

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


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

if TYPE_CHECKING:
					import pyspark

@dataclass
class 				a  (	datasets.BuilderConfig				):
						_lowercase    						=			None







def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  "pyspark.sql.DataFrame"  ,      lowerCAmelCase:  List[int]  ,      )   ->       List[str]:
				import pyspark

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

				return generate_fn

class 				a  (	_BaseExamplesIterable				):


						def __init__(       self						,					A_						,					A_=None						,					):







										'''simple docstring'''


										_UpperCAmelCase       :		int 		=    df
										_UpperCAmelCase       :		Dict 		=    partition_order or range(self.df.rdd.getNumPartitions()	)
										_UpperCAmelCase       :		Dict 		=    _generate_iterable_examples(self.df						,					self.partition_order	)


						def __iter__(       self	):







										'''simple docstring'''


										yield from self.generate_examples_fn()


						def        _UpperCAmelCase				(       self						,					A_	):







										'''simple docstring'''


										_UpperCAmelCase       :		str 		=    list(range(self.df.rdd.getNumPartitions()	)	)
										generator.shuffle(A_	)
										return SparkExamplesIterable(self.df						,					partition_order=A_	)


						def        _UpperCAmelCase				(       self						,					A_						,					A_	):







										'''simple docstring'''


										_UpperCAmelCase       :		Tuple 		=    self.split_shard_indices_by_worker(A_						,					A_	)
										return SparkExamplesIterable(self.df						,					partition_order=A_	)





						@property
						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										return len(self.partition_order	)







class 				a  (	datasets.DatasetBuilder				):
						_lowercase    						=			SparkConfig


						def __init__(       self						,					A_						,					A_ = None						,					A_ = None						,					**A_						,					):







										'''simple docstring'''


										import pyspark

										_UpperCAmelCase       :		Dict 		=    pyspark.sql.SparkSession.builder.getOrCreate()
										_UpperCAmelCase       :		Optional[Any] 		=    df
										_UpperCAmelCase       :		Tuple 		=    working_dir

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


						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										def create_cache_and_write_probe(A_	):
														# 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=A_	)
														_UpperCAmelCase       :		List[str] 		=    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(A_						,					"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       :		Any 		=    (
														    self._spark.sparkContext.parallelize(range(1	)						,					1	).mapPartitions(A_	).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        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										return datasets.DatasetInfo(features=self.config.features	)


						def        _UpperCAmelCase				(       self						,					A_	):







										'''simple docstring'''


										return [datasets.SplitGenerator(name=datasets.Split.TRAIN	)]


						def        _UpperCAmelCase				(       self						,					A_	):







										'''simple docstring'''


										import pyspark

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

										_UpperCAmelCase       :		str 		=    self.df.count()
										_UpperCAmelCase       :		List[Any] 		=    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       :		List[str] 		=    (
										    self.df.limit(A_	)
										    .repartition(1	)
										    .mapInArrow(A_						,					"batch_bytes: long"	)
										    .agg(pyspark.sql.functions.sum("batch_bytes"	).alias("sample_bytes"	)	)
										    .collect()[0]
										    .sample_bytes
										    / sample_num_rows
										)
										_UpperCAmelCase       :		Union[str, Any] 		=    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       :		List[str] 		=    min(A_						,					int(approx_total_size / max_shard_size	)	)
														_UpperCAmelCase       :		Tuple 		=    self.df.repartition(A_	)


						def        _UpperCAmelCase				(       self						,					A_						,					A_						,					A_						,					):







										'''simple docstring'''


										import pyspark

										_UpperCAmelCase       :		Dict 		=    ParquetWriter if file_format == "parquet" else ArrowWriter
										_UpperCAmelCase       :		Optional[int] 		=    os.path.join(self._working_dir						,					os.path.basename(A_	)	) if self._working_dir else fpath
										_UpperCAmelCase       :		str 		=    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       :		str 		=    self.config.features
										_UpperCAmelCase       :		Any 		=    self._writer_batch_size
										_UpperCAmelCase       :		List[Any] 		=    self._fs.storage_options

										def write_arrow(A_	):
														# Within the same SparkContext, no two task attempts will share the same attempt ID.
														_UpperCAmelCase       :		str 		=    pyspark.TaskContext().taskAttemptId()
														_UpperCAmelCase       :		Optional[int] 		=    next(A_						,					A_	)
														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       :		Optional[int] 		=    0
														_UpperCAmelCase       :		Union[str, Any] 		=    writer_class(
														    features=A_						,					path=working_fpath.replace("SSSSS"						,					f'{shard_id:05d}'	).replace("TTTTT"						,					f'{task_id:05d}'	)						,					writer_batch_size=A_						,					storage_options=A_						,					embed_local_files=A_						,					)
														_UpperCAmelCase       :		str 		=    pa.Table.from_batches([first_batch]	)
														writer.write_table(A_	)
														for batch in it:
																		if max_shard_size is not None and writer._num_bytes >= max_shard_size:
																						_UpperCAmelCase       :		Optional[Any] 		=    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       :		Optional[int] 		=    writer_class(
																						    features=writer._features						,					path=working_fpath.replace("SSSSS"						,					f'{shard_id:05d}'	).replace("TTTTT"						,					f'{task_id:05d}'	)						,					writer_batch_size=A_						,					storage_options=A_						,					embed_local_files=A_						,					)
																		_UpperCAmelCase       :		str 		=    pa.Table.from_batches([batch]	)
																		writer.write_table(A_	)

