






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

import numpy as np

from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import TensorType, logging


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




class 		UpperCAmelCase_						(	SCREAMING_SNAKE_CASE__					):


						'''simple docstring'''




						A     :   str						=					['input_features']






						def __init__(	self		,				_SCREAMING_SNAKE_CASE=80		,				_SCREAMING_SNAKE_CASE=1_6000		,				_SCREAMING_SNAKE_CASE=160		,				_SCREAMING_SNAKE_CASE=30		,				_SCREAMING_SNAKE_CASE=400		,				_SCREAMING_SNAKE_CASE=0.0		,				_SCREAMING_SNAKE_CASE=False		,				**_SCREAMING_SNAKE_CASE		,				) ->				Dict:
								super().__init__(
								    feature_size=_SCREAMING_SNAKE_CASE		,				sampling_rate=_SCREAMING_SNAKE_CASE		,				padding_value=_SCREAMING_SNAKE_CASE		,				return_attention_mask=_SCREAMING_SNAKE_CASE		,				**_SCREAMING_SNAKE_CASE		,				)
								snake_case_				:							int						 =       n_fft
								snake_case_				:							List[Any]						 =       hop_length
								snake_case_				:							str						 =       chunk_length
								snake_case_				:							int						 =       chunk_length * sampling_rate
								snake_case_				:							Any						 =       self.n_samples // hop_length
								snake_case_				:							Any						 =       sampling_rate
								snake_case_				:							List[Any]						 =       mel_filter_bank(
								    num_frequency_bins=1 + n_fft // 2		,				num_mel_filters=_SCREAMING_SNAKE_CASE		,				min_frequency=0.0		,				max_frequency=8000.0		,				sampling_rate=_SCREAMING_SNAKE_CASE		,				norm="slaney"		,				mel_scale="slaney"		,				)






						def       _lowerCAmelCase       (	self		,				_SCREAMING_SNAKE_CASE				) ->				np.ndarray:
								snake_case_				:							List[Any]						 =       spectrogram(
								    _SCREAMING_SNAKE_CASE		,				window_function(self.n_fft		,				"hann"				)		,				frame_length=self.n_fft		,				hop_length=self.hop_length		,				power=2.0		,				mel_filters=self.mel_filters		,				log_mel="log10"		,				)
								snake_case_				:							Optional[int]						 =       log_spec[:, :-1]
								snake_case_				:							Union[str, Any]						 =       np.maximum(_SCREAMING_SNAKE_CASE		,				log_spec.max() - 8.0				)
								snake_case_				:							Dict						 =       (log_spec + 4.0) / 4.0
								return log_spec






						@staticmethod
						# Copied from transformers.models.wav2vec2.feature_extraction_wav2vec2.Wav2Vec2FeatureExtractor.zero_mean_unit_var_norm
						def       _lowerCAmelCase       (	_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE = 0.0				) ->				List[np.ndarray]:
								if attention_mask is not None:
										snake_case_				:							int						 =       np.array(_SCREAMING_SNAKE_CASE		,				np.intaa				)
										snake_case_				:							List[str]						 =       []

										for vector, length in zip(_SCREAMING_SNAKE_CASE		,				attention_mask.sum(-1				)				):
												snake_case_				:							int						 =       (vector - vector[:length].mean()) / np.sqrt(vector[:length].var() + 1e-7				)
												if length < normed_slice.shape[0]:
														snake_case_				:							Union[str, Any]						 =       padding_value

												normed_input_values.append(_SCREAMING_SNAKE_CASE				)
								else:
										snake_case_				:							Any						 =       [(x - x.mean()) / np.sqrt(x.var() + 1e-7				) for x in input_values]

								return normed_input_values






						def __call__(	self		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE = True		,				_SCREAMING_SNAKE_CASE = None		,				_SCREAMING_SNAKE_CASE = None		,				_SCREAMING_SNAKE_CASE = None		,				_SCREAMING_SNAKE_CASE = "max_length"		,				_SCREAMING_SNAKE_CASE = None		,				_SCREAMING_SNAKE_CASE = None		,				_SCREAMING_SNAKE_CASE = None		,				**_SCREAMING_SNAKE_CASE		,				) ->				BatchFeature:

