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


lowerCamelCase       :	str									=     datasets.utils.logging.get_logger(__name__)

if TYPE_CHECKING:
 import pyspark







@dataclass
class 						A(		datasets.BuilderConfig     ):



 '''simple docstring'''


 UpperCamelCase		     =		None

def     _SCREAMING_SNAKE_CASE    (							lowercase			: "pyspark.sql.DataFrame"			,							lowercase			: List[int]			,							):


      '''simple docstring'''



      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 						A(		_BaseExamplesIterable     ):



 '''simple docstring'''


 def __init__(       self      :						int		,							A_      :						"pyspark.sql.DataFrame"		,							A_      :						Optional[int]=None		,							)     ->			List[str]:

       """simple docstring"""






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

 def __iter__(       self      :						List[Any]						)     ->			str:

       """simple docstring"""






       yield from self.generate_examples_fn()

 def        a__				(       self      :						Optional[int]		,							A_      :						np.random.Generator						)     ->			List[Any]:

       """simple docstring"""






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

 def        a__				(       self      :						Optional[int]		,							A_      :						int		,							A_      :						int						)     ->			Optional[Any]:

       """simple docstring"""






       lowerCamelCase_	  =					self.split_shard_indices_by_worker(lowercase_		,							lowercase_						)
       return SparkExamplesIterable(self.df		,							partition_order=lowercase_						)





 @property
 def        a__				(       self      :						List[str]						)     ->			Dict:

       """simple docstring"""






       return len(self.partition_order						)







class 						A(		datasets.DatasetBuilder     ):



 '''simple docstring'''


 UpperCamelCase		     =		SparkConfig

 def __init__(       self      :						Tuple		,							A_      :						"pyspark.sql.DataFrame"		,							A_      :						str = None		,							A_      :						str = None		,							**A_      :						str		,							)     ->			Optional[Any]:

       """simple docstring"""






       import pyspark

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

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

 def        a__				(       self      :						str						)     ->			str:

       """simple docstring"""






       def create_cache_and_write_probe(A_      :						str						):
             # 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_						)
             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(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:
             lowerCamelCase_	  =					(
                 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        a__				(       self      :						Union[str, Any]						)     ->			Optional[int]:

       """simple docstring"""






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

 def        a__				(       self      :						List[Any]		,							A_      :						datasets.download.download_manager.DownloadManager						)     ->			Tuple:

       """simple docstring"""






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

 def        a__				(       self      :						List[str]		,							A_      :						Union[str, Any]						)     ->			Dict:

       """simple docstring"""






       import pyspark

       def get_arrow_batch_size(A_      :						Any						):
             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(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
       )
       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(lowercase_		,							int(approx_total_size / max_shard_size						)						)
             lowerCamelCase_	  =					self.df.repartition(lowercase_						)

 def        a__				(       self      :						Any		,							A_      :						str		,							A_      :						str		,							A_      :						int		,							)     ->			Dict:

       """simple docstring"""






       import pyspark

       lowerCamelCase_	  =					ParquetWriter if file_format == """parquet""" else ArrowWriter
       lowerCamelCase_	  =					os.path.join(self._working_dir		,							os.path.basename(lowercase_						)						) 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(A_      :						str						):
             # Within the same SparkContext, no two task attempts will share the same attempt ID.
             lowerCamelCase_	  =					pyspark.TaskContext().taskAttemptId()
             lowerCamelCase_	  =					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']		,							)
             lowerCamelCase_	  =					0
             lowerCamelCase_	  =					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_		,							)
             lowerCamelCase_	  =					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:
                         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=lowercase_		,							storage_options=lowercase_		,							embed_local_files=lowercase_		,							)
                   lowerCamelCase_	  =					pa.Table.from_batches([batch]						)
                   writer.write_table(lowercase_						)

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

       lowerCamelCase_	  =					(
           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        a__				(       self      :						Dict		,							A_      :						"datasets.SplitGenerator"		,							A_      :						str = "arrow"		,							A_      :						Optional[Union[str, int]] = None		,							A_      :						Optional[int] = None		,							**A_      :						List[str]		,							)     ->			Optional[Any]:

       """simple docstring"""






       self._validate_cache_dir()

       lowerCamelCase_	  =					convert_file_size_to_int(max_shard_size or MAX_SHARD_SIZE						)
       self._repartition_df_if_needed(lowercase_						)
       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		,							lowercase_						)

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

       for task_id, content in self._prepare_split_single(lowercase_		,							lowercase_		,							lowercase_						):
             (
                 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(lowercase_						)

