






'''simple docstring'''
from __future__ import annotations

a_					=	list[tuple[int, int]]

a_					=	[
    [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],
]

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






class        __SCREAMING_SNAKE_CASE :




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




	def        __magic_name__		( self		:							Tuple )		->  Union[str, Any]:
							SCREAMING_SNAKE_CASE__       :			Any													=abs(self.pos_x - self.goal_x )
							SCREAMING_SNAKE_CASE__       :			Any													=abs(self.pos_y - self.goal_y )
							return dx + dy




	def __lt__( self		:							Optional[int]   , __lowercase		:							int )		->  List[Any]:
							return self.f_cost < other.f_cost






class        __SCREAMING_SNAKE_CASE :




	def __init__( self		:							List[str]   , __lowercase		:							tuple[int, int]   , __lowercase		:							tuple[int, int] )		->  str:
							SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=Node(start[1]   , start[0]   , goal[1]   , goal[0]   , 0   , _lowercase )
							SCREAMING_SNAKE_CASE__       :			Optional[int]													=Node(goal[1]   , goal[0]   , goal[1]   , goal[0]   , 9_99_99   , _lowercase )

							SCREAMING_SNAKE_CASE__       :			Dict													=[self.start]
							SCREAMING_SNAKE_CASE__       :			List[str]													=[]

							SCREAMING_SNAKE_CASE__       :			Optional[int]													=False




	def        __magic_name__		( self		:							Tuple )		->  Tuple:
							while self.open_nodes:
													# Open Nodes are sorted using __lt__
													self.open_nodes.sort()
													SCREAMING_SNAKE_CASE__       :			int													=self.open_nodes.pop(0 )

													if current_node.pos == self.target.pos:
																			SCREAMING_SNAKE_CASE__       :			Tuple													=True
																			return self.retrace_path(_lowercase )

													self.closed_nodes.append(_lowercase )
													SCREAMING_SNAKE_CASE__       :			List[str]													=self.get_successors(_lowercase )

													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(_lowercase )
																			else:
																									# retrieve the best current path
																									SCREAMING_SNAKE_CASE__       :			List[str]													=self.open_nodes.pop(self.open_nodes.index(_lowercase ) )

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

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




	def        __magic_name__		( self		:							Dict   , __lowercase		:							Node )		->  List[Any]:
							SCREAMING_SNAKE_CASE__       :			Any													=[]
							for action in delta:
													SCREAMING_SNAKE_CASE__       :			Any													=parent.pos_x + action[1]
													SCREAMING_SNAKE_CASE__       :			Any													=parent.pos_y + action[0]

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

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

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




	def        __magic_name__		( self		:							Dict   , __lowercase		:							Node | None )		->  Dict:
							SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=node
							SCREAMING_SNAKE_CASE__       :			List[str]													=[]
							while current_node is not None:
													path.append((current_node.pos_y, current_node.pos_x) )
													SCREAMING_SNAKE_CASE__       :			List[Any]													=current_node.parent
							path.reverse()
							return path


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

		print('------')

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

				for elem in grid:
						print(elem)







'''simple docstring'''
import inspect

import jax
import jax.lax as lax
import jax.numpy as jnp

from ..utils import add_start_docstrings
from ..utils.logging import get_logger


a_					=	get_logger(__name__)


a_					=	R'\n    Args:\n        input_ids (`jnp.ndarray` of shape `(batch_size, sequence_length)`):\n            Indices of input sequence tokens in the vocabulary.\n\n            Indices can be obtained using [`PreTrainedTokenizer`]. See [`PreTrainedTokenizer.encode`] and\n            [`PreTrainedTokenizer.__call__`] for details.\n\n            [What are input IDs?](../glossary#input-ids)\n        scores (`jnp.ndarray` of shape `(batch_size, config.vocab_size)`):\n            Prediction scores of a language modeling head. These can be logits for each vocabulary when not using beam\n            search or log softmax for each vocabulary token when using beam search\n        kwargs (`Dict[str, Any]`, *optional*):\n            Additional logits processor specific kwargs.\n\n    Return:\n        `jnp.ndarray` of shape `(batch_size, config.vocab_size)`: The processed prediction scores.\n\n'






class        __SCREAMING_SNAKE_CASE :




 @add_start_docstrings(__lowercase )
 def __call__( self		:							Any   , __lowercase		:							jnp.ndarray   , __lowercase		:							jnp.ndarray )		->  jnp.ndarray:
       raise NotImplementedError(
           F"{self.__class__} is an abstract class. Only classes inheriting this class can be called." )






class        __SCREAMING_SNAKE_CASE :




