


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		:List[str]											=       get_tests_dir('''fixtures''')
SCREAMING_SNAKE_CASE		:Dict											=       os.path.join(dirname(get_tests_dir()))
SCREAMING_SNAKE_CASE		:List[Any]											=       dirname(os.path.abspath(__file__))

# default torch.distributed port
SCREAMING_SNAKE_CASE		:Union[str, Any]											=       """10999"""

SCREAMING_SNAKE_CASE		:int											=       """t5-small"""

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


# *** To Fix ***


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

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

# issues with tokenizer
SCREAMING_SNAKE_CASE		:Union[str, Any]											=       """hf-internal-testing/tiny-random-ctrl"""
SCREAMING_SNAKE_CASE		:Tuple											=       """hf-internal-testing/tiny-random-transfo-xl"""  # same as ctrl

# other issues with tiny models
SCREAMING_SNAKE_CASE		:Optional[int]											=       """hf-internal-testing/tiny-random-ibert"""  # multiple issues with either mlm/qa/clas
SCREAMING_SNAKE_CASE		:Optional[int]											=       """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		:int											=       """hf-internal-testing/tiny-random-dpr"""
# - "dpr"  examples/research_projects/rag-end2end-retriever/
SCREAMING_SNAKE_CASE		:Optional[Any]											=       """hf-internal-testing/tiny-random-rag"""
# - "rag" research_projects
SCREAMING_SNAKE_CASE		:Dict											=       """"""
# - "luke" Entities classes - no plan to make such example
SCREAMING_SNAKE_CASE		:str											=       """hf-internal-testing/tiny-random-lxmert"""
# - "lxmert" doesn't work with run_qa.py
SCREAMING_SNAKE_CASE		:str											=       """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		:List[str]											=       """hf-internal-testing/tiny-random-tapas"""



def 	_lowerCAmelCase  (							lowerCAmelCase_							:Any=False							)->int:
   '''simple docstring'''






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



def 	_lowerCAmelCase  (							)->Tuple:
   '''simple docstring'''






   snake_case_         =   F'''{FIXTURE_DIRECTORY}/tests_samples'''
   snake_case_         =   F'''{data_dir_samples}/wmt_en_ro'''
   snake_case_         =   F'''{data_dir_samples}/xsum'''
   snake_case_         =   "\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.
   snake_case_         =   {
       "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",
       ],
   }

   snake_case_         =   F'''{ROOT_DIRECTORY}/examples/pytorch'''

   snake_case_         =   {
       "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
        ''',
   }

   snake_case_         =   get_launcher(distributed=_UpperCAmelCase							)

   snake_case_         =   {}
   for task, args in tasks.items():
      snake_case_         =   args.split()
      for model in tasksamodels[task]:
         snake_case_         =   globals()[F'''{model.upper().replace('-'						,  '_'							)}_TINY''']
         snake_case_         =   F'''--model_name_or_path {model_name}'''.split()
         snake_case_         =   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		:Dict											=       make_task_cmds()

SCREAMING_SNAKE_CASE		:Dict											=       """zero2"""
SCREAMING_SNAKE_CASE		:Union[str, Any]											=       """zero3"""

SCREAMING_SNAKE_CASE		:Any											=       [ZEROa, ZEROa]



def 	_lowerCAmelCase  (							lowerCAmelCase_							:List[Any]						,  lowerCAmelCase_							:int						,  lowerCAmelCase_							:int							)->int:
   '''simple docstring'''






   snake_case_         =   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		:Optional[Any]											=       list(itertools.product(stages, task_cmds.keys()))



@slow
@require_deepspeed
@require_torch_gpu
class       __lowerCAmelCase       (	snake_case__   ):






  """simple docstring"""





  def    lowerCAmelCase__					(       self    :    Any      ,			_lowerCAmelCase    :    List[Any]      ,			_lowerCAmelCase    :    Union[str, Any]					)				->	Tuple:




     """simple docstring"""
     # 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()}'''					)

     snake_case_         =   task_cmds[task]
     snake_case_         =   F'''--deepspeed {self.test_file_dir_str}/ds_config_{stage}.json'''.split()

     snake_case_         =   self.get_auto_remove_tmp_dir()
     snake_case_         =   F'''--output_dir {output_dir}'''.split()

     cmd += args_ds + args_out

     return cmd, output_dir



  @parameterized.expand(_lowerCAmelCase      ,			name_func=_lowerCAmelCase					)
  def    lowerCAmelCase__					(       self    :    Optional[int]      ,			_lowerCAmelCase    :    Optional[Any]      ,			_lowerCAmelCase    :    Optional[Any]					)				->	Optional[int]:




     """simple docstring"""
     # testing the ability to do a run followed by recovery of full fp32 weights

     snake_case_         =   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
     snake_case_         =   F'''{output_dir}/checkpoint-1'''
     snake_case_         =   F'''{chkpt_dir}/out.bin'''
     snake_case_         =   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








from __future__ import annotations



def      _lowerCAmelCase  (							lowerCAmelCase_							:int							)->list[int]:
   '''simple docstring'''






   assert (
       isinstance(lowerCAmelCase_						,  lowerCAmelCase_							) and mask > 0
   ), F'''mask needs to be positive integer, your input {mask}'''
   snake_case_         =   []
   snake_case_         =   mask

   while submask:
      all_submasks.append(lowerCAmelCase_							)
      snake_case_         =   (submask - 1) & mask

   return all_submasks


if __name__ == "__main__":
   import doctest

   doctest.testmod()




