

'''simple docstring'''
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


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

if TYPE_CHECKING:
    import pyspark


@dataclass
class        a__  (							datasets.BuilderConfig  ):
   _SCREAMING_SNAKE_CASE   :					Optional[datasets.Features]     	=							None







def  _A		(     snake_case		,							snake_case		,							)      ->     List[Any]:
   import pyspark

   def generate_fn():
      _lowercase      :				Optional[Any]      					=       df.select("*"		,							pyspark.sql.functions.spark_partition_id().alias("part_id"					)					)
      for partition_id in partition_order:
         _lowercase      :				str      					=       df_with_partition_id.select("*"					).where(F'''part_id = {partition_id}'''					).drop("part_id"					)
         _lowercase      :				Dict      					=       partition_df.collect()
         _lowercase      :				int      					=       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      ,    _UpperCamelCase      ,    _UpperCamelCase=None      ,    ):





      """simple docstring"""





      _lowercase      :				List[str]      					=       df
      _lowercase      :				List[Any]      					=       partition_order or range(self.df.rdd.getNumPartitions()  )
      _lowercase      :				str      					=       _generate_iterable_examples(self.df      ,    self.partition_order  )


   def __iter__(			self  ):





      """simple docstring"""





      yield from self.generate_examples_fn()


   def 						_lowerCamelCase       (			self      ,    _UpperCamelCase  ):





      """simple docstring"""





      _lowercase      :				Optional[int]      					=       list(range(self.df.rdd.getNumPartitions()  )  )
      generator.shuffle(_UpperCamelCase  )
      return SparkExamplesIterable(self.df      ,    partition_order=_UpperCamelCase  )


   def 						_lowerCamelCase       (			self      ,    _UpperCamelCase      ,    _UpperCamelCase  ):





      """simple docstring"""





      _lowercase      :				Dict      					=       self.split_shard_indices_by_worker(_UpperCamelCase      ,    _UpperCamelCase  )
      return SparkExamplesIterable(self.df      ,    partition_order=_UpperCamelCase  )






   @property
   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      return len(self.partition_order  )




class        a__  (							datasets.DatasetBuilder  ):
   _SCREAMING_SNAKE_CASE   :					Dict     	=							SparkConfig


   def __init__(			self      ,    _UpperCamelCase      ,    _UpperCamelCase = None      ,    _UpperCamelCase = None      ,    **_UpperCamelCase      ,    ):





      """simple docstring"""





      import pyspark

      _lowercase      :				int      					=       pyspark.sql.SparkSession.builder.getOrCreate()
      _lowercase      :				Tuple      					=       df
      _lowercase      :				List[str]      					=       working_dir

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


   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      def create_cache_and_write_probe(_UpperCamelCase  ):
         # 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=_UpperCamelCase  )
         _lowercase      :				Dict      					=       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(_UpperCamelCase      ,    "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:
         _lowercase      :				str      					=       (
             self._spark.sparkContext.parallelize(range(1  )      ,    1  ).mapPartitions(_UpperCamelCase  ).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 						_lowerCamelCase       (			self  ):





      """simple docstring"""





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


   def 						_lowerCamelCase       (			self      ,    _UpperCamelCase  ):





      """simple docstring"""





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


   def 						_lowerCamelCase       (			self      ,    _UpperCamelCase  ):





      """simple docstring"""





      import pyspark

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

      _lowercase      :				int      					=       self.df.count()
      _lowercase      :				int      					=       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.
      _lowercase      :				Any      					=       (
          self.df.limit(_UpperCamelCase  )
          .repartition(1  )
          .mapInArrow(_UpperCamelCase      ,    "batch_bytes: long"  )
          .agg(pyspark.sql.functions.sum("batch_bytes"  ).alias("sample_bytes"  )  )
          .collect()[0]
          .sample_bytes
          / sample_num_rows
      )
      _lowercase      :				Union[str, 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.
         _lowercase      :				List[Any]      					=       min(_UpperCamelCase      ,    int(approx_total_size / max_shard_size  )  )
         _lowercase      :				Any      					=       self.df.repartition(_UpperCamelCase  )


   def 						_lowerCamelCase       (			self      ,    _UpperCamelCase      ,    _UpperCamelCase      ,    _UpperCamelCase      ,    ):





      """simple docstring"""





      import pyspark

      _lowercase      :				str      					=       ParquetWriter if file_format == "parquet" else ArrowWriter
      _lowercase      :				Any      					=       os.path.join(self._working_dir      ,    os.path.basename(_UpperCamelCase  )  ) if self._working_dir else fpath
      _lowercase      :				Optional[int]      					=       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.
      _lowercase      :				Union[str, Any]      					=       self.config.features
      _lowercase      :				List[Any]      					=       self._writer_batch_size
      _lowercase      :				Dict      					=       self._fs.storage_options

