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  __magic_name__  (		datasets.BuilderConfig    ):





			"""simple docstring"""




			lowerCAmelCase      :    Optional[datasets.Features]  			=   None




def       lowerCAmelCase_     (		lowercase:							"pyspark.sql.DataFrame"  ,					lowercase:							List[int]  ,					)    ->				Union[str, Any]:







	'''simple docstring'''
	import pyspark

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

	return generate_fn




class  __magic_name__  (		_BaseExamplesIterable    ):





			"""simple docstring"""




			def __init__(	self					:  List[Any]		,  _lowercase					:  "pyspark.sql.DataFrame"		,  _lowercase					:  Optional[Any]=None		,  ):



				"""simple docstring"""



				_UpperCamelCase:     Union[str, Any]							= df
				_UpperCamelCase:     Dict							= partition_order or range(self.df.rdd.getNumPartitions()							)
				_UpperCamelCase:     List[Any]							= _generate_iterable_examples(self.df		,  self.partition_order							)



			def __iter__(	self					:  List[str]							):



				"""simple docstring"""



				yield from self.generate_examples_fn()



			def   lowerCAmelCase						(	self					:  Tuple		,  _lowercase					:  np.random.Generator							):



				"""simple docstring"""



				_UpperCamelCase:     int							= list(range(self.df.rdd.getNumPartitions()							)							)
				generator.shuffle(_lowercase							)
				return SparkExamplesIterable(self.df		,  partition_order=_lowercase							)



			def   lowerCAmelCase						(	self					:  List[str]		,  _lowercase					:  int		,  _lowercase					:  int							):



				"""simple docstring"""



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



			@property
			def   lowerCAmelCase						(	self					:  Union[str, Any]							):



				"""simple docstring"""



				return len(self.partition_order							)




class  __magic_name__  (		datasets.DatasetBuilder    ):





			"""simple docstring"""




			lowerCAmelCase      :    str  			=   SparkConfig



			def __init__(	self					:  Tuple		,  _lowercase					:  "pyspark.sql.DataFrame"		,  _lowercase					:  str = None		,  _lowercase					:  str = None		,  **_lowercase					:  Optional[int]		,  ):



				"""simple docstring"""



				import pyspark

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

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



			def   lowerCAmelCase						(	self					:  List[str]							):



				"""simple docstring"""



				def create_cache_and_write_probe(_lowercase					:  Optional[int]							):
					# 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=_lowercase							)
					_UpperCamelCase:     List[str]							= 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(_lowercase		,  '''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:     Optional[int]							= (
					    self._spark.sparkContext.parallelize(range(1							)		,  1							).mapPartitions(_lowercase							).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					:  Union[str, Any]							):



				"""simple docstring"""



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



			def   lowerCAmelCase						(	self					:  Dict		,  _lowercase					:  datasets.download.download_manager.DownloadManager							):



				"""simple docstring"""



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



			def   lowerCAmelCase						(	self					:  int		,  _lowercase					:  int							):



				"""simple docstring"""



				import pyspark

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

				_UpperCamelCase:     Optional[Any]							= self.df.count()
				_UpperCamelCase:     Union[str, Any]							= 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:     Dict							= (
				    self.df.limit(_lowercase							)
				    .repartition(1							)
				    .mapInArrow(_lowercase		,  '''batch_bytes: long'''							)
				    .agg(pyspark.sql.functions.sum('''batch_bytes'''							).alias('''sample_bytes'''							)							)
				    .collect()[0]
				    .sample_bytes
				    / sample_num_rows
				)
				_UpperCamelCase:     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.
					_UpperCamelCase:     Tuple							= min(_lowercase		,  int(approx_total_size / max_shard_size							)							)
					_UpperCamelCase:     Optional[int]							= self.df.repartition(_lowercase							)



			def   lowerCAmelCase						(	self					:  Any		,  _lowercase					:  str		,  _lowercase					:  str		,  _lowercase					:  int		,  ):



