





import warnings
from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import TensorType, is_torch_available, logging


snake_case__					:							int													=     logging.get_logger(__name__)

snake_case__					:							str													=     {
    'facebook/bart-large': 'https://huggingface.co/facebook/bart-large/resolve/main/config.json',
    # See all BART models at https://huggingface.co/models?filter=bart
}





class A_					(			_lowerCamelCase   ):
   lowerCAmelCase__        = """bart"""
   lowerCAmelCase__        = ["""past_key_values"""]
   lowerCAmelCase__        = {"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}




   def __init__(self	:str    ,   _UpperCamelCase	:Optional[int]=5_0265    ,   _UpperCamelCase	:Any=1024    ,   _UpperCamelCase	:Dict=12    ,   _UpperCamelCase	:Optional[Any]=4096    ,   _UpperCamelCase	:str=16    ,   _UpperCamelCase	:int=12    ,   _UpperCamelCase	:Union[str, Any]=4096    ,   _UpperCamelCase	:Dict=16    ,   _UpperCamelCase	:str=0.0    ,   _UpperCamelCase	:Optional[int]=0.0    ,   _UpperCamelCase	:str="gelu"    ,   _UpperCamelCase	:Dict=1024    ,   _UpperCamelCase	:Any=0.1    ,   _UpperCamelCase	:int=0.0    ,   _UpperCamelCase	:List[str]=0.0    ,   _UpperCamelCase	:Optional[int]=0.0_2    ,   _UpperCamelCase	:Any=0.0    ,   _UpperCamelCase	:Union[str, Any]=False    ,   _UpperCamelCase	:Union[str, Any]=True    ,   _UpperCamelCase	:int=3    ,   _UpperCamelCase	:Any=1    ,   _UpperCamelCase	:Union[str, Any]=0    ,   _UpperCamelCase	:List[Any]=2    ,   _UpperCamelCase	:Any=True    ,   _UpperCamelCase	:Dict=2    ,   _UpperCamelCase	:int=2    ,   **_UpperCamelCase	:Optional[int]    ,   )->					str:
         __A          =				vocab_size
         __A          =				max_position_embeddings
         __A          =				d_model
         __A          =				encoder_ffn_dim
         __A          =				encoder_layers
         __A          =				encoder_attention_heads
         __A          =				decoder_ffn_dim
         __A          =				decoder_layers
         __A          =				decoder_attention_heads
         __A          =				dropout
         __A          =				attention_dropout
         __A          =				activation_dropout
         __A          =				activation_function
         __A          =				init_std
         __A          =				encoder_layerdrop
         __A          =				decoder_layerdrop
         __A          =				classifier_dropout
         __A          =				use_cache
         __A          =				encoder_layers
         __A          =				scale_embedding  # scale factor will be sqrt(d_model) if True

         super().__init__(
             num_labels=_UpperCamelCase    ,   pad_token_id=_UpperCamelCase    ,   bos_token_id=_UpperCamelCase    ,   eos_token_id=_UpperCamelCase    ,   is_encoder_decoder=_UpperCamelCase    ,   decoder_start_token_id=_UpperCamelCase    ,   forced_eos_token_id=_UpperCamelCase    ,   **_UpperCamelCase    ,   )

         # ensure backward compatibility for BART CNN models
         if self.forced_bos_token_id is None and kwargs.get('''force_bos_token_to_be_generated'''    ,   _UpperCamelCase ):
               __A          =				self.bos_token_id
               warnings.warn(
                   f"""Please make sure the config includes `forced_bos_token_id={self.bos_token_id}` in future versions. """
                   '''The config can simply be saved and uploaded again to be fixed.''' )





class A_					(			_lowerCamelCase   ):




   @property
   def _lowerCAmelCase      (self	:List[Any] )->					Mapping[str, Mapping[int, str]]:
         if self.task in ["default", "seq2seq-lm"]:
               __A          =				OrderedDict(
                   [
                       ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
                       ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
                   ] )

               if self.use_past:
                     __A          =				{0: '''batch'''}
                     __A          =				{0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
               else:
                     __A          =				{0: '''batch''', 1: '''decoder_sequence'''}
                     __A          =				{0: '''batch''', 1: '''decoder_sequence'''}

