





'''simple docstring'''






import os
import sys
from pathlib import Path

import torch

from transformers.testing_utils import TestCasePlus, execute_subprocess_async, require_torch_multi_gpu
from utils import load_json


snake_case_     			=						torch.cuda.is_available()
snake_case_     			=						[""" Sam ate lunch today.""", """Sams lunch ingredients."""]
snake_case_     			=						["""A very interesting story about what I ate for lunch.""", """Avocado, celery, turkey, coffee"""]
snake_case_     			=						{
    """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""": 42,
    """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""": 12,
    """max_target_length""": 12,
    """val_max_target_length""": 12,
    """test_max_target_length""": 12,
    """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 				_lowerCamelCase(	UpperCamelCase__     :			Path     ,							UpperCamelCase__     :			list		)		->	str:
    A							:							List[Any]				=    '''\n'''.join(UpperCamelCase__		)
    Path(UpperCamelCase__		).open('''w'''		).writelines(UpperCamelCase__		)
def 				_lowerCamelCase(	UpperCamelCase__     :			List[Any]		)		->	Dict:
    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 	_lowercase    (				a					):


   @classmethod
   def        snake_case					(			cls       ):
       return cls


   @require_torch_multi_gpu
   def        snake_case					(			self       ):
       A							:							Optional[int]				=    {
           '''no_teacher''': True,
           '''freeze_encoder''': True,
           '''gpus''': 2,
           '''overwrite_output_dir''': True,
           '''sortish_sampler''': True,
       }
       self._test_distiller_cli_fork(_UpperCAmelCase     , check_contents=_UpperCAmelCase       )


   def        snake_case					(			self     , _UpperCAmelCase     , _UpperCAmelCase=True       ):
       A							:							List[Any]				=    {
           '''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(_UpperCAmelCase       )
       A							:							dict				=    CHEAP_ARGS.copy()
       A							:							Union[str, Any]				=    make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()       )
       A							:							Optional[int]				=    self.get_auto_remove_tmp_dir()
       args_d.update(data_dir=_UpperCAmelCase     , output_dir=_UpperCAmelCase     , **_UpperCAmelCase       )

       def convert(_UpperCAmelCase     , _UpperCAmelCase       ):
           if k in ["tgt_suffix", "server_ip", "server_port", "out", "n_tpu_cores"]:
               return ""
           if v is False or v is None:
               return ""
           if v is True:  # or len(str(v))==0:
               return f'''--{k}'''
           return f'''--{k}={v}'''

       A							:							int				=    [x for x in (convert(_UpperCAmelCase     , _UpperCAmelCase       ) for k, v in args_d.items()) if len(_UpperCAmelCase       )]
       A							:							Union[str, Any]				=    [sys.executable, f'''{self.test_file_dir}/distillation.py'''] + cli_args
       execute_subprocess_async(_UpperCAmelCase     , env=self.get_env()       )

       A							:							Dict				=    os.listdir(_UpperCAmelCase       )
       A							:							Optional[int]				=    {os.path.basename(_UpperCAmelCase       ) for p in contents}
       A							:							Union[str, Any]				=    [p for p in contents if p.endswith('''ckpt'''       )]
       assert len(_UpperCAmelCase       ) > 0

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

       # get the following from the module, (we don't have access to `model` here)
       A							:							Optional[Any]				=    os.path.join(_UpperCAmelCase     , '''metrics.json'''       )
       A							:							List[Any]				=    '''rouge2'''

       A							:							Union[str, Any]				=    load_json(_UpperCAmelCase       )
       # {'test': [{'test_avg_loss': 10.63731575012207, 'test_avg_rouge1': 0.0, 'test_avg_rouge2': 0.0, 'test_avg_rougeL': 0.0, 'test_avg_gen_time': 0.1822289228439331, 'test_avg_gen_len': 142.0, 'step_count': 1}]}
       print(_UpperCAmelCase       )
       A							:							Optional[Any]				=    metrics['''val'''][-1]
       self.assertGreaterEqual(last_step_stats['''val_avg_gen_time''']     , 0.01       )
       self.assertIsInstance(last_step_stats[f'''val_avg_{val_metric}''']     , _UpperCAmelCase       )
       self.assertEqual(len(metrics['''test''']       )     , 1       )
       A							:							Tuple				=    int(args_d['''max_epochs'''] * (1 / args_d['''val_check_interval''']) / 2 + 1       )
       self.assertEqual(len(metrics['''val''']       )     , _UpperCAmelCase       )







'''simple docstring'''






import itertools
import os
import subprocess
from os.path import dirname

from parameterized import parameterized

from tests.trainer.test_trainer import TrainerIntegrationCommon  # noqa
from transformers import is_torch_available
from transformers.testing_utils import (
    TestCasePlus,
    execute_subprocess_async,
    get_gpu_count,
    get_tests_dir,
    require_deepspeed,
    require_torch_gpu,
    slow,
)
from transformers.trainer_utils import set_seed


if is_torch_available():
     from tests.trainer.test_trainer import (  # noqa
         RegressionModelConfig,
         RegressionPreTrainedModel,
         get_regression_trainer,
     )


set_seed(42)

