



'''simple docstring'''





# HF Trainer benchmarking tool
#
# This tool can be used to run and compare multiple dimensions of the HF Trainers args.
#
# It then prints a report once in github format with all the information that needs to be shared
# with others and second time in a console-friendly format, so it's easier to use for tuning things up.
#
# The main idea is:
#
#     ./trainer-benchmark.py --base-cmd '<cmd args that don't change>' \
#     --variations '--tf32 0|--tf32 1' '--fp16 0|--fp16 1|--bf16 1' \
#     --target-metric-key train_samples_per_second
#
# The variations can be any command line argument that you want to compare and not just dtype as in
# the example.
#
# --variations allows you to compare variations in multiple dimensions.
#
# as the first dimention has 2 options and the second 3 in our example, this will run the trainer 6
# times adding one of:
#
#    1. --tf32 0 --fp16 0
#    2. --tf32 0 --fp16 1
#    3. --tf32 0 --bf16 1
#    4. --tf32 1 --fp16 0
#    5. --tf32 1 --fp16 1
#    6. --tf32 1 --bf16 1
#
# and print the results. This is just a cartesian product - and more than 2 dimensions can be used.
#
# If you want to rely on defaults, this:
#    --variations '--tf32 0|--tf32 1' '--fp16 0|--fp16 1|--bf16 1'
# is identical to this:
#    --variations '--tf32 0|--tf32 1' '|--fp16|--bf16'
#
# the leading empty variation in the 2nd dimension is a valid variation.
#
# So here we get the following 6 variations:
#
#    1. --tf32 0
#    2. --tf32 0 --fp16
#    3. --tf32 0 --bf16
#    4. --tf32 1
#    5. --tf32 1 --fp16
#    6. --tf32 1 --bf16
#
# In this particular case we don't know what the default tf32 setting is as it's normally
# pytorch-version dependent). That's why it's best to do an explicit setting of each variation:
#    `--tf32 0|--tf32 1`
#
# Here is a full example of a train:
#
# CUDA_VISIBLE_DEVICES=0 python ./scripts/benchmark/trainer-benchmark.py \
# --base-cmd \
# ' examples/pytorch/translation/run_translation.py --model_name_or_path t5-small \
# --output_dir output_dir --do_train --label_smoothing 0.1 --logging_strategy no \
# --save_strategy no --per_device_train_batch_size 32 --max_source_length 512 \
# --max_target_length 512 --num_train_epochs 1 --overwrite_output_dir \
# --source_lang en --target_lang ro --dataset_name wmt16 --dataset_config "ro-en" \
# --source_prefix "translate English to Romanian: " --warmup_steps 50 \
# --max_train_samples 20000 --dataloader_num_workers 2 ' \
# --target-metric-key train_samples_per_second --repeat-times 1 --variations \
# '|--fp16|--bf16' '--tf32 0|--tf32 1' --report-metric-keys train_loss \
# --repeat-times 1 --base-variation '--tf32 0'
#
# and here is a possible output:
#
#
# | Variation       |     Train |   Diff |   Train |
# |                 |   samples |      % |    loss |
# |                 |       per |        |         |
# |                 |    second |        |         |
# |:----------------|----------:|-------:|--------:|
# | --tf32 0        |    285.11 |      0 |    2.51 |
# | --tf32 1        |    342.09 |     20 |    2.51 |
# | --fp16 --tf32 0 |    423.49 |     49 |    2.51 |
# | --fp16 --tf32 1 |    423.13 |     48 |    2.51 |
# | --bf16 --tf32 0 |    416.80 |     46 |    2.52 |
# | --bf16 --tf32 1 |    415.87 |     46 |    2.52 |
#
#
# So you can quickly compare the different outcomes.
#
# Typically running each experiment once is enough, but if the environment is unstable you can
# re-run each multiple times, e.g., 3 using --repeat-times 3 and it will report the averaged results.
#
# By default it'll use the lowest result as the base line to use as 100% and then compare the rest to
# it as can be seen from the table above, but you can also specify which combination is the one to use as
# the baseline, e.g., to change to another entry use: --base-variation '--tf32 1 --fp16 0'
#
# --target-metric-key is there to tell the program which metrics to compare - the different metric keys are
# inside output_dir/all_results.json. e.g., to measure eval performance instead of train use:
#    --target-metric-key eval_samples_per_second
# but of course you will need to adjust the --base-cmd value in the example to perform evaluation as
# well (as currently it doesn't)
#

