



"""simple docstring"""

# Note: if you intend to run this script make sure you look under scripts/fsmt/
# to locate the appropriate script to do the work correctly. There is a set of scripts to:
# - download and prepare data and run the conversion script
# - perform eval to get the best hparam into the config
# - generate model_cards - useful if you have multiple models from the same paper

import argparse
import json
import os
import re
from collections import OrderedDict
from os.path import basename, dirname

import fairseq
import torch
from fairseq import hub_utils
from fairseq.data.dictionary import Dictionary

from transformers import FSMTConfig, FSMTForConditionalGeneration
from transformers.models.fsmt.tokenization_fsmt import VOCAB_FILES_NAMES
from transformers.tokenization_utils_base import TOKENIZER_CONFIG_FILE
from transformers.utils import WEIGHTS_NAME, logging


logging.set_verbosity_warning()

lowercase__    :  Tuple							    =	2

# based on the results of a search on a range of `num_beams`, `length_penalty` and `early_stopping`
# values against wmt19 test data to obtain the best BLEU scores, we will use the following defaults:
#
# * `num_beams`: 5 (higher scores better, but requires more memory/is slower, can be adjusted by users)
# * `early_stopping`: `False` consistently scored better
# * `length_penalty` varied, so will assign the best one depending on the model
lowercase__    :  List[str]							    =	{
    # fairseq:
    '''wmt19-ru-en''': {'''length_penalty''': 1.1},
    '''wmt19-en-ru''': {'''length_penalty''': 1.15},
    '''wmt19-en-de''': {'''length_penalty''': 1.0},
    '''wmt19-de-en''': {'''length_penalty''': 1.1},
    # allenai:
    '''wmt16-en-de-dist-12-1''': {'''length_penalty''': 0.6},
    '''wmt16-en-de-dist-6-1''': {'''length_penalty''': 0.6},
    '''wmt16-en-de-12-1''': {'''length_penalty''': 0.8},
    '''wmt19-de-en-6-6-base''': {'''length_penalty''': 0.6},
    '''wmt19-de-en-6-6-big''': {'''length_penalty''': 0.6},
}

# this remaps the different models to their organization names
lowercase__    :  Tuple							    =	{}
for m in ["wmt19-ru-en", "wmt19-en-ru", "wmt19-en-de", "wmt19-de-en"]:
     lowercase__    :  Optional[int]							    =	'''facebook'''
for m in [
    "wmt16-en-de-dist-12-1",
    "wmt16-en-de-dist-6-1",
    "wmt16-en-de-12-1",
    "wmt19-de-en-6-6-base",
    "wmt19-de-en-6-6-big",
]:
     lowercase__    :  int							    =	'''allenai'''




def __lowercase			(							_a    ):
    # (1) remove word breaking symbol, (2) add word ending symbol where the word is not broken up,
    # e.g.: d = {'le@@': 5, 'tt@@': 6, 'er': 7} => {'le': 5, 'tt': 6, 'er</w>': 7}
    snake_case_ :     List[str]				=	dict((re.sub(r'''@@$'''		,			''''''		,			_a    ), v) if k.endswith('''@@'''    ) else (re.sub(r'''$'''		,			'''</w>'''		,			_a    ), v) for k, v in d.items()    )
    snake_case_ :     List[Any]				=	'<s> <pad> </s> <unk>'.split()
    # restore the special tokens
    for k in keep_keys:
        del da[f"{k}</w>"]
        snake_case_ :     Optional[int]				=	d[k]  # restore
    return da




def __lowercase			(							_a		,			_a    ):
    # prep
    assert os.path.exists(_a    )
    os.makedirs(_a		,			exist_ok=_a    )
    print(f"Writing results to {pytorch_dump_folder_path}"    )

