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

import black


lowercase						:      int									= 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.
lowercase						:      str									= '    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 				lowerCamelCase__							(       unittest.TestCase):




	'''simple docstring'''




	def      _lowerCamelCase       (      self	:str		)	->				Optional[Any]:
								__UpperCamelCase			:     int         =      tempfile.mkdtemp()
								os.makedirs(os.path.join(self.transformer_dir  ,     "models/bert/"		)		)
								__UpperCamelCase			:     List[Any]         =      self.transformer_dir
								shutil.copy(
								    os.path.join(a  ,     "src/transformers/models/bert/modeling_bert.py"		)  ,     os.path.join(self.transformer_dir  ,     "models/bert/modeling_bert.py"		)  ,     )







	def      _lowerCamelCase       (      self	:List[str]		)	->				Any:
								__UpperCamelCase			:     int         =      "src/transformers"
								shutil.rmtree(self.transformer_dir		)







	def      _lowerCamelCase       (      self	:str  ,     a	:Tuple  ,     a	:Union[str, Any]  ,     a	:int  ,     a	:List[str]=None		)	->				List[Any]:
								__UpperCamelCase			:     List[Any]         =      comment + f'\nclass {class_name}(nn.Module):\n' + class_code
								if overwrite_result is not None:
															__UpperCamelCase			:     int         =      comment + f'\nclass {class_name}(nn.Module):\n' + overwrite_result
								__UpperCamelCase			:     int         =      black.Mode(target_versions={black.TargetVersion.PYaa}  ,     line_length=1_1_9		)
								__UpperCamelCase			:     List[str]         =      black.format_str(a  ,     mode=a		)
								__UpperCamelCase			:     List[Any]         =      os.path.join(self.transformer_dir  ,     "new_code.py"		)
								with open(a  ,     "w"  ,     newline="\n"		) as f:
															f.write(a		)
								if overwrite_result is None:
															self.assertTrue(len(check_copies.is_copy_consistent(a		)		) == 0		)
								else:
															check_copies.is_copy_consistent(f.name  ,     overwrite=a		)
															with open(a  ,     "r"		) as f:
																						self.assertTrue(f.read()  ,     a		)







	def      _lowerCamelCase       (      self	:Optional[int]		)	->				str:
								__UpperCamelCase			:     Optional[int]         =      check_copies.find_code_in_transformers("models.bert.modeling_bert.BertLMPredictionHead"		)
								self.assertEqual(a  ,     a		)







	def      _lowerCamelCase       (      self	:Any		)	->				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"  ,     a  ,     )

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

								# Copy consistency with a really long name
								__UpperCamelCase			:     Any         =      "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"  ,     a  ,     a		)  ,     )

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







	def      _lowerCamelCase       (      self	:Any		)	->				Tuple:
								__UpperCamelCase			:     Any         =      check_copies.LOCALIZED_READMES["README_zh-hans.md"]

								__UpperCamelCase			:     Optional[Any]         =      (
								    "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."
								)
								__UpperCamelCase			:     Optional[int]         =      (
								    "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"
								)
								__UpperCamelCase			:     Optional[int]         =      (
								    "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"
								)

								__UpperCamelCase     ,		__UpperCamelCase			:     Tuple         =      check_copies.convert_to_localized_md(
								    a  ,     a  ,     localized_readme["format_model_list"]		)

								self.assertFalse(a		)
								self.assertEqual(a  ,     a		)

								__UpperCamelCase     ,		__UpperCamelCase			:     Tuple         =      check_copies.convert_to_localized_md(
								    a  ,     a  ,     localized_readme["format_model_list"]		)

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

								__UpperCamelCase			:     Any         =      (
								    "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."
								)
								__UpperCamelCase			:     str         =      (
								    "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"
								)
								__UpperCamelCase			:     Tuple         =      (
								    "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"
								)

								__UpperCamelCase     ,		__UpperCamelCase			:     Any         =      check_copies.convert_to_localized_md(
								    a  ,     a  ,     localized_readme["format_model_list"]		)

