






"""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)

A_     =				get_tests_dir('''fixtures''')
A_     =				os.path.join(dirname(get_tests_dir()))
A_     =				dirname(os.path.abspath(__file__))

# default torch.distributed port
A_     =				'10999'

A_     =				't5-small'

# *** Working Models ***
A_     =				'hf-internal-testing/tiny-albert'
A_     =				'sshleifer/bart-tiny-random'
A_     =				'hf-internal-testing/tiny-bert'
A_     =				'hf-internal-testing/tiny-random-bigbird_pegasus'
A_     =				'hf-internal-testing/tiny-random-big_bird'
A_     =				'hf-internal-testing/tiny-random-blenderbot'
A_     =				'bigscience/bigscience-small-testing'
A_     =				'hf-internal-testing/tiny-random-deberta'
A_     =				'hf-internal-testing/tiny-random-deberta-v2'
A_     =				'sshleifer/tiny-distilbert-base-cased'
A_     =				'hf-internal-testing/tiny-electra'
A_     =				'hf-internal-testing/tiny-random-flaubert'
A_     =				'stas/tiny-wmt19-en-de'
A_     =				'hf-internal-testing/tiny-random-funnel'
A_     =				'sshleifer/tiny-gpt2'
A_     =				'hf-internal-testing/tiny-random-gptj'
A_     =				'hf-internal-testing/tiny-random-gpt_neo'
A_     =				'hf-internal-testing/tiny-layoutlm'
A_     =				'hf-internal-testing/tiny-random-led'
A_     =				'hf-internal-testing/tiny-random-longformer'
A_     =				'stas/tiny-m2m_100'  # hf tiny model is unsuitable
A_     =				'sshleifer/tiny-marian-en-de'
A_     =				'sshleifer/tiny-mbart'
A_     =				'hf-internal-testing/tiny-random-mobilebert'
A_     =				'hf-internal-testing/tiny-random-mpnet'
A_     =				'stas/pegasus-cnn_dailymail-tiny-random'
A_     =				'hf-internal-testing/tiny-random-prophetnet'
A_     =				'sshleifer/tiny-distilroberta-base'
A_     =				'hf-internal-testing/tiny-random-squeezebert'
A_     =				'patrickvonplaten/t5-tiny-random'
A_     =				'hf-internal-testing/tiny-random-t5-v1.1'
A_     =				'hf-internal-testing/tiny-random-vit'
A_     =				'hf-internal-testing/tiny-xlm-roberta'
A_     =				'sshleifer/tiny-xlnet-base-cased'


# *** To Fix ***


# *** tiny model issues ***
# missing model files:
A_     =				'hf-internal-testing/tiny-random-mt5'
A_     =				'hf-internal-testing/tiny-random-camembert'
A_     =				'hf-internal-testing/tiny-random-openai-gpt'

# missing tokenizer files
A_     =				'hf-internal-testing/tiny-random-convbert'
A_     =				'hf-internal-testing/tiny-random-layoutlmv2'
A_     =				'hf-internal-testing/tiny-random-hubert'

# issues with tokenizer
A_     =				'hf-internal-testing/tiny-random-ctrl'
A_     =				'hf-internal-testing/tiny-random-transfo-xl'  # same as ctrl

# other issues with tiny models
A_     =				'hf-internal-testing/tiny-random-ibert'  # multiple issues with either mlm/qa/clas
A_     =				'hf-internal-testing/tiny-random-reformer'  # multiple issues with either mlm/qa/clas

