



'''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


lowerCAmelCase			,      lowerCAmelCase			,      lowerCAmelCase	:		List[str]				=   False, False, False






@dataclass
class 					UpperCAmelCase__	:
					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=UpperCamelCase__							,       repr=UpperCamelCase__ )



					def __call__(    self     )     ->			Optional[int]:
						return self.pa_type



					def        UpperCAmelCase_ (    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     ) / 3_2767
								else:
									__lowerCAmelCase												=  np.memmap(value["path"]			,				dtype="h"			,				mode="r"     ).astype(np.floataa     ) / 3_2767

								__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        UpperCAmelCase_ (    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        UpperCAmelCase_ (    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        UpperCAmelCase_ (    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        UpperCAmelCase_ (    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     )




'''simple docstring'''



from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCAmelCase	:		Optional[Any]				=   logging.get_logger(__name__)

lowerCAmelCase	:		Optional[int]				=   {
    '''facebook/dpr-ctx_encoder-single-nq-base''': (
        '''https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/config.json'''
    ),
    '''facebook/dpr-question_encoder-single-nq-base''': (
        '''https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/config.json'''
    ),
    '''facebook/dpr-reader-single-nq-base''': (
        '''https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/config.json'''
    ),
    '''facebook/dpr-ctx_encoder-multiset-base''': (
        '''https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/config.json'''
    ),
    '''facebook/dpr-question_encoder-multiset-base''': (
        '''https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/config.json'''
    ),
    '''facebook/dpr-reader-multiset-base''': (
        '''https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/config.json'''
    ),
}






class 					UpperCAmelCase__	(		UpperCamelCase__ ):
					a      :     Optional[Any]        =							"""dpr"""



					def __init__(    self			,				UpperCamelCase=3_0522			,				UpperCamelCase=768			,				UpperCamelCase=12			,				UpperCamelCase=12			,				UpperCamelCase=3072			,				UpperCamelCase="gelu"			,				UpperCamelCase=0.1			,				UpperCamelCase=0.1			,				UpperCamelCase=512			,				UpperCamelCase=2			,				UpperCamelCase=0.02			,				UpperCamelCase=1E-12			,				UpperCamelCase=0			,				UpperCamelCase="absolute"			,				UpperCamelCase = 0			,				**UpperCamelCase			,				)     ->			Tuple:
						super().__init__(pad_token_id=UpperCamelCase			,				**UpperCamelCase     )

						__lowerCAmelCase												=  vocab_size
						__lowerCAmelCase												=  hidden_size
						__lowerCAmelCase												=  num_hidden_layers
						__lowerCAmelCase												=  num_attention_heads
						__lowerCAmelCase												=  hidden_act
						__lowerCAmelCase												=  intermediate_size
						__lowerCAmelCase												=  hidden_dropout_prob
						__lowerCAmelCase												=  attention_probs_dropout_prob
						__lowerCAmelCase												=  max_position_embeddings
						__lowerCAmelCase												=  type_vocab_size
						__lowerCAmelCase												=  initializer_range
						__lowerCAmelCase												=  layer_norm_eps
						__lowerCAmelCase												=  projection_dim
						__lowerCAmelCase												=  position_embedding_type