





import argparse
import gc
import os
import sys
from pathlib import Path
from typing import List  # noqa: F401

import pytorch_lightning as pl
import torch
from finetune import SummarizationModule, TranslationModule
from finetune import main as ft_main
from make_student import create_student_by_copying_alternating_layers, get_layers_to_supervise
from torch import nn

from transformers import AutoModelForSeqaSeqLM, MBartTokenizer, TaForConditionalGeneration
from transformers.models.bart.modeling_bart import shift_tokens_right
from utils import calculate_bleu, check_output_dir, freeze_params, label_smoothed_nll_loss, use_task_specific_params


# need the parent dir module
sys.path.insert(2, str(Path(__file__).resolve().parents[1]))
from lightning_base import generic_train  # noqa





class 					lowerCAmelCase							(   lowercase_					):
  __lowerCamelCase		=			['loss', 'ce_loss', 'mlm_loss', 'hid_loss_enc', 'hid_loss_dec']







  def __init__(			self  :List[str]    ,							_lowercase  :Union[str, Any]					):






     '''simple docstring'''





     assert Path(hparams.data_dir					).exists()
     lowercase__    =  Path(hparams.output_dir					)
     self.output_dir.mkdir(exist_ok=_lowercase					)

     lowercase__    =  self.output_dir.joinpath("student"					)

     lowercase__    =  str(_lowercase					)  # Tell lightning we are training the student
     lowercase__    =  AutoModelForSeqaSeqLM.from_pretrained(hparams.teacher					).eval()
     use_task_specific_params(_lowercase    ,							hparams.task					)  # We copy good generation parameters to student by default
     if hparams.student is not None:
        lowercase__    =  AutoModelForSeqaSeqLM.from_pretrained(hparams.student					)
        use_task_specific_params(_lowercase    ,							hparams.task					)
        lowercase__   ,							lowercase__    =  None, None
     else:
        lowercase__   ,							lowercase__   ,							lowercase__    =  create_student_by_copying_alternating_layers(
            _lowercase    ,							e=hparams.student_encoder_layers    ,							d=hparams.student_decoder_layers    ,							save_path=_lowercase					)

     if hparams.length_penalty != -1:
        lowercase__    =  hparams.length_penalty
     lowercase__    =  hparams.teacher  # Use teacher's tokenizer
     super().__init__(_lowercase    ,							model=_lowercase    ,							config=student.config					)
     assert student.config.model_type == teacher.config.model_type, (
         f'''teacher, student model types should be the same, got {student.config.model_type} !='''
         f''' {teacher.config.model_type}'''
     )

     if student.config.model_type == "t5":
        lowercase__    =  len(student.get_encoder().block					)
        lowercase__    =  len(student.get_decoder().block					)
        lowercase__    =  len(teacher.get_encoder().block					)
        lowercase__    =  len(teacher.get_decoder().block					)
     else:
        lowercase__    =  student.config.encoder_layers
        lowercase__    =  student.config.decoder_layers
        lowercase__    =  teacher.config.encoder_layers
        lowercase__    =  teacher.config.decoder_layers

     lowercase__    =  not (hparams.student is None or hparams.teacher == hparams.student)
     lowercase__    =  (not self.different_base_models) and hparams.alpha_hid > 0
     lowercase__    =  self.different_base_models or (student_encoder_layers != teacher_encoder_layers)
     # self.different_encoder determines whether we need to run the teacher encoder
     lowercase__    =  teacher
     freeze_params(self.teacher					)

     if not self.different_encoder:  # To save RAM, delete teacher encoder and freeze student encoder.
        try:
           del self.teacher.model.encoder
        except AttributeError:  # T5
           del self.teacher.encoder

     if e_layer_ids is None:
        lowercase__    =  list(range(_lowercase					)					)
     if d_layer_ids is None:
        lowercase__    =  list(range(_lowercase					)					)

     lowercase__   ,							lowercase__    =  e_layer_ids, d_layer_ids  # type: List[int], List[int]

