
"""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


__A   :							str										=   logging.getLogger(__name__)
__A   :							Dict										=   list(MODEL_FOR_MASKED_LM_MAPPING.keys())
__A   :							Union[str, Any]										=   tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)
@dataclass
class 			_a :
			"""simple docstring"""
			UpperCamelCase__			      =	field(
			    default=snake_case__			,							metadata={
			        """help""": (
			            """The model checkpoint for weights initialization.Don't set if you want to train a model from scratch."""
			        )
			    }			,							)
			UpperCamelCase__			      =	field(
			    default=snake_case__			,							metadata={"""help""": """If training from scratch, pass a model type from the list: """ + """, """.join(snake_case__)}			,							)
			UpperCamelCase__			      =	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"""
			        )
			    }			,							)
			UpperCamelCase__			      =	field(
			    default=snake_case__			,							metadata={"""help""": """Pretrained config name or path if not the same as model_name"""})
			UpperCamelCase__			      =	field(
			    default=snake_case__			,							metadata={"""help""": """Pretrained tokenizer name or path if not the same as model_name"""})
			UpperCamelCase__			      =	field(
			    default=snake_case__			,							metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""}			,							)
			UpperCamelCase__			      =	field(
			    default=snake_case__			,							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=snake_case__			,							metadata={
			        """help""": (
			            """Will use the token generated when running `huggingface-cli login` (necessary to use this script """
			            """with private models)."""
			        )
			    }			,							)



			def  lowercase__						(		self     :    Optional[Any]   )->Optional[Any]:
							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 :
			"""simple docstring"""
			UpperCamelCase__			      =	field(
			    default=snake_case__			,							metadata={"""help""": """The name of the dataset to use (via the datasets library)."""})
			UpperCamelCase__			      =	field(
			    default=snake_case__			,							metadata={"""help""": """The configuration name of the dataset to use (via the datasets library)."""})
			UpperCamelCase__			      =	field(default=snake_case__			,							metadata={"""help""": """The input training data file (a text file)."""})
			UpperCamelCase__			      =	field(
			    default=snake_case__			,							metadata={"""help""": """An optional input evaluation data file to evaluate the perplexity on (a text file)."""}			,							)
			UpperCamelCase__			      =	field(
			    default=snake_case__			,							metadata={"""help""": """An optional input train ref data file for whole word masking in Chinese."""}			,							)
			UpperCamelCase__			      =	field(
			    default=snake_case__			,							metadata={"""help""": """An optional input validation ref data file for whole word masking in Chinese."""}			,							)
			UpperCamelCase__			      =	field(
			    default=snake_case__			,							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=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."""
			        )
			    }			,							)
			UpperCamelCase__			      =	field(
			    default=snake_case__			,							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=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  lowercase__						(		self     :    str   )->int:
							if self.train_file is not None:
											_UpperCAmelCase					=    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:
											_UpperCAmelCase					=    self.validation_file.split('''.'''   )[-1]
											assert extension in ["csv", "json", "txt"], "`validation_file` should be a csv, a json or a txt file."







def  lowercase	(      _SCREAMING_SNAKE_CASE   :						str					,     _SCREAMING_SNAKE_CASE   :						str				):






				'''simple docstring'''



				with open(_SCREAMING_SNAKE_CASE					,     '''r'''					,     encoding='''utf-8'''				) as f:
								_UpperCAmelCase					=    [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				)

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







def  lowercase	(      ):






				'''simple docstring'''



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

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

				_UpperCAmelCase					=    {
				    '''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:
								_UpperCAmelCase					=    AutoTokenizer.from_pretrained(model_args.tokenizer_name					,     **_SCREAMING_SNAKE_CASE				)
				elif model_args.model_name_or_path:
								_UpperCAmelCase					=    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:
								_UpperCAmelCase					=    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'''				)
								_UpperCAmelCase					=    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:
								_UpperCAmelCase					=    datasets['''train'''].column_names
				else:
								_UpperCAmelCase					=    datasets['''validation'''].column_names
				_UpperCAmelCase					=    '''text''' if '''text''' in column_names else column_names[0]

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

				def tokenize_function(_SCREAMING_SNAKE_CASE   :						int				):
								# Remove empty lines
								_UpperCAmelCase					=    [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				)

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

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

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

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

								_UpperCAmelCase					=    trainer.evaluate()

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

								_UpperCAmelCase					=    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  lowercase	(      _SCREAMING_SNAKE_CASE   :						Any				):






				'''simple docstring'''



				main()


if __name__ == "__main__":
					main()








"""simple docstring"""


class 			_a :
			"""simple docstring"""



			def __init__(		self     :    Tuple				,   __UpperCamelCase     :    list[int]   )->None:
							_UpperCAmelCase					=    len(__UpperCamelCase   )
							_UpperCAmelCase					=    [0] * len_array

							if len_array > 0:
											_UpperCAmelCase					=    array[0]

							for i in range(1				,   __UpperCamelCase   ):
											_UpperCAmelCase					=    self.prefix_sum[i - 1] + array[i]



			def  lowercase__						(		self     :    str				,   __UpperCamelCase     :    int				,   __UpperCamelCase     :    int   )->int:
							if start == 0:
											return self.prefix_sum[end]

							return self.prefix_sum[end] - self.prefix_sum[start - 1]



			def  lowercase__						(		self     :    Any				,   __UpperCamelCase     :    int   )->bool:
							_UpperCAmelCase					=    {0}
							for sum_item in self.prefix_sum:
											if sum_item - target_sum in sums:
															return True

											sums.add(__UpperCamelCase   )

							return False


if __name__ == "__main__":
					import doctest

					doctest.testmod()



