
"""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


A__ :       str				= datasets.utils.logging.get_logger(__name__)

if TYPE_CHECKING:
  import pyspark


@dataclass
class       __magic_name__							(							datasets.BuilderConfig			):
       UpperCamelCase_ =		None




def 			_lowerCAmelCase		(		_UpperCamelCase			,					_UpperCamelCase			,					):






  """simple docstring"""



  import pyspark

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

  return generate_fn


class       __magic_name__							(							_BaseExamplesIterable			):
       def __init__(  self			,					A_			,					A_=None			,					)  ->						Optional[int]:





         """simple docstring"""




         _lowercase:      Optional[Any]   			=  df
         _lowercase:      List[str]   			=  partition_order or range(self.df.rdd.getNumPartitions()							)
         _lowercase:      Optional[Any]   			=  _generate_iterable_examples(self.df			,					self.partition_order							)
       def __iter__(  self							)  ->						List[Any]:





         """simple docstring"""




         yield from self.generate_examples_fn()
       def 				lowercase_				(  self			,					A_							)  ->						"SparkExamplesIterable":





         """simple docstring"""




         _lowercase:      List[Any]   			=  list(range(self.df.rdd.getNumPartitions()							)							)
         generator.shuffle(A_							)
         return SparkExamplesIterable(self.df			,					partition_order=A_							)
       def 				lowercase_				(  self			,					A_			,					A_							)  ->						"SparkExamplesIterable":





         """simple docstring"""




         _lowercase:      str   			=  self.split_shard_indices_by_worker(A_			,					A_							)
         return SparkExamplesIterable(self.df			,					partition_order=A_							)



       @property
       def 				lowercase_				(  self							)  ->						int:





         """simple docstring"""




         return len(self.partition_order							)


class       __magic_name__							(							datasets.DatasetBuilder			):
       UpperCamelCase_ =		SparkConfig
       def __init__(  self			,					A_			,					A_ = None			,					A_ = None			,					**A_			,					)  ->						Optional[Any]:





         """simple docstring"""




         import pyspark

         _lowercase:      Union[str, Any]   			=  pyspark.sql.SparkSession.builder.getOrCreate()
         _lowercase:      Dict   			=  df
         _lowercase:      Tuple   			=  working_dir

         super().__init__(
             cache_dir=A_			,					config_name=str(self.df.semanticHash()							)			,					**A_			,					)
       def 				lowercase_				(  self							)  ->						int:





         """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_							)
           _lowercase:      Tuple   			=  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:
           _lowercase:      List[str]   			=  (
               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 				lowercase_				(  self							)  ->						List[Any]:





         """simple docstring"""




         return datasets.DatasetInfo(features=self.config.features							)
       def 				lowercase_				(  self			,					A_							)  ->						List[Any]:





         """simple docstring"""




         return [datasets.SplitGenerator(name=datasets.Split.TRAIN							)]
       def 				lowercase_				(  self			,					A_							)  ->						Optional[Any]:





         """simple docstring"""




         import pyspark

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

         _lowercase:      str   			=  self.df.count()
         _lowercase:      Optional[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.
         _lowercase:      int   			=  (
             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
         )
         _lowercase:      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.
           _lowercase:      Union[str, Any]   			=  min(A_			,					int(approx_total_size / max_shard_size							)							)
           _lowercase:      Union[str, Any]   			=  self.df.repartition(A_							)
       def 				lowercase_				(  self			,					A_			,					A_			,					A_			,					)  ->						Iterable[Tuple[int, bool, Union[int, tuple]]]:





         """simple docstring"""




         import pyspark

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

         def write_arrow(A_							):
           # Within the same SparkContext, no two task attempts will share the same attempt ID.
           _lowercase:      Any   			=  pyspark.TaskContext().taskAttemptId()
           _lowercase:      Dict   			=  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''']			,					)
           _lowercase:      Optional[int]   			=  0
           _lowercase:      List[str]   			=  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_			,					)
           _lowercase:      List[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:
               _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''']			,					)
               shard_id += 1
               _lowercase:      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_			,					)
             _lowercase:      Any   			=  pa.Table.from_batches([batch]							)
             writer.write_table(A_							)

           if writer._num_bytes > 0:
             _lowercase			,					_lowercase:      Optional[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_							)							):
               _lowercase:      Dict   			=  os.path.join(os.path.dirname(A_							)			,					os.path.basename(A_							)							)
               shutil.move(A_			,					A_							)

