






"""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)

_snake_case       :      Dict       =					get_tests_dir("fixtures")
_snake_case       :      Union[str, Any]       =					os.path.join(dirname(get_tests_dir()))
_snake_case       :      Tuple       =					dirname(os.path.abspath(__file__))

# default torch.distributed port
_snake_case       :      Any       =					"10999"

_snake_case       :      List[str]       =					"t5-small"

# *** Working Models ***
_snake_case       :      Union[str, Any]       =					"hf-internal-testing/tiny-albert"
_snake_case       :      int       =					"sshleifer/bart-tiny-random"
_snake_case       :      int       =					"hf-internal-testing/tiny-bert"
_snake_case       :      Any       =					"hf-internal-testing/tiny-random-bigbird_pegasus"
_snake_case       :      str       =					"hf-internal-testing/tiny-random-big_bird"
_snake_case       :      List[str]       =					"hf-internal-testing/tiny-random-blenderbot"
_snake_case       :      Dict       =					"bigscience/bigscience-small-testing"
_snake_case       :      Union[str, Any]       =					"hf-internal-testing/tiny-random-deberta"
_snake_case       :      Any       =					"hf-internal-testing/tiny-random-deberta-v2"
_snake_case       :      Any       =					"sshleifer/tiny-distilbert-base-cased"
_snake_case       :      Any       =					"hf-internal-testing/tiny-electra"
_snake_case       :      Optional[int]       =					"hf-internal-testing/tiny-random-flaubert"
_snake_case       :      int       =					"stas/tiny-wmt19-en-de"
_snake_case       :      List[Any]       =					"hf-internal-testing/tiny-random-funnel"
_snake_case       :      Any       =					"sshleifer/tiny-gpt2"
_snake_case       :      str       =					"hf-internal-testing/tiny-random-gptj"
_snake_case       :      Any       =					"hf-internal-testing/tiny-random-gpt_neo"
_snake_case       :      Union[str, Any]       =					"hf-internal-testing/tiny-layoutlm"
_snake_case       :      Optional[int]       =					"hf-internal-testing/tiny-random-led"
_snake_case       :      List[str]       =					"hf-internal-testing/tiny-random-longformer"
_snake_case       :      Union[str, Any]       =					"stas/tiny-m2m_100"  # hf tiny model is unsuitable
_snake_case       :      List[Any]       =					"sshleifer/tiny-marian-en-de"
_snake_case       :      Dict       =					"sshleifer/tiny-mbart"
_snake_case       :      Dict       =					"hf-internal-testing/tiny-random-mobilebert"
_snake_case       :      List[str]       =					"hf-internal-testing/tiny-random-mpnet"
_snake_case       :      Optional[int]       =					"stas/pegasus-cnn_dailymail-tiny-random"
_snake_case       :      int       =					"hf-internal-testing/tiny-random-prophetnet"
_snake_case       :      List[str]       =					"sshleifer/tiny-distilroberta-base"
_snake_case       :      List[Any]       =					"hf-internal-testing/tiny-random-squeezebert"
_snake_case       :      List[Any]       =					"patrickvonplaten/t5-tiny-random"
_snake_case       :      Tuple       =					"hf-internal-testing/tiny-random-t5-v1.1"
_snake_case       :      str       =					"hf-internal-testing/tiny-random-vit"
_snake_case       :      List[Any]       =					"hf-internal-testing/tiny-xlm-roberta"
_snake_case       :      str       =					"sshleifer/tiny-xlnet-base-cased"


# *** To Fix ***


# *** tiny model issues ***
# missing model files:
_snake_case       :      Any       =					"hf-internal-testing/tiny-random-mt5"
_snake_case       :      Tuple       =					"hf-internal-testing/tiny-random-camembert"
_snake_case       :      int       =					"hf-internal-testing/tiny-random-openai-gpt"

# missing tokenizer files
_snake_case       :      List[str]       =					"hf-internal-testing/tiny-random-convbert"
_snake_case       :      Optional[Any]       =					"hf-internal-testing/tiny-random-layoutlmv2"
_snake_case       :      List[Any]       =					"hf-internal-testing/tiny-random-hubert"

# issues with tokenizer
_snake_case       :      Tuple       =					"hf-internal-testing/tiny-random-ctrl"
_snake_case       :      Dict       =					"hf-internal-testing/tiny-random-transfo-xl"  # same as ctrl