    # handle various types of models

    snake_case_ :     str				=	basename(_a    )
    snake_case_ :     int				=	dirname(_a    )

    snake_case_ :     Any				=	fairseq.model_parallel.models.transformer.ModelParallelTransformerModel
    snake_case_ :     str				=	cls.hub_models()
    snake_case_ :     Optional[Any]				=	{'bpe': 'fastbpe', 'tokenizer': 'moses'}
    snake_case_ :     List[Any]				=	'.'
    # note: since the model dump is old, fairseq has upgraded its model some
    # time later, and it does a whole lot of rewrites and splits on the saved
    # weights, therefore we can't use torch.load() directly on the model file.
    # see: upgrade_state_dict(state_dict) in fairseq_model.py
    print(f"using checkpoint {checkpoint_file}"    )
    snake_case_ :     Any				=	hub_utils.from_pretrained(
        _a		,			_a		,			_a		,			archive_map=_a		,			**_a    )

    snake_case_ :     Optional[Any]				=	vars(chkpt['''args''']['''model''']    )

    snake_case_ :     Optional[Any]				=	args['source_lang']
    snake_case_ :     Dict				=	args['target_lang']

    snake_case_ :     int				=	dirname(_a    )
    snake_case_ :     Tuple				=	basename(_a    )

    # dicts
    snake_case_ :     int				=	os.path.join(_a		,			f"dict.{src_lang}.txt"    )
    snake_case_ :     str				=	os.path.join(_a		,			f"dict.{tgt_lang}.txt"    )

    snake_case_ :     str				=	Dictionary.load(_a    )
    snake_case_ :     Optional[int]				=	rewrite_dict_keys(src_dict.indices    )
    snake_case_ :     List[Any]				=	len(_a    )
    snake_case_ :     Optional[int]				=	os.path.join(_a		,			'''vocab-src.json'''    )
    print(f"Generating {src_vocab_file} of {src_vocab_size} of {src_lang} records"    )
    with open(_a		,			'''w'''		,			encoding='''utf-8'''    ) as f:
        f.write(json.dumps(_a		,			ensure_ascii=_a		,			indent=_a    )    )

    # detect whether this is a do_lower_case situation, which can be derived by checking whether we
    # have at least one uppercase letter in the source vocab
    snake_case_ :     int				=	True
    for k in src_vocab.keys():
        if not k.islower():
            snake_case_ :     Optional[Any]				=	False
            break

    snake_case_ :     Tuple				=	Dictionary.load(_a    )
    snake_case_ :     List[Any]				=	rewrite_dict_keys(tgt_dict.indices    )
    snake_case_ :     Any				=	len(_a    )
    snake_case_ :     str				=	os.path.join(_a		,			'''vocab-tgt.json'''    )
    print(f"Generating {tgt_vocab_file} of {tgt_vocab_size} of {tgt_lang} records"    )
    with open(_a		,			'''w'''		,			encoding='''utf-8'''    ) as f:
        f.write(json.dumps(_a		,			ensure_ascii=_a		,			indent=_a    )    )

    # merges_file (bpecodes)
    snake_case_ :     str				=	os.path.join(_a		,			VOCAB_FILES_NAMES['''merges_file''']    )
    for fn in ["bpecodes", "code"]:  # older fairseq called the merges file "code"
        snake_case_ :     Any				=	os.path.join(_a		,			_a    )
        if os.path.exists(_a    ):
            break
    with open(_a		,			encoding='''utf-8'''    ) as fin:
        snake_case_ :     Any				=	fin.read()
    snake_case_ :     Optional[int]				=	re.sub(r''' \d+$'''		,			''''''		,			_a		,			0		,			re.M    )  # remove frequency number
    print(f"Generating {merges_file}"    )
    with open(_a		,			'''w'''		,			encoding='''utf-8'''    ) as fout:
        fout.write(_a    )

