

"""simple docstring"""






import os

import jsonlines
import numpy as np
from tqdm import tqdm


lowerCAmelCase__      =    2048
lowerCAmelCase__      =    4096
lowerCAmelCase__      =    42
lowerCAmelCase__      =    os.environ.pop('''PROCESS_TRAIN''', '''false''')
lowerCAmelCase__      =    {'''null''': 0, '''short''': 1, '''long''': 2, '''yes''': 3, '''no''': 4}






def 	snake_case_  (				A_ :		Dict						):


       '''simple docstring'''





       def choose_first(A_ :		List[Any],    A_ :		List[str]=False						):
              assert isinstance(A_,    A_						)
              if len(A_						) == 1:
                     _lowerCamelCase						:    Optional[int]         =			answer[0]
                     return {k: [answer[k]] for k in answer} if is_long_answer else answer
              for a in answer:
                     if is_long_answer:
                            _lowerCamelCase						:    Dict         =			{k: [a[k]] for k in a}
                     if len(a['''start_token''']						) > 0:
                            break
              return a

       _lowerCamelCase						:    Tuple         =			{'''id''': example['''id''']}
       _lowerCamelCase						:    Optional[Any]         =			example['''annotations''']
       _lowerCamelCase						:    str         =			annotation['''yes_no_answer''']
       if 0 in yes_no_answer or 1 in yes_no_answer:
              _lowerCamelCase						:    Any         =			['''yes'''] if 1 in yes_no_answer else ['''no''']
              _lowerCamelCase						:    Union[str, Any]         =			[]
              _lowerCamelCase						:    List[Any]         =			[]
              _lowerCamelCase						:    Any         =			['''<cls>''']
       else:
              _lowerCamelCase						:    Dict         =			['''short''']
              _lowerCamelCase						:    str         =			choose_first(annotation['''short_answers''']						)
              if len(out['''start_token''']						) == 0:
                     # answer will be long if short is not available
                     _lowerCamelCase						:    Union[str, Any]         =			['''long''']
                     _lowerCamelCase						:    Union[str, Any]         =			choose_first(annotation['''long_answer'''],    is_long_answer=A_						)
                     _lowerCamelCase						:    Dict         =			[]
              answer.update(A_						)

       # disregard some samples
       if len(answer['''start_token''']						) > 1 or answer["start_token"] == answer["end_token"]:
              _lowerCamelCase						:    Union[str, Any]         =			True
       else:
              _lowerCamelCase						:    Tuple         =			False

       _lowerCamelCase						:    Union[str, Any]         =			['''start_token''', '''end_token''', '''start_byte''', '''end_byte''', '''text''']
       if not all(isinstance(answer[k],    A_						) for k in cols						):
              raise ValueError('''Issue in ID''',    example['''id''']						)

       return answer






def 	snake_case_  (				A_ :		List[Any],    A_ :		Dict=False						):


       '''simple docstring'''





       _lowerCamelCase						:    Union[str, Any]         =			_get_single_answer(A_						)
       # bytes are of no use
       del answer["start_byte"]
       del answer["end_byte"]

       # handle yes_no answers explicitly
       if answer["category"][0] in ["yes", "no"]:  # category is list with one element
              _lowerCamelCase						:    Optional[int]         =			example['''document''']['''tokens''']
              _lowerCamelCase						:    Optional[Any]         =			[]
              for i in range(len(doc['''token''']						)						):
                     if not doc["is_html"][i]:
                            context.append(doc['''token'''][i]						)
              return {
                  "context": " ".join(A_						),
                  "answer": {
                      "start_token": -1_00,  # ignore index in cross-entropy
                      "end_token": -1_00,  # ignore index in cross-entropy
                      "category": answer["category"],
                      "span": answer["category"],  # extra
                  },
              }

       # later, help in removing all no answers
       if answer["start_token"] == [-1]:
              return {
                  "context": "None",
                  "answer": {
                      "start_token": -1,
                      "end_token": -1,
                      "category": "null",
                      "span": "None",  # extra
                  },
              }

       # handling normal samples

       _lowerCamelCase						:    List[str]         =			['''start_token''', '''end_token''']
       answer.update({k: answer[k][0] if len(answer[k]						) > 0 else answer[k] for k in cols}						)  # e.g. [10] == 10

