


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


_UpperCAmelCase	:     Union[str, Any]		       = logging.get_logger(__name__)







class 				lowercase       ( lowercase_    ):
				__SCREAMING_SNAKE_CASE      :				List[Any]     =    ['''input_features''']




				def __init__(      self   ,     snake_case=80   ,     snake_case=1_6000   ,     snake_case=160   ,     snake_case=30   ,     snake_case=400   ,     snake_case=0.0   ,     snake_case=False   ,     **snake_case   ,     ):
											super().__init__(
											    feature_size=snake_case   ,     sampling_rate=snake_case   ,     padding_value=snake_case   ,     return_attention_mask=snake_case   ,     **snake_case   ,     )
											snake_case_           =					n_fft
											snake_case_           =					hop_length
											snake_case_           =					chunk_length
											snake_case_           =					chunk_length * sampling_rate
											snake_case_           =					self.n_samples // hop_length
											snake_case_           =					sampling_rate
											snake_case_           =					mel_filter_bank(
											    num_frequency_bins=1 + n_fft // 2   ,     num_mel_filters=snake_case   ,     min_frequency=0.0   ,     max_frequency=8000.0   ,     sampling_rate=snake_case   ,     norm='slaney'   ,     mel_scale='slaney'   ,     )




				def 					a							(      self   ,     snake_case ):
											snake_case_           =					spectrogram(
											    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_           =					log_spec[:, :-1]
											snake_case_           =					np.maximum(snake_case   ,     log_spec.max() - 8.0 )
											snake_case_           =					(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 					a							(      snake_case   ,     snake_case   ,     snake_case = 0.0 ):
											if attention_mask is not None:
																		snake_case_           =					np.array(snake_case   ,     np.intaa )
																		snake_case_           =					[]

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

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

											return normed_input_values




				def __call__(      self   ,     snake_case   ,     snake_case = True   ,     snake_case = None   ,     snake_case = None   ,     snake_case = None   ,     snake_case = "max_length"   ,     snake_case = None   ,     snake_case = None   ,     snake_case = None   ,     **snake_case   ,     ):

											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_           =					isinstance(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_           =					is_batched_numpy or (
											    isinstance(snake_case   ,     (list, tuple) ) and (isinstance(raw_speech[0]   ,     (np.ndarray, tuple, list) ))
											)

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

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

											snake_case_           =					BatchFeature({'input_features': raw_speech} )

											# convert into correct format for padding

											snake_case_           =					self.pad(
											    snake_case   ,     padding=snake_case   ,     max_length=max_length if max_length else self.n_samples   ,     truncation=snake_case   ,     pad_to_multiple_of=snake_case   ,     return_attention_mask=return_attention_mask or do_normalize   ,     )

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

											# make sure list is in array format
											snake_case_           =					padded_inputs.get('input_features' ).transpose(2   ,     0   ,     1 )

											snake_case_           =					[self._np_extract_fbank_features(snake_case ) for waveform in input_features[0]]

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

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

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

											return padded_inputs




				def 					a							(      self ):
											snake_case_           =					copy.deepcopy(self.__dict__ )
											snake_case_           =					self.__class__.__name__
											if "mel_filters" in output:
																		del output["mel_filters"]
											return output







import heapq
import sys

import numpy as np

_UpperCAmelCase	:     str		       = tuple[int, int]







class 				lowercase       :




				def __init__(      self ):
											snake_case_           =					[]
											snake_case_           =					set()




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




				def 					a							(      self ):
											return len(self.elements ) == 0




				def 					a							(      self   ,     snake_case   ,     snake_case ):
											if item not in self.set:
																		heapq.heappush(self.elements   ,     (priority, item) )
																		self.set.add(snake_case )
											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 					a							(      self   ,     snake_case ):
											if item in self.set:
																		self.set.remove(snake_case )
																		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 					a							(      self ):
											return self.elements[0][1]




				def 					a							(      self ):
											((snake_case_)       ,					(snake_case_))           =					heapq.heappop(self.elements )
											self.set.remove(snake_case )
											return (priority, item)





def       __lowerCamelCase			(				UpperCamelCase__     ,							UpperCamelCase__   ):







