





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     :     Tuple							=	datasets.utils.logging.get_logger(__name__)

if TYPE_CHECKING:
					import pyspark







@dataclass
class     __lowerCAmelCase	(   datasets.BuilderConfig):
						_lowercase    : Optional[datasets.Features]            =      None
def 					_A	(		SCREAMING_SNAKE_CASE					:       "pyspark.sql.DataFrame"	,	SCREAMING_SNAKE_CASE					:       List[int]	,	):





		"""simple docstring"""



		import pyspark

		def generate_fn():
				a__     : Dict  =df.select("*"	,	pyspark.sql.functions.spark_partition_id().alias("part_id"						)						)
				for partition_id in partition_order:
						a__     : Tuple  =df_with_partition_id.select("*"						).where(f'''part_id = {partition_id}'''						).drop("part_id"						)
						a__     : List[Any]  =partition_df.collect()
						a__     : Union[str, Any]  =0
						for row in rows:
								yield f'''{partition_id}_{row_id}''', row.asDict()
								row_id += 1

		return generate_fn







class     __lowerCAmelCase	(   _BaseExamplesIterable):







						def __init__(   self		,				lowerCAmelCase__		,				lowerCAmelCase__=None		,				)       ->						Any:



								'''simple docstring'''




								a__     : Dict  =df
								a__     : Tuple  =partition_order or range(self.df.rdd.getNumPartitions()							)
								a__     : Any  =_generate_iterable_examples(self.df		,				self.partition_order							)







						def __iter__(   self							)       ->						Dict:



								'''simple docstring'''




								yield from self.generate_examples_fn()







						def        _lowercase  (   self		,				lowerCAmelCase__							)       ->						"SparkExamplesIterable":



								'''simple docstring'''




								a__     : Any  =list(range(self.df.rdd.getNumPartitions()							)							)
								generator.shuffle(lowerCAmelCase__							)
								return SparkExamplesIterable(self.df		,				partition_order=lowerCAmelCase__							)







						def        _lowercase  (   self		,				lowerCAmelCase__		,				lowerCAmelCase__							)       ->						"SparkExamplesIterable":



								'''simple docstring'''




								a__     : str  =self.split_shard_indices_by_worker(lowerCAmelCase__		,				lowerCAmelCase__							)
								return SparkExamplesIterable(self.df		,				partition_order=lowerCAmelCase__							)







						@property
						def        _lowercase  (   self							)       ->						int:



								'''simple docstring'''




								return len(self.partition_order							)







class     __lowerCAmelCase	(   datasets.DatasetBuilder):
						_lowercase    : List[Any]            =      SparkConfig







						def __init__(   self		,				lowerCAmelCase__		,				lowerCAmelCase__ = None		,				lowerCAmelCase__ = None		,				**lowerCAmelCase__		,				)       ->						Any:



								'''simple docstring'''




								import pyspark

								a__     : Union[str, Any]  =pyspark.sql.SparkSession.builder.getOrCreate()
								a__     : Optional[int]  =df
								a__     : Optional[int]  =working_dir

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







						def        _lowercase  (   self							)       ->						Optional[int]:



								'''simple docstring'''




								def create_cache_and_write_probe(lowerCAmelCase__							):
										# 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=lowerCAmelCase__							)
										a__     : List[str]  =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(lowerCAmelCase__		,				"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:
										a__     : List[str]  =(
										    self._spark.sparkContext.parallelize(range(1							)		,				1							).mapPartitions(lowerCAmelCase__							).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        _lowercase  (   self							)       ->						Optional[int]:



								'''simple docstring'''




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







						def        _lowercase  (   self		,				lowerCAmelCase__							)       ->						str:



								'''simple docstring'''




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







						def        _lowercase  (   self		,				lowerCAmelCase__							)       ->						Tuple:



