
"""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_    							=					datasets.utils.logging.get_logger(__name__)

if TYPE_CHECKING:
						import pyspark




@dataclass
class  __lowerCamelCase  ( datasets.BuilderConfig			):
				a__:      Optional[datasets.Features]          =    None






def  lowercase						(	lowerCAmelCase__	,lowerCAmelCase__	,):
			import pyspark

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

			return generate_fn




class  __lowerCamelCase  ( _BaseExamplesIterable			):




				def __init__(				self							,						UpperCAmelCase							,						UpperCAmelCase=None							,						):
							lowerCamelCase_					=  df
							lowerCamelCase_					=  partition_order or range(self.df.rdd.getNumPartitions()   )
							lowerCamelCase_					=  _generate_iterable_examples(self.df							,						self.partition_order   )




				def __iter__(				self   ):
							yield from self.generate_examples_fn()




				def        UpperCAmelCase__						(				self							,						UpperCAmelCase   ):
							lowerCamelCase_					=  list(range(self.df.rdd.getNumPartitions()   )   )
							generator.shuffle(UpperCAmelCase   )
							return SparkExamplesIterable(self.df							,						partition_order=UpperCAmelCase   )




				def        UpperCAmelCase__						(				self							,						UpperCAmelCase							,						UpperCAmelCase   ):
							lowerCamelCase_					=  self.split_shard_indices_by_worker(UpperCAmelCase							,						UpperCAmelCase   )
							return SparkExamplesIterable(self.df							,						partition_order=UpperCAmelCase   )





				@property
				def        UpperCAmelCase__						(				self   ):
							return len(self.partition_order   )




class  __lowerCamelCase  ( datasets.DatasetBuilder			):
				a__:      Optional[Any]          =    SparkConfig




				def __init__(				self							,						UpperCAmelCase							,						UpperCAmelCase = None							,						UpperCAmelCase = None							,						**UpperCAmelCase							,						):
							import pyspark

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

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




				def        UpperCAmelCase__						(				self   ):
							# Returns the path of the created file.
							def create_cache_and_write_probe(UpperCAmelCase   ):
										# 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=UpperCAmelCase   )
										lowerCamelCase_					=  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(UpperCAmelCase							,						'''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:
										lowerCamelCase_					=  (
										    self._spark.sparkContext.parallelize(range(1   )							,						1   ).mapPartitions(UpperCAmelCase   ).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   ):
							return datasets.DatasetInfo(features=self.config.features   )




				def        UpperCAmelCase__						(				self							,						UpperCAmelCase   ):
							return [datasets.SplitGenerator(name=datasets.Split.TRAIN   )]




				def        UpperCAmelCase__						(				self							,						UpperCAmelCase   ):
							import pyspark

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

							lowerCamelCase_					=  self.df.count()
							lowerCamelCase_					=  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.
							lowerCamelCase_					=  (
							    self.df.limit(UpperCAmelCase   )
							    .repartition(1   )
							    .mapInArrow(UpperCAmelCase							,						'''batch_bytes: long'''   )
							    .agg(pyspark.sql.functions.sum('''batch_bytes'''   ).alias('''sample_bytes'''   )   )
							    .collect()[0]
							    .sample_bytes
							    / sample_num_rows
							)
							lowerCamelCase_					=  approx_bytes_per_row * df_num_rows
							if approx_total_size > max_shard_size:
										# Make sure there is at least one row per partition.
										lowerCamelCase_					=  min(UpperCAmelCase							,						int(approx_total_size / max_shard_size   )   )
										lowerCamelCase_					=  self.df.repartition(UpperCAmelCase   )




				def        UpperCAmelCase__						(				self							,						UpperCAmelCase							,						UpperCAmelCase							,						UpperCAmelCase							,						):
							import pyspark

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

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

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

							lowerCamelCase_					=  (
							    self.df.mapInArrow(UpperCAmelCase							,						'''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							,						UpperCAmelCase							,						UpperCAmelCase = "arrow"							,						UpperCAmelCase = None							,						UpperCAmelCase = None							,						**UpperCAmelCase							,						):
							self._validate_cache_dir()

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

							lowerCamelCase_					=  '''-TTTTT-SSSSS-of-NNNNN'''
							lowerCamelCase_					=  f"{self.name}-{split_generator.name}{SUFFIX}.{file_format}"
							lowerCamelCase_					=  path_join(self._output_dir							,						UpperCAmelCase   )

							lowerCamelCase_					=  0
							lowerCamelCase_					=  0
							lowerCamelCase_					=  0
							lowerCamelCase_					=  []
							lowerCamelCase_					=  []

							for task_id, content in self._prepare_split_single(UpperCAmelCase							,						UpperCAmelCase							,						UpperCAmelCase   ):
										(
										    (
										    lowerCamelCase_
										)		, (
										    lowerCamelCase_
										)		, (
										    lowerCamelCase_
										)		, (
										    lowerCamelCase_
										)		, 
										)					=  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(UpperCAmelCase   )

							lowerCamelCase_					=  total_num_examples
							lowerCamelCase_					=  total_num_bytes

