




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  __magic_name__						:
				'''simple docstring'''







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


				def     _A							(							self:   List[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  __magic_name__						:
				'''simple docstring'''







				SCREAMING_SNAKE_CASE__					:							Dict	      =					field(
				    default=_a    ,  metadata={"help": "The name of the dataset to use (via the datasets library)."})
				SCREAMING_SNAKE_CASE__					:							int	      =					field(
				    default=_a    ,  metadata={"help": "The configuration name of the dataset to use (via the datasets library)."})
				SCREAMING_SNAKE_CASE__					:							List[Any]	      =					field(default=_a    ,  metadata={"help": "The input training data file (a text file)."})
				SCREAMING_SNAKE_CASE__					:							Dict	      =					field(
				    default=_a    ,  metadata={"help": "An optional input evaluation data file to evaluate the perplexity on (a text file)."}    ,  )
				SCREAMING_SNAKE_CASE__					:							Optional[int]	      =					field(
				    default=_a    ,  metadata={"help": "An optional input train ref data file for whole word masking in Chinese."}    ,  )
				SCREAMING_SNAKE_CASE__					:							Optional[int]	      =					field(
				    default=_a    ,  metadata={"help": "An optional input validation ref data file for whole word masking in Chinese."}    ,  )
				SCREAMING_SNAKE_CASE__					:							Union[str, Any]	      =					field(
				    default=_a    ,  metadata={"help": "Overwrite the cached training and evaluation sets"})
				SCREAMING_SNAKE_CASE__					:							Optional[Any]	      =					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__					:							Optional[Any]	      =					field(
				    default=_a    ,  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__					:							List[str]	      =					field(
				    default=_a    ,  metadata={"help": "The number of processes to use for the preprocessing."}    ,  )
				SCREAMING_SNAKE_CASE__					:							Union[str, Any]	      =					field(
				    default=0.15    ,  metadata={"help": "Ratio of tokens to mask for masked language modeling loss"})
				SCREAMING_SNAKE_CASE__					:							List[str]	      =					field(
				    default=_a    ,  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:   Union[str, Any]		):
											if self.train_file is not None:
																		SCREAMING_SNAKE_CASE_   	=    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:
																		SCREAMING_SNAKE_CASE_   	=    self.validation_file.split('''.'''		)[-1]
																		assert extension in ["csv", "json", "txt"], "`validation_file` should be a csv, a json or a txt file."






def 			a   (_lowerCAmelCase  ,    _lowerCAmelCase     ):






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

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

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

							# Detecting last checkpoint.
							SCREAMING_SNAKE_CASE_   	=    None
							if os.path.isdir(training_args.output_dir     ) and training_args.do_train and not training_args.overwrite_output_dir:
														SCREAMING_SNAKE_CASE_   	=    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.
														SCREAMING_SNAKE_CASE_   	=    load_dataset(data_args.dataset_name  ,    data_args.dataset_config_name     )
														if "validation" not in datasets.keys():
																					SCREAMING_SNAKE_CASE_   	=    load_dataset(
																					    data_args.dataset_name  ,    data_args.dataset_config_name  ,    split=F"train[:{data_args.validation_split_percentage}%]"  ,    )
																					SCREAMING_SNAKE_CASE_   	=    load_dataset(
																					    data_args.dataset_name  ,    data_args.dataset_config_name  ,    split=F"train[{data_args.validation_split_percentage}%:]"  ,    )
							else:
														SCREAMING_SNAKE_CASE_   	=    {}
														if data_args.train_file is not None:
																					SCREAMING_SNAKE_CASE_   	=    data_args.train_file
														if data_args.validation_file is not None:
																					SCREAMING_SNAKE_CASE_   	=    data_args.validation_file
														SCREAMING_SNAKE_CASE_   	=    data_args.train_file.split('''.'''     )[-1]
														if extension == "txt":
																					SCREAMING_SNAKE_CASE_   	=    '''text'''
														SCREAMING_SNAKE_CASE_   	=    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.
							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,
							}
							if model_args.config_name:
														SCREAMING_SNAKE_CASE_   	=    AutoConfig.from_pretrained(model_args.config_name  ,    **UpperCAmelCase__     )
							elif model_args.model_name_or_path:
														SCREAMING_SNAKE_CASE_   	=    AutoConfig.from_pretrained(model_args.model_name_or_path  ,    **UpperCAmelCase__     )
							else:
														SCREAMING_SNAKE_CASE_   	=    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}"     )

