






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

				UpperCamelCase__						:      str								=					True
				UpperCamelCase__						:      int								=					""""""
except Exception as e:
				UpperCamelCase__						:      Optional[int]								=					False
				UpperCamelCase__						:      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 				lowerCamelCase_     (	a_ ,						a_     ):
							SCREAMING_SNAKE_CASE_						     =			'mel_config.json'
							@register_to_config
							def __init__(						self		:					List[str]							,__lowerCamelCase		:					int = 2_56							,__lowerCamelCase		:					int = 2_56							,__lowerCamelCase		:					int = 2_20_50							,__lowerCamelCase		:					int = 20_48							,__lowerCamelCase		:					int = 5_12							,__lowerCamelCase		:					int = 80							,__lowerCamelCase		:					int = 32							,):





								'''simple docstring'''



								a		=			hop_length
								a		=			sample_rate
								a		=			n_fft
								a		=			top_db
								a		=			n_iter
								self.set_resolution(__lowerCamelCase							,__lowerCamelCase					)
								a		=			None

								if not _librosa_can_be_imported:
									raise ValueError(_import_error					)
							def  SCREAMING_SNAKE_CASE_ (						self		:					Optional[Any]							,__lowerCamelCase		:					int							,__lowerCamelCase		:					int					):





								'''simple docstring'''



								a		=			x_res
								a		=			y_res
								a		=			self.y_res
								a		=			self.x_res * self.hop_length - 1
							def  SCREAMING_SNAKE_CASE_ (						self		:					Tuple							,__lowerCamelCase		:					str = None							,__lowerCamelCase		:					np.ndarray = None					):





								'''simple docstring'''



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

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





								'''simple docstring'''



								return len(self.audio					) // self.slice_size
							def  SCREAMING_SNAKE_CASE_ (						self		:					int							,__lowerCamelCase		:					int = 0					):





								'''simple docstring'''



								return self.audio[self.slice_size * slice : self.slice_size * (slice + 1)]
							def  SCREAMING_SNAKE_CASE_ (						self		:					Optional[Any]					):





								'''simple docstring'''



								return self.sr
							def  SCREAMING_SNAKE_CASE_ (						self		:					Dict							,__lowerCamelCase		:					int					):





								'''simple docstring'''



								a		=			librosa.feature.melspectrogram(
								    y=self.get_audio_slice(__lowerCamelCase					)							,sr=self.sr							,n_fft=self.n_fft							,hop_length=self.hop_length							,n_mels=self.n_mels					)
								a		=			librosa.power_to_db(__lowerCamelCase							,ref=np.max							,top_db=self.top_db					)
								a		=			(((log_S + self.top_db) * 2_55 / self.top_db).clip(0							,2_55					) + 0.5).astype(np.uinta					)
								a		=			Image.fromarray(__lowerCamelCase					)
								return image




							def  SCREAMING_SNAKE_CASE_ (						self		:					Tuple							,__lowerCamelCase		:					Image.Image					):





								'''simple docstring'''



								a		=			np.frombuffer(image.tobytes()							,dtype='''uint8'''					).reshape((image.height, image.width)					)
								a		=			bytedata.astype('''float'''					) * self.top_db / 2_55 - self.top_db
								a		=			librosa.db_to_power(__lowerCamelCase					)
								a		=			librosa.feature.inverse.mel_to_audio(
								    __lowerCamelCase							,sr=self.sr							,n_fft=self.n_fft							,hop_length=self.hop_length							,n_iter=self.n_iter					)
								return audio













import sys
from dataclasses import dataclass
from typing import TYPE_CHECKING, Dict, List, Optional, Tuple, Union

import pandas as pd
import pyarrow as pa

import datasets
import datasets.config
from datasets.features.features import require_storage_cast
from datasets.table import table_cast


if TYPE_CHECKING:
				import sqlitea

				import sqlalchemy


UpperCamelCase__						:      Tuple								=					datasets.utils.logging.get_logger(__name__)







@dataclass
class 				lowerCamelCase_     (	datasets.BuilderConfig     ):
							SCREAMING_SNAKE_CASE_						     =			None
							SCREAMING_SNAKE_CASE_						     =			None
							SCREAMING_SNAKE_CASE_						     =			None
							SCREAMING_SNAKE_CASE_						     =			True
							SCREAMING_SNAKE_CASE_						     =			None
							SCREAMING_SNAKE_CASE_						     =			None
							SCREAMING_SNAKE_CASE_						     =			None
							SCREAMING_SNAKE_CASE_						     =			1_00_00
							SCREAMING_SNAKE_CASE_						     =			None
							def  SCREAMING_SNAKE_CASE_ (						self		:					Optional[Any]					):





