






from __future__ import annotations

lowerCamelCase : Optional[int]						   =       [
    [-1, 0],  # left
    [0, -1],  # down
    [1, 0],  # right
    [0, 1],  # up
]
def       snake_case_		(     lowerCAmelCase_       :						list[list[int]]       ,       lowerCAmelCase_       :						list[int]       ,       lowerCAmelCase_       :						list[int]       ,       lowerCAmelCase_       :						int       ,       lowerCAmelCase_       :						list[list[int]]       ,       ):
							__lowercase      :							str				=     [
							    [0 for col in range(len(grid[0]      )      )] for row in range(len(lowerCAmelCase_      )      )
							]  # the reference grid
							__lowercase      :							str				=     1
							__lowercase      :							Optional[Any]				=     [
							    [0 for col in range(len(grid[0]      )      )] for row in range(len(lowerCAmelCase_      )      )
							]  # the action grid

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

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

							while not found and not resign:
														if len(lowerCAmelCase_      ) == 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()
																					__lowercase      :							Tuple				=     cell.pop()
																					__lowercase      :							Optional[int]				=     next_cell[2]
																					__lowercase      :							Tuple				=     next_cell[3]
																					__lowercase      :							List[Any]				=     next_cell[1]

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

							__lowercase      :							List[Any]				=     []
							for i in range(len(lowerCAmelCase_      )      ):
														path.append(invpath[len(lowerCAmelCase_      ) - 1 - i]      )
							return path, action


if __name__ == "__main__":
	lowerCamelCase : Any						   =       [
	    [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],
	]

	lowerCamelCase : Any						   =       [0, 0]
	# all coordinates are given in format [y,x]
	lowerCamelCase : str						   =       [len(grid) - 1, len(grid[0]) - 1]
	lowerCamelCase : List[Any]						   =       1

	# the cost map which pushes the path closer to the goal
	lowerCamelCase : str						   =       [[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])):
			lowerCamelCase : Tuple						   =       abs(i - goal[0]) + abs(j - goal[1])
			if grid[i][j] == 1:
				# added extra penalty in the heuristic map
				lowerCamelCase : str						   =       99

	lowerCamelCase   ,lowerCamelCase : Optional[Any]						   =       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])







import uuid
from typing import Any, Dict, List, Optional, Union

from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_tf_available():
	import tensorflow as tf

if is_torch_available():
	import torch


lowerCamelCase : List[Any]						   =       logging.get_logger(__name__)

class 		lowerCAmelCase     :

					'''simple docstring'''

					def __init__(  self				:	List[str]		,     __a				:	str = None		,     __a				:	uuid.UUID = None		,     __a				:	Any=None		,     __a				:	List[Any]=None							)			->  List[Any]:


												"""simple docstring"""

												if not conversation_id:
																			__lowercase      :							Any				=     uuid.uuida()
												if past_user_inputs is None:
																			__lowercase      :							Dict				=     []
												if generated_responses is None:
																			__lowercase      :							Dict				=     []

												__lowercase      :							uuid.UUID				=     conversation_id
												__lowercase      :							List[str]				=     past_user_inputs
												__lowercase      :							List[str]				=     generated_responses
												__lowercase      :							Optional[str]				=     text



					def __eq__(  self				:	Dict		,     __a				:	Dict							)			->  Any:


												"""simple docstring"""

												if not isinstance(__a		,     __a							):
																			return False
												if self.uuid == other.uuid:
																			return True
												return (
												    self.new_user_input == other.new_user_input
												    and self.past_user_inputs == other.past_user_inputs
												    and self.generated_responses == other.generated_responses
												)



					def       lowerCAmelCase				(  self				:	List[str]		,     __a				:	str		,     __a				:	bool = False							)			->  Dict:


												"""simple docstring"""

												if self.new_user_input:
																			if overwrite:
																										logger.warning(
																										    F"User input added while unprocessed input was existing: \"{self.new_user_input}\" was overwritten "
																										    F"with: \"{text}\"."							)
																										__lowercase      :							Optional[int]				=     text
																			else:
																										logger.warning(
																										    F"User input added while unprocessed input was existing: \"{self.new_user_input}\" new input "
																										    F"ignored: \"{text}\". Set `overwrite` to True to overwrite unprocessed user input"							)
												else:
																			__lowercase      :							Dict				=     text



					def       lowerCAmelCase				(  self				:	int							)			->  Optional[Any]:


												"""simple docstring"""

												if self.new_user_input:
																			self.past_user_inputs.append(self.new_user_input							)
												__lowercase      :							Dict				=     None



					def       lowerCAmelCase				(  self				:	Optional[int]		,     __a				:	str							)			->  List[Any]:


												"""simple docstring"""

												self.generated_responses.append(__a							)



					def       lowerCAmelCase				(  self				:	Any							)			->  Optional[Any]:


												"""simple docstring"""

												for user_input, generated_response in zip(self.past_user_inputs		,     self.generated_responses							):
																			yield True, user_input
																			yield False, generated_response
												if self.new_user_input:
																			yield True, self.new_user_input



					def __repr__(  self				:	int							)			->  str:


												"""simple docstring"""

												__lowercase      :							Optional[int]				=     F"Conversation id: {self.uuid} \n"
												for is_user, text in self.iter_texts():
																			__lowercase      :							Optional[Any]				=     """user""" if is_user else """bot"""
																			output += F"{name} >> {text} \n"
												return output







@add_end_docstrings(
    __a      ,       r'''
        min_length_for_response (`int`, *optional*, defaults to 32):
            The minimum length (in number of tokens) for a response.
        minimum_tokens (`int`, *optional*, defaults to 10):
            The minimum length of tokens to leave for a response.
    '''      ,       )
class 		lowerCAmelCase     ( __a			):

