


'''simple docstring'''



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     _UpperCamelCase      (		UpperCamelCase__ ,      UpperCamelCase__					):
			assert isinstance(UpperCAmelCase__ ,      UpperCAmelCase__					)
			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     _UpperCamelCase      (		UpperCamelCase__ ,      UpperCamelCase__ ,      UpperCamelCase__					):
			UpperCAmelCase__  :					int        =				tmp_path / """cache"""
			UpperCAmelCase__  :					Optional[Any]        =				{"""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__  :					Tuple        =				ParquetDatasetReader(UpperCAmelCase__ ,      cache_dir=UpperCAmelCase__ ,      keep_in_memory=UpperCAmelCase__					).read()
			_check_parquet_dataset(UpperCAmelCase__ ,      UpperCAmelCase__					)







@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     _UpperCamelCase      (		UpperCamelCase__ ,      UpperCamelCase__ ,      UpperCamelCase__					):
			UpperCAmelCase__  :					int        =				tmp_path / """cache"""
			UpperCAmelCase__  :					Tuple        =				{"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
			UpperCAmelCase__  :					List[Any]        =				features.copy() if features else default_expected_features
			UpperCAmelCase__  :					List[str]        =				(
			    Features({feature: Value(UpperCAmelCase__					) for feature, dtype in features.items()}					) if features is not None else None
			)
			UpperCAmelCase__  :					List[Any]        =				ParquetDatasetReader(UpperCAmelCase__ ,      features=UpperCAmelCase__ ,      cache_dir=UpperCAmelCase__					).read()
			_check_parquet_dataset(UpperCAmelCase__ ,      UpperCAmelCase__					)







@pytest.mark.parametrize("""split""" ,      [None, NamedSplit("""train"""					), """train""", """test"""]					)
def     _UpperCamelCase      (		UpperCamelCase__ ,      UpperCamelCase__ ,      UpperCamelCase__					):
			UpperCAmelCase__  :					List[str]        =				tmp_path / """cache"""
			UpperCAmelCase__  :					List[Any]        =				{"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
			UpperCAmelCase__  :					List[Any]        =				ParquetDatasetReader(UpperCAmelCase__ ,      cache_dir=UpperCAmelCase__ ,      split=UpperCAmelCase__					).read()
			_check_parquet_dataset(UpperCAmelCase__ ,      UpperCAmelCase__					)
			assert dataset.split == split if split else "train"







@pytest.mark.parametrize("""path_type""" ,      [str, list]					)
def     _UpperCamelCase      (		UpperCamelCase__ ,      UpperCamelCase__ ,      UpperCamelCase__					):






			if issubclass(UpperCAmelCase__ ,      UpperCAmelCase__					):
						UpperCAmelCase__  :					Union[str, Any]        =				parquet_path
			elif issubclass(UpperCAmelCase__ ,      UpperCAmelCase__					):
						UpperCAmelCase__  :					Tuple        =				[parquet_path]
			UpperCAmelCase__  :					Optional[int]        =				tmp_path / """cache"""
			UpperCAmelCase__  :					Dict        =				{"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
			UpperCAmelCase__  :					Any        =				ParquetDatasetReader(UpperCAmelCase__ ,      cache_dir=UpperCAmelCase__					).read()
			_check_parquet_dataset(UpperCAmelCase__ ,      UpperCAmelCase__					)







def     _UpperCamelCase      (		UpperCamelCase__ ,      UpperCamelCase__ ,      UpperCamelCase__=("train",)					):
			assert isinstance(UpperCAmelCase__ ,      UpperCAmelCase__					)
			for split in splits:
						UpperCAmelCase__  :					int        =				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     _UpperCamelCase      (		UpperCamelCase__ ,      UpperCamelCase__ ,      UpperCamelCase__					):
			UpperCAmelCase__  :					str        =				tmp_path / """cache"""
			UpperCAmelCase__  :					Optional[Any]        =				{"""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__  :					Any        =				ParquetDatasetReader(
						    {"""train""": parquet_path} ,      cache_dir=UpperCAmelCase__ ,      keep_in_memory=UpperCAmelCase__					).read()
			_check_parquet_datasetdict(UpperCAmelCase__ ,      UpperCAmelCase__					)







@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     _UpperCamelCase      (		UpperCamelCase__ ,      UpperCamelCase__ ,      UpperCamelCase__					):
			UpperCAmelCase__  :					Optional[int]        =				tmp_path / """cache"""
			UpperCAmelCase__  :					Union[str, Any]        =				{"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
			UpperCAmelCase__  :					Optional[Any]        =				features.copy() if features else default_expected_features
			UpperCAmelCase__  :					int        =				(
			    Features({feature: Value(UpperCAmelCase__					) for feature, dtype in features.items()}					) if features is not None else None
			)
			UpperCAmelCase__  :					Tuple        =				ParquetDatasetReader({"""train""": parquet_path} ,      features=UpperCAmelCase__ ,      cache_dir=UpperCAmelCase__					).read()
			_check_parquet_datasetdict(UpperCAmelCase__ ,      UpperCAmelCase__					)







@pytest.mark.parametrize("""split""" ,      [None, NamedSplit("""train"""					), """train""", """test"""]					)
def     _UpperCamelCase      (		UpperCamelCase__ ,      UpperCamelCase__ ,      UpperCamelCase__					):






