



"""simple docstring"""






import argparse
import logging
import os
import sys
import tempfile
from pathlib import Path

import lightning_base
import pytest
import pytorch_lightning as pl
import torch
from convert_pl_checkpoint_to_hf import convert_pl_to_hf
from distillation import distill_main
from finetune import SummarizationModule, main
from huggingface_hub import list_models
from parameterized import parameterized
from run_eval import generate_summaries_or_translations
from torch import nn

from transformers import AutoConfig, AutoModelForSeqaSeqLM
from transformers.testing_utils import CaptureStderr, CaptureStdout, TestCasePlus, require_torch_gpu, slow
from utils import label_smoothed_nll_loss, lmap, load_json


logging.basicConfig(level=logging.DEBUG)

lowerCamelCase				:     str								=  logging.getLogger()
lowerCamelCase				:     Union[str, Any]								=  torch.cuda.is_available()
lowerCamelCase				:     Dict								=  {
    """max_tokens_per_batch""": None,
    """supervise_forward""": True,
    """normalize_hidden""": True,
    """label_smoothing""": 0.2,
    """eval_max_gen_length""": None,
    """eval_beams""": 1,
    """val_metric""": """loss""",
    """save_top_k""": 1,
    """adafactor""": True,
    """early_stopping_patience""": 2,
    """logger_name""": """default""",
    """length_penalty""": 0.5,
    """cache_dir""": """""",
    """task""": """summarization""",
    """num_workers""": 2,
    """alpha_hid""": 0,
    """freeze_embeds""": True,
    """enc_only""": False,
    """tgt_suffix""": """""",
    """resume_from_checkpoint""": None,
    """sortish_sampler""": True,
    """student_decoder_layers""": 1,
    """val_check_interval""": 1.0,
    """output_dir""": """""",
    """fp16""": False,  # TODO(SS): set this to CUDA_AVAILABLE if ci installs apex or start using native amp
    """no_teacher""": False,
    """fp16_opt_level""": """O1""",
    """gpus""": 1 if CUDA_AVAILABLE else 0,
    """n_tpu_cores""": 0,
    """max_grad_norm""": 1.0,
    """do_train""": True,
    """do_predict""": True,
    """accumulate_grad_batches""": 1,
    """server_ip""": """""",
    """server_port""": """""",
    """seed""": 4_2,
    """model_name_or_path""": """sshleifer/bart-tiny-random""",
    """config_name""": """""",
    """tokenizer_name""": """facebook/bart-large""",
    """do_lower_case""": False,
    """learning_rate""": 0.3,
    """lr_scheduler""": """linear""",
    """weight_decay""": 0.0,
    """adam_epsilon""": 1E-08,
    """warmup_steps""": 0,
    """max_epochs""": 1,
    """train_batch_size""": 2,
    """eval_batch_size""": 2,
    """max_source_length""": 1_2,
    """max_target_length""": 1_2,
    """val_max_target_length""": 1_2,
    """test_max_target_length""": 1_2,
    """fast_dev_run""": False,
    """no_cache""": False,
    """n_train""": -1,
    """n_val""": -1,
    """n_test""": -1,
    """student_encoder_layers""": 1,
    """freeze_encoder""": False,
    """auto_scale_batch_size""": False,
    """overwrite_output_dir""": False,
    """student""": None,
}


def 							A__ (     UpperCamelCase__       ,							UpperCamelCase__    ):


  '''simple docstring'''


  _SCREAMING_SNAKE_CASE        =			'''\n'''.join(UpperCamelCase__    )
  Path(UpperCamelCase__    ).open('''w'''    ).writelines(UpperCamelCase__    )


lowerCamelCase				:     List[Any]								=  [""" Sam ate lunch today.""", """Sams lunch ingredients."""]
lowerCamelCase				:     List[Any]								=  ["""A very interesting story about what I ate for lunch.""", """Avocado, celery, turkey, coffee"""]
lowerCamelCase				:     Dict								=  """patrickvonplaten/t5-tiny-random"""
lowerCamelCase				:     List[str]								=  """sshleifer/t5-tinier-random"""
lowerCamelCase				:     Dict								=  """sshleifer/bart-tiny-random"""
lowerCamelCase				:     str								=  """sshleifer/tiny-mbart"""
lowerCamelCase				:     Union[str, Any]								=  """sshleifer/tiny-marian-en-de"""
lowerCamelCase				:     int								=  """stas/tiny-wmt19-en-de"""


lowerCamelCase				:     Union[str, Any]								=  logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
logging.disable(logging.CRITICAL)  # remove noisy download output from tracebacks


def 							A__ (     UpperCamelCase__    ):


