




import numpy as np  # noqa: E402

from ...configuration_utils import ConfigMixin, register_to_config
from ...schedulers.scheduling_utils import SchedulerMixin


try:
	import librosa  # noqa: E402

	__A						:		Optional[int]          =						True
	__A						:		Optional[int]          =						""
except Exception as e:
	__A						:		Dict          =						False
	__A						:		Any          =						(
	    f"""Cannot import librosa because {e}. Make sure to correctly install librosa to be able to install it."""
	)


from PIL import Image  # noqa: E402

class 		lowercase_       (	lowerCAmelCase__						,							lowerCAmelCase__				):
					__UpperCamelCase     	= "mel_config.json"




					@register_to_config
					def __init__(   self:		List[str],     _lowercase:		int = 256,     _lowercase:		int = 256,     _lowercase:		int = 22050,     _lowercase:		int = 2048,     _lowercase:		int = 512,     _lowercase:		int = 80,     _lowercase:		int = 32,     ):


									'''simple docstring'''


									__lowerCAmelCase					=  hop_length
									__lowerCAmelCase					=  sample_rate
									__lowerCAmelCase					=  n_fft
									__lowerCAmelCase					=  top_db
									__lowerCAmelCase					=  n_iter
									self.set_resolution(_lowercase,     _lowercase)
									__lowerCAmelCase					=  None

									if not _librosa_can_be_imported:
													raise ValueError(_import_error)




					def     _lowercase						(   self:		Optional[int],     _lowercase:		int,     _lowercase:		int):


									'''simple docstring'''


									__lowerCAmelCase					=  x_res
									__lowerCAmelCase					=  y_res
									__lowerCAmelCase					=  self.y_res
									__lowerCAmelCase					=  self.x_res * self.hop_length - 1




					def     _lowercase						(   self:		Optional[int],     _lowercase:		str = None,     _lowercase:		np.ndarray = None):


									'''simple docstring'''


									if audio_file is not None:
													__lowerCAmelCase	,				__lowerCAmelCase					=  librosa.load(_lowercase,     mono=_lowercase,     sr=self.sr)
									else:
													__lowerCAmelCase					=  raw_audio

									# Pad with silence if necessary.
									if len(self.audio) < self.x_res * self.hop_length:
													__lowerCAmelCase					=  np.concatenate([self.audio, np.zeros((self.x_res * self.hop_length - len(self.audio),))])




					def     _lowercase						(   self:		int):


									'''simple docstring'''


									return len(self.audio) // self.slice_size




					def     _lowercase						(   self:		Optional[int],     _lowercase:		int = 0):


									'''simple docstring'''


									return self.audio[self.slice_size * slice : self.slice_size * (slice + 1)]




					def     _lowercase						(   self:		Optional[Any]):


									'''simple docstring'''


									return self.sr




					def     _lowercase						(   self:		Dict,     _lowercase:		int):


									'''simple docstring'''


									__lowerCAmelCase					=  librosa.feature.melspectrogram(
									    y=self.get_audio_slice(_lowercase),     sr=self.sr,     n_fft=self.n_fft,     hop_length=self.hop_length,     n_mels=self.n_mels)
									__lowerCAmelCase					=  librosa.power_to_db(_lowercase,     ref=np.max,     top_db=self.top_db)
									__lowerCAmelCase					=  (((log_S + self.top_db) * 255 / self.top_db).clip(0,     255) + 0.5).astype(np.uinta)
									__lowerCAmelCase					=  Image.fromarray(_lowercase)
									return image







					def     _lowercase						(   self:		Union[str, Any],     _lowercase:		Image.Image):


									'''simple docstring'''


									__lowerCAmelCase					=  np.frombuffer(image.tobytes(),     dtype="""uint8""").reshape((image.height, image.width))
									__lowerCAmelCase					=  bytedata.astype("""float""") * self.top_db / 255 - self.top_db
									__lowerCAmelCase					=  librosa.db_to_power(_lowercase)
									__lowerCAmelCase					=  librosa.feature.inverse.mel_to_audio(
									    _lowercase,     sr=self.sr,     n_fft=self.n_fft,     hop_length=self.hop_length,     n_iter=self.n_iter)
									return audio











import csv
import os

import pytest

from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.csv import CsvDatasetReader, CsvDatasetWriter

from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases





def      UpperCAmelCase  (						UpperCamelCase__						,			UpperCamelCase__   )     ->     Tuple:







