


"""simple docstring"""





import datasets
import faiss
import numpy as np
import streamlit as st
import torch
from elasticsearch import Elasticsearch
from elia_utils import (
    embed_questions_for_retrieval,
    make_qa_sas_model,
    qa_sas_generate,
    query_es_index,
    query_qa_dense_index,
)

import transformers
from transformers import AutoModel, AutoModelForSeqaSeqLM, AutoTokenizer


snake_case__						:   List[Any]			 = '''bart'''
snake_case__						:   Union[str, Any]			 = True


@st.cache(allow_output_mutation=_snake_case	)
def    _snake_case  (  ):
      if LOAD_DENSE_INDEX:
            lowerCAmelCase    :		Dict				=      AutoTokenizer.from_pretrained('''yjernite/retribert-base-uncased'''	)
            lowerCAmelCase    :		List[str]				=      AutoModel.from_pretrained('''yjernite/retribert-base-uncased'''	).to('''cuda:0'''	)
            lowerCAmelCase    :		Optional[int]				=      qar_model.eval()
      else:
            lowerCAmelCase,					lowerCAmelCase    :		int				=      (None, None)
      if MODEL_TYPE == "bart":
            lowerCAmelCase    :		Tuple				=      AutoTokenizer.from_pretrained('''yjernite/bart_eli5'''	)
            lowerCAmelCase    :		Tuple				=      AutoModelForSeqaSeqLM.from_pretrained('''yjernite/bart_eli5'''	).to('''cuda:0'''	)
            lowerCAmelCase    :		Optional[Any]				=      torch.load('''seq2seq_models/eli5_bart_model_blm_2.pth'''	)
            sas_model.load_state_dict(save_dict['''model''']	)
            lowerCAmelCase    :		Any				=      sas_model.eval()
      else:
            lowerCAmelCase,					lowerCAmelCase    :		Any				=      make_qa_sas_model(
                model_name='''t5-small'''       ,      from_file='''seq2seq_models/eli5_t5_model_1024_4.pth'''       ,      device='''cuda:0'''	)
      return (qar_tokenizer, qar_model, sas_tokenizer, sas_model)


@st.cache(allow_output_mutation=_snake_case	)
def    _snake_case  (  ):
      if LOAD_DENSE_INDEX:
            lowerCAmelCase    :		List[str]				=      faiss.StandardGpuResources()
            lowerCAmelCase    :		Optional[Any]				=      datasets.load_dataset(path='''wiki_snippets'''       ,      name='''wiki40b_en_100_0'''	)['''train''']
            lowerCAmelCase    :		List[Any]				=      np.memmap(
                '''wiki40b_passages_reps_32_l-8_h-768_b-512-512.dat'''       ,      dtype='''float32'''       ,      mode='''r'''       ,      shape=(wikiaab_passages.num_rows, 128)       ,      )
            lowerCAmelCase    :		Union[str, Any]				=      faiss.IndexFlatIP(128	)
            lowerCAmelCase    :		int				=      faiss.index_cpu_to_gpu(_snake_case       ,      1       ,      _snake_case	)
            wikiaab_gpu_index_flat.add(_snake_case	)  # TODO fix for larger GPU
      else:
            lowerCAmelCase,					lowerCAmelCase    :		List[str]				=      (None, None)
      lowerCAmelCase    :		int				=      Elasticsearch([{'''host''': '''localhost''', '''port''': '''9200'''}]	)
      return (wikiaab_passages, wikiaab_gpu_index_flat, es_client)


@st.cache(allow_output_mutation=_snake_case	)
def    _snake_case  (  ):
      lowerCAmelCase    :		List[str]				=      datasets.load_dataset('''eli5'''       ,      name='''LFQA_reddit'''	)
      lowerCAmelCase    :		Any				=      elia['''train_eli5''']
      lowerCAmelCase    :		int				=      np.memmap(
          '''eli5_questions_reps.dat'''       ,      dtype='''float32'''       ,      mode='''r'''       ,      shape=(elia_train.num_rows, 128)	)
      lowerCAmelCase    :		Tuple				=      faiss.IndexFlatIP(128	)
      eli5_train_q_index.add(_snake_case	)
      return (elia_train, eli5_train_q_index)