				"""simple docstring"""



				import pyspark

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

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

					if writer._num_bytes > 0:
						_UpperCamelCase:     Dict							= 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(_lowercase							)							):
							_UpperCamelCase:     int							= os.path.join(os.path.dirname(_lowercase							)		,  os.path.basename(_lowercase							)							)
							shutil.move(_lowercase		,  _lowercase							)

				_UpperCamelCase:     List[Any]							= (
				    self.df.mapInArrow(_lowercase		,  '''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					:  Any		,  _lowercase					:  "datasets.SplitGenerator"		,  _lowercase					:  str = "arrow"		,  _lowercase					:  Optional[Union[str, int]] = None		,  _lowercase					:  Optional[int] = None		,  **_lowercase					:  Tuple		,  ):



				"""simple docstring"""



				self._validate_cache_dir()

				_UpperCamelCase:     Dict							= convert_file_size_to_int(max_shard_size or MAX_SHARD_SIZE							)
				self._repartition_df_if_needed(_lowercase							)
				_UpperCamelCase:     int							= not is_remote_filesystem(self._fs							)
				_UpperCamelCase:     Union[str, Any]							= os.path.join if is_local else posixpath.join

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

				_UpperCamelCase:     Dict							= 0
				_UpperCamelCase:     Optional[int]							= 0
				_UpperCamelCase:     Optional[int]							= 0
				_UpperCamelCase:     int							= []
				_UpperCamelCase:     Union[str, Any]							= []

				for task_id, content in self._prepare_split_single(_lowercase		,  _lowercase		,  _lowercase							):
					(
					    _UpperCamelCase							
					):     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(_lowercase							)

				_UpperCamelCase:     Dict							= total_num_examples
				_UpperCamelCase:     Tuple							= total_num_bytes

				# should rename everything at the end
				logger.debug(f"""Renaming {total_shards} shards."""							)
				if total_shards > 1:
					_UpperCamelCase:     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.
					_UpperCamelCase:     Tuple							= self._fs

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



			def   lowerCAmelCase						(	self					:  Dict		,  _lowercase					:  "datasets.SplitGenerator"		,  ):



				"""simple docstring"""



				return SparkExamplesIterable(self.df							)
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


UpperCAmelCase_			    =							{
    '''configuration_blenderbot''': [
        '''BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''BlenderbotConfig''',
        '''BlenderbotOnnxConfig''',
    ],
    '''tokenization_blenderbot''': ['''BlenderbotTokenizer'''],
}

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase_			    =							['''BlenderbotTokenizerFast''']

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase_			    =							[
      '''BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST''',
      '''BlenderbotForCausalLM''',
      '''BlenderbotForConditionalGeneration''',
      '''BlenderbotModel''',
      '''BlenderbotPreTrainedModel''',
  ]


try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase_			    =							[
      '''TFBlenderbotForConditionalGeneration''',
      '''TFBlenderbotModel''',
      '''TFBlenderbotPreTrainedModel''',
  ]


try:
  if not is_flax_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase_			    =							[
      '''FlaxBlenderbotForConditionalGeneration''',
      '''FlaxBlenderbotModel''',
      '''FlaxBlenderbotPreTrainedModel''',
  ]


if TYPE_CHECKING:
  from .configuration_blenderbot import (
      BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP,
      BlenderbotConfig,
      BlenderbotOnnxConfig,
  )
  from .tokenization_blenderbot import BlenderbotTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_blenderbot_fast import BlenderbotTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_blenderbot import (
        BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST,
        BlenderbotForCausalLM,
        BlenderbotForConditionalGeneration,
        BlenderbotModel,
        BlenderbotPreTrainedModel,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_blenderbot import (
        TFBlenderbotForConditionalGeneration,
        TFBlenderbotModel,
        TFBlenderbotPreTrainedModel,
    )

  try:
    if not is_flax_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_flax_blenderbot import (
        FlaxBlenderbotForConditionalGeneration,
        FlaxBlenderbotModel,
        FlaxBlenderbotPreTrainedModel,
    )

else:
  import sys

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