"""simple docstring"""
import math
from collections import defaultdict
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput





def 				lowerCAmelCase__      (     _UpperCamelCase    :			Union[str, Any]	,     _UpperCamelCase    :			str=0.9_99	,     _UpperCamelCase    :			List[str]="cosine"	,     )   ->			Tuple:





						"""simple docstring"""






						if alpha_transform_type == "cosine":

												def alpha_bar_fn(_UpperCamelCase    :			int   ):
																		return math.cos((t + 0.0_08) / 1.0_08 * math.pi / 2   ) ** 2

						elif alpha_transform_type == "exp":

												def alpha_bar_fn(_UpperCamelCase    :			Tuple   ):
																		return math.exp(t * -12.0   )

						else:
												raise ValueError(f"""Unsupported alpha_tranform_type: {alpha_transform_type}"""   )

						snake_case        =     []
						for i in range(_UpperCamelCase   ):
												snake_case        =     i / num_diffusion_timesteps
												snake_case        =     (i + 1) / num_diffusion_timesteps
												betas.append(min(1 - alpha_bar_fn(_UpperCamelCase   ) / alpha_bar_fn(_UpperCamelCase   )	,     _UpperCamelCase   )   )
						return torch.tensor(_UpperCamelCase	,     dtype=torch.floataa   )



class 					lowerCAmelCase_      (     lowerCAmelCase       ,      lowerCAmelCase   ):



				"""simple docstring"""
				_lowerCAmelCase       :      Any     					=  [e.name for e in KarrasDiffusionSchedulers]
				_lowerCAmelCase       :      Dict     					=  2

				@register_to_config
				def __init__(			self						,      lowerCAmelCase = 10_00						,      lowerCAmelCase = 0.0_00_85						,      lowerCAmelCase = 0.0_12						,      lowerCAmelCase = "linear"						,      lowerCAmelCase = None						,      lowerCAmelCase = "epsilon"						,      lowerCAmelCase = False						,      lowerCAmelCase = False						,      lowerCAmelCase = 1.0						,      lowerCAmelCase = "linspace"						,      lowerCAmelCase = 0						,      ):






										"""simple docstring"""

										if trained_betas is not None:
																snake_case        =     torch.tensor(lowerCAmelCase						,      dtype=torch.floataa      )
										elif beta_schedule == "linear":
																snake_case        =     torch.linspace(lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase						,      dtype=torch.floataa      )
										elif beta_schedule == "scaled_linear":
																# this schedule is very specific to the latent diffusion model.
																snake_case        =     (
																    torch.linspace(beta_start**0.5						,      beta_end**0.5						,      lowerCAmelCase						,      dtype=torch.floataa      ) ** 2
																)
										elif beta_schedule == "squaredcos_cap_v2":
																# Glide cosine schedule
																snake_case        =     betas_for_alpha_bar(lowerCAmelCase						,      alpha_transform_type='cosine'      )
										elif beta_schedule == "exp":
																snake_case        =     betas_for_alpha_bar(lowerCAmelCase						,      alpha_transform_type='exp'      )
										else:
																raise NotImplementedError(F"""{beta_schedule} does is not implemented for {self.__class__}"""      )

										snake_case        =     1.0 - self.betas
										snake_case        =     torch.cumprod(self.alphas						,      dim=0      )

										#  set all values
										self.set_timesteps(lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase      )
										snake_case        =     use_karras_sigmas

				def 						snake_case	(			self						,      lowerCAmelCase						,      lowerCAmelCase=None      ):






										"""simple docstring"""

										if schedule_timesteps is None:
																snake_case        =     self.timesteps

										snake_case        =     (schedule_timesteps == timestep).nonzero()

										# The sigma index that is taken for the **very** first `step`
										# is always the second index (or the last index if there is only 1)
										# This way we can ensure we don't accidentally skip a sigma in
										# case we start in the middle of the denoising schedule (e.g. for image-to-image)
										if len(self._index_counter      ) == 0:
																snake_case        =     1 if len(lowerCAmelCase      ) > 1 else 0
										else:
																snake_case        =     timestep.cpu().item() if torch.is_tensor(lowerCAmelCase      ) else timestep
																snake_case        =     self._index_counter[timestep_int]

										return indices[pos].item()

				@property
				def 						snake_case	(			self      ):






