






"""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__       :			List[Any]=						get_tests_dir("""fixtures""")
A__       :			List[str]=						os.path.join(dirname(get_tests_dir()))
A__       :			Tuple=						dirname(os.path.abspath(__file__))

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

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

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


# *** To Fix ***


# *** tiny model issues ***
# missing model files:
A__       :			str=						"""hf-internal-testing/tiny-random-mt5"""
A__       :			Optional[Any]=						"""hf-internal-testing/tiny-random-camembert"""
A__       :			int=						"""hf-internal-testing/tiny-random-openai-gpt"""

# missing tokenizer files
A__       :			Tuple=						"""hf-internal-testing/tiny-random-convbert"""
A__       :			str=						"""hf-internal-testing/tiny-random-layoutlmv2"""
A__       :			int=						"""hf-internal-testing/tiny-random-hubert"""

# issues with tokenizer
A__       :			Optional[Any]=						"""hf-internal-testing/tiny-random-ctrl"""
A__       :			Optional[Any]=						"""hf-internal-testing/tiny-random-transfo-xl"""  # same as ctrl

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







def 	lowerCAmelCase_(			SCREAMING_SNAKE_CASE=False       )       ->						List[str]:
    """simple docstring"""

    UpperCamelCase__								=     min(2			,						get_gpu_count()       ) if distributed else 1
    UpperCamelCase__								=     os.environ.get('DS_TEST_PORT'			,						_SCREAMING_SNAKE_CASE       )
    return F'deepspeed --num_nodes 1 --num_gpus {num_gpus} --master_port {master_port}'.split()







def 	lowerCAmelCase_(			)       ->						Optional[Any]:
    """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=_SCREAMING_SNAKE_CASE       )

    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


A__       :			int=						make_task_cmds()

A__       :			Dict=						"""zero2"""
A__       :			str=						"""zero3"""

A__       :			Any=						[ZEROa, ZEROa]







def 	lowerCAmelCase_(			SCREAMING_SNAKE_CASE			,						SCREAMING_SNAKE_CASE			,						SCREAMING_SNAKE_CASE       )       ->						str:
    """simple docstring"""

    UpperCamelCase__								=     parameterized.to_safe_name('_'.join(str(_SCREAMING_SNAKE_CASE       ) 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  __lowerCamelCase     (	snake_case_	):

   def     SCREAMING_SNAKE_CASE__			(       self			,					snake_case_			,					snake_case_   )		->							List[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()}'   )

       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(_lowerCAmelCase			,					name_func=_lowerCAmelCase   )
   def     SCREAMING_SNAKE_CASE__			(       self			,					snake_case_			,					snake_case_   )		->							int:
       # testing the ability to do a run followed by recovery of full fp32 weights

       UpperCamelCase__							,    UpperCamelCase__								=     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
       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(_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









def 				UpperCAmelCase__		(					_SCREAMING_SNAKE_CASE :						str					)->str:
      return " ".join(sorted(set(sentence.split()					)					)					)


if __name__ == "__main__":
    import doctest

    doctest.testmod()