

'''simple docstring'''
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import Audio, Features, Value
from .base import TaskTemplate







@dataclass(frozen=A__  )
class 							A__   (	A__  ):
  A__					=    field(default='automatic-speech-recognition'					,       metadata={'include_in_asdict_even_if_is_default': True}  )
  A__					=    Features({'audio': Audio()}  )
  A__					=    Features({'transcription': Value('string'  )}  )
  A__					=    "audio"
  A__					=    "transcription"





  def 						A  (		self				:     Optional[Any]							, _a				:     Dict			)     ->   List[str]:





       '''simple docstring'''






       if self.audio_column not in features:
            raise ValueError(f"Column {self.audio_column} is not present in features."			)
       if not isinstance(features[self.audio_column]							, _a			):
            raise ValueError(f"Column {self.audio_column} is not an Audio type."			)
       _SCREAMING_SNAKE_CASE							   =copy.deepcopy(self			)
       _SCREAMING_SNAKE_CASE							   =self.input_schema.copy()
       _SCREAMING_SNAKE_CASE							   =features[self.audio_column]
       _SCREAMING_SNAKE_CASE							   =input_schema
       return task_template





  @property
  def 						A  (		self				:     Tuple			)     ->   Dict[str, str]:





       '''simple docstring'''






       return {self.audio_column: "audio", self.transcription_column: "transcription"}



'''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			:				int 			=					logging.getLogger(__name__)

if __name__ == "__main__":
     lowerCamelCase			:				Optional[Any] 			=					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_0_5_2_2, type=int)
     lowerCamelCase			:				Optional[Any] 			=					parser.parse_args()

     logger.info(f'''Loading data from {args.data_file}''')
     with open(args.data_file, "rb") as fp:
          lowerCamelCase			:				Optional[int] 			=					pickle.load(fp)

     logger.info("Counting occurrences for MLM.")
     lowerCamelCase			:				Dict 			=					Counter()
     for tk_ids in data:
          counter.update(tk_ids)
     lowerCamelCase			:				Tuple 			=					[0] * args.vocab_size
     for k, v in counter.items():
          lowerCamelCase			:				Any 			=					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)
