


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__     					=			logging.get_logger(__name__)



class 						_UpperCAmelCase      (     lowercase							):
		lowerCamelCase_	:			Tuple       			=  ["""input_features""", """is_longer"""]






		def __init__(						self				:     Any   ,  UpperCAmelCase				:     int=64   ,  UpperCAmelCase				:     Optional[Any]=4_80_00   ,  UpperCAmelCase				:     Optional[int]=4_80   ,  UpperCAmelCase				:     Optional[Any]=10   ,  UpperCAmelCase				:     Dict=10_24   ,  UpperCAmelCase				:     int=0.0   ,  UpperCAmelCase				:     Tuple=False   ,  UpperCAmelCase				:     float = 0   ,  UpperCAmelCase				:     float = 1_40_00   ,  UpperCAmelCase				:     int = None   ,  UpperCAmelCase				:     str = "fusion"   ,  UpperCAmelCase				:     str = "repeatpad"   ,  **UpperCAmelCase				:     Dict   ,  ):
				super().__init__(
				    feature_size=UpperCAmelCase   ,  sampling_rate=UpperCAmelCase   ,  padding_value=UpperCAmelCase   ,  return_attention_mask=UpperCAmelCase   ,  **UpperCAmelCase   ,  )
				SCREAMING_SNAKE_CASE_				:Optional[int]				     =						top_db
				SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						truncation
				SCREAMING_SNAKE_CASE_				:Tuple				     =						padding
				SCREAMING_SNAKE_CASE_				:List[Any]				     =						fft_window_size
				SCREAMING_SNAKE_CASE_				:List[str]				     =						(fft_window_size >> 1) + 1
				SCREAMING_SNAKE_CASE_				:Dict				     =						hop_length
				SCREAMING_SNAKE_CASE_				:str				     =						max_length_s
				SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						max_length_s * sampling_rate
				SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						sampling_rate
				SCREAMING_SNAKE_CASE_				:int				     =						frequency_min
				SCREAMING_SNAKE_CASE_				:List[Any]				     =						frequency_max
				SCREAMING_SNAKE_CASE_				:List[Any]				     =						mel_filter_bank(
				    num_frequency_bins=self.nb_frequency_bins   ,  num_mel_filters=UpperCAmelCase   ,  min_frequency=UpperCAmelCase   ,  max_frequency=UpperCAmelCase   ,  sampling_rate=UpperCAmelCase   ,  norm=UpperCAmelCase   ,  mel_scale="htk"   ,  )
				SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						mel_filter_bank(
				    num_frequency_bins=self.nb_frequency_bins   ,  num_mel_filters=UpperCAmelCase   ,  min_frequency=UpperCAmelCase   ,  max_frequency=UpperCAmelCase   ,  sampling_rate=UpperCAmelCase   ,  norm="slaney"   ,  mel_scale="slaney"   ,  )






		def _snake_case		(						self				:     Dict):
				SCREAMING_SNAKE_CASE_				:List[str]				     =						copy.deepcopy(self.__dict__)
				SCREAMING_SNAKE_CASE_				:str				     =						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 _snake_case		(						self				:     List[Any]   ,  UpperCAmelCase				:     np.array   ,  UpperCAmelCase				:     Optional[np.array] = None):
				SCREAMING_SNAKE_CASE_				:str				     =						spectrogram(
				    UpperCAmelCase   ,  window_function(self.fft_window_size   ,  "hann")   ,  frame_length=self.fft_window_size   ,  hop_length=self.hop_length   ,  power=2.0   ,  mel_filters=UpperCAmelCase   ,  log_mel="dB"   ,  )
				return log_mel_spectrogram.T






		def _snake_case		(						self				:     Optional[Any]   ,  UpperCAmelCase				:     Optional[int]   ,  UpperCAmelCase				:     str   ,  UpperCAmelCase				:     int):
				SCREAMING_SNAKE_CASE_				:List[str]				     =						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
						SCREAMING_SNAKE_CASE_				:str				     =						[0]
				if len(ranges[2]) == 0:
						# if the audio is too short, we just use the first chunk
						SCREAMING_SNAKE_CASE_				:List[str]				     =						[0]
				# randomly choose index for each part
				SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						np.random.choice(ranges[0])
				SCREAMING_SNAKE_CASE_				:List[Any]				     =						np.random.choice(ranges[1])
				SCREAMING_SNAKE_CASE_				:Tuple				     =						np.random.choice(ranges[2])

