






import enum
import warnings

from ..tokenization_utils import TruncationStrategy
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

							from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING

if is_torch_available():
							from ..models.auto.modeling_auto import MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING

lowercase  :							List[str]			  =    logging.get_logger(__name__)




class 		UpperCAmelCase_						(	enum.Enum					):


						'''simple docstring'''




						A     :   Optional[Any]						=					0
						A     :   Dict						=					1




@add_end_docstrings(_UpperCAmelCase					)
class 		UpperCAmelCase_						(	_UpperCAmelCase					):


						'''simple docstring'''




						A     :   List[Any]						=					'generated'






						def __init__(	self		,				*_SCREAMING_SNAKE_CASE		,				**_SCREAMING_SNAKE_CASE				) ->				Any:
								super().__init__(*__UpperCamelCase		,				**__UpperCamelCase				)

								self.check_model_type(
								    TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
								    if self.framework == "tf"
								    else MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING				)






						def       _lowerCAmelCase       (	self		,				_SCREAMING_SNAKE_CASE=None		,				_SCREAMING_SNAKE_CASE=None		,				_SCREAMING_SNAKE_CASE=None		,				_SCREAMING_SNAKE_CASE=None		,				_SCREAMING_SNAKE_CASE=None		,				_SCREAMING_SNAKE_CASE=None		,				**_SCREAMING_SNAKE_CASE		,				) ->				Tuple:
								snake_case_				:							List[Any]						 =       {}
								if truncation is not None:
										snake_case_				:							Tuple						 =       truncation

								snake_case_				:							List[Any]						 =       generate_kwargs

								snake_case_				:							Optional[Any]						 =       {}
								if return_tensors is not None and return_type is None:
										snake_case_				:							Tuple						 =       ReturnType.TENSORS if return_tensors else ReturnType.TEXT
								if return_type is not None:
										snake_case_				:							Optional[Any]						 =       return_type

								if clean_up_tokenization_spaces is not None:
										snake_case_				:							int						 =       clean_up_tokenization_spaces

								if stop_sequence is not None:
										snake_case_				:							str						 =       self.tokenizer.encode(__UpperCamelCase		,				add_special_tokens=__UpperCamelCase				)
										if len(__UpperCamelCase				) > 1:
												warnings.warn(
												    "Stopping on a multiple token sequence is not yet supported on transformers. The first token of"
												    " the stop sequence will be used as the stop sequence string in the interim."				)
										snake_case_				:							Any						 =       stop_sequence_ids[0]

								return preprocess_params, forward_params, postprocess_params






						def       _lowerCAmelCase       (	self		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE				) ->				Any:
								return True






						def       _lowerCAmelCase       (	self		,				*_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE				) ->				Dict:
								snake_case_				:							Optional[int]						 =       self.model.config.prefix if self.model.config.prefix is not None else ""
								if isinstance(args[0]		,				__UpperCamelCase				):
										if self.tokenizer.pad_token_id is None:
												raise ValueError("Please make sure that the tokenizer has a pad_token_id when using a batch input"				)
										snake_case_				:							Dict						 =       ([prefix + arg for arg in args[0]],)
										snake_case_				:							str						 =       True

								elif isinstance(args[0]		,				__UpperCamelCase				):
										snake_case_				:							Any						 =       (prefix + args[0],)
										snake_case_				:							List[Any]						 =       False
								else:
										raise ValueError(
										    f''' `args[0]`: {args[0]} have the wrong format. The should be either of type `str` or type `list`'''				)
								snake_case_				:							str						 =       self.tokenizer(*__UpperCamelCase		,				padding=__UpperCamelCase		,				truncation=__UpperCamelCase		,				return_tensors=self.framework				)
								# This is produced by tokenizers but is an invalid generate kwargs
								if "token_type_ids" in inputs:
										del inputs["token_type_ids"]
								return inputs






						def __call__(	self		,				*_SCREAMING_SNAKE_CASE		,				**_SCREAMING_SNAKE_CASE				) ->				Optional[Any]:
								snake_case_				:							Dict						 =       super().__call__(*__UpperCamelCase		,				**__UpperCamelCase				)
								if (
								    isinstance(args[0]		,				__UpperCamelCase				)
								    and all(isinstance(__UpperCamelCase		,				__UpperCamelCase				) for el in args[0]				)
								    and all(len(__UpperCamelCase				) == 1 for res in result				)
								):
										return [res[0] for res in result]
								return result






