
"""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


_UpperCAmelCase :			List[Any]              =	datasets.utils.logging.get_logger(__name__)

if TYPE_CHECKING:
					import pyspark


@dataclass
class     lowercase (		datasets.BuilderConfig       ):
		__lowercase	:	Optional[datasets.Features]     					=    None






def 				A	( lowercase   ,	lowercase   ,	)       ->  Dict:
		'''simple docstring'''

		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       ):



		def __init__(			self    ,				A_    ,				A_=None    ,				)  -> Union[str, Any]:
				"""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	)  -> 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(A_	)
				return SparkExamplesIterable(self.df    ,				partition_order=A_	)



		def __UpperCamelCase			(			self    ,				A_    ,				A_	)  -> "SparkExamplesIterable":
				"""simple docstring"""
				UpperCamelCase          =					self.split_shard_indices_by_worker(A_    ,				A_	)
				return SparkExamplesIterable(self.df    ,				partition_order=A_	)



		@property
		def __UpperCamelCase			(			self	)  -> int:
				"""simple docstring"""
				return len(self.partition_order	)






class     lowercase (		datasets.DatasetBuilder       ):
		__lowercase	:	int     					=    SparkConfig



		def __init__(			self    ,				A_    ,				A_ = None    ,				A_ = None    ,				**A_    ,				)  -> Any:
				"""simple docstring"""
				import pyspark

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

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



		def __UpperCamelCase			(			self	)  -> Tuple:
				"""simple docstring"""
				# Returns the path of the created file.
				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_	)
						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(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:
						UpperCamelCase          =					(
						    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 __UpperCamelCase			(			self	)  -> Union[str, Any]:
				"""simple docstring"""
				return datasets.DatasetInfo(features=self.config.features	)



		def __UpperCamelCase			(			self    ,				A_	)  -> str:
				"""simple docstring"""
				return [datasets.SplitGenerator(name=datasets.Split.TRAIN	)]



		def __UpperCamelCase			(			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]}	)

				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(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
				)
				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(A_    ,				int(approx_total_size / max_shard_size	)	)
						UpperCamelCase          =					self.df.repartition(A_	)



		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(A_	)	) 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(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']    ,				)
						UpperCamelCase          =					0
						UpperCamelCase          =					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_    ,				)
						UpperCamelCase          =					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:
										UpperCamelCase     ,  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=A_    ,				storage_options=A_    ,				embed_local_files=A_    ,				)
								UpperCamelCase          =					pa.Table.from_batches([batch]	)
								writer.write_table(A_	)

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

				UpperCamelCase          =					(
				    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 __UpperCamelCase			(			self    ,				A_    ,				A_ = "arrow"    ,				A_ = None    ,				A_ = None    ,				**A_    ,				)  -> List[Any]:
				"""simple docstring"""
				self._validate_cache_dir()

				UpperCamelCase          =					convert_file_size_to_int(max_shard_size or MAX_SHARD_SIZE	)
				self._repartition_df_if_needed(A_	)
				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    ,				A_	)

				UpperCamelCase          =					0
				UpperCamelCase          =					0
				UpperCamelCase          =					0
				UpperCamelCase          =					[]
				UpperCamelCase          =					[]

				for task_id, content in self._prepare_split_single(A_    ,				A_    ,				A_	):
						(
						    (
						    UpperCamelCase  
						)     ,  (
						    UpperCamelCase  
						)     ,  (
						    UpperCamelCase  
						)     ,  (
						    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(A_	)

				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(
								    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}'''	)    ,				)

						UpperCamelCase          =					[]
						UpperCamelCase          =					0
						for i in range(len(A_	)	):
								UpperCamelCase     ,  UpperCamelCase          =					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
						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(A_    ,				''	)    ,				)



		def __UpperCamelCase			(			self    ,				A_    ,				)  -> SparkExamplesIterable:
				"""simple docstring"""
				return SparkExamplesIterable(self.df	)


import darl  # noqa
import gym
import tqdm
from diffusers.experimental import ValueGuidedRLPipeline


_UpperCAmelCase :			Optional[Any]              =	{
    "n_samples": 64,
    "horizon": 32,
    "num_inference_steps": 20,
    "n_guide_steps": 2,  # can set to 0 for faster sampling, does not use value network
    "scale_grad_by_std": True,
    "scale": 0.1,
    "eta": 0.0,
    "t_grad_cutoff": 2,
    "device": "cpu",
}


if __name__ == "__main__":
					_UpperCAmelCase :			int              =	"hopper-medium-v2"
					_UpperCAmelCase :			Tuple              =	gym.make(env_name)

					_UpperCAmelCase :			Any              =	ValueGuidedRLPipeline.from_pretrained(
					    "bglick13/hopper-medium-v2-value-function-hor32",
					    env=env,
					)

					env.seed(0)
					_UpperCAmelCase :			Optional[Any]              =	env.reset()
					_UpperCAmelCase :			Union[str, Any]              =	0
					_UpperCAmelCase :			Union[str, Any]              =	0
					_UpperCAmelCase :			Dict              =	1_000
					_UpperCAmelCase :			Tuple              =	[obs.copy()]
					try:
										for t in tqdm.tqdm(range(T)):
															# call the policy
															_UpperCAmelCase :			int              =	pipeline(obs, planning_horizon=32)

															# execute action in environment
															_UpperCAmelCase       ,_UpperCAmelCase       ,_UpperCAmelCase       ,_UpperCAmelCase :			List[Any]              =	env.step(denorm_actions)
															_UpperCAmelCase :			int              =	env.get_normalized_score(total_reward)

															# update return
															total_reward += reward
															total_score += score
															print(
															    F'''Step: {t}, Reward: {reward}, Total Reward: {total_reward}, Score: {score}, Total Score:'''
															    F''' {total_score}'''
															)

															# save observations for rendering
															rollout.append(next_observation.copy())

															_UpperCAmelCase :			Union[str, Any]              =	next_observation
					except KeyboardInterrupt:
										pass

					print(F'''Total reward: {total_reward}''')
