





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


_SCREAMING_SNAKE_CASE 		=						logging.getLogger(__name__)
_SCREAMING_SNAKE_CASE 		=						list(MODEL_FOR_MASKED_LM_MAPPING.keys())
_SCREAMING_SNAKE_CASE 		=						tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)
@dataclass
class SCREAMING_SNAKE_CASE_  :
						__magic_name__:     Optional[str]	 =    field(
						    default=snake_case_    ,			metadata={
						        "help": (
						            "The model checkpoint for weights initialization.Don't set if you want to train a model from scratch."
						        )
						    }    ,			)
						__magic_name__:     Optional[str]	 =    field(
						    default=snake_case_    ,			metadata={"help": "If training from scratch, pass a model type from the list: " + ", ".join(snake_case_   )}    ,			)
						__magic_name__:     Optional[str]	 =    field(
						    default=snake_case_    ,			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"
						        )
						    }    ,			)
						__magic_name__:     Optional[str]	 =    field(
						    default=snake_case_    ,			metadata={"help": "Pretrained config name or path if not the same as model_name"}   )
						__magic_name__:     Optional[str]	 =    field(
						    default=snake_case_    ,			metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}   )
						__magic_name__:     Optional[str]	 =    field(
						    default=snake_case_    ,			metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"}    ,			)
						__magic_name__:     bool	 =    field(
						    default=snake_case_    ,			metadata={"help": "Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."}    ,			)
						__magic_name__:     str	 =    field(
						    default="main"    ,			metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."}    ,			)
						__magic_name__:     bool	 =    field(
						    default=snake_case_    ,			metadata={
						        "help": (
						            "Will use the token generated when running `huggingface-cli login` (necessary to use this script "
						            "with private models)."
						        )
						    }    ,			)






						def UpperCAmelCase_			( self : str		)							->      str:







							"""simple docstring"""


							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 SCREAMING_SNAKE_CASE_  :
						__magic_name__:     Optional[str]	 =    field(
						    default=snake_case_    ,			metadata={"help": "The name of the dataset to use (via the datasets library)."}   )
						__magic_name__:     Optional[str]	 =    field(
						    default=snake_case_    ,			metadata={"help": "The configuration name of the dataset to use (via the datasets library)."}   )
						__magic_name__:     Optional[str]	 =    field(default=snake_case_    ,			metadata={"help": "The input training data file (a text file)."}   )
						__magic_name__:     Optional[str]	 =    field(
						    default=snake_case_    ,			metadata={"help": "An optional input evaluation data file to evaluate the perplexity on (a text file)."}    ,			)
						__magic_name__:     Optional[str]	 =    field(
						    default=snake_case_    ,			metadata={"help": "An optional input train ref data file for whole word masking in Chinese."}    ,			)
						__magic_name__:     Optional[str]	 =    field(
						    default=snake_case_    ,			metadata={"help": "An optional input validation ref data file for whole word masking in Chinese."}    ,			)
						__magic_name__:     bool	 =    field(
						    default=snake_case_    ,			metadata={"help": "Overwrite the cached training and evaluation sets"}   )
						__magic_name__:     Optional[int]	 =    field(
						    default=5    ,			metadata={
						        "help": "The percentage of the train set used as validation set in case there's no validation split"
						    }    ,			)
						__magic_name__:     Optional[int]	 =    field(
						    default=snake_case_    ,			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."
						        )
						    }    ,			)
						__magic_name__:     Optional[int]	 =    field(
						    default=snake_case_    ,			metadata={"help": "The number of processes to use for the preprocessing."}    ,			)
						__magic_name__:     float	 =    field(
						    default=0.1_5    ,			metadata={"help": "Ratio of tokens to mask for masked language modeling loss"}   )
						__magic_name__:     bool	 =    field(
						    default=snake_case_    ,			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 UpperCAmelCase_			( self : int		)							->      Optional[int]:







							"""simple docstring"""


							if self.train_file is not None:
								snake_case_						:     Optional[int]     =		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:
								snake_case_						:     Optional[Any]     =		self.validation_file.split('.'		)[-1]
								assert extension in ["csv", "json", "txt"], "`validation_file` should be a csv, a json or a txt file."