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







def     _UpperCamelCase      (		UpperCamelCase__ ,      UpperCamelCase__					):
			UpperCAmelCase__  :					Optional[int]        =				ParquetDatasetWriter(UpperCAmelCase__ ,      tmp_path / """foo.parquet"""					)
			assert writer.write() > 0
			UpperCAmelCase__  :					int        =				pq.ParquetFile(tmp_path / """foo.parquet"""					)
			UpperCAmelCase__  :					List[str]        =				pf.read()
			assert dataset.data.table == output_table







def     _UpperCamelCase      (		UpperCamelCase__ ,      UpperCamelCase__					):
			UpperCAmelCase__  :					Optional[int]        =				str(shared_datadir / """test_image_rgb.jpg"""					)
			UpperCAmelCase__  :					int        =				{"""image""": [image_path]}
			UpperCAmelCase__  :					Optional[int]        =				Features({"""image""": Image()}					)
			UpperCAmelCase__  :					str        =				Dataset.from_dict(UpperCAmelCase__ ,      features=UpperCAmelCase__					)
			UpperCAmelCase__  :					List[Any]        =				ParquetDatasetWriter(UpperCAmelCase__ ,      tmp_path / """foo.parquet"""					)
			assert writer.write() > 0

			UpperCAmelCase__  :					Dict        =				Dataset.from_parquet(str(tmp_path / """foo.parquet"""					)					)
			assert dataset.features == reloaded_dataset.features

			UpperCAmelCase__  :					Union[str, Any]        =				ParquetDatasetReader(str(tmp_path / """foo.parquet"""					) ,      streaming=UpperCAmelCase__					).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     _UpperCamelCase      (		UpperCamelCase__ ,      UpperCamelCase__					):
			assert get_writer_batch_size(UpperCAmelCase__					) == expected



'''simple docstring'''



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


__A	=logging.get_logger(__name__)


class 		_snake_case					(     a__	):
					lowerCAmelCase      :int   =					['''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							,							)
								UpperCAmelCase__  :					Dict        =     top_db
								UpperCAmelCase__  :					int        =     truncation
								UpperCAmelCase__  :					Optional[Any]        =     padding
								UpperCAmelCase__  :					Union[str, Any]        =     fft_window_size
								UpperCAmelCase__  :					List[Any]        =     (fft_window_size >> 1) + 1
								UpperCAmelCase__  :					Optional[int]        =     hop_length
								UpperCAmelCase__  :					int        =     max_length_s
								UpperCAmelCase__  :					Tuple        =     max_length_s * sampling_rate
								UpperCAmelCase__  :					List[Any]        =     sampling_rate
								UpperCAmelCase__  :					Tuple        =     frequency_min
								UpperCAmelCase__  :					Dict        =     frequency_max
								UpperCAmelCase__  :					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"""							,							)
								UpperCAmelCase__  :					Optional[int]        =     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 				snake_case__						(      self):
								UpperCAmelCase__  :					List[Any]        =     copy.deepcopy(self.__dict__)
								UpperCAmelCase__  :					Optional[int]        =     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							,							_lowerCamelCase							,							_lowerCamelCase = None):
								UpperCAmelCase__  :					str        =     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 				snake_case__						(      self							,							_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase):
								UpperCAmelCase__  :					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
											UpperCAmelCase__  :					List[Any]        =     [0]
								if len(ranges[2]) == 0:
											# if the audio is too short, we just use the first chunk
											UpperCAmelCase__  :					Union[str, Any]        =     [0]
								# randomly choose index for each part
								UpperCAmelCase__  :					List[str]        =     np.random.choice(ranges[0])
								UpperCAmelCase__  :					Optional[int]        =     np.random.choice(ranges[1])
								UpperCAmelCase__  :					List[str]        =     np.random.choice(ranges[2])

								UpperCAmelCase__  :					str        =     mel[idx_front : idx_front + chunk_frames, :]
								UpperCAmelCase__  :					Optional[int]        =     mel[idx_middle : idx_middle + chunk_frames, :]
								UpperCAmelCase__  :					Tuple        =     mel[idx_back : idx_back + chunk_frames, :]

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




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

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

											if truncation == "fusion":
														UpperCAmelCase__  :					List[Any]        =     self._np_extract_fbank_features(_lowerCamelCase							,							self.mel_filters)
														UpperCAmelCase__  :					Dict        =     np.stack([input_mel, input_mel, input_mel, input_mel]							,							axis=0)
											else:
														UpperCAmelCase__  :					int        =     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							,							):
								UpperCAmelCase__  :					Dict        =     truncation if truncation is not None else self.truncation
								UpperCAmelCase__  :					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.""")

								UpperCAmelCase__  :					int        =     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}''')
								UpperCAmelCase__  :					List[str]        =     is_batched_numpy or (
								    isinstance(_lowerCamelCase							,							(list, tuple)) and (isinstance(raw_speech[0]							,							(np.ndarray, tuple, list)))
								)

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

								# always return batch
								if not is_batched:
											UpperCAmelCase__  :					int        =     [np.asarray(_lowerCamelCase)]

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

								UpperCAmelCase__  :					int        =     []
								UpperCAmelCase__  :					List[str]        =     []
								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
											UpperCAmelCase__  :					Optional[int]        =     np.random.randint(0							,							len(_lowerCamelCase))
											UpperCAmelCase__  :					Tuple        =     True

								if isinstance(input_mel[0]							,							_lowerCamelCase):
											UpperCAmelCase__  :					str        =     [np.asarray(_lowerCamelCase							,							dtype=np.floataa) for feature in input_mel]

								# is_longer is a list of bool
								UpperCAmelCase__  :					str        =     [[longer] for longer in is_longer]

								UpperCAmelCase__  :					Optional[int]        =     {"""input_features""": input_mel, """is_longer""": is_longer}
								UpperCAmelCase__  :					Optional[int]        =     BatchFeature(_lowerCamelCase)

								if return_tensors is not None:
											UpperCAmelCase__  :					str        =     input_features.convert_to_tensors(_lowerCamelCase)

								return input_features