snake_case__ ,   snake_case__ ,   snake_case__						:   Optional[Any]			 = load_indexes()
snake_case__ ,   snake_case__ ,   snake_case__ ,   snake_case__						:   str			 = load_models()
snake_case__ ,   snake_case__						:   Union[str, Any]			 = load_train_data()


def    _snake_case  (  _snake_case :	int       ,      _snake_case :	Dict=10	):
      lowerCAmelCase    :		Tuple				=      embed_questions_for_retrieval([question]       ,      _snake_case       ,      _snake_case	)
      lowerCAmelCase,					lowerCAmelCase    :		Any				=      eli5_train_q_index.search(_snake_case       ,      _snake_case	)
      lowerCAmelCase    :		str				=      [elia_train[int(_snake_case	)] for i in I[0]]
      return nn_examples


def    _snake_case  (  _snake_case :	List[Any]       ,      _snake_case :	str="wiki40b"       ,      _snake_case :	List[str]="dense"       ,      _snake_case :	Union[str, Any]=10	):
      if source == "none":
            lowerCAmelCase,					lowerCAmelCase    :		List[str]				=      (''' <P> '''.join(['''''' for _ in range(11	)]	).strip(), [])
      else:
            if method == "dense":
                  lowerCAmelCase,					lowerCAmelCase    :		Tuple				=      query_qa_dense_index(
                      _snake_case       ,      _snake_case       ,      _snake_case       ,      _snake_case       ,      _snake_case       ,      _snake_case	)
            else:
                  lowerCAmelCase,					lowerCAmelCase    :		List[str]				=      query_es_index(
                      _snake_case       ,      _snake_case       ,      index_name='''english_wiki40b_snippets_100w'''       ,      n_results=_snake_case       ,      )
      lowerCAmelCase    :		int				=      [
          (res['''article_title'''], res['''section_title'''].strip(), res['''score'''], res['''passage_text''']) for res in hit_lst
      ]
      lowerCAmelCase    :		Any				=      '''question: {} context: {}'''.format(_snake_case       ,      _snake_case	)
      return question_doc, support_list


@st.cache(
    hash_funcs={
        torch.Tensor: (lambda _snake_case	: None),
        transformers.models.bart.tokenization_bart.BartTokenizer: (lambda _snake_case	: None),
    }	)
def    _snake_case  (  _snake_case :	str       ,      _snake_case :	Dict       ,      _snake_case :	Dict       ,      _snake_case :	List[Any]=64       ,      _snake_case :	int=256       ,      _snake_case :	List[str]=False       ,      _snake_case :	Any=2       ,      _snake_case :	List[Any]=0.95       ,      _snake_case :	Tuple=0.8	):
      with torch.no_grad():
            lowerCAmelCase    :		Union[str, Any]				=      qa_sas_generate(
                _snake_case       ,      _snake_case       ,      _snake_case       ,      num_answers=1       ,      num_beams=_snake_case       ,      min_len=_snake_case       ,      max_len=_snake_case       ,      do_sample=_snake_case       ,      temp=_snake_case       ,      top_p=_snake_case       ,      top_k=_snake_case       ,      max_input_length=1024       ,      device='''cuda:0'''       ,      )[0]
      return (answer, support_list)


st.title('''Long Form Question Answering with ELI5''')

# Start sidebar
snake_case__						:   Dict			 = '''<img src=\'https://huggingface.co/front/assets/huggingface_logo.svg\'>'''
snake_case__						:   Tuple			 = '''
<html>
  <head>
    <style>
      .img-container {
        padding-left: 90px;
        padding-right: 90px;
        padding-top: 50px;
        padding-bottom: 50px;
        background-color: #f0f3f9;
      }
    </style>
  </head>
  <body>
    <span class="img-container"> <!-- Inline parent element -->
      %s
    </span>
  </body>
</html>
''' % (
    header_html,
)
st.sidebar.markdown(
    header_full,
    unsafe_allow_html=True,
)

