






"""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)

UpperCamelCase			=      get_tests_dir("""fixtures""")
UpperCamelCase			=      os.path.join(dirname(get_tests_dir()))
UpperCamelCase			=      dirname(os.path.abspath(__file__))

# default torch.distributed port
UpperCamelCase			=      """10999"""

UpperCamelCase			=      """t5-small"""

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


# *** To Fix ***


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

# missing tokenizer files
UpperCamelCase			=      """hf-internal-testing/tiny-random-convbert"""
UpperCamelCase			=      """hf-internal-testing/tiny-random-layoutlmv2"""
UpperCamelCase			=      """hf-internal-testing/tiny-random-hubert"""

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

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

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






def     A(  snake_case_=False		):
       """simple docstring"""





       lowercase__:    Optional[int]        =      min(2						,       get_gpu_count()		) if distributed else 1
       lowercase__:    Tuple        =      os.environ.get("DS_TEST_PORT"						,       snake_case_		)
       return F"""deepspeed --num_nodes 1 --num_gpus {num_gpus} --master_port {master_port}""".split()






def     A(  ):
       """simple docstring"""





       lowercase__:    Optional[Any]        =      F"""{FIXTURE_DIRECTORY}/tests_samples"""
       lowercase__:    Union[str, Any]        =      F"""{data_dir_samples}/wmt_en_ro"""
       lowercase__:    Optional[int]        =      F"""{data_dir_samples}/xsum"""
       lowercase__:    Any        =      "\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.
       lowercase__:    List[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",
           ],
       }

       lowercase__:    Dict        =      F"""{ROOT_DIRECTORY}/examples/pytorch"""

       lowercase__:    List[str]        =      {
           "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
        """,
       }

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

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


UpperCamelCase			=      make_task_cmds()

UpperCamelCase			=      """zero2"""
UpperCamelCase			=      """zero3"""

UpperCamelCase			=      [ZEROa, ZEROa]






def     A(  snake_case_						,       snake_case_						,       snake_case_		):
       """simple docstring"""





       lowercase__:    Any        =      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
UpperCamelCase			=      list(itertools.product(stages, task_cmds.keys()))







@slow
@require_deepspeed
@require_torch_gpu
class 		_a       (						lowercase_   ):





   '''simple docstring'''






   def __lowercase    (   self			,				UpperCAmelCase_			,				UpperCAmelCase_)	-> List[str]:




          '''simple docstring'''




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

          lowercase__:    Tuple        =      task_cmds[task]
          lowercase__:    Union[str, Any]        =      F"""--deepspeed {self.test_file_dir_str}/ds_config_{stage}.json""".split()

          lowercase__:    Dict        =      self.get_auto_remove_tmp_dir()
          lowercase__:    Optional[Any]        =      F"""--output_dir {output_dir}""".split()

          cmd += args_ds + args_out

          return cmd, output_dir





   @parameterized.expand(UpperCAmelCase_			,				name_func=UpperCAmelCase_)
   def __lowercase    (   self			,				UpperCAmelCase_			,				UpperCAmelCase_)	-> Optional[Any]:




          '''simple docstring'''




          lowercase__  ,     lowercase__:    Tuple        =      self.get_task_cmd(UpperCAmelCase_			,				UpperCAmelCase_)

          # 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(UpperCAmelCase_			,				env=self.get_env())

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





UpperCamelCase			=      {
    """0""": 1,
    """1""": 1,
    """2""": 2,
    """3""": 6,
    """4""": 24,
    """5""": 1_20,
    """6""": 7_20,
    """7""": 50_40,
    """8""": 4_03_20,
    """9""": 36_28_80,
}

UpperCamelCase			=      {1_45: 1_45}

UpperCamelCase			=      {
    1_45: 0,
    1_69: 3,
    3_63_01: 3,
    14_54: 3,
    8_71: 2,
    4_53_61: 2,
    8_72: 2,
}






def     A(  snake_case_		):
       """simple docstring"""





       if n in CACHE_SUM_DIGIT_FACTORIALS:
              return CACHE_SUM_DIGIT_FACTORIALS[n]
       lowercase__:    Any        =      sum(DIGIT_FACTORIALS[let] for let in str(snake_case_		)		)
       lowercase__:    Optional[Any]        =      ret
       return ret






def     A(  snake_case_						,       snake_case_ = None		):
       """simple docstring"""





       lowercase__:    Optional[int]        =      previous or set()
       if n in CHAIN_LENGTH_CACHE:
              return CHAIN_LENGTH_CACHE[n]
       lowercase__:    Tuple        =      sum_digit_factorials(snake_case_		)
       if next_number in previous:
              lowercase__:    List[Any]        =      0
              return 0
       else:
              previous.add(snake_case_		)
              lowercase__:    Optional[int]        =      1 + chain_length(snake_case_						,       snake_case_		)
              lowercase__:    Any        =      ret
              return ret






def     A(  snake_case_ = 60						,       snake_case_ = 1000000		):
       """simple docstring"""





       return sum(1 for i in range(1						,       snake_case_		) if chain_length(snake_case_		) == num_terms		)


if __name__ == "__main__":
  print(F"{solution() = }")