       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(
                 A_      :						int		,							A_      :						int		,							A_      :						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}"""						)		,							)

             lowerCamelCase_	  =					[]
             lowerCamelCase_	  =					0
             for i in range(len(lowercase_						)						):
                   lowerCamelCase_	  =					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 A_						: _rename_shard(*lowercase_						)						).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(lowercase_		,							''						)		,							)





 def        a__				(       self      :						Union[str, Any]		,							A_      :						"datasets.SplitGenerator"		,							)     ->			Optional[Any]:

       """simple docstring"""






       return SparkExamplesIterable(self.df						)









import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_tf


if is_tf_available():
 import tensorflow as tf
 from tensorflow.python.eager import context
 from tensorflow.python.framework import ops

 from transformers import GradientAccumulator, create_optimizer







@require_tf
class 						A(		unittest.TestCase     ):



 '''simple docstring'''


 def        a__				(       self      :						Optional[int]		,							A_      :						Tuple		,							A_      :						str		,							A_      :						int						)     ->			Any:

       """simple docstring"""






       self.assertEqual(len(A_						)		,							len(A_						)						)
       for a, b in zip(A_		,							A_						):
             self.assertAlmostEqual(A_		,							A_		,							delta=A_						)

 def        a__				(       self      :						int						)     ->			str:

       """simple docstring"""






       lowerCamelCase_	  =					GradientAccumulator()
       accumulator([tf.constant([1.0, 2.0]						)]						)
       accumulator([tf.constant([-2.0, 1.0]						)]						)
       accumulator([tf.constant([-1.0, 2.0]						)]						)
       with self.assertRaises(A_						):
             accumulator([tf.constant([1.0, 1.0]						), tf.constant([2.0, 2.0]						)]						)
       self.assertEqual(accumulator.step		,							3						)
       self.assertEqual(len(accumulator.gradients						)		,							1						)
       self.assertListAlmostEqual(accumulator.gradients[0].numpy().tolist()		,							[-2.0, 5.0]		,							tol=1E-2						)
       accumulator.reset()
       self.assertEqual(accumulator.step		,							0						)
       self.assertListAlmostEqual(accumulator.gradients[0].numpy().tolist()		,							[0.0, 0.0]		,							tol=1E-2						)





 def        a__				(       self      :						List[Any]						)     ->			List[Any]:

       """simple docstring"""






       lowerCamelCase_	  =					None
       ops.enable_eager_execution_internal()
       lowerCamelCase_	  =					tf.config.list_physical_devices('CPU'						)
       if len(A_						) == 1:
             tf.config.set_logical_device_configuration(
                 physical_devices[0]		,							[tf.config.LogicalDeviceConfiguration(), tf.config.LogicalDeviceConfiguration()]						)
       lowerCamelCase_	  =					tf.config.list_logical_devices(device_type='CPU'						)
       lowerCamelCase_	  =					tf.distribute.MirroredStrategy(devices=devices[:2]						)

       with strategy.scope():
             lowerCamelCase_	  =					GradientAccumulator()
             lowerCamelCase_	  =					tf.Variable([4.0, 3.0]						)
             lowerCamelCase_  ,       lowerCamelCase_	  =					create_optimizer(5E-5		,							10		,							5						)
             lowerCamelCase_	  =					tf.Variable([0.0, 0.0]		,							trainable=A_						)

       def accumulate_on_replica(A_      :						Any						):
             accumulator([gradient]						)

       def apply_on_replica():
             optimizer.apply_gradients(list(zip(accumulator.gradients		,							[variable]						)						)						)

       @tf.function
       def accumulate(A_      :						List[Any]		,							A_      :						Tuple						):
             with strategy.scope():
                   lowerCamelCase_	  =					strategy.experimental_local_results(A_						)
                   local_variables[0].assign(A_						)
                   local_variables[1].assign(A_						)
                   strategy.run(A_		,							args=(gradient_placeholder,)						)

       @tf.function
       def apply_grad():
             with strategy.scope():
                   strategy.run(A_						)

       def _check_local_values(A_      :						List[Any]		,							A_      :						str						):
             lowerCamelCase_	  =					strategy.experimental_local_results(accumulator._gradients[0]						)
             self.assertListAlmostEqual(values[0].value()		,							A_		,							tol=1E-2						)
             self.assertListAlmostEqual(values[1].value()		,							A_		,							tol=1E-2						)

       accumulate([1.0, 2.0]		,							[-1.0, 1.0]						)
       accumulate([3.0, -1.0]		,							[-1.0, -1.0]						)
       accumulate([-2.0, 2.0]		,							[3.0, -2.0]						)
       self.assertEqual(accumulator.step		,							3						)
       _check_local_values([2.0, 3.0]		,							[1.0, -2.0]						)
       apply_grad()
       self.assertListAlmostEqual(variable.value()		,							[4.0, 3.0]		,							tol=1E-2						)
       accumulator.reset()
       self.assertEqual(accumulator.step		,							0						)
       _check_local_values([0.0, 0.0]		,							[0.0, 0.0]						)