     if self.do_calc_hidden_loss:  # Intermediate supervision: Decide which layers to supervise
        if hparams.supervise_forward:
           lowercase__    =  get_layers_to_supervise(
               n_student=len(self.e_layer_ids					)    ,							n_teacher=_lowercase					)
           lowercase__    =  get_layers_to_supervise(
               n_student=len(self.d_layer_ids					)    ,							n_teacher=_lowercase					)
        else:  # student layer should emulate hidden states of the teacher layer it was copied from
           lowercase__    =  self.e_layer_ids
           lowercase__    =  self.d_layer_ids
     else:
        lowercase__    =  None
        lowercase__    =  None

     lowercase__    =  nn.KLDivLoss(reduction="batchmean"					)
     lowercase__    =  2.0
     lowercase__    =  hparams.alpha_mlm
     lowercase__    =  hparams.alpha_ce
     lowercase__    =  hparams.alpha_hid
     gc.collect()
     torch.cuda.empty_cache()







  def   UpperCAmelCase     (			self  :Union[str, Any]    ,							_lowercase  :Optional[int]    ,							_lowercase  :Optional[int]    ,							_lowercase  :Any					):






     '''simple docstring'''





     lowercase__    =  mask[:, :, None].expand_as(_lowercase					)
     lowercase__    =  s_logits.size(-1					)
     lowercase__    =  torch.masked_select(_lowercase    ,							_lowercase					)  # (bs * seq_length * voc_size) modulo the 1s in mask
     lowercase__    =  torch.masked_select(_lowercase    ,							_lowercase					)  # (bs * seq_length * voc_size) modulo the 1s in mask
     lowercase__    =  s_logits_slct.view(-1    ,							_lowercase					)  # (bs * seq_length, voc_size) modulo the 1s in mask
     lowercase__    =  t_logits_slct.view(-1    ,							_lowercase					)  # (bs * seq_length, voc_size) modulo the 1s in mask
     assert t_logits_slct.size() == s_logits_slct.size()
     lowercase__    =  (
         self.ce_loss_fct(
             nn.functional.log_softmax(s_logits_slct / self.temperature    ,							dim=-1					)    ,							nn.functional.softmax(t_logits_slct / self.temperature    ,							dim=-1					)    ,							)
         * (self.temperature) ** 2
     )
     return loss_ce







  @staticmethod
  def   UpperCAmelCase     (			_lowercase  :Tuple    ,							_lowercase  :List[Any]					):






     '''simple docstring'''





     SummarizationModule.add_model_specific_args(_lowercase    ,							_lowercase					)
     add_distill_args(_lowercase					)
     return parser







  def   UpperCAmelCase     (			self  :Optional[Any]    ,							_lowercase  :dict					):






     '''simple docstring'''





     lowercase__    =  self.tokenizer.pad_token_id
     lowercase__   ,							lowercase__   ,							lowercase__    =  batch["input_ids"], batch["attention_mask"], batch["labels"]
     if isinstance(self.model    ,							_lowercase					):
        lowercase__    =  self.model._shift_right(_lowercase					)
     else:
        lowercase__    =  shift_tokens_right(_lowercase    ,							_lowercase					)

     # noinspection PyCallingNonCallable
     lowercase__    =  self(
         _lowercase    ,							attention_mask=_lowercase    ,							decoder_input_ids=_lowercase    ,							output_hidden_states=self.do_calc_hidden_loss    ,							output_attentions=_lowercase    ,							use_cache=_lowercase    ,							)
     lowercase__    =  student_outputs["logits"]

     # Same cross entropy vs. label smoothing logic as finetune.py
     assert lm_logits.shape[-1] == self.model.config.vocab_size
     if self.hparams.label_smoothing == 0:
        # Same behavior as modeling_bart.py, besides ignoring pad_token_id
        lowercase__    =  nn.CrossEntropyLoss(ignore_index=_lowercase					)
        lowercase__    =  loss_fct(lm_logits.view(-1    ,							lm_logits.shape[-1]					)    ,							labels.view(-1					)					)
     else:
        lowercase__    =  nn.functional.log_softmax(_lowercase    ,							dim=-1					)
        lowercase__   ,							lowercase__    =  label_smoothed_nll_loss(
            _lowercase    ,							_lowercase    ,							self.hparams.label_smoothing    ,							ignore_index=_lowercase					)