										"""simple docstring"""

										if self.config.timestep_spacing in ["linspace", "trailing"]:
																return self.sigmas.max()

										return (self.sigmas.max() ** 2 + 1) ** 0.5

				def 						snake_case	(			self						,      lowerCAmelCase						,      lowerCAmelCase						,      ):






										"""simple docstring"""

										snake_case        =     self.index_for_timestep(lowerCAmelCase      )

										snake_case        =     self.sigmas[step_index]
										snake_case        =     sample / ((sigma**2 + 1) ** 0.5)
										return sample

				def 						snake_case	(			self						,      lowerCAmelCase						,      lowerCAmelCase = None						,      lowerCAmelCase = None						,      ):






										"""simple docstring"""

										snake_case        =     num_inference_steps

										snake_case        =     num_train_timesteps or self.config.num_train_timesteps

										# "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
										if self.config.timestep_spacing == "linspace":
																snake_case        =     np.linspace(0						,      num_train_timesteps - 1						,      lowerCAmelCase						,      dtype=lowerCAmelCase      )[::-1].copy()
										elif self.config.timestep_spacing == "leading":
																snake_case        =     num_train_timesteps // self.num_inference_steps
																# creates integer timesteps by multiplying by ratio
																# casting to int to avoid issues when num_inference_step is power of 3
																snake_case        =     (np.arange(0						,      lowerCAmelCase      ) * step_ratio).round()[::-1].copy().astype(lowerCAmelCase      )
																timesteps += self.config.steps_offset
										elif self.config.timestep_spacing == "trailing":
																snake_case        =     num_train_timesteps / self.num_inference_steps
																# creates integer timesteps by multiplying by ratio
																# casting to int to avoid issues when num_inference_step is power of 3
																snake_case        =     (np.arange(lowerCAmelCase						,      0						,      -step_ratio      )).round().copy().astype(lowerCAmelCase      )
																timesteps -= 1
										else:
																raise ValueError(
																    F"""{self.config.timestep_spacing} is not supported. Please make sure to choose one of 'linspace', 'leading' or 'trailing'."""      )

										snake_case        =     np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5      )
										snake_case        =     np.log(lowerCAmelCase      )
										snake_case        =     np.interp(lowerCAmelCase						,      np.arange(0						,      len(lowerCAmelCase      )      )						,      lowerCAmelCase      )

										if self.config.use_karras_sigmas:
																snake_case        =     self._convert_to_karras(in_sigmas=lowerCAmelCase						,      num_inference_steps=self.num_inference_steps      )
																snake_case        =     np.array([self._sigma_to_t(lowerCAmelCase						,      lowerCAmelCase      ) for sigma in sigmas]      )

										snake_case        =     np.concatenate([sigmas, [0.0]]      ).astype(np.floataa      )
										snake_case        =     torch.from_numpy(lowerCAmelCase      ).to(device=lowerCAmelCase      )
										snake_case        =     torch.cat([sigmas[:1], sigmas[1:-1].repeat_interleave(2      ), sigmas[-1:]]      )

										snake_case        =     torch.from_numpy(lowerCAmelCase      )
										snake_case        =     torch.cat([timesteps[:1], timesteps[1:].repeat_interleave(2      )]      )

										if str(lowerCAmelCase      ).startswith('mps'      ):
																# mps does not support float64
																snake_case        =     timesteps.to(lowerCAmelCase						,      dtype=torch.floataa      )
										else:
																snake_case        =     timesteps.to(device=lowerCAmelCase      )

										# empty dt and derivative
										snake_case        =     None
										snake_case        =     None

										# for exp beta schedules, such as the one for `pipeline_shap_e.py`
										# we need an index counter
										snake_case        =     defaultdict(lowerCAmelCase      )

				def 						snake_case	(			self						,      lowerCAmelCase						,      lowerCAmelCase      ):






										"""simple docstring"""

										snake_case        =     np.log(lowerCAmelCase      )

										# get distribution
										snake_case        =     log_sigma - log_sigmas[:, np.newaxis]

										# get sigmas range
										snake_case        =     np.cumsum((dists >= 0)						,      axis=0      ).argmax(axis=0      ).clip(max=log_sigmas.shape[0] - 2      )
										snake_case        =     low_idx + 1

										snake_case        =     log_sigmas[low_idx]
										snake_case        =     log_sigmas[high_idx]