    # model config
    snake_case_ :     Optional[Any]				=	os.path.join(_a		,			'''config.json'''    )

    # validate bpe/tokenizer config, as currently it's hardcoded to moses+fastbpe -
    # may have to modify the tokenizer if a different type is used by a future model
    assert args["bpe"] == "fastbpe", f"need to extend tokenizer to support bpe={args['bpe']}"
    assert args["tokenizer"] == "moses", f"need to extend tokenizer to support bpe={args['tokenizer']}"

    snake_case_ :     List[Any]				=	{
        'architectures': ['FSMTForConditionalGeneration'],
        'model_type': 'fsmt',
        'activation_dropout': args['activation_dropout'],
        'activation_function': 'relu',
        'attention_dropout': args['attention_dropout'],
        'd_model': args['decoder_embed_dim'],
        'dropout': args['dropout'],
        'init_std': 0.02,
        'max_position_embeddings': args['max_source_positions'],
        'num_hidden_layers': args['encoder_layers'],
        'src_vocab_size': src_vocab_size,
        'tgt_vocab_size': tgt_vocab_size,
        'langs': [src_lang, tgt_lang],
        'encoder_attention_heads': args['encoder_attention_heads'],
        'encoder_ffn_dim': args['encoder_ffn_embed_dim'],
        'encoder_layerdrop': args['encoder_layerdrop'],
        'encoder_layers': args['encoder_layers'],
        'decoder_attention_heads': args['decoder_attention_heads'],
        'decoder_ffn_dim': args['decoder_ffn_embed_dim'],
        'decoder_layerdrop': args['decoder_layerdrop'],
        'decoder_layers': args['decoder_layers'],
        'bos_token_id': 0,
        'pad_token_id': 1,
        'eos_token_id': 2,
        'is_encoder_decoder': True,
        'scale_embedding': not args['no_scale_embedding'],
        'tie_word_embeddings': args['share_all_embeddings'],
    }

    # good hparam defaults to start with
    snake_case_ :     Optional[Any]				=	5
    snake_case_ :     Tuple				=	False
    if model_dir in best_score_hparams and "length_penalty" in best_score_hparams[model_dir]:
        snake_case_ :     List[str]				=	best_score_hparams[model_dir]['length_penalty']
    else:
        snake_case_ :     int				=	1.0

    print(f"Generating {fsmt_model_config_file}"    )
    with open(_a		,			'''w'''		,			encoding='''utf-8'''    ) as f:
        f.write(json.dumps(_a		,			ensure_ascii=_a		,			indent=_a    )    )

    # tokenizer config
    snake_case_ :     Dict				=	os.path.join(_a		,			_a    )

    snake_case_ :     List[Any]				=	{
        'langs': [src_lang, tgt_lang],
        'model_max_length': 1_024,
        'do_lower_case': do_lower_case,
    }

    print(f"Generating {fsmt_tokenizer_config_file}"    )
    with open(_a		,			'''w'''		,			encoding='''utf-8'''    ) as f:
        f.write(json.dumps(_a		,			ensure_ascii=_a		,			indent=_a    )    )

    # model
    snake_case_ :     Optional[Any]				=	chkpt['models'][0]
    snake_case_ :     str				=	model.state_dict()

    # rename keys to start with 'model.'
    snake_case_ :     Tuple				=	OrderedDict(('''model.''' + k, v) for k, v in model_state_dict.items()    )

    # remove unneeded keys
    snake_case_ :     Optional[int]				=	[
        'model.model',
        'model.encoder.version',
        'model.decoder.version',
        'model.encoder_embed_tokens.weight',
        'model.decoder_embed_tokens.weight',
        'model.encoder.embed_positions._float_tensor',
        'model.decoder.embed_positions._float_tensor',
    ]
    for k in ignore_keys:
        model_state_dict.pop(_a		,			_a    )

    snake_case_ :     Dict				=	FSMTConfig.from_pretrained(_a    )
    snake_case_ :     Union[str, Any]				=	FSMTForConditionalGeneration(_a    )

