





from __future__ import annotations

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

_UpperCAmelCase				:       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],
]

_UpperCAmelCase				:       Tuple	   =				([-1, 0], [0, -1], [1, 0], [0, 1])  # up, left, down, right

class    lowerCAmelCase							:
 def __init__(      self  :      Optional[int]			,     UpperCAmelCase  :      int			,     UpperCAmelCase  :      int			,     UpperCAmelCase  :      int			,     UpperCAmelCase  :      int			,     UpperCAmelCase  :      float			,     UpperCAmelCase  :      Node | None			,     )			->	Optional[int]:
     lowerCamelCase__    :      Union[str, Any]					 =		pos_x
     lowerCamelCase__    :      List[Any]					 =		pos_y
     lowerCamelCase__    :      Union[str, Any]					 =		(pos_y, pos_x)
     lowerCamelCase__    :      Optional[Any]					 =		goal_x
     lowerCamelCase__    :      Optional[int]					 =		goal_y
     lowerCamelCase__    :      str					 =		g_cost
     lowerCamelCase__    :      int					 =		parent
     lowerCamelCase__    :      Union[str, Any]					 =		self.calculate_heuristic()
 def      A_	(      self  :      Optional[Any]	)			->	float:
     lowerCamelCase__    :      Dict					 =		abs(self.pos_x - self.goal_x	)
     lowerCamelCase__    :      int					 =		abs(self.pos_y - self.goal_y	)
     return dx + dy







 def __lt__(      self  :      int			,     UpperCAmelCase  :      int	)			->	bool:
     return self.f_cost < other.f_cost


class    lowerCAmelCase							:
 def __init__(      self  :      List[str]			,     UpperCAmelCase  :      tuple[int, int]			,     UpperCAmelCase  :      tuple[int, int]	)			->	Union[str, Any]:
     lowerCamelCase__    :      Tuple					 =		Node(start[1]			,     start[0]			,     goal[1]			,     goal[0]			,     0			,     UpperCAmelCase	)
     lowerCamelCase__    :      Tuple					 =		Node(goal[1]			,     goal[0]			,     goal[1]			,     goal[0]			,     99999			,     UpperCAmelCase	)

     lowerCamelCase__    :      List[str]					 =		[self.start]
     lowerCamelCase__    :      list[Node]					 =		[]

     lowerCamelCase__    :      Tuple					 =		False
 def      A_	(      self  :      Any	)			->	Path | None:
     while self.open_nodes:
         # Open Nodes are sorted using __lt__
         self.open_nodes.sort()
         lowerCamelCase__    :      Optional[Any]					 =		self.open_nodes.pop(0	)

         if current_node.pos == self.target.pos:
             lowerCamelCase__    :      Optional[int]					 =		True
             return self.retrace_path(UpperCAmelCase	)

         self.closed_nodes.append(UpperCAmelCase	)
         lowerCamelCase__    :      List[Any]					 =		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
                 lowerCamelCase__    :      Optional[Any]					 =		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	)

     if not self.reached:
         return [self.start.pos]
     return None
 def      A_	(      self  :      int			,     UpperCAmelCase  :      Node	)			->	list[Node]:
     lowerCamelCase__    :      List[Any]					 =		[]
     for action in delta:
         lowerCamelCase__    :      Union[str, Any]					 =		parent.pos_x + action[1]
         lowerCamelCase__    :      Any					 =		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      A_	(      self  :      Tuple			,     UpperCAmelCase  :      Node | None	)			->	Path:
     lowerCamelCase__    :      Optional[Any]					 =		node
     lowerCamelCase__    :      Any					 =		[]
     while current_node is not None:
         path.append((current_node.pos_y, current_node.pos_x)	)
         lowerCamelCase__    :      Optional[int]					 =		current_node.parent
     path.reverse()
     return path


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

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

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

        for elem in grid:
            print(elem)










'''simple docstring'''




from typing import List, Union

import numpy as np

from ..tokenization_utils import TruncationStrategy
from ..utils import add_end_docstrings, logging
from .base import PIPELINE_INIT_ARGS, ArgumentHandler, ChunkPipeline


lowerCamelCase							:Tuple				    =					logging.get_logger(__name__)







class 	_lowerCAmelCase							(			__UpperCAmelCase ):


	def     _a	(self       ,      lowercase							):
				if isinstance(lowercase       ,      lowercase							):
							A_	:						Tuple	=     [label.strip() for label in labels.split(""","""							) if label.strip()]
				return labels


	def __call__(self       ,      lowercase       ,      lowercase       ,      lowercase							):
				if len(lowercase							) == 0 or len(lowercase							) == 0:
							raise ValueError("""You must include at least one label and at least one sequence."""							)
				if hypothesis_template.format(labels[0]							) == hypothesis_template:
							raise ValueError(
							    (
							        """The provided hypothesis_template \"{}\" was not able to be formatted with the target labels. """
							        """Make sure the passed template includes formatting syntax such as {{}} where the label should go."""
							    ).format(lowercase							)							)

				if isinstance(lowercase       ,      lowercase							):
							A_	:						Optional[Any]	=     [sequences]

				A_	:						List[Any]	=     []
				for sequence in sequences:
							sequence_pairs.extend([[sequence, hypothesis_template.format(lowercase							)] for label in labels]							)

				return sequence_pairs, sequences







@add_end_docstrings(__UpperCAmelCase )
class 	_lowerCAmelCase							(			__UpperCAmelCase ):


	def __init__(self       ,      lowercase=ZeroShotClassificationArgumentHandler()       ,      *lowercase       ,      **lowercase							):
				A_	:						str	=     args_parser
				super().__init__(*lowercase       ,      **lowercase							)
				if self.entailment_id == -1:
							logger.warning(
							    """Failed to determine 'entailment' label id from the label2id mapping in the model config. Setting to """
							    """-1. Define a descriptive label2id mapping in the model config to ensure correct outputs."""							)


