






"""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)

_UpperCamelCase							:       Optional[int]       					=		get_tests_dir('fixtures')
_UpperCamelCase							:       Optional[int]       					=		os.path.join(dirname(get_tests_dir()))
_UpperCamelCase							:       List[Any]       					=		dirname(os.path.abspath(__file__))

# default torch.distributed port
_UpperCamelCase							:       Any       					=		'10999'

_UpperCamelCase							:       List[str]       					=		't5-small'

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


# *** To Fix ***


# *** tiny model issues ***
# missing model files:
_UpperCamelCase							:       Dict       					=		'hf-internal-testing/tiny-random-mt5'
_UpperCamelCase							:       Tuple       					=		'hf-internal-testing/tiny-random-camembert'
_UpperCamelCase							:       List[str]       					=		'hf-internal-testing/tiny-random-openai-gpt'

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

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

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




def      snake_case    (A_ :Any=False ):







  '''simple docstring'''

  a	:     str							=  min(2      ,     get_gpu_count() ) if distributed else 1
  a	:     Optional[Any]							=  os.environ.get('DS_TEST_PORT'      ,     A_ )
  return f'''deepspeed --num_nodes 1 --num_gpus {num_gpus} --master_port {master_port}'''.split()




def      snake_case    ():







  '''simple docstring'''

  a	:     str							=  f'''{FIXTURE_DIRECTORY}/tests_samples'''
  a	:     List[Any]							=  f'''{data_dir_samples}/wmt_en_ro'''
  a	:     Optional[int]							=  f'''{data_dir_samples}/xsum'''
  a	:     Tuple							=  '\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.
  a	:     Optional[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',
      ],
  }

  a	:     Dict							=  f'''{ROOT_DIRECTORY}/examples/pytorch'''

  a	:     Union[str, 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
        ''',
  }

  a	:     str							=  get_launcher(distributed=A_ )

  a	:     Tuple							=  {}
  for task, args in tasks.items():
    a	:     int							=  args.split()
    for model in tasksamodels[task]:
      a	:     Optional[Any]							=  globals()[f'''{model.upper().replace('-'      ,     '_' )}_TINY''']
      a	:     Tuple							=  f'''--model_name_or_path {model_name}'''.split()
      a	:     Optional[Any]							=  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


_UpperCamelCase							:       int       					=		make_task_cmds()

_UpperCamelCase							:       List[str]       					=		'zero2'
_UpperCamelCase							:       str       					=		'zero3'

_UpperCamelCase							:       int       					=		[ZEROa, ZEROa]




def      snake_case    (A_ :Optional[Any]      ,     A_ :int      ,     A_ :List[str] ):







  '''simple docstring'''

  a	:     Dict							=  parameterized.to_safe_name('_'.join(str(A_ ) for x in param.args ) )
  return f'''{func.__name__}_{param_based_name}'''


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






@slow
@require_deepspeed
@require_torch_gpu
class 		snake_case    (    UpperCAmelCase			):





    def 							lowerCamelCase__ (				self      :   Optional[Any]							,						A      :   List[Any]							,						A      :   Any     ):


      '''simple docstring'''




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

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

      a	:     Tuple							=  self.get_auto_remove_tmp_dir()
      a	:     Any							=  F'''--output_dir {output_dir}'''.split()

      cmd += args_ds + args_out

      return cmd, output_dir





    @parameterized.expand(A							,						name_func=A     )
    def 							lowerCamelCase__ (				self      :   Tuple							,						A      :   Union[str, Any]							,						A      :   Tuple     ):


      '''simple docstring'''




      a,	a	:     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
      a	:     Tuple							=  F'''{output_dir}/checkpoint-1'''
      a	:     List[Any]							=  F'''{chkpt_dir}/out.bin'''
      a	:     List[Any]							=  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












"""simple docstring"""


from __future__ import annotations




def      snake_case    (A_ :list[int | float]      ,     A_ :int      ,     A_ :int ):







  '''simple docstring'''

  if len(A_ ) == 0:
    raise ValueError('find_min() arg is an empty sequence' )
  if (
      left >= len(A_ )
      or left < -len(A_ )
      or right >= len(A_ )
      or right < -len(A_ )
  ):
    raise IndexError('list index out of range' )
  if left == right:
    return nums[left]
  a	:     Optional[Any]							=  (left + right) >> 1  # the middle
  a	:     List[str]							=  find_min(A_      ,     A_      ,     A_ )  # find min in range[left, mid]
  a	:     Union[str, Any]							=  find_min(A_      ,     mid + 1      ,     A_ )  # find min in range[mid + 1, right]

  return left_min if left_min <= right_min else right_min


if __name__ == "__main__":
   import doctest

   doctest.testmod(verbose=True)