							SCREAMING_SNAKE_CASE_   	=    {
							    '''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:
														SCREAMING_SNAKE_CASE_   	=    AutoTokenizer.from_pretrained(model_args.tokenizer_name  ,    **UpperCAmelCase__     )
							elif model_args.model_name_or_path:
														SCREAMING_SNAKE_CASE_   	=    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:
														SCREAMING_SNAKE_CASE_   	=    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'''     )
														SCREAMING_SNAKE_CASE_   	=    AutoModelForMaskedLM.from_config(UpperCAmelCase__     )

							model.resize_token_embeddings(len(UpperCAmelCase__     )     )

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

							SCREAMING_SNAKE_CASE_   	=    '''max_length''' if data_args.pad_to_max_length else False

							def tokenize_function(_lowerCAmelCase     ):
														# Remove empty lines
														SCREAMING_SNAKE_CASE_   	=    [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     )

							SCREAMING_SNAKE_CASE_   	=    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:
														SCREAMING_SNAKE_CASE_   	=    add_chinese_references(tokenized_datasets['''train''']  ,    data_args.train_ref_file     )
							if data_args.validation_ref_file is not None:
														SCREAMING_SNAKE_CASE_   	=    add_chinese_references(
														    tokenized_datasets['''validation''']  ,    data_args.validation_ref_file     )
							# If we have ref files, need to avoid it removed by trainer
							SCREAMING_SNAKE_CASE_   	=    data_args.train_ref_file or data_args.validation_ref_file
							if has_ref:
														SCREAMING_SNAKE_CASE_   	=    False

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

							# Initialize our Trainer
							SCREAMING_SNAKE_CASE_   	=    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:
																					SCREAMING_SNAKE_CASE_   	=    last_checkpoint
														elif model_args.model_name_or_path is not None and os.path.isdir(model_args.model_name_or_path     ):
																					SCREAMING_SNAKE_CASE_   	=    model_args.model_name_or_path
														else:
																					SCREAMING_SNAKE_CASE_   	=    None
														SCREAMING_SNAKE_CASE_   	=    trainer.train(resume_from_checkpoint=UpperCAmelCase__     )
														trainer.save_model()  # Saves the tokenizer too for easy upload

														SCREAMING_SNAKE_CASE_   	=    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
							SCREAMING_SNAKE_CASE_   	=    {}
							if training_args.do_eval:
														logger.info('''*** Evaluate ***'''     )

														SCREAMING_SNAKE_CASE_   	=    trainer.evaluate()

														SCREAMING_SNAKE_CASE_   	=    math.exp(eval_output['''eval_loss''']     )
														SCREAMING_SNAKE_CASE_   	=    perplexity

														SCREAMING_SNAKE_CASE_   	=    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 			a   (_lowerCAmelCase     ):
							main()


if __name__ == "__main__":
			main()








# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING

import numpy as np
import pyarrow as pa

from .. import config
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter


if TYPE_CHECKING:
			import torch






class  __magic_name__						(  TensorFormatter[Mapping, "torch.Tensor", Mapping]):
				'''simple docstring'''







				def __init__(							self:   int		,					_lowerCamelCase:   int=None		,					**_lowerCamelCase:   List[Any]		):
											super().__init__(features=_lowerCamelCase		)
											SCREAMING_SNAKE_CASE_   	=    torch_tensor_kwargs
											import torch  # noqa import torch at initialization


				def     _A							(							self:   Union[str, Any]		,					_lowerCamelCase:   Optional[Any]		):
											import torch

											if isinstance(_lowerCamelCase		,					_lowerCamelCase		) and column:
																		if all(
																		    isinstance(_lowerCamelCase		,					torch.Tensor		) and x.shape == column[0].shape and x.dtype == column[0].dtype
																		    for x in column		):
																									return torch.stack(_lowerCamelCase		)
											return column


				def     _A							(							self:   Tuple		,					_lowerCamelCase:   List[str]		):
											import torch

											if isinstance(_lowerCamelCase		,					(str, bytes, type(_lowerCamelCase		))		):
																		return value
											elif isinstance(_lowerCamelCase		,					(np.character, np.ndarray)		) and np.issubdtype(value.dtype		,					np.character		):
																		return value.tolist()

