






"""simple docstring"""
import os
import posixpath
import uuid
from dataclasses import dataclass
from typing import TYPE_CHECKING, Iterable, List, Optional, Tuple, Union

import numpy as np
import pyarrow as pa

import datasets
from datasets.arrow_writer import ArrowWriter, ParquetWriter
from datasets.config import MAX_SHARD_SIZE
from datasets.filesystems import (
    is_remote_filesystem,
    rename,
)
from datasets.iterable_dataset import _BaseExamplesIterable
from datasets.utils.py_utils import convert_file_size_to_int


lowerCamelCase_       			=   datasets.utils.logging.get_logger(__name__)

if TYPE_CHECKING:
			import pyspark
@dataclass
class     UpperCamelCase_ (datasets.BuilderConfig  ):
	__magic_name__			   =       None





def        snake_case   (     A__			,A__			,):
			import pyspark

			def generate_fn():
						UpperCAmelCase_  :				List[str]      =      df.select("*"			,pyspark.sql.functions.spark_partition_id().alias("part_id"		)		)
						for partition_id in partition_order:
									UpperCAmelCase_  :				List[str]      =      df_with_partition_id.select("*"		).where(F"""part_id = {partition_id}"""		).drop("part_id"		)
									UpperCAmelCase_  :				List[Any]      =      partition_df.collect()
									UpperCAmelCase_  :				Tuple      =      0
									for row in rows:
												yield F"""{partition_id}_{row_id}""", row.asDict()
												row_id += 1

			return generate_fn
class     UpperCamelCase_ (_BaseExamplesIterable  ):





	def __init__(   self      :				Optional[int]			,			lowerCAmelCase_      :				"pyspark.sql.DataFrame"			,			lowerCAmelCase_      :				List[str]=None			,			)		->    Union[str, Any]:
				UpperCAmelCase_  :				int      =      df
				UpperCAmelCase_  :				str      =      partition_order or range(self.df.rdd.getNumPartitions()		)
				UpperCAmelCase_  :				Tuple      =      _generate_iterable_examples(self.df			,			self.partition_order		)





	def __iter__(   self      :				Tuple		)		->    Union[str, Any]:
				yield from self.generate_examples_fn()





	def   _SCREAMING_SNAKE_CASE   (   self      :				List[Any]			,			lowerCAmelCase_      :				np.random.Generator		)		->    List[Any]:
				UpperCAmelCase_  :				Union[str, Any]      =      list(range(self.df.rdd.getNumPartitions()		)		)
				generator.shuffle(lowerCamelCase_		)
				return SparkExamplesIterable(self.df			,			partition_order=lowerCamelCase_		)





	def   _SCREAMING_SNAKE_CASE   (   self      :				Optional[Any]			,			lowerCAmelCase_      :				int			,			lowerCAmelCase_      :				int		)		->    Optional[Any]:
				UpperCAmelCase_  :				int      =      self.split_shard_indices_by_worker(lowerCamelCase_			,			lowerCamelCase_		)
				return SparkExamplesIterable(self.df			,			partition_order=lowerCamelCase_		)





	@property
	def   _SCREAMING_SNAKE_CASE   (   self      :				List[str]		)		->    str:
				return len(self.partition_order		)





class     UpperCamelCase_ (datasets.DatasetBuilder  ):
	__magic_name__			   =       SparkConfig





	def __init__(   self      :				Optional[Any]			,			lowerCAmelCase_      :				"pyspark.sql.DataFrame"			,			lowerCAmelCase_      :				str = None			,			lowerCAmelCase_      :				str = None			,			**lowerCAmelCase_      :				int			,			)		->    Optional[int]:
				import pyspark