     def zero_tensor():
        return torch.tensor(0.0					).type_as(_lowercase					)

     lowercase__    =  student_outputs[
         "encoder_last_hidden_state"
     ]  # use this unless self.different_base_models
     lowercase__   ,							lowercase__    =  zero_tensor(), zero_tensor()
     if self.different_encoder:  # compute encoder hidden state loss
        lowercase__    =  self.teacher.get_encoder()(
            _lowercase    ,							attention_mask=_lowercase    ,							output_hidden_states=self.do_calc_hidden_loss    ,							)
        if self.different_base_models:
           lowercase__    =  all_teacher_encoder_outputs["last_hidden_state"]
        elif self.do_calc_hidden_loss:
           lowercase__    =  self.calc_hidden_loss(
               _lowercase    ,							student_outputs["encoder_hidden_states"]    ,							all_teacher_encoder_outputs["hidden_states"]    ,							self.e_matches    ,							normalize_hidden=self.hparams.normalize_hidden    ,							)

     lowercase__    =  self.teacher(
         _lowercase    ,							attention_mask=_lowercase    ,							encoder_outputs=(teacher_enc_outputs,)    ,							decoder_input_ids=_lowercase    ,							output_hidden_states=self.do_calc_hidden_loss    ,							use_cache=_lowercase    ,							)
     lowercase__    =  decoder_input_ids.ne(_lowercase					)
     lowercase__    =  self.calc_ce_loss(_lowercase    ,							_lowercase    ,							teacher_outputs["logits"]					)
     if self.do_calc_hidden_loss:  # Intermediate supervision of decoder hidden states
        lowercase__    =  self.calc_hidden_loss(
            _lowercase    ,							student_outputs["decoder_hidden_states"]    ,							teacher_outputs["decoder_hidden_states"]    ,							self.d_matches    ,							normalize_hidden=self.hparams.normalize_hidden    ,							)

     lowercase__    =  (
         self.alpha_ce * loss_ce
         + self.alpha_mlm * student_lm_loss
         + self.hparams.alpha_hid * (hid_loss_enc + hid_loss_dec)
     )
     return blended_loss, loss_ce, student_lm_loss, hid_loss_enc, hid_loss_dec







  @staticmethod
  def   UpperCAmelCase     (			_lowercase  :int    ,							_lowercase  :Union[str, Any]    ,							_lowercase  :List[Any]    ,							_lowercase  :Any    ,							_lowercase  :Optional[Any]					):






     '''simple docstring'''





     lowercase__    =  "expected list or tuple for hidden_states, got tensor of shape: "
     assert not isinstance(_lowercase    ,							torch.Tensor					), f'''{msg}{hidden_states.shape}'''
     assert not isinstance(_lowercase    ,							torch.Tensor					), f'''{msg}{hidden_states_T.shape}'''
     lowercase__    =  attention_mask.to(hidden_states[0]					)
     lowercase__    =  mask.sum() * hidden_states[0].size(-1					)
     lowercase__    =  torch.stack([hidden_states[i] for i in range(len(_lowercase					)					)]					)
     lowercase__    =  torch.stack([hidden_states_T[j] for j in matches]					)
     assert student_states.shape == teacher_states.shape, f'''{student_states.shape} != {teacher_states.shape}'''
     if normalize_hidden:
        lowercase__    =  nn.functional.layer_norm(_lowercase    ,							student_states.shape[1:]					)
        lowercase__    =  nn.functional.layer_norm(_lowercase    ,							teacher_states.shape[1:]					)
     lowercase__    =  nn.functional.mse_loss(_lowercase    ,							_lowercase    ,							reduction="none"					)
     lowercase__    =  (mse * mask.unsqueeze(0					).unsqueeze(-1					)).sum() / valid_count
     return masked_mse