  '''simple docstring'''


  for split in ["train", "val", "test"]:
    _dump_articles(os.path.join(UpperCamelCase__       ,							F'''{split}.source'''    )       ,							UpperCamelCase__    )
    _dump_articles(os.path.join(UpperCamelCase__       ,							F'''{split}.target'''    )       ,							UpperCamelCase__    )
  return tmp_dir




class    __snake_case(					__A       ):




    @classmethod
    def   A   (   cls			):





      '''simple docstring'''



      logging.disable(logging.CRITICAL			)  # remove noisy download output from tracebacks
      return cls




    @slow
    @require_torch_gpu
    def   A   (   self			):





      '''simple docstring'''



      _SCREAMING_SNAKE_CASE        =			list_models()
      _SCREAMING_SNAKE_CASE        =			'''sshleifer'''
      _SCREAMING_SNAKE_CASE        =			[x.modelId for x in model_list if x.modelId.startswith(A_			)]
      _SCREAMING_SNAKE_CASE        =			['''sshleifer/blenderbot-3B''', '''sshleifer/blenderbot-90M''']
      _SCREAMING_SNAKE_CASE        =			[]
      for m in model_ids:
        if m in allowed_to_be_broken:
          continue
        try:
          AutoConfig.from_pretrained(A_			)
        except Exception:
          failures.append(A_			)
      assert not failures, F'''The following models could not be loaded through AutoConfig: {failures}'''




    def   A   (   self			):





      '''simple docstring'''



      _SCREAMING_SNAKE_CASE        =			{'''student_encoder_layers''': 2, '''student_decoder_layers''': 1, '''no_teacher''': True}
      self._test_distiller_cli(A_			)




    def   A   (   self			):





      '''simple docstring'''



      _SCREAMING_SNAKE_CASE        =			{
          '''student_encoder_layers''': 2,
          '''student_decoder_layers''': 1,
          '''max_epochs''': 4,
          '''val_check_interval''': 0.25,
          '''alpha_hid''': 2.0,
          '''model_name_or_path''': '''IGNORE_THIS_IT_DOESNT_GET_USED''',
      }
      _SCREAMING_SNAKE_CASE        =			self._test_distiller_cli(A_    ,							check_contents=A_			)

      _SCREAMING_SNAKE_CASE        =			list(Path(model.output_dir			).glob('''*.ckpt'''			)			)
      self.assertEqual(1    ,							len(A_			)			)
      _SCREAMING_SNAKE_CASE        =			list(Path(model.output_dir			).glob('''**/*.bin'''			)			)
      self.assertEqual(len(A_			)    ,							2			)
      _SCREAMING_SNAKE_CASE        =			lmap(str.strip    ,							Path(model.hparams.data_dir			).joinpath('''test.source'''			).open().readlines()			)
      _SCREAMING_SNAKE_CASE        =			tempfile.mktemp()  # XXX: not being cleaned up
      generate_summaries_or_translations(A_    ,							A_    ,							str(model.output_dir / '''best_tfmr'''			)			)
      self.assertTrue(Path(A_			).exists()			)

      _SCREAMING_SNAKE_CASE        =			self.get_auto_remove_tmp_dir()
      convert_pl_to_hf(ckpts[0]    ,							transformer_ckpts[0].parent    ,							A_			)
      assert os.path.exists(os.path.join(A_    ,							'''pytorch_model.bin'''			)			)




    def   A   (   self			):





      '''simple docstring'''



      _SCREAMING_SNAKE_CASE        =			AutoModelForSeqaSeqLM.from_pretrained(A_			)
      _SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE        =			model.dummy_inputs['''input_ids'''], model.dummy_inputs['''attention_mask''']
      _SCREAMING_SNAKE_CASE        =			torch.tensor([[0, 4, 8, 2], [0, 8, 2, 1]]    ,							dtype=torch.long    ,							device=model.device			)
      _SCREAMING_SNAKE_CASE        =			target_ids[:, :-1].contiguous()  # Why this line?
      _SCREAMING_SNAKE_CASE        =			target_ids[:, 1:].clone()  # why clone?
      _SCREAMING_SNAKE_CASE        =			model(
          A_    ,							attention_mask=A_    ,							decoder_input_ids=A_    ,							labels=A_    ,							use_cache=A_			).loss