							'''simple docstring'''






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





def       __lowerCamelCase			(				UpperCamelCase__     ,							UpperCamelCase__   ):







							'''simple docstring'''






							return consistent_heuristic(UpperCamelCase__     ,							UpperCamelCase__   ) // t





def       __lowerCamelCase			(				UpperCamelCase__     ,							UpperCamelCase__   ):







							'''simple docstring'''






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





def       __lowerCamelCase			(				UpperCamelCase__     ,							UpperCamelCase__     ,							UpperCamelCase__     ,							UpperCamelCase__   ):







							'''simple docstring'''






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





def       __lowerCamelCase			(				UpperCamelCase__     ,							UpperCamelCase__     ,							UpperCamelCase__   ):







							'''simple docstring'''






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

							for i in range(UpperCamelCase__   ):
														for j in range(UpperCamelCase__   ):
																					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(UpperCamelCase__   ):
														for j in range(UpperCamelCase__   ):
																					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(UpperCamelCase__     ,							end=' '   )
														snake_case_           =					back_pointer[x]
							print(UpperCamelCase__   )
							sys.exit()





def       __lowerCamelCase			(				UpperCamelCase__   ):







							'''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       __lowerCamelCase			(				UpperCamelCase__     ,							UpperCamelCase__     ,							UpperCamelCase__     ,							UpperCamelCase__     ,							UpperCamelCase__     ,							UpperCamelCase__     ,							UpperCamelCase__     ,							UpperCamelCase__     ,							):







							'''simple docstring'''






							for itera in range(UpperCamelCase__   ):
														open_list[itera].remove_element(UpperCamelCase__   )
							# 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(UpperCamelCase__   ) and neighbours not in visited:
																												# print("neighbour", neighbours)
																												visited.add(UpperCamelCase__   )
																												snake_case_           =					-1
																												snake_case_           =					float('inf'   )

																					if valid(UpperCamelCase__   ) 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(UpperCamelCase__     ,							key(UpperCamelCase__     ,							0     ,							UpperCamelCase__     ,							UpperCamelCase__   )   )
																																			if neighbours not in close_list_inad:
																																										for var in range(1     ,							UpperCamelCase__   ):
																																																	if key(UpperCamelCase__     ,							UpperCamelCase__     ,							UpperCamelCase__     ,							UpperCamelCase__   ) <= Wa * key(
																																																	    UpperCamelCase__     ,							0     ,							UpperCamelCase__     ,							UpperCamelCase__   ):
																																																								open_list[j].put(
																																																								    UpperCamelCase__     ,							key(UpperCamelCase__     ,							UpperCamelCase__     ,							UpperCamelCase__     ,							UpperCamelCase__   )   )





def       __lowerCamelCase			(				):







							'''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(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


_UpperCAmelCase	:     Tuple		       = {0: consistent_heuristic, 1: heuristic_a, 2: heuristic_a}

_UpperCAmelCase	:     List[str]		       = [
    (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),
]
_UpperCAmelCase	:     Tuple		       = make_common_ground()


_UpperCAmelCase	:     Dict		       = blocks_blk
# hyper parameters
_UpperCAmelCase	:     int		       = 1
_UpperCAmelCase	:     int		       = 1
_UpperCAmelCase	:     Tuple		       = 20
_UpperCAmelCase	:     Tuple		       = 3  # one consistent and two other inconsistent

# start and end destination
_UpperCAmelCase	:     Tuple		       = (0, 0)
_UpperCAmelCase	:     List[Any]		       = (n - 1, n - 1)

_UpperCAmelCase	:     Optional[int]		       = 1





def       __lowerCamelCase			(				UpperCamelCase__     ,							UpperCamelCase__     ,							UpperCamelCase__   ):







							'''simple docstring'''






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

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

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




