
"""simple docstring"""

from __future__ import annotations

import time
from math import sqrt

# 1 for manhattan, 0 for euclidean
UpperCamelCase			:       List[Any]									=				0

UpperCamelCase			:       Union[str, 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],
]

UpperCamelCase			:       Any									=				[[-1, 0], [0, -1], [1, 0], [0, 1]]  # up, left, down, right

UpperCamelCase			:       Dict									=				tuple[int, int]

class __lowerCAmelCase	:





     def __init__(			self			,  __UpperCAmelCase			,  __UpperCAmelCase			,  __UpperCAmelCase			,  __UpperCAmelCase			,  __UpperCAmelCase			,  __UpperCAmelCase			,  ):

      '''simple docstring'''




      __UpperCamelCase       						= pos_x
      __UpperCamelCase       						= pos_y
      __UpperCamelCase       						= (pos_y, pos_x)
      __UpperCamelCase       						= goal_x
      __UpperCamelCase       						= goal_y
      __UpperCamelCase       						= g_cost
      __UpperCamelCase       						= parent
      __UpperCamelCase       						= self.calculate_heuristic()
      __UpperCamelCase       						= self.g_cost + self.h_cost





     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      __UpperCamelCase       						= self.pos_x - self.goal_x
      __UpperCamelCase       						= self.pos_y - self.goal_y
      if HEURISTIC == 1:
       return abs(UpperCAmelCase__   ) + abs(UpperCAmelCase__   )
      else:
       return sqrt(dy**2 + dx**2   )







     def __lt__(			self			,  __UpperCAmelCase   ):

      '''simple docstring'''




      return self.f_cost < other.f_cost


class __lowerCAmelCase	:





     def __init__(			self			,  __UpperCAmelCase			,  __UpperCAmelCase   ):

      '''simple docstring'''




      __UpperCamelCase       						= Node(start[1]			,  start[0]			,  goal[1]			,  goal[0]			,  0			,  UpperCAmelCase__   )
      __UpperCamelCase       						= Node(goal[1]			,  goal[0]			,  goal[1]			,  goal[0]			,  9_9999			,  UpperCAmelCase__   )

      __UpperCamelCase       						= [self.start]
      __UpperCamelCase       						= []

      __UpperCamelCase       						= False





     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      while self.open_nodes:
       # Open Nodes are sorted using __lt__
       self.open_nodes.sort()
       __UpperCamelCase       						= self.open_nodes.pop(0   )

       if current_node.pos == self.target.pos:
        return self.retrace_path(UpperCAmelCase__   )

       self.closed_nodes.append(UpperCAmelCase__   )
       __UpperCamelCase       						= 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
         __UpperCamelCase       						= 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__   )

      return [self.start.pos]





     def 		UpperCAmelCase      (			self			,  __UpperCAmelCase   ):

      '''simple docstring'''




      __UpperCamelCase       						= []
      for action in delta:
       __UpperCamelCase       						= parent.pos_x + action[1]
       __UpperCamelCase       						= 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 		UpperCAmelCase      (			self			,  __UpperCAmelCase   ):

      '''simple docstring'''




      __UpperCamelCase       						= node
      __UpperCamelCase       						= []
      while current_node is not None:
       path.append((current_node.pos_y, current_node.pos_x)   )
       __UpperCamelCase       						= current_node.parent
      path.reverse()
      return path


class __lowerCAmelCase	:





     def __init__(			self			,  __UpperCAmelCase			,  __UpperCAmelCase   ):

      '''simple docstring'''




      __UpperCamelCase       						= AStar(UpperCAmelCase__			,  UpperCAmelCase__   )
      __UpperCamelCase       						= AStar(UpperCAmelCase__			,  UpperCAmelCase__   )
      __UpperCamelCase       						= False





     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      while self.fwd_astar.open_nodes or self.bwd_astar.open_nodes:
       self.fwd_astar.open_nodes.sort()
       self.bwd_astar.open_nodes.sort()
       __UpperCamelCase       						= self.fwd_astar.open_nodes.pop(0   )
       __UpperCamelCase       						= self.bwd_astar.open_nodes.pop(0   )

       if current_bwd_node.pos == current_fwd_node.pos:
        return self.retrace_bidirectional_path(
            UpperCAmelCase__			,  UpperCAmelCase__   )

       self.fwd_astar.closed_nodes.append(UpperCAmelCase__   )
       self.bwd_astar.closed_nodes.append(UpperCAmelCase__   )