 @add_start_docstrings(__lowercase )
 def __call__( self		:							List[str]   , __lowercase		:							jnp.ndarray   , __lowercase		:							jnp.ndarray )		->  jnp.ndarray:
       raise NotImplementedError(
           F"{self.__class__} is an abstract class. Only classes inheriting this class can be called." )






class        __SCREAMING_SNAKE_CASE (     lowerCamelCase			):




 @add_start_docstrings(__lowercase )
 def __call__( self		:							Any   , __lowercase		:							jnp.ndarray   , __lowercase		:							jnp.ndarray   , __lowercase		:							int   , **__lowercase		:							List[str] )		->  jnp.ndarray:
       for processor in self:
             SCREAMING_SNAKE_CASE__       :			Optional[Any]													=inspect.signature(processor.__call__ ).parameters
             if len(__lowercase ) > 3:
                   if not all(arg in kwargs for arg in list(function_args.keys() )[2:] ):
                         raise ValueError(
                             F"Make sure that all the required parameters: {list(function_args.keys() )} for "
                             F"{processor.__class__} are passed to the logits processor." )
                   SCREAMING_SNAKE_CASE__       :			List[Any]													=processor(__lowercase   , __lowercase   , __lowercase   , **__lowercase )
             else:
                   SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=processor(__lowercase   , __lowercase   , __lowercase )
       return scores






class        __SCREAMING_SNAKE_CASE (     lowerCamelCase			):




 def __init__( self		:							List[str]   , __lowercase		:							float )		->  Tuple:
       if not isinstance(__lowercase   , __lowercase ) or not (temperature > 0):
             raise ValueError(F"`temperature` has to be a strictly positive float, but is {temperature}" )

       SCREAMING_SNAKE_CASE__       :			Optional[int]													=temperature




 def __call__( self		:							Optional[int]   , __lowercase		:							jnp.ndarray   , __lowercase		:							jnp.ndarray   , __lowercase		:							int )		->  jnp.ndarray:
       SCREAMING_SNAKE_CASE__       :			int													=scores / self.temperature
       return scores






class        __SCREAMING_SNAKE_CASE (     lowerCamelCase			):




 def __init__( self		:							Tuple   , __lowercase		:							float   , __lowercase		:							float = -float('''Inf''' )   , __lowercase		:							int = 1 )		->  List[str]:
       if not isinstance(__lowercase   , __lowercase ) or (top_p < 0 or top_p > 1.0):
             raise ValueError(F"`top_p` has to be a float > 0 and < 1, but is {top_p}" )
       if not isinstance(__lowercase   , __lowercase ) or (min_tokens_to_keep < 1):
             raise ValueError(F"`min_tokens_to_keep` has to be a positive integer, but is {min_tokens_to_keep}" )

       SCREAMING_SNAKE_CASE__       :			Tuple													=top_p
       SCREAMING_SNAKE_CASE__       :			List[str]													=filter_value
       SCREAMING_SNAKE_CASE__       :			Optional[Any]													=min_tokens_to_keep




 def __call__( self		:							int   , __lowercase		:							jnp.ndarray   , __lowercase		:							jnp.ndarray   , __lowercase		:							int )		->  jnp.ndarray:
       SCREAMING_SNAKE_CASE__						,		SCREAMING_SNAKE_CASE__       :			List[Any]													=lax.top_k(__lowercase   , scores.shape[-1] )

       SCREAMING_SNAKE_CASE__       :			List[str]													=jnp.full_like(__lowercase   , self.filter_value )
       SCREAMING_SNAKE_CASE__       :			Tuple													=jax.nn.softmax(__lowercase   , axis=-1 ).cumsum(axis=-1 )
       SCREAMING_SNAKE_CASE__       :			Dict													=cumulative_probs < self.top_p

       # include the token that is higher than top_p as well
       SCREAMING_SNAKE_CASE__       :			Optional[Any]													=jnp.roll(__lowercase   , 1 )
       score_mask |= score_mask.at[:, 0].set(__lowercase )

       # min tokens to keep
       SCREAMING_SNAKE_CASE__       :			Optional[Any]													=score_mask.at[:, : self.min_tokens_to_keep].set(__lowercase )

