
from __future__ import annotations

import time
from math import sqrt

# 1 for manhattan, 0 for euclidean
__magic_name__            =  0

__magic_name__            =  [
    [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],
]

__magic_name__            =  [[-1, 0], [0, -1], [1, 0], [0, 1]]  # up, left, down, right

__magic_name__            =  tuple[int, int]




class 					_SCREAMING_SNAKE_CASE  :
      def __init__(			self		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase		,	):
          snake_case__						=				pos_x
          snake_case__						=				pos_y
          snake_case__						=				(pos_y, pos_x)
          snake_case__						=				goal_x
          snake_case__						=				goal_y
          snake_case__						=				g_cost
          snake_case__						=				parent
          snake_case__						=				self.calculate_heuristic()
          snake_case__						=				self.g_cost + self.h_cost
      def A_			(			self ):
          snake_case__						=				self.pos_x - self.goal_x
          snake_case__						=				self.pos_y - self.goal_y
          if HEURISTIC == 1:
              return abs(lowerCamelCase ) + abs(lowerCamelCase )
          else:
              return sqrt(dy**2 + dx**2 )



      def __lt__(			self		,	lowerCamelCase ):
          return self.f_cost < other.f_cost





class 					_SCREAMING_SNAKE_CASE  :
      def __init__(			self		,	lowerCamelCase		,	lowerCamelCase ):
          snake_case__						=				Node(start[1]		,	start[0]		,	goal[1]		,	goal[0]		,	0		,	lowerCamelCase )
          snake_case__						=				Node(goal[1]		,	goal[0]		,	goal[1]		,	goal[0]		,	9_99_99		,	lowerCamelCase )

          snake_case__						=				[self.start]
          snake_case__						=				[]

          snake_case__						=				False
      def A_			(			self ):
          while self.open_nodes:
              # Open Nodes are sorted using __lt__
              self.open_nodes.sort()
              snake_case__						=				self.open_nodes.pop(0 )

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

              self.closed_nodes.append(lowerCamelCase )
              snake_case__						=				self.get_successors(lowerCamelCase )

              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(lowerCamelCase )
                  else:
                      # retrieve the best current path
                      snake_case__						=				self.open_nodes.pop(self.open_nodes.index(lowerCamelCase ) )

                      if child_node.g_cost < better_node.g_cost:
                          self.open_nodes.append(lowerCamelCase )
                      else:
                          self.open_nodes.append(lowerCamelCase )

          return [self.start.pos]
      def A_			(			self		,	lowerCamelCase ):
          snake_case__						=				[]
          for action in delta:
              snake_case__						=				parent.pos_x + action[1]
              snake_case__						=				parent.pos_y + action[0]
              if not (0 <= pos_x <= len(grid[0] ) - 1 and 0 <= pos_y <= len(lowerCamelCase ) - 1):
                  continue

              if grid[pos_y][pos_x] != 0:
                  continue

              successors.append(
                  Node(
                      lowerCamelCase		,	lowerCamelCase		,	self.target.pos_y		,	self.target.pos_x		,	parent.g_cost + 1		,	lowerCamelCase		,	) )
          return successors



      def A_			(			self		,	lowerCamelCase ):
          snake_case__						=				node
          snake_case__						=				[]
          while current_node is not None:
              path.append((current_node.pos_y, current_node.pos_x) )
              snake_case__						=				current_node.parent
          path.reverse()
          return path





class 					_SCREAMING_SNAKE_CASE  :
      def __init__(			self		,	lowerCamelCase		,	lowerCamelCase ):
          snake_case__						=				AStar(lowerCamelCase		,	lowerCamelCase )
          snake_case__						=				AStar(lowerCamelCase		,	lowerCamelCase )
          snake_case__						=				False
      def A_			(			self ):
          while self.fwd_astar.open_nodes or self.bwd_astar.open_nodes:
              self.fwd_astar.open_nodes.sort()
              self.bwd_astar.open_nodes.sort()
              snake_case__						=				self.fwd_astar.open_nodes.pop(0 )
              snake_case__						=				self.bwd_astar.open_nodes.pop(0 )

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

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

              snake_case__						=				current_bwd_node
              snake_case__						=				current_fwd_node

              snake_case__						=				{
                  self.fwd_astar: self.fwd_astar.get_successors(lowerCamelCase ),
                  self.bwd_astar: self.bwd_astar.get_successors(lowerCamelCase ),
              }

              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(lowerCamelCase )
                      else:
                          # retrieve the best current path
                          snake_case__						=				astar.open_nodes.pop(
                              astar.open_nodes.index(lowerCamelCase ) )