						def       _lowerCAmelCase       (	self		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE=TruncationStrategy.DO_NOT_TRUNCATE		,				**_SCREAMING_SNAKE_CASE				) ->				Optional[int]:
								snake_case_				:							List[Any]						 =       self._parse_and_tokenize(__UpperCamelCase		,				truncation=__UpperCamelCase		,				**__UpperCamelCase				)
								return inputs






						def       _lowerCAmelCase       (	self		,				_SCREAMING_SNAKE_CASE		,				**_SCREAMING_SNAKE_CASE				) ->				Tuple:






								if self.framework == "pt":
										snake_case_ ,       snake_case_				:							Optional[Any]						 =       model_inputs["input_ids"].shape
								elif self.framework == "tf":
										snake_case_ ,       snake_case_				:							Tuple						 =       tf.shape(model_inputs["input_ids"]				).numpy()

								snake_case_				:							str						 =       generate_kwargs.get("min_length"		,				self.model.config.min_length				)
								snake_case_				:							str						 =       generate_kwargs.get("max_length"		,				self.model.config.max_length				)
								self.check_inputs(__UpperCamelCase		,				generate_kwargs["min_length"]		,				generate_kwargs["max_length"]				)
								snake_case_				:							List[str]						 =       self.model.generate(**__UpperCamelCase		,				**__UpperCamelCase				)
								snake_case_				:							str						 =       output_ids.shape[0]
								if self.framework == "pt":
										snake_case_				:							str						 =       output_ids.reshape(__UpperCamelCase		,				out_b // in_b		,				*output_ids.shape[1:]				)
								elif self.framework == "tf":
										snake_case_				:							Tuple						 =       tf.reshape(__UpperCamelCase		,				(in_b, out_b // in_b, *output_ids.shape[1:])				)
								return {"output_ids": output_ids}






						def       _lowerCAmelCase       (	self		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE=ReturnType.TEXT		,				_SCREAMING_SNAKE_CASE=False				) ->				Any:
								snake_case_				:							Tuple						 =       []
								for output_ids in model_outputs["output_ids"][0]:
										if return_type == ReturnType.TENSORS:
												snake_case_				:							Any						 =       {f'''{self.return_name}_token_ids''': output_ids}
										elif return_type == ReturnType.TEXT:
												snake_case_				:							Any						 =       {
												    f'''{self.return_name}_text''': self.tokenizer.decode(
												        __UpperCamelCase		,				skip_special_tokens=__UpperCamelCase		,				clean_up_tokenization_spaces=__UpperCamelCase		,				)
												}
										records.append(__UpperCamelCase				)
								return records




@add_end_docstrings(_UpperCAmelCase					)
class 		UpperCAmelCase_						(	_UpperCAmelCase					):


						'''simple docstring'''




						A     :   List[Any]						=					'summary'






						def __call__(	self		,				*_SCREAMING_SNAKE_CASE		,				**_SCREAMING_SNAKE_CASE				) ->				Any:
								return super().__call__(*__UpperCamelCase		,				**__UpperCamelCase				)






						def       _lowerCAmelCase       (	self		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE				) ->				bool:






								if max_length < min_length:
										logger.warning(f'''Your min_length={min_length} must be inferior than your max_length={max_length}.'''				)

								if input_length < max_length:
										logger.warning(
										    f'''Your max_length is set to {max_length}, but your input_length is only {input_length}. Since this is '''
										    "a summarization task, where outputs shorter than the input are typically wanted, you might "
										    f'''consider decreasing max_length manually, e.g. summarizer(\'...\', max_length={input_length//2})'''				)




@add_end_docstrings(_UpperCAmelCase					)
class 		UpperCAmelCase_						(	_UpperCAmelCase					):


						'''simple docstring'''




