			)		-> List[Any]:
		"""simple docstring"""







		if not self.empty():
			return self.elements[0][0]
		else:
			return float("""inf"""			)







	def  _a   (  self      :							Dict			)		-> Optional[Any]:
		"""simple docstring"""







		return len(self.elements			) == 0







	def  _a   (  self      :							str       ,				_lowerCAmelCase      :							Union[str, Any]       ,				_lowerCAmelCase      :							Optional[Any]			)		-> Dict:
		"""simple docstring"""







		if item not in self.set:
			heapq.heappush(self.elements       ,				(priority, item)			)
			self.set.add(_lowerCAmelCase			)
		else:
			# update
			# print("update", item)
			__lowercase             =  []
			((__lowercase)       ,						(__lowercase))             =  heapq.heappop(self.elements			)
			while x != item:
				temp.append((pri, x)			)
				((__lowercase)       ,						(__lowercase))             =  heapq.heappop(self.elements			)
			temp.append((priority, item)			)
			for pro, xxx in temp:
				heapq.heappush(self.elements       ,				(pro, xxx)			)







	def  _a   (  self      :							List[str]       ,				_lowerCAmelCase      :							Optional[Any]			)		-> Dict:
		"""simple docstring"""







		if item in self.set:
			self.set.remove(_lowerCAmelCase			)
			__lowercase             =  []
			((__lowercase)       ,						(__lowercase))             =  heapq.heappop(self.elements			)
			while x != item:
				temp.append((pro, x)			)
				((__lowercase)       ,						(__lowercase))             =  heapq.heappop(self.elements			)
			for prito, yyy in temp:
				heapq.heappush(self.elements       ,				(prito, yyy)			)







	def  _a   (  self      :							Any			)		-> List[Any]:
		"""simple docstring"""







		return self.elements[0][1]







	def  _a   (  self      :							Optional[int]			)		-> List[Any]:
		"""simple docstring"""







		((__lowercase)       ,						(__lowercase))             =  heapq.heappop(self.elements			)
		self.set.remove(_lowerCAmelCase			)
		return (priority, item)





def 				snake_case  (     lowerCamelCase				,  lowerCamelCase					):




	'''simple docstring'''







	__lowercase             =  np.array(lowerCamelCase					)
	__lowercase             =  np.array(lowerCamelCase					)
	return np.linalg.norm(a - b					)





def 				snake_case  (     lowerCamelCase				,  lowerCamelCase					):




	'''simple docstring'''







	return consistent_heuristic(lowerCamelCase				,  lowerCamelCase					) // t





def 				snake_case  (     lowerCamelCase				,  lowerCamelCase					):




	'''simple docstring'''







	return abs(p[0] - goal[0]					) + abs(p[1] - goal[1]					)





def 				snake_case  (     lowerCamelCase				,  lowerCamelCase				,  lowerCamelCase				,  lowerCamelCase					):




	'''simple docstring'''







	__lowercase             =  g_function[start] + Wa * heuristics[i](lowerCamelCase				,  lowerCamelCase					)
	return ans





def 				snake_case  (     lowerCamelCase				,  lowerCamelCase				,  lowerCamelCase					):




	'''simple docstring'''







	__lowercase             =  np.chararray((n, n)					)
	for i in range(lowerCamelCase					):
		for j in range(lowerCamelCase					):
			__lowercase             =  """*"""

	for i in range(lowerCamelCase					):
		for j in range(lowerCamelCase					):
			if (j, (n - 1) - i) in blocks:
				__lowercase             =  """#"""

	__lowercase             =  """-"""
	__lowercase             =  back_pointer[goal]
	while x != start:
		((__lowercase)       ,						(__lowercase))             =  x
		# print(x)
		__lowercase             =  """-"""
		__lowercase             =  back_pointer[x]
	__lowercase             =  """-"""

	for i in range(lowerCamelCase					):
		for j in range(lowerCamelCase					):
			if (i, j) == (0, n - 1):
				print(grid[i][j]				,  end=""" """					)
				print("""<-- End position"""				,  end=""" """					)
			else:
				print(grid[i][j]				,  end=""" """					)
		print()
	print("""^"""					)
	print("""Start position"""					)
	print()
	print("""# is an obstacle"""					)
	print("""- is the path taken by algorithm"""					)
	print("""PATH TAKEN BY THE ALGORITHM IS:-"""					)
	__lowercase             =  back_pointer[goal]
	while x != start:
		print(lowerCamelCase				,  end=""" """					)
		__lowercase             =  back_pointer[x]
	print(lowerCamelCase					)
	sys.exit()





def 				snake_case  (     lowerCamelCase					):




	'''simple docstring'''







	if p[0] < 0 or p[0] > n - 1:
		return False
	if p[1] < 0 or p[1] > n - 1:
		return False
	return True





def 				snake_case  (     lowerCamelCase				,  lowerCamelCase				,  lowerCamelCase				,  lowerCamelCase				,  lowerCamelCase				,  lowerCamelCase				,  lowerCamelCase				,  lowerCamelCase				,  ):