       __UpperCamelCase       						= current_bwd_node
       __UpperCamelCase       						= current_fwd_node

       __UpperCamelCase       						= {
           self.fwd_astar: self.fwd_astar.get_successors(UpperCAmelCase__   ),
           self.bwd_astar: self.bwd_astar.get_successors(UpperCAmelCase__   ),
       }

       for astar in [self.fwd_astar, self.bwd_astar]:
        for child_node in successors[astar]:
         if child_node in astar.closed_nodes:
          continue

         if child_node not in astar.open_nodes:
          astar.open_nodes.append(UpperCAmelCase__   )
         else:
          # retrieve the best current path
          __UpperCamelCase       						= astar.open_nodes.pop(
              astar.open_nodes.index(UpperCAmelCase__   )   )

          if child_node.g_cost < better_node.g_cost:
           astar.open_nodes.append(UpperCAmelCase__   )
          else:
           astar.open_nodes.append(UpperCAmelCase__   )

      return [self.fwd_astar.start.pos]







     def 		UpperCAmelCase      (			self			,  __UpperCAmelCase			,  __UpperCAmelCase   ):

      '''simple docstring'''




      __UpperCamelCase       						= self.fwd_astar.retrace_path(UpperCAmelCase__   )
      __UpperCamelCase       						= self.bwd_astar.retrace_path(UpperCAmelCase__   )
      bwd_path.pop()
      bwd_path.reverse()
      __UpperCamelCase       						= fwd_path + bwd_path
      return path


if __name__ == "__main__":
 # all coordinates are given in format [y,x]
 UpperCamelCase			:       Dict									=				(0, 0)
 UpperCamelCase			:       List[str]									=				(len(grid) - 1, len(grid[0]) - 1)
 for elem in grid:
  print(elem)

 UpperCamelCase			:       int									=				time.time()
 UpperCamelCase			:       int									=				AStar(init, goal)
 UpperCamelCase			:       List[Any]									=				a_star.search()
 UpperCamelCase			:       List[Any]									=				time.time() - start_time
 print(f'''AStar execution time = {end_time:f} seconds''')

 UpperCamelCase			:       Optional[int]									=				time.time()
 UpperCamelCase			:       str									=				BidirectionalAStar(init, goal)
 UpperCamelCase			:       Any									=				time.time() - bd_start_time
 print(f'''BidirectionalAStar execution time = {bd_end_time:f} seconds''')





"""simple docstring"""

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

from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging


UpperCamelCase			:       Optional[int]									=				logging.get_logger(__name__)

UpperCamelCase			:       int									=				{
    "EleutherAI/gpt-j-6B": "https://huggingface.co/EleutherAI/gpt-j-6B/resolve/main/config.json",
    # See all GPT-J models at https://huggingface.co/models?filter=gpt_j
}

class __lowerCAmelCase	(   __SCREAMING_SNAKE_CASE    ):
     lowercase		     =		"gptj"
     lowercase		     =		{
         "max_position_embeddings": "n_positions",
         "hidden_size": "n_embd",
         "num_attention_heads": "n_head",
         "num_hidden_layers": "n_layer",
     }





     def __init__(			self			,  __UpperCAmelCase=5_0400			,  __UpperCAmelCase=2048			,  __UpperCAmelCase=4096			,  __UpperCAmelCase=28			,  __UpperCAmelCase=16			,  __UpperCAmelCase=64			,  __UpperCAmelCase=None			,  __UpperCAmelCase="gelu_new"			,  __UpperCAmelCase=0.0			,  __UpperCAmelCase=0.0			,  __UpperCAmelCase=0.0			,  __UpperCAmelCase=1E-5			,  __UpperCAmelCase=0.0_2			,  __UpperCAmelCase=True			,  __UpperCAmelCase=5_0256			,  __UpperCAmelCase=5_0256			,  __UpperCAmelCase=False			,  **__UpperCAmelCase			,  ):

