



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


UpperCAmelCase							 =					logging.get_logger(__name__)

UpperCAmelCase							 =					{
    """google/realm-cc-news-pretrained-embedder""": (
        """https://huggingface.co/google/realm-cc-news-pretrained-embedder/resolve/main/config.json"""
    ),
    """google/realm-cc-news-pretrained-encoder""": (
        """https://huggingface.co/google/realm-cc-news-pretrained-encoder/resolve/main/config.json"""
    ),
    """google/realm-cc-news-pretrained-scorer""": (
        """https://huggingface.co/google/realm-cc-news-pretrained-scorer/resolve/main/config.json"""
    ),
    """google/realm-cc-news-pretrained-openqa""": (
        """https://huggingface.co/google/realm-cc-news-pretrained-openqa/aresolve/main/config.json"""
    ),
    """google/realm-orqa-nq-openqa""": """https://huggingface.co/google/realm-orqa-nq-openqa/resolve/main/config.json""",
    """google/realm-orqa-nq-reader""": """https://huggingface.co/google/realm-orqa-nq-reader/resolve/main/config.json""",
    """google/realm-orqa-wq-openqa""": """https://huggingface.co/google/realm-orqa-wq-openqa/resolve/main/config.json""",
    """google/realm-orqa-wq-reader""": """https://huggingface.co/google/realm-orqa-wq-reader/resolve/main/config.json""",
    # See all REALM models at https://huggingface.co/models?filter=realm
}

class  __snake_case      (   SCREAMING_SNAKE_CASE):







							'''simple docstring'''






							UpperCamelCase__				:      Any         =					"""realm"""


							def __init__(							self  ,  a_=30_522  ,  a_=768  ,  a_=128  ,  a_=12  ,  a_=12  ,  a_=8  ,  a_=3_072  ,  a_="gelu_new"  ,  a_=0.1  ,  a_=0.1  ,  a_=512  ,  a_=2  ,  a_=0.02  ,  a_=1E-1_2  ,  a_=256  ,  a_=10  ,  a_=1E-3  ,  a_=5  ,  a_=320  ,  a_=13_353_718  ,  a_=5_000  ,  a_=1  ,  a_=0  ,  a_=2  ,  **a_  ,  ):
											super().__init__(pad_token_id=a_  ,  bos_token_id=a_  ,  eos_token_id=a_  ,  **a_		)

											# Common config
											a__				      =		vocab_size
											a__				      =		max_position_embeddings
											a__				      =		hidden_size
											a__				      =		retriever_proj_size
											a__				      =		num_hidden_layers
											a__				      =		num_attention_heads
											a__				      =		num_candidates
											a__				      =		intermediate_size
											a__				      =		hidden_act
											a__				      =		hidden_dropout_prob
											a__				      =		attention_probs_dropout_prob
											a__				      =		initializer_range
											a__				      =		type_vocab_size
											a__				      =		layer_norm_eps

											# Reader config
											a__				      =		span_hidden_size
											a__				      =		max_span_width
											a__				      =		reader_layer_norm_eps
											a__				      =		reader_beam_size
											a__				      =		reader_seq_len

											# Retrieval config
											a__				      =		num_block_records
											a__				      =		searcher_beam_size






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


UpperCAmelCase ,       UpperCAmelCase ,       UpperCAmelCase							 =					False, False, False

@dataclass
class  __snake_case      :







							'''simple docstring'''






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


							def __call__(							self		):
											return self.pa_type


							def _a      (							self  ,  a_		):
											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
															a__				      =		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!)
																							a__				      =		np.frombuffer(value["""bytes"""]  ,  dtype=np.intaa		).astype(np.floataa		) / 32_767
																			else:
																							a__				      =		np.memmap(value["""path"""]  ,  dtype="""h"""  ,  mode="""r"""		).astype(np.floataa		) / 32_767

																			a__				      =		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 _a      (							self  ,  a_  ,  a_ = None		):
											if not self.decode:
															raise RuntimeError("""Decoding is disabled for this feature. Please use Audio(decode=True) instead."""		)

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

															with xopen(a_  ,  """rb"""  ,  use_auth_token=a_		) as f:
																			a__      ,			a__				      =		sf.read(a_		)

											else:
															a__      ,			a__				      =		sf.read(a_		)

											a__				      =		array.T
											if self.mono:
															a__				      =		librosa.to_mono(a_		)
											if self.sampling_rate and self.sampling_rate != sampling_rate:
															a__				      =		librosa.resample(a_  ,  orig_sr=a_  ,  target_sr=self.sampling_rate		)
															a__				      =		self.sampling_rate

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


							def _a      (							self		):
											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  ,  a_		):
											if pa.types.is_string(storage.type		):
															a__				      =		pa.array([None] * len(a_		)  ,  type=pa.binary()		)
															a__				      =		pa.StructArray.from_arrays([bytes_array, storage]  ,  ["""bytes""", """path"""]  ,  mask=storage.is_null()		)
											elif pa.types.is_binary(storage.type		):
															a__				      =		pa.array([None] * len(a_		)  ,  type=pa.string()		)
															a__				      =		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__				      =		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:
																			a__				      =		storage.field("""bytes"""		)
															else:
																			a__				      =		pa.array([None] * len(a_		)  ,  type=pa.binary()		)
															if storage.type.get_field_index("""path"""		) >= 0:
																			a__				      =		storage.field("""path"""		)
															else:
																			a__				      =		pa.array([None] * len(a_		)  ,  type=pa.string()		)
															a__				      =		pa.StructArray.from_arrays([bytes_array, path_array]  ,  ["""bytes""", """path"""]  ,  mask=storage.is_null()		)
											return array_cast(a_  ,  self.pa_type		)







							def _a      (							self  ,  a_		):

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

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