						A     :   Optional[int]						=					'translation'






						def       _lowerCAmelCase       (	self		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE				) ->				Any:






								if input_length > 0.9 * max_length:
										logger.warning(
										    f'''Your input_length: {input_length} is bigger than 0.9 * max_length: {max_length}. You might consider '''
										    "increasing your max_length manually, e.g. translator('...', max_length=400)"				)
								return True






						def       _lowerCAmelCase       (	self		,				*_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE=TruncationStrategy.DO_NOT_TRUNCATE		,				_SCREAMING_SNAKE_CASE=None		,				_SCREAMING_SNAKE_CASE=None				) ->				Tuple:






								if getattr(self.tokenizer		,				"_build_translation_inputs"		,				__UpperCamelCase				):
										return self.tokenizer._build_translation_inputs(
										    *__UpperCamelCase		,				return_tensors=self.framework		,				truncation=__UpperCamelCase		,				src_lang=__UpperCamelCase		,				tgt_lang=__UpperCamelCase				)
								else:
										return super()._parse_and_tokenize(*__UpperCamelCase		,				truncation=__UpperCamelCase				)






						def       _lowerCAmelCase       (	self		,				_SCREAMING_SNAKE_CASE=None		,				_SCREAMING_SNAKE_CASE=None		,				**_SCREAMING_SNAKE_CASE				) ->				int:
								snake_case_ ,       snake_case_ ,       snake_case_				:							List[str]						 =       super()._sanitize_parameters(**__UpperCamelCase				)
								if src_lang is not None:
										snake_case_				:							int						 =       src_lang
								if tgt_lang is not None:
										snake_case_				:							Union[str, Any]						 =       tgt_lang
								if src_lang is None and tgt_lang is None:
										# Backward compatibility, direct arguments use is preferred.
										snake_case_				:							List[Any]						 =       kwargs.get("task"		,				self.task				)
										snake_case_				:							List[str]						 =       task.split("_"				)
										if task and len(__UpperCamelCase				) == 4:
												# translation, XX, to YY
												snake_case_				:							Union[str, Any]						 =       items[1]
												snake_case_				:							Dict						 =       items[3]
								return preprocess_params, forward_params, postprocess_params






						def __call__(	self		,				*_SCREAMING_SNAKE_CASE		,				**_SCREAMING_SNAKE_CASE				) ->				int:
								return super().__call__(*__UpperCamelCase		,				**__UpperCamelCase				)








import heapq
import sys

import numpy as np

lowercase  :							str			  =    tuple[int, int]




class 		UpperCAmelCase_						:


						'''simple docstring'''




						def __init__(	self				) ->				Optional[int]:
								snake_case_				:							int						 =       []
								snake_case_				:							int						 =       set()






						def       _lowerCAmelCase       (	self				) ->				List[Any]:
								if not self.empty():
										return self.elements[0][0]
								else:
										return float("inf"				)






						def       _lowerCAmelCase       (	self				) ->				Optional[int]:
								return len(self.elements				) == 0






						def       _lowerCAmelCase       (	self		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE				) ->				str:
								if item not in self.set:
										heapq.heappush(self.elements		,				(priority, item)				)
										self.set.add(_SCREAMING_SNAKE_CASE				)
								else:
										# update
										# print("update", item)
										snake_case_				:							Any						 =       []
										((snake_case_) ,       (snake_case_))				:							Optional[Any]						 =       heapq.heappop(self.elements				)
										while x != item:
												temp.append((pri, x)				)
												((snake_case_) ,       (snake_case_))				:							Optional[int]						 =       heapq.heappop(self.elements				)
										temp.append((priority, item)				)
										for pro, xxx in temp:
												heapq.heappush(self.elements		,				(pro, xxx)				)






						def       _lowerCAmelCase       (	self		,				_SCREAMING_SNAKE_CASE				) ->				Any:
								if item in self.set:
										self.set.remove(_SCREAMING_SNAKE_CASE				)
										snake_case_				:							Optional[Any]						 =       []
										((snake_case_) ,       (snake_case_))				:							Tuple						 =       heapq.heappop(self.elements				)
										while x != item:
												temp.append((pro, x)				)
												((snake_case_) ,       (snake_case_))				:							Dict						 =       heapq.heappop(self.elements				)
										for prito, yyy in temp:
												heapq.heappush(self.elements		,				(prito, yyy)				)






						def       _lowerCAmelCase       (	self				) ->				Optional[Any]:
								return self.elements[0][1]






						def       _lowerCAmelCase       (	self				) ->				Optional[int]:
								((snake_case_) ,       (snake_case_))				:							Any						 =       heapq.heappop(self.elements				)
								self.set.remove(_SCREAMING_SNAKE_CASE				)
								return (priority, item)




def      lowerCAmelCase__	(    _a       :			TPos  ,      _a       :			TPos    ):
		# euclidean distance
		snake_case_				:							Optional[Any]						 =       np.array(_a    )
		snake_case_				:							Dict						 =       np.array(_a    )
		return np.linalg.norm(a - b    )

