

import os
from dataclasses import dataclass, field
from io import BytesIO
from typing import TYPE_CHECKING, Any, ClassVar, Dict, Optional, Union

import numpy as np
import pyarrow as pa

from .. import config
from ..download.streaming_download_manager import xopen, xsplitext
from ..table import array_cast
from ..utils.py_utils import no_op_if_value_is_null, string_to_dict


if TYPE_CHECKING:
					from .features import FeatureType


SCREAMING_SNAKE_CASE_    ,SCREAMING_SNAKE_CASE_    ,SCREAMING_SNAKE_CASE_        =      False, False, False

@dataclass
class 				a  :
						_lowercase    						=			None
						_lowercase    						=			True
						_lowercase    						=			True
						_lowercase    						=			None
						# Automatically constructed
						_lowercase    						=			"dict"
						_lowercase    						=			pa.struct({"bytes": pa.binary(), "path": pa.string()}				)
						_lowercase    						=			field(default="Audio"							, init=UpperCAmelCase							, repr=UpperCAmelCase				)


						def __call__(       self	):







										'''simple docstring'''


										return self.pa_type


						def        _UpperCAmelCase				(       self						,					A_	):







										'''simple docstring'''


										try:
														import soundfile as sf  # soundfile is a dependency of librosa, needed to decode audio files.
										except ImportError as err:
														raise ImportError("To support encoding audio data, please install 'soundfile'."	) from err
										if isinstance(A_						,					A_	):
														return {"bytes": None, "path": value}
										elif isinstance(A_						,					A_	):
														return {"bytes": value, "path": None}
										elif "array" in value:
														# convert the audio array to wav bytes
														_UpperCAmelCase       :		str 		=    BytesIO()
														sf.write(A_						,					value["array"]						,					value["sampling_rate"]						,					format="wav"	)
														return {"bytes": buffer.getvalue(), "path": None}
										elif value.get("path"	) is not None and os.path.isfile(value["path"]	):
														# we set "bytes": None to not duplicate the data if they're already available locally
														if value["path"].endswith("pcm"	):
																		# "PCM" only has raw audio bytes
																		if value.get("sampling_rate"	) is None:
																						# At least, If you want to convert "PCM-byte" to "WAV-byte", you have to know sampling rate
																						raise KeyError("To use PCM files, please specify a 'sampling_rate' in Audio object"	)
																		if value.get("bytes"	):
																						# If we already had PCM-byte, we don`t have to make "read file, make bytes" (just use it!)
																						_UpperCAmelCase       :		Union[str, Any] 		=    np.frombuffer(value["bytes"]						,					dtype=np.intaa	).astype(np.floataa	) / 32767
																		else:
																						_UpperCAmelCase       :		Union[str, Any] 		=    np.memmap(value["path"]						,					dtype="h"						,					mode="r"	).astype(np.floataa	) / 32767

																		_UpperCAmelCase       :		Any 		=    BytesIO(bytes()	)
																		sf.write(A_						,					A_						,					value["sampling_rate"]						,					format="wav"	)
																		return {"bytes": buffer.getvalue(), "path": None}
														else:
																		return {"bytes": None, "path": value.get("path"	)}
										elif value.get("bytes"	) is not None or value.get("path"	) is not None:
														# store the audio bytes, and path is used to infer the audio format using the file extension
														return {"bytes": value.get("bytes"	), "path": value.get("path"	)}
										else:
														raise ValueError(
														    f'An audio sample should have one of \'path\' or \'bytes\' but they are missing or None in {value}.'	)


						def        _UpperCAmelCase				(       self						,					A_						,					A_ = None	):







										'''simple docstring'''


										if not self.decode:
														raise RuntimeError("Decoding is disabled for this feature. Please use Audio(decode=True) instead."	)

										_UpperCAmelCase		,							_UpperCAmelCase       :		Optional[Any] 		=    (value["path"], BytesIO(value["bytes"]	)) if value["bytes"] is not None else (value["path"], None)
										if path is None and file is None:
														raise ValueError(f'An audio sample should have one of \'path\' or \'bytes\' but both are None in {value}.'	)

										try:
														import librosa
														import soundfile as sf
										except ImportError as err:
														raise ImportError("To support decoding audio files, please install 'librosa' and 'soundfile'."	) from err

										_UpperCAmelCase       :		List[str] 		=    xsplitext(A_	)[1][1:].lower() if path is not None else None
										if not config.IS_OPUS_SUPPORTED and audio_format == "opus":
														raise RuntimeError(
														    "Decoding 'opus' files requires system library 'libsndfile'>=1.0.31, "
														    "You can try to update `soundfile` python library: `pip install \"soundfile>=0.12.1\"`. "	)
										elif not config.IS_MP3_SUPPORTED and audio_format == "mp3":
														raise RuntimeError(
														    "Decoding 'mp3' files requires system library 'libsndfile'>=1.1.0, "
														    "You can try to update `soundfile` python library: `pip install \"soundfile>=0.12.1\"`. "	)

										if file is None:
														_UpperCAmelCase       :		Optional[int] 		=    token_per_repo_id or {}
														_UpperCAmelCase       :		List[Any] 		=    path.split("::"	)[-1]
														try:
																		_UpperCAmelCase       :		Optional[Any] 		=    string_to_dict(A_						,					config.HUB_DATASETS_URL	)["repo_id"]
																		_UpperCAmelCase       :		str 		=    token_per_repo_id[repo_id]
														except (ValueError, KeyError):
																		_UpperCAmelCase       :		List[Any] 		=    None

														with xopen(A_						,					"rb"						,					use_auth_token=A_	) as f:
																		_UpperCAmelCase		,							_UpperCAmelCase       :		Tuple 		=    sf.read(A_	)

										else:
														_UpperCAmelCase		,							_UpperCAmelCase       :		str 		=    sf.read(A_	)

