




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       a					(       datasets.BuilderConfig      ):

					'''simple docstring'''





					lowerCAmelCase    :	Optional[datasets.Features]       		=  None




def  SCREAMING_SNAKE_CASE     (	__UpperCamelCase      :						"pyspark.sql.DataFrame"	,					__UpperCamelCase      :						List[int]	,					)  ->							List[Any]:
				import pyspark

				def generate_fn():
								UpperCAmelCase_										=					df.select('''*'''	,					pyspark.sql.functions.spark_partition_id().alias('''part_id'''				)				)
								for partition_id in partition_order:
												UpperCAmelCase_										=					df_with_partition_id.select('''*'''				).where(f'part_id = {partition_id}'				).drop('''part_id'''				)
												UpperCAmelCase_										=					partition_df.collect()
												UpperCAmelCase_										=					0
												for row in rows:
																yield f'{partition_id}_{row_id}', row.asDict()
																row_id += 1

				return generate_fn



class       a					(       _BaseExamplesIterable      ):

					'''simple docstring'''





					def __init__(     self       :      int ,   __snake_case       :      "pyspark.sql.DataFrame" ,   __snake_case       :      Optional[int]=None ,   ):
									UpperCAmelCase_										=					df
									UpperCAmelCase_										=					partition_order or range(self.df.rdd.getNumPartitions()    )
									UpperCAmelCase_										=					_generate_iterable_examples(self.df ,   self.partition_order    )
					def __iter__(     self       :      List[str]    ):
									yield from self.generate_examples_fn()
					def   lowerCamelCase_       (     self       :      Optional[Any] ,   __snake_case       :      np.random.Generator    ):
									UpperCAmelCase_										=					list(range(self.df.rdd.getNumPartitions()    )    )
									generator.shuffle(__snake_case    )
									return SparkExamplesIterable(self.df ,   partition_order=__snake_case    )
					def   lowerCamelCase_       (     self       :      Dict ,   __snake_case       :      int ,   __snake_case       :      int    ):
									UpperCAmelCase_										=					self.split_shard_indices_by_worker(__snake_case ,   __snake_case    )
									return SparkExamplesIterable(self.df ,   partition_order=__snake_case    )




					@property
					def   lowerCamelCase_       (     self       :      Tuple    ):
									return len(self.partition_order    )



class       a					(       datasets.DatasetBuilder      ):

					'''simple docstring'''





					lowerCAmelCase    :	Union[str, Any]       		=  SparkConfig
					def __init__(     self       :      int ,   __snake_case       :      "pyspark.sql.DataFrame" ,   __snake_case       :      str = None ,   __snake_case       :      str = None ,   **__snake_case       :      Tuple ,   ):
									import pyspark

									UpperCAmelCase_										=					pyspark.sql.SparkSession.builder.getOrCreate()
									UpperCAmelCase_										=					df
									UpperCAmelCase_										=					working_dir

									super().__init__(
									    cache_dir=__snake_case ,   config_name=str(self.df.semanticHash()    ) ,   **__snake_case ,   )
					def   lowerCamelCase_       (     self       :      int    ):
									# Returns the path of the created file.
									def create_cache_and_write_probe(__snake_case       :      Union[str, Any]    ):
													# 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=__snake_case    )
													UpperCAmelCase_										=					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(__snake_case ,   '''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_										=					(
													    self._spark.sparkContext.parallelize(range(1    ) ,   1    ).mapPartitions(__snake_case    ).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   lowerCamelCase_       (     self       :      int    ):
									return datasets.DatasetInfo(features=self.config.features    )
					def   lowerCamelCase_       (     self       :      Optional[Any] ,   __snake_case       :      datasets.download.download_manager.DownloadManager    ):
									return [datasets.SplitGenerator(name=datasets.Split.TRAIN    )]
					def   lowerCamelCase_       (     self       :      str ,   __snake_case       :      Union[str, Any]    ):
									import pyspark

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

									UpperCAmelCase_										=					self.df.count()
									UpperCAmelCase_										=					df_num_rows if df_num_rows <= 1_00 else 1_00
									# Approximate the size of each row (in Arrow format) by averaging over a max-100-row sample.
									UpperCAmelCase_										=					(
									    self.df.limit(__snake_case    )
									    .repartition(1    )
									    .mapInArrow(__snake_case ,   '''batch_bytes: long'''    )
									    .agg(pyspark.sql.functions.sum('''batch_bytes'''    ).alias('''sample_bytes'''    )    )
									    .collect()[0]
									    .sample_bytes
									    / sample_num_rows
									)
									UpperCAmelCase_										=					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_										=					min(__snake_case ,   int(approx_total_size / max_shard_size    )    )
													UpperCAmelCase_										=					self.df.repartition(__snake_case    )
					def   lowerCamelCase_       (     self       :      Optional[int] ,   __snake_case       :      str ,   __snake_case       :      str ,   __snake_case       :      int ,   ):
									import pyspark

