
"""simple docstring"""



import collections
import importlib.util
import os
import re
from pathlib import Path


UpperCAmelCase__          = 'src/transformers'


# Matches is_xxx_available()
UpperCAmelCase__          = re.compile(r'is\_([a-z_]*)_available()')
# Catches a one-line _import_struct = {xxx}
UpperCAmelCase__          = re.compile(r'^_import_structure\s+=\s+\{([^\}]+)\}')
# Catches a line with a key-values pattern: "bla": ["foo", "bar"]
UpperCAmelCase__          = re.compile(r'\s+"\S*":\s+\[([^\]]*)\]')
# Catches a line if not is_foo_available
UpperCAmelCase__          = re.compile(r'^\s*if\s+not\s+is\_[a-z_]*\_available\(\)')
# Catches a line _import_struct["bla"].append("foo")
UpperCAmelCase__          = re.compile(r'^\s*_import_structure\["\S*"\]\.append\("(\S*)"\)')
# Catches a line _import_struct["bla"].extend(["foo", "bar"]) or _import_struct["bla"] = ["foo", "bar"]
UpperCAmelCase__          = re.compile(r'^\s*_import_structure\[\S*\](?:\.extend\(|\s*=\s+)\[([^\]]*)\]')
# Catches a line with an object between quotes and a comma:     "MyModel",
UpperCAmelCase__          = re.compile('^\s+"([^"]+)",')
# Catches a line with objects between brackets only:    ["foo", "bar"],
UpperCAmelCase__          = re.compile('^\s+\[([^\]]+)\]')
# Catches a line with from foo import bar, bla, boo
UpperCAmelCase__          = re.compile(r'\s+from\s+\S*\s+import\s+([^\(\s].*)\n')
# Catches a line with try:
UpperCAmelCase__          = re.compile(r'^\s*try:')
# Catches a line with else:
UpperCAmelCase__          = re.compile(r'^\s*else:')
def 					_UpperCAmelCase      ( __lowerCamelCase   :       Dict		)     ->					Any:
  if _re_test_backend.search(__lowerCamelCase		) is None:
    return None
  _snake_case					     =       [b[0] for b in _re_backend.findall(__lowerCamelCase		)]
  backends.sort()
  return "_and_".join(__lowerCamelCase		)
def 					_UpperCAmelCase      ( __lowerCamelCase   :       List[Any]		)     ->					Optional[Any]:
  with open(__lowerCamelCase ,					'''r''' ,					encoding='''utf-8''' ,					newline='''\n'''		) as f:
    _snake_case					     =       f.readlines()

  _snake_case					     =       0
  while line_index < len(__lowerCamelCase		) and not lines[line_index].startswith('''_import_structure = {'''		):
    line_index += 1

  # If this is a traditional init, just return.
  if line_index >= len(__lowerCamelCase		):
    return None

  # First grab the objects without a specific backend in _import_structure
  _snake_case					     =       []
  while not lines[line_index].startswith('''if TYPE_CHECKING'''		) and find_backend(lines[line_index]		) is None:
    _snake_case					     =       lines[line_index]
    # If we have everything on a single line, let's deal with it.
    if _re_one_line_import_struct.search(__lowerCamelCase		):
      _snake_case					     =       _re_one_line_import_struct.search(__lowerCamelCase		).groups()[0]
      _snake_case					     =       re.findall('''\[([^\]]+)\]''' ,					__lowerCamelCase		)
      for imp in imports:
        objects.extend([obj[1:-1] for obj in imp.split(''', '''		)]		)
      line_index += 1
      continue
    _snake_case					     =       _re_import_struct_key_value.search(__lowerCamelCase		)
    if single_line_import_search is not None:
      _snake_case					     =       [obj[1:-1] for obj in single_line_import_search.groups()[0].split(''', '''		) if len(__lowerCamelCase		) > 0]
      objects.extend(__lowerCamelCase		)
    elif line.startswith(''' ''' * 8 + '''"'''		):
      objects.append(line[9:-3]		)
    line_index += 1

