




from __future__ import annotations

from collections.abc import Iterator
from typing import Any






class      A     :
     """simple docstring"""

     def __init__(      self					:				List[str]		,					A__					:				Any     )			->    List[str]:
           lowerCamelCase__ 					=			data
           lowerCamelCase__ 					=			None




     def __str__(      self					:				Any     )			->    Optional[int]:
           return F"""{self.data}"""






class      A     :
     """simple docstring"""

     def __init__(      self					:				List[Any]     )			->    Union[str, Any]:
           lowerCamelCase__ 					=			None
           lowerCamelCase__ 					=			None




     def __iter__(      self					:				Optional[int]     )			->    Optional[Any]:
           lowerCamelCase__ 					=			self.front
           while node:
                 yield node.data
                 lowerCamelCase__ 					=			node.next




     def __len__(      self					:				List[str]     )			->    Union[str, Any]:
           return len(tuple(iter(self     )     )     )




     def __str__(      self					:				List[str]     )			->    Optional[int]:
           return " <- ".join(str(_lowerCamelCase     ) for item in self     )




     def 	__snake_case				(      self					:				Optional[Any]     )			->    List[Any]:
           return len(self     ) == 0




     def 	__snake_case				(      self					:				Any		,					A__					:				Any     )			->    Optional[int]:
           lowerCamelCase__ 					=			Node(_lowerCamelCase     )
           if self.is_empty():
                 lowerCamelCase__ 					=			lowerCamelCase__ 					=			node
           else:
                 assert isinstance(self.rear		,					_lowerCamelCase     )
                 lowerCamelCase__ 					=			node
                 lowerCamelCase__ 					=			node




     def 	__snake_case				(      self					:				Dict     )			->    str:
           if self.is_empty():
                 raise IndexError('dequeue from empty queue'     )
           assert isinstance(self.front		,					_lowerCamelCase     )
           lowerCamelCase__ 					=			self.front
           lowerCamelCase__ 					=			self.front.next
           if self.front is None:
                 lowerCamelCase__ 					=			None
           return node.data




     def 	__snake_case				(      self					:				Optional[Any]     )			->    Tuple:
           lowerCamelCase__ 					=			lowerCamelCase__ 					=			None


if __name__ == "__main__":
      from doctest import testmod

      testmod()





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      :				Optional[Any]				      =get_tests_dir("""fixtures""")
__SCREAMING_SNAKE_CASE      :				int				      =os.path.join(dirname(get_tests_dir()))
__SCREAMING_SNAKE_CASE      :				List[str]				      =dirname(os.path.abspath(__file__))

# default torch.distributed port
__SCREAMING_SNAKE_CASE      :				Tuple				      ="""10999"""

__SCREAMING_SNAKE_CASE      :				List[Any]				      ="""t5-small"""

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


# *** To Fix ***


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

# missing tokenizer files
__SCREAMING_SNAKE_CASE      :				List[str]				      ="""hf-internal-testing/tiny-random-convbert"""
__SCREAMING_SNAKE_CASE      :				Optional[Any]				      ="""hf-internal-testing/tiny-random-layoutlmv2"""
__SCREAMING_SNAKE_CASE      :				List[str]				      ="""hf-internal-testing/tiny-random-hubert"""

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

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

def      lowerCAmelCase_     (					lowerCamelCase:    str=False  )   ->     str:


      '''simple docstring'''




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

def      lowerCAmelCase_     (					)   ->     int:


      '''simple docstring'''




      lowerCamelCase__ 					=			f"""{FIXTURE_DIRECTORY}/tests_samples"""
      lowerCamelCase__ 					=			f"""{data_dir_samples}/wmt_en_ro"""
      lowerCamelCase__ 					=			f"""{data_dir_samples}/xsum"""
      lowerCamelCase__ 					=			'\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.
      lowerCamelCase__ 					=			{
          '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',
          ],
      }

      lowerCamelCase__ 					=			f"""{ROOT_DIRECTORY}/examples/pytorch"""

      lowerCamelCase__ 					=			{
          '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
        """,
      }

      lowerCamelCase__ 					=			get_launcher(distributed=lowerCamelCase  )

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

__SCREAMING_SNAKE_CASE      :				Optional[Any]				      ="""zero2"""
__SCREAMING_SNAKE_CASE      :				List[Any]				      ="""zero3"""

__SCREAMING_SNAKE_CASE      :				str				      =[ZEROa, ZEROa]

def      lowerCAmelCase_     (					lowerCamelCase:    Optional[Any]       ,			lowerCamelCase:    List[str]       ,			lowerCamelCase:    Any  )   ->     List[Any]:


      '''simple docstring'''




      lowerCamelCase__ 					=			parameterized.to_safe_name('_'.join(str(lowerCamelCase  ) for x in param.args  )  )
      return f"""{func.__name__}_{param_based_name}"""


# Cartesian-product of zero stages with models to test
__SCREAMING_SNAKE_CASE      :				List[str]				      =list(itertools.product(stages, task_cmds.keys()))






@slow
@require_deepspeed
@require_torch_gpu
class      A     (						_lowerCAmelCase						):
     """simple docstring"""

     def 	__snake_case				(      self					:				int		,					A__					:				List[str]		,					A__					:				List[Any]     )			->    Any:
           # 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()}"""     )

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

           lowerCamelCase__ 					=			self.get_auto_remove_tmp_dir()
           lowerCamelCase__ 					=			F"""--output_dir {output_dir}""".split()

           cmd += args_ds + args_out

           return cmd, output_dir




     @parameterized.expand(A__		,					name_func=A__     )
     def 	__snake_case				(      self					:				Union[str, Any]		,					A__					:				List[Any]		,					A__					:				Union[str, Any]     )			->    Any:
           # testing the ability to do a run followed by recovery of full fp32 weights

           lowerCamelCase__					,						lowerCamelCase__ 					=			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
           lowerCamelCase__ 					=			F"""{output_dir}/checkpoint-1"""
           lowerCamelCase__ 					=			F"""{chkpt_dir}/out.bin"""
           lowerCamelCase__ 					=			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