
"""simple docstring"""







from __future__ import annotations

lowerCamelCase__   					=		list[tuple[int, int]]

lowerCamelCase__   					=		[
    [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],
]

lowerCamelCase__   					=		([-1, 0], [0, -1], [1, 0], [0, 1])  # up, left, down, right




class  A__       :

		def __init__(      self       ,		_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE       ,		):
			__lowerCAmelCase			:						int									=      pos_x
			__lowerCAmelCase			:						Optional[Any]									=      pos_y
			__lowerCAmelCase			:						Optional[int]									=      (pos_y, pos_x)
			__lowerCAmelCase			:						Union[str, Any]									=      goal_x
			__lowerCAmelCase			:						Any									=      goal_y
			__lowerCAmelCase			:						Optional[Any]									=      g_cost
			__lowerCAmelCase			:						Any									=      parent
			__lowerCAmelCase			:						Union[str, Any]									=      self.calculate_heuristic()

		def 							__lowerCamelCase							(      self ):
			__lowerCAmelCase			:						str									=      abs(self.pos_x - self.goal_x )
			__lowerCAmelCase			:						str									=      abs(self.pos_y - self.goal_y )
			return dx + dy







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




class  A__       :

		def __init__(      self       ,		_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE ):
			__lowerCAmelCase			:						Optional[int]									=      Node(start[1]       ,		start[0]       ,		goal[1]       ,		goal[0]       ,		0       ,		_SCREAMING_SNAKE_CASE )
			__lowerCAmelCase			:						Any									=      Node(goal[1]       ,		goal[0]       ,		goal[1]       ,		goal[0]       ,		9_99_99       ,		_SCREAMING_SNAKE_CASE )

			__lowerCAmelCase			:						Tuple									=      [self.start]
			__lowerCAmelCase			:						list[Node]									=      []

			__lowerCAmelCase			:						str									=      False

		def 							__lowerCamelCase							(      self ):
			while self.open_nodes:
				# Open Nodes are sorted using __lt__
				self.open_nodes.sort()
				__lowerCAmelCase			:						Optional[int]									=      self.open_nodes.pop(0 )

				if current_node.pos == self.target.pos:
					__lowerCAmelCase			:						Union[str, Any]									=      True
					return self.retrace_path(_SCREAMING_SNAKE_CASE )

				self.closed_nodes.append(_SCREAMING_SNAKE_CASE )
				__lowerCAmelCase			:						Optional[Any]									=      self.get_successors(_SCREAMING_SNAKE_CASE )

				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(_SCREAMING_SNAKE_CASE )
					else:
						# retrieve the best current path
						__lowerCAmelCase			:						Optional[Any]									=      self.open_nodes.pop(self.open_nodes.index(_SCREAMING_SNAKE_CASE ) )

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

			if not self.reached:
				return [self.start.pos]
			return None

		def 							__lowerCamelCase							(      self       ,		_SCREAMING_SNAKE_CASE ):
			__lowerCAmelCase			:						Dict									=      []
			for action in delta:
				__lowerCAmelCase			:						Optional[int]									=      parent.pos_x + action[1]
				__lowerCAmelCase			:						Union[str, Any]									=      parent.pos_y + action[0]

				if not (0 <= pos_x <= len(grid[0] ) - 1 and 0 <= pos_y <= len(_SCREAMING_SNAKE_CASE ) - 1):
					continue

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

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







		def 							__lowerCamelCase							(      self       ,		_SCREAMING_SNAKE_CASE ):
			__lowerCAmelCase			:						Union[str, Any]									=      node
			__lowerCAmelCase			:						Optional[int]									=      []
			while current_node is not None:
				path.append((current_node.pos_y, current_node.pos_x) )
				__lowerCAmelCase			:						int									=      current_node.parent
			path.reverse()
			return path


if __name__ == "__main__":
							lowerCamelCase__   					=		(0, 0)
							lowerCamelCase__   					=		(len(grid) - 1, len(grid[0]) - 1)
							for elem in grid:
														print(elem)

							print("""------""")

							lowerCamelCase__   					=		GreedyBestFirst(init, goal)
							lowerCamelCase__   					=		greedy_bf.search()
							if path:
														for pos_x, pos_y in path:
																					lowerCamelCase__   					=		2

														for elem in grid:
																					print(elem)






