




'''simple docstring'''





import heapq
import sys

import numpy as np

_SCREAMING_SNAKE_CASE							:   Optional[int]      		= tuple[int, int]




class  _snake_case		:




			def __init__(					self							)				->				List[Any]:

										'''simple docstring'''




										snake_case_						=      []
										snake_case_						=      set()




			def    lowerCAmelCase__				(					self							)				->				Optional[int]:

										'''simple docstring'''




										if not self.empty():
																	return self.elements[0][0]
										else:
																	return float("inf"							)




			def    lowerCAmelCase__				(					self							)				->				List[str]:

										'''simple docstring'''




										return len(self.elements							) == 0




			def    lowerCAmelCase__				(					self				,							a__				,							a__							)				->				List[str]:

										'''simple docstring'''




										if item not in self.set:
																	heapq.heappush(self.elements				,							(priority, item)							)
																	self.set.add(a__							)
										else:
																	# update
																	# print("update", item)
																	snake_case_						=      []
																	((snake_case_)					,      (snake_case_))						=      heapq.heappop(self.elements							)
																	while x != item:
																								temp.append((pri, x)							)
																								((snake_case_)					,      (snake_case_))						=      heapq.heappop(self.elements							)
																	temp.append((priority, item)							)
																	for pro, xxx in temp:
																								heapq.heappush(self.elements				,							(pro, xxx)							)




			def    lowerCAmelCase__				(					self				,							a__							)				->				int:

										'''simple docstring'''




										if item in self.set:
																	self.set.remove(a__							)
																	snake_case_						=      []
																	((snake_case_)					,      (snake_case_))						=      heapq.heappop(self.elements							)
																	while x != item:
																								temp.append((pro, x)							)
																								((snake_case_)					,      (snake_case_))						=      heapq.heappop(self.elements							)
																	for prito, yyy in temp:
																								heapq.heappush(self.elements				,							(prito, yyy)							)




			def    lowerCAmelCase__				(					self							)				->				List[str]:

										'''simple docstring'''




										return self.elements[0][1]




			def    lowerCAmelCase__				(					self							)				->				Optional[int]:

										'''simple docstring'''




										((snake_case_)					,      (snake_case_))						=      heapq.heappop(self.elements							)
										self.set.remove(a__							)
										return (priority, item)


def      UpperCamelCase_(	snake_case							:       TPos							,				snake_case							:       TPos     ):

							'''simple docstring'''






							snake_case_						=      np.array(snake_case     )
							snake_case_						=      np.array(snake_case     )
							return np.linalg.norm(a - b     )


def      UpperCamelCase_(	snake_case							:       TPos							,				snake_case							:       TPos     ):

							'''simple docstring'''






							return consistent_heuristic(snake_case							,				snake_case     ) // t


def      UpperCamelCase_(	snake_case							:       TPos							,				snake_case							:       TPos     ):

							'''simple docstring'''






							return abs(p[0] - goal[0]     ) + abs(p[1] - goal[1]     )


def      UpperCamelCase_(	snake_case							:       TPos							,				snake_case							:       int							,				snake_case							:       TPos							,				snake_case							:       dict[TPos, float]     ):

							'''simple docstring'''






							snake_case_						=      g_function[start] + Wa * heuristics[i](snake_case							,				snake_case     )
							return ans


def      UpperCamelCase_(	snake_case							:       List[Any]							,				snake_case							:       List[Any]							,				snake_case							:       int     ):

							'''simple docstring'''






							snake_case_						=      np.chararray((n, n)     )
							for i in range(snake_case     ):
														for j in range(snake_case     ):
																					snake_case_						=      "*"

							for i in range(snake_case     ):
														for j in range(snake_case     ):
																					if (j, (n - 1) - i) in blocks:
																												snake_case_						=      "#"

							snake_case_						=      "-"
							snake_case_						=      back_pointer[goal]
							while x != start:
														((snake_case_)					,      (snake_case_))						=      x
														# print(x)
														snake_case_						=      "-"
														snake_case_						=      back_pointer[x]
							snake_case_						=      "-"

							for i in range(snake_case     ):
														for j in range(snake_case     ):
																					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_						=      back_pointer[goal]
							while x != start:
														print(snake_case							,				end=" "     )
														snake_case_						=      back_pointer[x]
							print(snake_case     )
							sys.exit()


def      UpperCamelCase_(	snake_case							:       TPos     ):

							'''simple docstring'''






							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      UpperCamelCase_(	snake_case							:       List[Any]							,				snake_case							:       int							,				snake_case							:       Tuple							,				snake_case							:       int							,				snake_case							:       List[str]							,				snake_case							:       Tuple							,				snake_case							:       Dict							,				snake_case							:       Dict							,				):

