






import io
import json

import fsspec
import pytest

from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.json import JsonDatasetReader, JsonDatasetWriter

from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases





def 	SCREAMING_SNAKE_CASE__    ( snake_case_,	snake_case_	) ->	Any:


	"""simple docstring"""



	assert isinstance(lowerCamelCase__,	lowerCamelCase__	)
	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 	SCREAMING_SNAKE_CASE__    ( snake_case_,	snake_case_,	snake_case_	) ->	Any:


	"""simple docstring"""



	a												=      tmp_path / '''cache'''
	a												=      {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
	with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
		a												=      JsonDatasetReader(lowerCamelCase__,	cache_dir=lowerCamelCase__,	keep_in_memory=lowerCamelCase__	).read()
	_check_json_dataset(lowerCamelCase__,	lowerCamelCase__	)





@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 	SCREAMING_SNAKE_CASE__    ( snake_case_,	snake_case_,	snake_case_	) ->	Optional[int]:


	"""simple docstring"""



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





@pytest.mark.parametrize(
    '''features''',	[
        None,
        {'''col_3''': '''float64''', '''col_1''': '''string''', '''col_2''': '''int64'''},
    ],	)
def 	SCREAMING_SNAKE_CASE__    ( snake_case_,	snake_case_,	snake_case_	) ->	List[Any]:


	"""simple docstring"""



	a												=      tmp_path / '''cache'''
	a												=      {'''col_3''': '''float64''', '''col_1''': '''string''', '''col_2''': '''int64'''}
	a												=      features.copy() if features else default_expected_features
	a												=      (
	    Features({feature: Value(lowerCamelCase__	) for feature, dtype in features.items()}	) if features is not None else None
	)
	a												=      JsonDatasetReader(lowerCamelCase__,	features=lowerCamelCase__,	cache_dir=lowerCamelCase__	).read()
	assert isinstance(lowerCamelCase__,	lowerCamelCase__	)
	assert dataset.num_rows == 2
	assert dataset.num_columns == 3
	assert dataset.column_names == ["col_3", "col_1", "col_2"]
	for feature, expected_dtype in expected_features.items():
		assert dataset.features[feature].dtype == expected_dtype





def 	SCREAMING_SNAKE_CASE__    ( snake_case_,	snake_case_	) ->	str:


	"""simple docstring"""



	a												=      {'''col_2''': '''int64''', '''col_3''': '''float64''', '''col_1''': '''string'''}
	a												=      features.copy()
	a												=      (
	    Features({feature: Value(lowerCamelCase__	) for feature, dtype in features.items()}	) if features is not None else None
	)
	a												=      tmp_path / '''cache'''
	a												=      JsonDatasetReader(lowerCamelCase__,	features=lowerCamelCase__,	cache_dir=lowerCamelCase__	).read()
	assert isinstance(lowerCamelCase__,	lowerCamelCase__	)
	assert dataset.num_rows == 2
	assert dataset.num_columns == 3
	assert dataset.column_names == ["col_2", "col_3", "col_1"]
	for feature, expected_dtype in expected_features.items():
		assert dataset.features[feature].dtype == expected_dtype





@pytest.mark.parametrize('''split''',	[None, NamedSplit('''train'''	), '''train''', '''test''']	)
def 	SCREAMING_SNAKE_CASE__    ( snake_case_,	snake_case_,	snake_case_	) ->	Optional[int]:


	"""simple docstring"""



	a												=      tmp_path / '''cache'''
	a												=      {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
	a												=      JsonDatasetReader(lowerCamelCase__,	cache_dir=lowerCamelCase__,	split=lowerCamelCase__	).read()
	_check_json_dataset(lowerCamelCase__,	lowerCamelCase__	)
	assert dataset.split == split if split else "train"





@pytest.mark.parametrize('''path_type''',	[str, list]	)
def 	SCREAMING_SNAKE_CASE__    ( snake_case_,	snake_case_,	snake_case_	) ->	Any:


	"""simple docstring"""



	if issubclass(lowerCamelCase__,	lowerCamelCase__	):
		a												=      jsonl_path
	elif issubclass(lowerCamelCase__,	lowerCamelCase__	):
		a												=      [jsonl_path]
	a												=      tmp_path / '''cache'''
	a												=      {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
	a												=      JsonDatasetReader(lowerCamelCase__,	cache_dir=lowerCamelCase__	).read()
	_check_json_dataset(lowerCamelCase__,	lowerCamelCase__	)





def 	SCREAMING_SNAKE_CASE__    ( snake_case_,	snake_case_,	snake_case_=("train",)	) ->	Union[str, Any]:


	"""simple docstring"""



	assert isinstance(lowerCamelCase__,	lowerCamelCase__	)
	for split in splits:
		a												=      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 	SCREAMING_SNAKE_CASE__    ( snake_case_,	snake_case_,	snake_case_	) ->	str:


