'''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)

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

# default torch.distributed port
__SCREAMING_SNAKE_CASE :  Optional[Any]  =						"""10999"""

__SCREAMING_SNAKE_CASE :  Optional[Any]  =						"""t5-small"""

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


# *** To Fix ***


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

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

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

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

def 			UpperCamelCase_							(      _UpperCAmelCase						:   List[Any]=False					)					->					List[Any]:


       """simple docstring"""







       _UpperCAmelCase  :						Optional[Any]							   =					min(2    ,			get_gpu_count()					) if distributed else 1
       _UpperCAmelCase  :						List[str]							   =					os.environ.get("DS_TEST_PORT"    ,			_UpperCAmelCase					)
       return F"""deepspeed --num_nodes 1 --num_gpus {num_gpus} --master_port {master_port}""".split()

def 			UpperCamelCase_							(      )					->					Any:


       """simple docstring"""







       _UpperCAmelCase  :						int							   =					F"""{FIXTURE_DIRECTORY}/tests_samples"""
       _UpperCAmelCase  :						Any							   =					F"""{data_dir_samples}/wmt_en_ro"""
       _UpperCAmelCase  :						Tuple							   =					F"""{data_dir_samples}/xsum"""
       _UpperCAmelCase  :						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.
       _UpperCAmelCase  :						Any							   =					{
           "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  :						List[Any]							   =					F"""{ROOT_DIRECTORY}/examples/pytorch"""

       _UpperCAmelCase  :						Union[str, Any]							   =					{
           "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
        """,
       }

       _UpperCAmelCase  :						Union[str, Any]							   =					get_launcher(distributed=_UpperCAmelCase					)

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


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

__SCREAMING_SNAKE_CASE :  Any  =						"""zero2"""
__SCREAMING_SNAKE_CASE :  Optional[Any]  =						"""zero3"""

__SCREAMING_SNAKE_CASE :  List[Any]  =						[ZEROa, ZEROa]

def 			UpperCamelCase_							(      _UpperCAmelCase						:   Optional[Any]    ,			_UpperCAmelCase						:   Union[str, Any]    ,			_UpperCAmelCase						:   List[str]					)					->					int:


       """simple docstring"""







       _UpperCAmelCase  :						Optional[int]							   =					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
__SCREAMING_SNAKE_CASE :  Dict  =						list(itertools.product(stages, task_cmds.keys()))


@slow
@require_deepspeed
@require_torch_gpu
class 			lowerCamelCase_			(snake_case__					):
      '''simple docstring'''

      def   _A			( self		:       List[str]		,      A		:       int		,      A		:       Union[str, 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  :						List[Any]							   =					task_cmds[task]
             _UpperCAmelCase  :						Any							   =					F"""--deepspeed {self.test_file_dir_str}/ds_config_{stage}.json""".split()

             _UpperCAmelCase  :						Tuple							   =					self.get_auto_remove_tmp_dir()
             _UpperCAmelCase  :						int							   =					F"""--output_dir {output_dir}""".split()

             cmd += args_ds + args_out

             return cmd, output_dir





      @parameterized.expand(A		,      name_func=A )
      def   _A			( self		:       int		,      A		:       Optional[int]		,      A		:       Optional[Any] ):
             # testing the ability to do a run followed by recovery of full fp32 weights

             _UpperCAmelCase  :						Union[str, Any]							   =					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  :						Tuple							   =					F"""{output_dir}/checkpoint-1"""
             _UpperCAmelCase  :						List[Any]							   =					F"""{chkpt_dir}/out.bin"""
             _UpperCAmelCase  :						Optional[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(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'''




import abc
import copy
import dataclasses
from dataclasses import dataclass
from typing import ClassVar, Dict, Type, TypeVar

from ..features import Features


__SCREAMING_SNAKE_CASE :  Any  =						TypeVar("""T""", bound="""TaskTemplate""")

@dataclass(frozen=snake_case__					)
class 			lowerCamelCase_			(abc.ABC					):
      '''simple docstring'''

      __UpperCamelCase:						str
      __UpperCamelCase:						ClassVar[Features]
      __UpperCamelCase:						ClassVar[Features]




      def   _A			( self		:       T		,      A		:       Features ):
             return copy.deepcopy(self )




      @property
      def   _A			( self		:       List[str] ):
             return Features(**self.input_schema		,      **self.label_schema )




      @property
      @abc.abstractmethod
      def   _A			( self		:       Optional[int] ):
             raise NotImplementedError





      @classmethod
      def   _A			( cls		:       Type[T]		,      A		:       dict ):
             _UpperCAmelCase  :						Optional[Any]							   =					{f.name for f in dataclasses.fields(cls )}
             return cls(**{k: v for k, v in template_dict.items() if k in field_names} )