											SCREAMING_SNAKE_CASE_   	=    {}

											if isinstance(_lowerCamelCase		,					(np.number, np.ndarray)		) and np.issubdtype(value.dtype		,					np.integer		):
																		SCREAMING_SNAKE_CASE_   	=    {'''dtype''': torch.intaa}
											elif isinstance(_lowerCamelCase		,					(np.number, np.ndarray)		) and np.issubdtype(value.dtype		,					np.floating		):
																		SCREAMING_SNAKE_CASE_   	=    {'''dtype''': torch.floataa}
											elif config.PIL_AVAILABLE and "PIL" in sys.modules:
																		import PIL.Image

																		if isinstance(_lowerCamelCase		,					PIL.Image.Image		):
																									SCREAMING_SNAKE_CASE_   	=    np.asarray(_lowerCamelCase		)
											return torch.tensor(_lowerCamelCase		,					**{**default_dtype, **self.torch_tensor_kwargs}		)


				def     _A							(							self:   Union[str, Any]		,					_lowerCamelCase:   Dict		):
											import torch

											# support for torch, tf, jax etc.
											if hasattr(_lowerCamelCase		,					'''__array__'''		) and not isinstance(_lowerCamelCase		,					torch.Tensor		):
																		SCREAMING_SNAKE_CASE_   	=    data_struct.__array__()
											# support for nested types like struct of list of struct
											if isinstance(_lowerCamelCase		,					np.ndarray		):
																		if data_struct.dtype == object:  # torch tensors cannot be instantied from an array of objects
																									return self._consolidate([self.recursive_tensorize(_lowerCamelCase		) for substruct in data_struct]		)
											elif isinstance(_lowerCamelCase		,					(list, tuple)		):
																		return self._consolidate([self.recursive_tensorize(_lowerCamelCase		) for substruct in data_struct]		)
											return self._tensorize(_lowerCamelCase		)


				def     _A							(							self:   str		,					_lowerCamelCase:   dict		):
											return map_nested(self._recursive_tensorize		,					_lowerCamelCase		,					map_list=_lowerCamelCase		)


				def     _A							(							self:   Optional[int]		,					_lowerCamelCase:   pa.Table		):
											SCREAMING_SNAKE_CASE_   	=    self.numpy_arrow_extractor().extract_row(_lowerCamelCase		)
											SCREAMING_SNAKE_CASE_   	=    self.python_features_decoder.decode_row(_lowerCamelCase		)
											return self.recursive_tensorize(_lowerCamelCase		)


				def     _A							(							self:   Optional[Any]		,					_lowerCamelCase:   pa.Table		):
											SCREAMING_SNAKE_CASE_   	=    self.numpy_arrow_extractor().extract_column(_lowerCamelCase		)
											SCREAMING_SNAKE_CASE_   	=    self.python_features_decoder.decode_column(_lowerCamelCase		,					pa_table.column_names[0]		)
											SCREAMING_SNAKE_CASE_   	=    self.recursive_tensorize(_lowerCamelCase		)
											SCREAMING_SNAKE_CASE_   	=    self._consolidate(_lowerCamelCase		)
											return column





				def     _A							(							self:   Optional[Any]		,					_lowerCamelCase:   pa.Table		):
											SCREAMING_SNAKE_CASE_   	=    self.numpy_arrow_extractor().extract_batch(_lowerCamelCase		)
											SCREAMING_SNAKE_CASE_   	=    self.python_features_decoder.decode_batch(_lowerCamelCase		)
											SCREAMING_SNAKE_CASE_   	=    self.recursive_tensorize(_lowerCamelCase		)
											for column_name in batch:
																		SCREAMING_SNAKE_CASE_   	=    self._consolidate(batch[column_name]		)
											return batch


