



import torch
from transformers import AutoModelForSequenceClassification, AutoTokenizer, Trainer, TrainingArguments

import datasets


UpperCamelCase_									=   '''\
@article{eddine2021frugalscore,
  title={FrugalScore: Learning Cheaper, Lighter and Faster Evaluation Metrics for Automatic Text Generation},
  author={Eddine, Moussa Kamal and Shang, Guokan and Tixier, Antoine J-P and Vazirgiannis, Michalis},
  journal={arXiv preprint arXiv:2110.08559},
  year={2021}
}
'''

UpperCamelCase_									=   '''\
FrugalScore is a reference-based metric for NLG models evaluation. It is based on a distillation approach that allows to learn a fixed, low cost version of any expensive NLG metric, while retaining most of its original performance.
'''


UpperCamelCase_									=   '''
Calculates how good are predictions given some references, using certain scores.
Args:
    predictions (list of str): list of predictions to score. Each predictions
        should be a string.
    references (list of str): list of reference for each prediction. Each
        reference should be a string.
    batch_size (int): the batch size for predictions.
    max_length (int): maximum sequence length.
    device (str): either gpu or cpu
Returns:
    scores (list of int): list of scores.
Examples:
    >>> frugalscore = datasets.load_metric("frugalscore")
    >>> results = frugalscore.compute(predictions=[\'hello there\', \'huggingface\'], references=[\'hello world\', \'hugging face\'])
    >>> print([round(s, 3) for s in results["scores"]])
    [0.631, 0.645]
'''







@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION ,							_KWARGS_DESCRIPTION )
class       _snake_case				(     datasets.Metric ):






   '''simple docstring'''






   def       A__    (    self:     Any							)	->      Optional[int]:
       return datasets.MetricInfo(
           description=_DESCRIPTION  ,citation=_CITATION  ,inputs_description=_KWARGS_DESCRIPTION  ,features=datasets.Features(
               {
                   """predictions""": datasets.Value("""string"""							),
                   """references""": datasets.Value("""string"""							),
               }							)  ,homepage="""https://github.com/moussaKam/FrugalScore"""  ,)


   def       A__    (    self:     Union[str, Any]  ,lowerCamelCase_:     str							)	->      Tuple:
       if self.config_name == "default":
           UpperCAmelCase_     :       Optional[Any]  		=					"""moussaKam/frugalscore_tiny_bert-base_bert-score"""
       else:
           UpperCAmelCase_     :       str  		=					self.config_name
       UpperCAmelCase_     :       int  		=					AutoModelForSequenceClassification.from_pretrained(lowerCamelCase_							)
       UpperCAmelCase_     :       List[str]  		=					AutoTokenizer.from_pretrained(lowerCamelCase_							)






   def       A__    (    self:     int  ,lowerCamelCase_:     Any  ,lowerCamelCase_:     Dict  ,lowerCamelCase_:     Tuple=32  ,lowerCamelCase_:     Tuple=128  ,lowerCamelCase_:     Optional[int]=None  ,)	->      List[str]:
       assert len(lowerCamelCase_							) == len(
           lowerCamelCase_							), "predictions and references should have the same number of sentences."
       if device is not None:
           assert device in ["gpu", "cpu"], "device should be either gpu or cpu."
       else:
           UpperCAmelCase_     :       Dict  		=					"""gpu""" if torch.cuda.is_available() else """cpu"""
       UpperCAmelCase_     :       Union[str, Any]  		=					TrainingArguments(
           """trainer"""  ,fpaa=(device == """gpu""")  ,per_device_eval_batch_size=lowerCamelCase_  ,report_to="""all"""  ,no_cuda=(device == """cpu""")  ,log_level="""warning"""  ,)
       UpperCAmelCase_     :       Optional[int]  		=					{"""sentence1""": predictions, """sentence2""": references}
       UpperCAmelCase_     :       Optional[int]  		=					datasets.Dataset.from_dict(lowerCamelCase_							)

       def tokenize_function(lowerCamelCase_:     str							):
           return self.tokenizer(
               data["""sentence1"""]  ,data["""sentence2"""]  ,max_length=lowerCamelCase_  ,truncation=lowerCamelCase_  ,padding=lowerCamelCase_							)

       UpperCAmelCase_     :       Union[str, Any]  		=					raw_datasets.map(lowerCamelCase_  ,batched=lowerCamelCase_							)
       tokenized_datasets.remove_columns(["""sentence1""", """sentence2"""]							)
       UpperCAmelCase_     :       Optional[Any]  		=					Trainer(self.model  ,lowerCamelCase_  ,tokenizer=self.tokenizer							)
       UpperCAmelCase_     :       Optional[int]  		=					trainer.predict(lowerCamelCase_							)
       return {"scores": list(predictions.predictions.squeeze(-1							)							)}





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

from parameterized import parameterized

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


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


set_seed(42)

UpperCamelCase_									=   get_tests_dir('''fixtures''')
UpperCamelCase_									=   os.path.join(dirname(get_tests_dir()))
UpperCamelCase_									=   dirname(os.path.abspath(__file__))

# default torch.distributed port
UpperCamelCase_									=   '''10999'''

UpperCamelCase_									=   '''t5-small'''

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


# *** To Fix ***


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

# missing tokenizer files
UpperCamelCase_									=   '''hf-internal-testing/tiny-random-convbert'''
UpperCamelCase_									=   '''hf-internal-testing/tiny-random-layoutlmv2'''
UpperCamelCase_									=   '''hf-internal-testing/tiny-random-hubert'''