# *** Lacking official examples to test with ***
# or not working with examples
A_     =				'hf-internal-testing/tiny-random-dpr'
# - "dpr"  examples/research_projects/rag-end2end-retriever/
A_     =				'hf-internal-testing/tiny-random-rag'
# - "rag" research_projects
A_     =				''
# - "luke" Entities classes - no plan to make such example
A_     =				'hf-internal-testing/tiny-random-lxmert'
# - "lxmert" doesn't work with run_qa.py
A_     =				'hf-internal-testing/tiny-random-clip'
# - "clip" nothing under pytorch examples - XXX: Suraj is working on adding some - check by end of Sep
A_     =				'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
A_     =				'hf-internal-testing/tiny-random-tapas'




def   _lowerCAmelCase		(				UpperCAmelCase__							:       Tuple=False   )		->Union[str, Any]:
						A__    :	List[str]			=	min(2,    get_gpu_count()   ) if distributed else 1
						A__    :	str			=	os.environ.get("""DS_TEST_PORT""",    snake_case_   )
						return f'deepspeed --num_nodes 1 --num_gpus {num_gpus} --master_port {master_port}'.split()




def   _lowerCAmelCase		(				)		->Dict:
						A__    :	List[str]			=	f'{FIXTURE_DIRECTORY}/tests_samples'
						A__    :	int			=	f'{data_dir_samples}/wmt_en_ro'
						A__    :	List[Any]			=	f'{data_dir_samples}/xsum'
						A__    :	Tuple			=	"""\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__    :	str			=	{
						    """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__    :	Optional[Any]			=	f'{ROOT_DIRECTORY}/examples/pytorch'

						A__    :	List[str]			=	{
						    """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        ',
						}

						A__    :	Optional[int]			=	get_launcher(distributed=snake_case_   )

						A__    :	str			=	{}
						for task, args in tasks.items():
												A__    :	str			=	args.split()
												for model in tasksamodels[task]:
																		A__    :	Dict			=	globals()[f'{model.upper().replace("-",    "_"   )}_TINY']
																		A__    :	Optional[int]			=	f'--model_name_or_path {model_name}'.split()
																		A__    :	str			=	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


A_     =				make_task_cmds()

A_     =				'zero2'
A_     =				'zero3'

A_     =				[ZEROa, ZEROa]




def   _lowerCAmelCase		(				UpperCAmelCase__							:       Optional[Any],    UpperCAmelCase__							:       Dict,    UpperCAmelCase__							:       Dict   )		->List[str]:
						A__    :	int			=	parameterized.to_safe_name("""_""".join(str(snake_case_   ) for x in param.args   )   )
						return f'{func.__name__}_{param_based_name}'


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






@slow
@require_deepspeed
@require_torch_gpu
class 				__SCREAMING_SNAKE_CASE    (					UpperCAmelCase__		):


							def     _UpperCamelCase							(      self   :    List[Any]      ,     snake_case   :    List[str]      ,     snake_case   :    Union[str, Any]				):
													'''simple docstring'''





													if task not in task_cmds:
																			raise ValueError(F'don\'t know of task {task}, have {task_cmds.keys()}'				)

													A__    :	List[Any]			=	task_cmds[task]
													A__    :	int			=	F'--deepspeed {self.test_file_dir_str}/ds_config_{stage}.json'.split()

													A__    :	Union[str, Any]			=	self.get_auto_remove_tmp_dir()
													A__    :	List[str]			=	F'--output_dir {output_dir}'.split()

													cmd += args_ds + args_out

													return cmd, output_dir


							@parameterized.expand(snake_case__      ,     name_func=snake_case__				)
							def     _UpperCamelCase							(      self   :    Optional[int]      ,     snake_case   :    str      ,     snake_case   :    List[Any]				):
													'''simple docstring'''





													A__	,	A__    :	List[Any]			=	self.get_task_cmd(snake_case__      ,     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(snake_case__      ,     env=self.get_env()				)

													# 2. test that the fp32 weights get reconsolidated
													A__    :	str			=	F'{output_dir}/checkpoint-1'
													A__    :	int			=	F'{chkpt_dir}/out.bin'
													A__    :	List[Any]			=	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(snake_case__      ,     shell=snake_case__				)
													assert os.path.exists(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












def 				__SCREAMING_SNAKE_CASE    (     snake_case_		):

						'''simple docstring'''


						return int(snake_case_		) if x - int(snake_case_		) >= 0 else int(snake_case_		) - 1


if __name__ == "__main__":
				import doctest

				doctest.testmod()