				SCREAMING_SNAKE_CASE_				:Any				     =						mel[idx_front : idx_front + chunk_frames, :]
				SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						mel[idx_middle : idx_middle + chunk_frames, :]
				SCREAMING_SNAKE_CASE_				:List[Any]				     =						mel[idx_back : idx_back + chunk_frames, :]

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






		def _snake_case		(						self				:     List[str]   ,  UpperCAmelCase				:     np.array   ,  UpperCAmelCase				:     Optional[Any]   ,  UpperCAmelCase				:     Optional[int]   ,  UpperCAmelCase				:     int):
				if waveform.shape[0] > max_length:
						if truncation == "rand_trunc":
								SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						True
								# random crop to max_length (for compatibility) -> this should be handled by self.pad
								SCREAMING_SNAKE_CASE_				:List[str]				     =						len(UpperCAmelCase) - max_length
								SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						np.random.randint(0   ,  overflow + 1)
								SCREAMING_SNAKE_CASE_				:Optional[int]				     =						waveform[idx : idx + max_length]
								SCREAMING_SNAKE_CASE_				:str				     =						self._np_extract_fbank_features(UpperCAmelCase   ,  self.mel_filters_slaney)[None, :]
						elif truncation == "fusion":
								SCREAMING_SNAKE_CASE_				:Any				     =						self._np_extract_fbank_features(UpperCAmelCase   ,  self.mel_filters)
								SCREAMING_SNAKE_CASE_				:int				     =						max_length // self.hop_length + 1  # the +1 related to how the spectrogram is computed
								SCREAMING_SNAKE_CASE_				:str				     =						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.
										SCREAMING_SNAKE_CASE_				:List[Any]				     =						np.stack([mel, mel, mel, mel]   ,  axis=0)
										SCREAMING_SNAKE_CASE_				:List[str]				     =						False
								else:
										SCREAMING_SNAKE_CASE_				:List[str]				     =						self._random_mel_fusion(UpperCAmelCase   ,  UpperCAmelCase   ,  UpperCAmelCase)
										SCREAMING_SNAKE_CASE_				:Tuple				     =						True
						else:
								raise NotImplementedError(F"data_truncating {truncation} not implemented")

				else:
						SCREAMING_SNAKE_CASE_				:Dict				     =						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":
										SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						int(max_length / len(UpperCAmelCase))
										SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						np.stack(np.tile(UpperCAmelCase   ,  n_repeat + 1))[:max_length]
								if padding == "repeatpad":
										SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						int(max_length / len(UpperCAmelCase))
										SCREAMING_SNAKE_CASE_				:str				     =						np.stack(np.tile(UpperCAmelCase   ,  UpperCAmelCase))
								SCREAMING_SNAKE_CASE_				:Dict				     =						np.pad(UpperCAmelCase   ,  (0, max_length - waveform.shape[0])   ,  mode="constant"   ,  constant_values=0)

						if truncation == "fusion":
								SCREAMING_SNAKE_CASE_				:List[Any]				     =						self._np_extract_fbank_features(UpperCAmelCase   ,  self.mel_filters)
								SCREAMING_SNAKE_CASE_				:Optional[int]				     =						np.stack([input_mel, input_mel, input_mel, input_mel]   ,  axis=0)
						else:
								SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						self._np_extract_fbank_features(UpperCAmelCase   ,  self.mel_filters_slaney)[None, :]

				return input_mel, longer







		def __call__(						self				:     Optional[Any]   ,  UpperCAmelCase				:     Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]   ,  UpperCAmelCase				:     str = None   ,  UpperCAmelCase				:     Optional[str] = None   ,  UpperCAmelCase				:     Optional[int] = None   ,  UpperCAmelCase				:     Optional[int] = None   ,  UpperCAmelCase				:     Optional[Union[str, TensorType]] = None   ,  **UpperCAmelCase				:     int   ,  ):
				SCREAMING_SNAKE_CASE_				:Tuple				     =						truncation if truncation is not None else self.truncation
				SCREAMING_SNAKE_CASE_				:List[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.")