def      lowerCAmelCase__	(    _a       :			TPos  ,      _a       :			TPos    ):
		# integer division by time variable
		return consistent_heuristic(_a  ,      _a    ) // t

def      lowerCAmelCase__	(    _a       :			TPos  ,      _a       :			TPos    ):
		# manhattan distance
		return abs(p[0] - goal[0]    ) + abs(p[1] - goal[1]    )

def      lowerCAmelCase__	(    _a       :			TPos  ,      _a       :			int  ,      _a       :			TPos  ,      _a       :			dict[TPos, float]    ):
		snake_case_				:							List[Any]						 =       g_function[start] + Wa * heuristics[i](_a  ,      _a    )
		return ans

def      lowerCAmelCase__	(    _a       :			Tuple  ,      _a       :			Union[str, Any]  ,      _a       :			List[Any]    ):
		snake_case_				:							Dict						 =       np.chararray((n, n)    )
		for i in range(_a    ):
				for j in range(_a    ):
						snake_case_				:							List[str]						 =       "*"

		for i in range(_a    ):
				for j in range(_a    ):
						if (j, (n - 1) - i) in blocks:
								snake_case_				:							Optional[Any]						 =       "#"

		snake_case_				:							Optional[int]						 =       "-"
		snake_case_				:							Tuple						 =       back_pointer[goal]
		while x != start:
				((snake_case_) ,       (snake_case_))				:							Any						 =       x
				# print(x)
				snake_case_				:							List[str]						 =       "-"
				snake_case_				:							Dict						 =       back_pointer[x]
		snake_case_				:							str						 =       "-"

		for i in range(_a    ):
				for j in range(_a    ):
						if (i, j) == (0, n - 1):
								print(grid[i][j]  ,      end=" "    )
								print("<-- End position"  ,      end=" "    )
						else:
								print(grid[i][j]  ,      end=" "    )
				print()
		print("^"    )
		print("Start position"    )
		print()
		print("# is an obstacle"    )
		print("- is the path taken by algorithm"    )
		print("PATH TAKEN BY THE ALGORITHM IS:-"    )
		snake_case_				:							List[Any]						 =       back_pointer[goal]
		while x != start:
				print(_a  ,      end=" "    )
				snake_case_				:							int						 =       back_pointer[x]
		print(_a    )
		sys.exit()

def      lowerCAmelCase__	(    _a       :			TPos    ):
		if p[0] < 0 or p[0] > n - 1:
				return False
		if p[1] < 0 or p[1] > n - 1:
				return False
		return True

def      lowerCAmelCase__	(    _a       :			int  ,      _a       :			str  ,      _a       :			Optional[int]  ,      _a       :			List[str]  ,      _a       :			Optional[Any]  ,      _a       :			Dict  ,      _a       :			Tuple  ,      _a       :			Any  ,      ):
		for itera in range(_a    ):
				open_list[itera].remove_element(_a    )
		# print("s", s)
		# print("j", j)
		((snake_case_) ,       (snake_case_))				:							Tuple						 =       s
		snake_case_				:							Dict						 =       (x - 1, y)
		snake_case_				:							Union[str, Any]						 =       (x + 1, y)
		snake_case_				:							List[str]						 =       (x, y + 1)
		snake_case_				:							int						 =       (x, y - 1)

		for neighbours in [left, right, up, down]:
				if neighbours not in blocks:
						if valid(_a    ) and neighbours not in visited:
								# print("neighbour", neighbours)
								visited.add(_a    )
								snake_case_				:							Optional[int]						 =       -1
								snake_case_				:							int						 =       float("inf"    )

						if valid(_a    ) and g_function[neighbours] > g_function[s] + 1:
								snake_case_				:							int						 =       g_function[s] + 1
								snake_case_				:							Any						 =       s
								if neighbours not in close_list_anchor:
										open_list[0].put(_a  ,      key(_a  ,      0  ,      _a  ,      _a    )    )
										if neighbours not in close_list_inad:
												for var in range(1  ,      _a    ):
														if key(_a  ,      _a  ,      _a  ,      _a    ) <= Wa * key(
														    _a  ,      0  ,      _a  ,      _a    ):
																open_list[j].put(
																    _a  ,      key(_a  ,      _a  ,      _a  ,      _a    )    )

def      lowerCAmelCase__	(    ):
		snake_case_				:							Union[str, Any]						 =       []
		for x in range(1  ,      5    ):
				for y in range(1  ,      6    ):
						some_list.append((x, y)    )

		for x in range(15  ,      20    ):
				some_list.append((x, 17)    )

		for x in range(10  ,      19    ):
				for y in range(1  ,      15    ):
						some_list.append((x, y)    )