# Long Form QA with ELI5 and Wikipedia
snake_case__						:   List[Any]			 = '''
This demo presents a model trained to [provide long-form answers to open-domain questions](https://yjernite.github.io/lfqa.html).
First, a document retriever fetches a set of relevant Wikipedia passages given the question from the [Wiki40b](https://research.google/pubs/pub49029/) dataset,
a pre-processed fixed snapshot of Wikipedia.
'''
st.sidebar.markdown(description, unsafe_allow_html=True)

snake_case__						:   str			 = [
    '''Answer the question''',
    '''View the retrieved document only''',
    '''View the most similar ELI5 question and answer''',
    '''Show me everything, please!''',
]
snake_case__						:   List[Any]			 = st.sidebar.checkbox('''Demo options''')
if demo_options:
 snake_case__						:   Tuple			 = st.sidebar.selectbox(
     '''''',
     action_list,
     index=3,
 )
 snake_case__						:   List[Any]			 = action_list.index(action_st)
 snake_case__						:   List[str]			 = st.sidebar.selectbox(
     '''''',
     ['''Show full text of passages''', '''Show passage section titles'''],
     index=0,
 )
 snake_case__						:   List[Any]			 = show_type == '''Show full text of passages'''
else:
 snake_case__						:   Tuple			 = 3
 snake_case__						:   List[Any]			 = True

snake_case__						:   List[str]			 = st.sidebar.checkbox('''Retrieval options''')
if retrieval_options:
 snake_case__						:   str			 = '''
    ### Information retriever options

    The **sparse** retriever uses ElasticSearch, while the **dense** retriever uses max-inner-product search between a question and passage embedding
    trained using the [ELI5](https://arxiv.org/abs/1907.09190) questions-answer pairs.
    The answer is then generated by sequence to sequence model which takes the question and retrieved document as input.
    '''
 st.sidebar.markdown(retriever_info)
 snake_case__						:   Union[str, Any]			 = st.sidebar.selectbox('''Which Wikipedia format should the model use?''', ['''wiki40b''', '''none'''])
 snake_case__						:   Union[str, Any]			 = st.sidebar.selectbox('''Which Wikipedia indexer should the model use?''', ['''dense''', '''sparse''', '''mixed'''])
else:
 snake_case__						:   List[Any]			 = '''wiki40b'''
 snake_case__						:   Union[str, Any]			 = '''dense'''

snake_case__						:   int			 = '''beam'''
snake_case__						:   str			 = 2
snake_case__						:   Dict			 = 64
snake_case__						:   List[str]			 = 256
snake_case__						:   Dict			 = None
snake_case__						:   List[str]			 = None
snake_case__						:   List[str]			 = st.sidebar.checkbox('''Generation options''')
if generate_options:
 snake_case__						:   List[Any]			 = '''
    ### Answer generation options

    The sequence-to-sequence model was initialized with [BART](https://huggingface.co/facebook/bart-large)
    weights and fine-tuned on the ELI5 QA pairs and retrieved documents. You can use the model for greedy decoding with
    **beam** search, or **sample** from the decoder\'s output probabilities.
    '''
 st.sidebar.markdown(generate_info)
 snake_case__						:   List[str]			 = st.sidebar.selectbox('''Would you like to use beam search or sample an answer?''', ['''beam''', '''sampled'''])
 snake_case__						:   List[str]			 = st.sidebar.slider(
     '''Minimum generation length''', min_value=8, max_value=256, value=64, step=8, format=None, key=None
 )
 snake_case__						:   Optional[Any]			 = st.sidebar.slider(
     '''Maximum generation length''', min_value=64, max_value=512, value=256, step=16, format=None, key=None
 )
 if sampled == "beam":
  snake_case__						:   Dict			 = st.sidebar.slider('''Beam size''', min_value=1, max_value=8, value=2, step=None, format=None, key=None)
 else:
  snake_case__						:   int			 = st.sidebar.slider(
      '''Nucleus sampling p''', min_value=0.1, max_value=1.0, value=0.9_5, step=0.0_1, format=None, key=None
  )
  snake_case__						:   int			 = st.sidebar.slider(
      '''Temperature''', min_value=0.1, max_value=1.0, value=0.7, step=0.0_1, format=None, key=None
  )
  snake_case__						:   List[str]			 = None

