

"""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	:       Optional[int]        =						get_tests_dir('fixtures')
__A	:       str        =						os.path.join(dirname(get_tests_dir()))
__A	:       int        =						dirname(os.path.abspath(__file__))

# default torch.distributed port
__A	:       Union[str, Any]        =						'10999'

__A	:       Optional[Any]        =						't5-small'

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


# *** To Fix ***


# *** tiny model issues ***
# missing model files:
__A	:       List[Any]        =						'hf-internal-testing/tiny-random-mt5'
__A	:       int        =						'hf-internal-testing/tiny-random-camembert'
__A	:       List[Any]        =						'hf-internal-testing/tiny-random-openai-gpt'

# missing tokenizer files
__A	:       Tuple        =						'hf-internal-testing/tiny-random-convbert'
__A	:       List[Any]        =						'hf-internal-testing/tiny-random-layoutlmv2'
__A	:       Any        =						'hf-internal-testing/tiny-random-hubert'

# issues with tokenizer
__A	:       Dict        =						'hf-internal-testing/tiny-random-ctrl'
__A	:       Optional[int]        =						'hf-internal-testing/tiny-random-transfo-xl'  # same as ctrl

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


__A	:       str        =						make_task_cmds()

__A	:       Tuple        =						'zero2'
__A	:       List[Any]        =						'zero3'

__A	:       List[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
__A	:       Tuple        =						list(itertools.product(stages, task_cmds.keys()))





@slow
@require_deepspeed
@require_torch_gpu
class      __UpperCamelCase					(  A__		):


		def    SCREAMING_SNAKE_CASE__  (self					:						Any	,	__SCREAMING_SNAKE_CASE					:						Tuple	,	__SCREAMING_SNAKE_CASE					:						Optional[Any]):
							# 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(_lowerCAmelCase	,	name_func=_lowerCAmelCase)
		def    SCREAMING_SNAKE_CASE__  (self					:						int	,	__SCREAMING_SNAKE_CASE					:						List[str]	,	__SCREAMING_SNAKE_CASE					:						Optional[int]):
							# testing the ability to do a run followed by recovery of full fp32 weights

							A     ,       A     =   self.get_task_cmd(_lowerCAmelCase	,	_lowerCAmelCase)

							# 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(_lowerCAmelCase	,	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(_lowerCAmelCase	,	shell=_lowerCAmelCase)
							assert os.path.exists(_lowerCAmelCase), 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'''


from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available, is_torch_available


_lowerCamelCase					:						str					     =							{
    'configuration_ernie_m': ['ERNIE_M_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ErnieMConfig'],
}

try:
				if not is_sentencepiece_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_lowerCamelCase					:						List[str]					     =							['ErnieMTokenizer']

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_lowerCamelCase					:						Union[str, Any]					     =							[
				    'ERNIE_M_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'ErnieMForMultipleChoice',
				    'ErnieMForQuestionAnswering',
				    'ErnieMForSequenceClassification',
				    'ErnieMForTokenClassification',
				    'ErnieMModel',
				    'ErnieMPreTrainedModel',
				    'ErnieMForInformationExtraction',
				]


if TYPE_CHECKING:
				from .configuration_ernie_m import ERNIE_M_PRETRAINED_CONFIG_ARCHIVE_MAP, ErnieMConfig

				try:
								if not is_sentencepiece_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_ernie_m import ErnieMTokenizer

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_ernie_m import (
								    ERNIE_M_PRETRAINED_MODEL_ARCHIVE_LIST,
								    ErnieMForInformationExtraction,
								    ErnieMForMultipleChoice,
								    ErnieMForQuestionAnswering,
								    ErnieMForSequenceClassification,
								    ErnieMForTokenClassification,
								    ErnieMModel,
								    ErnieMPreTrainedModel,
								)


else:
				import sys

				_lowerCamelCase					:						int					     =							_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)