               if self.use_past:
                     self.fill_with_past_key_values_(_UpperCamelCase    ,   direction='''inputs''' )
         elif self.task == "causal-lm":
               # TODO: figure this case out.
               __A          =				OrderedDict(
                   [
                       ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
                       ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
                   ] )
               if self.use_past:
                     __A   ,						__A          =				self.num_layers
                     for i in range(_UpperCamelCase ):
                           __A          =				{0: '''batch''', 2: '''past_sequence + sequence'''}
                           __A          =				{0: '''batch''', 2: '''past_sequence + sequence'''}
         else:
               __A          =				OrderedDict(
                   [
                       ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
                       ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
                       ('''decoder_input_ids''', {0: '''batch''', 1: '''decoder_sequence'''}),
                       ('''decoder_attention_mask''', {0: '''batch''', 1: '''decoder_sequence'''}),
                   ] )

         return common_inputs




   @property
   def _lowerCAmelCase      (self	:List[Any] )->					Mapping[str, Mapping[int, str]]:
         if self.task in ["default", "seq2seq-lm"]:
               __A          =				super().outputs
         else:
               __A          =				super(_UpperCamelCase    ,   self ).outputs
               if self.use_past:
                     __A   ,						__A          =				self.num_layers
                     for i in range(_UpperCamelCase ):
                           __A          =				{0: '''batch''', 2: '''past_sequence + sequence'''}
                           __A          =				{0: '''batch''', 2: '''past_sequence + sequence'''}
         return common_outputs




   def _lowerCAmelCase      (self	:Dict    ,   _UpperCamelCase	:PreTrainedTokenizer    ,   _UpperCamelCase	:int = -1    ,   _UpperCamelCase	:int = -1    ,   _UpperCamelCase	:bool = False    ,   _UpperCamelCase	:Optional[TensorType] = None    ,   )->					Mapping[str, Any]:
         __A          =				self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
             _UpperCamelCase    ,   _UpperCamelCase    ,   _UpperCamelCase    ,   _UpperCamelCase    ,   _UpperCamelCase )

         # Generate decoder inputs
         __A          =				seq_length if not self.use_past else 1
         __A          =				self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
             _UpperCamelCase    ,   _UpperCamelCase    ,   _UpperCamelCase    ,   _UpperCamelCase    ,   _UpperCamelCase )
         __A          =				{f"""decoder_{name}""": tensor for name, tensor in decoder_inputs.items()}
         __A          =				dict(**_UpperCamelCase    ,   **_UpperCamelCase )

         if self.use_past:
               if not is_torch_available():
                     raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.''' )
               else:
                     import torch
               __A   ,						__A          =				common_inputs['''input_ids'''].shape
               __A          =				common_inputs['''decoder_input_ids'''].shape[1]
               __A   ,						__A          =				self.num_attention_heads
               __A          =				(
                   batch,
                   num_encoder_attention_heads,
                   encoder_seq_length,
                   self._config.hidden_size // num_encoder_attention_heads,
               )
               __A          =				decoder_seq_length + 3
               __A          =				(
                   batch,
                   num_decoder_attention_heads,
                   decoder_past_length,
                   self._config.hidden_size // num_decoder_attention_heads,
               )

               __A          =				torch.cat(
                   [common_inputs['''decoder_attention_mask'''], torch.ones(_UpperCamelCase    ,   _UpperCamelCase )]    ,   dim=1 )

               __A          =				[]
               # If the number of encoder and decoder layers are present in the model configuration, both are considered
               __A   ,						__A          =				self.num_layers
               __A          =				min(_UpperCamelCase    ,   _UpperCamelCase )
               __A          =				max(_UpperCamelCase    ,   _UpperCamelCase ) - min_num_layers
               __A          =				'''encoder''' if num_encoder_layers > num_decoder_layers else '''decoder'''

               for _ in range(_UpperCamelCase ):
                     common_inputs["past_key_values"].append(
                         (
                             torch.zeros(_UpperCamelCase ),
                             torch.zeros(_UpperCamelCase ),
                             torch.zeros(_UpperCamelCase ),
                             torch.zeros(_UpperCamelCase ),
                         ) )
               # TODO: test this.
               __A          =				encoder_shape if remaining_side_name == '''encoder''' else decoder_shape
               for _ in range(_UpperCamelCase    ,   _UpperCamelCase ):
                     common_inputs["past_key_values"].append((torch.zeros(_UpperCamelCase ), torch.zeros(_UpperCamelCase )) )
         return common_inputs




   def _lowerCAmelCase      (self	:str    ,   _UpperCamelCase	:PreTrainedTokenizer    ,   _UpperCamelCase	:int = -1    ,   _UpperCamelCase	:int = -1    ,   _UpperCamelCase	:bool = False    ,   _UpperCamelCase	:Optional[TensorType] = None    ,   )->					Mapping[str, Any]:
         __A          =				self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
             _UpperCamelCase    ,   _UpperCamelCase    ,   _UpperCamelCase    ,   _UpperCamelCase    ,   _UpperCamelCase )

