
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  =	False, False, False





@dataclass
class       _lowerCAmelCase   :
				_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=__a   , repr=__a			)




				def __call__(   self	)							-> Any:
							return self.pa_type




				def 	__a		(   self							,							_UpperCamelCase	)							-> 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(_UpperCamelCase							,							_UpperCamelCase	):
										return {"bytes": None, "path": value}
							elif isinstance(_UpperCamelCase							,							_UpperCamelCase	):
										return {"bytes": value, "path": None}
							elif "array" in value:
										# convert the audio array to wav bytes
										lowerCAmelCase_         =      BytesIO()
										sf.write(_UpperCamelCase							,							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!)
																lowerCAmelCase_         =      np.frombuffer(value["bytes"]							,							dtype=np.intaa	).astype(np.floataa	) / 32_767
													else:
																lowerCAmelCase_         =      np.memmap(value["path"]							,							dtype="h"							,							mode="r"	).astype(np.floataa	) / 32_767

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

							lowerCAmelCase_      ,    lowerCAmelCase_         =      (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

							lowerCAmelCase_         =      xsplitext(_UpperCamelCase	)[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:
										lowerCAmelCase_         =      token_per_repo_id or {}
										lowerCAmelCase_         =      path.split("::"	)[-1]
										try:
													lowerCAmelCase_         =      string_to_dict(_UpperCamelCase							,							config.HUB_DATASETS_URL	)["repo_id"]
													lowerCAmelCase_         =      token_per_repo_id[repo_id]
										except (ValueError, KeyError):
													lowerCAmelCase_         =      None

										with xopen(_UpperCamelCase							,							"rb"							,							use_auth_token=_UpperCamelCase	) as f:
													lowerCAmelCase_      ,    lowerCAmelCase_         =      sf.read(_UpperCamelCase	)

							else:
										lowerCAmelCase_      ,    lowerCAmelCase_         =      sf.read(_UpperCamelCase	)

							lowerCAmelCase_         =      array.T
							if self.mono:
										lowerCAmelCase_         =      librosa.to_mono(_UpperCamelCase	)
							if self.sampling_rate and self.sampling_rate != sampling_rate:
										lowerCAmelCase_         =      librosa.resample(_UpperCamelCase							,							orig_sr=_UpperCamelCase							,							target_sr=self.sampling_rate	)
										lowerCAmelCase_         =      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							,							_UpperCamelCase	)							-> pa.StructArray:
							if pa.types.is_string(storage.type	):
										lowerCAmelCase_         =      pa.array([None] * len(_UpperCamelCase	)							,							type=pa.binary()	)
										lowerCAmelCase_         =      pa.StructArray.from_arrays([bytes_array, storage]							,							["bytes", "path"]							,							mask=storage.is_null()	)
							elif pa.types.is_binary(storage.type	):
										lowerCAmelCase_         =      pa.array([None] * len(_UpperCamelCase	)							,							type=pa.string()	)
										lowerCAmelCase_         =      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"	):
										lowerCAmelCase_         =      pa.array([Audio().encode_example(_UpperCamelCase	) 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:
													lowerCAmelCase_         =      storage.field("bytes"	)
										else:
													lowerCAmelCase_         =      pa.array([None] * len(_UpperCamelCase	)							,							type=pa.binary()	)
										if storage.type.get_field_index("path"	) >= 0:
													lowerCAmelCase_         =      storage.field("path"	)
										else:
													lowerCAmelCase_         =      pa.array([None] * len(_UpperCamelCase	)							,							type=pa.string()	)
										lowerCAmelCase_         =      pa.StructArray.from_arrays([bytes_array, path_array]							,							["bytes", "path"]							,							mask=storage.is_null()	)
							return array_cast(_UpperCamelCase							,							self.pa_type	)




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

							@no_op_if_value_is_null
							def path_to_bytes(_UpperCamelCase	):
										with xopen(_UpperCamelCase							,							"rb"	) as f:
													lowerCAmelCase_         =      f.read()
										return bytes_

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





from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union

import numpy as np
import PIL
from PIL import Image

from ...utils import BaseOutput, is_torch_available, is_transformers_available





@dataclass
class       _lowerCAmelCase   (      __a			):
				_lowercase     						=42
				_lowercase     						=42


if is_transformers_available() and is_torch_available():
			from .pipeline_semantic_stable_diffusion import SemanticStableDiffusionPipeline



