code: 
  """simple docstring"""
  import itertools
  import os
  import subprocess
  from os.path import dirname
  
  from parameterized import parameterized
  
  from tests.trainer.test_trainer import TrainerIntegrationCommon  # noqa
  from transformers import is_torch_available
  from transformers.testing_utils import (
      TestCasePlus,
      execute_subprocess_async,
      get_gpu_count,
      get_tests_dir,
      require_deepspeed,
      require_torch_gpu,
      slow,
  )
  from transformers.trainer_utils import set_seed
  
  
  if is_torch_available():
  			from tests.trainer.test_trainer import (  # noqa
  			    RegressionModelConfig,
  			    RegressionPreTrainedModel,
  			    get_regression_trainer,
  			)
  
  
  set_seed(42)
  
  __magic_name__		       =				get_tests_dir('''fixtures''')
  __magic_name__		       =				os.path.join(dirname(get_tests_dir()))
  __magic_name__		       =				dirname(os.path.abspath(__file__))
  
  # default torch.distributed port
  __magic_name__		       =				'''10999'''
  
  __magic_name__		       =				'''t5-small'''
  
  # *** Working Models ***
  __magic_name__		       =				'''hf-internal-testing/tiny-albert'''
  __magic_name__		       =				'''sshleifer/bart-tiny-random'''
  __magic_name__		       =				'''hf-internal-testing/tiny-bert'''
  __magic_name__		       =				'''hf-internal-testing/tiny-random-bigbird_pegasus'''
  __magic_name__		       =				'''hf-internal-testing/tiny-random-big_bird'''
  __magic_name__		       =				'''hf-internal-testing/tiny-random-blenderbot'''
  __magic_name__		       =				'''bigscience/bigscience-small-testing'''
  __magic_name__		       =				'''hf-internal-testing/tiny-random-deberta'''
  __magic_name__		       =				'''hf-internal-testing/tiny-random-deberta-v2'''
  __magic_name__		       =				'''sshleifer/tiny-distilbert-base-cased'''
  __magic_name__		       =				'''hf-internal-testing/tiny-electra'''
  __magic_name__		       =				'''hf-internal-testing/tiny-random-flaubert'''
  __magic_name__		       =				'''stas/tiny-wmt19-en-de'''
  __magic_name__		       =				'''hf-internal-testing/tiny-random-funnel'''
  __magic_name__		       =				'''sshleifer/tiny-gpt2'''
  __magic_name__		       =				'''hf-internal-testing/tiny-random-gptj'''
  __magic_name__		       =				'''hf-internal-testing/tiny-random-gpt_neo'''
  __magic_name__		       =				'''hf-internal-testing/tiny-layoutlm'''
  __magic_name__		       =				'''hf-internal-testing/tiny-random-led'''
  __magic_name__		       =				'''hf-internal-testing/tiny-random-longformer'''
  __magic_name__		       =				'''stas/tiny-m2m_100'''  # hf tiny model is unsuitable
  __magic_name__		       =				'''sshleifer/tiny-marian-en-de'''
  __magic_name__		       =				'''sshleifer/tiny-mbart'''
  __magic_name__		       =				'''hf-internal-testing/tiny-random-mobilebert'''
  __magic_name__		       =				'''hf-internal-testing/tiny-random-mpnet'''
  __magic_name__		       =				'''stas/pegasus-cnn_dailymail-tiny-random'''
  __magic_name__		       =				'''hf-internal-testing/tiny-random-prophetnet'''
  __magic_name__		       =				'''sshleifer/tiny-distilroberta-base'''
  __magic_name__		       =				'''hf-internal-testing/tiny-random-squeezebert'''
  __magic_name__		       =				'''patrickvonplaten/t5-tiny-random'''
  __magic_name__		       =				'''hf-internal-testing/tiny-random-t5-v1.1'''
  __magic_name__		       =				'''hf-internal-testing/tiny-random-vit'''
  __magic_name__		       =				'''hf-internal-testing/tiny-xlm-roberta'''
  __magic_name__		       =				'''sshleifer/tiny-xlnet-base-cased'''
  
