






# 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


__magic_name__   :  Optional[Any]        =							float('''nan''')





class 					A__     :

     '''simple docstring'''






     def __init__( self      :    Optional[Any]   ,						_SCREAMING_SNAKE_CASE      :    Optional[Any] ):






           """simple docstring"""




           UpperCamelCase					       =  sys.stdout
           UpperCamelCase					       =  open(_SCREAMING_SNAKE_CASE   ,						'a' )







     def __getattr__( self      :    Any   ,						_SCREAMING_SNAKE_CASE      :    Optional[Any] ):






           """simple docstring"""




           return getattr(self.stdout   ,						_SCREAMING_SNAKE_CASE )







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






           """simple docstring"""




           self.stdout.write(_SCREAMING_SNAKE_CASE )
           # strip tqdm codes
           self.file.write(re.sub(r'^.*\r'   ,						''   ,						_SCREAMING_SNAKE_CASE   ,						0   ,						re.M ) )

def 		lowercase__  (				_UpperCamelCase=80       ,							_UpperCamelCase=False) ->			Dict:
      """simple docstring"""

      UpperCamelCase					       =  []

      # deal with critical env vars
      UpperCamelCase					       =  ['CUDA_VISIBLE_DEVICES']
      for key in env_keys:
            UpperCamelCase					       =  os.environ.get(_UpperCamelCase       ,							_UpperCamelCase)
            if val is not None:
                  cmd.append(F'{key}={val}')

    # python executable (not always needed if the script is executable)
      UpperCamelCase					       =  sys.executable if full_python_path else sys.executable.split('/')[-1]
      cmd.append(_UpperCamelCase)

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

      # split up into up to MAX_WIDTH lines with shell multi-line escapes
      UpperCamelCase					       =  []
      UpperCamelCase					       =  ''
      while len(_UpperCamelCase) > 0:
            current_line += F'{cmd.pop(0)} '
            if len(_UpperCamelCase) == 0 or len(_UpperCamelCase) + len(cmd[0]) + 1 > max_width - 1:
                  lines.append(_UpperCamelCase)
                  UpperCamelCase					       =  ''
      return "\\\n".join(_UpperCamelCase)
def 		lowercase__  (				_UpperCamelCase       ,							_UpperCamelCase) ->			Optional[Any]:
      """simple docstring"""

      UpperCamelCase					       =  re.sub(r'[\\\n]+'       ,							' '       ,							args.base_cmd)

      # remove --output_dir if any and set our own
      UpperCamelCase					       =  re.sub('--output_dir\s+[^\s]+'       ,							''       ,							args.base_cmd)
      args.base_cmd += F' --output_dir {output_dir}'

      # ensure we have --overwrite_output_dir
      UpperCamelCase					       =  re.sub('--overwrite_output_dir\s+'       ,							''       ,							args.base_cmd)
      args.base_cmd += " --overwrite_output_dir"

      return [sys.executable] + shlex.split(args.base_cmd)
def 		lowercase__  (				_UpperCamelCase       ,							_UpperCamelCase       ,							_UpperCamelCase       ,							_UpperCamelCase       ,							_UpperCamelCase       ,							_UpperCamelCase       ,							_UpperCamelCase) ->			Tuple:
      """simple docstring"""

      if 0:
            import random
            from time import sleep

            sleep(0)
            return dict(
                {k: random.uniform(0       ,							1_00) for k in metric_keys}       ,							**{target_metric_key: random.choice([nan, 1_0.3_1, 1_0_0.2, 5_5.6_6_6_6, 2_2_2.2_2_2_2_2_2_2_2])}       ,							)

      UpperCamelCase					       =  subprocess.run(_UpperCamelCase       ,							capture_output=_UpperCamelCase       ,							text=_UpperCamelCase)

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

      # save the streams
      UpperCamelCase					       =  variation.replace(' '       ,							'-')
      with open(Path(_UpperCamelCase) / F'log.{prefix}.stdout.txt'       ,							'w') as f:
            f.write(result.stdout)
      with open(Path(_UpperCamelCase) / 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:
            UpperCamelCase					       =  json.load(_UpperCamelCase)

      # filter out just the keys we want
      return {k: v for k, v in metrics.items() if k in metric_keys}
def 		lowercase__  (				_UpperCamelCase       ,							_UpperCamelCase       ,							_UpperCamelCase       ,							_UpperCamelCase       ,							_UpperCamelCase       ,							_UpperCamelCase       ,							_UpperCamelCase       ,							_UpperCamelCase       ,							_UpperCamelCase       ,							_UpperCamelCase       ,							) ->			Tuple:
      """simple docstring"""