				'''simple docstring'''


				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__   )     ->     Union[str, Any]:







				'''simple docstring'''


				__lowerCAmelCase					=  tmp_path / """cache"""
				__lowerCAmelCase					=  {"""col_1""": """int64""", """col_2""": """int64""", """col_3""": """float64"""}
				with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
								__lowerCAmelCase					=  CsvDatasetReader(UpperCamelCase__						,			cache_dir=UpperCamelCase__						,			keep_in_memory=UpperCamelCase__   ).read()
				_check_csv_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__   )     ->     Tuple:







				'''simple docstring'''


				__lowerCAmelCase					=  tmp_path / """cache"""
				# CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
				__lowerCAmelCase					=  {"""col_1""": """int64""", """col_2""": """int64""", """col_3""": """float64"""}
				__lowerCAmelCase					=  features.copy() if features else default_expected_features
				__lowerCAmelCase					=  (
				    Features({feature: Value(UpperCamelCase__   ) for feature, dtype in features.items()}   ) if features is not None else None
				)
				__lowerCAmelCase					=  CsvDatasetReader(UpperCamelCase__						,			features=UpperCamelCase__						,			cache_dir=UpperCamelCase__   ).read()
				_check_csv_dataset(UpperCamelCase__						,			UpperCamelCase__   )





@pytest.mark.parametrize("""split"""						,			[None, NamedSplit("""train"""   ), """train""", """test"""]   )
def      UpperCAmelCase  (						UpperCamelCase__						,			UpperCamelCase__						,			UpperCamelCase__   )     ->     Optional[Any]:







				'''simple docstring'''


				__lowerCAmelCase					=  tmp_path / """cache"""
				__lowerCAmelCase					=  {"""col_1""": """int64""", """col_2""": """int64""", """col_3""": """float64"""}
				__lowerCAmelCase					=  CsvDatasetReader(UpperCamelCase__						,			cache_dir=UpperCamelCase__						,			split=UpperCamelCase__   ).read()
				_check_csv_dataset(UpperCamelCase__						,			UpperCamelCase__   )
				assert dataset.split == split if split else "train"





@pytest.mark.parametrize("""path_type"""						,			[str, list]   )
def      UpperCAmelCase  (						UpperCamelCase__						,			UpperCamelCase__						,			UpperCamelCase__   )     ->     List[Any]:







				'''simple docstring'''


				if issubclass(UpperCamelCase__						,			UpperCamelCase__   ):
								__lowerCAmelCase					=  csv_path
				elif issubclass(UpperCamelCase__						,			UpperCamelCase__   ):
								__lowerCAmelCase					=  [csv_path]
				__lowerCAmelCase					=  tmp_path / """cache"""
				__lowerCAmelCase					=  {"""col_1""": """int64""", """col_2""": """int64""", """col_3""": """float64"""}
				__lowerCAmelCase					=  CsvDatasetReader(UpperCamelCase__						,			cache_dir=UpperCamelCase__   ).read()
				_check_csv_dataset(UpperCamelCase__						,			UpperCamelCase__   )





def      UpperCAmelCase  (						UpperCamelCase__						,			UpperCamelCase__						,			UpperCamelCase__=("train",)   )     ->     Optional[int]:







				'''simple docstring'''


				assert isinstance(UpperCamelCase__						,			UpperCamelCase__   )
				for split in splits:
								__lowerCAmelCase					=  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__   )     ->     List[Any]:







				'''simple docstring'''


				__lowerCAmelCase					=  tmp_path / """cache"""
				__lowerCAmelCase					=  {"""col_1""": """int64""", """col_2""": """int64""", """col_3""": """float64"""}
				with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
								__lowerCAmelCase					=  CsvDatasetReader({"""train""": csv_path}						,			cache_dir=UpperCamelCase__						,			keep_in_memory=UpperCamelCase__   ).read()
				_check_csv_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__   )     ->     List[Any]:







				'''simple docstring'''