							'''simple docstring'''






							for itera in range(snake_case     ):
														open_list[itera].remove_element(snake_case     )
							# print("s", s)
							# print("j", j)
							((snake_case_)					,      (snake_case_))						=      s
							snake_case_						=      (x - 1, y)
							snake_case_						=      (x + 1, y)
							snake_case_						=      (x, y + 1)
							snake_case_						=      (x, y - 1)

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

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


def      UpperCamelCase_(	):

							'''simple docstring'''






							snake_case_						=      []
							for x in range(1							,				5     ):
														for y in range(1							,				6     ):
																					some_list.append((x, y)     )

							for x in range(1_5							,				2_0     ):
														some_list.append((x, 1_7)     )

							for x in range(1_0							,				1_9     ):
														for y in range(1							,				1_5     ):
																					some_list.append((x, y)     )

    # L block
							for x in range(1							,				4     ):
														for y in range(1_2							,				1_9     ):
																					some_list.append((x, y)     )
							for x in range(3							,				1_3     ):
														for y in range(1_6							,				1_9     ):
																					some_list.append((x, y)     )
							return some_list


_SCREAMING_SNAKE_CASE							:   Optional[int]      		= {0: consistent_heuristic, 1: heuristic_a, 2: heuristic_a}

_SCREAMING_SNAKE_CASE							:   int      		= [
    (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),
]
_SCREAMING_SNAKE_CASE							:   Optional[int]      		= make_common_ground()


_SCREAMING_SNAKE_CASE							:   List[str]      		= blocks_blk
# hyper parameters
_SCREAMING_SNAKE_CASE							:   Tuple      		= 1
_SCREAMING_SNAKE_CASE							:   Optional[int]      		= 1
_SCREAMING_SNAKE_CASE							:   int      		= 20
_SCREAMING_SNAKE_CASE							:   Tuple      		= 3  # one consistent and two other inconsistent

# start and end destination
_SCREAMING_SNAKE_CASE							:   List[str]      		= (0, 0)
_SCREAMING_SNAKE_CASE							:   Optional[int]      		= (n - 1, n - 1)

_SCREAMING_SNAKE_CASE							:   Tuple      		= 1


def      UpperCamelCase_(	snake_case							:       TPos							,				snake_case							:       TPos							,				snake_case							:       int     ):

							'''simple docstring'''






							snake_case_						=      {start: 0, goal: float("inf"     )}
							snake_case_						=      {start: -1, goal: -1}
							snake_case_						=      []
							snake_case_						=      set()

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

							snake_case_						=      []
							snake_case_						=      []
							while open_list[0].minkey() < float("inf"     ):
														for i in range(1							,				snake_case     ):
																					# 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(snake_case							,				snake_case							,				snake_case     )
																												else:
																																			snake_case_					,      snake_case_						=      open_list[i].top_show()
																																			visited.add(snake_case     )
																																			expand_state(
																																			    snake_case							,				snake_case							,				snake_case							,				snake_case							,				snake_case							,				snake_case							,				snake_case							,				snake_case							,				)
																																			close_list_inad.append(snake_case     )
																					else:
																												if g_function[goal] <= open_list[0].minkey():
																																			if g_function[goal] < float("inf"     ):
																																										do_something(snake_case							,				snake_case							,				snake_case     )
																												else:
																																			snake_case_						=      open_list[0].top_show()
																																			visited.add(snake_case     )
																																			expand_state(
																																			    snake_case							,				0							,				snake_case							,				snake_case							,				snake_case							,				snake_case							,				snake_case							,				snake_case							,				)
																																			close_list_anchor.append(snake_case     )
							print("No path found to goal"     )
							print()
							for i in range(n - 1							,				-1							,				-1     ):
														for j in range(snake_case     ):
																					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)









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

import numpy as np
import torch

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


SCREAMING_SNAKE_CASE_:Dict             =						logging.get_logger(__name__)






class SCREAMING_SNAKE_CASE__     (		SCREAMING_SNAKE_CASE__    ):




    '''simple docstring'''






