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


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

if TYPE_CHECKING:
						import pyspark






@dataclass
class      __lowercase					(    datasets.BuilderConfig    ):
						lowercase					 =     None






def        __UpperCAmelCase    (       UpperCAmelCase,		UpperCAmelCase,		)->       Dict:



			"""simple docstring"""







			import pyspark

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

			return generate_fn






class      __lowercase					(    _BaseExamplesIterable    ):

						def __init__(	self				:      Union[str, Any]							,							__lowerCamelCase				:      "pyspark.sql.DataFrame"							,							__lowerCamelCase				:      List[str]=None							,							)							->		Optional[int]:


									'''simple docstring'''

									lowercase							=		df
									lowercase							=		partition_order or range(self.df.rdd.getNumPartitions()   )
									lowercase							=		_generate_iterable_examples(self.df							,							self.partition_order   )

						def __iter__(	self				:      Any   )							->		Optional[int]:


									'''simple docstring'''

									yield from self.generate_examples_fn()

						def        __a		(	self				:      Dict							,							__lowerCamelCase				:      np.random.Generator   )							->		"SparkExamplesIterable":


									'''simple docstring'''

									lowercase							=		list(range(self.df.rdd.getNumPartitions()   )   )
									generator.shuffle(__lowerCamelCase   )
									return SparkExamplesIterable(self.df							,							partition_order=__lowerCamelCase   )

						def        __a		(	self				:      List[Any]							,							__lowerCamelCase				:      int							,							__lowerCamelCase				:      int   )							->		"SparkExamplesIterable":


									'''simple docstring'''

									lowercase							=		self.split_shard_indices_by_worker(__lowerCamelCase							,							__lowerCamelCase   )
									return SparkExamplesIterable(self.df							,							partition_order=__lowerCamelCase   )



						@property
						def        __a		(	self				:      Union[str, Any]   )							->		int:


									'''simple docstring'''

									return len(self.partition_order   )






class      __lowercase					(    datasets.DatasetBuilder    ):
						lowercase					 =     SparkConfig

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


									'''simple docstring'''

									import pyspark

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

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

						def        __a		(	self				:      Tuple   )							->		Union[str, Any]:


									'''simple docstring'''

									def create_cache_and_write_probe(__lowerCamelCase				:      Dict   ):
												# 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   )
												lowercase							=		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:
												lowercase							=		(
												    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        __a		(	self				:      str   )							->		Tuple:


									'''simple docstring'''

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

						def        __a		(	self				:      List[str]							,							__lowerCamelCase				:      datasets.download.download_manager.DownloadManager   )							->		List[Any]:


									'''simple docstring'''

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

						def        __a		(	self				:      Optional[int]							,							__lowerCamelCase				:      Dict   )							->		List[Any]:


									'''simple docstring'''

									import pyspark

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

									lowercase							=		self.df.count()
									lowercase							=		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.
									lowercase							=		(
									    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
									)
									lowercase							=		approx_bytes_per_row * df_num_rows
									if approx_total_size > max_shard_size:
												# Make sure there is at least one row per partition.
												lowercase							=		min(__lowerCamelCase							,							int(approx_total_size / max_shard_size   )   )
												lowercase							=		self.df.repartition(__lowerCamelCase   )

						def        __a		(	self				:      Optional[Any]							,							__lowerCamelCase				:      str							,							__lowerCamelCase				:      str							,							__lowerCamelCase				:      int							,							)							->		Iterable[Tuple[int, bool, Union[int, tuple]]]:


									'''simple docstring'''

									import pyspark

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

									def write_arrow(__lowerCamelCase				:      Dict   ):
												# Within the same SparkContext, no two task attempts will share the same attempt ID.
												lowercase							=		pyspark.TaskContext().taskAttemptId()
												lowercase							=		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''']							,							)
												lowercase							=		0
												lowercase							=		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							,							)
												lowercase							=		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:
																		lowercase		,lowercase							=		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
																		lowercase							=		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							,							)
															lowercase							=		pa.Table.from_batches([batch]   )
															writer.write_table(__lowerCamelCase   )

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

									lowercase							=		(
									    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        __a		(	self				:      Optional[Any]							,							__lowerCamelCase				:      "datasets.SplitGenerator"							,							__lowerCamelCase				:      str = "arrow"							,							__lowerCamelCase				:      Optional[Union[str, int]] = None							,							__lowerCamelCase				:      Optional[int] = None							,							**__lowerCamelCase				:      Optional[int]							,							)							->		Tuple:


									'''simple docstring'''

									self._validate_cache_dir()

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

									lowercase							=		'''-TTTTT-SSSSS-of-NNNNN'''
									lowercase							=		f'{self.name}-{split_generator.name}{SUFFIX}.{file_format}'
									lowercase							=		path_join(self._output_dir							,							__lowerCamelCase   )

									lowercase							=		0
									lowercase							=		0
									lowercase							=		0
									lowercase							=		[]
									lowercase							=		[]

									for task_id, content in self._prepare_split_single(__lowerCamelCase							,							__lowerCamelCase							,							__lowerCamelCase   ):
												(
												    (
												    lowercase
												)		,(
												    lowercase
												)		,(
												    lowercase
												)		,(
												    lowercase
												)		,
												)							=		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   )

									lowercase							=		total_num_examples
									lowercase							=		total_num_bytes

									# should rename everything at the end
									logger.debug(f'Renaming {total_shards} shards.'   )
									if total_shards > 1:
												lowercase							=		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.
												lowercase							=		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}'   )							,							)

												lowercase							=		[]
												lowercase							=		0
												for i in range(len(__lowerCamelCase   )   ):
															lowercase		,lowercase							=		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
												lowercase							=		0
												lowercase							=		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        __a		(	self				:      Union[str, Any]							,							__lowerCamelCase				:      "datasets.SplitGenerator"							,							)							->		SparkExamplesIterable:


