




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


UpperCAmelCase_		:					Optional[Any]          =  logging.getLogger(__name__)
UpperCAmelCase_		:					Union[str, Any]          =  list(MODEL_FOR_MASKED_LM_MAPPING.keys())
UpperCAmelCase_		:					List[str]          =  tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)




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

						def      A__    (		self :Union[str, Any]			):
										'''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 		__A   :
						UpperCamelCase      =       field(
						    default=UpperCamelCase__       ,    metadata={"""help""": """The name of the dataset to use (via the datasets library)."""}      )
						UpperCamelCase      =       field(
						    default=UpperCamelCase__       ,    metadata={"""help""": """The configuration name of the dataset to use (via the datasets library)."""}      )
						UpperCamelCase      =       field(default=UpperCamelCase__       ,    metadata={"""help""": """The input training data file (a text file)."""}      )
						UpperCamelCase      =       field(
						    default=UpperCamelCase__       ,    metadata={"""help""": """An optional input evaluation data file to evaluate the perplexity on (a text file)."""}       ,    )
						UpperCamelCase      =       field(
						    default=UpperCamelCase__       ,    metadata={"""help""": """An optional input train ref data file for whole word masking in Chinese."""}       ,    )
						UpperCamelCase      =       field(
						    default=UpperCamelCase__       ,    metadata={"""help""": """An optional input validation ref data file for whole word masking in Chinese."""}       ,    )
						UpperCamelCase      =       field(
						    default=UpperCamelCase__       ,    metadata={"""help""": """Overwrite the cached training and evaluation sets"""}      )
						UpperCamelCase      =       field(
						    default=5       ,    metadata={
						        """help""": """The percentage of the train set used as validation set in case there's no validation split"""
						    }       ,    )
						UpperCamelCase      =       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."""
						        )
						    }       ,    )
						UpperCamelCase      =       field(
						    default=UpperCamelCase__       ,    metadata={"""help""": """The number of processes to use for the preprocessing."""}       ,    )
						UpperCamelCase      =       field(
						    default=0.15       ,    metadata={"""help""": """Ratio of tokens to mask for masked language modeling loss"""}      )
						UpperCamelCase      =       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      A__    (		self :Optional[int]			):
										'''simple docstring'''

										if self.train_file is not None:
														__magic_name__						:						Dict										=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:
														__magic_name__						:						Union[str, 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 		lowerCAmelCase_ (						lowerCamelCase						,  lowerCamelCase ):
				with open(lowerCamelCase						,  """r"""						,  encoding="""utf-8""" ) as f:
								__magic_name__						:						Any										=[json.loads(lowerCamelCase ) for line in f.read().splitlines() if (len(lowerCamelCase ) > 0 and not line.isspace())]
				assert len(lowerCamelCase ) == len(lowerCamelCase )

				__magic_name__						:						Optional[Any]										={c: dataset[c] for c in dataset.column_names}
				__magic_name__						:						List[str]										=refs
				return Dataset.from_dict(lowerCamelCase )




def 		lowerCAmelCase_ (						):
				# 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.

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

				# Detecting last checkpoint.
				__magic_name__						:						List[str]										=None
				if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir:
								__magic_name__						:						Optional[int]										=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"""						,  lowerCamelCase )

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

				__magic_name__						:						Any										={
				    """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:
								__magic_name__						:						List[str]										=AutoTokenizer.from_pretrained(model_args.tokenizer_name						,  **lowerCamelCase )
				elif model_args.model_name_or_path:
								__magic_name__						:						Dict										=AutoTokenizer.from_pretrained(model_args.model_name_or_path						,  **lowerCamelCase )
				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:
								__magic_name__						:						List[str]										=AutoModelForMaskedLM.from_pretrained(
								    model_args.model_name_or_path						,  from_tf=bool(""".ckpt""" in model_args.model_name_or_path )						,  config=lowerCamelCase						,  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""" )
								__magic_name__						:						List[str]										=AutoModelForMaskedLM.from_config(lowerCamelCase )

				model.resize_token_embeddings(len(lowerCamelCase ) )

