
import doctest
import glob
import importlib
import inspect
import os
import re
from contextlib import contextmanager
from functools import wraps
from unittest.mock import patch

import numpy as np
import pytest
from absl.testing import parameterized

import datasets
from datasets import load_metric

from .utils import for_all_test_methods, local, slow


# mark all tests as integration
lowercase   =	pytest.mark.integration


lowercase   =	{'''comet'''}
lowercase   =	importlib.util.find_spec('''fairseq''') is not None

lowercase   =	{'''code_eval'''}
lowercase   =	os.name == '''nt'''

lowercase   =	{'''bertscore''', '''frugalscore''', '''perplexity'''}
lowercase   =	importlib.util.find_spec('''transformers''') is not None



def   __lowerCAmelCase    (				UpperCAmelCase__					:   Optional[int]							)	->     Tuple:
							@wraps(UpperCAmelCase__							)
							def wrapper(self					:   Tuple      ,					UpperCAmelCase__					:   str							):
														if not _has_fairseq and metric_name in REQUIRE_FAIRSEQ:
																					self.skipTest("""\"test requires Fairseq\""""							)
														else:
																					test_case(self      ,					UpperCAmelCase__							)

							return wrapper



def   __lowerCAmelCase    (				UpperCAmelCase__					:   Any							)	->     str:
							@wraps(UpperCAmelCase__							)
							def wrapper(self					:   Optional[Any]      ,					UpperCAmelCase__					:   int							):
														if not _has_transformers and metric_name in REQUIRE_TRANSFORMERS:
																					self.skipTest("""\"test requires transformers\""""							)
														else:
																					test_case(self      ,					UpperCAmelCase__							)

							return wrapper



def   __lowerCAmelCase    (				UpperCAmelCase__					:   List[Any]							)	->     Optional[int]:
							@wraps(UpperCAmelCase__							)
							def wrapper(self					:   str      ,					UpperCAmelCase__					:   List[str]							):
														if _on_windows and metric_name in UNSUPPORTED_ON_WINDOWS:
																					self.skipTest("""\"test not supported on Windows\""""							)
														else:
																					test_case(self      ,					UpperCAmelCase__							)

							return wrapper



def   __lowerCAmelCase    (				)	->     str:
							lowerCamelCase_					=       [metric_dir.split(os.sep							)[-2] for metric_dir in glob.glob("""./metrics/*/"""							)]
							return [{"testcase_name": x, "metric_name": x} for x in metrics if x != "gleu"]  # gleu is unfinished





@parameterized.named_parameters(get_local_metric_names()							)
@for_all_test_methods(
    UpperCAmelCase  ,		UpperCAmelCase  ,		UpperCAmelCase							)
@local
class    __A(					parameterized.TestCase							):
					SCREAMING_SNAKE_CASE           =     {}
					SCREAMING_SNAKE_CASE           =     None

					@pytest.mark.filterwarnings("""ignore:metric_module_factory is deprecated:FutureWarning""" )
					@pytest.mark.filterwarnings("""ignore:load_metric is deprecated:FutureWarning""" )
					def  lowercase__   (						self       :  Any ,       __UpperCamelCase       :  Any ):
												lowerCamelCase_					=       """[...]"""
												lowerCamelCase_					=       importlib.import_module(
												    datasets.load.metric_module_factory(os.path.join("""metrics""" ,       __UpperCamelCase ) ).module_path )
												lowerCamelCase_					=       datasets.load.import_main_class(metric_module.__name__ ,       dataset=__UpperCamelCase )
												# check parameters
												lowerCamelCase_					=       inspect.signature(metric._compute ).parameters
												self.assertTrue(all(p.kind != p.VAR_KEYWORD for p in parameters.values() ) )  # no **kwargs
												# run doctest
												with self.patch_intensive_calls(__UpperCamelCase ,       metric_module.__name__ ):
																			with self.use_local_metrics():
																										try:
																																	lowerCamelCase_					=       doctest.testmod(__UpperCamelCase ,       verbose=__UpperCamelCase ,       raise_on_error=__UpperCamelCase )
																										except doctest.UnexpectedException as e:
																																	raise e.exc_info[1]  # raise the exception that doctest caught
												self.assertEqual(results.failed ,       0 )
												self.assertGreater(results.attempted ,       1 )

