



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


lowerCamelCase       ,   lowerCamelCase       ,   lowerCamelCase		       =      False, False, False


@dataclass
class 				_a						:





							'''simple docstring'''

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



							def __call__(							self				):







											"""simple docstring"""



											return self.pa_type



							def 		_A	(							self ,					__UpperCAmelCase				):







											"""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(__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
															a__	:							Any      						= 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!)
																							a__	:							Optional[int]      						= np.frombuffer(value["bytes"] ,					dtype=np.intaa				).astype(np.floataa				) / 3_2767
																			else:
																							a__	:							Any      						= np.memmap(value["path"] ,					dtype="h" ,					mode="r"				).astype(np.floataa				) / 3_2767

																			a__	:							List[str]      						= 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				):







											"""simple docstring"""



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

											a__						,   a__	:							Union[str, 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

											a__	:							Dict      						= 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:
															a__	:							Any      						= token_per_repo_id or {}
															a__	:							str      						= path.split("::"				)[-1]
															try:
																			a__	:							Tuple      						= string_to_dict(__UpperCAmelCase ,					config.HUB_DATASETS_URL				)["repo_id"]
																			a__	:							List[str]      						= token_per_repo_id[repo_id]
															except (ValueError, KeyError):
																			a__	:							List[Any]      						= None

															with xopen(__UpperCAmelCase ,					"rb" ,					use_auth_token=__UpperCAmelCase				) as f:
																			a__						,   a__	:							Optional[int]      						= sf.read(__UpperCAmelCase				)

											else:
															a__						,   a__	:							Any      						= sf.read(__UpperCAmelCase				)

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

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



							def 		_A	(							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 		_A	(							self ,					__UpperCAmelCase				):







											"""simple docstring"""



											if pa.types.is_string(storage.type				):
															a__	:							Dict      						= pa.array([None] * len(__UpperCAmelCase				) ,					type=pa.binary()				)
															a__	:							List[Any]      						= 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(__UpperCAmelCase				) ,					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__	:							Optional[Any]      						= 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:
																			a__	:							Dict      						= storage.field("bytes"				)
															else:
																			a__	:							Union[str, Any]      						= pa.array([None] * len(__UpperCAmelCase				) ,					type=pa.binary()				)
															if storage.type.get_field_index("path"				) >= 0:
																			a__	:							str      						= storage.field("path"				)
															else:
																			a__	:							List[Any]      						= pa.array([None] * len(__UpperCAmelCase				) ,					type=pa.string()				)
															a__	:							Optional[int]      						= 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				):







											"""simple docstring"""



											@no_op_if_value_is_null
											def path_to_bytes(__UpperCAmelCase				):
															with xopen(__UpperCAmelCase ,					"rb"				) as f:
																			a__	:							Dict      						= f.read()
															return bytes_

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







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowerCamelCase		       =      {
    """configuration_instructblip""": [
        """INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """InstructBlipConfig""",
        """InstructBlipQFormerConfig""",
        """InstructBlipVisionConfig""",
    ],
    """processing_instructblip""": ["""InstructBlipProcessor"""],
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCamelCase		       =      [
						    """INSTRUCTBLIP_PRETRAINED_MODEL_ARCHIVE_LIST""",
						    """InstructBlipQFormerModel""",
						    """InstructBlipPreTrainedModel""",
						    """InstructBlipForConditionalGeneration""",
						    """InstructBlipVisionModel""",
						]

if TYPE_CHECKING:
						from .configuration_instructblip import (
						    INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
						    InstructBlipConfig,
						    InstructBlipQFormerConfig,
						    InstructBlipVisionConfig,
						)
						from .processing_instructblip import InstructBlipProcessor

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_instructblip import (
												    INSTRUCTBLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
												    InstructBlipForConditionalGeneration,
												    InstructBlipPreTrainedModel,
												    InstructBlipQFormerModel,
												    InstructBlipVisionModel,
												)

else:
						import sys

						lowerCamelCase		       =      _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


