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 __snake_case    (						__a		):
   lowerCAmelCase_														=							field(default="automatic-speech-recognition"       ,		metadata={"include_in_asdict_even_if_is_default": True}		)
   lowerCAmelCase_														=							Features({"audio": Audio()}		)
   lowerCAmelCase_														=							Features({"transcription": Value("string"		)}		)
   lowerCAmelCase_														=							"audio"
   lowerCAmelCase_														=							"transcription"




   def      __a						(   self      :    Dict	,     _lowercase      :    Optional[Any]				):



     """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]	,     UpperCamelCase__				):
       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      :    str				):



     """simple docstring"""






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

import os
from argparse import ArgumentParser
from typing import List

import torch.utils.data

from datasets import Dataset, IterableDataset
from datasets.distributed import split_dataset_by_node


__lowerCamelCase      :			int							       =		4
__lowerCamelCase      :			Dict							       =		3







class __snake_case    (						lowerCamelCase_		):
   pass





def      __SCREAMING_SNAKE_CASE  ( __UpperCamelCase   :	List[str]     )							-> List[str]:







  """simple docstring"""





  for shard in shards:
    for i in range(__UpperCamelCase     ):
      yield {"i": i, "shard": shard}



def      __SCREAMING_SNAKE_CASE  ( )							-> Union[str, Any]:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__      							=				int(os.environ["""RANK"""]     )
  SCREAMING_SNAKE_CASE__      							=				int(os.environ["""WORLD_SIZE"""]     )

  SCREAMING_SNAKE_CASE__      							=				ArgumentParser()
  parser.add_argument("""--streaming"""			,					type=__UpperCamelCase     )
  parser.add_argument("""--local_rank"""			,					type=__UpperCamelCase     )
  parser.add_argument("""--num_workers"""			,					type=__UpperCamelCase			,					default=0     )
  SCREAMING_SNAKE_CASE__      							=				parser.parse_args()
  SCREAMING_SNAKE_CASE__      							=				args.streaming
  SCREAMING_SNAKE_CASE__      							=				args.num_workers

  SCREAMING_SNAKE_CASE__      							=				{"""shards""": [f"""shard_{shard_idx}""" for shard_idx in range(__UpperCamelCase     )]}
  SCREAMING_SNAKE_CASE__      							=				IterableDataset.from_generator(__UpperCamelCase			,					gen_kwargs=__UpperCamelCase     )
  if not streaming:
    SCREAMING_SNAKE_CASE__      							=				Dataset.from_list(list(__UpperCamelCase     )     )

  SCREAMING_SNAKE_CASE__      							=				split_dataset_by_node(__UpperCamelCase			,					rank=__UpperCamelCase			,					world_size=__UpperCamelCase     )
  SCREAMING_SNAKE_CASE__      							=				torch.utils.data.DataLoader(__UpperCamelCase			,					num_workers=__UpperCamelCase     )

  SCREAMING_SNAKE_CASE__      							=				NUM_SHARDS * NUM_ITEMS_PER_SHARD
  SCREAMING_SNAKE_CASE__      							=				full_size // world_size
  expected_local_size += int(rank < (full_size % world_size)     )

  SCREAMING_SNAKE_CASE__      							=				sum(1 for _ in dataloader     )
  if local_size != expected_local_size:
    raise FailedTestError(f"""local_size {local_size} != expected_local_size {expected_local_size}"""     )


if __name__ == "__main__":
   main()