					@slow
					def  lowercase__   (						self       :  str ,       __UpperCamelCase       :  Any ):
												lowerCamelCase_					=       """[...]"""
												lowerCamelCase_					=       importlib.import_module(
												    datasets.load.metric_module_factory(os.path.join("""metrics""" ,       __UpperCamelCase ) ).module_path )
												# run doctest
												with self.use_local_metrics():
																			lowerCamelCase_					=       doctest.testmod(__UpperCamelCase ,       verbose=__UpperCamelCase ,       raise_on_error=__UpperCamelCase )
												self.assertEqual(results.failed ,       0 )
												self.assertGreater(results.attempted ,       1 )

					@contextmanager
					def  lowercase__   (						self       :  Optional[int] ,       __UpperCamelCase       :  List[Any] ,       __UpperCamelCase       :  Dict ):
												if metric_name in self.INTENSIVE_CALLS_PATCHER:
																			with self.INTENSIVE_CALLS_PATCHER[metric_name](__UpperCamelCase ):
																										yield
												else:
																			yield

					@contextmanager
					def  lowercase__   (						self       :  Union[str, Any] ):
												def load_local_metric(__UpperCamelCase       :  int ,       *__UpperCamelCase       :  List[Any] ,       **__UpperCamelCase       :  Tuple ):
																			return load_metric(os.path.join("""metrics""" ,       __UpperCamelCase ) ,       *__UpperCamelCase ,       **__UpperCamelCase )

												with patch("""datasets.load_metric""" ) as mock_load_metric:
																			lowerCamelCase_					=       load_local_metric
																			yield



					@classmethod
					def  lowercase__   (						cls       :  Union[str, Any] ,       __UpperCamelCase       :  Dict ):
												def wrapper(__UpperCamelCase       :  Optional[Any] ):
																			lowerCamelCase_					=       contextmanager(__UpperCamelCase )
																			lowerCamelCase_					=       patcher
																			return patcher

												return wrapper



@LocalMetricTest.register_intensive_calls_patcher("""bleurt"""							)
def   __lowerCAmelCase    (				UpperCAmelCase__					:   Any							)	->     Optional[Any]:
							import tensorflow.compat.va as tf
							from bleurt.score import Predictor

							tf.flags.DEFINE_string("""sv"""      ,					""""""      ,					""""""							)  # handle pytest cli flags





							class    __A(					UpperCAmelCase							):

												def  lowercase__   (						self       :  Optional[int] ,       __UpperCamelCase       :  Dict ):
																			assert len(input_dict["""input_ids"""] ) == 2
																			return np.array([1.03, 1.04] )

    # mock predict_fn which is supposed to do a forward pass with a bleurt model
							with patch("""bleurt.score._create_predictor"""							) as mock_create_predictor:
														lowerCamelCase_					=       MockedPredictor()
														yield



@LocalMetricTest.register_intensive_calls_patcher("""bertscore"""							)
def   __lowerCAmelCase    (				UpperCAmelCase__					:   Optional[Any]							)	->     List[str]:
							import torch

							def bert_cos_score_idf(UpperCAmelCase__					:   List[str]      ,					UpperCAmelCase__					:   Dict      ,					*UpperCAmelCase__					:   int      ,					**UpperCAmelCase__					:   Tuple							):
														return torch.tensor([[1.0, 1.0, 1.0]] * len(UpperCAmelCase__							)							)

							# mock get_model which is supposed to do download a bert model
							# mock bert_cos_score_idf which is supposed to do a forward pass with a bert model
							with patch("""bert_score.scorer.get_model"""							), patch(
							    """bert_score.scorer.bert_cos_score_idf"""							) as mock_bert_cos_score_idf:
														lowerCamelCase_					=       bert_cos_score_idf
														yield



@LocalMetricTest.register_intensive_calls_patcher("""comet"""							)
def   __lowerCAmelCase    (				UpperCAmelCase__					:   List[Any]							)	->     List[str]:
							def load_from_checkpoint(UpperCAmelCase__					:   Union[str, Any]							):





														class    __A:

																			def  lowercase__   (						self       :  int ,       __UpperCamelCase       :  Optional[int] ,       *__UpperCamelCase       :  List[str] ,       **__UpperCamelCase       :  List[Any] ):
																										assert len(__UpperCamelCase ) == 2
																										lowerCamelCase_					=       [0.19, 0.92]
																										return scores, sum(__UpperCamelCase ) / len(__UpperCamelCase )
														return Model()