# start main text
snake_case__						:   str			 = [
    '''<MY QUESTION>''',
    '''How do people make chocolate?''',
    '''Why do we get a fever when we are sick?''',
    '''How can different animals perceive different colors?''',
    '''What is natural language processing?''',
    '''What\'s the best way to treat a sunburn?''',
    '''What exactly are vitamins ?''',
    '''How does nuclear energy provide electricity?''',
    '''What\'s the difference between viruses and bacteria?''',
    '''Why are flutes classified as woodwinds when most of them are made out of metal ?''',
    '''Why do people like drinking coffee even though it tastes so bad?''',
    '''What happens when wine ages? How does it make the wine taste better?''',
    '''If an animal is an herbivore, where does it get the protein that it needs to survive if it only eats grass?''',
    '''How can we set a date to the beginning or end of an artistic period? Doesn\'t the change happen gradually?''',
    '''How does New Zealand have so many large bird predators?''',
]
snake_case__						:   Union[str, Any]			 = st.selectbox(
    '''What would you like to ask? ---- select <MY QUESTION> to enter a new query''',
    questions_list,
    index=1,
)
if question_s == "<MY QUESTION>":
 snake_case__						:   Optional[Any]			 = st.text_input('''Enter your question here:''', '''''')
else:
 snake_case__						:   int			 = question_s

if st.button('''Show me!'''):
 if action in [0, 1, 3]:
  if index_type == "mixed":
   snake_case__ ,   snake_case__						:   str			 = make_support(question, source=wiki_source, method='''dense''', n_results=10)
   snake_case__ ,   snake_case__						:   Tuple			 = make_support(question, source=wiki_source, method='''sparse''', n_results=10)
   snake_case__						:   int			 = []
   for res_d, res_s in zip(support_list_dense, support_list_sparse):
    if tuple(res_d) not in support_list:
     support_list += [tuple(res_d)]
    if tuple(res_s) not in support_list:
     support_list += [tuple(res_s)]
   snake_case__						:   List[str]			 = support_list[:10]
   snake_case__						:   int			 = '''<P> ''' + ''' <P> '''.join([res[-1] for res in support_list])
  else:
   snake_case__ ,   snake_case__						:   Union[str, Any]			 = make_support(question, source=wiki_source, method=index_type, n_results=10)
 if action in [0, 3]:
  snake_case__ ,   snake_case__						:   List[str]			 = answer_question(
      question_doc,
      sas_model,
      sas_tokenizer,
      min_len=min_len,
      max_len=int(max_len),
      sampling=(sampled == '''sampled'''),
      n_beams=n_beams,
      top_p=top_p,
      temp=temp,
  )
  st.markdown('''### The model generated answer is:''')
  st.write(answer)
 if action in [0, 1, 3] and wiki_source != "none":
  st.markdown('''--- \n ### The model is drawing information from the following Wikipedia passages:''')
  for i, res in enumerate(support_list):
   snake_case__						:   int			 = '''https://en.wikipedia.org/wiki/{}'''.format(res[0].replace(''' ''', '''_'''))
   snake_case__						:   List[Any]			 = res[1].strip()
   if sec_titles == "":
    snake_case__						:   Tuple			 = '''[{}]({})'''.format(res[0], wiki_url)
   else:
    snake_case__						:   Optional[int]			 = sec_titles.split(''' & ''')
    snake_case__						:   Optional[Any]			 = ''' & '''.join(
        ['''[{}]({}#{})'''.format(sec.strip(), wiki_url, sec.strip().replace(''' ''', '''_''')) for sec in sec_list]
    )
   st.markdown(
       '''{0:02d} - **Article**: {1:<18} <br>  _Section_: {2}'''.format(i + 1, res[0], sections),
       unsafe_allow_html=True,
   )
   if show_passages:
    st.write(
        '''> <span style="font-family:arial; font-size:10pt;">''' + res[-1] + '''</span>''', unsafe_allow_html=True
    )
 if action in [2, 3]:
  snake_case__						:   int			 = find_nearest_training(question)
  snake_case__						:   List[Any]			 = nn_train_list[0]
  st.markdown(
      '''--- \n ### The most similar question in the ELI5 training set was: \n\n {}'''.format(train_exple['''title'''])
  )
  snake_case__						:   Dict			 = [
      '''{}. {}'''.format(i + 1, '''  \n'''.join([line.strip() for line in ans.split('''\n''') if line.strip() != '''''']))
      for i, (ans, sc) in enumerate(zip(train_exple['''answers''']['''text'''], train_exple['''answers''']['''score''']))
      if i == 0 or sc > 2
  ]
  st.markdown('''##### Its answers were: \n\n {}'''.format('''\n'''.join(answers_st)))


snake_case__						:   Any			 = '''
---

**Disclaimer**

*The intent of this app is to provide some (hopefully entertaining) insights into the behavior of a current LFQA system.
Evaluating biases of such a model and ensuring factual generations are still very much open research problems.
Therefore, until some significant progress is achieved, we caution against using the generated answers for practical purposes.*
'''






st.sidebar.markdown(disclaimer, unsafe_allow_html=True)






