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


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

if TYPE_CHECKING:
					import pyspark



@dataclass
class     _A   (    datasets.BuilderConfig							):
		SCREAMING_SNAKE_CASE_	:	Optional[datasets.Features]						=None







def  __UpperCamelCase    (						A ,							A ,							):
					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     _A   (    _BaseExamplesIterable							):






		def __init__(self   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_=None   ,				)		->      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	)		->      int:





							'''simple docstring'''





							yield from self.generate_examples_fn()






		def 		_a					(self   ,				SCREAMING_SNAKE_CASE_	)		->      "SparkExamplesIterable":





							'''simple docstring'''





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






		def 		_a					(self   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_	)		->      "SparkExamplesIterable":





							'''simple docstring'''





							UpperCamelCase__										=      self.split_shard_indices_by_worker(SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_	)
							return SparkExamplesIterable(self.df   ,				partition_order=SCREAMING_SNAKE_CASE_	)






		@property
		def 		_a					(self	)		->      int:





							'''simple docstring'''





							return len(self.partition_order	)



class     _A   (    datasets.DatasetBuilder							):
		SCREAMING_SNAKE_CASE_	:	List[str]						=SparkConfig






		def __init__(self   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_ = None   ,				SCREAMING_SNAKE_CASE_ = None   ,				**SCREAMING_SNAKE_CASE_   ,				)		->      Union[str, Any]:





							'''simple docstring'''





							import pyspark

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

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






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





							'''simple docstring'''





							def create_cache_and_write_probe(SCREAMING_SNAKE_CASE_	):
												# 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=SCREAMING_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(SCREAMING_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(SCREAMING_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 		_a					(self	)		->      Any:





							'''simple docstring'''





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






		def 		_a					(self   ,				SCREAMING_SNAKE_CASE_	)		->      int:





							'''simple docstring'''





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






		def 		_a					(self   ,				SCREAMING_SNAKE_CASE_	)		->      List[str]:





							'''simple docstring'''





							import pyspark

							def get_arrow_batch_size(SCREAMING_SNAKE_CASE_	):
												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(SCREAMING_SNAKE_CASE_	)
							    .repartition(1	)
							    .mapInArrow(SCREAMING_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(SCREAMING_SNAKE_CASE_   ,				int(approx_total_size / max_shard_size	)	)
												UpperCamelCase__										=      self.df.repartition(SCREAMING_SNAKE_CASE_	)






		def 		_a					(self   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				)		->      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(SCREAMING_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(SCREAMING_SNAKE_CASE_	):
												# Within the same SparkContext, no two task attempts will share the same attempt ID.
												UpperCamelCase__										=      pyspark.TaskContext().taskAttemptId()
												UpperCamelCase__										=      next(SCREAMING_SNAKE_CASE_   ,				SCREAMING_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=SCREAMING_SNAKE_CASE_   ,				path=working_fpath.replace('''SSSSS'''   ,				F"{shard_id:05d}"	).replace('''TTTTT'''   ,				F"{task_id:05d}"	)   ,				writer_batch_size=SCREAMING_SNAKE_CASE_   ,				storage_options=SCREAMING_SNAKE_CASE_   ,				embed_local_files=SCREAMING_SNAKE_CASE_   ,				)
												UpperCamelCase__										=      pa.Table.from_batches([first_batch]	)
												writer.write_table(SCREAMING_SNAKE_CASE_	)
												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=SCREAMING_SNAKE_CASE_   ,				storage_options=SCREAMING_SNAKE_CASE_   ,				embed_local_files=SCREAMING_SNAKE_CASE_   ,				)
																	UpperCamelCase__										=      pa.Table.from_batches([batch]	)
																	writer.write_table(SCREAMING_SNAKE_CASE_	)