import argparse
import datetime
import io
import itertools
import json
import math
import os
import platform
import re
import shlex
import subprocess
import sys
from pathlib import Path
from statistics import fmean

import pandas as pd
import torch
from tqdm import tqdm

import transformers


__a          =		float("nan")







class     UpperCAmelCase_       :
    """simple docstring"""







    def __init__(    self					:     str ,     snake_case_					:     str						):
          snake_case__  :    Union[str, Any]    						=      sys.stdout
          snake_case__  :    int    						=      open(snake_case_ ,     """a"""						)




    def __getattr__(    self					:     Tuple ,     snake_case_					:     Optional[Any]						):
          return getattr(self.stdout ,     snake_case_						)




    def lowerCamelCase       (    self					:     Any ,     snake_case_					:     List[str]						):
          self.stdout.write(snake_case_						)
          # strip tqdm codes
          self.file.write(re.sub(r"""^.*\r""" ,     """""" ,     snake_case_ ,     0 ,     re.M						)						)





def 						__snake_case(				_lowerCAmelCase=80     ,   _lowerCAmelCase=False		)		-> List[str]:
      snake_case__  :    Union[str, Any]    						=      []

      # deal with critical env vars
      snake_case__  :    Optional[int]    						=      ["""CUDA_VISIBLE_DEVICES"""]
      for key in env_keys:
            snake_case__  :    List[str]    						=      os.environ.get(_lowerCAmelCase     ,   _lowerCAmelCase		)
            if val is not None:
                  cmd.append(f"{key}={val}"		)

    # python executable (not always needed if the script is executable)
      snake_case__  :    Optional[Any]    						=      sys.executable if full_python_path else sys.executable.split("""/"""		)[-1]
      cmd.append(_lowerCAmelCase		)

      # now the normal args
      cmd += list(map(shlex.quote     ,   sys.argv		)		)

      # split up into up to MAX_WIDTH lines with shell multi-line escapes
      snake_case__  :    List[Any]    						=      []
      snake_case__  :    Any    						=      """"""
      while len(_lowerCAmelCase		) > 0:
            current_line += f"{cmd.pop(0		)} "
            if len(_lowerCAmelCase		) == 0 or len(_lowerCAmelCase		) + len(cmd[0]		) + 1 > max_width - 1:
                  lines.append(_lowerCAmelCase		)
                  snake_case__  :    int    						=      """"""
      return "\\\n".join(_lowerCAmelCase		)





def 						__snake_case(				_lowerCAmelCase     ,   _lowerCAmelCase		)		-> int:
      # unwrap multi-line input
      snake_case__  :    Dict    						=      re.sub(r"""[\\\n]+"""     ,   """ """     ,   args.base_cmd		)

      # remove --output_dir if any and set our own
      snake_case__  :    Any    						=      re.sub("""--output_dir\s+[^\s]+"""     ,   """"""     ,   args.base_cmd		)
      args.base_cmd += f" --output_dir {output_dir}"