'''simple docstring'''

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

import black


__lowerCAmelCase							:	Any			=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.
__lowerCAmelCase							:	Optional[int]			="    def __init__(self, config):\n        super().__init__()\n        self.transform = BertPredictionHeadTransform(config)\n\n        # The output weights are the same as the input embeddings, but there is\n        # an output-only bias for each token.\n        self.decoder = nn.Linear(config.hidden_size, config.vocab_size, bias=False)\n\n        self.bias = nn.Parameter(torch.zeros(config.vocab_size))\n\n        # Need a link between the two variables so that the bias is correctly resized with `resize_token_embeddings`\n        self.decoder.bias = self.bias\n\n    def forward(self, hidden_states):\n        hidden_states = self.transform(hidden_states)\n        hidden_states = self.decoder(hidden_states)\n        return hidden_states\n"






class    UpperCAmelCase	(       unittest.TestCase				):
 def      UpperCAmelCase_							(				self      :Tuple							)->							Union[str, Any]:
     A__				=			tempfile.mkdtemp()
     os.makedirs(os.path.join(self.transformer_dir						,       "models/bert/"							)							)
     A__				=			self.transformer_dir
     shutil.copy(
         os.path.join(lowercase_						,       "src/transformers/models/bert/modeling_bert.py"							)						,       os.path.join(self.transformer_dir						,       "models/bert/modeling_bert.py"							)						,       )
 def      UpperCAmelCase_							(				self      :Optional[int]							)->							Tuple:
     A__				=			"src/transformers"
     shutil.rmtree(self.transformer_dir							)
 def      UpperCAmelCase_							(				self      :List[Any]						,       lowercase_      :List[str]						,       lowercase_      :Union[str, Any]						,       lowercase_      :int						,       lowercase_      :Tuple=None							)->							Optional[Any]:
     A__				=			comment + F"\nclass {class_name}(nn.Module):\n" + class_code
     if overwrite_result is not None:
         A__				=			comment + F"\nclass {class_name}(nn.Module):\n" + overwrite_result
     A__				=			black.Mode(target_versions={black.TargetVersion.PYaa}						,       line_length=1_19							)
     A__				=			black.format_str(lowercase_						,       mode=lowercase_							)
     A__				=			os.path.join(self.transformer_dir						,       "new_code.py"							)
     with open(lowercase_						,       "w"						,       newline="\n"							) as f:
         f.write(lowercase_							)
     if overwrite_result is None:
         self.assertTrue(len(check_copies.is_copy_consistent(lowercase_							)							) == 0							)
     else:
         check_copies.is_copy_consistent(f.name						,       overwrite=lowercase_							)
         with open(lowercase_						,       "r"							) as f:
             self.assertTrue(f.read()						,       lowercase_							)
 def      UpperCAmelCase_							(				self      :str							)->							Optional[Any]:
     A__				=			check_copies.find_code_in_transformers("models.bert.modeling_bert.BertLMPredictionHead"							)
     self.assertEqual(lowercase_						,       lowercase_							)
 def      UpperCAmelCase_							(				self      :List[str]							)->							Optional[int]:
     # 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"						,       lowercase_						,       )

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

     # Copy consistency with a really long name
     A__				=			"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"						,       lowercase_						,       lowercase_							)						,       )

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



 def      UpperCAmelCase_							(				self      :Dict							)->							Any:
     A__				=			check_copies.LOCALIZED_READMES["README_zh-hans.md"]

     A__				=			(
         "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."
     )
     A__				=			(
         "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"
     )
     A__				=			(
         "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"
     )

     A__,	A__				=			check_copies.convert_to_localized_md(
         lowercase_						,       lowercase_						,       localized_readme["format_model_list"]							)

     self.assertFalse(lowercase_							)
     self.assertEqual(lowercase_						,       lowercase_							)

     A__,	A__				=			check_copies.convert_to_localized_md(
         lowercase_						,       lowercase_						,       localized_readme["format_model_list"]							)

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

     A__				=			(
         "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."
     )
     A__				=			(
         "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"
     )
     A__				=			(
         "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"
     )

     A__,	A__				=			check_copies.convert_to_localized_md(
         lowercase_						,       lowercase_						,       localized_readme["format_model_list"]							)

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