							# mock load_from_checkpoint which is supposed to do download a bert model
							# mock load_from_checkpoint which is supposed to do download a bert model
							with patch("""comet.download_model"""							) as mock_download_model:
														lowerCamelCase_					=       None
														with patch("""comet.load_from_checkpoint"""							) as mock_load_from_checkpoint:
																					lowerCamelCase_					=       load_from_checkpoint
																					yield



def   __lowerCAmelCase    (				)	->     Optional[int]:
							lowerCamelCase_					=       load_metric(os.path.join("""metrics"""      ,					"""seqeval"""							)							)
							lowerCamelCase_					=       """ERROR"""
							lowerCamelCase_					=       F'''Scheme should be one of [IOB1, IOB2, IOE1, IOE2, IOBES, BILOU], got {wrong_scheme}'''
							with pytest.raises(UpperCAmelCase__      ,					match=re.escape(UpperCAmelCase__							)							):
														metric.compute(predictions=[]      ,					references=[]      ,					scheme=UpperCAmelCase__							)







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

from datasets import Dataset, load_dataset

import transformers
from transformers import (
    CONFIG_MAPPING,
    MODEL_FOR_MASKED_LM_MAPPING,
    AutoConfig,
    AutoModelForMaskedLM,
    AutoTokenizer,
    DataCollatorForWholeWordMask,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
)
from transformers.trainer_utils import get_last_checkpoint, is_main_process


lowercase   =	logging.getLogger(__name__)
lowercase   =	list(MODEL_FOR_MASKED_LM_MAPPING.keys())
lowercase   =	tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)





@dataclass
class    __A:
					SCREAMING_SNAKE_CASE           =     field(
					    default=UpperCAmelCase  ,		metadata={
					        '''help''': (
					            '''The model checkpoint for weights initialization.Don\'t set if you want to train a model from scratch.'''
					        )
					    }  ,		)
					SCREAMING_SNAKE_CASE           =     field(
					    default=UpperCAmelCase  ,		metadata={'''help''': '''If training from scratch, pass a model type from the list: ''' + ''', '''.join(UpperCAmelCase							)}  ,		)
					SCREAMING_SNAKE_CASE           =     field(
					    default=UpperCAmelCase  ,		metadata={
					        '''help''': (
					            '''Override some existing default config settings when a model is trained from scratch. Example: '''
					            '''n_embd=10,resid_pdrop=0.2,scale_attn_weights=false,summary_type=cls_index'''
					        )
					    }  ,		)
					SCREAMING_SNAKE_CASE           =     field(
					    default=UpperCAmelCase  ,		metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''}							)
					SCREAMING_SNAKE_CASE           =     field(
					    default=UpperCAmelCase  ,		metadata={'''help''': '''Pretrained tokenizer name or path if not the same as model_name'''}							)
					SCREAMING_SNAKE_CASE           =     field(
					    default=UpperCAmelCase  ,		metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''}  ,		)
					SCREAMING_SNAKE_CASE           =     field(
					    default=UpperCAmelCase  ,		metadata={'''help''': '''Whether to use one of the fast tokenizer (backed by the tokenizers library) or not.'''}  ,		)
					SCREAMING_SNAKE_CASE           =     field(
					    default='''main'''  ,		metadata={'''help''': '''The specific model version to use (can be a branch name, tag name or commit id).'''}  ,		)
					SCREAMING_SNAKE_CASE           =     field(
					    default=UpperCAmelCase  ,		metadata={
					        '''help''': (
					            '''Will use the token generated when running `huggingface-cli login` (necessary to use this script '''
					            '''with private models).'''
					        )
					    }  ,		)

					def  lowercase__   (						self       :  str ):
												if self.config_overrides is not None and (self.config_name is not None or self.model_name_or_path is not None):
																			raise ValueError(
																			    """--config_overrides can't be used in combination with --config_name or --model_name_or_path""" )





