






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					:		Dict	  =						logging.getLogger(__name__)
__UpperCamelCase					:		Dict	  =						list(MODEL_FOR_MASKED_LM_MAPPING.keys())
__UpperCamelCase					:		List[str]	  =						tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)


@dataclass
class   __SCREAMING_SNAKE_CASE:

							'''simple docstring'''





							_UpperCAmelCase     =       field(
							    default=SCREAMING_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=SCREAMING_SNAKE_CASE__    ,  metadata={"help": "If training from scratch, pass a model type from the list: " + ", ".join(SCREAMING_SNAKE_CASE__	)}    ,  )
							_UpperCAmelCase     =       field(
							    default=SCREAMING_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=SCREAMING_SNAKE_CASE__    ,  metadata={"help": "Pretrained config name or path if not the same as model_name"}	)
							_UpperCAmelCase     =       field(
							    default=SCREAMING_SNAKE_CASE__    ,  metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}	)
							_UpperCAmelCase     =       field(
							    default=SCREAMING_SNAKE_CASE__    ,  metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"}    ,  )
							_UpperCAmelCase     =       field(
							    default=SCREAMING_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=SCREAMING_SNAKE_CASE__    ,  metadata={
							        "help": (
							            "Will use the token generated when running `huggingface-cli login` (necessary to use this script "
							            "with private models)."
							        )
							    }    ,  )
							def lowerCAmelCase_ (     self:      Union[str, Any]						)		->		List[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   __SCREAMING_SNAKE_CASE:

							'''simple docstring'''





							_UpperCAmelCase     =       field(
							    default=SCREAMING_SNAKE_CASE__    ,  metadata={"help": "The name of the dataset to use (via the datasets library)."}	)
							_UpperCAmelCase     =       field(
							    default=SCREAMING_SNAKE_CASE__    ,  metadata={"help": "The configuration name of the dataset to use (via the datasets library)."}	)
							_UpperCAmelCase     =       field(default=SCREAMING_SNAKE_CASE__    ,  metadata={"help": "The input training data file (a text file)."}	)
							_UpperCAmelCase     =       field(
							    default=SCREAMING_SNAKE_CASE__    ,  metadata={"help": "An optional input evaluation data file to evaluate the perplexity on (a text file)."}    ,  )
							_UpperCAmelCase     =       field(
							    default=SCREAMING_SNAKE_CASE__    ,  metadata={"help": "An optional input train ref data file for whole word masking in Chinese."}    ,  )
							_UpperCAmelCase     =       field(
							    default=SCREAMING_SNAKE_CASE__    ,  metadata={"help": "An optional input validation ref data file for whole word masking in Chinese."}    ,  )
							_UpperCAmelCase     =       field(
							    default=SCREAMING_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=SCREAMING_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=SCREAMING_SNAKE_CASE__    ,  metadata={"help": "The number of processes to use for the preprocessing."}    ,  )
							_UpperCAmelCase     =       field(
							    default=0.1_5    ,  metadata={"help": "Ratio of tokens to mask for masked language modeling loss"}	)
							_UpperCAmelCase     =       field(
							    default=SCREAMING_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 lowerCAmelCase_ (     self:      List[Any]						)		->		List[str]:

											if self.train_file is not None:
															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:
															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_ (      _A      ,   _A  )			->			Dict:







				"""simple docstring"""






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

				snake_case__									=     {c: dataset[c] for c in dataset.column_names}
				snake_case__									=     refs
				return Dataset.from_dict(__UpperCamelCase  )
def      a_ (      )			->			List[Any]:







				"""simple docstring"""
				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.
								snake_case__									=     parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]  )  )
				else:
								snake_case__									=     parser.parse_args_into_dataclasses()

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

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

				snake_case__									=     """max_length""" if data_args.pad_to_max_length else False

				def tokenize_function(_A  ):
								# Remove empty lines
								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  )

				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:
								snake_case__									=     add_chinese_references(tokenized_datasets['train']      ,   data_args.train_ref_file  )
				if data_args.validation_ref_file is not None:
								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
				snake_case__									=     data_args.train_ref_file or data_args.validation_ref_file
				if has_ref:
								snake_case__									=     False

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

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

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

								snake_case__									=     trainer.evaluate()

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

								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_ (      _A  )			->			Optional[Any]:







				"""simple docstring"""
				main()


if __name__ == "__main__":
							main()












def      a_ (      _A      ,   _A  )			->			str:







				"""simple docstring"""
				if number < 0 or shift_amount < 0:
								raise ValueError('both inputs must be positive integers'  )

				snake_case__									=     str(bin(_A  )  )
				binary_number += "0" * shift_amount
				return binary_number
def      a_ (      _A      ,   _A  )			->			str:







				"""simple docstring"""
				if number < 0 or shift_amount < 0:
								raise ValueError('both inputs must be positive integers'  )

				snake_case__									=     str(bin(_A  )  )[2:]
				if shift_amount >= len(_A  ):
								return "0b0"
				snake_case__									=     binary_number[: len(_A  ) - shift_amount]
				return "0b" + shifted_binary_number
def      a_ (      _A      ,   _A  )			->			str:







				"""simple docstring"""
				if number >= 0:  # Get binary representation of positive number
								snake_case__									=     '0' + str(bin(_A  )  ).strip('-'  )[2:]
				else:  # Get binary (2's complement) representation of negative number
								snake_case__									=     len(bin(_A  )[3:]  )  # Find 2's complement of number
								snake_case__									=     bin(abs(_A  ) - (1 << binary_number_length)  )[3:]
								snake_case__									=     (
								    '1' + '0' * (binary_number_length - len(_A  )) + binary_number
								)

				if shift_amount >= len(_A  ):
								return "0b" + binary_number[0] * len(_A  )
				return (
				    "0b"
				    + binary_number[0] * shift_amount
				    + binary_number[: len(_A  ) - shift_amount]
				)


if __name__ == "__main__":
							import doctest

							doctest.testmod()