      '''simple docstring'''




      __UpperCamelCase       						= vocab_size
      __UpperCamelCase       						= n_positions
      __UpperCamelCase       						= n_embd
      __UpperCamelCase       						= n_layer
      __UpperCamelCase       						= n_head
      __UpperCamelCase       						= n_inner
      __UpperCamelCase       						= rotary_dim
      __UpperCamelCase       						= activation_function
      __UpperCamelCase       						= resid_pdrop
      __UpperCamelCase       						= embd_pdrop
      __UpperCamelCase       						= attn_pdrop
      __UpperCamelCase       						= layer_norm_epsilon
      __UpperCamelCase       						= initializer_range
      __UpperCamelCase       						= use_cache

      __UpperCamelCase       						= bos_token_id
      __UpperCamelCase       						= eos_token_id

      super().__init__(
          bos_token_id=__UpperCAmelCase			,  eos_token_id=__UpperCAmelCase			,  tie_word_embeddings=__UpperCAmelCase			,  **__UpperCAmelCase   )


class __lowerCAmelCase	(   __SCREAMING_SNAKE_CASE    ):





     def __init__(			self			,  __UpperCAmelCase			,  __UpperCAmelCase = "default"			,  __UpperCAmelCase = None			,  __UpperCAmelCase = False			,  ):

      '''simple docstring'''




      super().__init__(__UpperCAmelCase			,  task=__UpperCAmelCase			,  patching_specs=__UpperCAmelCase			,  use_past=__UpperCAmelCase   )
      if not getattr(self._config			,  'pad_token_id'			,  __UpperCAmelCase   ):
       # TODO: how to do that better?
       __UpperCamelCase       						= 0





     @property
     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      __UpperCamelCase       						= OrderedDict({'input_ids': {0: 'batch', 1: 'sequence'}}   )
      if self.use_past:
       self.fill_with_past_key_values_(__UpperCAmelCase			,  direction='inputs'   )
       __UpperCamelCase       						= {0: 'batch', 1: 'past_sequence + sequence'}
      else:
       __UpperCamelCase       						= {0: 'batch', 1: 'sequence'}

      return common_inputs





     @property
     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      return self._config.n_layer





     @property
     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      return self._config.n_head





     def 		UpperCAmelCase      (			self			,  __UpperCAmelCase			,  __UpperCAmelCase = -1			,  __UpperCAmelCase = -1			,  __UpperCAmelCase = False			,  __UpperCAmelCase = None			,  ):

      '''simple docstring'''




      __UpperCamelCase       						= super(__UpperCAmelCase			,  self   ).generate_dummy_inputs(
          __UpperCAmelCase			,  batch_size=__UpperCAmelCase			,  seq_length=__UpperCAmelCase			,  is_pair=__UpperCAmelCase			,  framework=__UpperCAmelCase   )

      # We need to order the input in the way they appears in the forward()
      __UpperCamelCase       						= OrderedDict({'input_ids': common_inputs['input_ids']}   )

      # Need to add the past_keys
      if self.use_past:
       if not is_torch_available():
        raise ValueError('Cannot generate dummy past_keys inputs without PyTorch installed.'   )
       else:
        import torch

        __UpperCamelCase						,			__UpperCamelCase       						= common_inputs['input_ids'].shape
        # Not using the same length for past_key_values
        __UpperCamelCase       						= seqlen + 2
        __UpperCamelCase       						= (
            batch,
            self.num_attention_heads,
            past_key_values_length,
            self._config.hidden_size // self.num_attention_heads,
        )
        __UpperCamelCase       						= [
            (torch.zeros(__UpperCAmelCase   ), torch.zeros(__UpperCAmelCase   )) for _ in range(self.num_layers   )
        ]

      __UpperCamelCase       						= common_inputs['attention_mask']
      if self.use_past:
       __UpperCamelCase       						= ordered_inputs['attention_mask'].dtype
       __UpperCamelCase       						= torch.cat(
           [ordered_inputs['attention_mask'], torch.ones(__UpperCAmelCase			,  __UpperCAmelCase			,  dtype=__UpperCAmelCase   )]			,  dim=1   )

      return ordered_inputs







     @property
     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      return 13



