




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__    :							List[Any]       		=     os.path.join(dirname(get_tests_dir()))
UpperCAmelCase__    :							List[Any]       		=     dirname(os.path.abspath(__file__))

# default torch.distributed port
UpperCAmelCase__    :							Optional[int]       		=     '10999'

UpperCAmelCase__    :							Dict       		=     't5-small'

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


# *** To Fix ***


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

# missing tokenizer files
UpperCAmelCase__    :							Union[str, Any]       		=     'hf-internal-testing/tiny-random-convbert'
UpperCAmelCase__    :							Dict       		=     'hf-internal-testing/tiny-random-layoutlmv2'
UpperCAmelCase__    :							Dict       		=     'hf-internal-testing/tiny-random-hubert'

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

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


def 		_lowerCamelCase					(       UpperCamelCase=False				)      ->		Optional[int]:





  """simple docstring"""





  _lowercase:			Tuple      =     min(2       ,  get_gpu_count()				) if distributed else 1
  _lowercase:			int      =     os.environ.get('DS_TEST_PORT'       ,  _A				)
  return F'deepspeed --num_nodes 1 --num_gpus {num_gpus} --master_port {master_port}'.split()


def 		_lowerCamelCase					(       )      ->		Optional[int]:





  """simple docstring"""





  _lowercase:			int      =     F'{FIXTURE_DIRECTORY}/tests_samples'
  _lowercase:			Any      =     F'{data_dir_samples}/wmt_en_ro'
  _lowercase:			Optional[Any]      =     F'{data_dir_samples}/xsum'
  _lowercase:			Optional[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:			Optional[Any]      =     {
      '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:			str      =     F'{ROOT_DIRECTORY}/examples/pytorch'

  _lowercase:			List[str]      =     {
      'trans': F'\n        {scripts_dir}/translation/run_translation.py\n        --train_file {data_dir_wmt}/train.json\n        --source_lang en\n        --target_lang ro\n        ',
      'sum': F'\n        {scripts_dir}/summarization/run_summarization.py\n        --train_file {data_dir_xsum}/sample.json\n        --max_source_length 12\n        --max_target_length 12\n        --lang en\n        ',
      'clm': F'\n        {scripts_dir}/language-modeling/run_clm.py\n        --train_file {FIXTURE_DIRECTORY}/sample_text.txt\n        --block_size 8\n        ',
      'mlm': F'\n        {scripts_dir}/language-modeling/run_mlm.py\n        --train_file {FIXTURE_DIRECTORY}/sample_text.txt\n        ',
      'qa': F'\n        {scripts_dir}/question-answering/run_qa.py\n        --train_file {data_dir_samples}/SQUAD/sample.json\n        ',
      'clas': F'\n        {scripts_dir}/text-classification/run_glue.py\n        --train_file {data_dir_samples}/MRPC/train.csv\n        --max_seq_length 12\n        --task_name MRPC\n        ',
      'img_clas': F'\n        {scripts_dir}/image-classification/run_image_classification.py\n            --dataset_name hf-internal-testing/cats_vs_dogs_sample\n            --remove_unused_columns False\n            --max_steps 10\n            --image_processor_name {DS_TESTS_DIRECTORY}/vit_feature_extractor.json\n        ',
  }

  _lowercase:			Union[str, Any]      =     get_launcher(distributed=_A				)

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

UpperCAmelCase__    :							int       		=     'zero2'
UpperCAmelCase__    :							Union[str, Any]       		=     'zero3'

UpperCAmelCase__    :							int       		=     [ZEROa, ZEROa]


def 		_lowerCamelCase					(       UpperCamelCase       ,  UpperCamelCase       ,  UpperCamelCase				)      ->		str:





  """simple docstring"""





  _lowercase:			List[Any]      =     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__    :							str       		=     list(itertools.product(stages, task_cmds.keys()))





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






   def 			_a    (						self :							Any    ,       UpperCamelCase_ :							Tuple    ,       UpperCamelCase_ :							List[str]  )->      List[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()}'  )

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

     _lowercase:			List[Any]      =     self.get_auto_remove_tmp_dir()
     _lowercase:			Optional[int]      =     f'--output_dir {output_dir}'.split()

     cmd += args_ds + args_out

     return cmd, output_dir






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

     _lowercase  ,       _lowercase:			Tuple      =     self.get_task_cmd(lowercase__    ,       lowercase__  )

     # 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(lowercase__    ,       env=self.get_env()  )

     # 2. test that the fp32 weights get reconsolidated
     _lowercase:			List[str]      =     f'{output_dir}/checkpoint-1'
     _lowercase:			Union[str, Any]      =     f'{chkpt_dir}/out.bin'
     _lowercase:			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(lowercase__    ,       shell=lowercase__  )
     assert os.path.exists(lowercase__  ), 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






import io
import json
import subprocess


UpperCamelCase__       				=	[
    ['''en''', '''ru'''],
    ['''ru''', '''en'''],
    ['''en''', '''de'''],
    ['''de''', '''en'''],
]

UpperCamelCase__       				=	8






def 				UpperCAmelCase__  (							_A      ,      _A			):






  """simple docstring"""


  a_		    =  {}
  for src, tgt in pairs:
    a_		    =  f"{src}-{tgt}"
    a_		    =  f"sacrebleu -t wmt19 -l {pair} --echo src".split()
    a_		    =  subprocess.run(_A      ,      stdout=subprocess.PIPE			).stdout.decode('''utf-8'''			).splitlines()
    a_		    =  f"sacrebleu -t wmt19 -l {pair} --echo ref".split()
    a_		    =  subprocess.run(_A      ,      stdout=subprocess.PIPE			).stdout.decode('''utf-8'''			).splitlines()
    a_		    =  {'''src''': src_lines[:n_objs], '''tgt''': tgt_lines[:n_objs]}
  return text


UpperCamelCase__       				=	get_all_data(pairs, n_objs)
UpperCamelCase__       				=	'''./fsmt_val_data.json'''
with io.open(filename, '''w''', encoding='''utf-8''') as f:
  UpperCamelCase__       				=	json.dump(text, f, indent=2, ensure_ascii=False)