  _snake_case					     =       {'''none''': objects}
  # Let's continue with backend-specific objects in _import_structure
  while not lines[line_index].startswith('''if TYPE_CHECKING'''		):
    # If the line is an if not is_backend_available, we grab all objects associated.
    _snake_case					     =       find_backend(lines[line_index]		)
    # Check if the backend declaration is inside a try block:
    if _re_try.search(lines[line_index - 1]		) is None:
      _snake_case					     =       None

    if backend is not None:
      line_index += 1

      # Scroll until we hit the else block of try-except-else
      while _re_else.search(lines[line_index]		) is None:
        line_index += 1

      line_index += 1

      _snake_case					     =       []
      # Until we unindent, add backend objects to the list
      while len(lines[line_index]		) <= 1 or lines[line_index].startswith(''' ''' * 4		):
        _snake_case					     =       lines[line_index]
        if _re_import_struct_add_one.search(__lowerCamelCase		) is not None:
          objects.append(_re_import_struct_add_one.search(__lowerCamelCase		).groups()[0]		)
        elif _re_import_struct_add_many.search(__lowerCamelCase		) is not None:
          _snake_case					     =       _re_import_struct_add_many.search(__lowerCamelCase		).groups()[0].split(''', '''		)
          _snake_case					     =       [obj[1:-1] for obj in imports if len(__lowerCamelCase		) > 0]
          objects.extend(__lowerCamelCase		)
        elif _re_between_brackets.search(__lowerCamelCase		) is not None:
          _snake_case					     =       _re_between_brackets.search(__lowerCamelCase		).groups()[0].split(''', '''		)
          _snake_case					     =       [obj[1:-1] for obj in imports if len(__lowerCamelCase		) > 0]
          objects.extend(__lowerCamelCase		)
        elif _re_quote_object.search(__lowerCamelCase		) is not None:
          objects.append(_re_quote_object.search(__lowerCamelCase		).groups()[0]		)
        elif line.startswith(''' ''' * 8 + '''"'''		):
          objects.append(line[9:-3]		)
        elif line.startswith(''' ''' * 12 + '''"'''		):
          objects.append(line[13:-3]		)
        line_index += 1

      _snake_case					     =       objects
    else:
      line_index += 1

    # At this stage we are in the TYPE_CHECKING part, first grab the objects without a specific backend
  _snake_case					     =       []
  while (
      line_index < len(__lowerCamelCase		)
      and find_backend(lines[line_index]		) is None
      and not lines[line_index].startswith('''else'''		)
  ):
    _snake_case					     =       lines[line_index]
    _snake_case					     =       _re_import.search(__lowerCamelCase		)
    if single_line_import_search is not None:
      objects.extend(single_line_import_search.groups()[0].split(''', '''		)		)
    elif line.startswith(''' ''' * 8		):
      objects.append(line[8:-2]		)
    line_index += 1

  _snake_case					     =       {'''none''': objects}
  # Let's continue with backend-specific objects
  while line_index < len(__lowerCamelCase		):
    # If the line is an if is_backend_available, we grab all objects associated.
    _snake_case					     =       find_backend(lines[line_index]		)
    # Check if the backend declaration is inside a try block:
    if _re_try.search(lines[line_index - 1]		) is None:
      _snake_case					     =       None

    if backend is not None:
      line_index += 1

      # Scroll until we hit the else block of try-except-else
      while _re_else.search(lines[line_index]		) is None:
        line_index += 1

      line_index += 1

      _snake_case					     =       []
      # Until we unindent, add backend objects to the list
      while len(lines[line_index]		) <= 1 or lines[line_index].startswith(''' ''' * 8		):
        _snake_case					     =       lines[line_index]
        _snake_case					     =       _re_import.search(__lowerCamelCase		)
        if single_line_import_search is not None:
          objects.extend(single_line_import_search.groups()[0].split(''', '''		)		)
        elif line.startswith(''' ''' * 12		):
          objects.append(line[12:-2]		)
        line_index += 1

      _snake_case					     =       objects
    else:
      line_index += 1

  return import_dict_objects, type_hint_objects
def 					_UpperCAmelCase      ( __lowerCamelCase   :       Dict ,					__lowerCamelCase   :       Union[str, Any]		)     ->					Union[str, Any]:

  def find_duplicates(__lowerCamelCase   :       Dict		):
    return [k for k, v in collections.Counter(__lowerCamelCase		).items() if v > 1]

  if list(import_dict_objects.keys()		) != list(type_hint_objects.keys()		):
    return ["Both sides of the init do not have the same backends!"]