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

          return [self.fwd_astar.start.pos]



      def A_			(			self		,	lowerCamelCase		,	lowerCamelCase ):
          snake_case__						=				self.fwd_astar.retrace_path(lowerCamelCase )
          snake_case__						=				self.bwd_astar.retrace_path(lowerCamelCase )
          bwd_path.pop()
          bwd_path.reverse()
          snake_case__						=				fwd_path + bwd_path
          return path


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

     __magic_name__            =  time.time()
     __magic_name__            =  AStar(init, goal)
     __magic_name__            =  a_star.search()
     __magic_name__            =  time.time() - start_time
     print(F'''AStar execution time = {end_time:f} seconds''')

     __magic_name__            =  time.time()
     __magic_name__            =  BidirectionalAStar(init, goal)
     __magic_name__            =  time.time() - bd_start_time
     print(F'''BidirectionalAStar execution time = {bd_end_time:f} seconds''')





import os
import re
import shutil
import sys
import tempfile
import unittest

import black


__magic_name__            =  os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, '''utils'''))

import check_copies  # noqa: E402


# This is the reference code that will be used in the tests.
# If BertLMPredictionHead is changed in modeling_bert.py, this code needs to be manually updated.
__magic_name__            =  '''    def __init__(self, config):
        super().__init__()
        self.transform = BertPredictionHeadTransform(config)

        # The output weights are the same as the input embeddings, but there is
        # an output-only bias for each token.
        self.decoder = nn.Linear(config.hidden_size, config.vocab_size, bias=False)

        self.bias = nn.Parameter(torch.zeros(config.vocab_size))

        # Need a link between the two variables so that the bias is correctly resized with `resize_token_embeddings`
        self.decoder.bias = self.bias

    def forward(self, hidden_states):
        hidden_states = self.transform(hidden_states)
        hidden_states = self.decoder(hidden_states)
        return hidden_states
'''




class 					_SCREAMING_SNAKE_CASE  (      unittest.TestCase ):
      def A_			(			self ):
          snake_case__						=				tempfile.mkdtemp()
          os.makedirs(os.path.join(self.transformer_dir		,	"models/bert/" ) )
          snake_case__						=				self.transformer_dir
          shutil.copy(
              os.path.join(lowerCamelCase		,	"src/transformers/models/bert/modeling_bert.py" )		,	os.path.join(self.transformer_dir		,	"models/bert/modeling_bert.py" )		,	)
      def A_			(			self ):
          snake_case__						=				"src/transformers"
          shutil.rmtree(self.transformer_dir )
      def A_			(			self		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase=None ):
          snake_case__						=				comment + F"""\nclass {class_name}(nn.Module):\n""" + class_code
          if overwrite_result is not None:
              snake_case__						=				comment + F"""\nclass {class_name}(nn.Module):\n""" + overwrite_result
          snake_case__						=				black.Mode(target_versions={black.TargetVersion.PYaa}		,	line_length=1_19 )
          snake_case__						=				black.format_str(lowerCamelCase		,	mode=lowerCamelCase )
          snake_case__						=				os.path.join(self.transformer_dir		,	"new_code.py" )
          with open(lowerCamelCase		,	"w"		,	newline="\n" ) as f:
              f.write(lowerCamelCase )
          if overwrite_result is None:
              self.assertTrue(len(check_copies.is_copy_consistent(lowerCamelCase ) ) == 0 )
          else:
              check_copies.is_copy_consistent(f.name		,	overwrite=lowerCamelCase )
              with open(lowerCamelCase		,	"r" ) as f:
                  self.assertTrue(f.read()		,	lowerCamelCase )
      def A_			(			self ):
          snake_case__						=				check_copies.find_code_in_transformers("models.bert.modeling_bert.BertLMPredictionHead" )
          self.assertEqual(lowerCamelCase		,	lowerCamelCase )
      def A_			(			self ):
          # Base copy consistency
          self.check_copy_consistency(
              "# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead"		,	"BertLMPredictionHead"		,	REFERENCE_CODE + "\n"		,	)

          # With no empty line at the end
          self.check_copy_consistency(
              "# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead"		,	"BertLMPredictionHead"		,	lowerCamelCase		,	)

          # Copy consistency with rename
          self.check_copy_consistency(
              "# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead with Bert->TestModel"		,	"TestModelLMPredictionHead"		,	re.sub("Bert"		,	"TestModel"		,	lowerCamelCase )		,	)

          # Copy consistency with a really long name
          snake_case__						=				"TestModelWithAReallyLongNameBecauseSomePeopleLikeThatForSomeReason"
          self.check_copy_consistency(
              F"""# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead with Bert->{long_class_name}"""		,	F"""{long_class_name}LMPredictionHead"""		,	re.sub("Bert"		,	lowerCamelCase		,	lowerCamelCase )		,	)