      # ensure we have --overwrite_output_dir
      snake_case__  :    str    						=      re.sub("""--overwrite_output_dir\s+"""     ,   """"""     ,   args.base_cmd		)
      args.base_cmd += " --overwrite_output_dir"

      return [sys.executable] + shlex.split(args.base_cmd		)





def 						__snake_case(				_lowerCAmelCase     ,   _lowerCAmelCase     ,   _lowerCAmelCase     ,   _lowerCAmelCase     ,   _lowerCAmelCase     ,   _lowerCAmelCase     ,   _lowerCAmelCase		)		-> Optional[Any]:
      # Enable to debug everything but the run itself, to do it fast and see the progress.
      # This is useful for debugging the output formatting quickly - we can remove it later once
      # everybody is happy with the output
      if 0:
            import random
            from time import sleep

            sleep(0		)
            return dict(
                {k: random.uniform(0     ,   100		) for k in metric_keys}     ,   **{target_metric_key: random.choice([nan, 10.31, 100.2, 55.6666, 222.2222_2222]		)}     ,   )

      snake_case__  :    Union[str, Any]    						=      subprocess.run(_lowerCAmelCase     ,   capture_output=_lowerCAmelCase     ,   text=_lowerCAmelCase		)

      if verbose:
            print("""STDOUT"""     ,   result.stdout		)
            print("""STDERR"""     ,   result.stderr		)

      # save the streams
      snake_case__  :    Dict    						=      variation.replace(""" """     ,   """-"""		)
      with open(Path(_lowerCAmelCase		) / f"log.{prefix}.stdout.txt"     ,   """w"""		) as f:
            f.write(result.stdout		)
      with open(Path(_lowerCAmelCase		) / f"log.{prefix}.stderr.txt"     ,   """w"""		) as f:
            f.write(result.stderr		)

      if result.returncode != 0:
            if verbose:
                  print("""failed"""		)
            return {target_metric_key: nan}

      with io.open(f"{output_dir}/all_results.json"     ,   """r"""     ,   encoding="""utf-8"""		) as f:
            snake_case__  :    Dict    						=      json.load(_lowerCAmelCase		)

      # filter out just the keys we want
      return {k: v for k, v in metrics.items() if k in metric_keys}





def 						__snake_case(				_lowerCAmelCase     ,   _lowerCAmelCase     ,   _lowerCAmelCase     ,   _lowerCAmelCase     ,   _lowerCAmelCase     ,   _lowerCAmelCase     ,   _lowerCAmelCase     ,   _lowerCAmelCase     ,   _lowerCAmelCase     ,   _lowerCAmelCase     ,   )		-> Dict:
      snake_case__  :    Any    						=      []
      snake_case__  :    int    						=      []
      snake_case__  :    Tuple    						=      f"{id}: {variation:<{longest_variation_len}}"
      snake_case__  :    Optional[Any]    						=      f"{preamble}: "
      snake_case__  :    Optional[Any]    						=      set(report_metric_keys + [target_metric_key]		)
      for i in tqdm(range(_lowerCAmelCase		)     ,   desc=_lowerCAmelCase     ,   leave=_lowerCAmelCase		):
            snake_case__  :    Dict    						=      process_run_single(
                _lowerCAmelCase     ,   _lowerCAmelCase     ,   _lowerCAmelCase     ,   _lowerCAmelCase     ,   _lowerCAmelCase     ,   _lowerCAmelCase     ,   _lowerCAmelCase		)
            snake_case__  :    Tuple    						=      single_run_metrics[target_metric_key]
            if not math.isnan(_lowerCAmelCase		):
                  metrics.append(_lowerCAmelCase		)
                  results.append(_lowerCAmelCase		)
                  outcome += "✓"
            else:
                  outcome += "✘"
      snake_case__  :    str    						=      f"\33[2K\r{outcome}"
      if len(_lowerCAmelCase		) > 0:
            snake_case__  :    List[Any]    						=      {k: fmean([x[k] for x in metrics]		) for k in metrics[0].keys()}
            snake_case__  :    Any    						=      round(mean_metrics[target_metric_key]     ,   2		)
            snake_case__  :    Optional[Any]    						=      f"{outcome} {mean_target}"
            if len(_lowerCAmelCase		) > 1:
                  results_str += f" {tuple(round(_lowerCAmelCase     ,   2		) for x in results		)}"
            print(_lowerCAmelCase		)
            snake_case__  :    Optional[Any]    						=      variation
            return mean_metrics
      else:
            print(_lowerCAmelCase		)
            return {variation_key: variation, target_metric_key: nan}