         if self.use_past:
               if not is_torch_available():
                     raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.''' )
               else:
                     import torch
               __A   ,						__A          =				common_inputs['''input_ids'''].shape
               # Not using the same length for past_key_values
               __A          =				seqlen + 2
               __A   ,						__A          =				self.num_layers
               __A   ,						__A          =				self.num_attention_heads
               __A          =				(
                   batch,
                   num_encoder_attention_heads,
                   past_key_values_length,
                   self._config.hidden_size // num_encoder_attention_heads,
               )

               __A          =				common_inputs['''attention_mask'''].dtype
               __A          =				torch.cat(
                   [common_inputs['''attention_mask'''], torch.ones(_UpperCamelCase    ,   _UpperCamelCase    ,   dtype=_UpperCamelCase )]    ,   dim=1 )
               __A          =				[
                   (torch.zeros(_UpperCamelCase ), torch.zeros(_UpperCamelCase )) for _ in range(_UpperCamelCase )
               ]
         return common_inputs




   def _lowerCAmelCase      (self	:List[Any]    ,   _UpperCamelCase	:PreTrainedTokenizer    ,   _UpperCamelCase	:int = -1    ,   _UpperCamelCase	:int = -1    ,   _UpperCamelCase	:bool = False    ,   _UpperCamelCase	:Optional[TensorType] = None    ,   )->					Mapping[str, Any]:
         # Copied from OnnxConfig.generate_dummy_inputs
         # Did not use super(OnnxConfigWithPast, self).generate_dummy_inputs for code clarity.
         # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
         __A          =				compute_effective_axis_dimension(
             _UpperCamelCase    ,   fixed_dimension=OnnxConfig.default_fixed_batch    ,   num_token_to_add=0 )

         # If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
         __A          =				tokenizer.num_special_tokens_to_add(_UpperCamelCase )
         __A          =				compute_effective_axis_dimension(
             _UpperCamelCase    ,   fixed_dimension=OnnxConfig.default_fixed_sequence    ,   num_token_to_add=_UpperCamelCase )

         # Generate dummy inputs according to compute batch and sequence
         __A          =				[''' '''.join([tokenizer.unk_token] ) * seq_length] * batch_size
         __A          =				dict(tokenizer(_UpperCamelCase    ,   return_tensors=_UpperCamelCase ) )
         return common_inputs




   def _lowerCAmelCase      (self	:Union[str, Any]    ,   _UpperCamelCase	:PreTrainedTokenizer    ,   _UpperCamelCase	:int = -1    ,   _UpperCamelCase	:int = -1    ,   _UpperCamelCase	:bool = False    ,   _UpperCamelCase	:Optional[TensorType] = None    ,   )->					Mapping[str, Any]:
         if self.task in ["default", "seq2seq-lm"]:
               __A          =				self._generate_dummy_inputs_for_default_and_seqaseq_lm(
                   _UpperCamelCase    ,   batch_size=_UpperCamelCase    ,   seq_length=_UpperCamelCase    ,   is_pair=_UpperCamelCase    ,   framework=_UpperCamelCase )

         elif self.task == "causal-lm":
               __A          =				self._generate_dummy_inputs_for_causal_lm(
                   _UpperCamelCase    ,   batch_size=_UpperCamelCase    ,   seq_length=_UpperCamelCase    ,   is_pair=_UpperCamelCase    ,   framework=_UpperCamelCase )
         else:
               __A          =				self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
                   _UpperCamelCase    ,   batch_size=_UpperCamelCase    ,   seq_length=_UpperCamelCase    ,   is_pair=_UpperCamelCase    ,   framework=_UpperCamelCase )

         return common_inputs






   def _lowerCAmelCase      (self	:str    ,   _UpperCamelCase	:List[str]    ,   _UpperCamelCase	:Union[str, Any]    ,   _UpperCamelCase	:str    ,   _UpperCamelCase	:int )->					List[Any]:
         if self.task in ["default", "seq2seq-lm"]:
               __A          =				super()._flatten_past_key_values_(_UpperCamelCase    ,   _UpperCamelCase    ,   _UpperCamelCase    ,   _UpperCamelCase )
         else:
               __A          =				super(_UpperCamelCase    ,   self )._flatten_past_key_values_(
                   _UpperCamelCase    ,   _UpperCamelCase    ,   _UpperCamelCase    ,   _UpperCamelCase )












