


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






@dataclass
class      UpperCamelCase:
					snake_case_		:					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."""
					        )
					    }  ,				)
					snake_case_		:					Optional[str]    					=						field(
					    default=UpperCAmelCase__  ,				metadata={"""help""": """If training from scratch, pass a model type from the list: """ + """, """.join(UpperCAmelCase__ )}  ,				)
					snake_case_		:					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"""
					        )
					    }  ,				)
					snake_case_		:					Optional[str]    					=						field(
					    default=UpperCAmelCase__  ,				metadata={"""help""": """Pretrained config name or path if not the same as model_name"""} )
					snake_case_		:					Optional[str]    					=						field(
					    default=UpperCAmelCase__  ,				metadata={"""help""": """Pretrained tokenizer name or path if not the same as model_name"""} )
					snake_case_		:					Optional[str]    					=						field(
					    default=UpperCAmelCase__  ,				metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""}  ,				)
					snake_case_		:					bool    					=						field(
					    default=UpperCAmelCase__  ,				metadata={"""help""": """Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."""}  ,				)
					snake_case_		:					str    					=						field(
					    default="""main"""  ,				metadata={"""help""": """The specific model version to use (can be a branch name, tag name or commit id)."""}  ,				)
					snake_case_		:					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 				SCREAMING_SNAKE_CASE_	(    self						:		int    )      ->						str:




											'''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:
					snake_case_		:					Optional[str]    					=						field(
					    default=UpperCAmelCase__  ,				metadata={"""help""": """The name of the dataset to use (via the datasets library)."""} )
					snake_case_		:					Optional[str]    					=						field(
					    default=UpperCAmelCase__  ,				metadata={"""help""": """The configuration name of the dataset to use (via the datasets library)."""} )
					snake_case_		:					Optional[str]    					=						field(default=UpperCAmelCase__  ,				metadata={"""help""": """The input training data file (a text file)."""} )
					snake_case_		:					Optional[str]    					=						field(
					    default=UpperCAmelCase__  ,				metadata={"""help""": """An optional input evaluation data file to evaluate the perplexity on (a text file)."""}  ,				)
					snake_case_		:					Optional[str]    					=						field(
					    default=UpperCAmelCase__  ,				metadata={"""help""": """An optional input train ref data file for whole word masking in Chinese."""}  ,				)
					snake_case_		:					Optional[str]    					=						field(
					    default=UpperCAmelCase__  ,				metadata={"""help""": """An optional input validation ref data file for whole word masking in Chinese."""}  ,				)
					snake_case_		:					bool    					=						field(
					    default=UpperCAmelCase__  ,				metadata={"""help""": """Overwrite the cached training and evaluation sets"""} )
					snake_case_		:					Optional[int]    					=						field(
					    default=5  ,				metadata={
					        """help""": """The percentage of the train set used as validation set in case there\'s no validation split"""
					    }  ,				)
					snake_case_		:					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."""
					        )
					    }  ,				)
					snake_case_		:					Optional[int]    					=						field(
					    default=UpperCAmelCase__  ,				metadata={"""help""": """The number of processes to use for the preprocessing."""}  ,				)
					snake_case_		:					float    					=						field(
					    default=0.15  ,				metadata={"""help""": """Ratio of tokens to mask for masked language modeling loss"""} )
					snake_case_		:					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 				SCREAMING_SNAKE_CASE_	(    self						:		Tuple    )      ->						Optional[Any]:




											'''simple docstring'''

											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 						_lowerCAmelCase							(     _lowerCAmelCase   ,						_lowerCAmelCase			)    ->						Optional[Any]:


						'''simple docstring'''






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

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





def 						_lowerCAmelCase							(     )    ->						Optional[int]:


						'''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		,       __snake_case		,       __snake_case				=		parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]			)			)
						else:
												__snake_case		,       __snake_case		,       __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"   ,						_lowerCAmelCase			)