    # check that it loads ok
    model_new.load_state_dict(_a		,			strict=_a    )

    # save
    snake_case_ :     int				=	os.path.join(_a		,			_a    )
    print(f"Generating {pytorch_weights_dump_path}"    )
    torch.save(_a		,			_a    )

    print('''Conversion is done!'''    )
    print('''\nLast step is to upload the files to s3'''    )
    print(f"cd {data_root}"    )
    print(f"transformers-cli upload {model_dir}"    )


if __name__ == "__main__":
     lowercase__    :  str							    =	argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         '''--fsmt_checkpoint_path''',
         default=None,
         type=str,
         required=True,
         help=(
             '''Path to the official PyTorch checkpoint file which is expected to reside in the dump dir with dicts,'''
             ''' bpecodes, etc.'''
         ),
     )
     parser.add_argument(
         '''--pytorch_dump_folder_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
     )
     lowercase__    :  Optional[int]							    =	parser.parse_args()
     convert_fsmt_checkpoint_to_pytorch(args.fsmt_checkpoint_path, args.pytorch_dump_folder_path)






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

if TYPE_CHECKING:
   import pyspark





@dataclass
class 		_lowercase						(					datasets.BuilderConfig       ):
 lowercase				     =   None





def    __lowercase     (						lowerCamelCase					:     "pyspark.sql.DataFrame"  ,  lowerCamelCase					:     List[int]  ,  ):
 import pyspark

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

 return generate_fn





class 		_lowercase						(					_BaseExamplesIterable       ):

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

  """simple docstring"""





  UpperCamelCase_					:						Dict =					df
  UpperCamelCase_					:						int =					partition_order or range(self.df.rdd.getNumPartitions()			)
  UpperCamelCase_					:						Optional[Any] =					_generate_iterable_examples(self.df   ,      self.partition_order			)

 def __iter__( self	:							Optional[int]			)   ->						Any:

  """simple docstring"""





  yield from self.generate_examples_fn()

 def 						SCREAMING_SNAKE_CASE__   ( self	:							Any   ,      snake_case	:							np.random.Generator			)   ->						"SparkExamplesIterable":

  """simple docstring"""





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

 def 						SCREAMING_SNAKE_CASE__   ( self	:							int   ,      snake_case	:							int   ,      snake_case	:							int			)   ->						"SparkExamplesIterable":

  """simple docstring"""





  UpperCamelCase_					:						Tuple =					self.split_shard_indices_by_worker(snake_case   ,      snake_case			)
  return SparkExamplesIterable(self.df   ,      partition_order=snake_case			)


 @property
 def 						SCREAMING_SNAKE_CASE__   ( self	:							int			)   ->						int:

  """simple docstring"""





  return len(self.partition_order			)





class 		_lowercase						(					datasets.DatasetBuilder       ):
 lowercase				     =   SparkConfig

 def __init__( self	:							List[Any]   ,      snake_case	:							"pyspark.sql.DataFrame"   ,      snake_case	:							str = None   ,      snake_case	:							str = None   ,      **snake_case	:							Optional[Any]   ,      )   ->						List[str]:

  """simple docstring"""





  import pyspark

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

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

 def 						SCREAMING_SNAKE_CASE__   ( self	:							Any			)   ->						Dict:

  """simple docstring"""





  def create_cache_and_write_probe(snake_case	:							str			):
   # 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=snake_case			)
   UpperCamelCase_					:						Tuple =					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(snake_case   ,      '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:
   UpperCamelCase_					:						Tuple =					(
       self._spark.sparkContext.parallelize(range(1			)   ,      1			).mapPartitions(snake_case			).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	:							int			)   ->						Union[str, Any]:

  """simple docstring"""





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

 def 						SCREAMING_SNAKE_CASE__   ( self	:							Optional[Any]   ,      snake_case	:							datasets.download.download_manager.DownloadManager			)   ->						Optional[int]:

  """simple docstring"""





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

 def 						SCREAMING_SNAKE_CASE__   ( self	:							Optional[Any]   ,      snake_case	:							Optional[int]			)   ->						List[Any]:

  """simple docstring"""





  import pyspark

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

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

 def 						SCREAMING_SNAKE_CASE__   ( self	:							Union[str, Any]   ,      snake_case	:							str   ,      snake_case	:							str   ,      snake_case	:							int   ,      )   ->						Iterable[Tuple[int, bool, Union[int, tuple]]]:

  """simple docstring"""





  import pyspark

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

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

   if writer._num_bytes > 0:
    UpperCamelCase_, UpperCamelCase_					:						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(snake_case			)			):
     UpperCamelCase_					:						Dict =					os.path.join(os.path.dirname(snake_case			)   ,      os.path.basename(snake_case			)			)
     shutil.move(snake_case   ,      snake_case			)

  UpperCamelCase_					:						int =					(
      self.df.mapInArrow(snake_case   ,      '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	:							int   ,      snake_case	:							"datasets.SplitGenerator"   ,      snake_case	:							str = "arrow"   ,      snake_case	:							Optional[Union[str, int]] = None   ,      snake_case	:							Optional[int] = None   ,      **snake_case	:							Any   ,      )   ->						int:

  """simple docstring"""





  self._validate_cache_dir()

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

  UpperCamelCase_					:						Optional[int] =					'-TTTTT-SSSSS-of-NNNNN'
  UpperCamelCase_					:						Dict =					f"{self.name}-{split_generator.name}{SUFFIX}.{file_format}"
  UpperCamelCase_					:						int =					path_join(self._output_dir   ,      snake_case			)

  UpperCamelCase_					:						int =					0
  UpperCamelCase_					:						Optional[int] =					0
  UpperCamelCase_					:						Union[str, Any] =					0
  UpperCamelCase_					:						Optional[Any] =					[]
  UpperCamelCase_					:						Any =					[]

  for task_id, content in self._prepare_split_single(snake_case   ,      snake_case   ,      snake_case			):
   (
       (
       UpperCamelCase_
   ), (
       UpperCamelCase_
   ), (
       UpperCamelCase_
   ), (
       UpperCamelCase_
   ), 
   )					:						Optional[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(snake_case			)

  UpperCamelCase_					:						Optional[Any] =					total_num_examples
  UpperCamelCase_					:						Any =					total_num_bytes

  # should rename everything at the end
  logger.debug(f"Renaming {total_shards} shards."			)
  if total_shards > 1:
   UpperCamelCase_					:						List[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.
   UpperCamelCase_					:						int =					self._fs

   # use the -SSSSS-of-NNNNN pattern
   def _rename_shard(
       snake_case	:							int   ,      snake_case	:							int   ,      snake_case	:							int   ,      ):
    rename(
        snake_case   ,      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}"			)   ,      )

   UpperCamelCase_					:						Any =					[]
   UpperCamelCase_					:						Optional[int] =					0
   for i in range(len(snake_case			)			):
    UpperCamelCase_, UpperCamelCase_					:						Union[str, Any] =					task_id_and_num_shards[i]
    for shard_id in range(snake_case			):
     args.append([task_id, shard_id, global_shard_id]			)
     global_shard_id += 1
   self._spark.sparkContext.parallelize(snake_case   ,      len(snake_case			)			).map(lambda snake_case			: _rename_shard(*snake_case			)			).collect()
  else:
   # don't use any pattern
   UpperCamelCase_					:						Tuple =					0
   UpperCamelCase_					:						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(snake_case   ,      ''			)   ,      )


 def 						SCREAMING_SNAKE_CASE__   ( self	:							Tuple   ,      snake_case	:							"datasets.SplitGenerator"   ,      )   ->						SparkExamplesIterable:

  """simple docstring"""





  return SparkExamplesIterable(self.df			)