def 						__snake_case(				)		-> Any:
      snake_case__  :    int    						=      torch.cuda.get_device_properties(torch.device("""cuda"""		)		)
      return f"\nDatetime    : {datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S'		)}\n\nSoftware:\ntransformers: {transformers.__version__}\ntorch       : {torch.__version__}\ncuda        : {torch.version.cuda}\npython      : {platform.python_version()}\n\nHardware:\n{torch.cuda.device_count()} GPUs      : {properties.name}, {properties.total_memory/2**30:0.2f}GB\n"





def 						__snake_case(				_lowerCAmelCase     ,   _lowerCAmelCase     ,   _lowerCAmelCase     ,   _lowerCAmelCase     ,   _lowerCAmelCase		)		-> Optional[int]:
      snake_case__  :    List[Any]    						=      pd.DataFrame(_lowerCAmelCase		)
      snake_case__  :    Union[str, Any]    						=      """variation"""
      snake_case__  :    int    						=      """diff_%"""

      snake_case__  :    List[Any]    						=      nan
      if base_variation is not None and len(df[df[variation_key] == base_variation]		):
            # this may still return nan
            snake_case__  :    Tuple    						=      df.loc[df[variation_key] == base_variation][target_metric_key].item()
      if math.isnan(_lowerCAmelCase		):
            # as a fallback, use the minimal value as the sentinel
            snake_case__  :    Optional[int]    						=      df.loc[df[target_metric_key] != nan][target_metric_key].min()

      # create diff column if possible
      if not math.isnan(_lowerCAmelCase		):
            snake_case__  :    Optional[Any]    						=      df.apply(
                lambda _lowerCAmelCase		: round(100 * (r[target_metric_key] - sentinel_value) / sentinel_value		)
                if not math.isnan(r[target_metric_key]		)
                else 0     ,   axis="""columns"""     ,   )

      # re-order columns
      snake_case__  :    str    						=      [variation_key, target_metric_key, diff_key, *report_metric_keys]
      snake_case__  :    int    						=      df.reindex(_lowerCAmelCase     ,   axis="""columns"""		)  # reorder cols

      # capitalize
      snake_case__  :    Any    						=      df.rename(str.capitalize     ,   axis="""columns"""		)

      # make the cols as narrow as possible
      snake_case__  :    Optional[Any]    						=      df.rename(lambda _lowerCAmelCase		: c.replace("""_"""     ,   """<br>"""		)     ,   axis="""columns"""		)
      snake_case__  :    Optional[Any]    						=      df.rename(lambda _lowerCAmelCase		: c.replace("""_"""     ,   """\n"""		)     ,   axis="""columns"""		)

      snake_case__  :    Optional[int]    						=      ["""""", """Copy between the cut-here-lines and paste as is to github or a forum"""]
      report += ["----------8<-----------------8<--------"]
      report += ["*** Results:", df_github.to_markdown(index=_lowerCAmelCase     ,   floatfmt=""".2f"""		)]
      report += ["```"]
      report += ["*** Setup:", get_versions()]
      report += ["*** The benchmark command line was:", get_original_command()]
      report += ["```"]
      report += ["----------8<-----------------8<--------"]
      report += ["*** Results (console):", df_console.to_markdown(index=_lowerCAmelCase     ,   floatfmt=""".2f"""		)]

      print("""\n\n""".join(_lowerCAmelCase		)		)