						# 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(_lowerCAmelCase   ,						data_files=_lowerCAmelCase			)
						# 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   ,						**_lowerCAmelCase			)
						elif model_args.model_name_or_path:
												__snake_case				=		AutoConfig.from_pretrained(model_args.model_name_or_path   ,						**_lowerCAmelCase			)
						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   ,						**_lowerCAmelCase			)
						elif model_args.model_name_or_path:
												__snake_case				=		AutoTokenizer.from_pretrained(model_args.model_name_or_path   ,						**_lowerCAmelCase			)
						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=_lowerCAmelCase   ,						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(_lowerCAmelCase			)

						model.resize_token_embeddings(len(_lowerCAmelCase			)			)

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

						__snake_case				=		datasets.map(
						    _lowerCAmelCase   ,						batched=_lowerCAmelCase   ,						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=_lowerCAmelCase   ,						mlm_probability=data_args.mlm_probability			)

						# Initialize our Trainer
						__snake_case				=		Trainer(
						    model=_lowerCAmelCase   ,						args=_lowerCAmelCase   ,						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=_lowerCAmelCase   ,						data_collator=_lowerCAmelCase   ,						)

						# 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=_lowerCAmelCase			)
												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(_lowerCAmelCase   ,						"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(_lowerCAmelCase   ,						"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							(     _lowerCAmelCase			)    ->						Optional[Any]:


						'''simple docstring'''






						main()


if __name__ == "__main__":
	main()











'''simple docstring'''


import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_electra import ElectraTokenizer


_lowercase  =			{"""vocab_file""": """vocab.txt""", """tokenizer_file""": """tokenizer.json"""}

_lowercase  =			{
    """vocab_file""": {
        """google/electra-small-generator""": (
            """https://huggingface.co/google/electra-small-generator/resolve/main/vocab.txt"""
        ),
        """google/electra-base-generator""": """https://huggingface.co/google/electra-base-generator/resolve/main/vocab.txt""",
        """google/electra-large-generator""": (
            """https://huggingface.co/google/electra-large-generator/resolve/main/vocab.txt"""
        ),
        """google/electra-small-discriminator""": (
            """https://huggingface.co/google/electra-small-discriminator/resolve/main/vocab.txt"""
        ),
        """google/electra-base-discriminator""": (
            """https://huggingface.co/google/electra-base-discriminator/resolve/main/vocab.txt"""
        ),
        """google/electra-large-discriminator""": (
            """https://huggingface.co/google/electra-large-discriminator/resolve/main/vocab.txt"""
        ),
    },
    """tokenizer_file""": {
        """google/electra-small-generator""": (
            """https://huggingface.co/google/electra-small-generator/resolve/main/tokenizer.json"""
        ),
        """google/electra-base-generator""": (
            """https://huggingface.co/google/electra-base-generator/resolve/main/tokenizer.json"""
        ),
        """google/electra-large-generator""": (
            """https://huggingface.co/google/electra-large-generator/resolve/main/tokenizer.json"""
        ),
        """google/electra-small-discriminator""": (
            """https://huggingface.co/google/electra-small-discriminator/resolve/main/tokenizer.json"""
        ),
        """google/electra-base-discriminator""": (
            """https://huggingface.co/google/electra-base-discriminator/resolve/main/tokenizer.json"""
        ),
        """google/electra-large-discriminator""": (
            """https://huggingface.co/google/electra-large-discriminator/resolve/main/tokenizer.json"""
        ),
    },
}

_lowercase  =			{
    """google/electra-small-generator""": 5_12,
    """google/electra-base-generator""": 5_12,
    """google/electra-large-generator""": 5_12,
    """google/electra-small-discriminator""": 5_12,
    """google/electra-base-discriminator""": 5_12,
    """google/electra-large-discriminator""": 5_12,
}