def 							SCREAMING_SNAKE_CASE__  (      __a				,	__a				):
	with open(__a				,	'r'				,	encoding='utf-8'				) as f:
		snake_case_						:     Dict     =		[json.loads(__a				) for line in f.read().splitlines() if (len(__a				) > 0 and not line.isspace())]
	assert len(__a				) == len(__a				)

	snake_case_						:     Tuple     =		{c: dataset[c] for c in dataset.column_names}
	snake_case_						:     int     =		refs
	return Dataset.from_dict(__a				)


def 							SCREAMING_SNAKE_CASE__  (      ):
	# 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.

	snake_case_						:     Optional[Any]     =		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.
		snake_case_  ,snake_case_  ,snake_case_						:     int     =		parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]				)				)
	else:
		snake_case_  ,snake_case_  ,snake_case_						:     Any     =		parser.parse_args_into_dataclasses()

	# Detecting last checkpoint.
	snake_case_						:     Optional[int]     =		None
	if os.path.isdir(training_args.output_dir				) and training_args.do_train and not training_args.overwrite_output_dir:
		snake_case_						:     Union[str, Any]     =		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'				,	__a				)

	# 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.
		snake_case_						:     List[str]     =		load_dataset(data_args.dataset_name				,	data_args.dataset_config_name				)
		if "validation" not in datasets.keys():
			snake_case_						:     Optional[int]     =		load_dataset(
			    data_args.dataset_name				,	data_args.dataset_config_name				,	split=f"""train[:{data_args.validation_split_percentage}%]"""				,	)
			snake_case_						:     Any     =		load_dataset(
			    data_args.dataset_name				,	data_args.dataset_config_name				,	split=f"""train[{data_args.validation_split_percentage}%:]"""				,	)
	else:
		snake_case_						:     Any     =		{}
		if data_args.train_file is not None:
			snake_case_						:     str     =		data_args.train_file
		if data_args.validation_file is not None:
			snake_case_						:     List[Any]     =		data_args.validation_file
		snake_case_						:     Dict     =		data_args.train_file.split('.'				)[-1]
		if extension == "txt":
			snake_case_						:     Dict     =		'text'
		snake_case_						:     Optional[int]     =		load_dataset(__a				,	data_files=__a				)
	# 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.
	snake_case_						:     Tuple     =		{
	    '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:
		snake_case_						:     str     =		AutoConfig.from_pretrained(model_args.config_name				,	**__a				)
	elif model_args.model_name_or_path:
		snake_case_						:     Dict     =		AutoConfig.from_pretrained(model_args.model_name_or_path				,	**__a				)
	else:
		snake_case_						:     List[str]     =		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}"""				)

	snake_case_						:     Optional[int]     =		{
	    '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:
		snake_case_						:     List[str]     =		AutoTokenizer.from_pretrained(model_args.tokenizer_name				,	**__a				)
	elif model_args.model_name_or_path:
		snake_case_						:     List[Any]     =		AutoTokenizer.from_pretrained(model_args.model_name_or_path				,	**__a				)
	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:
		snake_case_						:     Dict     =		AutoModelForMaskedLM.from_pretrained(
		    model_args.model_name_or_path				,	from_tf=bool('.ckpt' in model_args.model_name_or_path				)				,	config=__a				,	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'				)
		snake_case_						:     List[str]     =		AutoModelForMaskedLM.from_config(__a				)

	model.resize_token_embeddings(len(__a				)				)

	# Preprocessing the datasets.
	# First we tokenize all the texts.
	if training_args.do_train:
		snake_case_						:     Union[str, Any]     =		datasets['train'].column_names
	else:
		snake_case_						:     Tuple     =		datasets['validation'].column_names
	snake_case_						:     Union[str, Any]     =		'text' if 'text' in column_names else column_names[0]

	snake_case_						:     str     =		'max_length' if data_args.pad_to_max_length else False