									'''simple docstring'''

									return SparkExamplesIterable(self.df   )






from dataclasses import dataclass
from typing import Optional, Tuple, Union

import flax
import jax.numpy as jnp
from jax import random

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .scheduling_utils_flax import FlaxSchedulerMixin






@flax.struct.dataclass
class      __lowercase					:
						# setable values
						lowercase					 =     None
						lowercase					 =     None
						lowercase					 =     None  # sigma(t_i)

						@classmethod
						def        __a		(	cls				:      List[str]   )							->		Dict:


									'''simple docstring'''

									return cls()






@dataclass
class      __lowercase					(    _A    ):
						lowercase					 =     42
						lowercase					 =     42
						lowercase					 =     42






class      __lowercase					(    _A       ,     _A    ):

						@property
						def        __a		(	self				:      Union[str, Any]   )							->		Optional[Any]:


									'''simple docstring'''

									return True

						@register_to_config
						def __init__(	self				:      int							,							__lowerCamelCase				:      float = 0.02							,							__lowerCamelCase				:      float = 1_00							,							__lowerCamelCase				:      float = 1.007							,							__lowerCamelCase				:      float = 80							,							__lowerCamelCase				:      float = 0.05							,							__lowerCamelCase				:      float = 50							,							)							->		List[Any]:


									'''simple docstring'''

									pass

						def        __a		(	self				:      Dict   )							->		Optional[int]:


									'''simple docstring'''

									return KarrasVeSchedulerState.create()

						def        __a		(	self				:      str							,							__lowerCamelCase				:      KarrasVeSchedulerState							,							__lowerCamelCase				:      int							,							__lowerCamelCase				:      Tuple = ()   )							->		KarrasVeSchedulerState:


									'''simple docstring'''

									lowercase							=		jnp.arange(0							,							__lowerCamelCase   )[::-1].copy()
									lowercase							=		[
									    (
									        self.config.sigma_max**2
									        * (self.config.sigma_min**2 / self.config.sigma_max**2) ** (i / (num_inference_steps - 1))
									    )
									    for i in timesteps
									]

									return state.replace(
									    num_inference_steps=__lowerCamelCase							,							schedule=jnp.array(__lowerCamelCase							,							dtype=jnp.floataa   )							,							timesteps=__lowerCamelCase							,							)

						def        __a		(	self				:      Optional[Any]							,							__lowerCamelCase				:      KarrasVeSchedulerState							,							__lowerCamelCase				:      jnp.ndarray							,							__lowerCamelCase				:      float							,							__lowerCamelCase				:      random.KeyArray							,							)							->		Tuple[jnp.ndarray, float]:


									'''simple docstring'''

									if self.config.s_min <= sigma <= self.config.s_max:
												lowercase							=		min(self.config.s_churn / state.num_inference_steps							,							2**0.5 - 1   )
									else:
												lowercase							=		0

									# sample eps ~ N(0, S_noise^2 * I)
									lowercase							=		random.split(__lowerCamelCase							,							num=1   )
									lowercase							=		self.config.s_noise * random.normal(key=__lowerCamelCase							,							shape=sample.shape   )
									lowercase							=		sigma + gamma * sigma
									lowercase							=		sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)

									return sample_hat, sigma_hat

						def        __a		(	self				:      str							,							__lowerCamelCase				:      KarrasVeSchedulerState							,							__lowerCamelCase				:      jnp.ndarray							,							__lowerCamelCase				:      float							,							__lowerCamelCase				:      float							,							__lowerCamelCase				:      jnp.ndarray							,							__lowerCamelCase				:      bool = True							,							)							->		Union[FlaxKarrasVeOutput, Tuple]:


									'''simple docstring'''

									lowercase							=		sample_hat + sigma_hat * model_output
									lowercase							=		(sample_hat - pred_original_sample) / sigma_hat
									lowercase							=		sample_hat + (sigma_prev - sigma_hat) * derivative

									if not return_dict:
												return (sample_prev, derivative, state)

									return FlaxKarrasVeOutput(prev_sample=__lowerCamelCase							,							derivative=__lowerCamelCase							,							state=__lowerCamelCase   )

						def        __a		(	self				:      str							,							__lowerCamelCase				:      KarrasVeSchedulerState							,							__lowerCamelCase				:      jnp.ndarray							,							__lowerCamelCase				:      float							,							__lowerCamelCase				:      float							,							__lowerCamelCase				:      jnp.ndarray							,							__lowerCamelCase				:      jnp.ndarray							,							__lowerCamelCase				:      jnp.ndarray							,							__lowerCamelCase				:      bool = True							,							)							->		Union[FlaxKarrasVeOutput, Tuple]:


									'''simple docstring'''

									lowercase							=		sample_prev + sigma_prev * model_output
									lowercase							=		(sample_prev - pred_original_sample) / sigma_prev
									lowercase							=		sample_hat + (sigma_prev - sigma_hat) * (0.5 * derivative + 0.5 * derivative_corr)

									if not return_dict:
												return (sample_prev, derivative, state)

									return FlaxKarrasVeOutput(prev_sample=__lowerCamelCase							,							derivative=__lowerCamelCase							,							state=__lowerCamelCase   )



						def        __a		(	self				:      int							,							__lowerCamelCase				:      KarrasVeSchedulerState							,							__lowerCamelCase				:      str							,							__lowerCamelCase				:      str							,							__lowerCamelCase				:      Dict   )							->		Tuple:


									'''simple docstring'''

									raise NotImplementedError()





