



"""simple docstring"""






_lowercase					:					str 		=		{
    0: "underwear",
    1: "pants",
    2: "belt",
    3: "suit",
    4: "shoe",
    5: "socks",
    6: "shirt",
    7: "tie",
    8: "watch",
}

_lowercase					:					Dict 		=		[[1, 4], [2, 4], [3], [], [], [4], [2, 7], [3], []]

_lowercase					:					Union[str, Any] 		=		[0 for x in range(len(graph))]
_lowercase					:					Union[str, Any] 		=		[]






def 	snake_case__  (						__lowerCamelCase							:       Optional[int]				,						__lowerCamelCase							:       Dict      ):
			"""simple docstring"""
			lowerCamelCase__						:						int						      =1
			while stack:
						lowerCamelCase__						:						int						      =stack.pop()
						print(__lowerCamelCase				,						clothes[current_clothing]      )
						order += 1






def 	snake_case__  (						__lowerCamelCase							:       Optional[Any]				,						__lowerCamelCase							:       str				,						__lowerCamelCase							:       Tuple      ):
			"""simple docstring"""
			lowerCamelCase__						:						Dict						      =1
			for v in graph[u]:
						if not visited[v]:
									depth_first_search(__lowerCamelCase				,						__lowerCamelCase				,						__lowerCamelCase      )

			stack.append(__lowerCamelCase      )






def 	snake_case__  (						__lowerCamelCase							:       Optional[Any]				,						__lowerCamelCase							:       Optional[Any]      ):
			"""simple docstring"""
			for v in range(len(__lowerCamelCase      )      ):
						if not visited[v]:
									depth_first_search(__lowerCamelCase				,						__lowerCamelCase				,						__lowerCamelCase      )


if __name__ == "__main__":
		topological_sort(graph, visited)
		print(stack)
		print_stack(stack, clothes)







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							:  Tuple     =				get_tests_dir("fixtures")
_lowerCamelCase							:  List[Any]     =				os.path.join(dirname(get_tests_dir()))
_lowerCamelCase							:  List[Any]     =				dirname(os.path.abspath(__file__))

# default torch.distributed port
_lowerCamelCase							:  Any     =				"10999"

_lowerCamelCase							:  Dict     =				"t5-small"

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


# *** To Fix ***


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

# missing tokenizer files
_lowerCamelCase							:  Tuple     =				"hf-internal-testing/tiny-random-convbert"
_lowerCamelCase							:  Any     =				"hf-internal-testing/tiny-random-layoutlmv2"
_lowerCamelCase							:  str     =				"hf-internal-testing/tiny-random-hubert"

# issues with tokenizer
_lowerCamelCase							:  List[str]     =				"hf-internal-testing/tiny-random-ctrl"
_lowerCamelCase							:  Any     =				"hf-internal-testing/tiny-random-transfo-xl"  # same as ctrl

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

def      a__	(						UpperCAmelCase				:     Any=False     )    ->		Optional[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)
  UpperCAmelCase			:				Union[str, Any]										=					min(2							,					get_gpu_count()     ) if distributed else 1
  UpperCAmelCase			:				List[Any]										=					os.environ.get('''DS_TEST_PORT'''							,					UpperCAmelCase     )
  return f'''deepspeed --num_nodes 1 --num_gpus {num_gpus} --master_port {master_port}'''.split()

def      a__	(						)    ->		List[Any]:
  UpperCAmelCase			:				List[str]										=					f'''{FIXTURE_DIRECTORY}/tests_samples'''
  UpperCAmelCase			:				Optional[Any]										=					f'''{data_dir_samples}/wmt_en_ro'''
  UpperCAmelCase			:				int										=					f'''{data_dir_samples}/xsum'''
  UpperCAmelCase			:				int										=					'''
        --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.
  UpperCAmelCase			:				str										=					{
      '''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			:				int										=					f'''{ROOT_DIRECTORY}/examples/pytorch'''

  UpperCAmelCase			:				int										=					{
      '''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			:				Dict										=					get_launcher(distributed=UpperCAmelCase     )

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


_lowerCamelCase							:  Optional[Any]     =				make_task_cmds()

_lowerCamelCase							:  Any     =				"zero2"
_lowerCamelCase							:  Optional[int]     =				"zero3"

_lowerCamelCase							:  Optional[int]     =				[ZEROa, ZEROa]

def      a__	(						UpperCAmelCase				:     Dict							,					UpperCAmelCase				:     Dict							,					UpperCAmelCase				:     Dict     )    ->		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
  UpperCAmelCase			:				List[str]										=					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
_lowerCamelCase							:  Dict     =				list(itertools.product(stages, task_cmds.keys()))






@slow
@require_deepspeed
@require_torch_gpu
class    __UpperCAmelCase	(			lowerCamelCase__		):



   def 			__magic_name__       (				self				:	Optional[int],						__A				:	Union[str, Any],						__A				:	str		):
     # 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			:				Optional[Any]										=					task_cmds[task]
     UpperCAmelCase			:				int										=					F'''--deepspeed {self.test_file_dir_str}/ds_config_{stage}.json'''.split()

     UpperCAmelCase			:				Optional[int]										=					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 			__magic_name__       (				self				:	Tuple,						__A				:	List[Any],						__A				:	List[str]		):
     # testing the ability to do a run followed by recovery of full fp32 weights

     UpperCAmelCase					,		UpperCAmelCase			:				List[str]										=					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			:				List[str]										=					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