	"""simple docstring"""



	a												=      tmp_path / '''cache'''
	a												=      {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
	with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
		a												=      JsonDatasetReader({'''train''': jsonl_path},	cache_dir=lowerCamelCase__,	keep_in_memory=lowerCamelCase__	).read()
	_check_json_datasetdict(lowerCamelCase__,	lowerCamelCase__	)





@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 	SCREAMING_SNAKE_CASE__    ( snake_case_,	snake_case_,	snake_case_	) ->	Tuple:


	"""simple docstring"""



	a												=      tmp_path / '''cache'''
	a												=      {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
	a												=      features.copy() if features else default_expected_features
	a												=      (
	    Features({feature: Value(lowerCamelCase__	) for feature, dtype in features.items()}	) if features is not None else None
	)
	a												=      JsonDatasetReader({'''train''': jsonl_path},	features=lowerCamelCase__,	cache_dir=lowerCamelCase__	).read()
	_check_json_datasetdict(lowerCamelCase__,	lowerCamelCase__	)





@pytest.mark.parametrize('''split''',	[None, NamedSplit('''train'''	), '''train''', '''test''']	)
def 	SCREAMING_SNAKE_CASE__    ( snake_case_,	snake_case_,	snake_case_	) ->	Tuple:


	"""simple docstring"""



	if split:
		a												=      {split: jsonl_path}
	else:
		a												=      '''train'''
		a												=      {'''train''': jsonl_path, '''test''': jsonl_path}
	a												=      tmp_path / '''cache'''
	a												=      {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
	a												=      JsonDatasetReader(lowerCamelCase__,	cache_dir=lowerCamelCase__	).read()
	_check_json_datasetdict(lowerCamelCase__,	lowerCamelCase__,	splits=list(path.keys()	)	)
	assert all(dataset[split].split == split for split in path.keys()	)





def 	SCREAMING_SNAKE_CASE__    ( snake_case_	) ->	int:


	"""simple docstring"""



	return json.load(lowerCamelCase__	)





def 	SCREAMING_SNAKE_CASE__    ( snake_case_	) ->	Tuple:


	"""simple docstring"""



	return [json.loads(lowerCamelCase__	) for line in buffer]







class 					lowerCamelCase_  :
							@pytest.mark.parametrize('''lines, load_json_function'''				,[(True, load_json_lines), (False, load_json)]       )
							def 			SCREAMING_SNAKE_CASE_			(    self		:       List[str]				,__lowerCamelCase		:       Dict				,__lowerCamelCase		:       List[Any]				,__lowerCamelCase		:       Optional[int]       ):





								'''simple docstring'''



								with io.BytesIO() as buffer:
									JsonDatasetWriter(__lowerCamelCase				,__lowerCamelCase				,lines=__lowerCamelCase       ).write()
									buffer.seek(0       )
									a												=      load_json_function(__lowerCamelCase       )
								assert isinstance(__lowerCamelCase				,__lowerCamelCase       )
								assert isinstance(exported_content[0]				,__lowerCamelCase       )
								assert len(__lowerCamelCase       ) == 10
							@pytest.mark.parametrize(
							    '''orient, container, keys, len_at'''				,[
							        ('''records''', list, {'''tokens''', '''labels''', '''answers''', '''id'''}, None),
							        ('''split''', dict, {'''columns''', '''data'''}, '''data'''),
							        ('''index''', dict, set('''0123456789'''       ), None),
							        ('''columns''', dict, {'''tokens''', '''labels''', '''answers''', '''id'''}, '''tokens'''),
							        ('''values''', list, None, None),
							        ('''table''', dict, {'''schema''', '''data'''}, '''data'''),
							    ]				,)
							def 			SCREAMING_SNAKE_CASE_			(    self		:       Dict				,__lowerCamelCase		:       Union[str, Any]				,__lowerCamelCase		:       int				,__lowerCamelCase		:       str				,__lowerCamelCase		:       Dict				,__lowerCamelCase		:       int       ):





								'''simple docstring'''



								with io.BytesIO() as buffer:
									JsonDatasetWriter(__lowerCamelCase				,__lowerCamelCase				,lines=__lowerCamelCase				,orient=__lowerCamelCase       ).write()
									buffer.seek(0       )
									a												=      load_json(__lowerCamelCase       )
								assert isinstance(__lowerCamelCase				,__lowerCamelCase       )
								if keys:
									if container is dict:
										assert exported_content.keys() == keys
									else:
										assert exported_content[0].keys() == keys
								else:
									assert not hasattr(__lowerCamelCase				,'''keys'''       ) and not hasattr(exported_content[0]				,'''keys'''       )
								if len_at:
									assert len(exported_content[len_at]       ) == 10
								else:
									assert len(__lowerCamelCase       ) == 10
							@pytest.mark.parametrize('''lines, load_json_function'''				,[(True, load_json_lines), (False, load_json)]       )
							def 			SCREAMING_SNAKE_CASE_			(    self		:       Optional[int]				,__lowerCamelCase		:       List[Any]				,__lowerCamelCase		:       Tuple				,__lowerCamelCase		:       Optional[int]       ):