      _SCREAMING_SNAKE_CASE        =			model(A_    ,							attention_mask=A_    ,							decoder_input_ids=A_    ,							use_cache=A_			).logits

      _SCREAMING_SNAKE_CASE        =			nn.functional.log_softmax(A_    ,							dim=-1			)
      _SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE        =			label_smoothed_nll_loss(
          A_    ,							A_    ,							0.1    ,							ignore_index=model.config.pad_token_id			)
      with self.assertRaises(A_			):
        # TODO: understand why this breaks
        self.assertEqual(A_    ,							A_			)




    def   A   (   self			):





      '''simple docstring'''



      _SCREAMING_SNAKE_CASE        =			{
          '''student_encoder_layers''': 2,
          '''student_decoder_layers''': 1,
          '''num_train_epochs''': 4,
          '''val_check_interval''': 0.25,
          '''alpha_hid''': 2.0,
          '''task''': '''translation''',
          '''model_name_or_path''': '''IGNORE_THIS_IT_DOESNT_GET_USED''',
          '''tokenizer_name''': MBART_TINY,
          '''teacher''': MBART_TINY,
          '''src_lang''': '''en_XX''',
          '''tgt_lang''': '''ro_RO''',
      }
      _SCREAMING_SNAKE_CASE        =			self._test_distiller_cli(A_    ,							check_contents=A_			)
      assert model.model.config.model_type == "mbart"

      _SCREAMING_SNAKE_CASE        =			list(Path(model.output_dir			).glob('''*.ckpt'''			)			)
      self.assertEqual(1    ,							len(A_			)			)
      _SCREAMING_SNAKE_CASE        =			list(Path(model.output_dir			).glob('''**/*.bin'''			)			)
      _SCREAMING_SNAKE_CASE        =			list(Path(model.output_dir			).glob('''best_tfmr/*'''			)			)
      assert len(A_			) > 2
      self.assertEqual(len(A_			)    ,							2			)




    def   A   (   self			):





      '''simple docstring'''



      _SCREAMING_SNAKE_CASE        =			{
          '''student_encoder_layers''': 1,
          '''student_decoder_layers''': 1,
          '''alpha_hid''': 2.0,
          '''teacher''': T5_TINY,
          '''model_name_or_path''': T5_TINY,
          '''tokenizer_name''': T5_TINY,
      }
      self._test_distiller_cli(A_			)




    def   A   (   self			):





      '''simple docstring'''



      _SCREAMING_SNAKE_CASE        =			{
          '''teacher''': T5_TINY,
          '''student''': T5_TINIER,
          '''model_name_or_path''': T5_TINIER,
          '''tokenizer_name''': T5_TINIER,
      }
      self._test_distiller_cli(A_			)




    def   A   (   self    ,							A_    ,							A_=True			):





      '''simple docstring'''



      _SCREAMING_SNAKE_CASE        =			{
          '''label_smoothing''': 0.0,
          '''early_stopping_patience''': -1,
          '''train_batch_size''': 1,
          '''eval_batch_size''': 2,
          '''max_epochs''': 2,
          '''alpha_mlm''': 0.2,
          '''alpha_ce''': 0.8,
          '''do_predict''': True,
          '''model_name_or_path''': '''sshleifer/tinier_bart''',
          '''teacher''': CHEAP_ARGS['''model_name_or_path'''],
          '''val_check_interval''': 0.5,
      }
      default_updates.update(A_			)
      _SCREAMING_SNAKE_CASE        =			CHEAP_ARGS.copy()
      _SCREAMING_SNAKE_CASE        =			make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()			)
      _SCREAMING_SNAKE_CASE        =			self.get_auto_remove_tmp_dir()

      args_d.update(data_dir=A_    ,							output_dir=A_    ,							**A_			)
      _SCREAMING_SNAKE_CASE        =			distill_main(argparse.Namespace(**A_			)			)
      if not check_contents:
        return model
      _SCREAMING_SNAKE_CASE        =			os.listdir(A_			)
      _SCREAMING_SNAKE_CASE        =			{os.path.basename(A_			) for p in contents}
      _SCREAMING_SNAKE_CASE        =			[p for p in contents if p.endswith('''ckpt'''			)]
      assert len(A_			) > 0

      self.assertIn('''test_generations.txt'''    ,							A_			)
      self.assertIn('''test_results.txt'''    ,							A_			)

