
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


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


class        lowercase      (	_SCREAMING_SNAKE_CASE     ):
		__lowercase : Optional[int]			  =       ["input_features", "is_longer"]



		def __init__(				self       ,   A_=64       ,   A_=48_000       ,   A_=480       ,   A_=10       ,   A_=1_024       ,   A_=0.0       ,   A_=False       ,   A_ = 0       ,   A_ = 14_000       ,   A_ = None       ,   A_ = "fusion"       ,   A_ = "repeatpad"       ,   **A_       ,   )						->      Tuple:
				"""simple docstring"""
				super().__init__(
				    feature_size=A_       ,   sampling_rate=A_       ,   padding_value=A_       ,   return_attention_mask=A_       ,   **A_       ,   )
				UpperCamelCase						=  top_db
				UpperCamelCase						=  truncation
				UpperCamelCase						=  padding
				UpperCamelCase						=  fft_window_size
				UpperCamelCase						=  (fft_window_size >> 1) + 1
				UpperCamelCase						=  hop_length
				UpperCamelCase						=  max_length_s
				UpperCamelCase						=  max_length_s * sampling_rate
				UpperCamelCase						=  sampling_rate
				UpperCamelCase						=  frequency_min
				UpperCamelCase						=  frequency_max
				UpperCamelCase						=  mel_filter_bank(
				    num_frequency_bins=self.nb_frequency_bins       ,   num_mel_filters=A_       ,   min_frequency=A_       ,   max_frequency=A_       ,   sampling_rate=A_       ,   norm=A_       ,   mel_scale='htk'       ,   )
				UpperCamelCase						=  mel_filter_bank(
				    num_frequency_bins=self.nb_frequency_bins       ,   num_mel_filters=A_       ,   min_frequency=A_       ,   max_frequency=A_       ,   sampling_rate=A_       ,   norm='slaney'       ,   mel_scale='slaney'       ,   )



		def __UpperCamelCase				(				self )						->      Dict[str, Any]:
				"""simple docstring"""
				UpperCamelCase						=  copy.deepcopy(self.__dict__ )
				UpperCamelCase						=  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 __UpperCamelCase				(				self       ,   A_       ,   A_ = None )						->      np.ndarray:
				"""simple docstring"""
				UpperCamelCase						=  spectrogram(
				    A_       ,   window_function(self.fft_window_size       ,   'hann' )       ,   frame_length=self.fft_window_size       ,   hop_length=self.hop_length       ,   power=2.0       ,   mel_filters=A_       ,   log_mel='dB'       ,   )
				return log_mel_spectrogram.T



		def __UpperCamelCase				(				self       ,   A_       ,   A_       ,   A_ )						->      str:
				"""simple docstring"""
				UpperCamelCase						=  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
						UpperCamelCase						=  [0]
				if len(ranges[2] ) == 0:
						# if the audio is too short, we just use the first chunk
						UpperCamelCase						=  [0]
				# randomly choose index for each part
				UpperCamelCase						=  np.random.choice(ranges[0] )
				UpperCamelCase						=  np.random.choice(ranges[1] )
				UpperCamelCase						=  np.random.choice(ranges[2] )

				UpperCamelCase						=  mel[idx_front : idx_front + chunk_frames, :]
				UpperCamelCase						=  mel[idx_middle : idx_middle + chunk_frames, :]
				UpperCamelCase						=  mel[idx_back : idx_back + chunk_frames, :]

				UpperCamelCase						=  torch.tensor(mel[None, None, :] )
				UpperCamelCase						=  torch.nn.functional.interpolate(
				    A_       ,   size=[chunk_frames, 64]       ,   mode='bilinear'       ,   align_corners=A_ )
				UpperCamelCase						=  mel_shrink[0][0].numpy()
				UpperCamelCase						=  np.stack([mel_shrink, mel_chunk_front, mel_chunk_middle, mel_chunk_back]       ,   axis=0 )
				return mel_fusion



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

				else:
						UpperCamelCase						=  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":
										UpperCamelCase						=  int(max_length / len(A_ ) )
										UpperCamelCase						=  np.stack(np.tile(A_       ,   n_repeat + 1 ) )[:max_length]
								if padding == "repeatpad":
										UpperCamelCase						=  int(max_length / len(A_ ) )
										UpperCamelCase						=  np.stack(np.tile(A_       ,   A_ ) )
								UpperCamelCase						=  np.pad(A_       ,   (0, max_length - waveform.shape[0])       ,   mode='constant'       ,   constant_values=0 )

						if truncation == "fusion":
								UpperCamelCase						=  self._np_extract_fbank_features(A_       ,   self.mel_filters )
								UpperCamelCase						=  np.stack([input_mel, input_mel, input_mel, input_mel]       ,   axis=0 )
						else:
								UpperCamelCase						=  self._np_extract_fbank_features(A_       ,   self.mel_filters_slaney )[None, :]

				return input_mel, longer



		def __call__(				self       ,   A_       ,   A_ = None       ,   A_ = None       ,   A_ = None       ,   A_ = None       ,   A_ = None       ,   **A_       ,   )						->      BatchFeature:
				"""simple docstring"""
				UpperCamelCase						=  truncation if truncation is not None else self.truncation
				UpperCamelCase						=  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.' )