	@property
	def     _a	(self							):
				for label, ind in self.model.config.labelaid.items():
							if label.lower().startswith("""entail"""							):
										return ind
				return -1


	def     _a	(self       ,      lowercase       ,      lowercase=True       ,      lowercase=True       ,      lowercase=TruncationStrategy.ONLY_FIRST       ,      **lowercase							):
				A_	:						Optional[int]	=     self.framework
				if self.tokenizer.pad_token is None:
							# Override for tokenizers not supporting padding
							logger.error(
							    """Tokenizer was not supporting padding necessary for zero-shot, attempting to use """
							    """ `pad_token=eos_token`"""							)
							A_	:						Any	=     self.tokenizer.eos_token
				try:
							A_	:						Optional[Any]	=     self.tokenizer(
							    lowercase       ,      add_special_tokens=lowercase       ,      return_tensors=lowercase       ,      padding=lowercase       ,      truncation=lowercase       ,      )
				except Exception as e:
							if "too short" in str(lowercase							):
										# tokenizers might yell that we want to truncate
										# to a value that is not even reached by the input.
										# In that case we don't want to truncate.
										# It seems there's not a really better way to catch that
										# exception.

										A_	:						str	=     self.tokenizer(
										    lowercase       ,      add_special_tokens=lowercase       ,      return_tensors=lowercase       ,      padding=lowercase       ,      truncation=TruncationStrategy.DO_NOT_TRUNCATE       ,      )
							else:
										raise e

				return inputs


	def     _a	(self       ,      **lowercase							):
				if kwargs.get("""multi_class"""       ,      lowercase							) is not None:
							A_	:						Union[str, Any]	=     kwargs["""multi_class"""]
							logger.warning(
							    """The `multi_class` argument has been deprecated and renamed to `multi_label`. """
							    """`multi_class` will be removed in a future version of Transformers."""							)
				A_	:						Tuple	=     {}
				if "candidate_labels" in kwargs:
							A_	:						Optional[int]	=     self._args_parser._parse_labels(kwargs["""candidate_labels"""]							)
				if "hypothesis_template" in kwargs:
							A_	:						List[str]	=     kwargs["""hypothesis_template"""]

				A_	:						Union[str, Any]	=     {}
				if "multi_label" in kwargs:
							A_	:						Optional[int]	=     kwargs["""multi_label"""]
				return preprocess_params, {}, postprocess_params


	def __call__(self       ,      lowercase       ,      *lowercase       ,      **lowercase       ,      ):
				if len(lowercase							) == 0:
							pass
				elif len(lowercase							) == 1 and "candidate_labels" not in kwargs:
							A_	:						Any	=     args[0]
				else:
							raise ValueError(F'Unable to understand extra arguments {args}'							)

				return super().__call__(lowercase       ,      **lowercase							)


	def     _a	(self       ,      lowercase       ,      lowercase=None       ,      lowercase="This example is {}."							):
				A_,    A_	:						Any	=     self._args_parser(lowercase       ,      lowercase       ,      lowercase							)

				for i, (candidate_label, sequence_pair) in enumerate(zip(lowercase       ,      lowercase							)							):
							A_	:						List[Any]	=     self._parse_and_tokenize([sequence_pair]							)

							yield {
							    "candidate_label": candidate_label,
							    "sequence": sequences[0],
							    "is_last": i == len(lowercase							) - 1,
							    **model_input,
							}


	def     _a	(self       ,      lowercase							):
				A_	:						Dict	=     inputs["""candidate_label"""]
				A_	:						Any	=     inputs["""sequence"""]
				A_	:						List[Any]	=     {k: inputs[k] for k in self.tokenizer.model_input_names}
				A_	:						Optional[int]	=     self.model(**lowercase							)

				A_	:						Optional[Any]	=     {
				    """candidate_label""": candidate_label,
				    """sequence""": sequence,
				    """is_last""": inputs["""is_last"""],
				    **outputs,
				}
				return model_outputs


	def     _a	(self       ,      lowercase       ,      lowercase=False							):
				A_	:						Tuple	=     [outputs["""candidate_label"""] for outputs in model_outputs]
				A_	:						Optional[int]	=     [outputs["""sequence"""] for outputs in model_outputs]
				A_	:						Union[str, Any]	=     np.concatenate([output["""logits"""].numpy() for output in model_outputs]							)
				A_	:						List[str]	=     logits.shape[0]
				A_	:						Optional[int]	=     len(lowercase							)
				A_	:						int	=     N // n
				A_	:						int	=     logits.reshape((num_sequences, n, -1)							)

				if multi_label or len(lowercase							) == 1:
							# softmax over the entailment vs. contradiction dim for each label independently
							A_	:						Dict	=     self.entailment_id
							A_	:						Dict	=     -1 if entailment_id == 0 else 0
							A_	:						str	=     reshaped_outputs[..., [contradiction_id, entailment_id]]
							A_	:						Optional[Any]	=     np.exp(lowercase							) / np.exp(lowercase							).sum(-1       ,      keepdims=lowercase							)
							A_	:						Tuple	=     scores[..., 1]
				else:
							# softmax the "entailment" logits over all candidate labels
							A_	:						Optional[int]	=     reshaped_outputs[..., self.entailment_id]
							A_	:						Any	=     np.exp(lowercase							) / np.exp(lowercase							).sum(-1       ,      keepdims=lowercase							)

				A_	:						List[str]	=     list(reversed(scores[0].argsort()							)							)
				return {
				    "sequence": sequences[0],
				    "labels": [candidate_labels[i] for i in top_inds],
				    "scores": scores[0, top_inds].tolist(),
				}