      _SCREAMING_SNAKE_CASE        =			load_json(model.metrics_save_path			)
      _SCREAMING_SNAKE_CASE        =			metrics['''val'''][-1]
      self.assertGreaterEqual(last_step_stats['''val_avg_gen_time''']    ,							0.01			)
      self.assertGreaterEqual(1.0    ,							last_step_stats['''val_avg_gen_time''']			)
      self.assertIsInstance(last_step_stats[F'''val_avg_{model.val_metric}''']    ,							A_			)
      _SCREAMING_SNAKE_CASE        =			int(args_d['''max_epochs'''] * (1 / args_d['''val_check_interval''']) + 1			)
      self.assertEqual(len(metrics['''val''']			)    ,							A_			)
      self.assertEqual(len(metrics['''test''']			)    ,							1			)
      return model




class    __snake_case(					__A       ):




    @parameterized.expand(
        [T5_TINY, BART_TINY, MBART_TINY, MARIAN_TINY, FSMT_TINY]    ,							)
    def   A   (   self    ,							A_			):





      '''simple docstring'''



      _SCREAMING_SNAKE_CASE        =			CHEAP_ARGS.copy()
      _SCREAMING_SNAKE_CASE        =			'''translation''' if model in [MBART_TINY, MARIAN_TINY, FSMT_TINY] else '''summarization'''
      _SCREAMING_SNAKE_CASE        =			0.1 if task == '''translation''' else 0

      _SCREAMING_SNAKE_CASE        =			make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()			)
      _SCREAMING_SNAKE_CASE        =			self.get_auto_remove_tmp_dir()
      args_d.update(
          data_dir=A_    ,							model_name_or_path=A_    ,							tokenizer_name=A_    ,							train_batch_size=2    ,							eval_batch_size=2    ,							output_dir=A_    ,							do_predict=A_    ,							task=A_    ,							src_lang='''en_XX'''    ,							tgt_lang='''ro_RO'''    ,							freeze_encoder=A_    ,							freeze_embeds=A_    ,							)
      assert "n_train" in args_d
      _SCREAMING_SNAKE_CASE        =			argparse.Namespace(**A_			)
      _SCREAMING_SNAKE_CASE        =			main(A_			)

      _SCREAMING_SNAKE_CASE        =			module.model.get_input_embeddings()
      assert not input_embeds.weight.requires_grad
      if model == T5_TINY:
        _SCREAMING_SNAKE_CASE        =			module.model.lm_head
        assert not lm_head.weight.requires_grad
        assert (lm_head.weight == input_embeds.weight).all().item()
      elif model == FSMT_TINY:
        _SCREAMING_SNAKE_CASE        =			module.model.model
        _SCREAMING_SNAKE_CASE        =			fsmt.decoder.embed_positions
        assert not embed_pos.weight.requires_grad
        assert not fsmt.decoder.embed_tokens.weight.requires_grad
        # check that embeds are not the same
        assert fsmt.decoder.embed_tokens != fsmt.encoder.embed_tokens
      else:
        _SCREAMING_SNAKE_CASE        =			module.model.model
        _SCREAMING_SNAKE_CASE        =			bart.decoder.embed_positions
        assert not embed_pos.weight.requires_grad
        assert not bart.shared.weight.requires_grad
        # check that embeds are the same
        assert bart.decoder.embed_tokens == bart.encoder.embed_tokens
        assert bart.decoder.embed_tokens == bart.shared

      _SCREAMING_SNAKE_CASE        =			load_json(module.output_dir / '''text_batch.json'''			)
      assert isinstance(A_    ,							A_			)
      assert len(A_			) >= 4




    def   A   (   self			):





      '''simple docstring'''



      _SCREAMING_SNAKE_CASE        =			CHEAP_ARGS.copy()

      _SCREAMING_SNAKE_CASE        =			'''summarization'''
      _SCREAMING_SNAKE_CASE        =			make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()			)

      args_d.update(
          data_dir=A_    ,							tokenizer_name=A_    ,							train_batch_size=2    ,							eval_batch_size=2    ,							do_predict=A_    ,							task=A_    ,							src_lang='''en_XX'''    ,							tgt_lang='''ro_RO'''    ,							freeze_encoder=A_    ,							freeze_embeds=A_    ,							)

