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)
  
  lowercase__  :					List[str]												=	get_tests_dir('fixtures')
  lowercase__  :					Dict												=	os.path.join(dirname(get_tests_dir()))
  lowercase__  :					int												=	dirname(os.path.abspath(__file__))
  
  # default torch.distributed port
  lowercase__  :					Any												=	'10999'
  
  lowercase__  :					Optional[Any]												=	't5-small'
  
  # *** Working Models ***
  lowercase__  :					List[Any]												=	'hf-internal-testing/tiny-albert'
  lowercase__  :					List[Any]												=	'sshleifer/bart-tiny-random'
  lowercase__  :					List[Any]												=	'hf-internal-testing/tiny-bert'
  lowercase__  :					str												=	'hf-internal-testing/tiny-random-bigbird_pegasus'
  lowercase__  :					List[Any]												=	'hf-internal-testing/tiny-random-big_bird'
  lowercase__  :					List[str]												=	'hf-internal-testing/tiny-random-blenderbot'
  lowercase__  :					Dict												=	'bigscience/bigscience-small-testing'
  lowercase__  :					str												=	'hf-internal-testing/tiny-random-deberta'
  lowercase__  :					int												=	'hf-internal-testing/tiny-random-deberta-v2'
  lowercase__  :					str												=	'sshleifer/tiny-distilbert-base-cased'
  lowercase__  :					Union[str, Any]												=	'hf-internal-testing/tiny-electra'
  lowercase__  :					str												=	'hf-internal-testing/tiny-random-flaubert'
  lowercase__  :					Tuple												=	'stas/tiny-wmt19-en-de'
  lowercase__  :					Optional[Any]												=	'hf-internal-testing/tiny-random-funnel'
  lowercase__  :					Dict												=	'sshleifer/tiny-gpt2'
  lowercase__  :					Optional[int]												=	'hf-internal-testing/tiny-random-gptj'
  lowercase__  :					Union[str, Any]												=	'hf-internal-testing/tiny-random-gpt_neo'
  lowercase__  :					List[str]												=	'hf-internal-testing/tiny-layoutlm'
  lowercase__  :					Any												=	'hf-internal-testing/tiny-random-led'
  lowercase__  :					Union[str, Any]												=	'hf-internal-testing/tiny-random-longformer'
  lowercase__  :					Optional[Any]												=	'stas/tiny-m2m_100'  # hf tiny model is unsuitable
  lowercase__  :					List[str]												=	'sshleifer/tiny-marian-en-de'
  lowercase__  :					List[str]												=	'sshleifer/tiny-mbart'
  lowercase__  :					Tuple												=	'hf-internal-testing/tiny-random-mobilebert'
  lowercase__  :					Any												=	'hf-internal-testing/tiny-random-mpnet'
  lowercase__  :					Dict												=	'stas/pegasus-cnn_dailymail-tiny-random'
  lowercase__  :					Optional[int]												=	'hf-internal-testing/tiny-random-prophetnet'
  lowercase__  :					str												=	'sshleifer/tiny-distilroberta-base'
  lowercase__  :					str												=	'hf-internal-testing/tiny-random-squeezebert'
  lowercase__  :					Dict												=	'patrickvonplaten/t5-tiny-random'
  lowercase__  :					Optional[Any]												=	'hf-internal-testing/tiny-random-t5-v1.1'
  lowercase__  :					int												=	'hf-internal-testing/tiny-random-vit'
  lowercase__  :					Union[str, Any]												=	'hf-internal-testing/tiny-xlm-roberta'
  lowercase__  :					int												=	'sshleifer/tiny-xlnet-base-cased'
  
  
  # *** To Fix ***
  
  
  # *** tiny model issues ***
  # missing model files:
  lowercase__  :					List[Any]												=	'hf-internal-testing/tiny-random-mt5'
  lowercase__  :					List[Any]												=	'hf-internal-testing/tiny-random-camembert'
  lowercase__  :					Dict												=	'hf-internal-testing/tiny-random-openai-gpt'
  
  # missing tokenizer files
  lowercase__  :					Union[str, Any]												=	'hf-internal-testing/tiny-random-convbert'
  lowercase__  :					Optional[Any]												=	'hf-internal-testing/tiny-random-layoutlmv2'
  lowercase__  :					List[Any]												=	'hf-internal-testing/tiny-random-hubert'
  