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

from __future__ import annotations

lowercase						:      str									= list[tuple[int, int]]

lowercase						:      Optional[int]									= [
    [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],
]

lowercase						:      int									= ([-1, 0], [0, -1], [1, 0], [0, 1])  # up, left, down, right




class 				lowerCamelCase__							:




	'''simple docstring'''




	def __init__(      self	:Optional[int]  ,     a	:int  ,     a	:int  ,     a	:int  ,     a	:int  ,     a	:float  ,     a	:Node | None  ,     )	->				List[Any]:
								__UpperCamelCase			:     List[Any]         =      pos_x
								__UpperCamelCase			:     List[str]         =      pos_y
								__UpperCamelCase			:     str         =      (pos_y, pos_x)
								__UpperCamelCase			:     Optional[int]         =      goal_x
								__UpperCamelCase			:     str         =      goal_y
								__UpperCamelCase			:     int         =      g_cost
								__UpperCamelCase			:     Dict         =      parent
								__UpperCamelCase			:     str         =      self.calculate_heuristic()







	def      _lowerCamelCase       (      self	:List[Any]		)	->				float:
								__UpperCamelCase			:     Any         =      abs(self.pos_x - self.goal_x		)
								__UpperCamelCase			:     Tuple         =      abs(self.pos_y - self.goal_y		)
								return dx + dy







	def __lt__(      self	:Union[str, Any]  ,     a	:Dict		)	->				bool:
								return self.f_cost < other.f_cost




class 				lowerCamelCase__							:




	'''simple docstring'''




	def __init__(      self	:List[str]  ,     a	:tuple[int, int]  ,     a	:tuple[int, int]		)	->				List[str]:
								__UpperCamelCase			:     Any         =      Node(start[1]  ,     start[0]  ,     goal[1]  ,     goal[0]  ,     0  ,     a		)
								__UpperCamelCase			:     List[str]         =      Node(goal[1]  ,     goal[0]  ,     goal[1]  ,     goal[0]  ,     9_9_9_9_9  ,     a		)

								__UpperCamelCase			:     Optional[int]         =      [self.start]
								__UpperCamelCase			:     list[Node]         =      []

								__UpperCamelCase			:     Any         =      False







	def      _lowerCamelCase       (      self	:List[str]		)	->				Path | None:
								while self.open_nodes:
															# Open Nodes are sorted using __lt__
															self.open_nodes.sort()
															__UpperCamelCase			:     Optional[int]         =      self.open_nodes.pop(0		)

															if current_node.pos == self.target.pos:
																						__UpperCamelCase			:     Dict         =      True
																						return self.retrace_path(a		)

															self.closed_nodes.append(a		)
															__UpperCamelCase			:     Union[str, Any]         =      self.get_successors(a		)

															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(a		)
																						else:
																													# retrieve the best current path
																													__UpperCamelCase			:     Union[str, Any]         =      self.open_nodes.pop(self.open_nodes.index(a		)		)

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

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







	def      _lowerCamelCase       (      self	:str  ,     a	:Node		)	->				list[Node]:
								__UpperCamelCase			:     List[Any]         =      []
								for action in delta:
															__UpperCamelCase			:     Optional[Any]         =      parent.pos_x + action[1]
															__UpperCamelCase			:     List[Any]         =      parent.pos_y + action[0]

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

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

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







	def      _lowerCamelCase       (      self	:Optional[Any]  ,     a	:Node | None		)	->				Path:
								__UpperCamelCase			:     str         =      node
								__UpperCamelCase			:     int         =      []
								while current_node is not None:
															path.append((current_node.pos_y, current_node.pos_x)		)
															__UpperCamelCase			:     str         =      current_node.parent
								path.reverse()
								return path


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

		print('------')

		lowercase						:      Any									= GreedyBestFirst(init, goal)
		lowercase						:      List[str]									= greedy_bf.search()
		if path:
				for pos_x, pos_y in path:
						lowercase						:      Optional[int]									= 2

				for elem in grid:
						print(elem)