
"""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


_A						=			logging.get_logger(__name__)






class 	_lowercase       (    lowerCamelCase__		):




	def       _UpperCamelCase   (			self		,			UpperCAmelCase_ )		->							int:
			if isinstance(__A		,			__A ):
					lowerCamelCase					:      str          =   [label.strip() for label in labels.split(',' ) if label.strip()]
			return labels




	def __call__(			self		,			UpperCAmelCase_		,			UpperCAmelCase_		,			UpperCAmelCase_ )		->							Optional[int]:
			if len(__A ) == 0 or len(__A ) == 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(__A ) )

			if isinstance(__A		,			__A ):
					lowerCamelCase					:      int          =   [sequences]

			lowerCamelCase					:      Dict          =   []
			for sequence in sequences:
					sequence_pairs.extend([[sequence, hypothesis_template.format(__A )] for label in labels] )

			return sequence_pairs, sequences






@add_end_docstrings(lowerCamelCase__		)
class 	_lowercase       (    lowerCamelCase__		):




	def __init__(			self		,			UpperCAmelCase_=ZeroShotClassificationArgumentHandler()		,			*UpperCAmelCase_		,			**UpperCAmelCase_ )		->							str:
			lowerCamelCase					:      Optional[Any]          =   args_parser
			super().__init__(*__A		,			**__A )
			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       _UpperCamelCase   (			self )		->							List[str]:
			for label, ind in self.model.config.labelaid.items():
					if label.lower().startswith('entail' ):
							return ind
			return -1




	def       _UpperCamelCase   (			self		,			UpperCAmelCase_		,			UpperCAmelCase_=True		,			UpperCAmelCase_=True		,			UpperCAmelCase_=TruncationStrategy.ONLY_FIRST		,			**UpperCAmelCase_ )		->							Tuple:
			lowerCamelCase					:      Dict          =   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`' )
					lowerCamelCase					:      Optional[int]          =   self.tokenizer.eos_token
			try:
					lowerCamelCase					:      int          =   self.tokenizer(
					    __A		,			add_special_tokens=__A		,			return_tensors=__A		,			padding=__A		,			truncation=__A		,			)
			except Exception as e:
					if "too short" in str(__A ):
							# 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.

							lowerCamelCase					:      Dict          =   self.tokenizer(
							    __A		,			add_special_tokens=__A		,			return_tensors=__A		,			padding=__A		,			truncation=TruncationStrategy.DO_NOT_TRUNCATE		,			)
					else:
							raise e

			return inputs




	def       _UpperCamelCase   (			self		,			**UpperCAmelCase_ )		->							Optional[Any]:
			if kwargs.get('multi_class'		,			__A ) is not None:
					lowerCamelCase					:      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.' )
			lowerCamelCase					:      Tuple          =   {}
			if "candidate_labels" in kwargs:
					lowerCamelCase					:      List[str]          =   self._args_parser._parse_labels(kwargs['candidate_labels'] )
			if "hypothesis_template" in kwargs:
					lowerCamelCase					:      int          =   kwargs['''hypothesis_template''']

			lowerCamelCase					:      int          =   {}
			if "multi_label" in kwargs:
					lowerCamelCase					:      List[str]          =   kwargs['''multi_label''']
			return preprocess_params, {}, postprocess_params




	def __call__(			self		,			UpperCAmelCase_		,			*UpperCAmelCase_		,			**UpperCAmelCase_		,			)		->							str:
			if len(__A ) == 0:
					pass
			elif len(__A ) == 1 and "candidate_labels" not in kwargs:
					lowerCamelCase					:      int          =   args[0]
			else:
					raise ValueError(F"""Unable to understand extra arguments {args}""" )

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




	def       _UpperCamelCase   (			self		,			UpperCAmelCase_		,			UpperCAmelCase_=None		,			UpperCAmelCase_="This example is {}." )		->							Any:
			lowerCamelCase					:      List[Any]          =   self._args_parser(__A		,			__A		,			__A )

			for i, (candidate_label, sequence_pair) in enumerate(zip(__A		,			__A ) ):
					lowerCamelCase					:      Dict          =   self._parse_and_tokenize([sequence_pair] )

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




	def       _UpperCamelCase   (			self		,			UpperCAmelCase_ )		->							Dict:
			lowerCamelCase					:      str          =   inputs['''candidate_label''']
			lowerCamelCase					:      List[Any]          =   inputs['''sequence''']
			lowerCamelCase					:      int          =   {k: inputs[k] for k in self.tokenizer.model_input_names}
			lowerCamelCase					:      List[Any]          =   self.model(**__A )

			lowerCamelCase					:      List[str]          =   {
			    '''candidate_label''': candidate_label,
			    '''sequence''': sequence,
			    '''is_last''': inputs['''is_last'''],
			    **outputs,
			}
			return model_outputs




	def       _UpperCamelCase   (			self		,			UpperCAmelCase_		,			UpperCAmelCase_=False )		->							Optional[int]:
			lowerCamelCase					:      Union[str, Any]          =   [outputs['''candidate_label'''] for outputs in model_outputs]
			lowerCamelCase					:      Union[str, Any]          =   [outputs['''sequence'''] for outputs in model_outputs]
			lowerCamelCase					:      Any          =   np.concatenate([output['logits'].numpy() for output in model_outputs] )
			lowerCamelCase					:      List[Any]          =   logits.shape[0]
			lowerCamelCase					:      Union[str, Any]          =   len(__A )
			lowerCamelCase					:      str          =   N // n
			lowerCamelCase					:      Dict          =   logits.reshape((num_sequences, n, -1) )