										# interpolate sigmas
										snake_case        =     (low - log_sigma) / (low - high)
										snake_case        =     np.clip(lowerCAmelCase						,      0						,      1      )

										# transform interpolation to time range
										snake_case        =     (1 - w) * low_idx + w * high_idx
										snake_case        =     t.reshape(sigma.shape      )
										return t

				def 						snake_case	(			self						,      lowerCAmelCase						,      lowerCAmelCase      ):






										"""simple docstring"""

										snake_case        =     in_sigmas[-1].item()
										snake_case        =     in_sigmas[0].item()

										snake_case        =     7.0  # 7.0 is the value used in the paper
										snake_case        =     np.linspace(0						,      1						,      lowerCAmelCase      )
										snake_case        =     sigma_min ** (1 / rho)
										snake_case        =     sigma_max ** (1 / rho)
										snake_case        =     (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
										return sigmas

				@property
				def 						snake_case	(			self      ):






										"""simple docstring"""

										return self.dt is None

				def 						snake_case	(			self						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase = True						,      ):






										"""simple docstring"""

										snake_case        =     self.index_for_timestep(lowerCAmelCase      )

										# advance index counter by 1
										snake_case        =     timestep.cpu().item() if torch.is_tensor(lowerCAmelCase      ) else timestep
										self._index_counter[timestep_int] += 1

										if self.state_in_first_order:
																snake_case        =     self.sigmas[step_index]
																snake_case        =     self.sigmas[step_index + 1]
										else:
																# 2nd order / Heun's method
																snake_case        =     self.sigmas[step_index - 1]
																snake_case        =     self.sigmas[step_index]

										# currently only gamma=0 is supported. This usually works best anyways.
										# We can support gamma in the future but then need to scale the timestep before
										# passing it to the model which requires a change in API
										snake_case        =     0
										snake_case        =     sigma * (gamma + 1)  # Note: sigma_hat == sigma for now

										# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
										if self.config.prediction_type == "epsilon":
																snake_case        =     sigma_hat if self.state_in_first_order else sigma_next
																snake_case        =     sample - sigma_input * model_output
										elif self.config.prediction_type == "v_prediction":
																snake_case        =     sigma_hat if self.state_in_first_order else sigma_next
																snake_case        =     model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
																    sample / (sigma_input**2 + 1)
																)
										elif self.config.prediction_type == "sample":
																snake_case        =     model_output
										else:
																raise ValueError(
																    F"""prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`"""      )

										if self.config.clip_sample:
																snake_case        =     pred_original_sample.clamp(
																    -self.config.clip_sample_range						,      self.config.clip_sample_range      )

										if self.state_in_first_order:
																# 2. Convert to an ODE derivative for 1st order
																snake_case        =     (sample - pred_original_sample) / sigma_hat
																# 3. delta timestep
																snake_case        =     sigma_next - sigma_hat

																# store for 2nd order step
																snake_case        =     derivative
																snake_case        =     dt
																snake_case        =     sample
										else:
																# 2. 2nd order / Heun's method
																snake_case        =     (sample - pred_original_sample) / sigma_next
																snake_case        =     (self.prev_derivative + derivative) / 2

																# 3. take prev timestep & sample
																snake_case        =     self.dt
																snake_case        =     self.sample

																# free dt and derivative
																# Note, this puts the scheduler in "first order mode"
																snake_case        =     None
																snake_case        =     None
																snake_case        =     None

										snake_case        =     sample + derivative * dt

										if not return_dict:
																return (prev_sample,)

										return SchedulerOutput(prev_sample=lowerCAmelCase      )

				def 						snake_case	(			self						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase						,      ):






										"""simple docstring"""

										snake_case        =     self.sigmas.to(device=original_samples.device						,      dtype=original_samples.dtype      )
										if original_samples.device.type == "mps" and torch.is_floating_point(lowerCAmelCase      ):
																# mps does not support float64
																snake_case        =     self.timesteps.to(original_samples.device						,      dtype=torch.floataa      )
																snake_case        =     timesteps.to(original_samples.device						,      dtype=torch.floataa      )
										else:
																snake_case        =     self.timesteps.to(original_samples.device      )
																snake_case        =     timesteps.to(original_samples.device      )

										snake_case        =     [self.index_for_timestep(lowerCAmelCase						,      lowerCAmelCase      ) for t in timesteps]

										snake_case        =     sigmas[step_indices].flatten()
										while len(sigma.shape      ) < len(original_samples.shape      ):
																snake_case        =     sigma.unsqueeze(-1      )

										snake_case        =     original_samples + noise * sigma
										return noisy_samples

				def __len__(			self      ):