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

	snake_case_						:     Union[str, Any]     =		datasets.map(
	    __a				,	batched=__a				,	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:
		snake_case_						:     str     =		add_chinese_references(tokenized_datasets['train']				,	data_args.train_ref_file				)
	if data_args.validation_ref_file is not None:
		snake_case_						:     Optional[Any]     =		add_chinese_references(
		    tokenized_datasets['validation']				,	data_args.validation_ref_file				)
	# If we have ref files, need to avoid it removed by trainer
	snake_case_						:     str     =		data_args.train_ref_file or data_args.validation_ref_file
	if has_ref:
		snake_case_						:     Tuple     =		False

	# Data collator
	# This one will take care of randomly masking the tokens.
	snake_case_						:     Dict     =		DataCollatorForWholeWordMask(tokenizer=__a				,	mlm_probability=data_args.mlm_probability				)

	# Initialize our Trainer
	snake_case_						:     List[str]     =		Trainer(
	    model=__a				,	args=__a				,	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=__a				,	data_collator=__a				,	)

	# Training
	if training_args.do_train:
		if last_checkpoint is not None:
			snake_case_						:     Tuple     =		last_checkpoint
		elif model_args.model_name_or_path is not None and os.path.isdir(model_args.model_name_or_path				):
			snake_case_						:     Tuple     =		model_args.model_name_or_path
		else:
			snake_case_						:     str     =		None
		snake_case_						:     Optional[Any]     =		trainer.train(resume_from_checkpoint=__a				)
		trainer.save_model()  # Saves the tokenizer too for easy upload

		snake_case_						:     Dict     =		os.path.join(training_args.output_dir				,	'train_results.txt'				)
		if trainer.is_world_process_zero():
			with open(__a				,	'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
	snake_case_						:     Dict     =		{}
	if training_args.do_eval:
		logger.info('*** Evaluate ***'				)

		snake_case_						:     int     =		trainer.evaluate()

		snake_case_						:     int     =		math.exp(eval_output['eval_loss']				)
		snake_case_						:     Optional[int]     =		perplexity

		snake_case_						:     int     =		os.path.join(training_args.output_dir				,	'eval_results_mlm_wwm.txt'				)
		if trainer.is_world_process_zero():
			with open(__a				,	'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 							SCREAMING_SNAKE_CASE__  (      __a				):
	# For xla_spawn (TPUs)
	main()


if __name__ == "__main__":
						main()








import re
import string

import numpy as np

import datasets


_SCREAMING_SNAKE_CASE 		=						"""
Returns the rate at which the input predicted strings exactly match their references, ignoring any strings input as part of the regexes_to_ignore list.
"""

_SCREAMING_SNAKE_CASE 		=						"""
Args:
    predictions: List of predicted texts.
    references: List of reference texts.
    regexes_to_ignore: List, defaults to None. Regex expressions of characters to
        ignore when calculating the exact matches. Note: these regexes are removed
        from the input data before the changes based on the options below (e.g. ignore_case,
        ignore_punctuation, ignore_numbers) are applied.
    ignore_case: Boolean, defaults to False. If true, turns everything
        to lowercase so that capitalization differences are ignored.
    ignore_punctuation: Boolean, defaults to False. If true, removes all punctuation before
        comparing predictions and references.
    ignore_numbers: Boolean, defaults to False. If true, removes all punctuation before
        comparing predictions and references.
Returns:
    exact_match: Dictionary containing exact_match rate. Possible values are between 0.0 and 100.0, inclusive.
Examples:
    >>> exact_match = datasets.load_metric(\"exact_match\")
    >>> refs = [\"the cat\", \"theater\", \"YELLING\", \"agent007\"]
    >>> preds = [\"cat?\", \"theater\", \"yelling\", \"agent\"]
    >>> results = exact_match.compute(references=refs, predictions=preds)
    >>> print(round(results[\"exact_match\"], 1))
    25.0

    >>> exact_match = datasets.load_metric(\"exact_match\")
    >>> refs = [\"the cat\", \"theater\", \"YELLING\", \"agent007\"]
    >>> preds = [\"cat?\", \"theater\", \"yelling\", \"agent\"]
    >>> results = exact_match.compute(references=refs, predictions=preds, regexes_to_ignore=[\"the \", \"yell\"], ignore_case=True, ignore_punctuation=True)
    >>> print(round(results[\"exact_match\"], 1))
    50.0