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

				__magic_name__						:						Union[str, Any]										="""max_length""" if data_args.pad_to_max_length else False

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

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

				# Data collator
				# This one will take care of randomly masking the tokens.
				__magic_name__						:						Any										=DataCollatorForWholeWordMask(tokenizer=lowerCamelCase						,  mlm_probability=data_args.mlm_probability )

				# Initialize our Trainer
				__magic_name__						:						Tuple										=Trainer(
				    model=lowerCamelCase						,  args=lowerCamelCase						,  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=lowerCamelCase						,  data_collator=lowerCamelCase						,  )

				# Training
				if training_args.do_train:
								if last_checkpoint is not None:
												__magic_name__						:						str										=last_checkpoint
								elif model_args.model_name_or_path is not None and os.path.isdir(model_args.model_name_or_path ):
												__magic_name__						:						Union[str, Any]										=model_args.model_name_or_path
								else:
												__magic_name__						:						int										=None
								__magic_name__						:						Dict										=trainer.train(resume_from_checkpoint=lowerCamelCase )
								trainer.save_model()  # Saves the tokenizer too for easy upload

								__magic_name__						:						List[str]										=os.path.join(training_args.output_dir						,  """train_results.txt""" )
								if trainer.is_world_process_zero():
												with open(lowerCamelCase						,  """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
				__magic_name__						:						Dict										={}
				if training_args.do_eval:
								logger.info("""*** Evaluate ***""" )

								__magic_name__						:						Tuple										=trainer.evaluate()

								__magic_name__						:						Optional[int]										=math.exp(eval_output["""eval_loss"""] )
								__magic_name__						:						List[Any]										=perplexity

								__magic_name__						:						Optional[int]										=os.path.join(training_args.output_dir						,  """eval_results_mlm_wwm.txt""" )
								if trainer.is_world_process_zero():
												with open(lowerCamelCase						,  """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_ (						lowerCamelCase ):
				# For xla_spawn (TPUs)
				main()


if __name__ == "__main__":
							main()






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__lowerCAmelCase					   =       {
    'configuration_deberta': ['DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'DebertaConfig', 'DebertaOnnxConfig'],
    'tokenization_deberta': ['DebertaTokenizer'],
}

try:
							if not is_tokenizers_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__lowerCAmelCase					   =       ['DebertaTokenizerFast']

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__lowerCAmelCase					   =       [
							    'DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST',
							    'DebertaForMaskedLM',
							    'DebertaForQuestionAnswering',
							    'DebertaForSequenceClassification',
							    'DebertaForTokenClassification',
							    'DebertaModel',
							    'DebertaPreTrainedModel',
							]

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__lowerCAmelCase					   =       [
							    'TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST',
							    'TFDebertaForMaskedLM',
							    'TFDebertaForQuestionAnswering',
							    'TFDebertaForSequenceClassification',
							    'TFDebertaForTokenClassification',
							    'TFDebertaModel',
							    'TFDebertaPreTrainedModel',
							]


if TYPE_CHECKING:
							from .configuration_deberta import DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, DebertaConfig, DebertaOnnxConfig
							from .tokenization_deberta import DebertaTokenizer

							try:
														if not is_tokenizers_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_deberta_fast import DebertaTokenizerFast

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_deberta import (
														    DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
														    DebertaForMaskedLM,
														    DebertaForQuestionAnswering,
														    DebertaForSequenceClassification,
														    DebertaForTokenClassification,
														    DebertaModel,
														    DebertaPreTrainedModel,
														)

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_deberta import (
														    TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TFDebertaForMaskedLM,
														    TFDebertaForQuestionAnswering,
														    TFDebertaForSequenceClassification,
														    TFDebertaForTokenClassification,
														    TFDebertaModel,
														    TFDebertaPreTrainedModel,
														)


else:
							import sys

							__lowerCAmelCase					   =       _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)