				UpperCamelCase						=  isinstance(A_       ,   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}''' )
				UpperCamelCase						=  is_batched_numpy or (
				    isinstance(A_       ,   (list, tuple) ) and (isinstance(raw_speech[0]       ,   (np.ndarray, tuple, list) ))
				)

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

				# always return batch
				if not is_batched:
						UpperCamelCase						=  [np.asarray(A_ )]

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

				UpperCamelCase						=  []
				UpperCamelCase						=  []
				for mel, longer in padded_inputs:
						input_mel.append(A_ )
						is_longer.append(A_ )

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

				if isinstance(input_mel[0]       ,   A_ ):
						UpperCamelCase						=  [np.asarray(A_       ,   dtype=np.floataa ) for feature in input_mel]

				# is_longer is a list of bool
				UpperCamelCase						=  [[longer] for longer in is_longer]

				UpperCamelCase						=  {'input_features': input_mel, 'is_longer': is_longer}
				UpperCamelCase						=  BatchFeature(A_ )

				if return_tensors is not None:
						UpperCamelCase						=  input_features.convert_to_tensors(A_ )

				return input_features


import pyarrow.parquet as pq
import pytest

from datasets import Audio, Dataset, DatasetDict, Features, NamedSplit, Sequence, Value, config
from datasets.features.image import Image
from datasets.io.parquet import ParquetDatasetReader, ParquetDatasetWriter, get_writer_batch_size

from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases


def    A (      lowercase					, lowercase				)     ->						Optional[int]:
		'''simple docstring'''

		assert isinstance(lowercase					, lowercase				)
		assert dataset.num_rows == 4
		assert dataset.num_columns == 3
		assert dataset.column_names == ["col_1", "col_2", "col_3"]
		for feature, expected_dtype in expected_features.items():
				assert dataset.features[feature].dtype == expected_dtype


@pytest.mark.parametrize('keep_in_memory'					, [False, True]				)
def    A (      lowercase					, lowercase					, lowercase				)     ->						Tuple:
		'''simple docstring'''

		UpperCamelCase						=  tmp_path / 'cache'
		UpperCamelCase						=  {'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'}
		with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
				UpperCamelCase						=  ParquetDatasetReader(lowercase					, cache_dir=lowercase					, keep_in_memory=lowercase				).read()
		_check_parquet_dataset(lowercase					, lowercase				)


@pytest.mark.parametrize(
    'features'					, [
        None,
        {'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'},
        {'col_1': 'string', 'col_2': 'string', 'col_3': 'string'},
        {'col_1': 'int32', 'col_2': 'int32', 'col_3': 'int32'},
        {'col_1': 'float32', 'col_2': 'float32', 'col_3': 'float32'},
    ]					, )
def    A (      lowercase					, lowercase					, lowercase				)     ->						Tuple:
		'''simple docstring'''

		UpperCamelCase						=  tmp_path / 'cache'
		UpperCamelCase						=  {'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'}
		UpperCamelCase						=  features.copy() if features else default_expected_features
		UpperCamelCase						=  (
		    Features({feature: Value(lowercase				) for feature, dtype in features.items()}				) if features is not None else None
		)
		UpperCamelCase						=  ParquetDatasetReader(lowercase					, features=lowercase					, cache_dir=lowercase				).read()
		_check_parquet_dataset(lowercase					, lowercase				)


@pytest.mark.parametrize('split'					, [None, NamedSplit('train'				), 'train', 'test']				)
def    A (      lowercase					, lowercase					, lowercase				)     ->						Optional[int]:
		'''simple docstring'''

		UpperCamelCase						=  tmp_path / 'cache'
		UpperCamelCase						=  {'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'}
		UpperCamelCase						=  ParquetDatasetReader(lowercase					, cache_dir=lowercase					, split=lowercase				).read()
		_check_parquet_dataset(lowercase					, lowercase				)
		assert dataset.split == split if split else "train"


@pytest.mark.parametrize('path_type'					, [str, list]				)
def    A (      lowercase					, lowercase					, lowercase				)     ->						Union[str, Any]:
		'''simple docstring'''

		if issubclass(lowercase					, lowercase				):
				UpperCamelCase						=  parquet_path
		elif issubclass(lowercase					, lowercase				):
				UpperCamelCase						=  [parquet_path]
		UpperCamelCase						=  tmp_path / 'cache'
		UpperCamelCase						=  {'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'}
		UpperCamelCase						=  ParquetDatasetReader(lowercase					, cache_dir=lowercase				).read()
		_check_parquet_dataset(lowercase					, lowercase				)


def    A (      lowercase					, lowercase					, lowercase=("train",)				)     ->						Tuple:
		'''simple docstring'''

		assert isinstance(lowercase					, lowercase				)
		for split in splits:
				UpperCamelCase						=  dataset_dict[split]
				assert dataset.num_rows == 4
				assert dataset.num_columns == 3
				assert dataset.column_names == ["col_1", "col_2", "col_3"]
				for feature, expected_dtype in expected_features.items():
						assert dataset.features[feature].dtype == expected_dtype