    # L block
		for x in range(1  ,      4    ):
				for y in range(12  ,      19    ):
						some_list.append((x, y)    )
		for x in range(3  ,      13    ):
				for y in range(16  ,      19    ):
						some_list.append((x, y)    )
		return some_list


lowercase  :							Union[str, Any]			  =    {0: consistent_heuristic, 1: heuristic_a, 2: heuristic_a}

lowercase  :							Union[str, Any]			  =    [
    (0, 1),
    (1, 1),
    (2, 1),
    (3, 1),
    (4, 1),
    (5, 1),
    (6, 1),
    (7, 1),
    (8, 1),
    (9, 1),
    (10, 1),
    (11, 1),
    (12, 1),
    (13, 1),
    (14, 1),
    (15, 1),
    (16, 1),
    (17, 1),
    (18, 1),
    (19, 1),
]
lowercase  :							int			  =    make_common_ground()


lowercase  :							Optional[Any]			  =    blocks_blk
# hyper parameters
lowercase  :							Optional[Any]			  =    1
lowercase  :							str			  =    1
lowercase  :							Any			  =    20
lowercase  :							str			  =    3  # one consistent and two other inconsistent

# start and end destination
lowercase  :							Any			  =    (0, 0)
lowercase  :							int			  =    (n - 1, n - 1)

lowercase  :							str			  =    1

def      lowerCAmelCase__	(    _a       :			TPos  ,      _a       :			TPos  ,      _a       :			int    ):
		snake_case_				:							List[str]						 =       {start: 0, goal: float("inf"    )}
		snake_case_				:							List[Any]						 =       {start: -1, goal: -1}
		snake_case_				:							Optional[Any]						 =       []
		snake_case_				:							Dict						 =       set()

		for i in range(_a    ):
				open_list.append(PriorityQueue()    )
				open_list[i].put(_a  ,      key(_a  ,      _a  ,      _a  ,      _a    )    )

		snake_case_				:							list[int]						 =       []
		snake_case_				:							list[int]						 =       []
		while open_list[0].minkey() < float("inf"    ):
				for i in range(1  ,      _a    ):
						# print(open_list[0].minkey(), open_list[i].minkey())
						if open_list[i].minkey() <= Wa * open_list[0].minkey():
								global t
								t += 1
								if g_function[goal] <= open_list[i].minkey():
										if g_function[goal] < float("inf"    ):
												do_something(_a  ,      _a  ,      _a    )
								else:
										snake_case_ ,       snake_case_				:							Optional[Any]						 =       open_list[i].top_show()
										visited.add(_a    )
										expand_state(
										    _a  ,      _a  ,      _a  ,      _a  ,      _a  ,      _a  ,      _a  ,      _a  ,      )
										close_list_inad.append(_a    )
						else:
								if g_function[goal] <= open_list[0].minkey():
										if g_function[goal] < float("inf"    ):
												do_something(_a  ,      _a  ,      _a    )
								else:
										snake_case_				:							Tuple						 =       open_list[0].top_show()
										visited.add(_a    )
										expand_state(
										    _a  ,      0  ,      _a  ,      _a  ,      _a  ,      _a  ,      _a  ,      _a  ,      )
										close_list_anchor.append(_a    )
		print("No path found to goal"    )
		print()
		for i in range(n - 1  ,      -1  ,      -1    ):
				for j in range(_a    ):
						if (j, i) in blocks:
								print("#"  ,      end=" "    )
						elif (j, i) in back_pointer:
								if (j, i) == (n - 1, n - 1):
										print("*"  ,      end=" "    )
								else:
										print("-"  ,      end=" "    )
						else:
								print("*"  ,      end=" "    )
						if (j, i) == (n - 1, n - 1):
								print("<-- End position"  ,      end=" "    )
				print()
		print("^"    )
		print("Start position"    )
		print()
		print("# is an obstacle"    )
		print("- is the path taken by algorithm"    )


if __name__ == "__main__":
							multi_a_star(start, goal, n_heuristic)