								'''simple docstring'''



								with io.BytesIO() as buffer:
									JsonDatasetWriter(__lowerCamelCase				,__lowerCamelCase				,lines=__lowerCamelCase				,num_proc=2       ).write()
									buffer.seek(0       )
									a												=      load_json_function(__lowerCamelCase       )
								assert isinstance(__lowerCamelCase				,__lowerCamelCase       )
								assert isinstance(exported_content[0]				,__lowerCamelCase       )
								assert len(__lowerCamelCase       ) == 10
							@pytest.mark.parametrize(
							    '''orient, container, keys, len_at'''				,[
							        ('''records''', list, {'''tokens''', '''labels''', '''answers''', '''id'''}, None),
							        ('''split''', dict, {'''columns''', '''data'''}, '''data'''),
							        ('''index''', dict, set('''0123456789'''       ), None),
							        ('''columns''', dict, {'''tokens''', '''labels''', '''answers''', '''id'''}, '''tokens'''),
							        ('''values''', list, None, None),
							        ('''table''', dict, {'''schema''', '''data'''}, '''data'''),
							    ]				,)
							def 			SCREAMING_SNAKE_CASE_			(    self		:       str				,__lowerCamelCase		:       Dict				,__lowerCamelCase		:       Optional[int]				,__lowerCamelCase		:       Optional[Any]				,__lowerCamelCase		:       List[Any]				,__lowerCamelCase		:       int       ):





								'''simple docstring'''



								with io.BytesIO() as buffer:
									JsonDatasetWriter(__lowerCamelCase				,__lowerCamelCase				,lines=__lowerCamelCase				,orient=__lowerCamelCase				,num_proc=2       ).write()
									buffer.seek(0       )
									a												=      load_json(__lowerCamelCase       )
								assert isinstance(__lowerCamelCase				,__lowerCamelCase       )
								if keys:
									if container is dict:
										assert exported_content.keys() == keys
									else:
										assert exported_content[0].keys() == keys
								else:
									assert not hasattr(__lowerCamelCase				,'''keys'''       ) and not hasattr(exported_content[0]				,'''keys'''       )
								if len_at:
									assert len(exported_content[len_at]       ) == 10
								else:
									assert len(__lowerCamelCase       ) == 10
							def 			SCREAMING_SNAKE_CASE_			(    self		:       List[str]				,__lowerCamelCase		:       Union[str, Any]       ):





								'''simple docstring'''



								with pytest.raises(__lowerCamelCase       ):
									with io.BytesIO() as buffer:
										JsonDatasetWriter(__lowerCamelCase				,__lowerCamelCase				,num_proc=0       )




							@pytest.mark.parametrize('''compression, extension'''				,[('''gzip''', '''gz'''), ('''bz2''', '''bz2'''), ('''xz''', '''xz''')]       )
							def 			SCREAMING_SNAKE_CASE_			(    self		:       List[str]				,__lowerCamelCase		:       int				,__lowerCamelCase		:       Dict				,__lowerCamelCase		:       Dict				,__lowerCamelCase		:       str				,__lowerCamelCase		:       Dict       ):





								'''simple docstring'''



								a												=      tmp_path_factory.mktemp('''data'''       ) / F"""test.json.{extension}"""
								a												=      str(shared_datadir / F"""test_file.json.{extension}"""       )
								JsonDatasetWriter(__lowerCamelCase				,__lowerCamelCase				,compression=__lowerCamelCase       ).write()

								with fsspec.open(__lowerCamelCase				,'''rb'''				,compression='''infer'''       ) as f:
									a												=      f.read()
								with fsspec.open(__lowerCamelCase				,'''rb'''				,compression='''infer'''       ) as f:
									a												=      f.read()
								assert exported_content == original_content









from math import ceil
from typing import List, Optional, Union

import numpy as np

from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import BatchFeature, SequenceFeatureExtractor
from ...utils import TensorType, logging


UpperCAmelCase   :    Any     =		logging.get_logger(__name__)







class        __lowercase       (							a_  ):



		"""simple docstring"""

		UpperCamelCase     :					List[Any]			       =		["audio_values", "audio_mask"]







		def __init__( self		,      A=20_48		,      A=1		,      A=[16, 16]		,      A=1_28		,      A=4_41_00		,      A=86		,      A=20_48		,      A=0.0		,      **A		,      )  -> Dict:







					'''simple docstring'''







					super().__init__(
					    feature_size=A		,      sampling_rate=A		,      padding_value=A		,      **A		,      )

					lowerCamelCase    		=  spectrogram_length
					lowerCamelCase    		=  num_channels
					lowerCamelCase    		=  patch_size
					lowerCamelCase    		=  feature_size // self.patch_size[1]
					lowerCamelCase    		=  n_fft
					lowerCamelCase    		=  sampling_rate // hop_length_to_sampling_rate
					lowerCamelCase    		=  sampling_rate
					lowerCamelCase    		=  padding_value
					lowerCamelCase    		=  mel_filter_bank(
					    num_frequency_bins=1 + n_fft // 2		,      num_mel_filters=A		,      min_frequency=0.0		,      max_frequency=22050.0		,      sampling_rate=A		,      norm="""slaney"""		,      mel_scale="""slaney"""		,      ).T







		def 					__A   ( self		,      A	)  -> np.ndarray:







					'''simple docstring'''







					lowerCamelCase    		=  spectrogram(
					    A		,      window_function(self.n_fft		,      """hann"""	)		,      frame_length=self.n_fft		,      hop_length=self.hop_length		,      power=2.0		,      mel_filters=self.mel_filters.T		,      log_mel="""dB"""		,      db_range=80.0		,      )
					lowerCamelCase    		=  log_spec[:, :-1]
					lowerCamelCase    		=  log_spec - 20.0
					lowerCamelCase    		=  np.clip(log_spec / 40.0		,      -2.0		,      0.0	) + 1.0
					return log_spec







		def __call__( self		,      A		,      A = None		,      A = True		,      A = None		,      A = False		,      A = False		,      **A		,      )  -> BatchFeature:







					'''simple docstring'''







					if sampling_rate is not None:
								if sampling_rate != self.sampling_rate:
											raise ValueError(
											    """This feature extractor is set to support sampling rate"""
											    F' of {self.sampling_rate}. Please make sure that the provided `raw_speech` input was sampled'
											    F' 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."""	)

					lowerCamelCase    		=  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}'	)
					lowerCamelCase    		=  is_batched_numpy or (
					    isinstance(A		,      (list, tuple)	) and (isinstance(raw_speech[0]		,      (np.ndarray, tuple, list)	))
					)
					if is_batched:
								lowerCamelCase    		=  [np.asarray([speech]		,      dtype=np.floataa	).T for speech in raw_speech]
					elif not is_batched and not isinstance(A		,      np.ndarray	):
								lowerCamelCase    		=  np.asarray(A		,      dtype=np.floataa	)
					elif isinstance(A		,      np.ndarray	) and raw_speech.dtype is np.dtype(np.floataa	):
								lowerCamelCase    		=  raw_speech.astype(np.floataa	)
					# always return batch
					if not is_batched:
								lowerCamelCase    		=  [np.asarray([raw_speech]	).T]

					# Convert audio signals to log mel spectrograms, truncate by time axis
					lowerCamelCase    		=  [
					    self._np_extract_fbank_features(waveform.squeeze()	).T[: self.spectrogram_length] for waveform in raw_speech
					]
					if isinstance(audio_features[0]		,      A	):
								lowerCamelCase    		=  [np.asarray(A		,      dtype=np.floataa	) for feature in audio_features]

					# Create audio attention mask
					lowerCamelCase    		=  max(
					    [ceil(feature.shape[0] / self.patch_size[0]	) * self.freq_len for feature in audio_features]	)  # The maximum number of audio patches in a batch
					if return_attention_mask:
								lowerCamelCase    		=  [
								    (ceil(feature.shape[0] / self.patch_size[0]	) * self.freq_len) * [1]
								    + (max_patch_len - ceil(feature.shape[0] / self.patch_size[0]	) * self.freq_len) * [0]
								    for feature in audio_features
								]
								lowerCamelCase    		=  np.array(A	).astype(np.floataa	)

					# convert into correct format for padding
					lowerCamelCase    		=  max_patch_len // self.freq_len * self.patch_size[0]  # The maximum audio size in a batch
					lowerCamelCase    		=  np.ones([len(A	), 1, max_time_len, self.feature_size]	).astype(np.floataa	)
					lowerCamelCase    		=  padded_audio_features * self.padding_value
					for i in range(len(A	)	):
								lowerCamelCase    		=  audio_features[i]
								lowerCamelCase    		=  feature

					# return as BatchFeature
					if return_attention_mask:
								lowerCamelCase    		=  {"""audio_values""": padded_audio_features, """audio_mask""": audio_mask}
					else:
								lowerCamelCase    		=  {"""audio_values""": padded_audio_features}

					lowerCamelCase    		=  BatchFeature(data=A		,      tensor_type=A	)
					return encoded_inputs