  # issues with tokenizer
  lowercase__  :					List[str]												=	'hf-internal-testing/tiny-random-ctrl'
  lowercase__  :					Tuple												=	'hf-internal-testing/tiny-random-transfo-xl'  # same as ctrl
  
  # other issues with tiny models
  lowercase__  :					Optional[Any]												=	'hf-internal-testing/tiny-random-ibert'  # multiple issues with either mlm/qa/clas
  lowercase__  :					Optional[int]												=	'hf-internal-testing/tiny-random-reformer'  # multiple issues with either mlm/qa/clas
  
  # *** Lacking official examples to test with ***
  # or not working with examples
  lowercase__  :					List[Any]												=	'hf-internal-testing/tiny-random-dpr'
  # - "dpr"  examples/research_projects/rag-end2end-retriever/
  lowercase__  :					Dict												=	'hf-internal-testing/tiny-random-rag'
  # - "rag" research_projects
  lowercase__  :					Optional[int]												=	''
  # - "luke" Entities classes - no plan to make such example
  lowercase__  :					Tuple												=	'hf-internal-testing/tiny-random-lxmert'
  # - "lxmert" doesn't work with run_qa.py
  lowercase__  :					List[Any]												=	'hf-internal-testing/tiny-random-clip'
  # - "clip" nothing under pytorch examples - XXX: Suraj is working on adding some - check by end of Sep
  lowercase__  :					Dict												=	'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
  lowercase__  :					Dict												=	'hf-internal-testing/tiny-random-tapas'
  def      __SCREAMING_SNAKE_CASE		(		lowercase__=False   ):
  					"""simple docstring"""
  					A    =      min(2    ,							get_gpu_count()   ) if distributed else 1
  					A    =      os.environ.get("DS_TEST_PORT"    ,							lowercase__   )
  					return F"""deepspeed --num_nodes 1 --num_gpus {num_gpus} --master_port {master_port}""".split()
  def      __SCREAMING_SNAKE_CASE		(		):
  					"""simple docstring"""
  					A    =      F"""{FIXTURE_DIRECTORY}/tests_samples"""
  					A    =      F"""{data_dir_samples}/wmt_en_ro"""
  					A    =      F"""{data_dir_samples}/xsum"""
  					A    =      "\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.
  					A    =      {
  					    "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",
  					    ],
  					}
  
  					A    =      F"""{ROOT_DIRECTORY}/examples/pytorch"""
  
  					A    =      {
  					    "trans": F"""
          {scripts_dir}/translation/run_translation.py
          --train_file {data_dir_wmt}/train.json
          --source_lang en
          --target_lang ro
          """,
  					    "sum": F"""
          {scripts_dir}/summarization/run_summarization.py
          --train_file {data_dir_xsum}/sample.json
          --max_source_length 12
          --max_target_length 12
          --lang en
          """,
  					    "clm": F"""
          {scripts_dir}/language-modeling/run_clm.py
          --train_file {FIXTURE_DIRECTORY}/sample_text.txt
          --block_size 8
          """,
  					    "mlm": F"""
          {scripts_dir}/language-modeling/run_mlm.py
          --train_file {FIXTURE_DIRECTORY}/sample_text.txt
          """,
  					    "qa": F"""
          {scripts_dir}/question-answering/run_qa.py
          --train_file {data_dir_samples}/SQUAD/sample.json
          """,
  					    "clas": F"""
          {scripts_dir}/text-classification/run_glue.py
          --train_file {data_dir_samples}/MRPC/train.csv
          --max_seq_length 12
          --task_name MRPC
          """,
  					    "img_clas": F"""
          {scripts_dir}/image-classification/run_image_classification.py
              --dataset_name hf-internal-testing/cats_vs_dogs_sample
              --remove_unused_columns False
              --max_steps 10
              --image_processor_name {DS_TESTS_DIRECTORY}/vit_feature_extractor.json
          """,
  					}
  
  					A    =      get_launcher(distributed=lowercase__   )
  