												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(SCREAMING_SNAKE_CASE_	)	):
																						UpperCamelCase__										=      os.path.join(os.path.dirname(SCREAMING_SNAKE_CASE_	)   ,				os.path.basename(SCREAMING_SNAKE_CASE_	)	)
																						shutil.move(SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_	)

							UpperCamelCase__										=      (
							    self.df.mapInArrow(SCREAMING_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 		_a					(self   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_ = "arrow"   ,				SCREAMING_SNAKE_CASE_ = None   ,				SCREAMING_SNAKE_CASE_ = None   ,				**SCREAMING_SNAKE_CASE_   ,				)		->      Optional[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(SCREAMING_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   ,				SCREAMING_SNAKE_CASE_	)

							UpperCamelCase__										=      0
							UpperCamelCase__										=      0
							UpperCamelCase__										=      0
							UpperCamelCase__										=      []
							UpperCamelCase__										=      []

							for task_id, content in self._prepare_split_single(SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_	):
												(
												    (
												    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(SCREAMING_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(
												    SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				):
																	rename(
																	    SCREAMING_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(SCREAMING_SNAKE_CASE_	)	):
																	UpperCamelCase__						,       UpperCamelCase__										=      task_id_and_num_shards[i]
																	for shard_id in range(SCREAMING_SNAKE_CASE_	):
																						args.append([task_id, shard_id, global_shard_id]	)
																						global_shard_id += 1
												self._spark.sparkContext.parallelize(SCREAMING_SNAKE_CASE_   ,				len(SCREAMING_SNAKE_CASE_	)	).map(lambda SCREAMING_SNAKE_CASE_	: _rename_shard(*SCREAMING_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(SCREAMING_SNAKE_CASE_   ,				''''''	)   ,				)






		def 		_a					(self   ,				SCREAMING_SNAKE_CASE_   ,				)		->      SparkExamplesIterable:





							'''simple docstring'''





							return SparkExamplesIterable(self.df	)




import numpy as np
import pandas as pd
from sklearn.preprocessing import MinMaxScaler
from tensorflow.keras.layers import LSTM, Dense
from tensorflow.keras.models import Sequential

if __name__ == "__main__":
     __magic_name__								=pd.read_csv('''sample_data.csv''', header=None)
     __magic_name__								=df.shape[:1][0]
     # If you're using some other dataset input the target column
     __magic_name__								=df.iloc[:, 1:2]
     __magic_name__								=actual_data.values.reshape(len_data, 1)
     __magic_name__								=MinMaxScaler().fit_transform(actual_data)
     __magic_name__								=10
     __magic_name__								=5
     __magic_name__								=20
     __magic_name__								=len_data - periods * look_back
     __magic_name__								=actual_data[:division]
     __magic_name__								=actual_data[division - look_back :]
     __magic_name__		,  __magic_name__								=[], []
     __magic_name__		,  __magic_name__								=[], []

     for i in range(0, len(train_data) - forward_days - look_back + 1):
          train_x.append(train_data[i : i + look_back])
          train_y.append(train_data[i + look_back : i + look_back + forward_days])
     for i in range(0, len(test_data) - forward_days - look_back + 1):
          test_x.append(test_data[i : i + look_back])
          test_y.append(test_data[i + look_back : i + look_back + forward_days])
     __magic_name__								=np.array(train_x)
     __magic_name__								=np.array(test_x)
     __magic_name__								=np.array([list(i.ravel()) for i in train_y])
     __magic_name__								=np.array([list(i.ravel()) for i in test_y])

     __magic_name__								=Sequential()
     model.add(LSTM(128, input_shape=(look_back, 1), return_sequences=True))
     model.add(LSTM(64, input_shape=(128, 1)))
     model.add(Dense(forward_days))
     model.compile(loss='''mean_squared_error''', optimizer='''adam''')
     __magic_name__								=model.fit(
         x_train, y_train, epochs=150, verbose=1, shuffle=True, batch_size=4
     )
     __magic_name__								=model.predict(x_test)



