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						:   Tuple         =			logging.getLogger(__name__)
__SCREAMING_SNAKE_CASE						:   List[str]         =			list(MODEL_FOR_MASKED_LM_MAPPING.keys())
__SCREAMING_SNAKE_CASE						:   Optional[Any]         =			tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)



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



							def 	UpperCamelCase	(						self    ):




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




													if self.train_file is not None:
																			_snake_case						:					List[str]    =   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						:					List[str]    =   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						)	->	Optional[Any]:

						'''simple docstring'''



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

						_snake_case						:					Tuple    =   {c: dataset[c] for c in dataset.column_names}
						_snake_case						:					List[str]    =   refs
						return Dataset.from_dict(__lowercase						)
def  snake_case						()	->	Union[str, Any]:

						'''simple docstring'''



						_snake_case						:					int    =   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						:					Dict    =   parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]						)						)
						else:
												_snake_case    ,_snake_case    ,_snake_case						:					Optional[int]    =   parser.parse_args_into_dataclasses()

						# Detecting last checkpoint.
						_snake_case						:					Any    =   None
						if os.path.isdir(training_args.output_dir						) and training_args.do_train and not training_args.overwrite_output_dir:
												_snake_case						:					List[Any]    =   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.
												_snake_case						:					Optional[Any]    =   load_dataset(data_args.dataset_name ,      data_args.dataset_config_name						)
												if "validation" not in datasets.keys():
																		_snake_case						:					List[Any]    =   load_dataset(
																		    data_args.dataset_name ,      data_args.dataset_config_name ,      split=F"""train[:{data_args.validation_split_percentage}%]""" ,      )
																		_snake_case						:					str    =   load_dataset(
																		    data_args.dataset_name ,      data_args.dataset_config_name ,      split=F"""train[{data_args.validation_split_percentage}%:]""" ,      )
						else:
												_snake_case						:					Optional[int]    =   {}
												if data_args.train_file is not None:
																		_snake_case						:					Any    =   data_args.train_file
												if data_args.validation_file is not None:
																		_snake_case						:					Optional[int]    =   data_args.validation_file
												_snake_case						:					int    =   data_args.train_file.split("."						)[-1]
												if extension == "txt":
																		_snake_case						:					int    =   "text"
												_snake_case						:					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.
						_snake_case						:					str    =   {
						    "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						:					str    =   AutoConfig.from_pretrained(model_args.config_name ,      **__lowercase						)
						elif model_args.model_name_or_path:
												_snake_case						:					Dict    =   AutoConfig.from_pretrained(model_args.model_name_or_path ,      **__lowercase						)
						else:
												_snake_case						:					List[Any]    =   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						:					int    =   {
						    "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						:					Union[str, Any]    =   AutoTokenizer.from_pretrained(model_args.tokenizer_name ,      **__lowercase						)
						elif model_args.model_name_or_path:
												_snake_case						:					List[str]    =   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:
												_snake_case						:					Dict    =   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"						)
												_snake_case						:					Optional[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:
												_snake_case						:					Union[str, Any]    =   datasets["train"].column_names
						else:
												_snake_case						:					Optional[int]    =   datasets["validation"].column_names
						_snake_case						:					Optional[int]    =   "text" if "text" in column_names else column_names[0]

						_snake_case						:					Tuple    =   "max_length" if data_args.pad_to_max_length else False

						def tokenize_function(__lowercase						):
												# Remove empty lines
												_snake_case						:					Dict    =   [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						)

						_snake_case						:					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:
												_snake_case						:					Dict    =   add_chinese_references(tokenized_datasets["train"] ,      data_args.train_ref_file						)
						if data_args.validation_ref_file is not None:
												_snake_case						:					List[Any]    =   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						:					List[str]    =   data_args.train_ref_file or data_args.validation_ref_file
						if has_ref:
												_snake_case						:					List[Any]    =   False

						# Data collator
						# This one will take care of randomly masking the tokens.
						_snake_case						:					Union[str, Any]    =   DataCollatorForWholeWordMask(tokenizer=__lowercase ,      mlm_probability=data_args.mlm_probability						)

						# Initialize our Trainer
						_snake_case						:					Optional[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:
																		_snake_case						:					Dict    =   last_checkpoint
												elif model_args.model_name_or_path is not None and os.path.isdir(model_args.model_name_or_path						):
																		_snake_case						:					List[str]    =   model_args.model_name_or_path
												else:
																		_snake_case						:					Optional[Any]    =   None
												_snake_case						:					Any    =   trainer.train(resume_from_checkpoint=__lowercase						)
												trainer.save_model()  # Saves the tokenizer too for easy upload

												_snake_case						:					Union[str, Any]    =   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
						_snake_case						:					Tuple    =   {}
						if training_args.do_eval:
												logger.info("*** Evaluate ***"						)

												_snake_case						:					List[Any]    =   trainer.evaluate()

												_snake_case						:					Tuple    =   math.exp(eval_output["eval_loss"]						)
												_snake_case						:					Tuple    =   perplexity

												_snake_case						:					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						)	->	Union[str, Any]:

						'''simple docstring'''



						main()


if __name__ == "__main__":
				main()






# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


__SCREAMING_SNAKE_CASE						:   Any         =			{'configuration_mra': ['MRA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MraConfig']}

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__SCREAMING_SNAKE_CASE						:   Optional[Any]         =			[
				    'MRA_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'MraForMaskedLM',
				    'MraForMultipleChoice',
				    'MraForQuestionAnswering',
				    'MraForSequenceClassification',
				    'MraForTokenClassification',
				    'MraLayer',
				    'MraModel',
				    'MraPreTrainedModel',
				]


if TYPE_CHECKING:
				from .configuration_mra import MRA_PRETRAINED_CONFIG_ARCHIVE_MAP, MraConfig

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_mra import (
								    MRA_PRETRAINED_MODEL_ARCHIVE_LIST,
								    MraForMaskedLM,
								    MraForMultipleChoice,
								    MraForQuestionAnswering,
								    MraForSequenceClassification,
								    MraForTokenClassification,
								    MraLayer,
								    MraModel,
								    MraPreTrainedModel,
								)
else:
				import sys

				__SCREAMING_SNAKE_CASE						:   str         =			_LazyModule(__name__, globals()['__file__'], _import_structure)