






"""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            =	False, False, False


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






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






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

																		lowercase    :      Optional[int]       = BytesIO(bytes()				)
																		sf.write(__magic_name__			, __magic_name__			, 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      __snake_case      (		self  :List[str]			, __magic_name__  :dict			, __magic_name__  :Optional[Dict[str, Union[str, bool, None]]] = None				)      ->dict:
										if not self.decode:
														raise RuntimeError("""Decoding is disabled for this feature. Please use Audio(decode=True) instead."""				)

										lowercase      ,      lowercase    :      Tuple       = (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

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

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

										else:
														lowercase      ,      lowercase    :      Union[str, Any]       = sf.read(__magic_name__				)

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

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






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






						def      __snake_case      (		self  :str			, __magic_name__  :pa.StructArray				)      ->pa.StructArray:

										@no_op_if_value_is_null
										def path_to_bytes(__magic_name__  :Optional[int]				):
														with xopen(__magic_name__			, """rb"""				) as f:
																		lowercase    :      Tuple       = f.read()
														return bytes_

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








"""simple docstring"""







import argparse
import logging
import pickle
from collections import Counter


logging.basicConfig(
    format='%(asctime)s - %(levelname)s - %(name)s - %(message)s', datefmt='%m/%d/%Y %H:%M:%S', level=logging.INFO
)
_lowerCAmelCase            =	logging.getLogger(__name__)

if __name__ == "__main__":
				_lowerCAmelCase            =	argparse.ArgumentParser(
				    description='Token Counts for smoothing the masking probabilities in MLM (cf XLM/word2vec)'
				)
				parser.add_argument(
				    '--data_file', type=str, default='data/dump.bert-base-uncased.pickle', help='The binarized dataset.'
				)
				parser.add_argument(
				    '--token_counts_dump', type=str, default='data/token_counts.bert-base-uncased.pickle', help='The dump file.'
				)
				parser.add_argument('--vocab_size', default=3_05_22, type=int)
				_lowerCAmelCase            =	parser.parse_args()

				logger.info(F'Loading data from {args.data_file}')
				with open(args.data_file, 'rb') as fp:
								_lowerCAmelCase            =	pickle.load(fp)

				logger.info('Counting occurrences for MLM.')
				_lowerCAmelCase            =	Counter()
				for tk_ids in data:
								counter.update(tk_ids)
				_lowerCAmelCase            =	[0] * args.vocab_size
				for k, v in counter.items():
								_lowerCAmelCase            =	v

				logger.info(F'Dump to {args.token_counts_dump}')
				with open(args.token_counts_dump, 'wb') as handle:
								pickle.dump(counts, handle, protocol=pickle.HIGHEST_PROTOCOL)