       _lowerCamelCase						:    Optional[int]         =			example['''document''']['''tokens''']
       _lowerCamelCase						:    Dict         =			answer['''start_token''']
       _lowerCamelCase						:    Optional[Any]         =			answer['''end_token''']

       _lowerCamelCase						:    Union[str, Any]         =			[]
       for i in range(len(doc['''token''']						)						):
              if not doc["is_html"][i]:
                     context.append(doc['''token'''][i]						)
              else:
                     if answer["start_token"] > i:
                            start_token -= 1
                     if answer["end_token"] > i:
                            end_token -= 1
       _lowerCamelCase						:    Tuple         =			''' '''.join(context[start_token:end_token]						)

       # checking above code
       if assertion:
              _lowerCamelCase						:    Dict         =			doc['''is_html'''][answer['''start_token'''] : answer['''end_token''']]
              _lowerCamelCase						:    Tuple         =			doc['''token'''][answer['''start_token'''] : answer['''end_token''']]
              _lowerCamelCase						:    Any         =			''' '''.join([old[i] for i in range(len(A_						)						) if not is_html[i]]						)
              if new != old:
                     print('''ID:''',    example['''id''']						)
                     print('''New:''',    A_,    end='''\n'''						)
                     print('''Old:''',    A_,    end='''\n\n'''						)

       return {
           "context": " ".join(A_						),
           "answer": {
               "start_token": start_token,
               "end_token": end_token - 1,  # this makes it inclusive
               "category": answer["category"],  # either long or short
               "span": new,  # extra
           },
       }






def 	snake_case_  (				A_ :		int,    A_ :		List[Any],    A_ :		Dict=20_48,    A_ :		Dict=40_96,    A_ :		Optional[int]=True						):


       '''simple docstring'''





       _lowerCamelCase						:    Union[str, Any]         =			get_context_and_ans(A_,    assertion=A_						)
       _lowerCamelCase						:    Optional[Any]         =			out['''answer''']

       # later, removing these samples
       if answer["start_token"] == -1:
              return {
                  "example_id": example["id"],
                  "input_ids": [[-1]],
                  "labels": {
                      "start_token": [-1],
                      "end_token": [-1],
                      "category": ["null"],
                  },
              }

       _lowerCamelCase						:    Any         =			tokenizer(example['''question''']['''text'''],    out['''context''']						).input_ids
       _lowerCamelCase						:    Union[str, Any]         =			input_ids.index(tokenizer.sep_token_id						) + 1

       # return yes/no
       if answer["category"][0] in ["yes", "no"]:  # category is list with one element
              _lowerCamelCase						:    List[str]         =			[]
              _lowerCamelCase						:    int         =			[]
              _lowerCamelCase						:    Tuple         =			input_ids[:q_len]
              _lowerCamelCase						:    str         =			range(A_,    len(A_						),    max_length - doc_stride						)
              for i in doc_start_indices:
                     _lowerCamelCase						:    List[str]         =			i + max_length - q_len
                     _lowerCamelCase						:    Any         =			input_ids[i:end_index]
                     inputs.append(q_indices + slice						)
                     category.append(answer['''category'''][0]						)
                     if slice[-1] == tokenizer.sep_token_id:
                            break

              return {
                  "example_id": example["id"],
                  "input_ids": inputs,
                  "labels": {
                      "start_token": [-1_00] * len(A_						),
                      "end_token": [-1_00] * len(A_						),
                      "category": category,
                  },
              }

       _lowerCamelCase						:    Optional[int]         =			out['''context'''].split()
       _lowerCamelCase						:    List[str]         =			splitted_context[answer['''end_token''']]
       _lowerCamelCase						:    Dict         =			len(
           tokenizer(
               ''' '''.join(splitted_context[: answer['''start_token''']]						),    add_special_tokens=A_,    ).input_ids						)
       _lowerCamelCase						:    Optional[int]         =			len(
           tokenizer(''' '''.join(splitted_context[: answer['''end_token''']]						),    add_special_tokens=A_						).input_ids						)

       answer["start_token"] += q_len
       answer["end_token"] += q_len

       # fixing end token
       _lowerCamelCase						:    Any         =			len(tokenizer(A_,    add_special_tokens=A_						).input_ids						)
       if num_sub_tokens > 1:
              answer["end_token"] += num_sub_tokens - 1

