


"""simple docstring"""







from math import ceil
def 					a__			(				SCREAMING_SNAKE_CASE    :    List[str]				,      SCREAMING_SNAKE_CASE    :    Tuple				):


  '''simple docstring'''







  lowerCAmelCase     :     Union[str, Any]								=							list(range(0				,      SCREAMING_SNAKE_CASE				)				)

  lowerCAmelCase     :     Union[str, Any]								=							[item for sublist in list(device_map.values()				) for item in sublist]

  # Duplicate check
  lowerCAmelCase     :     Optional[Any]								=							[]
  for i in device_map_blocks:
    if device_map_blocks.count(SCREAMING_SNAKE_CASE				) > 1 and i not in duplicate_blocks:
      duplicate_blocks.append(SCREAMING_SNAKE_CASE				)
    # Missing blocks
  lowerCAmelCase     :     Any								=							[i for i in blocks if i not in device_map_blocks]
  lowerCAmelCase     :     Any								=							[i for i in device_map_blocks if i not in blocks]

  if len(SCREAMING_SNAKE_CASE				) != 0:
    raise ValueError(
        "Duplicate attention blocks specified in device_map. Attention blocks must be specified to one device."
        " These attention blocks were specified more than once: " + str(SCREAMING_SNAKE_CASE				)				)
  if len(SCREAMING_SNAKE_CASE				) != 0:
    raise ValueError(
        "There are attention blocks for this model that are not specified in the device_map. Add these attention "
        "blocks to a device on the device_map: " + str(SCREAMING_SNAKE_CASE				)				)
  if len(SCREAMING_SNAKE_CASE				) != 0:
    raise ValueError(
        "The device_map contains more attention blocks than this model has. Remove these from the device_map:"
        + str(SCREAMING_SNAKE_CASE				)				)





def 					a__			(				SCREAMING_SNAKE_CASE    :    Dict				,      SCREAMING_SNAKE_CASE    :    Any				):


  '''simple docstring'''







  lowerCAmelCase     :     List[Any]								=							list(range(SCREAMING_SNAKE_CASE				)				)
  lowerCAmelCase     :     Union[str, Any]								=							int(ceil(n_layers / len(SCREAMING_SNAKE_CASE				)				)				)
  lowerCAmelCase     :     int								=							[layers[i : i + n_blocks] for i in range(0				,      SCREAMING_SNAKE_CASE				,      SCREAMING_SNAKE_CASE				)]

  return dict(zip(SCREAMING_SNAKE_CASE				,      SCREAMING_SNAKE_CASE				)				)













from typing import Optional

import pyspark

from .. import Features, NamedSplit
from ..download import DownloadMode
from ..packaged_modules.spark.spark import Spark
from .abc import AbstractDatasetReader
class       _UpperCAmelCase					(       A__	):

				"""simple docstring"""




				def __init__(     self						:    Dict,  lowerCamelCase						:    pyspark.sql.DataFrame,  lowerCamelCase						:    Optional[NamedSplit] = None,  lowerCamelCase						:    Optional[Features] = None,  lowerCamelCase						:    bool = True,  lowerCamelCase						:    str = None,  lowerCamelCase						:    bool = False,  lowerCamelCase						:    str = None,  lowerCamelCase						:    bool = True,  lowerCamelCase						:    str = "arrow",  **lowerCamelCase						:    str,  ):

								'''simple docstring'''
								super().__init__(
								    split=lowerCamelCase,  features=lowerCamelCase,  cache_dir=lowerCamelCase,  keep_in_memory=lowerCamelCase,  streaming=lowerCamelCase,  **lowerCamelCase,  )
								lowercase__    =			load_from_cache_file
								lowercase__    =			file_format
								lowercase__    =			Spark(
								    df=lowerCamelCase,  features=lowerCamelCase,  cache_dir=lowerCamelCase,  working_dir=lowerCamelCase,  **lowerCamelCase,  )




				def 				lowercase__   (     self						:    List[Any]  ):

								'''simple docstring'''
								if self.streaming:
												return self.builder.as_streaming_dataset(split=self.split  )
								lowercase__    =			None if self._load_from_cache_file else DownloadMode.FORCE_REDOWNLOAD
								self.builder.download_and_prepare(
								    download_mode=lowerCamelCase,  file_format=self._file_format,  )
								return self.builder.as_dataset(split=self.split  )