				UpperCAmelCase_  :				Any      =      pyspark.sql.SparkSession.builder.getOrCreate()
				UpperCAmelCase_  :				List[str]      =      df
				UpperCAmelCase_  :				Optional[Any]      =      working_dir

				super().__init__(
				    cache_dir=lowerCamelCase_			,			config_name=str(self.df.semanticHash()		)			,			**lowerCamelCase_			,			)





	def   _SCREAMING_SNAKE_CASE   (   self      :				Dict		)		->    Optional[Any]:



				def create_cache_and_write_probe(lowerCAmelCase_      :				str		):
							# makedirs with exist_ok will recursively create the directory. It will not throw an error if directories
							# already exist.
							os.makedirs(self._cache_dir			,			exist_ok=lowerCamelCase_		)
							UpperCAmelCase_  :				Union[str, Any]      =      os.path.join(self._cache_dir			,			"fs_test" + uuid.uuida().hex		)
							# Opening the file in append mode will create a new file unless it already exists, in which case it will not
							# change the file contents.
							open(lowerCamelCase_			,			"a"		)
							return [probe_file]

				if self._spark.conf.get("spark.master"			,			""		).startswith("local"		):
							return

				# If the cluster is multi-node, make sure that the user provided a cache_dir and that it is on an NFS
				# accessible to the driver.
				# TODO: Stream batches to the driver using ArrowCollectSerializer instead of throwing an error.
				if self._cache_dir:
							UpperCAmelCase_  :				Optional[Any]      =      (
							    self._spark.sparkContext.parallelize(range(1		)			,			1		).mapPartitions(lowerCamelCase_		).collect()
							)
							if os.path.isfile(probe[0]		):
										return

				raise ValueError(
				    "When using Dataset.from_spark on a multi-node cluster, the driver and all workers should be able to access cache_dir"		)





	def   _SCREAMING_SNAKE_CASE   (   self      :				Tuple		)		->    Tuple:
				return datasets.DatasetInfo(features=self.config.features		)





	def   _SCREAMING_SNAKE_CASE   (   self      :				Union[str, Any]			,			lowerCAmelCase_      :				datasets.download.download_manager.DownloadManager		)		->    Union[str, Any]:
				return [datasets.SplitGenerator(name=datasets.Split.TRAIN		)]





	def   _SCREAMING_SNAKE_CASE   (   self      :				List[str]			,			lowerCAmelCase_      :				int		)		->    List[str]:
				import pyspark

				def get_arrow_batch_size(lowerCAmelCase_      :				int		):
							for batch in it:
										yield pa.RecordBatch.from_pydict({"batch_bytes": [batch.nbytes]}		)

				UpperCAmelCase_  :				Union[str, Any]      =      self.df.count()
				UpperCAmelCase_  :				int      =      df_num_rows if df_num_rows <= 100 else 100
				# Approximate the size of each row (in Arrow format) by averaging over a max-100-row sample.
				UpperCAmelCase_  :				str      =      (
				    self.df.limit(lowerCamelCase_		)
				    .repartition(1		)
				    .mapInArrow(lowerCamelCase_			,			"batch_bytes: long"		)
				    .agg(pyspark.sql.functions.sum("batch_bytes"		).alias("sample_bytes"		)		)
				    .collect()[0]
				    .sample_bytes
				    / sample_num_rows
				)
				UpperCAmelCase_  :				Dict      =      approx_bytes_per_row * df_num_rows
				if approx_total_size > max_shard_size:
							# Make sure there is at least one row per partition.
							UpperCAmelCase_  :				Union[str, Any]      =      min(lowerCamelCase_			,			int(approx_total_size / max_shard_size		)		)
							UpperCAmelCase_  :				Union[str, Any]      =      self.df.repartition(lowerCamelCase_		)





	def   _SCREAMING_SNAKE_CASE   (   self      :				Tuple			,			lowerCAmelCase_      :				str			,			lowerCAmelCase_      :				str			,			lowerCAmelCase_      :				int			,			)		->    Optional[int]:
				import pyspark