def _A   (    __magic_name__       ):
   # NOTE: if --student argument was specified and the teacher and student base models
   # are different, the models still have to have the same tokenizer, specified by
   # --tokenizer_name. So, for example, you can distill from t5_large to t5_small but not
   # from bart to t5. This s because if the tokenizers are different, the output space
   # for the two models is also different and their logits are not comparable.
   parser.add_argument("--teacher"  ,					type=__magic_name__       )
   parser.add_argument("--alpha_ce"  ,					default=0.8  ,					type=__magic_name__       )
   parser.add_argument("--alpha_mlm"  ,					default=0.2  ,					type=__magic_name__       )
   parser.add_argument("--alpha_hid"  ,					default=0.0  ,					type=__magic_name__  ,					required=__magic_name__       )
   parser.add_argument("--student"  ,					type=__magic_name__  ,					required=__magic_name__       )
   parser.add_argument("--student_decoder_layers"  ,					default=12  ,					type=__magic_name__  ,					required=__magic_name__       )
   parser.add_argument("--student_encoder_layers"  ,					default=12  ,					type=__magic_name__  ,					required=__magic_name__       )
   parser.add_argument("--no_teacher"  ,					action="store_true"  ,					default=__magic_name__       )
   parser.add_argument("--length_penalty"  ,					type=__magic_name__  ,					default=-1       )
   parser.add_argument("--supervise_forward"  ,					action="store_true"  ,					default=__magic_name__       )
   parser.add_argument("--normalize_hidden"  ,					action="store_true"  ,					default=__magic_name__       )





class 					lowerCAmelCase							(   lowercase_					):
  __lowerCamelCase		=			'translation'
  __lowerCamelCase		=			['bleu']
  __lowerCamelCase		=			'bleu'







  def __init__(			self  :int    ,							_lowercase  :int    ,							**_lowercase  :Union[str, Any]					):






     '''simple docstring'''





     super().__init__(_lowercase    ,							**_lowercase					)
     assert hparams.src_lang is not None
     assert hparams.tgt_lang is not None
     lowercase__    =  hparams.src_lang
     lowercase__    =  hparams.tgt_lang
     if self.model.config.decoder_start_token_id is None and isinstance(self.tokenizer    ,							_lowercase					):
        lowercase__    =  self.tokenizer.lang_code_to_id[hparams.tgt_lang]







  def   UpperCAmelCase     (			self  :str    ,							_lowercase  :List[str]    ,							_lowercase  :Optional[int]					):






     '''simple docstring'''





     return calculate_bleu(_lowercase    ,							_lowercase					)







  @staticmethod
  def   UpperCAmelCase     (			_lowercase  :str    ,							_lowercase  :List[Any]					):






     '''simple docstring'''





     TranslationModule.add_model_specific_args(_lowercase    ,							_lowercase					)
     add_distill_args(_lowercase					)
     return parser


def _A   (    __magic_name__       ):
   if args.no_teacher:
      lowercase__    =  TranslationModule if "translation" in args.task else SummarizationModule
   else:  # DISTILL WITH TEACHER
      lowercase__    =  TranslationDistiller if "translation" in args.task else SummarizationDistiller
   lowercase__    =  module_cls.__name__
   print(f'''using module {args.setup_cls}'''       )
   lowercase__    =  module_cls(__magic_name__       )
   return model


def _A   (    __magic_name__       ):
   Path(args.output_dir       ).mkdir(exist_ok=__magic_name__       )
   check_output_dir(__magic_name__  ,					expected_items=3       )

   lowercase__    =  create_module(__magic_name__       )
   return ft_main(__magic_name__  ,					model=__magic_name__       )


if __name__ == "__main__":
  _snake_case				= argparse.ArgumentParser()
  _snake_case				= pl.Trainer.add_argparse_args(parser)
  _snake_case				= SummarizationDistiller.add_model_specific_args(parser, os.getcwd())
  _snake_case				= parser.parse_args()

  distill_main(args)













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)

_snake_case				= get_tests_dir("""fixtures""")
_snake_case				= os.path.join(dirname(get_tests_dir()))
_snake_case				= dirname(os.path.abspath(__file__))

# default torch.distributed port
_snake_case				= """10999"""

_snake_case				= """t5-small"""