				__lowerCAmelCase					=  tmp_path / """cache"""
				# CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
				__lowerCAmelCase					=  {"""col_1""": """int64""", """col_2""": """int64""", """col_3""": """float64"""}
				__lowerCAmelCase					=  features.copy() if features else default_expected_features
				__lowerCAmelCase					=  (
				    Features({feature: Value(UpperCamelCase__   ) for feature, dtype in features.items()}   ) if features is not None else None
				)
				__lowerCAmelCase					=  CsvDatasetReader({"""train""": csv_path}						,			features=UpperCamelCase__						,			cache_dir=UpperCamelCase__   ).read()
				_check_csv_datasetdict(UpperCamelCase__						,			UpperCamelCase__   )





@pytest.mark.parametrize("""split"""						,			[None, NamedSplit("""train"""   ), """train""", """test"""]   )
def      UpperCAmelCase  (						UpperCamelCase__						,			UpperCamelCase__						,			UpperCamelCase__   )     ->     Tuple:







				'''simple docstring'''


				if split:
								__lowerCAmelCase					=  {split: csv_path}
				else:
								__lowerCAmelCase					=  {"""train""": csv_path, """test""": csv_path}
				__lowerCAmelCase					=  tmp_path / """cache"""
				__lowerCAmelCase					=  {"""col_1""": """int64""", """col_2""": """int64""", """col_3""": """float64"""}
				__lowerCAmelCase					=  CsvDatasetReader(UpperCamelCase__						,			cache_dir=UpperCamelCase__   ).read()
				_check_csv_datasetdict(UpperCamelCase__						,			UpperCamelCase__						,			splits=list(path.keys()   )   )
				assert all(dataset[split].split == split for split in path.keys()   )





def      UpperCAmelCase  (						UpperCamelCase__   )     ->     str:







				'''simple docstring'''


				with open(UpperCamelCase__						,			encoding="""utf-8"""   ) as csvfile:
								yield from csv.reader(UpperCamelCase__   )





def      UpperCAmelCase  (						UpperCamelCase__						,			UpperCamelCase__   )     ->     List[Any]:







				'''simple docstring'''


				__lowerCAmelCase					=  tmp_path / """cache"""
				__lowerCAmelCase					=  os.path.join(UpperCamelCase__						,			"""tmp.csv"""   )
				__lowerCAmelCase					=  CsvDatasetReader({"""train""": csv_path}						,			cache_dir=UpperCamelCase__   ).read()
				CsvDatasetWriter(dataset["""train"""]						,			UpperCamelCase__						,			num_proc=1   ).write()

				__lowerCAmelCase					=  iter_csv_file(UpperCamelCase__   )
				__lowerCAmelCase					=  iter_csv_file(UpperCamelCase__   )

				for rowa, rowa in zip(UpperCamelCase__						,			UpperCamelCase__   ):
								assert rowa == rowa





def      UpperCAmelCase  (						UpperCamelCase__						,			UpperCamelCase__   )     ->     Union[str, Any]:







				'''simple docstring'''


				__lowerCAmelCase					=  tmp_path / """cache"""
				__lowerCAmelCase					=  os.path.join(UpperCamelCase__						,			"""tmp.csv"""   )
				__lowerCAmelCase					=  CsvDatasetReader({"""train""": csv_path}						,			cache_dir=UpperCamelCase__   ).read()
				CsvDatasetWriter(dataset["""train"""]						,			UpperCamelCase__						,			num_proc=2   ).write()

				__lowerCAmelCase					=  iter_csv_file(UpperCamelCase__   )
				__lowerCAmelCase					=  iter_csv_file(UpperCamelCase__   )

				for rowa, rowa in zip(UpperCamelCase__						,			UpperCamelCase__   ):
								assert rowa == rowa





def      UpperCAmelCase  (						UpperCamelCase__						,			UpperCamelCase__   )     ->     Dict:







				'''simple docstring'''


				__lowerCAmelCase					=  tmp_path / """cache"""
				__lowerCAmelCase					=  os.path.join(UpperCamelCase__						,			"""tmp.csv"""   )
				__lowerCAmelCase					=  CsvDatasetReader({"""train""": csv_path}						,			cache_dir=UpperCamelCase__   ).read()
				with pytest.raises(UpperCamelCase__   ):
								CsvDatasetWriter(dataset["""train"""]						,			UpperCamelCase__						,			num_proc=0   )