										_UpperCAmelCase       :		Any 		=    array.T
										if self.mono:
														_UpperCAmelCase       :		Optional[int] 		=    librosa.to_mono(A_	)
										if self.sampling_rate and self.sampling_rate != sampling_rate:
														_UpperCAmelCase       :		Dict 		=    librosa.resample(A_						,					orig_sr=A_						,					target_sr=self.sampling_rate	)
														_UpperCAmelCase       :		Tuple 		=    self.sampling_rate

										return {"path": path, "array": array, "sampling_rate": sampling_rate}


						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										from .features import Value

										if self.decode:
														raise ValueError("Cannot flatten a decoded Audio feature."	)
										return {
										    "bytes": Value("binary"	),
										    "path": Value("string"	),
										}


						def        _UpperCAmelCase				(       self						,					A_	):







										'''simple docstring'''


										if pa.types.is_string(storage.type	):
														_UpperCAmelCase       :		int 		=    pa.array([None] * len(A_	)						,					type=pa.binary()	)
														_UpperCAmelCase       :		Optional[Any] 		=    pa.StructArray.from_arrays([bytes_array, storage]						,					["bytes", "path"]						,					mask=storage.is_null()	)
										elif pa.types.is_binary(storage.type	):
														_UpperCAmelCase       :		Tuple 		=    pa.array([None] * len(A_	)						,					type=pa.string()	)
														_UpperCAmelCase       :		Any 		=    pa.StructArray.from_arrays([storage, path_array]						,					["bytes", "path"]						,					mask=storage.is_null()	)
										elif pa.types.is_struct(storage.type	) and storage.type.get_all_field_indices("array"	):
														_UpperCAmelCase       :		str 		=    pa.array([Audio().encode_example(A_	) if x is not None else None for x in storage.to_pylist()]	)
										elif pa.types.is_struct(storage.type	):
														if storage.type.get_field_index("bytes"	) >= 0:
																		_UpperCAmelCase       :		str 		=    storage.field("bytes"	)
														else:
																		_UpperCAmelCase       :		Optional[Any] 		=    pa.array([None] * len(A_	)						,					type=pa.binary()	)
														if storage.type.get_field_index("path"	) >= 0:
																		_UpperCAmelCase       :		str 		=    storage.field("path"	)
														else:
																		_UpperCAmelCase       :		Optional[int] 		=    pa.array([None] * len(A_	)						,					type=pa.string()	)
														_UpperCAmelCase       :		Union[str, Any] 		=    pa.StructArray.from_arrays([bytes_array, path_array]						,					["bytes", "path"]						,					mask=storage.is_null()	)
										return array_cast(A_						,					self.pa_type	)





						def        _UpperCAmelCase				(       self						,					A_	):







										'''simple docstring'''


										@no_op_if_value_is_null
										def path_to_bytes(A_	):
														with xopen(A_						,					"rb"	) as f:
																		_UpperCAmelCase       :		int 		=    f.read()
														return bytes_

										_UpperCAmelCase       :		str 		=    pa.array(
										    [
										        (path_to_bytes(x["path"]	) if x["bytes"] is None else x["bytes"]) if x is not None else None
										        for x in storage.to_pylist()
										    ]						,					type=pa.binary()						,					)
										_UpperCAmelCase       :		Tuple 		=    pa.array(
										    [os.path.basename(A_	) if path is not None else None for path in storage.field("path"	).to_pylist()]						,					type=pa.string()						,					)
										_UpperCAmelCase       :		List[Any] 		=    pa.StructArray.from_arrays([bytes_array, path_array]						,					["bytes", "path"]						,					mask=bytes_array.is_null()	)
										return array_cast(A_						,					self.pa_type	)





from __future__ import annotations

from collections.abc import Callable

def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  Callable[[int | float], int | float]  ,      lowerCAmelCase:  int | float  ,      lowerCAmelCase:  int | float  ,      lowerCAmelCase:  int = 100  ,      )   ->       float:
				_UpperCAmelCase       :		str 		=    x_start
				_UpperCAmelCase       :		Union[str, Any] 		=    fnc(lowerCAmelCase )
				_UpperCAmelCase       :		Tuple 		=    0.0
				for _ in range(lowerCAmelCase ):
								# Approximates small segments of curve as linear and solve
								# for trapezoidal area
								_UpperCAmelCase       :		Union[str, Any] 		=    (x_end - x_start) / steps + xa
								_UpperCAmelCase       :		Optional[Any] 		=    fnc(lowerCAmelCase )
								area += abs(fxa + fxa ) * (xa - xa) / 2
								# Increment step
								_UpperCAmelCase       :		Any 		=    xa
								_UpperCAmelCase       :		Optional[Any] 		=    fxa
				return area


if __name__ == "__main__":

					def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  List[str] )   ->       List[Any]:
									return x**3 + x**2

					print('f(x) = x^3 + x^2')
					print('The area between the curve, x = -5, x = 5 and the x axis is:')
					SCREAMING_SNAKE_CASE_        =      10
					while i <= 100000:
										print(F'''with {i} steps: {trapezoidal_area(f, -5, 5, i)}''')
										i *= 10