									UpperCAmelCase_										=					ParquetWriter if file_format == '''parquet''' else ArrowWriter
									UpperCAmelCase_										=					os.path.join(self._working_dir ,   os.path.basename(__snake_case    )    ) if self._working_dir else fpath
									UpperCAmelCase_										=					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_										=					self.config.features
									UpperCAmelCase_										=					self._writer_batch_size
									UpperCAmelCase_										=					self._fs.storage_options

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

													if writer._num_bytes > 0:
																	UpperCAmelCase_			,							UpperCAmelCase_										=					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(__snake_case    )    ):
																					UpperCAmelCase_										=					os.path.join(os.path.dirname(__snake_case    ) ,   os.path.basename(__snake_case    )    )
																					shutil.move(__snake_case ,   __snake_case    )

									UpperCAmelCase_										=					(
									    self.df.mapInArrow(__snake_case ,   '''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   lowerCamelCase_       (     self       :      List[str] ,   __snake_case       :      "datasets.SplitGenerator" ,   __snake_case       :      str = "arrow" ,   __snake_case       :      Optional[Union[str, int]] = None ,   __snake_case       :      Optional[int] = None ,   **__snake_case       :      int ,   ):
									self._validate_cache_dir()

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

									UpperCAmelCase_										=					'''-TTTTT-SSSSS-of-NNNNN'''
									UpperCAmelCase_										=					F'{self.name}-{split_generator.name}{SUFFIX}.{file_format}'
									UpperCAmelCase_										=					path_join(self._output_dir ,   __snake_case    )

									UpperCAmelCase_										=					0
									UpperCAmelCase_										=					0
									UpperCAmelCase_										=					0
									UpperCAmelCase_										=					[]
									UpperCAmelCase_										=					[]

									for task_id, content in self._prepare_split_single(__snake_case ,   __snake_case ,   __snake_case    ):
													(
													    (
													    UpperCAmelCase_							
													)			,							(
													    UpperCAmelCase_							
													)			,							(
													    UpperCAmelCase_							
													)			,							(
													    UpperCAmelCase_							
													)			,														
													)										=					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(__snake_case    )

									UpperCAmelCase_										=					total_num_examples
									UpperCAmelCase_										=					total_num_bytes

									# should rename everything at the end
									logger.debug(F'Renaming {total_shards} shards.'    )
									if total_shards > 1:
													UpperCAmelCase_										=					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_										=					self._fs

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




					def   lowerCamelCase_       (     self       :      Optional[Any] ,   __snake_case       :      "datasets.SplitGenerator" ,   ):
									return SparkExamplesIterable(self.df    )











from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


_lowerCamelCase      			=						{
    'configuration_conditional_detr': [
        'CONDITIONAL_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'ConditionalDetrConfig',
        'ConditionalDetrOnnxConfig',
    ]
}

try:
 if not is_vision_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _lowerCamelCase      			=						['ConditionalDetrFeatureExtractor']
 _lowerCamelCase      			=						['ConditionalDetrImageProcessor']

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _lowerCamelCase      			=						[
     'CONDITIONAL_DETR_PRETRAINED_MODEL_ARCHIVE_LIST',
     'ConditionalDetrForObjectDetection',
     'ConditionalDetrForSegmentation',
     'ConditionalDetrModel',
     'ConditionalDetrPreTrainedModel',
 ]


if TYPE_CHECKING:
 from .configuration_conditional_detr import (
     CONDITIONAL_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP,
     ConditionalDetrConfig,
     ConditionalDetrOnnxConfig,
 )

 try:
  if not is_vision_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .feature_extraction_conditional_detr import ConditionalDetrFeatureExtractor
  from .image_processing_conditional_detr import ConditionalDetrImageProcessor

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_conditional_detr import (
      CONDITIONAL_DETR_PRETRAINED_MODEL_ARCHIVE_LIST,
      ConditionalDetrForObjectDetection,
      ConditionalDetrForSegmentation,
      ConditionalDetrModel,
      ConditionalDetrPreTrainedModel,
  )

else:
 import sys

 _lowerCamelCase      			=						_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