'''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


__A					:  Optional[Any]											=      datasets.utils.logging.get_logger(__name__)

if TYPE_CHECKING:
  import pyspark






@dataclass
class    __snake_case       (   datasets.BuilderConfig):







    """simple docstring"""



    lowercase    =					None







def 				UpperCamelCase_			(    A__					:   "pyspark.sql.DataFrame"							,    A__					:   List[int]							,    ):
  '''simple docstring'''





  import pyspark

  def generate_fn():
    lowerCAmelCase_			:      Union[str, Any]			=   df.select("""*"""							,    pyspark.sql.functions.spark_partition_id().alias("""part_id"""					)					)
    for partition_id in partition_order:
      lowerCAmelCase_			:      int			=   df_with_partition_id.select("""*"""					).where(f'part_id = {partition_id}'					).drop("""part_id"""					)
      lowerCAmelCase_			:      Union[str, Any]			=   partition_df.collect()
      lowerCAmelCase_			:      str			=   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    :  Optional[int]    ,						lowerCamelCase    :  "pyspark.sql.DataFrame"    ,						lowerCamelCase    :  List[Any]=None    ,						)   ->  Optional[Any]:
      lowerCAmelCase_			:      str			=   df
      lowerCAmelCase_			:      List[Any]			=   partition_order or range(self.df.rdd.getNumPartitions()		)
      lowerCAmelCase_			:      List[str]			=   _generate_iterable_examples(self.df    ,						self.partition_order		)

    def __iter__(   self    :  Dict		)   ->  int:
      yield from self.generate_examples_fn()

    def __lowercase   (   self    :  Any    ,						lowerCamelCase    :  np.random.Generator		)   ->  "SparkExamplesIterable":
      lowerCAmelCase_			:      Optional[int]			=   list(range(self.df.rdd.getNumPartitions()		)		)
      generator.shuffle(lowerCamelCase		)
      return SparkExamplesIterable(self.df    ,						partition_order=lowerCamelCase		)

    def __lowercase   (   self    :  Optional[Any]    ,						lowerCamelCase    :  int    ,						lowerCamelCase    :  int		)   ->  "SparkExamplesIterable":
      lowerCAmelCase_			:      Tuple			=   self.split_shard_indices_by_worker(lowerCamelCase    ,						lowerCamelCase		)
      return SparkExamplesIterable(self.df    ,						partition_order=lowerCamelCase		)





    @property
    def __lowercase   (   self    :  str		)   ->  int:
      return len(self.partition_order		)







class    __snake_case       (   datasets.DatasetBuilder):







    """simple docstring"""



    lowercase    =					SparkConfig

    def __init__(   self    :  Union[str, Any]    ,						lowerCamelCase    :  "pyspark.sql.DataFrame"    ,						lowerCamelCase    :  str = None    ,						lowerCamelCase    :  str = None    ,						**lowerCamelCase    :  int    ,						)   ->  Tuple:
      import pyspark

      lowerCAmelCase_			:      Dict			=   pyspark.sql.SparkSession.builder.getOrCreate()
      lowerCAmelCase_			:      List[str]			=   df
      lowerCAmelCase_			:      List[Any]			=   working_dir

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

    def __lowercase   (   self    :  Tuple		)   ->  Union[str, Any]:
      # Returns the path of the created file.
      def create_cache_and_write_probe(lowerCamelCase    :  int		):
        # 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		)
        lowerCAmelCase_			:      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:
        lowerCAmelCase_			:      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    :  Any		)   ->  List[Any]:
      return datasets.DatasetInfo(features=self.config.features		)

    def __lowercase   (   self    :  str    ,						lowerCamelCase    :  datasets.download.download_manager.DownloadManager		)   ->  Optional[int]:
      return [datasets.SplitGenerator(name=datasets.Split.TRAIN		)]

    def __lowercase   (   self    :  Any    ,						lowerCamelCase    :  Tuple		)   ->  Optional[Any]:
      import pyspark