       _lowerCamelCase						:    List[Any]         =			input_ids[answer['''start_token'''] : answer['''end_token'''] + 1]  # right & left are inclusive
       _lowerCamelCase						:    int         =			answer['''start_token''']
       _lowerCamelCase						:    Tuple         =			answer['''end_token''']

       if assertion:
              _lowerCamelCase						:    List[str]         =			tokenizer.decode(A_						)
              if answer["span"] != new:
                     print('''ISSUE IN TOKENIZATION'''						)
                     print('''OLD:''',    answer['''span''']						)
                     print('''NEW:''',    A_,    end='''\n\n'''						)

       if len(A_						) <= max_length:
              return {
                  "example_id": example["id"],
                  "input_ids": [input_ids],
                  "labels": {
                      "start_token": [answer["start_token"]],
                      "end_token": [answer["end_token"]],
                      "category": answer["category"],
                  },
              }

       _lowerCamelCase						:    Tuple         =			input_ids[:q_len]
       _lowerCamelCase						:    Union[str, Any]         =			range(A_,    len(A_						),    max_length - doc_stride						)

       _lowerCamelCase						:    str         =			[]
       _lowerCamelCase						:    Union[str, Any]         =			[]
       _lowerCamelCase						:    List[str]         =			[]
       _lowerCamelCase						:    List[Any]         =			[]  # null, yes, no, long, short
       for i in doc_start_indices:
              _lowerCamelCase						:    List[Any]         =			i + max_length - q_len
              _lowerCamelCase						:    Union[str, Any]         =			input_ids[i:end_index]
              inputs.append(q_indices + slice						)
              assert len(inputs[-1]						) <= max_length, "Issue in truncating length"

              if start_token >= i and end_token <= end_index - 1:
                     _lowerCamelCase						:    Optional[int]         =			start_token - i + q_len
                     _lowerCamelCase						:    Union[str, Any]         =			end_token - i + q_len
                     answers_category.append(answer['''category'''][0]						)  # ["short"] -> "short"
              else:
                     _lowerCamelCase						:    Tuple         =			-1_00
                     _lowerCamelCase						:    Optional[Any]         =			-1_00
                     answers_category.append('''null'''						)
              _lowerCamelCase						:    Tuple         =			inputs[-1][start_token : end_token + 1]

              answers_start_token.append(A_						)
              answers_end_token.append(A_						)
              if assertion:
                     if new != old and new != [tokenizer.cls_token_id]:
                            print('''ISSUE in strided for ID:''',    example['''id''']						)
                            print('''New:''',    tokenizer.decode(A_						)						)
                            print('''Old:''',    tokenizer.decode(A_						),    end='''\n\n'''						)
              if slice[-1] == tokenizer.sep_token_id:
                     break

       return {
           "example_id": example["id"],
           "input_ids": inputs,
           "labels": {
               "start_token": answers_start_token,
               "end_token": answers_end_token,
               "category": answers_category,
           },
       }






def 	snake_case_  (				A_ :		Optional[int],    A_ :		int,    A_ :		Any=20_48,    A_ :		Union[str, Any]=40_96,    A_ :		Optional[Any]=False						):


       '''simple docstring'''





       _lowerCamelCase						:    Union[str, Any]         =			get_strided_contexts_and_ans(
           A_,    A_,    doc_stride=A_,    max_length=A_,    assertion=A_,    )

       return example






def 	snake_case_  (				A_ :		List[str],    A_ :		Union[str, Any]						):


       '''simple docstring'''





       with jsonlines.open(A_,    '''a'''						) as writer:
              for example in tqdm(A_,    total=len(A_						),    desc='''Saving samples ... '''						):
                     _lowerCamelCase						:    Optional[Any]         =			example['''labels''']
                     for ids, start, end, cat in zip(
                         example['''input_ids'''],    labels['''start_token'''],    labels['''end_token'''],    labels['''category'''],    ):
                            if start == -1 and end == -1:
                                   continue  # leave waste samples with no answer
                            if cat == "null" and np.random.rand() < 0.6:
                                   continue  # removing 50 % samples
                            writer.write(
                                {
                                    '''input_ids''': ids,
                                    '''start_token''': start,
                                    '''end_token''': end,
                                    '''category''': CATEGORY_MAPPING[cat],
                                }						)


if __name__ == "__main__":
   from datasets import load_dataset

   from transformers import BigBirdTokenizer

   lowerCAmelCase__      =    load_dataset('''natural_questions''')
   lowerCAmelCase__      =    BigBirdTokenizer.from_pretrained('''google/bigbird-roberta-base''')