								'''simple docstring'''




								import pyspark

								def get_arrow_batch_size(lowerCAmelCase__							):
										for batch in it:
												yield pa.RecordBatch.from_pydict({"batch_bytes": [batch.nbytes]}							)

								a__     : Dict  =self.df.count()
								a__     : Union[str, Any]  =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.
								a__     : Dict  =(
								    self.df.limit(lowerCAmelCase__							)
								    .repartition(1							)
								    .mapInArrow(lowerCAmelCase__		,				"batch_bytes: long"							)
								    .agg(pyspark.sql.functions.sum("batch_bytes"							).alias("sample_bytes"							)							)
								    .collect()[0]
								    .sample_bytes
								    / sample_num_rows
								)
								a__     : List[Any]  =approx_bytes_per_row * df_num_rows
								if approx_total_size > max_shard_size:
										# Make sure there is at least one row per partition.
										a__     : Tuple  =min(lowerCAmelCase__		,				int(approx_total_size / max_shard_size							)							)
										a__     : List[Any]  =self.df.repartition(lowerCAmelCase__							)







						def        _lowercase  (   self		,				lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__		,				)       ->						Iterable[Tuple[int, bool, Union[int, tuple]]]:



								'''simple docstring'''




								import pyspark

								a__     : Optional[Any]  =ParquetWriter if file_format == "parquet" else ArrowWriter
								a__     : Dict  =os.path.join(self._working_dir		,				os.path.basename(lowerCAmelCase__							)							) if self._working_dir else fpath
								a__     : Dict  =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.
								a__     : Tuple  =self.config.features
								a__     : Optional[Any]  =self._writer_batch_size
								a__     : Dict  =self._fs.storage_options

								def write_arrow(lowerCAmelCase__							):
										# Within the same SparkContext, no two task attempts will share the same attempt ID.
										a__     : Optional[Any]  =pyspark.TaskContext().taskAttemptId()
										a__     : Optional[Any]  =next(lowerCAmelCase__		,				lowerCAmelCase__							)
										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"]		,				)
										a__     : Optional[Any]  =0
										a__     : List[str]  =writer_class(
										    features=lowerCAmelCase__		,				path=working_fpath.replace("SSSSS"		,				F'''{shard_id:05d}'''							).replace("TTTTT"		,				F'''{task_id:05d}'''							)		,				writer_batch_size=lowerCAmelCase__		,				storage_options=lowerCAmelCase__		,				embed_local_files=lowerCAmelCase__		,				)
										a__     : List[Any]  =pa.Table.from_batches([first_batch]							)
										writer.write_table(lowerCAmelCase__							)
										for batch in it:
												if max_shard_size is not None and writer._num_bytes >= max_shard_size:
														a__     : Tuple  =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
														a__     : List[Any]  =writer_class(
														    features=writer._features		,				path=working_fpath.replace("SSSSS"		,				F'''{shard_id:05d}'''							).replace("TTTTT"		,				F'''{task_id:05d}'''							)		,				writer_batch_size=lowerCAmelCase__		,				storage_options=lowerCAmelCase__		,				embed_local_files=lowerCAmelCase__		,				)
												a__     : Dict  =pa.Table.from_batches([batch]							)
												writer.write_table(lowerCAmelCase__							)

										if writer._num_bytes > 0:
												a__     : Any  =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(lowerCAmelCase__							)							):
														a__     : List[str]  =os.path.join(os.path.dirname(lowerCAmelCase__							)		,				os.path.basename(lowerCAmelCase__							)							)
														shutil.move(lowerCAmelCase__		,				lowerCAmelCase__							)

								a__     : Optional[Any]  =(
								    self.df.mapInArrow(lowerCAmelCase__		,				"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        _lowercase  (   self		,				lowerCAmelCase__		,				lowerCAmelCase__ = "arrow"		,				lowerCAmelCase__ = None		,				lowerCAmelCase__ = None		,				**lowerCAmelCase__		,				)       ->						Dict:



								'''simple docstring'''




								self._validate_cache_dir()

								a__     : Optional[Any]  =convert_file_size_to_int(max_shard_size or MAX_SHARD_SIZE							)
								self._repartition_df_if_needed(lowerCAmelCase__							)
								a__     : Optional[Any]  =not is_remote_filesystem(self._fs							)
								a__     : Dict  =os.path.join if is_local else posixpath.join

								a__     : List[str]  ="-TTTTT-SSSSS-of-NNNNN"
								a__     : Union[str, Any]  =F'''{self.name}-{split_generator.name}{SUFFIX}.{file_format}'''
								a__     : Union[str, Any]  =path_join(self._output_dir		,				lowerCAmelCase__							)

								a__     : Union[str, Any]  =0
								a__     : List[Any]  =0
								a__     : Dict  =0
								a__     : Optional[Any]  =[]
								a__     : int  =[]

								for task_id, content in self._prepare_split_single(lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__							):
										(
										    a__							
										)     : List[Any]  =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(lowerCAmelCase__							)

								a__     : str  =total_num_examples
								a__     : int  =total_num_bytes

								# should rename everything at the end
								logger.debug(F'''Renaming {total_shards} shards.'''							)
								if total_shards > 1:
										a__     : Dict  =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.
										a__     : List[str]  =self._fs