      UpperCamelCase					       =  []
      UpperCamelCase					       =  []
      UpperCamelCase					       =  F'{id}: {variation:<{longest_variation_len}}'
      UpperCamelCase					       =  F'{preamble}: '
      UpperCamelCase					       =  set(report_metric_keys + [target_metric_key])
      for i in tqdm(range(_UpperCamelCase)       ,							desc=_UpperCamelCase       ,							leave=_UpperCamelCase):
            UpperCamelCase					       =  process_run_single(
                _UpperCamelCase       ,							_UpperCamelCase       ,							_UpperCamelCase       ,							_UpperCamelCase       ,							_UpperCamelCase       ,							_UpperCamelCase       ,							_UpperCamelCase)
            UpperCamelCase					       =  single_run_metrics[target_metric_key]
            if not math.isnan(_UpperCamelCase):
                  metrics.append(_UpperCamelCase)
                  results.append(_UpperCamelCase)
                  outcome += "✓"
            else:
                  outcome += "✘"
      UpperCamelCase					       =  F'\33[2K\r{outcome}'
      if len(_UpperCamelCase) > 0:
            UpperCamelCase					       =  {k: fmean([x[k] for x in metrics]) for k in metrics[0].keys()}
            UpperCamelCase					       =  round(mean_metrics[target_metric_key]       ,							2)
            UpperCamelCase					       =  F'{outcome} {mean_target}'
            if len(_UpperCamelCase) > 1:
                  results_str += F' {tuple(round(_UpperCamelCase       ,							2) for x in results)}'
            print(_UpperCamelCase)
            UpperCamelCase					       =  variation
            return mean_metrics
      else:
            print(_UpperCamelCase)
            return {variation_key: variation, target_metric_key: nan}
def 		lowercase__  (				) ->			Dict:
      """simple docstring"""

      UpperCamelCase					       =  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 		lowercase__  (				_UpperCamelCase       ,							_UpperCamelCase       ,							_UpperCamelCase       ,							_UpperCamelCase       ,							_UpperCamelCase) ->			Tuple:
      """simple docstring"""

      UpperCamelCase					       =  pd.DataFrame(_UpperCamelCase)
      UpperCamelCase					       =  'variation'
      UpperCamelCase					       =  'diff_%'

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

      # create diff column if possible
      if not math.isnan(_UpperCamelCase):
            UpperCamelCase					       =  df.apply(
                lambda _UpperCamelCase: round(1_00 * (r[target_metric_key] - sentinel_value) / sentinel_value)
                if not math.isnan(r[target_metric_key])
                else 0       ,							axis='columns'       ,							)

      # re-order columns
      UpperCamelCase					       =  [variation_key, target_metric_key, diff_key, *report_metric_keys]
      UpperCamelCase					       =  df.reindex(_UpperCamelCase       ,							axis='columns')  # reorder cols

      # capitalize
      UpperCamelCase					       =  df.rename(str.capitalize       ,							axis='columns')

      # make the cols as narrow as possible
      UpperCamelCase					       =  df.rename(lambda _UpperCamelCase: c.replace('_'       ,							'<br>')       ,							axis='columns')
      UpperCamelCase					       =  df.rename(lambda _UpperCamelCase: c.replace('_'       ,							'\n')       ,							axis='columns')

      UpperCamelCase					       =  ['', '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=_UpperCamelCase       ,							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=_UpperCamelCase       ,							floatfmt='.2f')]

      print('\n\n'.join(_UpperCamelCase))
def 		lowercase__  (				) ->			List[str]:
      """simple docstring"""

      UpperCamelCase					       =  argparse.ArgumentParser()
      parser.add_argument(
          '--base-cmd'       ,							default=_UpperCamelCase       ,							type=_UpperCamelCase       ,							required=_UpperCamelCase       ,							help='Base cmd'       ,							)
      parser.add_argument(
          '--variations'       ,							default=_UpperCamelCase       ,							type=_UpperCamelCase       ,							nargs='+'       ,							required=_UpperCamelCase       ,							help='Multi-dimensional variations, example: \'|--fp16|--bf16\' \'|--tf32\''       ,							)
      parser.add_argument(
          '--base-variation'       ,							default=_UpperCamelCase       ,							type=_UpperCamelCase       ,							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=_UpperCamelCase       ,							type=_UpperCamelCase       ,							required=_UpperCamelCase       ,							help='Target metric key in output_dir/all_results.json, e.g., train_samples_per_second'       ,							)
      parser.add_argument(
          '--report-metric-keys'       ,							default=''       ,							type=_UpperCamelCase       ,							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=_UpperCamelCase       ,							help='How many times to re-run each variation - an average will be reported'       ,							)
      parser.add_argument(
          '--output_dir'       ,							default='output_benchmark'       ,							type=_UpperCamelCase       ,							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=_UpperCamelCase       ,							action='store_true'       ,							help='Whether to show the outputs of each run or just the benchmark progress'       ,							)
      UpperCamelCase					       =  parser.parse_args()