	'''simple docstring'''







	for itera in range(lowerCamelCase					):
		open_list[itera].remove_element(lowerCamelCase					)
	# print("s", s)
	# print("j", j)
	((__lowercase)       ,						(__lowercase))             =  s
	__lowercase             =  (x - 1, y)
	__lowercase             =  (x + 1, y)
	__lowercase             =  (x, y + 1)
	__lowercase             =  (x, y - 1)

	for neighbours in [left, right, up, down]:
		if neighbours not in blocks:
			if valid(lowerCamelCase					) and neighbours not in visited:
				# print("neighbour", neighbours)
				visited.add(lowerCamelCase					)
				__lowercase             =  -1
				__lowercase             =  float("""inf"""					)

			if valid(lowerCamelCase					) and g_function[neighbours] > g_function[s] + 1:
				__lowercase             =  g_function[s] + 1
				__lowercase             =  s
				if neighbours not in close_list_anchor:
					open_list[0].put(lowerCamelCase				,  key(lowerCamelCase				,  0				,  lowerCamelCase				,  lowerCamelCase					)					)
					if neighbours not in close_list_inad:
						for var in range(1				,  lowerCamelCase					):
							if key(lowerCamelCase				,  lowerCamelCase				,  lowerCamelCase				,  lowerCamelCase					) <= Wa * key(
							    lowerCamelCase				,  0				,  lowerCamelCase				,  lowerCamelCase					):
								open_list[j].put(
								    lowerCamelCase				,  key(lowerCamelCase				,  lowerCamelCase				,  lowerCamelCase				,  lowerCamelCase					)					)





def 				snake_case  (     ):




	'''simple docstring'''







	__lowercase             =  []
	for x in range(1				,  5					):
		for y in range(1				,  6					):
			some_list.append((x, y)					)

	for x in range(15				,  20					):
		some_list.append((x, 17)					)

	for x in range(10				,  19					):
		for y in range(1				,  15					):
			some_list.append((x, y)					)

    # L block
	for x in range(1				,  4					):
		for y in range(12				,  19					):
			some_list.append((x, y)					)
	for x in range(3				,  13					):
		for y in range(16				,  19					):
			some_list.append((x, y)					)
	return some_list


__UpperCamelCase      :      Optional[int]     						=						{0: consistent_heuristic, 1: heuristic_a, 2: heuristic_a}

__UpperCamelCase      :      Optional[Any]     						=						[
    (0, 1),
    (1, 1),
    (2, 1),
    (3, 1),
    (4, 1),
    (5, 1),
    (6, 1),
    (7, 1),
    (8, 1),
    (9, 1),
    (10, 1),
    (11, 1),
    (12, 1),
    (13, 1),
    (14, 1),
    (15, 1),
    (16, 1),
    (17, 1),
    (18, 1),
    (19, 1),
]
__UpperCamelCase      :      Optional[Any]     						=						make_common_ground()


__UpperCamelCase      :      Dict     						=						blocks_blk
# hyper parameters
__UpperCamelCase      :      Union[str, Any]     						=						1
__UpperCamelCase      :      Union[str, Any]     						=						1
__UpperCamelCase      :      Optional[int]     						=						20
__UpperCamelCase      :      List[str]     						=						3  # one consistent and two other inconsistent

# start and end destination
__UpperCamelCase      :      str     						=						(0, 0)
__UpperCamelCase      :      str     						=						(n - 1, n - 1)

__UpperCamelCase      :      Optional[Any]     						=						1





def 				snake_case  (     lowerCamelCase				,  lowerCamelCase				,  lowerCamelCase					):




	'''simple docstring'''







	__lowercase             =  {start: 0, goal: float("""inf"""					)}
	__lowercase             =  {start: -1, goal: -1}
	__lowercase             =  []
	__lowercase             =  set()

	for i in range(lowerCamelCase					):
		open_list.append(PriorityQueue()					)
		open_list[i].put(lowerCamelCase				,  key(lowerCamelCase				,  lowerCamelCase				,  lowerCamelCase				,  lowerCamelCase					)					)

	__lowercase             =  []
	__lowercase             =  []
	while open_list[0].minkey() < float("""inf"""					):
		for i in range(1				,  lowerCamelCase					):
			# print(open_list[0].minkey(), open_list[i].minkey())
			if open_list[i].minkey() <= Wa * open_list[0].minkey():
				global t
				t += 1
				if g_function[goal] <= open_list[i].minkey():
					if g_function[goal] < float("""inf"""					):
						do_something(lowerCamelCase				,  lowerCamelCase				,  lowerCamelCase					)
				else:
					__lowercase       ,						__lowercase             =  open_list[i].top_show()
					visited.add(lowerCamelCase					)
					expand_state(
					    lowerCamelCase				,  lowerCamelCase				,  lowerCamelCase				,  lowerCamelCase				,  lowerCamelCase				,  lowerCamelCase				,  lowerCamelCase				,  lowerCamelCase				,  )
					close_list_inad.append(lowerCamelCase					)
			else:
				if g_function[goal] <= open_list[0].minkey():
					if g_function[goal] < float("""inf"""					):
						do_something(lowerCamelCase				,  lowerCamelCase				,  lowerCamelCase					)
				else:
					__lowercase             =  open_list[0].top_show()
					visited.add(lowerCamelCase					)
					expand_state(
					    lowerCamelCase				,  0				,  lowerCamelCase				,  lowerCamelCase				,  lowerCamelCase				,  lowerCamelCase				,  lowerCamelCase				,  lowerCamelCase				,  )
					close_list_anchor.append(lowerCamelCase					)
	print("""No path found to goal"""					)
	print()
	for i in range(n - 1				,  -1				,  -1					):
		for j in range(lowerCamelCase					):
			if (j, i) in blocks:
				print("""#"""				,  end=""" """					)
			elif (j, i) in back_pointer:
				if (j, i) == (n - 1, n - 1):
					print("""*"""				,  end=""" """					)
				else:
					print("""-"""				,  end=""" """					)
			else:
				print("""*"""				,  end=""" """					)
			if (j, i) == (n - 1, n - 1):
				print("""<-- End position"""				,  end=""" """					)
		print()
	print("""^"""					)
	print("""Start position"""					)
	print()
	print("""# is an obstacle"""					)
	print("""- is the path taken by algorithm"""					)


if __name__ == "__main__":
						multi_a_star(start, goal, n_heuristic)