  
  # *** To Fix ***
  
  
  # *** tiny model issues ***
  # missing model files:
  __magic_name__		       =				'''hf-internal-testing/tiny-random-mt5'''
  __magic_name__		       =				'''hf-internal-testing/tiny-random-camembert'''
  __magic_name__		       =				'''hf-internal-testing/tiny-random-openai-gpt'''
  
  # missing tokenizer files
  __magic_name__		       =				'''hf-internal-testing/tiny-random-convbert'''
  __magic_name__		       =				'''hf-internal-testing/tiny-random-layoutlmv2'''
  __magic_name__		       =				'''hf-internal-testing/tiny-random-hubert'''
  
  # issues with tokenizer
  __magic_name__		       =				'''hf-internal-testing/tiny-random-ctrl'''
  __magic_name__		       =				'''hf-internal-testing/tiny-random-transfo-xl'''  # same as ctrl
  
  # other issues with tiny models
  __magic_name__		       =				'''hf-internal-testing/tiny-random-ibert'''  # multiple issues with either mlm/qa/clas
  __magic_name__		       =				'''hf-internal-testing/tiny-random-reformer'''  # multiple issues with either mlm/qa/clas
  
  # *** Lacking official examples to test with ***
  # or not working with examples
  __magic_name__		       =				'''hf-internal-testing/tiny-random-dpr'''
  # - "dpr"  examples/research_projects/rag-end2end-retriever/
  __magic_name__		       =				'''hf-internal-testing/tiny-random-rag'''
  # - "rag" research_projects
  __magic_name__		       =				''''''
  # - "luke" Entities classes - no plan to make such example
  __magic_name__		       =				'''hf-internal-testing/tiny-random-lxmert'''
  # - "lxmert" doesn't work with run_qa.py
  __magic_name__		       =				'''hf-internal-testing/tiny-random-clip'''
  # - "clip" nothing under pytorch examples - XXX: Suraj is working on adding some - check by end of Sep
  __magic_name__		       =				'''hf-internal-testing/tiny-random-speech_to_text'''
  # - "speech_to_text", nothing under pytorch examples
  
  
  # *** Reactive mode ***
  # models with low usage, unstable API, things about to change - do nothing about the following until someone runs into a problem
  __magic_name__		       =				'''hf-internal-testing/tiny-random-tapas'''
  
  
  
  
  def     __lowerCamelCase  (		UpperCamelCase__=False      ):
  
  
  	"""simple docstring"""
  
  
  
  
  
  
  	_UpperCAmelCase =						min(2				,     get_gpu_count()      ) if distributed else 1
  	_UpperCAmelCase =						os.environ.get("DS_TEST_PORT"				,     UpperCamelCase__      )
  	return f"deepspeed --num_nodes 1 --num_gpus {num_gpus} --master_port {master_port}".split()
  
  
  
  
  def     __lowerCamelCase  (		):
  
  
  	"""simple docstring"""
  
  
  
  
  
  
  	_UpperCAmelCase =						f"{FIXTURE_DIRECTORY}/tests_samples"
  	_UpperCAmelCase =						f"{data_dir_samples}/wmt_en_ro"
  	_UpperCAmelCase =						f"{data_dir_samples}/xsum"
  	_UpperCAmelCase =						"\n        --do_train\n        --max_train_samples 4\n        --per_device_train_batch_size 2\n        --num_train_epochs 1\n        --fp16\n        --report_to none\n        --overwrite_output_dir\n        ".split()
  