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


# *** To Fix ***


# *** tiny model issues ***
# missing model files:
_snake_case				= """hf-internal-testing/tiny-random-mt5"""
_snake_case				= """hf-internal-testing/tiny-random-camembert"""
_snake_case				= """hf-internal-testing/tiny-random-openai-gpt"""

# missing tokenizer files
_snake_case				= """hf-internal-testing/tiny-random-convbert"""
_snake_case				= """hf-internal-testing/tiny-random-layoutlmv2"""
_snake_case				= """hf-internal-testing/tiny-random-hubert"""

# issues with tokenizer
_snake_case				= """hf-internal-testing/tiny-random-ctrl"""
_snake_case				= """hf-internal-testing/tiny-random-transfo-xl"""  # same as ctrl

# other issues with tiny models
_snake_case				= """hf-internal-testing/tiny-random-ibert"""  # multiple issues with either mlm/qa/clas
_snake_case				= """hf-internal-testing/tiny-random-reformer"""  # multiple issues with either mlm/qa/clas

# *** Lacking official examples to test with ***
# or not working with examples
_snake_case				= """hf-internal-testing/tiny-random-dpr"""
# - "dpr"  examples/research_projects/rag-end2end-retriever/
_snake_case				= """hf-internal-testing/tiny-random-rag"""
# - "rag" research_projects
_snake_case				= """"""
# - "luke" Entities classes - no plan to make such example
_snake_case				= """hf-internal-testing/tiny-random-lxmert"""
# - "lxmert" doesn't work with run_qa.py
_snake_case				= """hf-internal-testing/tiny-random-clip"""
# - "clip" nothing under pytorch examples - XXX: Suraj is working on adding some - check by end of Sep
_snake_case				= """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
_snake_case				= """hf-internal-testing/tiny-random-tapas"""


def _A   (    __magic_name__=False       ):
   # 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)
   lowercase__    =  min(2  ,					get_gpu_count()       ) if distributed else 1
   lowercase__    =  os.environ.get("DS_TEST_PORT"  ,					__magic_name__       )
   return f'''deepspeed --num_nodes 1 --num_gpus {num_gpus} --master_port {master_port}'''.split()


def _A   (    ):
   lowercase__    =  f'''{FIXTURE_DIRECTORY}/tests_samples'''
   lowercase__    =  f'''{data_dir_samples}/wmt_en_ro'''
   lowercase__    =  f'''{data_dir_samples}/xsum'''
   lowercase__    =  "\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__    =  {
       "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__    =  f'''{ROOT_DIRECTORY}/examples/pytorch'''

   lowercase__    =  {
       "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
        ''',
   }

   lowercase__    =  get_launcher(distributed=__magic_name__       )

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


_snake_case				= make_task_cmds()

_snake_case				= """zero2"""
_snake_case				= """zero3"""

_snake_case				= [ZEROa, ZEROa]


def _A   (    __magic_name__  ,					__magic_name__  ,					__magic_name__       ):
   # 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
   lowercase__    =  parameterized.to_safe_name("_".join(str(__magic_name__       ) for x in param.args       )       )
   return f'''{func.__name__}_{param_based_name}'''


# Cartesian-product of zero stages with models to test
_snake_case				= list(itertools.product(stages, task_cmds.keys()))







@slow
@require_deepspeed
@require_torch_gpu
class 					lowerCAmelCase							(   lowercase_					):







  def   UpperCAmelCase     (			self  :Tuple    ,							_lowercase  :int    ,							_lowercase  :Any					):






     '''simple docstring'''





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

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

     lowercase__    =  self.get_auto_remove_tmp_dir()
     lowercase__    =  f'''--output_dir {output_dir}'''.split()

     cmd += args_ds + args_out

     return cmd, output_dir







  @parameterized.expand(_lowercase    ,							name_func=_lowercase					)
  def   UpperCAmelCase     (			self  :str    ,							_lowercase  :Dict    ,							_lowercase  :Tuple					):






     '''simple docstring'''





     lowercase__   ,							lowercase__    =  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__    =  f'''{output_dir}/checkpoint-1'''
     lowercase__    =  f'''{chkpt_dir}/out.bin'''
     lowercase__    =  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