								'''simple docstring'''



								if self.sql is None:
									raise ValueError('''sql must be specified'''					)
								if self.con is None:
									raise ValueError('''con must be specified'''					)
							def  SCREAMING_SNAKE_CASE_ (						self		:					Any							,__lowerCamelCase		:					dict							,__lowerCamelCase		:					Optional[datasets.Features] = None							,):





								'''simple docstring'''



								a		=			config_kwargs.copy()
								# We need to stringify the Selectable object to make its hash deterministic

								# The process of stringifying is explained here: http://docs.sqlalchemy.org/en/latest/faq/sqlexpressions.html
								a		=			config_kwargs['''sql''']
								if not isinstance(__lowerCamelCase							,__lowerCamelCase					):
									if datasets.config.SQLALCHEMY_AVAILABLE and "sqlalchemy" in sys.modules:
										import sqlalchemy

										if isinstance(__lowerCamelCase							,sqlalchemy.sql.Selectable					):
											a		=			sqlalchemy.create_engine(config_kwargs['''con'''].split('''://'''					)[0] + '''://'''					)
											a		=			str(sql.compile(dialect=engine.dialect					)					)
											a		=			sql_str
										else:
											raise TypeError(
											    F"""Supported types for 'sql' are string and sqlalchemy.sql.Selectable but got {type(__lowerCamelCase					)}: {sql}"""					)
									else:
										raise TypeError(
										    F"""Supported types for 'sql' are string and sqlalchemy.sql.Selectable but got {type(__lowerCamelCase					)}: {sql}"""					)
								a		=			config_kwargs['''con''']
								if not isinstance(__lowerCamelCase							,__lowerCamelCase					):
									a		=			id(__lowerCamelCase					)
									logger.info(
									    F"""SQL connection 'con' of type {type(__lowerCamelCase					)} couldn't be hashed properly. To enable hashing, specify 'con' as URI string instead."""					)

								return super().create_config_id(__lowerCamelCase							,custom_features=__lowerCamelCase					)




							@property
							def  SCREAMING_SNAKE_CASE_ (						self		:					Dict					):





								'''simple docstring'''



								a		=			{
								    '''index_col''': self.index_col,
								    '''columns''': self.columns,
								    '''params''': self.params,
								    '''coerce_float''': self.coerce_float,
								    '''parse_dates''': self.parse_dates,
								}
								return pd_read_sql_kwargs







class 				lowerCamelCase_     (	datasets.ArrowBasedBuilder     ):
							SCREAMING_SNAKE_CASE_						     =			SqlConfig
							def  SCREAMING_SNAKE_CASE_ (						self		:					Optional[Any]					):





								'''simple docstring'''



								return datasets.DatasetInfo(features=self.config.features					)
							def  SCREAMING_SNAKE_CASE_ (						self		:					Union[str, Any]							,__lowerCamelCase		:					int					):





								'''simple docstring'''



								return [datasets.SplitGenerator(name=datasets.Split.TRAIN							,gen_kwargs={}					)]
							def  SCREAMING_SNAKE_CASE_ (						self		:					Tuple							,__lowerCamelCase		:					pa.Table					):





								'''simple docstring'''



								if self.config.features is not None:
									a		=			self.config.features.arrow_schema
									if all(not require_storage_cast(__lowerCamelCase					) for feature in self.config.features.values()					):
										# cheaper cast
										a		=			pa.Table.from_arrays([pa_table[field.name] for field in schema]							,schema=__lowerCamelCase					)
									else:
										# more expensive cast; allows str <-> int/float or str to Audio for example
										a		=			table_cast(__lowerCamelCase							,__lowerCamelCase					)
								return pa_table




							def  SCREAMING_SNAKE_CASE_ (						self		:					str					):





								'''simple docstring'''



								a		=			self.config.chunksize
								a		=			pd.read_sql(
								    self.config.sql							,self.config.con							,chunksize=__lowerCamelCase							,**self.config.pd_read_sql_kwargs					)
								a		=			[sql_reader] if chunksize is None else sql_reader
								for chunk_idx, df in enumerate(__lowerCamelCase					):
									a		=			pa.Table.from_pandas(__lowerCamelCase					)
									yield chunk_idx, self._cast_table(__lowerCamelCase					)