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

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








"""simple docstring"""

from __future__ import annotations

_A						=			[
    [-1, 0],  # left
    [0, -1],  # down
    [1, 0],  # right
    [0, 1],  # up
]
def 		UpperCAmelCase (		a_,						a_,						a_,						a_,						a_,						):





		'''simple docstring'''


		lowerCamelCase					:      Dict          =   [
		    [0 for col in range(len(grid[0]      )      )] for row in range(len(a_      )      )
		]  # the reference grid
		lowerCamelCase					:      Union[str, Any]          =   1
		lowerCamelCase					:      Any          =   [
		    [0 for col in range(len(grid[0]      )      )] for row in range(len(a_      )      )
		]  # the action grid

		lowerCamelCase					:      List[str]          =   init[0]
		lowerCamelCase					:      Optional[Any]          =   init[1]
		lowerCamelCase					:      List[Any]          =   0
		lowerCamelCase					:      List[str]          =   g + heuristic[x][y]  # cost from starting cell to destination cell
		lowerCamelCase					:      Union[str, Any]          =   [[f, g, x, y]]

		lowerCamelCase					:      Union[str, Any]          =   False  # flag that is set when search is complete
		lowerCamelCase					:      str          =   False  # flag set if we can't find expand

		while not found and not resign:
				if len(a_      ) == 0:
						raise ValueError('Algorithm is unable to find solution'      )
				else:  # to choose the least costliest action so as to move closer to the goal
						cell.sort()
						cell.reverse()
						lowerCamelCase					:      int          =   cell.pop()
						lowerCamelCase					:      str          =   next_cell[2]
						lowerCamelCase					:      Union[str, Any]          =   next_cell[3]
						lowerCamelCase					:      List[str]          =   next_cell[1]

						if x == goal[0] and y == goal[1]:
								lowerCamelCase					:      Any          =   True
						else:
								for i in range(len(a_      )      ):  # to try out different valid actions
										lowerCamelCase					:      Tuple          =   x + DIRECTIONS[i][0]
										lowerCamelCase					:      Union[str, Any]          =   y + DIRECTIONS[i][1]
										if xa >= 0 and xa < len(a_      ) and ya >= 0 and ya < len(grid[0]      ):
												if closed[xa][ya] == 0 and grid[xa][ya] == 0:
														lowerCamelCase					:      str          =   g + cost
														lowerCamelCase					:      Tuple          =   ga + heuristic[xa][ya]
														cell.append([fa, ga, xa, ya]      )
														lowerCamelCase					:      Union[str, Any]          =   1
														lowerCamelCase					:      Any          =   i
		lowerCamelCase					:      Any          =   []
		lowerCamelCase					:      Optional[int]          =   goal[0]
		lowerCamelCase					:      Dict          =   goal[1]
		invpath.append([x, y]      )  # we get the reverse path from here
		while x != init[0] or y != init[1]:
				lowerCamelCase					:      Dict          =   x - DIRECTIONS[action[x][y]][0]
				lowerCamelCase					:      Dict          =   y - DIRECTIONS[action[x][y]][1]
				lowerCamelCase					:      Optional[Any]          =   xa
				lowerCamelCase					:      Union[str, Any]          =   ya
				invpath.append([x, y]      )

		lowerCamelCase					:      Optional[int]          =   []
		for i in range(len(a_      )      ):
				path.append(invpath[len(a_      ) - 1 - i]      )
		return path, action


if __name__ == "__main__":
					_A						=			[
					    [0, 1, 0, 0, 0, 0],
					    [0, 1, 0, 0, 0, 0],  # 0 are free path whereas 1's are obstacles
					    [0, 1, 0, 0, 0, 0],
					    [0, 1, 0, 0, 1, 0],
					    [0, 0, 0, 0, 1, 0],
					]

					_A						=			[0, 0]
					# all coordinates are given in format [y,x]
					_A						=			[len(grid) - 1, len(grid[0]) - 1]
					_A						=			1

					# the cost map which pushes the path closer to the goal
					_A						=			[[0 for row in range(len(grid[0]))] for col in range(len(grid))]
					for i in range(len(grid)):
										for j in range(len(grid[0])):
															_A						=			abs(i - goal[0]) + abs(j - goal[1])
															if grid[i][j] == 1:
																				# added extra penalty in the heuristic map
																				_A						=			9_9

					_A					,  _A						=			search(grid, init, goal, cost, heuristic)

					print('ACTION MAP')
					for i in range(len(action)):
										print(action[i])

					for i in range(len(path)):
										print(path[i])