@pytest.mark.parametrize('keep_in_memory'					, [False, True]				)
def    A (      lowercase					, lowercase					, lowercase				)     ->						Optional[Any]:
		'''simple docstring'''

		UpperCamelCase						=  tmp_path / 'cache'
		UpperCamelCase						=  {'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'}
		with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
				UpperCamelCase						=  ParquetDatasetReader(
				    {'train': parquet_path}					, cache_dir=lowercase					, keep_in_memory=lowercase				).read()
		_check_parquet_datasetdict(lowercase					, lowercase				)


@pytest.mark.parametrize(
    'features'					, [
        None,
        {'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'},
        {'col_1': 'string', 'col_2': 'string', 'col_3': 'string'},
        {'col_1': 'int32', 'col_2': 'int32', 'col_3': 'int32'},
        {'col_1': 'float32', 'col_2': 'float32', 'col_3': 'float32'},
    ]					, )
def    A (      lowercase					, lowercase					, lowercase				)     ->						List[Any]:
		'''simple docstring'''

		UpperCamelCase						=  tmp_path / 'cache'
		UpperCamelCase						=  {'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'}
		UpperCamelCase						=  features.copy() if features else default_expected_features
		UpperCamelCase						=  (
		    Features({feature: Value(lowercase				) for feature, dtype in features.items()}				) if features is not None else None
		)
		UpperCamelCase						=  ParquetDatasetReader({'train': parquet_path}					, features=lowercase					, cache_dir=lowercase				).read()
		_check_parquet_datasetdict(lowercase					, lowercase				)


@pytest.mark.parametrize('split'					, [None, NamedSplit('train'				), 'train', 'test']				)
def    A (      lowercase					, lowercase					, lowercase				)     ->						Union[str, Any]:
		'''simple docstring'''

		if split:
				UpperCamelCase						=  {split: parquet_path}
		else:
				UpperCamelCase						=  'train'
				UpperCamelCase						=  {'train': parquet_path, 'test': parquet_path}
		UpperCamelCase						=  tmp_path / 'cache'
		UpperCamelCase						=  {'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'}
		UpperCamelCase						=  ParquetDatasetReader(lowercase					, cache_dir=lowercase				).read()
		_check_parquet_datasetdict(lowercase					, lowercase					, splits=list(path.keys()				)				)
		assert all(dataset[split].split == split for split in path.keys()				)


def    A (      lowercase					, lowercase				)     ->						List[Any]:
		'''simple docstring'''

		UpperCamelCase						=  ParquetDatasetWriter(lowercase					, tmp_path / 'foo.parquet'				)
		assert writer.write() > 0
		UpperCamelCase						=  pq.ParquetFile(tmp_path / 'foo.parquet'				)
		UpperCamelCase						=  pf.read()
		assert dataset.data.table == output_table


def    A (      lowercase					, lowercase				)     ->						Optional[int]:
		'''simple docstring'''

		UpperCamelCase						=  str(shared_datadir / 'test_image_rgb.jpg'				)
		UpperCamelCase						=  {'image': [image_path]}
		UpperCamelCase						=  Features({'image': Image()}				)
		UpperCamelCase						=  Dataset.from_dict(lowercase					, features=lowercase				)
		UpperCamelCase						=  ParquetDatasetWriter(lowercase					, tmp_path / 'foo.parquet'				)
		assert writer.write() > 0

		UpperCamelCase						=  Dataset.from_parquet(str(tmp_path / 'foo.parquet'				)				)
		assert dataset.features == reloaded_dataset.features

		UpperCamelCase						=  ParquetDatasetReader(str(tmp_path / 'foo.parquet'				)					, streaming=lowercase				).read()
		assert dataset.features == reloaded_iterable_dataset.features


@pytest.mark.parametrize(
    'feature, expected'					, [
        (Features({'foo': Value('int32'				)}				), None),
        (Features({'image': Image(), 'foo': Value('int32'				)}				), config.PARQUET_ROW_GROUP_SIZE_FOR_IMAGE_DATASETS),
        (Features({'nested': Sequence(Audio()				)}				), config.PARQUET_ROW_GROUP_SIZE_FOR_AUDIO_DATASETS),
    ]					, )
def    A (      lowercase					, lowercase				)     ->						Union[str, Any]:
		'''simple docstring'''

		assert get_writer_batch_size(lowercase				) == expected