      # test models whose config includes the extra_model_args
      _SCREAMING_SNAKE_CASE        =			BART_TINY
      _SCREAMING_SNAKE_CASE        =			self.get_auto_remove_tmp_dir()
      _SCREAMING_SNAKE_CASE        =			args_d.copy()
      args_da.update(
          model_name_or_path=A_    ,							output_dir=A_    ,							)
      _SCREAMING_SNAKE_CASE        =			('''encoder_layerdrop''', '''decoder_layerdrop''', '''dropout''', '''attention_dropout''')
      for p in extra_model_params:
        _SCREAMING_SNAKE_CASE        =			0.5
      _SCREAMING_SNAKE_CASE        =			argparse.Namespace(**A_			)
      _SCREAMING_SNAKE_CASE        =			main(A_			)
      for p in extra_model_params:
        assert getattr(model.config    ,							A_			) == 0.5, F'''failed to override the model config for param {p}'''

      # test models whose config doesn't include the extra_model_args
      _SCREAMING_SNAKE_CASE        =			T5_TINY
      _SCREAMING_SNAKE_CASE        =			self.get_auto_remove_tmp_dir()
      _SCREAMING_SNAKE_CASE        =			args_d.copy()
      args_da.update(
          model_name_or_path=A_    ,							output_dir=A_    ,							)
      _SCREAMING_SNAKE_CASE        =			'''encoder_layerdrop'''
      _SCREAMING_SNAKE_CASE        =			0.5
      _SCREAMING_SNAKE_CASE        =			argparse.Namespace(**A_			)
      with pytest.raises(A_			) as excinfo:
        _SCREAMING_SNAKE_CASE        =			main(A_			)
      assert str(excinfo.value			) == F'''model config doesn\'t have a `{unsupported_param}` attribute'''




    def   A   (   self			):





      '''simple docstring'''



      _SCREAMING_SNAKE_CASE        =			CHEAP_ARGS.copy()

      _SCREAMING_SNAKE_CASE        =			'''summarization'''
      _SCREAMING_SNAKE_CASE        =			make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()			)

      _SCREAMING_SNAKE_CASE        =			BART_TINY
      _SCREAMING_SNAKE_CASE        =			self.get_auto_remove_tmp_dir()

      args_d.update(
          data_dir=A_    ,							model_name_or_path=A_    ,							output_dir=A_    ,							tokenizer_name=A_    ,							train_batch_size=2    ,							eval_batch_size=2    ,							do_predict=A_    ,							task=A_    ,							src_lang='''en_XX'''    ,							tgt_lang='''ro_RO'''    ,							freeze_encoder=A_    ,							freeze_embeds=A_    ,							)

      # emulate finetune.py
      _SCREAMING_SNAKE_CASE        =			argparse.ArgumentParser()
      _SCREAMING_SNAKE_CASE        =			pl.Trainer.add_argparse_args(A_			)
      _SCREAMING_SNAKE_CASE        =			SummarizationModule.add_model_specific_args(A_    ,							os.getcwd()			)
      _SCREAMING_SNAKE_CASE        =			{'''--help''': True}

      # --help test
      with pytest.raises(A_			) as excinfo:
        with CaptureStdout() as cs:
          _SCREAMING_SNAKE_CASE        =			parser.parse_args(A_			)
        assert False, "--help is expected to sys.exit"
      assert excinfo.type == SystemExit
      _SCREAMING_SNAKE_CASE        =			lightning_base.arg_to_scheduler_metavar
      assert expected in cs.out, "--help is expected to list the supported schedulers"

      # --lr_scheduler=non_existing_scheduler test
      _SCREAMING_SNAKE_CASE        =			'''non_existing_scheduler'''
      _SCREAMING_SNAKE_CASE        =			{F'''--lr_scheduler={unsupported_param}'''}
      with pytest.raises(A_			) as excinfo:
        with CaptureStderr() as cs:
          _SCREAMING_SNAKE_CASE        =			parser.parse_args(A_			)
        assert False, "invalid argument is expected to sys.exit"
      assert excinfo.type == SystemExit
      _SCREAMING_SNAKE_CASE        =			F'''invalid choice: \'{unsupported_param}\''''
      assert expected in cs.err, F'''should have bailed on invalid choice of scheduler {unsupported_param}'''

