


'''simple docstring'''



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	:						str       =	logging.getLogger(__name__)
__lowercase	:						Optional[int]       =	list(MODEL_FOR_MASKED_LM_MAPPING.keys())
__lowercase	:						Optional[Any]       =	tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)





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




	def       __UpperCAmelCase							(   self							):

						'''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 							__UpperCamelCase				:
	A_				=    field(
	    default=lowerCAmelCase_		,      metadata={"help": "The name of the dataset to use (via the datasets library)."}					)
	A_				=    field(
	    default=lowerCAmelCase_		,      metadata={"help": "The configuration name of the dataset to use (via the datasets library)."}					)
	A_				=    field(default=lowerCAmelCase_		,      metadata={"help": "The input training data file (a text file)."}					)
	A_				=    field(
	    default=lowerCAmelCase_		,      metadata={"help": "An optional input evaluation data file to evaluate the perplexity on (a text file)."}		,      )
	A_				=    field(
	    default=lowerCAmelCase_		,      metadata={"help": "An optional input train ref data file for whole word masking in Chinese."}		,      )
	A_				=    field(
	    default=lowerCAmelCase_		,      metadata={"help": "An optional input validation ref data file for whole word masking in Chinese."}		,      )
	A_				=    field(
	    default=lowerCAmelCase_		,      metadata={"help": "Overwrite the cached training and evaluation sets"}					)
	A_				=    field(
	    default=5		,      metadata={
	        "help": "The percentage of the train set used as validation set in case there's no validation split"
	    }		,      )
	A_				=    field(
	    default=lowerCAmelCase_		,      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."
	        )
	    }		,      )
	A_				=    field(
	    default=lowerCAmelCase_		,      metadata={"help": "The number of processes to use for the preprocessing."}		,      )
	A_				=    field(
	    default=0.15		,      metadata={"help": "Ratio of tokens to mask for masked language modeling loss"}					)
	A_				=    field(
	    default=lowerCAmelCase_		,      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							):

						'''simple docstring'''





						if self.train_file is not None:
											__a			:       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:
											__a			:       int					    =  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							(_SCREAMING_SNAKE_CASE      :   int						,      _SCREAMING_SNAKE_CASE      :   Optional[int]   ):
					with open(_SCREAMING_SNAKE_CASE						,      'r'						,      encoding='utf-8'   ) as f:
										__a			:       str					    =  [json.loads(_SCREAMING_SNAKE_CASE   ) for line in f.read().splitlines() if (len(_SCREAMING_SNAKE_CASE   ) > 0 and not line.isspace())]
					assert len(_SCREAMING_SNAKE_CASE   ) == len(_SCREAMING_SNAKE_CASE   )

					__a			:       List[str]					    =  {c: dataset[c] for c in dataset.column_names}
					__a			:       Optional[int]					    =  refs
					return Dataset.from_dict(_SCREAMING_SNAKE_CASE   )






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.

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

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

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

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

					model.resize_token_embeddings(len(_SCREAMING_SNAKE_CASE   )   )

					# Preprocessing the datasets.
					# First we tokenize all the texts.
					if training_args.do_train:
										__a			:       List[Any]					    =  datasets['train'].column_names
					else:
										__a			:       Any					    =  datasets['validation'].column_names
					__a			:       Optional[int]					    =  'text' if 'text' in column_names else column_names[0]

					__a			:       int					    =  'max_length' if data_args.pad_to_max_length else False

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

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

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

					# Initialize our Trainer
					__a			:       Optional[int]					    =  Trainer(
					    model=_SCREAMING_SNAKE_CASE						,      args=_SCREAMING_SNAKE_CASE						,      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=_SCREAMING_SNAKE_CASE						,      data_collator=_SCREAMING_SNAKE_CASE						,      )

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

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

										__a			:       List[Any]					    =  trainer.evaluate()

										__a			:       Optional[Any]					    =  math.exp(eval_output['eval_loss']   )
										__a			:       Union[str, Any]					    =  perplexity

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


if __name__ == "__main__":
	main()







'''simple docstring'''