      UpperCamelCase					       =  args.output_dir
      Path(_UpperCamelCase).mkdir(exist_ok=_UpperCamelCase)
      UpperCamelCase					       =  get_base_command(_UpperCamelCase       ,							_UpperCamelCase)

      # split each dimension into its --foo variations
      UpperCamelCase					       =  [list(map(str.strip       ,							re.split(r'\|'       ,							_UpperCamelCase))) 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
      UpperCamelCase					       =  list(map(str.strip       ,							map(' '.join       ,							itertools.product(*_UpperCamelCase))))
      UpperCamelCase					       =  max(len(_UpperCamelCase) for x in variations)

      # split wanted keys
      UpperCamelCase					       =  args.report_metric_keys.split()

      # capture prints into a log file for convenience
      UpperCamelCase					       =  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}')

      UpperCamelCase					       =  Tee(_UpperCamelCase)

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

      UpperCamelCase					       =  'variation'
      UpperCamelCase					       =  []
      for id, variation in enumerate(tqdm(_UpperCamelCase       ,							desc='Total completion: '       ,							leave=_UpperCamelCase)):
            UpperCamelCase					       =  base_cmd + variation.split()
            results.append(
                process_run(
                    id + 1       ,							_UpperCamelCase       ,							_UpperCamelCase       ,							_UpperCamelCase       ,							_UpperCamelCase       ,							args.target_metric_key       ,							_UpperCamelCase       ,							args.repeat_times       ,							_UpperCamelCase       ,							args.verbose       ,							))

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


if __name__ == "__main__":
       main()











class 					A__     :

     '''simple docstring'''






     def __init__( self      :    Optional[int]   ,						_SCREAMING_SNAKE_CASE      :    str = ""   ,						_SCREAMING_SNAKE_CASE      :    bool = False ):






           """simple docstring"""




           UpperCamelCase					       =  {}

           # A node will be a leaf if the tree contains its word
           UpperCamelCase					       =  is_leaf

           UpperCamelCase					       =  prefix







     def   _SCREAMING_SNAKE_CASE     ( self      :    Optional[int]   ,						_SCREAMING_SNAKE_CASE      :    str ):






           """simple docstring"""




           UpperCamelCase					       =  0
           for q, w in zip(self.prefix   ,						_SCREAMING_SNAKE_CASE ):
                 if q != w:
                       break

                 x += 1

           return self.prefix[:x], self.prefix[x:], word[x:]







     def   _SCREAMING_SNAKE_CASE     ( self      :    Any   ,						_SCREAMING_SNAKE_CASE      :    list[str] ):






           """simple docstring"""




           for word in words:
                 self.insert(_SCREAMING_SNAKE_CASE )







     def   _SCREAMING_SNAKE_CASE     ( self      :    Union[str, Any]   ,						_SCREAMING_SNAKE_CASE      :    str ):






           """simple docstring"""




           if self.prefix == word:
                 UpperCamelCase					       =  True

           # Case 2: The node has no edges that have a prefix to the word
           # Solution: We create an edge from the current node to a new one
           # containing the word
           elif word[0] not in self.nodes:
                 UpperCamelCase					       =  RadixNode(prefix=_SCREAMING_SNAKE_CASE   ,						is_leaf=_SCREAMING_SNAKE_CASE )

           else:
                 UpperCamelCase					       =  self.nodes[word[0]]
                 UpperCamelCase       ,  UpperCamelCase       ,  UpperCamelCase					       =  incoming_node.match(
                     _SCREAMING_SNAKE_CASE )

                 # Case 3: The node prefix is equal to the matching
                 # Solution: We insert remaining word on the next node
                 if remaining_prefix == "":
                       self.nodes[matching_string[0]].insert(_SCREAMING_SNAKE_CASE )

                 # Case 4: The word is greater equal to the matching
                 # Solution: Create a node in between both nodes, change
                 # prefixes and add the new node for the remaining word
                 else:
                       UpperCamelCase					       =  remaining_prefix

