





"""simple docstring"""




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


a	, a	, a						: Optional[Any]     	=			False, False, False


@dataclass
class        __UpperCamelCase				:
							lowerCamelCase   :					Optional[int]      					=None
							lowerCamelCase   :					bool      					=True
							lowerCamelCase   :					bool      					=True
							lowerCamelCase   :					Optional[str]      					=None
							# Automatically constructed
							lowerCamelCase   :					ClassVar[str]      					="dict"
							lowerCamelCase   :					ClassVar[Any]      					=pa.struct({"""bytes""": pa.binary(), """path""": pa.string()}						)
							lowerCamelCase   :					str      					=field(default="""Audio"""		,       init=a__		,       repr=a__						)






							def __call__(   self      )	-> Union[str, Any]:
								return self.pa_type






							def     __a   (   self      ,					lowerCAmelCase__      )	-> dict:
								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(lowerCAmelCase__      ,					lowerCAmelCase__      ):
									return {"bytes": None, "path": value}
								elif isinstance(lowerCAmelCase__      ,					lowerCAmelCase__      ):
									return {"bytes": value, "path": None}
								elif "array" in value:
									# convert the audio array to wav bytes
									a      :		Any	=				BytesIO()
									sf.write(lowerCAmelCase__      ,					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!)
											a      :		Union[str, Any]	=				np.frombuffer(value["bytes"]      ,					dtype=np.intaa      ).astype(np.floataa      ) / 3_2767
										else:
											a      :		List[Any]	=				np.memmap(value["path"]      ,					dtype="h"      ,					mode="r"      ).astype(np.floataa      ) / 3_2767

										a      :		str	=				BytesIO(bytes()      )
										sf.write(lowerCAmelCase__      ,					lowerCAmelCase__      ,					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     __a   (   self      ,					lowerCAmelCase__      ,					lowerCAmelCase__ = None      )	-> dict:
								if not self.decode:
									raise RuntimeError("Decoding is disabled for this feature. Please use Audio(decode=True) instead."      )

								a,       a      :		Optional[int]	=				(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

								a      :		Union[str, Any]	=				xsplitext(lowerCAmelCase__      )[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:
									a      :		Dict	=				token_per_repo_id or {}
									a      :		Any	=				path.split("::"      )[-1]
									try:
										a      :		Any	=				string_to_dict(lowerCAmelCase__      ,					config.HUB_DATASETS_URL      )["repo_id"]
										a      :		Optional[Any]	=				token_per_repo_id[repo_id]
									except (ValueError, KeyError):
										a      :		List[Any]	=				None

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

								else:
									a,       a      :		Tuple	=				sf.read(lowerCAmelCase__      )

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

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






							def     __a   (   self      )	-> Union["FeatureType", Dict[str, "FeatureType"]]:
								from .features import Value

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






							def     __a   (   self      ,					lowerCAmelCase__      )	-> pa.StructArray:
								if pa.types.is_string(storage.type      ):
									a      :		Tuple	=				pa.array([None] * len(lowerCAmelCase__      )      ,					type=pa.binary()      )
									a      :		Dict	=				pa.StructArray.from_arrays([bytes_array, storage]      ,					["bytes", "path"]      ,					mask=storage.is_null()      )
								elif pa.types.is_binary(storage.type      ):
									a      :		str	=				pa.array([None] * len(lowerCAmelCase__      )      ,					type=pa.string()      )
									a      :		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"      ):
									a      :		Union[str, Any]	=				pa.array([Audio().encode_example(lowerCAmelCase__      ) 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:
										a      :		Union[str, Any]	=				storage.field("bytes"      )
									else:
										a      :		Dict	=				pa.array([None] * len(lowerCAmelCase__      )      ,					type=pa.binary()      )
									if storage.type.get_field_index("path"      ) >= 0:
										a      :		List[Any]	=				storage.field("path"      )
									else:
										a      :		Tuple	=				pa.array([None] * len(lowerCAmelCase__      )      ,					type=pa.string()      )
									a      :		List[Any]	=				pa.StructArray.from_arrays([bytes_array, path_array]      ,					["bytes", "path"]      ,					mask=storage.is_null()      )
								return array_cast(lowerCAmelCase__      ,					self.pa_type      )






							def     __a   (   self      ,					lowerCAmelCase__      )	-> pa.StructArray:

								@no_op_if_value_is_null
								def path_to_bytes(lowerCAmelCase__      ):
									with xopen(lowerCAmelCase__      ,					"rb"      ) as f:
										a      :		List[str]	=				f.read()
									return bytes_

								a      :		int	=				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()      ,					)
								a      :		Optional[int]	=				pa.array(
								    [os.path.basename(lowerCAmelCase__      ) if path is not None else None for path in storage.field("path"      ).to_pylist()]      ,					type=pa.string()      ,					)
								a      :		Tuple	=				pa.StructArray.from_arrays([bytes_array, path_array]      ,					["bytes", "path"]      ,					mask=bytes_array.is_null()      )
								return array_cast(lowerCAmelCase__      ,					self.pa_type      )









"""simple docstring"""




def       _SCREAMING_SNAKE_CASE		(	_lowercase						:    list					) ->int:






	'''simple docstring'''






	if not grid or not grid[0]:
		raise TypeError("The grid does not contain the appropriate information"					)

	for cell_n in range(1    ,       len(grid[0]					)					):
		grid[0][cell_n] += grid[0][cell_n - 1]
	a      :		Union[str, Any]	=				grid[0]

	for row_n in range(1    ,       len(_lowercase					)					):
		a      :		Optional[Any]	=				grid[row_n]
		a      :		Tuple	=				fill_row(_lowercase    ,       _lowercase					)
		a      :		List[Any]	=				grid[row_n]

	return grid[-1][-1]






def       _SCREAMING_SNAKE_CASE		(	_lowercase						:    list    ,       _lowercase						:    list					) ->list:






	'''simple docstring'''






	current_row[0] += row_above[0]
	for cell_n in range(1    ,       len(_lowercase					)					):
		current_row[cell_n] += min(current_row[cell_n - 1]    ,       row_above[cell_n]					)

	return current_row


if __name__ == "__main__":
		import doctest

		doctest.testmod()