								if sampling_rate is not None:
										if sampling_rate != self.sampling_rate:
												raise ValueError(
												    f'''The model corresponding to this feature extractor: {self.__class__.__name__} was trained using a'''
												    f''' sampling rate of {self.sampling_rate}. Please make sure that the provided `raw_speech` input'''
												    f''' was sampled with {self.sampling_rate} and not {sampling_rate}.'''				)
								else:
										logger.warning(
										    "It is strongly recommended to pass the `sampling_rate` argument to this function. "
										    "Failing to do so can result in silent errors that might be hard to debug."				)

								snake_case_				:							Optional[Any]						 =       isinstance(_SCREAMING_SNAKE_CASE		,				np.ndarray				) and len(raw_speech.shape				) > 1
								if is_batched_numpy and len(raw_speech.shape				) > 2:
										raise ValueError(f'''Only mono-channel audio is supported for input to {self}'''				)
								snake_case_				:							Optional[Any]						 =       is_batched_numpy or (
								    isinstance(_SCREAMING_SNAKE_CASE		,				(list, tuple)				) and (isinstance(raw_speech[0]		,				(np.ndarray, tuple, list)				))
								)

								if is_batched:
										snake_case_				:							Dict						 =       [np.asarray([speech]		,				dtype=np.floataa				).T for speech in raw_speech]
								elif not is_batched and not isinstance(_SCREAMING_SNAKE_CASE		,				np.ndarray				):
										snake_case_				:							List[str]						 =       np.asarray(_SCREAMING_SNAKE_CASE		,				dtype=np.floataa				)
								elif isinstance(_SCREAMING_SNAKE_CASE		,				np.ndarray				) and raw_speech.dtype is np.dtype(np.floataa				):
										snake_case_				:							Dict						 =       raw_speech.astype(np.floataa				)

								# always return batch
								if not is_batched:
										snake_case_				:							Tuple						 =       [np.asarray([raw_speech]				).T]

								snake_case_				:							List[Any]						 =       BatchFeature({"input_features": raw_speech}				)

								# convert into correct format for padding

								snake_case_				:							Union[str, Any]						 =       self.pad(
								    _SCREAMING_SNAKE_CASE		,				padding=_SCREAMING_SNAKE_CASE		,				max_length=max_length if max_length else self.n_samples		,				truncation=_SCREAMING_SNAKE_CASE		,				pad_to_multiple_of=_SCREAMING_SNAKE_CASE		,				return_attention_mask=return_attention_mask or do_normalize		,				)

								# zero-mean and unit-variance normalization
								if do_normalize:
										snake_case_				:							Dict						 =       self.zero_mean_unit_var_norm(
										    padded_inputs["input_features"]		,				attention_mask=padded_inputs["attention_mask"]		,				padding_value=self.padding_value		,				)
										snake_case_				:							List[Any]						 =       np.stack(padded_inputs["input_features"]		,				axis=0				)

								# make sure list is in array format
								snake_case_				:							Optional[Any]						 =       padded_inputs.get("input_features"				).transpose(2		,				0		,				1				)

								snake_case_				:							Tuple						 =       [self._np_extract_fbank_features(_SCREAMING_SNAKE_CASE				) for waveform in input_features[0]]

								if isinstance(input_features[0]		,				_SCREAMING_SNAKE_CASE				):
										snake_case_				:							int						 =       [np.asarray(_SCREAMING_SNAKE_CASE		,				dtype=np.floataa				) for feature in input_features]
								else:
										snake_case_				:							Dict						 =       input_features

								if return_attention_mask:
										# rescale from sample (48000) to feature (3000)
										snake_case_				:							Optional[int]						 =       padded_inputs["attention_mask"][:, :: self.hop_length]

								if return_tensors is not None:
										snake_case_				:							Dict						 =       padded_inputs.convert_to_tensors(_SCREAMING_SNAKE_CASE				)

								return padded_inputs






						def       _lowerCAmelCase       (	self				) ->				Dict[str, Any]:
								snake_case_				:							List[str]						 =       copy.deepcopy(self.__dict__				)
								snake_case_				:							List[str]						 =       self.__class__.__name__
								if "mel_filters" in output:
										del output["mel_filters"]
								return output








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)