@dataclass
class    __A:
					SCREAMING_SNAKE_CASE           =     field(
					    default=UpperCAmelCase  ,		metadata={'''help''': '''The name of the dataset to use (via the datasets library).'''}							)
					SCREAMING_SNAKE_CASE           =     field(
					    default=UpperCAmelCase  ,		metadata={'''help''': '''The configuration name of the dataset to use (via the datasets library).'''}							)
					SCREAMING_SNAKE_CASE           =     field(default=UpperCAmelCase  ,		metadata={'''help''': '''The input training data file (a text file).'''}							)
					SCREAMING_SNAKE_CASE           =     field(
					    default=UpperCAmelCase  ,		metadata={'''help''': '''An optional input evaluation data file to evaluate the perplexity on (a text file).'''}  ,		)
					SCREAMING_SNAKE_CASE           =     field(
					    default=UpperCAmelCase  ,		metadata={'''help''': '''An optional input train ref data file for whole word masking in Chinese.'''}  ,		)
					SCREAMING_SNAKE_CASE           =     field(
					    default=UpperCAmelCase  ,		metadata={'''help''': '''An optional input validation ref data file for whole word masking in Chinese.'''}  ,		)
					SCREAMING_SNAKE_CASE           =     field(
					    default=UpperCAmelCase  ,		metadata={'''help''': '''Overwrite the cached training and evaluation sets'''}							)
					SCREAMING_SNAKE_CASE           =     field(
					    default=5  ,		metadata={
					        '''help''': '''The percentage of the train set used as validation set in case there\'s no validation split'''
					    }  ,		)
					SCREAMING_SNAKE_CASE           =     field(
					    default=UpperCAmelCase  ,		metadata={
					        '''help''': (
					            '''The maximum total input sequence length after tokenization. Sequences longer '''
					            '''than this will be truncated. Default to the max input length of the model.'''
					        )
					    }  ,		)
					SCREAMING_SNAKE_CASE           =     field(
					    default=UpperCAmelCase  ,		metadata={'''help''': '''The number of processes to use for the preprocessing.'''}  ,		)
					SCREAMING_SNAKE_CASE           =     field(
					    default=0.15  ,		metadata={'''help''': '''Ratio of tokens to mask for masked language modeling loss'''}							)
					SCREAMING_SNAKE_CASE           =     field(
					    default=UpperCAmelCase  ,		metadata={
					        '''help''': (
					            '''Whether to pad all samples to `max_seq_length`. '''
					            '''If False, will pad the samples dynamically when batching to the maximum length in the batch.'''
					        )
					    }  ,		)

					def  lowercase__   (						self       :  Tuple ):
												if self.train_file is not None:
																			lowerCamelCase_					=       self.train_file.split(""".""" )[-1]
																			assert extension in ["csv", "json", "txt"], "`train_file` should be a csv, a json or a txt file."
												if self.validation_file is not None:
																			lowerCamelCase_					=       self.validation_file.split(""".""" )[-1]
																			assert extension in ["csv", "json", "txt"], "`validation_file` should be a csv, a json or a txt file."



def   __lowerCAmelCase    (				UpperCAmelCase__					:   Any      ,					UpperCAmelCase__					:   Tuple							)	->     Dict:
							with open(UpperCAmelCase__      ,					"""r"""      ,					encoding="""utf-8"""							) as f:
														lowerCamelCase_					=       [json.loads(UpperCAmelCase__							) for line in f.read().splitlines() if (len(UpperCAmelCase__							) > 0 and not line.isspace())]
							assert len(UpperCAmelCase__							) == len(UpperCAmelCase__							)

							lowerCamelCase_					=       {c: dataset[c] for c in dataset.column_names}
							lowerCamelCase_					=       refs
							return Dataset.from_dict(UpperCAmelCase__							)



def   __lowerCAmelCase    (				)	->     Any:
							# 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.

							lowerCamelCase_					=       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.
														lowerCamelCase_	, lowerCamelCase_	, lowerCamelCase_					=       parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]							)							)
							else:
														lowerCamelCase_	, lowerCamelCase_	, lowerCamelCase_					=       parser.parse_args_into_dataclasses()

							# Detecting last checkpoint.
							lowerCamelCase_					=       None
							if os.path.isdir(training_args.output_dir							) and training_args.do_train and not training_args.overwrite_output_dir:
														lowerCamelCase_					=       get_last_checkpoint(training_args.output_dir							)
														if last_checkpoint is None and len(os.listdir(training_args.output_dir							)							) > 0:
																					raise ValueError(
																					    F'''Output directory ({training_args.output_dir}) already exists and is not empty. '''
																					    """Use --overwrite_output_dir to overcome."""							)
														elif last_checkpoint is not None:
																					logger.info(
																					    F'''Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change '''
																					    """the `--output_dir` or add `--overwrite_output_dir` to train from scratch."""							)

    # Setup logging
							logging.basicConfig(
							    format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s"""      ,					datefmt="""%m/%d/%Y %H:%M:%S"""      ,					handlers=[logging.StreamHandler(sys.stdout							)]      ,					)
							logger.setLevel(logging.INFO if is_main_process(training_args.local_rank							) else logging.WARN							)

							# Log on each process the small summary:
							logger.warning(
							    F'''Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}'''
							    + F'''distributed training: {bool(training_args.local_rank != -1							)}, 16-bits training: {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"""      ,					UpperCAmelCase__							)

