
"""simple docstring"""




import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional

import transformers
from transformers import (
    AutoConfig,
    AutoModelForQuestionAnswering,
    AutoTokenizer,
    DataCollatorWithPadding,
    HfArgumentParser,
    SquadDataset,
    Trainer,
    TrainingArguments,
)
from transformers import SquadDataTrainingArguments as DataTrainingArguments
from transformers.trainer_utils import is_main_process


snake_case    =		logging.getLogger(__name__)




@dataclass
class      UpperCamelCase	:

				"""simple docstring"""



				UpperCAmelCase_				:      str						  =				field(
				    metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}      )
				UpperCAmelCase_				:      Optional[str]						  =				field(
				    default=__magic_name__  ,      metadata={"help": "Pretrained config name or path if not the same as model_name"}      )
				UpperCAmelCase_				:      Optional[str]						  =				field(
				    default=__magic_name__  ,      metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}      )
				UpperCAmelCase_				:      bool						  =				field(default=__magic_name__  ,      metadata={"help": "Set this flag to use fast tokenization."}      )
				# If you want to tweak more attributes on your tokenizer, you should do it in a distinct script,
				# or just modify its tokenizer_config.json.
				UpperCAmelCase_				:      Optional[str]						  =				field(
				    default=__magic_name__  ,      metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"}  ,      )






def   UpperCamelCase_  (		):
					# See all possible arguments in src/transformers/training_args.py
					# or by passing the --help flag to this script.
					# We now keep distinct sets of args, for a cleaner separation of concerns.

					SCREAMING_SNAKE_CASE				=	HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)			)

					if len(sys.argv			) == 2 and sys.argv[1].endswith('.json'			):
										# If we pass only one argument to the script and it's the path to a json file,
										# let's parse it to get our arguments.
										SCREAMING_SNAKE_CASE	,    SCREAMING_SNAKE_CASE	,    SCREAMING_SNAKE_CASE				=	parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]			)			)
					else:
										SCREAMING_SNAKE_CASE	,    SCREAMING_SNAKE_CASE	,    SCREAMING_SNAKE_CASE				=	parser.parse_args_into_dataclasses()

					if (
					    os.path.exists(training_args.output_dir			)
					    and os.listdir(training_args.output_dir			)
					    and training_args.do_train
					    and not training_args.overwrite_output_dir
					):
										raise ValueError(
										    f'''Output directory ({training_args.output_dir}) already exists and is not empty. Use'''
										    ' --overwrite_output_dir to overcome.'			)

					# Setup logging
					logging.basicConfig(
					    format='%(asctime)s - %(levelname)s - %(name)s - %(message)s',     datefmt='%m/%d/%Y %H:%M:%S',     level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN,     )
					logger.warning(
					    'Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s',     training_args.local_rank,     training_args.device,     training_args.n_gpu,     bool(training_args.local_rank != -1			),     training_args.fpaa,     )
					# Set the verbosity to info of the Transformers logger (on main process only):
					if is_main_process(training_args.local_rank			):
										transformers.utils.logging.set_verbosity_info()
										transformers.utils.logging.enable_default_handler()
										transformers.utils.logging.enable_explicit_format()
					logger.info('Training/evaluation parameters %s',     SCREAMING_SNAKE_CASE_			)

					# Prepare Question-Answering task
					# Load pretrained model and tokenizer
					#
					# Distributed training:
					# The .from_pretrained methods guarantee that only one local process can concurrently
					# download model & vocab.

					SCREAMING_SNAKE_CASE				=	AutoConfig.from_pretrained(
					    model_args.config_name if model_args.config_name else model_args.model_name_or_path,     cache_dir=model_args.cache_dir,     )
					SCREAMING_SNAKE_CASE				=	AutoTokenizer.from_pretrained(
					    model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path,     cache_dir=model_args.cache_dir,     use_fast=SCREAMING_SNAKE_CASE_,     )
					SCREAMING_SNAKE_CASE				=	AutoModelForQuestionAnswering.from_pretrained(
					    model_args.model_name_or_path,     from_tf=bool('.ckpt' in model_args.model_name_or_path			),     config=SCREAMING_SNAKE_CASE_,     cache_dir=model_args.cache_dir,     )

					# Get datasets
					SCREAMING_SNAKE_CASE				=	hasattr(model.config,     'lang2id'			)
					SCREAMING_SNAKE_CASE				=	(
					    SquadDataset(
					        SCREAMING_SNAKE_CASE_,     tokenizer=SCREAMING_SNAKE_CASE_,     is_language_sensitive=SCREAMING_SNAKE_CASE_,     cache_dir=model_args.cache_dir			)
					    if training_args.do_train
					    else None
					)
					SCREAMING_SNAKE_CASE				=	(
					    SquadDataset(
					        SCREAMING_SNAKE_CASE_,     tokenizer=SCREAMING_SNAKE_CASE_,     mode='dev',     is_language_sensitive=SCREAMING_SNAKE_CASE_,     cache_dir=model_args.cache_dir,     )
					    if training_args.do_eval
					    else None
					)

					# Data collator
					SCREAMING_SNAKE_CASE				=	DataCollatorWithPadding(SCREAMING_SNAKE_CASE_,     pad_to_multiple_of=8			) if training_args.fpaa else None

					# Initialize our Trainer
					SCREAMING_SNAKE_CASE				=	Trainer(
					    model=SCREAMING_SNAKE_CASE_,     args=SCREAMING_SNAKE_CASE_,     train_dataset=SCREAMING_SNAKE_CASE_,     eval_dataset=SCREAMING_SNAKE_CASE_,     data_collator=SCREAMING_SNAKE_CASE_,     )

					# Training
					if training_args.do_train:
										trainer.train(
										    model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path			) else None			)
										trainer.save_model()
										# For convenience, we also re-save the tokenizer to the same directory,
										# so that you can share your model easily on huggingface.co/models =)
										if trainer.is_world_master():
															tokenizer.save_pretrained(training_args.output_dir			)

def   UpperCamelCase_  (		SCREAMING_SNAKE_CASE_			):
					# For xla_spawn (TPUs)
					main()


if __name__ == "__main__":
	main()







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