    __lowerCamelCase			: Union[str, Any]					=      ["input_features", "is_longer"]






    def __init__(       self,		lowerCamelCase__=64,		lowerCamelCase__=4_8000,		lowerCamelCase__=480,		lowerCamelCase__=10,		lowerCamelCase__=1024,		lowerCamelCase__=0.0,		lowerCamelCase__=False,		lowerCamelCase__ = 0,		lowerCamelCase__ = 1_4000,		lowerCamelCase__ = None,		lowerCamelCase__ = "fusion",		lowerCamelCase__ = "repeatpad",		**lowerCamelCase__,		):
        super().__init__(
            feature_size=lowerCamelCase__,		sampling_rate=lowerCamelCase__,		padding_value=lowerCamelCase__,		return_attention_mask=lowerCamelCase__,		**lowerCamelCase__,		)
        A :       Tuple          =   top_db
        A :       Dict          =   truncation
        A :       int          =   padding
        A :       Optional[Any]          =   fft_window_size
        A :       Optional[Any]          =   (fft_window_size >> 1) + 1
        A :       List[Any]          =   hop_length
        A :       Tuple          =   max_length_s
        A :       str          =   max_length_s * sampling_rate
        A :       List[str]          =   sampling_rate
        A :       Dict          =   frequency_min
        A :       str          =   frequency_max
        A :       Union[str, Any]          =   mel_filter_bank(
            num_frequency_bins=self.nb_frequency_bins,		num_mel_filters=lowerCamelCase__,		min_frequency=lowerCamelCase__,		max_frequency=lowerCamelCase__,		sampling_rate=lowerCamelCase__,		norm=lowerCamelCase__,		mel_scale="""htk""",		)
        A :       Union[str, Any]          =   mel_filter_bank(
            num_frequency_bins=self.nb_frequency_bins,		num_mel_filters=lowerCamelCase__,		min_frequency=lowerCamelCase__,		max_frequency=lowerCamelCase__,		sampling_rate=lowerCamelCase__,		norm="""slaney""",		mel_scale="""slaney""",		)






    def 			_lowerCAmelCase					(       self		):
        A :       Any          =   copy.deepcopy(self.__dict__		)
        A :       Tuple          =   self.__class__.__name__
        if "mel_filters" in output:
            del output["mel_filters"]
        if "mel_filters_slaney" in output:
            del output["mel_filters_slaney"]
        return output






    def 			_lowerCAmelCase					(       self,		lowerCamelCase__,		lowerCamelCase__ = None		):
        A :       Optional[Any]          =   spectrogram(
            lowerCamelCase__,		window_function(self.fft_window_size,		"""hann"""		),		frame_length=self.fft_window_size,		hop_length=self.hop_length,		power=2.0,		mel_filters=lowerCamelCase__,		log_mel="""dB""",		)
        return log_mel_spectrogram.T






    def 			_lowerCAmelCase					(       self,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__		):
        A :       Any          =   np.array_split(list(range(0,		total_frames - chunk_frames + 1		)		),		3		)
        if len(ranges[1]		) == 0:
            # if the audio is too short, we just use the first chunk
            A :       Any          =   [0]
        if len(ranges[2]		) == 0:
            # if the audio is too short, we just use the first chunk
            A :       Union[str, Any]          =   [0]
        # randomly choose index for each part
        A :       str          =   np.random.choice(ranges[0]		)
        A :       str          =   np.random.choice(ranges[1]		)
        A :       Tuple          =   np.random.choice(ranges[2]		)

        A :       List[str]          =   mel[idx_front : idx_front + chunk_frames, :]
        A :       Union[str, Any]          =   mel[idx_middle : idx_middle + chunk_frames, :]
        A :       Any          =   mel[idx_back : idx_back + chunk_frames, :]

        A :       Any          =   torch.tensor(mel[None, None, :]		)
        A :       Union[str, Any]          =   torch.nn.functional.interpolate(
            lowerCamelCase__,		size=[chunk_frames, 64],		mode="""bilinear""",		align_corners=lowerCamelCase__		)
        A :       List[Any]          =   mel_shrink[0][0].numpy()
        A :       Any          =   np.stack([mel_shrink, mel_chunk_front, mel_chunk_middle, mel_chunk_back],		axis=0		)
        return mel_fusion






    def 			_lowerCAmelCase					(       self,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__		):
        if waveform.shape[0] > max_length:
            if truncation == "rand_trunc":
                A :       List[Any]          =   True
                # random crop to max_length (for compatibility) -> this should be handled by self.pad
                A :       int          =   len(lowerCamelCase__		) - max_length
                A :       Optional[int]          =   np.random.randint(0,		overflow + 1		)
                A :       List[Any]          =   waveform[idx : idx + max_length]
                A :       Optional[Any]          =   self._np_extract_fbank_features(lowerCamelCase__,		self.mel_filters_slaney		)[None, :]
            elif truncation == "fusion":
                A :       List[str]          =   self._np_extract_fbank_features(lowerCamelCase__,		self.mel_filters		)
                A :       List[str]          =   max_length // self.hop_length + 1  # the +1 related to how the spectrogram is computed
                A :       Optional[Any]          =   mel.shape[0]
                if chunk_frames == total_frames:
                    # there is a corner case where the audio length is larger than max_length but smaller than max_length+hop_length.
                    # In this case, we just use the whole audio.
                    A :       str          =   np.stack([mel, mel, mel, mel],		axis=0		)
                    A :       Union[str, Any]          =   False
                else:
                    A :       Dict          =   self._random_mel_fusion(lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__		)
                    A :       Tuple          =   True
            else:
                raise NotImplementedError(f'''data_truncating {truncation} not implemented'''		)