							# Set seed before initializing model.
							set_seed(training_args.seed							)

							# Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below)
							# or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/
							# (the dataset will be downloaded automatically from the datasets Hub).
							#
							# For CSV/JSON files, this script will use the column called 'text' or the first column if no column called
							# 'text' is found. You can easily tweak this behavior (see below).
							#
							# In distributed training, the load_dataset function guarantee that only one local process can concurrently
							# download the dataset.
							if data_args.dataset_name is not None:
														# Downloading and loading a dataset from the hub.
														lowerCamelCase_					=       load_dataset(data_args.dataset_name      ,					data_args.dataset_config_name							)
														if "validation" not in datasets.keys():
																					lowerCamelCase_					=       load_dataset(
																					    data_args.dataset_name      ,					data_args.dataset_config_name      ,					split=F'''train[:{data_args.validation_split_percentage}%]'''      ,					)
																					lowerCamelCase_					=       load_dataset(
																					    data_args.dataset_name      ,					data_args.dataset_config_name      ,					split=F'''train[{data_args.validation_split_percentage}%:]'''      ,					)
							else:
														lowerCamelCase_					=       {}
														if data_args.train_file is not None:
																					lowerCamelCase_					=       data_args.train_file
														if data_args.validation_file is not None:
																					lowerCamelCase_					=       data_args.validation_file
														lowerCamelCase_					=       data_args.train_file.split("""."""							)[-1]
														if extension == "txt":
																					lowerCamelCase_					=       """text"""
														lowerCamelCase_					=       load_dataset(UpperCAmelCase__      ,					data_files=UpperCAmelCase__							)
							# See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
							# https://huggingface.co/docs/datasets/loading_datasets.html.

							# Load pretrained model and tokenizer
							#
							# Distributed training:
							# The .from_pretrained methods guarantee that only one local process can concurrently
							# download model & vocab.
							lowerCamelCase_					=       {
							    """cache_dir""": model_args.cache_dir,
							    """revision""": model_args.model_revision,
							    """use_auth_token""": True if model_args.use_auth_token else None,
							}
							if model_args.config_name:
														lowerCamelCase_					=       AutoConfig.from_pretrained(model_args.config_name      ,					**UpperCAmelCase__							)
							elif model_args.model_name_or_path:
														lowerCamelCase_					=       AutoConfig.from_pretrained(model_args.model_name_or_path      ,					**UpperCAmelCase__							)
							else:
														lowerCamelCase_					=       CONFIG_MAPPING[model_args.model_type]()
														logger.warning("""You are instantiating a new config instance from scratch."""							)
														if model_args.config_overrides is not None:
																					logger.info(F'''Overriding config: {model_args.config_overrides}'''							)
																					config.update_from_string(model_args.config_overrides							)
																					logger.info(F'''New config: {config}'''							)

							lowerCamelCase_					=       {
							    """cache_dir""": model_args.cache_dir,
							    """use_fast""": model_args.use_fast_tokenizer,
							    """revision""": model_args.model_revision,
							    """use_auth_token""": True if model_args.use_auth_token else None,
							}
							if model_args.tokenizer_name:
														lowerCamelCase_					=       AutoTokenizer.from_pretrained(model_args.tokenizer_name      ,					**UpperCAmelCase__							)
							elif model_args.model_name_or_path:
														lowerCamelCase_					=       AutoTokenizer.from_pretrained(model_args.model_name_or_path      ,					**UpperCAmelCase__							)
							else:
														raise ValueError(
														    """You are instantiating a new tokenizer from scratch. This is not supported by this script."""
														    """You can do it from another script, save it, and load it from here, using --tokenizer_name."""							)

							if model_args.model_name_or_path:
														lowerCamelCase_					=       AutoModelForMaskedLM.from_pretrained(
														    model_args.model_name_or_path      ,					from_tf=bool(""".ckpt""" in model_args.model_name_or_path							)      ,					config=UpperCAmelCase__      ,					cache_dir=model_args.cache_dir      ,					revision=model_args.model_revision      ,					use_auth_token=True if model_args.use_auth_token else None      ,					)
							else:
														logger.info("""Training new model from scratch"""							)
														lowerCamelCase_					=       AutoModelForMaskedLM.from_config(UpperCAmelCase__							)

							model.resize_token_embeddings(len(UpperCAmelCase__							)							)