def 						__snake_case(				)		-> Any:
      snake_case__  :    int    						=      argparse.ArgumentParser()
      parser.add_argument(
          """--base-cmd"""     ,   default=_lowerCAmelCase     ,   type=_lowerCAmelCase     ,   required=_lowerCAmelCase     ,   help="""Base cmd"""     ,   )
      parser.add_argument(
          """--variations"""     ,   default=_lowerCAmelCase     ,   type=_lowerCAmelCase     ,   nargs="""+"""     ,   required=_lowerCAmelCase     ,   help="""Multi-dimensional variations, example: '|--fp16|--bf16' '|--tf32'"""     ,   )
      parser.add_argument(
          """--base-variation"""     ,   default=_lowerCAmelCase     ,   type=_lowerCAmelCase     ,   help="""Baseline variation to compare to. if None the minimal target value will be used to compare against"""     ,   )
      parser.add_argument(
          """--target-metric-key"""     ,   default=_lowerCAmelCase     ,   type=_lowerCAmelCase     ,   required=_lowerCAmelCase     ,   help="""Target metric key in output_dir/all_results.json, e.g., train_samples_per_second"""     ,   )
      parser.add_argument(
          """--report-metric-keys"""     ,   default=""""""     ,   type=_lowerCAmelCase     ,   help="""Report metric keys - other metric keys from output_dir/all_results.json to report, e.g., train_loss. Use a single argument e.g., 'train_loss train_samples"""     ,   )
      parser.add_argument(
          """--repeat-times"""     ,   default=1     ,   type=_lowerCAmelCase     ,   help="""How many times to re-run each variation - an average will be reported"""     ,   )
      parser.add_argument(
          """--output_dir"""     ,   default="""output_benchmark"""     ,   type=_lowerCAmelCase     ,   help="""The output directory where all the benchmark reports will go to and additionally this directory will be used to override --output_dir in the script that is being benchmarked"""     ,   )
      parser.add_argument(
          """--verbose"""     ,   default=_lowerCAmelCase     ,   action="""store_true"""     ,   help="""Whether to show the outputs of each run or just the benchmark progress"""     ,   )
      snake_case__  :    int    						=      parser.parse_args()

      snake_case__  :    Dict    						=      args.output_dir
      Path(_lowerCAmelCase		).mkdir(exist_ok=_lowerCAmelCase		)
      snake_case__  :    Dict    						=      get_base_command(_lowerCAmelCase     ,   _lowerCAmelCase		)

      # split each dimension into its --foo variations
      snake_case__  :    Dict    						=      [list(map(str.strip     ,   re.split(r"""\|"""     ,   _lowerCAmelCase		)		)		) for x in args.variations]
      # build a cartesian product of dimensions and convert those back into cmd-line arg strings,
      # while stripping white space for inputs that were empty
      snake_case__  :    List[str]    						=      list(map(str.strip     ,   map(""" """.join     ,   itertools.product(*_lowerCAmelCase		)		)		)		)
      snake_case__  :    List[str]    						=      max(len(_lowerCAmelCase		) for x in variations		)

      # split wanted keys
      snake_case__  :    int    						=      args.report_metric_keys.split()

      # capture prints into a log file for convenience
      snake_case__  :    str    						=      f"benchmark-report-{datetime.datetime.now().strftime('%Y-%m-%d-%H-%M-%S'		)}.txt"
      print(f"\nNote: each run's output is also logged under {output_dir}/log.*.std*.txt"		)
      print(f"and this script's output is also piped into {report_fn}"		)

      snake_case__  :    Optional[int]    						=      Tee(_lowerCAmelCase		)

      print(f"\n*** Running {len(_lowerCAmelCase		)} benchmarks:"		)
      print(f"Base command: {' '.join(_lowerCAmelCase		)}"		)

      snake_case__  :    Any    						=      """variation"""
      snake_case__  :    str    						=      []
      for id, variation in enumerate(tqdm(_lowerCAmelCase     ,   desc="""Total completion: """     ,   leave=_lowerCAmelCase		)		):
            snake_case__  :    str    						=      base_cmd + variation.split()
            results.append(
                process_run(
                    id + 1     ,   _lowerCAmelCase     ,   _lowerCAmelCase     ,   _lowerCAmelCase     ,   _lowerCAmelCase     ,   args.target_metric_key     ,   _lowerCAmelCase     ,   args.repeat_times     ,   _lowerCAmelCase     ,   args.verbose     ,   )		)