         _lowercase:      Tuple   			=  (
             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 				lowercase_				(  self			,					A_			,					A_ = "arrow"			,					A_ = None			,					A_ = None			,					**A_			,					)  ->						str:





         """simple docstring"""




         self._validate_cache_dir()

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

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

         _lowercase:      Union[str, Any]   			=  0
         _lowercase:      Dict   			=  0
         _lowercase:      List[Any]   			=  0
         _lowercase:      Tuple   			=  []
         _lowercase:      List[str]   			=  []

         for task_id, content in self._prepare_split_single(A_			,					A_			,					A_							):
           (
               (
               _lowercase
           )			,					(
               _lowercase
           )			,					(
               _lowercase
           )			,					(
               _lowercase
           )			,					
           ):      Optional[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(A_							)

         _lowercase:      List[str]   			=  total_num_examples
         _lowercase:      List[Any]   			=  total_num_bytes

         # should rename everything at the end
         logger.debug(f'''Renaming {total_shards} shards.'''							)
         if total_shards > 1:
           _lowercase:      List[Any]   			=  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:      Union[str, Any]   			=  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}'''							)			,					)

           _lowercase:      str   			=  []
           _lowercase:      List[str]   			=  0
           for i in range(len(A_							)							):
             _lowercase			,					_lowercase:      List[str]   			=  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
           _lowercase:      Optional[int]   			=  0
           _lowercase:      Optional[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(A_			,					''''''							)			,					)



       def 				lowercase_				(  self			,					A_			,					)  ->						SparkExamplesIterable:





         """simple docstring"""




         return SparkExamplesIterable(self.df							)


"""simple docstring"""




import os
from datetime import datetime as dt

from github import Github


A__ :       Tuple				= [
    'good first issue',
    'good second issue',
    'good difficult issue',
    'enhancement',
    'new pipeline/model',
    'new scheduler',
    'wip',
]




def 			_lowerCAmelCase		(		):






  """simple docstring"""



  _lowercase:      Optional[int]   			=  Github(os.environ['''GITHUB_TOKEN''']    )
  _lowercase:      Optional[int]   			=  g.get_repo('''huggingface/diffusers'''    )
  _lowercase:      Union[str, Any]   			=  repo.get_issues(state='''open'''    )

  for issue in open_issues:
    _lowercase:      List[Any]   			=  sorted(issue.get_comments()			,					key=lambda _UpperCamelCase    : i.created_at			,					reverse=_UpperCamelCase    )
    _lowercase:      Optional[Any]   			=  comments[0] if len(_UpperCamelCase    ) > 0 else None
    if (
        last_comment is not None
        and last_comment.user.login == "github-actions[bot]"
        and (dt.utcnow() - issue.updated_at).days > 7
        and (dt.utcnow() - issue.created_at).days >= 30
        and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels()    )
    ):
      # Closes the issue after 7 days of inactivity since the Stalebot notification.
      issue.edit(state='''closed'''    )
    elif (
        "stale" in issue.get_labels()
        and last_comment is not None
        and last_comment.user.login != "github-actions[bot]"
    ):
      # Opens the issue if someone other than Stalebot commented.
      issue.edit(state='''open'''    )
      issue.remove_from_labels('''stale'''    )
    elif (
        (dt.utcnow() - issue.updated_at).days > 23
        and (dt.utcnow() - issue.created_at).days >= 30
        and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels()    )
    ):
      # Post a Stalebot notification after 23 days of inactivity.
      issue.create_comment(
          '''This issue has been automatically marked as stale because it has not had '''
          '''recent activity. If you think this still needs to be addressed '''
          '''please comment on this thread.\n\nPlease note that issues that do not follow the '''
          '''[contributing guidelines](https://github.com/huggingface/diffusers/blob/main/CONTRIBUTING.md) '''
          '''are likely to be ignored.'''    )
      issue.add_to_labels('''stale'''    )


if __name__ == "__main__":
  main()