   lowerCAmelCase__      =    data['''train''' if PROCESS_TRAIN == '''true''' else '''validation''']

   lowerCAmelCase__      =    {
       '''tokenizer''': tokenizer,
       '''doc_stride''': DOC_STRIDE,
       '''max_length''': MAX_LENGTH,
       '''assertion''': False,
   }
   lowerCAmelCase__      =    data.map(prepare_inputs, fn_kwargs=fn_kwargs)
   lowerCAmelCase__      =    data.remove_columns(['''annotations''', '''document''', '''id''', '''question'''])
   print(data)

   np.random.seed(SEED)
   lowerCAmelCase__      =    '''nq-training.jsonl''' if PROCESS_TRAIN == '''true''' else '''nq-validation.jsonl'''
   save_to_disk(data, file_name=cache_file_name)







"""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


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

if TYPE_CHECKING:
   import pyspark






@dataclass
class 	__snake_case	(     datasets.BuilderConfig):
  snake_case__    :       Optional[datasets.Features]        =						None






def 	snake_case_  (				A_ :		"pyspark.sql.DataFrame",    A_ :		List[int],    ):


       '''simple docstring'''





       import pyspark

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

       return generate_fn






class 	__snake_case	(     _BaseExamplesIterable):




  def __init__(			self					:						Tuple						, __lowerCAmelCase					:						"pyspark.sql.DataFrame"						, __lowerCAmelCase					:						Optional[int]=None						, ):






         """simple docstring"""






         _lowerCamelCase						:    Dict         =			df
         _lowerCamelCase						:    Union[str, Any]         =			partition_order or range(self.df.rdd.getNumPartitions()		)
         _lowerCamelCase						:    Dict         =			_generate_iterable_examples(self.df						, self.partition_order		)




  def __iter__(			self					:						List[Any]		):






         """simple docstring"""






         yield from self.generate_examples_fn()




  def 						SCREAMING_SNAKE_CASE    (			self					:						Tuple						, __lowerCAmelCase					:						np.random.Generator		):






         """simple docstring"""






         _lowerCamelCase						:    Optional[Any]         =			list(range(self.df.rdd.getNumPartitions()		)		)
         generator.shuffle(__lowerCAmelCase		)
         return SparkExamplesIterable(self.df						, partition_order=__lowerCAmelCase		)




  def 						SCREAMING_SNAKE_CASE    (			self					:						Optional[Any]						, __lowerCAmelCase					:						int						, __lowerCAmelCase					:						int		):






         """simple docstring"""






         _lowerCamelCase						:    List[Any]         =			self.split_shard_indices_by_worker(__lowerCAmelCase						, __lowerCAmelCase		)
         return SparkExamplesIterable(self.df						, partition_order=__lowerCAmelCase		)




  @property
  def 						SCREAMING_SNAKE_CASE    (			self					:						Union[str, Any]		):






         """simple docstring"""






         return len(self.partition_order		)






class 	__snake_case	(     datasets.DatasetBuilder):
  snake_case__    :       List[Any]        =						SparkConfig




  def __init__(			self					:						Union[str, Any]						, __lowerCAmelCase					:						"pyspark.sql.DataFrame"						, __lowerCAmelCase					:						str = None						, __lowerCAmelCase					:						str = None						, **__lowerCAmelCase					:						int						, ):






         """simple docstring"""






         import pyspark

         _lowerCamelCase						:    Optional[int]         =			pyspark.sql.SparkSession.builder.getOrCreate()
         _lowerCamelCase						:    int         =			df
         _lowerCamelCase						:    Any         =			working_dir

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




  def 						SCREAMING_SNAKE_CASE    (			self					:						List[Any]		):






         """simple docstring"""






         def create_cache_and_write_probe(__lowerCAmelCase					:						Optional[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						:    Optional[int]         =			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						:    Optional[Any]         =			(
                    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 						SCREAMING_SNAKE_CASE    (			self					:						Any		):






         """simple docstring"""






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




  def 						SCREAMING_SNAKE_CASE    (			self					:						int						, __lowerCAmelCase					:						datasets.download.download_manager.DownloadManager		):






         """simple docstring"""






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




  def 						SCREAMING_SNAKE_CASE    (			self					:						Tuple						, __lowerCAmelCase					:						Union[str, Any]		):