  	# try to cover as many models as possible once (it's enough to run on one task per model)
  	# but need a tiny model for each
  	#
  	# should have "{model_type.upper()}_TINY" corresponding vars defined, e.g., T5_TINY, etc.
  	_UpperCAmelCase =						{
  	    "trans": [
  	        "bart",
  	        "fsmt",
  	        "m2m_100",
  	        "marian",
  	        "mbart",
  	        "t5",
  	        "t5_v1",
  	        # "mt5", missing model files
  	    ],
  	    "sum": [
  	        "pegasus",
  	    ],
  	    "clm": [
  	        "big_bird",
  	        "bigbird_pegasus",
  	        "blenderbot",
  	        "bloom",
  	        "gpt2",
  	        "gpt_neo",
  	        "gptj",
  	        "xlm-roberta",
  	        "prophetnet",
  	        # "camembert", missing model files
  	    ],
  	    "mlm": [
  	        "albert",
  	        "deberta",
  	        "deberta-v2",
  	        "distilbert",
  	        "electra",
  	        "flaubert",
  	        "funnel",
  	        "layoutlm",
  	        # "reformer", # multiple issues with either mlm/qa/clas
  	    ],
  	    "qa": [
  	        "led",
  	        "longformer",
  	        "mobilebert",
  	        "mpnet",
  	        "roberta",
  	        "squeezebert",
  	        # "convbert", # missing tokenizer files
  	        # "layoutlmv2", missing model files
  	    ],
  	    "clas": [
  	        "bert",
  	        "xlnet",
  	        # "hubert", # missing tokenizer files
  	        # "ibert", # multiple issues with either mlm/qa/clas
  	        # "transfo-xl", # tokenizer issues as ctrl
  	        # "ctrl", # tokenizer issues
  	        # "openai-gpt", missing model files
  	        # "tapas", multiple issues
  	    ],
  	    "img_clas": [
  	        "vit",
  	    ],
  	}
  
  	_UpperCAmelCase =						f"{ROOT_DIRECTORY}/examples/pytorch"
  
  	_UpperCAmelCase =						{
  	    "trans": f"\n        {scripts_dir}/translation/run_translation.py\n        --train_file {data_dir_wmt}/train.json\n        --source_lang en\n        --target_lang ro\n        ",
  	    "sum": f"\n        {scripts_dir}/summarization/run_summarization.py\n        --train_file {data_dir_xsum}/sample.json\n        --max_source_length 12\n        --max_target_length 12\n        --lang en\n        ",
  	    "clm": f"\n        {scripts_dir}/language-modeling/run_clm.py\n        --train_file {FIXTURE_DIRECTORY}/sample_text.txt\n        --block_size 8\n        ",
  	    "mlm": f"\n        {scripts_dir}/language-modeling/run_mlm.py\n        --train_file {FIXTURE_DIRECTORY}/sample_text.txt\n        ",
  	    "qa": f"\n        {scripts_dir}/question-answering/run_qa.py\n        --train_file {data_dir_samples}/SQUAD/sample.json\n        ",
  	    "clas": f"\n        {scripts_dir}/text-classification/run_glue.py\n        --train_file {data_dir_samples}/MRPC/train.csv\n        --max_seq_length 12\n        --task_name MRPC\n        ",
  	    "img_clas": f"\n        {scripts_dir}/image-classification/run_image_classification.py\n            --dataset_name hf-internal-testing/cats_vs_dogs_sample\n            --remove_unused_columns False\n            --max_steps 10\n            --image_processor_name {DS_TESTS_DIRECTORY}/vit_feature_extractor.json\n        ",
  	}
  
  	_UpperCAmelCase =						get_launcher(distributed=UpperCamelCase__      )
  
  	_UpperCAmelCase =						{}
  	for task, args in tasks.items():
  		_UpperCAmelCase =						args.split()
  		for model in tasksamodels[task]:
  			_UpperCAmelCase =						globals()[f"{model.upper().replace('-'				,     '_'      )}_TINY"]
  			_UpperCAmelCase =						f"--model_name_or_path {model_name}".split()
  			_UpperCAmelCase =						launcher + args + args_model + args_main
  