       SCREAMING_SNAKE_CASE__       :			Dict													=jnp.where(__lowercase   , __lowercase   , __lowercase )
       SCREAMING_SNAKE_CASE__       :			int													=jax.lax.sort_key_val(__lowercase   , __lowercase )[-1]

       return next_scores






class        __SCREAMING_SNAKE_CASE (     lowerCamelCase			):




 def __init__( self		:							List[str]   , __lowercase		:							int   , __lowercase		:							float = -float('''Inf''' )   , __lowercase		:							int = 1 )		->  List[Any]:
       if not isinstance(__lowercase   , __lowercase ) or top_k <= 0:
             raise ValueError(F"`top_k` has to be a strictly positive integer, but is {top_k}" )

       SCREAMING_SNAKE_CASE__       :			str													=max(__lowercase   , __lowercase )
       SCREAMING_SNAKE_CASE__       :			List[Any]													=filter_value




 def __call__( self		:							List[Any]   , __lowercase		:							jnp.ndarray   , __lowercase		:							jnp.ndarray   , __lowercase		:							int )		->  jnp.ndarray:
       SCREAMING_SNAKE_CASE__						,		SCREAMING_SNAKE_CASE__       :			Dict													=scores.shape
       SCREAMING_SNAKE_CASE__       :			str													=jnp.full(batch_size * vocab_size   , self.filter_value )

       SCREAMING_SNAKE_CASE__       :			List[Any]													=min(self.top_k   , scores.shape[-1] )  # Safety check
       SCREAMING_SNAKE_CASE__						,		SCREAMING_SNAKE_CASE__       :			Optional[int]													=lax.top_k(__lowercase   , __lowercase )
       SCREAMING_SNAKE_CASE__       :			int													=jnp.broadcast_to((jnp.arange(__lowercase ) * vocab_size)[:, None]   , (batch_size, topk) ).flatten()
       SCREAMING_SNAKE_CASE__       :			str													=topk_scores.flatten()
       SCREAMING_SNAKE_CASE__       :			int													=topk_indices.flatten() + shift

       SCREAMING_SNAKE_CASE__       :			str													=next_scores_flat.at[topk_indices_flat].set(__lowercase )
       SCREAMING_SNAKE_CASE__       :			Any													=next_scores_flat.reshape(__lowercase   , __lowercase )
       return next_scores






class        __SCREAMING_SNAKE_CASE (     lowerCamelCase			):




 def __init__( self		:							Any   , __lowercase		:							int )		->  Optional[int]:
       SCREAMING_SNAKE_CASE__       :			List[str]													=bos_token_id




 def __call__( self		:							Tuple   , __lowercase		:							jnp.ndarray   , __lowercase		:							jnp.ndarray   , __lowercase		:							int )		->  jnp.ndarray:
       SCREAMING_SNAKE_CASE__       :			Tuple													=jnp.full(scores.shape   , -float('''inf''' ) )

       SCREAMING_SNAKE_CASE__       :			str													=1 - jnp.bool_(cur_len - 1 )

       SCREAMING_SNAKE_CASE__       :			Optional[int]													=jnp.where(__lowercase   , new_scores.at[:, self.bos_token_id].set(0 )   , __lowercase )

       return scores






class        __SCREAMING_SNAKE_CASE (     lowerCamelCase			):




 def __init__( self		:							Union[str, Any]   , __lowercase		:							int   , __lowercase		:							int )		->  List[str]:
       SCREAMING_SNAKE_CASE__       :			Optional[int]													=max_length
       SCREAMING_SNAKE_CASE__       :			int													=eos_token_id




 def __call__( self		:							Any   , __lowercase		:							jnp.ndarray   , __lowercase		:							jnp.ndarray   , __lowercase		:							int )		->  jnp.ndarray:
       SCREAMING_SNAKE_CASE__       :			Optional[int]													=jnp.full(scores.shape   , -float('''inf''' ) )

       SCREAMING_SNAKE_CASE__       :			str													=1 - jnp.bool_(cur_len - self.max_length + 1 )

       SCREAMING_SNAKE_CASE__       :			Optional[int]													=jnp.where(__lowercase   , new_scores.at[:, self.eos_token_id].set(0 )   , __lowercase )

       return scores






class        __SCREAMING_SNAKE_CASE (     lowerCamelCase			):




 def __init__( self		:							Union[str, Any]   , __lowercase		:							int   , __lowercase		:							int )		->  Tuple:
       if not isinstance(__lowercase   , __lowercase ) or min_length < 0:
             raise ValueError(F"`min_length` has to be a positive integer, but is {min_length}" )

       if not isinstance(__lowercase   , __lowercase ) or eos_token_id < 0:
             raise ValueError(F"`eos_token_id` has to be a positive integer, but is {eos_token_id}" )