  _snake_case					     =       []
  for key in import_dict_objects.keys():
    _snake_case					     =       find_duplicates(import_dict_objects[key]		)
    if duplicate_imports:
      errors.append(f'''Duplicate _import_structure definitions for: {duplicate_imports}'''		)
    _snake_case					     =       find_duplicates(type_hint_objects[key]		)
    if duplicate_type_hints:
      errors.append(f'''Duplicate TYPE_CHECKING objects for: {duplicate_type_hints}'''		)

    if sorted(set(import_dict_objects[key]		)		) != sorted(set(type_hint_objects[key]		)		):
      _snake_case					     =       '''base imports''' if key == '''none''' else f'''{key} backend'''
      errors.append(f'''Differences for {name}:'''		)
      for a in type_hint_objects[key]:
        if a not in import_dict_objects[key]:
          errors.append(f'''  {a} in TYPE_HINT but not in _import_structure.'''		)
      for a in import_dict_objects[key]:
        if a not in type_hint_objects[key]:
          errors.append(f'''  {a} in _import_structure but not in TYPE_HINT.'''		)
  return errors
def 					_UpperCAmelCase      ( )     ->					List[Any]:
  _snake_case					     =       []
  for root, _, files in os.walk(__lowerCamelCase		):
    if "__init__.py" in files:
      _snake_case					     =       os.path.join(__lowerCamelCase ,					'''__init__.py'''		)
      _snake_case					     =       parse_init(__lowerCamelCase		)
      if objects is not None:
        _snake_case					     =       analyze_results(*__lowerCamelCase		)
        if len(__lowerCamelCase		) > 0:
          _snake_case					     =       f'''Problem in {fname}, both halves do not define the same objects.\n{errors[0]}'''
          failures.append('''\n'''.join(__lowerCamelCase		)		)
  if len(__lowerCamelCase		) > 0:
    raise ValueError('''\n\n'''.join(__lowerCamelCase		)		)
def 					_UpperCAmelCase      ( )     ->					Dict:
  _snake_case					     =       []
  for path, directories, files in os.walk(__lowerCamelCase		):
    for folder in directories:
      # Ignore private modules
      if folder.startswith('''_'''		):
        directories.remove(__lowerCamelCase		)
        continue
      # Ignore leftovers from branches (empty folders apart from pycache)
      if len(list((Path(__lowerCamelCase		) / folder).glob('''*.py'''		)		)		) == 0:
        continue
      _snake_case					     =       str((Path(__lowerCamelCase		) / folder).relative_to(__lowerCamelCase		)		)
      _snake_case					     =       short_path.replace(os.path.sep ,					'''.'''		)
      submodules.append(__lowerCamelCase		)
    for fname in files:
      if fname == "__init__.py":
        continue
      _snake_case					     =       str((Path(__lowerCamelCase		) / fname).relative_to(__lowerCamelCase		)		)
      _snake_case					     =       short_path.replace('''.py''' ,					''''''		).replace(os.path.sep ,					'''.'''		)
      if len(submodule.split('''.'''		)		) == 1:
        submodules.append(__lowerCamelCase		)
  return submodules


UpperCAmelCase__          = [
    'convert_pytorch_checkpoint_to_tf2',
    'modeling_flax_pytorch_utils',
]
def 					_UpperCAmelCase      ( )     ->					List[Any]:
  # This is to make sure the transformers module imported is the one in the repo.
  _snake_case					     =       importlib.util.spec_from_file_location(
      '''transformers''' ,					os.path.join(__lowerCamelCase ,					'''__init__.py'''		) ,					submodule_search_locations=[PATH_TO_TRANSFORMERS] ,					)
  _snake_case					     =       spec.loader.load_module()