      process_results(_lowerCAmelCase     ,   args.target_metric_key     ,   _lowerCAmelCase     ,   args.base_variation     ,   _lowerCAmelCase		)


if __name__ == "__main__":
       main()





from unittest.mock import patch

import pyspark

from datasets.packaged_modules.spark.spark import (
    Spark,
    SparkExamplesIterable,
    _generate_iterable_examples,
)

from ..utils import (
    require_dill_gt_0_3_2,
    require_not_windows,
)



def 			_lowerCAmelCase       (					__lowerCAmelCase  ,  __lowerCAmelCase      )   ->					Tuple:


				"""simple docstring"""

				snake_case__	:      Union[str, Any]     		=	[]
				for part_id in partition_order:
								snake_case__	:      Any     		=	df.where(f"""SPARK_PARTITION_ID() = {part_id}"""      ).collect()
								for row_idx, row in enumerate(__lowerCAmelCase      ):
												expected_row_ids_and_row_dicts.append((f"""{part_id}_{row_idx}""", row.asDict())      )
				return expected_row_ids_and_row_dicts



@require_not_windows
@require_dill_gt_0_3_2
def 			_lowerCAmelCase       (					)   ->					Tuple:


				"""simple docstring"""

				snake_case__	:      Any     		=	pyspark.sql.SparkSession.builder.master('''local[*]'''      ).appName('''pyspark'''      ).getOrCreate()
				snake_case__	:      Optional[Any]     		=	spark.range(100      ).repartition(1      )
				snake_case__	:      Optional[int]     		=	Spark(__lowerCAmelCase      )
				# The id ints will be converted to Pyarrow int64s, so each row will be 8 bytes. Setting a max_shard_size of 16 means
				# that each partition can hold 2 rows.
				spark_builder._repartition_df_if_needed(max_shard_size=16      )
				# Given that the dataframe has 100 rows and each partition has 2 rows, we expect 50 partitions.
				assert spark_builder.df.rdd.getNumPartitions() == 50



@require_not_windows
@require_dill_gt_0_3_2
def 			_lowerCAmelCase       (					)   ->					Optional[int]:


				"""simple docstring"""

				snake_case__	:      Any     		=	pyspark.sql.SparkSession.builder.master('''local[*]'''      ).appName('''pyspark'''      ).getOrCreate()
				snake_case__	:      Dict     		=	spark.range(10      ).repartition(2      )
				snake_case__	:      Any     		=	[1, 0]
				snake_case__	:      Tuple     		=	_generate_iterable_examples(__lowerCAmelCase  ,  __lowerCAmelCase      )  # Reverse the partitions.
				snake_case__	:      Any     		=	_get_expected_row_ids_and_row_dicts_for_partition_order(__lowerCAmelCase  ,  __lowerCAmelCase      )

				for i, (row_id, row_dict) in enumerate(generate_fn()      ):
								snake_case__		, snake_case__	:      Union[str, Any]     		=	expected_row_ids_and_row_dicts[i]
								assert row_id == expected_row_id
								assert row_dict == expected_row_dict



@require_not_windows
@require_dill_gt_0_3_2
def 			_lowerCAmelCase       (					)   ->					Any:


				"""simple docstring"""

				snake_case__	:      Any     		=	pyspark.sql.SparkSession.builder.master('''local[*]'''      ).appName('''pyspark'''      ).getOrCreate()
				snake_case__	:      List[Any]     		=	spark.range(10      ).repartition(1      )
				snake_case__	:      int     		=	SparkExamplesIterable(__lowerCAmelCase      )
				assert it.n_shards == 1
				for i, (row_id, row_dict) in enumerate(__lowerCAmelCase      ):
								assert row_id == f"""0_{i}"""
								assert row_dict == {"id": i}



