



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


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




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







					def  _lowercase			(       self      :     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 							lowerCAmelCase__  :
					__A     :    Optional[str]           =						field(
					    default=UpperCamelCase					,							metadata={'help': 'The name of the dataset to use (via the datasets library).'}						)
					__A     :    Optional[str]           =						field(
					    default=UpperCamelCase					,							metadata={'help': 'The configuration name of the dataset to use (via the datasets library).'}						)
					__A     :    Optional[str]           =						field(default=UpperCamelCase					,							metadata={'help': 'The input training data file (a text file).'}						)
					__A     :    Optional[str]           =						field(
					    default=UpperCamelCase					,							metadata={'help': 'An optional input evaluation data file to evaluate the perplexity on (a text file).'}					,							)
					__A     :    Optional[str]           =						field(
					    default=UpperCamelCase					,							metadata={'help': 'An optional input train ref data file for whole word masking in Chinese.'}					,							)
					__A     :    Optional[str]           =						field(
					    default=UpperCamelCase					,							metadata={'help': 'An optional input validation ref data file for whole word masking in Chinese.'}					,							)
					__A     :    bool           =						field(
					    default=UpperCamelCase					,							metadata={'help': 'Overwrite the cached training and evaluation sets'}						)
					__A     :    Optional[int]           =						field(
					    default=5					,							metadata={
					        'help': 'The percentage of the train set used as validation set in case there\'s no validation split'
					    }					,							)
					__A     :    Optional[int]           =						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.'
					        )
					    }					,							)
					__A     :    Optional[int]           =						field(
					    default=UpperCamelCase					,							metadata={'help': 'The number of processes to use for the preprocessing.'}					,							)
					__A     :    float           =						field(
					    default=0.15					,							metadata={'help': 'Ratio of tokens to mask for masked language modeling loss'}						)
					__A     :    bool           =						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  _lowercase			(       self      :     Optional[int]):
											if self.train_file is not None:
																	A__	:							Any						    = 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__	:							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 	snake_case__       (					__lowercase							,     __lowercase       )		->		Any:







						"""simple docstring"""

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

						A__	:							str						    = {c: dataset[c] for c in dataset.column_names}
						A__	:							str						    = refs
						return Dataset.from_dict(__lowercase       )
def 	snake_case__       (					)		->		int:







						"""simple docstring"""

						A__	:							Dict						    = 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__	:							Any						    = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]       )       )
						else:
												A__    ,       A__    ,       A__	:							List[str]						    = parser.parse_args_into_dataclasses()

						# Detecting last checkpoint.
						A__	:							Any						    = None
						if os.path.isdir(training_args.output_dir       ) and training_args.do_train and not training_args.overwrite_output_dir:
												A__	:							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"							,     __lowercase       )

						# 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__	:							Union[str, Any]						    = 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__	:							Any						    = load_dataset(
																		    data_args.dataset_name							,     data_args.dataset_config_name							,     split=F'train[{data_args.validation_split_percentage}%:]'							,     )
						else:
												A__	:							List[Any]						    = {}
												if data_args.train_file is not None:
																		A__	:							Tuple						    = data_args.train_file
												if data_args.validation_file is not None:
																		A__	:							List[Any]						    = data_args.validation_file
												A__	:							Dict						    = data_args.train_file.split("."       )[-1]
												if extension == "txt":
																		A__	:							Dict						    = "text"
												A__	:							Union[str, Any]						    = load_dataset(__lowercase							,     data_files=__lowercase       )
						# 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__	:							Tuple						    = AutoConfig.from_pretrained(model_args.config_name							,     **__lowercase       )
						elif model_args.model_name_or_path:
												A__	:							Any						    = AutoConfig.from_pretrained(model_args.model_name_or_path							,     **__lowercase       )
						else:
												A__	:							Optional[int]						    = 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__	:							Union[str, 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:
												A__	:							Optional[Any]						    = AutoTokenizer.from_pretrained(model_args.tokenizer_name							,     **__lowercase       )
						elif model_args.model_name_or_path:
												A__	:							List[Any]						    = AutoTokenizer.from_pretrained(model_args.model_name_or_path							,     **__lowercase       )
						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__	:							Optional[int]						    = AutoModelForMaskedLM.from_pretrained(
												    model_args.model_name_or_path							,     from_tf=bool(".ckpt" in model_args.model_name_or_path       )							,     config=__lowercase							,     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__	:							Union[str, Any]						    = AutoModelForMaskedLM.from_config(__lowercase       )

						model.resize_token_embeddings(len(__lowercase       )       )

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

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

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

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

						# Data collator
						# This one will take care of randomly masking the tokens.
						A__	:							int						    = DataCollatorForWholeWordMask(tokenizer=__lowercase							,     mlm_probability=data_args.mlm_probability       )

						# Initialize our Trainer
						A__	:							Union[str, Any]						    = Trainer(
						    model=__lowercase							,     args=__lowercase							,     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=__lowercase							,     data_collator=__lowercase							,     )

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

												A__	:							Optional[int]						    = os.path.join(training_args.output_dir							,     "train_results.txt"       )
												if trainer.is_world_process_zero():
																		with open(__lowercase							,     "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__	:							Optional[int]						    = trainer.evaluate()

												A__	:							str						    = math.exp(eval_output["eval_loss"]       )
												A__	:							List[str]						    = perplexity

												A__	:							Optional[Any]						    = os.path.join(training_args.output_dir							,     "eval_results_mlm_wwm.txt"       )
												if trainer.is_world_process_zero():
																		with open(__lowercase							,     "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 	snake_case__       (					__lowercase       )		->		int:







						"""simple docstring"""

						main()


if __name__ == "__main__":
		main()




from collections.abc import Callable

import numpy as np
def 	snake_case__       (					__lowercase							,     __lowercase							,     __lowercase							,     __lowercase							,     __lowercase       )		->		np.array:







						"""simple docstring"""

						A__	:							Any						    = int(np.ceil((x_end - xa) / step_size       )       )
						A__	:							Union[str, Any]						    = np.zeros((n + 1,)       )
						A__	:							Any						    = ya
						A__	:							Union[str, Any]						    = xa

						for k in range(__lowercase       ):
												A__	:							Any						    = y[k] + step_size * ode_func(__lowercase							,     y[k]       )
												A__	:							Any						    = y[k] + (
												    (step_size / 2) * (ode_func(__lowercase							,     y[k]       ) + ode_func(x + step_size							,     __lowercase       ))
												)
												x += step_size

						return y


if __name__ == "__main__":
		import doctest

		doctest.testmod()