							# Preprocessing the datasets.
							# First we tokenize all the texts.
							if training_args.do_train:
														lowerCamelCase_					=       datasets["""train"""].column_names
							else:
														lowerCamelCase_					=       datasets["""validation"""].column_names
							lowerCamelCase_					=       """text""" if """text""" in column_names else column_names[0]

							lowerCamelCase_					=       """max_length""" if data_args.pad_to_max_length else False

							def tokenize_function(UpperCAmelCase__					:   Any							):
														# Remove empty lines
														lowerCamelCase_					=       [line for line in examples["""text"""] if len(UpperCAmelCase__							) > 0 and not line.isspace()]
														return tokenizer(examples["""text"""]      ,					padding=UpperCAmelCase__      ,					truncation=UpperCAmelCase__      ,					max_length=data_args.max_seq_length							)

							lowerCamelCase_					=       datasets.map(
							    UpperCAmelCase__      ,					batched=UpperCAmelCase__      ,					num_proc=data_args.preprocessing_num_workers      ,					remove_columns=[text_column_name]      ,					load_from_cache_file=not data_args.overwrite_cache      ,					)

							# Add the chinese references if provided
							if data_args.train_ref_file is not None:
														lowerCamelCase_					=       add_chinese_references(tokenized_datasets["""train"""]      ,					data_args.train_ref_file							)
							if data_args.validation_ref_file is not None:
														lowerCamelCase_					=       add_chinese_references(
														    tokenized_datasets["""validation"""]      ,					data_args.validation_ref_file							)
							# If we have ref files, need to avoid it removed by trainer
							lowerCamelCase_					=       data_args.train_ref_file or data_args.validation_ref_file
							if has_ref:
														lowerCamelCase_					=       False

							# Data collator
							# This one will take care of randomly masking the tokens.
							lowerCamelCase_					=       DataCollatorForWholeWordMask(tokenizer=UpperCAmelCase__      ,					mlm_probability=data_args.mlm_probability							)

							# Initialize our Trainer
							lowerCamelCase_					=       Trainer(
							    model=UpperCAmelCase__      ,					args=UpperCAmelCase__      ,					train_dataset=tokenized_datasets["""train"""] if training_args.do_train else None      ,					eval_dataset=tokenized_datasets["""validation"""] if training_args.do_eval else None      ,					tokenizer=UpperCAmelCase__      ,					data_collator=UpperCAmelCase__      ,					)

							# Training
							if training_args.do_train:
														if last_checkpoint is not None:
																					lowerCamelCase_					=       last_checkpoint
														elif model_args.model_name_or_path is not None and os.path.isdir(model_args.model_name_or_path							):
																					lowerCamelCase_					=       model_args.model_name_or_path
														else:
																					lowerCamelCase_					=       None
														lowerCamelCase_					=       trainer.train(resume_from_checkpoint=UpperCAmelCase__							)
														trainer.save_model()  # Saves the tokenizer too for easy upload

														lowerCamelCase_					=       os.path.join(training_args.output_dir      ,					"""train_results.txt"""							)
														if trainer.is_world_process_zero():
																					with open(UpperCAmelCase__      ,					"""w"""							) as writer:
																												logger.info("""***** Train results *****"""							)
																												for key, value in sorted(train_result.metrics.items()							):
																																			logger.info(F'''  {key} = {value}'''							)
																																			writer.write(F'''{key} = {value}\n'''							)

            # Need to save the state, since Trainer.save_model saves only the tokenizer with the model
																					trainer.state.save_to_json(os.path.join(training_args.output_dir      ,					"""trainer_state.json"""							)							)

    # Evaluation
							lowerCamelCase_					=       {}
							if training_args.do_eval:
														logger.info("""*** Evaluate ***"""							)

														lowerCamelCase_					=       trainer.evaluate()

														lowerCamelCase_					=       math.exp(eval_output["""eval_loss"""]							)
														lowerCamelCase_					=       perplexity

														lowerCamelCase_					=       os.path.join(training_args.output_dir      ,					"""eval_results_mlm_wwm.txt"""							)
														if trainer.is_world_process_zero():
																					with open(UpperCAmelCase__      ,					"""w"""							) as writer:
																												logger.info("""***** Eval results *****"""							)
																												for key, value in sorted(results.items()							):
																																			logger.info(F'''  {key} = {value}'''							)
																																			writer.write(F'''{key} = {value}\n'''							)

							return results



def   __lowerCAmelCase    (				UpperCAmelCase__					:   Optional[Any]							)	->     Optional[Any]:
							# For xla_spawn (TPUs)
							main()


if __name__ == "__main__":
		main()