  _snake_case					     =       [
      module
      for module in get_transformers_submodules()
      if module not in IGNORE_SUBMODULES and module not in transformers._import_structure.keys()
  ]
  if len(__lowerCamelCase		) > 0:
    _snake_case					     =       '''\n'''.join(f'''- {module}''' for module in module_not_registered		)
    raise ValueError(
        '''The following submodules are not properly registered in the main init of Transformers:\n'''
        f'''{list_of_modules}\n'''
        '''Make sure they appear somewhere in the keys of `_import_structure` with an empty list as value.'''		)


if __name__ == "__main__":
 check_all_inits()
 check_submodules()





"""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


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

if TYPE_CHECKING:
 import pyspark

@dataclass
class       lowerCAmelCase__				(						datasets.BuilderConfig	):
     __a						=					None






def 					_UpperCAmelCase      ( __lowerCamelCase   :       "pyspark.sql.DataFrame" ,					__lowerCamelCase   :       List[int] ,					)     ->					Optional[int]:
  import pyspark

  def generate_fn():
    _snake_case					     =       df.select('''*''' ,					pyspark.sql.functions.spark_partition_id().alias('''part_id'''		)		)
    for partition_id in partition_order:
      _snake_case					     =       df_with_partition_id.select('''*'''		).where(f'''part_id = {partition_id}'''		).drop('''part_id'''		)
      _snake_case					     =       partition_df.collect()
      _snake_case					     =       0
      for row in rows:
        yield f'''{partition_id}_{row_id}''', row.asDict()
        row_id += 1

  return generate_fn

class       lowerCAmelCase__				(						_BaseExamplesIterable	):







     def __init__(							self			:			Optional[int]    ,      _lowerCamelCase			:			"pyspark.sql.DataFrame"    ,      _lowerCamelCase			:			List[Any]=None    ,      ):
       _snake_case					     =       df
       _snake_case					     =       partition_order or range(self.df.rdd.getNumPartitions()			)
       _snake_case					     =       _generate_iterable_examples(self.df    ,      self.partition_order			)







     def __iter__(							self			:			Optional[int]			):
       yield from self.generate_examples_fn()







     def      lowercase	(							self			:			Any    ,      _lowerCamelCase			:			np.random.Generator			):
       _snake_case					     =       list(range(self.df.rdd.getNumPartitions()			)			)
       generator.shuffle(_lowerCamelCase			)
       return SparkExamplesIterable(self.df    ,      partition_order=_lowerCamelCase			)







     def      lowercase	(							self			:			List[Any]    ,      _lowerCamelCase			:			int    ,      _lowerCamelCase			:			int			):
       _snake_case					     =       self.split_shard_indices_by_worker(_lowerCamelCase    ,      _lowerCamelCase			)
       return SparkExamplesIterable(self.df    ,      partition_order=_lowerCamelCase			)







     @property
     def      lowercase	(							self			:			List[str]			):
       return len(self.partition_order			)






class       lowerCAmelCase__				(						datasets.DatasetBuilder	):
     __a						=					SparkConfig







     def __init__(							self			:			str    ,      _lowerCamelCase			:			"pyspark.sql.DataFrame"    ,      _lowerCamelCase			:			str = None    ,      _lowerCamelCase			:			str = None    ,      **_lowerCamelCase			:			List[str]    ,      ):
       import pyspark

       _snake_case					     =       pyspark.sql.SparkSession.builder.getOrCreate()
       _snake_case					     =       df
       _snake_case					     =       working_dir

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







     def      lowercase	(							self			:			str			):
       # Returns the path of the created file.
       def create_cache_and_write_probe(_lowerCamelCase			:			List[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=_lowerCamelCase			)
         _snake_case					     =       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:
         _snake_case					     =       (
             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			:			Dict			):
       return datasets.DatasetInfo(features=self.config.features			)







     def      lowercase	(							self			:			Union[str, Any]    ,      _lowerCamelCase			:			datasets.download.download_manager.DownloadManager			):
       return [datasets.SplitGenerator(name=datasets.Split.TRAIN			)]







     def      lowercase	(							self			:			Dict    ,      _lowerCamelCase			:			List[Any]			):
       import pyspark