      # --lr_scheduler=existing_scheduler test
      _SCREAMING_SNAKE_CASE        =			'''cosine'''
      _SCREAMING_SNAKE_CASE        =			args_d.copy()
      _SCREAMING_SNAKE_CASE        =			supported_param
      _SCREAMING_SNAKE_CASE        =			argparse.Namespace(**A_			)
      _SCREAMING_SNAKE_CASE        =			main(A_			)
      assert (
          getattr(model.hparams    ,							'''lr_scheduler'''			) == supported_param
      ), F'''lr_scheduler={supported_param} shouldn\'t fail'''





"""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(4_2)

lowerCamelCase				:     Union[str, Any]								=  get_tests_dir("""fixtures""")
lowerCamelCase				:     str								=  os.path.join(dirname(get_tests_dir()))
lowerCamelCase				:     Union[str, Any]								=  dirname(os.path.abspath(__file__))

# default torch.distributed port
lowerCamelCase				:     Optional[Any]								=  """10999"""

lowerCamelCase				:     Optional[Any]								=  """t5-small"""

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


# *** To Fix ***


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

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

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

# other issues with tiny models
lowerCamelCase				:     Tuple								=  """hf-internal-testing/tiny-random-ibert"""  # multiple issues with either mlm/qa/clas
lowerCamelCase				:     Dict								=  """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				:     List[str]								=  """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				:     Dict								=  """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[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
lowerCamelCase				:     Union[str, Any]								=  """hf-internal-testing/tiny-random-tapas"""


def 							A__ (     UpperCamelCase__=False    ):


  '''simple docstring'''


  _SCREAMING_SNAKE_CASE        =			min(2       ,							get_gpu_count()    ) if distributed else 1
  _SCREAMING_SNAKE_CASE        =			os.environ.get('''DS_TEST_PORT'''       ,							UpperCamelCase__    )
  return F'''deepspeed --num_nodes 1 --num_gpus {num_gpus} --master_port {master_port}'''.split()


def 							A__ (     ):


  '''simple docstring'''


  _SCREAMING_SNAKE_CASE        =			F'''{FIXTURE_DIRECTORY}/tests_samples'''
  _SCREAMING_SNAKE_CASE        =			F'''{data_dir_samples}/wmt_en_ro'''
  _SCREAMING_SNAKE_CASE        =			F'''{data_dir_samples}/xsum'''
  _SCREAMING_SNAKE_CASE        =			'''
        --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.
  _SCREAMING_SNAKE_CASE        =			{
      '''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''',
      ],
  }

  _SCREAMING_SNAKE_CASE        =			F'''{ROOT_DIRECTORY}/examples/pytorch'''

  _SCREAMING_SNAKE_CASE        =			{
      '''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
        ''',
  }

  _SCREAMING_SNAKE_CASE        =			get_launcher(distributed=UpperCamelCase__    )

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

lowerCamelCase				:     Any								=  """zero2"""
lowerCamelCase				:     List[Any]								=  """zero3"""

lowerCamelCase				:     int								=  [ZEROa, ZEROa]


def 							A__ (     UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__    ):


  '''simple docstring'''


  _SCREAMING_SNAKE_CASE        =			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				:     Optional[Any]								=  list(itertools.product(stages, task_cmds.keys()))







@slow
@require_deepspeed
@require_torch_gpu
class    __snake_case(					__A       ):




    def   A   (   self    ,							A_    ,							A_			):





      '''simple docstring'''



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

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

      _SCREAMING_SNAKE_CASE        =			self.get_auto_remove_tmp_dir()
      _SCREAMING_SNAKE_CASE        =			F'''--output_dir {output_dir}'''.split()

      cmd += args_ds + args_out

      return cmd, output_dir




    @parameterized.expand(A_    ,							name_func=A_			)
    def   A   (   self    ,							A_    ,							A_			):





      '''simple docstring'''



      _SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE        =			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
      _SCREAMING_SNAKE_CASE        =			F'''{output_dir}/checkpoint-1'''
      _SCREAMING_SNAKE_CASE        =			F'''{chkpt_dir}/out.bin'''
      _SCREAMING_SNAKE_CASE        =			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