					'''simple docstring'''

					def __init__(  self				:	Any		,     *__a				:	int		,     **__a				:	str							)			->  Union[str, Any]:


												"""simple docstring"""

												super().__init__(*__a		,     **__a							)
												if self.tokenizer.pad_token_id is None:
																			__lowercase      :							List[Any]				=     self.tokenizer.eos_token



					def       lowerCAmelCase				(  self				:	Union[str, Any]		,     __a				:	int=None		,     __a				:	Tuple=None		,     __a				:	Any=None		,     **__a				:	List[str]							)			->  Optional[int]:


												"""simple docstring"""

												__lowercase      :							List[str]				=     {}
												__lowercase      :							Tuple				=     {}
												__lowercase      :							List[str]				=     {}

												if min_length_for_response is not None:
																			__lowercase      :							Dict				=     min_length_for_response
												if minimum_tokens is not None:
																			__lowercase      :							Union[str, Any]				=     minimum_tokens

												if "max_length" in generate_kwargs:
																			__lowercase      :							Union[str, Any]				=     generate_kwargs["""max_length"""]
																			# self.max_length = generate_kwargs.get("max_length", self.model.config.max_length)
												if clean_up_tokenization_spaces is not None:
																			__lowercase      :							Union[str, Any]				=     clean_up_tokenization_spaces

												if generate_kwargs:
																			forward_params.update(__a							)
												return preprocess_params, forward_params, postprocess_params



					def __call__(  self				:	Optional[int]		,     __a				:	Union[Conversation, List[Conversation]]		,     __a				:	Dict=0		,     **__a				:	List[Any]							)			->  Tuple:


												"""simple docstring"""

												__lowercase      :							List[Any]				=     super().__call__(__a		,     num_workers=__a		,     **__a							)
												if isinstance(__a		,     __a							) and len(__a							) == 1:
																			return outputs[0]
												return outputs



					def       lowerCAmelCase				(  self				:	Union[str, Any]		,     __a				:	Conversation		,     __a				:	Tuple=32							)			->  Dict[str, Any]:


												"""simple docstring"""

												if not isinstance(__a		,     __a							):
																			raise ValueError("""ConversationalPipeline, expects Conversation as inputs"""							)
												if conversation.new_user_input is None:
																			raise ValueError(
																			    F"Conversation with UUID {type(conversation.uuid							)} does not contain new user input to process. "
																			    """Add user inputs with the conversation's `add_user_input` method"""							)
												if hasattr(self.tokenizer		,     """_build_conversation_input_ids"""							):
																			__lowercase      :							List[Any]				=     self.tokenizer._build_conversation_input_ids(__a							)
												else:
																			# If the tokenizer cannot handle conversations, we default to only the old version
																			__lowercase      :							Tuple				=     self._legacy_parse_and_tokenize(__a							)

												if self.framework == "pt":
																			__lowercase      :							List[Any]				=     torch.LongTensor([input_ids]							)
												elif self.framework == "tf":
																			__lowercase      :							List[str]				=     tf.constant([input_ids]							)
												return {"input_ids": input_ids, "conversation": conversation}



					def       lowerCAmelCase				(  self				:	Any		,     __a				:	Dict		,     __a				:	Any=10		,     **__a				:	Dict							)			->  Optional[Any]:


												"""simple docstring"""

												__lowercase      :							Optional[int]				=     generate_kwargs.get("""max_length"""		,     self.model.config.max_length							)

												__lowercase      :							List[Any]				=     model_inputs["""input_ids"""].shape[1]
												if max_length - minimum_tokens < n:
																			logger.warning(F"Conversation input is to long ({n}), trimming it to ({max_length} - {minimum_tokens})"							)
																			__lowercase      :							Any				=     max_length - minimum_tokens
																			__lowercase      :							int				=     model_inputs["""input_ids"""][:, -trim:]
																			if "attention_mask" in model_inputs:
																										__lowercase      :							Dict				=     model_inputs["""attention_mask"""][:, -trim:]
												__lowercase      :							Union[str, Any]				=     model_inputs.pop("""conversation"""							)
												__lowercase      :							Tuple				=     max_length
												__lowercase      :							int				=     self.model.generate(**__a		,     **__a							)
												if self.model.config.is_encoder_decoder:
																			__lowercase      :							Optional[int]				=     1
												else:
																			__lowercase      :							str				=     n
												return {"output_ids": output_ids[:, start_position:], "conversation": conversation}



					def       lowerCAmelCase				(  self				:	int		,     __a				:	Tuple		,     __a				:	List[Any]=True							)			->  List[str]:


												"""simple docstring"""

												__lowercase      :							int				=     model_outputs["""output_ids"""]
												__lowercase      :							Union[str, Any]				=     self.tokenizer.decode(
												    output_ids[0]		,     skip_special_tokens=__a		,     clean_up_tokenization_spaces=__a		,     )
												__lowercase      :							List[str]				=     model_outputs["""conversation"""]
												conversation.mark_processed()
												conversation.append_response(__a							)
												return conversation



					def       lowerCAmelCase				(  self				:	int		,     __a				:	Conversation							)			->  Dict:


												"""simple docstring"""

												__lowercase      :							Optional[int]				=     self.tokenizer.eos_token_id
												__lowercase      :							Optional[Any]				=     []
												for is_user, text in conversation.iter_texts():
																			if eos_token_id is not None:
																										input_ids.extend(self.tokenizer.encode(__a		,     add_special_tokens=__a							) + [eos_token_id]							)
																			else:
																										input_ids.extend(self.tokenizer.encode(__a		,     add_special_tokens=__a							)							)

												if len(__a							) > self.tokenizer.model_max_length:
																			__lowercase      :							List[Any]				=     input_ids[-self.tokenizer.model_max_length :]
												return input_ids