                       UpperCamelCase					       =  self.nodes[matching_string[0]]
                       UpperCamelCase					       =  RadixNode(_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE )
                       UpperCamelCase					       =  aux_node

                       if remaining_word == "":
                             UpperCamelCase					       =  True
                       else:
                             self.nodes[matching_string[0]].insert(_SCREAMING_SNAKE_CASE )







     def   _SCREAMING_SNAKE_CASE     ( self      :    Any   ,						_SCREAMING_SNAKE_CASE      :    str ):






           """simple docstring"""




           UpperCamelCase					       =  self.nodes.get(word[0]   ,						_SCREAMING_SNAKE_CASE )
           if not incoming_node:
                 return False
           else:
                 UpperCamelCase       ,  UpperCamelCase       ,  UpperCamelCase					       =  incoming_node.match(
                     _SCREAMING_SNAKE_CASE )
                 # If there is remaining prefix, the word can't be on the tree
                 if remaining_prefix != "":
                       return False
                 # This applies when the word and the prefix are equal
                 elif remaining_word == "":
                       return incoming_node.is_leaf
                 # We have word remaining so we check the next node
                 else:
                       return incoming_node.find(_SCREAMING_SNAKE_CASE )







     def   _SCREAMING_SNAKE_CASE     ( self      :    List[Any]   ,						_SCREAMING_SNAKE_CASE      :    str ):






           """simple docstring"""




           UpperCamelCase					       =  self.nodes.get(word[0]   ,						_SCREAMING_SNAKE_CASE )
           if not incoming_node:
                 return False
           else:
                 UpperCamelCase       ,  UpperCamelCase       ,  UpperCamelCase					       =  incoming_node.match(
                     _SCREAMING_SNAKE_CASE )
                 # If there is remaining prefix, the word can't be on the tree
                 if remaining_prefix != "":
                       return False
                 # We have word remaining so we check the next node
                 elif remaining_word != "":
                       return incoming_node.delete(_SCREAMING_SNAKE_CASE )
                 else:
                       # If it is not a leaf, we don't have to delete
                       if not incoming_node.is_leaf:
                             return False
                       else:
                             # We delete the nodes if no edges go from it
                             if len(incoming_node.nodes ) == 0:
                                   del self.nodes[word[0]]
                                   # We merge the current node with its only child
                                   if len(self.nodes ) == 1 and not self.is_leaf:
                                         UpperCamelCase					       =  list(self.nodes.values() )[0]
                                         UpperCamelCase					       =  merging_node.is_leaf
                                         self.prefix += merging_node.prefix
                                         UpperCamelCase					       =  merging_node.nodes
                    # If there is more than 1 edge, we just mark it as non-leaf
                             elif len(incoming_node.nodes ) > 1:
                                   UpperCamelCase					       =  False
                             # If there is 1 edge, we merge it with its child
                             else:
                                   UpperCamelCase					       =  list(incoming_node.nodes.values() )[0]
                                   UpperCamelCase					       =  merging_node.is_leaf
                                   incoming_node.prefix += merging_node.prefix
                                   UpperCamelCase					       =  merging_node.nodes

                             return True







     def   _SCREAMING_SNAKE_CASE     ( self      :    Tuple   ,						_SCREAMING_SNAKE_CASE      :    int = 0 ):






           """simple docstring"""




           if self.prefix != "":
                 print('-' * height   ,						self.prefix   ,						'  (leaf)' if self.is_leaf else '' )

           for value in self.nodes.values():
                 value.print_tree(height + 1 )

def 		lowercase__  (				) ->			bool:
      """simple docstring"""

      UpperCamelCase					       =  'banana bananas bandana band apple all beast'.split()
      UpperCamelCase					       =  RadixNode()
      root.insert_many(_UpperCamelCase)

      assert all(root.find(_UpperCamelCase) for word in words)
      assert not root.find('bandanas')
      assert not root.find('apps')
      root.delete('all')
      assert not root.find('all')
      root.delete('banana')
      assert not root.find('banana')
      assert root.find('bananas')

      return True
def 		lowercase__  (				) ->			None:
      """simple docstring"""

      assert test_trie()
def 		lowercase__  (				) ->			None:
      """simple docstring"""

      UpperCamelCase					       =  RadixNode()
      UpperCamelCase					       =  'banana bananas bandanas bandana band apple all beast'.split()
      root.insert_many(_UpperCamelCase)

      print('Words:'       ,							_UpperCamelCase)
      print('Tree:')
      root.print_tree()


if __name__ == "__main__":
       main()