         """simple docstring"""






         import pyspark

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

         _lowerCamelCase						:    Any         =			self.df.count()
         _lowerCamelCase						:    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.
         _lowerCamelCase						:    List[Any]         =			(
             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						:    Dict         =			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						:    List[str]         =			min(__lowerCAmelCase						, int(approx_total_size / max_shard_size		)		)
                _lowerCamelCase						:    Optional[int]         =			self.df.repartition(__lowerCAmelCase		)




  def 						SCREAMING_SNAKE_CASE    (			self					:						Union[str, Any]						, __lowerCAmelCase					:						str						, __lowerCAmelCase					:						str						, __lowerCAmelCase					:						int						, ):






         """simple docstring"""






         import pyspark

         _lowerCamelCase						:    Optional[Any]         =			ParquetWriter if file_format == '''parquet''' else ArrowWriter
         _lowerCamelCase						:    List[Any]         =			os.path.join(self._working_dir						, os.path.basename(__lowerCAmelCase		)		) if self._working_dir else fpath
         _lowerCamelCase						:    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.
         _lowerCamelCase						:    str         =			self.config.features
         _lowerCamelCase						:    Dict         =			self._writer_batch_size
         _lowerCamelCase						:    List[str]         =			self._fs.storage_options

         def write_arrow(__lowerCAmelCase					:						List[str]		):
                # Within the same SparkContext, no two task attempts will share the same attempt ID.
                _lowerCamelCase						:    List[str]         =			pyspark.TaskContext().taskAttemptId()
                _lowerCamelCase						:    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						:    Optional[int]         =			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						:    int         =			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						:    Optional[int]         =			pa.Table.from_batches([batch]		)
                       writer.write_table(__lowerCAmelCase		)

                if writer._num_bytes > 0:
                       _lowerCamelCase      ,   _lowerCamelCase						:    Optional[int]         =			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						:    Optional[Any]         =			os.path.join(os.path.dirname(__lowerCAmelCase		)						, os.path.basename(__lowerCAmelCase		)		)
                              shutil.move(__lowerCAmelCase						, __lowerCAmelCase		)

         _lowerCamelCase						:    List[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 						SCREAMING_SNAKE_CASE    (			self					:						Tuple						, __lowerCAmelCase					:						"datasets.SplitGenerator"						, __lowerCAmelCase					:						str = "arrow"						, __lowerCAmelCase					:						Optional[Union[str, int]] = None						, __lowerCAmelCase					:						Optional[int] = None						, **__lowerCAmelCase					:						Tuple						, ):






         """simple docstring"""






         self._validate_cache_dir()

         _lowerCamelCase						:    str         =			convert_file_size_to_int(max_shard_size or MAX_SHARD_SIZE		)
         self._repartition_df_if_needed(__lowerCAmelCase		)
         _lowerCamelCase						:    str         =			not is_remote_filesystem(self._fs		)
         _lowerCamelCase						:    Tuple         =			os.path.join if is_local else posixpath.join

         _lowerCamelCase						:    int         =			'''-TTTTT-SSSSS-of-NNNNN'''
         _lowerCamelCase						:    Tuple         =			f'''{self.name}-{split_generator.name}{SUFFIX}.{file_format}'''
         _lowerCamelCase						:    List[Any]         =			path_join(self._output_dir						, __lowerCAmelCase		)

         _lowerCamelCase						:    List[Any]         =			0
         _lowerCamelCase						:    Any         =			0
         _lowerCamelCase						:    str         =			0
         _lowerCamelCase						:    int         =			[]
         _lowerCamelCase						:    List[str]         =			[]

         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						:    int         =			total_num_examples
         _lowerCamelCase						:    str         =			total_num_bytes

         # should rename everything at the end
         logger.debug(f'''Renaming {total_shards} shards.'''		)
         if total_shards > 1:
                _lowerCamelCase						:    Optional[Any]         =			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						:    str         =			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						:    Union[str, Any]         =			[]
                _lowerCamelCase						:    Any         =			0
                for i in range(len(__lowerCAmelCase		)		):
                       _lowerCamelCase      ,   _lowerCamelCase						:    Dict         =			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						:    Any         =			0
                _lowerCamelCase						:    List[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 						SCREAMING_SNAKE_CASE    (			self					:						Any						, __lowerCAmelCase					:						"datasets.SplitGenerator"						, ):






         """simple docstring"""






         return SparkExamplesIterable(self.df		)