import heapq
import sys

import numpy as np

snake_case__					:							Tuple													=     tuple[int, int]





class A_					:




   def __init__(self	:Union[str, Any] )->					Union[str, Any]:
         __A          =				[]
         __A          =				set()




   def _lowerCAmelCase      (self	:List[str] )->					str:
         if not self.empty():
               return self.elements[0][0]
         else:
               return float('''inf''' )




   def _lowerCAmelCase      (self	:Any )->					Any:
         return len(self.elements ) == 0




   def _lowerCAmelCase      (self	:List[str]    ,   _UpperCamelCase	:Optional[Any]    ,   _UpperCamelCase	:Optional[int] )->					Optional[int]:
         if item not in self.set:
               heapq.heappush(self.elements    ,   (priority, item) )
               self.set.add(_UpperCamelCase )
         else:
               # update
               # print("update", item)
               __A          =				[]
               ((__A)   ,						(__A))          =				heapq.heappop(self.elements )
               while x != item:
                     temp.append((pri, x) )
                     ((__A)   ,						(__A))          =				heapq.heappop(self.elements )
               temp.append((priority, item) )
               for pro, xxx in temp:
                     heapq.heappush(self.elements    ,   (pro, xxx) )




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




   def _lowerCAmelCase      (self	:Optional[Any] )->					int:
         return self.elements[0][1]






   def _lowerCAmelCase      (self	:List[str] )->					List[Any]:
         ((__A)   ,						(__A))          =				heapq.heappop(self.elements )
         self.set.remove(_UpperCamelCase )
         return (priority, item)





def _a				(	lowerCamelCase:   TPos					,				lowerCamelCase:   TPos )	->						Any:







      '''simple docstring'''






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



def _a				(	lowerCamelCase:   TPos					,				lowerCamelCase:   TPos )	->						List[str]:







      '''simple docstring'''






      return consistent_heuristic(lowerCamelCase					,				lowerCamelCase ) // t



def _a				(	lowerCamelCase:   TPos					,				lowerCamelCase:   TPos )	->						Any:







      '''simple docstring'''






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



def _a				(	lowerCamelCase:   TPos					,				lowerCamelCase:   int					,				lowerCamelCase:   TPos					,				lowerCamelCase:   dict[TPos, float] )	->						Tuple:







      '''simple docstring'''






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



def _a				(	lowerCamelCase:   Tuple					,				lowerCamelCase:   Optional[int]					,				lowerCamelCase:   Optional[int] )	->						Tuple:







      '''simple docstring'''






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

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

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

      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:-''' )
      __A          =				back_pointer[goal]
      while x != start:
            print(lowerCamelCase					,				end=''' ''' )
            __A          =				back_pointer[x]
      print(lowerCamelCase )
      sys.exit()



def _a				(	lowerCamelCase:   TPos )	->						Optional[Any]:







      '''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 _a				(	lowerCamelCase:   int					,				lowerCamelCase:   Optional[int]					,				lowerCamelCase:   Tuple					,				lowerCamelCase:   Tuple					,				lowerCamelCase:   Optional[int]					,				lowerCamelCase:   str					,				lowerCamelCase:   List[Any]					,				lowerCamelCase:   Dict					,				)	->						Dict:







      '''simple docstring'''






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

                  if valid(lowerCamelCase ) and g_function[neighbours] > g_function[s] + 1:
                        __A          =				g_function[s] + 1
                        __A          =				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 _a				(	)	->						str:







      '''simple docstring'''






      __A          =				[]
      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


snake_case__					:							Union[str, Any]													=     {0: consistent_heuristic, 1: heuristic_a, 2: heuristic_a}

snake_case__					:							List[str]													=     [
    (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),
]
snake_case__					:							List[str]													=     make_common_ground()


snake_case__					:							int													=     blocks_blk
# hyper parameters
snake_case__					:							Optional[int]													=     1
snake_case__					:							List[Any]													=     1
snake_case__					:							int													=     20
snake_case__					:							Optional[int]													=     3  # one consistent and two other inconsistent

# start and end destination
snake_case__					:							Tuple													=     (0, 0)
snake_case__					:							Any													=     (n - 1, n - 1)

snake_case__					:							List[Any]													=     1



def _a				(	lowerCamelCase:   TPos					,				lowerCamelCase:   TPos					,				lowerCamelCase:   int )	->						Union[str, Any]:







      '''simple docstring'''






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

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

      __A          =				[]
      __A          =				[]
      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:
                              __A   ,						__A          =				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:
                              __A          =				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)