    >>> exact_match = datasets.load_metric(\"exact_match\")
    >>> refs = [\"the cat\", \"theater\", \"YELLING\", \"agent007\"]
    >>> preds = [\"cat?\", \"theater\", \"yelling\", \"agent\"]
    >>> results = exact_match.compute(references=refs, predictions=preds, regexes_to_ignore=[\"the \", \"yell\", \"YELL\"], ignore_case=True, ignore_punctuation=True)
    >>> print(round(results[\"exact_match\"], 1))
    75.0

    >>> exact_match = datasets.load_metric(\"exact_match\")
    >>> refs = [\"the cat\", \"theater\", \"YELLING\", \"agent007\"]
    >>> preds = [\"cat?\", \"theater\", \"yelling\", \"agent\"]
    >>> results = exact_match.compute(references=refs, predictions=preds, regexes_to_ignore=[\"the \", \"yell\", \"YELL\"], ignore_case=True, ignore_punctuation=True, ignore_numbers=True)
    >>> print(round(results[\"exact_match\"], 1))
    100.0

    >>> exact_match = datasets.load_metric(\"exact_match\")
    >>> refs = [\"The cat sat on the mat.\", \"Theaters are great.\", \"It's like comparing oranges and apples.\"]
    >>> preds = [\"The cat sat on the mat?\", \"Theaters are great.\", \"It's like comparing apples and oranges.\"]
    >>> results = exact_match.compute(references=refs, predictions=preds)
    >>> print(round(results[\"exact_match\"], 1))
    33.3

"""

_SCREAMING_SNAKE_CASE 		=						"""
"""
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION    ,			_KWARGS_DESCRIPTION   )
class SCREAMING_SNAKE_CASE_  (   datasets.Metric   ):






						def UpperCAmelCase_			( self : Union[str, Any]		)							->      Optional[Any]:







							"""simple docstring"""


							return datasets.MetricInfo(
							    description=_DESCRIPTION						,				citation=_CITATION						,				inputs_description=_KWARGS_DESCRIPTION						,				features=datasets.Features(
							        {
							            'predictions': datasets.Value('string'						,				id='sequence'		),
							            'references': datasets.Value('string'						,				id='sequence'		),
							        }		)						,				reference_urls=[]						,				)






						def UpperCAmelCase_			( self : List[Any]						,				_A : Optional[Any]						,				_A : Optional[int]						,				_A : Optional[int]=None						,				_A : Dict=False						,				_A : Dict=False						,				_A : Optional[Any]=False						,				)							->      List[str]:







							"""simple docstring"""


							if regexes_to_ignore is not None:
								for s in regexes_to_ignore:
									snake_case_						:     List[str]     =		np.array([re.sub(_A						,				''						,				_A		) for x in predictions]		)
									snake_case_						:     int     =		np.array([re.sub(_A						,				''						,				_A		) for x in references]		)
							else:
								snake_case_						:     Optional[Any]     =		np.asarray(_A		)
								snake_case_						:     Optional[Any]     =		np.asarray(_A		)

							if ignore_case:
								snake_case_						:     int     =		np.char.lower(_A		)
								snake_case_						:     List[str]     =		np.char.lower(_A		)

							if ignore_punctuation:
								snake_case_						:     str     =		string.punctuation.maketrans(''						,				''						,				string.punctuation		)
								snake_case_						:     str     =		np.char.translate(_A						,				table=_A		)
								snake_case_						:     Any     =		np.char.translate(_A						,				table=_A		)

							if ignore_numbers:
								snake_case_						:     int     =		string.digits.maketrans(''						,				''						,				string.digits		)
								snake_case_						:     Tuple     =		np.char.translate(_A						,				table=_A		)
								snake_case_						:     Optional[Any]     =		np.char.translate(_A						,				table=_A		)

							snake_case_						:     Optional[Any]     =		predictions == references

							return {"exact_match": np.mean(_A		) * 100}