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 				_lowerCamelCase(	UpperCamelCase__     :			str=False		)		->	List[str]:
    # 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)
    A							:							int				=    min(2     ,							get_gpu_count()		) if distributed else 1
    A							:							Dict				=    os.environ.get('''DS_TEST_PORT'''     ,							UpperCamelCase__		)
    return F'''deepspeed --num_nodes 1 --num_gpus {num_gpus} --master_port {master_port}'''.split()
def 				_lowerCamelCase(	)		->	Any:
    A							:							Tuple				=    F'''{FIXTURE_DIRECTORY}/tests_samples'''
    A							:							int				=    F'''{data_dir_samples}/wmt_en_ro'''
    A							:							List[str]				=    F'''{data_dir_samples}/xsum'''
    A							:							int				=    '''
        --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.
    A							:							List[str]				=    {
        '''trans''': [
            '''bart''',
            '''fsmt''',
            '''m2m_100''',
            '''marian''',
            '''mbart''',
            '''t5''',
            '''t5_v1''',
            # "mt5", missing model files
        ],
        '''sum''': [
            '''pegasus''',
        ],
        '''clm''': [
            '''big_bird''',
            '''bigbird_pegasus''',
            '''blenderbot''',
            '''bloom''',
            '''gpt2''',
            '''gpt_neo''',
            '''gptj''',
            '''xlm-roberta''',
            '''prophetnet''',
            # "camembert", missing model files
        ],
        '''mlm''': [
            '''albert''',
            '''deberta''',
            '''deberta-v2''',
            '''distilbert''',
            '''electra''',
            '''flaubert''',
            '''funnel''',
            '''layoutlm''',
            # "reformer", # multiple issues with either mlm/qa/clas
        ],
        '''qa''': [
            '''led''',
            '''longformer''',
            '''mobilebert''',
            '''mpnet''',
            '''roberta''',
            '''squeezebert''',
            # "convbert", # missing tokenizer files
            # "layoutlmv2", missing model files
        ],
        '''clas''': [
            '''bert''',
            '''xlnet''',
            # "hubert", # missing tokenizer files
            # "ibert", # multiple issues with either mlm/qa/clas
            # "transfo-xl", # tokenizer issues as ctrl
            # "ctrl", # tokenizer issues
            # "openai-gpt", missing model files
            # "tapas", multiple issues
        ],
        '''img_clas''': [
            '''vit''',
        ],
    }

    A							:							int				=    F'''{ROOT_DIRECTORY}/examples/pytorch'''

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

    A							:							int				=    get_launcher(distributed=UpperCamelCase__		)

    A							:							Optional[Any]				=    {}
    for task, args in tasks.items():
        A							:							Dict				=    args.split()
        for model in tasksamodels[task]:
            A							:							Dict				=    globals()[F'''{model.upper().replace('-'     ,							'_'		)}_TINY''']
            A							:							List[str]				=    F'''--model_name_or_path {model_name}'''.split()
            A							:							List[Any]				=    launcher + args + args_model + args_main

            # # generation special case
            # if task == "gen":
            #     launcher = f"deepspeed --num_nodes 1 --num_gpus 1".split()
            #     args_model += f"--model_type {model}".split()
            #     cmds[f"{task}_{model}"] = launcher + args + args_model
            # else:

    return cmds


snake_case_     			=						make_task_cmds()

snake_case_     			=						"""zero2"""
snake_case_     			=						"""zero3"""

snake_case_     			=						[ZEROa, ZEROa]
def 				_lowerCamelCase(	UpperCamelCase__     :			Tuple     ,							UpperCamelCase__     :			int     ,							UpperCamelCase__     :			Optional[int]		)		->	Any:
    # 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
    A							:							Union[str, Any]				=    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
snake_case_     			=						list(itertools.product(stages, task_cmds.keys()))





@slow
@require_deepspeed
@require_torch_gpu
class 	_lowercase    (				a					):


   def        snake_case					(			self     , _UpperCAmelCase     , _UpperCAmelCase       ):
       # 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()}'''       )

       A							:							List[str]				=    task_cmds[task]
       A							:							Dict				=    f'''--deepspeed {self.test_file_dir_str}/ds_config_{stage}.json'''.split()

       A							:							Optional[Any]				=    self.get_auto_remove_tmp_dir()
       A							:							Union[str, Any]				=    f'''--output_dir {output_dir}'''.split()

       cmd += args_ds + args_out

       return cmd, output_dir


   @parameterized.expand(_UpperCAmelCase     , name_func=_UpperCAmelCase       )
   def        snake_case					(			self     , _UpperCAmelCase     , _UpperCAmelCase       ):
       # testing the ability to do a run followed by recovery of full fp32 weights

       A,						A							:							List[str]				=    self.get_task_cmd(_UpperCAmelCase     , _UpperCAmelCase       )

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

       # 2. test that the fp32 weights get reconsolidated
       A							:							Dict				=    f'''{output_dir}/checkpoint-1'''
       A							:							Optional[Any]				=    f'''{chkpt_dir}/out.bin'''
       A							:							Optional[Any]				=    f'''{chkpt_dir}/zero_to_fp32.py {chkpt_dir} {recovered_model_path}'''
       # keep for quick debug
       # print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] +cmd)); die
       subprocess.check_call(_UpperCAmelCase     , shell=_UpperCAmelCase       )
       assert os.path.exists(_UpperCAmelCase       ), 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