				SCREAMING_SNAKE_CASE_				:str				     =						isinstance(UpperCAmelCase   ,  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}")
				SCREAMING_SNAKE_CASE_				:Dict				     =						is_batched_numpy or (
				    isinstance(UpperCAmelCase   ,  (list, tuple)) and (isinstance(raw_speech[0]   ,  (np.ndarray, tuple, list)))
				)

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

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

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

				SCREAMING_SNAKE_CASE_				:Tuple				     =						[]
				SCREAMING_SNAKE_CASE_				:Optional[int]				     =						[]
				for mel, longer in padded_inputs:
						input_mel.append(UpperCAmelCase)
						is_longer.append(UpperCAmelCase)

				if truncation == "fusion" and sum(UpperCAmelCase) == 0:
						# if no audio is longer than 10s, then randomly select one audio to be longer
						SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						np.random.randint(0   ,  len(UpperCAmelCase))
						SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						True

				if isinstance(input_mel[0]   ,  UpperCAmelCase):
						SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						[np.asarray(UpperCAmelCase   ,  dtype=np.floataa) for feature in input_mel]

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

				SCREAMING_SNAKE_CASE_				:Dict				     =						{"input_features": input_mel, "is_longer": is_longer}
				SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						BatchFeature(UpperCAmelCase)

				if return_tensors is not None:
						SCREAMING_SNAKE_CASE_				:Optional[int]				     =						input_features.convert_to_tensors(UpperCAmelCase)

				return input_features










def     lowercase	(    a     , a     , a     , a ):



		'''simple docstring'''




		SCREAMING_SNAKE_CASE_				:int				     =						[False] * len(a )
		SCREAMING_SNAKE_CASE_				:List[Any]				     =						[]
		queue.append(a )
		SCREAMING_SNAKE_CASE_				:int				     =						True

		while queue:
				SCREAMING_SNAKE_CASE_				:int				     =						queue.pop(0 )
				for ind in range(len(graph[u] ) ):
						if visited[ind] is False and graph[u][ind] > 0:
								queue.append(a )
								SCREAMING_SNAKE_CASE_				:Tuple				     =						True
								SCREAMING_SNAKE_CASE_				:Optional[int]				     =						u

		return visited[t]


def     lowercase	(    a     , a     , a ):



		'''simple docstring'''




		SCREAMING_SNAKE_CASE_				:Any				     =						[-1] * (len(a ))
		SCREAMING_SNAKE_CASE_				:Tuple				     =						0
		while bfs(a     , a     , a     , a ):
				SCREAMING_SNAKE_CASE_				:List[Any]				     =						float("Inf" )
				SCREAMING_SNAKE_CASE_				:str				     =						sink

				while s != source:
						# Find the minimum value in select path
						SCREAMING_SNAKE_CASE_				:str				     =						min(a     , graph[parent[s]][s] )
						SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						parent[s]

				max_flow += path_flow
				SCREAMING_SNAKE_CASE_				:Dict				     =						sink

				while v != source:
						SCREAMING_SNAKE_CASE_				:int				     =						parent[v]
						graph[u][v] -= path_flow
						graph[v][u] += path_flow
						SCREAMING_SNAKE_CASE_				:Any				     =						parent[v]
		return max_flow


SCREAMING_SNAKE_CASE__     					=			[
    [0, 16, 13, 0, 0, 0],
    [0, 0, 10, 12, 0, 0],
    [0, 4, 0, 0, 14, 0],
    [0, 0, 9, 0, 0, 20],
    [0, 0, 0, 7, 0, 4],
    [0, 0, 0, 0, 0, 0],
]

SCREAMING_SNAKE_CASE__					,    SCREAMING_SNAKE_CASE__     					=			0, 5
print(ford_fulkerson(graph, source, sink))