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

										# use the -SSSSS-of-NNNNN pattern
										def _rename_shard(
										    UpperCAmelCase							,						UpperCAmelCase							,						UpperCAmelCase							,						):
													rename(
													    UpperCAmelCase							,						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}"   )							,						)

										lowerCamelCase_					=  []
										lowerCamelCase_					=  0
										for i in range(len(UpperCAmelCase   )   ):
													lowerCamelCase_		, lowerCamelCase_					=  task_id_and_num_shards[i]
													for shard_id in range(UpperCAmelCase   ):
																args.append([task_id, shard_id, global_shard_id]   )
																global_shard_id += 1
										self._spark.sparkContext.parallelize(UpperCAmelCase							,						len(UpperCAmelCase   )   ).map(lambda UpperCAmelCase   : _rename_shard(*UpperCAmelCase   )   ).collect()
							else:
										# don't use any pattern
										lowerCamelCase_					=  0
										lowerCamelCase_					=  task_id_and_num_shards[0][0]
										self._rename(
										    fpath.replace('''SSSSS'''							,						f"{shard_id:05d}"   ).replace('''TTTTT'''							,						f"{task_id:05d}"   )							,						fpath.replace(UpperCAmelCase							,						''''''   )							,						)





				def        UpperCAmelCase__						(				self							,						UpperCAmelCase							,						):
							return SparkExamplesIterable(self.df   )





from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import KarrasVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


class 			lowercase_		(      A       ):
       __lowerCamelCase				       =						42
       __lowerCamelCase				       =						42


       def __init__(    self						,       __A						,       __A				)						->    List[str]:
              super().__init__()
              self.register_modules(unet=__A						,       scheduler=__A				)





       @torch.no_grad()
       def __call__(    self						,       __A = 1						,       __A = 50						,       __A = None						,       __A = "pil"						,       __A = True						,       **__A						,       )						->    Union[Tuple, ImagePipelineOutput]:
              SCREAMING_SNAKE_CASE_							:				Dict		=self.unet.config.sample_size
              SCREAMING_SNAKE_CASE_							:				Tuple		=(batch_size, 3, img_size, img_size)

              SCREAMING_SNAKE_CASE_							:				int		=self.unet

              # sample x_0 ~ N(0, sigma_0^2 * I)
              SCREAMING_SNAKE_CASE_							:				Tuple		=randn_tensor(__A						,       generator=__A						,       device=self.device				) * self.scheduler.init_noise_sigma

              self.scheduler.set_timesteps(__A				)

              for t in self.progress_bar(self.scheduler.timesteps				):
                     # here sigma_t == t_i from the paper
                     SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=self.scheduler.schedule[t]
                     SCREAMING_SNAKE_CASE_							:				List[Any]		=self.scheduler.schedule[t - 1] if t > 0 else 0

                     # 1. Select temporarily increased noise level sigma_hat
                     # 2. Add new noise to move from sample_i to sample_hat
                     SCREAMING_SNAKE_CASE_	,     SCREAMING_SNAKE_CASE_							:				Dict		=self.scheduler.add_noise_to_input(__A						,       __A						,       generator=__A				)

                     # 3. Predict the noise residual given the noise magnitude `sigma_hat`
                     # The model inputs and output are adjusted by following eq. (213) in [1].
                     SCREAMING_SNAKE_CASE_							:				Tuple		=(sigma_hat / 2) * model((sample_hat + 1) / 2						,       sigma_hat / 2				).sample

                     # 4. Evaluate dx/dt at sigma_hat
                     # 5. Take Euler step from sigma to sigma_prev
                     SCREAMING_SNAKE_CASE_							:				List[Any]		=self.scheduler.step(__A						,       __A						,       __A						,       __A				)

                     if sigma_prev != 0:
                            # 6. Apply 2nd order correction
                            # The model inputs and output are adjusted by following eq. (213) in [1].
                            SCREAMING_SNAKE_CASE_							:				int		=(sigma_prev / 2) * model((step_output.prev_sample + 1) / 2						,       sigma_prev / 2				).sample
                            SCREAMING_SNAKE_CASE_							:				Dict		=self.scheduler.step_correct(
                                __A						,       __A						,       __A						,       __A						,       step_output.prev_sample						,       step_output['''derivative''']						,       )
                     SCREAMING_SNAKE_CASE_							:				Any		=step_output.prev_sample

              SCREAMING_SNAKE_CASE_							:				List[Any]		=(sample / 2 + 0.5).clamp(0						,       1				)
              SCREAMING_SNAKE_CASE_							:				Any		=sample.cpu().permute(0						,       2						,       3						,       1				).numpy()
              if output_type == "pil":
                     SCREAMING_SNAKE_CASE_							:				Optional[Any]		=self.numpy_to_pil(__A				)

              if not return_dict:
                     return (image,)

              return ImagePipelineOutput(images=__A				)