				UpperCAmelCase_  :				List[str]      =      ParquetWriter if file_format == """parquet""" else ArrowWriter
				UpperCAmelCase_  :				Tuple      =      os.path.join(self._working_dir			,			os.path.basename(lowerCamelCase_		)		) if self._working_dir else fpath
				UpperCAmelCase_  :				List[Any]      =      file_format == """parquet"""

				# Define these so that we don't reference self in write_arrow, which will result in a pickling error due to
				# pickling the SparkContext.
				UpperCAmelCase_  :				int      =      self.config.features
				UpperCAmelCase_  :				Dict      =      self._writer_batch_size
				UpperCAmelCase_  :				Tuple      =      self._fs.storage_options

				def write_arrow(lowerCAmelCase_      :				List[Any]		):
							# Within the same SparkContext, no two task attempts will share the same attempt ID.
							UpperCAmelCase_  :				Any      =      pyspark.TaskContext().taskAttemptId()
							UpperCAmelCase_  :				str      =      next(lowerCamelCase_			,			lowerCamelCase_		)
							if first_batch is None:
										# Some partitions might not receive any data.
										return pa.RecordBatch.from_arrays(
										    [[task_id], [0], [0]]			,			names=["task_id", "num_examples", "num_bytes"]			,			)
							UpperCAmelCase_  :				Any      =      0
							UpperCAmelCase_  :				Any      =      writer_class(
							    features=lowerCamelCase_			,			path=working_fpath.replace("SSSSS"			,			f"""{shard_id:05d}"""		).replace("TTTTT"			,			f"""{task_id:05d}"""		)			,			writer_batch_size=lowerCamelCase_			,			storage_options=lowerCamelCase_			,			embed_local_files=lowerCamelCase_			,			)
							UpperCAmelCase_  :				Tuple      =      pa.Table.from_batches([first_batch]		)
							writer.write_table(lowerCamelCase_		)
							for batch in it:
										if max_shard_size is not None and writer._num_bytes >= max_shard_size:
													UpperCAmelCase_  :				Tuple      =      writer.finalize()
													writer.close()
													yield pa.RecordBatch.from_arrays(
													    [[task_id], [num_examples], [num_bytes]]			,			names=["task_id", "num_examples", "num_bytes"]			,			)
													shard_id += 1
													UpperCAmelCase_  :				List[str]      =      writer_class(
													    features=writer._features			,			path=working_fpath.replace("SSSSS"			,			f"""{shard_id:05d}"""		).replace("TTTTT"			,			f"""{task_id:05d}"""		)			,			writer_batch_size=lowerCamelCase_			,			storage_options=lowerCamelCase_			,			embed_local_files=lowerCamelCase_			,			)
										UpperCAmelCase_  :				Optional[int]      =      pa.Table.from_batches([batch]		)
										writer.write_table(lowerCamelCase_		)

							if writer._num_bytes > 0:
										UpperCAmelCase_  :				str      =      writer.finalize()
										writer.close()
										yield pa.RecordBatch.from_arrays(
										    [[task_id], [num_examples], [num_bytes]]			,			names=["task_id", "num_examples", "num_bytes"]			,			)

							if working_fpath != fpath:
										for file in os.listdir(os.path.dirname(lowerCamelCase_		)		):
													UpperCAmelCase_  :				Optional[int]      =      os.path.join(os.path.dirname(lowerCamelCase_		)			,			os.path.basename(lowerCamelCase_		)		)
													shutil.move(lowerCamelCase_			,			lowerCamelCase_		)

