

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


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

if TYPE_CHECKING:
 import pyspark



@dataclass
class        lowercase		(			datasets.BuilderConfig   ):



 '''simple docstring'''


 __SCREAMING_SNAKE_CASE   				=							None







def 		UpperCamelCase_(     snake_case__:    "pyspark.sql.DataFrame"    ,       snake_case__:    List[int]    ,       )			->					Optional[int]:
 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        lowercase		(			_BaseExamplesIterable   ):



 '''simple docstring'''


 def __init__(self			,			__a			,			__a=None			,			)					->   List[str]:






  """simple docstring"""





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




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






  """simple docstring"""





  yield from self.generate_examples_fn()




 def     UpperCamelCase__				(self			,			__a					)					->   "SparkExamplesIterable":






  """simple docstring"""





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




 def     UpperCamelCase__				(self			,			__a			,			__a					)					->   "SparkExamplesIterable":






  """simple docstring"""





  UpperCAmelCase__     							=							self.split_shard_indices_by_worker(snake_case__			,			snake_case__					)
  return SparkExamplesIterable(self.df			,			partition_order=snake_case__					)




 @property
 def     UpperCamelCase__				(self					)					->   int:






  """simple docstring"""





  return len(self.partition_order					)







class        lowercase		(			datasets.DatasetBuilder   ):



 '''simple docstring'''


 __SCREAMING_SNAKE_CASE   				=							SparkConfig




 def __init__(self			,			__a			,			__a = None			,			__a = None			,			**__a			,			)					->   Optional[int]:






  """simple docstring"""





  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     UpperCamelCase__				(self					)					->   Tuple:






  """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=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     UpperCamelCase__				(self					)					->   Dict:






  """simple docstring"""





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




 def     UpperCamelCase__				(self			,			__a					)					->   Dict:






  """simple docstring"""





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




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






  """simple docstring"""





  import pyspark

  def get_arrow_batch_size(__a					):
   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 <= 100 else 100
  # 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     UpperCamelCase__				(self			,			__a			,			__a			,			__a			,			)					->   Iterable[Tuple[int, bool, Union[int, tuple]]]:






  """simple docstring"""





  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(__a					):
   # 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__     							=							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__     							=							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     UpperCamelCase__				(self			,			__a			,			__a = "arrow"			,			__a = None			,			__a = None			,			**__a			,			)					->   int:






  """simple docstring"""





  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__   
   )     							=							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(
       __a			,			__a			,			__a			,			):
    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__     							=							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 __a					: _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     UpperCamelCase__				(self			,			__a			,			)					->   SparkExamplesIterable:






  """simple docstring"""





  return SparkExamplesIterable(self.df					)





import secrets
from random import shuffle
from string import ascii_letters, ascii_lowercase, ascii_uppercase, digits, punctuation


def      UpperCamelCase   (       __lowerCamelCase	:					int = 8       ):
       snake_case			:  int     							= ascii_letters + digits + punctuation
       return "".join(secrets.choice(__lowerCamelCase       ) for _ in range(__lowerCamelCase       )       )


def      UpperCamelCase   (       __lowerCamelCase	:					str							,   __lowerCamelCase	:					int       ):
       # Password Generator = full boot with random_number, random_letters, and
       # random_character FUNCTIONS
       # Put your code here...
       i -= len(__lowerCamelCase       )
       snake_case			:  Any     							= i // 3
       snake_case			:  Optional[int]     							= i % 3
       # chars = chars_incl + random_letters(ascii_letters, i / 3 + remainder) +
       #     random_number(digits, i / 3) + random_characters(punctuation, i / 3)
       snake_case			:  Tuple     							= (
           chars_incl
           + random(__lowerCamelCase							,   quotient + remainder       )
           + random(__lowerCamelCase							,   __lowerCamelCase       )
           + random(__lowerCamelCase							,   __lowerCamelCase       )
       )
       snake_case			:  Optional[Any]     							= list(__lowerCamelCase       )
       shuffle(__lowerCamelCase       )
       return "".join(__lowerCamelCase       )

       # random is a generalised function for letters, characters and numbers


def      UpperCamelCase   (       __lowerCamelCase	:					str							,   __lowerCamelCase	:					int       ):
       return "".join(secrets.choice(__lowerCamelCase       ) for _ in range(__lowerCamelCase       )       )


def      UpperCamelCase   (       __lowerCamelCase	:					List[str]							,   __lowerCamelCase	:					str       ):
       pass  # Put your code here...


def      UpperCamelCase   (       __lowerCamelCase	:					Optional[int]							,   __lowerCamelCase	:					List[str]       ):
       pass  # Put your code here...


def      UpperCamelCase   (       __lowerCamelCase	:					Any							,   __lowerCamelCase	:					List[Any]       ):
       pass  # Put your code here...


def      UpperCamelCase   (       __lowerCamelCase	:					str							,   __lowerCamelCase	:					int = 8       ):

       if len(__lowerCamelCase       ) < min_length:
              # Your Password must be at least 8 characters long
              return False

       snake_case			:  Dict     							= any(char in ascii_uppercase for char in password       )
       snake_case			:  Optional[int]     							= any(char in ascii_lowercase for char in password       )
       snake_case			:  str     							= any(char in digits for char in password       )
       snake_case			:  str     							= any(char in punctuation for char in password       )

       return upper and lower and num and spec_char
       # Passwords should contain UPPERCASE, lowerase
       # numbers, and special characters


def      UpperCamelCase   (       ):
       snake_case			:  int     							= int(input("Please indicate the max length of your password: "       ).strip()       )
       snake_case			:  Union[str, Any]     							= input(
           "Please indicate the characters that must be in your password: "       ).strip()
       print("Password generated:"							,   password_generator(__lowerCamelCase       )       )
       print(
           "Alternative Password generated:"							,   alternative_password_generator(__lowerCamelCase							,   __lowerCamelCase       )							,   )
       print("[If you are thinking of using this passsword, You better save it.]"       )


if __name__ == "__main__":
   main()





