
"""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






"""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() = }''')