  					A    =      {}
  					for task, args in tasks.items():
  										A    =      args.split()
  										for model in tasksamodels[task]:
  															A    =      globals()[F"""{model.upper().replace("-"    ,							"_"   )}_TINY"""]
  															A    =      F"""--model_name_or_path {model_name}""".split()
  															A    =      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
  
  
  lowercase__  :					List[str]												=	make_task_cmds()
  
  lowercase__  :					Optional[int]												=	'zero2'
  lowercase__  :					List[str]												=	'zero3'
  
  lowercase__  :					Union[str, Any]												=	[ZEROa, ZEROa]
  def      __SCREAMING_SNAKE_CASE		(		lowercase__    ,							lowercase__    ,							lowercase__   ):
  					"""simple docstring"""
  					A    =      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
  lowercase__  :					int												=	list(itertools.product(stages, task_cmds.keys()))
  
  
  
  
  
  @slow
  @require_deepspeed
  @require_torch_gpu
  class     __UpperCamelCase				(     _A		):
  
  
  		def       SCREAMING_SNAKE_CASE__     (self				:	List[str]  ,  __SCREAMING_SNAKE_CASE				:	Dict  ,  __SCREAMING_SNAKE_CASE				:	str):
  							# 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()}""")
  
  							A    =      task_cmds[task]
  							A    =      F"""--deepspeed {self.test_file_dir_str}/ds_config_{stage}.json""".split()
  
  							A    =      self.get_auto_remove_tmp_dir()
  							A    =      F"""--output_dir {output_dir}""".split()
  
  							cmd += args_ds + args_out
  
  							return cmd, output_dir
  
  
  		@parameterized.expand(__SCREAMING_SNAKE_CASE  ,  name_func=__SCREAMING_SNAKE_CASE)
  		def       SCREAMING_SNAKE_CASE__     (self				:	List[str]  ,  __SCREAMING_SNAKE_CASE				:	List[Any]  ,  __SCREAMING_SNAKE_CASE				:	List[Any]):
  							# testing the ability to do a run followed by recovery of full fp32 weights
  
  							A    ,					A    =      self.get_task_cmd(__SCREAMING_SNAKE_CASE  ,  __SCREAMING_SNAKE_CASE)
  
  							# 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(__SCREAMING_SNAKE_CASE  ,  env=self.get_env())
  
  							# 2. test that the fp32 weights get reconsolidated
  							A    =      F"""{output_dir}/checkpoint-1"""
  							A    =      F"""{chkpt_dir}/out.bin"""
  							A    =      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(__SCREAMING_SNAKE_CASE  ,  shell=__SCREAMING_SNAKE_CASE)
  							assert os.path.exists(__SCREAMING_SNAKE_CASE), 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"""
  
  
  
  
  from typing import TYPE_CHECKING
  
  from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
  
  
  __A  :					int												=	{
      'configuration_nystromformer': ['NYSTROMFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'NystromformerConfig'],
  }
  
  try:
  	if not is_torch_available():
  		raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  	pass
  else:
  	__A  :					Any												=	[
  	    'NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
  	    'NystromformerForMaskedLM',
  	    'NystromformerForMultipleChoice',
  	    'NystromformerForQuestionAnswering',
  	    'NystromformerForSequenceClassification',
  	    'NystromformerForTokenClassification',
  	    'NystromformerLayer',
  	    'NystromformerModel',
  	    'NystromformerPreTrainedModel',
  	]
  
  
  if TYPE_CHECKING:
  	from .configuration_nystromformer import NYSTROMFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, NystromformerConfig
  
  	try:
  		if not is_torch_available():
  			raise OptionalDependencyNotAvailable()
  	except OptionalDependencyNotAvailable:
  		pass
  	else:
  		from .modeling_nystromformer import (
  		    NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
  		    NystromformerForMaskedLM,
  		    NystromformerForMultipleChoice,
  		    NystromformerForQuestionAnswering,
  		    NystromformerForSequenceClassification,
  		    NystromformerForTokenClassification,
  		    NystromformerLayer,
  		    NystromformerModel,
  		    NystromformerPreTrainedModel,
  		)
  
  
  else:
  	import sys
  
  	__A  :					Optional[Any]												=	_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
  
  
  
  
  
  