														if writer._num_bytes > 0:
																		_UpperCAmelCase       :		List[Any] 		=    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(A_	)	):
																						_UpperCAmelCase       :		int 		=    os.path.join(os.path.dirname(A_	)						,					os.path.basename(A_	)	)
																						shutil.move(A_						,					A_	)

										_UpperCAmelCase       :		str 		=    (
										    self.df.mapInArrow(A_						,					"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        _UpperCAmelCase				(       self						,					A_						,					A_ = "arrow"						,					A_ = None						,					A_ = None						,					**A_						,					):







										'''simple docstring'''


										self._validate_cache_dir()

										_UpperCAmelCase       :		Union[str, Any] 		=    convert_file_size_to_int(max_shard_size or MAX_SHARD_SIZE	)
										self._repartition_df_if_needed(A_	)
										_UpperCAmelCase       :		str 		=    not is_remote_filesystem(self._fs	)
										_UpperCAmelCase       :		List[Any] 		=    os.path.join if is_local else posixpath.join

										_UpperCAmelCase       :		str 		=    "-TTTTT-SSSSS-of-NNNNN"
										_UpperCAmelCase       :		Dict 		=    f'{self.name}-{split_generator.name}{SUFFIX}.{file_format}'
										_UpperCAmelCase       :		Optional[int] 		=    path_join(self._output_dir						,					A_	)

										_UpperCAmelCase       :		int 		=    0
										_UpperCAmelCase       :		Any 		=    0
										_UpperCAmelCase       :		Tuple 		=    0
										_UpperCAmelCase       :		Tuple 		=    []
										_UpperCAmelCase       :		List[Any] 		=    []

										for task_id, content in self._prepare_split_single(A_						,					A_						,					A_	):
														(
														    _UpperCAmelCase							
														)       :		Tuple 		=    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(A_	)

										_UpperCAmelCase       :		Tuple 		=    total_num_examples
										_UpperCAmelCase       :		List[str] 		=    total_num_bytes

										# should rename everything at the end
										logger.debug(f'Renaming {total_shards} shards.'	)
										if total_shards > 1:
														_UpperCAmelCase       :		int 		=    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       :		Optional[int] 		=    self._fs

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





						def        _UpperCAmelCase				(       self						,					A_						,					):







										'''simple docstring'''


										return SparkExamplesIterable(self.df	)




from ...configuration_utils import PretrainedConfig
from ...utils import logging


SCREAMING_SNAKE_CASE_        =      logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_        =      {
    'facebook/timesformer': 'https://huggingface.co/facebook/timesformer/resolve/main/config.json',
}

class 				a  (	UpperCAmelCase				):
						_lowercase    						=			"timesformer"


						def __init__(       self						,					A_=224						,					A_=16						,					A_=3						,					A_=8						,					A_=768						,					A_=12						,					A_=12						,					A_=3072						,					A_="gelu"						,					A_=0.0						,					A_=0.0						,					A_=0.02						,					A_=1e-6						,					A_=True						,					A_="divided_space_time"						,					A_=0						,					**A_						,					):







										'''simple docstring'''


										super().__init__(**A_	)

										_UpperCAmelCase       :		Union[str, Any] 		=    image_size
										_UpperCAmelCase       :		Tuple 		=    patch_size
										_UpperCAmelCase       :		Any 		=    num_channels
										_UpperCAmelCase       :		List[Any] 		=    num_frames

										_UpperCAmelCase       :		str 		=    hidden_size
										_UpperCAmelCase       :		Any 		=    num_hidden_layers
										_UpperCAmelCase       :		Dict 		=    num_attention_heads
										_UpperCAmelCase       :		Optional[int] 		=    intermediate_size
										_UpperCAmelCase       :		Union[str, Any] 		=    hidden_act
										_UpperCAmelCase       :		Optional[Any] 		=    hidden_dropout_prob
										_UpperCAmelCase       :		Tuple 		=    attention_probs_dropout_prob
										_UpperCAmelCase       :		Union[str, Any] 		=    initializer_range
										_UpperCAmelCase       :		Optional[Any] 		=    layer_norm_eps
										_UpperCAmelCase       :		str 		=    qkv_bias

										_UpperCAmelCase       :		Tuple 		=    attention_type
										_UpperCAmelCase       :		List[Any] 		=    drop_path_rate