       SCREAMING_SNAKE_CASE__       :			str													=min_length
       SCREAMING_SNAKE_CASE__       :			Tuple													=eos_token_id




 def __call__( self		:							Tuple   , __lowercase		:							jnp.ndarray   , __lowercase		:							jnp.ndarray   , __lowercase		:							int )		->  jnp.ndarray:
       # create boolean flag to decide if min length penalty should be applied
       SCREAMING_SNAKE_CASE__       :			List[str]													=1 - jnp.clip(cur_len - self.min_length   , 0   , 1 )

       SCREAMING_SNAKE_CASE__       :			Tuple													=jnp.where(__lowercase   , scores.at[:, self.eos_token_id].set(-float('''inf''' ) )   , __lowercase )

       return scores






class        __SCREAMING_SNAKE_CASE (     lowerCamelCase			):




 def __init__( self		:							int   , __lowercase		:							Optional[Any]   , __lowercase		:							Tuple )		->  List[str]:
       SCREAMING_SNAKE_CASE__       :			Dict													=list(__lowercase )
       SCREAMING_SNAKE_CASE__       :			Optional[int]													=begin_index




 def __call__( self		:							Optional[Any]   , __lowercase		:							Optional[Any]   , __lowercase		:							Dict   , __lowercase		:							int )		->  List[str]:
       SCREAMING_SNAKE_CASE__       :			Any													=1 - jnp.bool_(cur_len - self.begin_index )

       SCREAMING_SNAKE_CASE__       :			Optional[int]													=jnp.where(__lowercase   , scores.at[:, self.begin_suppress_tokens].set(-float('''inf''' ) )   , __lowercase )

       return scores






class        __SCREAMING_SNAKE_CASE (     lowerCamelCase			):




 def __init__( self		:							Optional[Any]   , __lowercase		:							list )		->  Dict:
       SCREAMING_SNAKE_CASE__       :			int													=list(__lowercase )




 def __call__( self		:							str   , __lowercase		:							jnp.ndarray   , __lowercase		:							jnp.ndarray   , __lowercase		:							int )		->  jnp.ndarray:
       SCREAMING_SNAKE_CASE__       :			Optional[int]													=scores.at[..., self.suppress_tokens].set(-float('''inf''' ) )

       return scores






class        __SCREAMING_SNAKE_CASE (     lowerCamelCase			):




 def __init__( self		:							List[Any]   , __lowercase		:							List[str] )		->  Optional[Any]:
       SCREAMING_SNAKE_CASE__       :			List[str]													=dict(__lowercase )
       # Converts the dictionary of format {index: token} containing the tokens to be forced to an array, where the
       # index of the array corresponds to the index of the token to be forced, for XLA compatibility.
       # Indexes without forced tokens will have a negative value.
       SCREAMING_SNAKE_CASE__       :			Any													=jnp.ones((max(force_token_map.keys() ) + 1)   , dtype=jnp.intaa ) * -1
       for index, token in force_token_map.items():
             if token is not None:
                   SCREAMING_SNAKE_CASE__       :			Tuple													=force_token_array.at[index].set(__lowercase )
       SCREAMING_SNAKE_CASE__       :			Dict													=jnp.intaa(__lowercase )




 def __call__( self		:							Optional[Any]   , __lowercase		:							jnp.ndarray   , __lowercase		:							jnp.ndarray   , __lowercase		:							int )		->  jnp.ndarray:
       def _force_token(__lowercase		:							Union[str, Any] ):
             SCREAMING_SNAKE_CASE__       :			Dict													=scores.shape[0]
             SCREAMING_SNAKE_CASE__       :			Optional[int]													=self.force_token_array[generation_idx]

             SCREAMING_SNAKE_CASE__       :			Optional[int]													=jnp.ones_like(__lowercase   , dtype=scores.dtype ) * -float('''inf''' )
             SCREAMING_SNAKE_CASE__       :			Tuple													=jnp.zeros((batch_size, 1)   , dtype=scores.dtype )
             SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=lax.dynamic_update_slice(__lowercase   , __lowercase   , (0, current_token) )
             return new_scores

