





'''simple docstring'''






# XXX: we want transformers master here - in the absense of conftest manipulating sys.path:
# hack it in for now:
import sys
from pathlib import Path


lowerCAmelCase									=   Path(__file__).resolve().parents[3] / "src"
sys.path.insert(1, str(git_repo_path))

import dataclasses  # noqa
import io  # noqa
import itertools  # noqa
import json  # noqa
import os  # noqa
import unittest  # noqa
from copy import deepcopy  # noqa

from parameterized import parameterized  # noqa
from transformers import TrainingArguments, is_torch_available  # noqa
from transformers.deepspeed import is_deepspeed_available  # noqa
from transformers.file_utils import WEIGHTS_NAME  # noqa
from transformers.testing_utils import (  # noqa
    CaptureLogger,
    ExtendSysPath,
    TestCasePlus,
    execute_subprocess_async,
    get_gpu_count,
    mockenv_context,
    require_deepspeed,
    require_torch_gpu,
    require_torch_multi_gpu,
    slow,
)
from transformers.trainer_utils import set_seed  # noqa


set_seed(42)

lowerCAmelCase									=   {"base": "patrickvonplaten/wav2vec2_tiny_random", "robust": "patrickvonplaten/wav2vec2_tiny_random_robust"}

lowerCAmelCase									=   "zero2"
lowerCAmelCase									=   "zero3"
lowerCAmelCase									=   [ZEROa, ZEROa]






def   __A    (				a_    :	int   ,a_    :	Tuple   ,a_    :	int						):
		lowerCAmelCase  :     str       						= parameterized.to_safe_name("_".join(str(__lowercase						) for x in param.args						)						)
		return f'''{func.__name__}_{param_based_name}'''


# Cartesian-product of zero stages with models to test
lowerCAmelCase									=   list(itertools.product(stages, models.keys()))

@slow
@require_deepspeed
@require_torch_gpu
class 		lowerCamelCase	(   lowercase_							):



	@parameterized.expand(UpperCamelCase_		,   name_func=UpperCamelCase_						)
	def 	_lowerCamelCase     ( self		,   a_		,   a_						):
			self.run_and_check(
			    stage=UpperCamelCase_		,   model=UpperCamelCase_		,   distributed=UpperCamelCase_		,   fpaa=UpperCamelCase_		,   )



	@require_torch_multi_gpu
	@parameterized.expand(UpperCamelCase_		,   name_func=UpperCamelCase_						)
	def 	_lowerCamelCase     ( self		,   a_		,   a_						):
			self.run_and_check(
			    stage=UpperCamelCase_		,   model=UpperCamelCase_		,   distributed=UpperCamelCase_		,   fpaa=UpperCamelCase_		,   )



	@parameterized.expand(UpperCamelCase_		,   name_func=UpperCamelCase_						)
	def 	_lowerCamelCase     ( self		,   a_		,   a_						):
			self.run_and_check(
			    stage=UpperCamelCase_		,   model=UpperCamelCase_		,   distributed=UpperCamelCase_		,   fpaa=UpperCamelCase_		,   )



	@require_torch_multi_gpu
	@parameterized.expand(UpperCamelCase_		,   name_func=UpperCamelCase_						)
	def 	_lowerCamelCase     ( self		,   a_		,   a_						):
			self.run_and_check(
			    stage=UpperCamelCase_		,   model=UpperCamelCase_		,   distributed=UpperCamelCase_		,   fpaa=UpperCamelCase_		,   )



	def 	_lowerCamelCase     ( self		,   a_						):
			pass



	def 	_lowerCamelCase     ( self		,   a_		,   a_		,   a_ = 10		,   a_ = True		,   a_ = True		,   a_ = True		,   ):
			lowerCAmelCase  :     Optional[int]       						= models[model]

			lowerCAmelCase  :     int       						= self.run_trainer(
			    stage=UpperCamelCase_		,   model_name=UpperCamelCase_		,   eval_steps=UpperCamelCase_		,   num_train_epochs=1		,   distributed=UpperCamelCase_		,   fpaa=UpperCamelCase_		,   )

			self.do_checks(UpperCamelCase_						)

			return output_dir



	def 	_lowerCamelCase     ( self		,   a_		,   a_		,   a_ = 10		,   a_ = 1		,   a_ = True		,   a_ = True		,   ):
			lowerCAmelCase  :     Any       						= self.get_auto_remove_tmp_dir("./xxx"		,   after=UpperCamelCase_						)
			lowerCAmelCase  :     Any       						= F'''\n            --model_name_or_path {model_name}\n            --dataset_name hf-internal-testing/librispeech_asr_dummy\n            --dataset_config_name clean\n            --train_split_name validation\n            --validation_split_name validation\n            --output_dir {output_dir}\n            --num_train_epochs {str(UpperCamelCase_						)}\n            --per_device_train_batch_size 2\n            --per_device_eval_batch_size 2\n            --evaluation_strategy steps\n            --learning_rate 5e-4\n            --warmup_steps 8\n            --orthography timit\n            --preprocessing_num_workers 1\n            --group_by_length\n            --freeze_feature_extractor\n            --report_to none\n            --save_steps 0\n            --eval_steps {eval_steps}\n            --report_to none\n        '''.split()

			if fpaa:
					args.extend(["--fp16"]						)

			# currently ds_config_wav2vec2_zero.json requires "zero_optimization.find_unused_parameters": true,
			# hence the separate config files
			lowerCAmelCase  :     Dict       						= F'''--deepspeed {self.test_file_dir_str}/ds_config_wav2vec2_{stage}.json'''.split()
			lowerCAmelCase  :     str       						= [F'''{self.examples_dir_str}/research_projects/wav2vec2/run_asr.py''']
			lowerCAmelCase  :     Optional[int]       						= self.get_launcher(UpperCamelCase_						)

			lowerCAmelCase  :     Any       						= launcher + script + args + ds_args
			# keep for quick debug
			# print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] +cmd)); die
			execute_subprocess_async(UpperCamelCase_		,   env=self.get_env()						)

			return output_dir





	def 	_lowerCamelCase     ( self		,   a_=False						):
			lowerCAmelCase  :     Union[str, Any]       						= min(2		,   get_gpu_count()						) if distributed else 1
			return F'''deepspeed --num_nodes 1 --num_gpus {num_gpus}'''.split()











def       _SCREAMING_SNAKE_CASE    (			__lowercase :      List[Any]     )    ->   Any:





    """simple docstring"""

    stooge(__lowercase      ,					0      ,					len(__lowercase     ) - 1     )
    return arr



def       _SCREAMING_SNAKE_CASE    (			__lowercase :      str      ,					__lowercase :      Dict      ,					__lowercase :      Dict     )    ->   int:





    """simple docstring"""

    if i >= h:
        return

    # If first element is smaller than the last then swap them
    if arr[i] > arr[h]:
        __A     ,						__A       =						arr[h], arr[i]

    # If there are more than 2 elements in the array
    if h - i + 1 > 2:
        __A       =						(int)((h - i + 1) / 3     )

        # Recursively sort first 2/3 elements
        stooge(__lowercase      ,					__lowercase      ,					(h - t)     )

        # Recursively sort last 2/3 elements
        stooge(__lowercase      ,					i + t      ,					(__lowercase)     )

        # Recursively sort first 2/3 elements
        stooge(__lowercase      ,					__lowercase      ,					(h - t)     )


if __name__ == "__main__":
  __a   :							List[Any]						= input("Enter numbers separated by a comma:\n").strip()
  __a   :							List[str]						= [int(item) for item in user_input.split(",")]
  print(stooge_sort(unsorted))