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

       _snake_case					     =       self.df.count()
       _snake_case					     =       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.
       _snake_case					     =       (
           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
       )
       _snake_case					     =       approx_bytes_per_row * df_num_rows
       if approx_total_size > max_shard_size:
         # Make sure there is at least one row per partition.
         _snake_case					     =       min(_lowerCamelCase    ,      int(approx_total_size / max_shard_size			)			)
         _snake_case					     =       self.df.repartition(_lowerCamelCase			)







     def      lowercase	(							self			:			Dict    ,      _lowerCamelCase			:			str    ,      _lowerCamelCase			:			str    ,      _lowerCamelCase			:			int    ,      ):
       import pyspark

       _snake_case					     =       ParquetWriter if file_format == '''parquet''' else ArrowWriter
       _snake_case					     =       os.path.join(self._working_dir    ,      os.path.basename(_lowerCamelCase			)			) if self._working_dir else fpath
       _snake_case					     =       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.
       _snake_case					     =       self.config.features
       _snake_case					     =       self._writer_batch_size
       _snake_case					     =       self._fs.storage_options

       def write_arrow(_lowerCamelCase			:			Tuple			):
         # Within the same SparkContext, no two task attempts will share the same attempt ID.
         _snake_case					     =       pyspark.TaskContext().taskAttemptId()
         _snake_case					     =       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''']    ,      )
         _snake_case					     =       0
         _snake_case					     =       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    ,      )
         _snake_case					     =       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:
             _snake_case     ,					_snake_case					     =       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
             _snake_case					     =       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    ,      )
           _snake_case					     =       pa.Table.from_batches([batch]			)
           writer.write_table(_lowerCamelCase			)

         if writer._num_bytes > 0:
           _snake_case     ,					_snake_case					     =       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			)			):
             _snake_case					     =       os.path.join(os.path.dirname(_lowerCamelCase			)    ,      os.path.basename(_lowerCamelCase			)			)
             shutil.move(_lowerCamelCase    ,      _lowerCamelCase			)

       _snake_case					     =       (
           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			:			int    ,      _lowerCamelCase			:			"datasets.SplitGenerator"    ,      _lowerCamelCase			:			str = "arrow"    ,      _lowerCamelCase			:			Optional[Union[str, int]] = None    ,      _lowerCamelCase			:			Optional[int] = None    ,      **_lowerCamelCase			:			List[Any]    ,      ):
       self._validate_cache_dir()

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

       _snake_case					     =       '''-TTTTT-SSSSS-of-NNNNN'''
       _snake_case					     =       f'''{self.name}-{split_generator.name}{SUFFIX}.{file_format}'''
       _snake_case					     =       path_join(self._output_dir    ,      _lowerCamelCase			)

       _snake_case					     =       0
       _snake_case					     =       0
       _snake_case					     =       0
       _snake_case					     =       []
       _snake_case					     =       []

       for task_id, content in self._prepare_split_single(_lowerCamelCase    ,      _lowerCamelCase    ,      _lowerCamelCase			):
         (
             (
             _snake_case
         )     ,					(
             _snake_case
         )     ,					(
             _snake_case
         )     ,					(
             _snake_case
         )     ,					
         )					     =       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			)

       _snake_case					     =       total_num_examples
       _snake_case					     =       total_num_bytes

       # should rename everything at the end
       logger.debug(f'''Renaming {total_shards} shards.'''			)
       if total_shards > 1:
         _snake_case					     =       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.
         _snake_case					     =       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}'''			)    ,      )

         _snake_case					     =       []
         _snake_case					     =       0
         for i in range(len(_lowerCamelCase			)			):
           _snake_case     ,					_snake_case					     =       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
         _snake_case					     =       0
         _snake_case					     =       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			:			List[str]    ,      _lowerCamelCase			:			"datasets.SplitGenerator"    ,      ):
       return SparkExamplesIterable(self.df			)



