

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      				=     datasets.utils.logging.get_logger(__name__)

if TYPE_CHECKING:
		import pyspark





@dataclass
class     snake_case_			(				datasets.BuilderConfig							):


							"""simple docstring"""





							__UpperCAmelCase       =None


def        __lowercase    (  UpperCAmelCase__		,					UpperCAmelCase__		,					):

					"""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     snake_case_			(				_BaseExamplesIterable							):


							"""simple docstring"""





							def __init__( self					,						_A					,						_A=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        A__  ( self					,						_A							):
												__lowerCAmelCase					=							list(range(self.df.rdd.getNumPartitions()							)							)
												generator.shuffle(_A							)
												return SparkExamplesIterable(self.df					,						partition_order=_A							)




							def        A__  ( self					,						_A					,						_A							):
												__lowerCAmelCase					=							self.split_shard_indices_by_worker(_A					,						_A							)
												return SparkExamplesIterable(self.df					,						partition_order=_A							)




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





class     snake_case_			(				datasets.DatasetBuilder							):


							"""simple docstring"""





							__UpperCAmelCase       =SparkConfig




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

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

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




							def        A__  ( self							):
												# 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							)
																	__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(_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:
																	__lowerCAmelCase					=							(
																	    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        A__  ( self							):
												return datasets.DatasetInfo(features=self.config.features							)




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




							def        A__  ( self					,						_A							):
												import pyspark

												def get_arrow_batch_size(_A							):
																	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 <= 1_0_0 else 1_0_0
												# Approximate the size of each row (in Arrow format) by averaging over a max-100-row sample.
												__lowerCAmelCase					=							(
												    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
												)
												__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(_A					,						int(approx_total_size / max_shard_size							)							)
																	__lowerCAmelCase					=							self.df.repartition(_A							)




							def        A__  ( self					,						_A					,						_A					,						_A					,						):
												import pyspark

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

																	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(_A							)							):
																											__lowerCAmelCase					=							os.path.join(os.path.dirname(_A							)					,						os.path.basename(_A							)							)
																											shutil.move(_A					,						_A							)

												__lowerCAmelCase					=							(
												    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        A__  ( self					,						_A					,						_A = "arrow"					,						_A = None					,						_A = None					,						**_A					,						):
												self._validate_cache_dir()

												__lowerCAmelCase					=							convert_file_size_to_int(max_shard_size or MAX_SHARD_SIZE							)
												self._repartition_df_if_needed(_A							)
												__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					,						_A							)

												__lowerCAmelCase					=							0
												__lowerCAmelCase					=							0
												__lowerCAmelCase					=							0
												__lowerCAmelCase					=							[]
												__lowerCAmelCase					=							[]

												for task_id, content in self._prepare_split_single(_A					,						_A					,						_A							):
																	(
																	    (
																	    __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(_A							)

												__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					,						_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}"""							)					,						)

																	__lowerCAmelCase					=							[]
																	__lowerCAmelCase					=							0
																	for i in range(len(_A							)							):
																						__lowerCAmelCase,	__lowerCAmelCase					=							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
																	__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(_A					,						''							)					,						)




							def        A__  ( self					,						_A					,						):
												return SparkExamplesIterable(self.df							)









# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import torch

from ..models.auto import AutoModelForSequenceClassification, AutoTokenizer
from .base import PipelineTool





class     snake_case_			(				_a							):


							"""simple docstring"""





							__UpperCAmelCase       ="""facebook/bart-large-mnli"""
							__UpperCAmelCase       =(
							    """This is a tool that classifies an English text using provided labels. It takes two inputs: `text`, which """
							    """should be the text to classify, and `labels`, which should be the list of labels to use for classification. """
							    """It returns the most likely label in the list of provided `labels` for the input text."""
							)
							__UpperCAmelCase       ="""text_classifier"""
							__UpperCAmelCase       =AutoTokenizer
							__UpperCAmelCase       =AutoModelForSequenceClassification

							__UpperCAmelCase       =["""text""", ["""text"""]]
							__UpperCAmelCase       =["""text"""]




							def        A__  ( self							):
												super().setup()
												__lowerCAmelCase					=							self.model.config
												__lowerCAmelCase					=							-1
												for idx, label in config.idalabel.items():
																	if label.lower().startswith('entail'							):
																						__lowerCAmelCase					=							int(_A							)
												if self.entailment_id == -1:
																	raise ValueError('Could not determine the entailment ID from the model config, please pass it at init.'							)




							def        A__  ( self					,						_A					,						_A							):
												__lowerCAmelCase					=							labels
												return self.pre_processor(
												    [text] * len(_A							)					,						[F"""This example is {label}""" for label in labels]					,						return_tensors='pt'					,						padding='max_length'					,						)




							def        A__  ( self					,						_A							):
												__lowerCAmelCase					=							outputs.logits
												__lowerCAmelCase					=							torch.argmax(logits[:, 2]							).item()
												return self._labels[label_id]