				UpperCAmelCase_  :				Dict      =      (
				    self.df.mapInArrow(lowerCamelCase_			,			"task_id: long, num_examples: long, num_bytes: long"		)
				    .groupBy("task_id"		)
				    .agg(
				        pyspark.sql.functions.sum("num_examples"		).alias("total_num_examples"		)			,			pyspark.sql.functions.sum("num_bytes"		).alias("total_num_bytes"		)			,			pyspark.sql.functions.count("num_bytes"		).alias("num_shards"		)			,			pyspark.sql.functions.collect_list("num_examples"		).alias("shard_lengths"		)			,			)
				    .collect()
				)
				for row in stats:
							yield row.task_id, (row.total_num_examples, row.total_num_bytes, row.num_shards, row.shard_lengths)





	def   _SCREAMING_SNAKE_CASE   (   self      :				str			,			lowerCAmelCase_      :				"datasets.SplitGenerator"			,			lowerCAmelCase_      :				str = "arrow"			,			lowerCAmelCase_      :				Optional[Union[str, int]] = None			,			lowerCAmelCase_      :				Optional[int] = None			,			**lowerCAmelCase_      :				List[Any]			,			)		->    Tuple:
				self._validate_cache_dir()

				UpperCAmelCase_  :				Any      =      convert_file_size_to_int(max_shard_size or MAX_SHARD_SIZE		)
				self._repartition_df_if_needed(lowerCamelCase_		)
				UpperCAmelCase_  :				Dict      =      not is_remote_filesystem(self._fs		)
				UpperCAmelCase_  :				Any      =      os.path.join if is_local else posixpath.join

				UpperCAmelCase_  :				Optional[int]      =      """-TTTTT-SSSSS-of-NNNNN"""
				UpperCAmelCase_  :				Optional[int]      =      f"""{self.name}-{split_generator.name}{SUFFIX}.{file_format}"""
				UpperCAmelCase_  :				List[str]      =      path_join(self._output_dir			,			lowerCamelCase_		)

				UpperCAmelCase_  :				str      =      0
				UpperCAmelCase_  :				str      =      0
				UpperCAmelCase_  :				Any      =      0
				UpperCAmelCase_  :				Optional[int]      =      []
				UpperCAmelCase_  :				Optional[int]      =      []

				for task_id, content in self._prepare_split_single(lowerCamelCase_			,			lowerCamelCase_			,			lowerCamelCase_		):
							(
							    UpperCAmelCase_       
							)  :				Union[str, Any]      =      content
							if num_bytes > 0:
										total_num_examples += num_examples
										total_num_bytes += num_bytes
										total_shards += num_shards
										task_id_and_num_shards.append((task_id, num_shards)		)
										all_shard_lengths.extend(lowerCamelCase_		)

				UpperCAmelCase_  :				List[Any]      =      total_num_examples
				UpperCAmelCase_  :				Optional[Any]      =      total_num_bytes

				# should rename everything at the end
				logger.debug(f"""Renaming {total_shards} shards."""		)
				if total_shards > 1:
							UpperCAmelCase_  :				List[str]      =      all_shard_lengths

							# Define fs outside of _rename_shard so that we don't reference self in the function, which will result in a
							# pickling error due to pickling the SparkContext.
							UpperCAmelCase_  :				List[str]      =      self._fs

							# use the -SSSSS-of-NNNNN pattern
							def _rename_shard(
							    lowerCAmelCase_      :				int			,			lowerCAmelCase_      :				int			,			lowerCAmelCase_      :				int			,			):
										rename(
										    lowerCamelCase_			,			fpath.replace("SSSSS"			,			f"""{shard_id:05d}"""		).replace("TTTTT"			,			f"""{task_id:05d}"""		)			,			fpath.replace("TTTTT-SSSSS"			,			f"""{global_shard_id:05d}"""		).replace("NNNNN"			,			f"""{total_shards:05d}"""		)			,			)