										# use the -SSSSS-of-NNNNN pattern
										def _rename_shard(
										    lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__		,				):
												rename(
												    lowerCAmelCase__		,				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}'''							)		,				)

										a__     : Optional[Any]  =[]
										a__     : Any  =0
										for i in range(len(lowerCAmelCase__							)							):
												a__     : List[Any]  =task_id_and_num_shards[i]
												for shard_id in range(lowerCAmelCase__							):
														args.append([task_id, shard_id, global_shard_id]							)
														global_shard_id += 1
										self._spark.sparkContext.parallelize(lowerCAmelCase__		,				len(lowerCAmelCase__							)							).map(lambda lowerCAmelCase__							: _rename_shard(*lowerCAmelCase__							)							).collect()
								else:
										# don't use any pattern
										a__     : List[str]  =0
										a__     : Optional[Any]  =task_id_and_num_shards[0][0]
										self._rename(
										    fpath.replace("SSSSS"		,				F'''{shard_id:05d}'''							).replace("TTTTT"		,				F'''{task_id:05d}'''							)		,				fpath.replace(lowerCAmelCase__		,				""							)		,				)







						def        _lowercase  (   self		,				lowerCAmelCase__		,				)       ->						SparkExamplesIterable:



								'''simple docstring'''




								return SparkExamplesIterable(self.df							)













import warnings

from transformers import AutoTokenizer
from transformers.utils import is_torch_available
from transformers.utils.generic import ExplicitEnum

from ...processing_utils import ProcessorMixin


if is_torch_available():
     import torch







class     __lowerCAmelCase	(   UpperCamelCase__):
      _lowercase    : Any            =      """char"""
      _lowercase    : str            =      """bpe"""
      _lowercase    : List[Any]            =      """wp"""
UpperCAmelCase     :     List[Any]							=	(DecodeType.CHARACTER, DecodeType.BPE, DecodeType.WORDPIECE)







class     __lowerCAmelCase	(   UpperCamelCase__):
      _lowercase    : Tuple            =      ["""image_processor""", """char_tokenizer"""]
      _lowercase    : Any            =      """ViTImageProcessor"""
      _lowercase    : Optional[Any]            =      """MgpstrTokenizer"""







      def __init__(   self		,				lowerCAmelCase__=None		,				lowerCAmelCase__=None		,				**lowerCAmelCase__							)       ->						Optional[Any]:



        '''simple docstring'''




        a__     : Optional[int]  =None
        if "feature_extractor" in kwargs:
          warnings.warn(
              "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
              " instead."		,				lowerCAmelCase__		,				)
          a__     : List[str]  =kwargs.pop("feature_extractor"							)

        a__     : List[str]  =image_processor if image_processor is not None else feature_extractor
        if image_processor is None:
          raise ValueError("You need to specify an `image_processor`."							)
        if tokenizer is None:
          raise ValueError("You need to specify a `tokenizer`."							)

        a__     : str  =tokenizer
        a__     : List[str]  =AutoTokenizer.from_pretrained("gpt2"							)
        a__     : Optional[int]  =AutoTokenizer.from_pretrained("bert-base-uncased"							)

        super().__init__(lowerCAmelCase__		,				lowerCAmelCase__							)







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



        '''simple docstring'''




        if images is None and text is None:
          raise ValueError("You need to specify either an `images` or `text` input to process."							)

        if images is not None:
          a__     : Union[str, Any]  =self.image_processor(lowerCAmelCase__		,				return_tensors=lowerCAmelCase__		,				**lowerCAmelCase__							)
        if text is not None:
          a__     : int  =self.char_tokenizer(lowerCAmelCase__		,				return_tensors=lowerCAmelCase__		,				**lowerCAmelCase__							)

        if text is None:
          return inputs
        elif images is None:
          return encodings
        else:
          a__     : Tuple  =encodings["input_ids"]
          return inputs







      def        _lowercase  (   self		,				lowerCAmelCase__							)       ->						Optional[Any]:



        '''simple docstring'''




        a__ ,							a__ ,							a__     : Any  =sequences
        a__     : Union[str, Any]  =char_preds.size(0							)

        a__ ,							a__     : Dict  =self._decode_helper(lowerCAmelCase__		,				"char"							)
        a__ ,							a__     : List[Any]  =self._decode_helper(lowerCAmelCase__		,				"bpe"							)
        a__ ,							a__     : Optional[int]  =self._decode_helper(lowerCAmelCase__		,				"wp"							)

        a__     : List[Any]  =[]
        a__     : Dict  =[]
        for i in range(lowerCAmelCase__							):
          a__     : int  =[char_scores[i], bpe_scores[i], wp_scores[i]]
          a__     : Tuple  =[char_strs[i], bpe_strs[i], wp_strs[i]]
          a__     : Any  =scores.index(max(lowerCAmelCase__							)							)
          final_strs.append(strs[max_score_index]							)
          final_scores.append(scores[max_score_index]							)

        a__     : Dict  ={}
        a__     : str  =final_strs
        a__     : Optional[int]  =final_scores
        a__     : Union[str, Any]  =char_strs
        a__     : List[str]  =bpe_strs
        a__     : Union[str, Any]  =wp_strs
        return out







      def        _lowercase  (   self		,				lowerCAmelCase__		,				lowerCAmelCase__							)       ->						Tuple:



        '''simple docstring'''




        if format == DecodeType.CHARACTER:
          a__     : Optional[Any]  =self.char_decode
          a__     : Dict  =1
          a__     : Tuple  ="[s]"
        elif format == DecodeType.BPE:
          a__     : str  =self.bpe_decode
          a__     : Dict  =2
          a__     : Optional[int]  ="#"
        elif format == DecodeType.WORDPIECE:
          a__     : Union[str, Any]  =self.wp_decode
          a__     : List[Any]  =1_0_2
          a__     : Dict  ="[SEP]"
        else:
          raise ValueError(F'''Format {format} is not supported.'''							)

        a__ ,							a__     : Any  =[], []
        a__     : str  =pred_logits.size(0							)
        a__     : Optional[Any]  =pred_logits.size(1							)
        a__ ,							a__     : Optional[int]  =pred_logits.topk(1		,				dim=-1		,				largest=lowerCAmelCase__		,				sorted=lowerCAmelCase__							)
        a__     : Optional[Any]  =preds_index.view(-1		,				lowerCAmelCase__							)[:, 1:]
        a__     : Dict  =decoder(lowerCAmelCase__							)
        a__ ,							a__     : Any  =torch.nn.functional.softmax(lowerCAmelCase__		,				dim=2							).max(dim=2							)
        a__     : int  =preds_max_prob[:, 1:]

        for index in range(lowerCAmelCase__							):
          a__     : Optional[Any]  =preds_str[index].find(lowerCAmelCase__							)
          a__     : Optional[int]  =preds_str[index][:pred_eos]
          a__     : List[Any]  =preds_index[index].cpu().tolist()
          a__     : List[Any]  =pred_index.index(lowerCAmelCase__							) if eos_token in pred_index else -1
          a__     : Union[str, Any]  =preds_max_prob[index][: pred_eos_index + 1]
          a__     : List[Any]  =pred_max_prob.cumprod(dim=0							)[-1] if pred_max_prob.nelement() != 0 else 0.0
          dec_strs.append(lowerCAmelCase__							)
          conf_scores.append(lowerCAmelCase__							)

        return dec_strs, conf_scores







      def        _lowercase  (   self		,				lowerCAmelCase__							)       ->						str:



        '''simple docstring'''




        a__     : int  =[seq.replace(" "		,				""							) for seq in self.char_tokenizer.batch_decode(lowerCAmelCase__							)]
        return decode_strs







      def        _lowercase  (   self		,				lowerCAmelCase__							)       ->						Tuple:



        '''simple docstring'''




        return self.bpe_tokenizer.batch_decode(lowerCAmelCase__							)







      def        _lowercase  (   self		,				lowerCAmelCase__							)       ->						Optional[Any]:



        '''simple docstring'''




        a__     : List[Any]  =[seq.replace(" "		,				""							) for seq in self.wp_tokenizer.batch_decode(lowerCAmelCase__							)]
        return decode_strs