       SCREAMING_SNAKE_CASE__       :			Tuple													=lax.cond(
           cur_len >= self.force_token_array.shape[0]   , lambda: scores   , lambda: lax.cond(
               self.force_token_array[cur_len] >= 0   , lambda: _force_token(__lowercase )   , lambda: scores   , )   , )
       return scores






class        __SCREAMING_SNAKE_CASE (     lowerCamelCase			):




 def __init__( self		:							List[str]   , __lowercase		:							List[Any]   , __lowercase		:							List[str]   , __lowercase		:							Any )		->  Tuple:
       SCREAMING_SNAKE_CASE__       :			Optional[int]													=generate_config.eos_token_id
       SCREAMING_SNAKE_CASE__       :			List[Any]													=generate_config.no_timestamps_token_id
       SCREAMING_SNAKE_CASE__       :			List[Any]													=generate_config.no_timestamps_token_id + 1

       SCREAMING_SNAKE_CASE__       :			Dict													=decoder_input_length + 1

       if generate_config.is_multilingual:
             # room for language token and task token
             self.begin_index += 2
       if hasattr(__lowercase   , '''max_initial_timestamp_index''' ):
             SCREAMING_SNAKE_CASE__       :			int													=generate_config.max_initial_timestamp_index
       else:
             SCREAMING_SNAKE_CASE__       :			Optional[int]													=model_config.vocab_size
       if self.max_initial_timestamp_index is None:
             SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=model_config.vocab_size




 def __call__( self		:							List[Any]   , __lowercase		:							Union[str, Any]   , __lowercase		:							Any   , __lowercase		:							str )		->  Optional[Any]:
       # suppress <|notimestamps|> which is handled by without_timestamps
       SCREAMING_SNAKE_CASE__       :			Any													=scores.at[:, self.no_timestamps_token_id].set(-float('''inf''' ) )

       def handle_pairs(__lowercase		:							Any   , __lowercase		:							Optional[int] ):
             SCREAMING_SNAKE_CASE__       :			Optional[int]													=jnp.where((cur_len - self.begin_index) >= 1   , __lowercase   , __lowercase )
             SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=jnp.where(
                 input_ids_k[cur_len - 1] >= self.timestamp_begin   , True and last_was_timestamp   , __lowercase   , )

             SCREAMING_SNAKE_CASE__       :			List[str]													=jnp.where((cur_len - self.begin_index) < 2   , __lowercase   , __lowercase )
             SCREAMING_SNAKE_CASE__       :			Any													=jnp.where(
                 input_ids_k[cur_len - 2] >= self.timestamp_begin   , __lowercase   , __lowercase   , )

             return jnp.where(
                 __lowercase   , jnp.where(
                     penultimate_was_timestamp > 0   , scores_k.at[self.timestamp_begin :].set(-float('''inf''' ) )   , scores_k.at[: self.eos_token_id].set(-float('''inf''' ) )   , )   , __lowercase   , )

       SCREAMING_SNAKE_CASE__       :			List[str]													=jax.vmap(__lowercase )(__lowercase   , __lowercase )

       SCREAMING_SNAKE_CASE__       :			Optional[Any]													=jnp.where(cur_len == self.begin_index   , __lowercase   , __lowercase )
       SCREAMING_SNAKE_CASE__       :			int													=jnp.where(
           self.max_initial_timestamp_index is not None   , True and apply_max_initial_timestamp   , __lowercase   , )

       SCREAMING_SNAKE_CASE__       :			Optional[int]													=self.timestamp_begin + self.max_initial_timestamp_index

       SCREAMING_SNAKE_CASE__       :			int													=jnp.where(
           __lowercase   , scores.at[:, last_allowed + 1 :].set(-float('''inf''' ) )   , __lowercase   , )

       # if sum of probability over timestamps is above any other token, sample timestamp
       SCREAMING_SNAKE_CASE__       :			Any													=jax.nn.log_softmax(__lowercase   , axis=-1 )

       def handle_cumulative_probs(__lowercase		:							Optional[int]   , __lowercase		:							str ):
             SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=jax.nn.logsumexp(logprobs_k[self.timestamp_begin :]   , axis=-1 )
             SCREAMING_SNAKE_CASE__       :			List[Any]													=jnp.max(logprobs_k[: self.timestamp_begin] )
             return jnp.where(
                 timestamp_logprob > max_text_token_logprob   , scores_k.at[: self.timestamp_begin].set(-float('''inf''' ) )   , __lowercase   , )

       SCREAMING_SNAKE_CASE__       :			Any													=jax.vmap(__lowercase )(__lowercase   , __lowercase )

       return scores