							UpperCAmelCase_  :				Optional[Any]      =      []
							UpperCAmelCase_  :				Optional[int]      =      0
							for i in range(len(lowerCamelCase_		)		):
										UpperCAmelCase_  :				List[str]      =      task_id_and_num_shards[i]
										for shard_id in range(lowerCamelCase_		):
													args.append([task_id, shard_id, global_shard_id]		)
													global_shard_id += 1
							self._spark.sparkContext.parallelize(lowerCamelCase_			,			len(lowerCamelCase_		)		).map(lambda lowerCAmelCase_		: _rename_shard(*lowerCamelCase_		)		).collect()
				else:
							# don't use any pattern
							UpperCAmelCase_  :				str      =      0
							UpperCAmelCase_  :				int      =      task_id_and_num_shards[0][0]
							self._rename(
							    fpath.replace("SSSSS"			,			f"""{shard_id:05d}"""		).replace("TTTTT"			,			f"""{task_id:05d}"""		)			,			fpath.replace(lowerCamelCase_			,			""		)			,			)





	def   _SCREAMING_SNAKE_CASE   (   self      :				int			,			lowerCAmelCase_      :				"datasets.SplitGenerator"			,			)		->    List[Any]:
				return SparkExamplesIterable(self.df		)











"""simple docstring"""
from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowerCamelCase_       			=   logging.get_logger(__name__)

lowerCamelCase_       			=   {
    '''facebook/levit-128S''': '''https://huggingface.co/facebook/levit-128S/resolve/main/config.json''',
    # See all LeViT models at https://huggingface.co/models?filter=levit
}







class     UpperCamelCase_ (__A  ):
	__magic_name__			   =       '''levit'''





	def __init__(   self      :				List[str]			,			lowerCAmelCase_      :				int=224			,			lowerCAmelCase_      :				Union[str, Any]=3			,			lowerCAmelCase_      :				Tuple=3			,			lowerCAmelCase_      :				int=2			,			lowerCAmelCase_      :				str=1			,			lowerCAmelCase_      :				Optional[Any]=16			,			lowerCAmelCase_      :				Tuple=[128, 256, 384]			,			lowerCAmelCase_      :				Optional[int]=[4, 8, 12]			,			lowerCAmelCase_      :				str=[4, 4, 4]			,			lowerCAmelCase_      :				Dict=[16, 16, 16]			,			lowerCAmelCase_      :				int=0			,			lowerCAmelCase_      :				Optional[int]=[2, 2, 2]			,			lowerCAmelCase_      :				Any=[2, 2, 2]			,			lowerCAmelCase_      :				int=0.0_2			,			**lowerCAmelCase_      :				List[Any]			,			)		->    List[str]:
				super().__init__(**lowerCAmelCase_		)
				UpperCAmelCase_  :				int      =      image_size
				UpperCAmelCase_  :				List[Any]      =      num_channels
				UpperCAmelCase_  :				str      =      kernel_size
				UpperCAmelCase_  :				List[Any]      =      stride
				UpperCAmelCase_  :				List[str]      =      padding
				UpperCAmelCase_  :				Any      =      hidden_sizes
				UpperCAmelCase_  :				List[str]      =      num_attention_heads
				UpperCAmelCase_  :				List[str]      =      depths
				UpperCAmelCase_  :				int      =      key_dim
				UpperCAmelCase_  :				List[str]      =      drop_path_rate
				UpperCAmelCase_  :				str      =      patch_size
				UpperCAmelCase_  :				Tuple      =      attention_ratio
				UpperCAmelCase_  :				Optional[int]      =      mlp_ratio
				UpperCAmelCase_  :				Union[str, Any]      =      initializer_range
				UpperCAmelCase_  :				List[Any]      =      [
				    ["Subsample", key_dim[0], hidden_sizes[0] // key_dim[0], 4, 2, 2],
				    ["Subsample", key_dim[0], hidden_sizes[1] // key_dim[0], 4, 2, 2],
				]







class     UpperCamelCase_ (__A  ):
	__magic_name__			   =       version.parse('''1.11'''  )





	@property
	def   _SCREAMING_SNAKE_CASE   (   self      :				int		)		->    Mapping[str, Mapping[int, str]]:
				return OrderedDict(
				    [
				        ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
				    ]		)





	@property
	def   _SCREAMING_SNAKE_CASE   (   self      :				Dict		)		->    float:
				return 1e-4