_lowercase  =			{
    """google/electra-small-generator""": {"""do_lower_case""": True},
    """google/electra-base-generator""": {"""do_lower_case""": True},
    """google/electra-large-generator""": {"""do_lower_case""": True},
    """google/electra-small-discriminator""": {"""do_lower_case""": True},
    """google/electra-base-discriminator""": {"""do_lower_case""": True},
    """google/electra-large-discriminator""": {"""do_lower_case""": True},
}
class 						a_				(			UpperCAmelCase__						):
	lowercase_       :     Tuple			  =      VOCAB_FILES_NAMES
	lowercase_       :     Optional[int]			  =      PRETRAINED_VOCAB_FILES_MAP
	lowercase_       :     int			  =      PRETRAINED_INIT_CONFIGURATION
	lowercase_       :     str			  =      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	lowercase_       :     List[Any]			  =      ElectraTokenizer




	def __init__(       self     :  Optional[Any]       ,		__lowerCAmelCase     :  Optional[Any]=None       ,		__lowerCAmelCase     :  str=None       ,		__lowerCAmelCase     :  Union[str, Any]=True       ,		__lowerCAmelCase     :  int="[UNK]"       ,		__lowerCAmelCase     :  str="[SEP]"       ,		__lowerCAmelCase     :  Optional[Any]="[PAD]"       ,		__lowerCAmelCase     :  Optional[Any]="[CLS]"       ,		__lowerCAmelCase     :  str="[MASK]"       ,		__lowerCAmelCase     :  List[Any]=True       ,		__lowerCAmelCase     :  Optional[int]=None       ,		**__lowerCAmelCase     :  List[Any]       ,		):
							super().__init__(
							    __lowerCAmelCase       ,		tokenizer_file=__lowerCAmelCase       ,		do_lower_case=__lowerCAmelCase       ,		unk_token=__lowerCAmelCase       ,		sep_token=__lowerCAmelCase       ,		pad_token=__lowerCAmelCase       ,		cls_token=__lowerCAmelCase       ,		mask_token=__lowerCAmelCase       ,		tokenize_chinese_chars=__lowerCAmelCase       ,		strip_accents=__lowerCAmelCase       ,		**__lowerCAmelCase       ,		)

							__snake_case						 =   json.loads(self.backend_tokenizer.normalizer.__getstate__()      )
							if (
							    normalizer_state.get('lowercase'       ,		__lowerCAmelCase      ) != do_lower_case
							    or normalizer_state.get('strip_accents'       ,		__lowerCAmelCase      ) != strip_accents
							    or normalizer_state.get('handle_chinese_chars'       ,		__lowerCAmelCase      ) != tokenize_chinese_chars
							):
													__snake_case						 =   getattr(__lowerCAmelCase       ,		normalizer_state.pop('type'      )      )
													__snake_case						 =   do_lower_case
													__snake_case						 =   strip_accents
													__snake_case						 =   tokenize_chinese_chars
													__snake_case						 =   normalizer_class(**__lowerCAmelCase      )

							__snake_case						 =   do_lower_case




	def   lowercase__   (       self     :  Union[str, Any]       ,		__lowerCAmelCase     :  str       ,		__lowerCAmelCase     :  Tuple=None      ):
							__snake_case						 =   [self.cls_token_id] + token_ids_a + [self.sep_token_id]

							if token_ids_a:
													output += token_ids_a + [self.sep_token_id]

							return output




	def   lowercase__   (       self     :  Any       ,		__lowerCAmelCase     :  List[int]       ,		__lowerCAmelCase     :  Optional[List[int]] = None      ):
							__snake_case						 =   [self.sep_token_id]
							__snake_case						 =   [self.cls_token_id]
							if token_ids_a is None:
													return len(cls + token_ids_a + sep      ) * [0]
							return len(cls + token_ids_a + sep      ) * [0] + len(token_ids_a + sep      ) * [1]




	def   lowercase__   (       self     :  List[Any]       ,		__lowerCAmelCase     :  str       ,		__lowerCAmelCase     :  Optional[str] = None      ):
							__snake_case						 =   self._tokenizer.model.save(__lowerCAmelCase       ,		name=__lowerCAmelCase      )
							return tuple(__lowerCAmelCase      )