@require_not_windows
@require_dill_gt_0_3_2
def 			_lowerCAmelCase       (					)   ->					Dict:


				"""simple docstring"""

				snake_case__	:      List[str]     		=	pyspark.sql.SparkSession.builder.master('''local[*]'''      ).appName('''pyspark'''      ).getOrCreate()
				snake_case__	:      Tuple     		=	spark.range(30      ).repartition(3      )
				# Mock the generator so that shuffle reverses the partition indices.
				with patch('''numpy.random.Generator'''      ) as generator_mock:
								snake_case__	:      Union[str, Any]     		=	lambda __lowerCAmelCase      : x.reverse()
								snake_case__	:      List[str]     		=	_get_expected_row_ids_and_row_dicts_for_partition_order(__lowerCAmelCase  ,  [2, 1, 0]      )

								snake_case__	:      List[str]     		=	SparkExamplesIterable(__lowerCAmelCase      ).shuffle_data_sources(__lowerCAmelCase      )
								assert shuffled_it.n_shards == 3
								for i, (row_id, row_dict) in enumerate(__lowerCAmelCase      ):
												snake_case__		, snake_case__	:      Optional[Any]     		=	expected_row_ids_and_row_dicts[i]
												assert row_id == expected_row_id
												assert row_dict == expected_row_dict



@require_not_windows
@require_dill_gt_0_3_2
def 			_lowerCAmelCase       (					)   ->					List[Any]:


				"""simple docstring"""

				snake_case__	:      Union[str, Any]     		=	pyspark.sql.SparkSession.builder.master('''local[*]'''      ).appName('''pyspark'''      ).getOrCreate()
				snake_case__	:      Dict     		=	spark.range(20      ).repartition(4      )

				# Partitions 0 and 2
				snake_case__	:      List[Any]     		=	SparkExamplesIterable(__lowerCAmelCase      ).shard_data_sources(worker_id=0  ,  num_workers=2      )
				assert shard_it_a.n_shards == 2
				snake_case__	:      int     		=	_get_expected_row_ids_and_row_dicts_for_partition_order(__lowerCAmelCase  ,  [0, 2]      )
				for i, (row_id, row_dict) in enumerate(__lowerCAmelCase      ):
								snake_case__		, snake_case__	:      Dict     		=	expected_row_ids_and_row_dicts_a[i]
								assert row_id == expected_row_id
								assert row_dict == expected_row_dict

				# Partitions 1 and 3
				snake_case__	:      List[str]     		=	SparkExamplesIterable(__lowerCAmelCase      ).shard_data_sources(worker_id=1  ,  num_workers=2      )
				assert shard_it_a.n_shards == 2
				snake_case__	:      List[Any]     		=	_get_expected_row_ids_and_row_dicts_for_partition_order(__lowerCAmelCase  ,  [1, 3]      )
				for i, (row_id, row_dict) in enumerate(__lowerCAmelCase      ):
								snake_case__		, snake_case__	:      Optional[int]     		=	expected_row_ids_and_row_dicts_a[i]
								assert row_id == expected_row_id
								assert row_dict == expected_row_dict





@require_not_windows
@require_dill_gt_0_3_2
def 			_lowerCAmelCase       (					)   ->					Dict:


				"""simple docstring"""

				snake_case__	:      int     		=	pyspark.sql.SparkSession.builder.master('''local[*]'''      ).appName('''pyspark'''      ).getOrCreate()
				snake_case__	:      Dict     		=	spark.range(100      ).repartition(1      )
				snake_case__	:      Tuple     		=	Spark(__lowerCAmelCase      )
				# Choose a small max_shard_size for maximum partitioning.
				spark_builder._repartition_df_if_needed(max_shard_size=1      )
				# The new number of partitions should not be greater than the number of rows.
				assert spark_builder.df.rdd.getNumPartitions() == 100

