




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       _A				(		datasets.BuilderConfig     ):
		__a					    =       None




def        _SCREAMING_SNAKE_CASE      (		snake_case			,     snake_case			,     )						->				Optional[int]:
						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						,		):
								_UpperCAmelCase      =			df
								_UpperCAmelCase      =			partition_order or range(self.df.rdd.getNumPartitions()			)
								_UpperCAmelCase      =			_generate_iterable_examples(self.df						,		self.partition_order			)






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






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






		def 				UpperCAmelCase (    self						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE			):
								_UpperCAmelCase      =			self.split_shard_indices_by_worker(_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE			)
								return SparkExamplesIterable(self.df						,		partition_order=_SCREAMING_SNAKE_CASE			)






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




class       _A				(		datasets.DatasetBuilder     ):
		__a					    =       SparkConfig






		def __init__(    self						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE = None						,		_SCREAMING_SNAKE_CASE = None						,		**_SCREAMING_SNAKE_CASE						,		):
								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 				UpperCAmelCase (    self			):
								# Returns the path of the created file.
								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 				UpperCAmelCase (    self			):
								return datasets.DatasetInfo(features=self.config.features			)






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






		def 				UpperCAmelCase (    self						,		_SCREAMING_SNAKE_CASE			):
								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 				UpperCAmelCase (    self						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE						,		):
								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 				UpperCAmelCase (    self						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE = "arrow"						,		_SCREAMING_SNAKE_CASE = None						,		_SCREAMING_SNAKE_CASE = None						,		**_SCREAMING_SNAKE_CASE						,		):
								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 				UpperCAmelCase (    self						,		_SCREAMING_SNAKE_CASE						,		):
								return SparkExamplesIterable(self.df			)





import jax.numpy as jnp

from ...utils import logging
from ..ta.modeling_flax_ta import FlaxTaEncoderModel, FlaxTaForConditionalGeneration, FlaxTaModel
from .configuration_mta import MTaConfig


a													=			logging.get_logger(__name__)

a													=			"T5Config"




def        _SCREAMING_SNAKE_CASE      (		snake_case			,     snake_case			,     snake_case       )						->				jnp.ndarray:
						_UpperCAmelCase      =			jnp.zeros_like(snake_case       )
						_UpperCAmelCase      =			shifted_input_ids.at[:, 1:].set(input_ids[:, :-1]       )
						_UpperCAmelCase      =			shifted_input_ids.at[:, 0].set(snake_case       )

						_UpperCAmelCase      =			jnp.where(shifted_input_ids == -1_0_0			,     snake_case			,     snake_case       )
						return shifted_input_ids




class       _A				(		__lowercase     ):
		__a					    =       """mt5"""
		__a					    =       MTaConfig




class       _A				(		__lowercase     ):
		__a					    =       """mt5"""
		__a					    =       MTaConfig




class       _A				(		__lowercase     ):
		__a					    =       """mt5"""
		__a					    =       MTaConfig