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

      lowerCAmelCase_			:      str			=   self.df.count()
      lowerCAmelCase_			:      Any			=   df_num_rows if df_num_rows <= 1_00 else 1_00
      # Approximate the size of each row (in Arrow format) by averaging over a max-100-row sample.
      lowerCAmelCase_			:      str			=   (
          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
      )
      lowerCAmelCase_			:      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.
        lowerCAmelCase_			:      Tuple			=   min(lowerCamelCase    ,						int(approx_total_size / max_shard_size		)		)
        lowerCAmelCase_			:      Dict			=   self.df.repartition(lowerCamelCase		)

    def __lowercase   (   self    :  Optional[int]    ,						lowerCamelCase    :  str    ,						lowerCamelCase    :  str    ,						lowerCamelCase    :  int    ,						)   ->  Iterable[Tuple[int, bool, Union[int, tuple]]]:
      import pyspark

      lowerCAmelCase_			:      List[str]			=   ParquetWriter if file_format == """parquet""" else ArrowWriter
      lowerCAmelCase_			:      int			=   os.path.join(self._working_dir    ,						os.path.basename(lowerCamelCase		)		) if self._working_dir else fpath
      lowerCAmelCase_			:      Tuple			=   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_			:      str			=   self.config.features
      lowerCAmelCase_			:      Tuple			=   self._writer_batch_size
      lowerCAmelCase_			:      Any			=   self._fs.storage_options

      def write_arrow(lowerCamelCase    :  Optional[int]		):
        # Within the same SparkContext, no two task attempts will share the same attempt ID.
        lowerCAmelCase_			:      Union[str, Any]			=   pyspark.TaskContext().taskAttemptId()
        lowerCAmelCase_			:      Union[str, 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"""]    ,						)
        lowerCAmelCase_			:      List[Any]			=   0
        lowerCAmelCase_			:      Dict			=   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    ,						)
        lowerCAmelCase_			:      str			=   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:
            lowerCAmelCase_,							lowerCAmelCase_			:      Any			=   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_			:      Optional[int]			=   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    ,						)
          lowerCAmelCase_			:      Tuple			=   pa.Table.from_batches([batch]		)
          writer.write_table(lowerCamelCase		)

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

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

      lowerCAmelCase_			:      List[str]			=   convert_file_size_to_int(max_shard_size or MAX_SHARD_SIZE		)
      self._repartition_df_if_needed(lowerCamelCase		)
      lowerCAmelCase_			:      Optional[int]			=   not is_remote_filesystem(self._fs		)
      lowerCAmelCase_			:      Dict			=   os.path.join if is_local else posixpath.join

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

      lowerCAmelCase_			:      Dict			=   0
      lowerCAmelCase_			:      int			=   0
      lowerCAmelCase_			:      Dict			=   0
      lowerCAmelCase_			:      Any			=   []
      lowerCAmelCase_			:      Dict			=   []

      for task_id, content in self._prepare_split_single(lowerCamelCase    ,						lowerCamelCase    ,						lowerCamelCase		):
        (
            (
            lowerCAmelCase_
        ),							(
            lowerCAmelCase_
        ),							(
            lowerCAmelCase_
        ),							(
            lowerCAmelCase_
        ),							
        )			:      str			=   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		)

      lowerCAmelCase_			:      Union[str, Any]			=   total_num_examples
      lowerCAmelCase_			:      int			=   total_num_bytes

      # should rename everything at the end
      logger.debug(F'Renaming {total_shards} shards.'		)
      if total_shards > 1:
        lowerCAmelCase_			:      Optional[int]			=   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_			:      List[Any]			=   self._fs

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

        lowerCAmelCase_			:      Any			=   []
        lowerCAmelCase_			:      str			=   0
        for i in range(len(lowerCamelCase		)		):
          lowerCAmelCase_,							lowerCAmelCase_			:      Tuple			=   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
        lowerCAmelCase_			:      List[Any]			=   0
        lowerCAmelCase_			:      str			=   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    :  Dict    ,						lowerCamelCase    :  "datasets.SplitGenerator"    ,						)   ->  SparkExamplesIterable:
      return SparkExamplesIterable(self.df		)