        else:
            A :       int          =   False
            # only use repeat as a new possible value for padding. you repeat the audio before applying the usual max_length padding
            if waveform.shape[0] < max_length:
                if padding == "repeat":
                    A :       Optional[int]          =   int(max_length / len(lowerCamelCase__		)		)
                    A :       Union[str, Any]          =   np.stack(np.tile(lowerCamelCase__,		n_repeat + 1		)		)[:max_length]
                if padding == "repeatpad":
                    A :       Dict          =   int(max_length / len(lowerCamelCase__		)		)
                    A :       Dict          =   np.stack(np.tile(lowerCamelCase__,		lowerCamelCase__		)		)
                A :       Any          =   np.pad(lowerCamelCase__,		(0, max_length - waveform.shape[0]),		mode="""constant""",		constant_values=0		)

            if truncation == "fusion":
                A :       Union[str, Any]          =   self._np_extract_fbank_features(lowerCamelCase__,		self.mel_filters		)
                A :       Tuple          =   np.stack([input_mel, input_mel, input_mel, input_mel],		axis=0		)
            else:
                A :       List[str]          =   self._np_extract_fbank_features(lowerCamelCase__,		self.mel_filters_slaney		)[None, :]

        return input_mel, longer






    def __call__(       self,		lowerCamelCase__,		lowerCamelCase__ = None,		lowerCamelCase__ = None,		lowerCamelCase__ = None,		lowerCamelCase__ = None,		lowerCamelCase__ = None,		**lowerCamelCase__,		):
        A :       Any          =   truncation if truncation is not None else self.truncation
        A :       Union[str, Any]          =   padding if padding else self.padding

        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."""		)

        A :       str          =   isinstance(lowerCamelCase__,		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}'''		)
        A :       Any          =   is_batched_numpy or (
            isinstance(lowerCamelCase__,		(list, tuple)		) and (isinstance(raw_speech[0],		(np.ndarray, tuple, list)		))
        )

        if is_batched:
            A :       Optional[int]          =   [np.asarray(lowerCamelCase__,		dtype=np.floataa		) for speech in raw_speech]
        elif not is_batched and not isinstance(lowerCamelCase__,		np.ndarray		):
            A :       int          =   np.asarray(lowerCamelCase__,		dtype=np.floataa		)
        elif isinstance(lowerCamelCase__,		np.ndarray		) and raw_speech.dtype is np.dtype(np.floataa		):
            A :       int          =   raw_speech.astype(np.floataa		)

        # always return batch
        if not is_batched:
            A :       List[Any]          =   [np.asarray(lowerCamelCase__		)]

        # convert to mel spectrogram, truncate and pad if needed.
        A :       Tuple          =   [
            self._get_input_mel(lowerCamelCase__,		max_length if max_length else self.nb_max_samples,		lowerCamelCase__,		lowerCamelCase__		)
            for waveform in raw_speech
        ]

        A :       Optional[Any]          =   []
        A :       Optional[Any]          =   []
        for mel, longer in padded_inputs:
            input_mel.append(lowerCamelCase__		)
            is_longer.append(lowerCamelCase__		)

        if truncation == "fusion" and sum(lowerCamelCase__		) == 0:
            # if no audio is longer than 10s, then randomly select one audio to be longer
            A :       Tuple          =   np.random.randint(0,		len(lowerCamelCase__		)		)
            A :       str          =   True

        if isinstance(input_mel[0],		lowerCamelCase__		):
            A :       int          =   [np.asarray(lowerCamelCase__,		dtype=np.floataa		) for feature in input_mel]

        # is_longer is a list of bool
        A :       Optional[int]          =   [[longer] for longer in is_longer]

        A :       Union[str, Any]          =   {"""input_features""": input_mel, """is_longer""": is_longer}
        A :       Tuple          =   BatchFeature(lowerCamelCase__		)

        if return_tensors is not None:
            A :       List[str]          =   input_features.convert_to_tensors(lowerCamelCase__		)

        return input_features


