


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				):
 __lowercase			:							Optional[datasets.Features]       					=			None





def        snake_case_					(  snake_case      ,							snake_case      ,							)  ->    str:
       import pyspark

       def generate_fn():
              lowercase__:       Tuple        =       df.select('*'      ,							pyspark.sql.functions.spark_partition_id().alias('part_id'					)					)
              for partition_id in partition_order:
                     lowercase__:       List[str]        =       df_with_partition_id.select('*'					).where(f'part_id = {partition_id}'					).drop('part_id'					)
                     lowercase__:       Tuple        =       partition_df.collect()
                     lowercase__:       Tuple        =       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    ,			lowerCAmelCase__    ,			lowerCAmelCase__=None    ,			) ->    Any:




        '''simple docstring'''
        lowercase__:       Any        =       df
        lowercase__:       List[Any]        =       partition_order or range(self.df.rdd.getNumPartitions()			)
        lowercase__:       Tuple        =       _generate_iterable_examples(self.df    ,			self.partition_order			)
 def __iter__(       self			) ->    List[Any]:




        '''simple docstring'''
        yield from self.generate_examples_fn()
 def 		SCREAMING_SNAKE_CASE__       (       self    ,			lowerCAmelCase__			) ->    "SparkExamplesIterable":




        '''simple docstring'''
        lowercase__:       Any        =       list(range(self.df.rdd.getNumPartitions()			)			)
        generator.shuffle(lowerCAmelCase__			)
        return SparkExamplesIterable(self.df    ,			partition_order=lowerCAmelCase__			)
 def 		SCREAMING_SNAKE_CASE__       (       self    ,			lowerCAmelCase__    ,			lowerCAmelCase__			) ->    "SparkExamplesIterable":




        '''simple docstring'''
        lowercase__:       Dict        =       self.split_shard_indices_by_worker(lowerCAmelCase__    ,			lowerCAmelCase__			)
        return SparkExamplesIterable(self.df    ,			partition_order=lowerCAmelCase__			)
 @property
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    int:




        '''simple docstring'''
        return len(self.partition_order			)





class   __a      (   datasets.DatasetBuilder				):
 __lowercase			:							Any       					=			SparkConfig
 def __init__(       self    ,			lowerCAmelCase__    ,			lowerCAmelCase__ = None    ,			lowerCAmelCase__ = None    ,			**lowerCAmelCase__    ,			) ->    Optional[Any]:




        '''simple docstring'''
        import pyspark

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

        super().__init__(
            cache_dir=lowerCAmelCase__    ,			config_name=str(self.df.semanticHash()			)    ,			**lowerCAmelCase__    ,			)
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    Optional[Any]:




        '''simple docstring'''
        # Returns the path of the created file.
        def create_cache_and_write_probe(lowerCAmelCase__			):
               # 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__:       Dict        =       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__:       Dict        =       (
                   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			) ->    Union[str, Any]:




        '''simple docstring'''
        return datasets.DatasetInfo(features=self.config.features			)
 def 		SCREAMING_SNAKE_CASE__       (       self    ,			lowerCAmelCase__			) ->    List[Any]:




        '''simple docstring'''
        return [datasets.SplitGenerator(name=datasets.Split.TRAIN			)]
 def 		SCREAMING_SNAKE_CASE__       (       self    ,			lowerCAmelCase__			) ->    List[Any]:




        '''simple docstring'''
        import pyspark

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

        lowercase__:       List[str]        =       self.df.count()
        lowercase__:       Tuple        =       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.
        lowercase__:       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
        )
        lowercase__:       Tuple        =       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__:       Optional[int]        =       min(lowerCAmelCase__    ,			int(approx_total_size / max_shard_size			)			)
               lowercase__:       Dict        =       self.df.repartition(lowerCAmelCase__			)
 def 		SCREAMING_SNAKE_CASE__       (       self    ,			lowerCAmelCase__    ,			lowerCAmelCase__    ,			lowerCAmelCase__    ,			) ->    Iterable[Tuple[int, bool, Union[int, tuple]]]:




        '''simple docstring'''
        import pyspark

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

        def write_arrow(lowerCAmelCase__			):
               # Within the same SparkContext, no two task attempts will share the same attempt ID.
               lowercase__:       str        =       pyspark.TaskContext().taskAttemptId()
               lowercase__:       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']    ,			)
               lowercase__:       Dict        =       0
               lowercase__:       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__    ,			)
               lowercase__:       str        =       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__:       Dict        =       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__:       List[Any]        =       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__:       Optional[int]        =       pa.Table.from_batches([batch]			)
                      writer.write_table(lowerCAmelCase__			)

               if writer._num_bytes > 0:
                      lowercase__      ,						lowercase__:       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(lowerCAmelCase__			)			):
                             lowercase__:       int        =       os.path.join(os.path.dirname(lowerCAmelCase__			)    ,			os.path.basename(lowerCAmelCase__			)			)
                             shutil.move(lowerCAmelCase__    ,			lowerCAmelCase__			)

        lowercase__:       List[str]        =       (
            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    ,			lowerCAmelCase__    ,			lowerCAmelCase__ = "arrow"    ,			lowerCAmelCase__ = None    ,			lowerCAmelCase__ = None    ,			**lowerCAmelCase__    ,			) ->    int:




        '''simple docstring'''
        self._validate_cache_dir()

        lowercase__:       Tuple        =       convert_file_size_to_int(max_shard_size or MAX_SHARD_SIZE			)
        self._repartition_df_if_needed(lowerCAmelCase__			)
        lowercase__:       List[str]        =       not is_remote_filesystem(self._fs			)
        lowercase__:       int        =       os.path.join if is_local else posixpath.join

        lowercase__:       List[str]        =       '-TTTTT-SSSSS-of-NNNNN'
        lowercase__:       Tuple        =       F'{self.name}-{split_generator.name}{SUFFIX}.{file_format}'
        lowercase__:       Union[str, Any]        =       path_join(self._output_dir    ,			lowerCAmelCase__			)

        lowercase__:       List[Any]        =       0
        lowercase__:       Tuple        =       0
        lowercase__:       Tuple        =       0
        lowercase__:       Optional[Any]        =       []
        lowercase__:       List[Any]        =       []

        for task_id, content in self._prepare_split_single(lowerCAmelCase__    ,			lowerCAmelCase__    ,			lowerCAmelCase__			):
               (
                   (
                   lowercase__
               )      ,						(
                   lowercase__
               )      ,						(
                   lowercase__
               )      ,						(
                   lowercase__
               )      ,						
               ):       str        =       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__:       Optional[int]        =       total_num_examples
        lowercase__:       int        =       total_num_bytes

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

               # use the -SSSSS-of-NNNNN pattern
               def _rename_shard(
                   lowerCAmelCase__    ,			lowerCAmelCase__    ,			lowerCAmelCase__    ,			):
                      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__:       str        =       []
               lowercase__:       int        =       0
               for i in range(len(lowerCAmelCase__			)			):
                      lowercase__      ,						lowercase__:       Tuple        =       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__:       List[str]        =       0
               lowercase__:       Dict        =       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    ,			lowerCAmelCase__    ,			) ->    SparkExamplesIterable:




        '''simple docstring'''
        return SparkExamplesIterable(self.df			)






import torch

from diffusers import StableDiffusionPipeline


__lowerCAmelCase			=							'''path-to-your-trained-model'''
__lowerCAmelCase			=							StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.floataa).to('''cuda''')

__lowerCAmelCase			=							'''A photo of sks dog in a bucket'''
__lowerCAmelCase			=							pipe(prompt, num_inference_steps=50, guidance_scale=7.5).images[0]

image.save('''dog-bucket.png''')


