

'''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)

_snake_case     						=						get_tests_dir('fixtures')
_snake_case     						=						os.path.join(dirname(get_tests_dir()))
_snake_case     						=						dirname(os.path.abspath(__file__))

# default torch.distributed port
_snake_case     						=						'10999'

_snake_case     						=						't5-small'

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


# *** To Fix ***


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

# missing tokenizer files
_snake_case     						=						'hf-internal-testing/tiny-random-convbert'
_snake_case     						=						'hf-internal-testing/tiny-random-layoutlmv2'
_snake_case     						=						'hf-internal-testing/tiny-random-hubert'

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

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

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







def       _A	(			snake_case=False  )    ->			Dict:
   # 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)
   _lowercase    :   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	(			)    ->			Union[str, Any]:
   _lowercase    :   List[str]      =     F'''{FIXTURE_DIRECTORY}/tests_samples'''
   _lowercase    :   List[str]      =     F'''{data_dir_samples}/wmt_en_ro'''
   _lowercase    :   Union[str, Any]      =     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    :   int      =     {
       "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    :   int      =     F'''{ROOT_DIRECTORY}/examples/pytorch'''

   _lowercase    :   List[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
        ''',
   }

   _lowercase    :   List[str]      =     get_launcher(distributed=snake_case  )

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


_snake_case     						=						make_task_cmds()

_snake_case     						=						'zero2'
_snake_case     						=						'zero3'

_snake_case     						=						[ZEROa, ZEROa]







def       _A	(			snake_case	,							snake_case	,							snake_case  )    ->			List[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
   _lowercase    :   str      =     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
_snake_case     						=						list(itertools.product(stages, task_cmds.keys()))


@slow
@require_deepspeed
@require_torch_gpu
class      a__						(							lowerCamelCase_						):


   def 		_lowerCamelCase     (				self    ,       _UpperCamelCase    ,       _UpperCamelCase						):





      """simple docstring"""





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

      _lowercase    :   List[Any]      =     task_cmds[task]
      _lowercase    :   List[Any]      =     f'''--deepspeed {self.test_file_dir_str}/ds_config_{stage}.json'''.split()

      _lowercase    :   str      =     self.get_auto_remove_tmp_dir()
      _lowercase    :   int      =     f'''--output_dir {output_dir}'''.split()

      cmd += args_ds + args_out

      return cmd, output_dir






   @parameterized.expand(_UpperCamelCase    ,       name_func=_UpperCamelCase						)
   def 		_lowerCamelCase     (				self    ,       _UpperCamelCase    ,       _UpperCamelCase						):





      """simple docstring"""





      _lowercase  ,   _lowercase    :   Optional[int]      =     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    :   Any      =     f'''{output_dir}/checkpoint-1'''
      _lowercase    :   Any      =     f'''{chkpt_dir}/out.bin'''
      _lowercase    :   Tuple      =     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'''
class      a__						:


   def __init__(				self    ,       _UpperCamelCase						):





      """simple docstring"""





      _lowercase    :   Tuple      =     data
      _lowercase    :   int
      _lowercase    :   Node







def       _A	(			snake_case  )    ->			None:
   _lowercase    :   Any      =     0
   _lowercase    :   str      =     x







def       _A	(			snake_case	,							snake_case  )    ->			None:
   _lowercase  ,   _lowercase    :   Any      =     find_set(snake_case  ), find_set(snake_case  )
   if x == y:
      return

   elif x.rank > y.rank:
      _lowercase    :   Dict      =     x
   else:
      _lowercase    :   Optional[int]      =     y
      if x.rank == y.rank:
         y.rank += 1







def       _A	(			snake_case  )    ->			Node:
   if x != x.parent:
      _lowercase    :   Optional[int]      =     find_set(x.parent  )
   return x.parent







def       _A	(			snake_case  )    ->			set:
   _lowercase    :   Any      =     ({0, 1, 2}, {3, 4, 5})
   for s in sets:
      if node.data in s:
         return s
   _lowercase    :   List[Any]      =     F'''{node.data} is not in {sets}'''
   raise ValueError(snake_case  )







def       _A	(			)    ->			None:
   _lowercase    :   Union[str, Any]      =     [Node(snake_case  ) for i in range(6  )]
   for v in vertex:
      make_set(snake_case  )

   union_set(vertex[0]	,							vertex[1]  )
   union_set(vertex[1]	,							vertex[2]  )
   union_set(vertex[3]	,							vertex[4]  )
   union_set(vertex[3]	,							vertex[5]  )

   for nodea in vertex:
      for nodea in vertex:
         if find_python_set(snake_case  ).isdisjoint(find_python_set(snake_case  )  ):
            assert find_set(snake_case  ) != find_set(snake_case  )
         else:
            assert find_set(snake_case  ) == find_set(snake_case  )


if __name__ == "__main__":
    test_disjoint_set()