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

         if writer._num_bytes > 0:
            _lowercase					,				_lowercase      :				Union[str, 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(_UpperCamelCase  )  ):
               _lowercase      :				Dict      					=       os.path.join(os.path.dirname(_UpperCamelCase  )      ,    os.path.basename(_UpperCamelCase  )  )
               shutil.move(_UpperCamelCase      ,    _UpperCamelCase  )

      _lowercase      :				int      					=       (
          self.df.mapInArrow(_UpperCamelCase      ,    "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 						_lowerCamelCase       (			self      ,    _UpperCamelCase      ,    _UpperCamelCase = "arrow"      ,    _UpperCamelCase = None      ,    _UpperCamelCase = None      ,    **_UpperCamelCase      ,    ):





      """simple docstring"""





      self._validate_cache_dir()

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

      _lowercase      :				str      					=       "-TTTTT-SSSSS-of-NNNNN"
      _lowercase      :				Optional[int]      					=       f'''{self.name}-{split_generator.name}{SUFFIX}.{file_format}'''
      _lowercase      :				List[str]      					=       path_join(self._output_dir      ,    _UpperCamelCase  )

      _lowercase      :				List[str]      					=       0
      _lowercase      :				List[Any]      					=       0
      _lowercase      :				int      					=       0
      _lowercase      :				Tuple      					=       []
      _lowercase      :				str      					=       []

      for task_id, content in self._prepare_split_single(_UpperCamelCase      ,    _UpperCamelCase      ,    _UpperCamelCase  ):
         (
             (
             _lowercase
         )					,				(
             _lowercase
         )					,				(
             _lowercase
         )					,				(
             _lowercase
         )					,				
         )      :				Dict      					=       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(_UpperCamelCase  )

      _lowercase      :				str      					=       total_num_examples
      _lowercase      :				List[Any]      					=       total_num_bytes

      # should rename everything at the end
      logger.debug(f'''Renaming {total_shards} shards.'''  )
      if total_shards > 1:
         _lowercase      :				str      					=       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.
         _lowercase      :				Any      					=       self._fs

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

         _lowercase      :				str      					=       []
         _lowercase      :				List[str]      					=       0
         for i in range(len(_UpperCamelCase  )  ):
            _lowercase					,				_lowercase      :				List[Any]      					=       task_id_and_num_shards[i]
            for shard_id in range(_UpperCamelCase  ):
               args.append([task_id, shard_id, global_shard_id]  )
               global_shard_id += 1
         self._spark.sparkContext.parallelize(_UpperCamelCase      ,    len(_UpperCamelCase  )  ).map(lambda _UpperCamelCase  : _rename_shard(*_UpperCamelCase  )  ).collect()
      else:
         # don't use any pattern
         _lowercase      :				Dict      					=       0
         _lowercase      :				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(_UpperCamelCase      ,    ""  )      ,    )






   def 						_lowerCamelCase       (			self      ,    _UpperCamelCase      ,    ):





      """simple docstring"""





      return SparkExamplesIterable(self.df  )









'''simple docstring'''
import os
from typing import List, Optional, Union

from ...image_processing_utils import BatchFeature
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
from ..auto import AutoTokenizer


class        a__  (							lowerCamelCase_  ):
   _SCREAMING_SNAKE_CASE   :					Any     	=							['image_processor', 'tokenizer']
   _SCREAMING_SNAKE_CASE   :					Dict     	=							'BlipImageProcessor'
   _SCREAMING_SNAKE_CASE   :					Dict     	=							'AutoTokenizer'


   def __init__(			self      ,    _UpperCamelCase      ,    _UpperCamelCase      ,    _UpperCamelCase  ):





      """simple docstring"""





      super().__init__(_UpperCamelCase      ,    _UpperCamelCase  )

      # add QFormer tokenizer
      _lowercase      :				List[Any]      					=       qformer_tokenizer


   def __call__(			self      ,    _UpperCamelCase = None      ,    _UpperCamelCase = None      ,    _UpperCamelCase = True      ,    _UpperCamelCase = False      ,    _UpperCamelCase = None      ,    _UpperCamelCase = None      ,    _UpperCamelCase = 0      ,    _UpperCamelCase = None      ,    _UpperCamelCase = None      ,    _UpperCamelCase = False      ,    _UpperCamelCase = False      ,    _UpperCamelCase = False      ,    _UpperCamelCase = False      ,    _UpperCamelCase = False      ,    _UpperCamelCase = True      ,    _UpperCamelCase = None      ,    **_UpperCamelCase      ,    ):