          # Copy consistency with overwrite
          self.check_copy_consistency(
              "# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead with Bert->TestModel"		,	"TestModelLMPredictionHead"		,	lowerCamelCase		,	overwrite_result=re.sub("Bert"		,	"TestModel"		,	lowerCamelCase )		,	)



      def A_			(			self ):
          snake_case__						=				check_copies.LOCALIZED_READMES["README_zh-hans.md"]

          snake_case__						=				(
              "1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (from Google Research and the"
              " Toyota Technological Institute at Chicago) released with the paper [ALBERT: A Lite BERT for"
              " Self-supervised Learning of Language Representations](https://arxiv.org/abs/1909.11942), by Zhenzhong"
              " Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut.\n1."
              " **[DistilBERT](https://huggingface.co/transformers/model_doc/distilbert.html)** (from HuggingFace),"
              " released together with the paper [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and"
              " lighter](https://arxiv.org/abs/1910.01108) by Victor Sanh, Lysandre Debut and Thomas Wolf. The same"
              " method has been applied to compress GPT2 into"
              " [DistilGPT2](https://github.com/huggingface/transformers/tree/main/examples/distillation), RoBERTa into"
              " [DistilRoBERTa](https://github.com/huggingface/transformers/tree/main/examples/distillation),"
              " Multilingual BERT into"
              " [DistilmBERT](https://github.com/huggingface/transformers/tree/main/examples/distillation) and a German"
              " version of DistilBERT.\n1. **[ELECTRA](https://huggingface.co/transformers/model_doc/electra.html)**"
              " (from Google Research/Stanford University) released with the paper [ELECTRA: Pre-training text encoders"
              " as discriminators rather than generators](https://arxiv.org/abs/2003.10555) by Kevin Clark, Minh-Thang"
              " Luong, Quoc V. Le, Christopher D. Manning."
          )
          snake_case__						=				(
              "1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (来自 Google Research and the"
              " Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of"
              " Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian"
              " Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n"
          )
          snake_case__						=				(
              "1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (来自 Google Research and the"
              " Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of"
              " Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian"
              " Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n1."
              " **[DistilBERT](https://huggingface.co/transformers/model_doc/distilbert.html)** (来自 HuggingFace) 伴随论文"
              " [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and"
              " lighter](https://arxiv.org/abs/1910.01108) 由 Victor Sanh, Lysandre Debut and Thomas Wolf 发布。 The same"
              " method has been applied to compress GPT2 into"
              " [DistilGPT2](https://github.com/huggingface/transformers/tree/main/examples/distillation), RoBERTa into"
              " [DistilRoBERTa](https://github.com/huggingface/transformers/tree/main/examples/distillation),"
              " Multilingual BERT into"
              " [DistilmBERT](https://github.com/huggingface/transformers/tree/main/examples/distillation) and a German"
              " version of DistilBERT.\n1. **[ELECTRA](https://huggingface.co/transformers/model_doc/electra.html)** (来自"
              " Google Research/Stanford University) 伴随论文 [ELECTRA: Pre-training text encoders as discriminators rather"
              " than generators](https://arxiv.org/abs/2003.10555) 由 Kevin Clark, Minh-Thang Luong, Quoc V. Le,"
              " Christopher D. Manning 发布。\n"
          )

          snake_case__						,					snake_case__						=				check_copies.convert_to_localized_md(
              lowerCamelCase		,	lowerCamelCase		,	localized_readme["format_model_list"] )

          self.assertFalse(lowerCamelCase )
          self.assertEqual(lowerCamelCase		,	lowerCamelCase )

          snake_case__						,					snake_case__						=				check_copies.convert_to_localized_md(
              lowerCamelCase		,	lowerCamelCase		,	localized_readme["format_model_list"] )

          # Check whether the number of models is equal to README.md after conversion.
          self.assertTrue(lowerCamelCase )

          snake_case__						=				(
              "1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (from Google Research and the"
              " Toyota Technological Institute at Chicago) released with the paper [ALBERT: A Lite BERT for"
              " Self-supervised Learning of Language Representations](https://arxiv.org/abs/1909.11942), by Zhenzhong"
              " Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut."
          )
          snake_case__						=				(
              "1. **[ALBERT](https://huggingface.co/transformers/main/model_doc/albert.html)** (来自 Google Research and"
              " the Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of"
              " Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian"
              " Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n"
          )
          snake_case__						=				(
              "1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (来自 Google Research and the"
              " Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of"
              " Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian"
              " Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n"
          )

          snake_case__						,					snake_case__						=				check_copies.convert_to_localized_md(
              lowerCamelCase		,	lowerCamelCase		,	localized_readme["format_model_list"] )

          # Check if the model link is synchronized.
          self.assertEqual(lowerCamelCase		,	lowerCamelCase )