  			# # generation special case
  			# if task == "gen":
  			#     launcher = f"deepspeed --num_nodes 1 --num_gpus 1".split()
  			#     args_model += f"--model_type {model}".split()
  			#     cmds[f"{task}_{model}"] = launcher + args + args_model
  			# else:
  
  	return cmds
  
  
  __magic_name__		       =				make_task_cmds()
  
  __magic_name__		       =				'''zero2'''
  __magic_name__		       =				'''zero3'''
  
  __magic_name__		       =				[ZEROa, ZEROa]
  
  
  
  
  def     __lowerCamelCase  (		UpperCamelCase__				,     UpperCamelCase__				,     UpperCamelCase__      ):
  
  
  	"""simple docstring"""
  
  
  
  
  
  
  	_UpperCAmelCase =						parameterized.to_safe_name("_".join(str(UpperCamelCase__      ) for x in param.args      )      )
  	return f"{func.__name__}_{param_based_name}"
  
  
  # Cartesian-product of zero stages with models to test
  __magic_name__		       =				list(itertools.product(stages, task_cmds.keys()))
  
  
  
  @slow
  @require_deepspeed
  @require_torch_gpu
  class    _lowerCAmelCase						(						lowerCamelCase						):
  
  
  
  							def       _a   (      self ,						a_ ,						a_   )							->							Tuple:
  								# return a ready to run train cmd
  								if task not in task_cmds:
  									raise ValueError(f"don't know of task {task}, have {task_cmds.keys()}"   )
  
  								_UpperCAmelCase =						task_cmds[task]
  								_UpperCAmelCase =						f"--deepspeed {self.test_file_dir_str}/ds_config_{stage}.json".split()
  
  								_UpperCAmelCase =						self.get_auto_remove_tmp_dir()
  								_UpperCAmelCase =						f"--output_dir {output_dir}".split()
  
  								cmd += args_ds + args_out
  
  								return cmd, output_dir
  
  
  
  							@parameterized.expand(a_ ,						name_func=a_   )
  							def       _a   (      self ,						a_ ,						a_   )							->							int:
  								# testing the ability to do a run followed by recovery of full fp32 weights
  
  								_UpperCAmelCase ,      _UpperCAmelCase =						self.get_task_cmd(a_ ,						a_   )
  
  								# 1. generate the checkpoint
  								cmd += "--save_steps 1".split()
  								# keep for quick debug
  								# print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] + cmd)); die
  								execute_subprocess_async(a_ ,						env=self.get_env()   )
  
  								# 2. test that the fp32 weights get reconsolidated
  								_UpperCAmelCase =						f"{output_dir}/checkpoint-1"
  								_UpperCAmelCase =						f"{chkpt_dir}/out.bin"
  								_UpperCAmelCase =						f"{chkpt_dir}/zero_to_fp32.py {chkpt_dir} {recovered_model_path}"
  								# keep for quick debug
  								# print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] +cmd)); die
  								subprocess.check_call(a_ ,						shell=a_   )
  								assert os.path.exists(a_   ), f"{recovered_model_path} was not found"
  
  								# possibly could also test that the resulting saved model is usable but given that we use
  								# random models we won't know if it's any good
  
  
  
  
  
style_context: 
  """simple docstring"""
  def     __lowerCamelCase  (		UpperCamelCase__ = 400_0000      ):
  
  
  	"""simple docstring"""
  
  
  
  
  
  
  	_UpperCAmelCase =						1
  	_UpperCAmelCase =						2
  	_UpperCAmelCase =						0
  	while j <= n:
  		if j % 2 == 0:
  			total += j
  		_UpperCAmelCase ,      _UpperCAmelCase =						j, i + j
  
  	return total
  
  
  if __name__ == "__main__":
  			print(f'''{solution() = }''')
  
  
  
  
  