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

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

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


# *** Reactive mode ***
# models with low usage, unstable API, things about to change - do nothing about the following until someone runs into a problem
UpperCamelCase_									=   '''hf-internal-testing/tiny-random-tapas'''







def       lowerCamelCase_  (    _a       :						Any=False    ):



    '''simple docstring'''




    UpperCAmelCase_     :       Optional[Any]  		=					min(2   ,		get_gpu_count()    ) if distributed else 1
    UpperCAmelCase_     :       Optional[Any]  		=					os.environ.get("""DS_TEST_PORT"""   ,		_a    )
    return F'''deepspeed --num_nodes 1 --num_gpus {num_gpus} --master_port {master_port}'''.split()







def       lowerCamelCase_  (    ):



    '''simple docstring'''




    UpperCAmelCase_     :       Dict  		=					F'''{FIXTURE_DIRECTORY}/tests_samples'''
    UpperCAmelCase_     :       Optional[int]  		=					F'''{data_dir_samples}/wmt_en_ro'''
    UpperCAmelCase_     :       Dict  		=					F'''{data_dir_samples}/xsum'''
    UpperCAmelCase_     :       Optional[Any]  		=					"""
        --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.
    UpperCAmelCase_     :       List[Any]  		=					{
        """trans""": [
            """bart""",
            """fsmt""",
            """m2m_100""",
            """marian""",
            """mbart""",
            """t5""",
            """t5_v1""",
            # "mt5", missing model files
        ],
        """sum""": [
            """pegasus""",
        ],
        """clm""": [
            """big_bird""",
            """bigbird_pegasus""",
            """blenderbot""",
            """bloom""",
            """gpt2""",
            """gpt_neo""",
            """gptj""",
            """xlm-roberta""",
            """prophetnet""",
            # "camembert", missing model files
        ],
        """mlm""": [
            """albert""",
            """deberta""",
            """deberta-v2""",
            """distilbert""",
            """electra""",
            """flaubert""",
            """funnel""",
            """layoutlm""",
            # "reformer", # multiple issues with either mlm/qa/clas
        ],
        """qa""": [
            """led""",
            """longformer""",
            """mobilebert""",
            """mpnet""",
            """roberta""",
            """squeezebert""",
            # "convbert", # missing tokenizer files
            # "layoutlmv2", missing model files
        ],
        """clas""": [
            """bert""",
            """xlnet""",
            # "hubert", # missing tokenizer files
            # "ibert", # multiple issues with either mlm/qa/clas
            # "transfo-xl", # tokenizer issues as ctrl
            # "ctrl", # tokenizer issues
            # "openai-gpt", missing model files
            # "tapas", multiple issues
        ],
        """img_clas""": [
            """vit""",
        ],
    }

    UpperCAmelCase_     :       Union[str, Any]  		=					F'''{ROOT_DIRECTORY}/examples/pytorch'''

    UpperCAmelCase_     :       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
        ''',
    }

    UpperCAmelCase_     :       List[Any]  		=					get_launcher(distributed=_a    )

    UpperCAmelCase_     :       Tuple  		=					{}
    for task, args in tasks.items():
        UpperCAmelCase_     :       Optional[int]  		=					args.split()
        for model in tasksamodels[task]:
            UpperCAmelCase_     :       Optional[Any]  		=					globals()[F'''{model.upper().replace('-'   ,		'_'    )}_TINY''']
            UpperCAmelCase_     :       str  		=					F'''--model_name_or_path {model_name}'''.split()
            UpperCAmelCase_     :       Optional[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


UpperCamelCase_									=   make_task_cmds()

UpperCamelCase_									=   '''zero2'''
UpperCamelCase_									=   '''zero3'''

UpperCamelCase_									=   [ZEROa, ZEROa]







def       lowerCamelCase_  (    _a       :						List[str]   ,		_a       :						int   ,		_a       :						List[Any]    ):



    '''simple docstring'''




    UpperCAmelCase_     :       Optional[Any]  		=					parameterized.to_safe_name("""_""".join(str(_a    ) for x in param.args    )    )
    return F'''{func.__name__}_{param_based_name}'''


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







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






   '''simple docstring'''






   def       A__    (    self:     Any  ,lowerCamelCase_:     int  ,lowerCamelCase_:     int							)	->      int:
       # 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()}'''							)

       UpperCAmelCase_     :       List[Any]  		=					task_cmds[task]
       UpperCAmelCase_     :       Union[str, Any]  		=					F'''--deepspeed {self.test_file_dir_str}/ds_config_{stage}.json'''.split()

       UpperCAmelCase_     :       int  		=					self.get_auto_remove_tmp_dir()
       UpperCAmelCase_     :       str  		=					F'''--output_dir {output_dir}'''.split()

       cmd += args_ds + args_out

       return cmd, output_dir






   @parameterized.expand(lowerCamelCase_  ,name_func=lowerCamelCase_							)
   def       A__    (    self:     Union[str, Any]  ,lowerCamelCase_:     Optional[int]  ,lowerCamelCase_:     str							)	->      Tuple:
       # testing the ability to do a run followed by recovery of full fp32 weights

       UpperCAmelCase_		, UpperCAmelCase_     :       int  		=					self.get_task_cmd(lowerCamelCase_  ,lowerCamelCase_							)

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

       # 2. test that the fp32 weights get reconsolidated
       UpperCAmelCase_     :       str  		=					F'''{output_dir}/checkpoint-1'''
       UpperCAmelCase_     :       Optional[int]  		=					F'''{chkpt_dir}/out.bin'''
       UpperCAmelCase_     :       int  		=					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(lowerCamelCase_  ,shell=lowerCamelCase_							)
       assert os.path.exists(lowerCamelCase_							), 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