										"""simple docstring"""

										return self.config.num_train_timesteps



"""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


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

if TYPE_CHECKING:
					import pyspark



@dataclass
class 					lowerCAmelCase_      (     datasets.BuilderConfig   ):



				"""simple docstring"""
				_lowerCAmelCase       :      Optional[datasets.Features]     					=  None





def 				lowerCAmelCase__      (     _UpperCamelCase    :			"pyspark.sql.DataFrame"	,     _UpperCamelCase    :			List[int]	,     )   ->			Dict:





						"""simple docstring"""






						import pyspark

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

						return generate_fn



class 					lowerCAmelCase_      (     _BaseExamplesIterable   ):



				"""simple docstring"""

				def __init__(			self						,      lowerCAmelCase						,      lowerCAmelCase=None						,      ):






										"""simple docstring"""

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

				def __iter__(			self      ):






										"""simple docstring"""

										yield from self.generate_examples_fn()

				def 						snake_case	(			self						,      lowerCAmelCase      ):






										"""simple docstring"""

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

				def 						snake_case	(			self						,      lowerCAmelCase						,      lowerCAmelCase      ):






										"""simple docstring"""

										snake_case        =     self.split_shard_indices_by_worker(lowerCAmelCase						,      lowerCAmelCase      )
										return SparkExamplesIterable(self.df						,      partition_order=lowerCAmelCase      )

				@property
				def 						snake_case	(			self      ):






										"""simple docstring"""

										return len(self.partition_order      )





class 					lowerCAmelCase_      (     datasets.DatasetBuilder   ):



				"""simple docstring"""
				_lowerCAmelCase       :      List[str]     					=  SparkConfig

				def __init__(			self						,      lowerCAmelCase						,      lowerCAmelCase = None						,      lowerCAmelCase = None						,      **lowerCAmelCase						,      ):






										"""simple docstring"""

										import pyspark

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

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

				def 						snake_case	(			self      ):






										"""simple docstring"""

										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      )
																snake_case        =     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:
																snake_case        =     (
																    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 						snake_case	(			self      ):






										"""simple docstring"""

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

				def 						snake_case	(			self						,      lowerCAmelCase      ):






										"""simple docstring"""

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

				def 						snake_case	(			self						,      lowerCAmelCase      ):






										"""simple docstring"""

										import pyspark

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

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

				def 						snake_case	(			self						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase						,      ):






										"""simple docstring"""

										import pyspark

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

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

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

										snake_case        =     (
										    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 						snake_case	(			self						,      lowerCAmelCase						,      lowerCAmelCase = "arrow"						,      lowerCAmelCase = None						,      lowerCAmelCase = None						,      **lowerCAmelCase						,      ):






										"""simple docstring"""

										self._validate_cache_dir()

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

										snake_case        =     '-TTTTT-SSSSS-of-NNNNN'
										snake_case        =     F"""{self.name}-{split_generator.name}{SUFFIX}.{file_format}"""
										snake_case        =     path_join(self._output_dir						,      lowerCAmelCase      )

										snake_case        =     0
										snake_case        =     0
										snake_case        =     0
										snake_case        =     []
										snake_case        =     []

										for task_id, content in self._prepare_split_single(lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase      ):
																(
																    (
																    snake_case
																)   ,(
																    snake_case
																)   ,(
																    snake_case
																)   ,(
																    snake_case
																)   ,
																)        =     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      )

										snake_case        =     total_num_examples
										snake_case        =     total_num_bytes

										# should rename everything at the end
										logger.debug(F"""Renaming {total_shards} shards."""      )
										if total_shards > 1:
																snake_case        =     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.
																snake_case        =     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}"""      )						,      )

																snake_case        =     []
																snake_case        =     0
																for i in range(len(lowerCAmelCase      )      ):
																						snake_case   ,snake_case        =     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
																snake_case        =     0
																snake_case        =     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 						snake_case	(			self						,      lowerCAmelCase						,      ):






										"""simple docstring"""

										return SparkExamplesIterable(self.df      )