# other issues with tiny models
_snake_case       :      Union[str, Any]       =					"hf-internal-testing/tiny-random-ibert"  # multiple issues with either mlm/qa/clas
_snake_case       :      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
_snake_case       :      Union[str, Any]       =					"hf-internal-testing/tiny-random-dpr"
# - "dpr"  examples/research_projects/rag-end2end-retriever/
_snake_case       :      Optional[Any]       =					"hf-internal-testing/tiny-random-rag"
# - "rag" research_projects
_snake_case       :      Union[str, Any]       =					""
# - "luke" Entities classes - no plan to make such example
_snake_case       :      Any       =					"hf-internal-testing/tiny-random-lxmert"
# - "lxmert" doesn't work with run_qa.py
_snake_case       :      str       =					"hf-internal-testing/tiny-random-clip"
# - "clip" nothing under pytorch examples - XXX: Suraj is working on adding some - check by end of Sep
_snake_case       :      Any       =					"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
_snake_case       :      Optional[int]       =					"hf-internal-testing/tiny-random-tapas"




def 						lowerCamelCase_				(  __A  :		List[str]=False			):
					# 1. explicitly set --num_nodes=1 just in case these tests end up run on a multi-node setup
					# - it won't be able to handle that
					# 2. for now testing with just 2 gpus max (since some quality tests may give different
					# results with mode gpus because we use very little data)
					lowerCamelCase__				=min(2			,	get_gpu_count()			) if distributed else 1
					lowerCamelCase__				=os.environ.get('DS_TEST_PORT'			,	__A			)
					return f"deepspeed --num_nodes 1 --num_gpus {num_gpus} --master_port {master_port}".split()




def 						lowerCamelCase_				(  ):
					lowerCamelCase__				=f"{FIXTURE_DIRECTORY}/tests_samples"
					lowerCamelCase__				=f"{data_dir_samples}/wmt_en_ro"
					lowerCamelCase__				=f"{data_dir_samples}/xsum"
					lowerCamelCase__				='\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.
					lowerCamelCase__				={
					    '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',
					    ],
					}

					lowerCamelCase__				=f"{ROOT_DIRECTORY}/examples/pytorch"

					lowerCamelCase__				={
					    '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        ",
					}

					lowerCamelCase__				=get_launcher(distributed=__A			)

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


_snake_case       :      Union[str, Any]       =					make_task_cmds()

_snake_case       :      Dict       =					"zero2"
_snake_case       :      Optional[int]       =					"zero3"

_snake_case       :      List[str]       =					[ZEROa, ZEROa]




def 						lowerCamelCase_				(  __A  :		int			,	__A  :		Tuple			,	__A  :		Tuple			):
					# customize the test name generator function as we want both params to appear in the sub-test
					# name, as by default it shows only the first param
					lowerCamelCase__				=parameterized.to_safe_name('_'.join(str(__A			) for x in param.args			)			)
					return f"{func.__name__}_{param_based_name}"


# Cartesian-product of zero stages with models to test
_snake_case       :      Union[str, Any]       =					list(itertools.product(stages, task_cmds.keys()))




@slow
@require_deepspeed
@require_torch_gpu
class        __lowercase(       _UpperCamelCase  ):






						def 		lowerCAmelCase   (					self    ,						_SCREAMING_SNAKE_CASE    ,						_SCREAMING_SNAKE_CASE)			->  List[str]:




											"""simple docstring"""





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

											lowerCamelCase__				=task_cmds[task]
											lowerCamelCase__				=F"--deepspeed {self.test_file_dir_str}/ds_config_{stage}.json".split()

											lowerCamelCase__				=self.get_auto_remove_tmp_dir()
											lowerCamelCase__				=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 		lowerCAmelCase   (					self    ,						_SCREAMING_SNAKE_CASE    ,						_SCREAMING_SNAKE_CASE)			->  Union[str, Any]:




											"""simple docstring"""





											lowerCamelCase__				,     lowerCamelCase__				=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
											lowerCamelCase__				=F"{output_dir}/checkpoint-1"
											lowerCamelCase__				=F"{chkpt_dir}/out.bin"
											lowerCamelCase__				=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








"""simple docstring"""



_snake_case       :      Dict       =					[0.485, 0.456, 0.406]
_snake_case       :      int       =					[0.229, 0.224, 0.225]
_snake_case       :      Optional[int]       =					[0.5, 0.5, 0.5]
_snake_case       :      str       =					[0.5, 0.5, 0.5]
_snake_case       :      str       =					[0.4814_5466, 0.457_8275, 0.4082_1073]
_snake_case       :      str       =					[0.2686_2954, 0.2613_0258, 0.2757_7711]