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


__lowercase	:						str       =	{
    'configuration_data2vec_audio': ['DATA2VEC_AUDIO_PRETRAINED_CONFIG_ARCHIVE_MAP', 'Data2VecAudioConfig'],
    'configuration_data2vec_text': [
        'DATA2VEC_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'Data2VecTextConfig',
        'Data2VecTextOnnxConfig',
    ],
    'configuration_data2vec_vision': [
        'DATA2VEC_VISION_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'Data2VecVisionConfig',
        'Data2VecVisionOnnxConfig',
    ],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__lowercase	:						str       =	[
	    'DATA2VEC_AUDIO_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'Data2VecAudioForAudioFrameClassification',
	    'Data2VecAudioForCTC',
	    'Data2VecAudioForSequenceClassification',
	    'Data2VecAudioForXVector',
	    'Data2VecAudioModel',
	    'Data2VecAudioPreTrainedModel',
	]
	__lowercase	:						Tuple       =	[
	    'DATA2VEC_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'Data2VecTextForCausalLM',
	    'Data2VecTextForMaskedLM',
	    'Data2VecTextForMultipleChoice',
	    'Data2VecTextForQuestionAnswering',
	    'Data2VecTextForSequenceClassification',
	    'Data2VecTextForTokenClassification',
	    'Data2VecTextModel',
	    'Data2VecTextPreTrainedModel',
	]
	__lowercase	:						Dict       =	[
	    'DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'Data2VecVisionForImageClassification',
	    'Data2VecVisionForMaskedImageModeling',
	    'Data2VecVisionForSemanticSegmentation',
	    'Data2VecVisionModel',
	    'Data2VecVisionPreTrainedModel',
	]

if is_tf_available():
	__lowercase	:						Optional[Any]       =	[
	    'TFData2VecVisionForImageClassification',
	    'TFData2VecVisionForSemanticSegmentation',
	    'TFData2VecVisionModel',
	    'TFData2VecVisionPreTrainedModel',
	]

if TYPE_CHECKING:
	from .configuration_dataavec_audio import DATA2VEC_AUDIO_PRETRAINED_CONFIG_ARCHIVE_MAP, DataaVecAudioConfig
	from .configuration_dataavec_text import (
	    DATA2VEC_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP,
	    DataaVecTextConfig,
	    DataaVecTextOnnxConfig,
	)
	from .configuration_dataavec_vision import (
	    DATA2VEC_VISION_PRETRAINED_CONFIG_ARCHIVE_MAP,
	    DataaVecVisionConfig,
	    DataaVecVisionOnnxConfig,
	)

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_dataavec_audio import (
		    DATA2VEC_AUDIO_PRETRAINED_MODEL_ARCHIVE_LIST,
		    DataaVecAudioForAudioFrameClassification,
		    DataaVecAudioForCTC,
		    DataaVecAudioForSequenceClassification,
		    DataaVecAudioForXVector,
		    DataaVecAudioModel,
		    DataaVecAudioPreTrainedModel,
		)
		from .modeling_dataavec_text import (
		    DATA2VEC_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    DataaVecTextForCausalLM,
		    DataaVecTextForMaskedLM,
		    DataaVecTextForMultipleChoice,
		    DataaVecTextForQuestionAnswering,
		    DataaVecTextForSequenceClassification,
		    DataaVecTextForTokenClassification,
		    DataaVecTextModel,
		    DataaVecTextPreTrainedModel,
		)
		from .modeling_dataavec_vision import (
		    DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST,
		    DataaVecVisionForImageClassification,
		    DataaVecVisionForMaskedImageModeling,
		    DataaVecVisionForSemanticSegmentation,
		    DataaVecVisionModel,
		    DataaVecVisionPreTrainedModel,
		)
	if is_tf_available():
		from .modeling_tf_dataavec_vision import (
		    TFDataaVecVisionForImageClassification,
		    TFDataaVecVisionForSemanticSegmentation,
		    TFDataaVecVisionModel,
		    TFDataaVecVisionPreTrainedModel,
		)

else:
	import sys

	__lowercase	:						int       =	_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



