



import importlib.metadata
import subprocess
import sys




def       UpperCAmelCase__	(							__snake_case = False							)				->	List[Any]:
  _A          =      False
  if "IPython" in sys.modules:
    _A          =      '''google.colab''' in str(sys.modules['''IPython'''].get_ipython()							)

  if in_colab:
    if upgrade:
      _A          =      ['''pip''', '''install''', '''-U''', '''torch''']
      subprocess.run(__snake_case  ,			check=__snake_case							)
    # get the current version of torch
    _A          =      importlib.metadata.version('''torch'''							)
    _A          =      torch_version[: torch_version.rindex('''.'''							)]
    _A          =      F'''https://storage.googleapis.com/tpu-pytorch/wheels/colab/torch_xla-{torch_version_trunc}-cp37-cp37m-linux_x86_64.whl'''
    _A          =      ['''pip''', '''install''', xla_wheel]
    subprocess.run(__snake_case  ,			check=__snake_case							)
  else:
    raise RuntimeError('''`install_xla` utility works only on google colab.'''							)




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(4_2)

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

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

__lowerCamelCase								=   """t5-small"""

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


# *** To Fix ***


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

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

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

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

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




def       UpperCAmelCase__	(							__snake_case=False							)				->	List[Any]:
  # 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)
  _A          =      min(2  ,			get_gpu_count()							) if distributed else 1
  _A          =      os.environ.get('''DS_TEST_PORT'''  ,			__snake_case							)
  return F'''deepspeed --num_nodes 1 --num_gpus {num_gpus} --master_port {master_port}'''.split()




def       UpperCAmelCase__	(							)				->	Dict:
  _A          =      F'''{FIXTURE_DIRECTORY}/tests_samples'''
  _A          =      F'''{data_dir_samples}/wmt_en_ro'''
  _A          =      F'''{data_dir_samples}/xsum'''
  _A          =      '''
        --do_train
        --max_train_samples 4
        --per_device_train_batch_size 2
        --num_train_epochs 1
        --fp16
        --report_to none
        --overwrite_output_dir
        '''.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=__snake_case							)

  _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


__lowerCamelCase								=   make_task_cmds()

__lowerCamelCase								=   """zero2"""
__lowerCamelCase								=   """zero3"""

__lowerCamelCase								=   [ZEROa, ZEROa]




def       UpperCAmelCase__	(							__snake_case  ,			__snake_case  ,			__snake_case							)				->	str:
  # 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
  _A          =      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
__lowerCamelCase								=   list(itertools.product(stages, task_cmds.keys()))



@slow
@require_deepspeed
@require_torch_gpu
class 						_snake_case (     lowerCamelCase			):





 """simple docstring"""



 def lowercase_						(  self		,      a		,      a   )		->							Dict:




   """simple docstring"""







   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(a		,      name_func=a   )
 def lowercase_						(  self		,      a		,      a   )		->							Optional[Any]:




   """simple docstring"""







   _A      ,				_A          =      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
   _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(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