      """simple docstring"""





      if images is None and text is None:
         raise ValueError("You have to specify at least images or text."  )

      _lowercase      :				str      					=       BatchFeature()

      if text is not None:
         _lowercase      :				Dict      					=       self.tokenizer(
             text=_UpperCamelCase      ,    add_special_tokens=_UpperCamelCase      ,    padding=_UpperCamelCase      ,    truncation=_UpperCamelCase      ,    max_length=_UpperCamelCase      ,    stride=_UpperCamelCase      ,    pad_to_multiple_of=_UpperCamelCase      ,    return_attention_mask=_UpperCamelCase      ,    return_overflowing_tokens=_UpperCamelCase      ,    return_special_tokens_mask=_UpperCamelCase      ,    return_offsets_mapping=_UpperCamelCase      ,    return_token_type_ids=_UpperCamelCase      ,    return_length=_UpperCamelCase      ,    verbose=_UpperCamelCase      ,    return_tensors=_UpperCamelCase      ,    **_UpperCamelCase      ,    )
         encoding.update(_UpperCamelCase  )
         _lowercase      :				Dict      					=       self.qformer_tokenizer(
             text=_UpperCamelCase      ,    add_special_tokens=_UpperCamelCase      ,    padding=_UpperCamelCase      ,    truncation=_UpperCamelCase      ,    max_length=_UpperCamelCase      ,    stride=_UpperCamelCase      ,    pad_to_multiple_of=_UpperCamelCase      ,    return_attention_mask=_UpperCamelCase      ,    return_overflowing_tokens=_UpperCamelCase      ,    return_special_tokens_mask=_UpperCamelCase      ,    return_offsets_mapping=_UpperCamelCase      ,    return_token_type_ids=_UpperCamelCase      ,    return_length=_UpperCamelCase      ,    verbose=_UpperCamelCase      ,    return_tensors=_UpperCamelCase      ,    **_UpperCamelCase      ,    )
         _lowercase      :				Union[str, Any]      					=       qformer_text_encoding.pop("input_ids"  )
         _lowercase      :				List[Any]      					=       qformer_text_encoding.pop("attention_mask"  )

      if images is not None:
         _lowercase      :				List[Any]      					=       self.image_processor(_UpperCamelCase      ,    return_tensors=_UpperCamelCase  )
         encoding.update(_UpperCamelCase  )

      return encoding


   def 						_lowerCamelCase       (			self      ,    *_UpperCamelCase      ,    **_UpperCamelCase  ):





      """simple docstring"""





      return self.tokenizer.batch_decode(*_UpperCamelCase      ,    **_UpperCamelCase  )


   def 						_lowerCamelCase       (			self      ,    *_UpperCamelCase      ,    **_UpperCamelCase  ):





      """simple docstring"""





      return self.tokenizer.decode(*_UpperCamelCase      ,    **_UpperCamelCase  )


   @property
   # Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      _lowercase      :				Dict      					=       self.tokenizer.model_input_names
      _lowercase      :				Optional[Any]      					=       self.image_processor.model_input_names
      return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names  )  )


   def 						_lowerCamelCase       (			self      ,    _UpperCamelCase      ,    **_UpperCamelCase  ):





      """simple docstring"""





      if os.path.isfile(_UpperCamelCase  ):
         raise ValueError(f'''Provided path ({save_directory}) should be a directory, not a file'''  )
      os.makedirs(_UpperCamelCase      ,    exist_ok=_UpperCamelCase  )
      _lowercase      :				Union[str, Any]      					=       os.path.join(_UpperCamelCase      ,    "qformer_tokenizer"  )
      self.qformer_tokenizer.save_pretrained(_UpperCamelCase  )
      return super().save_pretrained(_UpperCamelCase      ,    **_UpperCamelCase  )






   @classmethod
   def 						_lowerCamelCase       (			cls      ,    _UpperCamelCase      ,    **_UpperCamelCase  ):





      """simple docstring"""





      _lowercase      :				List[Any]      					=       AutoTokenizer.from_pretrained(_UpperCamelCase      ,    subfolder="qformer_tokenizer"  )
      _lowercase      :				Optional[Any]      					=       cls._get_arguments_from_pretrained(_UpperCamelCase      ,    **_UpperCamelCase  )
      args.append(_UpperCamelCase  )
      return cls(*_UpperCamelCase  )






